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PMC10000584

Bhuvana Plakkot,Ashley Di Agostino,Madhan Subramanian

Implications of Hypothalamic Neural Stem Cells on Aging and Obesity-Associated Cardiovascular Diseases

28-02-2023

aging,cardiovascular conditions,hypothalamus,neural stem cells,neuroinflammation,obesity

The hypothalamus, one of the major regulatory centers in the brain, controls various homeostatic processes, and hypothalamic neural stem cells (htNSCs) have been observed to interfere with hypothalamic mechanisms regulating aging. NSCs play a pivotal role in the repair and regeneration of brain cells during neurodegenerative diseases and rejuvenate the brain tissue microenvironment. The hypothalamus was recently observed to be involved in neuroinflammation mediated by cellular senescence. Cellular senescence, or systemic aging, is characterized by a progressive irreversible state of cell cycle arrest that causes physiological dysregulation in the body and it is evident in many neuroinflammatory conditions, including obesity. Upregulation of neuroinflammation and oxidative stress due to senescence has the potential to alter the functioning of NSCs. Various studies have substantiated the chances of obesity inducing accelerated aging. Therefore, it is essential to explore the potential effects of htNSC dysregulation in obesity and underlying pathways to develop strategies to address obesity-induced comorbidities associated with brain aging. This review will summarize hypothalamic neurogenesis associated with obesity and prospective NSC-based regenerative therapy for the treatment of obesity-induced cardiovascular conditions.

Implications of Hypothalamic Neural Stem Cells on Aging and Obesity-Associated Cardiovascular Diseases The hypothalamus, one of the major regulatory centers in the brain, controls various homeostatic processes, and hypothalamic neural stem cells (htNSCs) have been observed to interfere with hypothalamic mechanisms regulating aging. NSCs play a pivotal role in the repair and regeneration of brain cells during neurodegenerative diseases and rejuvenate the brain tissue microenvironment. The hypothalamus was recently observed to be involved in neuroinflammation mediated by cellular senescence. Cellular senescence, or systemic aging, is characterized by a progressive irreversible state of cell cycle arrest that causes physiological dysregulation in the body and it is evident in many neuroinflammatory conditions, including obesity. Upregulation of neuroinflammation and oxidative stress due to senescence has the potential to alter the functioning of NSCs. Various studies have substantiated the chances of obesity inducing accelerated aging. Therefore, it is essential to explore the potential effects of htNSC dysregulation in obesity and underlying pathways to develop strategies to address obesity-induced comorbidities associated with brain aging. This review will summarize hypothalamic neurogenesis associated with obesity and prospective NSC-based regenerative therapy for the treatment of obesity-induced cardiovascular conditions. Neural stem cells (NSCs) in an adult brain are responsible for neurogenesis and regeneration of brain functions. The two primary NSC reservoirs (neurogenic niches) in an adult mammalian brain are the sub-ventricular zone (SVZ) of the lateral ventricles and the hippocampal dentate gyrus (DG) [1,2,3]. In recent times, a third NSC pool, hypothalamic neural stem cells (htNSCs), were discovered [4,5,6]. The htNSC population is sensitive to variations in nutrient intake and signaling. An increase in neurogenesis in the hypothalamus was observed upon acutely feeding a high fat diet (HFD) [7], whereas a reduced neurogenesis in the hypothalamus was noticed as a result of chronic HFD feeding [8], and ‘inflammation’ was suggested as a major factor in causing such pronounced changes in neurogenesis. Upon htNSCs culturing, we observed a significant increase in htNSCs after eight months in HFD-fed C57BL/6J male adult mice compared to the chow-fed controls (unpublished). Cellular senescence is an irreversible growth arrest in proliferating cells, which has been implicated in several neurodegenerative diseases [9,10]. During the process of senescence, the NSCs lose their ability to proliferate and generate neurons [11,12,13,14]. Supplementing mono-unsaturated fatty acids, such as oleic acid, in the diet caused lipid droplets to develop in ependymal cells and contributed to a decrease in neurogenesis in SVZ in the Alzheimer’s disease mouse model, 3xTg-AD [15]. Likewise in obesity, SVZ showed an increase in senescent glial cells carrying excessive fat deposits, and genetically ablating these senescent glial cells restored neurogenesis [16]. Thus, modifying the lipid content in the diet can replenish the old neurogenic pool. In this review, we will summarize hypothalamic neurogenesis associated with obesity and aging and explore the possibilities of NSC-based regenerative therapy to treat obesity-induced cardiovascular conditions. NSCs are multipotent and they generate neurons, oligodendrocytes, and glia in the nervous system [17]. Varied levels of neural inflammation are observed in many neurological disorders or neurodegenerative diseases in human beings [18,19]. Their progression involves mediators of inflammation that are synthesized and secreted by various CNS cells, such as astrocytes, microglia, and oligodendrocytes [20]. Both beneficial and detrimental effects are observed in inflammatory conditions, which makes it unclear to specify the exact role of inflammation on NSCs. Certain pathways, after long term activation, cause energy imbalance, abnormal nutrient metabolism, restricted neurogenesis, proliferation, and differentiation of neural stem cells leading to metabolic and cognitive abnormalities. In the hypothalamus, the medio-basal hypothalamus (MBH) and the 3rd ventricle wall are observed to be the NSC niches [8]. Some studies state that mainly adult NSCs are observed in the MBH [7,21]. The MBH is a predominant region for physiological homeostasis of the entire body. Many neural progenitors or specialized ependymal cells that line the 3rd ventricle are observed to be glia-like tanycytes. They send processes to the arcuate nucleus and ventro-medial nucleus of the hypothalamus. Functionally these tanycytes are observed to be glucosensitive, reacting to metabolic stimulation and signal variations caused by feeding and energy balance [4,7,22]. Properties of tanycytes include ATP release, purinergic P2Y1 receptors, ectonucleoside triphosphate diphosphohydrolase 2 (NTPDase2) expression [23], and reacting to the activation of these receptors by the means of intense Ca2+ waves [24]. This is similar to the signaling mechanisms in stem cells. Expression of doublecortin-like [25] proteins, nestin [26,27,28] and vimentin [29,30,31], linked to neural precursor cells are observed in humans and rodent tanycytes. The expression of Sox2 [7,8], a nuclear transcription factor and NSC marker, is found in a few of the tanycytes, especially in the subventricular zone and dentate gyrus. In adult mice, it is mainly expressed in a group of cells in the MBH, particularly within the hypothalamic third-ventricle wall [8]. However, a few studies have shown rare occurrences of proliferating neurogenic progenitors in the human dentate gyrus [32,33]. One of the studies also observed human paralaminar nuclei of the amygdala showing persistence of immature excitatory neurons for decades [34]. Thus, the possibility of observing immature non-proliferative hypothalamic neurons cannot be denied and future studies focusing on confirming their ability to proliferate and differentiate could possibly reveal their normal functionality. The MBH regulates body weight, feeding, and glucose balance via melanocortin signals based in the arcuate nucleus (ARC), mainly via orexigenic agouti-related peptide (AGRP) neurons and anorexigenic proopiomelanocortin (POMC) neurons [35,36,37,38]. Leptin and insulin, which vary with different fat mass conditions and feeding patterns, affect these two neurons and the process is crucial for body weight homeostasis [36,39,40,41]. The studies also showed decrease in responsiveness to leptin and insulin by these neurons upon chronic feeding of a high-fat diet (HFD), resulting in type-2 diabetes (T2D) and HFD-induced obesity. A 10% loss in POMC neurons was observed in the hypothalamus upon long term HFD feeding [8,21,42]. Neural precursors giving rise to different neurons were observed to have POMC gene expression [43]. Considering these data and mechanisms, there is evidence of dysregulation of neurogenesis in the hypothalamus of obese subjects. Based on many recent studies, neurogenesis has been observed in adult rodents [7,22,44,45] and htNSCs in adult MBH contribute to the regulation of metabolic physiology [8]. Hence, future studies could be focused on developing htNSCs as a treatment regimen for obesity and its related disorders, such as diabetes. Microglia are brain-resident macrophages that contribute to reduced neurogenesis in aging and play a predominant role in the inflammatory response [46]. Through microglia sorting studies, we observed a significant elevation of activated microglia in the hypothalamus of four-month HFD-fed young adult male mice compared to the chow-fed controls (unpublished). Activated microglia have the potential to release proinflammatory cytokines that can be harmful to NSCs, neurons and other glial cells. Among the complex neural immune reactions in adult NSCs, inflammatory cytokines are observed to majorly affect differentiation, proliferation, migration, and survival [47]. Inhibition of neurogenesis is achieved by pro-inflammatory cytokines whereas an increase in neurogenesis is observed by anti-inflammatory cytokines [48]. The gene expression studies in our lab revealed a significant increase in proinflammatory markers, such as IL1β, MCP1, and TNFα, in the whole hypothalamus of middle-aged, eight-month HFD-fed male mice compared to controls (unpublished). However, an anti-inflammatory cytokine, such as the transforming growth factor-beta (TGFβ), can enhance endothelial cells of adult NSC during aging [49,50]. In addition to these, a chemokine, CCL11, was observed to be increased in aged mice, both in blood and cerebrospinal fluid (CSF), which further caused a decline in neurogenesis leading to cognitive function impairment [51]. Exercise and restriction of calories can cause variations in systemic factors, and hence, act as adult NSC function modulators [52]. Upon over-nutrition, the IκB kinase-β/nuclear transcription factor NF-κB (IKKb/NF-κB) pathway, that plays a crucial role in many physiological processes, gets activated; this can cause SOCS3, a suppressor of cytokine signaling-3 gene upregulation in the hypothalamus, to inhibit insulin and leptin signaling, leading to resistance [53]. Studies have confirmed that, in the neurons of the hypothalamus in mice, SOCS3 knockout leads to an improvement in central leptin signaling and reduced obesity [54,55,56]. Similar effects were observed in central IKKb knockout mice and, in the MBH, SOCS3 overexpression decreased the neural IKKb inhibition effect on obesity reduction [53]. Like SOCS3, protein tyrosine phosphatase 1B (PTP1B) causes inhibition of leptin and insulin signaling and was observed to have a role in the IKKb/NF-kB inflammatory pathway. PTP1B expression in the hypothalamus can be increased by TNF-a by activating the IKKb/NF-kB pathway, mainly by being a transcriptional target [57]. Inhibition of PTP1B in neurons resolved leptin resistance, glucose disorders, and obesity induced by over-nutrition [58,59,60]. It is assumed that neural PTP1B may form a link with metabolic disease pathways and neurodegenerative diseases as it had an effect on genetic mouse models of Alzheimer’s disease [61]. In the forebrain, degeneration of GABAergic interneurons was mediated by an overproduction of the cytokine interleukin-6 in diabetes and obesity, which leads to NF-kB activation and release of neurotoxic inflammatory products [62]. Therefore, alleviating chronic diet-induced neuroinflammation by exploring the pathways associated with the metabolic control function of htNSC and identifying their therapeutic potential is essential. Various factors affect NSC populations in obesity, including hormonal factors, transcription factors, inflammatory factors such as cytokines and chemokines, epigenetic changes and chromatin stability, oxidative stress, DNA damage, hyperlipidemia/hyperglycemia, etc. Nuclear factor E2-related factor 2 (Nrf2) is a major transcription factor that regulates basal and induced expression of antioxidant response element genes in response to oxidative stress. Functions of Nrf2 also include stem cell survival, apoptosis, autophagy, mitochondrial biogenesis, and many more, in addition to aging processes [63,64,65,66,67]. Studies in our lab observed an elevated expression of Nrf2 in the hypothalamus of adult obese male mice, along with a significant increase in htNSCs (unpublished). In a previous study, increased oxidation, or reactive oxygen species in adult mouse NSCs, promoted their ability to generate neurons and proliferate [68]. Self-renewal of stem cells was observed to be regulated by Nrf2, along with differentiation initiation with the support of epigenetic factors and transcription regulators [69]. Nrf2 expression and transcriptional activity steadily increased during the induced oluripotent stem cells (iPSC) differentiation process that peaked in later stages [70]. Restoration of age-related loss of hippocampal function was evidenced by transplanting Nrf2-overexpressing young NSCs [71], indicating the critical role of Nrf2 in mediating NSC/neural progenitor cell (NPC)- dependent neurogenesis in aging. Redox homeostasis by Nrf2 critically mediates the differentiation ability of different stem cell types to survive oxidative stress, which could gradually reduce during aging [69]. Thus, obtaining insight into one of the main transcription factors, Nrf2, that can resist oxidative stress, could provide fundamental knowledge about changes in htNSCs during neuroinflammation and lead to development of an associated therapeutic strategy. A continuous decline in physiological integrity is observed during aging. Characteristic intertwining factors that contribute to the complex aging process include deregulated nutrient sensing, cellular senescence, epigenetic alterations, genomic instability, loss of proteostasis, mitochondrial dysfunction, telomere attrition, change in intercellular communication, and exhaustion of stem cells [72,73]. It has been observed in various research that the hypothalamus is particularly important in aging [74,75,76,77] but the underlying cellular mechanism is not known in depth. The IκB kinase-β (IKKβ) pro-inflammatory axis in the hypothalamus and its downstream nuclear transcription factor, NF-κB, (IKKβ/NF-κB signaling) is over-stimulated in over-nutrition or aging [77,78,79]. Systemic aging is directed by the hypothalamic IKKβ/NF-κB pathway via inflammatory crosstalk between neurons and microglia by inhibiting gonadotropin-releasing hormone (GnRH) production, and so counteracting inflammation or GnRH therapy could partly regress degenerative signs of aging [77]. Maternal inflammation has been observed to cause reduced ventricular cell proliferation in developing fetal mouse brain [80]. In young mice, a high number of cells co-expressing Sox2 and the polycomb complex protein, Bmi-1, a nuclear protein [81] that is vital for self-renewal of NSCs and hematopoietic stem cells [82], were observed in the third-ventricle wall, whereas the ones in the MBH were found to be sparse. However, a gradual decrease in these cells was observed as age increased, which was initiated in the ventral region of 3rd ventricle wall within the MBH in 11–16-month-old mice and was totally lost in 22-months-and-older ones. Thus, various studies that aim to evaluate the exact time required to intervene in an inflammatory condition/pathway in the brain will provide more understanding upon which to formulate therapeutic clinical strategies for different neural stem cell niches. Senescent glial cell accumulation is observed in proximity to the lateral ventricles along with excessive fat deposition within them. Upon removal of senescent cells from HFD or obese mice deficient in leptin receptors, neurogenesis being restored and a decline in anxiety-related behavior was observed [16]. Hence from subsequent studies, they concluded that the topmost contributors to obesity-induced anxiety are senescent cells. Therefore, senolytic drugs have opened a novel therapeutic pathway to treat neuropsychiatric disorders. Alterations in mitochondrial structure and function may cause deleterious effects in adult NSC, which could drive the aging process [83]. Abnormal toxic by-product accumulation, including of reactive oxygen species (ROS), accompanies this event [84]. SOD2, an antioxidant enzyme that is regulated by FoxO3, a transcription factor associated with longevity [85], protects adult NSCs in mice [86]. An increased level of ROS and a decrease in the potential for self-renewal of adult NSCs was observed in mice that were deficient in FoxO1, 3, and 4 [87]. Other dysfunctions of mitochondria that contribute to the aging of NSCs include mitochondrial protein oxidation, variations in mitochondrial membrane composition, and abnormal mitophagy [83,88,89]. During mammalian NSC division, protein segregation is affected by age, mainly by means of diffusion barrier alteration. The stem cells are kept free of damage by the diffusion barrier that facilitates asymmetric segregation of damaged proteins among daughter cells [90]. Like yeast, efficient compartmentalization of cellular damage is achieved in young rodent NSCs and that can protect these proliferative cells. As age advances, this efficiency is reduced, causing aged NSCs to be exposed to excessive cell damage [90]. A mitochondrial function regulator, hypoxia-inducible factor-1α (HIF-1α), is essential for the maintenance of adult NSCs in their hypoxic niches. HIF-1α plays a major part in cell adaptation under hypoxia by inducing transcriptional responses. Thus, for proper adult NSC proliferation and subsequent differentiation, oxygen availability is critically important [91]. An abnormal oxygen-sensing pathway may be responsible for the neurogenic decline in aging [92]. Thus, the use of anti-inflammatory agents along with senolytic and associated htNSC therapy have the potential to strategically counteract diet-induced chronic neuroinflammation and aging. This could possibly pave way to new therapeutic regimens in obesity-induced cardiovascular conditions. Certain nutrient-sensing mechanisms that can be associated with aging have been considered modifiers of adult NSCs. Adult NSC proliferation and differentiation can be stimulated by insulin-like growth factor 1 (IGF-1) [93], and a reduced IGF-1 level has been associated with cognitive aging [94]. However, lifelong IGF-1 exposure may be the reason for an age-related reduction in adult neurogenesis [95]. An important metabolic regulation coordinator is the mammalian target of rapamycin (mTOR), which has two types, viz., mTORC1 and mTORC2 [96,97]. Regulation of body weight and feeding behavior is primarily controlled by mTOR1 using ghrelin and leptin signaling, in addition to control of gluconeogenesis and adipogenesis peripherally in many tissues [97]. Size, morphology, and neuronal cell numbers are controlled by mTORC2, along with energy balance regulation in the hypothalamus. In POMC neurons in aged mice, an elevation in mTOR activity was observed [98], which can indirectly lead to POMC neuronal soma enlargement and a decline in the projection of neurites to the paraventricular nucleus (PVN), which causes age-dependent obesity [99]. It has been observed that, upon intracerebral injection, rapamycin causes mTOR inhibition which further leads to neurite projection and neuronal excitability in POMC, establishing a decline in body weight and food consumption; hence, age acceleration is achieved by the mTOR pathway. Therefore, to delay aging and improve the lifespan, this pathway can be considered a potential target for therapeutic intervention. As previously discussed, during aging, a decrease in htNSCs was observed [81]. In addition, mice models with gene silencing mediating Bmi1+ depletion in stem cells showed a significant reduction in cognition, sociality, muscle endurance, coordination, and spatial memory. In other mice models, a decline in lifespan was observed in Sox2+ stem cell-depleted animals. Hence, replenishing new htNSC from a newborn mouse into the MBH of a middle-aged mouse could enhance the lifespan and delay age-associated physiological decline [81]. Exogenous implantation of stem cells into the hypothalamus caused secretion of microRNA-containing exosomes, which delayed physiological deficits in aging. Suppression of NF-kB activation was achieved in neurons due to these microRNAs, and GnRH secretion was also restored [81]. As a result, during aging, htNSC loss might cause systemic physiological changes due to underlying inflammation. Through Wingless-related integration site (Wnt)-mediated signaling by astrocytes, adult NSC expansion is induced in a paracrine manner [100]. As age increases, Wnt3 expression reduces in astrocytes, which causes further neurogenic decline [101]. Expression of survivin is decreased in adult NSCs due to an age-associated decline in Wnt-mediated signaling in the astrocytes that leads to a quiescent phase in adult NSCs [102]. Release of the Wnt inhibitor, DKK1, from astrocytes is increased in NSC niches during aging, which decreases neurogenesis [103]. Extracellular matrix composition, mechanical properties, and arrangement have a role in adult NSC function, which varies with injury, disease, and aging [104,105]. A high fat mass expression and obesity associated gene (FTO) was observed in adult NSCs [106]. A smaller number of BrdU+ and Ki67+ cells was also observed during FTO loss, showing a decline in adult NSC and reduced proliferating capacity, along with a decline in glial and neuronal differentiation, making adult NSC less multipotent. In addition, in adult mice, FTO loss was observed to decrease adult NSC proliferation and caused inhibition of neuronal differentiation in both SGZ and SVZ regions. Thus, adult NSC activity modulation is achieved by FTO through m6A modification regulation of selective transcripts that can indirectly affect the gene expression [107,108,109]. Alteration of important signaling for neurodevelopment is observed when a change in nutrient or neurotrophic environment is observed. Brain abnormalities and decreased brain weight, along with altered glial and neuronal protein expression is observed in mice that have a paucity of leptin signaling and varied expression of neuronal and glial proteins [110]. Elevated proliferation and decline in neural stem/progenitor cells (NPC) are observed in adult rats with type II diabetes [111] and in rats showing hyperglycemia. NPCs do not respond to growth factors and form neurospheres (NS) that are smaller in size. In addition, a decline in neurogenesis is observed in type I diabetic mice or rats treated with streptozotocin [112]. Along with anorexigenic response signaling, during fetal life, insulin and leptin help in neuronal development and their neurotrophic effects are mediated by the MAPK (ERK/MAPK) pathway that resulted in phosphorylation of ERK1/2 [113]. Significant neuronal differentiation was induced by leptin in differentiation conditions along with elevated early and late neuronal marker expression [114]; whereas the late neuronal marker neuronal nuclei (NeuN) showed no significant increase and a normal elevation in early neuronal markers, such as doublecortin (DCX) and neuron-specific class III β tubulin (Tuj1), were observed upon insulin exposure [114]. According to these studies, it was concluded that maternal diabetes and differential exposure of the fetus to insulin and leptin could result in reduced growth or macrosomia that could have a significant effect on the development of a fetal brain. In various studies of the effects of leptin on the hypothalamus, it has been observed that α-melanocyte-stimulating hormone (α-MSH) or melanotan II (agonist of MC3/4R (MTII)), upon intracerebroventricular (ICV) administration, enhanced sympathetic nerve activity (SNA), however agouti-related protein or MC3/4R broad brain inhibition with ICV SHU9119 blocked leptin’s sympathoexcitatory effect [115,116]. This is based on the understanding that α-MSH and glutamate are two major excitatory signals to the PVN, a cardiogenic center in the hypothalamus (see Figure 1), that can mediate leptin’s sympathoexcitatory effects. POMC neurons synthesize and release α-MSH. These neurons are in arcuate nucleus (ArcN), which projects to various sites in the hypothalamus, including the PVN [117,118,119], and regulates autonomic activity; however, the role of PVN MC3/4 is ambiguous. Glutamatergic signals are received by the PVN from various regions that include the dorsal medial hypothalamus, ventral medial hypothalamus, lateral hypothalamus, and ArcN, wherein elevated SNA is observed due to the action of leptin [120]. A small group of POMC neurons in the ArcN also expresses the glutamate vesicular transporter (VGLUT-2) [121]. PVN glutamate receptors blockade decreases the ArcN’s non-specific chemical stimulation-mediated sympathoexcitatory effects [122,123]. Along with this excitatory signaling, inhibitory neurons, such as neuropeptide Y (NPY) neurons of the ArcN, are projected into the PVN [122,123,124]. NPY neurons are inhibited by leptin in the ArcN [125,126] and PVN neuron firing is inhibited by NPY, which gets stimulated by α-MSH or plasma leptin elevation. By various studies it has been identified that elevated SNA is observed in obesity, especially in the kidney and hindlimb, for which a leptin increase and hypothalamic melanocortin activity elevation are predominant activities [127]. In mice and rats, expression of NPY in the ArcN/PVN is reduced by diet-induced obesity or, in the ArcN, by NPY mRNA levels [128,129,130]. In the PVN region, obesity-prone rats that were inbred showed a reduction in agouti-related protein/NPY processes [131]. Tonic NPY inhibition decline is essential for leptin-induced sympathetic outflow driven via PVN MC3/4R. It is inferred from this that obesity plays a role in SNA inhibition and it is due to tonic activity of NPY, which further reveals an elevated α-MSH excitation [132]. htNSCs are predominantly found adjacent to the PVN of the hypothalamus (See Figure 2) lining the 3rd ventricle [133]. Based on these studies, there is a need for detailed investigation into the link between the variation in NSC levels associated with different conditions, such as age, diet etc., and sympathoexcitatory activity. Reduced energy consumption without any effect on nutritional value is characteristic of dietary restriction (DR). It can be alternatively described as caloric restriction (CR) and, in a broader way, termed as periodic fasting, short-term starvation, intermittent fasting (IF), and fasting-mimetic diets [134]. In maintaining proper health and physiology, a crucial role is played by the type and amount of diet [135]. Adult stem cells are important for tissue regeneration and homeostasis and these stem cells can differentiate and self-renew into specialized cell types. Dietary changes, environment, and nutrient variation influence the stem cells via function alteration. In various studies, a positive effect was observed in stem cells when calories were restricted, especially an increase in the function of intestine and skeletal muscle stem cells, in addition to an elevated quiescence of hematopoietic stem cells (HSCs). In addition, time-restricted feeding has been shown to protect neuronal stem cells, intestinal stem cells, and HSCs from injury, especially stroke and neurodegenerative diseases in the brain [136,137,138]. HFD impairs neurogenesis and hematopoiesis, and it can create opportunities for tumorigenesis. Characteristic changes in metabolic pathways in the brain are achieved by IF, mainly by ketogenic amino acid and fatty acid breakdown and an elevation in stress resistance [139,140]. A neuroprotective effect can be achieved via IF by activation of many signaling pathways [141]. IF in rodents has shown an increase in long-term potentiation (LTP) at synapses in the hippocampus and an increase in hippocampal neurogenesis [138] in comparison with animals with a sedentary lifestyle that are fed ad libitum (AL) diet. BrdU-labeled cell number in the dentate gyrus was elevated in the mice that were intermittently fasted [138]. They also used Ki67 as a marker to evaluate cell proliferation by identifying an increase in dentate gyrus Ki67-labeled cells in mice that were fed with an IF diet. Mice subjected to IF for three months showed an elevated level of hippocampal nestin and NeuN (protein markers for precursor/neuronal stem cells), and also PSD95 (a scaffolding protein that is a potent regulator of synaptic strength) compared to AL mice [141], which demonstrated an increase in hippocampal neurogenesis and a strengthening of synaptic connections after IF. The researchers also showed that a pathway essential for neural stem cell maintenance in the mammalian brain [142], the Notch 1 signaling pathway, was shown to become activated mainly by upregulation of full-length Notch 1, Notch intracellular domain (NICD1), and transcription factor HES5 (involved in the formation of neurospheres) after IF. The stress resistance ability of brain cells is activated by IF by causing various changes in brain metabolic pathways [140]. The changes in metabolic pathways during IF can be injurious to the brain and through activation of the brain-derived neurotrophic factor (BDNF) signaling pathway, a neuroprotective state is achieved. Downstream transcription factor activation that helps in energy balance and neurogenesis is made possible by BDNF, and one such transcription factor is cAMP response element-binding protein (CREB). To differentiate stem cells into matured neurons, collaboration between the Notch signaling pathway and the CREB and BDNF signaling pathways is essential [143,144,145]. An increase in BDNF and p-CREB expression has been seen in IF compared to AL animals [141]. Without leading to malnutrition, CR is a 20–40% reduction in intake of calories. It is known to cause life-span increase, prolonged onset of diseases that are age related, and decrease in the incidence of cancer in different tissues and organisms [146,147,148,149]. The link between CR and longevity is under the influence of the downregulation of major nutrient sensing pathways, including those of insulin or IGF-1, and signaling by mTOR [84,147,150]. Very few studies have been documented on the positive and negative effects of CR on NSCs. Two-days-a-week fasting or alternate-days fasting (IF) in animals have been shown to decrease clinical symptoms caused by age-related neuronal diseases such as Alzheimer’s disease, and the animals that were fasted also perform better after stroke, which is an acute injury [151]. After three weeks of a three-month period of IF, an elevated NSC proliferation in the rats and mice dentate gyrus was observed [152,153]. An elevated BDNF was associated with these positive effects. However, various studies showed that neuronal survival ability was altered by fasting rather than induction of NSC proliferation. In the dentate gyrus of mice, an increase in neuron and glia numbers was observed within 72 h of feeding a fasting-mimicking diet (FMD), along with a reduced IGF-1/PKA signaling [152,154]. In addition, an increase in mesenchymal stem and progenitor cell number and proliferation were observed on FMD repeated feeding in aged animals, and in aged mice; rebalanced output from HSCs and progenitors were also observed [154,155]. Therefore, time restricting feeding can be a neuroprotective strategy for replenishing lost NSCs in chronic neuroinflammatory conditions. HtNSCs have a distinct endocrine function, to release excessive amounts of microRNAs (miRNAs)-containing exosomes [81]. In addtion, they have certain long non-coding RNAs (lncRNAs) that possess the ability to maintain pluripotency and embryonic stem cell neurogenesis [156], self-renewal of cancer stem cells [157], and reprogramming of pluripotent stem cells [158]. LncRNAs may play a unique role in determining the fate of these stem cells in cellular senescence regulation [159]. An abundant lncRNA, Hnscr in the htNSCs of young mice, drastically reduces as the mice age [159]. Hnscr regulates htNSC senescence and mouse aging by binding to YB-1, a multi-functional protein [160] that controls protein translation [161], and also regulates DNA repair [162], protecting it from protein degradation and ubiquitination. YB-1 acts as a repressor of transcription, inhibiting p16INK4A expression in htNSCs [159,163], and hence could be targeted to modulate senescence in htNSC. According to [159], TF2A treatment, isomeric theaflavin monomer, and a black tea derivative [164], improved YB-1 stability, diminished htNSC senescence, and decreased the level of aging related physiological downturn in mice. By various studies it has been observed that htNSC loss causes systemic aging within a short time and the exosomal miRNAs secreted by these cells (See Figure 3) mediate anti-aging properties [81]. Aging can be correlated with modulation of some gene expressions by certain non-coding RNAs. In aged adult NSC, heterochronic micro-RNA let-7b upregulation is observed. Repression of Hmga2, a high mobility group transcriptional regulator, is observed upon let-7b overexpression, which indirectly potentiates p16lnk4a (an inhibitor of cyclin-dependent kinase and activator of Rb) and p19Arf expression, improving the stability of p53 protein [165]. Therefore, it slows down the progression of cell cycle and induces senescence [166], leading to reduced adult NSC functioning and neurogenesis. However, deficiency of p16INK4a in aged mice diminished this effect [167]. Let-7b initiates differentiation and inhibits proliferation of neural stem cells by targeting Tlx and cyclin D1 in adult NSC and embryonic brains [168]. A higher-to --lower/quiescent shift in NSC proliferative state from fetus to adult is contributed to by Imp1, a different let-7b target, even though it is not expressed in adult NSC [169]. As a result, changes in let-7b may initiate aging in adult NSC. The gene regulation mediated by micro RNAs impacts healthy aging as well as aging associated with neurodegenerative diseases [170]. Administering exosomes derived from NSCs (exo-NSCs) could restore BDNF signaling and memory in HFD mice [171], providing suggestive evidence of the potential therapeutic effect of exo-NSCs on HFD-induced NSC dysregulation in obesity. Hence, further studies on differential expression of certain exosomal non-coding RNAs must be performed to form an understandable association with pathological and healthy aging. Immunological rejection is one of the major difficulties in stem cell therapy. This could be addressed by isolating NSCs from the same subjects that require the therapy to prevent immunological reaction to the newly transplanted stem cells. Administering immunosuppressive drugs could be an additional or alternative option even though it has a lot of side effects. Another challenge is to make certain that the transplanted cells grow enough without causing tumor development and karyotypic instability. According to [172], there is a critical challenge in isolating multipotent NSCs from cell culture for transplantation as the majority of neurospheres in vitro are heterogenic with varying developmental stages and gene expression mainly due to ex vivo culture conditions. Overcoming these challenges and establishing NSC-based therapy for obesity-induced comorbidities, especially cardiovascular conditions, to improve functional outcomes through associated multimodal mechanisms is tremendously foresighted. Based on the difficulty in accessing the brain to collect tissues for processing from live animals, using induced pluripotent stem cell (iPSC) technology is a solution that could produce in vitro NSCs or neurons for transplantation. As iPSCs can be non-invasively obtained from live subjects, and to reduce the risk of immune rejection, reprogramming these cells to NSCs or neurons could provide autologous engraftments [173]. HtNSCs could be a potential therapy in obesity-induced cardiovascular diseases. Exosomes derived from htNSCs could be an alternative to or a conjunction with NSC therapy, being a minimally invasive technique to reverse aging and degenerative changes in the CNS. The relationship between htNSC dysregulation and sympathetic nerve response in obesity has never been studied. As brain microglia activation is a predominant indicator of neuroinflammation in hypertension, restoring a normal population of glia and neurons within the cardiogenic centers of the brain cannot be ruled out. Thus, identifying associated htNSC mechanisms and pathways could bring novel insight to therapeutic strategies in obesity-associated hypertension or sympathetic nerve overactivity.

PMC10000590

Lesly J. Bueno-Urquiza,Marcela G. Martínez-Barajas,Carlos E. Villegas-Mercado,Jonathan R. García-Bernal,Ana L. Pereira-Suárez,Maribel Aguilar-Medina,Mercedes Bermúdez

The Two Faces of Immune-Related lncRNAs in Head and Neck Squamous Cell Carcinoma

24-02-2023

HNSCC,tumor microenvironment,LncRNAs,cancer-associated fibroblasts

Head and neck squamous cell carcinoma (HNSCC) is a group of cancers originating from the mucosal epithelium in the oral cavity, larynx, oropharynx, nasopharynx, and hypopharynx. Molecular factors can be key in the diagnosis, prognosis, and treatment of HNSCC patients. Long non-coding RNAs (lncRNAs) are molecular regulators composed of 200 to 100,000 nucleotides that act on the modulation of genes that activate signaling pathways associated with oncogenic processes such as proliferation, migration, invasion, and metastasis in tumor cells. However, up until now, few studies have discussed the participation of lncRNAs in modeling the tumor microenvironment (TME) to generate a protumor or antitumor environment. Nevertheless, some immune-related lncRNAs have clinical relevance, since AL139158.2, AL031985.3, AC104794.2, AC099343.3, AL357519.1, SBDSP1, AS1AC108010.1, and TM4SF19-AS1 have been associated with overall survival (OS). MANCR is also related to poor OS and disease-specific survival. MiR31HG, TM4SF19-AS1, and LINC01123 are associated with poor prognosis. Meanwhile, LINC02195 and TRG-AS1 overexpression is associated with favorable prognosis. Moreover, ANRIL lncRNA induces resistance to cisplatin by inhibiting apoptosis. A superior understanding of the molecular mechanisms of lncRNAs that modify the characteristics of TME could contribute to increasing the efficacy of immunotherapy.

The Two Faces of Immune-Related lncRNAs in Head and Neck Squamous Cell Carcinoma Head and neck squamous cell carcinoma (HNSCC) is a group of cancers originating from the mucosal epithelium in the oral cavity, larynx, oropharynx, nasopharynx, and hypopharynx. Molecular factors can be key in the diagnosis, prognosis, and treatment of HNSCC patients. Long non-coding RNAs (lncRNAs) are molecular regulators composed of 200 to 100,000 nucleotides that act on the modulation of genes that activate signaling pathways associated with oncogenic processes such as proliferation, migration, invasion, and metastasis in tumor cells. However, up until now, few studies have discussed the participation of lncRNAs in modeling the tumor microenvironment (TME) to generate a protumor or antitumor environment. Nevertheless, some immune-related lncRNAs have clinical relevance, since AL139158.2, AL031985.3, AC104794.2, AC099343.3, AL357519.1, SBDSP1, AS1AC108010.1, and TM4SF19-AS1 have been associated with overall survival (OS). MANCR is also related to poor OS and disease-specific survival. MiR31HG, TM4SF19-AS1, and LINC01123 are associated with poor prognosis. Meanwhile, LINC02195 and TRG-AS1 overexpression is associated with favorable prognosis. Moreover, ANRIL lncRNA induces resistance to cisplatin by inhibiting apoptosis. A superior understanding of the molecular mechanisms of lncRNAs that modify the characteristics of TME could contribute to increasing the efficacy of immunotherapy. Cancer, a group of multifactorial diseases, is considered one of the main public health problems, being the second cause of death worldwide [1]. According to GLOBOCAN, HNSCC incidence and mortality are about 800,000 and 400,000 cases, respectively, positioning it as the sixth most common cause of cancer death around the world [2]. HNSCC develops from squamous cells in the mucosal epithelium lining the oral cavity, larynx, oropharynx, nasopharynx, and hypopharynx [3,4]. This type of cancer is more common in men, with a 3:1 ratio compared with women [5], and occurs mainly after the age of 55 [6,7]. The main factors related to the development of this type of cancer are the consumption of alcohol and tobacco [8], whose effect is proportional to the intensity of exposure [9]. Additionally, it has been described that infection with high-risk human papillomavirus (HPV), mainly genotypes 16, 18, 31, 33, and 35, acts synergistically in carcinogenesis. In this regard, HNSCC can be classified as HPV-negative and HPV-positive [10,11,12]. HPV infection is responsible for up to 60% of HNSCC cases, as it participates in the development of oropharyngeal tumors, being the 90% of HPV-positive tumors related to HPV 16 infection. Interestingly, HPV infection, in addition to being an etiological factor, is related to the prognosis of patients. It has been observed that HPV-positive cases show a favorable prognosis, unlike those that are not [10,12]. The TME is a very complex construct composed of extracellular matrices (ECM) and cellular components such as tumor cells, immune cells, and cancer-associated fibroblasts (CAFs), among others [13,14]. For this, tumors can be classified according to the cellular infiltrate as inflamed tumors and immune-excluded and immune-desert tumors. Inflamed tumors are characterized by abundant intratumoral and stromal immunological infiltrate. Immune-excluded tumors have immunological infiltrate restricted to the stroma. Immune-deserts lack infiltrate both in the tumor and in the stroma [15]. Even though in inflamed tumors there is an infiltrate of immune cells, in an immunosuppressive environment the tumor can evade the host response and progress [15,16]; this also depends on the infiltrate and its relationship with a positive or negative prognosis. The most common model to explain the tumor behavior is “cancer immunoediting”, which refers to a dual action that the immune system can take, one of which is the protection towards the host by eliminating tumor cells, the other is the programming of, those cells of the immune system that are associated with the tumor and help tumor progression [17]. This process can be divided into three phases that are called the “three E” (elimination, equilibrium, and escape). First, elimination refers to immunosurveillance mediated by the immune cells; second, equilibrium is where the immune system promotes the generation of tumor cells that survive the attack; finally, once immunological anergy and tolerance are achieved, escape leads to cancer cells that can form tumors [17,18]. The immune infiltrate in TME includes cells from the adaptive immunity such as cytotoxic T lymphocytes (CTL) that recognize and kill tumor cells through the release of granzymes and perforins, CD4+ T cells that are essential for the proliferation and differentiation of CD8+ T cells that infiltrate the tumor, innate immune cells such as natural killer (NK) cells that have cytotoxic and cytokine-producing activity, tumor-associated macrophages (TAMs) classified into two subpopulations (M1 with antitumor activity and M2 with protumor activity and an immunosuppressive profile), mast cells that release preformed inflammatory mediators in their granules, and finally stromal cells such as CAFs that are fibroblasts functionally different from the normal population and participate in the remodeling of the extracellular matrix and the production of protumoral cytokines [15,16,19,20,21,22]. The antitumor immune response is characterized by an infiltrate of CTL, B lymphocytes, CD4+ Th1 lymphocytes, regulatory T cells (Treg), M1 macrophages, and NK cells, while CD4+ Th2 lymphocytes, M2 macrophages, neutrophils, and CAFs, among others, participate in the protumoral immune response [16,19,20]. These cell populations have intercellular communication through cytokines, chemokines, and non-coding RNAs (ncRNAs) [22,23,24,25], which will modulate the characteristics of TME [26]. ncRNAs represent a large percentage of the genome with relevant functions in biological processes since they control the expression of genes. ncRNAs can be classified according to their length in microRNAs (miRNA), which have a length of approximately 22 nucleotides, and the lncRNA, which are longer than 200 nucleotides [25,27]. LncRNAs are non-coding chains of 200 to 100,000 nucleotides transcribed by RNA polymerase II [28]. Generally, they have a poly-A tail and can be subjected to splicing processes [27,29]. Their mechanisms of action are diverse both in the cytoplasm and in the nucleus. In the cytosol, they are related to the regulation of mRNA decay as well as its stability, functioning as sponges for miRNAs. Meanwhile, in the nucleus, they are associated with promoter sites, participating in transcriptional repression, epigenetic regulation, and nuclear architecture [30,31]. LncRNAs play an important role in both innate and adaptive immune responses; it has been shown that they affect essential processes such as differentiation or the immune function [11,32,33]. It has been observed that some of the biological processes they regulate are cell activation, proliferation, metabolism, and death [28,34]. In recent years, the participation of lncRNAs in the tumorigenesis of cancer cells involving the tumor microenvironment has gained relevance given that some of the lncRNAs are associated with poor prognosis (Table 1). In this regard, the lncRNA MIR31HG is associated with poor prognosis since its expression is significantly correlated with advanced stages in laryngeal squamous cell carcinoma (LSCC) samples and in vitro and in vivo analysis found that it promotes cancer cell growth [35]. In addition, USP2-AS1 promotes progression through proliferation, tumor growth, invasion, and the transition from G0/G1 to the S phase of the cell cycle in both in vitro and in vivo models [36]. On the other hand, the lncRNA TM4SF19-AS1 acts as a sponge for miR-153-3p since it binds to LAMC1 (laminin gamma 1 subunit), which has been reported to be upregulated in patients with HNSCC [37]; thus, TM4SF19-AS1 enhances proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) through the expression of mesenchymal markers (vimentin, N-cadherin) [38]. Furthermore, LINC00460 is associated with the regulation of proliferation, migration, invasion, and mesenchymal marker expression in vitro [40]. In the case of HCG18, lncRNA is overexpressed in cell lines and patients with HNSCC regulating migration, invasion, and modulating progression through the expression of cyclin D, which is a key protein in the WNT signaling pathway and is directly associated with a poor prognosis of patients [41]. The lncRNAs not only act in the progression of cancer but also in tumor suppression, being associated with a good prognosis. For instance, HNSCAT1 is downregulated in samples of advanced HNSCC, meanwhile its overexpression is associated with the formation of minor tumors in vivo [43]. Due to the heterogeneity of CAFs, several pathways participate in their activation. Recently, the role of some lncRNAs that participate in the modulation of their activation has been described as finding the stimulus with the factor PDGF-BB (platelet-derived growth factor-BB), associated with differentiation towards CAFs [67], also increases the expression of the lncRNA LURAP1L-AS1 (leucine-rich adaptor protein 1-like antisense RNA 1) as well as the classical markers of CAFs (α-SMA (α-smooth muscle actin), FSP-1 (fibroblast-specific protein 1), and FAP (fibroblast activation protein)).When LURAP1L-AS1 silencing is performed, the expression of the markers decreases; it also participates in the regulation of NF-κB through the LURAP1L-AS1/LURAP1L/IKKa/IκBa/NF-κB axis [44]. Another lncRNA overexpressed is FLJ22447 or lncRNA-CAF that, in conjunction with IL-33, participates in NF activation toward CAFs. LncRNA-CAF silencing has an impact on the decreased expression of classical CAF markers and lncRNA-CAF functions as a lncRNA scaffold to maintain IL-33 protein stability and inhibit its degradation [45]. Recently, the lncRNA LOC100506114 was found to be overexpressed in the tumor stroma, indicating that expression is driven by mesenchymal cells. Subsequently, increased expression of LOC100506114 was found in CAFs isolated from patients in comparison with NF [46]. Furthermore, functional analysis on tumor cells co-cultured with CAF-conditioned medium determined the increase in migration, proliferation, and expression of mesenchymal markers. Briefly, the studies showed that growth differentiation factor 10 (GDF10) promotes the functional transformation of an NF to a CAF via LOC100506114 that binds to the transcription factor RUNX2, which, in turn, participates in tumor growth, invasion, and metastasis [46]. Some lncRNAs participate in the regulation of glucose metabolism of oral CAFs, as reported by Yang et al., where lncRNA H19 was identified to modulate glucose metabolism [47]. When its expression is suppressed, it decreases glucose uptake and lactate secretion. It also regulates fundamental processes such as proliferation and migration. It has been reported that H19 exerts sponge or precursor functions of various miRNAs. In this case, it was reported to be a precursor of Hsa-miR-675 that interacts with the PFKFB3 gene in the glycolysis pathway in oral CAFs [48]. Important processes such as angiogenesis and metastasis are regulated by lncRNAs in CAFs. However, they can result in a better or worse prognosis for patients, depending on the regulation at the gene level. For instance, patients who overexpress FOXF1 adjacent noncoding developmental regulatory RNA (FENDRR) have a better prognosis, because, when it is overexpressed, there is less migration in vitro and it also can regulate proangiogenic activity through the PI3K/AKT pathway [66]. Conversely, a lncRNA associated with a poor prognosis is the new one called TIRY, which indirectly regulates cancer cells due to the effect of CAF-conditioned medium on tumor cells, where it was reported that TIRY is upregulated and facilitates increased invasion, migration, and metastasis in addition to acting as a miRNA sponge of miR-14 and inducing activation of the WNT/b-catenin pathway resulting in increased EMT [49]. There are molecules secreted by CAFs that regulate the expression of lncRNAs in tumor cells as reported by Zhang et al., reporting that the Midkine molecule (MK) secreted by the tumor stroma regulates the expression of the lncRNA ANRIL and participates directly in the resistance to cisplatin, showing that CAF-conditioned medium in stimulated cancer cells induces cisplatin resistance, thus suggesting that the MK secreted by CAFs in a paracrine manner towards tumor cells regulates the resistance to cisplatin by inhibiting apoptosis [68]. The use of lncRNAs as therapeutic targets has gained relevance in recent years since they could act in response to chemotherapy. For instance, it has been reported that, when lncRNA IL7R is silenced and a TLR3 inhibitor is used, tumor cells are more sensitive to treatment and apoptosis increases in epithelial cells cocultured with CAFs, in addition to increasing the immune infiltrate with immune cells associated with a better prognosis such as dendritic cells and CD8+ lymphocytes [51]. In TME, tumor cells interact with other cell populations such as CAFs, endothelial cells, and cells of the immune system [33] through complex communication networks, enhancing tumor modulation of the microenvironment. Thus, TME plays an essential role in the initiation, tumor growth, invasion, and metastasis (Figure 1). In addition, the HNSCC TME is highly infiltrated by immune cells, which, depending on tumor biology, may mediate immune surveillance or evasion through various mechanisms [3]. Recently, increasing evidence has revealed that lncRNAs regulate the immune response in TME by controlling the type of cellular infiltration, differentiation, and functions of immune cells [32,69], which can suppress or favor the progression of cancer. Hence, the study of the involvement of immune-related lncRNAs on the evolution of HNSCC has gained importance. Recent studies have identified immune-related lncRNAs in HNSCC impacting the prognosis of patients. Using bioinformatic tools, Chen et al. selected seven immune-related lncRNAs associated with OS: AL139158.2, AL031985.3, AC104794.2, AC099343.3, AL357519.1, SBDSP1, and AC108010.1. With these lncRNAs, they built a prognostic signature and classified HNSCC patients as low- or high-risk. Furthermore, they identified that low-risk cases have a more significant infiltration of immune cells and enrichment of pathways associated with the immune response. In contrast, high-risk cases are related to the enrichment of metabolic pathways [70]. This result is consistent with previous reports that identified nine immune-related lncRNAs in nasopharyngeal carcinoma, where low-risk patients have active pathways associated with the immune response and a greater intratumoral infiltrate of CD8+ T cells and B cells. In contrast, in high-risk patients, there is an association with pathways involved in metabolism [71]. In the case of OSCC, previous research divided samples according to the expression of eight ferroptosis-related lncRNAs with implications in the prognosis. In the low-risk group, a significant decrease in AL512274.1, MIAT, and AC079921.2 was found, related to a more intense immune response compared with the high-risk group, where the expression of FIRRE, AC099850.3, and AC090246.1 increased [72]. Multiple reports relate the cases of HNSCC that present a better prognosis with an active immune response, which can be associated with an abundant infiltrate of immune cells [33]. However, there are tumor characteristics that can modify the expression of specific immune-related lncRNAs and, with this, induce an immunosuppressive TME. Mutations in the tumor suppressor genes TP53 and CDKN2A are frequent in HNSCC, and tumor cells that present these modifications can alter their pattern of expression of lncRNAs. At the same time, conditions such as hypoxia induce the expression of specific lncRNAs in immune cells. This crosstalk between tumor cells and immune cells induces the formation of an immunosuppressive TME [73]. On the other hand, it has been observed that there are lncRNAs expressed in tumor cells that promote immune activation. PRINS, an overexpressed lncRNA in some cases of HPV-positive HNSCC, is related to the activation of genes involved in the immune response. Among HPV-positive tumors, those with higher PRINS levels are associated with a better prognosis [52]. Due to this, research is focused on studying how lncRNAs regulate the differentiation and function of specific populations of immune cells in TME, which ultimately impacts tumor progression. Bioinformatic analysis of HNSCC samples has allowed the identification of various lncRNAs associated with genomic instability [74] or with the immune response [75,76] and related to the prognosis of patients for grouping the cases in low and high risk. Furthermore, since the inflammatory infiltrates in TME can exert a dual anti- or protumor function, the types of immune cell populations that infiltrate both groups of tumors have been studied. Different reports agree that in the low-risk group, there is a greater infiltrate of activated CD8+ T cells, activated CD4+ T cells, T follicular helper (T fh) cells [20,77], Treg cells [20,76], NK cells, B cells [74], and resting mast cells [76], as well as decreased numbers of M0 macrophages, activated mast cells [20], and CAFs [74]. In contrast, the high-risk group is characterized by increased eosinophil infiltration, naive CD4+ T cells, resting NK cells, M0 macrophages [76], M2 macrophages [78], activated mast cells [76], and CAFs [74], accompanied by the decrease in the expression of human leukocyte antigen (HLA) molecules [76] necessary to sustain the activation of the immune response. These findings show that the cell populations in low-risk cases are associated with an anti-tumor immune response, whereas an immunosuppressive microenvironment predominates in high-risk tumors. For a better understanding of the factors involved in the modulation of TME characteristics, the role played by some lncRNAs in the differentiation or polarization of immune cells has been studied. In LSCC, a considerable infiltrate of M2 macrophages plays a protumor role. HOTAIR is a lncRNA expressed in LSCC tumor cells and can be released into exosomes, which is related to M2 macrophage polarization via the downregulation of PTEN and the upregulation of PI3K and AKT expression. In in vitro analysis, the co-culture of M2 macrophages polarized with exosomes and LSCC cell lines increased the proliferation and migration of tumor cells. Interestingly, the in vivo injection of exosome-treated macrophages promoted an increase in tumor size, downregulation of the epithelial marker E-cadherin, and increased expression levels of the mesenchymal marker N-cadherin related to the EMT [54]. Regarding M1 macrophages, with bioinformatic analysis of databases, it has been identified that in OSCC, LINC00460 is positively correlated with this cell phenotype and CASC9 is negatively correlated; both have a strong correlation with the prognosis of patients [75]. Within the group of innate immune cells present in TME are neutrophils, the study of which has gained importance in recent years due to the impact that the functions of these cells have on tumor progression [79]. In an evaluation of the lncRNAs associated with NETosis (formation of neutrophil extracellular traps (NETs)), in HNSCC, it was found that low-risk patients present enrichment of pathways associated with the immune response. At the same time, high-risk cases correspond to cold tumors associated with NETosis activation. Among the lncRNAs identified, LINC00426 is a protective factor. When nasopharyngeal carcinoma cell lines are transfected with this lncRNA, its overexpression significantly increased the expression levels of p-STING, p-TBK1, and p-IRF3. In addition, activation of the STING signaling pathway promotes the secretion of cytokines necessary for the recruitment of T cells and B cells, such as CXCL10, CCL5, ISG15, and ISG56 [11]. MANCR is highly expressed in HNSCC tissue and cell lines, related to poor OS and disease-specific survival. In vitro, MANCR silencing inhibits the proliferation, migration, and invasion of HNSCC cell lines. However, in addition to acting as an oncogene, bioinformatic analyses have revealed that its expression is positively correlated with the infiltrate of neutrophils and γδ T cells but negatively with the presence of CD8+ T cells and B cells [55]. The type of inflammatory infiltrate is modulated by the cytokines secreted in the TME; this cytokine secretion can, in turn, be affected by the expression of lncRNAs. For example, BARX1-DT, KLHL7-DT, and LINC02154 are expressed in LSCC. These immune-related lncRNAs can promote an immunosuppressive TME by decreasing the expression of CCR3, CXCL9, and CXCL10, decreasing the recruitment of CD8+ T cells [56]. There are immune-related lncRNAs that, in addition to being involved with the secretion of cytokines, are also related to the expression of other molecules necessary to mount the antitumor immune response. For example, TRG-AS1 is expressed in warm tumors with a high infiltration of cytotoxic cells, related to a better prognosis. It has been shown in vitro that the silencing of TRG-AS1 in an OSCC cell line suppresses the expression of HLA-A, HLA-B, and HLA-C molecules necessary for antigen presentation, as well as CXCL9, CXCL10, and CXCL11 [32]. On the other hand, LINC02195 has high expression in the nucleus and cytoplasm of HNSCC cells, which is associated with a good prognosis as it has a positive correlation with the infiltrate of CD4+ T cells and CD8+ T cells, in addition to being involved in the expression of the MHC-I, antigen processing, and presentation [58]. The set of molecules that regulate the immune response includes costimulatory and coinhibitory molecules that are also targets for modification by lncRNAs. IFITM4P progressively increases its expression from premalignant lesions such as OL to OSCC, thus acting as an oncogene. In a murine model of carcinogenesis in the tongue, lipopolysaccharides (LPS) bind to its receptor TLR4, which induces an increase in the expression of IFITM4P, acceleration of the carcinogenesis process, and immune escape through overexpression of the PD-L1 immunoregulatory ligand. IFITM4P induces PD-L1 expression in two different ways. In the cytoplasm it acts as a scaffold for the recruitment of SASH1, which binds and phosphorylates TAK1; this increases NF-κB phosphorylation, which ultimately induces PD-L1 expression. In the nucleus, IFITM4P reduces the transcription of PTEN by increasing the binding of KDM5A to its promoter and, with this, it upregulates PD-L1. In contrast, the overexpression of IFITM4P increases the sensitivity to treatment with PD-1 mAb [59]. LncRNAs are associated with tumor immune evasion. LINC01123 is overexpressed in HNSCC tissue and cell lines, mainly in the cytoplasm of the cells, which, together with the overexpression of the immune checkpoint B7-H3, is associated with a poor prognosis by promoting tumor immune evasion. Furthermore, LINC01123 is competitively bound to miR-214-3p, and miR-214-3p, specifically targeting B7–H3; this inhibits CD8+ T cell activation and favors tumor progression. By silencing LINC01123 in HNSCC cell lines, the cytotoxic activity of CD8+ T cells increased, thereby decreasing tumorigenicity and increasing the secretion of factors associated with immune activation in vivo [60]. LncRNAs also participate in sculpting the TME and include the activation or inhibition of specific pathways. For example, LINC01355 is overexpressed in OSCC and is associated with antitumor evasion by inhibiting the activity of CD8+ T cells through activation of the Notch pathway. Conversely, by deleting LINC01355 in OSCC cells, apoptosis of CD8+ T cell is retrained, proliferation and cytolysis activity is enhanced, and tumor cell proliferation, migration, and invasion are decreased [61]. The expression of lncRNAs in TME immune system cells has been less studied. However, the reported evidence shows that they have an impact on tumor progression due to the bidirectional communication that exists between tumor cells and stromal cells. DCST1-AS1 is overexpressed in OSCC tumor cells and M2 macrophages. Silencing this lncRNA has been shown in vitro and in vivo to block NF-κB signaling, therefore repressing tumor cell emergence, migration, and invasion, as well as protumor M2 polarization of macrophages [62]. In the case of CRNDE, it is expressed in OSCC, mainly in advanced stages in tumor cells and tumor-infiltrating T lymphocytes (TILs). Its expression in cancer cells exerts a protumor function by sponging miR-545-5p, which leads to increased expression of the immune checkpoint TIM-3 and suppresses the cytotoxicity of CD8+ T cells by contributing to their depletion [63]. In a mouse model, injecting CD8+ T cells with CRNDE-knockdown decreases tumor size, increases the number of IFN-γ and TNF-α-producing CD8+ T cells, decreases TIM-3 expression, and increases miR expression -545-5p, activating the antitumor immune response of CD8+ T lymphocytes [63]. Finally, HOTTIP is a lncRNA expressed by HNSCC tumor cells and present in the exosomes of M1 macrophages. Although it has been associated with a protumor function, one study reported that exosomes from M1 macrophages, primarily through HOTTIP, inhibit HNSCC progression by activating the TLR5/NF-κB signaling pathway by competitively sponging miR-19a-3p and miR-19b-3p. In addition, they polarize circulating monocytes and TAMs toward an antitumor M1 phenotype, inducing positive feedback [65]. Despite significant advances in the treatment of HNSCC, the mortality rate remains around 50% [32]. It is essential to explore new therapeutic strategies to improve patients’ time and quality of life. Lately, immunotherapy has received rising attention in cancer treatment for the OS advantages it offers; however, the overall response rate to immunotherapy in patients with HNSCC is less than 20% [80]. The understanding of the molecular mechanisms that modify the characteristics of the TME can contribute to the detection of lncRNAs as novel biomarkers to provide new ideas for clinical diagnosis, immune-targeted therapy, and drug discovery [33]. For example, identifying that HOTTIP polarizes circulating monocytes towards an antitumor M1 phenotype and suppresses HNSCC progression through the upregulation of the TLR5/NF-κB signaling pathway may provide novel insight into HNSCC immunotherapy [65]. LncRNAs may function as potential therapeutic targets (Figure 2), as it has been reported that, when their lnc-IL7R function is suppressed, there is better sensitivity to chemotherapy in oral cancer cell lines [51]. The mechanism by which IFITM4P induces PD-L1 expression is known, so this lncRNA may serve as a new therapeutic target in the blockage of oral carcinogenesis [59]. However, for most of the lncRNAs that show alteration in expression in HNSCC, the exact mechanism by which TME conditions are modified remains to be unknown; for this reason, more studies are required to clarify this information to develop new therapeutic strategies [20]. Hereby, we present data that show that some immune-related lncRNAs have clinical relevance, since AL139158.2, AL031985.3, AC104794.2, AC099343.3, AL357519.1, SBDSP1 [70], and AC108010.1 TM4SF19-AS1 [39] have been associated with overall survival (OS). MANCR [55] is also related to poor OS and disease-specific survival. MiR31HG [35], TM4SF19-AS1 [39], and LINC01123 [60] are associated with poor prognosis. Meanwhile, LINC02195 [58] and TRG-AS1 [32] overexpression is associated with favorable prognosis. Moreover, ANRIL [68] lncRNA induces resistance to cisplatin by inhibiting apoptosis. A superior understanding of the molecular mechanisms of lncRNAs that modify the characteristics of TME could contribute to increasing the efficacy of immunotherapy. LncRNAs involved in TME are clinically relevant, being indicators of survival, acting in important processes such as chemoresistance and being indicators of prognosis. The study of lncRNAs in cancer can contribute to a better understanding of the molecular mechanisms that modify the characteristics of TME, allowing the detection of possible therapeutic targets and biomarkers that contribute to the best selection of patients who are candidates for immunotherapy, resulting in the increase in efficacy of this type of treatment in HNSCC.

PMC10000595

Alessia Garufi,Valerio D’Orazi,Giuseppa Pistritto,Mara Cirone,Gabriella D’Orazi

HIPK2 in Angiogenesis: A Promising Biomarker in Cancer Progression and in Angiogenic Diseases

02-03-2023

VEGF,HIF-1,hypoxia,micro-RNA,circular HIPK2,p53,cancer,diabetes,diabetic retinopathy,wound healing

Simple Summary Dysregulated angiogenesis contributes to cancer progression and to many chronic inflammatory diseases. Many efforts in the field of angiogenesis have been made to discover new potential molecular targets to be used as biomarkers or to improve the anti-angiogenic therapies. HIPK2, an oncosuppressor able to regulate multiple molecular pathways, has been shown lately to play a role in angiogenesis both in cancer and in other angiogenic diseases. Therefore, HIPK2 emerges as a potential new biomarker of angiogenic diseases. Abstract Angiogenesis is the formation of new blood capillaries taking place from preexisting functional vessels, a process that allows cells to cope with shortage of nutrients and low oxygen availability. Angiogenesis may be activated in several pathological diseases, from tumor growth and metastases formation to ischemic and inflammatory diseases. New insights into the mechanisms that regulate angiogenesis have been discovered in the last years, leading to the discovery of new therapeutic opportunities. However, in the case of cancer, their success may be limited by the occurrence of drug resistance, meaning that the road to optimize such treatments is still long. Homeodomain-interacting protein kinase 2 (HIPK2), a multifaceted protein that regulates different molecular pathways, is involved in the negative regulation of cancer growth, and may be considered a “bona fide” oncosuppressor molecule. In this review, we will discuss the emerging link between HIPK2 and angiogenesis and how the control of angiogenesis by HIPK2 impinges in the pathogenesis of several diseases, including cancer.

HIPK2 in Angiogenesis: A Promising Biomarker in Cancer Progression and in Angiogenic Diseases Dysregulated angiogenesis contributes to cancer progression and to many chronic inflammatory diseases. Many efforts in the field of angiogenesis have been made to discover new potential molecular targets to be used as biomarkers or to improve the anti-angiogenic therapies. HIPK2, an oncosuppressor able to regulate multiple molecular pathways, has been shown lately to play a role in angiogenesis both in cancer and in other angiogenic diseases. Therefore, HIPK2 emerges as a potential new biomarker of angiogenic diseases. Angiogenesis is the formation of new blood capillaries taking place from preexisting functional vessels, a process that allows cells to cope with shortage of nutrients and low oxygen availability. Angiogenesis may be activated in several pathological diseases, from tumor growth and metastases formation to ischemic and inflammatory diseases. New insights into the mechanisms that regulate angiogenesis have been discovered in the last years, leading to the discovery of new therapeutic opportunities. However, in the case of cancer, their success may be limited by the occurrence of drug resistance, meaning that the road to optimize such treatments is still long. Homeodomain-interacting protein kinase 2 (HIPK2), a multifaceted protein that regulates different molecular pathways, is involved in the negative regulation of cancer growth, and may be considered a “bona fide” oncosuppressor molecule. In this review, we will discuss the emerging link between HIPK2 and angiogenesis and how the control of angiogenesis by HIPK2 impinges in the pathogenesis of several diseases, including cancer. Angiogenesis is the formation of new blood capillaries taking place from preexisting functional vessels. In the adult, a physiologic vessels formation is transiently activated for tissue growth and regeneration during processes such as wound healing and the female reproductive cycle. However, angiogenesis may also have a pathologic role as it fuels inflammatory and malignant diseases [1]. Deregulation of the normal vessels’ growth is observed in many diseases including diabetic retinopathy, autoimmune diseases, rheumatoid arthritis, atherosclerosis, cerebral ischemia, cardiovascular diseases, psoriasis, and delayed wound healing [2]. In many solid cancers, angiogenesis is constantly activated by the “angiogenic switch” that causes normally quiescent vasculature to continually sprouts new vessels. In this way, angiogenesis helps to sustain expanding neoplastic growth. For that reason, angiogenesis is considered a hallmark of cancer progression [3,4]. The interaction between neoplastic cells by means of the angiogenic factors produced by them, and the newly formed vessels promotes the growth of solid tumors and the metastases formation, as well as the impairment of the efficacy of anticancer therapies [5]. Interestingly, other than the tumor cells, there are also stromal cells such as tumor-associated macrophages (TAM) which can produce the angiogenic factors that promote angiogenesis and metastasis [6]. Many reviews have extensively summarized the steps through which the vascular bed expands by sprouting and matures into a system of stable vessels in normal and pathological conditions; therefore, here the main molecules regulating angiogenesis will be only briefly described. Angiogenesis is regulated by the balance of many positive and negative factors released into the microenvironment [7,8]. The positive regulators include vascular endothelial growth factors (VEGFs); A, B, and C fibroblasts growth factors (FGFs); 1 and 2 platelet-derived growth factor (PDGF); hepatocyte growth factor (HGF); and angiopoietins, while the negative regulators include angiostatin, endostatin, thrombospondin, and interferons [8]. However, the most important mediator of angiogenesis is VEGFA, which acts on endothelial cells by binding two different receptors (R), namely VEGFR-1 and VEGFR-2 [9]. The binding of VEGF to its receptor activates the PI3K/Akt, MEK, or FAK signaling pathways leading to the expression of genes whose proteins induce vascular permeability, cell proliferation, and motility, thus promoting angiogenesis [10]. Since its discovery, VEGF has revolutionized the comprehension of the angiogenesis process in normal tissue development and in health conditions, as well as in the course of many diseases [11]. The targeting of VEGF is therefore a therapeutic approach of high interest and, so far, hundreds of thousands of patients have been treated with blockers of VEGF even if the limited therapeutic efficacy due to the activation of resistance mechanisms remains an outstanding problem [8,12]. A key driver of angiogenesis is the hypoxia-inducible factor-1 (HIF-1), a heterodimeric transcription factor that consists of two subunits: the oxygen-sensitive subunit HIF-1α (or its analogs HIF-2α and HIF-3α) that undergoes quick degradation under normoxic conditions and the constitutively expressed HIF-1β, also known as aryl hydrocarbon nuclear translocator (ARNT) [13,14]. Under hypoxia, HIF-1α is stabilized via several post-translational modifications involving hydroxylation, acetylation, and phosphorylation. Following activation, HIF-1α translocates into the nucleus to bind HIF-1β and induce the transcription of several target genes involved in many aspects of cancer progression including angiogenesis (e.g., VEGF, PDGFB), metabolic adaptation (e.g., GLUT1, PDK1), apoptosis resistance (e.g., Bcl-2, MDR), invasion, and metastasis (e.g., CXCR4, MMP9) [15,16]. When a tumor mass grows beyond 1–2 mm, it undergoes hypoxia because of the distance from the host microvasculature, which makes it difficult to efficiently supply the tumor with nutrients and oxygen. In order to survive to the hypoxia, tumors activate HIF-1 [16]. Other than hypoxia, many genetic alterations inactivating tumor suppressors or activating oncoproteins have been reported to increase the basal levels of HIF-1α in cancers and contribute to tumor progression and angiogenesis [17]. Homeodomain-interacting protein kinase-2 (HIPK2) is a serine/threonine kinase which belongs to a family that includes four members (HIPK1, HIPK2, HIPK3, and HIPK4) of corepressors for homeodomain transcription factors whose structures and functions have been extensively summarized (for a review, see references [18,19,20]). HIPK2 modulates the activity of many transcriptional regulators and chromatin modifiers and, depending on the cell context, it can repress or promote the gene transcription [21]. HIPK2 regulates the expression of several genes involved in cell development, cytokinesis, protein stability, apoptosis, and DNA damage response [22]. One of the most important targets of HIPK2 is the oncosuppressor p53 that is phosphorylated by HIPK2 in Serine 46 to specifically activate the p53 apoptotic function essential for the success of the anticancer therapies [23,24,25,26]. HIPK2 may also regulate p53-independent pathways and, for this reason, HIPK2 dysregulation is associated with neurological diseases and fibrosis other than with cancer progression [20,27]. Recently, a role for HIPK2 in angiogenesis has been pointed out, not only in cancer, but also in other angiogenic diseases, and will be summarized below. Our previous studies showed that HIPK2 binds, along with histone deacetylase 1 (HDAC1), to the HIF-1α gene promoter repressing the HIF-1-mediated transcription of many target genes including VEGF, therefore restraining tumor growth [28]. As a proof of principle, HIPK2 silencing with small interfering RNA upregulated HIF-1α in cancer cells, inducing a pseudohypoxic tumor phenotype in normoxic conditions, fueling tumor progression, and chemoresistance [28]. To examine whether the effect of HIPK2 on the modulation of HIF-1α/VEGF pathway was associated with endothelial cell sprouting, the growth of human umbilical vein endothelial cells (HUVEC) was evaluated in vitro in the presence of conditioned media (CM) derived from colon cancer cells depleted or not of the HIPK2 function, and the results confirmed that HIPK2 silencing increases tumor vascularity in vitro [28]. Interestingly, hypoxia-driven mechanisms lead to HIPK2 protein degradation [29]. Therefore, a regulatory loop exists between HIPK2 and HIF-1α that affects the multiple downstream molecular pathways, including p53 and VEGF, regulated by both proteins [30,31,32], impinging on tumor growth and angiogenesis and/or on tumor regression (Figure 1). Thus, HIPK2 silencing increases the xenograft tumor growth and the physiologic relevance was assessed by analyzing the HIPK2 gene expression in human specimens collected from patients with the familial adenomatous polyposis (FAP) and with sporadic colorectal cancer (CRC). HIPK2 mRNA levels were lower in sporadic CRC tissues compared to FAP tissues and the HIPK2 expression in human CRC inversely correlate with the staging of the tumors [33], although the molecular mechanisms leading to HIPK2 mRNA downregulation were not unveiled. In the attempt to target hypoxia and restrain tumor angiogenesis, we have shown that zinc chloride induces HIF-1α protein degradation and inhibits the HIF-1-induced transcription of VEGF and angiogenesis, in vitro and in animal studies [34]. In addition, zinc counteracts the hypoxia-induced HIPK2 deregulation restoring p53 apoptotic response to chemotherapy, underscoring the potential use of zinc supplementation in combination with chemotherapy to improve the efficacy of the anticancer treatments [35,36]. The balance between HIPK2 and HIF-1 in angiogenesis was recently confirmed in a study on hepatocellular carcinomas (HCC) [37]. In two independent patients’ cohorts with, respectively, 90 and 52 paired HCC and adjacent normal tissues, the authors analyzed using immunohistochemistry (IHC) of the expression levels of HIPK2 protein and found that they were lower in the cancer tissues compared to the adjacent normal tissues [37]. Studies performed in animal models showed that HIPK2 overexpression reduces tumor xenografts growth and metastasis formation. IHC analysis of the xenograft tumor tissues derived from Huh7 or BEL-7404 cancer cells, with or without HIPK2 overexpression, showed that HIPK2 downregulation significantly increases VEGFα level in the subcutaneous tumor and in the corresponding new blood vessels [37]. Following this, in vitro studies evaluated the tube formation of HUVEC cultured with the conditioned medium (CM) of HCC cells with or without HIPK2 overexpression in hypoxic condition. The tube formation was reduced when HUVEC were cultured with the medium derived from cells with HIPK2 overexpression compared with the medium derived from the control cells [37], strengthening the finding that HIPK2 may inhibit hypoxia-induced angiogenesis in the HCC tumor, as observed in the above reported study on CRC [28]. Mechanistically, the authors found that HIPK2 directly binds to and downregulates HIF-1α protein by inducing its proteasomal degradation as demonstrated by the use of the proteasome inhibitor MG132 [37]. To further evaluate the antiangiogenic role of HIPK2 in HCC samples, the authors analyzed data retrieved from the Gene Expression Omnibus (GEO) database. They found lower HIPK2 expression in both metastasis tissues and the primary lesions with metastasis compared to the primary lesions without metastasis. After this, by knocking out with the CRISPR-Cas9 system the HIF-1α and HIPK2 genes individually or simultaneously, the authors determined the in vivo tumor growth capacities of Hepa1-6 cells in a xenograft mice model. They observed enhanced in vivo tumor growth of the HIPK2−/− cells while the tumor cells with HIF-1α knockout grew significantly slower compared to the control tumors [37]. The double HIF-1α and HIPK2 knockout greatly counteracted the tumor growth caused by the HIPK2 knockout, suggesting that the effect of HIPK2 depletion on HCC progression was mediated by HIF-1α and by the HIF-1-induced angiogenesis [37], further strengthening the effect of the HIPK2/HIF-1α balance. Consistently, HIPK2 overexpression reduced the hypoxia-induced angiogenesis in vitro as well as the brain and bone metastasis of the highly metastatic HCC cell line CSQT-2 in a mouse model [37], thus connecting angiogenesis with metastasis [38]. The above reported findings suggest that the lower expression of HIPK2 in cancer tissues, compared to the normal ones, could serve as a novel biomarker of HCC progression due to the HIF-1-induced angiogenesis, although the mechanisms leading to HIPK2 downregulation (e.g., hypoxia or microRNAs) in HCC have not been elucidated and might deserve further studies. The findings also confirm the key role of angiogenesis in the HCC progression and metastasis and highlight how its targeting might represent an efficacious strategy in the clinical treatment of HCC [39]. Antiangiogenic drugs such as sorafenib and regorafenib or the multikinase inhibitors for VEGF receptors, PDGF receptors, and c-Kit, have been shown to be promising therapeutic agents against the HCC, although drug resistance may occur and contribute to the chemotherapeutic failure [40]. Therefore, uncovering novel molecular mechanisms driving angiogenesis in HCC could provide novel potential therapeutical strategies. In this regard, it is tempting to hypothesize that combined therapies including zinc supplementation could, on one hand, inhibit the HIF-1-induced angiogenesis and, on the other hand, restore the HIPK2/p53 antitumor axis, as previously shown [34,35,36]. Another mechanism that underscores the role of HIPK2 in tumor angiogenesis is the microRNA (miRNAs)-induced HIPK2 modulation [41]. miRNAs are non-coding single strand RNAs of about 19–25 nucleotides which bind to the 3′ untranslated region (3′UTR) of target mRNAs to inhibit the translation and induce degradation of the target mRNAs at the post-transcriptional level [42,43]. miRNAs can be included into exosomes, a type of extracellular vesicles that are secreted by many cell types and that contain, other than miRNAs, all the main biomolecules including lipids, proteins, circulating tumor DNA (ctDNA), messenger RNAs, and oncoproteins [44]. Tumor-derived exosomes (TEXs) perform intercellular transfer of components, locally and systemically, interacting with the surrounding cells in the tumor microenvironment. They are considered new players in tumor growth and invasion, tumor-associated angiogenesis, tissue inflammation, and immunologic remodeling [44]. In this regard, it has been found that patients with colorectal cancer (CRC) show high levels of circulating exosomal (exo) miR-1229 which correlated with tumor size, lymphatic metastasis, angiogenesis, and poor survival [45]. Mechanistically, exomiR-1229 targets the HIPK2 3′UTR. Thus, HIPK2 mRNA expression was found to be significantly downregulated in CRC tissues compared to the adjacent normal tissues [45], in agreement with the above-described study showing reduced HIPK2 mRNA expression in CRC tissues compared to the familial adenomatous polyposis (FAP) samples [33]. Circulating exomiR-1229 reduced the HIPK2 protein levels in HUVECs leading to upregulation of the downstream VEGFA, VEGFR1, and p-Akt, thereby stimulating angiogenesis [45]. Hu et al. showed that HIPK2 overexpression counteracts the exomiR-1229-induced upregulation of VEGFA, VEGFR1, and p-Akt, reducing both the extracellular release of VEGF and angiogenesis [45]. The authors identified in the VEGF promoter a potential binding site for myocyte enhancer factor (MEF)-2C, a transcription factor that regulates sprouting angiogenesis directly downstream from VEGFA [46], and demonstrated that the MEF2-mediated activation of VEGF luciferase reporter may be suppressed by HIPK2 [45]. In agreement, a previous study has shown that HIPK2 represses MEF2C-mediated transcriptional activation of VEGF and MMP10, regulating vascular morphogenesis [47]. The authors concluded that the CRC-secreted exomiR-1229 can induce tumor angiogenesis by blocking the HIPK2-mediated suppression of VEGF expression. Hence, lower HIPK2 mRNA or protein levels in CRC tissues compared to the adjacent normal ones can be considered potential novel prognostic biomarkers of CRC progression. In addition, high levels of circulating exomiR-1229, associated with HIPK2 downregulation, could be also considered a potential prognostic biomarker in addition to being a potential therapeutic target for inhibiting tumor angiogenesis in CRC [45]. Interestingly, exomiR-1229 was found to be upregulated in breast cancer and to trigger tumorigenesis by activating the Wnt/β-catenin pathway following targeting the key negative regulators of β-catenin such as glycogen synthase kinase (GSK)-3β, adenomatous polyposis coli (APC), and ICAT [48]. The Wnt/β-catenin pathway is an evolutionarily conserved cellular signaling system involved in different biologic processes such as organogenesis, tissue homeostasis, as well as in the pathogenesis of many human diseases [49]. The β-catenin transcription factor indeed induces the expression of several target genes involved in cell growth and angiogenesis including c-myc, cyclin D1, and VEGF [50]. The β-catenin transcription factor is strongly involved in the early and stepwise events of the colon tumorigenesis and an aberrant activation of the Wnt/β-catenin signaling has been linked to the progression of many other cancer types [51]. Interestingly, HIPK2 has been shown to phosphorylate and degrade β-catenin protein [52], therefore repressing the β-catenin-induced VEGF expression and tumorigenesis [53,54]. Hence, it is tempting to speculate that high levels of exomiR-1229 might induce tumor angiogenesis not only by blocking the HIPK2-mediated suppression of VEGF expression [45] but also by blocking the HIPK2-mediated inhibition of the β-catenin/VEGF pathway, although this latter hypothesis needs to be supported by further studies. Among the tumor-derived exosomes (TEXs), exomiR-1260b has been shown to target HIPK2 in HUVECs and promote angiogenesis, migration, invasion, and chemoresistance of non-small cell lung cancer (NSCLC) cells [55]. Although the authors did not unveil the molecular mechanisms leading to the increased angiogenesis by exomiR-1260b-induced HIPK2 downregulation, they found a relationship between miR-1260b and HIPK2 and its clinical meaning. They found an inverse correlation between miR-1260b and HIPK2 by analyzing 124 paired NSCLC tissues and adjacent noncancerous lung tissues using quantitative Reverse Transcription (qRT)-PCR. The expression levels of HIPK2 transcripts were significantly lower in NSCLC tissues compared to the corresponding noncancerous lung tissues, while miR-1260b expression was higher in NSCLC tissues compared to the noncancerous lung tissues [55]. Interestingly, HIPK2 downregulation and miR-1260b upregulation correlated with the presence of lymph node and distant metastasis, although the molecular mechanisms were not investigated. Further analyses showed that the level of exomiR-1260b was higher in the plasma of patients with NSCLC compared to that of healthy donors. In addition, Kaplan–Meier survival analysis showed that patients with high exomiR-1260b levels had worse overall survival rates than those with low exomiR-1260b levels [55]. These findings suggest an inverse association between miR-1260b and HIPK2 and underline the new role of low HIPK2 levels as a prognostic indicator or predictor of metastasis in NSCLC. In addition, high levels of exomiR-1260b, associated with HIPK2 downregulation, could be considered a potential prognostic biomarker and a therapeutic target to inhibit NSCLC progression. Interestingly, it has been shown that exomiR-1260b promotes cell invasion through the Wnt/β-catenin signaling pathway in lung adenocarcinoma [56]. Therefore, it can be hypothesized that exomiR-1260b-induced HIPK2 downregulation can consequently inhibit also the β-catenin signaling leading to angiogenesis and metastasis, as reported above for the exomiR-1229 [48]. miRNAs can be regulated by circular RNAs (circRNAs), a large family of non-coding (nc) RNAs which are produced by “back splicing” of primary transcripts, and are more stable in vivo because they are protected from exonuclease degradation [57,58]. Dysregulation of circRNAs is associated with the development of many diseases; hence, they are considered potentially useful biomarkers [59]. In this regard, high expression of circHIPK2 was found in cisplatin (DDP)-resistant NSCLC cells and tissues [60]. Bioinformatic analyses predicted that miR-1249–3p was the downstream target of circHIPK2, and the authors found that miR-1249–3p was indeed downregulated in NSCLC tissues and cells [60]. The VEGFA expression positively correlated with circHIPK2 while negatively correlating with miR-1249–3p expression, as assessed by tumor xenograft studies. The authors showed that miR-1249–3p is a regulator of VEGFA expression and that VEGF was responsible of induction of angiogenesis and resistance to cisplatin. At the biological level, circHIPK2 silencing in lung cancer A549-DDP-resistant cells reduced their proliferation and inhibited the tube formation of HUVEC, leading to reduced tumor growth in vivo [60]. They concluded that circHIPK2 has the malignant property to induce angiogenesis in NSCLC via miR-1249–3p/VEGF axis [60]. High levels of circHIPK2 are starting to be found in a few other tumors and are being associated with increased tumor progression, although angiogenesis was not always analyzed in those studies [61,62,63]. A high level of circHIPK2 has been found in CRC tissues compared to the adjacent normal tissue, and has been associated with lower overall and disease-free survival rate [64]. CircHIPK2 has been found remarkably upregulated in nasopharyngeal carcinoma (NPC) tissues [65]. In vitro and in vivo studies in animal models showed that circHIPK2 promotes proliferation of NPC cells while knockdown of circHIPK2 dampens the growth of NPC cells [65]. Mechanistically, circHIPK2 downregulated HIPK2 at the protein levels and consequently increased the β-catenin protein expression. Hence, high levels of circHIPK2 have potential clinical significance in CRC and NPC progression; therefore, analysis of circHIPK2 may be worth of further studies also in other tumor types. The biological consequences of miRNAs-induced HIPK2 targeting and of the high circHIPK2 levels in tumors are summarized in Table 1. Gestational hypertension is the second leading cause of maternal death in developed countries [66]. Angiogenesis plays a role in gestational hypertension through upregulation of angiopoietin-1 (ANG-1) or activation of the renin angiotensin system (RAS) that causes high circulating levels of angiotensin-II (ANG-II) [67]. HIPK2 has been shown to play a role in angiogenesis of a model of gestational hypertension induced by hypoxia and reoxygenation (H/R). Human placental microvascular endothelial cells (HPMECs) undergoing H/R showed downregulation of miR-100-5p along with reduced concentrations of ANG-1 and ANG-2 and reduced VEGFA, TGF-β, and PLGF protein levels that correlated with reduced viability and angiogenesis of HPMECs [66]. Rescue assays showed that miR-100-5p overexpression promoted HPMECs viability and angiogenesis restoring the levels of ANG-1, ANG-2, VEGFA, TGF-β, and PLGF inhibited by H/R [68]. Interestingly, miR-100-5p overexpression significantly downregulated the expression levels of HIPK2 in HPMECs and, indeed, HIPK2 was found to targeted and negatively modulated by miR-110-5p [68]. The authors showed that HIPK2 overexpression decreases the expression of VEGFA and TGF-β while increases the expression of anti-angiogenetic proteins (e.g., sFLT1 and sENG). Such overexpression reversed the effect induced by overexpression of miR-100-5p in terms of viability and angiogenesis in HPMECs exposed to the H/R. Mechanistically, miR-100-5p-induced HIPK2 downregulation led to the activation of the PI3K/AKT signaling pathway and such activation was reversed by HIPK2 overexpression [68]. A link between HIPK2 and PI3K/AKT has been previously suggested. In that study, the authors found that the oncogene SPEN induces miR-4652-3p expression in nasopharyngeal carcinoma (NPC) by activation of the PI3K/AKT/c-JUN signaling and that miR-4652-3p targets and downregulates HIPK2 [69]. The PI3K/AKT inhibitor LY294002 counteracted the increase in HPMEC viability and angiogenesis induced by miR-100-5p overexpression, an effect that was further strengthened by HIPK2 overexpression [68]. Given the lack of effective therapies against pregnancy-induced hypertension, the discovery of new potential therapeutic targets such as the miR-100-5p could help to reduce the overall risk of cardiovascular, cerebrovascular, kidney diseases, and diabetes during pregnancy. Another complication that may occur during pregnancy is the increased risk of bronchopulmonary dysplasia (BPD) promoted by smoking [70]. In a mouse model of gestational exposure to sidestream cigarette smoke (SS), the BPD-like condition correlated with impaired angiogenesis, suppression of VEGF, and increase in the alveolar cells’ apoptosis [71]. Mechanistically, gestational SS inhibited HIF-1α and increased pro-apoptotic factors including HIPK2 [71], in line with the role of HIF-1α in inducing the HIPK2 degradation, an interplay that is known to affect both apoptosis and angiogenesis [31]. Among the miRNAs that regulate angiogenesis there is miR-126 that has been shown to directly repress the negative regulators of the VEGF pathway, including the Sprouty-related protein SPRED1 and the phosphoinositol-3 kinase regulatory subunit 2 (PIK3R2) [72]. More recently, miR-126-5p was investigated in a model of myocardial infarction (MI), the most common cardiovascular disease in which hypoxia induces endothelial injury [73]. The authors found that miR-126-5p was upregulated in hypoxia-treated HUVECs undergoing oxidative stress and apoptosis, an effect that was counteracted by inhibiting miR-126-5p via negative regulation of HIPK2 which was predicted as a target of miR-126-5p. However, the molecular mechanisms of miR-126-5p-induced HIPK2 regulation were not investigated in this study, neither was the ability of HUVEC to undergo angiogenesis [74]. The biological consequences of miRNAs-induced HIPK2 targeting in angiogenic diseases are summarized below in Table 2. Diabetic retinopathy (DR) is the primary cause of blindness in the world. It is a complication of diabetes characterized by hyperglycemia that damages retina [75,76] and has limited treatments options [77]. A key role in the vascular complications of DR has been described for several classes of non-coding RNA including miRNAs [78]. It has been shown that miR-4235-5p can increase proliferation, migration, and angiogenesis of retinal endothelial cells (RECs) cultured in high glucose (HG) condition [79]. In agreement, elevated miR-423-5p levels were found to be present in the plasma of DR patients [79]. RECs cultured in HG showed E2F1-dependent miR-423-5p upregulation that was responsible of HIPK2 downregulation and of HIF-1α and VEGF upregulation [79]. Knockdown of E2F1 or miR-423-5p suppressed the HG-induced angiogenesis and restored the HIPK2 levels [79]. In vivo studies in a mouse model of streptozotocin (STZ)-induced diabetes confirmed that VEGF was upregulated in the retina in correlation with the upregulation of E2F1 and miR-423-5p and the downregulation of HIPK2 [79]. These data suggest that HIPK2 acts as a suppressor of angiogenesis in DR, likely through downregulation of the HIF-1α/VEGF axis, a role played also in angiogenesis during cancer development [31]. The above reported study shed some light into the mechanisms driving DR progression and identified promising biomarkers, such as low HIPK2 levels, and potential targets, such as elevated miR-423-5p levels in the plasma, to predict the disease progression and to eventually design novel therapeutic strategies. The low expression of the HIPK2 levels in DR is also in agreement with our recent study showing that hyperglycemia triggers HIPK2 degradation via HIF-1, increasing tumor progression [80,81,82,83]. Another frequent complication of hyperglycemia is the diabetic foot ulcer [84,85], a consequence of neurological disorders and peripheral vascular complications due to impaired angiogenesis that leads to reduced wound healing and increased risk of infections [86]. It has been previously shown that endothelial progenitor cell-derived exosomes containing miR-221-3p alleviate diabetic ulcers improving wound healing [87]. Subsequently, it has been shown that HG inhibited HUVEC migration and capillary formation, effects that could be reversed by treatment with miR-221-3p that promoted angiogenesis and improved the wound healing [85]. The authors of this study also found an increased expression of HIPK2 in skin tissues of diabetic mice when compared to normal ones, as well as in HG-cultured HUVECs [88]. This is in agreement with a finding showing that HG, by downregulating Siah1, increases HIPK2 expression in glomerular mesangial cells of a mouse model of diabetic nephropathy [89]. HIPK2 inhibition with small interfering (si) RNA rescued HUVEC migration and tube formation under HG condition, while it did not affect HUVEC migration and tube formation in normal metabolic condition [88]. HIPK2 was found to be targeted and negatively regulated by miR-221-3p. Subcutaneous injection of miR-221-3p agomir (which upregulates miRNA activity) into diabetic mice, suppressed HIPK2 expression in wound margin tissues and promoted wound healing [88]. These findings indicate that HG condition reduces angiogenesis and impairs wound healing, effects that correlated with the increased expression of HIPK2. Although the authors did not elucidate the molecular mechanisms through which miR-221-3p/HIPK2 may affect angiogenesis in diabetic condition, they suggest that miR-221-3p analogs may be potentially useful for treating diabetic foot ulcers and for improving wound healing [88]. The biological consequences of miRNAs-induced HIPK2 targeting in angiogenic diseases are summarized in Table 2. The studies performed in more than twenty years since its discovery have depicted HIPK2 as a central hub in a molecular network that controls several signaling pathways involved in cell death and proliferation and that restrain tumor growth. In this scenario, HIPK2 downregulation by hypoxia-driven mechanisms plays a key role in inducing tumor angiogenesis and solid tumor progression. The role of HIPK2 in restraining tumor angiogenesis has been strengthened by several studies also showing that miRNAs may induce HIPK2 downregulation. Based on these findings, mostly obtained in pre-clinical studies, we can hypothesize that low HIPK2 mRNA or protein levels in cancer tissues compared to the adjacent normal ones can be considered a potential novel prognostic biomarkers of cancer progression, especially if correlated with increased angiogenesis. Interestingly, HIPK2 downregulation by some miRNAs has been shown to be involved in diabetic retinopathy, diabetic wound ulcer, and gestational hypertension, by limiting HIF-1-induced VEGF, and/or β-catenin-induced-VEGF or by activating p53. Defining the molecular basis of angiogenic disorders in greater detail may provide new avenues to improve the prognosis of angiogenic diseases including cancer and to develop more tailored therapeutic strategies. In this regard, the lower expression of HIPK2 in angiogenic tissues compared to the normal ones could become a novel biomarker of angiogenic diseases that deserves to be supported by further studies.

PMC10000606

Prajish Iyer,Lili Wang

Emerging Therapies in CLL in the Era of Precision Medicine

03-03-2023

CLL,emerging therapies,metabolism,splicing,whole-exome,transcriptome

Simple Summary Despite being a slow-proliferating disease, chronic lymphocytic leukemia (CLL) is an incurable and frequently reoccurring adult leukemia. Although large-scale genome-wide next-generation sequencing studies have provided insights into CLL’s transcriptome and mutational landscape, the molecular mechanisms underlying disease progression remain incompletely understood. Over time, the treatment landscape in CLL has shifted from chemoimmunotherapies (CIT) to targeted therapies, but resistance mechanisms have emerged, leading to progression such as Richter’s transformation (RT). As a result, there remains an unmet clinical need to identify new therapeutic strategies. In our review article, we aim to evaluate the past and current state of CLL treatment in both frontline and relapsed/refractory settings and also explore mitochondrial reprogramming, metabolic alterations, and RNA splicing as potential novel therapeutic targets. Abstract Over the past decade, the treatment landscape of CLL has vastly changed from the conventional FC (fludarabine and cyclophosphamide) and FCR (FC with rituximab) chemotherapies to targeted therapies, including inhibitors of Bruton tyrosine kinase (BTK) and phosphatidylinositol 3-kinase (PI3K) as well as inhibitors of BCL2. These treatment options dramatically improved clinical outcomes; however, not all patients respond well to these therapies, especially high-risk patients. Clinical trials of immune checkpoint inhibitors (PD-1, CTLA4) and chimeric antigen receptor T (CAR T) or NK (CAR NK) cell treatment have shown some efficacy; still, long-term outcomes and safety issues have yet to be determined. CLL remains an incurable disease. Thus, there are unmet needs to discover new molecular pathways with targeted or combination therapies to cure the disease. Large-scale genome-wide whole-exome and whole-genome sequencing studies have discovered genetic alterations associated with disease progression, refined the prognostic markers in CLL, identified mutations underlying drug resistance, and pointed out critical targets to treat the disease. More recently, transcriptome and proteome landscape characterization further stratified the disease and revealed novel therapeutic targets in CLL. In this review, we briefly summarize the past and present available single or combination therapies, focusing on potential emerging therapies to address the unmet clinical needs in CLL.

Emerging Therapies in CLL in the Era of Precision Medicine Despite being a slow-proliferating disease, chronic lymphocytic leukemia (CLL) is an incurable and frequently reoccurring adult leukemia. Although large-scale genome-wide next-generation sequencing studies have provided insights into CLL’s transcriptome and mutational landscape, the molecular mechanisms underlying disease progression remain incompletely understood. Over time, the treatment landscape in CLL has shifted from chemoimmunotherapies (CIT) to targeted therapies, but resistance mechanisms have emerged, leading to progression such as Richter’s transformation (RT). As a result, there remains an unmet clinical need to identify new therapeutic strategies. In our review article, we aim to evaluate the past and current state of CLL treatment in both frontline and relapsed/refractory settings and also explore mitochondrial reprogramming, metabolic alterations, and RNA splicing as potential novel therapeutic targets. Over the past decade, the treatment landscape of CLL has vastly changed from the conventional FC (fludarabine and cyclophosphamide) and FCR (FC with rituximab) chemotherapies to targeted therapies, including inhibitors of Bruton tyrosine kinase (BTK) and phosphatidylinositol 3-kinase (PI3K) as well as inhibitors of BCL2. These treatment options dramatically improved clinical outcomes; however, not all patients respond well to these therapies, especially high-risk patients. Clinical trials of immune checkpoint inhibitors (PD-1, CTLA4) and chimeric antigen receptor T (CAR T) or NK (CAR NK) cell treatment have shown some efficacy; still, long-term outcomes and safety issues have yet to be determined. CLL remains an incurable disease. Thus, there are unmet needs to discover new molecular pathways with targeted or combination therapies to cure the disease. Large-scale genome-wide whole-exome and whole-genome sequencing studies have discovered genetic alterations associated with disease progression, refined the prognostic markers in CLL, identified mutations underlying drug resistance, and pointed out critical targets to treat the disease. More recently, transcriptome and proteome landscape characterization further stratified the disease and revealed novel therapeutic targets in CLL. In this review, we briefly summarize the past and present available single or combination therapies, focusing on potential emerging therapies to address the unmet clinical needs in CLL. CLL is one of the most common forms of adult leukemia in the western world, characterized by the accumulation of CD19+CD5+ cells in bone marrow, lymph nodes, the spleen, and peripheral blood [1]. Typically, CLL occurs in the older age group with a median age at diagnosis of 72. In 2020, it was estimated that 21,040 new cases and 4060 deaths in the USA per year [2]. Despite all the treatment advancements in the past decade, CLL remains incurable. Approximately 10% of patients progress to an aggressive form of lymphoma called RT, and at least 20% develop chemorefractory disease or resistance to targeted therapies [3]. Thus, novel treatment options are still needed to cure this disease. In the past decade, large-scale genome-wide whole-exome and whole-genome sequencing studies of primary CLL samples have revealed the mutational landscape of CLL and its vast genetic heterogeneity [4,5,6]. Integration of somatic mutations and clinical annotation enabled the identification of genetic drivers and the improvement of the prognostication of CLL patients. Recurrent alterations have been identified in genes related to significant pathways such as RNA splicing and metabolism (SF3B1, U1, RPS15, DDX3X), DNA damage (ATM, TP53), MAPK-ERK (KRAS, BRAF, NRAS), B-cell receptor and Toll-like receptor signaling (MYD88, PAX5, BCOR, IKZF3), the cell cycle (CDKN1B, CDKN2A), NF-KB signaling (BIRC3, TRAF2, TRAF3), and chromatin modification (CHD2, SETD2, KMT2D, and ASXL1) [4,7,8,9,10,11]. Genetic heterogeneity functions as a fuel for clonal evolution and is implicated in disease progression and poor prognosis [12]. More in-depth studies uncovered that genetic heterogeneity is influenced by the cell of origin; for example, some mutations appear majorly in immunoglobulin heavy chain variable region gene (IGHV) unmutated CLL (U-CLL) (U1snRNA, NOTCH1, POT1), whereas others present predominantly in IGHV mutated CLL (M-CLL)(MYD88, PAX5), in the presence of sub-clonal mutations that are acquired during the disease evolution, according to the age of CLL patients (young patients have MYD88 mutations) [13,14], or according to the course of the disease (MYC amplification is acquired during transformation to aggressive lymphoma). Identification of mutations and copy number variations on independent cohorts of patients undergoing targeted therapies will unravel the predictive value of CLL management and develop personalized treatments to combat disease resistance and improve progression-free survival (PFS) outcomes. Studies of CLL biology have also revealed novel aspects of this disease. Splicing factor mutations were identified in ~20% of CLL samples, and splicing dysregulation is reviewed as a characteristic feature of CLL [11,15,16]. Targeting of RNA splicing dysregulation was explored recently [16]. Metabolic reprogramming is a hallmark of cancer and underlies disease progression and relapse. In the past few years, various studies have uncovered glucose, lipid, and glutamine metabolic dependencies in CLL samples [17,18,19]. However, the exploitation of metabolic dependencies in clinical settings is still minimal. This review summarizes past, present, and emerging therapies to combat CLL in frontline and relapsed settings. Observation without cytotoxic therapy is usually the approach for asymptomatic patients in earlier stages of CLL (Rai stage 0), while therapy is recommended for those with symptomatic CLL [20]. For over a decade, chlorambucil, a purine analog, has been the preferred treatment for CLL patients. From 1997 to 2006, the German CLL study group conducted several trials to improve the survival of CLL patients [21,22,23]. The CLL5 trial demonstrated that older patients did not benefit from first-line therapy with fludarabine, as their median overall survival (OS) was 46 months, whereas it was 64 months with chlorambucil. Nonetheless, fludarabine is more effective than chlorambucil [23]. The CLL4 trial revealed that combining fludarabine with cyclophosphamide enhanced the quality and duration of response in younger patients under the age of 65 when compared to fludarabine alone [21]. The FCR300 trial (fludarabine-cyclophosphamide combined with the CD20-monoclonal antibody, rituximab) showed an overall response rate (ORR) of 95% with a complete remission (CR) of 72% and a PFS of 6 years [24]. Long-term outcomes from the FCR300 trial showed that one-half of the patients who had mutated IGHV and received FCR achieved negative status for minimal residual disease (MRD) with a PFS rate of 79.8 at 12.8 years [20]. Overall, in patients with or without mutated IGHV, the PFS rate at 12.8 years was 53% and 8%, respectively [25]. In phase III of the ECOG-ACRIN 1912 clinical trial, 529 treatment-naïve CLL patients aged 70 years were randomly assigned to receive ibrutinib-rituximab (IR)/six cycles of FCR [26]. With a median follow-up of 70 months, the results showed superior PFS with IR compared to FCR in patients with mutated IGHV (HR, 0.27; 95% CI, 0.11–0.62). Of note, ibrutinib treatment was mainly discontinued in one out of five patients due to grade three adverse events, and 60% of patients were randomized to receive ibrutinib-based therapy with approximately six years of follow-up [26]. FCR is also associated with side effects such as myelosuppression and infections. As a result, FCR therapy is not very well-tolerated in older patients aged >65 years. A phase III CLL10 trial showed that bendamustine and rituximab (BR) are tolerable in older patients with comparable efficacy. The results showed that FCR was superior to BR in terms of PFS at 55.2 months in the FCR arm vs. 41.7 months in the BR arm; however, patients > 65 years had increased infections and cytopenias in the FCR arm [27]. For over a decade, monoclonal antibodies, such as CD20-targeting rituximab and ofatumumab or CD52-targeting alemtuzumab, have been available for CLL patients. Ofatumumab has been shown to bind a specific epitope in CD20 [28], improving complement-dependent cytotoxicity over rituximab. In the PROLONG trial, single-agent ofatumumab improved the PFS and remission in relapsed patients when given maintenance therapy compared to the current standard of care (observation) [29]. Combining ofatumumab with FC or pentostatin and cyclophosphamide has shown equivalent efficacy to FCR [30,31]. Another monoclonal CD52-targeting antibody, alemtuzumab, has demonstrated superiority over chlorambucil in the frontline setting in CAM 307 trials [32]. A combination of alemtuzumab with FCR was tested in 60 high-risk CLL patients as a frontline treatment and showed an OR of 92% of CR 3 grade and grade 3–4 myelotoxicity [33]. Due to significant toxicities such as immunosuppression, cytokine storm, opportunistic infections, and neutropenia, alemtuzumab has limited use in CLL. Recently, a novel monoclonal CD20 antibody called obinutuzumab, humanized glycoengineered (lack of sugar moiety in the Fc region) antibody, has been added to the armamentarium [34]. In the GAUGUIN monotherapy trial, obinutuzumab (Obi) demonstrated an ORR of 62% in phase I and 30% in phase II [34]. In the phase III CLL11 trial conducted by GCLLSG, which involved 781 previously untreated patients with a cumulative illness rating scale score of >6, patients were divided into three arms: obinutuzumab + chlorambucil (Obi-Chl, arm I) compared to rituximab + chlorambucil (Rix-Chl, arm II) and chlorambucil alone (arm III). The median PFS was 26.7 months in arm I compared to 11.1 months in arm III and 16.3 months in arm II (p < 0.001). Overall, there was an improvement in ORR, PFS (p < 0.001), and OS (p = 0.002) in arm I as compared to arm III. Additionally, there was an improvement in PFS (p > 0.001) and CR rates (20.7% vs. 7%) when comparing arm I with arm II. Obi has improved efficacy in older patients with coexisting conditions [35]. Based on the results of the CLL11 trial, Obi has been approved by the FDA with chlorambucil for treating previously untreated patients with comorbidities. The GAIA (CLL13) study assessed the effectiveness and safety of three frontline treatments consisting of venetoclax (BCL2 inhibitor) combined with CD20 antibody compared to standard CIT for patients with CLL who were fit and without the TP53 mutation/deletion. The study followed patients for a median of 38.8 months and found that the median PFS was not reached in patients treated with venetoclax (Ven), obinutuzumab (Obi), and ibrutinib (Ven-Obi-Ibr, HR = 0.32) or Ven and Obi (Ven-Obi, HR = 0.42). By contrast, the median PFS for standard chemotherapy was 52 months. The combination of Ven and rituximab had a similar PFS to CIT. Ven-Obi-Ibr reduced the risk of CLL progression by 68%, and Ven-Obi reduced the risk by 58%. The rates of three-year PFS were 90.5% and 87.7% for Ven-Obi-Ibr and Ven-Obi, respectively. The study also found that patients treated with venetoclax-based regimens had higher rates of MRD negativity. However, overall survival rates were similar across all treatment arms. The CLL14 randomized phase III trial evaluated the combination of Obi with Ven, particularly in elderly patients with coexisting conditions who cannot tolerate intensive CIT such as FCR [36]. About 432 patients were enrolled, and patients were randomized to either 12 cycles of chlorambucil/obinutuzumab (Clb-Obi) or 12 cycles of Ven-Obi, and the main objective was improving PFS. Overall, patients treated with Ven-Obi showed longer PFS compared to those treated with Clb-Obi and had undetectable minimal residual disease (uMRD) levels of 76% at the end of treatment. The adverse events were minimal in both arms, and patients treated with Ven-Obi demonstrated improved global health status, insomnia, fatigue, and quality of life [36]. The BCR is composed of a surface immunoglobulin (Ig) molecule non-covalently associated with an Ig-α and Ig-β (CD79a/CD79b) (Figure 1). In normal B-cells, antigenic stimulation leads to signalosome formation, a complex of scaffold proteins and kinases tethered at the plasma membrane at the sites of sIg activation [37]. The antigenic binding leads to the phosphorylation of SRC family kinase LYN, which is further followed by a series of kinases such as SYK, which further recruit B-cell linker protein (BLNK) and other adaptor molecules. This signalosome complex activates phospholipase C-γ2 (PLC-γ2), and Ras. Ras binds to and leads to the activation of Raf, which activates ERK (Extracellular Regulated Kinase). PLC-γ2 activation releases intracellular calcium, which activates PKC (Protein Kinase C). PKC activation leads to subsequent activation of various kinases—MAPK (Mitogen-activated Protein Kinases), c-JUN NH2-terminal kinase (JNK), and p38 MAPK and transcription factors such as MYC and NF-κB. BCR is negatively regulated by molecules such as CD22, CD5, CD72, and FcγRIIB that control the duration and intensity of the signaling [38]. These negative regulators contain immunoreceptor tyrosine-based inhibitory motifs [37,39]. Although CLL cells express low levels of surface immunoglobulin [40,41], these cells were found to be driven by antigen-independent autonomous signaling for growth and proliferation, which is dependent on the unique heavy-chain complementarity-determining region (CDR) and an internal epitope of the BCR [42]. It is well-known that a striking aspect of CLL is that the immunoglobin heavy chain (IgVH) and immunoglobin light chain (IgVL) have a very limited repertoire with similar gene rearrangements. Approximately one-third of CLL patients carry quasi-identical BCR sequences that can be classified into stereotyped BCR subsets based on CDR structure. These findings support the idea that tonic BCR signaling is a critical pathogenic mechanism driving CLL [43]. In addition, a strong correlation between BCR signaling and IGHV mutation status was observed [44]. Strong down-modulation of BCR signaling is observed in M-CLL due to a lack of sIgM expression. Less dramatic reduction of sIgM in U-CLL leads to partial activation of downstream pathways. In addition to IgM modulation, other factors also contribute to BCR signaling. ZAP70 expression correlates with BCR signaling, and its overexpression augments BCR signaling capacity [45]. In CLL, ZAP70 overexpression is associated with poor clinical outcomes and expresses unmutated IgHV [46]. Additional evidence suggests that ZAP70 may modulate cell migration-associated pathways. CD38 is another prognostic indicator whose expression correlates with sIgM signaling capacity [47]. Reports indicate that CD38–CD31 interactions contribute to cell migration and homing, enhancing CLL survival via inducing BCL2 and BCL XL [48,49]. Overall, BCR signaling is essential for CLL pathogenesis; thus, targeting this pathway is vital for treating CLL patients. Targeted tyrosine kinase inhibitors have been of particular interest in treating CLL due to the success of using tyrosine kinase inhibitors in CML. Dasatinib, which has been used to treat CML and Ph+ ALL acute lymphoblastic leukemia (ALL), can also inhibit the Src family of kinases, including Lyn, which is often dysregulated in CLL B-cells. Dasatinib induces apoptosis in CLL B-cells in vitro, with U-CLL cells being more sensitive [50], and has also been shown to sensitize tumor cells to chlorambucil and fludarabine to overcome CD40-mediated drug resistance in vitro [51]. A phase II study of dasatinib in high-risk relapsed or refractory CLL patients has shown a partial response with side effects of myelosuppression in two-thirds of the cases [52]. A recent in vitro study with 53 CLL patient samples treated with dasatinib showed that 17.7% of samples were apoptotic, indicating that dasatinib has anti-leukemic effects [53]; however, clinical use of dasatinib has been unclear. Ibrutinib, a covalent inhibitor for Bruton’s tyrosine kinase (BTK), was approved by the FDA as one of the first inhibitors for treating relapsed or refractory (R/R) CLL after showing positive results in the RESONATE trial, a randomized phase III trial that compared ibrutinib with a single agent ofatumumab in R/R CLL patients [54]. The 18-month PFS was similar regardless of the genetic mutations, including NOTCH1, BIRC3, and ATM, or other factors such as del(17p) and del(11q) [54]. In addition, a phase Ib/II single-arm trial compared ibrutinib with single-agent PCYC-1102 treatment, where patients with R/R and TN settings were treated with ibrutinib as a single agent. A pooled analysis from the RESONATE and PCYC-1102 trials showed that patients who received ibrutinib and no prior treatment or bulky disease (lymph node ≥ 5 cm) achieved a CR. Another RESONATE-2 trial compared ibrutinib with chlorambucil in 269 treatment-naïve older (65+) patients with CLL/SLL, where ibrutinib showed large improvements at ORR [55]. RESONATE-17 was a single-arm study of ibrutinib in 144 R/R CLL or SLL del(17p). The results showed that the 24-month PFS, OS, and ORR were 63%, 75%, and 83%, respectively, after a median follow-up of 11.5 months [56]. Notably, complex karyotype (CK) may be a strong predictor of inferior outcomes compared to del(17p) among patients with R/R CLL in the setting of ibrutinib therapy. The MD Anderson Cancer Center analyzed 88 patients with R/R CLL who received ibrutinib-based therapies between 2010 and 2013. Investigators found that only fludarabine refractoriness and complex karyotype were statistically significant prognostic factors associated with lower OS, with an HR of 6.9 and 5.9, respectively. Interestingly, del(17p) was not associated with a decreased OS [57]. In light of the success of ibrutinib, second-generation BTK inhibitors (BTKi) such as acalabrutinib and zanubrutinib have been developed aiming to reduce grade 2–4 adverse events, bleeding, and cardiac toxicities. In the ASCEND trial, a phase III trial focused on cytogenetic high-risk R/R patients, acalabrutinib demonstrated superior PFS compared to CIT and the PI3Kδ inhibitor idelalisib [58]. Furthermore, the ELEVATE-TN trial examined first-line treatment in elderly CLL patients with comorbidities, which showed that single-agent acalabrutinib or in combination with the anti-CD20 mAb obinutuzumab can prolong PFS [59]. In a recent ELEVATE-RR phase III-based clinical trial dedicated to R/R CLL patients, acalabrutinib treatment was non-inferior in terms of PFS (38.6 months) compared to ibrutinib (38.4 months PFS) and had an improved safety profile with fewer AF events [60]. Zanubrutinib has demonstrated an ORR of 85% in phase II clinical trials with treatment-naïve (TN) and R/R CLL with TP53 mutations [61]. In a randomized phase III controlled clinical trial, zanubrutinib single treatment was compared with ibrutinib and zanubrutinib in R/R CLL patients [62]. Zanubrutinib has shown a superior response rate and improved PFS with a lower rate of atrial fibrillation than ibrutinib [63]. Similar to other kinase inhibitors, resistance was developed by either acquired mutations in the BTK domain, such as mutations in the drug binding site (BTK Cys481) [64] PLCG2 mutations [65], or alterations, such as del(8p) and additional mutations in EP300, MLL2, and EIF2A. To overcome the BTKi resistance, pirtobrutinib has been developed as an orally available, highly selective reversible BTKi with equal potency against wildtype and Cys481 mutated BTK. It has shown to have an overall good safety profile with an ORR of 62% in phase I/II studies of R/R CLL patients [66]. Mechanisms for non-genetic events-related resistance were under investigation in CLL and other B-cell malignancies with candidate treatment options proposed [67,68]. The p110δ isoform of PI3K is responsible for transducing downstream signals of BCR signaling. Although idelalisib has been approved, many concerns exist regarding the toxicity and modest outcomes associated with treatment. In particular, compared to the relatively safer ibrutinib, the use of idelalisib in patients with CLL has been limited. Thus, it is critical to exercise a high degree of vigilance for these adverse events in patients receiving idelalisib. Idelalisib (IDEL) is a selective PI3Kδ inhibitor that promotes the apoptosis of B-cells. Activating the BCR signaling increases the calcium, which increases the diacylglycerol and IP3, thus activating the PI3 kinase pathway. Hence, treatment of CLL cells with PI3Kδ inhibitors causes a reduction in lymph node size and concomitant enhanced lymphocytosis, which can be mitigated using rituximab. In phase III, a randomized, double-blind, placebo-controlled trial (220 high-risk R/R CLL patients) of idelalisib + rituximab (IDEL-RIX) versus a placebo + rituximab, the IDEL-RIX arm had significantly improved ORR compared to the placebo arm (81% vs. 13% p < 0.001), as well as OS (92% vs. 80% at 12 months p = 0.02), irrespective of the presence of poor prognostic factors including del17p [69]. In this phase III trial, the adverse events were similar in both the idelalisib and placebo groups; in the idelalisib group, there were five common adverse events like fatigue, pyrexia, nausea, chills, and diarrhea. Grade 3–4 elevation of hepatic transaminases occurred more in the idelalisib group. Gastrointestinal and skin disorders led to discontinuation in the idelalisib group [70]. In another phase II trial involving 64 patients, IDEL-RIX showed an ORR of 97% with a CR of 19%. Despite its efficacies, grade 2–3 AEs (adverse events) such as transaminitis, rash, and diarrhea have been reported [71]. All these AEs are managed with a drug hold; hence, the US FDA approved idelalisib in combination with rituximab for high-risk R/R CLL patients. Duvelisib is an inhibitor that targets two forms of the phosphatidyl 3-kinase (PI3K) enzyme, specifically the PI3Kδ and γ forms, and has been used to manage high-risk CLL patients. In September 2018, the US FDA approved duvelisib for treating R/R CLL patients after having undergone two prior lines of therapy. In a phase I dose-escalation trial involving 52 R/R CLL patients and 15 older previously untreated patients with CLL, duvelisib showed an ORR of 48% with 89% nodal responses [72]. Currently, duvelisib is undergoing testing in a phase III trial in comparison with ofatumumab for relapsed CLL patients. In a randomized phase III DUO trial, duvelisib was compared with ofatumumab monotherapy in patients with R/R CLL. Duvelisib showed a significantly higher ORR (74%) than the ofatumumab arm (45%; p = 0.0001). The median PFS was 13.3 months in the duvelisib arm versus 9.9 months in ofatumumab, respectively, among all patients. The median PFS of del(17p) or patients with the TP53 mutation was 12.7 months on treatment with duvelisib, while the median survival of patients without such lesions was not reported [73]. The most common adverse events are diarrhea, neutropenia, pyrexia, nausea, anemia, and opportunistic infections such as P jirovecii pneumonia in patients who did not receive prophylactic treatment. These data indicate that duvelisib may be effective for R/R CLL patients. Umbralisib is a novel PI3K inhibitor with efficacy similar to idelalisib is umbralisib, which has been shown to have low toxicity in R/R CLL patients. In the phase I–II trials comprising a triplet combination of umbralisib with ublituximab plus venetoclax, the ORR was 90% with a CR of 29% and a two-year PFS of 90%. In the peripheral blood and bone marrow, 58% of patients had undetectable MRD [74]. Due to findings from the UNITY-CLL clinical trial (NCT02612311) continuing to show a possible increased risk of death in patients receiving umbralisib, TG Therapeutics withdrew it from the market in June 2022. B-cell leukemia/lymphoma-2 (BCL2) is a protein often overexpressed in CLL patients and plays a crucial role in regulating the apoptotic pathway. Venetoclax is a highly selective, second-generation small-molecule inhibitor of BCL2 that can effectively shift the balance of proteins in CLL cells towards apoptosis. In the phase I clinical trial that included 116 high-risk R/R CLL patients, venetoclax achieved an ORR of 79%, with 20% of patients experiencing a CR. Notably, patients with del(17p) and those who were refractory to fludarabine achieved even higher ORRs of 82% and 89%, respectively. The most common grade 3–4 adverse event reported was neutropenia, in 41% of patients [75]. Currently, there are several combination trials underway involving venetoclax. In one study that examined the combination of venetoclax and rituximab (Ven-Rix) in high-risk R/R CLL patients, approximately half of the patients achieved CR, with 57% achieving uMRD activity [76]. The estimated two-year PFS was 82% (95% CI 66–91); however, two patients progressed after 24 months of therapy [76]. The phase III MURANO trial compared a combination of Ven-Rix versus bendamustine/rituximab (BR) in R/R CLL patients. The two-year PFS rate was 84.9% and 36.3% in the Ven-Rix arm and the BR arm, respectively, with a median PFS significantly higher in the Ven-Rix arm [77]. Tumor lysis syndrome (TLS) is one of the most common adverse events associated with venetoclax treatment. Hence, appropriate patient selection, strategies to reduce TLS risk, and following the standard ramp-up will be critical for successful treatment with low TLS risk. Preclinical studies investigating the synergy of BTK and BCL2 inhibitors and single-center studies examining the effectiveness of ibrutinib in combination with venetoclax with or without obinutuzumab have shown promising results in CLL treatment. A phase II international study named CAPTIVATE (NCT02910583) examined the use of fixed-duration treatment with ibrutinib plus venetoclax (Ibr-Ven) in patients aged 70 years who had not received any prior treatment for CLL [78]. Despite a median follow-up of only 27.9 months, the results seen in this trial are remarkable. The treatment regimen demonstrated exceptional efficacy with a 56% CR, and with 76% and 62% of patients achieving uMRD in the blood and bone marrow. At the 24-month mark, 95% of patients were alive and free of progression. Notably, patients with high genomic risk diseases such as TP53 abnormalities had excellent outcomes. Additionally, patients with unmutated IGHV showed a trend toward achieving uMRD [78]. A phase III trial named GLOW (n = 211) evaluated the efficacy and safety of Ibr-Ven in older patients and/or those with comorbidities with previously untreated CLL. After a median follow-up of 46 months, the Ibr-Ven treatment was found to reduce the risk of disease progression or death by 79% compared to chlorambucil with Obi (Clb-Obi) (Hazard Ratio (HR) 0.214; 95% Confidence Interval (CI), 0.138–0.334; p < 0.0001). This marks the first instance of a fixed-duration novel combination demonstrating an OS advantage compared to Clb-Obi as a first-line treatment for CLL (HR 0.487; 95% CI, 0.262–0.907; nominal p = 0.0205) with the Ibr-Ven treatment given once daily orally. Estimated data suggests that 74.6% of previously untreated older and/or comorbid patients remained alive and progression-free after 3.5 years of fixed-duration Ibr-Ven treatment, compared to 24.8% of patients in the Clb-Obi cohort. CLL is a disease of the mature B-cells; however, recent reports indicate the role of T-cells in the disease pathogenesis and progression [79,80]. In CLL, T-cell exhaustion is mediated by upregulation of co-inhibitory signals such as programmed death-1 (PD1), lymphocyte activation gene-3 (LAG-3), cytotoxic T-lymphocyte-associated protein-4 (CTLA4), and T-cell immunoglobin-3 (TIM-3). These findings have led to the development of immune-checkpoint inhibitors for managing CLL treatment [81]. However, PD1 inhibitors used as single agents have failed to produce promising results in CLL [82]. The anti-PD1 mAb pembrolizumab has shown selective efficacy in CLL patients progressing to RT. Recent trials in RT have demonstrated that combining ibrutinib with anti-PD1 mAb nivolumab has produced considerable results with acceptable levels of toxicity [83]. Moreover, a triplet combination of umbralisib, ublituximab, and pembrolizumab has reported durable responses [84]. As evidenced by high response rates in previously untreated RT patients, the combination of the Bcl2-inhibitor venetoclax, next-generation anti-CD20 mAb obinutuzumab, and anti-PDL1 mAb atezolizumab offer immunotherapy as a promising treatment approach. Bispecific antibodies (bsAbs) are a promising approach that combines antibody therapies with cellular-mediated immunotherapy. BsAbs are antibodies with two binding sites directed at two different antigens or two different epitopes on the same antigen. There are two types of bsAbs: bispecific T-cell engagers (BiTEs) and dual-affinity targeting antibodies (DARTs). BiTEs are a subtype of bispecific antibodies that link two single-chain variable fragments with a flexible linker. One fragment binds to the tumor-associated antigen, and the other binds to a T-cell-specific antigen to activate the T-cell to kill the cancer cell to which it is linked. DART consists of two variable fragments that connect the opposite heavy chain variable regions through a disulfide bond, improving stability. Blinatumomab, a CD19/CD3 bsAb designed in a BiTE format, was one of the first bsAbs tested in CLL and has been shown to eliminate CLL cells in a mouse xenograft model [85]. Blinatumomab was tested in vitro on 28 freshly treated naïve CLL patients. The antibody treatment induced tumor cell death via T-cell activation and granzyme-mediated cytotoxicity [86]. Clinical trials currently underway involve combining lenalidomide (NCT02568553) or blinatumomab-expanded T-cells (NCT03823365) in patients with a broad spectrum of NHL, including CLL. Promising results have emerged from preclinical studies of another bsAb called MGD011 CD3 X CD19 DART, which has shown the ability to effectively engage CLL-derived T-cells and promote the killing of tumor cells in vitro. Further, MGD011 also indicated an impact on eliminating CLL resistance to venetoclax [87]. Recently, preclinical studies have explored the potential of a bispecific antibody that targets leukemic cells and Vγ9Vδ2 T-cells, a conserved T-cell subset with intrinsic anti-tumor activity. Furthermore, a CD40-bispecific γδ T-cell engager has been found to trigger apoptosis through a powerful Vγ9Vδ2 T-cell-dependent anti-leukemic response [88]. Altogether, bsAbs may be a potentially valid option for high-risk patients resistant to previous therapies. Natural killer cells are hypofunctional in CLL, affecting target cell recognition and cellular toxicity [81]. BiKEs (Bi-specific Killer Engagers) and TriKEs (Tri-specific Killer Engagers) recruit NK cells to target tumor antigens. The NKG2D receptor-ligand ULBP2 has been targeted by TriKEs (ULBP2/aCD19/aCD19 and ULBP2/aCD19/aCD33) and has demonstrated in vitro and in vivo activity against CLL [89]. Further, a CD16/CD19 BiKE and a CD16/CD19/CD22 TriKE have been shown to trigger NK cell activation via CD16 signaling, for which CD16/CD19 TriKE induced better killing of CLL cells in vitro compared to rituximab [90]. Overall, inducing an NK cell response against CLL cells is compelling to explore as a therapeutic option. Chimeric antigen receptor T-cells (CAR T-cells) represent a promising area of investigation in adoptive cellular therapies, combining the strengths of T-cells and antibodies to boost T-cell anti-tumor activity. To date, there have been four generations of CAR T constructs developed. These constructs typically consist of an antigen binding domain, such as a single chain variable fragment (scFv) derived from immunoglobulin directed against the tumor antigen, as well as the intracellular domain from the CD3 chain, and a costimulatory domain, which is generally identified as the intracellular domain of a costimulatory molecule (CD28/4-1BB) [91]. One of the key advantages of CARs is their ability to identify and target tumor antigens in an HLA-independent manner, thus targeting tumor cells in a tumor-evasive environment [92]. CD19 CAR T-cells have been widely used in B-cell malignancies; however, their usage in CLL is controversial due to exhausted T-cell phenotype in CLL [93,94] and the loss of CD19 upon CAR T therapy resistance. In the CLL 4 trial, CD19 CAR T-cells were used as a single agent, resulting in an ORR of 82%, with a 45% CR and a high rate of uMRD observed in heavily pre-treated CLL patients, including high-risk patients who were refractory to BTKi and venetoclax [95]. Other studies of CD19 CAR T-cells in CLL have also shown an ORR of 50–70% and a CR of 20–30% [96,97]. Kappa or lambda light chains can be attractive targets for CLL patients, as it allows high target specificity for the leukemia cells while avoiding the normal B-cells. Recently, a new CAR T has been investigated against the Ig light chain, as CLL cells mainly express the Ig light chain compared to their normal counterparts [98]. Preclinical studies showed CAR T-cells have been effective against Igκ or Igλ in vitro or in vivo CLL models, and there is an ongoing clinical trial investigating anti-Ig kappa on CLL (NCT04223765). Combination therapies have also been tested. A recent study suggested increased viability and expansion in human CAR T-cells in the presence of ibrutinib. In line with this, administering ibrutinib with anti-CD19 CAR T has increased tolerability with a lower incidence of severe side effects. One of the impediments to CAR T treatment in CLL is decreased fitness and activity of CAR T-cells due to immune subversion. Thus, using allogenic CAR T-cells from healthy donors has been an attractive option; however, strategies are evolving, such as gene editing-based strategies to knock out endogenous αβTCR to prevent graft-versus-host disease and donor-mediated rejection [99]. Similarly, acalabrutinib has been shown to improve the in vitro and in vivo anti-tumor function of CD19 CAR T-cells [100]. These studies’ results will guide future treatment when including CAR T-cells. Even though CD19 as an antigen has been a promising target, there are resistance mechanisms, such as a CD19 loss, leading to exploration for novel targets. Another good target is CD20. Anti-CD20 CAR has been investigated in non-Hodgkin lymphomas. Among the three patients who received anti-CD20 CAR, two did not develop the evaluable disease with a progression-free survival of 12 and 24 months. The third patient achieved partial remission and relapsed after 12 months post infusions [101]. An ongoing trial evaluates anti-CD20 CAR in R/R B-cell malignancies, including CLL (NCT0327779). Another attractive antigen expressed on CLL cells but not on normal B-cells is the ROR1 (Receptor tyrosine kinase-like orphan receptor 1) receptor [102]. In vitro, promising data shows CAR T’s effect on ROR1 and an ongoing trial evaluating anti-ROR1 CART against ROR1 malignancy, including CLL (NCT02706392). FcμR is expressed highly in CLL cells while at minor levels in normal healthy B-cells. Anti-FcμR CAR T has been investigated in CLL cells, which affects CLL cells without affecting normal healthy B-cells [103]. Splicing is a highly precise and stepwise process that converts pre-mRNA into mature RNA, facilitated by a group of proteins called the spliceosome. The spliceosome is composed of over 300 proteins, which includes more than 100 accessory proteins that process the U2-type introns. The core of the spliceosome includes U1, U2, U4, U5, and U6 small nuclear ribonucleoproteins (snRNPs), as well as seven Sm proteins or Lsm (U6-specific) proteins and other snRNP-specific factors [104]. Each snRNP contains a small nuclear RNA (snRNA), enabling interactions between RNA–RNA and RNA–protein during the dynamic splicing process [104,105]. Typically, cells generate various mRNA forms via alternative splicing, which occurs through multiple mechanisms such as alternative 5′ or 3′ splice sites, exon skipping, alternative promoter, intron retention, and alternative polyadenylation [106]. Genome-wide cancer sequencing studies have identified recurrent mutations in RNA splicing factor proteins (SF3B1, U1 snRNA, SRSF2, U2AF1, ZRSR2) myeloid neoplasms, clonal hematopoiesis, mantle cell lymphoma, and CLL [11,15,107,108,109,110,111,112]. All these mutations lead to transcriptome-wide RNA splicing dysregulation. Additionally, RNA sequencing studies of primary cancer cells across various cancer types have revealed that aberrant RNA splicing is a common feature of cancer. In TCGA (The Cancer Genome Atlas) analysis of 33 different cancer types, mutations in 119 splicing factors were reported, which comprise half of the splicing factor proteins [113]. Moreover, 70% of splicing factors and 84% of RNA binding proteins are dysregulated at mRNA levels in various cancers, resulting in dysregulated splicing events. The discovery of splicing factor mutations has generated an interest in therapeutic targeting of the splicing factor mutant tumor cells. One interesting feature in splicing factor mutant cases is the solid mutual exclusivity. Several reports also suggest that co-expression of the most common splicing factor mutations in SF3B1, SRSF2, or U2AF1 is not tolerated in cells [114]. Similarly, expression of a single wild type encoding these factors is tolerated, while deletion of the wild type in splicing factor mutant cell lines leads to cell death [115]. This evidence further motivated the development of inhibitors to target the splicing catalysis function to kill splicing factor mutant tumors. Among the various inhibitors developed are a class of natural products and their synthetic analogs that bind to the Sf3b complex and prevent interaction with the branch point. Among the widely studied compounds, the pladienolide analogs (A-G and synthetic analog E7107) target Sf3b, which binds the U2 snRNP to disrupt splicing [116]. PLAD-B and FD-895 have been shown to induce apoptosis and overcome the protective effect of the microenvironment in CLL in vitro, indicating that these inhibitors may work in R/R CLL patients. Another pladienolide analog, E7107, has been tested in phase I clinical trials and showed limited efficacy; however, due to adverse events, the trial was terminated [117]. A recent report suggests the combination of E7107 with venetoclax sensitized both human and murine CLL cells in vitro and can overcome venetoclax resistance in vivo [16]. Another class of inhibitors similar to pladienolides is FR901464 and its methylated derivative spliceostatin A (SSA), which inhibit the Sf3b subcomplex [118]. Bcl2 family member Mcl1, an apoptosis regulator, is highly expressed in CLL samples with progressive disease. Reports suggest that spliceosome inhibitor spliceostatin (SSA) altered the splicing of Mcl1 and led to the downregulation of Mcl1, resulting in apoptosis [119]. Notably, the microenvironmental signals such as CD40L and IL4 treatment of CLL cells offered resistance to spliceostatin. This resistance was reversed using a combination of spliceostatin with ABT-199/263-BCL2 family inhibitor, indicating that the combination may work for CLL cells resistant to spliceostatin [119]. Another synthetic analog of FR901464, sudemycin, has been shown to induce apoptosis in CLL samples without affecting the normal B-cell counterparts in vitro and in vivo [120]. Further, combining sudemycin with ibrutinib confers enhanced sensitivity to ibrutinib by modulating the loss of regulatory function of IBTK over BTK via alternative splicing regulation [120]. Despite the promising results with splicing inhibitors in CLL, none of the inhibitors have been approved by the FDA for treatment, hence further understanding is needed to modulate splicing catalysis in vivo with an acceptable therapeutic index. Metabolic changes enable the tumor cells to sustain proliferation and adapt to stressful conditions. Hanahan and Weinberg noted that metabolic rewiring is a hallmark of cancer cells [121]. Even though CLL cells are known to be quiescent, they have been shown to have high mitochondrial respiration and reactive oxygen species and enhanced antioxidant activity compared to normal B-cells [17,122,123]. Accumulating evidence indicates that CLL cells undergo spontaneous apoptosis, and a gradual increase in the size of the CLL clone results from the newly proliferating lymphocytes. CLL cells are generally slowly proliferating, with approximately 0.1% to 1.75% of CLL cells proliferating daily compared to resting B-cells [123,124]. Very few studies are investigating the role of metabolism in CLL and exploiting it as a therapeutic approach. We will briefly discuss the recent works related to metabolism in CLL. Mitochondria play an important role in energy metabolism, as they regulate oxidative phosphorylation, reactive oxygen species, and ATP production via the TCA cycle. Mitochondria also regulate other metabolic processes, such as amino acid and fatty acid metabolism. CLL cells have higher mitochondrial mass, ROS, and activity than normal B-cells [18,19,123]. Primary CLL samples have been shown to have high basal respiration via seahorse-based assays [19]. Recent omics analyses indicate that CLL proliferation is linked to the mTOR-MYC-OXPHOS pathway [122]. Overexpression of oxoglutarate dehydrogenase and isocitrate dehydrogenase was commonly found in CLL cells [125]. In line with this, CLL cells are sensitive to the pharmacological inhibition of oxidative phosphorylation by OXPHOS inhibitors (PK11195, oligomycin A, and metformin) (Figure 2) [126]. Glucose can be converted to pyruvate via glycolysis and ribose via the pentose phosphate pathway. Glucose is further utilized to synthesize glycogen, fatty acid, and serine. An increase in glycolytic flux to produce ATP meets the energy demands of highly proliferating cells, rendering the cells addicted to glucose. CLL cells have been shown to have high glucose metabolism and uptake. Glucose uptake inhibitors (ritonavir) and glycolysis inhibitors (2-DG) were reported to induce in vitro cytotoxicity in CLL [127]. ATM and TP53 have been shown to regulate central carbon metabolism. ATM deletion or 11q deletion in CLL cells led to increased insulin receptor expression and glucose uptake [128]. Given this evidence, 11q-deleted CLL cells are more sensitive to glycolysis inhibition. Currently, there are clinical trials targeting the mitochondrial OXPHOS via metformin (NCT01750567) alone or in combination with GLUT4 (glucose transporter) via ritonavir (NCT02948283) [126]. Glutamine is the most abundant non-essential amino acid in the blood at a concentration of ~0.5–1 mM. Even though cells can synthesize glutamine via GLUL, many tumors are addicted to glutamine, especially KRAS and Myc-driven tumors [129,130]. Glutamine is converted to glutamate by a rate-limiting step in glutamine catabolism via glutaminase (GLS1). Glutaminase is overexpressed in CLL samples, targeted in vitro by CB-839. Del11q CLL patients exhibit higher glutamine synthesis and metabolism than their negative counterparts. Del11q CLL samples are susceptible to glutaminase inhibitors indicating the pivotal role of glutamine metabolism in Del11q CLL [126]. Glutamine is taken up by the cells via glutamine transporters such as SLC1A5, SLC38A1, and SLC38A2 [131]. CLL cell proliferation is inhibited by targeting the glutamine transporters via the V9302 inhibitor, indicating the vital role of glutamine transport in CLL [17]. To evaluate glutamine incorporation in CLL cells, there is an ongoing clinical trial (NCT04785989) in low disease burden CLL testing the glutamine incorporation via in vivo labeling. Metabolomic analysis of CLL samples shows a differential abundance of lipids compared to other metabolites in CLL, indicating the dependency of CLL on fatty acid metabolism [18,124]. Lipoprotein lipase mRNA levels are highly expressed in CLL compared to normal B-cells [132]. BCR stimulation further increases the LPL expression, indicating the function of BCR signaling in regulating fatty acid metabolism in CLL. Fatty acid synthesis and oxidation genes overexpressed in CLL are reported [133]. Orlistat, a fatty acid synthesis inhibitor, is cytotoxic for CLL cells in vitro. CPT-1, a mitochondrial fatty acid transporter, is highly expressed in CLL [134]. In line with this, CLL cells are sensitive to etomoxir, which targets fatty acid oxidation via CPT-1. Recent reports also suggest that ibrutinib affects fatty acid metabolism via inhibiting free fatty acid synthesis [135]; however, the mechanism is poorly understood. Based on extensive CIT studies, in 2016 the European Medicine Agency accepted the use of uMRD, defined as <1 CLL cell per 10,000 leukocytes, as an intermediate endpoint and independent prognostic factor for PFS and OS [136,137,138]. However, uMRD and CR are not commonly achieved with targeted agents such as ibrutinib, and several combinations are being tried to achieve increased efficacy. The HELIOS trial involved 578 R/R CLL patients who were randomly assigned to receive BR plus ibrutinib or BR plus a placebo. The results showed that adding ibrutinib led to a significantly higher ORR (83 vs. 68%), longer median PFS, and a higher rate of MRD negativity (13% vs. 5%). In total, ibrutinib improved the efficacy of the treatment in both naïve patients with del(17p) and R/R patients compared to CIT treatment [139,140]. However, some off-target ibrutinib may be responsible for its unique toxicities, such as atrial fibrillation and bleeding. Similarly, PI3Kδ idelalisib has been tested with BR. The triple combination of PI3K with BR produced a significant increase in PFS (20.8 vs. 11.1 months) compared to BR alone; however, the triple combination was associated with adverse events such as increased infections, limiting its clinical use [141]. Another trial tested the combination of ibrutinib with FC and obi in young treatment-naïve patients with a favorable genetic profile (IGHV mutated and no TP53 aberrations). MRD was used to guide frontline therapy [142]. The patients in the study received a quadruple combination for three courses, followed by either ibr plus obi for nine cycles or ibr plus obi for three cycles and ibrutinib for six cycles, based on their MRD status post-chemoimmunotherapy. Of the 28 patients who completed the 12-month treatment, all achieved undetectable MRD and a CR rate of 86%. Though these kinase inhibitors achieve impressive undetectable MRDs and CR, treating elderly patients with comorbidities becomes difficult due to increased toxicity rates. The ALLIANCE randomized phase III trial compared ibrutinib and ibrutinib plus rituximab to bendamustine plus rituximab in older patients and found the efficacy to be almost identical for both the ibrutinib-containing arms [143]. In the iLLUMINATE trial, Ibr-Obi was compared to Chl-Obi, with the former achieving superior PFS at 30 months (79% vs. 31% p < 0.0001). In the relapsed/refractory R/R setting, rituximab plus venetoclax, not ibrutinib, has produced improved MRD-negative rates, leading to its broad approval in the R/R setting [144,145]. In the CLL2-BAG trial, a combination of bendamustine, venetoclax, and obinutuzumab was used, and an MRD-guided maintenance phase led to an 87% rate of MRD negativity [41,45]. CLARITY is a phase II trial in which a combination of ibrutinib plus venetoclax was tested in 40 patients with R/R CLL. The results showed a CR rate of 58% (23/40) and no detectable MRD in peripheral blood after 12 months of treatment. In another trial of treatment-naïve CLL patients (n = 80), the same combination was tested for 24 months. Ninety-six percent of patients treated with a combination of venetoclax and ibrutinib achieved a complete response (CR) after 12 months, and 69% had no undetectable MRD in the bone marrow [146]. Combined treatment can achieve better efficacies; however, optimal treatment selection for each patient is challenging. Lenalidomide has been used as an immunomodulatory agent, which has been shown to induce T-cell activation resulting in CLL cell apoptosis. A combination of lenalidomide with idelalisib can potentially reduce lenalidomide-induced flare, as lenalidomide-induced cytokine release and immune activation are PI3K dependent [147]. The most common toxicities are fatigue, thrombocytopenia, and neutropenia, noted in 83%, 78%, and 78% of patients, respectively [148]. The ORR in different studies has been 32–54% with monotherapy and is better (66%) in combination with rituximab. In another trial, lenalidomide was evaluated for maintenance therapy post-chemotherapy in high-risk patients (NCT01556776). Allo-SCT has been considered an option for high-risk CLL patients in the CIT era and remains one of the potentially curative treatments for CLL. In this era of novel agents, allo-SCT remains an option for high-risk patients who progress after at least BTKi or venetoclax treatment. Due to substantial toxicities and morbidities, myeloablative-based treatment strategies were discontinued in CLL patients. However, recently large-scale prospective studies conducted with a median follow-up of 6 years [149,150,151] have shown that RIC (reduced-intensity conditioning) allo-SCT can provide long-term disease control in 40% of patients and overcome TP53-based negative prognostication and refractoriness associated with fludarabine. In different studies, OS has been 50% with a PFS of ~40% [152]. Non-relapse mortality (NRM) remains significant, affecting 15–25% of patients. In a prospective trial conducted on 55 patients, adding rituximab peri-transplant improved response rates compared to 157 historical control patients. The NRM rate in patients with no comorbidities was less than 12% [153,154]. Limited data is available on allo-SCT efficacy; hence, more collaborative efforts are needed to understand the effectiveness of novel agents in this era. In the last year’s ASH, several new targets were discussed. As patients treated with Ibr-Ven show resistance, these authors explored Protac-based BCL2/BCL-XL degrader PZ18753b to overcome Ibr-Ven resistance. The authors used OSU-CLL cells to generate BCL2 mutant (G101V, F104, and R107-110dup) cells. They showed rapid apoptosis with Protac-based degrader PZ118753b with efficient degradation of BCL-xL and partial degradation of BCL2, suggesting that venetoclax resistant cells retain dependency on BCL2/BCL-xL [155]. As CDK9 is a master transcription regulator and a potential target in hematologic malignancies, the authors explored PRT2527 as a novel low nanomolar potent CDK9 inhibitor in B-ALL and CLL primary samples. Their studies showed that PRT2527 treatment has potent anti-leukemic activity in primary CLL and B-ALL samples evaluated ex vivo and two systemic models of B-ALL in vivo [156]. Another group explored bromodomain-containing protein 9 (BRD9), a chromatin remodeling complex, as a potential target in CLL. The authors showed that BRD9i could improve the survival of orthotopic CLL xenograft mice with significantly reduced expression of targets related to the NRF2 pathway [157]. CD70, a TNF family member, and its receptor CD27 were highly expressed in CLL cells compared to normal B-cells, suggesting immune deregulation in CLL. Using preclinical models, the authors showed that targeting CD70 with anti-CD70 antibodies in CLL altered BCR, CD40L, IL4, and TNFR signaling and delayed CLL disease progression, thus suggesting the use of anti-CD70 immunotherapy in combination therapies in CLL [158]. As RT studies need more bona fide models, from our group, we reported the establishment of a new RT murine model driven by Mga KO and OXPHOS, where targeting the OXPHOS regulation along with CDK9i could improve RT mice survival [159]. As RT has an unmet clinical need, the Phase Ib/II EPCORE CLL-1 trial explored Epcoritamab, a novel subcutaneously (SC) administered CD3/CD20 bispecific antibody in CD20+ RS LBCL (large B-cell lymphoma). Among the ten patients who received treatment, preliminary findings show that SC-Epcoritamab has encouraging single-agent activity with high overall and complete response rates. Most were seen at the first six-week assessment [160]. This is an ongoing trial; updated data will be presented in future studies. The treatment of CLL has been transformed with the increasing availability of innovative agents that are now favored over traditional CIT in all settings. However, there are still many questions to be answered through well-designed studies. Areas of active research include RT metabolism, new targets, combinational treatments in CLL, splicing-related inhibitors, and the best treatment approach for high-risk patients. The cost-effectiveness of different treatments must also be taken into account. Although there are many obstacles, CLL therapy is still hopeful, with new agents recently approved by the FDA and more promising ones coming. Based on the ongoing recently completed accrual from several cooperative groups and international phase III trials (EA9161: NCT03701282; A041702: NCT03737981; CLL17: NCT04608318; MAJIC: NCT05057494), the proposed treatment can include treatment arms combining venetoclax and BTKi in the frontline setting. A viable strategy for relapsed or refractory CLL patients without identified BTK/PLCG2/BCL2 mutations may involve retreatment with either ibrutinib/non-covalent BTKi, venetoclax, or both. Lastly, advances in disease biology have revealed new targets and brought us closer to a cure for CLL by shifting from chemotherapy to more patient-friendly treatments.

PMC10000620

Ioana-Miruna Stanciu,Andreea Ioana Parosanu,Cristina Orlov-Slavu,Ion Cristian Iaciu,Ana Maria Popa,Cristina Mihaela Olaru,Cristina Florina Pirlog,Radu Constantin Vrabie,Cornelia Nitipir

Mechanisms of Resistance to CDK4/6 Inhibitors and Predictive Biomarkers of Response in HR+/HER2-Metastatic Breast Cancer—A Review of the Literature

05-03-2023

CDK4/6 inhibitors,advanced/metastatic breast cancer,biomarkers of response,progression on CDK4/6 inhibitors,resistance mechanisms,endocrine therapy

The latest and newest discoveries for advanced and metastatic hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancer are the three cyclin-dependent kinases 4 and 6 inhibitors (CDK4/6i) in association with endocrine therapy (ET). However, even if this treatment revolutionized the world and continued to be the first-line treatment choice for these patients, it also has its limitations, caused by de novo or acquired drug resistance which leads to inevitable progression after some time. Thus, an understanding of the overview of the targeted therapy which represents the gold therapy for this subtype of cancer is essential. The full potential of CDK4/6i is yet to be known, with many trials ongoing to expand their utility to other breast cancer subtypes, such as early breast cancer, and even to other cancers. Our research establishes the important idea that resistance to combined therapy (CDK4/6i + ET) can be due to resistance to endocrine therapy, to treatment with CDK4/6i, or to both. Individuals’ responses to treatment are based mostly on genetic features and molecular markers, as well as the tumor’s hallmarks; therefore, a future perspective is represented by personalized treatment based on the development of new biomarkers, and strategies to overcome drug resistance to combinations of ET and CDK4/6 inhibitors. The aim of our study was to centralize the mechanisms of resistance, and we believe that our work will have utility for everyone in the medical field who wants to deepen their knowledge about ET + CDK4/6 inhibitors resistance.

Mechanisms of Resistance to CDK4/6 Inhibitors and Predictive Biomarkers of Response in HR+/HER2-Metastatic Breast Cancer—A Review of the Literature The latest and newest discoveries for advanced and metastatic hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancer are the three cyclin-dependent kinases 4 and 6 inhibitors (CDK4/6i) in association with endocrine therapy (ET). However, even if this treatment revolutionized the world and continued to be the first-line treatment choice for these patients, it also has its limitations, caused by de novo or acquired drug resistance which leads to inevitable progression after some time. Thus, an understanding of the overview of the targeted therapy which represents the gold therapy for this subtype of cancer is essential. The full potential of CDK4/6i is yet to be known, with many trials ongoing to expand their utility to other breast cancer subtypes, such as early breast cancer, and even to other cancers. Our research establishes the important idea that resistance to combined therapy (CDK4/6i + ET) can be due to resistance to endocrine therapy, to treatment with CDK4/6i, or to both. Individuals’ responses to treatment are based mostly on genetic features and molecular markers, as well as the tumor’s hallmarks; therefore, a future perspective is represented by personalized treatment based on the development of new biomarkers, and strategies to overcome drug resistance to combinations of ET and CDK4/6 inhibitors. The aim of our study was to centralize the mechanisms of resistance, and we believe that our work will have utility for everyone in the medical field who wants to deepen their knowledge about ET + CDK4/6 inhibitors resistance. Breast cancer has the highest number of new cases for both sexes and all ages, according to GLOBOCAN 2020. It is the second leading cause of mortality among women, and it has become a global health challenge. It is estimated that about 7.8 million women were diagnosed in 2021 [1]. Unfortunately, the global burden of breast cancer is increasing both in developed countries and in developing ones [2]. Breast cancer is grouped into four categories based on the immunohistochemical expression of hormone receptors: estrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor receptor positive (HER2+), and triple-negative (TNBC), which is characterized by the lack of expression of any of the above receptors [3]. We found it of great interest and intriguing that one of the latest studies [4] on the regulation on signaling pathways, which highlighted that even natural products obtained from plants, fruits and vegetables (such as viridiflorol, verminoside, novel phloroglucinol derivatives, genistein, vulpinic acid, calcitrinone A, kaempferol, protopanaxadiol, thymoquinone, arctigenin, glycyrrhizin, 25-OCH3-PPD, oridonin, apigenin, wogonin, fisetin, curcumin, berberine, cimigenoside, and resveratrol) show anticancer activities against breast cancer through the inhibition of angiogenesis, cell migrations, proliferations, and tumor growth, as well as cell cycle arrest by inducing apoptosis and cell death, the downstream regulation of signaling pathways (such as Notch, NF-κB, PI3K/Akt/mTOR, MAPK/ERK, and NFAT-MDM2), and the regulation of EMT processes [4]. The investigators actually concluded that natural products also act synergistically to overcome the drug resistance issue, thus improving their efficacy as an emerging therapeutic option for breast cancer therapy. However, in this review we stay focused on molecular resistance to the treatment of HR+/HER2- breast cancer. One of the most common subtypes (20–25% of all breast cancers) is HR+/HER2- breast cancer [5]. Endocrine therapy (ET) is the main treatment for the HR+ luminal subtype of breast cancer, in association with targeted therapy. Cyclin-dependent kinases 4 and 6 inhibitors (CDK4/6i) restore the cell cycle by selectively inhibiting cyclin-dependent kinases 4 and 6, and block cell proliferation in a variety of tumor cells, including those of breast cancer [6]. There are three CDK4/6 inhibitors approved by the US Food and Drug Administration that are transforming the treatment landscape nowadays: palbociclib, ribociclib, and abemaciclib (Table 1). They all have similar mechanisms of action and properties, with few differences in their preclinical and pharmacological settings and toxicity profiles [7]. There is a need for a personalized approach to overcome the growing financial burden for health care systems through more effective patient selection. Palbociclib, ribociclib and abemaciclib are expensive anticancer drugs because they are currently protected by drug patents, and hence the need for predictive biomarkers of response beyond estrogen receptor positivity [8]. The management of breast cancer CDK4/6 inhibitor resistance is one of the most important clinical issues to be overcome, indicating a clear need for continuous discovery-based preclinical and clinical approaches. In order to assess these issues, we performed a systematic review of the published literature. The two key objectives were to identify resistance biomarkers and to understand molecular mechanisms underpinning drug resistance for CDK4/6 inhibition in breast cancer patients. Every single biomarker and signaling pathway was taken and discussed in separate paragraphs, highlighting the mechanism of possible resistance and its clinical and therapeutical implication. The databases used to gather information for this review include Pubmed.gov and Clinicaltrials.gov. We reviewed the PubMed database from January 2013 to January 2023 and selected all relevant articles. The inclusion criteria for this literature review encompassed studies that examined resistance to CDK4/6 inhibitors. The inclusion criteria were studies that evaluated and validated biomarkers of predictive response to therapy and potential mechanisms of resistance. Studies that addressed future directions after the progression of inhibitors were also assessed. Exclusion criteria were articles with unavailable abstracts, non-English-written articles, and conference presentations. Keywords used to search for references included CDK4/6 inhibitor, biomarker, progression, and resistance in order to achieve the most specific results. The search generated 75 results, but only 25 articles met our criteria. The malignant transformation of normal cells begins with chaotic cellular proliferation, which takes place due to cell cycle dysregulation [9]. The cell cycle has four important stages: G1 (cells grow, increasing in size), S (synthesis of the DNA), G2 (cells grow more and make proteins), and M (mitosis). In the end, the cell splits into two daughter cells [10]. One of the most important cell cycle malfunctions starts right at the beginning of the cell cycle, which is controlled by the retinoblastoma protein (pRb). When in its active state, it stops the cell from progressing in the S phase by binding and suppressing E2F transcription factors. Phosphorylation of the Rb protein, which can be undertaken by the cyclin D–CDK4/6 complex, leads to E2F release. Thus, the cell can enter the S phase, and the cell cycle continues [11]. In turn, the complex is activated through the PI3K/AKT/mTOR and RAS/MAPK pathways by the activation of hormone receptors (including the estrogen receptor (ER)) and growth factors [12]. Obviously, the complex itself is downregulated by endogenous CDK inhibitors: the INK4 and Cip/Kip protein families [12]. A schematic representation of how CDK4/6 inhibitors work can be found in Figure 1. There are several resistance mechanisms and potential biomarkers of response to CDK4/6 inhibitor regimens, which we will review in the upcoming paragraphs. The most frequently encountered resistance to CDK4/6i is the upregulation of the Cyclin D–CDK4/6–pRb pathway [13]. In a study conducted by Yang et al. in 2017, the majority of cells that were resistant to abemaciclib contained an amplification of CDK6 [14]. While CDK6 amplification was demonstrated to have an impact on potential treatment resistance, both high and low levels of CDK4 have been seen in resistance models [11]. In the same year, Gong et al. [15] demonstrated that cells with the highest sensitivity to abemaciclib showed increased cyclin D activity, which promotes cyclin D1 turnover [12]. Additionally, the overexpression of Cyclin D1 in breast cancer cells showed higher sensitivity to palbociclib [16]. However, many studies demonstrated that the overexpression of Cyclin D, with or without Cyclin D1 gene amplification, occurred in more than 50% of breast cancer cells [17]. Cyclin D1 is also a direct transcriptional target of ER [18], so the activation of the Cyclin D–CDK4/6 complex also contributes to endocrine therapy resistance [12]. Another important down-regulatory component of the complex is the p16 protein (a member of the CDKN2/INK family), whose inactivity could also contribute to aggressive breast cancer [17]. It is a tumor-suppressor protein that inhibits the activity of CDK4/6, and its expression correlates with a better prognosis in breast cancer patients. Low activity of p16 is correlated with increased CDK4/6 activity and increased sensitivity to palbociclib [19]. The loss of G1/S control is a hallmark of cancer, and is often caused by the inactivation of the retinoblastoma pathway [20]. As shown above, the integrity of the retinoblastoma protein is an important condition for the cells to be sensitive to CDK4/6 inhibitors, as it is at the center of the action mechanism. RB1 is the gene that encodes pRb, one of the most studied and reported biomarkers to date. Its loss or mutation is one of the most observed resistance mechanisms for CDK4/6i [21]. However, pRb function loss prior to CDK4/6i treatment is uncommon in metastatic breast cancer with HR+/HER2- [22]. In the PALOMA-3 study, only six out of 127 patients developed an RB1 loss of function after treatment with palbociclib and fulvestrant [23]. Another study conducted by Li et al. found a statistically significant difference in progression-free survival (PFS) regarding treatment with CDK4/6i; 3.6 months for patients who had a loss of the RB1 gene, compared to 10.1 months for patients with intact RB1 [16]. The first examples of acquired resistance were reported by Condorelli et al. [24], where acquired RB1 mutations were detected in ER-positive breast cancer patients treated with palbociclib and fulvestrant or ribociclib and letrozole. To determine the function of Rb phosphorylation by cyclin D-CDK4/6, Topacio and colleagues sought to generate variants of Rb that could no longer interact with cyclin D-Cdk4,6 while preserving all the other interactions with other cyclin-Cdk complexes [25]. They analyzed the docking interactions between Rb and cyclin D-CDK4/6 complexes and found that cyclin D-CDK4/6 targets the Rb family of proteins for phosphorylation, primarily by docking a C-terminal alpha-helix, which is not recognized by the other major cell-cycle cyclin-CDK complexes cyclin E-CDK2, cyclin A-CDK2, and cyclin B-CDK1 [25]. Their results showed that cyclin D-CDK4/6 phosphorylates and inhibits Rb via a C-terminal helix, and that this interaction is a major driver of cell proliferation [25]. During a normal cell cycle, cyclin E1 and cyclin E2 can bind to and activate CDK2 in order to phosphorylate pRb, but only after it has already been phosphorylated by the cyclin D–CDK4/6 complex as a second wave of signaling [11]. The activation of the cyclin E1/cyclin E2-CDK2 complex permits cells to bypass the inhibiting activity of CDK4/6 and encourage growth and proliferation [13]. Therefore, the overexpression of cyclin E1, cyclin E2, and CDK2 can subvert the CDK4/6 inhibition [11]. An interesting study conducted by Guarducci et al. showed that the ratio of cyclin E1 to RB1 level (not only cyclin E1 amplification and RB1 loss) is a poor prognostic factor and predicts palbociclib de novo resistance in HR+ breast cancer [26]. Herrera-Abreu et al. demonstrate in a study from 2016 that cyclin E1 is upregulated via CDK2 activation in palbociclib-resistant cells (that were generated via chronic exposure to the drug and named palbociclib-resistant MCF-7 breast cancer cells) [27]. In a phase II study (the NeoPalAna trial), researchers studied palbociclib resistance in patients with high levels of cyclin E1 [28]. Cyclin E1 overexpression was also predictive of an abemaciclib response to targeted therapy, as shown in the study conducted by Gong X et al. [29]. Next, gene expression analysis of 302 ER+ breast cancer samples from PALOMA-3 trial revealed that lower Cyclin E1 (CCNE1) mRNA levels were associated with a better response to palbociclib [30]. This association was confirmed in a preoperative setting, in the cohort of POP (PreOperative Palbociclib) trial [31]. Taking all this together, cyclin E1, cyclin E2, and CDK2 are upregulated in the CDK4/6 inhibitor resistance models [11]. This signaling pathway activation is another mechanism for both de novo and acquired resistance to CDK4/6i, with the hyperactivity of PI3K playing a role in endocrine-resistant mechanisms [17]. PIK3CA mutations could be identified in almost 40% of breast cancers with hormonal receptors [32]. Activating PIK3CA mutations could be a biomarker of either intrinsic resistance or acquired resistance. However, PI3KCA mutations have not been associated with resistance to CDK inhibitors in clinical studies to date [12]. One study identified that the PI3K pathway kinase (PDK1) was overexpressed in ribociclib-resistant cells [21]. Not only in ribociclib-resistant cell lines, but also in palbociclib-resistant cell lines, PIK3CA loss led to reduced proliferation of all cell lines regardless of RB status, as shown by Attia and colleagues in a 2020 study [21,33]. There are works in the literature that suggest adding a PI3K inhibitor, such as alpelisib, to CDK4/6i in order to circumvent the resistance mechanisms that develop for CDK4/6. It could be added after progression on CDK4/6i and ET (endocrine therapy), or from the start in triple combination to prevent the onset of resistance to the combination of CDK4/6i and ET (via modulation of early adaptive response) [34]. The mammalian target of rapamycin (mTOR) is implicated in cell cycle processes such as cell growth, size control, division, and proliferation, and it could be one of the reasons for CDK4/6i resistance. mTORC1 and mTORC2 are two different complexes that are formed by the mTOR kinase. A study conducted by Michaloglou and colleagues demonstrates that an mTORC1/mTORC2 inhibitor (vistusertib) could prevent early adaptive resistance to palbociclib in HR-positive breast cancer cells [35]. According to the specialty literature, the most frequent therapy used after progression on CDK4/6i is the mTOR inhibitor (everolimus) [36]. The AKT (serine/threonine kinase of the AGC kinase family) is activated via phosphorylation, which induces growth and survival. For this process, PDK1 (3-phosphoinositide dependent kinase 1) has an important role in the PI3K–AKT pathway. A low level of PDK1 makes tumor cells more sensitive to CDK4/6i [37]. On the other hand, a high level of AKT1 activity was seen in palbociclib-resistant cells [38]. Fibroblast growth factor receptor 1 (FGFR1) is a protein of the tyrosine kinase family that plays an important role in the cell cycle, being implicated in the migration, proliferation, differentiation, and survival of the cells. In more than 15% of breast cancers with hormone receptors present, a mutation of FGFR1 is found [39]. Thus, the causal relationship between FGFR1 mutations and endocrine therapy resistance has already been explained and demonstrated [13]. It is also important to find out if there is a connection between these mutations and resistance to CDK4/6 inhibitors. In order to do this, Formisano and colleagues showed that the cells that overexpressed FGFR1 were resistant to ribociclib and fulvestrant, and they also demonstrated that the cells that received an FGFR1 tyrosine kinase inhibitor (lucitanib) reversed the resistance. Moreover, the study highlighted a shorter PFS rate in those with FGFR overexpression among patients enrolled in the MONALEESA-2 clinical trial [38]. Surprisingly, the patients enrolled in the PALOMA-2 trial with FGFR2 amplification in the palbociclib + letrozol arm benefited from a longer PFS than those who were given placebo and letrozole [40]. In a study from 2019, Drago and colleagues [39] evaluated the clinical response to endocrine and targeted therapies in a cohort of 110 patients with HR+/HER2− metastatic breast cancer and validated the functional role of FGFR1-amplification in mediating response/resistance to hormone therapy in vitro. The investigators concluded that, while FGFR1 amplification confers broad resistance to ER, PI3K, and CDK4/6 inhibitors, mTOR inhibitors might have a unique therapeutic role in the treatment of patients with ER+/FGFR1+ metastatic breast cancer [39]. Another study conducted by Mouron et al. [41] included 251 patients with HR+ breast cancer and studied the role of ER, CDK4/6, and/or FGFR1 blockade alone or in combinations in Rb phosphorylation, cell cycle, and survival. They showed how hormonal deprivation leads to FGFR1 overexpression, thus being associated with resistance to hormonal monotherapy or in combination with palbociclib. Both resistances have been reverted with triple ER, CDK4/6, and FGFR1 blockade [41]. The RAS family of protooncogenes encodes three oncogenes, KRAS, NRAS, and HRAS, each with important roles in the cell cycle, such as apoptosis, growth, and differentiation. Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) is the most frequently mutated RAS gene [13]. Many studies over the last years have revealed how the engagement of RAS function might result in mandatory downstream varied oncogenic alterations for progression, metastatic dissemination, and therapy resistance in breast cancers [42]. In this direction, we found a review from 2019 conducted by Galie where he underlined the major studies over the last 30 years which have explored the role of RAS proteins and their mutation in breast cancer patients [42]. An overexpression of KRAS has been associated over the years with many types of cancer growth and development, including breast cancer resistance to CDK4/6i. A study from 2021 by Raimondi et al., who enrolled 106 patients with HR+ metastatic breast cancer, showed resistance to palbociclib and fulvestrant in the cells that developed a KRAS amplification. Moreover, the PFS was just three months for the subjects with KRAS mutations, whereas, in the other arm, the PFS had not even been reached by the 18-month follow-up [8,13]. Cells with KRAS, NRAS, and HRAS activating mutations are, therefore, susceptible to CDK4/6 inhibitor resistance [8,13]. Another important and well-studied biomarker of possible resistance to CDK4/6i is the loss of FAT1. FAT atypical cadherin 1 (FAT1) is among the most frequently mutated genes in many types of cancer [43]. This is a tumor suppressor gene, a member of the cadherin superfamily, which interacts with beta-catenin and Hippo signaling pathways. It is found in 6% of metastatic HR+ breast cancers [44]. Chen and colleagues performed a literature review on the diverse functions of FAT1 in cancer progression and presented the phenotypic alterations due to FAT1 mutations, several signaling pathways and tumor immune systems known or proposed to be regulated by this protein [43]. A study conducted by Li et al. on 348 patients treated with CDK4/6 inhibitors highlighted, after genetic sequencing, that patients with loss of FAT1 had a lower PFS compared to those with intact FAT1 (2.4 months and 10.1 months, respectively) and rendered cells resistant to all three CDK4/6i. The investigators highlighted that FAT1 loss is also associated with CDK6 overexpression via downregulation of the Hippo signaling pathway (through YAP and TAZ transcription factors) [45]. The role of FAT1 deleterious mutations was then confirmed in vivo. Cells with FAT1 knockout or knockdown did not stop cell growth upon exposure to abemaciclib, and MCF7-implanted xenografts experienced much less sensitivity to abemaciclib than mice with a non-mutated FAT1 gene [13,21]. PTEN is a tumor suppressor gene and one of the frequently mutated genes in human cancers [46]. The increased expression of PTEN leads to the inactivation of CDK, which enables the Rb1 to keep dephosphorylating, while binding to transcription factor E2F, which ultimately inhibits cell proliferation [47]. The overexpression of AKT could reduce PTEN expression and render breast cancer cells resistant to CDK4/6i [48]. Costa and colleagues performed an analysis of serial biopsies, which uncovered both RB and PTEN loss as mechanisms of acquired resistance to CDK4/6i. The investigators demonstrated that, in breast cancer cells, the ablation of PTEN through increased AKT activation was sufficient to promote resistance to CDK4/6 inhibition (ribociclib and letrozole) in vitro and in vivo; PTEN loss resulted in the exclusion of p27 from the nucleus, leading to increased activation of both CDK4 and CDK2 [49]. PTEN loss is rare in treatment-naïve ER-positive tumors [50,51]. The loss of PTEN confers resistance to PI3K inhibitors (alpelisib) [48], as well as cross-resistance to CDK4/6i and PI3K inhibitors [52]. Lee and colleagues conducted a retrospective analysis using real-world data, molecular biomarkers such as FGFR1 amplification, PTEN loss, and DNA repair pathway gene mutations, and showed a significant association of shorter PFS with CDK4/6i therapy [53]. S6K1 is a conserved serine/threonine protein kinase that belongs to the family of protein kinases, being the principal kinase effector downstream of the mammalian target of rapamycin complex 1 (mTORC1) [54]. S6K1 is an important regulator of cell size control, protein translation and cell proliferation [55]. S6K1 is one of the best-characterized downstream targets of mTORC1, and rapamycin treatment results in rapid dephosphorylation and inactivation of S6K1 [56]. The hyperactivation of mTORC1/S6K1 signaling may be closely related to ER-positive status in breast cancer, and may be utilized as a marker for prognosis and a therapeutic target [54]. A study from 2012 highlights that the S6K1–ER relationship creates a positive feed-forward loop in the control of breast cancer cell proliferation and, furthermore, the co-dependent association between S6K1 and ERα may be exploited in the development of targeted breast cancer therapies [57]. During the literature review, we found of interest a recent research article from August 2022 conducted by Mo and colleagues [58] regarding S6K1 amplification. The Chinese investigators demonstrated that S6K1 amplification confers innate resistance to palbociclib and ET through activating c-Myc pathway in 36 patients with ER+ breast cancer. In those who had received palbociclib, patients with high-expressed S6K1 had significantly worse progression-free survival and significantly worse relapse-free survival than those with low S6K1 expression. S6K1 overexpression was sufficient to promote resistance to palbociclib. S6K1 overexpression increased the expression levels of G1/S transition-related proteins and the phosphorylation of Rb, mainly through the activation of the c-Myc pathway. Mo et al. showed that this resistance could be abrogated by the addition of the mTOR inhibitor, which blocked the upstream of S6K1, in vitro and in vivo [58]. Aurora kinase A (AURKA) belongs to the family of serine/threonine kinases, whose activation is necessary for cell division processes via the regulation of mitosis. AURKA shows significantly higher expression in cancer tissues than in normal control tissues for multiple tumor types [59]. The amplification of the mitotic kinase AURKA has been identified in 11 out of 41 HR+ breast cancer biopsies from tumors resistant to CDK4/6 inhibitors, including examples of both intrinsic and acquired resistance, with no alterations detected in sensitive samples [60]. Aurora A has been previously shown to mediate endocrine resistance through the downregulation of ER expression in an SMAD5-dependent manner [61]. Two studies have shown that Aurora kinase A/B inhibition is synthetically lethal with RB1 deficiency in breast cancer and small-cell lung cancer cell lines [62,63], suggesting alternative therapeutic strategies for RB1-null tumors or new combinatorial strategies to prevent acquired resistances to CDK4/6 inhibitors [57]. c-Myc is a member of a family of protooncogenes that code for transcription factors, and is often overexpressed in cancer [64]. It is activated by phosphorylation, and in this form c-Myc is stable and allows cells to escape senescence. CDK2 and CDK4/6 inhibition decreases the phosphorylation of c-Myc, which destabilizes the gene and allows cells to enter the apoptosis process [13]. Mateyak et al. performed a comprehensive analysis and found that the largest defect observed in c-myc-/- cells was a 12-fold reduction in the activity of cyclin D1-CDK4/6 complexes during the G0 to S transition. The investigators suggested that c-Myc affects the cell cycle at multiple independent points, because the restoration of the CDK4 and 6 defect does not significantly increase growth rate [65]. Pandey et al. concluded in a study from 2020 that overexpression of c-Myc leads to palbociclib-resistant cells [66]. In the MONARCH-3 trial, 5% of patients with newly acquired c-Myc mutations were associated with resistance to abemaciclib + ET, and 9% of patients treated with abemaciclib alone in the MONARCH-1 trial acquired new Myc alterations [13,67]. MicroRNAs are non-coding RNA molecules involved in the post-transcriptional regulation of gene expression and regulate 30–60% of the human genome. MicroRNAs regulate the cell cycle through cyclin-dependent kinases and cyclins. The downregulation of miRNAs negatively regulates CDK6, which leads to CDK6 activation. CDK6 activation results in palbociclib resistance, as shown by Li and colleagues in a study from 2020 [44,68]. Moreover, in a retrospective analysis of 44 patients treated with CDK4/6i, microRNA levels were higher in those with intrinsic or acquired CDK4/6i resistance [69]. Krasniqi et al. summarized in their study that some miRNAs (such as miR-326, miR-29b-3p, miR-126, and miR3613-3p) are associated with sensitivity to CDK4/6 inhibitors, whereas others (such as miR-432-5p, miR-223, and miR-106b) appear to confer treatment resistance [70]. Identifying specific expression patterns of miRNAs could be a promising approach to study tumor response to CDK 4/6 inhibitors and exploit them as novel biomarkers [70]. Non-coding RNAs have been demonstrated to be strictly lineage-specific; their expression may therefore determine cell phenotype, allowing for the identification of specific tumor sub-populations resistant to CDK inhibitors [71]. Thymidine kinase 1 (TK1) is a DNA salvage pathway enzyme involved in regenerating thymidine for DNA synthesis and DNA damage [72]. It catalyzes the conversion of thymidine to deoxythymidine monophosphate, which is further phosphorylated to di- and triphosphates before its use for DNA synthesis [73]. In resting cells, observable TK1 activity is low to absent, increasing during G1/S transcription and peaking at S phase [74]. In healthy subjects, levels of TK1 are low to absent, with contrastingly elevated levels observed in patients with a range of malignancies, including breast cancer [75]. TK1 is a phosphotransferase that plays a role in DNA replication, is regulated by the E2F pathway, and is downstream of CDK4/6. Its activity is a marker of tumor proliferation. TKs’ activity has been shown to be a prognostic marker in patients with metastatic breast cancer, both when measured at baseline and during treatment. There are some clinical studies that support this statement [36,44]. A prospective monitoring trial (ClinicalTrials.gov NCT01322893) from Sweden, in which 156 metastatic breast cancer patients planned to start first-line systemic therapy, has reported that the TK1 activity level is prognostic for survival (decreases in TK1 levels from 3 to 6 months correlate to improved survival PFS and OS) in patients with newly diagnosed metastatic breast cancer [76]. McCartney and colleagues reported that intense TK1 activity is seen in cell lines resistant to palbociclib. The phase II TRend study also reported a shorter PFS for patients with high levels of TK1 than in the other arm [22,66] (3 months vs. 9 months) [77]. Another study (the ECLIPS trial) reported progressive disease in patients with metastatic breast cancer treated with palbociclib [78]. In the NeoPalAna trial, investigators observed an important reduction in TK1 activity after the initiation of palbociclib, suggesting a reduction in tumor proliferation [28,79]. Endocrine treatment is one of the most important approaches when it comes to ER+ breast cancers, and for metastatic disease it becomes the physician’s first choice, along with other targeted therapies (except in the case of a visceral crisis scenario, when chemotherapy should be the first choice). To date, some endocrine-resistant mechanisms have been described, including the upregulation of ER coactivators (e.g., FOXA1), cyclins (cyclin D and E), CDK proteins (CDK2 and CDK6), mitogen signaling pathways (PI3K and RAS pathways), or the downregulation of CDK inhibitor proteins (p16) [11]. As already known, CDK4/6 inhibition acts downstream of endocrine therapy; therefore, some resistance mechanisms are common to both types of treatments (endocrine therapy and CDK4/6 inhibitors) [11]. Among these resistance mechanisms, many studies and clinical trials have found a connection between estrogen receptor 1 (ESR1) mutations and acquired resistance to endocrine therapy. ESR1 mutations are the most important alterations resulting in resistance to aromatase inhibitor treatment, and can be found in almost 40% of metastatic breast cancer patients [80] and in approximately 20% of patients with endocrine-resistant breast cancer [81]. However, no association was found between ESR1 and CDK4/6i resistance. In the MONALEESA-2 trial, there was no correlation between ESR1 levels and response to ribociclib [80], and neither was there in the PALOMA-3 trial, where there was no link between ESR1 mutations and response to palbociclib [9]. Moreover, the PFS was improved both for patients with ESR1 mutations and for patients with non-mutated ESR1, demonstrating that this mutation does not affect treatment response. However, in the PALOMA-3 trial, at the end of the treatment 12.8% of patients developed new mutations in the ESR1 gene, with the Y537S mutation in particular [12]. Different results were observed in MONARCH-2, in which patients with ERS1 mutations showed an overall survival benefit [82]. O’Leary and colleagues also investigated PIK3CA mutations and concluded that both PIK3CA and ESR1 mutations were evenly distributed in both arms of the study, which leads to the idea that these mutations are more likely to affect the response to fulvestrant than to palbociclib [23]. The PALOMA-3 trial highlights the idea that ET resistance should be taken into consideration when talking about resistance to combination regimens in HR+/HER2-breast cancer. There is also an ongoing trial from Johns Hopkins University (NCT03439735) that studies the association between ESR1 mutations and clinical outcomes in patients treated with palbociclib and aromatase inhibitor as a first-line treatment regimen; its reported results should be available in June 2024. However, all three pivotal clinical trials (PALOMA-3, MONARCH-2, and MONALEESA-3) demonstrated that CDK4/6 inhibitors prolong PFS even after ET resistance, which demonstrates that CDK4/6i maintain effectiveness regardless of the endocrine-resistant disease. Additionally, endocrine-resistant tumors maintain sensitivity to CDK4/6 inhibitors, particularly when they are used in association with ET [11]. CDK4/6 inhibitors remain a landmark for the treatment of hormone receptor-positive and human epidermal growth factor receptor 2-negative metastatic breast cancer, being the most significant advance in the last decade. Various preclinical and translational research efforts have begun to shed light on the genomic and molecular landscape of resistance to these agents [83]. As we showed above, it is important to understand the mechanism of action of CDK4/6 inhibitors in order to target specific signaling pathways and predictive biomarkers of response, taking into consideration that intrinsic and acquired resistance could limit the activity of these inhibitors. In addition, one of the greatest challenges is distinguishing between mechanisms causing resistance to CDK4/6 inhibition and endocrine resistance. Approximately 10% of patients will have primary resistance to CDK4/6 inhibitors [84]. For instance, patients with evidence of functional Rb loss at baseline are not likely to benefit from CDK4/6 inhibition, or from increased cyclin E1/E2 expression. A rise in TK1 activity may also provide a marker of early resistance [84]. Mutations in RB1, resulting in the activation of other cell cycle factors, such as E2F and the Cyclin E-CDK2 axis, have been demonstrated in cases of acquired resistance [84]. In the table below (Table 2), we summarized the main resistance mechanisms and biomarkers of resistance, which we have previously reviewed. Following progression, no prospective randomized data exist to help guide second-line treatment [85]. While prospective data are needed, analysis of real-world data suggests a survival benefit for the continuation of CDK4/6i beyond a frontline progression for patients with HR+/HER2- metastatic breast cancer [85]. Several ongoing Phase 1 and 2 trials (MAINTAIN NCT02632045, PACE NCT03147287, NCT01857193, NCT 02871791, and TRINITI-1 NCT 02732119) are investigating the potential benefit of continuing CDK4/6i beyond progression [84]. For more successful treatment, biomarkers are of potential interest in order to identify patients who might be responsive or not to CDK4/6 inhibitors, facilitating an early switch to a more efficacious treatment. To date, no biomarker has been studied enough to be approved as a predictor of response to treatment or a targeted signaling pathway. Personalized treatment based on an individual’s response and tumor genomics represents the future of oncology. Therefore, it is a justification for future clinical trials because the identification of biomarkers of resistance is still a problem universally, and there is still more to be discovered about CDK4/6 inhibitor resistance. The optimum management of HR+/HER2-metastatic breast cancer is essential for patients as they might have only one more card to play, so future therapeutic targets should be examined in clinical trials to delay or overcome treatment resistance to combinations of ET and CDK4/6 inhibitors. In conclusion, we strongly believe that the validation of proposed biomarkers should be an option to consider before starting treatment with CDK4/6 inhibitors and hormonal therapy. This can be carried out via whole exome and targeted sequencing of solid and liquid biopsies, in order to reveal several possible genomic alterations that could change the course of treatment. In Romania, unfortunately there are few patients who can afford the costs of this type of testing. After doing such exhaustive research for this review, our personal opinion is that some biomarkers are worth testing more than others, such as loss of retinoblastoma protein. Some mechanisms of resistance, such as PI3K/AKT/mTOR or Cyclin E–CDK2 pathway activation, have already had their implication validated in resistance to CDK4/6i + ET; therefore it would be a worthy idea to take into consideration before starting the treatment. Breast cancer patients, maybe more than any other patients, are susceptible to depression and self-esteem loss, thus making any kind of treatment more difficult. We believe that a good start is always a better start and we do hope that in the near future breast cancer patients would benefit from the best personalized treatment.

PMC10000625

Kathryn J. Brayer,Huining Kang,Adel K. El-Naggar,Simon Andreasen,Preben Homøe,Katalin Kiss,Lauge Mikkelsen,Steffen Heegaard,Daniel Pelaez,Acadia Moeyersoms,David T. Tse,Yan Guo,David Y. Lee,Scott A. Ness

Dominant Gene Expression Profiles Define Adenoid Cystic Carcinoma (ACC) from Different Tissues: Validation of a Gene Signature Classifier for Poor Survival in Salivary Gland ACC

22-02-2023

oral cancer,biomarker,MYB oncogene,transcriptome analysis,bioinformatics,survival analysis

Simple Summary Adenoid cystic carcinoma (ACC) is a pathologically distinctive tumor that most often occurs in major or minor salivary glands, but can also occur in other tissues. We compared the gene expression profiles of ACC tumor samples from salivary gland, lacrimal gland, breast or skin. Despite their different tissues of origin, the ACC tumors displayed highly related patterns of gene expression. Indeed, gene expression patterns could not distinguish ACC tumors from different tissues, suggesting that genetic and epigenetic regulatory events induce a dominant ACC ‘phenotype’. We also used the new cohort of salivary gland ACC tumors to validate a gene expression biomarker developed with a previously analyzed cohort. The 49-gene classifier correctly identified 98% of the poor survival patients, validating the biomarker and suggesting that a clinical test should be developed so patients at highest risk of poor survival can be identified and provided additional treatment. Abstract Adenoid cystic carcinoma (ACC) is an aggressive malignancy that most often arises in salivary or lacrimal glands but can also occur in other tissues. We used optimized RNA-sequencing to analyze the transcriptomes of 113 ACC tumor samples from salivary gland, lacrimal gland, breast or skin. ACC tumors from different organs displayed remarkedly similar transcription profiles, and most harbored translocations in the MYB or MYBL1 genes, which encode oncogenic transcription factors that may induce dramatic genetic and epigenetic changes leading to a dominant ‘ACC phenotype’. Further analysis of the 56 salivary gland ACC tumors led to the identification of three distinct groups of patients, based on gene expression profiles, including one group with worse survival. We tested whether this new cohort could be used to validate a biomarker developed previously with a different set of 68 ACC tumor samples. Indeed, a 49-gene classifier developed with the earlier cohort correctly identified 98% of the poor survival patients from the new set, and a 14-gene classifier was almost as accurate. These validated biomarkers form a platform to identify and stratify high-risk ACC patients into clinical trials of targeted therapies for sustained clinical response.

Dominant Gene Expression Profiles Define Adenoid Cystic Carcinoma (ACC) from Different Tissues: Validation of a Gene Signature Classifier for Poor Survival in Salivary Gland ACC Adenoid cystic carcinoma (ACC) is a pathologically distinctive tumor that most often occurs in major or minor salivary glands, but can also occur in other tissues. We compared the gene expression profiles of ACC tumor samples from salivary gland, lacrimal gland, breast or skin. Despite their different tissues of origin, the ACC tumors displayed highly related patterns of gene expression. Indeed, gene expression patterns could not distinguish ACC tumors from different tissues, suggesting that genetic and epigenetic regulatory events induce a dominant ACC ‘phenotype’. We also used the new cohort of salivary gland ACC tumors to validate a gene expression biomarker developed with a previously analyzed cohort. The 49-gene classifier correctly identified 98% of the poor survival patients, validating the biomarker and suggesting that a clinical test should be developed so patients at highest risk of poor survival can be identified and provided additional treatment. Adenoid cystic carcinoma (ACC) is an aggressive malignancy that most often arises in salivary or lacrimal glands but can also occur in other tissues. We used optimized RNA-sequencing to analyze the transcriptomes of 113 ACC tumor samples from salivary gland, lacrimal gland, breast or skin. ACC tumors from different organs displayed remarkedly similar transcription profiles, and most harbored translocations in the MYB or MYBL1 genes, which encode oncogenic transcription factors that may induce dramatic genetic and epigenetic changes leading to a dominant ‘ACC phenotype’. Further analysis of the 56 salivary gland ACC tumors led to the identification of three distinct groups of patients, based on gene expression profiles, including one group with worse survival. We tested whether this new cohort could be used to validate a biomarker developed previously with a different set of 68 ACC tumor samples. Indeed, a 49-gene classifier developed with the earlier cohort correctly identified 98% of the poor survival patients from the new set, and a 14-gene classifier was almost as accurate. These validated biomarkers form a platform to identify and stratify high-risk ACC patients into clinical trials of targeted therapies for sustained clinical response. Adenoid cystic carcinoma (ACC) is one of the most common salivary gland malignancies, arising mainly in minor and major salivary glands, but ACC also occurs less frequently in other organs, and the clinical behavior of non-salivary ACC varies widely. This suggests that the ACC tumors arising in different organs may be biologically distinct or that they are affected by different host factors. Molecular analyses have shown that most ACC tumors have recurrent chromosomal translocations that activate the MYB oncogene or the related MYBL1 gene [1,2,3,4], resulting in characteristic gene expression changes [5,6]. The translocations frequently relocate a distant, salivary gland-specific enhancer in proximity to the MYB or MYBL1 genes, leading to their overexpression [7]. Many of the translocations occur within the MYB or MYBL1 genes, leading to truncation and overexpression of the genes and their gene products [5,6]. The MYB and MYBL1 genes encode the DNA-binding transcription factor proteins Myb (c-Myb) and A-Myb, which are important for normal development [8,9]. Relatively small changes in these proteins, such as truncations of the N- or C-terminal domains, can lead to profound differences in the genes they regulate [10,11,12,13], suggesting that the proteins perform complex regulatory functions. Indeed, the Myb protein can function as a ‘pioneer’ transcription factor capable of initiating the formation of new enhancers that can modify the expression of distant genes [14,15,16]. Thus, Myb or A-Myb proteins activated by C-terminal truncations may induce a specific ACC tumor phenotype [17], similar to the actions of Myb proteins in other types of cells and malignancies [18,19,20,21,22,23,24]. Identifying the ACC-specific regulatory mechanisms that induce an ‘ACC phenotype’ could lead to new types of therapies. ACC patients often have a slow clinical course with a poor long-term prognosis [4,25]. However, clinical outcomes can vary dramatically; unpredictable aggressive and progressive disease is not uncommon. Post-surgical survival ranges from just a few months to 15 years or longer. The protracted temporal progression of ACC tumors necessitates using archived samples at least 5–10 years old for studies linking genomic changes to outcomes. However, standard genomic methods are largely unsuitable for reliable RNA-sequencing (RNA-seq) analysis of archived samples, because the recovered RNA is often highly fragmented, necessitating the use of specialized approaches [5,6,26]. Despite these complications, several studies have identified subgroups of ACC patients with distinct molecular features linked to differences in prognosis and survival [5,6,27,28,29,30], suggesting that applying these approaches to well-structured retrospective cohorts of ACC tumors could produce biomarkers for identifying poor prognosis patients and recommending them for targeted therapy. In previous studies, we were able to use optimized RNA-seq approaches to successfully analyze the transcriptomes of archived, formalin-fixed, paraffin-embedded ACC tumor samples up to 25 years old, which led to the identification of the first ACC tumors with MYBL1 translocations [5]. Extending those studies to a larger cohort of 68 samples (the TX cohort) led to the identification of several subgroups of ACC tumors with unique gene expression signatures, including one subgroup with poor survival and a ‘No Myb’ group that expressed neither MYB nor MYBL1 [6]. Although we identified gene expression signatures that correlated with poor survival, it was not possible to validate the results using only one cohort of samples. Here we describe the analysis of a new cohort of ACC tumor samples, from Denmark and Florida (the DK cohort), primarily from salivary gland but also including some ACC tumors from the lacrimal gland, breast, and skin. The availability of the large cohorts allowed us to designate a training set for defining a gene expression classifier to distinguish poor survival samples, which we validated using the second cohort. These results set the stage for using clinical RNA-seq assays for identifying patients who are likely to be in the poor survival subgroup, so they can be offered clinical trials or additional treatment to improve their outcomes. De-identified adenoid cystic carcinoma tumor samples were obtained from several institutions: the Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital; the Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Rigshospitalet; the Department of Pathology, Rigshospitalet, University of Copenhagen; and the Department of Ophthalmology, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark. Some lacrimal gland samples were obtained from the Dr. Nasser Al-Rashid Orbital Vision Research Center and the Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine. All samples were provided Formalin-Fixed and Paraffin-Embedded (FFPE) as 5-micron sections baked onto glass slides. Salivary gland samples with survival information had at least 5-year follow-up. All samples were collected in accordance with the principle of the Declaration of Helsinki and with Institutional Review Board-approved protocols: Danish Regional Ethics Committee (H-6-2014-086) and the Danish Data Protection Agency (Journal no. REG-94-2014). Total RNA was isolated from one or two 5-micron slide-mounted FFPE sections using the RNeasy FFPE kit (Qiagen, 19300 Germantown Rd Germantown MD 20874, USA). cDNA synthesis and library preparation were performed using the SMARTer Universal Low Input RNA Kit for Sequencing (Takara 1290 Terra Bella Avenue Mountain View, CA 94043, USA) and the Ion Plus Fragment Library Kit (ThermoFisher, 168 Third Avenue, Waltham, MA 02451, USA), as previously described [5,6]. Ion Torrent sequencing using Ion S5/XL systems (ThermoFisher) was performed in the Analytical and Translational Genomics Shared Resource at the University of New Mexico Comprehensive Cancer Center. Data are available for download from the NCBI BioProject database using study accession number PRJNA287156. The TX cohort of samples has been described previously [6]. Low quality and non-human RNA-seq reads were filtered and removed using the Kraken2 suite [31,32,33]. High-quality reads were aligned to the human genome (hg38) using TMAP (v5.10.11), and gene counts were calculated using HT-Seq, as previously described [5,6]. Poor quality samples with fewer than 10% of the median number of reads of all samples were removed. Samples that failed other quality control tests were also removed. The same parameters were used when the data from the new (DK) cohort were combined with the previously described samples in the TX cohort [6]. (Software versions are provided in File S1). For identifying clusters, analyses were limited to genes that were expressed above a threshold level in a number of samples (e.g., 250 reads in at least 10 samples). These thresholds were reduced (e.g., to 50 reads in at least 10 samples) to generate the final heatmaps, to include as many relevant genes as possible, while retaining the clusters and the sample order. Multi-dimensional scaling was performed using plotMDS from the limma package version 3.48.0. Hierarchical clustering was performed using hclust from the stats package (R/Bioconductor version 4.1.0) [34]. Overall survival (OS), defined as time from the date of diagnosis to the date of death, was the primary endpoint for outcome. Subjects who were lost to follow-up or alive within the follow-up period were censored at the date of the last contact. OS was estimated using the Kaplan–Meier method. Differences in OS were examined using the log-rank test. All the statistical analyses were performed using statistical software R (version 4.1.0) [34]. Gene Ontology analyses were performed with Bioconductor package GO.db version 3.13.0, as previously described [5,6,10,26]. Personalized logistic regression with the elastic net and LASSO regulations implemented in R package glmnet [35] was used to develop the classifiers that distinguish between the poor prognosis group and the remaining samples. ROC curve and the area under the curve (AUC) were used to evaluate the performance of the classifiers. The prognostic models were built using the training data set, i.e., the TX cohort, through a 10-fold cross-validation (CV) procedure. An unbiased estimate for each of the final models was obtained by performing a nested CV procedure that included the full cycle of the 10-fold inner loop CV followed by a 100 × 6-fold outer loop CV using the training set. The prediction accuracy of each model was further validated using an independent test set, i.e., the DK cohort. Although they most commonly occur in major and minor salivary glands, ACC tumors can also arise in lacrimal, bronchial, mammary, or skin adnexal glands. To assess the similarities and differences in gene expression patterns in ACC tumors from different tissues, we performed RNA-sequencing (RNA-seq) on a cohort of 113 ACC tumors, comprised of 17 samples from breast tissue, 24 from cutaneous tissue, 16 from lacrimal glands, and 56 from salivary glands (Table 1). Most of the samples came from Denmark (DK cohort), with the exception of 6 lacrimal gland samples from Florida (FL). We used optimized methods for RNA analysis and Ion Proton sequencing that we developed previously [5,26]. For the DK cohort, 91 samples (81%) clearly expressed MYB, while 13 (12%) expressed MYBL1 and 9 (8%) expressed neither MYB nor MYBL1. We first performed Multi-Dimensional Scaling (MDS, i.e., principal component analysis) for the ACC samples. As shown in Figure 1A, the dots representing ACC tumor samples from different organs are shaded with different colors (see legend). For comparison, we included RNA-seq results from several normal salivary gland tissues (shaded black) and several from a histologically different salivary gland tumor, acinic cell carcinoma (shaded gray), that have been described previously [5,28]. All the ACC tumor samples clustered into a large group at the right, suggesting that the transcription profiles of the ACC tumors are more similar to each other than they are to normal tissue or other salivary gland tumors, despite the different tissues of origin. Next, we used unsupervised hierarchical clustering to group samples that were similar and generated the heatmap comparing the transcriptional profiles shown in Figure 1B. Interestingly, as shown by the dendrogram at the top of the heatmap, the ACC tumor samples formed several large subgroups, but each cluster contained samples from all the tissue types: salivary gland, lacrimal gland, breast and cutaneous (dark blue, orange, pink and green in the color bar at top, respectively). Thus, although the ACC samples in this cohort are heterogeneous and formed distinct subgroups, the groups are not defined by the tissue of origin. Instead, the subgroups could represent biological differences amongst the ACC samples, irrespective of the tissue from which they were derived. These results differ somewhat from a previous report showing that microRNA expression profiles could distinguish ACC tumors from different tissue types [36], suggesting that the biological mechanisms leading to the formation of ACC tumors may have a more profound impact on the overall mRNA transcriptional profile than on microRNAs, which may be more tissue-specific. Several genes that are known to be important in ACC tumors are highlighted with black dots at right, including (from top to bottom) EN1, GABRP, MYB, MYBL1 and NFIB, all of which are expressed more highly in the ACC tumors than in the normal salivary gland or acinic cell carcinoma samples (at left). Since our initial hierarchical clustering did not separate ACC tumors by tissue type, we reanalyzed the data to see if we could identify tissue-specific gene expression patterns in the tumors. We specifically selected genes that were differentially expressed in the ACC tumors originating in different tissues. The heatmap in Figure 2 summarizes the results when the most tissue-specific genes are chosen for display (and the normal salivary gland and acinic cell carcinoma samples are left out). This type of analysis led to better clustering of the ACC samples from lacrimal gland (orange, left), salivary gland (blue), cutaneous (green) and breast (pink). There were specific genes that were up-regulated in some ACC samples compared to the others. For example, at the far left of the heatmap (labeled A at bottom, orange color bar at top) is a group of ACC tumors, mostly from the lacrimal gland, that overexpress the OPRPN gene, which encodes the Opiorphin Proline-Rich Lacrimal Protein 1 (previously named PROL1). The next group (labeled B) are salivary ACC (blue color bar at top) and overexpress several salivary-gland specific genes including MUC19, CA6, MUC7, SMR3B, LPO, BPIFA2, CST5, CST2, CST1 and CST4 (marked by blue dots at lower right of figure). Group C also contains salivary ACC samples with a few from other tissues, and several overexpress KRT4 and KRT13. Most of the cutaneous samples clustered in a group (D, green color bar) and are identified by overexpression of FLG, KRT10, FLG2, KRT6A and KRT1. However, the remaining ACC tumors formed a large cluster (labeled E), which included most of the breast ACC tumors as well as samples from salivary, lacrimal and cutaneous adnexal glands. The last group was notable because the samples failed to express the gland-specific marker genes that defined the other groups, suggesting that they had a more de-differentiated or perhaps more stem cell-like phenotype. Thus, while we were able to identify tissue-specific marker genes in some ACC tumors, the specificity was not absolute and there remained significant heterogeneity in the gene expression patterns of different samples. Also, since tumor samples always contain some normal cells, the tissue-specific differences that were detected could be due to the non-tumor cells in the samples. We conclude that an ACC-specific gene expression pattern dominated the tumors, apparently overriding the tissue-specific differences. Previous studies of ACC tumor samples identified subgroups of tumors with distinct gene expression and survival characteristics [6]. We carefully evaluated the new cohort of salivary gland ACC tumors for evidence of subgroups with distinct gene expression patterns. Figure 3A shows a multi-dimensional scaling plot of the 56 DK cohort salivary gland samples. Most of the tumors (shaded light blue) form a large cluster but a small group of tumor samples (brown) formed a separate group at the upper left corner of the plot. Figure 3B shows a Kaplan–Meier survival analysis: the samples in the brown group had a median survival of only 8 months, compared to the main group (light blue), which showed a median survival of 80 months (p-value = 0.006). This is reminiscent of our previous results with the TX cohort, where a subgroup of ACC tumors displayed similarly poor survival [6]. Some ACC tumors display a ‘solid form’ morphology, which has been associated with worse prognosis [37,38]. Although ‘solid morphology’ ACC tumors were excluded from the TX cohort, the DK cohort contains 11 such samples: 5 in the poor prognosis group and 6 in other group. This suggests that the poor prognosis group is not defined simply by solid tumor morphology. The differential gene expression analysis identified 273 genes at least 2-fold up- or down-regulated in the brown subgroup (adjusted p-value < 0.05). The results for 85 of the genes are summarized in the heatmap in Figure 3C (The poor-survival subgroup cluster is at the left side of the heatmap, marked by the brown color bar at the top). In the heatmap, all the genes that were regulated in similar directions both in this new DK cohort and also in the previously described TX cohort (e.g., up-regulated in both poor survival groups) are marked by bars along the right edge of the figure. Genes that were up-regulated in both poor-survival subgroups include (from bottom of the heatmap) CD37, SERPINE2, CDK19, PRLR, and RPL23. Down-regulated genes include AQP3, SCNN1A, LTF, ELL2, and DKK3. These results further indicate that a subgroup of ACC tumors from patients with poor survival display a unique gene expression profile that could potentially be used for prognostication [6]. The results described above suggest that the new DK cohort of ACC tumor samples contains subgroups of patients that are very similar to the subgroups we identified previously in the TX cohort [6]. To compare the subgroups we combined the RNA-seq results of the two independent cohorts and performed a unified analysis of 124 salivary gland ACC samples (56 from DK and 68 from TX). Figure 4A shows the multi-dimensional scaling plot of the combined data sets, which form three main groups. The largest group, in the middle of the plot, have been shaded dark blue or cyan to indicate that they overexpress MYB or the related MYBL1 gene, respectively. A group at the upper left is shaded red and contains the poor survival samples from both cohorts, all of which express MYB. Finally, a group of samples at the right, shaded orange, express neither MYB nor MYBL1 (‘no MYB’). This group was described previously, and the ‘driver’ oncogenes or mutations responsible for that group remain unknown [6]. Although these cohorts of ACC samples were completely independent and the patients came from different countries, both cohorts formed similar major subgroups when analyzed together. A Kaplan–Meier survival analysis of these groups is shown in Figure 4B. The overall survival for patients in the orange ‘no Myb’ group was similar to the main group of samples expressing either MYB or MYBL1. As described above, the red group displayed much worse survival compared to the other patients. While the median survival for most patients exceeded 120 months, including the orange ‘no MYB’ group, median survival for the red group was only 16.8 months (p-value < 1 × 10−6). These groups were segregated using only their different gene expression characteristics, suggesting that biomarkers could be developed to identify the patients in the poor survival group at the time of surgery. The MDS plot in Figure 4C shows the large overlap in the DK and TX cohorts, despite being analyzed separately and several years apart. Most ACC tumors have recurrent chromosomal translocations that activate the MYB oncogene or the related MYBL1 gene [1,2,3,4], but this raises questions about the underlying biology and driver genes active in the remaining ACC tumors that do not express MYB or MYBL1. As shown in Figure 4B, the ‘no MYB’ subgroup of tumors (orange line) had survival similar to the bulk of ACC samples (blue and cyan lines). To further explore the potential driver genes in these samples, we compared them to the rest of the ACC samples in the combined cohort and performed a differential gene expression analysis. In the heatmap shown in Figure 5, the dendrogram at the top shows the hierarchical clustering that was used to arrange the samples from left to right. The ‘no MYB’ samples are at the far right (marked by orange at the top). The heatmap summarizes the gene expression differences for 124 of the 881 genes that were differentially expressed (at least 2-fold up- or down-regulated, adjusted p-value < 0.05) when the ‘no MYB’ samples were compared to all the others. The top 10 up- or down-regulated genes are listed in Table 2, and the full list is provided in Supplementary Data (Table S1). There are several important conclusions from this analysis. First, as described previously [5,6], the ACC samples that express MYBL1 do not form their own subgroup, but mix in with the samples expressing MYB, suggesting that the two oncogenes have similar effects on gene expression patterns [6]. Second, each of the three main groups (orange, red, blue) contains samples from both the TX and DK cohorts, suggesting that these subgroups are consistent in ACC tumors and are not a characteristic unique to just one cohort or one analysis. Several interesting genes are marked along the right side of the heatmap, including AFF1, EBF1, EMP1, ZFP36, FOXO1, and SFRP2, which are all up-regulated in the ‘no MYB’ tumors (marked by orange dots). The SHANK2, NFIB, GABRP, MEX3A, PRLR, and MYB genes were down-regulated in the ‘no MYB’ tumors (marked by blue dots). A gene set enrichment analysis identified a number of Gene Ontology Cellular Process categories that were over-represented in the differentially expressed genes. The top six categories are described in Table 3. The finding that the ‘no MYB’ samples have such a dramatically different gene expression profile reinforces the conclusion that the ACC phenotype can be achieved through different regulatory pathways. For the combined cohorts, the poor survival subgroup, marked by red at the top of the heatmap in Figure 5 and in the survival plot in Figure 4B, displayed a median survival of only 22 months, compared to greater than 123 months for the other patients. This suggests that a gene expression panel could be developed to identify patients at highest risk of poor survival. To characterize the poor survival subgroup in more detail, we performed in depth analysis of the gene expression patterns of tumors from these patients. The heatmap in Figure 6 summarizes the results of a differential gene expression analysis comparing the poor survival subgroup samples to all the other ACC tumor samples in the combined cohort. The samples are arranged in the same left-to-right order as in Figure 5, using the dendrogram generated by hierarchical clustering, and the poor-survival samples are indicated by the red color bar at the top. The samples from the TX and DK cohorts are indicated by the gray and purple color bar at the bottom, respectively. The heatmap summarizes the relative expression of the 124 most differentially expressed genes out of the 729 genes that were at least 2-fold up- or down-regulated (with adjusted p-values > 0.05). Several notable up- or down-regulated genes are marked by red or gray dots, respectively, along the right side of the heatmap. The genes that were up-regulated in the poor survival samples include EZH2, HDAC2, PRLR, SOX8, NFIB, SHANK2, and ADARB1. The down-regulated genes include CND2, TP63, AQP3, NTRK3, and ADARB2. The top 10 up- or down-regulated genes are listed in Table 4 and the full list is provided in Supplementary Data (Table S2). Interestingly, there is no single gene that is specifically up- or down-regulated only in the poor survival samples, or that could be used to identify either the poor survival or better survival patients, suggesting that a multi-gene biomarker could be developed to identify the patients in the poor-survival subgroup. As shown in Figure 3, the DK cohort of ACC samples contained a subgroup of patients with poor survival, similar to one that was originally identified in the TX cohort [5,6]. Having patients from two independent cohorts allowed us to use the original TX cohort as a training set to develop a multi-gene biomarker panel, which could be validated with the DK cohort. Starting with expression data for 3597 genes expressed above a threshold level in the training set (TX cohort), we used an elastic net type of penalized logistic regression model to identify genes that could distinguish the poor prognosis cohort from the rest of the patients. The model selection was performed with a 10-fold cross-validation and yielded a 49-gene classifier developed solely with data from the TX cohort (Table 5). The ROC curve analysis was used to evaluate the classifier’s accuracy. As shown in Figure 7, the elastic net classifier could distinctly separate the poor prognosis samples from others in the training set (TX cohort, left panel) as AUC = 1. An unbiased estimate for the AUC (AUC = 1) was also achieved through a double loop nested cross-validation, which showed a perfect classification performance. However, the accuracy achieved was expected because the same gene expression data were used to develop and test the classifier. Importantly, the classifier developed with the TX cohort also gave nearly perfect (AUC = 0.984) separation on the independent DK cohort test set (right panel). We also generated a 14-gene subset of the classifier using a Least Absolute Shrinkage Selection Operator (LASSO) approach. The 14-gene classifier containing genes A2M, ACTA2, ANO1, APOL6, DMD, IPO9, LIMCH1, MAMLD1, MIR205HG, PLAT, RASSF6, SEMA3C, SLPI, and TP63 (shown in bold in Table 4) was only slightly less accurate, with AUC = 0.976. These results suggest that a gene classifier can be used to identify ACC patients with poor prognosis. To illustrate their usefulness, we tested the genes in the classifiers on the combined DK and TX cohorts of salivary gland ACC samples. We limited the data sets to only the 49-gene or 14-gene lists (except that MYB, MYBL1, and NFIB were added back for comparison), and performed hierarchical clustering, which identified the two major clusters shown in the dendrograms in Figure 8A,B. The heatmaps display the differences in gene expression. Interestingly, most of the classifier genes were down-regulated in the poor prognosis tumors compared to the other samples. As an example, the TP63 tumor suppressor gene is significantly down-regulated in the poor prognosis group. The poor prognosis tumors appear to lack the expression of specific genes that are expressed by the other ACC samples. Notably, only 4 of the 11 ‘solid form’ morphology samples from the DK cohort were in the poor prognosis subgroup, suggesting that solid morphology is insufficient to classify samples as poor prognosis [37,38]. As shown by the color bar at the top of each heatmap, some of the samples that were in the poor prognosis subgroup described in Figure 4 (marked red) did not cluster with the poor survival samples identified by the gene classifiers, and a few samples that were not included above did cluster in the poor survival group in this analysis. However, as shown in the Kaplan–Meier survival plots in Figure 8C,D, the classifiers did identify a poor-prognosis group with median survival of less than 20 months, compared to a median of 125 months for the rest of the samples. In addition, none of the poor-prognosis patients identified by the classifiers survived 10 years, while more than half of the other patients survived at least 10 years. Thus, the multi-gene classifiers identified using the TX cohort samples were able to identify a subset of ACC patients in the independent DK cohort, which validates the classifiers and suggests that adapting them to the clinic could be useful. To examine whether the gene classifier provides more information for survival outcomes beyond that contained in the clinical covariates, we performed univariate and multivariate Cox regression analyses, with the gene classifier and clinical covariables deemed to be the risk factors as predictors. (Details of the analysis are in File S1). The available clinical covariables include Margins (free or close), Vascular Invasion (yes or no), Radiotherapy (yes or no), Cribriform (tubular or solid), and Stage (I-II or III-IV). The analyses were restricted to 56 samples; a union of the subsets to which the data of each variable are available. However, the number of samples used by each Cox regression analysis varied subject to data availability. The univariate and bivariate analyses are in the Supplementary Materials, and the multivariate analysis is reported in Table 6. The univariate analysis (see Supplementary Materials) showed that the two variables, Vascular Invasion and Cribriform, were significantly associated with survival outcomes (p < 0.05), while the variable Stage was marginally significant (p = 0.084). We compared these three variables with the gene classifier through bivariate Cox regression (see Supplementary Materials). The result showed a remarkable association between our gene classifier and survival after adjusting for each clinical covariate’s effect. We further performed a multivariate Cox regression (Table 6), and our gene classifier was still significantly correlated with the survival outcomes after adjusting for Vascular Invasion and Stage effects. Note that the Cox regression with three or more variables will not converge if we include Cribriform in the model, which limits our ability to conduct further investigation in this respect. However, the results have given sufficient statistical evidence that our gene classifier provided more information about the survival outcome than the available clinical parameters. We compared the transcription profiles of ACC tumor samples that arose in very different tissues: salivary gland, lacrimal gland, breast, and skin. Despite being from different tissues, all ACC tumors had markedly similar gene expression profiles. Indeed, the ACC samples were much more similar to each other than they were to normal salivary gland tissue or another type of salivary gland tumor, acinic cell carcinoma [28]. These results demonstrate that ACC tumors arising in different tissues are highly related and are difficult to distinguish using gene expression patterns alone. Interestingly, different types of ACC tumors were shown previously to have distinct patterns of microRNA expression [36]—a result that we could not reproduce using gene expression results. This suggests that the activated MYB or MYBL1 oncogenes may induce an ACC-specific gene expression pattern that affects protein-coding genes much more than microRNAs. This is a fascinating biological difference that could be important for explaining tumor phenotypes and some aspects of tissue differentiation. Having RNA-seq data from a new set of ACC samples provided us with the opportunity to perform a validation cohort analysis. Despite the challenges that exist for translating RNA sequencing (RNA-seq) results into widely used clinical assays [39], several types of gene expression signatures have been developed for clinical use [40,41,42]. In this study, we used RNA-seq data from a previous cohort of 68 salivary gland ACC samples to develop a 49-gene expression classifier for identifying a subgroup of patients with poor survival. We then validated the result using results from the new cohort of 56 salivary ACC samples, finding that the biomarker was able to distinguish 98% of the poor survival patients. A smaller 14-gene classifier achieved similar results with slightly less accuracy. Salivary gland ACC patients display widely variable outcomes, with some patients surviving decades after surgery and others succumbing after only a few months [5,30,43]. It seems clear that the development of new clinical trials should be targeted to the ACC patients that are most likely to have a recurrence and die from the disease. The validated biomarker we have described should have important utility, if it can be developed into an assay suitable for clinical laboratory use. Although our results do not suggest a new or modified treatment for ACC patients, they do suggest that developing a suitable biomarker assay to identify the worse prognosis patients is worthwhile so a new therapeutic strategy could be developed for them. Clinical RNA-seq is fast becoming a routine assay for cancer patients, so these biomarkers should be adaptable to clinical laboratories. Some ACC tumors display a ‘solid form’ morphology, which has been associated with worse prognosis [37,38]. Other clinical features, such as advanced tumor stage, lack of clean margins during surgery, or vascular invasion, might also be used to identify higher risk patients. However, in our analysis, none of these other markers were able to identify the poor prognosis group of patients that we identified using gene expression patterns. Therefore, we conclude that the gene classifiers provide a novel and independent means of distinguishing poor prognosis ACC patients that should be pursued and studied further. The next step will be to develop assays that work in clinical laboratories so that these classifiers can be used to identify patients that should be targeted for clinical trials or more aggressive therapy to improve their survival. In addition to identifying and validating a multi-gene classifier for ACC patients, analyzing a new cohort of 56 salivary gland ACC samples from Denmark (DK) also validated important biological results that we described previously using 68 ACC patient samples from the Salivary Gland Tumor Bank in Texas (TX) [5,6]. The main result is that ACC patients can be divided into at least three distinct groups based on gene expression signatures. These groups are easily discernable in the multi-dimensional scaling plots (e.g., see Figure 4). The samples in the main group, comprising 76% of the total, express either MYB or MYBL1 and have a median survival of more than 10 years after surgery. A second group, about 10% of the samples with survival similar to the main group, express neither MYB nor MYBL1. These samples have a unique gene expression signature, suggesting a different mechanism driving the malignancy. The samples in the final group, about 14% of the total, are the focus of the multi-gene classifier because they have much worse survival than the rest of the ACC patients. Although the detailed transcriptome analyses that we performed were able to discern distinct subgroups of ACC tumor types, the bulk RNA-sequencing does not provide information on cell lineage composition within tumors. Thus, it is not clear if the different subgroups result from the unique features of different types of ACC tumor cells or whether the subgroups are due to differences in cellular composition in the tumors. Addressing those questions will require using single-cell genomics assays or spatial genomics approaches that can discern different cell types in the tumors. Our somewhat surprising result is that ACC tumors arising in different tissues or organs have remarkably similar transcriptional profiles. Indeed, we were unable to identify gene signatures that distinguished the ACC tumors from different organs. This may point to an important underlying biology in ACC tumors that makes them so similar. Since the majority of ACC tumors overexpress the MYB or MYBL1 genes, the dominant ACC phenotype may be induced by the activated Myb transcription factors. A second, but very important finding is that RNA sequencing analysis can be used to identify a subgroup of MYB-expressing salivary gland ACC patients with poor prognosis. We were able to use the new DK cohort of ACC samples to validate a biomarker developed with an earlier (TX) cohort. This is especially important for diseases like ACC, in which many patients survive more than 10 years post-surgery. Our results provide a tool for identifying the patients that should be enrolled in clinical trials of targeted therapies to improve their outcomes.

PMC10000627

Irène Tatischeff

Extracellular Vesicle-DNA: The Next Liquid Biopsy Biomarker for Early Cancer Diagnosis?

24-02-2023

extracellular vesicles (EVs),exosomes (EXs),liquid biopsy (LB),EV-associated DNA (EV-DNA),cancer diagnosis

Simple Summary Each human cancer is a specific disease, but all cancers share the necessity of an early diagnosis for providing the optimal outcome for the patient. Liquid biopsy in blood, as a substitute to invasive tissue biopsies, brought the first important breakthrough for cancer diagnosis. The race for efficient cancer biomarkers was first focused on the few circulating tumor cells released in the bloodstream, then on circulating cell-free tumor DNAs in plasma and serum. The last decade’s discovery of the ubiquitous cell-derived extracellular vesicles (EVs) brought a new “treasure chest” for the worldwide search of cancer biomarkers among the many tumor EVs-associated biological components. The aim of this review is to follow the different steps—mostly in vitro and preclinical liquid biopsies—which focused the current interest on tumor EVs-associated DNA as a promising cancer biomarker that still has many challenges yet to be solved before reaching the clinic. Abstract After a short introduction about the history of liquid biopsy, aimed to noninvasively replace the common tissue biopsy as a help for cancer diagnosis, this review is focused on extracellular vesicles (EVs), as the main third component, which is now coming into the light of liquid biopsy. Cell-derived EV release is a recently discovered general cellular property, and EVs harbor many cellular components reflecting their cell of origin. This is also the case for tumoral cells, and their cargoes might therefore be a “treasure chest” for cancer biomarkers. This has been extensively explored for a decade, but the EV-DNA content escaped this worldwide query until recently. The aim of this review is to gather the pilot studies focused on the DNA content of circulating cell-derived EVs, and the following five years of studies about the circulating tumor EV-DNA. The recent preclinical studies about the circulating tEV-derived gDNA as a potential cancer biomarker developed into a puzzling controversy about the presence of DNA into exosomes, coupled with an increased unexpected non vesicular complexity of the extracellular environment. This is discussed in the present review, together with the challenges that need to be solved before any efficient clinical transfer of EV-DNA as a quite promising cancer diagnosis biomarker.

Extracellular Vesicle-DNA: The Next Liquid Biopsy Biomarker for Early Cancer Diagnosis? Each human cancer is a specific disease, but all cancers share the necessity of an early diagnosis for providing the optimal outcome for the patient. Liquid biopsy in blood, as a substitute to invasive tissue biopsies, brought the first important breakthrough for cancer diagnosis. The race for efficient cancer biomarkers was first focused on the few circulating tumor cells released in the bloodstream, then on circulating cell-free tumor DNAs in plasma and serum. The last decade’s discovery of the ubiquitous cell-derived extracellular vesicles (EVs) brought a new “treasure chest” for the worldwide search of cancer biomarkers among the many tumor EVs-associated biological components. The aim of this review is to follow the different steps—mostly in vitro and preclinical liquid biopsies—which focused the current interest on tumor EVs-associated DNA as a promising cancer biomarker that still has many challenges yet to be solved before reaching the clinic. After a short introduction about the history of liquid biopsy, aimed to noninvasively replace the common tissue biopsy as a help for cancer diagnosis, this review is focused on extracellular vesicles (EVs), as the main third component, which is now coming into the light of liquid biopsy. Cell-derived EV release is a recently discovered general cellular property, and EVs harbor many cellular components reflecting their cell of origin. This is also the case for tumoral cells, and their cargoes might therefore be a “treasure chest” for cancer biomarkers. This has been extensively explored for a decade, but the EV-DNA content escaped this worldwide query until recently. The aim of this review is to gather the pilot studies focused on the DNA content of circulating cell-derived EVs, and the following five years of studies about the circulating tumor EV-DNA. The recent preclinical studies about the circulating tEV-derived gDNA as a potential cancer biomarker developed into a puzzling controversy about the presence of DNA into exosomes, coupled with an increased unexpected non vesicular complexity of the extracellular environment. This is discussed in the present review, together with the challenges that need to be solved before any efficient clinical transfer of EV-DNA as a quite promising cancer diagnosis biomarker. Cancer is a major burden on humanity, as recapitulated by Globocan, the Global Cancer Statistics 2020, concerning 36 cancers, with regard to their respective incidence and mortality in men and women from 185 countries worldwide (https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21660, accessed on 31 October 2021). Human cancer is still a mysterious multiform disease, and each organ-specific cancer has to be considered as a unique disease, with some common hallmarks. They also share in common the necessity of an early diagnosis for an optimal outcome for the patient. Besides the many sophisticated technologies now available for asserting a cancer diagnosis, liquid biopsy, first in blood (serum, plasma) and now in many other body fluids (urine, cerebrospinal fluid, saliva), has brought the hope of an efficient cancer signature for significantly helping an early diagnosis. Many components released from the tumor cell machinery during life and death might be candidates for being noninvasive cancer “whistleblowers”, which explain the already long-lasting query about the most promising liquid biopsy biomarkers. Liquid biopsy in blood already has a long history as a promising substitute for tissue biopsy for cancer diagnosis. Cancer biomarkers were first focused on rare circulating tumor cells (CTCs), followed by cell-free tumor DNAs (cf-tDNAs). Recently, circulating tumor extracellular vesicles (cir-tEVs) became the third most interesting resource for cancer liquid biopsy [1,2]. The high EVs heterogeneity has been classified into three main EV categories, according to their size, biogenesis, composition and biological properties [3,4]: apoptotic bodies (ABs) (50 nm–5 µm in diameter), microvesicles (MVs) (100 nm–1 µm) and exosomes (EXs) (30 nm–150 nm). Due to the lack of specific vesicle biomarkers and the EVs overlapping size properties, it is presently difficult to efficiently discriminate the different EVs; therefore, they currently share the generic name of extracellular vesicles (EVs) [5]. The release of different types of extracellular vesicles (EVs) is recognized as a new important common cell property, extending each cell influence well beyond its plasma membrane. For about a decade, EVs have been recognized as important messengers of intercellular communication, and nowadays, their major biological functions in human health and disease are highly investigated. With regard to their recent involvement as circulating EVs (cirEVs) in many body fluids for diagnosis of human diseases including cancers, the smallest vesicles, i.e., mainly exosomes, are the most considered. As recently reviewed [1,2], an increasing worldwide search is focused on finding the most relevant biomarkers to achieve early diagnosis of different human cancers among the many macromolecular components, which are specifically carried inside the rich cargoes of the numerous circulating tumor EVs (cir-tEVs). Although cf-tDNAs was the second important resource for cancer liquid biopsy, EV-DNA remained long ignored as a tumoral biomarker. The aim of the present review is to point out the recent studies which shed light on the potential capacity of cir-tEV-DNA as a new, interesting biomarker candidate for early diagnosis and prognosis of human cancers. The current knowledge evolution about the composition of the extracellular medium will also be discussed, as well as the challenges to solve before any usable routine clinical transfer. After the mere observation that tumor cells release more EVs (tEVs) than their normal cell counterparts (nEVs), it was obviously interesting to check the comparative cargo composition of tEVs and nEVs. This EV cargo comparison was first focused on EV proteins, then on EV-RNAS (coding messenger RNAs (mRNAs), and mainly noncoding RNAs (microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (cirRNAs)). At first, small EVs were not supposed to harbor DNAs, which was only assumed as a known property of apoptotic bodies. However, in 2011, Balaj et al. [6] asserted that tumor cells release an abundance of microvesicles containing a selected set of proteins and RNAs. However, they also carry DNA, which reflects the genetic status of the tumor, including a significant sequence amplification of the c-Myc oncogene for three medulloblastoma cell lines compared with normal fibroblasts and other tumor cell types. ExoDNA appeared to be primarily single stranded (ssDNA). Tumor microvesicles contain genetic information available for horizontal gene transfer and provide a potential source of tumor biomarkers. In 2012, Waldenström et al. [7], after having previously revealed that human prostasomes contain chromosomal DNA, successfully searched DNA in microvesicles/exosomes derived from a murine cardiomyocite cell line; they also showed that these EVs, containing DNA/RNA, could transfer chromosomal DNA sequences to the cytosol or nuclei of target fibroblasts. These two pioneering works on MVs initiated the interest in EV-DNA. In 2013, six years after the noticeable observation of Valadi et al. [8], claiming that “exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells”, Cai et al. [9] showed that “extracellular vesicle-mediated transfer of donor genomic DNA to recipient cells is a novel mechanism for genetic influence between cells”. They first examined the existence of genomic DNA (gDNA) in EVs derived from human plasma and from vascular smooth muscle cells (VSMCs) in culture. They found at least 16,434 gene fragments in the human plasma, ranging in size from 1 to 20 kilobases (kb), but mostly around 17 kb. They showed with VSMCs that apoptosis was not the source of EV-DNA. Moreover, they observed that DNA was present only inside the thoroughly washed EVs and that EVs contain double-stranded DNA (dsDNA). Then, they investigated the function of transferable DNA in the recipient cells. To determine the pathophysiological significance of EV-gDNA transfer into cells, they further examined the transfer of BCL/ABL hybrid gene in EVs from K562 cells to normal human neutrophils isolated from human peripheral blood. They found that the numerous gDNA fragments in EVs are transportable between the same or different types of cells and increase the gDNA-coding mRNA and protein expressions in the recipient cells. This immediately boosted the interest of circulating EV-DNA as a new cancer biomarker in liquid biopsy. In 2014, independently of the three pilot studies detailed above, Kahlert et al. [10] investigated whether exosomes from two pancreatic cancer cell lines and serum from (a few) patients with pancreatic ductal adenocarcinoma (PDAC) contain gDNA. They provided evidence that exosomes contain >10 kb fragments of ds-gDNA spanning all chromosomes. They showed that the known specific KRAS and p53 DNA mutations found in the pancreatic tumor cells were recovered in the serum exosomes of patients with pancreatic cancer. Therefore, serum exosomes might be used to determine gDNA mutations for cancer prediction. Moreover, their data suggested that the majority of circulating DNAs from the serum samples may come from inside the exosomes and are not present as free-floating circulating DNA. This important preliminary study opened the way to a preclinical liquid biopsy study, involving a larger number of PDAC patients, compared to the appropriate healthy controls. At the same time, Thakur et al. [11] used a quite interesting approach to evidence, for the first time, dsDNA in exosomes derived from two human cancer cell lines (myeloid leukemia K562 and colorectal carcinoma HCT116) and one murine melanoma cell model. They extracted DNA from exosomes either intact or pretreated with DNAse. However, instead of using the nonspecific DNAse I, they used either S1 nuclease, specific for ssDNA, or shrimp-dsDNAse, specific for dsDNA. Thus, they convincingly demonstrated that the majority of exosome-dsDNA with a size greater than 2.5 kb is associated with the outer membrane, whereas internal exosome-dsDNA depicts a size between 100 bp and 2.5 kb. This first observation of exosome-associated dsDNA was confirmed by atomic force microscopy (AFM) and extended a broad panel of human tumor cell lines to the analysis. A predominant dsDNA form of internal exoDNA was detected in all exosomes, but with lower amounts in most pancreatic and lung cancer cell lines. It is noticeable that exosomes from two normal fibroblast stromal cell lines exhibited about 20-fold less exoDNA than the one isolated from tumor cells. Furthermore, exosomes derived from murine B16-F10 melanoma revealed that only a (10%) subset of exosomes contained DNA, suggesting a specific targeting of DNA into exosomes. Another very important point of this work is that exoDNA represents the entire genome and might then mirror the tumor state. Focusing on a major modification of nuclear DNA, i.e., the methylation of 5’-cytosine, exoDNA was found methylated to a similar level to gDNA. ExoDNAwas also tested for some cancer-specific mutations such as the BRAF (V600E) mutation, present in 50% of malignant melanoma. They detected the mutant alleles in exoDNA of all cell lines containing the mutation and only the wild type (WT) in exoDNA originating from the cell lines with non mutated BRAF. The same search was performed with the epidermal growth factor receptor (EGFR), which is mutated in several cancers, including non small cell lung cancer (NSCLC), and respective EGFR mutations were also detected in 100% of exoDNA isolated from the NSCLC cell lines, harboring EGFR mutations. Thus, Thakur et al. [11] showed that double-stranded DNA in exosomes reflects the mutational status of parental tumor cells, illustrating its significant translational potential as a novel circulating biomarker candidate in cancer detection. Lee et al. [12] used (RAT-1), an immortalized nontumorigenic rat intestinal epithelial cell line (IEC-18) and its tumoral derivative (RAS-3) transfected with the V12 mutant c-H-ras human oncogene. By whole genome sequencing (WGS), these EVs, containing chromatin-associated dsDNA large fragments (777 bp, 2200 bp), were shown to cover the entire rat genome, including the full-length H-ras oncogene (3308 bp). Moreover, these EVs could transfer this oncogene to nontumorigenic cells and induce their increased proliferation. After evidencing gDNA inside microvesicles/exosomes [6,7,9,10,11,12], the presence of DNA was also questioned in other EVs [13,14]. Shelke et al. [13] claimed that the EV-DNA released by human mast cells is mostly associated with the outside of EVs and cause their aggregation. Fisher et al. [14] showed that EVs (50–150 nm in size) released from human bone marrow-derived mesenchymal stromal cells (BM-hMSC) also carry high-molecular DNA. This DNA, which is not derived from apoptotic or necrotic cells, was mainly associated with the outer EV membrane and, to a smaller degree, inside the EVs. The DNA isolated from EVs was not organized in nucleosomes. The EV-gDNA amount was sufficient for next-generation sequencing (NGS) and virtually covered the complete human genome. After transducing a plant-DNA into BM-hMSCs, the released EVs were tagged with the Arabidopsis thaliana-DNA (A.t.-DNA) and able to rarely perform the (A.t.-DNA) EV-mediated transfer to naïve BM-hMSCs. As previously observed with rat cells [12], this is a confirmation of the EV-mediated horizontal DNA gene transfer to recipient cells as a new important EV biological function. In 2016, Kalluri and Lebleu summarized the discovery of double-stranded genomic DNA in circulating exosomes [15], focusing on studies related to the origin of gDNA in exosomes and its utility in cancer diagnosis and disease monitoring. Lastly, Jin et al. [16] proved that EVs extracted from serum are stable under different storage conditions (at 4 °C for 24 h, 72 h, 168 h; at room temperature for 6 h, 12 h, 24 h, 48 h; and after one-time-, three-time-, five-time-repeated freeze–thaw cycles). DNA in serum EVs is also stable under different storage conditions. Serum DNA is mainly present in exosomes, and EVs-DNA stayed stable for 1 week at 4 °C, 1 day at room temperature, and after fewer than three-time-repeated freeze–thaw cycles. The observed stability of serum EVs and EVs-DNA is the premise for using cirEVs for the search of new potential genetic DNA biomarkers for cancer diagnostics. A summary of these precursor studies on the DNA content of cell-released EVs is given in Table 1. First, Lazaro-Ibanez et al. [17] showed different gDNA fragments in the subpopulations of EVs (Abs, MVs, and EXs) with prostate cancer (PCa) cell lines (LNCaP, PC-3, and R92a/hTERT) in vitro. Derived from morphologically heterogeneous cancer cells, their respective MVs and EXs had comparable sizes and concentrations (1.36–2.52 × 108 particles/mL per million cells) for MVs (n = 16) and (0.56–1.93 × 108 particles/mL per million cells) for EXs (n = 16). However, for each of the three cell lines, the MVs’ total protein content (6 µg protein/106 cells) was about twice that of EXs (3.2 µg protein/106 cells). Besides very rare MLH1 mutations in prostate cancer (PCa), TP53 and PTEN were the only significantly mutated genes in both localized PCa and castration-resistant (CRPC) tumors. The number of amplified gDNA fragments of MLH1 (108 bp), PTEN (225 bp), and TP53 (316 bp) were almost double between MVs and EXs (n = 12), showing that different types of EVs carried different gDNA contents, which suggests a selective gDNA package into the different PCa cell-derived EV subtypes. Moreover, they demonstrated that the EV-derived gDNA fragments from the LNCaP cells had no MLH1 mutation but a frame-shift PTEN mutation and a (C > G) TP53 mutation, showing that EV-gDNA could even harbor specific gDNA mutations of the parent cells. Then, they provided evidence that plasma-derived EVs are more abundant in PCa patients (n = 4) than in healthy donors (n = 4) and that human plasma-derived EVs also carry double-stranded gDNA fragments. However, they did not observe any significant differences in the MVs and EXs or in the total EV population isolated from human plasma samples of PCa patients compared with healthy controls. Moreover, the previously described gDNA mutations for the LNCaP cell-derived EVs were not detected from the small studied cohort of plasma EVs. Thus, the promising in vitro observations are to be confirmed by other extended preclinical studies, before asserting EV-DNA as a valuable biomarker for PCa diagnostics. After previous isolation from tumor cells with high migratory and invasive abilities of new, unusually large (1 µm in diameter) EVs (L-EVs), also named large oncosomes (LO), Vagner et al. [18] first characterized the DNA in large L-EVs (LO surrogate) and small S-EVs (EX surrogate) from the same PC3 (PCa) or U87 (glioblastoma) cancer cell lines, as well as from plasma of a PCa mouse model. L-EVs emerged as the EV subpopulation containing most of the circulating DNA, which was quantified as a high molecular weight (up to 2 Mb) chromatinized DNA. Then, they isolated L-EVs and S-EVs from human plasma of patients (n = 40) with metastatic castration-resistant PCa (mCRPC). As observed in vitro, and despite a pronounced interpatient variability in the amount of EV DNA, L-EVs contained significantly more DNA than S-EVs, whereas DNA was totally absent from both L-EVs and S-EVs in controls. Moreover, L-EVs isolated from human mCRPC patients contained large-size dsDNA, covering the entire tumor genome, with reported cancer-specific genomic alterations (MYC/PTEN imbalance). It is noticeable that, in line with their in vitro and in vivo results, the ssDNA/dsDNA ratio was 5/1 in three out of four patients and the amount of EV-free DNA was comparable or higher than the amount of DNA in L-EVs in two patients. This points out the necessity for further preclinical studies to shed light on the relationship between disease progression and the composition of the DNA cargo in L-EVs. Pancreatic cancer, in urgent need of early diagnosis, was also considered under the light of DNA biomarker. Allenson et al. [19] compared exosome-derived DNA (exoDNA) to cfDNA in liquid biopsies of patients with pancreatic ductal adenocarcinoma (PDAC) on 263 individuals, including a discovery cohort of 68 PDAC patients of all stages, 20 PDAC patients with localized tumor after curative resection, and 54 healthy controls. A validation cohort of 39 cancer patients and 82 healthy controls was studied to validate KRAS detection rates in early-stage PDAC patients. KRAS mutations were more detectable in exoDNA than in cfDNA. However, mutant KRAS was also detected in a substantial minority of healthy samples, which limits its utility as a cancer-screening method. Yang et al. [20] added to the search of KRASG12D mutation in serum exosomal DNA, the associated search of TP53R273H mutation from patients with pancreatic cancer and healthy individuals. The minimal exosomal DNA used for digital PCR analyses was 0.663 ng. A sufficient amount of exosomal DNA for the KRASG12D and TP53R273H mutations search was obtained for 49% (76/156) of patients and 66% (114/171) of healthy serum samples. In 39.6% of the serum samples of PDAC patients (n = 48), the KRASG12D mutation was identified, whereas the TP53R273H mutation appeared in 4.2% of the serum samples, leaving 27 samples without these two specific mutations. With the frequency of the KRASG12D mutation being measured as about 40–50% in PDAC tumor tissue, this exosomal DNA study likely captures most of the KRASG12D mutation in PDAC patients. This also appears to be the case for the TP53R273H mutation. Thus, this study showed that exosomal DNA can be used as a substitute for less convenient tissue biopsy to identify mutations using digital PCR. Moreover, whereas KRASG12D mutation was detected in 2.6% of a large cohort (n = 114) of healthy individuals, TP53R273H mutation was never detected in healthy subjects. On the other hand, in vitro and in vivo studies [21] showed the interest of engineered exosomes (iExosomes) to carry short interfering RNA (siRNA) or short hairpin RNA (shRNA), specific to oncogenic KrasG12D, for efficiently targeting KRAS. Mendt et al. [22] reported a bioreactor-based generation and testing of large-scale production of clinical-grade iExosomes for targeting KRAS in pancreatic cancer. These iExosomes were thoroughly tested in vitro with many cell lines and in vivo on several mouse models with pancreatic cancer. These studies confirmed the suppression of oncogenic Kras and an increase in the survival of mouse with pancreatic cancer, illustrating their therapeutic potentialities. Garcia-Romero et al. [23] showed that all three types of EVs (Abs, MVs, and EXOs) secreted by human glioma cells contained gDNA sequences. Some sequences appeared in all EVs, whereas a few sequences appeared exclusively in one type of EVs. IDH1, harboring the most relevant mutation for human glioma diagnostic, was detected only in MVs and EXOs. Moreover, in vivo studies demonstrated that all types of tumor-derived EVs cross the intact blood–brain barrier and can be detected in the peripheral blood. In a small cohort of glioma patients, they demonstrated that the IDH1G395A mutation could be successfully detected in the peripheral blood EVs cargo as a minimally invasive method compared to liquid biopsy from cerebrospinal fluid. In 2019, Kahlert [24] wondered whether the exosomal gDNA, discovered only some years ago, might be a better choice as a cancer biomarker in liquid biopsy than the cfDNA discovered six decades before. After recapitulating the origin of both DNAs and their respective advantages and disadvantages, he concluded that both are currently complementary. Whereas cfDNA can be detected in healthy individuals and patients with nonmalignant or malignant disease, mutated cfDNA is more tumor-specific and enriched in smaller fragments between 90 and 150 bp and in the size range 250 to 320 bp, originating from cell death remnants insufficiently cleared by infiltrating phagocytes; therefore, cfDNA, with an easier amount of accessible DNA and higher copy numbers of some cancer-specific mutations, is more efficient for prognosis of late tumor stages. By contrast, exosomal gDNA can be found less fragmented (with a size range between 2.5–10 kb [10,11]) not only in exosomes, but in all EV types, more frequently in MVs and EXOs and sometimes only in some specific EXO subsets [11], with an apparent distribution depending on the tumor type. Although in a smaller amount, exosomal gDNA, spanning all the chromosomes, is sufficient to obtain the significant tumor-specific mutated DNA sequences by using the most recent PCR technologies. Thus, exosomal DNA might be a better potential biomarker for early cancer diagnosis than cfDNA. However, the clinical translation of exosomal DNA as a cancer biomarker is greatly hampered by the urgent need for finding a valuable substitute to the “gold standard” of differential ultracentrifugation for EVs extraction from human body fluids. The greatest promise for using the tumoral EV-specific gDNAs as an early cancer diagnosis biomarker might be to specifically extract tumor EVs from the whole circulating EV population by capture on “lab-on-chip” solutions, for example, by targeting some tumor-specific EV outer membrane proteins, such as glypican-1, followed by the use of the new PCR technologies for reaching the cancer-specific mutation(s) of interest. To define EV component(s) as potential biomarker(s) for a given human cancer diagnosis by liquid biopsy, three steps are generally undertaken: in vitro studies with specific tumor cell lines, in vivo studies with murine tumor models, and preclinical studies on circulating tumor-derived EVs from a few patients’ plasma or serum. Whereas two-dimensional (2D) cell cultures are generally used as “gold standard” in vitro models, Thippabhotla et al. [25] intended to compare the EVs respectively released by an immortalized HeLa (2D) cell culture, issued from a cervical cancer patient, and a three-dimensional (3D) organoid culture, elaborated on peptide hydrogel with the same HeLa cells. They found that the EV secretion dynamics were significantly different for both culture types. Moreover, their respective EV-RNA and EV-DNA compositions were also quite different. The 3D-culture-derived EV-small RNA profile (<200 nt) showed a much higher similarity (about 96%) than the 2D culture-derived EVs to plasma EV-small RNA profile from two cervical cancer patients with one healthy control. In contrast with RNA, analysis of the cir-tEV-DNA sequencing data showed that culture or growth conditions do not affect the genomic DNA information carried by EV secretion. Therefore, at least for cervical cancer, 2D culture seems to remain a valuable in vitro tool for the search of human cir-tEV-gDNA cancer biomarker, whereas the 3D culture system may constitute a more useful in vitro model for the search of cir-tEV-RNA cancer biomarkers. Yokoi et al. [26] were the first to question the mechanisms of nuclear content loading to exosomes. Upon induction of genomic instability with genotoxic drugs, they identified a link between micronuclei (MN) formation and the generation of some specific exosomal loading with gDNA and other nuclear contents. On the other hand, Lazaro-Ibanez et al. [27], using two human mast (HMC-1) and erythroleukemic (TF-1) cell lines, prepared, by ultracentrifugation, exosome-enriched small extracellular vesicles (sEVs). The amount of sEVs for TF-1 cells was over 2.5-fold more than that for HMC-1. By further using a high-resolution iodixanol density gradient on the two sEVs populations, the authors discriminated two novel heterogeneous subpopulations with different DNA content and topology. Each sEVs fraction was separated in nine 1 mL fractions (F1–F9) with measured densities from top to bottom. For both cell lines, the respective (F1 = F7) fractions were clustered in two low-density (LD) (F1–F3) and high-density (HD) (F4–F7) sEV subsets. The majority of the classical exosome-like sEVs were contained in the LD fractions. DNA was less abundant than RNA, and DNA was mainly present as ssDNA in the HD fractions for both cell types. The (HMC-1) HD fraction had a DNA-to-RNA ratio of 2.2/1, while the (TF-1) HD fraction was enriched in RNAs with a 1/2.9 DNA-to-RNA ratio. The LD fractions had the most prominent rRNA peaks and least DNA, while the HD fractions had most of the DNA cargo and small RNAs with no ribosomal rRNA peaks. DNA was predominantly localized on the outside or surface of sEVs, with only a small portion inside the vesicles. The entire human genome was represented both on the inside and outside of the sEVs. When sEVs were analyzed in bulk, whole-genome sequencing identified gDNA fragments of various lengths (from 500 to 10,000 bp), spanning both mitochondrial DNA and all chromosomes. These interesting and somewhat amazing observations have to be further explained, especially the cell mechanisms for the sEV specific loading before release and the curious DNA topology. In 2019, Jeppesen et al. [28] questioned the heterogeneity of the exosome-enriched crude sEVs sample. From their in-depth studies published in Cell, the authors claimed the necessary reassessment of the “classical” exosome composition both with regard to their assumed biogenesis and to their widely admitted global composition. The most “iconoclast” assertion for the topic of the present review was that extracellular dsDNA was not associated with exosomes or any other types of sEVs. Reviewing the ongoing studies from 2020 might perhaps clarify this pending question concerning exosomal DNA, which is important for keeping the current assumed interest of EVs as a potential rich tumor DNA resource for early cancer diagnosis (cf. detailed discussion in part 5.). A Summary of these (2014–2019) studies about circulating tumor EVs-DNAs can be found in Table 2. In line with the prestigious, newly reassessed exosome description [28], Hoshino and 116 coauthors [29] brought, also in Cell, a more-medical insight by investigating the proteomic profile of potential new liquid biopsy cancer biomarkers in 426 human cancer and non cancer samples derived from various cells, tissues, and body fluids. However, instead of using two-pooled LD and HD density fractions of the crude sEVs [27], the authors categorized the crude sEVs into three prominent subpopulations: small exosomes (Exo-S 50–70 nm), large exosomes (Exo-L 90–120 nm), and exomeres (non vesicular (NV) particles <50 nm), collectively referred to as extracellular vesicles and particles (EVPs), with the aim of defining EVP protein signatures that distinguish cancer patients from healthy individuals. Exomeres were identified in 2018 as nanoparticles distinct from EVs by using asymmetric flow field-flow fractionation (AF-4) for EV analysis [30]. Among their in-depth studies [29], the authors analyzed 120 plasma-derived EVP proteomes from 77 cancer patients with 16 different cancer types and 43 healthy controls (HC). They highlighted the identification of EVP markers, characterized EVP markers in human tissues and plasma, and suggested that EVP proteins can be useful for cancer detection and determination of cancer type. For the present review focused on exosomal DNAs, it is noticeable that not only is the choice of the EV-transported components (proteins/RNAs/DNAs) as the best type of cancer biomarkers still widely questioned, but even the more appropriate nature of the circulating extracellular transporter (EVs and/or NV materials) is also becoming a matter of debate. Although aware of the recent reassessment of the composition of EVs and the overturn of some previous findings [28,29], Teng and Fussenegger [31] kept the EV common classification in three main types (Exos, MVs, and ABs) for extensively reviewing the EV biogenesis, focusing mainly on exosomes and microvesicles. They detailed the current knowledge about the three distinct steps concerning exosomes biogenesis and release, initiated from the endosomal pathway, with further intracellular transport of the multivesicular bodies (MVBs) containing intraluminal vesicles, and fusion of some MVBs with the plasma membrane for exosomes release. Likewise, they detailed the mechanisms of biogenesis and release of microvesicles and discussed the current knowledge upon EV uptake and cell–cell communication, as well as upon the cargo sorting into EVs. Lastly, with all this accumulated knowledge, they concluded by recapitulating the many possible EV bioengineering methodologies for therapy improvements in the future. Besides the increasing knowledge about EVs’ biogenesis and composition, some recent reviews were focused on potential EV-derived DNAs as liquid biopsy biomarkers applied on a few specific cancers. Thus, Kim et al. [32] were concerned with lung adenocarcinoma. After summarizing older liquid biopsy approaches to overcome the small tissue availability in lung cancer patients, they advocated for EVs as ideal carriers of cancer biomarkers. They recalled that, contrary to the passively released fragmented cfDNAs (about 200 bp), cirEV-DNAs consist of both large-sized ds-gDNAs (up to 10 kb) and fragmented mutated DNAs, giving an active image of both the viable and dying tumor cells. Moreover, a higher sensitivity can be achieved by using EV-DNAs obtained from bronchoalveolar lavage fluid (BALF) than those from blood. Compared with the short half-life (2–2.5 h) of cfDNAs, the membrane-protected EV-DNAs also have a high stability. In conclusion, cirEV-DNAs are expected to be more widely used in the future, when their current sophisticated isolation methods will become clinically adapted. By contrast, Sun et al. [33] claimed an improved detection of cell-free tumor DNAs (cf-tDNAs) in EVs-depleted plasma of cancer patients. It is to be stressed that exosomes were prepared either by mere precipitation using ExoQuick (System Biosciences, CA, USA) or fractionated by using five sequential centrifugations and ExoQuick instead of ultracentrifugation. However, preparing exosomes by a precipitation method might not be a guarantee for keeping the exosomal DNA cargo intact, and it is noticeable that, in this case, the exosomal fraction 5 was dominated by small (~160 bp) nucleosome-like DNAs [33]. It is also noticeable that an older research article [34], using two different methods for exosomes isolation, brought contradictory evidence that more than 90% of cfDNA in human blood plasma is localized in exosomes. However, agarose gel electrophoresis of DNA isolated from plasma exosomes showed two prominent bands, one high intensity and high molecular weight, and the other of low molecular weight (less than 200 bp in length). By RNase treatment, the first band turned out to be exosome copurified RNA, with a 5-fold higher amount than the exosomal dsDNA, corresponding to the second band. It would be worth performing some in vitro studies about the exosomal DNA yield and size as a function of the methods used for collecting the exosomes. Cambier et al. [35] aimed to identify circulating nucleic acid sequences associated with serum EVs as a step toward an osteosarcoma (OS) early detection assay. qPCR analysis of PEG-precipitated EVs revealed the over-representation of some repetitive element DNAs in OS patient versus control sera. Taken into account that, in these serum EVs the OS-associated repetitive element DNAs were sensitive to DNase I, they were not in a protected EV cargo. Moreover, the repetitive DNA elements were copurified with EVs in PEG precipitation and size exclusion chromatography (SEC), but not in CD81 or CD9 EV immunoaffinity capture. These observations were taken as supporting the recent exosome reassessment [28], claiming that exosomes do not contain DNA, or tightly associate with other non vesicular entities containing dsDNAs that are extruded from cancer cells. Ruhen et al. [36] aimed to use low-pass whole-genome sequencing to identify copy number variants (CNVs) in serial samples of both cf-tDNA and EV-DNA from plasma of a patient with metastatic breast cancer. Of the 52 CNVs identified in tDNA, 36 (69%) were detected in at least one cf-tDNA sample and 13 (25%) in at least one EV-DNA sample. Variants ranged in size from 0.3 to 106.5 Mb and were distributed randomly throughout the genome. Both kinds of noninvasive liquid biopsy depicted a CNV increase with disease progression, but this case study demonstrated that cf-tDNA, shed from apoptotic tumor cells, had a greater sensitivity for serial monitoring of breast cancer than EV-DNA actively secreted from viable neoplastic cells. Elzanowska et al. [37] summarized the biological and clinical aspects of EV-DNA and examined the current role of EV-DNA specifically in cancer. Overall, they emphasized that EV-DNA as a biomaterial for liquid biopsies is a new but definitely promising area of study, but its study in the clinical context is still quite open for further validation. Lee et al. [38] performed targeted NGS of DNA derived from bronchoalveolar lavage fluid (BALF-EV DNA) of 20 patients with EGFR-mutated non small cell lung cancer (NSCLC) and DNA from matched formalin-fixed paraffin-embedded (FFPE) tissue samples. EVs from BALF were heterogeneous (100–300 nm in size); EV-DNAs from the BALF existed in short and long sizes, but mostly in about 11 kb; and EVs contained DNAs from both vesicle surface and inside. The DNA yield from BALF-EVs was 100-times less than tissue DNA but had enough tumor-specific DNA for use in NGS analysis for the identification of actionable genetic alterations. This approach has a high potential clinical feasibility and utility. Kim et al. [39], also enrolling NSCLC patients after tyrosine kinase inhibitor therapy, compared different technological tools to detect EGFR mutations in 54 plasma samples and 13 pleural fluids. They demonstrated that combined tumor nucleic acid analysis (exoTNA+cfTNA) in the plasma and exoTNa in the pleural fluid allowed for the detection of target mutations more sensitively than that using cfDNA or total DNA alone. Amintas et al. [40], claiming that “dsDNA in EVs might be the latest most promising biomarker of tumor presence and complexity”, focused on the recent knowledge on the DNA inclusion in vesicles, the technical aspects of EV-DNA detection and quantification, and the use of EV-DNA as a clinical biomarker. They recapitulated the cell-free DNA cell sources by active or passive mechanisms (cf. their Figure 1) and summarized the tumor genome hallmarks reflected by EV-DNA, as well as the results of the main clinical studies assessing the performance of EV-DNA biomarkers (cf. their Table 1). Although suggesting EV-tDNA as an alternative to reach the promise of cftDNA, they concluded by enumerating the many challenging questions remaining to be solved before reaching this goal. Maire et al. [41] investigated whether the DNA in glioblastoma cell-derived EVs reflects genome-wide tumor methylation and mutational profiles and allows noninvasive tumor subtype classification. They found that DNA is present in the vast majority of EVs, with a major localization to the EV surface. Genome-wide methylation profiling identified with high accuracy in EV-DNA the methylation of the parental tumor-specific mutations and copy number variations (CNVs). Interestingly, the methylation profiling and CNV results were not affected by the EV isolation techniques. This showed that EV-DNA reflects the genome methylation, CNV, and mutational status of glioblastoma cells. Likewise, Baris et al. [42] compared epigenetic alterations in the target gene Enhancer of Zeste Homolog-2 (EZH-2) between plasma-derived exosomes and matched primary tumor tissues of 21 patients with aggressive diffuse large B cell lymphoma (DLBCL). They showed, for the first time, the presence of DNA in plasma exosomes of DLBCL patients and found that CDKN2A and CDKN2B were methylated in both plasma exosomes and primary tumor tissue samples. Compared to 21 healthy individuals, exosome concentration was approximately six-times higher in DLBCL patients, but the exosomal dsDNA content was extremely low compared to RNA contents. Zavridou et al. [43] were also the first to perform a direct comparison of gene expression and DNA methylation markers in CTCs and paired plasma-derived exosomes. This revealed a remarkable heterogeneity on gene expression and DNA methylation markers between EpCAM-positive CTCs and paired plasma-derived exosomes in metastatic castration-resistant prostate cancer (mCRPC) patients, with a significantly higher positivity in CTCs. Lastly, Hur and Lee [44] extensively reviewed the properties of extracellular vesicle-derived DNA for future clinical applications. They examined the biogenesis of DNA-containing EVs, their DNA methylation, and the use of next-generation sequencing (NGS). They questioned the use of EV-DNA as a biomarker in clinical settings, the modality of EV-DNA gene transfer, and its therapeutic potential. They hypothesized that DNA might exist inside an EV in a protected nucleosome or supercoiled form, which would enable the packaging of long dsDNA. Taking into account the nucleosome’s 11 nm size, long dsDNA would more likely be present in larger EVs. However, the presence and topology of DNA in extracellular EVs will continue to be controversial until the development of a method for isolating pure EV subsets. Nonetheless, the authors recalled that the (100 bp to 20 kbp) EV-dsDNA fragments can represent the entire genome and reflect the mutational status of tumor parental cells. Lastly, mentioning several recent liquid biopsy studies in different body fluids of EVs associated-dsDNA for cancer patients, they also expressed the strong interest of EV-DNA as a new potential cancer biomarker. A summary of the discussed preclinical studies (2020–2021), about the circulating tumor-derived EV- gDNA as a potential cancer biomarker, is given in Table 3. During many years, the exosome concept was “the tree that hid the forest of EVs”. However, EVs “came on stage” about one decade ago and have been studied worldwide since 2012, reaching a huge, still-uncontrolled complexity in heterogeneity. An EV classification into three main categories as a function of their size and biogenesis, i.e., apoptotic bodies (ABs), microvesicles (MVs), and exosomes (EXOs), obtained a general long-lasting consensus until Jeppesen et al. [28] recently proposed a complete reassessment of exosome composition, with a new classification of low-density (LD) “exosome-like” small extracellular vesicles (sEVs), without any DNAs in their cargos, and a much more significant high-density (HD) non-EV extracellular mixed component associated with DNAs. Therefore, they used different cell lines, human plasma, and tissue for preparing sEVs samples by the commonly used differential centrifugations. Then, they further used a density discrimination by means of a discontinuous iodixanol gradient density. Being aware of the ultracentrifugation-induced aggregation artefacts, they kept, in parallel, parts of the 15,000× g filtered supernatants as precleared media. These media were submitted to direct immunoaffinity-capture (DIC) of exosomes by means of magnetic beads conjugated to exosomes-specific tetraspanins antibodies. The crude sEVs, their different density fractions, and the scarce directly captured CD63-, CD81-, or CD9-specific EVs were submitted to the same immunoblots. Different studies aimed to give an insight upon the proteins, RNA, and DNA composition of two-pooled low-density (LD) and high-density (HD) fractions of the crude sEVs samples. Surprisingly, many of the presumed components of exosomes were absent from the “classical” exosomes expressing CD63, CD81, and CD9. Many of the most abundant miRNAs were associated with extracellular nonvesicular (NV) fractions rather than with purified sEVs. Moreover, extracellular dsDNA was stressed as being not associated with exosomes or any other types of sEVs. An autophagy- and multivesicular endosome-related pathway was supposed to be the driver of extracellular DNA secretion instead of the exosome-dependent pathway. These assertions were sufficiently “iconoclast” to be seriously questioned before entering into the many details suggested for supporting the new exosome model. The results in [27] were indeed “interesting and amazing”, but when compared with those recalled in [28], both taken together were quite perturbing. The methods used for flotation, although with the same technology of discontinuous iodixanol gradient density, were not exactly the same (12–36% and 6–30% for the [28] gradients and 20–45% for the [27] one). Neither was on the same samples, and they used different means of sample deposit to the bottom of the centrifugation tube, i.e., 1 mL of crude sEVs suspension in PBS was mixed with iodixanol to a final 36% concentration [28] or mixed with 3 mL of a 60% iodixanol solution [27]. Moreover, the resulting increasing densities from top to bottom were differently measured, either with a refractometer on a mock identical gradient without sample [28] or directly on all the 1 mL collected fractions by absorbance at 340 nm [27]. The final results were indeed analogous for the LD fractions covering the “classical” exosomal sEVs. However, they were so different with regard to the HD fractions, corresponding either to a sum of many nonvesicular extracellular materials [28] or to another “non-classical” exosome subset [27], that it would be worth further questioning the properties of the discontinuous iodixanol gradient density method as a function of the chosen parameters on the same crude sEV sample. Although quite new and highly cited by further publications [29,31,33,35,36,37,40,41,44,45,46,47], the conclusions asserted by Jeppesen et al. [28] were only poorly confirmed [29,35]. Their claimed absence of exosomal DNA [28] did not appear to be quite convincing [33,37,40,41,44,45,46,47], especially when compared with the observations of Lazaro-Ibanez et al. [27]. These authors, also using a discontinuous iodixanol gradient density separation of crude sEVs, reached only two heterogeneous (LD and HD) subpopulations of sEVs and only a small discarded heavier fraction of non-EV material. Sun et al. [33], taking into account the suggestion that extracellular DNA may not be associated with exosomes, but copurifies with the sEV fraction during standard isolation protocols [28], elaborated a clinically feasible protocol to analyze the cirEVs influence on the whole plasma cf-tDNAs’ measurements. The authors selected nine small-cell lung cancer (SCLC) patients with a known relatively high cf-tDNA content; for each patient, they prepared, from a 1 mL blood sample, a platelet-poor conventional-plasma and, from another ml of the same blood sample, four pelleted fractions by successive light centrifugations, with replacement of the usual last ultracentrifugation by an ExoQuick exosome precipitation. Thus, the fractionated plasma corresponded, respectively, to “cells and larger debris” (fraction 1); crude “large microvesicles” (fraction 3); exosomes (fraction 5), which were characterized by transmission electron microscopy (TEM); nanoparticle tracking analysis (NTA); and by CD63/CD81 ratio using flow cytometry. The last supernatant (fraction 6) corresponded to the conventional plasma depleted of EVs. Then, the DNA yield and size distribution were compared in the whole plasma and in the different fractions for the nine (SCLC) patients. From 1 mL starting plasma, the average DNA yield was 5.3 ng in fraction 1, 1.73 ng in fraction 2, 0.99 ng in fraction 3, 0.68 ng in fraction 4, 4.17 ng in fraction 5, and 4.28 ng in fraction 6, and the average summed DNA yields in fractions 1, 5, and 6 accounted for 79.9% of the total DNA yields. Comparatively, whole plasma showed an average of 23.84% cftDNA in the same group of patients. The DNA size distribution was also measured in each DNA sample and showed a peak size of 7000–10,000 bp in fraction 1 and gradually reduced in fractions 2–3, while smaller fragments (about 160 bp) gradually increased from fractions 3 to 6. They also estimated cir-tDNA content in the different fractions and showed that the copy number variations (CNVs) were more detectable in fractions 3 (large EVs), 5 (exosomes), and 6 (EV-depleted plasma). Interestingly, the authors “were not able to remove any DNA copurified with exosomes”, as previously suggested [28], and therefore, they questioned the origin of cir-tDNA detected in fraction 5. Maire et al. [41] observed, in glioblastoma cell-derived EVs, that even after robust digestion of surface-associated DNA and any possibly contaminating free-floating DNA, they still detected DNA in 76.4% of the CD63/CD81-positive vesicles, strongly supporting the notion that EVs contained DNA inside. Some others tried reserved contradictory comments toward the suggested reassessment of exosome composition [28]. Thus, Elzanowska et al. [37] pointed out “the unreported amount of exosomes used in the study, as well as a limited cell lines included in the report”. For Hur et al. [44], the inconsistency about the presence or absence of exosomal DNA can be attributed to the preparation method and size of the isolated EVs. Zhou et al. [45] advocated exosomal DNA as possessing more abundant biological information and higher accuracy for prognosis prediction than cf-DNA in liquid biopsy. However, they recognized that it is unclear whether gDNA exists in exosomes in all mentioned studies with different DNA detection methods. Shen et al. [46] asserted that “too strict an exosome isolation strategy may result in the loss of DNA-containing vesicles”. Kalluri and Lebleu [47], summarizing the hallmarks of exosomes as being “a cell-to-cell transit system in the human body with pleiotropic functions”, mentioned the current controversy about exosomal DNA and gave a negative appreciation of ref. [28], which “did not specify the quantity of exosomes used in its analytical assays, leading to ambitious conclusions”. However, the pioneering studies of Jeppesen et al. [28], stressing the importance of extracellular nonvesicular particles as DNA biomarkers, was highly comforted by the discovery of exomeres, using the new technology of asymmetric flow-field fractionation (AF-4) for identification of subsets of extracellular vesicles [30]. Furthermore, Zhang et al. demonstrated the exosome-like ability of exomeres to transfer functional cargoes [48]. Malkin and Bratman [49], focusing on the increasing huge heterogeneity of the extracellular medium, brought an outstanding review article about “the nomenclature of EVs and extracellular particles (EPs), the physical and structural characteristics of EV/EP DNA, the physiological roles of EV/EP DNA in health and disease and the emerging potential of EV/EP DNA as a molecular biomarker.” Interestingly, they extended the consensual long-lasting EV classification to nonvesicular EPs and modified the current nomenclature of extracellular components into large EVs (100 to >1000 nm), including apoptotic bodies (ABs), large oncosomes (LOs), microvesicles (MVs), originating from the plasma membrane; small EVs, including 50 to 130 nm exosomes (EXOs) of endosomal origin; and extracellular particles (<50 nm), including exomeres, with mean diameter of 35 nm, and chromatimers, both of yet unknown origin. Thus DNA, the overlooked component of EV/EPS is now becoming the central actor of many pending unanswered questions [49]. The assets of circulating EV-DNAs, as a new promising biomarker for cancer diagnosis and prognosis, have been convincingly demonstrated. However, the clinical transfer of the accumulated preclinical knowledge that began about one decade ago is highly hampered by some important challenging questions needing to be solved as a priority. The suppression of the main “bottlenecks”, both biological and medical, in the present knowledge about the extremely heterogeneous tumor-derived EVs/EPs populations, is highly dependent upon the future technological advances about their specific isolation and characterization. All the cells present in a human body, whether procaryotes or eukaryotes, are potentially equipped with the general cell property of releasing extracellular material, aimed either to remove no-longer-employed cell components or to send important epigenetic messengers into blood and/or into the many other minor subpopulation body fluids for modifying the fate of some specific recipient cells. Among this newly discovered “stellar” complexity of active extracellular material, it is not yet possible to precisely define the few tumor-specific subpopulations. At a smaller level of complexity, a given tumor cell population releases a quasicontinuum of EVs, with partly overlapping sizes and some common outer membrane protein markers. Therefore, the necessary classification of EV subsets without any specific biomarker is currently out of reach, which precludes further evidence for any of their specific biological functions. Moreover, the mechanisms used for specifically loading the multi components (proteins, lipids, nucleic acids, metabolites) into each EV cargo are almost completely unknown. This is also true for the EV-transported DNA, with some supplementary controversial questions about its topological localization inside the EV, outside on the EV membrane, or in both positions, and also on its size and nature as ssDNA, dsDNA, gDNA, or nucleosomes. The same questions will probably arise with the more recently discovered EPs, together with the one about the part played by the older known proteins such as Argonaute in the protected intercellular DNA transport. This detailed picture is aimed to show the huge problem of EV/EP heterogeneity, which has to be at least partly solved before efficiently facing the medical validation of a few promising EV-derived biomarkers for cancer diagnosis by well-standardized protocols for a given cancer, undertaken with important patient cohorts in different cancer centers worldwide. Some recent technological reviews have been selected to give an insight into the current state of the art concerning EV isolation and characterization [50,51,52,53,54,55,56,57,58,59]. Valencia and Montuenga [50] focused on the biological properties of exosomes and especially on their heterogeneity, which is due to the association of five factors: the cell of EVs origin, the EVs size and number, their molecular composition, and their functionality transferred in recipient cells. Different combinations of these factors result in highly complex EV heterogeneity. Moreover, in an oncologic patient, tumor-derived exosomes are estimated to be no more than 10% of all the circulating exosomes. Nevertheless, the authors suggest that exosomal DNA might become the future liquid biopsy gold standard. However, to become a clinical reality, every single procedure (EV isolation and characterization and all analytical protocols) remains to be standardized for a valid comparison of the different EV-DNA biomarker studies. Saad et al. [51] detailed eight exosome-isolation methods and discussed the advantages and disadvantages associated with each method (cf. their Table 1). They also discussed the physical and chemical characterization and the detection techniques for exosomal samples. Widely studied since 2012, exosomes/EVs, with their potential to develop new clinical approaches of modern medicine, are also progressively entering the medical field, especially in cancer, cardiovascular disease, and central nervous system defects [52]. Hirata et al. [53] summarized the assets of liquid biopsy as a distinctive approach to the diagnosis and prognosis of cancer. They strongly advocated liquid biopsy compared with the usual tissue biopsy and its drawbacks. Although mentioning exosomes, they only actualized the comparison between the two older liquid biopsy circulating biomarkers CTCs and cell-free tDNAs as cancer diagnostic and prognostic tools. With regard to compared EV characterization between tumors and normal controls, Western blots and all the “omics” technologies, i.e., proteomic, transcriptomic, metabolomic, lipidomic, and genomic, gave, at each level, an interesting global insight of the tumor-induced modifications. Recently, Shaba et al. [54] reviewed the EV multiomics integrated approach and summarized the state of the art of EVs omic studies. The abundant information reached for each omic level has to be correctly deciphered, and this is even more necessary if the different omics levels interact together. One essential requisite for multiomics integration is, beyond the generation of different omic datasets from the same biological samples, the development of statistical and annotation tools, which is essential for the interpretation of data. Still, many issues are encountered in each step of EV multiomic analysis, starting from EV isolation to the data integration methods, suggesting that this field is at its early state and requires further improvements. However, considering the complex EVs as optimal targets for omic sciences, the authors predicted a future challenging milestone for a multiomic integrative approach, which might contribute to explore EV functions, their tissue-specific origin, and their potentiality. On the other hand, it is feasible that each cancer-related global EV description might be “blurring” minute but important EV subsets, specifically linked with the tumor processes. Therefore, the new recent analyses at the single extracellular vesicle level (SVA), summarized by Bordanaba-Florit et al. [55], seem to be a quite interesting complementary approach to unravel the heterogeneity of extracellular vesicles. The authors extensively described some of the current methods so far developed for single-vesicle analysis (SVA). They reviewed the assets of SVA methods on recent advances in the EV field of research. They also focused on prostate cancer (PCa) diagnostics, showing the important improvements brought by the SVA of EVs. Ultimately, they concluded that an entirely new cell-to-cell EV-mediated communication network will be founded by single-vesicle techniques. SVA is also bridging the “omic” studies, carried for deciphering the global EV properties and their further functional studies, to the clinical world, by participating in the elaboration of simpler and less time-consuming technologies for EV isolation, such as microfluidics. Recently, Mousavi et al. [56] extensively reviewed microfluidics for detection of exosomes and microRNAs in cancer. First introduced in the early 1990s, microfluidics manipulates microliter volumes in microchannels ranging in size only from 1 to 1000 µm. When compared with conventional studies, microfluidics platforms have many advantages, including enhanced reliability, sensitivity, accessibility, lower consumption of samples and reagents, reduced costs, quicker processing and response times, and the possibility of automated multiplexing. The authors summarized the microfluidic technologies used for exosome isolation and analysis and specifically applied for cancer studies. They also focused on microfluidic-based miRNA detection in human cancer. An increased interest has been shown in microfluidics use for biomarker discovery, but many challenges are yet to be faced, such as standardization and validation at a large scale, before any routine clinical application for cancer diagnosis. Recently, Campos-Silva et al. [57] described a simple immunoassay for extracellular vesicle liquid biopsy in microliters of unprocessed plasma. They demonstrated that many EVs in solution, being like stable colloidal suspensions, are therefore unable to interact with a stationary functionalized surface. A more efficient capture on antibody-coated surfaces was obtained by using flocculation methods with cationic polymers. This led to the optimization of a protocol allowing effective immunocapture of EVs in bead-assisted flow cytometry. Only a few microliters of plasma were necessary for easy detection of tumor markers without previous ultracentrifugation. This easily adaptable method has been validated using plasma from lung cancer patients, with detection of the epithelial cell marker EpCAM on EVs. This radically improves the efficiency of clinical EV detection in immunocapture assays, opening new possibilities for the validation of EV biomarkers with large cohorts of patients. To gain a new step toward the clinical practice, it is mandatory to deeply investigate the still controversial nature and topology of the EV-associated DNA and the largely unknown EP-associated DNA. Moreover, microfluidics should focus on new technologies for discriminating circulating tumor EVs/EPs from the wide panel of the numerous other circulating EVs/EPs, blurring the tumoral message. As observed in this review focused on EV-DNA, the extracellular world is now becoming even more complex by the recent introduction of extracellular particles (EPs) [28,49], competing with EVs for assuming the many important intercellular messenger functions involved in human health and disease. It stresses the fundamental importance of deeply deciphering the extracellular environment composition and functions to complement the current knowledge slowly accumulated during two centuries about the cell machinery. As already mentioned [54], using a multiomics integrative approach at the single EV level [55] is probably the ultimate goal to elucidate the most challenging EVs/EPs complexity in the far future. Therefore, it is probably only the very beginning of a long-standing scientific query, highly dependent on many future technological advances to control the EV/EP-epigenetic extracellular heterogeneity governing their putative, specific intercellular functions. Besides overcoming these major challenges, it will be necessary to define a standardized protocol for analyzing each given promising liquid biopsy biomarker for a given cancer type. Finally, the essential large-scale intercenter clinical validation might bring the putative biomarker to the long-awaited clinical practice. To give an optimistic insight into the huge interest in these hard future steps, one can mention a recent editorial about exosomes in cancer therapy [58] and a hopeful commentary upon liquid biopsy of extracellular biomarkers for prostate cancer personalized treatment decision [59]. Moreover, a recently published new EV data base (EV-ADD), the first one to be concerned with EV-associated DNA in human liquid biopsy samples [60], corroborates the current potential interest of this long-neglected EV component, not only for early cancer diagnosis but also possibly in the future for prognosis and disease monitoring after treatment, and even for EV-mediated therapy and resistance to therapy.

PMC10000641

Verena Damiani,Erika Pizzinato,Ilaria Cicalini,Gianmaria Demattia,Mirco Zucchelli,Luca Natale,Claudia Palmarini,Claudia Di Marzio,Luca Federici,Vincenzo De Laurenzi,Damiana Pieragostino

Development of a Method for Detection of SARS-CoV-2 Nucleocapsid Antibodies on Dried Blood Spot by DELFIA Immunoassay

27-02-2023

SARS-CoV-2,nucleocapsid antibodies,ELISA,DELFIA,dried blood spot

Antibodies against the SARS-CoV-2 nucleocapsid protein are produced by the immune system in response to SARS-CoV-2 infection, but most available vaccines developed to fight the pandemic spread target the SARS-CoV-2 spike protein. The aim of this study was to improve the detection of antibodies against the SARS-CoV-2 nucleocapsid by providing a simple and robust method applicable to a large population. For this purpose, we developed a DELFIA immunoassay on dried blood spots (DBSs) by converting a commercially available IVD ELISA assay. A total of forty-seven paired plasma and dried blood spots were collected from vaccinated and/or previously SARS-CoV-2-infected subjects. The DBS-DELFIA resulted in a wider dynamic range and higher sensitivity for detecting antibodies against the SARS-CoV-2 nucleocapsid. Moreover, the DBS-DELFIA showed a good total intra-assay coefficient of variability of 14.6%. Finally, a strong correlation was found between SARS-CoV-2 nucleocapsid antibodies detected by the DBS-DELFIA and ELISA immunoassays (r = 0.9). Therefore, the association of dried blood sampling with DELFIA technology may provide an easier, minimally invasive, and accurate measurement of SARS-CoV-2 nucleocapsid antibodies in previously SARS-CoV-2-infected subjects. In conclusion, these results justify further research to develop a certified IVD DBS-DELFIA assay for detecting SARS-CoV-2 nucleocapsid antibodies useful for diagnostics as well as for serosurveillance studies.

Development of a Method for Detection of SARS-CoV-2 Nucleocapsid Antibodies on Dried Blood Spot by DELFIA Immunoassay Antibodies against the SARS-CoV-2 nucleocapsid protein are produced by the immune system in response to SARS-CoV-2 infection, but most available vaccines developed to fight the pandemic spread target the SARS-CoV-2 spike protein. The aim of this study was to improve the detection of antibodies against the SARS-CoV-2 nucleocapsid by providing a simple and robust method applicable to a large population. For this purpose, we developed a DELFIA immunoassay on dried blood spots (DBSs) by converting a commercially available IVD ELISA assay. A total of forty-seven paired plasma and dried blood spots were collected from vaccinated and/or previously SARS-CoV-2-infected subjects. The DBS-DELFIA resulted in a wider dynamic range and higher sensitivity for detecting antibodies against the SARS-CoV-2 nucleocapsid. Moreover, the DBS-DELFIA showed a good total intra-assay coefficient of variability of 14.6%. Finally, a strong correlation was found between SARS-CoV-2 nucleocapsid antibodies detected by the DBS-DELFIA and ELISA immunoassays (r = 0.9). Therefore, the association of dried blood sampling with DELFIA technology may provide an easier, minimally invasive, and accurate measurement of SARS-CoV-2 nucleocapsid antibodies in previously SARS-CoV-2-infected subjects. In conclusion, these results justify further research to develop a certified IVD DBS-DELFIA assay for detecting SARS-CoV-2 nucleocapsid antibodies useful for diagnostics as well as for serosurveillance studies. Today, almost three years after the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in China, the COVID-19 pandemic continues spreading across the world. Indeed, despite a massive vaccination campaign that has been activated against the pandemic worldwide, new highly transmissible SARS-CoV-2 variants are continuously emerging, and it is becoming increasingly clear that they may evade neutralizing antibodies generated by previous infection and/or vaccination and thus contribute to the virus circulation [1,2,3,4]. SARS-CoV-2 is an enveloped, single-stranded, positive-sense RNA virus [5]. The four main structural proteins encoded by the genome include the spike (S), membrane (M), envelope (E), and nucleocapsid (N) proteins [6]. The S protein is a trimeric protein comprising two subunits, namely S1 and S2. The S1 subunit mediates binding to host cells via interactions between its receptor-binding domain (RBD) and the human receptor angiotensin-converting enzyme 2 (ACE2), whereas the S2 subunit is responsible for membrane fusion, which is required for virus entry [7]. The N protein, in which the viral genome is encapsulated, plays a fundamental function in viral RNA transcription, replication, and virion assembly. Although the N protein is highly immunogenic and a major target for antibody response [8,9], the S protein was employed to develop vaccines first. The M and E viral structural proteins have not been investigated as vaccine targets due to their inability to induce complete immune protection; indeed, only a significant cellular immune response was elicited, whereas no robust humoral immunity was detected [10]. Currently, the majority of vaccines available on the European market target the spike protein, which is the leading immunogenic protein [11,12]. However, recently, the N protein has attracted much attention for vaccine development because it is more conservative, more stable, and has fewer mutations than the S protein [13,14,15]. In response to SARS-CoV-2 infection or vaccination, most individuals develop antibodies to the N and S proteins within 1 or 2 weeks, and these antibodies can be measured as an indicator of COVID-19 prevalence; moreover, they allow for the monitoring of seroconversion in the population and are essential elements in developing strategies for SARS-CoV-2 infection prevention and control [16,17,18]. Plasma and sera isolated from venous blood represent the conventional sample types used for the evaluation of SARS-CoV-2 antibody responses. However, the collection of these samples is invasive and requires trained personnel and equipment for immediate processing. Once collected, plasma and sera must be stored and shipped refrigerated. Therefore, dried blood spot (DBS) testing, already applied in the fields of anti-doping, toxicology, newborn screening, and the diagnosis of infectious diseases, has been validated for the measurement of SARS-CoV-2 IgG antibodies against the N and S proteins [19,20,21,22,23,24]. The most commonly used method for serological tests is the enzyme-linked immunosorbent assay (ELISA). During the SARS-CoV-2 pandemic, several ELISA methods were developed to determine SARS-CoV-2 antigens and antibodies, qualitatively and quantitatively, with great sensitivity and specificity [25,26,27]. However, a colorimetric ELISA is affected by a narrow linear range for the optical density (OD), which is common to absorbance-based measurements. For this reason, an unknown sample concentration could fall outside the standard curve, introducing the challenge of testing multiple dilutions from the same, potentially limited, sample. The aim of this study was to develop a time-resolved fluorometry-based dissociation-enhanced lanthanide fluorescence immunoassay for detecting nucleocapsid antibodies to SARS-CoV-2 by using dried blood spots (DBS-DELFIA). The newly developed assay was compared to a commercially available, certified in vitro diagnostic (IVD) qualitative ELISA. The DBS-DELFIA test resulted in higher sensitivity and a wider dynamic range compared to the ELISA test. These results justify further research to develop a certified IVD for SARS-CoV-2 IgG anti-N detection by DBS-DELFIA technology. Forty-seven subjects were enrolled in this study. Sex, age, vaccination doses, and SARS-CoV-2 infection history are reported in Table S1. The study was conducted at the Center for Studies and Advanced Technologies (CAST), “G. D’Annunzio” of Chieti-Pescara, Italy, in accordance with the Declaration of Helsinki and the approval no. 16 of 1 July 2021 of the Ethics Committee of “G. D’Annunzio” University of Chieti-Pescara. Written informed consent forms were obtained from all the enrolled subjects. Whole blood was collected via venipuncture in Vacumed sodium citrate tubes (3.2%, FL MEDICAL s.r.l., Padova, Italy) to prevent coagulation, and processed within 6 h of collection. DBS samples were prepared from venous whole blood by transferring approximately 40 µL of blood to each circle of a filter paper card. Cards were then air-dried for at least two hours, placed into bags with a desiccant dehumidifier, and stored at −20 °C. The remaining whole blood was centrifuged at 3000 rpm for 12 min. Plasma aliquots were taken and transferred into sterile microtubes and stored at −80 °C until analysis. SARS-CoV-2 NP IgG ELISA kit [CE-IVD] (ImmunoDiagnostics, Hong Kong, China) was used following manufacturer’s recommendations. Briefly, 50 μL of negative control, 100 μL of the test sample (diluted plasma 1:100), and 100 μL of Assay Buffer (ImmunoDiagnostics) as blank were added onto the antigen-coated plate in duplicate, as the test recommended. Subsequently, the plate was incubated at room temperature (RT) for 1 h. Then, each well was manually washed 3 times with Wash Buffer (ImmunoDiagnostics) included in the kit. Next, 100 μL of Detection Solution (ImmunoDiagnostics) was added to each well and incubated for 1 h at RT. Then, after the wash step, 100 μL of Substrate Solution (ImmunoDiagnostics) was added to each well and incubated for 15 min at RT, protected from light. Finally, we added 100 μL of Stop Solution (ImmunoDiagnostics) to each well, and, after 10 min, absorbance was measured at 450 nm by Victor Nivo microplate reader (PerkinElmer, Turku, Finland). The antigen-coated plate from SARS-CoV-2 NP IgG ELISA kit (ImmunoDiagnostics) was used. DBS disks were punched out into 3.2 mm disks by using the PerkinElmer DBS Puncher, while plasma samples were diluted 1:100 with DELFIA Assay Buffer (PerkinElmer). Next, DBS disks were extracted with 100 μL DELFIA Assay Buffer directly onto the antigen-coated plate, whereas 100 μL of diluted plasma was transferred to the plate, and both DBS disks and plasma samples were incubated for 2 h at RT on a plate shaker set at 300 rpm. Then, after removing DBS disks and plasma samples, each well was manually washed 4 times with DELFIA Wash Solution (PerkinElmer). Next, 100 μL (200 ng/mL) of DELFIA Eu-labeled Anti-human IgG antibody (PerkinElmer) was added to each well and incubated for 1 h at RT on a plate shaker set at 300 rpm. Subsequently, each well was washed 6 times with DELFIA Wash Solution. Finally, 200 μL of DELFIA Enhancement Solution (PerkinElmer) was added, and the plate was read after 10 min of incubation time by Victor Nivo microplate reader using fluorescence (TRF) settings. To test the linearity of both ELISA and DBS-DELFIA immunoassays, a SARS-CoV-2 anti-N IgG positive sample was diluted sequentially 12 times with a seronegative plasma and tested in duplicate. Dilution percentages are listed in Table S2. Data for linearity and intra-assay precision were collected by the same operator. All statistical analyses were carried out using GraphPad Prism 9.0.2 software (GraphPad Software, La Jolla, CA, USA). The study population included fifteen subjects who had never tested positive for SARS-CoV-2 infection, thirty-two subjects who reported a positive nasopharyngeal swab (NPS) test, and forty-three individuals who had completed the vaccination schedule. The age of the subjects ranged from 25 to 60 (mean = 35.6), and 70% were female (Table S1). Firstly, we conducted an experiment to set up the method for converting ELISA into DBS-DELFIA by using paired plasma samples and dried blood spots from two subjects, one negative and one positive for SARS-CoV-2 infection. The cut-off value used by the qualitative SARS-CoV-2 nucleocapsid antibody ELISA kit was 0.2 OD. We analyzed, in parallel and in duplicate, the plasma diluted 100 times both by ELISA and by DELFIA, and the paired DBSs by DELFIA following the manufactures’ instructions (Supplementary Table S3). The positive subject, with 2.47 ± 0.06 OD, had almost comparable values for plasma DELFIA and DBS-DELFIA (510,238 ± 11,641.8 TRF and 496,748 ± 96,228.75 TRF, respectively). On the other hand, the negative subject showed different values between plasma DELFIA and DBS-DELFIA (24,067.5 ± 678.11 TRF and 93,547.5 ± 12,087.99 TRF). For this reason, we attempted to improve the DBS-DELFIA protocol using the commercially available ELISA-to-DELFIA conversion kit protocol distributed by PerkinElmer, which is fully described in the Section 2. To prove the linearity of the method for quantifying IgG anti-N, we performed sequential dilutions using two blood samples, one that tested negative (dilution percentage 0, 0.07 OD) and one that tested positive (dilution percentage 100, 3.33 OD) for anti-SARS-CoV-2 nucleocapsid antibodies by the ELISA method. We analyzed 12 paired dilutions by both ELISA and the new DBS-DELFIA method, using different sample matrices (plasma or DBS) (Table S2). The DELFIA method had a wider dynamic range than conventional ELISA (Figure 1). While the linear range of DELFIA covered dilution percentages up to 100 with an R2 equal to 0.97, the ELISA method only showed linearity below the 40-dilution percentage (R2 = 0.97). Next, we examined the cut-off value referred to in the ELISA kit and noted that for 1:100 (0.191 ± 0.03 OD) and 2:100 (0.241 ± 0.03 OD) dilutions, the test resulted in negative and positive results, respectively. However, the paired samples analyzed by DBS-DELFIA measured 30,351 ± 4912 TRF and 53,652 ± 17,674 TRF, showing a higher sensitivity potential. In order to evaluate intra-assay precision, we evaluated the coefficient of variability in percentage (CV%) of both obtained curves. We obtained 7% and 15.2 CV% for ELISA and DBS-DELFIA, respectively. Finally, we calculated the Limit of Detection (LOD) and Limit of Quantification (LOQ) for both methods by using blank sample replicates (n = 8), obtaining 0.09 and 0.18 values, respectively, for the ELISA immunoassay, and 2797 and 3787 values for the DBS-DELFIA one. All paired plasma samples and dried blood spots were collected and analyzed by both ELISA and DBS-DELFIA immunoassays for the detection of SARS-CoV-2 nucleocapsid antibodies (Table S4). A significant positive correlation (r = 0.9, p < 0.0001) between IgG anti-N measured on DBS and plasma with DELFIA and ELISA immunoassays, respectively, was observed (Figure 2). Fifteen subjects had never tested positive for SARS-CoV-2 infection using NPS. At the same time, thirty-two subjects tested positive at different times after being tested for SARS-CoV-2 nucleocapsid antibodies. All subjects without SARS-CoV-2 infection tested negative on the ELISA test (OD < 0.2). Overall, these subjects presented a mean TRF below 2.0 × 104. Despite significant differences in IgG anti-N levels evaluated by both ELISA and DBS-DELFIA between subjects who were negative or positive for SARS-CoV-2 infection (Figure 3A,C), the receiver operator characteristic (ROC) curve analysis revealed higher sensitivity (87.5%, AUC: 0.91, p < 0.0001) for the DBS-DELFIA assay compared to ELISA (71.88%, AUC: 0.92, p < 0.0001) while the specificity was 100% for both assays (Figure 3B,D). Moreover, the ROC analysis showed that 87.5% sensitivity was achieved above 20,120 TRF; therefore, we set the cut-off value for the DBS-DELFIA assay at 2.0 × 104 (Figure 3C). Five samples (PZ 24, 25, 32, 44, and 47) tested negative when analyzed with ELISA but appeared above the assessed cut-off value when analyzed with the DBS-DELFIA method. By calculating the percentage difference between the cut-off value and LOQ for both evaluated methods, we observed that the ELISA test’s positivity limit was 2% higher than the LOQ, while the DBS-DELFIA positivity limit was more than 80% higher than its relative LOQ. With thousands of new cases daily, the ongoing scenario indicates that the SARS-CoV-2 pandemic will continue to evolve [28]. Indeed, as SARS-CoV-2 continues to spread in human populations with fewer susceptible hosts, the risk of selecting more infectious variants or antibody-evasive mutations is expected to increase. Even to avoid undiagnosed cases of SARS-CoV-2 infection in emergencies [29], viral tests are used for the assessment of current infection with SARS-CoV-2 by testing respiratory tract specimens (throat swabs, sputum, nasopharyngeal swabs, nasal swabs, and bronchoalveolar lavage fluid). There are two main types of viral tests: nucleic acid amplification tests (NAATs, such as reverse transcription polymerase chain reaction) and antigen tests [30]. However, the collection of samples from the respiratory tract is relatively complicated and causes significant discomfort to subjects. Moreover, the costs of NAATs continue to remain high. Therefore, growing interest has been placed in developing serological tests for the detection of anti-SARS-CoV-2 antibodies to help identify people who have been infected with SARS-CoV-2, have recovered from COVID-19, or have been vaccinated [31,32]. Notably, DBS specimens have been seen to be reliably used as an alternative to serum samples for SARS-CoV-2 antibody measurement, facilitating serosurveillance efforts [33,34]. We have already validated the GSP®®/DELFIA®® Anti-SARS-CoV-2 IgG Kit for measuring anti-S1 antibodies by using DBS, demonstrating the feasibility of such serological tests for high-throughput serosurveillance [35]. However, to ascertain whether a recent SARS-CoV-2 infection occurred among subjects who have previously been vaccinated for the prevention of COVID-19, high-sensitivity immunoassays for SARS-CoV-2 anti-nucleocapsid antibodies are required. Notably, there is evidence of longer durability of anti-spike antibodies after vaccination with the mRNA vaccine in subjects with previous infection, and the risk of new SARS-CoV-2 infection appears to be higher in previously uninfected individuals [36,37]. This is why knowing the SARS-CoV-2 nucleocapsid antibody profile is extremely important. For this purpose, we established a dissociation-enhanced lanthanide fluorescence (DELFIA) immunoassay for the evaluation of SARS-CoV-2 anti-nucleocapsid IgG antibody status by analyzing dried blood spots (DBS-DELFIA). We converted a commercially available CE-IVD SARS-CoV-2 nucleocapsid protein IgG ELISA kit validated with serum or plasma samples into the DBS-DELFIA. Our results confirm a wider dynamic range and higher sensitivity of the DBS-DELFIA compared to the ELISA immunoassay in recognizing IgG anti-N against SARS-CoV-2, revealing lower amounts of antibodies even when several days have passed since the previous infection. Moreover, the total intra-assay coefficient of variability of the DBS-DELFIA was 14.6%, indicating good assay precision. Finally, a strong correlation between SARS-CoV-2 nucleocapsid antibodies detected by the DBS-DELFIA and ELISA immunoassays was found (r = 0.9). Therefore, the association of dried blood sampling with DELFIA technology, in addition to a simple, non-invasive approach and little time-consuming sample preparation, may provide a more sensitive and accurate measurement of SARS-CoV-2 nucleocapsid antibodies than tests currently available, particularly for low detectable or higher quantizable antibody levels among SARS-CoV-2-infected subjects, with the further potential of being fully automated [35]. In summary, we developed a new serological immunoassay specifically for the detection of SARS-CoV-2 nucleocapsid antibodies employing DELFIA technology. The assay showed increased sensitivity and appears to be particularly suitable for high-throughput serosurveillance studies, thus justifying further research aimed at developing a certified IVD DBS-DELFIA assay for detecting SARS-CoV-2 nucleocapsid antibodies.

PMC10000648

Manjula P. Mony,Kelly A. Harmon,Ryan Hess,Amir H. Dorafshar,Sasha H. Shafikhani

An Updated Review of Hypertrophic Scarring

21-02-2023

normal (acute) wound healing,hypertrophic scar,keloids,animal models,treatments

Hypertrophic scarring (HTS) is an aberrant form of wound healing that is associated with excessive deposition of extracellular matrix and connective tissue at the site of injury. In this review article, we provide an overview of normal (acute) wound healing phases (hemostasis, inflammation, proliferation, and remodeling). We next discuss the dysregulated and/or impaired mechanisms in wound healing phases that are associated with HTS development. We next discuss the animal models of HTS and their limitations, and review the current and emerging treatments of HTS.

An Updated Review of Hypertrophic Scarring Hypertrophic scarring (HTS) is an aberrant form of wound healing that is associated with excessive deposition of extracellular matrix and connective tissue at the site of injury. In this review article, we provide an overview of normal (acute) wound healing phases (hemostasis, inflammation, proliferation, and remodeling). We next discuss the dysregulated and/or impaired mechanisms in wound healing phases that are associated with HTS development. We next discuss the animal models of HTS and their limitations, and review the current and emerging treatments of HTS. Wound healing is a complex physiologic process in which the body attempts to replace destroyed and damaged tissue with newly generated tissue and restore the skin’s barrier functions. It is an overlapping and sequential process of hemostasis, inflammation, proliferation, and remodeling that involves communication between many different cell types [1]. When this process does not occur in a sequential and finite manner, aberrant wound healing with hypertrophic scarring (HTS) or keloids may occur. These fibroproliferative disorders can be appreciated as elevated scars above the skin level with abundant deposition of extracellular matrix (ECM) components, especially collagen [2]. Although HTS and keloids are often used interchangeably, they are not the same. In HTS, excess scarring is limited to the original site of injury, whereas in keloids, scarring can extend beyond the original wound and is often regarded as a form of benign skin tumor [3,4]. Scarring is a major clinical problem, affecting some 100 million patients in the developed world alone [5]. The reported prevalence of hypertrophic scarring ranges from 32 to 72% [6,7]. Hypertrophic scars are particularly prevalent among adult burn patients, with those with darker skin, younger age, female sex, burns greater than 20% of total body surface area (TBSA), and burns on the neck and upper limbs experiencing the highest risk [6,8]. Following burn injury, nearly 75% of patients develop neuropathic pain [9]. Factors such as scar height, pigmentation, vascularity, and hyperplasia have been associated with increased levels of pain [9]. In one study, nearly 60% of patients who underwent bilateral reduction mammoplasty or median sternotomy incision developed HTS postoperatively, with an increased risk in those who were young [10]. Keloids have been reported in all ethnic groups, but they are significantly more common in individuals of African, Asian, and to a lesser degree, Hispanic descent, with the incidence ranging from 0.09% amongst the European white population, to 16% in the black population in Africa [11,12,13]. Severe HTS may result in scar contractures which can be significantly disfiguring and disabling and may lead to loss of mobility and affect patients’ ability to carry out routine daily activities [14,15]. In patients with severe burns, HTS is associated with decreased quality of life and delayed reintegration into society, in part due to the effect on self-esteem and the resultant desire to hide the scarring [16]. Globally, the wound care cost is estimated to be nearly $20.8 billion annually, with $4 billion per year associated with HTS treatment in the United States alone [17]. Hypertrophic wound care remains one of the largest markets without definitive drug therapy. The global hypertrophic and keloid scar treatment market size is expected to reach $37.9 billion US dollars by 2026 with a compound annual growth rate (CAGR) of 9.9% [18]. Hypertrophic scars typically occur in the second to third decade of life and present 1–2 months following injury [7]. The scar experiences a rapid growth phase for the first 6 months, followed by regression [7]. HTS arises as increased induration and dyspigmentation limited to the site of initial injury in areas of high tension, such as the shoulders, neck, prosternum, knees, and ankles [7]. Diagnosis of hypertrophic scarring is made clinically. Scoring systems such as the Vancouver Scar Scale, Seattle Scar Scale, Hamilton Scar Scale, and Patient and Observer Scar Assessment Scale may be used to assess the degree of hypertrophy [19]. These scales are based on clinical parameters such as lesion thickness, color, pliability, pain, and itching; however, the resulting scar scores are variable, as they are based on subjective clinical assessment. Combining the scar scales with more objective data such as high-resolution ultrasound scanning may be beneficial [20]. In this review article, we provide an overview of normal (also known as, acute) wound healing phases; namely, hemostasis, inflammation, proliferation, and remodeling. We next provide an updated review of the dysregulated and/or impaired mechanisms of HTS associated with each phase of wound healing. We then discuss the animal models of HTS and their limitations, and review the current and emerging treatments of HTS. To gain better understanding of the pathophysiology underlying HTS, it is essential to appreciate the processes underlying normal (acute) wound healing in the acute setting. Normal wound healing occurs in four overlapping and complex phases; namely, hemostasis, inflammation, proliferation, and remodeling (Figure 1). Hemostasis begins immediately after injury and could last for several hours. As an immediate response to limit blood loss after injury, the blood vessels’ smooth muscle contracts via vasoconstrictors, such as endothelin, released by the damaged endothelial cells [21]. This is followed by blood clot formation, which occurs in two steps: primary hemostasis and secondary hemostasis. During primary hemostasis, rearrangement and transformation of the actin cytoskeleton occur in platelets, allowing a change in their morphology from disk-shaped to fried egg-shaped cells. This, in turn, causes platelets to interact with each other and the surrounding extracellular matrix (ECM) through activated integrins, allowing for the development of the platelet plug [21,22]. During secondary hemostasis, thrombin becomes activated via the intrinsic and extrinsic coagulation pathways [23]. Activated thrombin cleaves soluble fibrinogen into fibrin and cross-links them to form fibrin mesh, which is incorporated into the fibrin clot at the site of injury to form a thrombus, which enmeshes aggregated platelets and leukocytes into a stronger structure known as the platelet plug [22]. The platelet plug serves three important functions during wound healing: to prevent blood loss after injury, to serve as a source of chemokines and growth factors needed to initiate the inflammatory phase, and to function as a provisional scaffold for inflammatory leukocytes migration into damaged tissue [24,25]. Following injury, the inflammatory phase begins within minutes, peaks in 2–3 days, and can last 1–2 weeks, depending on the extent of the injury [1]. The primary functions of the inflammation phase during wound healing are to protect wounds against invading pathogens and to jumpstart the subsequent inflammatory and non-inflammatory responses needed for proper healing [26,27]. The inflammatory phase can be divided into early and late phases. During the early phase of inflammation, endothelial cells increase the expression of adhesion molecules, resulting in the recruitment and extravasation of inflammatory cells, such as neutrophils, monocyte, lymphocytes, and mast cells [28,29]. Leukocytes recruitment is mediated by pro-inflammatory cytokines, such as interleukin-1 (IL-1), IL-6, and tumor necrosis factor alpha (TNF-α), which are released from degranulating platelets, keratinocytes, endothelial cells, and tissue-resident macrophages [1,30,31]. Upon arrival into the wound, monocytes differentiate into pro-inflammatory M1 macrophages, which function to further amplify inflammatory responses by producing more pro-inflammatory cytokines, and assist neutrophils in destroying invading pathogens [32,33]. During the late phase of inflammation, the macrophages polarize into the anti-inflammatory M2 phenotype, which play pivotal roles in the resolution of the inflammatory responses and in the initiation of the proliferation phase through the production of a spectrum of angiogenic and growth factor mediators, such as vascular endothelial growth factor (VEGF), PDGF, and FGF2 [33,34,35]. The proliferative phase, also known as the new tissue regeneration phase, begins approximately 3 days after injury and lasts for about 2–3 weeks. The main events during the proliferative phase are provisional matrix replacement with granulation tissue, angiogenesis, and re-epithelization [36]. Initially, fibroblasts migrate to the site of injury in response to mediators released by platelets and macrophages, such as PDGF, transforming growth factor beta (TGF-β), and connective tissue growth factor (CTGF) [37]. To replace the provisional matrix with granulation tissue, fibroblasts release extracellular matrix (ECM) components (primarily type III collagen, fibronectin, proteoglycans, and hyaluronic acid) [38,39]. Granulation tissue is composed of ECM components, fibroblasts, proliferating blood vessels, macrophages, and lymphocytes, and it is an important indicator of wound healing progression [40]. During re-epithelization, M2 macrophages and keratinocytes produce and release EGF and TGF-β, which in turn induce proliferation and cell migration in epithelial cells bordering the wound edges to re-establish the epidermis integrity at the wound site [41]. Stem cells from hair follicles and sebaceous glands differentiate into keratinocytes to aid in this process [42]. Angiogenesis involves the creation of new vasculature that is 3 to 10 times denser than what is found in normal tissue [43]. It is critical in facilitating the transport of immune cells, oxygen, and nutrients for the cells participating in healing [43]. Angiogenesis is triggered by local hypoxia and several soluble factors, including VEGF (most prominent factor), PDGF, fibroblast growth factor-basic (bFGF), the serine protease thrombin, and members of the TGF-β family [44,45,46,47]. Following the completion of wound healing, most of the newly formed capillaries will regress [43]. The remodeling phase begins 2–3 weeks following injury and can last up to a year or even longer [48]. Matrix maturation and tissue remodeling depend on the balance between the degradation of extracellular matrix (ECM) components in granulation tissue and their replacement by connective tissue components, namely collagen I. Early in the remodeling phase, ECM components (e.g., collagen III, fibronectin, and hyaluronic acid) are degraded by matrix metalloproteinases (MMPs) [49]. Because of the destructive nature of the MMPs, they are tightly regulated by the tissue inhibitors of metalloproteinases (TIMPs) [50]. Moreover, fibroblasts differentiate into myofibroblasts which produce thick bundles of collagen I to replace most of the collagen III [51]. Over time, collagen I fiber bundles increase in diameter, resulting in increased wound tensile strength; however, the healed tissue never fully regains the properties of uninjured skin, resulting in a mostly acellular and avascular scar [50]. Scar tissue contains collagen bundles that are smaller and more disorganized and, thus, prone to dehiscence [52,53]. Over time, wound contraction occurs as the result of myofibroblasts bringing the wound edges together with the contractile function of their actin filaments [54]. A large body of evidence suggests that excessive inflammation generates pro-fibrotic molecules, which in turn activate fibroblasts, resulting in HTS [55]. In addition, excessive angiogenesis and prolonged re-epithelialization can extend the release of pro-fibrotic growth factors [56,57]. In the last few years, many biomolecules have been implicated in HTS; however, their exact mechanisms have yet to be fully elucidated, in part due to the complexity and overlapping nature of wound-healing processes. Here, we will examine each phase of wound healing with respect to HTS formation. The fibrin provisional matrix deposited during hemostasis has been implicated in the activation of myofibroblasts and the formation of HTS [58]. In fact, high-density fibrin clot deposition during the early phase of healing may predict the formation of HTS (Figure 2), as calculated by a multiscale mathematical model [59]; however, more studies related to fibrin content and rate of fibrinolysis in experimental models are required to validate the role of the fibrin provisional matrix in the formation of HTS. In addition, during hemostasis, platelets release a multitude of pro-fibrotic growth factors such as PDGF, VEGF, TGF-β1, and CTGF, which have been linked to the formation of HTS (Figure 3) [60]. Interestingly, platelet-rich plasma (PRP) obtained from platelets of the peripheral blood is considered to be a therapeutic option for HTS, as it reduces the expression of pro-fibrotic molecules such as TGF-β1 and CTGF [61]. These reports suggest that while naïve platelets may be anti-fibrotic in nature when activated excessively, they can contribute to HTS development. Clearly, more studies are needed to evaluate the role of naïve versus activated platelets in HTS. Excessive inflammation (Figure 2) is the best elucidated pathophysiological reason for HTS formation [55]. As such, many of the accepted therapeutics target inflammation [55]. Excessive infection and tissue necrosis in severe burn wounds cause increased pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), toll-like receptor (TLR) signaling, and infiltration of inflammatory cells to the wound site [62,63,64]. Surprisingly, studies of HTS have found chemokine expression to be variable. In a study using the rabbit ear as a model for HTS, the expression of chemokines such as Chemokine (C-C motif) ligand 3 (CCL3), CCL7, and CCL13 maintained increased expression for 21 to 35 days, while CCL2, CCL4, CCL5, and chemokine (C-X3-C motif) ligand 1 (CX3CL1) were maintained at high levels for 21 to 56 days [65]. Another study reported that the expression of CCL3, CXCL1, CXCL2, CXCR2, C3, and Interleukin 10 (IL-10) was reduced in human HTS, 52 weeks following surgery [66]. In another study, SDF1/CXCR4 signaling was found to be increased in human HTS tissue [67]. The underlying reasons for this variability remain unknown and require future investigation. Inflammatory cells release various factors such as interleukins, interferon, and growth factors [68]. Increased expression of pro-inflammatory and pro-fibrotic growth factors activate fibroblasts and are thus implicated in HTS formation [69]. Interestingly, in a study of HTS tissue at 3 h following surgery, the expression of certain pro-inflammatory factors such as IL-6, IL-8, and CCL2 was found to be reduced during the early phase of healing [70]. Intriguingly, inadequate pro-inflammatory responses have also been reported in hypofibrotic diabetic wounds early after injury, rendering them vulnerable to infection and impaired healing [71,72,73,74]. This delay in inflammatory responses during the acute phase of healing early after injury and its role in the formation of HTS should be further investigated. IL-6 is a major cytokine that influences the middle and late phases of healing, as it is involved in shifting inflammation from acute to chronic by enhancing monocyte recruitment, M2 macrophage polarization, and ECM deposition [75,76,77]. IL-6 is highly expressed in HTS and is considered to be a therapeutic target for the treatment of HTS [69,78]. The IL-6/STAT3 (Signal transducer and activator of transcription 3) pathway activates many of the genes required for ECM production and fibroblast proliferation, leading to HTS [79]. Other than the IL-6 and inflammatory chemokines, other inflammatory cytokines that are highly expressed in HTS include IL-1β, IL-4, IL-8, IL-17, IL-13, and IL-22 (Figure 3) [69,80,81]. Some of these cytokines, such as IL-4 and IL-13, have been under investigation as therapeutic targets for HTS [82]. The expression of IL-10, an anti-inflammatory cytokine and promising therapeutic molecule, has been found to be low in patients with hypertrophic scarring compared to those with non-hypertrophic scarring [66]. Some studies have suggested that IL-10 directly influences fibroblasts by activating the STAT3 or AKT signaling pathways [83]. It has also been reported that IL-10 reduces scar formation by regulating the TLR4/NF-kB pathway in dermal fibroblasts [84]. However, further investigation is required to elucidate the role of IL-10 in preventing HTS development. Additionally, the expression of other cytokines, such as IL-24, IL-36, IL-37, IL-1RA, and TNF-α, has been found to be low in HTS (Figure 3) [69]. TNF-stimulated gene 6 (TSG-6) has been found to suppress scarring by downregulating the IRE1α/TRAF2/NF-κB signaling pathway [85]. Moreover, alteration in the fatty acid metabolism influences inflammation and can result in excessive scarring [86,87]. In a recent study, the expression of sterol regulatory element-binding protein-1 (SREBP1) and fatty acid synthase (FASN) was shown to be reduced at mRNA and protein levels in pathological HTS and in HTS-derived fibroblasts [86]. In another study, the expression of fatty acid desaturase 1 and 2 (FAD1 and FAD2)—key enzymes in the polyunsaturated fatty acids (PUFAs) metabolism with demonstrated anti-inflammatory function [88]—were lower in keloids and keloid-derived fibroblasts [87]. However, the mechanism of altered lipid profile in HTS has not been explored. It is possible that alterations in lipid metabolism might influence HTS through changes in the inflammatory pathways, given that fatty acids play an important role in regulating inflammation [89,90]. Events of the proliferative phase, such as angiogenesis and ECM deposition, are highly active in HTS, whereas re-epithelialization is prolonged in HTS as keratinocytes remain continually activated (Figure 2) [91,92,93]. Consequently, the granulation tissue becomes denser during HTS formation than in normal scarring (Figure 2). In HTS, cells such as keratinocytes, endothelial cells, and fibroblasts release many pro-fibrotic growth factors such as TGF-β, PDGF, VEGF, and CTGF [94]. This pro-fibrotic environment, in turn, induces fibroblasts to produce more ECM proteins such as collagen, fibronectin, laminin, periostin, fibrillin, and tenascin; however, the expression of certain ECM proteins such as hyaluronic acid, dermatopontin, and decorin are found to be altered or reduced (Figure 3) [95]. Fibroblasts of the deep dermis are responsible for the production of additional factors such as osteopontin, angiotensin-II, and peroxisome proliferator-activated receptor (PPAR)-α and contribute to scarring more than fibroblasts of the superficial dermis [96]. Recently, it has been revealed that fibroblasts in the upper dermis also contribute to scarring by producing IL-11, which in turn activates myofibroblasts [97]. TGF-β plays an important role in the formation of HTS, and the TGF-β/SMAD (Suppressor of Mothers against Decapentaplegic) signaling pathway is considered to be a potential therapeutic target of HTS [98]. Molecules such as SMAD interacting protein and bacterial PAMPs such as lipopolysaccharides (LPS) may induce HTS by enhancing the TGF-β1/SMAD signaling pathway [99,100]. Endothelial cells isolated from porcine burn wounds show that endothelial dysfunction and altered expression of angiogenic genes such as endothelin-1, angiopoietin-1, angiopoietin-2, and angiogenin may result in HTS (Figure 3) [57,101]. In turn, angiogenesis is stimulated by the microvesicles released from the myofibroblast [102]. Factors released from these vesicles may result in HTS, as many of them are pro-fibrotic in nature [102,103]. During hypertrophic scarring, keratinocytes remain in their activated state for a prolonged duration of time [92]. Dysregulation in the Notch signaling of keratinocytes may also contribute to HTS formation [104]. Notch 1 signaling and intracellular domains such as Jagged1 and Hes1 are highly expressed in the epidermis of hypertrophic scar patients [104]. This leads to the enhanced expression of pro-fibrotic factors, such as TGF β1, TGF β2, CTGF, IGF-1, VEGF, and EGF (Figure 3) [104]. In addition, epithelial–mesenchymal transition increases ECM deposition and has been shown to contribute to HTS formation [105]. Moreover, keratinocytes produce HMGB1, which activates fibroblasts, resulting in HTS formation [106]. However, certain factors released from keratinocyte-like pigment epithelium-derived growth factor (PEDF) are associated with reduced angiogenesis and HTS formation (Figure 3) [107]. Interestingly, among different growth factors, FGF-2 has an anti-scarring effect since it up-regulates the expression of MMP-1 and hepatocyte growth factor (HGF), although further investigations are required to clarify its therapeutic potential [108,109]. In HTS, the balance of ECM synthesis and remodeling is dysregulated [110]. Both fibroblasts and myofibroblasts continue to deposit collagen III and collagen I in HTS [111]. The persistence of myofibroblasts due to defects in apoptosis results in the deposition of excessive fibrous collagen I and scarring (Figure 2) [112,113,114]. The presence of nodules containing myofibroblasts is a peculiar feature of HTS [50]. Mechanical stretch and TGF-β can stimulate the differentiation of fibroblasts to myofibroblasts, contributing to HTS formation [115,116]. Metalloproteinases (MMPs), such as MMP1 and MMP7, are downregulated during HTS formation, resulting in reduced degradation of ECM components such as collagen I, collagen III, and fibronectin ([117,118] and Figure 2). Administration of MMP1 has been shown to improve scarring [119]. The tissue inhibitors of metalloproteinases (TIMPs), such as TIMP1 and TIMP2, reduce the action of MMPs during HTS development (Figure 3) [110]. In contrast, expression of MMP2, MMP9, and MMP13 are shown to be increased in HTS (Figure 3) [110,118]. This upregulation may be a compensatory response to elevated levels of ECM in HTS, but it remains unclear and requires future investigation. In HTS, reduced expression of matrix remodeling proteins results in the disorganization of ECM components [50,113]. Treatment with decorin, a matricellular protein involved in collagen fiber organization, has been shown to reduce HTS formation [120]. In addition, targeting the lysil hydroxylase enzyme, involved in the formation of pyridinoline cross-links, reduces the activity of fibroblast proliferation by regulating TGF-β1 [121]. While two-dimensional and three-dimensional cell culture-based in vitro models can be useful for investigating the mechanism of fibroblast in producing excessive ECM and potential therapeutic molecules, the absence of immune and vascular components in these models limits the physiological relevance of the findings emerging from these studies with respect to the mechanisms underlying HTS formation in tissue [122,123]. In the past several decades, many attempts have been made to develop animal models of HTS in different species. Despite these attempts, there are no animal models that can fully recapitulate HTS in humans. Descriptions of each animal model for HTS, as well as their advantages and disadvantages, are summarized in Table 1. The rabbit ear model has been widely used to study HTS formation despite the involvement of chondrocytes during the healing, where skin and perichondrial layers are removed from the ventral side of the rabbit ear to generate an HTS-like condition [124,125]. The advantages of this model include the simplicity of the procedure, ease of handling of the animal, and ability to create multiple wounds; however, the ventral side of the ear is difficult to handle because of its low thickness, and precaution needs to be taken to avoid damage of the underlying cartilage during the procedure [124,125]. To reduce damage to the rabbit ear cartilage during the procedure, cryosurgery has been attempted to remove the perichondrial layer [126]. In another rabbit ear model for HTS, thermal burn injury has been attempted to create a more elevated scar within a shorter duration which better mimics an HTS condition in humans [127]. However, thermal injury has to be precisely controlled to avoid variability in scarring [127]. Injecting anhydrous alcohol into the subcutaneous and superficial fascia regions of the dorsal skin of a rabbit has been used to model HTS [128]; however, this model appears to be more appropriate for skin fibrosis than the HTS due to the absence of a healing response. Deep burn injury to the dorsal side of porcine skin creates raised scar tissue and has been used in some studies as a model for HTS [57]. Although there are structural similarities between human and pig skin, the high costs associated with the production of this animal model and the difficulty in handling it have lessened its popularity for HTS studies. Several groups have also attempted to develop rodent (mouse and rat) models for HTS [129,130,131]. These murine models are inexpensive to produce and easy to handle, but wound healing patterns in rodents differ from that of humans due to the rapid contraction of the panniculus carnosus muscles [132]. To mitigate the effect of rapid wound contraction in rodents, splinting excision wounds have been attempted [130]. For example, splinted full-thickness skin wounds in rodents recapitulate mechanical tension in the wound bed, and the lack of neo-epithelium in this model amplifies myofibroblast function, culminating in hypertrophic features, which are similar to HTS in humans [131]. Similarly, mechanical pressure applied to a wound by a biomechanical loading device also produces HTS-like features in mice [133]. C–X–C motif chemokine receptor 3 (CXCR3)-deficient mice develop thick keratinized scars and have been used in some studies to model HTS, but deficient dermal maturation with poor collagen content has been observed [134,135,136]. Hence, the role of CXCR-3 and its effect on matrix development require further investigation. By resecting the abdominal wall muscle on the ventral side of mice that produces contractile forces, another murine wound model for scarring has been created, but it is not comparable with the healing mechanism underlying HTS [137]. Some attempts have been made to develop a xenograft model of HTS by grafting tissue from human HTS onto nude mice [138,139]. These mice displayed scar thickness and collagen bundle orientation and morphology resembling human HTS [129]. However, a lack of an immune response and difficulty in maintaining nude mice may obstruct the study of therapeutic molecules in this model. Developing an ideal animal model for HTS is exceptionally challenging, as the scar endotype is difficult to control in experimental settings [140]. The aforementioned animal models all fall short; therefore, developing an ideal animal model is essential to support studies related to the formation of and therapy for HTS. Treatments of hypertrophic scars often focus on correction of factors that are associated with pathological scar development as described above. These include wound stabilization, minimizing mechanical irritation, balancing wound healing phases, attenuating pro-fibrotic mechanisms, inducing anti-fibrotic mechanisms, and promoting the remodeling of collagenous scar components. Published guidelines on the treatment of hypertrophic scars and keloids include many different modalities without one single, widely accepted protocol [3,141]. Several treatments and techniques have been shown to prevent the development of hypertrophic scar development. (These conventional treatments have been summarized in Table 2). Reduction in tension on the dermal layer when closing wounds is effective and can be achieved with fascial and subcutaneous tensile reduction sutures in wounds of adequate depth [142]. Additionally, dermal closure using sutures arranged in a zig-zag pattern or using z-plasties should be performed whenever possible [142,143]. Closure with 3–0 VLoc 90 barbed suture (VLoc, Covidien, North Haven, CT, USA) compared to interrupted suture with 4–0 nylon produced significant improvements in the Vancouver scar scale (VSS) and patient and observer scar assessment scale (POSAS) scores in patients undergoing anterolateral thigh flap procedures with identical methods of deep closure between groups [144]. Following the closure of initial wounds, several therapies can also be applied early in the healing process. Similarly to the aforementioned suturing techniques, wound stabilization using paper tape or silicone sheets can also prevent the dermal inflammation that contributes to hypertrophic scar and keloid formation [145]. Wound compression using pressure garment therapy at 15–40 mmHg has been shown to improve outcomes [146]. Regarding ideal pressure, one review of pressure garment therapy for the treatment of burn wounds found that the application of pressure at 17–24 mmHg resulted in improved scar height, softness, and cosmetic appearance compared to a pressure below 5 mmHg [147]. Cohesive silicone sheets that added pressure to the wound also outperformed silicone gel sheets in improving scar assessment scale scores [148]. Intermittent application of pressure through regular massage therapy has not been shown to improve outcomes, suggesting that constant pressure must be applied [149]. Topical agents applied to heal wounds have also been shown to reduce hypertrophic scar formation, including flavonoids and silicone cream [150,151]. The local injection of Botulinum toxin-A postoperatively has also been shown to significantly improve scar assessment scale scores compared to controls [152,153,154]. In a recent study of optimal dosing of Botulinum toxin-A, postoperative injections of 8 units showed significantly improved Stony Brook Scar Evaluation Scale (SBSES) scores compared to the injections of 4 units [155]. The culture of human fibroblasts with Botulinum toxin-A resulted in decreased proliferation, migration, and secretion of pro-fibrotic factors, while JNK phosphorylation levels were increased, providing evidence for possible mechanisms of this benefit [156]. Scar revision is the simplest method of treating pre-existing HTS and encompasses procedures aimed at excisional debulking of hypertrophic scar tissue ([157] and Table 2). Closure during these procedures is specifically directed at providing favorable cosmetic results and should employ the methods described above for prophylaxis against scar recurrence. To be effective, scar revisions should be performed over 1 year from the original injury to give adequate time for the scar to mature [150], as immature scars are prone to hypertrophic healing and give poor results after scar revision [158]. However, excision may not be necessary, as more conservative measures have proven to be effective. For example, in one study, mechanical disruption of existing hypertrophic scars using microneedle roller therapy improved scar pigmentation to resemble surrounding tissue more closely, and significantly improved both the mean patient satisfaction scale (PSS) and observer satisfaction scale (OSS) between preoperative and postoperative sampling [159]. Another study found that microneedle therapy improved modified Vancouver scar scale (mVSS) scores significantly more than carbon dioxide (CO2) laser therapy for hypertrophic scars [160]. This benefit may be explained by microneedle therapy disrupting existing collagen and stimulating the release of MMP-9 [161]. Pharmacologic agents have also been used frequently in the treatment of hypertrophic scars, with common agents including corticosteroids, chemotherapeutic agents, and Botulinum toxin-A. Corticosteroids provide benefits through their potent anti-inflammatory effects and are believed to induce local vasoconstriction when applied to hypertrophic scars and keloids. Tapes and plasters containing corticosteroids effectively treat hypertrophic scars and keloids when applied to these lesions and should be positioned to avoid contact with surrounding tissue [145]. The most common use of corticosteroids in the treatment of hypertrophic scars and keloids by far is the intralesional injection of triamcinolone (TAC). A recent literature review and meta-analysis of this therapy found that compared to 5-FU and verapamil, TAC alone improved scar vascularity [162]. However, TAC therapy also had higher rates of skin atrophy and telangiectasias, especially at the commonly used dose of 40 mg/mL [162]. Significant differences in favor of other agents were found for scar height (5-FU, TAC + 5-FU), scar pliability (TAC + 5-FU, Botulinum toxin-A), scar pigmentation (TAC + 5-FU), VSS score (TAC + 5-FU, TAC + platelet rich plasma), and POSAS score (bleomycin) when compared against TAC alone [163]. A study of TAC vs. TAC + 5-FU found significant differences favoring TAC + 5-FU in mean reduction in scar height, overall POSAS score, and the overall rate of efficacy. Rates of telangiectasias (commonly known as “spider veins”), skin atrophy, hypopigmentation, and recurrence were significantly higher in the group receiving TAC, while the rates of ulceration were significantly higher in the group receiving TAC + 5-FU [164]. A literature review and meta-analysis of intralesional Botulinum toxin-A injection found significantly improved visual analog scale (VAS) scores compared to intralesional corticosteroid and placebo injection [165]. In a split-scar study of patients with existing hypertrophic scars, injection of Botulinum toxin-A was found to significantly improve mean VSS score pre- and post-treatment as compared to the placebo control [166]. The energy-based therapy is well established as a treatment modality for hypertrophic scars and keloids, with its use dating back to the 1980s [167]. Lasers are the mainstay of energy-based treatments, with a multitude of different laser devices utilizing different wavelengths for specific targets [168]. Laser therapy is often used in the treatment of formed hypertrophic scars but can also be used preventatively in the early postoperative period. In a split-scar study of patients undergoing total knee arthroplasties, scar treatment with a 595 nm pulsed-dye laser was associated with significantly improved overall VSS scores compared to an untreated scar [169,170,171,172,173,174]. The guidelines for the use of energy-based treatment for acne scars have included specific recommendations for use with hypertrophic acne scars and keloids. In patients with active acne, a 1064 nm ND:YAG laser is preferred, and pulsed-dye vascular lasers are the laser treatment of choice for hypertrophic acne scars. Pulsed-dye lasers (PDL) may also be used to assist with the delivery of 5-FU and/or TAC. Non-laser devices, including Tixel (Novoxel, Ltd., Berlin, Germany) and EnerJet (PerfAction Technologies Ltd., Rehovot, Israel), were also recommended for the treatment of hypertrophic acne scars [175]. Similar guidelines for traumatic scars recommend non-ablative fractional laser (NAFL) for hypertrophic scars, except in the presence of significant thickness and textural irregularity, where ablative fractional laser (AFL) therapy is preferred [169]. In a study comparing no laser treatment, CO2 laser treatment alone, and intense pulsed light (IPL) + CO2 laser, both treatment groups had statistically significant improvements in POSAS score and Manchester scar scale (MSS) score compared to the placebo, without significant difference between the treatment groups. The only significant difference between treatment groups was in favor of the combination therapy for scar color and texture, indicating that CO2 alone is sufficient and IPL can be used for an additional benefit for these specific factors [176]. Regarding protocols for CO2 laser, a study of varying densities for fractional CO2 laser treatment found that high (25.6%) density significantly improved VAS and POSAS scores compared to low (7.4%) and medium (12.6%) densities in treating mature hypertrophic burn scars [170]. A split-scar study of low-energy CO2 fractional laser treatment showed significantly improved POSAS scores for all elements except for patient-scored irregularity compared to the control for pediatric patients with early-stage hypertrophic burn scars [171]. A study of CO2, PDL, and CO2 + PDL for the treatment of hypertrophic burn scars found significant improvements in posttreatment POSAS for all treatment groups. Focused analyses found that scar height was improved by PDL or CO2 + PDL for scars <0.3 cm, and a significant reduction in scar height was achieved by CO2 + PDL only for scars older than 9 months. Although the guidelines for hypertrophic acne scars include the use of laser-assisted delivery of corticosteroids, a study of fractional ER:YAG laser alone or in combination with topical clobetasol found no significant benefit from the addition of steroids, with both treatment groups achieving significant posttreatment improvements in scar thickness and POSAS scores [172,175]. Recently, studies have compared IPL to non-laser therapies. Significant differences in scar pliability, hyperpigmentation, and median VAS favored IPL vs. silicone sheet, but significant differences in VAS and histopathological characteristics favored cryotherapy vs. IPL [173,174]. Given the prevalence of hypertrophic scarring, new treatments are continually developed. Intralesional TAC, for example, was found to improve scar height, pliability, and pigmentation when combined with 5-FU and reduced the number of treatment sessions and remission time when combined with 1550 nm erbium glass fractional laser treatment (Table 3) [163,164,177,178]. While Botox A with TAC showed no difference in scar appearance, it significantly reduced pain and pruritis [179]. Scars treated with RFA plus verapamil and 5-FU experienced the fastest scar volume reduction with relief of symptoms and hyperemia compared to either agent alone [180]. Additionally, the combination of intense pulse light (IPL) and CO2 laser significantly improved scar color and texture [176]. The combination of lasers with 5-FU and/or TAC delivered intralesionally or via laser assistance has thus been recommended for the treatment of hypertrophic acne scars [169,175]. The role of angiotensin II in scar activity has recently been examined [181]. Human dermal fibroblasts treated with losartan, an angiotensin II type 1 receptor antagonist, displayed decreased contractile activity, fibroblast migration, gene expression of TGF-β1, type 1 collagen, and MCP-1, while reducing monocyte migration and adhesion [181]. In rat models, the consumption of losartan showed decreased cross-sectional area and elevation index in scars, with decreased α-SMA+ and CD68+ during immunostaining [181]. Another in vivo model demonstrated a reduced incidence of hypertrophic scarring with decreased inflammation, collagen and fibroblast cellularity, vascularization, and myofibroblast activity with the topical administration of oxandrolone and hyaluronic acid gel [182]. Clinically, the administration of dipeptidyl peptidase-4 inhibitors was shown to reduce the risk of hypertrophic scarring and keloid onset by less than half in patients who underwent sternotomy, while 1,4-diaminobutane (1,4 DAB) in breast reduction patients resulted in significantly greater scar satisfaction and less scar hardness measured by Rex Durometer [183,184]. Autologous fat grafting also presents as a novel therapy to improve the function and appearance of scars. While the underlying mechanism is unknown, exposure to adipocytes decreased the expression of the myofibroblast marker α-SMA and ECM components [185]. The reprogramming of myofibroblasts was found to be triggered by BMP-4 (bone morphogenetic protein 4) and activation of PPARγ (peroxisome proliferator-activated receptor gamma) signaling, which initiated tissue remodeling [185]. As is the case in many other fields of medicine, stem cells are also a promising therapeutic target for HTS. Mesenchymal stem cells (MSC) isolated from the mouse whisker hair follicle outer root sheath were applied to an in vivo full-thickness wound model [186]. A quantitative evaluation revealed reduced inflammation, cellularity, and collagen filaments, as well as thinner dermal and epidermal layers in the MSC-treated wounds, indicating a reduction in hypertrophic scars. Another study examined the effect of combined treatment with a non-ablative laser and human stem cell-conditioned medium on burn-induced hypertrophic scar formation [187]. The treatment group was found to have reduced erythema, trans-epidermal water loss, and scar thickness. Platelet-rich plasma (PRP) has also been identified as a promising therapy for scarring. In one study, primary dermal fibroblasts isolated from hypertrophic scars were cultured in a medium supplemented with 5% PRP or platelet-poor plasma (PPP) [61]. The PRP group was found to have reduced expression of TGF-β1 and connective tissue growth factor (CTGF) mRNA. Other studies have examined combination treatments with both PRP and ablative fractional CO2 lasers and have found the combination to be more beneficial than either treatment alone [188,189]. In addition, identifying the molecular targets for potential treatments is an ongoing source of investigation. Co-cultures of anti-inflammatory cluster of differentiation 206 (CD206)+ macrophages and fibroblasts showed decreased expression of fibrotic factors, such as type 1 and 2 collagen, alpha-smooth muscle actin, connective tissue growth factor, and TGF-β, with upregulation of MMP-1. IL-6 was also found to be increased in the medium, with an increase in anti-fibrotic gene expression when IL-6 was added to fibroblasts. Cytotherapy with cultured CD206+ macrophages or a direct administration of recombinant human IL-6 has been shown to dampen the expression of pro-fibrotic mediators (e.g., COL1A1 *, COL2A1 *, α-SMA *, CTGF *, and TGF-β1) in fibroblast in cell culture studies [190]. In vitro studies of fibroblasts have revealed that IFN-γ inhibits collagen synthesis [191]. IFN-γ knockout mice were found to have reduced wound closure, lower wound breaking strength, and dampened expression of collagen type 1A (COL1A1) and collagen type 3 A1 (COL3A1) mRNA, but a greater expression of MMP-2 (gelatinase) mRNA [191]. The study concluded IFN-γ may be involved in both the proliferation and maturation stages of wound healing and, therefore, may be a target for potential treatments. As this review illustrates, there has been significant knowledge gained in the field of hypertrophic scarring. A pro-fibrotic environment results in excessive collagen deposition and, therefore, hypertrophic scar formation. In this review article, and for the first time, we highlighted the defective and impaired mechanisms underlying HTS that are associated with each phase of wound healing (hemostasis, inflammation, proliferation, and remodeling). This was an attempt to demonstrate the multifaceted nature of the phase-specific dysregulations and impaired mechanisms that underlie HTS development. We further discussed the current animal models and their limitations in order to highlight the need for better animal models that can more closely reproduce the human condition with respect to HTS development. We also reviewed the current and emerging therapies, which further demonstrate the inadequacy of therapies to address HTS. There is still much to be discovered in regard to the underlying mechanisms contributing to HTS development. A better understanding of the impaired mechanisms underlying HTS would surely lead to the development of more effective targeted therapies to treat this debilitating and costly pathological condition.

PMC10000650

Márcia C. Coelho,Francisco Xavier Malcata,Célia C. G. Silva

Distinct Bacterial Communities in São Jorge Cheese with Protected Designation of Origin (PDO)

26-02-2023

cheese,microbiota,lactic acid bacteria,Leuconostoc,fermented foods,metagenomic analysis,bacterial diversity,high throughput sequencing

São Jorge cheese is an iconic product of the Azores, produced from raw cow’s milk and natural whey starter (NWS). Although it is produced according to Protected Designation of Origin (PDO) specifications, the granting of the PDO label depends crucially on sensory evaluation by trained tasters. The aim of this work was to characterize the bacterial diversity of this cheese using next-generation sequencing (NGS) and to identify the specific microbiota that contributes most to its uniqueness as a PDO by distinguishing the bacterial communities of PDO and non-PDO cheeses. The NWS and curd microbiota was dominated by Streptococcus and Lactococcus, whereas Lactobacillus and Leuconostoc were also present in the core microbiota of the cheese along with these genera. Significant differences (p < 0.05) in bacterial community composition were found between PDO cheese and non-certified cheese; Leuconostoc was found to play the chief role in this regard. Certified cheeses were richer in Leuconostoc, Lactobacillus and Enterococcus, but had fewer Streptococcus (p < 0.05). A negative correlation was found between contaminating bacteria, e.g., Staphylococcus and Acinetobacter, and the development of PDO-associated bacteria such as Leuconostoc, Lactobacillus and Enterococcus. A reduction in contaminating bacteria was found to be crucial for the development of a bacterial community rich in Leuconostoc and Lactobacillus, thus justifying the PDO seal of quality. This study has helped to clearly distinguish between cheeses with and without PDO based on the composition of the bacterial community. The characterization of the NWS and the cheese microbiota can contribute to a better understanding of the microbial dynamics of this traditional PDO cheese and can help producers interested in maintaining the identity and quality of São Jorge PDO cheese.

Distinct Bacterial Communities in São Jorge Cheese with Protected Designation of Origin (PDO) São Jorge cheese is an iconic product of the Azores, produced from raw cow’s milk and natural whey starter (NWS). Although it is produced according to Protected Designation of Origin (PDO) specifications, the granting of the PDO label depends crucially on sensory evaluation by trained tasters. The aim of this work was to characterize the bacterial diversity of this cheese using next-generation sequencing (NGS) and to identify the specific microbiota that contributes most to its uniqueness as a PDO by distinguishing the bacterial communities of PDO and non-PDO cheeses. The NWS and curd microbiota was dominated by Streptococcus and Lactococcus, whereas Lactobacillus and Leuconostoc were also present in the core microbiota of the cheese along with these genera. Significant differences (p < 0.05) in bacterial community composition were found between PDO cheese and non-certified cheese; Leuconostoc was found to play the chief role in this regard. Certified cheeses were richer in Leuconostoc, Lactobacillus and Enterococcus, but had fewer Streptococcus (p < 0.05). A negative correlation was found between contaminating bacteria, e.g., Staphylococcus and Acinetobacter, and the development of PDO-associated bacteria such as Leuconostoc, Lactobacillus and Enterococcus. A reduction in contaminating bacteria was found to be crucial for the development of a bacterial community rich in Leuconostoc and Lactobacillus, thus justifying the PDO seal of quality. This study has helped to clearly distinguish between cheeses with and without PDO based on the composition of the bacterial community. The characterization of the NWS and the cheese microbiota can contribute to a better understanding of the microbial dynamics of this traditional PDO cheese and can help producers interested in maintaining the identity and quality of São Jorge PDO cheese. The microbiota of raw milk cheeses is quite complex and includes many non-starter lactic acid bacteria (LAB) strains originally derived from the milk itself or introduced by the manufacturing environment; these bacteria are important for the ripening of the cheese and the development of the expected flavor [1,2]. Interest in the functional and structural diversity of the microbiota in raw milk cheeses has increased because these cheeses have a more intense and unique flavor compared to cheeses produced from pasteurized milk (Montel et al., 2014). Several studies have attempted to describe the microbiota of traditional cheeses and the distinct stages of cheese manufacture and ripening [3,4,5,6,7,8]. Culture-dependent methods have been the preferred choice, but they are labor-intensive and inherently biased [9]. Therefore, culture-independent techniques and next-generation sequencing (NGS) technology have played a key role in recent studies on microbial communities in traditional cheeses [10,11,12,13,14,15]. In addition, NGS methods can reveal the existence of subdominant populations within the cheese microbiota that are difficult to detect using culture-dependent methods. These populations may be responsible (at least in part) for the differentiating flavors of raw milk cheeses. The interactions of the subdominant (or rare) microbiota with the dominant microbiota also likely play an important role in the development of the key flavor and aroma notes of these cheeses [16,17,18]. São Jorge cheese is a very popular Portuguese cheese produced from raw cow’s milk on the island of São Jorge in the Azores. It bears major economic and social importance on the island. This cheese exhibits a yellowish, hard or semi-hard paste and a crumbly texture. It is produced from raw cow’s milk to which a natural whey starter (NWS) is added, obtained from the whey of the previous day’s cheese-making. The NWS is added in a ratio of about 1.5–2/1000 (1.5/1000, for NWS acidity 50–65 °D; 2/1000, for NWS acidity 40–50 °D). Acidification takes place at 30 °C and is followed by cooking the curd at 35–36 °C, draining the whey, shaping the curd, and salting and pressing the curd. This is followed by the ripening process, which lasts at least 3–4 months. By the end of the ripening, São Jorge cheese has small, irregular eyes, and its flavor is characterized by strong, clean and slightly spicy notes that become more intense as it matures. Therefore, the indigenous microbiota of the raw milk and the starter culture (NWS) is important for the subsequent ripening process, as both actively contribute to the characteristic aroma and spicy flavor of the final product. However, the variability of the final product—expected in view of its being manufactured from raw milk, is often sufficient to compromise the PDO seal. In order to obtain this seal, it is not enough for the cheese to be produced by a certified cheese maker according to PDO specifications; each batch of cheese is indeed also subjected to sensory testing by a trained panel from an independent certifying body (Confraria do Queijo de São Jorge). As a result, a large percentage of cheeses (40–60%) produced according to PDO specifications will be eventually denied PDO status, yet they will still be suitable for selling at lower prices. Although a few studies have attempted to identify and characterize the dominant bacteria in São Jorge cheese [19,20,21,22], they all relied on culture-dependent methods, unable to fully decipher the diversity associated with this type of dairy product. In addition, the growth media used in culture-dependent methods are not truly selective for differentiation among bacterial communities [23]. Therefore, complementary, more in-depth studies are needed to fully elucidate the role of the entire microbiota of this cheese. To achieve this goal, a detailed identification and characterization of the microbiota using culture-independent methods appears important for the eventual selection of tailor-made starter cultures (SLAB) and/or auxiliary cultures specifically designed to control the fermentation of this cheese, thus helping reduce variability, achieve the best sensory characteristics during ripening, and guarantee a higher percentage of PDO cheeses. The aim of this work was accordingly to apply culture-independent and NGS methods to characterize the bacterial communities in milk, NWS, curd, and final cheese under a concerted program to shed light on the dynamics of the microbiota and to rationalize the failure to receive the PDO seal on microbiological grounds. Samples of milk, NWS, curd and cheese (9 months ripening) from the traditional production of São Jorge cheese were taken aseptically on four different occasions at “UNIQUEIJO: Union of Agricultural Cooperatives” on the island of São Jorge (the main producer of PDO cheese). One milk sample and one sample of the NWS were taken from the respective tank in each sampling period. In addition, two samples of the curd were taken each time from different vats, so a total of 8 samples were taken. The milk samples (ca. 100 mL), the NWS (ca. 100 mL) and the cheese curd (ca. 250 g) were kept in sterile individual bottles, refrigerated (4 °C) during transport and stored at −20 °C until DNA extraction. From each of the four production dates, several batches of cheeses (from different vats) matured for 9 months were subjected to sensory analysis by the São Jorge Private Control Body and Cheese Certification. After classification, 8 cheeses granted PDO status and 8 cheeses without PDO certification were collected for analysis. The cheese samples (ca. 500 g) were vacuum packed and kept refrigerated (4 °C) until DNA extraction. Bacterial cells in milk and NWS samples were concentrated by centrifugation (10 mL) at 7000× g for 10 min (Beckman J2-HS centrifuge). The supernatant was discarded, and the pellet was washed twice with TE buffer (Tris-EDTA: 2M Tris HCl + 0.5M EDTA, pH 8.0); the pellet was then resuspended in 1 mL of TE buffer before DNA extraction. For the preparation of cheese and curd aliquots, 5 g of the sample was weighed and 45 mL of 2% sodium citrate buffer was added, followed by homogenization in a stomacher (400 Circulator, Seward Medical, London, UK) for 2 min at 230 rpm. Total genomic DNA was extracted using the UltraClean® extraction kit Microbial DNA Isolation Kit (MoBio, Carlsbad, CA, USA). The quantity and quality of extracted DNA were evaluated by measuring absorbance at 260 and 280 nm (LVis Plate, Fluorstar Omega, BMG Labtech). The quality of the extracted DNA was confirmed via 1.5% agarose (w/v) gel electrophoresis. Only two milk samples yielded good-quality DNA after extraction. Therefore, a total of 30 samples, including raw milk (n = 2), NWS (n = 4), curd (n = 8), PDO cheese (n = 8), and non-PDO cheese (n = 8) were analyzed. The samples were prepared for Illumina Sequencing by 16S rRNA gene amplification of the bacterial community. The DNA was amplified for the hypervariable V3–V4 region with specific primers and further reamplified in a limited-cycle PCR reaction to add sequencing adapters and dual indexes. PCR reactions were first performed for each sample using the KAPA HiFi HotStart PCR Kit according to the manufacturer’s recommendations: 0.3 μM of each PCR primer: forward primer Bakt_341F 5′–CCTACGGGNGGCWGCAG-3′ and reverse primer Bakt_805R 5′–GACTACHVGGGTATCTAATCC-3′ (Herlemann et al., 2011, Klindworth et al., 2013), and 12.5 ng of template DNA was collected accordingly in a total volume of 25 μL. PCR conditions included denaturation at 95 °C for 3 min, followed by 25 cycles of 98 °C for 20 s, 55 °C for 30 s, and 72 °C for 30 s and a final extension at 72 °C for 5 min. In the second PCR reactions, indexes and sequencing adapters were added to both ends of the amplified target region according to the manufacturer’s recommendations (Illumina, 2013). Negative PCR controls were used for all amplification procedures. PCR products were then purified in one step, normalized using the SequalPrep 96-well plate kit (ThermoFisher Scientific, Waltham, MA, USA) (Comeau et al., 2017), pooled, and pair-end sequenced in the Illumina MiSeq® sequencer using V3 chemistry, according to the manufacturer’s instructions (Illumina, San Diego, CA, USA) at Genoinseq (Cantanhede, Portugal). Sequence data were processed at Genoinseq (Cantanhede, Portugal). Raw reads were extracted from an Illumina MiSeq® System in the fastq format and quality-filtered using PRINSEQ v. 0.20.4 [24] to remove sequencing adapters and reads with fewer than 150 bases and trim bases with an average quality lower than Q25 in a 5-base window. The forward and reverse reads were merged by overlapping paired-end reads with AdapterRemoval v. 2.1.5 [25] using default parameters. The QIIME package v. 1.8.0 [26] was used for operational taxonomic unit (OTU) generation, taxonomic identification, sample diversity and richness index calculation. Sample IDs were assigned to the merged reads and converted to the fasta format. Chimeric merged reads were detected and removed using UCHIME [27] against the Greengenes database v. 13.8 (DeSantis et al., 2006). OTUs were selected at a 97% similarity threshold using the open reference strategy. Merged reads were pre-filtered by removing sequences with a similarity below 60% against Greengenes database v. 13.8, and the remaining merged reads were then clustered at 97% similarity against the same database. The merged reads that failed clustering in the previous step were de novo clustered into OTUs at 97% similarity. OTUs with fewer than two reads were removed from the OTU table. A representative sequence of each OTU was then selected for taxonomy assignment. Alpha diversity indices Chao1, dominance, equitability, goods coverage, observed species, Shannon and Simpson were calculated to reflect the diversity and richness of bacterial communities in the different samples. Chao1 rarefaction curves were also calculated. The OTU profiles of each sample were normalized (total sum normalization, TSS) and compared with the Bray–Curtis distance metric. The calculated Bray–Curtis distances were later used to sort the OTU profiles using principal coordinate analysis (PCoA). A Pearson correlation network was constructed based on the relative number of readings assigned to each genus in cheeses with and without PDO status. The underlying relationships between the genera observed in the cheeses were also analyzed using Spearman correlation. All analyses were performed using the program Calypso, v. 8.84 (http://bioinfo.qimr.edu.au/calypso, accessed on 29 January 2023). To determine which genera provided significant discrimination between cheeses with and without PDO status, a stepwise discriminant analysis was performed using Wilks’ lambda. The assumptions of normality and homogeneity of the variance–covariance matrices of each group were tested using the Kolmogorov–Smirnov test and Box’s M test, respectively. To evaluate possible differences between cheeses with and without PDO status for the main taxonomic genera, the nonparametric Wilcoxon-Mann–Whitney test (for α = 0.05) was also applied. Statistical tests were performed using IBM SPSS Statistics (v. 25, IBM Corporation, New York, NY, USA). Based on 97% similarity, a total of 1612 operational taxonomic units (OTUs) were identified (out of a total of 2,039,272 sequence reads), of which, 1580 OTUs were identified in the 30 analyzed samples of milk, NWS, curd, and cheese with and without PDO status (PDO cheese and non-PDO cheese, respectively). Only 32 OTUs (representing 0.01% of the total number of reads) could not be identified. The average value of sequence frequency per sample was 67,976 reads/sample from a minimum of 48,194 reads/sample (PDO cheese) to a maximum of 89,059 reads/sample (non-PDO cheese). Although only two milk samples produced DNA for NGS, they were included in the results to understand the microbial dynamics from milk to curd. The rarefaction curve (Figure S1) showed a tendency to flatten, indicating that bacterial communities were adequately covered in all samples analyzed. This finding was confirmed by the estimated coverage index of the samples (Good’s coverage), above 99% in all samples, indicating a good description of microbial diversity (Table 1). The richness and diversity of the bacterial community were assessed for the samples of raw milk, NWS, curd and ripened cheese with or without PDO status, and the assignment was performed using different alpha diversity indices (OTU, Chao1, dominance, equitability, Shannon index and Simpson index) as shown in Table 1. The Chao analysis, which estimates species richness, showed good richness in the samples. There were no significant differences in the Chao1 index (p > 0.05), in contrast to the other diversity indices (p < 0.05) between the species richness of milk and that of NWS, curd and ripened cheese (PDO and non-PDO). The dominance index showed a significantly higher value (p < 0.05) for NWS and curd than for milk and cheese (Table 1). This value indicates a several-fold lower diversity in NWS and curd, which is confirmed by the significantly lower values (p < 0.05) in these samples when considering the number of observed different OTUs, equitability, Shannon index and Simpson index. Conversely, greater species diversity was observed in milk, as indicated by the higher number of different OTUs (p < 0.05) and Shannon index (p < 0.05) compared to all other samples. In contrast, an increase in species diversity, reflected in Shannon and Simpson indices, was observed from NWS to cheese (p < 0.05). There were no significant differences (p > 0.05) in species diversity between cheeses with and without PDO, although cheese with PDO resulted in slightly higher Shannon and Simpson index values (Table 1). The relative abundance of sequences identified at the family and genus level is shown in Figure 1. The major families found in milk were Pseudomonadaceae (14–52%), Moraxellaceae (21–4%), Enterobacteriaceae (13–27%) and Streptococcaceae (11–12%). In the M1 sample, the dominant genus was Pseudomonas, whereas the genus Acinetobacter was found in a greater proportion in the M2 sample. Although the milk had a lower abundance of bacteria of the genus Streptococcus, the M2 sample exhibited a greater abundance of this genus than the M1 milk sample (Figure 1b). The dominant family in NWS was Streptococcaceae, with a relative abundance exceeding 99%. The Streptococcaceae family also dominated in curd (91 to 99%), although bacteria from the Enterobacteriaceae (1 to 7%), Moraxellaceae (<2%) and Staphylococcaceae (<1%) families were also detected in some samples (Figure 1a). Although communities from the Listeriaceae family were detected in milk (<0.3%), it should be noted that no OTUs from this family were found in NWS, curd or cheese samples. At the genus level, the bacterial population in NWS was dominated by the genus Streptococcus (69–92%), followed by the genera Lactococcus (8–31%) and Lactobacillus (0.004–0.9%). NWS samples W2 and W3 were characterized by a higher percentage of Lactococcus (Figure 1b); these samples were also characterized by the presence of bacteria belonging to the genus Acetobacter (0.2–0.3%). The genus Streptococcus was also dominant (65–91%) in the cheese curd (Figure 1b), followed by the genus Lactococcus (6–42%). The genera Acinetobacter (0.08–1.1%), Serratia (0–3.3%) and Macrococcus (0.005–1.4%) were detected in curd to a much lesser extent. Regarding the microbiota in aged cheeses (9 months), several differences can be observed between samples of non-PDO (nPDO) and PDO cheeses (Figure 1). The Streptococcaceae family was dominant in non-PDO cheese samples, with the exception of sample 7 (nPDO7). However, there was a marked decrease in the Streptococcaceae family from curd (>99%) to cheese (29–73%). The Lactobacillaceae family was second-most abundant in non-PDO cheeses (12–34%), except in sample 7 (64%). In all non-PDO cheeses, the relative abundance of the Leuconostocaceae family was less than 5% (Figure 1a). Bacteria of the families Staphylococcaceae (2–3%, in samples 1 and 2) and Enterococcaceae (0.2–1%) were still detected in some non-PDO cheeses. In contrast, the predominant families in PDO cheeses were: Lactobacillaceae (26–45%), Streptococcaceae (24–36%), Leuconostocaceae (13–31%) and Enterococcaceae (1.5–3%). Thus, according to the sensory evaluation by the trained tasters, there was a clear difference between the cheeses that obtained PDO status and those that did not. The most striking difference concerned the relative abundance of the Leuconostocaceae family, which was higher in all PDO cheeses (Figure 1a). In addition, the Lactobacillaceae family was more represented in the PDO cheeses, whereas the Streptococcaceae family dominated in the non-PDO cheeses. At the genus level, Streptococcus (14–58%), Lactobacillus (11–50%) and Lactococcus (9–39%) were the dominant genera of non-PDO (nPDO) cheeses. Among the subdominant microbiota, the following genera were detected: Leuconostoc (0.23–4.1%), Enterococcus (0.22–1.2%), Staphylococcus (0.01–1.75%), Pediococcus (0–1.75%), Macrococcus (0–1.6%), Acinetobacter (0–0.6%), Weissella (0–0.5%), Citrobacter (0–0.4%), Chryseobacterium (0–0.16%), Delftia (0–0.12%) and Enhydrobacter (0–0.11%). In PDO cheeses, the diversity of dominant genera increased, with the genus Lactobacillus standing out. In these cheeses, the dominant genera were Lactobacillus (25–55%), Streptococcus (9–27%), Leuconostoc (8–28%), Lactococcus (8–26%) and Enterococcus (1.5–3.3%), whereas the subdominant genera were Weissella (0.01–2.6%), Macrococcus (0.04–0.75%), Pediococcus (0.02–0.64%), Staphylococcus (0.04–0.56%), Chryseobacterium (0.02–0.25%), Vibrio (0–0.23%), Delftia (0.04–0.17%) and Acinetobacter (0.01–0.16%). Although the genus Lactobacillus was recently reclassified into 25 genera [28], the name of this genus is retained in this study to denote all organisms classified by 2020. The bacterial communities in the cheese, curd, NWS and milk used in cheese production differ significantly from each other, as shown by the principal coordinate analysis (PCoA, Figure 2). The first two PCoA axes accounted for 94% of the total variability, with PCoA1 and PCoA2 describing 63% and 31% of the variability, respectively. The first axis (PCoA1) refers to the differentiation of the NWS, curd and cheese populations. PCoA2 differentiates the bacterial community in milk. At both levels (family and genus), there was a high degree of dissimilarity between the bacterial community in the milk and the remaining samples. On the other hand, no differences were found between the NWS and curd samples, as they were grouped together. Some degree of dissimilarity was also found between the bacterial communities of the non-PDO and PDO cheeses, especially at the family level (Figure 2). At the genus level, one sample of cheese without PDO status (sample 7) was included in the PDO group (Figure 2b). Cluster analysis confirmed the differentiation observed between the samples at the genus level (Figure 3). A clear separation of the milk cluster—with the highest degree of dissimilarity—from the other clusters was evident. A cluster of NWS and curd samples showed a high degree of similarity and was dominated by the genus Streptococcus. The cluster for non-PDO cheese included six of the eight non-PDO cheese samples and shared the high relative abundance of Streptococcus with the cluster for NWS and curd. On the other hand, the genera Lactobacillus, Leuconostoc and Enterococcus were positively differentiated in the cluster for PDO cheeses. Two samples of non-PDO cheeses (samples 6 and 7) were also included in this cluster. Although they were included in the same cluster as the PDO cheeses, these samples differed in the low abundance of OTUs of the genus Leuconostoc (Figure 1b). PCoA based on Bray–Curtis distance matrix on cheese samples was performed to visualize the differences in the bacterial community between the non-PDO and PDO cheeses. As shown in Figure 4a, the bacterial communities in the PDO cheeses were closer, and more similar to each other than in the non-PDO cheeses. The results of the PCoA analysis were consistent with the network for the bacterial communities of the São Jorge cheeses (Figure 4b). The network with the interactions of OTUs identified at the genus level unfolded the difference between the PDO cheeses (blue circles) and the non-PDO cheeses (red circles). The genera Leuconostoc, Enterococcus, Lactobacillus, Weissella and Vibrio were associated with PDO cheeses, whereas Streptococcus, Lactococcus, Staphylococcus, Citrobacter, Serratia, Enhydrobacter and Acinetobacter were associated with non-PDO cheeses. To determine which genus best characterizes PDO cheese, a stepwise discriminant analysis was performed that identified the genus Leuconostoc as the variable that significantly differentiates PDO cheese (p < 0.05). These results were confirmed by a nonparametric analysis of the OTUs assigned to the dominant genera in these cheeses (Figure 5). Compared to the non-PDO cheeses, the cheeses bearing the PDO label had a higher proportion of OTUs of the genera Lactobacillus (p < 0.05), Leuconostoc (p < 0.001), and Enterococcus (p < 0.01). In contrast, the PDO cheeses had lower Streptococcus OTUs (p < 0.05) than non-PDO cheeses. The pattern of co-occurrence and exclusion of OTUs in the cheese samples is shown in Figure 6. A strong negative correlation is observed between Streptococcus and Lactobacillus, suggesting that a reduction in Streptococcus dominance is necessary for the development of Lactobacillus during cheese ripening. Negative correlations are also observed between Streptococcus and Leuconostoc and between Streptococcus and Enterococcus, although to a lesser extent. Conversely, the genera Staphylococcus and Acinetobacter exhibited a strong positive correlation with Streptococcus. Despite the small sample size, the results of α-diversity in milk are consistent with other studies that have found higher species diversity in raw milk compared to cheeses produced from it [7,17,29,30]. The high level of species diversity in milk decreases significantly when moving to NWS and curd. These samples have high dominance values associated with low equitability and lower Shannon and Simpson indices, indicating low diversity in bacterial community composition with dominant populations. NWSs were generally characterized by a relatively simple microbiota. This LAB community is generally thermophilic and well adapted to the particular physicochemical conditions of NWSs [31]. The decrease in biodiversity observed during the transition from milk to curd is expected because the lactic acid production of LAB from NWS lowers the pH, which contributes to cheese curd formation and inhibits pathogen growth from raw milk [32]. However, the biological richness of raw milk is of great importance as it can provide a desirable microbiota associated with specific characteristics of raw milk cheeses [32,33]. Similar results were obtained with Poro cheese, an artisanal Mexican cheese also produced from raw cow’s milk and inoculated with fermented NWS from the previous batch [29]. During the production of this cheese, the bacterial diversity in the milk was high and decreased significantly in the NWS and curd, although it increased again during cheese ripening [29]. Concerning the taxonomic composition of bacterial communities, the results were similar to those reported for milk and curd in the production of traditional Italian cheeses [34]. Other studies provided similar results to our work, with the phylum Proteobacteria predominant in milk, followed by Firmicutes and Bacteroidetes [17]. In contrast, Quigley et al. [35] reported that Firmicutes accounted for ca. 80% of the bacterial community in raw milk in Ireland. The presence of Proteobacteria may unfold hygiene problems in milk, as this phylum includes a wide range of Gram-negative pathogenic bacteria [36]. It should be noted that milk samples were collected from the cold storage tank, knowing that during storage, populations of psychotropic bacteria dominate, which have been reported to contribute to the spoilage of dairy products [32,37]. The present study also confirms the previous data of Kongo et al. [21], according to which Enterobacteriaceae were detected in the milk used for the production of São Jorge cheese. The presence of high numbers of these bacteria is generally considered an indicator of poor hygiene, and if pathogenic species are also present, this can pose a health risk; it also has a negative effect on the sensory quality of the finished cheese [35]. In contrast, the presence of the genera Lactococcus, Lactobacillus, Leuconostoc and Enterococcus in the milk samples, albeit at relatively low levels, may be critical to the development of desired flavor characteristics during cheese ripening [35]. These bacteria exhibit significant lipolytic and proteolytic activities, so they strongly influence the quality of cheese produced from raw milk [38]. As for the NWS, all samples had a bacterial community dominated by Streptococcaceae, which accounted for 99.1% to 99.9% of the total population. Thus, there was a significant change in the bacterial community during the transition from milk to NWS. This change was predictable since NWS was mainly associated with backslopping, and this method tends to favor the bacterial community best adapted to the fermentation of milk [39]. These results are also in agreement with those of Fontina PDO cheese, where a low correlation was found between the microbiota of raw milk and curd, which was influenced by the composition of the NWS added as a starter culture [40]. The microbial composition at the genus level of the NWS, which was dominated exclusively by Streptococcus and Lactococcus, was similar to starter cultures traditionally used in the production of aged cheese [41]. The bacteria of these genera are known to play a crucial role in acidifying milk at the beginning of cheese making. However, the less frequent presence of the genus Lactobacillus distinguishes this NWS from the one used in the manufacture of other artisanal cheeses [16,18,29,42,43]. In these cheeses, the NWS showed a microbiota dominated by the genera Lactobacillus and Streptococcus, as was also the case in Silter PDO cheese [6]. This difference is probably due to the heat treatment applied in the production of these cheeses (39–54 °C), i.e., higher temperatures than those commonly used for São Jorge cheese (35–36 °C). According to some authors [12,42,44], an increase in temperature during heat treatment leads to a decrease in Lactococcus spp. and an increase in Lactobacillus spp. Acetobacter was also detected in NWS, but is not common in this habitat, although it has been described in some traditional cheeses (Jin et al., 2018). In addition, the presence of Enterococcus in NWS has been reported by some authors (Giannino et al., 2009, Silvetti et al., 2017). However, this genus was essentially not detected in the NWS samples tested. Similar results were obtained in starter cultures used in the production of Italian and Mexican cheeses [29,31]. Although the genera Leuconostoc and Enterococcus were not detected in the NWS, they were present in lower proportions in the curd, likely imported from the milk. Should they find the right conditions in the cheese ecosystem, such LAB genera would become dominant in the cheese microbiota. The dominant microbiota in the NWS (Streptococcus and Lactococcus) was also found in the curd, whereas the dominant genera in the milk, namely, Pseudomonas and Acinetobacter, underwent a substantial reduction in the curd. These results are comparable to those reported in previous studies on different artisanal cheeses (Quigley et al., 2013, Aldrete-Tapia et al., 2014, De Pasquale et al., 2014). It is known that the changes in the food environment during the fermentation phase exert some selection pressure on the microbial populations present in raw milk, which ultimately favors the growth of LAB. As mentioned earlier, several studies have been published on the microbiota of São Jorge cheese [19,21,22,45]. However, all of these studies resorted to cultivation-dependent methods and did not attempt to distinguish between cheeses with and without PDO status. Although cultivation-dependent methods are essential for isolating microorganisms characteristic of cheese, they may underestimate some microbial communities—particularly species that are less well-adapted to growth under conditions commonly used for isolation in the laboratory. With the recent development of new sequencing techniques, it has become possible to assess the composition of bacterial communities in these ecosystems without the bias that their isolation represents. This is, in fact, the first study to apply these methods to gain a better understanding of the microbial community of São Jorge cheese. However, this technique is limited to the identification of bacterial communities at the genus level. In addition, NSG methodologies may also introduce some bias due to the methods used in sampling, DNA extraction, PCR amplification, and sequencing (reviewed by Hugerth and Andersson [46]). According to our results, the ripening of São Jorge cheese is dominated by the genera Lactobacillus, Streptococcus, Leuconostoc, and Lactococcus. In general, these dominant genera are similar to those previously found in ripened cheeses produced from raw milk [10,13,15,17,29,47,48,49,50,51]. Previous studies on the microbiota of São Jorge cheese also refer to Lactobacillus as the dominant genus at the end of ripening [19]; however, the genus Enterococcus accounted for 62% of isolates in the curd and 30–37% in the cheese. Given the high selection of Enterococcus by culture media commonly used for bacterial isolation [23], it is possible that the dominance of Enterococcus reported for this and other traditional cheeses was overestimated. In studies using culture-independent methods, this genus was not found expressively in the microbiota of ripening cheeses [10,17,29]. Among the lactobacilli, two species were described as dominant in an earlier study on São Jorge cheese: Lacticaseibacillus paracasei and Lacticaseibacillus rhamnosus [19]. In this study, Lactococcus lactis was also identified as dominant in the curd, but no Streptococcus spp. were isolated from the São Jorge cheese, despite the dominance of this genus observed in the present study. As for the subdominant microbiota, the genera Weissella, Macrococcus and Pediococcus should be highlighted as potential contributors to cheese texture and flavor [52]. The presence of the genus Vibrio has also been described in Herve PDO cheese, suggesting that these bacteria may play an important role in the ripening process [53]. However, due to their low abundance and sporadic occurrence, this genus is not expected to have a positive impact on the flavor of São Jorge cheese. To assess which genera best distinguished PDO cheeses, a discriminant analysis pointed to the genus Leuconostoc (p < 0.05). This result is not surprising since some species of the genus Leuconostoc are well-adapted to the cheese environment and may play an important role in flavor development during ripening [54,55]. Therefore, our results support a clear distinction between PDO and non-PDO cheeses in terms of the bacterial community. It should be noted that this classification depends solely on the evaluation of a group of tasting experts who grant (or do not) the PDO label based on the sensory characteristics of the cheese. Because this cheese is produced from raw milk without the addition of a well-defined starter culture, it is subject to wide batch-to-batch variations that often disqualify it for PDO status. When samples were taken for this work, the rejection of PDO status was over 50% of batches. Therefore, it seems crucial to know what is expected in terms of the microbiota of said PDO cheese in order to improve the sensory quality of the final product, which could eventually allow a higher percentage of PDO approval. As Leuconostoc has been found to be essential for the differentiation of PDO cheese, it is important to determine the factors that allow the development of these bacteria in the cheese during ripening. The differentiation resulting from the development of Leuconostoc may result from the environment created by the particular microbial ecology of each vat. The presence of a specific microbial community can favor the development of beneficial bacteria for the flavor development of the cheese, which guarantees the awarding of PDO status. It should also be noted that the genera characteristic of São Jorge cheese, such as Leuconostoc and Lactobacillus, showed negative correlations with bacteria considered contaminants, e.g., Staphylococcus and the proteobacteria Acinetobacter, Serratia, Klebsiella, Erwinia, Citrobacter, Enhydrobacter and Bacillus. Similar results were reported by Zheng et al. [56], who observed a negative correlation between Lactobacillus and Lactococcus, and Acinetobacter and Staphylococcus in Kazak artisan cheese. The pattern of co-occurrence and exclusion suggests that good milk quality, low levels of contaminating bacteria and good equipment hygiene may control the dominance of Streptococcus during cheese ripening. Such control would allow the growth of Leuconostoc spp. as well as Lactobacillus spp. and Enterococcus spp., thus ensuring the proper development of the intended characteristic flavors in São Jorge PDO cheese. Thus, our results indicate that LAB populations, especially of Leuconostoc and Lactobacillus, dominate the microbiota of São Jorge PDO cheese and limit the development of spoilage bacteria, as in other cheeses [30]. Recently, Lactobacillus and Lactococcus were also shown to positively correlate with cheese quality in traditional Chinese cheeses [57]. The unique characteristics of São Jorge PDO cheese are related to the microbiota present in its ingredients (milk and NWS), which in turn are controlled by the production process and the ripening period. Milk stored in tanks and used for cheese production is dominated by Gram-negative bacteria of the genera Pseudomonas and Acinetobacter, whereas Lactococcus and Streptococcus were detected in lower numbers. On the other hand, the microbial composition of NWS was dominated by Streptococcus, followed by Lactococcus, which should play a positive role in curd acidification. These genera were retained in the curd, with a decrease in Streptococcus and an increase in Lactococcus. However, during ripening, a decrease in Streptococcus and an increase in Lactobacillus and Leuconostoc communities were observed. Thus, the microbiota of São Jorge cheese was dominated by the genera Lactobacillus, Streptococcus, Leuconostoc and Lactococcus. This work contributed to clearly distinguishing between PDO and non-PDO cheeses in terms of bacterial community composition. PDO status is assigned using empirical methods based on sensory analysis by a tasting panel. PDO cheeses have been found to own a distinctive bacterial community in which the genus Leuconostoc is a distinguishing feature. Leuconostoc bacteria are associated with the development of flavor during the ripening process, so they should play a major role in the final sensory characteristics of São Jorge PDO cheese. In addition to the genus Leuconostoc, PDO cheeses were characterized by a higher occurrence of the genera Lactobacillus and Enterococcus and a lower occurrence of Streptococcus compared to non-PDO cheeses. The pattern of co-occurrence and exclusion of OTUs in cheese samples suggests that the presence of contaminating bacteria does not favor the development of bacteria associated with PDO status. Therefore, good milk quality appears to be essential for the development of a community rich in the genera Leuconostoc and Lactobacillus characteristic of São Jorge PDO cheese. The results of this study will allow a better understanding of the bacterial community of São Jorge cheese and its use to distinguish between non-PDO and PDO cheeses by applying culture-independent techniques. This information is important for developing strategies to increase the percentage of cheeses that can obtain the PDO label, which will ultimately have a positive impact on the economic aspects of São Jorge cheese production.

PMC10000660

Micael F. M. Gonçalves,Teresa Pina-Vaz,Ângela Rita Fernandes,Isabel M. Miranda,Carlos Martins Silva,Acácio Gonçalves Rodrigues,Carmen Lisboa

Microbiota of Urine, Glans and Prostate Biopsies in Patients with Prostate Cancer Reveals a Dysbiosis in the Genitourinary System

23-02-2023

prostate cancer,microbiota,urinary tract,glans penis,urine

Simple Summary Prostate cancer (PCa) is the most common cancer diagnosed among men aged 50 years and older. There is gaining interest in the genitourinary microbiome in developing ways to control this disease. In this work, an analysis of the bacterial microbiota was performed from urine samples, glans secretions, and prostate biopsies from patients with PCa, and the results were investigated and compared with non-PCa patients. Our results showed a distinct clustering of genera associated with urine samples and prostate biopsies of PCa and non-PCa patients. Observed microbial dysbiosis may increase chronic inflammation and ultimately prostate carcinogenesis. Future research on the biological functions of the uropathogens found is needed to understand their impact on the pathogenesis of PCa. Abstract Prostate cancer (PCa) is the most common malignant neoplasm with the highest worldwide incidence in men aged 50 years and older. Emerging evidence suggests that the microbial dysbiosis may promote chronic inflammation linked to the development of PCa. Therefore, this study aims to compare the microbiota composition and diversity in urine, glans swabs, and prostate biopsies between men with PCa and non-PCa men. Microbial communities profiling was assessed through 16S rRNA sequencing. The results indicated that α-diversity (number and abundance of genera) was lower in prostate and glans, and higher in urine from patients with PCa, compared to non-PCa patients. The different genera of the bacterial community found in urine was significantly different in PCa patients compared to non-PCa patients, but they did not differ in glans and prostate. Moreover, comparing the bacterial communities present in the three different samples, urine and glans show a similar genus composition. Linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed significantly higher levels of the genera Streptococcus, Prevotella, Peptoniphilus, Negativicoccus, Actinomyces, Propionimicrobium, and Facklamia in urine of PCa patients, whereas Methylobacterium/Methylorubrum, Faecalibacterium, and Blautia were more abundant in the non-PCa patients. In glans, the genus Stenotrophomonas was enriched in PCa subjects, while Peptococcus was more abundant in non-PCa subjects. In prostate, Alishewanella, Paracoccus, Klebsiella, and Rothia were the overrepresented genera in the PCa group, while Actinomyces, Parabacteroides, Muribaculaceae sp., and Prevotella were overrepresented in the non-PCa group. These findings provide a strong background for the development of potential biomarkers with clinical interest.

Microbiota of Urine, Glans and Prostate Biopsies in Patients with Prostate Cancer Reveals a Dysbiosis in the Genitourinary System Prostate cancer (PCa) is the most common cancer diagnosed among men aged 50 years and older. There is gaining interest in the genitourinary microbiome in developing ways to control this disease. In this work, an analysis of the bacterial microbiota was performed from urine samples, glans secretions, and prostate biopsies from patients with PCa, and the results were investigated and compared with non-PCa patients. Our results showed a distinct clustering of genera associated with urine samples and prostate biopsies of PCa and non-PCa patients. Observed microbial dysbiosis may increase chronic inflammation and ultimately prostate carcinogenesis. Future research on the biological functions of the uropathogens found is needed to understand their impact on the pathogenesis of PCa. Prostate cancer (PCa) is the most common malignant neoplasm with the highest worldwide incidence in men aged 50 years and older. Emerging evidence suggests that the microbial dysbiosis may promote chronic inflammation linked to the development of PCa. Therefore, this study aims to compare the microbiota composition and diversity in urine, glans swabs, and prostate biopsies between men with PCa and non-PCa men. Microbial communities profiling was assessed through 16S rRNA sequencing. The results indicated that α-diversity (number and abundance of genera) was lower in prostate and glans, and higher in urine from patients with PCa, compared to non-PCa patients. The different genera of the bacterial community found in urine was significantly different in PCa patients compared to non-PCa patients, but they did not differ in glans and prostate. Moreover, comparing the bacterial communities present in the three different samples, urine and glans show a similar genus composition. Linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed significantly higher levels of the genera Streptococcus, Prevotella, Peptoniphilus, Negativicoccus, Actinomyces, Propionimicrobium, and Facklamia in urine of PCa patients, whereas Methylobacterium/Methylorubrum, Faecalibacterium, and Blautia were more abundant in the non-PCa patients. In glans, the genus Stenotrophomonas was enriched in PCa subjects, while Peptococcus was more abundant in non-PCa subjects. In prostate, Alishewanella, Paracoccus, Klebsiella, and Rothia were the overrepresented genera in the PCa group, while Actinomyces, Parabacteroides, Muribaculaceae sp., and Prevotella were overrepresented in the non-PCa group. These findings provide a strong background for the development of potential biomarkers with clinical interest. According to the International Agency for Research on Cancer of the World Health Organization, prostate cancer (PCa) is the most common cancer diagnosed among men aged 50 years and older and the fifth leading cause of cancer-associated death [1]. In 2020, approximately 473,000 new cases of PCa and 108,000 deaths were estimated in Europe. Based on the reports, the prevalence of this neoplasm is increasing worldwide [2]. New cases of PCa in Portugal are estimated to be approximating 6700, and there have been 1900 cancer-associated deaths, whose circumstances nowadays pose an important and worrying public health problem [1]. In the past decades, PCa has aroused the attention of scientists due to its high mortality rates. Unfortunately, the etiology and pathology of PCa remain unknown [3]. However, it has been reported that PCa may be caused by genetic mutations and external risk factors, including environmental factors, such as dietary changes and lifestyle, microbial infections, and inflammatory stimuli [4,5,6]. Moreover, PCa treatment significantly affects men’s sexual health, reducing their quality of life [7]. Notably, all the above-mentioned factors are known to affect the composition of the resident microbiota in the skin, mucous membranes, fluids, and tissues of the human body [8]. Recent studies on the human microbiome have shown that mammary, lung, bladder, pancreas, intestine, and prostate microbial dysbiosis can enhance the progression of diseases, such as cancer and chronic inflammation [9,10,11,12,13,14,15,16]. The genitourinary microbiota is gaining a relevant role in the prostate carcinogenesis process: emerging evidence suggests that the resident microbiota may promote chronic prostate inflammation linked to the development of PCa [6,17]. Any imbalance in the composition of the genitourinary microbiota can lead to an increase in immune responses or a modification in the extracellular environment of the prostate [18]. Although it is well established that Helicobacter pylori affects gastric physiology which can lead, at last instance, to the development of gastric cancer [19], the relationship between specific pathogens and PCa is still poorly understood. Nevertheless, it has been reported that some pathogenic microorganisms may possibly induce symptomatic and asymptomatic infections in the prostate, including the bacterium Cutibacterium acnes (syn. Propionibacterium acnes) [20], and sexually transmitted infection-causing pathogens, such as Chlamydia trachomatis [21], Neisseria gonorrhoeae [22], Trichomonas vaginalis [23], and Mycoplasma genitalium [24]. Most of the above findings were obtained by performing conventional polymerase chain reaction (PCR), quantitative real-time PCR techniques, and Sanger sequencing technology. Meanwhile, some studies have applied metagenomic sequencing technologies for an accurate and complete characterization of the genitourinary microbiota, and its possible implications in the pathogenesis of PCa. The current studies carried out concerning the genitourinary microbiota are mainly focused on the role of the urinary [25,26], fecal [25,27,28], rectal [29], prostate [18,30,31,32], and prostatic fluid [33] microbiota in men with PCa. Taken together, these results strongly support the hypothesis that the microbiota might be involved in prostate carcinogenesis, its progression, and relapse. Until now, a detailed and comprehensive analysis that combines the microbiota in the urine, glans, and prostate biopsies of PCa and non-PCa patients has not been conducted. Therefore, the aims of our study were to characterize and compare the potential association between the urinary, glans, and prostate microbiotas of PCa and non-PCa men. This research contributes to improve our understanding of the role of the genitourinary microbiota in PCa risk and pathogenesis, as well as to elucidate potential biomarkers for diagnosis and eventually new therapeutic and prognostic options. This study was conducted under the project “SexHealth & Prostate Cancer, Psychobiological Determinants of Sexual Health in Men with Prostate Cancer”, approved by the Ethical Committee of the University Hospital Center of São João (Portugal). From the patients enrolled in this project with clinical suspicion of PCa admitted to the Department of Urology between February 2022 and August 2022, 30 males (15 positive and 15 negative cases for PCa) were randomly chosen for this study. Prostate cancer cases were histologically confirmed as prostate adenocarcinoma by the hospital. Negative cases for PCa were considered as the non-PCa group. Non-PCa patients were asymptomatic patients (no urogenital symptoms) referred to the Department of Urology due to elevated PSA. Patients did not report to have LUTS (lower urinary tract symptoms). Patients with other urinary pathologies that could introduce bias were excluded from this study. All participants signed a written informed consent to contribute their own anonymous information to this study. Age, clinical and analytical data such as PSA value, prostate volume evaluated by magnetic resonance imaging (MRI), Charlson’s index, and ISUP grade were recorded. All participants started antibiotic prophylaxis the day before the biopsy procedure according to the local protocol. Urine, glans swabs, and prostate biopsy specimens were collected from each male. Urine specimens were maintained at room temperature (less than 3 h after collection) and immediately processed in the laboratory. Glans secretions swabs and prostate biopsy tissues were maintained at 4 °C until further processing. First-void urine stream specimens were collected by the clean catch method, then centrifuged at 9000 × rpm for 15 min, and the pellets were immediately stored at −80 °C. Sterile swabs moistened with physiological saline solution were used for the collection of glans secretions, transferred to Eppendorf tubes with 1 mL of phosphate-buffered saline solution (PBS) and then vortexed for 1 min. Afterwards, the swabs were discarded, and the samples were centrifuged at 14,000 × rpm for 5 min and the pellets stored at −80 °C. Prostate biopsies were performed under transrectal ultrasound image, with 12 random cores including variable cores to a specific target if the MRI identified one or the Urologist found it adequate. Then, one random core was transferred to tubes with 1 mL of TripleXtractor solution (GRiSP, Porto, Portugal), and stored at −80 °C until DNA extraction. All specimens’ samples from PCA and non-PCa groups were maintained at room temperature (15–25 °C) for 2 min before DNA extraction. Prostate biopsy samples (n = 30) were cut into small pieces and transferred to tubes with 200 µL of tissue lysis buffer solution (Qiagen, Hilden, Germany). Urine (n = 30) and glans swab (n = 30) pellets were resuspended in PBS. Total genomic DNA was extracted using the QIAamp DNA Micro Kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. The quality of the DNA was assessed by agarose gel electrophoresis (0.8%). DNA purity and quantity were determined using a Qubit 2.0 fluorometer and Qubit dsDNA BR kit (Thermo Fisher Scientific, Waltham, MA, USA). The 16S rRNA gene microbiome profiling with Illumina MiSeq platform was performed by STAB Vida (Lisbon, Portugal) by amplifying the hypervariable V3–V4 region with primers 341F (5′ CCTACGGGNGGCWGCAG 3′) and 785R (5′ GACTACHVGGGTATCTAATCC 3′). The resulting data were analyzed according to STAB Vida standard protocols using QIIME2 v2021.4 [34]. The reads were denoised using the DADA2 plugin [35] as follows: trimming and truncating low quality regions, quality filtering (to remove reads with less than 300 bp, ambiguous bases (“N”), and sequences with an average quality lower than Q30), dereplicating the reads, and identification and removal of chimeric reads. High-quality reads were assigned to operational taxonomic units (OTUs), and then classified by taxon using a fitted classifier. The scikit-learn classifier was used to train the classifier using the SILVA database (release 138 QIIME) [36], with a dynamic clustering threshold of 99% similarity. For classification purposes, only OTUs containing at least 10 sequence reads were considered as significant. Given the nature of the samples and the low microbial biomass, an additional step, attempting to remove contaminant OTUs based on the prevalence and frequency of the determined OTUs, was applied. This was achieved using the Bioconductor’s decontam package [37]. The marker-gene data profiling module of the MicrobiomeAnalyst web-based platform [38] was used for community profiling and comparative analysis. The parameters of the low count filter (minimum count of 4 and 20% prevalence) and low variance filter (10% based on the interquartile range) were used as default. The data were normalized to rarefy the data to the minimum library size, scaled by total sum scaling, but we did not apply any data transformations. The α- and β-diversity metrics were calculated based on OTUs abundance table at the genus level. Number of observed OTUs (richness index) and diversity (Shannon–Wiener index) of bacteria taxonomic diversity were calculated as a measure of α-diversity. The distance between samples based on the difference in OTUs in each sample defined as β-diversity, was evaluated by the principal coordinates analysis (PCoA) and hierarchical clustering analysis using a Bray–Curtis dissimilarity index. Statistical analyses were performed using SPSS Statistics v26 (IBM Corporation, Endicott, NY, USA). Before the analysis, the data were checked for normality using the Shapiro–Wilk test. As the data met the analysis of variance (ANOVA) assumptions, one-way ANOVA followed by a t-test was used to determine differences between the two groups based on clinical characteristics and the significance of the taxonomy. Permutational multivariate analysis of variance (PERMANOVA), based on 999 permutations, was used to test for significant differences in sampling units between the PCa and non-PCa groups. This was performed separately for the urine, glans swabs, and prostate biopsy samples. A p-value less than 0.05 was considered statistically significant. OTUs at the genus level with a relative abundance of at least 0.01% and present in more than 60% of tested samples were used to calculate the core community at the genus level in each sample type and in each group. Linear discriminant analysis (LDA) effect size (LEfSe) was adopted to explore the significant differences in bacterial taxa abundance [39]. The LEfSe submodule in the MicrobiomeAnalyst platform was used with the default settings of an FDR-adjusted, Kruskal–Wallis p-value cutoff set to 0.1 and the logarithmic LDA score cut-off at 2.0. The clinical information of the 30 Caucasian men who participated in this study is presented in Table 1. Among them, 15 men were included in the PCa group (68 ± 9 years of age), and the other 15 men were included in the non-PCa group (69 ± 8 years of age). Both groups showed similar mean age and comparable comorbidities, as seen by Charlon’s index. PSA levels and prostate volumes were higher in the non-PCa group, since most of them have benign prostatic hyperplasia (BPH). A total of 5,654,185 raw sequence reads were obtained with an average of 62,824 ± 47,688 (average ± SD) reads per sample. A total of 3,946,884 effective reads were generated, and each sample produced an average of 45,366 ± 33,686 effective reads. Rarefaction curves are listed in Figure S1. After denoising, a total of 3486 unique features (OTUs) were identified. Boxplots of α-diversity in terms of urine, glans swabs, and prostate biopsies microbiota richness and diversity for PCa and non-PCa groups are shown in Figure 1A,B. When comparing the three biological samples, urine samples were the only ones to show low indices of species richness and diversity. Although no significant differences (p ≥ 0.05) were observed between PCa and non-PCa groups, the species richness was lower in prostate biopsies and glans swabs, and higher in urine from patients with PCa. Additionally, the bacterial diversity (Shannon–Wiener index) was lower in prostate biopsies and higher in urine and glans swabs from patients with PCa. The PCoA and hierarchical clustering analysis used to compare the similarity between the PCa and non-PCa groups in each sample type and all combined samples are shown in Figure 2. The results indicated that the composition of the bacterial community was significantly different in urine samples between the PCa and non-PCa groups (PERMANOVA, R2 = 0.068, p = 0.014) (Figure 2A), while in swabs of glans and prostate biopsy samples the composition of the bacterial community did not differ (PERMANOVA, R2 = 0.020, p = 0.946; R2 = 0.026, p = 0.689, respectively) (Figure 2B,C). The PCoA analysis revealed that the bacterial communities present in urine and glans swabs of both patient groups are similar to each other but distinct from the bacterial communities of the prostate (Figure 2D). Moreover, this observation is also notorious in the hierarchical cluster, where it is clear that urine and glans samples of both patient groups were grouped into a large cluster. Following initial evaluations for differences in overall bacterial community composition, we investigated the relative abundance of specific taxa between the PCa and non-PCa groups. A total of seven different phyla, eleven classes, twenty-six orders, thirty-four families, and fifty genera were determined across all samples. Assessment of the relative abundance of the top 25 bacteria was considered as the predominant bacteria at each taxon level (Figure 3). At the phylum level, Firmicutes, Proteobacteria, Actinobacteriota, Bacteroidota, Campilobacterota, Cyanobacteria, and Fusobacteriota were the dominant bacterial phyla in both PCa and non-PCa patients (Figure 3A) in each sample type. Among them, Firmicutes was significantly more abundant in urine samples of PCa patients (0.469%) than in non-PCa (0.212%), p = 0.014, while Proteobacteria was significantly more abundant in urine of non-PCa patients (0.338%), compared to those with PCa (0.083%), p = 0.014 (Table S1). At the class level, the dominant bacterial classes from both groups were Clostridia, Actinobacteria, Gammaproteobacteria, Bacteroidia, Bacilli, Negativicutes, Campylobacteria, Alphaproteobacteria, Cyanobacteria, Fusobacteria, and Coriobacteria (Figure 3B). Among them, Clostridia was significantly more abundant in urine samples of PCa patients (0.229%) than in non-PCa (0.078%), p = 0.023, while Gammaproteobacteria in non-PCa patients was significantly more abundant (0.320%) than in PCa patients (0.070%), p = 0.014 (Table S1). Among the top 10 genera more abundant, higher levels of Prevotella, Corynebacterium, Finegoldia, Peptoniphilus, Fenollaria, Streptococcus, Negativicoccus, Enterococcus, Porphyromonas, and Ezakiella were observed in the urine of PCa patients, compared to non-PCa patients (Figure 4A). On the contrary, Klebsiella was significantly more abundant in non-PCa patients (p = 0.043) (Supplementary Table S1). Although no statistically significant differences were found, urine samples from the non-PCa group tended to show a higher abundance of the genera Staphylococcus and Escherichia/Shigella (Figure 4A). Similar compositions of bacterial abundance were observed in both the PCa and non-PCa groups regarding the glans samples (Figure 4B). The ten dominant bacterial genera were Prevotella, Corynebacterium, Finegoldia, Porphyromonas, Campylobacter, Staphylococcus, Peptoniphilus, Fenollaria, Anaerococcus, and Mobiluncus (Figure 4B). Only Actinomyces was found to be more abundant in PCa patients (p = 0.041). Regarding prostate biopsies, similar bacterial composition was also observed in PCa and non-PCa patients but with differences in their relative abundances. The PCa group tended to show more abundance of the genera Pseudomonas, Faecalibacterium, Cutibacterium, Bacteroides, Corynebacterium, Turicella, Curvibacter, Sphingomonas, and Staphylococcus (Figure 4C). Cutibacterium (p = 0.014) and Lawsonella (p = 0.005) were statistically different. On the other hand, the genus Prevotella was revealed to have the higher mean relative abundance in non-PCa patients with statistically significant differences (p = 0.027). Differences in the bacterial communities between both groups and biological samples are also presented in a heatmap with all genera listed (Figure S2). The bacterial core community at the genus level with relative abundance of at least 0.01% and present in more than 60% of samples is shown in Figure 5. The most prevalent core OTUs in urine samples of PCa patients correspond to the genus Prevotella followed by Streptococcus, Corynebacterium, and Finegoldia, while in non-PCa patients, Corynebacterium and a not-assigned genus were the most prevalent (Figure 5A). The most dominant genera in glans were similar between PCa and non-PCa patients. Prevotella was the most prevalent genus in both groups, followed by Corynebacterium, Peptoniphilus, Finegoldia, and Anaerococcus in patients with PCa, while in non-PCa was followed by Finegoldia, Porphyromonas, Peptoniphylus, Corynebacterium, and Anaerococcus (Figure 5B). Regarding prostate biopsies, a higher prevalence of a not-assigned genus, followed by Pseudomonas, Cutibacterium, Curvibacter, Sphingomonas, Corynebacterium, Staphylococcus, Lawsonella, and Paracoccus were observed in PCa patients. On the other hand, Pseudomonas was the most prevalent genus in non-PCa patients, followed by a not-assigned genus, Sphingomonas, Curvibacter, Corynebacterium, and Cutibacterium (Figure 5C). The linear discrimination analysis (LDA) effect size (LEfSe) method was employed to search differentially abundant genera within the PCa and non-PCa groups (Figure 6A–C). This analysis revealed that the genera Streptococcus, Prevotella, Peptoniphilus, Negativicoccus, Actinomyces, Propionimicrobium (P. lymphophilum), and Facklamia were significantly increased in urine samples of PCa subjects, whereas Methylobacterium/Methylorubrum, Faecalibacterium, Blautia, and one not-assigned group were increased in non-PCa patients (Figure 6A). In the case of glans samples from PCa subjects, the genus Stenotrophomonas was enriched, while Peptococcus was underrepresented (Figure 6B). Analysis of the prostate biopsies’ microbiota also revealed Alishewanella, Paracoccus, Klebsiella, and Rothia as the most overrepresented genera in PCa subjects, while Actinomyces, Parabacteroides, Prevotella, and members of the family Muribaculaceae were enriched in non-PCa subjects (Figure 6C). LEfSe analysis for the top 30 significantly enriched genera, combining the urine, glans swabs, and prostate biopsies from subjects with PCa and non-PCa is shown in Figure 6D. This analysis showed which taxa are most abundant in each group. For example, Enhydrobacter and Anaerococcus were more abundant in the three types of samples in patients with PCa compared to patients with non-PCa. Nevertheless, the differences in the Anaerococcus abundance between both PCa and non-PCa groups were more evident in urine samples. Moreover, higher abundances of Peptoniphilus and Actinomyces in urine and swabs of patients with PCa was also evident. Regarding non-PCa patients, the genera Blautia and Faecalabacterium were more abundant in the three types of samples compared with patients with PCa. However, the abundance of these genera was clearer in urine and swabs samples. Microbiome composition and its function have been identified as contributing factors in the pathogenesis of several diseases [40]. An imbalance in microbiome homeostasis owing to persistent inflammation induced by the resident microbiota may be involved in the development of malignant diseases, such as cancer [41,42,43]. However, little is known regarding the differences in the genitourinary tract microecology in PCa and non-PCa patients. To the best of our knowledge, this study represents the first detailed and comprehensive analysis of the genitourinary tract bacterial microbiota of urine, glans, and prostate biopsies between PCa and non-PCa patients. The findings from this study show disparities in the structure and bacterial composition of urine, glans, and prostate biopsies between PCa and non-PCa patients. Nevertheless, these differences in the bacterial community are more evident in urine samples. Alpha diversity metrics revealed that bacterial diversity was lower in prostate biopsies from patients with PCa compared to non-PCa patients. In fact, Ma et al. [33] demonstrated that PCa subjects exhibited a reduced microbial diversity in the prostate when compared to the non-PCa subjects; they suggested that the bacterial diversity might have a role in the development of PCa. According to our results, the urine of patients from both groups were the only samples to show low indices of species richness and diversity. Such an outcome suggests that dysbiosis of the urine microbiota may be the origin of prostate inflammation [44]. Moreover, the similarity found in the bacterial community in the urine and glans, compared to that of the prostate, might be explained by their anatomic proximity. Various studies reported the genera Prevotella, Corynebacterium, Streptococcus, Finegoldia, Peptoniphilus, Anaerococcus, Propionibacterium, Staphylococcus, and Lactobacillus in the urinary microbiota of adult men [45] as well as in the male genital mucosa [46]. These genera have been considered as commensals of the genitourinary system [47,48,49]. In agreement to these previous reports, our results also demonstrated that Prevotella, Corynebacterium, Staphylococcus, Peptoniphilus, and Anaerococcus are among the top 10 genera in urine and glans samples of both PCa and non-PCa patients. Regarding PCa patients, LEfSe analysis revealed that Streptococcus, Prevotella, Peptoniphilus, Negativicoccus, Actinomyces, Propionimicrobium, and Facklamia were enriched in urine. Some of these genera, that were differentially abundant in PCa patients, harbor uropathogens that colonize the genital tract, including Propionimicrobium lymphophilum and Streptococcus anginosus that have been implicated in urogenital infections, including prostatitis [26,50,51]. Furthermore, anaerobic cocci, such as Peptoniphilus and Negativicoccus, have been reported in urinary tract microbiota and in cases of urinary tract infections, mostly in patients with comorbidities [52,53]. A previous study by Hurst et al. [54] identified also Propionimicrobium, Negativicoccus, Peptoniphilus, and Prevotella in the urine of patients with PCa. Garbas et al. [3] suggested that the presence of these uropathogens might be implicated in prostate inflammation progression. The prostate microbiota in patients from both the PCa and non-PCa groups was dominated by the genera Pseudomonas, Cutibacterium, Curvibacter, Sphingomonas, and Corynebacterium, although their abundances were more evident in patients with PCa. On the other hand, Pseudomonas was the most prevalent in non-PCa patients. Feng et al. [32] observed that the genus Pseudomonas was prevalent in prostatic cancer tissues along with a greater expression of human small RNAs in patients with low rates of metastases. The authors concluded that Pseudomonas infection hampers the progression to metastatic disease and might be negatively correlated with tumor–node–metastasis (TNM) stage. Our results also showed that Stenotrophomonas was significantly elevated in glans samples of PCa patients. The presence of this genus that often co-colonizes with Pseudomonas, is also found to be negatively correlated with TNM, thus hindering the progression of metastatic cancer [55]. However, in-depth studies need to corroborate this hypothesis. Cutibacterium is a pro-inflammatory bacterial genus that has been detected in prostatic tissues and has been widely studied in the genitourinary context [42,56]. Specifically, Cutibacterium acnes has been proposed to induce the expression of immunosuppressive genes in macrophages, which in turn influence the risk of PCa progression [57,58]. Additionally, Yu et al. [59] reported an abundance of the genus Sphingomonas in prostatic secretions of patients with PCa, while Yow et al. [60] observed that Curvibacter and Corynebacterium were the most abundant genera within aggressive PCa tissues. In our study, apart from the above-mentioned genera, we observed that Staphylococcus, Lawsonella, and Paracoccus were also prevalent in the prostate biopsies of PCa patients. The presence of the genus Staphylococcus in prostate tissues is corroborated by a study carried out by Cavarretta et al. [18]. Furthermore, Sarkar et al. [61] investigated the differential composition of commensal bacteria in prostate tissues among patients with BPH and PCa, and observed Prevotella copri, Cupriavidus campinensis, and C. acnes were the most abundant bacteria in diseased prostate tissues of PC patients. To our knowledge, there is no evidence of the association between Lawsonella and PCa; it has only been described in association with breast cancer and infection [62,63,64]. Fastidious Gram-positive taxa such as Actinomyces spp. have been recognized recently as etiological agents of urogenital infections, such as urethritis, cystitis, and prostatitis [65,66,67,68]. Actinomyces causes a slowly progressive infection with tissue destruction that often resembles malignancy. In the LEfSe analysis (Figure 6D), our results showed that Actinomyces was more abundant in the three biological samples of patients with PCa. Other genera were observed to be significantly elevated in prostatic tissues of patients with PCa, such as Alishewanella, Paracoccus, Klebsiella, Rothia, and Microbacterium. On the contrary, Sarkar et al. [61] observed that the genus Paracoccus was enriched in the prostate tissue of patients with BPH. Further investigations regarding the association of these genera with PCa are needed to confirm these results. Nevertheless, using a meta-transcriptomic approach, Salachan et al. [12] highlighted Microbacterium sp. as an interesting candidate for further investigation due to its association with PCa, as they revealed significantly higher abundances of Microbacterium sp. in samples from advanced PCa. Considering the bacterial abundance at the genus level, a higher number of bacterial genera were found to be overrepresented in the urine and biopsies from PCa subjects in comparison with the non-PCa ones, as shown in out LEfSe analysis. Therefore, these results strongly suggest the occurrence of alterations in the genitourinary system, which can induce a chronic inflammatory environment in the prostate and, ultimately, the development of cancer as previously advocated by Garbas et al. [3] and Shrestha et al. [26]. Similar to other age-related diseases, PCa is often characterized by enhanced oxidative stress and oxidative damage. Recent findings demonstrate the link between the enhanced indices of oxidative stress and PCa [69], and between radical PCa removal and the normalization of such indices revealed by measuring 8-OHdG and 8-Iso-PGF2α in the urine of patients with PCa [70]. Moreover, the measurement of such biomarkers in the urine before and after surgery helps to predict radicality (and perhaps local recurrence) following surgery [70]. It is worth mentioning that the classic sexually transmitted bacteria, such as Chlamydia trachomatis and Neisseria gonorrhoeae, were not identified in this study cohort. Nevertheless, recent studies reported that sexually transmitted microorganisms might be involved in prostate carcinogenesis [21,22,23,24]. Our study has some limitations. First, we did not test for possible sources of contamination during medical procedures as all analyses were based on existing total DNA. Additionally, despite the analysis of three sample types of each subject, the number of patients could be greater, and the lack of true control samples limits our conclusions between PCa and non-PCa groups. Finally, taking an antibiotic the day before the biopsy might influence our findings, but both groups of patients underwent this antibiotic prophylaxis which does not affect the differences found in the genitourinary microbiota. The detection of microorganisms in the urine or prostate opens a new and exciting field for science. Until recently, the urine was considered a sterile niche in the human body, and its normal flow prevents bacteria from infecting the urinary tract [71]. With the development of next-generation sequencing technologies, alterations in the urine or prostate microbiome (or even in adjacent sites) associated with PCa have increasingly become the focus of current research. However, the overall number of studies conducted in this field is still limited and difficult to compare the results obtained. One important factor that strongly influences the comparison of microbiome data from different studies is the method of collecting and processing the biological samples. To date, studies to determine the prostate microbiome of patients with PCa or BPH have analyzed both formalin-fixed paraffin-embedded tissue samples [18,30] and fresh material [55]. Another aspect is the region of the prostate where the biopsy is performed and how urine is collected. By analyzing a random fragment of the prostate and owing to its small size, it is difficult to control the distribution of microorganisms in the organ or determine the most colonized area, as highlighted by Alexeyev et al. [42]. Thus, standardization of the collection technique should be mandatory for further microbiome studies. In conclusion, our results show that there is microbial dysbiosis with an increase in overall species diversity in the urine of patients with PCa compared to non-PCa patients. This dysbiosis may promote chronic inflammation in the prostate and might be implicated in the development of PCa. Differential abundances of certain bacterial genera present in the urine, glans, and prostate could provide together with data from other studies, promising biomarkers for early diagnosis, and aid the scientific and clinical community to search for new therapeutic and prognostic options. Furthermore, this study did not prove any connection between bacterial sexually transmitted pathogens and PCa. The emerging human pathogens that colonize the genital tract should be carefully investigated once they have been implicated in urogenital infections. Such studies would provide a starting point for future investigations into the functional relevance of these uropathogens in PCa pathogenesis. These aspects should be further validated in future research, including a larger cohort, and other molecular and histological tools.

PMC10000663

Athanasios Armakolas,Maria Kotsari,John Koskinas

Liquid Biopsies, Novel Approaches and Future Directions

03-03-2023

cancer,diagnosis,prognosis,CTCs,ctDNA,miRNA,proteome,exosomes,clinical applications

Simple Summary The gold standard for detecting cancer and profiling tumors is tissue biopsies. Despite this, tissue biopsies have been associated with many limitations leading to the desire for less invasive and more accurate solutions. One very attractive candidate for the diagnosis and prognosis of cancer in patients is provided by liquid biopsies. The number of analytes circulating in the blood that may be used for liquid biopsy testing is enormous making it a promising technique for the clinical management of oncological patients. The goal of this study is to discuss in detail the clinical relevance of liquid biopsies, as well as the opportunities they might offer for cancer prognosis, diagnosis and monitoring. The isolation process and clinical use of the biological components of the liquid biopsy will also be explained, with specific focus placed on novel procedures that can be developed as well as the approach’s future possibilities. Abstract Cancer is among the leading causes of death worldwide. Early diagnosis and prognosis are vital to improve patients’ outcomes. The gold standard of tumor characterization leading to tumor diagnosis and prognosis is tissue biopsy. Amongst the constraints of tissue biopsy collection is the sampling frequency and the incomplete representation of the entire tumor bulk. Liquid biopsy approaches, including the analysis of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating miRNAs, and tumor-derived extracellular vesicles (EVs), as well as certain protein signatures that are released in the circulation from primary tumors and their metastatic sites, present a promising and more potent candidate for patient diagnosis and follow up monitoring. The minimally invasive nature of liquid biopsies, allowing frequent collection, can be used in the monitoring of therapy response in real time, allowing the development of novel approaches in the therapeutic management of cancer patients. In this review we will describe recent advances in the field of liquid biopsy markers focusing on their advantages and disadvantages.

Liquid Biopsies, Novel Approaches and Future Directions The gold standard for detecting cancer and profiling tumors is tissue biopsies. Despite this, tissue biopsies have been associated with many limitations leading to the desire for less invasive and more accurate solutions. One very attractive candidate for the diagnosis and prognosis of cancer in patients is provided by liquid biopsies. The number of analytes circulating in the blood that may be used for liquid biopsy testing is enormous making it a promising technique for the clinical management of oncological patients. The goal of this study is to discuss in detail the clinical relevance of liquid biopsies, as well as the opportunities they might offer for cancer prognosis, diagnosis and monitoring. The isolation process and clinical use of the biological components of the liquid biopsy will also be explained, with specific focus placed on novel procedures that can be developed as well as the approach’s future possibilities. Cancer is among the leading causes of death worldwide. Early diagnosis and prognosis are vital to improve patients’ outcomes. The gold standard of tumor characterization leading to tumor diagnosis and prognosis is tissue biopsy. Amongst the constraints of tissue biopsy collection is the sampling frequency and the incomplete representation of the entire tumor bulk. Liquid biopsy approaches, including the analysis of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating miRNAs, and tumor-derived extracellular vesicles (EVs), as well as certain protein signatures that are released in the circulation from primary tumors and their metastatic sites, present a promising and more potent candidate for patient diagnosis and follow up monitoring. The minimally invasive nature of liquid biopsies, allowing frequent collection, can be used in the monitoring of therapy response in real time, allowing the development of novel approaches in the therapeutic management of cancer patients. In this review we will describe recent advances in the field of liquid biopsy markers focusing on their advantages and disadvantages. As cancer continues to be one of the leading causes of death worldwide, continuous efforts are being made to diagnose and manage this disease. Although, tissue biopsies have been the most common methods for diagnosing cancer and profiling the tumor, they are associated with many limitations [1]. Typically, tissue biopsies are an invasive method and for some anatomical sites it is not easy to collect them. They also provide a limited picture for intratumoral and intermetastatic genetic heterogeneity, as tumors are heterogeneous entities containing various subpopulations of cells that feature different lesions [1,2]. Furthermore, cancer cells over time undergo genetic and epigenetic changes and can evolve dynamically, guided by microenvironmental stimuli and clonal selection due to therapy pressure. This results in further tumoral heterogeneity [1], thus affecting the accuracy of the examination and the therapeutic decisions made based on it. In addition, surgical biopsies have limitations in terms of time, repeatability, age of the patient, cost and sometimes can even cause harmful clinical complications [3]. Therefore, they are not suitable to highlight the overall tumor profile, to identify any lesions in different locations nor to be used for the longitudinal monitoring of the disease [4]. The solution to the above issues comes from liquid biopsies, which constantly gain ground in terms of prognosis, diagnosis and monitoring of the progression of the disease. This method offers the advantage of a less invasive nature, lower cost, real-time information on the state of the tumor and, in some cases, the ability to overcome the issue of tumor heterogeneity (or multiple metastatic alterations) [3]. Such biopsies include sampling and analysis of body fluids, usually blood, although other sources such as urine, saliva, cerebrospinal fluid (CSF) and bone marrow can be used [5]. Biologically, the targets for liquid biopsy can be divided into two categories. One category refers to large or small molecules without cells or without a subcellular structure in the body fluid; these include proteins, nucleic acids, lipids, carbohydrates and other small metabolites and metal ions. The second category includes targets with cellular or subcellular structures, including single or clustered circulating tumor cells (CTCs), circulating cancer-related fibroblasts (CAF), immune cells, tumor-educated platelets (TEP) [6], extracellular vesicles (EVs) and circulating mitochondria [7,8]. Recent evidence suggests that exosomes operate on numerous receptor cells via a range of bioactive chemicals in vesicles and play a significant role in immune surveillance, angiogenesis, tumor formation, metabolism and inflammatory responses [9]. However to date, only circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) are the components whose clinical application has been approved by the US Food and Drug Administration (FDA) [2]. The purpose of this review is to describe in detail the clinical significance of liquid biopsies as well as the possibilities it can offer to the prognosis, diagnosis and monitoring of cancer progression. The isolation method and the usefulness in clinical practice of the biological components of a liquid biopsy will be described as well, and special emphasis will be given to the new techniques that can be developed as well as to the future prospects of the method. Metastasis is a multi-step process that depends on the presence of CTCs in the blood stream and/or disseminated tumor cells (DTCs) that are found in the bone marrow [7,8]. In order for CTCs to be able to disseminate from primary tumors they must undergo phenotypic changes that will allow the cells to penetrate blood vessels [10]. The epithelial-mesenchymal transition (EMT) is a central process in metastasis where the cancer epithelial cells downregulate the expression of their epithelial markers, including the cell membrane proteins that are responsible for cell to cell adhesion, and they express the mesenchymal markers [11,12,13]. Mesenchymal cells do not possess cell adhesion molecules on their surface and, therefore, can easily detach from the main tumor. In addition these cells induce the expression of proteases and integrins that are central molecules in the intravasation and extravasation of mesenchymal cancer cells [14,15]. CTCs are a population of cancer cells that manage to detach from either the primary tumor or metastatic deposits in the periphery of patients, and they seem to have a short half-life of approximately 1h to 2.4 h. The presence of CTCs in the bloodstream consists of a very heterogenous population that varies greatly in number from patient to patient and even within the same patient at different time points. The presence of CTCs in the circulation is fundamental for the development of metastasis in various types of solid tumors [16,17] (Figure 1). The fact that CTCs are highly heterogeneous and circulate in low numbers renders them a very hard target to detect accurately enough to set the guidelines for patient treatment (Figure 1). Therefore, it can easily be understood that the first step of enrichment is critical for the analysis of the cancer cell load (metastatic or not) in the periphery of the patients. For this reason, a variety of techniques have been developed, based on both the biophysical and biological properties of these cells, in order to differentiate them from their background and enrich them, so that they are compatible with molecular analysis or imaging analysis. Similarly, many technologies have been developed for the capture, isolation and detection of CTCs [18,19,20,21,22]. Due to the small CTC concentration in the blood, analysis always starts with an enrichment step that aims to increase the concentration of these cells by several logarithmic units thus allowing an easier identification of single tumor cells. CTCs can be enriched by approaches that exploit differences between tumor and normal blood cells, based on biological properties such as the differential expression of protein markers or different physical properties of cells including size, density, deformability or electric charges, and these enrichment principles can be combined to optimize the yield of CTCs [17,24,25]. A variety of devices has been developed to enrich and detect CTCs, with emphasis on devices capable of selecting and detecting CTCs that have undergone EMT [25,26]. The EPCAM-based enrichment for CTC detection has provided a reliable prognostic tool in different carcinomas [27,28]. However other epithelial cell surface antigens including EGFR7 [29] and mucin 1 [30], and tissue specific antigens such as prostate specific membrane antigen (PSMA) [31] for prostate cancer cells and ERBB2 [32] for breast cancer cells have been exploited for this purpose (Table 1). CTCs present a very heterogeneous population of cells. Recent evidence indicates that in many cases CTCs could cease to express the selected marker, leading to markers escaping detection and, thus, to false negative results [33,34]. Consequently, the bias which might be introduced by positive selection can be avoided by negative selection. In this case non-malignant blood cells are depleted from the blood using antibodies that recognize the cell surface antigens expressed on leukocytes, usually CD45 and other cells in the bloodstream, including endothelial stem cells with markers such as CD146 and hematopoietic stem cells with markers such as CD34 [35,36]. Disadvantages of negative selection include the lower purity of isolated CTC populations compared to the techniques of positive selection and the risk of CTCs becoming trapped in a mass of blood cells and, thus, being included in the depleted cell fraction and ignored [37,38,39]. With regard to techniques based on the physical differences of tumor cells and non-malignant blood cells, it is worth noting that these characteristics are highly variable between CTCs and have substantial overlap with those of non-malignant cells; therefore, definitions of CTC size depend on the capture device. Microfiltration technologies have been developed where blood is passed through pores or microfluidic passageways with calibrated size to trap CTCs, resulting in size exclusion and, therefore, retention of large CTCs, albeit with possible loss of small CTCs [40]. Other microfluidic devices that rely on size separation use inertial focusing strategies to separate CTCs from other blood components, while dielectrophoresis (DEP) allows the separation of CTCs based on the different electrical charges of tumor and blood cells. Special microfiltration systems have also been developed to specifically capture CTC clusters based on size exclusion [41]. Most CTCs occur as single cells, but CTC clusters can be detected and their biology is still being investigated [42]. After enrichment, an identification step is required to detect CTCs surrounded by residual leukocytes at the single cell level by immunological, molecular or functional methods [66]. The dominant methods use antibodies against membrane and cytoplasmic antigens, including epithelial, mesenchymal, histospecific and tumor-related markers, with the aim of direct immunological detection [26]. Until now, the only clinical application of CTCs approved by the FDA is the CellSearch platform [28] and the most current CTC assays use the same identification step as this one (Table 1). Cells stained with fluorescently labeled antibodies to epithelial cytokeratin (CK) are visualized through fluorescence microscopy and used as a marker of CTCs, while staining of CD45 is used to exclude leukocytes [67]. Some of the markers used vary in different types of cancer, for example cytokeratins apply to breast, colon and prostate cancers, and other epithelial tumors, although specific tissue antigens can also be used, such as prostate specific antigen (PSA) or breast specific mammaglobin [17]. However, this technology has some limitations. First of all, it is mainly based on the expression of EpCAM which has been associated with localized cancer, but during metastasis its expression, along with that of CK, decreases amid the appearance of mesenchymal markers [68]. Second, cell isolation through the CellSearch system is followed by cell fixation for stabilization, which prevents further characterization of viable cells such as CTC cultures, while having a low sensitivity for CTC detection (one cell per 1 mL of blood sample). Finally, the CellSearch system offers low purity of captured cells, in the range of 60–70%, resulting in captured CTCs that are usually contaminated by blood cells or normally circulating epithelial cells (CECs) [42,65,69]. It is possible to manually isolate the identified CTCs by micromanipulation; however, it is laborious and time-consuming. An alternative approach for separation of CTCs in order to further genomic, molecular or functional analyses involves automated selection of single cells using a DEPArray, a device that allows trapping single CTCs in DEPcages [70]. Dielectrophoresis (DEP) is a liquid biopsy separation assay that is based on particles with different polarizations and that move differently under a non-uniform electric field [71]. Microchips that use the DEP method have multiple integrated electrodes which generate the non-uniform electric field in order to isolate and capture single CTCs. However, the low sample volumes and the varying dielectric features of cells due to ion leakage could limit the isolation time [72]. A new fluorescence-activated cell sorting (FACS) approach has also been used for CTC detection and phenotypic analysis, but this technology typically requires a pre-enrichment step to achieve sufficiently high initial CTC concentrations [73]. FACS is a cell-based analytic method where an immunomagnetically enriched blood sample is injected into a fluid stream, and single cells in the stream are interrogated by lasers as they flow into a capillary tube. The cells are then sorted based on light scattering and fluorescence patterns by comparison with negative (healthy blood cells) and positive control (EpCAM-expressing cancer cell lines) [74]. On the other hand, there are some restrictions to this method. Τhe use of expensive antibodies leads to high detection costs, whereas, in many cases CTCs cannot be further analyzed in real-time conditions since the cells are fixed or lysed during the assay process [75,76]. CTCs can also be detected by techniques that target the mRNA or DNA level. These techniques require the design of PCR tests with specific primers for tissue, organ, tumor-specific transcriptions, or for tumor-specific mutations, translocations or methylation patterns unique to the tumor [77]. Furthermore, these technologies also allow the quantification of CTC numbers. Reverse transcription PCR (RT-PCR) assays are the most user-friendly method for detecting low-abundance mRNA transcriptions. A limitation of this approach is that the CTC number can only be estimated due to the fact that gene expression levels vary between CTCs [78]. Currently, digital droplet PCR (ddPCR) allows detection and absolute quantification of low-abundance targets in shorter times without requiring a large number of replications [79,80]. ddPCR relies on water-oil emulsion droplet technology. In comparison with other digital PCR assays, this method has a lower sample requirement, thereby reducing costs and preserving valuable samples [81]. Functional assays like the epithelial ImmunoSPOT (EPISPOT) assay have been used for CTC in vitro detection in blood and bone marrow samples for more than two decades and have been validated at the clinical level for several different cancers [82]. This assay provides quantitative information about the number of viable CTCs present in the sample based on the fluorescence detection of specific epithelial proteins secreted by these cells, as well as qualitative information about which of these proteins are shed during cell culture. Currently, this technique has been further developed allowing for the capture and detection of CTCs at the single cell level. The so-called EPIDROP, as an abbreviation of ELISPOT in a drop, is a more rapid and sensitive form than the previous one [83]. In this assay, CTCs are immunostained prior to individual encapsulation in fluid microdroplets and, consequently, both the total number of CTCs (EPCAM+ or EPCAM−) and the number of functional CTCs can be imprinted. Indeed, viable CTCs can be distinguished from apoptotic CTCs, and EPCAM+ versus EPCAM− CTCs enable the assessment of EMT status. In the future, a subsequent molecular characterization of the captured CTCs will be incorporated into this innovative assay. Despite the fact that EPISPOT is a promising technique, there can be problems when antigen levels are lower or binding efficiency is reduced [84]. Furthermore, processing a single sample in an EPISPOT assay requires three days for analysis [85]. This, in combination with the finding that it may fail to isolate more heterogeneous cells because of its biomarker dependence [86], render EPISPOT unsuitable for clinical use. Aiming towards personalized cancer treatment, many innovative technologies have been developed in recent years intended for the characterization of CTCs. CTCs can be analyzed by cytogenetic analyses, such as in situ fluorescence hybridization (FISH), to identify chromosomal rearrangements [87] (Table 1). Multi-omics techniques have entered dynamically in the patient management of cancer. CTC single cell analysis is a novel approach where CTCs are isolated and the entire genome can be amplified in order to make subsequent assessments of duplicate number aberrations and specific mutations using array competitive genome hybridization or next-generation sequencing (NGS) techniques [88]. A physical disadvantage of this method is that the findings cannot be verified because single CTCs are found in limited quantities [89]. Though, the DNA amplification protocol requires careful technical validation to avoid false findings and, thus, ensures a low error rate [62]. In addition, strict bioinformatic approaches are needed to ensure reliable identification of tumor-specific changes in individuals’ CTCs. Therefore, it is a time-consuming technique that also involves high costs [90] (Figure 1). On the other hand, this method offers high sensitivity of CTCs from many tumor types, and the variety of selection markers allows for the possibility of characterizing cells for multiple markers all at the same time [91]. In addition, the use of NGS in CTC analysis offers the possibility of using genomic and transcriptional CTC profiles to improve the understanding of cancer heterogeneity [92]. RT-PCR transcription assays that are not expressed in non-malignant blood cells such as those encoding PSA or epithelial cytokeratins are sensitive enough to allow the detection of single CTCs but can also provide information on their phenotype (Table 1). However, special manipulations are required because low-level external expression of target transcript in infected leukocytes (or other non-malignant cells in the bloodstream) can lead to incorrect attribution of the results (false positives). As an alternative approach, the transcriptomic profile of single CTCs isolated by micromanipulation can be determined using multiplex quantitative RT-PCR [93] or RNA sequencing assays, NGS. These techniques may also allow the assessment of heterogeneity between single CTCs within the same patient [94]. The sensitivity of NGS-based technologies is lower than that of PCR-based technologies and inversely proportional to the number of sites analyzed, with the total exome sequence (WES) having the lowest sensitivity [95]. On the other hand, compared to ddPCR, NGS had a higher sensitivity for individual nucleotide variants, indels and selected rearrangements and has been shown to have a positive percentage agreement of 95% and a positive predictive value of 100% [96]. On the other hand, immunophenotyping with antibodies to proteins of interest (proliferation or apoptosis markers) is the most commonly used approach to CTC characterization but is currently limited to a few proteins of interest (beyond those required for the enrichment and detection). In many studies immunophenotyping has been used to confirm the epithelial [97] or mesenchymal [98] nature of the suspected circulating cells (Table 1). However, even among the epithelial markers typically used to conceive CTCs, such as EpCAM or cytokeratins, there is no consensus on specific markers that can more effectively identify clinically relevant CTCs. A micro-fluid single cell western blot (scWB) technology has also been developed for proteomic CTC phenotyping but is limited to evaluating only eight proteins [99].The rare cell scWB quantifies multiple surface and intracellular signaling proteins in each individual CTC, allowing estimates of the variation in biological protein expression between CTCs. This method is compatible with well-established CTC isolation tools and can successfully analyze CTC populations with just two primary cells. The monitoring of multiple regulated proteins in blood derived CTC may provide information about the treatment options to maximize the benefit for each specific patient at each specific time point [100]. With in vitro cultures of CTCs, in addition to transient expansion, some groups have been able to create permanent CTC cell lines obtained from patients with advanced-stage diseases. However, these cell lines have phenotypes that reflect those of cells in tumor tissue samples from patient donors, but they also have a special molecular signature that reflects the metastasizing capacity generally attributed to CTCs. In practice, cell lines derived from CTCs have germinality, a specific DNA repair phenotype and a high metabolic rate [101].These cell lines can also be used to test drugs in prospective discovery projects, but the process of determining these cell lines is not yet fast enough and CTCs capable of metastasis are a rare subset of the cell population, thus limiting the usefulness of this approach for decision-making in clinical practice. Furthermore, short-term CTC cultures could provide information quickly enough to potentially inform treatment decisions for the donor patient. They could also reveal new pathways specific to metastasis-causing CTCs and, therefore, new targets for drugs that specifically eliminate this more aggressive subset of CTCs. The evolution of CTCs presents another challenge for the development of cell lines that accurately reflect the disease, and the creation of multiple cell lines using CTCs isolated from sequential blood samples collected during disease and treatment can provide unique information [102]. Finally, CTCs can also be characterized through functional studies in patient-derived xenograft models (PDX) which can result in revealing the properties of these cells that are required for the transition to secondary sites and/or the outgrowth of diffuse cancer cells (DTCs) to form apparent metastases. In addition, these PDX models can be used to test drugs that may be interesting candidates for anticancer therapy [103] (Figure 1). The disadvantage of this method, however, is that the development of PDX models usually takes several months and the rate of successful CTC integration is generally very low due to the requirement of a large number of CTCs, which generally excludes the use of such models in making treatment decisions for individual patients. However, these models appear to recap the molecular and cellular characteristics of parent tumors as well as the response to chemotherapy [86,104]. Cell-free DNA (cfDNA) from cancer cells, known as circulating tumor DNA (ctDNA), can be tracked in the plasma of cancer patients. Since the first reporting of identical DNA mutations in plasma compared to a patient’s tumor, ctDNA has been investigated as a tool for diagnosis, detection, prognosis, treatment selection and monitoring [105]. Both the amount [106] and integrity [107] of circulating cfDNA can be used to distinguish between cancer patients and healthy individuals. Overall levels of cfDNA tend to be higher in cancer patients than in healthy individuals [108,109,110] and appear to increase with stage [111] and metastasis [112]. The increased concentration of cfDNA in these patients is believed to reflect the additional release of genetic material from tumor cells, but it could also be a result of defective clearance of ctDNA from phagocytes [113]. However, high levels of cfDNA are not specific to cancer and have been identified in other pathological and non-pathological conditions, such as exercise, trauma, and surgery, that may interfere with their immediate application for a cancer diagnosis [114] (Figure 1). Currently, highly sensitive and specific methods for the detection of ctDNA have been developed. Technologies leading to the detection of ctDNA can be separated into two main categories: (a) targeted techniques designed to detect mutations in a collection of predetermined genes, and (b) untargeted methods that attempt to screen the entire genome, such as whole-genome sequencing, exome sequencing, or array comparative genomic hybridization [115]. Allele-specific PCR was the first approach used in ctDNA detection, and a quantitative PCR variant (qPCR) of this technique is currently being adopted by the EGFR cobas® test [116]. Given that the proportion of ctDNA in total cfDNA is typically very low, frequently 0.01% [117], more sensitive technologies, such as digital PCR (dPCR) [118], droplet digital PCR (ddPCR) [119], and beads, emulsion, amplification, magnetics (BEAMing) [120], have been developed and successfully used for ctDNA analysis (Table 2). The inadequate multiplexing capability of PCR-based assays prevents them from analyzing more than a few loci concurrently, despite their high sensitivity, speed, and affordability. These techniques have ctDNA detection limits of 0.01%, making them more sensitive than non-targeted sequencing procedures. However, the need for extensive prior knowledge of the mutational spectrum of the tumor in the specific patient is a drawback of these methods [115]. The sensitivity of NGS-based technologies is negatively correlated with the number of loci tested, lower than that of PCR-based technologies, and lowest for whole-exome sequencing (WES) (5% mutant allele fraction (MAF)—the percentage of mutant allele in a given locus). Consideration of patient- or cancer-specific gene panels, as in the cancer personalized profiling by deep sequencing (CAPP-Seq) technology [121], or strategies to suppress background noise generated by random errors occurring during library preparation are approaches to improving NGS sensitivity. These methods include attaching distinct molecular identifiers to each template molecule (UMIs). Various NGS technologies, including improved tagged amplicon sequencing (eTAm-SeqTM), utilise these [105] (Table 2). Selective nuclease digestion of DNA that has not undergone mutation is another method to boost sensitivity. This method raises MAF and has made it possible to detect mutations down to 0.00003% MAF [108]. Despite its potential, using ctDNA as a liquid biopsy has a number of limitations. The sensitivity of detection is a serious concern, particularly in early cancer detection, where the low amount of ctDNA may result in a MAF lower than the detection limit of existing techniques (Figure 1). Other body fluids sampled near the putative site of the tumor can increase the detection rate, at least in individuals at risk due to hereditary predisposition for example. This is primarily because, particularly in the early stages, proximal body fluid may contain a higher concentration of tumor-derived DNA than blood [122]. Another issue in early detection is the predictive value of single or small groups of mutations, because cancer-associated mutations can be found in healthy people’s plasma as a result of clonal hematopoiesis. The CancerSEEK platform, which associates the analysis of eight tumor-derived proteins with ctDNA mutation profiling and has a specificity of >99%, is one approach to overcoming this challenge [123]. Another barrier to the widespread use of ctDNA analysis is the lack of standardized protocols for preanalytical sample preparation and ctDNA purification. Current procedures are complicated and have the potential to cause ctDNA degradation and blood cell lysis [124]. It is desirable to have a platform that allows for the quick, single-step purification of ctDNA from blood and lab-on-a-chip systems have the potential to meet this need [125]. Five tests have been approved by the FDA since the discovery of cfDNA. These tests include finding point mutations in cancer-related genes like KRAS, EGFR and PIK3CA, as well as assessing tumor mutation burden (TMB), microsatellite instability, ALK rearrangement, insertions and deletions, and methylation patterns [173] (Table 2). The results of these tests may have an immediate impact on the patient’s treatment. Early cancer detection, improved cancer staging, early detection of relapse, real-time monitoring of therapeutic efficacy, and detection of therapeutic targets and resistance mechanisms are all current clinical applications [174]. ctDNA analysis can provide both qualitative and quantitative information. The MAF measurement provides quantitative information and is a reflection of tumor burden. It is used to detect minimal residual disease (MRD) and occult metastases [175], as well as to monitor treatment response and therapeutic effectiveness [176]. Because ctDNA has a short half-life (2.5 h), ctDNA levels provide a ‘real-time’ snapshot of tumor bulk. The presence of ctDNA after treatment is a highly sensitive and specific predictor of relapse [177]. The profiling of mutations, amplifications, deletions and translocations in ctDNA can provide qualitative information, allowing the identification of genetic alterations associated with response and thus supporting decision-making for personalized management. Other qualitative information obtained from ctDNA analysis includes methylation status [178] (Figure 1). Other biofluids, besides blood, have been shown to contain ctDNA including urine, cerebrospinal fluid (CSF) and gastric washes. Depending on the type of cancer, tumors may come into closer contact with different fluids, resulting in higher ctDNA concentrations than blood [179]. CSF-derived ctDNA is particularly easy to investigate because it is not diluted by the normal DNA found in blood. A few studies have looked at CSF and paired plasma, tumor tissue from patients with central nervous system tumors (glioblastoma and medulloblastoma), as well as brain metastases from lung or breast cancer [180]. In patients with head and neck squamous-cell carcinoma, the presence of saliva-derived ctDNA has been used to detect HPV and genomic point mutations. Saliva ctDNA was found to be enriched for ctDNA from the oral cavity, whereas plasma ctDNA was found to be enriched for tumor DNA from other sites [130].Tumor-specific genomic and epigenomic alterations in urine-derived ctDNA have been observed in patients with urological, prostate, NSCLC, CRC, pancreatic cancer and other cancers. However, assessing urine-derived ctDNA is more difficult due to the massive amount of normal DNA constantly released by urinary epithelial cells [181]. The percentage of circulating cell-free RNA derived from cancer cells is known as ctRNA. In comparison to DNA, RNA is a rather unstable molecule, with a naked half-life in plasma of 15 s [182] (Figure 1). Its interaction with proteins [183], proteolipid complexes and EVs increases its stability [184]. Almost all known types of RNA have been detected in systemic circulation, and each has the potential to act as a cancer biomarker to some extent. ctRNA, like other components of the tumor circulome, provides both quantitative and qualitative information. In reality, whereas coding and noncoding RNA expression patterns are the most relevant source of information, the discovery of tumor-specific fusion transcripts or alternative splice events is also achievable [185] (Figure 1). The most important ctRNAs that might be used as biomarkers include mRNAs, miRNAs and long noncoding RNAs (lncRNAs) (Table 2). Their study is carried out using techniques ranging from qRT-PCR or dPCR-based evaluation of single or small panels of RNAs to RNA-Seq-based complete characterization of RNA (particularly miRNA) signatures [186]. Several miRNA levels are often changed in cancer patients, allowing for the identification of miRNA signatures with diagnostic and prognostic value. Tumors and their microenvironments produce miRNAs that are released in the circulation as ribonucleoprotein complexes or as EVs [187]. Circulating miRNA patterns appear to be consistent with tumor tissue profiles [188]. EV-incorporated miRNAs, on the other hand, appear to comprise just a tiny percentage of the miRNAs in circulation and to have different diagnostic performance [189]. Plasma exosomal miR-196a and miR-1246 levels have the potential for early pancreatic cancer detection [190], and panels of miRNAs have been demonstrated to be valid biomarkers for lung cancer diagnosis [191] or prognosis [156]. A serum exosomal miRNA profile was recently demonstrated to be a novel approach for the differential diagnosis of gliomas [192] (Table 2). Exosomal mRNA has been utilized to explore the mutational status of KRAS and BRAF in CRC patients [193], and exosomal EGFRvIII mRNA has the potential to be employed in the diagnosis of EGFRvIII-positive high-grade gliomas [194]. Numerous lung-cancer-related gene fusions have been found in both vesicular and nonvesicular mRNA and have potential as biomarkers [195] (Table 2). LncRNAs are a new and potential source of RNA biomarkers. Plasma exosome LINC00152 levels, for example, have been associated with gastric cancer, and the combination of two mRNAs and one lncRNA in serum exosomes has CRC diagnostic potential [196]. Furthermore, serum exosomal HOTAIR lncRNA can be used to help in the diagnosis and prognosis of glioblastoma multiforme [197]. Recently, a panel of five circulating lncRNAs were investigated as potential diagnostic biomarkers for gastric cancer [198] (Table 2). The most significant obstacles to the clinical use of ctRNAs concern the preanalytical and analytical phases. Although circulating RNAs are protected by their connection with various molecules and structures, they are unstable in plasma when held at 4 °C and are restricted by extraction speed. Furthermore, different extraction techniques provide varying recovery rates, and there is presently no agreement on the best extraction protocol [199] (Figure 1). EVs are membrane particles that are produced by all cell types under healthy and pathological situations as well as in response to various stimuli such as proteases, ADP, thrombin, inflammatory cytokines, growth factors, biomechanical shear and stress inducers, and apoptotic signals [200]. They may be present in nearly every physiological fluid, particularly blood. EVs are classified into two groups based on their biogenesis, composition and secretory pathways: microvesicles and exosomes [201]. Exosomes belong to a large class of EVs with a diameter ranging from 40 nm to 160 nm that are created by the inward budding of the limited multivesicular body (MVB) membrane, which is generated constitutively from late endosomes. Intraluminal vesicles (ILVs) occur within large MVBs as a result of late endosomal membrane invagination. Some proteins are integrated into the invaginating membrane during this process, whereas cytosolic components are absorbed and confined inside the ILVs. As ILVs fuse with the plasma membrane, the majority of them are discharged into the extracellular space as “exosomes”. Research suggests that the endosomal sorting complex required for transport (ESCRT) function is essential for the production of ILVs [202]. Notably, new research suggests that an alternate method for sorting exosomal cargo into MVBs that is ESCRT-independent appears to rely on raft-based microdomains for lateral cargo segregation inside the endosomal membrane. These microdomains are hypothesized to be rich in sphingomyelinases, which can be used to create ceramides by hydrolytic removal of the phosphocholine moiety. Ceramides are known to cause lateral phase separation and microdomain coalescence in model membranes. Moreover, ceramide’s cone-shaped structure may generate spontaneous negative curvature of the endosomal membrane, enhancing domain-induced budding. As a result, this ceramide-dependent method highlights the importance of exosomal lipids in exosome synthesis [203]. The mitogen-activated protein kinase pathway, which is up-regulated in most tumor cells, is considered to be involved in the active shedding of vesicles from tumor cells [132]. They are distinguished by characteristics such as CD9, CD63, CD81, ALIX and heat shock protein 70 (HSP70), which aid in their collection and enrichment [204]. Exosomes can mediate cell communication under healthy and pathological settings by transporting particular cargos (nucleic acid or protein) [205]. Exosomes, as evidenced by growing studies, play an important role in carcinogenesis, tumor development, metastasis and medication resistance [206]. The genetic makeup of the parent tumor cells is congruent with the cargos of tumor-derived exosomes [207]. As a result, exosomes and their transported cargos have progressively come to be recognized as new biomarkers for cancer diagnosis and prognosis prediction. Exosomes are also stable in circulation and can preserve their cargos from degradation [208]. The absence of established guidelines for sample handling and EV isolation and analysis, which could affect reproducibility in the clinical area, is an important limitation to the clinical application of EVs as liquid biopsies [209] (Figure 1). Mainstream EV isolation methods utilise physiological (density and size) and biological (expression of surface markers) characteristics [210]. Ultracentrifugation (UC) is now recognized as the “gold standard” approach for separating and concentrating exosomes from other components depending on densities. Protein contamination can be reduced by UC. Though, it has a limited throughput and may separate other particles of comparable size [211]. While the throughput is still limited, utilizing density gradient centrifugation can overcome the impurity of the UC technique [212]. Although commonly utilized, these procedures are costly, time-consuming, and do not guarantee pure yields, frequently resulting in a trade-off between purity and recovery. Filtration and size-exclusion chromatography (SEC) are two size-based approaches. Filtration can provide high yields and purity, but it is restricted by EV adhesion to filters and vesicle destruction caused by high pressure. Furthermore, its poor resolution is restricted by the presence of additional contamination such as virus and lipoprotein particles [213]. When compared to ultracentrifugation, SEC offers enhanced EV recovery [214]. Immunoaffinity-based separation techniques involve antibodies to target particular surface antigens of exosomes, which can greatly boost exosome purity and reduce isolation time [215]. Antibodies are often immobilized in ELISA plates or magnetic beads. However, it is expensive and occasionally afflicted by nonspecific antibody binding [216]. Polymer precipitation is another popular technique of isolation, particularly for exosomes. The use of polymers such as polyethylene glycol (PEG) to limit the solubility of EVs in order to precipitate them using quick low-speed centrifugation is used in this procedure. This approach has a low purity despite providing good recovery rates [217]. Electric fields are being used in new methods for EV isolation. Lewis et al. created an alternating-current electrokinetic (ACE) chip that can catch exosomes from the entire blood sample and perform in situ immunofluorescent analysis in 30 min. The scientists verified this chip by evaluating GPC-1 and CD63 levels as PDAC diagnostic indicators [218]. Finally, microfluidics is a promising area of prospective innovative techniques to EV isolation. The existing microfluidic techniques are based on EV characteristics including nanoscale size-based filtering [219], antibody-functionalized microfluidic channels [220] and spiral inertial microfluidic devices [221]. Exosomes must be measured and examined once they have been extracted. Exosomes are widely quantified using ELISA, fluorescence activated cell sorting (FACS), and nanoparticle tracking analysis (NTA). ELISA can capture certain proteins and generate a color change that is proportional to the concentration of the target protein. CD9, CD63 and CD81 have been identified as the most often utilized exosome-specific markers in the ELISA approach for exosome quantification [222]. Exosome-specific markers can also be employed in FACS to quantify and sort exosomes. However, FACS needs a somewhat sophisticated setup and costly equipment, making it unsuitable for clinical use. Another disadvantage of FACS is the lack of consistency in results due to the various optical and laser settings used to detect exosomes [223]. Another fluorescent-based approach for measuring and sorting exosomes is NTA. The idea is to use a laser beam to follow the movement of exosomes. NTA can identify smaller exosomes than FACS; however, it cannot be used in clinical settings due to the lengthy processing time [224]. As a result, various innovative ways for detecting and measuring exosomes arise that are more cost-effective and efficient. Lv et al., for example, coated nanoellipsoids with antiCD63 antibody as the substrate of localized surface plasmon resonance biosensors. The peak wavelength can be used to calculate exosome concentration. When compared to ELISA, this type of biosensor takes a fifth of the sample amount but can cut processing time in half. Furthermore, it is inexpensive, which makes it suitable for clinical use [225]. The molecular contents carried by EVs can be regarded as a molecular fingerprint of the cell of origin, making them suitable as cancer biomarkers. When compared to CTCs, EVs are frequently produced and liberated in higher numbers [226]. Similarly, the vesicular cargo’s stability is maintained by an outer protective lipid membrane. EVs can provide both quantitative and qualitative data. Quantitative data such as EV levels can reveal the existence of malignant etiology and tumor density. The most easily accessible qualitative information is gained by the molecular characterization of EV components, including nucleic acids and proteins. The RNA composition of EVs, including both coding and noncoding (nc)RNAs, has received a lot of attention. Proteins are carried by EVs in their lumen and membrane, and multiple studies have been published indicating the importance of EV proteins as potential cancer biomarkers [227] (Table 2). From a proteogenomic approach, evaluating the proteome is more technically and conceptually rigorous than analyzing the genome. To begin, the proteome is projected to have about one million diverse proteoforms via multiple epigenetic controls, variable RNA splicing and PTM, as opposed to a total of 22,000 to 25,000 protein-translatable genes inside the human genome. Furthermore, the dynamic range of proteins in cells or body fluids can reach up to 12 logs of size [228]. Finally, the proteome undergoes continual and fast shifts in protein quantities and/or alterations in response to a variety of stimuli. While it is impossible to test the identical proteome twice, the genome is generally stable, with gradual continuous alterations. Because of these difficulties, proteomics typically comes behind genetics in many applications [229]. However, as proteins are the primary mediators of most biological activities and the direct drug targets in the majority of existing cancer treatments, high-dimensional proteomic data are anticipated to yield unparalleled insights to contribute to the identification and practical use of new biomarkers (Figure 1). High-plex proteomics technologies that are applied in cancer liquid biopsy include mass spectrometry (MS), antibody/antigen arrays, aptamer-based assays, proximity extension assay (PEA) and reverse phase protein arrays (RPPA) [230]. MS-based proteomics constitute an effective method for cancer biomarker profiling in the context of various body fluids, with an emphasis on serum/plasma and urine. With technological and scientific advancements, current MS mostly employs purpose-designed sample preparation in conjunction with liquid chromatography (LC) prior to peptide ionization and tandem MS scans in liquid biopsy screening [231]. The ability to conduct non-hypothesis-driven proteome research (total proteins and modified forms) is a fundamental feature of MS for cancer liquid biopsy, making it a preferable technique at the early biomarker identification stage. Nowadays, for clinical proteomic analysis, a few hundred to over a thousand proteins may be described in an untargeted MS run in serum or plasma, but urine-based MS profiling can accomplish several thousand targets concurrently due to its considerably less complicated protein composition [232]. The main task in blood-based proteomics is to decrease noise or false discovery rates due to the huge dynamic range of blood protein concentration as well as pre-analytical fluctuations [233]. MS-based liquid biopsies, though, have been used in a variety of malignancies, including lung, breast, colorectal, ovarian, gastric, pancreatic, prostate, cervical, lymphoma and so on [234,235,236,237] (Table 2). Immobilizing particular antibodies onto modified planar substrates by covalent binding, affinity binding or physical trapping is a common scientific technique. Samples are typically tagged with fluorescent, chemiluminescent or oligo-coupled tags in high-plex (usually several hundred targets) profiling to allow for varied signal amplification and detection. This approach is capable of characterizing over a thousand proteins or modified proteoforms with low immunogenic cross-relativity caused by antibody reaction mixtures [238]. Because most TAP are low abundant cellular efflux, including hormones, cytokines, chemokines, intracellular signaling components and post-translational alterations, antibody arrays are especially effective for serological analysis [239]. Nonetheless, because of its inadequate quantification due to restricted dynamic ranges and signal saturation, sample labeling need, and inter-assay heterogeneity, it occupies a tiny methodological area for biofluid-based proteomic screening. Another high-throughput discovery in proteomics field is antigen arrays, also known as functional protein arrays [240]. They begin with the deposition of ectopically produced proteins/peptides with broad proteome coverage in the desired species which act as baits to collect analytes of interest inside the flowthrough. Protein interactions with proteins (protein PTMs), lipids, cells, tiny molecules, nucleic acids and antibodies may all be studied theoretically. In this regard, serological autoantibodies (AAbs) constitute a hotspot for cancer biomarker profiling [241]. Aptamers are short single-stranded DNA, RNA or peptides that bind to cognate protein targets in natural states with high affinity and specificity after folding into specified tertiary structures [242]. In the case of the slow off-rate modified aptamers (SOMA) scan assay, binding molecules (SOMAmers) are attached to photocleavable linkers and fluorescent labels, and those nucleic acid structures are then used to capture proteins of interest, followed by biotin-mediated purification, oligo release via ultraviolet (UV)-based cleavage, and biotin tagging of bound proteins. The protein-bound SOMAmers are then eluted and quantified using traditional DNA hybridization methods, representing the protein abundance in the system [243]. Aptamers are more beneficial than antibodies because they have stronger affinity and specificity, and they can be easily produced and chosen in vitro with little batch-to-batch variation, giving a cost-effective means to scale up their multiplexity [244]. Regardless of the fact that there are already over 7000 protein-specific aptamers available for commercial assay services, one constraint is the difficulty in creating high-quality aptamers for new targets. Aptamers are still scarce in the research community compared to antibodies [245]. Its wide dynamic range (scan 10 logs) and small sample size make PEA an ideal method for serological analysis. Multiple antibody pairs for proteins of interest are pooled in PEA. Each antibody in a pair is tagged with complementary DNA oligo sequences to enable for high-fidelity discriminative hybridization, which occurs only when real antibody pairs are brought together by binding to the target proteins. Following that, the proximity reactions proceed through a dilution phase, which replaces the washes used in typical sandwich immune tests. Oligonucleotides on pairs of antibodies that remain in close proximity due to binding the same protein molecule can subsequently be ligated (proximity ligation assay) or polymerized (DNA polymerization assay) (proximity extension assay). PCR is used to amplify the resulting double-stranded DNA sequences. The ligation or polymerization procedures produce amplifiable reporter DNA strands for sensitive readout using techniques such as real-time PCR or next-generation sequencing [246]. The most developed PEA assay offers standard measurement coverage of 3072 targets and avoids the cross-reactivity problem that multiplexed immunoassays generate [247]. RPPA is an open-source platform that may be constructed in a variety of ways. In a typical RPPA system, completely denatured protein lysates are immobilized onto solid substrates, often by a dilution series, and this procedure can be repeated to probe any number of targets (up to 500 targets). Sample-containing slides are probed with highly specialized antibodies that have been pre-validated for RPPA use, and quantifiable signals are collected via colorimetric amplification or fluorescence analysis. Due to its nature of measuring all samples in one test cycle, which typically runs from a few hundreds to over a thousand samples, RPPA is extremely resilient in parallel to big sample profiling [248]. RPPA necessitates a complicated experimental procedure that includes critical steps such as array printing, numerous phases of immunostaining and signal amplification, high-resolution data outputs, and custom data compilation and analysis [249]. Metabolomics is regarded as a potent high-throughput tool for detecting low molecular weight compounds in biological samples such as blood, urine, bile, ascites and tissue. So far, it has contributed to the clarification of biochemical processes involved in numerous human malignancies, while also providing a unique opportunity to identify novel biomarkers and carcinogenesis drivers in this field [250]. Cancer metabolomics, for example, has shown an upregulation in glycolysis, glutaminolysis, lipid metabolism, mitochondrial biogenesis and the pentose phosphate pathway, among other biosynthetic and bioenergetic pathways [251].The most forefront example of metabolomics supporting precision medicine is the use of a metabolomic method to categorize malignancies in order to later build personalized medicines [252]. Another main application area for metabolomics is the development of cancer medicines. Cancer immunotherapy, for example, has lately altered the paradigm in a number of solid and hematologic cancers. However, in a considerable proportion of instances the responses are limited, with cancers acquiring inherent or acquired resistance to checkpoint inhibition [253]. Certain immune-sensitive cancers develop immunity, resulting in tumor growth and disease progression. The tumor microenvironment is the most important contributor to immune resistance [254]. By modifying immune metabolism and reprogramming immune cells, nutrient shortage, hypoxia, acidity and the release of numerous inflammatory markers all contribute to pro- or anti-inflammatory phenotypes [255]. A wide range of matrices can be investigated from all available tissues and body fluids, such as plasma, serum, cerebrospinal fluid (CSF), saliva, feces, pus, cervicovaginal secretions and urine. Because of the chemical complexity of the metabolome, the dynamic range of metabolites, fluctuating quantities and the hard simultaneous quantification within complex mixtures, identifying a metabolome as a lengthy metabolite list by accurate spectrometry-quantification is complex [256]. However, the Metabolomics Society has set reporting criteria for biospecimen source, collection and processing details [257]. Nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS) or other combinations of these analytical methods can all be used. NMR can be used to identify metabolic signatures or biomarkers associated with homeostasis disorders. In cancer research, mass spectrometry imaging in combination with the rest of the methods can contribute to three possible applications: (i) establishing a chemical and morphological mapping of regions of interest to identify next-generation prognostic and therapeutic biomarkers, (ii) evaluating the molecular efficacy of chemotherapeutic agents, and (iii) classifying tissue types based on molecular patterns to recognize their pathways and therapeutic prognoses [258]. Liquid biopsy is a very informative and noninvasive method for the care of cancer patients because it offers information on the molecular properties of the tumor in real time, recapitulating the entire tumor complexity (Figure 1). Because blood collection may be done frequently, liquid biopsy is also highly essential in helping us understand how the tumor changes as it advances. This method becomes more useful when traditional biopsies are not possible. In spite of the therapeutic importance of liquid biopsy, which has been demonstrated in several trials in various forms of cancer, its clinical value is just now beginning to penetrate the clinic. Based on ctDNA analysis, the FDA has authorized liquid biopsy NGS companion diagnostic assays for numerous malignancies and biomarkers (Table 2). It is worth mentioning at this time that ctDNA analysis is progressing faster than CTC analysis and has already achieved significant clinical use in standard practice [259]. It is critical for the success of downstream applications to ensure that ctDNA samples are of appropriate number and quality. Contamination of samples with genomic DNA must be avoided for this purpose, for example, by employing white blood cell stabilizers. It is also preferential to isolate ctDNA from plasma samples rather than serum, as this avoids the release of cellular DNA from lysing cells during the clotting process. Furthermore, due to the low concentrations, extraction procedures must provide high ctDNA yields [260]. Because of the tiny amount and proportion of ctDNA in circulation, extremely sensitive detection methods such as droplet digital Polymerase Chain Reaction (ddPCR), Next Generation Sequencing (NGS) or BEAMing (beads, emulsion, amplification and magnetics) must be used [261]. Nevertheless, because of temporal variability, targeted sequencing restricts treatment response monitoring and identification of resistance mutations, highlighting the necessity for larger panels to assess ctDNA during follow-up, which may impair detection sensitivity [262]. In clinical practice, ctDNA profiling is still complicated and costly [263]. Furthermore, the isolation of CTCs, which are relatively scarce in circulation, is challenging and expensive as well [264]. CTC molecular characterization, on the other hand, can give extra information (Figure 1). Single CTC genomic analysis can show intrapatient heterogeneity, which may explain therapy resistance. Additionally, transcriptional plasticity may be a significant driver of cancer therapeutic resistance, and CTCs may be probed at the RNA and protein levels. Transcriptional analysis of CTCs may be able to predict which organ site will be colonized in the future. More specifically, different organ microenvironments can collect different types of tumor cells and induce different transcriptional activities as a result of crosstalk between tumor cells and surrounding organ cells. Single-cell CTC analysis might reveal intrapatient heterogeneity. Finally, CTC-derived cell lines or xenografts can be employed as novel models for screening tests, opening up a new route for functional investigation [102]. Exosomal nucleic acid and protein have been implicated in carcinogenesis and tumor progression in a rising number of studies in recent years, indicating that they might be used as a diagnostic or prognostic biomarker. However, research on exosomal lipids and metabolites as diagnostic or prognostic indicators is inadequate [205]. Despite the various advantages, the use of exosomes as cancer biomarkers is fraught with difficulties. To begin, conventional methods for separating and enriching exosomes have limited throughput and purity. There are significant differences in the procedures used to isolate exosomes (Figure 1). Thus, the current aim in this research is to enhance the exosome isolation and enrichment procedure, create more efficient characterization approaches and finally establish a standard exosome-based method. Furthermore, it is questionable if the relatively low quantity of exosomes in biofluids is adequate to detect minute abnormalities which are frequently overlooked in clinical detection. Evaluation of global abnormalities such as chromosomal instability may help to solve this problem [265]. Furthermore, the demonstration of the superiority of exosomes as a liquid biopsy is based on limited patient cohorts and lacks a clear therapeutic advantage [266]. As a result, it is critical to develop accurate exosomal biomarkers in large-scale samples for early-stage cancer detection and prognosis prediction that can be modified for therapeutic use. The advancement of the technological aspect of proteomics is one potential direction. Improving detection resolution, standardizing procedures and increasing high-quality antibodies with high sample throughput can improve overall detection accuracy, particularly during the early stages of discovery. This is due to the fact that most organ-specific biomarkers in the secretome are present in extremely low quantities and have yet to be found. Given the absence of a so-called ideal technology, balancing the benefits and drawbacks of multiple technologies throughout the development phases is critical [267]. This may be shown in recent research that employed MS or aptamers in conjunction with PEA to uncover cancer biomarkers [268]. Finally, as with liquid biopsy, single-cell proteomics is spreading across all domains of cancer biomarker research. MS has already opened the path for single-cell proteomics using flow-cytometry cell sorters and high-resolution TIMS-TOF [269]. Surface protein phenotypes and single-cell secretomes are both hotspots for finding novel biomarkers in liquid biopsy, notably in cancer immunotherapy [270]. The study of tumor genetic changes from tissue samples is one of the current criteria for patient classification and therapy selection. Tissue biopsies, while undeniable in their importance, have significant limitations in that they are very invasive procedures that fail to capture tumor clonal heterogeneity. Liquid biopsies, which include the examination of circulating tumor-derived components such as CTCs, ctDNA or ctRNA, and EVCs, are gaining popularity as a promising treatment option. The tumor circulome contains many kinds of tumor-derived biological components. Novel methods are being developed to improve tumor circulome analysis, with the goal of fully investigating the intricacy of the information obtained from a single blood sample. The potential of liquid biopsies and the advent of new technology enables researchers to define each individual component of the tumor circulome with greater precision. Liquid biopsies are being hailed as a game-changing technique in customized cancer care. Because of advancements in both omics’ technologies and the associated artificial intelligence elaboration of the data, liquid biopsies can overcome many limitations of tissue biopsies and can capture tumor heterogeneity in general, but mostly they can capture tumor evolution without being invasive to the patients. This will soon be converted into a more precise prognosis evaluation and the optimum therapy option based on a particular patient’s condition as it progresses, ushering in a genuine precision medicine approach. Nevertheless, its clinical implementation has been slowed by a number of technological obstacles. As a result, various issues need to be overcome before liquid biopsies may be used in clinical settings. Despite the considerable work that must be done to fully define the future role of liquid biopsies in cancer diagnosis, monitoring and prognosis, the key outcomes published so far indicate the promise of this method in changing current cancer management approaches.

PMC10000674

Georgios-Ioannis Verras,Levan Tchabashvili,David-Dimitris Chlorogiannis,Francesk Mulita,Maria-Ioanna Argentou

Updated Clinical Evidence on the Role of Adipokines and Breast Cancer: A Review

03-03-2023

breast cancer,adipokines,adiponectine,leptin,resistin,visfatin

Simple Summary Breast cancer is currently one of the most common types of cancer and the number one cause of cancer-related deaths in women worldwide. Despite significant advances involving cancer research in cancer biology, targeted treatments, and novel surgical approaches, breast cancer poses a constant, prominent challenge. In order to combat this reality, novel biomarkers and treatment targets are constantly on the rise. One of the known risk factors and survival predictors of breast cancer is obesity and obesity-related hormonal changes. The main effectors of said hormonal changes are a group of fatty tissue-related molecules, adipokines. Adipokines have many known and intertwined mechanisms of actions, many of which are known to enable carcinogenesis within the breast tissue. This review aims to summarize all available evidence of relationships between adipokines and the development of breast cancer, in order to emphasize their potential roles as novel biomarkers, predictive indicators, and possible future therapeutic targets of breast cancer. Abstract With the recent leaps in medicine, the landscape of our knowledge regarding adipose tissue has changed dramatically: it is now widely regarded as a fully functional endocrine organ. In addition, evidence from observational studies has linked the pathogenesis of diseases like breast cancer with adipose tissue and mainly with the adipokines that are secreted in its microenvironment, with the catalog continuously expanding. Examples include leptin, visfatin, resistin, osteopontin, and more. This review aims to encapsulate the current clinical evidence concerning major adipokines and their link with breast cancer oncogenesis. Overall, there have been numerous meta-analyses that contribute to the current clinical evidence, however more targeted larger-scale clinical studies are still expected to solidify their clinical utility in BC prognosis and reliability as follow-up markers.

Updated Clinical Evidence on the Role of Adipokines and Breast Cancer: A Review Breast cancer is currently one of the most common types of cancer and the number one cause of cancer-related deaths in women worldwide. Despite significant advances involving cancer research in cancer biology, targeted treatments, and novel surgical approaches, breast cancer poses a constant, prominent challenge. In order to combat this reality, novel biomarkers and treatment targets are constantly on the rise. One of the known risk factors and survival predictors of breast cancer is obesity and obesity-related hormonal changes. The main effectors of said hormonal changes are a group of fatty tissue-related molecules, adipokines. Adipokines have many known and intertwined mechanisms of actions, many of which are known to enable carcinogenesis within the breast tissue. This review aims to summarize all available evidence of relationships between adipokines and the development of breast cancer, in order to emphasize their potential roles as novel biomarkers, predictive indicators, and possible future therapeutic targets of breast cancer. With the recent leaps in medicine, the landscape of our knowledge regarding adipose tissue has changed dramatically: it is now widely regarded as a fully functional endocrine organ. In addition, evidence from observational studies has linked the pathogenesis of diseases like breast cancer with adipose tissue and mainly with the adipokines that are secreted in its microenvironment, with the catalog continuously expanding. Examples include leptin, visfatin, resistin, osteopontin, and more. This review aims to encapsulate the current clinical evidence concerning major adipokines and their link with breast cancer oncogenesis. Overall, there have been numerous meta-analyses that contribute to the current clinical evidence, however more targeted larger-scale clinical studies are still expected to solidify their clinical utility in BC prognosis and reliability as follow-up markers. The days in which adipose tissue (AT) was considered a fat-storage and thermoregulatory organ are long over. The extra endocrine nature of adipose tissue was solidified with the discovery of a biological compound in the face of leptin and its function in 1994 [1]. Years of consequent hormone discoveries have shed light on its true endocrine nature and thus replaced this simplistic belief with an understanding of adipose tissue as a complex organ that is involved in many physiological pathways from inflammation to carcinogenesis and especially breast cancer. Breast cancer (BC) is an umbrella term encompassing tumors arising from each different cell type of the breast tissue, with most of them being adenocarcinomas. The current epidemiological data places BC as the second most common malignancy in women in the United States and as the second most lethal type of female cancer. It is hypothesized that one in eight women in the US will eventually develop breast cancer. Its incidence has been slowly increasing, which is mainly attributed to a drop in fertility rates and, most importantly, to the Western lifestyle of fatty, rich, and processed food consumption and lack of exercise, leading to obesity [2]. Obesity is a pandemic in itself. However it is also closely correlated with an increased risk for the development of breast cancer; it also leads to worse patient outcomes when present [3]. Obesity has been positively correlated with worse prognoses [4] and is considered an independent risk factor for disease progression in major diseases like dyslipidemia, hypertension, cardiovascular disease (CVD), diabetes mellitus (DM) type 2, stroke, chronic kidney disease, and carcinogenesis [5]. Furthermore, the International Agency for Research on Cancer (IARC) concluded that there is plenty of available data to support the direct link [6] between excessive fat disposition and tumor development for multiple types of cancer like gastric cardia, esophageal adenocarcinoma, liver, gallbladder, pancreas, kidney, colon, and rectum, while low BMI was found to be protective against thyroid, gastric cardia, pancreas, liver, gallbladder, ovary, multiple myeloma, and meningioma. In addition, an inverse correlation between adult-type post-menopausal breast cancer risk and waist circumference has been well established [7]. Multiple mechanisms have been proposed to explain this dose-dependent relationship of fat disposition and oncogenesis, for example, inflammatory and immunologic pathways or epigenetic changes that result in DNA damage, alteration, and eventual malignant transformation. Recent studies have focused on the role of adipokines, hormones that are secreted by the adipose tissue, on oncogenesis that in an obesity-state can act directly on metabolic pathways such as Janus kinase–signal transducer and activator of transcription (JAK-STAT) or phosphoinositide 3-kinase (PI3K) and pathways or via tumor microenvironment alteration [8]. Furthermore, over ten adipokines have been associated with breast cancer, with their number steadily increasing [9]. The increase of the majority of circulating adipokines like adiponectin, leptin, resistin, visfatin, osteopontin, apelin, and lipocalin has been linked with breast cancer, while on the contrary reduced circulating levels of the adipokines adiponectin and iridine (also known as adipo-mycin) has been shown to bear a protective role against it. Obesity is a well-known alternator of tissue microenvironments, It has long been identified as a central link in the chain of pathophysiological events leading to carcinogenesis. All the above-mentioned intracellular pathways are activated following extracellular changes in the microenvironment via crosstalk mechanisms that transfer extracellular stimuli via a molecular cascade. One of the most prominent elements of epigenetic modification in cancer is epigenetics involving mitochondrial DNA (mitoepigenetics) that are affected primarily by oxidative stress [10]. Mitochondria also appear to play a central role in the production of metabolites that cause epigenetic modification of nuclear DNA that at times causes oncogenic mutations [11]. One of the key processes of oncogenic effects is DNA hypermethylation, which results in the inactivation of tumor-suppressor genes within the cell [11]. Examples within breast cancer include DNMT-1 gene disinhibition and SIRT4 loss, mediated by hypermethylation, which promotes breast cancer self-renewal [11]. Oxidative stress is known to be mediated by hormonal changes in obesity patients, in particular by upregulation of pro-inflammatory cytokines caused by altered levels of adipokines that in turn cause the production of more adipokines, creating a molecular loop [12]. It has been demonstrated that mismanagement of reactive oxygen species by cancer cells can result in altered mitoepigenetics, which lead to defects in the mitochondrial genome of breast adipose cells that are vital to tumor progression and infiltration [10]. Microenvironment alteration in the form of increased free ROS is directly associated with increased triglyceride deposition within adipose cells, which induces an inflammatory cellular response and endoplasmic reticulum stress [13]. In addition to carcinogenesis, obesity and adipokine-mediated microenvironment alterations have been found to be correlated with worse survival outcomes, with a significant proportion of obese patients being diagnosed with triple-negative breast carcinoma, a subtype known for inferior survival and increased resistance to chemotherapy [13]. Overall, a deeper understanding of the heterogenous role of adipokines in the breast cancer tumor microenvironment will aid the evidence-based practices of breast cancer treatment by revealing new markers for disease progression and potential therapeutic targets. Herein, we will review the current clinical evidence of the role of certain adipokines in cancer pathogenesis with an emphasis on meta-analyses and synopsize the underlying mechanisms of certain adipokines that contribute to the neoplastic development of breast cancer. A visual summary of current insights on the role of adipokines in breast cancer can be seen in Figure 1. The role of adipose tissue in breast cancer development was based on two observations. Firstly, from the plethora of data that have established the relationship of obesity and post-menopausal breast cancer development [14] and progression [15,16]. Secondly, adipose tissue represents the largest part of the microenvironment that nurtures breast cancer cells, irrespective of the patient’s BMI [17]. In obese states, the adipose tissue is in a continuous low-inflammatory state in which the fat cells secrete hormones that can directly alter metabolic pathways and promote mutations [18] or indirectly make changes in the microenvironment by recruiting inflammatory cells. The recruited immune cells in the tumor microenvironment not only fail to exhibit cancer suppression activities, they also paradoxically promote “immune-evasion” phenomena by promoting different receptor expression in tumor cells that ultimately make the immune cells unable to identify and destroy them [19]. The peritumoral adipose tissue, though interactions with cancer cells, dedifferentiate into pre-adipocytes or differentiate into cancer associated adipocytes (CAA) and continue to secrete adipokines that promote an infiltrating phenotype [20]. Moreover, it is theorized that CAAs, in order to support breast cancer cells, undergo reprogramming of their intracellular metabolic pathways through their direct interaction. Through the dynamic exchange of metabolites like fatty acids, cancer cells act like parasites and take advantage of this energy source through β-oxidation to meet their increased metabolic demands. Indeed, chemokines like chemokine (C–C motif) ligand 5 (CCL5), chemokine (C–C motif) ligand 2 (CCL2), interleukin-1β (IL-1β), tumor necrosis factor-alpha (TNF-α), and vascular endothelial growth factor (VEGF), secreted by CAAs, orchestrate tumor invasion of the basement membrane and distant metastasis through the circulatory system. Although changes in tumor microenvironments are often thought of as a local process, adiposity-induced changes can also stem from distant adipose mass. Systemic low-grade inflammation is a cornerstone in the relationship between obesity and carcinogenesis, a relationship best described by the actions of released adipokines (studied here), interleukin 6 (IL-6) and Tumor Necrosis Factor α (TNF-α) [21]. Adipose-tissue-derived factors engaged in the regulation of inflammatory response and the management of oxidative stress are also produced in more than just breast adipose cells, and can be found in visceral and subcutaneous adipose tissue [15]. Current evidence also suggest a degree of crosstalk between different adipose tissue subtypes, mediated by exosomes that can promote intracellular communication via the transportation of molecules that are pro-inflammatory and pro-fibrotic in nature [15]. Extracellular vehicles containing thrombospondin-5 (TSP-5) that promotes epithelial-to-mesenchymal transition of breast adipocytes have been identified as related to progression and infiltration of breast carcinoma [22]. Obesity, as a systemic condition, exerts oncogenic effects on breast tissue; cancer-associated fibroblasts that are stimulated from adipokines provide an altered extracellular matrix that favors the development of malignant adipocytes and has been shown to activate when co-cultured with adipose cells taken from subcutaneous tissue [16,21]. Clinical studies have also confirmed that visceral adiposity is closely related to breast cancer outcomes, with a 2021 study clearly indicating a correlation between the metabolically hyperactive visceral adipose tissue commonly found in obese patients and worse recurrence-free survival rates [23]. Adipokines (also known as adipocytokines) is a vast group of heterogenous soluble factors produced by adipose tissue that function in different pathways involving metabolism, inflammation, and vascular homeostasis (Table 1). The catalogue includes over 600 identified proteins [24], with the best known being adiponectin, leptin, resistin, visfatin, osteopontin, IL-6, NF-κB, etc. Adipokines interact with and activate different pathways that contribute to the hallmarks of breast cancer since they express respective receptors for the interaction. Adiponectin is the protein hormone of the C1q/TNF family. It is comprised of 244 amino acids and encoded by the ADIPOQ gene located in chromosome 3q27. It is mainly secreted by white adipose tissue and primarily has energy homeostasis and anti-inflammatory effects. It also promotes insulin sensitivity and cell proliferation. It exerts its function by binding to the AdipoR1 (mainly expressed in skeletal tissue and endothelial cells) and AdipoR2 receptors (mainly expressed in the liver) [25]. Recent studies have shown that CAA is a known effector of decreased adiponectin secretion in humans. Adiponectin has also been shown to act as a protective factor against tumor progression through its most prominent receptors: AdipoR1 and AdipoR2. Their interaction inhibits the growth and invasion of cancerous cells, including those of breast cancer. It is also known to induce cell apoptosis by enabling Adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) signaling and inhibiting PI3K/AKT signaling [9,25,26]. Adiponectin also seems to act in a contradictory manner to leptin. The adiponectin/leptin ratio is often used in literature to describe their interaction. Obesity development is characterized by the mis-differentiation of adipocytes. A result of this mis-differentiation is the induction of hypoxia-induced factor 1 (HIF-1) that in turn stimulates the expression of leptin and downregulates the expression of adiponectin [9]. In other words, the adiponectin/leptin ratio is decreased in the adipose tissue of obese people. The same receptor also promotes the activation and translocation of LKB1/STE20- related adaptor proteins and sc and scaffolding mouse 25 protein (MO25) from the nucleus all the way to the cytoplasm, which causes the subsequent activation of AMPK and at the same time the inhibition of MAPK, WNT-beta-cantenin, NFkB, and more [27]. Recent epidemiological data have shown adiponectin to have an inverse relationship to obesity-related cancers [28] (Table 2). Low serum adiponectin levels have been correlated with a high risk of breast cancer [29], while high serum levels may act protectively against it. This finding, however, has been subject to controversy since several individual studies did not find an increased risk. Ever since, adiponectin became a hot topic of extensive research and recent meta-analyses have tried to clarify the true association while also taking into account the ethnicity and dietary heterogeneity of different ethnic populations. As such Gu et al. [30] and Yu et al. [31] pooled the results of 31 and 27 studies, respectively, in order to obtain a reliable sample size. The results of these studies confirmed the inverse relationship of serum levels of adiponectin and breast cancer in pre-menopausal and post-menopausal women, with the included studies exhibiting a high heterogeneity. Subsequent subgroup analysis stratified by ethnic groups revealed that the association was stronger in women of Asian heritage than the Caucasian group. A different meta-analysis by Yoon et al. [32] studied the circulating levels of different adipokines in different cancer types and reported that the cancer type with the stronger connection with low serum adiponectin levels was breast cancer. Among the other reported actions of adiponectin, some of the most prominent are its protective function against obesity-related diseases such as metabolic syndrome, cardiovascular disease, type II diabetes, and malignancies. Hypoadiponectinemia has been correlated with insulin resistance, diabetes, and cardiovascular risk, in addition to malignancy development. A better understanding of the link between adiponectin levels and cancer cell proliferation and metastatic potential is needed, and it could provide insights to potential therapeutic targets. Adiponectin has been studied in vitro where it was found to suppress cell proliferation, invasion, and migration in estrogen-deficient breast cancer cell lines [33]. The literature suggests a somewhat inconsistent relationship between adiponectin and the development of breast cancer. A meta-analysis by Liu et al. found that adiponectin levels were inversely associated with breast cancer with an OR of 0.838 [34]. It must be noted, however, that these results were highly influenced by certain individual studies that caused a high degree of heterogeneity, and it was only after their removal from the analysis that a marginally significant result was obtained. Likewise, regarding the menopausal status of patients, the relationship seemed to be reversed. Postmenopausal women were more likely to develop breast cancer alongside high levels of adiponectin. Additionally, the study was not able to identify any significant associations between adiponectin and breast cancer in all stratified sub-analyses [35]. Leptin is a 146-amino acid type I cytokine and a member of the family of long-chain helical cytokines. It is encoded by the Ob gene located in chromosome 7q31.3. Leptin is predominantly produced in the adipose tissue. It serves as an indirect modulator of fat tissue mass by producing the signal of satiety in the hypothalamus and its production is proportionate to the total adipose tissue mass. Apart from its endocrine activities, leptin also exhibits various pre-oncogenic mitogenic actions through the LEPR receptor, which is widely expressed in breast cancer cells. Leptin activates PI3K/AKT and JAK/STAT pathways and induces their proliferation [36]. It also inhibits cancer cell apoptosis by inducing the expression of anti-apoptotic genes like bak, bax, and angiogenesis though VEFG production. Even though these proposed mechanisms have been widely accepted, there have been contradicting results regarding leptin’s role in the risk of breast cancer development. A meta-analysis conducted by Niu et al. was one of the first to establish this positive correlation [37]. Moreover, a different team [38] that examined the association of multiple adipokine levels in different cancer types underscored that ER+ cancer patients had significantly higher leptin levels than ER- cases (Table 2). The association between leptin levels and estrogen status in breast cancer has been previously studied as well. Raut et al. [39] proved that the suppression of estrogen receptor signaling downregulates leptin-induced cellular cycle progression and the induction of leptin-controlled autophagy process. Leptin is also known to activate estrogen receptors via transactivation stimulated by leptin through MAP-kinase signaling [40]. In addition, leptin levels were also significantly higher in lymph node metastasis (LNM) positive cases than in LNM negative cases, and considerably higher between post-menopausal and pre-menopausal women. Contrary to the effects of adiponectin, leptin seems to promote the proliferation and development of breast cancer cells. Production of leptin from the adipose tissue is directly proportional to the total adipose tissue mass of an adult. Moreover, studies have shown an additional leptin production site to be present in the form of fibroblasts associated with cancer [15]. Another interesting mechanism of action that promotes oncogenesis through leptin is its crosstalk with produced estrogens. Estrogen receptors and leptin receptors ObR were found to be coexpressed in malignant breast tissue, as well as cell lines from breast cancer specimens [41]. Other studies have proven that there is an association between leptin levels, estrogen, and progesterone receptor expression in breast cancer patients. Previous works have also proven a physiological mechanism through which estradiol levels upregulate leptin m-RNA expression in adipose tissue and increase leptin and ObR expression in breast cancer cell lines [33,40,41]. Leptin activates ER-alpha through the MAP-Kinase pathway in breast cancer cells, and in the process, reproduces the features of ER-alpha transactivation. One of the most popular concepts in breast cancer carcinogenesis is the epithelial-to-mesenchymal transition (EMT), which is otherwise a normal physiological process found mainly in wound healing and embryogenesis. EMT is a critical step in carcinogenesis. It represents the turning point of an epithelial cell towards a depolarized, mesenchymal-type cell [27]. As a consequence of the loss of cell polarity, these cells gain cancerous characteristics—survival, loss of adhesion, and potential for invasion—thus adding to the tumor’s overall aggressiveness. A recent study showed that ObR RNA expression and concomitant leptin secretion is found in cancer-associated fibroblasts (CAFs) and proposed that leptin is integral in mediating the crosstalk between potentially cancerous breast cells and CAFs, causing tumor growth and invasive characteristics [41]. The authors describe that, when breast cancer cells were treated with leptin, morphological phenotypical changes occurred that highly resembled fibroblast cells, with increased pseudopodia formation, actin reorganization, and stress fiber formation. The exposure of cells to leptin also altered E-cadherin expression by downregulation and caused the upregulation of mesenchymal markers. Laboratory studies, however, seem to differentiate from epidemiological studies as far as the role of leptin in breast cancer goes. Subsequent observational studies were not able to verify the risk for breast cancer development in post-menopausal women [42] or presented weak associations [43] A research team from Iran [44] also found no association with cancer in leptin’s gene and leptin’s gene receptor, polymorphism LEP rs7799039 and LEPR rs1137101, respectively. The previously established results were however recently overturned, largely due to a meta-analysis from Pan et al. [44] that presented solid evidence from multiple sources regarding the positive correlation of elevated leptin levels and incidence rates of breast carcinoma, especially in obese post-menopausal women. A subgroup analysis of the same study also identified a stronger association in patients of Asian descent, more specifically from China. Resistin, originally discovered in 2001, is a small 108-amino acid cystine-rich adipokine encoded by the RETN gene in chromosome 19 (19p13.2). Resistin is involved not only in glucose and insulin metabolic pathways (its name stems from resistance to insulin), but also in pro-inflammatory pathways (NF-κB, PI3K) through the activation of its receptor TLR4 (a member of the Toll-Like Receptor family) [45,46]. Thus, it has been found that resistin is secreted not only by adipocytes but also by inflammatory cells. An insight into the pathophysiology of resistin in the proliferation and invasion of breast cancer cells can be found in the work of Lee et al. [47]. Resistin was found to mediate breast cancer cell invasion through the c-Src pathway that resulted in an intracellular calcium influx, after which cancer cells exhibited invasive properties. Resistin was also found to induce PKC alpha phosphorylization, a pathway known for its ability to induce the invasive properties of cancer cells. One of the most prominent sites of action of the PKC alpha pathway is the increase of vimentin filaments, a process best known for its contribution to the epithelial to mesenchymal transition in carcinogenesis [27,41]. Clinical implications of hyperestistinemia include contribution to diabetes type 2 through increased insulin resistance, obesity, and, from the activation of the pro-inflammatory pathways in obesity, malignancies like colorectal and breast cancer [44,48]. However, in recent years there have been conflicting results regarding the relationship between RETN polymorphisms as a prognostic factor for breast cancer. A meta-analysis that pooled the data from nine individual studies showed an increased risk of breast and colon cancer in patients with the RETN rs1862513 variant, especially Caucasians. In addition, there was no data to support the association of the RETN rs3745367 variant with malignancy. Moreover, its role as a prognostic marker for breast cancer has also been studied with conflicting results. Yoon et al. reported that high levels of serum resistin did not bear statistical significance, with the studies included bearing high heterogeneity [32] (Table 2). Of note is that during sensitivity analysis, the exclusion of one study resulted in a positive association with breast cancer. On the contrary, the results from the study of Gui et al. underlined an increase in the mean concentrations of resistin in the breast cancer group [37]. However, this association was observed in the Asian group, with concomitant no significant difference between the non-Asian and control group. Visfatin, also known as nicotinamide phosphoribosyl-transferase (NAMPT) or pre-B-cell colony-enhancing factor 1 (PBEF1), originally discovered in 2005, is a large 52kD protein with enzymatic activity. It is encoded in the NAMPT gene located in chromosome 7 (7q22.2). Two forms have been identified, an intracellular form (iNampt) that is involved in NAD salvage pathways, and an extracellular form (eNampt) that is found in many tissues ranging from adipose to heart tissue. It is involved in beta oxidation, inflammatory pathways, and angiogenetic pathways [49]. Visfatin has attracted the attention of the scientific community due to its relationship with obesity-related cancers and, most specifically, post-menopausal breast cancer. Moreover, its hypersecretion is related with worse prognoses. It has been hypothesized that visfatin promotes breast cancer survival through the ABL proto-oncogene 1 (c-Abl) activator of the transcription 3 (STAT3) pathway and via upregulation of the mRNA levels of cyclin D1 and cyclin-dependent kinase 2 (CDK2). Many studies have examined the relation of serum visfatin levels with breast cancer. Of note are the results of a large meta-analysis, which included 27 studies, that indicated that malignant individuals have significantly different elevated serum visfatin levels compared to controlled cohorts [50] (Table 2). Consistent with these results was the recently reported outcome of a study by Hori Ghaneialvar et at [51] in which it was found that the levels of visfatin are different between breast cancer patients and healthy individuals. These results highlight visfatin’s potential future application as a screening and/or follow-up tool. Lastly, a study by Gui et al. found no strong difference in the serum levels of visfatin between subjects with BMI < 25 kg/m2 and those with BMI > 25 kg/m2. Lipocalin-2, also known as neutrophil gelatinase-associated lipocalin (NGAL), is a 198 amino acid adipocytokine, member of the lipocalin family. It is encoded by the LCN2 gene located in chromosome 9 (9q34.11). Lipocalin-2, as an acute phase reactant protein, is mainly expressed by neutrophils, with its main role being iron-sequestration and thusthe limiting of bacterial growth, while also elevated in the early stages of acute kidney injury. However, recent studies have reported that it is also secreted by adipose tissue and may contribute to breast cancer progression [52] by inducing EMT [53]. Its role as a biomarker was proposed by the results of a meta-analysis by Wang et al. in which NGAL levels were correlated with breast cancer diagnosis [54,55]. Subsequent studies have strengthened the association between elevated NGAL levels and breast cancer [55,56] and, more specifically, the upregulation of the LCN2 [56,57] with consequent activation of the e-cadherin pathway, starting from the activation of the transmembrane glycoprotein cadherin that is known to stimulate cellular invasion and metastasis properties via tumor-stroma interactions, a process that apparently leads to significantly poor disease outcomes [57] (Table 2). Lastly, silencing of the LCN2 gene was also proposed as a novel therapeutic target via small interference RNA molecules (siRNA). Ginette et al. assessed the biological efficacy of in vitro silencing molecules for inflammatory breast cancer, which resulted in decreased cell proliferation and invasiveness [58]. Chemerin, also known as retinoid acid receptor responder protein 2 (RARRES2), is a small 16-kDa protein. It is encoded by the RARRES2 gene in chromosome 7 (7q36.1). Chemerin has been shown to act as a chemoattractant by acting as a ligand to Chemerin/chemokine-like receptor (CMKLR-1) expressed in immune cells [59], while also exhibiting angiogenetic and proliferative inducing properties. There is a paucity of data with conflicting results concerning its value as a biomarker in cancer research. Some studies have shown that RARRES2 downregulation is associated with poor prognoses in certain cancer types, like hepatocellular carcinoma, mainly due to diminished leukocyte recruitment [60], while also acting as a favorable prognostic marker in lung cancer [61]. Within contrast to breast cancer, a study revealed higher expression of chemerin in malignant tissue in comparison with adjacent normal breast tissue and was associated with poor prognosis [62]. In addition, a study by Song et al. confirmed that elevated serum levels of chemerin (in combination with CA15-3) achieve better diagnostic performances in breast cancer and correlate to aggressive phenotypes [63] (Table 2). Lastly, a team led by Pachynski have proposed novel therapeutic strategies by studying the in vivo properties of chemerin and showing that it suppressed its growth by NK and T cell recruitment within the breast cancer’s tumor microenvironment [64]. Chemerin is most prominently known for its receptor CMKLR1, expressed in dendritic cells and other components of the immune system. Its role in chemotaxis and attraction of immune cells towards the inflammatory response site is well known and hypothesized to contribute to carcinogenesis indirectly through the induction of inflammatory responses that turn the tumor microenvironment into a favorable state for carcinogenesis [64]. Osteopontin, also known as bone/sialoprotein I (BSP-1), is a large adipokine derived from the bone as it was firstly found as part of the normal bone tissue extracellular matrix, but is also expressed in a variety of other tissues from adipocytes to the placenta [65]. It is encoded by the SPP1 gene located in chromosome 4 (4q1322.1). Its role through the activation of integrins is biomineralization, chemotaxis, inflammation, and cell activation. Osteopontin merits scientific research due to recent observations of its relationship to many diseases, from obesity and diabetes to a variety of cancer types [66] like breast cancer and hepatocellular carcinoma. Indeed, abundant expression of osteopontin has been associated with poor prognosis and low survival. In a meta-analysis by Hao et al. [67] (Table 2), the prognostic value of osteopontin was studied in breast cancer. The final analysis included 1567 breast cancer patients and the results underlined the strong correlation of high osteopontin levels and worse overall mortality. In addition, its splice variant-c expression appeared to be even more significantly associated with worse prognosis, making osteopontin and osteopontin-c candidates for future breast cancer prognostic markers. With obesity steadily overthrowing cigarettes as the leading preventable risk factor for cancer, it is of utmost importance that we decipher the significant connection of its consequences, like elevated circulating adipokines in a chronic inflammatory state, with breast cancer. While many recent meta-analyses revealed a strong association of high levels of certain adipokines (adiponectin, leptin) with breast cancer, inter-population variability may limit its generalizability as a biomarker for disease prognosis and progression. Further clinical studies are needed to produce more robust results and assess their prognostic reliability and novelty as therapeutic targets.

PMC10000676

Vladimir N. Binhi

Statistical Amplification of the Effects of Weak Magnetic Fields in Cellular Translation

24-02-2023

biological effect of magnetic field,ribosome,protein translation,incorporation error,the RPM,geomagnetic field

We assume that the enzymatic processes of recognition of amino acids and their addition to the synthesized molecule in cellular translation include the formation of intermediate pairs of radicals with spin-correlated electrons. The mathematical model presented describes the changes in the probability of incorrectly synthesized molecules in response to a change in the external weak magnetic field. A relatively high chance of errors has been shown to arise from the statistical enhancement of the low probability of local incorporation errors. This statistical mechanism does not require a long thermal relaxation time of electron spins of about 1 s—a conjecture often used to match theoretical models of magnetoreception with experiments. The statistical mechanism allows for experimental verification by testing the usual Radical Pair Mechanism properties. In addition, this mechanism localizes the site where magnetic effects originate, the ribosome, which makes it possible to verify it by biochemical methods. This mechanism predicts a random nature of the nonspecific effects caused by weak and hypomagnetic fields and agrees with the diversity of biological responses to a weak magnetic field.

Statistical Amplification of the Effects of Weak Magnetic Fields in Cellular Translation We assume that the enzymatic processes of recognition of amino acids and their addition to the synthesized molecule in cellular translation include the formation of intermediate pairs of radicals with spin-correlated electrons. The mathematical model presented describes the changes in the probability of incorrectly synthesized molecules in response to a change in the external weak magnetic field. A relatively high chance of errors has been shown to arise from the statistical enhancement of the low probability of local incorporation errors. This statistical mechanism does not require a long thermal relaxation time of electron spins of about 1 s—a conjecture often used to match theoretical models of magnetoreception with experiments. The statistical mechanism allows for experimental verification by testing the usual Radical Pair Mechanism properties. In addition, this mechanism localizes the site where magnetic effects originate, the ribosome, which makes it possible to verify it by biochemical methods. This mechanism predicts a random nature of the nonspecific effects caused by weak and hypomagnetic fields and agrees with the diversity of biological responses to a weak magnetic field. An extensive literature [1,2,3,4] is devoted to the biological effects of weak magnetic fields (MFs). As is believed, MF can cause various, including toxic, effects in organisms [5]. The difficulty lies in the fact that there is no convincing physical mechanism that would provide a discernible shift in the probability of an individual act of a chemical reaction in the MF on the order of the geomagnetic field, about T [6]. One of the mechanisms relates the biological effect of MFs to the presence of magnetic nanoparticles in organisms [7,8]. However, some cell cultures and plants that respond to MFs do not contain magnetic nanoparticles. Therefore, search for a general molecular mechanism of the biological response to MFs continues. The characteristic of the current state of the problem is that even the most plausible mechanism of magnetic biological effects, the Radical Pair Mechanism (RPM), provides only an insignificant, at best on the order of 0.01–0.1%, response to a change in the MF of the geomagnetic field level [9,10]. Several orders of magnitude are missing to explain the facts of animal magnetic navigation [6]. The difficulty is that the coherence of spin quantum states, which provides the magnetic effect at physiological temperatures, takes place over a short time interval. In weak MFs, however, a noticeable effect occurs only at a sufficiently long coherence relaxation time. These conflicting trends prevent the occurrence of a magnetic RPM effect sufficient to explain the observations. Noticeable magnetic effects in magnetochemistry usually arise in MFs exceeding 5 mT [11]. Accordingly, as is shown below, the thermal relaxation time of electron spins in biological media at physiological temperature should be about 1 ns in order of magnitude. At the same time, to explain the biological effects of MFs of the geomagnetic field level of 0.05 mT, the thermal relaxation time of electron spins must exceed a few hundred ns, i.e., be comparable with the Larmor precession period of about 700 ns. It is unknown where and whether such spin states could appear in biological tissue. Apparently, the small primary changes that occur as a result of the action of MF must somehow be amplified to cause noticeable changes in concentrations of biochemical agents. This idea is not new. The enzymatic reaction, highly responsive to the enzyme concentration, was proposed in [12]. A chemical reaction in a mode close to bifurcation instability, when the reaction path can change dramatically with a slight variation in the reactants content, was proposed in [13,14,15]. However, these approaches have not been developed—probably due to the impossibility of their experimental verification. The idea was put forward of a statistical amplification of parallel negligible magnetic signals from millions of photoreceptors by the brain. However, the gain appeared to be insufficient to explain the magnetic navigation of animals based on the RPM [16]. Moreover, this mechanism does not apply to nonspecific effects—in the absence of evolutionarily developed magnetoreceptors. In this paper, we pay attention to the fact that small initial signals can be statistically amplified—in the course of their accumulation in a long series of repeated events. It is known, for example, that a small physical effect of the effort of thought can become noticeable after millions of elementary acts of scattering in the quincunx or acts of generation of binary random events. The integrated deviation served as a tool for detecting the effect—as a tool that allowed one to accumulate minor regular deviations against the background of significant random variations, see, e.g., [17] p. 319. In the same way, one would expect that magnetic effects, if they are due to primary minor signals under the action of an MF, could be reliably detected where they accumulate in a long sequence of elementary events. The natural carrier of such an integrator in the cell is the process of gene expression. The essence of these processes is the multiple and almost error-free repetition of homogeneous acts of biochemical reactions involving biopolymers. Such cyclic processes are ideal integrators; they accumulate the probabilities of errors occurring in each step. Sooner or later, the accumulation of error probabilities leads to disruption of the functions of the synthesized and folding proteins, which one can record in the experiment. Therefore, a relatively stable magnetic effect is mainly expected where there are intensive processes of replication, transcription, translation, and folding—in cells under radiation or chemical stress. There are almost no data on the sensitivity of pre-biological systems to a weak MF in vitro. Additionally, if there are [18], then their relationship with the effects in vivo remains questionable [19]. If we do not consider the evolutionarily fixed magnetic sensitivity in seasonally migratory species, then marked and reproducible MF effects in biology occur in systems with intense gene expression. Such experimental observations include morphological changes during embryogenesis [20], neurite outgrowth [21], restoration of a severed head in planarians [22], response to heat shock [23], some phases of cell growth and gene expression in plants [24,25,26], effects of ionizing radiation [27], a direct controle of the DNA synthesis with magnetic ions [28], etc. It follows gene expression is almost mandatory for the observation of magnetic responses. Numerous data on the strong dependence of magnetic effects in cells on their genetic modifications also make it evident. All those works indicate that gene expression, which includes various processes of biopolymer synthesis, may be a prerequisite for the MF nonspecific effects. It is essential that cyclic processes of biopolymer synthesis are catalytic. They occur due to enzymes that directly produce elongation of the biopolymer chain. On the one hand, in such cyclic processes, the low probability of an error in one cycle accumulates and leads to a significant chance of functionality violations of the synthesized biopolymer. Other enzymes that do not elongate biopolymers do not have this feature—the low probability of error in a single act means an equally small fraction of erroneous product molecules. On the other hand, intermediate states of paired radicals with spin-correlated electrons can arise in the active sites of enzymes, e.g., [29,30]. Such quantum states are magnetically sensitive. The effect of MFs on enzymatic activity has been discussed for a long time. For example, in [31], 2-T MF caused a change in carboxydismutase activity from approximately 9 to 30%. The change occurred when the enzyme-substrate mixture was exposed to MF from 1 to 192 h. In [32], MF of 5 T did not cause statistically significant changes in DNA hydrolysis by ribonuclease and reduction in cytochrome-c by succinate-cytochrome-c reductase. No noticeable changes in the activity of ribonuclease, polyphenol oxidase, horseradish peroxidase, and aldolase were observed in [33] upon exposure of the enzyme-substrate mixture for up to 20 min to an MF of up to 17 T. It was shown in [34] that a 30 min exposure to an MF of 1.1 T caused a 12% decrease in the activity of ascorbic acid oxidase. In [35], MF affected the synthesis of adenosine triphosphate by creatine kinase with Mg ions in the catalytic centers. The rate of synthesis increased by 70% in the field of 80 mT. Replicative experiments are also known, which failed to confirm the effect of MF on the activity of creatine kinase [36]. More recent works have not made effects of MFs on enzyme activity clearer. In [37], an MF with a value slightly less than 1 mT could enhance or suppress the activities of superoxide dismutase and catalase in radish seedlings, depending on the lighting conditions. The effect of an MF of 10–160 mT for up to 20 s on several redox flavoprotein enzymes was studied in [38]; no changes were found. An MF of up to 500 mT and a duration of up to 3 h changed lipase activity for up to 50% in [39]; there was an increase or a decrease depending on the MF and other conditions. In [40], a 10% activity growth of fungal laccase was observed in a variable magnetic field of about 17 mT in the frequency range of 10–50 Hz. The effect of a static MF of up to 220 mT on the enzymatic DNA synthesis in the presence of magnesium ions was demonstrated in [41]; the activity of DNA polymerase in this study decreased by 2–4 times. The review [26] cites many other observations of MF-modulated enzyme activity. In general, these studies do not show any common pattern that controls the occurrence of the magnetic effect or links its magnitude with the parameters of magnetic exposure, especially in in vivo studies. The MF effect on enzymatic activity appears to be largely random. Apparently, the changes that occur at the molecular level—when they occur—depend significantly and ambiguously on many biochemical conditions and affect, also ambiguously, the measured characteristics [42]. Thus, on the one hand, the premises taken into account in this work are as follows. It is most likely that (i) the biological effects of a weak MF originate from the MF action on gene expression, and (ii) the primary mechanism for the weak MF effects in organisms is the RPM. On the other hand, the facts that one should interpret are (a) the observed nonspecific biological effects are orders of magnitude greater than the primary RPM signals that occur in response to a weak MF, and hence some amplification must exist of the primary signals; and (b) in the vast majority of cases, these observed effects are random. The purpose of this work was to build a statistical model of the accumulation of small primary signals in the probability of local translation errors under the action of a weak MF up to a level of more than a few percent. The process of local error amplification and the RPM are combined into a single model to evaluate a much greater possible magnetic effect. Such a mechanism would then explain the observed effects of weak MFs without contradictions. In the next section, we present the biochemical information that underpins the model presented in section Mathematical Model. In the Discussion, we analyze the properties of the model and discuss the consequences arising from it. We show that this statistical model has the properties necessary to explain the specific magnetoreception and nonspecific magnetic response. Section Appendix A describes the RPM in a simple form, which is embedded in the model. The processes of biopolymer synthesis are diverse. These are DNA replication, transcription—synthesis of complementary RNA, splicing, translation—protein synthesis from amino acids under the mRNA code, and post-translational folding of the protein chain into a globule and its maturation. At each stage, random errors can occur, but with significantly different probabilities. There are perfect biochemical mechanisms for correcting replication and transcription errors; the likelihood of these errors is therefore tiny, on the order of and , respectively, [43]. Translation errors occur much more frequently, with a probability of the order of – per added amino acid [44,45]. Then about –26% of synthesized molecules of 300 units contain at least one error. For such molecules, the chance of adopting a native conformation during folding significantly reduces. Often they are cytotoxic and cause harmful cellular effects [46]. Errors also occur at the folding stage—one of the causes is the intricate geometry and topological nodes of the folding trajectories [47]. However, misfolding due to translation errors, as is believed, is more likely than due to the actual folding. Therefore, translation errors mainly control the accuracy of gene expression. Consequently, the possible influence of MFs on the probability of translation errors is the process where magnetic effects could manifest themselves at the biological level. However, the possibility of MF influence on translation errors, as far as we know, was not previously considered in theoretical models. The translation is a complex multi-stage cyclic process that includes a variety of enzymatic reactions. The ribosome produces translation—it is a macromolecular machine assembling amino acids into proteins. Below is a statistical model of ribosomal translation in which there is a low probability of a local incorporation, or substitution, error—the appearance, in the synthesized chain, of a non-cognate amino acid residue that does not correspond to the mRNA blueprint. A simplification illustrating the occurrence of a noticeable translation error is as follows. Let a ribosome synthesize a protein chain of a large number n of links with an equal probability of incorporation error. A native functional protein globule implies the absence of local errors at all n links in the chain. Then is the probability of occurrence of error-free amino acid sequence, and is the probability of the appearance of a defective molecule, i.e., the probability of a translation error. The sensitivity of p to the local incorporation error probability is the derivative . Its magnitude is maximum under the condition and can reach large values of about at small . The probability of correct translation of the entire molecule does not exceed unity. In particular, for and we have . This fact means that the result of changing the error probability when varies is not that the probability (1) changes much, but that a change in the translation error p by a few tenths occurs when varying very small . The number of different proteins in the human body exceeds two million [48]. Most of them have a length of 100 to 500 amino acids. The probability of a local failure is unlikely to reach because almost all proteins would fold incorrectly otherwise. Therefore, the range is interesting. Since small probabilities of substitution errors lead to significant variations in the translation error, it makes sense to assume that MF can change those probabilities of local errors. Then, a magnetic effect, even being minor initially, could manifest itself in significant changes in the concentration of nonfunctional proteins. These, in turn, would lead to an additional load on biochemical adaptation mechanisms and the appearance of noticeable biological effects. How can MF affect the probability of local incorporation errors ? Cyclic processes of biopolymer synthesis are those produced by enzymes that elongate the biopolymer chain. In translation, the ribosome cyclically reads information from mRNA and attaches a suitable amino acid to the synthesized protein chain. We suggest that this enzymatic recognition/attachment in the ribosomal active site includes the electron transfer and formation of the intermediate radical pair in the singlet spin state. If a necessary amino acid enters the active site, then the process ends with chain elongation without error. If a non-cognate amino acid gets into the active site, this amino acid is not accepted. The radical pair decomposes into the initial state of the enzyme and amino acid, and the latter goes out into the cytoplasm. The picture in more detail is as follows. Protein biosynthesis is known to consist of two steps, e.g., [49]. First is the extraribosomal stage. In it, the enzymatic selection of amino acids occurs by attaching them to the corresponding specific transfer RNA—aminoacylation of tRNA by the enzyme aminoacyl-tRNA synthetase. Then the tRNA with a cognate amino acid enters the ribosome, where a comparison occurs of the specific tRNA with the instruction of the messenger mRNA. If there is a match, the enzyme detaches amino acid from the tRNA and attaches it to the end of the synthesized chain. If not, the tRNA-amino acid complex disintegrates and escapes into the cytoplasm. All these processes, in turn, can go in several stages. It is not yet clear at what stage the emergence of an intermediate magnetosensitive radical pair is more likely. Therefore, we consider the following general model regardless of where the radicals appear. The proposed kinetic scheme, Appendix A.1, combines the processes of different stages and distinguishes among them one that hypothetically occurs with the formation of an intermediate state—a temporary pair of radicals—and can end with an error. When developing the RPM-based kinetics, the MF is usually assumed to affect the radical pairs that arise in a “correct” biochemical reaction that leads to the formation of valid product molecules. Indeed, in the case of reagents that are not biopolymers, the likelihood of a product that is chemically different from the expected one is negligible, if at all possible. Another situation occurs in cyclic reactions involving biopolymers. Here, the occurrence of errors is the norm [50] and even necessary to ensure biological evolution [46]. In this case, it is reasonable to assume that the MF can influence such a process through the RPM. As mentioned above, the RPM is now considered the most likely molecular basis for magnetic effects in biology [3,51]. The fact that radical pairs often arise in enzymatic reactions supports this view. According to the RPM, an external MF causes a singlet-triplet (S-T) conversion in the spin state of the initial singlet radical pair and, thereby, reduces the probability of its decomposition. Then, there occurs a possibility of including an incorrect amino acid group in the protein chain. A local incorporation error appears. Thus, MF increases the likelihood of the appearance of defective protein chains that cannot further acquire the correct conformation or become functional globules. In a realistic scenario, independent random variables , on each link represent the failure probabilities. Let all of them have the same distribution with expectation and variance . In this case, the translation error becomes a random variable with mean and variance , respectively Expanding (2) into a series in , one can see that in the region of large n and small the relations and take place. The average incorporation error increases by a factor of n, and the fractional value of variations decreases as . Since the length of most proteins is about a hundred and more, the main amplification effects arise from the change in the average value of the errors and not from their variations. Next, we estimate and its dependence on the MF. To analyze the dependence of average probability P of a translation error on MF H, it is necessary to sequentially relate MF H to the S-T conversion rate , then relate rate to the incorporation error rate , further relate rate u to the probability of an incorporation error, and finally relate probability to the translation error probability . In what follows, it will be convenient to use dimensionless quantity , i.e., MF H in units of the geomagnetic field Oe that corresponds to the magnetic flux density of 45 T. All necessary relations look like . The regularities of S-T conversion in the spin state of a pair of radical electrons give the first dependence . The equation—in a simplified form—relating the rate of the S-T conversion with an external MF, is where a is a constant part, is a model parameter, G s is the gyromagnetic ratio of the electron, and is the time of its spin decoherence, , Appendix A.2. The rate of S-T conversion is a quadratic function of h, which means that the magnetic effect does not change at the MF reversal. The spin decoherence time is yet unknown at physiological temperature and a zero-field condition. This condition is relevant because the MFs in this study are less than the hyperfine MFs by orders of magnitude. For this reason, we consider a model parameter that can vary in a wide range, from usual spin-chemical values of about 1 ns to hypothetical values of about 1 s. The second dependence is derived from the equations of chemical kinetics, and has the form of Equation (A3), see Appendix A.1. With regard to the third dependence, we note that probability of an incorporation error upon elongation by one peptide bond is determined by the kinetic rate of occurrence of errors u. Since elongation by one peptide bond occurs in the time , then . Finally, Equation (2) determines dependence , whence, substituting the above relations for u, , and and taking into account that , where – is the experimental probability of incorporation error per peptide bond, we write In view of Equation (3), it is convenient to use dimensionless parameters and . With these parameters, the translation error probability has the form independent of the spin relaxation time. Figure 1 shows dependences of the average error probability in different forms on MF in the H range from 450 nT to 4.5 mT. The features of the dependences are clearly visible on the logarithmic scale of the MF. It can be seen from Figure 1a that the change in the error probability with increasing MF reaches significant values, far exceeding what is usually observed in magnetochemistry in such weak MFs. The magnitude of deviations increases with the length n of a synthesized chain. The position of the inflection region depends little on n, shifting towards smaller fields with increasing n. Figure 1b shows the shift of the inflection region in P for absolute values of parameters a and j as changes. The inflection region shifts approximately from 100 h to 1 h towards lower MFs with an increase in by three orders. It shifts even more at a lesser j. In other words, there is a wide range of values, in which the inflection region shifts directly with . Therefore, one could determine an unknown value of from dependence obtained experimentally. In the experiment, one usually works with normalized relative effects. The magnitude of a measured value in the geomagnetic field, i.e., at , often serves as “control”. Since the level of error in the geomagnetic field also depends on the parameters, the dependence of normalized effects on the MF may differ from . It is natural to define normalized magnetic effect as the fractional difference between the error probabilities in the MF h and in , i.e., , Figure 1c. It can be seen that the relative effect is larger, depends little on n, and has opposite signs, ±, in fields greater than and less than the geomagnetic one. The magnetic effect saturates both with increasing and decreasing MF. In the region of large MF values, the relative effect is weaker for longer chains, which might seem counterintuitive. This is because both and depend on n. To explain the decrease in W is easier by considering the relative effect in terms of expansion shown above. A more exact expansion of P in (2) reads . As n grows, the negative quadratic term in P starts to play a role, and this term is larger, as seen, for larger . Since monotonously increases with h, the relative effect W weakens with n. For small n, for any h reaches a limit that has no practical significance, since the absolute magnetic effect P, Figure 1b, tends to a value of the order of q, which means that there is no amplification. As follows from (3), the translation error probability depends on the ratio of the rate parameters a and , or . To represent this dependence, the span of the magnetic effect, defined as , is used. Figure 2 demonstrates that this dependence is significant. The magnitude of the magnetic effect decreases with an increase in the relative value A of the constant component in the S-T conversion rate. However, when the model parameters and , a 10% magnetic effect appears even at a relatively large value of . First, we emphasize that formula (4) describes the accumulation of probabilities, not physical changes. Physical changes happen suddenly at a random moment in translation. The transformation of small probabilities into a significant one does not occur in time as the chain elongates. It refers to the result of the synthesis—an entire molecule. Figure 2 demonstrates that the constant a, or A in dimensionless form, is crucial for the observability of magnetic effects. For , there is a convincing dependence of the translation error probability on the MF, while for , there is practically no such dependence. Equation (A5), , from which the S-T conversion rate (3) follows, establishes an observability criterion for magnetic effects in order of magnitude in the region . If , the relative magnetic effect in the MFs of the geomagnetic field level will be much less than unity. Additionally, this means the practical impossibility of observing the magnetic effect in biochemical or biological quantities due to the usually high random fluctuations in these quantities. The amplifying mechanism only partially removes this limitation since, while strengthening the absolute values of the probabilities, it can only weaken the relative values when absolute ones approach unity. For example, in the RPM, the probability of a triplet product in small MFs, much smaller than the HFI field, has, according to [52], the form of Equation (A4). For small values of the master parameter , the probability of a triplet product is proportional to , i.e., . Value , in this case, can be very large, depending on the relaxation time . There are only two ways to overcome the strict constraint imposed by the observability criterion (6). One should assume that either the spin coherence time is large or the constant a is sufficiently small. The RPM models of animal magnetic navigation use the first assumption as a rule. Even in this case, a reliable explanation fails since, in the case of magnetic navigation, the characteristic value of the MF is not the geomagnetic field but a thousand times lower geomagnetic variations of the order of tens of nT. It is these variations that some animals detect and use to survive in seasonal migrations. This being so, , where . Even for s, the increment remains small compared to the constant component, which is now . The ratio of these two is about for . That is, the requirement of the smallness of the constant component in w is far from being fulfilled. It is yet unclear whether small values of a satisfying (6) are possible. Small a would mean that some microscopic physical quantity in a zero external MF has only a fluctuation component. S-T transitions in the RPM do not satisfy this condition since they also happen in the absence of external MF—under the influence of sufficiently strong fields of the nearest magnetic nuclei. However, the idea of a play of quantum levels looks attractive. When the levels mix in a zero MF—when quantum selection rules lose their significance—some physical quantities could become very small. Interestingly, in addition to the RPM, there is another mechanism of the primary magnetic response—the so-called level mixing mechanism which utilizes a dynamics of a single magnetic moment [53,54], rather than that of a pair of moments as in the RMP. This mechanism is very abstract, and it is not yet clear whether it could operate within the enzymatic machine of cellular translation. The probability of an abstract chemical reaction in this mechanism is given by where is the average rate of the biophysical events initiated by the precessing magnetic moment. Can condition (6) be satisfied here? To answer, we need to verify the validity of inequality that follows from . One can show that this ratio does not fall below 0.8 for any parameter values, i.e., condition (6) is not satisfied. This mechanism has the same difficulty as in the RPM—the reaction takes place in zero MF, and changing the MF around the geomagnetic field can alter its rate insignificantly. Until now, to explain biological magnetic effects on the basis of the RPM, one had to assume a long thermal relaxation time of electron spins in biological tissues, on the order of 100 ns and more [55]. The presented model of a statistical amplification of weak primary magnetic signals is free from this presumption. To calculate the MF dependences Figure 1b, demonstrating an effect sized enough to be observed, thermal relaxation time of electron spins was set equal to 1–10 ns also. The statistical mechanism of increasing the probability of incorporation errors largely compensates for the lack of the effect size in the RPM. Combining these two mechanisms into a single one makes it possible to explain the relatively large observed magnetic effects at short spin relaxation times, even if the constant part in the S-T conversion rate is relatively high, as seen in Figure 2. As is known, strong MFs, of the order of 1 T and more, used in magnetic tomography, are safe at limited times of MF action of the order of 20–30 min [56]. This no harmful effects, of course, does not mean the absence of any biological effects of such an MF in general. The above-presented regularities explain why strong MFs exceeding the geomagnetic field by four or more orders of magnitude do not lead to likewise strong magnetic effects in comparison with those in the MF of the geomagnetic level. Due to the kinetic limitations of the RPM, the magnetic effect is saturated already at the molecular level. In the idealized model, saturation occurs in fields approximately an order of magnitude different from the geomagnetic field, reaching about two-fold effects in . In reality, the values of magnetic alterations measured in biological variables are unlikely to be very large due to various adaptive feedbacks. Due to the presence of systems in cells that destroy misfolded proteins, the final observed effects are unlikely to be as great as the model predicts. An idealized model can explain qualitative rather than quantitative patterns. In general, it is difficult to predict how the deviation in the level of translation errors from its natural level at will affect the observed biological characteristics. It is unclear what is the subsequent transduction pathway of the mistranslation signal to a measured reaction. Even the sign (±) of the body’s response to MF is not clear. For example, an increase in the concentration of reactive oxygen species, in response to an MF change, as a secondary biochemical effect could occur with both an increase and a decrease in MF relative to the natural level to which the body is adapted. Therefore, when discussing the connection between the aberrant translation and the values measured in the experiment, it would be reasonable not to pay attention to the sign of effects but to interpret only the qualitative features of the MF response. The list of qualitative features includes (a) independence of the direction of the external MF, (b) significant absolute and relative magnitudes of translation errors depending on the weak MF, (c) saturation of the relative magnetic effect both with increasing and decreasing MF from the geomagnetic level, (d) unpredictable sign of the effect observed by secondary changes in the body, i.e., the random nature of the measured effect. (e) Under in vitro conditions, the concentration of the defective product does not depend on the MF frequency in the low-frequency range. In particular, the responses to a constant MF H and to a variable one with an amplitude should be the same. Model validation would be possible not only in vivo, but also in vitro—in laboratory biochemical translation systems. They are being actively developed [57]. The dependence of the reaction yield on the MF proves the existence of an intermediate S-T state of a pair of electrons in magnetochemistry. Similarly, observation of the MF-dependent concentration of incorrectly synthesized molecules in vitro would be a direct evidence of the existence of an intermediate S-T state of electron pairs during translation. In calculations [55], the relative RPM magnetic effect in a simple configuration “two electrons, one proton” was about 10% when the MF changed by the value of the geomagnetic field. At first glance, this is enough to explain the nonspecific effects of the geomagnetic field. The problem, however, is that a long thermal phase relaxation time of electron spins, 1 s, was used in the calculations. The authors of [58,59] attempted to substantiate such a great value theoretically. However, there are no experiments so far that would confirm the existence of this long relaxation time, with the exception of exotic systems, such as fullerenes, which have nothing to do with biology. Reliable evidence of the spin relaxation time in radical pairs could be given by measurements of the EPR linewidth in the geomagnetic field or by measurements of the spin magnetic effects in vitro and their comparison with calculations. To the best of our knowledge, EPR signals in the geomagnetic field from the electron spins of transient pairs of radicals have not been observed in biochemical reactions. At the same time, the experience of spin chemistry, which agrees with the theory, indicates a faster spin relaxation at the conditions under discussion, about 1 ns. Perhaps, at the most, 10 ns. The magnetic effects fall in the same way, by a thousand or a hundred times, since they are roughly proportional to the spin relaxation time—unless the rate of chemical process is a “bottleneck” suppressing magnetic effects to an even greater extent. The correct value of the RPM effect is thus only 0.01–0.1% per 50 T—a figure that follows from the fundamental relation [10] for ordinary values of MF 1–5 mT, at which noticeable magnetic effects occur [11]. Many experiments support this conclusion; e.g., it is in perfect agreement with a recent study [41]. Even if the spin relaxation time in radicals was about 1 s, and even more so if noticeably less, the RPM in its usual form, as shown above, could not explain the specific sensitivity of some seasonally migratory species to geomagnetic variations in the MF at the level of tens of nT. There is also no explanation for often observed nonspecific effects of the geomagnetic storms on the state of organisms, e.g., [60]. This is a disappointing situation—we cannot explain these magnetic biological effects without reference to some obscure tricks of natural biological evolution. What new findings can the above-described statistical mechanism bring to this state of affairs? First, the statistical amplification of initially weak magnetic RPM signals in the process of cellular translation makes it possible to raise the effect to the level of at least a few percent. These magnitudes are observable and verifiable. The results in Figure 1 indicate such effect sizes. The statistical mechanism indicates a definite region in the biochemical machinery where the primary magnetic biological effects occur. This region is the active site of the tRNA–ribosome complex, where enzymatic processes of the recognition of cognate amino acids and their attachment to the growing protein chain take place. Thus, the statistical mechanism can also be validated by biochemical methods—in addition to testing the qualitative features that follow from the mathematical relation (4). Finally, we note that the magnetic influence on such a general molecular machine as the ribosome, which synthesizes many different proteins, is consistent with the experimentally observed fact that non-specific magnetic biological effects are mostly random effects [42], not allowing simple averaging. In general, the amplification of the probability of local errors is valid for any process, the result of which would be error-free only if there were no errors at each step in a long series. In addition to translation, these could be replication and transcription. As is known, cells have developed surprisingly perfect mechanisms for repairing replication and transcription errors [43]. However, errors still occur at individual steps. Their low probability can accumulate and lead to cellular stress. Although relatively strong MFs of the order of several mT or more change the activity of, e.g., DNA polymerase [41], it is not yet known whether an MF of the geomagnetic level can influence the probability of errors in that enzyme. The synthesis of protein molecules from a hundred or more units has been shown to occur with the accumulation of the probability of an error due to the stepwise cyclic process of cellular translation. This statistical amplification of local incorporation errors enhances the chance of translation failure and leads to a significant fraction of non-functional protein molecules. The amount of amplification is approximately proportional to the length of the synthesized biopolymer. The statistical accumulation of the probability of local errors in cellular translation is an autonomous phenomenon that helps to explain the biological effects of weak MFs. The enzymatic process of recognition of amino acids and their addition to the synthesized molecule in cellular translation has been proposed to include the formation of intermediate pair of radicals with spin-correlated electrons. This makes sense because many enzymatic reactions that involve an electron trasfer are magnetically sensitive. We present a mathematical model that combines the known mechanism of magnetic sensitivity of the intermediate pair of radicals, the RPM, and the statistical amplification mechanism described above. The model describes the changes in the probability of incorrectly synthesized molecules in response to a change in the weak MF of the geomagnetic field level. The model explains the biological effects of weak MFs without assuming an unlikely long spin relaxation time of 100 ns or more, often set by default to match the RPM models with the experiment. The combined mechanism allows for experimental verification by testing qualitative features that match those of the RPM. The combined mechanism predicts the active center of a ribosome as the primary site, where magnetic effects occur, creating the possibility of verification by biochemical methods; predicts a random nature of the observed response to a weak MF; holds promise for explaining biological response to a hypomagnetic field; and agrees with the diversity of cell responses to a weak MF.

PMC10000678

Konstantinos Arvanitakis,Ioannis Mitroulis,Antonios Chatzigeorgiou,Ioannis Elefsiniotis,Georgios Germanidis

The Liver Cancer Immune Microenvironment: Emerging Concepts for Myeloid Cell Profiling with Diagnostic and Therapeutic Implications

28-02-2023

The Liver Cancer Immune Microenvironment: Emerging Concepts for Myeloid Cell Profiling with Diagnostic and Therapeutic Implications Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. To date, systemic treatment for patients with unresectable or advanced disease was composed of sorafenib and other multikinase inhibitors, with limited efficacy and high toxicity. Nevertheless, immune checkpoint inhibitors (ICIs) have greatly broadened the treatment landscape of unresectable HCC [1]. Specifically, the IMbrave150 trial demonstrated that in patients with unresectable HCC, combined treatment with the programmed-death ligand-1 (PD-L1) inhibitor, atezolizumab, alongside the vascular endothelial growth factor (VEGF) inhibitor, bevacizumab, prolonged the median overall survival (OS) to 19.2 months as compared to sorafenib treatment alone [2]. However, around 20–25% of patients exhibit complete primary resistance to atezolizumab, plus bevacizumab, indicating that the identification of patients who might gain the most from this therapy is crucial. To date, there are no definite biomarkers in HCC that can accurately predict response or resistance to ICIs, while as the HCC treatment regimens have shifted towards immunotherapy, the identification of potent predictive and prognostic biomarkers has attracted attention. Although HCC formation has been attributed to certain viral or non-viral causes, nonalcoholic steatohepatitis (NASH), is a major driver of HCC as well [3]. Recent data suggest that NASH-related HCC might have decreased sensitivity to immunotherapy, due to the presence of CD8+ T cells and, especially, of the hepatic steatosis-induced CXCR6+ subset that was correlated with hepatocyte injury and potentiated the pathogenesis of NASH-related HCC via the secretion of pro-inflammatory cytokines and direct hepatocyte killing, mediated by the tumor necrosis factor (TNF) [4]. Moreover, local and systemic inflammation are considered hallmarks of cancer, and they have a pivotal role in HCC pathogenesis and progression [5]. An increased peripheral blood absolute neutrophil count and an elevated neutrophil to lymphocyte ratio (NLR ≥ 5) are considered markers of advanced disease, poor prognosis, and poor response to treatment with hepatic resection, transplantation, locoregional therapy, and tyrosine kinase inhibitors in patients with HCC. Indeed, systemic inflammation measured by NLR is independently a negative prognostic factor for patients with HCC under ICI therapy [6]. The measurement of the NLR across various time points could provide insight into how different values of this inflammatory marker could accurately predict patient response to systemic therapy, patient outcomes, or the development of adverse events (AEs). The tumor immune microenvironment (TIME), being heterogeneous and comprised of a multitude of immune and stromal cells, is an essential factor that leads to tumor metastasis and relapse, as well as resistance to therapy, whereas the way in which different TIME cell subtypes are connected with the clinical relevance in liver cancer remains unclear [7]. Indeed, cells of innate and adaptive immunity coexist and interact within the liver microenvironment during HCC, especially in the case of NASH, during which chronic hepatic inflammation preexists the emergence of HCC [8]. As far as the innate immune cells are concerned, in a mouse model of NASH-related HCC, neutrophils were shown to predominantly increase in the course of the disease, in comparison to other immune cells [9]. Indeed, in humans, high numbers of tumor-associated neutrophils (TANs) are a biomarker of poor prognosis in HCC and various other cancers [10]. Nevertheless, whether TANs, either within the HCC TIME or in a peritumoral hepatic location, account for this association remains unclear. However, these results should be carefully assessed, since they derive from HCC patients undergoing liver resection or liver transplantation, and it is typical for such patients to present with early, localized disease with preserved performance status and liver function. In countries with a low prevalence of viral hepatitis and a high prevalence of nonalcoholic fatty liver disease (NAFLD), approximately 15% of patients with HCC present with early disease and are considered candidates for curative resection. Consequently, the results of the aforementioned studies regarding TANs, might not be similar and comparable for individuals with more advanced HCC that are usually candidates for systemic treatment and account for the majority of patients. Neutrophils have considerable phenotypic plasticity and can exist in both tumor-promoting (TAN2) and tumor-suppressing (TAN1) states. Neutrophils may also have the ability to influence ICIs therapy. Recent data report that CXCR2+ neutrophils were found in human NASH and within the tumor of both human and mouse models of NASH-related HCC. The resistance of NASH-related HCC to anti-PD1 therapy is being overcome by co-treatment with a CXCR2 small molecule inhibitor, with evidence of reduced tumor burden and extended survival [11]. Anti-PD1 and CXCR2 inhibitors combine to selectively reprogram TANs from a protumor to an antitumor phenotype, which unlocks their potential for cancer therapy. The ability of CXCR2 antagonism to combine with ICI therapy in order to lead to enhanced therapeutic benefit in NASH-related HCC (and potentially in HCC related to other aetiologies) warrants further clinical investigation. Along the same line, a recent animal study added that ferroptosis, caused by a tumor-suppressive immune response, is characterised by a CXCL10-dependent infiltration of cytotoxic CD8+ T cells, which at the same time was counterbalanced by a PD-L1 upregulation on tumor cells, as well as by a marked myeloid-derived suppressor cell (MDSC) infiltration. A triple combination of a ferroptosis-inducing agent, a CXCR2 inhibitor, and an anti-PD1, greatly improved the survival of wild-type mice with liver tumors [12]. Furthermore, another important study [13], by integrated analyses on molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced patient samples of HCC collected within the phase Ib GO30140 and the phase III IMbrave150 trials, identified key molecular correlates of the combination therapy and highlighted that anti-VEGF might synergize with anti-PD-L1 by targeting angiogenesis, regulatory T-cells (Tregs) proliferation and myeloid cell inflammation. The presence of preexisting T-cell immunity is a key phenotypic characteristic that correlates with the response to atezolizumab plus bevacizumab. TIME potentiating the enrichment of the effector T-cell response over immunosuppressive Tregs identified patients that achieved significantly improved overall survival from the aforementioned combination. These findings were further validated by analyses of paired pre- and post-treatment biopsies, in situ analyses, and in vivo mouse models. Recent studies have also revealed the critical role of antigen non-specific auto-aggressive CD8+ T cells in instigating liver damage and promoting liver cancer in human NASH [14]. In addition, hepatic CD8+ PD1+ CXCR6+ T cells of humans with NASH, as well as neutrophil extracellular traps (NETs), contributed to the development of NASH-related HCC by promoting Treg differentiation, thus suppressing HCC immune surveillance [9,15] (Figure 1). Through in vitro induction of TANs and ex vivo analyses of human TANs, a recent study also showed that CCL4+ TANs can recruit macrophages and that PD-L1 + TANs can suppress T cell cytotoxicity [16]. Monocytes are recruited into the tumor site by the release of tumoral and stromal chemokines, such as CCL2 and CCL15. Monocytes can be polarized into different subtypes such as CD14+, CCR1+, and CD14+ [7], while macrophages are in the epicentre of the molecular pathways regulating NASH-related HCC pathogenesis [17]. All of these subtypes promote a strong immunosuppressive environment with the expression of ICIs (PD-L1/2, B7-H3, and TIM3) and cytokines (IL-10, CXCL2, and CXCL8), inhibiting natural killer (NK) cytotoxicity, inducing Tregs. They also interact with neutrophils to promote tumor invasiveness through the oncostatin M pathway. A way to control tumorigenesis through monocytes would be through the prevention of their recruitment to the tumor site by inhibiting the CCL15 pathway, via blockade of their polarization by the inhibition of the p38 pathway, or via repressing the IL-6 pathway in order to prevent the formation of Tregs. The CD68 marker is commonly used for liver tumor-associated macrophage (TAM) localization and distribution, while the expression levels of CD86 (M1), CD163 (M2), and CD206 (M2) are used to distinguish between M1-like (inflammatory) and M2-like (anti-inflammatory) macrophages in vitro [18]. Collectively, these data show that non-viral HCC, and particularly NASH-related HCC, might be less responsive to immunotherapy, at least partially due to the presence of TANs and TAMs in the TIME of HCC. However, an important unmet clinical need is demonstrated by the lack of accurate biomarkers that can influence therapeutic choices. This unfulfilled need is being met by very few novel studies that incorporate multiregional single cell-dissection landscape of tumor and immune cells in HCC with the sole purpose of shedding light on the biological tumor characteristics and identifying potential tumor and blood biomarkers, in order to categorize specific groups of TIME and identify patients who might have a benefit from a specific treatment option. In brief, the combination of two single-cell RNA sequencing technologies [19], produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites, and demonstrated an aggregate of LAMP3+ dendritic cells (DCs) that could modulate different subtypes of lymphocytes. Moreover, TAMs were correlated with poor prognosis, and the authors provided evidence of the inflammatory role of SLC40A1 and GPNMB in those cells. Additionally, Sun et al. [20], by performing single-cell profiling in relapsed HCC, remarkably found that CD8+ T cells in recurrent tumors overexpressed KLRB1 (CD161) and displayed an innate-like low cytotoxic state, with low clonal expansion, unlike the classical exhausted state observed in primary HCC. The enrichment of these cells was associated with a worse prognosis. In addition, by performing multiregional single-cell RNA sequencing (scRNA-seq) analysis, Ma et al. [21], identified and further validated the cellular dynamics of malignant cells and their communication networks with tumor-associated immune cells in terms of ligand-receptor interaction pairs associated with unique transcriptome. These molecular networks of malignant ecosystems, may open a path for therapeutic exploration. Very recently, too, the first proteogenomic characterization of hepatitis B virus (HBV)-related HCC using paired tumor and adjacent liver tissues from 159 patients was performed by Gao Q et al. [22], and two prognostic biomarkers, PYCR2 and ADH1A, which were related to proteomic subgrouping and were involved in HCC metabolic reprogramming, were identified. CTNNB1 and TP53 mutation-associated signaling and metabolic profiles were revealed, among which, mutated CTNNB1-associated ALDOA phosphorylation was demonstrated to promote glycolysis and cell proliferation. In a molecular study of HCC in patients with NASH, NASH-related HCCs were characterized by bile and fatty acid signaling, oxidative stress, and inflammation, and demonstrated an increased fraction of Wnt/TGF-β subclass of tumors and a decreased fraction of the CTNNB1 subclass. In comparison to other etiologies, NASH-related HCC had a considerably higher prevalence of an immunosuppressive cancer field [23]. Moreover, it was also demonstrated that the prognostic liver signature (PLS)-NAFLD predicted incident HCC over up to 15 years of longitudinal observation, while high-risk PLS-NAFLD was associated with IDO1+ dendritic cells and dysfunctional CD8+ T cells in fibrotic portal tracts, with impaired metabolic regulators. PLS-NAFLD was affected by bariatric surgery, lipophilic statins, and the use of IDO1 inhibitors, implicating that it could be utilized in pharmacotherapy and HCC chemoprevention [24]. Interestingly, treatment modalities aiming towards specific genomic alterations form the basis of personalized medicine and constitute the epitome of systemic treatment for many malignancies, but are still not available in HCC. Tools such as liquid biopsy and circulating tumor DNA (ctDNA), even though most studies have not analyzed HCC tissue concomitantly, may be of aid in identifying biomarkers of early diagnosis, response, or resistance to treatment, and their role in HCC represents an ongoing research field [25]. In addition, extracellular vesicles (EVs) or exosomes provide a critical mechanistic way of bidirectional intercellular communication in the TIME of various cancers and it would be very interesting to characterize tumor-derived versus immune-cell-TIME-derived EVs for HCC, in terms of their functionally important genomic, lipidomic, and proteomic cargo [26]. Finally, an integrative analysis of RNA and whole exome sequencing, T-cell receptor (TCR) sequencing, multiplex immunofluorescence, and immunohistochemistry was performed in a cohort of 240 patients with HCC and was validated in other cohorts of 660 patients in total [27]. A 20-gene signature, characterized by high interferon signalling and type I antigen-presenting genes, defined the inflamed class of HCC and was able to capture ~90% of these tumors and was associated with response to immunotherapy. Proteins identified in liquid biopsies recapitulated the inflamed class with an area under the ROC curve (AUC) of 0.91. Taking into account the aforementioned complex molecular omics alongside the heterogeneous cellular landscape of the TIME of HCC, future studies are expected to highlight in a simple way that is practical for clinical use, the hepatic and peripheral blood inflammatory and immunosuppressive tumorigenic function and composition of TANs and TAMs in NASH-related HCC in comparison with other aetiologies and, more importantly, to shed further light on potential prognostic or predictive molecular markers for future immunotherapies targeting TANs and TAMs, acting in synergy with ICIs, in order to overcome resistance and eventually improve the percentage of patients with HCC that respond to treatment. In addition, we underline the significance of predictive biomarkers of response to ICIs in order to (i) enhance the overall survival of patients that are likely to respond to therapy, (ii) reduce the risk of treatment-related adverse effects conveyed through the combination of drugs such as bevacizumab, (iii) maximize efficacious application, and therefore the cost-effectiveness of different treatment modalities, and (iv) characterize the molecular landscape of patients with advanced HCC responding to anti-PD1 therapy and define a novel tool for patient selection in future clinical trials. All of these data render HCC an oncological diagnosis in which spontaneous immunogenicity is critical for the efficacy of immunotherapy. Although the predictive value of histopathologic assessment is unparalleled, TIME immunogenicity is influenced by density, functional polarization, and distribution of the infiltrate. The diversity of the HCC TIME and the demand for readily applicable biomarkers rather than complex transcriptomics is still challenging, while the ultimate goal of expanding the reach of effective cancer immunotherapy to a wider proportion of patients via clinical stratification of trial participants or targeted testing of novel combinations prognostically modulating adverse traits, such as TANs and TAMs infiltration, is of the utmost importance.

PMC10000680

Jihui Lee,Hara Kang

Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under Hypoxia by Modulating miRNA Expression

06-03-2023

pulmonary artery smooth muscle cells,nucleolin,hypoxia,microRNA

Hypoxia induces the abnormal proliferation of vascular smooth muscle cells (VSMCs), resulting in the pathogenesis of various vascular diseases. RNA-binding proteins (RBPs) are involved in a wide range of biological processes, including cell proliferation and responses to hypoxia. In this study, we observed that the RBP nucleolin (NCL) was downregulated by histone deacetylation in response to hypoxia. We evaluated its regulatory effects on miRNA expression under hypoxic conditions in pulmonary artery smooth muscle cells (PASMCs). miRNAs associated with NCL were assessed using RNA immunoprecipitation in PASMCs and small RNA sequencing. The expression of a set of miRNAs was increased by NCL but reduced by hypoxia-induced downregulation of NCL. The downregulation of miR-24-3p and miR-409-3p promoted PASMC proliferation under hypoxic conditions. These results clearly demonstrate the significance of NCL–miRNA interactions in the regulation of hypoxia-induced PASMC proliferation and provide insight into the therapeutic value of RBPs for vascular diseases.

Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under Hypoxia by Modulating miRNA Expression Hypoxia induces the abnormal proliferation of vascular smooth muscle cells (VSMCs), resulting in the pathogenesis of various vascular diseases. RNA-binding proteins (RBPs) are involved in a wide range of biological processes, including cell proliferation and responses to hypoxia. In this study, we observed that the RBP nucleolin (NCL) was downregulated by histone deacetylation in response to hypoxia. We evaluated its regulatory effects on miRNA expression under hypoxic conditions in pulmonary artery smooth muscle cells (PASMCs). miRNAs associated with NCL were assessed using RNA immunoprecipitation in PASMCs and small RNA sequencing. The expression of a set of miRNAs was increased by NCL but reduced by hypoxia-induced downregulation of NCL. The downregulation of miR-24-3p and miR-409-3p promoted PASMC proliferation under hypoxic conditions. These results clearly demonstrate the significance of NCL–miRNA interactions in the regulation of hypoxia-induced PASMC proliferation and provide insight into the therapeutic value of RBPs for vascular diseases. Hypoxia induces changes in gene expression which can trigger adaptive processes, such as cell proliferation or motility [1]. The alterations in numerous RNA-binding proteins (RBPs) and microRNAs (miRNAs) under hypoxic conditions have been investigated to better understand the mechanisms underlying adaptive cellular processes [2,3,4,5]. Hypoxia-regulated RBPs or miRNAs bind to specific target mRNAs for the selective regulation of expression in response to hypoxia [6,7]. Hypoxia induces structural changes in the medial compartment of the pulmonary arterial wall, including pulmonary artery smooth muscle cell (PASMC) proliferation, hypertrophy, matrix protein production, and recruitment of adventitial or circulating cells. These changes contribute to pulmonary vascular remodeling and hypertension [8,9]. Several miRNAs have been shown to modulate gene expression and PASMC function during the pathogenesis of vascular disorders under hypoxia [10,11]. However, the functions of RBPs in vascular cells under hypoxic conditions are not fully understood. Recent evidence suggests that RBPs play a role in vascular smooth muscle cells (VSMCs). For example, Hu Antigen R (HuR) contributes to the proliferation of human aortic smooth muscle cells in response to platelet-derived growth factor (PDGF) signaling [12]. In addition, the downregulation of heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1) protects against atherosclerosis by suppressing VSMC proliferation [13]. These findings suggest that RBPs contribute to the regulation of PASMC function under hypoxic conditions. Elucidating the molecular mechanism by which RBPs mediate PASMC functions under hypoxia is expected to provide a basis for the development of therapeutic strategies for pulmonary vascular diseases. Nucleolin (NCL) is an RBP implicated in the response to hypoxia. Under hypoxia, NCL regulates the expression of matrix-metalloproteinase-9 (MMP-9) and collagen prolyl 4-hydroxylase-alpha(I) (C-P4H-alpha(I)), which are involved in ECM remodeling in human fibrosarcoma cells [14,15]. In addition to the hypoxic response, NCL is involved in a variety of biological processes, such as DNA transcription, ribosomal biogenesis, and the regulation of RNA stability [16,17,18,19,20,21]. Moreover, NCL regulates the expression of several miRNAs, including miR-15a/16, miR-21, miR-221, and miR-103. The abrogation of NCL expression affects the biogenesis of specific miRNAs, including miR-21, miR-221, and miR-103; however, the specific underlying mechanisms are unknown [22]. NCL is involved in the primary miRNA processing of miR-15a/16 through direct interactions with the microprocessor complex, DGCR8, and Drosha [23]. Interestingly, functional interactions between RBPs and miRNAs have been reported in various cancer cells [24]. Some miRNAs regulate RBP expression, and, conversely, some RBPs can modulate miRNA expression in cancer [25,26]. However, little is known about the functional relationships between RBPs and miRNAs in vascular diseases. We have hypothesized that NCL regulates the expression of hypoxia-responsive miRNAs in PASMCs and is associated with hypoxic vascular disorders. In this study, we observed that NCL levels in PASMCs are altered in response to hypoxia. To investigate the role of NCL in PASMCs in response to hypoxia, we examined its interactions with miRNAs and the functional relevance of NCL–miRNA interactions in the responses of PASMCs to hypoxia, such as their proliferation. To identify RBPs involved in the regulation of the PASMC phenotypes under hypoxia, we searched for RBPs with mRNA expression changes in the PASMCs in response to hypoxia in the next-generation RNA sequencing results from our previous studies [27]. Among the thirteen RBPs with established roles in the hypoxia-induced responses of various cells (CIRBP, CPEB1, CPEB2, HNRNPA2B1, HNRNPL, IREB2, NCL, PTBP1, PTBP3, RBM3, TIA1, ZFP36, and ZFP36L1), the NCL mRNA levels showed the greatest difference between the hypoxia-exposed and control PASMCs (Figure 1A) [14,15,28,29,30,31,32,33,34,35,36,37,38,39,40]. These results were confirmed using qRT-PCR analysis of the transcript levels in hypoxia-exposed PASMCs after 24 h (Figure 1B). Hypoxia significantly reduced the NCL mRNA levels to 49% of those in the control, which is consistent with the RNA sequencing data. None of the other 12 genes investigated showed significant changes in PASMCs under hypoxic conditions. A reduction in NCL protein levels following hypoxia was validated by immunoblotting (Figure 1C). The level of hypoxia-inducible factor 1-alpha (HIF1α) protein was examined by immunoblotting to confirm the hypoxic conditions in the PASMCs (Figure 1D). As expected, significant induction of HIF1α upon hypoxia was observed. To investigate whether the hypoxia-induced decrease in NCL is specific to PASMCs, a variety of cells, including pulmonary arterial endothelial cells (PAEC), HEK293, and HeLa, were exposed to hypoxia for 24 h, and the NCL mRNA and protein levels were examined using qRT-PCR and immunoblotting. Neither the mRNA nor protein levels of the NCL were changed by hypoxia (Figure 1E,F). The induction of HIF1α with hypoxia exposure was confirmed in the PAEC, HEK293, and HeLa cells (Figure 1F). These results indicate that NCL expression is downregulated by hypoxia in the PASMCs specifically, suggesting that it plays an important role in the regulation of PASMC functions in response to hypoxia. The unique responsiveness of PASMCs to hypoxia has been reported [41]. Hypoxia increases the proliferation of PASMCs, whereas it inhibits proliferation in many other cells [41]. PASMC-specific reduction of NCL expression may contribute to inducing the unique responsiveness of PASMCs to hypoxia. Repression of NCL can be mediated by histone deacetylation via histone deacetylase 1 (HDAC1) and HDAC2 [42]. We examined whether histone deacetylation is involved in the hypoxia-induced repression of NCL in PASMCs. PASMCs were treated with a histone deacetylase (HDAC) inhibitor, sodium butyrate (NaBu), and exposure to hypoxia. As determined by qRT-PCR (Figure 1G), the downregulation of NCL by hypoxia was abolished upon treatment with NaBu, suggesting that histone deacetylation is responsible for the repression of NCL under hypoxic conditions. To examine the role of HDAC1 or HDAC2 in the repression of NCL under hypoxia, endogenous HDAC1 and HDAC2 were reduced in PASMCs using small interfering RNAs (si-HDAC1 and si-HDAC2). The repression of NCL in response to hypoxia exposure was prevented in the PASMCs transfected with si-HDAC1 or si-HDAC2, suggesting that HDAC1 and HDAC2 are involved in the repression of NCL (Figure 1H). The knockdown of HDAC1 and HDAC2 was confirmed by qRT-PCR and immunoblotting (Figure 1I,J). According to previous studies, the HDAC1 expression levels were elevated in the lungs of patients with idiopathic pulmonary arterial hypertension and rats exposed to hypoxia, and HDAC inhibitors prevented hypoxia-induced pulmonary hypertension [43,44,45]. Therefore, hypoxia is likely to downregulate NCL expression specifically in PASMCs by histone deacetylation. As hypoxia stimulates the proliferation of PASMCs, the role of NCL in this process was investigated. First, NCL expression in PASMCs was downregulated using siRNAs. PASMCs transfected with siRNA against NCL (si-NCL) for 24 h were stained with a Ki-67 antibody to quantify the proliferating cells (Figure 2A). Hoechst dye was then used for nuclear staining. The percentage of Ki-67-positive cells among the si-NCL-transfected cells was approximately 1.85-fold higher than that in negative control siRNA-transfected cells, suggesting that the downregulation of NCL is sufficient to promote the proliferation of PASMCs. We then overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector (Lonza) for 48 h and examined the changes in the number of Ki-67-positive proliferating cells by immunofluorescence staining (Figure 2B). The empty pEGFP-N1 vector (Addgene) was used as a control. The percentage of proliferating cells decreased significantly to 48% when exogenous NCL mRNAs were overexpressed, indicating that NCL inhibited the proliferation of PASMCs. These results suggest that NCL is involved in the regulation of PASMC proliferation. To investigate the significance of the hypoxia-induced downregulation of NCL on PASMC proliferation, we examined whether the hypoxia-induced increase in proliferation was affected by NCL overexpression (Figure 2C). PASMCs transfected with exogenous NCL mRNA for 48 h were exposed to hypoxia for 24 h and then stained with a Ki-67 antibody. Approximately 5.1% of the control cells were Ki-67-positive under normoxia. The percentage of proliferating cells increased to 12% under hypoxia, and this increase in cell proliferation was not detected in NCL-overexpressing cells. The results suggest that the hypoxia-induced downregulation of NCL is essential for the promotion of PASMC proliferation under hypoxic conditions. Cell proliferation was also examined by the cell counting assay. The number of viable cells increased 24 h after the transfection of PASMCs with si-NCL (Figure 2D), whereas the number of cells decreased 24 h after the transfection of PASMCs with an NCL-overexpressing vector (Figure 2E). The number of cells increased by hypoxia decreased when NCL was overexpressed (Figure 2F). These results corroborate the observation of Ki-67 immunofluorescence staining. The efficiency of the NCL knockdown or overexpression was confirmed by qRT-PCR and immunoblotting analyses (Figure 2G,H). In PASMCs, miRNAs play important roles in the cellular responses to hypoxia [10,11]. Interestingly, recent reports have suggested that NCL is involved in the biogenesis of several miRNAs [22,23]. Therefore, we hypothesized that hypoxia-induced changes in NCL expression result in the modulation of specific miRNAs, thereby promoting the proliferation of PASMCs. As NCL has RNA-binding properties, we searched for miRNAs associated with NCL in the PASMCs. RNAs were immunoprecipitated with an antibody against NCL or rabbit IgG as a negative control, followed by NGS-based small RNA sequencing (GSE184972). The small RNA sequencing library was made from the total RNAs from 1.5 × 106 PASMCs and two samples per condition were sequenced. We identified 39 miRNAs that were specifically pulled down by NCL antibodies (≥2-fold change in pull-down samples using NCL antibodies in comparison to those using IgG, p < 0.05), supporting the potential role of miRNAs in NCL-induced changes in PASMCs (Table 1). The binding of NCL to these miRNAs was validated by qRT-PCR after immunoprecipitation with an NCL antibody or rabbit IgG. The four miRNAs most highly enriched in NCL pull-down samples from the small RNA sequencing data (i.e., miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p) showed approximately 5.5- to 33.5-fold higher levels in the pull-down samples using NCL antibodies than in those using IgG (Figure 3A). The level of miR-497-5p, which does not bind to NCL based on the results of sequencing data after immunoprecipitation, was also confirmed in the NCL pull-down sample. As expected, miR-497-5p was not enriched in either the NCL or IgG pull-down samples. To determine whether a conserved motif exists within the sequences of the 39 potential target miRNAs of NCL, we analyzed the miRNA sequences using the motif-based sequence analysis tool MEME Suite 5.1.1 (Figure 3B). The most frequently observed motif was 5′-G/UGCUC-3′, and its position along the miRNA sequences was not identical. Since NCL is known to regulate mRNA stability by binding to a GC-rich element, it is likely that NCL binds to miRNAs with high GC contents [46]. To further confirm the interactions between the NCL and miRNAs, PASMCs were transfected with biotinylated miRNAs (bio-miR), such as bio-miR-24-3p or bio-miR-409-3p with known roles in the regulation of PASMC function, followed by affinity purification using streptavidin beads and immunoblotting with an NCL antibody (Figure 3C) [47,48,49,50]. Biotinylated Caenorhabditis elegans miR-67 (bio-cel-miR-67) was used as a negative control. An immunoblot analysis indicated that NCL binds to bio-miR-24-3p and bio-miR-409-3p but not to bio-cel-miR-67 (Figure 3C). The expression of exogenous biotinylated miRNAs in PASMCs transfected with bio-miR-24-3p or bio-miR-409-3p was confirmed by qRT-PCR (Figure 3D). Taken together, these results further support the hypothesis that NCL binds to specific miRNAs. We subsequently examined whether the 5′-G/UGCUC-3′ motif is critical for the binding of NCL to miRNAs. Mutations were introduced in the motifs of bio-miR-24-3p and bio-miR-409-3p (bio-miR-24-3p mutant and bio-miR-409-3p mutant) (Figure 3B). PASMCs were transfected with these mutants or bio-cel-miR-67, followed by a pull-down assay (Figure 3C). Mutations in the motif abrogate the binding of NCL to miRNAs, suggesting that the 5′-G/UGCUC-3′ motif serves as an NCL-binding site. These results suggest that NCL binds to specific miRNAs via this binding site and selectively regulates miRNA expression. We examined whether NCL affects the expression levels of miRNAs that bind to NCL. PASMCs were transfected with negative control siRNA (control) or si-NCL for 24 h and miRNA levels were measured by qRT-PCR. When NCL was downregulated by siRNAs, the expression levels of the miRNAs, including miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p, were reduced by 51–70% compared with levels in the control (Figure 4A). We subsequently overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector (Lonza) for 24 h and examined the levels of these miRNAs (Figure 4B). The expression levels of four miRNAs were 1.3–2.8-fold higher in the NCL-overexpressed cells than in the control cells transfected with the empty pEGFP-N1 vector (control). The level of miR-497-5p, which does not bind to NCL, was not affected by the knockdown or overexpression of NCL. These results suggest that NCL binds to certain miRNAs and regulates their expression. Previous studies have shown that NCL can affect the biogenesis of miRNAs or promote targeted mRNA degradation of miRNAs via interactions with miRNA-associated proteins, such as DGCR8, Drosha, or Ago2 [23,51,52]. Thus, we investigated their interactions in PASMCs. The cellular NCL from the PASMCs was immunoprecipitated with an NCL antibody or IgG control and analyzed by Western blotting with antibodies against DGCR8, Ago2, or NCL (Figure 4C,D). Conversely, lysates of PASMCs were immunoprecipitated with antibodies against DGCR8, Ago2, or IgG, and Western blotting was used to determine whether NCL was present in the pull-down (Figure 4E,F). We found that NCL binds to DGCR8 and Ago2 in PASMCs. These results further support the role of NCL in the regulation of miRNA expression and activity. Given that hypoxia downregulates NCL expression, miRNAs regulated by NCL are expected to show lower expression under hypoxic conditions. We examined the changes in the expression levels of miRNAs, including miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p, under hypoxia using qRT-PCR. The expression levels of miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p were all reduced by exposure to hypoxia for 24 h (Figure 5A). These results suggest that the hypoxia-induced downregulation of NCL is responsible for the reduced expression of certain miRNAs. To determine whether the modulation of NCL expression influences the hypoxia-induced regulation of miRNA expression, we overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector for 24 h before hypoxia exposure and examined the levels of miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p using qRT-PCR. When NCL was overexpressed, the hypoxia-induced reduction in the miRNA levels was restored (Figure 5B). These results indicate that the hypoxia-induced modulation of NCL expression controls the expression of certain miRNAs. We determined the biological consequences of NCL-mediated miRNA regulation in hypoxic PASMCs. As NCL was observed to regulate PASMC proliferation (Figure 2), we further examined whether miRNAs regulated by NCL, such as miR-24-3p and miR-409-3p, would affect PASMC proliferation. PASMCs were transfected with control, miR-24-3p mimic, miR-409-3p mimic, miR-24-3p antisense inhibitor RNA (anti-miR-24-3p), or anti-miR-409-3p and stained with a Ki-67 antibody. miR-24-3p and miR-409-3p mimics significantly reduced the number of Ki-67-positive proliferating cells by 61% and 70%, respectively, compared to cell counts in the control (Figure 6A). Conversely, cells transfected with anti-miR-24-3p or anti-miR-409-3p showed increased numbers of proliferating cells (i.e., 1.83-fold or 1.62-fold higher than counts in the control) (Figure 6B). These results demonstrate that the downregulation of miR-24-3p and miR-409-3p is required to promote PASMC proliferation. To examine whether the modulation of miR-24-3p or miR-409-3p affects the hypoxia-induced proliferative response of PASMCs, PASMCs were transfected with control, miR-24-3p mimic, or miR-409-3p mimic prior to exposure to hypoxia and stained with a Ki-67 antibody. The hypoxia-induced increase in PASMC proliferation was inhibited in cells transfected with miR-24-3p mimic or miR-409-3p mimic (Figure 6C). Therefore, it is likely that the hypoxia-induced downregulation of NCL promotes PASMC proliferation by downregulating a subset of miRNAs, including miR-24-3p and miR-409-3p. We also carried out a cell counting assay to determine cell proliferation. Consistent with the results of Ki-67 immunostaining, the rate of proliferation decreased in PASMCs transfected with miR-24-3p, or miR-409-3p mimics (Figure 6D). In contrast, the proliferation of PASMCs was promoted by anti-miR-24-3p, or anti-miR-409-3p (Figure 6E). The hypoxia-induced increase in cell proliferation was inhibited by miR-24-3p or miR-409-3p (Figure 6F). Therefore, the downregulation of miR-24-3p and miR-409-3p is essential for promoting PASMC proliferation under hypoxia. To confirm the overexpression or downregulation of miR-24-3p and miR-409-3p, their levels were measured in the PASMCs at 24 h after transfection with the control, miR-24-3p mimic, miR-409-3p mimic, anti-miR-24-3p, or anti-miR-409-3p (Figure 6G,H). RBPs are important regulators of gene expression via post-transcriptional regulation. Under hypoxia, RBPs regulate the expression of hypoxia-inducible genes. However, the role of RBPs in the functions of PASMCs under hypoxic conditions and the molecular mechanisms underlying their effects are not yet fully understood. In this study, we identified NCL as an essential regulator of PASMC proliferation under hypoxia and characterized its molecular function. miRNAs act as critical mediators of the response to hypoxia in PASMCs. As recent studies have revealed that NCL is involved in the biogenesis of specific miRNAs, we evaluated NCL–miRNA interactions in PASMCs by immunoprecipitation and small RNA sequencing. Thirty-nine miRNAs enriched in NCL pull-down were identified. We further found that the hypoxia-induced downregulation of NCL affects the expression of these miRNAs and demonstrated that NCL-mediated miRNA regulation induces the proliferation of PASMCs under hypoxic conditions. Given that an RBP deficiency is associated with cardiovascular developmental defects, RBPs may play a critical role in maintaining cardiovascular health. Recently, RBPs have been implicated in systematic cardiovascular disease via the post-transcriptional regulation of target genes. For example, quaking (QKI) in VSMCs binds to myocardin and derives alternative splicing in response to vessel injury [53]. The identification of the modulation of NCL in PASMCs under hypoxia extends our understanding of the functions of RBPs in vascular conditions and provides new targets for the treatment of vascular diseases. NCL expression has been shown to be regulated by HuR and several miRNAs. HuR interacts with the 3′UTR of NCL and promotes its translation, whereas miR-494, miR-194, and miR-206 suppress NCL expression [54,55]. In this study, we observed that both the mRNA and protein levels of NCL were significantly reduced by hypoxia. We examined whether the levels of miR-494, miR-194, or miR-206 increased in hypoxia-exposed PASMCs to suppress NCL expression. Our previously generated small RNA sequencing data showed that miR-494 and miR-194 levels did not change in response to hypoxia, and the expression of miR-206 was not determined [27]. It is therefore unlikely that these three miRNAs are responsible for the decrease in NCL expression in PASMCs under hypoxia. Rather, we elucidated the role of HDAC in the transcriptional repression of NCL under hypoxia. NCL has been linked to a variety of pathologies, including carcinogenesis, and thus, elucidating the regulatory mechanisms underlying its expression should provide a basis for the development of new therapeutic strategies for a variety of diseases, including hypoxia-induced vascular diseases. There is emerging evidence of the involvement of RBPs in the regulation of miRNA biogenesis. For example, HNRNPA1 promotes Drosha cleavage by restructuring pri-miR-18a [56]. NCL has also been reported to enhance the maturation of specific miRNAs, including miR-21, miR-221, and miR-222, and is consequently involved in the pathogenesis of cancer [22]. We have demonstrated that NCL controls the fate of miRNAs in response to hypoxia in PASMCs. NCL binds to and regulates certain miRNAs, particularly those that contain the 5′-G/UGCUC-3′ sequence. We have provided the first evidence to elucidate the biochemical interactions between NCL and miRNAs in PASMCs and their role in the proliferation of PASMCs. The regulation of miRNA expression by NCL is essential for PASMC responses to hypoxic conditions. The proliferation of VSMCs is a hallmark of several vascular pathologies as well as hypoxia-induced remodeling [41,57]. Multiple miRNAs involved in the proliferation of VSMCs have also been explored [58,59,60]. For example, miR-24 inhibits high glucose-stimulated VSMC proliferation by targeting high mobility group box-1 (HMGB1) [49]. Overexpression of miR-24 reduced neointimal hyperplasia and VSMC proliferation by inhibiting the Wnt4 signaling pathway [47]. In addition, miR-24 suppressed the platelet-derived growth factor-BB (PDGF-BB) signaling pathway by decreasing the expression levels of activator protein 1 (AP-1) and the PDGF-receptor (PDGF-R), resulting in the inhibition of VSMC proliferation and vascular remodeling [50]. The results imply that miR-24 may also regulate VSMC proliferation under hypoxia. While few previous studies have explored the function of miR-409 in VSMCs, decreased miR-409 expression levels were observed during high phosphate-induced vascular calcification, triggering VSMC de-differentiation [48]. This finding suggests that miR-409 may be involved in the regulation of VSMC proliferation. We have demonstrated that the target miRNAs of NCL influence the proliferation of PASMCs. For example, miR-24-3p and miR-409-3p inhibit PASMC proliferation and their overexpression further prevents hypoxia-induced proliferation. These results add a layer of valuable information about a specific set of miRNAs that regulate the proliferation of PASMCs. In addition, as the target miRNA level is regulated by the level of NCL expression, it is clear that NCL–miRNA interactions are essential for the regulation of PASMC proliferation. As miRNAs are potent regulators of cellular function in pathophysiological conditions, our illustration of NCL–miRNA interactions and the role of NCL in PASMC functions via the regulation of miRNAs improves our general understanding of the mechanisms underlying the pathogenesis of vascular conditions related to hypoxia. To explore the potential therapeutic benefits of NCL or interacting miRNAs on pulmonary hypertension, it is necessary to investigate whether modulation of NCL or interacting miRNAs is effective in attenuating pulmonary vascular remodeling in animal models, such as a chronic hypoxia-induced rat model. In this study, we provide clear evidence for the role of the RBP nucleolin (NCL) in hypoxia-induced PASMC proliferation. NCL is downregulated by histone deacetylation under hypoxic conditions in PASMCs, which consequently promotes PASMC proliferation. Furthermore, we demonstrated that these effects of NCL are mediated by interactions with a subset of miRNAs using immunoprecipitation and NGS-based small RNA sequencing. Thirty-nine miRNAs were found to be enriched in NCL pull-down, and NCL regulates particular miRNA expressions via the 5′-G/UGCUC-3′ binding sites. Hypoxia-mediated regulation of NCL affects miRNA expression, and these miRNAs, such as miR-24-3p and miR-409-3p, are involved in the proliferation of PASMCs under hypoxia. Collectively, the identification of NCL-miRNA interactions in hypoxia-induced PASMC proliferation provides a basis for further studies of the molecular mechanisms underlying vascular diseases. Human primary pulmonary artery smooth muscle cells (PASMCs) were purchased from Lonza (CC-2581) and were maintained in Sm-GM2 medium (Lonza, Basel, Switzerland) containing 5% fetal bovine serum (FBS). For hypoxia, the cells were placed in fresh medium and incubated in a sealed modular incubator chamber (Billups-rothenberg Inc., San Diego, CA, USA) for 24 h at 37 °C after flushing with a mixture of 5% CO2, 1% O2 and 94% N2 for 4 min. NaBu was purchased from Sigma-Aldrich (St. Louis, MO, USA, #B5887). The cells were treated with 10 mM NaBu for 24 h. Quantitative analysis of the change in expression levels was performed using real-time PCR. The mRNA levels were normalized to 18S rRNA. The primers used were as follows: 18S rRNA, 5′-GTAACCCGTTGAACCCCATT-3′ and 5′-CCATCCAATCGGTAGTAGCG-3; CIRBP, 5′-CTTTTTGTTGGAGGGCTGAG-3′ and 5′-CTTGCCTGCCTGGTCTACTC-3′; CPEB1, 5′-TCTGCCCTTCCTGTCTCTGT-3′ and 5′-TATGCTGAAGGGGTCTTTGG-3′; CPEB2, 5′-GCGAGTTGCTTTCTCCAATC-3′ and 5′-CCTGGCATTCATCACACATC-3′; HNRNPA2B1, 5′-GGCTACGGAGGTGGTTATGA-3′ and 5′- ATAACCCCCACTTCCTCCAC -3′; HNRNPL, 5′-AGATCACCCCGCAGAATATG -3′ and 5′-CAAGCCATAGACCATGAGCA -3′; IREB2, 5′-GCACCGGATTCAGTTTTGTT-3′ and 5′-CTTAGCGGCAGCACTATTCC-3′; NCL, 5′-GAAGGAAATGGCCAAACAGA-3′ and 5′-ACGCTTTCTCCAGGTCTTCCA-3′; PTBP1, 5′-ACGGACCGTTTATCATGAGC-3′ and 5′-GTTTTTCCCCTTCAGCATCA-3′; PTBP3, 5′-CATTCCTGGGGCTAGTGGTA-3′ and 5′-CCATCTGAACCAAGGCATTT-3′; RBM3, 5′-CAGGCACTGGAAGACCACTT-3′ and 5′-CTCTCATGGCAACTGAAGCA-3′; TIA1, 5′-TGCTATTGGGGCAAAGAAAC-3′ and 5′-GCGGTTGCACTCCATAATTT-3′; ZFP36, 5′-TCCACAACCCTAGCGAAGAC-3′ and 5′-GAGAAGGCAGAGGGTGACAG-3′; and ZFP36L1, 5′-GAGGAAAACGGTGCCTGTAA-3′ and 5′-CTCTTCAGCGTTGTGGATGA-3′. For the quantification of mature miRNAs, such as miR-423-3p (MS00004179), miR-744-5p (MS00010549), miR-24-3p (MS00006552), miR-409-3p (MS00006895), and miR-497-5p (MS00004361), the miScript PCR assay kit (#218073, Qiagen, Hilden, Germany) was used according to the manufacturer’s instructions. Data analysis was performed using a comparative CT method in the Bio-Rad software 3.1. The levels of the miRNAs were normalized to U6 small nuclear RNA or U61 small nucleolar RNA (SNORD61). Three experiments were performed in triplicate, and the mean results with standard errors are presented. Chemically modified double-stranded RNAs designed to mimic the endogenous mature miR-24-3p and miR-409-3p were purchased from Genolution Pharmaceuticals (Seoul, Republic of Korea). Antisense inhibitor RNAs (anti-miR-24-3p and anti-miR-409-3p) and negative control miRNA were purchased from Bioneer (Daejeon, Republic of Korea). The miRNA mimics and anti-miRNA oligonucleotides were transfected at 5 nM and 25 nM, respectively, using RNAi Max (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol. Small interfering RNA (siRNA) duplexes were synthesized by Genolution Pharmaceuticals (Seoul, Republic of Korea) and Integrated DNA Technologies (Coralville, IA, USA). NCL siRNA (si-NCL): 5′-GGAUAGUUACUGACCGGGA-3′, HDAC1 siRNA (si-HDAC1): 5′-AGUUUCCUUUUUGAGAUACUAUUTT-3′, and HDAC2 siRNA (si-HDAC2): 5′-GAAUUUCUAUUCGAGCAUCAGACAA-3′. Negative control siRNA (Genolution) was used as a control. The cells were transfected with 100 nM si-NCL, 25 nM si-HDAC1, or si-HDAC2 using RNAi Max (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol. PASMC lysates were prepared in lysis buffer (20 mM Tris–HCl [pH 7.5], 100 mM KCl, 5 mM MgCl2, and 0.5% NP-40) containing a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland). The lysates were precleared with DynabeadsTM Protein G (#10007D, Invitrogen, Carlsbad, CA, USA) at 4 °C with gentle rotation for 1 h. The precleared lysates were incubated with DynabeadsTM Protein G coated with 2 μg each of rabbit anti-nucleolin antibody (#ab22758, Abcam, Cambridge, UK) or rabbit IgG (#2729, Cell Signaling Technology, Danvers, MA, USA) at 4°C for overnight. For DGCR8 IP and Ago2 IP, 5 μg of mouse anti-DGCR8 antibody (#60084-1-Ig, Proteintech, Rosemont, IL, USA) and mouse anti-Ago2 antibody (#ab57113, Abcam) were used, respectively. A reaction containing mouse IgG (#sc-2025, Santa Cruz Biotechnology, Dallas, TX, USA) served as a negative control. Unbound materials were washed off using NT2 buffer (50 mM Tris–HCl [pH 7.5], 150 mM NaCl, 1 mM MgCl2, and 0.05% NP-40). All collected protein complexes were eluted with 2X Laemmli sample buffer supplemented with β-mercaptoethanol and boiled. The boiled supernatants and input (2%) samples were resolved by SDS-PAGE and analyzed by immunoblotting with the anti-NCL antibody (#ab22758), anti-DGCR8 antibody (#60084-1-Ig), or anti-Ago2 antibody (#ab57113). PASMC lysates were prepared in lysis buffer (20 mM Tris–HCl (pH 7.5), 100 mM KCl, 5 mM MgCl2, and 0.5% NP-40) containing 40 U/μL RiboLock RNase Inhibitor (#EO0381, Thermo Fisher Scientific, Waltham, MA, USA) and a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland). The lysates were incubated with DynabeadsTM Protein G (#10007D, Invitrogen, Carlsbad, CA, USA) coated with 2 μg each of rabbit anti-NCL antibody (#ab22758, Abcam, Cambridge, UK) or rabbit IgG (#2729, Cell Signaling Technology, Danvers, MA, USA) at 4 °C for 2 h. Unbound materials were washed off using NT2 buffer (50 mM Tris–HCl (pH 7.5), 150mM NaCl, 1 mM MgCl2, and 0.05% NP-40). The pellet was subsequently incubated with NT2 buffer containing RNase-free Dnase I (1 U/μL) (#EN0521, Thermo Fisher Scientific, Waltham, MA, USA) at 30 °C for 15 min and NT2 buffer containing 0.1% SDS and 0.1 mg/mL Proteinase K (#25530049, Thermo Fisher Scientific, Waltham, MA, USA) at 55 °C for 15 min. The RNA was extracted using Trizol in the presence of glycoblue (#AM9515, Thermo Fisher Scientific, Waltham, MA, USA) and analyzed by NGS-based small RNA sequencing or qRT-PCR. The extracted RNA was qualified using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). cDNA libraries were constructed with the NEBNext Multiplex small RNA library prep kit (NEB, Ipswich, MA, USA) using the total RNA from RNA immunoprecipitation, according to the manufacturer’s instructions. Briefly, adapter ligation, reverse transcription, PCR amplification, and purification using a QIAquick PCR Purification Kit (Qiagen) and AMPure XP beads (Beckman Coulter, Brea, CA, USA) were conducted to generate a library product. The yield and size distribution of the small RNA libraries were assessed by high-sensitivity DNA analysis on an Agilent 2100 Bioanalyzer. High-throughput sequences were produced by single-end 75 sequencing using the NextSeq 500 system (Illumina, San Diego, CA, USA). Sequence reads were mapped using the Bowtie 2 software tool to obtain the BAM file (alignment file). A mature miRNA sequence was used as a reference for mapping. Read counts mapped onto the mature miRNA sequence were extracted from the alignment file using bedtools (v2.25.0) and Bioconductor, which uses R (version 3.2.2) statistical programming language (R development Core Team, 2011). The read counts were then used to determine the expression levels of miRNAs. The quantile normalization method was used to compare samples. The 3′-biotinylated miR-24-3p mimic (bio-miR-24-3p), 3′-biotinylated miR-409-3p mimic (bio-miR-409-3p), 3′-biotinylated miR-24-3p mutant (bio-miR-24-3p mutant), 3′-biotinylated miR-409-3p mutant (bio-miR-409-3p mutant), and 3′-biotinylated control Caenorhabditis elegans miR-67 mimic (bio-cel-miR-67) were synthesized by Integrated DNA Technologies (Coralville, IA, USA). PASMCs were transfected with 150 nM bio-miR-24-3p, bio-miR-409-3p, bio-miR-24-3p mutant, bio-miR-409-3p mutant, or bio-cel-miR-67 mimic using RNAi MAX (Invitrogen, Carlsbad, CA, USA). Twenty-four hours later, the cells were trypsinized and lysed in lysis buffer (20 mM Tris–HCl (pH 7.5), 100 mM KCl, 5 mM MgCl2, and 0.5% NP-40) containing 40 U/μL RiboLock RNase Inhibitor (#EO0381, Thermo Fisher Scientific, Waltham, MA, USA) and a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland) on ice for 20 min and centrifuged at 12,000 rpm for 10 min at 4 °C. The lysates were incubated with Streptavidin Mag Sepharose (#GE28-9857-38, Sigma-Aldrich, St. Louis, MO, USA) at 4 °C for 4 h, and unbound materials were washed off using NT2 buffer. The pull-down sample was boiled in 2X Laemmli sample buffer supplemented with β-mercaptoethanol. The boiled pull-down and input (1%) samples were resolved by SDS-PAGE and analyzed by immunoblotting with the anti-NCL antibody (#ab22758, Abcam). The plasmid GFP-NCL was a gift from Michael Kastan (Addgene plasmid #28176; http://n2t.net/addgene:28176; RRID: Addgene_28176) [61]. PASMCs were transfected with 1 μg of the GFP-NCL or the empty pEGFP-N1 vector (Addgene, Watertown, MA, USA) using the P1 Primary Cell 4D-NucleofectorTM X kit (Lonza, Basel, Switzerland) according to the manufacturer’s protocol. Cells were lysed in TNE buffer (50 mM Tris–HCl (pH 7.4), 100 mM NaCl, and 0.1 mM EDTA) and total cell lysates were separated by SDS-PAGE, transferred to PVDF membranes, immunoblotted with antibodies, and visualized using an enhanced chemiluminescence detection system (Bio-Rad Laboratories, Hercules, CA, USA). Antibodies against NCL (#ab22758), HIF1α (#ab2185), and Ago2 (#ab57113) were purchased from Abcam (Cambridge, UK). An anti-β-actin antibody (#sc47778), anti-HDAC1 antibody (#sc81598), anti-HDAC2 antibody (#sc-81599), and anti-DGCR8 antibody (#60084-1-Ig) were purchased from Santa Cruz Biotechnology (Dallas, TX, USA) and Proteintech (Rosemont, IL, USA). Equal amounts of PASMCs were seeded in chamber well slides and transfected with control mimic, miR-24-3p, miR-409-3p, anti-miR-24-3p, or anti-miR-409-3p. Cells were exposed to normoxia or hypoxia and then fixed in 2% paraformaldehyde, blocked in 3% BSA in PBS, and permeabilized in 0.1% Triton X-100 (Sigma-Aldrich, St. Louis, MO, USA) in PBS. The slides were sequentially probed with rabbit anti-human Ki-67 antibody (#ab16667, Abcam) and goat anti-rabbit IgG (H + L) cross-adsorbed secondary antibody Alexa flour 488 (#A-11008, Thermo Fisher Scientific). Nuclei were stained with Hoechst 33342 (#62249, Thermo Fisher Scientific). The slides were imaged by a Zeiss Axio Imager Z1 microscope (Oberkochen, Germany). At least 2000 cells were counted per condition, and the percentages of Ki-67-positive cells were presented. The results are the mean ± S.E. for triplicate assays. Equal amounts of PASMCs were seeded in plates and transfected with negative control siRNA, si-NCL, pEGFP-N1 vector, GFP-NCL, control mimic, miR-24-3p mimic, miR-409-3p mimic, anti-miR-24-3p, or anti-miR-409-3p. The cells were trypsinized and manually counted using a hemocytometer. The total cell numbers were compared and presented as a fold change. All experiments were performed with at least three independent repetitions. The results were presented as the mean with standard error. Statistical analyses were performed by an analysis of variance followed by Student’s t-test, multiple t-test, one-way ANOVA, or two-way ANOVA using Prism 8 software (GraphPad Software Inc., San Diego, CA, USA). p-values of <0.05 were considered significant and are indicated with asterisks. *, **, ***, and **** represent p-values less than 0.05, 0.005, 0.0005, and 0.0001, respectively. The RNA sequencing dataset generated during the current study is available from the corresponding author on reasonable request. The accession numbers for the data reported in this paper are GEO: GSE184972.

PMC10000683

Elio López-García,Antonio Benítez-Cabello,Javier Ramiro-García,Victor Ladero,Francisco Noé Arroyo-López

In Silico Evidence of the Multifunctional Features of Lactiplantibacillus pentosus LPG1, a Natural Fermenting Agent Isolated from Table Olive Biofilms

22-02-2023

probiotic,starter culture,genome overview,fermented vegetables,whole-genome sequencing

In recent years, there has been a growing interest in obtaining probiotic bacteria from plant origins. This is the case of Lactiplantibacillus pentosus LPG1, a lactic acid bacterial strain isolated from table olive biofilms with proven multifunctional features. In this work, we have sequenced and closed the complete genome of L. pentosus LPG1 using both Illumina and PacBio technologies. Our intention is to carry out a comprehensive bioinformatics analysis and whole-genome annotation for a further complete evaluation of the safety and functionality of this microorganism. The chromosomic genome had a size of 3,619,252 bp, with a GC (Guanine-Citosine) content of 46.34%. L. pentosus LPG1 also had two plasmids, designated as pl1LPG1 and pl2LPG1, with lengths of 72,578 and 8713 bp (base pair), respectively. Genome annotation revealed that the sequenced genome consisted of 3345 coding genes and 89 non-coding sequences (73 tRNA and 16 rRNA genes). Taxonomy was confirmed by Average Nucleotide Identity analysis, which grouped L. pentosus LPG1 with other sequenced L. pentosus genomes. Moreover, the pan-genome analysis showed that L. pentosus LPG1 was closely related to the L. pentosus strains IG8, IG9, IG11, and IG12, all of which were isolated from table olive biofilms. Resistome analysis reported the absence of antibiotic resistance genes, whilst PathogenFinder tool classified the strain as a non-human pathogen. Finally, in silico analysis of L. pentosus LPG1 showed that many of its previously reported technological and probiotic phenotypes corresponded with the presence of functional genes. In light of these results, we can conclude that L. pentosus LPG1 is a safe microorganism and a potential human probiotic with a plant origin and application as a starter culture for vegetable fermentations.

In Silico Evidence of the Multifunctional Features of Lactiplantibacillus pentosus LPG1, a Natural Fermenting Agent Isolated from Table Olive Biofilms In recent years, there has been a growing interest in obtaining probiotic bacteria from plant origins. This is the case of Lactiplantibacillus pentosus LPG1, a lactic acid bacterial strain isolated from table olive biofilms with proven multifunctional features. In this work, we have sequenced and closed the complete genome of L. pentosus LPG1 using both Illumina and PacBio technologies. Our intention is to carry out a comprehensive bioinformatics analysis and whole-genome annotation for a further complete evaluation of the safety and functionality of this microorganism. The chromosomic genome had a size of 3,619,252 bp, with a GC (Guanine-Citosine) content of 46.34%. L. pentosus LPG1 also had two plasmids, designated as pl1LPG1 and pl2LPG1, with lengths of 72,578 and 8713 bp (base pair), respectively. Genome annotation revealed that the sequenced genome consisted of 3345 coding genes and 89 non-coding sequences (73 tRNA and 16 rRNA genes). Taxonomy was confirmed by Average Nucleotide Identity analysis, which grouped L. pentosus LPG1 with other sequenced L. pentosus genomes. Moreover, the pan-genome analysis showed that L. pentosus LPG1 was closely related to the L. pentosus strains IG8, IG9, IG11, and IG12, all of which were isolated from table olive biofilms. Resistome analysis reported the absence of antibiotic resistance genes, whilst PathogenFinder tool classified the strain as a non-human pathogen. Finally, in silico analysis of L. pentosus LPG1 showed that many of its previously reported technological and probiotic phenotypes corresponded with the presence of functional genes. In light of these results, we can conclude that L. pentosus LPG1 is a safe microorganism and a potential human probiotic with a plant origin and application as a starter culture for vegetable fermentations. Lactobacillales, commonly called lactic acid bacteria (LAB), are an order of Gram-positive and acid-tolerant bacteria that produce lactic acid as the major metabolic end product obtained from carbohydrate fermentation. Their large genetic versatility allows them to colonize a wide range of ecological niches, ranging from the mammalian gut microbiota to a large number of fermented foods [1]. This is the case of table olives, one of the most important fermented vegetables in Mediterranean countries, with a worldwide production that exceeds 3 million tonnes/year [2]. Lactiplantibacillus pentosus and Lactiplantibacillus plantarum (formerly known as Lactobacillus pentosus and Lactobacillus plantarum), together with diverse species of yeasts, are among the main microorganisms and LAB species responsible for table olive fermentation, determining the flavor, quality, and safety of the final product [3,4,5]. Traditionally, the market for probiotic foods was dominated by fermented dairy products. However, in recent years, the demand for vegetal-based probiotic products has grown due to a shift in consumer preference toward healthier alternatives. In addition, vegetarian activism and lactose intolerance have driven the search for new dairy-free and vegan products. Therefore, the emergence of new vegetal-based probiotics should be encouraged to satisfy societal demand, contributing to the rapidly growing global market of approximately 50 billion USD [6]. In recent years, table olives have emerged as a solid alternative to dairy products as a carrier of beneficial microorganisms to consumers. Certain LAB strains have the ability to form biofilms on the olive epidermis, obtaining fermented olives with more than 10 million UFC/g [7]. L. pentosus LPG1 (hereinafter LPG1) is a fermentation agent obtained from table olive biofilms with proven probiotic and technological features based on previous in vitro and in vivo studies [8,9]. Thereby, LPG1 has shown anti-inflammatory, esterase, and phytase activities, a reduction in cholesterol levels, lactic acid production, and the inhibition of food-borne pathogens. It has also shown the ability to adhere to Caco-2 cells, as well as the absence of antibiotic resistance and hemolytic activity, among other features. However, the precise genomic mechanisms involving its functions are still unknown. Advances in genome sequencing techniques together with cost reduction have allowed an advancement of the knowledge, not only of the probiotic potential of the strains, but also of the technological potential. In recent years, several genomes of L. pentosus isolated from table olives have been sequenced [10,11,12,13,14], as well as from other vegetal-derived matrices [15]. Our aim in the present study is to expand our knowledge of the functional and safety features of the LPG1 strain. For this purpose, we have sequenced, annotated, and closed its full genome, including chromosome and plasmids, correlating the technological and probiotic features with the presence of functional genes after in silico analysis. Our work was performed under the recommendations of EFSA, which requires full genome sequencing and annotation of novel strains that are intended for biotechnological applications [16]. The LPG1 strain was previously isolated from Spanish-style table olive biofilms and identified by molecular methods [8]. LPG1 was grown in Man Rogosa and Sharpe agar (Oxoid, Basingstoke, UK) at 37 °C for 48 h prior to DNA isolation. Then, genomic and plasmid DNA was extracted and purified using the protocol described by Martín-Platero et al. [17]. The integrity of the extracted DNA was confirmed by visualization in agarose gel (0.9%), while its concentration was determined by a Qubit 4 fluorometer, obtaining a DNA concentration of 319 ng/µL. Whole-genome sequencing of LPG1 was carried out using two platforms, Illumina HiSeq (Eurofins, Luxemburg) and PacBio (FISABIO, Valencia, Spain). The raw reads obtained from both methodologies were curated by Trim Galore and the fastp tool, respectively. Then, the Illumina and PacBio reads were checked for quality control by FastQC and the MultiQC tool, respectively. A hybrid assembly was performed using Unicycler a recent assembler for bacterial genomes from a combination of short and long reads [18]. Finally, the genome annotation of the LPG1 strain was done using the Prokaryotic Genome Annotation System (Prokka) version 1.14.6 [19]. A circular genomic map was obtained through GView.js software [20]. Genomic data were deposited in the European Nucleotide Archive under Bioproject number PRJEB51357. To verify the taxonomic identity of the LPG1 strain, an analysis of the Average Nucleotide Identity (ANI) was performed that included different strains of L. plantarum, L. paraplantarum, L. pentosus, and other genera from the Lactobacillaceae family (Table S1, Supplementary Materials). ANI analysis was performed using the JSpecies Web Server after plasmid removal [21]. Clustering and heatmap were performed by using HemI version 2.0 [22]. Moreover, a total of 62 fully assembled genomes of L. pentosus strains were retrieved from the NCBI GenBank (https://www.ncbi.nlm.nih.gov/genbank/) (accessed on 10 July 2022) for pan-genome analysis (Table S2, Supplementary Materials). All genomes were re-annotated with the same tool (Prokka) to avoid biases of the annotation process in the comparisons due to different annotation methods. The output general feature format (GFF) was used to perform a pan-genome analysis to identify core, accessory, and unique genes using Roary by setting a threshold of 95% BLASTp [23]. Thus, four different classes of genes belonging to the core (99% < strain < 100%), soft-core (95% < strain < 99%), shell (15% < strain < 95%), and cloud (0% < strain < 15%) groups were obtained. The genome safety of LPG1 was assessed using several bioinformatics tools. First, the Comprehensive Antibiotic Resistance Database (CARD) and Resistance Gene Identifier (RGI) tool were used to identify antibiotic resistance genes [24]. Second, acquired antimicrobial resistance (AMR) genes were screened by ResFinder software version 4.1, with a threshold of 80% identity and 60% for minimum length [25,26,27]. In addition, PathogenFinder 1.1 and VirulenceFinder 2.0 were employed to predict the bacteria pathogenicity towards human hosts and the identification of acquired virulence genes, respectively [28,29,30]. Functional classification of genes into COGs (Cluster of Orthologous Groups) was performed by EggNOGmapper (version 2.0), a tool for functional annotation based on precomputed orthology assignments from the EggNOG database (version 5.0) [31]. Furthermore, KofamKOALA (version 2.2) was employed for the Kyoto Encyclopedia of Genes and Genomes Orthology (KEGG) assignment and mapping of the metabolic pathways [32]. Moreover, for the automated annotation of Carbohydrate-active enzymes (CAZymes), the dbCAN2 metaserver was used, which integrates three search tools, an HMMER search against the dbCAN HMM database, a DIAMOND search against the CAZy database, and an HMMER search against dbCAN-sub, a database of carbohydrate-active enzyme subfamilies for substrate annotation [33,34]. The annotation of CAZymes was considered valid when it was consistent across at least two tools. The genes clusters coding bacteriocins and ribosomal-synthesized and post-translationally modified peptides (RiPPs) were predicted by Bagel Database through Bagel4 and the antiSMASH version 6.1.1 web server tool [35,36]. The prediction of probiotic and technological genes in LPG1 was carried out by searching the National Center for Biotechnology Information (NCBI). The whole LPG1 genome was subjected to BLAST from the amino acid sequences of genes of interest using BLASTp, with identity percentage, E-value, and query percentage coverage threshold values of 70%, 1E-20 and 70%, respectively. Subsequently, the gene resulting from the BLAST, and not annotated by the Prokka and eggNOG annotation method, was exposed to a new BLAST search in UniProt, as many entries in this database are manually reviewed by experts. Table S3 (Supplementary Materials) shows the list of genes with technological and probiotic potential studied in the present study. The prophage regions were predicted using Phage Search Tool Enhanced Release (PHASTER) [37,38]. Thus, the length, localization, GC content, and gene annotation for each prophage were predicted. The predicted intact prophages were compared to the Virus-Host DB database, and a proteomics tree of the viral genome sequence was generated through the VIPtree tool, which was based on the genome-wide sequence similarities calculated by tBLASTx [39,40]. Moreover, the genomic islands (GI) were searched against several databases using IslandViewer 4 [41]. In addition, for the search of integrons, we used the software Integronfinder [42]. Finally, the ISFinder database and ISsaga tool were used for the annotation of transposase and mobile elements [43,44]. Coding sequences for Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated genes (Cas) were scanned using CRISPRCasFinder v.1.1.2-I2BC [45,46]. Then, CRISPR arrays were confirmed according to the CRISPRdb database. The genome of LAB contains mobile elements and repeat regions, which makes it difficult to close the full genome using exclusively short-read sequences obtained from the Illumina platform. For this reason, the LPG1 genome was sequenced and closed in this study by the hybrid assembly of the sequences obtained from both Illumina (short reads) and PacBio (long reads) technologies. The LPG1 complete genome consisted of 3,700,533 bp with a G+C content of 46.23%. This strain had a unique and circular chromosome of 3,619,252 bp with a G+C content of 46.34%, and two sequenced plasmids, designated as pl1LPG1 and pl2LPG1, with lengths of 72,578 and 8713 bp, respectively. The sequence blast results revealed that the plasmids pl1LPG1 and pl2LPG1 showed the highest similarity to the plasmid number 6 of L. pentosus KW2 and plasmid number 10 of L. plantarum M19, respectively. Further genome annotation showed that the sequenced genome consisted of 3,345 coding genes and 89 non-coding sequences (73 tRNA and 16 rRNA genes). Table S2 (Supplementary Materials) shows the length and GC content for a total of 62 L. pentosus genomes obtained from the NCBI database. The genome sizes ranged from 3,426,320 bp (KAC1 strain) to 4,036,510 (KW1 strain), while the GC content ranged from 44.9% (IG1 strain) to 46.34% (LPG1 strain). Therefore, LPG1 is currently the L. pentosus strain with the highest GC content. The genomic size and GC content of strains can be indicative of their lifestyle and preferred environmental niches. Thereby, the LPG1 genome size was higher than that of the other genera from the Lactobacillaceae family such as Lactobacillus helveticus (2 Mb), Lactobacillus acidophilus (2 Mb), or Lacticaseibacillus casei (3 Mb). This fact is due to the ecological flexibility of L. pentosus and the diversity of the ecological niches this organism can colonize. Throughout their evolution, Lactobacillaceae species reduced their genome size to adapt to specific ecological niches such as mammalian gut microbiota. However, free-living species such as L. pentosus and L. plantarum have larger genomes due to horizontal gene transfer through plasmids, transposons, or prophages [47,48,49]. An overview of the LPG1 chromosome and its two plasmids is shown in Figure 1. It is noteworthy that two clusters encoding bacteriocins, as well as two clusters of exopolysaccharide production, were found in the genome. Likewise, a total of four prophages and six GI were predicted to be introduced into the chromosome. Finally, two CRISPR-Cas systems were found, as well as four CRISPR arrays. Functional classification into COG categories performed by EggNOGmapper provided an overview of the genes that LPG1 contains. Protein-coding sequences were functionally divided into 19 COG categories; others were assigned to unknown functions or did not belong to COG. The category with the highest number of coding genes was transcription, which may be due to the genomic plasticity of the strain. Carbohydrate transport and metabolism was the second most abundant category in terms of genes coding for this function, which reflects the great capacity of LPG1 to capture sugars and transform them, either by catabolism or by anabolism to produce more complex carbohydrates. At the opposite extreme were the low numbers of genes coding for cell motility. Figure 2 shows the number of assigned genes by COG category. The complete genome assembly of LPG1 was used to verify its taxonomy and phylogenetic relationship through ANI analysis. Studies recently showed ANI analysis as a robust measure of the genetic and evolutionary distance, as ANI analysis is based on a large number of conserved genes [50]. Moreover, phylogenetic analysis based exclusively on the 16S ribosomal gene does not distinguish between L. pentosus, L. paraplantarum or L. plantarum species [51]. For this reason, in the ANI analysis, we also included different strains of L. pentosus, L. plantarum, L. paraplantarum, as well as other genera from the Lactobacillaceae family (Table S1). ANI analysis revealed that LPG1 was grouped with the rest of the L. pentosus strains with >94% of the ANI value, above the threshold value to consider two organisms as the same species [52]. Moreover, L. plantarum WCFS1 was grouped together with L. paraplantarum DSM10667 strain in another sub-cluster, which was very different from the rest of the Lactobacillaceae genomes included in the present analysis (Figure 3). The pan-genome of L. pentosus was analyzed, including our LPG1 strain as well as 62 other L. pentosus strains available in the NCBI database (https://www.ncbi.nlm.nih.gov/genbank/) (accessed on 10 July 2022) (Table S2, Supplementary Materials). For this purpose, we used the Roary program. The pan-genome composition consisted of 1407 core, 639 softcore, 2251 shell, and 5894 cloud genes (Table S4, Supplementary Materials). A total of 10,191 genes made up the full pan-genome; therefore, as genomes were added, the number of genes increased. The occurrence of a small number of core genes could be due to the small amount of L. pentosus genomes included in the analysis and to the fact that the threshold of presence in the analyzed sequences must be 99% for consideration as a core gene, which implies that if a gene is missing in any strain, it was not considered a core gene. Therefore, when the isolated strains originate from diverse environments such as fermented vegetables, female vaginas, fermented drink, soil, or fermented food, it is common for some genes to be lost. Figure 4 shows the hierarchical clustering and the heat map of the gene matrix (presence/absence) obtained after the pan-genome analysis of L. pentosus. As can be easily deduced from this figure, the L. pentosus strains more closely related to the LPG1 strain were IG8, IG9, IG11, and IG12, all of which were isolated from table olive biofilms. The approach used for pan-genome analysis was also used to search probiotic marker genes (PMGs) within the shell and cloud genes present in our LPG1 strain. Genes involved in the tolerance of bile salt, adhesion, gut persistence, response to stress, and carbohydrate metabolism were found in the LPG1 strain in both the shell and cloud genes categories, which are also the genes shared with the rest of the L. pentosus strains (Table 1). Finally, from the total of 62 L. pentosus strains assayed, 15 strains with two complete exopolysaccharide (EPS) clusters and 48 strains with one complete EPS cluster were obtained. The genome comparison indicated the presence of only 78 wzy genes, which encoded the polysaccharide polymerase protein, an essential protein for EPS biosynthesis, although other crucial genes were found [53,54]. However, previous studies showed incomplete EPS clusters would likely be compensated by the genes located in other clusters [55]. Many other studies have reported the involvement of EPS in functions as diverse as biofilm formation, adhesion to the intestinal cells, inhibition of pathogen adhesion to gut cells, as well as resistance to bile salt and toxic compounds such as metal ions, and resistance under adverse conditions such as desiccation, a high salt concentration, and varying pH [56,57,58,59]. Lactobacilli have been commonly recognized as generally regarded as safe (GRAS) organisms due to their high safety profile. Additionally, L. pentosus has the qualified presumption of safety (QPS) status from the European Food Safety Agency (EFSA). However, previous studies have shown that certain Lactobacillus strains have antimicrobial resistance genes (AMR) that are involved in antibiotic resistance mechanisms, e.g., against chloramphenicol, tetracycline, beta-lactam, or aminoglycoside [69,70,71]. Therefore, the resistome of LPG1 was evaluated by the RGI tool (CARD) matching only complete genes. The CARD prediction matched strict category 2 gen, vanT and vanY in the VanG cluster, which encode alanine racemase and D-alanyl-D-alanine carboxypeptidase protein, respectively. Both genes were present in a vancomycin resistance cluster. On the one hand, vanT converts L-serine to D-serine for peptidoglycan synthesis, and DD-carboxypeptidase catalyzes the release of D-Ala from the diacetyl-L-Lys-D-Ala-D-Ala tripeptide [72]. Of the remaining genes in the cluster, only one was predicted by the RGI tool as a loose category, the vanS gene in the vanG cluster. Therefore, the vanG operon was predicted as incomplete. However, previous in vitro tests showed that LPG1 was a vancomycin-resistant strain [8]. This may be due to the presence of the D-alanine-D-alanine ligase protein encoded by the ddlA gene, as the vanG gene encodes to form the same protein, creating the dipeptide D-Ala-D-Ala, which after several transformations, develops a high affinity for vancomycin, binding it to the membrane and preventing the penetration of the antibiotic into the cytoplasm [73]. It is noteworthy that vancomycin resistance is considered intrinsic in Lactobacilli and not transmissible and is therefore not considered a safety concern by EFSA. As can be seen in Table S3 (pan-genome analysis) all of the L. pentosus strains carry both genes encoding alanine racemase and D-alanyl-D-alanine. Therefore, resistance to vancomycin is expected. On the other hand, no other AMR genes were obtained by the ResFinder tool for LPG1. These data agree with previous results obtained by Benítez-Cabello et al. [8], who described this strain as very sensitive to a wide range of antibiotics. Furthermore, no gene was predicted to be virulent based on a search in VirulenceFinder. Finally, the PathogenFinder tool classified the strain as a non-human pathogen, with a probability of being a human pathogen of only 0.075%. L. pentosus and L. plantarum are species typically found in a wide range of vegetable fermentations. This ability to colonize different niches is due to the presence, in both species, of an arsenal of enzymes that degrade carbohydrates released during fermentation [47,74]. Regarding technological features, in silico analysis of the LPG1 strain showed a complete Embden–Meyenhorf pathway. Thereby, LPG1 is able to ferment six-carbon carbohydrates such as glucose and fructose, both present in table olive fermentation brine. The enzymatic arsenal predicted in the annotation of the LPG1 strain allows it to capture and hydrolyze sucrose, a compound of glucose and fructose linked through an O-glucosidic bond that is present in large quantities in table olive brine. Moreover, LPG1 contains the mannitol degradative operon. This sugar is also common in table olive fermentations. However, in the absence of six-carbon carbohydrates, LPG1 can ferment five-carbon sugars through the pentose phosphate pathway. Furthermore, the LPG1 strain was predicted to contain other enzymatic tools involved in the degradation of more complex carbohydrates such as galactose, glycogen, starch, cellulose, or xylan (Table S3). Moreover, CAZymes analysis revealed that this enzymatic arsenal was classified into five gene classes of CAZymes. The LPG1 genome contains 58 genes encoding the Glycosides Hydrolase family (GHs), 41 genes encoding Glucosyltransferase (GT), 3 Carbohydrates Esterases (CE), and 8 genes encoding Carbohydrate-Binding Modules (CBM). Similar results were described for other L. pentosus strains [65,75], which contrasts with the lower amount of CAZymes reported in other genera from the Lactobacillaceae family such as L. casei, L. rhamnosus, L. acidophilus, or L. helveticus [76]. This difference probably lies in the ability of L. pentosus to adapt to different niches and the use of diverse carbon sources as nutrients [4]. Therefore, species such as L. rhamnosus, L. casei, or L. acidophilus are more commonly found in host environments. In addition, to contribute to the fermentation process, the LPG1 strain contains three copies of the gene encoding for D-Lactate dehydrogenase and three copies of the genes encoding for L-Lactate dehydrogenase, supporting the results obtained in previous studies where the LPG1 strain was shown to be a major producer of lactic acid [8]. In these previous studies, this strain also showed high esterase activity, involved in aroma production and degradation of the bitter compound oleuropein. The subsequent in silico analysis confirmed the esterase activity, as the LPG1 strain is predicted to contain genes encoding esterase, carboxylesterase, gallate decarboxylase, p-coumaric acid decarboxylase, and the transcriptional regulator PadR, all of which are involved in the degradation of phenolic compounds [68]. On the contrary, no gene encoding β-glucosidase was predicted, also supporting the previous results that showed the phenotype was not positive for this activity [8]. Many microorganisms produce a diverse range of products with antibiotic, antifungal, or immunosuppressant activity such as bacteriocins and Ribosomally synthesized and Post translationally modified Peptides (RiPPs), encoded by different gene clusters [77]. The occurrence of bacteriocins in fermented food is very important because this can help bacteria to inhibit the growth or eliminate foodborne pathogens and spoilage bacteria [78]. After comprehensive bioinformatics analysis, two types of bacteriocins in the LPG1 genome were predicted; however, no RiPPs were predicted. A Pediocin gene cluster (PA-1) was identified between position 3.422.048 and 3.442.255 bp. Although bacteriocins commonly show more activity against Gram-positive bacteria, Escherichia coli and Salmonella Typhimurium have been shown to be sensitive to pediocin. Several studies have shown the relevant activity of Pediocin against Listeria monocytogenes [79]. In addition, the plantaricin EF gene cluster was predicted and encoded between position 420.305 and 435.375 bp. Plantaricin EF consists of two-peptide bacteriocins, and both genes were found side by side in the same operon. Both pediocin and plantaricin EF were found in other genomes of the Lactobacillus genus [80,81]. Previous studies showed the ability of LPG1 to inhibit E. coli and L. monocytogenes under in vitro conditions, improving the inhibitory activity shown by the recognized probiotics strains L. casei Shirota and L. rhamnosus GG [8]. All of these data give solid reasons for the commercial use of this microorganism as a starter culture in table olives (https://www.oleica.es/productos-y-servicios/oleicastarter/, accessed on 10 July 2022) and possibly other fermented vegetables. Regarding the probiotic potential, this activity is determined by several factors such as the capacity to survive digestion and persistence in the human gut, attachment in the intestinal cells, ability to carry out metabolic processes in a highly competitive environment, and finally to produce a beneficial effect on the host [60]. In silico analysis of LPG1 predicted a large number of genes with the ability to perform all of these functions (Table S3). Firstly, the LPG1 strain was predicted to harbor three genes encoding choloylglicine hydrolase, involved in resistance to bile salt. Although no choloylglicine hydrolase gene was annotated as the bile salt hydrolase gene (bsh), previous studies showed the ability of the LPG1 strain to decrease cholesterol levels [8]. The ability of microorganisms to deconjugate bile via the production of bile salt hydrolase has been widely associated with their cholesterol-lowering potential in the prevention of hypercholesterolemia [61]. Additionally, other genes with important roles regarding resistance to bile salt were predicted. In this way, two chaperones genes encoding clpC and clpE, four genes encoding methionine sulfoxide reductase (msrA_1, msrA_2, mrsA_3, and msrB), two genes encoding histidine protein kinase (HKP) along with response regulator (RR), and two efflux systems related to the multidrug resistance transporter family encoding by emrB_1 and lmrA were predicted [82,83]. A large number of genes involved in the response to acid stress and ultimately involved in persistence in the gut, such as the DnaK, DnaJ, GroEL, and GroES chaperones, as well as the GrpE protein, were found in the genome of the LPG1 strain. Previous assays related an upregulated response to the acid environment and bile exposure to genes coding for these proteins [84,85]. Furthermore, a total of 16 complete PTS sugar transporter systems were annotated, reflecting an adaptative and flexible behavior in carbohydrate metabolism. More specifically, cellobiose PTS has been confirmed to play a key role in the competitive ability of Lactobacillus in the gut [86]. Regarding the ability of the LPG1 strain to adhere to intestinal cells, a variety of related genes have been predicted. Thus, five genes encoding a mucus-binding protein (MucBP), two genes encoding a collagen binding-protein and fibronectin-binding domain-containing protein, and two genes encoding glyceraldehyde-3-phosphate dehydrogenase were predicted, all of them involved mainly in the colonization of the gut [62,63,64,87]. In addition, others genes involved in intestinal cell adhesion such as the strA, luxS, dltA, or tuf genes were annotated in the LPG1 genome [88,89,90,91]. In contrast, the gene encoding S-layer protein, an important protein involved in the adhesion to intestinal cells, was not predicted [62]. EPS production also plays an important role in the adhesion to the surface, as well as conferring a certain degree of protection to the bacteria themselves [92]. Moreover, bacterial EPS has been associated with the pathogen’s growth inhibition [93] and modulation of the immune system [94,95]. In this sense, up to two complete EPS clusters were predicted in the LPG1 genome. Alternatively, a certain degree of synergism of LPG1 with the gut microbiota could be expected due to a large number of enzymes producers of lactic acid that were predicted, especially butyrate-producing colon bacteria that transform lactic acid into butyric acid, which has been shown to be an excellent beneficial anti-inflammatory in the gut epithelium [96,97]. Previous studies showed the ability of this strain to modulate the immune response in human and murine cell lines and a colitis murine model, highlighting its anti-inflammatory activity [9]. Finally, some species of Lactobacillus are known to be producers of vitamin B9, also known as folate, an essential nutrient component of the human diet, that is involved in many metabolic pathways [98]. The efficiency of DNA replication, repair, and methylation is affected by folate; therefore, high amounts of folate are required by fast-proliferating cells such as leucocytes, erythrocytes, and enterocytes [99]. The LPG1 genome was predicted to harbor an active folate cluster including genes such as dfrA, fpgS_1, fpgS_2, folP, folE, folk, and folB. Bacteriophages are viruses that infect and replicate in bacterial cells, being ubiquitous and the most abundant biological agent in the environment. Similar to most viruses, bacteriophages are species-specific or even strain-specific. When a bacteriophage infects a bacterial cell, two different strategies of replication can be developed, a lysogenic or a lytic cycle. Usually, during a lysogenic cycle, the phage genome is integrated into the bacterial chromosome, called a prophage. The prophage contains the necessary information to induce a lytic cycle, a viral reproduction method that involves the destruction of infected cells and that is usually induced in response to stress conditions [100,101]. The presence of prophages in bacterial strains with probiotic potential isolated from the fermentation process is common [102,103,104]. Furthermore, prophages are a source of new genes added to the genome, in some cases providing new features in the bacterial genomes [105,106]. These features could include antibiotic resistance genes or virulence factors [107,108]. The LPG1 strain was predicted to contain four intact prophages according to PHASTER. A similar number of prophages have been found in other L. pentosus genomes [14,75,108]. The length of the prophages ranged from 39.4 Kb to 62.5 kB, and each prophage encoded about 50 proteins. Integrase was present in all prophages. Among genes predicted in the prophages sequence, no genes involved in the drug resistance mechanisms or virulence factors were detected. A comparison of the database and the proteomic tree was performed for each latent phage (Figure 5). The prophages analyzed were related to the Siphoviridae family, the main family responsible for infecting the Lactiplantibacillus genus along with the Myoviridae family [109]. The IslandViewer 4 tool was used to predict GI matched with whole prophages predicted through PHASTER. GI refer to discrete DNA segments that establish horizontally transferred genes in a population and frequently encode functions such as pathogenesis, symbiosis, and adaptation [110,111]. Six putative GI in the LPG1 genome were predicted by at least one of the following methods: IslandPick, SIGI-HMM, and IslandPath-DIMOB [112,113,114]. Some of the most interesting genes found in GI of the LPG1 strain were involved in polysaccharide biosynthesis, teichoic acid synthesis, metal ion transporters, and carbohydrate metabolism, such as beta-galactosidase and extracellular alpha-L-arabinofuranosidase. These findings showed similarities with other Lactobacillus where GI included genes coding for sugar metabolism and transport and exopolysaccharide biosynthesis [115,116]. Furthermore, a search for integrons was carried out using IntegronFinder, and no results were obtained for the presence of integrons. Perhaps the most important mobile genetic elements are the integrons, which play a key role in the dissemination of antibiotic-resistance genes [117]. The GI coding for L-arabinofuranosidase and beta-galactosidase, both found within the shell genes in the pan-genome analysis, were used to find the most likely bacterium that transferred these genes to LPG1. Thus, BLAST-p was performed for both genes, prior to transformation into proteins. Later, the alignment was held with the sequences of most similar genomes found in the BLAST-p results. The phylogenetic tree showed that Levilactobacillus was probably the genus responsible for the transfer of the GI to LPG1 (Figure 6). Interestingly, species of the Levilactobacillus genus have been also identified in Sicilian and Algerian table olive processing [118,119], with both species sharing the same habitats. In addition, L. pentosus IG8, IG9, IG10, and IG11, all of them from table olive processing [13], contain these genes in common with LPG1. Therefore, it is likely that the LPG1 strain shares a common ancestor with all of them. These results agree with the hierarchical clustering obtained from the pan-genome analysis. Finally, other mobile genetic elements such as the insertion sequence (IS) could play a role in gene disruption or activation due to promoter interaction to contribute to the genome plasticity [120]. Functional gene loss occurs in genomes with a large number of IS elements. Furthermore, IS elements with transposase genes flanked by two inverted repeats have the skill to move through transposition, causing DNA rearrangement [121,122]. Seventeen transposases were found in the chromosome of LPG1, and 13 were located in pl1LPg1, the most abundant plasmid in the LPG1 strain. A similar number was found in other Lactobacillus strains such as L. pentosus DSM20314 or L. reuteri PNW1 [67]. However, the number was lower than that of other L. pentosus with putative probiotic potential such as L. pentosus MP-10 or L. pentosus CF2-10N [14,123]. This could mean that the LPG1 strain has higher transcription stability than other genomes with a large amount of IS. The transposase included 10 different families, highlighting the ISL3, IS5, and IS30 families with multiple copies. CRISPR-Cas systems are an adaptive immune system present in prokaryotic organisms, both bacteria, and Archaea, conferring resistance to exogenous genetic material, whether from pathogens, other bacteria, phages, or any element that could lead to a risk to the organism at the expression levels [124,125]. Previous studies have shown the occurrence of the prophages was lower in strains with the CRISPR-Cas system than in strains without this immune system, suggesting that CRISPR-Cas systems play a key role in preventing phage infection and consequently avoiding prophage integration into the genome [100]. In the LPG1 genome, two CRISPR-Cas systems were identified, CRISPR-Cas type I-E and type II-A. CRISPR-Cas operon type I-E and type II-A were formed by eight Cas genes (cas1, cas2, cas3, cse1, cse2, cas7, cas5, and cas6) and four Cas genes (cas9, cas1, cas2, and csn2), respectively. These results were very similar to those found in other L. pentosus strains such as KCA1, MP-10, IG4, IG7, IG8, IG9, IG11, or IG12 [12,13,65,66]. Up to six CRISPR arrays were predicted by the CRISPRcasFinder tool but, only four were confirmed by CRISPRdb. A CRISPR array is composed of repeated elements and spacers, where spacer sequences are fragments of foreign DNA originating from plasmids, phages, or other exogenous genetic material that are incorporated into the host [125]. Therefore, the occurrence of the CRISPR-Cas system could prevent the LPG1 strain from acquiring antimicrobial resistance genes or virulence or pathogenic factors through the horizontal transfer of genes. Comprehensive bioinformatic analysis of the LPG1 genome has revealed the presence of a large number of genes related to multifunctional features, which could be allied with many of its technological and probiotic phenotypes obtained in previous studies. A safety assessment of the LPG1 genome also showed the absence of virulence and antibiotic resistance genes, as well as the presence of gene systems that could prevent their acquisition (CRISPR-Cas system). Conclusively, LPG1 is a safe microorganism with great potential for use as a human probiotic from plant origin or as a starter culture for vegetable fermentations.

PMC10000689

Aviral Kumar,Sosmitha Girisa,Mohammed S. Alqahtani,Mohamed Abbas,Mangala Hegde,Gautam Sethi,Ajaikumar B. Kunnumakkara

Targeting Autophagy Using Long Non-Coding RNAs (LncRNAs): New Landscapes in the Arena of Cancer Therapeutics

06-03-2023

lncRNAs,autophagy,cancer,therapeutics

Cancer has become a global health hazard accounting for 10 million deaths in the year 2020. Although different treatment approaches have increased patient overall survival, treatment for advanced stages still suffers from poor clinical outcomes. The ever-increasing prevalence of cancer has led to a reanalysis of cellular and molecular events in the hope to identify and develop a cure for this multigenic disease. Autophagy, an evolutionary conserved catabolic process, eliminates protein aggregates and damaged organelles to maintain cellular homeostasis. Accumulating evidence has implicated the deregulation of autophagic pathways to be associated with various hallmarks of cancer. Autophagy exhibits both tumor-promoting and suppressive effects based on the tumor stage and grades. Majorly, it maintains the cancer microenvironment homeostasis by promoting viability and nutrient recycling under hypoxic and nutrient-deprived conditions. Recent investigations have discovered long non-coding RNAs (lncRNAs) as master regulators of autophagic gene expression. lncRNAs, by sequestering autophagy-related microRNAs, have been known to modulate various hallmarks of cancer, such as survival, proliferation, EMT, migration, invasion, angiogenesis, and metastasis. This review delineates the mechanistic role of various lncRNAs involved in modulating autophagy and their related proteins in different cancers.

Targeting Autophagy Using Long Non-Coding RNAs (LncRNAs): New Landscapes in the Arena of Cancer Therapeutics Cancer has become a global health hazard accounting for 10 million deaths in the year 2020. Although different treatment approaches have increased patient overall survival, treatment for advanced stages still suffers from poor clinical outcomes. The ever-increasing prevalence of cancer has led to a reanalysis of cellular and molecular events in the hope to identify and develop a cure for this multigenic disease. Autophagy, an evolutionary conserved catabolic process, eliminates protein aggregates and damaged organelles to maintain cellular homeostasis. Accumulating evidence has implicated the deregulation of autophagic pathways to be associated with various hallmarks of cancer. Autophagy exhibits both tumor-promoting and suppressive effects based on the tumor stage and grades. Majorly, it maintains the cancer microenvironment homeostasis by promoting viability and nutrient recycling under hypoxic and nutrient-deprived conditions. Recent investigations have discovered long non-coding RNAs (lncRNAs) as master regulators of autophagic gene expression. lncRNAs, by sequestering autophagy-related microRNAs, have been known to modulate various hallmarks of cancer, such as survival, proliferation, EMT, migration, invasion, angiogenesis, and metastasis. This review delineates the mechanistic role of various lncRNAs involved in modulating autophagy and their related proteins in different cancers. Cancer has been hailed as one of the deadliest diseases of the 21st century, with a significant impact on patients’ economics and quality of life [1]. Different treatment regimens are available in the battle against cancer, but their effect is limited based on the tumor grade, stage, and type [2,3]. Novel methods have tried to determine the possible targets for early detection, but still, much research is required to firmly establish these targets to be used as effective prognostic and diagnostic tools. With the identification of several types of cancers, their mechanisms, pathways involved, and their causes, it has become even more imperative to identify further the causative factors that could help diagnose and combat the disease [4,5,6]. The shift towards more personalized approaches for the treatment of this disease has led to an increase in the overall survival of patients [7]. Autophagy is an evolutionary conserved, multi-step, biological process of cellular degradation and elimination of damaged or misfolded proteins and cellular organelles that occurs in response to stressful stimuli [8,9]. The word autophagy (from the Greek term “auto”, meaning oneself, and “phagy”, referring to eat) was first coined by Nobel laureate Christian de Duve in 1963 at the Ciba Foundation Symposium on Lysosomes in London [10]. Although many researchers have tried to delve into the molecular mechanism governing autophagy, only recently, in 2016, Yoshinori Ohsumi was awarded the Nobel Prize for Physiology or Medicine for his research on the mechanism of autophagy and its effect on human health and disease [11]. Autophagy helps to maintain cellular homeostasis by engulfing the aggregates or organelles in membrane vesicles, which are then transported to the lysosome for degradation [12]. Moreover, the basal activation of autophagy plays a vital role in the maintenance of organelle quality control [13]. It is also established that autophagy plays a crucial role in immune surveillance, as peptides of pathogens degraded by autophagic pathways can present antigens to immune cells, thereby regulating host defense and immunity [14,15,16]. In the absence of stress stimuli, such as hypoxia, nutrient deprivation, the presence of pathogens, and the accumulation of misfolded proteins, autophagy is active at basal levels to recycle the nutrients and maintain the energetics of the cell [17]. However, autophagy is upregulated in response to various stresses to preserve cellular homeostasis. Autophagy can be stratified into three major types depending upon the route of cargo delivery to the lysosome, which are macroautophagy, microautophagy, and chaperon-mediated autophagy (CMA) [18,19]. Macroautophagy is the most widely studied mechanism, wherein the formation of autophagosomes and autolysosomes takes place. The autophagosomes, double membrane vesicles, engulf the protein aggregates and (a portion of) organelles, and fuse with the lysosome to form autolysosomes [20]. Microautophagy, a process of cell eating, involves the direct engulfment of cytoplasmic contents into the lysozymes at the membrane boundary by autophagic tubes. The major functions of microautophagy in cellular homeostasis are the maintenance of organelle size, membrane dynamics, and survival under nitrogen restrictions [21,22]. In CMA, cytosolic proteins with the pentapeptide sequence KFERQ are identified by the heat shock cognate (HSC70; also known as HSPA8) to form a complex. LAMP2A or the lysosomal-associated membrane protein 2A facilitates the translocation of the chaperone complex into the lysosome [23]. The various autophagy processes are strictly regulated by a set of autophagy-related genes (ATGs) [24,25]. A plethora of studies have elucidated the crucial involvement of autophagy in various human disorders, including cancer [26,27]. As autophagy is a catabolic degradative process, it prevents the accumulation of cellular damage that could otherwise lead to cancer initiation. Autophagy acts as a double-edged sword in cancer development and progression by playing a multi-faceted role in both tumor-promoting and suppressing functions [12,28]. In the early stages of cancer, autophagy has tumor-suppressive functions through the degradation of harmful and damaged proteins/organelles, thus minimizing the accumulation and spread of damage. However, in the advanced stages of the disease, it exhibits tumor-promoting effects by maintaining the vitality and viability of the tumor under the stress microenvironment [29]. As tumors require sustained proliferation, nutrient demand is crucial for their progression. Metabolic reprogramming by the tumor activates autophagy to recycle the nutrients and channel the energy back to the tumor [30]. Initial evidence for a possible direct connection between autophagy and cancer came from the discovery of Beclin-1 as a haploinsufficient tumor suppressor with mutations in cancers of the ovary, small intestines, and skin [31]. Moreover, it has been reported that the activation of RAS, a well-known oncogene, induces autophagy through signaling pathways, such as Raf-1/ERK, PI3K/mTOR, and Rac1/JNK [32]. Various studies have linked autophagy to be involved in the maintenance of cancer dormancy [33,34,35,36]. Cellular dormancy refers to a halt in the proliferative abilities of the cells, where they enter into a quiescent-like state and rest in the G0–G1 phase of the cell cycle. The dormant cancer cells utilize autophagy to survive in hypoxic and nutrient-deficit conditions in the tumor microenvironment [33]. These dormant cells may reside in the tumor for a long time and not respond to therapies, leading to metastasis and disease recurrence [37]. Additionally, autophagy is thought to contribute to tumor cells’ lower vulnerability to NK cell eradication. According to Baginska et al., hypoxia-induced autophagy prevented the NK cell-mediated destruction of MCF-7 breast cancer cells [24]. The deprivation of glucose and amino acids triggers HIF-1-independent autophagy through AMPK activation and mTOR inhibition [38]. Autophagy can be employed with immunosurveillance for the non-cellular autonomous prevention of cancer. For instance, reduced autophagy is linked to regulatory T-cell infiltration, which suppresses the immune system and reduces the effectiveness of immunosurveillance, thus increasing tumor development [39]. Therefore, increasing autophagy in premalignant lesions might be a plausible approach for inhibiting tumor progression. Recently, there have been different clinical interventions specifically modulating or targeting autophagy in cancer therapy, with the vast majority focusing on inhibiting autophagy [12,30]. Thus, understanding the intricate molecular regulation of autophagy and its distinct functions is essential for the development of cutting-edge cancer treatments. Recently, there has been increased attention towards non-coding RNAs (ncRNAs) and their role in regulating various biological processes, such as survival, proliferation, differentiation, apoptosis, the immune response, metabolism, and homeostasis [40,41,42,43]. Among various ncRNAs, long non-coding RNAs (lncRNAs) have emerged as important players in modulating diverse molecular and biological functions [44]. lncRNAs are ncRNAs of more than 200 nucleotides in length and are involved in transcriptional and post-transcriptional gene regulation [45,46]. Like most non-coding RNAs, lncRNAs are transcribed by RNA polymerase II, containing a 5′ methyl-cytosine cap and 3′ poly(A) at the tail [47]. Various mechanisms are associated with the formation of lncRNAs, such as the generation of mature end by ribonuclease P (RNaseP), cleavage, and the formation of complex caps from small nucleolar RNA (snoRNA) and protein (snoRNP) [48,49]. In one study, sub-nuclear structures called “paraspeckles” were found to be present in the biogenesis of specific lncRNA [50]. Still, the exact mechanism of lncRNA synthesis remains unclear, and more in-depth studies are vital in understanding the functional role of lncRNAs in gene regulation. lncRNAs can act as scaffolds, decoys, enhancers, and guides to bind and regulate DNA, RNA, and proteins [51]. Further, lncRNAs can behave as competing endogenous RNAs (ceRNAs), which sequester the microRNAs (miRNA) by competing with and sharing similar miRNA-response elements, thus regulating the expression of miRNAs and the gene [52]. As the research on lncRNAs is constantly evolving, the functional classification of lncRNA is not present to date. Recent studies have linked lncRNA deregulation with various pathophysiological processes of different human disorders, including cancer [53,54,55,56]. lncRNAs can act as oncogenes and tumor suppressors, thereby modulating different hallmarks of cancer, such as survival, proliferation, apoptosis, migration, invasion, epithelial-to-mesenchymal transition (EMT), angiogenesis, and metastasis [57,58,59,60,61]. There have been reports in which lncRNAs regulate autophagy by modulating the expression of various ATG genes. Majorly, lncRNAs act as ceRNAs to sequester miRNAs targeting the autophagic process [62,63]. This review focuses on the functional role of different lncRNAs modulating autophagy in various cancers. Further, it provides insights into the autophagy-related lncRNAs and their regulation of ATG genes in defining the autophagic process of initiation, phagophore formation, autophagosome elongation/closure, and autolysosome fusion. Targeting autophagy through lncRNAs would be a novel treatment regimen in circumventing this deadly malady. Autophagy is a multi-step and dynamic process that eliminates accumulated misfolded proteins or damaged organelles. Although there are three major types of autophagy, macroautophagy has been extensively studied and well-researched [62]. Here, we have discussed the major phases of autophagy with an emphasis on lncRNAs as regulators of different ATG-related genes (Figure 1). Two major classes of sensing proteins that are involved in autophagy initiation are the mammalian target of rapamycin complex 1 (mTORC1) and adenine monophosphate-activated protein kinase (AMPK) [64,65,66]. During nutritional starvation, the phosphorylation of AMPK leads to the inhibition of mTORC1, which induces the ATG1/ULK1/2 complex to initiate the autophagic process [64,66]. In addition, it has been well-reported that a PI3K/AKT/mTOR signaling cascade is negatively associated with the initiation of autophagy [67]. Then, ATG17 forms a complex with ATG31 and ATG29 and other autophagy-related genes, ATG13 and ATG1, to form the pre-autophagosomal structure (PAS) in yeast systems [68,69]. A stable complex is formed in mammalian cells with the help of the ULK1/2 and ATG101 complex in conjugation with ATG13 and FIP200/RB1CC1, which is then transferred to omegasomes (the site of autophagosome synthesis) [69,70]. Zhou and his colleagues elucidated the functional role of lncRNA H19 in autophagy initiation. It was reported that high glucose levels decreased H19 expression leading to transcriptional inactivation of DIRAS3 via inhibition of the PI3K/AKT/mTOR signaling cascade. Further, the knockdown of H19 increased ATG7 and Beclin-1 levels, indicating a link between the H19 and major autophagy-related genes [71]. In another study, it was observed that lncRNA H19 activated autophagy via the repression of DUSP5, a type of mitogen-activated kinase phosphatase. It is well known that DUSP5 inhibits ERK1/2 phosphorylation, which can suppress autophagy [72]. Recently, a study reported that lncRNA neighbor of BRCA1 gene 2 (NBR2) could induce the activation of AMPK through direct binding. Moreover, the expression of NRB2 was found to be regulated by increasing AMPK activation under stress stimuli [73]. Furthermore, another study demonstrated the link between miR-19a and NRB2 in acute liver failure. It was observed that miR-19a suppresses autophagy by regulating the NBR2/AMPK axis [74]. It has been found that upregulating the artificial lncRNA Ad5-AlncRNA resulted in sponging multiple miRNAs (miR-216a, miR-21, miR-494, and miR-217), which targeted PTEN. This led to an increase in PTEN levels, inhibiting the AKT/mTOR pathway, thus activating autophagy in hepatocellular carcinoma (HCC) [75]. Studies have suggested that the knockdown of lncRNA HOXA transcript antisense RNA myeloid-specific 1 (HOTARIM1) results in the inhibition of autophagy and suppression of all-trans retinoic acid (ATRA)-induced degradation of RARA in promyelocytic leukemia cells [76,77]. It was observed that lncRNA maternally expressed gene 3 (MEG3) overexpression induces autophagy by directly binding to the ATG3 protein, thus preventing its degradation in ovarian carcinoma and inhibiting tumorigenesis [78]. Interestingly, lncRNA PTEN pseudogene-1 (PTENP1) has a similar 3′ UTR to that of PTEN, a tumor suppressor gene, and hence, it confers a protective role to PTEN from its candidate miRNAs [79]. The overexpression of PTENP1 results in increased levels of PTEN, leading to repression of the PI3K/Akt pathway and the activation of autophagy. Moreover, it has been observed that PTENP1 can sequester miR-20a and miR-17 to increase the expression of ATG7, ULK1, and p62/SQSTM1 [80]. After activation of the omegasome/PAS, the ATG1/ULK1 complex induces the PI3K complex, consisting of Vps15, Vps34, Beclin 1, and Barkor, to form phosphatidylinositol 3-phosphate (PI3P) [17,81,82]. PI3P promotes formation of the omegasome through the recruitment of double FYVE-containing protein 1 (DFCP1) [83]. In addition, various regulators of autophagy, such as VMP1 and ATG9, are active in the autophagic membrane [69,84]. It has been found that Bcl-2 and Rubicon are negative regulators of phagophore nucleation by modulating the formation of the class III PI3K complex [85,86]. The inhibiting long intergenic non-protein coding RNA, regulator of reprogramming (linc-ROR) can induce autophagy by increasing Beclin-1 expression and inducing gemcitabine and tamoxifen resistance in breast cancer. However, more research is required to determine how linc-ROR modulates the expression of Beclin-1 and whether linc-ROR could be used in clinical settings [87,88]. LncRNA loc146880 has been found to be closely associated with lung cancer pathogenesis and autophagy. Induction with PM2.5 (a particulate matter) results in high levels of reactive oxygen species (ROS), and it increases the expression of the lncRNA loc146880, which activates autophagy and consequently promotes lung cancer cell invasion and migration [89]. Treatment with cisplatin increases the expression of lncRNA AC023115.3 and facilitates cisplatin-induced apoptosis. Additional mechanistic investigations have shown that lncRNA AC023115.3 sponges miR-26a, thereby increasing glycogen synthase kinase-3 (GSK3) expression [90]. Together, linc-ROR and loc146880 can influence vesicle nucleation by regulating Beclin-1 gene or protein expression. Ubiquitin-like conjugation systems have an important function in the autophagosome elongation/closure of the membrane. ATG12 conjugates to ATG5 and subsequently interacts with ATG16 to form the ATG12–ATG5–ATG16 complex under the control of ATG7 (E1-like enzyme) and ATG10 (E2-like enzyme) [91,92]. Then, the newly formed ATG5–ATG12–ATG16 complex helps LC3B (ATG8) to transform into its soluble cytosolic form (LC3-I) to the membrane-anchored LC3-II [93]. The neighbor of BRCA1 gene 1 (NBR1), Nix, p62, and other adaptor proteins, such as ATG19 and ATG32, selectively mediate the degradation of proteins or organelles by attracting them to autophagosomes via binding to LC3-II [93]. Research on lncRNA TGFB2 overlapping transcript 1 (TGFB2-OT1) has revealed that vascular endothelial inflammation triggers the upregulation of TGFB2-OT1, which in turn increases the expression of ATG7, ATG3, and p62 expression, plausibly by increasing the LARP1 levels and sponging miR-4459 [94]. In another study, the overexpression of GAS5 resulted in decreased levels of ATG5, ATG7, Beclin-1, ATG3, ATG12, and LC3B expression, thereby repressing autophagy [95]. Another lncRNA, prostate cancer gene expression marker 1 (PCGEM1), promotes autophagy by increasing the mRNA levels of ATG3, ATG5, ATG12, and Beclin-1 [96]. It was found that lncRNA HNF1A-AS1 can prevent miR-30b-5p from interacting with its target ATG5 and thus repress autophagy in hepatocellular cancer (HCC) [97]. In another study, lncRNA HOX antisense intergenic RNA (HOTAIR) expression was upregulated in HCC and promoted proliferation by increasing ATG3 and ATG7. Since many miRNAs, including miR-34a, miR-331-3P, miR-130a, and miR-454-3p, can interact with HOTAIR, it can control autophagy in two different ways. Firstly, to prevent miRNA transcription, HOTAIR may work as a scaffold to draw in epigenetic modification enzymes. Secondly, HOTAIR may act as a sponge to capture miRNAs from their targets [98]. The last step of the autophagic process is the fusion of lysosomes to the autophagosomes to form an autolysosome, wherein the final degradation takes place [93]. The Rab–SNARE system and the lysosome membrane proteins LAMP1 and LAMP2 are the key molecules involved in autolysosome fusion [99,100]. Further, various adaptor proteins are crucial to connect the lysosome to the endocytic and autophagic processes. One such adaptor protein, known as pleckstrin homology domain-containing protein family M member 1 (Plekhm1), mediates the fusion of endosomes and autophagosomes with lysosomes by directly associating with the homotypic fusion and protein sorting complex and possessing an LC3-interacting region [101]. LncRNA cardiac hypertrophy-associated transcript (Chast) has been found to repress autophagy by decreasing the Plekhm1 levels and possibly ATG5 expression. This Chast/Plekhm1 axis regulates the fusion of autophagosomes to the lysosomes [102]. Accumulating evidence has implicated various lncRNAs regulating autophagy in different types of cancer [103,104,105,106,107,108,109,110]. In most cases, lncRNAs work as competing endogenous RNAs (ceRNAs) by sequestering the autophagy-related miRNAs, thereby regulating the ATG genes responsible for autophagy [53]. Recent investigations have elucidated the mechanistic role of different lncRNAs involved in autophagic regulation in tumorigenesis (Table 1). Herein, various lncRNAs that have been found to target the autophagy process have been described in cancers of the bladder, breast, cervical colon, lung, liver, blood, bone, brain, pancreas, and prostate, etc. (Figure 2; Table 2). Bladder cancer originates in the lining of the bladder, and it accounts for 212,536 cancer-related deaths worldwide [1]. Although it is a rare type of malignancy, recent statistics show its increasing prevalence worldwide with 573,278 cases reported in 2020 [1,111,112]. Very few lncRNAs have been found to play a vital role in regulating autophagy in bladder cancer [113]. Ying and their colleagues elucidated the functional role of lncRNA MEG3 in bladder tumorigenesis. LncRNA MEG3 was found to be downregulated in the bladder tumor tissues compared to levels in the normal tissue samples. It was also found that MEG3 was negatively correlated with levels of the autophagic marker LC3II; therefore, the knockdown of MEG3 activated autophagy by regulating LC3II levels. Moreover, MEG3 silencing inhibited apoptosis and induced T24 cell proliferation [114]. Another study reported that lncRNA urothelial carcinoma-associated 1 (UCA1) was involved in regulating ATG7 levels by sponging miR-582-5p. In addition, in vivo studies demonstrated that UCA1 was involved in bladder cancer progression, and the knockdown of UCA1 inhibited growth, migration, and invasion in bladder cancer cells [115]. Further, lncRNA ADAMTS9-AS1 was shown to regulate apoptosis and autophagy in bladder cancer. Silencing ADAMTS9-AS1 was found to induce autophagy and apoptosis by upregulating the LC3-II/I ratio, Beclin-1, Bax, and Caspase 9 levels while decreasing the expression of p62 and Bcl-2 in T24 and 5637 bladder cancer cell lines [116]. Hence, lncRNAs, such as MEG3, UCA1, and ADAMTS9-AS1, have been shown to regulate autophagy in bladder cancer; however, further research is crucial to elucidate their effects and mechanism in bladder carcinogenesis. Breast cancer is the most common malignancy among women and accounts for the highest number of incidences reported in the year 2020 worldwide [1,117,118]. Recent studies have found that lncRNAs are crucial regulators of autophagy in breast cancer [119,120,121]. A study conducted by Zhang and group showed that the knockdown of lncRNA differentiation antagonizing non-protein coding RNA (DANCR) promoted autophagy and apoptosis and inhibited breast cancer cell proliferation by increasing Caspase 3 and 9, Atg5, LC3B, and Bax/Bcl-2 while decreasing Beclin-1. Moreover, it was revealed that DANCR acts as a competitive endogenous RNA sponge to regulate the expression of PAX6 by targeting miR-758-3p [120]. LncRNA HOTAIR was also found to be an important regulator of autophagy in breast cancer. It was observed that there are many different mechanisms of interplay between autophagy and HOTAIR in breast cancer; however, further research is required to find the underlying mechanisms [122]. Another study revealed that lncRNA growth arrest-specific transcript 5 (GAS5) is a significant contributor to the pathogenesis of breast cancer. The overexpression of GAS5 led to an increase in LC3B and Beclin-1 expression and chemosenstivity towards cisplatin. This was mainly achieved by promoting autophagy in breast cancer cells through the regulation of ULK1/ULK2 [121]. Another study observed that H19 was overexpressed in tamoxifen-resistant breast cancer cells, and the knockdown of lncRNA H19 abrogated autophagy by decreasing LC3-II and Beclin-1 levels. Interestingly, the overexpression of H19 in MCF7 tamoxifen-sensitive cells could reiterate tamoxifen resistance [119]. Other studies have also demonstrated the functional role of different lncRNAs in regulating autophagy in breast cancer [123,124]. Hence, lncRNAs, such as DANCR, HOTAIR, H19, and GAS5, are identified as key regulators of autophagy in breast cancer, and further research needs to be carried out to identify their potential in breast cancer diagnosis and treatment. Cervical cancer is a malignant tumor that originates in the cervix cells and is one of the most common cancers occurring in women worldwide [203,204]. According to GLOBOCAN 2020, approximately 604,127 incidences were reported for cervical cancer [1]. Few studies have investigated the functional role of lncRNAs in modulating autophagy in cervical cancer. In line with this, Zhang and his group studied the role and effect of lncRNA ROR1 antisense RNA 1 (ROR1-AS1) in cervical cancer. It was observed that the silencing of ROR1-AS1 resulted in decreased proliferation, migration, invasion, and autophagy in cervical cancer. Moreover, ROR1-AS1 was found to regulate the expression of STC2 by sponging miR-670-3p [127]. In another study, lncRNA RP11-381N20.2 was significantly downregulated in the chemotherapy-insensitive cervical cancer patients compared with that in the chemotherapy-sensitive group. Moreover, treatment with RP11-381N20.2 was found to inhibit paclitaxel-induced autophagy in SiHa cells [128]. Thus, lncRNAs play a crucial role in regulating the process of autophagy in cervical cancer; however, further studies are required to understand its in-depth mechanistic role in modulating autophagy in cervical cancer. Colorectal cancer (CRC) is the third most common cancer diagnosed and one of the major causes of cancer-related deaths worldwide [1,205]. CRC is a heterogeneous disease characterized by the gradual compilation of genetic and epigenetic changes, leading to the modification of normal colonic mucosa into invasive cancer [206,207,208,209,210]. Recent studies have shown that lncRNAs play a crucial role in regulating the autophagic mechanisms involved in intestinal tumorigenesis [131,132,134,137]. For instance, it was observed that nuclear paraspeckle assembly transcript 1 (NEAT1) lncRNA was highly expressed in CRC cells and tissues and promoted autophagy by directly targeting miR-34a levels. Moreover, NEAT1 was also found to be involved in promoting 5-FU-chemoresistance in CRC by indirectly regulating HMGB1 levels [134]. Another study reported that lncRNA small nucleolar RNA host gene 6 (SNHG6) inhibited the expression of miR-26a-5p and promoted ULK1-induced autophagy in RKO, HCT116, and HT29 cell lines. Further, it was also found to play a vital role in chemoresistance by sponging miR-26a-5p expression [211]. In addition, Wang and Jin explored the role of lncRNA SLCO4A1 antisense RNA 1 (SLCO4A1-AS1) in colorectal tumorigenesis. It was found that SLCO4A1-AS1 is an important factor in inducing the proliferation of cancer cells through the enhancement of autophagy via the miR-508-3p/PARD3 axis [135]. Additionally, it was found that lncRNA H19 is a mediator of autophagy in colorectal cancer cells. It was observed that the overexpression of H19 led to increased LC3-II levels and decreased p62 expression, which triggered the autophagy signaling pathway [140]. Further, in the research conducted on lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), it was observed that it is an activator of autophagy and was found to promote cell proliferation and inhibit apoptosis by sponging miR-101 in CRC. It was also observed that the suppressive effects of miR-101 on proliferation and autophagy in CRC cell lines were abolished in rescue experiments by MALAT1 [138]. Another study on lncRNA small nucleolar RNA host gene 14 (SNHG14) showed that it stimulates autophagy by regulating the miR-186/ATG14 axis. Further, this study concluded that SNHG14 could have a crucial role in the regulation of cisplatin resistance in CRC via autophagy [136]. LncRNA CPS1 intronic transcript 1 (CPS-IT1) was identified as an important suppressor of EMT and metastasis in CRC. This tumor suppressor role was achieved by inhibiting hypoxia-induced autophagy through inactivating HIF-1α [139]. Moreover, another study has revealed that lncRNA UCA1 accelerates 5-FU resistance in CRC cells by inhibiting apoptosis and facilitating autophagy. Further, miR-23b-3p was identified as a key target of UCA1 in CRC cells. It was also found that the knockdown of miR-23b-3p resulted in reversing the effects of UCA1 interference. Thus, UCA1 was identified as a critical regulator of autophagy [137]. In addition, a study showed that lncRNA KCNQ1 opposite strand/antisense transcript 1 (KCNQ1OT1) enhances autophagy in colon cancer. It was also found to be a promoter of chemoresistance by sponging miR-34a, thereby modulating the expression of Atg4B. Due to these multifaceted roles, KCNQ1OT1 could be a promising target for colon cancer therapeutics [133]. Additionally, another study showed that inhibition of lncRNA eosinophil granule ontogeny transcript (EGOT) promoted autophagy in colon cancer and significantly suppressed cell growth and metastasis [130]. In another study, lncRNA CASC2 was found to be an inducer of autophagy and apoptosis by modulating the expression of TRIM16 in colon cancer [131]. Finally, lncRNA LINC00858 was found to inhibit the autophagy, apoptosis, and senescence of colon cancer cells via the activation of methylation at the WNK2 promoter [132]. Hence, many studies have led us to conclude that lncRNAs play a vital role in regulating the autophagic mechanisms in colorectal cancer. Gastric cancer (GC), also called stomach cancer, is the 5th most common cancer occurring worldwide, which primarily arises from the lining of the stomach [1,224]. Although early interventions have been successful in treating this malignancy, it still ranks as the 4th leading cause of cancer-related deaths worldwide [1,225,226]. Hence, it is necessary to identify plausible mechanisms and causative factors that could be developed into treatment regimens. There has been a plethora of studies indicating the functional role of various lncRNAs in modulating autophagy to promote or inhibit tumorigenesis in GC [143,144,145]. For example, Wu and his group explored the oncogenic role of lncRNA small nucleolar host gene 11 (SNHG11) in promoting GC. SNHG11 was observed to be upregulated in both tumor tissues and cell lines of GC and was correlated with poor clinical outcomes in patients. It was observed that inhibiting SNHG11 led to the abrogation of various hallmarks of gastric cancer, such as proliferation, migration, invasion, stemness, and EMT, by preventing autophagy. Moreover, the post-transcriptional expression of autophagy-related 12 (ATG12) and catenin beta 1 (CTNNB1) was regulated by SNHG11 through the modulation of miR-483-3p/miR-1276. Further, SNHG11 initiated the ubiquitination of GSK-3β by interacting with Cullin 4A (CUL4A) to promote the activation of Wnt/β-catenin signaling cascade [143]. In another study, lncRNA JPX transcript, XIST activator (JPX) was found to be upregulated while the miR-197 level was downregulated and was associated with poor overall survival in patients. Further, miR-197 was established as a direct target of JPX, and the knockdown of JPX resulted in decreased cell migration, invasion, and modulated autophagy by regulating CXCR6 and miR-197. Moreover, the overexpression of miR-197 reduced the expression of CXCR6, Beclin-1, and the ratio of LC3-II/LC3-I levels with an increase in p62 protein expression [144]. Another group sought to evaluate the underlying mechanism of cisplatin resistance in GC regulated by LINC01572. Expression analysis revealed LINC01572 was upregulated in cisplatin-resistant GC patient samples and resistant cell lines. It was found that LINC01572 acted as a sponge for miR-497-5p, and the overexpression of miR-497-5p led to the blockage of ATG14, an essential protein in the autophagic pathway leading to apoptosis in drug-resistant cell lines. These results were further validated in xenograft mouse models, where the downregulation of LINC01572 resulted in increased tumor volumes by inhibiting autophagy through the miR-497-5p/ATG14 axis [145]. Other studies have also corroborated the important role of lncRNAs in regulating autophagy in gastric cancer [146,147,148,149,150,151,152]. Taken together, lncRNAs have proven to be indispensable regulators of autophagy in gastric tumorigenesis. Glioma is a prevalent tumor of the primary central nervous system, and the prognosis for this disease is poor [227,228]. Recently, many studies have reported the potential of autophagic drugs to induce cell death in glioma [229,230]. Thus, modulators of the autophagic process in glioma hold immense prospects in the management of this disease. In line with this, recent investigations have gauged the mechanistic role of lncRNAs in autophagy in glioma [90,105,160]. A study on lncRNA MALAT1 showed that it is an important activator of autophagy and promotes glioma cell proliferation by sponging miR-101. Additionally, the knockdown of MALAT1 resulted in the downregulation of ATG4D, STMN1, and RAB5A expression in glioma cell lines [105]. Another study on lncRNA AC023115.3 demonstrated that it inhibits the chemoresistance of glioblastoma by significantly reducing autophagy and sponging miR-26a [90]. Moreover, a study by Huo and his group revealed that lncRNA GAS5 facilitates the sensitivity of glioma cells by suppressing excessive autophagy via activation of the mTOR signaling cascade [159]. In addition, research conducted by Zheng and colleagues showed that Linc-RA1 is an inhibitor of autophagy. Further, it also promotes radioresistance by preventing the H2Bub1/USP44 complex in glioma cell lines [160]. Thus, the above studies demonstrate the crucial role of various lncRNAs in regulating autophagy in glioma. Hepatocellular cancer (HCC) is the most common malignancy of the liver and is also the third largest cause of cancer-related deaths worldwide [1,231]. Autophagy, through its various molecular associations, plays a crucial role in the pathogenesis of HCC [232]. Recently, various studies have explored the role of lncRNAs in regulating autophagy in HCC [168,169,173,216,233]. For instance, in a study on lncRNA activated by transforming growth factor beta (ATB), it was found to promote autophagy by activating YAP and inducing ATG5 in HCC cells [175]. Moreover, experiments on lncRNA HOTAIR demonstrated that it induced the activation of autophagy in HCC. It was also found that the overexpression of HOTAIR leads to the upregulation of ATG3 and ATG7 expression in HCC cell lines [98]. Another study revealed that the knockdown of lncRNA H19 resulted in a decrease in autophagic markers, such as LC3-II and Beclin-1, leading to activation of the PI3K/AKT/mTOR pathway in HCC [170]. In addition, it was found that lncRNA SNHG11 promotes autophagy, apoptosis, and proliferation via the regulation of hsa-miR-184/AGO2 in HCC [165]. Additionally, research on lncRNA colon cancer-associated transcript-1 (CCAT1) showed that it aids I and promotes autophagy by functioning as a sponge for miR-181a-5p, thereby regulating the expression of ATG7 in HCC [167]. Further, lncRNA MEG3 was found to contribute towards the inhibition of autophagy by regulating the expression of Beclin-1 and PI3K/AKT/mTOR signaling pathways [215]. Another study discovered that the lncRNA hepatocyte nuclear factor 1α (HNF1A-AS1)/miR-30b-5p axis promotes autophagy, and ATG5 was identified as a target of miR-30b-5p [97]. Furthermore, lncRNA MCM3AP-AS1 was found to be involved in regulating autophagy and promoting HCC metastasis by interacting with miR-455 and regulating epidermal growth factor receptor (EGFR) [216]. Another study on lncRNA neighbor of BRCA1 lncRNA 2 (NBR2) revealed its tumor-suppressive role in HCC by inhibiting Beclin-1-dependent autophagy pathway via JNK and ERK signaling cascades [168]. In one study, Li and colleagues revealed that lncRNA DCST1 antisense RNA 1 (DCST1-AS1) is a crucial tumor-promoting factor in HCC. It was found to promote cell proliferation and inhibit autophagy and apoptosis via the AKT/mTOR signaling cascade [171]. Additionally, research on lncRNA nuclear-enriched abundant transcript 1 (NEAT1) demonstrated that it promotes autophagy via modulation of the miR-204/ATG3 pathway in HCC [169]. Further, Wei and colleagues elucidated the role of lncRNA HAGLR opposite strand (HAGLROS) in regulating autophagy in HCC. HAGLROS enhances autophagy and promotes cell proliferation by regulating the miR-5095/ATG12 axis [172]. Furthermore, a study on lncRNA highly upregulated in liver cancer (HULC) revealed that it inhibits PTEN via the ubiquitin-proteasome system mediated by the autophagy-related p62 protein, which leads to activation of the PI3K–AKT–mTOR pathway in HCC cells [174]. Further, a study on lncRNA DANCR showed that it promotes the expression of ATG7, which leads to autophagy and HCC cell proliferation by sponging miR-222-3p [173]. Taken together, the above studies elucidated the role of various lncRNAs in promoting autophagy in HCC. Hematological malignancies are commonly called cancers of the blood and its associated tissues and cells, such as bone marrow and immune cells [234,235]. Though the conventional therapeutic approaches have increased patients’ overall survival and quality of life, the clinical outcomes suffer with drug tolerance and tumor reoccurrence [236]. There is heterogeneity among the various subclasses of blood cancers, which can be stratified into lymphoma, leukemia, and myeloma [237]. Recent studies have delineated the functional role of different lncRNAs in modulating autophagy in various hematological malignancies [125,126,212]. For instance, a study by Zhang et al. elucidated the functional role of lncRNA LINC00265 in acute myeloid leukemia (AML). It was observed that LINC00265 was upregulated and miR-485-5p was significantly downregulated in AML patient serum and cell lines. Moreover, the overexpression of LINC00265 was found to promote autophagy and suppress apoptosis in AML cell lines by upregulating LC3-II/LC3-I and Beclin expression while decreasing p62 levels. In addition, LINC00265 showed a direct interaction with miR-485-5p, where lncRNA served as a ceRNA to regulate the expression of IRF2 [125]. Another study explored the oncogenic potential of lncRNA UCA1 in modulating AML proliferation and autophagy. It was reported that UCA1 acted as a sponge to bind miR-96-5p, which in turn induced the ATG7/autophagic pathway in AML cells. Moreover, UCA1 was found to promote the proliferation of AML cells through the activation of autophagy [212]. Additionally, Zhang and his group studied the tumor-promoting and drug resistance role of lncRNA DANCR in AML. The overexpression of DANCR resulted in an increase in cytarabine resistance in AML. Further, DANCR induces autophagy by sponging the miR-20a-5p levels and increasing the expression of ATG16L1 [126]. Thus, these studies have proved the potential of autophagic modulators, such as lncRNAs, as a treatment approach in different hematological malignancies. Lung cancer is one of the most frequent malignancies worldwide due to various risk factors, such as smoking, drinking, and air pollution, etc. [1,238,239,240,241,242]. Various studies have found that lncRNAs play a vital role in the progression of lung cancer [243,244,245]. With regard to this, a study on lncRNA misato homolog 2 pseudogene (MSTO2P) showed that it was considerably upregulated in lung cancer cells. The knockdown of MSTO2P resulted in decreased levels of LC3-I, LC3-II, AGT5, and EZH2, leading to impaired processes of proliferation and autophagy in lung cancer cells [180]. An analysis by Len Zhang and his group suggested that lncRNA promoter of CDKN1A antisense DNA damage activated RNA (PANDAR) inhibited the proliferation of NSCLC cells via the activation of autophagy, as well as apoptotic pathways, by upregulating the expression of BECN1 [182]. Moreover, another study indicated that lncRNA lung cancer progression-association transcript 1 (LCPAT1) was expressed in the presence of particulate matter (PM) 2.5 and cigarette smoke extracts. It stimulated the progression of lung cancer through RCC2 leading to the upregulation of autophagy [181]. Another study found that LINC00857 knockdown induced autophagy by increasing the phospho-AMP-activated protein kinase (p-AMPK). Thus, it was found that LINC00857/p-AMPKa signaling is vital for regulating autophagy, apoptosis, and cell proliferation. This could lead to a potential therapeutic target for lung cancer [218]. In addition, it was observed that the UCA1 inhibitory effect on miR-185-5p was significantly decreased after interference with lncRNA UCA1. This resulted in the downregulation of Beclin-1, β-catenin/TCF-4, and LC3II, leading to a reduction in the growth of cells and autophagy in non-small cell lung cancer (NSCLC) [220]. Apart from this, lncRNA neuroblastoma-associated transcript 1 (NBAT1) was also found as an autophagy inhibitor by suppressing ATG7 in NSCLC [185]. Additionally, lncRNA bladder cancer-associated transcript 1 (BLACAT1) was found to be upregulated in NSCLC. It promoted chemoresistance and autophagy of the cancer cells via the miR-17/ATG7 axis [186]. Further, a study suggested that lncRNA plasmacytoma variant translocation 1 (PVT1) might function as a ceRNA for miR-216b. It inhibited the cisplatin sensitivity of NSCLC by regulating autophagy and apoptosis. This might provide a novel target for improving the efficiency of chemotherapy in NSCLC [188]. Furthermore, one study showed that the downregulation of lncRNA GAS5 was related to cisplatin resistance in NSCLC. The knockdown of GAS5 decreased autophagy and promoted cisplatin resistance in NSCLC [187]. Thus, these studies demonstrate the importance and potential of lncRNAs in the treatment of lung cancer by modulating the autophagic pathways. Osteosarcoma is the most common type of bone cancer, which originates in the mesenchymal tissue and affects children and young adults worldwide [246]. A growing line of experimental evidence suggests that lncRNAs play a vital role in osteosarcoma [247,248,249]. In accordance with this, Wang and his group aimed to explore the functional role of lncRNA CTA in osteosarcoma chemoresistance. lncRNA CTA was found to be downregulated in osteosarcoma tissues as compared to levels i the normal adjacent tissues. It was observed that the overexpression of lncRNA CTA resulted in decreased cell proliferation with an increase in autophagy and DOX-induced apoptosis in MG63 and Saos-2 osteosarcoma cell lines. Treatment with DOX increased the accumulation of LC3B-II and BNIP3/BNIP3L levels, whereas the overexpression of lncRNA CTA reversed this effect [189]. Another study reported that lncRNA DICER1 antisense RNA 1 (DICER1-AS1) enhanced the proliferation and autophagy of osteosarcoma cells by regulating the miR-30b/ATG5 levels. The knockdown of DICER1-AS1 led to decreased proliferation, migration, and invasion of osteosarcoma cells. Further, ATG5 was found to be a target of miR-30b, which was regulated by lncRNA DICER1-AS1 [107]. Moreover, in another study, lncRNA SNHG15 was found to be negatively correlated with the miR-141 levels in osteosarcoma. The knockdown of SNHG15 abrogated LC3B-II expression while increasing p62 levels, and the introduction of miR-141 rescued autophagy-related protein expression [190]. In addition, Zhu et al. demonstrated the tumorigenic potential of lncRNA SNHG6 in osteosarcoma. SNHG6 was found to be overexpressed in osteosarcoma cells and tissues, and its high expression was correlated with poor survival and metastasis. Moreover, SNHG6 silencing decreased proliferation, migration, and autophagy and induced G0/G1 phase arrest and apoptosis in osteosarcoma [191]. Overall, these studies delineate the crucial role of lncRNAs in autophagy and could be used as a potential target in the treatment of osteosarcoma. Ovarian cancer is the most common gynecological cancer afflicting women worldwide [1,250]. Although treatment options, such as surgical resection and chemotherapy, have increased ovarian cancer patients’ survival, tumor relapse and chemoresistance are the major hurdles in efficacious treatments [251]. Studies have identified lncRNAs as playing a crucial role in modulating autophagy in ovarian cancer. For example, Chen and co-workers explored the role of lncRNA HOXA11-AS in the development of ovarian cancer. It was observed that HOXA11-AS was overexpressed in ovarian cancer, and the knockdown of HOXA11-AS resulted in the inhibition of proliferation and malignant transformation with the induction of cell cycle arrest, apoptosis, and autophagy in ovarian cancer. The silencing of this lncRNA was also found to elevate autophagy-related proteins, such as Beclin-1, and increase the LC3II/I ratio while reducing p62 protein expression [192]. Moreover, in another study, lncRNA TUG1 was found to be overexpressed in ovarian cancer tissue samples and cell lines. Silencing TUG1 decreased Beclin-1 and the conversion of LC3B-I to LC3B-II in ovarian cancer cells. Further, it was observed that the knockdown of TUG1 affected paclitaxel sensitivity in cancer cells by modulating autophagy through the sponging of miR-29b-3p levels [193]. In addition, Xia et al. deciphered the mechanistic role of lncRNA X-inactive specific transcript (XIST) in carboplatin resistance in ovarian cancer. lncRNA XIST was found to be upregulated in ovarian cancer and carboplatin-resistant cells. Moreover, the knockdown of XIST led to the suppression of proliferation and autophagy while inducing apoptosis in ovarian cancer cells. Further, miR-506-3p was found to be a target for XIST, and it mediated carboplatin resistance by modulating FOXP1 expression [194]. Taken together, lncRNAs have been found to modulate autophagy and chemotherapy resistance; however, further studies are required to decipher the better functional role of lncRNAs that modulate autophagy in ovarian cancer. Pancreatic cancer is one of the major causes of cancer-related deaths worldwide due to its poor prognosis and few therapeutic approaches at the advanced stages [252,253]. Therefore, understanding the molecular mechanism and the causative factors involved in this deadly malignancy would open newer possibilities for novel diagnostic, prognostic, and therapeutic interventions. Various studies have delineated the involvement of lncRNAs as crucial regulators in pancreatic cancer [42,254,255]. For example, the role of lncRNA LINC01207 was evaluated in modulating autophagy and apoptosis in pancreatic cancer. It was found that lncRNA LINC01207 was upregulated in pancreatic cancer, and silencing LINC01207 increased Beclin-1 and LC3II expression while decreasing the p62 levels and Bcl-2/Bax ratio. Moreover, LINC01207 was reported to modulate the expression of AGR2 by directly targeting miR-143-5p [195]. In addition, lncRNA PVT1 was observed to promote autophagy and gemcitabine resistance in pancreatic cancer by regulating miR-619-5p/ATG14 via the Wnt/β-catenin pathway. It was also found that the interaction of PVT1 and ATG14 facilitates the formation of the autophagy-specific complex I (PtdIns3K-C1) and class III PtdIns3K activity, thereby initiating autophagy in pancreatic cancer [196]. MALAT1 is a well-researched lncRNA known for its oncogenic role and for regulating the process of autophagy in different cancers, including pancreatic malignancy. For instance, MALAT1 was found to be positively correlated with LC3B expression. Further, silencing MALAT1 abrogated the autophagic flux by modulating LC3, P62, and LAMP-2. It was also speculated that MALAT1 would regulate autophagic processes by interacting with HuR via the regulation of TIA-1 (T-cell intracellular antigen-1) levels [197]. In addition, the lncRNA SNHG14 was found to enhance gemcitabine resistance and induce autophagy in pancreatic cancer by sponging its target, miR-101. It was also found that silencing SNHG14 markedly reduced autophagy-related protein 4D (ATG4D) and RAB GTPase 5 A (RAB5A) in SW1990 pancreatic ductal adenocarcinoma cells [198]. Further, Wang and his group investigated the chemoresistance effects of lncRNA antisense non-coding RNA in terms of the INK4 locus (ANRIL) in pancreatic cancer. It was found that ANRIL was overexpressed in pancreatic tissues while miR-181a was inversely downregulated. The same study also showed that ANRIL-induced HMGB1 mediated autophagy by targeting miR-181a [199]. Hence, a vivid understanding of the mechanistic role of lncRNAs in autophagy would be crucial in designing novel therapeutic regimens against pancreatic cancer. Prostate cancer is one of the most commonly diagnosed cancers in men worldwide [256,257]. According to GLOBOCAN 2020, approximately 1,414,259 new incidences were reported for prostate cancer [1]. Although there have been numerous reports of lncRNAs regulating prostate tumorigenesis, very few studies have shed light on the regulation of autophagy by lncRNAs in prostate cancer [104,201,222]. In line with this, Chen and his colleagues explored the mechanistic role of lncRNA HULC in prostate cancer treatment via irradiation. The knockdown of HULC resulted in a significant decrease in cellular proliferation and the induction of apoptosis in PC3 and LNCaP cells treated with irradiation. In addition, HULC inhibited autophagy by regulating the levels of Beclin-1 and targeting the mTOR pathway [222]. In addition, lncRNA SNHG1 was found to inhibit autophagy by binding to EZH2 and modulating the PI3K/AKT/mTOR and Wnt/β-Catenin pathways. Further, interference with SNHG1 resulted in the inhibition of proliferation, migration, and invasion in LNCaP and PC3 prostate cancer cells [201]. Further, LncRNA PRRT3-AS1 was found to be highly expressed in prostate cancer. Therefore, the knockdown of PRRT3-AS1 induced autophagy by decreasing COX2, S6K1, NF-κB1, and 4EPB1 and upregulating peroxisome proliferator-activated receptor γ (PPARγ), thereby inhibiting the mTOR pathway. Furthermore, PPARγ was found to be the direct target of lncRNA PRRT3-AS1 [104]. Hence, these studies clearly depict the vital role of lncRNA in regulating autophagy and suggest possible therapeutic interventions. Besides the aforementioned studies, lncRNAs have been found to play a crucial role in regulating autophagy in different cancers of the eye and thyroid [200,202,221,223]. Uveal melanoma (UM), a type of rare cancer, occurs in the melanocytes present in the uveal tract of the eyes [258]. LncRNA was also found to modulate autophagy in this cancer. For example, Li P and his group showed that the overexpression of lncRNA ZNNT1 induced autophagy by increasing the levels of ATG12 and the degradation of SQSTM1, which led to the suppression of growth and migration in OCM1 and OM431 uveal melanoma cell lines. Further, the knockdown of ZNNT1 resulted in the repression of PP242-induced autophagy [223]. Retinoblastoma, another type of eye cancer, starts at the retina (back of the eyes) and is usually common in children [259]. The modulatory effect of lncRNAs was also reported in retinoblastoma. With regard to this, one study elucidated the functional role of lncRNA MALAT1, a well-known oncogenic lncRNA, in retinoblastoma. It was found that MALAT1, through the direct targeting miR-124, modulated the expression of Syntaxin 17 (STX17), one of the SNARE proteins of the autophagosome [202]. There have been few reports where lncRNAs were found to be involved in autophagic regulation in papillary thyroid cancer [200,221]. In line with this, one study found that lncRNA RP11-476D10.1 was overexpressed in papillary thyroid cancer cells. Further, the knockdown of RP11-476D10.1 resulted in a decrease and increase in apoptosis and autophagy, respectively. Furthermore, miR-138-5p was found to be a direct target of RP11-476D10.1, where it activated the expression of LRRK2 [221]. Another study found that BRAF-activated lncRNA (BANCR) promoted papillary thyroid carcinoma by inducing proliferation and autophagy. The overexpression of BANCR led to upregulation of the LC3-II/LC3-I ratio, a marker for autophagy [200]. Taken together, lncRNAs are known to modulate the expression of autophagy-related genes by acting as ceRNAs, and targeting these lncRNAs might be a plausible approach on the path of cancer treatment. Cancer has become the most prevalent disorders of this century, accounting for millions of deaths worldwide. Though the current treatment approaches, such as surgical resection, chemotherapy, radiotherapy, and targeted therapies, have increased the overall survival of patients and the quality of life, there is no definite cure for the advanced stages of the diseases. Moreover, most treatment regimens suffer from poor clinical outcomes with severe side effects. Hence, there exists a need to understand the intricate molecular events underlying tumorigenesis in order to develop safe and targeted therapies. With the discovery of autophagy, a crucial cellular mechanism governing homeostasis, there has been huge attention towards understanding its mechanistic role in health and diseases. Autophagy, a catabolic degradative process, helps in the removal of unwanted protein aggregates and damaged organelles and thus maintains nutrient recycling under stress stimuli. A plethora of studies has demystified the crucial role of autophagy in various chronic diseases, including cancer. In cancer, autophagy plays a dual role; it acts as a tumor suppressor to degrade the damaged and accumulated proteins, thereby reducing cancer development, but in advanced stages of the disease, it sustains cancer cell proliferation and viability by providing energy during nutritional starvation. In some cases, autophagy may prevent the development of tumors, but mounting evidence points to this pathway’s pro-tumorigenic effects. In this context, autophagy causes tumor-intrinsic and tumor-extrinsic immunosuppression, metabolic alterations, and resistance to treatment. Various studies have shown that lncRNAs are essential for the initiation and development of different types of cancer. In particular, aberrant lncRNA expression may be present in cancer cells with DNA damage, immunological evasion, and cellular metabolic problems. The complex process of carcinogenesis is even more intriguing because of the diversity and heterogeneity of lncRNAs. Creative experimentation and advanced next-generation technologies have widened our understanding of lncRNAs’ role in cancer. Because the sensitivity and specificity are still not at the acceptable level, it is challenging to use lncRNAs as cancer biomarkers at present. However, developing diagnostics and tailored cancer therapies might be possible using the autophagy-modulating lncRNAs. We may conclude from the rising body of evidence on lncRNAs, autophagy, and malignancies that the majority of lncRNAs have a role in carcinogenesis by stimulating or inhibiting the autophagy process. In this review, we have discussed lncRNAs that modulate autophagy to either promote or inhibit different types of cancer. LncRNAs, members of various ceRNA types, employ the ceRNA network’s mechanism to regulate epigenetic control and vital post-transcriptional regulation. These days, mounting data show that intracellular lncRNA availability is sufficient in disease states, such as neoplasms, to induce ceRNA crosstalk of the lncRNA/miRNA/mRNA axis. Additionally, lncRNA could sponge miRNA for an extended period through incomplete complementary binding between miRNA-responsive elements and miRNA, changing the activity and availability of miRNA while regulating the expression of autophagy-related genes. We have also shed light on the mechanistic effects of the role of lncRNA based on the ceRNA network and how autophagic modulation brings about a change in different hallmarks of cancer. So far, all of the studies have been inclined towards the modulation of autophagy by lncRNAs. It would be a crucial question to ask whether autophagy can regulate the expression of lncRNA. To date, only PVT1 expression has been found to be modulated with autophagy in diabetes. Since their discovery, lncRNAs that modulate autophagy have been the subject of intense debate. Both autophagy and lncRNAs have the potential to either promote or inhibit the development of cancer. While increasing autophagy is a useful therapeutic approach for treating chronic inflammatory disorders, autoimmune disease, and neuroinflammation, its significance in cancer has remained pleiotropic. Sustained autophagy is recognized as a crucial mechanism for treating resistance and immune evasion in the context of cancer. Pharmacological strategies that safely reduce autophagic flux to support meaningful immune responses against advanced stages of cancer while simultaneously allowing for the emergence of long-lasting protection as determined by antigen-specific cellular immunity will be necessary for the modulation of autophagy to be successful. The conception to use non-coding RNA as a therapeutic regimen has gained immense attention from researchers worldwide. Many antisense oligonucleotides and small interfering RNAs have been used in the clinical use of RNA-based treatments against various diseases over the past ten years, and several of these have been approved by the Food and Drug Administration. The clinical trial results, however, have thus far been mixed, with some studies reporting strong potent effects and others showing little efficacy or toxicity. One of the biggest hurdles in employing lncRNAs is the efficient distribution of RNA across the cell membrane and reaching the site and cell type of interest to carry out their post-transcriptional regulation. Innovative technologies are coming up front with an unprecedented interdisciplinary approach that could provide plausible solutions to these problems. Still, there is a long way ahead for the lncRNAs to enter clinical trials. What we require now is more experimental evidence of the lncRNA regulation of autophagy in different pre-clinical cancer models. Further, randomized multi-centered clinical trials are paramount in establishing lncRNAs as a vital therapeutic approach targeting autophagy in cancers.

PMC10000693

Nikita Choudhary,Robert C. Osorio,Jun Y. Oh,Manish K. Aghi

Metabolic Barriers to Glioblastoma Immunotherapy

28-02-2023

glioblastoma,immunotherapy,metabolism,tumor microenvironment,glycolysis,glutamine metabolism,lipid metabolism,tryptophan metabolism

Simple Summary Glioblastoma (GBM) is an aggressive brain tumor with limited prognosis despite multimodal treatment approaches. Various immunotherapies have been investigated to address the need for novel therapeutic options in GBM with limited success. Recently, alterations in the metabolism of cancer cells which allow for tumor proliferation, but simultaneously alter immune populations leading to an immunosuppressive tumor microenvironment, have been investigated as contributory to therapeutic resistance. This review discusses metabolic alterations in GBM tumor cells which have been investigated as contributory to immunosuppression and resistance to immunotherapies. Abstract Glioblastoma (GBM) is the most common primary brain tumor with a poor prognosis with the current standard of care treatment. To address the need for novel therapeutic options in GBM, immunotherapies which target cancer cells through stimulating an anti-tumoral immune response have been investigated in GBM. However, immunotherapies in GBM have not met with anywhere near the level of success they have encountered in other cancers. The immunosuppressive tumor microenvironment in GBM is thought to contribute significantly to resistance to immunotherapy. Metabolic alterations employed by cancer cells to promote their own growth and proliferation have been shown to impact the distribution and function of immune cells in the tumor microenvironment. More recently, the diminished function of anti-tumoral effector immune cells and promotion of immunosuppressive populations resulting from metabolic alterations have been investigated as contributory to therapeutic resistance. The GBM tumor cell metabolism of four nutrients (glucose, glutamine, tryptophan, and lipids) has recently been described as contributory to an immunosuppressive tumor microenvironment and immunotherapy resistance. Understanding metabolic mechanisms of resistance to immunotherapy in GBM can provide insight into future directions targeting the anti-tumor immune response in combination with tumor metabolism.

Metabolic Barriers to Glioblastoma Immunotherapy Glioblastoma (GBM) is an aggressive brain tumor with limited prognosis despite multimodal treatment approaches. Various immunotherapies have been investigated to address the need for novel therapeutic options in GBM with limited success. Recently, alterations in the metabolism of cancer cells which allow for tumor proliferation, but simultaneously alter immune populations leading to an immunosuppressive tumor microenvironment, have been investigated as contributory to therapeutic resistance. This review discusses metabolic alterations in GBM tumor cells which have been investigated as contributory to immunosuppression and resistance to immunotherapies. Glioblastoma (GBM) is the most common primary brain tumor with a poor prognosis with the current standard of care treatment. To address the need for novel therapeutic options in GBM, immunotherapies which target cancer cells through stimulating an anti-tumoral immune response have been investigated in GBM. However, immunotherapies in GBM have not met with anywhere near the level of success they have encountered in other cancers. The immunosuppressive tumor microenvironment in GBM is thought to contribute significantly to resistance to immunotherapy. Metabolic alterations employed by cancer cells to promote their own growth and proliferation have been shown to impact the distribution and function of immune cells in the tumor microenvironment. More recently, the diminished function of anti-tumoral effector immune cells and promotion of immunosuppressive populations resulting from metabolic alterations have been investigated as contributory to therapeutic resistance. The GBM tumor cell metabolism of four nutrients (glucose, glutamine, tryptophan, and lipids) has recently been described as contributory to an immunosuppressive tumor microenvironment and immunotherapy resistance. Understanding metabolic mechanisms of resistance to immunotherapy in GBM can provide insight into future directions targeting the anti-tumor immune response in combination with tumor metabolism. Glioblastoma (GBM) is the most common primary brain tumor with a limited prognosis and a median survival of 15 months despite an aggressive standard of care treatment consisting of maximal safe surgical resection followed by radiation and chemotherapy with temozolomide [1]. Since the addition of temozolomide to the standard of care treatment in 2005, subsequent efforts to develop new therapeutic candidates have failed to outperform standard of care treatment in clinical trials [2]. Developing effective novel therapies for GBM therefore remains an unmet need. One novel emerging area of cancer therapeutics is immunotherapies, which target one of the hallmarks of cancer—the ability to evade cellular immunity that would otherwise result in immunological targeting of tumor cells [3]. While in recent years immunotherapies have become standard treatment options for several cancer types, a variety of immune-based therapies including checkpoint inhibitors, vaccines, CAR-T cells, oncolytic viruses, and myeloid-targeted therapies have failed to benefit patients with GBM in trials [4]. The uniquely immunosuppressive tumor microenvironment in GBM is thought to contribute significantly to immunotherapy resistance [4,5]. The tumor microenvironment is affected by unique cancer cell metabolism that not only promotes tumor cell growth but also alters the pH, oxygen, and metabolite contents that affect the survival and function of immune cells in the tumor microenvironment [6]. Metabolic reprogramming within tumor cells diminishes the function of effector immune cells through depletion of essential metabolites and promotes enrichment of suppressive immune populations [7]. More recently, therapies targeting metabolic factors in the tumor microenvironment that adversely impact the antitumor immune response such as low glucose, low pH, hypoxia, and the generation of suppressive metabolites have been explored as immunotherapeutic anticancer strategies [7]. Similar findings have also been reported in GBM, where metabolic reprogramming in tumor cells plays a significant role in driving survival, proliferation, and invasion. However these metabolic adaptations additionally alter the GBM tumor immune microenvironment [8]. In this review, we discuss how GBM tumor cell metabolism of four nutrients (glucose, glutamine, tryptophan, and lipids) leads to an immunosuppressive tumor microenvironment and the implications of these metabolic changes on immune based treatment strategies for GBM. Glycolysis is the most prominent metabolic pathway implicated in cancer metabolism as contributory to sustaining the energetic cost of growth and proliferation. During glycolysis, glucose is catabolized to pyruvate which is then converted to lactate to either be secreted or enter the TCA cycle generating ATP and NADH in the process. Glucose metabolism plays a significant role in the brain microenvironment given the high metabolic demand of the brain and lack of glycogen storage within the brain. High blood glucose levels and increased neuronal expression of glucose transporters have been linked to decreased survival in glioblastoma patients [9]. Altered glucose metabolism in tumor cells results in preferential aerobic glycolysis—increased glycolytic activity despite the presence of oxygen enabling alternate metabolic pathways, a phenomenon known as the Warburg effect. Though glycolysis is an inefficient pathway for energy production relative to mitochondrial oxidation, increased glycolysis in proliferating tumor cells generates metabolic precursors such as lactate which are thought to be the rate-limiting factors during cellular proliferation. In tumor cells, glucose transporters and glycolytic enzymes essential for the conversion of pyruvate to lactate are upregulated. In glioblastoma, the significantly increased rate of glycolysis drives energy production [10]. Tumor cells develop alterations to allow for this increased glycolysis and tumor growth [10]. Genome-wide transcriptomic analysis of patient-derived GBM cells demonstrate strong upregulation of glycolysis-related genes [11]. Hexokinase 2 (HK2), an isoform of the enzyme which catalyzes the conversion of glucose to glucose-6-phosphate in the first step of glycolysis is strongly expressed in GBM [12]. Knockdown or silencing of glycolytic genes such as HK2, PKFP, ALDOA, PGAM1, ENO1, ENO2, or PDK1 inhibits GBM tumor growth and prolongs survival in a mouse xenograft model [11]. These differentially regulated genes were involved in glycolysis and downstream hypoxia response signaling pathways, suggesting that the glycolytic enzymes encoded by these genes are essential for GBM growth [11]. More recently, attention has been given to the impact of alterations in glycolytic pathways on not only proliferating tumor cells but also the tumor microenvironment and resulting changes in immune cell metabolism and function (Figure 1) [13]. Glycolysis alters the immune response in cancer as shown by glycolysis-related genes with prognostic value found to be linked to varying immune cell infiltration and differential immune-related gene expression [14]. Glycolysis requires export of lactate from cells by transporters which co-transport lactate and protons (H+), leading to their accumulation in the tumor microenvironment and resulting in tumor acidosis which impacts the function of immune cells in the tumor microenvironment. Acidosis has been described in other cancers as contributory to immunosuppression [15]. Lactic acid produced by tumor cells inhibits the differentiation and activation of monocytes and T cells and regulates the expression and secretion of tumor-promoting cytokine interleukin 23 [16,17]. Lactate accumulation additionally inhibits type 1 interferon signaling and granzyme B expression which normally promotes cancer immunosurveillance through the activity of natural killer (NK) cells [18,19]. In melanoma, lactic acid production by tumor cells reduces the quantity and the cytotoxic activity of CD8 T cells and NK cells in culture and in vivo [20]. Activated T cells require the ability to co-transport lactate and protons as part of their own glycolytic metabolism. Increased lactic acid production by tumor cells has been shown to inhibit T cell glycolysis and function by altering the concentration gradient for lactate and proton export by the T cells [21]. In effect, increased glycolysis in tumor cells inhibits the ability of T cells to engage in glycolytic metabolism [20]. Strategies that free T cell glycolytic metabolism from the restrictions imposed on these cells by the tumor microenvironment have been evaluated in preclinical models. For example, genetic modification of tumor specific CD4 and CD8 T cells to overexpress phosphoenolpyruvate carboxykinase 1 (PCK1) increased the production of the glycolytic metabolite phosphoenolpyruvate, resulted in increased T cell glycolysis, increased T cell effector function, and restricted tumor growth and prolonged survival in a melanoma mouse model [22]. Further supporting the idea that increased tumor glycolysis results in a glucose-poor tumor microenvironment that diminishes T cell function, increased glycolytic metabolism in melanoma cells has been associated with resistance to adoptive T cell therapy and checkpoint blockade [23]. Another study in a mouse sarcoma model demonstrated that glucose consumption by tumors leads to metabolic restriction of T cells and reduced T cell glycolytic capacity allowing for tumor progression [24]. In this study, an antigenic model that enhanced glycolysis of T cells led to slower tumor growth [24]. Calcinotto et al. demonstrated that increased acidosis resulted in mouse and human CD8 T cell anergy [25]. Combining proton pump inhibitors lowering pH with adoptive transfer of antigen specific T cells or vaccines to melanoma specific antigens resulted in increased therapeutic efficacy in a mouse model of melanoma [25]. In a separate study of mouse models of multiple cancer types, neutralizing tumor acidity increased T cell infiltration and impaired tumor growth [26]. Furthermore, combining bicarbonate therapy for neutralization of tumor acidosis with checkpoint inhibitors or adoptive T cell transfer improved antitumor responses [26]. Lactic acid production has also been suggested to not only reduce anti-tumoral immune cell populations but also promote immunosuppressive populations. Notably, myeloid cells are resistant to lactic acid-induced apoptosis [20]. In fact, in some studies, these cells have not only been resistant to the effects of lactic acid, but the most aggressive pro-tumoral myeloid cells often thrive in response to lactic acid. For example, accumulation of lactic acid in pancreatic tumor cells was shown to increase the number of myeloid derived suppressor cells (MDSCs) in mice [27]. Colegio et al. showed that lactic acid promotes polarization of macrophages towards the tolerogenic M2 type [28]. Suppressive Treg cells are not impaired by the low lactate levels that impair the function of effector T cells. In fact, Treg cells are able to generate NAD+ through mitochondrial metabolism in high lactate environments [29]. Glycolytic alterations may also specifically impact neutrophils. While less is understood about the metabolic utilization of neutrophils in the tumor microenvironment than leukocytes, neutrophils are generally regarded as highly glycolytic. Neutrophil function has been described to highly depend on glucose availability with lack of glucose abrogating function [30]. suggesting that increased glucose metabolism by tumor cells may limit the availability of glucose to neutrophils limiting their function. However, counterintuitively to a perhaps reduced function in a tumor microenvironment with low levels of glucose, neutrophil recruitment to the tumor site has been regularly described as immunosuppressive and inhibitory of the activity of T cells [31]. An elevated circulating neutrophil to lymphocyte ratio (NLR) has been found to be a negative prognostic factor in glioblastoma patients [32]. Rice et al. suggest a potential mechanism in which neutrophils maintain local immune suppression in the glucose-limited tumor microenvironment through the adaptation of a neutrophil subpopulation to an oxidative mitochondrial metabolism [33]. Interestingly, the interplay between glycolytic tumor metabolism and immune cell function may be bidirectional with immune cells able to regulate metabolic pathways as well. Zhang et al. show that macrophages produce interleukin-6 which leads to downstream phosphorylation of the glycolytic enzyme phosphoglycerate kinase 1 (PGK1) and facilitates a PGK1-catalyzed reaction towards glycolysis rather than gluconeogenesis through altered substate affinity [34]. PGK1 phosphorylation correlated with increased macrophage infiltration, higher grade, and worse prognosis in human GBM samples [34]. Further work will be necessary to elucidate this metabolic crosstalk and metabolic competition between tumor cells and immune cells and to understand whether immune cells can themselves alter the metabolic environment to support tumor growth, including through mechanisms, such as post-translational modifications, which regulate the functions of many glycolytic enzymes. Studies of glycolysis in glioblastoma have paralleled the findings in other cancer types of the significance of increased glycolysis in creating an immunosuppressive tumor microenvironment. The shift to increased aerobic glycolysis from oxidative phosphorylation in glioblastoma is associated with immunosuppression and tumor progression [35]. In GBM, hypoxia-inducible factor 1α (HIF-1α) directs the metabolic switch for Tregs from glycolysis in the glucose-poor tumor environment to oxidative phosphorylation which drives immunosuppression [36]. One recent study determined a glycolytic score for glioblastoma utilizing seven genes involved in expression of glycolytic enzymes and found that T cells, B cells, and NK cells were depressed while there was high infiltration of immunosuppressive cells in patients with high glycolytic scores [37]. One of the genes utilized in the glycolytic score, ENO1, promoted M2 microglia polarization promoting immunosuppression and glioblastoma cell malignancy. Another recent study utilizing differentially expressed genes between high and low glycolytic activity to assign risk scores to classify high and low risk GBM patients found differential infiltration of immune cells and immune checkpoints, suggesting a relationship between glycolytic activity and immunosuppression in patients with GBM [38]. Glutamine, an amino acid highly expressed in cancer cells, plays a critical role for cellular function and the generation of energy and metabolic precursors for macromolecule synthesis which help sustain anabolic growth. Glutamine is converted by glutaminase into glutamate which is then converted to α-ketoglutarate, a critical component of the TCA cycle and in the production of metabolic intermediates utilized in the production of lipids, nucleic acids, and proteins. Upregulated glutamine metabolism in cancer cells promotes tumor growth through supporting macromolecule biosynthesis, altered signaling pathways, and cancer cell proliferation and survival. The metabolism of glutamine provides carbons for the TCA cycle to sustain accelerated anabolism in cancer cells and promotes tumor growth [39,40,41]. Glutamine is amongst the most prevalent amino acids in the brain as a precursor to the excitatory neurotransmitter glutamate [9]. Glutamate transporters are upregulated in gliomas allowing for increased glutamine uptake [42]. The absence of glutamine in culture medium leads to loss of viability as determined by a trypan blue dye exclusion test in glioma cell lines [43]. Increased and rapid glutamine utilization has been described as characteristic of glioblastoma cell proliferation through promoting generation of NADPH for anabolic processes such as nucleotide biosynthesis and providing a source of carbon for fatty acid synthesis [44]. Glutamate secretion in glioma cells results in a growth advantage in vivo, and targeting glutamate secretion or antagonizing glutamate target receptors resulted in slowed tumor expansion in C6Glu+ tumors in rats [45]. Glutamine metabolic pathways are also upregulated in glioblastoma. Glutamate dehydrogenase (GDH), an enzyme which catalyzes the conversion of L-glutamate into α-ketoglutarate as part of glutaminolysis, is upregulated in many human cancers and shown to promote tumor growth [46]. An isoenzyme of GDH, GDH1, maintains glioma cell survival in glucose depleted conditions through activation of glutamine metabolism and the α-ketoglutarate generated drives glucose uptake and cell survival under low glucose [47]. Glutamine synthetase expression in glioblastoma is associated with poor prognosis, with absent or low intensity expression of glutamine synthetase in neoplastic cells associated with longer survival [48]. Glutamine is hypothesized to be provided by surrounding astrocytes to feed GBM cells negative for glutamine synthetase cells [49]. Glutamine metabolism in cancer cells impacts the tumor microenvironment and the immune populations within it in ways similar to glucose metabolism (Figure 1). Cancer cells relying on exogenous glutamine synthesis utilize glucose, further depleting it in the tumor microenvironment and contributing to the reduced function of immunostimulatory effector T cells and NK populations that require glucose for function [7]. Glutaminolysis also results in the downstream production of lactate, mirroring the effect of aerobic glycolysis in generating an acidic tumor microenvironment which contributes to immunosuppression as described earlier in this review. Increased uptake of glutamine by tumor cells may result in its depletion in the tumor microenvironment and affect the function of immune cells which utilize glutamine for their own metabolic programs. Activated T cells upregulate glutamine metabolism to generate α-ketoglutarate to enter the TCA cycle and generate ATP to fulfill the energetic demands of T cell proliferation [50]. Glutamine depletion in the tumor microenvironment compromises activation-induced T cell growth and proliferation. Addition of the macromolecular products of glutamine synthesis (nucleotides and polyamines) does not rescue T cell growth in a glutamine depleted environment, implicating the specific role of glutamine in meeting the bioenergetic and biosynthetic precursor requirements of activated T cells [50]. Targeting glutamine metabolism in a mouse model of colon cancer through a glutamine antagonist 6-Diazo-5-oxo-L-norleucine, which broadly inhibits several glutamine-using enzymes, led to suppression of both oxidative phosphorylation and glycolytic metabolism in cancer cells and decreased tumor-related changes in the microenvironment with decreased hypoxia, acidosis, and nutrient depletion [51]. In contrast, glutamine blockade in effector T cells resulted in upregulated oxidative metabolism and increased survival and activation [51]. PD-1-targeted checkpoint blockade co-administered with glutamine antagonism resulted in a complete therapeutic response and a memory response with tumor rechallenge [51]. The efficacy of glutamine antagonism was entirely dependent on the activity of CD8 T cells, indicating that the mechanism through which glutamine antagonism promoted anti-tumoral activity was through enhancing cytotoxic T cell anti-tumor response [51]. These findings highlight a common theme for glutamine and glycolytic metabolism in which tumor cells and anti-tumor immune cells compete for metabolites to promote their individual function. It additionally offers insights for metabolic targeting in cancer that leverages the therapeutic window created by the differential metabolic plasticity of immune cells versus cancer cells in which cancer cells are highly metabolically interdependent (targeting glutamine metabolism leads to widespread metabolic inhibition), whereas T cells exhibit adaptive metabolic reprogramming (targeting glutamine metabolism activates upregulation of alternate pathways allowing survival). Additionally, glutamine metabolism by cancer cells leads to the enrichment of various immunosuppressive populations in cancer. Notably, α-ketoglutarate generated through glutaminolysis restricts anti-tumoral macrophage M1 activation [52]. A separate study also supports the role of α-ketoglutarate in promoting an immunosuppressive macrophage phenotype by showing that higher production of α-ketoglutarate results in M2 activation of macrophages (an immunotolerant phenotype) [53]. Targeting glutamine metabolism in cancers with known resistance to checkpoint blockade (triple negative breast cancer and lung carcinoma) with a small molecule inhibitor led to the marked inhibition of the generation and recruitment of immunosuppressive myeloid-derived suppressor cells (MDSCs) through apoptosis of these MDSCs [54]. Additionally, glutamine antagonism promoted the generation of antitumor inflammatory tumor-associated macrophages [54]. Combining glutamine antagonism with checkpoint blockade in immunotherapy-resistant tumors was shown to enhance the efficacy of checkpoint blockade [54]. Targeting glutamine metabolism may enhance endogenous anti-tumor immunity through independent mechanisms promoting the metabolic programs of cytotoxic populations while inhibiting immunosuppressive populations. Given the success of combining targeting glutamine metabolism with checkpoint inhibitors in other immunotherapy resistant tumors, it may be worthwhile to explore this combination in GBM. Tryptophan is an essential amino acid utilized for protein biosynthesis and a biochemical precursor to physiologically important compounds such as serotonin and melatonin. The majority of tryptophan which is not incorporated into proteins is broken down into degradation products (kynurenines) via the kynurenine pathway [55]. Physiologically, tryptophan degradation into kynurenines enables the generation of the essential metabolic cofactor nicotinamide adenine dinucleotide (NAD+). In cancer, the production of kynurenine metabolites by tumor cells contributes to tumor growth by generating an immunosuppressive tumor microenvironment through the recruitment and differentiation of immunosuppressive Treg cells and MDSCs [56]. Tryptophan is degraded into kynurenine metabolites by the two enzymes indoleamine-2,3-dioxygenase-1 (IDO1) or tryptophan-2,3-dioxygenase (TDO2) which catalyze the rate limiting reactions in the kynurenine pathway. Tryptophan catabolism and alterations in kynurenine pathway has been implicated in poor prognosis in several cancer types, including in GBM [57]. The correlation between overexpression of enzymes involved in tryptophan degradation and patient survival in primary and metastatic brain tumors has been well established [58,59]. Recurrent malignant gliomas are associated with increased levels of tryptophan metabolism compared to newly diagnosed patients in metabolic profiles obtained from CSF analysis [60]. IDO1 and TDO2 are highly expressed in glioma cells proportionally to glioma grade [55,61]. Additionally, amongst higher grade patients, those with strong IDO expression were noted to have significantly worse overall survival rates compared to patients with weak IDO expression [61]. IDO1 is expressed in the majority of malignant gliomas with mRNA and protein expression levels correlating with overall patient survival [59]. IDO1- and TDO2- mediated degradation of tryptophan by cancer cells is a driver of immune suppression in the tumor microenvironment through recruitment and activation of myeloid-derived suppressor cells (MDSCs) and induction of anergy of CD8+ T cells [62]. Degradation of tryptophan and reduced tryptophan availability within the tumor microenvironment resulting in arrest of T cell growth and activation has been well characterized [63]. Tryptophan-free media suppresses human T cell proliferation and activation [64]. Tryptophan catabolism by tumor cells allows for metabolic inhibition of T cells and promotes tumor evasion of immune destruction. Two mechanisms enable tumor cell tryptophan catabolism to inhibit anti-tumoral T cells: (1) tumor cell depletion of the essential metabolite tryptophan which is required for T cell metabolism (the competition for nutrients scenario between tumor cells and T cells described above for glucose and glutamine) and (2) generation of T cell inhibitory molecules from tryptophan metabolites such as kynurenine and its derivatives (Figure 2). Tryptophan utilization by tumor cells leads to metabolic starvation of T cells which are unable to utilize tryptophan for their own functions and thus promotes immunosuppression in the tumor microenvironment. Inhibition of tryptophan degrading enzymes blocks enzymatic activity and restores cytotoxic T cell activity in vitro and in vivo [65]. T cells undergo rapid growth arrest in low tryptophan conditions due to a tryptophan-sensitive checkpoint inhibiting the cell cycle in the G1 phase [66]. High IDO expression in colorectal cancer cell lines was associated with significant reduction of CD3+ infiltrating T cells and increased frequency of liver metastases [67]. Additionally, intratumoral immunosuppressive cells—such as MDSCs, tumor associated macrophages (TAMs), and Treg cells—upregulate production of IDO and metabolize tryptophan into suppressive kynurenine which reduces the availability of tryptophan for cytotoxic T cells in the tumor microenvironment [68]. This mechanism is so crucial to MDSC immunosuppression that IDO has been shown to be required for MDSCs’ immunosuppression of T cells with inhibition of IDO leading to decreased MDSC suppression of T cell proliferation in a murine melanoma model [68,69]. Given the role of IDO through kynurenine synthesis in generating the tumor microenvironment allowing for immune escape in cancer, IDO inhibition has been explored as an attractive therapeutic option in multiple cancers. Inhibition of IDO was found to effectively normalize plasma kynurenine levels in patients with various tumor types [70]. Interestingly, combinatorial inhibition of IDO1, IDO2, and TDO2 (together thought to be the predominant rate-limiting enzymes for the kynurenine pathway) did not impact tumor viability in patient derived GBM cells [55]. However, these findings are consistent with the mechanistic understanding that inhibition of the kynurenine pathway enzymes has anti-tumoral effects due to alterations in the survival and function of immune cells normally present in the tumor microenvironment that are not present in an in vitro model. Kynurenine metabolites activate a ligand-activated transcription factor, aryl hydrocarbon receptor (AhR) which results in increased expression of IDO1 and IDO2 in a positive feedback loop. Targeting AhR in vitro led to decreased glioma cell viability [55]. Opitz et al. also showed that kynurenines generated by TDO act to suppress antitumor responses by T cell suppression and promotion of tumor cell survival through AhR mediated signaling in a murine glioma model [71]. Increased expression of AhR target genes involved in signaling pathways related to immune tolerance correlated with decreased survival in patients with glioma [71]. Additionally, AhR activity was found to drive T cell dysfunction through promotion of a Treg-macrophage suppressive axis [62]. This alternate pathway of AhR agonism may circumvent the anti-tumoral effects of tryptophan degradation inhibition through AhR agonism independent of immune function. This also suggests a potential limitation of previous clinical trials of IDO1 inhibitors, some of which have been AhR agonists themselves [72]. Tryptophan catabolism and its downstream metabolic pathways are known to contribute to the immunosuppressive tumor microenvironment and contribute to resistance to novel immunotherapies for malignant gliomas. Increased expression of IDO and TDO has been suggested as an acquired resistance mechanism to PD-1 and CTLA blockade in pre-clinical models of multiple cancers, including GBM [73,74]. The effect of CTLA-4 blockade synergized with IDO inhibitors in metastatic melanoma in both IDO-expressing and non-IDO-expressing poorly immunogenic tumors, and was shown to be effector T cell dependent [75]. The combination of targeting tryptophan metabolism with immune checkpoint blockade has been also explored in glioblastoma. Combining 1-methyltryptophan, which inhibits IDO, with dual immune checkpoint blockade significantly improved survival in an orthotropic mouse GBM model correlating with increased T-cell survival and synergistic decrease of Treg infiltration [76]. Likewise, immunotherapy simultaneously targeting IDO, CTLA-4, and PD-L1 in a mouse glioma model demonstrated a survival benefit [57]. However, combinatorial effects of IDO inhibition with checkpoint inhibitors have been observed in mouse models of other cancers but have not necessarily translated to success in clinical trials. Most prominently, a large phase 3 trial of an IDO1 enzyme inhibitor plus a PD-1 inhibitor in metastatic melanoma did not result in greater clinical benefit compared to PD-1 inhibition alone [77]. Subsequently, multiple phase 3 trials of various IDO1 inhibitors in combination with immune checkpoint inhibitors in other cancers were halted. Potential limitations of IDO1 inhibition that led to failure in this phase 3 trial combining this approach with a PD-1 inhibitor include: insufficient inhibition of IDO1 at the doses being used; the ability of other enzymes involved in tryptophan metabolisms such as TDO2 or pathways downstream of IDO1 inhibition to compensate and still generate immunosuppressive tryptophan metabolites such as kynurenine and its derivatives when IDO1 is inhibited; and lack of patient selection based on IDO1 expression [65,78]. IDO1 may also suppress the antitumor immune response independent of its association with tryptophan metabolism [79]. Additionally, while overwhelming evidence suggests that IDO expression and tryptophan degradation results in immunosuppression and T cell dysfunction diminishing the efficacy of immunotherapy, the understanding of IDO interaction with immunotherapy remains incomplete. Counterintuitively, brain-tumor mice genetically deficient for IDO1 demonstrate decreased efficacy in dual and triple immunotherapy approaches [57]. Thus, IDO inhibition or deficiency may be evaded by alternate metabolic pathways that maintain continued immunosuppression. One study demonstrated that while IDO1 was identified as the top gene in determining low versus high tryptophan in GBM, another potential mediator of the high tryptophan metabolic phenotype in GBM, quinolinate phosphoribosyltransferase, was identified as well, suggesting alternate pathways that could be upregulated to evade IDO1 inhibition and maintain the high utilization of tryptophan in tumor cells [80]. Interestingly, targeting AhR in tumors with an active tryptophan catabolic pathway allows for the overcoming of immunosuppression and sensitization to anti-PD-1 therapy, suggesting a role for targeting alternate areas of tryptophan metabolism [62]. More work is needed to understand the role of compensatory pathways in targeting tryptophan metabolism. Targeting tryptophan metabolism may also have implications for vaccine related cancer therapies which rely on T cell mediated antitumoral responses. IDO expression correlated with lack of specific T cell enrichment at the tumor site and prevented the rejection of tumor cells in mice who have been preimmunized against tumor antigens with a vaccine [58]. This effect was partially reversible with systemic administration of an IDO inhibitor, holding implications for combining cancer vaccines which utilize tumor specific antigenic peptides to generate a T cell antitumor response with IDO inhibitors to enhance the antitumoral effect of immunomodulatory vaccines. Lipid metabolism physiologically functions to allow for cellular energy storage, synthesis of cellular membranes, and cellular signaling. In cancer, alterations in lipid metabolism help meet high bioenergetic demands by generating energy through beta-oxidation. Utilization of fatty acid oxidation in addition to increased glycolysis allows for bioenergetic flexibility in promoting aggressive tumor growth and metastasis [81]. Glioma cells utilize lipid oxidation and upregulate transport of ketones generated from lipid metabolism to sustain growth [82]. Lipid metabolism is abnormally regulated in gliomas with altered expression of lipid-related genes, altered lipid composition, and lipogenesis [83]. Lipids provide energy to fuel GBM cellular proliferation and also play a role in mitigation of oxidative damage that is increased during proliferation [84]. Evidence supporting the role of lipid metabolism in GBM biology comes from studies in which targeting lipid homeostasis inhibits GBM cell proliferation [85]. Lipids are also utilized through lipolysis to maintain stem cell self-renewal in GBM and to allow for propagation of orthotopic tumor xenografts from GBM stem cells in mice [86]. Differential expression of nine genes related to lipid metabolism has been shown to allow the classification of GBM patients into high and low risk for poor outcome [87]. Altered lipid metabolism in GBM impacts immune cell function, particularly that of T cells [88]. T cells utilize lipids to promote their proliferation and differentiation by taking up exogenous lipids and oxidizing intracellular stores of lipids [89]. In GBM, T cells are sequestered in the bone marrow away from the tumor microenvironment via T cell internalization of the lipid sphingosine-1-phosphate receptor, which has been suggested to play a protumoral role through promotion of angiogenesis in GBM [90]. Notably, Treg cells also primarily utilize fatty acid oxidation for their metabolism [91]. While less is understood about the metabolic requirements of Treg cells, utilizing fatty acid oxidation over glycolysis may promote Treg survival over the survival of CD4 and CD8 T cells (Figure 2). Lipid signaling in intratumoral Treg cells additionally allows for cell survival and induces signaling pathways to promote oxidative phosphorylation in Treg cells [92]. Another class of suppressive immune cells, MDSCs, have also been found to demonstrate increased fatty acid uptake and activated fatty acid oxidation in multiple murine tumor models [93]. Pharmacological inhibition of fatty acid oxidation led to decreased production of inhibitory cytokines by MDSCs, blocking of immune inhibitory pathways, delayed tumor growth in a T-cell dependent manner, and enhanced efficacy of adoptive T-cell therapy [93]. GBM cells are also able to evade the anti-tumor immune response due to altered lipid metabolism impacting the function of antigen presenting cells. Exogenous induction of lipid peroxidation and ferroptosis resulted in release of damage-associated molecular patterns from glioma cells that stimulate dendritic cell activation and maturation and can lead to activation of cytotoxic T lymphocytes by dendritic cells [94,95]. Recurrent GBM reprogram metabolic processes to enrich fatty acid oxidation which allow for adaptive tumor resistance and anti-phagocytosis [96]. Fatty acid oxidation by GBM cells activates CD47 to mediate immune escape, representing a potential target for immunotherapy [96]. Notably, one class of fatty acids, arachidonic acids, also contributes to tumor progression in GBM [97]. However, targeting these molecules with steroids has been shown to also correlate with tumor progression and inhibits responses to oncolytic adenoviral therapy and checkpoint immunotherapy [98,99]. Further understanding of the specific role of individual classes of lipids in altering the immune response in the tumor microenvironment will be needed to develop specific anti-tumoral immune strategies targeting lipid utilization in GBM. While initially the central nervous system was thought to be an immune-privileged site, current thought points to the presence of immune surveillance in the brain following findings revealing the presence of dedicated lymphatic channels running parallel to dural venous sinuses and allowing for lymphocyte priming from antigen presenting cells in the brain [96,100]. Despite this evidence supporting the possibility of immune responses in the central nervous system, immunotherapies have not met with anywhere near the level of success in GBM that they have encountered in other cancers. Indeed, GBMs have enriched immunosuppressive myeloid, microglia, and macrophage populations and depleted tumor infiltrating lymphocytes, and thus have been characterized as “cold” tumors due to their lack of response to immunotherapy [101]. Furthermore, each component of standard of care treatment (surgery, radiation, chemotherapy, steroids) for GBM elicits immunosuppressive effects as well [102]. The most studied immunotherapeutic approaches for GBM are vaccines, immune checkpoint inhibitors, and biologic therapies (Figure 3). The most advanced vaccine trial in GBM, a phase 3 trial of the peptide vaccine rindopepimut, which targets EGFR variant III (EGFRvIII), relied on adaptive immunity with the tumor based on a single immunogenic peptide and failed to demonstrate improvement in survival over standard of care in EGFRvIII positive patients [103]. Peptide vaccines such as rindopepimut have poor immunogenicity on their own and require an effective T cell response to have antitumoral effects, which may limit their efficacy in GBM due to metabolic alterations in the GBM microenvironment altering effector T cell proliferation and function (Figure 3). Another vaccine-based therapy in trials was based on utilizing dendritic cells with promising early phase 3 survival data. However, dendritic cells injected into the tumor may have altered function based on altered tumoral metabolism, such as changes in lipid metabolism [104]. Promisingly, there is much more limited evidence on metabolic alterations impacting dendritic cells as compared to T cells. However, these vaccines ultimately also depend on an effective T cell response. Immune checkpoint inhibitors targeting PD-1/PD-L1 and/or CTLA-4 are also being investigated in phase 3 trials in GBM, with initial results suggesting no clinical benefit [104,105]. While numerous factors may be responsible for the limited response observed thus far to immune checkpoint inhibitors, including the expression levels of the targets themselves, the reduced T cell infiltration in glioblastoma is a notable barrier (Figure 3). Both CTLA-4 and PD-1 mechanistically involve the T cell response—blocking CTLA-4 enhances T cell priming and blocking PD-1 enhances T cell differentiation. Addressing the metabolic restraints experienced by effector T cells within the tumor may have potential to enhance the respose to checkpoint inhibitors. Though combining metabolic targeting with immune checkpoint blockade has been investigated in other cancer types, this approach has not yet been explored in GBM. While enthusiasm for IDO inhibition combined with checkpoint inhibitors has diminished following failure in a metastatic melanoma phase 3 trial, alternate strategies could include targeting tryptophan metabolism, considering pathways that tumor cells may use to bypass IDO inhibition. Glycolytic targeting in combination with checkpoint inhibitors is another strategy that has been shown in preclinical studies as increasing T cell activation, viability, and effector function to improve the efficacy of checkpoint therapy [106]. Biologic therapies for GBM can be viral or cellular. Oncolytic viruses have overall met with limited success in GBM, and initial anti-tumor T cell immune responses generated by viral infiltration into tumor do not persist without serial treatment [107]. Metabolic alterations, particularly the limited glucose and acidosis in the tumor microenvironment, can inhibit viral replication as well as prevent the activation of CD8 T cells which are required for oncolytic viral stimulation of host anti-tumor immune responses [108]. Cellular therapies for GBM include chimeric antigen receptor (CAR) T cell therapy, in which T cells are engineered to express an activated phenotype and can be designed to recognize antigens not presented in the context of MHC molecules, potentially allowing this therapeutic approach to bypass some of the immunosuppressive metabolic limitations in the tumor microenvironment. However, in the first trial of CAR T cells directed at EGFRvIII in glioblastoma patients, tumor infiltration of CAR T cells was detected but overall survival was not affected [109]. Significantly, tumor samples from patients who underwent surgical resection after CAR T cell infusion revealed upregulation of IDO1 and increased T regulatory cells, implying the possibility of GBM escape mechanisms reinstating an immunosuppressive milieu. One potential strategy to enhance CAR T cell therapy involves culturing CAR T cells in metabolic conditions similar to the tumor microenvironment to allow for acclimation to low nutrient availability and potentially mititgate the metabolic immunosuppression in the tumor microenvironment (Figure 2). T cell expansion in media containing low levels of glutamine was shown to result in greater effective antitumor function compared to cells cultured in traditional media [110]. Ex vivo culturing of cells concurrent with glycolytic blockade demonstrated improved tumor clearance [111]. Targeting metabolic adaptations, such as increased fatty acid oxidation, in conjunction with adoptive T-cell therapy has also demonstrated success in preclinical models [93]. Metabolic pathways implicated in maintaining immunosuppression in the GBM microenvironment have been well elucidated. However, the potential for targeting these metabolic pathways to condition the tumor microenvironment to become more responsive to immunotherapies remains underexplored in GBM. Preclinical data targeting metabolic pathways in conjunction with immunotherapies largely come from models of more immunogenic cancers. This presents an attractive avenue for further study in GBM, in which modifying a largely immunosuppressive environment may meaningfully alter immunotherapeutic response. While this review discusses the most studied metabolic pathways with respect to immunosuppression in GBM, several other metabolic pathway alterations occur to meet the energetic demands of GBM progression. Arginine metabolism reprogramming in GBM leads to increased intake and decreased degradation of arginine by tumor cells and has been linked to impaired T cell responses due to altered bioavailablity of arginine [112]. Targeting arginine metabolism has been explored preclinically in GBM and found to synergize with radiotherapy while promoting a protumor immune population, suggesting potential to explore targeting this pathway in combination with immunotherapies in GBM [112]. Another metabolite, 2-hydroxuglutarate (2-HG), has also been implicated in both tumor growth and modulation of anti-tumor immunity through inhibition of T cell activity, though 2-HG alterations in GBM are heterogenous and epigenetically regulated [113]. In IDH-1 mutant tumors which produce 2-HG, antitumor immunity induced by an IDH-1 specific vaccine or checkpoint inhibition is improved by simultaneously downregulating 2-HG production through inhibition of the enzymatic function of mutant IDH [113]. Several other metabolites have been described as altered in GBM such as aspartate, α-ketoglutarate, and methionine; however, less evidence exists regarding the contribution of these metabolic alterations to immunosuppression. Other molecular targets in GBM have also been noted to contrastingly impact tumor cell metabolism and immunosuppression in which tumor cell metabolism is slowed while T cell activatation promotes pro-tumoral effects on the immune microenvironment [114]. These suggest a range of alternative pathways that may be implicated in the limited response to immunotherapies and warrant further preclinical study. While many immunotherapies are being investigated in GBM patients, none have resulted in major improvements in survival outcomes. Negative results from phase II and phase III clinical trials of vaccines and immune checkpoint inhibitors have challenged the potential of these approaches in GBM. Future directions for immune-based strategies for glioblastoma require treatment modalities that can convert a ‘cold’ tumor with significant local immunosuppression into a ‘hot’ tumor. The uniquely immunosuppressive environment generated by tumor cellular metabolism in GBM presents an opportunity for augmenting responses to immunotherapy. Combining immunotherapy with agents that target the metabolic alterations resulting in an immunosuppressive microenvironment may have greater success in generating an antitumor immune response. These combinations should be evaluated rigorously preclinically in order to ensure that the most biologically sound combination approaches addressing the antitumor immune response along with tumor metabolism are advanced to clinical trials.

PMC10000695

Cindy Barba,H. Atakan Ekiz,William Weihao Tang,Arevik Ghazaryan,Mason Hansen,Soh-Hyun Lee,Warren Peter Voth,Ryan Michael O’Connell

Interferon Gamma-Inducible NAMPT in Melanoma Cells Serves as a Mechanism of Resistance to Enhance Tumor Growth

23-02-2023

melanoma,NAMPT,interferon gamma

Simple Summary The tumor microenvironment is complex, with interacting immune and tumor cells. Immune cells release inflammatory cytokines, including interferons (IFNs), that drive tumor clearance. However, evidence suggests that tumor cells can also utilize IFNs to enhance growth and survival in certain cases. We demonstrate that interferon gamma (IFNγ) mediates the metabolic reprogramming of melanoma cells by inducing the essential NAD+ salvage pathway enzyme nicotinamide phosphoribosyltransferase (NAMPT) gene through STAT1 binding to the NAMPT locus. NAMPT is constitutively expressed in cells during normal homeostasis. However, melanoma cells have higher energetic demands and increased NAMPT. We show that IFNγ signaling upregulates NAMPT in melanoma cells, increasing cell proliferation and survival. Further, STAT1-inducible Nampt promotes melanoma growth in mice. We provide evidence that melanoma cells directly respond to IFNγ-activated STAT1 by increasing Nampt, which improves their fitness during tumor immunity. Elucidating mechanisms that regulate NAMPT expression can lead to enhanced therapeutic approaches with immunotherapies that utilize IFN signaling to improve patient outcomes. Abstract (1) Background: Immune cells infiltrate the tumor microenvironment and secrete inflammatory cytokines, including interferons (IFNs), to drive antitumor responses and promote tumor clearance. However, recent evidence suggests that sometimes, tumor cells can also harness IFNs to enhance growth and survival. The essential NAD+ salvage pathway enzyme nicotinamide phosphoribosyltransferase (NAMPT) gene is constitutively expressed in cells during normal homeostasis. However, melanoma cells have higher energetic demands and elevated NAMPT expression. We hypothesized that interferon gamma (IFNγ) regulates NAMPT in tumor cells as a mechanism of resistance that impedes the normal anti-tumorigenic effects of IFNγ. (2) Methods: Utilizing a variety of melanoma cells, mouse models, Crispr-Cas9, and molecular biology techniques, we explored the importance of IFNγ-inducible NAMPT during melanoma growth. (3) Results: We demonstrated that IFNγ mediates the metabolic reprogramming of melanoma cells by inducing Nampt through a Stat1 binding site in the Nampt gene, increasing cell proliferation and survival. Further, IFN/STAT1-inducible Nampt promotes melanoma in vivo. (4) Conclusions: We provided evidence that melanoma cells directly respond to IFNγ by increasing NAMPT levels, improving their fitness and growth in vivo (control n = 36, SBS KO n = 46). This discovery unveils a possible therapeutic target that may improve the efficacy of immunotherapies involving IFN responses in the clinic.

Interferon Gamma-Inducible NAMPT in Melanoma Cells Serves as a Mechanism of Resistance to Enhance Tumor Growth The tumor microenvironment is complex, with interacting immune and tumor cells. Immune cells release inflammatory cytokines, including interferons (IFNs), that drive tumor clearance. However, evidence suggests that tumor cells can also utilize IFNs to enhance growth and survival in certain cases. We demonstrate that interferon gamma (IFNγ) mediates the metabolic reprogramming of melanoma cells by inducing the essential NAD+ salvage pathway enzyme nicotinamide phosphoribosyltransferase (NAMPT) gene through STAT1 binding to the NAMPT locus. NAMPT is constitutively expressed in cells during normal homeostasis. However, melanoma cells have higher energetic demands and increased NAMPT. We show that IFNγ signaling upregulates NAMPT in melanoma cells, increasing cell proliferation and survival. Further, STAT1-inducible Nampt promotes melanoma growth in mice. We provide evidence that melanoma cells directly respond to IFNγ-activated STAT1 by increasing Nampt, which improves their fitness during tumor immunity. Elucidating mechanisms that regulate NAMPT expression can lead to enhanced therapeutic approaches with immunotherapies that utilize IFN signaling to improve patient outcomes. (1) Background: Immune cells infiltrate the tumor microenvironment and secrete inflammatory cytokines, including interferons (IFNs), to drive antitumor responses and promote tumor clearance. However, recent evidence suggests that sometimes, tumor cells can also harness IFNs to enhance growth and survival. The essential NAD+ salvage pathway enzyme nicotinamide phosphoribosyltransferase (NAMPT) gene is constitutively expressed in cells during normal homeostasis. However, melanoma cells have higher energetic demands and elevated NAMPT expression. We hypothesized that interferon gamma (IFNγ) regulates NAMPT in tumor cells as a mechanism of resistance that impedes the normal anti-tumorigenic effects of IFNγ. (2) Methods: Utilizing a variety of melanoma cells, mouse models, Crispr-Cas9, and molecular biology techniques, we explored the importance of IFNγ-inducible NAMPT during melanoma growth. (3) Results: We demonstrated that IFNγ mediates the metabolic reprogramming of melanoma cells by inducing Nampt through a Stat1 binding site in the Nampt gene, increasing cell proliferation and survival. Further, IFN/STAT1-inducible Nampt promotes melanoma in vivo. (4) Conclusions: We provided evidence that melanoma cells directly respond to IFNγ by increasing NAMPT levels, improving their fitness and growth in vivo (control n = 36, SBS KO n = 46). This discovery unveils a possible therapeutic target that may improve the efficacy of immunotherapies involving IFN responses in the clinic. Melanoma is the most common form of cancer in the U.S., increasing in prevalence over the last decade [1]. This form of skin cancer is characterized by the uncontrolled growth of melanocytes and is treated with surgical resection, chemotherapy, or immunotherapy, depending on the tumor stage [2]. Melanomas frequently display resistance to such therapeutic approaches; therefore, new avenues for treating melanoma are urgently needed [3]. In melanoma, immune cells infiltrate the tumor microenvironment (TME) and secrete inflammatory cytokines, such as interferons (IFNs) [4]. Type I and II IFNs reduce tumor growth and have significant anti-proliferative effects [4,5,6]. Type II IFNs, such as IFNγ, are hallmark cytokines produced by tumor-infiltrating lymphocytes (TILs), such as NK and T cells, and their activation leads to tumor clearance through multiple mechanisms [7]. Recently, there has been a new appreciation for mechanisms by which tumor cells evade immune responses. Although counterintuitive, IFNs also have pro-tumorigenic roles in cancer [5,7,8] and enhance tumor growth by promoting angiogenesis [9], upregulating co-inhibitory molecules involved in immune checkpoints, including the programmed death ligand 1 protein 1 (PD-L1) [10], and preventing apoptosis [11]. These contrasting findings require further characterization of IFNγ signaling to optimize therapeutic windows. Here, we examined a key nexus of IFNγ-mediated tumor cell fitness, metabolic reprogramming by upregulating the essential nicotinamide adenine dinucleotide (NAD+) salvage pathway enzyme, nicotinamide phosphoribosyltransferase (NAMPT) [12]. All cells utilize NAMPT as the rate-limiting enzyme in NAD+ production, and it is important in biological signaling and ATP production [12]. In addition, tumor cells divide rapidly, leading to increased metabolic demands and an increased dependence on NAD+, a cofactor central to metabolism [13,14]. Melanoma cells have increased NAMPT expression [15], and NAMPT inhibition significantly reduces melanoma cell proliferation [12,15,16]. Concordantly, NAMPT inhibitors, such as FK866, have been tested as a novel cancer therapy and showed reduced tumor burden in preclinical studies, leading to several clinical trials (NCT00432107, NCT00431912) [17]. NAMPT is clearly important for cell homeostasis, and we have identified a novel IFNγ-induced mechanism by which NAMPT enhances tumor burden. Our data elucidates a mechanism by which IFNγ upregulates NAMPT in both human and mouse melanoma cells by inducing the signal transducer and activator of transcription 1 (STAT1) transactivation via a conserved enhancer that we recently identified in tumor-associated macrophages [18]. Further, IFN-inducible NAMPT contributes to melanoma cell growth in vitro and in vivo. Despite the well-established anti-proliferative effects of IFNs on tumor cells, our findings reveal novel facets of IFNγ signaling in tumor cells that may contribute to the pathogenic effects mediated by antitumor immunity, as seen in recent studies [5]. Together, these data suggest an essential role for IFN-inducible NAMPT in melanoma progression. By identifying mechanisms by which IFNs and IFN-based therapies promote tumor growth, the efficacy of such treatments in the clinic will be enhanced. Mouse B16-F10 melanoma cell lines were grown in DMEM supplemented with 10% FBS, 1% penicillin-streptomycin, and 1% L-glutamine at 37 °C in a humidified atmosphere containing 5% CO2. Mouse YUMM (1.1, 3.2 and 5.2) melanoma cell lines were cultured in DMEM/F12 media containing 10% FBS and supplemented with 1% pen-strep and 1% non-essential amino acids. Lipofectamine RNAi MAX and Opti-MEM medium were purchased from Invitrogen. FK866 was obtained from Sigma, IFNγ was obtained from Ebioscience, and NMN was obtained from Sigma-Aldrich. NAD+ was measured using the NAD+/NADH quantitation kit from Sigma according to the manufacturer’s instructions. Tumor (B16-F10, YUMM 1.1, YUMM 3.2, YUMM 5.2, A375, A2058) mouse and human cell lines were cultured in their respective media, as mentioned under Cell cultures and reagents. For the IFNγ stimulation/NAMPT inhibition experiments, B16-F10 cells were stimulated with either mouse or human IFNγ (50 ng/mL mouse IFNγ, Ebioscience Cat # 14-8311-63; 50 ng/mL human IFNγ, Ebioscience Cat # 14-8319-80) and FK866 (50 nM, R&D Systems Cat # 4808/10) for 6 h for RNA isolation or 24 h for protein isolation. B16-F10 cells were also treated with 10 ng/mL of IFNβ in respective experiments. In experiments with IFNγ stimulation only, cells were stimulated with 50 ng/mL IFNγ, unless otherwise indicated. Where indicated, 100 µM NMN was added to cell culture at the beginning of each experiment to rescue the effects of FK866 on the depletion of NAD+. C1498 and MC38 cells were treated with IFNγ (50 ng/mL mouse IFNγ, Ebioscience Cat # 14-8311-63) for 6 h for RNA isolation qPCR analysis. Cells were lysed with Qiazol reagent prior to the isolation of total RNA with a miRNeasy RNA isolation kit (Qiagen, Germantown, MD, USA). An amount of 200–400 ng of RNA was utilized in a cDNA synthesis reaction with qScript kit reagents (Quanta Biosciences, Beverly, MA, USA) and was diluted to a final volume of 200 μL. Quantitative PCR (qPCR) was performed in a QuantStudio 6 (Thermo, Madison, WI, USA), utilizing Promega GoTaq qPCR master mix reagents, or the equivalent, and qPCR primers listed in Table S1. RT-qPCR data presented were representative of at least 2 independent experiments with at least 2 biological replicates per condition and 2 to 3 technical replicate PCR reactions per sample. CRISPR/Cas9 lentivector infections were performed by utilizing Trans-IT 293 (Mirus, Madison, WI, USA) to transfect 293T cells with the packaging plasmids pVSVg and psPAX2, along with a modified CRISPR plasmid based on lentiCRISPRv2 (Addgene plasmid # 52961), as described [19,20]. The deletion of the STAT1 binding site was made by targeting sequences immediately flanking the SBS region expressed from a single lentiCRISPRv2 vector containing a dual promoter system, designed at the University of Utah Mutation Generation and Detection Core, and consisting of the U6 promoter for one gRNA and the H1 promoter for the second. Clonal cell populations were obtained by plating single cells on a 96-well plate, and the expression of the NAMPT was quantified by IFNγ stimulation following RNA isolation and RT-qPCR to verify the knockout of the STAT1 binding site. Controls for each of these cell lines were generated in a similar manner as described above, with a CRISPR/Cas9 lentiCRISPRv2 vector plasmid that contained non-targeting sgRNA sequences. Cell pellets were lysed in RIPA buffer containing 150 mM Tris (pH7.4), 150 mM NaCl, 1% NP40, 1 mM EDTA, and 1% SDS. The purified protein was separated via SDS-PAGE and transferred to a 0.45 µM nitrocellulose membrane. All experimental samples and controls used for one comparative analysis were run on the same blot/gel. Antibody staining of NAMPT (Bethyl Laboratories) and ACTB (Santa Cruz sc-47778) was performed. Primary antibody binding was detected with IRDye-700- or IRDye-800-conjugated secondary antibodies (LI-COR, Lincoln, NE, USA) using a LI-COR Odyssey Infrared Flatbed Scanner. Antibodies used are detailed in Table S2. All mice were on a C57BL/6J genetic background obtained from The Jackson Laboratory (Bar Harbor, ME, USA), cataloged as C57BL/6J, Stock No: 000664. All experimental procedures using mice were performed with the approval of the Institutional Animal Care and Use Committee of the University of Utah. Mice were shaved, at most, 24 h prior to any injection. Mice were then injected subcutaneously in the rear flank with 5 × 105 B16-F10 melanoma cells on day 0. Tumor size measurements were taken on the indicated days, and tumors were weighed upon sacrifice at day 14. The DNA template for amplicon sequencing of SBS mutationswas obtained from the control and SBS KO B16-F10 cells that were cultured as mentioned above. Cells were treated with IFNγ (50 ng/mL mouse IFNγ, Ebioscience Cat # 14-8311-63) and/or FK866 (50 ng/mL, R&D Systems Cat # 4808/10) for 6 h. PCR using primers containing adaptor sequences according to manufacturer’s instructions and targeting the SBS region of NAMPT was performed. For each clone, 4 separated aliquots were individually amplified to reduce sampling error during PCR. After the confirmation of the correct amplicon by gel electrophoresis, separate aliquots were pooled together and then purified using PCR purification (Qiagen Cat # 28106). Individual SBS KO clones and pooled control cell clones were then submitted for amplicon sequencing (Amplicon-EZ sequencing, Genewiz Inc., South Plainfield, NJ, USA). After the mechanical disruption of tumor tissue, tumor cells were placed on an orbital shaker in Accumax (Innovative CellTechnologies) and incubated for 30 min at room temperature, followed by red blood cell lysis in ammonium–chloride–potassium buffer (Biolegend) and filtration through a 0.45 micron filter (18). Flow cytometric analyses were performed on single-cell suspensions isolated from tumors. Prior to staining with fluorophore-conjugated antibodies (all from Biolegend or Thermo/eBioscience), Fc receptors were blocked using αCD16/32 antibody (1:200, Biolegend) to reduce nonspecific staining. Staining was performed on ice for 15–30 min in PBS supplemented with 2% FBS and 0.1% sodium azide. Cells were stained with a combination of the following fluorophore-conjugated antibodies in Hanks’ balanced salt solution supplemented with 10% BSA, pyruvate, EDTA, and HEPES. Cultured cells were treated with various reagents as described and were collected off the plate via 5 mM EDTA in PBS in a non-enzymatic manner. For viability assays, Ghost Dye Red 780 (Tonbo) stain was applied. The cells were then washed, and flow cytometry was performed using a BD LSRFortessa II (BD Biosciences). For death assay, 7AAD-PerCP Cy5.5 (2 µL/test) and Annexin V-PeCy7 (2 µL/test) were stained in annexin binding buffer for 30 min. The cells were then washed, and flow cytometry was performed using a BD LSRFortessa II (BD Biosciences). Data were subsequently analyzed using FlowJo software, utilizing the gating strategy shown in Supplemental Figure S2 (Tree Star). Antibodies used are detailed in Table S2. Tumor cells were cultured at a seeding density of 5000 cells per well in a 96-well plate for 24 h to allow cell adherence. Media was then changed to add appropriate drug conditions: IFNγ (50 ng/mL mouse IFNγ, Ebioscience Cat # 14-8311-63; 50 ng/mL human IFNγ, Ebioscience Cat # 14-8319-80), FK866 (50 ng/mL, R&D Systems Cat # 4808/10), and NMN (100 µM, Sigma Aldrich, Inc. Product # N3501). Cells were then placed in the incubator containing the Incucyte at 37 °C in a humidified atmosphere containing 5% CO2 for up to 140 h. Phase confluence was determined using the IncuCyte Zoom software. Four images of each well were taken every 2 h and normalized to phase confluence plotted over time. T-tests were performed when comparing differences between two experimental groups in the in vitro experiments. Tumor growth rates were analyzed using 2-way ANOVA comparison. Unless otherwise noted, the following symbols were utilized in all figures to denote significant p-values: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001, and ns for not significant. Our previous work identified that IFNγ upregulates NAMPT in tumor-associated macrophages and is important for their anti-tumorigenic function [18]. Additionally, melanomas have higher levels of NAMPT [15]; therefore, we investigated whether IFNs induce NAMPT in melanoma cells. We treated B16-F10 mouse melanoma cells with IFNγ for 6 or 24 h, which significantly increased NAMPT mRNA expression (Figure 1A). In addition, 24 h of IFNγ treatment induced higher levels of the NAMPT protein in B16-F10 cells (Figure 1B), with Western blot quantification showing significant increases in protein levels (Figure 1C). To examine the IFN–NAMPT axis in other clinically relevant models, we used Yale University Mouse Melanoma (YUMM) cells expressing mutations commonly observed in melanoma patients [21]. We treated YUMM 1.1, 3.2, and 5.2 mouse melanoma cell lines (Figure 1D) and A375 and A2058 human melanoma cells (Figure 1E) with IFNγ. In all cases, IFNγ significantly induced NAMPT expression in mouse and human melanoma cells. Next, we determined if NAMPT was induced in other cancer types in response to IFNγ. We treated C1498, a leukemia cell line, and MC38, a colon cancer cell line, with IFNγ. NAMPT was upregulated in C1498 but not in MC38 cells, suggesting tumor type dependency in the IFNγ-induction of NAMPT (Figure 1F). In addition, we tested whether IFNβ induced NAMPT in melanoma cells, since it has been shown to be within the TME and can increase pro-tumor factors [10,22]. In all cases of cells treated with IFNγ, CXCL10 was measured to verify IFNγ induction (Supplemental Figure S1A–E). We found that B16-F10 cells treated with IFNβ had a significant upregulation of NAMPT (Figure 1G), suggesting that a variety of IFNs can induce NAMPT. Altogether, we showed strong evidence that IFNs induce NAMPT in mouse and human melanoma cells, suggesting that the IFN-mediated metabolic changes are, in part, mediated by differentially regulated NAMPT. Previous literature implicates NAMPT in melanoma proliferation [16]. After observing the IFN induction of NAMPT expression in melanoma cells, we investigated whether IFNγ-inducible NAMPT promoted proliferation in vitro. We treated B16-F10 cells with FK866, a chemical inhibitor of NAMPT enzymatic function, and nicotinamide mononucleotide (NMN), the direct byproduct of NAMPT, and measured the proliferation of cells with each individual treatment or with a combination of the three using Incucyte live imaging analysis for 6 days. Melanoma cells treated with either IFNγ or FK866 had a reduction in growth that was further exacerbated upon combination treatment with IFNγ and FK866 (Figure 2A). To rescue the effect of NAMPT, FK866, we treated cells with FK866 and NMN, restoring growth and validating the function of NAMPT (Figure 2A). These results were consistent with previous works and further suggest that IFNγ-inducible NAMPT is important for melanoma proliferation [16]. Melanoma cells also exhibited a significant decrease in cell confluency when treated with IFNγ and FK866, and we sought to identify if it was due to cell death or an inhibition of proliferation (Figure 2B). We treated B16-F10 cells with IFNγ, FK866, or a combination of the two and analyzed our data using flow cytometry (Figure 2C). To quantify proliferation, we used Cell Trace Violet (CTV), and to quantify cell death, we used 7AAD and Annexin V staining. The CTV fluorescent signal was stratified into high and low subsets using flow cytometry. IFNγ/FK866 combination-treated melanoma cells had the highest proportion of CTV-high (non-proliferating) cells and the lowest proportion of CTV-low (proliferating) cells, suggesting that IFNγ with FK866 decreased growth more significantly than either treatment alone (Figure 2D). In addition, the mean fluorescence intensity (MFI) of CTV was highest in the IFNγ/FK866 combination-treated cells, further suggesting that the combination treatment had the greatest reduction in melanoma cell proliferation (Figure 2E). Altogether, we conclude that IFNγ-inducible NAMPT is an important player in melanoma cell growth. After observing that IFNγ/FK866 combination-treated cells grew significantly slower, we wanted to investigate if cell death played a role in the growth reduction. To do so, we treated B16-F10 melanoma cells with IFNγ, FK866, or a combination of the two and analyzed our data using flow cytometry to quantify live and dead cell populations. IFNγ/FK866 combination-treated cells also exhibited the lowest proportion of live cells and the highest proportion of dead cells using a live/dead stain (Figure 2F). Additionally, we used this same technique with another stain combination to further characterize the type of cell death that was induced by IFNγ/FK866 treatment. Cells treated with the combination of IFNγ and FK866 had the highest proportion of early apoptotic and late apoptotic cells and the lowest proportion of necrotic cells (Figure 2G). Altogether, these data indicate that the inhibition of IFNγ-induced NAMPT leads to cell death in melanoma. IFNγ and FK866 combination treatment significantly decreased melanoma growth, but we wanted to assess the contribution of IFNγ-inducible NAMPT in melanoma growth, specifically. We previously identified a novel conserved STAT1 binding element in the NAMPT locus that was sufficient for the IFNγ-mediated induction of NAMPT in macrophages [18]. Our previous publication outlined the predicted Stat1 consensus binding sites in the first intron of the NAMPT gene (Figure 3A). Previously, we validated that deletion of the first two binding sites led to decreased IFNγ-inducible NAMPT while maintaining the baseline expression of NAMPT [18]. Therefore, we utilized this information and deleted the STAT1 binding sites in B16-F10 cells (SBS KO) with a lentiviral CRISPR/Cas9 knockout vector expressing two sgRNAs targeting flanking sequences of the SBS element. We compared cells with our CRISPR construct to control cells expressing non-targeting sgRNAs (Figure 3A). A spectrum of CRISPR-Cas9-driven mutations were seen in the analyzed clones, confirming the successful deletion of the Stat1 binding site of NAMPT. By analyzing multiple lines cloned from single cells, we confirmed that, upon IFNγ stimulation, SBS KO cells significantly reduced NAMPT induction (Figure 3B). Lastly, we sought to investigate whether IFN/Stat-inducible NAMPT regulates melanoma growth in vivo. To do so, we injected C57BL/6J WT mice with either control or SBS KO B16-F10 tumor cells subcutaneously and monitored tumor size and composition. Control and SBS KO tumors were measured every two days and weighed at the end of the experiment. Altogether, SBS KO cells grew significantly smaller tumors, compared to wild-type B16-F10 cells, over time by volume and mass (Figure 3C,D). In addition, we immunologically charactered the TME of wild-type and SBS-KO tumors using flow cytometry. We quantified the percent of overall immune cells (CD45+), T cells (CD3+), helper T cells (CD4+), cytotoxic T cells (CD8+), B cells (B220+), and macrophages (CD11b+ F4/80+) within tumors from control and SBS KO B16-F10-bearing mice and found no overall differences in immune composition (Figure 3E,F, Supplemental Figure S2). We also assessed programmed cell death protein 1 (PD1) expression on T cells and programmed cell death ligand protein 1 (PD-L1) within the tumor to rule out tumor growth differences attributed to the immune function within the TME. We found no differences in T cell PD1 expression between the control and SBS KO tumors (Figure 3G, Supplemental Figure S2). Additionally, we found no differences in non-immune cell PD-L1 (CD45- PD-L1+) expression between the control and SBS KO tumors and modest but significant increases in the mean fluorescence intensity (MFI) of non-immune cell PD-L1 (Figure 3H,I, Supplemental Figure S2). Overall, we saw no significant differences in major immune cell populations within the tumors of control and SBS KO cells, suggesting that the phenotypic differences in tumor growth were tumor cell-intrinsic and not due to an altered immune response in the TME. IFNγ’s effect on reprogramming has been studied in tumor-associated macrophages, whereby IFNγ affects many important metabolic regulators to alter metabolism [18,23]. Some of the known mechanisms by which IFNγ reprograms the metabolic state of immune cells is by the inhibition of the mammalian target of rapamycin complex 1 (mTORC1), 5′-AMP-activated protein kinase (AMPK), and glycogen synthase kinase 3 (GSK3) [24,25]. In this study, we found that IFNγ induces NAMPT in both human and mouse melanoma cells. Further, we identified the importance of IFN/Stat-inducible NAMPT in melanoma growth and proliferation. Moreover, we found that perturbation by IFNγ, in the presence of a NAMPT inhibitor, led to increased apoptosis. We also demonstrated that IFNγ-inducible NAMPT in melanoma cells counteracts the growth-suppressive effects of IFNγ. Notably, Stat1 can sometimes be activated by several other factors besides IFNγ within the TME, which may add valuable insight into the importance of Stat1-inducible NAMPT and will be of interest in future studies. Further, NAMPT is also known to be secreted from cells and is referred to as extracellular NAMPT (eNAMPT), and the mechanism by which IFNγ alters eNAMPT in melanoma will also be of interest for future studies [26,27]. Together, our work describes how IFN/STAT1 can induce NAMPT in human and mouse melanoma cells to increase growth both in vitro and in vivo. It is important to note that some of the mechanisms downstream of IFNγ signaling may be shared between melanoma cells and tumor-associated macrophages [18]. NAMPT orchestrates an effective antitumor immune response in macrophages within the TME, whereas selectively interfering with NAMPT in melanoma cells may improve the efficacy of IFN-based immunotherapies. STING agonists, immune checkpoint blockade (ICB), and other IFN-inducing therapies have been promising for patients with cancer; however, they are not always the frontline or the most effective and durable treatment strategies for melanoma, due to high rates of immune-related adverse events [22,28,29]. These immunotherapy approaches may indirectly upregulate IFN-inducible NAMPT in tumors cells, and we hypothesize that their efficacy can be enhanced by combining these therapies with NAMPT inhibitors that specifically target tumor cells. For instance, increased PD-L1 may lead to immune suppression of antitumor responses; however, increased PD-L1 expression correlates with enhanced immune checkpoint blockade responses, more specifically to anti-PD-1 therapy [30]. In our study, we saw trending increases in PD-L1 expression in non-immune tumor cells without the STAT1 binding site necessary for IFNγ, corroborating a recent study that found that IFN increased PD-L1 expression on liver cancer cells and required IFN-independent NAMPT [31]. Together, these observations implicate increased sensitivity to immune checkpoint blockade therapy and the IFNs induced by immune checkpoint blockade therapy with tumor cell-specific NAMPT inhibition, yet it may depend on the tumor type [31]. Ultimately, our understanding of IFN/Stat-inducible NAMPT may lead to better combinational therapies or improve the efficacy of NAMPT inhibitors. In our work, we were interested in the paradigm between IFNs’ antitumor and protumorigenic roles in melanoma. Altogether, our findings suggest a novel mechanism by which immune molecules alter a key tumor cell metabolic enzyme to enhance tumor survival, which may counteract some of the antitumor effects of IFNs. We believe that targeting protumorigenic IFN-mediated changes in tumor metabolism is critical to the enhancement of IFN-based immunotherapies.

PMC10000698

Hongxuan Li,Shiqian Fu,Danliangmin Song,Xue Qin,Wei Zhang,Chaoxin Man,Xinyan Yang,Yujun Jiang

Identification, Typing and Drug Resistance of Cronobacter spp. in Powdered Infant Formula and Processing Environment

03-03-2023

powdered infant formula,Cronobacter spp.,antibiotic resistance,multilocus sequence typing,transcriptomics

Cronobacter spp. is a food-borne pathogenic microorganism that can cause serious diseases such as meningitis, sepsis, and necrotizing colitis in infants and young children. Powdered infant formula (PIF) is one of the main contamination routes, in which the processing environment is an important source of pollution. In this investigation, 35 Cronobacter strains isolated from PIF and its processing environment were identified and typed by 16S rRNA sequencing and multilocus sequence typing (MLST) technology. A total of 35 sequence types were obtained, and three new sequence types were isolated for the first time. The antibiotic resistance was analyzed, showing that all isolates were resistant to erythromycin but sensitive to ciprofloxacin. Multi-drug resistant strains accounted for 68.57% of the total, among which Cronobacter strains with the strongest drug resistance reached 13 multiple drug resistance. Combined with transcriptomics, 77 differentially expressed genes related to drug resistance were identified. The metabolic pathways were deeply excavated, and under the stimulation of antibiotic conditions, Cronobacter strains can activate the multidrug efflux system by regulating the expression of chemotaxis-related genes, thus, secreting more drug efflux proteins to enhance drug resistance. The study of drug resistance of Cronobacter and its mechanism has important public health significance for the rational selection of existing antibacterial drugs, the development of new antibacterial drugs to reduce the occurrence of bacterial resistance, and the control and treatment of infections caused by Cronobacter.

Identification, Typing and Drug Resistance of Cronobacter spp. in Powdered Infant Formula and Processing Environment Cronobacter spp. is a food-borne pathogenic microorganism that can cause serious diseases such as meningitis, sepsis, and necrotizing colitis in infants and young children. Powdered infant formula (PIF) is one of the main contamination routes, in which the processing environment is an important source of pollution. In this investigation, 35 Cronobacter strains isolated from PIF and its processing environment were identified and typed by 16S rRNA sequencing and multilocus sequence typing (MLST) technology. A total of 35 sequence types were obtained, and three new sequence types were isolated for the first time. The antibiotic resistance was analyzed, showing that all isolates were resistant to erythromycin but sensitive to ciprofloxacin. Multi-drug resistant strains accounted for 68.57% of the total, among which Cronobacter strains with the strongest drug resistance reached 13 multiple drug resistance. Combined with transcriptomics, 77 differentially expressed genes related to drug resistance were identified. The metabolic pathways were deeply excavated, and under the stimulation of antibiotic conditions, Cronobacter strains can activate the multidrug efflux system by regulating the expression of chemotaxis-related genes, thus, secreting more drug efflux proteins to enhance drug resistance. The study of drug resistance of Cronobacter and its mechanism has important public health significance for the rational selection of existing antibacterial drugs, the development of new antibacterial drugs to reduce the occurrence of bacterial resistance, and the control and treatment of infections caused by Cronobacter. Cronobacter spp. is a facultatively anaerobic, Gram-negative foodborne pathogen [1]. The World Food and Agriculture Organization (FAO) and the World Health Organization (WHO) have classified Cronobacter spp. as one of the pathogenic bacteria in category A of infant formula (PIF). Newborns and infants are high-risk groups for Cronobacter infection, and PIF and its processing environment are the main pollution channels of Cronobacter spp. [2,3,4]. Cronobacter spp. includes seven species and three subspecies [5,6,7]. Among them, C. sakazakii and C. malonaticus are the main pathogenic bacteria in the clinic. This bacterium is related to meningitis and necrotizing enterocolitis and infects infants by contaminating powdered infant formula (PIF) [8,9], with mortality rates ranging from 40% to 80% [10,11]. Even if cured, there is a possibility of severe neurological sequelae. Multilocus sequence typing (MLST) is a molecular typing method based on DNA sequence. MLST method for Cronobacter spp. has become one of the most effective and common methods [12,13]. Currently, the Cronobacter MLST database (http://www.pubmlst.org/cronobacter accessed on 22 March 2022) has been established, which contains more than 3000 Cronobacter strains from different sources and is divided into more than 800 sequence types (STs). MLST technology has been gradually expanded in the typing research of Cronobacter spp., because of its simple operation, high identification, and a high degree of sharing. At present, the study found that the STs of Cronobacter have a certain relationship with its serotype [14,15]. In addition, Fei et al. used MLST technology to classify 70 Cronobacter strains isolated from PIF in China into 19 STs and found that the dominant STs of Cronobacter spp. from PIF in China were ST4, ST1, and ST64 [16]. It can be seen that the MLST method has been widely used in the identification and typing of Cronobacter spp. and has been well accepted. Currently, the most effective treatment for diseases caused by Cronobacter spp. is antibiotic therapy [17,18]. Many studies have been carried out to evaluate the drug resistance of Cronobacter spp. [19,20,21]. Research reports in recent years have confirmed that Cronobacter spp. has strong drug resistance and is increasing year by year [22,23,24]. After research, penicillin, first- and second-generation cephalosporins and other commonly used antibiotics in hospitals have lost their inhibitory effect on Cronobacter spp., and new Cronobacter isolates with multi-drug resistance spectrum have been gradually discovered [25,26]. Although the multidrug-resistant operon mar was found in the genome of Cronobacter spp., the drug resistance level of this genus is generally lower than that of other foodborne pathogens, and the drug resistance mechanism is not yet clear [27,28]. Cao et al. studied the reaction mechanism of C. sakazakii CICC 21,544 under the combined stress of citral and carvacrol by transcriptome sequencing and found 25 significantly differentially expressed genes, which were mainly involved in the metabolism of various small molecules, ribosome function, and transmembrane transport systems [29]. Xu et al. used transcriptome sequencing to explore the regulatory mechanism of pmrA mutation on the biofilm formation of C. sakazakii BAA-894 [30]. The transcriptome research method can be used to determine the known tolerance mechanism. We can also find a new regulatory mechanism related to the tolerance of the bacteria from these differentially expressed genes and reveal the gene function and its possible metabolic pathway. Thereby, contributing to the prevention and control of pathogenic microorganisms in food and its processing environment [31,32,33,34]. Thirty-five Cronobacter strains isolated from PIF and its processing environment were used as the research objects in this investigation. The strains were identified and molecularly typed by 16S rRNA sequencing and MLST technology. On this basis, strains with different STs were selected for antibiotic resistance evaluation and analyzed. The drug resistance-related genes and metabolic pathways of Cronobacter spp. under antibiotic conditions were mined with the transcriptome analysis, thereby, revealing the resistance mechanism. Thirty-five Cronobacter strains isolated from PIF and processing environments in different areas of China were selected in this investigation. The detailed information of strains was listed in Table S1. Meanwhile, eight type strains (C. sakazakii ATCC 29544T, C. sakazakii ATCC BAA-894T, C. malonaticus LMG 23826T, C. turicensis DSM 18703T, C. muytjensii ATCC 51329T, C.universalis NCTC 9529T, C. dublinensis DSM 18705T, C. condimenti LMG 26250T) and two reference strains (C. sakazakii ATCC 12868, C. sakazakii ATCC 29004) were also chosen for comparison. A 2× Taq PCR MasterMix and DNA Marker were purchased from TIANGEN Biotech Co., Ltd. (Beijing, China). Primers were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). EZNATM bacterial total RNA extraction kit was obtained from Omega Bio-Tek Inc. (Norcross, GA, USA). PrimeScriptTM reverse transcription kit was obtained from TaKaRa BIO Co., Ltd. (Dalian, China). Absorbance was measured by SpectraMax i3x multifunctional microplate reader (Molecular Devices Co., Ltd., Sunnyvale, CA, USA). The concentration and purity of the DNA was measured by NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific Co., Ltd., Shanghai, China). All strains were cultured at 37 °C, 150 r/min overnight, and then purified on tryptic soy agar (TSA) plates at 37 °C for 14–16 h. A single colony was selected in a tryptic soy broth (TSB) liquid medium and cultured for 8 h at 37 °C 150 r/min to obtain strains at the end of the logarithmic growth phase. In this experiment, TIANGEN bacterial genomic DNA extraction kit was used for rapid extraction of strain DNA and stored at −20 °C for further tests. The extracted DNA was amplified by PCR using bacterial 16S rRNA universal primers 27-F and 1492-R. The PCR assay was carried out in a 50 μL mixture system including 16 μL of 2× Taq PCR MasterMix, 27 μL of sterile distilled water, 1 μL of each primer with a concentration of 10 μM, and 5 μL of template DNA. The reaction conditions consisted of an initial predenaturation at 95 °C for 5 min, followed by 30 cycles of 94 °C for 30 s, an annealing at 55 °C for 30 s, an elongation at 72 °C for 30 s, and a final extension at 72 °C for 5 min. The amplified products were sequenced to complete the molecular identification of the strains. At the same time, 7 pairs of housekeeping gene primers of Cronobacter spp. were used to specifically amplify the extracted DNA of different strains, the genes were atpD, fusA, glnS, gltB, gyrB, infB, and pps, respectively. The amplification system and conditions were the same as the 16S rRNA PCR reaction. Then, the amplified product was sequenced and analyzed, and the unidirectional measurement was performed to obtain the nucleic acid sequences of the 7 housekeeping genes of the strains. The primer sequences were shown in Table S2. According to the 16S rRNA sequencing results and the 3036 bp nucleic acid sequence of 7 housekeeping genes spliced in sequence, 35 Cronobacter strains were analyzed by neighbor-joining phylogeny analysis using MEGA7.0 software, and the procedure was repeated 1000 times. At the same time, the type strain and reference strain were selected for comparison, and phylogenetic trees based on 16S rRNA and MLST were constructed, respectively. For all Cronobacter isolates, drug susceptibility tests of 20 commonly used clinical antibiotics in 9 categories were carried out. The operation was performed according to the implementation standard of the American Clinical and Laboratory Standards Institute (CLSI) M100 antimicrobial susceptibility test, and the susceptibility results were interpreted according to its specifications. The names and doses of antibiotics and the judgment results were shown in Table 1. The representative resistant strain was selected for transcriptome sequencing. The strain without antibiotic treatment was activated and cultured under normal conditions. The treatment conditions of the strain requiring antibiotic treatment were as follows: the frozen bacterial solution was removed from −20 °C and added to TSB liquid medium with 2% inoculum for overnight culture, and then reactivated in TSB liquid medium with 4 μg/mL erythromycin for 6 h. The strains before and after treatment were analyzed by transcriptome sequencing. RNA was extracted and purified from the samples and tested for concentration and purity (OD260/280 and OD260/230) along with RNA integrity number (RIN) for quality control (Table S4). RNA of acceptable quality was subjected to subsequent cDNA synthesis and library construction and then the libraries were double-end sequenced. The raw upland data are filtered to obtain high quality sequences and the filtered sequences are tested for quality using Q20 and Q30 values (Table S5). The high-quality filtered sequences are only then available for comparison with the reference genome of the species. The expression of each gene was calculated according to the comparison results. On this basis, the expression difference analysis, enrichment analysis, and cluster analysis were carried out. According to the screening results of transcriptomics tolerance-related genes, 10 genes with a high expression ratio and high correlation with drug resistance were selected for qRT-PCR verification. According to the results of tolerance evaluation, two strains with strong and weak drug resistance were selected for quantitative real-time PCR detection of related genes before and after treatment to investigate the expression differences of each gene in different strains. RNA from strains were extracted with a simple P total RNA extraction kit (Bioer Technology Co., Ltd., Hangzhou, China). NanoDrop spectrophotometer (Thermo, Wilmington, DE, USA) was used to measure the purity and integrity of RNA samples. A reverse transcription kit (Takara Bio, Shiga, Japan) was used for reverse transcription. Additionally, RT-qPCR was performed by SYBR Premix Ex Taq (Takara Bio, Shiga, Japan) according to the manufacturer’s instructions; the reaction was conducted with a QuantStudio™ 3 system. The value of 2−∆∆Ct was calculated to analyze and compare the changes in the expression levels of each gene before and after treatment to verify the accuracy of the omics analysis results. The target genes were matched according to the corresponding sequences in C. sakazakii ATCC 29544T genome in the NCBI database, and real-time quantitative primer design was performed in the software Primer Premier 5.0. The gene names and primer sequences were shown in Table S3. The 16S rRNA sequencing results of 35 isolates were compared and analyzed in the GenBank database, and the results showed that they all belonged to the genus Cronobacter spp., and the similarity between the sequences exceeded 99%, including C. sakazakii (n = 27, 77.14%), C. malonaticus (n = 5, 14.29%), C. turicensis (n = 2, 5.71%), and C. dublinensis (n = 1, 2.86%). On this basis, C. sakazakii ATCC 29544T, C. sakazakii ATCC 12868, C. malonaticus LMG 23826T, C. turicensis DSM 18703T, and C. dublinensis DSM 18705T were used as reference strains, and the phylogenetic tree was constructed according to the 16S rRNA sequencing results, as shown in Figure 1. It can be seen that the strains of the four species of Cronobacter were divided into two large clusters, in which C. sakazakii and C. malonaticus were divided into one cluster, and C. turicensis and C. dublinensis were divided into another cluster. The strains of the four species showed clear phylogenetic relationships, on the whole, indicating that 16S rRNA could be used for molecular identification and typing of Cronobacter spp. A total of 35 STs were obtained by MLST typing of Cronobacter spp., mainly distributed in 15 homologous complexes. Due to the different sample collection areas and sources, the sequence types were relatively scattered, and some strains could not find the homologous complex matching them in the database, which was the unique type. In addition, three strains of Cronobacter had not been recorded in the database, which were new sequence types discovered for the first time in the world. By uploading to the Cronobacter MLST database (http://www.pubmlst.org/cronobacter accessed on 22 March 2022), the corresponding sequence numbers were obtained, which were one strain of C. dublinensis (CD31, ST788) and two strains of C. sakazakii (CS32, ST789, and CS33, ST790). The allele numbers of each housekeeping gene and sequence types of all isolates were shown in Table 2. A phylogenetic tree was constructed based on the nucleic acid sequences of 35 sequence types of 3036 bp and 10 strains of Cronobacter were used as a reference to evaluate the genetic relationship among different sequence types of strains. The results were shown in Figure 2. All strains were divided into five large clusters and seven species kept a certain distance from each other, among which C. sakazakii and C. malonaticus showed a closer relationship. Within C. sakazakii species, ST4 and ST268, ST13 and ST789, ST64 and ST261, ST73, ST269 and ST790 had closer phylogenetic relationships. Among C. malonaticus species, ST7 and ST201 were closely related in phylogeny. According to the results of the MLST database, there was only one base difference between these similar sequence types. Therefore, the strains corresponding to each sequence type were likely to have similar phylogenetic relationships at the whole genome level. The drug susceptibility results of 35 Cronobacter strains were shown in Table 3. All Cronobacter isolates had the highest resistance rate to erythromycin (100%), followed by sulfamethoxazole/trimethoprim (45.71%). The others were neomycin (37.14%), cefazolin (28.57%), kanamycin (28.57%), ceftriaxone (25.71%), amikacin (25.71%), ampicillin (20%), ceftazidime (20%), cefuroxime (14.29%), polymyxin B (14.29%), gentamicin (11.43%), norfloxacin (11.43%), piperacillin (8.57%), cefoperazone (8.57%), tetracycline (8.57%), doxycycline (8.57%), ofloxacin (2.86%), and chloramphenicol (2.86%). All strains were sensitive to ciprofloxacin. The drug resistance profile of Cronobacter strains was shown in Table 4. There were six strains with one drug resistance spectrum (erythromycin), accounting for 17.14%. The double drug resistance spectrum showed three spectrum types and a total of five strains, accounting for 14.29%. There were six types of triple-drug resistance spectrum and seven strains in total, accounting for 20%. The quadruple drug resistance spectrum includes four spectrum types and a total of four strains, accounting for 11.43%. The five-fold drug resistance spectrum showed three spectrum types and a total of three strains, accounting for 8.57%. The six-fold drug resistance spectrum showed two spectrum types and a total of two strains, accounting for 5.71%. The seven-fold drug resistance spectrum showed six spectrum types and a total of six strains, accounting for 17.14%. There was one strain in each of the eleven and thirteen-fold drug resistance spectrums, accounting for 2.86%. According to the results, 24 strains were multidrug resistant (resistant to three or more classes of antibiotics), accounting for 68.57% of the total number of Cronobacter isolates. At the same time, C. sakazakii ATCC 29544 was only resistant to erythromycin and sulfamethoxazole/trimethoprim, which was a double drug resistance. In conclusion, compared with the reference strains, most Cronobacter isolates showed strong drug resistance and multi-drug resistance, among which the representative drug-resistant strains were CS14 (ST42) and CS17 (ST64). Based on the results of the drug resistance test, CS14, which has a high ability to survive under antibiotic conditions, was selected as the representative strain. The normal culture group (A) and the antibiotic treatment group (B) were compared with each other, and the overall situation of the screened differentially expressed genes is shown in Figure 3. Figure 3a as a volcano map of differentially expressed genes. Translated with www.DeepL.com/Translator (free version, accessed on 3 April 2022), a total of 2324 differentially expressed genes were screened, among which 1154 genes were upregulated and 1170 genes were downregulated. The cluster heat map of differentially expressed genes was obtained by two-way cluster analysis on the union of screened differentially expressed genes and samples. As can be seen in Figure 3b, the gene expression between the three duplicate samples of the control group and the treatment group was consistent, and the gene expression between the comparison groups was significantly different. KEGG enrichment analysis was performed on differentially expressed genes and the top 30 pathways with the most significant enrichment were selected for demonstration. The results were shown in Figure 4. Signaling pathways can be divided into three categories: environmental information processing, genetic information processing, and metabolism. The metabolic category contained 26 signaling pathways and 381 differentially expressed genes, which mainly focused on carbohydrate synthesis and metabolism, energy metabolism, amino acid metabolism, terpenoids and polyketides metabolism, lipid metabolism, degradation of heterogeneous organisms, and metabolic processes. The genetic information processing category included three signaling pathways and 75 differentially expressed genes, mainly focusing on ribosomal translation and RNA transcription signaling pathways. The environmental information processing category included one signaling pathway and 115 differentially expressed genes, focusing on membrane transport processes. According to the results of RNA-seq analysis under antibiotic treatment, a total of 94 genes related to drug resistance were screened, among which 77 genes were differentially expressed, 35 genes were upregulated, and 42 genes were downregulated. Some differentially expressed genes were screened in Table 5, listed in order of p value from smallest to largest. Ten differentially expressed genes related to drug resistance were screened and qRT-PCR was performed on CS14 and CS17 strains with multiple drug resistance, and CS6 and CS9 strains with only one double drug resistance. The results were shown in Table 6. The relative expression of CS14 genes was consistent with the transcriptome analysis results. Eight genes were upregulated and significantly upregulated (2−ΔΔCt ≥ 2, p < 0.05), and two genes were downregulated and significantly downregulated (2−ΔΔCt ≤ 0.5, p < 0.05). The relative gene expression of CS17 was consistent with that of CS14, among which six genes were significantly upregulated and two genes were significantly downregulated. For CS6 with weak drug resistance, eight out of ten genes were downregulated, among which four genes were significantly downregulated, and no genes were significantly upregulated. For another strain CS9 with weak drug resistance, seven out of ten genes were downregulated, including two significantly downregulated genes, and no genes that were significantly upregulated. In general, the results of qRT-PCR were consistent with the transcriptome, with higher upregulation or downregulation levels in the two resistant strains compared with the two less resistant strains. When the strain was challenged with antibiotics in the bacterial chemotaxis pathway (ko02030), the genes CSK29544_01507 and CSK29544_02725 encoding methyl receptor chemotactic proteins were significantly upregulated by 2.24 and 2.66-fold, respectively. The gene CSK29544_02809 encoding the methyl receptor chemotactic citrate sensor was significantly upregulated by 2.36-fold; the gene CSK29544_00467 encoding the methyl receptor chemosensory sensor was significantly upregulated by 2.06 times; and the genes fliG and fliM encoding the flagellar motor switch protein were significantly upregulated by 1.76 and 1.83 times, respectively. At the same time, the genes CSK29544_01807 and CSK29544_02659 encoding methyl receptor chemosensory sensors were significantly downregulated by 1.57 and 2.21 times, respectively. The gene cheR encoding chemotactic protein methyltransferase was significantly downregulated by 1.79 times. The gene cheB encoding the chemotactic family, protein-glutamate methyl esterase/glutaminase, was significantly down-regulated by 2.51 times. The gene encoding purine-binding chemotactic protein cheW was significantly downregulated by 2.20 times; and the gene encoding chemotactic protein cheZ was significantly downregulated by 2.22 times. It can be seen that Cronobacter can enhance the drug resistance of bacteria as a whole by regulating the partial upregulation or downregulation of chemotaxis-related genes. In the ABC transporter pathway (KO02010), 29 genes were significantly upregulated, and 86 genes were significantly downregulated. The gene mdlA encoding the ATP-binding protein of the multidrug efflux system was significantly upregulated by 2.05 times, and the gene yojI encoding the ATP-binding/permease protein of the multi-drug transport system was significantly upregulated by 1.96 times. The four coding genes potA, potB, potC, and potD involved in spermidine transport were significantly upregulated by 2.46, 3.10, 2.5,9 and 2.06 times, respectively. The gene proX encoding the glycine betaine/proline transport system substrate binding protein was significantly upregulated by 2.76 times; the gene proW encoding the glycine betaine/proline transport system permease protein was significantly upregulated by 2.60 times; and encoding the glycine betaine/proline gene proV of the acid transport system ATP-binding protein was significantly upregulated 4.23-fold. The genes togM and togN encoding the oligogalacturonic acid transport system permease proteins were significantly upregulated by 2.11 and 2.37 times, respectively, and the gene togB encoding the low-galacturonic acid transport system substrate-binding protein was significantly downregulated by 2.39 times. The three encoding genes mlaD, mlaE, and mlaF involved in phospholipid transport were significantly upregulated by 1.88, 2.63, and 1.88 times, respectively. The genes encoding glutamine transport glnP and glnQ were significantly upregulated; the genes involved in zinc ion transport znuB and znuC were significantly upregulated; the genes involved in lipoprotein transport lolC, lolD, and lolE were significantly upregulated; and the encoding genes lptF involved in lipopolysaccharide transport were significantly upregulated and lptG were significantly upregulated. In the two-component signaling pathway (ko02020), 25 genes were significantly upregulated and 46 genes were significantly downregulated. The gene baeR encoding the ompR family and response regulator was significantly downregulated by 2.20 times. The gene mdtA encoding the multidrug efflux system membrane fusion protein was significantly downregulated by 1.50 times. The gene mdtD encoding MFS transporter, DHA2 family, multidrug resistance protein was significantly downregulated by 2.26 times. The gene acrD encoding the MDR efflux system was significantly downregulated by 2.01-fold. In summary, under the stimulation of antibiotics, Cronobacter spp. secretes more drug efflux proteins, enhances drug resistance by activating the multidrug efflux system, and reduces the transport activity of nucleic acids, lipids, amino acids, and other molecular substances. The damaging effect of drugs on cells. In this study, 35 STs were obtained by MSLT on all isolated Cronobacter spp. strains, which were distributed in raw materials, semi-finished products, finished products, and the processing environment. The main sequence types were ST1 (14.42%), ST4 (18.27%), and ST64 (11.54%). This study found that ST4 was present in the finished product, and many studies have reported that ST4 was associated with infantile meningitis [35]. ST1 has also been isolated from clinically infected infants [36]. ST64 is one of the main contamination sequence types of PIF and its processing environment in China, enough to show its drug resistance and difficulty to disinfect [16]. Therefore, it is very important to study different sequence types of Cronobacter spp. to reduce the contamination of Cronobacter spp. in PIF. In addition, three of the isolates were not recorded in the database and were new sequence types found for the first time in the world. Phylogenetic trees were constructed based on the MLST results and one representative strain for each ST was selected for follow-up studies. The isolates showed 100% resistance to erythromycin, followed by 45.71% (16/35) to sulfamethoxazole/trimethoprim, indicating that Cronobacter spp. used in this study had a good tolerance rate to macrolides and sulfonamides antibiotics. Meanwhile, all tested isolates were sensitive to ciprofloxacin. We found that the strains were only 11.43% and 2.86% resistant to norfloxacin and ofloxacin (both belong to fluoroquinolones), so the contamination of Cronobacter spp. could be treated with fluoroquinolones. This study also found that strains with only one drug resistance spectrum (erythromycin) accounted for 17.14% (6/35), and multidrug-resistant strains accounted for 68.57% (24/35), indicating that the overall drug resistance of Cronobacter spp. was strong. Among them, CS14 (ST42) and CS17 (ST64) were the most representative drug-resistant strains, which were 13 and 11 multiple drug resistance, respectively. In recent years, many reports have found that a variety of antibiotics, such as gentamicin, ampicillin, kanamycin, and ciprofloxacin, can kill Cronobacter spp. [37]. Lai et al. found that Cronobacter spp. had consistent resistance to ampicillin, cefazolin, and extended-spectrum penicillin [38]. Kim et al. found that Cronobacter spp. isolated from food was resistant to ceftaroline and ampicillin [39]. It was further confirmed that even though antibiotics can be effective in eliminating the contamination caused by this bacteria, long-term use can cause multi-drug resistance in Cronobacter spp. Pakbin et al. showed that C. sakazakii isolates were completely resistant to ampicillin and amoxicillin and showed multidrug resistance, and moderate resistance to ciprofloxacin and tetracycline antibiotics [40]. Odeyemi et al. found that Cronobacter spp. was resistant to erythromycin and sulfamethoxazole, and all bacteria were able to form biofilms [41]. This finding is highly consistent with the findings of this experiment. In conclusion, multi-drug resistance of Cronobacter spp. is increasing year by year, and screening for effective antibiotics is one of the urgent research priorities. In this study, a total of 77 differentially expressed genes related to drug resistance were screened by transcriptome analysis of CS14 under antibiotic conditions, and some highly correlated genes included emrA, CSK29544_03309, yojI, mdfA, fliM, mdlA, emrB, pump, CSK29544_03853, and baeR. We found that the regulatory genes related to methyl-accepting chemotaxis were significantly upregulated by about two-fold, and the regulatory genes fliG and fliM related to flagellar motor switch protein were significantly upregulated by 1.76 and 1.83-fold, respectively. In addition, the regulatory genes mdlA and yojI related to the multidrug efflux system were also significantly upregulated by approximately two-fold, as were some genes involved in amino acid, sugar, and lipid transport. In summary, under the stimulation of antibiotic conditions, Cronobacter spp. can activate the multidrug efflux system by regulating the expression of chemotaxis-related genes, thereby, secreting more drug efflux proteins to enhance drug resistance. The results of the study also revealed that Cronobacter spp. can reduce cellular damage from drugs by reducing the transport activity of nucleic acids, lipids, amino acid, and other molecular substances. Some researchers found the multi-drug resistance operon mar in the genome of Cronobacter spp. Bao et al. also found that the polymyxin resistance gene pmrA was related to biofilm formation in Cronobacter spp., but, overall, the antibiotic resistance level of this genus was lower than that of other foodborne pathogens [42]. However, the results of this study showed that the Cronobacter strains had a strong drug resistance level and a multi-drug resistance spectrum, which may be due to the stronger resistance of the Cronobacter spp. from PIF and its processing environment. In conclusion, the 35 Cronobacter strains isolated from PIF and its processing environment showed high diversity. A total of 35 sequence types were obtained by MLST typing, among which three sequence types were discovered for the first time in the world: C. dublinensis CD31 (ST788), C. sakazakii CS32 (ST789), and CS33 (ST790), respectively. The resistance rate of all isolates to erythromycin was 100%, but they were all sensitive to ciprofloxacin, and the multidrug-resistant strains accounted for 68.57%, among which the most resistant strain reached 13 multiple drug resistance. Seventy-seven differentially expressed genes related to drug resistance were screened by transcriptome sequencing and the qRT-PCR verification results were consistent with the transcriptome results. Under the stimulation of antibiotic conditions, Cronobacter strains can activate the multidrug efflux system by regulating the expression of chemotaxis-related genes, thus, secreting more drug efflux proteins to enhance drug resistance. These studies can provide a theoretical basis for the prevention and treatment of Cronobacter.

PMC10000706

Marcel Kemper,Carolin Krekeler,Kerstin Menck,Georg Lenz,Georg Evers,Arik Bernard Schulze,Annalen Bleckmann

Liquid Biopsies in Lung Cancer

23-02-2023

liquid biopsy,lung cancer,ctDNA,CTC,miRNA,EV

Simple Summary Liquid biopsy has recently been introduced as a novel method in cancer diagnostics. It is less invasive for patients than conventional tissue biopsies, as the assay material is drawn from peripheral blood. The detected DNA fragments, as well as the cells and extracellular vesicles, can be used as biomarkers for cancer. Not all biomarkers are equally reliable in cancer diagnostics. In this review, we give an overview of the lung cancer biomarkers identified in liquid biopsy assays and discuss the differences, current applications, and future perspectives of liquid biopsies in lung cancer. Abstract As lung cancer has the highest cancer-specific mortality rates worldwide, there is an urgent need for new therapeutic and diagnostic approaches to detect early-stage tumors and to monitor their response to the therapy. In addition to the well-established tissue biopsy analysis, liquid-biopsy-based assays may evolve as an important diagnostic tool. The analysis of circulating tumor DNA (ctDNA) is the most established method, followed by other methods such as the analysis of circulating tumor cells (CTCs), microRNAs (miRNAs), and extracellular vesicles (EVs). Both PCR- and NGS-based assays are used for the mutational assessment of lung cancer, including the most frequent driver mutations. However, ctDNA analysis might also play a role in monitoring the efficacy of immunotherapy and its recent accomplishments in the landscape of state-of-the-art lung cancer therapy. Despite the promising aspects of liquid-biopsy-based assays, there are some limitations regarding their sensitivity (risk of false-negative results) and specificity (interpretation of false-positive results). Hence, further studies are needed to evaluate the usefulness of liquid biopsies for lung cancer. Liquid-biopsy-based assays might be integrated into the diagnostic guidelines for lung cancer as a tool to complement conventional tissue sampling.

Liquid Biopsies in Lung Cancer Liquid biopsy has recently been introduced as a novel method in cancer diagnostics. It is less invasive for patients than conventional tissue biopsies, as the assay material is drawn from peripheral blood. The detected DNA fragments, as well as the cells and extracellular vesicles, can be used as biomarkers for cancer. Not all biomarkers are equally reliable in cancer diagnostics. In this review, we give an overview of the lung cancer biomarkers identified in liquid biopsy assays and discuss the differences, current applications, and future perspectives of liquid biopsies in lung cancer. As lung cancer has the highest cancer-specific mortality rates worldwide, there is an urgent need for new therapeutic and diagnostic approaches to detect early-stage tumors and to monitor their response to the therapy. In addition to the well-established tissue biopsy analysis, liquid-biopsy-based assays may evolve as an important diagnostic tool. The analysis of circulating tumor DNA (ctDNA) is the most established method, followed by other methods such as the analysis of circulating tumor cells (CTCs), microRNAs (miRNAs), and extracellular vesicles (EVs). Both PCR- and NGS-based assays are used for the mutational assessment of lung cancer, including the most frequent driver mutations. However, ctDNA analysis might also play a role in monitoring the efficacy of immunotherapy and its recent accomplishments in the landscape of state-of-the-art lung cancer therapy. Despite the promising aspects of liquid-biopsy-based assays, there are some limitations regarding their sensitivity (risk of false-negative results) and specificity (interpretation of false-positive results). Hence, further studies are needed to evaluate the usefulness of liquid biopsies for lung cancer. Liquid-biopsy-based assays might be integrated into the diagnostic guidelines for lung cancer as a tool to complement conventional tissue sampling. In recent years, lung cancer has become the second most frequent, newly diagnosed malignant disease worldwide, and is the main cause of cancer-specific mortality [1]. The timely detection of these tumors is difficult, as early symptoms, such as cough or dyspnea, are unspecific or absent [2], and because current screening methods, such as low dose computed tomography (LD-CT), albeit effective, are not available on a large scale. Managing the tumor patient and adapting the therapy can be challenging due to the fact that lung cancer is a very heterogeneous disease, even in a single patient. For instance, PD-L1 expression can differ between the primary tumor, lymph node, and metastatic site tissue [3]. Currently, the standard sampling of suspected tumor tissue is resource consuming, painful, and potentially dangerous for the patient. In addition, the obtained material only represents the tissue at the specific biopsy site, and thus can entirely overlook the aspect of tumor heterogeneity, which is essential for effective therapy. This is especially true in patients undergoing targeted treatment of driver mutations as the treatment-induced selection of drug resistant cells [4] with the subsequent progression of such being a major prognostic issue. Progressive lesions in the CT scan might call for a therapeutic switch or treatment beyond disease progression (TBP), that also might be beneficial in immunotherapeutic or targeted therapy settings [5,6]. However, such decisions would be hard to make without definitive data on the actual makeup of the tumor. In this setting, the liquid-biopsy-based assay, or simply the liquid biopsy, a method for detecting, identifying, and quantifying DNA and cell fragments in body fluids, can be an important addition to the palette of methods for the diagnosis and management of lung cancer patients. The method itself has a long history—scientists first detected cell-free DNA (cfDNA) in blood in 1948 [7]. Others were able to identify specific mutations of cfDNA in 1994, but it wasn’t until 2016 that the FDA approved the first liquid biopsy test for detecting EGFR gene mutations in lung cancer. The foremost value of a liquid biopsy is that one can obtain a synopsis of the relevant tumor information, not only of the known tumors, but also of any yet undetected metastatic tumors in the body, by analyzing the sample for circulating tumor DNA (ctDNA). In brief, samples of peripheral blood are analyzed for cfDNA (150–200 base pairs [bp]) and ctDNA (90–150 bp), as well as for microRNA (miRNA), which directly originate from apoptotic or necrotic cells or are released from tumor-associated macrophages (TAMs). Circulating tumor cells (CTCs) may also be found in the blood samples, but the information from the circulating DNA fragments is more revealing than that from single tumor cells, since it represents the heterogeneity of all shed cells, including NSCLC cells at a specific time point. As mentioned previously, cfDNA consists of small DNA fragments that are either passively released from apoptotic or necrotic cells or released by phagocytes due to digestion [8]. ctDNA is a subentity of cfDNA that is shed by malignant cells [9]. Apoptosis promotes protein-bound cfDNA and ctDNA in peripheral blood, also known as oligo- or mono-nucleosomes. However, ctDNA can also be found in membrane-bearing EVs that are actively released by cells [10]. In the current guidelines for the management of patients with non-curatively treated lung cancer, the liquid biopsy has found its way into the routine diagnostics for EGFR-mutated tumors, once the disease has progressed under a non-T790 M-addressing EGFR tyrosine kinase inhibitor [11]. Due to the fact that the concentration of fragmented DNA in the blood plasma of cancer patients is orders of magnitude higher than that in non-cancer controls (5–1500 ng/mL vs. 1–5 ng/mL), it can be used as a diagnostic tool [12]. A liquid biopsy offers an altogether minimally invasive method for monitoring treatment responses (i.e., minimal residual disease [MRD] monitoring), and helps us to understand genomic tumor evolution and to identify resistance mutations earlier than is possible with radiological findings. The materials and methods currently used in liquid and tissue biopsies for diagnostic assessments of lung cancer can be found in Figure 1. Although the cfDNA content is higher in serum than in plasma [13], plasma is the preferred source for extraction, since there is less DNA contamination due to clotting, cell lysis, and the release of cfDNA from white blood cells [14]. A standard volume for a sample collection has not yet been defined, but 20 mL blood samples are usually employed for a cfDNA analysis in most isolation protocols [14]. The detection and identification of ctDNA is the preferred method of the liquid biopsy for lung cancer. As the levels of ctDNA in the plasma of NSCLC patients are very low (<0.5% of total cfDNA) [15], and it has only a short half-life < 3 h [16], time-sensitive isolation protocols should be implemented. The procedural steps in isolating ctDNA are: (1) drawing blood, (2) centrifugation, (3) DNA extraction, and (4) DNA analysis. Blood samples can be collected in either standard EDTA tubes or in specialized tubes containing a cfDNA preservative (see Table 1). EDTA tubes are widely available and considerably less inexpensive than the specialized tubes with the preservative. Disadvantageously, the samples from EDTA tubes have to be processed within 1–2 h to avoid their contamination with cfDNA from leukocyte lysis [17], but the samples from the tubes containing the cfDNA preservative can be stored at room temperature for up to 14 days [18]. However, for cfDNA preservative tubes, the contamination with leucocyte DNA can commence earlier so that processing should begin within three days [14]. The disadvantage with these tubes is that they require special processing and must often be sent to external laboratories, incurring delays and further costs. Even in immediately processed samples, tumor-specific somatic mutations often represent < 1% of the total cfDNA for the region of interest, and any increase in the contamination with “diluting” cfDNA from in vitro lysis may result in false-negative results [19]. Consequently, a two-step centrifugation of the EDTA samples is recommended to reduce the contamination from leukocyte DNA, prior to their freezing and storage [19]. In total, two established methods are available for analyzing nucleic acids (e.g., DNA and RNA): targeted polymerase chain reaction (PCR)-based, and next generation sequencing (NGS)-based assays. Table 2 highlights the differences between the methods. A qPCR is a widely used method for DNA analysis, which also allows for the semi-quantification of samples. The cost of the method has decreased markedly, and the results are easy to interpret. There are several commercially available FDA- and EMA-approved tests for detecting, activating, or resistance-causing EGFR mutations in NSCLC. Because only a single gene or an already known genetic alteration can be examined, the sensitivity is only 70–80% (detection limit 1–5%) [23,24], and using a qPCR alone carries the risk of false-negative results. A ddPCR is more sensitive than a qPCR (detection limit 0.1–1%) [23]. In ddPCR, a sample oil emulsion is fractionated into many thousands of droplets, upon which a PCR is subsequently performed in the individual compartments of a microtiter plate. This method allows for the detection of even rare events and quantification at the level of a single molecule. BEAMing (beads, emulsions, amplifications, and magnetics) has further improved the ddPCR technique using DNA templates that are bound to magnetic beads [25]. NGS is a high-throughput method for sequencing DNA and RNA that allows for the detection of single nucleotide polymorphisms (SNPs), and small (insertions and deletions) as well as large (insertions, deletions, amplifications, inversions, and translocations) genetic alterations. The method is very sensitive (detection limit 0.001–2%) and can detect even unknown genetic alterations in small DNA fragments [22]. Whole genome sequencing (WGS) and whole exome sequencing (WES) have not been established, as the amount of ctDNA obtained with liquid biopsies might be too low. So far, only hybrid capture-based and amplicon-based (PCR capture) NGS approaches are regularly used in routine testing for cancer diagnostics. The hybrid capture-based NGS approach is not based on primary amplification, and thus allows for a more reliable quantification of copy numbers than amplicon-based NGS. Targeted DNA sequences are hybridized (“captured”) to biotinylated probes, which are bound to magnetic beads. The beads are captured by magnets, and the non-hybridized DNA is washed off. Since the ctDNA plasma concentration is very low, and sequencing does not rely on prior amplification, this method is at a greater risk of sequencing errors, including false-positive results. This explains its low specificity (approx. 60%) [12]. The amplicon-based NGS approach is based on the primary PCR amplification of specific genomic regions of interest, especially hotspot genes. Before sequencing, the amplified DNA sequences (“amplicons”) are multiplexed and marked with a distinct molecular barcode for identification. As this approach uses primary PCR amplification, it is valuable for liquid biopsies with their low amounts of ctDNA [26]. The risk of false-positive results is effectively reduced using molecular barcodes. While amplification biases the quantification of allele frequencies and copy number variations (CNVs), this method is useful for the detection of SNPs, indels, or known gene fusions [12]. PCR-based methods (qPCR, ddPCR) are well established, require short turnaround times of 2–3 days, and are fairly inexpensive. However, they allow for the detection of only a limited number of genetic alterations at a time (e.g., no multiplexing across different genes) and detect neither previously unknown alterations, nor gene fusions. NGS-based methods, on the other hand, have a longer turnaround time of about 1–2 weeks [27], but are able to detect known and unknown genetic variants, including CNVs, SNPs, and gene fusions, which might promote the use of diagnostic algorithms for cancer diagnostics and treatment monitoring. Lately, the NGS of ALK-positive NSCLC has offered a more detailed characterization of fusion partners than standard pathological techniques like IHC or FISH [28]. For quantification analysis, NGS and ddPCR are more suitable than the semi-quantitative qPCR. Despite the high sensitivity of NGS-based methods, a single false-positive read (with an error rate of approximately 10−3) can impact the result [29]. Hence, error-proofing techniques and algorithms need to be implemented. Circulating tumor cells are rare in peripheral blood (1 CTC per 106−7 leukocytes), where they occur as single tumor cells in cell clusters, as so-called circulating tumor microemboli [30], or attached to stromal cells originating from the primary tumor [31]. Their plasma half-life (1–2.4 h) is short, so that they must be promptly separated from other blood cells [32]. CTCs can be isolated by using antibodies to identify the expression of intracellular (DNA) or transmembrane molecules (cytokeratins, epithelial adhesion molecules [EGDR and HER3], and CD45) [33]. They can also be identified according to their density or size [12]. Circulating epithelial cells are present in only 46% of blood samples from stage IV NSCLC patients, and circulating cells with an epithelial phenotype can also be found in 7% of healthy controls [34]. Thus, the presence of CTCs in the bloodstream alone is an inadequate marker for the tumor burden. However, some studies have shown that the baseline CTC count is a prognostic marker for tumors other than lung cancer [35,36,37]. In the CellSearch® CTC Test (Menarini Silicon Biosystems Inc., Castel Maggiore, Italy) the CTCs are separated magnetically from other blood cells using anti-EpCAM antibodies conjugated with magnetic nanoparticles. The separated cells are stained for nuclear DNA with 4′,6-diamidino-2-phenylindol (DAPI), and with fluorescently labeled antibodies for cytokeratins (CK) and CD45. They are then analyzed with automated fluorescence microscopy. CTCs are defined as DAPI+, CK+, and CD45−, while leukocytes are DAPI+ and CD45+ [33]. While this widely used assay is considered the gold standard for CTC detection, it has, so far, only been approved by the FDA for routine use in metastatic breast, prostate, and colorectal cancer, but not yet in lung cancer. A different commercially available test (AdnaTest® (Qiagen GmbH, Hilden, Germany)) uses a combination of different antibodies (e.g., anti-EpCAM, anti-MUC1, anti-HER2, and anti-EGFR) conjugated with magnetic beads for cell separation, which increases the sensitivity of the CTC detection when only the antibodies against EpCAM are used [38]. However, this test has not yet been approved by the FDA for routine use in cancer. Antigen-based isolation methods have a poor sensitivity and specificity [31] due to the cells’ tendency for epithelial-to-mesenchymal transition (EMT) [39], with a subsequent loss of epithelial surface markers [40]. Focusing on the physical and biological characteristics of the CTCs, instead of the epithelial cell surface antigens, would bypass this problem. CTCs can also be isolated by using the physical properties of the cells, i.e., size and density. One method employs a porous membrane (pore diameter 8 μm) (e.g., ISET® system,(Rarecells Diagnostics SAS, Paris, France)) to isolate the CTCs. Here, leukocytes are not totally eliminated. Thus, a subsequent further cell characterization with a cytomorphological or immunocytochemical analysis is required [41,42]. Another method is to centrifuge the plasma in a density gradient, e.g., a Ficoll-Paque® solution, to separate the mononuclear cells (including the CTCs) from other blood cells (e.g., OncoQuick® system, (Greiner BioOne International GmbH, Kremsmuenster, Austria)). The method is inexpensive but has a high rate of contamination with leukocytes [43,44]. Microfluidic technologies have recently evolved as a very appealing approach to CTC isolation [45]. Exemplarily, one such “CTC-chip” uses anti-EpCAM-coated microposts under precisely controlled laminar flow conditions to capture the CTCs [46], while the vortex technology uses microscale vortices to isolate the CTCs based on their physical characteristics, such as size or compressibility [12,47,48]. These approaches are antigen-independent, resulting in a high sensitivity (a low risk of false-negative results due to EMT) and high purity of the CTC isolates [12]. Apart from using the common morphological and biological features of malignant cells (e.g., nucleus size and chromatin structure) to discriminate the CTCs from other blood cells, immunocytochemistry can be used for CTC identification. Moreover, genomic analyses, such as DNA analysis, as well as RNA analysis (qPCR or RNA sequencing), can be performed on CTCs, and may reflect the tumor heterogeneity [33]. So far, proteomics using mass spectrometry and immunoblotting have not been implemented in CTC analysis. MicroRNAs (miRNAs) are noncoding RNAs involved in the posttranscriptional regulation of gene expression that can act as tumor suppressors or oncogenes. Cell-free miRNA can be detected in various body fluids. It is passively released during cell lysis (e.g., necrosis and apoptosis) and actively secreted by cells for intercellular communication [49]. miRNAs are usually packed into EVs (see below), or coupled with Argonaute2 (Ago2) protein [50] or high-density lipoprotein (HDL) [51], and dispersed into the extracellular environment. A genomic analysis can be performed using the same methods as those for cfDNA (see previous section). EVs are small membrane particles that are shed by all living cells and mediate intercellular communication by carrying proteins, lipids, and nucleic acids (e.g., DNA, RNA) from the secreting to the surrounding cells. A total of two distinct EV populations have so far been described: small EVs (sEVs, also called exosomes) with a diameter between 50–150 nm, and larger microvesicles (intermediate size vesicles, IEVs) with a diameter between 100–1000 nm [52]. sEVs are formed inside the cell through the inward budding of endosomal membranes, while IEVs bud directly from the plasma membrane [53]. Numerous studies have demonstrated that tumor-derived EVs are crucial in establishing a favorable tumor microenvironment, and thus pave the way for metastatic spread [54]. The large amount of EVs shed by tumor cells into the blood and other body fluids has opened up a new perspective on using EVs as cancer biomarkers in liquid biopsies, particularly in lung cancer. Due to their small size, the isolation and analysis of sEVs is time-consuming, and thus not suitable for clinical routine. In contrast, the extraction and analysis of IEVs with flow cytometry is less demanding and time-consuming [52]. Although EVs can be isolated from body fluids [55,56], an internationally standardized isolation method, which would be necessary for their clinical use, is lacking. Inherent to every method is the risk of co-isolating the other subtypes of EVs or lipoparticles, and the separation of different EV subtypes remains difficult. Further details are highlighted in the review “Extracellular vesicles in liquid biopsies” in this Special Issue. Liquid biopsies have a great potential for initial tumor diagnosis, as well as for monitoring the response to therapy. Here, we elucidate the current applications and future perspectives of blood-plasma-based assays in lung cancer. An overview of studies, partly using liquid biopsy in mutational diagnostics, cited in this review can be found in the Supplementary Materials (Table S1). The most robust data on all methods exist for the use and assessment of ctDNA, which has been integrated into the diagnostic work-up of NSCLC during the past decade. Current studies have shown the amount of ctDNA to correlate with the tumor stage (TNM), tumor burden, and metabolism [57,58,59]. Moreover, ctDNA assessment is suitable for monitoring therapeutic responses, as well as for detecting minimal residual disease (MRD). This would allow for the timely adjustments of individualized treatment, if needed. However, some key preanalytical considerations should be made ahead of ctDNA testing to improve the test performance. Most important is the identification of the correct patient cohort to be tested. As there is a variety of different tests available, each of them should be used carefully in the right clinical setting. The test requirements for a screening test of healthy individuals highly differ from the requirements for those of metastatic cancer to monitor the treatment response. In locally advanced and metastatic lung cancer, for example, state-of-the-art ctDNA tests focus on the evaluation of specific mutation sites, such as EGFR, ALK, and BRAF. However, upfront WGS might enable the detection of rare alterations and allow for an individualized treatment approach. A recent study evaluated the cost-effectiveness of a standard-of-care diagnostic approach vs. an upfront WGS for lung cancer patients [60]. If reimbursed and established, a liquid biopsy might allow for an easy and all-encompassing approach to evaluate for actionable mutations via ctDNA WGS. Another relevant state-of-the-art diagnostic focus in metastatic lung cancer is ctDNA testing for resistance genotyping under (targeted) therapy. Depending on the test method (e.g., targeted PCR-based assays vs. NGS), not only on-target resistance mutations, but also off-target resistance mutations can be detected (Table 2), thus implicating the importance of preanalytical considerations ahead of testing. Apart from that, a number of factors (e.g., disease status, tumor stage, histology, molecular pathology, and type of therapy, etc.) have an impact on the specific test performance, and should be considered carefully before choosing the right ctDNA test method. Due to the rising number of targetable oncogenic drivers, the current NSCLC guidelines include molecular testing for already known driver mutations in regular diagnostic work-ups [11]. The diagnostic assessment should be performed at initial diagnosis and in cases of relapse or progression. Tissue analysis was formerly the accepted standard, but biopsies are often scarce, difficult to obtain, and may not represent the heterogeneity of the tumor. The use of liquid biopsies in the assessment of NSCLC is increasing, as they are easy to obtain and can possibly overcome interspatial/intratumoral variations. However, liquid biopsies should be obtained prior to the beginning of cytotoxic or targeted therapies, since effective therapy reduces DNA shedding and thus lowers the amount of ctDNA below a detectable threshold within 1–2 weeks after the therapy induction [61]. A recent analysis of the German CRISP register revealed that only 75% of all patients were tested for EGFR mutations. Most unsettling was that 12.4% of patients were not tested for any marker by any method [62,63], mostly because they were unable to obtain tissue biopsies and were unaware of the liquid biopsy. Here, by analyzing both tissue and liquid biopsy DNA samples, targetable oncogenic driver mutations were detected in 30–60% of patients [64,65]. Multiple meta-analyses have revealed the feasibility of assessing the mutational status via a liquid biopsy [66,67]. Moreover, the subsequent prospective trial validation led to the recommendation of liquid biopsies within current guidelines in Europe and the US, especially in cases with insufficient or unattainable tissue biopsies [68,69]. Recently, the International Association for the Study of Lung Cancer (IASLC) underlined the outstanding potential of liquid biopsies for the evaluation of oncogenic drivers and proposed that ctDNA-based mutational analysis be used preferentially. Especially in patients with progressive disease, they facilitate the choice of a specific therapy (“plasma first”) [23]. Moreover, the IASLC recommended their concurrent use in cases of questionable adequacy of the accessible specimens. The currently ongoing LIQUIK Trial (NCT04703153) aims to prove the non-inferiority of liquid biopsies compared to tissue biopsies in detecting the guideline-recommended molecular biomarkers, and might further foster the role of liquid biopsies in NSCLC diagnostics. ctDNA analyses initially entered the diagnostic field with the detection of EGFR mutations and the subsequent management of NSCLC treatment with tyrosine kinase inhibitors (TKI). In total, two PCR-based tests have been approved by the FDA and EMA, respectively, for detecting EGFR mutations. The cobas EGFR mutation test ® detects 42 defined EGFR mutations with a high sensitivity [70], and allows a treatment selection from the companion drugs gefitinib and erlotinib or osimertinib. The therascreen® EGFR kit [12] permits the initiation of treatment with afatinib, gefitinib, and dacomitinib in cases of finding an EGFR mutation. Comparative molecular analyses of liquid and tissue biopsies showed concordance in some trials, but often resulted in contradictory findings [70]. However, PCR-based methods can only detect a limited number of known EGFR mutations, which must be predefined before testing (c.f., comparison of PCR-based and NGS-based methods). Thus, both tests are unable to discover new EGFR mutations. Furthermore, PCR-based methods showed a high specificity (97% in a meta-analysis of nine studies) in EGFR detection, albeit with a low sensitivity of 51% [71]. In addition, high rates of false-negative results when detecting T790M mutations, a harbored resistance to first-generation TKIs, and only a 51% agreement to the matched tissue samples were seen with the cobas® test in the AURA3 trial [72]. Hence, guidelines (e.g., ESMO practical guideline [73]) require a subsequent tissue biopsy in the case of a negative result from PCR-based liquid biopsies. Nevertheless, the less expensive PCR-based EGFR testing methods remain highly relevant as an initial step, especially for patients at risk of EGFR mutations. Still, unaddressed mutations can result in false-negative EGFR mutation test results, and a negative PCR-based test does not prove the absence of an EGFR mutation. NGS-based approaches might overcome the latter disadvantage, as they allow for the simultaneous detection of all actionable targets, facilitate the detection of actionable variants, and thus permit rapid genotype-matched therapeutic decision making. Furthermore, NGS-based methods achieve a higher sensitivity and specificity [71,74], are therefore currently preferred over PCR-based methods, and are the first choice for liquid biopsy testing in the current ESMO guidelines for metastatic NSCLC. Thus, they might supersede PCR-based methods in the upcoming years. A total of two NGS-based methods were approved by the FDA in 2020: Guardant360® CDx (Guardant Health, Palo Alto, CA, USA) and Foundation One® Liquid Cdx (Roche, Basel, Switzerland). The Guardant360® CDx-test, which allows for the use of osimertinib as a companion drug in the case of an EGFR mutation, showed a significantly increased number of molecular assessments compared to tissue-based standard diagnostics in a shorter time-period with a high (>98%) concordance between plasma and tissue [27]. The Foundation One® Liquid Cdx test can assess over 300 gene alterations, allows the companion use of first- and second-generation TKI after the detection of sensitizing EGFR mutations, and is also approved for other cancers (e.g., breast and colon cancer). A liquid biopsy also allows for the early detection of disease progression, which is eventually seen in most patients undergoing TKI therapy as an expression of an acquired (secondary) resistance to EGFR TKI. There is an urgent need to evaluate the on- and off-target mechanisms of resistance (MOR), in order to better understand the treatment-induced tumor evolution and to adjust further therapies. The most common MOR is the EGFR T790M mutation, which can be effectively targeted with the third generation TKI osimertinib. The assessment of T790M with liquid biopsies is very well examined, and its detection in plasma could obviate the need for tissue biopsies, as it predicts an excellent response to osimertinib, similar to that observed in tumor genotyping [75,76]. Moreover, a subsequent ctDNA analysis in patients undergoing TKI treatment detected the T790M mutation in a median time of two months prior to the clinical progression, in 45% of all the patients monitored [77,78], allowing for the treatment to be adjusted before the clinical tumor progressed and the detriment of patient performance statuses were evident. The results from the FLAURA-trial pointed out that osimertinib significantly improved the survival of treatment-naive patients compared to the comparator-TKI, and the application of first-line osimertinib led to a lower frequency of T790M mutations [79]. Nonetheless, a resistance to osimertinib is emerging, and an evaluation of the distinct on- and off-target MOR to EGFR TKI via NGS-based liquid biopsies is urgently needed to effectively treat osimertinib-resistant NSCLC. This is the aim of the APPLE trial [80]. Beyond this, the MELROSE study (NCT03865511) addresses the question of whether there is an association between ctDNA levels and the occurrence of resistance to osimertinib or therapeutic escape, in order to choose the ideal treatment for the individual patient [81]. For instance, the promising results of the use of the c-MET inhibitor crizotinib in NSCLC cell lines harboring MET amplification, a known MOR to osimertinib, has shown a potential to overcome the acquired resistance [82,83]. In patients with T790M-negative plasma samples, a tumor biopsy is still warranted due to the possibility of false-negative results and a limited sensitivity of the liquid biopsy. In addition, other acquired EGFR mutations besides T790M should be examined, as some of them can be targeted by switching to first- and second-generation TKIs [84]. Interestingly, even when no other mutations are found and no tumor material can be assessed, a small cohort of patients might nonetheless profit from treatment with osimertinib. In the AURA1-trial, patients with tumor progression after TKI treatment, in whom neither the original EGFR-sensitizing mutation nor a T790M mutation could be detected with ctDNA, showed an improved outcome under osimertinib compared to those patients with proof of their sensitizing mutation (progression-free survival [PFS] of 15.2 months vs. 4.4 months). The negative test results for the T790M mutation were thus misleading in these cases. The lack of any detectable mutation in the plasma test might be explained by low DNA shedding, lower aggressiveness, and/or a lower disease burden in those patients. ALK rearrangement preferentially occurs in young patients, as well as in never-smokers. Fortunately, the treatment inhibition of downstream signaling via TKI is effective, irrespective of the, so far, 19 known fusion partners [85,86]. Where ALK fusions have not been effectively detected by PCR-based methods, an NGS-based liquid biopsy attains a high specificity (100%) and sensitivity (79.2%) [87]. In 2011, the multi-kinase-inhibiting drug crizotinib was the first FDA-approved substance for ALK-rearranged NSCLC, achieving response rates of 60% and disease control rates of 90% [88,89]. However, most patients ultimately progress while on crizotinib treatment [14]. Consequently, the current ESMO guideline [11] endorses the first-line treatment of ALK-driven NSCLC with a second-generation TKI, such as alectinib or its comparators brigatinib, ceritinib, or ensartinib [90,91,92]. Likewise, each of these substances is able to induce a renewed therapeutic response in up to 55% of patients with progression [93]. Other than these, lorlatinib is a potent third-generation ALK-TKI that has shown efficacy in patients without a response to one or more second-generation TKIs, and was recently EMA-approved, even as a first-line treatment [94]. However, the various TKIs have individual binding affinities and therefore a sensitivity to the ALK resistance mechanisms. Thus, even if the exact characterization of the mutations under treatment with ALK-TKI is not required for a therapeutic switch, this characterization via NGS panels, covering a variety of mutations, might help with finding the optimum choice of TKI for the patient. Most recently, the FDA approved alectinib as a companion drug for ALK rearrangement detected with the Foundation One® Liquid Cdx test, since the first results of the ongoing BFAST trial not only underlined the suitability of NGS-based assays in detecting ALK-fusions, but also demonstrated that a subsequent treatment with alectinib yielded high overall response rates (ORR) [95]. NGS-based longitudinal monitoring also has prognostic value, as the detection of ctDNA (based on matched targeted NGS and shallow WGS) at the time of the initial diagnosis indicates a shorter time until progression, and the ctDNA levels might be highly elevated in some patients throughout TKI therapy until death [96,97]. Oncogenic ROS1 gene fusions that induce the relevant activation of the ROS-1 receptor tyrosine kinase can be detected with NGS-based liquid biopsies and subsequently targeted by crizotinib (with ORR of 73–93%) and entrectinib (ORR 41%, some cases of durable responses) in a first-line setting [98,99]. According to preliminary results, the consecutive ctDNA monitoring of a ROS1 rearrangement may help to predict the response to therapy [100], and allows for the early detection of resistance-mediating mutations in ROS1 genes [101] that would ultimately require an adjustment of therapy. The MET gene encodes for a receptor tyrosine kinase that, i.a., activates the signaling pathways involved in crucial cellular processes such as cell proliferation, survival, and growth [102]. Most prominently in NSCLC, MET exon 14 (METex14) skipping mutations are characterized by the fusion of exons 13 and 15, which ultimately impairs receptor degradation, thus overactivating the MET-mediated signaling [102]. METex14 alterations are usually observed in the absence of other oncogenic driver mutations [102]. NGS-based Guardant360® CDx and Foundation One® Liquid Cdx tests are FDA-approved for the assessment of METex14 skipping mutations in NSCLC. Encouraging results from the phase II GEOMETRY mono-1 trial led to the FDA approval of the MET-inhibiting oral agent capmatinib; patients with qPCR tissue-confirmed METex14 skipping NSCLC, that was retrospectively assessed by Foundation One® Liquid Cdx, achieved an ORR of 68% in first-line and 41% in second- or third-line treatment with capmatinib. More so, the study resulted in a 92% intracranial disease control and a 31% complete remission in patients with brain metastases [103]. Capmatinib was approved by the EMA in 2022. Most recently, the second-line use of tepotinib, another oral MET kinase inhibitor, was approved by the FDA and EMA, based on the results of the phase II VISION trial [104]. Here, METex14 skipping was either determined by Guardant360® CDx in plasma or with RNA from tissue specimens. Treatment with tepotinib induced an ORR rate of 43% over all therapeutic lines, with a median duration of response (mDOR) of approximately 11 months, and likewise demonstrated intracranial activity [105]. Furthermore, a high concordance of clinical response and decreasing ctDNA levels during treatment were observed [104,106]. Preliminary results of a second-line phase II trial (NCT02897479) with savolitinib indicated a mORR of 46%, while the mDOR was not reached in the interim analysis at 6.9 months [107]. RET rearrangements represent oncogenic drivers in young NSCLC patients with a non-smoking history. These patients are especially prone to brain metastases [108]. Several studies have shown liquid biopsies to be a reliable tool in identifying RET rearrangements [64,109,110]. The results of the LIBRETTO trial highlighted a high ORR after a RET-inhibiting selpercatinib administration in second- and first-line treatment (64% and 84%, respectively). While in both treatment lines, cases of durable response and intracranial activity [111] were observed, the approval was restricted to second-line treatment after platinum-based chemotherapy. With praseltinib being a highly effective treatment (an ORR of 70%, an mDOR not reached after 6 months, and the presence of CR in some cases), the ARROW trial [112] led to the approval of praseltinib in the first-line setting of RET-rearranged NSCLC. In line with other TKIs, the use of RET inhibitors is periodically limited, e.g., with acquired RET V480M gatekeeper resistance mutations [113] that are identifiable by a liquid biopsy [114]. With even greater impact, an analysis of over 32,000 samples showed that the finding of non-KIF5B-RET fusions contributed to anti-EGFR therapy resistance in cases with co-mutations [115]. BRAF mutations, and predominantly the V600E mutation, which accounts for over 50% of all cases and can reliably be detected in liquid biopsies [116], are associated with a dismal response to cytotoxic treatment [117]. In melanoma, in which BRAF mutations are more frequent, the BRAF inhibitors vemurafenib or dabrafenib have shown high response rates and have improved the survival of patients dramatically [118,119]. In parallel, the application of vemurafenib or dabrafenib alone, or with additional downstream MEK inhibition, has shown promising antitumor activity (an ORR of 33% for dabrafenib, 42% for vemurafenib, and 67% for dabrafenib/trametinib) in phase II trials for NSCLC [120]. Consequently, the EMA and FDA approved the combination therapy of dabrafenib and trametinib for BRAF-mutated, advanced NSCLC. ERBB-2/Her-2 alterations (overexpression, amplification, or point mutations) are common in breast cancer patients, where the monitoring of ctDNA under treatment has shown a high predictive value regarding the response to therapy [121]. ERBB2/Her-2-overexpressing NSCLC implies an inferior outcome [122], as ERBB2/Her-2 amplification is known to be one of the acquired EGFR TKI resistance mechanisms [122] that can be identified by NGS-based liquid biopsies [123]. Recently, the Her-2/neu-targeting antibody-drug conjugate trastuzumab deruxtecan has shown an impressive ORR of 54% in heavily pretreated NSCLC patients [124]. Activating mutations in the KRAS gene are the most prevalent mutations in NSCLC, with the KRAS p.G12C variant being most frequent. The phase II CodeBreaK100 trial recently showed promising results for the use of sotorasib, a small molecule that irreversibly inhibits KRAS G12C, in pretreated KRAS p.G12C-mutated NSCLC [125]. Here, sotorasib exhibited an ORR of 37.1%, a disease control of 80.6%, and an mDOR of 11.1 months. However, the median PFS (6.8 months) and median overall survival (OS, 12.5 months) were still low in a cohort of pretreated, advanced NSCLC patients. Most recently, the first results of CodeBreaK200 phase III trial demonstrated that treatment with sotorasib significantly improved the disease control rate, as well as the PFS, and had a more favorable safety profile in a head-to-head comparison with docetaxel (NCT04303780), while OS data have not been provided yet [126]. The ongoing CodeBreaK101 trial (NCT04185883) is currently investigating the role of sotorasib in various combination therapies, including first-line settings. The feasibility of the NGS-based KRAS mutation assessment of liquid biopsies from NSCLC patients has already been proven, especially in cases with insufficient or unavailable tissue samples [127]. Other than NGS, the Idylla qPCR platform, that has a 100% concordance with NGS and a high sensitivity, may expedite cost-effective tools to assess the KRAS p.G12C mutation [128]. So far, the FDA has only approved the NGS-based Guardant360® liquid biopsy CDx for tumor mutation profiling to identify KRAS p.G12C mutated patients with locally advanced or metastatic NSCLC, who may benefit from sotorasib. Rare NTRK fusions can effectively be targeted by two FDA- and EMA-approved drugs (larotrectinib and entrectinib). Although the FDA approved the NGS-based FoundationOne® CDx tissue test as a companion diagnostic to identify the fusions of NTRK genes in solid tumors, including NSCLC, so far, no NGS-based test for liquid biopsies has been approved. Immune checkpoint inhibitors (ICI), e.g., antibodies against PD-1 and PD-L1, have dramatically improved the outcome of advanced stage NSCLC patients. Independent of PD-L1 expression in tumor tissue, a combination of immunotherapy with platinum-based chemotherapy positively impacts the OS, more so than standard platinum-based chemotherapy alone, both in non-squamous [129] and squamous cell lung cancer [130]. However, NSCLC patients with a high tumor PD-L1 expression (i.e., their tumor proportion score [TPS]) and/or a high PD-L1 expression in tumor-infiltrating immune cells (i.e., their combined positive score [CPS]) benefit more from a single-agent immunotherapy compared to the previous standard-of-care platinum-based chemotherapy [131,132,133]. Unlike targeted therapies, ICIs reactivate cytotoxic T cells to overcome the tumor immune escape [134]. Although the tissue PD-L1 expression positively correlates with the treatment response, not all patients respond to immunotherapy, and to date, there is no alternative reliable cell-surface biomarker to predict the efficacy of ICI. The tumor mutational burden, which is usually assessed by whole exome sequencing (WES) or targeted NGS (the sequencing of cancer gene panels (CGP)) of tissue specimens (tTMB), refers to the total number of somatic coding mutations, base substitutions, short insertions, and deletions per tumor genome. A high TMB is positively correlated with an environment of a high tumor neoantigen load, which contributes to a lower PFS and OS [135]. Multiple retrospective trials have shown that a high tTMB (determined with a cut-off >20 mut/mb in the CHECKMATE-026 trial) predicts a greater benefit of the PD-1 inhibitors nivolumab or pembrolizumab, in both treatment-naive and pretreated NSCLC patients [136,137,138]. Furthermore, tTMB is non-overlapping with PD-L1 expression, as it constitutes another aspect of the immune phenotype, and thus enables the selection of ICI-treatable patients. Despite conflicting results regarding the relationship of the tTMB and ICI response in clinical trials [139,140], the determination of tTMB and the subsequent selection of patients for pembrolizumab treatment in advanced non-resectable or metastasized cancers, including NSCLC, was approved by the FDA in 2020 [141]. However, tTMB diagnosis is still limited to a minor proportion of NSCLC patients (34–59%), due to the mentioned difficulties in obtaining tissue specimens [142]. With established ctDNA panels or optimized gene panel algorithms for NGS-based blood TMB (bTMB) determination, a liquid biopsy may offer a valuable substitute for tissue. Here, bTMB has been demonstrated to have a positive concordance with tTMB [143,144], and was validated in several trials, confirming that a high bTMB predicts beneficial PFS and ORR when treated with immunotherapy [32,145]. bTMB can be measured precisely using targeted gene panels, but the accuracy is compromised when the bait size is less than 0.5 MB [139]. The FDA-approved tests, Guardant OMNI ® (500 genes, 2.1 MB) and Foundation Medicine Liquid Cdx® bTMB (394 genes, 1.14 MB), have been demonstrated to test for sufficiently large baits and to correlate with tissue [146]. In the MYSTIC phase III trial, ‘high’ bTMB was defined as a threshold of at least 20 mut/mb, assessed by the Guardant OMNI® test [147]. High bTMB similarly predicted a clinical benefit of ICI therapy, with durvalumab and CTLA-4-inhibiting tremelimumab, over chemotherapy (a median OS of 21.9 months vs. 10 months, respectively). In addition, a cut-off concentration of ≥ 16 mut/mb measured with the Foundation Medicine® bTMB test [145] identified ‘high’ bTMB in patients particularly prone to ICI [131,148]. This cut-off was defined in the POPLAR study and validated by the OAK trial [143,149]. It was likewise confirmed in the first randomized, prospective trial using bTMB (B-F1RST) as a predictor for the response to treatment with the PD-L1 inhibitor atezolizumab, showing a beneficial PFS and OS beyond the cut-off of ≥16 mut/mb [150]. Nonetheless, there is currently a high variability among techniques and their interpretations, and a lack of a unified threshold for “high TMB”. The preliminary findings of the TMB Harmonization Project [151], an initiative for standardization, recently demonstrated the feasibility of reference control lines to further align the estimation of TMB and identify targeted NGS assays, which must now be validated in prospective trials. Synoptically, the incorporation of both TMB and PD-L1 expression into multivariate predictive models [152] might be helpful in forecasting ICI response. One of the major epigenetic modifications is DNA methylation. In tumorigenesis especially, the aberrant methylation of gene promoters might enhance or silence the transcription of RNA; hence, this directs a certain biological behavior of the malignant cell. When focusing on methylation analysis, one has to consider the different approaches of the obtained tissue and sample: while the liquid biopsy and analysis of cfDNA resulted in relatively high levels of APCme and SLFN11me sites in lung cancer patients, e.g., its corresponding CTCs exhibited a significantly lower frequency of promoter methylation [153]. In comparison to solid tissue, cfDNA and CTCs had fewer methylation signatures than tumor tissue itself, but cfDNA-detected alterations might derive from tumor adjacent tissue (non-ctDNA) [153]. Thus, before implementing the liquid biopsy analysis with regards to the methylation analysis, a predefined standardization and normalization of the tissue observed (e.g., cfDNA and CTCs in a liquid biopsy versus tumor tissue and adjacent tissue from tissue samples) has to be implemented and validly evaluated. However, some early studies of cfDNA that evaluated DNA methylation analyses in cancer diagnostics allow a little glance at future perspectives. Here, Constâncio et al. detected a sensitivity of 64% and a specificity of 70% for a ‘PanCancer’ cfDNA analysis compiled of FOXA1me, RARβ2me, and RASSF1Ame in male early lung and prostate cancer patients. While positive results were also detected in 30% of the cfDNA healthy controls [154], its value as a potentially harmless method for cancer screening has to be verified by larger prospective trials. With a focus on lung cancer patient diagnostics and staging, a liquid biopsy gathered APCme and RASSF1Ame cfDNA methylation panel predicted the disease-specific mortality of lung cancer patients: compared to unmethylated sites, APCme plus RASSF1Ame resulted in a Hazards Ratio of 3.9 (1.9–7.9) in lung cancer patients [154]. Such data could help to predict the cancer course and be useful for treatment decisions in the future. Of current clinical relevance, TKI resistance mechanisms can be driven by DNA methylation. Here, a liquid biopsy cfDNA methylation analysis of prespecified sites (e.g., RASSF1A, RASSF10, APC, WIF-1, BRMS1, SLFN11, RARβ, SHISA3, and FOXA1) indicates the development of EGFR-TKI resistance in osimertinib-treated patients [155]. Likewise, a specific DNA methylation (i.e., 5-mC score) score was used to monitor the treatment efficacy and predict the disease progression in TKI-treated ALK-driven NSCLC patients with a liquid biopsy cfDNA analysis [156]. In conclusion, epigenetic changes such as DNA methylation might frequently be found in the liquid biopsies of cfDNA and CTCs. Still, adjacent tissue, tumor, and cfDNA analysis have to be normalized, and their deductive consequences must be well defined. CTCs provide parallel information on the mutational profile, CNVs, genomic rearrangements, and gene expression of a tumor. As they are released from the primary tumor site, as well as from metastases, the analysis of the CTCs might overcome the intratumor heterogeneity. Nevertheless, the CTC count does not correlate with the tumor burden, as only a fraction of NSCLCs shed CTCs [33], and the current data regarding the counting of CTCs and their correlation with the tumor stage are conflicting [157,158,159]. However, there is strong evidence that a high baseline CTC count predicts a poorer outcome in NSCLC patients, and is thus an independent prognostic factor in lung cancer [160]. Interestingly, monitoring the CTC count during therapy allows for an assessment of the disease development in real-time [33]. The maintenance of high CTC counts under therapy and during aftercare indicates a poorer prognosis, and identifies patients at risk of progression [161]. Even if mutational assessment is more established in the field of ctDNA, a molecular analysis of CTCs can provide additional information about the underlying driver mutations. Allele-specific PCR amplification in CTCs has been shown to confirm tissue EGFR-mutation in NSCLC in 11 of 12 patients (92%) [162]. Interestingly, in cases where no oncogenic drivers were found in the tissue-based examination, the assessment of CTCs was also able to identify EGFR mutations [163]. In addition to EGFR mutations, ALK rearrangements can be detected with a high sensitivity and high concordance with tissue [164] when analyzing CNVs, even though this is with a less broad range of mutations than with NGS-based methods in ctDNA [162,165,166]. The expression of MET in CTCs has been shown to correlate with their expression in the primary tumor tissues of NSCLC patients [167], expanding the number of molecular drivers that can be observed in CTCs. If a mutation is identified, CNVs can serve as a simple tool to monitor the genomic evolution of the tumor under TKI therapy, and predict the therapy resistance and clinical outcome [168]. PD-L1 is co-expressed with EpCAM on >80% of CTCs from patients with metastatic lung cancer [169], making CTCs a suitable tool for identifying patients that qualify for ICI treatment. Interestingly, PD-L1 expression on CTCs in NSCLC patients is more often positive than on that in tissue, thus highlighting the potential use of CTC PD-L1 expression as a biomarker under immunotherapy [170,171]. Generally, the presence of PD-L1-positive (>1%) CTCs prior to treatment is associated with a poor outcome [170,172,173] in most trials. The persistence of PD-L1-positive CTCs after three and six months of ICI treatment was proposed as a predictor of mortality [174]. While conclusive proof is lacking, it is believed that the expression of PD-L1 on CTCs mediates immune escape [175], as patients with PD-L1-positive CTCs are more often non-responders to nivolumab [170]. More importantly, Nicolazzo et al. found PD-L1-positive CTCs in all patients that developed a resistance under ICI therapy [174], supporting the latter hypothesis. Nonetheless, further studies are needed to prove that correlation. Comparing PD-L1 expression on CTCs and in tumor tissue yielded widely divergent results: while Ilie et al. described a 93% concordance in their tissue-matched study [176], others saw no correlation [170], which might be explained by the different methods and/or antibodies used to analyze the PD-L1 expression in those trials [170,177,178]. This illustrates the need for standardizing the methods, and for future trials to validate the current knowledge and enlighten future perspectives on CTCs in NSCLC diagnostics. miRNA profiles can reliably distinguish healthy controls from lung cancer patients [179,180,181]. Some profiles are specific for particular NSCLC entities [182,183,184], e.g., the detection of the highly specific hsa-miR-205 in plasma identifies a squamous cell carcinoma of the lung, with a high sensitivity and specificity [185]. In addition, the levels of blood-based miRNAs correlate with the disease stage and poor prognosis [186,187]. The analysis of miRNAs can also be used in early tumor stages for genotyping, monitoring TKI therapy [188], and selecting the patients that benefit from immunotherapy [189]. High expression levels of miR-195 and miR-122 were found to be associated with EGFR-mutant tumors, and were, moreover, independent predictors of survival [190]. In addition, increasing miR-21 levels were observed until there was a clinical progression under first-line TKI in EGFR-mutated NSCLC, anticipating TKI resistance and probably serving as MOR [191]. As miRNAs play a role in antitumor immunity by influencing the mRNA levels, and thus modulating the T-/NK-cell response [192] or PD-L1 expression [193], circulating miRNAs also have the potential to predict the response to ICI. Even though miRNAs have great potential, there is an urgent need for future trials to evaluate their role in disease progression, the evaluation of the therapeutic response, the prediction of progression, and the outcome of NSCLC patients. The analysis of EVs has emerged as a method that can potentially complement the current gold standard of ctDNA analysis in the field of liquid biopsies for NSCLC (c.f. review “Extracellular vesicles in liquid biopsies” in this Special Issue). They are released from the primary tumor into the blood already at early tumor stages, are not as scarce as CTCs, and have the ability to pass the blood–brain barrier, thus allowing early access to tumors and metastases and their molecular compositions [194]. In NSCLC, EVs incorporate miRNAs, which are solely expressed in cancer but not in healthy subjects [195], and of which levels rise during tumor progression [196]. The role of sEVs and their potential usefulness in diagnostics, disease monitoring, and therapeutic stratification have been highly studied over the past years. Regarding their mutational assessment in NSCLC especially, the analysis of EV-derived miRNA has been proven to detect oncogenic drivers with a higher sensitivity than ctDNA [197], thus extending the therapeutic options of TKI therapy for more patients. Interestingly, the combined evaluation of resistance mechanisms in ctDNA and EV-derived miRNA showed a high sensitivity in lung cancer patients [198]. The miRNA analysis of sEVs can also offer prognostic information with regards to the response to osimertinib, and might thus be a screening tool regarding the individual choice of TKI [199,200]. Furthermore, PD-L1-positive EVs can inhibit the activation of CD8+ T cells. The finding of PD-L1+-EVs in plasma, and a rapid decline of EV levels under ICI treatment, seems to be highly prognostic with regards to an expectable response [201] and the outcome of NSCLC patients [202]. Furthermore, PD-L1 expression in sEVs is correlated with the progression, tumor burden, TNM stages, and metastatic capacity of NSCLC [203]. The results regarding the correlation between PD-L1 expression in EVs and the tissue remain controversial, ranging from strong [204] to no correlation [203]. EVs are also involved in immune escape mechanisms and the resistance to chemo- and immunotherapy, e.g., by distinct miRNA expression patterns or the overexpression of PD-L1 [55]. Thus, the assessment of sEVs might also help to predict the response to ICI or the failure of immunotherapy [201,205]. lEVs have long been known for their involvement in tumor progression and metastasis formation [55]. lEV levels are elevated in lung cancer patients [206], tend to be increased in late stage cancers [55,206], and predict a poor outcome [207], which demonstrates their potential as diagnostic and prognostic biomarkers in liquid biopsies. The combined analysis of EV-associated transcripts and ctDNA improved the sensitivity of EGFR mutational detection [208], with a high concordance with tissue biopsies [209]. As an alternative approach, an analysis of sEV mRNA levels to determine the oncogenic driver mutations of NSCLC has demonstrated a greater sensitivity than that of ctDNA assessment [197]. However, lEVs seem to reflect the mutational status even better than sEVs [210]. Furthermore, lEVs might be involved in the induction of resistance to both EGFR and ALK inhibitors via the trafficking of EGFR or ALK-mutated isoforms, and the subsequent activation of oncogenic pathways [211,212]. In synopsis, the monitoring of the presence of tumor-derived EVs and the investigation of the vesicle-specific content have dramatically emerged over the last years, and are a promising methodology for a rapid and accurate diagnosis, early tumor detection, and the guidance of therapy. At present, their clinical applications are hampered by a lack of standardization and multicenter studies, as well as a lack of efficient and cost-effective methods for the separation of EVs from non-EV lipid particles. Since the clinical appearance of lung cancer is unspecific, the detection of tumors in early, potentially curable stages is challenging. With respect to liquid biopsies, little DNA is shed from small tumor masses and ctDNA concentrations are very low, even prior to treatment [23]. Thus, the low sensitivity of detecting ctDNA in early-stage lung cancer is a major challenge. The sensitivity has been increased by the use of NGS-panels [213] or targeted error sequencing (TEC-Seq) [214]. Several programs have fused ctDNA diagnostics with other parameters, and by integrating distinct molecular features, the lung cancer likelihood in the plasma (Lung-CLIP) algorithm was able to robustly discriminate cancer patients from the risk-matched controls [213]. The multi-analytic blood test CancerSEEK combines ctDNA detection with known protein biomarkers of early cancer stages [215,216]. As early cancer detection ultimately reduces the number of cancer deaths, these multi-analytic blood tests (such as CancerSEEK) have shown promising results for cancer screening. However, ongoing trials aim to increase the clinical sensitivity of those tests. Similar to the implementation of LD-CT scans as a screening method for lung cancer, the use of such multi-analytic blood tests should be limited to patients at risk for cancer, in order to increase the sensitivity. In lung cancer, factors like the patient’s smoking status, their history of exposure to carcinogens, and their age should be considered when selecting the most suitable group of patients for blood screening. Additionally, the prevalence of cancer should be taken into account in order to increase sensitivity [217]. However, CancerSEEK has been proven to be highly specific, e.g., only 7 of 812 healthy individuals tested positive [215]. Thus, it might even be possible in future to screen not only patients at risk, but also healthy individuals for cancer. Prospective trials, such as the DETECT-A trial [218], the SUMMIT trial (NCT03934866), or the ASCEND trial (NCT04213326) will provide new insights into a risk-stratified approach based on the results of the liquid biopsies (Table S2). Moreover, with few exceptions, such as Crohn disease [219], endometriosis [220], or pregnancy [221], CTCs might enable an early tumor detection in lung cancer with a high specificity. The detection of CTCs in patients with CT-confirmed suspicious pulmonary nodules reliably discriminates malignant from benign lesions [222]. More importantly, “sentinel” CTC detection in COPD patients identified those patients who developed cancer in the follow-up period [223]. The ongoing multi-center cohort AIR-trial [224] aims to prove whether the image-based monitoring of “sentinel” CTC-positive COPD patients allows for the early diagnosis of lung cancer and improves prognosis (Table S2). ctDNA and CTCs in liquid biopsies are also being investigated for their use as biomarkers to determine the MRD levels and monitor the follow-up after curative intended therapy and surgery. In a cohort of 41 NSCLC patients, 91.7% (22) of the pre-op-identified 24 plasma ctDNA mutations had a decrease in their mutation frequency within only two days after surgical tumor resection. Moreover, the presence of ctDNA had a higher positive predictive value of residual disease than that of six tumor biomarkers [225]. The ongoing randomized and controlled phase III SUPE_R trial (NCT03740126) is currently investigating the efficacy of ctDNA analysis using serial liquid biopsies every three months in stage I-III NSCLC patients after curative intended treatment [226]. In addition, the observational ORACLE (NCT05059444) study aims at demonstrating the ability of a novel ctDNA assay to detect recurrence in individuals treated for early-stage solid tumors. Newman et al. developed a method called CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) to identify cancer-specific genetic aberrations in ctDNA analysis [227]. In 94% of patients with tumor recurrence after the curative intended treatment for stage I-III lung cancer, ctDNA detection using CAPP-Seq was positive in the first post-treatment blood sample, indicating the reliable identification of MRD [228]. Post-treatment ctDNA detection preceded radiographic progression in 72% of patients by a median of 5.2 months [228]. Likewise, in the ongoing TRACERx (NCT01888601) study, pre- and post-surgical plasma ctDNA profiling is performed, blinded to relapse statuses to assess the clonal tumor evolution and tumor heterogeneity, and the mutational changes under therapy. The ctDNA profiling used in TRACERx was able to identify 93% (n = 13) of those patients with a confirmed relapse, prior to or at clinical relapse. The median interval between the ctDNA detection and the NSCLC relapse confirmed by a CT scan was 70 days [229]. Interestingly, it was shown that maintenance with immunotherapy in MRD-positive patients after curative intended therapy provided better outcomes [230]. Although patients with detectable ctDNA after surgery have a significantly lower PFS and OS than those with undetectable ctDNA after surgery [228], there are still some patients with undetectable ctDNA that ultimately recur [231]. Thus, interpreting the absence of ctDNA remains difficult. Although the preliminary data for using ctDNA after surgery to identify the patients at risk of relapse appear promising, further data, including the awaited MRD data from the ADAURA trial [232], are required to elucidate which patients would benefit from an adjuvant therapy such as osimertinib (EGFR-TKI). ctDNA detection can also be used to monitor the treatment response in advanced (metastatic) tumor stages. As one knows that the mutation load is correlated with the survival rate [233], the ctDNA-based monitoring of EGFR mutations might allow for an early prediction of the resistance to EGFR-TKI in NSCLC patients [234]. The FASTACT-2 study found that the changes in the cfDNA EGFR mutation status might predict clinical outcomes, even prior to routine CT scans [235]. In patients with EGFR mut+ at baseline, the median PFS and median OS were shorter if the patients were still EGFR mut+ at cycle 3, than if they were EGFR mut− at that point (7.2 vs. 12.0 months and 18.2 vs. 31.9 months, respectively.) [24]. The prognostic value of (i) the detection of the EGFR mutation in the plasma at baseline, as well as of (ii) the clearance of the EGFR mutated ctDNA under systemic therapy, was confirmed by several other studies, including the FLAURA trial [235,236,237,238]. The ongoing phase II APPLE trial (NCT02856893) on EGFR mutant, advanced NSCLC patients, compares the sequential T790M test using a ctDNA analysis of liquid biopsies to conventional radiological procedures, with regards to the prediction tumor progression [80]. Confirming the therapeutic response to ICI with imaging methods is challenging due to the induction of inflammatory processes with leukocyte infiltration that can be misinterpreted (pseudoprogression) in up to 6% of patients [239]. The subsequent monitoring of ctDNA levels via targeted NGS-based methods can predict the response to ICI [240]: After an initial peak due to the massive induction of apoptosis, a rapid decrease in the ctDNA levels under treatment, especially in the first four to eight weeks, correlates with a response and favorable outcome [241,242], while patients without a molecular response had a shorter PFS and OS compared to molecular responders [243]. There is also evidence from a small patient cohort that the increase in ctDNA might identify ICI non-responders prior to imaging [244]. The now-recruiting LIBERTYLUNG trial (NCT04790682) prospectively examines the suitability of ctDNA to predict the response to the PD-L1 inhibitor pembrolizumab (+/− chemotherapy) in treatment-naive metastatic NSCLC patients, with at least one detectable mutation. However, further studies are crucial to standardize the diagnostic thresholds and refine the methodology of liquid biopsies in this regard. Despite the great potential for the use of liquid biopsies in diagnostic and therapeutic decision making for patients with lung cancer, there are still several limitations of the current applications. Tumor tissue genotyping is generally associated with a higher sensitivity than liquid-biopsy-based genotyping [14,23]. The sensitivity of ctDNA analysis mainly depends on the amount of ctDNA shed by the tumor; hence, its stage and type. The sensitivity is increased in patients with extra-thoracic metastases (especially bone and liver metastases), [245] as well as in patients with a high tumor burden [246]. However, 20% of stage IV NSCLC patients do not shed ctDNA [62]. Currently, highly sensitive assays can achieve sensitivity levels of up to 85% in advanced stage lung cancer [14], which have been further improved with the use of deep sequencing methods. Although a positive result in ctDNA analysis using a validated test is usually sufficient to initiate targeted therapy [14], a negative result should be further clarified, e.g., with a tissue biopsy, due to the high risk of false-negative results, especially in slow-growing or early-stage tumors [71,247]. Although the evolvement of deep sequencing methods such as NGS has increased sensitivity, it brings the inherent risk of false-positive results—variants with low allele frequencies especially can result in a single false-positive result and impact the data interpretation. However, some technical adaptations have been developed to further improve specificity (e.g., molecular barcoding or error-proofing algorithms). False-positive results can also be caused by germline variants or the clonal hematopoiesis of indeterminate potentials (CHIP) that contribute to the bulk of cfDNA, and can interfere with the interpretation of ctDNA analysis results [247,248,249]. Some methods have been developed to avoid the false-positive detection of mutations caused by CHIP, e.g., a combined screening of matched white blood cells and ctDNA [250,251]. Moreover, a machine learning method, which can discriminate clonal hematopoiesis mutations from frequently recurring genetic alterations in NSCLC, has been validated [213,247]. It must be noted that conditions unrelated to cancer, such as infections, trauma, or inflammations, can increase the cfDNA levels in blood and might also lead to false-positive results [252]. Molecular testing in the early stages of NSCLC is not as widely reflected in current guidelines as it is for advanced stages. However, it has gained importance in the adjustment and individualization of therapy. The ADAURA trial demonstrated that adjuvant treatment with osimertinib in stage IB-IIIA EGFR-mutated NSCLC significantly improved the disease-free survival, thus leading to the approval of osimertinib in Ex19del or L858R EGFR-mutated NSCLC [232]. Consequently, testing for EGFR mutations, which were found in 30% of stage I-III NSCLCs in a meta-analysis [253], is now recommended in the current National Comprehensive Cancer Network (NCCN) guidelines, to determine whether a patient might benefit from adjuvant treatment with osimertinib [254]. CTCs can also be detected in the early NSCLC stages. Crosbie et al. outlined the large potential of CTCs for post-surgical risk stratification [255]. In their study, in 33 patients with NSCLC stage IA-IIIA, they found that the detection of tumor micro-emboli or ≥2 CTCs in the pulmonary vein draining the tumor was associated with an eight-fold increase in the risk of disease recurrence, and a seven-fold increased risk of death. Hence, the CTCs detected during surgery might identify the patients for whom an adjuvant therapy during early-stage disease would prevent the disease recurrence. As more patients are given targeted therapies, the outcome of the patients suffering from NSCLC has improved. An analysis in Canada showed that therapeutic treatments yielded a gain of 168 life years, but at a cost of 14.7 million US dollars [256]. Even if the cost of an initial broad NGS-based mutational assessment (e.g., using liquid biopsies) is now comparable to that of sequential testing or hotspot panels [257], standard-of-care tissue testing is significantly less expensive [23]. In addition, a reimbursement from national health systems or health insurance is lacking in many countries, e.g., in Spain. NGS-based methods for analyzing liquid biopsies require the newest equipment that is generally only available in First World countries, which increases the global social gap and disparity, and also hinders their broad clinical introduction. As a result of intensified research activity, the scope of the application of liquid biopsies is currently expanding. The new NGS-based techniques have enhanced the ability to analyze ctDNA, EVs, or CTCs, in order to assess an increasing number of accessible targets. Liquid biopsies, and ctDNA in particular, have already secured their position in clinical routine, where they are useful for the risk stratification of NSCLC patients, as well as for monitoring therapeutic efficacy, although further elucidation of the pathophysiological role and the implications of CTCs and EVs is required. However, there are several obstacles limiting the role of this technique. The absence of standardized protocols for extracting and analyzing the DNA, and interpreting the data, together with the low amount of targeted biomarkers in plasma, are impeding the further integration of liquid biopsies into diagnostic routine. Additionally, most studies in this field are based on small sample sizes, which gives them an inferior level of evidence. Hence, further large-scale, prospective, randomized, and controlled trials are needed to clarify the role of liquid biopsies, to extend the current knowledge, and to strengthen the weight of liquid biopsies in diagnostics, risk stratification, the assessment of individualized therapy, disease monitoring, and, ultimately, in achieving better outcomes for NSCLC patients.

PMC10000708

Jiabin Yang,Liangtang Zeng,Ruiwan Chen,Leyi Huang,Zhuo Wu,Min Yu,Yu Zhou,Rufu Chen

Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify Gene Signatures Related to Prognosis and Immunotherapy Response

24-02-2023

pancreatic cancer,immune microenvironment,prognosis,immunotherapy,molecular subtype,F2RL1,therapeutic target

Simple Summary Pancreatic ductal adenocarcinoma (PDAC) has an insidious onset and rapid progression, and its morbidity and mortality are increasing year by year. Currently, there are limited therapeutic methods and no effective therapeutic guidance. Tumor microenvironments (TME) of PDAC are highly specific and associated with the failure of chemotherapy, radiotherapy, and immunotherapy. Different TMEs have different sensitivities to treatment modalities. Therefore, constructing a prediction model based on TME classification and giving corresponding treatment measures according to the classification results will provide a new idea for clinical precision diagnosis and treatment. Further verification of gene function related to TME will greatly provide effective potential clinical treatment targets for personalized therapy. Abstract The hallmark of pancreatic ductal adenocarcinoma (PDAC) is an exuberant tumor microenvironment (TME) comprised of diverse cell types that play key roles in carcinogenesis, chemo-resistance, and immune evasion. Here, we propose a gene signature score through the characterization of cell components in TME for promoting personalized treatments and further identifying effective therapeutic targets. We identified three TME subtypes based on cell components quantified by single sample gene set enrichment analysis. A prognostic risk score model (TMEscore) was established based on TME-associated genes using a random forest algorithm and unsupervised clustering, followed by validation in immunotherapy cohorts from the GEO dataset for its performance in predicting prognosis. Importantly, TMEscore positively correlated with the expression of immunosuppressive checkpoints and negatively with the gene signature of T cells’ responses to IL2, IL15, and IL21. Subsequently, we further screened and verified F2R-like Trypsin Receptor1 (F2RL1) among the core genes related to TME, which promoted the malignant progression of PDAC and has been confirmed as a good biomarker with therapeutic potential in vitro and in vivo experiments. Taken together, we proposed a novel TMEscore for risk stratification and selection of PDAC patients in immunotherapy trials and validated effective pharmacological targets.

Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify Gene Signatures Related to Prognosis and Immunotherapy Response Pancreatic ductal adenocarcinoma (PDAC) has an insidious onset and rapid progression, and its morbidity and mortality are increasing year by year. Currently, there are limited therapeutic methods and no effective therapeutic guidance. Tumor microenvironments (TME) of PDAC are highly specific and associated with the failure of chemotherapy, radiotherapy, and immunotherapy. Different TMEs have different sensitivities to treatment modalities. Therefore, constructing a prediction model based on TME classification and giving corresponding treatment measures according to the classification results will provide a new idea for clinical precision diagnosis and treatment. Further verification of gene function related to TME will greatly provide effective potential clinical treatment targets for personalized therapy. The hallmark of pancreatic ductal adenocarcinoma (PDAC) is an exuberant tumor microenvironment (TME) comprised of diverse cell types that play key roles in carcinogenesis, chemo-resistance, and immune evasion. Here, we propose a gene signature score through the characterization of cell components in TME for promoting personalized treatments and further identifying effective therapeutic targets. We identified three TME subtypes based on cell components quantified by single sample gene set enrichment analysis. A prognostic risk score model (TMEscore) was established based on TME-associated genes using a random forest algorithm and unsupervised clustering, followed by validation in immunotherapy cohorts from the GEO dataset for its performance in predicting prognosis. Importantly, TMEscore positively correlated with the expression of immunosuppressive checkpoints and negatively with the gene signature of T cells’ responses to IL2, IL15, and IL21. Subsequently, we further screened and verified F2R-like Trypsin Receptor1 (F2RL1) among the core genes related to TME, which promoted the malignant progression of PDAC and has been confirmed as a good biomarker with therapeutic potential in vitro and in vivo experiments. Taken together, we proposed a novel TMEscore for risk stratification and selection of PDAC patients in immunotherapy trials and validated effective pharmacological targets. Pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging cancers in alimentary malignancies [1,2]. For most patients with PDAC, cytotoxic chemotherapy remains the mainstay of treatment. However, despite recent improvements in chemotherapeutic regimens and treatment modalities, their survival benefits remain limited [3]. In addition, many efforts have been made to develop targeted therapies for PDAC, but there has been no substantial improvement [4,5]. Progress in strategies targeting homologous recombination defects, while substantial, currently shows applicability and efficacy in only a small proportion of patients [6]. Furthermore, PDAC is known to lack an effective immune response with low immunogenicity, which results in rapid cancer progression and a limited response to cancer immunotherapy [7,8]. Despite the aggressive molecular behavior driven by intrinsic oncogenic genetic alterations, the tumor microenvironment (TME) of pancreatic cancer has been deemed to be responsible for the above dilemma [9,10,11,12]. PDAC is characterized by extensive deposition of desmoplastic stroma, which may comprise more than 80% of the whole tumor mass [13,14]. The extracellular matrix, vessels, and stromal cells comprise the TME of pancreatic cancer [15]. The cell component surrounding PDAC cells consists predominantly of cancer-associated fibroblasts, various immune cells, and endothelial cells. The complex interactions between TME cells and cancer cells contribute to tumor progression in a multifaceted way [16]. For example, cancer-associated fibroblasts, infiltrated inflammatory cells, and desmoplastic stroma enhance cancer growth, invasion, metastasis, and treatment resistance in direct or indirect ways [15,17]. The immune-suppressor cells in TME establish an immunosuppressive tumor microenvironment, which results in rapid cancer progression and a low immune response to immunotherapy [18]. Accumulating studies have revealed that the TME context correlates with clinical outcomes, therapy benefits, and immune response [19,20,21,22,23,24,25]. By now, although clinical decision-making based on molecular subtypes has been well established in some cancer types, subtypes of PDAC do not currently provide effective support for clinical decisions [26]. However, accumulating molecular subtypes have been defined in PDAC with the development of the genome project, which defined various PDAC subtypes with distinct tumor biological behaviors and clinical characteristics [27]. Although the several mechanisms associated with the role of TME have been highlighted in some previous subtypes [27,28,29,30,31], the comprehensive landscape of cells infiltrating the TME of PDAC has not yet been elucidated, as well as that there is a lack of a molecular subtype based on TME signatures to inform treatment decisions, including the applicability of immunotherapy. Therefore, in the present study, we evaluated the cell components of the TME in PDAC using computational algorithms and then established subtypes based on TME infiltration signatures. Finally, a robust TMEscore and an effective biomarker capable of risk stratification and informing treatment decisions were developed. The expression data (RPKM) of 182 PDAC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/ (accessed on 1 January 2020)) by using TCGAbiolinks. The fragments per kilobase of exon model per million (FPKM) data from TCGA were transformed into transcripts per kilobase per million (TPM) values. Additionally, other public PDAC datasets were obtained through the retrieval of the GEO database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 January 2020)) with the following retrieval strategy: (“pancreatic neoplasms” [MeSH Terms] OR pancreatic cancer [All Fields]) AND “Homo sapiens” [porgn] AND (“Expression profiling by array” [Filter]). Samples with survival data were retained for further analysis. The detailed information on the retrieved datasets was summarized in Supplementary Table S1. The ESTIMATE algorithm was used to calculate the level of stromal cell and immune cell infiltration in each sample and further infer tumor purity [32]. Single-sample gene set enrichment analysis (ssGSEA) [33,34], a deconvolution algorithm based on gene set enrichment analysis (GSEA), was used to qualify the relative abundance of 29 cell types within the TME. The ssGSEA was run using the GSVA R package. Signature genes of each cell type were obtained from previous publications [35,36]. The ssGSEA score was normalized to a unity distribution, in which zero is the minimum score and one is the maximal score for each cell type. In some analyses, the immune infiltrations were also quantified by the GSVA algorithm [37], for which the normalized GSVA scores were obtained from a recent publication [38]. Unsupervised consensus clustering was performed on normalized ssGSEA scores of TME cell components by using the ConsensusClusterPlus R package (parameters: reps = 1000, pItem = 0.8, pFeature = 1). The complete method and Manhattan distance were used as the clustering algorithm and distance metric, respectively. To identify the signature genes of TME, we first estimated the differentially expressed genes (DEGs) associated with TME subtypes determined by consensus clustering of TME infiltration. The DEGs among TME subtypes were obtained using the limma R package with the selection criteria of Log2FoldChange > 1 and an adjusted p-value < 0.05 (Benjamini–Hochberg correction). Next, the random forest method was used to evaluate the contribution of these DEGs to the cluster grouping of the TME cell population, and the genes that had less influence on the grouping were filtered out. Finally, 74 DEGs that influenced the prognosis were obtained. An unsupervised clustering method (K-means) with Ward.D and Euclidean distance was used to classify patients into subgroups based on the 74 DEGs. The ConsensusClusterPlus R package was adopted and used to annotate gene patterns and define gene clusters. After obtaining the two gene clusters in the above part, the genes in each cluster were extracted to serve as the TME gene signature sets, respectively: TME signature gene set A from cluster 1, and TME signature gene set B from cluster 2. The ssGSEA algorithm was used to calculate the enrichment score of each TME signature gene set for each sample. Thereafter, the TMEscore for each sample was obtained by using the following formula: TMEscore = TMEscoreB − TMEscoreA, where TMEscoreB stands for the ssGSEA score of TME signature gene set B, TMEscoreA stands for the ssGSEA score of TME signature gene set A. A schematic diagram illustrating the process of generation of the TMEscore was described in Supplementary Figure S1. A functional annotation analysis was conducted based on the GO database (http://geneontology.org/page/go-database (accessed on 1 January 2020)) and the KEGG database (http://www.kegg.jp/kegg/ko.html (accessed on 1 January 2020)). KEGG and GO term gene set enrichment analysis (GSEA) was conducted using the clusterProfiler R package. We also estimated the enrichment of pathways among TME gene clusters or samples with different TMEscores by running gene set enrichment analysis (GSEA) [33,39]. For GSEA analysis, gene sets for certain pathways were collected via searching the MSigDB database (https://www.gsea-msigdb.org/gsea/msigdb/ (accessed on 1 January 2020)). GSEA was performed using the GSEA software and visualized by the ggplot R package. TCGA-PAAD mutation data were downloaded in January 2020 from the GDC data portal. The copy number events were filtered for those with at least 10 supporting probes and a segment mean >0.2 (amplifications) or <−0.2 (deletions), as recommended by a previous study [40,41]. The waterfall plots of mutational landscapes were drawn using the maftools Bioconductor package [42]. Mutation types were ordered by their potential impact, from most deleterious to least. Human cell lines BxPC-3, PANC-1, AsPC-1, Capan-2, MIA-PaCa2, SW1990, and hTERT-HPNE were purchased from the ATCC (American Type Culture Collection, Rockville, MD, USA). Cells were cultured in DMEM (Gibco, Billings, MT, USA) or RPMI 1640 medium (Gibco, USA). All media were supplemented with 10% fetal bovine serum (FBS, BI, Israel) and 1% penicillin/streptomycin. All cells were cultured in a humid environment containing 5% CO2 at 37 °C. For cell transfection, the siRNAs for knocking down F2R-like Trypsin Receptor1 (F2RL1) and the F2RL1 overexpression plasmid were purchased from IGE (Guangzhou, China). Transfection was performed using the Lipofectamine 3000 kit (Invitrogen, Cat# L3000015, Waltham, MA, USA) according to the manufacturer’s instructions. For lentivirus infection, in order to construct stable knockdown cell lines, the shRNA sequence of F2RL1 was cloned into a pLKO.1-Puro vector by IGE, and then the lentivirus packaging plasmids containing psPAX2 and pMD2G were cotransfected into HEK-293T cells (ATCC, RRID: CVCL_0063). After transfection for 72 h, lentivirus was collected and concentrated. Subsequently, the cell lines were infected with lentivirus and selected by treatment with puromycin (Solarbio, Beijing, China) for 2 weeks. All the sequences of oligonucleotides are shown in Supplementary Table S9. The total RNA of PDAC cell lines was extracted using Trizol reagent (Takara Bio, Shiga, Japan) according to the instructions. Subsequently, the total RNA was reverse transcribed into cDNA using the Hiscript III Reverse Transcriptase Kit (Vazyme, Nanjing, China). Finally, qRT-PCR was used to detect the expression of RNA using the ChamQ Universal SYBR qPCR Master Mix kit (Vazyme, Nanjing, China). GAPDH was used as an internal control. The sequences of primers are listed in Supplementary Table S9. A total of 500 cells transfected with siRNA or stably overexpressing F2RL1 were cultured in 6-well plates in a humidified atmosphere containing 5% CO2 at 37 °C for 2 weeks. After that, the colonies were fixed in 4% paraformaldehyde for 20 min, stained with 0.1% crystal violet for 15 min, and washed twice with phosphate-buffered saline (PBS). Count colonies manually. Each group repeated the experiment at least three different times. The cells transfected with siRNA or stably overexpressed F2RL1 were pre-seeded in 24-well plates and cultured at 37 °C containing 5% CO2 for 24 h. Then, using BeyoClickTM EdU-555 detection kits (Beyotime, Shanghai, China) and according to the manufacturer’s instructions, the cells were stained with EdU for 2 h, fixed with 4% paraformaldehyde for 15 min, incubated with click reaction solution for 30 min and stained with Hoechst 33,342 for 10 min. The images were obtained by fluorescence microscope Nikon TI-S (Nikon, Tokyo, Japan). Each group repeated the experiment at least three different times. The cells transfected with siRNA or stably overexpressed F2RL1 were seeded in a 12-well plate according to 2 × 105 cells/well and cultured in a moist environment containing 5% CO2 at 37 °C until the cell density was about 90%. Then adherent cells were scraped with the 10 μL sterile pipette tips in a straight line, and the images were obtained by an inverted microscope, the Nikon TI-S (Nikon, Tokyo, Japan), at 0 h and 24 h, respectively. The cell migration distance was measured and calculated. Each group repeated the experiment at least three different times. The cells transfected with siRNA or stably overexpressed F2RL1 were added to 200 μL serum-free medium with or without Matrigel (BD Biosciences, Franklin Lakes, NJ, USA), respectively, and seeded in a Transwell chamber, then placed in a 24-well plate. 700 μL complete medium was added to each well in the lower layer of the 24-well plate in advance. PANC1 was incubated for about 8 h, and BxPC-3 was incubated for about 48 h. Then the Transwell chamber was removed, fixed with 4% paraformaldehyde for 15 min, and stained with 0.1% crystal purple for 15 min. Images were obtained by an inverted microscope, the Nikon TI-S (Nikon, Tokyo, Japan), and the number of cells that migrated or invaded was counted. Each group repeated the experiment at least three different times. To construct a subcutaneous tumorigenicity animal model, 5 × 106 BxPC-3 cells in suspension, stably overexpressing F2RL1 and Vector, were injected subcutaneously into the left dorsal side of BALB/c nude mice (n = 5) aged 4 to 5 weeks, purchased from the Guangdong Medical Laboratory Animal Center. Tumor growth was measured every 4 days, and tumor volume was recorded. Volume = 0.5 × length × width2. Four weeks later, all the mice were sacrificed, and the tumor tissue was dissected, weighed, and fixed with 37% formalin and embedded in paraffin. Immunohistochemical staining was performed on paraffin sections, which were first treated at 60 °C for 2 h, then dewaxed with xylene, rehydrated with different grades of ethanol, repaired antigen with EDTA, and blocked with normal goat serum. The sections were incubated with primary antibodies at 4℃ overnight and secondary antibodies at room temperature for 2 h. Finally, DAB chromogenic reagent was used to label the antigen, followed by counterstaining with hematoxylin. The staining was judged by two independent observers. Images were obtained by a microscope, the Nikon 80i (Nikon, Tokyo, Japan). The antibodies used in this study are listed in Supplementary Table S10. The distribution of two sets of continuous variables was compared using a t-test. If continuous variables did not follow a normal distribution, the Mann–Whitney U test was applied. Unless explicitly stated, the association between categorical variables was evaluated using Pearson’s chi-square test. To divide the samples assessed into groups according to high versus low TMEscore, the MaxStat R package was used. Survival curves were compared using the Kaplan–Meier method with a log-rank t-test. The influence of the TMEscore on survival was additionally evaluated through the Cox proportional hazard model. The independence of association was verified by a multivariate Cox regression model of survival. The resulting p-values of differently expressed genes between two groups were corrected for multiple testing by the Benjamini–Hochberg method. All reported p-values are two-sided. R (version 3.6.3) and SPSS (version 17.0; SPSS Inc., Chicago, IL, USA) were used to perform statistical analyses. Figures were generated with the ggplot R package and GraphPad Prism 8 (GraphPad Prism Software, San Diego, CA, USA). Two-sided p < 0.05 was considered significant. The flow chart of the study is shown in Figure 1A. First, we defined the tumor microenvironment (TME) infiltration pattern of each tumor as the relative abundance of an array of twenty-eight cell populations of immune cells and fibroblasts. TME cell profiles were estimated via the ssGSEA algorithm (Supplementary Table S2). To select the optimal cluster number, we grouped the ssGSEA scores of the resectable PDAC tumors from the TCGA dataset using hierarchical clustering. As a result, we obtained three robust subtypes of PDAC (named TMEgroups1–3) (Figure 1B) (Supplementary Table S3). The TMEgroup3 is defined as the smallest group of cases (23/177, 13.0%), followed by TMEgroup1 (71/177, 40.1%), and TMEgroup2 (83/177, 46.9%). TMEgroup3 was associated with better overall survival in comparison with the other two TMEgroups (Log-rank test, p = 0.017, Figure 1C). In many solid tumors, the degree of immune infiltration of the TME is highly correlated with immunotherapy efficacy [43]. Due to the high tumor heterogeneity and TME heterogeneity in PDAC, the specific biological characteristics are different from other solid tumors [44]. As previous studies have shown, the abundance of immune cells tends to predict a poor prognosis [45,46]. The overall survival rate of TMEgroup3, which had the fewest immune cells, was higher than that of the other two groups. To identify the signature genes associated with TMEgroups, differentially expressed genes (DEGs) between each TMEgroup and others were obtained using the limma R package, and the results are shown in Supplementary Figure S2 and Supplementary Table S4. As shown in the Venn diagram of Figure S2, there was no overlap between the DEGs from each TMEgroup, suggesting the high specificity of DEGs for each TMEgroup. Next, a random forest method was then used to estimate the contribution of these DEGs to the clustering of the TME cell population, and 74 genes with influence on the clustering were finally retained. By performing unsupervised hierarchical cluster analysis based on the 74 TME-related DEGs, we identified 2 robust groups for TCGA-PAAD samples: TMEgeneGroup1 and TMEgeneGroup2. The gene symbols for signature genes for each group were summarized in Supplementary Table S5. The patient-level annotation of the DEGs is visualized in Figure 2A. A significant decreased overall survival was found in TMEgeneGroup1 (Log-rank test, p = 0.0034, Figure 2B). The differences in class assignments between the two clustering methods were visualized with an alluvial diagram (Figure 2C). Most deaths occurred in TMEgroup3 and all deaths that occurred in TMEgroup2 were assigned to TMEgeneGroup1, suggesting signature genes had better prognostic discrimination values, such as the identification of patients at a high risk of death from subgroups with better prognosis. In addition, we analyzed the tumor purity of the TCGA dataset, and the results showed that there was a difference between TMEgeneGroup1 and TMEgeneGroup2 in the tumor purity, suggesting that the tumor may be related to immune infiltration of TME (Supplementary Figure S2G, Supplementary Table S7). Further, we found that tumor purity was negatively correlated with the immune ssGSEA (Supplementary Figure S2H). Moreover, we further analyzed the laser microdissected dataset from Maurer C et al. [47]. Through the comparison of stromal and epithelial cells, we obtained 6308 DEGs representing stromal regions, which were further intersected with our 74 DEGs. Interestingly, the results showed that there were 41 duplicate DEGs (Supplementary Figure S2E), suggesting that those 41 DEGs we analyzed had the characteristics of representing stromal cells in non-tumor regions. Subsequently, we also verified the effect of ssGSEA used in this project in reflecting TME characteristics (Supplementary Figure S2F). The above results show that the difference in TME can be better reflected by screening effective DEGs to distinguish cell components. To further explore the biological function and mechanism behind the signature genes, DEGs between TMEgeneGroup1 and TMEgeneGroup2 were determined (Supplementary Table S6). Functional annotation analysis of these DEGs was conducted. Significantly enriched KEGG pathways and GO biological processes were summarized in Supplementary Figure S3. We found TMEgeneGroup1 and TMEgeneGroup2 had distinct differences in the enriched pathways and biological processes. Genes overexpressed in TMEgeneGroup1 were involved in several well-known carcinogenesis mechanisms, such as the PI3K/Akt signaling pathway and the p53 signaling pathway. In addition, mechanisms involved in extracellular matrix composition, cell-extracellular matrix interaction, and cell adhesion were enriched in TMEgeneGroup1. In contrast, genes overexpressed in TMEgeneGroup2 were mainly involved in signal molecule transduction-related mechanisms, such as the cAMP signaling pathway, ligand-receptor interaction, single release, chemical synaptic transmission, and regulation of membrane potential. Furthermore, using disease network enrichment analysis with all DEGs between TMEgeneGroup1 and TMEgeneGroup2 (Figure 2D), we found enriched disease gene sets related to chronic pancreatitis, cholangiocarcinoma, and breast carcinoma stage IV. In addition, we identified seven core DEGs: GSTP1, ERBB2, MUC1, F2RL1, PTGS2, CCND1, and CXCL8, which were involved in all three gene sets. As mentioned above, the unsupervised hierarchical cluster analysis was based on the 74 most representative DEGs and separated the PDAC cohort into 2 distant patient clusters (Figure 2A). Subsequently, the 74 DEGs were divided into 2 distinct clusters, termed TME signature gene set A (enriched in TMEgeneGroup1) and TME signature gene set B (enriched in TMEgeneGroup2). Furthermore, based on the two TME signature gene sets, we estimated two TME-related scores using the ssGSEA algorithm as described in the “Methods” part: TMEscoreA from TME signature gene set A and TMEscoreB from TME signature gene set B, thus obtaining the final TMEscore through the following formula: TMEscore = TMEscoreB − TMEscoreA. After having identified the TMEscore for each patient in the TCGA-PAAD cohort, we sought to determine whether the TMEscore could effectively predict prognosis. As shown in Figure 3A–C, low TMEscoreA was correlated with improved survival (Log-rank test, p = 0.0005) in TCGA-PAAD patients, and a low TMEscoreB was associated with poor survival (Log-rank test, p = 0.00152). At last, survival analysis revealed that patients with a low TMEscore had a less favorable outcome (Log-rank test, p = 0.00065). Multivariate Cox models revealed that the TMEscore was an independent prognostic variable for overall survival (HR = 1.72. 95%CI 1.07–2.80, p = 0.025) (Figure 3D). Next, we tried to validate the prognostic value of TMEscore with seven external data sets obtained from the GEO database. As shown in Supplementary Figure S4, upon stratification of the samples according to TMEscore, significant differences in overall survival were found between the TMEscore low and high groups for all datasets except GSE28735 (p = 0.14, sample size = 43, the dataset with the lowest sample size), which confirmed the robust prognosis stratification ability of the TMEscore. To explore the underlying molecular mechanisms associated with the TMEscore, we first compared the profile of oncogenic mutations between patients with a low and high TMEscore (Figure 4A). We found significantly increased mutation rates in KRAS, TP53, and CDKN2A in the low TMEscore group. Afterward, the GSEA was performed to evaluate the pathways associated with the TMEscore (Figure 4B). The significantly enriched gene sets in samples with a low TMEscore were correlated with KRAS, NF-κβ, P53, MEK, AKT, and cell cycle signaling pathways, all directly associated with tumor development. On the other hand, genes up regulated in neurons and in response to overexpressing Src were enriched in samples with a high TMEscore. The GSEA results were consistent with the differences in mutation spectras between patients with a low and high TMEscore. Additionally, these results also indicated that the TME-based score could reflect tumor-intrinsic mechanisms at the level of driver gene profiles. Next, we analyzed the infiltration of cell populations between two groups. Figure 4C displays the differences in TME cell infiltration in the two groups with high and low TMEscores. Overall, most cell populations were increased in samples with a high TMEscore, especially since the infiltration of activated CD4/CD8 T cells was significantly increased. In light of the well-recognized close relationship between TME and immune status, we further compared the well-known biological mechanisms/pathways associated with TME and cancer-immune phenotypes between the two groups. The feature genes for each mechanism/pathway were summarized in Supplementary Table S8. As shown in Figure 4D, the ssGSEA analysis confirmed a significant enrichment of genes representing EMT, the TGF-β pathway, the Wnt pathway, homologous recombination, mismatch repair, and DNA damage repair in low TMEscore samples. This suggests that the TMEscore may reflect tumor environment infiltrations, tumor-intrinsic mechanisms, and immune status. To further delineate the link between TMEscore and immune status, we evaluated the relationship between a well-established immune phenotype and TMEscore. The immune phenotypes of 156 TCGA-PAAD samples were obtained from a previous publication, which classified tumors into three immune phenotypes: poor cytotoxicity, intermediate cytotoxicity, and high cytotoxicity on the basis of cytotoxic infiltration [38]. The abundance of cytotoxic cells was estimated by either ssGSEA (Figure 5A) or GSVA (Figure 5B). As shown in the heatmaps (Figure 5A,B) and the result of the chi-square test (Figure 5C), we observed that the cytotoxic level of the immune phenotype tended to increase with the elevated level of TMEscore. A previous study reported that tumors with a highly cytotoxic immune phenotype tend to show an increased abundance of cytotoxic infiltration with ectopic expression of negative immune checkpoints [38]. So, we next estimated the infraction of activated CD8 T cells, effector memory T cells, and γδ T cells by ssGSEA (Figure 5D) and GSVA (Figure 5E). Both algorithms showed increased infiltration of CD8 T cells and effector memory T cells in samples with high TMEscores. Additionally, we observed the same increase in the cytotoxic score (Figure 5F). These results collectively suggested that a high TMEscore indicates a TME with high cytotoxic activity. Therefore, we next estimated the enrichment of gene sets associated with the T cell–inflamed phenotype, which correlates with improved responsiveness to therapies dependent on T cell killing, such as checkpoint blockade and adoptive cell therapy [48]. We used three independent gene sets for each comparison, including gene sets that have been confirmed to be predictive of response to immunotherapy across different cancer types. Notably, all gene sets displayed significant enrichment in the high TMEscore group (Figure 5G). Meanwhile, we also observed the enrichment of immune checkpoints. At last, we assessed the enrichment of gene programs defining PDAC subtypes and their association with the TMEscore (Figure 5H). The expression of genes defining the ADEX (aberrantly differentiated endocrine exocrine) subtype [30] was increased in TMEscore-high tumors, while the genes defining the pancreatic progenitor subtype tended to increase in TMEscore-low tumors. TMEscore-high tumors were statistically enriched for the immune gene programs (GP6 and GP8) from Bailey and colleagues [30]. These two gene programs contain signature genes for CD8+ T cells and B cells, supporting the finding that TMEscore-high tumors had high cytotoxic infiltration. Furthermore, TMEscore-high tumors were enriched for the normal stroma gene program. The normal stroma gene signature contains markers of pancreatic stellate cells, which have been linked to an immunosuppressive tumor microenvironment through blocking antigen presentation [29,49,50]. In contrast, TMEscore-low tumors were enriched for the activated stroma gene program. The activated stroma was characterized by a more diverse set of genes associated with activated fibroblasts and activated inflammatory stromal responses, both of which were responsible for a low antitumor immune response [29]. The above data suggested that the TMEscore can stratify PDAC patients into distinct clusters or subtypes not only with different tumor-intrinsic characteristics but also with different stromal statuses and immune environments. To further validate this finding, we performed GSEA and found enrichment of immune sensing pathways involved in T cell priming (STING and NLRP3 inflammasome signaling) in TMEscore-low tumors (Figure 6A). We found that both STING and NLRP3 inflammasome signaling were enriched in samples with low TMEscore. Consistent with this, the antigen presentation activity, which was measured by the ssGSEA score of two independent gene sets reflecting the antigen presentation mechanism (APM), was elevated in samples with a low TMEscore (Figure 6B,C). Interestingly, the expression of signature genes of Batf3-dendritic cells, a key antigen-presenting cell population for driving T cell immunity and response to immunotherapy in PDAC [51,52], was increased in TMEscore-high tumors (Figure 6D). These results support the above hypothesis that a low TMEscore identified tumors with normal stroma status, which was characterized by blocked antigen presentation. In addition, we found both the CD8/CD4 ratio and the CD8/Treg ratio were significantly increased in TMEscore-high tumors (Figure 6E,F). Both of them were biomarkers of elevated cytotoxic activity. We also found that downregulated genes after IL15, IL2, or IL21 stimulation were enriched in TMEscore-high tumors (Figure 6A). IL15, IL2, and IL21 were responsible for the expansion of cytotoxic T cells; therefore, the negative correlation between TMEscore and the expression of these gene sets supported a cytotoxic environment with exhaustion in TMEscore-high tumors. Collectively, the stratification of patients with TMEscore based on transcriptional profiling may differentiate between tumors with different immune evasion mechanisms and different immunotherapy responses. Immune checkpoint blockade therapy, such as inhibitors targeting the PD1–PDL1 axis, shows promising prospects for cancer treatment. We subsequently explored the prognostic value of the TMEscore in patients who received immune-checkpoint therapy. As there is currently no available cohort with both transcriptome and survival information for immune therapy in pancreatic cancer, we used two well-known solid tumor cohorts receiving immune checkpoint blockade therapy. As shown in Figure 6G–J, patients with high TMEscores had significantly longer overall survival than those with low TMEscores in both the GSE78220 [53] cohort (anti-PD1) and IMvigor210 [54] cohort (anti-PDL1). In line with survival analysis, patients with a high TMEscore also showed an increased response rate to both anti-PD-1 (GSE78220) and anti-PD-L1 (IMvigor210) antibody treatment. Taken together, our data suggest that TMEscore could predict the response to checkpoint inhibitor immunotherapy. To further explore potential drug therapeutic targets for PDAC, we screened the aforementioned core DEGs: GSTP1, ERBB2, MUC1, F2RL1, PTGS2, CCND1, and CXCL8, and verified the expression between BxPC-3 and PANC-1 cell lines. The results showed that the expression of F2RL1 was significantly high (Figure 7A,B). Currently, the mechanism of F2RL1 in the malignant progression of pancreatic cancer remains unclear, and more scientific evidence is needed. TCGA and Genotype-Tissue Expression (GTEx) database analysis showed that F2RL1 was highly expressed in PDAC tumor tissues compared with non-tumor tissues (NAT) (Figure 7C). Then, univariate and multivariate Cox analyses revealed that F2RL1 was an independent influence factor on the overall survival (OS) and disease-free survival (DFS) of PDAC patients (Figure 7D, Supplementary Tables S11 and S12). Importantly, Kaplan–Meier analysis showed that PDAC patients with high F2RL1 expression had shorter OS and DFS, suggesting that F2RL1 is associated with the malignant progression of PDAC (Figure 7E,F). Notably, further ssGSEA correlation analysis showed that the expression of F2RL1 was related to some immune cells in TME (Figure 7G). Further, we used the TISCH platform (http://tisch.comp-genomics.org (accessed on 2 January 2023)) to analyze the expression of F2RL1 at the cellular level. The results showed that F2RL1 was mainly distributed in malignant cell subsets (Figure 7H–J). Moreover, through functional annotation analysis of malignant cells, the enrichment results of the KEGG pathway and GO biological process showed that F2RL1 was mainly related to extracellular matrix composition, signaling pathways, cell adhesion, and other mechanisms (Figure 7K,L). The above results suggested that the upregulation of F2RL1 could promote the malignant progression of PDAC. Given that F2RL1 was associated with a poor prognosis for PDAC, we further explored its biological function. We analyzed the expression of F2RL1 in PDAC cell lines, and the results showed that the expression of F2RL1 was significantly high in PANC-1 and BxPC-3 (Figure 8A). Further, we verified the transfection efficiency of F2RL1 in BxPC-3 and PANC-1 cell lines by knockdown and overexpression of F2RL1 (Figure 8B–E). The colony formation assay (Figure 8F–H, Supplementary Figure S5A–C) and EdU assay (Figure 8I–K, Supplementary Figure S5D–F) showed that the proliferation ability of cells, compared with the control group, was decreased after F2RL1 expression was down-regulated. The overexpression of F2RL1 showed the opposite effect. These results suggested that the upregulation of F2RL1 can promote the proliferation of PDAC cells. The transwell assay (Figure 8O–Q, Supplementary Figure S5J–L) and the wound healing assay (Figure 8L–N, Supplementary Figure S5G–I) showed that, compared with the control group, the cell invasion ability was weakened after F2RL1 was down-expressed, while the effect was opposite after F2RL1 was over-expressed. Therefore, these results indicated that the overexpression of F2RL1 could promote the proliferation and invasion of PDAC cells in vitro. Further, we verified the carcinogenic function of F2RL1 in a subcutaneous tumorigenicity mouse model (Figure 9A,B). Animal experiments showed that, compared with the sh-NC group (n = 5), the tumor volume and weight in the sh-F2RL1#1 group were lower (Figure 9C,D). Remarkably, IHC staining showed lower levels of Ki-67 expression in stable knockdown F2RL1 tissues (Figure 9E,F). Therefore, the downregulation of F2RL1 can strikingly inhibit the proliferation of PDAC in vivo. Collectively, F2RL1 may be a potential biomarker for predicting survival outcomes in patients with PDAC. It is well acknowledged that the TME is of vital importance in cancer progression and therapeutic responses [55]. In this context, we evaluated the infiltration pattern of TME cells through the computational integration of their signature genes and developed a TMEscore that robustly predicts the prognosis for PDAC patients. Through comparison with the established PDAC molecular subtypes, we found the TMEscore differed across multiple established PDAC subtypes. Overall, tumors with a high TMEscore tend to share transcriptional commonalities with ADEX/Exocrine-like subtypes, which were defined by the transcriptional expression of multiple genes associated with terminally differentiated pancreatic tissues [30]. Instead, tumors with a low TMEscore were enriched with squamous/classical subtypes which reflect the molecular characteristics of squamous tumors across multiple tissue types, such as hypoxia response, metabolic reprogramming, and TGF-β signaling [30]. The close relationship between the TMEscore and tumor essential character-based subtypes suggested a correlation between TME and tumor cell-intrinsic properties. On the other hand, as expected, TMEscore-low tumors showed a subtype of “activated stroma” [29], while tumors with a high TMEscore displayed a “normal stroma” subtype [29]. Beyond that, TME high-score tumors were enriched for the immune gene programs (GP6 and GP8) from Bailey and colleagues [30]. These two gene programs were associated with B cells and CD8+ T cell infiltration signatures and T cell co-inhibitory phenotypes, respectively [30]. Therefore, the TMEscore is a comprehensive index reflecting tumor intrinsic features, stromal states, and immunophenotype. PDAC is strikingly resistant to traditional treatment [56,57,58]. Immune checkpoint inhibitors, represented by PD-1/PD-L1 blockers, are widely believed to be a promising modality in pancreatic cancer, but the high prevalence of immunotherapy resistance of PDAC remains a main obstacle [59]. Effective identification in PDAC patients with potential benefits from immunotherapy could facilitate the translation of immunotherapy agents from preclinical research to clinical trials or applications. However, the biomarker for the efficiency of immunotherapy is thus far lacking [60]. A strong relationship between the tumor mutational burden (TMB) and the activity of immune checkpoint inhibitor (ICI) therapies has been identified across multiple cancers, but not pancreatic cancer [61]. MMR-D pancreatic cancer has been reported to respond to checkpoint inhibitor therapy, but it occurs in less than 1% of PDAC patients [62,63]. The MSI-H/dMMR phenotype is also very rare in PDAC [62]. The essential role of TME in immunosuppressants has been well known, and the impact of TME on the immune behavior of tumors has become a research focus in the immunotherapy of PDAC [9,64,65,66]. Therefore, personalized immunotherapy for solid tumors can be realized based on the characteristics of TME cell components. With the help of several computational algorithms, the TMEscore established in this study could represent the landscape of the whole infiltration. Notably, in the context of the lack of immunotherapy in the PDAC cohort, we also determined that a high TMEscore is associated with increased response to anti-PD-L1/anti-PD-1 agents and improved survival time in two cohorts of patients with other solid tumors. Therefore, our present TMEscore might help predict the response to immune checkpoint inhibitors and thus promote the precision immunotherapy of PDAC. The predictive value of our TMEscore was supported by some facts. The finding that cytolytic activity in PDA did not correlate with TMB or neoantigen load reveals the distinct difference between pancreatic and others in antitumor immunity [50]. It seems that the immune privilege of pancreatic cancer depends much more on intrinsic oncogenic processes than that of other cancers [50]. Several intrinsic oncogenic mechanisms responsible for the immunosuppression in pancreatic cancer have been identified, including the Kras mutation [67,68], the CDKN2A mutation [69], the TGF-beta within the TME [70], the activation of WNT/beta-catenin signaling [71], PTEN loss, and PI3K–AKT pathway activation [72,73,74,75,76], which were all highly enriched in TMEscore-low tumors. The essential role of TME as an immunosuppressant has been well known, and the heterogeneous TME shaped by infiltrated cells might illustrate the underlying mechanisms of altered response to immunotherapy. Importantly, a low TMEscore may indicate the presence of stubborn intrinsic immunosuppression. Taken together, different TME cell components are reflected according to different immune infiltration subtypes, which has significant clinical value for further guiding the refinement of immunotherapy. Despite the tumor-intrinsic acquired resistance, tumor-extrinsic acquired resistance also contributes to immunosuppression and resistance to immunotherapy [77,78]. The tumor-extrinsic immune microenvironment can be mostly reflected by the evaluation of the degree of T cell-inflamed phenotype. A baseline T cell-inflamed TME phenotype has been demonstrated to correlate with responsiveness to checkpoint blockade therapy and adoptive cell therapy [48]. Multiple T cell-inflamed gene signatures have been established and proven to be correlated with clinical response to PD-1PD-/L1 blockade across a variety of tumor types. Through assessing the T cell-inflamed status of PDAC tumors via the well-established signatures, including T cell-inflamed signatures [79], IFN-related genes [79], effector T cell signatures [80], and cytotoxic genes [81], we found significant enrichment of all signatures in TMEscore-high tumors. In addition, ratios of CD8+ to CD4 and Treg were also elevated in TMEscore-high tumors. All these results support the TMEscore-high tumors have T cell-inflamed phenotypes and vice versa for the TMEscore-low tumors. Despite the T cell-inflamed phenotype, there is other evidence providing support for a negative correlation between resistance to immunotherapy and TMEscore. It is known that NLRP3 signaling drives resistance to anti-PD-1 immunotherapy and is responsible for adaptive immune suppression through promoting the production of IL-1β in pancreatic carcinoma [82]. Our GSEA results indicated an underlying enrichment of NLRP3-related genes in TMEscore-low tumors. Furthermore, the gene sets associated with down-regulated genes in response to the stimulation of IL-2, IL-15, or IL-21 in T cells were negatively enriched. The impaired response to the proliferation stimulus suggested a dysfunctional state of infiltrated cytotoxic cells. The enrichment of multiple inhibitory checkpoint molecules in the group with a high TMEscore was also observed. Collectively, the TMEscore-high tumor has a series of characteristics suitable for immunotherapy. There are some interesting findings that should be noted in our present study. F2R-like Trypsin Receptor 1 (F2RL1), as a G-protein-coupled receptor, can be activated by serine proteases and plays a key role in tumor progression [83]. We have confirmed that the upregulation of F2RL1 in PDAC tumors can significantly promote tumor proliferation, invasion, and migration. Moreover, it has been reported that the enrichment of F2RL1 in the tumor matrix region is increased [84,85], which is also similar to our results, suggesting that it mediates TME matrix remodeling to further drive tumor malignant progression. Therefore, whether there are subsets of cells with F2RL1 as a marker in the TME of PDAC as a bridge or not, the interaction between the tumor and TME remains to be further explored. The exact site structure of receptors and ligands also needs to be clarified. This provides new insights into individualized treatment schemes based on risk stratification based on the TMEscore and targeted therapy for F2RL1. In addition, the STING pathway, a cytosolic DNA sensing pathway, is a proximal event required for optimal type I interferon production, dendritic cell activation, and priming of CD8+ T cells against tumor-associated antigens [86]. STING pathway activation with antigen-presenting cells in the tumor microenvironment leads to the spontaneous generation of antitumor CD8+ T-cell responses [86]. Interestingly, we found the STING pathway was enriched in low TMEscore samples, which was somewhat at odds with that. Low TMEscore was associated with low cytotoxic infiltration and low T cell-inflamed activity, but the high APM score of TMEscore-low samples was in coherence with the enrichment of the STING pathway. The different STING activity and antigen presentation scores between TMEscore high and low samples could potentially be explained by the difference within TME. For example, the normal stromal phenotype was considered associated with the immunosuppressive pancreatic stellate cells, which inhibit the function of dendritic cells [29]. A previous study has reported that combining STING-based agonists with checkpoint modulators could enhance antitumor immunity in murine pancreatic cancer [87]. Therefore, considering the character of the low STING activity, high TME group, the combination of STING pathway agonists with immune checkpoint inhibitors seems to be a promising strategy in these patients. Taken together, the present TMEscore was associated with a variety of molecular hallmarks of immunosuppression and antitumor immunity. The prognostic value of the TMEscore was validated in multiple PDAC cohorts and two cohorts of patients treated with immune checkpoint inhibitors, suggesting the TMEscore has the potential to improve precision immunotherapy. The distinct tumor cell-intrinsic and tumor-extrinsic characteristics among tumors with different TMEscores indicate different treatment strategies according to the TMEscore category. Due to the strong intrinsic immunosuppression mechanisms and low cytotoxic infiltration, patients with a low TMEscore are unlikely to benefit from treatment with checkpoint inhibitors alone, but their response to immunotherapy might be rescued through a combined blockade of other intrinsic suppressive molecules, such as TGF signaling or others. Patients with a high TMEscore might benefit more from the immune checkpoint blockage, and their sensitivity to immunotherapy might be enhanced through the association of chemotherapy and/or a STING agonist in order to promote antigen presentation. In summary, our study systematically analyzed TME-related genes and proposed a novel TMEscore for risk stratification and selection of PDAC patients in clinical practice. In terms of the future perspectives and implications of our study, it could be useful to further validate the predictive value of the TMEscore in prospective cohorts of patients receiving treatments based on immunotherapy. Furthermore, a comprehensive evaluation of TMEscores with other molecular and genetic markers may provide direction for developing new comprehensive treatment strategies.

PMC10000713

Rosa Calvello,Chiara Porro,Dario Domenico Lofrumento,Melania Ruggiero,Maria Antonietta Panaro,Antonia Cianciulli

Decoy Receptors Regulation by Resveratrol in Lipopolysaccharide-Activated Microglia

21-02-2023

microglia,resveratrol,decoy receptors,lipopolysaccharide,cytokines

Resveratrol is a polyphenol that acts as antioxidants do, protecting the body against diseases, such as diabetes, cancer, heart disease, and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s diseases (PD). In the present study, we report that the treatment of activated microglia with resveratrol after prolonged exposure to lipopolysaccharide is not only able to modulate pro-inflammatory responses, but it also up-regulates the expression of decoy receptors, IL-1R2 and ACKR2 (atypical chemokine receptors), also known as negative regulatory receptors, which are able to reduce the functional responses promoting the resolution of inflammation. This result might constitute a hitherto unknown anti-inflammatory mechanism exerted by resveratrol on activated microglia.

Decoy Receptors Regulation by Resveratrol in Lipopolysaccharide-Activated Microglia Resveratrol is a polyphenol that acts as antioxidants do, protecting the body against diseases, such as diabetes, cancer, heart disease, and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s diseases (PD). In the present study, we report that the treatment of activated microglia with resveratrol after prolonged exposure to lipopolysaccharide is not only able to modulate pro-inflammatory responses, but it also up-regulates the expression of decoy receptors, IL-1R2 and ACKR2 (atypical chemokine receptors), also known as negative regulatory receptors, which are able to reduce the functional responses promoting the resolution of inflammation. This result might constitute a hitherto unknown anti-inflammatory mechanism exerted by resveratrol on activated microglia. Resveratrol (3,5,4′-trihydroxy-trans-stilbene), also called polyphenol, is a stilbenoid belonging to the phytoalexin superfamily, mostly found in red grapes, blueberries, raspberries, mulberries, and peanuts [1]. Resveratrol has two isomers with trans and cis configurations. In this regard, the trans-resveratrol is the non-toxic stereoisomer that has been widely described to have beneficial effects on health [2]. Resveratrol is part of a group of compounds that act as antioxidants do, protecting the body against diseases, such as diabetes, cancer, heart disease, ileitis, obesity, and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s diseases (PD) [3,4,5,6]. In this respect, in experimental models of both AD and PD, it has been showed that resveratrol exerts neuroprotective actions; however, its application in therapeutic protocols is limited by its poor bioavailability due to quick metabolization in the intestine and liver [3,4,5,6,7]. Resveratrol is able to cross the blood–brain barrier (BBB) via tight junctions, thus carrying out a protective action in the brain tissue that could reduce the loss of neurons, which arises due to neurodegenerative diseases [4,5,6,7,8]. Many studies carried out in recent years have focused on researching the therapeutic potential for the additional treatment of neurodegenerative diseases of many natural compounds, in particular those extracted from plants [5,6,7,8,9,10]. Among the vast diversity of natural compounds that have been studied for their neuroprotective effects, there are polyphenolic compounds, such as curcumin, capsaicin, epigallocatechin gallate, and resveratrol too [7,8,9,11,12,13]. Apart from having antioxidant and anti-inflammatory actions, resveratrol modulates the intracellular signals involved in neurons survival and inhibits beta-amyloid (Aβ) protein aggregation [10,11,12,13,14]. Consistent with these data, it was reported in a mouse model of Parkinson’s-like disease that a resveratrol treatment protects the dopaminergic (DA) neurons of the Substantia Nigra pars compacta (SNpc) against neurotoxic insult by modulating inflammatory reactions through SOCS-1 activation [11,12,13,14,15]. Decoy receptors are involved in mechanisms of immune evasion adopted by pathogens, including IL-1R2 and atypical chemokine receptors (ACKRs). In IL-1R2, the lack of the intracellular TIR domain makes this receptor unable to initiate signal transduction following binding with IL-1 [12,16]. The main types of ACKRs are ACKR1, ACKR2, ACKR3, and ACKR4 [13,17]. These molecules are able to recognize and bind specific growth factors or inflammatory cytokines efficiently; however, they are structurally incapable of initiating and transducing signals, acting as a molecular trap for the agonist and for signaling receptor components. All of these members, also referred as chemokine-binding proteins, scavengers, receptor antagonists, negative regulatory receptors, anti-inflammatory ligands, and decoys, act as brakes in the functional responses [14,18]. IL-1R2 functions as a negative regulator of several IL-1 family members, as well as of TLRs, thus it is involved in several pathophysiological contexts in which inflammation and innate and adaptive immune responses play a significative role [19]. ACKR2, previously known as D6, through inhibiting inflammation, mediates the resolution of inflammation in various conditions such as infections, autoimmune diseases, cancer, and neurodegenerative conditions [14,15,16,17,18,19,20]. We reported in a previous work that in LPS-activated cells, the pre-treatment of microglia with resveratrol up-regulated the phosphorylation of JAK1 and STAT3, as well as the expression of the suppressor of cytokine signaling (SOCS)3, demonstrating that the JAK-STAT signaling pathway is involved in the anti-inflammatory effect exerted by resveratrol [15,16,17,18,19,20,21]. The aim of the present research was designed to determine the potential anti-inflammatory effects of resveratrol through the regulation of decoy receptor expression, IL-1R2 and ACKR2, on the activated microglia after prolonged exposure to LPS. The results obtained from this study provide, for the first time, evidence of a new anti-inflammatory mechanism exerted by resveratrol on the activated microglia. The murine microglial cell line N13 was grown in RPMI 1640 basal medium enriched with 10% heat-inactivated fetal bovine serum (FBS), 1% L-glutamine (2 mM), and 1% penicillin-streptomycin solution (100 U/mL penicillin; 100 μg/mL streptomycin) (Life Technologies-Invitrogen, Milan, Italy) in a CO2 incubator set to 5% CO2 at 37 °C in a humidified atmosphere until 70% confluence. For the treatments, we used 10 μM resveratrol (trans-3,40, 5-trihydroxystilbene; purity > 99% GC; Sigma Aldrich, St. Louis, MO, USA) and the cell wall component LPS of Salmonella typhimurium at a concentration of 100 ng/mL. N13 cells were submitted to a single treatment with LPS or resveratrol and to a combined treatment with resveratrol, followed up an hour later by LPS (Sigma Aldrich) for 72 h. Cell viability of N13 cells was evaluated by the MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay. The cells were seeded in 96-well multi-well plates at a density of 8 × 103/well to be treated with LPS alone or in presence of resveratrol. The MTT was solubilized in PBS 1X to be added to the wells at a working concentration of 0.5 mg/mL starting from a stock solution of 5 mg/mL. After 4 h of incubation in a CO2 incubator at 37 °C in a humidified atmosphere, the formazan crystals were solubilized in Dimethyl sulfoxide (DMSO) keeping the plates in agitation for 20 min. Since the amount of formazan is directly proportional to the number of viable cells, it is quantified by measuring the optical density at 560 nm and subtracting the background at 670 nm by using a Victor Multiplate Reader (Wallac, Perkin Elmer, Milan, Italy). Cells were harvested, and the total RNA was extracted by using the TRIzol isolation reagent (Invitrogen, Milan, Italy) according to the manufacturer’s instructions. Once isolated, the RNA was reverse transcribed back into cDNA, causing a reaction between 3 μg of total RNA, 40 U of RNase Out (Invitrogen), 40 mU of oligo dT, 0.5 mM dNTP (PCR Nucleotide Mix, Roche Diagnostics, Milan, Italy), and 40 U of Moloney Murine Leukemia Virus Reverse Transcriptase (Roche Diagnostics). The cDNA synthesis was initiated at 37 °C for 59 min and terminated at 95 °C for 5 min to remain at 4 °C. The cDNA was amplified by performing a polymerase chain reaction (PCR) for 30 cycles using a thermal cycler (Eppendorf, Milan, Italy) together with the cDNA of GAPDH, which was used as a reference gene. At the completion of the PCR, TriTrack Loading Dye 6X (Thermo Fisher, Waltham, MA, USA) was added to the amplified samples prior to be loaded onto the agarose gel. The DNA bands were quantified by densitometry with the ImageJ software, and the results were normalized with GAPDH. Primer sequences for the tested genes are reported in Table 1. After 72 h from the treatments, the cells were harvested and lysed with a lysis buffer (1% Triton X-100, 20 mM Tris-HCl, 137 mM NaCl, 10% glycerol, 2 mM EDTA, 1 mM phenylmethylsulfonyl fluoride (PMSF), 20 μM leupeptin hemisulfate salt, and 0.2 U/mL aprotinin) and subjected to many cycles of freezing and thawing to facilitate the lysis. The lysates were obtained by centrifugation at 12,800× g for 20 min at 4 °C, and the proteins were quantified by the Bradford’s protein assay [22,23,24,25,26,27,28,29,30,31,32,33,34,35]. A quantity of 25 μg of proteins from each sample were diluted with a sample buffer (0.5 M Tris-HCl pH 6.8, 10% glycerol, 10% (w/v) SDS, 5% β2-mercaptoethanol, and 0.05% (w/v) bromophenol blue), and then boiled for 3 min. At the end, the samples were loaded onto 4%–12% SDS precast polyacrylamide gels (BioRad Laboratories, Tokyo, Japan) and fractionated in relation to the size by applying a voltage of 200 V. Upon completion of electrophoresis, the resolved proteins were transferred onto ni-trocellulose membranes and blocked with 5% fat-free milk diluted in a solution containing 0.1% (v/v) Tween 20% and PBS to avoid nonspecific binding. After three ten-minute washes with 0.1% Tween 20-PBS (T-PBS), the membranes were incubated overnight at 4 °C with mouse monoclonal antibody (mAb) anti-CD11b (1:200), anti-iNOS (1:200) mAb, anti-COX-1 (1:200) mAb, anti-COX-2 (1:200) mAb, anti-phospho-cPLA2 (1:200), anti-phospho-IkBα (1:200) mAb, anti-IL-1R2 (1:100) mAb, and anti-ACKR2 receptor (1:100) mAb, and mouse polyclonal Ab anti β-actin (all from Santa Cruz Biotechnology, Inc., Milan, Italy) according to the manufacturer’s protocol. Then, the membranes were washed with 0.1% Tween 20-PBS (for 20 min, 3 times) and incubated with specific horseradish peroxidase (HRP)-conjugated secondary antibody anti-mouse (Santa Cruz Biotechnology, Milan, Italy) diluted to 1:10,000 for 1 h in agitation and in the dark. At the end, the protein bands were highlighted by chemiluminescence, and images were acquired using a ChemiDoc Imaging System. The bands were normalized against β-actin, and the results, expressed as means ± SD, were provide as the relative optical density. The sandwich ELISA was performed following the kit manufacturer’s instructions to measure the levels of TNF-α (Cat. #BMS607-3 Thermo Fisher—Invitrogen Technology, Milan, Italy) and IL-1β (Cat. # BMS6002 Thermo Fisher—Invitrogen Technology, Milan, Italy) cytokines in the cell culture supernatants withdrawn after 72 h from the treatments. Since the intensity of the signal is directly proportional to the concentration of the antigen, the concentration was quantified and expressed in pg/mL. The determinations were performed in triplicate. NO, quantified as the NO2− concentration in the cell culture supernatants, was determined by the Griess assay. The supernatants were collected after 72 h from the treatments and centrifuged to remove possible cellular residues. After adding the Griess Reactive (0.1% N-(1-naphthyl) ethylenediamine dihydrochloride and 1% sulfanilamide in 2.5% H3PO4) (1:1 v/v), the samples were incubated in the dark at room temperature for 10 min. At the end, the absorbance was spectrophotometrically measured at 540 nm by using the conditioned medium as a blank to clear the interference of nitrites. The NO2− concentration was calculated by interpolation on a standard curve of sodium nitrite (NaNO2) and is expressed as μmol/mL. To measure the PGE2 in the cell culture supernatants, we performed the PGE2 assay. N13 cells (3 × 106/well) were seeded in 6-well plates, pre-treated with resveratrol for 1 h, and then stimulated with LPS at a concentration of 100 ng/mL. The cultures were maintained at 37 °C for 72 h in a humidified air containing 5% CO2. The PGE2 levels were determined in the supernatant using the competitive binding immunoassay (Cayman Chemical, Ann Arbor, MI, USA) according to the manufacturer’s instructions. Unstimulated cells were included as a control. The optical density was measured at λ = 405–420 nm using a precision microplate reader, and the PGE2 concentration, expressed in ng/mL, was determined by using a PGE2 standard curve. The statistical analysis was carried out with the software package MINITAB Release 14.1 (Minitab Ltd., Coventry, UK). The results were analyzed by the ANOVA one-way followed by the Tukey test, assuming that the p-values ≤ 0.05 were significant. The effect of the pre-treatment with resveratrol on the N13 cells treated with LPS was verified by MTT cell viability test. We used an optimal concentration of LPS (100 ng/mL) and an optimal non-toxic resveratrol concentration (10 μM) selected on the basis of the experiments reported in our previous works [15,16,21,23]. Furthermore, in the experiments of this work, we used prolonged exposure to LPS by treating the N13 cells with LPS for 72 h. The viability of the cells exposed for 72 h to 100 ng/mL LPS was significantly reduced in comparison to that of the untreated cells; however, the pre-treatment with resveratrol was able to significantly increase the cell viability in the cells treated with LPS with respect to that of the cells treated with LPS alone (Figure 1). The pre-treatment with resveratrol of the N13 cells treated with LPS determined the modulation of the expression of the microglial activation marker CD11b both at transcriptional and post-transcriptional levels (Figure 2). In particular, resveratrol is able to determine a significant decrease in the mRNA expression levels of CD11b in the N13 cells treated with LPS in comparison to those observed in cells treated with LPS alone (Figure 2A). The same results were observed for CD11b protein expression. In this context, the treatment with LPS induced a significantly higher increase in the CD11b protein expression levels in the cells treated with LPS in comparison to that of the control cells. In addition, resveratrol showed the ability to significantly decrease the CD11b protein expression levels in the N13 cells treated with LPS compared to that observed in the cells treated with LPS alone (Figure 2B). These results confirm the role of resveratrol as a modulator of microglial activation even in case of prolonged exposure to LPS. To evaluate the effect of resveratrol on NO production in the N13 cells subjected to prolonged exposure to LPS, the levels of NO produced by the microglia treated for 72 h with LPS in the absence and in the presence of resveratrol were tested. The levels of NO released by the untreated cells and those treated with resveratrol alone were low. The treatment of microglia with LPS for 72 h, on the other hand, resulted in a significant increase in NO release compared to that which was observed in the control cells. Conversely, the cells treated with LPS pre-treated with resveratrol showed a significant reduction of NO production in comparison to that of the cells treated with LPS alone (Figure 3A). In addition, to evaluate whether the inhibitory effect of resveratrol on NO production could derive from an action of resveratrol on the inducible isoform of NO synthase (iNOS), the protein expression of iNOS after the different treatments was determined by the Western blot. Again, significantly higher levels of iNOS protein expression were found in the cells treated with LPS alone in comparison to that which was shown by the untreated cells. Similarly, as observed for NO release, the pre-treatment with resveratrol was able to significantly inhibit the expression of iNOS in the microglia submitted to prolonged exposure to LPS (Figure 3B). Pro-inflammatory cytokines production levels were assessed in culture supernatants by ELISA both in the presence and absence of resveratrol. As shown in Figure 4, there was a marked increase in TNF-a and IL-1β production in the microglial cells after 72 h of LPS stimulation. No effect by the treatment with resveratrol alone on the pro-inflammatory cytokine production was observed in the microglial cells. Moreover, we observed that the treatment with 10 μg/mL of resveratrol in the LPS-treated cells significantly down-regulated the production levels of pro-inflammatory cytokines in comparison to those of the N13 cells stimulated with LPS alone, suggesting that resveratrol was able to negatively modulate the production levels of pro-inflammatory cytokines in LPS-activated microglial cells. Cyclooxygenase-2 (COX-2) and phospholipase A2 (cPLA2) participate in eicosanoid production, such as prostaglandin E2 (PGE2), which is implicated in the Arachidonic Acid (AA) pathway and is a key factor in neuroinflammatory and neurodegenerative diseases. Moreover, it is well known that COX-1 could be an important player in neuroinflammation by being predominantly localized in the microglia, and thus, being implicated in the secretion of prostaglandins (PGs) in response to microglia activation [17,24]. For this reason, in microglial cells exposed for a prolonged time to LPS, we have verified the anti-inflammatory ability of resveratrol in terms of the modulation of the of COX-1, COX-2, and p-cPLA2 protein expression. In addition, the evaluation of COX activity by the quantification of the PGE2 production by the enzymatic conversion of AA has been widely used and is well accepted as a method to evaluate potential COX inhibitors [18,25]. Therefore, we also verified the inhibitory action of resveratrol on the release of PGE2 in N13 cells treated with LPS for 72 h. From our results, it appears to be evident that resveratrol is able to determine a significant decrease in the expression levels of COX-1, COX-2, and p-cPLA2 in the cells treated for 72 h with LPS that had undergone a pre-treatment of 1 h with resveratrol in comparison to those of the cells treated with LPS alone (Figure 5A–C). We observed similar results in the PGE2 release assay. Resveratrol, in fact, determined a significant decrease in the release of this inflammatory mediator in the microglia exposed to the prolonged treatment with LPS and pre-treated with resveratrol compared to those subjected to the treatment with LPS alone (Figure 5D). From these results, it is, therefore, possible to highlight that resveratrol, in cases of prolonged inflammation, is able to show an anti-inflammatory effect by inhibiting the AA pathway. In order to evaluate NF-kB activation, we measured the levels of the phosphorylated form of IkBα (p-IkBα), the inhibitory complex of NF-kB, since its phosphorylation is an essential step for NF-kB activation. In this regard, we determined the expression of p-IkB in cell lysates obtained from LPS-stimulated N13 microglial cells. In this context, we observed that the LPS treatment for 72 h significantly increased the expression level of phosphorylated IkB-α protein compared to that of the control cells, and the resveratrol pre-treatment significantly prevented this increase, as revealed by the densitometric analysis (Figure 6). These data indicate that resveratrol inhibited NF-kB activity in the LPS-treated N13 cells by suppressing the degradation of IkB-α, and consequentially, relieving the pro-inflammatory mediator’s expression. IL-1R2 is a decoy receptor that causes a block of signal transduction after IL-1 binding. By regulating IL-1R2 expression, cells can modulate inflammation in response to exogenous stimuli. It has been showed that the up-regulation of IL-1R2 in microglial cells and brain endothelial cells attenuates CNS inflammation [12,16]. ACKR2, also known as the D6 decoy receptor, scavenges various inflammatory chemokines, thus affecting the inflammatory microenvironment. In this regard it is thought that the D6 decoy receptor could be a resolving agent in the neuroinflammatory processes because of its capacity to scavenge chemokines, leading to the alleviation of inflammation in different situations, including neuroinflammatory-based neurological disorders [20]. Therefore, in our study, we verified the ability of resveratrol to modulate the expression of decoy receptor IL-1R2 and decoy receptor ACKR2 both in terms of mRNA and protein expression. The analysis of mRNA expression for both the decoy IL1-R2 receptor and the decoy ACKR2 receptor showed a significantly reduced expression of both these receptors in the microglial cells subjected to prolonged exposure to LPS in comparison to that of those cells treated with Resveratrol alone. Interestingly, in the cells exposed to LPS but pre-treated with resveratrol, there was a drastic and highly significant increase in mRNA expression for both of the decoy receptors studied in comparison to that of the cells treated with LPS alone (Figure 7A,B). These results were confirmed by the Western blotting analysis on IL1-R2 and ACKR2 protein expression. Additionally, in this case, resveratrol was able to cause a significant increase in the protein expression of both IL1-R2 and ACKR2 decoy receptors in the cells treated for 72 h with LPS that received a pre-treatment of 1 h with resveratrol in comparison to that of those cells treated with LPS alone (Figure 7C,D). All together, these results certainly confirm the already known anti-inflammatory effect that resveratrol elicits on microglial cells in case of neuroinflammation. At the same time, however, these experiments demonstrate, for the first time, the ability of resveratrol to modulate the expression of IL1-R2 and ACKR2 decoy receptors, which could represent a new potential therapeutic target especially in cases of the prolonged inflammation of the CNS. Based on the previous results evidencing that the resveratrol treatment on the LPS activated microglia responses exerts both an inhibition of pro-inflammatory mechanisms and an induction of anti-inflammatory responses [15,16,21,23], we aimed, in this study, to expand our knowledge regarding the other possible effects of this polyphenolic compound on the inflammatory responses of microglia submitted to a prolonged LPS treatment. Here, we demonstrated that resveratrol, without affecting the viability of these cells, is able to specifically interfere with the pro-inflammatory responses induced by LPS in terms of both the decreased production of IL-1β and the increased production of the IL-1β decoy receptor. IL-1β, a member of the IL-1 family, is a potent pro-inflammatory cytokine in the acute and chronic phases of inflammation, therefore, the reduced production of IL-1β after 72 h of incubation in resveratrol-treated cells demonstrates that this polyphenol could limit the amplification phase of inflammation. To analyze whether the resveratrol-treated microglia display a reduced ability to react to pro-inflammatory stimuli, we also investigated the response of the cells to LPS in terms of NO and of TNF-a release. In this regard, we detected that after 72 h of treatment, resveratrol was able to significantly reduce the production of both of these mediators. Moreover, we also demonstrated that after a prolonged incubation of microglia cells to LPS, the resveratrol treatment was able to counteract the pro-inflammatory processes down-regulating the IkB degradation, which resulted significantly reduced in comparison to that of the cells treated with LPS alone. NF-kB is considered to be the most important transcription factor involved in the inflammatory responses, thereby in the regulation of NO, TNF-α, and IL-1β [19,20,26,27]. Previously published papers have reported in other cell types [16,23,28,29] that resveratrol significantly inhibited the degradation of IκBα in microglia stimulated with LPS, as well as the subsequent iNOS expression and production of TNF-α, suggesting that resveratrol can modulate the signaling pathways triggered by pro-inflammatory stimuli, such as LPS. However, in the present study, we observed that this action of resveratrol on the production of TNF-α and the degradation of IκB-α is also evident after a more prolonged incubation time, evidencing how this compound is effective at modulating the inflammatory responses protracted over time and not only in the acute ones. In addition, we also demonstrated that the resveratrol treatment determined a significant reduction of COX-1, COX-2, and p-cPLA2, which are all mediators of pro-inflammatory responses. Cyclooxygenase exists as COX-1 and COX-2 distinct isoforms [23,24,30,31] and converts arachidonic acid (AA) released by PLA2 acting at the sn-2 position of membrane phospholipids into prostaglandins and other lipid mediators. Both isoforms are important pro-inflammatory enzyme, whose abnormal expression is a significant marker of neuroinflammation, as previously reported [24,31]. Moreover, AA plays also a key role in inflammation and neurodegenerative disorders [25,32]. In mammalians, there are the three major classes of PLA2s, secretory, calcium-independent, and calcium-dependent ones: among them, the calcium-dependent cytosolic PLA2α (cPLA2α) has received the most attention because the cPLA2-AA-COX-2 pathway is an important signaling pathway in different inflammatory paradigms and neurodegeneration [26,33]. In this regard, it has been demonstrated that the oxidative responses observed in many types of brain damage are associated with increased COX activity [27,34]. Moreover, it was reported that a treatment with COX inhibitors may significantly reduce in neuronal and microglial cell LPS- and IL-1β-induced oxidative damage [28,35]. The results of our study are in accordance with ones showing that in mouse microglial cells, the reduction of COX-2 expression observed after a resveratrol treatment could be determined by the inhibition of NF-κB activation [29,36]. Therefore, our data evidence that NF-κB pathway inhibition through the targeting of IκB phosphorylation by resveratrol ultimately may reduce a pro-inflammatory phenotype, thereby down-regulating different mediators, including COX-1, COX-2, and p-cPLA2. One aspect that is particularly important emerging from our study was the ability of resveratrol to modulate the expression of the so-called decoy receptors, such as IL-1R2 and ACKR2. IL-1R2, first identified on monocytes, neutrophils, dendritic and B cells, in both human and mice, has been reported to be largely involved in driving myeloid cells polarization, and consequently, orientating the immune response. In fact, anti-inflammatory M2 stimuli, such as IL-4, IL-13, IL-10, IL-27, and aspirin, lead to the up-regulation of IL-1R2 expression, whereas the M1 phenotype activated by pro-inflammatory molecules (such as LPS, IFNγ, and TNF-α) exhibits a down-regulation of IL-1R2 [12,16]. The modulation of IL-1R2 expression has been reported in many cell types as a way to counterbalance and limit sustained inflammation in response to exogenous stimuli. In this regard, IL-1R2 up-regulation in the microglia and brain endothelial cells reduced the brain inflammation in experimental models of IL-1β-induced neurotoxicity, as previously reported [30,31,32,37,38,39]. ACKRs are a group (four in humans) of proteins with a high degree of homology with chemokine receptors. ACKRs are chemotactic receptors; however, since they are devoid of the structural domains required to activate canonical G protein-dependent receptor signaling and chemotactic functions, they do not transduce signals through G proteins and lack chemotactic activity [33,40]. Consequently, ACKRs fail to initiate classical signaling pathways after ligand binding, playing a crucial role as regulatory components of chemokine networks in many physiological and pathological processes. Interestingly, the resveratrol treatment enhanced the expression of the anti-inflammatory IL-1β decoy receptor IL-1R2 and increased the expression of the other decoy receptor, ACKR2. IL-1R2 is the decoy receptor for IL-1; when IL-1R2 binds to IL-1β, signal transduction cannot be triggered, and consequently, the pro-inflammatory action of this cytokine is neutralized [34,41]. Therefore, the increased expression of IL-1R2 on the microglia surface indicates a reduced responsiveness of these cells to IL-1β stimulation, significantly dampening the pro-inflammatory profile. Moreover, IL-1R2 also exists in soluble form that can be rapidly shed, so the increased release of the soluble form by IL-1R2-overexpressing cells could neutralize the action of IL-1β on other cells, thus reducing the extent of the pro-inflammatory responses. The results of our pioneering work describe, for the first time, that the resveratrol treatment of the microglia exposed to a prolonged pro-inflammatory stimulus is able to counterbalance inflammatory responses through the regulation of decoy receptors. These findings suggest that the naturally occurring polyphenol resveratrol ability to drive microglial activation, thus regulating the inflammatory response, may help to explain its neuroprotective effects in several in vivo models of neuroinflammation. The results of the present in vitro study suggest that polyphenolic compounds, such as resveratrol, may be useful in the treatment of inflammation associated with neurodegeneration and that clinical studies may evaluate the possibility of their use as a therapeutic support strategy. The results of this study highlight the direct effects of resveratrol in the regulation of functional in vitro responses by microglial cells, therefore it would be of considerable importance to investigate the effect of this polyphenol in vivo for future clinical use also in nano-formulations or intranasal spray applications, for example, in order to overcome the bioavailability problems linked to the BBB or metabolism of endothelial cells. In light of these results, we plan to carry out further studies to clarify the modulation mechanisms of the decoy receptors underlying the neuroprotective effects of polyphenols.

PMC10000725

Kristin E. Cox,Shanglei Liu,Thinzar M. Lwin,Robert M. Hoffman,Surinder K. Batra,Michael Bouvet

The Mucin Family of Proteins: Candidates as Potential Biomarkers for Colon Cancer

27-02-2023

mucins,colorectal cancer,adenocarcinoma,mucinous carcinoma,hyperplastic polyps,adenomatous polyps,adenoma,serrated polyps,prognostics

Simple Summary Colorectal cancer is the second leading cause of cancer-related deaths in the United States with an overall 5-year survival of 65%. While there have been many advances in the treatment of this disease over the past few decades, there has been minimal change in the overall five-year survival in the past twenty years. Thus, there is still a need for the improved detection and treatment of this malignancy that affects many patients. Mucins are a family of glycoproteins (MUC1–MUC24) expressed by many epithelial tissues and some have been implicated in the progression of various malignancies. Mucins have diverse expression profiles amongst pre-malignant, malignant, and normal colonic tissues. This review article focuses on mucin expression profiles in normal and malignant colonic tissue as well as mucins’ role in diagnostics, therapeutics, and prognostication. Abstract Mucins (MUC1–MUC24) are a family of glycoproteins involved in cell signaling and barrier protection. They have been implicated in the progression of numerous malignancies including gastric, pancreatic, ovarian, breast, and lung cancer. Mucins have also been extensively studied with respect to colorectal cancer. They have been found to have diverse expression profiles amongst the normal colon, benign hyperplastic polyps, pre-malignant polyps, and colon cancers. Those expressed in the normal colon include MUC2, MUC3, MUC4, MUC11, MUC12, MUC13, MUC15 (at low levels), and MUC21. Whereas MUC5, MUC6, MUC16, and MUC20 are absent from the normal colon and are expressed in colorectal cancers. MUC1, MUC2, MUC4, MUC5AC, and MUC6 are currently the most widely covered in the literature regarding their role in the progression from normal colonic tissue to cancer.

The Mucin Family of Proteins: Candidates as Potential Biomarkers for Colon Cancer Colorectal cancer is the second leading cause of cancer-related deaths in the United States with an overall 5-year survival of 65%. While there have been many advances in the treatment of this disease over the past few decades, there has been minimal change in the overall five-year survival in the past twenty years. Thus, there is still a need for the improved detection and treatment of this malignancy that affects many patients. Mucins are a family of glycoproteins (MUC1–MUC24) expressed by many epithelial tissues and some have been implicated in the progression of various malignancies. Mucins have diverse expression profiles amongst pre-malignant, malignant, and normal colonic tissues. This review article focuses on mucin expression profiles in normal and malignant colonic tissue as well as mucins’ role in diagnostics, therapeutics, and prognostication. Mucins (MUC1–MUC24) are a family of glycoproteins involved in cell signaling and barrier protection. They have been implicated in the progression of numerous malignancies including gastric, pancreatic, ovarian, breast, and lung cancer. Mucins have also been extensively studied with respect to colorectal cancer. They have been found to have diverse expression profiles amongst the normal colon, benign hyperplastic polyps, pre-malignant polyps, and colon cancers. Those expressed in the normal colon include MUC2, MUC3, MUC4, MUC11, MUC12, MUC13, MUC15 (at low levels), and MUC21. Whereas MUC5, MUC6, MUC16, and MUC20 are absent from the normal colon and are expressed in colorectal cancers. MUC1, MUC2, MUC4, MUC5AC, and MUC6 are currently the most widely covered in the literature regarding their role in the progression from normal colonic tissue to cancer. Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States [1]. When detected early (T1), 5-year survival rates can be as high as 91%. Unfortunately, despite screening efforts, many colon cancers are still detected at advanced stages, making the overall 5-year survival 65% [2]. A series of well-defined malignant transformations has been described for the progression to CRC. These include the tubular adenoma (TA) and sessile serrated adenoma (SSA) pathways that form precancerous polyps and eventually progress to CRC [3,4,5,6,7]. TAs account for 65–70% of CRCs, while SSAs account for 15–30% of CRCs [8]. A hallmark of CRC is the inactivation of tumor suppressor genes such as adenomatous polyposis coli (APC), p53, and KRAS. Most CRCs (70–80%) possess a mutation in the APC gene, including both sporadic and germline mutations [9]. Mucins are a family of high-molecular-weight glycoproteins primarily synthesized by epithelial cells [10]. Mucins are characterized by tandem repeat structures and a high proportion of proline, threonine, and serine (the PTS domain) [11]. The human family of mucins consists of 24 members (MUC1 to MUC24), which can be subclassified into transmembrane or secreted mucins. Transmembrane mucins include MUC1, MUC3A/B, MUC4, MUC11-13, MUC15-17, MUC20, and MUC21. Secreted mucins include the gel-forming MUC2, MUC5AC/B, MUC6, and MUC19, and the non-gel-forming MUC7 [12,13] (Table 1). The molecular mechanisms and signaling pathways of mucins are diverse. Transmembrane mucins contain EGF domains that allow them to participate in signal transduction [14]. The involved signaling pathways include MUC1 and TAK1, MUC1/MUC4 and WNT/β-catenin, MUC13 and ERK, and MUC16 and JAK/STAT [15,16,17]. They also play important roles in forming protective barriers, antigen presentation, and the production of antimicrobial peptides [14,18]. Mucins have been implicated in chronic inflammatory states and the promotion of oncogenesis in numerous malignancies including breast, lung, gastric, biliary, pancreatic, colorectal, and ovarian cancers. A large body of work has been generated since the first international meeting on “carcinoma-associated mucins” that was held in San Francisco in 1990 [19]. It is hypothesized that the abnormal expression of mucins disrupts cell–cell adhesions, thus facilitating tumor invasion [20]. For this review, we focus on mucins’ role in colorectal cancers, exploring the research behind their role in promoting tumorigenesis and how they can be used for diagnosis, prognostication, and potential treatment. PubMed and Google Scholar were searched for publications on human mucins related to both normal colonic tissue and colon cancers published through January 2023. Inclusion criteria were as follows: (1) research concerning the investigation of human mucin proteins in colorectal cancer or their expression in the normal colon, (2) research with non-retracted findings, and (3) research accessible by the University of California, San Diego (UCSD) library. Exclusion criteria were as follows: (1) abstracts without published manuscripts available for review and (2) publications not available in English. For each mucin gene, the phrases “MUC#” OR “mucin#” AND “colon” OR “colorectal” were used as search terms (e.g., MUC3 colon). This returned 1356 entries, and each abstract was screened for possible inclusion; thus, 346 papers remained and were examined further. Upon reviewing their citations, an additional 9 papers were identified and a total of 129 papers were included in this review. MUC1 is a large and highly glycosylated transmembrane mucin that was originally termed milk mucin, given its high expression in mammary glands [21,22,23]. It is known to have minimal or absent expression within normal colonic tissues (reported up to 10%), while it is upregulated in 54.5–100% of colorectal cancers (CRCs) [24,25,26,27,28,29,30,31]. In a 1994 study, Nakamori et al. showed that MUC1 expression increased with the advanced stage of the disease and when a tumor had metastasized [27]. Of the patients with Dukes stage C or D, 33.3% (7 of 21) had MUC1 levels five times that of normal tissue according to Western blotting, while 91.6% (11 of 12) of patients with Duke’s stage A or B had MUC1 expression levels less than two times that of normal tissue. Wang et al. observed MUC1 expression in 34.5% (9 of 26) of patients’ tumors without lymph node metastasis, while MUC1 expression was seen in 84.2% (16 of 19) of patients’ tumors with lymph node metastasis [28]. Within the signet ring subtype of CRC, 42% (n = 12) of cases were found to express MUC1 [32]. Baeckström et al. showed that the CRC cell lines Colo205 and SW1116 express MUC1, while LoVo did not express MUC1 [33]. Additionally, Devine et al. reported MUC1 to be expressed by HT29 [34]. Conflicting data have been reported on LS174T, with a slightly higher proportion of groups reporting negative MUC1 expression [33,34,35,36,37]. Mukherejee et al. demonstrated the ability of a MUC1 vaccine to prevent tumor growth in mice [38]. In the mice treated with a combination of MUC1 vaccine, granulocyte macrophage colony-stimulating factor (GM-CSF), and CpG motifs 7 days prior to injection with the CRC cell line MC38, all eight mice failed to grow tumors. When rechallenged with tumor cells two months later, the tumors again failed to grow. Suprunuik et al. studied the synergistic effects of platinum-based chemotherapeutics and anti-MUC1 antibodies in mouse models of colon cancer [39]. Using two human colon cancer cell lines, they studied the rates of apoptosis and changes in both mRNA and protein expression after treatment with each drug alone or in combination. The rate of apoptosis, as noted by Annexin V and Propidium iodine staining, increased from 13.7% to 30% when PtPz6 (a pyrazole-platinum complex) was combined with an anti-MUC1 monoclonal antibody. They further showed that the mRNA of the pro-survival proteins Bcl-xL and Bcl-2 was suppressed after treatment, while the pro-apoptotic factors Bax, Bad, Bim, and Bid were increased. One conflicting finding, however, was that while Bcl-xL mRNA was suppressed, protein expression was increased. In 2006, Loveland et al. published a phase 1 clinical trial that utilized autologous dendritic cells treated with mannin-MUC1 fusion protein in ten patients with adenocarcinomas that were either stage IV or had progressed during prior therapy. They included patients with colorectal, esophageal, lung, ovarian, fallopian, breast, and renal cell cancers. They showed that this treatment elicited an IFNγ-mediated T-cell response in all patients, among which a patient with CRC had stable disease for 7 months, and two patients (breast and renal CA) were able to achieve 3-year remission with subsequent dendritic cell immunotherapy [40]. When the study progressed to phase 2 testing among ovarian cancer patients, a statistically significant improvement in progression free survival and overall survival was seen in the subset that had required second-line therapies to achieve remission [41,42]. Karanikas et al. attempted direct immunization with a mannin-MUC1 fusion protein in patients with primarily breast or colorectal cancer, though only 20% demonstrated cellular immunity following vaccination [43,44]. More recently, a phase 1 trial of an anti-MUC1 monoclonal antibody was completed in 2013 [45]. Multiple MUC1-expressing tumors were analyzed in the study, including colon (33.5%), ovarian (27%), breast (9.5%), non-small cell lung cancer (9.5%), and pancreatic cancer (6.8%). Unfortunately, when the tested antibody, Gatipotuzumab, was taken to phase 2 testing in ovarian cancer, no benefit was seen compared to the placebo [46]. A phase 2 trial of another MUC1 vaccine is currently underway to evaluate its ability to decrease recurrence rates in patients with a history of advanced adenoma (those with high-grade dysplasia, villous/tubulovillous features, or tumors larger than 1 cm) [47]. Another therapeutic use of MUC1 was explored by affixing MUC1 aptamers to exosomes containing doxorubicin for selective drug delivery. While in vitro studies did not show an improvement over doxorubicin alone, when implemented in in vivo mouse models, the tumor volume growth rate was significantly reduced. Additionally, all mice survived to 30 days compared to only two of the five mice surviving when treated with doxorubicin containing exosomes alone, while none of the control mice survived [48]. Li et al. performed a meta-analysis of 16 studies and found that high MUC1 expression was associated with worse overall survival (HR 1.51) (95% CI 1.30–1.75, p-value < 0.00001). Additionally, high MUC1 expression was associated with a higher stage (RR 1.44), depth of invasion (RR 1.30), and lymph node metastasis (RR 1.47) [49]. MUC2 is part of the secreted and gel-forming subset within the mucin family that is synthesized and secreted by goblet cells [50]. The intestinal epithelium is covered by a thick layer of mucus for protection, of which MUC2 is a major component [51]. MUC2 has been shown to be expressed in normal colonic tissue, while its decreased expression is associated with non-mucinous colon adenocarcinomas [52,53,54,55,56] (Figure 1). Although decreased MUC2 expression is associated with colorectal adenocarcinoma, its expression is always maintained in mucinous carcinomas [57]. A large study of 702 patients conducted by Walsh et al. revealed that 33% of the analyzed tumors expressed MUC2 [58]. Bu et al. found similar results, with MUC2 expression seen in 46.2% of colorectal adenocarcinoma (n = 26), 100% of mucinous carcinoma (n = 15), and 87.5% of signet-ring cell carcinoma (n = 8) [59]. MUC2 expression is maintained in hyperplastic polyps, sessile serrated polyps, and traditional serrated adenomas [60]. This pattern is not surprising, as normal colonic tissue expresses MUC2; therefore, these polyps have not yet lost their expression the way some non-mucinous adenocarcinomas have. Using multivariate analysis, Krishn et al. were able to demonstrate that the loss of MUC2 was a significant predictor of adenoma/adenocarcinoma vs. hyperplastic polyps [61]. In mice deficient in Muc2, colonic inflammation and superficial erosions are seen that mimic ulcerative colitis as early as 5 weeks old [62]. When allowed to survive for 6 months, they develop adenomas; at 1 year, the majority have progressed to adenocarcinomas [63]. Cecchini et al. evaluated multiple biomarkers’ ability to predict prognosis in stage II colon cancer [64]. They focused on stage II, as prior studies noted variable prognosis within this stage and sought to identify a subgroup for which a survival benefit might be found for adjuvant chemotherapy. They evaluated 210 cases of stage II colon cancer and found that the complete loss of MUC2 expression resulted in a hazard ratio of 3.32 (95% CI 1.20–9.20). Low levels of MUC2 expression have also been shown to correlate with lymph node metastasis [56]. Wang et al. evaluated low-MUC2- and high-MUC2-expressing colon tumors and found that 52.8% (38 of 72) of low-MUC2-expressing tumors had lymph node metastasis compared to 35.8% (24 of 67) of high-MUC2-expressing tumors (p-value < 0.05). Additionally, they showed a significantly reduced 5-year survival for low-MUC2-expressing tumors of 40.2% compared to 73.9% for high-MUC2-expressing tumors [56]. In a meta-analysis, Li et al. found low MUC2 expression corresponded to a worse overall survival with an HR of 1.67 [65]. MUC3, part of the membrane-bound subset within the mucin family, has two discrete yet very similar MUC3 genes (MUC3A and MUC3B) [66,67]. In addition to MUC1, MUC2, and MUC4, MUC3 is expressed in normal colonic tissue [52,68,69]. Throughout the body, the highest expression of MUC3 is seen in the duodenum [70]. Williams et al. evaluated ten CRC cell lines (Caco-2, LIM1215, LIM1899, HCT116, SW116, LoVo, LS174T, KM12SM, LISP-1, and SW620) and found that all lines except SW620 expressed MUC3 according to RT-PCR data [71]. However, when Gum et al. performed similar experiments, LS174T only expressed MUC3 (by RNA blot) when cultured in the presence of Butyrate (the main energy source of intestinal cells) [70]. When comparing the MUC3 expression levels in colon cancers to normal tissue, lower levels were seen in CRCs via immunohistochemistry and in situ hybridization [55]. MUC4, a transmembrane mucin protein, is normally expressed in respiratory and colonic epithelial cells [68,70,72]. Abnormal expression of MUC4 has been implicated in breast [73], ovarian [74], lung [75], gallbladder [76], and biliary malignancies [77]. Regarding CRC, there have been conflicting reports as to whether MUC4 is overexpressed or lost. Shanmugam et al. characterized 132 CRCs and found that 25% (n = 33) had high MUC4 expression while 6% (n = 8) had a complete loss of MUC4 expression [78] (Figure 2). However, Krishn et al. reported undetectable levels of MUC4 in 63% of colorectal adenocarcinomas, a large difference from the 6% loss of MUC4 that Shanmugam et al. reported, though only 16 samples were evaluated [61]. Krishn et al. evaluated 10 hyperplastic polyps and 30 adenomatous polyps, in which the majority were found to have lower expression of MUC4 compared to normal tissue, and only 13% of adenomas had strong expression [61]. In a study by Biemer-Hüttmann et al., hyperplastic polyps showed a reduction in MUC4 expression, with 6 of 12 completely negative for MUC4 and 4 showing reduced staining patterns. No change in the expression of tubular adenomas was seen compared to normal tissue, while serrated adenomas showed a complete loss of MUC4 [79]. In Muc4 knockout mouse studies (Muc4-/-), Muc2 mRNA was significantly increased compared to wild-type (WT) mice. When inflammation was induced with dextran sodium sulfate (DDS), Muc3 was also found to increase. Following DDS treatment, these Muc4-/- mice had improved survival compared to the wild type. Interestingly, both the Muc4-/- and WT female mice had improved survival compared to their male counterparts (survival at 21 days: Muc4-/- female 100%, WT female 30%, Muc4-/- male 10%, and WT male 0%) [80]. In orthotopic mouse models of CRC, MUC4 conjugated to a near-infrared dye (IR800) effectively targeted and labeled primary colorectal tumors and liver metastasis (Figure 3). Both a cancer cell line (LS174T) and a patient-derived tumor were used in these experiments. In vivo imaging was performed, and tumor-to-background ratios (TBR) of ~2 were observed for primary tumors, while TBRs of 1.56 were observed for liver metastasis [81]. MUC4 mutation has been shown to be an independent predictor of survival among CRC patients. Peng et al. evaluated the tumor mutational burden of over seven hundred samples from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) and found 17 genes that were commonly mutated amongst both groups (APC, TP53, TNN, KRAS, MUC16, MUC4, SYNE1, FLG, FAT4, OBSCN, FAT3, RYR2, PIK3CA, FBXW7, DNAH11, MUC5B, and ZFHX4). MUC4 mutation was the only gene associated with a significantly worse survival compared to wild type MUC4 for the patients in the TCGA (p-value of 0.009) [82]. Shanmugam et al. reported shorter disease-free survival for high-MUC4-expressing CRCs, with a hazard ratio of 2.07 (95% CI 1.14–3.75, p-value of 0.017). This shortened survival was most pronounced in early-stage (I and II) CRC with a hazard ratio of 3.77 (95% CI 1.46–9.73, p-value of 0.006) [78]. Additionally, in patients without distant metastasis, two single nucleotide polymorphisms (SNPs) of the MUC4 gene, rs3107764 and rs842225, were associated with differential overall survival and event-free survival [83]. Lu et al. demonstrated that the substitution of GG for CC at this SNP resulted in a reduction in 5-year survival from ~78% to ~42% [83]. MUC5AC and MUC5B are secreted, gel-forming mucin proteins. Both MUC5AC and MUC5B are absent (or have been found to be present at extremely low levels) in normal colonic tissue [52,61,84]. MUC5AC expression has been noted in many CRCs, most often in mucinous carcinomas [53]. A large study of 702 patients in Melbourne revealed that 50% of the tumors expressed MUC5AC, and 53% expressed MUC5B [58]. MUC5AC expression has also been reported in the HT29 and SW620 CRC cell lines [85]. When evaluating histopathologic subtypes of CRCs, Imai et al. found that 30.2% of well-to-moderately differentiated CRCs (n = 63) expressed MUC5AC while 51.6% of poorly differentiated CRCs (n = 91) expressed MUC5AC [86]. Numerous groups have reported varying percentages for MUC5AC expression amongst different subtypes of colonic polyps (Table 2, Figure 4). For hyperplastic polyps, these values have ranged from 11–100%, while traditional and sessile serrated adenomas (TSA and SSA) ranged from 31–43% and 61–100%, respectively [60,84,87]. An interesting subclass that Bartman et al. evaluated was that of adenomas with villous features. Of the 120 colonic polyps they evaluated, which ranged from 0.5 cm to greater than 2 cm, adenomas with villous features had a higher percentage of MUC5AC expression at 40.7% (n = 86) compared to that of 24% for tubular (n = 25) [84]. Kim et al. also further examined polyp subgroups, evaluating adenomas with low-grade vs. high-grade dysplasia, though no difference in MUC5AC expression was observed (12% for low-grade vs. 10% for high-grade). However, when evaluating 175 microsatellite-unstable (MSI-H) CRCs, of which 76 were sporadic and 99 were associated with lynch syndrome, MUC5AC-positive tumors were significantly associated with sporadic tumors (54% vs. 27% for lynch syndrome; p-value < 0.001). Another significant difference was observed regarding the location of the tumor. Of the MUC5AC-positive tumors, 46% were proximal compared to 25% that were distal (p-value of 0.005) [87]. MUC5AC has been used for detecting xenografts of human colon cancer lines via MRI. Rossez et al. screened peptides against MUC5AC for positive selection and MUC2 for negative selection, given the latter’s abundant expression in normal colonic tissue. By attaching the best peptide (the peptide with the highest affinity to MUC5AC and lowest or no affinity to MUC2) to small iron oxide particles, they could visualize tumors with MRI. They confirmed its specificity for labeling MUC5AC-producing tumors by using the HT29 cell line as a positive control and HCT116 as a negative control [89]. High-MUC5AC-expressing tumors have been shown to more commonly be poorly differentiated [56,86]. Additionally, Wang et al. demonstrated that these tumors were more likely to have higher rates of lymph node metastasis (p-value < 0.01) and higher tumor stages (p-value < 0.01). The 5-year survival was also significantly reduced for high-MUC5AC-expressing tumors at 29.8% compared to 69.9% for low-MUC5AC-expressing tumors (p-value < 0.001) [56]. While poorly differentiated CRCs have a worse prognosis than well-to-moderately differentiated CRCs, and are more likely to express MUC5AC, a lack of MUC5AC expression amongst poorly differentiated CRCs indicates a worse prognosis. Recurrence-free 5-survival was ~53% vs 20% for high- vs. low-MUC5AC-expressing tumors (p-value of 0.004). Overall survival showed a similar trend, though it was non-significant, with a p-value of 0.1 [86]. MUC6 is a member of the gel-forming secreted subset of mucins. It was originally called gastric mucin when it was first described in 1992 by Toribara et al. due to its high expression within the stomach [90]. MUC6 has minimal to no expression within normal colonic tissue [68,84]. Reports on MUC6 expression within CRCs range from 18.8–39% [58,61,68]. In the signet-ring cell subtype, 0% (n = 12) were found to express MUC6 [32]. Amongst microsatellite-unstable CRCs, Kim et al. reported that 13.7% (24 of 175) expressed MUC6 protein, and no significant difference was seen between sporadic and hereditary non-polyposis colorectal cancer [87]. High levels of MUC6 expression have been reported in the CRC cell line LS174T [91]. Numerous groups have reported varying percentages for MUC6 expression amongst different subtypes of colonic polyps, with values ranging from 0–16.9% for hyperplastic polyps, 0–16% for adenomas, and 20–100% for sessile serrated adenomas (SSA) (Table 3) [60,61,68,84,87,92,93]. A few of these studies noted significant differences in MUC6 expression based on the proximal vs. distal location of the polyps. Fujita et al. noted that 60% of proximal HPs expressed MUC6 compared to only 4% of distal polyps (p-value < 0.0001) [60]. Within SSAs, IHC revealed that 94.1% of proximal polyps expressed MUC6 compared to only 26.9% of distal polyps [93]. MUC7 and MUC8 expression have not been observed within the small bowel or colon [52]. MUC11 is a member of the transmembrane subtype of mucins. It was first described in 1999 by Williams et al. [94]. Via Northern blotting, they were able to show that MUC11 was expressed in normal colon samples and was either absent or significantly reduced in paired CRC samples. They also evaluated MUC11 mRNA expression in multiple CRC cell lines and found that HT29, LIM1215, LIM1899, and LIM1863 had very faint expression, while SW620 and SW480 had relatively high levels of MUC11 expression. In patient-derived tumor samples, 80% (12 of 15) had down-regulated MUC11 levels compared to paired normal colon samples. MUC12 is a transmembrane mucin that is expressed within the normal colon and weakly in the pancreas [94]. Multiple studies have shown the downregulation of MUC12 mRNA in some CRCs compared to normal colons [94,95,96,97]. Although, some CRCs still express relatively high levels of MUC12. Pham at el. found that 62.9% (39 of 62) of CRCs expressed MUC12, of which 35.9% (n = 14) had weak or mild staining and 64.1% (n = 25) had intense staining [98]. When only evaluating the 37 CRCs with metastatic disease, similar ratios were found for the staining patterns (13.5% weak/mild and 40.5% intense). When present in CRC, MUC12 localization is altered, with apical staining observed in normal colons and a loss of polarity seen in CRCs [98]. Additionally, Williams et al. found that MUC12 mRNA was not expressed in the colon cancer cell lines HT29, LIM1215, LIM1899, LIM1863, SW620, or SW480 [94]. Matsuyama et al. evaluated the MUC12 mRNA expression levels in 73 patients with stage II or III CRC and found that the 3-year disease-free survival was reduced to 66.1% for patients with low-MUC12-expressing tumors compared to 90.9% for those with high-MUC12-expressing tumors (p-value of 0.02). Worse survival for low-MUC12-expressing tumors of any stage was also demonstrated by Wu et al. [99]. Upon multivariate analysis, tumor MUC12 expression levels were found to be an independent prognostic factor [100]. MUC13 is a transmembrane mucin that is cleaved into two subunits and then undergoes homodimerization [101]. It has been shown to be expressed in the normal colon to varying degrees and is confined to the apical membrane [101,102,103]. There have been discrepancies regarding MUC13 expression in CRC; Packer et al. and Williams et al. both reported a downregulation of MUC13 mRNA levels [95,101], while Walsh et al. and Gupta et al. reported MUC13 expression equal to or greater than paired normal samples in 100% of tumors evaluated by IHC [102,103]. Additionally, Gupta et al. demonstrated that aberrant localization of MUC13 staining (i.e., expression on the basal surface, in the cytoplasm, or in the nucleus) was more commonly seen in metastatic CRCs. Cytoplasmic staining was seen in 23.7% of non-metastatic tumors compared to 89.3% for metastatic disease. Nuclear staining was observed in 10.5% of non-metastatic tumors and 64.3% of metastatic tumors. Variable expression has been observed within colon cancer cell lines. SW620, LoVo, T-84, and HT-29 had detectable MUC13 RNA levels, while these were faint or absent from SW48 and SW480 [103]. LIM2463, LS513, SW116, and SW620 were found to have high levels of MUC13 mRNA [91,101]. In subsequent work, Gupta et al. further characterized the role of MUC13 in tumorigenesis by creating CRC cell lines that had exogenous MUC13 expression (by transfecting a GFP-tagged MUC13 vector) or knock-down expression (with a shRNA lentivirus). The MUC13-overexpressing line showed statistically significant increases in cell growth, the ability to form colonies, and cell migration compared to the control. Importantly, the inverse was true for the MUC13 knock-down cell line, wherein a significant reduction was seen in all three tumorigenic features [104]. Using a Muc13 knockout mouse (Muc13-/-) and a colitis-associated colorectal (CAC) tumorigenesis model induced by AOM and followed by DDS, Sheng et al. showed that Muc13-/- mice had fewer and smaller tumors and decreased hyperplasia compared to wild-type CAC models [105]. They also found that the anti-apoptotic protein Bcl-xL was upregulated in WT mice but not in Muc13-/- mice, suggesting that Muc13 was important for the prevention of apoptosis. However, when Bcl-xL was blocked, no change in the number of tumors was seen in the WT or Muc13-/- mice, which conflicted with the prior hypothesis. Sheng et al. evaluated 88 cases of CRC and found that low MUC13 expression (by IHC) predicted a significantly reduced 5-year survival of 45% (n = 60) compared to 90% (n = 28) for high-MUC13-expressing tumors (p-value of 0.0006) [105]. However, Sojka et al. found improved survival with low MUC13 expression (n = 187) [106]. MUC14 is a transmembrane mucin protein that has been scarcely researched with respect to its expression profiles or roles in human tissues. In a genomic analysis, Reynolds et al. reported a higher rate of MUC14 mutations in microsatellite-stable mucinous CRC (4.44%) compared to non-mucinous CRC (0.24%) [107]. MUC15 is a transmembrane mucin of ~100–120 kDa that was first described by Pallesen et al. in 2002 [108]. Low levels of MUC15 mRNA have been reported in normal colonic tissue, while many CRCs exhibit overexpression (sometimes as high as 10-fold compared to matched normal tissue) [108,109]. Huang et al. found that 70.8% (51 of 72) of patient-derived CRCs had MUC15 overexpression (as determined by RT-PCR) while 82.7% (43 of 52) had overexpression determined via IHC [109]. MUC15 has also been shown to be more highly expressed in poorly differentiated CRC compared to well- or moderately differentiated CRC [110]. Huang et al. also utilized the transfection of MUC15 vectors into HCT116 (a low-MUC15-expressing CRC cell line) with or without shRNA to study its effects on cell proliferation, apoptosis, and tumor growth. They found that MUC15 overexpression led to a statistically significant increase in cell proliferation, which was blocked by treatment with shRNA; however, no effect on apoptosis was seen [109]. When the HCT116 cells transfected with MUC15 vectors were implanted subcutaneously in mice, the tumors were significantly larger (with a six-fold higher weight) compared to tumors grown from cells transfected with a mock vector. Additionally, Ki67 expression was significantly increased in the MUC15-positive cells compared to the control (74.3% versus 38.0%, p-value of 0.01), indicating increased cell proliferation [109]. MUC16 is the largest of the transmembrane mucins and is clinically known as cancer antigen 125 (CA125) [111]. Streppel et al. demonstrated that MUC16 is not expressed within normal colonic tissue, while 64.1% (25 of 39) of CRCs express MUC16 (Figure 5) [111]. A significant difference in the rates of MUC16 expression between right-sided CRCs (23%, n = 206) and left-sided CRCs (9.8%, n = 214) was observed by Ward et al. [112]. A significant difference was also seen in the MUC16 expression levels between stages A–B and stages C–D, with lower expression levels seen in the earlier stages of CRC (p-value of 0.037) [113]. Liu et al. also demonstrated that elevated levels of MUC16 mRNA can be detected in the peripheral blood of patients with CRC compared to healthy individuals [114]. Huang et al. investigated the predictive value of MUC16 serum levels towards the presence of peritoneal disease compared to CEA [115]. They found that MUC16 had improved specificity (89.2%) compared to CEA (62.8%), though its sensitivity was lower at 61.4% for MUC16 vs. 75.4% for CEA (p-value < 0.01). However, no significant difference in MUC16 levels was seen with increasing stages except for stage IV with peritoneal dissemination (PD). Interestingly, CEA levels predicted stage IV disease, although only if PD was absent [115]. Multiple groups have investigated the prognostic value of MUC16 expression towards CRC, the majority of which found a significantly worse prognosis for those with elevated serum MUC16 levels (Table 4 and Table 5). Giessen-Jung et al. did not find a statistically significant difference in 5-year survival based on MUC16 expression; however, they excluded patients with metastatic disease and patients who received neoadjuvant therapy [116]. Streppel et al. evaluated CRCs via IHC and found that absent MUC16 expression (n = 14) had significantly worse mean survival compared to CRCs with focal staining (n = 15), namely, 87.3 months (95% CI 34.0–140.5) vs. 182.6 months (95% CI 143.1–222.1), respectively [111]. MUC17 is a transmembrane mucin that is expressed in the normal colon and small intestine [61,70]. MUC17 is downregulated in inflammatory states such as ulcerative colitis and ischemic colitis [118]. Wolff et al. evaluated 148 CRC samples for mutations in 38 genes of interest and found that 21.6% (32 of 148) of CRCs had a MUC17 mutation [119]. Additionally, the human colon cancer cell line LS174T has consistently been shown to express MUC17 [70,118,120]. MUC17 has been shown to play a role in cell adhesion, adherence, and migration. Luu et al. used siRNA to MUC17 to silence its expression in LS174T cells. They saw reduced adhesion (96.25% vs. 91.67%; p-value < 0.002), reduced aggregation, and 67% less migration (p-value < 0.0001) compared to the controls. Additionally, when cells were treated with etoposide, those treated with MUC17 siRNA showed a significant increase in rates of apoptosis (6.52% vs. 1.75%; p-value < 0.002) [120]. Krishn et al. showed that 60% of adenomas (n = 30) had strong MUC17 expression while hyperplastic polyps (n = 10) showed similar staining levels to those of the normal colon [61]. The work by Delker et al. also highlighted MUC17 expression as a distinguisher between sessile serrated adenomas and hyperplastic polyps as they found an 82-fold increase in MUC17 RNA expression in SSAs compared to HPs [121]. In mouse models of induced colitis (using acetic acid or dextran sodium sulfate), Luu et al. reported a significant reduction in crypt damage scores and degrees of ulceration for mice treated with exogenous MUC17 compared to the controls [120]. MUC18 is a membrane-bound, mucin-like protein that is also known as CD146 and the melanoma cell adhesion molecule (MCAM). MUC18 is not expressed within the normal colonic mucosa [122]. Tian et al. found that 20% (n = 1080) of CRC samples expressed MUC18. Additionally, a higher proportion of MUC18 expression was seen in those with liver metastasis (39.2%, n = 102) vs. those without liver metastasis (18%, n = 978) [123]. Liu et al. also reported MUC18 expression in the CRC cell lines HT29 and SW948; this expression was absent from the SW480, SW620, and Colo205 lines [122]. In knockdown xenograft models of MUC18 using CRC cell lines transfected with MUC18 shRNA, tumors lacking MUC18 grew faster than the controls (tumors visible at ~20 days compared to ~36 days) [122]. MUC19 is a secreted mucin that has been limitedly researched with regard to colorectal cancer. However, amongst CRC lung metastasis samples, MUC19 mutations have been identified that are not present in the primary tumor, thus suggesting a role played by MUC19 concerning distant spread [124]. MUC20 is a membrane-bound mucin for which minimal research has been conducted compared to other mucins. Xiao et al. is the only group to publish research on MUC20 and its role in CRC [125]. They reported MUC20 expression in 61.7% (91 of 150) of CRC and only 12% (18 of 150) of adjacent normal colon cells (p-value < 0.05). They also transfected CRC cell lines (LoVo and SW620) with GFP-shRNA-MUC20 or GFP-MUC20 to silence or express MUC20, respectively. They reported significantly reduced cell migration in the cells transfected with the shRNA; the opposite was seen in the GFP-MUC20-transfected cells. Xiao et al. reported MUC20 overexpression correlated with both increased recurrence and death [125]. Of the 47 patients with recurrence, 76.6% (n = 36) had MUC20-expressing tumors, while only 23.4% (n = 11) had tumors lacking MUC20 (p-value of 0.016). The rates of MUC20 positivity were similar in the patients without recurrence (55.6% MUC20+ and 44.4% MUC20-). Of the 41 deaths, 78% (n = 32) had MUC20-positive tumors, while 21.9% (n = 9) did not express MUC20 (p-value of 0.015). MUC21 is a transmembrane mucin that Ito et al. first described in 2007 [126]. They reported MUC21 mRNA expression in the lung, thymus, and colon. However, when King et al. evaluated mRNA expression, they found MUC21 was absent from the normal colon and that its expression increased with the increasing stage of CRC [127]. Vymetalkova et al. evaluated microRNA binding site polymorphisms in multiple mucin genes and their relation to colorectal cancer [128]. They reported that after adjusting for sex, age, smoking status, and cancer stage, there was a statistically significant reduction in overall survival for the CC genotype of rs886403 in MUC21 (HR 2.63, 95% CI 1.69–4.10, and p-value < 0.0001). Mucins are a family of glycoproteins containing 24 members that play diverse roles in cell signaling, barrier protection, and cell migration in many organ systems. They have been implicated in chronic inflammatory states and the promotion of oncogenesis in numerous malignancies, including breast [73], lung [75], gastric [11,129], biliary [76,77], pancreatic [11,111], colon, and ovarian cancers [46,74]. It is hypothesized that they aid tumor invasion by disrupting cell–cell adhesions [13]. While there is variability amongst colorectal cancers, general expression patterns have been established for many of the mucins. Those found to be expressed in normal colons include MUC2, MUC3, MUC4, MUC11, MUC12, MUC13, and MUC15 (at low levels) [52,53,54,55,68,70,94,101,102,103,108,109]. Those absent from the normal colon, and, subsequentially, found to be aberrantly expressed in CRC, include MUC5, MUC6, MUC16, and MUC20 [52,61,68,84,111,121]. This group of mucins that is absent from normal colons, yet is abnormally expressed in colorectal cancer, is a potential target for continued research into imaging studies geared towards improved detection or the delivery of therapeutic agents. Unfortunately, of these four genes, the highest percentage of positivity within CRCs was only 61.7% for MUC20. Many mucins were also shown to be independent predictors of prognosis (Table 6), with worse prognosis for the low expression of MUC2, MUC12, and MUC13 [65,100,105] and the high expression of MUC4, MUC5, MUC16, and MUC20 [56,78,86,112,113,116,117,121]. When evaluating new biomarkers for colon cancer, MUC1, MUC2, MUC4, MUC5AC, and MUC6 are currently documented in the largest bodies of work regarding their role in the progression from normal colonic tissue to malignancy. Of these mucins, MUC6 seems promising in terms of its role as a biomarker of colorectal cancer, given its lack of expression in normal tissue and the relative consensus on its polyp expression profile (specifically, its expression is absent from benign hyperplastic polyps yet present in sessile serrated adenomas). However, MUC6 expression within CRC is lower (up to 39%) compared to other mucins such as MUC1, which is expressed in 84% of patients with lymph node metastases [28,68]. Thus, a combined approach utilizing multiple mucin proteins might be the next step for the specific labeling of pre-malignant and malignant tumors or for the targeted delivery of therapeutics. In this review, we provided an overview of the current data on the mucin expression profiles in normal colons, benign and pre-malignant polyps, and colon cancer. While there have been some variations in these expression patterns, trends regarding the presence or absence of mucins in various tissue types can be ascertained. This includes the absence of MUC5, MUC6, MUC16, and MUC20 from the normal colon; maintained MUC2 expression in polyps; aberrant MUC5AC expression in sessile serrated adenomas; and the lack of MUC6 expression in benign hyperplastic polyps. Overall, there has been extensive research into the roles mucins play in the progression of colorectal cancer, and this work provides promising material for future developments in mucin-related diagnostics or therapeutics.

PMC10000735

Iñigo Les,Mireia Martínez,Inés Pérez-Francisco,María Cabero,Lucía Teijeira,Virginia Arrazubi,Nuria Torrego,Ana Campillo-Calatayud,Iñaki Elejalde,Grazyna Kochan,David Escors

Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events

06-03-2023

immune-related adverse events,immune-checkpoint inhibitors,biomarkers,prediction,diagnosis

Simple Summary Immune-checkpoint inhibitors (ICIs) are increasingly used in the treatment of cancer, but they cause immune-related adverse events (irAEs) in around 40% of patients treated. Identifying biomarkers predictive of irAEs has become a priority for the optimal management of patients on ICIs. Herein, we review the state of the art regarding the most relevant biomarkers for predicting irAEs, distinguishing between biomarkers already clinically available and those under investigation. Although none of these biomarkers has been validated in prospective studies, there is growing evidence supporting their use for irAE prediction and clinical characterization, which depend on cancer type, ICI agent and organ affected by the toxicity. A better understanding of the pathogenic mechanisms underlying irAEs and the combination of different emerging biomarkers would allow us to improve the risk-benefit balance for patients who are candidates for ICI therapy. Abstract Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site.

Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events Immune-checkpoint inhibitors (ICIs) are increasingly used in the treatment of cancer, but they cause immune-related adverse events (irAEs) in around 40% of patients treated. Identifying biomarkers predictive of irAEs has become a priority for the optimal management of patients on ICIs. Herein, we review the state of the art regarding the most relevant biomarkers for predicting irAEs, distinguishing between biomarkers already clinically available and those under investigation. Although none of these biomarkers has been validated in prospective studies, there is growing evidence supporting their use for irAE prediction and clinical characterization, which depend on cancer type, ICI agent and organ affected by the toxicity. A better understanding of the pathogenic mechanisms underlying irAEs and the combination of different emerging biomarkers would allow us to improve the risk-benefit balance for patients who are candidates for ICI therapy. Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site. In recent years, treatment with immune checkpoint inhibitors (ICIs) has led to a paradigm shift in the treatment of various types of cancer [1,2]. The mechanism of action of ICIs consists of blocking certain inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), programmed death cell protein 1 (PD-1), and PD ligand 1 (PD-L1). By blocking these inhibitory pathways, ICIs induce an enhanced T-cell-mediated response aimed at eliminating tumor cells. As a result of this immune overactivation, ICIs may also trigger a wide range of toxic effects known as immune-related adverse events (irAEs) [3], which mimic traditional autoimmune disorders. In practical terms, an irAE can be defined as any symptom, sign, syndrome, or disease caused or exacerbated by an immune-activating mechanism during the administration of an ICI once other causes such as infectious diseases or tumor progression have been ruled out [4]. The burden of irAEs is high because they are common and, not infrequently, severe complications impact the quality of life and prognosis of patients receiving ICIs [5]. Furthermore, it remains unclear how best to manage irAEs without interfering with ICI-related antitumor response and long-term patient survival [6]. Indeed, patients who develop irAEs have a better cancer-related prognosis [7,8,9]. Therefore, it is of great interest to assess the individual risk of toxicity in advance, allowing earlier management of irAEs, which would help maintain ICIs in these patients susceptible to immune-mediated complications but who, paradoxically, benefit more from therapy. With the progressive expansion of ICI use in the oncology field, there is an increasing need for reliable and validated biomarkers able to predict irAEs [10]. In accordance with FDA guidelines, a biomarker is “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention” [11]. In line with this, a predictive biomarker can be defined as a factor that is “used to identify individuals who are more likely than similar individuals without the biomarker to experience a favorable or unfavorable effect from exposure to a medical product or an environmental agent” [12]. Recent years have seen a proliferation of studies on predictive biomarkers for irAEs. Nonetheless, the clinical benefit of reported biomarkers still needs to be confirmed by long-term prospective studies, preferably within randomized clinical trials or real-life studies. To date, most research on irAE biomarkers has had similar shortcomings: a short follow-up time, retrospective design, and a restricted focus on specific types of irAE, ICI or cancer. That is, there is a lack of long-term, multicenter, and prospective studies encompassing pan-tumor cohorts of patients treated with different ICI agents. Moreover, a cross-sectional use of generic predictors aimed at different tumors, irAEs and ICIs is not feasible because each organ or system damaged by immune toxicity is related to a specific biomarker. For instance, it is well known that the risk of developing nivolumab-induced destructive thyroiditis is higher in patients with antithyroid antibodies pre-treatment [13]. Strikingly, some studies have suggested that certain specific autoantibodies, such as antithyroid antibodies, may herald the risk of irAEs at other anatomic sites [14]. Overall, current knowledge gaps on the pathogenesis of irAEs prevent us from estimating individual patients’ risk of ICI-mediated toxicity and motivate us to search for more effective predictive biomarkers. Among the wide range of potentially useful biomarkers, we can distinguish between biomarkers that, though not validated, are available for routine clinical use and investigational biomarkers [15]. The aim of this paper is to review the literature on predictive biomarkers for irAEs from a practical approach, differentiating between biomarkers already available for use in daily practice and those still at the research stage. The search strategy is detailed in Supplementary Materials (Figure S1). First, it should be noted that no biomarkers have yet been validated as an irAE predictor in asymptomatic patients treated with ICIs [16]. Taking a pragmatic, clinically based approach, we have classified potentially predictive irAE biomarkers as those currently available for clinical use and those still under investigation (Figure 1). Within this classification, clinically available biomarkers would be easily accessible to attending physicians if validated for this purpose, while those still under investigation would require implementation in clinical practice in addition to validation. The potential use of autoantibodies as predictive biomarkers of irAEs has become an expanding field of research [17,18]. Currently, guidelines do not recommend testing every patient for autoantibodies before ICI initiation as this indicator has not been validated for irAE screening [19]. The association between autoantibodies and irAEs is, however, well documented in the case of organ-specific irAEs and the autoantibodies related to such events [20,21,22]. For instance, the risk of suffering ICI-induced thyroiditis is higher in patients with pre-existing antithyroid antibodies [13,23,24]. That is, on the one hand, some organ-specific autoantibodies are only useful for organ-specific irAEs, although not all reported irAEs have been paired with a specific autoantibody. On the other, generic and routinely available autoantibodies, such as antinuclear antibodies (ANA) or rheumatoid factor, may be useful in screening for any type of irAE, regardless of the tissue involved [25]. Furthermore, even certain organ-specific autoantibodies such as antithyroid antibodies, traditionally linked to the prognosis of immunogenic tumors [26], could also be useful in predicting irAEs at any site, indicating a marked overlap between generic and specific autoantibodies [14,25]. Nevertheless, the clinical heterogeneity and complex and diverse pathogenesis, as well as the generally low rates of autoantibody seropositivity associated with these events, mean that current autoantibody panels are not applicable to all patients developing ICI-related toxicity. In recent years, research on autoantibodies as irAE indicators has moved from retrospective towards prospective methods (Table 1) [14,25,27,28,29,30,31,32,33,34,35,36,37,38,39]. While testing positive for autoantibodies at baseline was considered a risk factor for irAE development in preliminary studies, the most recent reports have provided greater insight into changes in antibody levels over time. For example, a retrospective study by Toi et al. suggested that patients with pre-treatment ANAs, rheumatoid factor or antithyroid antibodies were at increased risk of developing irAEs [27]. De Moel et al. showed an association between seroconversion of any autoantibody included in a battery of 23 autoantibodies and irAEs during follow-up, especially when focusing on specific irAEs related to the battery under study; however, the presence of autoantibodies before ICI initiation was not associated with irAEs [30]. The value of autoantibody seroconversion was also highlighted by Giannicola et al., who found a higher risk of irAEs in patients who became positive for ANAs, anti-extractable nuclear antigens antibodies or anti-smooth muscle antibodies after starting nivolumab administration [31]. Confirming the significance of dynamic changes in autoantibody titer, a sub-study from a phase II clinical trial identified a low autoantibody titer at baseline and greater fold change in autoantibody titer after ICI initiation as independent risk factors for irAEs [39], in contrast with the pre-formed autoantibody theory. Moreover, Alsewaran et al. demonstrated that pre-treatment ANA positivity was not associated with irAE development. On the contrary, patients experiencing seroconversion to ANA positivity after ICI initiation developed more severe irAEs than patients who remained ANA-negative and patients who were ANA-positive before ICI initiation. This humoral response in the form of ANA seroconversion could be related to early B-cell changes induced by ICIs, namely a decline in circulating B cells and an increase in CD21 B cells and plasmablasts, which have been associated with a higher frequency of irAEs [40]. Furthermore, in up to 83% of patients with severe irAEs, modifications in ANA patterns preceded irAE onset [38]. In addition to ANA positivity, ANA patterns determined by immunofluorescence may be useful for discriminating between primary autoimmune diseases and irAEs. Although no studies designed to compare autoantibody profiles have yet been reported, patients who develop irAEs may be less likely to express disease-specific ANA patterns than patients with the corresponding classical autoimmune disease. In contrast, a nuclear speckled pattern may be more indicative of immune-related toxicity [38]. The use of blood cell counts for the early detection of irAEs may be of great interest to clinicians due to their wide availability, low cost, and easy interpretation. Although not completely consistent, there is supportive evidence suggesting that baseline absolute neutrophil, lymphocyte, monocyte, eosinophil and basophil counts, platelet counts, and increases in white blood cell, lymphocyte and eosinophil counts during follow-up are associated with a higher risk of irAEs (Table 2) [41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60]. In addition, several blood cell ratios, the most common being the neutrophil-to-lymphocyte ratio (NLR) and derived NLR (calculated as absolute neutrophil count/[white blood cell count–absolute neutrophil count]), could help to predict irAEs before and after ICI initiation. In a systematic review and meta-analysis including 6696 patients on ICIs from 25 studies, a high NLR was identified as an independent risk factor for developing irAEs [51]. In a prospective study including 1187 patients, an elevated NLR at the beginning of ICI therapy was predictive of very severe irAEs (grades 4 and 5) [42]. Similarly, it has been reported that peripheral CD8 T-cell expansion and diversification after ipilimumab initiation, as surrogate markers of autoreactivity against tissue self-antigens at the systemic level, allows us to predict irAE onset with very high sensitivity [61]. This diversification occurs early in follow-up, within the first two weeks after ICI administration [62]. Furthermore, patients experiencing ipilimumab-induced colitis showed higher absolute counts of peripheral CD4+ T cells and lower percentages of regulatory T cells at baseline [63]. Likewise, it has been suggested that changes in the percentage of peripheral CD4+ CD25+ Foxp3+ regulatory T cells, a cell subset in charge of maintaining immune tolerance in the tumor microenvironment, may be predictive of irAEs [64]. Recently, elevated levels of circulating low-density neutrophils, a myeloid subpopulation with immunosuppressive properties, have been associated with a poor response to pembrolizumab mediated by T-cell cytotoxicity down-regulation in patients with non-small cell lung cancer [65]. As in tumor response, different subpopulations may play a role in the pathogenesis of ICI toxicity. Despite these promising findings, most published studies on blood cell counts and ratios have been retrospective, time-limited (usually considering only baseline data or short follow-up periods) and constrained to either a specific type of cancer, irAE or ICI agent (Table 2). Notably, to date, few prospective studies have assessed the clinical value of blood cell count fluctuations for predicting irAEs in the long term. Baseline levels of thyroid-stimulating hormone in serum have been shown to predict immune-related thyroiditis before ICI initiation [66,67,68]. Likewise, thyroid stimulating hormone is the most efficient biomarker for monitoring thyroid dysfunction in patients on ICI therapy [6]. Similarly, serial measurements of serum brain natriuretic peptide and troponin, together with new-onset electrocardiographic abnormalities, help anticipate cardiovascular irAEs [69]. Fecal lactoferrin and calprotectin are commonly used as screening tools for ICI-induced colitis [70], calprotectin being a good non-invasive indicator for assessing treatment response and avoiding repetitive endoscopic procedures [71]. Among generic biomarkers, raised C-reactive protein levels correlate well with the risk of developing irAEs [51], in parallel with serum interleukin-6 (IL-6) levels [72]. With different cut-offs, high serum albumin levels have also been associated with irAEs [48,73]. In addition, elevated blood lactate dehydrogenase levels predispose patients to irAEs [74], especially high-severity events (grade ≥ 3) [48]. In contrast, a decrease in serum leptin levels at four weeks from ICI initiation was more common in patients who experienced irAEs than in irAE-free patients [75]. Levels of these generic proteins, which are acute-phase reactants or tumor burden-related markers, are relatively easy to interpret in patients with an indication for adjuvant treatment. In contrast, it may be less appropriate to use them as irAE biomarkers in patients with metastatic disease since cancer, especially in progressive phases, may alter protein levels. Interestingly, a reduction over time in the level of certain serum tumor markers, such as the melanoma-inhibitory activity protein, could help discriminate between toxicity and progression in patients with metastatic melanoma [72,76]. In recent years, the interest in cytokines to predict irAE susceptibility has grown steadily [77]. Certain cytokine profiles at baseline and dynamic fluctuations in cytokine levels over time have been associated with a higher risk of developing irAEs and better treatment outcomes [78]. Moreover, the uncoupled effect achieved by some anti-cytokine drugs in preclinical studies, consisting of decreased ICI-induced toxicity without sacrificing antitumor activity, makes it a priority to improve our understanding of the pathogenic role of cytokines [79,80]. Unfortunately, not all cytokines are currently assessed in clinical practice for diagnostic purposes. In addition, the cytokines involved in a particular irAE can differ from the corresponding autoimmune manifestation and with the ICI agent administered [81]. Tumor necrosis factor-α (TNF-α) is one of the most studied biomarkers in the field of irAE research [82]. Low baseline TNF-α levels may predispose patients to better antitumor immunity [83], while it is unknown whether fluctuations in TNF-α levels over time can anticipate irAE onset. In any case, various TNF-α blockers, such as infliximab, etanercept, adalimumab and certolizumab, have been used as rescue therapy for steroid-refractory cases of ICI-induced colitis, arthritis and pneumonitis [84,85,86,87]. Two concerns arising from TNF-α antagonism are the attenuation of antitumor immunity and promotion of tumorigenesis by anti-TNF-α drugs [88], which may depend on the dose and duration of treatment. Indeed, it is accepted that short courses of TNF-α inhibitors given at regular doses are safe for patients undergoing ICI therapy [89]. Moreover, preclinical data suggesting an antitumor benefit in mice combining ICIs and TNF-α inhibitors warrant the undertaking of clinical trials assessing this hypothesis (NCT03293784) [90,91]. Considered a “usual suspect”, IL-6 is a proinflammatory cytokine that is potentially involved in the pathogenesis of several immune-mediated disorders [92]. Low baseline levels of IL-6 were strongly associated with irAEs [93,94]. Combined with C-reactive protein, IL-6 has also been proposed as an early biomarker for irAE detection during follow-up [72,83]. Even in patients with elevated C-reactive protein, regardless of serum IL-6 levels, IL-6 blockade with tocilizumab has been tested as a therapeutic and pre-emptive drug for irAEs [95]. Recently, an uncoupled effect on induced toxicity and antitumor immunity exerted by immunotherapy has been achieved by the blockade of IL-6 in a murine model [96], which has warranted the launching of a phase II clinical trial to assess the efficacy of tocilizumab in patients receiving ICIs (NCT04940299). The favorable safety profile of tocilizumab and other anti-IL-6 agents, widely available in the clinical setting due to the SARS-CoV-2 pandemic, makes IL-6 a promising therapeutic target [97]. Interleukin-17 (IL-17) is another pro-inflammatory cytokine involved in the pathogenesis of inflammatory bowel disease, psoriasis, psoriatic arthritis, other types of spondyloarthritis, and even interstitial lung disease [98,99]. Unlike TNF-α and IL-6, high serum levels of IL-17 at baseline have been associated with severe colitis in patients on ipilimumab [100]. An increase in serum IL-17 levels was demonstrated following CTLA-4 blockade with ipilimumab in patients developing colitis, consistent with CTLA-4 inhibiting the production of IL-17 by type 17 T helper (Th17) cells [101,102]. These findings support the central role of IL-17 in the pathogenesis of irAEs involving Th17 cell-enriched tissues, such as colitis, psoriasiform dermatitis, pneumonitis and neuroendocrine toxicity [103,104,105]. Correspondingly, IL-17 antagonists have shown a clinical benefit in IL-17-dependent irAEs, opening the door to targeted anti-cytokine therapies [106]. The case of IL-1 is revealing and may represent a new therapeutic target. There is evidence suggesting that baseline elevated levels of IL-1β are related to thyroid dysfunction [107]. In a retrospective series, IL-1α was significantly elevated in patients who developed ICI-induced myositis [108]. Notably, patients treated with a combination of anti-CTLA-4 and anti-PD-1 drugs who developed ICI-induced colitis overexpressed mucosal IL-1β (as well as IL-17), but not TNF-α, with a higher abundance of Bacteroides intestinalis [109]. Moreover, fecal microbiota transplant of large numbers of Bacteroides intestinalis bacteria to mice induced overexpression of IL-1β after ICI administration [5]. These findings on the role of IL-1 regardless of TNF-α activity in immune-mediated colitis could be related to the subgroup of patients with inflammatory bowel disease who are refractory to standard therapy with TNF-α blockers [110]. In addition, IL-1β has been identified as an independent risk factor for irAEs in patients on PD-(L)1 inhibitors [111]. The pro-inflammatory interleukins IL-12 and IL-23 belong to the IL-12 family and are characterized by sharing a p40 subunit. Blocking both IL-12 and IL-23 with ustekinumab has been shown to be effective in ICI-induced refractory colitis [112]. On the other hand, interfering with the IL-12-dependent pathway may alter the antitumor effect associated with ICI therapy [113,114]. The use of guselkumab, a specific anti-IL-23 agent, has been proposed as a way of inhibiting both the pro-tumor and pro-inflammatory effects attributed to IL-23 without affecting the IL-12 pathway, although this hypothesis needs further testing [115]. Regarding IL-10, an anti-inflammatory interleukin with homeostatic properties [116], a retrospective study revealed that high baseline IL-10 levels and increases in these levels after the first cycle of ICI were the only independent factors predicting irAEs among a broad battery of cytokines [117]. Further insight is required into the mechanisms by which IL-10 may promote immune tolerance and their relationship with toxicity modulation [118]. Other potential cytokine-related biomarkers are being studied, such as the serum soluble IL-2 receptor, a biomarker of hyper-inflammatory status available in daily clinical practice [119]. In addition, low baseline values and decreases in interferon-γ release, commonly used for the detection of Mycobacterium tuberculosis (latent) infection, have been associated with ICI-induced pneumonitis [120]. The chemokine ligand 15 (CXCL15) and the soluble protein cluster of differentiation 163 (sCD163), as surrogate indicators of Th17 cell and tumor-associated macrophage activation, respectively, have been proposed as biomarkers for irAE prediction [121]. Lower levels of CXCL9, CXCL10, CXCL11 and CXCL19 at baseline and greater increases in CXCL9 and CXCL10 levels have been reported in patients who experienced irAEs [122]. Other biomarkers under investigation are angiopoietin-1 (Ang-1) and CD40 ligand, whose baseline high levels have been related to dermatitis [78]. Early decreases in granulocyte colony-stimulating factor have been associated with several irAEs, while lower baseline levels of this growth factor may predispose patients to colitis [78,107]. High baseline growth-regulated oncogene-1 and granulocyte macrophage colony-stimulating factor levels have been associated with generic irAEs and specifically with thyroid dysfunction and dermatitis, respectively [75,107]. Other organ-specific irAEs were associated with stem cell factor (colitis), leukemia inhibitory factor and placental growth factor (both with myositis), and B and T-lymphocyte attenuator (dermatitis) [108]. In addition, the pathogenic role and predictive value of other cytokines such as IL-2, IL-4, IL-5, IL-15, IL-27, IL-35, and interferon-α remain to be clarified [107,108,123,124]. Overall, the incorporation of several cytokines, both those available routinely and those under development, into already-designed toxicity risk scores such as the CYTOX score might provide a useful tool for irAE prediction [125]. We are currently witnessing a surge in precision medicine approaches based on personalized cytokine profiles depending on individual, pharmacologic and tissue-related factors, without undermining the antitumor response. Such molecular-focused strategies yield therapies focused on specific cytokine signatures rather than the targeted organ [126]. Monogenic mutations leading to autoimmune diseases have been identified for years [127]. For instance, we know about the existence of cases of systemic lupus erythematosus caused by monogenic mutations in the C1qA, B, C, C1R, DNASE1, DNASE1L3, and ACP5 genes [128], among many other predisposing genetic variations [129]. Furthermore, certain germline CTLA4 and PDCD1 (encoding for PD-1 protein) gene polymorphisms have been associated with both the development of autoimmune diseases and susceptibility to ICI-induced endocrine irAEs [130] (Table 3) [131,132,133,134,135,136,137,138,139,140,141,142,143,144]. In the same vein, two different PDCD1 gene single-nucleotide polymorphisms (SNPs), namely rs2227981 and rs10204525, have been identified as protective and susceptibility biomarkers for irAEs, respectively [135,136]; these apparently paradoxical findings are explained by the polymorphism in question, which determines the level of PD-1 expression (low in the case of rs2227981 and high in the case of rs10204525). In a comprehensive study, Abdel-Wahab et al. described as many as 30 SNPs related to irAEs, of which twelve led to a higher irAE risk, eighteen to a lower irAE risk, and nine involved genes associated with autoimmune or inflammatory diseases (GABRP, DSC2, BAZ2B, SEMA5A, ANKRD42, PACRG, FAR2, ROBO1 and GLIS3) [132]. These SNPs add to others previously described as isolated risk factors for irAEs or combined predictors of irAEs and autoimmune diseases [133,145]. In addition, genetic alterations other than SNPs such as small sequence variations and copy number variations (namely, duplications and depletions) have been detected in 16 genes (AIRE, TERT, SH2B3, LRRK2, IKZF1, SMAD3, JAK2, PRDM1, CTLA4, TSHR, FAN1, SLCO1B1, PDCD1, IL1RN, CD274, and UNG) and linked to irAEs affecting different organs and systems [131]. Moreover, patients showing modifications of CEBPA, FGFR4, MET or KMT2B genes detected in circulating tumor DNA before ICI initiation are at higher risk of experiencing irAEs [134]. Another gene-related biomarker for ICI-mediated toxicity is the expression of specific gene signatures. For example, Friedlander et al. proposed a 16-gene signature (involving CARD12, CCL3, CCR3, CXCL1, F5, FAM210B, GADD45A, IL18bp, IL2RA, IL5, IL8, MMP9, PTGS2, SOCS3, TLR9 and UBE2c genes) to discriminate between low- and high-grade tremelimumab-induced diarrhea [139]. Clinically relevant pathways, such as those of the inflammasome in ICI-induced myocarditis or the neutrophil activation cascade in gastrointestinal irAEs, have been identified through the overexpression of type 5 and 6 guanylate binding proteins and CD177 and CEACAM1 genes, respectively [140,141]. More specifically, IFI27 gene expression, related to the interferon-α pathway, has allowed ICI-associated T cell-mediated rejection to be distinguished from ICI-associated acute interstitial nephritis in kidney transplant patients [142]. With the assistance of pharmacovigilance, other over-represented genes in patients with irAEs have been identified through integrated bioinformatic analysis [143], molecular multi-omics data [144], and transcriptomic information of messenger RNA and alternative splicing features [146,147] (Table 3). Again, further studies are needed to validate these promising results in clinical practice. Among the genes most influential in irAEs are those in the major histocompatibility complex, also known as the human leucocyte antigen (HLA) system, which is a group of genes encoding for surface glycoproteins involved in antigen presentation that has been widely related to susceptibility to immune-mediated diseases and cancer [148,149]. Although clinically available, the use of HLA genotyping for diagnostic purposes is constrained to specific disorders such as celiac disease or axial spondyloarthritis [150,151]. Despite other confirmed genotypic-phenotypic associations, testing is not recommended for entities with more reliable diagnostic methods, because HLA variants indicate a genetic susceptibility rather than a diagnosis of certainty. The potential association between certain HLA genotypes and polymorphisms and the risk of immune-related toxicity has been mainly assessed in the context of endocrinologic irAEs, such as ICI-induced diabetes (overall, the irAE most studied from the point of view of the HLA system) [152], thyroid dysfunction and hypophysitis [13,153]. For instance, the development of ICI-induced type 1 diabetes mellitus [154,155], thyroiditis [156] and even autoimmune polyglandular syndrome type 2 [157] was observed in patients with HLA-DR4 more often than in patients with another HLA haplotype. Notably, Delivanis et al. demonstrated that ICI therapy can increase HLA-DR surface expression in activated monocytes leading to pembrolizumab-induced thyroiditis [158]. Other relevant associations of HLA alleles or proteins with irAEs previously reported are those of HLA-DRB1*04:05 with inflammatory arthritis [159], HLA-B27*05 with autoimmune encephalitis [160], HLA-Cw12 with hypophysitis [153,161], HLA-DQB1*03:01 with colitis [162], HLA-DRB3*01:01 with thrombocytopenia [163], HLA-A03 with pneumonitis [164], and HLA DRB*04:01, HLA-DRB1*15:01 and HLA-DQB1*06:02 with hepatitis [165] (Table 3) [74,131,152,153,154,155,156,157,159,160,161,162,164,165,166,167,168,169]. By contrast, some studies have reached negative results regarding the connection between irAEs and the HLA system [170,171]. Due to its growing availability, HLA genotyping could be considered an irAE biomarker on the boundary between clinical and investigational. A micro-RNA (miR) is a non-coding molecule of single-stranded RNA containing between 20 and 25 nucleotides, which can regulate the post-transcriptional expression of genes by blocking translation of targeted messenger RNA through a process known as ribo-interference [172]. Like CTLA-4 and PD-1, certain miRs, such as miR-146a, promote down-regulation of both innate and adaptive immune responses and may impact ICI-related survival [173], in part by counteracting cell escape mechanisms in the tumor microenvironment [174]. Indeed, the first phase 1 clinical trial which evaluated a liposomal mimic of miR-34a was halted early due to severe irAEs in participating patients [175]. Among the most relevant miRs (Table 3) [176,177,178], miR-146a is a miR family whose modified expression has been involved in the pathogenesis of several autoimmune diseases, including rheumatoid arthritis, psoriasis, and laboratory-induced colitis [179,180,181]. Moreover, specific miR-146a SNPs, such as rs2910164, predispose patients on ICIs to a higher risk of developing severe irAEs and reduced progression-free survival [177]. By contrast, exogenous administration of a miR-146a mimic may mitigate irAE intensity assessed by histopathologic criteria in mice [176]. Another micro-RNA called miR-34a-5p, related to cardiac injury by doxorubicin and cardiac senescence [182], was found to be involved in ICI-induced cardiotoxicity in an animal model [178]. Interestingly, miR-34a-5p has been shown to modulate the response of M1 macrophages, CD4+ and CD8+ T cells by downregulation of chemokine signaling, specifically of CXCR3 and its ligands CXCL10 and CXCL11 [183]. The term gastrointestinal, or gut, microbiome refers to a complex system of microorganisms, mainly bacteria, that inhabit the intestine establishing a symbiotic relationship with the host and participating in several homeostatic processes that contribute to the host’s health [184]. Already known to be involved in the pathogenesis of several immune-based disorders, especially those related to inflammatory bowel diseases [185,186,187], the gut microbiome has also been shown to modulate both intestinal and non-intestinal irAEs [188]. Gut dysbiosis, characterized by a reduction in microbiome diversity and resulting dominance of certain bacteria in the gut, may increase or decrease the anti-tumoral response and the risk of developing irAEs induced by ICIs [189]. As with other irAEs, patients experiencing ICI-induced colitis appear to have better antitumor responses and cancer-related prognosis [190]. An abundance of Bacteroidetes phylum has long been known to be a feature of colitis-resistant patients [191], while a microbiome rich in Faecalibacterium and other members of Firmicutes is associated with an elevated risk of ICI-related colitis [63]. Recently, a well-designed cohort study has confirmed the impact of specific microbial signatures, enriched with Lachnospiraceae spp. and Streptococcaceae spp., on certain irAEs [192]. In another prospective cohort study, Chau et al. found that the gut microbiome of patients not developing irAEs was relatively enriched with Bifidobacterium and Desulfovibrio species [193]. Indeed, over recent years, the collection of intestinal commensal micro-organisms potentially related to irAEs has expanded (Table 4) [63,109,186,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204]. There are many hypothetical underlying mechanisms, likely interconnected, explaining the contribution of the gut microbiome to immune-related toxicity: dysregulation between pro-inflammatory (i.e., IL-6) and anti-inflammatory (i.e., IL-10) interleukins at local and distant tissues [198]; differentiation, expansion and migration of gut mucosal Th17 cells; inactivation of gut-associated regulatory T cells leading to exacerbation of T-cell effector activities [205]; a role of microbiome metabolites such as short-chain fatty acids [205], polyamine transport system and group B vitamins [181], and microbial fragments such as polysaccharide A [206]. As already mentioned in the “Cytokine” section, overexpression of IL-1β and IL-17, but not of TNF-α, has been demonstrated by two independent groups in samples from patients with ICI-induced colitis [5,109]. All these findings open the door to gut microbiota manipulation using various strategies, such as the administration of antibiotics [86], prebiotics, probiotics, or postbiotics [196,201], or fecal microbiota transplantation [200,207,208,209,210], as well as cytokine-targeted therapies for specific irAEs [211]. Nonetheless, many questions remain unanswered in the complex phenomenon of interaction between the gut microbiome and ICI-related toxicity. Neoantigens are immunogenic peptides derived from tumor-specific genetic alterations and presented to T cells only on the malignant cell surface in the presence of the HLA system [212]. Nowadays, the detection of neoantigens, which are tumor and individual-specific, is being used as a way to design targeted therapies based on T-cell-mediated cytotoxicity with a low incidence of irAEs [213]. One of these neoantigens, namely, napsin A, has already been identified as a lung tumor self-antigen present in both lung malignant cells and ICI-induced inflammatory lung lesions [214]. Furthermore, Tahir et al. conducted a serological analysis of recombinant cDNA expression, a technique designed to identify tumor antigens, resulting in the detection of specific anti-CD74 autoantibodies related to pneumonitis, and anti-GNAL and anti-ITM2B autoantibodies related to hypophysitis [215]. In addition, autoantibody signatures profiled using the HuProt human proteome microarray system, which tests a massive number of proteins, may become a prominent tool for predicting toxicity [216], or even efficacy and toxicity simultaneously [217], in the short-to-medium term. Likewise, by means of a microarray autoantigen panel including 120 autoantibodies, Ghosh et al. showed that patients most likely to experience irAEs had lower baseline autoantibody titers and larger increases in these titers over time [39]. Given the complex nature of irAEs and the difficulty of predicting their onset, an approach based on the combination of different omics disciplines, including radiomics [218], together with real-time big data exploitation is on the horizon [219]. The field of ICI-related toxicity is evolving rapidly [220]. Nowadays, we are witnessing rapid growth in publications on potentially predictive irAE biomarkers [208]. Despite this boom in research, no biomarkers have yet been validated for clinical use. Except for routine laboratory testing, such as complete blood cell count with differential, glucose, renal and liver function tests and thyroid-stimulating hormone measurements, analysis of other laboratory parameters is not recommended before starting ICI therapy [221,222]. Hence, the first question is, Are any known biomarkers capable of predicting toxicity? and the answer is no, at least for most patients. In certain clinical scenarios, we can use some biomarkers for decision-making. Specifically, in the case of patients with a pre-existing autoimmune disease who would benefit from an ICI, measurement of autoantibodies known to be useful in assessing autoimmune disease activity may be indicated [19]. For instance, a high titer of anti-double-stranded DNA antibodies could represent a risk factor for developing a flare in a patient diagnosed with lupus before starting ICI therapy. Likewise, a progressive increase in anti-double-stranded DNA antibody titers could anticipate a lupus flare once an ICI has been initiated. The identification of versatile biomarkers is also made more complex by the pathogenic mechanisms involved in irAEs, which are diverse and heterogeneous [223]. Hence, not all biomarkers under study are equally applicable to all patients. A pragmatic approach would be to design risk toxicity scores that are cross-sectionally applicable to different settings and include accessible and understandable biomarkers [125,217]. To our knowledge, there are currently no multi-factor prediction models combining baseline patient characteristics with autoantibody titers, blood cell counts or ratios, and levels of easily measurable cytokines. Besides static predictive models, longitudinal data on biomarker fluctuations, such as blood cell counts, autoantibodies, and cytokines, may provide a more reliable approach to assessing the individual risk of experiencing irAEs. Moreover, incorporation into standard practice of more sophisticated but increasingly widespread diagnostic tools, such as HLA genotyping, and measurement of micro-RNA expression, genetic variation and gene expression, and gut microbiome signatures, will depend on their future validation and availability. In this regard, results from emerging research based on artificial intelligence, big data and machine learning as methods for creating predictive models of toxicity are particularly promising [219,224,225]. From a practical point of view, another question that arises is, Once toxicity appears in a particular patient treated with ICIs, would any of these potential biomarkers be reliable for categorizing this toxicity as immune-mediated? If that were to be the case, biomarkers could be recommended as a support tool for the differential diagnosis of a particular complication. In this clinical situation, certain hormone profiles already represent a valuable test for the identification of immune-related endocrinopathies in routine practice. In addition, some biomarkers such as fecal lactoferrin and calprotectin might suggest an immune-mediated process underlies new-onset diarrhea and reduce the number of endoscopic procedures, especially during the recovery phase from colitis. In a more general sense, the elevation of acute phase reactants, such as C-reactive protein, could also point to an immune-related etiology when faced with a nonspecific clinical picture without a clear diagnosis. Nonetheless, such generic biomarkers of inflammation can be difficult to interpret in the context of advanced cancer or concomitant infection. In conclusion, a better understanding of the pathogenic mechanisms linked to immune-mediated toxicity and the implementation of long-term, prospective, and real-life studies on irAEs are needed to confirm the validity of numerous biomarkers under investigation and enable their adoption in practice in a wide range of clinical scenarios.

PMC10000737

Zeltzin Alejandra Ceja-Galicia,Ana Karina Aranda-Rivera,Isabel Amador-Martínez,Omar Emiliano Aparicio-Trejo,Edilia Tapia,Joyce Trujillo,Victoria Ramírez,José Pedraza-Chaverri

The Development of Dyslipidemia in Chronic Kidney Disease and Associated Cardiovascular Damage, and the Protective Effects of Curcuminoids

22-02-2023

curcumin,curcuminoids,chronic renal disease,cardiovascular disease (CVD),dyslipidemia,CKD

Chronic kidney disease (CKD) is a health problem that is constantly growing. This disease presents a diverse symptomatology that implies complex therapeutic management. One of its characteristic symptoms is dyslipidemia, which becomes a risk factor for developing cardiovascular diseases and increases the mortality of CKD patients. Various drugs, particularly those used for dyslipidemia, consumed in the course of CKD lead to side effects that delay the patient’s recovery. Therefore, it is necessary to implement new therapies with natural compounds, such as curcuminoids (derived from the Curcuma longa plant), which can cushion the damage caused by the excessive use of medications. This manuscript aims to review the current evidence on the use of curcuminoids on dyslipidemia in CKD and CKD-induced cardiovascular disease (CVD). We first described oxidative stress, inflammation, fibrosis, and metabolic reprogramming as factors that induce dyslipidemia in CKD and their association with CVD development. We proposed the potential use of curcuminoids in CKD and their utilization in clinics to treat CKD-dyslipidemia.

The Development of Dyslipidemia in Chronic Kidney Disease and Associated Cardiovascular Damage, and the Protective Effects of Curcuminoids Chronic kidney disease (CKD) is a health problem that is constantly growing. This disease presents a diverse symptomatology that implies complex therapeutic management. One of its characteristic symptoms is dyslipidemia, which becomes a risk factor for developing cardiovascular diseases and increases the mortality of CKD patients. Various drugs, particularly those used for dyslipidemia, consumed in the course of CKD lead to side effects that delay the patient’s recovery. Therefore, it is necessary to implement new therapies with natural compounds, such as curcuminoids (derived from the Curcuma longa plant), which can cushion the damage caused by the excessive use of medications. This manuscript aims to review the current evidence on the use of curcuminoids on dyslipidemia in CKD and CKD-induced cardiovascular disease (CVD). We first described oxidative stress, inflammation, fibrosis, and metabolic reprogramming as factors that induce dyslipidemia in CKD and their association with CVD development. We proposed the potential use of curcuminoids in CKD and their utilization in clinics to treat CKD-dyslipidemia. Chronic kidney disease (CKD) is a global public health problem, with an incidence of >11.1% [1], corresponding to 843.6 million cases worldwide [2]. CKD significantly increases cardiovascular morbidity and mortality rates since CKD increases cardiovascular events by more than 50% [3,4,5,6]. Several risk factors are shared between CKD and cardiovascular disease (CVD), including diabetes, hypertension, lipid abnormalities, obesity, and smoking. CKD-induced dyslipidemia has been highlighted as a critical factor in CVD development [7,8]. CKD patient management involves using different drugs to reduce cardiovascular risk and prevent renal venous hypertension and congestion. These drugs include antihyperlipidemic combinations, renin-angiotensin-aldosterone system (RAAS) inhibitors, angiotensin receptor blockers, diuretics, vasodilators, inotropes, and β-blockers [8,9,10]. However, it has been reported that these drugs might cause side effects, doing more challenging to treat CKD patients [8]. Therefore, new treatment strategies are required to avoid or reduce dyslipidemia in CKD and the associated CVD without these side effects. Curcuminoids are compounds derived from turmeric (Curcuma longa) root, used in traditional medicine and as a pigment, additive, and spice for several years [11]. In CKD, curcuminoids have received significant interest due to their several health-beneficial properties, such as antioxidative, anti-inflammatory, antifibrotic, and others [12]. In addition, it has been hypothesized that curcuminoids can reduce dyslipidemia in CKD; however, the beneficial effects of curcuminoids on dyslipidemia in CKD and associated CVD are poorly explored. Therefore, this review aims to describe some mechanisms that lead to dyslipidemia in CKD and how these mechanisms promote CVD development. We also discuss the use of curcuminoids to attenuate CKD-induced dyslipidemia and the associated CVD. CKD is a chronic disorder characterized by kidney structure and function abnormalities for 3 months or more [13]. CKD is classified into five stages based on the estimated glomerular filtration rate (eGFR), serum creatinine, and albuminuria levels [14,15]. The advanced stages of CKD are characterized by a decreased eGFR of less than 60 mL/min per 1.73 m2, which leads to progressive glomerular, tubular, and interstitial damage [13]. Several etiologic factors predispose to CKD development, including diabetes, hypertension, vascular disease, and glomerulonephritis [16]. Mechanistically, the pathophysiology of CKD is characterized by overstimulation in the RAAS, oxidative stress, inflammation, fibrosis, and dyslipidemia [14,17]. Dyslipidemia is an unfavorable lipid profile that occurs in approximately one-third of patients [18], complicating their treatment [16,19]. Dyslipidemia results from the imbalance of lipids such as cholesterol, triglycerides, and lipoproteins. Lipoproteins are macromolecules that transport lipids into the bloodstream to deliver them to the organs [20]. These macromolecules are synthesized in the liver and are denominated according to their density as high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very-low-density (VLDL). VLDL and LDL mainly transport triglycerides to the tissues, while HDL transports cholesterol back to the liver [21]. Patients with CKD develop dyslipidemia since the early stages of renal dysfunction [19], which may increase the CKD progression rate [22]. Dyslipidemia in CKD is characterized by elevated triglycerides, cholesterol, VLDL, LDL, and low concentrations of HDL [23]. Furthermore, the size of LDL tends to be smaller and denser, related to atherogenic risk. The levels of apoproteins (proteins associated with lipoproteins) are also altered, characterized by the decrease of HDL apolipoprotein A1 (apoAI) and the accumulation of cholesterol. More specifically, CKD patients with dyslipidemia have elevated total cholesterol (above 240 mg/dL) and LDL-cholesterol levels (above 130 mg/dL), reduced HDL-cholesterol, and increased LDL-cholesterol/HDL-cholesterol ratio [24]. These alterations have been linked to the decrease in renal clearance and the altered enzyme activity of lipoprotein lipase, which induce alterations in triglyceride elimination [25]. In addition, the apolipoprotein B (apoB)/apoAI ratio is higher, causing the increase of LDL in plasma [26]. Therefore, dyslipidemia is a progressive disease with the potential need for additional lipid-lowering modifications in CKD patients [27]. Moreover, several studies have shown that dyslipidemia during CKD might lead to CVD development [7]. CVD is a growing condition that produces high rates of mortality and disability in the world [28,29]. CKD is an independent risk factor for CVD development, and CVD degree closely correlates with CKD severity [5,30]. For example, patients with an eGFR less than 60 mL/min/1.73 m2 have a three-fold increased risk of heart failure (HF) [31], while patients with end-stage renal disease or on dialysis have a 10–30-fold increased risk of cardiovascular events for all-cause [10,17]. In addition, the lipid accumulation in plasma causes atheroma, increasing the CVD risk [19]. It is well-recognized that dyslipidemia is a risk factor for CVD [32]. In the clinical context, two profiles are considered to determine dyslipidemia. In the first, total cholesterol, LDL, triglycerides, and apoB levels are above the 90th percentile of the general population. In the second, HDL and apoA1 levels are below the 10th percentile of the general population [33,34]. Low HDL levels have been associated with a significant risk factor for chronic and ischemic heart disease [35]. An updated statistical report from the American Heart Association in 2020 showed that 38% of the adult population had elevated triglyceride levels (>200 mg/dL), and 29% had elevated LDL levels (>130 mg/dL) [36]. The alterations in these lipids are a risk factor for the development of dyslipidemia. Dyslipidemia also produces alterations such as atherosclerotic CVD [37], which is the leading cause of death and disability in the elderly [38]. The patient’s condition worsens if other factors, such as smoking, body weight, hypertension, and diabetes, are involved [39]. In addition, atherosclerosis induces endothelial damage, leading to inflammation and the production of a fibrotic plaque that inhibits lipid metabolism [37]. The development of dyslipidemia is a common factor between CKD and CVD, which complicates the illness and its progression [26]. In clinical trials, it has been shown that the reduction in LDL-cholesterol levels in CKD patients is directly proportional to the decrease in CVD risk [40,41]. Dyslipidemia increases the risk of developing atheroma and arteriosclerotic plaques. These injuries increase vessel thickness and decrease resistance, promoting blood pressure changes. Moreover, the vessels can develop aneurysms, increasing the risk of internal bleeding and death [42]. Therefore, CVD because of dyslipidemia continues to be a factor that contributes to higher mortality and morbidity in CKD patients. CKD causes a systemic and permanent proinflammatory state that contributes to vascular and myocardial remodeling processes, vascular senescence, and myocardial fibrosis [43]. CKD patients manifest cardiovascular outcomes as coronary artery disease, HF, arrhythmias, and sudden cardiac death [43]. In addition, metabolic changes have also been proposed [44,45,46]. Although several guidelines exist to guide healthcare providers in treating dyslipidemia, there are no specific recommendations for the CKD population [19]. Furthermore, some preventive therapies to reduce lipid levels in patients with CKD, such as statins and other drugs, are not always optimal for treating CKD patients [18]. Thus, strategies to improve some of these symptoms could be crucial in treating CKD-induced dyslipidemia and the associated CVD. Oxidative stress is recognized as an imbalance between the production of reactive oxygen species (ROS) and their elimination. In the kidney, the primary ROS sources are mitochondria, nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) oxidases (NOX), peroxisomes, and endoplasmic reticulum; however, the main contributors in CKD are NOX and mitochondria, mainly in the tubular segments of the nephron [47,48]. Although mitochondria canonically generate 1–3% of electron leakage, inducing low ROS production, mitochondria dysfunction produces ROS in large amounts during CKD [49]. Furthermore, ROS produced by NOX is upregulated in CKD, which increases oxidative stress [50]. During CKD, hemodynamic changes and hypertrophy induce ROS overproduction, which might activate hypoxia-inducible factor (HIF)-1α, triggering lipid accumulation (Figure 1). This mechanism implies the repression of carnitine palmitoyl transferase 1 A (CPT1A), the rate-limiting enzyme of β-oxidation in mitochondria [51]. Additionally, high ROS levels promoting oxidative stress led to peroxisome proliferator-activated receptor γ co-activator 1α (PGC-1α) deactivation, downregulating β-oxidation, and contributing to fatty acid (FA) accumulation [52]. Indeed, dyslipidemia might be caused by oxidative stress due to high ROS levels leading to PGC-1α deactivation, downregulating β-oxidation, and contributing to FA accumulation [52]. This mechanism could be explained since PGC-1α interacts with peroxisome proliferator-activated receptor-alpha (PPAR)-α to regulate FA metabolism through genes involved in β-oxidation [52]. Thus, the dysregulation of PGC-1α leads to the downregulation of β-oxidation genes, inducing a decrease in FA oxidation into mitochondria (Figure 1). Additionally, ROS overproduction might lead to the oxidation of lipids in the membranes, which further increases cell damage [53]. Another protein affected by ROS is nuclear factor erythroid 2-related factor 2 (Nrf2), which is commonly downregulated in CKD [54]. In contrast, the levels of Kelch-like-ECH associated protein-1 (Keap-1), a negative regulator of Nrf2, are upregulated, possibly contributing to low levels of Nrf2 [55]. The decrease in Nrf2 has been related to FA metabolism alterations. In line with this, in type 2 diabetes, the low levels of CPT1A and acetyl-CoA carboxylase (ACC) were rescued by sulforaphane (SFN), a potent Nrf2 activator, suggesting that Nrf2 reduction decreases the levels of these proteins [55]. Supporting this, our group has recently reported that SFN alleviated FA metabolism dysfunction in the unilateral ureteral obstruction (UUO) model by downregulating cluster of differentiation 36 (CD36) levels and the FA synthesis proteins, such as FA synthase (FAS), sterol regulatory-element binding protein 1 (SREBP1) and diacylglycerol O-acyltransferase 1 (DGAT1), as well as triglyceride levels in the renal tissue [56]. These data suggest that the restoration of Nrf2 results in improving lipid metabolism impairment in the renal damage caused by obstruction. Interestingly, Nrf2 overactivation might have deleterious consequences in dyslipidemia [57]. This hypothesis is sustained due to Nrf2 regulating the transcription of CD36 by positioning in its promoter region. Following the latter, in a model of atherosclerosis, the upregulation of Nrf2 leads to the transcription of CD36, which causes free-cholesterol accumulation due to the presence of high levels of FA [58]. However, additional studies are required to determine the effect of Nrf2 overactivation in other kidney disease models. Oxidative stress promotes atherosclerosis by modifying the lipoproteins and proteins involved in FA metabolism. For instance, intermediate LDL and LDL are accumulated in uremia, which causes the oxidation, carbamylation, or glycation of apoB contained in these lipoproteins [59]. The oxidation of LDL-cholesterol produces oxidized (Ox)-LDL-cholesterol. 4-hydroxy-2-nonenal (4-HNE) is the most abundant aldehyde in Ox-LDL-cholesterol and malondialdehyde (MDA); MDA has been found in the plasma of CVD patients [59]. The accumulation of Ox-LDL-cholesterol can also damage the mitochondria, increasing the leakage and the subsequent production of ROS and later oxidative stress [60]. Additionally, macrophages induce Ox-LDL-cholesterol uptake, forming macrophage foam cells in the walls of the vessels, which also cause even more oxidative stress. In this way, oxidative stress promotes atherosclerotic plaque development [60]. In summary, hemodynamic changes and hypertrophy induce ROS, causing the inactivation of Nrf2. The decrease of Nrf2 decreases β-oxidation through the activation of HIF-1α. Moreover, Nrf2 low levels increase CD36 expression and the levels of FA biosynthesis enzymes. ROS also deactivates to PGC-1α, promoting the downregulation of β-oxidation genes. These alterations lead to LDL accumulation and oxidation. Ox-LDLs are the leading factors for atherosclerotic lesions development (Figure 1). Inflammation is present during CKD, supporting kidney damage through the release of cytokines, chemokines, and other molecules that lead to the recruitment of macrophages, neutrophils, and lymphocytes to the damage site [61]. These inflammatory cells secrete additional molecules, inducing a vicious cycle that contributes even more to damage. Fibrosis is a part of the repair process that develops in response to injury. However, the dysregulation of fibrosis causes an overproduction of extracellular matrix proteins, mainly collagen [22,62]. In kidney diseases, both inflammation and fibrosis go hand in hand. For example, secretion of tumor necrosis factor (TNF)-α, an activator of nuclear factor-kappa B (NF-κB), results in the production of transforming growth factor (TGF)-ß by fibroblasts [63]. In the same way, in renal interstitial fibrosis, the infiltration of inflammatory cells, mainly lymphocytes and macrophages, promotes fibrosis through the M2 CD206+ phenotype, leading to various degrees of renal failure [64]. In addition, macrophages can undergo the macrophage-to-myofibroblast transition process, contributing to the fibrotic process [65]. Thus, inflammation and fibrosis are CKD’s leading causes of kidney damage. Inflammation and fibrosis are closely related to metabolic disorders such as dyslipidemia [66]. This relationship is observed through CD36, an integral membrane protein that not only facilities FA uptake but is also related to inflammation and fibrosis [67]. Furthermore, this receptor is expressed in macrophages, inducing the capture of ligands such as apoAI, lipopolysaccharide, FA, and Ox-LDL [68]. A study reported that the overexpression of CD36 on macrophages contributes to foam cell formation and subsequent accumulation, leading to atherosclerotic lesions (Figure 2) [69]. These mechanisms are triggered primarily by CD36 increasing Ox-LDL consumption, which then induces interleukin (IL)-1ß secretion mediated by activation of the nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3) [69]. Furthermore, in hypercholesterolemia-induced CKD, the deletion of CD36 decreases NF-κB, preventing interstitial macrophage infiltration [70]. Additionally, CD36-/- mice showed less fibrosis compared to CD36 wild type, suggesting that the decreased lipid accumulation could prevent inflammation and fibrosis in this model [70]. Therefore, CD36 increase and overactivation promote the maintenance of inflammation and fibrosis in CKD models. Interestingly, the upregulation of CD36 in rodent models has been related to CVD caused by type II diabetes, obesity, and insulin resistance [71,72]. The association between inflammation and dyslipidemia has also been linked through TNF-α in a diabetic nephropathy urine model where the injection of TNF-α caused the accumulation of cholesterol and favored apoptosis [73]. This study indicates that inflammatory markers promote the dysregulation of lipid metabolism. In addition, the cytokine tumor necrosis factor-like weak inducer of apoptosis, a member of the TNF-α family, induces PGC-1α downregulation via NF-κB [74], suggesting that β-oxidation might be altered due to the upregulation of inflammatory pathways. The kidneys are highly energy-demanding organs [75,76], and the mitochondria principally sustain adenosine triphosphate (ATP) production in these organs to carry out the reabsorption process [77,78,79]. The principal substrates used by kidneys are FA, metabolized via β-oxidation [80,81,82,83]. In contrast, glycolytic pathway contribution is strongly limited under normal physiological conditions [84,85,86]. Mitochondrial dysfunction is a common pathology observed in several types of CKD [75,76,87]. In CKD, the activation of lipogenesis pathways decreases β-oxidation and mitochondrial biogenesis through PGC-1α and PPAR-α reduction [88]. In this context, mitochondria fail to respond to the CKD-induced ATP demand increase [87,89,90], which induces a metabolic reprogramming characterized by the shift from mitochondrial-based to anaerobic metabolism [91,92]. Additionally, the increase in triglyceride synthesis and FA uptake proteins has been observed since the early stages of CKD [76,93], favoring lipid deposition in nephrons [92,93,94]. Likewise, FA release from phospholipids is also stimulated [76]. Therefore, metabolic reprogramming has been suggested as a critical factor that allows dyslipidemia in CKD [76,95,96]. CKD patients and experimental models have shown that elevated protein levels of CD36 indicate an increase in lipid uptake [97]. CD36 also increases PPAR-γ abundance and produces positive feedback increasing CD36 and FA binding protein (FABP), favoring the lipid droplets formation and their later accumulation in the kidneys [97]. According to the latter, dyslipidemia also is developed in the nephrectomy model due to metabolic reprogramming, which increases FA synthesis and decreases mitochondrial β-oxidation in the kidney [98,99]. In experimental models, the reduction of mitochondrial electron transport complexes activity in the kidney [90] produces an increase in FA release to the bloodstream and their posterior accumulation in other organs, particularly the liver [96]. Furthermore, altered lipid metabolism in the liver is observed during CKD. Liver FA synthesis increases, followed by FAS and ACC abundance elevation. The β-oxidation is also decreased via PGC-1α/PPARα/CPT1A reduction [99]. In the liver, CD36 is also increased [96], promoting FA and VLDL synthesis. Together, these data suggest that impairing mitochondrial β-oxidation, electron transport system activities, and biogenesis favor metabolic reprogramming. This enhances renal lipids uptake and its accumulation, promoting dyslipidemia in CKD. CKD is a syndrome that involves a variety of symptoms that must be treated to prevent their progression and the development of other complications. Managing CKD requires reducing cardiovascular risk, arterial hypertension, nephrotoxins, acidosis, and dyslipidemia [100]. Although different therapies are used during CKD treatment, some cautions must be considered. For example, RAAS inhibitors are utilized to slow the progression of CKD; however, recent studies have found that these inhibitors might cause hyperkalemia. In contrast, discontinuation of RAAS inhibitors is associated with an increased risk of initiation of dialysis and cardiovascular mortality [101]. Diuretics are the first-line treatment in acute decompensated HF; however, close monitoring is needed to avoid dangerous side effects in patients [8]. Potassium-sparing diuretics, such as amiloride, are used primarily in combination with thiazide or loop diuretics to prevent hypokalemia, and their diuretic effect is low. In contrast, aldosterone receptor antagonists and potassium-sparing diuretics can induce hyperkalemia, mainly in patients with renal dysfunction [8]. Furthermore, when renal function declines to eGFR <30 mL/min, thiazide diuretics are ineffective and cause hypokalemia and nocturia [101]. Although loop diuretics are the most common for HF and acute renal dysfunction, their short half-life and hemodynamic changes are their principal limitations. In addition, these diuretics might produce ototoxicity. Moreover, high doses of diuretics are often associated with increased serum creatinine and mortality, but data are inconclusive [102]. Vasodilators are often used in patients with preserved or elevated blood pressure to alleviate symptoms and improve hemodynamics; however, vasodilators increase stroke volume and cardiac output [103]. Other approaches to reduce dyslipidemia include pharmacological therapy with statins to lower cholesterol [104], ezetimibe, fibrates to reduce FA and triglycerides, niacin (HDL-increasing drug), and bile acid-binding resins [27,105,106]. Statins and fibrates are the most common treatments for dyslipidemia; however, these drugs could produce myopathy in the long term or in combination. Moreover, these drugs do not correct the lipid problem [107]. In this sense, managing dyslipidemia implies lifestyle modification and dietary interventions, such as reducing sugars, saturated fats, and salts [32]. Following the latter, treating severe hypercholesterolemia and very high-risk atherosclerotic CVD involves combining dietary and pharmacological therapies. However, its exclusive use is sometimes the most effective [108]. Therefore, searching for treatments that help significantly reduce dyslipidemia without modifying other parameters in patients is urgently needed. Moreover, it is necessary to use better alternatives that correct CKD symptoms without damaging other organs, preventing its progression, and avoiding the consumption of different drugs by the patients. Turmeric (Curcuma longa) root has multiple properties, such as antioxidant and anti-inflammatory, showing beneficial effects on health [109]. Their principal active molecules are curcumin, bis-dimethoxy curcumin, demethoxycurcumin, and tetrahydro curcumin [110]. In addition, other synthetic curcumin derivates have shown high bioavailability and reabsorption [111]. Curcumin can be administered as concentrates or purified turmeric, curcuminoids (95%), or curcumin alone [112]. Most orally administered curcuminoids are excreted in the feces and urine. Therefore, very low is detected in blood plasma [113]. Low bioavailability has been linked to their lipophilic properties, difficulty absorbing water and acidic or neutral pH, and rapid metabolism to inactive metabolites [114]. The bioavailability of curcuminoids is a problem that prevents taking advantage of their benefits [115]. Therefore, different strategies have been implemented to increase curcuminoid availability [114]. Several formulations have been intended to enhance solubility and distribution to augment curcumin’s bioavailability [112]. Carriers or delivery systems’ synthetic compounds may increase curcuminoids’ bioavailability [114]. Some of the most common have included micelles, liposomes, phospholipids, microemulsions, nano-emulsions, emulsions, solid lipid nanoparticles, gelatin or polysaccharides, nanostructured lipid carriers, biopolymer nanoparticles and microgels [112,116,117]. Conjugated curcumin to phospholipidic carriers has increased its antioxidant capacities compared to when it is free [118]. Other strategies, such as liposomal curcumin (e.g., chitosan-coated curcumin and Lallemantia iberica seed gum nanoparticles), allow the correct encapsulation of curcumin and show an improvement in the mucoadhesive property [119]. The mucoadhesive property suggests prolonged adsorption in the gastrointestinal tract and has been shown to treat cancer effectively [120]. Recent techniques have been applied with outstanding results in different diseases. Magnetic nanoparticles provide multifunctional properties due to their controlled application. In this sense, magnetic-guide targeting in the delivery of curcumin diethyl γ-aminobutyrate, a carbamate prodrug of curcumin, has proved to be effective in cancer treatment due to its poor water solubility and improved delivery [121]. Other techniques include emulsion-based delivery systems used in the food industry to protect active ingredients against extreme conditions. One example is nanoemulsions formed with oil and emulsifiers that proved to augment the anti-inflammatory properties of curcumin in a model of 12-O-tetradecanoylphorbol-13-acetate-induced edema of mouse ear [122]. Curcuminoids’ complex formation is difficult due to their physicochemical features [114]. Therefore, more recent carriers have tried nanostructured lipid carriers with liquid and solid lipids. The ultrasonication method allows the encapsulation of whole turmeric into nanostructured lipid carriers. The technique can maintain turmeric’s physicochemical properties and stability. Moreover, nanostructured lipid carriers protected gastric conditions, suitability, and safety for oral delivery, improved release control, and bioaccessibility compared with free turmeric [114]. The beneficial role of delivery systems in curcuminoids has been extensively proven. Alkaline conditions and organic solvents do not mimic those of the gastrointestinal tract and are very susceptible to auto-degradation. Therefore, careful experiments must be carried out [112,122,123]. Furthermore, more experimental and clinical studies are obligatory to prove curcumin’s beneficial effects in other models. Taken together, the studies showed that the availability of curcuminoids could be more feasible, and their clinical and basic research study is plausible and reproducible. To date, the study of curcuminoid carriers to improve their bioavailability in CKD models has yet to be carried out. In current experimental models, the vehicles used include water [124], carboxymethyl cellulose [96], and yoghurt [125]. In patients, commercial curcumin is first given in juices, water [126], and capsules [127]. In line with this, curcumin is generally administered along with dietary lipids or lecithin to enhance its absorption. They are mainly found in food ingredients such as eggs, dairy products, or vegetable oils, facilitating tissue bioavailability and concentration [128] and making their use possible in clinical practice. It has been suggested in preclinical studies that curcumin could be a potent adjuvant to treat various disorders, including renal and cardiovascular damage and dyslipidemia [11]. Several factors might contribute to the progression of CKD, including parenchymal cell loss, chronic inflammation, fibrosis, and reduced regenerative capacity of the kidney [22]. The increased plasma creatinine and blood urea nitrogen (BUN) indicates that the kidney’s filtering capacity is diminished, and nitrogenous compounds are accumulating in the bloodstream [129]. In this context, curcumin could be a potential therapy to treat kidney damage at different levels. For example, at two different doses (60 and 120 mg/kg), curcumin improves renal function in rats with 5/6 nephrectomy (5/6NX), being the high doses the most effective [130]. Furthermore, curcumin reduces proteinuria, creatinine, and BUN levels by improving renal hemodynamics [130,131,132]. Similar results have been shown with tetrahydro curcumin at 1% given in food [133] (Table 1). During exercise or strenuous physical activities, water excretion and protein metabolism increase, which could further damage the kidney during CKD [137]. Curcumin (75 mg/kg/day) prevented increased creatinine, proteinuria, and BUN levels in a renal damage model induced by adenine and aerobic exercise stress [138]. Another critical aspect in managing CKD is balancing the diet because hypercaloric diets (western diets) produce metabolic stress, dyslipidemia, and severe damage to kidney tissue [139]. Curcumin (100 mg/kg) administration in mice with CKD exposed to a western diet showed a reduction in the urine ratio of albumin-creatinine compared to a control diet [140]. Thus, curcumin could be used as a potential treatment to prevent the consequences of diet management in CKD patients (Table 1). The improvement in renal function by curcumin also prevents tissue degeneration. Curcumin (120 mg/kg) reversed renal tubular atrophy in 5/6NX rats by promoting the reduction of the mesangial area and mesangial cell proliferation, avoiding the expansion of the glomerular matrix [130]. Moreover, curcumin, combined with other natural compounds at different concentrations, reduced the expression of smooth muscle actin (α-SMA) in NFK-49F cells proving its antifibrotic effect [141] (Table 1). At a 60 mg/kg dose, curcumin prevented renal hypertrophy by reducing interstitial fibrosis and 50% of glomerular and global sclerosis [131,142]. The same effect was observed with tetrahydro curcumin at 1% in food, which reduced approximately 20% of renal fibrosis [133]. The antifibrotic effect of curcumin has been associated with the inactivation of the mammalian target of rapamycin/HIF-1α/vascular endothelial growth factor (mTOR/HIF-1α/VEGF) signaling pathway in vitro (10 and 20 µM doses of curcumin) [136] and in vivo (100 and 200 mg/kg doses of curcumin) [143]. Furthermore, in a nephrosclerosis salt-sensitive model, the antifibrotic effect of curcumin was attributed to the inhibition of histone acetylation in histone 3 lysine 9 (H3K9) [135] (Table 1). On the other hand, it has been well-described that curcumin has an antioxidant effect [144]. For instance, the minimum curcumin antioxidant dose of 60 mg/kg is enough to induce the Keap1/Nrf2 signaling pathway and promote nuclei translocation of Nrf2. This increases the expression, protein levels, and activity of antioxidant enzymes like glutathione peroxidase, glutathione reductase, and superoxide dismutase 1 in the 5/6NX and adenine models [130,131,132,134]. In vitro studies showed that curcumin and demethoxycurcumin decrease ROS levels and MDA content, and increase superoxide dismutase activity, avoiding apoptosis in advanced glycation end products-induced oxidative stress in mesangial cells [145]. In line with this, it has been demonstrated in the 5/6NX model that curcumin reduces the NOX activity in the renal cortex and proximal tubules [132,133]. Other authors hypothesized that curcumin decreases oxidative stress by reducing endoplasmic reticulum stress, preventing apoptosis in podocytes, and improving renal function [146]. These mechanisms were associated with regulating the mitogen-activated protein kinase/extracellular signal-regulated kinase 1/2 (MAPK/ERK1/2) signaling pathway [147]. Thus, one of the principal mechanisms mediated by curcumin is its ability to reverse oxidative stress by avoiding ROS overproduction (Table 1). Curcuminoids also have anti-inflammatory effects in CKD. For instance, in the 5/6NX model, curcumin at 60 mg/kg reduced the interstitial inflammation in the remnant kidney, falling from 50 to 20 macrophages per field and preventing monocyte chemoattractant protein-1 (MCP-1) overexpression [131,132]. In addition, curcumin reduced plasmatic concentrations of TNF-α and IL-6 [140]. Reducing all mentioned cytokines decreases kidney inflammation and stabilizes kidney function. In the cisplatin model, an acute model, intraperitoneal curcumin at 100 mg/kg, prevented macrophage infiltration in the kidney at 24 h. The beneficial effect was achieved by blocking macrophage inducible Ca2+-dependent lectin receptor (Mincle), diminishing spleen tyrosine kinase (Syk)/NF-κB signaling and, therefore, reducing IL-1β, TNF-α, IL-6, and MCP-1 expression [148]. Concerning NF-κB signaling, its canonical activation is given by p65/p50 heterodimer [149], which translocates to the nucleus to induce the expression of proinflammatory cytokines like TNF-α, IL-1, IL-2, IL-6; adhesion molecules such as intercellular adhesion molecule (ICAM)-1, vascular cell adhesion molecule (VCAM)-1, E-selectin, chemokines (e.g., IL-8, MCP-1, regulated on activation, normal T cells expressed and secreted (RANTES)), and inducible enzymes such as cyclooxygenase (COX) 2 and inducible nitric oxide synthase (iNOS) [150]. On the other hand, curcumin also avoids inflammation through arachidonic acid hydrolyzation, inhibiting phospholipase A2 (cPLA2) phosphorylation and decreasing COX1 and COX2 [142]. In the immune nephritis model, 1 g/kg of curcumin for 15 days reduces the periglomerular and perivascular lymphocyte infiltration [151] (Table 1). In CKD patients, curcuminoid’s effects are poorly investigated; however, it has been found that in mononuclear cells isolated from CKD patients, 1–3 mM of curcumin decreases the secretion of Il-6 and IL-1ß and its procoagulant activity [152] (Table 2). A similar effect was seen in the plasma of CKD patients treated with 1 g per day of Meriva® (demethoxycurcumin), which reduced lipid peroxidation and plasma pro-inflammatory mediators like MCP-1, IFN-γ, and IL-4 [153]. In hemodialyzed patients, 2.5 g of turmeric (the whole root) decreased NF-κB in mononuclear cells, TNF-α plasma levels, and regulated gut microbiota [126,154,155]. In the early stages of renal failure, curcuminoids in combination with Boswellia serrata influenced IL-6 and prostaglandin E2 plasma concentrations, avoiding CKD progression [156]; however, more studies are required to determine the mechanisms involved in improving renal function by curcuminoids (Table 2). The protective role of curcuminoids has been probed in preclinical models of CKD and concurrent cardiovascular alterations [159]. For example, in the heart of nephrectomized rats, curcumin prevented macrophage infiltration and reduced the inflammasome component levels NLRP3, apoptosis-associated speck-like (ASC), and caspase-1, preventing inflammasome activation. The latter avoided IL-1β release, reducing inflammatory levels [160]. Administration of curcumin at doses of 60 or 120 mg/kg/day in rats after 5/6NX with or as a prophylactic treatment reverted glomerular and systemic hypertension and improved renal function and structure. The beneficial effects were similar to those of enalapril, an inhibitor of the angiotensin-converting enzyme 2 [130]. In addition, the chronic administration of Theracurmin® (100 mg/kg/day gavage for 5 weeks) in the 5/6NX rat model improved ventricular function and avoided fatal consequences such as heart hypertrophy and interstitial fibrosis by reducing beta myosin heavy chain (ß-MHC) and Col I levels [160]. In the same model, the administration of tetra hydro curcumin, at a dose of 1% in the food per 9 weeks, showed a decrease in systolic and diastolic blood pressure associated with hypertrophy prevention [133]. In line with this, ventricle hypertrophy and dilatation were prevented through the reduction of glycogen synthase kinase 3 beta (pGSK-3ß), ß-catenin and nuclear factor of activated T-cells (NFAT) levels [142] (Table 3). In the 5/6NX model, 100 mg/kg/day of curcumin for 16 weeks decreased arteriosclerotic lesions [140], while 120 mg/kg reduced necrotic lesions in mice exposed to a western diet [161] by preventing the tissular remodeling process through the reduction of matrix metalloproteinase 2 (MMP-2) and the activity of gelatinase. These processes might be related to activating the phosphatidylinositol 3 kinase/protein kinase B/extracellular signal-regulated kinase (IP3K/AKT/ERK) signaling pathway [142,162]. Therefore, the studies mentioned above suggest curcumin could also be used as an alternative adjuvant or therapy to prevent cardiovascular side effects related to hypertrophy, cardiac remodeling, and ventricular function during CKD (Table 3). The effect of curcumin on dyslipidemia has been determined in diabetes and obesity models, demonstrating beneficial results [163]. Since the liver is the main lipid metabolism organ, most studies have used it to assess the curcumin effect on dyslipidemia in this organ. For instance, curcumin prevents hepatic lipotoxicity in diabetic and obese models, modulating the metabolism of cholesterol by forming bile acids and increasing the oxidation of fatty acids. At the same time, curcumin increases serum HDL and the activity of lipases that prevents the increased uptake of fatty acids [163]. Recent studies in obese rats treated with curcumin (80 mg/kg) and Garcinia mangostana (400 mg/kg) for 6 weeks showed that curcumin reduces oxidative stress, increases HDLc, and decreases LDLc sera levels [164]. In the high-fat diet induced-diabetic mice model, treating tetra hydro curcumin at 100 mg/kg for 12 weeks decreased the renal damage markers and cholesterol and triglycerides levels. The authors proposed that tetra hydro curcumin deactivates the renin-angiotensin system, which reduces oxidative stress. The reduction in oxidative stress causes a decrease in lipid levels, attenuating dyslipidemia [165]. In diabetic patients, a meta-analysis suggests that curcumin supplementation could lower LDL, TG, and TC levels in complicated type two diabetes [166]. Furthermore, in metabolic syndrome patients, a syndrome associated with diabetes development, 200 mg/day of curcumin reduces HDLc, LDL, TG, and TC serum levels [167]. In the obesity and diabetes protocols, it also has been reported that curcumin decreased dyslipidemia, attributed to its binding to lipids in the intestine [168]. The proposed molecular mechanism is mediated by cyclic adenosine monophosphate (cAMP) responsive element binding protein (CREB)/PPAR signaling pathway, which increases cAMP concentrations to promote lipid oxidation [169]. In adipose tissue, curcumin inactivates the AKT/mTOR signaling pathway, preventing adipogenesis, FA uptake, and triglyceride formation [170]. Curcumin also increased paraoxonase 1 activity and lipoprotein lipase abundance in plasma and the liver, promoting lipoprotein lipids hydrolysis and their oxidation in the tissues [124,125]. In an in silico study, curcumin showed a particular interaction with ADIPOQ and PPARG genes, both are closely related to lipid metabolism [171]. In C57BL/6J mice with renal injury induced by a high-fat diet, the treatment with bisdemethoxycurcumin at 20 and 40 mg/kg avoided lipid accumulation, oxidative stress, and improved plasma lipid levels through Nrf2/Keap1 [172]. Thus, curcumin has an antihyperlipidemic role in CKD related to these pathways. Few studies have evaluated the effects of curcumin on serum lipids during CKD. Currently, some attempts have found that curcumin modulates lipid metabolism in renal tissue and decreases serum and liver triglycerides, cholesterol, free FA, and LDL levels. In experimental models such as the 5/6NX, the administration of 75 mg/kg of curcumin for 11 weeks corrected the serum lipid profile by decreasing LDL, total cholesterol, and total triglycerides and increasing HDL levels [173], suggesting that curcumin has a positive effect on serum lipids unbalance. Supporting the latter, in the adenine CKD model, curcumin treatment with 100 mg/kg increased HDL cholesterol while decreasing total cholesterol, triglycerides, LDL cholesterol, VLDL, and non-esterified FA (NEFA) [174]. The authors also found that triglycerides and NEFA levels in the liver decreased, but cholesterol levels increased. This could be partly explained because increased serum HDL concentrations led to increased cholesterol uptake in the liver, which produced further metabolization and elimination via the bile [174]. Interestingly, the authors reported that the atherogenic and the coronary risk index also decreased, suggesting that the correction of lipid profile by curcumin influences cardiovascular alterations [174]. According to the latter, the decrease of LDL and VLDL reduces the formation of atheroma, a severe consequence of dyslipidemia [175]. Recently, our group determined a possible mechanism in 5/6 NX-induced CKD. We found that curcumin corrects dyslipidemia by improving renal mitochondrial β-oxidation function. This prevents lipid accumulation, its distribution, and FA uptake by the liver [96], suggesting that the kidney is the origin of dyslipidemia (Table 4 and Figure 3). CKD is characterized by a progressive decline in renal function, which triggers several pathological mechanisms, resulting in CVD consequences. Among them, dyslipidemia plays a crucial role in CVD development. Dyslipidemia is strongly related to oxidative stress, inflammation, metabolic reprogramming, and fibrosis in renal tissues. These pathological processes worsen renal disease and increase the plasmatic lipid levels, which results in metabolic lipid alterations in other tissues, like the liver and heart. The current drugs used to overcome these pathophysiological mechanisms produce side effects and are only sometimes optimal for all CKD types and populations. Recent studies have shown that curcuminoids may improve lipid disorders in diabetes and obesity. Moreover, a potential therapy for CKD-induced hyperlipidemia has been given. The administration of curcuminoids reverses CKD-induced metabolic reprogramming, avoiding the decrease in β-oxidation and preventing mitochondrial damage. Therefore, curcuminoids might avoid the accumulation of lipids in renal tissue. In addition, curcuminoids reverse increased FA uptake and synthesis, closely related to the decrease in oxidative stress and pro-inflammatory and pro-fibrotic processes in the kidney, reducing the release of lipids into the bloodstream. The curcuminoid’s protection also might decrease the pathological processes associated with the development of CVD during CKD by regulating dyslipidemia. Although the decrease in cardiovascular damage has been shown in several CKD experimental models, further investigation should be generated to determine the effects of curcumin in patients with CKD.

PMC10000741

Elodie Villalonga,Christine Mosrin,Thierry Normand,Caroline Girardin,Amandine Serrano,Bojan Žunar,Michel Doudeau,Fabienne Godin,Hélène Bénédetti,Béatrice Vallée

LIM Kinases, LIMK1 and LIMK2, Are Crucial Node Actors of the Cell Fate: Molecular to Pathological Features

04-03-2023

LIMK,actin dynamics,cytoskeleton remodelling,signalling pathways

LIM kinase 1 (LIMK1) and LIM kinase 2 (LIMK2) are serine/threonine and tyrosine kinases and the only two members of the LIM kinase family. They play a crucial role in the regulation of cytoskeleton dynamics by controlling actin filaments and microtubule turnover, especially through the phosphorylation of cofilin, an actin depolymerising factor. Thus, they are involved in many biological processes, such as cell cycle, cell migration, and neuronal differentiation. Consequently, they are also part of numerous pathological mechanisms, especially in cancer, where their involvement has been reported for a few years and has led to the development of a wide range of inhibitors. LIMK1 and LIMK2 are known to be part of the Rho family GTPase signal transduction pathways, but many more partners have been discovered over the decades, and both LIMKs are suspected to be part of an extended and various range of regulation pathways. In this review, we propose to consider the different molecular mechanisms involving LIM kinases and their associated signalling pathways, and to offer a better understanding of their variety of actions within the physiology and physiopathology of the cell.

LIM Kinases, LIMK1 and LIMK2, Are Crucial Node Actors of the Cell Fate: Molecular to Pathological Features LIM kinase 1 (LIMK1) and LIM kinase 2 (LIMK2) are serine/threonine and tyrosine kinases and the only two members of the LIM kinase family. They play a crucial role in the regulation of cytoskeleton dynamics by controlling actin filaments and microtubule turnover, especially through the phosphorylation of cofilin, an actin depolymerising factor. Thus, they are involved in many biological processes, such as cell cycle, cell migration, and neuronal differentiation. Consequently, they are also part of numerous pathological mechanisms, especially in cancer, where their involvement has been reported for a few years and has led to the development of a wide range of inhibitors. LIMK1 and LIMK2 are known to be part of the Rho family GTPase signal transduction pathways, but many more partners have been discovered over the decades, and both LIMKs are suspected to be part of an extended and various range of regulation pathways. In this review, we propose to consider the different molecular mechanisms involving LIM kinases and their associated signalling pathways, and to offer a better understanding of their variety of actions within the physiology and physiopathology of the cell. In 1994, LIM kinase 1 (LIMK1) was discovered simultaneously by the teams of Mizuno [1] and Bernard [2]. It was then described as the first kinase protein seen to contain LIM domains. The LIM kinase family was extended a year later with the discovery of LIM kinase 2 (LIMK2), which has a shared sequence of nearly 51% with LIMK1. Even though they are closely related, LIM kinases display cell-type-specific expression and different subcellular localisation [3]. LIM kinase expression patterns were first established in 2006 by Sumi and by Acevedo [3,4]: LIMK1 is particularly expressed in the brain, heart, skeleton muscles, kidneys, and lungs [5,6], while LIMK2 is more widely expressed in adult and embryonic tissues [4]. Canonically, LIM kinases act as downstream effectors of the members of the Rho GTPase family, including Rho, Rac and Cdc42, which modulate LIMK activity via their effectors, Rho-associated protein kinases (ROCK), myotonic dystrophy kinase-related Cdc42-binding kinases (MRCKα), and p21-activated kinases (PAK), PAK1, PAK2, and PAK4. LIMK1 and LIMK2 are activated by phosphorylation of Thr508 and Thr505, respectively [7,8]. LIM kinases are involved in cytoskeleton dynamics by independently remodelling both actin filaments and microtubules. Their most extensively described substrates are members of the actin depolymerising factor/cofilin (ADF/cofilin) family: cofilin1 (non-muscle cofilin, or n-cofilin), cofilin2 (muscle cofilin, or m-cofilin), and destrin (also known as actin depolymerising factor, or ADF), usually regrouped under the term cofilin. Cofilin was discovered as the first substrate of LIMK1 and LIMK2 in 1998 [9,10] and 1999 [7], respectively. Once activated, LIM kinases inactivate the ADF/cofilin proteins by phosphorylating their Ser3, rendering them unable to sever actin polymers and inducing the accumulation of filamentous actin (F-actin), actin stress fibre formation, and impacting cytoskeleton dynamics [7,9,10]. Independently of their activity on the actin cytoskeleton, it has been shown that LIM kinases play a role in microtubule turnover by favouring free tubulin formation [11,12], but the molecular implication of the kinases in this process remains to be elucidated. As there is growing evidence that LIM kinases are crucial node actors of the cell life fate, in this review, we will recapitulate the role of LIMKs in different cellular events and pathologies, and emphasize the molecular actors involved in these processes. The LIM kinase (LIMK) family of proteins is composed of only two highly related members, LIMK1 and LIMK2, which are encoded by separate genes located on the human chromosomes 7q11.23 and 22q12.2, respectively [13]. There are alternative splicing results in the generation of two LIMK1 mRNAs: one encodes the full length protein, while the other one results in a truncated protein, missing the beginning of the N-terminal first LIM domain [14]. LIMK2 has three isoforms resulting from this alternative splicing: LIMK2a, LIMK2b, and LIMK2-1 [15,16]. While LIMK2a represents the full-length transcript, LIMK2b lacks half of the first LIM domain. LIMK2-1 differs from its two counterparts with the presence of an extra protein phosphatase 1 inhibitory (PP1i) domain in its C-terminal extremity and a slightly truncated kinase domain [16,17]. A testis-specific LIMK2 isoform, tLIMK2, which lacks LIM domains at the N-terminus due to the usage of a testis-specific alternative initiation exon, is highly expressed in male germ cells and has also been described in mice [18,19]. LIMK1 and LIMK2 have the same domain organisation, with two amino-terminal LIM domains, an adjacent PDZ domain, a serine/proline rich region, and a carboxyl-terminal kinase domain (Figure 1). LIMK1 and LIMK2 share a 50% overall sequence identity, and that percentage reaches 70% in the kinase domain [13,20]. The LIM domains (named for the transcription factors Linl1, Isl1, and Mec-3), each composed of two zinc fingers, could play a role in protein–protein interaction, along with the PDZ domain, as well as in protein-to-DNA interaction. The PDZ domain (named for the post-synaptic density protein 95, Drosophila disc large tumour suppressor, and Zonula occludens-1 protein), in addition to its protein–protein interaction function, contains two leucine-rich nuclear export signals (NES) and plays a role in nuclear/cytoplasmic shuttling. The C-terminal extremity of LIMKs contains a nuclear localisation sequence (NLS) that could indicate a preferential localisation in the nucleus [21,22]. It also contains an atypical kinase domain that is able to phosphorylate serine and threonine, as well as tyrosine, due to an unusual consensus sequence (DLNSHN motif) in the subdomain VIB of the catalytic site [13]. The structural aspects of LIMK regulation and pharmacology are more extensively described in the review of Chatterjee et al., which belongs to the Special Issue LIM Kinases: From Molecular to Pathological Features [23]. LIM kinases were initially identified as kinases that are located downstream of the members of the Rho family of small GTPases, RhoA, Rac1, and Cdc42. Canonically, LIMK1 and LIMK2 are activated by p-21 activated kinases (PAK1,2,4), myotonic dystrophy kinase-related Cdc42-binding kinases (MRCKα), and Rho-associated protein kinases (ROCK1 and ROCK2), via the direct phosphorylation of Thr508 and Thr505, respectively. The phosphorylation of LIMKs on their activation loop leads to an activation of their kinase activity, resulting in a higher amount of phosphorylated cofilin on their Ser3, and its subsequent inactivation and cytoskeleton remodelling [7,8] (Figure 2). LIMKs’ phosphorylation of cofilin is counteracted by phosphatases dephosphorylating phospho-cofilin: SSH (slingshot phosphatases), PP1 (protein phosphatase 1), PP2A (protein phosphatase 2A), and CIN (chronophin). Other activators of LIMK activity have been reported. They were extensively described in the review of Manetti [20], and we will proceed with an exhaustive update (Figure 3 and Figure 4). Protein kinase A (PKA) phosphorylates LIMK1 at Ser596, and, to a lesser extent, at Ser323. As Ser596 is not conserved in LIMK2, it may play a role in the distinct regulation of LIMK1 and LIMK2. Ser596 phosphorylation with PKA increases the LIMK1 kinase activity on cofilin and induces actin cytoskeleton remodelling [24]. Upon VEGF-A (vascular endothelial growth factor A) activation, p38-MAPK (mitogen-activated protein kinase) phosphorylates LIMK1 on its Ser310 without affecting its kinase activity on cofilin. MK2, a MAPKAPK-2 (mitogen-activated protein kinase-activated protein kinase 2) and a downstream kinase of p38-MAPK, also phosphorylates LIMK1, but on its Ser323, leading to an increase in its kinase activity on cofilin [25]. Upon DNA damage, p53 upregulates LIMK2 expression via its binding to an intronic consensus p53 binding site of LIMK2. p53 is a transcription factor that regulates the transcription of various genes that are implicated in cell cycle arrest, autophagy, and apoptosis, upon DNA damages [26]. This regulation is LIMK2-isoform-dependent, as LIMK2b and LIMK2-1, but not LIMK2a, are upregulated, and leads to G2/M arrest via a cofilin phosphorylation increase [26,27]. Birkenfeld et al. have shown an interaction between LIMK1 and 14-3-3ζ, a member of the 14-3-3 protein family that is involved in cell signalling, cycle control, and apoptotic death, via yeast two-hybrid screening and GST pull-down experiments [28]. 14-3-3ζ also interacts with cofilin. 14-3-3ζ was shown to preferentially interact with the phosphorylated form of cofilin and to protect it from phosphatases, thus prolonging its inactivation [29]. An interaction between LIMK1, but not LIMK2, and p57Kip2 (cyclin-dependant kinase inhibitor) was shown through co-immunoprecipitation experiments [30,31]. This interaction is independent of p57Kip2 activity and not mediated by ROCK activation. LIMK1–p57Kip2 interaction leads to an increase in cofilin phosphorylation and to actin cytoskeleton remodelling. LIMK1 was shown to autophosphorylate [6]. This autophosphorylation is rather a transphosphorylation, as it was shown to be promoted by homodimerization that was mediated by Hsp90. Indeed, interactions between LIMK1 and Hsp90, as well as between LIMK2 and Hsp90, were detected via co-immunoprecipitation experiments. A proline plays a crucial role in this interaction. The transphosphorylation of LIMK results in its stabilization and highly increases its lifespan. Upon BDNF (brain-derived neurotrophic factor) stimulation, TrkB (Tropomyosin-related kinase B, a tyrosine kinase receptor) was shown to dimerize, leading to LIMK1 dimerization, transphosphorylation, and stabilisation, with a re-localisation from cytoplasm to membrane, resulting in an increased level of phospho-cofilin. TrkB and LIMK1 were shown to interact together by yeast two-hybrid screening and by co-immunoprecipitation experiments. TrkB kinase activity is not required to induce LIMK1 dimerization. LIMK1 was also shown to interact with TrkA and TrkC, two TrkB homologues [32]. LIMK2 activation by Aurora kinase A (AURKA) has also been reported [33]. As AURKA is well known for its implication in cancer development, its role in LIMK2 activation will be more extensively discussed in the breast cancer subsection. LIM kinase activity is finely up- but also down-regulated, and several proteins are able to negatively regulate this (Figure 4). Nischarin, a protein involved in intracellular signalling, forms a complex with PAK and LIMK1. Nischarin was shown to interact specifically with the phosphorylated form of LIMK1 on Thr508 via co-immunoprecipitation experiments, leading to its dephosphorylation and inhibition, and resulting in lower amounts of phospho-cofilin and impaired cancer cell invasion [34]. Nischarin acts synergistically with LKB1 (liver kinase B1) to decrease the phosphorylation of PAK, LIMK, and cofilin, reducing stress fibres and inhibiting cell migration and invasion [35]. LATS1 (large tumour suppressor kinase 1) was shown to interact with LIMK1 by co-immunoprecipitation experiments, leading to a lower amount of phosphorylated cofilin. LATS1 and LIMK1 co-localise at the actomyosin contractile ring. The inhibition of LIMK1by LATS1 reverts the LIMK1-induced cytokinesis defects [36]. Slingshot phosphatase (SSH, also known as SSH-1 or SSH1), initially described as cofilin phosphatase, also dephosphorylates and inactivates LIMK1 and LIMK2. SSH was shown to interact with LIMKs by co-immunoprecipitation experiments, and neither the catalytic activity of LIMK2, nor that of SSH, are required for this interaction. SSH dephosphorylates LIMK1 on Thr508, but also on trans/auto-phosphorylated residues, leading to decreased cofilin phosphorylation. A complex SSH1/LIMK1/Actin/14-3-3ζ was identified, and PAK4 was shown to interact with SSH1, leading to its phosphorylation and inhibition [37]. Upon PAR-2 (protease activated receptor) activation, a complex between the scaffold β-arrestin, cofilin, chronophin, and LIMK is formed, leading to cofilin dephosphorylation and actin filament severing. The proteins β-arrestin-1 and 2 were shown to interact with LIMK through co-immunoprecipitation experiments. The PAR-2 inhibition of the LIMK activity towards cofilin requires β-arrestin and triggers the LIMK re-localisation to membrane protrusions [38]. The RING finger E3 ubiquitin ligase (Rnf6) is highly expressed in the axons of developing neurons during mouse embryogenesis, and can influence the axon outgrowth of cultured hippocampal neurons. Rnf6 binds to and catalyses the polyubiquitination of LIMK1, which leads to its degradation by the proteasome in growth cones. An interaction between Rnf6 and LIMK1 was shown by co-immunoprecipitation experiments. In the presence of Rnf6, the LIMK1 lifespan is reduced 5-fold (4 h vs. 20 h) [39]. Par-3, a member of the polarity proteins involved in the formation of tight junctions, was shown to inhibit LIMK2, but not LIMK1. Par-3 was shown to interact with LIMK2 by co-immunoprecipitation, leading to its inhibition, as phospho-cofilin levels were strongly reduced in its presence [40]. LIMKs were also shown to interact with other proteins without phosphorylating them (Figure 4). The orphan nuclear receptor Nurr1 was shown to interact with LIMK1 via GST pull-down. LIMK1 inhibits the Nurr1 transcriptional activity, but the molecular requirements of this inhibition have not been elucidated [41]. LIMK1 was also shown to interact with paxillin, but not with vinculin, through co-immunoprecipitation experiments, although it colocalizes with actin, paxillin, and vinculin at the focal adhesions in fibroblasts [5]. Fascin-1, an actin crosslinking protein, was shown to interact with LIMK1 and LIMK2 by FRET and co-immunoprecipitation experiments. The LIMK phosphorylation by ROCK on Thr505 and Thr508 is required for this interaction, but LIMK kinase activity is not [42]. LIM kinases were also shown to play a pivotal role in the coordination of microtubules and actin filament dynamics. They interact with both actin and tubulin. The ROCK phosphorylation of LIMK1 increases its interaction with actin, whereas it decreases its interaction with tubulin, as was shown by co-immunoprecipitation experiments [11]. LIMK1 overexpression leads to microtubule destabilisation, and its kinase activity is required for this process. LIMK1 was initially thought to phosphorylate TPPP1 (tubulin polymerisation promoting protein 1), but it was then shown that TPPP1 was the substrate of ROCK [43]. Actually, a trimeric complex between LIMK1, TPPP1, and HDAC6 (histone deacetylase 6) was identified. ROCK inhibition stabilises this complex, whereas its activation dissociates it. This trimeric complex leads to HDAC6 inhibition and a subsequent increase in microtubule Lys40 acetylation, which renders microtubules more resilient to mechanical breaks, leading to longer-lived MT [44]. On the contrary, it decreases the cofilin phosphorylation level, resulting in the destabilisation of actin stress fibres. LIMK overexpression leads to tubulin acetylation and microtubule stabilisation, whereas TPPP1 overexpression leads to lower levels of phosphorylated cofilin and stress fibre disruption [45]. Cofilin was the first substrate of LIMK that was identified [9,10]. The other substrates of LIMKs have been described since then (Figure 4). The transcription factor cAMP-responsive element binding protein (CREB), a transcription factor that regulates the genes responsible for cell proliferation, differentiation, and survival, is phosphorylated by LIMK1 on its Ser133. An LIMK1–CREB interaction was shown via co-immunoprecipitation experiments [46]. Upon VEGF activation, LIMK1 phosphorylates annexin 1, a calcium- and phospholipid-binding protein, in an in vitro assay (32P labelling). This interaction is thought to regulate endothelial cell migration upon VEGF stimulation [47]. A trimeric complex between LIMK, Orb2, and Tob was also described. Drosophila Orb2 belongs to the cytoplasmic polyadenylation element binding proteins (CPEB), and binds RNA and regulates translation. Tob (transducer of Erb-B2) is known to induce Orb2 oligomerisation. More recently, it was shown that LIMK phosphorylates Tob, then associates with Tob to phosphorylate Orb2, leading to its stabilisation and oligomerisation. Orb2 oligomerisation plays a major role in long-term memory [48]. The membrane type-1 matrix metalloproteinase (MT1-MMP/MMP14) interacts with LIMK1 and LIMK2 in co-immunoprecipitation experiments, which triggers its phosphorylation on Tyr573. This phosphorylation leads to MT1-MMP-positive endosome association with cortactin, an f-actin binding protein, resulting in invadopodia formation and matrix degradation. LIMK1 and LIMK2 seem to play different roles in this process, as LIMK1 is involved in the cortactin association with MT1-MMP in positive endosomes, while LIMK2 is implicated in invadopodia-associated cortactin [49]. As cytoskeleton remodelling plays a vital role in the life of the cell, LIM kinases are involved in several physiological processes, including cell migration, cell cycle, apoptosis, and neuronal differentiation, which will be developed in the following paragraphs. Cell migration is essential to numerous physiological processes, such as embryogenesis, neuronal development, immune response, and wound repair. Cell morphogenesis during migration requires the fine spatiotemporal remodelling of the cytoskeleton and can be divided into four steps: (i) the protrusion of the leading edge of the cell (filopodia and lamellipodia, resulting from cytoskeleton polymerisation), (ii) the attachment of the cell front via focal adhesions (FA), (iii) the contraction of the whole-cell body through the interaction between myosin and actin in FA-anchored stress fibres, and (iv) the detachment and retraction of the rear by FA disassembly [50]. The implication of the Rho family of small GTPases in cell migration is well documented [51,52,53]. As they are the downstream effectors of small Rho GTPases, LIMKs play a role in this process. Moreover, the initial characterisation of LIMKs as kinases that phosphorylate cofilin pointed out their role in actin cytoskeleton dynamics and cell migration [9,10]. The involvement of LIMKs in microtubule remodelling is another way for them to be involved in cell migration [11]. The role of LIMKs in the polarised migration of immune cells upon chemokine stimulation, i.e., chemotaxis, was particularly emphasized. Indeed, LIM kinases and SSH-1 appear to play a key role in the cofilin-driven assembly and disassembly of protrusions [54,55]. Nishita et al. showed that, upon LIM kinase knockdown, lamellipodium and cell migration are suppressed, while SSH-1-KD cells display an impaired directional cell migration. These authors defined that LIMK1 worked synergistically with SSH-1 to assure the deactivation of cofilin at the leading edge of the chemokine-stimulated Jurkat T cells. At the rear, the activation of cofilin allows for the renewal of G-actin for the actin polymerisation in protrusions [54,55]. LIMK1 is required for cell migration by stimulating the lamellipodium formation in the first stages of the cell response, whereas SSH1 restricts this migration to one direction. Rac activation is required for the LIMK1-mediated SDF-1 (stromal cell-derived factor-1) chemotactic response, but not Rho or Cdc42 [56]. Upon EGF stimulation, LIMK1 was also shown to be involved in actin nucleation at the leading edge and the subsequent lamellipod extension in the metastatic adenocarcinoma cell line [57]. Two other partners of LIMKs were shown to play a role in LIMK-induced cell migration. In response to VEGF, MAPK/MK2/LIMK1 pathway activation leads to annexin 1 phosphorylation and activation, which triggers endothelial cell migration and tubulogenesis. LIMK1 was shown to directly phosphorylate annexin 1 in an [γ32P]-ATP labelling on LIMK1 immunoprecipitation, upon VEGF stimulation of human umbilical vein endothelial cells (HUVECs). p38-MAPK is required for this phosphorylation [47]. Fascin-1, an actin crosslinking protein that plays a role in the assembly of cell protrusions, is also associated with a complex of activated LIM kinases, promoting filopodia stabilisation. Indeed, LIMK1/fascin-1 interaction was shown by FRET and FLIM experiments, as well as by His pulldown. LIMK1 activation via ROCK is required for this interaction and for the subsequent filopodia formation and stability [42,56]. Finally, cell migration is dysregulated in cancer, and LIM kinases have been associated with higher tumour invasion properties and metastasis, indicating that the dysregulation of LIMK-mediated cell migration can lead to tumorigenesis [58,59,60,61]. Indeed, many inhibitors targeting LIMK activity have been shown to affect cell migration, as it is described in Berabez et al.’s review, published in the Special Issue LIM Kinases: From Molecular to Pathological Features [62]. Cell division is a complex and highly regulated mechanism that requires the fine remodelling of actin filaments and microtubules [63]. As main regulators of cytoskeleton dynamics, LIM kinases are involved in this process. LIMK localisation was extensively studied during the cell cycle and it was shown that LIMKs display different cellular localisation, depending of the stage of the cell cycle of HeLa cells [3]. During interphase and prophase, LIMK1 is associated with cell–cell adhesion sites and re-localises at the spindle poles during metaphase [3]. It disappears from these regions during late anaphase and redistributes at the contractile ring and cleavage furrow during telophase/cytokinesis. During interphase, LIMK2 is diffused throughout the cytoplasm. It re-localises at the spindle pole during prophase and redistributes at the mitotic spindles during metaphase and early anaphase along the spindle microtubules. As the cells progress through late anaphase and telophase, the LIMK2 localises at the spindle midzone, where it was seen to co-localise with microtubules [3]. These observations indicate that LIMK1 and LIMK2 might play different roles during the course of the cell cycle. LIMK localisation during mitosis was also studied on other cell lines: LIMKs are located at the centrosomes and at the cleavage furrow of MDA-MB-231 breast cancer cells and DU145 prostate cancer cells. Phospho-LIMK, but not LIMK, co-localises with gamma-tubulin in centrosomes. An interaction between phospho-LIMK, but not LIMK, and gamma-tubulin, was detected by co-IP on crude nuclear extracts [64]. SSH1 localisation was also determined in HeLa cells during mitosis. It appears at the actin cortex during metaphase and redistributes at the cleavage furrow during anaphase and telophase [65]. The similar localisation of SSH1 and LIMKs suggests a fine regulation of cofilin phosphorylation/dephosphorylation, and thus, the inactivation/activation progresses through the cell cycle and actin filament remodelling. Furthermore, different studies have shown the transient activation of these proteins along the cell cycle progression [3,66,67]. In synchronised HeLa cells, LIMK1 is hyperphosphorylated and activated during prometaphase and metaphase, gradually returning to the basal levels of the phosphorylation and activity in telophase and during cytokinesis. As for LIMK, cofilin phosphorylation is increased during prometaphase and metaphase, with a gradual return to basal levels during telophase and cytokinesis, indicating that the LIMK1 hyperphosphorylation activated its kinase activity towards cofilin [67]. The activity of LIMK2 did not change after the cells were released from an S phase cell cycle block. However, when the cells were treated with nocodazole or taxol to disrupt the microtubules and induce an M-phase block, LIMK2 was activated, suggesting that LIMK2 might be responsive to a spindle checkpoint [3]. The SSH1 activity decreased during early mitosis and returned to basal levels during telophase and cytokinesis, resulting in cofilin dephosphorylation and activation [65]. LIMK2 hyperphosphorylation during the early stages of mitosis was also observed, but to a lesser extent than that of LIMK1 (1.8-fold versus 6.4). This hyperphosphorylation leads to the enhanced phosphorylation of cofilin. This activation seems to be mediated via the LIM/PDZ domains of LIMK1, as no increased activity on the cofilin by the restricted kinase domain of LIMK1 was observed. Furthermore, LIMK1-Thr508 phosphorylation is required for this process, but not via ROCK or PAK activation [67]. This activation may occur via cyclin-dependent kinase (Cdk), as it is eliminated in the presence of roscovitine, a Cdk inhibitor [66]. LIMK misexpression leads to an aberrant cell cycle progression. Indeed, the inhibition of the LIMK1 activity during mitosis leads to a delay in mitotic progression (metaphase to anaphase) and irregular spindle positioning [68,69,70]. On the contrary, the overexpression of LIMK1 or a phosphatase-inactive SSH1 results in increased levels of phosphorylated cofilin and the production of multi-nucleated cells [67]. Upon ROCK activation in NIH-3T3 mouse fibroblasts, LIMK2, but not LIMK1, induces cyclin A expression and decreases p27Kip1 expression, which is a Cdk inhibitor, thus regulating the progression through the G1 to S phase. p57Kip, another Cdk inhibitor, was shown to interact with LIMK1, but not LIMK2, by co-immunoprecipitation on HeLa cells. It directly promotes LIMK1 phosphorylation, but not on Thr508, and without going through ROCK. Furthermore, the Cdk inhibitory activity of p57Kip2 is not required for its LIMK-mediated kinase activation on cofilin. Therefore, this cell cycle progression control by p57Kip2 occurs via its direct activation of LIMK1 [30]. Furthermore, LIMK1, but not LIMK2, was shown to interact with LIC1 and LIC2 (dynein light chain) via its kinase domain, through co-immunoprecipitation. LIMK1 promotes LIC1 and LIC2 phosphorylation on tyrosine residues. LIC1 and LIC2 are two subunits of the huge complex that forms the cytoplasmic dynein 1 motor, and defines the cargo specificity of dynein. LIMK1 seems to affect dynein motor function, as the kinase dead mutant of LIMK1, D460A, reduces the speed of PLK1 trafficking (a cargo of the dynein motor). Furthermore, LIC1 and LIC2 rescued the aberrant cell cycle progression induced by LIMK1 depletion (multipolar spindle, centrosome spread length, and spindle pole density). LIMK1 could regulate the trafficking of pericentriolar proteins by dynein cargo transportation via LIC1 and LIC2 phosphorylation [69]. Apoptosis is a mechanism of tightly regulated programmed cell death, and an essential process in development and cell homeostasis. Apoptosis may be induced by extrinsic stimuli (death receptor pathway) or intrinsic stimuli (mitochondrial pathway), leading to caspase proteolytic pathway activation [71]. A drastic change in cell morphology occurs during apoptosis: shrinkage, bleb formation, and rounding-up. The actin cytoskeleton has been linked to these apoptotic phenotypes [72]. Fas receptor, also known as Fas, FasR, apoptosis antigen 1 (APO-1 or APT), cluster of differentiation 95 (CD95), or tumour necrosis factor receptor superfamily member 6 (TNFRSF6), is a cell death surface receptor. The treatment of Jurkat and HeLa cells with an anti-Fas antibody leads to the cleavage and activation of LIMK1. The produced LIMK1 N-terminally truncated fragments are constitutively active and stimulate membrane blebbing when they are overexpressed, ultimately leading to cell death. The pre-treatment of cells with benzyloxycarbonyl-Asp(OCH3)-Glu(OCH3)-Val-Asp(OCH3)-fluoromethylketone (z-DEVD-fmk), an inhibitor for caspase-3 or related proteases, blocked the appearance of these LIMK1 fragments, as well as LIMK1 activation, suggesting that the cleavage of LIMK1 is mediated by these caspases. Indeed, LIMK1 is cleaved at a short motif DEID within the PDZ domain, more precisely at aspartic acid 240, while the caspase-3 and related proteins cleave the protein at a DEXD site. Interestingly, LIMK2 does not possess the DEID motif, suggesting that the cleavage-mediated LIMK activation is LIMK1-specific. Moreover, LIMK1 silencing using siRNA partially suppresses membrane blebbing. The caspase-mediated specific cleavage and activation of LIMK1 might play a decisive role in the membrane bleb formation during apoptosis [73]. Apoptosis is induced in the prostate cancer cells LNCaP (hormone sensitive), as well as in DU145 (hormone insensitive), when stimulated with membrane androgen receptor agonist testosterone-BSA (a non-permeable steroid albumin conjugate). In these conditions, Rho, ROCK, and LIMK2 are activated. LIMK2 immunoprecipitated extracts from the testosterone-BSA-treated DU145 exhibit a higher kinase activity compared to untreated cells, with an increase in phospho-LIMK and phospho-destrine by [γ32P]-ATP labelling. This increase, as well as apoptosis induction, is lost when cells are pre-treated with the ROCK inhibitor Y27632 [74]. Apoptosis is also induced in the colon cell line HCT116 upon tumour necrosis factor (TNF) treatment. In these conditions, an increase in LIMK phosphorylation on its Thr508 and the subsequent phosphorylation of cofilin are observed. Death-associated protein kinase (DAPK), a protein implicated in programmed cell death, could act as a scaffold protein for the LIMK/cofilin complex in TNF-induced apoptosis. The DAPK/LIMK/cofilin complex is abrogated once the cells are committed to apoptosis [75]. Indeed, LIMK and cofilin are co-immunoprecipitated with DAPK in extracts from the cells treated with TNF. Thus, DAPK promotes a closer interaction between LIMK and cofilin, resulting in a higher phosphorylation of cofilin by LIMK. DAPK kinase activity is required for this increase, as it is lost when the cells are pre-treated with a DAPK inhibitor. Furthermore, TNF induces the re-localisation of the DAPK/LIMK/cofilin complex in the perinuclear compartment. p57Kip2 has been shown to enhance mitochondrial-mediated apoptosis via the activation of LIMK1 and the stabilisation of the actin cytoskeleton. It has been shown to interact with LIMK1, leading to LIMK1 activation in HeLa cells [30]. Apoptosis triggered by staurosporine, an alkaloid with antibiotic properties, is mediated by LIMK1 in the HeLa cells overexpressing p57Kip2. Indeed, in these conditions, the silencing of LIMK1 leads to a decrease in apoptotic nuclei, Cas3 activity, and PARP cleavage. Furthermore, hexokinase-1, an inhibitor of the mitochondrial voltage-dependent anion channel, is displaced from mitochondria, inducing mitochondrial depolarisation and apoptotic cell death [76]. Upon a genotoxic stress, increases in RhoGTP, phospho-LIMK, and phospho-cofilin are observed, as well as cytoskeleton rearrangement with cell flattening and enhanced stress fibres. An increase in LIMK2-1 and LIMK2b, but not LIMK2a, at the protein and mRNA levels is also observed, requiring p53 transcriptional activity and leading to cell survival. LIMK2 inhibition was shown to sensitise cells to DNA-damage-induced apoptosis [27]. Hence, the LIMK inhibitors associated with genotoxic compounds could constitute an efficient alternative therapy to treat the cancer cells that are resistant to chemotherapy. Indeed, LIMK2 has been shown to be mis-regulated in these resistant cells [12,77]. LIMK2 is also involved in a particular form of neuronal cell death (necrosis) that results from status epilepticus (SE). SE is defined by a single long-lasting or multiple successive life-threatening seizures. Kim et al. have shown that the neuronal death in rat CA1 neurons is driven by a cyclin D1-CDK4 complex upon SE, and that this complex leads to the overexpression of LIMK2. When overexpressed, the LIMK2 impairs dynamic-related protein-1 (DRP1)-mediated mitochondrial fission by stabilising F-actin and preventing DRP1-actin interaction. Thus, it induces mitochondrial elongation and neurotoxicity. LIMK2 silencing via siRNA prevents the downregulation of the DRP1 and mitochondrial elongation induced by SE [78]. These authors further elucidated the stimulus leading to this LIMK2-mediated neuronal necrosis in an SE context. ET-1, a vasoactive peptide produced by vascular endothelium, and its brain receptor ETB, are rapidly increased upon SE. BQ788, an ETB antagonist, diminished the SE-induced neuronal damage via the alleviation of ROCK1 upregulation and a reduction in the LIMK2 protein and mRNA expression [79]. A TE-1 injection into the hippocampus of a normal rat leads to an increase in the LIMK2 mediated by ETB, as well as in mitochondria elongation and sphere formation, confirming the data obtained upon SE. LIMKs have been shown to play a crucial role during development. Ribba et al. notably focus on embryonic development in their review, which was published in the Special Issue LIM Kinases: From Molecular to Pathological Features [80]. Here, we choose to concentrate on neurodevelopment, synaptic plasticity, memory, and brain functions, as they are among the most well described functions of LIM kinases, along with their function in cytoskeleton remodelling. These features have been extensively detailed in several exhaustive reviews [81,82,83]. We will focus on the most striking data, especially that in KO mouse models. Very quickly after their discovery, the implication of LIMKs in neurodevelopment was pointed out in knockout (KO) mice. Indeed, LIMK1 seems to be involved in dendritic spine regulation, because LIMK1 KO mice neurons exhibit abnormal dendritic spines, which are longer, thinner, and immature. Accordingly, the spine heads of these LIMK1 KO neurons display a reduced amount of actin filaments, indicating that LIMK1 plays a critical role in spine morphology through actin filament remodelling. Indeed, the phospho-cofilin levels were higher in LIMK1 KO mice neurons [84]. LIMK2 also seems to play a role in spine regulation. Although LIMK2 KO mice have mild synaptic dysfunction, LIMK1 and LIMK2 double KO mice exhibited aggravated effects compared to that of the LIMK1 KO on the synaptic function, suggesting that there might be a compensatory mechanism upon the loss of LIMK1 or LIMK2 [85]. Several studies suggest that PAKs, as well as ROCK2, could be the upstream activators of LIMKs, with regard to their function in spine regulation [86,87,88]. There is evidence that LIM kinases also play a role in long-term potentiation (LTP) and long-term depression (LTD), two mechanisms that underlie synaptic plasticity, learning, and memory processes. Meng et al. showed that LIMK1 KO mice exhibited enhanced hippocampal LTP, as well as altered fear responses and spatial learning. F-actin depolymerising toxins increased the early-phase LTP (E-LTP) in wild-type mice, an effect that was abolished in the LIMK1 KO mice, suggesting that the role of LIMK1 on E-LTP is mediated by the actin cytoskeleton [84]. In 2015, Todorovski et al. showed that LIMK1 KO mice were drastically impaired in long-term memory (LTM) but not short-term memory (STM), and were defective in late-phase long-term potentiation (L-LTP), a form of long-lasting synaptic plasticity that is specifically required for the formation of LTM. They also showed that L-LTP-deficient mice were rescued by a pharmacological increase in the cAMP response element binding protein (CREB) activity [89]. CREB, as a transcription factor that regulates the genes responsible for cell proliferation, differentiation, and survival, is critical for LTM establishment. LIMK1 and CREB were shown to interact by two-hybrid screening and co-IP experiments, as well as in different parts of the brain [46], and LIMK1 has been shown to phosphorylate CREB [89]. Membrane trafficking is a complex process that allows for the delivery of specific cargo (proteins and macromolecules) via transport vesicles towards the dedicated location. LIMK1 has been shown to be involved in membrane trafficking from the Golgi apparatus. Rosso et al. showed that LIMK1 is enriched in the Golgi apparatus of developing neurons, with its LIM domain triggering this localisation. LIMK1 seems to regulate Golgi dynamics, as it is involved in the tubule-vesicular process. Indeed, when overexpressed, LIMK1 abrogates the formation of trans-Golgi-derived tubules and prevents cytochalasin D-induced Golgi fragmentation, as well as the Golgi export of synaptophysin-containing vesicles. LIMK1 kinase dead mutant has the opposite effect. LIMK1 enhances the accumulation of Par3/Par6, insulin-like growth factor 1 (IGF-1) receptors, and the neural cell adhesion molecule (NCAM) at growth cones, suggesting that it plays a crucial role in the delivery of these proteins to growth cones. These results suggest that a key role is played by LIMK1 in the Golgi dynamics and membrane trafficking in neurons [90]. Salvarezza et al. also reported the implication of LIMK1 and cofilin in the trafficking of proteins out of the Golgi apparatus in Madin–Darby canine kidney cells (MDCK). They showed that LIMK1, but not LIMK2, is localised at the Golgi apparatus, where it induces a specialised population of actin filament apparatus that is required for the emergence of an apical cargo route to the plasma membrane (PM), with a high specificity. Indeed, LIMK1, but not LIMK2, regulates the exit from the trans-Golgi network (TGN) of the apical PM marker p75 neurotrophin receptor and NHR2, a related receptor, but is not involved in the exit of another apical PM marker glycosyl phosphatidylinositol (GPI), nor the basolateral PM marker neural vell adhesion molecule (NCAM). The overexpression of a kinase dead LIMK1 mutant, a constitutively activated cofilin, or the use of LIMK1 siRNA, selectively slowed down this exit from the TGN. These authors also showed that in p75 carrier vesicles, LIMK1 cooperates with dynamin 2, and cortactin and syndapin, two dynamin-interacting proteins for the fission processes of the vesicle from the TGN [91]. To our knowledge, the role of LIMK1 in membrane trafficking was only documented by these two papers. Because they play a crucial role in actin cytoskeleton remodelling, cell shape, proliferation, and motility, there is growing evidence that LIM kinases are involved in tumour cell invasion, tumour growth, and metastasis. Indeed, LIMK1 and LIMK2 have been shown to be upregulated in breast cancer [58], gastric cancer [59], prostate cancer [92,93], and malignant melanoma cells [94], and they seem to be involved in multiple non-canonical signalling pathways that, when dysregulated, actively participate in tumorigenesis. With 2.3 million diagnoses in 2020, breast cancer is the most common and deadliest form of cancer in women [95]. LIMK1 and LIMK2 have been reported to be overexpressed in breast cancer [58,96], and there is growing evidence of their implication in molecular pathways with interactors linked to breast cancer tumorigenesis (Figure 5). Aurora kinase A (AURKA) is overexpressed in several types of cancer, including breast cancers, where it plays a role by aberrantly phosphorylating the proteins implicated in the cell cycle, ultimately leading to cell malignant transformation [97]. LIMK2 directly interacts with AURKA via its LIM domains in coIP experiments, which increases its levels by inhibiting its ubiquitin-dependent degradation and restraining its localisation to the cytoplasm. AURKA is also able to activate LIMK2 by the phosphorylation of its Ser283, Thr494, and Thr505, resulting in: (i) the increased kinase activity on cofilin, (ii) higher levels of LIMK2 via the prevention of its ubiquitin-dependent degradation, and (iii) the restraining of its localisation in the cytoplasm. Reciprocally, LIMK2 is required for AURKA-mediated cellular transformation in breast cancer, indicating that LIMK2 is a key oncogenic effector of AURKA in breast cancer malignancy [33]. Serine-arginine protein kinase 1 (SRPK1) has also been described as a target of LIMK2, and this connection plays a major role in triple negative breast cancer metastasis. Through SILAC experiments that used LX7101, an inhibitor of LIMKs, Malvi et al. have shown a strong decrease in SRPK1 phosphorylation on several serines (Ser7, Ser9, Ser51, Ser309, and Ser311) [96]. SRPK1 is involved in the splicing of pre-messenger mRNA, a mechanism which is imbalanced in malignant tumour cells. When overexpressed, SPRK1 is responsible for resistance to apoptotic signals [98], resistance to cisplatin therapy [99], and enhanced metastasis [100]. LIMK2 is overexpressed in triple-negative breast cancer (TNBC) and pharmacological inhibition, with LX7101 or TH-257, leads to the inhibition of the metastatic characteristics of TNBC cells (migration, invasion, actomyosin contractility, and extracellular matrix degradation). No direct interaction by coIP was detected between SRPK1 and LIMK2. However, an in vitro kinase assay pointed out the serine phosphorylation of SPRK1 on a recombinant protein, validated by immunoblotting. The inhibition of SRPK1 blocked the metastatic properties of TNBC cells, which led to the thought that LIMK2 promotes the metastatic progression of triple-negative breast cancer by activating SRPK1. The pharmacological inhibition of LIMKs by LX7101 inhibits metastatic progression in mice, but has no effect on primary tumour growth [96]. As LX7101 is a dual inhibitor of LIMKs and their upstream regulating kinase ROCK, we cannot rule out a role of ROCK in this phenomenon. However, another assay with the LIMK inhibitor Pyr1 on xenografted mice that were developing breast tumours and paclitaxel resistance showed a blockage of primary tumour growth, but not of their spread [101]. Pyr1 prevents cofilin phosphorylation by inhibiting LIMK1 and LIMK2 in vitro and in cellulo on HeLa cells. Pyr1 is a selective inhibitor of LIMK1: on a panel of 66 kinases, there were only three hits (MLK1, NEK11, and LIMK1), with the highest inhibition observed for LIMK1 (4% residual in vitro kinase activity). A thermal stability shift assay confirmed Pyr1 selectivity for LIMK1 [70]. LIMK1 and LIMK2 were also shown to interact with the membrane-anchored type-1 matrix metalloproteinase (MT1-MMP, or MMP14) in triple-negative breast cancer. To be able to spread, cancer cells need to degrade the extracellular matrix (ECM). In breast carcinoma, the dissemination of metastasis involves MT1-MMP, which participates in the degradation of ECM and is overexpressed in several cancers. A direct interaction between LIMK1/LIMK2 and MT1-MMP has been shown by coIP via the “DVK” motif of MT1-MMP. MT1-MMP phosphorylation by LIMKs on Tyr573 modulates its interaction with cortactin, a F-actin-binding protein. Lagoutte et al. showed that LIMK1 regulates the cortactin association with MT1-MMP-positive endosomes and associates with the MT1-MMP in endosomes, while LIMK2 participates in the formation of invadopodia-associated cortactin pools, indicating that both are necessary for MT1-MMP-induced matrix degradation and the tumour cell invasion in breast tumour [49]. Prostate cancer is the second most common cancer in men after lung cancer [102]. LIMK1 [92] and LIMK2 [93] are overexpressed in prostate cancer and prostate cell lines, and numerous LIMK partners that are dysregulated in prostate cancer have been discovered over the last years. The group of Shah has been particularly active in demonstrating the role played by LIMK2 in prostate cancer over the last years. They pointed out several proteins that are directly phosphorylated by LIMK2, and then ubiquitinated and subsequently degraded by the proteasome. These different partners also regulate the LIMK2 stability by promoting its ubiquitination. Many feedbacks loops are described [93,103,104,105] (Figure 6). Nikhil et al. depicted Twist-related protein 1 (TWIST1) as a new partner of LIMK2. TWIST1 is a transcription factor involved in embryonic development and organogenesis [93]. It is overexpressed in many cancers, where it drives tumour initiation, angiogenesis, dissemination, and drug resistance [106]. In adults, TWIST1 expression is limited to the quiescent stem cells located in mesenchymal tissues, but it is upregulated following androgen deprivation therapy (ADT) via TGF-β signalling, mediating the cancer prostate aggressiveness and development of castration-resistant prostate cancer (CRPC), a metastatic form of prostate cancer. LIMK2 and TWIST1 are interconnected in a synergic feedback loop. In an in vitro kinase assay with purified proteins, the authors show that LIMK2 phosphorylates TWIST1 on four different serines (Ser45, Ser78, Ser95, and Ser199), resulting in a decreased level of the ubiquitination of TWIST1 and, thereby, its stabilisation. This TWIST1 phosphorylation by LIMK2 is required for cell growth and migration, promotes epithelial–mesenchymal transition (EMT), and is crucial for tumorigenesis in vivo in xenograft mice. TWIST1 phosphorylation and stabilisation via LIMK2 also increases LIMK2 levels by inhibiting its ubiquitination and thus, its degradation. LIMK2 silencing decreases the TWIST1 mRNA levels under hypoxic conditions, but not under normoxia, whereas it decreases the TWIST1 protein levels under both conditions. Furthermore, the sequential treatment of prostate cancer cell line C4-2 with docetaxel, a well-established treatment for CRPC, and then with an LIMK2-specific inhibitor [107], resulted in a significant cellular death [93]. This high synergy is very promising for the future treatment of hormone-independent CRPC. SPOP, an E3 ubiquitin ligase adapter that is involved in numerous cellular mechanisms, is another partner of LIMK2. SPOP is the most mutated gene in CRPC, and a vast majority of these identified mutations alter its ability to ubiquitinate some of its oncogenic targets [108]. No direct interaction between LIMK2 and SPOP was detected by coIP with endogenous proteins. However, in an in vitro assay on purified proteins, SPOP is directly phosphorylated by LIMK2 on Ser59, Ser171, and Ser226, which causes its retention in the nucleus, and decreases its stability by increasing its ubiquitination. SPOP phosphorylation with LIMK2 decreases its ability to: (i) degrade its targets cMyc, androgen receptor (AR), and androgen receptor splice variant-7 (Arv7) by ubiquitination, (ii) inhibit cell proliferation and migration, (iii) prevent tumorigenesis in mice, and (iv) reduce EMT in vivo. Conversely, SPOP targets LIMK2 for ubiquitination and participates in its degradation via the proteasome [103]. Hence, LIMK2 and SPOP are part of a double negative feedback loop. SPOP also interacts with AURKA through coIP on endogenous proteins, which directly phosphorylates it on three sites (Ser33, Thr56, and Ser105), causing its ubiquitination and degradation. SPOP also degrades AURKA via a feedback loop [104]. The positive feedback loop existing between AURKA and LIMK2 [33] could be an aggravating factor to prevent the SPOP tumorigenesis inhibition. Upon hypoxia, in 22Rv1, PC-3, and LN95 prostate cancer cell lines, LIMK2 is upregulated and phosphorylates the phosphatase and TENsin homolog protein (PTEN) at five sites (Ser207, Ser226, Ser360, Ser361, and Ser362), causing its ubiquitination and degradation. No direct interaction via coIP was reported, but an in vitro kinase assay with purified proteins depicts a direct phosphorylation of PTEN by LIMK2. PTEN is a phosphatase involved in the regulation of the cell cycle. When dysregulated because of gene mutations or post-translational modifications, PTEN will drive the development of CRPC. PTEN is also known to be downregulated in hypoxic tumours, following castration via androgen deprivation therapy (ADT). LIMK2 also inhibits its lipid phosphatase activity upon phosphorylation. They are engaged in a negative regulatory loop, since PTEN promotes the degradation of LIMK2 by ubiquitination as well [109]. Homeobox protein NKX-3.1 is a prostate-specific transcription factor and tumour suppressor protein, which plays a role in cell differentiation, maintenance, and lineage plasticity. Its genetic loss is strongly associated with prostate cancer development. Encoded by an androgen-responsive gene, NKX-3.1 is downregulated following ADT and the subsequent hypoxia. In an in vitro assay using purified proteins, Sooreshjani et al. showed that NKX-3.1 is phosphorylated by LIMK2 on its Ser185. This phosphorylation is required for NKX-3.1 ubiquitination and its subsequent degradation, leading to enhanced cellular growth and migration. Moreover, LIMK2 also regulates NKX-3.1 at the mRNA level. NKX-3.1, in return, promotes LIMK2 ubiquitination and degradation, linking both proteins in a negative feedback loop [105]. Bowen et al. also established a link between NKX-3.1 and PTEN, where PTEN dephosphorylates NKX-3.1 at Ser185, thus enhancing its stability [110]. PTEN, which is downregulated in prostate cancer, has been shown to be an LIMK2 substrate which phosphorylates it and participates in its degradation [109]. Thus, the relations existing between LIMK2, PTEN, and NKX-3.1 could be an aggravating factor in CRPC development. The LIMK2 targets described above are tumour suppressor proteins, whose degradation upon ubiquitination is promoted by LIMK2 phosphorylation, and is a major step in tumorigenesis progression. LIMK1, which is found to be upregulated in prostate cancer samples and cancer cell lines, also seems to play a role in prostate cancer pathogenesis [92]. Mardilovich et al. depicted a correlation between elevated LIMK1 expression and activity, high phospho-cofilin levels in prostate cancer patient samples, and poor survival in non-metastatic prostate cancer. LIMK2 expression in non-metastatic PC showed a similar trend, but further investigations are needed. However, LIMK inhibition with a selective LIMK inhibitor, LIMKi 3 (BMS-5), reduced cell motility, inhibited proliferation, and increased apoptosis in androgen-dependent PC cells more effectively than in androgen-independent PC cells. Indeed, LIMKi 3 (BMS-5) has an inhibitory effect on AR nuclear translocation and AR-αTubulin interaction, leading to its retention in the cytoplasm and its subsequent degradation. Since LIMKs are well-described effectors of cytoskeleton remodelling as they are implicated in both actin filament and microtubule rearrangements, an involvement of LIMKs in AR regulation is probable [111]. Hepatocyte growth factor (HGF) has also been linked to the enhancement of prostate carcinoma cell proliferation and invasiveness [112,113]. Ahmed et al. showed that HGF, to which PC-3 prostate cancer cells respond in a chemotactic way, was able to activate PAK4. This activation leads to a specific HGF-driven PAK4/LIMK1 interaction, LIMK1 phosphorylation and activation, cofilin phosphorylation, and an increase in cell motility. Thus, the interaction between PAK4 and LIMK1 could be an essential factor in prostate cancer invasiveness [114]. The term “leukaemia” regroups different kinds of malignant disorders of the blood and bone marrow. They are characterised by an abnormally high leucocyte count in blood and/or bone marrow and are subdivided in multiple subtypes [115]. The most common one is acute myeloid leukaemia (AML), an aggressive form of leukaemia for which there are very few therapies. LIMK1 and LIMK2 have been described as potential targets for treating AML. In the human acute monocytic leukaemia cell line THP-1, an interaction between PKCζ and LIMK1, but not LIMK2, was detected by coIP experiments upon CSF1 stimulation [116]. CSF-1 cytokine causes a hematopoietic stem cell differentiation into macrophages. PKCζ, an atypical PKC, is required in chemokine-triggered cell adhesion and the actin assembly in polymorphonuclear cells. Furthermore, the KO of PKCζ by siRNA blocked the CSF-1-induced LIMK1 and cofilin phosphorylation in THP-1 cell lines, resulting in impaired migration. All these data suggest that PKCζ may phosphorylate LIMK1 upon CSF-1 stimulation. LIMK1 might trigger the chemoattraction of macrophages by tumour cells upon CSF-1 release and PKCζ activation, leading to tumour invasion and metastasis [116] (Figure 7). Jensen et al. performed an RNA interference screening on three different AML cell lines (U937, U60 and OCI-AML3). They identified LIMK1 as the only gene whose transcripts reduced the cell viability in all these cell lines when targeted by four independent shRNAs. Furthermore, by analysing The Cancer Genome Atlas (TCGA) AML databank, they pointed out a significant association between the high expression of LIMK1 and a shorter survival, and data from the Microarray Innovations in Leukaemia and TCGA AML allowed them to correlate the high LIMK1 mRNA level with the normal karyotype and KMT2A-rearrangement AML. The KMT2A gene encodes for the histone-lysine N-methyltransferase 2A, which works as a positive regulator of gene transcription. As these genetic subtypes are well characterised for recurrent driver alterations, authors have established a significant correlation between a high LIMK1 expression, FLT3 and NMP1 mutations, and KMT2A-rearrangements by further analysing TCGA AML databank. Suppression by shRNA or KO by the CRISPR-Cas9 gene-editing tool of LIMK1 or LIMK2 reduced the colony formation, decreased proliferation, and induced the morphological changes of different AML cell lines and patient-derived xenograft (PDX) samples, indicating a role for LIMKs in leukaemia tumorigenesis. The suppression of LIMK1 by shRNA was also associated with the upregulation of several tumour suppressor genes (EGR1, BTG2, and BIN1) and the downregulation of mitosis-associated genes (HOXA9-associated genes, which are transcription factors). Finally, the authors showed a negative correlation between the LIM kinases and cell division protein kinase 6 (CDK6). CDK6 depletion via shRNA or pharmacological kinase inhibition by Palbociclib leads to a higher level of LIMK1, as well as a higher activity of LIMK1, with an increase in cofilin phosphorylation, and the LIMK1 and LIMK2 suppression leads to an increased CDK6 expression. This negative correlation between LIMKs and CDK6 may be detrimental upon the pharmacological inhibition of both proteins. Authors have shown that the combined inhibition of both CDK6 and LIMKs by Palbociclib and LIMKi 3 (BMS-5), respectively, leads to synergic effects, with decreased proliferation and differentiated morphology [117]. The pharmacological inhibition of LIMKs represents a good therapeutic opportunity in AML. LIMKi 3 (BMS-5), an LIMK inhibitor [118], selectively suppressed the growth of T cell leukaemia (Jurkat and ATN-1), and induced centrosome fragmentation and apoptosis, whereas it had no impact on peripheral blood mononuclear cells [119]. The pharmacological inhibition of LIMKs does appear to be efficient for leukaemia treatment as Pyr1, a selective potent inhibitor of LIMKs, induced a complete survival gain of B6D2F1 mice bearing leukaemia L1210 cells, with no apparent toxicity [70]. Djamai et al. investigated the therapeutic potential of the LIMK1/2 inhibitor CEL_Amide (LIMKi) in FLT3-ITD-mutated (FLT3-ITD+) acute myeloid leukemia (AML). Treatment with LIMKi decreased the LIMK1 protein levels and phosphorylation of cofilin in FLT3-ITD+, MOLM-13, and MV-4-11 cell lines. In MOLM-13 cells, a synergistic effect was obtained when LIMKi was used concomitantly with the FLT3 inhibitors midostaurin, crenolanib, or gilteritinib, while combination experiments with LIMKi and the FLT3 inhibitor quizartinib, hypomethylating agent azacytidine, or ROCK inhibitor fasudil, were additive. Moreover, in NOD-SCID mice that were engrafted with MOLM13-LUC cells, the FLT3 inhibitor midostaurin, or LIMKi alone, delayed the MOLM13-LUC engraftment, and their combination significantly prolonged the survival of leukemic mice. Taken together, these data suggest that the small molecule inhibitor CEL_Amide LIMKi might constitute a novel treatment strategy for FLT3-ITD+ AML, when used in combination with FLT3 inhibitors [120]. The same group evaluated the efficiency of CEL_Amide LIMKi in Philadelphia chromosome-positive (BCR::ABL+) acute lymphoblastic leukemia (ALL), another subtype of leukaemia. In Ph+ (BCR::ABL+) B-ALL, ROCK is constitutively activated, leading to LIMK phosphorylation and thereby cofilin inactivation, and the subsequent abrogation of its apoptosis-promoting activity. In BCR::ABL+ TOM-1 and BV-173 cell lines, and in patient cells, the LIMKi treatment decreased the LIMK1 protein expression, whereas the LIMK2 protein expression was unaffected. Cofilin dephosphorylation was nevertheless observed. The conjoint treatments of CEL_Amide and the BCR::ABL tyrosine kinase inhibitors (TKIs) imatinib, dasatinib, nilotinib, and ponatinib were synergistic for both TOM-1 and BV-173 cell lines. Mice transplanted with CDKN2Ako/BCR::ABL1+ B-ALL cells displayed a prolonged survival when treated with a combination of LIMKi and TKIs, indicating that CEL-Amide might be a promising new therapy for BCR::ABL+ ALL [121]. A comprehensive overview of the role of LIMKs in osteosarcoma is depicted in the review by Brion et al. [122] in this Special Issue, and we will focus here on the main molecular features. Osteosarcoma (OS) is the most common solid bone malignancy in children and adolescents, characterised by the proliferation of malignant mesenchymal cells which produce osteoid and/or immature bone. Lung metastasis is a frequent evolution of the disease and participates in its poor prognosis [123]. Both LIMKs have been shown to play a role in OS malignancy. Several studies have shown that LIMK1 is overexpressed in osteosarcoma through immunohistochemistry on patient tissues. A WB analysis and RTqPCR on different OS cell lines (MG63, U2OS, OS732, and SaOS-2) show the overexpression of LIMK proteins and mRNA, respectively, compared to normal osteoblasts (hFOB 1.19) [124,125,126]. This is correlated with higher migration and invasion propensities, and limited apoptosis. Li et al. have shown that the signalling cascade PAK4/LIMK1/cofilin is activated in OS and associated with metastasis and poor survival [125]. PAK4 knockdown or silencing in MG63 diminishes the cell viability, migration, and invasion, and limits the growth of subcutaneous transplanted tumour in nude mice [125]. Different peptides or proteins (Insulin, EGF, and VEGF) have been shown to promote OS proliferation, migration, and invasion. In trying to understand the molecular mechanism involved in these phenomena, the implication of LIMKs has been pointed out. Insulin promotes the proliferation of MG63 osteosarcoma cells in a time- and dose-dependent manner, and also induces LIMK1 and cofilin phosphorylation. Moreover, upon LIMK1 KO using shRNA, insulin-induced proliferation is significantly inhibited, as well as when the cells were treated with the PI3K inhibitor LY294002. In these conditions, LIMK1 phosphorylation is reduced, indicating a possible role for LIMK1 in regulating the osteosarcoma cell proliferation via the insulin/PI3K/LIMK1 signalling pathway [124]. The epidermal growth factor (EGF) also promotes MG63 osteosarcoma cell migration and invasion, as well as stress fibre formation via RhoA activation. All the proteins of the signal transduction pathway RhoA/ROCK1/LIMK2/cofilin are activated upon the EGF treatment of MG63 cells. Moreover, the selective inhibition of ROCK1, LIMK2, or cofilin in the MG63 cells by shRNA prevents actin stress fibre formation and cell migration. These data delineate a role for RhoA/ROCK1/LIMK2/cofilin signalling in actin microfilament formation, and the migration and invasion increase in the MG63 cells upon EGF activation [127]. OS cell metastasis in lungs is an early stage of the pathology, and VEGFR2 and PD-L2 are overexpressed in lung metastasis. VEGFR2 inhibition by shRNA or by apatinib, a small molecular tyrosine kinase inhibitor, reduced the osteosarcoma cell migration, invasion, and metastatic potential, and downregulated the STAT3 and RhoA/ROCK/LIMK2 pathways. STAT3 silencing decreased the LIMK and cofilin phosphorylation. Moreover, VEGFR2 inhibition by apatinibled reduced the PD-L2 expression in osteosarcoma cells and attenuated the osteosarcoma lung metastasis capacity in vivo [128,129,130]. Accordingly, Ren et al. showed that PD-L2 knockdown attenuated the migration and invasion of OS cells, and decreased the LIMK and cofilin phosphorylation, as well as RhoA activation. It also suppressed EMT and inhibited autophagy by decreasing the beclin-1 expression. Beclin-1 knockdown also led to the inactivation of RhoA, as well as an LIMK2 and cofilin phosphorylation decrease, while PD-L2 knockdown inhibited the OS cell metastasis in lungs but had no effect on the primary tumour size [128]. All these data show that VEGFR2 and PD-L2 promote OS lung metastasis via RhoA/ROCK/LIMK2/cofilin signalling pathways. Bone morphogenetic protein type II receptor (BMPR2) was found upregulated in a majority of the osteosarcoma tissues, and its overexpression has been correlated to a poor overall survival. The depletion of BMPR2 in 143B cells diminished the LIMK and cofilin phosphorylation, as well as the cell migration and invasion in vitro, and increased the EMT. In vivo, BMPR2 depletion has no impact on the primary tumour size, however, it inhibits lung metastasis. BMPR2 was shown to interact with LIMK2 by co-immunoprecipitation experiments on U2OS cells. BMPR2 depletion decreased the LIMK and cofilin phosphorylation, whereas BMPR2 overexpression increased the LIMK and cofilin phosphorylation and activated RhoA by promoting its GTP-bound form [131]. Another study has shown an interaction between LIMK1 and BMPR2 by a yeast two-hybrid screening and by the co-immunoprecipitation of the proteins overexpressed in COS7 (via their LIM and C-terminal domain, respectively), or of the endogenous proteins in fibroblasts. An in vitro phosphorylation assay suggested that BMPR2 inhibited cofilin phosphorylation via LIMK1. Furthermore, it was shown that BMPR2 and PAK4 compete to interact with LIMK1 [132]. The discrepancy between these two studies may be explained by different points: (i) LIMK1 and LIMK2 may behave differently towards BMPR2, and (ii) in OS cells, BMPR2 may interact with another partner, modulating its activity towards LIMKs. Drug resistance is a major problem in osteosarcoma treatment, leading to poor prognosis. Vincristine (VCR) is widely used and is an effective chemotherapeutic agent to treat osteosarcoma. Different studies have explored the molecular mechanism of drug resistance by developing different MG63 cell lines that are resistant to VCR, MG63/VCR [61,126,133]. MG63/VCR appeared to be resistant to many other anticancer drugs (multidrug resistance, MDR), exhibiting a higher migration capacity compared to that of MG63 cells. In MG63/VCR, LIMK1 is overexpressed at both the mRNA and protein levels, with a subsequent elevated cofilin phosphorylation. The knockdown of LIMK1 inhibited the migration of MG63/VCR cells, suggesting that LIMK1 dysregulation contributes to the invasion and metastasis potential of drug-resistant osteosarcoma [61]. MDR-associated genes (MDR1, MRP1, and BCL2) are upregulated in MG63/VCR. MDR1 expression was positively correlated with LIMK1 expression, as was LIMK1 silencing with an enhanced apoptosis in the MG63 cells treated with VCR. These data suggest that LIMK1 is implicated in the MDR of osteosarcoma, probably by inducing MDR1 expression and/or activation and by limiting apoptosis [126,133]. As Rac1/PAK1/LIMK1/cofilin and RhoA/ROCK/LIMK2/cofilin signalling pathways have been shown to be activated in OS pathology, these proteins, especially LIMKs, appear as promising therapeutic targets to treat this disease. Several inhibitors of these pathways have shown promising results. Sea cucumber polysaccharide fucoidan (Cf-Fuc) significantly diminished the migration and adhesion capacity of U2OS cells and the remodelling of the actin cytoskeleton. Cf-Fuc inhibited the phosphorylation of focal adhesion kinase (FAK) and paxillin, as well as LIMK1 and cofilin phosphorylation, and Rac1 activation [134]. 6-hydroxythiobinupharidine that was isolated from Nuphar pumilum also suppressed the migration of murine LM8 osteosarcoma cells by decreasing the expression of LIMK1 and cofilin phosphorylation [135]. Taken together, these results indicate a high potential for therapy-targeting LIM kinases. Glioblastomas (GBM) are the most common and aggressive primary brain tumour in adults, exhibiting a very high infiltration and a very poor prognosis, since their survival time is less than two years upon diagnosis. GBM derive from multiple cell types with neural stem-cell-like properties and require multi-modal therapies, including chemotherapy, radiotherapy, and surgical intervention, which are commonly used [136]. However, despite this tough treatment, most GBM recur; hence, it is necessary to understand the molecular mechanisms of the pathology and to develop new and specific therapies for GBM. Several studies have shown that LIMK1 and LIMK2 are overexpressed and overactivated in patient tissue and different GBM cell lines. Park et al. performed a microarray analysis of the normal brain versus mesenchymal GBM and observed an increase in LIMK and cofilin expression [137]. An analysis of the gene expression data from TCGA and REMBRANDT, and a clinically annotated dataset from several groups confirmed these data: the clinical features of GBM are correlated with changes in LIMK expression. LIMKs are upregulated at the mRNA and protein levels in GBM, and patients with gliomas that exhibit downregulated LIMKs have a better overall survival [60,137,138]. The upregulation of LIMKs is also observed in different GBM cell lines (U87, T98G and U118) [137,138]. As they have been shown to be upregulated in GBM cell lines and patients, and as they play important roles in cell polarisation, migration, and invasion, LIM kinases were thought to be suitable therapeutic targets for the treatment of GBM by inhibition. The knockdown of both the LIM kinases with shRNA significantly reduced the invasion of the GBM cell lines and of the human GBM tumour-initiating cells (TICs), which is a more clinically proximal culture model, as it forms infiltrative tumours when xenografted in mice. Moreover, the tumours derived from LIMK1/LIMK2-knockdowned TICs were significantly smaller, as they grew slower compared to the tumours derived from the control TICs. They also spread at lower rates (with a reduced invasion propensity), resulting in an increase in survival [60]. However, surprisingly, Erktulu et al.’s RNA analysis of the paraffinized tumour tissue from 98 patients that were diagnosed with GBM pointed out that LIMK1 mRNA upregulation is correlated with an increased survival (more than six months) upon surgery [139]. Several proteins have been shown to regulate the LIMK and cofilin in GBM: PKCζ and intersectin-1 [116,140]. PKCζ is an atypical PKC. An interaction between endogenous PKCζ and LIMK1, but not LIMK2, was shown in the LN229 glioblastoma cell line upon EGF stimulation. The knockdown of PKCζ by siRNA in cell lines, as well as in mouse xenografted tumours, resulted in the specific and important impairment of glioblastoma cell migration and invasion. PKCζ silencing impaired the phosphorylation of LIMK and cofilin, indicating that PKCζ regulates both the cytoskeleton rearrangement and cell adhesion, thereby contributing to cell migration via the LIMK-cofilin pathway [116]. Intersectin-1 coordinates the endocytic trafficking with the actin assembly machinery. Like PKCζ, the knockdown of intersectin-1 by siRNA in GBM cell lines leads to a decrease in PAK, LIMK, and cofilin phosphorylation, as well as in migration and invasion. Some partners of LIMKs, PTEN, and Nf1 could also trigger the implication of LIMKs in GBM [140]. A loss of PTEN function is associated with a poor survival in anaplastic astrocytoma and glioblastoma. An overexpression of the epidermal growth factor receptor (EGFR) in heterozygous PTEN KO mice leads to the development of invasive glioma, which is very similar to human glioblastoma, indicating that PTEN plays a role in glioma progression [141]. Since it has been shown in prostate cancer cell lines that LIMK2 and PTEN are engaged in a negative regulatory loop, where PTEN promotes the degradation of LIMK2 by ubiquitination and LIMK2 inhibits PTEN function and promotes its degradation, an implication of LIMK2 in PTEN-loss-driven GBM aggressiveness might be considered [109]. Neurofibromin 1 (Nf1) is one of the most mutated genes in GBM. An interaction between Nf1 and LIMK2 that results in the inhibition of LIMK2 with Nf1 has been shown [142]. Similarly, Nf1 was shown to inhibit the PAK4/LIMK1/cofilin pathway [143]. The Nf1 mutations encountered in GBM may result in a loss of its inhibitory activity on LIMKs, also contributing to the overactivation of LIMKs observed in GBM. Several chemical inhibitors of LIMKs were also used in GBM. Park et al. used LIMKi 3 (BMS-5) on two GBM cell lines and they showed significant decreases in the viability of the GBM cells treated, with no cytotoxic effects on the normal astrocytes [137]. BMS-5 increased the adhesion of GBM cells, and decreased their migration and invasion, as well as their cofilin phosphorylation [137]. Schulze et al. also used LIMKi 3 (BMS5) and observed different responses depending on the cell lines (chemo-sensitive NCH644 vs. chemo-resistant NCH421k) [144]. Cucurbitacin I, which inhibits cofilin phosphorylation through an unknown mechanism, and alantolactone, which inhibits cofilin in a dose-dependent manner, were also tested on different GBM cell lines, and similar effects were observed: a decrease in migration and invasion, and an increase in apoptosis and adhesion [137,145]. These results are in favour of the regulation of the GBM invasive motility and tumour progression of LIM kinases. Several studies on knockout animal models have made clear the implication of LIM kinases, and more particularly LIMK1, in neurodevelopment and synaptic plasticity. Integrating multiple pathways to regulate finely dendritic spines, synaptic plasticity [84], learning, memory [146], and neuron migration [147] through actin dynamics, LIM kinase dysregulations have been reported in many brain diseases. Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common form of dementia, accounting for 60 to 80 percent of cases [148]. It is characterised by a progressive loss of cognitive functions, such as memory and visuo-spatial impairments [148,149], accompanied by neuropsychiatric symptoms including depression, apathy, and anxiety [148,150,151]. AD is typically hallmarked by early synaptic loss, an accumulation of the extracellular deposits of beta-amyloid peptides (Aβ42) in senile plaques, and the development of neurofibrillary tangles composed of hyperphosphorylated tau [152,153,154]. Aβ42 results from the proteolysis of amyloid precursor proteins (APP) with secretases and their abnormal production and aggregation is directly linked to neurotoxicity, driving the synaptic damages and dendritic loss characteristic of AD [155,156]. The formation of aberrant focal adhesion structures, cofilin–actin rods [157], Hirano bodies composed of ADF/cofilin and actin [158,159], and abnormal actin stabilisation suggest that actin cytoskeleton remodelling has a role in pathological neuroplasticity [160,161]. Synaptic strength and activity are deeply linked to spine morphology [87]. Several studies have shown an increase in phospho-LIMKs and phospho-cofilin in the AD brain area of patients or AD animal models by using western blot or immunofluorescence [155], more particularly in the post-synaptic compartment of excitatory synapses [161], as well as an increase in the ROCK protein level [162,163]. As the antibodies against phospho-LIMKs recognise both the phospho-Thr508 of LIMK1 and the phospho-Thr505 of LIMK2, it is not possible to discriminate the role of the activation of each protein. Both are probably concerned. These studies also pointed out an aberrant remodelling of the actin filaments (with an increased stabilisation) within spines, leading to neuritic dystrophy, with a reduction in the neuritic network and an impairment of the synaptic strength and plasticity of rat hippocampal neuron structures [155]. On the other hand, the expression of LIMK1 in hippocampal excitatory neurons increases cofilin phosphorylation, rescues the impairments of long-term potentiation, and improves the social memory of 3-month-old APP/PS1 transgenic mouse models [164]. Fibrillar Aβ (fAβ) treatment also activates Cdc42/Rac1/PAK1 signalling pathway, linking fAβ to actin dynamics via a different pathway to the Rho/ROCK pathway. Mendoza-Naranjo et al. established a time course activation of Cdc42/Rac1/PAK1 upon Aβ treatment [156]. LIMK1 was activated (phosphorylated) concomitantly with PAK1. An activation of Cdk5 was also depicted, but later in the process, leading to hyperphosphorylation and the inactivation of PAK1. Surprisingly, no change in the cofilin phosphorylation level was observed at any time. The authors suggest an implication of the slingshot phosphatase SSH1 in this process and point out a fine balance between the kinase and phosphatase activation regulating cofilin phosphorylation, and subsequently actin remodelling upon Aβ treatment [156]. Moreover, the treatment of rat hippocampal neurons with fibrillar Aβ (fAβ) increased the level of inactivated cofilin (Ser-3 phosphorylated) and activated LIMK1 (Thr-508 phosphorylated) with abnormal actin remodelling, neuritic dystrophy, and cell death. The inhibition of cofilin phosphorylation with S3 peptide, a specific competitor of Ser3 cofilin phosphorylation, diminished the effect of fAβ on actin filament remodelling and neuronal degeneration, indicating that LIMK1 plays a role in fAβ-induced neurotoxicity [155,161,165]. The pharmacological inhibition of ROCK by fasudil counterbalances Aβ-induced features, especially synaptic loss [161]. Henderson et al. showed that ROCK2 is responsible for spine loss via LIMK1 activation, while ROCK1 proceeds via the myosin actin pathway [163]. Fasudil was also used on an AD rat model induced by streptozoticin treatment. Fasudil reverses learning and memory deficits, synaptic structure degeneration, and phospho-LIMK2 and phospho-cofilin increases [146]. The pharmacological inhibition of LIMK1 by SR7826 [166] on a hAPP mouse model of AD protects against Aβ-induced neuronal hyperexcitability, Aβ-induced spine degeneration, and rescued hippocampal thin spine loss. Synaptosome fractions of hippocampal tissue homogenates from a hAPP mouse treated with SR7826 showed reduced levels of phospho-cofilin. Interestingly, the ROCK knockdown by the siRNA in neurons or the ROCK heterozygous knockout mice suppressed the endogenous production of Aβ by enhancing APP lysosomal degradation [162,163]. The nuclear receptor Nurr1 was shown to be associated with AD [167]. Indeed, upon the Nurr1 knockdown by shRNA in 5xFAD, an AD mouse model, the features of AD pathology become worse, whereas the overexpression of Nurr1 (via lentiviral vector) or its pharmacological activation (amodiaquine agonist) reduce these features. LIMK1 has been shown to interact with Nurr1 by co-immunoprecipitation experiments, resulting in the inhibition of the Nurr1 transcriptional activity [41]. Further investigations on the LIMK1/Nurr1 relationship within an AD context could bring new insights into this field. Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease, and the most common movement disorder [81,168]. It is characterised by bradykinesia (slowed movements), resting tremor, rigidity, and postural instability [169]. These impairments are mainly due to the loss of dopaminergic neurons in the substantial nigra (SN) pars compacta, which can be imputed to the development of abnormal protein aggregates called “Lewy bodies” (LBs) and degenerated neurites (“Lewy neurites”, or LNs) [170]. LBs are mainly composed of misfolded alpha-synuclein (α-synuclein), an abundant protein of the central nervous system, which is highly enriched in pre-synaptic nerve terminals [171,172]. As actin plays a crucial role in synaptic function, a link between actin and alpha-synuclein was investigated [173]. A direct interaction between cofilin and alpha-synuclein was detected through the co-immunoprecipitation of rat brain homogenates [174]. In a cell-free actin polymerisation in vitro assay, alpha-synuclein was shown to decrease actin polymerisation and to increase actin depolymerisation by sequestering actin monomers. Furthermore, Yan et al. showed that a cofilin/alpha-synuclein interaction promotes its aggregation [175]. These mixed fibrils that associate alpha-synuclein and cofilin are more compact and more potent in seeding alpha-synuclein aggregation, as their uptake is increased and more deleterious effects are observed on neuron morphology (especially neurites). Alpha-synuclein is also released from neurons, and this extracellular alpha-synuclein was shown to activate the Rac/PAK2/LIMK/cofilin pathway via its interaction with GRP78, a chaperone heat shock protein [176]. Indeed, when hippocampal neurons are incubated with recombinant alpha-synuclein, an increase in phospho-PAK2 and phospho-cofilin was observed, along with an accumulation of lamellipodia-like actin protrusions along the neurites and at their tips. These effects were lost when the GRP78 was downregulated. The Parkin gene is mutated in autosomal recessive juvenile Parkinsonism (ARJP) and early-onset Parkinsonism. The Parkin protein acts as an E3 ubiquitin ligase. Lim et al. [177] have shown that Parkin interacts with LIMK1 in HEK293 transfected cells, with the C-terminal part of Parkin and the N-terminal part of LIMK1 (LIM-LIM-PDZ) being involved in this interaction. Even though they focus on LIMK1, they showed a similar interaction between LIMK2 and Parkin. The authors could not detect an endogenous interaction on the mouse brain lysates. Parkin ubiquitinates LIMK1 and reduces the LIMK1-induced cofilin phosphorylation in dopaminergic neuronal BE(2)-M17, but not in HEK293 cells. Parkin also reduces the LIMK1-induced actin stress fibres in COS7 cells. Although LIMK1 does not phosphorylate Parkin, it lowers its ubiquitin ligase activity on itself and on p38-MAPK, one of its substrates. Therefore, Parkin and LIMK1 regulate each other. Nurr1 is highly expressed in dopaminergic neurons, where it plays a role in cell differentiation and survival. Defects in Nurr1 expression have been associated with PD in animal models and in the brain samples from PD patients [178,179,180]. As mentioned earlier, LIMK1 interacts with Nurr1 and inhibits its activity upon phosphorylation [41]. Thus, the inhibition of the LIMK1-driven repression of Nurr1 activity could represent an opportunity for PD therapy. Autism spectrum disorders (ASD) is a term used to designate a multitude of early-appearing behaviour impairments regarding social communication. These disorders are also characterised by repetitive and unusual sensory–motor behaviours, even though individuals with ASD display very different features to one to another [181]. Fragile X syndrome (FXS) is the most common inherited form of intellectual disability and autism spectrum disorder. Patients with FXS display severe behavioural dysfunctions, a hypersensibility to sensory stimuli, anxiety, poor language development, and epileptic seizures. FXS is caused by the silencing of the FMR1 gene, which encodes the fragile X messenger ribonucleoprotein 1 protein (FMRP). FMRP has a central role in gene expression, through the regulation of the translation of mRNAs, which are suspected to be involved in the development and maintenance of neuronal synaptic connections [182,183]. Dysregulation of the synaptic actin cytoskeleton, dendritic spines morphology, and synaptic plasticity is particularly described in FXS subjects [184,185,186]. Mutations in the RAC1 gene were reported in ASD patients and several studies have shown that the disruption of Rac1 signalling in animal models leads to ASD-like behaviours [187]. Moreover, Rac1 levels and activity were shown to be significantly higher in FXS patients [188]. Pyronneau et al. showed that the Rac1/Pak1/LIMK/Cofilin pathway is implicated in the aberrant neuronal structures of patients with FXS [189]. Indeed, increased Rac1/PAK1/LIMK/Cofilin signalling in the somatosensory cortex of FMR1 KO mice has been linked to aberrant spine morphology and density. Notably, PAK1 inhibition rescued cofilin regulation, glutamatergic signalling, and sensory processing [189,190,191], which led to thinking that a deficiency in the PAK signalling pathway might play a role in human FXS pathogenesis. Moreover, PAK2+/− mice display decreased synapse densities, defective long-term potentiation, and autism-related behaviours, and PAK2 nonsense mutations and deletions that impaired the PAK2 function have been found in large cohorts of patients with ASD [86]. BMPR2 mRNA is a target of FMRP [192]. BMPR2 is known to bind and activate LIMK1 in a pathway that stimulates actin reorganisation to promote neurite outgrowth and synapse formation [193]. The depletion of FMRP increased the BMPR2 abundance, which was observed in Drosophila and mouse models of FXS. The morphological defects associated with BMPR2-LIMK1 signalling [192,194] were rescued by BMPR2 heterozygosity or LIMK1 inhibition. A BMPR2 increase was also found in the prefrontal cortex of FXS patients [192], suggesting that LIMK1 could play a preponderant role in the actin-driven anomalies within the neuronal development of FXS patients. Conversely, Yao et al. showed a downregulation of LIMK1 in the plasma samples of ASD patients [195], which happened to be contrary to the observations made regarding the BMPR2 increase in FXS. This difference could be the result of a different cellular context, since LIMK1 is mostly expressed in the central nervous system. The deregulation of LIMK1 expression might then be tissue-specific. Schizophrenia (SZ) is a common, severe psychiatric disorder characterised by core features such as delusions and hallucinations, and by behavioural impairments including social withdrawal and depressive moods. SZ is a multifactorial disease caused by genetic and/or environmental factors [196]. A disturbance in glutamatergic functions and the deregulation of the actin cytoskeleton, especially regarding neuronal and synaptic dysfunctions, has been well characterised within SZ [197,198]. In the dorso lateral prefrontal cortex (DLPFC) of patients with SZ, the CDC42 gene is notably underexpressed [199,200,201]. Monkey models that were chronically exposed to antipsychotic medications showed no alteration in their CDC42 expression levels [199]. An analysis of CDC42-related gene expression displayed increased mRNA levels of LIMK1 and LIMK2 in the DLPFC of subjects with schizophrenia, implying that the CDC42/PAK/LIMK pathway has a role in the spine deficits in the DLPFC of SZ subjects [201]. Accordingly, another study showed an overexpression of LIMK2 in the nucleus accumbens, prefrontal cortex, and hippocampus of a neonatal ventral-hippocampal lesion rat model of SZ [202,203]. The neuronal inhibition of the 14-3-3 protein in KO mice leads to behavioural defects that correspond to the core symptoms of SZ, which could be imputed to an alteration of the actin dynamics. Indeed, following this inhibition, the mice showed a reduction in dendritic complexity and spine density, as well as downregulated levels of phosphorylated cofilin [204]. Indeed, Gohla and Bokoch showed that the isoform 14-3-3ζ was able to bind phosphorylated cofilin at Ser3 and protect it from phosphatase-mediated dephosphorylation [29]. 14-3-3ζ was also shown to interact directly with LIMK1, which could prevent its kinase activity on cofilin [28] (Figure 8). LIMK1 seems to play another role in SZ [205] notably through its interaction with neuregulin 1 (NRG1), a protein involved in synaptic plasticity and in the expression/activation of neurotransmitter receptors, including glutamate receptors, which exerts its synaptic activity through LIMK1/cofilin-mediated actin reorganisation [206]. NRG1 mutations have been linked to SZ [207,208]. NRG1 transgenic mice, in which NRG1 is upregulated in the neurons of their forebrains, exhibited increased LIMK1 activity, reduced spine density, and glutamatergic impairment. The pharmacological inhibition of LIMK1 diminished these effects on spine density and ameliorated the glutamatergic impairments [208,209] (Figure 8). Gory-Fauré et al. showed the dysregulation of the LIMK1 expression in MAP6 KO mice, a mouse model of SZ which display social withdrawal and anxiety-like features [210]. Treatment with the LIM kinase inhibitor Pyr1 rescued these behavioural impairments [210], making LIMK1 a potential target for the treatment of schizophrenia. Williams–Beuren syndrome (WBS) is a rare genetic disorder that is characterised by distinctive craniofacial features, congenital heart disease, and cognitive and behavioural dysfunctions that include intellectual disability and hypersociability. WBS is caused by a heterozygous deletion of approximately 1.5 Mb at the chromosome 7q11.23, which leads to the loss of one copy of 25–27 genes, including LIMK1. The loss of LIMK1 is believed to be responsible for some of the neurological aspects of WBS, notably the impaired visual–spatial cognition and long-term memory dysfunctions [211]. Indeed, Gregory et al. showed that the hemideletion in Williams syndrome and the LIMK1 sequence variation in the general population alter the functional connectivity of the intraparietal sulcus, a visual processing region, in similar ways [212]. Moreover, LIMK1 KO mice displayed impairments in fear processing, long-term memory, and spatial learning [84,213], which are features that could be associated with WBS. Further studies would be necessary to determine the molecular pathways that lead to these cognitive impairments induced by the LIMK1 deletion in WBS. Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by progressive loss of motor neurons in the brain and spinal cord, leading to the paralysis of most of the body muscles. Death usually occurs upon respiratory paralysis, within 3 to 5 years after diagnosis [214]. ALS etiology is complex, with genetic and environmental components, but some genes, such as SOD1 and C9ORF72, have been markedly associated with ALS when dysregulated [214,215]. There is actually no efficient treatment for this disease [214], but a possible involvement of the dysregulation of the cytoskeleton in ALS pathogenesis opens up the way to new therapies [216]. First of all, C9ORF72 has been shown to interact with cofilin. And, C9ORF72-depleted cells and post-mortem brain samples from ALS patients exhibit enhanced cofilin phosphorylation. This increase results from the activation of the Rac1/PAK/LIMK/Cofilin pathway, since C9ORF72 modulates the activity of Rac1. In C9ORF72-depleted motor neurons, axonal actin dynamics are impaired, indicating that the Rac1/PAK/LIMK/Cofilin pathway has a role in C9ORF72-driven ALS [215] and that there is a therapeutic potential for LIMK inhibitors. BMP-TGF-β signalling has also been associated with neuronal function, playing a role in synaptogenesis, axonal and dendritic growth, synaptic transmission, and neuronal survival. Disruptions in BMP/TGF-β signalling have been reported in a variety of neurological diseases, notably in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS) [217]. BMPR2, a BMP receptor, is known to bind and activate LIMK1 in a pathway that stimulates actin reorganisation, in order to promote neurite outgrowth and synapse formation [193]. The upregulation of BMP-TGF-β signalling could thus lead to an impairment of actin dynamics, which could result in defective axon and dendrite functioning. Cdk5 is dysregulated in various neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS) [218]. When it is upregulated in AD and ALS, Cdk5 causes tau and neurofilament protein hyperphosphorylation, leading to neuronal cell death [219]. The Cdk5 inhibition of PAK1 has been shown to decrease the LIMK1 activity and to increase the active cofilin pool, leading to abnormal actin remodelling. Indeed, there is some evidence regarding the involvement of actin regulation as a causative and dysregulated process in ALS [216,218]. Myosin-binding protein H (MyBP-H) is a component of the thick filaments of the skeletal muscle that has strong affinity for myosin. It has been shown to be upregulated in ALS. A high MyBP-H expression level was also associated with the abnormal expression of Rho kinase 2 (ROCK2), LIM domain kinase 1 (LIMK1), and Cofilin2, indicating that the ROCK2/LIMK1/Cofilin2 pathway might play a role in ALS pathogenesis in muscles [220]. Neurofibromatosis are genetic orphan diseases without any connection to each other, except for the development of tumours in the nervous system. To date, three neurofibromatosis have been described: neurofibromatosis type 1 (NF1, or von Recklinghausen disease), neurofibromatosis type 2 (NF2), and schwannomatosis (NF3). An LIMK implication has been reported in NF1 and NF2 and will be discussed here. Neurofibromatosis type 1 (NF1) is an autosomal-dominant disease and the most frequently diagnosed cancer predisposition disorder that involves the nervous system [221,222]. It is caused by inherited or de novo mutations in the gene encoding neurofibromin 1 (Nf1) [223], a GTPase-activating protein, which is highly expressed in the neuronal cells and acts a tumour suppressor by negatively regulating Ras pathways [224,225]. Neurofibromatosis type 1 is hallmarked by the development of tumours in the central or peripheral nervous system, with high risk of malignancy. It is also characterised by cognitive dysfunctions, including learning impairments, attention deficits, and dysfunctional social behaviours [222,225]. The GAP-related domain (GRD) of Nf1 is the effector of the downregulation of Ras pathways, which are involved in cell growth. The pre-GAP domain was reported to play a role in cytoskeleton remodelling. Indeed, the N-terminal extremity of Nf1 affects cell adhesion and migration by negatively regulating the Rac1/Pak1/LIMK1/cofilin pathway [143]. Nf1 also plays a role in the Rho/ROCK/LIMK2/cofilin pathway, acting as a negative regulator of this pathway. Its deletion has indeed been shown to activate the signalling cascade, affecting the actin cytoskeleton [226]. The SecPH domain of Nf1 interacts with LIMK2, but not LIMK1, inhibiting its phosphorylation and activation via ROCK [142]. In Nf1−/− mouse embryonic fibroblasts (MEFs), an increase in phospho-LIMK and phospho-cofilin was shown [143] (Figure 9). Nf1 seems to play a role in the fine regulation of the actin cytoskeleton by regulating, in parallel, two signalling pathways that lead to ADF/cofilin phosphorylation. These pathways, when dysregulated, are known to be involved in various neuronal and synaptic pathological mechanisms, as well as in the progression of cancers. Taken together, these results could explain the pathological features associated with the disease, and LIMKs, as downstream effectors of these pathways, could be interesting therapeutic targets for NF1. Neurofibromatosis type 2 is an autosomal-dominant neoplasia disorder caused by mutations in the neurofibromatosis 2 (NF2) gene, encoding a tumour suppressor protein called Merlin. It is characterised by nervous system tumours, notably multiple schwannomas, especially in the vestibulocochlear nerve, meningioma, ependymomas, neurofibromas, peripheral neuropathy, ophthalmological lesion skin tumours, and hearing loss [227]. Merlin interacts with membrane-associated proteins and transmembrane receptors, where it regulates the formation of membrane domains and acts as a tumour suppressor by modulating signalling, as a scaffold protein, from the transmembrane receptors to the intracellular effectors, controlling cell proliferation and survival [228,229]. A Merlin loss of function is associated with the increased activity of Rac and p21-activated kinases (PAK), and the dysregulation of cytoskeletal organisation [230]. In mouse Schwann cells (MSC), in which NF2 exon2 is deleted (NF2ΔEx2) and Merlin function is lost, the levels of phosphorylated LIMK1, phosphorylated LIMK2, and the subsequent phosphorylated cofilin were upregulated. However, no direct interaction between Merlin and LIMKs has been shown so far. The reintroduction of wild-type NF2 into these MSCs reduced the LIMK1 and LIMK2 levels. A reduction in the LIMK activity and/or protein levels decreases the NF2-deficient MSC viability, and the pharmacological inhibition of LIMKs by BMS5 decreases the viability of NF2ΔEx2 MSC, blocks the cell cycle progression in the G2/M phase, and decreases AURKA activation [231]. Taken together, these results suggest that LIM kinases play a role in Merlin-loss-driven cytoskeletal dysfunctions and could act as potential therapeutic targets for the treatment of NF2. The three main steps of a viral infection are: (i) its entry into the host cell, (ii) its replication within this cell, and (iii) the release of many virions/viral particles. Viruses need the help of the host cell cytoskeleton to be able to carry out their replication cycle. Thus, they have evolved to hijack the cytoskeletal network and use it to their advantage [232,233]. The LIM kinase implication in the context of viral infection is quite new, but there is evidence that they play a crucial role in the viral life cycle and could be of a therapeutic interest for certain viral infections [234]. The human immunodeficiency virus type 1 (HIV-1) has been widely studied over the last years, in particular has its implication in the fine regulation of actin cytoskeleton dynamics during each step of its replication cycle. The actin cytoskeleton has been reported to be involved in viral entry, reverse transcription, nuclear migration, the shuttling of viral components to the membrane, assembly, budding, and cell–cell transfer. Moreover, HIV has developed strategies to spatiotemporally lever the actin cytoskeleton network by upregulating, inhibiting, changing gene expression, cellular localisation, and even modulating the function of certain effectors, such as cofilin and LIMK1 [234,235]. During the course of the HIV-1 infection, cofilin is either inactivated or activated, depending on the step of the viral cycle. During early infection, the HIV-1 envelope glycoprotein gp120 interacts with the CD4 receptor and CXCR4 (or CCR5) co-receptor, activating RhoA and the Rac-dependent pathway, which leads to LIM kinase activation and cofilin inactivation [236]. Vorster et al. showed that, upon the HIV infection of resting CD4 T cells, a transient phosphorylation and the subsequent activation of LIMK1 is observed within 1 min, followed by a deactivation at 5 min, and a reactivation at 10 min [237]. Moreover, they showed that gp120 alone was sufficient to trigger this activation. The same features were observed in active CD4 T cells and macrophages. This activation of LIMK1 was shown to be mediated by Rac, PAK1, and PAK2, but not by PAK4, RhoA, or Cdc42. When LIMK1 was knockdown in the transformed and primary CD4 T cells, as well as in the stable human CD4 T cell line, CEM-SS, increases in the CXCR4 receptor at the membrane, endocytosis, and exocytosis were observed. Furthermore, the stable knockdown of LIMK1 by shRNA in CD4 T cells renders the T cells resilient against HIV infection [237]. Thus, LIMK1 activation leads to the inhibition of cofilin actin severing activity; hence, for early cortical actin skeleton polymerisation and CD4/CXCR4 receptor clustering, two mechanisms are required for virus entry into the host cell [236]. In the following steps of the HIV-1 infection, there is evidence of viral-driven cofilin dephosphorylation, leading to the depolymerisation of cortical actin, a mechanism by which the viral core penetrates and navigates through the host cell to the nucleus [236,238]. This mechanism seems to be responsible for the establishment of HIV-1 latency in resting CD4 T cells [239]. Indeed, Wu et al. showed an elevated level of activated cofilin in the resting CD4 T cells of the peripheral blood of patients infected with HIV-1 [240]. Yoder et al. showed that the incubation of T cells with S3 peptide, corresponding to the 16 first amino acids of human cofilin, leads to the decreased activity of LIMK1 on full cofilin, and an enhanced viral replication [238]. These data suggest that the viral replication is increased when the cofilin is less phosphorylated by the LIMK1 and consequently more active [238]. Nef, an actin-modifying HIV-1 protein, was shown to activate LIMK1 by increasing its phosphorylation on its Thr508, leading to an increase in cofilin phosphorylation and resulting in the inhibition of retinoid receptor-mediated reporter activity, which plays a crucial role in immune response [241]. The implication of the actin cytoskeleton in retroviral assembly and budding has also been established [242]. In order to identify new genes involved in HIV-1 virion assembly and release, Wen et al. performed a screening by using a siRNA library and measuring the HIV-1 particle release. LIMK1 and ROCK1 were identified in this screening. They confirmed that, when they depleted the LIMK1 in HeLa cells using siRNA, they observed a reduction in particle output. The same results were obtained with stable HeLa transfected with LIMK1 shRNA. The transfection of a shRNA-resistant FLAG-LIMK1 cDNA, restoring the LIMK1 protein levels, could reverse this effect, whereas the incubation with S3 peptide, a synthetic peptide which acts as a specific competitor for the ADF/cofilin phosphorylation by LIMK1, also leads to a decrease in particle output. ROCK1 is involved in this process, but not PAK1/2/4, as the HeLa cells treated with ROCK siRNA also exhibited a lower particle output. In the HeLa cells silenced for LIMK1, the HIV-1 virions accumulated at the plasma membrane. LIMK1 was shown to co-localise with the HIV-1 particle assembly sites and be incorporated within the HIV particles. Furthermore, the silencing of LIMK1 also resulted in a decrease in the HIV cell transmission from HeLa to the Jurkat cell lines [243]. Altogether, these results show that the ROCK/LIMK/cofilin pathway is involved in the HIV-1 particle release and the spread of the virus. It is well documented that herpes viruses use cytoskeleton-regulating Rho GTPase signalling pathways during different phases of their replication cycle [244]. Moreover, herpes simplex virus 1 (HSV-1) seems to be able to trigger biphasic cofilin deactivation/activation in a way that is similar to HIV-1, to facilitate its entry into the host cell and its replication in neuronal cells, via RhoA and Cdc42 [245]. Indeed, the interaction between the virus envelope and host cell leads to EGFR/PI3K and Rho/ROCK pathway activation and, ultimately, to actin remodelling. The specific inhibition of these pathways significantly limits the virus infectivity [246]. Amentoflavone, a natural polyphenol compound found in many plants, which has a broad antiviral activity spectrum against several viruses, was tested on HSV-1. The treatment of neuronal cells with Amentoflavone induced a decrease in the phospho-cofilin, leading to an impaired reorganisation of the F-actin and viral infection. Amentoflavone also leads to a decrease in the transport of viral particles from the plasma membrane to the nucleus [247]. As all these studies have highlighted the direct involvement of LIMK1, via its activation, in the viral infection process, inhibitors of LIMK1 have been developed as a new approach to block viral infection. Yi et al. have designed a set of 25 new LIMK inhibitors, and 8 of them appeared to reduce HIV infection [248]. They further characterised their best lead, R10015, and showed that it blocks viral DNA synthesis, viral nuclear migration, and virion release. R10015 was shown to inhibit multiple viruses, including Zaire ebolavirus (EBOV), Rift Valley fever virus (RVFV), Venezuelan equine encephalitis virus (VEEV), and herpes simplex virus 1 (HSV-1), suggesting that the LIMK implication in the viral life cycle concerns many viruses [248]. Thus, LIMK inhibitors appear as a potential new class of broad-spectrum antiviral drugs. Furthermore, there is growing evidence about the implication of the HSV-1 infection in the occurrence of neurological diseases such as Alzheimer’s disease. As reviewed by Wan et al., HSV-1 DNA co-localises with the senile plaques in AD brains, and some HSV proteins interact with Aβ and facilitate its production, as well as that of its precursor proteins [249]. Since cofilin dysregulation is a major factor in AD pathogenesis, as well as in virus infection, it could be the link between HSV-1 infection and AD development. Targeting cofilin or LIMKs could be a new strategy for treating patients infected with HSV-1 and exhibiting AD features. LIMKs have been shown to play a role in the function of the male urogenital system, as well as in its associated defects. All of these features are well described in the review of Pak et al., which belongs to the Special Issue LIM Kinases: From Molecular to Pathological Features [250]. Because they are implicated in the dynamics of actin filaments and microtubules, LIM kinases play a crucial role in the remodelling of the cytoskeleton and, thus, in the physiology of the cell. Originally described as being located downstream of several signalling pathways involving members of the Rho family of GTPases, it appeared that LIMKs are, in fact, capable of integrating signals from multiple partners, some of them without an obvious link with their role in the cytoskeleton reorganisation. In fact, LIMKs are at the heart of a complex network of cell signalling pathways. The dysregulation of these interactions has linked them to severe pathologies such as cancer, neurological diseases, and viral infections, making LIMKs a node with strong therapeutic potential (Figure 10). These studies have paved the way for the development of small inhibitory molecules that are capable of modulating the activity of these kinases, but unfortunately, despite promising preliminary results with different mouse models, none have made it through clinical trials. It is now necessary to go further in the direction of understanding the molecular mechanisms that link LIM kinases to the pathogenic phenomenon, and to have a better understanding of their own functioning, in the hope of developing new, tailored, and innovative drugs that target LIM kinases.

PMC10000745

Dino Bekric,Daniel Neureiter,Celina Ablinger,Heidemarie Dobias,Marlena Beyreis,Markus Ritter,Martin Jakab,Johannes Bischof,Ulrich Koller,Tobias Kiesslich,Christian Mayr

Evaluation of Tazemetostat as a Therapeutically Relevant Substance in Biliary Tract Cancer

02-03-2023

biliary tract cancer,cholangiocarcinoma,EZH2 inhibitor,epigenetics,tazemetostat

Simple Summary Treating biliary tract cancer (BTC) successfully remains to be a difficult task. Standard therapeutic options encompass surgery, radiation and chemotherapy, but the median survival has not improved beyond one year. The reasons for this might be diagnosis at an already late stage and resistance towards current therapy. Therefore, novel strategies to combat this gastrointestinal disease need to be investigated. One alternative option may be to inhibit the enhancer of Zeste homolog 2 (EZH2), a histone-lysine-N-methyltransferase that was already shown to play a role in oncogenesis in BTC. Tazemetostat, an FDA-approved EZH2-inhibitor, seems to harbor promising anti-cancer properties in various tumor types. Therefore, in this study, we aim to investigate for the first time if tazemetostat might be a potential novel therapeutic strategy in biliary tract cancer. Abstract Biliary tract cancer (BTC) is a gastrointestinal malignancy associated with a poor survival rate. Current therapies encompass palliative and chemotherapeutic treatment as well as radiation therapy, which results in a median survival of only one year due to standard therapeutic ineffectiveness or resistance. Tazemetostat is an FDA-approved inhibitor of enhancer of Zeste homolog 2 (EZH2), a methyltransferase involved in BTC tumorigenesis via trimethylation of histone 3 at lysine 27 (H3K27me3), an epigenetic mark associated with silencing of tumor suppressor genes. Up to now, there are no data available regarding tazemetostat as a possible treatment option against BTC. Therefore, the aim of our study is a first-time investigation of tazemetostat as a potential anti-BTC substance in vitro. In this study, we demonstrate that tazemetostat affects cell viability and the clonogenic growth of BTC cells in a cell line-dependent manner. Furthermore, we found a strong epigenetic effect at low concentrations of tazemetostat, which was independent of the cytotoxic effect. We also observed in one BTC cell line that tazemetostat increases the mRNA levels and protein expression of the tumor suppressor gene Fructose-1,6-bisphosphatase 1 (FBP1). Interestingly, the observed cytotoxic and epigenetic effects were independent of the mutation status of EZH2. To conclude, our study shows that tazemetostat is a potential anti-tumorigenic substance in BTC with a strong epigenetic effect.

Evaluation of Tazemetostat as a Therapeutically Relevant Substance in Biliary Tract Cancer Treating biliary tract cancer (BTC) successfully remains to be a difficult task. Standard therapeutic options encompass surgery, radiation and chemotherapy, but the median survival has not improved beyond one year. The reasons for this might be diagnosis at an already late stage and resistance towards current therapy. Therefore, novel strategies to combat this gastrointestinal disease need to be investigated. One alternative option may be to inhibit the enhancer of Zeste homolog 2 (EZH2), a histone-lysine-N-methyltransferase that was already shown to play a role in oncogenesis in BTC. Tazemetostat, an FDA-approved EZH2-inhibitor, seems to harbor promising anti-cancer properties in various tumor types. Therefore, in this study, we aim to investigate for the first time if tazemetostat might be a potential novel therapeutic strategy in biliary tract cancer. Biliary tract cancer (BTC) is a gastrointestinal malignancy associated with a poor survival rate. Current therapies encompass palliative and chemotherapeutic treatment as well as radiation therapy, which results in a median survival of only one year due to standard therapeutic ineffectiveness or resistance. Tazemetostat is an FDA-approved inhibitor of enhancer of Zeste homolog 2 (EZH2), a methyltransferase involved in BTC tumorigenesis via trimethylation of histone 3 at lysine 27 (H3K27me3), an epigenetic mark associated with silencing of tumor suppressor genes. Up to now, there are no data available regarding tazemetostat as a possible treatment option against BTC. Therefore, the aim of our study is a first-time investigation of tazemetostat as a potential anti-BTC substance in vitro. In this study, we demonstrate that tazemetostat affects cell viability and the clonogenic growth of BTC cells in a cell line-dependent manner. Furthermore, we found a strong epigenetic effect at low concentrations of tazemetostat, which was independent of the cytotoxic effect. We also observed in one BTC cell line that tazemetostat increases the mRNA levels and protein expression of the tumor suppressor gene Fructose-1,6-bisphosphatase 1 (FBP1). Interestingly, the observed cytotoxic and epigenetic effects were independent of the mutation status of EZH2. To conclude, our study shows that tazemetostat is a potential anti-tumorigenic substance in BTC with a strong epigenetic effect. Biliary tract cancer (BTC) is a dismal gastrointestinal disease with a very poor 5-year survival rate [1]. A possible explanation for the poor survival rate of BTC might be that symptoms are very unspecific, leading to a diagnosis at an already advanced stage [2]. For instance, typical symptoms of BTC are abdominal pain, unspecific weight loss and painless jaundice which impairs an efficient clinical management of BTC [3]. Current therapies against BTC encompass palliative treatment, radiation therapy and a combinatorial chemotherapeutic treatment, consisting of cisplatin and gemcitabine. However, the median survival remains poor [2,4]. Additionally, second-line therapies for advanced BTC are not standardized [5]. Due to the lack of efficient treatments as well as the poor overall survival rate, the investigation of new therapeutic approaches is still necessary. Enhancer of Zeste homolog 2 (EZH2) is the catalytic subunit of the polycomb repressive complex 2 (PRC2), which is an epigenetic regulator, that specifically performs stepwise trimethylation of histone H3 at Lysine 27 (H3K27me3), using S-adenosyl methionine cofactor (SAM) as the methyl donor [6]. These methylations result in the formation of a heterochromatin complex and gene silencing [6]. Physiologically, EZH2 is involved in embryonic development by regulating the expression and maintenance of genes, of which are required for differentiation and development during the embryonic phase [6]. Besides EZH2, the PRC2 consists of the core components EED, LSD1, SUZ12, DNMT1 and JARID2, which are mandatory for the proper function of the PRC2 [6]. Besides its role in embryonic development, aberrant PRC2 and EZH2 activity has been described in several human cancer types. It was demonstrated that EZH2 is overexpressed and/or harbors a gain-of-function mutations in solid tumors such as breast and prostate cancer as well in lymphomas and that these changes in EZH2 function are associated with shorter overall survival, progression of disease with development of metastasis and a higher TNM stage [7,8,9,10]. In BTC, EZH2 was also shown to be overexpressed [8,11]. Liu et al. demonstrated via immunostaining that patients with higher EZH2 expression suffered from larger tumors, more frequent lymph node metastases and a poorer overall survival compared to patients with a lower or negative EZH2 expression [12]. Additionally, Sasaki et al. and Liu et al. demonstrated in BTC that on a molecular level, EZH2 expression was negatively correlated with the expression of the tumor suppressor genes PTEN and p16, whereas Yamaguchi et al. found that the Ki-67, as a marker of proliferation, was positively correlated with EZH2 expression [11,13,14]. Tang et al. could demonstrate that EZH2 was highly expressed in cholangiocarcinoma (CCA) cells and that the overexpression of EZH2 led to the inhibition of apoptosis and resulted in an elevated proliferation of CCA cells [15]. Furthermore, the study by Tang et al. showed that RUNX3, a well-known tumor suppressor, was downregulated by the EZH2-mediated methylation of H3K27. Additionally, EZH2 inhibition resulted in upregulated RUNX3 protein expression, induced apoptosis and reduced cell proliferation [15]. In another study, carried out by Zhang et al., it was shown that in a xenograft model, EZH2 knockdown was able to reduce the progression of CCA significantly, and the depletion of EZH2 in CCA cells reduced the colony and growth formation ability [16]. Therefore, EZH2 may represent an attractive target for pharmacological interventions. Tazemetostat (also known as E7438 or EPZ-6438) is a SAM competitive EZH2 inhibitor that is currently used in more than 40 clinical trials in different clinical settings [17], (https://clinicaltrials.gov/ct2/results?cond=&term=TAZEMETOSTAT&cntry=&state=&city=&dist=, accessed on 19 September 2022). On January 2020, the FDA approved tazemetostat (Tazverik) for locally advanced or metastatic epithelioid sarcoma that are not eligible for complete surgical removal [18]. In several other studies, the anti-tumorigenic properties of tazemetostat were demonstrated: For example, Zhou et al. demonstrated that tazemetostat was able to sensitize mouse oral squamous cell carcinoma model cells (MOC-esc1) to T-cell-mediated cytotoxicity [19]. Furthermore, tazemetostat was able to increase cytotoxicity in head and neck cancer cells compared to untreated cells by enhancing the antigen presentation of tumor cells. Likewise, Tan et al. observed an augmentation of the cytotoxic effect of the chemotherapeutic 5-Flourouracil (5-FU) in colorectal cancer when combined with tazemetostat [20]. In medulloblastoma, Zhang et al. could demonstrate that the inhibition of EZH2 by tazemetostat led to the activation of the tumor suppressor gene ADGRB1, which resulted in an anti-tumorigenic response [21]. Additionally, SAM competitors such as tazemetostat worked more efficiently in cells harboring a gain-of-function mutation in EZH2 at lysine at position 641/646, which is positioned in the SET domain of EZH2 [22]. In 2014, Knutson et al. demonstrated that non-Hodgkin lymphoma (NHL) cells which displayed an EZH2 point mutation were more susceptible towards tazemetostat than wild-type EZH2 cells [22]. Almost all used NHL cells that harbored a Y646 mutation of EZH2 displayed higher sensitivity towards tazemetostat compared to wild-type cells [22]. Interestingly, cell proliferation was inhibited via apoptosis induction and cell cycle arrest in EZH2-mutant lymphoma cells if tazemetostat was applied [22]. Based on the current literature, the EZH2 inhibitor tazemetostat harbors potential as an (adjuvant) anti-tumor drug. The involvement of EZH2 in BTC development and progression is well described. However, data regarding tazemetostat and BTC are missing. Therefore, our presented study aims to investigate the cytotoxic and epigenetic effects of tazemetostat using an in vitro model with different human BTC cells for the first time. Human BTC cell lines HuCCT1 (JCRB0425, [23]), KKU-055 (JCRB1551), NOZ (JCRB1033, [24]), OCUG-1 (JCRB0191, [25]) and OZ (JCRB1032, [26]) and non-cholangiocyte cell line MMNK-1 (JCRB1553) were purchased from the Japanese Collection of Research Bioresources Cell Bank (JCRB, Osaka, Japan). BTC cell lines (Human) EGI-1 (ACC-385, [27]) and TFK-1 (ACC-344, [28]) were purchased from the German Quotes from Collection of Microorganisms and Cell Culture (DSZM, Braunschweig, Germany). Cell lines were cultured in a humidified atmosphere (5% CO2, 37 °C) in Dulbecco’s modified Eagle’s medium with high glucose (DMEM; Gibco, ThermoFisher Scientific, Vienna, Austria), supplemented with 10% fetal bovine serum (FBS; Eximus, Catus Biotech, Germany), 1% antibiotic-antimycotic (Sigma-Aldrich, St. Louis, MO, USA), 10 mM HEPES (Pan Biotech, Aidenbach, Germany) and 1 mM sodium pyruvate (Pan Biotech). Dulbecco’s Phosphate Buffered Saline (DPBS; Pan Biotech) was used for washing steps. Cell harvesting was carried out with 0.25% trypsin-EDTA (Sigma-Aldrich). Cells were counted using a Spark multimode reader and Cell Counting Chips (Tecan, Grödig, Austria). Resazurin was purchased from Alfa Aesar (Kandel, Germany) and dissolved in DPBS. Cisplatin, purchased from Selleckchem (Houston, TX, USA), was dissolved in ddH2O to a stock concentration of 5 mM and stored in aliquots at −20 °C. Tazemetostat was purchased from Selleckchem, was dissolved in dimethyl sulfoxide (DMSO; Sigma Aldrich) to a stock concentration of 20 mM and stored in aliquots at −20 °C. Samples treated with solvent did not significantly differ from untreated samples. Optimal cell densities for a miniaturized clonogenic assay in 96-well microplates (Starlab, Hamburg, Germany) were determined as described [29]. The following seeding numbers per well were chosen: 80 cells for HuCCT1 and OCUG-1, 50 cells for KKU-055, 40 cells for EGI-1, and 30 cells for NOZ and the MMNK-1 cells. The seeding of OZ and TFK-1 cells at different cell numbers did not result in clonogenic growth. Therefore, these cell lines were excluded from the experiments. The determination of optimal cell density was carried out in biological and technical triplicates. For the investigation of clonogenic growth, cells were seeded according to the determined optimal seeding numbers in 96-well microplates and were grown overnight. Then, cells were washed with DMEM without serum and incubated with different concentrations of tazemetostat in DMEM with serum using a 1:2 dilution series (starting concentration 80 µM, 10 steps) for seven days. To avoid evaporation, empty spaces on the plate were filled with DPBS. Confluence was measured after seven days with the Spark multimode reader. The short-term cytotoxicity of tazemetostat was measured after 72 and 120 h of tazemetostat treatment using the resazurin assay. Cells were seeded in 96-well microplates (10,000 cells per well for 72 h time point; 6000 cells for 120 h time point) and let to grow overnight. Then, cells were washed with serum-free DMEM and incubated with tazemetostat in FBS-free DMEM using a serial dilution (starting from 100 µM, 1:2, 10 steps). After 72 h or 120 h incubation, respectively, resazurin was added and fluorescence was measured on a Spark multimode reader. Serum-free medium was used to avoid the interactions of serum components with tazemetostat. Based on Knutson et al., the long-term cytotoxic effects of tazemetostat (up to 360 h incubation time) were investigated as followed: 6000 cells for KKU-055 and 4000 cells for NOZ were seeded in a 96-well microplate and let to grow overnight [22]. Then, cells were washed with FBS-free DMEM and incubated with 0.3, 3 and 30 µM Tazemetostat, respectively. After 120 h of incubation time, resazurin was added to the selected wells for measurement of cell viability. For the remaining wells, cells were harvested with trypsin-EDTA, pooled (for each condition), counted, re-seeded at the described seeding densities and let to grow overnight without tazemetostat. Cells were again washed and then incubated with tazemetostat (0.3, 3 and 30 µM) for an additional 120 h to evaluate the viability after 240 h. The procedure was repeated an additional time to measure cell viability also after 360 h. KKU-055, NOZ and OCUG-1 were seeded in 60 mm dishes at a seeding density of 5.2 × 106 per dish and let to grow overnight. Cells were washed with FBS-free DMEM, incubated with 0.3 µM tazemetostat for 96 h, washed with DPBS, harvested with trypsin-EDTA, centrifuged, counted and stored as cell pellets at −20 °C. For protein expression analysis, pellets were thawed, DPBS was added to obtain a concentration of 107 cells per ml and cells were lysed via sonication with a Sonopuls HD70 (UW 70 ultrasound head, Bandelin; 10 pulses). Samples were then centrifuged (17,000× g, 10 min) and 10 µL of supernatant was mixed with 10 µL of 2× sodium dodecyl sulfate (SDS) containing a lysis buffer (SDS; Thermo Fisher Scientific, Waltham, MA, USA), incubated for 5 min at 95 °C and centrifuged again (400× g, 5 min at RT). Proteins, with each slot containing 200,000 cells, were separated on gradient SDS gels (20 µL of each sample; 4–20% Mini-PROTEAN gels, Biorad, Hercules, CA, USA) for 90 min at 100 V and transferred using a Trans-Blot® Turbo™ System and nitrocellulose membranes (Biorad). Unspecific binding was blocked using a Blotting-Grade Blocker (Biorad). Membranes were incubated overnight at 4 °C with primary antibodies: anti-H3 (1:2000), anti-H3K27me3 (1:1000), anti-FBP1 (1:1000) and anti-EZH2 (1:1000)—all diluted in Blotting-Grade Blocker and purchased from Cell Signaling Technology (Danvers, MA, USA). Blots were washed three times with TBS-T, incubated with the secondary antibody (anti-rabbit IgG HRP-linked, 1:1000, Cell Signaling Technology) for 90 min at room temperature and then incubated for 2 min with the Signal Fire ECL Reagent (Cell Signaling Technologies) for signal development. Chemiluminescence was analyzed with the ChemiDoc MP System and the Image Lab Software™ (Biorad). Grey densities of bands were calculated with ImageJ (V1.53, NIH, Bethesda, MD, USA) to evaluate the protein expression related to loading control H3. Fold regulation, the negative inverse of fold change, was calculated to demonstrate the up- or downregulation of genes. The BTC cell lines KKU-055 and NOZ were seeded in 60 mm dishes at a seeding density of 5.2 × 106 per dish, grown overnight, washed with FBS-free DMEM and incubated with 0.3 µM tazemetostat for 96 h, respectively. Total RNA was isolated with TRI Reagent (Merck, Rahway, NJ, USA) and a Direct-zol RNA Miniprep kit (Zymo Research, Irvine, CA, USA) according to the manufacturers’ instructions. cDNA synthesis was carried out using the GoScript™Reverse Transcriptase kit (Promega, Madison, WI, USA). Real Time PCR was performed with a ViiA7 real-time PCR system (Applied Biosystems, Thermo Fisher Scientific) using the GoTaq® Master Mix (SYBR® Green, Promega). mRNA expression levels were related to ß-actin (ΔCt). Changes in mRNA expression between treated and untreated samples were calculated according to the ΔΔCt method. Fold regulation, the negative inverse of fold change, was calculated to demonstrate the up- or downregulation of genes. All primers were purchased from Sigma Aldrich (KiCqStart® SYBR® Green Primers) and prepared as 100 µM stocks (in H2O)—sequences are listed in Supplementary Figure S1. EGI-1, KKU-055, NOZ, OZ, TFK-1, HuCCT1, MMNK-1 and OCUG-1 were seeded in 60 mm dishes using a seeding density of 5.2 × 106 per dish, let to grow overnight, harvested with trypsin-EDTA and centrifuged. Genomic DNA was extracted using a Wizard Genomic DNA Purification Kit (Promega), according to the manufacturer’s protocol. Concentration and quality of extracted DNA was measured with an Eppendorf Biophotometer® plus (Hamburg, Germany) and amplification of the region of interest was carried out using GoTaq HotStart Polymerase (Promega) and specific primers (see Supplementary Figure S2) on a Thermocycler Labcycler® Sensoquest (Göttingen, Germany). The PCR product was evaluated via gel electrophoresis and subsequently sequenced using Sanger sequencing. Evaluations of the sequenced files were carried out via Finch TV (v1.5, NIH, Geospiza, Inc.; Seattle, WA, USA). To investigate the possible synergistic cytotoxic effects of tazemetostat with the standard chemotherapeutic cisplatin, KKU-055 and NOZ cells were seeded with a seeding density of 5000 (KKU-055) and 10,000 (NOZ) in 96-well microplates. For the simultaneous treatment of cells with cisplatin and tazemetostat, cells were grown overnight, washed with serum-free DMEM and incubated with a sub-lethal concentration of tazemetostat (30 µM) and a cisplatin dilution series (1:2, 10 steps, highest concentration of 20 µM) for 72 h. Cell viability was then measured via the resazurin assay. In a second approach, cells were seeded as described and pre-treated with 30 µM tazemetostat for 96 h. Afterwards, cells were washed with FBS-free DMEM and incubated with a cisplatin dilution series (10-fold 1:2, highest concentration of 30 µM) without tazemetostat for an additional 72 h before the measurement of cell viability. The three human BTC cell lines KKU-055, NOZ and OCUG-1 were seeded in 60 mm dishes and let to grow overnight. Cell blocks were prepared using a 1:1 mix of citrate plasma and Thromborel S (Siemens Healthcare, Marburg, Germany). The prepared cell blocks of these BTC cell lines were immunohistochemically stained for CK7, EZH2 and Vimentin (see details of the used antibodies in Table 1). In brief, 4 µm sections were mounted on glass slides, deparaffinized using graded alcohols, subjected to antigen retrieval at pH 9 and stained using the primary antibodies listed below. Ultraview (Ventana, Oro Valley, AZ, USA) was used as an IHC detection kit. If not stated otherwise, all data points represent the mean values of at least three independent biological replicates ± SEM, where each biological replicate contained an appropriate number of technical replicates. The Student’s t-test as well as ANOVA test with Bonferroni correction were applied for the calculation of significances between control and treated samples. All calculations were performed using OriginPro 9.1 (OriginLab, Northampton, MA, USA). Statistical results were considered significant (*) or highly significant (**) at p < 0.05 and p < 0.01, respectively. The available biodata of EZH2 and FBP1 mRNA expression in human BTC samples were analyzed via GEPIA http://gepia.cancer-pku.cn, (accessed on 26 January 2023) [30]. DNA methylation status as well as the clinical significance of methylated FBP1 in BTC human samples were analyzed via DNMIVD http://www.unimd.org/dnmivd/ and the SMART App http:// http://www.bioinfo-zs.com/smartapp/, (accessed on 26 January 2023). In the first step, we investigated the effect of tazemetostat on the viability of BTC cells following 72 and 120 h of treatment, respectively. As shown in Figure 1A,B, tazemetostat reduced the viability of most cell lines only at a very high concentration (starting from a concentration of 50 to 100 µM). We additionally investigated the effect of different tazemetostat concentrations on the clonogenic growth of BTC cells as an in vitro surrogate marker of the tumorigenic potential of cancer cells. We found that tazemetostat reduces clonogenic growth in a cell line-dependent manner. Figure 1C shows confluence images of KKU-055, OCUG-1 and NOZ cells as representative cell lines for a minor, moderate or strong effect of tazemetostat on clonogenic growth (see Supplementary Figure S3 for EGI-1, HuCCT-1 and MMNK-1 cells; due to their specific growth patterns, OZ and TFK-1 cells were not suitable for assessment of clonogenic growth). The strongest effect of tazemetostat on clonogenic growth was observable in NOZ, where at concentrations ≥2.5 µM, clonogenic growth was almost completely inhibited. In contrast, in KKU-055 cells, only treatment with high concentrations (≥40 µM) of tazemetostat resulted in a reduction in clonogenic growth. Regarding OCUG-1, a reduction in clonogenic growth was visible at concentrations of tazemetostat of ≥10 µM. Based on the results of the clonogenic growth assay, we selected KKU-055 and NOZ cells for further experiments, as these cell lines are representative for cell lines with low and high sensitivity towards treatment with tazemetostat, respectively. The current literature suggests a potential latency of the cytotoxic effect of tazemetostat in cancer cells [22]. Therefore, in an additional approach, we expanded the total incubation time to 360 h and measured the cell viability of the selected BTC cell lines after 120, 240 and 360 h of incubation with tazemetostat, respectively (see Figure 1D). Interestingly, for KKU-055 cells, which only showed a reduction in clonogenic growth at high tazemetostat concentrations, we measured a significant reduction in cell viability after 360 h of incubation time, even with the lowest tazemetostat concentration (0.3 µM, see Figure 1E). In contrast, in NOZ cells, only treatment with 30 µM tazemetostat for 240 and 360 h resulted in a non-significant reduction in cell viability (240 and 360 h, Figure 1F). We also tested whether the co-treatment of BTC cells with tazemetostat and cisplatin leads to a synergistic cytotoxic effect. However, we found that neither the simultaneous treatment nor pre-incubation of cells with tazemetostat followed by treatment with cisplatin resulted in synergistic effects (Supplementary Figure S4). Next, we investigated the epigenetic effect of tazemetostat on BTC cells and measured H3K27me3 levels. The treatment of KKU-055, NOZ and OCUG-1 cells with 0.3 µM tazemetostat resulted in a reproducible significant -2-fold to -6-fold reduction in H3K27me3 levels (Figure 2A–C). We next checked whether treatment with tazemetostat altered (compensatory) the EZH2 expression. As shown in Figure 3A, the mRNA levels of EZH2 were not changed by treatment with tazemetostat in both BTC cell lines. Similarly, on a protein level, no significant changes in EZH2 protein levels could be observed (Figure 3B,C). To investigate potential molecular mechanisms associated with the observed effects of tazemetostat in BTC cells, we measured the changes in mRNA levels of a total of 21 genes that were previously reported as directly regulated by EZH2 or part of molecular pathways that are regulated by EZH2. The selected genes, as well as their role in cancer and the references are listed in the Supplementary Figure S5. KKU-055 and NOZ cells were treated with 0.3 µM tazemetostat for 96 h before measurement of mRNA levels. Genes with a fold regulation of +2 and −2 were considered as upregulated and downregulated, respectively. As shown in Figure 4A, treatment with tazemetostat resulted in a significant 7-fold upregulation of the tumor suppressor fbp1 in KKU-055 cells. In addition, we also observed an increase (fold change > 2) of mRNA levels of klf2 and abi3bp in KKU-055 cells (Figure 4A). Of note, in NOZ cells, changes of mRNA levels of all 21 genes remained under the threshold of 2 (Figure 4A). Since fbp1 mRNA levels were significantly enhanced in KKU-055 cells following tazemetostat treatment, we also measured protein levels of FBP1. In NOZ cells, the FBP1 protein expression was not affected following tazemetostat treatment, whereas in KKU-055, the FBP1 protein expression was significantly upregulated (Figure 4B,C). Furthermore, when analyzing the available biodata of the Gene Expression Profiling Interactive Analysis (GEPIA) platform [30] (see http://gepia.cancer-pku.cn (accessed on 19 December 2022)) using data of The Cancer Genome Atlas (TCGA) project for cholangiocarcinoma, the mRNA expression of EZH2 and FBP1 in BTC samples showed a diametral distribution in normal controls and cases of BTCs, as demonstrated in Supplementary Figure S6: EZH2 was significantly upregulated in the tumor cases (Supplementary Figure S6A), whereas FBP1 was significantly downregulated in the tumor cases (Supplementary Figure S6A) and vice versa. Additionally, this expression pattern of EZH2 and FBP1 could be related, but not significantly, to the overall clinical outcome too, as shown in Supplementary Figure S6B, respectively. Furthermore, when analyzing the available biodata of the SMART app platform (http://www.bioinfo-zs.com/smartapp, accessed on 26 January 2023) and the DNMIVD (DNA Methylation Interactive Visualization Database, http://www.unimd.org/dnmivd/ (accessed on 26 January 2023)) database regarding the DNA methylation status of FBP1 in BTC human samples, it can be seen that in BTC tumor samples, the DNA methylation of FBP1 is higher compared to non-tumor samples (see Supplementary Figure S6C). Additionally, patients with a high DNA methylation at the FBP1 promotor region are suffering a non-significantly worse clinical outcome (see Supplementary Figure S6D). According to previous studies, the effect of tazemetostat is dependent on the mutation status of EZH2 [22]. We, therefore, analyzed the mutation status of EZH2 in our BTC cell lines to investigate whether the observed cytotoxic and epigenetic effects of tazemetostat can be related to the mutation status of EZH2. As shown in Table 2, both KKU-055 and NOZ cells harbored no mutation in the EZH2 gene. However, we found a Y641S mutation in OCUG-1 and TFK-1 cells (see Supplementary Figure S7). In the current project, we provide first evidence that the FDA-approved EZH2 inhibitor tazemetostat possesses antitumor effects in BTC. We found that treatment with tazemetostat affected clonogenic growth in a cell line-dependent manner. Our data are in line with other findings regarding the effect of pharmacological EZH2 inhibition on clonogenic growth. Bate-Eya et al. could demonstrate that clonogenic growth was affected in neuroblastoma cell lines following tazemetostat treatment for 14 days [31]. Similar to our study, a reduction in clonogenic ability only occurred at much higher concentrations than needed for H3K27me3 reduction [31]. Interestingly, immunohistochemistry staining revealed that NOZ cell lines are epithelial, whereas KKU-055 and OCUG-1 display a mesenchymal phenotype, which might explain why the clonogenic ability of NOZ cells was more affected (see Supplementary Figure S8). Regarding the effect of tazemetostat on cell viability, we found no significant changes after the application of tazemetostat for 72 h and for 120 h, as seen within other studies [22,32]. Therefore, we observed that clonogenic ability was affected by tazemetostat, whereas cell viability did not change at all after treatment with tazemetostat for up to 120 h. However, other studies have pointed out that long-term incubation (>120 h) with tazemetostat is required since EZH2 inhibition, as an epigenetic intervention, has a certain latency before the manifestation of a reduction in cell viability by the delayed activation of specific tumor suppressor genes which are downstream targets of EZH2 [32,33]. For instance, Brach et al. could demonstrate that cytotoxic effects occurred only after treatment with tazemetostat for up to 240 h in diffuse large B-cell lymphomas [32]. Furthermore, Knutson et al. demonstrated that cell viability following tazemetostat treatment was reduced in NHL cells after 96 h of treatment, which might be explainable by the accompanied reduction in H3K27me3 levels in the same time frame [22]. In accordance with these studies, we found that a clear reduction in cell viability in selected BTC cell lines occurred only after 360 h of incubation with tazemetostat. However, several further investigations must be performed to clarify the possible underlying mechanisms of the heterogenic effect of tazemetostat in BTC cells, especially considering the mutational status of Y641. Furthermore, there is evidence that non-canonical PRC2s exist that contain the EZH2 homolog EZH1 as the histone methyltransferase [34]. The moderate effect of tazemetostat on the cell viability might be due to compensation of the inhibition of EZH2 by EZH1 [35]. For instance, Shinohara et al. could demonstrate, that in malignant rhabdoid tumor cells, EZH1 protein expression was upregulated, after tazemetostat treatment [36]. Additionally, lncRNAs as well as miRNAs, were also shown to interact with EZH2 in BTC, which might also be interesting for future investigations [37]. Although the effects of tazemetostat on clonogenic growth and cell viability were observable only at relative high concentrations, several studies demonstrated that the epigenetic effect occurs at significantly lower substance concentrations [22,31]. For example, in the study by Bate-Eya et al., relatively low nanomolar concentrations of tazemetostat were needed to reduce H3K27me3 levels significantly (62.5 nM), whereas clonogenic ability was impaired at relatively higher concentrations (1 µM) [31]. Likewise, in our study, in KKU-055 and NOZ cells, we were able to measure the effects of tazemetostat on cell growth only at concentrations in the (high) µM range and after long incubation times, whereas treatment with 0.3 µM tazemetostat resulted in a clear reduction in H3K27me3 levels after 96 h of treatment. This is in line with several other studies [22,32,38]. It is well established that PRC2 as an epigenetic master regulator is involved in the regulation of numerous genes [21,39]. Interestingly, we also observed a non-significant increase in EZH2 protein expression in KKU-055 cells in some of the biological replicates following tazemetostat treatment. Since tazemetostat inhibits only the enzymatic activity and not the EZH2 expression, this observed elevation of the EZH2 expression in KKU-055 might be due to a compensatory reaction. Based on the current literature, we therefore selected n = 21 EZH2 target genes and measured their mRNA levels after tazemetostat application. By doing this, we found in KKU-055 that mRNA and protein levels of FBP1, a key enzyme in the gluconeogenesis, were significantly upregulated. These findings are in accordance with the study carried out by Wang et al., which demonstrated that FBP1 is partly epigenetically silenced/regulated via EZH2 [40]. Furthermore, in silico analysis of the EZH2 and FBP1 mRNA expressions in human CCA samples revealed that the EZH2 mRNA expression was upregulated in tumor samples, whereas the FBP1 expression was downregulated. Interestingly previous studies already described a potential tumor suppressor role of FBP1 in BTC [40,41]. Wang et al. demonstrated that mRNA and the protein expression of FBP1 were lower in CCA tissue compared to adjacent non-tumor tissue [40]. Furthermore, in BTC cells, when the inhibition of FBP1 was abolished by si-EZH2, the proliferation and migration of CCA cells was depleted, whereas the forced overexpression of FBP1 inhibited proliferation, migration, metastasis as well as colony formation [40,41]. Additionally, Zhao et al. demonstrated that FBP1 might act as a possible tumor suppressor gene via the beta catenin way [41]. Thus, further studies are required to investigate the role of FBP1 in BTC cell lines. A potential marker for the susceptibility of tumor cells towards tazemetostat could be the mutation status of EZH2. In previous studies, it was demonstrated that cells containing a point mutation, Y641/S or H within EZH2 are more sensitive to tazemetostat than the wild-type cells [22,33]. Up to now, these gain-of-function-mutations were mostly found in lymphomas. However, there are some descriptions of such mutations also in solid tumors. Tiffen et al., for example, found that mutated EZH2 (Y641) was constitutively active in melanoma [42]. Furthermore, this mutation was responsible for the silencing of tumor suppressor genes in melanoma [42]. In our study, we found Y641S mutations in OCUG-1 and TFK-1 cells. This is the first-time investigation of mutated EZH2 in BTC and in a solid tumor beside melanoma. However, based on the results of cell viability, we could not find any correlation between the mutated EZH2 and the susceptibility towards tazemetostat in OCUG-1 and TFK-1 cells. Given the fact that there are already EZH2 mutation kits available to test if patients are eligible for tazemetostat therapy in metastatic and/or locally advanced epithelioid sarcoma, it might be interesting for future studies to investigate the role of EZH2 mutation in BTC for potential diagnostic and therapeutic purposes [17,18,43]. In this regard, Morschhauser et al. confirmed an increased response rate in patients with relapsed or refractory follicular lymphoma harboring EZH2 mutations [44]. Cisplatin, a commonly used chemotherapeutic agent, is part of the standard therapeutic option for metastatic or locally advanced BTC [4]. However, BTC cells are often resistant to this intervention [4,13]. There might be evidence that tazemetostat can be used as an adjuvant therapeutic approach [20]. Furthermore, it was already demonstrated in several cancer entities that EZH2 might be involved in cisplatin resistance [45]. Therefore, EZH2 inhibition might sensitize resistant cells and/or enhance the cytotoxic effect of chemotherapeutics [46,47,48]. For example, Hu et al. could demonstrate that EZH2 was overexpressed in cisplatin-resistant ovarian cells compared to non-cisplatin-resistant cells [47]. Furthermore, EZH2 knockdown sensitized resistant ovarian cells towards cisplatin [47]. In another study, carried out by Cao et al., tazemetostat could enhance the cisplatin-induced apoptosis and cytotoxicity [46]. In our experimental setup, the treatment of BTC cells with tazemetostat did not augment the cytotoxicity of cisplatin, which might be explainable by tumor-specific phenomena. It would be interesting in future studies to investigate the effect of tazemetostat in combination with other commonly used chemotherapeutics such as 5-FU, doxorubicin and gemcitabine in BTC. The current study represents the first approach to investigate the effect of tazemetostat on BTC cells. We found that tazemetostat impairs clonogenic growth, as well as cell viability following long-term incubation. Moreover, we found that tazemetostat has a strong epigenetic effect in BTC cells and significantly reduces H3K27me3 levels. Furthermore, we observed a cell line-specific up-regulation of the tumor suppressor gene FBP1 following tazemetostat application on mRNA and protein levels. Interestingly, we could also demonstrate that the EZH2 Y641 point mutations occur in BTC cells. To conclude, our results provide the first evidence of tazemetostat as a possible anti-BTC agent and should be used as a base for further detailed investigations as well as in vivo experimentations.

PMC10000746

Ryan C. Hall,Amita M. Vaidya,William P. Schiemann,Quintin Pan,Zheng-Rong Lu

RNA-Seq Analysis of Extradomain A and Extradomain B Fibronectin as Extracellular Matrix Markers for Cancer

21-02-2023

extracellular matrix,tumor microenvironment,fibronectin,extradomain A fibronectin,extradomain B fibronectin

Alternatively spliced forms of fibronectin, called oncofetal fibronectin, are aberrantly expressed in cancer, with little to no expression in normal tissue, making them attractive biomarkers to exploit for tumor-targeted therapeutics and diagnostics. While prior studies have explored oncofetal fibronectin expression in limited cancer types and limited sample sizes, no studies have performed a large-scale pan-cancer analysis in the context of clinical diagnostics and prognostics to posit the utility of these biomarkers across multiple cancer types. In this study, RNA-Seq data sourced from the UCSC Toil Recompute project were extracted and analyzed to determine the correlation between the expression of oncofetal fibronectin, including extradomain A and extradomain B fibronectin, and patient diagnosis and prognosis. We determined that oncofetal fibronectin is significantly overexpressed in most cancer types relative to corresponding normal tissues. In addition, strong correlations exist between increasing oncofetal fibronectin expression levels and tumor stage, lymph node activity, and histological grade at the time of diagnosis. Furthermore, oncofetal fibronectin expression is shown to be significantly associated with overall patient survival within a 10-year window. Thus, the results presented in this study suggest oncofetal fibronectin as a commonly upregulated biomarker in cancer with the potential to be used for tumor-selective diagnosis and treatment applications.

RNA-Seq Analysis of Extradomain A and Extradomain B Fibronectin as Extracellular Matrix Markers for Cancer Alternatively spliced forms of fibronectin, called oncofetal fibronectin, are aberrantly expressed in cancer, with little to no expression in normal tissue, making them attractive biomarkers to exploit for tumor-targeted therapeutics and diagnostics. While prior studies have explored oncofetal fibronectin expression in limited cancer types and limited sample sizes, no studies have performed a large-scale pan-cancer analysis in the context of clinical diagnostics and prognostics to posit the utility of these biomarkers across multiple cancer types. In this study, RNA-Seq data sourced from the UCSC Toil Recompute project were extracted and analyzed to determine the correlation between the expression of oncofetal fibronectin, including extradomain A and extradomain B fibronectin, and patient diagnosis and prognosis. We determined that oncofetal fibronectin is significantly overexpressed in most cancer types relative to corresponding normal tissues. In addition, strong correlations exist between increasing oncofetal fibronectin expression levels and tumor stage, lymph node activity, and histological grade at the time of diagnosis. Furthermore, oncofetal fibronectin expression is shown to be significantly associated with overall patient survival within a 10-year window. Thus, the results presented in this study suggest oncofetal fibronectin as a commonly upregulated biomarker in cancer with the potential to be used for tumor-selective diagnosis and treatment applications. Cancer kills upwards of 600,000 people in the United States every year, making it the second leading cause of death in the United States [1]. Early detection and cancer-specific treatment have great potential to significantly improve the survival of cancer patients and reduce cancer-related mortality [2]. Cancer cells are biologically heterogeneous and dynamic in nature, presenting tremendous challenges in the development of molecular imaging technologies and therapies that target cellular markers [3]. The tumor microenvironment (TME) plays a crucial role in cancer development, progression, and treatment sensitivity [4,5,6]. Many cancer types share similar TME features, including angiogenic tumor vasculature, connective tissues, immune microenvironment, and extracellular matrix (ECM) [7,8,9,10]. Oncogenic markers in the TME are attractive targets for the design and development of molecular imaging technologies and targeted cancer therapeutics for early cancer detection and imaging-guided precision healthcare for cancer patients [11,12]. Compared to normal tissue, the tumor ECM is often highly enriched with aberrantly expressed proteins [13]. While most ECM proteins are expressed in normal tissues and tumors, some undergo tissue-specific alternative splicing, where specific exons or mRNA fragments can be included or excluded from the final mRNA transcript and translated protein [14]. Fibronectin (FN) is an ECM glycoprotein essential to normal tissue biology and is known to undergo alternative splicing [15]. Two alternatively spliced FN exons—extradomain-A (EDA) and extradomain-B (EDB)—are involved in developmental and remodeling processes, such as embryogenesis, wound healing, and neovascularization [16,17,18]. Previous studies focusing on EDA- and EDB-containing FN (EDA-FN and EDB-FN, respectively) in human tumor specimens have found them to be aberrantly expressed in several cancers, including breast cancer, head and neck cancer, pancreatic cancer, and prostate cancer, among others [19,20,21,22,23,24,25]. Furthermore, high expression of EDA-FN and EDB-FN in tumors has been associated with angiogenesis, epithelial-to-mesenchymal transition (EMT), tumor cell migration and invasion, and therapy resistance [26,27,28,29]. On account of their significant roles in fetal development and tumor-related processes, EDA-FN and EDB-FN are called “oncofetal” FNs. Preclinical work in several cancer types has already exploited the aberrant expression of oncofetal FN in tumors for targeted diagnostic and therapeutic purposes with high tumor selectivity and efficacy [30,31,32]. However, while these studies and clinical evidence have explored specific cancer types based on limited sample sizes, there has yet to be a large-scale pan-cancer study focusing on alternative splicing characteristics of FN in the context of clinical diagnostics and prognostics to examine its potential as a targetable biomarker for molecular diagnostics and therapies. To assess FN expression and its oncofetal subtypes from a pan-cancer perspective, large, publicly available datasets can be explored. The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects have collected thousands of cancer and normal tissue samples for a variety of tissue sites for large-scale, big-data analysis (Table 1, Supplementary Table S1) [33,34]. These projects conducted RNA-Seq analysis on mRNA samples from the collected tissues, producing large arrays of data mapped to the Ensembl genome library [35]. To utilize the projects in a single dataset, the UCSC TOIL Recompute project reprocessed the RNA-Seq samples to ensure consistent meta-analysis between datasets [36,37]. Using the TOIL Recompute results, gene and transcript expression can be explored in the context of clinical information provided with the samples to analyze differences in alternative splicing characteristics between normal tissue samples and tumors. Thus, this study investigates the aberrant expression of FN and its alternative splicing characteristics in primary tumors and normal tissues, with emphasis on its utility as a targetable biomarker. FN expression was first explored at both the mRNA (FN1 gene) and protein levels to elucidate correlations between transcription and translation activity. In the TCGA database, FN is one of the only ECM proteins for which mRNA and protein expression data are both provided for primary tumors. FN mRNA expression in primary tumors follows a significant positive relationship with FN protein expression (Figure 1A, Supplementary Figure S1). This suggests that FN mRNA expression can be used as an approximate analog of downstream FN protein expression. Since protein expression data are not provided for normal tissue samples or specific FN isoforms, and given the positive correlation between FN mRNA and protein expression, further analyses in this study will focus on FN mRNA expression. FN is known to generally exhibit upregulated expression in tumor tissues compared with corresponding normal tissues. The GTEx library provides thousands of normal tissue samples that can be added to those provided by TCGA for a more rigorous comparison of normal tissues and primary tumors. The assignments of GTEx tissue sites to corresponding TCGA tissue sites are shown in Table 1, and sample sizes for all cohorts used in this study are detailed in Supplementary Table S1. Based on data provided by TCGA and GTEx, FN mRNA exhibits higher expression in primary tumors than in normal tissues in 17 out of 25 cancer types (68%) for 10 or more normal tissue and primary tumor samples (Figure 1B, Supplementary Table S4). On average, FN mRNA exhibits 5.23× overexpression in primary tumors compared to normal tissues among the cancer types analyzed, with the highest overexpression occurring in breast cancer (BRCA, 8.4×), glioblastoma multiforme (GBM, 18.5×), head and neck squamous cell carcinoma (HNSC, 7.4×), pancreatic adenocarcinoma (PAAD, 20.9×), and thyroid cancer (THCA, 41.2×) (Figure 1C). However, despite the differences in expression of FN mRNA between normal tissues and primary tumors, there remain issues with FN as a potential oncotarget in general. First and foremost, as a primary component of the ECM, FN also exhibits high expression in normal tissues, raising concerns about off-target effects. Second, while upregulated FN is associated with primary tumors and tumor progression, aberrant FN expression can be related to many other physiological responses and conditions, such as transitory and chronic inflammation, fibrosis, and tissue repair. Therefore, a target more selective to tumors than general FN is ideal. The FN gene contains 47 exons, 3 of which exhibit alternative splicing (Figure 2A). These three exons are EDA and EDB, which are either absent or present, and the variable region (IIICS), which can take on five isoforms of differing amino acid lengths (V0, V64, V89, V95, and V120) (Figure 2B). The EDA and EDB domains are of particular interest due to their association with oncogenic processes. In total, all potential combinations of alternative splicing (EDA−/+, EDB−/+, and V0/64/89/95/120) result in a maximum of 20 full-length FN proteins (Supplementary Table S2). The most recent mRNA mapping to the human genome conducted by Ensembl identified 27 FN transcripts, 10 of which correspond to full-length FN, called ECM fibronectin (ECM-FN) for the remainder of this study (Figure 2D). Of the 10 ECM-FNs, 5 contain EDA and 3 contain EDB. For analyses conducted in this study, the combined expressions of the 10 ECM-FNs, 5 full-length EDA-containing transcripts (EDA-FNs), and 3 full length EDB-containing transcripts (EDB-FNs) are examined in more detail (Figure 2C). ECM-FN accounts for approximately 21% of total FN mRNA in normal tissues, representing a significantly small proportion of total FN mRNA expression (Figure 3A). In addition, ECM-FN follows similar overexpression patterns in primary tumors to the FN1 gene as a whole. Of the 25 cancer types analyzed, 19 (76%) overexpress ECM-FN in primary tumors compared to normal tissue (Figure 3B, Supplementary Table S5). Furthermore, primary tumors exhibit 6.60× overexpression of ECM-FN relative to normal tissue, which is substantially higher than the 5.23× overexpression of the FN1 gene (Figure 3C). Of the remaining 17 FN transcripts, just 1 corresponds to a known truncated protein called migration stimulating factor (MSF), which is a soluble protein present in blood serum and the stroma (Supplementary Table S3). The rest are likely to represent truncated transcripts or misaligned reads. While these short transcripts do not have a corresponding full-length protein, three contain EDA and two contain EDB. For analyses conducted in this study, the expression of total FN mRNA (i.e., all transcripts/FN1 gene), all EDA-containing transcripts (Exon A; five full-length and three short) and all EDB-containing transcripts (Exon B; three full-length and two short) are also examined (Figure 2C). The expression of EDA and EDB in FN was next explored in normal tissue and primary tumors. When analyzing all available FN transcripts, both Exon A and Exon B represent significantly small proportions of the FN1 gene as a whole (Figure 4A). In primary tumors, whereas the FN1 gene exhibited an average overexpression of 5.23× compared to normal tissue, Exon A and Exon B both exhibited significantly higher overexpression at 6.77× and 6.03×, respectively (Figure 4B, Supplementary Table S4, Supplementary Figure S2). When looking at individual cancer types, Exon A exhibited higher normalized expression than the FN1 gene in 17 out of 25 (68%) cancer types, while Exon B exhibited higher normalized expression in 19 out of 25 (76%) cancer types (Figure 4C). When narrowing our analyses strictly to ECM-FNs, both EDA-FN and EDB-FN likewise represent significantly small proportions of ECM-FNs as a whole (Figure 4D). In primary tumors, whereas ECM-FN exhibits an average overexpression of 6.60× compared to normal tissues, EDA-FN and EDB-FN exhibited substantially higher overexpression at 7.45× and 9.28×, respectively (Figure 4E, Supplementary Table S5, Supplementary Figure S2). Examining individual cancer types revealed that EDA-FN exhibits higher normalized expression than ECM-FNs in 15 out of 25 (60%) cancer types and that EDB-FN likewise exhibits higher normalized expression in 16 out of 25 (64%) cancer types (Figure 4F). Under both analysis schemes, FN transcripts containing EDA and EDB exhibited a greater level of overexpression in primary tumors compared to the FN1 gene (in the cases of Exon A and Exon B) and ECM-FN (in the cases of EDA-FN and EDB-FN). This indicates that oncofetal FNs could serve as potential tumor-selective ECM targets. The remainder of this study will focus on the correlations between oncofetal FN expression and clinical diagnostic and prognostic factors. As the focus will be on FN expressed in the ECM, only ECM-FNs and the corresponding EDA-FNs and EDB-FNs containing transcripts will be investigated in more detail. One of the leading prognostic indicators for many cancer types is tumor stage at the time of diagnosis. Several cancers already have clinically viable screening mechanisms to detect in situ or early-stage disease, but many others do not. TCGA provides pathologic staging information for most of the samples in the database. After grouping patients by stage, it is readily visible that several cancer types in TCGA are diagnosed with stage I disease less than 20% of the time (Figure 5A). While breast cancer patients in this analysis exhibited low rates of early-stage diagnosis, the clinical rate of early-stage diagnosis for breast cancer is approximately 48% [38]. Looking strictly at the cancer types with low early-diagnosis rates, patient survival is significantly better when tumors are diagnosed at stage I than at all other stages (Figure 5B). In these cancer types, both EDA-FN and EDB-FN expression were borderline significantly higher on average in stage I tumors compared with corresponding normal tissues (Figure 5C). Pancreatic cancer, with the highest overexpression of oncofetal FN in stage I tumors, has no clinically viable screening procedures. As such, overexpression of EDA-FN and EDB-FN in early-stage pancreatic tumors presents an opportunity for targeting the ECM to provide better mechanisms and procedures for identifying tumors at an early stage. Similarly, although screening procedures exist for breast cancer and head and neck squamous cell carcinoma, the high overexpression of oncofetal FN in stage I tumors presents a potential ECM marker to exploit for supplementing current clinical screening procedures and improving early detection of disease. Lymph node status is another factor highly predictive of patient prognosis, which is especially true in head and neck squamous cell carcinoma (HNSC), where it represents the most significant prognostic indicator [39]. In the TCGA database, patients can be grouped into those diagnosed with negative lymph node activity (N0) and those diagnosed with positive activity (N+). HNSC patients diagnosed N+ exhibited significantly poorer overall survival than patients diagnosed N0, with median survival times of 2.99 and 7.41 years, respectively (Figure 6A). As such, ensuring that the clinical diagnostic processes for lymph node activity are as accurate and sensitive as possible is of the utmost importance for HNSC patients. Oncofetal FN expression was next compared between normal tissue samples and primary tumors based on pathologic N stage. Samples from patients diagnosed N0 exhibited significantly higher EDA-FN and EDB-FN expression than normal tissue samples. Furthermore, patients diagnosed N+ exhibited significantly higher EDA-FN and EDB-FN expression than N0 patients, thereby representing a stepped trend of increasing oncofetal FN expression with increasing pathologic N stage (Figure 6B). In addition, when expanding the analysis of pathologic lymph node activity to other cancers, N+ patients exhibit significantly higher EDA-FN and EDB-FN expression than N0 patients on average, suggesting the potential for oncofetal FNs as unique molecular targets for aiding in the predictive diagnosis of lymph node activity (Supplementary Figure S3). To further examine this, a binary classification test was performed. HNSC is the only cancer type in the TCGA database for which clinical and pathologic N stages are both supplied for a majority of samples in the dataset. In this test, pathologic N stage (pN), which is determined via lymph node dissection and subsequent histological examination, represents the ground truth, while clinical N stage (cN), which is determined via physical and radiological examination, is the clinical test. The binary classification included 400 HNSC patients, with a prevalence of positive lymph node activity of 58.3% (Figure 6C, Supplementary Figure S4). Clinical N staging correctly staged 139 patients as N0 and 175 patients as N+, yielding an accuracy of 78.5% (Figure 6C, Supplementary Figure S4). However, 58 patients who were clinically N0 were pathologically N+, representing understaged patients, while 28 patients who were clinically N+ were pathologically N0, representing overstaged patients (Figure 6C, Supplementary Figure S4). Looking strictly at patients who were incorrectly diagnosed, both EDA-FN and EDB-FN expression were borderline significantly higher in understaged patients compared to those that were overstaged (Figure 6D). This result is in agreement with the overall expression trends observed when analyzing all N0 and N+ patients shown in Figure 6B. With this in mind, targeting oncofetal FN presents on opportunity to enhance the diagnostic procedures for lymph node activity in head and neck squamous cell carcinoma and has the potential to be applied to other cancers with high rates of lymph node activity as well. Just like overall tumor stage, histological grade is closely correlated with patient prognosis. In all cases in which a histological grade is determined, the tumors must be biopsied, which is often a highly invasive procedure. Diagnostic methods that exploit the TME have the potential to provide additional information about tumor morphology under non-invasive conditions that could aid in the diagnostic process and help limit the number of invasive biopsies required. This is especially true in brain cancer, where tumors range from low-grade gliomas to high-grade glioblastomas. The TCGA database provides data for two brain cancers—low-grade glioma (LGG) and glioblastoma multiforme (GBM)—which can be compared to determine differences in FN expression associated with histological grade. Brain cancer prognosis strongly correlates with tumor grade at the time of diagnosis (Figure 7A). Examining the expression of oncofetal FN reveals that both EDA-FN and EDB-FN follow a stepped trend of significantly increasing expression as brain cancer grade increases (Figure 7B). Similarly, prostate cancer patients are nearly universally biopsied and diagnosed with a Gleason score based on tumor morphology, where higher Gleason scores generally correlate with poorer prognosis. Although prostate cancer patients have a high overall survival rate, there is a notable difference in survival between low-to-mid-grade tumors and high-grade tumors, demonstrating the significance of diagnosing Gleason score to help determine patient prognosis (Figure 7C). Examining oncofetal FN expression shows that both EDA-FN and EDB-FN exhibit a trend of increasing expression as Gleason score increases (Figure 7D). While the steps between Gleason scores are not significant, the overall trend of increasing expression approaches significance. Other cancers in the TCGA database also include histological grading information. When expanding the scope of analysis to include these other cancers, the trend of increasing oncofetal FN expression with increasing histological grade remains consistent (Supplementary Figure S5). Unsurprisingly, highly aggressive cancers, such as pancreatic cancer and head and neck squamous cell carcinoma, exhibited the largest increases in expression with tumor grade. Oncofetal FNs therefore present targetable ECM biomarkers to supplement the diagnostic process in cancers that routinely undergo highly invasive biopsies. Aside from correlations between FN expression and clinical diagnostic information, FN expression has been shown to correlate directly with patient survival in several cancer types. The TCGA database provides survival information for most of the patients included in the study, so patients can be grouped based on high (top 33%) or low (bottom 33%) FN expression within each cancer type. In this analysis, there is a clear and significant trend of reduced median survival in patients expressing high EDA-FN and EDB-FN compared to those with low expression (Figure 8A,B). On average, patients expressing high levels of EDA-FN exhibited approximately 29% reduced median survival time compared to the low-expression group. Similarly, patients expressing high levels of EDB-FN exhibited approximately 26% reduced median survival time on average. Some of the largest reductions in median survival time are found in bladder cancer, esophageal cancer, mesothelioma, stomach adenocarcinoma, lung squamous cell carcinoma, and low-grade glioma (Supplementary Table S6). The low- and high-oncofetal-FN-expression groups for all cancer types in the TCGA database, when combined, generate a pan-cancer dataset for analyzing overall survival among all cancers. It can be clearly seen that, over at least 10 years, the high-EDA-FN- and high-EDB-FN-expression groups exhibit significantly poorer survival than the respective low-expression groups (Figure 8C,D). The median survival time of patients expressing high EDA-FN is 6.23 years, representing a 2.1-year drop from the 8.35 years for the low-expression group (Figure 8C). Similarly, the median survival time for patients expressing high EDB-FN is 6.35 years, representing a 1.4-year drop from the 7.91 years for the low EDB-FN group (Figure 8D). These data suggest that oncofetal FN expression levels correlate with patient prognosis, indicating that they could serve as potential ECM targets for risk stratification and more accurate prognosis. Due to the heterogeneity and dynamic nature of tumor cells, there is generally no single cellular marker for targeting tumors. As such, tumor ECM markers have become objects of increased interest as more reliable targeting platforms than tumor cells themselves. FN has become an attractive ECM marker to explore for cancer molecular imaging and targeted therapy in light of its association with tumorigenesis and tumor progression. Indeed, oncofetal FNs have been investigated as tumor-selective targets due to their low expression levels in normal tissues, facilitating tissue-selective uptake and reducing negative off-target effects. Several targeting ligands, including the L19 and F8 antibodies, the ZD2 and PL1 peptides, and the EDB aptide, have been developed to selectively target oncofetal FN [21,40,41,42]. Interestingly, most ligands targeting oncofetal FN that have received extensive study have targeted EDB-FN. The L19 antibody and its corresponding small-chain variable fragment (scFv), specific to EDB-FN, have been used extensively for cancer imaging applications [30]. Several clinical trials have been conducted for the development of L19-conjugated iodine-based agents. The L19 antibody has also been extensively used in cancer therapy applications, including radiotherapy, immunotherapy, and chemotherapy, with several other clinical trials completed or under way [30]. Similarly, the F8 antibody, which is specific to EDA-FN, has been studied as a ligand for tumor-selective immunotherapies [30]. Peptides are also attractive ligand designs due to their small size and ease of conjugation. ZD2, a seven-amino acid peptide targeting EDB-FN, has been used extensively as a targeting ligand for imaging agents and therapeutics [21,43,44,45,46,47,48,49,50,51,52]. The ZD2-targeted MRI contrast agent, MT218, has demonstrated effectiveness at doses below clinical levels in several cancers and is undergoing clinical development [53]. Similarly, an aptide developed for EDB binding has been used for targeted chemotherapy, gene delivery, and imaging agents [42,54,55,56]. Furthermore, PL1, a 12-amino acid peptide simultaneously targeting both EDB-FN and tenascin-C, has recently been developed to deliver iron oxide nanoworms to brain cancers [41]. Molecular imaging of aberrantly expressed oncofetal FNs presents an opportunity to non-invasively measure their expression in primary tumors for early cancer detection and characterization. This is especially true for pancreatic cancer, which has no clinically viable screening techniques for early-stage disease. With a 5-year survival rate of approximately 11%, detection of pancreatic cancer at an early stage provides an overwhelming benefit in terms of patient survival [1]. Low survival rates in pancreatic cancer are due, in large part, to the inability to resect late-stage tumors, as only 10–20% of pancreatic cancers are suitable for resection—an issue that early-stage diagnosis can help ablate [57]. In this study, oncofetal FN was shown to exhibit significant upregulation in stage I pancreatic tumors, suggesting a potential avenue for targeting and localizing early-stage disease via molecular imaging. Similarly, oncofetal FN also exhibits significantly elevated expression in head and neck squamous cell carcinoma and other cancer types with positive lymph node activity, suggesting an application for diagnosing primary tumors likely to exhibit positive lymph nodes or identifying positive lymph nodes directly. As the most significant prognostic indicator for HNSC patients, the accuracy of lymph node diagnosis plays a key role in determining the courses of treatment and the expected outcomes for patients [39]. Unfortunately, clinical methodologies for determining lymph node activity in HNSC reach an accuracy of approximately 80%, meaning that upwards of a fifth of HNSC patients are incorrectly diagnosed [58]. The results of incorrect diagnoses often include unnecessary elective neck dissections for false-positive patients and the failure to provide adequate treatment for false-negative patients [58]. Thus, ensuring that diagnostic tests are as accurate as possible is of the utmost importance, and targeting oncofetal FN has the potential to provide tumor-selective markers to enhance the accuracy of these tests. Oncofetal FNs are known markers of EMT, which is associated with cancer invasiveness. Thus, they have the potential to serve as markers to monitor disease progression as well as provide risk assessment, even in cancers with low overall FN expression. For example, prostate cancer is the most common non-cutaneous cancer and the second most common cause of death in men [1]. It is highly heterogeneous, and early detection of high-risk prostate cancer is crucial for timely treatment. Although EDA-FN and EDB-FN expression in prostate cancer samples were similar to expression in normal tissues, clinically significant prostate cancers (Gleason score ≥ 7) show a trend of increased EDA-FN and EDB-FN expression with increasing Gleason score, indicating that they are potential markers for the risk stratification of prostate cancer. Furthermore, the association of oncofetal FN overexpression with cancer angiogenesis, EMT, and invasion suggests FN downregulation as a mechanism to control cell behavior and improve patient survival. Specifically, knocking down the expression of oncofetal FN in tumors has the potential to limit disease progression and spread, while also playing a role in improving tumor sensitivity to therapeutics. Several studies exploring the treatment of various cancers with microRNAs from the miR-200 family have demonstrated this [51,59,60,61]. FN represents one of the primary targets of the miR-200 family, among several others that directly affect pathways related to EMT [62]. In animal models, miR-200 treatment generally reduces tumor proliferation and metastasis, while also abrogating drug resistance. While these effects are not singly tied to FN expression, as miRNAs can simultaneously target dozens of mRNA transcripts, ex vivo analyses of miR-200-treated tumors often show strong knockdown of FN [51]. The results from these studies suggest that FN plays a key role in tumor progression, spread, and treatment sensitivity, which is in agreement with the clinical prognostic results of our TCGA study. Oncofetal FN can also be utilized as a biomarker to monitor the effectiveness of traditional chemotherapies. Upon consecutive doses of chemotherapy, drug resistance is commonly developed. The dynamic nature of tumor cells generally produces cells that are susceptible to chemotherapy at certain doses, while also producing cells that are able to resist the treatment [63]. This often results in a tumor mass that initially recedes when the sensitive cells die, after which the resistant cells proliferate [64]. In many cases where drug resistance develops, ECM proteins such as oncofetal FN are known to become substantially more upregulated than the pretreatment tumor, thereby producing a more aggressive and difficult-to-treat primary tumor [65]. The enhanced oncofetal FN expression after the development of resistance presents an opportunity to use ECM biomarkers to monitor the efficacy of the therapy. In this study, the RNA-Seq analysis of oncofetal FN expression grouped all primary tumors together, as too few post-treatment samples existed for robust comparisons with pre-treatment expression. Further studies can focus on assessing the expression of tumor ECM proteins at various stages of treatment and with various responses to treatment to establish oncofetal FNs as markers for monitoring tumor progression and response to therapies. This analytical study has multiple limitations. First and foremost, while the gene and protein expression of FN exhibit overall positive trends, there is no guarantee of direct correlation in all of the samples analyzed. Many factors contribute to protein translation from mature mRNA, which can result in large differences between mRNA and protein expression. Second, the TCGA database only provides protein expression for all collective FN proteins as a whole, so the mRNA expression of alternatively spliced transcripts cannot be verified by protein expression from the database. It is assumed, due to the positive correlation between FN gene and protein expression, that a similar trend would exist for the alternative splice variants as well. Finally, our analysis of the diagnostic and prognostic importance of FN focused strictly on ECM-FNs. While these transcripts correlate with known full-length FN proteins, the remainder of unconfirmed short or truncated FN transcripts may represent misaligned reads that originally derived from full-length transcripts. Future work will focus on additional analysis of mRNA and protein expression of specific alternatively spliced variants of FN to validate the results found in this study. Based on reprocessed RNA-Seq data from the TOIL Recompute project, which sourced data from the TCGA and GTEx projects, we demonstrated that FN and its alternatively spliced isoforms exhibit high levels of overexpression in many different sites of primary tumors relative to corresponding normal tissues. In addition, we demonstrated correlations between FN expression and several clinical diagnostic and prognostic categories, including early-stage disease, lymph node activity, histological grade, and patient survival. Since oncofetal FNs are known to exhibit little to no protein expression in healthy normal tissues, the results from this study suggest that oncofetal FN presents a tumor-selective biomarker that can be exploited for targeted diagnostic and therapeutic agents for a multitude of cancers. Furthermore, diagnostic agents targeted to oncofetal FN have the potential to provide valuable diagnostic and prognostic information via non-invasive means, making them potentially high-value supplements to current diagnostic practices. Bulk data, including RNA-Seq, protein expression, and clinical information, were obtained from the Toil Recompute project, available through the UCSC XENA web portal (https://xena.ucsc.edu/, accessed on 23 May 2020). The Toil Recompute project sourced their data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) [33,34]. All data extraction and organization were completed in MatLab (MathWorks, Natick, MA, USA) by first loading the bulk datasets and matching sample IDs between RNA-Seq expression, protein expression, and clinical information. Datasets were then organized first by cancer type and then by tissue type (i.e., normal tissue, primary tumor, etc.). Cohort groups consisting only of primary tumor samples were then generated by separating the primary tumor datasets into various groups according to clinical information, such as pathologic N stage (i.e., N0 or N+) or histological grade (i.e., grades 1–4). Data were then exported in comma-separated variable (.csv) format for further analysis and the generation of figures. RNA-Seq data from the Toil Recompute project are provided in the form of: To recover the raw TPM expression value for each sample, the data values provided by the Toil Recompute project were processed as follows: Using raw TPM values allows for the direct summation of multiple transcript expression values within a sample. The sum of the TPM values of all 27 FN transcripts within a sample equals the TPM value for the FN1 gene for that sample. Normalized TPM values were also produced when comparing the expression of various cohorts to a control reference across multiple cancer types. The normalization procedure was as follows: Depending on the application, refers to either the average expression in the respective normal tissue for the cancer type and the transcript group being analyzed, or the total expression of the FN1 gene or ECM-FN in a sample. After processing, data were exported in comma-separated variable (.csv) format for further analysis and the generation of figures. Statistical analyses were performed in GraphPad Prism (GraphPad Software, San Diego, CA, USA). When analyzing averages of two groups across multiple cancer types, a paired t-test was used. When analyzing samples of two groups within a cancer type, an unpaired Welch t-test was used. When analyzing averages of more than two groups across multiple cancer types, a repeated-measures one-way ANOVA was used with the Dunnett post hoc test. When analyzing samples of more than two groups within a cancer type, a Brown–Forsythe and Welch one-way ANOVA was used with the Games–Howell post hoc test. Survival curves were generated according to standard Kaplan–Meier survival analysis, followed by log-rank statistical testing to determine differences between survival curves.

PMC10000752

Ruth Aquino,Vidian de Concini,Marc Dhenain,Suzanne Lam,David Gosset,Laura Baquedano,Manuel G. Forero,Arnaud Menuet,Patrick Baril,Chantal Pichon

Intrahippocampal Inoculation of Aβ1–42 Peptide in Rat as a Model of Alzheimer’s Disease Identified MicroRNA-146a-5p as Blood Marker with Anti-Inflammatory Function in Astrocyte Cells

22-02-2023

microRNAs,miRNA-146a-5p,Alzheimer’s disease,Aβ1–42 peptide,biomarkers,diagnosis

Circulating microRNAs (miRNAs) have aroused a lot of interest as reliable blood diagnostic biomarkers of Alzheimer’s disease (AD). Here, we investigated the panel of expressed blood miRNAs in response to aggregated Aβ1–42 peptides infused in the hippocampus of adult rats to mimic events of the early onset of non-familial AD disorder. Aβ1–42 peptides in the hippocampus led to cognitive impairments associated with an astrogliosis and downregulation of circulating miRNA-146a-5p, -29a-3p, -29c-3p, -125b-5p, and-191-5p. We established the kinetics of expression of selected miRNAs and found differences with those detected in the APPswe/PS1dE9 transgenic mouse model. Of note, miRNA-146a-5p was exclusively dysregulated in the Aβ-induced AD model. The treatment of primary astrocytes with Aβ1–42 peptides led to miRNA-146a-5p upregulation though the activation of the NF-κB signaling pathway, which in turn downregulated IRAK-1 but not TRAF-6 expression. As a consequence, no induction of IL-1β, IL-6, or TNF-α was detected. Astrocytes treated with a miRNA-146-5p inhibitor rescued IRAK-1 and changed TRAF-6 steady-state levels that correlated with the induction of IL-6, IL-1β, and CXCL1 production, indicating that miRNA-146a-5p operates anti-inflammatory functions through a NF-κB pathway negative feedback loop. Overall, we report a panel of circulating miRNAs that correlated with Aβ1–42 peptides’ presence in the hippocampus and provide mechanistic insights into miRNA-146a-5p biological function in the development of the early stage of sporadic AD.

Intrahippocampal Inoculation of Aβ1–42 Peptide in Rat as a Model of Alzheimer’s Disease Identified MicroRNA-146a-5p as Blood Marker with Anti-Inflammatory Function in Astrocyte Cells Circulating microRNAs (miRNAs) have aroused a lot of interest as reliable blood diagnostic biomarkers of Alzheimer’s disease (AD). Here, we investigated the panel of expressed blood miRNAs in response to aggregated Aβ1–42 peptides infused in the hippocampus of adult rats to mimic events of the early onset of non-familial AD disorder. Aβ1–42 peptides in the hippocampus led to cognitive impairments associated with an astrogliosis and downregulation of circulating miRNA-146a-5p, -29a-3p, -29c-3p, -125b-5p, and-191-5p. We established the kinetics of expression of selected miRNAs and found differences with those detected in the APPswe/PS1dE9 transgenic mouse model. Of note, miRNA-146a-5p was exclusively dysregulated in the Aβ-induced AD model. The treatment of primary astrocytes with Aβ1–42 peptides led to miRNA-146a-5p upregulation though the activation of the NF-κB signaling pathway, which in turn downregulated IRAK-1 but not TRAF-6 expression. As a consequence, no induction of IL-1β, IL-6, or TNF-α was detected. Astrocytes treated with a miRNA-146-5p inhibitor rescued IRAK-1 and changed TRAF-6 steady-state levels that correlated with the induction of IL-6, IL-1β, and CXCL1 production, indicating that miRNA-146a-5p operates anti-inflammatory functions through a NF-κB pathway negative feedback loop. Overall, we report a panel of circulating miRNAs that correlated with Aβ1–42 peptides’ presence in the hippocampus and provide mechanistic insights into miRNA-146a-5p biological function in the development of the early stage of sporadic AD. Alzheimer’s disease (AD) is a complex and multifactorial pathology that affects millions of people around the world [1]. The appearance of amyloid plaques and the formation of neurofibrillary tangles (NFTs) are two representative features of AD, responsible for the gradual deterioration of cognitive functions such as loss of memory, language, and thinking ability. Amyloid plaques are deposits of amyloid beta (Aβ) peptide that accumulate in the extracellular matrix between nerve cells [2]. The Aβ peptides arise from the cleavage of the amyloid precursor protein (APP). Among the different Aβ species generated, Aβ1–42 peptides are the most hydrophobic and fibrillogenic and are the main species deposited in the brain [3]. Neurofibrillary tangles are intraneuronal acumulation of hyperphosphorylated tau protein (41) [4]. One of the main issues with AD is the long preclinical stage. This pathology begins 10–20 years before significant neuronal death and cognitive symptoms and behavior defects appear [5]. Although much is known about the disease, the early molecular events that trigger the pathogenesis of AD are not yet fully understood. It has been postulated that Aβ peptides, especially Aβ1–42 peptides, which are more prone to aggregation, initiate a cascade of pathological events that lead to aberrant phosphorylation of Tau, neuronal loss, and eventual dementia [6,7,8]. The aggregation of Aβ peptides can generate oligomers and fibrils that can both be neurotoxic. Today, there is no therapy able to cure or prevent AD [9]. There is a real need to predict the development of symptomatic AD for both mild cognitive impairment (MCI) and dementia in asymptomatic individuals [10]. The current AD diagnosis uses cumbersome and expensive methods such as structural magnetic resonance imaging (MRI) and molecular neuroimaging with positron emission tomography (PET) [9]. Therefore, the search for biomarkers for early diagnosis is still essential and is currently an active field of research. MiRNAs are a subclass of small noncoding RNAs that play important roles in the regulation of post-transcriptional gene expression by binding to complementary sequences of target messenger RNAs (mRNAs) and inducing translation repression and/or mRNA degradation [11,12]. Under different pathological conditions, there is a dysregulation of miRNAs present in most body tissues and fluids, including brain tissues, cerebrospinal fluid (CSF), and serum [12]. MiRNAs present in biofluids are called circulating miRNAs [13]. They are produced and secreted from cells present in tissues and organs, and so reflect the pattern of expression of the tissues of origin. Circulating miRNAs are now considered as disease biomarkers [14]. They are stable in different biological fluids, relatively easy to detect, and have an expression pattern that reflects the disease stages of pathologies such as AD [15,16,17]. All of these characteristics position miRNAs as potential AD biomarkers. Several animal models have been created for AD research, with transgenic animal models being the most popular [18]. As with any animal model, they must mimic all the cognitive, behavioral, and neuropathological characteristics of the disease to recapitulate the disease phenotype with high fidelity. Unfortunately, most of the AD models currently used are called partial models, as they mimic only some components of AD. Amyloid-producing transgenic animal models of AD have been created based on the genetic origins of familial AD or early-onset AD (EOAD), detected in patients under 60 years of age, which corresponds to only 5% of AD patients. These models have contributed significantly to better understanding the molecular mechanisms involved in the pathology, but they do not represent the majority of AD cases, called late-onset AD (LOAD), which represents 95% of cases that develop in patients over 60 years of age [19,20,21]. In addition, the different genetic backgrounds of these models constitute a real issue [22]. Due to these drawbacks, non-transgenic animal models have been developed to study the most common LOAD form of AD [21]. These models represent one or more distinctive features such as AD-like senile plaques, NFT, oxidative stress, and cognitive impairment [22]. Amongst them, one model consists in injecting an Aβ synthetic peptide into the brains of rodents. Previous studies have shown that this procedure causes learning and memory deficits in treated animals due to the formation of amyloid plaques and the disruption of long-term potentiation and behavior [22,23,24]. As for the transgenic model, this model is not perfect either, but it provides some insights into the early impact of Aβ peptides on AD pathogenesis. Different studies have investigated the expression of circulating miRNAs in the blood of AD patients and/or in transgenic animal models of AD. Results indicated that the most representative dysregulated miRNAs are miRNA-9a-5p, -146a-5p, -29a-3p, -29c-3p-125b-5p, -181c-5p, -191-5p, -106b-5p, and -135a-5p [25,26,27,28]. Those miRNAs have been associated with different stages of the progression of AD (for a review, see [29]). To the best of our knowledge, no studies have searched for the expression of a panel of several circulating miRNAs in rodent models of AD generated by intrahippocampal infusion of fibrillary forms of the Aβ1–42 peptide. Yet, it might be relevant to correlate them with a specific pattern of miRNA expression produced from cerebral adult tissues in response to the deposit of Aβ1–42 peptides. This work performed with an AD model generated by intrahippocampal injection of fibrillary forms of Aβ1–42 peptide aims to correlate the expression of particular miRNAs with the presence of aggregated Aβ1–42 in the brain of an adult rat. A Morris Maze Water test was applied to assess the cognitive deficit of the rat model, while immunohistochemical analysis of GFAP expression was conducted to evaluate the presence of astrogliosis in brain tissues. Then, we quantified the amount of circulating miRNA selected from a short list of the most relevant miRNAs found in the AD literature. The same quantification was performed with the sera of amyloid-bearing APPswe/PS1dE9 transgenic mice for comparison between these two different animal models of AD. Last, we focused our attention on miRNA-146a-5p, as this miRNA was found exclusively differentially dysregulated in sera of an FAβ-infused rat model of AD and also because the kinetics of its expression in these animals were different from other established miRNA kinetics. We conducted functional in vitro studies on rat primary astrocytes treated with the Aβ1–42 peptide. We investigated the involvement of the NF-κB signaling pathway on the induction of miRNA-146a-5p expression and, as a consequence, the regulation of Irak-1, Irak-2, and Traf-6 as transcriptional targets of this miRNA and as functional effectors of the NF-κB signaling pathway. Finally, we evaluated the impact of this axis of regulation on the production of pro-inflammatory cytokines and chemokines such as IL-1β, IL-6, TNF-α, and Cxcl1 genes in astrocytes in response to treatment with Aβ1–42 peptides. Sprague–Dawley rats (aged 8–12 weeks) with a body weight of 200–380 g were obtained from the Bioterium of the Research and Development Laboratory located in the Cayetano Heredia Peruvian University. Animals were maintained under controlled laboratory temperature (25 ± 2 °C) and humidity (60%) conditions, with a controlled light cycle (12 h light/12 h dark). Water and food were available ad libitum throughout the experiment. Ethics Committee of the Cayetano Heredia Peruvian University (CIEA-102069) approved the animal handling and experimental procedures. The animal care staff monitored the behavior of rats daily to ensure that the animals were safe and healthy. APPswe/PS1dE9 transgenic mice and their littermate mice were bred and hosted in the animal facility of Commissariat à l’Energie Atomique (CEA, Centre de Fontenay-aux-Roses; European Institutions (Agreement #B92-032-02)). These mice express a chimeric mouse/human APP with the Swedish mutation and a human presenilin-1 lacking exon 9. All experimental procedures were conducted in accordance with the European Community Council Directive 2010/63/UE and approved by local ethics committees (CEtEACEA DSV IdF N°44, France) and the French Ministry of Education and Research (APAFIS#21333-2019062611099838v2). Amyloid-β1–42 (Aβ1–42) peptides were from Sigma-Aldrich (Sigma-Aldrich, Saint-Quentin-Fallavier, France). The dry powder was solubilized in DMSO to generate a stock solution of Aβ1–42 peptides at concentration of 10 µg/µL (2.22 mM) in PBS. This stock solution was stored at −20 °C until use. For production of fibrillary forms of Aβ1–42 peptides (FAβ1–42), the working solutions were diluted in PBS at final concentrations of 0.5, 1, and 2.5 μg/μL and incubated for 5 days at 37 °C [30]. At the end of this incubation period, FAβ1–42 solutions were directly inoculated into the hippocampus of the rats as described below. The general procedure to infuse the FAβ1–42 solutions in the brains of rats derived from Wu et al. [31] with some modifications. Briefly described, anesthetized rats were placed on a stereotaxic frame (KOPF® 900, David Kopf Instruments, Tujunga, CA, USA) to infuse 3 μL of FAβ1–42 or PBS solutions into the two hemispheres of the hippocampus using a 10 μL syringe (Hamilton® glass syringe 700 series RN, Hamilton, OH, USA) connected to a 26-G needle (Hamilton®). The procedure to inoculate FAβ1–42 and PBS solutions consisted of a gradual infusion of solutions over a 6 min period, followed by an additional 5 min period to ensure optimal dispersion of solutions into the ventricles. The Bregma was used as reference. The coordinates to infuse the solutions were as follows: 2.6 mm lateral, 3.0 mm back, 3.0 mm deep, corresponding to the CA1 regions of the hippocampus [32]. After careful removal of the syringe, rats were returned to cages and monitored every day until the time of experimentation. A Morris Water Maze test (MWM) was used to evaluate the spatial memory of animals. The general procedure was from Wenk et al. [33] with some modifications. A circular pool (126 cm in diameter, 75 cm in high) was built, filled with water at 21–22 °C, and loaded with a transparent plastic platform (10 cm in diameter) placed in a constant position. The pool was divided with imaginary lines to delineate four quadrants: northeast (NE), northwest (NW), southeast (SE), southwest (SW). Animals were placed in all quadrants, and their swimming trajectories to reach the platform were monitored using a webcam connected to a computer. Videos were processed with a home-made RatsTrack plug-in system, designed for these experiments to accurately record distance, time, and velocity of animals in the pool. The MWM was performed 14 days after the infusion of FAβ1–42 or PBS solutions and was monitored using an arbitrarily fixed time period of 90 s for each trial. A reference memory protocol consisting of familiarization, acquisition, and memory sessions was set up. The familiarization session procedure consisted in placing rats in one quadrant of the pool and to allow the rats to find the platform over 4 trials. The acquisition session was performed the day after the familiarization session and lasted over four days. The objective of this session was to evaluate the spatial learning of animals by placing rats in all 4 quadrants of the pool and evaluating their velocities to reach the platform. A total of 8 trials per rat were recorded. Finally, the memory session was performed on the sixth day. The objective of this trial is to record the reference memory of rats by monitoring the swimming trajectories of rats placed into the pool without any platform. One trial per rat was used in this specific session. Blood samples were collected in BD Vacutainer® tubes coated with silica as coagulation activator according to procedures described by Vigneron et al. [34]. Cardiac punctures were performed to collect enough blood per animal at day 7 (n = 8), 14 (n = 8) and 21 (n = 8) post-injection of FAβ1–42 or PBS solutions [35]. The samples were left at room temperature for 40 min for the formation of blood clot, and then centrifugated at 1900× g for 10 min at 4 °C to collect the serum. A second centrifugation at 16,000× g for 10 min at 4 °C was used to clarify the serum. Hemolyzed samples, inspected visually, were discarded from the study. The clarified sera were stored at −80 °C until used. CSF was collected 14 days after the inoculation of FAβ1–42 solution from the cisterna magna of the rats according to procedure described by Pegg et al. [36]. Briefly, the procedure to extract CSF consisted of using an infusion system equipped with a 25-gauge butterfly needle to collect the CSF into the dura mater/Atlanto-occipital membrane. CSF samples (40 to 80 µL) were loaded in a 0.5 mL Eppendorf tube, incubated for 1 h at room temperature before centrifugation at 1000× g for 5 min at 4 °C to remove potential cell debris. A second centrifugation at 16,000× g for 10 min at 4 °C was used to clarify the samples. The clarified CSF samples were stored at −80 °C until use. The NucleoSpin® miRNA Plasma kit from Macherey-Nagel (Hoerdt, France) and the miRNeasy Serum/Plasma Kit (Qiagen, Hilden, Germany) were used to extract respectively total RNAs from sera and CSF samples. In the latter, 5 μg of a glycogen solution prepared at concentration of 0.1 µg/µL was added to each 50 µL of CSF fraction to optimize the yield of miRNA recovery [37]. Exogenous spike-in miRs (cel-miR 39-3p, -54-3p, and cel-miR 238-3p) were added to each sample to normalize the extraction procedure of miRNA according to standardized protocol described by Vigneron et al. [34]. Nucleotide’s sequence of spike-in miRs was taken from miRNA database and synthesized by Eurogentec (Eurogentec, Liège, Belgium) as SDS-PAGE purified oligonucleotides. These synthetic miRNAs were resuspended in nuclease-free water at a fixed concentration of 200 amol/μL. Then 2.5 µL per 100 µL of samples was added after the denaturation step of the extraction procedure, as recommended by the manufacturer’s instructions. Total RNA fractions were quantified using a nanodrop spectrophotometer (Nanodrop 2000, Thermo Scientific, Waltham, MA, USA). Samples with an RNA integrity number (RIN) greater than 8 were considered for the study. Total RNAs were reverse transcribed by using the miScript II RT Kit (Qiagen, Hilden, Germany) according to routine procedure described in [38,39]. Fifty ng of total RNAs prepared at concentration of 10 ng/µL were polyadenylated by a poly (A) polymerase and then reverse transcribed to cDNA using oligo-dT primers following recommendations from the manufacturer (Qiagen). The generated cDNAs were then stored at −20 °C until use. qRT-PCR was performed with the miScript SYBR® Green PCR Kit (Qiagen, Hilden, Germany) according to routine procedures described by Simion et al. [38,39]. A volume of 2.5 µL of cDNA corresponding to 50 ng of cDNA was loaded into a final volume of 10 μL containing 5 μL of 2X QuantiTect SYBR Green PCR Master Mix, 1 μL of 10X miScript Universal Primer, 10X miScript Primer Assay, and 0.5 µL of RNase-free water. All miRNA-specific forward primers (miScript Primer Assays) were purchased from Qiagen and are listed in Table S1 (Supplementary Materials). The quantification of PCR products was collected using the Light Cycler® 480 (Roche Diagnostics Corporation, Indianapolis, IN, USA). The relative miRNA expression was calculated according to the Livak and Schmittgen method [40] and expressed as 2−∆∆Ct. The mean of Ct from the 3 spike-in miRNAs was used to normalize the data as described by Vigneron et al. and Faraldi et al. [34,41]. Immunostaining was performed following the protocol described in [42] on 5 µm paraffin sections of brain tissues. After deparaffinization, sections were saturated (2h at room temperature in TBS containing 0.2% triton, 0.5% FBS, and 1% BSA), then incubated with primary antibodies; anti-GFAP (Dako, Agilent, Santa Clara, USA, Z0334; 1:500) at 4 °C overnight, washed, and incubated with a secondary anti-rabbit Alexa 488 antibody (Abcam, ab150077, 1:1000). The slides were stained with DAPI for 10 min, washed with PBS, mounted with Fluoromount-G (SouthernBiotech, Birmingham, UK), and inspected visually using a ZEISS AXIOVERT 200 M Apotome microscope (Zeiss, Oberkochen, Germany) connected to a digital camera. Serial sections were analyzed at 200× magnification to reconstruct the whole hippocampus volume of the brain using the ZEN2.1 software (Zeiss). Images were collected as serial Z stack series from 18 optical slices. GFAP-positive cells were counted manually from cornu ammonis (CA) 1/CA2, CA3, and the dentate gyrus (DG) using Image J (version 1.53q, Fiji software) [43]. A total of 40 slides were analyzed, corresponding to treated group (n = 5) and control group (n = 5). Primary astrocyte cultures were prepared following the protocols described by Galland et al. [44]. Six brains of new-born Sprague–Dawley rats of 3 days of age were collected aseptically before to manually isolate the cerebral hemispheres. After being carefully removed, the meninges and tissues were dissociated mechanically and rinsed with PBS. The cell suspension was centrifuged at 1000× g for 5 min at 4 °C. Cell pellets were resuspended in DMEM complete medium and seeded in 24-well plates at density of 1.5 × 105 cells/cm2 until they reached 80% confluency. The medium was changed every 3–4 days. The cells were thereafter maintained in tissue culture for approximately 15 days. Confluent cell monolayers were washed twice with PBS and then incubated with the FAβ1–42 solutions at final concentrations of 0.5, 1, and 2 µM for 3 days at 37 °C, 5% CO2. In parallel, cells were also treated with BMS-345541, a NF-κB inhibitor (Sigma-Aldrich, Saint-Quentin-Fallavier, France) used at a final concentration of 5 µM. When specified, the cells were pre-incubated for 1 h in culture with the BMS-345541 inhibitor before treatment with either the FAβ1–42 or LPS solution (100 ng/mL in complete media) for 3 days as described here [45,46]. Cells were also treated with commercially available synthetic miRNA inhibitors for miRNA-146a-5p (AMO-146a, MH10722, ThermoFisher Scientific, Waltham, MA, USA) or control (AMO-CTL, 4464076, ThermoFisher Scientific, Waltham, MA, USA) using the RNAimax transfection reageant (ThermoFisher Scientific) according to manufacturer’s recommendations. Briefly evoked, confluent cell monolayers were washed with PBS and then transfected with AMO-146a or AMO-CTL at 100 nM final concentration for 6 h in OPTIMEM medium (ThermoFisher Scientific). Then, the next day, cells were treated with FAβ1–42 or DMSO control solutions at indicated concentrations before being collected for tRNA extraction and qRT-PCR analysis. Cell viability analysis was performed with the Alamar Blue™ HS reagent according to manufacturer’s instructions (Invitrogen, Carlsbad, CA, USA, A50101). Briefly described, a 1/10 dilution of Alamar blue solution was added to each well of 24-wellplates for 2 h at 37 °C. Then, 50 µL of cell supernatant were monitored using a fluorescence microplate reader set up at 560 and 605 nm as excitation and emission wavelengths, respectively. The procedure to quantify the relative miRNA expression from the primary astrocyte culture was the same as described above, except that no miRNA spike-ins were added to the samples and that the relative expression of the small nuclear RNA 6 (U6) was used to normalize the data as described before [47].The procedure to quantify the relative mRNA expression was also derived from routine procedures described by Simion et al. and Ezine et al. [38,39,47]. Briefly described, total RNA was extracted from cells using the Trizol reagent (Invitrogen, Carlsbad, CA, USA) and reverse transcribed from 100 ng tRNA using the RevertAid RT Reverse Transcription Kit from ThermoFisher (Thermofisher Scientific, Waltham, MA, USA). Commercially available primers (Qiagen, Hilden, Germany) were used to monitor expression of Irak1, Traf6, Irak2, IL-6, IL-1β, and CXCL1 genes. The relative expression of GAPDH gene was used to normalize expression of mRNA transcripts [40]. A sandwich enzyme-linked immunosorbent assay (ELISA) system was used to quantify concentrations of IL-1β, IL-6, and CXCL1 from the culture medium of astrocytes, using antibodies paired according to routine procedure [48]. All data were expressed as the mean ± SEM. Statistical analyses of the Morris test were performed with Stata 13.0 software, and the non-parametric Kruskal–Wallis test was used to evaluate the difference between the groups. To analyze the relative expression of miRNAs and mRNAs, the statistical software XLSTAT by Addinsoft was used, and the non-parametric Mann–Whitney U test was used to compare the expression of miRNAs between the groups. Experimental conditions within each in vitro experiment were performed in triplicate by a minimum of three independent experiments. A Student’s t-test was used to evaluate the difference in the relative expression of mRNA. All graphs were made using GraphPrism 8.0 software. Statistical significance was set at 95% confidence interval, with p values set as * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001. The rat model of AD, induced by the intrahippocampal infusion of FAβ1–42 peptide solutions, was first challenged using the classic Morris Water Maze test to evaluate the memory and behavior performance of treated animals [33]. For each rat, we monitored the distance traveled and the escape latency as variables of cognitive performance using a video-based system to automatically extract trajectories and the time used by rats to reach the platform on the maze. During the acquisition session, no statistically significant difference was found at day 1 of the training session between all groups, indicating that animals from both groups have similar motor and visual abilities. In contrast, significant differences in path lengths were found from day 2 to day 5 (Figure 1A). The cognitive abilities were studied through the evaluation of performance in four quadrants. In the NW quadrant, path lengths showed that the FAβ1–42 treated rats used longer distances to find the platform over these 4 days of interval time compared with PBS-treated rats (Figure S1A). When the rats were placed in the NE quadrant, significant differences were only observed at day 4 and day 5 (Figure S1B). For rats placed in the SW quadrant, FAβ1–42-treated rats traveled longer distances compared with the control group of animals, but the differences were not statistically significant due to a great dispersion observed in this group (Figure S1C). In the SE quadrant, the path lengths of the FAβ1–42-treated group were significantly different from those of the control group from day 2 to day 4 (Figure S1D). The escape latency of each rat was evaluated in all four quadrants. The groups of animals infused with FAβ1–42 showed a longer escape latency compared with the control group, with a significant difference at day 4 and day 5 (p < 0.05) (Figure 1B), demonstrating a significant memory defect. Astrogliosis is a well-recognized feature of AD, characterized by cellular hypertrophy and an increase in glial fibrillar acid (GFAP) expression [49]. To evaluate whether cognitive impairment detected in rats inoculated with FAβ1–42 peptide solution (1 µg/µL) was correlated with astrogliosis, we performed GFAP fluorescence labeling of brain tissues of rats harvested at 14 days post-infusion. Representative immunofluorescence images are shown in Figure 2A. Compared with brain sections from the control group, a more pronounced fluorescence staining was detected in the whole hippocampal tissues, including the CA1/CA2 and CA3 and DG regions of the brain sections from FAβ1–42-treated rats. The quantitative analysis of the whole fluorescence staining indicated that there were 2.1-fold and 1.7-fold more GFAP positive cells respectively in CA1/CA2 and CA3 and DG hippocampal regions of FAβ1–42-infused rats compared with control rats (Figure 2B,C). Then, we evaluated whether the defective cognitive performance and astrogliosis detected in FAβ1–42-treated rats might be correlated with dysregulation of a pattern of circulating miRNAs. We selected from the bibliography a list of nine miRNAs (miRNA-125b-5p, -181c-5p, -191-5p, -106b-5p, -135a-5p, -146a-5p, -9a-5p, -29a-3p, and -29c-3p) as these miRNAs are documented to be most commonly dysregulated in sera samples of animal models of AD and/or AD patients [25,26,27,28,29,31]. Results from qRT-PCR indicated that amongst this list, only miRNA-146a-5p was found to be statistically significantly different in sera of rats infused with FAβ1–42 peptide solution (1 µg/µL) compared with rats treated with PBS (Figure 3, p < 0.05). The average of expression of miRNA-9a-5p, -29a-3p, and -29c-3p in the FAβ1–42 -treated rats was lower compared with the control group, and very close to a statistical p value of 0.05 (Figure 3). In contrast, the relative expression of the other five miRNAs (miRNA-125b-5p, -181c-5p, -191-5p, -106b-5p, and -135a-5p) was far from significant. We followed up our investigation by infusing 2.5-fold more FAβ1–42 peptide solution (e.g., 2.5 µg/µL) in the hippocampus of rats to evaluate whether the pattern of miRNA expression might be different. Data from qRT-PCR analysis made on serum samples collected at day 21 indicated that the expression of miRNA-146a-5p, -29a-3p, and -29c-3p were significantly dysregulated in the FAβ1–42 -treated group in this second cohort of animals. As for the first cohort, no significant change in the expression of miRNA-9a-5p was found. In contrast to the first cohort of rats infused with 1 µg/µL of FAβ1–42 peptides, miRNA-125b-5p and -191-5p were detected as the most dysregulated miRNAs in this second cohort of rats (Figure 4). Next, we postulated that the expression of these miRNAs might be differently dysregulated as a function of time post-infusion of the FAβ1–42 peptide solution. We included miRNA-9a-5p assuming that this miRNA might be dysregulated at earlier time points than at the 21-day post-infusion time used previously. Results of these kinetics are shown in Figure 5. The kinetics of miRNA-146a-5p expression in the treated group was different compared with that of the control group. No statistically significant reduction of expression was found at the 7 day-early time point, followed by statistically significant reductions of expression detected at both the 14- and 21-day time points (p = 0.022 and p = 0.027, respectively, Figure 5A). The expression patterns of miRNA-29a-3p and -29c-3p were statistically reduced at all times compared with the control group, although the most significant difference was found at day 7 (p < 0.01; Figure 5B,C). Concerning miRNA-9-5p in FAβ1–42-infused rats, its kinetics of expression tends to decrease as a function of time compared with the control group but was not statistically different at each time point (Figure 5D). Next, we sought to compare the expression pattern of circulating miRNAs detected in this FAβ1–42-infused animal model of AD with the widely used APPswe/PS1dE9 transgenic mice model of AD. This transgenic mice model expresses two constitutive mutant forms of APP and PSEN1 and produces Aβ1–42 deposits by 6 months of age, followed by abundant plaque apparition in the hippocampus and cortex by 9 months, which are increasing up to around 12 months of age [50]. We evaluated the expression of the above-mentioned miRNAs in serum samples of APPswe/PS1dE9 mice collected at 4 and 15 months of age. The results obtained indicated that circulating miRNAs detected in serum samples of 4-month-old APPswe/PS1dE9 mice were not significantly different from those of control littermate mice. In serum samples collected from 15-month-old APPswe/PS1dE9 transgenic mice, four miRNAs (miRNA-125b-5p, -29a-3p, -29c-3p, and -191-5p) were found to be significantly downregulated (Figure 6). Interestingly, these four miRNAs were also significantly downregulated in the cohort of animals infused with 2.5 µg/µL of FAβ1–42 peptide solution (Figure 4). In contrast, no statistically significant difference in terms of miRNA-146a-5p expression was detected in the sera of APPswe/PS1dE9 transgenic mice (p > 0.179) independent of their age, whereas it was found to be statistically significantly downregulated in the FAβ1–42-infused rats (p < 0.027). Moreover, miRNA-125b-5p was found statistically much more downregulated (*** p < 0.0004) in the FAβ-infused rats’ model of AD as compared with the APPswe/PS1dE9 transgenic mice (p < 0.02). The same could be stated for miRNA-181c-5p, which, although not statistically significantly dysregulated in the two animal models of AD, had a p-value close to significance (p < 0.070) in the rat model as compared with APPswe/PS1dE9 transgenic mice (p < 0.748). We were intrigued by the observation that the kinetics of miRNA-146a-5p expression detected in the FAβ1-42-infused animal model of AD were different from other miRNA kinetics and the absence of dysregulation found in APPswe/PS1dE9 mice. This prompted us to focus our next experiments on deciphering the biological impact of miR-146a induction in the pathogenesis of the FAβ1–42-infused animal model of AD. First, we evaluated the expression pattern of miRNA-146a-5p in the CSF sample collected 14 days after the intrahippocampal infusion of FAβ1–42 peptide solution (2.5 μg/μL). Results indicated that, in contrast to serum samples (Figure 4; p = 0.022), the amount of miRNA-146a-5p in CSF samples was detected as statistically significantly up-regulated (Figure 7; p = 0.004). This data highlights the discrepancy between the presence of miRNA-146a-5p in serum versus CSF samples. To gain insight into the biological function of miRNA-146a-5p in the pathogenesis of the FAβ-induced animal model of AD, we performed a mechanistic study with a primary culture of rat astrocytes treated with the FAβ1–42 peptide solution. Our objective was to recapitulate, at least partially, the AD-like environment induced by the infusion of FAβ1–42 peptides in the hippocampi of rats. Astrocytes are considered to promote the first line of the neuroinflammation response [51] by regulating the expression of key mediators of innate and adaptive immune responses in the central nervous system, which are responsible in part for the early onset of cognitive dysfunction. First, we searched for the minimal concentration of FAβ1–42 peptide solution that did not induce cytotoxicity in astrocytes to mimic early biological events associated with the presence of FAβ1–42 peptides in the hippocampi of animals. Results indicated that FAβ1–42 peptide solutions ranging from 0.5 to 2 µM were well tolerated by these cells, whereas at higher concentrations, significant toxicities were observed (Figure S2). Next, we monitored miRNA-146a-5p expression in these cells after treatment with 0.5, 1, and 2 µM of FAβ1–42 solutions. The qPCR data demonstrated that miRNA-146a-5p expression tended to increase with the FAβ1–42 peptide concentration used, with a maximum of 1.5-fold induction of miRNA-146a-5p expression detected when cells were incubated with 2 µM of FAβ1–42. The expression of miRNA-146a-5p has been reported to be upregulated in several central nervous cells in response to TNF-α, IL-1β, or LPS through the activation of the NF-κB cell signaling pathway [52,53,54]. In the next step, we evaluated whether FAβ1–42 treatment might induce the expression of miRNA-146a-5p in primary astrocytes using the same cell signaling pathway. For that, cells were treated for 3 days with 2 µM of FAβ1–42 peptide solution in the presence or absence of BMS-345541, a pharmacological inhibitor of the NF-κB pathway, and then miRNA-146a-5p expression was evaluated by qRT-PCR. As a positive control, cells were treated with LPS [55]. Results shown in Figure 8 confirm that miRNA-146a-5p expression was significantly upregulated in FAβ1–42-treated cells as compared with PBS-treated cells used as controls. This induction was dependent on NF-κB cell signaling. Indeed, pre-treatment of cells with BMS-345541 inhibitor prior to the incubation with FAβ1–42 peptide solution significantly dropped the expression level of miRNA-146a-5p to the basal expression level detected in non-treated cells (Figure 8B). As expected, LPS-treatment of primary astrocytes increased significantly the expression of miRNA-146a-5p, which can be significantly reversed by BMS-345541 inhibitor treatment. Those results indicate that FAβ1–42 peptide treatment increases the basal expression level of miRNA-146a-5p through the transcriptional regulation of the NF-κB pathway, as previously demonstrated with pro-inflammatory cytokines [53,54] and here with LPS. We next evaluated the expression of IRAK-1/2 and TRAF-6, three well-known transcriptional targets of miRNA-146a-5p involved in the TLR4/NF-κB cell signaling pathway [52,56]. As shown in Figure 8C, the relative expression of IRAK-1 was significantly downregulated in FAβ1–42-treated cells compared with control cells, while the expression of IRAK-2 was unchanged. Surprisingly, the relative expression of TRAF-6, a direct downstream effector of the IRAK-1/2 complex, was unchanged in these FAβ1–42-treated cells (Figure 8C). Based on the above results, we next checked whether the downregulation of IRAK-1 expression detected in astrocytes treated with FAβ1–42 peptides might be sufficient to induce the expression of pro-inflammatory cytokines. As shown in Figure 9, none of the pro-inflammatory cytokines evaluated (IL-6, IL-1β, and TNF-α) were induced in FAβ1–42-treated cells (Figure 9A), as was the case for CXCL1. In contrast, significant and high induction levels of IL-6; IL-1β and CXCL1 were detected in LPS-treated cells used as positive controls (Figure 9B). We hypothesized that interfering with the expression of miRNA-146a-5p in astrocytes might rescue IRAK-1 and TRAF-6 expression and then promote cytokine production in response to FAβ1–42 treatment. At first, we evaluated the performance of an anti-miRNA-146a-5p oligonucleotide (AMO-146a) to inhibit the expression of miRNA-146a-5p in astrocytes. As shown in Figure 10A, the transfection of AMO-146a in these cells led to a statistically significant down-regulation (p = 0.01) of miRNA-146a-5p expression, which was not observed with AMO control (AMO-CTL). Then, we evaluated the biological impact of AMO-146a on the expression of IRAK-1/2 and TRAF-6 as transcriptional targets of miRNA-146a-5p. Cells were first transfected with AMO-146a or AMO-CTL, then treated with FAβ1–42 peptides, and finally lysed 48 h later to quantify the relative expression of IRAK-1/2 and TRAF-6. As shown in Figure 10B, AMO-146a delivery significantly elevated the expression of IRAK-1 (p = 0.042) and TRAF-6 (p = 0.0035), while AMO-CTL did not change the expression of these transcriptional targets. In contrast, IRAK-2 expression remained unchanged following the transfection with both AMO-146a and AMO-CTL. Moreover, it should be noted that AMO-146a treatment led to increased IRAK-1 and TRAF-6 expression levels superior to those detected in non-treated cells (NT). These last results suggest that interfering with miRNA-146a-5p expression rescues not only the expression of IRAK-1 and TRAF-6 in FAβ1–42-astrocyte cells but also induces a higher expression of those targets in non-treated astrocytes (NT). Next, we evaluated whether rescuing expression of IRAK-1 and TRAF-6 by AMO-146a will be sufficient to induce the expression of cytokines in FAβ1–42-treated astrocytes. As shown in Figure 11, the transfection of AMO-146a before treatment with FAβ1–42 peptides resulted in a statistically significant induction of IL-6 (p = 0.003), IL-1β (p = 0.05), and CXCL1 (p = 0.01) but not TNF-α (p = 0.803). The transfection of AMO-CTL had no impact on the expression of these molecules. In this study, we sought to identify a panel of miRNAs that might be directly correlated with the production of Aβ in adult tissues of the brain of animals as an early diagnosis marker. Accumulation of Aβ in the brain is one of the first steps of AD pathogenesis that ultimately causes inflammation and cognitive dysfunction. Therefore, the injection of fibrillar forms of Aβ in the hippocampus of an adult animal could be used as a model to investigate the very early induced events of AD. To the best of our knowledge, no reports have described a panel of several circulating miRNAs in serum samples of adult rats infused with FAβ1–42 peptides into the hippocampus, and none of them have compared the pattern of miRNA expression that could be detected in the APPswe/PS1dE9 transgenic mice model of AD. To address this point, we selected a short list of nine circulating miRNAs frequently dysregulated in patients with AD and/or in transgenic animal models and performed a comparative study between these two animal models of AD. We found a striking difference in terms of the expression pattern of some of these miRNAs and demonstrated that miRNA-146a-5p was exclusively dysregulated in the FAβ1–42-infused rat model of AD. We then provided experimental evidence that this miRNA was enriched in the cerebrospinal fluid of FAβ1–42-infused adult rats and that its expression was induced in primary astrocytes following treatment with FAβ1–42 peptides. Data from loss-of-function studies combined with pharmacological inhibition of the NF-κB pathway suggest that this miRNA acts as an anti-inflammatory modulator in astrocytes through a negative feedback loop of the NF-κB pathway, impairing cytokines and chemokines production. Taken together, our data indicate that miRNA-146a-5p might be considered an early blood-circulating miRNA marker reflecting the presence of an aggregated form of Aβ in adult brain tissues, in which it plays anti-inflammatory roles. The hippocampal infusion of Aβ peptides has been shown to reduce neuronal density, increase expression of the glial fibrillar acid protein, and cause deficiencies in the behavioral performance of infused animals [57]. Borbely et al. demonstrated that intrahippocampal administration of synthetic Aβ peptides simultaneously decreases both the spatial learning capacity in MWM and the density of the dendritic column in the CA1 region of the rat hippocampus [30]. We did observe the same following the infusion of FAβ1–42 peptides into the CA1 region of the hippocampus and observed a deterioration of the learning capacity of these animals, mainly at day 14 post-surgery [58]. Abnormal accumulation and shedding of Aβ peptides can lead to localized inflammation involving reactive astrocytes with increased expression of GFAP [49]. This gliosis process occurs after brain injury and is characteristic of neurodegenerative disorders such as AD [59]. We validated the presence of reactive astrocytes by immunofluorescence staining of GFAP expression in brain sections of animals infused with FAβ1–42. They had a higher number of astrocytes in all regions of the hippocampus (CA1/CA2, CA3, and DG), suggesting a reactive state. Each area contained approximately twice the number of detectable astrocytes than that of control animals (Figure 2). These data are in line with those of different studies showing an increase in astrocytes in a model of injection of fibrillar Aβ in the hippocampus of a rat model of AD [57,60]. Since miRNAs are known to act as temporal regulators of different biological processes, we established the kinetics of the expression of miRNA-9-5p, -29a-3p, -29c-3p, and -146a-5p [61,62] at days 7, 14, and 21 following intrahippocampal infusion of FAβ1–42 in adult rats (Figure 5). Kinetic studies of those miRNAs revealed a clear downregulation tendency, although no statistically significant difference was detected for miR-9-5p expression. Members of the miRNA-29 family showed a strong dysregulation in the FAβ1–42-injected group at 7 days post-injection compared with the control group. At 14 and 21 days, the dysregulation was maintained, but the curve started to rise very mildly, suggesting a reverse phase likely resulting from the clearance of FAβ1–42 peptides by macrophages and/or microglia/astrocytes (Figure 5). The downregulation of miRNA-29a-3p and miRNA-29c-3p is consistent with many previous reports that have evaluated serum and/or plasma from patients with AD [31,63,64]. Interestingly, miRNA-29 a/b has been described as a regulator of BACE1/beta-secretase expression [29,65] through binding to targeting moieties located in the 3′UTR of BACE1 mRNA. The same observation can also be made for miRNA-191-5p, which is frequently downregulated in the sera of AD patients [16] as well as in our FAβ1–42-infused animal model. Assuming that BACE1/beta-secretase expression contributes to Aβ accumulation in the brain of other animal models of AD [66,67], it might be possible that lowering the expression of miRNA-29 a/b-3p and -191-5p in FAβ1–42-infused rats might result in an increase of BACE1 expression and consequently Aβ1–42 peptide production by neural cells. This process might directly participate in or even amplify the astrogliosis process revealed in our study by GFAP staining. The expression of miRNA-146a-5p was also downregulated in FAβ1–42-infused rats compared with the control group, even though the reduction was only statistically significant after 14 and 21 days. MiRNA-146a-5p has been shown to be an early and prevalent pathological biomarker of AD as it is involved in the inflammatory response and neuroinflammation [25,68]. Our results are in line with different studies that have evaluated the circulating expression profile of miRNA-146a-5p in AD animal models and in humans, although its temporal expression over the pathogenesis of AD was not reported [25,68]. Mechanistically, miRNA-146a-5p acts as a negative regulator of NF-κB signaling to prevent the overproduction of pro-inflammatory cytokines or chemokines [53,56,69]. Therefore, the peak of miRNA-146a-5p dysregulation detected at day 14 in the FAβ1–42-infused animal model could be correlated with the peak of an inflammatory reaction induced by the presence of the FAβ1–42 peptide, for which miRNA-146a-5p might exert its maximal anti-inflammatory function. Surprisingly, no significant difference in the expression of miRNA-146a-5p was detected in the serum samples of APPswe/PS1dE9 transgenic mice, although these mice develop Aβ deposits [70]. This discrepancy between these two animal models of AD can be explained by the fact that the FAβ1–42 infusion model represents a model of inflammation induced by the direct exposition of hippocampal tissues to FAβ1–42 peptide loads [71], while the APPswe/PS1dE9 transgenic model corresponds to a model of chronic inflammation induced by the gradual accumulation of Aβ deposits over the lifespan of transgenic animals, which express constitutive mutant forms of APP and PS1 [72]. Therefore, the inflammatory responses are expected to be different in these two models and should implicate different cellular pathways and molecular partners. This difference might also be accentuated by compensatory or adaptive responses developed in Knock-OUT or Knock-IN transgenic animals [73,74] that counteract the biological impact of mutants. To complement our data, we quantified the amount of miRNA-146a-5p in CSF obtained from FAβ1–42-infused rats at 14 days post-injection. The expression of miRNA-146a-5p was detected as significantly upregulated in contrast to the serum sample. Reasons for this difference are not clear and difficult to apprehend. The same trend of opposite regulation between sera samples and CSF was described for other miRNAs [16]. Neuroinflammation is one of the hallmarks of AD, and miRNA-146a-5p might be a key mediator of the immune response linked to a variety of inflammation processes. To understand further the role of miRNA-146a-5p, we conducted functional in vitro studies with primary rat astrocytes, the most abundant cell type in the CNS and important modulators of the neuronal innate and inflammatory immune responses [75]. We observed a statistically significant upregulation of miRNA-146a-5p expression in astrocytes treated with FAβ peptides compared with control cells (Figure 8). These findings are consistent with studies done on human astrocytes that had an upregulation of miRNA-146a-5p when exposed to Aβ peptides [51,53]. Similarly, Li et al. demonstrated that miRNA-146a-5p was positively regulated in human neuronal-glial (HNG), human astroglial (HAG), and human microglial (HMG) cells treated with Aβ1–42 peptides [53]. However, neuronal cells or astrocytes were cultured under oxidative stress or inflammatory conditions (H2O2, LPS, TNF-α) in addition to Aβ treatment in these studies. The expression of miRNA-146a-5p occurred through activation of the NF-κB pathway, which in turn induced the production of inflammation and cytokines. Since there are several NF-κB-binding sites in the promoter sequence analysis of miRNA-146a-5p, the direct effect of FAβ1–42 peptides on the expression of miRNA-146a-5p was not independently investigated in these studies [56]. We did also find that the expression of miRNA-146a-5p was dependent on activation of the NF-κB pathway, as pre-treatment of FAβ1–42-treated cells with the BMS-345541 pharmacological inhibitor of NF-κB significantly reduced miRNA-146a-5p expression. However, we did not detect the induction of IL-6, IL-1β, and TNF-α inflammatory cytokine production. This indicates that the production of cytokines in cells treated with Aβ in the presence of H2O2, LPS, or TNF-α is more likely driven by these inflammatory mediators than by Aβ peptides itself. To go further into the dissection of underlying molecular mechanisms governing expression of miRNA-146a-5p in FAβ1–42-treated cells, we quantified expression of downstream modulators of the NF-κB pathway as IRAK-1/2 and TRAF-6 that harbor miRNA-146a-5p binding sites in the 3′UTR mRNAs. These three genes encode key adaptor molecules downstream of Toll-like and cytokine receptors. Different reports [53,76,77] made with human astrocytes or nerve cells showed that, concurrent to miRNA-146a-5p upregulation in cells exposed to Aβ peptides and inflammatory molecules, a decrease of IRAK-1 associated with a compensatory increase in the expression of IRAK-2 was observed, as well as dysregulation of TRAF-6 [69,78]. Consistent with this observation, we found that in FAβ-treated cells, while IRAK-1 expression was downregulated whilst IRAK-2 and TRAF-6 expression was unchanged. TRAF-6 is described as one of the final effectors of NF-κB signaling, regulating the nuclear processing of IKKB for transcription of pro-inflammatory cytokines [79]. This latest result could explain the lack of cytokine production in FAβ-treated astrocytes. Therefore, it is tempting to believe that the treatment of astrocytes with FAβ1–42 peptides alone is sufficient to induce the transcription of the miRNA-146a-5p gene, which in turn might bind to IRAK-1 and, to lesser extent, IRAK-2 and TRAF-6. As a consequence of this retro-control loop of the NF-κB pathway, an abrogation of the expression of pro-inflammatory cytokines such as IL-6, TNF-α, and IL-1β might occur by keeping constant the expression level of TRAF-6. In contrast, in overactivated cells treated with FAβ1–42 peptides plus proinflammatory molecules, as reported by others [52,53,54], this retro-control loop of the NF-κB pathway by miRNA-146a-5p might be overridden, leading to a significant change in TRAF-6 expression and consequently the production of pro-inflammatory cytokines. This is in agreement with reports showing that miRNA expression can act as a negative feedback regulator of the same signaling pathway used for its own induction, thereby preventing an overstimulation of the inflammatory response [80]. To verify further this hypothesis, we transfected astrocyte cells with a miRNA-146a-5p inhibitor (AMO-146) prior to stimulation with FAβ peptide solution (Figure 11). As expected, a significant upregulation of IRAK-1 mRNA, a transcriptional target of the NF-κB pathway, was detected, whereas no change in IRAK-2 expression was found. In addition, the expression level of TRAF-6 was significantly upregulated. Remarkably, these changes in IRAK-1 and TRAF-6 expression were directly correlated with the production of de novo pro-inflammatory cytokines such as IL-6, IL-1β, and CXCL1. Therefore, the increased level of TRAF-6 expression induced by AMO-146 allows it to reach a threshold that might lead to an activation of upstream molecular mediators of NF-κB signaling and, as consequence, the production of inflammatory cytokines. Additional studies are required to elucidate in detail the exact molecular mechanisms by which TRAF-6 overexpression led to the activation of the NF-κB pathway. We do not exclude the existance or participation of other cell signaling pathways. Nevertheless, the observation that interfering with miRNA-146a-5p expression is sufficient to modulate the production of inflammatory cytokines in FAβ1–42-treated astrocytes tends to support our overall statement. In suboptimal conditions, e.g., the presence of FAβ1–42 peptides alone, mimicking the early stage of Aβ deposit, miRNA-146a-5p might play an anti-inflammatory role by down-regulating expression of IRAK-1, resulting in the maintenance of the steady state of TRAF-6 expression and consequently the inhibition of inflammatory cytokine production. On the contrary, in advanced stages of AD pathology characterized by hyperproduction of pro-inflammatory cytokines in response to Tau phosphorylation and alteration of ApoE expression, for instance, the anti-inflammatory function of miRNA-146a-5p might be countered by multiple upstream signaling events activated by these inflammatory mediators of AD pathology. As a consequence, the expression of miRNA-146a-5p might not be sufficient to maintain neural tissues in a non-inflammatory state. Although deeper investigations are required to validate this statement, overall, our data provide additional insights into the molecular mechanism of anti-inflammatory function of miRNA-146a-5p and support the potential therapeutic function of miRNA-146a-5p in the management of AD as described by Mai et al. [81]. Data from this study show that circulating miRNA-146a-5p, -29a-3p, -29c-3p, -191-5p, and -125b-5p in the sera of rats injected with Aβ aggregates inside the hippocampus area were downregulated compared with those of control rats. Interestingly, some of those circulating miRNAs except miRNA-146a-5p were also dysregulated in the widely used APPswe/PS1dE9 transgenic mice model. By contrast, miRNA-146a-5p was upregulated in the CSF of those rats that presented astrogliosis in their brain. The mechanistic study done on rat primary astrocytes revealed that their treatment with Aβ aggregates also led to the upregulation of miRNA-146a-5p via the NF-κB signaling pathway, which in turn downregulated the expression of IRAK-1 but without affecting the expression of TRAF-6, a key effector of this NF-κB signaling pathway. As a consequence, no change in the expression of IL-6, IL-1β, and TNF-α was detected. A loss-of-function study performed with a miRNA-146a-5p inhibitor reversed this process and led to the production of IL-6 and IL-1β cytokines, as well as CXCL1. Based on those data and those reported in the literature, we propose that miRNA-146a-5p upregulation in astrocytes is likely playing an anti-inflammatory role through a negative feedback of the NF-κB pathway. In summary, this study contributes to improving our knowledge of the rat model of AD generated by intrahippocampal injection of Aβ1–42 peptides. As this model is supposed to characterize the very early stages of AD, the pattern of dysregulated miRNAs that we reported in this study deserves additional investigation for potential early diagnostic purposes.

PMC10000757

Maximilian Vidovic,Lars Hendrik Müschen,Svenja Brakemeier,Gerrit Machetanz,Marcel Naumann,Sergio Castro-Gomez

Current State and Future Directions in the Diagnosis of Amyotrophic Lateral Sclerosis

24-02-2023

amyotrophic lateral sclerosis,ALS,motor neuron disease,MND,diagnosis,diagnostics

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by loss of upper and lower motor neurons, resulting in progressive weakness of all voluntary muscles and eventual respiratory failure. Non-motor symptoms, such as cognitive and behavioral changes, frequently occur over the course of the disease. Considering its poor prognosis with a median survival time of 2 to 4 years and limited causal treatment options, an early diagnosis of ALS plays an essential role. In the past, diagnosis has primarily been determined by clinical findings supported by electrophysiological and laboratory measurements. To increase diagnostic accuracy, reduce diagnostic delay, optimize stratification in clinical trials and provide quantitative monitoring of disease progression and treatment responsivity, research on disease-specific and feasible fluid biomarkers, such as neurofilaments, has been intensely pursued. Advances in imaging techniques have additionally yielded diagnostic benefits. Growing perception and greater availability of genetic testing facilitate early identification of pathogenic ALS-related gene mutations, predictive testing and access to novel therapeutic agents in clinical trials addressing disease-modified therapies before the advent of the first clinical symptoms. Lately, personalized survival prediction models have been proposed to offer a more detailed disclosure of the prognosis for the patient. In this review, the established procedures and future directions in the diagnostics of ALS are summarized to serve as a practical guideline and to improve the diagnostic pathway of this burdensome disease.

Current State and Future Directions in the Diagnosis of Amyotrophic Lateral Sclerosis Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by loss of upper and lower motor neurons, resulting in progressive weakness of all voluntary muscles and eventual respiratory failure. Non-motor symptoms, such as cognitive and behavioral changes, frequently occur over the course of the disease. Considering its poor prognosis with a median survival time of 2 to 4 years and limited causal treatment options, an early diagnosis of ALS plays an essential role. In the past, diagnosis has primarily been determined by clinical findings supported by electrophysiological and laboratory measurements. To increase diagnostic accuracy, reduce diagnostic delay, optimize stratification in clinical trials and provide quantitative monitoring of disease progression and treatment responsivity, research on disease-specific and feasible fluid biomarkers, such as neurofilaments, has been intensely pursued. Advances in imaging techniques have additionally yielded diagnostic benefits. Growing perception and greater availability of genetic testing facilitate early identification of pathogenic ALS-related gene mutations, predictive testing and access to novel therapeutic agents in clinical trials addressing disease-modified therapies before the advent of the first clinical symptoms. Lately, personalized survival prediction models have been proposed to offer a more detailed disclosure of the prognosis for the patient. In this review, the established procedures and future directions in the diagnostics of ALS are summarized to serve as a practical guideline and to improve the diagnostic pathway of this burdensome disease. The diagnosis of amyotrophic lateral sclerosis (ALS) remains an enormous challenge not only to general physicians, but also to specialized neurologists. ALS, a fatal neurodegenerative and the most frequent motor neuron disease (MND), is primarily characterized by progressive weakness of voluntary muscles due to degenerating motor neurons in the brain, brainstem and spinal cord. Considered to be a multisystem disorder, it can also be accompanied by non-motor symptoms, such as behavioral and cognitive impairment, and even manifest as an overlap syndrome with signs of frontotemporal dementia (FTD), known as ALS-FTD [1,2,3]. The clinical, genetic and neuropathological heterogeneity, and the resemblance to other neuromuscular diseases, especially early in the disease’s course, commonly described as ALS mimics, often requires the application of additional diagnostic methods. Given the short median survival of 2 to 4 years [3,4] and a diagnostic delay of 10 to 16 months [5], there is an urgent need for optimizing diagnostic accuracy to yield a faster and more reliable recognition of the disease. This, in turn, may provide access to novel therapeutic agents and participation in clinical trials at an early disease stage. In recent decades, the diagnosis of ALS was mainly based on clinical findings with support of electrophysiological, imaging and laboratory techniques to exclude other diseases. Recent efforts have focused on establishing simplified, more practice-oriented diagnostic guidelines, as well as investigating the potential of specific fluid biomarkers, functional brain imaging and additional measures, such as predictive models, to hasten diagnosis, improve diagnostic accuracy and help outline the disease trajectory for the individual patient. This review aims to present the current state of ALS diagnosis and outline how recent findings and evolving concepts may show where it is headed in the future. It is essential to record the clinical findings and their course as accurately as possible. Therefore, a thorough exploration of the patient’s history of symptoms and a complete physical and neurological examination should be considered as the first step in the diagnosis of ALS. The clinical hallmarks of ALS are related to the impairment of voluntary muscles, resulting in progressive weakness of the limbs, speech and swallowing dysfunction and respiratory failure with concomitant signs, such as muscle atrophy, fasciculations and increased muscle tone. These motor dysfunctions are derived from the combined impairment of the upper motor neurons (UMN) in the motor cortex of the brain and the lower motor neurons (LMN) in the brainstem and the spinal cord [1,3]. Though generally unaffected, there is also evidence of oculomotor, sphincter and autonomic dysfunction [6,7]. Initially, the disease typically presents itself either with asymmetrical focal muscle weakness in the upper or lower limbs in spinal onset ALS or with speech and swallowing difficulties due to facial, tongue and pharyngeal muscle weakness in bulbar-onset ALS [2]. Disease propagation preferably follows an organized contiguous pattern with first symptoms in one limb, subsequently spreading to the contralateral limb and, later, to adjacent regions [8]. However, disease progression is highly variable and frequently shows a non-linear decline [9]. The anatomically separated body regions (or segments) are defined as the bulbar, cervical, thoracic and lumbar regions, with the latter three comprising the spinal regions [10]. The clinical signs of UMN and LMN involvement according to each of the body regions are presented in Figure 1. The notable clinical heterogeneity of motor manifestations frequently leads to controversy in the description of phenotypes as opposed to the determination of the diagnosis of ALS. Distinguishing these phenotypes is of relevance, as they are associated with various disease progression rates and survival times [11]. The different clinical phenotypes of ALS are generally described with regard to the extent of UMN and LMN impairment, its distribution and progression to the different body segments [2,11]. ALS with spinal onset is defined by focal weakness in distal muscle groups of the limbs and simultaneous UMN and LMN involvement. With up to 82% of all ALS patients [12], it is the most common phenotype, termed typical or classical ALS. Distal segments of the upper or lower limbs are affected in a focal manner at the onset of the disease. Characteristically, thenar eminence with the abductor pollicis brevis (APB) and the first dorsal interosseus (FDI) muscles are more affected compared to the hypothenar muscle abductor digiti minimi (ADM), referred to as split-hand syndrome [13]. Onset in the dominant hand is thought to be predominant [14]. Lower-limb weakness typically becomes apparent as it causes an unsteady gait due to weak foot dorsiflexion [15]. Notably, UMN dysfunction is not always easily identified in wasted or atrophic muscles of the limbs, particularly in the early stages of the disease [16,17]. ALS with bulbar onset, or bulbar ALS, is the second leading phenotype with initial motor dysfunction in the bulbar region. Speech difficulties and frequent choking with concomitant hypersalivation are the cardinal presenting symptoms. Both LMN and UMN impairment are present, causing tongue wasting with fasciculations, facial spasticity and pseudobulbar affect in the early stages of the disease. Propagation to other spinal regions is evident later in the disease’s course [18]. Bulbar UMN involvement, also known as pseudobulbar palsy, can become clinically indicated by emotional lability, accompanied by excessive crying or laughing response to minor stimuli. This is termed the “pseudobulbar affect”. Other bulbar UMN signs include facial spasticity and slowed spastic movement of the tongue, whereas slurred speech, wasting of the tongue and fasciculations indicate bulbar LMN impairment [1,3]. Frontal release signs, such as the palmomental reflex, may also indicate bulbar involvement [19]. In contrast to the often used phrase “progressive bulbar palsy”, “ALS with bulbar onset” appears to be the more convenient term, as it represents a phenotypic description rather than a diagnostic label, suggesting no progression to other body regions [11]. Progressive muscular atrophy (PMA) presents with clinically isolated LMN impairment of the anterior horn cells and brainstem motor nuclei. However, subclinical UMN impairment can be detected in the early stages of the disease [20]. Although PMA tends to have a slower disease progression than classical ALS [21], 20 to 30% of the patients may develop ALS with clinically evident UMN impairment within 5 to 10 years from the disease’s onset. It is still controversially debated whether PMA should be considered a variant of ALS [20]. Primary lateral sclerosis (PLS) is characterized by progressive isolated UMN dysfunction detectable in at least two regions (thoracic region will not be considered) for at least two years. LMN dysfunction is absent, whereas minimal signs of denervation (positive sharp waves or fibrillation potentials) on electromyography (EMG) are permitted. Whether PLS constitutes a separate disease entity or rather represents a clinically benign variant of ALS remains controversial [22]. In flail-arm-syndrome (FAS), also known as Vulpian Bernhardt’s type, progressive, proximal and symmetrical weakness of both upper limbs caused by LMN impairment is predominantly apparent. Motor symptoms in bulbar muscles or lower limbs are unaffected from 12 to 20 months after the onset of upper limb symptoms [23,24]. LMN involvement is predominant, whereas UMN involvement can be occasionally present in lower limbs [25]. FAS represents a rather benign phenotype with a median survival time of 4 years and a 10 year survival rate of 17% [23]. Analogous to FAS, flail-leg-syndrome (FLS) presents itself with progressive and symmetrical weakness of both lower limbs, whereas distal muscle groups are typically affected and LMN involvement outweighs UMN involvement [24]. Other segments are clinically spared for a mean of 16 months after the disease’s onset. Unlike FAS, FLS has a similar prognosis as spinal-onset ALS, with a median survival time of 3 years and a 10-year survival rate of 13% [23]. Axial-onset ALS initially presents itself with weakness of trunk muscles. Typically, paravertebral muscles are affected, resulting in bent posture, axial instability and dropped head syndrome [26]. However, weakness of the thoracic muscles can be rather difficult to recognize [8]. In respiratory-onset ALS patients suffer from dyspnoea and orthopnoea at the beginning of the disease, caused by weakness of the respiratory muscles and the diaphragm, which is also anatomically related to the thoracic region [27]. The prognosis is poor due to early respiratory failure and complications such as pneumonia [23,26,28]. There is no reliable method to detect thoracic UMN involvement [29]. However, brisk and deep abdominal reflexes, particularly with diminished or absent superficial abdominal reflexes, might be suggestive signs [30]. This very rare phenotype is defined by slowly progressive, unilateral muscle weakness in the limbs alongside clinically predominant UMN signs, such as pathological deep tendon reflexes (DTR) and pyramidal tract signs. The onset may either occur in the upper limbs with subsequent descending propagation to the lower limbs or vice versa [31]. As is the case with PLS, there is an ongoing debate regarding whether Mill’s syndrome should be considered a distinct clinical entity in the spectrum of motor neuron diseases or an ALS variant [31,32,33]. The El Escorial criteria were proposed as the first consensus diagnostic criteria for ALS in 1994 and were primarily designed for clinical trials and scientific research purposes [10]. However, they were increasingly applied in clinical practice due to the lack of any more reliable diagnostic procedures [34]. According to the criteria, clinical evidence of UMN and LMN impairment in four anatomically segmented body regions (bulbar, cervical, thoracic, lumbar) is surveyed for the diagnosis. Additionally, the progressive spreading of symptoms and the absence of electrophysiological and neuroimaging evidence of other causing diseases must be fulfilled. Patients are stratified into four categories of diagnostic certainty based on the number of affected regions and the extent of motor neuron involvement: suspected, possible, probable and definite ALS [10]. The criteria were revised (termed as revised El Escorial/Airlie House criteria) in 2000 to improve diagnostic sensitivity (Table 1). Diagnostic categories were refined with the following terms: clinically definite, clinically probable, clinically probable-laboratory supported and clinically possible ALS, while the category of suspected ALS was excluded. The electrophysiological examination was implemented to support clinical findings and identify LMN involvement, with signs of active and chronic denervation by EMG and the exclusion of motor neuropathy by nerve conduction studies [35]. The Awaji criteria, published in 2008, elaborated on the electrophysiological findings in the diagnosis of ALS (Table 1). Adapting the basic principles of the revised El Escorial criteria, evidence for chronic neurogenic damage in needle EMG is of equal significance as clinical signs for LMN involvement. Also, fasciculation potentials are equivalent to fibrillation potentials and positive sharp waves, implying that acute denervation if chronic neurogenic change on needle EMG is present. Consequently, the diagnostic categories were redetermined as follows: clinical definite ALS, clinically probable ALS and clinically possible ALS. The category of probable laboratory-supported ALS was discarded [36]. A meta-analysis proved a better diagnostic performance of the Awaji criteria with a sensitivity of 81.1% compared to the 62.2% of the revised El Escorial criteria, facilitating earlier diagnosis of ALS [37]. Although achieving improvement in diagnostic accuracy, both the revised El Escorial and the Awaji criteria are associated with various difficult aspects. Their complexity can be rather misleading and vulnerable to erroneous in their application, confirmed by a low test-retest as well as inter-rater reliability [38]. Also, the division into diagnostic categories may falsely suggest a predictive value about the actual occurrence of the disease. Furthermore, both criteria are limited in providing a prognostic value, as their diagnostic categories do not provide any relation to disease progression [39]. Ultimately, the category of possible ALS with isolated UMN signs in two regions exacerbates the debate about PLS being a separate entity or a prolonged form of ALS. Considering these issues, the Gold Coast criteria were introduced to further simplify the diagnostic approach by dichotomizing diagnostic categories into ALS and non-ALS (Table 2) [40]. Studies evaluating the feasibility of the Gold Coast criteria to date have shown an increase in diagnostic sensitivity with largely preserved high specificity [41]. To evaluate the functional status of patients, the ALS functional rating scale (ALSFRS) and its revised form (ALSFRS-R) were established. This self-assessment questionnaire contains items on bulbar functions, fine motor tasks, gross motor tasks and respiratory functions rated on a five-point scale from 0 to 4, with a maximum of 48 points [42,43]. The ALSFRS-R has become the gold standard tool to assess physical decline and rate of disease progression not only in clinical routine, but also in therapeutic trials. New staging systems, such as King’s staging system [44], Milano Torino Staging System (MiToS) [45] or Rasch overall ALS disability scale (ROADS) [46], have been recently developed to overcome methodological issues of the ALSFRS-R and improve reliability and measurement of therapeutic monitoring [47]. Since most recent pharmacological studies have yet failed to demonstrate an improvement of motor function or extend survival, much effort has been made to identify ALS patients in the early stages of the disease. The concept of pre-symptomatic ALS with mild motor impairment (MMI) has been discussed. Benatar and colleagues proposed a concept of ALS as a biological entity with a continuum of symptoms rather than a clinical syndrome alone. Therefore, there may be a pre-symptomatic phase with mild and/or unspecific symptoms that do not allow for a distinct differential diagnosis of MND until definite clinical signs of ALS occur and the ALS diagnosis according to recent diagnostic criteria is established [48]. In a small cohort of 20 pre-symptomatic patients with a known ALS gene mutation, there was found different mild motor symptoms, i.e., very mild focal weakness not resulting in disability, deep tendon hyperreflexia or EMG abnormalities, such as ongoing denervation. According to the underlying gene mutation, MMI onset takes place 1 month up to 10 years before ALS diagnosis has been established. While for some mutations the duration of the prodromal phase and disease was consistent (e.g., SOD1 A4V and I113T), a rather long prodrome over 4 years followed by a rapid disease course within about 15 months of the disease’s onset to permanent ventilation was reported. Conversely, FUS mutation carriers exhibit a relatively short prodromal stage, shorter than 1 year, followed by a disease course over 2.4 years [49]. Outside familial ALS, it remains a major concern to identify patients according to potential risk factors for developing sporadic ALS. Prospective population-based studies may be a useful tool to explore MMI in potential future ALS patients. Historically defined as a pure motor neuron disease, ALS is now considered a complex multisystemic disorder not only occurring with motor impairment, but also with non-motor features, particularly cognitive and behavioral changes [50]. Cognitive and behavioral impairment have been extensively reported in ALS, occurring in up to 50% of patients, with approximately 10% exhibiting the full symptoms of FTD, predominantly the behavioral variant [51,52,53]. This is attributable to underlying aggregation of ubiquitinated transactive response DNA binding protein 43 (TDP-43), which has been identified as the common pathological hallmark and possible mechanism [54]. Foremost, deficits are observed in the domains of executive and language functions [51]. Since these cognitive and behavioral changes have an impact on prognosis, disease progression and caregiver burden, neuropsychological assessment has been emphasized in the diagnostic routine of ALS. As a result, the Strong criteria as diagnostic consensus criteria for the diagnosis of frontotemporal dysfunction in ALS were published. Patients meeting the criteria for cognitive or behavioral dysfunction are labelled as ALS with cognitive (ALS-ci) and behavioral impairment (ALS-bi) or both (ALS-bci) [55,56]. Criteria for FTD are implemented to stratify patients as ALS-FTD [57,58]. Many neuropsychological batteries, such as the Frontal Assessment Battery (FAB), the Montreal Cognitive Assessment Test (MoCa) or the Mini-mental State Assessment (MMSE) have been in clinical use. Disease-specific assessments have later been developed to counteract motor disabilities in ALS [59]. With the Edinburgh Cognitive and Behavioral ALS Screen (ECAS), a disease-specific assessment tool for determining the presence, severity and type of cognitive and behavioral changes in ALS, and its differentiation from other disorders is available. It consists of tasks testing for ALS-specific (executive function, language, fluency, social cognition) and non-ALS-specific (memory, visuospatial functions) cognitive changes and can be administered by neuropsychological and non-neuropsychological professionals within approximately 15 to 20 min [60]. A systematic review comparing different cognitive assessments for ALS entitled the ECAS promises to be the most suitable screen to detect cognitive or behavioral changes in ALS [59]. Electrodiagnostic (ED) studies are established and fundamental for the correct diagnosis of MND, particularly for the exclusion of disease mimics such as multifocal motor neuropathy (MMN). Nerve conduction studies are required to demonstrate an affection of motor nerves, indicated by reduced compound muscle action potential, prolonged distal motor latency and decreased conduction velocity without evidence for conduction blocks. This is complemented by needle EMG examination evaluating signs of acute and chronic denervation. In recent years, much effort has been invested in the development of more sophisticated approaches to diagnose MND with higher accuracy at an early stage in the course of the disease. As to the point of differentiation from disease mimics, the nerve ultrasound has become a powerful tool to aid in the diagnostic work-up when ED studies remain inconclusive [61]. While MMN, for instance, usually presents itself with enlarged cross-sectional areas (CSA) of the nerves, this is not found in MND [62]. Indeed, the CSA appears reduced in MND, which is associated with clinical affection of the respective innervated muscle, yielding a higher diagnostic accuracy [63]. Analysis of the vagus nerve (VN) has become another interesting application of nerve ultrasound in ALS, showing a reduced CSA of the VN on both sides in ALS, independent of clinical symptoms [64,65]. This could add another pathophysiological hint as to the involvement of the autonomous system, which was also reported to be affected in ALS, although only to a mild extent and without a correlation with disease severity [6,65]. To further determine structural deficits of the peripheral nervous system suggestive of axonal degeneration in ALS, different magnetic resonance imaging (MRI) techniques were reported to add diagnostic value and work as a surrogate for disease progression, as measured by the ALSFRS [66,67]. Beyond the pure structural point of view, modern MRI sequences allow a functional–connectivity assessment, such as DTI (diffusion tensor imaging), which has been thoroughly investigated as a potential biomarker for ALS in the past years [67]. It is a measure for the unguided diffusion of water according to the Brownian movement, allowing assessment of the direction of the flux. It is usually numerically expressed by the fractional anisotropy, which can vary between 0 (pure spherical diffusion) and 1 (diffusion is strictly directed in one direction). More importantly, this technique has been extensively investigated in the field of MND and applied to determine the deterioration of both the peripheral and central motor nervous systems in ALS [65,66,67]. While routine MRI of the central nervous system is essential for regular diagnostic work-up, it often appears unremarkable, shows T2 hyperintensities along the corticospinal tract (CST) or shows atrophy of the precentral gyrus, which are, however, of unclear specificity [68]. In contrast, defined multiparametric MRI measurements, including DTI, allow for a much more accurate and reliable diagnostic workflow. However, it needs to be highly standardized [69]. On the other hand, these different imaging modalities offer a vast range of different data types that can be integrated into a multiparametric model acquired by machine learning algorithms, which could be a very powerful approach to making an unsupervised diagnosis based on neuroimaging data [70]. However, such elaborated MRI approaches require a high level of expertise and technical equipment, which is a limiting factor. Nonetheless, multiparametric MRI assessment including DTI should be accounted as a facultative biomarker for the diagnosis of MND in the future. Furthermore, in addition to the growing importance of fluid biomarkers for neuroinflammation in ALS, positron emission tomography (PET) imaging with highly specialized tracers indicating glial activation has caught attention. Standard [18F]Fluorodeoxyglucose-PET (FDG-PET) demonstrates distinct patterns of hypometabolism in the frontotemporal cortex [71,72] and diverse areas of hypermetabolism (e.g., spinal cord, cerebellum [72,73,74]). However, tracers indicating inflammatory processes might reflect the disease course in a much more reliable way. The [11C]-PBR28 tracer binds to the 18pkD translocator protein (TSPO), which is located in glial cells and can indicate inflammatory activation. TSPO is enriched in the precentral gyrus of patients with ALS, correlating with the disease burden of upper motor neuron symptoms [74]. Although having shown no correlation to disease progression in a longitudinal observation of patients with ALS, it still shows potential as a very specific biomarker. Broader studies including patients with fast disease progression and correlations to neuroinflammatory biomarkers are required to foster the concept of neuroinflammation as a diagnostic tool in ALS. Most ALS cases are sporadic and cannot be accounted for by a single genetic mutation, but a multitude of monogenic causes for ALS have been identified [75]. They are thought to constitute 5 to 10% of all ALS cases. More than 30 potentially causative genes have been identified, and the large majority are inherited in an autosomal-dominant manner. Mutations in most of these genes are very infrequent and most Mendelian ALS cases are due to mutations in chromosome 9 open reading frame 72 (C9orf72), superoxide dismutase 1 (SOD1), fused-in sarcoma (FUS) and TAR DNA binding protein (TARDBP). It is important to note that up to 20% of patients with a negative family history have been reported to harbor a causative genetic variant [76,77]. In a survey published in 2017, 90% of caregivers reported offering genetic testing to patients with familial ALS and 50% to patients with sporadic ALS. Most commonly, SOD1, C9orf72, TARDBP, and FUS were tested [78]. With more widespread availability of genetic testing in general and next-generation sequencing (NGS) in particular, it seems likely that the number of caregivers offering genetic testing has increased and that NGS approaches are used more frequently. The diagnostic yield is certainly higher using these approaches and progress in the analysis of sequencing data will lead to the identification of even more variants with effect on ALS risk, especially structural variations and intronic variants [79,80]. In contrast to other rare diseases, in the vast majority of ALS cases genetic testing will not be used to establish a diagnosis. However, genetic testing in patients with ALS does have a number of important implications: Exclusion of genetic disorders mimicking ALS, such as spinal and bulbar muscular atrophy (SBMA) which is caused by a polyglutamine expansion in the androgen receptor (AR) gene and can be mistaken for LMN-predominant ALS [81]. Other examples include, but are not limited to, adult-onset spinal muscular atrophy, a number of hereditary spastic paraparesis (HSP) subtypes and adult polyglucosan body disease (APBD) [82]; Identification of patients who are eligible for trials targeting specific mutated genes, such as SOD1, C9orf72, Ataxin 2 (ATXN2) and FUS [83]. For patients with SOD1 mutations, tofersen, an antisense oligonucleotide reducing SOD1 protein synthesis, was shown to reduce neurofilament light chain levels in plasma and is already available in many countries in an early access program [84,85]; Counselling of family members concerning predictive testing. Predictive testing always needs thorough counselling, especially when there are no measures to prevent the disease, as is currently the case for ALS [86,87]. However, family members may benefit from the knowledge gained from genetic testing, either because they are relieved when they do not harbor the mutation or because they are able to plan ahead for specific life decisions. This does also include preimplantation genetic diagnosis if a desire to have children is present [88]. Also, the exemplary ATLAS trial is already trying to closely follow up asymptomatic carriers of disease-causing mutations to initiate therapy on the basis of biomarker-defined phenoconversion and before the advent of clinical symptoms for an optimal disease-modifying effect [89]. Approaches like these will likely be applied more often as more gene-specific therapies become available; Identification of genetic mutations may help in the prediction of the clinical course of the disease, which is of importance for patient and caregiver counselling (see prediction models section). It should be mentioned that, with the advent of NGS techniques, the identification of variants of uncertain significance (VUS) can be challenging when counselling patients and relatives [90]. In one study, 47% of variants in ALS patients with potential functional significance were classified as VUS [91]. Segregation analysis, consultation of population databases and even functional analysis will often leave uncertainty concerning the pathogenicity of VUS and thorough pre-test counselling is therefore of utmost importance so patients can anticipate possible results [92]. Apart from disease-causing mutations, genome-wide association studies (GWAS) have identified a number of variants that modify the risk to develop ALS. They offer valuable insights into ALS pathogenesis and may help in the identification of treatment targets, but are not currently of additional diagnostic value [93]. In the future, polygenic risk scores (PRS) may also be of help in predicting disease progression, as one study already hinted at an association of a PRS and cognitive decline in ALS, or help stratify ALS patients according to underlying disease processes using pathway-specific PRS, as has been suggested for Parkinson’s disease [94,95]. Neurofilaments (Nf) emerged as one of the most promising biomarkers of ALS in the recent past. Considered an indicator of ongoing neuronal or axonal injury, the role of Nf in the pathophysiology of ALS remains unclear [96]. Elevated Nf can be found in several neurological diseases, including neurodegenerative disorders such as atypical Parkinson syndromes or FTD, as well as infectious or inflammatory disorders, such as multiple sclerosis (MS) or Creutzfeld-Jakob disease (CJD) [97,98]. Therefore, interpretation of Nf elevation should always be performed in the context of the primary suspected diagnosis and clinical finding. As of now, examination of the neurofilament light chain (NfL) and the phosphorylated heavy chain of the neurofilament (pNfH), both in the cerebrospinal fluid (CSF) and serum, are established methods and comparable with regard to diagnostic utility. Both NfL and pNfH values in the CSF and serum strongly correlate [99,100,101]. Numerous studies found a significant elevation of Nf in ALS patients compared to both healthy and disease controls [100,101,102,103,104,105]. Additionally, the site of symptom onset or presence of ALS-related gene mutations, such as C9Orf72, SOD1 or FUS, are associated with higher Nf values. ALS patients with known bulbar onset or with a mutation in the C9Orf72 tend to have higher Nf levels [101,106,107]. The same applies to ALS patients with more regions involved or present upper motor neuron signs at the time of diagnosis [100]. One major issue in the diagnosis of ALS is the date of diagnosis, since a proper diagnosis of ALS is established up to 10 to 12 months after the first symptoms emerge [108]. In pre-symptomatic patients with a known ALS-related mutation, elevated Nf values can be found up to 12 months before patients develop the first ALS-related symptoms [99,109]. Before that, Nf values are comparable to healthy controls [99]. After symptom onset, the longitudinal analysis revealed almost stable values for neurofilaments over the course of the disease [99,101,109]. The increase of Nf from baseline prior to symptom onset depends on the respective mutation found, i.e., in ALS patients with known SOD1 mutation, approximately 6 to 12 months in advance of the first emergence of ALS symptoms. This is compared to 2 to 3.5 years in ALS patients with mutations in the FUS gene or C9Orf72 with hexanucleotide (G4C2)n repeat expansion (HRE) [109]. Although these measurements are currently only available for a few patients with inherited ALS, they can most likely apply to sporadic ALS patients to an extent, as neuronal and axonal injury precedes the onset of motor symptoms in sporadic patients as well. Nf may shorten the diagnostic delay by up to 3 months in patients with suspected ALS [110]. The diagnostic sensitivity and specificity of Nf measurement depend, among other things, on the measurement of either NfL or pNfH and the assay used, yet efforts have been made to standardize procedures for comparable results among different laboratories [103]. Steinacker and colleagues first demonstrated a sensitivity of 77% and a specificity of 88% for CSF NfL values using a cut-off of 2200 pg/mL to distinguish ALS patients from ALS mimics. For CSF pNfH values above 560 pg/mL, a robust sensitivity of 83% and a specificity of 80% were found. Subsequent studies confirmed the usability of NfL and pNfH by applying comparable cut-off values for CSF NfL and CSF pNfH in the differential diagnosis of ALS (Table 3). The examination of serum NfL demonstrated comparable results for sensitivity and specificity [111]. However, pNfH examination in the serum seems to be inferior regarding differential diagnosis [112]. Besides the differential diagnostic value of the Nf measurement, several studies addressed the prognostic and therapeutic aspects of Nf in ALS patients. Elevated Nf positively correlates with disease progression rate [100,104]. Conversely, ALS patients with longer disease duration display lower Nf levels [103]. Additionally, high Nf levels are associated with shorter survival [101,104]. However, findings considering the therapeutic implications of Nf are ambiguous. In both phase 1/2 and phase 3 of tofersen trials, an antisense oligonucleotide (ASO) targeting SOD1 messenger RNA (mRNA) transcripts in ALS associated with mutations in SOD1, there was a significant reduction of NfL levels in the CSF and plasma, but no improvement in clinical endpoints could be demonstrated [84,85]. On the other hand, Riluzole, currently the only approved treatment for ALS, does not alter Nf levels after treatment initiation [113]. Nevertheless, findings regarding Nf in the therapy of other neurologic disorders are encouraging. In relapse-remitting MS, Nf may serve as a marker for treatment response for different disease-modifying therapies [114]. In other motor neuron diseases, such as spinal muscular atrophy (SMA), a significant reduction of plasma pNfH was found after treatment initiation with nusinersen, an ASO targeting SMN1 splicing, in children with SMA [115]. Therefore, the role of Nf as a marker for therapeutic response remains to be determined. The pathophysiology of ALS is characterized not only by neurodegenerative but also by inflammatory processes involving glial cells of the central nervous system and peripheral circulating immune cells [116]. Genetic alterations linked to ALS, e.g., in SOD1 and C9orf72, are also associated with the dysregulation of immune processes [117,118]. However, the dysfunction of autophagy and glial cells was also present in ALS patients without these genetic alterations [119]. It is assumed that anti-inflammatory processes predominate at the onset of the disease, while proinflammatory processes become relevant in later stages, accelerating motor neuron injury [120,121]. This marks the relevance of inflammatory biomarkers, which promise to provide information on disease stage, progression rate as well as pathophysiological and potential protective mechanisms. Several studies have investigated patterns of blood immune cells, such as granulocytes and T cells, as possible diagnostic biomarkers in ALS patients, finding altered leukocyte phenotypes [122,123]. Others found differentially regulated soluble factors such as interleukin (IL)-6, IL-8, tumor necrosis factor (TNF) and interferons [124,125,126]. They generally suggest differential immune regulation in ALS compared to healthy individuals. However, findings are heterogeneous and quite variable between studies and different methodological approaches. It is especially difficult to distinguish ALS from mimics, as they are often inflammatory diseases. More detailed data were provided by studies on a transcriptional level, including the finding that proinflammatory gene profiles had higher expression levels of IL-8, FBJ murine osteosarcoma viral oncogene homolog B (FOSB), cluster of differentiation 83 (CD83), suppressor of cytokine signaling 3 (SOCS3), chemokine (C-X-C motif) ligand 1 (CXCL1) and CXCL2 in monocytes of ALS patients compared to healthy controls [127]. Nevertheless, these inflammatory diagnostic biomarkers are far from making their way into clinical use. Similarly, they have not been proven to provide a prognostic value. Most studies failed to find a significant correlation between common clinical progression parameters such as the ALSFRS-R and blood concentrations of pro- or anti-inflammatory cytokines in ALS patients [124,128]. Other groups found correlations between the ALSFRS-R and survival on the one hand and seemingly protective monocyte and T-cell immune profiles on the other [122]. In addition to this, a higher number of pro-inflammatory differentially-expressed genes in monocytes of ALS patients was associated with faster disease progression [127]. Another potential inflammatory biomarker for ALS is neopterin, which is secreted by macrophages under interferon-gamma influence from stimulated T lymphocytes, is an indicator of general immune system activation and is renally excreted. In urine, it can thus be examined non-invasively without great effort. Higher concentrations were found in ALS patients than in patients with other neurological diseases, such as multiple sclerosis, and in healthy controls [129]. A higher neopterin level in ALS patients was associated with more severe symptoms evaluated by the ALSFRS-R [130]. In addition to peripherally circulating inflammatory biomarkers, there are CNS-specific ones that represent microglial and astrocyte-derived inflammation, which, however, are more difficult to access and measure. Up-regulation of activated microglia and astrocytes producing pro-inflammatory cytokines was found in the spinal cord tissue of ALS patients [119]. SOD1-mutated mouse microglia were found to express predominantly anti-inflammatory markers like chitinase-like 3 (Ym1), cluster of differentiation 163 (CD163) and brain-derived neurotrophic factor (BDNF) mRNA and fewer proinflammatory markers like NADPH oxidase 2 (Nox2) mRNA at disease onset than later in disease progression, consistent with observations of other peripheral inflammatory markers during disease course [131]. Astrocytes and microglia have been shown to interact and alter each other’s phenotype through the release of inflammatory mediators affecting disease progression in a mouse model [132]. As a representation of neuroinflammatory involvement not only in the spinal cord, but also in the motor cortex in early stages of ALS with TDP-43 pathology, activated astrocytes and microglia were detected in this brain area in patients and a TDP-43 mouse model. It was also demonstrated that cells of the primary motor cortex express the monocyte chemoattractant protein-1 (MCP1), a ligand for C-C chemokine receptor 2+ (CCR2+) monocytes infiltrating the CNS, driving the immune response in this area [133]. Recently, some approaches detected the above-explained inflammatory processes non-invasively using functional imaging, e.g., using positron emission tomography [134]. Elevations of chitinases (CHIs) and chitinase-like proteins (CLPs) have also been found in the CSF of ALS patients. CHIs are hydrolytic enzymes, widely distributed in nature, that metabolize chitin, the most abundant polysaccharide in nature and essential structural component of several organisms, including arthropods, protozoan parasites, nematodes, bacteria and fungi. Despite the absence of endogenous chitin, mammals express true CHIs with enzymatic activity and homologous structurally related CLPs lacking enzymatic activity but bind chitin with high affinity [135]. Despite its implication in several neurological diseases, the function of CHIs and CLPs in the CNS is still not completely understood. Chitotriosidase-1 (CHIT1) has been found only in microglia and CNS infiltrating peripheral macrophages [136]. Chitinase-3-like protein 1 (CHI3L1) has been mostly found in reactive astrocytes [137]. Little is known about the role of Chitinase-3-like protein 2 (CHI3L2) in physiologic and pathologic conditions. Increased CHIT1, CHI3L2 and CHI3L2 expression or CSF levels have been reported in various neuroinflammatory conditions [138]. CHIs and CLPs have been recently investigated. Thompson and colleagues showed that CHIT1, CHI3L1 and CHI3L2 were elevated in the CSF of patients with ALS compared with healthy controls and ALS-mimics. CHIT1 and CHI3L2 were elevated in ALS compared with PLS [139,140]. Additionally, the CHIT1 response appears to be an attribute of the late pre-symptomatic to early symptomatic phases in patients carrying mutations in C9orf72 or SOD1 [141]. Other markers commonly used for the diagnosis of other neurodegenerative diseases have also been studied in the context of ALS. For example, the microtubule-associated protein Tau, commonly found elevated in Alzheimer’s disease, increases significantly in the CSF of ALS patients. Significantly higher levels of total Tau (tTau) and lower phosphorylated Tau (pTau)/tTau ratio have been found in ALS patients in comparison with healthy controls in observational studies [142]. β-Amyloid, another Alzheimer’s disease-related biomarker, is elevated in the CSF of ALS patients and seems to predict shorter survivals [143] correlating with the ALSFRS-R at baseline [144]. On the other hand, the soluble amyloid precursor protein (sAPPβ) appears reduced in the CSF of ALS and FTD patients, correlating with cognitive performance [145]. TDP-43 has also demonstrated some prognostic value in ALS patients. Several studies have reported elevated CSF TDP-43 levels in patients with ALS [146]. Similarly, significantly increased levels of TDP-43 and pTDP-43 have been found in plasma of the ALS patients. Especially, the pTDP-43/TDP-43 ratio appears to distinguish individuals with ALS from healthy controls [147]. High levels of miR-181, a highly conserved non-coding RNA molecule enriched in neurons, predict a greater than a two-fold risk of death in ALS patients. The molecule miR-181 performed similarly to NfL, and when combined, miR-181 + NfL show a superior prognostic value [148]. Concerning non-neuronal related biomarkers, higher levels of creatine kinase (CK) are often found in ALS patients, especially in those with slow progression, correlating to lower ALSFRS-R scores. Higher CK blood concentration is likely linked to longer survival [149]. In contrast to the cardiac troponin I (cTnI), serum concentration of cardiac troponin T (cTnT) is elevated in the serum of ALS patients. Both are common biomarkers in the initial approach of myocardial infarction. This is especially true in patients with a spinal onset (AUC 0.87; 0.78–0.94), and it can thus differentiate ALS from other neurodegenerative diseases and ALS mimics [150]. CSF levels of the basic fibroblast growth factor (bFGF) are increased in ALS and correlate with disease duration and survival [151]. Similarly, the perivascular fibroblast marker Secreted Phosphoprotein 1 (SSP1, Osteopontin) increased in plasma of ALS patients in four independent cohorts. Increased levels of SPP1 at disease diagnosis predicted shorter survival as well [152]. Despite the value as a diagnostic tool showed by increasing research on Nf, many other neurological disorders present elevated Nf in serum and CSF, decreasing its specificity in the ALS diagnosis. Currently, great effort is exerted in clinical research to find ALS-specific biomarkers that indicate the onset of pathological events in pre-symptomatic or prodromal phases of the disease. Among the most promising is the translation products of the C9orf72 intronic expansion, poly-GP dipeptide repeats, which are increased in the CSF of pre-symptomatic patients of C9orf72-associated ALS. Similarly, higher levels of the poly-GP proteins were also found in peripheral mononuclear cells of pre-symptomatic C9orf72 mutation carriers [153,154]. Recently, it was found that the nuclear TDP-43 suppresses cryptic exon-splicing events of some ALS-associated genes, such as UNC13A. This repression is lost in the ALS/FTD pathology, as extranuclear TDP-43 is a specific hallmark of these disorders. The early identification of such cryptic exon-splicing variants or their translational products represents one of the most promising and specific biomarkers for identifying disease onset [155]. “Prognosis can no longer be relegated behind diagnosis and therapy in high-quality neurologic care” [156]. With a diagnosis of ALS, the question of prognosis almost automatically arises. However, the disease course is highly variable. This poses a problem when discussing prognosis with the individual patient, but also when dealing with high variability in the design and evaluation of disease-modifying trials [157]. Biomarkers such as neurofilaments and rating scales such as the ALSFRS-R at baseline, the ALSFRS-R decline from disease onset to the test date, the initial clinical presentation or specific genetic mutations are all individually associated with disease progression and survival time [158,159,160,161,162]. The multitude of parameters that can be evaluated in patients with ALS calls for approaches that incorporate many factors influencing the disease course to provide a more accurate estimate of how the disease will likely progress in the individual patient. The ENCALS survival prediction model uses eight predictors to define five groups with distinct survival outcome, which was defined as the time between symptom onset and non-invasive ventilation > 23 h/day, tracheostomy or death [157]. Clinical parameters, such as bulbar vs. non-bulbar onset, forced vital capacity, the age of onset and the diagnostic delay and the presence of C9orf72 repeat expansion, were used in the model. Patients with a predicted brief disease course had a median of 17.7 months from symptom onset to the composite survival outcome, while the median was 91.0 months for the group with a very long course. A qualitative study looking at the impact of personalized prognosis using the ENCALS survival prediction model found that it can be discussed with “minimal adverse emotional impact” and may help facilitate planning of the future [163]. However, the quotes of patients, relatives and caregivers in reaction to the prediction, probably not unexpectedly, show varying reactions. More research is needed to assess the impact of personal prediction models in ALS. Additionally, while the ENCALS model at least incorporated one non-clinical parameter by using the C9orf72 repeat expansion as a predictor, many studies trying to predict ALS or trying to identify ALS subgroups are still only or mostly using clinical parameters [164,165]. We are convinced that, in the future, the combination of clinical signs, genetic testing and biomarkers, such as neurofilaments, will offer the patient an informed prognostic estimation after establishing a diagnosis of ALS. Establishing and refining experimental disease models will be crucial for obtaining information about the underlying heterogenous disease-causing mechanisms and identifying new potential diagnostic and therapeutic targets in ALS, particularly in its sporadic forms. While transgenic animal models for familiar ALS with the disease-causing mutation have provided insights into pathogenesis and potential therapeutic targets, identifying the underlying disease and causes of sporadic forms remains challenging. Currently, various in vitro cellular models with induced pluripotent stem cells (iPSCs) [166] and organoids [167] from donors with sporadic ALS show phenotypic differences in the pattern of neuronal organization and degeneration, protein aggregation, cell death, as well as in onset and progression [168]. The further development of these methods in combination with advanced omics technologies, such as single-cell sequencing and Deep Learning algorithms, will allow precise, accurate and reliable decoding of patient-specific cellular and genetic dysfunction, leading not only to an individual molecular diagnosis but a solid predictive model for applying personalized therapies [169,170]. The diagnosis of ALS is currently primarily based on clinical aspects as options for early detection, such as biomarkers with high diagnostic accuracy, are lacking. The ongoing development of new and innovative diagnostic tools, however, promises major advances that will fundamentally impact future clinical practice and research in this field (Figure 2). Expert clinical assessment remains essential in diagnosing ALS. Its heterogeneous clinical presentation and multisystemic complexity can be challenging and requires interdisciplinary assessment of cognitive and behavioral aspects in addition to pure motor impairments. Electrophysiological assessments as well as imaging techniques, such as functional MRI and PET, are becoming increasingly sensitive and can aid in early diagnosis and, in particular, differentiation from related diseases. In addition to established Nf in the diagnostic workup, other biomarkers of several systems, ranging from inflammatory to degenerative types, are emerging. Ideally, biomarkers need to be easy to obtain and minimally invasive, improve diagnostic accuracy, facilitate early detection of ALS and enable the monitoring of disease progression. This would aid in enrolling patients in new clinical trials even in pre-symptomatic phases, evaluating new treatments and optimizing treatment plans and disease management. Early identification of affected and pre-symptomatic individuals may prevent prolonged diagnostic procedures in the future and greatly improve the potential efficacy of therapeutics by extending and advancing the treatment window. Genetic testing is becoming more feasible and frequently used. It promises a better endophenotypic classification of patients based on their neurobiological disease correlates and thus may enable targeted treatment and improved prediction of individual disease courses. Ultimately, the integration of various diagnostic methods and all collected patient data will be key to deriving patterns towards more personalized diagnostics with the future opportunity of evaluating individual prognosis as well as directing patients towards treatment studies or approved therapeutic strategies.

PMC10000760

Mihaela Roșca,Gabriela Mihalache,Vasile Stoleru

Tomato responses to salinity stress: From morphological traits to genetic changes

10-02-2023

abiotic stress,PRISMA,salt stress,screening of salinity effects,tomato,alleviation of salinity effects

Tomato is an essential annual crop providing human food worldwide. It is estimated that by the year 2050 more than 50% of the arable land will become saline and, in this respect, in recent years, researchers have focused their attention on studying how tomato plants behave under various saline conditions. Plenty of research papers are available regarding the effects of salinity on tomato plant growth and development, that provide information on the behavior of different cultivars under various salt concentrations, or experimental protocols analyzing various parameters. This review gives a synthetic insight of the recent scientific advances relevant into the effects of salinity on the morphological, physiological, biochemical, yield, fruit quality parameters, and on gene expression of tomato plants. Notably, the works that assessed the salinity effects on tomatoes were firstly identified in Scopus, PubMed, and Web of Science databases, followed by their sifter according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and with an emphasis on their results. The assessment of the selected studies pointed out that salinity is one of the factors significantly affecting tomato growth in all stages of plant development. Therefore, more research to find solutions to increase the tolerance of tomato plants to salinity stress is needed. Furthermore, the findings reported in this review are helpful to select, and apply appropriate cropping practices to sustain tomato market demand in a scenario of increasing salinity in arable lands due to soil water deficit, use of low-quality water in farming and intensive agronomic practices.

Tomato responses to salinity stress: From morphological traits to genetic changes Tomato is an essential annual crop providing human food worldwide. It is estimated that by the year 2050 more than 50% of the arable land will become saline and, in this respect, in recent years, researchers have focused their attention on studying how tomato plants behave under various saline conditions. Plenty of research papers are available regarding the effects of salinity on tomato plant growth and development, that provide information on the behavior of different cultivars under various salt concentrations, or experimental protocols analyzing various parameters. This review gives a synthetic insight of the recent scientific advances relevant into the effects of salinity on the morphological, physiological, biochemical, yield, fruit quality parameters, and on gene expression of tomato plants. Notably, the works that assessed the salinity effects on tomatoes were firstly identified in Scopus, PubMed, and Web of Science databases, followed by their sifter according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and with an emphasis on their results. The assessment of the selected studies pointed out that salinity is one of the factors significantly affecting tomato growth in all stages of plant development. Therefore, more research to find solutions to increase the tolerance of tomato plants to salinity stress is needed. Furthermore, the findings reported in this review are helpful to select, and apply appropriate cropping practices to sustain tomato market demand in a scenario of increasing salinity in arable lands due to soil water deficit, use of low-quality water in farming and intensive agronomic practices. Tomatoes (Solanum lycopersicum L.) are widely consumed worldwide as fresh or processed food products (e.g. canned tomatoes, sauce, juice, ketchup, soup, etc.) (Campestrini et al., 2019; Li et al., 2021) ranking second in the top of the most consumed vegetables in the United States of America, after potatoes (Reimers and Keast, 2016). These fruits have a high content of nutrients and bioactive substances (De Sio et al., 2021; Ali et al., 2021a) that are beneficial for a healthy body, a healthy skin, and weight loss, and which may ameliorate or prevent various human chronic degenerative diseases (Ali et al., 2021a). Tomato fruits are rich in carotenoids (e.g. β-carotenoids and lycopene), ascorbic acid (vitamin C), tocopherol (vitamin E), and bioactive phenolic compounds such as quercetin, kaempferol, naringenin and lutein, caffeic, ferulic and chlorogenic acids (Dasgupta and Klein, 2014; Mihalache et al., 2020; Stoleru et al., 2020; Murariu et al., 2021). The carotenoids from tomatoes are known to display anticancer properties and to be excellent deactivators of reactive oxygen species (ROS) (e.g. for singlet oxygen (1O2) and peroxyl radical (ROO•)) (Campestrini et al., 2019; Stoleru et al., 2020). Lycopene, which is an antioxidant, might protect the cells against oxidative damage and prevent cardiovascular disease and various types of cancer (e.g. prostate, breast, lung, bladder, ovaries, colon, as well as pancreas cancer) (Dasgupta and Klein, 2014). Li et al. (2021) ascertained in their study that the consumption of tomatoes provides about 85% of the daily dose of lycopene required by the population of North America and 56–97% in five European countries. According to FAOSTAT database, in 2020 about 251,687,023 tonnes of tomatoes were harvested from 6,163,463 hectares worldwide, with a yield average of 40.84 tonnes/ha (FAOSTAT, 2022). In 2020, the European Community reported a production of 16,657,000 tonnes, of which 9,801,000 tonnes were processed and 6,856,000 tonnes were consumed fresh. Compared to the previous year, EU production increased by almost 1%. In the last 10 years, the average annual tomato production in the EU was 16,474,000 tonnes, with the lowest value recorded in 2012 and 2013 (15,082,000 tonnes) and the highest in 2016 (17,862,000 tonnes) (European Commission, 2021). Annually, a wide variety of factors can affect tomato yield and fruit nutritional quality (Inculet et al., 2019). Among these factors, the salt content in soil and water used in irrigation stands out. According to Shrivastava and Kumar (2015) “worldwide 20% of total cultivated and 33% of irrigated agricultural lands are afflicted by high salinity”. Furthermore, by the year 2050 more than 50% of the arable land will probably become saline soils as a consequence of weathering of native rocks, irrigation with saline water, climate change projections predicting increasing drought events forcing farmers to make use of salty water, and intensive agronomic practices. The Food and Agriculture Organization of the United Nations ascertained that every year soil salinization takes 1.5 million ha of farmland out of production and annually decreases the production potential by up to 46 million ha per year. In sum, soil salinization has been causing annual losses in agricultural productivity estimated to be US $ 31 million (FAO, 2022). Tanji (2002) defined the salinity as “concentration of dissolved mineral salts present in soils (soil solution) and waters”. In small amounts, the dissolved salts are vital for the normal plant growth and development, but at high levels, they become harmful and often cause the death of plants (Nebauer et al., 2013). Sodium chloride is the most common salt detected in salty soils and waters, along with the chloride, sulfate, and carbonate salts of calcium, magnesium, and sodium (Nebauer et al., 2013; Riaz et al., 2019). Soil and water salinization generally occurs naturally, but the human factor via land clearing and inappropriate irrigation practices emphasizes this phenomenon. The soil is generally considered salt-affected when its electrical conductivity (EC) is above 4 dS·m-1. The soil salinity can be also increased by rainwater, which according to Riaz et al. (2019) can contain even 650 mg·kg-1 NaCl. Salinity induces various deleterious effects on plants which are forced to react. Depending on the post-exposure phase, plant responses induced by salinity can be grouped into (Negrão et al., 2017; Isayenkov and Maathuis, 2019): (I) the ion-independent response which occurs in the first hours-days after exposure and is characterized by stomatal closure and inhibition of cell expansion mainly in the shoot, and general plant growth; (II) the ion-dependent response which takes place over days or even weeks and is characterized by the slowdown of the metabolic processes, premature senescence, and ultimately cell death. Plant adaptation to saline stress depends significantly on a multitude of physiological and molecular mechanisms which are classified into three main categories: osmotic tolerance, ion exclusion, and tissue tolerance (Munns and Tester, 2008; Roy et al., 2014; Isayenkov and Maathuis, 2019). Under salinity stress, the plants maintain their growth and development, by tolerating the water loss, preserving the leaf expansion and stomatal conductance (osmotic tolerance), avoiding the accumulation of Na+ ions in the shoots and leaves at toxic concentrations (by ion exclusion) and protecting the plant cells against the toxic action of Na+ through its removal from the cytosol and subsequent sequestration in vacuoles (tissue tolerance) (Munns and Tester, 2008; Hasegawa, 2013; Roy et al., 2014). A range of transporters and their controllers at both plasma membrane and tonoplast levels are involved in ion exclusion and tissue tolerance. The ways of plants react to salinity stress at molecular, cellular, metabolic, and physiological levels, as well as the mechanisms involved in salinity tolerance are far from being completely understood (Gupta and Huang, 2014; Maathuis, 2014). Under osmotic stress, the cell expansion in root tips and young leaves is immediately reduced and stomatal closure is induced. Plant tolerance to salt is mediated by various biochemical pathways that support water retention and/or acquisition, protection of chloroplast functions and the maintenance of ion homeostasis (Ludwiczak et al., 2021). Proline, glycine-betaine and soluble sugars are the main osmoprotectants synthesized by plants to balance the osmotic difference between the cell's surroundings and the cytosol and to protect the cell structure (Gupta and Huang, 2014; Sharma et al., 2019). According to Roy et al. (2014), the action of the tolerance mechanisms is highly dependent on the salinity level. For example, the Na+ exclusion is more effective in conditions of high salinity, while osmotic tolerance may be the most important tolerance mechanism at moderate salinity. In Figure 1 the possible adaptive responses of plants to salt stress is schematically shown (Horie et al., 2012; de Oliveira et al., 2013). Plant exposure to salinity causes negative effects on their growth and development, even leading to their death. The first visible sign of salinity stress in plants is usually stunted growth, with plant leaves often colored in bluish-green (Zahra et al., 2020). Toxicity of Na+ occurs with time and after a great concentration increase of these ions in the older leaves which causes their premature death (Hasegawa, 2013). Salinity induces osmotic stress, excessive uptake of sodium and chloride ions (cytotoxicity), and nutritional imbalance, impairing the plant growth and development (Zahra et al., 2020; Ludwiczak et al., 2021). Plant exposure to saline stress also causes oxidative stress due to the generation of reactive oxygen species (ROS) (Isayenkov and Maathuis, 2019). High levels of salt cause physiological dysfunctions, affect photosynthesis, respiration, starch metabolism, and nitrogen fixation, and lead to reduced crop yield (Zahra et al., 2020). Salt accumulation inside the plant tissues above the tolerance limits leads to several negative changes in plant morphology, physiology, biochemistry and crop productivity. Salinity reduces water availability for plant use and due to unfavorable osmotic pressure, the roots are unable to absorb the water (Shrivastava and Kumar, 2015). According to Hasegawa (2013), Na+ causes the destabilization of membranes and proteins and negatively affects the fundamental cellular and physiological processes, mainly the division and expansion, primary and secondary metabolism, and mineral nutrient homeostasis. In addition, Na+ competes with K+ uptake causing K+ deficiency. The adverse effects of soil salinity on plants have been proven to be caused not only by Na+ cations but also by Cl− anions (Acosta-Motos et al., 2017). It has been reported in various studies that Cl− apart from having a toxic effect on plants, it also is a beneficial element for higher plants. As a micronutrient, Cl− regulates the leaf osmotic potential and turgor, stimulates growth in plants by increasing the leaf area and biomass, and improves the photosynthetic performance of plants (Colmenero-Flores et al., 2019; Franco-Navarro et al., 2019; Wu and Li, 2019). Geilfus (2018) stated that 0.2–2 mg g–1 fresh weight of Cl− can act in stabilizing the oxygen-evolving complex of photosystem II, maintaining the electrical potential in cell membranes, regulating tonoplast H+-ATPase and enzyme activities. Na+ cations are usually more toxic than chlorine anions in plants, but Wu and Li (2019) asserted that the salinity effects observed in soybeans and avocado were mainly due to Cl− toxicity. High concentrations of Cl– caused nitrogen or phosphorus deficiency, interfered with photosystem II (PSII) quantum yield and photosynthetic electron transport rate, and induced necrotic lesions, resulting in the symptom of leaf-tip burning and impairment of photosynthesis and growth (Teakle and Tyerman, 2010; Wu and Li, 2019). Due to both Na+ and Cl− toxicity, high levels of salt can induce a large number of negative effects on tomato plants: alteration of phenological development, replacement of nutrients with sodium and chloride ions, osmotic inhibition, photosynthetic reduction, nutrient deficiencies or imbalances, changes in gene expression and protein synthesis, and negative effects on crop productivity ( Figure 2 ). Salinity affects almost all aspects of plant growth including germination, vegetative growth and reproductive development. Plants are generally more sensitive to salinity during germination and early growth, and excessive accumulation of sodium in cell can rapidly lead to osmotic stress and cell death (Shrivastava and Kumar, 2015). According to Ibrahim (2018) and Zaki and Yokoi (2016), tomato is a moderately tolerant species to salinity, and seed germination, plant growth and fruit development are just affected by high salinity levels. The response to salinity depends mainly on the tomato genotype (Zaki and Yokoi, 2016) and it has been demonstrated that salt tolerance is controlled by several gene families (Ali et al., 2021a). Studies conducted so far have highlighted that the different levels of salts in soil or in the irrigation water can induce changes in plant morphology, physiology, and biochemistry, with particular consequences on yield and fruit quality. The knowledge of the salinity effects on tomato plants and fruits is an asset in the selection and application of the appropriate crop practices to fulfill tomato market demand. The assessment of the tomato responses to salinity stress is the main focus of this review, which was achieved through: (i) identification in Scopus, PubMed, and Web of Science databases of research works that assessed the effects induced by salinity on tomatoes, followed by (ii) their sifter according to PRISMA guideline and (iii) emphasis of the salinity effects on morphology, physiology, biochemistry, yield, fruit quality and gene expression of tomato plants induced by different levels of salts in water and soil. The problem of plant salinity stress has attracted the attention of many researchers who have been focusing on this topic. The main research approaches refer both to the effects of salinity on plant growth and development and to the possible strategies to increase plant tolerance to salinity. In this study, only original scientific papers which were published in the last 10 years, in peer-reviewed journals, and underlying the individual salinity effects on morphology, physiology, biochemistry, yield, fruit quality, and gene expression of tomato plants induced by different levels of Na, K and Mg salts in water and soil were included. PRISMA guideline (Page et al., 2021) was used in this review to extract from Scopus, Web of Knowledge and PubMed databases the scientific papers focused on the assessment of the effects induced by salinity on tomato plants. The key expression “tomato salinity effects” was used to identify the scientific papers and the search returned 529, 751, and 178 articles in Scopus, Web of Science, and PubMed databases respectively, published in the last 10 years. According to the PRISMA flow diagram ( Figure 3 ) after repetitive publications removal, 964 scientific papers were considered in the screening step. Following a careful reading of titles and abstracts, 435 articles were identified as incompatible with the search topic. Subsequently, the full texts of the left papers were downloaded and assessed to identify the works eligible with the established criteria. After an extensive screening, 11 papers in another language than English, 23 articles without full text, 250 articles focused on the methods and practices that could increase the tomato tolerance to saline stress, and 99 items for other reasons (e.g., reviews, inadequate experimental criteria data, book chapters, conference papers, are not highlighted the salinity effects, etc.) were removed. Finally, only 146 original articles were eligible based on the inclusion criteria. The detailed analysis of these articles led to the following results ( Figure 4A ): 14 articles focused on salinity’s impact on seed germination; in 92 articles the plant/parts of the plant height, fresh/dry weight, leaf area, and/or flower/ branch number depending on salinity level in the soil or water were measured; in 87 articles the physiological parameters related to photosynthesis, osmosis, nutrients uptake, and water content in plant parts were evaluated; in 81 articles the biochemical activity of tomato plants under saline stress was assessed. The main parameters analyzed were enzymatic activity, proteins, sugars and other compound synthesis, hormonal levels, and/or molecular biology analyses. and only in 51 scientific papers, the impact of saline stress on yield and/or fruit quality was studied. Out of the 146 full articles assessed for eligibility, only 98 studies were included in the reference list, following the evaluation of the information reported by the proposed objectives. In the last 10 years, at least 12 articles focusing on the impact of salinity on tomato morphology, physiology, biochemistry, and yield have been published annually in Scopus, Web of Science, and PubMed databases, respectively ( Figure 4B ). Salinity strongly influences all the aspects of a tomato plant’s life, producing changes even in the morphological characteristics. In general, the morphology of a plant is a reflection of its environmental conditions, proving information about its metabolic function. Increases in salt content and in particular of sodium chloride in the growing environment can significantly affect the plant’s physical appearance, but also the germination traits of tomato seeds. In the study conducted by Sholi (2012), it was reported that the increase of NaCl concentration in the 1/2 MS solidified medium delayed the seed germination of all four tomato cultivars: Jenin 1, Hebron, Ramallah and Maramand. The experiments were done in Petri dishes and incubated in the light at 23°C. The medium with the corresponding salt concentration was solidified with 8 g L-1 agar. At 0 mM NaCl the time required for germination of 50 % of ‘Jenin 1’ seeds was 2.45 days, but at 100 mM NaCl the same germination rate was reached in 8.51 days. At 150 mM NaCl the germination of ‘Jenin 1’, ‘Hebron’ and ‘Maramand’ cultivar seeds were completely inhibited. Similar results were obtained by Abdel-Farid et al. (2020), who observed that a salinity level of 50, 100 and 200 mM, NaCl reduced significantly the germination rate of tomato seeds, while at 100 and 200 mM NaCl the germination of tomato seeds was completely inhibited. The authors explained that the delay in seed germination may be due to the impairment of enzyme activity by the partially osmotic or ion toxicity. González-Grande et al. (2020) found that 85 mM NaCl reduced the seed germination rate of tomato cultivar Río Grande by 6.4% compared with the control (0 mM). At 171 and 257 mM NaCl the germination was severely affected, the rate being lower than 2.8%. The experiments were done in sterile Petri dishes on filter papers. Paradoxically, at 100 mM NaCl, Tanveer et al. (2020) reported a germination rate of 80% for tomato seeds. In the study of Adilu and Gebre (2021), a delay in seed germination with salinity increase was observed, the mean germination times (days) for the four selected tomato varieties (Sirinka, Weyno, ARP D2, and Roma VF) were 10.70, 8.72, 7.31, and 6.85 days respectively at 4 dS m-1 and 5.79, 5.69, 4.68, 5.09 days respectively at 0 dS m-1. According to Adilu and Gebre (2021) a low level of NaCl induces seed dormancy while a high level inhibits seed germination. González-Grande et al. (2020); Abdel-Farid et al. (2020) and Adilu and Gebre (2021) explained that the reduction in germination rate and percentage under salt stress can be linked to a decrease in water potential gradient among seeds and their surrounding medium. Furthermore, the osmotic and toxic effects of NaCl affect the enzyme activation during seed germination and the gibberellin acid content. Regarding the salinity effects on plant morphology, changes can appear in all stages of plant development, affecting the plant height, root/shoot ratio, leaf area, number of branches, or the number of leaves/flowers per plant. The studies focusing on the salinity effects on tomato plants showed that the intensity of plant morphology changes depends on the salt level in the growing environment. In addition, each cultivar/hybrid responds differently to saline stress. Assimakopoulou et al. (2015) assessed the responses of three cultivars (Santorini Authentic, Santorini Kaisia and Chios) and four hybrids of cherry tomato (Cherelino F1, Scintilla F1, Delicassi F1, and Zucchero F1) at 0, 75 and 150 mM NaCl in a mix of loamy soil and perlite (3:1 v/v). The results of this study showed that cultivar Chios was the most affected at 150 mM and its total plant dry weight decreased by 65.37% and the root/upper plant part ratio in terms of fresh weight from 0.09 to 0.03. The total plant dry weight of the other cherry tomato cultivars was reduced by 52.52-56.52% at the highest salinity level compared to the lowest level. Assimakopoulou et al. (2015) stated that the growth inhibition was due to the toxicity of Cl- and Na+ ions and to the nutritional imbalance induced by salinity. Samarah et al. (2021) assessed the tomato seedling growth in response to four saline water solutions of NaCl (0, 5, 10, and 15 dS m-1). The seedlings at 15 dS m-1 had a mean length of 3.8 cm and a dry weight of 9 mg, showing a longer length and weight at 0 dS m-1 (16.2 cm and 45 mg/seedling, respectively). The harmful effects of salinity on leaf area, leaf number, and leaf length also increase with the salt concentration rise, according to the studies performed by Babu et al. (2012); De Pascale et al. (2012); Hossain et al. (2012); Lovelli et al. (2012); Sánchez et al. (2012); Martínez et al. (2014); Al Hassan et al. (2015); Abouelsaad et al. (2016); Parvin et al. (2016); Chaichi et al. (2017); Rahman et al. (2018); Abdelaziz and Abdeldaym, (2019); Maeda et al. (2020). The cultivar Raf exposed at a salinity level of 5.5 dS m-1 had 2708 cm2 for the leaf area, but at 11 dS m-1 the leaves were smaller, and their leaf area decreased to 1815 cm2 (Sánchez et al., 2012). According to De Pascale et al. (2012), the saline water with an electrical conductivity of 4.4 dS m-1 used in tomato irrigation reduced the leaf number per plant from 82.6 at 48.9 and their leaf area with 47.55%, compared to the control. In their study, Babu et al. (2012) assessed the morphological changes induced by salinity on tomato cultivar PKM 1 based on leaf area, dry matter weight percentage, plant height and number of fruits per plant. Irrigation during 90 days with water containing NaCl at the concentrations of 0, 25, 50, 100,150, and 200 mM immediately after sowing caused negative changes in tomato plants. For example, it was found that the treatment with 200 mM NaCl reduced the plant leaf area by 43.91% and the fruit number per plant to 4 compared to 15 in the control. In addition, at this concentration, the plant height was 76.17 cm shorter compared to the control. In another study, irrigation with water having EC between 1.75 and 10.02 dS m-1 produced significant effects on specific leaf area, number of nodes per stem, fresh weight of roots/shoots/leaves, and length of primary roots/stem of the tomato cultivars Roma and Rio Grande (Prazeres et al., 2013). Increasing the NaCl concentration, in the irrigation water up to 3.22 dS m-1 led to an increase in the fresh weight of cultivar Roma leaves (by 84.7 g per plant), but at a higher NaCl concentration the leaf weight was reduced by 2.98-31.33 g. At 5.02 dS m-1 the leaf weight per plant was 157.80 g, with a non-significant reduction induced by salinity compared to the control whose leaf weight was 160.78 g per plant. In contrast, the fresh weight of the stems and roots decreased with the NaCl content increase in irrigation water. For cultivar Rio Grande the water EC higher than 1.75 dS m-1 had a positive effect on the fresh weight of roots, shoots and leaves, on specific leaf areas, number of nodes per stem and length of primary roots and stem (Prazeres et al., 2013). Several other studies have shown that the salt variation in the growing medium caused negative or positive changes in fresh biomass, plant height, root/shoot ratio, leaf areas, number of branches, and number of leaves/flowers per plant. In this respect, the results of some studies which assessed the morphological changes in tomato plants under salinity stress have been reported in Table 1 . Reducing plant height, leaf area, leaf number, and leaf length under salt stress conditions may be an adaptive morphological strategy to limit the water loss through transpiration. However, it could also be the result of the toxicity of Na+ and Cl- ions accumulated in cells, which slow the cell growth of young leaves (Negrão et al., 2017). The same authors interestingly focused on tissue and cellular levels to assess the morphological alterations caused by salinity in tomato plants. In this respect, Bogoutdinova et al. (2016) investigated the cell organization of the epidermis and parenchyma cortical tissues of tomato hypocotyl under different levels of NaCl in vitro. The size of the intercellular spaces in the cortical parenchyma as well as the average cross-sectional areas and shape of epidermal and cortical parenchyma hypocotyl cells of tomato line YaLF and cultivar Rekordsmen were significantly affected by the addition of NaCl to the culture medium. At 250 mM NaCl, the highest increase in the cell areas of tomato line YaLF was observed and the epidermal cell became angular in contours. Plant physiological processes are very sensitive to all environmental changes. Variations in NaCl and other salt levels in soil or hydroponic cultivation have a strong impact on the physiology of plants. Depending on the stress duration and severity, changes that can occur in the physiological processes affect plant growth, development, and productivity. The studies done on tomatoes in the last 10 years highlighted a negative influence of salinity stress on the physiological parameters such as photosynthetic rate, transpiration, stomatal conductance, chlorophyll content and mineral uptake (Hossain et al., 2012; Lovelli et al., 2012; Giannakoula and Ilias, 2013; Maeda et al., 2020; Yang et al., 2021). For instance, Maeda et al. (2020) reported that the increase of Enshi nutrient solution EC from 1.2 to 6 dS m-1 caused the reduction of: photosynthetic rate by 10.2 % and 12.4 %, respectively, in tomato leaves of cultivars CF Momotaro York and Endeavour; transpiration rate and stomatal conductance by 26.9% and 23.4%, respectively, in the cultivar CF Momotaro York, and by 24.6% and 24.1%, respectively, in the cultivar Endeavour. At 6 dS m-1, the stomatal conductance of tomato leaves grown in silt loam soil was 0.03 mol m-2 s-1, i.e., 0.05 mol m-2 s-1 lower than in control (EC= 0 dS m-1 Na) (Parvin et al., 2016). Marsic et al. (2018) reported that the photosynthetic and transpiration rates as well as stomatal conductance were lower in the leaves of tomato cultivars Belle and Gardel raised in hydroponics with electrical conductivity of 6 dS m-1, compared to 2 dS m-1. The photosynthetic and transpiration rates and stomatal conductance of cultivar Belle leaves were lower by 44.1%, 52.9% and 90%, respectively, than the control, and by 40.3%, 48.6% and 91.3%, respectively compared to cultivar Gardel. According to Marsic et al. (2018), the decreased values of these parameters could be due to the stomatal closure induced by water deficit. Like the photosynthesis rate, the chlorophyll synthesis in tomato plant leaves can be negatively affected by the exposure to high salt levels (Giannakoula and Ilias, 2013; Taheri et al., 2020). This may happen due to metabolic disorders which result in decreased chloroplast activity and photosynthesis, increased chlorophyllase enzyme activity, and respiration, followed by reduced chlorophyll contents (Taheri et al., 2020). Singh et al. (2016) found in their study that the chlorophyll content in ‘Lakshmi’ tomato leaves was reduced from 0.996 mg g-1 to 0.751 mg g-1 when the NaCl level increased from 0 to 0.5 g kg-1 in soilless cultivation. The same trend was observed in chlorophyll b synthesis, whose content decreased by 27.73% compared to the control. In another study carried out on the tomato cultivar Super Chef grown in hydroponics, the total chlorophyll content decreased by 40.93% at 120 mM NaCl compared to the control (0 mM NaCl) (Taheri et al., 2020). The effects of salinity on photosynthesis processes in tomatowere evaluated in various studies by chlorophyll fluorescence. This type of analysis offers information on energy transfer in the photosynthetic apparatus and the related photosynthetic processes, mainly about the activity of photosystem II (PSII). PSII is a membrane protein complex whose active centers exist as dimers in the thylakoid membranes of grana stacks. It is known that PSII has the function to catalyze light-induced water oxidation in oxygenic photosynthesis and in this way light energy is converted into biologically useful chemical energy (Khorobrykh, 2019; Rantala et al., 2021). Shin et al. (2020) used chlorophyll fluorescence to assess the PSII activity in the leaves of cultivars ‘Dafnis’, ‘Maxifort’, ‘BKO’ and ‘B-blocking’ irrigated with saline water. At 400 mM (the maximum concentration of NaCl in saline water) the chlorophyll fluorescence decrease ratio (Rfd) was the parameter whose levels were most negatively affected, followed by the maximum quantum yield of PSII photochemistry (Fv/Fm). The chlorophyll fluorescence parameters, such as the coefficient of photochemical quenching of variable fluorescence based on the puddle model of PSII (qP) and coefficient of nonphotochemical quenching of variable fluorescence (qN) showed moderate negativechanges due to the salt level increase in irrigation water, whereas the quantum yield of nonregulated energy dissipation in PSII Y(NPQ) showed a significant increment at the higher salt concentration compared to control. Gong et al. (2013) reported that the values of Fv/Fm parameter and the actual quantum efficiency of photosynthetic system II (ФPSII) in cv. ‘Jinpeng No. 1’ decreased with increasing levels of salt in the hydroponic media. For the non-photochemical quenching (NPQ) parameter was noticed that an increase in salt level led to an increase in its value, the highest being recorded at 100 mM. According to Zhao et al. (2019) the qP parameter measures the openness of PSII centers and reflects the conversion efficiency of the captured light quantum into chemical energy, while qN assesses the rate constant for heat loss from PSII. Fv/Fm parameters give information about the maximum light energy conversion efficiency of PSII after adaptation to darkness and NPQ reflects the level of excess energy dissipation as heat. Using the ФPSII parameter of chlorophyll fluorescence is assessed the actual photochemical efficiency when the PSII reaction center is partly shut down under light. Thereby, as Tsai et al. (2019) and Zhao et al. (2019) stated, the changes observed in the chlorophyll fluorescence parameters under salt stress are the results of the membrane system stability disturbance (especially the damage of thylakoid membrane), the aggravation of the PSII reaction center and disturbances in PSII performance, which diminished the photosynthesis. More results on the changes induced by saline stress on photosynthetic rate, transpiration, stomatal conductance and chlorophyll content in tomato leaves have been included in Table 2 . Frequently, salinity increase can lead to a reduction in the essential minerals content such as calcium, potassium or magnesium and, consequently, to a nutritional imbalance. Calcium is one of the structural components of cell walls and membranes and serves as a second messenger in a variety of processes (Thor, 2019; Bang et al., 2021). By transduction, integration and incoming signals multiplication, the calcium links the environmental stimuli with the physiological responses of plants (Bang et al., 2021). Potassium ensures optimal plant growth, acts as an activator of dozens of important enzymes and enhances plant yield. For example, potassium plays an important role in protein synthesis, sugar transport, N (nitrogen) and C (carbon) metabolism, photosynthesis, cell osmotic pressure regulation and maintaining the balance between cations and anions in the cytoplasm (Xu et al., 2020). Magnesium in plant tissue is the central element of the tetrapyrrole ring of the chlorophyll molecule and, therefore, its deficiency leads to a chlorophyll synthesis decrease and to the impairment of normal plant growth and development. Magnesium also acts as an activator or cofactor of enzymes involved in carbohydrate metabolism (Guo et al., 2015; Bang et al., 2021). Therefore, a deficiency of these minerals in the plant tissues can cause negative effects on growth and development (Bang et al., 2021). In tomato plants, the essential mineral uptake in soil or hydroponic cultivation can be significantly affected by saline stress (Sánchez et al., 2012; Nebauer et al., 2013; Assimakopoulou et al., 2015; Javeed et al., 2021). The results of studies presented in Table 3 show that high salt levels in the growing culture can cause a lower uptake of calcium, potassium and sometimes of magnesium ions (Sánchez et al., 2012; Nebauer et al., 2013; Assimakopoulou et al., 2015; Parvin et al., 2016). Nebauer et al. (2013) reported in their study that regardless of the salt applied (NaCl, Na2SO4, MgCl2 or MgSO4), a level of 100 mM in soil reduced the Ca uptake by 48.75 to 71.26% in tomato cultivar Marmande RAF and by 12.28 to 38.60% in cultivar Daniela. Moreover, the amount of K in plants was lower by up to 68.05% at 100 mM MgSO4 in cv. Marmande RAF leaves and by up to 42.67% at 100 mM MgCl2 or 100 mM MgSO4 in cv. Daniela leaves. Decreases in the content of aforementioned minerals were also reported by Manan et al. (2016); Gharbi et al. (2017a); Rodríguez-Ortega et al. (2019) or Borbély et al. (2020). Therefore, it can be stated that salinity limits the assimilation of essential minerals in the tomato plant tissue and the physiological processes are adversely affected by these deficiencies. However, there are studies that showed that potassium, calcium and magnesium content in tomato leaves increased under salt stress (Costan et al., 2020; Javeed et al., 2021). For example, the content of calcium increased from 6.66 mg g-1 to 11.03 mg g-1 and of potassium from 36.68 mg g-1 to 71.51 mg g-1 in the fresh leaves of cultivar Rio Grande, grown in hydroponics with nutrients solution and seawater (5%, 10 % and 20%), and an EC of the growing media between 0.41 and 8.14 dS m-1 (Javeed et al., 2021). The high content of calcium and magnesium ions in tomato leaves under saline stress could be due to the higher uptake affinity for these ions rather than for Na+ or Cl- (Al-Ghumaiz et al., 2017). According to Al-Ghumaiz et al. (2017), the tolerant plants under salinity stress can exclude the Na+ ions from their shoots or blades while maintaining high levels of K+. Besides affecting the morphological and physiological status, saline stress can also influence the biochemical reactions of plants. Many studies have shown that high salt concentrations cause biochemical imbalances resulting in low plant productivity (Kusvuran et al., 2016). Tomato plants, though considered moderately sensitive to saline stress, show many changes at the biochemical level such as increases or decreases in the accumulation of hormones, reactive oxygen species (ROS) or antioxidants. These changes have been mainly recorded when NaCl has been used as a salt stressor, in concentrations varying between 25 and 600 mM ( Table 4 ). In general, the plants respond to the salinity stress in two phases: in the first, which lasts for days or weeks, the effect of osmotic stress is predominant; in the second, of weeks to months duration, the ionic toxicity effect of leaf salt accumulation affects plant growth. In the first phase, the phytohormones play an important role in regulating plant growth. For instance, abscisic acid (ABA) under saline conditions can accumulate in tomato leaves and/or roots, as a response to the low soil water potential, causing stomatal closure, thus affecting the photosynthesis or enhancing the root growth (Babu et al., 2012; Lovelli et al., 2012; Gharbi et al., 2017a; de la Torre-González et al., 2017b). Indole acetic acid (IAA) is another hormone that is usually highly synthesized under saline stress, alleviating the negative effects of osmotic and oxidative stress, being involved in all aspects of the plant, from germination to vegetative growth and flowering. The accumulation of IAA was recorded in tomato leaves exposed to salt concentrations varying from 25 mM NaCl to 100 mM NaCl (Babu et al., 2012; de la Torre-González et al., 2017b). However, decreases or no change in the total auxins were found by Gharbi et al. (2017a), in S. chilense and cultivar Ailsa Craig at 125 mM NaCl or by de la Torre-González et al. (2017b) in cultivar Marmande at 100 mM NaCl. Other phytohormones studied in relation to saline stress in tomato are salicylic acid, polyamines (Put, Spd and Spm), ethylene, benzoic acid, total jasmonates, total gibberellins, cytokinins or aminocyclopropane-1-carboxylic acid (ACC, the ethylene precursor), whose content has shown very changeable responses to salinity. The content of phytohormones has been found highly dependent on the cultivar, salt concentration or plant part. For instance, the bioactive gibberellin GA4 accumulated in the cultivar Grand Brix, but not in Marmande; the total jasmonates increased in the leaves of cultivar Ailsa Craig, but remained unchanged in the roots ( Table 4 ) (de la Torre-González et al., 2017b; Gharbi et al., 2017b, 2017a). Under salinity stress, but not only, plants increased the content of ROS, causing oxidative damages. Regarding tomato, the studies have mainly focused on the activity of malondialdehyde (MDA, a lipid peroxidation marker), carbonyl groups, H2O2, or lipoxygenase (LOX). Their accumulation can lead to the inhibition of plant growth and development, and plant death. Increases in ROS content in tomato plants were reported at low levels of salinity (25 mM NaCl), in cultivar Ciettaicale, for hydrogen peroxide, but also at high levels of salinity (450 mM NaCl) in the variety cerasiforme for MDA (Al Hassan et al., 2015; Moles et al., 2019). The duration of exposure to salinity is an important factor in ROS accumulation, as suggested by Al Hassan et al. (2015), who recorded a significant increase in MDA content 33 days after starting the treatment but not after 25 days. Cultivar also plays a key role: the exposure of tomato cultivar Micro-Tom to NaCl (120 mM) or of Marmande and Grand Brix (100 mM NaCl) led to an increase in MDA and carbonyl groups or H2O2 and LOX contents, while at 40, 80 and 160 mM NaCl the MDA content in S. chilense Dun. and variety cerasiforme was not affected (Manai et al., 2014; Martínez et al., 2014; de la Torre-González et al., 2017b). In order to prevent the negative effects of ROS, plants produce enzymatic and non-enzymatic compounds such as: ascorbic acid, phenols, ascorbate peroxidase (APX), superoxide dismutase (SOD), glutathione reductase (GR), catalase (CAT), peroxidase (POD), glutathione peroxidase (GPx), plasma glutathione peroxidase (GSHPx) etc., which play a key role in cell protection against the oxidative stress (Kusvuran et al., 2016). In tomato subjected to saline stress, the antioxidant production can vary depending on cultivar, salt concentration, plant age or part. For instance, in a study done on cerasiforme variety subjected to 40, 80 and 160 mM NaCl, the enzymatic activity of SOD increased at 40 and 80 mM NaCl, then decreased at 160 mM, while the APX activity decreased regardless of the salt concentration (Martinez et al., 2012). In another study, where tomato cultivar Micro-Tom was subjected to 120 mM NaCl, the activity/content of ascorbate, glutathione (GSH), NADP-isocitrate dehydrogenase (NADP-ICDH), glucose-6-phosphate dehydrogenase (G6PDH), 6-phosphogluconate dehydrogenase (6PGDH), S-nitrosoglutathione (GSNO) reductase and CAT decreased, while the activity of GR and GPx increased, suggesting a negative impact of the salinity stress on the redox status and NO metabolism (Manai et al., 2014). Interesting findings were made by Srineing et al. (2015), in a study in vitro on the cultivar Puangphaka treated with NaCl at concentrations ranging between 5 – 100 mM. The authors analyzed the activity of SOD, CAT and GPx (roots and stem) at different time intervals: 7, 14, 21 days after incubation. The results showed differences in enzyme activity depending on plant age and part (roots or stems) ( Table 4 ). The influence of the salt and the exposure time on total carotenoids, total phenolics, total flavonoids and TSS was also analyzed by Al Hassan et al. (2015) in cerasiforme variety exposed to 150, 300 and 450 mM NaCl. The results showed that regardless of the time of treatment (25 or 33 days) the content of total carotenoids significantly decreased at all the concentrations, except for 150 and 300 mM, 25 days after treatment, while the content of the total phenolics and flavonoids significantly increased at all the salt concentrations, except for 150 mM, 25 days after treatment, in the case of phenolics. In another study, where the tomato plants of cultivar Microtom were exposed shorter to NaCl stress (14 days) the phenols increased to 150 mM NaCl (Bacha et al., 2017). Changes in the antioxidant activity were also reported by Martínez et al. (2014); Manan et al. (2016) and de la Torre-González et al. (2017b), included in Table 4 . Salinity stress is known to produce a C shortage in plants, stimulating the synthesis of C-rich compounds such as trehalose, mannitol, sorbitol or proline, involved in the osmotic adjustment mechanism to stressful conditions. Moreover, the N status is affected because of the influence on and uptake. Hossain et al. (2012) and Manai et al. (2014) reported that the activity of enzymes involved in the N absorption was affected by saline stress: a decrease was recorded for nitrate and nitrite reductase or nitric oxide (NO), suggesting a negative impact on the NO metabolism under salinity stress, while an increase was recorded for protease, glutamate synthase and Fd-dependent glutamate synthase, NADP-dependent isocitrate dehydrogenase, and glutamate dehydrogenase. No change was observed for NADH-dependent glutamate synthase. Most of the studies carried out on different tomato cultivars, varieties or genotypes (e.g. BINATomato-5, PKM1, Cerasiforme, Rio grande, Savera, Ciettaicale or San Marzano) reported increases in the proline, glycine betaine, serine, alanine, or total soluble sugars contents under different NaCl concentrations, as a result of osmotic adjustments (Babu et al., 2012; Hossain et al., 2012; Al Hassan et al., 2015; Manan et al., 2016; Moles et al., 2016). Increases in the proline content in the roots, stems and leaves of tomato plants, but not of the total soluble sugars, were also recorded in the case of combined salt stress, consisting of NaCl:Na2SO4 in a molar ratio of 9:1 (Wang et al., 2015). By contrast, a decrease in the proline content was reported by Abdel-Farid et al. (2020), in a pot experiment, where tomato plants were treated with 25, 50, 100, 200 mM NaCl. The decrease was explained by taking into consideration the replacement of the proline by another osmoprotectant under saline conditions. The salinity stress can also affect the protein content of plants. A study performed on two tomato cultivars (Castle rock and Edkawi) with different tolerance to salinity showed an accumulation of proteins (the large chloroplast subunit (RbcL), structural maintenance of chromosomes (SMC) protein, a protein from the plasma membrane, and transcription factors) at 50 mM NaCl in both cultivars, a gradual decrease at higher salt concentration for Castle rock and an approximately constant accumulation for Edkawi at 100, 150, 200 mM NaCl, followed by a decrease to 300 mM NaCl. According to the authors, the accumulation of RbcL at 50 mM NaCl in the cultivar Castle rock might be the result of Rubisco degradation under saline stress, as this cultivar is more sensitive to salinity. The better tolerance to salt stress of cultivar Edkawi is demonstrated by better retention of Rubisco content, chromosome segregation and up-regulation of ion pump proteins (Khalifa, 2012). In another study carried out on the cultivar BINATomato-5 the soluble protein content decreased by 25.64% at 60 mM NaCl and by 42.75% at 120 mM NaCl (Hossain et al., 2012). A decrease in protein content was also observed by Manaa et al. (2013a) in the leaves of two tomato cultivars (Roma – salt tolerant, SuperMarmande – salt sensitive), at 100 and 200 mM NaCl. The same author conducted leaf proteomic analysis, identifying 26 proteins involved in energy and carbon metabolism, photosynthesis, ROS scavenging and detoxification, stress defense and heat shock proteins, amino acid metabolism and electron transport. The majority of the proteins identified were upregulated as a consequence of saline stress. Variations in protein abundance were also reported in the fruits of two tomato cultivars (Cervil and Levovil), which were correlated to the salt treatments and the fruit ripening stage. Most of the proteins identified were associated with carbon and energy metabolism, salt stress, oxidative stress, and the ripening process (Manaa et al., 2013b). In general, the content of soluble proteins represents an indicator of plant physiological status under stress, having an important role in osmotic adjustments, and providing storage for different forms of nitrogen. Depending on the cultivar, the soluble proteins can decrease as a result of protein synthesis inhibition and/or protein hydrolysis or can increase through the production of new stress-related proteins (Ahmad et al., 2016). Salinity stress can also have no impact on the protein content, as recorded by Martínez et al. (2014), in a study done on S. chilense Dun. and variety cerasiforme at 40, 80, or 160 mM NaCl. Salinity can also affect the carboxylate metabolism and organic acid production, depending on the cultivar as demonstrated by (de la Torre-González et al., 2017a) ( Table 4 ). High activity of the enzymes involved in the carboxylate metabolism enhances tomato resistance to salinity due to the activation of osmotic adjustments mechanism of response which helps the plant to adapt to stressful conditions. Also, high organic acid concentrations are necessary for enhancing the plant’s tolerance to salinity, taking into account their important role in different biochemical pathways, such as energy production or amino-acid biosynthesis. In addition, Moles et al. (2019) showed that NaCl can influence the activity of the cell wall enzymes (endo-β-mannanase, β-mannosidase, α-galactosidase) involved in seed germination. Under 25 mM NaCl, the concentration of endo-β-mannanase and β-mannosidase increased in cultivar Ciettaicale, and decreased in cultivar San Marzano affecting the seed germination. Reyes-Pérez et al. (2019) stated that acid and alkali phosphatase, trypsin, lipase, β-galactosidase, and esterase can be used as biomarkers for NaCl-stress tolerance in tomato. In general, salinity stress, like other abiotic stresses, determines changes in the gene expression of plants. The knowledge of the gene expression as a result of salt stress is still limited, but mostly refers to changes in transcription factors (Devkar et al., 2020). Tomato research regarding the effect of salinity on gene expression has been carried out on different cultivars and focused mostly on the effect of NaCl applied at the concentration range between 50 and 500 mM ( Table 5 ). The results suggested changes in the expressions of genes involved in cell wall construction, biosynthesis of volatiles and secondary metabolites, protein synthesis, transport activity, etc. for the plants subjected to salinity stress. In a study with the cultivar Micro-Tom subjected to NaCl at 100, 200 and 400 mM, the genes responsible for the phenylpropanoid pathway (4CL3 = 4-coumarate-CoA ligase, PAL6 = phenylalanine ammonia lyase, CHI1 and CHI2 = chalcone isomerase, HQT = hydroxycinnamoyl-CoA quinate transferase), xyloglucan endo-transglucosylase or hydrolase (XTH4, XTH20, XTH16) activities, or enzymatic response to reactive oxygen species (ROS, SOD genes), were up-regulated in the top younger leaflets as compared to the older ones situated at the bottom of tomato plants, indicating an increase in the lignification process and flavonoid synthesis, a strengthening in the mechanical cell wall properties and an intensification in SOD production, an enzyme involved in the response to ROS as a result of the salinity stress. Furthermore, in the top leaflets of stressed plants, the expression of expansins (EXPA4, EXPA5, EXPA18), genes involved in cell wall reshaping, fasciclin-like arabinogalactan proteins (FLA 2, FLA10, FLA11) involved in keeping the plasma membrane and cell wall in close contact, and volatile organic compounds’ synthesis (TPS, FPS) were down-regulated, suggesting an increase in the salt sensitivity, as plant growth was stopped, as well as the production of terpene synthase (TPS) or farnesyl pyrophosphate synthase (FPS). Changes in the gene expression were also recorded in the bottom leaflets, with the LEA and LOX genes up-regulated, indicating an accumulation in late embryogenesis abundant (LEA) proteins responsible for membrane maintenance and ion-sequestering properties, as well as in lipoxygenases, markers for cell membrane damage. Other up-regulated genes in the salt-stressed tomato plants were those coding for heat shock transcription factor HSF30 (Hoffmann et al., 2021). In another experiment, in which tomato cultivar Yanfen 210 was treated with seawater at different concentrations (10%, 20% and 30%), a significant differential change was recorded in the expression of 509 genes, 40.67% of which were up-regulated, while 59.33% down-regulated. The highlighted genes were responsible for biological processes (i.e. metabolic process, cellular process or single organism process), cellular components (i.e. cell, cell part, membrane, organelle, etc.) or molecular functions (i.e. catalytic activity, binding, transporter activity, etc.). Notably, the SlGA20OX1gene expression was down-regulated, thus affecting the production of gibberellin and plant growth. Down-regulations were also observed for SlMYB13, part of MYB family transcription factors involved in biological and developmental processes, cell morphology, biological stress response, primary and secondary metabolism adjustment, SlCI-2 gene involved in the inhibition of proteinase activity or SlHYD gene responsible for the activity of cell membrane. On the other hand, over-expressions were observed for SlPCC27-04 gene coding for plant desiccation-related proteins, SlMYB48 gene responsible for ABA signaling, SlAPRR5 gene known to control the time of the flowering process, the circadian rhythms or the photomorphogenesis, or SlMFS gene involved in the membrane activity (Mu et al., 2021). Zhang et al. (2018), investigating the effect of NaCl on the volatile compound emission of tomato plants, found the expression of 18 genes down-regulated, thus affecting the biosynthesis of isopentenyl diphosphate isomerase, geranyl pyrophosphate synthase, sesquiterpene synthase, β-phellandrene synthase, terpene synthase 1, 28, 38 or farnesyl pyrophosphate synthase 1. Out of a total 7210 differentially expressed after NaCl exposure, of which 1208 were over-expressed and 6200 were down-expressed, other 3454 genes were related to plant-pathogen interaction, RNA-transport or hormone signal transduction. Changes in the expression of hormone-related genes were also recorded by Pye et al. (2018) in the roots of the cultivar New Yorker. The treatment with NaCl and CaCl2 led to an increased expression of two ABA-related genes: NCED and TAS14. An interesting finding was made by Coyne et al. (2019), who observed a correlation between the expression of some genes and the circadian rhythms. The gene coding for sodium or hydrogen antiporter and an enzyme for proline synthesis, SlSOS2 and P5CS, were expressed only in the morning, while SlDREB2 encoding a transcription factor responsible for the response of tomatoes to salinity was expressed only in the evening. Due to this behavior, tomato, but also other species, might be able to keep the balance of the endogenous systems to circadian rhythms. Almeida et al. (2014b) also reported an overexpression of P5CS gene which led to an accumulation of proline and Na+ in the leaves of five weeks old tomato plants, but not in the roots. The same authors observed a higher expression of NHX1 and NHX3 genes correlated with a lower Na+ accumulation in leaves, and a higher Na+ accumulation in roots; the expression of HKT1;2 gene in the roots was positively correlated with the amount of Na+ in leaves and stems, but not in the roots, where other genes were responsible for the accumulation of Na+ (HKT1;1). Changes in the expression of HKT1;2 gene due to salinity stress was also recorded in the cultivar Arbasson where an increase in the gene expression in stems and roots was recorded along with increased salinity stress. In leaves, the accumulation of Na+ was correlated with a low expression of HKT1;2 genes (Almeida et al., 2014a). The role of HKT1;1 and HKT1;2 in the ion homeostasis in tomato leaves and stems was also confirmed by Asins et al. (2013). Jaime-Pérez et al. (2017) demonstrated in transgenic tomato plants the importance of HKT1;2 gene in Na+ homeostasis and salinity tolerance. The same genes (HKT1;1 and HKT1;2) along with LeNHX1, LeNHX3, LeNHX4, SIWRKY8, SIWRKY31, SIWRKY39 (WRKY gene family) and ERF transcription factors were reported to be highly expressed in a study carried out by Gharsallah et al. (2016) on three tomato genotypes. The salinity stress can also affect the expression of genes related to nitrogen uptake and transport. In this respect, Abouelsaad et al. (2016) demonstrated a decrease in the expression of mRNA of nitrate transporters NRT1.1 and NRT1.2 in both cultivars Manitoba and S. pennellii. The same authors observed a higher expression of remarkable affinity ammonium transporters (AMT1.1 and AMT1.2) in Manitoba and a down-regulation of the Gs1 gene (cytosolic glutamine synthetase) in S. pennellii. Other genes whose expression was changed by salt stress are: SlERF5 gene, part of ERF family gene, which has an important role in the ethylene and abscisic acid signaling pathway (Pan et al., 2012); SlGSTU23, SlGSTU26, SlGSTL3, SlGSTT2, SlDHAR5, SlGSTZ2 involved in primary metabolism, regulation of plant growth and development, anthocyanin’s absorption, detoxification of toxic compounds (xenobiotic, lipid peroxides), etc. (Csiszár et al., 2014); LeHAK5 gene whose expression was significantly decreased when the Na+ concentration was increased (Bacha et al., 2015); SlARF1, SlARF4, SlARF8A, SlARF19 and SlARF24 which were upregulated in response to salinity stress (Bouzroud et al., 2018). The gene RBCL (large subunit RUBISCO) whose level of expression was not different as a result of salinity stress, in the presence or absence of ABA synthesis, but whose protein it encodes, showed a significant decrease (Poór et al., 2019). High levels of sodium chloride in soil or in nutritional medium highly affect plant physiological and biochemical processes as well as gene expression, with effects on plant morphology, but also on yield and fruit quality. Most of the research carried out with tomato suggested a positive or no impact of salinity on fruit quality ( Table 6 ). Therefore, increases are reported in the lycopene content (De Pascale et al., 2012; Islam et al., 2018; Sellitto et al., 2019), sugar (De Pascale et al., 2012; Islam et al., 2018; Marsic et al., 2018; Botella et al., 2021), total soluble solids (TSS), titratable acidity (TA), organic acids (OA), fruit firmness (Cantore et al., 2012; De Pascale et al., 2012; Martínez et al., 2012; Liu et al., 2014; Zhai et al., 2015; Pengfei et al., 2017; Islam et al., 2018; Rodríguez-Ortega et al., 2019; Maeda et al., 2020; Botella et al., 2021) or cuticle thickness (Agius et al., 2022). According to Agius et al. (2022) a salinity level of up to 5 dS m−1 in nutrient solutions may enhance the fruit quality. In a study conducted by Cantore et al. (2012) on two tomato cultivars, salinity increased the content of TSS and had no significant effect on the ascorbic acid content or the TA. Martínez et al. (2012) showed no change in the TSS and TA content at 40 or 80 mM NaCl. At a salinity level of 6.8 dS m-1 in soil, the TSS and TA contents in fruits of Buran F1 grafted on Maxifort are higher compared to the values determined in fruits grown in soil with the EC of 1.7 dS m-1 (Pašalić et al., 2016). Zhang et al. (2016) reported that the salt enrichment in nutrient solution also leads to an increase in the acidity of the tomato fruit. Islam et al. (2018); Costan et al. (2020) and De Pascale et al. (2012) found in their studies that the total soluble solids (Brix index) and citric acid content increased in tomato fruits with salinity increase. In the fruits of tomato cultivar Unicorn the total soluble solids (Brix index) and citric acid content increased by 22% and 20% per dS m-1 (Islam et al., 2018). Improvement of fruit quality as a result of salinity was also reported by: Ahmed et al. (2017); Pengfei et al. (2017) in cultivar Pepe; Rodríguez-Ortega et al. (2019) in tomato cultivar Optima; Maeda et al. (2020) in the two tomato cultivars CF Momotaro York and Endeavour. The main factors influencing the fruit quality under salinity stress are harvest day, salinity distribution in the soil or the growth stage (Iglesias et al., 2015; Chen et al., 2016; Zhang et al., 2017). In a study conducted with 4 tomato varieties (Raf, Delizia, Conquista, Tigre) subjected to salinity stress, the content of TSS was significantly decreased when the fruits were harvested 136 days after transplant for cultivar Raf and 90 and 104 days for Delizia; a significant increase of TSS was recorded for Conquista 150 days after transplant and Tigre 136 days (Iglesias et al., 2015). By testing the effect of the uneven vertical distribution of soil salinity on the tomato quality of cultivar Yazhoufenwang, Chen et al. (2016) showed that the content of TSS, OA and vitamin C increased with the soil salt concentration in the upper layer. Zhang et al. (2017), demonstrated that the salinity stress applied from flowering until the fruiting stage improves the TSS content. However, negative effects of high salt levels can be found in the mineral content of tomato fruits. Studies conducted by De Pascale et al. (2012); Hernández-Hernández et al. (2018); Islam et al. (2018); Costan et al. (2020) showed that under salinity stress, the mineral content in tomato fruits ( Table 7 ), especially of calcium and potassium, can decrease. Regarding tomato yield under saline stress, the Division of Agriculture and Natural Resources of University of California specifies that a soil salinity of 7.6 dS m-1 may reduce both tomato plant emergence and crop yield by 50% (Division of Agriculture and Natural Resources, 2022), but these effects are closely related to the tomato cultivar. The study performed by De Pascale et al. (2012) showed that at 4.4 dS m-1 the mean fruit weight, the number of fruits per plant and the total yield of tomato decreased compared to the control (0.5 dS m-1) by 19.68%, 20.74%, and 23.07%, respectively. According to Islam et al. (2018) an increase in soil salinity from 2.5 at 7.5 dS m-1 causes a 14.81% reduction in the mean fruit weight of the cultivar Unicorn. In addition, Liu et al. (2014) reported that the yield of three cherry tomato cultivars grown inpeat moss, perlite and sand mix (2:1:1) was affected differently by the same levels of salinity. At 150 mM NaCl the mean fruit weight of Tainan ASVEG No. 19, Hualien ASVEG No. 21 and Taiwan Seed ASVEG No. 22 was reduced by 26.03%, 47.13%, and 55.56% respectively, compared to the control, and the total yield decreased from 243.9, 78.7 and 155.5 g/plant to 48.8, 6.9, and 19.3 g/plant, respectively. Costan et al. (2020) reported that, although the number of fruits per plant increased with the salinity rises in the hydroponic system (from 0 at 50 mM), the yield of the tomato cultivar Belladonna was reduced by more than 36%. Noshadi et al. (2013) found the highest yield (47.15 t·ha-1) was recorded when the irrigation water EC was of 2 dS m-1. At 0.6 dS m-1, 38.02 t·ha-1 were harvested and at 4 dS m-1 about 31.57 t·ha-1, whereas the lowest yield was at 8 dS m-1 EC (21.20 t·ha-1). Therefore, according to the results of the latter study, a slightly saline soil or hydroponic cultivation can enhance tomato yield. The negative effects of salinity on tomato plants can be alleviated by using different strategies like plant priming or genetic modification. Plant priming represents a promising method to reduce the time required for a plant exposed to abiotic stress to respond efficiently to the stressor and, thereby, to increase the tolerance to stress conditions (Aranega-Bou et al., 2014). Effective priming agents against salt stress in tomato, which have been studied over years are elements (Fe, Si, K, N), plant growth regulators (ACC, IAA, SA, melatonin), reactive species (S-nitrosoglutathione, sodium hydrosulfide, sodium nitroprusside), vitamins (ascorbic acid - AsA), aminoacids, natural extracts (seaweed), polymers (chitosan), osmoprotectants (glycine betaine, proline), polyamines (spermidine) or plant growth promoting microorganisms (bacteria, fungi or arbuscular mycorrhizal fungi) (Choudhary et al., 2022; Gedeon et al., 2022; Zulfiqar et al., 2022). The results showed in most of the cases an enhancement of the tolerance of plants to various concentrations of salt, by decreasing the osmotic stress, enhancing the activity of the antioxidant system, increasing the growth and yield or by improving the fruit quality. For instance, the application of Fe increased the ascorbic acid content in the fruits of tomato along with the increment in salinity level; the Si addition stimulated an early accumulation of TSS in the fruits of tomatoes, but did not influence the quality of the taste; in another study, the presence of Si decreased the SOD activity, suggesting a reduction in ROS production; also, the treatment with Si increased the β-carotene and vitamin C content; the addition of 5 mM K+ regulated the ascorbate–glutathione cycle, the activity of antioxidant enzymes, the carbohydrate metabolism and increased the proline content; nitrogen applied at different concentrations (25, 75, 150 kg N ha−1) had a positive impact on the proline content and on the activity of P5CS enzyme, also affected the activity of various enzymes: proline dehydrogenase, nitrate reductase, nitrite reductase, glutamine synthetase and glutamate synthase, glutamate dehydrogenase under NaCl stress (Tantawy et al., 2013; Iglesias et al., 2015; Muneer and Jeong, 2015; Singh et al., 2016; Costan et al., 2020; Khan et al., 2021). The application of plant growth regulators such as ACC decreased the osmotic stress in ‘Ailsa Craig’ tomato cultivar; spraying the tomato plants with IAA (100 and 200 ppm) increased the TSS content of fruit juice and the chlorophyll content of the leaves; the exogenous application of salicylic acid decreased the ethylene synthesis and increased the polyamine endogenous concentration; in another study, salicylic acid applied foliar increased the TSS and the vitamin C content; the treatment of the seeds with salicylic acid (1 mM) and H2O2 (50 mM) increased the TSS, proteins, POD, CAT, SOD and MDA content; the treatment with 20 and 50 µM melatonin improved the activity of the antioxidant system, the proline and carbohydrate metabolism, also the ascorbate/reduced glutathione cycle in ‘Five Start’ tomato cultivar; in another studies, melatonin improved the root architecture, reduced the production of reactive oxygen species, enhanced the activity of enzymatic antioxidants and the photosynthesis (Gharbi et al., 2016; Gaba et al., 2018; Siddiqui et al., 2019; Alam et al., 2020; Altaf et al., 2020, 2021; 2022b; Borbély et al., 2020; Naeem et al., 2020; Hu et al., 2021; Ali et al., 2021b). The application of S-nitrosoglutathione and NaHS promoted the accumulation of NO and H2S, alleviating the deleterious effects of oxidative stress; the use of sodium nitroprusside increased the content of non-enzymatic and enzymatic antioxidants, up-regulated the NO level in leaves, enhanced the activity of Calvin cycle, overcame the stomatal limitations and protected the photosystem II from damages (da-Silva et al., 2018; Taheri et al., 2020; Li et al., 2022). Alves et al. (2021) by soaking the tomato ‘Micro-Tom’ seeds for one hour in 100 mM AsA, observed that the tolerance of plants to salt stress was enhanced by modulating the antioxidant mechanisms. The content of CAT, APX, POX, GPX, GR, GSH, SOD, chlorophyll, and carotenoids in the leaves of primed plants was higher than in the control. Chen et al. (2021) by spraying 0.5 mmol/L AsA solution on the leaves of cv. ‘Ligeer87-5’ exposed at 100 mmol/L NaCl reported an attenuation of the photoinhibition and oxidative stress damage in chloroplasts, dissipation of excitation energy in PSII antennae, stimulation of chlorophyll synthesis and reduction of damaging effects on photosynthesis in tomato leaves. The foliar application of an aminoacid (Botamisol as free L-amino acids) at different concentrations (0, 2, 4 g·L-1) increased the proline level in the leaves of tomato plants exposed to salinity (8 and 10 dS·m-1) (Jannesari et al., 2016). The application of a seaweed extract (100 mL of P. gymnospora 0.2% w/v) improved the growth, yield and quality of ‘Rio Fuego’ tomato cultivar (Hernández-Herrera et al., 2022). The use of chitosan solution at different concentrations (0.03% and 0.05% or 50, 100 and 150 mg/L) for spraying the tomato leaves, enhanced the salt tolerance of tomato at 100 mM NaCl applied as a root drench, promoted the growth and development of plants and increased the chlorophyll contents (Ullah et al., 2020; Özkurt and Bektaş, 2022). The exogenous application of spermidine (Spd) on tomato cv. ‘Ailsa Craig’ seedlings grown under salt stress resulted in higher photosynthesis and biomass, better ionic and osmotic homeostasis, and enhanced ROS scavenging capacity (Raziq et al., 2022). Siddiqui et al. (2017) found that the chlorophyll a and b contents, proline, activity of CAT, SOD, POD, GR and APX were increased and H2O2 and MDA production in tomato var. Five Star was reduced as a result of exogenous spermidine application on seedlings. The foliar application of 10 and 20 mg/L proline during the flowering stage of cultivars ‘Rio Grande’ and ‘Heinz-227’ led to an increase in the dry mass of leaves, stems and roots, improved various chlorophyll fluorescence parameters, increased the potassium and phosphorous content and reduced the accumulation of Na+ in different organs, compared with control (Kahlaoui et al., 2014). The effects of the exogenous application of glycine betaine (GB) on different tomato cultivars have been assessed in a few studies and both positive and negative correlations were found between GB exogenous application and salt tolerance in tomato. Chen et al. (2009) found that the exogenous use of 5 mM GB in half-strength Hoagland could alleviate the salt stress effects in tomato cv. ‘F144’ and cv. ‘Patio’ through changing the expression abundance of some proteins. Sajyan et al. (2019) irrigated the tomato ‘Sila’ plants with saline water (with EC between 2 and 10 dS m-1) and exogenous GB in various doses (4.5, 6 and 7.5 g/L) and observed a positive effects on leaf number, stem diameter, number of flowers, number of fruits, no evident effects on the number of clusters, fruit set, the weight of individual fruit, yield and fruit diameter were observed and a reduction in the fruit ripening process at 7.5 g/L GB. Plant growth-promoting rhizosphere bacteria (PGPB) can alleviate the effects induced by salt stress by production of phytohormone (e.g. auxin, cytokinin, and abscisic acid), ACC-deaminase, ammonia, IAA, extracellular polymeric substance (EPS), induction of synthesis of plant osmolytes and antioxidant activity, increasing the essential nutrient uptake or/and by reducing ethylene production (Kumar et al., 2020). Sphingobacterium BHU-AV3 (Vaishnav et al., 2020), Bacillus megaterium strain A12 (Akram et al., 2019), Enterobacter 64S1 and Pseudomonas 42P4 (Pérez-Rodriguez et al., 2022), Bacillus aryabhattai H19-1 and Bacillus mesonae H20-5 (Yoo et al., 2019) are some of the PGPB that have been proved to increase tomato tolerance to salt stress. For example, inoculation of tomato cv. ‘Kashi amrit’ plants with Sphingobacterium BHU-AV3 exhibited a less senescence in plants exposed to 200 mM NaCl, being determined that the proline content was increased, ion balance was maintained and the ROS was lower compared to the non-inoculated plants. In BHU-AV3-inoculated plant leaves superoxide content, cell death and lipid peroxidation were significantly reduced (Vaishnav et al., 2020). Enterobacter 64S1 and Pseudomonas 42P4 under salt stress reduced electrolyte leakage and lipid peroxidation and increased chlorophyll quantum efficiency (Fv/Fm), proline and antioxidant nonenzymatic compounds (carotenes and total phenolic compounds) contents in tomato leaves (Pérez-Rodriguez et al., 2022). A combination of arbuscular mycorrhizal fungi (Claroideoglomus etunicatum, Funneliformis mosseae, Glomus aggregatum, Rhizophagus intraradices), bacteria and fungi (Trichoderma, Streptomyces, Bacillus, Pseudomonas) improved the tomato fruit quality and the antioxidant content of ‘Pixel F1’ tomato cultivar exposed to soils electrical conductivity of 1.5, 3.0, 4.5, and 6.0 (Sellitto et al., 2019). Some researchers have focused not only on assessing the individual effects of a potential priming agent against salt stress in tomato plants, but also their combined effect. For example, Attia et al. (2021) studied the effects of foliar application of chitosan dissolved in acetic acid (Ch ACE), ascorbic acid (Ch ASC), citric acid (Ch CIT) and malic acid chitosan (Ch MAL) on tomato cultivar 023 irrigated with saline water (100 mM NaCl). These treatments alleviated the negative effects of salinity on tomato plants by increasing the photosynthetic pigments, osmoprotective compounds, and potassium content and lowering MDA, H2O2 and Na+ levels in leaves. Chanratana et al. (2019) used as a bioinoculant chitosan-immobilized aggregated Methylobacterium oryzae CBMB20 to improve the salt tolerance of cv. ‘Yeoreum Mujeok Heukchima’ and the results showed that plant dry weight, nutrient uptake, photosynthetic efficiency, and the accumulation of proline have been enhanced. Furthermore, the oxidative stress exerted by salt stress was alleviated and the electrolyte leakage and the excess Na+ influx into the plant cell were reduced. Tomato genetic modification techniques have already proven their efficiency and accuracy in protecting plants against salinity stress by improving their genome. Gene transformation, gene editing, quantitative trait loci (QTLs) analysis, gene-pyramiding, and genetic engineering (overexpression) are some examples of molecular genetic tools that have helped in the development of salt-tolerant tomato plants. Gene transformation has mainly focused on transferring genes of various origins, which can be good candidates to increase the tolerance to salinity stress, into tomato plants. Salt tolerant tomato plants were successfully obtained by Gilbert et al. by transferring the gene HAL1 from Saccharomyces cerevisiae, involved in Na+ transport and K+ regulation, which improved the in vivo and in vitro salt tolerance of transgenic tomato plants, by promoting the retention of K+ and the growth of the plants (Gisbert et al., 2000); by Goel et al., who demonstrated that by transforming the tomato cultivar ‘Pusa Ruby’ with the bacterial codA gene from Arthrobacter globiformis encoding for choline oxidase, the production of glycine betaine was induced, the content of relative water, chlorophyll and proline increased, also the overall tolerance of the plants under saline stress was improved (Goel et al., 2011); by Jia et al., who transferred the BADH gene from Atriplex hortensis in ‘Bailichun’ tomato cultivar, obtaining a normal growth and development of the plants treated with 120 mM NaCl (Jia et al., 2002); by Li et al., who isolated the SpPKE1 a lysine-, glutamic- and proline-rich type gene from the abiotic resistant Solanum pennellii LA0716 and transferred it to S. lycopersicum cv. M82 or by transferring the Osmotin gene from tobacco into tomato plants, an increased tolerance to salt stress was obtained, highlighted by better cell signaling, ROS scavenging, the content of carbohydrates, amino acids, polyols and performance of the antioxidant and photosynthetic systems (Goel et al., 2011; Li et al., 2019a; Rao et al., 2020). The only genetic editing technique that has been reported to be used in improving the tomato tolerance to salinity is clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9 (CRISPR-associated nuclease 9) a modern, easy and very effective genome editing tool (Salava et al., 2021; Altaf et al., 2022a). However, the researches on increasing tomato tolerance to salt stress by using CRISPR/Cas9 are still limited. So far, this tool was used to precisely edit the hybrid proline-rich proteins domain (HyPRP1) involved in different biotic and abiotic responses. The deletion of the SlHyPRP1 negative-response domain led to salt tolerance as high as 150 mM NaCl, improving the germination and the growth of the plants (Tran et al., 2021). The same results were obtained earlier by Li et al., who also observed that by silencing the negative regulator HyPRP1 the expression of the genes responsible for the production of SOD and CAT was enhanced (Li et al., 2016). In addition, CRISPR/Cas9 technology was used to knock out the SlABIG1 gene in tomato exposed to salinity, resulting plants with improved chlorophyll and proline content, photosynthetic system, root dry weight and decreased concentrations of ROS, MDA and Na+ (Ding et al., 2022). By using the same tool, Wang et al., demonstrated the importance of the plasma membrane Na+ /H+ antiporter SlSOS1 in the salt tolerance of tomato, by creating two mutant alleles (Slsos1-1 and Slsos1-2) which showed a significant increase in the Na+/K+ ratio and the salt sensitivity, as compared with the wild type (Wang et al., 2021). Bouzroud et al., by generating tomato SlARF4-crispr (arf4-cr) plants showed the importance of Auxin Response Factor 4 (ARF4) in the tolerance of tomato plants to salinity (Bouzroud et al., 2020). Regarding the other two known genetic editing techniques (zinc finger nucleases - ZFNs and Transcription Activator-Like Effector Nucleases - TALENs) no reports are available on tomato tolerance (Salava et al., 2021; Altaf et al., 2022a). Due to the QTLs mapping, different loci related to the oxidative defence system, Na+/K+ homeostasis, or developmental stages were identified in playing an important role in increasing the tomato tolerance to salinity. Therefore, Frary et al., identified 125 QTLs for antioxidant compounds under saline and non-saline conditions in S. pennellii tomato introgression lines, and their parental lines, salt-resistant wild tomato (S. pennellii LA716) and the salt sensitive cultivated S. lycopersicum Mill. cv. M82 that could be beneficial in developing salt-tolerant cultivars. Under the salt stress (150 mM NaCl), the wild tomato and different introgression lines accumulated more antioxidant compounds (phenolics, flavonoids, SOD, CAT, APX) than the cultivated tomato (Frary et al., 2010). The same wild tomato ascension, the wild S. lycopersicoides LA2951 and two introgression lines derived from them were used to identify QTLs for tolerance to salinity in the seedling stage by Li et al. Four major QTLs were detected on chromosomes 6, 7 and 11 in S. pennellii IL library, while in S. lycopersicoides IL library, six major QTLs were found on chromosomes 4, 6, 9 and 12. The authors concluded the possibility to create hybrids with QTLs coming from these two ascensions (Li et al., 2011). Foolad et al., detected and validated a number of five QTLs for tomato salt tolerance during vegetative growth in a population (BC1) resulted from the crosses between the breeding line NC84173 (Lycopersicon esculentum Mill.) and L. pimpinellifolium (Jusl.) Mill. accession LA722. One minor QTLs was identified on chromosome 3 in the interval CT82–TG515, two major QTLs on chromosomes 1 and 5, and the other two on chromosomes 6 and 11 (Foolad et al., 2001). Villalta el al., found QTLs for salt tolerance during reproductive stage in two populations of F7 tomato lines (P and C) resulted from ‘cerasiforme’ variety (salt sensitive genotype), as female parent, and two lines tolerant to salt tolerant, as male parents: S. pimpinellifolium, the P population (142 lines), and S. cheesmaniae, the C population (116 lines). The authors suggested that the QTLs detected by them can be used to increase the fruit yield of tomato plants under salt stress, being good candidates for increasing the tomato tolerance to salinity. The QTLs for fruit yield were detected in chromosome 5, the specific loci being fn5.2 and tw8.1 found in C population and fn10.1 which overlaps tw10.1 and fw8.1 loci in P population. Under saline conditions the fruits set percentage per truss, fruit number per plant and the total fruit weight per plant increased (Villalta et al., 2007). Other candidates for QTL can be those associated with Na+/K+ homeostasis are the genes encoding HKT1-like transporters (SlHKT1;1 and SlHKT1;2), with tonoplast NHX Na+/H+-antiporters (SlNHX3 and SlNHX4), with the content of α-tocopherol in tomato fruits (chromosomes 6 and 9), or with tocopherol biosynthesis (chromosomes 7, 8, and 9) (Egea et al., 2022). Gene pyramiding, which consists in combining multiple traits in a single genotype, represents another method that can help to obtain tomato plants tolerant to salinity stress, but the researches are still limited. Some strategies that have been proposed refer to pyramiding the ascorbic acid (AsA) biosynthetic pathway, the ascorbate–glutathione pathway, or different QTLs. For improving the AsA content in tomato, Li et al. pyramided the biosynthetic genes involved in the D-Man/L-Gal pathway of ascorbate, resulting the pyramiding lines GDP-Mannose 3′,5′-epimerase (GME) × GDP-d-mannose pyrophosphorylase (GMP), GDP-l-Gal phosphorylase (GGP) × l-Gal-1-P phosphatase (GPP) and GME × GMP × GGP × GPP. The results showed increased concentrations of total ascorbate in leaves and fruits and improved AsA transport capacity, light response and salinity stress tolerance. In addition, the fruit weight (significantly decreased in GGP × GPP lines), fruit size (significantly decreased in GMP × GME and GGP × GPP lines), and soluble solid (significantly increased in GMP × GME and GMP × GME × GGP × GPP lines) were affected by pyramiding maybe because of the influence of different primary metabolism pathways (sugar, acid, and cell wall metabolism) as stated by the authors (Li et al., 2019b). By pyramiding the genes of ascorbate-glutathione pathway, isolated from Pennisetum glaucoma (Pg) (PgSOD, PgAPX, PgGR, PgDHAR and PgMDHAR) Raja et al., obtained tomato lines with better germination rate, survival rate, photosynthetic and antioxidant activity, reduced ROS production, and membrane disruption, under 200 mM NaCl (Raja et al., 2022). Pyramiding QTLs can be an effective method to improve the tomato salt tolerance. The pyramiding of QTLs takes place by using a marker assisted selection (MAS). Some authors proposed the use of different QTLs associated with salt tolerance during seed germination or vegetative growth in tomato (Foolad, 2004). Another way to enhance the tomato salt tolerance is to overexpress specific genes that can increase the tomato tolerance to salt stress. Some authors highlighted the importance of various genes in the salt stress in transgenic plants and, in this respect, Hu et al. (2014) demonstrated that the overexpression of LeERF1 and LeERF2 genes have a positive impact on tomato plants exposed to salinity stress. Good results regarding different physiological and biochemical parameters (i.e. root length, chlorophyll, proline and antioxidant enzymes contents) were obtained in the transgenic tomato, where the expression of other genes related to salinity stress was up-regulated (RBOHC, TAS14, HVA22, PR5 and LHA1). The overexpression of SlERF5 gene (ethylene response factor) in transgenic tomato led to an increased tolerance to salinity by improving the relative water content (Rao et al., 2020). Albacete et al. (2014) recorded improved fruit yield, hormone concentrations, and sugar content in transgenic tomato due to the overexpression of a gene coding for isopentenyl transferase, an enzyme involved in cytokines biosynthesis – IPT gene and a cell wall invertase gene – CIN1. Cai et al., 2016 showed the importance of SlDof22 gene, coding for Dof proteins responsible for abiotic stress response, gibberellins regulation, and evolution of cell cycle, in improving the tomato tolerance to salinity stress. Other genes whose expression increased the tomato plant biomass production and yield under salinity stress were CDF3, which regulated important genes for redox homeostasis, photosynthesis process or primary metabolism (Renau-Morata et al., 2017). NAC transcription factor SlTAF1 is another gene described as a good candidate for increasing the salinity tolerance of tomato and other species. It’s silencing in transgenic plants increased the damages related to salinity (Devkar et al., 2020). Soil salinity represents one of the main causes of agricultural yield losses worldwide. Natural factors such as topography, and type of geological material, but especially anthropogenic activities like inappropriate agricultural practices (i.e. excessive fertilization, irrigation without proper drainage, and leaching) intensify the soil salinization process. Plants are directly impacted by the increases in soil salt concentration through reduced water and nutrient uptake by roots. In tomato plants, salinity stress affects positively ornegatively the germination process, the morphological traits, the physiological features, the biochemical and molecular parameters, and also the yield. Usually, the germination, morphology and physiology of tomato plants are negatively influenced by the saline stress. When the soil salinity increases, its water potential drops to a point close to the root water potential, slowing down the process of water uptake by roots, thus causing drought stress-related symptoms. Also, in saline soils, nutrients in the form of cations (Mg+, Ca+, K+, ) and anions (, ) compete with Na+ and Cl− to be transported inside the plant. Na+ competes with and K+ cations decreasing their absorption, while Cl− competes with anions decreasing its uptake. Therefore, along with a deficiency in the nutrient uptake, ion toxicity takes place due to excessive concentrations of Na+ and Cl−, consequently affecting plant growth and development. Regarding the effects on gene expression, the salinity stress can down-regulate or up-regulate the expression of genes in tomato plants. A similar situation also occurs with regard to the biochemical parameters which can either be enhanced by the saline stress or can be decreased. Generally, most of the increases and the decreases recorded for the biochemical parameters and the up- or down-regulation of genes represent adaptive responses to stress by plants that try to improve their homeostasis and resistance. However, the decreases can also be the result of biochemical pathways dysregulations. The quality of tomato fruit benefits from saline conditions in most cases, maybe due to lower water content and accumulation of biomolecules such as sugars, amino acids, and inorganic solutes that contribute to osmotic adjustments. The results of the studies carried out over the last 10 years have shed more light on the impact that saline stress can have on tomato plants. However, for a clearer image of the effects of salinity on tomato plants, more studies should be carried out in the field, in salt-affected soils, taking into account the individual and cumulative interactions of the factors involved. The deleterious effects of salinity on tomato plants can be alleviated by using different strategies like plant priming or genetic modification techniques. The results are very promising, but at this moment, they are relatively limited and at their beginnings. In addition, most of the research has focused on developing salt-resistant tomato plants and testing them for the needed characters, but to develop commercial lines, research carried out in saline fields are needed. Considering the FAO predictions that by 2050 more than 50% of arable land will become saline, urgent measures should also be taken to reduce the salinization process such as better water drainage and leaching of salts; a decrease in the quantity and number of fertilizers applied and water used in irrigation; proper crop selection or reduction of the degree of tillage systems. Therefore, researchers should focus more their attention on methods to desalinate the soils, on studies regarding the development of fertigation schemes that promote a better management of water and fertilizers applied according to the plant requirements, on the production of new varieties resistant to salinity, or in improving the existing species. VS, GM and MR: Conceptualization. MR and GM: Formal analysis. MR and GM: Investigation. MR and GM: Writing—original draft preparation. VS: Supervision and validation. VS, GM and MR: Writing - review & editing. MR and GM contributed equally to this work and share first authorship. All authors have read and agreed to this version of the manuscript. All authors contributed to the article and approved the submitted version.

PMC10000761

Johnny Atallah,Musie Ghebremichael,Kyle D. Timmer,Hailey M. Warren,Ella Mallinger,Ellen Wallace,Fiona R. Strouts,David H. Persing,Michael K. Mansour

Novel Host Response-Based Diagnostics to Differentiate the Etiology of Fever in Patients Presenting to the Emergency Department †

02-03-2023

host-response,diagnostics,gene signatures,fever,febrile syndromes

Fever is a common presentation to urgent-care services and is linked to multiple disease processes. To rapidly determine the etiology of fever, improved diagnostic modalities are necessary. This prospective study of 100 hospitalized febrile patients included both positive (FP) and negative (FN) subjects in terms of infection status and 22 healthy controls (HC). We evaluated the performance of a novel PCR-based assay measuring five host mRNA transcripts directly from whole blood to differentiate infectious versus non-infectious febrile syndromes as compared to traditional pathogen-based microbiology results. The FP and FN groups observed a robust network structure with a significant correlation between the five genes. There were statistically significant associations between positive infection status and four of the five genes: IRF-9 (OR = 1.750, 95% CI = 1.16–2.638), ITGAM (OR = 1.533, 95% CI = 1.047–2.244), PSTPIP2 (OR = 2.191, 95% CI = 1.293–3.711), and RUNX1 (OR = 1.974, 95% CI = 1.069–3.646). We developed a classifier model to classify study participants based on these five genes and other variables of interest to assess the discriminatory power of the genes. The classifier model correctly classified more than 80% of the participants into their respective groups, i.e., FP or FN. The GeneXpert prototype holds promise for guiding rapid clinical decision-making, reducing healthcare costs, and improving outcomes in undifferentiated febrile patients presenting for urgent evaluation.

Novel Host Response-Based Diagnostics to Differentiate the Etiology of Fever in Patients Presenting to the Emergency Department † Fever is a common presentation to urgent-care services and is linked to multiple disease processes. To rapidly determine the etiology of fever, improved diagnostic modalities are necessary. This prospective study of 100 hospitalized febrile patients included both positive (FP) and negative (FN) subjects in terms of infection status and 22 healthy controls (HC). We evaluated the performance of a novel PCR-based assay measuring five host mRNA transcripts directly from whole blood to differentiate infectious versus non-infectious febrile syndromes as compared to traditional pathogen-based microbiology results. The FP and FN groups observed a robust network structure with a significant correlation between the five genes. There were statistically significant associations between positive infection status and four of the five genes: IRF-9 (OR = 1.750, 95% CI = 1.16–2.638), ITGAM (OR = 1.533, 95% CI = 1.047–2.244), PSTPIP2 (OR = 2.191, 95% CI = 1.293–3.711), and RUNX1 (OR = 1.974, 95% CI = 1.069–3.646). We developed a classifier model to classify study participants based on these five genes and other variables of interest to assess the discriminatory power of the genes. The classifier model correctly classified more than 80% of the participants into their respective groups, i.e., FP or FN. The GeneXpert prototype holds promise for guiding rapid clinical decision-making, reducing healthcare costs, and improving outcomes in undifferentiated febrile patients presenting for urgent evaluation. Fever is among the most common presentations to healthcare facilities and urgent care services [1]. Fever has been linked to multiple disease processes that include malignancy, autoimmune diseases, sterile inflammatory processes, and, most commonly, infectious etiologies [2]. Confirming the definitive etiology of fever in hospital-admitted adult patients remains a significant challenge for patient management and represents a large investment in hospital resource utilization [3]. When suspecting an infectious process, it is particularly challenging that clinical manifestations of microbial infection can range across a broad spectrum of nonspecific symptoms. Clinically, the detrimental impact of missed or delayed diagnoses can lead to antibiotic overuse, increased antimicrobial resistance, increased length of stay, and ultimately unfavorable health effects [4,5]. Economically, increased healthcare costs, prolonged hospitalization periods, and the excessive utilization of hospital resources directly result from poorly performing diagnostics [6]. The need to determine the etiology of fever in febrile patients at an accurate and rapid rate necessitates improved diagnostic modalities for better medical decision-making and management [7]. Pathogen-based diagnostic testing remains the current standard of care when dealing with suspected infections. However, as with any other diagnostic modality, pathogen-specific diagnostics have shortcomings. Depending on the pathogen-based diagnostic test chosen, these challenges include both low sensitivity or low specificity, especially for the commonly used blood cultures, often leaving the healthcare provider with a conundrum regarding the nature of the microbe detected, whether it is commensal or contaminant versus a true invasive pathogen [8,9]. Thus, as an adjunct to pathogen-specific diagnostics, host immunodiagnostics shows promise. These techniques involve assays such as reverse transcription-polymerase chain reaction (RT-PCR) to measure specific host gene expression signatures and transcripts, which can inform the host’s susceptibility and response to infection [10], allowing for the integration of multiple determinations into single predictive models for accurate diagnosis and disease prognosis. Numerous gene signatures have been linked to the direct response of the immune system to different etiologies, including viral and bacterial infections. For example, the Interferon Regulatory Factor 9 (IRF-9) and Lymphocyte Antigen 6 Family Member E (LY6E) play an essential role in anti-viral immunity, including virus-mediated activation of interferons [11,12]. On the other hand, other genes, such as Integrin Subunit Alpha M (ITGAM), have been shown to regulate neutrophil migration and mediate the adhesion of neutrophils to pathogens leading to pathogen clearance in bacterial infections [13]. Other assays aimed at understanding the host response to different etiologies include flow cytometry. Flow cytometry can serve as an assay for stratification and differentiating the etiology of fever. Surface markers such as CD64 on neutrophils and CD169 on monocytes have been shown to serve as sensitive markers to differentiate bacterial infections from viral infections [14] This prospective study aims to understand the transcriptional gene changes of circulating white blood cells in admitted patients with undifferentiated febrile syndromes. Using a PCR-based prototype assay (GeneXpert) capable of accurately measuring five mRNA transcripts directly from whole blood, the primary objective of this study is to determine the ability to differentiate infectious versus non-infectious febrile syndromes in patients as compared to the results of traditional pathogen-based microbiology tests in an adjudicated cohort. Additionally, we explored surface maker determination using flow cytometry to detect potential surface markers that predict the etiology of fever, we aim to evaluate the sensitivity and accuracy of such markers to understand febrile syndromes better. Finally, patient pathways from the Emergency Department (ED) arrival to the inpatient unit admission were collected to map out and determine critical time points where such host-based diagnostic may have the highest potential for improving clinical decision-making for febrile patients. The study included 100 febrile patients recruited from a single site at Massachusetts General Hospital (MGH), and 22 healthy controls enrolled through an on-campus primary care clinic. The study was conducted according to the guidelines of the Declaration of Helsinki, and approval was obtained from the MGH institutional review boards (IRB) protocol, approval number: 2021P0003374. Informed consent was obtained from all subjects participating in the study. The research investigators designed the study, collected the data, and performed the analysis. DH Diagnostics LLC provided unrestricted funding and the GeneXpert® system required to complete the study but was not involved in data interpretation, analysis, or assembling the manuscript. Potential subjects were detected daily through generated screening EPIC reports that detect patients admitted to the MGH ED with a documented fever. Patients were enrolled within 24 h after ED presentation, and blood samples were collected within 48 h after ED presentation. Patients were considered eligible for recruitment if they were greater than 18 years of age, had a documented fever >38 °C, and had an ongoing work-up to determine the etiology of the fever initiated. Patients were excluded from the study in the case of pregnancy. Subjects were enrolled from March 2022 till October 2022. A prototype host response assay that integrates sample preparation and RT-PCR to measure 6-mRNA genes on the GeneXpert system was developed. The prototype assay measures five mRNA targets (RUNX1, LY6E, IRF9, ITGAM, PSTPIP2) and a control (ABL1) chosen from the literature based on their combined ability to distinguish infectious from non-infectious illness in patients with fever [15,16,17,18,19,20,21]. Assay testing was performed in the Division of Infectious Diseases at Massachusetts General Hospital in Boston, MA. A total of 20 cc of venous blood was collected in ethylenediaminetetraacetic acid (EDTA) tubes from the enrolled subject. A total of 200 μL of blood was added to the Xpert Lysis Reagent. Next, 1 mL of blood/lysis mixture was added to the GeneXpert cartridge and loaded into the GeneXpert system for processing. The turnaround time for assay results is 35 min. (Figure 1). Healthy blood donors consented under the MGH IRB protocol, approval number: 2018P001283. Whole blood was collected in EDTA-coated vacutainers (Beckton Dickinson, Franklin Lakes, NJ, USA) and subsequently centrifuged at 1500× g for 15 min. Buffy coat was collected, and neutrophil isolation was performed using the negative selection EasySep Direct Human Neutrophil Isolation Kit, according to the manufacturer’s instructions (STEMCELL Technologies, Seattle, WA, USA). Wright-Giemsa staining was performed after the isolation process to confirm neutrophil purity from the isolation kit. Flow cytometry was also used to verify a high neutrophil purity from the isolation procedure (≥94% neutrophil purity). Cell concentration and viability were measured by staining the cells with a 1:10 dilution of acridine orange/propidium iodide followed by automatic cell counting using the LUNA fl Dual Fluorescence Cell Counter (Logos Biosystems, Annandale, VA, USA) (≥99% live). Isolated neutrophils (50,000 cells) were stained in fluorescent activated cell sorting (FACS) buffer containing 2% heat-inactivated fetal bovine serum (FBS) (Life Technologies, Dun Laoghaire, Ireland) and 1 mM EDTA (Life Technologies) in phosphate buffer saline (PBS) without calcium and magnesium (Corning, Corning, NY, USA). Cells were stained with antibodies for 30 min at 4 °C in FACS buffer containing (BV421) anti-CD10 (1:200 dilution; clone HI10a; BioLegend, San Diego, CA, USA) and (BV786) anti-CXCR4 (1:100 dilution; clone12G5; BioLegend) and (AF647) anti-CD64 (1:100 dilution; clone 10.1; BioLegend) or (BV786) anti-CD63 (1:100 dilution; clone H5C6; BioLegend) and (APC) anti-CD66b (1:800 dilution; clone G10F5; BioLegend), or (BV421) anti-CD62L (1:200 dilution; clone DREG-56; BioLegend), and (PE/Dazzle) anti-CD32 (1:200 dilution; clone FUN-2; BioLegend). Data were acquired on a BD FACSCelesta (BD Biosciences, San Jose, CA, USA) with a BVR laser configuration (488 nm, 405 nm, 640 nm). Before recording data, gates were prepared so that 10,000 neutrophil events could be collected. FCS files were exported from BD FACSDiva Software (BD Biosciences) in a 3.0 format. FCS files were analyzed using FlowJo v.10 software (BD Biosciences). The primary outcome of the study is to determine the efficiency of the GeneXpert prototype assay monitoring host-response gene signatures in correctly predicting the etiology of fever when compared to microbiology results. The expression of the genes stratified by the different groups measured by delta Ct (ABL1 Ct value—Target Ct value), the correlation of the five genes with each other, and their predictive performance of the etiology of fever will be evaluated. Microbiology results were defined as the results of blood and urine cultures and viral panels. Febrile patients were categorized among two groups based on their infection status, and healthy controls were categorized as a third group. Febrile Positive (FP) group (n = 74): Febrile patients with confirmed positive microbiology results and clinically adjudicated febrile patients with suspected infections by clinical assessment despite the absence of positive microbiology results were categorized into one group of positive composite outcomes labeled FP. Clinical adjudication was performed by manual physician chart review. Febrile Negative (FN) group (n = 26): Febrile patients with negative microbiology results and the absence of suspected infection by clinical assessment were categorized into a second group labeled FN. Healthy Control (HC) group (n = 22): Healthy subjects presenting to the primary clinic for routine annual laboratory testing were categorized into a third group labeled HC (See Figure 2). Descriptive measures such as median, interquartile range, frequencies, and percentages were used to summarize the data. Exact binomial confidence intervals were used to estimate confidence intervals for sensitivities and specificities. Wilcoxon rank-sum and Fisher exact tests were used to compare continuous and categorical study variables, respectively, between febrile positive and negative patients. For comparing continuous outcomes among the three groups (healthy, febrile negative, and febrile positive patients), the Kruskal–Wallis test with Dunn’s post-hoc analysis was used. Spearman’s rank correlation was used to assess the strength and direction of association between the study variables. Univariate logistic regression models were utilized to assess the predictors of being a febrile positive. The least absolute shrinkage and selection operator (Lasso) logistic regression algorithm was performed to select the most predictive variables of febrile positive. We estimated the predictive accuracy of the selected variables in distinguishing class membership (febrile negative or positive) using several machine-learning algorithms, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), support vector machine (SVM), classification tree (CART), AdaBoost (ADA), neural networks (NNET), random forest (RF), Gaussian process and logistic regression. The leave-one-out cross-validation procedure was used to estimate the performance of the classifier algorithms. We used the algorithm with the highest cross-validated area under the receiver operating curves to evaluate the performance of the selected variables as biomarkers of febrile-positive patients. Statistical analyses were performed using the R package version 4.2.1 and SAS software version 9.4 (SAS Institute, Cary, NC, USA). All p values were 2-sided and considered statistically significant if <0.05. The median time for running the assay for all study subjects after blood collection was 3 h, and the average time was 3.4 h. The baseline characteristics of the patients are shown in Table 1. A total of n = 122 subjects were enrolled in the study and divided into three groups. The first group was the FP group, which comprised 74 patients (60.7%). Among the FP group, 45 patients (60.8%) were males. The confirmed infections included 57 bacterial and 17 viral infections. The bacterial infections included 17 urinary tract infections, 24 bloodstream infections, three cellulitis infections, two cholangitis infections, two pelvic inflammatory disease infections, one hepatic abscess infection, seven community-acquired pneumonia infections, and one endocarditis infection. The urinary tract and bloodstream infections were caused by Gram-positive (43%) and Gram-negative pathogens (57%). On the other hand, the viral infections included eight SARS-CoV-2 (COVID-19) infections, three influenza A infections, three rhinovirus infections, one human metapneumovirus infection, one Epstein-Barr virus (EBV) infection, and one parainfluenza virus infection. The median age was 63 (interquartile range, 44–73). Thirty percent (29.7%) of the patients had a body-mass index (the weight in kilograms divided by the square of the height in meters) >30. In total, 13.5% of the patients had known diabetes mellitus, and 10.9% had a history of previous lung disease (e.g., asthma, COPD). A total of 15 patients (20.3%) had a history of malignancy, 22 patients (29.7%) had an active malignancy, 17 patients (23%) had congestive heart failure, 20 patients (27%) had a history of recurrent infections, and two patients (2.7%) had liver disease/cirrhosis. A total of 48 patients (64.9%) did not require supplemental oxygenation at day 1, 23 patients (31.1%) were receiving supplemental oxygen at ≤6 L per minute, delivered by nasal cannula, to maintain an oxygen saturation >92%, and three patients (4%) were receiving high-flow oxygen. The median white blood cell (WBC) count for the FP group was 12.71 × 109/L (interquartile range: 6.2–16.2 × 109), and the median C-reactive protein (CRP) was 149.25 mg/L (interquartile range: 54.3–261.3 mg/L). The second group was the FN group, which comprised 26 patients (21.3%). Among the FN group, 17 patients (65.4%) were males. The median age was 57.5 (interquartile range, 27–66). The number of patients with a body-mass index (the weight in kilograms divided by the square of the height in meters) >30 was 7 (26.9%). In total, 15.4% of the patients had known diabetes mellitus, and 3.8% had a history of previous lung disease (e.g., asthma, COPD). One patient (3.8%) had a history of malignancy, ten patients (38.5%) had an active malignancy, two patients (7.7%) had congestive heart failure, four patients (15.4%) had a history of recurrent infections, and two patients (7.7%) had liver disease/cirrhosis. A total of 23 patients (88.4%) did not require supplemental oxygenation on day 1, 2 patients (7.7%) were receiving supplemental oxygen at ≤6 L per minute, delivered by nasal cannula, to maintain an oxygen saturation >92%, and 1 patient (3.8%) required mechanical ventilation. The median white blood cell (WBC) count for the FN group was 9.37 × 109/L (interquartile range: 4.72–12.56 × 109), and the median C-reactive protein (CRP) was 96.15 mg/L (interquartile range: 47.55–148.1 mg/L). The third group was the HC group, consisting of 22 patients (18%). Among the HC group, six patients (27.3%) were males. The median age was 57 (interquartile range, 43–68). The number of patients with a body-mass index (the weight in kilograms divided by the square of the height in meters) >30 was 8 (36.4%). In total, 18.2% of the patients had known diabetes mellitus, three patients (13.64%) had a history of malignancy, and one patient (4.5%) had an active malignancy. (See Table 1). To understand the network structure of the five genes, correlation plots between the gene signatures delta Ct in the three different groups were performed. The results showed that the network structure representing the correlation of the five genes for febrile patients with positive microbiology (FP) or with negative microbiology (FN) using the GeneXpert prototype assay was significantly more robust than the network structure of the five genes in the HC group. In the febrile patients’ (FP and FN) group, a strong correlation with a positive correlation coefficient is demonstrated between the five genes. The FP group’s five genes (ITGAM, IRF-9, LY6E, PSTPIP2, and RUNX1) were significantly correlated with each other (p-value < 0.001). Similarly, the FN group’s five genes (ITGAM, IRF-9, LY6E, PSTPIP2, and RUNX1) were also significantly correlated with each other (p-value < 0.001). No significant difference in the association among the five genes was noted between the two groups of febrile patients. On the other hand, in the HC group, a weak network structure with low correlation coefficients was noted between the five genes. Except for LY6E and IRF-9, which showed a significant correlation with a p-value < 0.05, the other gene signatures do not demonstrate any significant correlation. (See Figure 3.) To further investigate how the etiology of fever changes the host immune response, we next compared the gene expression profiles of matched subjects from the three different patient groups. We compared the expression of the five genes across the three different groups. Our analysis revealed that patients with confirmed infections (FP group) form a distinct cluster with higher expression of the five genes, demonstrating the effect of bacterial and viral infections on modifying the host response in febrile subjects. The analysis identified higher expression of the five genes in the FP group as compared to the FN and HC groups. Two clusters of genes were noted in the FP group. ITGAM, RUNX1, and PSTPIP2 formed one cluster with high expression in subjects with confirmed bacterial infections, whereas LY6E and IRF-9 formed another cluster with high expression, mainly in patients with confirmed viral infections. Moreover, for patients in the HC group, the expression levels of the five genes are lower than in other groups and are detected at low levels, implicating the role of fever on the immune response. (See Figure 4). Figure 5 displays the odds of being febrile with positive microbiology. In addition to the five genes, several other covariates, including age, gender, disease severity, cancer status, durations of fever, presence of fever on blood collection day, and antibiotics administration duration, were considered. The higher expression of the five genes was associated with higher odds of being febrile-positive patients: IRF-9 (OR = 1.750, 95% CI = 1.16 to 2.638), ITGAM (OR = 1.533, 95% CI = 1.047 to 2.244), PSTPIP2 (OR = 2.191, 95% CI = 1.293 to 3.711), and RUNX1 (OR = 1.974, 95% CI = 1.069 to 3.646) had a significant expression with positive infection status. The odds of being febrile-positive were associated with higher values of the LY6E gene, although it did not reach statistical significance (OR = 1.11, 95% CI: 0.91–1.35). Considering the differences in antibiotic exposure duration from the ED admission to the time of blood collection, and the potential effects of longer antibiotic usage on altering the gene expression profiles, stratification by antibiotic duration was also performed. Two groups of subjects were evaluated: those receiving antibiotics for two or fewer days before collecting blood and running the assay, and those receiving antibiotics for more than two days before collecting blood and running the assay. The results demonstrate that subjects receiving antibiotics for two or fewer days before running the assay (OR = 6.682, 95% CI = 2.452 to 18.206) were more significantly associated with positivity in infection status. The difference in the distribution of the fives genes varied by the duration of antibiotics usage, as shown in the density plots displayed in Figure 6. As expected, the differences between gene expression of febrile-positive and febrile-negative subjects are more noticeable in the shorter antibiotic usage category. The other parameters did not demonstrate any statistically significant association with infection status. We used machine-learning algorithms to develop a classifier model to classify study participants into groups based on the significant variables presented in Figure 5. The classifier model assessing the performance of the five genes in subjects receiving antibiotics for less than two days correctly classified more than 80% of the participants into their respective groups, i.e., FP or FN groups. The classifier correctly classified 65% [95% CI: 0.54–0.75] of the FP subjects and 89% [95% CI: 0.71–0.96] of the FN subjects. Using flow cytometry to detect specific markers that can predict infection in 20 febrile patients of our study subjects (13 FP and 7 FN) and 9 HC subjects, three different surface markers, CD10 (percent positive), CD64 mean fluorescence intensity (MFI), and CXCR4 MFI demonstrated a significant distinction between the FP and FN groups. The expression of CD10 and CXCR4 were significantly lower in the FP group compared to the FN group. In contrast, the expression of CD64 was significantly higher in the FP group compared to the FN group (See Figure 7). Different pathways mapping the clinical course of patients (n = 70) from the point of the initial evaluation in the ED arrival until the inpatient unit admission were collected to determine critical time points in clinical decision-making. Such pathways provided a framework where the implementation of the GeneXpert prototype assay could be best utilized. Average times to first labs, imaging, cultures, and antibiotics were collected for FP patients with bacterial infections (n = 44), FP patients with viral infections (n = 12), and FN patients (n = 14). The following timepoints were collected for the enrolled subjects prior to the establishment of a definite diagnosis. All patients were being managed for possible infections based on their febrile illness. The results show that the average times to the first labs are 1.09 h, 1.3 h, and 2 h for patients with bacterial, viral, and no infections, respectively. The average times for the first cultures performed were 2 h, 2.1 h, and 2.8 h for patients with bacterial infections, viral infections, and no infections, respectively. The average times to first imaging performed were 2.4 h, 2.6 h, and 3.5 h for patients with bacterial infections, viral infections, and no infections, respectively. Finally, the average times to first antibiotics administration were 4.9 h, 4.2 h, and 3.9 h for patients with bacterial infections, viral infections, and no infections, respectively. There were no significant differences between any of the average times among the three different groups indicating the need for earlier diagnostic to guide better management of febrile etiologies. It is noteworthy to mention that out of twelve patients with viral infections, eight patients (67%) received antibiotics in the ED prior to viral diagnostics indicating a viral pathogen. Four out of these eight (50%) were continued on antibiotics after the viral diagnostics turned positive due to concern for bacterial superinfection. Out of 14 patients with no infections (FN), 7 patients (50%) received antibiotics in the ED before ruling out infectious etiologies, and out of 44 patients with bacterial infections, 43 patients (97.7%) received antibiotics in the ED. (See Figure 8a). Moreover, the average time from ED arrival to inpatient admission was collected for these patients. The average times to admission were 21.1 h, 18.6 h, and 23.4 h for patients with bacterial infections, viral infections, and no infections, respectively. (See Figure 8b). In this prospective study, a robust predictive performance in differentiating the etiology of febrile syndromes has been demonstrated by the GeneXpert prototype assay. Such results reveal promise for using this assay for accurate diagnostic outcomes in the future. The rapid turnaround time of the assay (35 min) and the simple sample preparation protocol could provide additional potential for future implementation of such a modality from both clinical and possibly economic perspectives. Notably, the role of each of the five genes (ITGAM, IRF-9, LY6E, PSTPIP2, and RUNX1) has been linked to response to infection in the literature. To start with, ITGAM is known to promote the adherence of monocytes and macrophages and to mediate the uptake of opsonized particles and pathogens [22]. In fact, in one recent study, using a four-gene signature that includes ITGAM and three other genes has demonstrated a promising model to diagnose patients with sepsis [23]. Additionally, IRF-9 has also been shown to be an integral transcription factor in mediating the type I interferon antiviral response, and the expression of IRF-9 plays an essential role in antiviral immunity [24]. In a recent case report, a five-year-old child with IRF-9 deficiency experienced severe influenza pneumonitis, further highlighting IRF-9′s role in antiviral immunity [25]. Similarly, Ly6E genes have also been shown to possess an antiviral regulation response. Ly6E confers critical antiviral functions by restricting the entry of human coronaviruses, including SARS-CoV, MERS-CoV, and SARS-CoV-2, by interfering with spike protein-mediated membrane fusion [26]. Interestingly, our results revealed two gene expression clusters in patients with confirmed infections. ITGAM, RUNX1, and PSTPIP2 formed one cluster with high expression in subjects with confirmed bacterial infections, whereas LY6E and IRF-9 formed another cluster with high expression in patients with confirmed viral infections. These findings further support the different roles of the host-response gene signatures regarding the etiology of infections. In this study, flow cytometry was performed to evaluate the role of host surface markers in response to infections. The results showed that three different surface markers, CD10, CD64, and CXCR4, significantly differentiate between the presence and absence of infection in patients with febrile illnesses. In a previous study, the role of CD10 expression in sepsis patients was described, where CD10 and CD66b were shown to be effective biomarkers and good predictors for early bacterial infections in patients with suspected sepsis. When compared to the performance of procalcitonin and CRP, the accuracy of CD10 and CD66b expression for predicting bacterial infections was significantly higher, with a sensitivity of 86.5% and a specificity of 90.3%. [27]. Other studies have described the role of CD64 in differentiating bacterial and viral infections [28]. The immunologic assays performed in this study were exploratory. Consequently, future considerations directed at understanding the role of cell surface markers in the setting of infections can provide insight into developing a combined model that detects gene signatures and surface markers for more accurate diagnostic performance. Larger sample sizes will be ultimately needed to run such a classifier model. Thus, from a clinical perspective, the development of this promising five-gene signature assay has the potential to serve as a guide for better patient outcomes. The rapid and accurate differentiation between infectious and non-infectious etiologies in patients presenting with fever can lead to more optimal administration of antibiotics and a reduction in antimicrobial resistance [29,30]. Additionally, the results from this study suggest a potential effect of longer antibiotic administration duration on the expression profiles of the five genes. The prototype assay demonstrated more accurate performance for febrile subjects receiving shorter antibiotic duration before running the assay [31], attributed to possible gene expression changes in response to exposure to antimicrobials. Moreover, patient pathways collected in this study show that 50% of patients with no infections and 67% of patients with viral infections receive unnecessary antibiotics in the ED due to the absence of a rapid modality that can differentiate the etiology of fever [32]. The implications of our findings suggest that the optimal implementation of such a diagnostic would be in the ED setting before the administration of antibiotic therapy. Such an implementation has the potential to improve patient outcomes and provide optimal antibiotic use. The reduction in antibiotic usage, in addition to reducing antimicrobial resistance, ultimately leads to a reduction in antibiotics-associated gastrointestinal, dermatologic, musculoskeletal, hematologic, renal, cardiac, and neurologic adverse events [33]. Finally, from an economic perspective, such a modality could provide valuable decision-making guidance regarding unnecessary hospital admissions for the management of infections for patients with low suspicion of infectious etiologies. In a recent study of inpatient hospital costs for COVID-19 patients in the United States, the overall median cost of a hospital stay per day was shown to be more than $1700 USD, while the overall median cost of an ICU admission per day was shown to be approximately $3000 USD [34]. Thus, the reduction in patients’ hospital length of stay duration could potentially lead to significant healthcare savings [35]. Additional reductions in healthcare costs associated with adopting such an assay include a decline in the number of blood cultures drawn and fewer antibiotics being prescribed, decrease in unnecessary laboratory tests, imaging, and procedures [36,37]. Future interventional trials will be required for validation. In short, attempts have been focused on using the host response as a reference for a more personalized approach to precision medicine, taking into account its impact. By understanding the mechanisms underlying the host-immune response using specific signatures and robust diagnostics, we can achieve better medical decision-making guidance that ultimately leads to better patient outcomes. Our study should be interpreted in the setting of several limitations. One significant limitation is regarding the classification of subjects into different groups. Patients with suspected, and often apparent, infections such as cellulitis or community-acquired pneumonia had no cultures taken and no confirmed positive results. Thus, for the accurate classification of these patients, the clinical assessment of the medical team was followed. Moreover, the variability in the duration of antibiotic therapy received by the patients before the blood sample collection may present a confounding variable that alters the host’s immune response. Additionally, the GeneXpert prototype demonstrated lower predictive performance for patients on longer antibiotic duration suggesting a limited optimal timing for implementation of this diagnostic. Another limitation of the study is that the GeneXpert is designed to detect the expression of genes that point toward bacterial and viral infections. Its performance in detecting fungal and parasitic infections is yet to be evaluated. The lack of an adequate sample size to perform subgroup analysis between bacterial and viral infections poses an additional limitation. Larger cohorts will be required to perform any subgroup analysis for differentiating the microbiological etiology. Finally, the results presented in this manuscript are based on a single study center. A multi-center study might be essential to attain a larger and more diverse sample size. The implementation of novel rapid host-based diagnostics, such as the GeneXpert prototype assay and flow cytometry for patients with febrile syndromes has the potential to reduce adverse events, decrease the misuse of antibiotics, and lower the rate of emerging antimicrobial resistance. Furthermore, such modalities have the potential to reduce healthcare costs and inform clinicians about the optimal utilization of resources from an economic perspective. Future directions could be directed toward launching an interventional trial to assess the efficacy of the assay in reducing healthcare costs and patient adverse outcomes. Additionally, enrolling a larger sample size for subgroup analysis to assess the performance of the GeneXpert in differentiating bacterial vs. viral infections will be necessary. Finally, the future development of an exploratory model using the multi-modality host-based assays including flow markers and best-performing gene signatures may provide improved diagnostic accuracy in the management of the undifferentiated febrile patient.

PMC10000766

Mona A. Amin,Halla M. Ragab,Nabila Abd El Maksoud,Wafaa Abd Elaziz

CD24 Gene Expression as a Risk Factor for Non-Alcoholic Fatty Liver Disease

04-03-2023

NAFLD,gene expression,CD24

In light of increasing NAFLD prevalence, early detection and diagnosis are needed for decision-making in clinical practice and could be helpful in the management of patients with NAFLD. The goal of this study was to evaluate the diagnostic accuracy of CD24 gene expression as a non-invasive tool to detect hepatic steatosis for diagnosis of NAFLD at early stage. These findings will aid in the creation of a viable diagnostic approach. Methods: This study enrolled eighty individuals divided into two groups; a study group included forty cases with bright liver and a group of healthy subjects with normal liver. Steatosis was quantified by CAP. Fibrosis assessment was performed by FIB-4, NFS, Fast-score, and Fibroscan. Liver enzymes, lipid profile, and CBC were evaluated. Utilizing RNA extracted from whole blood, the CD24 gene expression was detected using real-time PCR technique. Results: It was detected that expression of CD24 was significantly higher in patients with NAFLD than healthy controls. The median fold change was 6.56 higher in NAFLD cases compared to control subjects. Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients but without significant difference (p = 0.588). ROC curve analysis showed that CD24 ∆CT had significant diagnostic accuracy in the diagnosis of NAFLD (p = 0.034). The optimum cutoff for CD24 was 1.83 for distinguishing patients with NAFLD from healthy control with sensitivity 55% and specificity 74.4%; and an area under the ROC curve (AUROC) of 0.638 (95% CI: 0.514–0.763) was determined. Conclusion: In the present study, CD24 gene expression was up-regulated in fatty liver. Further studies are required to confer its diagnostic and prognostic value in the detection of NAFLD, clarify its role in the progression of hepatocyte steatosis, and to elucidate the mechanism of this biomarker in the progression of disease.

CD24 Gene Expression as a Risk Factor for Non-Alcoholic Fatty Liver Disease In light of increasing NAFLD prevalence, early detection and diagnosis are needed for decision-making in clinical practice and could be helpful in the management of patients with NAFLD. The goal of this study was to evaluate the diagnostic accuracy of CD24 gene expression as a non-invasive tool to detect hepatic steatosis for diagnosis of NAFLD at early stage. These findings will aid in the creation of a viable diagnostic approach. Methods: This study enrolled eighty individuals divided into two groups; a study group included forty cases with bright liver and a group of healthy subjects with normal liver. Steatosis was quantified by CAP. Fibrosis assessment was performed by FIB-4, NFS, Fast-score, and Fibroscan. Liver enzymes, lipid profile, and CBC were evaluated. Utilizing RNA extracted from whole blood, the CD24 gene expression was detected using real-time PCR technique. Results: It was detected that expression of CD24 was significantly higher in patients with NAFLD than healthy controls. The median fold change was 6.56 higher in NAFLD cases compared to control subjects. Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients but without significant difference (p = 0.588). ROC curve analysis showed that CD24 ∆CT had significant diagnostic accuracy in the diagnosis of NAFLD (p = 0.034). The optimum cutoff for CD24 was 1.83 for distinguishing patients with NAFLD from healthy control with sensitivity 55% and specificity 74.4%; and an area under the ROC curve (AUROC) of 0.638 (95% CI: 0.514–0.763) was determined. Conclusion: In the present study, CD24 gene expression was up-regulated in fatty liver. Further studies are required to confer its diagnostic and prognostic value in the detection of NAFLD, clarify its role in the progression of hepatocyte steatosis, and to elucidate the mechanism of this biomarker in the progression of disease. NAFLD is a clinico-pathologic syndrome that encompasses various medical entities, including simple fatty liver or simple steatosis, nonalcoholic steatohepatitis (NASH), cirrhosis, and its complications [1]. NAFLD now affects up to 25% of people around the world. The highest prevalence rate is in the Middle East (32%), followed by South America (30%), while the lowest is in Africa (13%). It also accounts for 2% of total deaths [2]. The increase in NAFLD prevalence parallels the rise in obesity and is tightly associated with metabolic comorbidities (diabetes, hypertension, insulin resistance, and dyslipidemia). It also places patients at higher risk for progressive liver disease [3]. It became clear that, as with different complex multisystem disorders, NAFLD is triggered by a variety of underlying mechanisms; the most important one of them is the alterations in hepatic and extra-hepatic lipid metabolism [4]. The study of genetic factors in NAFLD is a rapidly growing field, as they determine not only the response of different individuals to excess caloric consumption, but also the resulting metabolic derangements [5]. Cluster of differentiation 24 (CD24) is a glycophosphatidylinositol (GPI)-anchored mucin-like cell surface glycoprotein, encoded by a gene located on chromosome 6. It is expressed on mature granulocytes and B cells and regulates growth and differentiation signals to these cells. Accumulating evidence showed that abnormal over-expression of this protein is a prognostic factor in many types of cancers, resulting in cancer cell growth, proliferation, and metastasis [6]. The expression of the cell surface molecule CD24 has previously been shown to identify a subset of adipocyte progenitor cells that is crucial for the reconstitution of white adipose tissue (WAT) function in vivo, as well as a particular regulator of adipogenesis in vitro [7]. Recently, CD24 has been identified as a possible biomarker for distinguishing NAFLD/NASH. It was concluded that the mRNA expression of CD24 is upregulated in the fatty liver [8]. Additionally, Feng et al., (2021) detected that CD24 was positively associated with NAFLD severity, and it could also differentiate mild NAFLD patients from severe NAFLD patients [9]. Therefore, the present study aimed to identify the association between gene expression of CD24 and early stage of NAFLD. The present study is a prospective study that was carried out on 80 subjects who attended outpatient clinics of the Internal Medicine Department of Kasr Al Ainy Hospital Cairo, Egypt during the period from May 2019 to December 2020 either for general health checks or to identify and treat the complications of other metabolic disorders such as diabetes or obesity. The selected subjects were divided into two groups according to the sonographic findings of steatosis: 40 NAFLD patients with bright liver echogenicity and 40 healthy subjects with normal liver echogenicity. All cases have age ranging between 19 to 56 years old. Those with clinical, biochemical, or histological evidence of cirrhosis, those with known causes of liver disease [viral hepatitis B and C, autoimmune hepatitis, primary biliary cirrhosis, haemochromatosis or Wilson disease], those with history of current or past excessive alcohol drinking as defined by an average daily consumption of more than 20 g alcohol, drug-induced liver disease, pregnant women and patients on hormonal contraceptive drugs (oral, parenteral), hormone replacement therapy were excluded from the study. The study was approved by Medical Research Ethical Committee of the National Research Center, Cairo, Egypt (Approval No.19-001), and informed consent was obtained from all patients. All patients were evaluated by history and clinical examination and measurement of anthropometric parameters, such as weight (kg), height (m), body mass index (BMI; kg/m2), waist circumference (cm), and mid-arm circumference (cm). Body mass index (BMI) was determined by dividing weight by square height (kg/m2). BMI is calculated as weight in kilograms divided by the height in metres squared. According to WHO, People with BMI = 18.5–24.9 have normal weight, people with BMI = 25.0–29.9 were classified overweight, while people with BMI ≥ 30 kg/m2 defines obese. BMI is calculated as weight in kilograms divided by the height in metres squared. According to WHO, in adults, overweight is defined as a BMI of 25–29.9, while a BMI ≥ 30 kg/m2 defines obesity. Waist circumference (WC) was obtained from each subject by measuring at the midpoint between the lower rib margin and the iliac crest using a conventional tape graduated in centimeters (cm). Mid-arm circumference was measured as the right upper arm measured at the midpoint between the tip of the shoulder and the tip of the elbow (olecranon process and the acromium). Cases were divided according to their previous diagnosis or levels of fasting blood sugar: a fasting blood sugar level less than 115 mg/dL is considered normal or prediabetes. While, if the fasting blood sugar level is 126 mg/dL or higher, the patient was diagnosed diabetic. Complete blood count was determined using the automated hematology analyzer SF-300 (Sysmex Corporation, Japan). Additionally, liver enzymes (ALT, AST, ALP, GGT), serum albumin, prothrombin time, INR, serum creatinine, lipid profile, and fasting blood sugar were measured to all individuals according to the manufacture instructions. The reagents were purchased from Spectrum Company, Cairo, Egypt. NAFLD fibrosis score (NFS), FIB-4, and Fast score were calculated as mentioned previously by Angulo et al. (2007) and Calès et al. (2009) [10,11] to assess fibrosis of the NAFLD patients’ group. NFS score = −1.675 + 0.037 × age [y] + 0.094× BMI [kg/m2] + 1.13 × IFG/diabetes [yes = 1, no = 0] + 0.99 × AST/ALT ratio − 0.013 × platelet count [×109/L] − 0.66 × albumin [g/dL] FIB-4 score = Age [y] × AST [U/L]/platelet [×109/L] × ALT [U/L] FAST score was calculated according to Newsome et al., (2020) [12] as: Abdominal ultrasonography was performed to all individuals using the 3.5 MHz probe of Logic 6 of a General Electric machine. Fibroscan (M probe, Echosens, Paris) was carried out by an experienced examiner in all patients (with at least 6 h of fasting) in left lateral position and the median liver stiffness of the 10 successful measurements fulfilling the criteria (success rate of greater than 60% and interquartile range/median ratio of <30%) were noted (in kPa). The final CAP value, which ranges from 100 to 400 (dB/m), is the median of individual measurements. As an indicator of variability, the ratio of the IQR of CAP values to the median (IQR/MCAP) was calculated. The operator was blinded to the patients’ clinical data. According to the manufacturer’s instructions, in addition to previous studies, the stages of fibrosis (F0: 1–6, F1: 6.1–7, F2: 7–9, F3: 9.1–10.3, and F4: ≥10.4) were defined in kPa [13]. Moreover, steatosis stages (S0: <215, S1: 216– 252, S2: 253–296, S3: >296) were defined in dB/m [13]. 10 mL venous blood were drawn from all study participants in the morning after a 12 h fast; a portion of the blood was collected on EDTA tube for the extraction of RNA and for the determination of routine blood pictures (CBC) by Sysmex, the automated hematology analyzer SF-300, which was produced by Sysmex Corporation, Japan. The other portion was left to clot at room temperature. Serum was separated by centrifuging for 10 min at 3000 rpm. Sera were used immediately for other biochemical investigations including aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin, serum albumin, fasting blood glucose, cholesterol, triglycerides, HDL-C, and LDL-C according to the manufacturer’s instructions. The reagents were purchased from Spectrum Company, Cairo, Egypt. Total RNA was isolated from whole blood using GeneJET Whole Blood RNA Purification Mini Kit (Thermo Scientific, Lithuania) following the manufacturer’s suggestions. Reverse transcription (RT) was performed to obtain cDNA from 400 ng of purified RNA using the High-Capacity cDNA Reverse Transcription Kits (Applied Biosystem, Lithuania) with random hexamers according to the manufacturer’s suggestions. A value of 10 µL of the 2X-RT master mix was pipetted into each tube and then 10 µL of RNA sample was added to it and mixed well. The tubes were centrifuged to spin down the content and to eliminate any air bubbles. After that, the tubes were placed on the PCR machine (Cleaver Scientific, UK) programmed as follows: 25 °C, 10 min, 37 °C, 120 min, and 85 °C, 5 min. After detection of cDNA concentration and purity, they were stored in −20 °C until carryover quantitative real-time PCR (QRT-PCR). CD-24 gene expression for enrolled samples was quantified using PowerUp SYBR Green master mix (2×) (ThermoFisher Scientific, Lithuania). The sequences for used primers were as follows: PrimerPrimer SequenceCD24 Forward primer5′-ACC CAC GCA GAT TTA TTC CA-3′CD24 Reverse primer5′-ACC ACG AAG AGA CTG GCT GT-3′β-actin Forward primer5′-TGA GCG CGG CTA CAG CTT-3′β-actin Reverse primer5′-TCC TTA ATG TCA CGC ACG ATT T-3′ PCR amplification was carried out in 20 μL reaction volume containing 1 µL cDNA, 10 µL PowerUp SYBR Green master mix, 7 μL nuclease-free water, and 1 µL of gene-specific forward and reverse primers as mentioned in table. The reaction was run in the Rotor-Gene Q instrument, (QIAGEN). Fluorescence measurements were made in every cycle, and the thermal profile was used as the follows: The amplification program included a UDG activation at 50 °C with a 2-min hold, and a dual-lock DNA polymerase at 94 °C with a 3-min hold, followed by 45 cycles with denaturation at 94 °C for 30-s, annealing at 55 °C for 30-s, and extension at 72 °C for 30-s. The expression levels of CD-24 in tested samples were expressed in the form of ∆∆CT (cycle threshold) value, which was calculated based on threshold cycle (Ct) values, corrected by β-actin expression, with the following equation. The relative amount of CD-24 = 2–ΔΔCt; ΔΔCt = [ΔCt of cases − ΔCt of control]; [ΔCt = Ct (CD-24) − Ct (β-actin)]. The following primers were used in the quantitative real-time PCR analyses. SPSS version 16.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis with a two-side significant criterion at p < 0.05. The clinical data were expressed as mean ± SD (continuous, normally distributed variables). Categorical data were summarized as percentages. The significance for the difference between groups was determined by using a two-tailed Student’s t-test. Additionally, qualitative variables were assessed by chi-squared χ2-test. Correlations between different parameters were performed using Pearson’s and spearman’s correlation coefficients. A receiver operating characteristic (ROC) curve was plotted to assess the diagnostic power of CD24 in NAFLD and controls, and the area under the curve (AUC) greater than 0.5 considered to be statistically significant. The probability (p) values of ≤0.05 were considered statistically significant and indicated, while p > 0.05 was considered statistically not significant and indicated NS. The present study is a case-control study recruited 80 adult subjects, (28 males and 52 females). Their age ranged from 19 to 56 years. The demographic, anthropometric, clinical, and biochemical characteristics of both groups (NAFLD and controls) are summarized in Table 1. Patients with NAFLD were significantly older than controls (mean age 42.18 ± 11.1 4 y vs. 29.65 ± 6.63 y, p < 0.0001). There were more males in the control group (45%), but the majority was females in the NAFLD group (75%). NAFLD patients exhibited a higher mean BMI (31.8 ± 2.9 kg/m2) than the control group (23.76 ± 1.4 kg/m2) (p < 0.001). Patients with NAFLD had a higher prevalence of hypertension and diabetes mellitus in comparison to healthy controls (p < 0.001) (Table 1). Among studied NAFLD patients, 22.5% had a family history of diabetes, and 30% had family history of liver disease, and 62.5% of NAFLD cases (n = 25) have enlarged liver size on ultrasound. The mean serum fasting blood glucose was significantly higher in NAFLD patients than that in controls (122.6 ± 40.97 vs. 96.03 ± 7.77); (p < 0.001). In addition, hemoglobin levels were lower in NAFLD cases (11.56 ± 1.4 (g/dL) than in healthy controls (12.81 ± 1.06 (g/dL), (p < 0.001). No significant difference was observed in total leucocytic count (TLC) and platelet count between the NAFLD and control groups (p > 0.05). NAFLD patients had significantly higher serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT) compared to healthy controls (p < 0.001). On the other hand, the mean albumin level was almost normal (3.8 ± 0.38 g/dL) in the NAFLD group. There was a significant elevation in total cholesterol, triglycerides, and LDL-cholesterol among NAFLD patients compared to controls, while there was significant decrement in HDL in the NAFLD group as opposed to controls (p < 0.05). Table 2 shows clinical and biochemical characteristics of participants stratified by sex and presence/absence of NAFLD. In both sexes, participants with NAFLD were older, had a higher BMI, as well as a higher prevalence of diabetes. Levels of hemoglobin was significantly lower in female cases compared to male cases in NAFLD group (p = 0.001). However, ALT and AST levels were significantly higher in male NAFLD cases compared to female NAFLD casess (p = 0.009 and p = 0.038; respectively) (Table 2). The mean Fibroscan value in all NAFLD patients was 5.1 ± 0.99 (kPa), indicating that all patients had mild fibrosis with a stage less than 2. Thirty patients had fibrosis belonging to stage 0, while the rest had fibrosis stage 1. Mean Fibroscan values for cases with fibrosis stages 0 and 1 were 4.7 ± 0.67 and 6.5 ± 0.3 (kPa), respectively. There was a statistically significant difference in liver stiffness measurements in patients with stage 0 fibrosis as compared to stage 1 fibrosis (p < 0.001). In addition, there was a stepwise increase in Cap score parallel to the increase in severity of liver fibrosis (p < 0.001) (Table 3). This study showed that both NFS and FIB-4 score were similar in patients with fibrosis stages 0 and those with fibrosis stages 1 (p > 0.05). This may be due to that all cases included in our study have mild fibrosis. Additionally, performances of FIB-4 and NFS to rule in advanced fibrosis are rather inadequate, meaning that further assessment with another test is needed in case of positive results. According to the RT-PCR results, it was detected that expression of CD24 was significantly higher in patients with NAFLD than healthy controls. The median fold change in the expression of CD24 was 6.56 higher in NAFLD cases compared to control subjects (Figure 1). The present study showed higher expression of CD24 in female cases with NAFLD compared to male cases (fold change was 6.9 in females vs 4.4 in males, but without significant difference; p = 0.262) (Figure 2). Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients, but without significant difference (p = 0.588). Furthermore, there was no difference in CD24 fold change between overweight patients (median fold change = 9) and obese cases (median fold change = 5.89) (p = 0.447) (Figure 3). Additionally, the median fold change in CD24 in diabetic cases was seven compared to 5.13 in non-diabetic cases (p = 0.609) (Figure 4). Figure 5 illustrates the ROC plots to assess the diagnostic accuracy of CD24 ∆CT to distinguish patients with NAFLD from healthy controls. ROC curve analysis showed that CD24 ∆CT had significant diagnostic accuracy in the diagnosis of NAFLD (p = 0.034). ROC curve showed the optimum cutoff for CD24 was 1.83 for distinguishing patients with NAFLD from healthy control with sensitivity 55% and specificity 74.4%; and an area under the ROC curve (AUROC) 0.638 (95% CI: 0.514–0.763). Table 4 shows the correlation of Kpa, CAP, FAST, NFS, and FIB-4 with CD 24 gene expression. Pearson’s correlation test showed positive significant correlation between CD24 and NFS (r = 0.356, p = 0.001). By binary logistic regression analysis, none of the examined parameters found to be significant determinant of NAFLD after adjusting the effects of potential cofounders of age, gender, suffering of diabetes, and BMI, respectively (Table 5). NAFLD is known nowadays as the most common liver disorder in the 21st century. It is diagnosed by the presence of more than 5% fat accumulation in liver cells without excess alcohol consumption or secondary causes of fat accumulation in the background. Approximately 25% of the world’s adult’s population has NAFLD, and the prevalence is still increasing [13]. NAFLD may eventually deteriorate to HCC as a result of excessive lipid accumulation, liver cell damage, immune system dysfunction, which leads to scarring, and permanent liver damage [14]. In light of increasing NAFLD prevalence, early detection and diagnosis are needed for decision-making in clinical practice and could be helpful in the management of patients with NAFLD. The present study showed a significant trend of elder age with the progression of non-alcoholic fatty liver disease. This finding substantiates previous findings in the literature, which suggested that the prevalence of NAFLD increases with increasing age [15]. The present study showed that, regarding gender distribution, there were more males in the control group (45%) compared to the NAFLD group (25%), but the majority was females in the NAFLD group (75%). These results revealed that there was no statistically significant difference between both studied groups according to gender as p = 0.061. The explanation for the gender difference is different distributions of fat mass by gender, e.g., more abdominal visceral adipose tissue in male and more subcutaneous adipose tissue mass in female. Additionally, previous results showed that Hispanic women having a higher risk for NAFLD compared to men, whereas, for the non-Hispanic population, the prevalence of NAFLD is more frequent in males [16]. Additionally, Lonardo et al. mentioned that gender is one of the main cause of variation in NAFLD risk factors. They also detected that NAFLD is more common and more severe in men than women. However, it is more common in women after menopause, indicating that estrogen may be beneficial [17]. In the current study, the incidence of NAFLD has been increasing in concert with the presence of multiple metabolic disorders, such as dyslipidemia, diabetes, hypertension, and visceral obesity. As expected, the incidence of diabetes and hypertension was significantly higher in patients suffering from NAFLD. This is in good agreement with previous studies that mentioned impaired glucose tolerance as an independent risk factor for the progression of NAFLD [18,19]. According to the International Diabetes Federation (IDF), the prevalence of DM among Egyptian adults is 15.2%, which may be an underestimation [20]. Lonardo et al. reported that patients with T2DM had 80% higher liver fat contents compared to non-diabetic patients [21]. Additionally, Lee, et al., (2019), mentioned that compared to the general population (around 25%), 50% to 70% of people with diabetes have NAFLD, and NAFLD severity (including fibrosis) tends to be worsened by the presence of diabetes [22]. Additionally, another study carried out on the Egyptian college students showed that around 1 in 3 had steatosis, and 1 in 20 had fibrosis. The prevalence of NAFLD in young adults was estimated to be 31.6%, which is perfectly in line with the 31.8% prevalence rate found in a meta-analysis of numerous epidemiological studies across general Middle Eastern populations. It is known that the Middle East and North Africa region has one of the highest prevalence rates of NAFLD globally, and that Egypt ranked among the highest 10 nations with obesity prevalence. Combing both may explain our unexpected observation. In our cohort, 59 (49.2%) of participants were overweight or obese [23]. NAFLD is caused by a variety of different molecular pathways and cellular alterations. The molecular pathways of NAFLD pathogenesis in the liver have been identified in several studies. The major genes linked to illness development and the underlying functional pathways are yet unknown, and whether the differentially expressed CD24 is involved in hepatic lipid metabolism is still unclear. Microarray technologies have revealed a large number of new molecular markers (DNA, RNA, and protein) in recent years. Further research is needed to confirm the clinical utility of these impending novel indicators in relation to hepatic steatosis. CD24 is one of these markers, which was recently reported by Huang et al. as a possible biomarker in the course of hepatocyte steatosis [8]. Various studies have recently discovered that CD24 expression is relatively high in many human malignancies, including HCC [24,25,26,27,28]. Additionally, CD24 overexpression has been correlated with increased invasiveness, proliferation, and metastasis [29]. It was previously identified that a subpopulation of adipocyte progenitor cells with the expression of the cell surface molecule CD24 being necessary for reconstitution of white adipose tissue function in vivo as well as being a key regulator of adipogenesis in vitro [30]. In our study, we investigated the association between CD24 gene expression and the prevalence of NAFLD. The current study found that CD24 gene expression was considerably greater in NAFLD cases compared to controls, and the normalized CD24 gene expression in NAFLD was up-regulated 6.56-fold. These findings suggest that the CD24 gene is important in the development of NAFLD. This could be related to CD24 gene expression’s impact on the immune/inflammatory response via T-cell activation [31]. Several immune cell-mediated inflammatory processes are involved in NAFLD and its progression to NASH. They also influence the generation of cytokines by necrotic liver cells [32]. This confirms the previous results detected by Feng et al., who observed the up-regulation of CD24 gene expression in the livers of HFD-induced NAFLD mice and in cultured HepG2 cells exposed to glucolipotoxicity (palmitic acid or advanced glycation end products) [9]. Up until now, the precise role and the underlying mechanisms of CD24 in NAFLD progression remain unclear. However, Huang and his colleague identified the prominent correlation between CD24 and NAFLD/NASH. They mentioned that CD24 could play a key role in one of the pathways that may cause IR and may induce NAFLD/NASH in humans including [“glycolysis/gluconeogenesis”, “p53 signaling pathway” and “glycine”, serine and threonine metabolism [8]. Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients, but without significant difference (p = 0.588). This may be because that all cases included in the present study have mild fibrosis. This results most be confirmed by other studies based on large number of samples and on patients with severe stage of fibrosis. The changes in liver tissue-transcriptome in a subset of 25 mild-NAFLD and 20 NASH biopsies were examined in a cross-sectional study. CD24 was revealed to be one of five differentially expressed genes (DEGs) positively linked with disease severity and to be main classifiers of mild and severe NAFLD [33]. Additionally, CD24-positive cells isolated from hepatocellular carcinoma cell lines exhibited stemness properties, such as self-renewal, chemotherapy resistance, metastasis, and tumorigenicity [34]. These results indicate that CD24 may play a role in hepatocyte injury and promote regeneration during the development and progression of NAFLD. Another Egyptian study detected that CD24 polymorphism 170 CT/TT may affect the incidence of infection with CHC, as well as HCC [35]. They revealed that the P170T allele, which is expressed at a higher level than P170C, encodes a certain protein, which is responsible for the progression of chronic HCV infection by affecting the efficiency of cleavage of posttranslational GPI. Additionally, Robert and Pelletier (2018) showed that the P170T allele affects the progression of chronic HCV infection through posttranslational mechanisms [36]. Another study by Kristiansen et al. (2010) also suggested that CD24 SNPs are prognostic markers for hepatic carcinoma [37]. Interestingly, CD24 was also up-regulated in the NAFLD patients with type 2 diabetes than its expression in non-diabetic cases, but without significant difference. Another study carried out by Shapira et al. (2021) reported that CD24 may negatively regulate peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in male mice. This gene is a regulator of adipogenesis that plays a role in insulin sensitivity, lipid metabolism, and adipokine expression in adipocytes. Furthermore, they concluded the association between the CD24 and insulin sensitivity, suggesting its possible mechanism for diabetes [38]. The current study found CD24 gene expression was considerably greater in NAFLD cases compared to controls. This could indicate that CD24 may contribute to hepatic steatosis, but a current study showed that it cannot be used as an independent predictor of NAFLD. Further studies are required to confer its diagnostic and prognostic value in the detection of NAFLD, as well as to clarify its role in the progression of hepatocyte steatosis in patients with advanced stage of fibrosis and to elucidate the mechanism of this biomarker in the progression of disease. However, our study is limited because of the small sample size, because all participants in this study have early stage of NAFLD, and because accurate diagnosis of liver fibrosis or hepatocellular injury are invasive and very expensive. Although abdominal ultrasonography has low sensitivity for detecting mild-NAFLD as reported in the previous literature, it is the best low-cost available non-invasive technique to detect NAFLD. Because of ethical considerations, we did not rely on the liver biopsy for diagnosis, as none of the patients had clinical manifestations. Moreover, the studied patients considered themselves healthy and refused to undergo further invasive investigations, including pathological examinations via liver biopsy to detect fibrosis.

PMC10000776

Hitha Gopalan Nair,Aneta Jurkiewicz,Damian Graczyk

Inhibition of RNA Polymerase III Augments the Anti-Cancer Properties of TNFα

27-02-2023

RNA polymerase III,cancer,TNFα,NF-κB

Simple Summary Tumour necrosis factor alpha (TNFα) is a cytokine that plays an important role in apoptosis, cell survival, as well as in inflammation and immunity. Although named for its antitumor properties, TNFα has tumour-promoting properties. TNFα is often present in large quantities in tumours, and cancer cells frequently acquire resistance to this cytokine. Identifying the means to sensitise the cancer cells to TNFα would have therapeutical benefits. We, therefore, sought to determine whether inhibition of RNA polymerase III (Pol III), which synthesises several essential components of the protein biosynthetic machinery, would affect the response of cancer cells to TNFα. Here we show that Pol III inhibition augments the cytotoxic and cytostatic effects of TNFα. Our data suggest that targeting Pol III may be a potential therapeutic intervention to treat colorectal cancer. Abstract Tumour necrosis factor alpha (TNFα) is a multifunctional cytokine that plays a pivotal role in apoptosis, cell survival, as well as in inflammation and immunity. Although named for its antitumor properties, TNFα also has tumour-promoting properties. TNFα is often present in large quantities in tumours, and cancer cells frequently acquire resistance to this cytokine. Consequently, TNFα may increase the proliferation and metastatic potential of cancer cells. Furthermore, the TNFα-driven increase in metastasis is a result of the ability of this cytokine to induce the epithelial-to-mesenchymal transition (EMT). Overcoming the resistance of cancer cells to TNFα may have a potential therapeutic benefit. NF-κB is a crucial transcription factor mediating inflammatory signals and has a wide-ranging role in tumour progression. NF-κB is strongly activated in response to TNFα and contributes to cell survival and proliferation. The pro-inflammatory and pro-survival function of NF-κB can be disrupted by blocking macromolecule synthesis (transcription, translation). Consistently, inhibition of transcription or translation strongly sensitises cells to TNFα-induced cell death. RNA polymerase III (Pol III) synthesises several essential components of the protein biosynthetic machinery, such as tRNA, 5S rRNA, and 7SL RNA. No studies, however, directly explored the possibility that specific inhibition of Pol III activity sensitises cancer cells to TNFα. Here we show that in colorectal cancer cells, Pol III inhibition augments the cytotoxic and cytostatic effects of TNFα. Pol III inhibition enhances TNFα-induced apoptosis and also blocks TNFα-induced EMT. Concomitantly, we observe alterations in the levels of proteins related to proliferation, migration, and EMT. Finally, our data show that Pol III inhibition is associated with lower NF-κB activation upon TNFα treatment, thus potentially suggesting the mechanism of Pol III inhibition-driven sensitisation of cancer cells to this cytokine.

Inhibition of RNA Polymerase III Augments the Anti-Cancer Properties of TNFα Tumour necrosis factor alpha (TNFα) is a cytokine that plays an important role in apoptosis, cell survival, as well as in inflammation and immunity. Although named for its antitumor properties, TNFα has tumour-promoting properties. TNFα is often present in large quantities in tumours, and cancer cells frequently acquire resistance to this cytokine. Identifying the means to sensitise the cancer cells to TNFα would have therapeutical benefits. We, therefore, sought to determine whether inhibition of RNA polymerase III (Pol III), which synthesises several essential components of the protein biosynthetic machinery, would affect the response of cancer cells to TNFα. Here we show that Pol III inhibition augments the cytotoxic and cytostatic effects of TNFα. Our data suggest that targeting Pol III may be a potential therapeutic intervention to treat colorectal cancer. Tumour necrosis factor alpha (TNFα) is a multifunctional cytokine that plays a pivotal role in apoptosis, cell survival, as well as in inflammation and immunity. Although named for its antitumor properties, TNFα also has tumour-promoting properties. TNFα is often present in large quantities in tumours, and cancer cells frequently acquire resistance to this cytokine. Consequently, TNFα may increase the proliferation and metastatic potential of cancer cells. Furthermore, the TNFα-driven increase in metastasis is a result of the ability of this cytokine to induce the epithelial-to-mesenchymal transition (EMT). Overcoming the resistance of cancer cells to TNFα may have a potential therapeutic benefit. NF-κB is a crucial transcription factor mediating inflammatory signals and has a wide-ranging role in tumour progression. NF-κB is strongly activated in response to TNFα and contributes to cell survival and proliferation. The pro-inflammatory and pro-survival function of NF-κB can be disrupted by blocking macromolecule synthesis (transcription, translation). Consistently, inhibition of transcription or translation strongly sensitises cells to TNFα-induced cell death. RNA polymerase III (Pol III) synthesises several essential components of the protein biosynthetic machinery, such as tRNA, 5S rRNA, and 7SL RNA. No studies, however, directly explored the possibility that specific inhibition of Pol III activity sensitises cancer cells to TNFα. Here we show that in colorectal cancer cells, Pol III inhibition augments the cytotoxic and cytostatic effects of TNFα. Pol III inhibition enhances TNFα-induced apoptosis and also blocks TNFα-induced EMT. Concomitantly, we observe alterations in the levels of proteins related to proliferation, migration, and EMT. Finally, our data show that Pol III inhibition is associated with lower NF-κB activation upon TNFα treatment, thus potentially suggesting the mechanism of Pol III inhibition-driven sensitisation of cancer cells to this cytokine. Colorectal cancer (CRC) is the third-most commonly diagnosed cancer and the second leading cause of cancer deaths worldwide [1]. CRC is very heterogeneous molecularly, and a wide range of causative genetic aberrations have been identified, including mutations, loss of heterozygosity and epigenetic changes. Although CRC has a substantial heritable component, most CRC cases are sporadic [2]. Moreover, CRC is one of the best examples of the involvement of chronic inflammation in the development of sporadic and heritable forms of this disease [3]. Chronic inflammation triggered by microbial infection, autoimmune diseases, or other pathologies raises the risk of tumorigenesis. Inefficient clearance of infection during chronic inflammation is a major cause of tissue damage and reconstitution. During this process, reactive oxygen species accumulate, leading to DNA damage and mutation. Moreover, cells are continuously proliferating to maintain tissue homeostasis under inflammatory conditions, which can be a major driving force for transforming initial tumour cells [4]. Finally, it is now clear that the tumour microenvironment, which is primarily orchestrated by inflammatory cells, is an indispensable participant in the neoplastic process. Tumour-infiltrating immune cells produce cytokines that activate various transcription factors, which regulate cancer cell survival, growth, proliferation, epithelial-mesenchymal transition (EMT), and metastasis [4]. Interleukin-6 and tumour necrosis factor alpha (TNFα) are cytokines considered to be important players in colorectal cancer development and progression [5]. TNFα is a multifunctional cytokine primarily produced by macrophages and other immune system cells, as well as some non-immune cells, although to a lesser extent [6]. TNF-α was initially discovered and named according to its ability to induce the necrosis of transplanted sarcomas in mice [7]. TNFα appeared to work when injected directly into tumours in high doses, however, its severe toxicity, when administrated systemically, almost entirely hampered its usage in cancer therapy. The only successful therapeutic intervention is the local administration of TNFα (usually in combination with chemotherapy) via isolated limb perfusion to treat soft tissue sarcomas [8]. Apart from its toxicity, TNFα is now believed to have also pro-tumorigenic properties [6]. Consistently this cytokine is frequently present in the tumour microenvironment, and tumour cells usually acquire resistance to TNFα-induced cell death [6]. TNFα signals through two cell surface receptors, TNFR1 and TNFR2, which differ in their signalling activity and expression pattern [9]. TNFR1 is expressed in almost all cell types, whereas the expression of TNFR2 is limited to immune cells and a few other cell types. TNFR1 and TNFR2 have similar extracellular TNF-binding domains, which equally efficiently bind both transmembrane and soluble forms of TNFα. Interestingly, transmembrane TNFα strongly activates signalling through both TNFR1 and TNFR2, and soluble TNFα triggers signalling only through TNFR1 [10]. Signalling through TNFR1 is usually pro-apoptotic, whereas signalling through TNFR2 is usually anti-apoptotic [10], which is a result of structural differences in the cytoplasmic domains of these receptors. TNFR1 contains a cytoplasmic death domain, which is not present in TNFR2 [10]. Upon TNF-α binding to TNFR1, the adaptor protein TNFR1-associated death domain protein (TRADD) is recruited to the cytoplasmic death domain of the receptor along with Receptor Interacting Protein Kinase 1 (RIPK1) and TNF receptor-associated factor 2 (TRAF2). Then, the ubiquitin ligase cellular inhibitors of apoptosis 1/2 (cIAP1/2) are recruited to form the so-called complex I. Within this complex, cIAPs attach ubiquitin chains to themselves and other subunits, leading to the recruitment of the linear ubiquitin chain assembly complex (LUBAC). Linear ubiquitin chains deposited by LUBAC on the complex I components constitute a docking platform for Transforming growth factor-β-activated kinase 1 (TAK1), TAK1-binding proteins 2/3 (TAB2/3), and the inhibitor of κB kinase (IKK) subunit, NEMO. Subsequently, TAK1 phosphorylates IKK, which leads to the activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) transcription factor (for review, see [10]). NF-κB is a crucial transcription factor mediating inflammatory signals. It has also been suggested to have a wide-ranging role in tumour progression, including acceleration of cell proliferation, inhibition of apoptosis, promotion of cell migration and invasion, and stimulation of angiogenesis and metastasis [11]. There are five mammalian members of the NF-κB family of transcription factors, RelA (p65), RelB, c-Rel, NF-κB1 (p50/p105), and NF-κB2 (p52/p100) [12]. NF-κB DNA binding activity consists of many possible homo- and heterodimers, although p50/RelA heterodimers are most commonly observed [13]. Under normal cellular conditions, NF-κB binds to and is negatively regulated by the inhibitor of kappa B (IκB) in the cytoplasm. Following an inflammatory stimulus, IκB is phosphorylated by IκB kinase (IKK) and undergoes proteasomal degradation. This allows NF-κB to translocate to the nucleus, where it regulates the transcription of a wide variety of target genes that induce inflammation, proliferation, and cell survival [12]. Notably, inhibition of this NF-κB-mediated pro-survival response may destabilise the TNFR1-bound complex I and lead to the formation of the pro-apoptotic complex II, consisting of TRADD, RIPK1, FAS-associated death domain protein (FADD), and caspase-8 [14]. When formed, complex II triggers apoptosis. The pro-inflammatory and pro-survival function of NF-κB can be disrupted by blocking macromolecule synthesis. Consistently, general RNA or protein synthesis inhibitors Actinomycin D (ActD) or cycloheximide (CHX), respectively, strongly sensitise cells to TNFα-induced cell death [15]. In mammalian cells, there are three DNA-dependent RNA polymerases (Pols). Pol I synthesises ribosomal RNA (rRNA), whereas Pol II synthesises mostly mRNAs and long non-coding RNA and micro RNAs. Pol III synthesises several essential components of the protein biosynthetic machinery, including tRNA, 5S rRNA, 7SL RNA, and a subset of small non-coding RNAs required for the maturation of other RNA molecules (U6 RNA). These untranslated RNAs are essential for cell growth, proliferation, and immune responses [16,17]. Moreover, an elevated Pol III activity is a recurring feature of murine and human tumours, and inhibition of Pol III has anti-tumorigenic effects [17,18,19,20]. ActD inhibits the activity of all three RNA polymerases, with Pol I being the most sensitive [21]. It is believed that this drug sensitises the cells to TNFα mainly through the prevention of Pol II-dependent gene expression (regulated by NF-κB) [22,23,24]. While this is most likely the case, no studies have directly explored the possibility that specific inhibition of Pol III activity sensitises cancer cells to TNFα. In the current study, we show that in colorectal cancer cells, Pol III inhibition augments the cytotoxic and cytostatic effects of TNFα. We also show that Pol III inhibition blocks TNFα-induced migration and EMT. Importantly, Pol III inhibition alone has very little effect on the cells. Finally, we show that Pol III inhibition impinges on NF-κB activity, which may potentially explain the sensitisation of cancer cells to TNFα. Cells were cultured in a humidified incubator with 5% CO2 at 37 °C. HCT116 (ATCC® CCL-247™) and LoVo (ATCC® CCL-229™) colorectal cancer cells were grown in DMEM supplemented with 2 mM L-glutamine, penicillin (100 U/mL), streptomycin (100 U/mL), and 10% foetal bovine serum (FBS), unless otherwise stated. Generated cell lines were cultured in the same medium. Cell lines were routinely tested for mycoplasma presence using the MycoSpy detection kit (Biontex, Munich, Germany, Cat. No. M020-025). When indicated, cells were treated with DMSO (PanReac, Darmstadt, Germany, Cat. No. A3672,0100), TNF-α (Peprotech, London, UK, Cat. No. 300-01A), and ML-60218 (Merck, Darmstadt, Germany, Cat. No. 557403). The cells were plated 24 h before the experiment, followed by treatment with DMSO, TNFα, ML-60218 alone, or TNFα and ML-60218 simultaneously for the indicated time. Cell metabolic activity was evaluated using the MTT assay. This assay is based on the conversion of MTT (3-(4,5-dimethyl thiazolyl)-2,5-diphenyltetrazolium bromide) to a blue/purple formazan crystal by NADPH or NADH produced by dehydrogenase enzymes in metabolically active cells. HCT116 and LoVo cells were seeded at a density of 5 × 103 cells per well in 96-well plates and incubated at 37 °C overnight. After 24 h, cells were treated with DMSO, TNFα, ML-60218 alone, or TNFα and ML-60218 simultaneously. The MTT assay was performed according to the manufacturer’s instructions (Promega, Madison, WI, USA, Cat. No. PRG8080). Briefly, MTT reagents were added (final concentration of 0.5 mg/mL) to each well. The microplate was incubated at 37 °C in 5% CO2 for 4 h (until the formazan crystals appeared). After incubation, 100 µL of solubilisation buffer was added to each well. Following complete solubilisation, the plate was read at 590 nm using a microplate reader (Beckman Coulter, Brea, CA, USA, DTX880). The cells were seeded at a density of 5 × 104 cells for each condition on a 6 cm dish. The following day, the cells were treated with DMSO, TNFα, ML-60218 alone, or TNFα and ML-60218 simultaneously. Following the removal of the medium, the cells were gently rinsed with PBS, and a new medium devoid of the medication was introduced. The plates were then kept in the incubator in the regular medium for 7 days. Then, the medium was removed, and the cells were washed with PBS, fixed, and stained with a crystal violet solution (0.05% crystal violet by volume, 1% formaldehyde, 1× PBS, and 1% methanol). Following a PBS washing, the cells were allowed to dry at room temperature before being imaged. HCT116 cell lines stably overexpressing tRNAiMet or tRNAeMet were generated using lentiviral transduction. HEK293T cells were transfected with pLHCX, pLHCX-tRNAiMet, or pLHCX-tRNAeMet plasmids [25] along with the lentiviral packaging vectors psPAX2 and pDM2.G. psPAX2 and pDM2.G were a gift from Didier Trono (Addgene plasmid #12260; https://www.addgene.org/12260 (Accessed on 1 December 2022); RRID: Addgene_12260, Addgene plasmid #12259; https://www.addgene.org/12259 (Accessed on 1 December 2022); RRID: Addgene_12259). After 48 h, the medium containing lentiviral particles was collected and filtrated using sterile 0.45-μm filters (Merck, Darmstadt, Germany, Cat. No SLHP033RS or Sarstedt, Nümbrecht, Germany, Cat. No. 83.1826). The medium filtrated was used to infect HCT116 cells that were cultured in 6-well plates. The cell lines stably expressing pLHCX, pLHCX-tRNAiMet, and pLHCX-tRNAiMet were selected with puromycin (1 µg/mL) until there were no live cells on the control plate. Pools of cells were used for the experiments. Cells were washed with ice-cold phosphate-buffered saline (PBS) and harvested by scraping directly into lysis buffer (100 mM NaCl, 50 mM HEPES (pH 7.9), 1 mM EDTA, protease and phosphatase inhibitor, 0.05% NP-40, 0.1% SDS). Extracts were sonicated using a Bioruptor (Diagenode) and spun for 20 min at 13,000 rpm at 4 °C. Supernatants were collected, and protein concentration was assessed using the Pierce BCA protein assay (Thermo Scientific, Waltham, MA, USA, Cat. No. 23225). A total of 25 µg of proteins was separated on SDS polyacrylamide gels, transferred to a PVDF (G.E. Healthcare, Chicago, IL, USA, Cat. No. 10600021) or nitrocellulose membrane (G.E. Healthcare, Cat. No. GE10600001), and incubated with antibodies in 5% w/v skimmed milk in Tris-buffered saline–Tween (TBST) and then probed with the appropriate antibodies. The antibodies used are listed in Supplementary Table S1. Original Western blots can be found in Supplementary Figure S1. Cells were plated at a density of 4 × 106 cells/dish in a 10 cm dish, treated as described in the figure legend, and harvested. Fractionation and nuclei isolation was performed as described in [26]. Total RNA was isolated from cells using RNA Extracol (EURx, Gdańsk, Poland, Cat. No. E3700) according to the manufacturer’s instructions. Then, 100 ng of RNA was used for cDNA synthesis using a QuantiTect reverse transcriptase kit (Qiagen, Hilden, Germany, Cat. No. 205314). To increase the efficiency of cDNA synthesis from tRNAs, oligonucleotides specific to the 3′ end of tRNA were added to the reaction mixture (Supplementary Table S2), each at the final concentration of 1 µM as described before [16]. The oligonucleotide sequences used for qPCR are listed in Supplementary Table S3. Quantitative PCR was performed using a Roche Light Cycler 480 System. The thermal cycling conditions were composed of 20 s at 95 °C, 30 s at 61 °C, and 20 s at 72 °C. After PCR amplification, each sample was subjected to a melting curve analysis to confirm that a single product with the predicted melting curve characteristics was achieved. Each sample was run in technical duplicate or triplicate. A non-reverse transcriptase control, a no-template control, and cDNA dilutions for the standard curve were all present on each plate. The effectiveness of PCR ranged from 90% to 100%. The Light Cycler 480 Software was used to process the data, and Microsoft Excel was used for further analysis. HCT116 and LoVo cells were seeded at a density of 5 × 104 cells/well (100 µL/well) in each well of the Image-lock 96-well plate (Sartorius, Göttingen, Germany). The cells were allowed to settle at ambient temperature for 10 min, and then the plates were kept in a 37 °C incubator with 5% CO2 overnight. The next day the Image-lock plate was carefully removed from the incubator, and a 96-well Wound-maker (Sartorius) was used to create wounds in all wells simultaneously. Immediately after making the wound, cells were washed twice with PBS and replenished with a medium containing DMSO, TNFα, ML-60218 alone, or TNFα and ML-60218. The cells were then kept in the IncuCyte Live cell imaging system and monitored for 72 h at 3 h intervals. Cell migration was analysed using IncuCyte 2019B Rev2 software (Sartorius). The cells were seeded at a density of 5 × 103 cells per well in a 96-well plate and allowed to attach overnight at 37 °C in the incubator. Then, the cells were treated with DMSO, TNFα, ML-60218 alone, or TNFα and ML-60218 simultaneously, and the plates were then kept in the IncuCyte S3 Live-Cell Analysis System (Sartorius) to monitor the confluency for 48 h at 3 h intervals. Cell confluency was analysed using IncuCyte 2019B Rev2 software. For cell death assessment, HCT116 cells were treated with DMSO, TNFα, ML-60218 alone, or TNFα and ML-60218 simultaneously for 24 h. After treatment, both the adherent and floating cells were harvested into 15 mL falcon tubes. The cells were then washed with ice-cold PBS. Then, the cells were resuspended in 1 mL of PBS solution containing propidium iodide and transferred into a FACS tube. The flow cytometry analysis was performed using an Attune NxT flow cytometer (Thermo Scientific). Where required, the cells were pre-treated for 3 h with broad-spectrum caspase inhibitor Q-VD-OPh and processed for cell death analysis. The cells were seeded at a density of 5 × 103 cells per well in a 96-well plate and allowed to attach overnight at 37 °C in the incubator. The cells were then treated with DMSO, TNFα, ML-60218 alone, or TNFα and ML-60218 in the presence of Sytox Green (Thermo Scientific, Cat. No. S34860) in the medium. The plates were then kept in the IncuCyte S3 Live-Cell Analysis System (Sartorius). The number of Sytox green positive (dead) cells was acquired at 48 h of treatment. The cell death was calculated by normalising the number of Sytox green positive cells to the cell confluency. The analysis was performed using IncuCyte 2019B Rev2 software. HCT116-Dual™ cells (Invivogen, San Diego, CA, USA) were used to assess NF-κB activity. HCT116-Dual™ cells express a secreted embryonic alkaline phosphatase (SEAP) reporter gene under the control of NF-κB binding sites. HCT116-Dual™ cells were cultured in a medium containing heat-inactivated (56 °C for 30 min) serum, seeded on a 96-well plate, and allowed to attach overnight in the incubator. Following incubation, the cells were treated with DMSO, TNFα, ML-60218 alone, TNFα, and ML-60218 simultaneously for 24 h. Then, the samples were processed according to manufacturer’s instructions. The absorbance at 655 nm was measured using a microplate reader (Beckman Coulter, DTX880). Immunofluorescence staining was used to detect E-cadherin expression in HCT116 and LoVo cells. Cells were grown on coverslips for 24 h. Following appropriate treatment, immunostaining was performed according to a previously described procedure [27]. The E-cadherin antibodies were purchased from Cell Signaling Technology (Mouse mAb #14472), and the Alexa Fluor™ 488-conjugated secondary antibodies were purchased from Thermo Scientific (Goat anti-mouse IgG1# A-21121). Nuclei were stained with Hoechst dye (1:5000, Hoechst 33342). High-content cell imaging was performed using a ScanR automated microscope (Olympus) with a UPlanSApo 20.0× objective. Image analysis was performed using ScanR 2.7.2 software (Olympus, Tokyo, Japan). Representative images were taken with a Nikon C1 confocal laser scanning microscope with Plan Apo 60.0×/1.40 NA with an oil objective. The images were processed using Nikon EZ-C1 software. To test whether Pol III inhibition sensitises colorectal cancer cells to TNFα treatment, we first investigated the viability of cells using an MTT colorimetric assay. HCT116 and LoVo human colorectal cancer cells were treated with DMSO, TNFα, RNA polymerase III inhibitor (ML-60218) alone, or a combination of TNFα and ML-60218. Treatment of HCT116 cells with TNFα alone slightly, and not statistically significantly, increased their viability (Figure 1a). ML-60218 treatment alone did not affect the cells, although it downregulated Pol III activity (Supplementary Figure S2). The combination of TNFα and ML-60218, however, markedly reduced the viability of HCT116 cells. In LoVo cells, similar results were obtained, with the exception that TNFα significantly increased cell viability (Figure 1b). We also monitored cell proliferation in real time. In HCT116 cells, the treatment with TNFα or ML-60218 alone led to a slight decrease in proliferation (Figure 1c). However, the combination of TNFα and ML-60218 strongly inhibited cell proliferation. In LoVo cells, TNFα substantially increased, whereas ML-60218 modestly decreased cell proliferation (Figure 1d). Notably, the combination of TNFα and ML-60218 strongly suppressed the proliferation of LoVo cells. In the case of HCT116 cells, the proliferation results do not fully overlap with MTT assay data, which showed a modest and statistically non-significant increase in cell viability upon TNFα treatment. In proliferation experiments, we observed the reverse effect. We speculate that in these cells a combination of effects occurs: a slight increase in proliferation and concomitant induction of cell death in some cells (see below). As a consequence, during the early stages of cell death, some cells detach from the bottom of the plate under TNFα treatment (we see an increase in the number of floating cells). Floating cells, not necessarily dead yet, are out of the focus in the IncuCyte device while still contributing to overall metabolic activity in the MTT assay. Nevertheless, MTT and proliferation assay results are consistent in both cell lines upon concomitant treatment with TNFα and ML-60218 and show the detrimental effect of the treatment. To further support our observations, we also performed clonogenic assays. Treatment with TNFα or ML-60218 alone slightly, and consistently with proliferation results, decreased colony formation by HCT116 cells (Figure 1e,f). Furthermore, the combination of TNFα and ML-60218 led to even more potent inhibition of the colony-forming ability of these cells. In LoVo cells, treatment with TNFα alone significantly increased the number of colonies, whereas the addition of ML-60218 had a slightly opposite effect (Figure 1g,h). ML-60218 alone had no apparent effect on colony formation in LoVo cells. Altogether, these data suggest that RNA Polymerase III inhibition augments the cytostatic/cytotoxic effect of TNFα in HCT116 cells and completely reverses the proliferation-stimulating effect of this cytokine in LoVo cells. The observed decrease in the overall viability of the cells (the MTT assay) or the cell number (proliferation monitoring) may stem from a reduction of cell proliferation, increased cell death, or a combination of both. Indeed, upon microscopic inspection, we noticed some cell death in HCT116 cells treated with TNFα, which was substantially increased upon concomitant treatment with ML-60218. Therefore, we sought to look in more detail whether ML-60218 augments the cytotoxic effects of TNFα. To this end, HCT116 and LoVo cells were treated as described above. The propidium iodide (PI)-exclusion method combined with FACS showed that in HCT116 cells, treatment with ML-60218 does not affect cell viability, whereas TNFα treatment slightly induces cell death (Figure 2a). However, the combination of TNFα and ML-60218 leads to a significant induction of cell death. When treated with TNFα, LoVo cells are difficult to detach from tissue culture dishes. The attempts to prolong the incubation with trypsin lead to a substantial decrease in cell viability, which hinders the usage of PI-exclusion combined with FACS. We, therefore, monitored the cell death in LoVo cells using the IncuCyte live-cell imaging system. In this case, as a reference, we included cells treated with a combination of TNFα and cycloheximide, which triggers rapid cell death [24] (Figure 2b). Of note, we did not use IncuCyte for monitoring cell death in HCT116 cells, because when they die, they detach from the bottom of the plate, become out of focus, and cannot be counted. The results showed that in LoVo cells, TNFα treatment alone slightly induces cell death and that the combination of TNFα and ML-60218 does not further potentiate this effect (Figure 2b). ML-60218 alone had minimal impact on the survival of LoVo cells. Thus, in LoVo cells, ML-60218 treatment does not potentiate TNFα-induced cell death as in HCT116 cells but instead has a cytostatic effect. We then sought to investigate further the type of death the HCT116 cells undergo. TNFα induces apoptosis mediated by caspases [28], and we explored this possibility. Western blotting analysis showed that concomitant treatment of cells with ML-60218 and TNFα leads to the cleavage of caspase 8, the most upstream protease participating in the activation cascade responsible for death receptor-induced cell death (Figure 2c) [29]. We also observed executioner caspase 7 and PARP cleavage, a hallmark of apoptosis (Figure 2c,d) [30]. Furthermore, while TNFα alone slightly induced PARP and caspase 7 and 8 cleavage, ML-60218 had no effect. These results are in agreement with PI-exclusion experiments. Finally, to further validate that the observed cell death type is apoptosis, the HCT116 cells were pre-treated with a broad-spectrum caspase inhibitor, Q-VD-Oph (Quinoline-Val-Asp-Difluorophenoxymethylketone) [31]. Then, the cells were treated as above. PI-exclusion experiments showed that inhibition of caspases blocked the cell death induced by TNFα and, most importantly, by concomitant treatment of cells with TNFα and ML-60218 (Figure 2e). Altogether, these data suggest that inhibition of RNA Polymerase III enhances TNFα-induced apoptosis in HCT116 cells. The data presented so far show that in the HCT116 cells, which are slightly sensitive to TNFα, Pol III inhibition strongly augments the cytotoxicity of this cytokine. On the other hand, in the LoVo cells where TNFα does not induce cell death but rather stimulates their proliferation, Pol III inhibition has a cytostatic effect, only marginally causing cell death. TNFα is known to promote cancer cell proliferation [32,33], which we could observe in our experiments with LoVo cells. More importantly, we could also see a decrease in cell proliferation when additional treatment with Pol III inhibitor was introduced (Figure 1d,f). Cyclin D1 is directly implicated in stimulating the proliferation of cells, and it was shown to be upregulated by TNFα [33,34]. We, therefore, asked whether ML-60218 treatment can block TNFα-induced upregulation of cyclin D1 protein levels. To address this, we treated LoVo and HCT116 cells with DMSO, TNFα, ML-60218, or TNFα simultaneously with ML-60218. Please note that since HCT116 cells are slightly sensitive to TNFα, from this point forward, the experiments with these cells were performed using a lower concentration of this cytokine to avoid the confounding effect of cell death. The results showed that while TNFα treatment induced cyclin D1 protein levels both in LoVo and HCT116 cells, the addition of ML-60218 completely blocked this effect (Figure 3a–d). ML-60218 treatment alone has a very modest, if any, impact on cyclin D1 protein levels. These data show that inhibition of RNA Polymerase III blocks TNFα-driven induction of cell proliferation marker, cyclin D1, in CRC. The results also suggest that the inhibition of cell proliferation upon combined treatment of cells with TNFα and ML-60218 may result from decreased cyclin D1 protein levels. The ability of cancer cells to migrate is crucial for metastasis [35]. It has been reported that TNFα enhances the migration of cancer cells, including colorectal cancer cells [36,37]. We employed a scratch wound-healing assay to investigate whether Pol III inhibition affects TNFα-induced alterations in cell migration. HCT116 and LoVo cells were treated with DMSO, TNFα, ML-60218 alone, or TNFα simultaneously with ML-60218. Please note that for the HCT116 cell line, similarly as for proliferation assays, the lower concentration of TNFα was used to decrease potential cell death. Consistently with literature data, we observed that TNFα enhanced the speed of wound closure for both cell lines tested, although it was less apparent for the LoVo cell line (Figure 4). ML-60218 treatment alone had little effect on HCT116 cell migration (statistically non-significant downregulation was observed at 48 h and 72 h of treatment). In LoVo cells, ML-60218 treatment led to the modest inhibition of cell migration. Importantly, the combination of TNFα with ML-60218 significantly decreased the migratory potential of HCT116 cells (Figure 4a,b). In this case, the migration was even slower than in control, DMSO-treated samples. In LoVo cells, although a similar effect was observed, ML-60218 only partially blocked the TNFα-induced increase of migration (Figure 4c,d). Overall, these data suggest that Pol III inhibition decreases TNFα-induced migration of CRC cells. The additive effect of TNFα and Pol III inhibitor in HCT116 cells may result from the higher sensitivity of these cells to the combination of treatments. During the epithelial-to-mesenchymal transition (EMT), the expression of epithelial markers decreases while the expression of mesenchymal markers increases [38]. E-cadherin is an adherens junction protein that is one of the critical elements involved in forming intercellular contacts in epithelial cells. Downregulation of E-cadherin is frequently observed in epithelial tumours and is a hallmark of EMT [39]. TNFα potentiates EMT in several cancers by downregulating epithelial markers (e.g., E-cadherin) and stimulating the expression of mesenchymal markers [36,37]. Given our results showing an effect of Pol III inhibition on TNFα-induced cell migration, we sought to determine whether it is associated with changes in EMT. To address this, we monitored the E-cadherin levels. HCT116 and LoVo cells were treated with DMSO, TNFα, ML-60218 alone, or TNFα simultaneously with ML-60218. In agreement with the literature data, Western blotting and immunofluorescence experiments showed that TNFα markedly downregulates the expression of E-cadherin in HCT116 cells (Figure 5a–d). Notably, this effect was entirely blocked by ML-60218. In LoVo cells, we could not detect E-cadherin by Western blotting, and only a weak signal using immunofluorescence was observed. This is consistent with previous data showing much lower levels of E-cadherin in LoVo cells as compared to HCT116 cells [40]. Nevertheless, a slight downregulation of E-cadherin was observed in TNFα-treated cells (Figure 5e,f). Similarly, as in HCT116 cells, ML-60218 blocked the effect of TNFα in LoVo cells (Figure 5f). There is a clear difference in regards to E-cadherin levels between HCT116 and LoVo cells. It is, therefore, plausible that LoVo cells have partially gone through EMT and have more mesenchymal-like characteristics as compared to HCT116 cells, which display a more epithelial-like phenotype [40]. We, therefore, tested the mRNA levels of Fibronectin 1, a mesenchymal marker, which is an essential component of the extracellular matrix that links collagen fibres to integrins on the surface of the cells [41]. RT-qPCR experiments showed that in HCT116 cells, Fibronectin 1 mRNA levels were below the detection threshold. In LoVo cells, however, we could observe an increase in Fibronectin 1 mRNA levels in TNFα-treated cells (Figure 5g). Notably, concomitant treatment of cells with ML-60218 blocked the effect of TNFα. ML-60218 alone also slightly reduced the levels of Fibronectin 1 mRNA. Overall, these data and data regarding cell migration show that inhibition of Pol III activity blocks TNFα-induced EMT in colorectal cancer cells. Of note, the presence of easily detectable Fibronectin 1 mRNA in LoVo cells and its lack in HCT116 cells further suggests that LoVo cells have more mesenchymal-like characteristics as compared to HCT116 cells. We previously showed that TNFα induces Pol III activity in macrophages [16]. It has also been reported that increased initiator methionine tRNA (tRNAiMet) expression may drive cancer cell proliferation and migration [25,42]. Therefore, we speculated that the increased proliferation and migration of CRC cells treated with TNFα may also result from increased tRNAiMet levels and that ML-60218 would act by preventing tRNAiMet upregulation. We first assessed whether the levels of tRNAiMet in HCT116 and LoVo cells treated with TNFα were upregulated. We also tested whether ML-60218 blocks this effect, if any. The data show that in HCT116 cells, TNFα treatment significantly upregulated tRNAiMet levels, which was blocked by Pol III inhibitor (Supplementary Figure S3a). In LoVo cells, this effect was also visible. However, it was less pronounced and did not reach statistical significance (Supplementary Figure S3b). We then sought to determine whether overexpression of tRNAiMet in HCT116 can stimulate their proliferation and migration. We prepared cell lines stably overexpressing tRNAiMet or, as controls, cells overexpressing elongator methionine tRNA (tRNAeMet) or cells harbouring an empty vector. Using RT-qPCR, we confirmed the efficiency of tRNAiMet overexpression (Supplementary Figure S3c). However, our results show that tRNAiMet overexpression did not affect either the proliferation or migration of HCT116 cells (Supplementary Figure S3d–f). Thus, these data suggest that it is unlikely that the observed effects of TNFα on colorectal cancer cell proliferation and migration are solely a result of higher tRNAiMet expression. Consequently, we also conclude that the impact of ML-60218 on the TNFα-treated cells is most likely not exclusively dependent on the prevention of tRNAiMet upregulation. NF-κB is an inducible nuclear transcription factor involved in immune responses, cell proliferation, and apoptosis. NF-κB is strongly activated in response to TNFα and contributes to cell survival and proliferation [13,23,24,43,44]. NF-κB controls the expression of several genes encoding anti-apoptotic proteins, such as c-FLIP, Bcl-2, Bcl-xL, and cIAP2 [45], as well as proteins involved in proliferation, such as cyclin D1 [43,44]. Disruption of NF-κB activity either by genetic manipulations or by chemical inhibition of IKK kinase, which activates this transcription factor, renders the cells highly susceptible to TNFα-induced cell death [22,23,24]. We thus speculated that Pol III inhibition might affect the activity of NF-κB and abolish its protective and pro-proliferative functions. To validate this possibility, nuclear extracts were prepared from HCT116 cells treated with DMSO, TNFα, ML-60218, or TNFα simultaneously with ML-60218 and analysed by Western blotting. The results show that a small amount of p65, an NF-κB subunit, is present in the nucleus, even in the unstimulated control cells (Figure 6a,b), which is consistent with the notion that some cancer cells may display constitutive activity of the NF-κB pathway [12]. Nevertheless, we could observe even higher nuclear levels of p65 in TNFα-treated cells as compared to the control (Figure 6a,b). Notably, ML-60218 treatment blocked the TNFα-induced localisation of p65 to the nucleus in HCT116 cells. ML-60218 alone had a minimal and not statistically significant effect on p65 localisation to the nucleus. Neither ML-60218 alone nor the combination of ML-60218 and TNFα downregulated total cellular levels of p65. We further validated these observations using HCT116-Dual™ cells (Invivogen) designed to monitor the NF-κB signal transduction pathway. The cells were treated as above, and the activity of the secreted embryonic alkaline phosphatase was assessed colorimetrically. In response to TNFα, the NF-κB activity was strongly induced in these cells (Figure 6c), whereas the addition of ML-60218 partially blocked this effect. Please note that the effect in reporter cells is less robust as compared to the effect on p65 localisation. This is most likely because secreted embryonic alkaline phosphatase accumulates in the medium over time, whereas the fractionation shows a snapshot of p65 localisation. Nevertheless, these data suggest that Pol III inhibition partially blocks the activation of NF-κB in response to TNFα. We also assessed the nuclear localisation of p65 in LoVo cells. In contrast to HCT116, the nuclear levels of p65 were high in the control cells, and we did not see an increase after TNFα treatment (Figure 6d,e). The constitutive activation of NF-κB in these cells was observed previously [46,47]; thus, it is possible that TNFα treatment is not able to stimulate p65 nuclear localisation further. Nevertheless, treatment of cells with the combination of TNFα and ML-60218 significantly downregulated nuclear p65 levels. We also observed slight downregulation of nuclear p65 levels in ML-60218-treated LoVo cells. These data further suggest that the inhibition of Pol III affects NF-κB activity. Of note, the high activity of NF-κB in LoVo cells may explain their resistance to TNFα-induced cell death and low levels of E-cadherin. cFLIP and cIAP1/2 encode anti-apoptotic proteins, and their expression is regulated by NF-κB [45]. Given the altered localisation of the NF-κB subunit, p65, we asked whether the expression of cFLIP and cIAP1/2 would also be altered. We could not detect cFLIP or cIAP2 proteins using Western blotting. We also tested the mRNA levels of cFLIP and cIAP2 using RT-qPCR and found the Cp values were very high. We thus concluded they are not expressed or are expressed at very low levels in HCT116 cells. We could, however, detect cIAP1 protein. The Western blotting showed that cIAP1 is slightly but not statistically significantly downregulated upon TNFα treatment (Figure 6e). ML-60218 alone did not affect cIAP1 protein levels. However, the combination of ML-60218 and TNFα substantially downregulated the levels of cIAP1 protein. Thus, the strong downregulation of cIAP1 may potentially explain increased cell death upon concomitant treatment with ML-60218 and TNFα. TNFα treatment alone very rarely induces cancer cell death, as the cells acquire resistance to this cytokine [6]. The cytotoxicity of TNFα can be enhanced by treating cells with, for example, translation or transcription inhibitors. TNFα is frequently present in tumours in large quantities, which can be exploited by delivering drugs that sensitise cells to this cytokine. Our results show that inhibition of Pol III may serve as a potential therapeutic intervention. Importantly, our data suggest that Pol III inhibitor alone has minimal impact on the CRC cells and is mainly limited to anti-proliferative and anti-migratory properties, resembling the effects of some commonly used anti-cancer drugs [48]. In our hands, Pol III inhibition does not induce cell death in colorectal cancer cells. These observations may be somewhat counterintuitive, given the housekeeping role of Pol III products. However, it is essential to note that Pol III products are abundant and relatively stable. Thus, the effect of Pol III inhibition may be modest, not immediate, and observed under specific environmental conditions, e.g., inflammatory response. Indeed, we previously showed that inhibition of Pol III in macrophages hampers their pro-inflammatory response upon treatment with lipopolysaccharides, a cell wall component of gram-negative bacteria [16]. Thus, Pol III inhibition may affect a specific subset of proteins, for example, those with a high turnover rate (short half-life). This would be significantly exacerbated in conditions of higher protein synthesis demand, like immune response triggered by TNFα. The Pol III inhibition would then rather affect cellular signalling pathways. The plausibility of this scenario is substantiated by the fact that in the case of E-cadherin, we observe a lack of its TNFα-induced downregulation when cells are additionally treated with a Pol III inhibitor (Figure 5). The lower NF-κB nuclear localisation in cells treated with a combination of TNFα and ML-60218 may also partially explain the phenotypes observed in our current work. Firstly, NF-κB is known to positively regulate cyclin D1 through direct binding within the CCND1 gene promoter [43,44]. Thus, NF-κB inactivation may prevent cyclin D1 upregulation and cell cycle acceleration, a phenomenon observed by us and previously by others [43,44]. Secondly, our results show that in cells treated with TNFα alone, there is a substantial downregulation of E-cadherin, which is blocked by additional treatment of cells with Pol III inhibitor. This observation is consistent with previous evidence showing that NF-κB negatively regulates E-cadherin levels by inducing the expression of transcriptional repressors ZEB1 and TWIST1 [36,49]. Moreover, TWIST1 promotes EMT and enhances the motility of several cancer cells [50,51]. Thus, it is possible that ML-60218 indirectly affects TWIST1 by blocking NF-κB activity and altering the migratory potential of colorectal cancer cells. Whether this is the case remains to be elucidated. It has been known for over two decades now that inhibition of NF-κB strongly sensitises cells to TNFα-induced cell death [22,23,24]. Generally, the fate of the cell treated with TNFα depends on the balance between pro-apoptotic and anti-apoptotic signalling. High activity of NF-κB confers the resistance of cells to this cytokine, and insufficient NF-κB activation tips the balance towards cell death. This is because NF-κB controls the expression of several anti-apoptotic genes, including c-FLIP, B-cell lymphoma-2 (BCL-2), BCL-xL, and cIAP1/2 [45]. Thus, the enhanced apoptosis we observe in cells upon concomitant treatment with TNFα and ML-60218 may result from lower levels of anti-apoptotic proteins, whose gene expression is regulated by NF-κB. Alternatively, decreased Pol III activity may directly impinge on the synthesis of these anti-apoptotic proteins. Pol III inhibition could especially affect MCL-1 and BCL-xL, which have short half-lives [52,53]. A subtle change in tRNA repertoire could slow down the synthesis of these proteins, tip the balance, and push the cells towards cell death. Further studies are needed to unequivocally determine the mechanism whereby ML-60218 sensitizes the cells to TNFα and whether the downregulation of NF-κB contributes to the observed phenotypes. The current study shows that treating colorectal cancer cells with ML-60218 augments prototypical functions of TNFα. In particular, we showed that ML-60218 treatment increases the cytotoxic and cytostatic effects of TNFα. Furthermore, ML-60218 blocks TNFα-induced cell migration and EMT. The observed effects correlate with the lower activity of NF-κB. Notably, ML-60218 seems not to have a cytotoxic effect on colorectal cancer cells. Given that TNFα is present in the tumour microenvironment, the administration of ML-60218 systemically could have an effect locally in the tumours, thus having a lower impact on other tissues. Indeed, this is a beneficial setting from the standpoint of potential therapeutical application. Interestingly, in recent years, RNA polymerase I (Pol I), which is responsible for synthesising ribosomal RNA, and Pol III, considered a housekeeping enzyme, have emerged as a promising anti-cancer target [54]. Given the stability of Pol I and Pol III products, inhibiting these key enzymes is not necessarily linked with extensive cytotoxicity. The high dependence of cancer cells on increased translation rates may render them particularly vulnerable to Pol I/III inhibition while sparing the normal cells. Moreover, the tumour microenvironment, where inflammatory and proliferative signalling dominates and increased translation occurs, may also be a susceptibility spot where the inhibition of Pol I/III would have a beneficial outcome. We hope our results will encourage other scientists to investigate further Pol III inhibitors as potential anti-cancer drugs. Of particular need now is research involving animal models.

PMC10000778

Pusheng Yang,Jiawei Lu,Panpan Zhang,Shu Zhang

Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism- and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma

24-02-2023

lipid metabolism,ferroptosis,immunotherapy,prognostic marker,uterine corpus endometrial carcinoma

(1) Background: The effect of tumor immunotherapy is influenced by the immune microenvironment, and it is unclear how lipid metabolism and ferroptosis regulate the immune microenvironment of uterine corpus endometrial carcinoma (UCEC). (2) Methods: Genes associated with lipid metabolism and ferroptosis (LMRGs-FARs) were extracted from the MSigDB and FerrDb databases, respectively. Five hundred and forty-four UCEC samples were obtained from the TCGA database. The risk prognostic signature was constructed by consensus clustering, univariate cox, and LASSO analyses. The accuracy of the risk modes was assessed through receiver operating characteristic (ROC) curve, nomogram, calibration,, and C-index analyses. The relationship between the risk signature and immune microenvironment was detected by the ESTIMATE, EPIC, TIMER, xCELL, quan-TIseq, and TCIA databases. The function of a potential gene, PSAT1, was measured by in vitro experiments. (3) Results: A six-gene (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) risk signature based on MRGs-FARs was constructed and evaluated with high accuracy in UCEC. The signature was identified as an independent prognostic parameter and it divided the samples into high- and low-risk groups. The low-risk group was positively associated with good prognosis, high mutational status, upregulated immune infiltration status, high expression of CTLA4, GZMA and PDCD1, anti-PD-1 treatment sensitivity, and chemoresistance. (4) Conclusions: We constructed a risk prognostic model based on both lipid metabolism and ferroptosis and evaluated the relationship between the risk score and tumor immune microenvironment in UCEC. Our study has provided new ideas and potential targets for UCEC individualized diagnosis and immunotherapy.

Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism- and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma (1) Background: The effect of tumor immunotherapy is influenced by the immune microenvironment, and it is unclear how lipid metabolism and ferroptosis regulate the immune microenvironment of uterine corpus endometrial carcinoma (UCEC). (2) Methods: Genes associated with lipid metabolism and ferroptosis (LMRGs-FARs) were extracted from the MSigDB and FerrDb databases, respectively. Five hundred and forty-four UCEC samples were obtained from the TCGA database. The risk prognostic signature was constructed by consensus clustering, univariate cox, and LASSO analyses. The accuracy of the risk modes was assessed through receiver operating characteristic (ROC) curve, nomogram, calibration,, and C-index analyses. The relationship between the risk signature and immune microenvironment was detected by the ESTIMATE, EPIC, TIMER, xCELL, quan-TIseq, and TCIA databases. The function of a potential gene, PSAT1, was measured by in vitro experiments. (3) Results: A six-gene (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) risk signature based on MRGs-FARs was constructed and evaluated with high accuracy in UCEC. The signature was identified as an independent prognostic parameter and it divided the samples into high- and low-risk groups. The low-risk group was positively associated with good prognosis, high mutational status, upregulated immune infiltration status, high expression of CTLA4, GZMA and PDCD1, anti-PD-1 treatment sensitivity, and chemoresistance. (4) Conclusions: We constructed a risk prognostic model based on both lipid metabolism and ferroptosis and evaluated the relationship between the risk score and tumor immune microenvironment in UCEC. Our study has provided new ideas and potential targets for UCEC individualized diagnosis and immunotherapy. Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies, with an increasing incidence of about 1% per year [1]. Approximately 15% of UCEC patients are diagnosed at an advanced stage, and approximately 15–20% of patients will experience relapse after primary surgical treatment [2,3]. Although surgery, carboplatin/paclitaxel systemic chemotherapy, and hormone therapy are effective treatments, patients with advanced disease, recurrence, or drug resistance still have poor prognoses [4,5]. In recent years, it has been reported that patients with advanced endometrial cancer may benefit from immunotherapy. The main immunotherapy approaches include immune checkpoint inhibitors (ICIs), adoptive cell transfer (ACT), cancer vaccines, and lymphocyte-promoting cytokines. For example, dostarlimab, a drug that inhibits the programmed cell death 1 and programmed cell death ligand 1 pathway, can improve the prognosis of patients receiving platinum chemotherapy or progressive mismatch repair deficiency endometrial cancer [6]. However, the effect of immunotherapy is not ideal due to the complexity of the immune microenvironment and differences in the response to immunotherapy [7,8]. Therefore, it is vital to identify potential diagnostic and prognostic targets or risk signatures and to tailor individualized immunotherapy strategies for improving the outcomes of UCEC patients. Obesity is an independent risk factor for UCEC [9]. Almost all UCEC patients with obesity have altered lipid metabolism [10]. Tan et al. built an 11 lipid metabolism gene (LMG) signature to reflect the prognosis of UCEC patients [11]. Lipids are susceptible to oxidation by oxygen free radicals. Overproduction and elimination failure of lipid peroxidation are the main reasons for the novel iron-dependent cell death ferroptosis [12,13,14]. Liu et al., Wang et al., and Wei et al. constructed a ferroptosis-related gene signature to predict the prognosis of UCEC patients [15,16,17]. Lipid synthesis, storage, and degradation processes can be regulated by ferroptosis [18,19]. Iron depletion leads to a large amount of lipid accumulation in breast cancer cells [20]. Iron accumulation is due to altered lipid metabolism associated with increased oxidative stress in myelodysplastic syndromes [21]. Ferroptosis is closely associated with lipid metabolism pathways [22,23]. Inhibiting β-oxidation can restore tumor cell sensitivity to ferroptosis [24]. Upregulating stearoyl CoA desaturase 1 (SCD1), the rate-limiting enzyme in fatty acid synthesis, increases the resistance of tumor cells to ferroptosis. Increasing evidence suggests that lipid metabolism and ferroptosis closely affect each other [25,26]. However, the interaction and shared role of ferroptosis and lipid metabolism in UCEC remains unclear. In the present study, we aimed to construct a prognostic risk signature based on both lipid metabolism and ferroptosis to comprehensively analyze their combined effects on UCEC. We screened six risk genes (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) as reliable diagnostic and prognostic biomarkers and divided UCEC patients into high- and low-risk groups based on their risk score. Then, we estimated differences in immune score, immune infiltration, immune checkpoint, immunotherapy, and chemotherapy response between the high- and low-risk groups. The findings provide a new idea for individualized therapy strategies to improve the prognosis of UCEC patients. Sequencing RNA data (HTSeq-FPKM) and clinical information were obtained from The Cancer Genome Atlas (TCGA) database, and 579 cases were selected for study, including 544 UCEC samples and 35 normal samples. The detailed clinical information of the UCEC patients is shown in Table S1. Lipid-metabolism-related genes (LMRGs) were collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signatures Database (MSigDB), including the GSEA, HALLMARK, and REACTOME databases [27]. The detailed gene sets are shown in Table S2. A total of 1457 genes were selected for analyses after removing duplicate genes (Table S3). In addition, we downloaded 288 ferroptosis-associated genes (FAGs) from the FerrDb database (http://zhounan.org/ferrdb/legacy/index.html, accessed on 1 June 2022). After removing the replicates, 259 individual FAGs were used for further investigation. The evaluation of the differentially expressed LMRGs (DE-LMRGs) was performed using the default settings for the “lmFit”, “eBayes”, and “topTable” functions in the “limma” R package. The screening criteria were p < 0.05, |Log2 Fold Change (FC)| > 1, and a false discovery rate (FDR) < 0.05. Then, univariate Cox regression analysis was applied to determine LMRGs with overall survival (OS) in UCEC by using the coxph function in the “survival” R package at p < 0.05. The molecular classification of DE-LMRGs in UCEC was analyzed by the “ConsensusClusterPlus” R package. Principal component analysis (PCA) was performed to identify the grouping ability of our model with the R package “stats”. Then, the FAGs interacted with the results of the consensus clustering approach, and the genes of interaction were selected for further study. We performed univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to identify significant prognostic genes based on both LMRGs and FARs with a threshold of p < 0.05. Then, a risk score signature was created by considering the estimated cox regression correlation coefficients and the expression values of the optimized LMRGs and FARs. The formula is risk score = Σi1expGenei*coeffi. According to the median value of the calculated risk scores from the TCGA-UCEC, the patients were divided into low- and high-risk groups. The prognostic ability and stability of the signature was measured by the Kaplan–Meier (K–M) analysis, multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve with the “Survival” and “sevivalROC” R package (p < 0.05). To examine the distinction between the high- and low-risk group of our model, we further carried out gene set variation analysis (GSVA) using the “GSVA” function with method parameters (min.sz = 10, max.sz = 500, verbose = TRUE) of the “GSVA” R package, and conducted KEGG pathway analysis and Gene Ontology (GO) analysis via the “clusterProfiler (version 3.14.3)” R package (p < 0.05). We downloaded the somatic mutation data from TCGA. Using Perl, we calculated the TMB value of each sample and divided all samples into high- and low-TMB groups based on the median TMB [28]. Then, K–M analysis was used to assess survival differences between the groups. We also calculated the expression differences in TMB between the high- and low-risk groups and analyzed the relationship between TMB and the risk score (p < 0.05) The CIBERSORT algorithm was utilized to evaluate the 22 types of immune fractions between the high- and low-risk groups, and the results were visualized with the “vioplot” R package. Then, we used the Tumor Immune Estimation Resource (TIMER) to evaluate correlations between expression of six model genes and the immune infiltration level of tumor-infiltrating immune cells. We also analyzed the relationship between innovative targeted therapy and risk prognostic models. The Wilcoxon test was used to detect expression of potential immune checkpoints between the high-risk and low-risk groups (p < 0.05). Furthermore, we downloaded clinical data from The Cancer Immunome Atlas (TCIA) to predict the response to immune checkpoint blockade (CTLA-4 and PD-1) in patients in the high- and low-risk groups by the immunophenoscore. In addition, according to the Genomics of Drug Sensitivity in Cancer (GDSC) database, the R package “pRRophetic” was used to measure the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs. The UCEC cell lines Ishikawa, HEC-1A, HEC-1B, and ECC-1 were obtained from the American Type Culture Collection (ATCC). The HEC-1A cell lines were cultured in McCoy’s 5A (Gibco, New York, NY, USA) supplemented with 10% fetal bovine serum (FBS, Biological Industries, Kibbutz Beit-Haemek, Israel) and 1% penicillin/streptomycin (P/S); the others were cultured in RPMI 1640 culture medium with 10% FBS and 1% P/S. All of the cells were cultured at 37 °C in a humidified incubator under 5% CO2. The siRNA PSAT1 and scrambled control sequences were obtained from Gene Pharma (Shanghai, China). The details of the sequences are as follows: si-PSAT1-1: forward 5′-CAGUGUUGUUAGAGAUACAdTdT-3′, reverse 5′-UGUAUCUCUAACAACACUGdTdT-3′; si-PSAT1-2: forward 5′-GCUGUUCCAGACAACUAUAdTdT-3′, reverse 5′-UAUAGUUGUCUGGAACAGCdTdT-3′. siRNA transfection was carried out using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). Total RNA was extracted using TRIzol reagent (Sangon Biotech, Shanghai, China) after transfecting siRNA for 48 h, and reverse transcription was performed using PrimeScriptTM RT Reagent Kit (TAKARA, RR047A). QRT–PCR was conducted with the SYBR Green qPCR Supermix kit (Invitrogen). The primers used were purchased from Tsingke Biotechnology Co (Beijing, China), as follows: PSAT1 Forward 5′-ACTTCCTGTCCAAGCCAGTGGA-3′; PSAT1 Reverse 5′-CTGCACCTTGTATTCCAGGACC-3′; GAPDH Forward 5′-GGAGCGAGATCCCTCCAAAAT-3′; GAPDH Reverse 5′-GGCTGTTGTCATACTTCTCATGG-3′. Total proteins were obtained from cells using PIPA buffer (New Cell & Molecular Biotech, Suzhou, China) at 72 h after siRNA transfection, separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE), and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, New York, NY, USA). The membranes were blocked using 5% BSA for at least 1 h at room temperature and incubated with PSAT1 (10501-1-AP, Proteintech, Wuhan, China) or GAPDH (10494-1-AP, Proteintech) at 4 °C overnight. The next day, the membranes were incubated with secondary antibody (GB23303, Servicebio, Shanghai, China) for 1 h at room temperature, and bands were detected by chemiluminescence. Cell proliferation was detected by the Cell Counting Kit-8 assay (CCK-8) and colony formation assay. For CCK-8, the cells were seeded into 96-well plates at a density of 2000 cells/well after 72 h of transfection. At the indicated time, CCK-8 solution (10 μL) was added to each well of the culture medium. Cell viability was measured using an automatic enzyme-linked immune detector after incubation for 1 h. For the colony formation assay, 1000 transfected cells were seeded into six-well plates for 10–14 days, and the culture medium was changed every three days. After staining with 0.1% crystal violet and photographing, cell colonies were statistically analyzed by the t-test. Cell migration and invasion were assessed using 24-well transwell chambers (8 μm; Millipore). In brief, a sample of 4 × 104 cells suspended in 200 μL serum-free medium was seeded in the upper chamber, and the lower chamber contained 600 μL medium with 10% FBS. After 48 h, the chambers were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet dye for 30 min. The upper chamber cells were wiped off and then photographed and counted under a microscope. For the invasion assays, Matrigel (BD, biocoat, #358248) was used to coat the upper chamber, after which the cells were seeded; the next step was the same as above. Bioinformatic statistical analyses were performed using R (v.3.6.1) software. Pearson correlation analysis was employed for correlation analysis between TMB and the risk model. All of the in vitro experiments were independently performed in triplicate and analyzed by the t-test. Data were analyzed using the IBM SPSS Statistics 22 and visualized in GraphPad Prism 9. The values were presented as the mean ± standard deviation (SD). p < 0.05 was considered statistically significant. A brief workflow of this research is presented in Figure S1. We screened 1457 LMRGs for differential expression analysis and identified 88 differentially expressed LMRGs (DE-LMRGs) with the “limma” R package based on 544 UCEC samples and 35 normal samples from TCGA. The boxplot of the expression patterns of the 88 DE-LMRGs is shown in Figure 1A. KEGG analysis and GO analysis showed that these significant genes mainly participate in lipid metabolic processes (Figure S2A,B). Then, univariate cox hazards regression and Kaplan–Meier (K–M) analyses were utilized to screen out prognostic LMRGs based on TCGA, and we obtained six risk genes and three protective genes for survival (Figure S2C,D). The consensus clustering approach was used to divide the UCEC samples with the non-negative matrix factorization (NMF) algorithm. Based on LMRGs expression, the optimal clustering stability was confirmed when K = 3 (Figure 1B and Figure S2E,F). We also performed principal component analysis (PCA), which showed the good grouping ability of our clustering (Figure 1C). Therefore, all of the UCEC samples were divided into three clusters, and the heatmap showed lower expression for the DE-LMRG genes in Cluster A (Figure S2G). Moreover, K–M analysis indicated a significant difference in OS among the three subgroups, with the patients in Cluster A having the best prognosis (Figure 1D, p < 0.05). By further analyzing the clinical characteristics among the three clusters, we found that patients in Cluster C had an older age and a higher grade and stage (Figure S2H–J, p < 0.05). Differentially expressed genes among the three clusters were obtained from consensus clustering analysis and intersected with FAGs. Then, we obtained both lipid metabolism-related and ferroptosis-associated genes (LMG-FAGs) (Figure 2A). We performed overall-survival-based univariate regression analysis on the lipid-metabolism-related and ferroptosis-associated genes (LMG-FAGs) obtained through consensus clustering analysis. This approach revealed 211 LMG-FAGs associated with the prognosis of endometrial cancer, and we classified them into 87 risk genes and 124 protective genes according to the hazard ratio (HR) and p value (Table S4, p < 0.05). To avoid overfitting and bias, the results of univariate regression analysis were subjected to LASSO regression analysis using the “glmnet” R package, and the accuracy of the model was tested by cross-validation (Figure 2B,C). Hence, a six-gene prognostic risk model was established by the following formula: risk score = [CDKN1A expression × (−0.02353)] + [CDKN2A expression × (0.11554)] + [ESR1 expression × (−0.05874)] + [PGR expression × (−0.11493)] + [PSAT1 expression × (0.05505)] + [RSAD2 expression × (0.01431)]. We analyzed the relationship between different risk scores and patient follow-up times, events, and expression changes of individual genes, and it was observed that with an increase in the risk score, the survival rate of patients decreased significantly. CDKN1A, ESR1, and PGR were found to be protective factors that showed downregulated expression with increased risk scores; CDKN2A, PSAT1, and RSAD2 showed the opposite result (Figure 2D p < 0.05). Furthermore, we detected expression levels and performed multivariate Cox regression and K-M survival analyses on the six independent prognostic genes. The results indicated that high expression of CDKN1A, ESR1, and PGR was related to better prognosis, whereas high expression of CDKN2A, PSAT1, and RSAD2 was not (Figure S3A–C, p < 0.05). According to the median cut-off value of the risk score, the high- and low-risk groups were established to differentiate the UCEC patients in TCGA, and the high-risk patients had a worse prognosis than the low-risk patients (Figure 2E, p < 0.05). Then, time-dependent ROC analysis was applied to evaluate the prediction capacity of the signature, with an area under the receiver operating characteristic curve (AUC) of 0.67, 0.70, and 0.70 at 365, 1905, and 1825 days, respectively (Figure 2F, p < 0.05). To assess the accuracy of the model, we evaluated the performance of this signature with regard to pathological features (age, grade, and stage). The results indicated that high risk was significantly associated with older age and higher grade and stage (Figure 3A–C, p < 0.05). Then, the pathological features were added for univariate and multivariate cox regression, and the forest plot showed that age, grade, and stage were still independent prognostic factors, which means that the signature had high accuracy (Figure 3D, p < 0.05). In addition, we built a nomogram to predict the 1-year, 3-year, and 5-year survival probability of UCEC patients based on all of the above prognostic elements (Figure 3E, p < 0.05), and the calibration plot showed a C-index of 0.767 (0.741–0.793), indicating that the nomogram had good predictive ability (Figure 3F, p < 0.05). To investigate the relationship between the six genes in the risk model, we constructed a protein–protein interaction (PPI) network (Figure S4A) and analyzed the correlations (Figure S4B). The results showed that PSAT1 and RSAD2 were more independent and less associated with other genes. Next, a volcano plot and heatmap showed the DEGs between the two risk groups; 81 genes were upregulated and 195 genes were downregulated (Figure 4A,B). The PPI network of the DEGs is depicted in Figure S4C. To reveal the underlying biological characteristics associated with the risk scores, KEGG and GO analyses were performed based on DEGs between the high- and low-risk groups. The results indicated that pathways such as kinase and peptidase regulation, apparatus morphogenesis, cell cycle regulation, viral infection, and antiviral innate immune response were highly enriched (Figure 4C,D, p < 0.05). In addition, we performed GSVA to probe differences in pathways between the two risk groups. As illustrated in the heatmap in Figure 4E, pathways related to lipid metabolism and ferroptosis, such as “tyrosine metabolism”, “fatty acid metabolism”, “alpha linolenic acid metabolism”, and “DNA replication”, were significantly enriched (p < 0.05). TMB, the somatic coding errors, is generally considered high when >10 or >16 mutations/megabase DNA are present [28]. Recently, TMB is thought to be closely related to the survival prognosis of tumor patient [29]. To examine in more depth how well the risk-prognosis model predicts tumor development, we investigated its relationship with TMB. First, correlation analysis showed that the TMB level had a negative association with the LMRG-FAG risk score (Figure 5A, p < 0.05), and the high-risk group showed lower TMB levels (Figure 5B, p < 0.05). We also investigated the survival of patients with different TMB statuses by K-M analysis, and the results demonstrated that the patients in the low-TMB group had poor prognostic outcomes (Figure 5C, p < 0.05). In addition, mutation information of the genes in the low- and high-TMB groups was explored using a waterfall chart, and PTEN (58.2%), PIK3CA (48.7%), TTN (44.5%), ARID1A (43.5%), and TP53 (36.4%) were the top five mutated genes (Figure 5D). We further studied and classified the mutation information, variant type, and SNV class, and the results demonstrated that missense mutations, single nucleotide polymorphism (SNP), and C > T accounted for the largest proportion (Figure S5A–C). The number of altered bases in each sample and the mutation types in different colors are shown in Figure S5D,E; mutation information for the six risk genes [PGR (37%), ESR1 (33%), RSAD2 (27%), PSAT1 (18%), CDKN1A (14%), and CDKN2A (5%)] is provided in Figure S5F. Recently, multiple pieces of research have illustrated that TMB is closely associated with tumor immune cell infiltration and affects the efficacy of immunotherapy [30,31]. Therefore, we evaluated the value of TBM in the complexity of the tumor immune microenvironment. We discovered that most immune cells had a positive correlation with the TMB level, especially T cells CD8+, T cells CD4+, and B cells (Figure S5G). In addition, T cells CD8+, T cells CD4+ memory activated, T cells CD4+ memory resting, and T cells regulatory had higher expression in the high-TMB group compared to the low-TMB group (Figure S5F, p < 0.05), suggesting that TMB may have an effect on the immune response. Recent studies have shown that lipid metabolism and ferroptosis are important components of the tumor microenvironment and are strongly associated with tumor immune activities [32,33,34,35]. We first used ESTIMATE to determine the relationship of tumor immune infiltration between the two risk groups. The stromal, immune score, and ESTIMATE score were significantly downregulated in the high-risk group (Figure 6A–C, Wilcoxon p < 0.05). Then, the CIBERSORT algorithm was applied to detect the composition of the 22 immune cells in UCEC patients (Figure S6A). A boxplot demonstrated that the difference in the distribution of the 10 immune-infiltrating cells between the two risk groups was significant. The naive B cells, memory B cells, resting CD4 memory T cells, regulatory T cells (Tregs), and resting dendritic cells had low expression in the high-risk group compared to the low-risk group. Meanwhile, the follicular helper T cells, monocytes, M1 macrophages, activated dendritic cells, and M2 macrophages were significantly upregulated in the high-risk group compared to the low-risk group (Figure 6D, p < 0.05). We also analyzed immune infiltration using the EPIC, TIMER, xCELL, and quanTIseq databases, which fully confirmed the six-gene prognostic risk signature to be closely related to immune activity (Figure S6B–E). In addition, the TIMER database was utilized to assess the relationship between the six risk genes and tumor-infiltrating immune cells. The results showed that only RSAD2 correlated positively with B cells (cor = 0.1858, p = 0.0015); except for RSAD2, the other genes were significantly associated with CD8+ T cells (Figure S7, p < 0.05). Recently, immune checkpoints have been identified as key targets of immunotherapy, and immune checkpoint inhibitors (ICIs) are regarded as an effective therapeutic strategy for patients with advanced disease [36,37]. Therefore, we identified potential relationships between the expression of immune checkpoint molecules and our risk model. The results showed that IDO1 and LAG3 expression was significantly increased in the high-risk group compared with the low-risk group, while the expression of CTLA4, GZMA and PDCD1 was obviously decreased in the high-risk group compared with the low-risk group (Figure 6E, p < 0.05). Then, we conducted immunophenoscore (IPS) analysis to predict immunotherapy response. As shown in Figure 6F, low-risk patients were more sensitive to anti-PD-1 therapy (p < 0.05), suggesting that immunotherapy of blocking CTLA-4 and PDCD1 may be more beneficial for patients in the low-risk group. Since chemotherapy is the main treatment for advanced and recurrent UCEC, we evaluated the response of chemotherapeutics to UCEC patients using the pRRophetic algorithm based on our signature and found that the estimated IC50 of typical chemotherapy drugs (cisplatin, paclitaxel, doxorubicin, and etoposide, etc.) were significantly higher in the low-risk group (Figure 6G, p < 0.05). For the other 40 chemotherapy and small molecule drugs, such as lenalidomide, gefitinib, AMG.706, and JNK inhibitor VIII, patients in the high-risk group were identified as being more sensitive (Figure S8, p < 0.05). Thus, we indicated that patients with low risk scores were more resistant to chemotherapy than those with high risk scores, but they were more sensitive to anti-PD-1 therapy. In addition, patients in the high-risk group were better suited for chemotherapy. These results may have important implications for individualized immunotherapy in patients with advanced UCEC. To further validate the ability of risk signatures to predict prognosis, we investigated protein expression of the six risk genes between normal and UCEC tissues with the CPTAC and HPA (Human Protein Atlas) databases (Figure S9A,B, p < 0.05), and the results corresponded with previous analysis. Combined with prognostic analysis and literature searches, we selected PSAT1 for further in vitro functional assays. We identified the mRNA and protein expression of PSAT1 in four UCEC cell lines (Ishikawa, HEC-1A, HEC-1B, and ECC1), and Ishikawa and HEC-1B cells were selected for subsequent studies (Figure 7A,B). Next, we knocked down PSAT1 with siRNA, and the efficiency was verified by qPCR (Figure 7C, p < 0.05) and Western blot analysis (Figure 7D). CCK-8 and colony formation assays showed that knockdown of PSAT1 significantly suppressed the proliferation of Ishikawa and HEC-1B cells (Figure 7E,F, p < 0.05). In addition, the migration and invasion of the two cell lines were also apparently inhibited after PSAT1 knockdown, as determined by transwell assays (Figure 7G). These results demonstrate that the risk gene PSAT1 significantly promotes progression of UCEC and may affect the prognosis of UCEC patients. UCEC is one of the most lethal gynecological malignancies. Although many studies over the past decades have sought to improve treatment efficacy, patients with advanced and recurrent disease still have poor prognosis [38]. With the rise and application of immunotherapy, it is insufficient to estimate the prognosis of UCEC patients based on traditional clinicopathological stage [39]. Therefore, our study included the tumor immune microenvironment and immunotherapy in UCEC based on both lipid metabolism and ferroptosis to select more effective prognostic targets and guide individualized treatment of patients. Previous studies have established prognostic models of lipid metabolism or ferroptosis in UCEC [11,15,16,17]. However, they only took a single influencing factor into account, and the complex tumor microenvironment was not considered. In our study, we comprehensively considered the interrelationship between lipid metabolism and ferroptosis, based on which a prognostic model of six genes was constructed. We deeply explored the relationship between the model risk score and the tumor immune microenvironment. We found that infiltration of B cells, T cells, and NK cells and expression of the immune checkpoints (CTLA4, GZMA, and PDCD1), as well as sensitivity and chemotherapy resistance to anti-PD-1 treatment in UCEC patients were closely related to the risk scores of the prognostic model. Moreover, in vitro experiments demonstrated that one of the potential targets, PSAT1, promoted the proliferation, migration, and invasion of UCEC cells. Our experiments provide new ideas and a basis for individualized immunotherapy for UCEC patients and provide a potential target for UCEC therapy. In the present study, we obtained genes associated with both lipid metabolism and ferroptosis by consensus clustering analysis. After LASSO Cox regression, we constructed a prognostic signature containing six risk genes (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) based on LMG-FAGs. K-M survival analysis, ROC curves, a nomogram, and calibration identified that the signature had high predictive ability. Estrogen receptor 1 (ESR1) and a progesterone receptor (PGR) were reported to participate in lipid metabolism by encoding estrogen or steroid receptors to promote tumor progression [40,41,42]. Cyclin-dependent kinase inhibitors 1A and 2A (CDKN1A and CDKN2A) have been identified as ferroptosis-related genes in recent studies and can be regarded as biomarkers that influence the tumor microenvironment [43,44,45]. Radical s-adenosyl methionine domain containing 2 (RSAD2) is an interferon-stimulated gene that exerts antiviral effects by dysregulating cellular lipid metabolism [46,47]. Phosphoserine aminotransferase 1 (PSAT1) has been reported to affect the progression of various cancers by participating in lipid metabolism processes [48,49,50]. In conclusion, the six-gene prognostic model showed a significant correlation with lipid metabolism or ferroptosis. In our study, these six genes were used for risk scoring, and each UCEC patient was categorized into two risk groups according to the risk score. We then explored the pathological features of the risk signature, with the high-risk group being related to older age and higher grade and stage. We also found that knockdown of PSAT1 inhibited the proliferation, migration, and invasion of UCEC cells, enhancing the reliability of our model. Subsequently, we comprehensively analyzed the impact of the risk signature on UCEC. A total of 276 genes were identified to be closely related to the risk score. GO, KEGG, and GSVA analyses based on the signature demonstrated that pathways associated with lipid metabolism and ferroptosis were significantly enriched, which also confirmed the accuracy of our signature. TMB is reported to correlate highly with tumor progression; for example, gastrointestinal tumor patients with low TMB have lower objective response rates and shorter progression-free survival [51], and high TMB is a poor prognostic factor for non-small cell lung cancer [52]. We found that TMB levels had a negative relationship with the LMRG-FAG risk model, which means that patients with low risk and high a mutational burden have a better prognosis in UCEC. Because surgery and chemoradiotherapy have limited effects in patients with advanced and recurrent UCEC and traditional pathological staging has an insufficient ability to estimate prognosis, we focused on the relationship of the LMRG-FAG-based risk model with immunotherapy. Stromal, immune, and ESTIMATE scores were significantly downregulated in the high-risk group, indicating that lipid metabolism and ferroptosis are significantly associated with the immune status of UCEC. CIBERSORT algorithm analysis showed that the distribution of 10 immune cells varied between the high- and low-risk groups, with antitumor cells (B cells, T cell CD8, and monocytes, etc.) present at higher abundance in the low-risk group. According to the results, we suggest that the risk score is associated with immune infiltration and immune status in UCEC. Adverse T cell regulatory pathways tend to be overactive when cancer occurs. CTLA-4 inhibits the immune response at the early stage of T cell induction, and PDCD1 prevents T cell function in peripheral tissues in the later stages [53,54]. Recently, immune checkpoint blockade, one of the major immunotherapy methods, has proven to be an effective strategy for enhancing the effector activity and clinical impact of anti-tumor T cells [55]. Among the ICIs, blocking CTLA-4 and PDCD1 are the two most eminent approaches. CTLA-4 and PDCD1 blockade could induce tumor immunity by improving effector T cell activity or consuming Treg [56]. In 2011, Ipilimumab, a CTLA-4 inhibitor, was approved for melanoma [57]. In 2017, the PDCD1 inhibitor pembrolizumab was approved for UCEC patients with microsatellite instability, and half of the patients benefited from it [58]. Since the predictive value of immune checkpoints has been demonstrated in a variety of human malignancies, we then explored immune checkpoint expression between the two risk groups to guide individualized immunotherapy for UCEC patients. The expression of CTLA4, GZMA and PDCD1 was significantly upregulated in patients with low risk scores, and IDO1 and LAG3 were increased in the high-risk group. Therefore, we indicated that specially blocking CTLA-4 and PDCD1 immunotherapy would be more effective for patients in the low-risk group. Meanwhile, we detected the difference in sensitivity to PD-1 and CTLA-4 inhibitors, and the results indicated that low-risk patients were more sensitive to anti-PD-1 therapy, meaning that immunotarget therapy was more effective in low-risk patients. Accordingly, our risk signature has a certain guiding role in the anti-PD-1 immunotherapy of UCEC patients. Interestingly, high-risk patients were more sensitive to traditional chemotherapeutic agents and small molecule inhibitors such as cisplatin, paclitaxel, AMG.706, and ABT.888. Hence, patients in the high-risk group were more likely to benefit from chemotherapy and our signature can be used to guide personalized treatment of UCEC patients. However, it is undeniable that our study also has some limitations. First, the study data were obtained from only TCGA, and we did not verify the accuracy of our model with more cohorts. Second, immunotherapy and chemosensitivity analyses were only derived from the transcriptome, and we still need to obtain more prospective experimental data to support the findings. Finally, as a potential therapeutic target, the molecular mechanism underlying the risk-related gene PSTA1 needs to be further clarified. Consequently, we constructed a risk prognostic model based on both lipid metabolism and ferroptosis to deeply analyze the relationship between lipid metabolism, ferroptosis and gene mutation, immune infiltration, immunotherapy, and chemotherapy in UCEC patients and provided potential biomolecules and a preliminary basis for individualized treatment of patients.

PMC10000782

Anna Citarella,Giuseppina Catanzaro,Zein Mersini Besharat,Sofia Trocchianesi,Federica Barbagallo,Giorgio Gosti,Marco Leonetti,Annamaria Di Fiore,Lucia Coppola,Tanja Milena Autilio,Zaira Spinello,Alessandra Vacca,Enrico De Smaele,Mary Anna Venneri,Elisabetta Ferretti,Laura Masuelli,Agnese Po

Hedgehog-GLI and Notch Pathways Sustain Chemoresistance and Invasiveness in Colorectal Cancer and Their Inhibition Restores Chemotherapy Efficacy

25-02-2023

colorectal cancer,signaling pathways,chemoresistance,epithelial-to-mesenchymal transition,organoids

Simple Summary Colorectal cancer is a leading cause of cancer-related deaths, mainly caused by resistance to therapy and metastatic spread, in turn sustained by the activation of mechanisms such as the epithelial-to-mesenchymal transition (EMT). We investigate here the role of the Hedgehog-GLI and NOTCH signaling pathways, already associated with poor prognosis in CRC, in the mechanism of chemoresistance and EMT, using monolayer and organoids from two models of common mutations in CRC: KRAS and BRAF. Our results show that treatment with the chemotherapeutic drug 5-fluorouracil activated both pathways in the investigated contexts. However, we observed a different behavior in the investigated models: in KRAS-mutated CRC, the inhibition of both the HH-GLI and NOTCH pathways is necessary to enhance chemosensitivity, while in BRAF-mutated CRC the inhibition of HH-GLI is sufficient to impair both signaling pathways and promote chemosensitivity. Abstract Colorectal cancer (CRC) is a leading cause of cancer-related mortality and chemoresistance is a major medical issue. The epithelial-to-mesenchymal transition (EMT) is the primary step in the emergence of the invasive phenotype and the Hedgehog-GLI (HH-GLI) and NOTCH signaling pathways are associated with poor prognosis and EMT in CRC. CRC cell lines harboring KRAS or BRAF mutations, grown as monolayers and organoids, were treated with the chemotherapeutic agent 5-Fluorouracil (5-FU) alone or combined with HH-GLI and NOTCH pathway inhibitors GANT61 and DAPT, or arsenic trioxide (ATO) to inhibit both pathways. Treatment with 5-FU led to the activation of HH-GLI and NOTCH pathways in both models. In KRAS mutant CRC, HH-GLI and NOTCH signaling activation co-operate to enhance chemoresistance and cell motility, while in BRAF mutant CRC, the HH-GLI pathway drives the chemoresistant and motile phenotype. We then showed that 5-FU promotes the mesenchymal and thus invasive phenotype in KRAS and BRAF mutant organoids and that chemosensitivity could be restored by targeting the HH-GLI pathway in BRAF mutant CRC or both HH-GLI and NOTCH pathways in KRAS mutant CRC. We suggest that in KRAS-driven CRC, the FDA-approved ATO acts as a chemotherapeutic sensitizer, whereas GANT61 is a promising chemotherapeutic sensitizer in BRAF-driven CRC.

Hedgehog-GLI and Notch Pathways Sustain Chemoresistance and Invasiveness in Colorectal Cancer and Their Inhibition Restores Chemotherapy Efficacy Colorectal cancer is a leading cause of cancer-related deaths, mainly caused by resistance to therapy and metastatic spread, in turn sustained by the activation of mechanisms such as the epithelial-to-mesenchymal transition (EMT). We investigate here the role of the Hedgehog-GLI and NOTCH signaling pathways, already associated with poor prognosis in CRC, in the mechanism of chemoresistance and EMT, using monolayer and organoids from two models of common mutations in CRC: KRAS and BRAF. Our results show that treatment with the chemotherapeutic drug 5-fluorouracil activated both pathways in the investigated contexts. However, we observed a different behavior in the investigated models: in KRAS-mutated CRC, the inhibition of both the HH-GLI and NOTCH pathways is necessary to enhance chemosensitivity, while in BRAF-mutated CRC the inhibition of HH-GLI is sufficient to impair both signaling pathways and promote chemosensitivity. Colorectal cancer (CRC) is a leading cause of cancer-related mortality and chemoresistance is a major medical issue. The epithelial-to-mesenchymal transition (EMT) is the primary step in the emergence of the invasive phenotype and the Hedgehog-GLI (HH-GLI) and NOTCH signaling pathways are associated with poor prognosis and EMT in CRC. CRC cell lines harboring KRAS or BRAF mutations, grown as monolayers and organoids, were treated with the chemotherapeutic agent 5-Fluorouracil (5-FU) alone or combined with HH-GLI and NOTCH pathway inhibitors GANT61 and DAPT, or arsenic trioxide (ATO) to inhibit both pathways. Treatment with 5-FU led to the activation of HH-GLI and NOTCH pathways in both models. In KRAS mutant CRC, HH-GLI and NOTCH signaling activation co-operate to enhance chemoresistance and cell motility, while in BRAF mutant CRC, the HH-GLI pathway drives the chemoresistant and motile phenotype. We then showed that 5-FU promotes the mesenchymal and thus invasive phenotype in KRAS and BRAF mutant organoids and that chemosensitivity could be restored by targeting the HH-GLI pathway in BRAF mutant CRC or both HH-GLI and NOTCH pathways in KRAS mutant CRC. We suggest that in KRAS-driven CRC, the FDA-approved ATO acts as a chemotherapeutic sensitizer, whereas GANT61 is a promising chemotherapeutic sensitizer in BRAF-driven CRC. Colorectal cancer (CRC) is the third most frequent cancer and the second cause of cancer-related death worldwide [1]. Mutations in KRAS and BRAF oncogenes represent the most common genetic drivers in CRC. Indeed, KRAS and BRAF mutations occur in 40% and 10% of CRC, respectively [2], and they are both associated with a poor outcome [3]. Even though they both belong to the MAPK pathway, KRAS and BRAF mutations are mutually exclusive in CRC and these two types of cancer are characterized by distinct clinical and molecular features. BRAF-mutant CRC often displays genome-wide hypermethylation, high microsatellite instability and mutation rates, while KRAS mutant CRC is associated with lower levels of microsatellite instability and gene methylation [2]. First-line and palliative treatments for metastatic CRC, bearing KRAS or BRAF mutations, include the cytotoxic chemotherapeutic agent 5-fluorouracil (5-FU) [1,4]; however, patients often present disease recurrence after 5-FU therapy [5]. Chemoresistance is conferred by a plethora of mechanisms, including the modulation of signaling pathways involved in the emergence of the cancer stem features and epithelial-to-mesenchymal transition (EMT) [6]. Other mechanisms for resistance to therapy include the inhibition of apoptosis driven by upregulation of autophagy [7], metabolic reprogramming [8], upregulation of molecules involved in drug efflux and drug metabolism and activation of alternative pathways [9]. Hedgehog-GLI (HH-GLI) and NOTCH signaling are pivotal developmental pathways involved in the regulation of multiple biological and pathological processes. The canonical HH-GLI pathway is activated upon the interaction between the extracellular ligands Shh, Ihh and Dhh and the receptor Patched (PTCH), which in turn derepresses Smoothened (Smo), thus activating the transcription factors GLI1, GLI2 and GLI3. Activated GLI translocate into the nucleus where they bind to DNA and activate the transcription of target genes [10]. In cancers, GLI1 can also be activated in a non-canonical way by the “oncogenic load” of the cancer cell [11]. NOTCH cascade is activated upon binding of ligands Jag1, Jag2, Dll1, Dll3 and Dll4 to NOTCH receptors (from 1 to 4). The binding leads to proteolytic cleavages of the NOTCH receptor, releasing the NOTCH intracellular domain (ID) into the cytoplasm. Then, NOTCH ID migrates into the nucleus where, in complex with CBF1 (also known as RPBJ), it activates its transcriptional program [12]. Downstream target genes include HES1, which is involved in EMT and transcriptionally regulates ATP-binding cassettes transporters (ABC transporters), involved in multidrug resistance [13]. Interestingly, deregulation of the NOTCH pathway was described in numerous cancerous and non-cancerous diseases, with its role being highly context-dependent [14]. Deregulated HH-GLI is involved in the development and maintenance of numerous cancers [10] and, together with NOTCH signaling, plays a crucial role in the maintenance of stem cells of the intestinal epithelia [15]. The crosstalk of HH-GLI and NOTCH signaling is fundamental for spinal cord patterning [16], and several previous reports highlighted how several molecules belonging to the NOTCH pathway regulate the key components of the HH-GLI pathway and vice versa, as reviewed Kumar et al. [17]. Both HH-GLI and NOTCH pathways were described as deregulated and associated with poor prognosis in CRC [18,19]. In this context, our previous work has described a chemoresistance mechanism operated by the HH-GLI signaling in CRC, where chemotherapy treatment resulted in aberrant activation of the HH-GLI pathway which in turn led to the transcription of ATP-binding cassette transporters (ABC transporters), involved in multidrug resistance [20]. Therefore, our current work aimed to evaluate the role of HH-GLI and NOTCH signaling pathways as regulatory molecular mechanisms responsible for chemotherapy resistance in models of KRAS- or BRAF-driven CRC. HCT116 (KRAS G13D mutant) and HT29 (BRAF V600E mutant) were obtained from American Type Culture Collection (ATCC) and grown in DMEM high glucose (supplemented with 10% (v/v) fetal bovine serum, 1% (v/v) penicillin (50 U mL−1)—streptomycin (50 U mL−1)—and 2 mM L-glutamine. Cells were routinely checked for mycoplasma contamination by testing with PCR Mycoplasma Detection Kit (Cat. G238, ABM, Richmond, BC, Canada). Cells were treated with 10 μM GANT61 (ENZO Lifesciences, New York, NY, USA), 10 μM DAPT (Merk Life Science S.r.l., Milan, Italy), 10 μM Arsenic Trioxide (ATO) (Merck, Merk Life Science S.r.l., Milan, Italy) and 10 μM 5-Fluorouracil (5-FU). For combined treatments, GANT61 and DAPT or Arsenic Trioxide (ATO) were administered to the cells 24 h before 5-FU. Cell proliferation was assessed by trypan blue dye exclusion test using 0.4% (w/v) Trypan Blue solution (Merk Life Science S.r.l., Milan, Italy). Blue-stained cells were scored as non-viable and unstained cells were scored as viable cells. The percentage of viable cells was obtained as the ratio between the percentage of viable cells in treated cells versus control. Transwell invasion assay was performed using Corning® Transwell® chambers (8 μm pore size, Corning®). HCT116 and HT29 cells (2.5 × 104 in each well) were seeded in the upper chambers of the 48-well plates (Corning, Somerville, MA, USA) while lower chambers were filled with 1 mL of medium with indicated treatments. Cells in the lower chambers were fixed with 95% ethanol for 10 min, stained with crystal violet and counted. Cells were lysed as previously described [20]. Lysates were separated on 8% acrylamide gel and immunoblotted using standard procedures [21]. Primary antibodies were Anti-GLI1 (L42B10, Cell Signalling Technology Inc., Boston, MA, USA), anti-PARP p85 Fragment (G7341, Promega, Madison, WI, USA) and anti-Cleaved NOTCH1 (D3B8, Cell Signalling Technology Inc., Boston, MA, USA). HRP-conjugated secondary antisera (Santa Cruz Biotechnology, Shanghai, China) were used, followed by enhanced chemiluminescence (ECL Amersham, Merk Life Science S.r.l., Milan, Italy). cDNA was obtained as described earlier [20]. RNA expression was analyzed on cDNAs using the ViiA™ 7 Real-Time PCR System, SensiFAST™ Probe Lo-ROX (Bioline, Memphis, TN, USA), TaqMan gene expression assay according to the manufacturer’s instructions (Life Technologies, Waltham, MA, USA). mRNA quantification was expressed in arbitrary units, as the ratio of the sample quantity to the calibrator or to the mean values of control samples. All values were normalized to three endogenous controls: HPRT, GAPDH and β-ACTIN. Primers for gene expression are listed in Supplementary Table S1. Gene expression of GLI1, HES1, c-MET, ABCG2, CD133, KRAS, BRAF, HPRT, GAPDH and β-ACTIN was assessed using Life technologies “best coverage” assays (Life Technologies, Waltham, MA, USA). Organoids were produced by seeding 1500 cells per well. Cells were mixed with 33% growth-factor-reduced phenol red-free Matrigel (Corning, Somerville, MA, USA). Cultures were grown using a flat-bottom 24-well microplate in advanced DMEM-F12 (Cat. 12634010, Gibco, Waltham, MA, USA) supplemented with Epidermal growth factor and Fibroblast growth factor both at final concentrations of 20 ng/µL. For in vivo live imaging experiments, GFP-labelled organoids were obtained by transducing HCT116 with PLKO lentiviral particles carrying pTWEEN-GFP vector. Transduced green fluorescent cells were selected by cell sorting and used for organoids production. Organoids were fixed with 4% paraformaldehyde and permeabilized with Triton X-100 in PBS (Sigma-Aldrich, St. Louis, MO, USA). Organoids were stained with anti-vimentin (ab11256, ABCAM, Cambridge, UK) antibody. Nuclei were DAPI-counterstained. Phalloidin was used for f-actin staining. Images were acquired using an LSM 900 (Zeiss, Milan, Italy) laser scanning confocal microscope with 40×/0.75 NA objective. Images were analyzed by using the program Zeiss ZEN 2.3 blue edition (https://www.zeiss.com/microscopy/int/products/microscope-software/zen-lite.html (accessed on 10 September 2022)). Datasets available on R2 platform (https://hgserver1.amc.nl/cgi-bin/r2/main.cgi (accessed on 15 December 2021)) were interrogated to evaluate GLI1 and NOTCH1 correlation in patients carrying BRAF or KRAS mutations. In detail, Tumor Colon Mutation status (Core Exon)—Sieber—211—rma_sketch—huex10p investigated gene correlation between GENE/REPORTER1: GLI1 and GENE/REPORTER2: NOTCH1, in 29 samples of CRC-carrying braf_v600e mutation; Tumor Colon (after surgery)—Beissbarth—363—custom—4hm44k investigated gene correlation between GENE/REPORTER1: GLI1 and GENE/REPORTER2: NOTCH1, in 32 samples of CRC carrying kras_g13d mutation. Single-particle tracking (SPT) diffusibility analysis was performed in five steps. In the first step, single cells were detected from the time-lapse movies of oHCT116-treated and control group organoids using Imaris spot model. Spots were taken in each frame and were linked to the spots corresponding to the same cell in the successive frame. Frame-to-frame tracking was implemented using the linear assignment problem (LAP) method [22,23]. In the third step, MSD, the mean square distance travelled by a cell given a certain time interval (see Supplementary Figure S3), was computed from single trajectories, as described by Michalet X [24]. In the fourth step, the diffusion parameter D was calculated for each tracked cell. To this end, the MSD was plotted for different time intervals (Δt) for each cell trajectory and the slope was computed using the Least Squares Method. In the fifth step, the Kolmogorov–Smirnov test was applied on the diffusion parameters D, obtained from 5-FU-treated oHCT116 and control group organoids. Results are representative of at least three independent experiments and are expressed as means +/− SD. Differences were analyzed using One-way ANOVA and Two-way ANOVA tests where appropriate, using the GraphPad Prism software Version 8.0. Adjusted p-values of less than 0.05 were considered as statistically significant. 5-fluorouracil (5-FU) is a chemotherapeutic agent used for adjuvant and palliative treatment of CRC; however, patients often present disease recurrence [5]. Therefore, we evaluated the role of HH-GLI and NOTCH signaling pathways as molecular mechanisms responsible for chemotherapy resistance. KRAS mutant HCT116 CRC cells were treated with 5-FU, alone or in combination with the HH-GLI inhibitor GANT61 and/or the NOTCH inhibitor DAPT. Our results showed that GLI1 and NOTCH1 ID were significantly upregulated after 5-FU treatment (Figure 1A). HH-GLI inhibition by GANT61 resulted in the downregulation of GLI1 and, interestingly, in the upregulation of NOTCH1 ID; vice versa, NOTCH inhibition by DAPT resulted in the downregulation of NOTCH1 ID and the upregulation of GLI1 (Figure 1A). We then determined the effects of treatments with the combination of HH-GLI inhibitor, NOTCH inhibitor and 5-FU. NOTCH1 ID expression was impaired in all combined treatments that included DAPT (DAPT+GANT61, DAPT+5-FU and DAPT+GANT61+5FU), while it was unaffected by the combination of 5-FU+GANT61. On the other hand, GLI1 was downregulated by GANT61 alone, as well as when combined with 5-FU and 5-FU+DAPT, while the combination of NOTCH inhibition and 5-FU failed to inhibit GLI1. Overall, our results show that GLI1 and NOTCH1 ID levels were concomitantly significantly downregulated only after the combined treatment of the chemotherapeutic agent 5-FU together with the inhibition of HH-GLI and NOTCH. In addition, we observed that the combination of HH-GLI and NOTCH pathway inhibition prevents the GLI1 upregulation and NOTCH1 activation induced by 5-FU. To determine the effects of treatments on apoptosis, levels of cleaved PARP (c-PARP) were evaluated; our results show that c-PARP was significantly induced by the combination of 5-FU and GANT61 or DAPT, and the three drugs combined (Figure 1A). We further analyzed the effects of treatments on cell viability, and we found it significantly impaired in cells treated with the combination of GANT61 and DAPT and with the combination of 5-FU with either GANT61 or DAPT or the combination of the three drugs (Figure 1B). To discern potential interdependence between the HH-GLI and NOTCH signaling pathways in mutant KRAS CRC cells, we analyzed GLI1 and NOTCH1 levels in an available cohort of CRC patients carrying this mutation (Tumor Colon (after surgery)—Beissbarth—363—custom—4hm44k; https://hgserver1.amc.nl/cgi-bin/r2/main.cgi, accessed on 15 December 2021) and no correlation was found (Figure 1C) We therefore envisioned a model where the oncogenic force of the driver gene KRASG13D sustains both HH-GLI and NOTCH pathways and both pathways need to be targeted to achieve a successful impairment of cells after chemotherapy. Hence, to clarify if the KRASG13D driver mutation sustained expression of GLI1 and NOTCH, we performed silencing of KRAS in HCT116 (Supplementary Figure S1A), which resulted in the significant downregulation of GLI1 and NOTCH1 ID protein levels (Supplementary Figure S1B). KRAS silencing was also accompanied by a significant downregulation of ABCG2 and HES1, target genes of HH-GLI and NOTCH1 ID, respectively (Supplementary Figure S1C). Arsenic Trioxide (ATO) is an organic compound approved for the therapy of adult patients with acute promyelocytic leukemia [25] and was shown to successfully inhibit both HH-GLI and NOTCH pathways [26]. ATO’sability to inhibit both GLI1 and NOTCH ID levels was confirmed in the KRASG13D-driven CRC model (Figure 1D). As previously shown, 5-FU alone was able to upregulate both GLI1 and NOTCH1 ID, while the combination with ATO impaired both signaling pathways (Figure 1D). Cleaved-PARP levels showed that apoptosis was significantly increased by the combination of ATO and 5-FU, while we observed a non-significant trend in ATO-treated cells (Figure 1D). A pivotal feature of CRC aggressiveness relies on the epithelial-to-mesenchymal transition (EMT), a process that includes the acquisition by cancer cells of properties including motility and migration, early steps in cancer invasion and metastasis. Therefore, we investigated whether the targeting of HH-GLI and NOTCH could impair KRAS mutant CRC’s migratory ability. We investigated the effects of the combined treatment of 5-FU and ATO on the migration ability of HCT116 cells. We observed that the migration was unaffected by 5-FU treatment, while it was impaired with ATO treatment and was completely abrogated after ATO plus 5-FU combined treatment (Figure 1E). Then, we evaluated epithelial differentiation through E-cadherin levels, which increased after the combined treatment of ATO and 5-FU (Supplementary Figure S1D). BRAF V600E is the activating driving mutation in 10% of CRC and correlates with poor prognosis, however targeted therapy against the mutation was proven ineffective and first-line treatment includes cytotoxic chemotherapy [1]; thus, we investigated the role of the HH-GLI and NOTCH signaling pathways in 5-FU chemotherapy resistance in BRAF mutant HT29 cells. HT29 cells were treated with 5-FU alone or in combination with the HH-GLI inhibitor GANT61 and the NOTCH1 inhibitor DAPT (Figure 2A). We observed that 5-FU induced upregulation of GLI1 and NOTCH1. GANT61 treatment resulted in the downregulation of both GLI1 and NOTCH1 ID, while DAPT treatment caused the downregulation only of NOTCH1 ID, without exerting any effect on GLI1 levels compared to control cells. The combination of GANT61 and DAPT successfully targeted both GLI1 and NOTCH1 ID. The combined treatment of GANT61 plus 5-FU was able to revert the 5-FU-induced upregulation of GLI1 and NOTCH1 ID, and the combined treatment of DAPT and 5-FU was able to revert the 5-FU-induced upregulation of NOTCH1 ID and partially of GLI1. Only when both HH-GLI and NOTCH pathways were inhibited together with 5-FU treatment were both GLI1 and NOTCH1 ID significantly downregulated (Figure 2A). Apoptosis was evaluated through c-PARP levels; treatment with 5-FU and single inhibition of HH-GLI and NOTCH1 failed to induce apoptosis; c-PARP levels indeed increased only when cells were treated with GANT61 in combination with 5-FU, or with the combination of the three drugs (Figure 2A). We then investigated cell viability and our results showed a significant impairment after GANT61 treatment, alone or in combination with 5-FU (Figure 2B). Based on these results, chemotherapy resistance to apoptosis in BRAF V600E mutated cells seems to be driven by the HH-GLI signaling, which in turn sustains the activation of the NOTCH pathway. To gain more insight into the interdependence between the HH-GLI and NOTCH pathways, we interrogated GLI1 and NOTCH1 levels in a cohort of CRC patients carrying BRAFV600E mutation (Mutation status (Core Exon)—Sieber—211—rma_sketch—huex10p; https://hgserver1.amc.nl/cgi-bin/r2/main.cgi, accessed on 15 December 2021) and found a positive and significant correlation between GLI1 and NOTCH1 (Figure 2C). The above presented data suggest an upstream role of HH-GLI in the regulation of NOTCH signaling in the BRAF-driven CRC model. To investigate whether BRAFV600E acted as a driver on the regulation of HH-GLI and NOTCH, we performed BRAF silencing (Supplementary Figure S2A). BRAF silencing resulted in decreased GLI1 and NOTCH1 ID protein levels (Supplementary Figure S2B). We also evaluated mRNA levels of HH-GLI and NOTCH1 ID readout, ABCG2 and HES1, respectively, and both were significantly decreased after BRAF silencing (Supplementary Figure S2C). The above-reported data demonstrate that chemotherapy stress induced increased levels of both HH-GLI and NOTCH1 pathways in the BRAF-driven CRC model. Interestingly, we observed that the GLI1 inhibitor GANT61 was also able to decrease NOTCH1 ID levels; conversely, the NOTCH1 inhibitor DAPT did not affect GLI1 levels. Since we observed that the targeting of HH-GLI was able to indirectly also target the NOTCH pathway, we wondered if the combination of 5-FU and GANT61 could affect cell motility, a key feature of EMT and therefore of CRC aggressiveness. Our experiments showed that 5-FU did not affect cell motility, while GANT61 resulted in decreased cell motility, which was further impaired by the combination of GANT61 with 5-FU (Figure 2D). Then, we investigated the expression of two HT29 cell-specific epithelial differentiation markers, Axin and Muc2. We observed upregulation of Axin only after combined treatment, while Muc2 was affected by both 5-FU and GANT61 alone and by their combination (Supplementary Figure S2D), suggesting that treatments enhance the differentiated phenotype. The previous set of experiments allowed us to point out the role of HH-GLI and NOTCH pathways as regulators of EMT in KRAS mutant and BRAF mutant CRC, a key feature of chemoresistance [27]. Organoid models in pre-clinical studies have become widespread due to their high reproducibility and high similarity to in vivo models [28,29]. Indeed, cell features and behavior depend on the architecture of the cell population, e.g., the cell–cell contact, the stiffness of the extracellular matrix and the interaction with the microenvironment. All these conditions concur with specific characteristics related to cell polarity, stemness and differentiation status. Thus, to obtain CRC organoids, we seeded HCT116 and HT29 cells in Matrigel and after 7 days we observed organoid growth, as shown in Figure 3A,B. We compared basal levels of GLI1 and NOTCH1 in organoids and in 2D monolayer and our results reported higher GLI1 and HES1 expression levels in organoids, indicating that both pathways were more active in organoids compared to monolayer cellular models (Figure 3C). We then evaluated levels of the EMT marker c-MET in both organoids and monolayers and observed that c-MET was expressed at higher levels in organoids (Figure 3C). Since our results showed that 5-FU was not able to impair the migratory ability of CRC (Figure 1 and Figure 2), and that CRC patients often present disease progression despite chemotherapy, we wondered if 5-FU itself favored aggressiveness in organoids, unleashing the migratory potential. Increased motility and migration capacity are features of EMT, thus we performed in vivo live cell imaging in the KRASG13D-driven CRC organoid model, the HCT116-derived organoids (oHCT116) at basal state and after 5-FU treatment (Supplementary Material Supplementary Video S1). To investigate the behavior of CRC cells within organoids, we investigated the diffusion parameters that allow the motility of individual cells to be quantified. The diffusion parameters from the oHCT116 control or 5-FU-treated organoids are reported (Figure 3E) along with the single cell trajectories that were used for the calculation of the diffusion parameters (Supplementary Figure S3). Interestingly, 5-FU-treated oHCT116 cells mostly present lower diffusion parameters compared with CTRL (Figure 3D), with a long tail corresponding to a sub-group of cells presenting very high diffusion (Figure 3E). Based on these results, we believe that cells with augmented motility after chemotherapy represent a subset of aggressive cells able to initiate the metastatic process. We then proceeded to investigate the inhibition of HH-GLI and NOTCH by using ATO in combination with 5-FU in KRAS-driven CRC organoids, oHCT116. Treatment with 5-FU alone did not affect organoid growth, while organoids treated with ATO were significantly smaller; the association of 5-FU and ATO further impaired organoid growth (Figure 4A). Expression levels of the EMT marker c-MET, cancer stemness markers ABCG2 and CD133a, which is both HH-GLI target and cancer stemness marker, were significantly decreased in the combined treatment of 5-FU and ATO (Figure 4B). Interestingly, ATO was able to counteract the 5-FU-driven upregulation of ABCG2. Mesenchymal features were also investigated by the immunofluorescence of the EMT marker vimentin, whose levels increased after 5-FU treatment and were reduced when organoids were treated with ATO alone or in combination with 5-FU. Of note F-actin, revealed by phalloidin staining, underwent a marked rearrangement in 5-FU-treated oHCT116, where cells lost their pseudopodia, probably due to a modification in the cell polarity (Figure 4C). We then investigated the effects of 5-FU alone or in combination with the HH-GLI blockade in the BRAFV600E-driven CRC organoids (oHT29). Our results showed that the size of oHT29 treated with 5-FU did not differ from the control group, while organoids treated with GANT61 were smaller in size and the combination of 5-FU and GANT61 strongly impaired organoid growth (Figure 5A). Gene expression analysis showed that the levels of cancer stem cell and EMT markers ABCG2, CD133 and c-MET significantly increased after chemotherapy treatment and were impaired by HH-GLI inhibition and the combination of 5-FU and GANT61 (Figure 5B). To better investigate EMT, we performed whole-mount immunofluorescence staining for the mesenchymal marker vimentin and observed that vimentin levels were upregulated in 5-FU-treated organoids, they decreased with GANT61 and were strongly impaired in the combined treatment (Figure 5C). Altogether, our experiments show that in KRAS-driven and BRAF-driven CRC, the HH-GLI and NOTCH pathways sustain the resistance to 5-FU through the activation of the EMT. Of note, ATO, the drug targeting both HH-GLI and NOTCH pathways, reverted the mesenchymal phenotype, therefore supporting the action of the chemotherapeutic drug. Despite recent advances in cancer therapy, CRC is still among the prevalent causes of cancer-related death [30]. Even though medical research has focused on identifying genetic mutations linked to CRC progression and tumor prognosis to improve patient treatment, drug resistance often occurs. One of the mechanisms conferring drug resistance is the misactivation of evolutionarily conserved pathways, such as Wingless (WNT) [31,32], phosphoinositide-3-kinase [33,34], extracellular signal-regulated kinase (ERK) [35,36], nuclear factor-κB (NF-κB) [37,38] and the Hedgehog-GLI (HH-GLI) signaling pathway [20]. The HH-GLI pathway has a crucial role in correct embryonic development and plays a role in the physiological maintenance of many tissues, including the colonic mucosa [39,40]. While canonical activation of the HH-GLI pathway transduces the signal through the Hedgehog/PTCH/SMO/GLI axis, non-canonical regulation of GLI is external to Hedgehog signaling. Of note, it was demonstrated that transforming growth factor-beta (TGF-β) [41], epidermal growth factor receptor (EGFR) [42], mitogen-activated protein kinases (MAPK) [11], β-arrestin [43] and WNT/β-catenin [44,45] were able to induce the expression of GLI, regardless of SMO activation. Since both canonical and non-canonical routes culminate with the activation of the GLI1 transcriptional program, GLI1 inhibition could be useful to prevent chemoresistance in cancer cells. Our group has previously demonstrated that HH-GLI signaling regulates the expression of ATP-binding cassette transporters (ABC transporters), which are correlated to multidrug resistance in cancer cells, providing a rationale for the consideration of the HH-GLI pathway as a therapeutic target in CRC [20]. NOTCH signaling has been reported to play a crucial role in the development of the normal mucosa [15] and its aberrant activation is related to carcinogenesis in CRC. HH-GLI and NOTCH signaling pathways together with the WNT and BMP pathways are responsible for the development of intestinal mucosa, which is the innermost layer of the colon. Stem cells, transit amplifying cells and terminally differentiated secretory cells or enterocytes, concur in the formation of the structural unit of the colon, known as the crypt of Lieberkuhn [46]. A recent paper showed that the HH-GLI blockade with GANT61 was able to inhibit NOTCH and WNT/β-catenin in cellular models of CRC [47]. Since the HH-GLI and NOTCH pathways play a fundamental role in the correct patterning of the colonic mucosa and HH-GLI is upregulated by chemotherapeutic stress, we wondered whether HH-GLI and NOTCH crosstalk could be involved in the resistance mechanism of CRC cells related to 5-FU chemotherapeutic stress. The results of this study show how the HH-GLI and NOTCH pathways sustain CRC chemoresistance in different ways depending on the driver oncogene mutation. In detail, in KRASG13D-driven HCT116 cells we observed an upregulation of HH-GLI and NOTCH pathways after 5-FU and the inhibition of HH-GLI resulted in increased levels of NOTCH1 ID and vice versa (Figure 1A). These results, coupled with the interrogation of public datasets (Figure 1C) suggested that the HH-GLI and NOTCH signaling pathways are connected in a positive feedback loop aiming to escape apoptosis induced by 5-FU (Figure 6). Importantly, the combined inhibition of HH-GLI and NOTCH was able to impair EMT, shown both as an impairment of transwell migration ability and with EMT markers in organoids (Figure 1E and Figure 4). ATO, which was used to target both HH-GLI and NOTCH pathways, has been approved by the FDA for the therapy of adult patients with acute promyelocytic leukemia (APL). A phase I trial investigating the co-administration of ATO and 5-FU/Leucovorin in patients with advanced/relapsed CRC showed that ATO was well tolerated and that in some patients it was associated with therapeutic response and increased survival; a later study investigated GLI1 levels in biopsies from the above-mentioned clinical trial and found that it resulted to be down-modulated after ATO administration. Of note, data on the mutational status of enrolled patients are not available [48,49]. In BRAFV600E-driven CRC, both pathways were upregulated after 5-FU treatment, but importantly GANT61 downregulated not only its specific target GLI1 but also NOTCH (Figure 2A), suggesting an upstream role of HH-GLI over the NOTCH pathway (Figure 6), thus explaining the positive correlation between these two signaling pathways (Figure 2C). Importantly, HH-GLI inhibition was able to impair EMT features, both in monolayer and organoids (Figure 2D and Figure 5). In conclusion, our study describes for the first time two distinct models for KRAS- and BRAF-driven CRC where the HH-GLI and NOTCH signaling pathways play different roles in the chemoresistance and mesenchymal phenotype of CRC (Figure 6). Indeed, we described that in KRASG13D-driven CRC, chemotherapy resistance is directed by the concurrent activation of the HH-GLI and NOTCH pathways and the inhibition of both is crucial to revert the resistant phenotype. Conversely, in BRAFV600E-mutated CRC, the resistance to apoptosis induced by chemotherapy is mainly sustained by the HH-GLI signaling pathway. The implications of this novel information can be far-reaching if taken into consideration for the management of CRC patients, providing clinicians with further tools for the development of more effective treatment plans.

PMC10000786

Shayan Shafiee,Jaidip Jagtap,Mykhaylo Zayats,Jonathan Epperlein,Anjishnu Banerjee,Aron Geurts,Michael Flister,Sergiy Zhuk,Amit Joshi

Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer

25-02-2023

cancer,tumor microenvironment modifier,notch-DLL4,consomic xenograft model,machine learning,binary classification,dynamic enhanced NIR imaging,indocyanine green,time series,tumor detection

Simple Summary Breast cancer is a disease that is affected by both the tumor cells and the host environment. It is well known that the tumor blood vessels are aberrant in structure and function due to rapid angiogenesis, and this aberrant vasculature plays a major role in drug delivery and therapy response of breast cancer. Dll4 is a protein that helps control the growth of blood vessels in tumors. This study used near-infrared optical imaging and a novel machine learning framework to determine if Dll4 levels can be predicted from simple noninvasive imaging assays. The eventual results of this study may help physicians decide if a given triple-negative breast cancer patient will benefit from a Dll4 targeted therapy. Abstract Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above 90%. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.

Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer Breast cancer is a disease that is affected by both the tumor cells and the host environment. It is well known that the tumor blood vessels are aberrant in structure and function due to rapid angiogenesis, and this aberrant vasculature plays a major role in drug delivery and therapy response of breast cancer. Dll4 is a protein that helps control the growth of blood vessels in tumors. This study used near-infrared optical imaging and a novel machine learning framework to determine if Dll4 levels can be predicted from simple noninvasive imaging assays. The eventual results of this study may help physicians decide if a given triple-negative breast cancer patient will benefit from a Dll4 targeted therapy. Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above 90%. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy. Breast cancer heterogeneity has been extensively studied and it has enabled the classification and categorization of tumors into molecular subtypes depending on the overexpression of antigens or hormone receptors on tumor cells [1]. Identification of tumor subtypes improves cancer patients’ care and prognosis by tailoring therapies to the subtypes [2,3,4]. Breast and many other cancers are highly heritable, yet most causative variants are unknown, and most of the known risk variants are considered tumor-cell-autonomous, with far less emphasis placed on identifying the role of germline variants impacting the tumor microenvironment (TME). The TME is a complex and dynamic system that includes cancer cells, stromal cells, blood vessels, and extracellular matrix [5] and plays a significant role both in tumor cell proliferation and in chemo- or radiotherapy delivery and response [5,6,7]. There is growing evidence that heritable modifiers of the tumor microenvironment can profoundly impact tumor behavior and response to diagnostic and therapeutic interventions [8,9,10,11,12]. Tumor blood vessels have abnormal structure and function, which leads to heterogeneity in blood perfusion both temporally and spatially [13]. This heterogeneity has multiple adverse consequences, including limiting the access of blood-borne drugs and effector immune cells to poorly perfused regions of tumors [14]. As a result, these areas become hypoxic and have low extracellular pH [15]. Hypoxia has been shown to play a significant role in tumor progression and metastasis by inducing genetic instability, angiogenesis, immunosuppression, inflammation, and resistance to cell death by apoptosis and autophagy [16,17]. Anti-angiogenic drugs are designed to target the vasculature in order to starve tumors and prevent them from growing. However, recent studies have shown that the efficacy of these drugs may be limited by specific biomarkers and pathways associated with resistance [15]. For example, it has been shown that some patients may not benefit from anti-VEGF therapies if they have elevated levels of plasma sVEGFR1 [18]. Similar outcomes have been observed with increased levels of SDF1α and anti-VEGF therapies [19]. Further the vascular TME and therapy response differs in primary tumor and metastasis sites [20] and the anatomic location [21,22,23]. Thus, characterizing angiogenesis in tumors holistically may have therapeutic implications. The process of tumor angiogenesis is closely regulated by a balance between promoting and suppressing angiogenic factors [24,25]. Delta like canonical notch ligand 4 (Dll4) is a protein-coding gene that provides instructions for making a protein part of a signaling pathway known as the notch pathway, which is essential for the normal development of many tissues throughout the body, affecting cell functions [26,27], modulating tumor angiogenesis [28], promoting vessel maturation, and inhibiting vessel sprouting by inducing apoptosis of tip endothelial cells (TECs) [28,29,30]. Dll4 is overexpressed in various types of cancer, including breast, ovarian, and colorectal cancer, and has been shown to promote tumor angiogenesis, growth, and metastasis by interacting with receptors on endothelial cells (ECs) [31,32,33,34]. Blockade of Dll4 activity results in enhanced vessel sprouting and increased vascular permeability [29,30,35], but anti-Dll4 therapy has not been universally successful, as Dll4 has been shown to have both pro-tumorigenic and anti-tumorigenic effects depending on the context of its expression [34,36,37]. We recently reported that Dll4 expression on the host TME rather than on tumor cells determines the EPR or enhanced permeation and retention effect in breast tumor xenografts and thus governs nanomedicine delivery and therapy response [38]. Despite the increasing evidence about the function of germline genetic modifiers, such as Dll4, in TME heterogeneity and enhanced permeability and retention (EPR) effects, the underlying influencers have mainly remained unexplored because of the lack of a systematic approach to studying them. Therefore, we developed the Consomic Xenograft Model (CXM) as a strategy for mapping heritable modifiers of TME heterogeneity. In the CXM, human breast cancer cells are orthotopically implanted into consomic xenograft host strains. These strains are derived from two parental strains with different susceptibilities to breast cancer. Salt-sensitive (SS) rats were employed as a tumor promoting strain, while Brown Norway (BN) rats were used as a tumor suppressing strain. A sequence of consomic strains were generated with chromosomes of SS rats replaced by those of BN rats one at a time and used for breast tumor xenograft studies [7,38,39]. Because the host backgrounds genetically differ by one chromosome, whereas the tumor cells are unvaried, any observed phenotypic differences are due to TME modifier(s) and can be linked to a single chromosome. These modifiers can be further localized by congenic mapping (inbred strains containing a given sub-chromosomal region in their genome). By combining CXM with dynamic epifluorescence near-infrared (DE-NIR) imaging, systemic injections of indocyanine green (ICG) through a tail vein in tumor-bearing rats, and multiparametric analysis of pharmacokinetic modeling, we localized and identified the function of the vascular-specific Dll4 allele on rat chromosome 3 (RNO3) as a heritable host TME modifier of EPR [38]. The SS.BN3IL2Rγ− CXM strain with low-level expression of Dll4 (referred to as Dll4−) had significant tumor growth inhibition compared with the parental SSIL2Rγ− strain with higher expression of Dll4 (Dll4+), despite a paradoxical increase in tumor blood vessel density in Dll4+. Further analysis revealed that the changes in the Dll4+ tumors were accompanied by altered expression of Dll4, which was previously linked with nonproductive angiogenesis. Additionally, Dll4 was found to be co-localized within a host TME modifier locus (Chr3: 95–131 Mb) identified by congenic mapping and correlated with the phenotypic differences observed at the consomic level [7,39,40]. The inheritance of functionally different Dll4 alleles can influence the efficacy of nanoparticle (NP) therapy, and previous results indicate that inherited microvascular distribution patterns, rather than overall NP uptake, ultimately determine the effectiveness of NP-mediated photothermal therapy (PTT). Consequently, patients with high endothelial Dll4 expression can be selected for treatment with anti-Dll4 targeted nanoparticles as opposed to patients with low Dll4 expression, where PEGylated nanoparticles will provide sufficient therapy response [38]. Recent advances in dynamic vascular imaging techniques, such as DCE-MRI and perfusion computed tomography, have facilitated the investigation of the time kinetics of a contrast agent to extract multiple vascular parameters and have been successfully applied in clinical trials of anti-angiogenic drugs [41,42]. However, these techniques have certain drawbacks, including a lack of high temporal resolution and the need for a heavy hardware system with sophisticated analysis software. Dynamic NIR fluorescence imaging, on the other hand, offers a sufficient and effective alternative to other dynamic vascular imaging techniques for characterizing germline-dependent vascular phenotypes [7,43]. This has led to the combination of these modalities, such as in the paired agent MRI-coupled fluorescence tomography approach for noninvasive quantification of paired-agent uptake in response to anti-angiogenesis therapy in vivo [44]. As the field of artificial intelligence continues to advance, researchers are increasingly utilizing AI techniques, particularly machine learning, to develop predictive models that can support effective decision making in various domains including cancer therapy selection [45,46]. Previous research has investigated the use of machine learning algorithms to analyze near-infrared (NIR) signal intensity and perfusion patterns to differentiate between healthy, benign, and malignant tissue [47]. This work demonstrated that the signal intensity time course of an FDA-cleared near-infrared dye ICG inflow during the wash-in phase and ICG outflow during the wash-out phase could serve as significant markers for tissue distinction. This finding offers a new method for noninvasive tissue distinction and has prognostic potential [48,49]. However, there remains a need for further exploration of the use of machine learning for classifying host genetic tumor microenvironment (TME) modifiers and predicting therapy responses based on dynamic contrast-enhanced imaging of tumors, particularly DE-NIR fluorescence imaging data [47]. We hypothesize that the observation of subtle differences in vasculature structure and perfusion patterns characterized by ICG inflow and outflow using DE-NIR imaging could be used to differentiate between inherited tumor vascular microenvironment differences, such as Dll4 expression levels. We propose an experimental framework to noninvasively assess Dll4 expression levels in tumors based on the NIR signal intensity time course of perfusion patterns characterized by ICG time kinetics to develop a predictive model to support effective decision making in cancer therapy. Herein, we used two rat-based CXM strains of breast cancer, SSIL2Rγ−(Dll4+) and SS.BN3IL2Rγ− (Dll4−) [7,38,50], as well as eight congenic xenograft strains, CG1–CG8 (Figure 1a,b), to assess the impact of germline TME vascular heterogeneity on the signal intensity of DE-NIR imaging with systemically delivered ICG. Principal component analysis (PCA)-based decomposition of time-dependent epifluorescence videos (image stacks) was used for visualization and anatomical segmentation of tumors, liver, lungs, and fat pads [7]. In addition, we utilized modified principal component analysis (PCA)-based anatomical segmentation techniques to identify and analyze regions of interest (ROIs) representing potential tumors within the current dataset. To gather further information, we calculated the average NIR intensity for each ROI by analyzing the brightness of individual pixels at each time interval, resulting in a series of intensity measurements for each ROI. From this analysis, several easily interpretable features were extracted, including the slope of the initial uptake of ICG, the time it takes to reach peak perfusion, and the rate of ICG intensity changes once the half-maximum intensity is reached (which, to the best of our knowledge, has not been previously reported in the literature). We then applied a subset of machine learning algorithms, including Support Vector Machines (SVMs), Naive Bayesian Classifiers (NBCs), Generalized Additive Models (GAMs), Decision Trees (DTs), Nearest Neighbors (NN), and Logistic Regression (LR) to select the most discriminative features for classification. The performance of the model was evaluated using confusion matrix, receiver operating characteristic curve (ROC), and the area under the curve. To further evaluate our hypothesis of detecting Dll4 expression levels from DE-NIR imaging and test the generalizability of our framework, we conducted a secondary performance evaluation method using congenic groups with high and low Dll4 expression levels. The classification models were trained based on the selected features, and the performance of the model was tested on the remaining congenic groups. We demonstrate that robust ML methods can identify the alterations in host Dll4 expression from the tumor dynamic imaging datasets, and thus these methods can potentially stratify patients for Dll4 targeted therapies. All methods have been carried out in accordance with relevant guidelines and regulations. Approved protocols by the Medical College of Wisconsin Institutional Biosafety Committee (IBC) and Institutional Animal Care and Use Committee (IACUC) were followed. All live animal experiments are reported per the ARRIVE guidelines’ recommendations [51]. All results were rigorously adjusted for multiple comparisons. All animal protocols employed in this study were approved by the Institutional Animal Care and Use Committee (IACUC), Medical College of Wisconsin (MCW). The MCW has an Animal Welfare Assurance (Assurance number D16-00064 (A3102-01)) on file with the Office of Laboratory Animal Welfare, National Institutes of Health (NIH). Animal experiments were performed according to the relevant guidelines and regulations and in compliance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH publication no.85–23, revised 1996). SS.BN3 rats were developed as part of the Consomic Xenograft Model at the Medical College of Wisconsin (MCW) [40]. SS and SS.BN3 rats were purchased from the Rat Research Models Service Center at the Medical College of Wisconsin [52]. All rats were provided reverse osmosis hyper-chlorinated water ad libitum. All animal experiments were performed on anesthetized animals. The animal was placed in a transparent induction chamber to induce anesthesia. Isoflurane was delivered through a precision vaporizer and compressed O2 to the chamber. For induction, the percentage of isoflurane was up to 5%. Once the animal was unconscious, it was removed from the chamber. The unconscious animal was then placed on a warm surface and fitted with a nose cone attached to the vaporizer in the presence of a scavenging system and oxygen source. At this point, the concentration of isoflurane was reduced to this level that maintained the correct anesthesia plane, usually between 0.5 and 3%. After the end of the experiment, or when other criteria for animal protocols were justified, rats were euthanized. Rats were placed in an approved euthanasia chamber and exposed to CO2 from a compressed gas cylinder until the animal was no longer breathing. To ensure death in rats, a pneumothorax was created via thoracotomy for rats weighing more than 200 g. For rats weighing less than 200 g, a pneumothorax was created, or a cervical dislocation was performed. As previously described [39,40], consomic strains (SS and SS.BN3 rats) were generated by sequentially replacing SS chromosomes with the outbred wild-type and tumor-resistant strain of Brown Norway (BN) rats referred to as SS.BN#, which are reported for their tumorigenic potential, where # refers to the chromosome number. These parental SS and consomic SS.BN# strains were genetically ablated by knocking down the IL2Rγ gene to allow the grafting and growth of human cancer cell lines. Such immunocompromised strains are labeled as SSIL2Rγ−(Dll4+) and SS.BN#IL2Rγ−(Dll4−). Previous research has localized inherited modifier(s) of TME vascular heterogeneity to RNO3 by CXM mapping and further narrowed by congenic mapping to a 36 Mb locus containing Dll4 alleles with distinct vascular expression patterns in the SS.BN3IL2Rγ− consomic (Dll4−) and SSIL2Rγ− (Dll4+) rat strains, and verified via species-specific RNA sequencing and immunohistochemistry that strains inheriting the SS Dll4 allele on chromosome 3 have higher Dll4 expression on tumor-associated endothelium and that the blood vessel tortuosity and dysfunction increased in Dll4− strains [38,39]. Since there are many other candidate alleles on chromosome 3 that could also potentially account for the observed differences in therapeutic efficacy between the SS.BN3IL2Rγ− and SSIL2Rγ− strains [38], to further investigate the potential contribution of Dll4 to inherited tumor vascular heterogeneity, eight novel SS.BN3IL2Rγ− congenic xenograft host strains (CG1 to CG8) were constructed by introgression of the F1 progeny and F2 generation to capture different regions of RNO3 by marker-assisted selection, as described previously [50,53]. The exclusion congenic mapping localized a 7.9 Mb candidate region (marked by the SSLP marker D3Mgh11) that was associated with inherited tumor vascular heterogeneity and contained the Dll4 locus. As a result, eight new congenic strains CGN(s-e) were generated, where N (1 to 8) refers to the congenic group, while s and e refer to the starting and ending of Simple Sequence Length Polymorphism (SSLP) marker regions, respectively. This resulted in generating CG1(D3Rat26-D3Mgh30), CG2(D3Rat222-D3Got42), CG3(D3Rat222-D3Mco33), CG4(D3Rat164-D3Rat218), CG5(D3Rat26-D3Mco218), CG6(D3Rat86-D3Rat218), CG7(D3Mgh13-D3Rat218), and CG8(D3Rat160-D3Rat218) congenic groups (Figure 1a,b). As previously described [38], triple-negative breast cancer MDA-MB-231 cells were maintained in DMEM media (Sigma, Burlington, MA, USA) supplemented with 10% FBS (Gibco, New York, NY, USA) and 1% penicillin and streptomycin (Lonza, Cohasset, MN, USA) and incubated in 5% CO2 at 37 °C. These cells (6 × 106) in 50% Matrigel were orthotopically implanted into the mammary fat pads (MFP) of 4- to 6-week-old female Dll4+ (n = 8) and Dll4– (n = 17) rats and eight congenic strains CG1 (n = 19), CG2 (n = 2), CG3 (n = 26), CG4 (n = 12), CG5 (n = 28), CG6 (n = 12), CG7 (n = 5), and CG8 (n = 4) (Figure 1c) [54]. Tumors were treated after 10 days of implantation at an approximate size of 600 mm3, consistent across all rat strains. A customized NIR imaging system was assembled for imaging the rats. A bifurcated optical fiber bundle was used to deliver 785 nm excitation light (~5 mW/cm2 power at the surface, diode laser, ThorLabs Inc., Newton, NJ, USA) from two positions for uniform illumination of the entire rat body surface. A 16-bit deep-cooled intensified charge-coupled device camera (PIMAX4 ICCD, Princeton Instruments, Trenton, NJ, USA) equipped with 830 nm long-pass filter positioned following a holographic notch rejection filter in the optical path (ThorLabs Inc.) was used to image the rats through computer-controlled LightField® software (Teledyne Princeton Instruments, Trenton, NJ, USA) (Figure 1d). Dynamic contrast-enhanced NIR fluorescence imaging was performed on anesthetized rats, as reported previously for 800 nm NIR imaging [7]. In this study, the setup was used for imaging the whole body. A total of 133 rats (Dll4+ (n = 8), Dll4– (n = 17)) and eight congenic strains CG1 (n = 19), CG2 (n = 2), CG3 (n = 26), CG4 (n = 12), CG5 (n = 28), CG6 (n = 12), CG7 (n = 5), and CG8 (n = 4) were imaged. NIR imaging was performed for approximately 6 min following ICG injection with the CCD array hardware binned to 256 × 256 with a frame rate of 10.6 fps. A total of 3000 frames were captured for each imaging case, including about 50 frames for background correction. ICG (MP Biomedicals) was delivered in an intravenous bolus of 1200 µM ICG/200 g body weight into the tail vein via a catheter with a 32-gauge needle tip connected to a syringe pump (Harvard Apparatus PHD 2000 syringe pump, Holliston, MA, USA) operated at a speed of 0.2 mL/s. The injected volume was calibrated to provide a body-weight-equilibrated dose to each rat. Image processing and data analysis were performed in MATLAB (R2021b MATHWORKS Inc.) software. The time-dependent image frames were assembled as 3D arrays (two spatial and one temporal dimensions) for all animals. A custom-designed breathing correction method with a low-pass temporal filter combined with a 1D wavelet-based denoising was used to filter the high-frequency jitter generated by the animal’s respiratory motion from the fluorescence kinetic sequences of each pixel, as described previously [7]. An average of pre-ICG injection frames (acquired in the ~5 s before ICG injection) was used as background, incorporating contributions from CCD noise and excitation light leakage from emission filters and subtracted from all the frames. (Refer to Video S1 for respiratory motion corrected time course images of ICG biodistribution). First, motion correction and background subtraction were performed on the imaging data described in the previous steps. This was carried out to remove any potential artifacts or noise that could affect the accuracy of the principal component analysis. Next, the data were decomposed by PCA along the time dimension using MATLAB software following the previously published methods [55,56]. This resulted in converting the imaging data to a k-component vector for each pixel, where k is the number of time-frames in the original dataset. The PCA on the dynamic fluorescence image was used to extract spatial patterns of internal organs linked to statistically similar kinetic behaviors. This was carried out by comparing the k-component vectors for each pixel and identifying those that displayed similar patterns over time. The contribution of the first six principal components on a time basis is illustrated in Figure S1. The ROI detection module employed three steps in order to identify the tumor area in images (Figure 2a): (1) spatial alignment, (2) PCA ranking and selection, and (3) ROI selection and masking. First, images were registered to a reference image using a rigid body transformation in order to ensure consistent spatial alignment for the detection of the tumor area in subsequent steps. The variability in the visual appearance of internal organs or tumors within certain principal components necessitated the adaptation of existing methods for ranking normalized components based on their two-dimensional cross-correlation (2DCC) with a reference image containing the tumor. This was achieved by generating a stack of the first ten normalized principal components and applying a two-dimensional cross-correlation function (xcorr2 in MATLAB) to each component. The principal components were ranked according to the correlation scores obtained and a semi-automatic algorithm was then utilized to carry out the subsequent two steps for tumor ROI generation. To select the appropriate principal component for tumor identification, the algorithm prioritized the PC with the highest likelihood of containing the tumor tissue based on the ranking from the previous step. Once the proper PC was selected, we used 2DCC to estimate the tumor’s location in the frame. This was performed by creating a stack of four reference images using PCs that clearly visualized the tumor and generated a bounding box around the tumor. Next, we applied basic morphological dilation and image arithmetic operations to emphasize the tumor’s boundaries. A threshold was then applied to separate the tumor from the background. Finally, we used Blob Analysis (Computer Vision Toolbox™, MATLAB, MathWorks, MA, USA) to extract the centroid and exact location of the tumor in the frame, generating a region of interest (ROI). The use of a graphic user-interface-enabled semi-automatic platform allowed for real-time evaluation and adjustment of the algorithm’s performance if necessary. The underlying assumption for the NIR fluorescence intensity video analysis is that intensity was proportional to the ICG concentration in the tissue, which has been shown in the literature [57]. From each available video, , between 4 and 5 fluorescence time series are extracted, one based on the tumor ROI generated from earlier steps, and between three and four from mammary fat pads (MFPs) (Figure 2b). For each ROI, , and each included time step, the mean brightness of the pixels inside that ROI in the NIR video is stored as . The result is a collection of time series , where p ranges from 1 to (the number of animals in each group in our dataset), ranges from 4 to the number of ROIs generated in the specific animal (up to 5, 1 tumor and 3 or 4 fat pads), and t from 0 to 3000 (Figure 2b). First, we started with smoothing the data to exclude any potential noise from motion artifacts. The smoothing was conducted using a Savitzky–Golay filter of order 3 with window length 31 [58]. The parameters for this filter were determined through manual annotation of peak and latency in a subset of the data, with the goal of minimizing average estimation error. Subsequently, we used a MATLAB function to identify the time point of maximum intensity, which corresponded to the peak of the fluorescence signal. We employed a custom latency detector script that analyzed the smoothed derivative of the curves and identified the first “robust” zero crossing as the latency point () [48]. The features were chosen in two steps as previously described (Figure 3) [47,48]: first, the following characteristic numbers were chosen for each normalized time series individually. The time to peak () is simply the time difference between the peak intensity time point and the latency . The upslope () is computed as which is the average slope between initial ICG arrival and the peak. The downslopes () are the downslopes between the peak and seconds further: The time ratio () is the ratio between and when reaches half the peak values. The half intensity forward () is the average slopes between when reaches half the peak values () and seconds further, so To increase the robustness of estimated downslope-based and -based features, we introduced a window around and time steps taking the median of those downslopes: These features provide insight into the tumor’s ICG uptake and decay. and relate to the initial uptake of ICG, while relates to the decay of ICG fluorescence [48]. is a measure of the temporal inhomogeneity of the initial uptake, and is a measure of the temporal inhomogeneity of both the initial uptake and decay of ICG fluorescence. To address inter-animal variation, we propose a feature design that relates the features of tumor intensity () to the features of healthy tissue intensity () in the same animal. The median value of a feature across the healthy tissue (fat pad) is chosen as a reference value, and each feature is defined as its percentage difference to that reference value. This results in a single normalized feature for each animal. By using the median as an average that is robust to outliers, our feature design allows for a more accurate comparison of tumor intensity across different animals. The feature extraction and design required specialized knowledge of the tumor microenvironment (TME) and its impact on near-infrared intensity. However, the classification based on the inheritances of Dll4 can be considered a standard binary classification problem with feature selection as a sub-problem: given the features of an animal for which the Dll4 inheritance is unknown, we want to assign the label “Dll4high” or “Dll4low” to it. We also want to investigate which small subset of features performs best [44]. We restricted ourselves to a subset of available ML algorithms which were reported to perform best with intensity time series classification. [48], We excluded neural networks from consideration and evaluated Support Vector Machines (SVMs), Naive Bayesian Classifiers (NBs), Generalized Additive Models (GAMs), Decision Trees (DTs), Nearest Neighbors (NN), and Logistic Regression (LR). DTs and SVMs using the full set of 86 features were trained. Each ML model was tuned to the training set in an internal cross-validation procedure of 10-fold. This process was repeated 20 times, and the performance metrics are reported as classifier performance. One of the key metrics used to evaluate the performance of a classification algorithm is accuracy, which measures the proportion of correctly classified instances in the dataset. Other important metrics include sensitivity, which measures the proportion of positive instances that were correctly classified, and specificity, which measures the proportion of negative instances that were correctly classified. In our case, we are interested in classifying animals as either Dll4high or Dll4low, and we use the average metric (Ascore) for each pair of groups to evaluate the performance of the different classification algorithms. The Ascore for a given pair of groups is calculated as the average of accuracy, sensitivity, and specificity, as follows: In this study, we describe a two-step method for selecting congenic pairs with high and low Dll4 expression levels for feature selection and hypothesis testing. This method was used to identify well-behaved pairs for binary classification and to identify the pair with the highest classification performance: The primary selection of congenic pairs is based on their primary classification scores () and their performance against parental strains using all available features. The secondary selection is based on the classification performance using the 2 best-performing features. The congenic pair selection process involved the selection of all possible combinations of congenic pairs with high and low Dll4 expression levels with n > 5, resulting in a total of 12 pairs: Dll4+|Dll4−, Dll4+|CG3−, Dll4+|CG4−, Dll4−|CG1+, Dll4−|CG5+, Dll4−|CG6−, CG1+|CG3−, CG1+|CG4−, CG3−|CG5+, CG3−|CG6+, CG4−|CG5+, CG4−|CG6+. It should be noted that we only included subgroups with n > 5 in our analysis, to avoid introducing noise into the feature selection process. This is because smaller sample sizes can be more prone to variability and may not represent the larger population [59]. Therefore, we focused on more significant subgroups to ensure our feature selection process was robust and reliable. These 12 pairs have gone through our primary classification algorithm with a split of 75%–25% for testing and training with seeded randomization and proportional distribution of each group in training and testing datasets. Each ML model was tuned to the training set in an internal cross-validation procedure of 10-fold and evaluated by its performance on the test set. This process was repeated 20 times, and the best Ascore was reported as the metric of classifier performance. The result of this step was used to identify well-behaved congenic pairs for binary classification by calculating the separation score (Sscore) for each pair of congenic groups with CG#+ and CG#− as below, which is bound between 0 and 1 and reported in Table 1: The congenic pairs with Sscore above 80% were selected for secondary congenic pair selection. For the secondary selection, we evaluated the classification performance () of each pair determined this time by using the two most effective features as described in Section 3.13. We used a 75%–25% split for testing and training, with randomization to ensure a proportionate representation of each group in both datasets. The pair with the highest classification performance, Ascore, based on the two most effective features was selected for the final classification model training. To ensure a high-quality classification model, we set a minimum threshold for the Ascore of 0.70 for inclusion in the feature selection process. Congenic groups with an Ascore below 0.70 were considered to have an insignificant contribution to the classification model and were excluded from further consideration in the feature selection process. This approach allowed us to focus on the most informative feature pairs, improving the overall classification performance of our machine learning model. Each machine learning model was tuned to the training set using a 10-fold cross-validation procedure and evaluated based on its performance on the test set. We repeated this process 20 times and reported the best Ascore as the classifier’s performance. We selected the best pair of features in terms of achieved sensitivity, specificity, and accuracy by a two-step procedure as previously reported: DTs and SVMs using the full set of 86 features were trained, and recursive feature elimination (RFE) was performed to refine a much smaller set of best-performing features [60]. RFE is a widely used machine learning classification algorithm that helps in reducing the dimensionality of feature space and selecting a small subset of features that yield the best classification performance. This was achieved through an iterative procedure that uses a ranking criterion to eliminate features one or more at a time. The RFE algorithm started by selecting a subset of features and training a model on this subset. The features were then ranked based on their contribution to the model’s performance, and the least important feature was eliminated. The process was then repeated with the remaining features, and the best subset of features was selected based on a model selection criterion [61]. One of the main advantages of RFE is that it helps to reduce the risk of overfitting when the number of features is large, and the number of training patterns is comparatively small [62]. This is because the algorithm selects only a subset of features that are relevant to the classification task, and this helps to avoid the inclusion of irrelevant and redundant features. RFE can be used in conjunction with other techniques such as regularization and support vector machines (SVMs) to further improve the performance of the classification model. In addition, projection methods such as principal component analysis can reduce the feature space’s dimensionality before applying RFE [55]. We used a k-fold cross-validation strategy to assess the performance of our model. We also reserved a portion of the training data for primary testing of the model after hyperparameter optimization. Our experiments were conducted with 20 random splits of the training and test datasets, and the mean performance metrics were reported for sensitivity, specificity, and accuracy as Ascore. To facilitate the interpretation of our results, we limited the number of final features to two. Furthermore, given the small size of our dataset, there was no justification for using high-dimensional feature spaces. The use of data augmentation has become a popular technique in machine learning and deep learning, especially in the field of computer vision. Data augmentation involves applying random transformations to the training dataset to increase its diversity and improve the performance of a model. In this study, we used data augmentation on raw near-infrared (NIR) image stacks to evaluate the robustness of a classification model. We used a dataset of 3000 frames of the original raw 256 × 256 NIR images for this part of our study. These images were augmented using TensorFlow and the Keras API, which allowed us to apply random transformations to the dataset. The transformations included random rotation followed by a horizontal flipping, and up to 2% rescaling. The final training and testing dataset for the machine learning models was determined by the outcome of the congenic pair selection and feature selection steps. This dataset included all congenic groups except for Dll4+ and Dll4−, as well as the selected CG#+ and CG#− groups in the previous step. This step was conducted separately for the original dataset and augmented dataset. The models were trained using 10-fold cross-validation and a portion of the training data was reserved for testing after hyperparameter optimization, with 25% for the original dataset and 20% for the augmented dataset. The performance of the models was assessed using a confusion matrix, receiver operating characteristic curve (ROC), and the area under the curve. Repeated measures models are a powerful tool in statistical analysis that allow researchers to study the effects of different factors on a given outcome while accounting for the inherent dependence of multiple measurements taken on the same subject. In this study, a mixed effects model with appropriate time varying covariates was used to analyze the average fluorescence intensity of indocyanine green (ICG) in the tumor with multiple measurements per subject, with the subject number serving as the repeated measure indicator and the rat strain serving as a covariate. This allows for flexible time-based modeling when using multiple measures, likely dependent from the same animal [63,64]. Customized scripts in MATLAB were used to generate the fitted coefficients, covariance parameters, design matrix, error degrees of freedom, and between- and within-subjects factor names for the repeated measures model. The output was then analyzed with a multiple comparison of the estimated marginal means based on the variable strain, using the Tukey–Kramer test statistic [65]. This allowed estimation of multiplicity-adjusted p-values for the post hoc comparisons, which indicate whether the groups significantly differed with respect to strain. The data were then visualized as a p-value matrix, providing a clear illustration of the significant differences between groups. This study employed the established consomic rat models SS and SS.BN3 as well as our congenic strains CG1 to CG8. The publicly accessible and NIH-supported Rat Genome Database (rgd.mcw.edu) catalogs has tools to explore the genotype and phenotype information for the SS (Dll4+) and SS.BN3 (Dll4−) and congenic strains under strain records: Dll4+ (RGDID:61499), Dll4− (RGDID:1358154), CG1 (RGDID:155782881), CG2 (RGDID:155782883), CG3 (RGDID:155782884), CG4 (RGDID:155791428), CG5 (RGDID:155791426), CG6 (RGDID:155791430), CG7 (RGDID:155791429), and CG8 (RGDID:155791427). Dynamic contrast-enhanced NIR fluorescence imaging has been widely used for tumor detection in various studies [66,67,68]. The use of NIR imaging allows for the visualization of internal organs and tissues without the need for invasive procedures, which can be particularly useful in detecting tumors due to their vascular heterogeneity compared to surrounding healthy tissues. In previous studies, the use of principal component analysis (PCA) on the time domain of dynamic fluorescence images was utilized to extract spatial patterns of internal organs linked to statistically similar kinetic behaviors, such as liver, kidneys, lungs, and various tumors [7,56]. However, this technique required manual inspection and selection of proper principal components, which was time consuming and prone to human error and bias. In order to overcome the limitations present in the current dataset, we implemented a modified method that utilizes near-infrared imaging and principal component analysis to detect tumors with high accuracy and without the need for manual correction (Figure 2 and Figure S1). The use of principal component analysis in this context not only allows for dimensionality reduction and noise removal but also enhances the robustness and efficiency of the method. Our study also implemented a novel method of ranking PCA components based on the 2D cross-correlation of a reference image containing the tumor. This added to the simplicity and computational efficiency of the framework. However, it should be noted that this method may not be effective for detecting tumors with random locations. On the other hand, it could be useful for detecting tumors or tissues of interest with high localization, such as the lungs, liver, and kidney, and lesions in breast tissue. Overall, our method shows potential for improving the accuracy and efficiency of tumor detection using NIR imaging and PCA (Figure 4a). However, further experimentation is needed to expand the framework to a general tumor detection algorithm. The analysis of the average fluorescence intensity of indocyanine green (ICG) in the tumor tissue of Dll4+ and Dll4− rats bearing triple-negative breast cancer (TNBC) tumors revealed that ICG uptake occurred more rapidly in Dll4− tissues and was retained for longer periods of time compared to Dll4+ hosts (Figure 4b). This indicates systemic differences in vascular function between the two rat strains. Our previous histological data showed that Dll4+ tumors have a higher vascular density and tortuosity, indicating a genetic microenvironment that promotes nonproductive angiogenesis [38]. This is further supported by the slower ICG wash-out observed in the Dll4+ tumors. These findings provide insight into the effects of host genetics on tumor angiogenesis and suggest potential therapeutic targets for TNBC. In order to further investigate the role of Dll4 in vascular function in tumors, we divided chromosome 3 into regions with and without the Dll4 gene in congenic rat strains (Figure 1b) and then examined the ICG fluorescence intensity of tumors in Dll4-high and Dll4-low rats (Video S2) (Figure 4c). Our findings reveal significant systemic differences in vascular function between tumors in Dll4+ and Dll4− rats (parental strains), indicating the critical role of the Dll4 gene in tumor angiogenic response [38]. However, analysis of the ICG fluorescence intensity of tumors for individual strains (Figure 4d) reveals more complex behavior than the obvious differences in wash-in and wash-out patterns observed between Dll4+ and Dll4−. This supports the need for further investigation into the impact of Dll4 on NIR time series signatures and the potential use of Dll4-directed therapies for cancer treatment. It is worth noting that although there are significant differences in Dll4-low vs. Dll4-high rat strains (when all the strains of Dll4 expression levels are combined), they are inconsistent with the observations made in Dll4+ and Dll4− rats. These results have significant implications for developing novel therapies that target Dll4 and other host TME modifiers involved in angiogenesis, as they demonstrate the critical role of these genes in tumor vascular function and angiogenic response. Additionally, our research further highlights the capricious nature of the NIR signal, which is influenced by various heritable tumor microenvironments across different groups, as shown in Figure 4b,c. We aim to illustrate and categorize the impact of the Dll4 expression level on the NIR signal through this erratic behavior. We used a repeated measures model to analyze the average fluorescence intensity of ICG in the tumor over time, with the rat strain serving as a covariate. Figure 5a,b show the estimated response covariances matrix, which is the covariance of the repeated measures. The higher values in this matrix indicate the time points at which groups experience the greatest differences. By projecting the diagonal of the covariance matrix onto the time axis (Figure 5c), we were able to visualize the amount of difference between groups over time. This projection, when compared to the average fluorescence intensity of ICG in the tumor (Figure 4b,d), showed the strongest differences between groups at the points where the NIR signal regions from half of its peak value to the peak value and at the tail of the curve, which are measures of the temporal inhomogeneity of the initial uptake and the decay of ICG fluorescence, were found to be particularly useful in discriminating between groups with different levels of Dll4 expression. This projection of the diagonal of the estimated response covariances matrix on the time curve can be used in feature design to focus on regions with the maximum amount of useful information for discriminating between groups and, subsequently, between classes with different levels of Dll4 expression. This could potentially improve the accuracy of tumor classification and ultimately improve therapy outcomes. Our repeated measures model, which included responses as measurements and strains as predictor variables, allowed us to conduct multiple comparisons of estimated marginal means between groups. The resulting p-value matrix (Figure 5d) revealed significant differences in estimated marginal means between the Dll4+ and Dll4− groups, with a p-value of 4.71 × 10−7. In addition, we observed significant differences between Dll4+ and CG3, CG4, and CG8, with p-values of 1.67 × 10−5, 7.03 × 10−7, and 2.18 × 10−3, respectively. For each group pair with high and low Dll4 expression levels, the separation score was calculated. First each of Dll4+|CG#−, CG#+|Dll4−, and CG#+|CG#− went through our classification algorithm with 10-fold cross-validation using Nearest Neighborhood, Linear SVM, RBF SVM, Decision Tree, Naive Bayes, and Logistic Regression models. The highest average classification metrics (Accuracy + Specificity + Sensitivity)/3 for Dll4+|CG#−, CG#+|Dll4− and CG#+|CG#−) was used to calculate the separation score (Score Dll4+|CG#− + Score CG#+|Dll4− + 2 × Score CG#+|CG#−)/4. Furthermore, our analysis showed significant differences between Dll4− and CG5 and CG6, with p-values of 8.70 × 10−3 and 2.58 × 10−4, respectively. This supports the hypothesis that Dll4 expression levels can act as a heritable TME modifier on NIR time series intensity. However, the smallest p-value between Dll4+ and Dll4− suggests that there are other factors on chromosome 3, in addition to Dll4, that contribute to the observed differences in the NIR time series signature between these groups. In contrast, no significant differences were found between Dll4− and the congenic strains with low levels of Dll4 expression (CG2, CG3, CG4, CG7, and CG8). This further supports the notion that Dll4 plays a crucial role in determining tumor vascular function and NIR time series intensity. Among the congenic groups, the most significant differences were observed between CG5, CG6, and CG3, CG4 from the Dll4-high and Dll4-low groups, respectively. Notably, the differences were most significant between CG4 and CG6, with a p-value of 0.0003. This suggests that very narrow regions of differences on chromosome 3 between these two groups, one containing Dll4 and the other lacking it, have a significant effect on the NIR time series signature. The relationship between Dll4 expression and classification performance was analyzed using a total of 12 congenic pairs with n > 5 based on their levels of Dll4 expression (Dll4+|Dll4−, Dll4+|CG3, Dll4+|CG4, Dll4−|CG1, Dll4−|CG5, Dll4−|CG6, CG1|CG3, CG1|CG4, CG3|CG5, CG3|CG6, CG4|CG5, and CG4|CG6). The pairs were then subjected to a primary classification algorithm and their mean performance metrics, the Ascore, were calculated and reported in Table 1. The congenic pairs with low levels of Dll4 expression showed a mean Ascore of 0.91 +/− 0.01, indicating a high level of classification performance when compared to the Dll4+ parental strain. In contrast, the congenic pairs with high levels of Dll4 expression showed a mean Ascore of 0.8 +/− 0.05 when classified against the Dll4− consomic strain. Among the congenic pairs, the CG5|CG4 pair demonstrated the highest Ascore of 0.8, followed by the CG6|CG4 and CG6|CG3 pairs with Ascore values of 0.78 and 0.77, respectively. The results of the Ascore calculation are visualized in Figure 5e through a Sankey diagram. To account for potential differences between the congenic pairs and the parental pairs, the Sscore was calculated. The CG5|CG4, CG6|CG3, and CG6|CG4 pairs showed the highest Sscore values of 0.84, 0.84, and 0.85, respectively, and were selected for the feature selection step. These results align with the multiple comparison of estimated marginal means between groups, indicating that CG5|CG4, CG6|CG3, and CG6|CG4 show the strongest differences in classification performance. RFE is a wrapper method that evaluates the entire classification algorithm and has shown improved classification accuracy and reduced overfitting compared to other feature selection methods [69]. However, RFE can be sensitive to noise and irrelevant features, leading to suboptimal feature subsets and reduced classification performance. Additionally, RFE is computationally intensive, which can pose a challenge for large datasets with a high number of features. Despite these limitations, RFE remains a valuable tool for selecting an optimal subset of features that maximizes classification performance [70,71]. To address these limitations, we performed feature selection in two steps to optimize the selection process and improve the performance of the classifier. First, we used RFE to select only two features out of the 86 available features for congenic pairs Dll4+|Dll4−, Dll4+|CG4, CG5|Dll4−, CG5|CG4, CG6|Dll4−, and CG6|CG4. The CG3 and its combinations (CG6|CG3, CG5|CG3, and Dll4+|CG3) were dropped from the feature selection process as the performances of the classifiers, the Ascore, using only two features were below 0.70, and lower than the other strains. The congenic pairs CG5|CG4 and CG6|CG4 as well as the parental and consomic group Dll4+|Dll4− went through our feature selection algorithm, and for each pair the two best-performing classification algorithms based on Ascore and associated feature pair were reported (Table 2). The highest Ascore for the two best-performing models for CG5|CG4 was 0.78 ± 0.04 compared to CG6|CG4 with an Ascore of 0.72 ± 0.19 and 0.72 ± 0.22, resulting in the selection of CG5|CG4 for final congenic pair selection. Finally, from each pair of Dll4+|Dll4−, Dll4+|CG4, CG5|Dll4, and CG4|CG5, four of the best performing features regardless of the ML model were chosen and were used as a collection of features for the final feature selection (Table 2). A combination of parental and consomic groups and the final selected congenic pair (Dll4+, Dll4−, CG5, and CG4) was used to select the final feature pair out of the 16 selected features, resulting in the selection of HIF5_avg and HIF50_avg as the best-performing features. To evaluate the performance of the selected features, we trained datasets consisting of all the remaining congenic groups excluding the Dll4+, Dll4−, CG4, and CG5 (CG1 to CG3 and CG5 to CG8) using 10-fold cross-validation and keeping 25% of the dataset for testing the trained models. This allowed us to assess the generalizability of our model and test it on previously unseen datasets. The results of this step are reported as a confusion matrix, ROC curve, and AUC (Figure 6), as well as general classification metrics (Table 3). The best-performing models based on the selected features were SVM and KNN, with sensitivity and specificity of 1.00 and 0.81 and 1.00 and 0.75, respectively. In order to further assess the effectiveness of our model, the selected features, and the generated congenic pair, we generated an augmented dataset consisting of all remaining congenic pairs excluding Dll4+, Dll4−, CG4 and CG5 (CG1 to CG3 and CG5 to CG8, with random variations in rotation, horizontal flip, and limited scaling (up to ±2%) to increase the diversity of the dataset. This resulted in a total of 606 data points. The performance of the models was evaluated using 10-fold cross-validation and a 20% hold out. The results of this step were reported as a confusion matrix, ROC curve, AUC (Figure 7), and overall classification metrics (Table 4). The best-performing models based on the selected features were SVM and KNN, with sensitivity and specificity of 0.97 and 0.91, and 0.97 and 0.92, respectively. These results align closely with the performance of the models over the original dataset, indicating the generalizability of our framework. It is noteworthy that of the 16 most contributing features used to select the final feature pair, 12 were the newly proposed HIF features, and the other 4 were DS features, which we previously reported [48]. Additionally, the HIF features were amongst the best features for identifying genetic TME modifiers. The relationship between covariance of the repeated measures and optimal feature design in machine learning classification algorithms is an essential factor in developing effective classification algorithms. Combined with our recent report [47], our analysis found that the DS and HIF features, which are generated in regions where the NIR signal varies from half of its peak value to the peak value and at the tail of the curve, were particularly effective in discriminating between benign/malignant tumors (DS features) and groups with different levels of Dll4 expression (HIF features). Furthermore, the projection of the covariance matrix onto the time axis revealed similar regions, indicating a relationship between this projection and optimal feature design. These findings have significant implications for feature design in machine learning classification algorithms. By focusing on the regions with the greatest amount of useful information for discrimination, we can design features specifically to capture these differences and improve the accuracy of tumor classification. This can ultimately lead to better therapy outcomes for patients. It is worth noting that this relationship between the covariance of the repeated measures and optimal feature design is not limited to HIF features and the specific context of our analysis. In general, considering the covariance of repeated measures can provide valuable information for identifying key regions and designing effective features for machine learning classification algorithms. Dynamic vascular imaging techniques such as DCE-MRI and perfusion CT are used to extract multiple vascular parameters and have been used in clinical trials of anti-angiogenic drugs. However, these techniques have limitations, such as low temporal resolution and the need for specialized hardware and software [41,42]. To overcome these limitations, dynamic near-infrared (NIR) fluorescence imaging can serve as an effective alternative for characterizing germline-dependent vascular phenotypes. It can be combined with other modalities, such as in a paired-agent or multimodal MRI and fluorescence tomography approaches for noninvasive quantification of response to anti-angiogenesis therapy and classifying in vivo vascular phenotypes [7,43,44,67]. Furthermore, DE-NIR imaging, as a potential alternative for characterizing germline-dependent vascular phenotypes in preclinical models, can be extended to clinical modalities upon validation with cross-sectional dynamic contrast enhanced imaging. The present study proposes that by combining DE-NIR imaging and machine learning algorithms with consomic xenograft models with human tumors, the role of inherited notch protein Dll4 (rat variant of delta like canonical ligand 4) expression specifically in the host vascular microenvironment can be studied. Specifically, in the context of breast cancer, where different genetic subtypes can impact treatment outcomes, identifying patients with high or low DLL4 (human variant of delta like canonical ligand 4) expression levels through noninvasive imaging could assist in selecting personalized treatment options. Nonetheless, the study authors acknowledge notable differences between the rat model utilized in the study and the human system, which could affect the generalization of the findings to human cases, as in human tumors DLL4 expression maybe be both on tumor cells and host vasculature, whereas in our CXM model, we focused specifically on the inherited variation in rat-derived host vasculature Dll4 expression in human xenograft tumors. Such differences may result in amplification or suppression of vascular phenotype responses if both tumor cells and the host microenvironment express high levels or contrasting levels of DLL4. However, even in that case, dynamic imaging will be useful in identifying patients likely to respond better to DLL4 targeted therapies. Future studies will be necessary to validate this study’s findings, to assess the reliability and validity of the developed imaging and machine learning algorithms in a large and diverse patient population to determine if contrast agent kinetic profiles observed in human DCE-MRI or dyna-CT imaging datasets for primary and/or metastatic disease differ in human patients with high vs. low DLL4 expression. In the metastatic setting, where surgery is no longer an option, a machine-learning-enabled analysis of dynamic contrast-enhanced imaging will be valuable to assess the expression levels of DLL4 and guide therapy selection, especially in cases where a biopsy is not taken or if biopsy results are inconclusive [72,73]. Human anti-DLL4 antibodies have been reported for cancer treatment [54,74,75,76]. In one study, immunotoxin DLL4Nb-PE was developed, potentially as a cell cytotoxic agent and angiogenesis maturation inhibitor [76]. Another study successfully developed a bispecific monoclonal antibody that targets both human DLL4 and VEGF and showed efficacy in inhibiting proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVEC) [74]. In a phase 1a trial, navicixizumab, a bispecific antibody that inhibits DLL4 and VEGF, was tested in refractory solid tumor patients and showed the potential to inhibit tumor growth [75]. While DLL4 blockade is an attractive therapy, long-term extended use of DLL4 mAbs has demonstrated concerning off-target effects [77,78]. Pharmacokinetic modulation of DLL4 mAbs may reduce off-target effects [77], such as via short-term administration or by focusing on patients where dynamic contrast imaging indicates a high DLL4 vascular phenotype. As we have shown in prior work, high vascular DLL4 expressing tumors may also be susceptible to DLL4 targeted nanomedicine [38] or as combination therapy with anti-DLL4 monoclonal antibodies with nanomedicine drugs such as nab-paclitaxel (Abraxane) or Liposomal Doxorubicin (DoxilTM). The use of noninvasive DE-NIR imaging to detect heritable TME modifiers is significant for several reasons. First, this method allows for the identification of potential modifiers without the need for invasive procedures, reducing the potential for discomfort and complications for patients. Second, the use of machine learning and DE-NIR imaging to develop a predictive model for cancer nanomedicine therapy can support effective decision making in the treatment process. While data processing and preparation and algorithm training can be complex, the resulting algorithms are simple and allow for the prediction of heterogeneity in a single step using ROI brightness measurements. Interestingly, traditional features such as time-to-peak and upslope do not appear in our selection of the most discriminative features. However, two novel features derived from (HIF5_avg and HIF50_avg), which is a measure of the temporal inhomogeneity of both the initial uptake and decay of ICG fluorescence, were identified. It is important to note that the training and testing sets used in this study are minimal, and therefore the high accuracy rates obtained should be interpreted with caution. Further research with larger datasets will be necessary to assess the reliability and validity of these findings with confidence. We have reported novel dynamic enhanced near-infrared (NIR) fluorescence imaging and machine learning algorithms to noninvasively assess Dll4 expression levels in tumors. Our results showed that observation of subtle differences in vasculature structure and perfusion patterns characterized by ICG time kinetics could be used to differentiate between inherited tumor vascular microenvironment differences, such as Dll4 expression levels. Additionally, our analysis demonstrated the importance of considering the covariance of the repeated measures in the design of features for machine learning classification algorithms. By utilizing this information, we can improve the accuracy of tumor classification and ultimately improve therapy outcomes for patients. To summarize, based on our recent study, we investigated the impact of genetically heterogeneous notch-Dll4 inheritance on the contrast agent uptake and clearance in triple-negative breast cancer xenografts. The differences in Dll4 inheritance have been shown to impact nanomedicine biodistribution, pharmacokinetics, and therapy response in our prior work. Thus, our results indicated that imaging can be potentially employed for selecting patients for Dll4-directed therapies by identify host microenvironments with high- or low-expressing Dll4 inheritance. This further suggests that the success of nanomedicine might depend on hereditary tumor microenvironment genes, regardless of tumor type. Additionally, host genes such as Dll4 can affect individual differences in NP uptake and response to NP-mediated therapies, providing the potential for more effective personalized Dll4 targeted nanomedicine for therapy-resistant hosts. Further studies are needed to validate these findings and explore the potential clinical applications of this approach.

PMC10000814

Hussein Awada,Valeria Visconte

The Heterogeneous Complexity of Myeloid Neoplasm: Multi-Level Approaches to Study the Disease

24-02-2023

The Heterogeneous Complexity of Myeloid Neoplasm: Multi-Level Approaches to Study the Disease Myeloid neoplasms (MNs) include a spectrum of bone marrow malignancies that result from the clonal expansion and arrest of differentiation of myeloid progenitor cells [1]. MNs account for around 25% of all hematological malignancies and typically arise in older patients who are in their seventh or eight decade as they accumulate genetic alterations throughout their lifetime [2]. Perturbation of normal genetic and epigenetic regulation is mostly due to the disruption of key cellular processes such as self-renewal, proliferation, and differentiation [1]. The rise of population aging and exposure to environmental carcinogens have been attributed to triggering bone marrow insults [3,4]. Myelodysplastic syndromes (MDS), acute myeloid leukemia (AML) and myeloproliferative neoplasm (MPN) remain the most frequently encountered MNs and hence are the subject of the foremost research efforts as well. However, despite the mortality risks of these diseases, their courses are highly variable in terms of response to therapies and survival, which range from weeks to several years. While novel clinical markers were long considered in the past to better characterize these malignancies, the heterogeneous genetic nature of these disorders has left us with more questions. Therefore, seeking explanations behind the underlying pathogenetic processes driving clonal trajectories has prompted us to shift our focus more toward the understanding of the landscape of genetic aberrations instigating these malignancies [5]. Technologies to detect genetic defects have been largely used over the years, spanning from the gold standard karyotype analysis and fluorescence in situ hybridization to plasmid cloning and sequencing, single nucleotide polymorphism (SNP) arrays, comparative genomic hybridization arrays (CGH) and classical bidirectional sequencing [6,7]. The latter has been replaced by large-volume DNA segment amplified sequencing via next-generation sequencing (NGS), either through whole exome and genome or targeted deep sequencing [8,9]. The introduction of RNA sequencing (RNA-seq) via NGS further added new dimensions by which sequencing techniques detect polymorphisms spanning from differential expression or alternative splicing (AS). By converting extracted messenger RNAs (mRNA) into complementary DNA (cDNA), RNA-seq provides more accurate differential quantification of highly or lowly expressed genes while examining the function of cellular transcriptome through changes in gene expression, AS, or isoforms [10]. However, RNA-seq may obscure small yet significant differences between individual cell subsets when large amounts of cells are sequenced. Such differences may be critical in the early processes of clonal evolution of myeloid disorders into more aggressive diseases, disease relapse, as well as the assessment of minimal residual disease [11]. Further integration of single-cell DNA sequencing (scDNA-seq) to study the methylome complements scRNA-seq by uncovering methylation patterns influencing the levels of expression detected by scRNA-seq in leukemia cells at single-cell level. Further insights on gene regulatory landscapes may also be inferred by the assay for transposase-accessible chromatin using ATAC-seq which isolates and quantifies non-coding DNA regions. Hence, it provides a wider view of possible targetable active genomic areas of transcriptional influence and subsequent myeloid disease evolution [12]. Diagnostic NGS mutational panels are today an integral part of the management of myeloid disorders as they have been included in diagnostic tests. Along with the techniques mentioned above, diagnostic NGS has yielded the identification of several classes of leukemogenic drivers. Among others, the most relevant include epigenetic DNA methylation regulators (e.g., ASXL1, DNMT3A, EZH2, IDH1, IDH2, TET2), tumor suppressors (e.g., TP53), signaling pathways activators (e.g., FLT3, KRAS, NRAS, JAK2), transcription factors (e.g., CEBPA, ETV6, RUNX1), splicing factors (e.g., SF3B1, SRSF2, U2AF1) as well as shuttling proteins (e.g., NPM1) [1,13,14,15]. The hallmark of unmasking these leukemogenic mutations is to determine the unique genetic profile dictating the course and prognosis of each patient. Indeed, molecular mutations set the groundwork for improved classifications as reflected in the 2022 European Leukemia Net (ELN) recommendations which have further incorporated mutations in BCOR, EZH2, SF3B1, SRSF2, STAG2, U2AF1 and ZRSR2 genes into its adverse risk category of AML on top of their 2017′s recommendations for inclusion of ASXL1, RUNX1 and TP53 [16,17]. Moreover, the International Prognostic Scoring System (IPSS) and its revised version (IPSS-R) are now falling in favor of the molecularly upgraded IPSS-M which incorporates the lesions in 31 genes of confirmed independent impact on MDS prognosis [18]. The new mutation system divides MDS patients into six survival strata [18]. Similarly, a molecular version of the chronic myelomonocytic leukemia (CMML)-specific prognostic scoring system (CPSS-Mol) entailing ASXL1, NRAS, RUNX1 and SETBP1 mutations provides better clinical insights than the original CPSS [19]. Unveiling these mutations further serves as the cornerstone for new differential hematological entities, including benign conditions such as clonal hematopoiesis of indeterminate potential (CHIP) and clonal cytopenia of undermined significance (CCUS). However, perhaps the biggest clinical benefit of understanding the genetics and epigenetics of MNs pertains to identifying targetable mutations whose inhibition may improve disease outcomes. As more and more mutations are discovered, targeted panels are further expanding and thus leave us with more options for precision target therapy. Indeed, the identification of IDH1 and IDH2 as targetable mutations led to the introduction of the IDH1 inhibitors, Enasidenib and Ivosidenib, and the IDH2 inhibitor, Olutasidenib, which are now approved in full effect for refractory/relapsed MN [20]. As the use of modern genomic sequencing techniques has become the tested workhorse in characterizing new myeloid disease diagnoses, further advancement is still seeking to understand molecular interactions while also minimizing the role of human bias. In this context, the application of machine learning (ML) algorithms is becoming one approach to attempt simplification of complexity. As opposed to supervised learning, unsupervised learning prevents human bias and hence may contribute to increasing the accuracy of analyses by eliminating unnoticed errors perpetrated in prioritizing unrepresentative datasets, effect measurements and reporting [21]. Traditional unsupervised Artificial Intelligence (AI) techniques, especially Deep Learning (DL) algorithms, allow the integration of molecular signatures for disease subclassification, prognostication, and prediction of treatment response. More importantly, it allows the integration of multi-omics in one model (Figure 1). This is the case of novel studies performed in our laboratory by combining genomic and transcriptomic data of MNs [22]. For example, DL techniques succeeded in the novel subcategorization of newly diagnosed AML patients regardless of their primary or secondary subtypes into four clusters of invariant molecular features and unique prognoses [23]. In this multicenter study of 6,788 AML patients, the unsupervised analysis resulted in 97% cross-validation accuracy, far superior to the 74% accuracy yielded when applying standard supervised analysis attempting to use molecular patterns to predict traditional path-morphologic classifications [23]. Similar unsupervised learning approaches have identified 14 distinct molecular clusters of clinical heterogeneity in MDS [24]. Each of these clusters has its unique pathobiological associations, treatment responses, and prognosis [24]. ML approaches are also capable of unmasking the morphological consequences of specific molecular signatures while also tracing the evolutionary origins of MNs [25]. In another study, 1079 MDS patient specimens were sequenced in order to define an association between molecular profiles and bone marrow morphologic characteristics and clinical traits [25]. Five unique morphological profiles with distinct clinical features were identified, among which profile 1 was mostly high-risk MDS while the low-risk disease predominant profiles 2, 3, 4 and 5 were characterized by pancytopenia, monocytosis, megakaryocytosis and erythroid dysplasia, respectively [25]. In turn, the low-risk MDS group was classified into eight genetic groups, which served as the basis for subsequent geno-morphologic combinations [25]. Six geno-morphologic signature associations were yielded and hence improved our understanding of the impact of genetic alterations on clinic-morphologic traits [25]. So far, the diagnosis of MN fairly relies on bone marrow studies, yet these studies are subject to inter-observer variability bias as the diagnosis might be challenging in certain scenarios, especially in patients with pancytopenia or minimal dysplasia [26]. Radakovich et al. instead developed an ML model for MN diagnosis based on genomic and clinical data only; thus, it can be used to empower diagnostic decision-making in cases of uncertainty. In this international multicenter study, a gradient-boosted DL strategy was adopted as the geno-clinical features of 2697 patients with MDS or one of the MPN disorders were used to train their model [26]. The final model retained 15 geno-clinical variables, including JAK2, KRAS, and SF3B1 status, and then proved to accurately differentiate MDS, MPN subclasses, as well as benign conditions like CHIP, CCUS and Idiopathic cytopenia of undetermined significance with AUROC of 0.951 and 0.926 for the test and training cohorts, respectively [26]. Moreover, ML techniques may be helpful in developing personalized models that predict treatment response and thus aid in personalizing treatment selection according to each patient’s characteristics. Along with this line of research, Nazha et al. screened the genomic architecture of a cohort of 433 MDS patients with varying responses to the hypomethylating agents (HMA) azacitidine and decitabine [27]. By utilizing an unbiased ML recommender system, mutational signatures composed of two to three mutations in ASXL1, BCOR, EZH2, NF1, RUNX1, SRSF2 and TET2, were identified as an association with HMA resistance [27]. The result had an accuracy rate of 87% and 93% in the training and validation cohorts, respectively [27]. Such finding is further enforced by the estimation of around 30% of HMA-treated MDS patients had at least one of the signatures, and thus using ML techniques could have accurately predicted HMA resistance and the need to consider other therapeutic options [27]. Despite its proven utility, the implementation of AI methods in the daily clinical setting certainly remains challenging. It requires physicians to understand AI basics, as well as institutions to be capable of implementing its networks in their medical records. The latter brings about several hurdles, including the need for electronic types of medical records in order for AI to easily recall the large volumes of patient data that are necessary for its proper functioning. In addition, having the appropriate logistics, such as experienced data scientists, dedicated clinician-scientists, availability of the required technological tools, the volume and adequacy of data used to develop AI models, as well as clinically meaningful needs and standards for AI development are all prerequisites for its successful implementation in clinics.

PMC10000817

David Dubayle,Arnaud Vanden-Bossche,Tom Peixoto,Jean-Luc Morel

Hypergravity Increases Blood–Brain Barrier Permeability to Fluorescent Dextran and Antisense Oligonucleotide in Mice

24-02-2023

blood–brain barrier,permeability,centrifugation,hypergravity,dextran,antisense oligonucleotide

The earliest effect of spaceflight is an alteration in vestibular function due to microgravity. Hypergravity exposure induced by centrifugation is also able to provoke motion sickness. The blood–brain barrier (BBB) is the crucial interface between the vascular system and the brain to ensure efficient neuronal activity. We developed experimental protocols of hypergravity on C57Bl/6JRJ mice to induce motion sickness and reveal its effects on the BBB. Mice were centrifuged at 2× g for 24 h. Fluorescent dextrans with different sizes (40, 70 and 150 kDa) and fluorescent antisense oligonucleotides (AS) were injected into mice retro-orbitally. The presence of fluorescent molecules was revealed by epifluorescence and confocal microscopies in brain slices. Gene expression was evaluated by RT-qPCR from brain extracts. Only the 70 kDa dextran and AS were detected in the parenchyma of several brain regions, suggesting an alteration in the BBB. Moreover, Ctnnd1, Gja4 and Actn1 were upregulated, whereas Jup, Tjp2, Gja1, Actn2, Actn4, Cdh2 and Ocln genes were downregulated, specifically suggesting a dysregulation in the tight junctions of endothelial cells forming the BBB. Our results confirm the alteration in the BBB after a short period of hypergravity exposure.

Hypergravity Increases Blood–Brain Barrier Permeability to Fluorescent Dextran and Antisense Oligonucleotide in Mice The earliest effect of spaceflight is an alteration in vestibular function due to microgravity. Hypergravity exposure induced by centrifugation is also able to provoke motion sickness. The blood–brain barrier (BBB) is the crucial interface between the vascular system and the brain to ensure efficient neuronal activity. We developed experimental protocols of hypergravity on C57Bl/6JRJ mice to induce motion sickness and reveal its effects on the BBB. Mice were centrifuged at 2× g for 24 h. Fluorescent dextrans with different sizes (40, 70 and 150 kDa) and fluorescent antisense oligonucleotides (AS) were injected into mice retro-orbitally. The presence of fluorescent molecules was revealed by epifluorescence and confocal microscopies in brain slices. Gene expression was evaluated by RT-qPCR from brain extracts. Only the 70 kDa dextran and AS were detected in the parenchyma of several brain regions, suggesting an alteration in the BBB. Moreover, Ctnnd1, Gja4 and Actn1 were upregulated, whereas Jup, Tjp2, Gja1, Actn2, Actn4, Cdh2 and Ocln genes were downregulated, specifically suggesting a dysregulation in the tight junctions of endothelial cells forming the BBB. Our results confirm the alteration in the BBB after a short period of hypergravity exposure. Astronauts are exposed to successive phases of hypergravity phases during the takeoff and landing of spaceflights and due to microgravity in space. The most important and earliest reported symptom, related to days 1–3 of the spaceflight, is space motion sickness due to vestibular dysfunction [1,2,3,4,5,6,7]. The fluid shift is the shift in the distribution of human body fluids due to microgravity exposure. It was proposed to be responsible for space motion sickness. Several ground devices and protocols were rapidly developed to reproduce this phenomenon, such as centrifugation and parabolic flights [8,9]. Moreover, the decreases in plasma volume and cardiac performance, and the increase in intracranial blood pressure participate in vascular deterioration, as recently reviewed [10,11,12]. Furthermore, the alterations in gravity induce cardiovascular adaptations via the modifications in endothelial and smooth muscle vascular cell functions [13,14,15,16,17]. It is noticeable that the effects of centrifugation are only partially described in humans [18,19,20,21,22]. Like during microgravity exposure, hypergravity exposure, from 1.5 to 5 g, affects the vestibular functions [23,24,25,26] and modifies gene expression in the brain [27,28,29] and cognitive performances [30,31,32]. The use of hypergravity by centrifugation is required to qualify the biological effects of space motion sickness. Likewise, centrifugation, close to 2× g, is also proposed as a countermeasure against the deleterious effects of microgravity seen in humans [33,34]. Therefore, before the exposure of humans to centrifugation, it is important to study its biological impacts. The cerebral blood vessels are crucial in brain functions regarding oxygen supply and exchanges of nutrients and wastes. The endothelial cells of brain capillaries are organized to form the blood–brain barrier (BBB), assuming the fine-tuning of these exchanges to maintain brain homeostasis [35,36]. The efficacy of the BBB is regulated by the nychthemeral rhythms [37,38,39,40]. BBB alterations are clearly implicated in stroke and neurodegenerative disorders [41,42,43,44]. Gravity changes are able to modify endothelial cell functions [45]. Many in vitro models have been developed to reproduce the BBB [46], and experiments that exposed endothelial cells to gravity modifications revealed miscellaneous results, as reviewed [47]. Depending on hypergravity levels from 3 g to 20 g, endothelial cells modify their gene expression, angiogenesis, cytoskeleton architecture and tube formation [48,49,50,51]. Moreover, in devices that reproduce the barrier function, the effects of short-term exposure to hypergravity remain unclear. In fact, exposure (2 g and 4 g) increases the barrier efficacy, shown by resistance measurements of the endothelial cell culture [52], whereas a higher level (10 g) decreases it, as shown by the increase in fluorescent molecules passing through the culture monolayer [53]. The effects of hypergravity on the capacity of endothelial cells to form a barrier in vitro are insufficient to interpret the modifications in the BBB observed in vivo. More information should be collected in vivo. In mice exposed to hypergravity at 2 g for 24 h, we measured the transit through the BBB of different fluorescent molecules with different sizes, such as dextrans and antisense oligonucleotides (AS). We also investigated the regulation of expression of genes involved in junctions between endothelial cells. In accordance with the principles of the European community, the experimental protocols were validated by the local ethics committee (CEEA-Loire, APAFIS #38819), the animal welfare committee of PLEXAN (PLateforme d’EXpérimentations et d’ANalyses, Faculty of Medicine, Université Jean Monnet, Saint Etienne, France, agreement n°42-18-0801) and the French Ministry of Research. In this study, 86 male C57BL/6JRJ mice (8 weeks old, 22.5 ± 0.1 g, Janvier Labs, France) were used. The animals were housed (3 mice per cage) in standard conditions (22 °C, humidity 55%; 12 h/12 h day/night cycle; unlimited access to food and water). They were familiarized with the centrifugation room the week before the experiments and monitored by video in the centrifuge. In order to expose all the animals to the same environmental conditions, the mice were centrifuged at 2× g for 24 h, and the control mice in normogravity at 1 g for 24 h were placed simultaneously in the experimental room. The centrifugation protocol was detailed in our previous publication. All the fluorescent molecules were diluted in saline solution (sodium chloride 9 g/L) and retro-orbitally injected in the blood, under isoflurane anesthesia (5%). In our hands, this route of administration is safer (more rapid, efficient and reproducible) than other routes of i.v. administration. Sham mice were injected with vehicle solution. Mice received only one injection with one fluorescent tracer. Phosphorothioate antisense oligonucleotide directed against angiopoietin-2 (Angpt2, named AS, GCG-TTA-GAC-ATG-TAG-GG, 6084.9 g/mol, Eurogentec) was coupled to 5-carboxyfluorescein (excitation: 492 nm, blue light; emission: 518 nm, green light) and injected (18 mg/Kg). Fluorescein isothiocyanate-dextrans D40, D70 and D150 (FD40-100MG, FD70S-100MG, FD150S-1G, respectively, Sigma-Aldrich, St. Louis, MI, USA) were solubilized in vehicle (2× g/100 mL to be injected retro-orbitally at 150 mg/Kg, near 200 µL/mouse). Fluorescein isothiocyanate-dextrans were maximally excited at 490 nm (blue) and maximally emitted at 525 nm (green). Mice were randomly killed by lethal intraperitoneal injection of sodium pentobarbital (Euthasol, 175 mg/Kg, i.p.), within 2 h after stopping the centrifuge. Before intracardiac perfusion, a catheter was introduced in the right atrium, and blood samples were collected and placed in microtubes. Finally, mice were perfused intracardiacally (5 mL/min) with 30 mL of phosphate-buffered saline (0.01 M PBS, pH: 7.4) to discard blood cells and residual fluorescence of the injected tracers into vessel lumen. This step was followed by 30 mL formalin solution (10%, Merck, HT501128) to fix the tissues. Brains and the left lobe of livers were dissected and post-fixed for 24 h in a formalin solution at room temperature, placed for 48 h in a 30% sucrose–PBS solution at 4 °C and cryopreserved before being sliced. The microtubes containing blood samples were centrifuged (10 min at 2000× g) and 20 µL of serum was collected. Some serum samples were excluded due to hemolysis. The others (n = 60) were used for corticosterone assay (ELISA kit, K014, Arbor Assays, Ann Arbor, MI, USA), following the protocol of the supplier. Using a freezing microtome (frigomobil, Reichert-Jung), coronal sections of the brain (40 μm thick) were made. Olfactory bulbs were removed, and 3.2 mm after beginning the rostro-caudal slicing, the new slices were collected and placed individually in 48-well plates. To ensure reproducibility, we anatomically selected three similar brain slices for each mouse. Using a binocular device, the slices corresponding to interaural 1.98 mm; Begma −1.82 mm of the Atlas of the mouse brain in stereotaxic coordinates [54] were retained. Indeed, the medial habenular nuclei and mammillothalamic tract were anatomical landmarks, as well as the form and volume of the hippocampus. In the same manner, the left lobes of the liver were sliced (40 µm), and three slices per mouse were mounted. All the floating sections were incubated for 10 min in DAPI (4′,6-diamidino-2-phenylindole, 1:250,000, Interchim, Mannheim, Germany) and rinsed twice in PBS (10 and 20 min, respectively). Finally, they were mounted on glass slides (Superfrost) with a handmade medium based on Mowiol. All slices were DAPI-labeled and mounted on the same day. Slices presenting red blood cells in capillaries in ROI were excluded to reduce experimental bias [55]. The fluorescence of labeled brain slices was observed by confocal microscopy (SP5, Leica Microsystems, Wetzlar, Germany) and the slide scanner Nanozoomer (2.0 HT, Hamamatsu Photonics, Shizuoka Prefecture, Japan). The Nanozoomer contains a fluorescence imaging module using objective UPS APO 20X NA 0.75 combined with an additional lens 1.75X. Virtual slides were acquired with a TDI-3 CCD camera. The fluorescent acquisitions were conducted with a mercury lamp (LX2000 200W—Hamamatsu Photonics, Massy, France), and the set of filters adapted for DAPI and FITC/FAM fluorescence were usable for both fluorescein isothiocyanate-dextrans and 5-carboxyfluorescein antisense oligonucleotide. The DAPI labeling, revealing the double strain of DNA in the cell nuclei, was used for the automated focus required for Nanozoomer imaging. To reduce bias, all images (slices from control and centrifuged mice) were performed randomly in one batch. To localize antisense oligonucleotides in the brain and liver tissues, some images were acquired with SP5 confocal microscope. In this case, fluorescent molecules were excited with the 488 nm line of Argon laser and all acquisition parameters were kept constant. Several types of fluorescence analyses were double-blindly performed on Nanozoomer images. To evaluate the intensity level of fluorescence, the ndpi files generated by Nanozoomer were converted into tiff images with NDPI software (version 2). The tiff files were opened with Fiji software 2.9.0, and the intensity levels were measured in regions of interest (ROI defined as red circle of 960 µm2 in Figures and placed on the hippocampus (HPC), dorsal thalamic nuclei (THAL) and the retrosplenial and ectorhinal cortices (DCx and LCx, respectively) on both hemispheres of the three slices. No filter settings were applied to the images and we checked that the images did not have any saturated dots. The mean of fluorescence was calculated for each mouse and reported in the statistical analysis. A similar analysis was performed in three liver slices. Five ROI were randomly placed on each slice. Moreover, the image analysis of fluorescent spots was performed with QuPath directly on the ndpi files. The software is able to identify and localize fluorescent spots. We empirically determined parameters to segregate fluorescent spots in brain slices from 5 mice (control and centrifuged mice) and we applied these parameters to the project containing the entire sample. The parameters were: pixel size 0.5 µm, background radius 30 µm, median filter radius 0, sigma 1, minimum area 5 µm2, maximal area 1000 µm2 and threshold 7. The collected data were attributed to experimental groups (2 g vs. 1 g) and compared statistically. The analyses, reported, were performed on the ROI anatomically defined as HPC (hippocampus), THAL (grouping all medio-dorsal and lateral thalamic nuclei), DCx (containing retrosplenial cortices), SoCx (containing somatosensorial cortices) and PirCx (containing piriform cortices). A similar analysis was performed on the left lobe liver slices. For this experiment, 16 mice were used (8 were exposed to 2 g and 8 to 1 g, as described before). They were anesthetized with isoflurane 5% and decapitated, and the brains were directly frozen and stored at −80 °C. Hippocampus were dissected on ice and placed in 2 mL tubes containing 500 µL of Tri-reagent (MRCgene) and 10 ceramic beads (diameter 1.5 mm). Samples were mashed in a Beadbug6 shaker (Benchmark, 3 cycles, level of speed 4350 and 60 s time). RNA was isolated, following the instruction of the protocol elaborated by MRCgene. The concentration of RNA was measured with Nanodrop (Thermoscientific, Waltham, MA, USA) and adjusted close to 100 ng/µL. The cDNA was produced with the RT-i-script gDNA clear cDNA synthesis kit (Bio-Rad’s reference 1725035), using 100 ng of RNA and following the protocol from the supplier. The qPCR was performed using the endothelial cell contacts by junction M96 (predesigned for use with SYBR green; Bio-Rad’s plate reference 10029202) and the Sso-advanced universal SYBR green PCR kit (Bio-Rad’s reference 1725275). The qPCR was performed with CFX96 thermocycler (Bio-Rad). Samples were allocated randomly in plates, and some of them were tested twice to verify the quality of the experiment. The validation of Hprt and Gapdh as reference genes was evaluated with CFX Maestro software (Bio-Rad). The analysis of gene expression was performed on Actb, Actg1, Actn1, Actn2, Actn4, Cdh2, Cdh5, Cldn1, Cldn3, Cldn5, Ctnna1, Ctnnb1, Ctnnd1, Dsp, F11r, Gja1, Gja4, Gja5, Jam2, Jup, Ocln, Tjp1, Tjp2 and Vim. The threshold of the regulation by hypergravity on gene expression was chosen at 1.5. To discuss the RT-qPCR results, we checked the brain localization, cell types expressing genes and function of proteins encoded by these genes in endothelial cells using databases: https://www.proteinatlas.org; http://mousebrain.org; http://betsholtzlab.org and https://www.informatics.jax.org (accessed on 26 January 2023). The data were statistically compared using paired t-tests, non-parametric Mann–Whitney test, or one- and two-way ANOVA with post hoc comparisons when applicable. The normogravity (1 g) is the control condition. The software used was GraphPad Prism V9, which calculated the p value as the probability of observing two identical conditions. If p < 0.05, the two compared conditions were considered statistically different. The body weight gain, expressed as the difference in weight in a 24 h period (Figure 1), is the difference in body weight measured before and after exposure to centrifugation (2× g) or control conditions (1 g). As expected, the exposure to centrifugation induced a decrease in body weight (Figure 1A, p < 0.0001). More precisely, the decrease in body weight was similar in mice injected with saline solution (Sham) and solution containing fluorescent antisense oligonucleotide directed against angiopoietin-2 (AS) (two-way ANOVA coupled with Sidak post hoc test, interaction p = 0.0009; 1 g vs. 2 g: p < 0.0001; sham vs. AS p = 0.589; Figure 1B). The decrease in body weight gain due to centrifugation was similarly observed in mice injected with dextrans (D40, D70 and D150, one-way ANOVA coupled with Sidak post hoc test, 1 g vs. 2 g: p < 0.0001; p > 0.05 for comparison of 1 g groups as well as for 2 g groups, Figure 1C). The effects observed in mice injected with AS or dextrans (D40, D70 and D150) were similar (statistical analysis shown in Figure 1C). In conclusion, the injection of fluorescent tracers did not influence the effect of centrifugation on body weight gain. To explain the decrease in body weight, we also measured the food and water consumption. As shown in Figure 1D and 1E, the comparison of food and water consumption, respectively, during the day before the centrifugation with the consumption during the 24 h of centrifugation exposure showed that both food and water consumption specifically decreased in the group exposed to the centrifugation (two-way ANOVA, time x gravity p = 0.0001 for both parameters). The stress was evaluated by the concentration of corticosterone in plasma. The comparison between 1 g and 2 g conditions, including all the samples, did not reveal a variation in corticosterone concentration (Figure 2A, Mann–Whitney test, p = 0.255). We also separately analyzed the corticosterone concentration in each experimental group. In mice injected with saline solution (Sham), AS, D40, D70 and D150, the centrifugation had no significant effect on the corticosterone concentration (Figure 2B, one-way ANOVA, p = 0.278). In conclusion, centrifugation at 2× g did not modify the plasma concentration of corticosterone in mice injected with fluorescent tracers. The extravasation of dextrans through the BBB were firstly evaluated by the analysis of fluorescence intensities of several brain areas. To minimize local variations, we performed all analyses on slices containing similar anatomical landmarks. The regions of interest were distributed in different cerebral areas (red ROI in thalamus, hippocampus and dorsal and lateral cortices, Figure 3). The centrifugation was not able to modify the fluorescent levels in THAL (Figure 3A, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40, p = 0.93; for D70 p = 0.29 and for D150 p = 0.069), HPC (Figure 3B, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 p = 0.53; for D70 p = 0.76 and for D150 p = 0.089) and LCx (Figure 3C, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 p = 0.50; for D70 p = 0.08 and for D150 p = 0.16). In DCx, the hypergravity can increase the level of fluorescence only in D70 (Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 p = 0.051; for D70 p = 0.040 and for D150 p = 0.050; Figure 3D). The differences in D70 fluorescence across brain sections are illustrated in Figure 3E. More marked fluorescence diffusion is observed in the DCx of 2 g-exposed mice. In conclusion, these sets of data analysis suggested that centrifugation significantly increased the presence of D70 in DCx. We tested the ability of hypergravity to promote the passage of a molecule that can be captured by liver parenchyma cells. To test this hypothesis, we injected mice with fluorescent antisense oligonucleotides and compared the 1 g and 2 g conditions. The same quantification methods used for dextrans were applied on images obtained with Nanozoomer (Figure 4A). A significant increase in fluorescence in liver parenchyma was revealed in mice exposed to hypergravity (Figure 4B, Mann–Whitney tests, 1 g vs. 2 g p = 0.0291). Moreover, the number of areas containing fluorescence evaluated with QuPath was higher in 2 g in comparison with 1 g (Figure 4C, Mann–Whitney tests, 1 g vs. 2 g p < 0.0001). With confocal microscopy, the presence of AS was qualitatively revealed as spots of fluorescence close to vessel walls in the liver parenchyma. Taken together, these results strongly suggest that hypergravity increased the AS extravasation in the liver parenchyma. The qualitative analysis of images obtained with Nanozoomer and confocal SP5 showed fluorescent spots in the brain parenchyma only in slices from 2 g-exposed mice (Figure 5A and Figure 6A). The confocal images also revealed that these fluorescent spots were more localized in the brain parenchyma close to the vessel walls (Figure 5A, right panel). The quantitative analyses of images from Nanozoomer showed an increase in fluorescence level in HPC and DCx due to hypergravity exposure (Mann–Whitney tests, 1 g vs. 2 g in THAL p = 0.369, in HPC p = 0.033, in DCx p = 0.016 and in LCx p = 0.265; Figure 5B). The analysis with QuPath software was used to segregate fluorescent areas from the background in several brain regions (Figure 6A) using the same filtering parameters in both 1 g and 2 g conditions. The analyses confirmed that the exposure to hypergravity increased the number of fluorescent spots in HPC and DCx, but not in THAL (Mann–Whitney tests, 1 g vs. 2 g in THAL p = 0.0536, in HPC p = 0.0003 and in DCx p < 0.0001; Figure 6B). Moreover, it also revealed an increase in the number of fluorescent spots in SoCx and PirCx (Mann–Whitney tests, 1 g vs. 2 g p <0.0001 and p = 0.0024, respectively; Figure 6B). In conclusion, our data suggest that hypergravity induced a BBB leakage able to increase the presence of AS in brain parenchyma. Using Hprt and Gapdh as reference genes, the RT-qPCR analysis of the expression of genes involved in the regulation of endothelial cells interactions revealed that Gja4, Ctnnd1 and Actn1 were upregulated. Cdh2 was downregulated more than 2-fold and Ocln, Actn2, Jup, Actn4, Tjp2 and Gja1 were downregulated between 1.5- and 2-fold (Figure 7). The expressions of Actb, Actg1, Cdh5, Cldn1, Cldn5, Ctnna1, Ctnnd1, Dsp, F11r, Gja5, Jam2, Tjp1 and Vim were considered not altered (less than 1.5-fold modification), and Cldn3 appeared not expressed. The names, functions and cell types expressing these genes are summarized in Table 1 and supplementary Table S1. In the present study, our results suggest that hypergravity induces an increase in BBB permeability, allowing the passage of antisense oligonucleotides as well as dextran from blood to brain parenchyma. Moreover, the RT-qPCR experiments suggested an alteration in the expression of genes involved in endothelial cell junctions. In a ground model of hindlimb unloaded animals without [56] or in combination with radiation [57], as well as during spaceflight [58], the BBB was altered, suggesting that vestibular regulations were involved. As reviewed recently, the increase in gravity by centrifugation modifies vestibular function and induces motion sickness [59]. Our experiments confirm the decrease in body weight generated by centrifugation [18,60]. It is linked with the decrease in food intake [61], and probably linked to vestibular impairments [18,62]. Hypergravity exposure at 2 g increases the corticosterone concentration when it is measured during the first hour of exposure [63]. The increase in the hypergravity level can transiently increase the plasma corticosterone level [64]. Nevertheless, as our data showed, after 24 h of weak exposure at 2 g, the corticosterone levels were not altered in the first hour following the stop of the centrifuge [65]. The stress induced by the centrifugation is controversial and probably depends on the design of the centrifuge and experimental procedure with animals [18,27,63]. Moreover, our data showed a large spread of individual values of corticosterone concentration, confirming other studies [18,23,65]. In motion sickness, the relationship between brain and intestinal functions were known and clearly demonstrated, including microgravity and hypergravity models [66,67,68]. The most probable link is hypophagia. In mice and rats, the decrease in food intake was observed at the beginning of the 2 g exposure (first two days) and depended on the vestibular organ [18,69]. The hypophagia could have several causes, including: 1. modifications in microbiota [70] that can decrease the gastric acid synthesis [71], 2. metabolism dysregulation, such as decreases in leptin and insulin plasma concentrations [60] and 3. modifications in the expression of the starvation-induced genes [72]. Moreover, the serotonin pathways are involved in this phenomenon [69,73]. In conclusion, our results also confirm that the hypophagia induced a decrease in body weight. This is more related to the hypergravity and not related to an increase in corticosterone levels [30,60,62,65]. Fluorescent polysaccharides such as dextrans are safe at low concentrations, available in sizes from 3 to 2000 kDa. They can be used to study BBB permeability [74,75,76] and to determine the size of a BBB leak [77,78,79]. After 24 h exposure to 2 g hypergravity, our results demonstrated that 70 kDa dextran can be exported in cortex parenchyma, but not 40 or 150 kDa dextran. The lower molecular weight dextrans, the faster they are excreted. In fact, in less than one hour, dextrans between 30 and 40 kDa were excreted in urine, whereas the 62 kDa dextran was always present in the blood circulation and not highly present in urine [80]. This suggests that after 24 h, the 40 kDa dextran would be excreted. Thereby, the BBB leak required more than one hour of hypergravity exposure, confirming our previous data showing that short exposure (1–9 min at 5 g) was not efficient in destabilizing the BBB [65]. Because we cannot exclude an alteration in urine excretion of dextran in the hypergravity context, our data should be completed by the evaluation of the kinetics of dextran excretion in centrifuged mice. Because of the molecule shape, 150 kDa dextran was unable to flow from the circulation to the tissues in physiological conditions [80]. Our results showed that the BBB leak is not sufficient for 150 kDa dextran extravasation, suggesting that this leak was not comparable to the BBB disruption induced by stroke or acute hypertension [81]. In our previous study [65], the extravasation of IgG (around 150 kDa) was measured, suggesting that the nature of the molecule is also a crucial parameter. Moreover, our data showing the extravasation of antisense oligonucleotide in the cortex and hippocampus confirm that the BBB properties depend on the brain areas and the chemical nature of the markers [82,83,84]. In conclusion, our results showed an increase in the transfer of fluorescent molecules from blood to tissues, suggesting a global modification in effluxes due to hypergravity. To assess the alterations in the BBB in centrifuged mice, we focused the molecular investigation on gene expression using a set of primers targeting consensual genes involved in BBB efficacy. As reviewed recently [85,86], all of the proteins encoded by the genes studied here are involved in the scaffoldings required to maintain endothelial cell interactions to create the BBB, as well as in the initiation of angiogenesis and/or vascular repair. The database queries concerning the expression level in non-neuronal cells of the brain indicated that the proteins encoded by studied genes are also expressed in endothelial cells, but not exclusively (Supplementary Table S2). As expected, the modifications in gene expression are related to the durations of centrifugation and the levels of hypergravity, as suggested by the comparison between this current study and the RNAseq performed previously on the same device and the same mouse strains [29]. Moreover, the regulation of gene expression is not comparable to acute and chronic stress (Supplementary Table S3). Globally, the observed modifications could be interpreted as a specific dysregulation of gene expression that can alter the turnover and replacement of proteins involved in BBB efficacy as observed in BBB disruption models such as stroke, middle cerebral artery occlusion or hypoxia. This work suggests that the modification in gravity, which is accompanied by a modification in the vestibular functions, leads to an alteration in the BBB via a modification in the expression of genes which code the proteins in the junctions between the endothelial cells. As now studied, an alteration in the BBB, and not its destruction, allows the passage of molecules defined by their sizes and their chemical natures. Our work insists on this point; an alteration in the BBB is characterized according to the means of study, i.e., markers and measurement methods. This can be considered in two antagonistic ways, either as a minimally invasive physical means of crossing the BBB by molecules of therapeutic interest or, on the contrary, as something deleterious that can be found in the pathology of alterations in vestibular functions during spaceflight. The most important limit of this study is that the RT-qPCR was performed on RNA extracted from whole brain, and the query of hipposeq.janelia.org indicated that we cannot exclude the alteration in molecular scaffolding of synapses also implicating these genes. Finally, our study can be considered an extension of studies relating to the effectiveness of molecules to modulate the passage across the BBB. In a hypergravity context, but also in other models of alteration in vestibular functions, the transduction pathways involved in alterations in the BBB should also be investigated. For example, the angiopoietin-2 pathway is crucial for endothelial cell disassembly [87], and GPCR internalization in endothelial cells [88] should be considered in the context of centrifugation. The last topic that we can investigate is the effects of gravity modulation on angiogenesis, which is required to renovate the endothelium and form new brain capillaries. In fact, experiments on cultured endothelial cells have suggested that hypergravity reduces their capacity to form tubes and alters their responses to angiogenic factors [48,49,50,51]. In centrifugation as well as during parabolic flights, the in vivo responses to angiogenic factors have not yet been investigated. Moreover, it has been shown that during the takeoff and landing of a space module (BION-M 1), hypergravity induces cardiovascular changes [89]. More experiments should be conducted to precise how these cardiovascular changes can modify the structure of the BBB and neurovascular unit functions. To restore physiological functions after spaceflight or bed-rest in humans, a daily sequence of short exposure to centrifugation close to 2× g has been proposed. It is crucial to verify if this protocol has any effect on the BBB. Recently, biomarkers of BBB alteration have been listed [90], and their investigation in the spaceflight context should be performed. Finally, centrifugation can be considered to potentiate vectorization and should be used to investigate cell functions with antisense oligonucleotides in pathophysiological contexts.

PMC10000819

Walter Mancino,Paola Carnevali,Valeria Terzi,Pascual García Pérez,Leilei Zhang,Gianluca Giuberti,Lorenzo Morelli,Vania Patrone,Luigi Lucini

Hierarchical Effects of Lactic Fermentation and Grain Germination on the Microbial and Metabolomic Profile of Rye Doughs

27-02-2023

LAB,yeast,fermentation,rye,metagenomics,dough,metabolomics,germination

A multi-omics approach was adopted to investigate the impact of lactic acid fermentation and seed germination on the composition and physicochemical properties of rye doughs. Doughs were prepared with either native or germinated rye flour and fermented with Saccharomyces cerevisiae, combined or not with a sourdough starter including Limosilactobacillus fermentum, Weissella confusa and Weissella cibaria. LAB fermentation significantly increased total titrable acidity and dough rise regardless of the flour used. Targeted metagenomics revealed a strong impact of germination on the bacterial community profile of sprouted rye flour. Doughs made with germinated rye displayed higher levels of Latilactobacillus curvatus, while native rye doughs were associated with higher proportions of Lactoplantibacillus plantarum. The oligosaccharide profile of rye doughs indicated a lower carbohydrate content in native doughs as compared to the sprouted counterparts. Mixed fermentation promoted a consistent decrease in both monosaccharides and low-polymerization degree (PD)-oligosaccharides, but not in high-PD carbohydrates. Untargeted metabolomic analysis showed that native and germinated rye doughs differed in the relative abundance of phenolic compounds, terpenoids, and phospholipids. Sourdough fermentation promoted the accumulation of terpenoids, phenolic compounds and proteinogenic and non-proteinogenic amino acids. Present findings offer an integrated perspective on rye dough as a multi-constituent system and on cereal-sourced bioactive compounds potentially affecting the functional properties of derived food products.

Hierarchical Effects of Lactic Fermentation and Grain Germination on the Microbial and Metabolomic Profile of Rye Doughs A multi-omics approach was adopted to investigate the impact of lactic acid fermentation and seed germination on the composition and physicochemical properties of rye doughs. Doughs were prepared with either native or germinated rye flour and fermented with Saccharomyces cerevisiae, combined or not with a sourdough starter including Limosilactobacillus fermentum, Weissella confusa and Weissella cibaria. LAB fermentation significantly increased total titrable acidity and dough rise regardless of the flour used. Targeted metagenomics revealed a strong impact of germination on the bacterial community profile of sprouted rye flour. Doughs made with germinated rye displayed higher levels of Latilactobacillus curvatus, while native rye doughs were associated with higher proportions of Lactoplantibacillus plantarum. The oligosaccharide profile of rye doughs indicated a lower carbohydrate content in native doughs as compared to the sprouted counterparts. Mixed fermentation promoted a consistent decrease in both monosaccharides and low-polymerization degree (PD)-oligosaccharides, but not in high-PD carbohydrates. Untargeted metabolomic analysis showed that native and germinated rye doughs differed in the relative abundance of phenolic compounds, terpenoids, and phospholipids. Sourdough fermentation promoted the accumulation of terpenoids, phenolic compounds and proteinogenic and non-proteinogenic amino acids. Present findings offer an integrated perspective on rye dough as a multi-constituent system and on cereal-sourced bioactive compounds potentially affecting the functional properties of derived food products. Rye bread is one of the most consumed cereal-based foods in northern Europe, China, and North America [1]. In these regions, rye (Secale cereale L.) is a valuable crop because of its resistance towards cold temperatures and northern climates [1]. From a nutritional perspective, rye flour is gaining attention for its health-promoting potential considering its hypocholesterolemic, anti-diabetic, anti-inflammatory, and cardioprotective properties [1]. Whole grain rye is characterized by a high content of dietary fibers, such as arabinoxylans and cellulose, and bioactive compounds with antioxidant properties, such as polyphenols [2]. Driven by consumer demand for sustainable and healthier products, the utilization of rye in cereal-based, functional foods has been widely explored [3]. In this context, seed germination or sprouting has gained popularity in cereal processing as an effective practice to improve grains’ nutritional quality and functional value. Sprouting involves the activation of endogenous hydrolytic enzymes that results in augmented digestibility of cereal proteins and starch [4]. Moreover, it increases the bioavailability of simple sugars, amino acids, phenolic compounds, minerals, and certain vitamins [4]. Likewise, sourdough fermentation is a traditional process that has been shown to affect different attributes of baked goods due to metabolic activities of indigenous yeasts and lactic acid bacteria (LAB). In addition, LAB can positively affect the nutritional value of fermented cereals by increasing the content of bioactive compounds, vitamins, and minerals, and decreasing the amount of anti-nutritional factors [5]. Lactic fermentation has emerged as a promising alternative to improve gut health, preventing digestion-related issues such as gluten sensitivity, and playing a role in the detoxification of common food mycotoxins [6,7,8]. Previous research had shown that both germination of grains before fermentation and the type of fermentation markedly affected the structure and the potential bioactive properties of rye constituents [9,10]. However, to the best of our knowledge, no comprehensive multi-omics study has yet been made to evaluate the impact of seed sprouting and lactic acid fermentation on rye dough composition and quality. Consequently, we took advantage of targeted metagenomics to assess the evolution of the inoculated starters and their overall impact on bacterial ecology. Untargeted metabolomics analyses were applied to uncover microbial contribution to the biochemical profile of grain doughs. We sought to unravel dynamic relationships between microorganisms and food matrix components and identify potential markers of functional capacity important for developing value-added rye products. The use of advanced metagenomics techniques can be important to verify and validate the ecological success of certain starters, which can be interesting for large-spectrum industrial productions [11]. In this context, this approach may be also of interest from an industrial perspective to gain further insight into the effect of traditional technologies such as germination and fermentation on rye flour microbial, chemical, and technological multiple changes aiming to encourage the production of newly rye-based wholesome ingredients and related food products. Finally, the study of oligosaccharide profiles as a function of fermentation, can give a fundamental contribution to understanding how the substrates are modified and which carbohydrate components are present within the dough system [12]. Limosibacillus fermentum UC3641, Weissella confusa UC4052, and Weissella cibaria UC405, previously isolated from sorghum sourdough [13], were used as LAB starters. The strains were grown in MRS medium in anaerobic conditions, using a jar and the anaerocult P reagent (Merck, Germany) at 37 °C. Cultures were harvested by centrifugation (4000× g × 10 min), washed twice with sterile saline solution (0.9% NaCl), and re-suspended in 5 mL of water. Saccharomyces cerevisiae was a commercial, compressed fresh baker’s yeast (Lessafre, Marcq-en-Barœul, France), reintegrated in water before use. Unprocessed commercial rye grains (SU Bendix winter rye) were subjected to a preliminary sieving step. Kernel size fractions between 2 and 2.5 mm were obtained using an Octagon 200 test sieve shaker (Endecotts Ltd., London, UK). Malting was performed on 100 g sieved seed batches with an Automatic Micromalting System (Phoenix Biosystems, Adelaide, SA, Australia) (Figure S1). The following malting cycle (144 h in total) was applied: 15-min wash at room temperature; 7-h and 15-min steep at 15 °C; 8-h rest at 19 °C; 9-h steep at 15 °C; 6-h germination at 19 °C; 30-min steep at 15 °C; 88-h and 30-min germination at 15 °C; 7-h kilning from 30 to 40 °C; 6-h kilning from 40 to 60 °C; 6-h and 30-min kilning from 60 to 70 °C; 4-h and 30-min kilning from 70 to 80 °C; and 30-min kilning at 25 °C. The chemical composition of native rye flour was: dry matter (DM): 90.2 g/100 g; total starch: 59.0 g/100 g DM; crude protein: 9.3 g/100 g DM; total dietary fiber: 17.6 g/100 g DM, free glucose: 0.28 g/100 g DM. The chemical composition of sprouted rye flour was: DM: 93.1 g/100 g; total starch: 49.3 g/100 g DM; crude protein: 11.1 g/100 g DM; total dietary fiber: 24.1 g/100 g DM, free glucose: 5.6 g/100 g DM. In brief, 250 g of native or sprouted rye flours were mixed with 250 mL of tap water and 1% (w/v) of NaCl. The kneading process was preformed through a commercial kneading machine (IMETEC ZERO-GLU KM 1500, Tenacta Group Spa, Azzano S. Paolo, Italy), initially without inoculum, at machine speed 1 for 2 min. As for fermentation, three different experimental conditions were tested: (i) S. cerevisiae fermentation, in which S. cerevisiae was inoculated at a final concentration of 2% (w/w), SC; (ii) mixed fermentation, in which a mix of the three LAB strains (109 CFU/mL) plus S. cerevisiae (2% w/w) was added to the dough, LAB + SC; and (iii) spontaneous fermentation, where non-inoculated doughs were prepared by replacing the inoculum with an equal volume of plain water, considered as the control. The initial concentration of LAB in each inoculated dough was between 1.89–3.00 × 107 cfu/g, as expected, whereas the initial concentration of S. cerevisiae in inoculated doughs was 1.30–1.68x106 cfu/g. After inoculation, the kneading process was carried out for 5 min at machine speed 3. All doughs were maturated for 24 h; for the mixed fermentation, an initial maturation with only inoculated LAB was performed for 8 h (30 °C and 60% relative humidity), after which yeast was added and the dough was left to leaven for the remaining 16 h (35 °C and 65% relative humidity). The same temperature and humidity conditions were applied for SC and control. Dough samples were prepared in duplicate and fermentations were repeated twice. All the doughs were analyzed for pH (pH meter Hanna Edge), total titratable acidity (TTA) [14], water activity (aw) (Aqualab Serie 4; Steroglass, Perugia, Italy), dough rise, total yeast, and LAB count. Dough rise was determined as follows: after mixing, 20 g of the dough was transferred into a graduated cylinder. The height of the dough was measured before and after fermentation, and the dough increase in volume was calculated as previously reported [15]. Yeast and LAB counts were performed in duplicate on YPD agar supplemented with chloramphenicol and MRS agar with 1% of cycloheximide, respectively. Microbial cells were harvested from doughs as previously described [16]. Total DNA was extracted from bacterial pellets using the Fast DNA Spin Kit for Soil (MP biomedicals, Irvin, CA, USA) according to the protocol supplied. DNA quantity was determined by a Qubit fluorimeter (Life Technologies, Carlsbad, CA, USA), while DNA integrity was checked through 0.8% agarose gel electrophoresis. Samples were prepared according to the guidelines for preparing SMRTbell template for sequencing on the PacBio Sequel I System (PacBio, Menlo Park, CA, USA) at Macrogen (Seoul, Republic of Korea). The library was constructed with SMRTbell® Express Template Prep Kit 2.0 including 27F-1492R primers [17] along with barcode according to the manufacturer’s instructions (Pacific Biosciences, Menlo Park, CA, USA); library purification was carried out using Ampure® PB bead (Pacific Biosciences, Menlo Park, CA, USA). Purified SMRTbell library from the pooled and barcoded samples was sequenced on a single PacBio Sequel cell using a SMRT Cell 1M v3 Tray. The resulting data were processed using the rDNATools pipeline [18]. Ambiguous reads and short reads (<1199 bp) were filtered out, and extra-long tails were trimmed according to the target size of the bacterial 16S rRNA gene. Chimeras were identified and removed using the software Vsearch v2.14.2 using the default settings. Then, a distance matrix was generated using the Unweighted/Weighted UniFrac distance, and reads were clustered using the average neighbor method. Operational Taxonomy Unit (OTU) picking was based on the de novo method; sequences that shared over 99% similarity were assigned to a single OTU. Taxonomic assignment of OTUs was obtained using QIIME2 bioinformatic pipeline v 2022.2 [19], which provides functionality for working with and visualizing taxonomic annotations of features. OTUs were aligned with the representative sequence of the NCBI 16S Database with a similarity cutoff of 97% for species differentiation. The oligosaccharide semi-quantification of the different rye doughs was performed by high-performance anion exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD) approach. All samples were subjected to a previous extraction, in which 1 g of sample was mixed with 10 mL of deionized water, and the resulting mixture was homogenized by a high-speed rotor (Polytron PT 1600-E) for 1 min and centrifuged at 8000× g for 10 min at 4 °C (Eppendorf 5810R, Hamburg, Germany). The supernatants were collected, syringe filtered (0.22 µm pore size), and transferred into vials for the subsequent analysis. The experimental conditions applied for HPAEC-PAD analysis were previously described [20]. The equipment employed consisted of a Dionex ICS-5000+ (Thermo Fisher Scientific, Waltham, MA, USA), containing an electrochemical cell with a gold working electrode combined with a pH-Ag/AgCl reference electrode as detection system. The chromatographic separation was achieved through a Dionex CarboPac PA200 column (3 × 250 mm) coupled to a guard column (3 × 50 mm), as the stationary phase (both purchased from Thermo Scientific), which provides a high-resolution separation of monosaccharides and linear oligosaccharides. The mobile phase consisted in a binary solvent system that included 100 mM NaOH (eluent A) and 1 M sodium acetate in 100 mM NaOH (eluent B). The experimental runs presented a total time of 75 min, the flow rate was adjusted at 0.4 mL min−1 and temperature for both column and detector compartments were set at 27 °C, following a multi-gradient elution system: 0–10 min, 98% A; 10–35 min, 55% A; 65–75 min, 98% A. The amperometric detector was set in terms of several potentials and durations as follows: E1 = 0.10 V (t1 = 0.40 s); E2 = −2.00 V (t2 = 0.01 s); E3 = 0.60 V (t3 = 0.01 s); E4 = −0.10 V (t4 = 0.06 s). The semi-quantification of oligosaccharides was achieved according to their polymerization degree (PD), with respect to individualized standards that are representative of three different well-separated structural classes: monosaccharides, low-PD oligosaccharides, and high-PD oligosaccharides. Thus, xylose was applied as the reference standard for monosaccharides (y = 3.8348x + 1.007, R2 = 0.9918), arabinotriose was selected as the reference standard for low-PD oligosaccharides (y = 3.7960x + 1.656, R2 = 0.9878), and arabinooctaose was chosen as the reference standard for high-PD oligosaccharides (y = 0.9963x + 0.8871, R2 = 0.9842). The results were expressed as carbohydrate content in mg g−1 of sourdough. The standards and reagents employed for oligosaccharide profiling were purchased from Sigma Aldrich®, Darmstadt, Germany (xylose) and Megazyme®, Bray, Ireland (arabinotriose and arabinooctaose). The untargeted metabolomic profiling of the different rye doughs was obtained through an ultra-high performance liquid chromatography instrument (Agilent 1200 series), presenting a binary pump and JetStream electrospray source, coupled to a quadrupole time-of-flight mass spectrometer (UHPLC/QTOF-MS; Agilent iFunnel 6550). Before the analytical determination, 1 g of each rye doughs was mixed with 10 mL of the extraction solvent MeOH:H2O:HCOOH (80:19.9:0.1, v/v/v) and the extraction was performed under the same conditions described earlier [21]. All analytical conditions were set as described elsewhere [21,22]. Briefly, the injection volume was 6 µL, the chromatographic separation was achieved through an Agilent Zorbax Eclipse Plus C18 column (50 × 2.1 mm, 1.8 µm), applying an AcN:H2O binary mobile phase with a gradient elution: 6%–94% organic phase for a 33 min run, with a flow rate of 200 µL min−1. The analytical conditions for the QTOF performance were employed as follow: N2 as sheath gas with a flow of 10 L min−1 at 350 °C, drying gas was applied with a flow of 8 L min−1 at 330 °C, nebulizer pressure was set at 60 psi, nozzle voltage at 300 V, and capillary voltage at 3.5 kV. The mass spectrometer was adjusted in positive polarity and SCAN mode, with a detection range of 100–1200 m/z, with a nominal resolution of 40,000 FWHM. Moreover, quality control pooled samples were obtained and further analyzed through data-dependent MS/MS mode (12 precursors per cycle at 1 Hz, 50–1000 m/z, positive polarity), applying different collision energies: 10, 20 and 40 eV. The acquired raw data were later processed by the Agilent MassHunter Profinder v.10.0 using the “find-by-formula” algorithm, through mass and retention time alignments (5-ppm and 0.05 min tolerance, respectively). The annotation of the obtained chemical features was based on their accurate mass and isotopic patterns, given by the exact masses, relative abundance and m/z spacing, using the FooDB database (available at foodb.ca) to achieve their identification. Data reduction was achieved by applying the “filter-by-frequency” feature, exclusively retaining the features observed in all the replicates within the same treatment. As a result, the untargeted compound annotation obtained was in compliance with identification level 2 provided by the COSMOS Metabolomics Standard Initiative (putatively annotated compounds). To improve the confidence in the compound annotation raw data were processed by MS-DIAL software (v. 4.90), achieving the identification of chemical features through MS-MS spectral data, according to mass accuracy data, mass isotopic patterns, and spectral alignment matching. The parameters set for identification were: retention time range, 1–32 min; mass range, m/z 80–1200; mass tolerance, 0.05 Da. For data reduction, a filter step was performed, removing the identities that were not acquired in, at least the 80% of replicates. Finally, a score cut-off of 80% was selected to retain those compounds with the highest identification fidelity according to MS2 level. All chemicals used for extraction and chromatographic equipment were LC-MS grade, purchased from VWR (Milan, Italy). A two-way analysis of variance (ANOVA) was performed to analyze the effect of starter and germination on dough general parameters using GraphPad Prism version 8.0.0 for Windows (GraphPad Software, San Diego, CA, USA). Metagenomic data were processed using the QIIME2 v.2020.2 platform for diversity analysis of dough microbial communities. Observed species, Chao1, Shannon and Gini-Simpson indices were calculated to assess within sample diversity; sequencing depth was characterized by Good’s coverage. Weighted and unweighted Unifrac distances were calculated, and Principal Coordinate Analysis (PCoA) was performed on the distance matrices to visualize community variation across samples. The OTU table was uploaded to the Microbiomeanalyst server for compositional profiling and comparative analysis, using 10% prevalence in samples for the low count filter, the default settings for other filtering and total sum scaling for data normalization [23]. To test for significance in differential bacterial taxa abundance according to starter and flour, respectively, the algorithm DeSeq2 was used [24]. The name of the samples was as follows: nDLY (native rye–Dough–LAB + SC); sDLY (sprouted rye–Dough–LAB + SC); nDY (native rye–Dough–SC); sDY (sprouted rye–Dough SC); nDCTR (native rye–Dough–Control); sDCTR (sprouted rye–Dough–Control); nF (native rye–Flour); sF (sprouted rye–Flour). For both oligosaccharide and untargeted metabolomics profiling approaches, all sourdough samples were extracted in triplicate, and two technical determinations were carried out for each replicate (n = 6). The results for carbohydrate content of rye sourdoughs were statistically analyzed by one-way ANOVA followed by Duncan’s post hoc test, setting a significance value of α = 0.05, using the software SPSS 25 (IBM). The Agilent Mass Profile Professional v. 15.1 software analyzed the metabolomics data as previously indicated compounds were filtered by abundance, log2-transformed, and normalized at the 75th percentile [22]. The abundance of each compound was further baselined against the median abundance among all samples. Afterwards, an unsupervised multivariate hierarchical cluster analysis (HCA) was performed to evaluate the similarities and dissimilarities of all factors as a function of their metabolic profile (Euclidean distance, Ward’s linkage rule). Later, a Chemical Similarity Enrichment Analysis for Metabolomics (ChemRICH, available at chemrich.fiehnlab.ucdavis.edu) was performed to define the chemical composition of sourdoughs due to the addition of LAB. To that aim, compounds were filtered on Volcano plot and only the compounds showing a significantly different accumulation between treatments (p < 0.05) and with fold-change values > 2 were considered [25]. Finally, a supervised multivariate orthogonal projection to latent structures discriminant analysis (OPLS-DA) was carried out by the SIMCA v. 16.0.2 software (Umetrics). Model quality was evaluated according to goodness-of-fit parameters (R2X and R2Y), and goodness-of-prediction parameter (Q2Y). OPLS-DA predictive models were further statistically validated by cross-validation ANOVA (CV-ANOVA), and model overfitting was discarded through the development of permutation tests (n = 100). Such approach was followed by a variable importance in projection (VIP) analysis, providing insight into the compounds exhibiting the highest influence on the discrimination between treatments, known as VIP markers, according to their given VIP score [22]. Key technological parameters of the different doughs after fermentation are presented in Table 1. Rye doughs fermented with LAB + yeast showed total LAB counts reaching approximately 10 log CFU/g after 24-h fermentation; this value significantly exceeded the numbers of total LAB in either SC or control rye doughs, suggesting the actual growth of inoculated LAB starters. No significant difference in final LAB abundance was found between native and sprouted rye flour. A similar trend was observed for total yeast counts. Before fermentation, the pH was 6.23 ± 0.04 for doughs made with native flour and 5.88 ± 0.07 for doughs with sprouted rye flour, respectively. As expected, the addition of LAB starters caused a decrease in the pH value to about 4 both in sprouted and native rye doughs, as compared to either SC or control. However, the difference between final pH values was significant only when comparing LAB + SC vs. SC (p = 0.002) in sprouted rye samples. Consistently, the application of LAB starters significantly increased total acid concentration in rye doughs as compared to either yeast or control (Table 1). A two-way ANOVA revealed that there was a significant interaction between the effects of starter and germination as concerns TTA value (p-value interaction < 0.0001). Notably, the TTA level of LAB + SC doughs made with sprouted rye flour was higher than that of the corresponding doughs produced from native rye flour (2.08 ± 0.05 vs. 0.82 ± 0.06, p-value < 0.0001). The inclusion of LAB as a starter for rye flour fermentation displayed no significant impact on dough aw. Significant differences were observed for yeast (alone or in combination with LAB) versus control in native rye flour doughs (p = 0.0004 and p-value = 0.006, respectively). Concerning dough volume, LAB seemed to contribute to dough rise during leavening markedly. The volume increase was higher for LAB + SC vs. SC or control regardless of the type of rye flour used; differences were all statistically significant but for LAB + SC vs. SC in native rye dough (220% ± 18.87% vs. 143.3% ± 4.71%; p-value = 0.01). Analogously to aw, statistical analysis indicated that the type of fermentation had the same effect in sprouted and native rye doughs (p-value = 0.22). PacBio SMRT sequencing of the complete bacterial 16S rRNA gene resulted in 167,207 total filtered high-quality reads, with numbers ranging from 4,188 to 16,844 reads per sample (mean 10,450 reads). Clustering to 99% similarity yielded 5,657 distinct OTUs; the mean number of OTUs per sample was 353 (range 58–609; Table 2). To assess sample diversity, different indexes were calculated including Chao1, Shannon, and Gini-Simpson (Table 2). No significant differences were found among dough samples in any alpha diversity indexes. As expected, flours displayed the highest bacterial diversity among all tested samples, suggesting that fermentation exerted a selection pressure on the community structure of the dough microbiota (Table 2). Overall, native rye flour and doughs tended to have a lower bacterial richness as compared to their counterparts obtained from sprouted rye. Good’s coverage ranged from 97% to 99% suggesting that a high percentage of the total species was represented in each sample (Table 2). Beta diversity, based on an unweighted UniFrac distance matrix, highlighted three distinct bacterial clusters in the PCoA plot (Figure 1). Differences in microbial composition between the samples allowed a clear discrimination between flour samples and dough samples obtained by mixed fermentation, respectively. A further group, including both spontaneously fermented doughs and doughs produced by S. cerevisiae fermentation, could be identified. More than 93% of the sequences obtained by the PacBio SMRT sequencing were associated with known taxa; unclassified reads were 6.5% of the total sequences. Weissella, Limosillactobacillus, Salmonella, Latilactobacillus, and Microcoleus were the dominant genera in the metagenomic dataset, with an average abundance of 28.48%, 19.89%, 13.86%, 12.53%, and 6.36% of the total reads, respectively. At species level, reads were assigned to 56 different taxa, and the species with an average greater than 0.25% were 12 taxa (Figure 2). The bacterial community composition of flours differed between sprouted and native rye. Salmonella enterica represented more than 65% of bacterial microbiota in sprouted rye flour followed by Microcoleus anatoxicus, Delftia acidovorans, and others (Figure 2). Conversely, the most abundant bacterial species in native rye flour was the cyanobacterium Microcoleus anatoxicus, accounting for about 43% of the total sequences; less common species included Pantoea agglomerans and Cutibacterium acnes. Our results indicated that, sprouted rye doughs had greater uniformity in the bacterial community structure for all the 3 fermentation conditions than the native rye counterparts. L. fermentum and W. confusa/cibaria were the only bacterial species detected in dough samples inoculated with the lactic acid starter. As expected, the two closely related Weissella species were indistinguishable using 16S rRNA gene sequencing. L. fermentum sequences greatly outnumbered those ones classified under the species pair W. confusa/W. cibaria (Figure 2), reaching 93.56 ± 4.73% in native rye doughs, and 65.26 ± 0.27% in sprouted rye doughs. Yeast-leavened doughs were enriched in cereal-sourced LAB with a different species composition based on the rye flour used. Latilactobacillus curvatus was dominant (relative abundance > 89%) in sprouted rye doughs, while Lacticaseibacillus paracasei and Latilactobacillus graminis were found at low relative abundance. Native rye doughs harbored a bacterial community consisting of Lactoplantibacillus plantarum (with an average of 42.6%) followed by Lacticaseibacillus paracasei (relative abundance > 16%); Lactobacillus curvatus was detected with a relative abundance over 4%, while W. confusa/cibaria accounted for 38.5% of total sequences in nDY (Figure 2). Control doughs resulted in a microbiota that differed from that of other doughs and was broadly dominated by indigenous W. confusa/cibaria strains. Differential analysis of the abundance of microbial species revealed several features that varied significantly according to either starter or germination. When considering the impact of starter, L. fermentum was higher in LAB + SC samples compared to either SC or control samples, regardless of rye germination (Figure 3A,B). As for native rye doughs (Figure 3A), control samples were enriched in W. confusa/cibaria and P. agglomerans with respect to native doughs inoculated with LAB and yeast. Moreover, the species L. plantarum and L. paracasei were more abundant in yeast-leavened samples than doughs obtained by mixed fermentation, or native spontaneous fermentation samples. Among sprouted rye doughs, spontaneously fermented samples displayed significantly higher levels of W. confusa/cibaria and S. enterica than those observed in SC samples and in LAB + SC samples, respectively. Notably, the inoculation S. cerevisiae alone in sprouted rye samples was associated with higher proportions of endogenous LAB species including L. curvatus, L. graminis and L. paracasei. In fact, these species resulted in being higher in abundance in SC samples with respect to both LAB + SC samples and control samples. As regards the comparison between sprouted and native rye flours, L. curvatus was significantly higher in sprouted rye doughs as compared to native rye doughs (p-value = 0.045). Conversely, L. plantarum was significantly higher in native rye samples to sprouted samples (p-value = 0.045). The oligosaccharide semi-quantification of rye flour doughs is shown in Figure 4. In general, germination played a critical role on carbohydrate compositions since native rye doughs exhibited a lower carbohydrate content to the sprouted counterparts. In parallel, adding fermentation starters played a significant role on the carbohydrate content of rye doughs (Figure 4). Thus, for native rye doughs, SC-mediated fermentation promoted a significant decrease of both monosaccharides and low-PD oligosaccharides that were further significantly decreased in the LAB + SC fermentation by whereas high-PD carbohydrates were not affected (Figure 4A). In total, the combination of LAB with SC led to a harsh carbohydrate content reduction of 55.5% with respect to control. In contrast, the fermentation of sprouted rye doughs caused only a significant decrease in the carbohydrate content in the case of mixed fermentation, as SC fermentation did not promote any significant difference in comparison to control (Figure 4B). As a result, concerning total carbohydrate content, the LAB + SC treatment led to a 22.8% decrease with respect to control, suggesting a lower impact of fermentation than that observed for native-derived dough. Concerning the different carbohydrates, the high-PD oligosaccharides content was not affected by the type of fermentation, whereas the contents of both monosaccharides and low-PD oligosaccharides was significantly decreased after the LAB + SC combined fermentation (Figure 4B). Rye doughs were subjected to metabolic profiling via UHPLC/QTOF-MS, providing 1909 annotated chemical features (Table S1). From these annotated compounds, 158 were identified according to their MS2 spectral features (Table S2). The effect of grain germination and the addition of starters on the metabolic profile of rye doughs were evaluated by an unsupervised multivariate hierarchical cluster analysis (HCA), and the results are displayed in Figure 5. Among the factors involved in this study, germination was the most prevalent factor affecting the metabolome of samples since it ruled the establishment of two major clusters. Secondarily, within both clusters, fermentation starters played a significant role, providing three subclusters according to the different experimental conditions involved in dough production: non-inoculated, SC, and LAB + SC (Figure 5). Due to the heterogeneous metabolic profile of fermented rye doughs, an additional supervised multivariate orthogonal projection to latent structures discriminant analysis (OPLS-DA) was performed. It was followed by a variable importance in projection (VIP) analysis, with the aim of discriminating the effect of germination and starters on the metabolome of these matrices, providing insight on the metabolic markers mostly involved in such discrimination (VIP markers). The Figure 6 shows the OPLS-DA models and the proportion of VIP markers according to their chemical class for the discrimination between germination conditions (Figure 6A,B, respectively), fermentation starters on native rye-derived doughs (Figure 6C,D, respectively), and fermentative starters on sprouted rye-derived doughs (Figure 6E,F, respectively). Moreover, the full list of VIP markers associated with all models, together with their VIP score, logFC values, and chemical class are provided in Tables S4–S6, respectively. In all cases, the obtained OPLS-DA models showed high-quality parameters in terms of goodness-of-fit, given by R2X and R2Y coefficients, and predictability, given by the Q2Y coefficient (Q2Y > 0.5; Figure 6). Concerning germination, the OPLS-DA model spotted a definitive role of this factor on the metabolome of rye doughs, indicating a clear discrimination between native rye- and sprouted rye-derived doughs (Figure 6A). Phenolic compounds, terpenoids, and phospholipids (Figure 6B) predominantly represent the VIP markers provided for the discrimination between native and sprouted-derived doughs. In general, and regarding the logFC values (Table S3), sprouted rye-derived doughs exhibited an enhanced accumulation of phenolic compounds, including flavonoids like mulberrin, zapotinin, and isoferreirin (logFC = 13.5), and phenolic acids, mostly represented by spermidine esters, and resorcinols. In the case of terpenoids, triterpenoid acids like 3-benzoyloxy-6-oxo-12-ursen-28-oic acid and 2, 3, 23-triacetylsericic acid (log FC = 13.5) were accumulated in sprouted rye-derived doughs (Table S3). To a lesser extent, amino acids like His and Phe derivatives and oligopeptides, as well as glucosinolates were found to be differentially up accumulated in sprouted rye doughs, providing evidence on the metabolic richness of this matrix. Conversely, the accumulation of lipid metabolites did not follow a clear pattern. Metabolites like docosan-1-ol, phosphatidylethanolamines, and 3-hydroxy-9-hexadecenoylcarnitine exhibited a decrease in native rye doughs (log FC = ™9.4; Table S3), whereas saturated fatty acids 22-hydroxydocosanoate and 10-hydroxymyristic acid methyl ester and some di-glycerides were mostly measured in sprouted rye doughs (log FC = 13.5; Table S3). As germination played such a discriminant effect on the metabolome of rye sourdoughs, two additional OPLS models were performed to evaluate the impact of starters on either native rye or sprouted rye doughs (Figure 6C,E, respectively). In both cases, a clear discrimination between SC-fermented doughs and LAB + SC-fermented doughs was obtained, and phenolic compounds, terpenoids, and lipid metabolites were mostly identified as VIP markers (Figure 6D,F, respectively). Firstly, in the case of native rye doughs, the inoculation of LAB promoted a general up-regulation of the metabolome, since 54% of VIP markers possessed logFC = 8.6, and only 17% of markers were found to be down-accumulated as compared to SC-fermented doughs (Table S4). Thus, terpenoids were generally accumulated, ranging from triterpenoids and sesquiterpenoids to monoterpenoids, including sterols and carotenoids (Table S4). Likewise, phenolic compounds were all up-accumulated due to LAB addition, involving flavonoids, phenolic acids like p-coumaroyl derivatives, and spermine and putrescine esters, stilbenes, coumarins and lignans (Table S4). In parallel, amino acids were also selected as VIP markers accumulated in LAB + SC fermented doughs, represented by both proteinogenic amino acids, such as Gln (logFC = 3.3), Gly (logFC = 3.4), and Cys derivatives (log FC = 8.6), and non-proteinogenic amino acids, like ornithine (logFC = 2.0; Table S4). Peptides were found to be accumulated as a result of LAB inclusion (log FC = 2.3–8.6; Table S4) as well, suggesting an intense proteolytic activity. In contrast, lipid metabolites showed an unclear pattern of accumulation between LAB + SC fermentation and SC fermentation, as given by log FC values. Notably, lysophospholipids were mainly accumulated in LAB + SC-fermented doughs (log FC = 2.0–8.6), whereas fatty acids were heterogeneously detected (Table S4). A similar trend was observed for the inclusion of LAB as fermentation starters on the metabolome of sprouted rye doughs, as indicated by the corresponding OPLS-DA (Figure 6E). A metabolic stimulation was shown by the inclusion of LAB, with 76% of VIP markers up regulated in LAB + SC-fermented matrices to those fermented exclusively with SC (Table S5). Again, phenolic compounds constituted the class with the highest contribution to VIP markers, followed by terpenoids and lipid metabolites (Figure 6F). Considering phenolic compounds, lignans were the compounds presenting the highest accumulation (log FC = 10.0 for schidigeragenin B and clusin, Table S5), together with ferulic, caffeic acid esters (log FC = 10.0), whereas flavonoids presented much lower log FC values (Table S5). Considering terpenoids, LAB inclusion promoted the accumulation of high-isoprene subunits terpenoids, including steroids, triterpenoids and sesquiterpenoids (log FC = 10.0, Table S5), whereas the accumulation of mono- and diterpenoids was reduced (log FC < −3.3). In parallel, in the case of amino acids and peptides, the accumulation and down-accumulation did not follow a clear pattern. While saturated fatty acids, especially octadecanoic acids (log FC = −12.8), and sulfur-containing compounds, like 1-methoxyspirobrassinin (log FC = −12.8), were harshly down accumulated upon the addition of sourdough LAB starters, lysophospholipids were found accumulated upon the inclusion of LAB (log FC = 3.7–10.0; Table S5). This study explored the microbial, chemical, and technological profiles of rye doughs made with either native or sprouted flour and fermented with S.cerevisiae in combination or not with selected LAB starters. Two complementary approaches were applied to assess the metabolic profiling of rye doughs after fermentation. Firstly, the carbohydrate profile of rye doughs was assessed, and results indicate that grain germination and LAB fermentation played a significant role in the composition of these constituents. The carbohydrate content of sprouted rye doughs was significantly higher than that of native rye doughs, due to the induction of hydrolytic enzymes during seed germination, which includes amylases, pentosanases and glucanases [26]. Due to hydrolytic activity, insoluble fiber is mainly converted into soluble sugars, such as monosaccharides, that were spotted as the major sugar constituents of rye doughs in this work. Notably, VIP analysis indicated that characteristic oligosaccharides of sprouted rye doughs were maltopentaose and maltotetraose, functional maltodextrins potentially involved in glycemic control response and enterocyte differentiation [27]. Considering the fermentation starters, including LAB promoted a significant decrease in carbohydrate content in terms of monosaccharides and low-PD oligosaccharides. This can be explained considering the heterofermentative metabolism of sourdough LAB, which relies on the activity of a wide range of catabolic enzymes [7]. Analysis of technological parameters revealed important features of experimental doughs connected to the evolution of bacterial ecology during fermentation and the interplay between starter inoculation and germination. As expected, the application of sourdough LAB starters led to a substantial reduction of pH, especially in comparison to yeast-leavened doughs, as a result of LAB extensive exploitation of carbohydrates for organic acids biosynthesis [28,29]. Indeed, the germination-related enzymatic breakdown of carbohydrates into simple sugars can boost fermentative metabolism by sourdough LAB resulting in the accumulation of organic acids [9,30]. Interestingly, LAB + SC fermentation was also associated to a greater dough rise as compared to either SC or control. Heterofermentative LAB activity can affect dough leavening through the production of CO2 [31]. In mixed LAB + SC samples, metagenomics analysis highlighted a strong dominance of inoculated L. fermentum over Weissella strains at the end of the fermentation. On the other hand, endogenous W. confusa/cibaria was the predominant taxon in control samples. The latter finding is not surprising since several studies identified Weissella alone, or in combination with other LAB, as the dominant bacterial genus in rye sourdough after 24 h fermentation which may include or not refreshments [31,32,33,34,35]. Indeed, the ecological fitness of sourdough microorganisms is largely dependent on the interplay between strain-specific traits and process conditions including temperature, pH, dough hydration, fermentation time, and type of flour [36,37]. All these parameters can contribute to affect the growth rate of organisms, their competitiveness in sourdough fermentation and eventually their impact on product quality. It is thus presumably to suppose that a long fermentation at elevated temperature (i.e., 35 °C) as applied in the present study selected for L. fermentum owing to the thermophilic behavior and high acid resistance of this Limosilactobacillus species [38]. Consistent with this hypothesis, the cultivable microbiota of sourdoughs fermented at 37 °C was constituted by L. fermentum strains exclusively [39]. Notably, the VIP analysis on the metabolomics profile of doughs revealed that in both native and sprouted rye doughs there was an accumulation of mannitol when LAB were added as starters. Conversion of fructose to mannitol by heterofermentative LAB has been reported in sourdough fermentations [39]. Consistent with this, L. fermentum UC3641 has in its genome two Open Reading Frames (ORFs) encoding for a NAD(P)H-dependent mannitol/alcohol dehydrogenase [13]. In addition to oligosaccharide semi-quantification, a metabolomics approach was employed to investigate the overall effect of germination and fermentation on the metabolome of rye sourdoughs. The unsupervised HCA analysis of doughs revealed that germination of rye grains played a major role on the metabolic profile than fermentation, which was also supported by the results from the OPLS-DA models. Germination has been previously assessed as a physiological process in which phytohormones can play a critical role on the development and metabolome of rye grains, which may affect further processing, including fermentation [26]. Furthermore, germinated grains show a high biosynthetic potential and promote the activity of hydrolytic enzymes that lead to structural modifications [9], which could reflect in greater accessibility or diversity of fermentative bacteria. We assessed the presence of LAB species that could be differentially associated to either native or sprouted rye flour regardless of the fermentation conditions. Metagenomic data revealed that the species L. plantarum was typical of the microbiota of native rye doughs, whereas L. curvatus was significantly higher in doughs made with sprouted rye. As for L. plantarum, this species is known to metabolize a wide range of different carbohydrates of varying complexity, thanks to its rich repertoire of lytic enzymes [40]. Furthermore, among the significant compounds responsible for the discrimination between native- and sprouted-derived rye doughs obtained from OPLS-DA model, several compounds were spotted as VIP markers, especially primary metabolites as amino acids, peptides, and lipid metabolites. Concerning phenolics, the accumulation of flavonoids and phenolic acids was mostly modulated by fermentation, with yeast as the sole fermenting agent or in combination with LAB, which agrees with the previous study by Katina et al. [9] who reported increased levels of phenolic compounds after fermentation, especially in germinated rye. In parallel, sourdough fermentation contributed to increase significantly the content of total phenolic compounds, especially phenolic acids and alkylresorcinols, because of the pH reduction caused during fermentation [7]. Such compounds were found in this work as discriminant metabolites associated to LAB fermentation, as it is the case of feruloyl, caffeoyl, and coumaroyl derivatives, thus being in line with the results provided by other authors [10]. Ferulic and p-coumaric acids are the most prevalent phenolics attributed to rye, reaching a proportion of about the 95% of total phenolic compounds [41]. Notably, all the LAB starters used in our experimental conditions presented in their genomes ORFs encoding for esterases, phenolic acid decarboxylases and phenolic acid transferases [13], suggesting the possible involvement of these enzymes in the conversion of p-coumaric acid and ferulic acid in their esterified derivatives. It is important to note that phenolic acids have been previously reported in their esterified forms with diverse biogenic amines [10] as reflected by our results with spermine, spermidine, and putrescine. Moreover, the same authors reported an increase in the accumulation of flavonoids due to mixed fermentation, agreeing with present findings. Overall, the polyphenols enrichment associated with LAB + SC fermentation may suggest an enhancement of the nutritional value of rye doughs, given the biological activities of these compounds as multifaceted bioactive compounds, acting as antioxidant, anti-inflammatory, antitumor and antimicrobial agents, among other health-promoting properties [42]. In the case of terpenoids, little is known about the effect of fermentation on biosynthesis of these compounds in rye sourdough [43] that were widely identified during the current research as triterpenoids. Nevertheless, the presence of terpenoids may improve the shelf life and safety of these matrices, due to their associated antibacterial properties [44]. Concerning protein-derived metabolites, both germination and fermentation played a critical role on the catabolites determined in rye doughs, which agrees with the existing literature. Germination can increase the total proteolytic activity in rye whereas acidification mediated by both LAB and yeasts in sourdough fermentation triggers cereal protease activity by shifting the dough pH to the optimum of aspartic proteases, which represent the major proteases in rye and wheat [45]. Even more important for proteolysis is the activity of strain-dependent intracellular peptidases of sourdough lactobacilli, which enhances the accumulation of amino acids in fermented doughs providing key sources of nitrogen for yeast growth [40,45]. Thus, all these factors contribute to the plethora of free amino acids and peptides spotted in this work. The amount and type of peptides and amino acids occurring in cereal doughs mostly account for the overall quality of bread in that many of these compounds act as taste-active components and flavor precursors. However, as a result of the proteolytic activity of sourdough starters, non-proteinogenic amino acids, like citrulline or ornithine, were previously spotted [10] as well as in this case. L. fermentum is among the Lactobacillus species that can convert arginine to ornithine via the arginine-deiminase (ADI) pathway [46]. Notably both native and sprouted rye doughs fermented by sourdough LAB were enriched in γ-glutamyl dipeptides such as γ-glutamylglutamic acid and γ-l-glutamyl-l-pipecolic acid in the present study. Besides being naturally presented in certain foods, the synthesis of γ-glutamyl dipeptides may occur in fermented foods via microbial γ-glutamyl transpeptidases and γ-glutamyl cysteine synthetases. Formation of γ-glutamyl dipeptides in sourdough fermented by Limosilactobacillus reuteri was attributed to strain-specific biosynthetic capabilities and consistently improved the sensory attributes of the resulting bread [47]. Lipid metabolites play a minor role on the composition of rye sourdoughs motivated by the low-fat content of rye flour [7]. According to our results, the accumulation of lipid metabolites did not show a clear pattern, regardless of the germination and fermentation conditions, with the exception of lysophospholipids, which were harshly accumulated as a result of mixed fermentation. This finding could be explained by the activity of hydrolytic enzymes, such as lipases and phospholipases acting on di- and tryacylglycerides, which were found to be heterogeneously accumulated during fermentation. These enzymes could be sourced from rye flour as well as sourdough starter microorganisms. Nevertheless, the results reported by Koistinen et al. [10] on the untargeted metabolomic profile of rye sourdoughs indicate that fermentation promoted the accumulation of phosphatidylcholines, whereas oxidized fatty acids were found to be down-accumulated. Remarkably, in the present study, a higher level of hydroxy fatty acids was detected in doughs fermented with sourdough LAB as compared to S. cerevisiae alone. It is known that hydratases by sourdough lactobacilli can convert oleic acid, linoleic acid, and linolenic acid to hydroxy fatty acids. The results of the present study provide a comprehensive view of multiple compositional changes induced by germination and lactic fermentation in cereal flour, which may have implications for the nutritional value, sensory attributes, and functional characteristics of rye bakery products. Fermentation by selected sourdough lactic acid bacteria in addition to baker’s yeast resulted in lower levels of simple sugars and increased levels of mannitol in the dough system, and could thus represent a relevant strategy to reduce sugar in baked goods. Grain germination promoted the accumulation of maltooligosaccharides, a class of molecules displaying several potential biological capabilities. Overall, the combination of rye germination with the combined fermentation of S. cerevisiae and LAB promoted the accumulation of nutritionally important metabolites, such as polyphenols, terpenoids, hydroxy fatty acids, and peptides, which also contribute to the enhancement of the technological and sensorial properties associated with rye flour. Indeed, the integrated information provided by metagenomics and untargeted metabolomics offered new insights into the impact of processing technologies on dough quality, which can guide the design and development of novel, health-promoting rye foods.

PMC10000829

Giuseppe Schepisi,Caterina Gianni,Michela Palleschi,Sara Bleve,Chiara Casadei,Cristian Lolli,Laura Ridolfi,Giovanni Martinelli,Ugo De Giorgi

The New Frontier of Immunotherapy: Chimeric Antigen Receptor T (CAR-T) Cell and Macrophage (CAR-M) Therapy against Breast Cancer

04-03-2023

chimeric antigen receptor (CAR),macrophages,T cells,immunotherapy,breast cancer

Simple Summary To date, different therapeutic strategies, including immunotherapies, have been shown to prolong survival in breast cancer patients, representing one of the most common malignancies. Our article deals with chimeric antigen receptor-based immunotherapy in breast cancer. Abstract Breast cancer represents one of the most common tumor histologies. To date, based on the specific histotype, different therapeutic strategies, including immunotherapies, capable of prolonging survival are used. More recently, the astonishing results that were obtained from CAR-T cell therapy in haematological neoplasms led to the application of this new therapeutic strategy in solid tumors as well. Our article will deal with chimeric antigen receptor-based immunotherapy (CAR-T cell and CAR-M therapy) in breast cancer.

The New Frontier of Immunotherapy: Chimeric Antigen Receptor T (CAR-T) Cell and Macrophage (CAR-M) Therapy against Breast Cancer To date, different therapeutic strategies, including immunotherapies, have been shown to prolong survival in breast cancer patients, representing one of the most common malignancies. Our article deals with chimeric antigen receptor-based immunotherapy in breast cancer. Breast cancer represents one of the most common tumor histologies. To date, based on the specific histotype, different therapeutic strategies, including immunotherapies, capable of prolonging survival are used. More recently, the astonishing results that were obtained from CAR-T cell therapy in haematological neoplasms led to the application of this new therapeutic strategy in solid tumors as well. Our article will deal with chimeric antigen receptor-based immunotherapy (CAR-T cell and CAR-M therapy) in breast cancer. Breast cancer (BC) is the most common female cancer worldwide. According to Globocan, it is the number one diagnosed cancer with an estimated 2.3 million new cases (11.7%) globally in 2020, and is the fifth leading cause of cancer mortality [1]. The BC incidence is increasing, especially in highly developed countries where screening strategies help to reduce cancer mortality while in poor-developing countries, the BC incidence remains low but the mortality rate is still higher [1]. However, in advanced countries, the diagnosis of de novo metastatic BC still represents approximately 3% to 6% of new BC diagnoses and has not declined despite the wide diffusion of mammography screening [2]. BC is a very heterogeneous disease, clinically distinguished into several subtypes according to the expression of hormone receptors (HRs) and the human epidermal growth factor receptor 2 (HER-2) status: luminal BC, HER-2 positive BC, and triple-negative BC (TNBC). HR and HER-2 are the targets for numerous specific treatments in early and advanced settings. TNBC is defined by the lack of expression of HR and HER-2 and accounts for approximately 15% of all BCs [3]. TNBC has long been considered a major unmet need due to its aggressive behavior and poor prognosis related to its deficiency of specific therapeutic targets. These tumors tend to relapse early and rapidly metastasize in the lungs, liver, and central nervous system, determining a worse survival [4]. For these reasons, chemotherapy is still a cornerstone in the treatment of this BC subtype. Immunotherapy with checkpoint inhibitors was shown to be effective in TNBC, especially in the case of programmed death 1 (PD-1) expression in tumor tissue [5]. Furthermore, TNBC has the highest tumor mutational burden (TMB) among all BC subtypes [6]. A high mutational level can lead to the production of tumor “neoantigens” which could be recognized by antigen-presenting cells in the tumoral microenvironment (TME) enhancing antitumor immune response [6]. Even if BC has always been considered a poorly immunogenic tumor, TN and HER2+ subtypes show considerable immune infiltration. As a demonstration, tumor-infiltrating lymphocytes (TILs) are frequently present in TN and HER2+ tumor samples and are associated with good prognosis and are predictive of immunotherapy efficacy [7,8,9]. Otherwise high immune infiltration has a completely different effect in the luminal subtypes and lobular BC, suggesting a bad prognosis [5,10,11,12]. Especially for TNBC, the intrinsic molecular characteristics (determined by mRNA profiles, gene expression, and proteomics) can distinguish different intrinsic subtypes of TNBCs defined as basal-like 1 or 2, luminal androgen receptor, and mesenchymal tumors [13]. Each intrinsic subtype is associated with an individual TME, shaped by the molecular features and genomic signatures of cancer cells [14]. The quality of immune cells and distribution in the tumoral tissue is also important in TNBC, distinguishing between “cold tumors” and “inflamed tumors” and inflammation areas in the stromal tissue, margins, or fully inflamed tumoral tissue [15,16]. Generally, immune-rich early TNBCs have less clonal heterogeneity, somatic mutation, and a minor expression of neoantigens but a high expression of TILs, CD8 + T cells, or memory T cells [17]. Conversely, metastatic sites seem more heterogeneous and immunodepleted with fewer TILs, CD8 + T cells, or dendritic cells, a low TMB, and increased clonal diversity [18,19]. In the metastatic environment, there is instead a greater presence of metastasis-associated macrophages (MAMs) with a pro-tumoral phenotype that is able to increase immune escape strategies and cancer diffusion [20]. Tumor-mediated immune suppression is a real issue responsible for acquired resistance to active immunotherapy (ICIs) [21]. Targeting the immune system with a combination of different targets, especially in advanced BC, will become a valuable therapeutic strategy to achieve the best survival results. This strategy aims to convert non-responders to responder patients, maintain an achievable lasting response and overcome acquired immune resistance. Interfering with immune evasion, promoting the antitumor phenotype of immune cells, or enhancing antitumor immunity are the expected goals [22]. Many early-phase clinical trials are ongoing in several solid tumors (including BC patients) with new active compounds targeting macrophages or neutrophils [23]. New emerging treatments in solid tumors are now being used in immunotherapy after the incredible results that have been achieved in the onco-hematology field. Among these is adoptive cell therapy (ACT), that exploits TILs or T cells genetically engineered to express modified T-cell receptors (TCR) or chimeric antigen receptors (CAR). CAR-based therapies with T cells or natural killer (NK) cells are promising as potential practice-changing effectors in BC, especially for tumors with poor targetable antigens (like TNBC), even if there are still significant limitations depending on the resistance of an unfavorable TME and side effects [24,25]. The presence of an extracellular matrix (ECM) in the tumor stroma constitutes a physical barrier to the transfer of CAR-T cells. Macrophages engineered with CAR (CAR-M) may overcome this barrier by producing metalloproteinases and can enhance the antitumor effect thanks to antigen-specific phagocytosis [26,27]. In our review, we give an overview of the potentiality of CAR-based therapies in BC. A CAR is an artificial fusion protein that is composed of an extracellular antigen binding domain which includes an antigen recognition domain, for example, a mAb-derived single chain variable fragment (scFv) that is involved in the binding between the T cell and a tumor-associated antigen (TAA) [28]. A hinge region is linked with the scFv, providing CAR flexibility; its length can be modified to optimize the distance between CAR-T cells and targeted cancer cells and ameliorate the signal transduction process [29]. Moreover, a transmembrane domain is involved in intracellular signal transmission pathways. For this purpose, this region includes both costimulatory and signaling domains (e.g., CD3ζ, also called CD247) responsible for CAR-T cell activation [28]. Ameliorating CAR vectors can improve the safety and efficacy of CAR-T-cell therapy [30]. For this purpose, several CAR generations have been conceived, and currently, the fifth generation is already being tested [31,32]. The principal differences among the CAR generations consist of specific costimulatory molecules. The first generation contains only the CD3ζ signaling end domain, whose linking with the extracellular scFv modifies and activates T cells [33]. However, due to its short survival time and incomplete T-cell activation, it was necessary to conceive a second and third generation of CARs, which include one or two additional costimulatory molecules (respectively), such as CD27, CD28, 41BB, ICOS, and OX-40. These molecules increase the cells’ persistence and cytolytic capacity [34,35,36]. T cells redirected for universal cytokine-mediated killing (TRUCKs) or “armored CAR-Ts” represent the fourth generation of CARs, which includes a nuclear factor of the activated T cells (NFAT) domain [37]. The domain promotes cytokine secretion, mainly interleukin (IL)-12, IL-15, and the granulocyte–macrophage colony-stimulating factor (GM-CSF), which aims at modulating the anti-tumor microenvironment. In fact, armored CAR-Ts carry out a simultaneous antitumor activity directed to both tumor cells expressing and not expressing the CAR-targeting antigen. The other advantage of this strategy is determined by the local release of IL-12, with a lower risk of systemic toxicity related to the cytokine secretion [38]. Such CAR-Ts can be tested for targeting TNBC-related antigens. In recent years, a fifth generation of CARs has been developed. It contains a fragment of IL-2 receptor β (IL-2Rβ), which induces the secretion of Janus kinases (JAKs) and signal transducer and activator of transcription (STAT)-3/5 [31,39]. Such novel CAR generation aims to avoid terminal phenotypic differentiation of effector cells; consequently, fifth generation CAR is able to promote their expansion in vitro, and their persistent cytotoxicity in vivo [40]. The development of CARs has led to searching for new targets for cancer therapy, especially for histologies without ERBB2 and HR expression [24], such as TNBC (Figure 1). All studies testing potential targets for CAR-T cell therapy in BC tumors are shown in Table 1. Abbreviations: CAR: chimeric antigen receptor; CEA: carcinoembryonic antigen; EpCAM: epithelial cell adhesion molecule; FR: folate receptor; HER2: human epidermal growth factor receptor 2; MUC1: Mucin1; PRLR: prolactin receptor; ROR1: receptor tyrosine kinase-like orphan receptor; TEM8: tumor endothelial marker 8; TNBC: triple-negative breast cancer; VEGFR1: vascular endothelial growth factor receptor 1. In our research, we paid attention to the membrane receptors to identify a specific target for CAR molecules. In this context, Integrins represent a potential target for CARs because of their proven involvement in cell proliferation and metastatization and because of their high expression in BC [41]. In particular, CAR molecules targeting αvβ3-integrin were conceived and tested, showing their cytolytic activity against different tumors in vitro, including MDA-MB-231 TNBC cell lines. After testing these molecules in vivo, some complete responses were reported in mice that were affected by metastatic melanoma [43]. Moreover, as reported by some studies, this therapy demonstrated selective cytotoxicity against αvβ3-expressing cell lines without involving normal cells [42,43,44]. Therefore, based on what has been reported, αvβ3CAR-T cell therapy seems promising and deserves further study to verify its efficacy in BCs. Another potential target for CAR molecule development is Mesothelin, a tumor differentiation glycoprotein that is involved in cell adhesion, which is normally expressed on the mesothelial cells but is overexpressed in several solid neoplasms including TNBC [64,65]. Its activity in oncogenesis through different cell signaling pathways such as MAPK, PI3K, and NF-kB has been reported [83]. Since its overexpression has been reported in 67% of TNBCs, with a limited expression in normal breast cells [84], Mesothelin represents an appealing target for CAR molecule development. In this regard, Hu et al., evaluating the Mesothelin expression on three TNBC cell lines, such as MDA-MB-231, BT-549, and Hs578T, reported that only BT-549 cells expressed the molecule. Then, the authors produced second-generation mesothelin-redirected CAR-Ts and tested it in vitro and in vivo. It is noteworthy that the researchers disrupted the gene locus of PD-1 in T cells before CAR transgene insertion. Their CAR-Ts demonstrated an interesting increase in antitumor activity and cytokine secretion against PD-L1-expressing tumor cells in culture [85]. This is probably due to the high expression of PD-L1 in TNBC cells [86], suggesting a potential use of these CAR-Ts in order to overcome the suppressive effects of PD-1/PD-L1 axis in BCs [85]. Thanks to these interesting data, some clinical trials have been developed and are currently underway, with the aim of evaluating the activity of CAR molecules in TNBCs. In particular, a Phase I clinical trial (NCT02792114) is evaluating the safety and tolerability of Mesothelin-redirected CAR-Ts in metastatic/advanced Mesothelin-expressing BCs, including TNBCs. Another Phase I/II clinical trial (NCT02414269) is testing second generation Mesothelin-redirected CAR-Ts in different tumors, including BC. Moreover, two other clinical studies (NCT02580747 and NCT01355965, the latter only in Mesothelioma) have been completed but no official data have been published yet. Some studies have demonstrated that the endothelium of several neoplasms often overexpresses an integrin-like protein called tumor endothelial marker 8 (TEM8), also known as ANTXR1, and is usually expressed during endothelial cell development but rarely in adults [47,48]. As evidence of this, an elevated expression of TEM8 was found both in invasive/relapsed BC [49,50] and in several BC cell lines [51], so the upregulation of this molecule could represent a potential target for CAR-T cell development [87,88]. In this regard, a single dose of a specific L2CAR-T cell therapy, derived from the L2 Monoclonal Antibody (Mab) against TEM8, showed a complete response against TNBC in vitro and significant cancer reduction in vivo and TNBC xenografts [51]. For these reasons, TEM8 represents a promising target for CAR-T cell therapy against TNBC. In TNBC, MUC1 represents a highly selective overexpressed target [66]. It is a glycosylated transmembrane molecule with altered epithelia [66]. It produces mucin, which protects cells against pathogens [67,68,69]. Tumor cells overexpress MUC1, activating the intracellular pathways involved in cancer proliferation [66,69]. Jiang et al.’s recent cohort study, involving more than 5800 BC patients, demonstrated the predictive role of MUC1 and its correlation with a poor prognosis [89]. In particular, neoplasms overexpress a hypo-glycosylated variant of MUC1, also known as tumor-specific MUC1 (tMUC1) which exposes new epitopes for the immune system [90]. For this purpose, antibodies specifically binding to tMUC1 have been developed and tested [91]. One of these molecules, the so-called TAB004, served as a reference point for creating a CAR molecule containing its extracellular scFv. The derived CAR-T cells, known as MUC28ζ CAR-T cells demonstrated their efficacy in heightening the expression of both leukocyte activation markers and cytokines in vitro. These effects led to significant cell lysis in vitro and a reduction in cancer cell growth in vivo [66]. Recently, a new CAR molecule targeting tMUC1, known as huMNC2-CAR44, has been activated in a clinical trial recruiting 69 (HER2-positive, HER2-negative, triple-negative) BC patients; the estimated study completion date is 15 January 2035 (NCT04020575). Another Phase I study is currently testing the safety, tolerability, feasibility, and preliminary efficacy of the administration of CAR-T cells targeting tMUC1 in 112 patients with advanced tMUC1-positive solid tumors (including BC) and multiple myeloma. The estimated study completion date is 31 October 2036 (NCT04025216). Receptor tyrosine kinase-like orphan receptor (ROR)1 is a highly expressed molecule during embryogenesis but not in adults. BC cells highly express ROR1, especially in cases with a poor prognosis; ROR1 overexpression was found in some TNBC cell lines (e.g., in MDA-MB-231) but not others [70]. ROR1-based CAR-T cell therapy was also shown to induce MDA-MB-231 apoptosis in tumor models through significant IL-2 and IFNγ production [92]. A Phase I trial is testing the ROR1-specific CAR-T cells’ efficacy in 60 subjects with hematological and solid tumors, including triple negative BC. Patients will be followed up for approximately 15 years after study completion. The estimated study completion date is 1 December 2023 (NCT02706392). The first study results were recently published, suggesting a better CAR-T cell therapy efficacy from adding Oxaliplatin to the lymphodepletion regimen [93]. Another Chinese Phase I study is currently recruiting 40 patients with advanced solid tumors (including BC) to investigate the efficacy of TILs and CAR-TILs against several molecular targets, including ROR1, MUC1, HER-2, Mesothelin, PSCA, EGFR, GD1, GPC3, Lewis-Y, AXL, Claudin18.2/6, and B7-H3. The estimated study completion date is 1 January 2035 (NCT04842812). Under certain pathological conditions, both innate and adaptive immune cells (including CD8+ and some CD4+ T cells, NK cells, γδ T cells) express a type II transmembrane protein, called natural killer group 2, member D (NKG2D) [74], which in turn contribute to enhancing cytotoxicity and production of cytokines by effector cells and promoting their proliferation and survival. Moreover, NKG2D can cooperate with other receptors (including TCR in T cells or NKp46 in NK cells) by acting as a costimulator for their responses [94]. In tumor cells, including TNBC cells, an upregulation of stress-induced ligands has frequently been reported; NKG2D can naturally recognize these ligands [95] so it has been considered as a potential target for immunotherapy in several studies. CAR molecules that were obtained by fusion of the full-length NKG2D with the CD3z cytoplasmic region together with endogenous DAP10 costimulation, were demonstrated to react with NKG2DL-expressing tumor cells through cytokine and chemokine production, thus enhancing cytotoxicity [96]. These results were also confirmed by in vivo studies [97,98]. More recently, NKG2DL-redirected CAR-Ts were tested by Han et al. in TNBC cell lines and TNBC mouse models [99]. In this case, NKG2DL-redirected CAR-Ts were obtained by the fusion between the extracellular domain of human NKG2D and the TCR CD3z alone or co-stimulatory domains, such as 4-1BB or CD27. The authors demonstrated that the elevated expression of CD25 and the presence of IL-2 were required to promote CAR-T expansion in vitro in the absence of any costimulatory domains. Moreover, NKG2DL-redirected CAR-Ts were able to recognize and kill TNBC NKG2DL-expressing MDA-MB-231 and MDA-MB-468 cell lines [99]. Based on these results, a Phase I clinical trial (NCT04107142) evaluated the safety and tolerability of NKG2DL-redirected CAR-T cells in patients with various solid neoplasms including TNBC, but, to date, no results have been reported yet. CSPG4 is a hyperglycosylated transmembrane protein with a low expression in normal tissues and hyperexpressed in several tumor types, including TNBC. It has been suggested that CSPG4 is involved in the neuronal network regulation and epidermal stem cells homeostasis [55]. Second-generation CSPG4-redirected CAR-Ts were tested in various CSPG4-expressing cell lines (including SENMA, UACC-812, CLB, MDA-MB-231, MILL, PHI, and PCI-30), and demonstrated a significant capacity in cell growth suppression [56]. The same results were obtained in preclinical mouse models of several human tumors (including BCs). In another study, second-generation CSPG4-redirected CAR-Ts using murine-based scFvs reported target antigen-dependent cytotoxicity and cytokine secretion against several tumor (including BC) cell lines [100]. Epithelial cell adhesion molecule (EpCAM) represents a well-known molecule whose expression has been related to poor prognosis and tumor metastatization [71]. Several treatment strategies targeting EpCAM have shown benefits for different tumor types. Currently, a Chinese clinical trial is recruiting patients with nasopharynx neoplasms and BC, which aims to evaluate the safety of the engineered CAR-T cells recognizing EpCAM. These molecules were developed through lentiviral transduction of the third generation of CAR genes. Different patient cohorts will receive the experimental treatment in a dose-escalating manner; the estimated study completion date was set for July 2022 (NCT02915445). ICAM-1 is a transmembrane protein that is involved in white blood cell diapedesis. It is overexpressed on the surface of many cancer cells, including TNBC cells [60]. ICAM-1 plays a role in tumor growth, invasion, and metastasis [61]. In order to avoid CAR-T-related cytotoxicity in normal cells, Park et al. generated CAR-Ts with micromolar (instead of nanomolar) affinity, and demonstrated that these ICAM-1-redirected CAR-Ts were more efficacious and safe than their higher affinity homologs [62]. More recently, the same results were confirmed in preclinical models [63]. BC with HER2 overexpression represents the tumor subgroup for which CAR-T cells have been designed [101]. Several clinical trials are currently ongoing to test CAR molecules targeting HER-2. One of them is a Phase I trial, testing the safety and preliminary therapeutic efficacy of CCT303-406 cells in 15 patients with HER-2-positive stage IV solid tumors (that have failed standard treatment of relapsed or difficult-to-treat), including BC. The estimated study completion date is April 1, 2023 (NCT04511871). An American, multicenter, Phase I/II trial is currently ongoing, recruiting 220 patients with HER-2-positive tumors, including BC, to evaluate the safety, tolerability, and clinical activity of HER2-specific dual-switch CAR-T cells, BPX-603, administered with rimiducid. The estimated study completion date is January 2, 2025 (NCT04650451). Another American dose-escalation study is being conducted at the City of Hope Medical Center (Duarte, CA) to investigate the side effects and the best dose of HER2-CAR-T cells in treating patients with BC metastasized to the brain or leptomeninges. For this purpose, 39 patients will receive HER2-CAR-T cells via intraventricular administration over five minutes once weekly for three doses, which could be implemented at the principal investigator’s discretion. Patients will be followed up at the end of treatment at 4 weeks, 3, 6, 8, 10, and 12 months, and then for up to 15 years. The estimated study completion date is 31 August 2023 (NCT03696030). A third American Phase I trial is currently studying the safety and efficacy of combining HER2-specific CAR-T cells with an intra-tumor injection of CAdVEC, an oncolytic adenovirus designed to help the immune system activation against cancer. Our trial is recruiting 45 patients with HER-2 positive tumors. The estimated study completion date is 30 December 2038 (NCT03740256). However, although most patients did not have significant complications. In some cases, the non-specificity of HER-2 expression between tumor cells and healthy cells can lead to serious side effects; a case of cardiopulmonary failure from excessive T-cell activation has been reported [102]. Therefore, to avoid drawbacks related to the non-tumor-specificity of the marker, the research sought to deepen its understanding of the receptor to evaluate whether it was possible to find a more specific variant in BC. In this regard, a potential antigen is p95HER2, a truncated version of HER2 was found in 40% of HER2-positive BCs. This variant is more tumor-specific than the constitutive form since it was not found in normal breast cells. p95HER2 has already been evaluated as a target for a bispecific antibody against cancer cells in vitro and in vivo without significant side effects. Due to the encouraging results reported, this variant could, therefore, be a future target for developing CAR-based therapies [103,104]. Vascular endothelial growth factor receptor (VEGFR)1 is a Tyrosin-kinase receptor that is involved in the migration and survival of hematopoietic stem cells, and its overexpression is related to the process of BC metastatization [105,106]. Therefore, VEGFR1 represents a potential candidate for immunotherapy. To date, VEGFR1 was tested as a part of a VEGFR1-CD3 bispecific antibody and demonstrated promising results against MDA-MB-231 and MDA-MB-435 TNBC cell lines. These results warrant further studies on VEGFR1 activity, for example as a target for CAR-based therapies [107]. Moreover, because the main functions of the normal endothelial cells are VEGFR2-dependent [105], VEGFR1 inhibition could prevent endothelial toxicity. At present, this is only a hypothesis, so further investigation is needed. Hepatocyte growth factor receptor, also called c-Met, is a cell-membrane protein tyrosine kinase that is expressed in several types of solid neoplasms, including BC [108]. Onartuzumab, an anti-c-Met monoclonal antibody, has been administered in patients with metastatic, solid tumors [109,110,111,112]. Tchou et al. tested c-Met as a potential target for CAR-T cell therapy; for this purpose, the scFv of the CD19 binding domain of a CD19-CAR molecule was substituted for that of onartuzumab, and then its effectiveness against BC cells was confirmed in vitro and in vivo [113]. Subsequently, the new c-Met CAR-T cells were administered through a single intratumoral mRNA injection in a BC patient cohort (NCT01837602). The injections were well tolerated, and no significant drug–related adverse events were reported. Moreover, analyzing tumor specimens (four TNBC and two ER+ HER2-negative BCs) in which the CAR-T cells were injected, there was wide tumor necrosis at the injection site and macrophage infiltrates within the necrotic areas [113]. AXL, a receptor of the TAM tyrosine kinase receptor family, and its high-affinity ligand, called growth arrest-specific protein 6 (GAS6), are involved in cancer cell expansion, metastasization, and survival; moreover, AXL low expression in adult normal cells and its overexpression in several tumor types (including BC) and some cell lines (including MDA-MB-231 [114]) make AXL a potential target for CAR molecule development [52,115,116]. Wei et al. developed AXL-redirected CAR-Ts using an AXL-specific scFv; these CAR-Ts were tested in AXL-expressing TNBC cell line MDA-MB-231, and demonstrated an antigen-dependent cytotoxicity and cytokine production; these results were confirmed in an in vivo evaluation in MDA-MB-231-established xenograft models [53]. Other researchers generated AXL-redirected CAR-Ts with a constitutively activated IL-7 receptor (C7R); they demonstrated a significant tumor cell killing capacity, which was more efficacious than by using conventional AXL-redirected CAR-Ts, in TNBC MDA-MB-231 and MDA-MB-468 cell lines. This improvement was probably due to the co-expression of C7R, which helped to prolong survival and reduce rates of tumor relapse [54]. However, further investigations are required to confirm these results. GD2 is a surface protein that is normally expressed only in peripheral nociceptors, neurons, and melanocytes; consequently, GD2 expression has been demonstrated in neuroectoderm-derived tumors, such as melanomas and neuroblasomas [117]. Its cell-type restriction render GD2 a potential target for CAR molecule development. In fact, GD2 has been studied mainly as a target for treatments against neuroblastoma [118]. However, more recently, Seitz et al. used the scFv derived from anti-GD2 mAb dinutuximab to produce GD2-redirected CAR-Ts. The researchers evaluated the GD2 expression in several TNBC cell lines, demonstrating a very low expression in MDA-MB-231, whereas Hs578T and BT-549 uniformly expressed GD2. However, in an in vitro assay, these CAR-Ts did not demonstrate any specific tumor cell killing activity towards MDA-MB-231, whereas they induced specific cytotoxicity and cytokine production upon co-cultivation with the Hs578T and BT-549 cell lines [59]. To date, one study is currently testing the feasibility, safety, and efficacy of multiple fourth generation CAR-T cells targeting Her2, GD2, and CD44v6 surface antigen in BC (NCT04430595), but no results have been published yet. In mammals, prolactin is an important hormone for milk secretion and mammary tissue growth by binding with the prolactin receptor (PRLR) in the breast glands [119]. PRLR is overexpressed in some BC histotypes, especially in the MDA-MB-231 TNBC cell line and even more in T47DHER2+ and SKBR-3 cell lines [120]. This correlation between PRLR and HER2 expression could led to the development of a CAR-based therapy targeting PRLR against BC. However, in a combination of these two targets, a bispecific antibody cytotoxicity was reported, mainly caused by the combinatory inhibition of the two rather than the effect of T-mediated cytotoxicity [121]. Although the results seem promising, future studies on CAR-based therapies targeting PRLR must avoid causing cross-toxicity of other organs expressing the same receptor, such as the prostate, liver, etc. [122]. CEA is a well-known tumor marker that is expressed in several solid neoplasms [75]. In normal cells, only a small amount of CEA is expressed in the cell membrane, especially toward the cell cavity, under physiological conditions to avoid recognition by CAR-T cells targeting CEA. To date, a Phase I-II study is recruiting 40 patients with different solid tumors to test the efficacy and safety, recommended dose, and infusion plan of CEA-targeted CAR-T cells therapy. The estimated study completion date is 30 April 2023 (NCT04348643). CD44 variant domain 6 (CD44v6), a CD44 family member, has demonstrated its role in tumorigenesis, tumor cell invasion, and metastasis. In normal tissue, its presence is reported only on epithelial and hematopoietic cell subgroups, especially during embryogenesis and hematopoiesis [76]. In contrast, it is expressed in multiple squamous cell carcinomas, in a proportion of adenocarcinomas of differing origin, a proportion of lymphoma and melanoma, so it represents an attractive target for cancer therapy. A Phase I-II clinical trial is currently investigating the feasibility, safety, and efficacy of CD44v6 CAR-T cell therapy in 100 patients with several tumors, including BC. Understanding more about 4SCAR-CD44v6 T cell functions is another objective of this trial. The estimated study completion date is 31 December 2023 (NCT04427449). TROP2 is a transmembrane protein that is expressed on human trophoblast cell surface and is often present in several epithelial tumor types (including TNBC), in which it is associated with poor prognosis [57]. Zhao et al. developed and tested in vitro (against gastric cancer cell lines) and in vivo bispecific TROP2- and PD-L1-redirected CAR-Ts [58]. The authors reported a higher antitumor activity with bispecific CAR-Ts compared with monospecific CAR-Ts. In spite of CAR-T-mediated TROP2 targeting in TNBC not being comprehensively investigated, the results obtained in gastric cancer cell lines warrant further investigation in other tumor types, including TNBC. EGFR is a transmembrane glycoprotein belonging to the ERBB receptor tyrosine kinase family, that is involved in tumor growth and metastatization [72]. Its overexpression has been reported in several tumor types, including TNBC. Li et al. produced EGFR-redirected CAR-Ts using the non-viral piggy Bac transposon system, and demonstrated their antitumor activity firstly in human lung tumor xenografts and then in a phase I clinical trial (NCT03182816) against non-small cell lung tumors [123,124]. With regards to TNBCs, Liu et al. tested EGFR-redirected CAR-T antitumor activity in vitro and in vivo, reporting EGFR overexpression in several tumor cell lines, such as Hs578T, MDA-MB-468, and MDA-MB-231, respectively. These CAR-Ts demonstrated antitumor activity and cytokine secretion in these cell lines [125] Recently, Xia et al. generated third-generation EGFR-redirected CAR-Ts, which demonstrated antitumor activity, specific cytokine production. Moreover, the authors found that T cell activation markers (such as CD25 and CD69) were upregulated in case of co-cultivation with EGFR-positive TNBC cell lines [126]. However, more preclinical and clinical studies are needed to confirm these findings. Several clinical trials (NCT03182816, NCT02873390, NCT02862028, and NCT03170141), are testing the effects of EGFR-redirected CAR-Ts in regards to the production of anti-CTLA-4, anti-PD-1, or anti-PD-L1 antibodies in EGFR-positive solid tumors [127]. With regards to TNBC, two studies are ongoing. The first one (NCT05341492) is evaluating the safety and efficacy of EGFR/B7H3 CAR-T cell therapy in EGFR/B7H3-positive advanced solid cancers (including TNBC), and the second one is testing the anti-tumor activities and safety profiles of CAR-EGFR-TGFβR-KO T cell therapy in previously treated advanced EGFR positive solid tumors (including TNBC) However, no results regarding TNBC patients have yet been officially reported Although at first glance it does not appear to be related to BC, it has recently been demonstrated that this molecule is present in circulating breast cancer cells, and is related to a worse prognosis [77]. Although PSMA is expressed in normal prostate and is upregulated in prostate tumors, it is not prostate cancer restricted. In TNBC, PSMA is currently under evaluation as a target for CAR molecule development. In an ongoing, open label, Phase I trial, an anti-PSMA/CD70 bi-specific CAR-T cell therapy has been tested in several cancer types (including TNBC) expressing PSMA or CD70, another potential tumor target, that is overexpressed in many cancer types and scarcely expressed in normal tissue (NCT05437341). A second Phase I trial testing the feasibility, safety, and efficacy of anti-GD2/PSMA bi-specific CAR-T cell therapy in patients with GD2 and PSMA-positive tumors (including TNBC), is currently ongoing (NCT05437315). Moreover, in China, a third Phase I trial is currently verifying the feasibility, safety, and efficacy of PSMA-specific CAR-T cell therapy in patients with PSMA positive neoplasms, including TNBC (NCT04429451). To date, no results have been published yet. In normal cells, the DNA synthesis pathway is efficient in the presence of folate, which is conducted inside the cell through a suitable receptor called the folate receptor (FRα). FRα is often overexpressed in BC, especially in TNBC and that correlates with poor clinical outcomes [95,109]. FRα-CAR-T cells were demonstrated to target FRα + TNBCs and to reduce tumor growth in MDA-MB-231 tumor xenograft [110]. To limit toxicity, Lanitis et al. designed a trans-signaling CAR with two different signaling domains (CD3ζ and CD28) located in two different CARs and one T cell to link with mesothelin and FRα in tumor cells. In these conditions, the activation occurs only in the case of simultaneous antigen linkage, and that in turn may activate T cell activity [111]. Therefore, FRα could become a potential target for immunotherapy in BC. To date, CAR-T cell therapy has shown efficacy against hematological neoplasms, but conversely, its results against solid tumors were disappointing. The reason for this difference probably lies in the presence of a TME around solid tumors, which modulates the immune response against malignant cells, preventing the penetration of CAR-T cells [128,129,130]. To overcome the obstacle constituted by the TME, CAR molecules were conceived, taking inspiration from gamma-delta (γδ) T and natural killer (NK) cells for their specific biological features. Indeed, these cells can identify a wide range of tumor-associated antigens (TAAs) independently of the major histocompatibility complex (MHC) [131,132], with a consequently lower impact in terms of immune-mediated toxicity [133]. However, problems in the expansion process of these immune cells limit their application in clinical practice [134]. On the other hand, the phenomenon of “T cell exhaustion” represents a widely known and not a fully resolved topic [135,136,137]. Therefore, it was necessary to look for other strategies, for example, by exploring other immune cells [138]. In this context, macrophages seem to be an interesting option for immunotherapy development. Indeed, they present a wide range of immune activities, including their role as antigen-presenting cells which may modulate adaptive immune responses, phagocytosis, and pro-inflammatory cytokine secretion [139]. Moreover, they constitute a considerable percentage of immune cells within the TME of solid tumors, which recruit peripheral blood monocytes and then promote their differentiation into tumor-associated macrophages (TAMs) [140,141]. TAMs are often reported as both M1 (pro-inflammatory) and M2 (anti-inflammatory) phenotypes, but a higher M2 concentration is more frequently associated with a poor prognosis [142]. Their presence is considered crucial for TME regulation, especially regarding stimulation of tumor growth, angiogenesis, and metastatization [143,144]. Moreover, it was reported that TAMs have a role in cytotoxic lymphocyte recruitment in the TME [145]. In light of all of this, targeting TAMs has become the goal of numerous immunological approaches, such as TAM depletion, repolarization, or inhibition of TAM-secreted suppressive molecules [146]. Moreover, instead of directly targeting TAMs, different studies have evaluated the role of macrophages in cancer therapy. For example, antibody-dependent cellular phagocytosis is an interesting strategy (ADCP) [146]. It uses antibodies against specific tumor-associated antigens via the Fab region, which are internalized through the binding of Fc receptors (such as CD16a or CD32a) on macrophages. Moreover, macrophages may stimulate phagocytosis through these receptors and other surface molecules, including Mac1 or LRP1, whose intracellular mechanism of action is similar to that played by CD16a and CD32a. Indeed, their cytoplasmic region is rich in tyrosine-based activation motifs, which can activate MAPK and PI3K/AKT signaling pathways, with a consequential phagocytosis process against cancer cells [147]. Bispecific antibodies targeting different TAAs or macrophage receptors is another option [148]. Engaging phagocytosis checkpoint inhibitors, such as CD47, could enhance phagocytosis mechanisms by blocking the macrophage-inactivating signals [138]. However, using antibodies is not so simple; to date, some challenges must be overcome before they are clinically developed. Firstly, macrophages expose, on their extracellular membrane, the inhibitory FC receptor (FcγRIIb), which counteracts cell activation. Secondly, Mab-therapy cannot discriminate between antitumoral and/or protumoral TAMs [149]. For these reasons, another option is adoptive cell therapy based on ex vivo genetically engineered CAR macrophages (CAR-Ms). Among the different types of CAR molecules, second and third-generation CARs are preferred because of their capacity to potentiate phagocytic activation signals [150,151]. CAR-Ms provide some advantages with regards to T cells: (1) a lower risk of GVHD, which allows CAR production in advance for “on-demand” use; and (2) a significant production of MMPs, which allows macrophages to degrade ECMs and, consequently, get close to the tumor cells [152]. However, some problems are still unresolved: (1) Although their efficacy and safety profile have been reported in animal studies, in humans, it is still unclear; and (2) the use of viral transfection in CAR gene transfer could promote insertions with an unforeseeable impact on treatment. In this context, the CRISPR/Cas9 genome targeting system could represent a valuable option to overcome this problem [153]. Moreover, regenerative medicine could represent a potential strategy for limiting the high cost of CAR therapy by providing a sustainable source of CAR-Ms. Delivering CARs to induced pluripotent stem cell (iPSC)-derived macrophages may extend CAR-M cell therapy to a larger-patient population. In a recent study, iPSC-derived CAR-Ms reduced tumor growth by activating phagocytosis in leukemia, ovarian, and pancreatic cancer cell lines. Moreover, the same results were reported in vivo in an ovarian cancer mouse model [154]. To date, several researchers have attempted to employ CAR-M against solid tumors and BC. Different CAR-phagocytes (CAR-P) have been designed to guide macrophages against specific targets. In particular, CAR-P expressing the FcRv or Megf10 intracellular region was shown to stimulate phagocytosis of TAA by the TCR-CD3ζ-mediated recruitment of SYK kinase. Usually, complete phagocytosis is uncommon, suggesting that the CAR-P macrophage link with target cells is insufficient to obtain that. In this context, it is worth remembering that the PI3K signal pathway demonstrated its involvement in target internalization and phagocytosis enhancement in macrophages [152]. For this reason, a “tandem” CAR (CAR-P tandem) has been conceived by connecting the PI3K p85 subunit with CAR-P-FcRv. This molecule demonstrated an increase in the phagocytic activity of CAR-P, especially in terms of whole-cell phagocytosis [150]. CAR-147 is a CAR molecule that is composed of a single-strand antibody fragment targeting HER2, a murine hinge region of IghG1, and a trans-membrane and intracellular domain of mice-derived CD147. Co-culturing CD147 with HER2 + human BC cells led to an intense MMP expression, demonstrating the capacity of CAR-147 to target HER2 and effectively promote MMP production in macrophages. Indeed, CAR-147 macrophages were shown to increase the amount of T cells close to tumor cells compared with those in tumors that were treated with controlled macrophages, demonstrating their potential to destroy the extracellular matrix into tumors. Moreover, CAR-147 macrophages have shown an antitumor effect by increasing IL-12 and IFNγ levels in tumor tissue [155]. An intravenous CAR-147 injection significantly inhibited cancer growth in 4T1 BC mouse models, but the same was not shown in vitro. Recently, at the University of Pennsylvania, an adenovirus-induced CAR-M composed of an anti-HER2 CAR and the CD3ζ intracellular domain was designed, demonstrating in vitro its specificity in terms of antigen-specific phagocytosis against HER2-positive tumor cells. A single injection of anti-HER2 CAR-M was shown to reduce tumor load and prolong survival in mice. It was also able to transform M2 macrophages into M1 macrophages, stimulate an inflammatory TME and promote anti-tumor cytotoxicity. In addition, HER2 CAR-M may produce epitope diffusion, which could become another solution for avoiding tumor immune escape [151,156]. Another study combined an anti-HER2 CAR with transduced primary human CD14+ peripheral blood monocyte-derived macrophages. These CAR-Ms promoted phagocytosis of the HER2+ ovarian cancer cell line SKOV3 in a dose-dependent manner. The authors further demonstrated that macrophage transduction is unaffected by the anticancer effect since their transduction with a control CAR lacked antitumor activity [151]. Moreover, in vivo, the SKOV3 tumor burden in NOD-SCID mice was considerably lower in the cases that were treated with primary human anti-HER2 CAR-Ms. The authors also demonstrated that CAR-Ms survived and resisted the immunosuppressive cytokines that were secreted by the TME. On the contrary, CAR-Ms secreted pro-inflammatory cytokines, determining a macrophage conversion from an M2 to an M1 phenotype, and consequently transforming TME into a proinflammatory environment. Furthermore, a combination of donor-derived T cells with CAR-Ms increased the antitumor response in vivo [151]. Pierini et al. demonstrated that the infusion of murine-derived anti-HER2 CAR-Ms determined an inhibition of tumor growth, a prolongation of overall survival, and an increase of CD4+ and CD8+ T cells, NK cells, and dendritic cells in the TME. The authors also reported that CAR-Ms have a critical role in regulating the TME through the upregulation of MHC I/II expression on the cancer cells [157]. Until December 2022, three clinical trials have evaluated a CAR-M-based strategy in solid tumors, two of which achieved FDA approval [152] (Table 2). The first one (a Phase I clinical trial) tested CT-0508 (CARISMA Therapeutics Inc., Philadelphia, PA, USA), a therapy consisting of anti-HER2 CAR macrophages infused in 18 patients with relapsed/refractory HER2 over-expression tumors. The study evaluated the effects of adenovirus transduction CAR-M. The estimated study completion date is February 2023 (NCT04660929). The second trial tested MCY-M11 (MaxCyte Inc., Gaithersburg, MD, USA), consisting of mRNA-targeted PBMCs (not only CAR-M) which express mesothelin-CAR, in patients with relapsed/refractory ovarian cancer and peritoneal mesothelioma (NCT03608618). A third trial (CARMA-2101), not yet recruiting, will be conducted at the Centre Oscar Lambret (Lille, France). This observational study aims to determine the antitumor activity of new CAR-Ms in 100 BC patients’ derived organoids. In particular, researchers will test the CAR-M activity against organoids that are derived from HER2-negative, HER2 low, and HER2-positive BC, and then they will compare the activity of CAR-Ms and non-modified macrophages. The estimated study completion date is 1 September 2023 (NCT05007379). Recently, the remarkable advances reported in the field of immunotherapy have profoundly changed our approach toward many types of cancer. By gaining a better understanding of the role immune cells play in tumor progression mechanisms, it was possible to develop both drugs directed against specific immunological targets and forms of immune cell-based therapy, such as CAR technologies that led to the creation of CAR-T cells that are also effective in the clinical setting (especially in the field of haematological malignancies). To date, several problems significantly limit the application of CAR-T cells especially toward solid tumors. Due to this, further studies of CAR-M in tumor therapy are interesting because of the known adaptability of these immune cells to solid tumors. Indeed, the first results have shown that CAR-M is very promising in the fight against cancer; preclinical data have confirmed their efficacy (in terms of tumor phagocytosis and growth inhibition both in vitro and in vivo) and also in several solid tumors, including TNBC. The latter represents a heterogeneous BC subtype that is usually resistant to standard therapies. However, its immunogenic nature led to favorable clinical benefits from the new immune checkpoint inhibitors, such as atezolizumab, recently approved by the FDA in combination with nab-paclitaxel against metastatic TNBC [158]. With regards to CAR-based therapy in TNBC, this is an emerging field, whose improvement depends on discovering the suitable and targetable TAAs, mostly in preclinical and early clinical stages. In our article, we discussed different novel CAR-based target antigens evaluated against BC. A lot of them have only been tested in “in vitro” and “in vivo” studies, and a small part of them were also evaluated in humans, as summarized in Table 1 and Table 2. Furthermore, scientific research is discovering other potential targets, such as specific embryonic antigen-4, which was evaluated in “in vitro” and “in vivo” studies in BC patients, but with less data to date in comparison with the aforementioned targets [159]. Therefore, we still have little clinical data to judge the effective validity of these new therapeutic approaches against BC (and not only), which need further studies. Due to this shortage of relevant data on small series, there are still many unanswered questions. For example, is CAR-based (M or T cell) therapy more effective than standard treatments against TNBC? Is it enough to consider a mono-immunotherapy, or should it be combined with other strategies, perhaps even with other types of immunotherapy (e.g., with immune checkpoint inhibitors)? Regarding the latter question, it appears that combination regimens may lead to better efficacy, especially in terms of overcoming the TME. Indeed, in a recent study that was conducted in immunocompetent mouse models of HER2+ solid tumors (including BC), a combination of anti-PD-1 with HER2 CAR-M cell therapy demonstrated better OS and tumor control than monotherapy strategies [78]. In this regard, other strategies were evaluated. For example, ECM- or cancer-associated fibroblasts (CAF)-targeting and macrophage- or monocyte-eliminating agents were tested with the aim to enhance CAR-T antitumor effects in TNBC also [160]. It seems clear that the success of CAR-based therapy in BC will depend on the ability to select the best antigens to be used as a basis for the development of more effective, and at the same time more manageable and less toxic CAR molecules. In the near future, it is hoped that some more data can be obtained from ongoing studies.

PMC10000854

Yi Xu,Somaira Nowsheen,Min Deng

DNA Repair Deficiency Regulates Immunity Response in Cancers: Molecular Mechanism and Approaches for Combining Immunotherapy

06-03-2023

DNA damage response,cancer therapy,immunotherapy,cell death,biomarker,tumor microenvironment,DNA repair,cell death

Simple Summary DNA repair pathways play a crucial role in maintaining the stability of a cell’s genetic material. When these pathways are defective, it can lead to genomic instability in cancer cells, which can increase their ability to stimulate an immune response. Inhibiting DNA damage response, the process that helps repair DNA damage, has been shown to increase the effectiveness of anticancer immunotherapies. In this review, we will explore how deficits in the DNA repair pathway can affect the immune system’s ability to fight cancer. We will also examine clinical trials that have combined inhibition of DNA damage response with immune-oncology treatments. A better understanding of these pathways could help improve the effectiveness of cancer immunotherapies and other treatments for various types of cancer. Abstract Defects in DNA repair pathways can lead to genomic instability in multiple tumor types, which contributes to tumor immunogenicity. Inhibition of DNA damage response (DDR) has been reported to increase tumor susceptibility to anticancer immunotherapy. However, the interplay between DDR and the immune signaling pathways remains unclear. In this review, we will discuss how a deficiency in DDR affects anti-tumor immunity, highlighting the cGAS-STING axis as an important link. We will also review the clinical trials that combine DDR inhibition and immune-oncology treatments. A better understanding of these pathways will help exploit cancer immunotherapy and DDR pathways to improve treatment outcomes for various cancers.

DNA Repair Deficiency Regulates Immunity Response in Cancers: Molecular Mechanism and Approaches for Combining Immunotherapy DNA repair pathways play a crucial role in maintaining the stability of a cell’s genetic material. When these pathways are defective, it can lead to genomic instability in cancer cells, which can increase their ability to stimulate an immune response. Inhibiting DNA damage response, the process that helps repair DNA damage, has been shown to increase the effectiveness of anticancer immunotherapies. In this review, we will explore how deficits in the DNA repair pathway can affect the immune system’s ability to fight cancer. We will also examine clinical trials that have combined inhibition of DNA damage response with immune-oncology treatments. A better understanding of these pathways could help improve the effectiveness of cancer immunotherapies and other treatments for various types of cancer. Defects in DNA repair pathways can lead to genomic instability in multiple tumor types, which contributes to tumor immunogenicity. Inhibition of DNA damage response (DDR) has been reported to increase tumor susceptibility to anticancer immunotherapy. However, the interplay between DDR and the immune signaling pathways remains unclear. In this review, we will discuss how a deficiency in DDR affects anti-tumor immunity, highlighting the cGAS-STING axis as an important link. We will also review the clinical trials that combine DDR inhibition and immune-oncology treatments. A better understanding of these pathways will help exploit cancer immunotherapy and DDR pathways to improve treatment outcomes for various cancers. Cancer has become a leading cause of death in many countries and is still a major public health problem worldwide [1]. The classical and primary therapies are surgery, radiotherapy, and chemotherapy. Along with a better understanding of the molecular biology of the tumor cells, molecularly targeted therapies are designed to inhibit a target that is abnormal in malignant tissues when compared with normal tissues [2,3]. In comparison, most target drugs have shown limited efficacy against solid tumors, largely due to the fact that tumors frequently develop resistance to these therapies [4]. In recent years, immunotherapy has had remarkable clinical success, including immune checkpoint blockade (ICB) and adoptive cell therapy. The antibodies targeting programmed cell death 1 (PD1), PD1 ligand 1 (PDL1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) as ICBs have been approved for broad application to treat solid tumors [5]. Anti-PD therapy dominates ICB therapies and has been shown to be superior to anti-CTLA4 therapy in a wide variety of tumors [6,7]. However, the response rate of anti-PD therapy alone is usually only 20% in advanced-stage cancers, and adaptive immune resistance mechanisms also help cancer cells to escape attacks by the immune system. Thus, combining immunotherapy with other approaches to improve the anti-tumor effect is reasonable. Researchers have proposed the promising approach of utilizing DNA repair deficiency to enhance anti-tumor immunity [8]. The DNA damage response (DDR) is essential for maintaining genomic stability by repairing different types of DNA damage [9]. Cancer cells with high underlying levels of DNA damage are more dependent on DDR for survival when compared to normal cells [10]. Deficiencies in DDR result in the accumulation of DNA damage and enhance immunogenicity in tumors. Numerous studies have identified that DNA damage agents modify systemic immune functions [11,12,13]. In addition, clinical data show that a loss of mismatch repair could be a predictive biomarker for ICB response [14]. Thus, combining DDR network inhibitors with immunotherapy attracts more attention to clinical testing. Here, we review the mechanism of DDR and discuss its interactions with anti-tumor immunotherapy. We also present the clinical implications of DDR, including combination with immune-oncology treatment in clinical trials and immune response prediction as a biomarker. Finally, we evaluate the opportunities and development of DDR-immunotherapy combinations in anticancer therapies. DNA injuries occur as a result of intrinsic or extrinsic agents and can include modifications to bases and sugars, single- and double-strand breaks (SSBs, DSBs), DNA-protein crosslinks, and base-free sites [15]. While some specific DNA lesions can lead to mutations that cause cancer, the main consequence of DNA injuries is the threat they pose to DNA integrity and stability [16]. To prevent accumulated DNA lesions from causing irreversible harm, cells initiate DDR, which senses the DNA damage, signals its presence, and mediates its repair. DDR kinases, including DNA-dependent protein kinase (DNA-PK), ataxia telangiectasia mutated (ATM), and ataxia telangiectasia and Rad3-related (ATR), are activated at DNA lesions, which then mediate cell cycle arrest and DNA repair [17]. In the cell cycle arrest pathway, ATM and DNA-PK are mainly activated by DSBs, while ATR is activated by SSBs. These kinases phosphorylate downstream cell cycle checkpoint kinases. The active CHK1 and CHK2 then phosphorylate p53, CDC25, and WEE1, which increases the expression of p21 (p53), inhibits CDK activity and leads to cell cycle arrest at G1/S and G2/M transition (CDC25 and WEE1) [9,18]. In addition, the molecular pathways of primary DNA repair mechanisms that function in common types of DNA damage are introduced below (Figure 1) [19,20,21]. Base excision repair (BER): Base damage occurs when chemical bonds within the DNA molecule are formed abnormally. BER can remove a single damaged base. At the beginning of BER, a series of lesion-specific DNA glycosylases remove the damaged base by cleaving the N-glycosidic bond linking the base to its corresponding deoxyribose [22]. Apurinic/apyrimidinic endonuclease 1 (APE1) and poly ADP-ribose polymerase 1 (PARP1) can sense and bind to the damage site. This catalyzes poly ADP-ribosylation (PAR) and some other protein substrates, which allows for the recruitment of repair proteins. The next synthesis/ligation step of BER is divided into two sub-pathways—short-patch and long-patch [23]. In short-patch BER, the polymerase beta (Pol β) fills the generated gap with the correct nucleotide [24]. The successive ligation of the DNA ends demands either DNA ligase I (LIG1) or the complex of DNA ligase III (LIG3) and X-ray repair cross-complementing protein 1 (XRCC1). In long-patch BER, proliferating cell nuclear antigen (PCNA), replication factor-C (RFC), flap endonuclease-1 (FEN1), Pol δ/ε, and LIG1 are included [24]. Nucleotide excision repair (NER): This pathway removes bulky lesions, which involves removing the damaged base and several adjacent nucleotides [25]. The significant lesions initiating NER are pyrimidine dimers, such as cyclobutene pyrimidine dimers (CPD), and 6–4 photo-products induced by ultraviolet light- cisplatin-DNA intra-strand crosslinks [26,27]. In the recognition step, there are two different pathways, termed global genome NER (GG-NER) and transcription-coupled NER (TC-NER), whose recognition factor is the XPC/HR23B/CEN2 (XP complementation group C/Rad23 homolog B/Centrin-2) protein complex and CSA/B (Cockayne syndrome A and B, displacing the stalled RNA polymerase II), respectively [28,29]. The following excision and polymerization steps are all the same. XPB and XPD orchestrate the asymmetric unwinding of the DNA helix, accompanied by XPA and RPA binding to the damaged region. Then, the structure-specific endonucleases XPG and XPF/ERCC1 lead to nucleotide excision. Lastly, the resulting gap is resynthesized by Pol δ/ε and sealed by LIG1 [30]. Mismatch repair (MMR): This corrects mis-incorporated bases and strand crosslinks that occur during DNA replication. Defective MMR (dMMR) causes microsatellite instability (MSI) and an increased mutation frequency, which increases the risk of certain cancers such as Lynch syndrome and colon cancer. The MLH/MSH/PMS gene family plays a critical role in MMR [31,32]. The MSH2-MSH6 heterodimer preferentially recognizes base-base mismatches and small insertion/deletion loops (IDLs), while the MSH2-MSH3 heterodimer recognizes larger IDLs. MLH1 and PMS2, which contain the primary endonuclease activity (~90%), facilitate downstream events. The degradation of the error-containing strand is performed by Exo1 [32,33]. Then, polymerized DNA (synthesized by Pol δ), accompanied by PCNA and RPA, resynthesizes a vast gap, and LIG1 or LIG4 seals the remaining nick [34]. Inter-strand crosslink (ICL) repair: ICLs are a form of DNA damage in which two complementary DNA strands are covalently linked. To resolve ICLs, Fanconi Anemia (FA) proteins are primarily involved during the S phase of the cell cycle [35,36]. FANCM and its interacting partners (FAAP24 and MFH) recognize the lesions and recruit the FA core complex and UBE2T/FANC, to monoubiquitinate the ID2 complex (FANCI and FANCD2 heterodimer) [37,38,39]. Then, the monoubiquitinated central complex activates FANCP/SLX4-FANCQ/XPF to unhook ICLs, generating different types of lesions. These ICL-associated lesions are repaired by other DNA repair pathways, including translesion synthesis (TLS) and homologous recombination (HR) [35,40,41]. TLS repair: TLS, an DNA damage tolerance mechanism, uses specialized DNA Pols to bypass DNA damage or fill single-strand DNA (ssDNA) gaps by inserting and/or extending nucleotides [42]. It can be error-prone or error-free. Two models have been proposed to explain TLS: the Pol switching model and the gap-filling model [21,43]. In the former, the inserter TLS enzyme (usually a Pol h, Pol i, or Pol j), which incorporates a nucleotide opposite the DNA lesion, is replaced by extender TLS enzyme (usually Pol ζ (REV3 and REV7), in some cases by Pol j) [44,45]. The Rev1-Pol ζ complex is the most efficient among TLS Pols [44,45,46,47], initiated by monoubiquitinated PCNA [46,48]. In the latter, TLS polymerases (Rev1, Rev3, etc.) repair ssDNA to protect cells from replication stress, though the exact order of events is still unknown [49,50]. The TLS pathway has also been implicated in other DDRs, including HR, NER, and non-homologous end joining (NHEJ) [51,52,53,54]. SSB repair (SSBR): SSBs arise either directly or indirectly (e.g., during BER of base damage) [55]. Therefore, SSBR shares several enzymatic steps with the BER pathway. In the long-patch SSBR pathway, SSBs are detected by PARP1, following end processing by APE1/PNKP (poly-nucleotide kinase 30-phosphate)/APTX (aprataxin). Next, FEN1 removes the damaged termini, following which Pol β and LIG1 repair the gap [56,57,58,59]. Different from this, APE1 recognizes the lesion and LIG3 catalyzes ligation in the short-patch SSBR pathway, while TDP1 (tyrosyl-DNA phosphodiesterase 1) executes the end-processing function in the TOP1-SSB pathway [60,61]. DSBs repair: The main processes are HR, single-strand annealing (SSA), classical NHEJ (cNHEJ), and alternative end joining (A-EJ) [62,63,64,65]. HR repair is mostly error-free [66] and only happens during the S phase and subsequent G2/M phases [67]. Firstly, the Mre11-Rad50-Nbs1 (MRN) complex senses DSBs and stably recruits ATM [68,69], which can phosphorylate itself and downstream cellular targets, including MDC1. Then, RNF8 recognizes MDC1 and promotes the ubiquitylation of histone H1 [70,71]. RNF168 recognizes ubiquitylated H1 and recruits BRCA1 and 53BP1 to mediate the HR and NHEJ pathways, respectively [72,73]. In the next step, CtIP, Exo1, and BRCA1 are implicated in the DNA end resection. The emergent ssDNA protected by replication protein A (RPA), which BRCA2 displaces, invades duplex DNA molecules through the assistance of RAD51 and BRCA1–BARD1–PALB2. With sister chromatid DNA as a template, DNA Pol δ/ε chiefly mediates the nascent strand synthesis [74,75,76], while the SSA pathway directly joins two homologous 3′ ssDNA ends after extensive DNA end resection and RPA displacement, requiring RAD52, XPF–ERCC1 and LIG1 [77,78,79]. NHEJ does not require template DNA for repair, which distinguishes it from HR. It is an error-prone means of repair which can operate throughout the cell cycle. The Ku heterodimer (Ku70 and Ku80 subunits) is needed to recognize DSB termini [80]. Then, DNA-PK is recruited by binding Ku80 [81,82]. Finally, the XRCC4-XLF, Pol μ, and LIG4 complex joins the DNA ends together to complete the damage repair [83]. When the key NHEJ components are lacking, the A-EJ pathway, also known as microhomology-mediated end joining, is enhanced in the DDR [80,84]. It requires PARP1 and Pol θ (encoded by POLQ) to elicit the re-joining of the two DNA ends by using very short homologous sequences (2–20 bp). Due to the synthetic lethal relationship between HR and the A-EJ pathway, Pol θ is a novel druggable target for cancer therapy [85,86,87]. Figure 2 depicts the complex interaction between DDR deficiency and immune response. Genome instability is the hallmark of all forms of cancer [88], providing opportunities for intervention due to weak genome maintenance. DDR deficiency enhances genetic instability and imperfections [89], thus increasing endogenous nucleus-derived DNA generation in the cancer cell cytoplasm, which elicits an innate immune response. The formation of cytosolic DNA includes cytosolic nucleosome-free DNA fragments, cytosolic chromatin fragments (CCF), and micronuclei (MN) [90,91,92], derived from nuclear DNA, mitochondrial DNA, or even extracellular nucleosomes as a result of DNA damage [93,94,95]. However, the molecular mechanisms of cytosolic DNA accumulation are still under exploration. Defects in the DDR pathway cause replication forks to stall or collapse, leading to loss of chromosomal integrity maintenance and generating DNA fragments. For instance, a defect in MLH1 in the MMR system leads to a loss of regulation of Exo1. This causes unrestrained DNA end resection, leading to increased formation of ssDNA. Ultimately, this leads to chromosomal abnormalities and the release of nuclear DNA into the cytoplasm [34,96]. Similarly, MRE11 excessively degrades unprotected newly replicated genomes following RAD51 or BRCA2 dysfunction, resulting in increased fragmentation of nascent DNA [75,76,97,98]. On the other hand, the depletion of SAMHD1, which promotes the degradation of nascent DNA by stimulating the exonuclease activity of MRE11, leads to the release of ssDNA fragments [99]. This suggests a double-edged sword characteristic. In PARP-dependent DNA repair pathways, DNA structure-specific endonuclease MUS81 (a member of the XPF family) cleaves aberrant DNA structures at sites of stalled replication forks to preserve genome integrity [100,101]. Replication stress and unrepaired dsDNA also contribute to chromosomal instability [102,103]. Deletion of the interferon-stimulated gene (ISG15), which plays critical roles in the DDR to modulate p53 signaling and error-free DNA replication, was associated with CCF formation [104,105]. Interestingly, BLM RecQ-like helicase limits ISG induction to prevent genome instability [106]. Meanwhile, chromosomal instability leads to a preponderance of MN [107,108], which also results in the persistence of unrepaired DSBs during mitosis [91]. Mitochondrial DNA is also part of cytosolic DNA. Activation of intrinsic BAK and BAX–mediated apoptosis leads to the appearance of the BAK/BAX macropores, which allow the inner mitochondrial membrane to herniate into the cytosol, carrying matrix components, including the mtDNA [109]. Aberrant mtDNA packaging can also promote its escape into the cytosol, such as the loss of function of TFAM, an mtDNA packaging protein, which can elicit moderate mtDNA stress [93]. It is generally accepted that mtDNA activates DNA sensors upon its release into the cytoplasm [93,110]. To prevent host cytosolic DNA from accumulating and being recognized by DNA sensors, DNases degrade DNA molecules to maintain homeostatic conditions. For instance, DNase II rapidly degrades DNA derived from pathogens or apoptotic cells within endolysosomes [111], and three prime repair exonuclease (Trex1), a major DNA-specific 3′-5′ exonuclease in mammalian cells, degrades endogenous retroviruses and byproducts of DNA replication [112]. Pattern recognition receptors (PRRs), which include pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), detect cytosolic DNA and trigger innate immunity. When pathogenic nucleic acids are detected, the DNA sensor transduces a signal to the nucleus to produce proinflammatory cytokines. Among the downstream signaling for innate immune response, the cGAS-STING-IFN (cyclic GMP-AMP synthase, stimulator of interferon genes, interferon) pathway has been demonstrated to play an important role [113,114,115,116,117,118]. Defects in SWI/SNF subunits, including PBRM1, ARID1A, and SMARCA4, lead to replication stress and accumulation of cytosolic DNA, which facilitates cGAS–STING pathway activation following DNA damage [119,120,121]. Recognition of ruptured micronuclei or chromatin fragments by cGAS links genome instability to the innate immune response [3,122]. Upon the binding of cytosolic DNA, cGAS, as one of the most significant PRRs, catalyzes the synthesis of cyclic-dinucleotide 2′3′-cGAMP (cGAMP), which binds to STING at which point STING translocates from the endoplasmic reticulum to the Golgi apparatus, activating a variety of downstream signaling molecules [117,123]. It recruits and activates tank-binding kinase 1 (TBK1), which in turn phosphorylates STING to activate the interferon regulatory factor 3 (IRF3). Then, IRF3 translocases to the nucleus to induce ISGs and type 1 IFN (IFN-I) expression [124,125]. In parallel, it also activates IKK, which triggers the nuclear factor κB (NF-κB) signaling pathway to produce IFN-I, ISG, and proinflammatory cytokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1β and IL-6 [107,123,126,127,128]. Considerable evidence now suggests that IFN-induced immune responses are crucial for cancer immunotherapy. The produced IFNs, binding to the heterodimer type I IFN receptors (IFNAR1/IFNAR2), activate the JAK/STAT signaling pathway on the dendritic cells (DCs) to produce ISGs and proinflammatory cytokines, such as IFNγ and IP-10 (CXCL10), which influence adaptive immunity. IFNs also regulate the maturation, migration, and activation of various innate and adaptive immune cells, such as natural killer (NK) cells, macrophages, plasma B cells, CD8+ cytotoxic, and CD4+ helper T cells [129]. Moreover, cGAS also localizes to the nucleus, where it plays a role in regulating the DDR. When in the nucleus, cGAS is recruited to dsDNA and interacts with PARP1 to suppress HR progression [130,131]. It also acts as a decelerator of DNA replication forks to suppress replication-associated DNA damage [132]. However, nucleosomes have a higher binding affinity for cGAS than dsDNA, but they have significantly lower potency for activating cGAS [94]. The above suggests a complex connection between cytoplasmic and nuclear functions of cGAS in DDR-immunity interplay. There are several other cytosolic DNA sensors that regulate type I IFNs and cytokine production, including DDX41 [133,134], DDX60, IFNγ-inducible protein 16 (IFI16) [135], DNA-PK, and MRE11, which converge on STING [136]. Meanwhile, DNA-dependent activators of IFN-regulatory factors (DAI) directly trigger TBK1 activation [137]. In addition, absent in melanoma (AIM2)-like receptors, sensing dsDNA activates the ASC/Caspase1 inflammasome pathway to produce IL-1b instead [138]. There are also other pathways in which RNA polymerase III synthesizes 5′-PPP RNA from the AT-rich dsDNA or RNA: DNA hybrid, which induces IFN-β through the RIG-I (retinoic acid-induced gene I)- MAVS (mitochondrial antiviral signaling) pathway [139,140]. Furthermore, DDR activation prevents tumor cells from evading immunosurveillance of NK cells and/or CD8(+) T cells by shedding membrane ligands (through poorly understood mechanisms). Stimulation of ATR or ATM—major DNA damage checkpoints—can upregulate the ligands, which activate NKG2D receptors to alert the innate immune system [141]. Likewise, inhibition of DNA damage pathway components can also prevent the upregulation of major histocompatibility complex class I-related molecules A and B (MICA and MICB), which serve as membrane ligands [142,143]. Cellular senescence could be triggered by DNA damage, causing mammalian cells to enter an irreversible growth arrest that prevents abnormal cells from proliferating. This process is dependent on DDR regulators such as ATM/ATR, as well as the p53/p16 (INK4a) dependent pathway [144,145]. One key feature of senescence is the senescence-associated secretory phenotype (SASP), which involves the expression and secretion of various proinflammatory cytokines and chemokines. These secreted factors can stimulate the immune system and promote chronic inflammation either directly or indirectly, offering potential therapeutic opportunities [146,147,148]. Defects in DNA repair may increase the number of neoantigens in the tumor. For instance, low BER/SSBR gene expression leads to high neoantigen production, which enables a higher probability of recognition by the immune system [149,150]. The number of neoantigens is directly proportional to the number of non-synonymous mutations, which would be increased by a deficiency of multiple DNA repair pathways, including MMR, POLE/POLD1 (encoding the catalytic and proofreading subunits of Pol ε and Pol δ), and HR [151,152,153,154]. Studies have shown that neoantigen-reactive T cells [112,113] may be a key factor in the effectiveness of immunotherapy, particularly in tumors with a high tumor mutational burden (TMB). Cancer-associated antigens, including neoantigens derived from genetic alterations, are presented to CD8+ T cells through the major histocompatibility complex (MHC) on DCs, and professional antigen-presenting cells (APCs). However, most neoantigens are usually not recognized by the immune system, so identifying highly tumor-specific antigens is crucial for the development of personalized immunotherapy [155,156]. Recent technological advances allow new strategies to emerge in predicting, identifying, and validating neoantigens, with the ultimate goal of creating personalized vaccines for cancer treatment [157,158,159]. Additionally, tumors with high TMB resulting from dysfunction in the DDR process may have better clinical outcomes when treated with ICBs such as CTLA4 and PD1 in certain types of cancer [160]. This has been retrospectively validated in patients with advanced lung cancer, gastrointestinal carcinomas, ovarian cancer, skin melanomas, and glioma [159,161,162,163,164,165], suggesting that increased neoantigen burden is a predictive factor for a better outcome when using ICBs [115]. On the other hand, tumor aneuploidy, which is derived from chromosome instability, also provides an independent prognostic value as a biomarker [166,167]. A higher aneuploidy score is associated with poor prognosis among patients with lower-TMB (<80th percentile) tumors treated with immunotherapy [167] and non-small cell lung cancer (NSCLC) treated with radiotherapy and ICB [168]. Now, aneuploidy has been determined to affect immune cell action against the tumor adversely. However, the mechanisms underlying this observation are not well understood, with one proposed explanation being that most tumors with extensive aneuploidy often have fewer infiltrating immune cells [169,170]. DDR defects have been shown to modulate the expression of immune checkpoints and other co-stimulatory molecules. PDL1 is one of the hot spots for immune checkpoint blockade, with links to DDR defects. Specifically, tumoral PDL1 expression is more common in dMMR cancers relative to MMR-intact tumors, which have been identified in colorectal and endometrial carcinomas [171,172,173]. Nevertheless, the loss of MMR proteins seems to be less correlated with tumoral PDL1 expression in breast carcinoma, where MMR gene mutations are less common [174]. PDL1 is primarily induced by IFNγ [175] through the JAK1/JAK2-STAT1/STAT2/STAT3-IRF1 axis [176]. This pathway is activated by innate immunity in response to damaged DNA [150]. PARP inhibitors (PARPi) have been shown to potentiate IFN-γ-induced PDL1 expression in NSCLC cell lines and pancreatic cancer [177,178]. PDL1 upregulation, mediated by DNA damage signaling [179], has been linked to ATM/ATR-CHK1 pathway activation in BER- or BRCA2-depleted cells, for example [150,180,181], or the cGAS-STING-TBK1-IRF3 pathway [182,183]. Furthermore, the greater release of DAMPs from excessive DNA damage promoted by DDR deficiency could also upregulate PDL1 expression in the neighboring surviving tumor cells, due to the TLR4/MyD88/TRIF signaling mediated by HMGB1 [184,185]. Expression of PDL1 in tumors can serve as a potent mechanism for potentially immunogenic tumors to escape from host immune responses by negatively regulating T-cell antigen receptor signaling by binding PD1 [8,175,186,187,188,189]. Finally, blockading PDL1-PD1 binding may result in the remission of advanced-stage cancer, although it does not necessarily mean that PDL1+ tumors have higher response rates [8]. On the other hand, intracellular PDL1 can protect the mRNA of NBS1, BRCA1, and other DNA damage-related genes from degradation, thereby increasing cellular resistance to DNA damage [190]. Moreover, the expression of a co-stimulatory molecule related to DDR has implications for the immune system, as it is required to activate CD8+ T cells [191]. Co-stimulatory B7-1/B7-2 signals on antigen-presenting cells, which interact with CD28 molecules on the T-cell surface, may induce clonal expansion and activation of cytotoxic T cells (CTLs). Increasing CD8(+) CD28(−) T-cell apoptosis compared to CD8(+) CD28(+) T cells is correlated with an impaired DDR following treatment with etoposide, a topoisomerase II inhibitor [192]. Similarly, CTLA4 can exacerbate the DDR and induces T-cell apoptosis [193]. Fas ligand (FasL/CD95L), triggering apoptotic cell death following ligation to Fas (CD95/APO-1), helps to maintain tumor cells in a state of immune privilege by inducing apoptosis of anti-tumor immune effector cells [194]. Therefore, FasL in tumor cells may decrease lymphocyte infiltration, reduce anti-tumor immunity in vivo and promote tumor development [195,196]. Conversely, Fas expression in various human cancer cells enhances the anti-tumor efficiency of CD8+ T or NK cells. In human colon cancer cohorts, Fas expression has been strongly correlated with dMMR and MSI-high (MSI-H) tumors, and it also induced senescence caused by chronic DNA damage [197]. DDR kinases activated by purposeful genotoxic insults can regulate cell type-specific processes: variable gene segment recombination (VDJ), class-switch recombination (CSR), and somatic hypermutation (SHM) [198]. These processes are required for the normal development and function of immune responses [199], in which programmed DNA damage occurs at a specific site [200,201]. Multiple components of the DDR pathway are involved with these intermediates. For instance, DNA-PK, XLF4, SHLD1, and LIG4 participate in RAG-induced (in VDJ) or AID-initiated (in CSR) DSBs repair [202,203,204]. During SHM, error-prone non-canonical BER and/or MMR help to diversify mutations in the variable region of immunoglobulin genes to create high-affinity antibodies [205,206]. DNA repair is critical for antibody diversification and influences the development of the adaptive immune system [207,208]. Disturbances in the balance between enzymatic mutagenesis and DNA repair are at the basis of lymphoid malignancies [209,210]. This raises the intriguing possibility that therapeutic agents that target DDR proteins may be used to manipulate immune responses. Tumor immunotherapy, including ICB and adoptive cell transfer, can manipulate specific components of the immune system to reverse immunity suppression and target various cancers. PD1/PDL1 inhibitors and CTLA4 inhibitors have shown encouraging therapeutic effects in these approaches [211,212]. Nevertheless, only a minority of cancer patients respond to ICB in the clinic. Even among dMMR/MSI-H mCRC (metastatic colorectal cancer) patients for whom PD1 blockade is a guideline-recommended, first-line treatment option, response rates range between 30% and 50% [213,214]. These data suggest the existence of intrinsic resistance mechanisms, which are often contingent on the tumor microenvironment (TME) [215]. As a consequence, the development of novel therapeutic designs, as well as the discovery of biomarkers, are currently areas of intense research activity [216,217]. Combination regimens of traditional DNA-damaging approaches, such as chemotherapy drugs and radiotherapy, have been shown to enhance immunity by increasing antigens to stimulate T-cell-mediated immunity and modulating certain aspects of the immunosuppressive milieu [218,219]. Moreover, there is evidence to suggest that lower DDR factor expression in tumors may be associated with a better response to anticancer immunity, implying substantial potential benefits from DNA repair inhibitors [220]. Thus, there is considerable interest in combining ICB with DDR inhibition (DDRi), in order to enhance genomic instability and immunotherapy activity and potentially achieve additional anti-tumor responses [8] (Figure 2). DDR kinase inhibitors, such as those targeting PARP, ATM, ATR, DNA-PK, CHK1/2, BER, and WEE1, have been tested in clinical trials as a way to kill tumor cells, as cancer cells are more sensitive to compromised repair systems compared to normal cells (Table 1) [89,221]. With the expectation that the combination of DDRis with ICBs will show high potency, multiple studies exploring this combination are ongoing (Table 2). An archetypal example is PARPi, which have shown significant therapeutic efficacy in BRCA-deficient cancers by blocking BRCA-independent DNA repair in ovarian and breast cancer [222,223]. However, PARPi have only improved progression-free survival without reaching statistical significance in cancer-specific mortality in patients with germline BRCA mutations [224,225,226]. ICB has been proposed to optimize these clinical outcomes. In the BRCA1(−) tumor model, CTLA4 blockades combined with PARPi induce protective anti-tumor immunity and significant survival benefit by locally inducing anti-tumor immunity and increasing levels of IFNγ [227]. Accumulating evidence has also suggested that olaparib, a type of PARPi, triggers robust local and systemic anti-tumor immunity through a STING-dependent anti-tumor immune response independent of BRCA deficiency. This response can be further augmented by combining olaparib with PD1 blockade [228,229,230]. The clinical results of combining PARPi with an ICB, such as in advanced triple-negative breast cancer and advanced or metastatic non-small cell lung cancer [231,232], support further research on using this strategy in various cancers. Moreover, in PARP inhibitor-resistant cancers, PARG inhibitors may impair cancer cell survival by suppressing replication fork progression and show comparable killing ability [233,234]. This offers the potential for combining PARG inhibitors with ICB. Although other DDRis are being evaluated as monotherapies or in combination with cytotoxic or molecularly targeted agents in solid tumors, only a few early-phase trials currently focus on combining them with ICB [221]. Clinical trials with AZD6738 [235], an ATR inhibitor, and AZD1775 (NCT02617277), a WEE1 inhibitor, individually as well as in combination with durvalumab in patients with advanced cancers, are currently ongoing. New DDRis are being developed, such as WRN inhibitors, which have shown promising synthetic lethal interaction with MSI tumors [236]. As dMMR cancers are exceptionally responsive to ICB [237,238], the viability of WRN inhibition plus ICB deserves further exploration. As our understanding of the relationship between DDR and immune responses continues to grow, it is expected that additional DDR-related biomarkers will be identified to predict a patient’s response more accurately to immunotherapy. Several clinical trials have demonstrated that dMMR/MSI-H is significantly associated with long-term responses to immunotherapy and better prognosis in colorectal and non-colorectal malignancies treated with ICBs. Compared to chemotherapy, pembrolizumab has fewer treatment-related adverse events without compromising overall survival, supporting it as an efficacious first-line therapy [239,240]. In practice, pembrolizumab (anti-PD1) has been approved for dMMR/MSI-H refractory or metastatic solid tumors, and nivolumab (anti-PD1) for dMMR/MSI-H CRC [241,242,243]. One plausible hypothesis is that dMMR contributes to high TMB, though the specific mechanisms remain unclear [161]. TMB also has emerged as a promising biomarker of immunotherapy response across multiple cancer types. A high TMB may be a biomarker for identifying patients who will benefit from ICBs, irrespective of PDL1 expression level [244,245,246]. In many cases, TMB is a more reliable predictive marker for PD1 and PDL1 blockade immunotherapy response than PD1 or PDL1 expression; for example, the presence of ten or more mut/Mb was associated with improved response and prolonged progression-free survival, irrespective of tumor PDL1 expression in NSCLC [247,248]. Though higher TMB has been reported frequently in tumors with deleterious DDR gene alterations, mutations in different types of DDR pathways do not always exhibit high mutational load. In addition, clinical outcomes among patients with low TMB tumors are heterogeneous, with TMB status showing no ability to predict ICB-response in melanoma patients [162]. Recently, DDR scores quantifying the tumor signature of DDR pathways in tumors have provided new insights for guiding immunotherapeutic strategies. This is because DDR scores are not just closely associated with TMB and genome alteration, but also provide information regarding real-time DNA repair function [249]. There is evidence that patients with low DDR pathway signature scores might not benefit from a monoclonal anti-PD1 therapy, making these scores potentially useful for predicting treatment response in tumor tissues [163,164]. Similarly, studies have found that patients with high DDR scores have significantly higher survival rates after receiving ICBs compared to those with low DDR scores, while the reverse is true for traditional treatments [250]. Furthermore, tumor aneuploidy has been found to predict prognosis independently among patients with lower TMB (<80th percentile) tumors treated with immunotherapy [166,167]. It has been reported that DDRi can enhance immune signaling within the TME and complement neoantigens. However, different forms of DDR defects may have varying effects on tumor immunogenicity. DDRi may not generate sufficient neoantigens in tumors with low neoantigen burden to stimulate an immune response. Meanwhile, it can also be challenging to reduce the immune-suppressive effects of DDRi. For example, PARPis and ICB have not produced dramatic responses in patients with BRCA1mut- and HR-deficient high-grade serous ovarian cancer, as PARPis can mediate immune resistance and tumor progression by upregulating VEGF-A. This has led to the development of combination therapies using PARPis, ICB, and bevacizumab (anti-VEGF) [251]. Combining DDRi with ICB, radiotherapy, or chemotherapy may also be a promising means for achieving a favorable balance between immunogenicity and TME. DNA-PK inhibitors are being studied in combination with radiation and ICB in clinical trials (NCT04068194, NCT03724890). It is important to pay careful attention to specific therapeutic approaches for combination treatments. Optimizing the dose and schedule of DDRi agents may allow for increased tumor damage while sparing normal tissue by taking advantage of the differences in DDR and immune response between cancer and normal cells. The order in which combination drugs are administered and the line of therapy should also be considered. It is important to consider the toxicities of combination treatments versus monotherapy, as these can limit the development of combination therapies. Some DDR members are broad-spectrum and are necessary for maintaining homeostasis in normal tissues, which means that severe adverse events may occur when combined with ICBs. This is also the reason why many DDRis are eliminated in preclinical or phase I clinical trials. To optimize the use of combination therapies involving DDRis and ICBs, more specific and sensitive biomarkers are needed to identify the most suitable patient population and predict treatment outcomes. DDR scores are likely to be important predictive factors, but the definition of DDR deficiency genes varies across different tumor types. It is controversial as to which mutated genes (distinguished as heterozygous or homozygous, germline or somatic) should be used to characterize DDR status in tumors. Under conditions of active anti-tumor immunity, DDR scores have been found to positively correlate with immune-related biomarkers, such as the number of T cells (such as CD4+ activated memory cells, CD8+ cells), T-cell receptor repertoire, PDL1 expression, and broad immune infiltrate. Thus, integrating immune biomarkers into the DDR score may improve its predictive ability. In this review, we discuss the classical mechanism of DDR and its interplay with the immune system. We also present a compilation of studies on the combination of DDRi and ICBs for various cancer types, with the goal of inspiring new ideas for improving the efficacy of anti-tumor therapies and sparking innovation. While combination therapy has achieved impressive results in the clinic, increasing the success rate of treatment remains a challenge, and the rate of failure is still relatively high. A deeper understanding of the role of DDR in the immune system will be crucial for the design of future clinical trials.

PMC10000856

Zheyuan Hu,Penghui Zhao,Aimei Liao,Long Pan,Jie Zhang,Yuqi Dong,Jihong Huang,Weiwei He,Xingqi Ou

Fermented Wheat Germ Alleviates Depression-like Behavior in Rats with Chronic and Unpredictable Mild Stress

22-02-2023

FWG,depression,behavior,neurotransmitters,intestinal microbes

Depression is a chronic mental illness with devastating effects on a person’s physical and mental health. Studies have reported that food fermentation with probiotics can enrich the nutritional values of food and produce functional microorganisms that can alleviate depression and anxiety. Wheat germ is an inexpensive raw material that is rich in bioactive ingredients. For example, gamma-aminobutyric acid (GABA) is reported to have antidepressant effects. Several studies concluded that Lactobacillus plantarum is a GABA-producing bacteria and can alleviate depression. Herein, fermented wheat germs (FWGs) were used to treat stress-induced depression. FWG was prepared by fermenting wheat germs with Lactobacillus plantarum. The chronic unpredictable mild stress (CUMS) model was established in rats, and these rats were treated with FWG for four weeks to evaluate the effects of FWG in relieving depression. In addition, the study also analyzed the potential anti-depressive mechanism of FWG based on behavioral changes, physiological and biochemical index changes, and intestinal flora changes in depressed rats. The results demonstrated that FWG improved depression-like behaviors and increased neurotransmitter levels in the hippocampus of CUMS model rats. In addition, FWG effectively altered the gut microbiota structure and remodeled the gut microbiota in CUMS rats, restored neurotransmitter levels in depressed rats through the brain–gut axis, and restored amino acid metabolic functions. In conclusion, we suggest that FWG has antidepressant effects, and its potential mechanism may act by restoring the disordered brain–gut axis.

Fermented Wheat Germ Alleviates Depression-like Behavior in Rats with Chronic and Unpredictable Mild Stress Depression is a chronic mental illness with devastating effects on a person’s physical and mental health. Studies have reported that food fermentation with probiotics can enrich the nutritional values of food and produce functional microorganisms that can alleviate depression and anxiety. Wheat germ is an inexpensive raw material that is rich in bioactive ingredients. For example, gamma-aminobutyric acid (GABA) is reported to have antidepressant effects. Several studies concluded that Lactobacillus plantarum is a GABA-producing bacteria and can alleviate depression. Herein, fermented wheat germs (FWGs) were used to treat stress-induced depression. FWG was prepared by fermenting wheat germs with Lactobacillus plantarum. The chronic unpredictable mild stress (CUMS) model was established in rats, and these rats were treated with FWG for four weeks to evaluate the effects of FWG in relieving depression. In addition, the study also analyzed the potential anti-depressive mechanism of FWG based on behavioral changes, physiological and biochemical index changes, and intestinal flora changes in depressed rats. The results demonstrated that FWG improved depression-like behaviors and increased neurotransmitter levels in the hippocampus of CUMS model rats. In addition, FWG effectively altered the gut microbiota structure and remodeled the gut microbiota in CUMS rats, restored neurotransmitter levels in depressed rats through the brain–gut axis, and restored amino acid metabolic functions. In conclusion, we suggest that FWG has antidepressant effects, and its potential mechanism may act by restoring the disordered brain–gut axis. Depression is a chronic mental illness that negatively affects a person’s physical and mental health. Recurrent episodes of depression reduce the life expectancy and quality of life of an individual and enhance suicidal tendencies [1]. The global estimate of people suffering from depression is more than 350 million [2] and is expected to increase due to the increased stress in life. Depression is anticipated to top the total global burden of disease by 2030 [3], thus prioritizing the development of a treatment for depression. Standard pharmacological regimens, such as antidepressants, have limited efficacy, significant adverse effects, and rapid resistance [4], which might be attributed to the hitherto unclear pathogenesis of depression. Recently, brain–gut axis disorders, caused by dysbiosis of gut flora, have emerged as the main pathogenesis pathway of depression. Restoration of the gut microbiota in depressed patients can alleviate their depressive symptoms [5,6]. Fermented probiotic foods are emerging as healthy therapeutics that are associated with restoring the gut microbiota in depressive patients. Gut symbionts are found in gut–brain signaling, immunological homeostasis, and hormone regulation, and are known to reduce stress- and depression-related symptoms by regulating brain function [7]. Likewise, probiotics modulate the interaction between the gut and brain, thereby managing the effects of depression [8]. According to several studies, probiotic fermentation of raw materials can result in enriched nutritious products. These products can facilitate the proliferation of good gut bacteria and bioactive ingredients that can reduce depression and anxiety [9]. Wheat germs are a rich and inexpensive source of bioactive ingredients, containing high-quality proteins, amino acids, fats, vitamins, minerals, and other components [10]. Moreover, γ-aminobutyric acid (GABA), also found in wheat germs, has reported antidepressive effects [11]. The findings of several studies revealed that Lactobacillus plantarum is a GABA-producing bacteria that can alleviate depression [12,13]. Fermented wheat germs (FWGs) are obtained from the fermentation of wheat germs by Lactobacillus plantarum and comprise probiotics, prebiotics, protein, short-chain fatty acids, and GABA. Although there are a number of studies on probiotics for depression relief, there are still fewer studies on probiotic fermented foods for depression relief, especially Lactobacillus plantarum-FWG for depression relief, which has not been demonstrated by any other method before. In addition, most of the drugs developed for depression relief are based on the monoamine hypothesis using chemical methods, which usually have some side-effects on human health. In contrast, the study of Lactobacillus plantarum-FWG for depression alleviation adopts in a novel way a popular probiotic fermented food health therapy, in recent years, to develop new antidepressant functional foods that are healthy, green, and safe for the human body, providing a new direction for clinical treatment of depression. Finally, this study can fill the gap of fermented cereal products in the field of depression alleviation and provide new ideas for the development of cereal foods in the field of nutritional foods and functional foods. Herein, this study investigated the anti-depressive effects and potential mechanisms of Lactobacillus plantarum-FWG in depressed rats (Figure 1a). With a CUMS rat model, the animal behaviors, neurotransmitter index, and gut microbes were evaluated to verify the therapeutic efficacy of FWG to alleviate depression. Lactobacillus plantarum (M618) was preserved in our laboratory. Bainong 207 defatted wheat germ powder was purchased from Henan Kunhua Biotechnology Co. (Anyang, China). Date palm GABA-positive compound tablets were obtained from MCOK BIOTECHNOLOGY (USA). The elevated plus maze and GABA content kit (plant source) were supplied by Zhengzhou Yixinyuan Instruments (Zhengzhou, China). 5-hydroxytryptamine (5-HT), 5-hydroxyindoleacetic acid (5-HIAA), and acetylcholine (ACH) assay kits were obtained from Equipment Co. Elabscience Biotechnology Co. GABA assay kits were supplied from Wuhan Fine Biotech Co. (Wuhan, China). The ZQZY-98 oscillating incubator was purchased from Shanghai Zhichu Instruments Co. (Shanghai, China). The digital display PHS-3C laboratory PH meter was purchased from Hefei Zhuoer Instrument Co. (Hefei, China). The XFH-40MA stainless-steel autoclave was purchased from Zhejiang Xinfeng Medical Instrument Co. (Shaoxing, China). The YSW-CB-V vertical purification bench was purchased from Shenzhen Yongshengwang Industrial Co., Ltd. (Shenzhen, China). The TG16-WS desktop high-speed centrifuge was purchased from Changsha Xiangyi Centrifuge Instrument Co. (Changsha, China). The FDU-21 freeze dryer was purchased from Tokyo RIKEN, Wakō, Japan. The SynergyHTX multifunctional enzyme labeler was purchased from BioTek, Winooski, VT, USA. The Lactobacillus plantarum was cultured in De Man Rogosa Sharpe(MRS) liquid media at 37 °C for 24 h to obtain a seed solution. The number of active bacteria in the seed solution of Lactobacillus plantarum was determined to be about 2 × 108 CFU/mL by the dilution coating plate method. After drawing on Wu’s study [14] as well as the results of single-factor (Lactobacillus plantarum inoculum, fermentation time, ratio of material, pH) experiments and response surface experiments in the pre-laboratory, FWG preparation conditions were determined with the goal of optimizing the GABA content in FWG. Wheat germ powder and 50 mmol/L of acetic acid-sodium acetate buffer salt were mixed in a ratio of 1:7, shaken well, and adjusted to pH 4.45 with NaOH/HCl. They were then sterilized using an autoclave at 105 °C for 30 min and cooled to room temperature, and 3.82% of the weight of wheat germ powder was inoculated with Lactobacillus plantarum seed solution (mL/g) in the ultra-clean table and mixed evenly. This was followed by fermentation in a shaker (37 °C, 100 rpm/min) for 24 h. After fermentation, the fermentation broth was centrifuged at 5000 r/min for 15 min to obtain FWG. The FWG was stored at 4 °C and subsequently poured into several culture media plates (FWG, about 1/3 of each plate). Each plate was covered with cling film and dense and small holes were made on the surface of the cling film with a toothpick; then, the plates were placed in a refrigerator at −80 °C overnight to ensure that the FWG had been frozen into a solid form. Finally, the FWG was lyophilized according to the freeze dryer procedure. The lyophilized powder was analyzed for GABA content, using a GABA content kit. The results are displayed in Table 1. Excess lyophilized powder was subsequently stored in a −80 °C refrigerator. Male SD rats weighing 170–190 g (SCXK(Yu)2019-0002) were obtained from the Henan Huaxing Experimental Animal Farm in China. Rats were kept in a room with a humidity of 40–60% and a temperature of 22–26 °C with water and meals readily available [15]. The animals were treated according to the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. The Henan University of Technology ethics committee permitted the animal experiments conducted in this study. Six groups were formed with seven male SD rats in each: blank control (no stress), depression model (CUMS), positive control (GABA), low-dose FWG, medium-dose FWG, and high-dose FWG. A week before the experiments, the rats were acclimatized and fed with 1% sucrose water. Following acclimatization feeding, the rats were weighed and evaluated for 1% sucrose preference. The rats were then subjected to seven stressors over four weeks, except for the blank control group. The stressors included day and night reversal, inclined cage at 45° (24 h), water fasting (24 h), wet bedding (400 mL of water spread onto the bedding for 10 h), tail clamping (1 min), swimming (4 °C water for 5 min), and strobing (300 times/min for 12 h) [16,17]. The animals were randomly given a stressor per day, and the same stressor was not delivered for three successive days. Their body weights were measured weekly. The elevated plus maze experiment (EPM), forced swimming test (FST), open field test (OFT), and sugar preference test (SPT) were conducted four weeks after concluding the modeling phase to examine the anxiety-depression-like behavior of the rats. The behavioral findings of the remaining groups were compared to the blank control group to determine the efficiency of the model. If the model responded poorly, the modeling period was further extended in two-week intervals (with a maximum of two months) until the optimum model response. The CUMS group continued with the stressors for another four weeks, while the other groups stopped all stress stimulation. The following doses were used for gavage treatment intervention over four weeks: blank control group (normal saline 7 mL/kg·d), depression model group (normal saline 7 mL/kg·d), positive control group (26 mg/kg·d), low-dose FWG group (43 mg GABA/kg·d), medium-dose FWG group (43 mg GABA/kg·d), and high-dose FWG group (60 mg GABA/kg·d). The above experimental procedures are illustrated in Figure 1b. EPM is an unconditioned reflex model that uses the animal’s exploratory nature in a different environment and the fear of an elevated open arm to create a conflicting state for the anxiolytic assessment of drugs. The rat was positioned in the middle of the maze with its head facing the open arm. Each experimental animal was placed in the same position thereafter, while the camera monitor was turned on to record the following indexes within five minutes: frequency of entries in the open arm (OE) and the closed arm (CE). The frequency with which the rat’s two forepaws fully enter into the corresponding arm was the criterion for determining the OE and CE frequencies [18]. Open-arm dwell time (OT) and closed-arm dwell time (CT) were measured in seconds. The feces of the rats were removed after each test and the maze was cleaned with 75% ethanol to mask the smell. The first and second elevated plus maze experiments were performed after the modeling and treatment interventions, respectively. The behavioral test used in this experiment was developed by Porsolt et al. in 1977 for the evaluation of behavioral despair [19]. The experiment was split into two parts. The first part was pre-swimming, in which rats were confined in a transparent cylindrical container (water depth of 25–30 cm, water temperature 23 ± 1 °C) to swim for 15 min. The rats were then blown dry after pre-swimming and returned to their original cages. Experimental water was changed after each experiment to prevent external effects on subsequent experiments. The rats were placed in the pool again for a 6 min “test swim” after 24 h from the pre-swimming test and monitored. The duration of the static state of rats (e.g., body slightly curled, only nostrils exposed to maintain breathing, forepaws stopped digging, and hind paws occasionally paddled) during the last four minutes was observed. The static state is an animal response to abandon the thought of fleeing after it is unable to escape, which depicts a desperate predicament and behavioral despair. The first and second forced swimming experiments were performed after the modeling and treatment interventions, respectively. The OFT was performed to study the spontaneous activity and exploratory behavior of rats [20]. The rats were placed in a 1 m × 1 m × 0.4 m observation box with black walls and a white floor, and red and blue lines separating the bottom surface into 16 equal squares (i.e., four red squares in the center and 12 blue squares in the periphery). Rats were put in the middle of the observation box to evaluate their horizontal and vertical activity scores. Within five minutes, the center region residence time, the number of squares traversed, and the frequency of upright times were collected [21]. To avoid interference from previous experiments, the experimental apparatus was washed with 75% ethanol after each experiment. The first and second OFTs were conducted after modeling and treatment interventions. The rats were trained with 1% sucrose water during laboratory acclimatization: Two bottles of 1% sucrose water were given for 24 h and then switched to two bottles of water for 24 h for the rats to acclimate to the sucrose water intake [22]. Following a 12 h fast, the first SPTwas performed. The rats were fed in respective cages, and two water bottles of similar shape were placed in each cage, 1% sucrose water and pure water. The 24 h experiment was conducted and the bottle positions were switched every 12 h [23]. Following that, both bottles were removed and weighed to determine each rat’s rate of sucrose preference. Sugar water preference rate = [sucrose water consumed/(sucrose water consumed + pure water expended)] 100%. The second and third sugar preference experiments were performed according to the above procedures after four weeks of modeling and treatment intervention. The rats were fasted for 24 h after the behavioral test and deeply anesthetized with ether. Blood was collected from the eyeballs, and the rats were executed by severing their heads [24]. The rats were dissected on ice, and their heart, liver, spleen, lungs, kidneys, and cecum contents were removed, as well as the intact brain tissue, from which the brain, cerebellum, and cortex were separated. The aforementioned organs and tissues were preserved in liquid nitrogen for fast freezing and then in a −80 degrees Celsius refrigerator until usage. The rat hippocampus was dissected, weighed, and placed in a sterilized homogenization tube by reviewing the data combined, performing ELISA, and adding pre-chilled PBS buffer (in 2 additions) and Phenylmethylsulfonyl fluoride (PMSF) protease inhibitors at a working concentration of 1 nm/mL at a ratio of 1:9. The homogenization tube was rotated for 1–2 min to produce a 10% tissue homogenate. The levels of 5-hydroxytryptamine, 5-hydroxyindoleacetic acid, acetylcholine, and GABA in the hippocampus tissue homogenate supernatant were measured by the double antibody sandwich method [25]. To obtain microbial genomic DNA from samples of rat cecum contents, we employed the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). A 1% agarose gel was utilized to investigate DNA extracts, and a NanoDrop 2000 UV-Vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA) was used for measuring DNA concentration and purity. The thermal cycle PCR machine (Gene Amp 9700, ABI, USA) amplified the highly variable V3-V4 regions of the bacterial 16S rRNA gene 27 times with primers 338F (5′-ACTCCTACGGGAGGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR reaction system was composed of 4 μL of 5× TransStart FastPfu buffer,10 ng of DNA template, 2 μL of 2.5 mM dNTPs, 0.8 μL of each upper and lower primer (5 μM), 0.4 μL of TransStart FastPfu DNA polymerase, and topped up to 20 μL with H2O. PCR reaction was carried out three times. The final PCR products were purified, quantified, and sequenced using Illumina’s MiseqPE300 platform. For microbiome analysis, raw sequences were quality-controlled using fastp (version 0.20.0) and spliced using FLASH (version 1.2.7) [26,27]. Chimeras were removed using UPARSE (version 7.1) [28]. Operable taxonomic units (OTUs) were constructed, and each sequence was classified and annotated using the RDP classifier (version 2.2), with a 70% comparison threshold set against the SILVA 16S rRNA database (version 138) [29]. The richness and diversity of samples were assessed using diversity analysis, and the results were subjected to the Wilcoxon rank sum test between the exponential groups. The principal coordinate analysis (PCoA) distance visualization approach was employed to investigate β-diversity in the microbial community. To investigate the variations in species composition across groups, colony bar plots at the family level and Wilcoxon rank-sum tests of substantially different colonies were utilized. PICRUSt2 predicted the effect of FWG on fecal microbiota function in CUMS rats. Experimental data on body weight, behavioral assessments, and neurotransmitter levels within the hippocampus were presented as mean ± standard error (SEM) of at least three independent experimental data samples. One-way ANOVA was used for statistical analysis. GraphPad Prism version 8.0 was used to run one-way ANOVA between groups (GraphPad software, Inc., La Jolla, CA, USA). In addition, ANOSIM analysis was performed using QIIME scripts. The Wilcoxon rank sum test was based on R’s stats package (version 3.3.1) and the Python scipy module. In order to ensure that the GABA content in FWG lyophilized powder would not exceed the detection range of the kit when using the kit, different masses of FWG lyophilized powder were dissolved in 1 mL of water (mg/mL) for the determination, and the results in Table 1 showed that 10 mg of FWG lyophilized powder dissolved in 1 mL of water gave the best detection results, and the GABA content in FWG lyophilized powder was 19,797.54 ± 2719.322 (μg/g). The influence of FWG on the weight of CUMS rats is depicted in Figure 2a. Before the test, there was no appreciable difference in the weight of rats in all groups. Rats stimulated by CUMS exhibited considerably lower body mass indexes than the control group after four weeks of CUMS modeling. The body weight indices of CUMS rats improved dramatically after four weeks of FWG administration. There was no significant difference between the high-dose FWG group and the blank control group. The anxiolytic effect of drugs is usually assessed in the elevated plus maze experiment by OE and OT metrics, which are negatively correlated with anxiety. As demonstrated in Figure 2b, the OE of rats in the CUMS group was noticeably lower when compared to the control group (**** p < 0.0001). On the contrary, four weeks of treatment intervention restored the OE in the low-, medium-, and high-dose FWG groups to the level of the control group (N.S, # p > 0.05). Among these, the difference in the OE and OT of the high-dose FWG group was the largest as compared to the control group (**** p < 0.0001), thus indicating that FWG could improve the anxiety of depressed rats. FST was designed to assess the level of despair in CUMS rats in a static state, and the study revealed that CUMS rats had a longer swimming immobility time and more pronounced despair [30]. As depicted in Figure 2c, CUMS rats had significantly more immobility time as compared to healthy controls (#### p < 0.0001). Following four weeks of intervention, the immobility time of rats in the low- and high-dose FWG groups decreased (**** p < 0.0001) and returned to a normal level (N.S), thus indicating that the high-dose FWG could improve the despair in depressed rats. The number of traversal squares indicated the motor ability and exploratory behavior of rats, and the depressed rats have a lower number of traversed lattices. According to Figure 2d, the number of traversing lattices was appreciably lesser in CUMS rats than in the blank control group (#### p< 0.0001). After four weeks of FWG treatment, the low-, medium-, and high-dose FWG groups reported an increase in the frequency of traversing lattices in comparison to the CUMS model group. However, only the high-dose FWG group reported statistical significance (*** p < 0.001). Thus, a high-dose FWG treatment could improve the motor activity and independent exploratory behavior of depressed rats. A lack of pleasure is a critical depressive trait that is assessed mainly by the percentage of sugar water ingested in the SPT [31]. In Figure 2e, the percentage of sugar water intake in each group of rats was not significantly different (N.S, p > 0.05). CUMS rats had a significantly lower percentage of sugar water intake than the blank controls (#### p < 0.0001). After four weeks of treatment intervention, rats in the low-, medium-, and high-dose FWG groups reported a significantly higher percentage of sugar water consumption than the CUMS model group (**** p < 0.0001, ## p < 0.001), indicating that FWG could improve the pleasure deficit in CUMS rats, but not fully restore them to normal levels. The levels of 5-HT, 5-HIAA, Ach, and GABA in the hippocampus of CUMS rats were significantly reduced in comparison with the normal control (## p < 0.01) (Figure 3). The low-dose FWG group exhibited a substantial increase in 5-HT and 5-HIAA levels after four weeks of treatment intervention as compared with the CUMS model group (* p < 0.05), which returned to normal levels that were comparable to the blank control group (N.S, # p > 0.05). The recovery of the other two neurotransmitters displayed an upward trend, but it was not significantly different. The medium-dose FWG group reported a significant increase in the levels of 5-HT, 5-HIAA, Ach, and GABA as compared with the CUMS model group (* p < 0.05, ** p < 0.01). The concentrations of 5-HT, 5-HIAA, and Ach were all restored to levels comparable to the control group (N.S, # p > 0.05), while GABA did not return to the normal level (# p < 0.05). In comparison to the model set, the high-dose FWG group exhibited slightly higher levels of 5-HT,5-HIAA, Ach, and GABA than the blank control group (* p < 0.05, **** p < 0.0001, # p > 0.05). Figure 2 and Figure 3 demonstrate that the CUMS model was successfully established, and FWG had a similar impact on depression-like behaviors and neurotransmitter levels in the hippocampus of depressed rats. Furthermore, different doses of FWG had different degrees of improvement in depression, and the combined behavioral improvement results and neurotransmitter restoration effects highlighted that a high dose of FWG exhibited the strongest effect. The dilution curves in Figure 4a,b reached a plateau, indicating that the sequencing depth and coverage of the samples could be further analyzed. The α-diversity is reflected by community richness (Sobs index), while the species diversity of the samples is reflected by the community diversity index (Shannon index). There was a significant decrease in both species richness and diversity in the CUMS model group as compared with the blank control group (p < 0.05) (Figure 4c,d), and both species richness and diversity increased after treatment intervention with GABA and FWG (p > 0.05). To differentiate samples with different microbial communities, we employed PCoA analysis based on β-diversity analysis. The rats in the CUMS model group had significantly different fecal microbiota than rats in the control group, and the fecal microbiotas of the GABA and FWG groups were similar to that of the blank control (Figure 4e). The results of an analysis of similarity (ANOSIM), based on PCoA scores, indicated a statistically remarkable divergence between all four groups (Figure 4f, R = 0.4492, p = 0.001). Differences in the species composition of gut microorganisms at the family level in different groups of samples are depicted in Figure 5. The model group had a higher abundance of both Lactobacillaceae and Erysipelotrichaceae and a lower abundance of Peptostreptococcaceae, Lachnospiraceae, and Oscillospiraceae than the other groups (Figure 5a). To determine the significance of their differences in different groups, the aforementioned families were treated to a multigroup rank sum test analysis. Figure 5b,c indicate that the abundance of Lactobacillaceae and Erysipelotrichaceae in the model set was considerably greater than in the blank control group (p < 0.01). After the treatment with GABA or FWG, the abundance of Lactobacillaceae decreased significantly until the level reached that of the blank control group (p > 0.05), while the abundance of Erysipelotrichaceae only decreased slightly. The abundance of Peptostreptococcaceae was lower in the model group in comparison to the blank control group (p > 0.05). Likewise, treatment with GABA or FWG significantly increased the abundance of Peptostreptococcaceae until the level reached that of the blank control group (p > 0.05) (Figure 5d). Meanwhile, the abundance of Lachnospiraceae was lower in the model group than in the blank control group (p < 0.01). After treatment with GABA or WFG, the abundance of Lachnospiraceae increased significantly until the level reached that of the blank control group (p > 0.05) (Figure 5e). The abundance of Oscillospiraceae was significantly lower in the model group relative to the control group, but the difference was not statistically significant (p > 0.05). The abundance of Oscillospiraceae increased significantly after GABA or FWG treatment until the level reached that of the blank control group (p > 0.05) (Figure 5f). Figure 6 demonstrates that FWG regulated the composition of the gut microbiota at the genus level. The abundance of Lactobacillus and Marvinbryantia in the CUMS model group was higher than those in all the other groups, while the abundance of unclassified_f__Lachnospiraceae and Romboutsia was lower than those in all the other groups (Figure 6a). The Kruskal–Wallis H test was performed on the above genera, and the boxplot results are displayed in Figure 6b–e. The abundance of Lactobacillus was significantly higher in the model group than in the blank control group (p < 0.01) (Figure 6b). After treatment with GABA or FWG, the abundance of Lactobacillus significantly decreased until the level of the blank control group (p > 0.05). Likewise, the abundance of Romboutsia and unclassified_f__Lachnospiraceae in the model group was significantly lower than that of the normal control (p < 0.05, p < 0.01) (Figure 6c,d). After treatment with the FWG intervention, the abundance of Romboutsia and unclassified_f__Lachnospiraceae returned to levels that were close to that of the blank control group. The CUMS model group reported an increased abundance of Marvinbryantia compared to the control group (p > 0.05) (Figure 6e). After treatment with FWG, its abundance decreased to levels similar to the blank control group (p > 0.05). The analysis revealed 28 strong correlations between neurotransmitters and specific gut microflora (Figure 7). Among them, Ach was significantly and positively correlated with Romboutsia, g__norank_f__Eubacterium_coprostanoligenes_group, and NK4A214_group, while it was significantly and negatively correlated with Lactobacillus. Besides that, 5-HIAA was positively correlated with Romboutsia and negatively correlated with Lactobacillus. GABA and norank_f__Lachnospiraceae reported a significant positive correlation. The analysis identified 29 KEGG pathways, with significant differences in KEGG Path Level 2 for each group of rats (Figure 8a). Among them, the carbohydrate and amino acid metabolic pathways were considered to be dominant, and depressed rats were usually characterized by a dysfunctional amino acid metabolism [32,33]. Hence, a rank sum test was performed on the amino acid metabolic pathways for each group of rats (Figure 8b). The results revealed that the CUMS model group had lower levels of amino acid metabolism than the normal group did (p < 0.01), implying dysfunctional amino acid metabolism. After treatment with GABA or FWG, the level of amino acid metabolism greatly improved and returned to levels near the blank control group (p > 0.05). The results of the KEGG Module for each group of rats were subjected to a rank sum test to further explore amino acid metabolism in depression (Figure 8c,d). The results demonstrated that there were 14 differential modules of amino acid metabolic functions among the groups of rats, including M00525, M00017, M00026, M00570, M00023, M00609, M00846, M00015, M00845, M00028, M00025, M00024, M00135, and M00038 (Table 2). The biosynthetic functions of lysine and proline were upregulated, while those of tryptophan, GABA, glutamate, isoleucine, and histidine were decreased in the CUMS model group. After GABA or FWG intervention, the effects were reversed. FWG improved the depression-like behaviors of CUMS rats in the current study, including improved weight loss, increased percentage of OT, decreased despair, improved mobility, and alleviated pleasure deficit symptoms. These findings are consistent with the results from previous studies, involving fermented American red ginseng (ARG) and fermented GABA oolong tea [34,35]. Neurotransmitter deficiency is the root cause of depression. Most antidepressants function by enhancing monoamine neurotransmitters in the brain [36,37]. However, these drugs have their limitations and side-effects in clinical practice. We evaluated the levels of 5-HT, 5-HIAA, Ach, and GABA in the hippocampus tissue to study the effect of FWG on neurotransmitter release. FWG was observed to increase the levels of 5-HT, 5-HIAA, Ach, and GABA in rat hippocampus tissues, which is consistent with earlier research [38]. Hence, FWG is generally recognized as safe (GRAS) and may be a potential alternative to treat depression-like behavior without side-effects. According to emerging evidence, depression development may be linked to the gut flora [39,40,41]. Thus, we postulated that the anti-depressive effect of FWG might be correlated with intestinal microbiota and gut microbial function. The α-diversity data revealed that there was a remarkable decrease in both species’ richness and diversity in the CUMS model group in comparison with the blank control group (p < 0.05). Moreover, there was a general upward trend in both species’ richness and diversity after therapeutic intervention with GABA or FWG (p > 0.05). The PCoA results revealed that the intestinal flora of the CUMS model group differed significantly from that of the blank control group, validating the effects of chronic stress on the gut microbiota. The intestinal flora distribution in FGW rats was similar to that of the blank control group, suggesting that FWG intervention could change the structure of the intestinal flora. These findings were consistent with a recent study that discovered a noticeable change in the microbiota of depressed individuals after GABA-rich fermented milk intake [42]. Our findings are also congruent with previous research that found significant changes in the gut flora composition in rats with irritable bowel syndrome and depressed mice, which were fed with a barley and soybean fermentation mixture [43]. These results suggested that the anti-depressive effects of FWG might be mediated by the intestinal flora. We observed that the abundance of Lactobacillaceae and Erysipelotrichaceae increased in the rat fecal microbiota of the CUMS model group. In addition, there was a lower abundance of Peptostreptococcaceae, Lachnospiraceae, and Oscillospiraceae in the CUMS model group than in the blank control group. There was also an increase in the abundance of Lactobacillus and Marvinella in the rat fecal microbiota of the CUMS model group, and a significant decrease in the abundance of Romboutsia and unclassified _f__Lachnospiraceae in the CUMS model group. However, the Lactobacillus strain might damage the host’s neurological function and lead to dysbiosis [44]. In some studies, the abundance of the highly immunogenic and inflammation-related Erysipelotrichaceae significantly increased in depressed patients, as well as those with IBS and neurodegenerative diseases [45,46]. Furthermore, there is a correlation between IBS and depression [47]. The study discovered that patients with depression had remarkably lower levels of Peptostresptococcaceae in their gut than healthy controls [48]. In a separate study, the abundance of Peptostreptococcaceae was lower in the CUMS model than in the control group [49]. From the above findings, we inferred that Peptostreptococcaceae is a group of depression-associated bacteria. On the contrary, the abundance of Lachnospiraceae can affect the metabolism of short-chain fatty acids, intestinal permeability, and neurotransmitter secretion [48], and its abundance was significantly reduced in CUMS rats [50]. The unclassified _f__Lachnospiraceae belonged to the Lachnospiraceae family, and a study on the intestinal flora of Chinese children with autism spectrum disorders (ASDs) reported that the abundance of unclassified _f__Lachnospiraceae was significantly reduced in ASD patients [51]. In summary, we concluded that Lachnospiraceae might be an important marker for identifying neurological diseases, especially depression. According to a study on the gut microbiota of depressed people, the abundance of Oscillospiraceae was reduced [52]. In a study on fish oil improving depression-like behavior and intestinal flora dysbiosis in chronically mildly stressed rats, CMS increased the abundance of Marvinbryantia [53]. In contrast, a reduced abundance of Romboutsia in the intestinal flora of patients had been reported in studies on neurological diseases, such as Alzheimer’s disease and neurodegenerative disorders [54,55]. It could be observed that FWG downregulated the abundance of Lactobacillaceae, Erysipelotrichaceae, Lactobacillus, and Marvinbryantia, while increasing the abundance of Peptostreptococcaceae, Lachnospiraceae, Oscillospiraceae, Romboutsia, and unclassified__f__Lachnospiraceae (Figure 5 and Figure 6). Taken together, our findings and the corroborating evidence from the literature uncovered a relationship between changes in the intestinal flora and depression-like symptoms, as well as the efficacy of FWG treatment in restoring the intestinal microbiota in CUMS rats. Furthermore, the results of our Spearman correlation analysis (Figure 7) revealed an important role of gut microbiota in alleviating depression. A strong correlation between neurotransmitter levels and gut microbiota genus was also established, thus providing strong support for the brain–gut axis theory. PICRUSt2 functional prediction data disclosed that the model group had dysfunctional amino acid metabolism as compared to the blank control group, with increased biosynthesis of lysine and proline and decreased biosynthesis of tryptophan, GABA, glutamate, isoleucine, and histidine. GABA or FWG intervention decreased the biosynthetic functions of lysine and proline and increased the biosynthetic functions of tryptophan, GABA, glutamate, and isoleucine. According to several studies, the genes related to lysine biosynthesis were significantly elevated in the fecal flora of depressed rats [56]. Hence, proline is significantly and positively correlated with the severity of depression. In addition, proline supplementation was reported to aggravate depression and microbial translocation in mice [57], as the accumulation of proline disrupted GABA production, glutamate release, and synaptic transmission. On the other hand, tryptophan affects the body’s mood, sleep quality, and appetite, and tryptophan levels were significantly reduced in depressed patients [58]. GABA, a naturally occurring non-protein amino acid, is an essential inhibitory neurotransmitter in the mammalian central nervous system. Studies have reported that GABA affects the monoamine levels in the brain, such that GABA deficiency triggers emotions such as anxiety, restlessness, fatigue, and depression [59]. Likewise, GABA supplementation alleviated sleep disturbance in mice [60]. Glutamate, an acidic amino acid, is a key excitatory neurotransmitter in the central nervous system, and its dysregulation might be a major cause of depression [61]. Low glutamate levels manifest as decreased excitability, resulting in a low mental state, depression, tiredness, and sleeplessness. Isoleucine aids in blood sugar regulation, and its deficiency can cause fatigue, melancholy, disorientation, and irritability. In summary, we speculated that FWG can alleviate depressive symptoms in CUMS rats by restoring the metabolic balance of lysine, proline, tryptophan, GABA, glutamate, and isoleucine. It should be noted that the functional analysis of the intestinal flora in this study was performed by 16S rRNA sequencing technology to sequence and analyze very small DNA fragments in DNA extracts of the sample gut microbiota. Further mathematical computations were performed based on the sequencing data to predict the metabolic pathways. As a result, these data should be regarded as guidelines, and not as substantial evidence. With a rich database of whole-genome sequences of intestinal bacteria and a thorough annotation of their metabolic pathways, a precise understanding of intestinal microbial functions can be established. The involvement of lysine, proline, tryptophan, GABA, glutamate, and isoleucine in this study justified the requirement of future investigations, whereby untargeted metabolomics can be used to analyze more relevant metabolomics in cecum content samples. Overall, our findings are consistent with previous research, indicating that CUMS resulted in gut microbiota dysregulation and that FWG could alleviate depressive symptoms. More research is required to fully elucidate the mechanisms of FWG in relieving depression and other neurological conditions. In this study, we demonstrated that FWG improved depression-like behavior and increased neurotransmitter levels in the hippocampus of CUMS model rats. Our findings provided support for the role of intestinal microbiota in brain health and the anti-depressive effects of FWG in the gut-microbiota–brain axis. In particular, FWG could effectively change the intestinal microbiota structure of CUMS rats, remodel the intestinal microbiota, restore the neurotransmitter levels in depressed rats through the brain–gut axis, and recover amino acid metabolism functions. In conclusion, we believe that FWG has antidepressant potential. Our findings provide new ideas for the clinical treatment of depression and theoretical support for the development of safe novel antidepressant functional foods.

PMC10000868

Jurate Valciukiene,Kestutis Strupas,Tomas Poskus

Tissue vs. Fecal-Derived Bacterial Dysbiosis in Precancerous Colorectal Lesions: A Systematic Review

04-03-2023

intestinal microbiota,colorectal adenoma,colorectal neoplasm,polyp,gut,carcinoma in situ,tissue-derived,fecal-derived dysbiosis,mucosa samples,stool,bacteria

Simple Summary Although alterations of intestinal bacterial microbiota have been admitted as playing one of the most important roles in colorectal carcinogenesis, the links between microbiota compositional changes and premalignant colorectal polyps have still not been fully examined. Furthermore, there is a lack of knowledge in terms of defining the precise differences and the correct interpretation of tissue-derived and stool-based bacterial dysbiosis in patients with precancerous colorectal lesions. Thus, this systematic review aims, firstly, to assess whether and how the tissue-derived intestinal microbiota structure differs from the bacterial dysbiosis in fecal samples of patients with simple, advanced colorectal adenoma and carcinoma in situ, and, secondly, to propose the correct selection of each matrix in order to increase sampling accuracy and applicability in future microbiota studies and clinical practice. Abstract Alterations in gut microbiota play a pivotal role in the adenoma-carcinoma sequence. However, there is still a notable lack of the correct implementation of tissue and fecal sampling in the setting of human gut microbiota examination. This study aimed to review the literature and to consolidate the current evidence on the use of mucosa and a stool-based matrix investigating human gut microbiota changes in precancerous colorectal lesions. A systematic review of papers from 2012 until November 2022 published on the PubMed and Web of Science databases was conducted. The majority of the included studies have significantly associated gut microbial dysbiosis with premalignant polyps in the colorectum. Although methodological differences hampered the precise fecal and tissue-derived dysbiosis comparison, the analysis revealed several common characteristics in stool-based and fecal-derived gut microbiota structures in patients with colorectal polyps: simple or advanced adenomas, serrated lesions, and carcinomas in situ. The mucosal samples considered were more relevant for the evaluation of microbiota’s pathophysiological involvement in CR carcinogenesis, while non-invasive stool sampling could be beneficial for early CRC detection strategies in the future. Further studies are required to identify and validate mucosa-associated and luminal colorectal microbial patterns and their role in CRC carcinogenesis, as well as in the clinical setting of human microbiota studies.

Tissue vs. Fecal-Derived Bacterial Dysbiosis in Precancerous Colorectal Lesions: A Systematic Review Although alterations of intestinal bacterial microbiota have been admitted as playing one of the most important roles in colorectal carcinogenesis, the links between microbiota compositional changes and premalignant colorectal polyps have still not been fully examined. Furthermore, there is a lack of knowledge in terms of defining the precise differences and the correct interpretation of tissue-derived and stool-based bacterial dysbiosis in patients with precancerous colorectal lesions. Thus, this systematic review aims, firstly, to assess whether and how the tissue-derived intestinal microbiota structure differs from the bacterial dysbiosis in fecal samples of patients with simple, advanced colorectal adenoma and carcinoma in situ, and, secondly, to propose the correct selection of each matrix in order to increase sampling accuracy and applicability in future microbiota studies and clinical practice. Alterations in gut microbiota play a pivotal role in the adenoma-carcinoma sequence. However, there is still a notable lack of the correct implementation of tissue and fecal sampling in the setting of human gut microbiota examination. This study aimed to review the literature and to consolidate the current evidence on the use of mucosa and a stool-based matrix investigating human gut microbiota changes in precancerous colorectal lesions. A systematic review of papers from 2012 until November 2022 published on the PubMed and Web of Science databases was conducted. The majority of the included studies have significantly associated gut microbial dysbiosis with premalignant polyps in the colorectum. Although methodological differences hampered the precise fecal and tissue-derived dysbiosis comparison, the analysis revealed several common characteristics in stool-based and fecal-derived gut microbiota structures in patients with colorectal polyps: simple or advanced adenomas, serrated lesions, and carcinomas in situ. The mucosal samples considered were more relevant for the evaluation of microbiota’s pathophysiological involvement in CR carcinogenesis, while non-invasive stool sampling could be beneficial for early CRC detection strategies in the future. Further studies are required to identify and validate mucosa-associated and luminal colorectal microbial patterns and their role in CRC carcinogenesis, as well as in the clinical setting of human microbiota studies. With more than 1.9 million new cases and 935,000 deaths, colorectal cancer (CRC) is the third most diagnosed and the second leading cause of death among cancers worldwide [1]. Several risk factors are associated with the development of CRC through a conventional adenoma-carcinoma sequence and serrated pathways. Such factors include genetical mutations, environmental, lifestyle, and dietary habits. Nevertheless, compositional changes in gut microbiota and a shift in the diversity and distribution of bacterial communities determine increased mucosal permeability, bacterial translocation, and the activation of the immune system, causing chronic inflammation and CRC [2,3,4,5]. The collection of bacteria, archaea and eukarya colonizing the human GI tract is termed the human gut microbiota, while the entire habitat (intestines), including the microorganisms, their genes, and the surrounding environmental conditions, is commonly called the gut microbiome. As for gut bacterial dysbiosis, it describes the altered composition and reduced diversity of core bacterial communities living in the gut [2,4]. Recently, several clinical trials investigating the role of intestinal bacterial dysbiosis in the stages of colorectal carcinogenesis have been published [4,5,6,7]. Multiple studies have shown a strong link between alterations in the human intestinal microbiota and the presence of carcinoma lesions in the colorectum [5,6,7,8,9]. Furthermore, some of the microbes, such as Fusobacterium nucleatum (F. nucleatum), Streptococcus gallolyticus (S. bovis), enterotoxigenic Bacteroides fragilis (ETBF), pks (polyketide synthase) + Escherichia coli, Enterococcus faecalis, Peptostreptococcus anaerobius and Parvimonas micra, etc., were accepted as CRC-associated bacteria [10,11,12,13,14,15]. Colorectal adenoma (CRA) is a precancerous lesion of CRC, and recent research also finds it to be associated with an altered gut bacterial community structure, a lower richness, and a higher abundance of proinflammatory bacteria [15,16,17,18,19]. While emerging evidence suggests a link between the gut microbiota and CRC, it is hard to say that certain bacteria play an exceptional causal role in CRC, where secondary alterations in the local gut microbiota due to chronic inflammation dominate. In contrast, CR adenoma as a premalignant state does not induce severe local inflammation and consequent changes in the gut microbiota. Therefore, the indisputable detection of a statistically significant correlation between adenomatous colorectal (CR) polyps, as an early stage of the adenoma-carcinoma cascade, and intestinal microbial dysbiosis, would potentially imply primary microbiota’s role in CR tumorigenesis [20]. Sample collection is another challenging step in human microbiome studies. With the expansion of research in the field, advanced invasive and non-invasive examination models have been engaged in the detection of CRC-associated bacteria and the overall bacterial composition in human samples. Since the 1990s, molecular tools targeting the bacterial 16S ribosomal RNA (rRNA) gene have been applied for the explicit evaluation of the gut microbiota from both feces and tissues. Although both tissue and fecal specimens provide useful information about the composition of gut bacterial communities, most of the studies on the gut microbiome, including those related to CRC and CR adenoma, are still based only on fecal samples, as an easy and non-invasive procedure [21,22]. On the contrary, other clinical trials find no statistically significant association between premalignant CR polypoid lesions and an increase in CRC-associated bacteria in stool samples compared with gut mucosal biopsies, where an increased F. nucleatum abundance has been recognized [23]. Similarly, while some researchers state that bacterial community compositions in feces and mucosa differ completely [23,24], others believe that similar variations in CRC bacterial species can be identified between stool samples and gut mucosal biopsies [25,26]. Moreover, several studies suggest that tissue samples are more relevant for the evaluation of microbiota’s pathophysiological involvement in CR carcinogenesis, while stool samples are more powerful for identifying noninvasive diagnostic or prognostic markers of CRC [23,24,27]. Thus, a discussion arises from the detection of different intestinal microbiota shifts in mucosal and fecal samples of patients with CR neoplasia. Moreover, the proper employment of a sampling matrix and its accurate analysis for the examination of intestinal microbiota’s structural and functional composition is still lacking. Therefore, there is a need for a profound systematic literature review, firstly, to assess the difference between mucosa-associated (tissue) and luminal (fecal) intestinal microbiota alterations in patients with precancerous colorectal lesions (simple and advanced conventional adenoma, serrated adenoma) and preinvasive cancer (carcinoma in situ (Ca in situ)), compared with healthy control and/or self-control groups, and, secondly, to suggest the potentially correct implementation and assessment of tissue and stool samples in human gut microbiota studies and in the clinical field. The following could induce a new research era based on a comprehensive methodology and the accurate use of a selected type of matrix for the precise analysis of CR carcinogenesis. This would also contribute to the validation of preventive measures for the early detection of colorectal neoplasms, as well as the management of the affected gut microbiota in premalignant mucosal changes prior to the development of CRC. The present systematic review was performed according to the Cochrane collaboration-specific protocol [28] and was reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist [29]. The PRISMA checklist was completed according to the mentioned recommendations (Table S1). The present systematic review was prospectively registered in PROSPERO (ID No.: CRD42022376106). Studies that examined the link between CRA or Ca in situ, as precancerous colorectal lesions, and the intestinal tissue- and/or fecal-derived intestinal microbiota composition were eligible for inclusion. The search was restricted to human studies published in the English language from 2012 until November 2022. These studies included adult patients (≥18 years) with a diagnosed CR advanced/non-advanced adenoma, serrated polyps, or Ca in situ undergoing a complete examination of the tissue and/or stool-based bacterial microbiota community structure. Advanced adenomas were defined as those with high-grade dysplasia, villous or tubulovillous histology, or a diameter ≥1 cm, while serrated polyps included sessile serrated (SSA) and traditional serrated adenomas (TSA). The analysis included only studies with a complete bacterial community assessment and healthy or the same patients’ paired normal samples as controls. A literature search was performed in the PubMed and Web of Science online databases to identify original comparative studies analyzing the colorectal mucosa-associated and/or luminal microbiota composition in patients with premalignant (adenoma) and preinvasive (Ca in situ) colorectal neoplasia. The most recent search was performed in November 2022. We used the following combination of Medical Subject Headings (MeSH) and keywords with the employment of “AND” or “OR” Boolean operators: “Colorectal adenoma” OR “Colorectal polyp” OR “Colorectal polypoid lesion” OR “Colorectal precancerous lesion” OR “Colorectal neoplasms” OR “Colorectal neoplasia” OR “Colonic neoplasia” AND “Serrated adenoma” OR “Serrated polyp” AND “Colorectal carcinogenesis” OR “Colorectal tumorigenesis” OR “Adenoma-carcinoma sequence” AND “Gut microbiome” OR “Gut microbiota” OR “Intestinal microbiome” OR “Intestinal microbiota” OR “Gut dysbiosis” OR “Intestinal dysbiosis” AND “Mucosa-adherent” OR “Tissue-derived” OR “Mucosa-associated” OR “Stool-based” OR “Luminal” OR “Fecal-derived”. All titles and abstracts identified in the electronic databases were screened by two experienced reviewers independently of one another using a piloted electronic database (Microsoft Excel). Following the identification of relevant abstracts, full-text articles were retrieved and re-reviewed. Comments on articles, short notes, letters, conference abstracts, systematic reviews, meta-analyses, review articles, preclinical studies, and duplicates were manually excluded. A manual search was performed to identify additional primary studies and minimize search bias. The literature review was completed with an extensive search using the “related articles” function in PubMed. Studies which did not analyze tissue- and/or stool-based bacterial gut microbiota’s structure in patients with colorectal premalignant or preinvasive neoplasms in comparison with healthy or self-controls and/or CRC were excluded. The endpoint measures of the current review consisted of tissue and fecal-derived microbiota’s bacterial compositional diversity in CRA and Ca in situ. The following data were extracted from each study: first, the author’s name, the date of publication, the sample size, including the number of cases and controls, the microbiota examination method and the matrix type, the abundance and/or prevalence of CRC-associated and other bacteria, α- and β-diversity, and the other main findings of the study. The term α-diversity was described as the variation of microbes in a single sample and expressed by richness (that is, the number of taxa present in a sample) and evenness (that is, how evenly distributed the taxa are within a sample). Contrarily, β-diversity was determined by the variation in microbial communities between the samples in terms of ecological distance, likely reflecting the presence and absence of some rare species. The extracted data was only evaluated at the end of the reviewing process to reduce the selection bias. The methodological quality of the selected trials was assessed using the Cochrane Handbook method [28]. For evaluating the quality of non-randomized trials, items of risk in patient selection, baseline comparability, and outcomes/exposure selection and measurement were judged using the Newcastle–Ottawa scale (NOS) [30]. We rated the quality of the studies by awarding stars in each domain as follows: a “good” quality score required 3 or 4 stars in selection, 1 or 2 stars in comparability, and 2 or 3 stars in outcomes; a “fair” quality score required 2 stars in selection, 1 or 2 stars in comparability, and 2 or 3 stars in outcomes; a “poor” quality score was reflected by 0 or 1 star(s) in selection, or 0 stars in comparability, or 0 or 1 star(s) in outcomes. Only good and fair quality studies were included in the further analysis. A summary of the quality evaluation process has been visualized in Table 2 and Table S2 (extended version). The initial search yielded 292 results; after removing duplicates, 286 articles were screened for eligibility based on the title and abstract, and 64 articles were retrieved for a full-text evaluation. These were assessed for eligibility. A total of 35 were excluded as ineligible for inclusion: 4—review articles, 3—editorials, 1—video vignette, 3—conference abstracts, 5—inadequate data, 3—overlapping data, 4—no appropriate control, and 12—animal/cells study. Three studies were included additionally after the search update. All the included studies were observational: cohort, cross sectional, and case-control studies. No randomized control trials were identified. A total of 32 studies fulfilled the inclusion criteria and were finally selected for a qualitative analysis (Figure 1). The included studies were grouped according to the utilized samples and study goals, as follows: (a) studies investigating the composition of gut bacterial tissue-derived microbiota (n = 11), (b) studies examining the structure of gut bacterial stool-based microbiota (n = 14), and (c) those investigating the composition of both tissue- and/vs. fecal-derived gut bacterial microbiota (n = 7) in CRA and Ca in situ. Most of the studies included used the same ‘human gut microbiota’ term for the evaluation of bacterial communities prevailing in the gut, and the term ‘bacterial dysbiosis’ for the examination of gut bacterial composition and diversity changes. Very few studies referred to the ‘microbiome’ [27,31,32,33,34] and ‘metabolome’ [32,35,36] as study outcomes, and these generally served as a data supplementing factor for the review. Of the studies included, the comparison of the microbiota was often between conventional adenoma without further specification of type [18,23,24,25,27,30,33,36,37,38,39,40,41,42,43,44,45,46,47,48] or adenoma classified as advanced and non-advanced [32,34,35,49,50,51,52,53], and healthy controls. However, there were few studies that investigated the microbiota’s composition in sessile serrated polyps (SSPs) and traditional serrated adenomas (TSAs) in the serrated pathway specifically [31,51,54]. Only one study aimed to compare microbiota between patients with Ca in situ vs. healthy controls [55]. Eleven studies used DNA analysis with 16S rRNA gene sequencing [27,33,35,37,38,40,45,48,49,51,52] and one was a metagenome-wide association study (MGWAS) [50]. One study used shotgun metagenomic sequencing [39], one used qPCR with liquid (LC−TOFMS) and gas (GC−TOFMS) chromatography time-of-flight mass spectrometry [36], and one used terminal restriction fragment length polymorphism (T-RFLP) with next-generation sequencing (NGS) [55]. The remaining study used ENTERO-test 24 plus MALDI-TOF mass spectrometry [46]. Five other studies also used 454-pyrosequencing [18,25,41,43,44], while five used qPCR [23,24,25,26,47], one used high-throughput sequencing (HTS) and fluorescence in situ hybridization (FISH) [42], one used PCR and FISH [54], and one used only HTS [53]. One study used high-performance liquid chromatography (HPLC) [34], one used ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) metabolomics [32], one used internal transcribed spacer (ITS) ribosomal RNA sequencing and whole-genome shotgun sequencing (WGS) [31], and one used metagenomic sequencing [30] in addition to 16S rRNA gene sequencing. All the included studies investigated the association between gut bacterial dysbiosis in fecal and/or intestinal tissue samples and precancerous colorectal lesions (and/or CRC). Two parts of the included studies additionally examined the following: the relationship between metabolites and the metagenome-metabolome [32,34,35,36]; genetic mutations [48]; the presence of mucosal biofilm [42]; diagnostic biomarkers [26,27,33,39,40,52,53]; enterotypes and clusters [24,38]; the intracellular microbiota structure [46]; location-specific microbiota [24,54]; “on” and “off” tumor bacterial differences [24]; and the fungal community composition [31] in patients with colorectal polyps. Twelve studies were evaluated as representative with an estimate of more than 100 subjects per case group. Most of the studies used tissue specimens directly from the lesion [23,26,42,43,44,45,46,47,48,53,54], while others preferred non-tumor colon or rectal mucosa sampling [25,36,41] for the case group analysis. In several studies both “on” and “off” tumor sampling was planned [24,27,30,31]. Seventy-eight percent of the included studies used stool samples and/or intact colorectal mucosa specimens from healthy patients for the control group [18,23,24,25,26,27,30,31,32,33,34,35,36,37,38,39,40,41,43,45,49,50,51,52,54,55]. In other studies, paired adjacent normal mucosa samples were employed for self-control group formation [47,48]. The remaining studies used both controls [42,44,46,53]. One trial also included adenoma up to 1 cm in addition to neoplasia-free colon tissue sampling for the control group [30]. The characteristics and outcomes of the studies are displayed in Table 1. Additionally, the extended version of the table explicitly describing the type of matrix, the gut microbiota’s structure, and its compositional shifts in the fecal and tissue samples of patients with colorectal adenoma, and/or colorectal cancer vs. healthy controls is provided in a supplementary material file (Table S3). Based on the NOS assessment [56], 16 studies had a score of 5/9, 9 studies scored 6/9, and 7 studies scored 7/9 (Table 2). Overall, a high heterogeneity was observed in the study designs, study populations, and the examination methods of the gut microbiota’s composition. This systematic review revealed certain differences in the gut microbiota diversity and abundance of bacteria in patients with colorectal adenomas and Ca in situ compared to healthy adults. This is supported by most of the included studies. However, several studies reported no significant difference in microbiota diversity [45,48,52], while others did not report any difference in the microbiota’s bacterial composition between subjects with precancerous colorectal lesions and healthy controls [32,55]. Among microbiota’s compositional alterations, the most common were an increased abundance of Fusobacterium, Escherichia-Shigella, Coprococcus, Streptococcus, Enterococcus, and/or Ruminoccocus spp. and a reduction in Actinobacteria, Firmicutes, Eubacteria, Bifidobacterium, Lactobacillus, and butyrate-producing bacteria (Clostridium, Roseburia, Eubacterium, Blautia, and Dorea spp). These were evident in both mucosal and fecal samples in colorectal adenoma vs. healthy controls [23,25,27,37,49,51,55]. No consensus in the α-diversity and β-diversity was evident between the patterns of tissue- and fecal-derived microbiota in preneoplastic colorectal lesions. Overall, a reduction in the diversity/richness of bacterial species in the intestinal microbial community was detected in both tissue and stool samples. While comparing stool-based microbiota composition between the case and control, eight bacterial species (Actinomyces odontolyticus, Bacteroides fragilis, Clostridium nexile, Fusobacterium varium, Heamophilus parainfluenzae, Prevotella stercorea, Streptococcus gordonii, and Veillonella dispar) and four bacterial genera (Actinomyces, Atopobium, Fusobacterium, and Heamophilus) were significantly associated with the Ca in situ group [55]. Here, the control group significantly differed with the predominant genus being Slackia and sp. Eubacterium coprostanoligens, which is a cholesterol-reducing bacteria and potentially acts as an inhibitor of CRC. However, this observational study was the only one included in the review that examined microbiota changes in patients with colorectal Ca in situ. In addition, there were only six patients forming the case group, leading to some debate on its scientific weight. Laterally spreading tumors (LSTs) as primary precursors of CRC, due to its special morphology and growth pattern, are extremely difficult to identify during a simple colonoscopy. Thus, there is a need for new sensitive early detection methods, e.g., fecal biomarkers. Interestingly, LSTs are rarely investigated in the light of microbiota signatures. For instance, Shen et al. have demonstrated an increased fecal abundance of the three bacteria ETBF, Peptostreptococcus stomatis (P. stomatis), and Parvimonas micra (P. micra) with considerably high sensitivity and specificity in detecting LST, while tissue-derived microbiota’s composition was shown to be associated with an increase in genus Lactobacillus-Streptococcus and the spp. ETBF–P. stomatis–P. micra [26]. These oral bacteria are defined as early noninvasive biomarkers of LSTs and potentially could also predict the adenoma recurrence risk after resections. It is worth noting that the number of included studies that differentiated non-advanced adenomas (NAA) from advanced (AA) [32,34,35,49,50,51,52,53], and conventional adenomas from sessile serrated polyps (SSPs) and traditional serrated adenomas (TSAs) was rather low [31,51,54]. Different quantities of bacterial abundance were present at AA in comparison to NAA and HC. Several of the included studies demonstrated a statistically significant decrease in the butyrate producing bacteria, Roseburia, Eubacteria, and Clostridia [49]. Others found a considerable increase in Fusobacterium, Enteroccocus, and Bacteroidetes [50], while Firmicutes phylum and the Firmicutes: Bacteroidetes ratio were depleted in fecal samples of AA, though with no significant difference among the three groups (AA, CRC, and HC) [32]. Hale et al., with an estimated 780 patients included in their trial, reported a statistically significant increase in four genera: Bilophila, Desulfovibrio, Sutterella, and Mogibacterium in the stool samples of patients with diagnosed AA compared to healthy individuals. Bilophilia and Desulfovibrio are known to produce H2S and secondary bile acids, which act as a catalyzer in the A development of CRC [35]. A consistent increase in the genera Fusobacterium, Tyzzerella 4, Phascolarctobacterium, Clostridium sensu stricto 1; Streptococcus, Gemella, Actinomyces, and Terrisporobacter was observed in the fecal microbiota signatures of AA patients vs. healthy controls in a large sample size (n = 758) study. These microbial patterns could potentially supplement fecal immunohistochemical tests for the early non-invasive detection of CRA [52]. The most prominent change in colon tissue specimens of AA vs. healthy controls revealed increased Halomonadaceae and Shewanella algae and depleted Coprococcus and Bacteroides ovatus [53]. Peters et al. divided lesions into proximal and distal, AA and NAA, conventional adenomas (CA) and serrated polyps (SP). Their results showed a lower richness in CA, and especially in AA, and an enrichment of the genera Actinomyces and Streptococcus and a decrease of Erysipelotrichi and Clostridia in SSA compared to healthy controls. Colorectal serrated lesions were linked to the proximal colon location and microbiota dysbiosis was directly dependent on the severity of the lesion along the adenoma-carcinoma sequence and serrated pathway [51]. An increase in the abundance of the genus Fusobacterium was observed in people with serrated colorectal lesions in the reviewed studies [51,54], which was consistent with the literature on F. nucleatum’s primary role in the serrated neoplasia pathway [57]. Moreover, the analysis revealed well-known variations in the CRC-related bacteria found in tissue and stool samples. Lactobacillales were enriched in tumor tissue, Fusobacterium, Porphyromonas, Peptostreptococcus, Gemella, Mogibacterium, and Klebsiella were present in mucosa-adherent flora, while Erysipelotrichaceae, Prevotellaceae, and Coriobacteriaceae were highly abundant in the gut lumen of CRC patients. These prevailing bacterial communities may be related to secondary alterations in the microenvironment of CRC rather than playing a primary role in colorectal carcinogenesis. In contrast, precancerous colorectal lesions, having fewer genetic mutations and only subtle biochemical mucosal changes, have more potential to relate to the discovery of dysbiosis with initiation and acceleration processes in CRC development. Either way, these microbial signatures may resemble those presumably less severe microbiota compositional changes in precancerous colorectal lesions, adjacent tissue, and colon lumen [49,58]. The recent meta-analysis from Mo et al. concluded that the dysbiosis of the off-site (adjacent) tissue in CRC is distinctive and predictive. Tumor-adjacent tissue should not be regarded as healthy tissue and should not be used for self-controls, especially without a healthy control group [59]. In our systematic review, only a few studies employed paired adjacent tissue as self-control samples [47,48], while others used self-controls in addition to healthy patient control groups [42,44,46,53]. Several trials used normal rectal or colon mucosa samples for the case group formation [25,36,41]. Another controversial issue is the formation of biofilm in the colorectum. Biofilm is known as aggregations of microbial communities in a polymeric matrix that adhere to either biological or nonbiological surfaces, especially the colonic mucus layer coming into close contact with the mucosal epithelium itself. This contact eventually leads to altered epithelial functions and procarcinogenic tissue inflammation. One study included in the systematic review revealed a clear association between the presence of biofilm and diminished colonic epithelial cell E-cadherin, enhanced epithelial cell IL-6, and Stat3 activation. Moreover, biofilms were detected not only in tumors, both CRA and CRC, but also on tumor-free mucosa far distant from the tumors. Biofilm detection correlated with bacterial tissue invasion and changes in tissue biology with activated cellular proliferation and microbial dysbiosis [42]. Though the findings were inconsistent, ultimately, the majority of the studies reported statistically significant changes in microbial communities in patients with preneoplastic lesions after examining both tissue and stool samples. Regarding gut microbiota patterns and diversity in fecal vs. tissue samples in people with premalignant colorectal lesions compared to healthy controls, the results remain inconclusive. Several of the included studies declared similar variations in the microbial communities [25,26], while others reported fecal and mucosa-associated microbiotas to be completely distinct and different in composition and diversity [23,24,27]. The most common microbial signatures are displayed in Figure 2. The current literature links microbial dysbiosis with CRC and colorectal precursor lesions. Through exploring the gut microbiota’s structural composition, interactions with the genome, immunome and metabolome, the main goal is to enable the creation of novel and tailored prevention, screening, and therapeutic interventions [59]. According to the included studies’ aims, objectives and outcomes, there is a certain methodology and distinct recommendations for the right selection of matrix. Here we list the main pros and cons for each type of the aforementioned specimens used in gut microbiota studies (Figure 3). All the studies included were observational. We did not identify any randomized controlled trials which would meet the eligibility criteria and would be positively quality evaluated for inclusion. The efforts to avoid bias could have been hindered by the fact that non-English trials were not included in the review. Moreover, due to the low number of studies examining gut microbiota composition in both, tissue and/or/vs. fecal samples, we additionally included trials investigating mucosa-associated microbiota alone and those with the aim of examining luminal microbiota (marked accordingly, see Table 1). Similarly, considering the small number of studies looking only at precancerous lesions, we included those looking at premalignant lesions, preinvasive cancer, and CRC along the adenoma-carcinoma sequence. In addition, the results could be hampered by the different study sample sizes, different study populations (according to age, gender, diet, BMI, geographic location, and behavioral factors such as smoking, alcohol consumption, physical activity), different controls (healthy and/or paired normal tissue as self-controls), and different methodologies for the examination of the microbiota composition. The outcomes between the trials, including microbial diversity as well as the abundance of bacteria at phyla, family, and genus taxonomic levels in patients with precancerous colorectal lesions, were inconsistent and at some points, incomparable. Therefore, large sample-size studies examining the composition of gut microbiota in tissue and/or/vs. fecal samples and sharing their metadata are necessary in the future. Considering the large amount of upcoming clinical trials in the field, it is time to rethink our methods and the standardization of specific research practices. There is a significant need for overall recommendations for metagenomic studies which could ensure conceptual results, better comparability, the re-use of metadata, and thus more valuable research input. The main suggestions are as follows: seek larger sample sizes; use both stool and tissue samples; examine all stages of CRC carcinogenesis; think of both conventional and serrated pathways; continue studies in comprehensive methodology; keep the important data on the type of lesions and the site of sampling performed; consider examination which provides researchers with metabolic data (shotgun sequencing) as well; keep metadata open for the availability of the research community [19,59,60]. Complete network studies investigating the interactions among gut microbiota, diet, the metabolome, genetical alterations, and local immunity responses are paramount for better CRC diagnosis and prevention strategies [60,61,62]. Likewise, understanding the tissue- and fecal-pattern of gut microbiota structure may contribute to novel strategies, such as the early noninvasive stool-based detection of colorectal adenomas and appropriate additional treatment with pre/probiotics, or immunotherapy in people with colorectal neoplasms [63,64]. Emerging evidence suggests that gut dysbiosis is one of the major players in the initiation and development of CRC. Ultimately, the findings of this systematic review demonstrate that precancerous colorectal lesions are associated with alterations in gut microbiota composition in both mucosal and fecal samples, in comparison to healthy and self-controls. The majority of studies examining the tissue-associated and/vs. fecal-based structure of microbiota declare a higher presence of Fusobacterium, Escherichia-Shigella, Coprococcus, Streptococcus, Enterococcus, and/or Ruminoccocus spp. and a lower abundance of Actinobacteria, Firmicutes, Eubacteria, Bifidobacterium, Lactobacillus, and butyrate-producing bacteria (Clostridium, Roseburia, Eubacterium, Blautia, and Dorea spp.) in both fecal and tissue specimens, and Faecalibacterium in stool samples from patients with precancerous colorectal lesions compared to healthy controls. Mucosa samples are becoming more relevant in the evaluation of microbiota’s pathophysiological involvement in CR carcinogenesis, while stool samples are more powerful for identifying noninvasive diagnostic or prognostic markers in CRC. Due to the high heterogeneity in terms of methodology and sample size among the included studies, the results are inconclusive. Therefore, further studies with a larger sample size, comprehensive study design, and precise sampling selection are paramount to identify and validate tissue- and fecal-derived colorectal microbial patterns and their role in CRC carcinogenesis. Understanding both the mucosa-associated and luminal pattern of gut microbiota composition could also contribute to CRC diagnosis, prevention, and treatment.

PMC10000871

Phuong-Thu Mai,Daejin Lim,EunA So,Ha Young Kim,Taner Duysak,Thanh-Quang Tran,Miryoung Song,Jae-Ho Jeong,Hyon E. Choy

Constitutive Expression of a Cytotoxic Anticancer Protein in Tumor-Colonizing Bacteria

27-02-2023

Escherichia coli,Salmonella enterica serovar Gallinarum,anticancer protein expression,host response,bacterial cancer therapy

Simple Summary This study examined the biodistribution of Escherichia coli and an attenuated strain of Salmonella enterica serovar Gallinarum with defective ppGpp synthesis after injection into tumor-bearing mice through the tail vein. Bacteria targeting tumor tissues, but not those in the liver and spleen, were metabolically active and proliferated substantially. Recombinant bacteria derived from the attenuated Salmonella enterica serovar Gallinarum that constitutively expressed transforming growth factor α (TGFα) fused to a modified Pseudomonas exotoxin A (PE38) showed marked antitumor effects on tumor-bearing mice without any notable systemic toxicity. Abstract Bacterial cancer therapy is a promising next-generation modality to treat cancer that often uses tumor-colonizing bacteria to deliver cytotoxic anticancer proteins. However, the expression of cytotoxic anticancer proteins in bacteria that accumulate in the nontumoral reticuloendothelial system (RES), mainly the liver and spleen, is considered detrimental. This study examined the fate of the Escherichia coli strain MG1655 and an attenuated strain of Salmonella enterica serovar Gallinarum (S. Gallinarum) with defective ppGpp synthesis after intravenous injection into tumor-bearing mice (~108 colony forming units/animal). Approximately 10% of the injected bacteria were detected initially in the RES, whereas approximately 0.01% were in tumor tissues. The bacteria in the tumor tissue proliferated vigorously to up to 109 colony forming units/g tissue, whereas those in the RES died off. RNA analysis revealed that tumor-associated E. coli activated rrnB operon genes encoding the rRNA building block of ribosome needed most during the exponential stage of growth, whereas those in the RES expressed substantially decreased levels of this gene and were cleared soon presumably by innate immune systems. Based on this finding, we engineered ΔppGpp S. Gallinarum to express constitutively a recombinant immunotoxin comprising TGFα and the Pseudomonas exotoxin A (PE38) using a constitutive exponential phase promoter, the ribosomal RNA promoter rrnB P1. The construct exerted anticancer effects on mice grafted with mouse colon (CT26) or breast (4T1) tumor cells without any notable adverse effects, suggesting that constitutive expression of cytotoxic anticancer protein from rrnB P1 occurred only in tumor tissue.

Constitutive Expression of a Cytotoxic Anticancer Protein in Tumor-Colonizing Bacteria This study examined the biodistribution of Escherichia coli and an attenuated strain of Salmonella enterica serovar Gallinarum with defective ppGpp synthesis after injection into tumor-bearing mice through the tail vein. Bacteria targeting tumor tissues, but not those in the liver and spleen, were metabolically active and proliferated substantially. Recombinant bacteria derived from the attenuated Salmonella enterica serovar Gallinarum that constitutively expressed transforming growth factor α (TGFα) fused to a modified Pseudomonas exotoxin A (PE38) showed marked antitumor effects on tumor-bearing mice without any notable systemic toxicity. Bacterial cancer therapy is a promising next-generation modality to treat cancer that often uses tumor-colonizing bacteria to deliver cytotoxic anticancer proteins. However, the expression of cytotoxic anticancer proteins in bacteria that accumulate in the nontumoral reticuloendothelial system (RES), mainly the liver and spleen, is considered detrimental. This study examined the fate of the Escherichia coli strain MG1655 and an attenuated strain of Salmonella enterica serovar Gallinarum (S. Gallinarum) with defective ppGpp synthesis after intravenous injection into tumor-bearing mice (~108 colony forming units/animal). Approximately 10% of the injected bacteria were detected initially in the RES, whereas approximately 0.01% were in tumor tissues. The bacteria in the tumor tissue proliferated vigorously to up to 109 colony forming units/g tissue, whereas those in the RES died off. RNA analysis revealed that tumor-associated E. coli activated rrnB operon genes encoding the rRNA building block of ribosome needed most during the exponential stage of growth, whereas those in the RES expressed substantially decreased levels of this gene and were cleared soon presumably by innate immune systems. Based on this finding, we engineered ΔppGpp S. Gallinarum to express constitutively a recombinant immunotoxin comprising TGFα and the Pseudomonas exotoxin A (PE38) using a constitutive exponential phase promoter, the ribosomal RNA promoter rrnB P1. The construct exerted anticancer effects on mice grafted with mouse colon (CT26) or breast (4T1) tumor cells without any notable adverse effects, suggesting that constitutive expression of cytotoxic anticancer protein from rrnB P1 occurred only in tumor tissue. Bacterial cancer therapy relies on the inherent traits of certain facultative anaerobic bacteria that are capable of intratumoral penetration and localization in hypoxic areas presumably because of chemotaxis toward molecules produced by tumors and the immune-privileged environment of tumors [1,2,3]. Among gram-negative anaerobes, Salmonella spp. has prevailed as a therapeutic candidate because it is amenable to genetic manipulation and can trigger an immune response [4,5,6,7,8,9]. The initial response to the bacterial colonization of tumors is the secretion of the proinflammatory cytokine TNFα by innate immune cells, which causes a hemorrhage in the tumor and the formation of large necrotic regions [10,11]. The hemorrhage and necrosis formation induce tumor growth retardation. This is followed by the strong adjuvant effect of tumor-specific T cells that are activated by the colonizing bacteria [12,13,14,15,16]. CD8+ cytotoxic T cells are the main type of cells that counteract tumor growth. Tumor-colonizing bacteria can be used as a delivery system for therapeutic molecules that promote tumor elimination. However, this type of application is problematic for pathogenic Salmonella spp. that can invade animal cells and reside within membrane-bound compartments, namely, Salmonella-containing vacuoles (SCVs) [17,18]. The transportation of anticancer proteins expressed by SCV-bound Salmonella into the cancer cell cytosol is another potential complication; therefore, Salmonella should be prevented from invading host cells. Another challenging aspect of this approach is the specific targeting of cytotoxic anticancer proteins to solid tumors but not to the reticuloendothelial system (RES), the liver and spleen, where most of the bacteria are initially trapped [19,20,21]. To overcome this problem, bacteria are often genetically engineered to express a specific cytotoxic anticancer gene only when they accumulate in tumor tissues after being eliminated from the RES to prevent damage to these organs by toxic therapeutic molecules. We previously used the PBAD promoter from the E. coli arabinose operon, which can be activated by L-arabinose to induce the selective expression of cytotoxic anticancer proteins, although multiple injections of the inducer can cause problems for patients [20,22,23,24,25]. In this study, we determined the fate of the E. coli K12 strain MG1655 and an attenuated strain of Salmonella enterica serovar Gallinarum (S. Gallinarum), in which ppGpp synthesis was disabled after intravenous injection into tumor-bearing mice. The results showed that the bacteria that colonized the tumor tissue proliferated vigorously, whereas those in the RES were metabolically inert and were cleared in a short time by the innate immune cells. The ΔppGpp strain of avian-specific S. Gallinarum, the antitumor characteristics of which will be described in a separate manuscript, is defective in host cell invasion or intracellular survival [26]. We engineered ΔppGpp S. Gallinarum to express an immunotoxin (TGFα-PE38, TP) using the constitutive exponential phase promoter, the ribosomal RNA promoter rrnB P1, and found that it has remarkable antitumor effects on tumor-bearing mice without causing any adverse effects. Bacterial strains and plasmids are listed in Table 1. E. coli K-12 MG1655 and S. enterica serovar Gallinarum clinical isolate (SG4021) were used as wild-type strains and cultured in Luria Bertani broth (LB, Difco Laboratories, Franklin Lakes, NJ, USA). The ΔppGpp S. Gallinarum (SG4023) was constructed from SG4021 using the λ red system to disrupt the relA and spoT genes as described previously [26]. The ΔppGpp ΔglmS S. Gallinarum (SG4030) was constructed by p22 phage transduction as described previously [27] using 10% N-acetyl-D-glucosamine in LB medium. To monitor rrnB P1 promoter activity, the prrnBP1-gfpOVA plasmid was constructed by Gibson assembly as follows: first, the rrnB P1 promoter containing Fis-binding sites in the upstream activation region (−154–+3) was amplified from the E. coli K-12 MG1655 chromosome [28] and cloned into a reporter gene (gfpOVA) by replacing the katG promoter sequence in the pkatG::gfpOVA plasmid [29]. The rrnB P1 promoter replaced the araBAD promoter sequence in the pSEC-TGFα-PE38 plasmid [25] with the specific primer set listed in Table 2, generating the prrnBP1-psp-TP plasmid. The balanced-lethal system based on the glmS gene was introduced into prrnBP1-psp-TP in the claI site to maintain the plasmid in vivo [23,27]. Plasmids were confirmed via DNA sequencing (Macrogen, Seoul, Republic of Korea). Each plasmid was introduced into E. coli by heat shock or Salmonella by electroporation. Bacteria strains carrying the plasmid were grown in LB medium with 1% NaCl at 37 °C with vigorous shaking. When necessary, ampicillin (Sigma-Aldrich, Darmstadt, Germany) was added at a concentration of 100 µg/mL. To identify amino acids required for growth, wild-type S. Gallinarum and ΔppGpp S. Gallinarum were cultured on M9 Minimal Medium plates (Welgene Precision SolutionTM, Gyeongsan, Republic of Korea) supplemented with glucose (0.2 g/mL), thiamine (5 mg/mL), magnesium sulfate (1 M), calcium chloride (1 M), and a mixture of 19 amino acids (100 µg/mL, each) with 1 omitted from 20 essential amino acids. Female BALB/c mice (6–8 weeks, 18–20 g) were obtained from Orient Bio (South Korea). CT26 colon cancer cells and 4T1 murine mammary carcinoma cells were purchased from ATCC Korea and cultured in high-glucose DMEM supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells (1 × 106) in 30 µL of 1× PBS were subcutaneously injected into the right thigh of each mouse. Bacterial injections were executed when tumors reached a size of 100–150 mm3. For confirmation of rrnB P1 promoter activity in the tumor targeted bacteria, E. coli K-12 MG1655 and ΔppGpp S. Gallinarum, mice carrying CT26 xenografts were intravenously injected with bacteria carrying prrnBP1-gfpOVA (1 × 108 colony forming units [CFU]/mouse). To examine the antitumor effects of the TP immunotoxin, the mice grafted with CT26 and 4T1 cells were injected with ΔppGpp ΔglmS S. Gallinarum carrying prrnBP1-psp-TP through the tail vein. Tumor size was determined by measuring the length, width, and height of each tumor every 2 days after the injection (V = length × width × height × 0.5). For bacterial distribution in vivo, solid tumors and other organs were extracted from mice and homogenized in 1× PBS using a homogenizer (IKA, Ultra–Turrax T10). The bacteria counting method was previously described [25]. All mouse experiments were performed by following the guidelines of Chonnam National University–Institutional Animal Use and Care Committee (CNU IACUC-H-2020-7). The protocol requires sacrifice of the mice when the implanted tumor volume reaches > 1500 mm3. For in vivo experiments, excised tissues were stored at −80 °C in 1 mL tubes containing RNA protection reagent (Qiagen, Hilden, Germany). For RNA isolation, 50–100 mg tissue was homogenized in 1 mL Trizol (Gene All, Seoul, Republic of Korea, RiboEx, cat. no. 301–001). RNA extraction procedures were performed according to the manufacturer’s recommendations. RNA samples were treated with DNase I to minimize genomic DNA contamination, and RNA integrity and quantity were confirmed by agarose gel electrophoresis and NanoDrop (Eppendorf, Tokyo, Japan, BioSpectrometer). cDNA was synthesized from 1–5 µg total RNA using reverse transcriptase with random hexamer primers (Enzynomics, Daejeon, Republic of Korea, TOPscriptTM cDNA Synthesis Kit, cat. no. EZ005S). The qPCR mixtures (20 µL) consisted of the template cDNA (30 ng), a primer set (0.25 µM, each), and qPCR 2× PreMix (10 µL) (Enzynomics, TOPrealTM qPCR 2× PreMix, SYBR Green with lox ROX). To measure the expression level of gfp derived by the rrnB P1 promoter, cDNA was amplified with the forward primer 5′-GCAGACCATTATCAACA AAATACTCC-3′ and the reverse primer 5′-CTTTCGAAAGGGCAGATTGTGT-3′. As a reference gene, rpoB was used for qPCR using the forward primer 5′-CGCGTATGTCCAATCGAAA-3′ and the reverse primer 5′-GAGTCTCAAGGAAGCC GTATTC-3′ for E. coli, and the forward primer 5′-GCGTCTCAAGGAAGCCATATTC-3′ and the reverse primer 5′-GTCGCGTATGTCCTATCGAAAC-3′ for S. Gallinarum. The analysis was performed with a Rotor-GenQ real-time PCR system (Qiagen, Rotor-GenQ series software, v.2.2.3). The 40 PCR cycles were conducted as follows: initial denaturation at 95 °C for 15 min, denaturation at 95 °C for 10 s, annealing at 60 °C for 15 s, and elongation at 72 °C for 15 s. The cycle threshold (Ct) values obtained from amplifying the cDNA of the gfp gene were normalized to Ct values of the reference gene rpoB by the 2−ΔΔCt method in triplicate. At 1 and 3 days after E. coli injection, total RNA was extracted from the indicated organs and tumor tissues as described above. RNA quantification and purity assessment were performed using a 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany). A sequencing library was prepared with 1 μg total RNA for each sample using the Illumina TruSeq Stranded Total RNA LT Sample Prep Kit (Illumina, San Diego, CA, USA). The resulting cDNA libraries were sequenced using the NovaSeq platform (Illumina), generating approximately 2.78 billion paired end reads of 101 nucleotides in length. To obtain high-quality clean reads from the sequenced raw reads, quality-based filtering and trimming were performed using Trimmomatic (v.0.36) with the following parameters: ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10LEADING:3TRAILING:3SLIDINGWINDOW:4:15 MINLEN:36. To analyze the E. coli transcriptome in the mouse liver and tumor tissues, clean reads were mapped to the mouse reference genome (mm10) using HISAT (v.2.1.1) with default parameters. Then, unmapped reads were extracted using Samtools (v.1.9) and remapped to the E. coli K-12 MG16555 reference genome. To identify the read coverage of the rrnB operon, the alignment results were input to the Samtools depth command with the following range of E. coli chromosome 4: 165,658–4,172,756. The number of total RNA-seq reads was used for normalization. Flamma® Fluors 552 N-hydroxysuccinimide (NHS) ester, a labeling fluorescent dye, was purchased from BioActs (Incheon, Republic of Korea, cat. no. PWS1122) and dissolved in DMSO (Biosesang, DR1022-500-00). The overnight culture of ΔppGpp S. Gallinarum (1 × 109 CFU/mL) in 2 mL 1× PBS was conjugated with the above fluorescent dye (final concentration: 100 µg/µL) under slow-speed rotation at room temperature overnight. The stained bacteria were subcultured in LB broth (ratio: 1:100) in a 37 °C shaking incubator. Bacterial growth at A600 and red fluorescence intensity at λexcitation = 550 nm and λemission = 610 nm were measured every hour using a spectrophotometer (Shimazu, Kyoto, Japan, UV-1800) and a fluorometer (Thermo Scientific, Waltham, MA, USA, VarioskanLux). For in vivo analysis, the NHS-conjugated bacteria were injected into CT26-grafted mice through the tail vein (n = 5 per group, 1 × 108 CFU/mouse). To examine bacterial conditions in mice, the tumors, livers, and spleens were excised at the indicated times after bacterial infection and processed for the detection of bacteria and F4/80+ macrophages by confocal microscope. NHS-conjugated Salmonella was collected at the indicated times, washed with 1× PBS, fixed with 3.9% formaldehyde, and placed on glass slides. The samples were incubated with an anti-Salmonella antibody (antirabbit, Abcam (Cambridge, UK), ab35165, 1:50) overnight at 4 °C. After washing with 1× PBS, the samples were treated with the secondary antibody, Alexa Fluor® 488-conjugated goat antirabbit antibody (Invitrogen (Waltham, MA, USA), REF. A11008, 1:100). In vivo analysis of tumor targeting bacteria was performed using the isolated organs from tumor-bearing mice treated with each bacterial strain. The organs from the mice were fixed with 3.9% formaldehyde overnight at room temperature and embedded in 20% sucrose (Sigma-Aldrich) to remove formaldehyde. Tissues were then frozen in OCT compound (Optimal Cutting Temperature, Tissue-Tek, Torrance, CA, USA) and sliced into 7 µm-thick sections using a microtome (Thermo Scientific, Cryostat Microm HM525). To remove the OCT compound, the slices were dried for 15 min at room temperature, washed three times with 1× PBS, and fixed with absolute acetone. The slides were incubated in 1× PBS containing rabbit anti-Salmonella (Abcam, ab35165, 1:100) and rat anti-F4/80+ macrophage (Abcam, ab6640, 1:100) primary antibodies overnight at 4 °C, followed by washing and incubation in 1× PBS with Alexa Fluor® 633-conjugated goat antirabbit antibody (Life Technologies, Carlsbad, CA, USA, REF. A21071, 1:100) and Alexa Fluor® 488-conjugated goat antirat antibody (Life Technologies, REF. A11006, 1:100) for 2 h at room temperature. Nuclei were stained with DAPI for 10 min at room temperature (Invitrogen, 1:1000), and slides were covered with antifade DAPI (Invitrogen, REF. P36935). Samples were visualized using a confocal microscope, and images were acquired using ZEN blue edition 2.6 V7.0. To examine the expression of the TP protein in vitro, overnight cultures of ΔppGpp S. Gallinarum (SG4023) and ΔppGpp ΔglmS S. Gallinarum harboring the plasmid prrnBP1-psp-TP (SMP4003) were subcultured in LB broth (1:100) and grown for 7 h. At the indicated time points, bacterial pellets were collected and sonicated in 1× PBS. The supernatants were collected and filtered through 0.2 µm filters (GVS Filter Technology, USA). For animal experiments, SMP4003 (1 × 108 CFU/mouse) was injected into the mice grafted with CT26 tumors through the tail vein when tumors reached 130–150 mm3. Tumors were excised at the indicated days and homogenized in 1 mL RIPA buffer (Intron Biotechnology, Seongnam, Republic of Korea) containing 1× Protease & Phosphatase Inhibitor Cocktail and 1× EDTA (Thermo Scientific). The filtered supernatants were mixed with 6× SDS and boiled at 95 °C for 10 min. The proteins were loaded onto 10% SDS-PAGE gels and transferred to nitrocellulose membranes (GE Healthcare, Solingen, Germany, cat no. 10600002). TP expression was determined by western blot analysis using a primary polyclonal antibody against PE38 (Sigma-Aldrich, P2318, antirabbit, 1:5000). Spontaneous bacterial lysis was examined by detecting GroEL using a specific antibody (Sigma-Aldrich, G6532, antirabbit, 1:5000). The level of β-actin was determined using a specific rabbit polyclonal antibody (Abcam, ab8227, 1:2000). Membranes were incubated with primary antibodies diluted in 5% skim milk in TBST at 4 °C overnight, followed by incubation in mouse antirabbit IgG-HRP (Santa Cruz Biotechnology, Dallas, TX, USA, sc-2357, 1:2000) for 1 h at room temperature. The proteins were visualized using ECL (Thermo Scientific, REF. 32209). Data were analyzed using GraphPad Prism v.8.0.2 software. The differences between the mean values of the two groups were analyzed using the unpaired two-tailed Student’s t-test. Two-way analysis of variance (ANOVA) was used for time-course studies. The survival rates are shown in Kaplan–Meier curves with log-rank (Mantel-Cox) test. Differences with p < 0.05 indicated statistical significance. The fate of bacteria injected into tumor-bearing mice through the tail vein was examined using a common laboratory strain of E. coli, MG1655. E. coli MG1655 (1 × 108 CFU/mouse) was injected into BALB/c mice bearing CT26 colon cancer xenograft tumors. At the indicated times after the injection, bacterial numbers were counted in the RES, in the liver and spleen, and in tumors using plating methods (Figure 1A). At 2 h after the injection, there were approximately 1 × 107 CFU in the RES, and this number decreased gradually in a time-dependent manner, reaching approximately 5 × 104 CFU at 120 h. The bacterial number in tumors was 1 × 104 CFU at 2 h after the injection, and this increased to approximately 5 × 108 CFU at 72 h. This result indicates that although ~0.01% of the injected bacteria accumulated in tumor tissues initially, the immunocompromised environment allowed substantial proliferation of those bacteria, whereas those in the RES were cleared presumably by phagocytic immune cells (see below). Among the most activated genes the most highly induced was the rrnB operon, consisting of the transcription unit rrsB-gltT-rrlB-rrfB encoding the three major rRNA building blocks of ribosomes [32]. The expression profile of the rrnB operon (number of reads) was determined by RNA sequencing (Figure 1B). The normalized read coverages in the liver and tumor were 58,220 and 1,148,213 reads at 1 dpi, respectively. At 3 dpi, these values changed drastically because of a shortfall of reads in the liver, showing 67 and 1,780,236 reads for the liver and tumor, respectively. We hypothesized that the reads on day 1 in the liver were remnants of those from overnight culture. By day 3, the bacteria in the liver were perishing as rrnB expression ceased, whereas those in the tumor proliferated. To confirm these findings, we measured the activity of the rrnB P1 promoter, which is the major promoter driving the rrnB operon. This promoter is active during the early exponential phase of growth, when ribosomes are needed most, and declines sharply thereafter during the stationary phase [33]. Three Fis-binding sites in the upstream activation region are responsible for the activation of the rrnB P1 promoter (Figure 1C) [28]. A gene reporter system was constructed using the unstable GFP variant gfpOVA [29], which was cloned downstream of rrnB P1 in pBR322, generating prrnBP1-gfpOVA. E. coli transformed with this plasmid were used to monitor rrnB P1 activity. During growth in vitro (Figure S2A), fluorescence intensity determined at 488–522 nm indicated activation of rrnB P1 during the exponential phase of growth in LB medium, in agreement with the results of qPCR analysis of gfpOVA structural RNA. Then, we attempted to determine rrnB P1 activity in the E. coli injected into tumor-bearing mice using a fluorescence microscope; however, this failed due to weak emission of fluorescence. Alternatively, we measured rrnB P1 activity by qPCR analysis of the gfpOVA structural RNA relative to rpoB RNA, which is maintained at constant levels (Figure 1D) [34]. The activity of rrnB P1 increased by up to 40-fold in the bacteria in tumor tissues at 120 h, whereas those in the liver and spleen decreased over time. This result supports that those bacteria in tumor tissues proliferated, whereas those in the RES perished. We did not quantify the rRNA from the genomic rrnB operon because there are seven rrn operons in E. coli with similar sequences, and the ribosomal RNAs that provide the foundation for ribosomes are extremely stable. For bacteria-mediated cancer therapy, Salmonella spp. that trigger effective IL-1β/TNF-α-mediated immune responses in the tumor leading to tumor regression are preferred over E. coli [9]. An attenuated strain of avian host-specific S. enterica serovar Gallinarum was constructed by deleting relA and spoT, which encode enzymes that synthesize the bacterial signaling molecule ppGpp [26]. The ppGpp defect causes amino acid auxotrophy in S. Typhimurium and in E. coli [35]. We observed that the ppGpp-defective S. Gallinarum also required several amino acids to grow, including branched chain amino acids in addition to lysine and serine (Table S1). The ΔppGpp strain of S. Gallinarum was attenuated by approximately 1000-fold in mice, which allowed injection of 108 CFU/mouse, resulting in regression of various tumors grafted in mice (manuscript in preparation). In this study, we examined the fate of ΔppGpp S. Gallinarum after its injection into the tail vein of BALB/c mice bearing CT26 xenograft tumors. Similar to the E. coli, the bacterial counts in the tumor increased, whereas those in the RES decreased in a time-dependent manner (Figure 2). To obtain a clear picture of the fate of ΔppGpp S. Gallinarum injected into tumor-bearing mice, we measured cell division using bacteria that were cross-linked with the reactive form of a fluorescent dye (Flamma® Fluors 552: NHS), which reacts readily with amine-modified oligonucleotides or amino groups of proteins on the bacterial surface [36,37]. The bacteria incubated with the dye initially emitted strong red fluorescent signals when excited with a 550 nm laser light, which were visible under a fluorescence microscope (Figure 2A, 0 h). These bacteria were diluted in fresh LB medium (1/50) and grown with vigorous aeration (Figure 2B). Fluorescent signals from the bacterial cultures were detected at the indicated times and cell number was also estimated by determining optical density (A600). As the bacteria divided, the fraction of red fluorescent bacteria decreased, disappearing after approximately 2 h (four generations assuming g = 30 min) (Figure 2A,B). BALB/c mice bearing CT26 xenografts were injected with the fluorescent bacteria, and samples of the RES and tumor tissues were collected at the indicated times for the measurement of bacterial numbers and fluorescent signals using a fluorescence microscope (Figure 2C,D). Red fluorescent bacteria were observed in the RES even at 72 h after the injection, whereas they were rarely detected in tumor tissues after 12 h, indicating that the bacteria in the tumor tissue divided and diluted out the fluorescent dye (Figure 2E). In this experiment, the same tissue samples were stained for F4/80+ macrophages, and the results showed that most of the bacteria in the RES were associated with macrophages, whereas those in the tumor were not (Figure S3). Lastly, the activity of the exponential phase promoter rrnB P1 in ΔppGpp S. Gallinarum was measured in vitro (Figure S2B) and in vivo by qPCR analysis (Figure 2F). The activity of rrnB P1 in the tumor increased up to 72 h, whereas that in RES decreased over time. Taken together, these results suggest that the S. Gallinarum accumulating in tumor tissues proliferated, whereas those in the RES were cleared by phagocytic macrophages. To determine whether the rrnB P1 promoter could drive the expression of cytotoxic anticancer proteins, the rrnB P1 promoter sequence was cloned in place of the araBAD promoter in pBAD24 [38], which was fused to the open reading frame of the immunotoxin TP [25,27]. This immunotoxin (TP) comprising TGFα and a modified Pseudomonas exotoxin A (PE38) derived from Pseudomonas aeruginosa was developed for the treatment of EGFR-expressing malignant tumors such as brain tumors [39,40,41]. PE38 acts by inactivating protein synthesis in mammalian cells [42,43]. PE38, which lacks an intrinsic cell-binding domain, binds to EGFR-expressing cancer cells via the TGFα moiety in the recombinant toxin. The TP protein is cytotoxic to EGFR-expressing tumor cells in vitro and in xenograft mouse models [25,44]. In this study, we used the ribosomal RNA promoter rrnB P1 to express TP constitutively. The psp secretion signal peptide composed of 32 amino acids [25] was fused in-frame to the N’ end of TGFα-PE38 in the plasmid named prrnBP1-psp-TP. In addition, the plasmid contained the glmS gene to ensure the maintenance of the plasmid by a balanced-lethal host vector system in GlmS- mutant bacteria [27]. This mutant undergoes lysis when grown in the absence of N-acetyl-D-glucosamine (GlcNac) unless complemented by a plasmid carrying the glmS gene. The ΔppGpp strain of S. Gallinarum carrying the mutation in glmS was transformed with prrnBP1-psp-TP (SMP4003), grown in LB broth, and harvested at the indicated times to quantify the expression of TP. The bacterial cells and supernatant were separated and subjected to western blotting to detect TP expression (Figure 3A). Under the control of the rrnB P1 promoter, TP was expressed at high levels in the pellet in a constitutive manner, whereas it was detected in the supernatant at later time points, indicating that TP was secreted via the psp signal after a certain time. Next, we investigated the cytotoxic effect of the immunotoxin TP secreted from SMP4003 on cancer cell lines overexpressing EGFR, i.e., CT26 mouse colon carcinoma and 4T1 murine breast cancer cells (Figure S4) [25,45]. The bacteria were grown in LB medium and harvested when the culture entered the stationary phase. The cultures were centrifuged, and the supernatants were collected and concentrated. The CT26 and 4T1 cancer cell lines were treated with PBS or concentrated bacterial supernatant (1 µg protein). Approximately 70% of CT26 cells and 60% of 4T1 cells were killed after treatment with the supernatant of SMP4003 for 24 h. The supernatant from ΔppGpp S. Gallinarum only (SG4023) was included as a control and showed a moderate effect. These data indicate that TP released from SMP4003 is cytotoxic to these cancer cells. The expression and secretion of TP from tumor targeted ΔppGpp S. Gallinarum carrying the prrnBP1-psp-TP (SMP4003) plasmid were evaluated in BALB/c mice grafted with mouse colon cancer CT26 cells (Figure 3B). At the indicated days after tail vein injection of the bacteria (1 × 108 CFU), the grafted tumors were isolated and homogenized, and the supernatant was separated by centrifugation and filtered through 0.2 µm pores. The filtrate was analyzed for TP protein (43.3 kDa) expression by western blotting. TP was detected constantly throughout the course of the experiment from 1 to 5 dpi, suggesting that the protein was expressed constitutively from the rrnB P1 promoter and released from bacteria through the psp signal peptide. Next, we evaluated the antitumor effects of the immunotoxin on CT26 and mouse breast cancer 4T1 cells implanted into BALB/c mice (Figure 4). All mice received an intravenous injection of (i) PBS, (ii) ΔppGpp S. Gallinarum (SG4023) alone, or (iii) SG4030 carrying prrnBP1-psp-TP (SMP4003). Administration of SG4023 bacteria alone inhibited tumor growth for up to approximately 10 days compared with that in the PBS-treated group in both tumor models. Expression of the immunotoxin by the rrnB P1 promoter decreased tumor growth further. Average tumor size changes are shown in Figure 4A,D and the representative pictures are appeared in Figure 4B,E. The tumor sizes of individual mouse were also checked (Figure S5A,C). Negative effects on the health of mice were rarely observed, and there was no significant difference in the body weight of mice between the groups (Figure S5B,D). The mice treated with ΔppGpp S. Gallinarum expressing the immunotoxin survived 10–15 days longer than the mice treated with PBS or ΔppGpp S. Gallinarum alone (Figure 4C,F). Taken together, the results suggest that TP expressed from the constitutive rrnB P1 promoter in ΔppGpp S. Gallinarum effectively suppressed tumor growth without any additional manipulation and without causing side effects. In this study, we determined the fate of attenuated noninvasive ΔppGpp S. Gallinarum and the common laboratory strain of E. coli MG1655 after intravenous injection into tumor-bearing mice (108 CFU). Approximately 10% of the injected bacteria were detected initially in the RES, whereas approximately 0.01% were in tumor tissues. The bacteria in the tumor tissue proliferated vigorously to up to 109 CFU/g tissue, whereas those in the RES died off. (Figure 1 and Figure 2). The proliferation of bacteria in tumor tissues can be attributed to the unique immunosuppressive and biochemical environment of the tumor [46]. The rrnB P1 promoter in proliferating bacteria in tumor tissues was active, whereas that in the dying bacteria in the RES was not. The rrnB P1 promoter controls an operon that includes rrsB (16S rRNA), gltT (tRNA-glu), rrlB (23S rRNA), and rrfB (5S rRNA), which encode the three major rRNA building blocks of ribosomes [32]. In rapidly dividing bacteria, a large fraction of cellular energy and matter is devoted to the synthesis of ribosomes, accounting for 47% of the cell mass in E. coli when grown fast with a generation time of <30 min [47]. Because the rate of ribosome synthesis is determined solely by the availability of rRNA, the decrease observed suggests that bacteria in the RES ceased to grow and thus did not need ribosomes for de novo protein synthesis. It is likely that these bacteria were processed by phagocytic leukocytes, i.e., polymorphonuclear leukocytes (PMNs or neutrophils) and mononuclear phagocytes (monocytes, macrophages, and dendritic cells), providing a front line of defense against bacterial infection [48]. In this study, we showed that most of the bacteria in the RES were associated with phagocytic macrophages (Figure S3). These innate immune cells promote bacterial clearance through phagocytosis, generation of reactive oxygen and nitrogen species, extracellular trap formation, and production of proinflammatory cytokines [48]. The results presented in this study suggest that the confined expression of therapeutic payloads using a controllable system could be replaced by a constitutive expression system (Figure 3 and Figure 4). Metabolically inert bacteria that died out in macrophages detected in the RES discontinued protein synthesis, and leakage of therapeutic cargo into the circulation would thus be impossible. Several inducible promoter systems have been developed for the controlled expression of therapeutic cargo, including an E. coli promoter (pBAD) inducible with L-arabinose [20,49,50] and a tet promoter inducible with tetracycline [24,49]. Any transgene under these promoters is expressed upon the concurrent delivery of the inducer, although the expression is transient: the expression of cargo protein (TP) by pBAD lasted only 1 day after administration of L-arabinose into the peritoneal cavity (Figure S6). In this case, daily administration of L-arabinose would be required to prolong the expression. Another approach would be to use hypoxia-responsive promoters that are activated in tumor-colonizing bacteria [51]. Nevertheless, if the purpose of the controlled expression is to prevent toxic substances from harming healthy organs such as the liver and spleen, which are responsible for 60% and 30% of the immunological removal of bacteria from the circulation, respectively [52], such practice is no longer needed. Administration of S. Gallinarum constitutively expressing cytolysin A, a potent pore-forming hemolytic protein of S. enterica serovar Typhi [20], into tumor-bearing mice had no adverse effect on the animals. When an extension of anti-cancer cargo expression is needed, a multiple bacteria injection could be an option. In bacterial cancer therapy, the bacteria detected in the RES should be considered inert. In this study, we used the exponential phase promoter rrnB P1 to express TP in S. Gallinarum proliferating in tumor tissues exclusively, which conferred considerable antitumor effects without any systemic toxicity. This study demonstrated that bacteria that reside in tumor tissues actively proliferate, whereas those in the RES die off after injection into tumor-bearing mice. A cytotoxic anticancer protein gene fused to a constitutive promoter was expressed only in the bacteria residing in the tumor tissue, resulting in tumor suppression.

PMC10000876

Thimios A. Mitsiadis,Pierfrancesco Pagella,Helder Gomes Rodrigues,Alexander Tsouknidas,Liza L. Ramenzoni,Freddy Radtke,Albert Mehl,Laurent Viriot

Notch Signaling Pathway in Tooth Shape Variations throughout Evolution

27-02-2023

notch signaling,Jagged1,RNA analysis,mouse,human,tooth,dental morphology,evolution,differentiation

Evolutionary changes in vertebrates are linked to genetic alterations that often affect tooth crown shape, which is a criterion of speciation events. The Notch pathway is highly conserved between species and controls morphogenetic processes in most developing organs, including teeth. Epithelial loss of the Notch-ligand Jagged1 in developing mouse molars affects the location, size and interconnections of their cusps that lead to minor tooth crown shape modifications convergent to those observed along Muridae evolution. RNA sequencing analysis revealed that these alterations are due to the modulation of more than 2000 genes and that Notch signaling is a hub for significant morphogenetic networks, such as Wnts and Fibroblast Growth Factors. The modeling of these tooth crown changes in mutant mice, via a three-dimensional metamorphosis approach, allowed prediction of how Jagged1-associated mutations in humans could affect the morphology of their teeth. These results shed new light on Notch/Jagged1-mediated signaling as one of the crucial components for dental variations in evolution.

Notch Signaling Pathway in Tooth Shape Variations throughout Evolution Evolutionary changes in vertebrates are linked to genetic alterations that often affect tooth crown shape, which is a criterion of speciation events. The Notch pathway is highly conserved between species and controls morphogenetic processes in most developing organs, including teeth. Epithelial loss of the Notch-ligand Jagged1 in developing mouse molars affects the location, size and interconnections of their cusps that lead to minor tooth crown shape modifications convergent to those observed along Muridae evolution. RNA sequencing analysis revealed that these alterations are due to the modulation of more than 2000 genes and that Notch signaling is a hub for significant morphogenetic networks, such as Wnts and Fibroblast Growth Factors. The modeling of these tooth crown changes in mutant mice, via a three-dimensional metamorphosis approach, allowed prediction of how Jagged1-associated mutations in humans could affect the morphology of their teeth. These results shed new light on Notch/Jagged1-mediated signaling as one of the crucial components for dental variations in evolution. Identifying the molecular mechanisms that have driven evolutionary changes in tissues and organs is a critical challenge in current biology. Teeth are the most mineralized tissues in vertebrates and therefore constitute the best-preserved part of the skeleton following fossilization. Consequently, studies of mammalian evolution often rely on the analyses of tooth shape, since subtle changes in tooth crown morphology usually constitute a criterion of speciation events. The remarkable diversity of tooth crown shapes results from differences in number, position, arrangement and interrelation of cusps, as well as on the dimensions of the dental crown [1,2]. Variations in these traits reveal a wide range of adaptations that occurred in relation to numerous episodes of diversification over 200 million years of mammal evolutionary history [3,4]. Hence, the study of the genetic regulation of tooth morphology is important to understand the mechanisms underlying changes in tooth crown shape during evolution. In this context, the mouse dentition is one of the most widely used mammalian models in paleo-evo-devo investigations [5,6,7,8,9,10]. Mammals develop species-specific dentitions whose form and function are directly related to the activation of defined epithelial and mesenchymal signals at various locations of the developing tooth germs [11,12]. A combination of key signaling molecules, including Bone Morphogenic Protein (BMP), Fibroblast Growth Factor (FGF), Sonic hedgehog (Shh) and Wnt families, are produced and secreted by the developing dental tissues. These molecules are also expressed during specific stages of odontogenesis in restricted areas of the dental epithelium that represent tooth exclusive signaling centers comparable to those localized in a variety of other developing organs in vertebrates [11,13]. These signals regulate the proliferative activity of epithelial and mesenchymal cells, leading to dental epithelial folding and the formation of cusps, which constitute the earliest developmental sign of species-specific tooth patterning. Spatial arrangement and the interconnection of cusps is closely linked to the diversity and evolution of dietary habits [4,5,13]. The Notch signaling pathway encompasses a group of evolutionary conserved trans-membrane protein receptors known to be involved in tooth formation and morphology [14,15,16,17]. Four mammalian Notch homologues (Notch1, Notch2, Notch3 and Notch4), which interact with five trans-membrane-bound ligands (Jagged1, Jagged2, Delta1, Delta-like3 and Delta-like4), have been identified [18,19,20,21,22,23]. These molecules have been shown to play crucial roles in binary cell-fate decisions mediated by the lateral inductive cell signaling in many developmental systems. In humans, mutations in the NOTCH1 and NOTCH3 are associated with a neoplasia (T-cell acute lymphoblastic leukemia lymphoma) and CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy), respectively [24]. JAGGED1 mutations have been associated with Alagille syndrome, an inherited autosomal dominant disorder that affects the face and various organs and tissues, including liver, heart, axial skeleton and eyes [25,26]. The functional role of Notch molecules has been investigated in detail by their targeted inactivation in mice. Notch1−/− [27], Notch2−/− [28], Jagged1−/− [29], Jagged2−/− [30] and Delta1−/− [31] homozygous null mice die either at early embryonic stages or after birth. Notch deregulation in mice severely affects the development of the kidney, heart, intestine, eyes and somites, as well as neurogenesis and angiogenesis [18,27,28,29,30,31]. Components of the Notch pathway are also expressed during odontogenesis and play an essential role in the patterning and formation of dental tissue matrices [15,17,32]. Our previous findings have shown that Jagged2-mediated Notch signaling is required for proper tooth morphology [30]. Our previous studies have shown that Jagged1 is expressed from the very first stages of odontogenesis in the developing dental epithelium [14]. Loss of Jagged1 is embryonically lethal due to defects in the embryonic and yolk sac vasculature remodeling [29]. Therefore, to investigate the role of this gene in dental epithelium, we used a tissue-specific deletion strategy (K14Cre;Jagged1fl/fl; GenBank accession number AF171092). We examined the effects of Jagged1 loss in tooth epithelium and its involvement in tooth crown shape modifications. Animal housing and experimentation were performed according to the Swiss Animal Welfare Law and in compliance with the regulations of the Cantonal Veterinary Office, Zurich, Switzerland (licenses: 151/2014, 146/2017, 197/17). The animal facility provided standardized housing conditions, with a mean room temperature of 21 ± 1 °C, relative humidity of 50 ± 5%, and 15 complete changes of filtered air per hour (HEPA H14 filter); air pressure was controlled at 50 Pa. The light/dark cycle in the animal rooms was set to a 12 h/12 h cycle (lights on at 07:00 a.m., lights off at 19:00 p.m.) with artificial light of approximately 40 Lux in the cage. The animals had unrestricted access to sterilized drinking water, and ad libitum access to a pelleted and extruded mouse diet in the food hopper (Kliba No. 3436; Provimi Kliba/Granovit AG, Kaiseraugst, Switzerland). Mice were housed in a barrier-protected specific pathogen-free unit and were kept in groups of max. 5 adult mice per cage in standard IVC cages (Allentown Mouse 500; 194 mm × 181 mm × 398 mm, floor area 500 cm2; Allentown, NJ, USA) with autoclaved dust-free poplar bedding (JRS GmbH + Co. KG, Rosenberg, Germany). A standard cardboard house (Ketchum Manufacturing, Brockville, ON, Canada) served as a shelter, and tissue papers were provided as nesting material. Additionally, crinklets (SAFE® crinklets natural, JRS GmbH + Co. KG, Rosenberg, Germany) were provided as enrichment and further nesting material. The specific viral, bacterial and parasitic pathogen-free status of the animals was monitored frequently and confirmed according to FELASA guidelines by a sentinel program [33]. K14Cre;Jagged1fl/fl conditional knockout mice were generated by crossing K14:Cre (MGI: 2445832, Tg(KRT14-Cre)1Amc/J(#004782)) and Jagged1flox (MGI: 3577993, 129/Sv-Jag1<tm1Frad>) [34] mice. The animals were genotyped using the following primers: Cre Fw, 5′-CTG TTT TGC CGG GTC AGA AA-3′; Cre Rv, 5′-CCG GTA TTG AAA CTC CAG CG-3′; Jag1 Fw, 5′-GCA AGT CTG TCT GCT TTC ATC-3′; Jag1 Rv, 5′-AGG TTG GCC ACC TCT AAA TC-3′. The age of embryos was determined according to the vaginal plug (E0.5) and confirmed by morphological criteria. Animals were killed by cervical dislocation and E18.5 embryos were surgically removed and fixed overnight in 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS), pH 7.4. Newborn and adult animals were sacrificed by intracardiac perfusion with 4% PFA. Heads were then post-fixed in 4% PFA overnight at 4 °C, thoroughly rinsed with PBS, and placed in 70% ethanol. Embryos and newborn animals were washed in PBS, incubated in sucrose 30%, embedded in Tissue Tek® O.C.T.TM (4583, Sakura, Alphen aan den Rijn, The Netherlands) and serially sectioned at 10 µm. The dental nomenclature used here is specific to murid rodents: Mn refers to the nth upper molar, Mn to the nth lower molar and Mn to both the nth upper and lower molars. Cusps are respectively symbolized by a cn and cn for each upper and lower molar. Main cusps are numbered from 1 to 9 in upper molars and from 1 to 7 in lower molars from the mesio-lingual edge to the disto-vestibular edge of the tooth. High quality images of one wild-type (WT) mouse skull (D1404) and of two mutant mice skulls (D1437 and M12) were obtained using X-ray synchrotron microtomography at the European Synchrotron Radiation Facility (ESRF, Grenoble, France), beamline BM5, with a monochromatic beam at energy of 20 keV and using a cubic voxel of 7.45 µm. This method has been proven to be very useful for very precise imaging of small elements as teeth [35]. Three-dimensional renderings were then performed using VG Studio Max 2.0 software. Dental morphological variations in location, size and interconnection of cusps were analyzed in the WT and K14Cre;Jagged1fl/fl mice. Length (L) and width (W) for each molar were extracted at 0.001 mm using the LAS Core software (Leica®, 4132 Muttenz, Switzerland). The Student’s t-test and Fischer’s F-test was used on each dental measurement (W, L and d1–d5) for WT and K14Cre;Jagged1fl/fl mice to check mean and variance equality. In order to quantify the shape variations within M1 and M1 mesial parts, five other distances (d1–d5) were measured using LAS Core. Overall tooth shapes were investigated by using an outline analysis. By registering the relative size and position of each cusp, this method appears suitable for tooth shape study. Fourier methods, notably Elliptic Fourier Transform (EFT), allow the description of complex outlines approximating them by a sum of trigonometric functions of decreasing wavelength (i.e., harmonics). x and y coordinates of 64 points equally spaced along dental outline were calculated to quantitatively describe the shape of M1 and M1. We applied EFTs to these data using EFAwin software (version 11794, New York State University at Stony Brook, NY 11794, USA) [36], extracting Fourier coefficients from the original outline, and normalizing these shape variables. This method considers the separate Fourier decomposition of the incremental change in x and y coordinates as a function of the cumulative length along the outline [37]. For EFT, any harmonic n yields four Fourier coefficients: An and Bn for x, and Cn and Dn for y, which all contribute to describe the initial outline. We retained the first nine harmonics for M1 and the first five for M1, which represent the best compromise between measurement error and information content for these murine molars [38]. However, the four coefficients of the first harmonic (A1–D1) were not included in the subsequent analyses because they were poorly discriminant and constituted background noise after the standardization step (size and orientation) [38,39]. We performed the Student’s t-test and Fischer’s F-test on each dental measurement (W, L and D1–5) for WT and K14Cre;Jagged1fl/fl mice to respectively check mean and variance equality. A principal component analysis (PCA) allowed the evaluating of a possible outline variation between WT and K14Cre;Jagged1fl/fl mice. Variables were represented by the coefficients of each harmonics previously selected for M1 and M1 (respectively 32 and 16). A multivariate analysis of variations (MANOVA) allowed the researching of a potential significant difference between WT and K14Cre;Jagged1fl/fl mice cohorts. This test included the coordinates of first axes of the PCA for which the sum met at least 95% of the total variation. These data were previously rank transformed since they did not fulfill the required parameters (i.e., normality, homoscedasticity of variances) for such statistical tests [40]. Lower molars were dissected from n = 4 E18.5 K14Cre;Jagged1fl/fl embryos and n = 4 WT littermates. Left and right lower first molars from the same animal were pooled. RNA was isolated using the RNeasy Plus Mini Kit (Qiagen AG, 8634 Hombrechtikon, Switzerland) and subsequently purified by ethanol precipitation. The quality of the isolated RNA was determined with a Qubit® (1.0) Fluorometer (Life Technologies, South San Francisco, CA 94080, USA) and a Bioanalyzer 2100 (Agilent, Waldbronn, Germany). Only those samples with a 260 nm/280 nm ratio between 1.8–2.1 and a 28S/18S ratio within 1.5–2 were further processed. The TruSeq RNA Sample Prep Kit v2 (Illumina, Inc., San Diego, CA 92122, USA) was used in the succeeding steps. Briefly, total RNA samples (100–1000 ng) were poly A enriched and then reverse transcribed into double-stranded cDNA. The cDNA samples were fragmented, end repaired and polyadenylated before ligation of TruSeq adapters containing the index for multiplexing Fragments containing TruSeq adapters on both ends were selectively enriched with PCR. The quality and quantity of the enriched libraries were validated using Qubit® (1.0) Fluorometer and the Caliper GX LabChip® GX (Caliper Life Sciences Inc, Hanover, MD 21076, USA). The product is a smear with an average fragment size of approximately 260 bp. The libraries were normalized to 10nM in Tris-Cl 10 mM, pH8.5 with 0.1% Tween 20. The TruSeq PE Cluster Kit HS4000 or TruSeq SR Cluster Kit HS4000 (Illumina, Inc., San Diego, CA 92122, USA) was used for cluster generation using 10 pM of pooled normalized libraries on the cBOT. Sequencing was performed on the Illumina HiSeq 4000 single-end 125 bp using the TruSeq SBS Kit HS4000 (Illumina, Inc., San Diego, CA 92122, USA). Reads were quality checked with FastQC. Sequencing adapters were removed with Trimmomatic [41] and reads were hard-trimmed by 5 bases at the 3′ end. Successively, reads at least 20 bases long, and with an overall average phred quality score greater than 10 were aligned to the reference genome and transcriptome of Mus Musculus (FASTA and GTF files, respectively, downloaded from Ensembl, GRCm38) with STAR v2.5.1 [42] with default settings for single-end reads. Distribution of the reads across genomic isoform expression was quantified using the R package GenomicRanges [43] from Bioconductor Version 3.0. Differentially expressed genes were identified using the R package edgeR [44] from Bioconductor Version 3.0. A gene is marked as DE if it possesses the following characteristics: (i) at least 10 counts in at least half of the samples in one group; (ii) p ≤ 0.05; (iii) fold change ≥ 0.5. Finally, gene sets were used to interrogate the GO Biological Processes database for an exploratory functional analysis. Contingency tables were constructed based on the number of significant and non-significant genes in the categories and we reported statistical significance using Fisher’s exact test. Oligonucleotide sequences used in the study are listed in the Table 1. For RT-PCR analysis, lower molars were dissected from n = 8 E18.5 K14Cre;Jagged1fl/fl embryos and n = 8 WT littermates. Left and right lower first molars from the same animal were pooled. RNA was isolated using the RNeasy Plus Mini Kit (Qiagen AG, 8634 Hombrechtikon, Switzerland) and subsequently purified by ethanol precipitation. Reverse transcription of the isolated RNA was performed using the iScript™ cDNA Synthesis Kit and according to the instructions given (Bio-Rad Laboratories, 1785 Cressier, Switzerland). Briefly, 1000 ng of RNA were used for reverse transcription into cDNA. Nuclease-free water was added to add up to a total of 15 μL; 4 μL of 5× iScript reaction mix and 1 μL of iScript reverse transcriptase were added per sample in order to obtain a total volume of 20 μL. The reaction mix was then incubated for 5 min at 25 °C, for 30 min at 42 °C and for 5 min at 85 °C using a Biometra TPersonal Thermocycler (Biometra AG, Göttingen, Germany). The 3-step quantitative real-time PCRs were performed using an Eco RT-PCR System (Illumina Inc., San Diego, CA, USA). The reaction mix was composed of 5 μL of SYBR® Green PCR Master Mix reverse and forward primers (200 nM), and 2 ng of template cDNA. The thermocycling conditions were 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s, 55 °C for 30 s and 60 °C for 1 min. Melt curve analysis was performed at 95 °C for 15 s, 55 °C for 15 s and 95 °C for 15 s. Expression levels were calculated by the comparative ΔCt method (2−ΔCt formula), normalizing to the Ct-value of the 36B4 housekeeping gene. Cryosections were air dried for 1 h at room temperature, then washed with PBS to remove excess of Tissue Tek® O.C.T.TM. Endogenous peroxidases were inhibited by incubating the sections in a solution composed of 3% H2O2 in Methanol at −20 °C for 20 min. Specimens were then blocked with PBS supplemented with 2% fetal bovine serum and thereafter incubated with primary antibodies for 1 h at room temperature. The following primary antibodies were used: Rabbit anti-Notch1-ICD (1:50, ab8925, Abcam, Cambridge, UK), Rabbit anti-Hes1 (1:50, 19988, Cell Signaling, Danvers, MA, USA), Rabbit anti-Hes5 (1:50, ab194111, Abcam, Cambridge, UK), Rabbit anti-β-catenin (1:50, 8480S, Cell Signaling, Danvers, MA, USA), Rabbit anti-Ki67 (1:100, ab15580, Abcam, Cambridge, UK), Rabbit anti-Amelogenin (1:100, ab153915, Abcam, Cambridge, UK). For negative controls, primary antibody was omitted. The sections were then incubated with a biotinylated secondary antibody (Vector Vectastain ABC kit PK-4001-1; Vector Laboratories LTD, Peterborough, UK). Sections were then incubated with AEC (3-amino-9-ethylcarbazole; AEC HRP substrate Kit—SK4200; Vector Laboratories LTD, Peterborough, UK) to reveal the staining, counterstained with Toluidine Blue, mounted with Glycergel (C0563, Agilent Technologies, Santa Clara, CA, USA) and imaged with a Leica DM6000 light microscope (Leica Microsystems, Schweiz AG, Heerbrugg, Switzerland). The 3D morphing of all tooth geometries was conducted in ANSA by BETA CAE Systems A.G. (CH-6039 Switzerland) and the methodological approach can be broken down into the following 5 steps. Tooth surface generation. Average tooth morphologies were computed for the M1 of WT rodent (based on two characteristic teeth), a mutant one (considering nine Jagged1-mutant crown morphologies) and a human M1 (resulting from 246 molars). This process facilitated the detection and association of the prevalent features, to compute realistic reference models. Especially for the human molar M1, a large existing database was available, which was used as the starting point for calculating the respective average tooth morphology. The data set is based on a set of existing impressions of carious-free and intact tooth surfaces of West European young people within the ages from 16 to 20 years. From the impressions, stone replicas were made, and the clinical visible parts of their surfaces were measured with a 3D-scanning device. The resolution of the measuring process was 50 µm × 50 µm (x,y). All data sets were aligned in position and orientation within the same coordinate system: For this, a representative molar with an appropriate orientation was chosen. All other molars were superimposed with this representative molar by a least-square fitting routine (Match3D) [45,46,47]. This guaranteed that all occlusal features had the same orientation. The pattern of cusps and grooves varies considerably across individuals, even though some morphological properties, such as the overall layout of the main cusps and fissures, are shared by all samples. These common features allow to establish correspondence between the scans z(x,y) with a modified optical flow algorithm in an automated procedure [48]. Based on this correspondence, the x, y, z—coordinates can be averaged and result in a typical representation of the human M1, which was used further in this process [45,46]. Pre-processing. Prior to applying the morphing algorithms to the teeth, proxy geometries were created, by scaling all teeth (mouse WT and mutant M1 and human M1) to comparable dimensions. Teeth were then aligned based on characteristic morphological patterns (e.g., tooth cusps and grooves). Mouse and human teeth were positioned by a point-to-point spatial relation of their functional features: the 4 cusps of the human M1 (protocone, metacone, paracone, hypocone) were aligned to the 4 distal cusps (c5, c6, c8, c9) of the mouse M1. Creation of a dense 3D correspondence (Features mapping). The non-uniform triangulated meshes of the *.stl files, were used as bi-linear maps for the morphing algorithm, facilitating the determination of a dense correspondence across both source (WT) and target (mutant) tooth geometry. The mouse mutant M1 consisted of 249.797 triangular elements whereas the mouse WT M1 and human M1 consisted of 252.296 and 51.476, respectively. A correspondence was algorithmically established, by identifying the minimum projected distance between each node of the source mesh to a node or an interpolation of multiple nodes on the target geometry. Source nodes that are not paired with correspondences, are handled as “in-between positions” and placed as simple linear interpolations of the vertex positions in the target mesh. The use of these nodes in the mesh triangulation increases the quality of the morph function, despite not being visible on the target. To provide a reference plane for the translation matrix, pairs of points were placed on the source and target geometry to identify locations that should be in correspondence. These boundary locations were selected below the functional surfaces of the molars (in proximity to the cervical margin line) to ensure the unobstructed morphing of the molar crown morphology. Areas of interest were isolated based on the statistically significant variations found in mouse WT M1 vs. mutant M1. The computed transformation matrix was then applied to the 3D data set of the human M1, to predict the crown morphology in individuals carrying Jagged1 mutations. Construction of the 3D morphs. Polygonal surface approaches, such as 3D morphing, require mapping of the characteristic topological landmarks (as described above), followed by re-meshing of the morphed surfaces to achieve a realistic convergence. The quality-oriented reconstruction of the source model’s grid produces a robust 3D morph. Application of the 3D morphs to human molars. The transformation vectors required to morph the human healthy M1 into a M1 in individuals carrying Jagged1 mutations were formulated as an algorithmic matrix. This matrix was then stored as a geometry-independent parameter, reflecting the differences of the crown surface between WT and mutant M1. Transferring the deformation map of this parameter to the 3D data set of the human M1 (average model) facilitates the computation of the M1 crown morphology, which is expected to be representative both in terms of morphology and size of the human mutant M1. The teeth of the K14Cre;Jagged1fl/fl mice exhibited crown morphological differences when compared to the teeth of WT mice, mainly regarding the location and interconnection of the cusps (Figure 1Aa; red arrows). The most striking variations on the upper molars (i.e., M1, M2, M3) were observed in their first cusp (c1 cusp). In the WT mice, the c1-c2 connection of the M1 seen in the side-view was usually achieved by a high V-shaped crest, which contrasts to the frequently observed (in approximately 60% of analyzed samples) low U-shaped profile in M1 of the K14Cre;Jagged1fl/fl mice (Figure 1Aa,b; red lines). This implies that the c1-c2 connection was weak to absent in the K14Cre;Jagged1fl/fl mutant mice (Table 2). This variation was confirmed by height measurements of the c1–c2 connection (Figure 1B; d1), which were significantly different between the two cohorts. In addition, these measurements revealed that the spacing between c1 and c2 cusps was significantly greater in the M1 of mutant mice (Figure 1B; d2). The c1 cusp of the M1 had a more linguo-distal position in the majority of the K14Cre;Jagged1fl/fl mice, and this feature explained its greater spacing from c2. On the M2 of mutant mice, the vestibular extension of the cusp c1 spur was absent in about 60% of specimens (Figure 1Aa; Table 2). The c1 cusp of the M3 was sometimes reduced and tended to merge with the c4 cusp in 30% of the K14Cre;Jagged1fl/fl specimens (Figure 1Aa; Table 2). The central cusps (c5 and c8) of the mutant M1 and M2 had a rather angular or even pointed mesial edge, while this part was always smooth in the molars of WT mice (Figure 1Aa). This latter morphotype occurred in approximately 60% of the upper molars of K14Cre;Jagged1fl/fl mice (Table 2), but was not always simultaneously present on each cusp. The lower molars (i.e., M1, M2, M3) of the mutant mice showed little variation compared to the upper molars, yet the c1 and c2 cusps of the mutant M1 appeared closer to each other compared to the WT M1 (Figure 1Aa). Three measurements revealed significant differences in d3-d5 mean lengths (Table 3) and confirmed that c1 and c2 cusps tended to partly merge in the M1 of mutant mice, while the cusp c1 was less protruding. The first two axes of the PCA on M1 outlines were poorly discriminant because WT and K14Cre;Jagged1fl/fl specimens plotted together, but the third axis was more discriminant although morphospaces partly overlapped (Figure 1Ca). The main dental trend expressed on the third component was the variation of the cusp c1 location, according to extreme outlines on the negative and positive sides. This cusp was indeed located in a more distal-lingual position in the extreme outline of the negative side where a majority of K14Cre;Jagged1fl/fl specimens plots. The results of the principal component analysis (PCA) were also confirmed by a multivariate analysis of variations (MANOVA), pointing out a significant difference in M1 outline between WT and K14Cre;Jagged1fl/fl mice (Table 4). Contrary to M1, the first component of the PCA on M1 outlines represented the most discriminant axis, and the second one was more discriminant than the third (Figure 1Ca). Nonetheless, the M1 morphospaces overlapped as well. The main difference between WT and K14Cre;Jagged1fl/fl was linked to the mesial protrusion of the c1 cusps, which was less important on extreme outlines from the negative to the positive side of the first component. A significant difference between the two cohorts was also displayed by the MANOVA (Table 4). Mean sizes (L and W) of all molars were significantly lower in K14Cre;Jagged1fl/fl mice compared to WT specimens (Figure 1Cb; t-test), while there was no significant difference concerning the variances (Figure 1Cb; Fischer’s F-test). To understand how the epithelial deletion of Jagged1 modulates the whole dental developmental program we compared the transcriptome of first molars isolated from E18 K14Cre;Jagged1fl/fl embryos and WT littermates (Figure 2A). RNA sequencing analysis showed a significant change in the expression of more than 2000 genes upon epithelial deletion of Jagged1 (Figure 2A,B). These genes encode diverse categories of proteins, including proteins linked to binding, catalytic and transported activities (Figure 2C). Unbiased Gene Ontology (GO) Enrichment Analysis identified several networks affected by the loss of Jagged1 (Figure 2D). We found a significant upregulation of genes involved in mineralization, autophagy, ion transport and cell cycle arrest (Figure 2D), which are critical processes for enamel and dentin formation [49,50]. Several genes encoding for proteins necessary for enamel formation, such as Amelx, Ambn, Enam, Mmp20, and Klk4 were upregulated in K14Cre;Jagged1fl/fl molars (Figure 2E). However, these results varied between the K14Cre;Jagged1fl/fl molars (Figure 2F). In addition, the expression of mesenchymal genes was also affected in K14Cre;Jagged1fl/fl molars. For example, genes associated with odontoblast differentiation such as Dspp and Dmp1 were upregulated, while Pax9, Barx1, Dlx1 and Dlx2 were significantly downregulated in the mutant molars (Figure 2D,F). Furthermore, we showed that genes encoding for molecules of the Wnt and FGF signaling pathways were affected (Figure 2E,F). Concerning the Wnt pathway, we observed major downregulation of genes encoding Wnt11, Wnt10b, Wnt9b ligands, Frizzled receptors and Tcf transcription factors (Figure 2C–E), while the genes encoding Wnt3a, Wnt6, Wnt7a and Wnt10a ligands, as well as Apc were upregulated (Figure 2E). Regarding the FGF pathway, Spry1, Spry2 and Spry4 were significantly downregulated (Figure 2E,F and Figure 3). Notch pathway members, such as Jagged1, Hes5, Hes6, Lfng and Maml2 were downregulated, while others, such as Hey1 and Dll4 were upregulated (Figure 2D,E and Figure 3). Apart from the upregulation of several cell cycle arrest-related genes, no significant alterations were observed in specific networks linked to cell proliferation events (Figure 2Da). To validate the above results and analyze whether the site of expression of different genes and proteins was affected by Jagged1 epithelial deletion, we performed real-time PCR (RT-PCR) analysis, in situ hybridization, and immunohistochemistry on cryosections of E18 K14Cre;Jagged1fl/fl mouse embryos (Figure 4). RT-PCR analysis confirmed Jag1 downregulation as well as Notch1, Notch2 and Hes5 downregulation in K14Cre;Jagged1fl/fl molars (Figure 4A). We further demonstrated overexpression of ameloblast (e.g., Amelx and Ambn) and odontoblast (e.g., Dspp and Dmp1) differentiation markers in K14Cre;Jagged1fl/fl molars (Figure 4A). RT-PCR also confirmed alterations in the expression of genes coding for components of the Wnt signaling pathway, namely the upregulation of Wnt10a and Apc, and downregulation of Lef1, Tcf3 and Fzd2 (Figure 4A). In situ hybridization analysis in E18 WT molars showed intense Jagged1 expression in cells of the inner dental epithelium, which was drastically reduced in this cell layer in E18 mutant molars (Figure 4B). Although strong Notch1 expression was observed in all cells of the stratum intermedium in WT molars, its expression was downregulated in cells located at the cusp territories of K14Cre;Jagged1fl/fl molars (Figure 4B). Similarly, Notch2 expression was downregulated in cells of the outer enamel epithelium and stellate reticulum of E18 mutant molars, when compared to the gene expression in WT molars (Figure 4B). The expression of Jagged2 in inner dental epithelial cells was comparable between the mutant and WT molars (Figure 4B), thus suggesting a Jagged2 compensation for the loss of Jagged1 that explains the mild morphological changes observed in the mutant molars. Immunohistochemistry confirmed the deregulation of the Notch signaling pathway in the dental epithelium of K14Cre;Jagged1fl/fl molars. Labeling against the active form of Notch1 (i.e., Notch1 intracellular domain) showed its distribution in cells of the stratum intermedium of E18 WT molars, contrasting the lack of immunostaining in mutant molars (Figure 4C). Similarly, although a strong Hes1 staining was observed in the stratum intermedium of WT molars, in the cusps of the mutant molars the staining was not obvious (Figure 4C). Hes5 staining was mainly detected in inner dental epithelium (preameloblasts), stellate reticulum and odontoblasts of the E18 WT molars (Figure 4C). Comparison of Hes5 immunoreactivities between WT and mutant molars indicated a slight reduction in the staining in the inner dental epithelium and odontoblasts in the cusp areas (Figure 4C). Amelogenin was distributed in preameloblasts located at the cusps of the WT molars, as well as in parts of the dental pulp (Figure 4C). A similar pattern for Amelogenin was observed in the K14Cre;Jagged1fl/fl molars, although the staining appeared more expanded when compared to that of WT teeth (Figure 4C). Staining with the proliferation marker Ki67 showed mitotic activity in few cells of the stratum intermedium located in the cusps of the WT molars (Figure 4C). In mutant teeth, increased Ki67 immunoreactivity was observed in cells of the stratum intermedium and some preameloblasts (Figure 4C). The understanding of the molecular bases underlying fine variations in mouse dental morphology can help us to unravel the common factors that shaped teeth throughout evolution. Therefore, we investigated how the deregulation of Jagged1 function would affect the morphology of human molars. The statistically significant variations between WT and mutant rodent teeth can be mathematically applied to the human teeth, thus allowing to predict the crown morphology in humans carrying Jagged1 mutations. For building this model, we opted for M1 where the most accentuated modifications were observed in the mutant mice. A polygonal surface technique (3D direct morphing, using ANSA by BETA CAE Systems S.A.) was applied to compute a translation matrix. The transformation vectors of the translation matrix contain all the important information that would allow approximation of the WT M1 crown morphology to the K14Cre;Jagged1fl/fl one (Figure 4Aa,b), while providing an overview of the required node displacements (Figure 5Ac). These node displacements can be described by transformation vectors (Figure 5Ad), which can be transferred to the human M1. Consequently, we superimposed WT mouse M1 and human M1 morphologies (Figure 5B). Despite the fact that significant variations in mouse M1 also exist in its mesial part that is not comparable to the human M1, an analogue was drawn to the remaining variations in the distal part (containing the c5, c6, c8 and c9) of the mouse M1. The transformation matrix of each of these four cusps in the mouse M1 was applied to the 3D data set of the average human M1 (Figure 5C), to predict tooth morphology in individuals carrying Jagged1 mutations (Figure 5Cb,Da–d). The most significant morphological alterations were observed in the occlusal surface of M1, with a mesial-lingual/palatal shift of the hypocone and a mesial displacement of the metacone (arrows in Figure 5Cb,Da,b). The morphologies of the protocone and paracone were only marginally affected by the mutation. The distal ridge of the mutant M1 is predicted to have a notable deeper and narrower profile (Figure 5Dd) when compared to a typical M1 morphology (Figure 5Dc). The distal-lingual/palatal groove was also computed as slightly widened in its mesial part (Figure 5Db), while no significant changes were observed in the vestibular surface of the mutant M1. The characteristics of the K14Cre;Jagged1fl/fl mouse molars provide relevant information in terms of tooth crown microevolution in rodents. The evolution of the rodent dentition is well understood from an abundant and intensively studied fossil record [1,2,8,51]. The transition from ancestral to descendant species within most evolutionary lineages usually involves minor modifications of tooth shapes. The subtle changes in the interconnections between cusps, their varying frequency and the overlapping tooth size range are reminiscent of cases of dental variations among muroid rodents. Pleistocene and Holocene species of field mice (Apodemus) have been shown to possess upper and lower first molars with variable interconnections between their first and second cusps, and these variations occur both at intraspecific and close interspecific levels [52]. Variations in the interrelationships between mesial cusps of the first upper and lower molars have also been described in different Miocene species of Democricetodon [53] and Megacricetodon [54]. Similar cases of interspecific overall morphological similarities of first molars are also described in early murids, such as Progonomys species, showing only subtle differences [55]. In most of these examples, the tooth size range remains nearly identical. The loss of Jagged1 function in dental epithelium can thus cause morphological changes in the same cusps that are modified during murid evolution. Consequently, an alteration in Jagged1 expression might be responsible for subtle tooth crown modifications that underlie processes of dental phenotype adaptation during population splitting or even speciation events over evolution. Tooth morphogenesis requires the orchestrated activity of several molecular networks, and the Notch pathway plays a pivotal role in this process, acting as a hub regulator of main signaling pathways [56]. Jagged1 epithelial loss induces important modifications of numerous genes that are involved in an important and diverse number of signaling pathways. Loss of Jagged1 in mice did not result in dramatic changes in the activity of these signaling networks, but rather fine and discrete modulations, which still significantly impacted crown morphology. This indicates that Jagged1 fine tunes a sensitive balance between various gene networks whose interactions govern organogenesis. Disturbance of this genetic equilibrium might ultimately lead to the generation of subtle morphological modifications in most tissues and organs. The perturbed expression levels of Wnt signaling molecules are expected due to the well-known genetic interaction with the Notch signaling pathway [57,58,59]. The Wnt signaling is a powerful morphogen [60] involved in the establishment of planar cell polarity (PCP) that plays a crucial role in tissue patterning during all developmental stages [61]. It has been already shown that Wnt signaling is fundamental for tooth development since mutations in this pathway are associated with dental anomalies [62,63,64,65,66,67]. Significant alterations in the Wnt signaling pathway in the dental epithelium of Jagged1 mutants suggest that tooth crown morphological changes are mostly due to PCP dysfunction. This hypothesis is further reinforced by the deregulation of genes that are crucial for the establishment of PCP, such as Vangl2 and Celsr1 [61], as well as by the altered expression patterns of Notch1 and Notch2 in mutant teeth. Misexpression of the Notch receptors and ligands in dental epithelium, in combination with the deregulation of various Notch downstream effectors, will also affect the fate of dental epithelial cells [18]. Jagged1 mutation in dental epithelium also induces downregulation of the FGF inhibitors Spry1, Spry2 and Spry4, which are important genes affecting tooth number [68] and enamel formation [69]. No substantial changes occurred in genes related to cell proliferation, despite the known correlation between Notch signaling and cell proliferation [70]. However, the upregulation of genes associated with cell cycle arrest, together with the upregulation of genes involved in ameloblast (e.g., Amelx, Ambn, Enam, Mmp20, and Klk4) and odontoblast (e.g., Dspp and Dmp1) differentiation, indicates premature cytodifferentiation events in mutant molars. As a consequence, the accelerated deposition of enamel and dentin by the ameloblasts and odontoblasts, respectively, will prematurely stabilize tooth crown shape in the mutants. Advanced cytodifferentiation in mutant molars could thus lead to the observed reduced size and subtle morphological alterations of their crown. Such dental morphology does not resemble any particular murid rodent or any specific dental pattern that can be found in the evolution of Muridae. This is different from changes observed upon Fgf3 deregulation in mice that greatly affected tooth morphology [6]. Therefore, the present results should be evaluated from a microevolutionary perspective rather than from a macroevolutionary viewpoint. Indeed, the trends observed here for some characters (e.g., first and second cusp connection, first and second cusp complex), their variable frequency and the overlapping of both size range and shape reminds us of cases of wild sibling species of Muroidea [71,72]. It was shown that two extant field mice (i.e., Apodemus sylvaticus and Apodemus flavicollis) share dental morphologies and nearly the same size ranges [71]. Moreover, the extinct “hamster-like” species, Eucricetodon asiaticus and Eucricetodon jilantaiensis, from the Oligocene of Ulantatal (Inner Mongolia, China), displayed some overall shape variations even closer to the K14Cre;Jagged1fl/fl and WT case. The size range of these species is indeed nearly identical, they share intermediary dental morphotypes, their global shapes overlap and their main size distinction relies on mean L/W ratio [72]. Consequently, the modulation of Jagged1-mediated signaling could be the driving force for minor dental modifications that contribute to speciation phenomena, despite the reported negative effect of a haploinsufficiency of this gene in some organs [26,34,73,74]. The extent to which genetic changes contribute to morphological variations in human dentition remains a fundamental question in evolutionary biology. Despite the obvious differences between mouse and human molars (in cusp number, size and interconnections as well as in relative dental proportions), different studies have indicated some possible equivalences [75]. The creation of a model, based on mammalian evolution of common ancestral origin [76] and using computing 3D metamorphosis techniques [77], predicted meaningful morphological changes in the crown of molars in humans carrying Jagged1 mutations. In conclusion, our results suggest that the systematic effect of Jagged1 deletion provides a basis for dental variations in evolution. Mutation morphologically similar to Jagged1 deletion sheds a completely new light on the mechanisms of development, which could reconcile microevolutionary and macroevolutionary processes. Here, we have demonstrated that the deletion of Jagged1 from the dental epithelium results in changes to the morphology of the tooth crown, and we suggest the involvement of the Notch signaling pathway in subtle evolutionary tooth shape changes. Although this is a simple, plausible hypothesis, our data do not provide direct evidence of this event. We also propose that these shape modifications are due to the modulation of significant morphogenetic networks that are affected upon Jagged1 deletion. However, it is challenging to analyze separately and in detail the cascade of the molecular events leading to tooth morphological modifications. Finally, we predicted tooth shape alterations in individual Jagged1 mutations, using a computing mathematical model. Nevertheless, morphological variations in the teeth of humans carrying Jagged1 mutations remain to be demonstrated.

PMC10000881

Olivia G. Huffman,Danielle B. Chau,Andreea I. Dinicu,Robert DeBernardo,Ofer Reizes

Mechanistic Insights on Hyperthermic Intraperitoneal Chemotherapy in Ovarian Cancer

22-02-2023

hyperthermia,ovarian cancer,immunity,chemotherapy,HIPEC

Simple Summary Advanced ovarian cancer is the leading cause of gynecological death with a high rate of reoccurrence indicating the critical need for improved therapeutics. Hyperthermic intraperitoneal chemotherapy (HIPEC) treatment for ovarian cancer has shown efficacy in extending patient overall survival yet the mechanism of benefit remains unknown. The aim of this review is to address the impact of hyperthermia, providing insights into HIPEC efficacy. Here we review reports of HIPEC treatment in ovarian and peritoneal cancers as well as discussion of animal models used for emulating clinical HIPEC. Abstract Epithelial ovarian cancer is an aggressive disease of the female reproductive system and a leading cause of cancer death in women. Standard of care includes surgery and platinum-based chemotherapy, yet patients continue to experience a high rate of recurrence and metastasis. Hyperthermic intraperitoneal chemotherapy (HIPEC) treatment in highly selective patients extends overall survival by nearly 12 months. The clinical studies are highly supportive of the use of HIPEC in the treatment of ovarian cancer, though the therapeutic approach is limited to academic medical centers. The mechanism underlying HIPEC benefit remains unknown. The efficacy of HIPEC therapy is impacted by several procedural and patient/tumor factors including the timing of surgery, platinum sensitivity, and molecular profiling such as homologous recombination deficiency. The present review aims to provide insight into the mechanistic benefit of HIPEC treatment with a focus on how hyperthermia activates the immune response, induces DNA damage, impairs DNA damage repair pathways, and has a synergistic effect with chemotherapy, with the ultimate outcome of increasing chemosensitivity. Identifying the points of fragility unmasked by HIPEC may provide the key pathways that could be the basis of new therapeutic strategies for ovarian cancer patients.

Mechanistic Insights on Hyperthermic Intraperitoneal Chemotherapy in Ovarian Cancer Advanced ovarian cancer is the leading cause of gynecological death with a high rate of reoccurrence indicating the critical need for improved therapeutics. Hyperthermic intraperitoneal chemotherapy (HIPEC) treatment for ovarian cancer has shown efficacy in extending patient overall survival yet the mechanism of benefit remains unknown. The aim of this review is to address the impact of hyperthermia, providing insights into HIPEC efficacy. Here we review reports of HIPEC treatment in ovarian and peritoneal cancers as well as discussion of animal models used for emulating clinical HIPEC. Epithelial ovarian cancer is an aggressive disease of the female reproductive system and a leading cause of cancer death in women. Standard of care includes surgery and platinum-based chemotherapy, yet patients continue to experience a high rate of recurrence and metastasis. Hyperthermic intraperitoneal chemotherapy (HIPEC) treatment in highly selective patients extends overall survival by nearly 12 months. The clinical studies are highly supportive of the use of HIPEC in the treatment of ovarian cancer, though the therapeutic approach is limited to academic medical centers. The mechanism underlying HIPEC benefit remains unknown. The efficacy of HIPEC therapy is impacted by several procedural and patient/tumor factors including the timing of surgery, platinum sensitivity, and molecular profiling such as homologous recombination deficiency. The present review aims to provide insight into the mechanistic benefit of HIPEC treatment with a focus on how hyperthermia activates the immune response, induces DNA damage, impairs DNA damage repair pathways, and has a synergistic effect with chemotherapy, with the ultimate outcome of increasing chemosensitivity. Identifying the points of fragility unmasked by HIPEC may provide the key pathways that could be the basis of new therapeutic strategies for ovarian cancer patients. Epithelial ovarian, fallopian tube, and primary peritoneal cancers (EOC) are a leading cause of cancer death in women, highlighting the critical clinical need for therapeutic development [1]. The majority (80%) of EOC patients present with advanced stage (III–IV) disease and have a poor prognosis (5-year cancer stage-specific survival 42% and 26%, respectively). Standard of care treatment for advanced EOC involves a combination of debulking surgery and chemotherapy. Hyperthermia has been utilized in cancer treatment for centuries and continues in modern medicine [2]. The therapeutic strategy known as hyperthermic intraperitoneal chemotherapy (HIPEC) in EOC patients at the time of interval debulking surgery (IDS) shows promise as patients experience an extension in overall survival (OS) of nearly 12 months compared to patients undergoing interval debulking surgery (IDS) alone [3]. While this represents the most significant extension of overall survival in EOC patients in over a decade [3], HIPEC mechanisms of action have yet to be understood, thereby limiting further optimization of HIPEC for patients with advanced EOC. Mishra et al. reviewed the history of HIPEC including its adoption in ovarian cancer treatment [2]. Our review focuses on the clinical evidence in support of HIPEC’s benefit in ovarian cancer followed by an analysis of the mechanisms underlying the benefit of hyperthermia in combination with chemotherapy in cancer. Epithelial ovarian cancer (EOC) is an aggressive disease of the female reproductive system, often arising from the fallopian tubes, involving the surface lining (epithelial tissue) of the ovaries. A total of 1 in 78 women will experience ovarian cancer in their lifetime [4]. It is expected that more than 22,000 new cases will be reported annually, of which 14,000 will succumb to the disease [5]. EOC has the highest mortality rate of any gynecological cancer with a case-to-death ratio equivalent to lung cancer [6]. Nearly 80% of patients present in late stage (III–IV) thus resulting in poor prognosis [5]. A combination of cytotoxic platinum-paclitaxel-based chemotherapy and debulking surgery remains the standard of care for advanced EOC. While standard treatments have shown initial beneficial outcomes, 70% of patients with advanced disease will experience recurrence within five years, ultimately ending in mortality [7]. The goal of surgery for these patients is to achieve complete macroscopic cytoreduction, as this optimizes overall survival benefit for this intervention [8,9,10]. In patients for whom upfront or primary debulking surgery (PDS) is not safe or complete macroscopic resection is not feasible, neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) and postoperative chemotherapy allows for initial reduction of disease burden to optimize patients for surgical resection. Randomized clinical trials report no significant difference in progression-free survival (PFS) and overall survival (OS) with this approach compared to primary debulking surgery followed by adjuvant chemotherapy [11]. Despite several new chemotherapy agents demonstrating efficacy against EOC, minimal strides have been made to improve patient OS [11]. The need for new clinical therapeutic strategies is crucial in fighting this disease. Hyperthermic intraperitoneal chemotherapy (HIPEC) is a promising approach to treating advanced EOC, prolonging the overall survival of patients. HIPEC treatment involves abdominal perfusion of heated chemotherapy via catheter insertion at the time of cytoreductive surgery (CRS) (Figure 1). Perfusion machines maintain a constant infusion temperature through the abdominal cavity. Van Driel and colleagues performed a phase 3 randomized controlled trial (OVHIPEC-1) to test the benefits of HIPEC on newly diagnosed EOC patients, comparing results to treatment without HIPEC [3]. Patients with extensive disease who were not ideal candidates for primary debulking surgery (PDS) or patients with residual tumor after PDS were referred for NACT with or without HIPEC as study participants. Three cycles of NACT were completed prior to entry into the trial. Cytoreductive surgery was completed with or without intraoperative administration of HIPEC using perfusion of cisplatin heated to 40 °C for 90 min via an open abdomen technique. Following surgery, patients in both groups received an additional three cycles of chemotherapy. Results revealed patients receiving HIPEC had an extended OS by nearly 12 months, with no increased rate of adverse effects [3]. To answer the question of whether HIPEC extends patient survival regardless of the timing of cytoreductive surgery, a single-blinded randomized study was performed including patients with stage III or IV ovarian cancer planned for either PDS or IDS [12]. Patients randomized to the HIPEC arm received cisplatin heated to 41.5 °C for 90 min using the closed perfusion Belmont Hyperthermia Pump System. The results reveal an extended PFS and OS in the HIPEC cohort, with an OS increase in 8.2 months in HIPEC patients. Further exploration into any differences between HIPEC at the time of PDS or IDS revealed an increase in PFS and OS in the patients receiving HIPEC after IDS, by 2 and 13 months respectively. Notably, HIPEC at the time of PDS did not extend patient OS and PFS (Table 1). Consistent with Van Driel, these results indicate that HIPEC at the time of IDS prolonged patient survival and improved time to recurrence, providing further evidence of the benefit of HIPEC on extending patient survival against EOC [12]. The standard of care for advanced EOC includes cytotoxic platinum- and paclitaxel-based chemotherapy. In cases of HIPEC, however, single-agent platinum-based chemotherapies, particularly cisplatin or carboplatin, can be used [13]. Several studies have outlined variations in the efficacy of treatment based on the type of chemotherapy utilized in HIPEC. A recent prospective analysis found that PFS was significantly increased with paclitaxel/cisplatin-based HIPEC compared to single-agent cisplatin-based HIPEC [13]. These preliminary findings suggest that the combination of both chemotherapies may be superior to cisplatin alone. Overall survival data is not yet mature. Along the same line, though carboplatin and cisplatin have similar mechanisms of action [13], they can result in different patient outcomes. Zivanovic et al. demonstrated that carboplatin and cisplatin had similar safety profiles in the use of HIPEC for the treatment of recurrent ovarian cancer during secondary cytoreductive surgery [14]. Nevertheless, HIPEC with carboplatin at the time of IDS was not superior to IDS alone in terms of clinical outcomes in this study. These results illustrate that platinum-based HIPEC chemotherapy regimens have varying efficacies, particularly when used alone and when used with additional chemotherapeutic agents. While the majority of EOC patients initially respond to platinum-based therapy, they often become platinum-resistant (PR) over time, defined as experiencing a disease recurrence within six months of platinum-based therapy [15]. The determination of platinum resistance confers poor prognosis for patients as remaining therapeutic options have limited efficacy. Several studies have suggested that PR patients receiving HIPEC had no alteration in survival rate after HIPEC compared to that of platinum-sensitive (PS) patients [16,17,18]. A randomized study by Spiliotis et al. compared OS in patients undergoing CRS with or without HIPEC for recurrent EOC [16]. Patients who received HIPEC at the time of surgery for recurrence had an OS of 26.7 months compared to 13.4 months for patients who did not receive HIPEC. Furthermore, in the HIPEC group, there was no difference in OS among PS and PR patients (26.8 vs. 26.6 months), while a statistically significant difference in OS was noted between PS and PR patients in the non-HIPEC group (15.2 vs. 10.2 months). This data suggests that HIPEC may overcome the resistance to platinum-based chemotherapy exhibited by the stem cells harbored within recurrent disease [16]. More recently, a retrospective study compared PFS and OS in platinum-sensitive and platinum-resistant EOC patients after cytoreductive surgery (CRS) and HIPEC to determine if CRS with HIPEC in PR patients can overcome PR treatment disadvantages [18]. Patients showed an improved treatment-free interval (TFI) when treated with a combination of HIPEC and secondary CRS, regardless of platinum sensitivity. PS patients had an improved survival to a higher degree than PR patients. Complete tumor resection resulted in significantly increased PFS in PS patients. (Complete cytoreduction was associated with longer survival.) Study limitations included the low number of PR patients and lack of complete resection in nearly half the PR patients. Results suggested that the combination of CRS and HIPEC in PR patients extends the TFI and thus this combination could be a treatment option for patients with PR EOC [18]. Further inquiry is needed to evaluate the role of HIPEC in improving OS for PR patients. It has been demonstrated that homologous recombination repair (HRR) mutations extend EOC patient PFS and OS [19]. Homologous recombination (HR) is a double-stranded DNA repair mechanism in which damaged chromosomes are repaired and cells are protected from chromosomal aberrations. Disruptions in this pathway result in homologous recombination deficiency (HRD), which impairs a cell’s ability to repair the DNA damaged by chemotherapy [20]. The process of HR includes several mediator genes including BRCA1 and BRCA2; however, these are also among the most mutated HR genes and commonly present in ovarian cancer [21]. Mutations in BRCA1/2 increase the lifetime risk of ovarian cancer development by 40% [22]. Studies show EOC patients with a BRCA mutation have increased chemosensitivity, specifically to platinum-based therapeutics. BRCA mutational status similarly impacts EOC patient response to HIPEC treatment, as hyperthermia impairs the BRCA protein function [23]. An exploratory analysis of the OVHIPEC-1 trial performed by Koole et al. found that patients without BRCA mutations had an increased benefit from HIPEC when compared to those with BRCA mutations [24]. The researchers evaluated tissue samples and tumor DNA from 200 patients with stage III ovarian cancer originally enrolled in the trial and categorized them by BRCA status and HRD status based on copy number variation profile. This study found no significant recurrence-free survival (RFS) benefit or OS benefit to HIPEC among patients with BRCA mutations, HR 1.25 (99%CI 0.48–3.29) and 1.94 (99%CI 0.42–9.16), respectively. Conversely, patients with HRD/BRCA wild-type tumors demonstrated an RFS benefit from HIPEC, HR 0.44 (99%CI 0.21–0.91) without OS benefit 0.55 (99%CI 0.23–1.30). HRD classification may play an increasing role in selecting optimal patients for HIPEC therapy. The reduction of recurrence seen from HIPEC treatment is promising as the majority of patients with advanced disease experience recurrence within five years [25]. Patients with recurrent disease report a significant impact on their overall quality of life compared to that of women without recurrence, including daily pain, increased emotional burden, activity limitations, and issues concentrating [26]. A single institution cohort study of advanced or recurrent EOC patients receiving CRS and HIPEC was analyzed to identify patterns of recurrence (pelvic, upper abdominal, or extraperitoneal) and whether there exists an association between location of recurrence and patient survival [27]. Results revealed half of the patients analyzed had recurrence outside the peritoneal cavity after HIPEC following CRS. Recurrence location did not impact PFS or OS in HIPEC patients. As HIPEC in ovarian cancer therapy specifically targets the peritoneal cavity, this pattern of spread suggests that HIPEC maintains local control of EOC and may reduce recurrence within the peritoneal cavity [27]. Skepticism surrounds HIPEC as it is perceived to be highly toxic, causing complications [28]. Current HIPEC trials have not reported any adverse effects yet further analysis into patient quality of life post-HIPEC is necessary for the continuation of HIPEC as a safe therapeutic. In a phase-III randomized trial, patients diagnosed with advanced-stage EOC were assessed for any alterations in their health-related quality of life after CRS with and without HIPEC [29]. The study followed patients from before randomization into the trial through 12 months post-treatment including analysis after several rounds of adjuvant chemotherapy. Patient health-related quality of life was assessed via questionnaires at various time points. In patients receiving HIPEC during CRS, no impairment in health-related quality of life was observed. A secondary analysis of PFS and OS confirmed that HIPEC patients after interval CRS had both an extended PFS and OS, consistent with previous findings [3,12]. In summary, an extension in patient survival and reduction in recurrence rate is evident, yet the mechanistic benefit of HIPEC in advanced EOC remains unknown. Studies are highly supportive of the use of HIPEC in the treatment of advanced EOC and indicate the extension of patient survival (Table 1). Based on existing data, the efficacy of HIPEC can be impacted by procedural factors, such as the timing of surgery in the patient’s treatment course and the type of chemotherapy utilized. As previously outlined, different chemotherapy regimens may have altered efficacy when used alone vs in combination with other agents. Similarly, platinum sensitivity is a patient-related factor that affects the utility of HIPEC therapy. Molecular tumor-related factors, including deficiencies in homologous recombination and BRCA status, further influence how patients respond to HIPEC therapy. Additional research evaluating the mechanistic benefits of HIPEC is warranted. A critical factor in deciding patient eligibility for HIPEC treatment is the presence of peritoneal metastases (PM), which is common among ovarian cancer patients. Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) is considered a safe localized treatment for PM. PIPAC is an alternative method of intraperitoneal drug delivery via aerosolized drugs. A prospective PIPAC study enrolled 110 PM patients, 14 of which had a primary ovarian diagnosis, and administered several rounds of PIPAC with or without palliative chemotherapy and bidirectional treatment [30]. The Peritoneal Regression Grading score (PRGS) was utilized to investigate histological treatment response to PIPAC, with a primary outcome of complete or major histological response from three treatments. PIPAC with oxaliplatin or cisplatin and doxorubicin confirmed the primary outcome, PIPAC induced a major or complete histological response, a result independent of patient survival. Quality of life declined post-PIPAC with significantly worsened global health scores despite improvement in fatigue, nausea, constipation, and appetite. PIPAC is known to enhance postoperative pain, yet it cannot be concluded that exacerbated pain is the source of the decline in global health scores [30,31]. PIPAC efficacy warrants additional evaluation for use in primary ovarian cancer patients. Malignancy is highly reported in primary ovarian cancer patients with a common complication of ascites. Continuous hyperthermic intraperitoneal perfusion chemotherapy (CHIPC) is thought to be advantageous over HIPEC due to the combination of hyperthermia treatment with local chemotherapy via laparoscopic administration [32]. To evaluate CHIPC efficacy in presence of malignant ascites, a 36-patient study was performed, of which 12 patients had primary ovarian cancer [32]. Results reveal successful CHIPC with completely resolved ascites in a majority of patients. No significant adverse effects were reported, and improvement in quality of life was associated with the control of ascites. CHIPC involves the administration of significantly lower doses of chemotherapy compared to systemic treatment, hence the reports of CHIPC being advantageous over HIPEC with respect to the treatment of PM [32]. PIPAC and CHIPC are used as a palliative treatment modality specifically for cancers involving peritoneal metastases. Reports of these therapies being advantageous over HIPEC in cases of primary ovarian cancer with respect to overall survival have yet to be reported. An important aspect in elucidating the mechanistic benefit of HIPEC is the development of an animal model to effectively recapitulate clinical HIPEC. Helderman et al. reviewed the current in vivo HIPEC models including the challenges and clinical relevance of each experimental design [33]. Current study designs involve invasive murine models emulating the human surgical technique. Murine models involve either an open or closed perfusion pump system (Figure 2). The open (coliseum) perfusion system involves exposure of the abdominal cavity via a vertical midline laparotomy, securing skin to a ring stand while maintaining sterility. The closed perfusion system involves the introduction of double inflow and outflow catheters through the upper and lower quadrants of the abdomen. Constant temperature is ideally maintained throughout the study duration. Coliseum and closed perfusion systems have shown success in mimicking clinical HIPEC, although neither method of perfusion is without complication. Coliseum perfusion is beneficial as intraoperative organ manipulation is feasible and several studies report total animal survival using the coliseum system; however, reported heat loss limits total clinical recapitulation [34,35]. Simultaneous studies utilizing the closed perfusion system reported a multitude of complications including organ suction into outflow catheters, perfusate leakage, and blood loss at catheter insertion sites [34,35]. Closed and open perfusion systems both permit only one animal treatment at a time, limiting study cohorts to very few animals. Studies report no animal deaths prior to the study endpoint, though most studies follow animals for only days post-HIPEC [36]. Miailhe et al. sought to develop a less-invasive ovarian cancer HIPEC mouse model while limiting complications observed in previous reports [37]. Ten tumor-bearing mice were utilized in a closed perfusion system, in which inflow and outflow catheters were placed at specific locations. A single inflow catheter into the left hypochondria and a single outflow catheter into the left iliac fossa were introduced. Twelve minutes of 43 °C oxaliplatin was infused while mice were kept under constant general anesthesia. All animals survived the duration of treatment with no reported complications. Study limitations include one mouse treatment at a time and the inability to manually stir the perfusate in the abdomen as is possible in the coliseum system. A key component in clinical HIPEC is the combination of CRS prior to HIPEC treatment. The lack of debulking primary tumor in the animals prior to heat is a major study limitation. This improved model of HIPEC showed limited morbidity as only one mouse died prior to the study endpoint [37]. The need for a functional non-invasive animal model for total recapitulation of clinical HIPEC remains, though success in current modalities has reported HIPEC benefit in murine models. Studies using primarily rats and mice have reported that the HIPEC procedure is possible in animal models, though limited data exist on the mechanistic benefits that HIPEC provides. HIPEC perfusion in colorectal tumor-bearing rats resulted in significantly reduced tumor load in the HIPEC group compared to that of the control and chemotherapeutic-only groups [38]. HIPEC targeting ovarian cancer stem-like cells (CSCs) showed a significant therapeutic effect in immunocompetent mice compared to that of immunodeficient mice [39]. CSCs are a subpopulation of cancer cells exhibiting chemoresistance, thus CSCs may be enriched by chemotherapy [40]. Using the coliseum perfusion system, IP hyperthermia (heated PBS) was infused into the peritoneal cavity for 20 min, maintaining a constant temperature. IP injection of chemotherapeutics was administered immediately after hyperthermia treatment in the treatment group. Mice were then kept under a heat lamp until awake from anesthesia. Results reveal the combination of chemotherapy and IP hyperthermia showed antitumor effects as tumor size was significantly decreased after treatment compared to that of hyperthermia and control groups. Enhancement of antitumor effects was related to the enrichment of chemotherapy by hyperthermia thus reducing the proportion of CSCs in immunocompetent mice. Hyperthermia overcame the chemoresistance, reducing the CSC proportion, in presence of immune system [39]. Challenges in the study of HIPEC in murine models include the difficulty in recapitulating the clinical HIPEC setting. Clinical HIPEC involves several rounds of neoadjuvant chemotherapy followed by interval debulking surgery and a 90 min heated chemotherapy pumped through the peritoneal cavity. In reported murine HIPEC models, study cohorts are very small due to the nature of the procedure not allowing for multiple animals to be treated simultaneously. Procedure complications have been reported in nearly all cases, including organ suction into outflow catheters, bleeding, and heat loss. Though clinical HIPEC is not completely without complication, heat loss during murine HIPEC poses a major limitation as constant heat is the main premise of HIPEC treatment. The closed perfusion system is a promising model to mimic clinical HIPEC and has successfully shown HIPEC efficacy in reducing murine tumor burden [38,39,41,42]. Studies report the use of heated PBS as IP hyperthermia, though analysis of heated chemotherapeutics would more closely follow human HIPEC. The current unmet need in the current murine models is the low throughput to permit larger cohorts to investigate the impact of the immune system more robustly in HIPEC benefit. Hyperthermic treatment involves the administration of controlled heat above the physiologically normal range, ideally targeting the malignant tissue [43,44]. Studies have demonstrated that hyperthermia enhances the cytotoxic effect of chemotherapeutics when temperatures of 40.5–43.0 °C are applied [43]. The synergistic effect is seen as a linear impact as with increasing temperature, the rate at which cells are killed also increases, notably within platinum-based drugs [43]. Issels et al. reported a comprehensive study of clinical trial results representing standard chemotherapy enhancement by the addition of hyperthermia [45]. The additive effect of hyperthermia on chemotherapy increased median patient survival by over nine years, with a ten-year survival increase of nearly 10% compared to chemotherapy alone [45]. Hyperthermic enhancement of the chemotherapeutic effect may be linked to altered tumor blood flow [44,46]. Blood circulation through the tumor tissue results in enhanced vascular permeability, a physiologically normal pH, and increased oxygenation, thus improving chemotherapy drug distribution throughout the tumor [44]. The molecular mechanism of the synergy between hyperthermia and chemotherapy involves an increase in reactive oxygen species (ROS) with a multitude of downstream effects including an enhancement of drug uptake [47]. An increase in ROS synthesizes DNA damage either directly causing apoptosis or increasing p53 (a gene vital for cell division control and cell death) expression resulting in cell cycle arrest, thus initiating apoptosis [47]. Combined hyperthermia and chemotherapy treatment shows increased apoptotic events via a decrease in heat shock protein (HSP) production, specifically Hsp70 and Hsp90 [47], which are further discussed below in Heat Shock Response. Stressful conditions including heat shock and tumor presence increase the synthesis of a family of intracellular HSPs [48]. These molecular chaperones are expressed in all cells and are critical for a multitude of functions including protein folding, promotion of immune response, and enhancement of signal pathways essential for cell survival [49,50]. The release of intracellular HSPs in response to heat is dependent on heat shock transcription factor 1 (HSF1), which upon activation by stressors binds to heat shock gene promotors. These extracellular HSPs express pro-immunity function and have been shown to promote antitumor immunity [51]. Extracellular HSPs promote the maturation of dendritic cells (DCs) thus activating the innate immune system [52]. In response to heat shock (42–45 °C), HSPs are released from cells and bind to peptides forming HSP-peptide complexes [53]. The HSP-peptide complexes shuttle antigenic peptides into the major histocompatibility complex (MHC) class I pathway of antigen-presenting cells (APCs) [48] (Figure 3). The MHC-I APC peptide complex binds to the T-cell antigen receptor (TCR) on the surface of T cells, leading to stimulation of the adaptive immune response via activation of CD8+ T cells. CD8+ T cells have shown significant anticancer effects as they produce cytokines targeting tumor tissue. The picture of the role of HSPs is complex as many cancers exhibit overexpression of Hsp70 and Hsp90, known to be associated with tumor promotion [54]. Due to involvement in multidrug resistance, metastasis, and tumor progression, Hsp90 has been identified as a target for anticancer therapy [54]. Inhibition of Hsp90 stimulates dissociation of HSF1 from Hsp90, activating the heat shock response, with increased expression of heat shock response genes. Simultaneous inhibition of HSF1 is suggested to improve Hsp90 inhibitor anticancer activity due to the HSF1 target genes containing drug resistance and anti-apoptotic properties [54]. Inactivation of Hsp90 increases antitumor immune response thus making Hsp90 inhibitors a promising cancer therapeutic. Histone deacetylases (HDACs) are enzymes responsible for catalyzing the removal of acetyl functional groups [55]. Deacetylation decreases drug effectiveness, thus HDAC inhibitors are anticancer agents that play a role in the induction of apoptosis and cell cycle arrest [56]. HDAC inhibitors have shown cytotoxic effects on ovarian cancer as HDACs are upregulated after chemotherapy treatment [57]. Sensitizing ovarian cancer cells to Hsp90 inhibitors via histone deacetylase (HDAC) may improve prognosis. A fever response is a key component to the presence of infection and inflammation and plays a vital role in immune activation, increasing pathogen defense mechanisms [58]. Although HSPs are induced via heat shock, febrile temperatures (38–41 °C) are sufficient to promote HSP production [58]. Clinical results reveal antitumor immunity in the presence of hyperthermia via HSP production and activation of antigen-presenting cells (APCs), resulting in lymphocyte trafficking to the tumor site [59]. Hyperthermia generates the release of HSP-peptide complexes and increases tumor antigens. Febrile temperatures are associated with the activation of circulating neutrophils, which are then recruited to local and distant sites such as tumors, though once temperatures surpass the febrile range neutrophil function will be impaired [58]. The adaptive immune response is heightened during hyperthermia in that NK cells are recruited to the tumor sites under febrile temperatures with enhanced cytotoxicity [58]. The elevated cytotoxicity in NK cells can be linked to increased Hsp70, heat shock protein present in major cellular components, and decreased MHC-I expression by the tumor cells. Tumor cells have upregulated HSP production in response to heat resulting in enhanced antigen-specific cytotoxic T lymphocyte production [58]. The immune system is comprised of two components, innate and adaptive immunity, which work to prevent and limit the invasion of unhealthy cells. The innate immune response is the immediate defense mechanism and provides a general response to foreign substances. The adaptive immune response is a slower, highly specific response that is long-lasting. Immune cells stem from precursor cells found in bone marrow. Myeloid progenitor stem cells are precursors for innate immune cells and include neutrophils, monocytes, DCs, and macrophages. Lymphoid progenitor stem cells are precursors for adaptive immune cells and include B cells, T cells, and natural killer (NK) cells, broadly categorized as lymphocytes. Antigens are foreign substances unrecognizable by the body, thus activating an immune response. Tumors possess a set of specific antigens recognizable by the immune system. APCs at tumor sites uptake the antigens and can create an immune response by activating lymphocytes. Cytotoxic lymphocytes then target tumor cells for destruction. Hyperthermia has the ability to improve this process by the generation of HSPs and activation of APCs, heightening the immune response [59]. The cGAS-STING pathway is an innate immune system component [60] (Figure 4). Hyperthermia has been shown to promote the cGAS-STING pathway in macrophage-like cells [61]. Cyclic GMP-AMP synthase (cGAS) is a protein-coding gene that detects cytosolic DNA and activates the Stimulator of Interferon Genes (STING) pathway with a downstream effect of cytokine activation [62]. DNA is typically localized to the nucleus allowing for control of specialized functions including DNA damage repair and replication [63]. DNA crossing the plasma membrane must translocate across the cytoplasm for nuclear entry through the nuclear envelope [64]. DNA found in the cytoplasm, therefore, is a trigger for immune response activation, as the body recognizes cytosolic DNA as viral entry [65]. Cytosolic DNA is detected resulting in the expression of inflammatory genes, activating defense mechanisms. The cGAS-STING pathway has been discovered to play a vital role in detecting DNA in response to immune defense mechanisms [66]. cGAS interacts with double-stranded DNA (dsDNA) causing DNA ligands to bind with cGAS. Ligand binding induces conformational changes which allow for the catalyzation of ATP and GTP into cyclic GMP-AMP (cGAMP). cGAMP is a second messenger which binds to the surface receptor on the endoplasmic reticulum (ER), activating the Stimulator of Interferon Genes (STING) [67]. STING translocates from the ER to the ER-Golgi intermediate compartments at which TANK binding kinase-1 (TBK1) and interferon regulatory factor 3 (IRF3) are recruited. IRF3 translocates from the Golgi to the nucleus where transcription takes place resulting in the expression of immune-stimulated genes and type 1 interferons. Additionally, STING activates IκB kinase. IκB phosphorylates, mediating the activation of nuclear factor kappa B (NF-κB) activated inflammatory genes including Interleukin 6 (IL-6) and tumor necrosis factor (TNF) [67]. The activation of inflammatory genes elicits an immune response thus hyperthermia is implicated in the promotion of immunity. The hallmarks of cancer include genome instability and mutation. Heat causes DNA and protein damage and inhibits cell cycle progression, triggering apoptosis [68]. Hyperthermia induces DNA damage and in combination with chemotherapy has a synergistic effect with chemotherapy increasing sensitivity to chemotherapeutics [69]. Increased chemosensitivity has been attributed to impaired DNA damage repair mechanisms. Chemotherapy alone induces DNA damage, thus in combination with heat, HR is impaired, increasing cancer cell death. To elucidate the effect of hyperthermia on HR, HR-proficient mouse embryonic stem (ES) cells were radiosensitized at normothermic and hyperthermic temperatures and compared to HR-deficient ES cells [70]. Quantification of genes showed that HR-mediated gene targeting had significantly reduced efficiency in ES cells incubated at an elevated temperature. Results suggest hyperthermia inactivates the HR repair mechanism [70] leaving cells reliant on other repair mechanisms such as Poly(ADP-ribose) polymerase-mediated DNA repair. Poly(ADP-ribose) polymerase (PARP) is a family of proteins involved in DNA repair that when inhibited increases chemotherapy cytotoxicity [71]. PARP enzymes detect single-stranded DNA breaks and bind to the DNA-binding domain. This binding allows the synthesis and transfer of poly(ADP) ribose to acceptor proteins, thus recruiting repair proteins to the site of damaged DNA [58]. Poly(ADP) ribose is involved in the repair of both single-stranded and double-stranded DNA breaks [58]. PARP1 is an enzyme involved in the repair of single-stranded DNA breaks, making PARP1 inhibitors prominent in cancer therapy. BRCA is an HR mediator gene commonly mutated in ovarian cancer. A mutation in BRCA wouldn’t allow for tumor suppression protein release. Hyperthermia on tumor cells resulted in BRCA degradation and HR inhibition [70]. The synergy between hyperthermia and chemotherapy may increase HIPEC benefit for BRCA-positive patients via inhibition of PARP-1-dependent DNA replication [72]. The degradation of BRCA induces increased sensitivity of tumor cells to PARP-1 inhibitors [70]. The studies indicate hyperthermia causes HR-proficient tumors to become sensitive to PARP-1 inhibitors, enhanced by HSP inhibition [70]. The combination of PARP-1 and HSP inhibition with HR inactivation via hyperthermia may be a promising therapeutic in cancer treatment. Hyperthermia induces an immune response with the downregulation of DNA repair pathways, allowing for tumor suppression. To analyze transcriptomic profile changes induced by HIPEC, pre- and post-HIPEC tumor samples were collected from ovarian cancer patients and compared to normal tissue [73]. HIPEC was given with carboplatin to the four patients included. Samples were analyzed using RNA sequencing. HIPEC induced upregulation of HSPs in tumor tissue with expression changes of Hsp90, Hsp70, Hsp40, and Hsp60 in both normal and tumor tissue. HIPEC with carboplatin induces increased immune-related gene expression in normal tissue with increased protein folding in tumor tissue. Results support the contention that a combination of HIPEC with HSP inhibitors may provide increased therapeutic benefit as some HSPs inhibit protein misfolding thus promoting tumor survival [73]. Tumors of EOC patients receiving HIPEC were collected for whole-transcriptomic analysis to elucidate HIPEC-induced molecular changes [74]. Tumor samples from advanced-stage EOC patients undergoing HIPEC were harvested before and after the procedure. Whole-transcriptomic sequencing, differential gene expression analysis, and gene enrichment analysis were performed. HIPEC induced upregulation of TNFα via the NF-κB pathway [74]. NF-κB is known to be activated through the cGAS-STING pathway and enhanced via hyperthermia [61]. Notably, HIPEC tumors had increased T cell activation as indicated by elevated expression of programmed cell death protein 1 (PD-1), a protein found on the surface of T cells and has a role in immune regulation. PD-1 expression was significantly increased in CD8+ T cells in the post-HIPEC tumor microenvironment. Elevated PD-1 expression post-HIPEC was correlated with improved patient PFS. Post-HIPEC tumors showed an upregulation of immune-related pathways and a downregulation of HR [74]. This analysis references in vitro analyses as preclinical data validation for comparison to human specimens, posing a major limitation of the analyses [74]. Significant elevation in CD8+ T cells, NK cells, and B lymphocyte cells has been observed 30 days after the HIPEC procedure via analysis of peripheral blood in patients with peritoneal metastasis [75]. Results are consistent with other studies showing stimulation of adaptive and innate immune response and inhibition of DNA repair mechanisms via HIPEC. Advanced-stage EOC causes more deaths in women than any other gynecological malignancy. Although the standard of care shows initial benefit in treating disease, most patients experience reoccurrence and will ultimately succumb to the disease, indicating a critical need for improved therapy. HIPEC in the treatment of EOC shows significant extension in patient overall survival. Mechanistic insights as to how HIPEC improves patient overall survival provide the opportunity for clinical therapeutic advancement. Hyperthermia induces a multitude of effects making thermotherapy a promising aspect of cancer treatment. Heat activates an immune response, impairs DNA damage repair while inducing DNA damage, and has a synergistic effect with chemotherapy making cancer cells more sensitive to chemotherapeutics. Therapies reliant on DNA damage need also to consider the inhibition of DNA repair mechanisms. In parallel, heat induces the synthesis of HSPs triggering innate and adaptive immunity via the activation of cytotoxic T cells, DCs, and NK cells. Future therapeutic strategies need to include hyperthermic activation of the cGAS-STING pathway, apparently a key component in HIPEC efficacy. Increased cGAS-STING expression would promote increased activation of inflammatory genes leading to increased immune response and targeting the tumor for destruction. Additionally, simultaneous inhibition of Hsp90 and PARP-1 via hyperthermia could sensitize tumors to HR inactivation, impairing tumor cell repair mechanisms. A question for the field is why HIPEC-treated tumors recur. Future studies need to address mechanisms and identify therapeutics to prolong efficacy perhaps by targeting immune surveillance. Animal models have exhibited significant improvement in tumor burden following HIPEC, yet none have reported mechanisms of benefit. HIPEC efficacy is ostensibly reliant on immune system involvement. Studies to elucidate the role of the immune system in HIPEC would provide a starting point for explaining the mechanistic benefit of HIPEC, which could be translated into clinical medicine.

PMC10000883

Iwona A. Ciechomska,Kamil Wojnicki,Bartosz Wojtas,Paulina Szadkowska,Katarzyna Poleszak,Beata Kaza,Kinga Jaskula,Wiktoria Dawidczyk,Ryszard Czepko,Mariusz Banach,Bartosz Czapski,Pawel Nauman,Katarzyna Kotulska,Wieslawa Grajkowska,Marcin Roszkowski,Tomasz Czernicki,Andrzej Marchel,Bozena Kaminska

Exploring Novel Therapeutic Opportunities for Glioblastoma Using Patient-Derived Cell Cultures

02-03-2023

glioblastoma,cancer stem cells,EMT,MGMT,temozolomide,doxorubicin,STAT3,EGFR inhibitor (AG1478)

Simple Summary Glioblastomas (GBM) are aggressive brain tumors with poor prognosis that need effective treatment. GBMs are characterized by extensive cellular and molecular heterogeneity which are reflected in patient-derived cell cultures, frequently used in testing potential therapeutics. Here, we established GBM-derived cell cultures from fresh tumor specimens and characterized them at the protein and molecular levels. We confirmed the considerable intertumor heterogeneity of GBMs. As the epidermal growth factor receptor (EGFR) is a subject of common oncogenic alterations in GBM, we tested anti-EGFR therapy combined with temozolomide (first-choice medication for GBM) or with doxorubicin (common therapeutic for various solid and blood cancers). We found that GBM-derived cells were more sensitive to a combined therapy than to monotherapy, particularly cells with inactive DNA repair mechanisms. Abstract Glioblastomas (GBM) are the most common, primary brain tumors in adults. Despite advances in neurosurgery and radio- and chemotherapy, the median survival of GBM patients is 15 months. Recent large-scale genomic, transcriptomic and epigenetic analyses have shown the cellular and molecular heterogeneity of GBMs, which hampers the outcomes of standard therapies. We have established 13 GBM-derived cell cultures from fresh tumor specimens and characterized them molecularly using RNA-seq, immunoblotting and immunocytochemistry. Evaluation of proneural (OLIG2, IDH1R132H, TP53 and PDGFRα), classical (EGFR) and mesenchymal markers (CHI3L1/YKL40, CD44 and phospho-STAT3), and the expression of pluripotency (SOX2, OLIG2, NESTIN) and differentiation (GFAP, MAP2, β-Tubulin III) markers revealed the striking intertumor heterogeneity of primary GBM cell cultures. Upregulated expression of VIMENTIN, N-CADHERIN and CD44 at the mRNA/protein levels suggested increased epithelial-to-mesenchymal transition (EMT) in most studied cell cultures. The effects of temozolomide (TMZ) or doxorubicin (DOX) were tested in three GBM-derived cell cultures with different methylation status of the MGMT promoter. Amongst TMZ- or DOX-treated cultures, the strongest accumulation of the apoptotic markers caspase 7 and PARP were found in WG4 cells with methylated MGMT, suggesting that its methylation status predicts vulnerability to both drugs. As many GBM-derived cells showed high EGFR levels, we tested the effects of AG1478, an EGFR inhibitor, on downstream signaling pathways. AG1478 caused decreased levels of phospho-STAT3, and thus inhibition of active STAT3 augmented antitumor effects of DOX and TMZ in cells with methylated and intermediate status of MGMT. Altogether, our findings show that GBM-derived cell cultures mimic the considerable tumor heterogeneity, and that identifying patient-specific signaling vulnerabilities can assist in overcoming therapy resistance, by providing personalized combinatorial treatment recommendations.

Exploring Novel Therapeutic Opportunities for Glioblastoma Using Patient-Derived Cell Cultures Glioblastomas (GBM) are aggressive brain tumors with poor prognosis that need effective treatment. GBMs are characterized by extensive cellular and molecular heterogeneity which are reflected in patient-derived cell cultures, frequently used in testing potential therapeutics. Here, we established GBM-derived cell cultures from fresh tumor specimens and characterized them at the protein and molecular levels. We confirmed the considerable intertumor heterogeneity of GBMs. As the epidermal growth factor receptor (EGFR) is a subject of common oncogenic alterations in GBM, we tested anti-EGFR therapy combined with temozolomide (first-choice medication for GBM) or with doxorubicin (common therapeutic for various solid and blood cancers). We found that GBM-derived cells were more sensitive to a combined therapy than to monotherapy, particularly cells with inactive DNA repair mechanisms. Glioblastomas (GBM) are the most common, primary brain tumors in adults. Despite advances in neurosurgery and radio- and chemotherapy, the median survival of GBM patients is 15 months. Recent large-scale genomic, transcriptomic and epigenetic analyses have shown the cellular and molecular heterogeneity of GBMs, which hampers the outcomes of standard therapies. We have established 13 GBM-derived cell cultures from fresh tumor specimens and characterized them molecularly using RNA-seq, immunoblotting and immunocytochemistry. Evaluation of proneural (OLIG2, IDH1R132H, TP53 and PDGFRα), classical (EGFR) and mesenchymal markers (CHI3L1/YKL40, CD44 and phospho-STAT3), and the expression of pluripotency (SOX2, OLIG2, NESTIN) and differentiation (GFAP, MAP2, β-Tubulin III) markers revealed the striking intertumor heterogeneity of primary GBM cell cultures. Upregulated expression of VIMENTIN, N-CADHERIN and CD44 at the mRNA/protein levels suggested increased epithelial-to-mesenchymal transition (EMT) in most studied cell cultures. The effects of temozolomide (TMZ) or doxorubicin (DOX) were tested in three GBM-derived cell cultures with different methylation status of the MGMT promoter. Amongst TMZ- or DOX-treated cultures, the strongest accumulation of the apoptotic markers caspase 7 and PARP were found in WG4 cells with methylated MGMT, suggesting that its methylation status predicts vulnerability to both drugs. As many GBM-derived cells showed high EGFR levels, we tested the effects of AG1478, an EGFR inhibitor, on downstream signaling pathways. AG1478 caused decreased levels of phospho-STAT3, and thus inhibition of active STAT3 augmented antitumor effects of DOX and TMZ in cells with methylated and intermediate status of MGMT. Altogether, our findings show that GBM-derived cell cultures mimic the considerable tumor heterogeneity, and that identifying patient-specific signaling vulnerabilities can assist in overcoming therapy resistance, by providing personalized combinatorial treatment recommendations. Glioblastoma (GBM) is a primary brain tumor, known to be one of the most aggressive human tumors. Despite maximal safe resection followed by radiation with adjuvant chemotherapy, the average survival of patients is 15 months after diagnosis and tumors recur within 6 months after therapy [1]. Temozolomide (TMZ) is an alkylating drug widely used as a first-choice chemotherapeutic agent in GBM [2], however, 50% of patients develop resistance to TMZ, which limits therapy outcomes. O6-methylguanine-DNA methyltransferase (MGMT) is responsible for removing the methyl group from O6-methylguanine in DNA, thereby diminishing the overall efficacy of TMZ. The expression of MGMT negatively correlates with promoter methylation and correlates with prolonged survival of GBM patients. In contrast, tumors with unmethylated MGMT (MGMT active) exhibit resistance to TMZ [3]. MGMT methylation allows for predicting TMZ effectiveness [4]. Additional molecular mechanisms may contribute to TMZ resistance, such as other DNA repair systems, epigenetic modifications, aberrant signaling pathways or molecular- and cellular heterogeneity in malignant glioma [5,6]. Therefore, there is an urgent need to discover a novel approach to increase glioma cell sensitivity to TMZ and other drugs. Anthracycline antibiotic doxorubicin (DOX) is widely used in the treatment of various solid and blood cancers [7]. DOX is cytotoxic towards cultured glioma cells [8] and in animal models of malignant gliomas [9,10]. Unfortunately, DOX has low of blood-brain barrier (BBB) penetration, and causes side effects in healthy tissues, including dose-limiting cardiotoxicity. Various formulations such as nanoparticles, liposomes, exosomes and polymer conjugates were developed to improve transport of DOX through the BBB and achieve the desired drug concentration within tumors [11,12]. Complementary approaches such as combinatory treatment and/or intratumoral delivery of DOX have been used in GBM therapy to reduce side effects [11,13,14]. GBM is characterized by high inter- and intrapatient heterogeneity. Integrated genomic and transcriptomic analyses identified clinically relevant major subtypes of GBMs: classical (CL), mesenchymal (MES) and proneural (PN) [15]. These subtypes are tightly associated with genomic abnormalities. Platelet-derived growth factor receptor alpha (PDGFRA) amplifications and mutations in genes coding for isocitrate dehydrogenase 1 (IDH1) and tumor protein 53 (TP53) were most frequently found in the PN group. Epithelial growth factor receptor (EGFR) alterations were found in the CL group, while neurofibromin 1 (NF1) gene mutations occur preferentially in MES GBMs. Moreover, the therapy provides the greatest benefits in the CL-GBMs, and less/no benefits in the PN-GBMs [16]. Multi-region tumor sampling has shown co-existence of multiple subtypes in different regions of the same tumor. These subtypes can change over time and through therapy. Single-cell RNA-sequencing (scRNA-seq) indicated that distinct cells in the same tumor recapitulate programs from distinct subtypes [15,17,18]. Studies by Patel et al. [18] showed that cells from the same tumor had variable ‘stemness’ and expressed different receptor tyrosine kinases (RTKs). Markedly, several studies indicated the presence of different cells, including glioma stem cells (GSCs) (also called tumor-initiating cells), within a tumor and their contribution to tumor growth, recurrence and resistance to radio- and chemotherapies [19,20,21]. The amplifications and mutations of EGFR are detected in about half of GBM tumors and in 95% of CL-GBMs [22,23]. Amplification of EGFR is often accompanied by the appearance of an EGFR variant III (EGFRvIII), which lacks the extracellular domain, causing a constitutive ligand-independent activity [24]. EGFR and its downstream signaling networks contribute to GBM cell proliferation and diffused invasion [25]. Many EGFR-targeting therapies are in development or in clinical trials of many tumors, including GBMs [26,27]. Although EGFR kinase inhibitors had shown promising results in some tumors (e.g., non-small lung cancer), gefitinib and erlotinib had insignificant outcome in clinical trials [28,29]. Among mechanisms of therapy resistance to EGFR inhibitors are: PTEN (phosphatase and tensin homolog) alterations, deregulated PI3K (phosphatidylinositol 3-kinase) pathway [29,30], compensatory signaling pathways, tumor heterogeneity and ineffective BBB penetration [31]. One of the signaling pathways downstream EGFR involves a signal transducer and an activator of transcription 3 (STAT3), an oncogenic transcription factor [32] regulating the transcription of several genes involved in cell cycle progression, resistance to apoptosis, angiogenesis, invasiveness and immune escape [33,34]. GBM patients with high levels of activated (phosphorylated) STAT3 have more aggressive disease and poorer clinical outcomes [35]. Targeting STAT3 sensitizes glioma cells to anti-EGFR (Iressa/gefitinib) and alkylating agents [36]. Concurrent inhibition of EGFR and JAK2/STAT3 was highly effective in a panel of molecularly heterogeneous glioma stem cells (GSC) and in orthotopic EGFRvIII GSC xenografts [37]. Afatinib (a second generation of EGFR-inhibitor) combined with TMZ synergistically inhibited cell proliferation, clonogenicity, invasion and motility of cultured glioma cells expressing EGFRvIII and prevented progression of intracranially implanted U87-MG EGFRvIII cells [38]. Cetuximab (an anti-EGFR antibody) augmented radiation and chemotherapy effects in GBM cells in vitro and in vivo [39,40]. TMZ and cetuximab were tested in a phase I/II clinical trial of primary GBMs [41]. Depatuxizumab mafodotin (ABT-414), an EGFR-targeting antibody–drug conjugate [42], selectively killed tumor cells overexpressing wild-type or mutant forms of EGFR and reduced glioma growth in mice [43]. DOX conjugated with ultrasmall nanoparticles showed a significant efficacy in patient-derived xenografts harboring EGFR mutations and/or amplification after intravenous administration [14]. The anti-EGFR-doxorubicin-loaded immunoliposomes (ILs-DOX) displayed highly efficient binding and internalization in a panel of EGFR and EGFRvIII overexpressing cells [44,45]. A small trial with anti-EGFR ILs-DOX on relapsed GBMs with EGFR amplification showed positive response in one patient [46]. The lack of effective conventional GBM therapy encourages researchers to search for new therapeutic strategies based upon the combination or repurposing of drugs. We tested the effects of an EGFR inhibitor AG1478 combined with TMZ or DOX using molecularly diverse patient-derived cell cultures, especially with a different status of MGMT. We established a quick and reliable method for generating patient-derived primary glioma cell cultures and performed their molecular characterization. We demonstrate that blocking EGFR signaling together with TMZ or DOX decreased cell viability and induced apoptosis of GBM-derived cells with no or low expression of MGMT. Mechanistic studies showed that although AG1478 inhibits phosphorylation of STAT3 in patient-derived cells, it was not sufficient to sensitize primary cells with the unmethylated MGMT promoter. The data define cell-type specific responses to the EGFR inhibitor in combination with TMZ or DOX and highlight a role of the MGMT promoter methylation in predicting cell responses to chemotherapeutics. WG0, WG1, WG3, WG4, WG5, WG6, WG9, WG10, WG13, WG14, WG15, WG16, WG16, WG17, WG18, WG19 primary glioma cultures originated from surgically resected glioblastoma samples (grade 4, according to WHO 2016 classification) [47]. The use of tissues was approved by the Research Ethics Board at Institute of Psychiatry and Neurology in Warsaw, Poland, and informed consents were obtained from the patients. All methods were carried out in accordance with the relevant guidelines and regulations. Freshly resected tumor tissues were washed in Hank’s balanced sodium solution (HBSS; Gibco Life Technologies, Rockville, MD, USA) and subjected to mechanical and enzymatic dissociation using a Neural Tissue Dissociation Kit (Miltenyi Biotec, Bergisch Glasbach, Germany) according to the manufacturer’s instructions. Some samples were processed without enzymatic digestion, in favor of accurate tissue cutting in DMEM/F12 medium until a smooth, milky single-cell suspension was achieved. To remove undissociated pieces and debris, the cell suspension was filtered through 100- and 40-micron cell strainers. The blood cells were discarded during serial passage and medium exchange, instead of using Lympholyte-M [48]. Tumor cells were resuspended in DMEM/F-12 medium (Gibco Life Technologies, Rockville, MD, USA) supplemented with 10% fetal bovine serum for adherent cultures, or DMEM/F-12 serum-free medium for sphere cultures, and plated at a density of 1–2 × 106 cells/T75 flask. Every 4 days, 50% of the fresh medium was replaced. The L0125 and L0627 GBM GSC lines were provided by Dr Rossella Galli (San Raffaele Scientific Institute, Milan, Italy) [49]. L0125 and L0627 were expanded in vitro in serum-free medium for sphere culture. Normal human astrocytes (NHA) were purchased from Lonza (Walkersville, MD, USA) and cultured in ABM Basal Medium (Lonza) supplemented with 3% fetal bovine serum, 1% L-glutamine, 0.1% ascorbic acid, 0.1% human EGF, 0.1% gentamicin, and 0.0025% recombinant human insulin. NTERA-2 cl.D1 were purchased from ATCC (Manassas, VA, USA) and cultured in DMEM with GlutaMax-1 and supplemented with 10% fetal bovine serum. All cell cultures were grown in a humidified atmosphere of CO2/air (5%/95%) at 37 °C. For sphere cultures, cells were seeded at a low density (3000 viable cells/cm2) onto non-adherent plates and cultured in serum-free DMEM/F-12 medium, supplemented with 2% B27 (Gibco Life Technologies, Rockville, MD, USA), 20 ng/mL recombinant human bFGF (Miltenyi Biotec, Bergisch Gladbach, Germany), 20 ng/mL recombinant human EGF (StemCell Technologies, Vancouver, BC, Canada), 0.0002% heparin (StemCell Technologies, Vancouver, BC, Canada) and antibiotics (100 U/mL penicillin, 100 µg/mL streptomycin, Gibco Life Technologies, Rockville, MD, USA). Every 3 days, 25% of the medium was replaced. After 7–14 days of culturing, the spheres were collected by centrifugation at 110× g and lysed in Qiagen RLT lysis buffer for RNA isolation or lysed in buffer supplemented with complete protease inhibitor cocktail (Roche Applied Science, Indianapolis, IN, USA) for blotting. Temozolomide (TMZ), doxorubicin (DOX) and AG1478 (AG) were dissolved in DMSO. Cells were treated with single drugs: TMZ (1 mM) for 72 h, DOX (50–1000 nM) for 48 h and AG (10 µM) for 6 h, or with a combination of TMZ + AG (1 mM + 10 µM) for 72 h, and DOX + AG (500 nM + 10 µM) for 48 h. DMSO was added at respective concentrations and served as a control condition. Cell viability was evaluated using the MTT metabolism test, as described previously [50]. Briefly, 1.5–2 × 104 cells were seeded onto 24-well plates and the MTT solution (0.5 mg/mL; Sigma-Aldrich, Taufkirchen, Germany) was added 24, 48, 72 and 96 h after cell seeding. After 1 h of incubation at 37 °C, water-insoluble formazan was dissolved in DMSO and optical densities were measured at 570 nm and 620 nm using a scanning multi-well spectrophotometer. Cell viability after AG, DOX or TMZ treatments was evaluated using the PrestoBlue Cell Viability Reagent (Invitrogen, Eugene, OR, USA). Diluted PrestoBlue reagent was added to each well for 1.5 h at 37 °C. After collecting samples, fluorescence was measured at 570 nm and 620 nm using a multi-well spectrophotometer. Whole cell lysates were prepared in a buffer containing phosphatase and protease inhibitors, separated by SDS-PAGE and transferred onto nitrocellulose membranes as described [51]. After blocking with 5% nonfat milk in a blocking buffer, the membranes were incubated overnight with primary antibodies and then with the appropriate secondary antibodies for 1 h. Immunocomplexes were visualized using an enhanced chemiluminescence detection system (SuperSignal West Pico PLUS; ThermoFisher Scientific, Rockford, IL, USA). Blots were visualized with a Chemidoc imaging system (Bio-Rad, Hercules, CA, USA). The molecular weight of proteins was estimated with prestained protein markers (Sigma-Aldrich, St. Louis, MO, USA). Densitometric analysis of the blots and quantification of the results from independent experiments were performed, and the levels of a protein of interest were compared to its levels in NHA, taken as 1, and marked by a solid black line. Cells were seeded onto a glass coverslip at a density of 2–3 × 104 cells. After 24 h, cells were fixed with 4% PFA at pH 7.2, washed, permeabilized with 0.1% Triton-X100 and blocked in a mix of 2% donkey serum and 1.5% fetal bovine serum, followed by overnight incubation with primary antibodies diluted in PBS containing 1% bovine serum albumin (BSA) and 0.1% Triton X-100. Cells were then washed in PBS, incubated with Alexa Fluor A555 secondary antibodies diluted in PBS for 2 h, counterstained with DAPI and mounted. For reagent specifications, catalogue numbers, and concentrations, see Supplementary Table S1. Cells were seeded onto 60-mm culture dishes at a density of 8 × 104 cells, in duplicates. When cells reached 80% confluency, a scratch was gently made using a p200 pipette tip. Pictures of the area were taken immediately after a wound was inflicted to the cells (0 h) and after 18 h. The migration rate was estimated from the distance that the cells moved, as determined microscopically. The area between the edges of the wound was measured by using Image J software. Six measurements were taken for each experimental condition. A mobility rate is expressed as percentage of wound closure as compared to 0 h time point. Migration rates were calculated using the following equation: (initial distance − final distance/initial distance) × 100%. DNA was extracted using standard phenol/chloroform methods. The purity and concentration of the DNA were estimated by measuring absorbance at 260/280 nm. DNA (2 μg) was treated with bisulfite (EpiTect Bisulfite Kit, Qiagen, Hilden, Germany). The modified DNA was amplified using primers specific for the methylated or unmethylated MGMT gene promoter, as listed in Supplementary Table S1. Each PCR mixture contained 1 μL of DNA, 500 nM of primers, 1 reaction buffer containing 1.5 mM MgCl2, and 1 U HotStarTaq DNA polymerase and 250 mM dNTPs (Promega, Madison, WI, USA). PCR was performed with thermal conditions as follows: 95 °C for 10 min, 45 cycles of 95 °C for 30 s, 57 °C for 30 s and 72 °C for 30 s, with a final extension of 72 °C for 10 min. PCR products were visualized using Agilent TapeStation system (Agilent Technologies, Santa Clara, CA, USA) yielding a band of 81 bp for a methylated product and 93 bp for an unmethylated product. Positive methylated and positive unmethylated controls (EpiTect PCR Control DNA Set Qiagen, Germany) were included. Total RNA was extracted using an RNeasy Mini kit (Qiagen, Germany) and purified using RNeasy columns. The integrity of the RNA was determined using an Agilent 2100 Bioanalyzer. For qRT-PCR, the total RNA from cells was used to synthesize cDNA by extension of oligo (dT)15 primers with SuperScript reverse transcriptase (Thermo Fisher Scientific, USA). Real-time PCR experiments were performed in duplicates using a cDNA equivalent of 22.5 ng RNA in a 10 μL reaction volume containing 2x SYBR Green Fast PCR Master Mix (Applied Biosystems, Foster City, CA, USA) and a set of primers. Sequences of the primers are listed in Table S1. Data were analyzed by the relative quantification method using StepOne Software (Applied Biosystems, Foster City, CA, USA). The expression of each product was normalized to 18S rRNA and presented as a dCt value. The quality and quantity of isolated nucleic acids were determined by Nanodrop (Thermo Fisher Scientific, Waltham, MA, USA). mRNA libraries were prepared using a KAPA Stranded mRNA-seq Kit (Kapa Biosystems, Cape Town, South Africa) according to manufacturer’s protocol. Briefly, mRNAs were enriched from 500 ng total RNAs using poly-T oligo-attached magnetic beads (Kapa Biosystems). Enriched mRNA was fragmented, and then the first and second strands of cDNA were synthesized. Adapters were ligated and the loop structure of each adapter was cut by a USER enzyme (NEB, Ipswich, MA, USA). Finally, the amplification of obtained dsDNA fragments that contained a specific adapter sequence was performed using NEB starters. Quality control of the final libraries was performed using an Agilent Bioanalyzer High Sensitivity dsDNA Kit (Agilent Technologies, Waldbronn, Germany). The concentration of the final libraries was measured using the Quantus Fluorometer and QuantiFluor ONE Double Stranded DNA System (Promega, Madison, WI, USA). Libraries were sequenced on a HiSeq 1500 (Illumina, San Diego, CA, USA) on the rapid run flow cell with a paired-end setting (2 × 76 bp). Data analysis: RNA sequencing reads were aligned to the human genome reference with the STAR algorithm [52], a fast gap-aware mapper. Then, gene counts were obtained by featurecounts [53] using human transcriptomic annotations. The counts were then imported to R and processed by DESeq2 [54]. The counts were normalized for gene length and library size. TCGA public data analysis: TCGA level 3 RNA-seq data (aligned by STAR and gene expression counted by HTseq) were uploaded to R. Data from TCGA GBM (glioblastoma, WHO grade 4) and LGG (lower-grade gliomas, WHO grades 2/3) repositories were uploaded. Gene expression values as FPKM (fragments per kilobase of exon per million) were used for further analysis. The curated sets of genes characteristic for each GBM subtype, categorized originally by Verhaak et al. [16], were downloaded from the Molecular Signatures Database v7.5.1. The analysis for the following gene sets was performed: VERHAAK_GLIOBLASTOMA_PRONEURAL, VERHAAK_GLIOBLASTOMA_NEURAL, VERHAAK_GLIOBLASTOMA_CLASSICAL, VERHAAK_GLIOBLASTOMA_MESENCHYMAL. All biological experiments were performed on 3–4 independent cell passages. Results were expressed as means ± standard deviation (SD). p-values were calculated using a two-tailed t-test or a one-way ANOVA followed by an appropriate post hoc test using GraphPad Prism v6 (GraphPad Software, Boston, MA, USA). Differences were considered statistically significant for p values < 0.05. The effect size, Cohen’s ‘d’ and Hedge’s ‘g’ [55] were calculated as follows: , , where is the mean of the group, is the error mean square, and is the sample size. Patient-derived cell cultures represent more reliable cellular models for testing new cytotoxic drugs in comparison with commercially established cell lines. Thus, we aimed to establish primary cell cultures from freshly resected high- or low-grade gliomas. Following dissociation of the tissue, cells were cultured in the presence of serum as adherent cells or as spheres in defined serum-free media. We succeeded in obtaining adherent cell cultures from the vast majority of glioma samples, whereas only two tumor samples gave rise to spheres, enriched in GSCs. Cell cultures with unchanged proliferation after eight subsequent passages were considered as glioma cell lines. Three cell cultures (WG0, WG3 and WG6) underwent two passages and stopped growing. Altogether, we established 13 GBM-derived cell lines out of 16 WHO high-grade glioma surgical specimens. We managed to develop two cell cultures from a WHO grade 1 tumor, and two cell lines from WHO grade 2 or 3 tumors. Information concerning age, sex and histopathological diagnosis of patients is presented in the table (Supplementary Figure S1). The mean age of men was 65, and 45 in the case of women (Figure 1B). Our studies have shown that GBMs were more frequent in men than in women in a 1.67:1 proportion (Figure 1A,B). Sex differences in GBM incidence have been previously reported [56]. We analyzed the doubling time of 13 patient-derived cell cultures (Figure 1C,D) within 4 days. Cells with a doubling time of less than 60 h were considered as highly proliferating cells, where as those dividing every 60–150 h were designated as intermediate-proliferating cells. Finally, cell lines with a doubling time of more than 150 h were marked as slowly proliferating cells. Only WG4 and WG9 cells had a high proliferative index. Most of the cells were intermediate-proliferating cells, with an average doubling time of 100 h. WG18, WG10, WG2 and WG1 were slowly dividing cells. There was no correlation between the doubling time of patient-derived cell cultures and patient age (Figure 1C,D; Supplementary Figure S1). The doubling time of normal human astrocytes (NHA), used as a non-malignant control, was approximately 150 h. The proliferation rate of commercial glioma cell lines U251, U87-MG, LN229 and LN18 were 57, 43, 41 and 40 h, respectively, indicating that established glioma cell lines grew much faster than primary glioma cell cultures. For better characterization of primary cell cultures, we applied the RNA-seq and the unsupervised analysis of mRNA expression profiles, which compared 13 GBM-derived primary cell cultures to the TCGA (The Cancer Genomic Atlas) glioma datasets [16]. The results were mapped using PCA (principal component analysis). The resulting PCA (data not shown) showed a clear separation of low- and high-grade gliomas in the TCGA dataset, and primary GBM-derived cell cultures were more similar to high-grade gliomas in terms of transcriptional profiles. Gliomas from TCGA and the primary glioma cell cultures clustered separately (data not shown). Next, we attempted to assign each individual primary GBM cell culture to four molecular subclasses. Most of the patient-derived cell cultures represented mixed subtypes, without a dominant gene expression signature, with the exception of WG13 and WG17 showing mostly the MES signature, and WG4 and WG14 represented by mixed, PN and CL subtypes (Figure 1E). Then we performed cell culture subtyping by assessing protein expression, as previously demonstrated [57,58]. We analyzed the levels of seven proteins by Western blotting (Figure 1F,G). Detection of mutant IDH1R132H and high levels of TP53 and PDGFRα suggested the PN subtype, high levels of EGFR evidenced the CL subtype, and high levels of CHI3L1/YKL40, CD44 and phospho-STAT3 were typical for the MES subtype. We found that WG4, WG14 and WG17 were positive for the mutant IDH1R132H and TP53, but had low expression of PDGFRα. High levels of EGFR were detected in approximately 50% of the tumor cell cultures (WG0, WG1, WG2, WG4, WG9, WG10, WG13, WG18). Immunoblot analysis with antibodies specifically recognizing mesenchymal markers demonstrated elevated CD44 levels in all tested glioma cell cultures, whereas high expression of CHI3L1/YKL40 was found only in WG4 and WG14 cells. Elevated levels of phosphorylated STAT3 (active form of STAT3) were observed in the WG9, WG10, WG13, WG14, and WG18 primary glioma cell lines. The global gene profiling and protein-based classification of primary glioblastoma cell cultures revealed that WG4 and WG14 could be classified into the PN subtype; however, exome sequencing of the WG4 cells did not detect any mutation in IDH1 (data not shown). GBMs contain a rare population of glioma stem-like cells (GSCs, also called glioma-initiating cells) with capacities for self-renewal, multi-lineage differentiation, and resistance to therapies [20]. The expression of selected pluripotency and differentiation markers was examined. Transcriptomic analysis revealed high expression of both types of markers in WG4 and WG14 cells in comparison with other primary cell cultures (Figure 2A). We found high expression of pluripotency markers such as PROM1, OLIG2, SOX2 and NESTIN (a marker of neural precursors), as well as overexpression of markers for astrocytes (GFAP, S100) and neurons (MAP2, TUBB3). These results were validated by quantitative RT-PCR analysis, Western blotting and immunostaining (Figure 2B–E). High expression of GFAP at the mRNA and protein levels was observed not only in WG4 and WG14 but also in WG9, WG10, WG13 and WG17. Interestingly, these cell cultures exhibited high expression of TUBB3, SOX2 and NESTIN mRNA. Elevated levels of GFAP, β-Tubulin III, SOX2 and NESTIN proteins were detected in WG9, WG13, and WG17 cells (Figure 2B–E, Supplementary Figure S2). Using Western blotting analysis and immunostaining, we did not detect the OLIG2 protein expression in all tested glioma cell lines growing in serum-containing media (Supplementary Figure S2). Overexpression of both pluripotency and differentiation markers was observed in the same cell lines and confirmed that the tumor cells are aberrantly differentiated. Moreover, these results revealed the inter- and intratumoral heterogeneity of GBM-derived cell cultures. To obtain a subpopulation enriched in GSCs, cells were cultured at low density, without serum and in the presence of epidermal growth factor (EGF) and fibroblast growth factor (bFGF). We found that only two primary cell cultures, WG4 and WG14, were capable of forming spheres, as evidenced by using light microscopy (Figure 3A, Supplementary Figure S3) [51]. We have previously shown high expression of NANOG, POU5F1, SOX2 and PROM1 in WG4 spheres [51]. Here, we demonstrate that GCS-enriched spheres from WG14 expressed significantly higher levels of OLIG2 and lower levels of astrocytic (GFAP) and neuronal (β-Tubulin III) markers as compared with the adherent tumor cells (Figure 3B–D). Similar profiles were demonstrated in other GSCs originating from human GBMs (L0125 and L0627 cell lines) [49,59]. L0125 and L0627 spheres quickly attached to the cell cultures plates and branched out in serum-containing medium. Increased expression of GFAP and β-Tubulin III were observed upon the addition of serum containing media (Figure 3B–D). Interestingly, all studied glioblastoma-derived spheres (WG14, L0125, L0627) did not express NANOG or OCT4A, essential transcription factors that regulate self-renewal and pluripotency of embryonic stem cells [60]. NTERA-2 cells, a pluripotent human embryonic carcinoma cell line [61], were NANOG-, OCT4A- and SOX2-positive (Figure 3B,C). These results confirmed that GBM-derived spheres are lineage-restricted cells that could express some differentiation markers. Indeed, the presence of serum increased the levels of astrocytic and neuronal markers, and NESTIN and SOX2 were highly expressed (Figure 3B–D). During epithelial-to-mesenchymal transition (EMT) cells acquire mesenchymal features, resulting in increased motility and invasiveness that are crucial for tumor progression [62]. Although gliomas do not undergo the classical EMT program, the majority of molecules involved in EMT play a role in the glial-to-mesenchymal transition, GMT [63], a process in which a subpopulation of glioma cells becomes highly motile and more resistant to treatment, and invades the brain parenchyma, generating micrometastases [64]. The analysis of RNA-seq data from TCGA databases showed significant upregulation of genes encoding mesenchymal markers and EMT regulators such as CDH2 (coding N-cadherin), VIM (coding Vimentin), SNAIs and TWISTs in tumor samples, particularly in high-grade human gliomas (grade 4) and MES-GBMs (Supplementary Figure S4A,B). The CDH1 expression (coding E-cadherin) was relatively low, and the lowest level was found in MES-GBMs (Supplementary Figure S4A,B). Patients with low expression of CDH2, VIM, SNAIs and TWISTs had longer survival compared with those of high expression (Supplementary Figure S4C). We analyzed the expression of EMT-related genes (top 30 genes from the web-based EMTome portal [65]) in GBM-derived cell cultures. The heatmap shows different expressions of the EMT gene signature in primary cell cultures (Supplementary Figure S5A). Interestingly, the mesenchymal markers VIMENTIN and CD44 were upregulated at mRNA and protein levels in all tested cell cultures (Figure 1F and Figure 4A, Supplementary Figure S5B). An elevated level of CDH2 was found in GBM-derived cells (Supplementary Figure S5B). Immunoblot analysis showed higher levels of N-CADHERIN in the WG0, WG4, WG5, WG13, WG14, WG15 and WG17 cultures (Figure 4A,B). The expression of CDH1 (an epithelial marker) was low in tested cell lines (Supplementary Figure S5B). The analysis of EMT-inducing transcription factors showed elevated levels of SNAIL in WG4, WG9, and WG13 cells, and similar levels of SLUG in most of the studied cell cultures, with the exception of WG4 cells having the highest SLUG protein level (Figure 4A,B). We determined the migratory properties of human GBM-derived cells using a scratch assay. A majority of cells (WG1, WG2, WG4, WG10, WG13, WG15, WG16, WG17 and WG19) closed 30–50% of the wound in less than 18 h (Figure 4C, Supplementary Figure S5C). These data show that the majority of GBM-derived cells have high migratory capacity. We analyzed the MGMT promoter methylation in 12 cell cultures by using methylation-specific (MS)-PCR. The unmethylated MGMT gene promoter was found in seven cell cultures (WG1, WG5, WG9, WG15, WG16, WG17 and WG19 cells), whereas the methylated MGMT promoter was detected only in WG4 cells. The intermediate status of the MGMT, both methylated and unmethylated, was found in WG10, WG13, WG14 and WG19 cell cultures (Figure 5A,B). These results were corroborated by MGMT expression data (Figure 5C). The lowest expression was found in WG4 cells, intermediate in WG14 cells and high in WG9 cells and other tested cells. We tested the effects of 1 mM TMZ on GBM-derived cells with different statuses of the MGMT gene promoter: WG4 (MGMT low), WG14 (MGMT intermediate) and WG9 (MGMT high). The viability of glioma cells after 72 h treated with TMZ was significantly reduced to 75% (Figure 5D,E). The strongest accumulation of apoptosis markers—cleaved caspase 3 and caspase 7 and fragments of PARP—was found in TMZ-treated WG4 cells. The cytotoxic effect of TMZ was less prominent in WG14 cells, whereas WG9 cells were resistant to the treatment (Figure 5F,G). Next we tested how the cells responded to doxorubicin (DOX) added at different concentrations (50–1000 nM) for 48 h (Supplementary Figure S7). DOX reduced cell viability in a dose-dependent manner, with 25% of WG9 cells and 50% of WG4 and WG14 cells killed after drug administration. These results showed that WG9 primary cultures were more resistant to DOX. The half-maximal-effective concentration (EC50) values were calculated for WG4, WG14 and WG9 cells and were 1.09, 1.47 and 1.85 mM, respectively (Supplementary Figure S7). Evaluation of caspase-cascade protein levels showed that WG4 and WG14 cells were more sensitive to the drug than WG9 cells (Figure 7A,B). Altogether, the results support the notion that MGMT promoter methylation defines cell responses not only to TMZ but also DOX treatment. Due to the minor effect of TMZ or DOX alone on glioma cell viability, we explored whether blocking EGFR signaling with the specific inhibitor AG1478 (AG) would modify the cell response to the drugs. First, the effects of inhibition of EGFR on glioma cells were analyzed (Figure 6A,B). AG efficiently blocked EGFR activation in WG4, WG14 and WG9 cells was evidenced by a reduction of phospho-EGFR levels (Figure 6A,B). Interestingly, treatment with AG for 6 h resulted in the reduction of phospho-STAT3 levels without significant decrease phospho-ERK and phospho-AKT levels, and altering total ERK and AKT levels (Figure 6A,B; Supplementary Figure S6B). Cell viability was not affected after exposure to AG for 6 h (Supplementary Figure S6A); however, longer exposure (72 h) significantly reduced the viability of WG4 and WG14 cells (Figure 6C). To study whether AG sensitizes to TMZ-induced cell death, we calculated the effect size (Hedge’s g) for single AG or TMZ treatment, and combined AG + TMZ treatment. Hedge’s g = 0.2, 0.5 and 0.8 are often cited as indicative of a small, medium and large effect, respectively. The effects for WG4 were as follows: AG (4.5), TMZ (1.5), AG and TMZ (4.9); on WG14: AG (3.0), TMZ (2.7), AG and TMZ (3.6); and on WG9: AG (0.3), TMZ (0.2), AG + TMZ (0.5). Notably, in the WG4 and WG14 cells we observed additive effects of AG + TMZ and decreased cell viability, whereas WG9 cells were the most resistant to the treatments. The analysis of apoptosis markers confirmed that the combined treatment with AG + TMZ resulted in the strongest accumulation of cleaved caspase 7 and PARP in WG4 and WG14 cells in comparison with WG9 cells. The additive effect of AG + TMZ was observed in WG4 cells (Figure 6D,E). While phospho-STAT3 levels were reduced in AG + TMZ-treated cells, levels of phospho-ERK and phospho-AKT were not affected in treated cells (Supplementary Figures S6D,E and S17 for whole Western Blots). Similar changes were detected after the AG + DOX treatment (Figure 7C). We calculated the effect size (Hedge’s g) for all of the treatments to determine the effectiveness of the treatments. The effects on WG4 were as follows: AG (1.7), DOX (0.7), AG + DOX (1.8); on WG14: AG (2.0), DOX (2.3), AG + DOX (2.9); and on WG9: AG (0.8), DOX (0.4), AG + DOX (1.0). An additive effect between AG and DOX was visible in the tested cell lines, with a minor effect on WG9 cells. Markedly, the analysis of cell death markers by Western blotting confirmed those results (Figure 7D,E). The accumulation of cleaved caspase 7 and cleaved PARP was found in WG4 and WG14 cells but not in WG9 cells. We introduced small modifications into existing protocols [48,66] that resulted in producing a simple procedure to culture both adherent cells and GSCs derived from glioma patient tumor samples. We generated 13 adherent cell cultures out of 16 GBM specimens, including two sphere cultures, enriched in GSCs. We established two cell lines from a WHO grade 1 tumor, and two cell lines from a WHO grade 2/3 tumor, which is a rare event, as lower-grade patient-derived glioma (LGG) is challenging [67]. Virtually all LGG cell lines generated to date from adult patients represent oligodendroglioma WHO grade 3 [68,69,70]. The developed GBM-derived cell cultures were more similar to high- grade gliomas from TCGA than to LGG in terms of transcriptional profiles. The low frequency of establishing GSC-enriched spheres likely stems from the fact that only the WG4 and WG14 cells had a high expression of neural stem and precursors markers responsible for self-renewal of GSCs (OLIG2, SOX2 and NESTIN), while embryonic stem cell markers (NANOG and OCT4A) were not expressed [60]. This low frequency is in agreement with recent findings showing that a significant part of GBM samples did not form long-term serum-free cell cultures [19,71]. The expression of astrocytic (GFAP, S100) and neuronal (MAP2, TUBB3) markers in the same cells suggests that GBM-derived spheres undergo aberrant differentiation, as was shown previously [72]. The detected gene expression profiles and expressed proteins reveal a high degree of intertumoral heterogeneity among GBM-derived cell cultures, representing mixed subtypes within each GBM-derived cell culture. Indeed, Patel et al., using single cell RNA sequencing, reported that distinct cells in the same tumor exhibit transcriptional programs from distinct subtypes [18]. GBM cells exist in four main cellular states and show state transition, or plasticity [73]. WG4 and WG14 cells expressed PN and CL signatures and, consistently with CL and PN subtypes, were highly proliferating cells. Transcriptional analysis is not routinely feasible in clinical setting, therefore a simplified method based on the expression of some proteins was proposed. We evaluated seven proteins specific to distinct GBM subtypes: PN (OLIG2, IDH1R132H, TP53 and PDGFRα), CL (EGFR) and MES (CHI3L1/YKL40, CD44 and phospho-STAT3). EGFR alterations were found in 55% of patients in the TCGA-GBM dataset. It was also found in most of the presented cultures, with high levels of phosphorylated EGFR in WG4, WG9 and WG14 cells. WG4, WG14 and WG17 cells were positive for the mutant IDH1R132H. Increased TP53 protein levels in WG4, WG14 and WG17 cells suggests non-functional TP53, as wild-type TP53 is rapidly degraded and mutant forms are stabilized in tumor cells [74]. Mutation in the TP53 gene was confirmed by deep sequencing in WG4 cells. This observation is consistent with recent reports [16,23] showing that WHO grade 4 gliomas with IDH1 mutations harbor TP53 mutations. WG13 and WG17 cells were assigned with the MES subtype and analysis of EMT proteins showed that a majority of cells express elevated level of VIMENTIN, CD44 and SLUG. High expression of CHI3L1/YKL40 was found only in WG4 and WG14 cells. Recent findings indicate that CHI3L1/YKL40 is highly expressed only in a small fraction of the primary tumor samples [75]. N-CADHERIN, SNAIL and phospho-STAT3 levels varied in cell cultures. The acquisition of mesenchymal phenotypes by a majority of cultures could be due to clonal selection under in vitro conditions [71,76]. Our results demonstrate that using a small set of markers, we can define major GBM subtypes and the patient-derived cell cultures recapitulate the GBM heterogeneity of GBM, although gene expression was not fully recapitulated at protein levels. Indeed, Brennan et al. using a targeted proteomic profiling showed that the impact of specific genomic alterations on downstream pathway signaling is not linear and not always concordant with a genotype [23]. The classification of GBM may assist in selecting therapies, for example CL-GBMs are more responsive to radiation and chemotherapy, as typically, intact TP53 may control DNA-damage-induced cell death. The MES subtype is the most aggressive and strongly associated with a poor prognosis when compared to the PN subtype [77], and a shift from a PN to an MES subtype can occur in patients following radiotherapy and chemotherapy [78]. EGFR-targeted therapy has attracted much attention due to frequent alterations in malignant gliomas. A specific EGFR inhibitor AG1478 [N-(3-chlorophenyl)-6,7-dimethoxyquinazolin-4-amine] competitively binds to the ATP pocket of EGFR and inhibits its activity [79,80]. Previous data showed anti-proliferative effects of AG1478 and enhancement of sensitivity to cytotoxic drugs, such as cisplatin, etoposide and DOX in different cancer cells [81,82], including human EGFRvIII glioma cells in vivo [83,84] that resulted in preclinical studies [85]. We used molecularly diverse GBM-derived cell cultures with MGMT promoter methylation to determine if inhibition of EGFR with AG1478 would sensitize GBM cells to TMZ or DOX. We found that AG-treated WG4 cells (with methylated MGMT promoter) or WG14 cells (with intermediate MGMT methylation status) were more sensitive to DOX than WG9 cells (with the unmethylated MGMT). AG1478 + TMZ resulted in the strongest accumulation of apoptosis markers in WG4 cells. Our data suggest that MGMT promoter methylation could predict the response not only to TMZ but also to DOX. While TMZ and DOX induce different DNA damage responses, and most alkylating drugs (TMZ) are sensitive to MGMT-mediated repair, other studies showed that MGMT promoter methylation affects responses to radiotherapy and other cytotoxic drugs [4,86,87]. A recent systematic review and meta-analysis confirmed a MGMT methylation status as a clinical biomarker in GBM patients, showing association with better overall and progression-free survival in patients treated with alkylating agents [88] and tyrosine kinase inhibitors [89]. The mechanism of action of DOX is known and has been described as independent from MGMT promoter status; however, recent studies link the MGMT expression with the response to non-alkylating drug treatment [90,91]. AG1478-induced blockade of EGFR signaling shows that the levels of active, phosphorylated STAT3 were reduced without changing phospho-AKT and ERK levels. Phosphorylation of STAT3 requires nuclear entry of EGFRvIII and formation of an EGFRvIII-STAT3 nuclear complex [92]. An inactive PI3K-AKT pathway could result from PTEN alterations, mutations within the gene encoding the p110 catalytic subunit of PI3K (PIK3CA), AKT amplification [93] or pathway activation via PDGFRs [94]. Indeed, DNA sequencing analysis revealed mutations in PTEN and PIK3CA genes in WG14 and WG9 cells, respectively (data not shown). Our results show that EGFR inhibition via reducing STAT3 phosphorylation sensitize some GBM cells to the treatment with TMZ or DOX, providing a new modality for those chemotherapeutics resistant cells. DOX is a cytotoxic, anti-cancer drug with well-known pharmacokinetics that, due to low penetration of the BBB and serious side effects, is not used in GBM therapy. However, many new formulations of DOX with nanoparticles and liposomes may improve its delivery to glioblastomas [14,15,16,17,18]. Interestingly, co-delivery of DOX and EGFR siRNA in intracranial U87MG xenografts prolonged the life span of glioma-bearing mice and induced apoptosis in gliomas [11], which is exactly the combination we recommend based on our results. Disrupting the peritumoral BBB with focused ultrasound (FUS) and interstitial thermal therapy (LITT) improves the accumulation of DOX in GBM-bearing mice [12]. LITT combined with low-dose DOX results in longer survival of recurrent GBM patients. Low doses of DOX were safe for patients, even with extended (>6 weeks) dosing [95], and showed promising results in a phase I trial (GBM-LIPO trial) in which patients with relapsed glioblastoma harboring an EGFR amplification were treated with anti-EGFR doxorubicin-loaded immunoliposomes (anti-EGFR ILs-DOX) [46]. These results support using a combinatorial approach in well-defined GBM-derived cell cultures and advocate for the use of DOX together with EGFR-targeted therapy for GBM-patients. The results show that such a combination would be effective in GBM patients with amplified/mutated EGFR and with the methylated MGMT promoter. The present study provides a simplified protocol to generate glioma-patient-derived cultures and glioma stem-like spheres, expressing to some extent features and subtypes of the original tumors. We provided transcriptional and marker-protein-based characterization of the cell cultures that allowed classification into major subtypes. We determined the response of WG4, WG9 and WG14 cells bearing the different MGMT promoter methylation to various cytotoxic drugs and evaluated a potential additive effect of EGFR blockade. Our results show that EGFR inhibition via reducing STAT3 phosphorylation sensitizes some GBM cells to treatment with TMZ or DOX, providing a new modality for those chemotherapeutic-resistant cells. We emphasized the need for proper characterization of the cells to obtain the most reliable results, especially when the new drugs or their combinations will be tested for clinical settings.

PMC10000888

Leonie T. D. Wuerger,Felicia Kudiabor,Jimmy Alarcan,Markus Templin,Oliver Poetz,Holger Sieg,Albert Braeuning

Okadaic Acid Activates JAK/STAT Signaling to Affect Xenobiotic Metabolism in HepaRG Cells

28-02-2023

okadaic acid,HepaRG cells,CYP enzymes,NF-κB,JAK/STAT,inflammation

Okadaic acid (OA) is a marine biotoxin that is produced by algae and accumulates in filter-feeding shellfish, through which it enters the human food chain, leading to diarrheic shellfish poisoning (DSP) after ingestion. Furthermore, additional effects of OA have been observed, such as cytotoxicity. Additionally, a strong downregulation of the expression of xenobiotic-metabolizing enzymes in the liver can be observed. The underlying mechanisms of this, however, remain to be examined. In this study, we investigated a possible underlying mechanism of the downregulation of cytochrome P450 (CYP) enzymes and the nuclear receptors pregnane X receptor (PXR) and retinoid-X-receptor alpha (RXRα) by OA through NF-κB and subsequent JAK/STAT activation in human HepaRG hepatocarcinoma cells. Our data suggest an activation of NF-κB signaling and subsequent expression and release of interleukins, which then activate JAK-dependent signaling and thus STAT3. Moreover, using the NF-κB inhibitors JSH-23 and Methysticin and the JAK inhibitors Decernotinib and Tofacitinib, we were also able to demonstrate a connection between OA-induced NF-κB and JAK signaling and the downregulation of CYP enzymes. Overall, we provide clear evidence that the effect of OA on the expression of CYP enzymes in HepaRG cells is regulated through NF-κB and subsequent JAK signaling.

Okadaic Acid Activates JAK/STAT Signaling to Affect Xenobiotic Metabolism in HepaRG Cells Okadaic acid (OA) is a marine biotoxin that is produced by algae and accumulates in filter-feeding shellfish, through which it enters the human food chain, leading to diarrheic shellfish poisoning (DSP) after ingestion. Furthermore, additional effects of OA have been observed, such as cytotoxicity. Additionally, a strong downregulation of the expression of xenobiotic-metabolizing enzymes in the liver can be observed. The underlying mechanisms of this, however, remain to be examined. In this study, we investigated a possible underlying mechanism of the downregulation of cytochrome P450 (CYP) enzymes and the nuclear receptors pregnane X receptor (PXR) and retinoid-X-receptor alpha (RXRα) by OA through NF-κB and subsequent JAK/STAT activation in human HepaRG hepatocarcinoma cells. Our data suggest an activation of NF-κB signaling and subsequent expression and release of interleukins, which then activate JAK-dependent signaling and thus STAT3. Moreover, using the NF-κB inhibitors JSH-23 and Methysticin and the JAK inhibitors Decernotinib and Tofacitinib, we were also able to demonstrate a connection between OA-induced NF-κB and JAK signaling and the downregulation of CYP enzymes. Overall, we provide clear evidence that the effect of OA on the expression of CYP enzymes in HepaRG cells is regulated through NF-κB and subsequent JAK signaling. Okadaic acid (OA) is a lipophilic marine biotoxin produced by dinoflagellates. It accumulates in the fatty tissue of filter-feeding shellfish and can lead to diarrheic shellfish poisoning (DSP) after ingestion of contaminated shellfish [1]. With the rise in the occurrence of so-called harmful algae blooms due to climate change and industrial waste, the abundance of DSP-toxin-producing dinoflagellates is also rising. DSP is especially a problem in Europe and Japan, but also occurs in other countries around the world [2]. Leading symptoms include diarrhea, stomach pain, and vomiting. To prevent DSP, the European Union implemented a limit of 160 OA equivalents/kg shellfish meat, based on acute toxic effects, such as diarrhea [3]. Moreover, there are multiple reported properties of OA, such as cytotoxicity [4,5,6] and embryotoxicity in vitro [7,8]. However, no embryotoxic effect of OA in humans has been evaluated so far. It was shown to pass the placental barrier in mice [9]. The toxin also acts as a tumor promotor in various organs [10,11] and a correlation between frequent consumption of contaminated shellfish and colon cancer was reported [12,13,14]. OA was first discovered in the black sponge Halichondria okadaic, and in 1981 the structure was published [15]. OA was very early reported as a phosphatase inhibitor; its main target on the molecular level is protein phosphatase 1 and 2A [16], but it is also able to target other serine/threonine phosphatases. Therefore, OA exposure can lead to hyperphosphorylation, which can lead to modifications in signal transduction [17]. Because of DSP symptoms, most previous studies focused on the intestine; however, liver toxicity was also reported in mice [18]. Dietrich et al. were able to demonstrate a disruption of the tight junction proteins, such as claudins and occludin, in the intestine after OA exposure at food-relevant concentrations [19]. OA is therefore able to pass the mechanical barrier of the intestine. Furthermore, we recently demonstrated an effect of OA on xenobiotic metabolism in liver cells [20]. In a recent publication, we were able to show that OA is able to downregulate several cytochrome P450 (CYP) enzymes at the RNA and protein level and to decrease their activity. Furthermore, OA is able to influence the expression of several transporter proteins and transcription factors associated with xenobiotic metabolism, which indicates that OA is able to interact with the biochemical barrier in the liver. However, the underlying mechanism of CYP regulation remains to be elucidated. It has already been demonstrated that OA is able to activate nuclear factor kappa B (NF-κB) in different cell types [21,22,23,24,25]. NF-κB activation leads to elevated levels of pro-inflammatory cytokines [26], and therefore plays a major role in inflammation (cp. Figure 1). Keller et al. demonstrated that elevated cytokine levels in primary human hepatocytes activate the Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway to affect the expression of xenobiotic-metabolizing enzymes [27]. The JAK/STAT pathway is involved in several inflammatory and autoimmune diseases, but also in cell proliferation, differentiation, and apoptosis. It is therefore an essential pathway in human cells [28]. In this study, we provide an advanced characterization of the OA-mediated inflammatory response. We propose an activation pathway involving NF-κB and JAK/STAT signaling (cp. Figure 1) and determine if OA is able to activate the JAK/STAT signaling pathway in HepaRG cells through activation of NF-κB and if activation of the pathway is connected to CYP downregulation. We used differentiated HepaRG cells and incubated them with OA and several inhibitors, targeting several steps in the proposed activation pathway. HepaRG cells originate from human hepatocarcinoma. With the addition of DMSO to the cell culture medium, they can be differentiated into hepatocyte- and biliary-epithelium-like cells. Differentiated HepaRG cells express a variety of liver-specific enzymes and also xenobiotic metabolizing enzymes at a similar level to primary hepatocytes. Therefore, they are a very common model in studies examining xenobiotic metabolism in vitro [29,30]. OA was purchased from Enzo Life Sciences GmbH (Loerrach, Germany). JSH-23 and Methysticin were purchased from Merck KGaA (Darmstadt, Germany) and Decernotinib and Tofacitinib were obtained from Selleck Chemicals (Munich, Germany). All other standard chemicals and materials were purchased from Sigma-Aldrich (Taufkirchen, Germany) or Roth (Karlsruhe, Germany) in the highest available purity. HepaRG cells (Biopredic International, Saint-Grégoire, France) were cultivated at 37 °C for 14 days in William’s E medium supplemented with 10% fetal bovine serum (FBS), 5 μg/mL insulin (medium and both supplements from PAN-Biotech GmbH, Aidenbach, Germany), 50 μM hydrocortisone hemisuccinate (Sigma-Aldrich, Taufkirchen, Germany), 100 U/mL penicillin, and 100 μg/mL streptomycin (Capricorn Scientific, Ebsdorfergrund, Germany), according to established protocols. After 14 days, the medium was further supplemented with 1% dimethyl sulfoxide (DMSO) for 2 days, and afterwards, the DMSO content was increased to 1.7% for another 12 days. Then, the medium was changed to serum free assay medium (SFM), which consisted of William’s E medium without phenol red (PAN-Biotech GmbH, Aidenbach, Germany), containing 100 U/mL penicillin and 100 μg/mL streptomycin, 2.5 μM hydrocortisone hemisuccinate, 10 ng/mL human hepatocyte growth factor (Biomol GmbH, Hamburg, Germany), 2 ng/mL mouse epidermal growth factor (Sigma-Aldrich, Taufkirchen, Germany) and 0.5% DMSO. This treatment medium was adapted from Klein et al. [31]. Cultivation of HepG2 cells (ECACC, Porton Down, UK) was performed as described before [32]. HepG2 cells were used for development of the confocal microscopy method. The human embryonic kidney cell line HEK-T was obtained from the European Collection of Cell Cultures (ECACC, Porton Down, UK). The cells were cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM, Pan-Biotech GmbH, Aidenbach, Germany) supplemented with 10% fetal calf serum (Pan-Biotech GmbH, Aidenbach, Germany), 100 U/mL penicillin, and 100 g/mL streptomycin (PAA Laboratories GmbH, Pasching, Austria). The cells were passaged every 2–4 days (80–90% confluence) and seeded at 5 × 104 cells/cm2 in 96-well plates. In total, 2 × 105 cells/well were seeded in 6-well plates and treated with 11, 33, and 100 nM OA. Furthermore, they were treated with 30 µM JSH-23 and 30 µM Methysticin, two NF-κB activation inhibitors, and 40 µM Decernotinib and 40 µM Tofacitinib, two JAK inhibitors, for 24 h each. Afterwards, the gene expression of several cytokines, SOCS3, and 3 representative CYP enzymes was determined using quantitative real-time reverse transcriptase polymerase chain reaction (qPCR), which was performed as described before [20]. The used primers can be found in Table S1. The STAT1 and STAT3 proteins were analyzed using Western blotting. Cell cultivation in 6-well plates and the incubation was performed as described above for the gene expression analysis. After 24 h incubation, the cells were harvested in PBS after a washing step. The cells were centrifuged (5 min, 2000× g, 4 °C) and the supernatant was discarded. The proteins were then isolated from the pellets using RIPA lysis buffer (pH 7.5; 50 mM Tris-HCl, 150 mM NaCl, 2 µM EGTA, 0.1% sodium dodecyl sulfate (SDS), and 0.5% desoxycholic acid) containing 1:50 protease inhibitor (Complete Protease Inhibitor Cocktail Tablets, Roche, Mannheim, Germany) and 1% Triton X-100. The cell pellets were then rotated at 4 °C for 15 min and homogenized using an ultrasonic homogenizer (Sonopuls HD 2070, BANDELIN electronic GmbH & Co. KG, Berlin, Germany, 25% power, pulse 2). The homogenized lysates were then centrifuged at 4 °C and 13,200× g for 30 min. The cell pellet was discarded. The protein content in the supernatants was determined using Bradford assay according to the manual of the Biorad protein assay (Bio-Rad Laboratories GmbH, Feldkirchen, Germany) against a bovine serum albumin standard curve. The samples were diluted with MilliQ water, mixed with 5× Laemmli buffer (1:5; 320 mM Tris-HCl, 15% SDS (w/v), 35% glycerine, 0.5% bomophenol blue, and 25% 2-mercaptoethanol) and denatured at 96 °C for 5 min. SDS polyacrylamide gel electrophoresis was performed using a 5% stacking gel and a 10% separation gel, followed by a transfer of the separated proteins to a nitrocellulose membrane (Amersham™ Protran™ Premium NC, Cytiva Europe GmbH, Freiburg, Germany) using the wet blot method. Unspecific binding sites were then blocked using 5% milk powder in Tris-buffered saline with Tween-20 (TBS-T, pH 7.6, 20 mM Tris-HCl, 137 mM NaCl, and 0.1% Tween-20) for 1 h at room temperature. The first antibody against phosphoSTAT1 (Phospho-STAT1 (Tyr701) Recombinant Rabbit Monoclonal Antibody (15H13L67), Thermo Fisher Scientific, Waltham, MA, USA, dilution factor 1:1000) or phosphoSTAT3 (Phospho-STAT3 (Ser727) Recombinant Rabbit Monoclonal Antibody (SY24-09), Thermo Fisher Scientific, Waltham, MA, USA, dilution factor 1:1000) was then incubated overnight at 4 °C. The membrane was then washed 3× with TBS-T and incubated with the secondary antibody (Anti-rabbit, HRP-conjugated, R&D systems, Minneapolis, MN, USA, dilution factor: 1:1000) for 1 h at room temperature. For detection, the membrane was incubated with the SuperSignal West Fento Maximum Sensitivity Substrate Kit according to the included protocol (Thermo Fisher Scientific, Waltham, MA, USA). The chemiluminescence was detected using a Molecular Imager Versadoc MP 4000 (BioRad Laboratories GmbH, Feldkirchen, Germany). As loading control, GAPDH was afterwards stained as well. Therefore, the membrane was incubated with the Anti-GAPDH antibody [6C5] (abcam, Cambridge, UK, dilution factor 1:7500) for 1 h at room temperature. For detection, Sheep anti-Mouse-IgM-HRP antibody was incubated for 1 h (HRP-conjugated, Seramun Diagnostica GmbH, Heidesee, Germany, dilution factor 1:10,000), followed by protein detection as described above. For quantification of the band intensities, ImageLab 6.0.1 software was used. Intensities were then referred to the loading control GAPDH and normalized to the respective solvent control. First, 0.1 × 106 cells were seeded in 12-well plates containing a coverslip in each well. After differentiation, the cells were treated with 33 nM and 100 nM OA, 40 µM Decernotinib and 40 µM Tofacitinib, and the respective solvent control for 24 h. Cells were fixed for 20 min using 3.7% formaldehyde and permeabilized for 10 min with 0.5% Triton-X in PBS. Afterwards, they were incubated with DAPI (AppliChem, Darmstadt, Germany), which was diluted 1:1000 in PBS for 20 min at room temperature. Actin was stained with the ActinGreen™ 488 ReadyProbes™ reagent (Invitrogen™, Waltham, MA, USA). Two drops of ActinGreen were dissolved in 1 mL PBS and incubated on the cells at room temperature for 30 min. Afterwards, the samples were blocked with blocking solution (1% BSA in PBS-T) for 1 h at room temperature. The primary antibody, Anti-NF-kB p65 antibody ab16502 (Abcam, Cambridge, UK) for NF-κB and Phospho-STAT3 (Ser727) Recombinant Rabbit Monoclonal Antibody (SY24-09) (Invitrogen™, Waltham, MA, USA) for phosphoSTAT3 was then diluted 1:1000 in blocking solution and incubated on the cells at 4 °C overnight. Afterwards, the secondary antibody, Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor™ 633 (Life Technologies™, Carlsbad, CA, USA) was incubated for 1 h at room temperature. The coverslips with the cells were then flipped onto a glass slide containing a drop of Vectashield® HardSet™ Antifade Mounting Medium (Vector Laboratories, Newark, New Jersey, CA, USA) and dried overnight at 4 °C. Fluorescence was detected using the confocal laser scanning microscope LSM 700 (Zeiss, Oberkochen, Germany) at ex wavelengths of 405 nm (DAPI, blue), 488 nm (ActinGreen, green), and 633 nm (NF-κB or phosphoSTAT3, red). Z-stacks spanning through the entire cell layer were recorded at 63× magnification. For analysis, 6 images of each condition were collected and analyzed using the ImageJ 1.53e software (Laboratory for Optical and Computational Instrumentation (LOCI)) at the University of Wisconsin–Madison, Madison, WI, USA). For quantification of the colocalization, the nuclear fluorescence ratio of the target protein to the DAPI signal was determined for each single nucleus. The nuclei were marked as region of interest (ROI) and the fluorescence intensity of the red and blue channels in each ROI was determined separately. A ratio of red/blue signal intensity was then formed and normalized to the mean of the solvent controls. The nuclei were then divided into groups based on their signal intensity. Proteins were quantified using the Luminex xMAP® technology. In total, 2 × 105 cells/well were seeded in 6-well plates and treated with 11, 33, and 100 nM OA. Furthermore, they were treated with 30 µM JSH-23, 30 µM Methysticin, and 40 µM Decernotinib or 40 µM Tofacitinib for 24 h. After incubation, the cell culture supernatant was collected and frozen at −80 °C until further use. After thawing, the supernatants were treated as described in the instruction manual of the ProcartaPlex™ Basiskit, human (Invitrogen™, Waltham, MA, USA) using the IL-1 alpha Human ProcartaPlex™ Simplex Kit, IL-1 beta Human ProcartaPlex™ Simplex Kit, IL-6 Human ProcartaPlex™ Simplex Kit, IL-8 (CXCL8) Human ProcartaPlex™ Simplex Kit, IL-12 p70 Human ProcartaPlex™ Simplex Kit, and the TNF alpha Human ProcartaPlex™ Simplex Kit (all Invitrogen™, Waltham, MA, USA). The plates were measured on a FLEXMAP 3D® instrument (Luminex, Austin, TX, USA). The data were evaluated in Origin 2017 (OriginLab, Northampton, MA, USA) as described in the instruction manual. DigiWest® was performed as described before [33]. DigiWest® was performed for p100/p52 and RelB subunits of NF-κB, IκB kinase (IκBα), IκB kinase subunit IKKβ, Jak1, and Jak2. Three independent replicates were combined before measuring. Transactivation assays were conducted as previously described [34]. Briefly, 24 h after seeding, HEK-T cells were transiently transfected using TransIT-LT1 (Mirus Bio, Madison, WI, USA) according to the manufacturer’s protocol. For each well, the transfection mixture contained 40 ng pGAL4-(UAS)5-TK-luc, 40 ng pGAL4-hPXR-LBD, 1 ng pcDNA3-Rluc for PXR assay and 40 ng pGAL4-(UAS)5-TK-luc, 40 ng pCMX-GAL4-hRXRα, and 1 ng pcDNA3-Rluc for RXRα assay. pcDNA3-Rluc was used as an internal control for normalization. Four to six hours after transfection, the cells were incubated with different concentrations of OA dissolved in culture medium (0.1% MeOH). PXR agonist SR12813 (10 µM) and RXRα agonist CD2608 (100 nM) were used as positive controls. After 24 h, the culture medium was removed and the cells were lysed after addition of 50 µL lysis buffer (100 mM potassium phosphate with 0.2% (v/v) Triton X-100, pH 7.8) for 15 min on an orbital shaker. After centrifugation (5 min, 2000× g), 5 µL of the supernatant was analyzed for luciferase activity as previously described [35]. Firefly luciferase values were normalized to Renilla luciferase values and expressed as fold-induction normalized against solvent control. Statistical analysis was performed using the software SigmaPlot (SYSTAT Software Inc., San Jose, CA, USA). To determine the statistical significance, one-way ANOVA followed by Dunnett’s post-hoc test (* p < 0.05; ** p < 0.01; *** p < 0.001) was performed to compare the different sample groups. For confocal microscopy, Wilcoxon rank-sum test (* p < 0.05; ** p < 0.01; *** p < 0.001) was performed to compare the sample groups against a control. In this study, we chose three different OA concentrations based on previously published results of cell viability testing [20], where we incubated HepaRG cells with OA concentrations between 5 and 500 nM for 24 h. We chose 11 nM, 33 nM, and 100 nM as non-toxic concentrations. In this study, we also added different inhibitors, targeting different points of the proposed signaling pathway. JSH-23 and Methysticin inhibit the activation of NF-κB, while Decernotinib and Tofacitinib inhibit the activation of JAK. Figure 2A shows the analysis of the RNA expression of some CYP enzymes. CYP1A1, CYP2B6, and CYP3A4 were selected as representative CYP enzymes and analyzed using qPCR. As previously shown in Wuerger et al. [20], OA was able to strongly inhibit the expression of these CYPs. Furthermore, we investigated the effect of OA on the transcription factors PXR and RXRα. Those factors directly influence the expression of various CYPs, importantly CYP3A4 and CYP2B6. As shown in Figure 2B, OA was also able to strongly downregulate the RNA expression of the two transcription factors PXR and RXRα in HepaRG cells, as well as their transcriptional activity, as shown in HEK-T cells using reporter gene transactivation assays. In summary, OA was able to strongly downregulate the expression of CYP1A1, CYP2B6, and CYP3A4 and also able to downregulate the expression and activity of the transcription factors PXR and RXRα. Based on these data and information from the literature as detailed in the introduction section, we hypothesized a possible mechanism induced by OA involving the activation of NF-κB- and subsequent JAK/STAT signaling pathways (Figure 1). Activation of NF-κB consequently activates the expression of several cytokines released into the surrounding cell culture medium. Activated cytokine receptors recruit JAKs, which become activated to subsequently activate STAT proteins, which translocate into the nucleus, where they can act as transcription factors for SOCS3 [36,37], or for other genes related to xenobiotic metabolism. SOCS3 is a direct target of activated STAT3 and able to inhibit JAK/STAT, especially if activated via Interleukin-6- (IL-6) dependent signaling in a negative feedback loop [38]. To verify the activation of NF-κB by OA, we developed a confocal microscopy method. After activation of NF-κB, p65 translocates into the nucleus, which can be visualized using a specific primary antibody against p65 (Figure 3A). The activation and translocation of NF-κB by OA was observed in HepG2 cells (Figure S2). Figure 3A shows the activation of NF-κB by OA in a concentration-dependent manner in HepaRG cells. To visualize a colocalization of p65 and the nucleus, a cross-section through the entire cell layer was visualized. Figure 3B shows an increase in activated NF-κB with an increase of the OA concentration. Furthermore, DigiWest analysis, an advanced digital fluorescence-based Western blotting method, showed that the protein expression of another NF-κB subunit, RelB, was strongly upregulated after OA incubation. Furthermore, protein expression of IκBα and IKKβ were downregulated by OA, which is shown in Figure 3C. In summary, Figure 3 shows the activation of NF-κB in HepaRG cells after incubation with OA. The NF-κB-inhibitors JSH-23 and Methysticin were also evaluated in combination with OA but showed no effect on the translocation (Figure S3). Activation of NF-κB leads to expression and release of cytokines. We were able to show that the mRNA expression of several interleukins was upregulated in HepaRG cells upon exposure to OA. To show a direct involvement of NF-κB activation through OA with these effects, we then added NF-κB inhibitors. The NF-κB inhibitors were able to reverse the observed effect of OA on the ILs (Figure 4A). This suggests an involvement of NF-κB signaling in IL mRNA expression. To verify the effects of OA on the RNA expression of ILs in HepaRG cells, we then investigated the protein expression of IL-6 and IL-8 using Luminex xMAP® technology. Figure 4B shows the release of IL-6 and IL-8 into the cell culture supernatant. IL-1α, IL-1β, IL-12, and TNF-α were also analyzed. Results for IL-1α can be found in the supplementary material (Figure S4), while the levels of the other cytokines did not exceed the LLOQ (IL-1β: 2.76 pg/mL; IL-12: 7.74 pg/mL; TNFα: 6.15 pg/mL). OA exposure clearly led to a concentration-dependent release of cytokines from HepaRG cells. Furthermore, combined incubation with OA and the NF-κB inhibitor Methysticin was able to counteract the cytokine release induced by OA, which is shown in Figure 4C. Results of cytokine release after incubation with JAK inhibitors can also be found in the supplementary material (Figure S3). The addition of NF-κB inhibitors further influenced the RNA expression of the CYP enzymes CYP1A1, CYP2B6, and CYP3A4. Figure 4D shows that their expression is decreased with OA, which could be partially reversed with the NF-κB inhibitors, thus indicating that OA exerts its effects on the CYPs via NF-κB activation. Figure 5 shows the activation of the JAK/STAT signaling pathway in HepaRG cells after exposure to OA. Figure 5A was obtained using a similar method as in Figure 3, with a specific antibody against phosphorylated STAT3 (phosphoSTAT3), the activated form of STAT3. Figure 5A,B show that more activated STAT3 is present in the cells incubated with OA than in the solvent control. Furthermore, the JAK inhibitors Decernotinib and Tofacitinib (images shown in Figure S5) were inhibiting STAT3 activation by OA (Figure 5F). This result was further validated using Western blotting. Figure 5C shows a representative Western blot using the antibody against phosphoStat3 (pSTAT3). A strong activation of STAT3 was observed following OA incubation in HepaRG cells. We were able to reverse this effect using the JAK inhibitor Tofacitinib. On the contrary, no activation was detected for STAT1 using Western blotting (Figure S6). Furthermore, using DigiWest, we could show a higher protein expression of JAK1 upon OA incubation in HepaRG cells, whereas JAK2 expression was downregulated (Figure 5E). These results point towards an activation of JAK1 and subsequent activation of STAT3 by OA. Additionally, we again evaluated the RNA expression of CYP enzymes CYP1A1, CYP2B6, and CYP3A4 after OA exposure in combination with the JAK inhibitors. The strong CYP downregulation mediated by OA was significantly reversed using the inhibitors (Figure 5D), thus demonstrating that JAK signaling is involved in mediating the effects of OA. We recently demonstrated an effect of OA on the metabolic barrier in the liver: OA is able to downregulate the expression of several xenobiotic-metabolizing enzymes on the RNA and protein levels as well as their enzymatic activity (this work, [20]). Many studies have shown that inflammation can alter the metabolic function of xenobiotic metabolism [27,39]. While most studies have focused on only one inflammatory effector (eg., NF-κB activation or Cytokine release), our study provides deeper insight into the molecular mechanism of OA-mediated inflammatory response. Firstly, we evaluated the expression and transcriptional activity of PXR and RXRα, two transcription factors regulating CYP expression. Both endpoints were significantly downregulated upon exposure of cells to OA. We postulated a possible mechanism as underlying signaling pathway for the altered expression of xenobiotic-metabolizing enzymes (Figure 1). As already mentioned, OA is a potent activator of NF-κB in a variety of different cell types [21,22,23,24,25]. We verified this in HepaRG cells. Our findings show an activation of NF-κB in a concentration-dependent manner. Phosphorylation of the NF-κB inhibitor IκB is facilitated by the IκB-kinase (IKK), which is itself regulated by PP2A [40,41,42]. The activation of NF-κB thereby appears to be a direct consequence of the inhibition of PP2A by OA. NF-κB plays a major role in inflammation. Its target genes include several cytokines, such as IL-1, IL-6, IL-8, and TNFα [26,43]. Our findings show an increase in cytokine release and expression after OA exposure. Inhibited NF-κB activation through NF-κB-inhibitors did not result in an inhibition of translocation of p65 but led to a decreased cytokine release. This might be caused by the fact that the NF-κB-inhibitor JSH-23 is known to inhibit the transcriptional activity of NF-κB and does not only exert its effect by affecting the translocation of the transcription factor [44]. The second NF-κB-inhibitor used, Methysticin, also did not show an inhibition of NF-κB translocation. However, our data also suggest an inhibition of the transcriptional activity of NF-κB, which is in accordance with previous studies [45,46]. However, kinetic aspects might also play a role here. Based on this, the data suggest a NF-κB-dependent activation of cytokine release upon OA exposure in HepaRG cells. The cytokine IL-6, a major NF-κB target and mediator of inflammatory processes, binds to its respective cytokine receptor to activate JAKs and, subsequently, STATs [36]. JAKs are tyrosine kinases that are associated to a cytokine receptor. After binding of the ligand, the receptors dimerize, which leads to an activation of the pathway. JAKs phosphorylate each other, which enables the binding of STATs. STATs are also phosphorylated and thereby activated. They then dimerize and translocate into the nucleus where they act as transcription factors [36,37] (cp. Figure 1). Our results show an activation of STAT3 in HepaRG cells after exposure to OA, as evidenced in the confocal microscopy experiments. This was confirmed by Western blotting. Simultaneous incubation with JAK inhibitors reversed this activation, which further strengthens the key role of STAT3 in OA-mediated toxicity. Tanner et al. demonstrated that elevated levels of the cytokine IL-6 in human liver cells lead to a decrease in PXR and constitutive androstane receptor (CAR) activity, two nuclear receptors that play a major role in CYP expression after activation by xenobiotics and dimerization with the RXRα [39]. Furthermore, the activated JAK/STAT signaling pathway can influence RXRα. RXRα plays a central role in the regulation of CYP enzymes, as it associates with many other transcription factors influencing CYP expression [27]. It has been shown that an increased level of proinflammatory cytokines leads to a decreased expression of various drug-metabolizing enzymes and reduced CYP activity [47]. In particular, CYP3A4 is known to be downregulated during inflammation and sepsis. Furthermore, OA is able to strongly downregulate PXR, a main regulator of CYP3A4 [48]. There is clear evidence that PXR regulation occurs through NF-κB and subsequent cytokine signaling [49]. This suggests a connection between NF-κB and CYP3A4 via cytokine release, JAK/STAT and PXR. The CYP1A family (1A1, 1A2, and 1B1), on the contrary, is regulated through the aryl hydrocarbon receptor (AhR). While CAR and PXR can dimerize with RXRα, AhR dimerizes with the aryl hydrocarbon nuclear translocator (ARNT) protein before targeting the regulatory region of their respective target genes [50,51,52]. RNA expression of AhR is upregulated after exposure to OA [20]. AhR expression is controlled by NF-κB and it is able to directly influence the activation of STAT3 [49]. Therefore, a connection between JAK signaling and AhR-mediated CYP expression can be assumed, which remains to be elucidated in future studies. Our findings demonstrate a connection of the proposed OA/NF-κB/JAK/STAT pathway to the downregulation of CYP enzymes already reported in an earlier study [20]. OA was able to downregulate the expression of CYP enzymes. With the addition of NF-κB inhibitors or JAK inhibitors, this effect was reversed. This clearly shows a connection between inhibited CYP expression in OA-exposed cells and activation of NF-κB and JAK/STAT signaling by OA. Future research will help to elucidate the consequences of these findings with respect to the inhibition of the metabolic barrier function of the liver and intestine in organisms exposed to OA.

PMC10000895

Sabine Weiskirchen,Sarah K. Schröder,Eva Miriam Buhl,Ralf Weiskirchen

A Beginner’s Guide to Cell Culture: Practical Advice for Preventing Needless Problems

21-02-2023

contamination,mycoplasma,retrovirus,STR profiling,misidentification,cell authentication,conditional reprogramming

The cultivation of cells in a favorable artificial environment has become a versatile tool in cellular and molecular biology. Cultured primary cells and continuous cell lines are indispensable in investigations of basic, biomedical, and translation research. However, despite their important role, cell lines are frequently misidentified or contaminated by other cells, bacteria, fungi, yeast, viruses, or chemicals. In addition, handling and manipulating of cells is associated with specific biological and chemical hazards requiring special safeguards such as biosafety cabinets, enclosed containers, and other specialized protective equipment to minimize the risk of exposure to hazardous materials and to guarantee aseptic work conditions. This review provides a brief introduction about the most common problems encountered in cell culture laboratories and some guidelines on preventing or tackling respective problems.

A Beginner’s Guide to Cell Culture: Practical Advice for Preventing Needless Problems The cultivation of cells in a favorable artificial environment has become a versatile tool in cellular and molecular biology. Cultured primary cells and continuous cell lines are indispensable in investigations of basic, biomedical, and translation research. However, despite their important role, cell lines are frequently misidentified or contaminated by other cells, bacteria, fungi, yeast, viruses, or chemicals. In addition, handling and manipulating of cells is associated with specific biological and chemical hazards requiring special safeguards such as biosafety cabinets, enclosed containers, and other specialized protective equipment to minimize the risk of exposure to hazardous materials and to guarantee aseptic work conditions. This review provides a brief introduction about the most common problems encountered in cell culture laboratories and some guidelines on preventing or tackling respective problems. Cell culture experiments are widely used in biomedical research, regenerative medicine, and biotechnological production. Due to restrictions on the use of laboratory animals by animal protection laws and the strict implementation of the 3Rs (Replacement, Reduction, and Refinement) formulated by William Russell and Rex Burch to improve the welfare of animals, it can be expected that the general use of cell lines will further increase during the next years to substitute animal-based research [1]. However, it should be noted that cell culture experiments, when not properly conducted, are prone to errors. Therefore, it is essential that cell culture studies are performed with good cell culture practice (GCCP) to assure the reproducibility of in vitro experimentation [2]. In particular, inter- and intra-specific cross-contamination and cell misidentification, genetic drift, contamination with bacteria, fungi, yeast, viruses, or chemicals, and lack of quality control testing are widespread fatal cell culture problems that contaminate the literature with false and irreproducible results [3]. Rough estimates suggest that the number of published papers that used problematic cell lines is about 16.1% [4]. Moreover, the International Cell Line Authentication Committee (ICLAC) lists 576 misidentified or cross-contaminated cell lines in its latest register released in June 2021 [5]. Although it is hard to estimate how much misguided articles are actually affected, there is still an urgent need to better sensitize scientists to this problem. Furthermore, biosafety and ethical aspects are not in the public awareness or are even ignored when working with cell lines. Exemplarily, several continuous growing cell lines were established by transformation with the Simian virus 40 (SV40) large T-antigen (SV40T) or other agents with oncogenic potential, immortalized by introducing telomerase reverse transcriptase (TERT) activity, derived from genetically modified animals, or by novel technologies such as CRISPR/Cas9 gene editing [6,7,8]. Consequently, many cell lines need to be classified as genetically modified cell lines (GMCLs) that need sufficient safety attention. During the last decades, scientists have established many guidelines on good cell and tissue culture practice (GCCP) that provide continuously updated guidance on the main principles to consider when performing cell culture. The GCCP guidelines highlight issues of quality management, background on culture systems, documentation and reporting, general safety instructions, information about education and training, and ethical issues associated with the performance of cell culture experiments [2,9]. Moreover, these expert documents try to promote the harmonization, rationalization, and standardization of laboratory practices including manufacture and testing to foster the compliance of researchers’ work with laws, regulations, and ethical principles. In addition, these guidelines provide extensive information about essential, beneficial, and useful additional equipment for setting up and furnishing a cell culture laboratory environment with a focus on national and international agreed standards. These guidelines are rather complex and comprehensive, so they will be of interest primarily to trained researchers, who have extensive experience in performing cell culture experiments for many years. In this article, some general aspects of working with cell lines are discussed on a more simplified level. In particular, tests for the authentication of cell lines, potential cell culture contaminants, and brief information about ethical issues and biological safety guidelines for use of cell lines in biomedical research are summarized. As such, the information provided should be useful for those that will start to conduct cell culture experiments. Cell lines can be roughly classified into three groups, namely (i) finite cell lines, (ii) continuous cell lines, also known as immortalized or indefinite cell lines, and (iii) stem cell lines [2]. Finite cell lines are normally derived from primary cultures and have slow growth rates. As such, they can be grown for a limited number of cell generations in culture before finally undergoing aging and senescence, a process that is indicated by loss of the typical cell shape and enrichment of cytoplasmic lipids. Importantly, finite cell lines are contact-inhibited and arrested in the G0, G1, or G2 phase after forming monolayers [10]. In contrast, continuous growing cell lines are typically obtained from transformed or cancerous cells and divide rapidly and achieve much higher cell densities in culture than finite cell lines. In some cases, these cell lines exhibit aneuploidy (i.e., one or more chromosomes being present in greater or lesser number than the others) or heteroploidy (i.e., having a chromosome number that is other than a simple multiple of the haploid number). They often can be grown under reduced serum concentrations, are not contact-inhibited, and might form multilayers. Stem cells are an undifferentiated or partially differentiated pluripotent cell type originating from a multicellular organism. These cells can be extended to indefinitely more cells of the same type or alternatively can be triggered under the right conditions to produce cells with specialized functions. As such, they can act as a kind of multipotent precursor for many different cell types. In all cases, the growth of cells from various sources requires an artificial but controlled environment, in which sometimes highly specialized media, supplements, and growth factors are needed for proper cell growth. A cell type can either grow adherent (attached to a surface) requiring a detaching agent for passaging, or alternatively can be free floating in suspension. Adherent cells can be further divided into fibroblast-like cells having an elongated shape and epithelial-like cells characterized by a polygonal shape. Similarly, each cell culture can have unique properties in regard to morphology, viability, doubling time, and genetic stability and their handling and maintenance may require different media, culture conditions, and additives or processing agents including antibiotics, detachment solutions, or surface coating for cell attachment [2]. Non-adherent or suspension cells grow either as single cells or as free-floating clumps in liquid medium that do not require enzymatic or mechanical dissociation during passaging. However, in some cases, these cells demand shaking or stirring for adequate gas exchange and proper growth. Typical examples of non-adherent cells are hematopoietic cell lines derived from blood, spleen, or bone marrow that proliferate without being attached to a substratum. Nevertheless, also some adherent cell lines can be adapted to grow in suspension, which allows for more manageable cell culturing at larger scales with higher yields in special applications [11,12]. In addition, compared to adherent cells, cells grown in suspension are generally easier to handle. Exemplarily, when adherent cells should be analyzed by analytical flow cytometry or fluorescence-activated cell sorting (FACS), the cells must first be detached from their substratum. However, enzymatic digestion or the usage of non-enzymatic cell dissociation buffers can result in the degradation of surface proteins, which might prevent their subsequent identification and cell separation in respective protocols. This makes it extremely challenging to use flow cytometry for phenotyping and characterization of adherent cells [13]. Trypsin, for example, is frequently used for detaching adherent cells. It time-dependently degrades most cell surface proteins by cleaving peptides after lysine or arginine residues that are not followed by proline [14]. This results in degradation of most surface proteins during cellular dissociation. Similarly, other enzymes such as extracellular matrix-specific collagenases, the serine protease elastase cleaving the peptide bond of C-terminal neutral, non-aromatic amino acid residues [15], the peptidase dispase hydrolyzing N-terminal peptide bonds of non-polar amino acid residues [16], and many other detachment agents provoke the significant break down of proteins. Therefore, several milder enzyme mixtures such as Accutase and Accumax or non-enzymatic cell dissociation reagents such as a mixture of ethylenediaminetetraacetic acid (EDTA) and nitrilotriacetic acid (NTA) chelating divalent metal cations have been introduced for routine cell passaging and manipulation of sensitive cells. These formulations are less toxic and preserve most epitopes for subsequent flow cytometry analysis [17,18]. More recently, the culturing of cells in a three-dimensional microenvironment has become the focus of researchers. These 3D cell cultures are either produced by culturing cells within a defined scaffold such as hydrogel or polymeric materials derived from extracellular matrix proteins or agarose or as self-assembly systems in which the cells grow in clusters or spheroids. It is well-accepted that these in vitro cell models offer the possibility to study cellular reactions in a closed system that better resembles the physiological situation than cell culture technologies that rely on two dimensions. As such, these models are particularly interesting for those studying aspects of cell-to-cell interactions, tumor formation, drug discovery, stem cell research, and metabolic interactions [19]. In comparison to 2D systems, 3D models have the potential to completely change the way drug efficacy testing, disease modeling, stem cell research, and tissue engineering research take place [19]. Finally, these systems will substantially decrease the use of laboratory animals in some research areas, which is a key aspect of the 3R principle [1]. Proper cell culture media are critical in the maintenance and growth of cell cultures and to allow the reproducibility of experimental results. Some cells additionally need non-essential amino acids (alanine, asparagine, aspartic acid, glutamic acid, glycine, proline, and serine) for effective growth and the reduction of the metabolic burden of cells. The most common standard media used to preserve and maintain the growth of a broad spectrum of mammalian cell types are, for example, Dulbecco’s modified Eagle medium (DMEM) and Roswell Park Memorial Institute (RPMI) media. Typically, these media contain carbohydrates, amino acids, vitamins, salts, and a pH buffer system (Table 1). Common media such as DMEM are available in a ready-to-use liquid form or alternatively in powdered media for easier storage and a longer shelf life. Moreover, many media can be obtained with different glucose concentrations (low or high glucose) as well as in formulations with and without L-glutamine or alternatively with stabilized glutamine. Finally, they are sold with or without a pH indicator such as phenol red. Importantly, basal media typically contain no proteins, lipids, hormones, or growth factors. Therefore, these media require supplementation with fetal bovine serum (FBS), or often referred to as fetal calf serum (FCS), commonly at a concentration of 5–20% (v/v). FBS is obtained from the blood of fetuses of healthy, pre-partum bovine dams. The final serum is depleted of cells, fibrin, and clotting factors by centrifugation of the clotted blood. It should be noted that FBS from different sources might differ in growth factor and hormone profiles, virus content, endotoxin load, osmolality, total protein and metal content, sugars, and final processing (e.g., filtration, testing for potential contaminations). Therefore, most scientists prefer to buy traceable FBS batches with reliable lot-to-lot consistency to obtain reproducible results during experimentation. In addition, newborn calf serum (NBCS) obtained from calves less than 20 days of age, calf bovine serum (CBS) sourced from calves aged between 3 weeks to 12 months, and adult bovine serum (ABS) isolated from adult cows more than 12 months old are frequently used to supplement cell culture media. Some researchers routinely heat-inactivate the serum at 56 °C for 15–30 min to inactivate the complement and to destroy potential bacterial contaminants. However, in most cases this is not necessary and should be omitted because heat inactivation also reduces the concentration or biological activity of growth factors that are required for proper cell growth. However, it should be noted that serum-containing media has a number of disadvantages. Serum is complex, has an indefinite composition leading to batch-to-batch variation, increasing the risk of contamination, and the use of serum is commonly associated with ethical concerns in terms of avoiding the suffering of fetuses and animals [21,22]. Therefore, the development of serum-free media (SFM) has become a research hotspot during the last decades [21]. In principle, SFM can be divided into five types, namely (i) common SFM, (ii) xeno-free medium containing human-source but no animal components, (iii) animal-free medium, (iv) protein-free medium, and (v) chemically defined medium [21,23]. All these media contain key components (e.g., energy sources, vitamins, amino acids, lipids, trace elements, and inorganic salt ions) and are often enriched with special supplements such as anti-shear protectants, nucleic acids, and other ingredients that are required to improve the culture performance for certain cell types or applications [21]. Unfortunately, certain companies and suppliers of SFM often provide incomplete or no information at all about the composition of their media. Therefore, researchers already started a decade ago to install an online serum-free online database for the interactive exchange of information and experiences concerning SFM [22]. Biological contamination arising from bacteria, yeast, fungi, and mycoplasma can be better prevented by the addition of antibiotics and anti-mycotics to cell culture media. Most of them act by either inhibiting cell-wall synthesis (e.g., penicillin), interfering with membrane permeability (e.g., amphotericin B), or by inhibiting protein synthesis by preventing the assembly of the bacterial initiation complex between mRNA and the bacterial ribosome (e.g., streptomycin). However, the routine usage of antibiotics might develop slow growing persistent/resistant bacterial contaminants that may cause subtle alterations of cell differentiation and behavior [24]. In addition, antibiotics such as penicillin, streptomycin, and gentamycin can significantly alter gene expression and regulation and could modify the results of studies focused on drug response, cell regulation, and differentiation [25,26]. For example, a concise review has recently highlighted numerous publications that have shown the impact of antibiotics and antimycotics such as penicillin/streptomycin, gentamicin, and amphotericin B on in vitro properties of cells including proliferation, differentiation, survival, and genetic stability [27]. Similarly, a comprehensive literature search has found a number of reported side effects that are induced by different antibiotics, again supporting the notion that antibiotic-free culture media are recommended when possible to ensure the reliability and reproducibility of cell culture findings [28]. Consequently, researchers should avoid the permanent use of antibiotics in cell culture and should better try to implement strict aseptic working conditions to prevent bacterial contaminants in cell culture. Phenol red also known as phenolsulfonphthalein is the most frequent pH indicator in cell cultures. This water-soluble dye is a yellow zwitterion at low pH, while it changes to a red-colored anion or a fuchsia-colored di-anion at more basic conditions (Figure 1). Therefore, this dye has been used as an inert pH indicator dye in many tissue culture media to detect pH shifts, waste products of dying cells, or overgrowth of contaminants that typically cause an acidification of the medium. However, based on its structural resemblance to some non-steroidal estrogens, it has the capacity to bind to estrogen receptors with an affinity of 0.001% of that of estradiol, thereby stimulating the proliferation of estrogen receptor positive cells [20]. Thus, it is advisable to dispense phenol red during experimentation when working with estrogen-responsive cell systems. Both biological and chemical agents might lead to contamination in cell cultures (Table 2). In most cases, slow cellular growth, change in morphology, fast change of pH in media, and elevated quantities of death or floating cells in the culture are the consequence. Therefore, cell cultures should be routinely screened for respective contaminants to prevent inconsistent results and other serious consequences. The most important contaminants are discussed in the following table. Mycoplasmas are the smallest self-replicating organisms belonging to the bacterial class Mollicutes. They consist of a lipoprotein plasma membrane, ribosomes, and a genome consisting of a circular, double-stranded DNA molecule that ranges in size from 580 to 2200 kb [36] (Figure 2A). Mycoplasmas have limited biosynthetic capabilities and need to enter an appropriate host in which they multiply and survive for long periods of time [36]. Their tiny size (~0.1–0.2 µm) makes their identification impossible under a standard bright-field microscope, which is the reason why they are often go undetected in many laboratories. They lack a cell wall and are resistant to many common antibiotics that are used in cell cultures such as penicillin or streptomycin. Most important, mycoplasma contamination does not generate the turbidity that is characteristic for contamination by other bacteria or fungi. Systemic studies have identified typical ways in which mycoplasmas can spread into cell culture. They include the introduction of mycoplasma cross-contamination from infected cultures, media, sera, or reagents that were obtained from other research laboratories or commercial suppliers. Moreover, the usage of non-sterile supplies, the infection by laboratory personnel who are carriers of mycoplasma, diffusion of mycoplasmas in incubators or hoods, contamination of cell cultures in liquid nitrogen tanks, transmission via airborne particles and aerosols, overuse of antibiotics, and improper sealing of culture dishes are other sources favoring mycoplasma contamination [37]. Although mycoplasma compete with the host cell for biosynthetic precursors and nutrients, the observed alterations in growth rates in affected cell cultures are often minimal, which is the reason why respective contamination is not readily detected. Nevertheless, mycoplasmas can extensively affect the host’s DNA, RNA, and protein metabolisms, impact intracellular amino acid and available ATP levels, modify cellular surface antigens, and can provoke fragmentation of DNA and other significant chromosomal alterations [37]. Consequently, regular testing and quick identification for such contaminants is highly crucial to prevent falsified research results, misleading publications, and the waste of research money. To enable this, a number of sensitive and specific tests were developed that allow the detection of respective infections, often without considering the origin or species. In the gold standard to detect mycoplasma contamination, a conditioned culture supernatant sample is added first to a liquid medium for mycoplasma culture and incubated after a few days on broth, agar, or indicator cells [37]. However, this method is time consuming and other faster detection methods were introduced. They include, among others, specific DNA staining by fluorochromes such as 4′,6-diamidino-2-phenylindole (DAPI) or Hoechst 33342 (Figure 2B), ELISA testing, RNA labeling with mycoplasma-specific ribosomal probes, enzymatic procedures, flow-cytometric methods, colorimetric or strip-based mycoplasma detection assays, sensitive PCR-based assays detecting the bacterial 16S rRNA, and sophisticated Fourier transform infrared (FITR) microspectroscopy methods [38,39,40,41,42]. The staining with fluorochromes is rather non-specific, while most of the other assays have high analytical sensitivity (i.e., the ability of the test to identify the contaminant) and specificity (i.e., the ability to measure mycoplasmas and not closely related non-mycoplasma species). In particular, commercially available kit systems that rely on conventional (or endpoint) PCR often have the capacity to simultaneously detect more than 70–90 mycoplasma species because the primer set included in these kits is most often designed to specifically target and amplify the highly conserved 16S rRNA coding region of the mycoplasma genome (Figure 2C). Mycoplasmas can produce a virtually unlimited variety of effects in infected cultures [43]. Therefore, a wide spectrum of agents and methods for eradication of respective contaminants was developed. There are different physical, chemical, immunological, and chemotherapeutic treatments available to eliminate mycoplasma contaminants [43]. However, many of these methods are impractical because they are either time-consuming, require special equipment, capture only a limited number of mycoplasma species, or have an overall low efficiency. In the beginning, several antibiotics that suppress mycoplasma growth were introduced, but most of them were only moderately effective, had detrimental effects on eukaryotic cells, or had only a limited efficacy because of the development of resistance against respective agents. Nevertheless, several more selective and effective anti-mycoplasma agents are now available, which allow the elimination of mycoplasma infections in most cases already by one round of treatment (Table 3). In most cases, these removal agents either contain macrolides, tetracyclines, quinolones, or combinations thereof. The different quinolones included in the common compounds Baytril®, Ciprobay®, and Zagam® have similar chemical structures (Figure 3). They exert their inhibitory activity by blocking bacterial nucleic acid synthesis through disrupting the bacterial topoisomerase II, inhibiting the catalytic activities of topoisomerase IV and DNA gyrase, and by causing breakage of bacterial chromosomes [44]. The macrolide Tiamulin included in BM-Cyclin acts by inhibiting protein synthesis by targeting the 50S subunit of the bacterial ribosome and is further a strong inhibitor of the peptidyl transferase [45]. Its activity against mycoplasma is significantly enhanced by tetracycline [46]. Other ready-to-use removal agents contain combinations of antibiotics and antimicrobial acting peptides or combinations of three different bactericidal components belonging to different antibiotic families. The different bactericidal components have a high effectiveness in eliminating mycoplasma infections. Typically, these substances are already effective against all common mycoplasma strains when administered for two or three weeks [47]. Exemplarily, the complete eradication of mycoplasma in a rat cell line as assessed by electron microscopy is shown in Figure 4. Nevertheless, treatment or prevention of mycoplasma infections with these products can provoke significant cytotoxic and genotoxic effects [48]. Minocycline, for example, was shown to induce Bcl-2, which accumulated in mitochondria and interacted with death-promoting molecules including Bax, Bak, and Bid, thereby protecting against cell death [49]. Similarly, Tiamulin inhibited growth and metastasis of human breast cancer cell line MDA-MB-231 and mouse breast cancer cell line 4T1 by blocking the activity of 5′-nucleotidase (CD73) that catalyzes the conversion of purine 5′ mononucleotides to nucleosides [50]. Therefore, the application of compounds used for the removal of Mycoplasma infections should be used carefully and well-considered because they have the potential to alter the outcome of experimental studies. Compared with mycoplasma infections, viral contaminants in cell cultures present a more serious threat because of the difficulty in detecting them and the lack of methods of treating affected cell cultures [51]. Moreover, some viruses have the capacity to integrate their genome into the host cell, which in some cases results in the permanent production of new viral particles. This is a potential health risk for operators and might be the source for horizontal virus transmission into other cell lines. Accordingly, this has direct implications on the biological safety classification of an infected cell line. Unfortunately, generic tests for the systemic evaluation of viral contaminants are rather complex and undirected. Without precise knowledge of the virus authenticity, these detection methods are limited to electron microscopy and assays for retroviral reverse transcriptase [52,53]. In particular, the versatility of electron microscopy is an effective, universal, and unbiased means when an infectious viral agent is suspected [54]. This technique is suitable for obtaining high resolution images, thereby providing an immediate overview of the actual cell infection status and shape of the viruses. The observed morphology and size further allow the immediate preliminary classification of the viral type [55]. Exemplarily, mature retroviruses are generally spherical enveloped particles with an average diameter ranging between 100 and 200 nm, displaying a distinct morphology that differs between the six retrovirus genera [56]. Particularly, transmission electron microscopy (TEM) is suitable for direct visualization of viruses in biological samples without the need of prior assumptions about the infectious agent. Illustratively, the retroviral load of the continuous growing murine hepatic stellate cell line GRX that is widely used in hepatology research was first demonstrated by TEM [53,57]. After all these years, this cell line has still the capacity to produce large quantities of retroviral particles (Figure 5). Any non-living compound evoking undesirable effects in a cell culture needs critical attention. Even essential nutrients can have toxic effects when the concentration is high enough. Impurities can be introduced into cell cultures by media, sera, and water. However, plasticizers in plastic tubing, cell culture disks, and storage bottles, as well as free radicals formed in media by fluorescent or UV light or deposits on glassware and pipettes, can result in contaminations. In addition, impurities in the CO2 flow and residues from germicides or pesticides used to disinfect incubators or hoods can be critical. Most critical are endotoxins, which are derived from the outer cell membrane of most Gram-negative bacteria. These are composed of rather stable lipopolysaccharides (LPS) that are shed from bacteria and are released, in a much greater quantity, during the lysis of bacteria [58]. Consequently, the removal of pyrogens from glassware requires extensive heating at high temperatures, while these substances are rather resistant to autoclaving. Therefore, most commercially available ready-to-use cell culture media and supplements are prepared in such a manner that they contain no or rather low endotoxin levels. Furthermore, reliable producers of cell culture media and supplements provide certificates of analysis indicating the endotoxin levels in respective solutions or compounds. Some culture conditions (e.g., low serum concentration, high light exposure) provoke the formation of free radicals that are highly reactive, potentially leading to DNA damage, protein cross-links, lipid hydroperoxides, and the induction of apoptosis [59]. Therefore, antioxidants such as ascorbic acid, N-acetyl-L-cysteine, or vitamins (vitamin E, vitamin A, vitamin C) with free radical-scavenger activities are often added to cell culture media to prevent oxidative stress and its consequences [60,61]. Alternatively, the trace element selenium that functions as a cofactor in antioxidant enzymes, such as glutathione peroxidase, can be added to the media to annihilate reactive peroxides [62]. Additionally, high concentrations of heavy metals such as lead, cadmium, and mercury are toxic to many cell types. Therefore, it is important that water and solvents used to prepare media or supplements are tested for heavy metals prior to use. Alternatively, pure water from a commercial vendor that provides a certificate of analysis should be used if the laboratory water is not suitable for cell cultures. Contamination of cell lines with unrelated cells from the same species (intra-species contamination) or cells from another species (inter-species contamination) is a common and recurrent problem [33,34]. In particular, when the contaminant is a rapidly dividing cell line, it will overgrow and replace the original culture. If the contamination is undetected, this may result in unreliable and irreproducible findings that falsify the biomedical literature [3]. The problem of cell line cross-contamination has been known for decades, commencing with the controversies around HeLa cells in the 1960s [45,46]. A first synopsis published in 2010 that was drawn from 68 references listed 360 cross-contaminated cell lines [33]. Subsequently, in 2021, the International Cell Line Authentication Committee (ICLAC) that aims to bring researchers’ attention to this problem listed 576 misidentified cell lines from different species resulting from cross-contamination or other means [5] (Table 4). The sources for cross-contamination are manifold and include unwanted spreading via aerosols or unplugged pipettes, sharing media and reagents among different cell lines, and careless usage of conditioned medium [34]. Nowadays, different methods for determination of cell line cross-contamination are available. Inter-species cross-contamination can be detected by isoenzyme analysis, which utilizes electrophoretic binding patterns to examine slight differences from species to species in the structure and mobility of individual isoforms for a set of intracellular enzymes [63]. Intra-species cross-contamination of human cells can be identified by typing of the human lymphocyte antigen (HLA) locus, which is a complex of genes located on chromosome 6 encoding highly polymorphic cell-surface proteins involved in immune system regulation. Moreover, molecular serological methods, genetic tests using synthetic probes or primers, and sequence-based typing with direct DNA sequencing can discriminate between different HLA genotypes [64]. Similar to cell cross-contamination, the misidentification of cell lines has resulted in thousands of misleading and erroneous papers [65]. Usage of illegible labels and mislabeling of cell culture vessels during routine manipulation evoked by lack of attention, high operator workload, or distractions during experimental work is the most straightforward cause of misidentification [66]. It is comprehensible that a contamination with a more rapidly dividing contaminant cell can rapidly overgrow the original culture in a few passages and provokes cell line misidentification at later steps. Such an overgrowth can result when re-feeding operations and manipulations of multiple cultures are conducted with the same pipette or at the same time, as a consequence of intentional co-cultivation of different cell types, or by a lack of awareness when working with feeder cells. Nowadays, the most common method to identify cross-contamination and cell misidentification is short tandem repeat (STR) profiling. This method can compare the number of allele repeats at specific loci in DNA between different samples. Although the respective allelic variants of these repeats are rather polymorphic, the number of alleles is very small. Therefore, multiple STR loci are analyzed simultaneously in a multiplex PCR assay for making different STR profiles effective for identification or discrimination purposes with a high discriminative statistical power. In STR analysis, the amplified variable microsatellite regions obtained from the template DNA are separated on a genetic analyzer and subsequently analyzed with software that calculates the number of repeats at each variant site. Nowadays, effective and standardized STR panels are established for many species. In this regard, the Consortium for Mouse Cell Line Authentication that has established a multiplex PCR assay comprising 19 mouse STR markers is pioneering [66,67]. This multiplex PCR provides a unique STR profile for different mouse cell lines, including closely related cell lines. Representative chromatograms of four STR markers obtained for the widely used immortalized murine cell line AML12 are depicted in Figure 6. Importantly, when comparing the 19 mouse STR markers between three different mouse cell lines, each cell line has a unique constellation that allows the unequivocal discrimination of each cell line from the others (Table 5). Nowadays, databases are available in which primer information for the setup of STR testing for mouse, cat, dog, cattle, horse, men, and others are deposited [69]. Moreover, the Cellosaurus resource provides an incredible wealth of information and offers routines such as the STR Similarity Search Tool (Cell Line Authentication using STR, CLASTR) for comparing STR profiles [70]. Typically, immortalized cell lines are grown in the lab for many generations. However, a cell line cultured at high passage number or for prolonged times can show chromosomal duplications or rearrangements, mutations, and epigenetic changes [71]. This phenomenon is commonly known as genetic drift. Consequently, the morphology, proliferation rate, metabolic capacity, or general cell health can change dramatically, affecting experimental outcomes [72]. Therefore, the documentation of cell line passage number, which reflects the number of times the cells have been subcultured into a new vessel, is an important consideration when performing an experiment. It was also reported that the passage number can increase the risk of viral contamination [73]. Furthermore, over-passaging of cells selects faster growing cells that in some cases show reduced secretion rates, carrier-mediated transport, and paracellular permeability, while having increased transcellular permeability [74]. Consequently, similar or even the same investigations performed in different laboratories might have completely different experimental outcomes when the passage number differs by hundreds of passages. Although there are no specific guidelines regarding the optimal passage range, common practice is to not use cells after 20 to 30 passages. Unfortunately, the precise knowledge of passage number is often not known, especially when the cells were obtained from a source other than a cell repository, which usually provides data on the cell passage number [75]. In some cases, researchers argue that even the passage number is imprecise because different laboratories may use different initial cell seeding densities or splitting rates during passing, which affect the number of times cells divide in cultures. Therefore, a formula for the calculation of precise population doubling level (PDL), which is synonymous with the cell generation time, was introduced, which is used particularly for primary cells. In the respective formula, the PDL, which is the total number of times the cells in the population have doubled since their primary isolation in vitro, is calculated as follows: PDL = 3.32 (log Xe − log Xb) + S, where Xb is the number of seeded cells at the beginning of the incubation time, Xe is the cell number at the end of the incubation time, and S is the starting PDL before splitting [75,76,77]. Cell cultures may have the ability to cause harm to human health and the environment and need to be assigned to a biosafety level that takes into consideration a multitude of factors [78]. Before working with a cell line, it is necessary to have an accurate knowledge about these risks, taking into account the intrinsic properties, type of (genetic) manipulation, and the resulting biological hazard inherent with the respective cell line that may be significantly increased by contaminating pathogens. Although these estimates must be conducted on a case-by-case basis, there are some general guidelines that need to be followed. First, the closer the genetic relationship of a cell under investigation is to humans, the higher the risk is to humans because contaminating pathogens usually have a specific species barrier. Nevertheless, care should be taken because some contaminating organisms have the potential to cross the usual species barrier [79,80,81]. Second, the tumor-inducing potential of a cell line is strongly dependent on its origin. While, for example, epithelial and fibroblastic cells have a low tumor-inducing potential, hematopoietic cells have a significantly higher one [82]. Third, well-characterized cell lines that are already used in many laboratories for many years have an overall lower risk than uncharacterized continuous growing cell lines or primary cells. After identifying and evaluating potential risks, it is essential to define ways of avoiding or minimizing these risks by containment, restricting the movement of staff and equipment into and out of cell culture laboratories, working according to standard operating procedures (SOPs), avoiding formation of aerosols or splashes during working, regular cell culture training, and by the implementation and following of the general guidelines of good laboratory practice (GLP) [2,78]. In addition, vaccination against Hepatitis B virus is advisable when working with primary human cell cultures. However, global norms and international standards for biosafety and biosecurity are often highly variable between different countries and should be noted before starting with the work [83]. As discussed, established cell lines can undergo genetic drift or phenotypic alterations after long-term passaging. Therefore, they may no longer faithfully represent all the molecular features that were characteristic of the initial cell type they originated as. Consequently, scientists have developed techniques that allow the establishment of primary cells from either tissue or blood from healthy donors or subjects suffering from a defined disease. For humans, these patient-derived cell lines have high translational clinical relevance [84]. However, spontaneous immortalization is commonly a rare event and the establishment of respective cells was most often performed in the past by transformation with viral oncoproteins that partially deregulate the cell cycle or by overexpression of TERT that replaces short DNA segments that are lost during cell replication and are involved in control of cell senescence [6]. Similarly, the CRISPR-Cas9-mediated targeting of oncogenes that can be used to immortalize cells in vitro has been identified as an effective tool for establishing immortalized cell lines [7,8]. However, it should be critically noted that these manipulations can exert transcriptional and cell cycle effects and, further, that the inhibition of DNA damage signaling pathways by respective agents leads to the accumulation of mutations [85,86]. More recently, conditional reprogramming (CR) has emerged as a powerful tool for the establishment of long-term cultures of primary cells [86]. The technique of CR that was first established in 2012 allows the induction of normal and tumor epithelial cells from many tissues to proliferate indefinitely in vitro. In this technique, cells can be conditionally reprogrammed by co-culturing them with irradiated fibroblast feeder cells and the Rho kinase inhibitor Y-27632 [87]. This technology is now widely used to establish patient-derived cell cultures from both normal and diseased cells. In this procedure, the epithelial cells are reprogrammed to acquire an adult stem cell character by transferring the cells from standard culture medium to a CR medium that reverses their differentiation state and allows the generation of large numbers of cells for use in patient-derived models [87]. As such, CR offers exciting possibilities in precision medicine, regenerative medicine, drug testing, gene expression profiling, xenograft studies, and to define genetic, epigenetic, and metabolic alterations occurring during the transition from a normal to a tumor cell phenotype [88]. Importantly, sophisticated protocols are now available that further allow the use of CR for the rapid and efficient expansion of non-epithelial cells including those of neural, neuroendocrine, endocrine, and mesenchymal origin that conditionally can be grown for long periods [89]. In personalized medicine, organoids, which are self-organized 3D tissues typically derived from pluripotent fetal or adult stem cells, have gained enormous interest [90]. They are a kind of miniaturized and simplified version of an organ that forms in a selective 3D medium that includes a set of growth factors [91]. In particular, patient-derived organoids (PDOs) have been widely introduced in cancer research. They recapitulate basic features of primary tumors including histological complexity and genetic characteristics and are therefore ideally useful to predict the sensitivity toward antitumor drugs or aspects of tumor progression [92]. Similarly, patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be experimentally monitored [93]. PDXs are cancer models established by implanting and growing a patient’s tumor cells in a suitable animal host. In most cases, the recipient is an immunodeficient mouse engrafted with a human immune system [94]. These models have become a useful experimental tool for the study of molecular interactions between human immunity and cancer cells. Particularly, these models have become highly attractive in basic research to understand aspects of cancer progression and metastasis. In addition, PDXs are frequently used in preclinical cancer research to identify novel predictive cancer biomarkers, test the efficacy of cancer drugs, investigate intra-tumoral heterogeneity and clonal dynamics, evaluate personalized therapy options, and to test the general translational hypothesis [94,95]. All these advanced cell culture techniques that more closely mimic the cellular microenvironment are nowadays an integral part of basic and clinical research. However, the usage of these patient-derived models (PDMs) requires extensive expertise, training, and quality control. To foster the development of respective models, several international consortia have been established with the aim to generate novel human tumor-derived culture models with associated genomic and clinical data. Representative initiatives have been launched by the National Cancer Institute (NCI) and the Human Cancer Models Initiative (HCMI) [96,97]. Biosafety depends on the cleanness of the laboratory. The general guideline is to strictly follow all possible safety rules. Ignoring or failing to follow any safety regulations can result in laboratory-associated infections and environmental contamination. Therefore, the biological risks need to be reduced by decontamination of biological agents that were used during laboratory operations. In proper waste management, the inactivation methods should be appropriately validated whenever possible. In principal, there are four main categories for decontamination, namely (i) sterilization by heat, (ii) disinfection with liquids, (iii) disinfection by vapors and gases, and (iv) exposure to UV radiation. Autoclaving is the most effective and reliable method to sterilize laboratory materials and decontaminate or inactivate biological agents. It is a sterilization method that uses high-pressure water steam and is the method of choice for decontamination of culture media, glassware, and pipette tips. However, it is essential that sufficient high temperature and pressure are maintained for a period of time that also guarantees spore inactivation. Typically, autoclaving for 60–90 min at 121 °C is sufficient to achieve a waste temperature of at least 115 °C for 20 min. Moreover, the operation and maintenance of autoclaves should be performed only by trained individuals and the success of autoclaving should be regularly checked by biological indicators. It is essential that waste or materials subjected to autoclaving are placed in containers or sealed autoclave bags that permit good heat penetration [98]. Hazardous chemical substances or radioactive waste should not be autoclaved. Contaminated scalpel blades, hypodermic needles, knives, and broken glass should be collected in puncture-proof containers with covers. Large volumes of liquid waste and contaminated media should be decontaminated before disposal in the sanitary water. Chemical disinfection is usually the preferred method for decontamination of surfaces and furniture. For the optimal effectiveness of a disinfectant, several factors have to be considered. First, a disinfectant should have specificity for the biological agent to be removed. Second, a disinfectant should be suitable for the field of application because there can be significant activity differences when applied to surfaces or liquids. Finally, disinfectants may differ in their general application conditions (e.g., required contact time, working concentration) and in their effectiveness in the presence of other influencing factors (e.g., acids, organic load). Similar vapors and gases applied in closed systems can provide excellent disinfection. Aerosols of hydrogen peroxide, chlorine dioxide, glutaraldehyde, paraformaldehyde, ethylene oxide, and peracetic acid are used in some laboratories to decontaminate biosafety cabinets or incubators [98,99]. However, all these chemicals are hazardous and disinfection with these substances should only be conducted by experienced and trained personnel. Finally, exposure to ultraviolet (UV) radiation can effectively destroy most microorganisms. In particular, the UV-C light (spectral range: 100–280 nm), which is absorbed by the atmosphere, has the most destructive power for a wide spectrum of microorganisms [99]. Therefore, this UV region is often used in biological safety cabinets to reduce surface contamination [98]. However, the exposure to UV can cause burns to the eyes and skin of operators and should therefore be applied only with precaution. For cell culture beginners, it is therefore advisable to receive theoretical and hands-on training by experts before using UV-C radiation devices for cleaning and disinfection. Cell culture plays an important role in biomedical research. However, cell misidentification, intra- and inter-species cross-contamination, and infection by bacteria, fungi, yeast, or viruses can lead to fatal consequences and pollution of the scientific literature. Therefore, regular testing for contamination and cell authentication testing should be mandatory in each laboratory working with cell cultures. Biological contamination with bacteria, molds, and yeast can be effectively removed by diverse bactericidal or fungicidal acting components. The ICLAC register of misidentified cell lines and the linked Cellosaurus knowledge resource are extremely important knowledge resources and provide helpful search tools such as CLASTR for comparing STR profiles. In addition, the implementation of good cell culture practice and aseptic techniques are essential to increase cell culture safety, promote the generation of reproducible scientific data, and facilitate comparability of results established in different laboratories. In addition, proper staff training and standardization of documentation and reporting of cell culture procedures are further effective means to promote high-quality work and safety in a cell culture laboratory. Recent advances in the generation of more physiologically relevant PDMs such as PDOs and PDXs have revolutionized common cell culture methods and helped to better understand human biology and pathophysiology. In this regard, CR is an attractive technique that can be used to rapidly and efficiently establish patient-derived cell cultures for basic and clinical studies and further significantly substitute animal-based research. Nevertheless, it should be kept in mind that the best designed and most well engineered cell culture laboratory is only as good as its least competent worker.

PMC10000902

Huriye Ercan,Ulrike Resch,Felicia Hsu,Goran Mitulovic,Andrea Bileck,Christopher Gerner,Jae-Won Yang,Margarethe Geiger,Ingrid Miller,Maria Zellner

A Practical and Analytical Comparative Study of Gel-Based Top-Down and Gel-Free Bottom-Up Proteomics Including Unbiased Proteoform Detection

26-02-2023

top-down proteomics,2D-DIGE,bottom-up proteomics,shotgun proteomics,proteoforms,post-translational modification (PTM)

Proteomics is an indispensable analytical technique to study the dynamic functioning of biological systems via different proteins and their proteoforms. In recent years, bottom-up shotgun has become more popular than gel-based top-down proteomics. The current study examined the qualitative and quantitative performance of these two fundamentally different methodologies by the parallel measurement of six technical and three biological replicates of the human prostate carcinoma cell line DU145 using its two most common standard techniques, label-free shotgun and two-dimensional differential gel electrophoresis (2D-DIGE). The analytical strengths and limitations were explored, finally focusing on the unbiased detection of proteoforms, exemplified by discovering a prostate cancer-related cleavage product of pyruvate kinase M2. Label-free shotgun proteomics quickly yields an annotated proteome but with reduced robustness, as determined by three times higher technical variation compared to 2D-DIGE. At a glance, only 2D-DIGE top-down analysis provided valuable, direct stoichiometric qualitative and quantitative information from proteins to their proteoforms, even with unexpected post-translational modifications, such as proteolytic cleavage and phosphorylation. However, the 2D-DIGE technology required almost 20 times as much time per protein/proteoform characterization with more manual work. Ultimately, this work should expose both techniques’ orthogonality with their different contents of data output to elucidate biological questions.

A Practical and Analytical Comparative Study of Gel-Based Top-Down and Gel-Free Bottom-Up Proteomics Including Unbiased Proteoform Detection Proteomics is an indispensable analytical technique to study the dynamic functioning of biological systems via different proteins and their proteoforms. In recent years, bottom-up shotgun has become more popular than gel-based top-down proteomics. The current study examined the qualitative and quantitative performance of these two fundamentally different methodologies by the parallel measurement of six technical and three biological replicates of the human prostate carcinoma cell line DU145 using its two most common standard techniques, label-free shotgun and two-dimensional differential gel electrophoresis (2D-DIGE). The analytical strengths and limitations were explored, finally focusing on the unbiased detection of proteoforms, exemplified by discovering a prostate cancer-related cleavage product of pyruvate kinase M2. Label-free shotgun proteomics quickly yields an annotated proteome but with reduced robustness, as determined by three times higher technical variation compared to 2D-DIGE. At a glance, only 2D-DIGE top-down analysis provided valuable, direct stoichiometric qualitative and quantitative information from proteins to their proteoforms, even with unexpected post-translational modifications, such as proteolytic cleavage and phosphorylation. However, the 2D-DIGE technology required almost 20 times as much time per protein/proteoform characterization with more manual work. Ultimately, this work should expose both techniques’ orthogonality with their different contents of data output to elucidate biological questions. A central goal of proteome research is to understand the composition and function of the proteins in a biological sample. The completion of the human genome sequencing project in 2003 and the surprising identification of only about 20,300 distinct genes [1] made the one-gene-one-protein dogma [2] even more unlikely. Thus, the size of the human proteome is still debatable, with estimates ranging from 20,000 to several million different proteins and their proteoforms in the literature [3]. These facts indicate that much of the complexity created by biological machinery is at the level of different variants of the respective proteins and is not based on gene diversity [4]. Variations in a protein can occur as a result of different concentrations, genetic mutations, and alternative splicing of DNA-RNA transcripts. Further subsequent changes can result from proteolytic cleavage and numerous covalently linked chemical functional groups on dedicated “vulnerable” amino acids, which are then termed post-translational modifications (PTMs). To date, approximately 400 different PTMs are known in biology (http://www.unimod.org, accessed on 3 September 2022), the most common of which are lysine acetylation, C- and N-terminal cleavage [5], phosphorylation, methylation, glycation, lipidation, and ubiquitination, which dynamically modify proteins throughout their lifespan. PTMs have essential regulatory properties, such as switching a protein from its inactive to its active or thereafter inactivated state, or regulating a protein’s half-life following ubiquitination or acetylation, thus defining its functional property in a cell and tissue-specific context that ultimately determines the resulting cellular phenotype and its biological significance [6]. The term “proteoform” was defined in 2013 to provide a uniform definition for all these different possible protein variations [4]. A specific designation for proteins with PTMs has previously been defined with the term “protein species” [7]. To record all these regulatory processes at the protein level, an exact quantification of the proteins together with all their proteoforms is necessary. The number of proteoforms that a protein can have is theoretically impossible to predict. Based on the two-dimensional gel electrophoresis (2D-GE) data, it was assumed that each protein has, on average, three different proteoforms in eukaryotes [8]. A more recent work, the Blood Proteoform Atlas [9], found about 17.5 proteoforms per human gene using highly complex technical MS-based top-down proteomics. However, it is noteworthy that this MS-based top-down proteomics analysis mainly recognises proteoforms with a molecular weight of less than 20 kDa, of which lysine acetylation (32.9%) and the C- and N- terminal cleavage (30.6%) are the two most common [5]. Thus, an immense variety of proteoforms is currently not sufficiently considered in analytics, and no analytical method can fully decode the entire proteomic diversity of a complex biological sample. The general strategy pursued in proteomics is to compare related samples from different states (e.g., healthy vs. diseased/exposed/treated) since differences in their proteome should reflect the particular state of a biological sample. In the two main different proteomics approaches, gel-based and gel-free, the quantification of biological differences is done at different steps in the workflow: in gels immediately after separation and at the protein spot level. Identification of the respective proteins is not yet required for this step. In the gel-free LC-MS approach, also called shotgun proteomics, it is essential to know protein identity before quantification, as peptides need to be related to each other and to their parent proteins; only then is protein quantification possible. Hence, gel-based proteomics usually only identifies proteins with different abundance, while LC-MS has to identify all detected proteins. Today, most of the proteome analysis is performed with label and label-free shotgun proteomics. Label-free proteomics is more commonly used in large-scale biological studies because it requires less manual work, can be automated to some extent, and requires only minute amounts of the sample [10]. Overall, it is, therefore, faster and cheaper and enables quantitative high-throughput sample analysis [11]. However, the limited stability of the instrument components, liquid chromatography (LC), and mass spectrometry (MS) aggravate reproducibility. For shotgun analysis, intact proteins are enzymatically disassembled into peptides (e.g., primarily by trypsin) to facilitate separation by reversed-phase liquid chromatography, followed by directly coupled analysis in mass spectrometers. Intact proteins can no longer be examined, hence the term bottom-up proteomics. All measured peptides are reassembled in silico into the putative proteins or protein groups for quantitative profiling of the respective proteomic sample [10,11]. This evaluation method must therefore go back to the outdated “one gene, one protein dogma,” and the results describe only the qualitative and quantitative composition of so-called “theoretical” or “canonical proteins” in the samples [11]. Thus, this peptide-centric approach has lost all essential qualitative and quantitative information from the corresponding proteoforms. For shotgun analysis experts, this protein inference is a significant and well-known problem [12,13,14]. Interestingly, this significant disadvantage of shotgun proteomics is still almost wholly ignored in numerous analytical applications. [15]. The dynamic complexity of a proteome is currently best demonstrated by the top-down method 2D-GE. [11,16,17,18,19,20,21]. The term top-down proteomics in 2D-GE refers to intact proteins and their intact proteoforms being detected with this method [12,13,19,20,21]. This methodology allows the qualitative separation of intact proteins and their proteoforms based on their physico-chemical properties, which are determined by their respective isoelectric point (pI) and molecular weight (MW) [22,23,24,25]. Since each proteoform has a specific pI and MW, they can be readily separated and detected using 2D-GE. A protein’s mobility in the pI and MW dimensions can be altered, for example, by proteolytic cleavage, phosphorylation and the substitution of an amino acid due to an SNP, etc. However, in order to decode a proteoform´s identity, the protein has to be excised from the gel, digested and finally analysed by MS. Although this procedure can be automated to a certain extent, it is still very time-consuming and thus a limiting factor in the application of this technology. Two-dimensional (2D) fluorescence difference gel electrophoresis (2D-DIGE) is currently a widely used variant of quantitative 2D-GE electrophoresis analysis with the best quantitative precision. Direct labelling of a protein’s lysine with differentially spectrally resolvable cyanine fluorescent dyes (e.g., 488 nm/520 nm, 532 nm/580 nm, 633 nm/670 nm, 736 nm/760 nm) prior to 2D fractionation of proteoforms into 2D spots enables the simultaneous analysis of two to four different proteomic samples in one analytical 2D run. In this 2D analysis, one specific dye (e.g., Cy2) is often reserved for an internal standard (IS). This IS is usually a pool of all samples measured in all gels of the respective analytical study, allowing for perfect qualitative and quantitative comparability between the different 2D-GE runs and all the separated proteome samples [26]. With all these technical advantages and disadvantages, a systematic comparison of both techniques is required for their synergetic use to deepen knowledge in biological investigations. So far, however, these two proteomic methods have rarely been used in combination to investigate the proteomic composition of biological samples as comprehensively and deeply as possible. In these few available studies, both methods were used to increase the probability of finding as many proteins [27] or condition-dependent protein abundance changes as possible in the respective biological samples but without taking care of proteoforms [28,29,30,31,32,33]. Two other studies combined these proteomics technologies to characterize potentially robust method-independent biomarkers, such as in liver tumour samples [34] or frozen-thawed curled octopus [35]. Another study used both methods to determine which proteins co-occur in different cell types and can be detected using various proteomics technologies. Thus, this protein repertoire should serve as quality control for the sensitivity of the respective proteomics experiment [36]. However, none of these studies attempted to improve the profiling of proteins and their proteoforms by combining these two proteomics technologies. We could only find two publications in which the presence of different proteoforms of the respective proteins was deliberately and application-relatedly included in the parallel analysis by evaluation of the biological sample using 2D-DIGE and LC-MS/MS shotgun [37,38]. In addition, despite the utmost care in the analysis, the measured qualitative presence and quantitative amounts of proteins and their proteoforms from the same sample can also differ from gel to gel and from MS to MS run. A crucial parameter for the reliable detection of qualitative and quantitative changes in the abundance of proteins and their proteoforms of a biological sample is the evaluation of the system-specific technical and biological variation, which thus represents the total variation. Surprisingly, no study has yet directly compared these qualitative and quantitative properties, as well as the possible synergistic properties of both proteomics methods in a practical experiment. Therefore, this study focuses on the technical variability and orthogonality in the respective technical and biological data outputs of top-down or bottom-up proteomics analysis. For this purpose, identical technical and biological replicates were analysed using the 2D-DIGE and label-free shotgun technologies. Subsequently, coefficients of variation (CV) were determined from the respective quantitative data and comparatively evaluated, particularly considering the aspects of proteoforms and phosphorylation in specific biological examples. In parallel, all these strengths and limitations of the two proteomics techniques were considered and discussed together with the aspect of workload and time. DU145 human prostate carcinoma cells (ACC 261) were purchased from Leibniz Institute DSMZ (Braunschweig, Germany) and cultivated at 37 °C in RPMI 1640 (Gibco, Carlsbad, CA, USA), supplemented with 10% heat-inactivated foetal bovine serum (Sigma, St. Louis, MO, USA), 100 U/mL penicillin and 0.1 mg/mL streptomycin (Fisher Scientific, Waltham, MA, USA) at 37 °C in a humidified incubator in an atmosphere of 5% CO2, 20% O2 and 75% N2. DU145 cells were subsequently propagated in T75 vessels and seeded in 6-well plates, grown to 90% confluency, washed two times with PBS and dry plates were stored at −80 °C until cell lysis for proteomic analysis. For the preparation of cell lysates, 6-well microplates were allowed to reach ambient temperature (15–20 °C) to prevent precipitation of 2D-DIGE buffer (7 M urea, 2 M thiourea, 4% CHAPS, 20 mM Tris, pH 8.7), aliquots of 400 µL 2D-DIGE buffer were added and cells were scraped and collected in 1.5 mL tubes, solubilised for 2 h at 4 °C at 850 rpm to facilitate complete protein solubilization and centrifuged at 15,000× g for 10 min to remove insoluble material. The total protein concentration of the lysates was quantified by using a Bradford Coomassie Plus kit (Pierce Thermo Scientific, Rockford, IL, USA). Three biological replicates were generated from three different cell passages for parallel top-down and bottom-up proteomics to evaluate the total (biological + technical) variation of the particular method. A pool of these biological replicates was made for an IS sample commonly used in 2D-DIGE analysis to standardize the 2D spot signals across different gel runs. The six technical replicates of the 2D-DIGE analysis were all performed with this IS sample, labelled with Cy2, Cy3 and Cy5, in three paired gel runs (Figure 1A) of the pH 4–7 and pH 6–9 range, respectively. This IS sample was also used for label-free shotgun analysis to evaluate the technical and total variation as well as the performance in detecting phosphopeptides. Both gel-based and gel-free proteomic methods were used to analyse these samples’ technical and biological replicates in parallel. The gel-based method was performed by 2D-DIGE, followed by an MS protein identification. Twelve µg of technical or biological replicate was minimally labelled with fluorescent cyanine dyes (5 pmol of CyDyes per µg of protein; GE Healthcare, Uppsala, Sweden), as already described in [39]. Three technical replicates were labelled with either Cy3, Cy5 or Cy2. For the acidic protein range, immobilised pH gradient (IPG) strips (24 cm, pH 4–7, GE Healthcare, Uppsala, Sweden) were passively rehydrated for 11 h with rehydration buffer (7 M urea, 2 M thiourea, 4% CHAPS, 70 mM dithiothreitol (DTT), 0.5% pH 4–7 ampholyte (Serva, Heidelberg, Germany)) mixed with a total of 36 µg of alternatively Cy-labelled sample. Isoelectric focusing (IEF) was performed on a Protean I12 IEF unit (Bio-Rad) with gel side down until 30 kVh were reached. For the alkaline protein range, IPG-strips (24 cm, pH 6–9, GE Healthcare, Uppsala, Sweden) were soaked in rehydration solution (7 M urea, 2 M thiourea, 4% CHAPS, 150 mM DTT, 2% ampholyte pH 6–10 (GE Healthcare, Uppsala, Sweden)) prior to isoelectric focusing (IEF). Samples were applied by cup loading on the acidic side and DTT (325 mM) loading on the cathode of the IPG strip. IEF was performed on an IPGphor unit (GE Healthcare, Uppsala, Sweden) with gel side up until 30 kVh were reached. After IEF, IPG-strips were then each equilibrated with equilibration buffer (buffer 1: 1% DTT, 50 mM Tris-HCl pH 8.68, 6 M urea, 30% glycerol and 2% sodium dodecyl sulfate (SDS), for 20 min; buffer 2: 2.5% iodoacetamide (IAA), 50 mM Tris-HCl pH 8.68, 6 M urea, 30% glycerol and 2% SDS, for 15 min) under slow shaking. Each of the IPG-strips was transferred on 11.5% polyacrylamide gel (26 × 20 cm, 1 mm gel thickness) and sealed with low melting agarose sealing solution (375 mM Tris-HCl pH 8.68, 1% SDS, 0.5% agarose). The SDS-PAGE was performed using an Ettan DALTsix electrophoresis chamber (GE Healthcare, Uppsala, Sweden) under the following conditions: 35 V for 1 h, 50 V for 1.5 h and finally 110 V for 16.5 h at 10 °C. The gels were scanned with a resolution of 100 µm using a Typhoon 9410 laser scanner (GE Healthcare, Uppsala, Sweden) at excitation/emission wavelengths of 532/670 nm (Cy3), 633/670 nm (Cy5) and 488/520 nm (Cy2). It was a time interval of two months between the analysis of the technical and biological replicates. The IS sample was taken for the characterisation of the phosphorylated protein spots in the 2D-DIGE map of the DU145 cell line. Two aliquots of 90 µg of each sample (IS) were mixed with 5 µL of 10% SDS and vortexed for 10 s. Then, samples were filled up to 500 µL with a reaction mix containing 2 mM MnCl2, 5 mM DTT, 1× lambda-phosphatase (λ-PPase) buffer, and dH2O. One sample was incubated overnight (14 h) with 100 units of λ-PPase (30 °C, under gentle agitation). Subsequently, all samples (±λ-PPase) were precipitated with TCA containing 80 mM DTT for 1 h at 4 °C, pelleted at 20,000× g for 20 min at 4 °C, and washed four times (20,000× g for 10 min, 4 °C) with acetone containing 20 mM DTT. The protein concentration was again determined to calculate the 2D-DIGE buffer volume to solubilise the samples with a concentration of 2.5 µg/µL after TCA-precipitation. For MS-based identifications, 250 µg unlabelled proteins were separated by the same 2D-DIGE equipment that was used for the fluorescently labelled samples described above. Proteins were visualised by MS-compatible silver staining [40]. Protein spots of interest were excised manually from the gels, de-stained, disulphide was reduced, and afterwards, derivatised with iodoacetamide, and the proteins were digested with a concentration of 12.5 ng/µL sequencing grade modified trypsin (Promega, Madison, WI, USA). Electrospray ionization (ESI) quadrupole time-of-flight (QTOF; Compact, Bruker, Billerica, MA, USA) coupled to an Ultimate 3000 Nano HPLC system (Dionex, Sunnyvale, CA, USA) was used for LC-MS/MS-based identification of spot digests. In this system, a PepMap100 C18 trap column (300 μm × 5 mm) and PepMap100 C18 analytic column (75 μm × 250 mm) were used for reverse phase (RP) chromatographic separation with a flow rate of 500 nL/min. The two buffers used for the RP chromatography were 0.1% formic acid/water and 0.08% formic acid/80% acetonitrile (ACN)/water with a linear gradient for 90 min. Eluted peptides were then directly sprayed into the MS, and the MS/MS spectra were interpreted with the Mascot search engine (version 2.7.0, Matrix Science, London, UK) against the Swissprot database (564,277 sequences, released in January 2021) and the taxonomy was restricted to homo sapiens (human; 20,397 sequences). The search parameters were used with a mass tolerance of 10 ppm and an MS/MS tolerance of 0.1 Da. Carbamidomethylation (Cys), oxidation (Met), phosphorylation (Ser, Thr and Tyr), acetylation (Lys and N-term) and deamidation (Asn and Gln) were allowed with 2 missing cleavage sites. The Mascot cut-off score was set to 15, and proteins identified with two or more peptides were considered [41]. Furthermore, a protein was considered as reliably identified only when its associated peptide counts were at least five times higher than those from other protein identifications of this 2D spot. For shotgun proteomic analysis of DU145 cells, 50 µg protein lysates of the IS and the three biological replicate samples in 2D-DIGE buffer were subjected to methanol-chloroform-water (MCW) precipitation to remove detergents and salts. In brief, protein samples were diluted to 100 µL with dH2O, 400 µL methanol was added, vortexed for 1 min, 100 µL of chloroform was added and vortexed and finally, 300 µL of dH2O was added and samples were vortexed. Samples were centrifuged at 14,000× g at 4 °C for 15 min. The upper phase was removed, and the protein-interface was precipitated by the addition of 300 µL methanol. Samples were vortexed and left for 15 min at −20 °C, followed by centrifugation as before. The protein pellet was washed with methanol, air-dried and dissolved in 0.1% RapiGest SF (Waters, Milford, MA, USA) in 50 mM triethylammonium bicarbonate (TEAB), reduced by DTT (5 mM, 30 min at 60 °C) and alkylated in the dark by IAA (15 mM, 30 min, room temperature). Samples were digested using mass-spec grade Trypsin/Lys-C mix as suggested by the manufacturer (Promega, Madison, WI, USA) overnight; digests (16 h) were stopped by the addition of trifluoroacetic acid (TFA) (1% final concentration). Peptides were desalted and concentrated following the stage-tip protocol by Rappsilber et al. [42] using 3 layers of reversed-phase Empore Octadecyl C18 solid phase extraction disk stacked in a 200 µL pipet tip and stored at −20 °C until MS analysis. Peptides were eluted twice with 10 µL acetonitrile (ACN) and 10 µL 0.1% TFA, dried in a SpeedVac and solubilised in 12 µL peptide resuspension buffer (2% ACN and 0.1% FA). The technical and biological replicates of the tryptic peptide DU145 samples were separated by a 70 min gradient on a C18 µPAC (µ-Pillar-Arrayed-Column, PharmaFluidics, Ghent, Belgium) mounted on a nano RSLC UltiMate3000 (Thermo Fisher Scientific, Waltham, Massachusetts, USA) separation system. Peptides were detected as described earlier [43,44]. In brief, peptides (2 µL) were introduced into the nano electrospray source (ESI) after the UV cell, and the ionization was performed using a steel needle with a 20 µm inner diameter and 10 µm tip. Needle voltage was set to 2 kV in positive mode, and the top 10 ions were selected for MS/MS analysis (fragmentation). The resolution was set to 70,000 for full MS scans, a mass range of 350–1700 m/z, ions with single charge were excluded from MS/MS analysis and fragmented ions were excluded for 60 s from further fragmentation. During each run, the lock mass ion 445.12002 from ambient air (polysiloxane) was used for real-time mass calibration. Raw MS/MS files were analysed with MaxQuant version 1.6.0.1 with default settings for “label-free quantification” (LFQ), and match between runs was enabled, variable modifications were set to oxidation (M), acetyl (protein N-term) and phospho STY [44] against the human proteome (http://www.uniprot.org/proteomes/UP000005640_9606, (accessed on 30 September 2018), version from September 2018). LFQ and match between runs were enabled, and variable modifications were set to oxidation (M), acetyl (protein N-term) and phospho STY. The label-free quantification approach is based on the computational methodology described by Jürgen Cox et al. 2014 [45], where the intensities of the precursors (MS1) are used to quantify across the technical and biological replicate samples. This data output of the label-free LC-MS/MS shotgun analysis method was used, as shown in Figure 2, to document the workload and technical and total variation of this bottom-up method compared to the 2D-DIGE analysis. It is also important to mention that there was a time interval of several months between the analyses of the technical and biological replicates. To evaluate the performance for detecting phosphopeptides and their corresponding technical variation (CVtech) in label-free shotgun measurement, 10 µg aliquots of the IS standard sample were subjected to λ-PPase treatment as described above or not and subjected to MCW-precipitation. Proteins were solubilised in 8 M urea and 2 M thiourea, reduced and alkylated. Afterwards, sequential digestion was made by LysC (2 h), followed by trypsin (overnight, 16 h). Desalted peptides were diluted in 25 µL loading buffer (2% ACN, 0.05% TFA) and subjected to LC-MS/MS analysis as follows. To this end, 5 µL of peptide sample were injected into the Dionex Ultimate3000 nanoLC system (Thermo Fisher Scientific, Waltham, MA, USA). For sample pre-concentration, a pre-column (2 cm × 75 µm C18 Pepmap100; Thermo Fisher Scientific) run at a flow rate of 10 µL/min using mobile phase A (99.9% H2O, 0.1% FA) was used. Chromatographic separation was performed on a 25 cm × 75 µm Aurora Series emitter column (IonOpticks, Fitzroy, Australia) by applying a flow rate of 300 nL/min and using a gradient of 8% to 40% mobile phase B (79.9% ACN, 20% H2O, 0.1% FA) over 95 min, resulting in a total LC run time of 135 min per sample. For mass spectrometric analyses, the timsTOF Pro MS (Bruker) equipped with a captive spray ion source was used. The capillary voltage was set to 1700 V, and the MS/MS spectra were generated in the Parallel Accumulation-Serial Fragmentation (PASEF) mode with a moderate MS data reduction applied. The scan range (m/z) from 100–1700 for recording the MS and MS/MS spectra was applied. The mobility range was set to 1/k0 from 0.60–1.60 V.s/cm2 and the ramp time and accumulation time were set to 100 ms. All experiments were performed with 10 PASEF MS/MS scans per cycle, leading to a total cycle time of 1.16 s. Furthermore, the collision energy was ramped as a function of increasing ion mobility from 20 to 59 eV, and the quadrupole isolation width was set to 2 Th for m/z < 700 and 3 Th for m/z > 700. All samples were analysed as technical replicates. MaxQuant version 2.0.3.0 (Computational Systems Biochemistry, Max-Planck Institute for Biochemistry, Martinsried, German) was used to analyse Bruker d.folders with default settings and analytical replica set at single fractions, LFQ and match between runs was enabled, variable modifications were set to oxidation (MP), acetyl (protein N-term), deamidation (N) and phosphor (STY) and the fasta database was the same as described above. MaxQuant result outputs (proteinGroups.txt, Phospho(STY)Sites.txt, evidence.txt and peptides.txt) were analysed and visualised in Perseus version. 1.6.14.0 (Computational Systems Biochemistry, Max-Planck Institute for Biochemistry, Martinsried, German). For one-dimensional Western Blot (1D-WB), a total of 12 μg of the urea-dissolved DU145 protein extract was mixed with a sample buffer (150 mM Tris-HCl pH 8.68, 7.5% SDS, 37.5% glycerol, bromophenol blue, 125 mM DTT) to obtain a final volume of 20 μL. These samples were boiled for 4 min at 95°C and centrifuged for 3 min at 20,000× g. The samples were then run in an 11.5% SDS gel (50 V, 20 min and 100 V, 150 min), separated and blotted (75 V, 120 min) onto a polyvinylidene difluoride membrane (PVDF; FluoroTrans RW, Pall, East Hills, NY, USA). The molecular weight separation and the transfer to the membrane of the DU145 protein samples were monitored with a protein molecular weight marker (PageRuler, Prestained Protein Ladder, Life Technologies Limited Inchinnan, Renfrew PA4, UK). For detection of the blotted proteins, the total protein on the membrane was stained using ruthenium-(II)-tris-(bathophenanthroline disulphonate) (RuBPS; dilution 1:100,000 overnight at 4 C; Sigma-Aldrich St. Louis, MI, USA). For two-dimensional Western Blot (2D-WB) analysis, 30 µg of the urea-solubilised Cy5-labeled proteins were separated by isoelectric focusing on a 7 cm pH 3–10 IPG-strip (GE Healthcare, Uppsala, Sweden) in the first dimension and according to the MW by 11.5% SDS-PAGE, 10 × 8 cm, (50 V for 20 min and 100 V for 150 min). Then, proteins were semidry-blotted (1.0 A, 25 V, 40 min) onto a polyvinylidene difluoride membrane (PVDF) (FluoroTrans®W, Pall, East Hills, NY, USA), followed by scanning with a Typhoon FLA 9500 imager (GE Healthcare, Uppsala, Sweden). Subsequently, membranes were blocked in 5% non-fat dry milk (Bio-Rad, Hercules, CA, USA) in PBS containing 0.3% Tween-20 (PBS-T) overnight at 4 °C. On the next day, membranes were washed (3× PBS-T for 5 min, each) and incubated for 2 h at room temperature with primary detection antibodies (diluted in PBS-T containing 3% non-fat dry milk) for pyruvate kinase M2 (PKM2; #4053S; Cell Signaling Technology, Boston, MA, USA; 1:1000), 14-3-3 protein γ (YWHAG; #MA1-16587; clone KC21; Pierce, Rockford, IL, USA; 1:10,000), protein disulfide-isomerase A1 (P4HB/PDIA1; #ab2792; clone RL90; Abcam, Cambridge, MA, USA; 1:1000), glyceraldehyde-3-phosphate dehydrogenase (GAPDH; #NBP1-47339; clone 1A10; Novus Biologicals, Littleton, CO, USA; 1:3000), calmodulin (CALM1; #NB110-55649 (EP799Y); Novus Biologicals, Littleton, CO, USA; 1:2000), adenylate cyclase-associated protein 1 (CAP1; #H00010487; clone D01; Abnova, Taipei, Taiwan; 1:1000), eukaryotic initiation factor 4A-I (EIF4A1; # ab31217; Abcam, Cambridge, MA, USA; 1:500), prostaglandin E synthase 3 (PTGES3; #sc-101496; Santa Cruz Biotechnology, Dallas, Texas; 1:1000), transaldolase (H-4) (TALDO1; #sc-166230 Santa Cruz Biotechnology, Dallas, Texas; 1:500), cathepsin B (CTSB; #AF953-SP; R&D Systems, Minneapolis, MN, USA; 1:500), cathepsin D (CTSD; #AF1014-SP; R&D Systems, Minneapolis, MN, USA; 1:500). After washing (3× PBS-T for 5 min, each), membranes were incubated for 1.5 h at room temperature with the appropriate horseradish peroxidase (HRP)-conjugated secondary detection antibodies diluted 1:20,000 in PBS-T containing 3% non-fat dry milk. After two further washing steps in PBS-T and one in PBS, the immunoreactive bands were developed using SuperSignal Western Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific, Waltham, MA, USA). Chemiluminescence signals were detected on a UVP ChemStudio imager (Analytik Jena, Jena, Germany). The experimental design and sample sizes are indicated in Figure 1. The images of 2D-DIGE were analysed using DeCyder software (version 7.2, GE Healthcare, Uppsala, Sweden). The standardised abundance (SA) was calculated for protein spot quantifications according to the manual of the DeCyder software [46]. Detailed information about the image analysis was described previously [47]. MaxQuant (version 2.0.3.0, (Computational Systems Biochemistry, Max-Planck Institute for Biochemistry, Martinsried, German)) was used to identify and quantify canonical proteins of label-free shotgun LC-MS/MS runs and phosphorylated peptides and proteoforms. For the calculation of technical and total variation, the latter consisting of technical and biological variation, the SA values were taken from the 2D-DIGE analysis (Figure 1A), and MaxQuant LFQ protein intensities [45] were taken from label-free shotgun LC-MS/MS runs. The coefficient of variation (CV) was used to calculate the variability of each quantitative analysis system relative to its standard deviation and is presented here as a percentage. Since the SA values of the individual 2D spots are calculated using the normalised volume value from IS of the respective spot, these values no longer contain any information about the volume of the respective proteoform. Therefore, the spot sizes had to be calculated from the normalised spot volume values, which also came from the data output of the DeCyderTM software. Thus, each included spot’s representative spot volume value was calculated from the mean of all technical and biological replicates of the DIGE analysis and the mean of the LFQ values for each included canonical protein range. Mean spot size values from the 2D-DIGE and mean LFQ values from the label-free shotgun analysis are used for Spearman’s rank correlation (rs), which was made to determine how well the quantification of these two measurement systems compares to each other. The statistical analyses and graphs were made with GraphPad Prism 7 (GraphPad Software, Inc., San Diego, CA, USA). This study evaluates the workload, reproducibility, proteomic information output, and synergy of two routine applications of top-down and bottom-up proteome analysis. The prostate cell line DU145 served as the biological sample, although this selection is of secondary importance for the content of this comparative study on basic proteomics technologies. As outlined in the experimental design and workflow in Figure 1, six technical replicates of the 2D-DIGE and six label-free shotgun analysis runs were performed from the same DU145 protein sample to determine the CVtech of the method’s qualitative and quantitative data output. For the evaluation of top-down proteomics, six technical replicates were analysed through three 2D-DIGE gel runs, running one Cy3 and one Cy5 stained replicate on each 2D gel and normalised over the third Cy2 labelled replicate, which represented the IS. Thus, the same sample was separated into nine separate 2D images in these three runs. To cover the entire pH range of gel-based proteome analysis, each replicate was performed in the pH range of 4–7 and 6–9 and assembled into one replicate. To assess the technical reproducibility of a commonly used bottom-up proteome method, a label-free shotgun analysis of six technical replicates from three different digests of the same DU145 samples, each analysed in duplicate by LC-MS/MS, was performed. Furthermore, cell extracts from three different DU145 cell passages were prepared and analysed in parallel using 2D-DIGE and label-free shotgun runs to measure the total variation (technical + biological) in the proteome of a cell culture system (Figure 1). Depending on the proteomics technique, the analysis process requires significantly different amounts of time. Therefore, an important decision criterion for planning a proteomics study is to recognize the time factor in the workflow of the respective proteomics technology compared to an ample yield of well-reproduced and functional, informative data sets. Figure 2 illustrates the time required for a comparative proteomics analysis using 2D-DIGE or label-free shotgun. Good reproducibility of the quantitative data obtained was defined in this comparative study by including only protein events that were found in all analysis runs of the respective proteomics methods. In the six technical and three biological replicates of the DU145 cell extracts, a total of 1923 protein spots could be detected over the entire pH range with 100% reproducibility, summarised by six 2D-DIGE runs in the pH range of 4–7 and six associated 2D-DIGE runs in the pH range of 6–9 (Figure 1A). In shotgun-proteomics analysis, 703 canonical proteins with 100% reproducibility in the six technical and three biological replicates of the same set of DU145 samples as used for 2D-DIGE analysis were identified (Figure 1B). Detailed data on the frequency of the protein events detected in each run of the two proteomics technologies are given in Figure 1A,B. As outlined above and summarised in Figure 2, the preparative and analytical workflows in 2D-DIGE and label-free shotgun proteomics are entirely different. In the top-down 2D-DIGE method, the intact proteome is first separated and then quantitatively analysed. These proteomic working steps of the 12 necessary 2D-DIGE analysis runs took 122 h, from protein labelling of the samples with fluorescent dyes to computer-assisted image analysis. In contrast, bottom-up technologies, such as label-free shotgun analysis, first require tryptic digestion of the samples. In this case, together with the LC-MS/MS runs of these digests, a total analysis time of 77 h in the shotgun approach was necessary for the protein identification and quantitative proteome analysis of 703 canonical proteins. In contrast, after almost twice the working time compared to the shotgun analysis, only the quantitative data of 1923 protein spots were determined by 2D-DIGE without having any protein identifications. In our labour settings, generally preparative 2D silver gels with 250 µg in the pH range 4–7 and 150 µg in the pH range 6–9 of the protein sample are prepared to identify the protein spots in the gel. Optionally higher or medium abundant protein spots may also be directly cut out from the DIGE-gels after staining the gels with an MS-compatible silver stain. The processing time for these preparative gels in these two pH ranges with the DU145 samples was 61 h. Protein spots were manually excised from the gel and tryptically digested for LC-MS/MS analysis in this laboratory setting. With our devices, 48 protein spots for LC-MS/MS can be prepared per run. Thus, the identification of 48 proteins, including manual spot-picking and MS analysis, took about 45 h. Converted per protein spot, representing one proteoform, from cutting to LC-MS/MS analysis with our laboratory equipment, the analysis time is about 1 h to identify one spot in the 2D proteome map of the DU145 cells. Because of this significant amount of workload and time, all spots of a 2D gel analysis are rarely identified with LC-MS/MS. In order to enable a protein-to-protein comparison with the shotgun data output, 144 different protein spots from the DU145 2D map were first randomly identified in our current study. Therefore, the actual time for this 2D-DIGE proteome analysis, together with the identification of 144 protein spots (=proteoforms), is about 327 h (=13.6 days; 183 h gel work and 144 h spot digestion and MS-based identification) or 136 min per protein quantification and identification for the 2D gel-based method (Figure 2). The label-free shotgun analysis of 703 reliably quantified canonical proteins took 77 h (3–4 days), resulting in a time of 6.6 min (~7 min) per protein quantification and identification. Thus, label-free shotgun analysis was 20 times faster per protein quantification and identification in this specific example. This is one reason why this method has largely replaced 2D technology today. Apart from this direct comparison of the analysis times of these two proteomics systems, the 2D-DIGE setup requires more manual intervention than the label-free shotgun proteomics. In the 77 h of the label-free shotgun experiment, about 10% is hands-on time for sample preparation, as long as the LC and mass spectrometer run without technical problems. On the other hand, in the 2D-DIGE analysis, 50% of the 327 h of the current study are manual. One highly time-consuming process was the manual picking of the protein spots from the preparative silver 2D gel and their tryptic digestion. Spot-picking and digestion robots developed years ago could process 200–300 protein spots per hour [48]. However, these automated robotic systems have not caught on because they are expensive, and 2D gel-based studies typically do not have enough throughput for efficient use. Essential quality features for a knowledge-generating proteomic analysis of biological samples are sensitivity, specificity, functional insights and the reproducibility of the entire experimental setup. Therefore, it is first and fundamentally important to recognize the total variation of the experimental system, which consists of technical and biological variations. In this chapter, the analytical variability of 2D-DIGE and label-free shotgun analysis is evaluated using parallel measurements of the same DU145 samples. A major challenge in proteomics analysis arises from missing detections of proteins, which reduce the number of comparable proteins in multiple analysis runs. Therefore, these missing values in the respective protein abundance data are one of the main problems in proteomics, as they severely impair the statistical evaluation of 2D gel and shotgun analyses and thus reduce the biological significance [49,50]. For the final comparisons with the shotgun data, the qualitative variability analysis of the current 2D-DIGE runs refers to the six technical and three biological replicates, each proteomic gel data set, composed from the pH range 4–7 and 6–9 (Supplementary Figure S1). The number of matched protein spots from each 2D-DIGE run to the master gel ranged from 1787–2752 at pH 4–7 and from 1861–3224 at pH 6–9 (data not shown). No significant difference in the number of detected spots was observed between the respective technical and biological replicates. A protein spot was considered reproducible if it was matched in each of the six replicate 2D runs of the respective pH range. Accordingly, 100% reliably matched spots were 1070 for pH 4–7 and 853 for pH 6–9 (Figure 1A). Thus, 1923 protein spots in the 2D-DIGE proteome of the DU145 samples could be quantified with 100% reproducibility in this study (Figure 1A). However, there were more “missing values” at the technically difficult pH 6–9 than at pH 4–7. The reasons for these considerable amounts of missing values in the 2D analysis are that some spots are too weak, are often randomly subdivided by the computer-aided image recognition, some spots are not always equally well separated, or some artefacts in the gels, such as dust, are detected as spots. The qualitative analysis of variability of the LC-MS/MS runs for the six technical replicates revealed between 823 and 973 different protein identifications per run, and 758 of these proteins were recovered in all these runs. Between 2556 and 2616 proteins were identified in the three biological replicates, of which 2540 were detected in all runs. Across all of these nine replicates, 703 proteins could be found in all of these LC-MS/MS runs (Figure 1B). This significant difference between the detected number of different proteins in the measurement of the technical and biological replicates can be that several months had passed between these two analytical runs, and the LC in the technical replicates did not run in the same quality modus. The high variability of the detected proteins between the different LC-MS/MS runs shows that “missing values” are also a fundamental problem with the gel-free proteomics technology. Apart from the analytical variability of liquid chromatography in the sufficiently reproducible separation of the peptides, the additional variable of this study setting was the still common “data-dependent-acquisition” (DDA) mode of MS analysis. That is, i.e., the 10 most abundant peptides (top 10 methods) with a certain m/z within a specific retention time (scan time) are subjected to MS/MS fragmentation within a particular time window (cycle time). They are, therefore, explicitly identified at their molecular amino-acid composition level. Accordingly, this stochastic selection of peptide-precursors in MS1 is intrinsically not 100% reproducible, and neither is the MS/MS fragmentation (MS2). Computational imputation of missing quantitative information at the peptide or protein level by assuming a normality distribution is often used to “compensate” for the missing quantitative information to have sufficient data points for statistical analysis. On the other hand, in MS instrumentation (quadrupole and time-of-flight (ToF) mass analyzers), improved chromatographic peptide-precursor separation technologies such as gas-phase and ion-mobility-based fractionation subsequent to reversed-phase LC realised “inside” of the mass spectrometer, in combination with artificial intelligence-supported computational decoding of detected masses, data-independent-acquisition methods (DIA) are more and more frequently used. In DIA, each precursor-peptide (MS1) is isolated and accumulated (MS2) to yield an amount sufficient to detect peptide fragments (MS3). Thus, the problem of missing value in the shotgun analysis will be sufficiently improved in the future. A general limiting factor of all proteomics methods is that detecting all proteins in a complex biological sample is impossible. The main reason for this is the very wide concentration range of the various proteins in a complex biological sample. The resolution and staining techniques of the 2D gels are not as sensitive as the well-resolving bottom-up proteomics; therefore, more proteins from the respective proteome will be missing in top-down gel-based proteomics. Most biological functions and regulations are finally based on quantitative changes in proteins and corresponding proteoforms. However, technical and biological variations in the analysis system can mask these quantitative regulations. Quantitatively accurate proteomics technologies are required to detect as many changes as possible between different biological systems. Therefore, the CVtech of the respective proteomics technique and the biological system’s variability should be known to capture the significant differences of the respective biological question with sufficient sample size and statistical power. To evaluate the respective quantitative variability of the two proteomics analysis systems in the current work, only the protein events detected in all technical and biological replicates of the respective methods were included. Thus, 1923 protein spots were included in evaluating the quantitative variability assessment of the 2D-DIGE analysis and 703 canonical proteins in the label-free shotgun analysis, as shown in Figure 1A,B. The 2D protein spots have a median quantitative CVtech of 7.6% at pH 4–7 and 8.2% at pH 6–9 (Figure 3). Thus, the median technical, quantitative variation in the current 2D-DIGE analysis of DU145 samples is of the same order of magnitude as we found several years ago with the same method and the same sample size of technical replicates from a human platelet extract [51] and another research group using environmental bacteria [52]. In label-free shotgun LC-MS/MS, the included 703 proteins had a median CVtech of 24% (Figure 3). This higher variability is caused mainly because no internal reference proteins (to control for variations in tryptic digestions) or peptides (to control for variations in retention time or mass deviation) were used. At the same time, in 2D-DIGE, the Cy2-labelled IS sample corrects for technical variations. Literature for the technical variance of the label-free shotgun analysis generally describes significantly smaller CVs [53,54,55,56]. This discrepancy is mainly due to the log2-transformed quantitative intensity values (i.e., LFQ intensities) commonly used for quantitative differential statistical analysis. However, the incorrect application of this log2 transformation to calculate also the CV leads to significantly lower and wrong CV values [55,57,58,59]. Nonetheless, such log2 data transformations are still taken to calculate the CV from technical or biological replicates [60,61], or occasionally the computational route to CV value calculations remains enigmatic [62]. To examine the biological variation (CVbio) of our experimental setup, we analysed three different cell passages of the DU145 cell line with both proteomics technologies, 2D-DIGE, and label-free shotgun. In this case, the CV consisting of CVtech and CVbio is defined as the total coefficient of variation (CVtotal; Figure 3). The observed CVtotal between the three DU145 passages was 13% with the 2D-DIGE and 59% with the shotgun-system. Thus, the CVtotal for both proteomics technologies was higher than the respective CVtech (Figure 3). These results show that three different cell passages of the same cell line exhibit a biological variation in the proteome that contributes to the total variation. However, as with the CVtech (24%), the CVtotal (59%) was significantly higher than the label-free shotgun analysis of the biological replicates. With a stable mean quantitative CVtech of the 2D-DIGE system of 7 to 8% in the current as well as in our previous platelet proteomics study, we have a total mean quantitative variation of 13% in different passages of the DU145 samples and a slightly higher total mean variation with 18% was previously observed in the platelet proteome of 20 elderly healthy volunteers [51] as well as in a larger cohort of 238 volunteers [49]. As expected, these observations of the 2D-DIGE system also indicate that the mean quantitative CVbio of the proteome between cell passages of the same cell line is smaller than between platelet samples from different individuals. The higher CVtotal of the shotgun may also be due to the higher CVtech of these runs and/or a different biological variability of the quantified canonical proteins compared to their different proteoforms. Therefore, to assess the comparability of quantification as well as the technical and biological quantitative variability of specific proteins from the top-down and bottom-up analysis of the DU145 samples, 144 different protein spots were randomised evenly across pI and MW from preparative silver-stained 2D gels, picked out and analysed by LC-MS/MS. Among these, 138 protein spots were successfully identified. The six other identifications were unassignable and therefore unclear as they identified multiple nearly identical amounts of peptides from different proteins in these “protein spots”. This result also shows one methodological limitation of 2D-GE: Not all proteins/proteoforms can always be sufficiently separated based on their MW and pI. However, since a high-resolution 2D-GE was carried out in this work with two pH gradients (pH 4–7 and pH 6–9) and a broad separation distances of 24 cm in the pI and 20 cm for the MW separation, this problem is reduced, which is shown here with only 4% non-unique assignment of the protein identifications. On average, the unambiguous protein identifications have peptide counts with our 2D-GE separation protocols that are twenty-fold higher than other parallel protein identifications from the respective 2D spot digests. Significant portions of these clearly identified protein spots were assigned to the same UniProt accession numbers, thus representing the respective proteins’ proteoforms in the examined DU145 proteome. With this random selection, 103 different proteins with a total of 138 different proteoforms were identified in the 2D proteome of the DU145 cells. Eighty-four of the 103 different proteins were also detected with the shotgun analysis and were present with 119 proteoforms (=protein spots) in the top-down 2D-DIGE method (Figure 4, Supplementary Table S1 and Figure S2). These results show that the 2D method in the DU145 sample alone randomly captures 16% of the proteins with their phenotype-dependent proteoforms qualitatively and quantitatively. So far, however, proteomics studies, primarily using bottom-up technologies, have mainly published statistics on the quantitative changes of canonical proteins of different biological samples. The extent of quantitative variation and condition-dependent biological differences in their corresponding proteoforms has hardly been considered until now. For the first assessment of how comparable the quantification of bottom-up and top-down methods of the DU145 proteome is, the LFQ values of the canonical proteins were correlated with all corresponding individual protein spot (=proteoform) volumes of the 2D-DIGE analysis and showed only a weak correlation (rS = 0.34; n = 119). This feeble quantitative relationship between the two methods is likely due to the shotgun LFQ value of a “canonical” protein being contrasted with several different amounts of its proteoforms. The correlation was improved if the proteoform 2D spot volumes of the respective canonical proteins were also summed (rS = 0.55; n = 84). Since it was not validated for this comparison whether all detectable proteoforms were also captured in the 2D-DIGE analysis, a selection of 10 proteins was made, and 2D-WBs in the pH range 3–10 were performed to search for further proteoforms with specific pan antibodies. This proteoform-to-protein comparison of mutually identified entities by 2D-DIGE and shotgun is presented in Table 1. This selection of specifically detected proteoforms with 2D-WB showed again that a quantitative relationship of LFQ values was only present when the respective 2D spot volumes were grouped into the sum of their canonical proteins (Figure 5B; rS = 0.758; n = 10). When this critical factor of several proteoform abundance levels of protein was not taken into account, this correlation substantially decreased (Figure 5A; rS = 0.375; n = 34). Thus, at-a-glance visualisation of individual proteins and proteoforms from a total cell protein lysate, as performed in 2D-GE, is virtually impossible in shotgun analysis, as tryptic digestion reduces the complexity of a sample’s proteoform composition by several orders of magnitude, as shown in Figure 6 and Figure 7. In particular, while a protein/proteoform appears as specifiable spot(s) in 2D-GE (Figure 6A), provided that any PTM or alternative (proteolytic) processing event causes a change of the protein´s pI and/or MW, corresponding tryptic peptides of the same protein/proteoform are spread across the entire range of mass-to-charge and retention-time plane (Figure 6B and Figure 7C). Consequently, not only the complexity of a proteome and its quality can be evaluated by a trained eye or image libraries at a glance in 2D-GE, but also a protein´s “flavour” is readily traceable, thereby facilitating the detection of potentially interesting proteoforms characterising environmental, disease and/or drug-treatment response for example. In contrast, such signifying information is virtually lost following the digestion of the biological sample and LC-based peptide separations. After extensive computational analysis, some proteoform information becomes accessible again in bottom-up shotgun proteomics. Variable modifications on peptides can be partially captured if the correspondingly deduced mass differences on specific amino acids are included in the database-search of the shotgun approach. Similarly, proteoform-specific peptides termed “unique peptides” or “proteotypic peptides”, present the following, i.e., tissue-specific alternative splicing of the corresponding transcript and causing a minute change in pI and/or MW are not readily accessible to quantitation in bottom-up shotgun proteomics. Thus, the relative proportion of such a “non-canonical” modified protein to its “canonical” version in a proteome remains mostly elusive unless proteotypic isotope-labelled peptides are included in the shotgun runs for selective reaction monitoring [63]. The following section aims to illustrate the above-described scenario on the basis of a comparative top-down and bottom-up proteome analysis and data output for the glycolytic enzyme pyruvate kinase (PKM). This protein was chosen as an example because it had the most proteoforms from the 2D spots randomly chosen for identification, vividly illustrating the complexity of protein inference. Furthermore, in tumorigenesis, the increasing translational synthesis and level of PKM2 compared to PKM1 is crucial for tumour aggressiveness and has also recently been shown to be diagnostically valuable for prostate cancer progression. This switch to the PKM2 expression is responsible for the Warburg effect of cancer cells [64]. On 2D gels, proteins are often separated in a horizontal chain of their proteoforms, such as spot 15 pI 5.80 and spot 16 pI 6.08 with MW 58 kDa, as shown in Figure 7A. Excision, digestion and MS-analysis confirmed that both of these spots came from the same canonical protein, PKM. In this case, unique proteotypic peptides of the PKM2 protein isoform were detected in both spots along with additional tryptic PKM peptides, clearly showing that PKM2 is the major proteoform in the DU145 cells. In Figure 7B, this sequence coverage map is presented. PKM2 originates from the PKM gene, and PKM1 and PKM2 proteoforms are produced by alternative splicing. The PKM1 and PKM2 proteoforms differ in the canonical sequence only in amino acids 389–433. The MS identification of these two 2D PKM2 spots with MW 58 kDa covers exactly this sequence region to 100%. In shotgun-proteomics, information about the same protein appears totally different, as illustrated in Figure 7C,D. In the quantitative shotgun data analysis, 29 different PKM peptides, including the PKM2 peptides, were assigned to each other, whereby no intact proteoforms of PKM could be distinguished. From the compilation of these PKM peptides, the quantitative mean (LFQ) of the canonical PKM protein group was calculated (Table 1). The tryptic peptides of PKM2 were distributed throughout the m/z versus retention time two-dimensional space, and proteoform-specific information for PKM2 (peptide numbers coloured red) was lost like a needle in a haystack (Figure 7C). Therefore, information on the qualitative and quantitative composition of the PKM2 proteoforms in the DU145 sample cannot be found in the peptide fragments (Supplementary Table S2) and is, therefore, also not present in the MaxQuant data output of the shotgun analysis. To validate the 2D spots’ MS identifications of the PKM2 and to detect possible additional PKM2 proteoforms via a complementary methodology, a 2D-WB was performed in the pH range of 3–10, using a PKM2-specific antibody (Figure 7A). This PKM2 antibody recognised a chain of more than two PKM2 spots at MW 58 kDa between pI 5.80 and 8.20 and a cleavage proteoform of PKM2 at an MW of 42 kDa with a pI of 7.32. Subsequent MS analysis confirmed these other PKM2 proteoforms found immunologically (Figure 7B). A cleavage product of PKM2 with a similar MW of about 42 kDa was recently found as an enzymatic product from the cysteine proteases cathepsin B and S in pancreatic tumours. This cathepsin-mediated cleavage reduces PKM2 activity and is associated with increased tumour cell proliferation [65]. Therefore, this 42 kDa proteoform of PKM2 can be partially responsible for the Warburg effect and may be a potential biomarker for tumour growth aggressiveness. However, the 42 kDa cleavage product of the current PKM2 proteoform had a protein sequence coverage of 48% with MS analysis, whereby at the N-terminus start, a region of about 32 amino acids is missing and a region of 40 amino acids at the C-terminal end. Two cleavage sites are found for these cathepsins, Q16↓Q17 and Y390↓H391 [62], where only the Q16↓Q17 position can correspond to the amino acid sequence of the current PKM2 cleavage product in the DU145 cells (Figure 7B). Thus, unbiasedly, only the gel-based top-down proteomics methods could identify the tumour-associated PKM2 as the main PKM proteoforms in DU145 prostate cancer cell lysates. Cleavage of the 42 kDa fragment of PKM2 may be responsible for its reduced enzymatic activity and, thus, in part, for the reduced citric acid cycle-mediated oxidative phosphorylation of the Warburg effect [65]. The presence of this proteolytically processed, relatively unknown proteoform of PKM2 would have been over-looked by a conventional shotgun analysis, as done in the current study, unless specific targeted sample preparation methods, such as the terminal amine isotopic labelling of substrates (TAILS) methodologies, capture novel N-termini following protease-mediated cleavages, would be employed [65,66]. As shown for PKM2 (Figure 7A), screening for additional proteoforms of a given protein by complementary immunological methods, such as antibody-based immunological detection by 1D- or 2D-WB, is valuable as long as specific antibodies are available. In this way, possible proteoforms of a protein can be detected with 1D-and 2D-WBs. For all of these antibodies used for further identification of the respective proteoforms by 2D-WB analysis, their specificity was first validated by 1D-WB with the three biological DU145 replicates of this proteomics study (Supplementary Figure S3). Further examples are shown in Figure 8. For a conceivable comparison of how these data look in the bottom-up proteome analysis, the shotgun results, which just show protein groups, evidence, peptides and MS/MS data for these selected proteins, are summarised in Supplementary Table S3. In these examples, such as the glycolytic enzyme GAPDH, we found numerous different proteoforms in the alkaline pH region of the DU145 proteome using a 2D-WB (Figure 8A). It is also worth noting that the GAPDH proteoforms show a higher biological quantitative variation than the mean average of the DU145-2D proteome. Because GAPDH is defined as a housekeeping gene, it is believed to have low biological variation. It is therefore used as a normalizing protein for WB analysis to compensate for unevenly applied amounts of protein. Interestingly, we have previously shown in human platelet proteomes that GAPDH exhibits higher biological variation than many other proteins and is, therefore, not a well-suited normalising protein [47]. An example of a protein with only one 2D-detectable proteoform in the DU145 cell lysates was the adapter protein YWHAG. Immunological validation with a specific YWHAG antibody recognised only this spot and no other (Figure 8B). Consistent with this observation, significantly fewer PTMs are reported on the amino acid sequence of YWHAG than for PKM2, with nine in UniProt and three in the Consortium for the Proteoform Atlas (http://repository.topdownproteomics.org/Proteoforms?query=P61981, accessed on 21 October 2022). The electrophoretically clearly separated individual YWHAG spot shows a low quantitative variability with a CVtech of 2.6% and a CVtotal of 5.5%. A typical loading control, GAPDH, shows a CVtech of 5.6% and a CVtotal of 20.6%. We have also previously identified this protein with only one proteoform and very low biological variability in the platelet proteome of a large study cohort of 238 subjects [47]. In label-free shotgun analysis, CVtech and CVtotal of YWHAG were higher at 19% and 44%, respectively, but also here below are the respective mean CVs of all proteins. The typical loading control, GAPDH, shows a CVtech of 14% and a CVtotal of 54% with the shotgun analysis. Again, the shotgun data could not provide any information about the expected number of proteoforms of GAPDH or YWHAG. Further examples of proteins with different numbers of proteoforms in the DU145 proteome are CTSB, EIF4A1, P4HB and CTSD, with their comparative quantitative proteome data output of 2D-DIGE and label-free shotgun analysis (Figure 8 and Table 1). The MS-based LFQ data of these protein samples can show the overall abundance of their canonical proteins at a glance, and pathway analysis with many of their interaction partners can be better done with shotgun data output. However, a protein’s different amounts of potential regulatory proteoforms can currently only be determined with 2D electrophoresis. Although, in some cases, identifying the PTMs from the respective 2D spots is problematic, if not impossible, since the MS analysis of the respective proteoform rarely achieves 100% coverage. Different concentration of the various proteoforms of a protein also leads to different numbers of MS-identified peptides and, thus, to a differently covered protein sequence. Despite these analytical challenges in distinguishing the PTM-based differences between the different spot proteoforms of a protein, 2D electrophoresis can be expected to be much more likely to uncover new proteoform-based protein regulations than shotgun analysis. For example, we detected an increased amount of a previously unknown N-terminal cleavage product of the coagulation factor XIII (F13A1) in the platelet proteome from patients with lung cancer [39]. These observations finally indicated that the increased risk of thrombosis in lung cancer could also be related to the altered processing and inactivation of this fibrin-stabilizing coagulation factor, thereby providing a new target for antithrombotic treatment. Moreover, the amount of a proteoform with pI 5.60 of F13A1 correlates positively and another with pI 5.85 negatively with its enzymatic activity. These proteomics results also help elucidate this vital coagulation factor’s previously unknown mechanisms in regulating the enzymatic activity of F13A1. Another example first discovered using 2D electrophoresis is the major genetic risk factor for Alzheimer’s disease, apolipoprotein E4 [67]. However, it took several years until the single nucleotide polymorphism (rs429358) and thus the exchanged amino acid, cysteine, for arginine at position 112 of this protein could be assigned to this apolipoprotein E4 proteoform [68]. Other proteoform alterations, such as beta-amyloid and hyperphosphorylated tau protein, are also central to Alzheimer’s disease pathology. Thus, we also identified four proteins by a platelet proteomics study using 2D-DIGE as biomarkers for diagnosing Alzheimer’s disease from blood. For three of them, only some of their proteoforms have been modified disease-dependently, such as a splice variant of tropomyosin 1 [69,70]. Therefore, it would be paramount to supplement many shotgun studies and valuable protein information databases, such as the Top-Down Initiative and the Protein Atlas, with 2D gel and 2D-WB proteoform analysis, as presented in the current work. Thus, one would have a quick first unbiased overview of the proteoform profile of the respective proteins. This immunological 2-DE-based fine-tuning of proteoform detection would be an advanced 2-DE database like the USC-OGP 2-DE database introduced and maintained by Angel Garcia [71], which can be found linked in the UniProt database. Besides information on the workload, reproducibility and quantification of proteoforms, it is also essential to be aware of the different approaches and types of results that can be expected after 2D-DIGE or label-free shotgun analysis when targeting information on PTMs, such as phosphorylation, which are needed to obtain treatment and/or disease-specific biologically relevant information. According to Uniprot, the protein database which integrates and curates available information on proteins, only 13.0, 31.7 and 36.3% of all proteins which have a serine (Ser), threonine (Thr) and tyrosine (Tyr) are marked as phosphoproteins. Given the highly dynamic nature of these, and most likely all PTMs, the “true” proportion of a phosphorylated protein to its non-phosphorylated one in a biological sample remains largely elusive. Traditional methods in detecting phosphorylated proteins in top-down proteomic approaches are metabolic labelling with the radioactive phosphor (32P and 33P isotopes in tri-, di-, monophospho (A/G/T/C)-nucleosides), phospho-specific fluorescent dyes [72], and the use of phosphor-specific antibodies against the respective phosphosites. Phosphorylation can also be identified at a protein’s exact amino acid position in MS analysis using today’s routine search engine algorithms based on the specific mass difference of the neutral loss of HPO3/H3PO4 (80 and 98 mass units (Da)) detected on amino acids tyrosine (Y), serine (S) and threonine (T), respectively or diagnostic ions (78.959 Da). However, quantitative statistical evaluations of phosphorylated proteoform would require MS analysis to reproducibly detect the particular phosphorylated peptide in a complex tryptic digest of a biological sample or from a much less complex peptide mixture such as a 2D spot digest, but at a DDA setting, this is hardly possible. To increase the sensitivity and reliability for the detection of phosphorylated peptides in bottom-up shotgun analysis, the specific enrichment of phosphopeptides by affinity chromatography, e.g., immobilised metal affinity chromatography or metal oxide affinity chromatography (typically with TiO2), is necessary [73,74]. However, the quantitative ratios of phosphorylated to their other non-phosphorylated proteoforms are lost. For a first unbiased look at the sample in question, it is very instructive to investigate what the phosphorylation profiles look like in the original proteome. The use of the λ-PPase, which hydrolyses the phosphate groups of Ser, Thr, Tyr, and His residues [75], is very attractive for the 2D-DIGE system [76]. The loss of a phosphate group increases the pI, resulting in an altered position of phosphoproteins in the 2D map. This effect can be used well with the 2D-DIGE system since the differently fluorescence-labelled original and dephosphorylated samples can be ideally detected in the image analysis. In addition, the information on phosphorylated proteoforms is preserved in the 2D map of the respective biological sample, such as that of DU145 cells, provided that protein preparation and 2D conditions are not changed. Such an enzymatic cleavage of PTMs from proteins (and peptides) is also occasionally used in shotgun proteomics, i.e., to unmask cysteine reactivity [77], investigate phosphorylation-dependent protein-interactions [78] or to aid detection of glycoprotein-detection [79]. However, PTM-enrichment strategies are much more commonly used in shotgun approaches. As already mentioned, an inherent problem of such enrichment strategies is the loss of stoichiometric information about the different abundance of “native” versus PTM-modified proteoforms. In this study, we evaluated how λ-PPase treatment of the same DU145 protein lysates assists the detection of phosphorylated proteoforms by 2D-DIGE and phospho-peptide detection in a traditional, “direct” label-free shotgun approach in the original proteome without phosphopeptide enrichment. Using the 2D-DIGE method, which calculated the ratio of the fluorescent spot signals from the phosphorylated to the dephosphorylated DU145 sample, 81 potentially phosphorylated proteoforms could be detected with a ratio of more than 1.5. This would account for 4% of all protein spots as phosphorylated by this method. The most extensively visible λ-PPase-dephosphorylated protein spots, 13 in number, were selected, excised, in-gel digested, and identified by LC-MS/MS. These proteoforms are indicated in Figure 9A and Supplementary Table S4. Among them is a very well-known phosphorylation substrate, MYL6, the myosin light chain kinase, a critical regulator for tissue contraction. Both a phosphorylated and a non-phosphorylated proteoform of MYL6 could be detected in the 2-DE proteome of the DU145 cell line. In contrast, the phosphorylation profiles of CALM1 and EEF1B2 in this 2D-DIGE analysis show that these proteins could only be detected in the phosphorylated state in the proteome of the DU145 sample. Even with a 2D-WB, only the phosphorylated proteoform of CALM1 could be detected. Both phosphorylated and non-phosphorylated proteoforms could also be detected for the CAP1, PTGES3 and TALDO1. Again, 2D-WB analysis confirmed the presence of phosphorylated and non-phosphorylated spots of these spots (Figure 10A–D). Furthermore, the 2D profiles of CAP1 and PTGES3 show that accumulating phosphorylation events give rise to these spot chains (reflecting multiple proteoforms). We have previously observed phosphorylation patterns similar to CAP1 for the well-known platelet inactivation marker VASP in the 2D proteome of prostacyclin-treated platelets, using the same method of 2D-DIGE-based analysis of dephosphorylation by λ-PPase. The sequential phosphorylation at different amino acid positions S157 and S256 of the proteoforms causes this phenomenon, visible by their decreasing pI in the 2D-GE. Specific VASP antibodies to detect phosphorylation at S157 and S256 confirmed these observations of λ-PPase treatment [80]. Only recently, it was shown that the amount and the phosphorylation profile of CAP1 are altered in patients with lung cancer and other types of cancer and correlate with the degree of metastasis. Two-dimensional proteomic profiling of CAP1 proteoforms can be helpful in further investigations of the pathological role of CAP1 in cancer [81]. In contrast to 2D-DIGE analysis, the identification of phosphorylated proteoforms by “direct” shotgun proteomics, that is, without preceding selective affinity enrichment of negatively charged phosphate groups on a proteoform’s peptides to positively charged immobilised metal ions (commonly referred to as immobilised metal affinity chromato-graphy-IMAC), is very limited due to the low abundance of phosphorylated peptides in a complex peptide mixture as described above. To illustrate this, λ-PPase treated protein lysates were digested, and peptides were analysed directly by label-free shotgun on an ion mobility mass spectrometer (timsToF). As expected, while a large number of proteins were consistently identified in DU145 samples (3687 in the absence and 3528 in the presence of phosphatase, respectively, Supplementary Table S4), the proportion of phosphorylated proteins was less than 1%, illustrating that detection of phosphorylated peptides assignable to respective proteins by a “direct” shotgun approach is almost circumstantial. As summarised in the Venn diagrams (Figure 9B), only 49 versus 40 detected, quantified (numbers of identifications even without intensity are given in parenthesis) and phospho-site localised phosphopeptides on 39 versus 33 proteins were identified and quantitated in samples in the absence and presence of phosphatase, respectively. However, the reproducibility of phospho identification was also poor (i.e., only 1 peptide was reproducibly identified (n = 4) in non-phosphatase samples, detailed data in Supplementary Table S4). Puzzlingly, 28 phosphopeptides belonging to 24 proteins were detected only after phosphatase treatment. In general, estimating the ratio of the phosphorylated to an unphosphorylated abundance of a protein proves to be complicated in shotgun analysis, regardless of whether direct—as in this study—or phospho enrichment approaches are used. Nevertheless, one of the advantages of the label-free shotgun proteomics over 2D-DIGE is the concomitant identification and quantitation of proteins. To exploit this quantitative information also at the phosphopeptide level, we analysed to which extent the phosphatase treatment is deducible from the reported phosphopeptide intensities of commonly (−/+ λ-PPase) identified phosphopeptides (12 phosphopeptides assigned to nine proteins). We found that phosphopeptide abundance levels identified throughout this direct shotgun approach were, with some exceptions, rather low when not influenced by phosphatase treatment (Supplementary Figure S4B,C). Only two peptides on two different proteins were found with higher intensity, IFT27 phosphorylated at position 154 and FAM214A phosphorylated at position 879 (Supplementary Figure S4A source data summarised in Supplementary Table S4, Sheet 03). Here, phosphatase treatment impacted the phosphopeptide´s intensity as phosphopeptide counts (n = 4) and total peptide counts (7 and 25 for IFT27 and FAM214A in both −/+ λ-PPase, respectively) were identical. As mentioned above, information to which extent (“ratio”) a protein is phosphorylated or not is not directly deducible in this peptide-centric approach, which is in sharp contrast to 2D-DIGE. Using the top-down approach, we could show that the ratio of the phosphorylated to the unphosphorylated MYL6 is 2.7. In contrast, only the phosphorylated proteoform of CALM1 was detectable by 2D-DIGE. Instead, in shotgun analysis, for modification-specific peptides, such as phosphopeptides, individual peptide intensities are reported, and modified/unmodified peptide ratios are reported only when the corresponding unmodified peptide is recognised in most search algorithms, including MaxQuant. However, this is not always the case, as in this proteomics study. Especially if an enrichment step was included, a proteome and a phosphoproteome measurement are needed for each sample to estimate a ratio of modified/unmodified protein/proteoform. In our direct-shotgun approach, we analysed whether individual phosphopeptide intensities can be normalised to MaxQuant protein-LFQ intensities, obtained by summing up individual protein/proteoform-specific peptide intensities into a protein-abundance value. As depicted in Supplementary Figure S5A,B, in our direct analysis, the modified peptide intensity contributed to variable degrees, and for some proteins substantially, to the LFQ protein abundance (HMCN1, TRA28, NPM1 and SLIT1, IK in samples without and with λ-PPase-treatment, respectively). In contrast, quantification on the basis of iBAQ intensity (the sum of a protein’s measured peptide intensities is divided by the number of theoretically measurable tryptic peptides) reflects the abundance of the phosphorylated versus the unphosphorylated protein much better as iBAQ/p-peptide ratios are mostly lower than 1 in samples without and with phosphatase treatment with one striking exception—nucleophosmin 1/NPM1. This 294AA long protein has 38 trypsin-cleavage sites, theoretically, 12 iBAQ peptides, of which we detected eight, and obviously, the phosphopeptide contributed significantly to the overall iBAQ ratio. These evaluations show that proteoform quantification is more complicated with bottom-up proteomics. In summary, this study aims to provide life scientists with valuable, practical examples of the performance of the two established proteome analysis methods, 2D-DIGE and label-free shotgun. This variable quality of data obtained should be considered when planning a comprehensive, unbiased analysis of the functional protein repertoire of a biological sample. 2D-DIGE, a classic top-down proteomics approach, is increasingly considered a “low-throughput” technique compared to seemingly “high-throughput” bottom-up approaches such as shotgun analysis. The latter proteomics technology has obvious advantages, such as immediate protein identification, automation and ongoing advancement of data processing capacity. Despite these breakthrough analytical and technological advances, the underlying biological functions and questions should still guide the analytical approach. As expected, the evaluation of the current work confirmed that the shotgun analysis facilitates a timely and direct proteome profiling over a large abundance range of canonical proteins and enables a high sample throughput. Nevertheless, our data demonstrate that much biological information can be lost due to the higher technical variability and the low probability of reproducible and quantitative stoichiometry detection of proteoforms in a label-free bottom-up approach. Thus, this comparative study shows that the top-down 2D-GE methods remain a robust and highly accurate technique for the unbiased large-scale study of proteoforms and their condition-related qualitative and quantitative changes in biological samples. Summarising the comparative analytical investigations of our work, we propose that a bottom-up approach is advisable to take a quick, comprehensive look at a biological sample to find changes that are more likely to be transcriptionally or translationally based. However, if modifications at the proteoform level are also expected, such as proteolytic activation, like activation of blood coagulation factors, single nucleotide polymorphisms or short-term modifications in signal transduction pathways, the top-down 2D-DIGE method can be more advantageous. In hypothesis-generating proteomics studies, the alterations in the particular biological samples are often unknown, so it would be ideal to use both methods together in a complementary manner. Using two techniques in coalition can provide interesting new insights into the mutual abundance of proteoforms and their stoichiometric relationships, along with a comprehensive annotated proteome.

PMC10000907

Yang Wang,Muhammad Ali,Qi Zhang,Qiannan Sun,Jun Ren,Wei Wang,Dong Tang,Daorong Wang

ATF4 Transcriptionally Activates SHH to Promote Proliferation, Invasion, and Migration of Gastric Cancer Cells

23-02-2023

transcriptional factor,ATF4,SHH,gastric cancer

Simple Summary Gastric cancer (GC) is the world’s third greatest cause of cancer-related death. Since the underlying pathogenic mechanisms are still unclear and only a limited number of specialized drugs have been developed, treating GC patients in clinical practice remains challenging. We observed that ATF4 was markedly upregulated in gastric cancer (GC) using immunohistochemistry and Western blotting assays in 80 paraffin-embedded GC samples and 4 fresh samples and para-cancerous tissues. The mechanism of ATF4 as a transcription factor in gastric cancer remains unclear. ATF4 knockdown using lentiviral vectors strongly inhibited the proliferation and invasion of GC cells. ATF4 upregulation using lentiviral vectors promoted the proliferation and invasion of GC cells. We observed that transcription factor ATF4 is bound to the promoter region of SHH to activate the Sonic Hedgehog pathway. Mechanistically, rescue assays showed that ATF4 regulated gastric cancer cells’ proliferation and invasive ability through SHH. Abstract Activating transcription factor 4 (ATF4) is a DNA-binding protein widely generated in mammals, which has two biological characteristics that bind the cAMP response element (CRE). The mechanism of ATF4 as a transcription factor in gastric cancer affecting the Hedgehog pathway remains unclear. Here, we observed that ATF4 was markedly upregulated in gastric cancer (GC) using immunohistochemistry and Western blotting assays in 80 paraffin-embedded GC samples and 4 fresh samples and para-cancerous tissues. ATF4 knockdown using lentiviral vectors strongly inhibited the proliferation and invasion of GC cells. ATF4 upregulation using lentiviral vectors promoted the proliferation and invasion of GC cells. We predicted that the transcription factor ATF4 is bound to the SHH promoter via the JASPA database. Transcription factor ATF4 is bound to the promoter region of SHH to activate the Sonic Hedgehog pathway. Mechanistically, rescue assays showed that ATF4 regulated gastric cancer cells’ proliferation and invasive ability through SHH. Similarly, ATF4 enhanced the tumor formation of GC cells in a xenograft model.

ATF4 Transcriptionally Activates SHH to Promote Proliferation, Invasion, and Migration of Gastric Cancer Cells Gastric cancer (GC) is the world’s third greatest cause of cancer-related death. Since the underlying pathogenic mechanisms are still unclear and only a limited number of specialized drugs have been developed, treating GC patients in clinical practice remains challenging. We observed that ATF4 was markedly upregulated in gastric cancer (GC) using immunohistochemistry and Western blotting assays in 80 paraffin-embedded GC samples and 4 fresh samples and para-cancerous tissues. The mechanism of ATF4 as a transcription factor in gastric cancer remains unclear. ATF4 knockdown using lentiviral vectors strongly inhibited the proliferation and invasion of GC cells. ATF4 upregulation using lentiviral vectors promoted the proliferation and invasion of GC cells. We observed that transcription factor ATF4 is bound to the promoter region of SHH to activate the Sonic Hedgehog pathway. Mechanistically, rescue assays showed that ATF4 regulated gastric cancer cells’ proliferation and invasive ability through SHH. Activating transcription factor 4 (ATF4) is a DNA-binding protein widely generated in mammals, which has two biological characteristics that bind the cAMP response element (CRE). The mechanism of ATF4 as a transcription factor in gastric cancer affecting the Hedgehog pathway remains unclear. Here, we observed that ATF4 was markedly upregulated in gastric cancer (GC) using immunohistochemistry and Western blotting assays in 80 paraffin-embedded GC samples and 4 fresh samples and para-cancerous tissues. ATF4 knockdown using lentiviral vectors strongly inhibited the proliferation and invasion of GC cells. ATF4 upregulation using lentiviral vectors promoted the proliferation and invasion of GC cells. We predicted that the transcription factor ATF4 is bound to the SHH promoter via the JASPA database. Transcription factor ATF4 is bound to the promoter region of SHH to activate the Sonic Hedgehog pathway. Mechanistically, rescue assays showed that ATF4 regulated gastric cancer cells’ proliferation and invasive ability through SHH. Similarly, ATF4 enhanced the tumor formation of GC cells in a xenograft model. Gastric cancer (GC) is the world’s third greatest cause of cancer-related death, despite the fact that its incidence and mortality have reduced drastically over the previous 50 years [1]. There are around 1.2 million newly diagnosed instances of gastric cancer worldwide, with China accounting for 40%. Only 20% of gastric cancers are discovered in their early stages, with the majority being advanced, and the total 5-year survival rate is less than 50% [2]. A comprehensive therapy approach utilizing effective molecular target medicines should be investigated to improve patient prognosis significantly. Since the underlying pathogenic mechanisms are still unclear and only a limited number of specialized drugs have been developed, treating GC patients in clinical practice remains challenging [3]. Due to the fact that the underlying pathogenic mechanisms are still unclear and that only a limited number of specialized drugs have been developed, treating GC patients in clinical practice remains very challenging [4]. The activating transcription factor family shares a conserved basic-region leucine zipper domain and was first discovered in E1A-mediated transcriptional activation in 1987 [5]. It has been reported that over 20 ATF/CREB members exist in mammals, and some of these members play important roles in cancer progression. The activating transcription factor 2 (ATF2) is a member of the family of bZIP transcription factors that regulate transcription, remodel chromatin, and respond to DNA damage [6]. As one of the ATF/CREB transcription factors, activating transcription factor 3 (ATF3) can respond to a variety of stress signals, including anoxia, carcinogens, and genetically modified foods [7]. ATF6 protects from DNA damage and cell death in colon cancer cells [8]. Activating transcription factor 4 (ATF4) is a DNA-binding protein that is widely produced in mammals. The transcription factor has two biological characteristics that bind the cAMP response element (CRE): during an integrated stress response (ISR), as a master transcription factor, and as a regulator of metabolic and redox processes in normal cellular conditions [4,9,10]. As a transcriptional activator, ATF4 is abnormally expressed in many types of tumors by promoting the expression of downstream molecules. For example, breast cancer, colorectal cancer, prostate cancer, and ATF4 are all involved in tumorigenesis [11,12,13,14]. There is some evidence suggesting that ATF4 regulates eral metabolites, including amino acids and glucose, which promote cancer development [15]. Furthermore, evidence suggests that long-term leucine deprivation might inhibit mTORC1 activity through ATF4-mediated REDD1 and Sestrin2 upregulation [16]. Stress is an inevitable part of the growth process for cancer cells. The need for protein synthesis is increased by hyperproliferation [17]. The unfolded protein response (UPR), which is triggered by ER stress; the cellular response to low oxygen levels; and the amino acid response (AAR), which is triggered by amino acid deficiency, all share ATF4 as a common critical downstream effector protein [18,19,20]. Consequently, this contributes to the growth of malignancies. ATF4 is a critical regulator of the transcription of important genes required for the management of the adaptive function. According to studies, endoplasmic reticulum stress or amino acid deficiency triggers the eIF2α/ATF4 pathway, which controls the transcription [21]. However, the precise biochemical mechanism underlying ATF4’s contribution to gastric cancer is poorly understood. Here, we discovered that the genomic DNA of ATF4 was commonly amplified in GC by evaluating GEPIA, whose RNA sequencing expression data of cancers and normal samples are from TCGA public databases (http://gepia.cancer-pku.cn/ (accessed on 15 May 2022)). Then, after analyzing the JASPA transcriptome database, we discovered that the SHH promoter region contains a large number of binding sites for the transcription factor ATF4. The Hedgehog (Hh) gene family is known to regulate the development of stem cells. In addition, activation is responsible for the induction of GLI1 proto-oncogene and subsequent cellular proliferation. Sonic Hedgehog (SHH), one of the Hh family members, promotes carcinogenesis in the airway, and pancreatic epithelia are expressed in colonic stem cells. Some research demonstrated increased expression of SHH mRNA in human colonic adenocarcinomas and in a colorectal cell line with downstream increased expression of GLI1 mRNA, known to promote cell proliferation [22]. The deregulation of SHH signaling is often observed during tumor formation and progression and is detrimental to the cancerous process. The deregulation of the Hh signaling pathway is associated with developmental anomalies and cancer, including Gorlin syndrome, and sporadic cancers, such as basal cell carcinoma, medulloblastoma, pancreatic, breast, colon, ovarian, and small-cell lung carcinomas. The aberrant activation of the Hh signaling pathway is caused by mutations in the related genes or by the excessive expression of the Hh signaling molecules [23]. Significantly, SHH controls the development of vertebrate organs, such as the architecture of the brain and the creation of fingers on limbs. SHH continues to play a significant role in adult cells. It also regulates adult stem cells’ ability to divide, and several types of cancer have been linked to it [24]. A few studies found an increased level of the Hh pathway components in CRC. In addition, in CRC cells in vivo, increased SHH expression was detected at both the mRNA and protein levels. In this regard, several significant genes are associated with the SHH pathway in malignancies of the digestive system. SHH signaling is dormant in the tissues of adult mammals. However, it becomes active during differentiation, proliferation, and maintenance in various adult tissues [25]. It is yet unknown how ATF4 contributes to gastric cancer on a cellular level or whether it stimulates the SHH protein through transcription. These issues are what we work to address. The GEPIA database provided the differential expression analysis of ATF4 bioinformatics in the neighboring normal tissues and gastric cancer tissues. Eighty pairs of paraffin tissues and four pairs of fresh surgical tumor specimens were utilized as clinical specimens in the experiment and were procured from North Jiangsu People’s Hospital. This study was supported by the Ethics Committee of North Jiangsu People’s Hospital with the ethics number 2019KY-022. A tissue microarray (TMA) that included samples from 80 patients with histologically confirmed gastric cancer and 80 controls was generated according to a previously described method [26]. The sections were deparaffinized in xylene, rehydrated in a graded alcohol series and citrate buffer, and then blocked with 3% hydrogen peroxide. Subsequently, the sections were incubated with a primary antibody directed against ATF4 (1:100; 11815, Cell Signaling Technology, Danvers, MA, USA) and then with a biotin-conjugated secondary antibody (SA1050; Boster, Wuhan, China), followed by incubation with a streptavidin–peroxidase complex. Five high-power fields (400× magnification) were selected randomly and photographed for each slide. The protein expression scoring was evaluated by taking both the proportion of positive cells (0 (<5%), 1 (5–25%), 2 (26–50%), 3 (51–75%), and 4 (>75%)) and the intensity of cell staining (0 (negative), 1 (weak), 2 (moderate), and 3 (strong)) into account. The final staining scores were calculated by multiplying the staining intensity by the degree of staining. ATF4 staining was considered low or high using a cutoff value of 5 based on the analysis from the receiver operating characteristic (ROC) curve. A final score greater than 5 was defined as a high expression of ATF4.2.2.P. The GES-1 Normal Gastric Epithelial Cell Line and the GC cell lines, AGS, MGC803, and HGC27, were all developed by Shanghai Gene Chemical Company (Chinese Academy of Science, Shanghai, China). The cells were tested regularly for mycoplasma contamination to ensure they were uncontaminated. MGC803 and HGC27 in RPMI-1640 medium (Invitrogen, Carlsbad, CA, USA) and AGS in F12K (Invitrogen, Carlsbad, CA, USA) and GES1 in DMEM (Invitrogen, Carlsbad, CA, USA) were cultured supplemented with 10% fetal bovine serum (Invitrogen, Carlsbad, CA, USA) and 100 units/mL of penicillin 100 mg/mL streptomycin (Hyclone SV30010) at 37 °C in a humidified incubator containing 5% CO2. We employed lentivirus to knock down ATF4 in AGS cells and overexpress ATF4 in the MGC803 cell line to create gastric cancer cell lines with stable ATF4 knockdown or overexpression. Four recombinant lentiviral vectors were constructed: Vector1 (lentivirus-EGFP-Puro), ATF4 (lentivirus-ATF4-EGFP-Puro), Si-ATF4 (lentivirus-EGFP-Si-ATF4-Puro), and Vector2 (lentivirus-EGFP-pRNAi-Puro) from GeneChem (Shanghai, China). In a 24-well plate, 1 × 104 cells were planted into each well 12 h before virus infection. Lentivirus should be added to each well for 72 h, followed by puromycin screening to identify stable cell lines and fluorescence microscopy detection of the fluorescence signal in the cells. GenePharma (Shanghai, China) provided 1-SHH and pcDNA3.1-NC, the negative control. Twenty-four-well plates were seeded with a total of 1 × 104 cells per well for 12 h, and Lipofectamine® 2000 (Thermo Fisher, Waltham, MA, USA) was used to transfect those cells. Cells that had undergone a two-day transfection were collected for the subsequent studies, utilizing a Weston blot to measure transfection effectiveness. GC tissues or entire cells were used to extract equivalent amounts of protein, which were then separated with 10% SDS-PAGE gel and electro-transferred onto polyvinylidene fluoride (PVDF) membranes (Thermo Fisher Scientific, Waltham, MA, USA). The antibodies were incubated with various primary antibodies overnight at 4 °C after blocking in 5% milk for 2 h; rabbit anti-ATF4-1 (1:3000, Cell Signaling Technology, Danvers, MA, USA), rabbit anti-SHH (1:3000, Cell Signaling Technology, Danvers, MA, USA), mouse anti-Gli1 (1:3000 Santa Cruz Bicycles, California, CA, USA), rabbit anti-GAPDH (1:5000, Cell Signaling Technology, Danvers, MA, USA), and mouse-anti GAPDH (1:5000, from Abclonal, Wuhan, China) antibodies were used as internal controls. Signals were then detected using an upgraded chemiluminescence substrate after incubating with a secondary antibody bound to peroxidase (Millipore, Schwalbach, Germany). Image J was used to measure the intensity of the bands on the Western blot. A 96-well plate was filled with 1500 cells in each well and then incubated at 37 °C overnight. Each well received 10 μL of the Cell Counting Kit-8 (CCK-8) (Beyotime Institute of Biotechnology, Shanghai, China), which was then added to the empty media and incubated for two hours at 37 °C. At 24, 48, and 72 h, absorbance was measured at 450 nm using a microplate reader. The plate cloning test assessed the cells’ ability to multiply. A 6-well plate was filled with 500 cells per well and cultured for two weeks. The colonies were stained with 0.1% crystal violet after being fixed with 4% paraformaldehyde. We used a digital camera to count the colonies. At least three duplicates of each experiment were carried out. Transwell with an 8-um pore and Matrigel (Corning Co, Corning, NY, USA) was used to assess the GC cell lines’ capacity for invasion. Using 200 μL of DMEM without FBS indicated 2 × 104 cells per well were planted into the upper chamber after 48 h of transfection. The lower chambers were then filled with 500 μL of the medium, which included 10% FBS, a chemoattractant. After twenty-four hours, the invading cells were preserved and a cotton swab was used to remove any cells left on the upper membrane. Cells that had been invaded were stained with 1% crystal violet after being fixed in 4% formaldehyde. Ten random visual fields were counted using an inverted Nikon microscope. An 8-um pore Transwell insert without Matrigel was used for the migration experiment. The invasion assay was conducted similarly. Wound healing assay and AGS and MGC-803 cells were seeded into 6-well plates and cultured in a complete medium to produce a confluent monolayer. Then, a 200 µL pipette tip was used to scratch a straight wound, and collected tissues were washed three times with PBS to remove debris. Moreover, the medium containing 3% FBS was replaced to culture the remaining cells. Photos were taken at 0 and 24 h after the scratching. Wound closure was evaluated by ImageJ software. The Simple ChIP Plus Enzymatic Chromatin IP Kit (Magnetic Beads) was used to perform chromatin immunoprecipitation (ChIP) (9005S; Cell Signalling Technology, Danvers, MA, USA). In a nutshell, 3 plates per treatment were utilized to seed 4 × 106 AGS and MGC-803 cells in 150 mm diameter dishes. The Simple ChIP Plus Enzymatic Chromatin IP Kit (Magnetic Beads) instructions (9005S; Cell Signalling Technology, Danvers, MA, USA) were strictly adhered to. Each plate contained 20 mL of medium that had been cross-linked with 1% formaldehyde at room temperature for 10 min. The cross-linking was then stopped by adding 2 mL of glycine, which was harvested in ice-cold PBS containing protease and phosphatase inhibitors after three ice-cold PBS washes per dish. According to the manufacturer’s instructions, chromatin was prepared and dispersed by partial digestion with Micrococcal Nuclease, followed by a mild sonication (Bioruptor Diagenode, Seraing, Belgium). ATF4 rabbit monoclonal antibody (11815, Cell Signaling Technology, Danvers, MA, USA), common rabbit IgG antibody, and ChIP-Grade Protein G Magnetic Beads were used in chromatin immunoprecipitations. Utilizing spin columns, DNA was purified following the reversal of protein–DNA cross-links. DNA was amplified using standard PCR with the addition of a hot-start Taq enzyme Kit (AG11201; Accurate Biology, Changsha, China); the results were detected using agarose electrophoresis. Positive and negative control primers were from the kit: SHH Primer F: GAGGAGTCTCTGCACTACGAG, R: GATATGTGCCTTGGACTCGTAG. A total of 10 4-week-old BALB/c male nude mice (weight, 18–22 g) were purchased from GemPharmatech Co. Ltd. (Nanjing, China) and raised in a pathogen-free laminar flow cabinet throughout the experiments under the following conditions: Controlled humidity (30–40%), a constant temperature of 25 °C, a 12-h light/dark cycle and free access to food and water. The ethical approval (approval no. 202111020) to perform the animal experiments was obtained from the Ethics Committee for Animal Experiments of the Yangzhou University (Yangzhou, China). The experimental protocol was performed in accordance with the Laboratory Animal Guideline for Ethical Review of Animal Welfare (26). 4-week-old male BALB/c nude mice were randomly divided into Vector and shATF4 groups (n = 5 in both groups). Under isoflurane inhalation anesthesia (1–2%), ~1 × 106 AGS cells of stably transfected strains Vector/shATF4 resuspended in 100 µl PBS were subcutaneously into the left armpit of the mice. The health and behaviour of the mice were monitored every 2 days to determine if there were difficulties eating or drinking, unrelieved pain or distress without recovery. If the tumor reached 2000 mm3, the animal would be euthanized as a humane endpoint. The following formula was used to calculate the tumor volume (V) every week: V = (Width2 × Length)/2. Four weeks post-inoculation, all the mice were sacrificed by cervical dislocation under anesthesia. The method of anesthesia used for the mice was CO2 asphyxiation (CO2 was introduced into the chamber at a rate of 40–70% of the chamber volume per min to minimize distress). Dilated pupils were then used to verify death. Then, the tumors were removed and weighed. The GraphPad Prism 8 program (GraphPad 8.0.1 Software, La Jolla, CA, USA) was used for statistical analysis, and the T-test was used. The correlation between gene expression was carried out using Spearman statistical methods. All data are expressed as the means ± SEM of at least three independent experiments; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 are considered significant. We initially identified the expression of ATF4 in gastric and surrounding tissues in the GEPIA database (http://gepia.cancer-pku.cn/ (accessed on 15 May 2022)) to evaluate the role of ATF4 in the pathogenesis of gastric cancer. ATF4 was considerably higher in gastric cancer than in surrounding tissues when we examined 408 gastric tumors and 211 normal tissues from the GEPIA database (Figure 1A). The expression of ATF4 and SHH was then found in four pairs of fresh gastric cancer tumor tissues. Fresh tumor tissue from gastric cancer showed similar outcomes (Figure 1B). We used immunohistochemical (IHC) analysis to examine the expression of ATF4 in 80 pairs of gastric cancer tissues and nearby normal tissues to better understand how ATF4 is expressed in gastric cancer. Compared to the surrounding non-cancerous tissues, the expression of ATF4 and SHH in stomach cancer tissues was greater, and the correlation between ATF4 and SHH was detected in 80 clinical gastric cancer tissues by immunohistochemistry score. There is a positive correlation between ATF4 and SHH protein expression. The results are presented in Figure 1C. It is statistically significant that gastric cancer has a high expression of ATF4 (Figure 1D). ATF4 and SHH are significantly expressed in gastric cancer cell lines, as seen in our examination of its expression in Human Gastric Mucosal Epithelial Cells (GES1) (Figure 1E). The increased expression of ATF4 in gastric cancer has been verified to assess ATF4’s contribution to the emergence of gastric cancer. To reduce endogenous ATF4 in AGS, lentivirus-mediated ATF4-specific short hairpin RNAs (shRNAs) were transfected. The green fluorescent protein sequence is present in the lentiviral vector, and cells that have been successfully transfected will produce the protein (Figure 2A). After puromycin selection, stable ATF4 knockdown cells were produced. The expression of ATF4 was dramatically reduced in cells transfected with the knockdown of ATF4 compared to cells transfected with an empty vector (Figure 2B). In order to determine whether ATF4 has an impact on the proliferation of AGS cells, a CCK8 and colony formation test were performed. The findings demonstrated that knocking down ATF4 considerably decreased the proliferation rate of cells in comparison to the control group (Figure 2C,D). After that, we looked at how AGS cells migrated and invaded after having ATF4 knocked down. The findings of transwell and wound-healing assays revealed that the migration in the sh-ATF4 group compared with the Vector group in AGS cell was dramatically reduced (Figure 2E,F). The transwell with Matrigel results revealed that the sh-ATF4 group’s capacity for invasion was decreased (Figure 2E). The sh-ATF4 group was compared with the growth, migration, and invasion capacities of the control and siRNA treated AGS with HGC27 cell lines naturally under-expressing ATF4. There was no significant difference in growth, migration, and invasion capacity between the sh-ATF4 AGS group and the HGC27 group. These results are presented in Supplementary S1A,B. To determine if ATF4 overexpression promotes gastric cancer cell proliferation and invasion, lentiviral transfection was used to create MGC803 cells that are continuously overexpressing ATF4 (Figure 3A,B). CCK8 and plate clone data demonstrate that ATF4 overexpression can encourage the proliferation of AGS cells (Figure 3C,D). Additionally, as seen in Figure 3E,F, MGC803 cells overexpressing ATF4 have improved abilities for migration and invasion. There was no significant difference in growth, migration, and invasion capacity between the oe-ATF4 MGC803 group and AGS group (Supplementary S1C,D). The transcription factor ATF4 is predicted to be able to bind to the SHH promoter sequence in the JASPA database that predicts transcription factor binding sites (https://jaspar.genereg.net/ (accessed on 20 May 2022)), and the predicted binding site is GGCTAGAGCGGCCC (Figure 4A). We created primers for the SHH promoter region to confirm the anticipated results. ATF4 recruitment to the SHH promoter was further demonstrated by the findings of chromatin immunoprecipitation (ChIP) tests in AGS and MGC-803 (Figure 4B). We treated AGS, MGC-803 with ATF4 siRNA and ATF4 overexpression to ascertain if ATF4 operates upstream of SHH in GC. Intriguingly, in the AGS and MGC-803 cell line, the knockdown of ATF4 dramatically decreased SHH protein and downstream Gli1 protein in the Sonic Hedgehog pathway (Figure 4C). The AGS and MGC-803 greatly boosted the expression of SHH and GLI proteins when we overexpressed ATF4 (Figure 4D). Quantification of Western blot analysis by Image J software (Figure 4E,F). ATF4 binding to the UHMK1 promoter in GC cell lines was further demonstrated by the findings of chromatin immunoprecipitation (ChIP) tests. Consequently, ATF4 encourages SHH transcription in GC, creating a positive feedback loop. We devised a series of rescue studies to show whether ATF4 controls the proliferation and invasion capacity of gastric cancer cell lines through SHH protein. pcDNA 3.1-SHH and the si-ATF4 lentivirus were co-transfected into AGS cells. After SHH overexpression, the proliferative capacity of AGS knockdown ATF4 cells was nearly restored to its initial level as compared to the group transfected with si-ATF4 lentivirus and pcDNA 3.1 (Figure 5A). Additionally, the invasive potential of AGS cells produced comparable outcomes (Figure 5B). We simultaneously transfected siSHH and oe-ATF4 lentivirus in MGC-803 cells. As seen in Figure 5C, SHH protein expression was decreased in MGC-803 cells that were stably overexpressing ATF4, and this dramatically decreased the cells’ ability to proliferate. When SHH expression is knocked down in MGC803 cells using an empty vector, the cells’ capacity to proliferate is drastically diminished. According to data from a study on cell invasion, MGC803 cells that were stably overexpressing ATF4 had considerably less ability to invade after SHH expression was knocked down (Figure 5D). In addition to these, CCK8 studies demonstrate that reversing SHH expression considerably improves colony-forming capacity (Figure 5E). The findings above imply that ATF4 may control gastric cancer cell invasion and proliferation through changing the SHH protein. Immunodeficient BALB/c mice bearing AGS cells that had been stably transfected with the vector or sh-ATF4 lentivirus were utilized to determine the involvement of ATF4 in the carcinogenesis of gastric cancer in vivo in order to confirm further whether ATF4 functions in in vivo models. Nude mice aged 5 weeks were subcutaneously injected with AGS NC cells and AGS KD cells. A palpable mass appeared at the injection site one week following the injection. After 4 weeks, the tumors were obtained and examined (Figure 6A). The mass’s largest and smallest diameters were then measured weekly. As anticipated, beginning in the third week, the silencing of ATF4 greatly reduced the growth of the AGS tumor in mice when compared to the control group (Figure 6B). After the fourth week, the nude mice were slaughtered, and tumor weights and volumes were measured. The average volume of the shATF4 group was considerably smaller than that of the shCtrl group (Figure 6C, p < 0.0001) (111.8 ± 23.66 vs. 636.3 ± 86.70 mm3). The average weight of the shATF4 group (0.1017 ± 0.01493 g) was significantly lower than that of the shCtrl group (0.5600 ± 0.08145 g) (Figure 6D, p < 0.0001). The outcomes showed that suppressing ATF4 prevented stomach cancer. According to reports, ATF4 is crucial in ER-negative breast malignancies, lung cancer, colorectal cancer, prostate cancer, and other types of cancer. ATF4 is highly expressed in triple-negative breast cancer, and ATF4 could promote breast cancer cell proliferation. In triple-negative breast cancer, a high ATF4 expression was shown to be correlated with low OS after diagnosis (37 months for high ATF4 expression and 46 months for low ATF4 expression) [28]. According to previous studies, higher nuclear ATF4 expression was detected in lung cancer cells compared to cytoplasmic ATF4 expression. Lung cancer cells overexpress ATF4 and localize it primarily to the nucleus, which leads to an increase in lung cancer cell proliferation and invasion [29]. Studies have reported that the transcriptional activation of ATF4 promotes colorectal cancer proliferation. ATF4 is a downstream target of URB1 and is involved in the oncogenic role of URB1 in colorectal cancer [30].Our study demonstrated, for the first time, how transcriptional control of the SHH protein by ATF4 affects the development of gastric cancer. In order to stimulate the expression of the SHH protein, which activates the Sonic Hedgehog signaling pathway, ATF4 binds to the SHH promoter region, as our study demonstrated for the first time. By comparing gastric cancer tissues and cells to healthy control tissues and cells, we were able to show that ATF4 is considerably increased in gastric cancer tissues and cancer cells. ATF4 positively regulated the proliferation, invasion, and migration of gastric cancer cells. Furthermore, ATF4 regulates the proliferation, invasion, and migration ability of gastric cancer cells through transcriptional activation of SHH. Cancer cells usually have an increased level of the stress-induced transcription protein activating transcription factor 4 (ATF4). ATF4 is overexpressed in ER-negative breast tumors and regulates the GSK3/-catenin/cyclin D1 pathway to advance the cell cycle. During glutaminolysis inhibition in colorectal cancer, activating transcription factor 4 (ATF4) is increased to reduce mTOR signaling by transcriptionally activating the mTOR suppressor DNA damage-inducible transcript 4 (DDIT4) [31]. ATF4 is strongly linked to autophagy and the mTORC1 pathway, which Yongxiang Li et al. found to enhance the growth of gastric cancer [32]. The direct interaction between SLFN5 and ATF4 in prostate cancer results in mTOR activation. ATF4 is not identified to regulate SHH, inhibiting the Sonic Hedgehog signaling pathway and preventing the onset and spread of gastric cancer. The development of the central nervous system during fetal development is mostly correlated with the Sonic Hedgehog (SHH) pathway, an evolutionary conserved molecular cascade. The ligands of the SHH signaling pathway control the activation of this pathway. Gli1 is a marker of Shh pathway activation. As a target gene of the Shh pathway and as a transcription activator downstream of Shh signaling, Gli1 autoregulates and increases Shh signaling output. Our design leaves something to be desired; tumor growth in nude mice could be studied with or without the treatment of animals using a Gli1 inhibitor; for example, GANT61. Such an experiment could bring strong evidence for the importance of developing anticancer therapies targeting the ATF4/SHH pathway. Numerous malignancies, such as retinoblastoma, breast, colorectal, and non-small cell lung cancer, are frequently linked to the growth of SHH pathway components, especially GLI transcription factors [33,34]. SHH was found to be effective in reducing tumor size and angiogenesis in a mouse model of pancreatic cancer [35]. One important molecule that turns on SHH signaling is the SHH ligand. Therefore, it should come as no surprise that research has been conducted on the upstream control of the SHH gene to fully comprehend the significance of the SHH pathway in carcinogenesis [36]. In a prior study, the KRAS proto-oncogene of the MAPK/ERK pathway was found to boost the transcriptional activity of GLI1 and the expression of SHH pathway target genes in gastric cancer [37]. The Hedgehog pathway is one of the most common signal transduction pathways used by mammalian cells. Most studies have focused on its role during development, primarily of the nervous system, skin, bone, and pancreas. SHH plays a significant role during epithelial development and differentiation, homeostasis, and neoplastic transformation of the stomach. Significant levels of Sonic Hedgehog are expressed in the gastric mucosa, which has served to direct analysis of its role during organogenesis, gastric acid secretion, and neoplastic transformation [38,39,40]. Due to the activation of this pathway during proliferation and neoplastic transformation, more recent studies have examined its role in adult tissues. In summary, we found that ATF4 binds to the promoter region of SHH to activate the Sonic Hedgehog pathway for the first time. The biological function of ATF4 in vivo and in vitro by activating SHH has been established, and a more detailed molecular mechanism of ATF4 and SHH regulating the occurrence of gastric cancer should be investigated in the future. We identify ATF4 as a key protein in mediating proliferation, invasion, and migration. This study provides a new understanding of the critical underlying mechanism of ATF4 leading to the proliferation, invasion, and migration of gastric cancer cells through transcriptionally activating SHH.

PMC10000908

Nesrine Ben Mhenni,Giulia Alberghini,Valerio Giaccone,Alessandro Truant,Paolo Catellani

Prevalence and Antibiotic Resistance Phenotypes of Pseudomonas spp. in Fresh Fish Fillets

23-02-2023

Pseudomonas spp.,fish fillets,food microbiology,antimicrobial resistance,multi-drug resistance

In fresh fish products, excessive loads of Pseudomonas can lead to their rapid spoilage. It is wise for Food Business Operators (FBOs) to consider its presence both in whole and prepared fish products. With the current study, we aimed to quantify Pseudomonas spp. in fresh fillets of Salmo salar, Gadus morhua and Pleuronectes platessa. For all three fish species, we detected loads of presumptive Pseudomonas no lower than 104–105 cfu/g in more than 50% of the samples. We isolated 55 strains of presumptive Pseudomonas and carried out their biochemical identification; 67.27% of the isolates were actually Pseudomonas. These data confirm that fresh fish fillets are normally contaminated with Pseudomonas spp. and the FBOs should add it as a “process hygiene criterion” according to EC Regulation n.2073/2005. Furthermore, in food hygiene, it is worth evaluating the prevalence of antimicrobial resistance. A total of 37 Pseudomonas strains were tested against 15 antimicrobials, and they all were identified as being resistant to at least one antimicrobial, mainly penicillin G, ampicillin, amoxicillin, tetracycline, erythromycin, vancomycin, clindamycin and trimethoprim. As many as 76.47% of Pseudomonas fluorescens isolates were multi-drug resistant. Our results confirm that Pseudomonas is becoming increasingly resistant to antimicrobials and thus should be continuously monitored in foods.

Prevalence and Antibiotic Resistance Phenotypes of Pseudomonas spp. in Fresh Fish Fillets In fresh fish products, excessive loads of Pseudomonas can lead to their rapid spoilage. It is wise for Food Business Operators (FBOs) to consider its presence both in whole and prepared fish products. With the current study, we aimed to quantify Pseudomonas spp. in fresh fillets of Salmo salar, Gadus morhua and Pleuronectes platessa. For all three fish species, we detected loads of presumptive Pseudomonas no lower than 104–105 cfu/g in more than 50% of the samples. We isolated 55 strains of presumptive Pseudomonas and carried out their biochemical identification; 67.27% of the isolates were actually Pseudomonas. These data confirm that fresh fish fillets are normally contaminated with Pseudomonas spp. and the FBOs should add it as a “process hygiene criterion” according to EC Regulation n.2073/2005. Furthermore, in food hygiene, it is worth evaluating the prevalence of antimicrobial resistance. A total of 37 Pseudomonas strains were tested against 15 antimicrobials, and they all were identified as being resistant to at least one antimicrobial, mainly penicillin G, ampicillin, amoxicillin, tetracycline, erythromycin, vancomycin, clindamycin and trimethoprim. As many as 76.47% of Pseudomonas fluorescens isolates were multi-drug resistant. Our results confirm that Pseudomonas is becoming increasingly resistant to antimicrobials and thus should be continuously monitored in foods. The microbiota of fishery products is made up of a set of microbial populations (bacteria, yeasts and/or molds), and the bacteria belonging to the Pseudomonadaceae family are one microbial population, alongside enterobacteria, coliforms or lactic acid bacteria. The genus Pseudomonas counts over 200 species divided into 11 subspecies. These bacteria are widely spread in the terrestrial and aquatic environment, both in fresh water and salt water. Based on currently available references, the species of Pseudomonas (hereafter sometimes only “P.”) most commonly isolated in fish are P. fluorescens, P. lundensis, P. fragi, P. anguilliseptica and P. putida [1,2,3]. The species of Pseudomonas that is mainly considered a human pathogen is P. aeruginosa, but it is not so common in food products [4]. Pseudomonads are one of the most relevant specific spoiling microorganisms (SSOs) because with their excessive proliferation, they enhance the splitting of nitrogenous compounds leading to product deterioration [5]. It is widely demonstrated that Gram-negative bacteria belonging to the Pseudomonadaceae family are one of the most active microbial populations in the production of proteolytic, lipolytic and saccharolytic enzymes (also extracellular ones). Furthermore, many species of Pseudomonas spp. are able to synthesize yellow, green fluorescent, blue and red pigments that can lead to the development of anomalous colors [5,6,7,8]. The discoloration and the development of unpleasant off-odors and slime coats characterize the spoilage of fresh fish, whole or portioned if they are stored in the air and even if stored at refrigeration temperatures [5,9]. In fact, Pseudomonadaceae are aerobic microorganisms and grow only in the presence of molecular oxygen. In vacuum- and CO2-packed stored fish, the number of Pseudomonas spp. is reduced [5]. Grooming and filleting operations bring additional loads of microbial community to the product being processed, with the risk of shortening the commercial life of the product itself [10]. Pseudomonas spp. can produce tough microbial biofilms on all surfaces they come into contact with, and these biofilms can offer shelter and protection to foodborne disease bacteria. The presence of high loads of Pseudomonas spp. in food or in any other substrate can favor the survival of Listeria monocytogenes, Staphylococcus aureus and enteropathogenic strains of E. coli (STEC) [11,12,13,14]. Due to the biofilm produced by Pseudomonas spp., it is not easy to eliminate the bacteria that are hosted there and, on the contrary, they are able to resist various environmental stresses, including chlorine disinfectants. Nikel and co-authors reported that P. putida possesses metabolic mechanisms that allow it to resist oxidative stress [15]. It is also known that Pseudomonadaceae are able to withstand a certain osmotic pressure (salinity of the medium) thanks to specific molecules present in their cytoplasm that can hook osmoprotective compounds such as choline, betaine and carnitine from the environment in which the bacteria are found, favoring the survival of species such as P. syringae and P. aeruginosa [16]. In general, it is accepted that a low number of Pseudomonas spp. does not pose a health problem for the consumer. Instead, Pseudomonadaceae can produce histamine and become a risk to human health if they proliferate excessively in fish that are inherently high in free histidine [17]. For this and for the reasons considered above, it is therefore wise that FBOs take into due consideration the presence of Pseudomonas spp. in whole fresh fish as well as in fresh fillets, formally giving to it the qualification of “process hygiene criterion” pursuant to EC Regulation n.2073/2005 [18] that establishes some “microbiological criteria for foodstuffs”. The aforementioned Regulation is addressed to the FBOs who use it as a reference to check and validate their self-monitoring plan as well as the safety level of their products as required by European laws. The process hygiene criteria currently provided by the Regulation n.2073/2005 for fishery products are the loads of E. coli and coagulase-positive staphylococci; in addition, only shelled and shucked products of cooked crustaceans and shellfish are the fishery products considered. The consumption of fishery products is regularly increasing, especially in low- and middle-income countries. Between 1961 and 2016, the demand for fish products increased by about 3.2% per year, while the need for meat from terrestrial animals increased by only 2.8% [19,20]. According to the FAO Report 2022 on “The State of World Fisheries and Aquaculture”, in recent years, there has been a sharp increase in the consumption of fresh, chilled and frozen fish products, while the consumption of salted, smoked or canned products remains stable. At the same time, aquatic food production is increasing; China, Norway, Vietnam, Chile and India are the major producers and exporters in the world [19]. In 2020, the global production of fishery products reached 178 million tons (with aquaculture representing 49.2% of the total), and it is forecasted to grow another 15% by 2030 [19]. Aquatic food systems are under growing pressure, and many reports document that the use of antimicrobials in fish farming is consequently growing. Researchers estimate that in 2030, about 13,495 tons of antimicrobials will be used in aquaculture, which, therefore, represents 5.7% of the total amount used in the world (by adding those used in humans and in all other animals) [21]. This also draws much attention to the fact that most classes of antimicrobials are used for the treatment of bacterial infections in both veterinary and human medicine [22]. This increases the speed of selection of drug-resistant bacteria [23], and antimicrobial-resistant bacteria can reduce the effectiveness of treatments, especially in Gram-negative bacterial infections [24]. Thus, the careless use of antibiotics in aquaculture is a contributing factor to the rise in antimicrobial resistance (AMR), leading to potential animal, human and ecosystem consequences [21]. The relationship between the detection of antibiotic-resistant bacteria in humans and antibiotic use in food animals continues to be a topic of discussion among researchers. Various studies have suggested that the use of antibiotics in animals can directly affect human health through direct contact with antibiotic-resistant bacteria from food animals [23]. Other authors argue indirect effects that result from contact with resistant organisms that have spread to various components of the ecosystem (e.g., water, soil, etc.), and so they can indirectly pass from animals to humans and vice versa. This indirect effect can be greatly increased by the horizontal transfer of mobile genetic elements such as conjugative plasmids, phages and transposons [25]. From a holistic view of AMR in different sectors, there are few studies that directly consider foods and not clinical isolates. Moreover, current AMR research, surveillance programs and dietary risk assessment studies (as the potential transmission of AMR), focus mainly on food-producing terrestrial animals and a few indicator bacteria. So, considering that world seafood production is increasing every year, our study aims to provide additional data to implement those already known regarding Pseudomonas spp. which is considered a dominant bacterial genus in food processing facilities and in the microbial community of many fish products [4]. Many strains of Pseudomonas have been shown to possess a high level of intrinsic resistance to most antibiotics [26]. This intrinsic resistance is mainly conferred by concurrent mechanisms, such as low outer membrane permeability, efflux systems that pump antibiotics out of the cell and the production of antibiotic-inactivating enzymes such as β-lactamases [17,27]. For now, various resistance genes have been described in the literature, and as it is a possible source of new resistance genes, Pseudomonas spp. should be better monitored [28] in their evolution. Considering only the strains of Pseudomonas spp. isolated from food, previous studies reported in the literature confirm that various strains are able to resist some antimicrobial agents of different classes, especially β-lactams such as penicillins, cephalosporins, carbapenems, and monobactams [29,30]. For example, the study reported by Kačániová and co-authors [31] reveals a high proportion of resistant strains among the Pseudomonas spp. isolated originated from fish. Moreover, all Pseudomonas were resistant to meropenem. According to Fazeli and Momtaz [32], bacterial strains exhibited the highest level of resistance to penicillin (100%) followed by tetracycline (90.19%), streptomycin (64.70%) and erythromycin (43.13%). Shabana and co-authors [33] showed that P. fluorescens strains were resistant to more than two classes of antibiotics and, therefore, they are considered multidrug-resistant (MDR) strains and accounted for 29.7%. The aim of our study was to determine and characterize a Pseudomonas spp. population in fresh fish fillets at the beginning of their shelf life and evaluate the degree of antimicrobial resistance of the isolated strains in order to provide useful data for monitoring its development from the point of view of food safety. A total of 75 fish fillets intended for large retailers were collected from a fish industry located in the Venetian area (Italy) in 5 different sampling times from March to May 2022. Each time, 15 samples were analyzed, namely: Five salmon (Salmo salar) fillets reared in Norway; Five plaice (Pleuronectes platessa) fillets caught using trawling in the Northeast Atlantic Ocean; Five northern cod (Gadus morhua) fillets caught using longlining in the Northeast Atlantic Ocean. The fish arrived in Italy by ship and under melting ice or frozen, as established by the EC Regulation n.853/2004. The samples of salmon and plaice were fresh, while the cod fillets were defrosted at the processing site. The fillets were collected from the fish industry early in the morning after packaging and immediately transferred to the laboratory. During transport (less than one hour), they were preserved at refrigeration temperature in expanded polystyrene containers with eutectic plates. In the laboratory, the fillets were immediately subjected to microbiological analysis for Total Viable Count and Pseudomonas spp. count using the following methods: UNI EN ISO 6887-1: 2017, UNI EN ISO 7218: 2013, UNI EN ISO 4833-1: 2013, UNI EN ISO 11133: 2014, UNI EN ISO 13720:2010. According to the guidelines provided by UNI EN ISO 6887-1:2017, 10 g of each sample was moved into a sterilized container with 90 mL of Buffered Peptone Water (BPW) under strict hygienic measures, and homogenization was performed using a Stomacher 400 Lab blender. From this mixture, 1 mL was transferred to a sterilized test tube containing 9 mL of BPW, from which ten-fold serial dilutions were processed up to 10−5. The prepared samples were subjected to the determination of Pseudomonas counts using Pseudomonas Agar Base (PAB) supplemented with CFC Supplement (Liofilchem®) and Total Viable Count (TVC) using Plate Count Agar (Merck®). The Pseudomonas Agar Base inoculated plates were incubated in a fridge-thermostat at 25 °C for 48 h, while the Plate Count Agar inoculated plates were incubated at 31 °C for 48–72 h. The colonies were counted using the UNI EN ISO 7218:2013 standard as a reference. The results were expressed in the colony-forming unit (cfu)/g and then subjected to the determination of arithmetic mean and statistical analysis. Log-transformed data (Total Viable Count and Pseudomonas spp.) were analyzed using a linear ANOVA model that included the fixed effects of fish type and sample ID (nested within the fish type). Least squares means (ls-means) were calculated and post hoc pairwise comparisons were performed using the Bonferroni correction. Data expressed as an index [formula: number of figures-1; 10^ (order of magnitude with powers of 10)] were submitted to non-parametric analysis (Kruskall–Wallis test) to assess the effect of the type of fish. Post hoc pairwise comparisons were estimated using the Steel–Dwass–Critchlow–Flinger correction. p < 0.05 was considered significant. All the analyses were carried out with SAS (SAS Institute Inc, Cary, NC, USA 2017) and XLStat (XLSTAT statistical and data analysis solution. New York, NY, USA). At least 5 colonies grown on PAB were selected from each plate and their oxidase and catalase activities were tested. The oxidase test was performed by smearing a fresh colony of each isolate onto a sterile oxidase filter paper disc soaked in distilled water. The appearance of purple color indicates a positive result of oxidase activity. The catalase test was carried out using hydrogen peroxide and the positive reaction was indicated by immediate bubbling. At this stage, if the colonies gave positive results to the two tests mentioned, the non-fermentation activity was evaluated using the Kligler Iron test. In the Kligler Iron Agar infixion test, bacterial growth was evaluated after 24 h of incubation at 37 °C (time and temperature are those written in the technical data sheet of the media). Presumptive Pseudomonas spp. developed only on the surface because they are strictly aerobic bacteria. Furthermore, this bacterium does not ferment glucose or lactose but can split peptones, so the indicator turned purple (alkaline pH) on the surface. Finally, the counts were corrected proportionally to the results of the tests provided by the ISO mentioned above. Some of the colonies which proved positive for the 3 tests were identified by a unique alphanumeric code, purified on Brain Heart Infusion agar (Thermo Scientific™ Oxoid™), inoculated into Plate Count Agar tubes and stored in the refrigerator at 4 °C. Then, the isolated colonies (n = 55) were subjected to biochemical identification with the BIOLOG® system. Biolog’s carbon source utilization technology identifies microorganisms depending on their characteristic metabolic pattern produced as a consequence of some pre-selected test. The reading is performed by comparing the results to an extensive database. The analyses were performed following the recommendation of EUCAST 2022 for the method of execution [34]. Antimicrobial resistance was tested using the standard disc diffusion method. Suspensions of Pseudomonas spp. were cultivated on Mueller Hinton agar (Merck®) with modifications regarding incubation temperature for the psychrotrophy of the bacterium itself. Then, antimicrobial discs (Liofilchem®) were placed on the agar surface taking care to preserve as much sterility as possible. All the strains of Pseudomonas spp. were tested against 15 different commercial antimicrobial agents chosen among the most used and important in human medicine and aquaculture worldwide [21,35,36]; the concentrations were instead chosen on the basis of preliminary studies. The antimicrobials were ciprofloxacin (5 μg/disk), enrofloxacin (5 μg/disk), tetracycline (30 μg/disk), rifampicin (30 μg/disk), erythromycin (15 μg/disk), vancomycin (30 μg/disk), clindamycin (10 μg/disk), meropenem (10 μg/disk), trimethoprim (5 μg/disk), penicillin G (10 IU/disk), ampicillin (10 μg/disk), amoxicillin (30 μg/disk), flumequine (30 μg/disk), florfenicol (30 μg/disk) and sulfadiazine (300 μg/disk). Inoculated plates were incubated at 31 °C for 24 h. Then, the diameters were measured in millimeters with a caliper. For the interpretation of the results, neither EUCAST nor CLSI (Clinical and Laboratory Standards Institute) provides criteria and reference values for Pseudomonas spp., so, it was decided to consider as “highly resistant” those cases in which the diameter of the inhibition zone was ≤10 mm. The load of Pseudomonas spp. in the samples analyzed has been reported in Figure A1. Indeed, in the salmon fillets, in 19 samples out of 25 (76%), the initial loads were between 104 and 105 cfu/g, a rather significant load considering that these are fresh fish products. In the remaining samples, the charges of Pseudomonas spp. were between 103 and 104 cfu/g. In only one sample, 106 cfu/g has been detected. In the plaice fillets, 13 out of 25 (52%) of the samples analyzed showed a load of Pseudomonas spp. between 104 and 105 cfu/g, and the remaining 12 samples showed loads between 105 and 106 cfu/g since the beginning of their shelf life. According to Silbande and co-authors [37], Pseudomonas loads equal to or greater than 106 cfu/g may already be sufficient to compromise the organoleptic qualities of fresh fish products. Therefore, in the salmon and plaice fillets, the loads of Pseudomonas spp. were, on average, between 104 and 105 cfu/g; nevertheless, some of the samples presented an initial load between 105 and 106 cfu/g of the fillet. Higher loads in some fillets could be due to transport or to the processing environment and also to the moment of processing (beginning vs. end) of that specific product compared to the other products belonging to the same batch. In the defrosted northern cod fillets, the number of samples that contained 104–105 cfu/g was 14 out of 25 (56%), while the number of samples that showed amounts of Pseudomonas spp. between 105 and 106 cfu/g was 10. In only 1 sample of northern cod fillet, an exceptionally low load of Pseudomonas spp. (i.e., 102 cfu/g) was detected. In addition to the reasons described above for salmon and plaice, the remarkable loads of cod fillets could be also explained by the fact that cod fillets were a defrosted product, so the load of Pseudomonas spp. could have been affected by the specific pre-existing microbial population in the fish prior to freezing, although it is admitted that freezing may have inactivated part of this specific microbial population [38]. It is also interesting to note that loads of Pseudomonas spp. in plaice and cod fillets have progressively increased with the progress of the season and, therefore, going towards higher environmental temperatures. Indeed, the loads of Pseudomonas spp. were relatively low in the samples analyzed in March and April, while they increased in the April/May period [39]. However, this was not observed in salmon fillets that presented a rather “homogeneous” microbial distribution and, in any case, further and larger studies should be performed to confirm this hypothesis. Generally, the average for the counts of pseudomonads detected in all three species analyzed is around 104 cfu/g (see Table 1) with some occasional increases towards higher loads (105–106 cfu/g) rather than lower levels. Regarding the Total Viable Count (TVC) in the three species of fillets analyzed (see Figure A2), the values were in most cases comprised between 104 and 105 cfu/g or between 105 and 106 cfu/g. In 10 samples out of 75, the Total Viable Count was higher, between 106 and 107 cfu/g. The arithmetic mean values were around 105 cfu/g (see Table 1). These values lead to the conclusion that a large part of the microbiota found in the analyses was made up of Pseudomonadaceae with other minor microbial populations. This confirms that the loads of Pseudomonas spp. have an important influence on the shelf-life of the fish fillets considered. The ANOVA model using log-transformed Total Viable Count data showed a significant effect of the fish type (p < 0.001). Back transformed ls-means pointed out that the loads of Pseudomonas spp. in salmon fillets were significantly lower than those in cod and, respectively, in plaice fillets (1.07 × 105 vs. 2.57 × 105 vs. 2.56 × 105 cfu/g, p < 0.01, see Table 1). The effect of fish type was also significant for Pseudomonas spp. loads, with salmon being significantly lower than plaice (2.33 × 104 vs. 6.71 × 104 cfu/g, p = 0.028, Table 1) and cod being between the two above (4.67 × 104 cfu/g). The Kruskall–Wallis test showed a significant effect of fish type only for Pseudomonas spp. (p = 0.021) with the same results as the previous linear ANOVA model (Figure 1). During our study, from the analyzed fish fillets, we selected 55 strains of presumptive Pseudomonas spp. that have been biochemically identified with the BIOLOG® system. Overall, 67.27% (37 up to 55) of the strains were definitively confirmed as Pseudomonas spp., while the other genera were all Gram-negative bacteria, such as Kluyvera, Proteus, Citrobacter, Klebsiella, Serratia, Roseomonas and Providencia. This finding is very similar to the one of another recent study carried out on the salmon processing environment where, out of 222 presumptive Pseudomonas isolates, 68% were confirmed as Pseudomonas [40]. This means that the identification of species on a certain number of bacterial colonies isolated is necessary to establish a higher degree of precision in the loads recorded. The bias of this methodological approach is represented by the economic costs and waiting times to obtain the final results. Pseudomonas isolates originating from salmon, cod and plaice fillets are diverse with many species represented. Our isolated Pseudomonas species were P. fluorescens, P. fragi, P. lundensis, P. marginalis, P. syringae, P. taetrolens, P. chlororaphis, P. tolaasii and P. viridilivida. Indeed, 45.95% of the strains are represented by P. fluorescens, which largely dominates the Pseudomonadaceae population in the analyzed fillets, followed by P. fragi (21.62%), P. marginalis (10.81%), P. taetrolens (8.11%), P. lundensis, P. syringae, P. tolaasii, P. viridilivida and P. chlororaphis (2.70% each one). It should be noted that this study has some limitations, mainly the lack of molecular identification using 16S rRNA and some housekeeping genes. It is, however, true that the genus Pseudomonas is very large and includes several hundred unclassified strains. So, for this complex, genus sequencing of the 16S rRNA gene can often only identify the three main lineages (P. aeruginosa, P. pertucinogena and P. fluorescens) but cannot give information about the species [40]. These data confirm what has already been described in the literature, which indicates that among the major components of Pseudomonadaceae in fishery products, there are P. fluorescens and P. fragi [1,2,3]. It should be noted that P. aeruginosa was never found among the strains isolated, and this is in line with what is reported by other studies [4] which highlight a low prevalence of the P. aeruginosa species in foods, including in drinking water. As it is known, P. fluorescens and P. marginalis were able to produce fluorescent pigments on culture medium when illuminated with a Wood’s lamp light (Figure A3). Additionally, some strains of P. chlororaphis also produced fluorescent pigments. In order to ensure the best growing conditions, in our study we changed the temperature of incubation of the Mueller–Hinton plates as the tested isolates were psychrophiles and could not grow well at high temperatures. The assay for these was conducted at 31 °C for 24 h. It was decided to consider as “highly resistant” those cases in which the diameter of the inhibition zone was ≤10 mm as it was impossible to find guidelines that provided identification criteria. The reasons were that, on the one hand, the incubation temperature was different and, on the other hand, neither EUCAST nor CLSI give enough information about the Pseudomonas species. Resistance was found with a prevalence greater than 50% (see Figure A4) in the following cases: penicillin G (penicillins), ampicillin (penicillins), amoxicillin (penicillins), tetracycline (tetracyclines), erythromycin (macrolides), vancomycin (glycopeptides), clindamycin (lincosamides) and trimethoprim (diaminopyrimidines). It should be noted that, according to the WHO classification (2019) [36], macrolides and glycopeptides are CIAs (Critically Important Antimicrobials), while lincosamides and diaminopyrimidines are HIAs (Highly Important Antimicrobials). Unlike Kačániová and co-authors [31], who found resistance of all Pseudomonas strains to meropenem, in our study, only 16.22% of the isolates were resistant to this antibiotic. Instead, according to Fazeli and Momtaz [32], the bacterial strains showed a high level of resistance to penicillin (86.49%), tetracycline and erythromycin but with greater resistance to erythromycin (64.86%) rather than to tetracycline (54.05%). In our case, these two were surpassed (or equaled) by clindamycin (70.27%), vancomycin and trimethoprim (both 64.86%). In the literature, various resistance mechanisms are described that could explain some of the cases detected in our study, such as β-lactamases against penicillins, efflux pump against erythromycin and dihydrofolate reductase against trimethoprim [24,27,28,41]. Countless acquired resistance genes have also been discovered [28]; however, few studies on the possible mechanisms of the resistance owned by Pseudomonas spp. against clindamycin and vancomycin are reported in the scientific literature. In our research, we noticed that 76.47% of the strains of P. fluorescens were resistant to more than two antibiotic classes and so we considered them as multi-drug resistant (MDR) according to Shabana and co-authors’ definition of “MDR strains” [33]. Based on the data obtained and the statistical analyses mentioned above, we can conclude that, under the basic hygienic conditions of production established by the EC Regulations n.852/2004 and 853/2004, fresh fish fillets (salmon, plaice, cod) intended for large-scale retailers have a load of Pseudomonas spp. of about, on average, 104 cfu/g. In salmon fillets, the counts of Pseudomonas spp. recorded have sporadically reached even 105 and 106 cfu/g. In contrast, in plaice and cod fillets, the upward swing of Pseudomonas loads to between 105 and 106 cfu/g were more frequent. Moreover, in our study, we noted a significant effect of the fish type on the Pseudomonas population of the fillets. The loads of Pseudomonas spp. in salmon fillets were significantly lower than in plaice fillets (p = 0.028), while in cod fillets, they ranged between the two above mentioned species. Considering the Total Viable Count of around 105 cfu/g (mean value), we can also conclude that a large part of the microbial community found on the analyzed fish fillets was made up of Pseudomonadaceae with other minor microbial populations. The most frequently isolated Pseudomonas species was P. fluorescens. As Pseudomonas is the main SSO of fresh fish fillets, it is certainly worth keeping an eye on the colony-forming units per gram of fish muscle. In our opinion, fresh fish fillets should contain low loads of Pseudomonas spp. during production in order to avoid rapid degradation of the product during its shelf life. It would be advisable that in the fresh fish fillets, the initial Pseudomonas load should be lower than 104 or 105 cfu/g to not exceed the concretely spoiling loads of these bacteria. In general, in the literature [37,42], it is admitted that up to 106 cfu/g of SSO, the organoleptic quality is fine. Furthermore, considering the normal growth potential of Pseudomonas spp. in refrigerated fresh fish products, it is possible to act both on conservation techniques (for example vacuum packing) and on greater hygiene in the fish processing environment to limit their proliferation. It would be advisable that, at the refrigeration temperature, fresh fish fillets have a shelf life of no more than six days; five days would be even better. Moreover, our antimicrobial resistance study on the 37 isolated strains of Pseudomonas spp. showed that these bacteria have a high level of phenotypic resistance to various antimicrobial classes. More specifically, 8 out of 15 antimicrobials tested proved ineffective in more than 50% of the strains. These eight antimicrobials are penicillin G 10 IU, ampicillin 10 μg, amoxicillin 30 μg, tetracycline 30 μg, erythromycin 15 μg, vancomycin 30 μg, clindamycin 10 μg and trimethoprim 5 μg. This highlights that 76.47% of P. fluorescens strains were multi-drug resistant. In the literature, various resistance mechanisms are described (both intrinsic and acquired) such as low outer membrane permeability, β-lactamases synthesis and efflux pump systems. However, it is worth noting that few studies on the possible mechanisms of the resistance of Pseudomonas spp. against clindamycin and vancomycin are available. In order to deepen our research and obtain more relevant results, our recommendation is to complete this study with a genomic analysis by performing a whole genome sequencing of the isolated strains of Pseudomonas (WGS). Therefore, our results suggest that Pseudomonas spp. should be monitored as possible source of other resistance genes. It would be preferable to complete this not only on strains isolated from clinical specimens but also on the strains isolated from food samples.

PMC10000909

Ehsan Gharib,Parinaz Nasri Nasrabadi,Gilles A. Robichaud

Circular RNA Expression Signatures Provide Promising Diagnostic and Therapeutic Biomarkers for Chronic Lymphocytic Leukemia

01-03-2023

cancer,chronic lymphocytic leukemia,circular RNA,diagnosis,prognosis,drug sensitivity prediction

Simple Summary This study aimed to evaluate the potential of circular RNA (circRNA) expression profiles for the early detection of chronic lymphocytic leukemia (CLL) using bioinformatic algorithms on verified CLL patient datasets. We analyzed and validated the diagnostic performance of circRNAs as potential biomarkers in different CLL sample sets, which reveal better prognostic value than existing clinical risk scales for the prediction of 5-year overall survival. The identification of specific circRNAs from our biomarker panel are also involved in cancer-related pathways, which possess druggable targets for therapeutic interests. Our findings therefore suggest that circRNA signatures represent significant biomarkers for the early detection and surveillance of CLL in addition to providing pharmacogenomic value to personalized medicine. Abstract Chronic lymphocytic leukemia (CLL) is a known hematologic malignancy associated with a growing incidence and post-treatment relapse. Hence, finding a reliable diagnostic biomarker for CLL is crucial. Circular RNAs (circRNAs) represent a new class of RNA involved in many biological processes and diseases. This study aimed to define a circRNA-based panel for the early diagnosis of CLL. To this point, the list of the most deregulated circRNAs in CLL cell models was retrieved using bioinformatic algorithms and applied to the verified CLL patients’ online datasets as the training cohort (n = 100). The diagnostic performance of potential biomarkers represented in individual and discriminating panels, was then analyzed between CLL Binet stages and validated in individual sample sets I (n = 220) and II (n = 251). We also estimated the 5-year overall survival (OS), introduced the cancer-related signaling pathways regulated by the announced circRNAs, and provided a list of possible therapeutic compounds to control the CLL. These findings show that the detected circRNA biomarkers exhibit better predictive performance compared to current validated clinical risk scales, and are applicable for the early detection and treatment of CLL.

Circular RNA Expression Signatures Provide Promising Diagnostic and Therapeutic Biomarkers for Chronic Lymphocytic Leukemia This study aimed to evaluate the potential of circular RNA (circRNA) expression profiles for the early detection of chronic lymphocytic leukemia (CLL) using bioinformatic algorithms on verified CLL patient datasets. We analyzed and validated the diagnostic performance of circRNAs as potential biomarkers in different CLL sample sets, which reveal better prognostic value than existing clinical risk scales for the prediction of 5-year overall survival. The identification of specific circRNAs from our biomarker panel are also involved in cancer-related pathways, which possess druggable targets for therapeutic interests. Our findings therefore suggest that circRNA signatures represent significant biomarkers for the early detection and surveillance of CLL in addition to providing pharmacogenomic value to personalized medicine. Chronic lymphocytic leukemia (CLL) is a known hematologic malignancy associated with a growing incidence and post-treatment relapse. Hence, finding a reliable diagnostic biomarker for CLL is crucial. Circular RNAs (circRNAs) represent a new class of RNA involved in many biological processes and diseases. This study aimed to define a circRNA-based panel for the early diagnosis of CLL. To this point, the list of the most deregulated circRNAs in CLL cell models was retrieved using bioinformatic algorithms and applied to the verified CLL patients’ online datasets as the training cohort (n = 100). The diagnostic performance of potential biomarkers represented in individual and discriminating panels, was then analyzed between CLL Binet stages and validated in individual sample sets I (n = 220) and II (n = 251). We also estimated the 5-year overall survival (OS), introduced the cancer-related signaling pathways regulated by the announced circRNAs, and provided a list of possible therapeutic compounds to control the CLL. These findings show that the detected circRNA biomarkers exhibit better predictive performance compared to current validated clinical risk scales, and are applicable for the early detection and treatment of CLL. Chronic lymphocytic leukemia (CLL) is one the most frequent hematologic cancers in the western world, and is responsible for one-third of all adult leukemia cases [1]. The National Institutes of Health’s (NIH) official estimation for CLL in the United States is more than 20,000 new cases and 4000 deaths for 2022 [2]. At the molecular level, CLL is characterized by the clonal expansion of neoplastic CD5+, CD19+, and CD23+ B-cells [3], which can elicit a wide range of heterogeneous clinical features from indolent to highly aggressive disease manifestations in CLL patients [4]. Suspected individuals are diagnosed with CLL if they express >5 × 109/L mature lymphocytes co-expressing CD5, CD19, and CD23 [4], along with a potential mutation in the immunoglobulin heavy-chain variable-region (IGHV) [5] or in the ζ-associated protein 70 (ZAP-70) gene [6,7]. Validated clinical staging scales developed by Binet [8] and Rai [9] are the other prognosis assessment tools currently used for CLL diagnosis. However, due to the lack sufficient sensitivity and asymptomaticity in CLL patients, these techniques do not distinguish patients with early CLL from those in advanced stages of the disease [10], which is why the treatment process in patients with CLL is ineffective. Therefore, finding a fast and reliable diagnostic biomarker for CLL will bring significant benefits, and justifies further work in this area. Circular RNAs (circRNAs) are a class of noncoding RNAs with a covalently continuous loop structure. They are mainly generated by back-splicing or lariat introns of two or more exons or introns [11]. Features such as abundancy [12], stability [13], and conservation [14] have created great consideration for circRNAs in various human disorders such as immune responses [15], cancer malignancies [16], cardiovascular events [17], neurological deficits [18], and metabolic diseases [19]. Mechanistically, circRNAs participate in signaling cascades through the sponging of small RNAs such as microRNAs [20]; the regulation of protein production by sequestering RNA-binding proteins (RBPs) [21], and even templating for protein synthesis through their open reading frames (ORFs) [22]. These properties support the potential analytical validity of circRNAs as better biomarkers over the canonical linear forms of RNAs in human diseases. Over the past few years, clinical studies have highlighted the diagnostic performance of circRNAs in hematopoietic malignancies, including acute myeloid leukemia (AML) [23,24], acute lymphoblastic leukemia (ALL) [25,26], and chronic myeloid leukemia (CML) [27,28,29]. With regard to CLL, fewer attempts have been made to establish circRNA prognosis potency. So far, circRNAs circ-CBFB [30], mitochondrial genome-derived mc-COX2 [31], and plasma Circ-RPL15 [32] have shown good analytical value as biomarkers in CLL. Despite these efforts, a reliable biomarker capable of efficiently dissociate the early stages of CLL from fully developed cancer malignancies has yet to be identified. Considering the paucity of studies on dependable CLL biomarkers, we set out to find a signature gene circRNA profile capable of distinguishing early detection of CLL. Our data showed that the combination of circKAT6A, circLNPEP, circMDM2, and circMYH9 could successfully discern CLL cells from healthy B-lymphocytes and differentiate patients with early CLL from those in advanced stages. The sample size for the study was calculated using the chi-square test with a significance level of α = 0.05 and a power of β = 0.2 [33]. Results from the training cohort revealed that the proportion of periodontitis was 0.31 in normal B-cells and 0.6 in CLL samples. The ratio of cancer cases to healthy controls was set at 1:1.9. Additionally, the sample size for the validation set I was increased by 10% due to the presence of a confounding variable (circRNA panel). The study population consisted of >600 CLL lymphocytes and B-cell RNA-sequencing data collected from the Gene Expression Omnibus (GEO) dataset series (GSE): GSE151159, GSE92626, GSE66117, GSE111014, GSE119103, GSE66121, GSE11154, GSE95352, GSE161711, GSE109085, GSE113386, GSE70830, GSE100026, GSE66228, GSE66167, GSE216288, GSE192685, GSE198454, GSE197811, GSE196741, GSE165087, GSE176141, GSE130385, GSE162427, GSE136634, GSE123777, and GSE111015, along with EMBL’s European Bioinformatics Institute (EBI) biostudies E-MTAB-12124 and -1176. Details regarding the number, sex, and staging of each set are shown in Table 1. First, the adaptor sequences of the Illumina paired-end reads were trimmed prior to mapping by Cutadapt [34] and then aligned to the UCSC human reference genome (GRCh37/hg19) using Bowtie2 [35]. The coding-protein reads were detected based on criteria defined by Coding-Noncoding-Index v2.0 [36], the Calculator-2 (CPC2) tool [37], InterPro [38], and PhyloCSF [39]. The Ensembl BioMart web-tool v101.0 (https://www.ensembl.org/Biomart, accessed on 5 October 2022) was used to sort the coding-reads based on the type (kinases, transcription factors, and other proteins). To identify the circRNAs structures, 20mers from each end of the unmapped reads were extracted and aligned in an independent reversed mode (head-to-tail) to detect the back-spliced junction. Completed circular sequences were retrieved by extending the identified anchor alignments and flanking the GU/AG splice sites accordingly [40]. The circular reads were then annotated against the Circbase hg19 assembly reference file to identify the validated circRNAs [41]. CircRNA abundance was estimated by counting the total number of back-splices. The spliced reads per Billion Mapping (SRPBM) formula was used to assess the relative expression of reads [the total number of circular reads/(number of mapped reads × read length)]. As for the other genes, the expression level of reads was estimated by Cufflink software v2.2.0 and the Cuffdiff2 package 2.2.1 as reads per kilobase of transcript per million mapped reads (RPKM), indicating the total exon reads/mapped reads in millions × exon length in kb [42]. Differences were considered significant if the false discovery rate (FDR) and q-value (p-adjusted) < 0.05. The interaction between CircRNAs and target miRNAs, kinases, transcription factors (TFs), and other proteins were analyzed based on StarBase v2.0 (http://starbase.sysu.edu.cn/, accessed on 22 October 2022), iCircRBP-DHN [43], and the BisoGenet v3.0 [44] algorithms, and were illustrated on Cytoscape platform v3.9.1 [45]. Network topology was visualized by CentiScaPe plugin v2.2 [46]. Annotations were deemed to be as significant if p < 0.05. The response rate of the identified circRNA-related CLL signaling pathways to medicinal drugs and toxic chemicals was analyzed by the U.S. National Toxicology Program DrugMatrix (https://ntp.niehs.nih.gov/data/drugmatrix, accessed on 25 October 2022) and ToxicoDB (https://www.toxicodb.ca, accessed on 28 October 2022) databases. Estimations were assumed as statistically significant if p < 0.05. Statistical analyses were done by IBM SPSS Statistics software v26 (IBM, USA). Clinical variables among the studied cohorts were estimated using chi-square and unpaired unequal variance two-tailed Student’s t-tests. The Wilcoxon rank sum test was employed to assess the median differences of the genes. Spearman’s rank correlation coefficient analyzed the correlation between the differentially expressed genes and the clinicopathologic features of patients. Receiver operating characteristic (ROC) curves were plotted to determine the performance of circRNAs in diagnosing CLL. The calibration and discriminative abilities of the panel were calculated with the Hosmer–Lemeshow test and Harrell’s concordance index, respectively. The Kaplan–Meier survival curve was drawn to estimate patients’ 5-year overall survival (OS). We used univariate/multivariate Cox proportional hazards regression to detect the factors that independently affect the patients’ OS. All data are represented as the mean ± S.D. (Standard deviation) and taken as significant if p < 0.05 (*). The initial population study comprised 635 CLL patients’ data profiles and 39 healthy B-cells, including 349 men and 325 women. However, due to insufficient clinical data, 64 CLL samples were removed from the final study. The remaining cohort was then divided into three independent sets, including a training set (100 CLLs) and two validation sets containing 220 (set I) and 251 (set II) CLL samples, respectively. The healthy B-cell data profiles were considered normal controls for each set. Additional details are shown in Table 1. The identification of circRNAs in processed CLL RNA profiles was made in accordance with the reference file provided by the Circbase database. Using a Log2 ≥ 2 as criteria, 52 circRNAs were labeled as differentially expressed in CLL samples versus healthy B-lymphocytes, including circC5orf25, circCHPT1, circPDE4B, circDENND2D, circRHOC, circPSMB4, circSEMA4A, circTP53BP2, circEIF4G2, circSRPR, circRAPGEF3, circPFDN5, circNCKAP1L, circGCN1L1, circSFSWAP, circMETTL17, circCCNDBP1, circGNB5, circVPS4A, circCYB5B, circCDK10, circNLRP1, circTP53I13, circSTARD3, circTCF3, circPRKCSH, circKLF1, circSAMD1, circNDUFA13, circNUCB1, circDNTTIP1, circTH1L, circPRIC285, circZMAT5, circMYH9, circLNPEP, circCD164, circCHMP7, circZNF395, circVCP, circMDM2, circKTN1, circVASH1, circKAT8, circTCF4, circPRDM2, circRALGPS2, circENTPD6, circCAB39, circHPS3, circKAT6A, and circGBA2 (Figure 1A,B). The interactions between these circRNAs and corresponding genes, kinase enzymes, TFs, and miRNAs were then estimated and visualized by bioinformatic algorithms (Figure 1C). According to the data, four circular RNAs: circKAT6A, circLNPEP, circMDM2, and circMYH9, play a crucial role in CLL signaling cascades. To better understand their impact on cancer progression, the obtained genes list was subsequently submitted for pathway analysis. Gene ontology (GO) enrichment assessment of these genes showed that they are actively involved in biological process pathways (Figure 1D) such as mRNA processing (GO:0006397), protein phosphorylation (GO:0006468), the regulation of intracellular signal transduction (GO:1902531), and cellular response to DNA damage stimulus (GO:0006974). Meanwhile, another enrichment tool, WikiPathways (https://www.wikipathways.org, 10 December 2022 Release) highlighted (Figure 1E) the gene network impact in the mRNA processing pathway (WP411), EGF/EGFR signaling pathway (WP437), T-cell receptor (TCR) signaling pathway (WP69), and the VEGFA-VEGFR2 signaling pathway (WP3888). Per these findings, we made a list of medicinal drugs and toxic chemicals with a potency to suppress the identified signaling pathways (Figure 1F). Our model showed that compounds such as lomustine, galactosamine, bortezomib, N-nitrosomorpholine, and cycloheximide have an inhibitory impact on CLL progression and can be considered for therapeutic interventions. Details regarding the expression comparisons, pathways annotations, and drug sensitivity prediction are provided in Supplementary Materials S1–S4, respectively. Next, we tested the diagnostic performance of candidate circRNAs as individual biomarkers for dissociating CLL lymphocytes from healthy B-cells in the training set. According to the results, four circRNAs, including circKAT6A, circLNPEP, circMDM2, and circMYH9, had a good predictor value (AUC > 0.7) among the examined RNAs (Table 2). These circRNAs were thus considered for subsequent experiments. To develop the diagnostic circRNA-biomarker panel, we combined the individual circRNA biomarkers and determined a new biomarker risk score (BRS) with the regression model as previously reported [47,48,49]. To this point, the expression level of each circRNA was recalculated as a log2-transformed variable to lower the variation between each biomarker and the logistic regression coefficient generation. The BRS of each sample was then determined as the sum of each circRNA risk score yielded by multiplying the level of a circRNA by its corresponding coefficient (Risk score = ∑ logistic regression coefficient of circRNA × expression level of circRNA). The median BRS was then used as the cutoff point for dividing the high-risk samples from the low-risk group. The most optimal combination of the candidate circRNA biomarkers was identified by establishing a stepwise logistic regression coefficients model between CLLs and healthy B-lymphocytes in the training set. We then constructed the ROC curve using the log it model of 0.7262 + 0.7690 × CircKAT6A + 0.1786 × CircLNPEP − 0.2246 × CircMDM2 + 0.9938 × CircMYH9 (Table 3). Considering 0.7262 as the optimal cutoff point, the training set CLL samples were divided into a high-risk and a low-risk score group. The obtained results indicated a higher diagnostic accuracy of the combination of these circRNAs as a panel compared to their performance as individual biomarkers, along with a suitable adjustment of the model to the data (Hosmer–Lemeshow test, p = 0.13) (Figure 2). For all Binet stages (A–C), the area under the roc curve (AUC) of the circRNA-biomarker panel was 0.8763 (95% CI: 0.8361–0.9165, sensitivity: 75.42% and specificity: 84.81%, Figure 2A). Meanwhile, the analysis of the Binet stage A CLL samples showed an AUC of 0.8441 (95% CI: 0.7980–0.8902, sensitivity: 91.14% and specificity: 70.95%, Figure 2B). As for Binet stage B, the obtained AUC was 0.8868 (95% CI: 0.8485–0.9251), with a sensitivity of 86.08% and a specificity of 76.54% (p < 0.001, Figure 2C). Accordingly, the ROC curve analysis of CLL samples with Binet stage C yielded an AUC of 0.8810 (95% CI: 0.8406–0.9214) followed by a 94.94% sensitivity and 77.65% specificity (p < 0.001, Figure 2D). The diagnostic accuracy of the circRNA-biomarker panel was then examined in the validation set I and II, consisting of 220 and 251 CLL samples, respectively. The corresponding AUC for all Binet stages (A–C stages) in validation set I was 0.8421 (95% CI: 0.7926–0.8916; sensitivity: 93.62% and specificity: 73.18%, Figure 3A). The analyzing of CLL lymphocytes with Binet stage A resulted in an AUC of 0.8561 (95% CI: 0.8087–0.9034) with a sensitivity and specificity of 91.84% and 70.39%, respectively (p < 0.001, Figure 3B). The AUC of Binet stage B-CLLs was 0.8965 (95% CI: 0.8570–0.9359; sensitivity: 85.71% and specificity: 79.33%, Figure 3C). As for the CLLs with Binet stage C, the median AUC was higher than the other conditions (0.9124, 95% CI: 0.8765–0.9482), with a sensitivity of 89.80% and a specificity of 83.80% (p < 0.001, Figure 3D). ROC curve analysis of the proposed circRNA-biomarker panel in validation set II indicated a similar diagnostic efficacy in a larger sample population. The corresponding AUC for all CLLs (Binet A–C stages) was 0.8852 (95% CI: 0.8534–0.9171), with a sensitivity of 93.86% and a specificity of 73.33% (p < 0.001, Figure 4A). Accordingly, the diagnostic accuracy of the panel in Binet stage A samples was 0.8491 (95% CI: 0.8127–0.8855; sensitivity: 97.47% and specificity: 72.06%, Figure 4B), while CLLs with Binet stage B showed an AUC of 0.8737 (95% CI: 0.8403–0.9071; sensitivity: 88.61% and specificity: 77.14%, Figure 4C). Meanwhile, the analyzing of CLL data profiles with Binet stage C resulted in an overall AUC of 0.9255 (95% CI: 0.9009–0.9502) along with a sensitivity of 89.87% and a specificity of 83.18% (p < 0.001, Figure 4D). A Cox’s proportional hazards model determined the prognostic value of the circRNA-biomarker panel in CLL lymphocytes. To this point, the clinical features of patients, including gender, age, and Binet stage, were added to the model for a better conclusion (Table 4). According to the univariate data of clinical variables, Binet staging had good prognostic efficacy in validation sets I (HR: 2.829, p < 0.01) and II (HR: 3.492, p < 0.01), but not in the training set (HR: 2.119, p = 0.071). Other parameters did not show any statistically significant prognostic impact in the studied groups. We evaluated the prognostic performance of the circRNA-biomarker panel as unadjusted and in combination with the Binet stage parameter by multivariate analysis (Table 5). The univariate Cox regression model reported that the combination of circKAT6A, circLNPEP, circMDM2, and circMYH9 deregulations had a significant impact on OS in the training set (HR (95% CI): 6.083 (5.152–8.023), p < 0.001), validation set I (HR: 7. 517 (6.117–8.886), p <0.001), and validation set II (HR: 8.294 (7.503–9.167), p < 0.001). Multivariate Cox regression analyses adjusting for the circRNA-biomarker panel also resulted in a good prognostic value of 4.193 in the training set (95% CI: 3.122–5.243, p < 0.001), along with HR values of 5.432 (95% CI: 4.716–6.915) and 6.992 (5.9365–8.049) in validation set I and II, respectively (p < 0.001, Table 5). The correlation between the abnormal level of circKAT6A, circLNPEP, circMDM2, and circMYH9 RNAs and 5-year OS of CLL patients was determined using the Kaplan–Meier survival analysis. The median follow-up of patients in the studied cohorts was 58 months with an average of 83.8% (Training set), 85.1% (Validation set I), and 88.3% (Validation set II). The analysis of the target circRNAs in the training set showed an HR value of 0.25 (CircKAT6A, p = 0.563), 0.4286 (CircLNPEP, p = 0.2032), 0.4451 (CircMDM2, p = 0.5081), and 0.6667 (CircMYH9, p = 0.5246), indicating their poor impact on patients’ survival as individual biomarkers (Figure 5A–D). On the contrary, the OS analysis of candidate circRNAs in larger population cohorts proved their correlation with worse clinical outcomes of the CLL patients in such a way that abnormal levels of circKAT6A, circLNPEP, circMDM2, and circMYH9 resulted in an HR of 0.3158, 0.4444, 0.2381, and 0.3684, respectively, in validation set I (p < 0.05, Figure 6A–D). Similar observations were obtained in the validation set II, and patients with a high level of circKAT6A (HR: 0.3103), circLNPEP (HR: 0.3448), circMDM2 (HR: 0.3103), and circMYH9 (HR: 0.2903) had a worse OS than those with a low-expression group (p < 0.01, Figure 7A–D). On the other hand, the combination of these deregulations as a panel had a statistically significant impact on CLL patients’ OS and yielded an overall HR of 0.1111 (p < 0.05, training set, Figure 5E), 0.1304 (p < 0.001, validation set I, Figure 6E), and 0.1133 (p < 0.001, validation set II, Figure 7E). These data showed that the combination of circKAT6A, circLNPEP, circMDM2, and circMYH9 as a panel has higher prognostic accuracy than the other clinical parameters used in this study. Chronic lymphocytic leukemia (CLL) is the most common form of leukemia, and accounts for approximately 30% of all adult leukemias [50]. Pathologically, CLL is a B-cell malignancy that is characterized by the relentless accumulation of slow proliferating (or quiescent), immunologically dysfunctional, mature B-lymphocytes that fail to undergo apoptosis (reviewed in [51]). Although the first clinical description of CLL was published over 150 years ago, it is still considered an “enigma” of modern hematology, as complete remission for this disease remains elusive today [52]. The course of disease progression can also vary significantly amongst individuals. For instance, some CLL patients experience indolent disease that progresses very slowly over several years and does not require immediate treatment. On the other hand, others characterized with cytogenetic biomarkers associated with CLL manifest more aggressive progression combined with drug therapy resistance. CLL malignancy is clinically determined by prognostic factors determined by genotyping analyses. The latter genetic alterations are also used to determine the therapeutic plan of CLL patients, such as the FCR regimen (fludarabine, cyclophosphamide, rituximab) [53]. Despite the characterization of these CLL biomarkers, genotyping approaches have proven to be complex (ex: IGHv status), and CLL is still not associated with a specific cytogenetic defect [54]. Consequently, CLL remains uncurable due to disease recidivism caused by chemoresistance. The study of predictive biomarkers for CLL is thus pivotal for early diagnosis, monitoring, and to define the most appropriate therapeutic approach to improve disease outcome [51,53]. The purpose of this study was to determine circRNA expression profiles in CLL patients to establish specific signatures associated with CLL disease. In addition, we wanted to determine the potential role of characterized circRNAs in the onset of disease, as well as their relationship with other prognostic markers and patient clinical status. CircRNAs represent a new class of non-coding RNAs, which are formed through the back-splicing of linear mRNAs, which results in the binding of both extremities to form a continuous circular RNA molecule [11]. CircRNAs have thus garnered interest as biomarker candidates given that they are highly resistant to RNase activity (due to the lack of 5′ and 3′ ends), and they are often expressed in tissue- and developmental stage-specific manners [55,56]. Studies have also shown that the aberrant expression of circRNAs is associated with cancer processes leading to disease progression [57,58,59]. More importantly, circRNA signaling pathways and function have been linked to tumor drug chemoresistance, including leukemia [60,61,62]. In this study, we tested 571 online CLL datasets and found 52 circRNAs where their expression is significantly modulated in cancer lymphocytes when compared to normal B-cells. CircRNA expression levels are usually tightly regulated as they control a broad network of genes, miRNAs, kinases, and TFs through direct or indirect molecular interactions. By minimizing the obtained network, we revealed four circRNAs with high betweenness degrees that significantly impacted cancer-related signaling cascades. A more in-depth analysis revealed that their diagnostic and prognostic efficacies, individually and in the form of a panel, were higher than standard clinical parameters, and might be considered for the early detection of CLL. Amongst the relevant differentially expressed circRNAs in CLLs, four circRNAs, including circKAT6A, circLNPEP, circMDM2, and circMYH9, have notably received prior focus for their regulation of cancer pathways. For example, circKAT6A is a circRNA derived from the Lysine acetyltransferase-6A (KAT6A) gene coding sequence. The KAT6A gene itself encodes an MYST-type histone acetyltransferase (HAT) enzyme, which is essential for the maintenance of hematopoietic stem cells [63], controlling cell cycle progression [64], and inducing cell senescence [65]. Subsequently, the oncogenic role of KAT6A has been highlighted in leukemia [66,67,68], breast cancer [69], and glioma [70]. As for the circKAT6A, this isoform was recently detected as one of the most abundantly expressed circRNAs in the oral squamous cell carcinoma (OSCC) cells, and is involved in survival responses [71]. However, the exact involvement of circKAT6A in cancer development is still not well defined. Accordingly, our network model showed that circKAT6A could control the platelet-derived growth factor (PDGF) signaling pathway, cholecystokinin (CCKR) signaling, and B-cell activation in CLL. These findings indicated that circKAT6A upregulation in CLL lymphocytes directly associates with cancer initiation and development. Similar to circKAT6A, circLNPEP was also associated with CLL progression. At the protein level, the Leucyl and cystinyl aminopeptidase (LNPEP) gene encodes a zinc-dependent aminopeptidase that facilitates the antigen transportation and processing [72] involved in inflammatory responses in cardiovascular complications and diabetes mellitus [73,74]. In cancer, LNPEP deregulation has been shown to contribute to immune infiltration of ovarian cancer [75]. In the circular RNA form, LNPEP was reported to enhance Ras-related protein Rab-9A expression levels by sponging miR-532–3p under hypoxia and promoting invasiveness in hepatocellular carcinoma cells [76]. The role of circLNPEP in ovarian cancer development was recently reported by Wang et al. (2021), where the authors claim that circLNPEP sponging of miR-876-3p diminished its inhibitory impact on WNT5A, which results in cancer cell growth and survival [77]. In line with these findings, our data demonstrated that circLNPEP could sponge the tumor suppressor miRNAs miR-15a-5p, miR-19a-3p, miR-135a-5p, and miR-138-5p, which are involved in specific activation pathways supporting protein acetylation, RNA polymerase II-dependent transcription, and the regulation of intracellular signal transduction in CLL cells, respectively. Our findings thus introduce circLNPEP as a key player in CLL growth and progression. The next circRNA in our network with a high impact on CLL was circMDM2, a circular RNA from the mouse double minute 2 homolog (MDM2) locus [78]. The MDM2 gene encodes a cellular phosphoprotein that acts as a negative regulator of the p53 tumor suppressor, and is upregulated in many human cancer malignancies [79]. As with its protein counterpart, the circular form of MDM2 is involved in cancer development by decreasing p53 and p21 levels [79]. More interestingly, it has been shown that elevated levels of circMDM2 in response to DNA damage in colorectal cancer (CRC) cell lines lowered MDM2 protein production, suggesting that this circular isoform is a derivative of the original pre-mRNA [79]. Nevertheless, the oncogenic impact of circMDM2 is not limited to p53 inhibition. A study by Zhang et al. (2020) on OSCC showed that the circular isoform of MDM2 could provoke proliferation and glycolysis in cancer cells through the sponging of the miR-532-3p, thus impacting downstream hexokinase 2 (HK2) levels [80]. The authors also showed that patients with a higher level of circMDM2 had poorer survival than those expressing low circMDM2 levels. Following these observations, the current investigation has identified a wide range of signaling mediators, including Nuclear factor I X (NFIX), Syntrophin Beta-2 (SNTB2), and RAF1, which interact with the circMDM2 directly or indirectly through miR-137, the miR-193 family, or miR-7, respectively. These interactions enable circMDM2 to actively manipulate the cell cycle, apoptosis, and PDGF signaling pathways in CLL cells, and to subsequently boost cancer development. The final member of our list is circMYH9, which derives from an intron of the Myosin Heavy Chain-9 (MYH9) transcripts sequence [81]. The MYH9 gene is a well-known oncogene that is directly associated with progression, invasiveness, and drug resistance in many human cancers [82]. Accordingly, the MYH9 circular product has recently been reported to play a similar role in cancer cells’ fate by increasing the mRNA stability of Karyopherin α2 (KPNA2), another known oncogene in hepatocellular carcinoma [83]. CircMYH9 also promotes CRC development by degrading p53 pre-mRNA and altering cell metabolism and redox homeostasis [81]. More interestingly, the enhanced level of circMYH9 in CRC cells has been shown to increase hepatoma-derived growth factor (HDGF) mRNA stability by sponging the tumor suppressing miRNA miR-761, leading to increased cancer cells survival against the anticancer compound Baicalin [84]. In corroboration, our analysis expands on the underlining gene network of circMDM2 to reveal a possible impact on angiogenesis, B-cell activation, and the EGF receptor signaling pathway through the interaction with NCK2, Casitas B-lineage lymphoma (CBL), and the Signal transducing adaptor molecule-2 (STAM2) oncogene. These findings demonstrate a direct correlation between the aberrant expression of circMYH9 and CLL cancer progression. A common challenge following the detection of the deregulated signaling pathways in cancer cells such as CLL is how to utilize these data for the development of strategic therapeutic and/or diagnostic interventions. To shed insight into the potential application of our list, we analyzed the yielded gene networks with different algorithms and made a list of potential drugs, such as lomustine, galactosamine, and bortezomib, that could target essential pathways identified in our gene networks. In fact, a comprehensive literature survey showed that these drugs had previously been applied for hematopoietic malignancies [85,86]. For example, Pigneux et al. (2010) used the alkylating agent lomustine as a therapeutic intervention for elderly patients with de novo AML. The authors reported that combining lomustine with idarubicin and cytarabine as standard therapy improved patients’ complete remission (CR) and survival [85]. Similarly, a regimen consisting of lomustine, mitoxantrone, and vinblastine was shown to be effective in patients with Hodgkin’s disease without confronting the adverse side effects and toxicity associated to compounds like bleomycin [86]. Despite its potency, lomustine has not been tested in human CLL subjects. Still, promising evidence has been published regarding the efficacy of lomustine combined with vincristine, procarbazine, and prednisolone in animal-harbored T-CLL [87,88,89], suggesting lomustine as a reliable chemotherapeutic agent for the control of hematological cancer growth. In addition, our drug sensitivity model based on circRNA interaction networks predicted over 900 medicinal drugs and toxic chemicals that could be used for therapeutic interventions in CLL. Alternatively, our data could also support the pharmacogenetic evaluation of patients to develop a more personalized therapeutic approach and benefit the outcome of CLL patients. Given the fact that CLL diagnosis uses a combination of criteria, the disease may encompass multiple related conditions [52,90]. This heterogeneity is reflected by the variability in CLL biology in terms of the location of the disease, potential cell of origin, CLL-related symptoms, rate of progression, response to treatments, and overall survival time from diagnosis [91]. The clinical presentation of CLL biomarkers and their use are therefore our most effective and promising tools for disease outcome [92,93]. A limitation of current biomarker tests is that they produce continuous rather than binary results, meaning that marker levels do not correspond to a precise or predicted clinical outcome for the patient. For example, while ZAP-70 is commonly used as a biomarker for CLL diagnosis [7], the expression levels of this kinase can range from 0 to 100% in CLL cells from an individual patient. Therefore, elevated ZAP-70 levels would at best only partially correlate with CLL progression risk [94]. Another challenge in CLL biomarker profiling is that biosignatures and the reliability of their predictive value are constantly influenced by various biological characteristics such as microenvironment interactions, bioenergetic constraints, risk factors (behavioral, demographic, and environmental), and levels of other biomarkers [94]. Therefore, features like stability, abundance, and tissue specificity better qualify circRNAs as worthy biomarkers for cancer prognosis [23,24,25,26,27,28,29]. Cancer bioinformatics play a crucial role in identifying and validating biomarkers related to early diagnosis, disease progression, therapy response, and quality of life. It involves the study of biomarkers such as genes, proteins, peptides, and chemical and physical variables in cancer, from single to multiple markers, from expression to function, and from network to dynamic network. Network biomarkers (which are based on protein-protein interactions) are being investigated by integrating protein annotations, interactions, and signaling pathways. Dynamic network biomarkers (which can be monitored and evaluated at various stages of disease development) are expected to be linked to clinical informatics, including patient symptoms, history, therapies, clinical exams, biochemistry, imaging, pathology, and other measurements [95]. Systems clinical medicine is seen as a new approach to developing cancer biomarkers. This approach integrates systems biology, clinical phenotypes, high-throughput technologies, bioinformatics, and computational science to improve disease diagnosis, treatment, and prognosis. For a cancer biomarker to be effective, it should possess properties such as a network, dynamics, interaction, and specificity to disease. Understanding the relationship between clinical informatics and bioinformatics is the key to developing new diagnostics and treatments. This approach has been applied to other diseases, such as acute renal transplant rejection and lung disease [96,97]. In essence, human samples from clinical studies are collected and analyzed with a complete profile of clinical informatics. Gene and/or protein profiles are then analyzed, and dynamic networks and interactions between genes and/or proteins are determined through bioinformatics and systems biology. The correlation between disease-specific and dynamic networks of genes/proteins with clinical phenotypes is achieved through computational analysis in order to validate and optimize disease-specific biomarkers. However, various challenges still persist in the implementation of systems clinical medicine, including the optimal translation of clinical descriptions into clinical informatics, the bioinformatics analysis that takes into consideration the disease severity, duration, location, and sensitivity to therapies, as well as the integration of clinical and high-throughput data for accurate conclusions. Additionally, determining the variations and significance between molecular networks, between molecular networks and clinical phenotypes, and between gene/protein interactions and expressions is also a challenge. Therefore, incorporating protein network and interaction data can enhance the interpretation of gene signatures, as demonstrated before by the efficacy of R-weighted Recursive Feature Elimination and average pathway expression in stratifying breast cancer patients [98]. Some limitations should be acknowledged when interpreting the results of this study. The main limitation arises from the lack of a mutual clinical parameter list. Although all of the samples studied had been classified based on their Binet stages, CLL diagnosis benefits from new prognostic markers such as IGHv variabilty or del(17p)/TP53 mutational status in CLL patients. For instance, an unmutated status of IGHV is determined when the immunoglobulin heavy-chain sequence of the CLL has less than a 2% difference in base pair sequences compared to a reference germline sequence [99]. This status is linked to a poorer prognosis and is present in approximately 40% of CLL cases upon diagnosis [54]. Conversely, mutated IGHv occurs when the CLL sequence has a difference of 2% or greater from the germline heavy-chain sequence, and is associated with a much better clinical prognosis [54,99]. The difference in overall survival between these two categories is significant. Due to its apparent association with the median survival of CLL patients, IGHv is now being used to assist clinicians with treatment decisions and to identify individuals who may benefit from modern therapies, such as ibrutinib, a BTK inhibitor [100]. Therefore, it would be important and interesting if we could determine the correlation between our circRNA biomarkers and these new prognostic markers to better understand the mechanisms involved in the pathogenetic process of CLL. Altogether, our study assessed the diagnostic and prognostic value of circRNAs in CLL samples, and found that a panel of circKAT6A, circLNPEP, circMDM2, and circMYH9 has the potency to differentiate the B-CLLs from healthy lymphocytes and could potentially discriminate between patients with early benign cases from those with advanced stages of disease. Specifically, we showed that CLL cases with an increased level of circKAT6A, circLNPEP, circMDM2, and circMYH9 reveal lower overall survival rates. To our knowledge, this study is also the first to report the prognostic impact of circKAT6A and circMYH9 in human cancers. Further studies are thus warranted to elucidate the role of circKAT6A, circLNPEP, circMDM2, and circMYH9 in CLL disease to further establish their reliability as prominent biomarkers for prognostics, therapeutic planning, and the monitoring of disease progression.

PMC10000922

Eishin Morita,Hiroaki Matsuo,Kunie Kohno,Tomoharu Yokooji,Hiroyuki Yano,Takashi Endo

A Narrative Mini Review on Current Status of Hypoallergenic Wheat Development for IgE-Mediated Wheat Allergy, Wheat-Dependent Exercise-Induced Anaphylaxis

23-02-2023

deamidation,enzymic degradation,hypoallergenic wheat,ω5-gliadin,thioredoxin,wheat-dependent exercise-induced anaphylaxis

Immunoglobulin E (IgE)-mediated food allergies to wheat that develop after school age typically shows a type of wheat-dependent exercise-induced anaphylaxis (WDEIA). At present, avoidance of wheat products or postprandial rest after ingesting wheat is recommended for patients with WDEIA, depending on the severity of the allergy symptoms. ω5-Gliadin has been identified as the major allergen in WDEIA. In addition, α/β-, γ-, and ω1,2-gliadins, high and low molecular weight-glutenins, and a few water-soluble wheat proteins have been identified as IgE-binding allergens in a small proportion of patients with IgE-mediated wheat allergies. A variety of approaches have been manufactured to develop hypoallergenic wheat products that can be consumed by patients with IgE-mediated wheat allergies. In order to analyze such approaches, and to contribute to the further improvement, this study outlined the current status of these hypoallergenic wheat productions, including wheat lines with a reduced allergenicity that are mostly constructed for the patients sensitized to ω5-gliadin, hypoallergenic wheat by enzymic degradation/ion exchanger deamidation, and hypoallergenic wheat by thioredoxin treatment. The wheat products obtained by these approaches significantly reduced the reactivity of Serum IgE in wheat-allergic patients. However, either these were not effective on some populations of the patients, or low-level IgE-reactivity to some allergens of the products was observed in the patients. These results highlight some of the difficulties faced in creating hypoallergenic wheat products or hypoallergenic wheat lines through either traditional breeding or biotechnology approaches in developing hypoallergenic wheat completely safe for all the patients allergic to wheat.

A Narrative Mini Review on Current Status of Hypoallergenic Wheat Development for IgE-Mediated Wheat Allergy, Wheat-Dependent Exercise-Induced Anaphylaxis Immunoglobulin E (IgE)-mediated food allergies to wheat that develop after school age typically shows a type of wheat-dependent exercise-induced anaphylaxis (WDEIA). At present, avoidance of wheat products or postprandial rest after ingesting wheat is recommended for patients with WDEIA, depending on the severity of the allergy symptoms. ω5-Gliadin has been identified as the major allergen in WDEIA. In addition, α/β-, γ-, and ω1,2-gliadins, high and low molecular weight-glutenins, and a few water-soluble wheat proteins have been identified as IgE-binding allergens in a small proportion of patients with IgE-mediated wheat allergies. A variety of approaches have been manufactured to develop hypoallergenic wheat products that can be consumed by patients with IgE-mediated wheat allergies. In order to analyze such approaches, and to contribute to the further improvement, this study outlined the current status of these hypoallergenic wheat productions, including wheat lines with a reduced allergenicity that are mostly constructed for the patients sensitized to ω5-gliadin, hypoallergenic wheat by enzymic degradation/ion exchanger deamidation, and hypoallergenic wheat by thioredoxin treatment. The wheat products obtained by these approaches significantly reduced the reactivity of Serum IgE in wheat-allergic patients. However, either these were not effective on some populations of the patients, or low-level IgE-reactivity to some allergens of the products was observed in the patients. These results highlight some of the difficulties faced in creating hypoallergenic wheat products or hypoallergenic wheat lines through either traditional breeding or biotechnology approaches in developing hypoallergenic wheat completely safe for all the patients allergic to wheat. Considering the immune response to wheat, wheat allergies can be divided into immunoglobulin E (IgE)- and non-IgE-mediated reactions [1]. The pathophysiology of the IgE-mediated wheat allergy involves the production of specific IgE to wheat allergens and the subsequent activation of mast cells and basophils by cross-linking IgE with wheat allergens. In contrast, a non-IgE-mediated reaction is an esophageal or gastrointestinal inflammation caused by T lymphocytes and eosinophils activated in response to wheat allergens. However, the precise mechanism underlying the reaction is unclear. The IgE-mediated wheat allergy is caused either by ingestion of wheat (food allergy) or inhalation of wheat (airway allergy called Baker’s asthma). The clinical features of IgE-mediated food allergies due to wheat are characterized by the age of the patient during the onset of the allergy. In infancy, it develops mainly in association with atopic dermatitis (AD), whereas after school age, it can affect individuals without any history of AD [2]. The IgE-mediated food allergies due to wheat that appear during childhood generally develop resistance at a high rate [2]. According to a study at the Johns Hopkins Pediatric Allergy Clinic, 65% of the children become resistant to wheat allergies up to the age of 12 years [3]. In contrast, IgE-mediated wheat allergies that develop after school age typically shows a type of wheat-dependent exercise-induced anaphylaxis (WDEIA), a life-threatening type of IgE-mediated wheat allergy. WDEIA causes allergic symptoms through a combination of secondary factors, such as exercise, drugs, alcohol, and stress, in addition to wheat ingestion, and often causes anaphylactic shock, whereas patients with WDEIA usually ingest wheat products safely without such co-factors [4]. The roles of these co-factors have been hypothesized to increase gastrointestinal permeability, tissue transglutaminase activation in the gut mucosa, blood flow redistribution, plasma osmolarity causing basophil histamine release, and acidosis causing mast cell degranulation in the tissues [5]. In order to clarify the pathophysiology of WDEIA, wheat allergens involved in the sensitization of WDEIA have been intensively investigated. The biochemical analyses indicated that ω5-gliadin is the major allergen among wheat gluten proteins, and an allergen-specific IgE test using recombinant ω5-gliadin identified the patients with WDEIA with a high sensitivity and specificity [6]. A natural history observational study reported that sensitization to wheat allergens in WDEIA continues for a long period once it develops [7]. Recent studies by Gupta et al. estimated that the prevalence of IgE-mediated wheat allergies was 0.8% in adults in the US [8]. According to an epidemiological study of local adult residents in Shimane Prefecture, Japan, the prevalence of this disease was 0.21% [9]. Allergen immunotherapy has been used for the IgE-mediated food allergies and can increase the threshold of reactivity to a variety of foods [10]. Wheat oral immunotherapy can be an effective and safe treatment modality for the children with a history of wheat anaphylaxis [11]. However, no immunotherapy or prophylaxis has been established for WDEIA. Avoiding wheat or postprandial rest is currently recommended, depending on the severity of the allergy symptoms, for patients with WDEIA [12]. Wheat is used in a variety of market foods due to its high processing characteristics; therefore, there is still a risk of life-threatening anaphylaxis due to accidental exposure, and the burden on the patients and their families is high. A variety of approaches have been made to develop wheat products that can be consumed by patients with IgE-mediated wheat allergies. However, hypoallergenic wheat products to meet the patient’s needs have not been supplied yet. In order to analyze such approaches, and to contribute to the further improvement, this study outlined the current status of the hypoallergenic wheat developed for IgE-mediated wheat allergies typically showing a type of WDEIA. According to their chemical properties, wheat proteins are classified as water-soluble albumin, salt-soluble globulins, aqueous alcohol-soluble gliadins, and diluted acid- or alkali-soluble glutenins [13,14]. Gliadins are further classified as α/β-, γ-, and ω-gliadins based on electrophoretic mobility, whereas glutenins are classified as high-molecular weight (HMW) (67,000–88,000 Da) and low-molecular weight (LMW) (32,000–35,000 Da) subunits. Further, based on the N-terminal amino acid sequences, ω-gliadins are classified as ω1,2-gliadins (sequences beginning with ARE/KELQS) and ω5-gliadin (sequences beginning with SRLL). To date, a variety of allergens and their IgE-binding epitopes have been identified according to the type of wheat allergy [15]. Table 1 contains a list of the wheat allergens relevant to WDEIA. ω5-Gliadin and HMW-glutenin have been identified as major allergens associated with WDEIA [16,17,18,19,20,21]. cDNA cloning of ω5-gliadin revealed that the major IgE-binding sites (epitopes) of ω5-gliadin were QQX1PX2QQ (X1 is L, F, S or I and X2 is Q, E or G) [19,20,21,22]. The major IgE epitopes of HMW-glutenin are QQPGQ, QQPGQGQQ, and QQSGQGQ [19,20]. The evaluation of sensitization rates using ω5-gliadin- and HMW-glutenin-specific IgE tests showed that more than 80% of patients with WDEIA were sensitized with ω5-gliadin, and approximately 10% of patients were sensitized with HMW-glutenin [23]. Since the sensitization rate to ω5-gliadin accounts for more than 90% of adult patients with WDEIA, WDEIA has been referred to as the “ω5-gliadin allergy” [24]. α/β-Gliadin, γ-gliadin, and ω1,2-gliadin have also been identified as allergens in patients with wheat allergy, including children with AD [15,22]. The LMW glutenin subunit was reported to be reactive with IgE in the adult patients with WDEIA, carrying specific IgE epitopes independent of ω5-gliadin epitopes [25]. Owing to their close phylogenetic relationship, secalins of rye and hordeins of barley share closely related amino acid sequences with wheat gluten proteins [14]. HMW-glutenins, HMW-secalins, and D-hordeins are classified into the HMW group, ω1,2-gliadin, ω5-gliadin, ω-secalins, and C-hordeins are classified into the medium MW group, and LMW-glutenins, α-gliadins, γ-gliadins, γ-75k-secalins, γ-40k-secalins, B-hordeins, and γ-hordeins are classified into the LMW groups [28]. Using an enzyme-linked immunosorbent assay (ELISA) with monoclonal antibodies against gluten, cross-reactivity among gluten proteins, secalins, and hordeins was established [28]. The cross-reactivity of γ-70 (γ-75k) and γ-35 (γ-40k) secalins in rye and γ-3 hordein in barley with ω5-gliadin was shown by ELISA using sera from patients with WDEIA and a skin prick test in these patients [29], and the existence of IgE binding epitopes of ω5-gliadin were shown in the γ-75k and γ-35 (γ-40k) secalins and γ-3 hordein [30]. An outbreak of wheat allergy (mostly WDEIA) caused by hydrolyzed wheat protein (HWP) occurred in Japan from 2008 to 2010 [31,32,33]. This was caused by cutaneous sensitization during the use of soap bars containing HWP. IgE against HWP cross-reacts with orally ingested wheat products. These hydrolyzed wheat allergies differ from conventional WDEIA with respect to the negative or low levels of ω5-gliadin-specific IgE. Yokooji et al. used recombinant allergens of wheat constituent proteins to study wheat proteins recognized by IgE in the serum of patients with hydrolyzed wheat allergies. They found that γ-gliadin was the major allergen and its major epitope was QPQQPFPQ [26]. This epitope is consistent with the IgE epitope QPEEPFPE of the hydrolysates of γ-gliadin and ω2-gliadin identified in patients with hydrolyzed wheat allergies in Europe [34]. Recently, wheat peroxidase-1 and β-glucosidase have been identified as specific IgE-binding allergens that cross-react with grass pollen allergens in the patients with WDEIA who developed a grass pollen allergy [27,35]. In order to develop wheat products that can be consumed by patients with IgE-mediated wheat allergies, especially WDEIA, a variety of approaches were developed to remove the major wheat allergens described above. These are wheat lines with a reduced allergenicity that are mostly constructed for the patients sensitized to ω5-gliadin, hypoallergenic wheat by enzymic degradation/ion exchanger deamidation, and hypoallergenic wheat by thioredoxin treatment. Wheat lines with a reduced allergenicity were established using either natural occurring wheat deletion lines of allergen genomes or transgenic wheat lines with RNA interference technique. Hypoallergenic wheat were produced by either epitope degradation with specific enzymes after identifying their epitopes using serum IgE of the patients with wheat allergy complicated with AD, or deaminating amino groups of glutamine and/or asparagine within the epitopes by treating with cation exchange resin. A subsequent attempt was made to dissociate disulfide bonds in gluten proteins, such as gliadin and glutenin, to reduce allergenicity using reducing agent thioredoxin. These methods to develop hypoallergenic wheat, and their outcomes, are summarized in Table 2. The major allergen in WDEIA is ω5-gliadin, which accounts for only a minor proportion of gluten. Among several ω-gliadins, ω1,2-gliadins are encoded by Gli-A1 and Gli-D1loci on the short arms of the Group 1 chromosome, whereas ω5-gliadin is encoded on the Gli-B1 locus on chromosome 1B [65,66]. Denery-Papini, et al. investigated 13 wheat cultivars with genetic variability at the Gli-B1 locus for reactivity to rabbit ω5-gliadin-specific antiserum and IgE from 10 patients, including those with WDEIA. They found that 1BL/1RS translocated wheat, in which a part of the short arm of the 1B chromosome was replaced with a portion of the short arm of the 1R chromosome of rye, lost reactivity to ω5-gliadin when tested using rabbit ω5-gliadin-specific antiserum [36]. In addition, the reactivity of Serum IgE from the patients against its gliadin preparation was mostly lost, except for one patient with anaphylaxis, who had IgE reacting with a 44 kDa band corresponding to 1RS-encoded ω-secalin, indicating that this line may be beneficial for patients with WDEIA. Gabler et al. compared the allergenicity of gluten prepared from another wheat/rye translocation line, Pamier (ω5-gliadin content; 2.40 mg/g protein), and gluten prepared from a conventional wheat line (22.3 mg/g protein) using the CD63-monitored basophil activation test in 12 patients with WDEIA. However, no significant difference was observed in the basophil activation between these two gluten preparations [37,38]. These findings suggest that non-gluten proteins, possibly ω-secalin derived from 1RS, are relevant because of their cross-reactivity with ω5-gliadin. Wheat lines carrying this translocation generally had poor bread-making qualities [67]. Lombardo et al. examined the allergenicity of Triticum monococcus, an earliest cultivated A genome diploid einkorn, in 14 patients with WDEIA sensitized with ω5-gliadin. A skin prick test using soluble and insoluble extracts failed to induce reactivity in almost all the patients tested. IgE-immunoblotting showed an absence of ω5-gliadin in the proteins of Triticum monococcus, although a variety of reactions were observed with limited cross-reactivity to ω5-gliadin [39]. As some einkorn accessions possess good bread-making characteristics, Triticum monococcus might be a potential candidate for the production of hypoallergenic bakery products in patients sensitized to ω5-gliadin [68]. Using a traditional breeding method, Waga, et al. established a winter wheat line possessing hybrid genotypes lacking all ω-gliadin coding loci (Gli A1, Gli B1 and Gli D1) and investigated the reactivity of Serum IgE in several patients with wheat allergies (including asthma, rhinitis, urticaria/angioedema, anaphylaxis, and AD). Although IgE binding to 60 kDa ω5-gliadin was lost in the hybrid line only in one patient with possible WDEIA, the hybrid line was immune-reactive against patients’ IgE with many gluten proteins, including α/β-, γ-gliadins, LMW-glutenin, and non-gluten proteins [40,41]. Kohno et al. found that one of the deletion lines of Chinese Spring wheat (1BS-18), in which the end of the short arm of chromosome 1B was deleted, lacked the Gli B1 locus. They confirmed the lack of ω5-gliadin protein in this line using immunoblotting with rabbit polyclonal antibodies against the ω5-gliadin epitope peptide, as well as a reversed phase-high performance liquid chromatography. They also confirmed the low allergenicity of 1BS-18 in a guinea pig challenge model [42]. Furthermore, this line (an experimental line unsuitable for practical use) was backcrossed with the Japanese practical wheat cultivar Hokushin, and the ω5-gliadin-deficient wheat line (1BS-18 Hokushin) was established. Yokooji et al. found that the ω5-gliadin content of 1BS-18 Hokushin was 1.21 mg/g gluten, and this is much lower than that of euploid Hokushin (5.17 mg/g gluten) and another Japanese wheat cultivar Norin 61 (5.65 mg/g gluten); ELISA with rabbit polyclonal antibodies specifically recognizing the IgE-binding epitope sequences (KQQSPEQQQFPQQQIPQQQ) of ω5-gliadin was used for the assessment. They speculated that the slight detection of ω5-gliadin in the 1BS-Hokushin was due to cross-reactivity with gliadin components other than ω5-gliadin [43]. Yamada et al. evaluated the allergenicity of 1BS-18 Hokushin using a rat wheat-anaphylaxis model and found that gluten proteins of 1BS-18 Hokushin elicited no allergic reaction in ω5-gliadin-sensitized rats and had less sensitization ability to ω5-gliadin than those of euploid Hokushin wheat [44]. In addition, they found that early consecutive ingestion of 1BS-18 Hokushin prevents subcutaneous immunization against ω5-gliadin protein using the rat wheat-anaphylaxis model, suggesting that 1BS-18 Hokushin induces oral tolerance to wheat allergens [45]. The hypoallergenicity of 1BS-18 Hokushin and another wheat line, 1BS-18 Minaminokaori, was investigated using immunoblotting, and the lack of ω5-gliadin was determined using Serum IgE, which was obtained from patients with WDEIA as well as rabbit polyclonal antibodies against ω5-gliadin epitope peptide. The evaluation for allergenicity showed faint or no reaction bands corresponding to ω5-gliadin (Figure 1). Although the safety of 1BS-18 wheat products for the patients with WDEIA should be confirmed by clinical studies, the introduction of wheat lacking ω5-gliadin into wheat products could reduce the chance of exposure of consumers to ω5-gliadin and, hence, the population of patients with WDEIA. Lee et al. investigated the allergenicity of wheat mutant line DH20, with a defect in the chromosome B Glu-B3 and Gli-B1 loci having selective deletions in ω5-gliadin, as well as some LMW glutenins and γ-gliadins using IgE-immunoblotting with sera from patients with WDEIA, and found that the gliadin and glutenin fractions of DH2 had less binding of IgE from the patients compared with that of the wild type wheat line [46,47]. In addition, the ELISA inhibition assay showed that 50% inhibitory concentrations of DH2 fractions against gliadin- or glutenin-IgE reactivity were approximately 4-fold higher than those of wild-type wheat line. Notably, two-dimensional immunoblot analysis revealed the existence of several minor but highly immunogenic ω5-gliadin proteins in a wheat mutant line, DH20, with a deletion of 5.8 Mb, including the Gli-B1 locus of Chromosome 1B [48]. These proteins were found to be encoded in the ω5-gliadin gene of Chromosome 1D and to have a high homology, including repetitive IgE binding epitopes to the ω5-gliadin of the Gli-B1 locus, although they have a TRQ N-terminal amino acid sequence and altered C-terminal amino acid sequence compared with the Gli-B1 ω5-gliadin [49]. It is not known whether other hexaploid wheat cultivars contain such active ω5-gliadin genes on the 1D chromosome. These findings indicate the importance of a detailed understanding of the gluten protein genes in individual cultivars for exploring hypoallergenic wheat. Altenbach et al. showed that the ω5-gliadin protein was either undetectable or depleted in two transgenic wheat lines with reduced levels of ω5-gliadin using the RNA interference technique [50,51]. IgE-immunoblotting using the products of these transgenic wheat lines revealed that the reactivity to ω5-gliadin of the serum IgE was greatly reduced in seven of 11 patients with WDEIA [52]. These findings indicate that the response of patients with WDEIA to ω5-gliadin can be effectively eliminated by changing only the protein encoded by the Gli-B1 locus. In the short-to-medium term, these wheat products are expected to be costly because of the high cost of the cultivar development using the transgenic processing. Therefore, it may be more appropriate to supply these products to ω5-gliadin-sensitized patients with WDEIA, who are restricted from using wheat products. Based on the results of IgE-binding epitope analysis of wheat allergens, hypoallergenic wheat flour was subjected to enzymatic modification or deamidation. Tanabe et al. identified IgE epitopes of gluten protein allergens using Serum IgE from patients with AD. They first identified a 30-mer peptide corresponding to a part of the amino acid sequence of LMW glutenin, and then determined that QQQPP is the smallest unit recognized by the patient’s Serum IgE. Substitution experiments with glycine revealed that the amino acids essential for IgE binding are the first Q, and the fourth and the fifth P [69]. Pastorello et al. also reported that LMW glutenin is one of the causative allergens of wheat allergy in children with AD [70]. Watanabe et al. searched for various proteases to cleave the IgE epitope QQQPP and devised a two-step method for producing a hypoallergenic wheat product, cupcake, using cellulase and actinase [53]. The hypoallergenic wheat flour consists mainly of oligopeptides and amino acids, and its average molecular weight was lower than 1000 [54]. The safety of the hypoallergenic wheat flour was evaluated using a DNA microarray in rats, and no groups of genes known to be involved in the carcinogenesis or oxidative stress were affected [55]. When the cupcakes prepared by this method were ingested by 15 children with AD, a systemic urticaria was induced in two children, whereas 13 children were able to ingest cupcakes without adverse effects [56,57]. Furthermore, an open study of oral immunotherapy with continuous cupcake intake showed that most of the 20 children with a history of wheat allergy became able to consume normal wheat products, suggesting that the continuous intake of hypoallergenic wheat products could induce oral immunotolerance [57]. Future studies should examine whether the hypoallergenic wheat products produced by the two-step method using cellulase and actinase are effective in inducing immunotolerance in the children with wheat allergies developed in association with AD. Recently, it has been clarified that the food allergies observed in children with AD are not caused by food allergens ingested orally, but by the cutaneous sensitization caused by a trace amount of food allergens that invade the lesions of AD [71]. Since a variety of food allergens exist in the environment, the sensitizing food allergens in children with AD are very diverse. The use of emollients in infancy does not always prevent the development of AD and food allergies [72]. The early initiation of food intake in infants is also not always effective in preventing food allergies [71]. A tight control of dermatitis using topical corticosteroids at the early stage of AD decreases the risk of development of food allergies [73,74]. Some reports have shown that the deamidation of wheat gliadin decreases allergenicity. Kumagai et al. performed the deamidation of gliadin using a cation exchange resin without affecting peptide-bond and investigated its allergenicity using a rat model as well as patients’ Serum IgE. Compared with undeamidated gliadin, the deamidated gliadin showed lower reactivity against Serum IgE obtained from patients with high levels of wheat-specific IgE, and induced lower levels of gliadin-specific IgE in the rat allergy model of oral administration [58]. Abe et al. examined the allergenicity of deamidated gliadin in a mouse model of wheat-gliadin allergy. An oral administration of the deamidated gliadin suppressed intestinal permeability, serum allergen levels, serum allergen-specific IgE levels, mast cell-surface expression of FcεRI, and serum and intestinal histamine levels [59]. On the other hand, Abe et al. also clarified that deamidated and hydrolyzed gliadin induced severe allergic reactions, while deamidated-only and hydrolyzed-only gliadin showed almost no allergic response in the transdermal administration model of mice [75]. These findings are compatible with the outbreak of hydrolyzed wheat protein allergies described above [26,31,32,33,34]. Disulfide bonds usually provide a digestion-resistant feature and increase the allergenicity of food proteins [76]. Using a canine allergy model, Buchanan et al. showed that thioredoxin, reduced by NADPH via NADP-thioredoxin reductase, mitigated the allergenicity of wheat proteins, particularly gliadin and glutenin, by reducing their disulfide bonds. This decrease occurred along with an increased susceptibility to proteolysis, heat denaturation, and altered biochemical activity [60]. Yano et al. explored a technique to identify the target proteins of thioredoxin using electrophoresis after labelling with a fluorescent probe, providing information to produce practical hypoallergenic wheat products by thioredoxin treatment [61,62]. Waga et al. analyzed 10 winter wheat genotypes treated with thioredoxin using ELISA with Serum IgE obtained from patients and found that the reduction by thioredoxin strongly decreased gliadin immunoreactivity but did not significantly affect dough rheological properties [63]. Matsumoto et al. investigated effects of thioredoxin on the allergenicity of salt-soluble wheat proteins in six patients with IgE-mediated wheat allergy and found that the thioredoxin-treated wheat proteins mitigated both the reaction of the skin prick test and the binding to serum IgE by inhibition assay using fluorescence enzyme immunoassay using ImmunoCAP (CAP-FEIA) [64]. An overexpression of thioredoxin in the wheat endosperm was found to increase the solubility and decrease the allergenicity of gliadins in a canine allergy model, indicating that the high expression in seeds by gene recombination reduces their allergenicity [77]. Since ω-gliadins contain no cysteine residues, they do not participate in the formation of the disulfide bridges that stabilize the gluten protein structure. However, the ω-gliadins interact with other proteins via weak, low-energetic hydrogen bonds. Stawoska et al. suggested that the elimination of ω-fractions from the gliadin complex causes minor modifications to the secondary structures of the remaining gliadin proteins, facilitating the interaction of IgE epitopes with IgE antibodies [78]. NADPH and reductase, which activate thioredoxin, are required to act on thioredoxin. It has also been reported that thioredoxin itself may be allergenic [79,80,81]. At present, consumers tend not to like the use of genetic modification or additives in food [82]. The use of thioredoxin in food products should be carried out carefully while monitoring costs and consumer awareness. The limitations of this study are that we have not presented details of the methods that have been used for developing the hypoallergenic wheat, and that the references written in English have been analyzed while the five references written in non-English languages have been omitted in the analysis among 61 references extracted using PubMed with the keyword “hypoallergenic wheat.” Several approaches have been used to establish hypoallergenic wheat products that can be consumed by patients with IgE-mediated wheat allergies. These approaches are divided globally into two methods. One is the alteration of allergen epitopes by enzymatic degradation, deamidation, or the addition of reducing agents, and the other is the production of wheat lines that do not contain major allergen epitopes by traditional breeding or biotechnology. In particular, hypoallergenic wheat lines, which lack ω5-gliadin with transgenic or natural breeding techniques, significantly reduced the reactivity of Serum IgE in wheat-allergic patients while retaining the characteristics of flour, such as bread-making properties. However, low-level reactivity to wheat allergens in the wheat-allergic patients has also been observed in these wheat lines. This may be due to cross-reactivity between the epitopes of ω5-gliadin and similar amino acid sequences of other gluten proteins. It is also possible that the wheat-allergic patients have Serum IgE that reacts primarily with α/β-, γ-, and ω1,2-gliadins, or HMW- and LMW-glutenins. Given the complexity of the immune response of patients with WDEIA and the severity of the allergic reaction, it is not feasible for the patients with WDEIA to consume flour from wheat lines with a reduced allergenicity without a thorough analysis of the IgE reactivity of each patient’s serum. Better diagnostic methods should be developed to define sensitization conditions precisely in conjunction with hypoallergenic wheat products. These results highlight some of the difficulties faced in creating new hypoallergenic wheat lines through either traditional breeding or biotechnology approaches in developing hypoallergenic wheat for patients allergic to wheat. Nevertheless, the provision of new wheat lines with a reduced allergenicity in general populations may reduce the number of subjects sensitized to wheat proteins in the future. Subjects possesing HLA-DPB1∗02:01:02, a susceptibility gene for WDEIA, could especially avoid a sensitization to ω5-gliadin if they would consume these hypoallergenic wheat products in their daily meals [83]. Hypoallergenic wheat could also be applied in new immunotherapy protocols aimed at desensitizing patients to specific wheat allergens.

PMC10000926

Mengyuan Li,Angela Quintana,Elena Alberts,Miu Shing Hung,Victoire Boulat,Mercè Martí Ripoll,Anita Grigoriadis

B Cells in Breast Cancer Pathology

28-02-2023

B cells,tumour-infiltrating lymphocytes,breast cancer,tertiary lymphoid structures,lymph nodes,germinal centres

Simple Summary B cells in the tumour microenvironment and lymph nodes have affirmed their role in breast cancer pathology. Multiplex imaging, single cell, and spatial transcriptomics of cancer patients’ breast carcinomas and lymph nodes have illustrated the diversity and spatial context of B cells in this disease. Their anti-tumoural and pro-tumoural functions make B cells an attractive research area to improve chemo- and immuno-therapy responses for breast cancer patients. Abstract B cells have recently become a focus in breast cancer pathology due to their influence on tumour regression, prognosis, and response to treatment, besides their contribution to antigen presentation, immunoglobulin production, and regulation of adaptive responses. As our understanding of diverse B cell subsets in eliciting both pro- and anti-inflammatory responses in breast cancer patients increases, it has become pertinent to address the molecular and clinical relevance of these immune cell populations within the tumour microenvironment (TME). At the primary tumour site, B cells are either found spatially dispersed or aggregated in so-called tertiary lymphoid structures (TLS). In axillary lymph nodes (LNs), B cell populations, amongst a plethora of activities, undergo germinal centre reactions to ensure humoral immunity. With the recent approval for the addition of immunotherapeutic drugs as a treatment option in the early and metastatic settings for triple-negative breast cancer (TNBC) patients, B cell populations or TLS may resemble valuable biomarkers for immunotherapy responses in certain breast cancer subgroups. New technologies such as spatially defined sequencing techniques, multiplex imaging, and digital technologies have further deciphered the diversity of B cells and the morphological structures in which they appear in the tumour and LNs. Thus, in this review, we comprehensively summarise the current knowledge of B cells in breast cancer. In addition, we provide a user-friendly single-cell RNA-sequencing platform, called “B singLe cEll rna-Seq browSer” (BLESS) platform, with a focus on the B cells in breast cancer patients to interrogate the latest publicly available single-cell RNA-sequencing data collected from diverse breast cancer studies. Finally, we explore their clinical relevance as biomarkers or molecular targets for future interventions.

B Cells in Breast Cancer Pathology B cells in the tumour microenvironment and lymph nodes have affirmed their role in breast cancer pathology. Multiplex imaging, single cell, and spatial transcriptomics of cancer patients’ breast carcinomas and lymph nodes have illustrated the diversity and spatial context of B cells in this disease. Their anti-tumoural and pro-tumoural functions make B cells an attractive research area to improve chemo- and immuno-therapy responses for breast cancer patients. B cells have recently become a focus in breast cancer pathology due to their influence on tumour regression, prognosis, and response to treatment, besides their contribution to antigen presentation, immunoglobulin production, and regulation of adaptive responses. As our understanding of diverse B cell subsets in eliciting both pro- and anti-inflammatory responses in breast cancer patients increases, it has become pertinent to address the molecular and clinical relevance of these immune cell populations within the tumour microenvironment (TME). At the primary tumour site, B cells are either found spatially dispersed or aggregated in so-called tertiary lymphoid structures (TLS). In axillary lymph nodes (LNs), B cell populations, amongst a plethora of activities, undergo germinal centre reactions to ensure humoral immunity. With the recent approval for the addition of immunotherapeutic drugs as a treatment option in the early and metastatic settings for triple-negative breast cancer (TNBC) patients, B cell populations or TLS may resemble valuable biomarkers for immunotherapy responses in certain breast cancer subgroups. New technologies such as spatially defined sequencing techniques, multiplex imaging, and digital technologies have further deciphered the diversity of B cells and the morphological structures in which they appear in the tumour and LNs. Thus, in this review, we comprehensively summarise the current knowledge of B cells in breast cancer. In addition, we provide a user-friendly single-cell RNA-sequencing platform, called “B singLe cEll rna-Seq browSer” (BLESS) platform, with a focus on the B cells in breast cancer patients to interrogate the latest publicly available single-cell RNA-sequencing data collected from diverse breast cancer studies. Finally, we explore their clinical relevance as biomarkers or molecular targets for future interventions. Breast cancer is the most common cancer in women, with 2.3 million new cases diagnosed per year, attributing to 690,000 deaths annually [1]. Patient outcome is worsened when the tumour disseminates to distant organs, as the five-year overall survival rate in breast cancer patients is reduced from 90% to 29% [1,2]. To date, disease prognosis and treatment stratification are heavily reliant on tumour-, nodal-, and metastasis (TNM) staging and breast cancer subtypes. Based on immunohistochemical (IHC) staining for oestrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2), the majority of the breast cancer cases are classified as ER-positive (~65%), followed by HER2-positive (~20%) and triple-negative breast cancers (TNBC; ~15%) [3]. As a result of their intrinsic molecular heterogeneity, different breast cancer subtypes express varying quantities and assortments of tumour-associated antigens (TAA) and tumour-specific antigens (TSA). These antigens may influence the recruitment and expansion of tumour-infiltrating lymphocytes (TILs) that impact tumour progression. In recent years, the levels of TILs at the primary tumour lesion have been repeatedly shown to correlate positively with better prognosis in TNBCs and HER2-positive cancers [4]. TILs assessment has been demonstrated to be superior to TNM staging when predicting response to chemotherapy, anti-HER2 therapy, and immunotherapy [4,5,6]. Additional clinical observations collectively led to updated recommendations by the St Gallen International Consensus Guidelines 2019 and the upcoming ESMO Early Breast Guidelines 2023. They suggest evaluating stromal TILs in specific carcinomas of TNBC patients, but not to take treatment decisions alone or to escalate or de-escalate treatment. Combining this with stage, age, tumour size, and LN status can help better determine the prognosis [7] thus emphasising the biological relevance of TILs in advancing breast cancer pathology and therapeutic understanding. Due to the current shortage of pathologists and their ever-increasing workload, histological scoring of TILs is currently not performed routinely [8]. To address this discrepancy, several machine-learning algorithms for digital pathology, including the TILs in breast cancer (TIGER) challenge [9], have been developed, in which immune infiltrates at the primary tumour lesion are spatially identified, annotated, and quantified. In the future, these research efforts may streamline the process of TILs scoring and in turn, add valuable information for the treatment regimens for certain breast cancer patients. Despite these efforts, pathological TILs assessment may, however, not capture the nuances of the immune cell populations present. As such, studies aim to dissect the diverse TILs subsets and their miscellaneous functions in the hope of unveiling additional targetable biomarkers. Tumour-infiltrating T cells (TIL-T) have historically been in the spotlight of tumour immunology networks as they are cardinal in immune-recruitment and eliciting cytotoxic anti-tumour responses. In concordance, the enrichment of intra-tumoral CD4+ helper T cells and CD8+ cytotoxic T cells correlates with a better prognosis and treatment response [10,11,12]. In contrast, many breast tumours exhibit high levels of regulatory T cells (Tregs), which promote the infiltration of immunosuppressive tumour-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), and neutrophils [13]. These cell populations can facilitate TIL-T exhaustion in situ and are associated with an inferior outcome [14]. The diverse functions of immune cell subsets therefore, highlight the necessity to investigate TILs with greater granularity and clarity. Both TIL-T and tumour-infiltrating B cell (TIL-B) levels demonstrate prognostic value for disease-free and overall survival in cancer, especially for TNBC and HER2-positive breast cancer patients [15]. TIL-B represents around 20% of all immune infiltrates at the primary breast carcinoma, which is elevated compared to normal breast tissue [16,17,18]. High quantities of infiltrating B cells have been reported in around 20% of breast cancers [19], and their levels are highly correlated with the density of TIL-T [15,17]. Within the TME, B cells aid T cell function through the presentation of antigens and secrete antibodies that provide independent anti-tumour cytotoxicity. So far, scoring of immune infiltrates in breast cancer has rarely included B cell populations. Given B cells’ fundamental antigen-presentation capacities, their propensity to secrete anti-tumour antibodies [20], spatial distribution, and their integral role in adaptive responses, B cells are likely to play a crucial role in anti-tumour immunity. As such, the interference of B cells could impact immunotherapeutic approaches [21,22,23], and they must not be overlooked when assessing TILs diagnostically in breast cancer patients. To appreciate the diversity of B cell populations, including their antibody production, maintenance in immunological memory, and their regulation of immune responses, one needs to consider their origin and maturation (Figure 1). The early development of B cells begins in the bone marrow, where haematopoietic stem cells (HSC) (CD34+ CD19−) gradually differentiate to pro-B cells (CD19+ CD10+ CD34+ IgM−), and subsequently pre-B cells (CD19+ CD10+ CD34− IgM−). During this process, immunoglobulin heavy and light chains are synthesised sequentially in the pre-B and pro-B stages, owing to V(D)J recombination. Immunoglobulin chains are later assembled and expressed as the IgM isotype of the B cell receptor (BCR) in immature B cells (IgM+ CD19+ CD20+ CD21+ CD40+). Immature B cells undergo a negative clonal selection for auto-reactivity before exiting the bone marrow and entering the periphery as mature B cells (IgM+ IgD+ CD19+ CD20+ CD21+ CD40+). Chemotactic gradients such as the CCL2/CCR2 and CCR7/CCL19/21 axes recruit circulating B cells into secondary lymphoid organs (SLOs) for further maturation, namely the spleen, tonsils, and LNs. After exposure to antigens, naïve B cells within SLOs progressively mature into germinal centre (GC) B cells (CD19+ CD20+ CD27+ CD38+ CD40+ CD83+), memory B cells (CD19+ CD20+ CD21+ CD40+), plasma cells (CD10− CD20− CD38+) or mantle zone (MZ) B cells (IgM+ IgD− CD1+ CD21+) (Figure 1). Antigen-presenting cells (APCs), including dendritic cells and macrophages from the periphery gain access to the SLOs via the subcapsular sinus. Here, they migrate into the T cell zone, where the presentation of cognate antigens drives the differentiation of CD4+ naïve T cells into pre-follicular T helper (pre-Tfh) cells. The cooperation with naïve B cells at the T-B border subsequently induces the final differentiation into T follicular helper cells (Tfh) that will fully activate B cells, directing them towards either an extrafollicular or a GC response, depending on the BCR-antigen affinity. Activated B cells will undergo class switch recombination to express IgA, IgE, and IgG depending on the nature of the antigenic stimuli [24]. B cells that enter GCs will transit between two functionally distinct and polarised areas, the dark zone (DZ) and light zone (LZ), in which somatic hypermutation and affinity maturation take place, respectively [25,26,27] (Figure 1). Somatic hypermutation describes the process by which point mutations accumulate within Ig variable-region encoding genes to generate antibodies. After iterative rounds of proliferation, selection, and tolerance checks, high-affinity B cells will survive and exit the GC as memory B cells and plasma cells, tailored to the nature of the antigenic stimulation. Regulatory B cells (Bregs) have been described as an IL-10+ CD1d+ CD5+ CD19+ immunoregulatory B cell population that resemble Tregs in maintaining the balance between self-tolerance and immune activation. The current lack of consensus on Breg-defining markers is also reflected in the ongoing debate with regards to their functions. Bregs primarily secrete cytokines such as IL-10, IL-35, and TGF-β, that restrict the differentiation of pro-inflammatory Th1/Th17 cells [21,22], inhibit the cytotoxic functions of CD8+/NK cells [28], and stimulate the expansion of Tregs [29]. The physiological diversity of B cells, their intertwined activities with other immune and non-immune cells, and their spatially defined development stages, asks for extensive characterisation and quantification of these subsets in the context of breast cancer immune-oncology. Distribution of B cell subpopulations within the TIL-B reported in the TME of TNBCs include naïve B cells (~10% of B cells), memory B cells (~80% of B cells), and plasma cells (~20% of B cells) [15,17], suggesting a local presence of adaptive immunity. Over recent years, B cell infiltration has mostly been associated with improved prognosis in breast cancer patients [15,30,31,32,33,34,35]. In particular, memory B cells are significantly enriched in breast cancers compared with healthy tissue [36,37] and are consistently found to be associated with good prognosis in TNBC patients [38]. Upon entering the TME, TIL-B cells encounter both tumour and immune cells, which generates the activation and expansion of specific B cell clones. Cross-linking of the BCR promotes the formation of immune complexes by upregulating BCR signalling pathway molecules JUN and FOS, lymphocyte activation marker CD69, and GC chemokine regulator RGS1, which are subsequently associated with a superior outcome in TNBC [34]. In contrast, the presence of intra-tumoral Bregs has been demonstrated as a poor prognostic feature in breast cancer. A study of breast cancer patients in 2019 showed that the abundance of IL10+ Bregs was increased proportionally with Tregs in primary tumours, which coincided with shorter relapse-free disease intervals [39]. This enrichment of Bregs was attributed to the infiltration and regulation by CD33+ MDSCs which contributed to an immunosuppressive TME [40,41]. Similarly, the expansion of IL-10+ CD1d+ CD5+ CD138+ CD19+ Bregs within TNBC may enhance the differentiation of anti-inflammatory M2 macrophages and Tregs to tone down anti-tumour responses [23]. Supporting this, T cells pre-conditioned with Bregs exacerbated lung metastasis in breast cancer xenograft models [42]. The inhibitory activity of Bregs is mediated by CD80-CD86 interactions, the PD-1/PD-L1 axis, in addition to TGF-β and IL-10 production [43]. TGF-β secreted by Bregs has been shown to promote Treg expansion in TNBC mouse models. Upon neoadjuvant chemotherapy treatment, the level of IL-10+ B cells in breast tumours is dramatically reduced and B cells upregulate ICOSL, opting for a more immunostimulatory phenotype. An increase in this ICOSL+ B cell subset is subsequently associated with improved disease-free and overall survival [44]. Although understudied in breast cancer, mouse models and clinical trials in other solid tumours including renal cell carcinoma and melanoma have shown IL-10 inhibition upregulates anti-tumour CD8+ T cell responses and subsequently potentiates immunotherapy treatment [45]. Together, these observations, therefore, present the possibility of targeting Breg functions, thereby preventing immune-cell exhaustion at the primary lesion and distant metastases development. Studies have further highlighted the importance of CD38+ plasma cell infiltration as an independent factor for progression-free survival and overall survival [46]. Plasma cells in TNBCs exhibit the most significant frequency of IgH somatic hypermutation compared to other TIL-B subsets, with most clones sharing identical IgH sequences [17]. This suggests a high Ig specificity against tumour antigens. However, the rate of plasma cell IgH somatic hypermutation is downregulated in primary TNBC compared to those present in peripheral blood mononuclear cell (PBMC) samples, indicating the potential ability of tumour cells to suppress local humoral response [17]. Given that antibody deficiency in xenograft mouse models accelerates tumour growth [24,47,48,49], it has become apparent that humoral activation by plasma cells is fundamental in halting breast cancer progression. Immunoglobulin isotype is thought to play an essential role in regulating cancer growth. Profiling immunoglobulins within the TNBC TME indicates that IgG+ TIL-B are more prominent than TIL-B of any other immunoglobulin isotype [17], and IgG isotype switching correlates with a better prognosis [34]. In alignment, the adoptive transfer of tumour-draining LN-derived IgG-secreting plasma cells led to their migration to tumour sites and effectively limited metastatic progression in breast tumour mouse models [24,47,48,49]. An IgG gene expression array has recently been incorporated into the novel HER2DX assay. This signature has been extracted from transcriptomic profiles and patient clinicopathological data of HER2-positive breast carcinoma patients, to estimate the likelihood of recurrence and achieving pathological complete response (pCR) [50,51,52]. Furthermore, over 80% of breast tumour tissues contain either IgG or IgA autoantibodies against a known antigen in a 91-antigen array, namely CTAG1B, MAGEA1, TP53, MUC1, SOX2, BRCA2, TERT [53]. These autoantibodies are rarely detectable in normal adjacent breast tissue and the absence of many of these from patients’ plasma antibody repertoire points to in situ antibody production, presumably by tumour-infiltrating plasma cells. Higher levels of breast-cancer-specific IgG autoantibodies are associated with shorter disease-free survival which is reflected in IgA autoantibodies [53]. Conversely, increased IgG responses are concurrent with lower cytotoxic CD8+ TIL-T and poorer outcomes in some breast cancer patients [16]. IgG antibodies within breast cancer mouse models have been reported to facilitate LN metastasis by targeting the tumour antigen HSPA4. This activates the Src/NF-κB pathway and facilitates metastasis through the CXCR4/SDF-1α axis [11]. More frequently, compelling observations point to tumour-derived IgG having a role in promoting tumour progression [54,55,56], therefore, it remains to be determined whether plasma cells contribute to the secretion of such pathogenic IgG antibodies. A deeper investigation into the contribution of different antibody isotypes on different TIL-B subsets is warranted, especially given the routine use of antibody-based therapies in breast cancer. High-throughput sequencing technologies have become indispensable to comprehensively capture the molecular features of B cell populations in the TME of breast carcinomas and addressed several aforementioned research gaps. Single-cell (sc)-based signatures can delineate TIL-B subsets more precisely, and have shown superior performance in predicting TNBC patient survival compared to single marker expression e.g., CD20 [17]. Considering the volume of available scRNA-sequencing data and its potential for exponential growth in the B cell field, we have developed a user-friendly “B singLe cEll rna-Seq browSer” (BLESS) platform (https://github.com/cancerbioinformatics/BLESS/ (accessed on 19 February 2023)) [36,57,58,59,60,61,62,63,64,65,66,67,68]. BLESS provides the ability to interrogate and visualise human datasets comprised of B cells from primary breast tumours, patient-paired normal breast tissue, PBMCs, and LN samples. By building a comprehensive database (Supplementary Table S1), B cell subsets in different immunological sites of breast cancer patients can be investigated pre-, during, or post-treatment. BLESS can delineate their phenotype and their communication networks with adjacent cells, such as endothelial cells, fibroblasts, macrophages, and other TILs in breast tumours. Of note, the source code of BLESS is suitable for sharing and assisting analysis. BLESS’ expandable functionalities provide the opportunity to inspect and compare TIL-B in different clinical settings and will contribute to and elucidate further the multi-functional role of B cells in breast cancer pathology. Unique spatial arrangements of B cells in complex cellular contexts have been observed in breast carcinomas by utilising diverse multiplex imaging technologies. B cells in TNBCs are often depleted along the tumour border, and dispersed infiltration of B cells is associated with a lower incidence of recurrence within five years [16,50]. Patients who exhibit heterogeneous clusters containing both B and T cells near cancer cells have superior disease trajectories than those patients who exhibit fewer immune cell aggregates that are larger and further from malignant cell islands [50]. These observations suggest that the distance between immune and tumour cells may potentially influence the capacity of immune-mediated tumour cell killing [69]. TLS are immune cell aggregates typically located in the periphery of the tumour and have recently gained attraction in cancer immunology due to their pan-cancer association with a favourable prognosis [70]. In breast cancer patients, the molecular, cellular, and histological presence of TLS has been associated with a better outcome. A 12-chemokine gene signature which can predict the presence of TLS in multiple cancer types was shown to be prognostic for improved survival in breast cancer patients [70]. Moreover, the histological detection of TLS in tumour samples confers improved disease-free survival [71] and overall survival [72] in multiple subtypes of breast cancer. Tumour-infiltrating CXCL13-expressing Tfh cells are closely associated with TLS in breast tumours. CXCL13 is a B cell chemoattractant that selectively binds to CXCR5, and triggers the formation and structural organisation of B cells in TLS. During the maturation of TLS, CXCL13 expression shifts from primarily CD4+ Tfh to CD21+ follicular DC [73]. A gene signature which predicts the presence of these Tfh cells, and CXCL13 expression alone, is prognostic for the survival of untreated breast cancer patients and associated with a higher rate of pCR [74]. TLS are more prevalent in high-grade and early-stage carcinomas, present within as many as 77% of TNBC tumours [11]. TLS are characterised by CD20+ B cells, CD3+ T cells, and mature dendritic cells (DC-LAMP+), however, this can range from disorganised clusters to defined GC-like structures [75]. Additional markers to clarify these distinctions, including Ki67, CD21, CD23, BCL-6, and AID, have identified follicular DCs and Tfh cells within mature TLS, as well as GC-B cell-like centrocyte and centroblast subsets [76,77,78] (Figure 2). These transient structures bear similar morphological characteristics, chemotactic profiles, and B cell maturation features to GCs, with a definitive mantle zone and evidence of polarised chemokine expression [79]. Moreover, the presence of somatically mutated immunoglobulin genes and antibody-producing plasma cells within TLS signifies potentially localised evolution of the B cell response [80]. TLS have also been observed in tumours with an immune excluded or desert phenotype, in which lymphocytes are restricted to the periphery or are completely absent, respectively. This ability to form TLS within immune-cold tumours could indicate some level of immune activation and in turn, a positive influence on the disease trajectory for low TILs breast cancer patients, thus warranting further investigation. Currently, TLS and lymphocytes present within the periphery of the tumour are not included in TIL scoring guidelines recommended by the International TILs Working Group [81]. We hypothesise that for TNBC patients with low TILs, TLS scoring may provide additional information. This is concurrent with recent guidelines from the National Institute for Health and Care Excellence (NICE) recommending immunotherapy as an option in the neoadjuvant and adjuvant setting for locally advanced TNBC patients at high risk of recurrence [82]. The initial seeding site for metastatic cells of breast carcinomas is the nearby LN, and as such, the number of involved LNs is a fundamental prognostic factor [83]. However, the role of the LN as an SLO and a site for potential anti-tumour adaptive immune responses has hardly been considered. We have repeatedly shown that the formation of GCs in LNs is associated with a lower risk of developing distant metastasis in TNBC patients with involved LNs, even for TNBC patients with low TILs at their primary lesion [84,85]. Moreover, the frequency of GCs in cancer-free LNs was increased in TNBC compared to non-TNBC patients [85]. As GCs act as the immunological hubs for B cell maturation, this may be reflective of LN-mediated immune responses that are conducive to a superior disease trajectory. Furthermore, patients with more and larger GCs exhibit more frequent TLS formation and higher TILs in their tumours [78], indicating potential crosstalk responsible for the activation of B cells between the primary tumour and the LNs. Thus, the LN as an SLO and its mechanisms to facilitate anti-tumour B cell responses needs to be examined. Morphological features indicative of B cell activation, namely the formation of GC in LNs and TLS in the primary tumour, are ideally suited for robust quantification using deep learning approaches on digitised whole slide images of H&E-stained tumours and LNs. We and others have begun to implement such deep learning frameworks, and demonstrated that an increased frequency of GCs in cancer-free and involved LNs is associated with longer time to distant metastasis in TNBC patients [86]. Tools to automatically annotate TLS in breast carcinomas have not yet been developed. For lung cancer patients, deep learning-based approaches to capture the presence and frequency of TLS have shown their potential as a predictor of tumour recurrence [87,88,89]. Besides the pathological assessments of GCs in LNs, scRNA sequencing studies of LN immune cells have demonstrated both immune-suppressive and immune-promoting roles for B cells [49]. Shariati et al. revealed that an enhanced CD86+CD39+PD-1+PD-L1+ B cell population may hold immunosuppressive properties through association with poor prognostic factors [90], whilst CD40-activated B cells as APCs can activate and expand anti-tumour naïve and memory T cells [91]. Further, interactions between CD45 and the inhibitory receptor CD22 negatively regulated B cell immunity and were reported to be associated with LN metastasis in breast cancer [62]. Tumour-draining LNs may also provide a rich source for tumour-reactive B cells, that give rise to circulating autoantibodies. BCR sequencing in sentinel LNs of breast cancer patients demonstrates hallmarks of affinity maturation against tumour antigen NY-ESO-1 [92]. However, as previously mentioned, tumour-draining LN B cells can also secrete pathogenic IgG that contributes to nodal metastasis in a mouse model of breast cancer [11]. The application of spatial transcriptomics has recently identified ten distinct B cell subtypes spatially distributed across a LN from breast cancer patients, including naïve B cells, activated B cells, GC B cells, and plasma cells [93]. The combination of spatial transcriptomics with scRNA-sequencing data will further deconvolute how these different immune cells may communicate with each other and other cells, and will provide valuable information on B cell subsets and their interaction within GCs, TLS, and the TME of primary breast tumours [94,95]. By following the same explorative translational studies as TILs, B cell markers and the presence of TLS have been evaluated as potential prognostic and predictive biomarkers in breast cancer patients from clinical trials and translational studies. B cell markers, such as CD20, CXCL13, IgG, and the presence of TLS correlate with a higher pCR rate in neoadjuvant-treated patients (Table 1). This is predominantly associated with improved disease-free survival and overall survival in breast cancer patients receiving a different range of treatment regimens [96,97]. Given the recent approval for the addition of immunotherapeutic drugs as a treatment option in the early and metastatic setting for TNBC, we can explore how different B cell populations are affected by the addition of immunotherapeutic drugs. Pembrolizumab, an antibody targeting PD-1, combined with chemotherapy has been approved to treat early TNBC patients independently of their PD-L1 status [98,99] and as a first-line treatment combined with chemotherapy for advanced TNBC patients expressing PD-L1 (combined positive score [CPS] of at least 10) [100]. Most of the current immunotherapies are developed to target T cells to reverse tumour-mediated exhaustion. However, it remains to be determined whether immune checkpoint inhibitors (ICI) have any direct effect on B cells, and in turn on drug efficacy and patient prognosis. Immunotherapy-related side effects are termed immune-related adverse events (irAEs) and resemble clinical patterns similar to what has been observed in autoimmune diseases whilst being mechanistically distinct [101]. An increase of irAE-related autoantibodies has been observed [102], coupled with a decrease of CD19+ B cells and an increase in CD21lo B cells and plasmablasts [103] in the blood of immunotherapy-treated patients. Conversely, B cell levels are decreased in the organs affected by irAEs, accompanied by high infiltration of CD4+ and CD8+ T cells [102,104,105]. Patients who developed irAEs but did not succumb to these adverse events had an overall better prognosis than those who had not experienced irAEs [106]. B cells are often depleted after chemotherapy, which is potentially a side effect of the treatment and might cause patients to be less responsive to immune targeted therapies [107]. In the BIG 02-98 trial, a higher presence of B cells, particularly memory B cells, was reported in HER2+ and TNBC compared to normal breast tissue, which correlated with higher levels of TILs. They also observed that B cells in the tumour expressed a multitude of cytokines, except for IL-17A, IL-21, and IL-22—which were very low—and higher levels of IFNγ and TNFα in comparison to LN and tonsils [16]. IL-17, IL-21, and IL-22 trigger the creation and assembly of TLS [115,116]. Thus, low levels of those cytokines may hinder or halt their formation. Studies in TNBC mouse models treated with ICI have shown that this will induce the activation of B cells via Tfh to facilitate the anti-tumour response, and subsequently promote the generation of class-switched plasma cells [48]. In breast cancer patients, scRNA-sequencing analyses revealed that responders to combined anti-PD-L1 and chemotherapy treatment exhibited increased baseline levels of intratumoural colocalised B cells and CXCL13+ T cells compared to non-responders. Amongst those patients enriched with B cells, antigen presentation and T cell activation genes are upregulated in responders, in contrast to immunoglobulin production and humoral response genes in non-responders [63]. So far, B cell-associated structures such as GC in LNs, the expression of different immune checkpoints with regards to B cell populations, or other B cell markers in the LNs of patients after treatment have not been elucidated. PD-1 is distinctively expressed in Tfh cells of GCs in LNs and TLS in primary tumours, which drives the formation of these structures and creates long-lived plasma cells by interacting with PD-L1 and PD-L2 expressed on B cells. Memory B cells have upregulated PD-L2 in comparison to naïve B cells [117]. We have shown a statistically significant higher expression of PD-L2 in the GCs of LNs from TNBC patients with high TIL levels at their primary tumours [78], potentially being associated with an improved long-term prognosis. In the TME, PD-L1 is expressed by tumour cells, macrophages, B cells, and T cells, whilst PD-1 is upregulated on immune cells, mainly T cells. PD-L2 is also present in the malignant epithelial cancer cells of some tumours (but not as widely as PD-L1) and antigen-presenting cells (macrophages and dendritic cells). It is known that anti-PD-1 drugs have more efficacy but also more toxicity than anti-PD-L1 drugs [118]. This distinction may be linked to the difference in expression of these markers not only in the TME but also in the cells of the GC from tumour draining LNs. Antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) induced by anti-HER2 therapeutics have been well-known for a long time, but recent drugs with the same mechanism have stronger effects on the immune system than the classic anti-HER2 therapies. New anti-HER2 therapies, namely Margetuximab or KN026 enhance ADCC more potently than the combination of trastuzumab and pertuzumab [119,120], whilst BDC-1001, a trastuzumab biosimilar immune-stimulating antibody conjugate (ISAC) chemically conjugated to a toll-like receptor (TLR) 7/8 agonist, has shown promising results [121]. ADCC and ADCP eliminate the tumour mass via cells of the innate immune system, including natural killer cells or macrophages, due to their affinity with the Fc fragment of the antibody on those cells. ADCC and ADCP mechanisms could potentially increase antigen presentation and promote adaptive immunity but not with the same strength of activation as ICIs, which specifically target lymphocytes. This difference in the mechanism of action towards the immune system also translates into differences in the toxicity profile, as anti-HER2 treatments do not cause irAEs. It is probable that the activation of the adaptive immune system follows the natural pathway due to a more indirect effect of these drugs. Other promising immunotherapeutic drugs are cancer vaccines, currently under preclinical and clinical development although not approved for breast cancer treatment. The most common modalities for breast cancer are peptide/protein and tumour cell vaccines, and more recently dendritic cell-based and DNA and RNA vaccines. Most of them are directed against the HER2 epitope, and recently developed ones also target HER3, EGFR (epidermal growth factor receptor), VEGF (vascular endothelial growth factor), and IGF-1R (insulin-like growth factor 1). Despite some of these vaccines inducing detectable immune responses with relatively low side effects compared to ICIs and anti-HER treatments, none have demonstrated any significant clinical benefit [122]. There are still many pieces missing how B cells can respond to different treatments, interact with other immune cells, and become activated to promote anti-tumour growth. When all these puzzle pieces fit together, we will be able to see the whole picture of the diverse roles of B cells in breast cancer. Breast cancer, in particular TNBC, is still an unmet medical need which requires improved treatments with a tolerable safety profile. TILs have consistently been shown to provide prognostic and predictive information, but there are patients with low TILs levels that also show excellent prognoses. Adding B cell markers or the structures in which they take part may add valuable information to estimate responses to different treatments and assess patient outcome. New technologies such as digital pathology, scRNA-sequencing and spatial transcriptomics will undoubtedly play their part in unravelling and exploring known and newly defined B cell populations. These high-throughput methods within a spatial context at the primary tumour site and in the LNs provide exciting entry points as a future research focus in this area.

PMC10000927

Benson Chellakkan Selvanesan,Alvaro de Mingo Pulido,Sheelu Varghese,Deepak Rohila,Daniel Hupalo,Yuriy Gusev,Sara Contente,Matthew D. Wilkerson,Clifton L. Dalgard,Geeta Upadhyay

NSC243928 Treatment Induces Anti-Tumor Immune Response in Mouse Mammary Tumor Models

25-02-2023

LY6K,mammary tumor model,NKT cells,B1 cells,myeloid derived suppressor cells,NSC243928,4T1,E0771

Simple Summary This study used two different syngeneic mouse mammary tumor models to determine the effect of a small molecule NSC243928 on intra-tumoral immune cells. We observed that NSC243928 treatment reduced the tumor burden in vivo and altered the wide range of immune cell infiltration in both models. These results pave the path for further study of the role of NSC243928 in immuno-oncology drug development for triple-negative breast cancer. Abstract NSC243928 induces cell death in triple-negative breast cancer cells in a LY6K-dependent manner. NSC243928 has been reported as an anti-cancer agent in the NCI small molecule library. The molecular mechanism of NSC243928 as an anti-cancer agent in the treatment of tumor growth in the syngeneic mouse model has not been established. With the success of immunotherapies, novel anti-cancer drugs that may elicit an anti-tumor immune response are of high interest in the development of novel drugs to treat solid cancer. Thus, we focused on studying whether NSC243928 may elicit an anti-tumor immune response in the in vivo mammary tumor models of 4T1 and E0771. We observed that NSC243928 induced immunogenic cell death in 4T1 and E0771 cells. Furthermore, NSC243928 mounted an anti-tumor immune response by increasing immune cells such as patrolling monocytes, NKT cells, B1 cells, and decreasing PMN MDSCs in vivo. Further studies are required to understand the exact mechanism of NSC243928 action in inducing an anti-tumor immune response in vivo, which can be used to determine a molecular signature associated with NSC243928 efficacy. NSC243928 may be a good target for future immuno-oncology drug development for breast cancer.

NSC243928 Treatment Induces Anti-Tumor Immune Response in Mouse Mammary Tumor Models This study used two different syngeneic mouse mammary tumor models to determine the effect of a small molecule NSC243928 on intra-tumoral immune cells. We observed that NSC243928 treatment reduced the tumor burden in vivo and altered the wide range of immune cell infiltration in both models. These results pave the path for further study of the role of NSC243928 in immuno-oncology drug development for triple-negative breast cancer. NSC243928 induces cell death in triple-negative breast cancer cells in a LY6K-dependent manner. NSC243928 has been reported as an anti-cancer agent in the NCI small molecule library. The molecular mechanism of NSC243928 as an anti-cancer agent in the treatment of tumor growth in the syngeneic mouse model has not been established. With the success of immunotherapies, novel anti-cancer drugs that may elicit an anti-tumor immune response are of high interest in the development of novel drugs to treat solid cancer. Thus, we focused on studying whether NSC243928 may elicit an anti-tumor immune response in the in vivo mammary tumor models of 4T1 and E0771. We observed that NSC243928 induced immunogenic cell death in 4T1 and E0771 cells. Furthermore, NSC243928 mounted an anti-tumor immune response by increasing immune cells such as patrolling monocytes, NKT cells, B1 cells, and decreasing PMN MDSCs in vivo. Further studies are required to understand the exact mechanism of NSC243928 action in inducing an anti-tumor immune response in vivo, which can be used to determine a molecular signature associated with NSC243928 efficacy. NSC243928 may be a good target for future immuno-oncology drug development for breast cancer. Lymphocyte antigen 6K (LY6K), a cancer-testis protein, is highly expressed in 70% of clinical cases of triple-negative breast cancer and the expression of LY6K is associated with poor survival outcome in breast cancers [1]. NSC243928 is part of the NCI small molecule library, which is composed of 2000 anti-cancer molecules (https://dtp.cancer.gov/, accessed on 4 December 2022). We identified that small molecule NSC243928 binds with LY6K specifically [2]. NSC243928 was first identified as a compound with anticancer properties in leukemic models in 1979 [3] and was shown to be effective in inducing cell death in ovarian spheroid cultures in vitro [4]. We discovered that NSC243928 induces cell death in multiple triple-negative cancer cell lines that express high levels of the LY6K protein [2]. We observed that the downregulation of LY6K using shRNA can reduce in vivo tumor growth via signaling pathways associated with immune pathways [5]. A precise mechanism of NSC243928 in cancer cell death is not yet known. Thus, we wanted to see whether a pharmacological agent that binds with LY6K to induce cell death in vitro could also inhibit tumor cell growth in vivo, and whether this inhibition is accompanied by changes in the tumor microenvironment in the context of immune cell infiltration. To test whether NSC243928, a binder of LY6K, may reduce in vivo tumor growth, we selected two immune-competent syngeneic mammary tumor models, 4T1 and E0771, both models that are well used in immuno-oncology drug development. The 4T1 model, a triple negative mammary tumor model, originates from Balb/c mice, and the E0771 model, a luminal B mammary tumor model, originates from C57BL6 mice. Since the models are available as syngeneic mouse models, they offer a unique opportunity to test the effect of novel therapies on immune cells relevant to sustained tumor growth [6,7]. Here, we tested whether treatment with NSC243928 could induce an anti-tumor immune response in these two mammary tumor models in vivo. We found that NSC243928 could indeed induce immunogenic cell death in the 4T1 and E0771 cell lines in vitro and induce an anti-tumor immune response in vivo, as seen by the immunophenotyping of tumor isografts from the control and treated mice. The analysis of the bulk RNA sequencing supports these findings. These data suggest that the NSC243928 small molecule is a valid anti-cancer agent that can be used to develop novel targeted therapeutics that can mount an effective anti-tumor response in triple-negative breast cancer. E0771 and 4T1 cells were obtained from American Type Culture Collection (ATCC), Manassas, VA, USA. The cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS), 2 mM glutamine, 1× non-essential amino acids, 1 mM sodium pyruvate, and 100 U/mL penicillin/streptomycin, henceforth referred to as DMEM complete medium. All cell culture reagents were purchased from Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA. Cells were seeded overnight in DMEM complete medium as described in Section 2.1. Cells were serum starved for four hours before treatment with the indicated drugs for 24 h and followed by flow cytometry analysis for the cell surface expression of APC-CRT (Novus Biologicals, Centennial, CO, USA). A live–dead zombie dye (Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA) was used to discriminate between the live and dead cells. Cells were labeled as per the manufacturer’s protocol and gated on live cells for the cell surface expression of CRT using a CytoFLEX flow cytometer (Beckman Coulter Life Sciences, Indianapolis, IN, USA). The flow cytometry data were analyzed using FLOWJO software (Becton, Dickinson and Company, Ashland, OR, USA). Cells were seeded overnight before treatment in DMEM supplemented with 1× insulin-transferrin-selenium (ITS-G) (100×), 2 mM glutamine, 1× non-essential amino acids, 1 mM sodium pyruvate, and 100 U/mL pen/strep. All cell culture reagents were purchased from Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA. Cells were treated with the indicated drugs for 48 h and conditioned medium (CM) was collected. The CM was centrifuged for 10 min at 3000 rpm to ensure the removal of the cell debris. The CM was subjected to protein precipitation using acetone (Sigma-Aldrich, Inc. St. Louis, MO, USA). For acetone precipitation of the proteins, four times the sample volume of cold (−20 °C) acetone was added to the CM. Precipitation was allowed for 1 h in −20 °C and the CM was centrifuged for 10 min at 13,000–15,000× g. The precipitated protein was resuspended in 1× RIPA buffer (Cat # 20–188, Sigma-Aldrich, Inc. St. Louis, MO, USA) and protein was quantified using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA) according to the manufacturer’s protocol. A total of 50 mg of protein from each sample was separated on a 4–12% SDS-PAGE gel and Western blotting was conducted using a rabbit polyclonal HMGB1 antibody (Novus Biologicals, Centennial, CO, USA) and the bands were visualized using HRP conjugated anti-rabbit IgG (Cell signaling Technology, Danvers, MA, USA). The chemiluminescence substrate was used to detect the signals on an iBright Imaging System (Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA). Equal loading of the protein was ensured by staining the transferred protein on the membrane with Ponceau S (Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA) staining prior to developing the Western blot for HMGB1 proteins in the CM. Cells were seeded overnight in DMEM complete medium, as described in Section 2.1. Cells were serum starved for four hours before treatment with indicated drugs and intervals. Extracellular ATP release was measured using a RealTime-Glo™ Extracellular ATP Assay (Cat #GA5011, Promega Corporations, Madison, WI, USA) according to the manufacturer’s instructions and the luminescence was recorded on a Promega™ GloMax® microplate plate reader (Promega Corporations, Madison, WI, USA). The cell viability was monitored by the CellTiter-Glo® Luminescent Cell Viability Assay (Cat #G7570, Promega Corporations, Madison, WI, USA), which measures intracellular ATP as a direct measure of cellular health or metabolic activity. The assay was carried out according to the manufacturer’s instructions and the luminescence was recorded on a Promega™ GloMax® microplate plate reader (Promega Corporations, Madison, WI, USA). All animal experiments were approved by The Uniformed Services University of the Health Sciences Institutional Animal Care and Use Committee (PAT-21-060). The 5 × 105 E0771 or 4T1 cells were injected subcutaneously into the ventral abdominal mammary chain of C57BL6 or Balbc 5 to 8-week-old female mice (Charles River Laboratories, Wilmington, DE). Five mice per group for E0771 and ten mice per group in 4T1 were used. Tumors were measured using Vernier calipers by measuring the width (W) and length (L). The length was considered along the body axis. Tumor volumes (V) were calculated using the formula V = (W2 × L)/2 [8]. The tumor growth rate was calculated as volume per day as described [9]. Tumor isografts were grown to be larger than 50 mm3, followed by drug treatment. Mice were treated with 50 mg/kg of NSC243928 via IV route the first time, the IP route the second time, and the IV route was used the third time. As per guidance from the IACUC, an IP route was used in between IV injections to minimize distress to the animal tail. Mice were euthanized after the indicated treatments to harvest the tumor tissue. Blood was collected by cardiac puncture in a terminal procedure. ACD solution (Sigma Aldrich, St. Louis, MO, USA) was added to prevent the coagulation. Red blood cells were lysed using ACK lysis buffer (Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA) [10]. Next, the cells were stained with live/dead viability dye (Zombie Yellow, BioLegend, San Diego, CA, USA), followed by Fc block and stained with APC tagged CD11b and APC-Cy7 tagged Gr-1 (Ly-6G/Ly-6C), the double positive cells were taken as MDSC. The cells were analyzed using a CytoFLEX flow cytometer (Beckman Coulter Life Sciences, Indianapolis, IN, USA). The data were analyzed using FlowJo software, version 10.8.1 (Becton, Dickinson and Company, Ashland, OR, USA). Tumor isografts were collected in RPMI medium (Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA). Tissue was enzymatically dissociated in collagenase solution containing 1.5 mg/mL collagenase IV (Thermo Fisher Scientific, Invitrogen Corporation, Waltham, MA, USA) and DNase solution at 0.1 mg/mL (Roche Holdings, Basel, Switzerland) at 37 °C for 20 min, filtered through a 70-micron filter, and divided into three parts for immunostaining with antibodies in a myeloid panel, lymphoid panel, and in vitro stimulation with PMA/ionomycin and staining to analyze the T-cell activation. The myeloid and lymphoid panel were composed of the following antibodies; BV711 tagged CD45, BV421 tagged CD3, BUV737 tagged CD19, APV-R700 tagged CD11b, BV650 tagged CD11c, APC tagged F4/80, PE-Cy7 tagged MHC II, PerCP-Cy5 tagged Ly6C, BUV395 tagged Ly6G, AF488 tagged CD49b, PE tagged CD103, PE-Dazzle tagged CD4, and BV785 tagged CD8 from BioLegend, San Diego, CA. The single cells subjected to PMA/ionomycin stimulation were analyzed for T-cell activation using the following antibodies: BV711 tagged CD45, BV421 tagged CD3, BUV395 tagged CD4, AF647 tagged CD8, AF488 tagged CD107, BV650 tagged IFNγ, and PE-CY7 tagged TNFα from BioLegend, San Diego, CA. The live CD45 cells were gated for the quantification of various immune cell populations (Figure 1). An equal number of CD45 cells was selected to compare the cell population in the control vs. the treated isografts to remove the tumor size bias from the study. The flow cytometry was performed on a BD LSRII flow cytometer and the flow cytometry data were analyzed using FLOWJO software (Becton, Dickinson and Company, Ashland, OR, USA). Total RNA was isolated using the RNeasy Kit and subjected to on-column DNA digest (Qiagen Inc., Germantown, MD, USA). The RNA was quantified using the Quant-IT RiboGreen RNA Reagent (Thermo Scientific, Waltham, MA, USA) and measured with a Spectramax Gemini XPS plate reader (Molecular Devices, San Jose, CA, USA). RNA integrity was assessed using automated capillary electrophoresis on a Fragment Analyzer (Advanced Analytical Technologies Inc., Ankeny, IA, USA) with samples passing quality control for RIN values >9. A total RNA input of 200 ng was ideally used for library preparation using the TruSeq Stranded mRNA Library Preparation Kit (Illumina, San Diego CA, USA). Sequencing libraries were sequenced on a NovaSeq sequencer (Illumina, San Diego CA, USA). Paired-end reads were aligned to the mouse reference genome (mm10) using MapSplice (version v2.2.2) with fusion transcript detection enabled [11]. Gene read counts against GENCODE (version 28) “basic” gene models were calculated by HTSeq (version 0.9.1) with parameters: -s reverse -t exon -m intersection-nonempty [12]. Multi-mapping reads were discarded from the analysis. Sample read counts per gene were then normalized using EdgeR by log2 transformation with a minimum CPM of 0.5 [13]. Missing values within the cohort were imputed using the gene median value. Differential expression as performed between each case and control pair using DESeq2 with a FDR cutoff of 0.1, and a minimum fold change of 2 [14]. The most differentially expressed genes were further narrowed down using a fold change cutoff of at least 2 and FDR cutoff of 0.05. The intersection was taken to identify the genes commonly differentially expressed between all experiments and cell lines. The list of differentially expressed genes was used for pathway enrichment analysis using Pathway Studio. Paired-end reads were analyzed using the Nextflow nf-core/rnaseq pipeline [15]. Read count matrices for the control and treated isografts of each model were extracted from Star-Salmon quant.sf files and analyzed on the ImmuCC server using the local linear semi-supervised regression method. This method has been validated to predict mouse immune cell composition in RNA-Seq data [16]. The Wilcoxon rank sum test was used for statistical analysis using the RStudio program. The data were considered significant with p < 0.05 with a confidence interval not including 1 or 0 [17]. Previously, we reported that NSC243928 induces cell death in multiple triple-negative breast cancer cell lines [2]. However, the molecular mechanism of cell death was not explored. Cell death, known as immunogenic cell death (ICD), can induce an anti-tumor immune response in vivo [18]. ICD is characterized by the exposure of calreticulin (CRT) on the cell surface and the extracellular release of high mobility group box 1 (HMGB1) and ATP [19,20]. To test whether NSC243928 can directly facilitate the cell surface exposure of CRT, E0771 and 4T1 cells were treated with increasing concentrations of NSC243928. We used doxorubicin as a positive control that has been shown to induce ICD [21]. We observed that the cell surface expression of CRT in the live cells was increased in the E0771 and 4T1 cells in a dose dependent manner (Figure 2A,B). The treatment with NSC243928 led to the release of the HMGB1 protein (Figure 2C) and increased levels of extracellular ATP (Figure 2D,E) in the E0771 and 4T1 cell lines. NSC243928 induced cell death at these concentrations (Figure 2F,G). Induction of ICD is linked to the activation of the immune system in the in vivo tumor microenvironment [20]. Therefore, we tested the effect of NSC243928 treatment in vivo. We observed an immediate significant reduction in tumor growth rate and weight following two treatments in E0771 and three treatments in the 4T1 model (Figure 3A–F). These data are also described in a separate study focused on the non-immunogenic effects of NSC243928 (manuscript in preparation). Myeloid derived suppressor cells (MDSCs), defined by positive labeling of CD11b and Gr1 antibodies, are major tumor immune suppressor cells [10]. We found that peripheral MDSCs (CD11b+Gr1+) were significantly downregulated in drug-treated E0771 and 4T1 tumor models (Figure 3G–J). Systemic downregulation of MDSCs upon drug treatment indicated that treatment with NSC243928 was able to trigger an anti-tumor immune response in vivo. Thus, we looked into differential gene expression analysis to identify a broad transcriptional effect due to NSC243928 treatment in vivo. We found that a total of 228 unique genes were differentially expressed in the NSC243928 treated E0771 isograft (Table S1), and 372 unique genes were differentially expressed in the NSC243928 treated 4T1 isograft (Table S2), while 89 genes were common to the NSC243928 treated E0771 and 4T1 isografts (Table S3). Pathway Studio analysis of the differentially expressed gene list revealed that pathways associated with immune regulation and oncogenic signaling were altered in drug treated isografts (Table 1). We used an online bioinformatic tool, the seq-ImmuCC program, which uses RNA-Seq data to predict the distribution of a pre-defined set of immune cell populations [16]. NSC243928 treated E0771 isografts showed increases in B-cells, dendritic cells, mast cells, NK cells, and a decrease in the monocyte population (Figure 4A). NSC243928 treated 4T1 isografts showed an increase in mast and NK cells (Figure 4B). Tumor isografts were subjected to single cell dissociation and analyzed for the presence of intra−tumoral immune cell populations. Immune cell populations with a distinct phenotype were identified with specific markers using the gating strategy described in Figure 1. Equal numbers of live CD45 cells were used for the quantitative intra-tumoral immune cell populations in the control and treated isografts to remove the isograft bias size. Peripheral MDSCs were identified using CD11b+ and GR1+ markers. The Gr1 marker is a composite epitope between the Ly6C and Ly6G antigens. MDSCs can be further subdivided into granulocytes or polymorphonuclear MDSCs (PMN MDSCs) identified by CD11b+Ly6G+Ly6Clow cells and monocytic MDSCs (mMDSCs) identified by CD11b+Ly6C+/Ly6G− cells. PMN MDSCs are terminally differentiated MDSCs, which reside in the tumor microenvironment and suppress the antitumor immune response [38,39]. We observed that the PMN-MDSC cells were significantly downregulated in the NSC243928 treated E0771 model but not in the 4T1 model (Figure 5A). We observed that the tumor residing M-MDSC populations were not significantly altered by the NSC243928 treatment of isografts in either model (Figure 5B). Patrolling monocytes (CD11b+, Ly6Clow, Ly6G−) are associated with anti-tumor immune response [40]. Patrolling monocyte levels were significantly increased by NSC243928 treatment in both the E0771 and 4T1 models (Figure 5C). NKT (CD3+, CD49b+) cells were significantly increased in the NSC243928 treated tumor isografts from E0771 but not in the 4T1 model (Figure 5D). A subpopulation of B− cells, namely B1 cells (MHCII+, CD19+, CD11b++), which are important for anti-tumor immune response [41], was significantly increased in the NSC243928 treated tumor isografts from the E0771 model (Figure 5E). NK cells (CD3−, CD49b+) showed an upward trend but did not reach significance in the NSC243928 treated mice from both models (Figure S1A). The total B-cell (MHCII+, CD19+) levels were not altered in the NSC243928 treated tumor isografts from the E0771 model and were not detected in the NSC243928 treated tumor isografts from the 4T1 model (Figure S1B). The total T-cell (CD3+, CD11b−) levels were not altered in the NSC243928 treated tumor isografts from the E0771 and 4T1 models (Figure S1C). MHCII−TAMS (F4/80+ MHCII−, CD11b+) were not significantly altered in the NSC243928 treated tumor isografts from E0771 and were found to be slightly elevated in the 4T1 model (Figure S1D). MHCII+TAMS (F4/80+ MHCII+, CD11b+) was reduced in the NSC243928 treated tumor isografts from the E0771 and 4T1 models (Figure S1E). Flow cytometry analysis revealed that many immune cell types involved in anti-tumor response such as PMN−MDSCs, patrolling monocytes, and NKT cells were increased in the drug treated isografts. Anti-tumor immune cells are known to produce an array of cytokines such as TNFα, IFNγ, and CD107 as a measure of their activity [42]. To assess whether the immune cells that infiltrated the tumor microenvironment in the drug-treated isografts have the capability to produce cytokines, the single cell suspensions from the control and NSC243928 treated tumor isografts were subjected to an ex vivo treatment with PMA and ionomycin for 4 h before flow cytometry analysis for TNFα, IFNγ, and CD107(LAMP1) on the CD4 and CD8 positive (+) T cells. We observed increased levels of TNFα on CD4+ cells in the NSC243928-treated E0771 isografts but not from the 4T1 isografts (Figure 6A). Similarly, we observed a trend of increased TNFα producing CD8+ cells in the NSC243928-treated E0771 isografts but not in the 4T1 isografts (Figure 6B). Increased IFNγ production in the CD4+ and CD8+ cells was observed, but it did not reach significance in the NSC243928 treated E0771 isografts (Figure 6C,D). Intra-tumoral immune cells from NSC243928 treated E0771 isografts but not from 4T1 isografts showed a trend of a higher stimulation of CD107(LAMP1) CD4+ cells, but it did not reach significance (Figure 6E,F). These data indicate that CD4+ and CD8+ T−cells from the NSC243928-treated E0771 isografts were able to generate increased cytokine production compared to the NSC243928-treated 4T1 isografts. Breast cancer is one of the malignancies still to see the benefits of immunotherapy advances [43]. Mammary tumor E0771 and 4T1 syngeneic mouse models have been implemented to study drug efficacy including novel immune-oncology drugs for human luminal B, Her2 positive, and stage IV triple negative breast cancer [6,7,44,45,46,47,48]. Thus, we selected these models to determine whether NSC243928 may suppress tumor growth and induce an anti-tumor immune response. Previously, we observed that NSC243928 induced cell death in cancer cells. Here, we showed that NSC243928 was able to induce a specific form of cell death known as immunogenic cell death (ICD) in E0771 and 4T1. Although the precise mechanisms of NSC243928induced ICD remain to be discovered, we were able to show that NSC243928 induced a tumor reduction and anti-tumor immune response in both models. It was shown that TGFβ signaling associated with fibrotic regulation of the extracellular matrix requires CRT expression, a hallmark of ICD, during ER stress [49]. We previously showed that LY6K is required for canonical TGFβ/Smad signaling, but the effect is still to be determined in fibrotic TGFβ signaling, leading directly or indirectly to ICD. This line of investigation will be pursued in the future to determine whether the NSC243928 effect on TGFβ signaling is associated with ICD response in vitro and in vivo. We observed that the functional effect of ICD manifested as systemic anti-tumor immune response in drug treated isografts. NSC243928 treatment led to reduced MDSCs (CD11b+ Gr1+) in the peripheral blood, which supports the anti-tumor immune effects of NSC243928 in vivo. MDSCs (CD11b+ Gr1+) can differentiate into PMN MDSCs (CD11b+Ly6G+Ly6Clow) and M MDSCs (CD11b+Ly6G+Ly6C−) in the tumor microenvironment [38]. PMN MDSCs are terminally differentiated cells that have potent immunosuppressive function leading to sustained tumor growth [50,51]. We observed that NSC243928 treatment significantly decreased intra-tumoral PMN MDSCs in E0771 model but not in the 4T1 model. It is plausible that NSC243928 treatment modulates the tumor microenvironment specific to E0771 resulting in suppression of pro-tumorigenic PMN−−MDSCs. Monocytes are important immune cell components, with the classical monocytes having tumor promoting action and the non−classical, or patrolling monocytes with CD11b+, Ly6Clow, Ly6G− phenotype having potent anti-cancer and anti-metastatic properties [40]. They help to recruit and activate NK cells and play an important role in immunosurveillance [52,53,54]. NSC243928 treatment led to significantly increased patrolling monocytes in both models, suggesting that this can be an important pathway mechanism associated with the in vivo action of NSC243928. NKT and B1 (MHCII+, CD19+, CD11b+) cells are of emerging interest in the immune-oncology field as these cells have the potential to mount a direct cancer cell death response, so they are part of the adaptive immune response and they have an innate immune response component [41,55]. The NKT cells were significantly increased in the drug treated E0771 model; this was also increased in the drug treated 4T1 model, but the level of significance was not reached. B1 cells were not detected in the 4T1 model. These data indicate that the E0771 mammary tumor model may have a robust anti-tumor response upon NSC243928 treatment. We did not observe the expected effects of NSC243928 treatment on the tumor associated macrophages (TAMs) (F4/80+ MHCII+, CD11B+, CD11c int.) that are associated with the tumor microenvironment [56,57]. Cytokine release from immune cells in response to activation stimuli is considered as a surrogate marker for their activity. To test the activity of immune cells, the single cell suspension from isografts were stimulated with PMA-ionomycin and we observed that increased TNFα, IFNγ, and CD107 were expressed by the CD4 and CD8 positive immune cells in the E0771 model, although significance was not reached. In summary, we observed that NSC243928 treatment of the E0771 isograft tumor model led to significant changes in many immune markers that are desired to be employed in immuno-oncology (IO) therapies including PMN-MDSCs (neutrophils), NKT, and B1 cells. These changes were not observed in the NSC243928 treatment of 4T1 isograft tumor models. These results suggest that intrinsic properties of tumors will dictate the anti-tumor immune response triggered by NSC243928. E0771 represents a luminal B subtype and more precisely, the ERα−, ERβ +, PR+, and ErbB2 + phenotypes, which respond to anti-estrogen treatments [7]. The 4T1 model represents a triple negative phenotype, which is not responsive to anti-estrogen treatments [6]. Because NSC243928 treatment led to significantly increased patrolling monocytes in both models, this suggests that it could be a more important immune cell type of NSC243928 action. Clinical development for this novel drug like molecule may focus on testing whether NSC243928 treatment can increase the anti-tumor immune response in combination with anti-estrogen therapy for luminal B tumors and in combination with chemotherapy in triple negative breast cancer. Future studies are warranted to delineate the immune vs. non-immune effects of NSC243928 in vivo using mouse models.

PMC10000933

Artem Pilunov,Dmitrii S. Romaniuk,Anton Shmelev,Savely Sheetikov,Anna N. Gabashvili,Alexandra Khmelevskaya,Dmitry Dianov,Ksenia Zornikova,Naina T. Shakirova,Murad Vagida,Apollinariya Bogolyubova,Grigory A. Efimov

Transgenic HA-1-Specific CD8+ T-Lymphocytes Selectively Target Leukemic Cells

03-03-2023

adoptive transfer,acute myeloid leukemia,transgenic TCR,allo-HSCT,minor histocompatibility antigens,HA-1

Simple Summary A relapse of the malignant disease frequently occurs after allogeneic hematopoietic stem cell transplantation. Immune recognition of minor histocompatibility antigens, the polymorphic peptides that differ between donor and recipient, often triggers a beneficial graft-versus-leukemia response. The transgenic donor-derived cytotoxic T cells, which recognize patient-specific minor histocompatibility antigens presented by hematopoietic cells, allow precise elimination of malignant recipient cells while sparing both donor and non-hematopoietic patient cells. We generated the MiHA-specific T cells by gene editing to knock out the endogenous T cell receptor, followed by lentiviral transduction of HA-1-specific T cell receptors. Modified T cells demonstrated cytotoxicity against leukemia cells from HA-1+ donors with acute myeloid leukemia, acute T-cell, and B-cell lymphoblastic leukemia. Transgenic T cells showed no cytotoxicity against donor cells lacking HA-1 surface presentation. The proposed therapeutic approach could be used after allogeneic hematopoietic stem cell transplantation to prevent and treat leukemia relapse. Abstract A significant share of allogeneic hematopoietic stem cell transplantations (allo-HSCT) results in the relapse of malignant disease. The T cell immune response to minor histocompatibility antigens (MiHAs) promotes a favorable graft-versus-leukemia response. The immunogenic MiHA HA-1 is a promising target for leukemia immunotherapy, as it is predominantly expressed in hematopoietic tissues and presented by the common HLA A*02:01 allele. Adoptive transfer of HA-1-specific modified CD8+ T cells could complement allo-HSCT from HA-1- donors to HA-1+ recipients. Using bioinformatic analysis and a reporter T cell line, we discovered 13 T cell receptors (TCRs) specific for HA-1. Their affinities were measured by the response of the TCR-transduced reporter cell lines to HA-1+ cells. The studied TCRs showed no cross-reactivity to the panel of donor peripheral mononuclear blood cells with 28 common HLA alleles. CD8+ T cells after endogenous TCR knock out and introduction of transgenic HA-1-specific TCR were able to lyse hematopoietic cells from HA-1+ patients with acute myeloid, T-, and B-cell lymphocytic leukemia (n = 15). No cytotoxic effect was observed on cells from HA-1- or HLA-A*02-negative donors (n = 10). The results support the use of HA-1 as a target for post-transplant T cell therapy.

Transgenic HA-1-Specific CD8+ T-Lymphocytes Selectively Target Leukemic Cells A relapse of the malignant disease frequently occurs after allogeneic hematopoietic stem cell transplantation. Immune recognition of minor histocompatibility antigens, the polymorphic peptides that differ between donor and recipient, often triggers a beneficial graft-versus-leukemia response. The transgenic donor-derived cytotoxic T cells, which recognize patient-specific minor histocompatibility antigens presented by hematopoietic cells, allow precise elimination of malignant recipient cells while sparing both donor and non-hematopoietic patient cells. We generated the MiHA-specific T cells by gene editing to knock out the endogenous T cell receptor, followed by lentiviral transduction of HA-1-specific T cell receptors. Modified T cells demonstrated cytotoxicity against leukemia cells from HA-1+ donors with acute myeloid leukemia, acute T-cell, and B-cell lymphoblastic leukemia. Transgenic T cells showed no cytotoxicity against donor cells lacking HA-1 surface presentation. The proposed therapeutic approach could be used after allogeneic hematopoietic stem cell transplantation to prevent and treat leukemia relapse. A significant share of allogeneic hematopoietic stem cell transplantations (allo-HSCT) results in the relapse of malignant disease. The T cell immune response to minor histocompatibility antigens (MiHAs) promotes a favorable graft-versus-leukemia response. The immunogenic MiHA HA-1 is a promising target for leukemia immunotherapy, as it is predominantly expressed in hematopoietic tissues and presented by the common HLA A*02:01 allele. Adoptive transfer of HA-1-specific modified CD8+ T cells could complement allo-HSCT from HA-1- donors to HA-1+ recipients. Using bioinformatic analysis and a reporter T cell line, we discovered 13 T cell receptors (TCRs) specific for HA-1. Their affinities were measured by the response of the TCR-transduced reporter cell lines to HA-1+ cells. The studied TCRs showed no cross-reactivity to the panel of donor peripheral mononuclear blood cells with 28 common HLA alleles. CD8+ T cells after endogenous TCR knock out and introduction of transgenic HA-1-specific TCR were able to lyse hematopoietic cells from HA-1+ patients with acute myeloid, T-, and B-cell lymphocytic leukemia (n = 15). No cytotoxic effect was observed on cells from HA-1- or HLA-A*02-negative donors (n = 10). The results support the use of HA-1 as a target for post-transplant T cell therapy. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is widely used for the treatment of acute myeloid leukemia (AML) and lymphoblastic leukemia (ALL) [1,2]. The therapeutic effect of allo-HSCT is largely mediated by the graft-versus-leukemia (GVL) response, in which residual recipient malignant cells are eliminated by the donor lymphocytes [3,4,5]. However, a significant proportion of the patients experience disease relapse [6,7,8,9,10]. Anti-relapse therapies include tyrosine kinase inhibitors, monoclonal antibodies, and antibody-based conjugates specific for CD19, CD20, CD22, and CD52 for lymphoblastoid leukemias; CD33, CD123, CD13, CLL-1, and CD38 [11,12] for myeloid malignancies [11,12]. However, pharmaceutical interventions remain challenging due to low efficacy and adverse side effects [13]. Patients with relapsed and refractory AML have a particularly poor prognosis [14]. Chemotherapy is still the most common treatment for relapsed and refractory AML [15]. A combination of venetoclax and hypomethylating agents was beneficial in the relapsed/refractory AML although the effect of such therapy is transient and it is best used as bridge therapy before HSCT [16,17]. Therapies targeting to IDH1/2 or FLT3 have significantly expanded the arsenal of treatment options, but they are still not curative [18,19]. Among antibody-based therapies, gemtuzumab ozogamicin had the highest efficacy, but its approval was withdrawn due to toxicity [20]. Therefore, allo-HSCT remains the most reliable option for patients with AML. Patients who relapse after transplantation are particularly in need of novel therapies [21]. Donor and recipient cells could be distinguished by their cell surface presentation of minor histocompatibility antigens (MiHAs), the peptides derived from proteins with polymorphic amino acids and presented by HLA molecules [22,23]. The donor immune response directed against the recipient MiHAs expressed predominantly or exclusively in the hematopoietic tissue could result in a beneficial GVL response without graft-versus-host disease (GVHD) [22,24]. The minor histocompatibility antigen HA-1 is a promising target for several reasons. First, it is presented by the HLA allele (HLA-A*02:01) common in the Caucasian population [25]. Second, its encoding gene ARGHAP45 (HMHA1) is exclusively expressed in hematopoietic tissue, including hematological malignancies [26,27,28]. HA-1 is derived from a histidine-encoding allelic variant (VLHDDLLEA) of the polymorphism rs1801284 [25]. Its arginine-encoding allelic counterpart VLRDDLLEA is non-immunogenic due to insufficient binding affinity with HLA-A*02:01 [29]. The genotype frequencies of rs1801284 for the A/A, A/G (immunogenic) and G/G (non-immunogenic) HA 1 variants are 16%, 36%, and 48%, respectively. Therefore, approximately half of the allo-HSCT recipients carry at least one HA-1 allele resulting in 25% of transplants being mismatched by this antigen [27]. It has been observed that recipients receiving HA-1-mismatched grafts in HLA-A*02-matched transplantations have lower relapse rates compared to the patients with HA 1 matched pairs [30,31]. Furthermore, the presence of HA-1-specific T cell clones after infusion of the donor lymphocytes was associated with a better outcome [32,33]. Therefore, the therapeutic strategy based on the adoptive transfer of HA 1 specific T cells could potentially be used as a targeted method to eradicate the residual disease while sparing the healthy hematopoietic system of the donor origin and non-hematopoietic tissues of the patient [22,24]. To generate HA-1-specific T cells, donor CD8+ T cells could be modified by lentiviral transduction to express a high-avidity transgenic HA-1-specific T cell receptor (TCR) [34,35]. In this study, we report the development of HA-1-specific T cell immunotherapy. Using the Jurkat J76 reporter T cell line [36], we have determined a set of functional HA-1-specific TCRs, estimated their affinity, and investigated their cross-reactivity against the panel of peripheral mononuclear blood cells (PBMC) with common HLA alleles. Three TCRs selected for their high affinity and the lack of cross-reactivity showed a response to PBMC with endogenously processed HA-1 peptide. Endogenous TCR was knocked out by CRISPR/Cas in primary CD8+ T cells; a selected HA-1-specific TCR was then introduced by lentiviral transduction. Such HA-1-specific CD8+ T cells showed in vitro cytotoxicity against blood cells from HA-1–positive leukemia patients, but not against cells from HA-1 or HLA-A*02-negative patients. The described pipeline for the selection of the HA-1-specific TCRs and the production of the MiHA-specific CD8+ cells could be applied to a wide variety of different MiHAs presented by the other HLA alleles. The study was approved by the Research Ethics Committee of the National Research Center for Hematology (Protocol № 126, 25 February 2022). All donors and patients gave informed consent before enrollment. Blood samples were collected during the patients’ hospitalization. Peripheral mononuclear blood cells (PBMC) were isolated from a whole blood sample by Ficoll gradient centrifugation (Paneco, Moscow, Russia). PBMC were stored frozen in FBS (Gibco, Paisley, UK) in the presence of 7% DMSO at −80 °C. DNA from the blood samples was extracted using QIAamp DNA Blood Mini kit and QIAcube system (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. HLA typing was performed either by next-generation sequencing (NGS) [37] or by flow cytometry (Supplementary Table S1) [38] (HLA-A*02 status only). The method used for the HLA testing for each sample is listed in Supplementary Table S4. Samples that were determined to be HLA-A*02-positive by flow cytometry but did not have HLA-A*02:01 allele, were discriminated by the J76 stimulation assay (Materials and methods, determining functionality and affinity of HA-1-specific TCR) and excluded from the analysis. The NGS libraries were prepared using AllType NGS amplification kits (One Lambda, Los Angeles, CA, USA) and sequenced using MiSeq Reagent Kit v2 (Illumina, San Diego, CA, USA). HLA genotyping was performed using the TypeStream Visual Software v2.0.0.68 (TSV) (One Lambda, Los Angeles, CA, USA) and the IPD-IMGT/HLA 3.40.0.1 database [39]. HA-1 genotyping was performed as previously described [40]. Naive CD8+ T cells from HA-1 donors (rs1801284 G/G) were isolated from PBMC using a naive T cell immunomagnetic isolation kit (130-045-201, Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s protocol. Cells were cultured in RPMI medium (Gibco, Paisley, UK) supplemented with 10% FBS, 50 U/mL IL-2 (Biotech, Moscow, Russia), 0.5 U/mL IL-7 and 0.8 U/mL IL-15 (130-095-362 and 130-095-765, Miltenyi Biotec, Bergisch Gladbach, Germany), and 100 U/mL penicillin/streptomycin (Gibco, Paisley, UK). For the isolation of dendritic cells (DCs), monocytes from the same donor were purified using anti-CD14 immunomagnetic beads (130-050-201, Miltenyi Biotec, Bergisch Gladbach, Germany) and cultured in RPMI medium supplemented with 10%FBS, 100 U/mL penicillin/streptomycin, 800 U/mL GM-CSF (130-093-864, Miltenyi Biotec, Bergisch Gladbach, Germany), 100 U/mL IL-4 (130-095-765, Miltenyi Biotec, Bergisch Gladbach, Germany), 10 ng/mL lipopolysaccharide (L2630-10MG, Sigma Aldrich, Taufkirchen, Germany), and 100 U/mL IFN-γ (130-093-864, Miltenyi Biotec, Bergisch Gladbach, Germany). After 3 days, DCs were detached with a cell scraper and irradiated for 50 min with a total dose of 50 Gy using Biobeam GM 8000 (Gamma-Service Medical, Leipzig, Germany). Irradiated DCs were pulsed with synthetic HA-1 peptide (LifeTein, Somerset, USA) at a final concentration of 5000 ng/mL (4.88 nmol/mL). Naive CD8+ T cells were co-cultured with the irradiated DCs of the same donor (or, in the case of HLA-A*02:01- donors, with allogeneic DCs from HLA-A*02:01+ donors) for 10 days as described previously [41]. Briefly, cells were seeded in 48-well suspension plates at a density of 2 × 105 to 1 × 106 per well in RPMI media supplemented with 10% FBS, 100 U/mL penicillin/streptomycin, 30 ng/mL IL-21 (130-095-784, Miltenyi Biotec, Bergisch Gladbach, Germany). The ratio of plated CD8+ naive T cells to DC cells was 2:1 or 4:1. Both cytokines IL-7 and IL-15 (Miltenyi Biotec, Bergisch Gladbach, Germany) were added at concentrations of 100 U/mL each on days 3, 5, and 7. After co-culture, the T cell cultures were screened for antigen specificity by restimulation followed by CD137 or direct tetramer staining as described in the Supplementary Methods (CD8+ T cell activity assays and flow cytometry analysis). Tetramer+ or CD137+ cells of T cell expansions were selected using anti-PE immunomagnetic beads (Miltenyi Biotec, Bergisch Gladbach, Germany), and RNA was purified using RNEasy mini columns (Qiagen, Hilden, Germany) followed by cDNA library synthesis as described previously [42]. Briefly, a universal primer specific for the α or β TCR constant region was used to prime a reverse transcription reaction using high precision Moloney murine leukemia virus reverse transcriptase (SMARTScribe, Takara, Kusatsu, Japan), which proceeds according to the method of rapid amplification of complementary DNA (cDNA) 5′ end (5′RACE) [43]. After reverse transcription, a unique molecular identifier (UMI) and a sample barcode were inserted at the 3′ end of the first cDNA strand via a template switch. Next, the cDNA chains were amplified using two-step nested PCR. In the second step of the nested PCR, Illumina sequencing adaptors were introduced. α and β TCR libraries were generated separately from a single cDNA library. The PCR libraries were then sequenced using the Illumina MiSeq Kit v2 and the MiSeq system (Illumina, San Diego, CA, USA). NGS data were analyzed using the MiXCR and VDJtools frameworks [44,45]. The number of reads of each CDR3 sequence in the antigen-specific fraction and flowthrough was compared for statistically significant (exact Fischer test, p < 0.05) and strong (10-fold or greater) enrichment. α- and β-chains enriched in CD137+ or tetramer+ fractions from the same expansion well were used for cloning into lentiviral modules as described below. For transgenic TCR reactivity assays, the J76 reporter cell line was used [36]. This cell line lacks endogenous TCR and is engineered to express fluorescent proteins under promoters activated by TCR signaling. In our readouts, we measured the GFP expression driven by the NFAT promoter. To increase the sensitivity of the system, we generated a J76 cell expressing transgenic CD8, CD2, and CD28 by lentiviral transduction (see Supplementary Materials, lentiviral transduction of cell lines). K562 cells (ATCC CCL-243™) with transgenic HLA-A*02:01 were used for activation assays. J76 cells harboring a transgenic TCR were prepared as described (Materials and methods, Transgenic TCR assembly, and Supplementary Materials, Lentiviral transduction of cell lines). For the TCR stimulation assay, 5 × 105 of the obtained TCR transgenic J76 cells were incubated overnight with 1 × 106 K562-HLA-A*02 cells that were pulsed with HA-1 peptide to a final concentration of 4,88 nmol/mL. Activation of J76 cells was determined by GFP expression using flow cytometry (Figure 1C). GFP-positive J76 cells were considered to have functional HA-1-specific TCR. Cell lines with functional TCRs were enriched by immunomagnetic separation for CD3 (130-050-101, Miltenyi Biotec, Bergisch Gladbach, Germany) and then sorted using anti-TCRα antibody (Sony, Tokyo, Japan, 2133590) staining on the BD FACS Aria III cell sorter. The sorted J76 cell lines were used to measure TCR affinity. J76 cells, previously labeled with lipophilic DID stain (Invitrogen, Waltham, USA), were stimulated with 10 fivefold serial dilutions of HA-1 peptide, starting with the highest concentration of 2.5 × 104 ng/mL (24.4 nmol/mL). We co-cultured 1.25 × 105 of J76 cells with K562-A*02:01 cells in a 1:2 ratio with an indicated amount of peptide in a 96-well plate in duplicate. After overnight stimulation, the percentage of GFP-expressing DID-positive J76 cells was analyzed using a MACS Quant flow cytometer (Miltenyi Biotec, Bergisch Gladbach, Germany). EC50 values for the plotted TCR titration curves were estimated using GraphPad Prizm 9 software (GraphPad, San Diego, CA, USA). Stimulation of sorted J76 cells by PBMC from healthy donors and leukemia patients was performed as follows: PBMC were seeded in a 96-well plate at a density of 2.5 × 105 cells/well and 1.25 × 105 DID-labeled J76 cells were added. Stimulation was performed in triplicate. As a positive control for each triplicate, exogenous HA-1 peptide at a concentration of 4.88 nmol/mL was added to the fourth well. After overnight cocultivation at 37 °C and 5% CO2, GFP expression was measured by flow cytometry using MACS Quant. Samples that were determined to be HLA-A*02 positive by flow cytometry but failed to elicit a J76 response in a positive control assay were excluded from the analysis as HLA-A*02:01 negative. The cytotoxic assay to evaluate the functional activity of CD8+ T cells modified with transgenic TCRs was performed by monitoring caspase 3/7 levels by flow cytometry analysis and IFN-γ by ELISA. For the caspase-killing assay, 2.5 × 105 PBMC were seeded in a 48-well plate and 1.25 × 106 DID-labeled effector CD8+ T cells were added. The experiment was performed in triplicate. As a positive control, 4.88 nmol/mL HA-1 peptide (LifeTein, Somerset, NJ, USA) was added and as a negative control, mock (PBS) transduced effectors were added. After overnight cocultivation at 37 °C and 5% CO2, cells were transferred to a round-bottomed 96-well plate, pelleted by centrifugation at 300× g for 5 min, and stained using the CellEvent Caspase-3/7 Green Flow Cytometry Assay Kit (eBioscience, San Diego, CA, USA) according to the manufacturer’s protocol. Plates were analyzed on a MACS Quant flow cytometer. The percentage of caspase-3/7 and 7AAD-positive events in the DID-negative fraction was measured to determine the cytotoxic effect. The functional activity of TCR-modified CD8+ T cells was followed by the amount of IFN-γ produced in the media, measured by the IFN-γ ELISA assay (Hema, Moscow, Russia) according to the manufacturer’s protocol. Cells in the functional assay were stimulated with serial dilutions of HA-1 peptide as described above. Expansions of CD8+ T clones were screened with irradiated B lymphoblastoid cells (B-LCL) from the same donor. The B-LCL cells (Supplementary Methods, LCL generation) were irradiated (50 min, 5 Gy) and tested for antigen specificity loaded with 4.88 nmol/mL HA-1 peptide. CD137 expression was measured by flow cytometry after 16 h of stimulation (Supplementary Methods, flow cytometry analysis). For rapid cloning of the desired TCR, we developed a modular Golden Gate assembly system (Figure 1B). Human constant TCR chains were synthesized by RT-PCR from mRNA isolated from the PBMC of a healthy donor. Murine constant TCR chains [46] were codon-optimized for human expression using the iCodon algorithm and synthesized by Evrogen LLC, Russia. To stabilize the recombinant TCR receptors, the cysteine substitutions were additionally introduced into all constant chains [47]. The TCR α and β chains were spanned by the P2A peptide to ensure chain separation during translation [48]. The variable α and β chains selected for TCR cloning were amplified using a pair of primers for specific V and J segments flanked by the BpiI sites and the corresponding NGS libraries as templates. The PCR products were analyzed and purified by 1% agarose gel electrophoresis using the Gene Jet Gel Extraction Kit (Thermo Fisher Scientific, Waltham, MA, USA) and either used directly for Golden Gate assembly of the final lentiviral constructs or subcloned into the pJet1.2 plasmid (Thermo Scientific, Waltham, MA, USA). Unwanted BpiI sites were removed from the variable chains by PCR mutagenesis. The final plasmid was constructed from modules containing two variable and two constant chain regions and the lentiviral backbone vector by the Golden Gate assembly reaction [49] using BpiI restriction endonuclease (Thermo Fisher Scientific, Waltham, MA, USA) and T4 ligase (Thermo Fisher Scientific, Waltham, USA). The Golden Gate assembly was performed by 9 to 30 cycles of 15 min restriction at 37 °C followed by 10 min ligation at 16 °C. The reaction was terminated by 5 min at 55 °C. The reaction mix was then transformed into NEB stable competent cells (NEB, Ipswich, USA), and transformants were screened by PCR. Plasmid constructs were isolated using the Gene Jet Miniprep kit (Thermo Fisher Scientific, Waltham, MA, USA) and sequenced using the SeqStudio Genetic Analyzer (Applied Biosystems, Waltham, MA, USA). The purified primary CD8+ T cells were activated using T cell anti-CD3/2/28 activation/expansion kit (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s protocol, and further cultivated for 72h in the RPMI media supplemented with 10% FBS, 500 U of IL-2, 100 U of IL-15, and 100 U/mL penicillin/streptomycin. The non-treated 24-well culture plates (Sarstedt, Nümbrecht, Germany) were coated with 30 µg/mL retronectin (Takara, Kusatsu, Japan) and blocked with 2% BSA solution (Sigma Aldrich, Taufkirchen, Germany) at 37 °C and 5% CO2 overnight to be used for lentiviral transduction. Activated CD8+ T cells (1 × 106/well) were seeded into retronectin-coated plates, and thawed lentiviral supernatant (Supplementary Materials, lentivirus manufacturing) was added to achieve MOI~6. Plates were then centrifuged at 1000× g during 45 min at 32 °C and cultured at 37 °C and 5% CO2 for 72h. Cells expressing transgenic murine TCR were purified by staining with anti-mouse TCR β antibody, followed by the anti-APC immunomagnetic separation on beads (130-090-855, Miltenyi Biotec, Bergisch Gladbach, Germany), and further expanded for a total of 4–5 weeks with fresh medium changed every week. A total of 2.5 × 106 activated T cells were electroporated with ribonucleoprotein (RNP) complexes (see Supplementary Materials, Ribonucleoprotein complexes) using the Neon transfection system (Thermo Fisher Scientific, Waltham, MA, USA). We added 20 µL of RNP complexes to T cells in 90 µL of electroporation buffer T supplemented with 11 µM electroporation enhancer (IDT, San Diego, MA, USA). Electroporation pulse parameters were 1600 V, 10 ms, 3 pulses. If further lentiviral transduction was required, T cells were transferred to retronectin-coated plates with virus-containing medium immediately after electroporation and were then proceeded to spinfection as described previously. Alternatively, T cells were immediately transferred into 1.5 mL of pre-warmed medium in a 24-well plate and incubated for 72 h at 37 °C and 5% CO2 for further flow cytometric analysis to determine TCR knockout efficacy. For the gRNA efficacy screening, Jurkat E6-1 cells (ATCC TIB-152™) were electroporated as described above. We expanded naive CD8+ T cells of five healthy HLA-A02:01+ HA-1 donors. We complemented them with expansions of T cells from three HLA-A02:01 donors (Figure 1A and Figure S1), as allogeneic expansions have been previously reported to be a source of TCRs [50,51]. The antigen-specific T cells were detected in 16–50% of the individual expansion wells (Supplementary Table S2), indicating that the naive HA-1−specific T cells were relatively rare. TCRs were sequenced in the flow-through and antigen-specific fractions (Materials and methods, Supplementary Materials). Sequencing revealed that 50 α and 68 β chains were strongly (10-fold or higher) and significantly (exact Fischer test, p < 0.05) enriched in the antigen-specific fraction, indicating they belonged to the HA-1–specific TCRs (Supplementary Figure S2). The CDR3 regions discovered here, plus previously published [35,52] HA-1−specific α and β TCR chains, were clustered according to the Levenshtein distance (Figure 2A). The analysis showed a low level of homology; the majority of CDR3 sequences were unique and did not belong to any homology cluster. TRBV7–9 was the most abundant V-gene among TCR β chains (Figure 2B), suggesting the importance of its C-terminal amino acids for HA-1 peptide recognition. No significant bias in V-gene usage was found among α chains. Analysis of V and J gene combinations did not reveal any V–J pairs that were significantly more frequent than others. Compared to TCR repertoires specific for the well-studied epitopes of cytomegalovirus (KLG and NLV) and Epstein–Barr virus (AFV) (data from VDJdb), TRBV genes of the HA-1−specific TCR repertoire had significantly lower diversity (Shannon diversity index 1.6) (Supplementary Figure S3), and a similar low diversity was observed in the GIL-specific repertoire (Influenza A). For TRAV genes, the diversity index did not differ significantly between the repertoires (Shannon diversity 3.3). The CDR3 length varied from 8 to 19 and 11 to 17 amino acids for α and β chains, respectively (Supplementary Figure S4). The length distribution was normal with a mode of 13 amino acids for β chains (n = 35) and 14 amino acids for α chains (n = 14). We cloned 25 β and 29 α bioinformatically discovered TCR chains and assembled 48 recombinant TCRs combining chains obtained from the same expansion well (supplementary Table S3). Using the J76 reporter cell line [36], we identified 13 functional HA-1-specific TCRs. The EC50 values calculated from the peptide–titration curves (Supplementary Figure S5) are shown in Figure 3A. The measured affinities of the TCRs mostly varied in the typical for TCR range of 10–100 µM [53]. Staining with recombinant pMHC dextramer showed some discrepancy with the results of the functional titration assay (Figure 3B). For example, ER6 and ER8, both determined to have high affinity in the functional titration assay, showed low levels of dextramer binding. The medium affinity receptor ER29 and the low affinity receptors ER4, PKS3, ER17, and ER23 showed no dextramer binding. However, it is known that functional avidity is a better representation of TCR activity and does not correlate uniformly with the affinity of the TCR to the peptide–MHC complex [54]. Surprisingly, some TCRs derived from the tetramer-enriched population (ER17, ER29) failed to bind dextramer when expressed in J76 cells. The most plausible explanation could be the different expression levels of transgenic TCR and CD8 expression in J76 cells compared to primary CD8+ T cells. TCR PKS11 obtained from the allogeneic expansion showed the highest level of dextramer binding. However, functional titration showed its activation even at the lowest peptide concentration (Supplementary Figure S5), suggesting peptide-independent HLA recognition. Stimulation with healthy donor PBMC confirmed that PKS11 was alloreactive to HLA-A*02:01 (Supplementary Figure S6). For further analysis, we selected three TCRs that showed both high affinity in titration assays, high dextramer binding, and were not alloreactive to HLA-A*02: ER6, ER12, and ER28. The transgenic J76 cells modified with the ER6, ER12, and ER28 TCR receptors were stimulated by PBMC from healthy donors or leukemia patients with HLA-A*02:01+ HA-1+/+, HA-1+/−, HA-1−/− and HLA-A*02:01− (Figure 3C,D). PBMCs from the majority of HA-1+/+ and HA-1+/− healthy donors were able to elicit a reporter cell line response, whereas no activation was observed upon stimulation by the cells from HA-1−/− or HLA-A*02:01− donors (Figure 3C). HA-1+/+ healthy donor cells activated a higher percentage of reporter T cells compared to HA-1+/− cells, which probably could be explained by higher abundance of HA-1-HLA complexes on the cell surface. No such difference in reporter activation was observed for PBMC from leukemia patients (Figure 3D). Furthermore, PBMC from leukemia patients were found to have lower HLA-A*02:01 expression levels than PBMC from healthy donors (Supplementary Figure S7); downregulation of HLA expression is one of the mechanisms of tumor immune evasion [55]. At the same time, we did not observe any obvious correlation between the diagnosis or the percentage of blast cells in the sample and the level of reporter activation (Supplementary Table S4). Of the three HA-1-specific receptors tested, ER28 demonstrated the highest level of activation upon antigen stimulation. To confirm the absence of cross-reactivity, we incubated J76 reporter cells with PBMC samples from 21 healthy donors with the most frequent HLA alleles (Supplementary Table S5). Neither cell line was activated by PBMC from 21 donors that were negative for HA-1 or HLA-A*02:01 (Supplementary Table S5). Murinization of transgenic TCR constant chains and cysteine modification are widely used in T cell therapy; they increase the expression level of transgenic TCR and allow direct measurement of transduction efficiency by flow cytometry analysis [46,56,57]. Primary CD8+ T cells from an HLA-A*02:01− donor were transduced with three murinized HA-1-specific TCRs; transgenic TCR expression in the cells with intact endogenous TCR as assessed by flow cytometry was 8–9% for all three cultures (Supplementary Figure S8). Transduced cultures secreted IFN-γ in a dose-dependent manner upon exogenous HA-1 peptide stimulation, with cultures carrying each of the three TCRs secreting the same amount of IFN-γ at the maximum peptide concentration (Figure 4A). Considering the results of dextramer staining, peptide titration experiments, and stimulation with donor and patient-derived PBMC, the TCR ER28 was selected as the most promising TCR candidate for further analysis. First, we compared the knockout efficiency of previously published gRNAs and a set of gRNAs provided by the Synthego CRISPR design tool (Supplementary Table S6) [58,59]. Published gRNAs demonstrated the most efficient TCR knockout in the Jurkat E6-1 cell line (Supplementary Figure S9A) and primary CD8+ T cells from healthy donors, reaching 80–90% knockout efficiency (Supplementary Figure S9B). Second, TCR knockout increased the MFI of HA-1-HLA-A*02:01 dextramer staining of primary CD8+ T cells transduced with HA-1-specific TCR ER28 by 40–90% (Figure 4B). The increase in dextramer binding intensity after TCR knockout suggests that transgenic TCR expression was enhanced due to reduced competition with endogenous TCR chains. Finally, we demonstrated the cytotoxic effect of HA-1 TCR-transgenic CD8+ T cells on PBMC from leukemia patients. CD8+ T cells from two HLA-A*02:01− donors were subjected to endogenous αβ TCR knockout, transduced with ER28 TCR containing murine constant chains, magnetically sorted for murine TCR expression, and expanded more than tenfold (Supplementary Figure S10). HA-1 dextramer staining after two weeks in culture showed that > 90% of cells in enriched cultures were HA-1-specific (Supplementary Figure S11). HA-1-specific CD8+ T-lymphocytes exhibited marked cytotoxicity against PBMC from leukemia patients with HA-1+/+ and HA-1+/− genotypes, while no cytotoxic effect was observed against cells without HA-1 or HLA-A*02:01 expression (Figure 4C,D). The addition of exogenous HA-1 peptide increased the killing, confirming that the cytotoxic response was directed against the HA-1 peptide (Figure 4E). While the majority of PBMC samples belonged to patients with AML (Supplementary Table S4), the cytotoxic effect was also demonstrated in other diagnoses and was independent of blast percentage (Supplementary Figure S12), supporting the evidence that HA-1 is a universal hematological target suitable for immunotherapy of a broad range of hematological malignancies. Among all types of T cell immunotherapy, chimeric antigen receptor T cells (CAR-T) currently dominate, with more than 600 active clinical trials underway [60] and more than 80 potential targets identified [61]. Compared to CAR-T, TCR-T therapy is still in its infancy, with approximately 100 clinical trials targeting a total of 19 antigens [62,63]. This discrepancy could be explained by the complexity of TCRs and their antigen identification, which requires a laborious ex vivo culture of T cells, their expansion, and subsequent analysis of the TCR repertoire [64]. However, the therapeutic application of CAR-T is limited for certain diagnoses due to the lack of suitable cell surface targets. The therapy-induced myelotoxicity limits the use of CAR-T to a bridging therapy prior to allo-HSCT [65]; therefore, other therapeutic approaches are needed [66]. Another area where TCR-T has some advantages over CAR-T is in the treatment of solid tumors [67]. The factors that complicate the use of T cell therapy in solid tumors are poor tumor infiltration, immunosuppressive microenvironment, and tumor antigen heterogeneity [68]. Many surface antigens are not essential for cancer progression and their expression could be easily ablated by cancer cells, or the targetable antigens are expressed at low levels; in addition, most antigens available for CAR-T are TAAs, which significantly increases the chance of on-target off-tumor toxicity [69]. TCR-T therapy has the advantage of targeting a diverse set of solid tumor antigens: in contrast to the CAR-T approach, which exclusively targets surface molecules, TCR-T could potentially target any protein if its peptides are presented by the appropriate HLA [70]. Thus, the patient-specific TCR repertoire of tumor-infiltrating lymphocytes could be used to advantage in TCR-T therapy [71]. As shown in a recent study, claudin-specific CAR-T cells could be efficiently stimulated by an mRNA vaccine that induced dendritic cells to express CLDN6 on their surface [72]. The same strategy could be applied more efficiently with TCR-T therapy, since TCR antigens are much smaller and can be delivered by peptide, mRNA, or oncolytic virus vaccine [73] The efficiency of antigen presentation by HLA is the key factor to consider in the design of an efficient TCR-T therapy. In addition, the immunogenicity of the peptide and the differences in the expression pattern of the antigen source in healthy and tumor cells are two other factors influencing the effect of TCR-T therapy. These limitations drastically limit the choice of antigens: the majority of TCR-T therapies use the NY-ESO-1 and WT-1 targets against solid and hematological malignancies, respectively [63]. The high immunogenicity of NY-ESO-1 is rather an exception [74]. In contrast, tumor neoantigens are more immunogenic; only a few are shared by a large number of patients [75]. Therefore, the search for novel and more immunogenic peptides derived from TAAs, as well as the enhancement of TCR affinity [76] are required for the improvement of new generations of the TCR-T therapy in the future. The use of MiHAs as immunotherapy targets may be more advantageous in the context of allo-HSCT therapy than targeting the TAA and tumor neoantigens. MiHAs are more immunogenic than TAA because they are completely foreign to the MiHA-negative immune system of the donor [77]. In addition, MiHAs are much safer targets because they have less on-target off-tumor toxicity [78]. MiHA-targeted therapy is not patient-specific compared to the majority of tumor neoantigens. Good targets for MiHA-targeted therapy can be identified without extensive tumor genome and transcriptome sequencing [79]. In addition, when MiHAs arise from germline polymorphisms in genes whose function is important to the malignant cell, tumors are less likely to escape the MiHA-directed immune response due to the downregulation of the source gene. Potential tumor escape is most likely due to loss of HLA [80]. This is particularly important in the treatment of AML, as this type of leukemia is thought to have many subclones at baseline, and targeting a germline polymorphism such as MiHA seems to be an appropriate strategy [80]. Therefore, the transfer of T cells modified with the transgenic TCRs seems to be a promising method of immunotherapy to complement allo-HSCT for efficient relapse prevention, applicable to the treatment of a wide spectrum of hematological malignancies [81,82]. Currently, only a few clinical trials are focused on the use of MiHA-specific transgenic T cells for the treatment of relapsed and refractory hematologic diseases, especially AML [34]. Taking into account the frequency of HLA and MiHA variants, it could be estimated that the development of 50 MiHA-based therapies would be sufficient to effectively treat 35% of patients undergoing allo-HSCT [83]. The pipeline of the antigen-specific expansion and TCR discovery outlined in our work (Figure 1) could be efficiently applied to generate the TCR repertoires specific to the other therapeutically promising MiHAs such as HA-2 [84] and ACC-1Y [85], as well as any other T cell antigen. This study is the first systemic analysis of an MiHA-specific TCR repertoire, combining our newly generated data with those published previously [34,35]. Our results differ from the previously published data on well-studied virus-specific TCR repertoires, such as SARS-CoV2 and CMV. The HA-1-specific TCR repertoire demonstrated a low degree of overall sequence homology and low diversity of the β V-genes with the TRBV7–9 gene, being the most commonly used. Analysis of the antigen-specific repertoire may be useful for design of TCR-T therapy; some of the identified V-genes were reported to be “weak” due to less efficient folding, which affects the transgenic TCR exposure on the cell surface [86]. The efficient expression and exposure of the transgenic TCR could be improved not only by the choice of the V-gene, but also by the use of the murine TCR constant chains [46,59]. Inevitably, this approach raises concerns about the safety and immunogenicity of the T cell product, as foreign parts of the TCR could trigger an immune response [87]. Indeed, clinical trials showed that the murine TCR-specific antibodies were detected in approximately 23% of patients after infusion of the autologous T cells modified with the murinized transgenic TCR [57]. However, the generated antibodies were neutralizing in only half of the reported cases, which could have affected the efficacy of the therapy. More importantly, the antibodies generated after transfer were specific for a variable part of the TCRs exposed from the cell surface and more accessible for immune recognition. We used only constant TCR chains of murine origin in our constructs to minimize the immunogenicity of the transgenic TCRs. However, there is still the possibility of the recipient’s CD8+ T cells developing an immune response to the peptides derived from the foreign sequences of the genetic construct [88]. While little is known about CD8+ responses directed against murinized TCR, there is evidence that such responses impair the efficacy of CAR-T therapy [89,90]. Therefore, elimination of T cell-immunogenic epitopes of the murine TCR may be required for improved therapeutic efficacy. The generated transgenic TCR-T cell products pose a potential safety risk because transgenic and endogenous TCR chains could form heterodimers of unknown reactivity [91]. To circumvent this problem, the CRISPR/Cas knockout of endogenous TCR could be used [92,93] in addition to the introduction of murine constant TCR chains. Genetic modification of cells by electroporation of RNP complexes proved to be safer compared to lentiviral methods of CRISPR/Cas delivery. The Cas9 protein did not elicit an immune response due to its short-lived presence in the organism. The off-target activity of Cas9 was shown to be insignificant to undermine the genetic stability of the T cell therapeutic product [94]. Moreover, the increasing commercial availability of Cas9 protein and the upcoming availability of clinical-grade electroporation systems are likely to make CRISPR/Cas disruption of the endogenous TCR a standard procedure for the TCR T cell product manufacturing. The persistence of modified T cells is essential for therapeutic efficacy [70]. CAR-T cells with 4–1BB costimulatory domain showed better persistence than CAR-T with CD28 domain, which is attributed to a more moderate level of receptor activation and less exhaustion [95]. Transgenic TCR induces physiological levels of cell activation compared to CAR, and a direct comparison of CAR-T and TCR-T revealed that although CAR-T are more potent effectors in the short term, TCR-T cells perform better under high antigenic pressure, showing less exhaustion and expanding more efficiently [96]. The reported in vivo persistence of TCR-T cells that underwent ex vivo expansion and adoptive transfer could vary from 1 week for expanded tumor-infiltrating lymphocytes [97] to more than 430 days in some patients [98]. Transgenic TCR-T have been reported to persist for at least 1–2 months [99,100] and were detectable for more than 6 months in some cases [101]. The use of high doses of IL-2 during ex vivo culture and the resulting effector memory phenotype of the infused cells are considered to be the main factors that negatively influence the persistence of the transferred cells and the efficacy of the therapy, as concluded from the HA-1-specific adoptive transfer clinical trials [102]. The strategy to increase the persistence of CAR-T by modification of naïve and stem-cell memory populations [103] may also be applicable to TCR-T. In some cases, CAR-T persistence reached 10 years after infusion, and it is reasonable to expect no less persistence from TCR-T [104]. Advanced methods of gene engineering and novel bioinformatic tools for peptide immunogenicity prediction have great potential to make TCR T therapy a highly efficient and specific method of choice when other approaches fail, particularly for the therapy of relapsed and refractory AML. MiHAs are particularly promising targets for this purpose because, unlike other classes of antigens, they allow efficient discrimination between donor and recipient cells. We reported the experimental pipeline for the development of MiHA HA-1 specific TCR-T therapy. We described the repertoire of HA-1-specific TCRs, identified a number of functional/TCR chain combinations, and then selected several TCR receptors with sufficient affinity and no alloreactivity. The ability of the cloned TCRs to effectively recognize endogenously processed HA-1 antigens on the surface of cells derived from healthy and leukemia patient PBMCs was demonstrated. We proposed a method for rapid TCR cloning and demonstrated its use for subsequent modification of CD8+ T cells with HA-1-specific transgenic TCRs after CRISPR/Cas knockout of an endogenous TCR. The resulting T cells showed clear cytotoxic activity against PBMC of patients with various hematological malignancies, including AML, B-, and T-cell ALL. The described pipeline could be applied to the development of novel therapies targeting other minor histocompatibility antigens (MiHAs), which represent a promising class of antigens for the treatment of hematological malignancies.

PMC10000935

Reza Zarinshenas,Arya Amini,Isa Mambetsariev,Tariq Abuali,Jeremy Fricke,Colton Ladbury,Ravi Salgia

Assessment of Barriers and Challenges to Screening, Diagnosis, and Biomarker Testing in Early-Stage Lung Cancer

03-03-2023

lung cancer,screening,biomarkers,LDCT,COVID-19

Simple Summary Lung cancer management continues to evolve with improvements in survival across all stages. The review highlights the current data supporting screening and discusses barriers to screening and potential opportunities to improve screening access. Further, the review discusses the current challenges in diagnosis and biomarker testing in early stage lung cancer and ways in which these can be improved upon. Abstract Management of lung cancer has transformed over the past decade and is no longer considered a singular disease as it now has multiple sub-classifications based on molecular markers. The current treatment paradigm requires a multidisciplinary approach. One of the most important facets of lung cancer outcomes however relies on early detection. Early detection has become crucial, and recent effects have shown success in lung cancer screening programs and early detection. In this narrative review, we evaluate low-dose computed tomography (LDCT) screening and how this screening modality may be underutilized. The barriers to broader implementation of LDCT screening is also explored as well as approaches to address these barriers. Current developments in diagnosis, biomarkers, and molecular testing in early-stage lung cancer are evaluated as well. Improving approaches to screening and early detection can ultimately lead to improved outcomes for patients with lung cancer.

Assessment of Barriers and Challenges to Screening, Diagnosis, and Biomarker Testing in Early-Stage Lung Cancer Lung cancer management continues to evolve with improvements in survival across all stages. The review highlights the current data supporting screening and discusses barriers to screening and potential opportunities to improve screening access. Further, the review discusses the current challenges in diagnosis and biomarker testing in early stage lung cancer and ways in which these can be improved upon. Management of lung cancer has transformed over the past decade and is no longer considered a singular disease as it now has multiple sub-classifications based on molecular markers. The current treatment paradigm requires a multidisciplinary approach. One of the most important facets of lung cancer outcomes however relies on early detection. Early detection has become crucial, and recent effects have shown success in lung cancer screening programs and early detection. In this narrative review, we evaluate low-dose computed tomography (LDCT) screening and how this screening modality may be underutilized. The barriers to broader implementation of LDCT screening is also explored as well as approaches to address these barriers. Current developments in diagnosis, biomarkers, and molecular testing in early-stage lung cancer are evaluated as well. Improving approaches to screening and early detection can ultimately lead to improved outcomes for patients with lung cancer. Lung cancer has a low five-year survival rate due in part to delays in diagnosis and presentation of advanced disease, making early detection critical [1]. In the US, less than 20% of those diagnosed with lung cancer presented with localized disease [2]. The 2011 National Lung Screening Trial (NLST) reported a relative reduction of mortality from lung cancer of 20.0% with low-dose computed tomography (LDCT) screening in comparison to chest radiography [1,3]. The European Nederlands Leuvens Longkanker Screenings Onderzoek (NELSON) trial showed LDCT screening for high-risk patients led to a 26% reduction in lung cancer mortality. The Multicentric Italian Lung Detection (MILD) trial demonstrated a 39% reduction in lung cancer mortality at the 10-year mark as a result of screening over five years [4]. After the NLST’s publication, the United States Preventative Task Force (USPSTF), American Cancer Society, and National Comprehensive Cancer Network (NCCN) developed recommendations for annual LDCT screening for high-risk patients [1]. The USPSTF currently supports annual screening for lung cancer with LDCT for adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years (B recommendation) [5]. The American Academy of Family Physicians reports a B recommendation for the same population [6]. Current NCCN guidelines recommend annual LDCT for individuals 50–80 years old with greater than or equal to a 20 pack-year history of smoking [7]. Despite these recommendations, LDCT screening has been underutilized [8]. The goal of this narrative review is to evaluate and address the potential barriers to screening and diagnosis as well as to explore biomarkers and molecular testing approaches in early-stage non-small cell lung cancer (NSCLC). The percentage of eligible smokers who reported LDCT screening only increased from 3.3% in 2010 to 3.9% in 2015 (p = 0.60) according to respondents of the National Health Interview Survey in the United States [9]. Of note, in 2015 the Centers for Medicare and Medicaid Services (CMS) approved insurance coverage for LDCT for high-risk patients. In 2016, only 1.9% of eligible patients were screened [1]. Further, a Center for Disease Control analysis of 10 states in 2017 found that 12.5% of eligible candidates received LDCT in the 12 months prior [8]. There are multiple reasons why there is not greater incorporation of LDCT screening in the United States. Physician referral for LDCT is one primary barrier which is multifactorial and may be related to busy practice patterns, lack of awareness of screening guidelines, and patient-specific barriers discussed below. Other barriers to LDCT include lack of notifications from the electronic medical record (EMR), patient refusal, time constraints, varying provider knowledge about insurance coverage, cost concerns, concern for false positives and overtreatment, lack of patient knowledge of screening guidelines, lack of patient access to screening, concerns about radiation exposure, inadequate staffing, and inconsistent lung cancer screening recommendations across organizations [1,10]. Screening techniques have improved significantly in recent years. Regarding risks related to radiation exposure due to CT lung cancer screening (CTLS), the risk appears to be too low to measure. Further, there have been no definitive cases of radiation-induced malignancies that have been reported. However, in one study, it was estimated that 10 annual LDCTs produce a 0.26 to 0.81 lifetime risk of major cancers for every 1000 people screened [2]. Over-diagnosis and false-positive rates leading to additional workup do not appear to significantly impact lung cancer patients either and should not be a barrier for screening [11]. One analysis of the NLST trial demonstrated that 779 per 1000 people would have a normal screen and no diagnosis of lung cancer based on Lung-RADS criteria and 180 per 1000 people were found to have a false positive. Of the 180 false positives, 13 would require an invasive procedure to exclude lung cancer, and 1 per 2500 people would have a major complication from an invasive procedure. Further, they reported that 1 per 5000 screened would die within 60 days of the invasive procedure from any cause [12]. Secondary analysis of the Danish Lung Cancer Screening Trial revealed an over-diagnosis rate of 67.2% [13,14]. However, secondary analysis of the NLST showed that complications from invasive procedures were low overall [15]. Standardized screening systems could help reduce rates of over-diagnosis and complications [11]. Geography can be a barrier to screening (Table 1). In one study from the US, the South had the greatest eligible number of smokers and the most screening sites. Nonetheless, it had one of the lowest screening rates at 1.7%, compared with 2.1% in the Midwest, and 3.9% in the Northeast. The West had the lowest screening rate at 1.1% but also had the second fewest eligible smokers. Higher screening rates in the Northeast could partially be linked to having a higher density of physicians per capita, rate of insurance, per capita income, and education level. The South had the lowest rate of insured patients. Furthermore, there is a lower likelihood of having screening sites in rural areas outside of the Northeast [10]. In the US, 14.9% of patients did not have a lung cancer screening center within 30 miles, a problem particularly prevalent in rural areas (Table 1). The shortage of LDCT screening centers in rural regions is particularly concerning given the number of at-risk individuals in rural regions. Expanding telehealth coverage is one approach to address this challenge [16]. Health insurance status continues to factor into those who undergo screening in the US. While Medicare and most commercial insurers started covering LDCT starting in 2015, Medicaid (which covers 19% of the US population) may not always cover. Moreover, 9% of the population remains uninsured [10]. Greater than half the patients who are eligible for lung cancer screening according to the USPTF screening guidelines are either uninsured or have Medicaid insurance. Uninsured patients are 72% less likely to get screened compared with insured patients [17,18]. Medicaid coverage of lung cancer screening depends on whether the state adopts Medicaid expansion [17]. One approach to address this limitation would be to expand Medicaid coverage of lung cancer screening to all states [16]. Other approaches for improving LDCT include making lung cancer screening a Center for Medicare and Medicaid Services (CMS) national quality measure and increasing awareness and compliance among physicians and patients [10]. Another proposal to address screening disparities in underserved populations is to link American College of Radiology (ACR) screening sites with Federally Qualified Health Centers (FQHC) [19]. Race and ethnicity also impact rates of screening. One limitation of the NELSON and NLST studies was that their populations consisted mainly of white men. Nonetheless, minority groups are disproportionately impacted by lung cancer and are not well represented in studies [20]. In particular, African Americans have the highest rates of lung cancer mortality despite similar smoking rates compared with whites [19]. In the NLST trial, only 4% of participants were African American and yet a secondary analysis suggested that African Americans had a significant benefit from LDCT screening [21]. One approach to address disparities faced by African Americans and other at-risk populations, is to have local and state organizations partner to develop outreach programs, educational materials, and increase awareness. In addition, developing strategies to address stigma, clinician implicit bias, and nihilism is imperative [19]. There is evidence to suggest that individuals of a higher socioeconomic status and men are overrepresented in lung cancer screening programs. Finding approaches to engage women and individuals of lower socioeconomic statuses is critical [22]. Despite the underutilization of LDCT screening, efforts should be made to educate patients on both the risks and benefits of LDCT. The USPSTF recommends that shared decision-making be included in discussions regarding the pros and cons of LDCT screening. Shared decision-making is required for Medicare reimbursement, which could be a barrier given the shortage of time in visits [10]. Nonetheless, shared decision-making surrounding lung cancer screening is often underutilized, and the potential harms of screening are not sufficiently explained (should there be a comma between 22 and 23) [23,24]. Lung cancer screening in non-smokers remains challenging and to date there are no standardized screening guidelines for non-smokers [25]. Although lung cancer is strongly associated with smoking, the rate of lung cancer in non-smokers has been increasing in the US [26]. In one study using the United States (US), it was found that 12.5% of patients with lung cancer were never smokers. Women were more likely than men to be never smokers with lung cancer. From a race/ethnicity perspective, Asian/Pacific Islanders comprised the highest percentage of never smokers with lung cancer [26]. Risk factors for lung cancer in non-smokers include increased age, secondhand smoke, environmental exposures, radon exposure, genetic factors, underlying lung disease, and oncogenic viruses [25]. The racial discrepancies in lung cancer among non-smokers deserves further investigation. As one example of this, the majority of Han Chinese women with lung cancer have been non-smokers. Exposure to cooking oil fumes has been explored as one potential explanation for this discrepancy [27]. The upcoming FANS (Female Asian Never Smokers) Study will further explore potential causes of lung cancer in never-smoker Asian American women, which could in turn inform screening strategies for never smokers with lung cancer [28]. In the initial stages of the COVID-19 pandemic, the American College of Chest Physicians recommended delaying initial and repeat annual lung cancer screening to avoid COVID-19 exposure in the general population [29,30]. One study attempted to measure the impact of COVID-19 on lung cancer screening through a prospectively maintained database recording patients in a lung cancer screening program when LDCT screening was temporarily suspended during the initial stages of the pandemic. The percentage of patients with lung nodules suspicious of malignancy when screening resumed increased to 29% from their 8% institution baseline levels, causing a sudden rise in referrals to thoracic surgery or interventional pulmonology [29]. Despite the challenges that the COVID-19 pandemic brought, the pandemic also led to an increased use of telehealth services which offered added convenience for the shared decision-making required for lung cancer screening [31]. One broad approach to help categorize and summarize the LDCT screening barriers discussed is to view them as barriers for providers, barriers for patients, screening and treatment concerns, healthcare disruptions, and screening limitations (Figure 1). From there a number of potential solutions can be applied to improve LDCT screening access including more screening centers, expanding insurance coverage for LDCT, identifying populations that have limited access to screening or have hesitations to undergo screening, and better education primary care physicians on the importance of LDCT for those who meet criteria for screening (Figure 2). One area of advancement within LDCT is the integration of artificial intelligence (AI) for early detection of lung cancer. A review article, published in 2021, evaluated comparative studies between AI algorithms and humans. These studies, many of which were from over a decade ago, showed that AI algorithms were either a little worse or equivalent to their human counterparts. However, the false-positive rate for the AI algorithms was higher [32]. Newer approaches have been developed to reduce false positives. For example, computer-aided detection (CAD) systems for detecting pulmonary nodules is being evaluated as a second reader for radiologists. CAD systems have also been used to distinguish between six nodule types (perifissural, spiculated, solid, part-solid, non-solid, calcified), and they showed similar performance to a human expert when the deep learning (DL) system was trained on data from the Italian MILD screening trial and validated on data from the Danish LCS trial [33]. In one study, a deep learning model for the analysis of malignancy risk in lung cancer screening CTs was developed and applied to 6716 NLST cases for which a 94.4% area under the curve was achieved. This model outperformed six radiologists and had an 11% absolute reduction in false positives and a 5% reduction in false negatives when prior CT imaging was not available. The model’s performance was comparable to the same radiologists when prior imaging was available [34]. Furthermore, AI can be implemented to identify smoking-related diseases on LDCT, including coronary artery calcification and cardiovascular events, emphysema, and osteoporosis and fragility fractures. As one example of this expanded role, the AI-RAD Companion by Siemens Healthineers uses DL algorithms to provide assessments of emphysema and coronary artery calcification in addition to detecting lung nodules. DL algorithms that can assess for smoking-related diseases could broaden the impact of lung cancer screening [33]. One challenge for lung cancer diagnosis is tumor heterogeneity, which makes molecular analysis important [7]. The Clinical Lung Cancer Genome Project and Network Genomic Medicine evaluated 1255 lung tumors and found that 55% had at minimum one genomic alteration that is possibly amenable to targeted therapy. Diagnostic platforms available for genomic profiling of lung cancers have increased in recent years given the importance of genomic testing before initiating treatment for advanced lung cancer patients. Barriers to broader adoption of molecular testing globally include availability of tissue, cost of molecular testing, quality and standards, access to molecular testing within their institution, awareness of the most recent guidelines, turnaround time for results, and inconclusive results [35]. Appropriate staging may be another barrier in treatment diagnosis. CT imaging is the most common imaging modality for the staging of lung cancer. However, CT of the thorax only has a sensitivity of 55% and specificity of 81% in detecting malignant mediastinal lymph nodes. PET-CT offers higher accuracy (sensitivity 80% and specificity of 88%) for detecting metastasis to the mediastinal lymph nodes. However, PET-CT can face challenges differentiating between inflammation, infection, and malignant disease, which raises the importance of tissue sampling. PET-CT can also yield a high false-negative rate in situations in which lymph nodes are moderately enlarged or not at all [36]. Therefore, in particular for mediastinal/hilar staging, mediastinoscopy or EBUS are recommended, and a PET-CT should not obviate the need for these procedures. Additionally, PET radiotracers are currently being evaluated that may be more cancer-specific than the current fluorodeoxyglucose (FDG) that is used. For example, fibroblast activation protein inhibitor (FAPI), which is expressed in the vast majority of epithelial cancers, has been investigated in the setting of lung cancer imaging. The fibroblast activation protein inhibitor (FAPI), binds to FAP. FAPI derivatives can be radiolabeled with Gallium-68 [68Ga]. For detecting of lung metastasis, there is evidence to suggest that [68Ga]Ga-FAPI PET/CT offers higher maximum standardized uptake value and tumor-to-background ratio compared with [18F]FDG PET/CT making this modality potentially useful for staging. Furthermore, prognostic applications of [68Ga]Ga-FAPI PET are being explored [37]. Currently molecular biomarker testing is standard for advanced and metastatic NSCLC as it impacts selection of systemic therapy. Broad panel-based Next Generation Sequencing (NGS) is usually recommended for patients as it includes the most common molecular alterations with specific therapy options. If the results of the broad panel-based NGS approach are unrevealing, then a ribonucleic acid (RNA)-based NGS is recommended. Other techniques that can be employed are real-time PCR (polymerase chain reaction), Sanger sequencing, fluorescence in situ hybridization, and Immunohistochemistry (IHC). ALK (Anaplastic Lymphoma Kinase) rearrangements can be detected via IHC or fluorescence in situ hybridization (FISH). ROS1 rearrangements are screened with IHC and validated with FISH. BRAF (B-Raf proto-oncogene) point mutations and KRAS (KRAS proto-oncogene) point mutations can be detected with real-time PCR, Sanger sequencing, and NGS. NGS is recommended for the detection of MET (mesenchymal–epithelial transition) exon 14 (METex14) skipping variants. FISH can be used to detect RET (rearranged during transfection) Gene Rearrangements. NTRK1/2/3 (neurotrophic tyrosine receptor kinase) gene fusions can be detected through NGS, FISH, IHC, and PCR [7]. IHC for PD-L1 (Programmed death ligand 1) can be implemented to find a disease that is likely to respond to first-line anti-PD-1/PD-L1 and can be viewed as a predictive biomarker [20]. Plasma cell-free/circulating tumor DNA (ctDNA) has high specificity but compromised sensitivity. For this reason, ctDNA should not replace histologic tissue diagnosis [7]. Current guidelines from the College of American Pathologists, International Association for the Study of Lung Cancer (IASLC), and the Association of Molecular Pathology recommend testing for EGFR (Epidermal Growth Factor Receptor) mutations, ALK rearrangements, ROS1 rearrangements, BRAF, RET rearrangements, and MET exon 14 skipping mutations for newly diagnosed lung adenocarcinoma [20]. Liquid biopsy is an alternative form of biopsy which complements current tissue biopsy. A liquid biopsy is an analysis of any tumor-derived product in the blood or serum. Compared with tissue biopsies, they assess the spatial and temporal heterogeneity of the tumor and may be a useful option when tissue is limited or unavailable [38]. Analyzing ctDNA in patients with NSCLC is a common application of liquid biopsy and can offer similar utility as tissue biopsy in many settings [39]. Liquid biopsies can help alleviate challenges and delays in getting additional tissue often needed for molecular analysis. Liquid biopsy assays have been studied in the EGFR population. One meta-analysis comparing EGFR mutations in plasma and tumor tissue found a sensitivity of 67% and specificity of 94% for plasma measurements [40]. While there are US Food and Drug Administration (FDA) approved tests for EGFR mutations (cobas EGFR Mutation Test v2), NGS allows the ability to analyze multiple genes at the same time or the entire cancer genome [41]. There are a number of studies evaluating the role of ctDNA. The Galleri multicancer early detection (MCED) test identifies circulating cell-free DNA (cfDNA) in blood using next-generation sequencing to detect DNA methylation for early detection of cancer as well as identifying the organ of origin for the cancer. The Circulating Cell-free Genome Atlas (CCGA), STRIVE, SUMMIT, and PATHFINDER studies are four trials using this technology, with the CCGA being the initial development of the test [42]. The CCGA was used to validate an MCED test using cfDNA with machine learning to detect cancer signals with a single blood draw. This study was divided into three substudies. In the first substudy, whole genome methylation was identified as the method to be used for further development. The second study supported further cfDNA sequencing evaluation in a prospective population-level study [43]. In the third study, a pre-specified subset analysis of 4077 participants (2823 with cancer and 1254 non-cancer) was used for clinical validation of this test [44]. The STRIVE trial has a goal of validating the Galleri test for early detection of cancer. The SUMMIT trial has the goal of validating the Galleri test by measuring cancer incidence. The PATHFINDER evaluates implementation of the test in clinical practice [42]. The combination of blood testing (liquid biopsies) with imaging has been investigated as a screening option for cancer. The DETECT-A (Detecting cancers Earlier Through Elective mutations-based blood Collection and Testing) trial consisted of a blood test designed to detect DNA mutations and protein biomarkers in a cohort of 10,006 women from the ages 65 to 75 years of age [45]. Positive blood tests in this study were followed by a PET-CT scan. Of the 96 cancers detected in this cohort, 26 were first detected by a blood test (of which nine were lung cancers). Twenty-four cancers (of which three were lung cancers) were detected through standard-of-care screening [45]. In 2020, the US FDA approved Guardant 360 and FoundationOne Liquid CDx as ctDNA assays to detect genomic alterations in patients with advanced-stage solid malignancies and are commonly used at many institutions. Another avenue being explored is the use of ctDNA for detecting minimal residual disease (MRD). In a study published in 2017, it was found that ctDNA detected after curative-intent NSCLC treatment could predict relapse often prior to radiologic evidence of relapse. In 2020, updated results were presented, and it was determined that among those with disease recurrence, 82% of patients had ctDNA that was detected at or before clinical relapse [46]. Some other example biomarkers include autoantibodies, complement fragments, microRNA, methylated DNA, and proteins [47]. Autoantibodies are in response to tumor antigens and can appear even in the preclinical phase. They have the benefit of being specific but not sensitive [48]. Lung cancer can also activate the classic complement pathway [48]. Studies have suggested complement factors, such as factors H, C5a, and C4d as lung cancer biomarkers in lung cancer cell lines [47]. Like autoantibodies, complement fragments suffer from high specificity and low sensitivity [48]. Circulating microRNA, which has been explored for cancer diagnosis and prognosis, has been shown to reduce LDCT false-positive rates in two large retrospective studies. Serum antigens have also been explored to improve diagnostic accuracy. Liquid chromatography–mass spectrometry (LC–MS) has been explored for biomarker research in lung cancer. In one case-control study involving 24 bronchoalveolar lavage extracellular vesicle samples, state-of-the-art LC–MS applied to bronchoalveolar lavage small vesicles found that there is evidence to suggest proteome complexity is correlated with stage 4 lung cancer and mortality [49]. Furthermore, in this study a possible therapeutic target, Cytosine-5-methyltransferase 3β (DNMT3B) complex protein, which is an epigenetic modifier, was found to be upregulated in tumor tissues and bronchoalveolar lavage extracellular vesicles [49]. Additionally, in a real-life cohort study of 97 patients, LC–MS was used to identify 34 proteins in pleural effusions that are associated with survival. The proteins identified could inform research into more personalized therapeutic options [50]. Other future directions for biomarkers include exhaled breath (EB) biomarkers, sputum cell-based image analysis, metabolomics, genomic predispositions, radiomics, and artificial intelligence [48]. Biosensors are also being explored as an avenue for lung cancer diagnoses [51]. Optical, electro-conductive, piezoelectric, and amperometric biosensors are types of biosensors. In the realm of optical biosensors: surface plasmon resonance (SPR)-based sensors, SPR-based sensor-on-chip, and quantum dot-based sensors are some examples [47]. Despite the rapid growth of biomarker testing, many challenges remain [52]. For example, biopsy tissue samples are often inadequate for biomarker testing, or the tests themselves may face technical problems. Coordination with services such as pulmonology and interventional radiology can help, and taking steps to improve tissue handling can be taken as well. Additionally, as discussed earlier, blood biomarker testing may complement tissue testing and potentially one day obviate the need for tissue or, at a minimum, repeat biopsies for additional tissue for molecular testing [53]. Access to personalized medicine can be more challenging in the community setting. A cloud-based virtual molecular tumor board (VMTB) can be an effective way to establish a relationship between community oncologists and academic site physicians [54]. Furthermore, the molecular tumor board (MTB) model can be utilized at the community level, either through virtual or physical collaborations, and can be especially useful in settings such as the current COVID-19 pandemic. Furthermore, the use of vendor-based oncology clinical pathways can be one way to guide physicians in decision-making regarding a patient case amidst the rapidly changing guidelines in oncology. Another way to optimize the delivery of care in the community setting is to use a standardized electronic health record system in both the academic and community settings [55] (Figure 2). In this review, we explore lung cancer screening with LDCT and the advantages and potential disadvantages of LDCT screening. Barriers to wider implementation of LDCT screening were investigated, including socioeconomic, racial, geographic factors, and healthcare interruptions, and potential strategies to try to reduce these barriers were discussed (Figure 2). Furthermore, current paradigms in lung cancer biomarkers were also discussed, as well as the future avenues and opportunities with lung cancer biomarkers. Finally, potential approaches to encourage wider adoption of personalized medicine approaches were discussed. As the field validates the current use of biomarkers and incorporates radiomic features found on imaging, including those from LDCT scans, tools can be developed to better detect early-stage NSCLC. AI may play a major role in storing and interpreting the large amounts of molecular and digital data to aid clinicians in improving early detection of lung cancer. In conclusion, in the current era of early-stage detection, while LDCT plays a pivotal role, the additional biomarkers discussed could further stratify low- and high-risk patients, leading to quicker diagnoses and potential treatment options.

PMC10000936

Henriett Butz,Éva Saskői,Lilla Krokker,Viktória Vereczki,Alán Alpár,István Likó,Erika Tóth,Erika Szőcs,Mihály Cserepes,Katalin Nagy,Imre Kacskovics,Attila Patócs

Context-Dependent Role of Glucocorticoid Receptor Alpha and Beta in Breast Cancer Cell Behaviour

01-03-2023

glucocorticoid receptor,glucocorticoid receptor alpha,glucocorticoid receptor beta,breast cancer,proliferation,migration,breast cancer progression,metastasis

Background. The dual role of GCs has been observed in breast cancer; however, due to many concomitant factors, GR action in cancer biology is still ambiguous. In this study, we aimed to unravel the context-dependent action of GR in breast cancer. Methods. GR expression was characterized in multiple cohorts: (1) 24,256 breast cancer specimens on the RNA level, 220 samples on the protein level and correlated with clinicopathological data; (2) oestrogen receptor (ER)-positive and -negative cell lines were used to test for the presence of ER and ligand, and the effect of the GRβ isoform following GRα and GRβ overexpression on GR action, by in vitro functional assays. Results. We found that GR expression was higher in ER− breast cancer cells compared to ER+ ones, and GR-transactivated genes were implicated mainly in cell migration. Immunohistochemistry showed mostly cytoplasmic but heterogenous staining irrespective of ER status. GRα increased cell proliferation, viability, and the migration of ER− cells. GRβ had a similar effect on breast cancer cell viability, proliferation, and migration. However, the GRβ isoform had the opposite effect depending on the presence of ER: an increased dead cell ratio was found in ER+ breast cancer cells compared to ER− ones. Interestingly, GRα and GRβ action did not depend on the presence of the ligand, suggesting the role of the “intrinsic”, ligand-independent action of GR in breast cancer. Conclusions. Staining differences using different GR antibodies may be the reason behind controversial findings in the literature regarding the expression of GR protein and clinicopathological data. Therefore, caution in the interpretation of immunohistochemistry should be applied. By dissecting the effects of GRα and GRβ, we found that the presence of the GR in the context of ER had a different effect on cancer cell behaviour, but independently of ligand availability. Additionally, GR-transactivated genes are mostly involved in cell migration, which raises GR’s importance in disease progression.

Context-Dependent Role of Glucocorticoid Receptor Alpha and Beta in Breast Cancer Cell Behaviour Background. The dual role of GCs has been observed in breast cancer; however, due to many concomitant factors, GR action in cancer biology is still ambiguous. In this study, we aimed to unravel the context-dependent action of GR in breast cancer. Methods. GR expression was characterized in multiple cohorts: (1) 24,256 breast cancer specimens on the RNA level, 220 samples on the protein level and correlated with clinicopathological data; (2) oestrogen receptor (ER)-positive and -negative cell lines were used to test for the presence of ER and ligand, and the effect of the GRβ isoform following GRα and GRβ overexpression on GR action, by in vitro functional assays. Results. We found that GR expression was higher in ER− breast cancer cells compared to ER+ ones, and GR-transactivated genes were implicated mainly in cell migration. Immunohistochemistry showed mostly cytoplasmic but heterogenous staining irrespective of ER status. GRα increased cell proliferation, viability, and the migration of ER− cells. GRβ had a similar effect on breast cancer cell viability, proliferation, and migration. However, the GRβ isoform had the opposite effect depending on the presence of ER: an increased dead cell ratio was found in ER+ breast cancer cells compared to ER− ones. Interestingly, GRα and GRβ action did not depend on the presence of the ligand, suggesting the role of the “intrinsic”, ligand-independent action of GR in breast cancer. Conclusions. Staining differences using different GR antibodies may be the reason behind controversial findings in the literature regarding the expression of GR protein and clinicopathological data. Therefore, caution in the interpretation of immunohistochemistry should be applied. By dissecting the effects of GRα and GRβ, we found that the presence of the GR in the context of ER had a different effect on cancer cell behaviour, but independently of ligand availability. Additionally, GR-transactivated genes are mostly involved in cell migration, which raises GR’s importance in disease progression. In women, breast cancer is the most common cancer type worldwide (estimated 2.3 million new cases per year) [1]. Early-diagnosed breast cancer accounts for more than 90% of all cases, but despite the availability of modern treatment options, approximately one-third of these patients develop cancer recurrence/progression at a later time [2]. Locally advanced/metastatic breast cancer has a median overall survival of ~3 years, and the 5-year survival is only ~25% [3]. The optimal therapy is selected based on the immunophenotype of the tumour, determined by immunostaining of the oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Hormone receptors (ER and PR) are expressed in most (~75%) breast cancers, indicating the responsiveness to hormonal therapy, and their presence represents a better prognosis [4]. HER2 overexpression can be detected in ~15% of breast cancers due to gene amplification, and it is an important predictive marker for the response to anti-HER2 therapy. Additionally, HER2-enriched tumours are associated with a more aggressive clinical course and poorer prognosis. Glucocorticoids (GCs), e.g., dexamethasone (dex), are routinely administered as adjuvant therapy to prevent hypersensitivity reactions and to manage the side effects of cytotoxic chemotherapy, due to their antiemetic and orexigenic effects. Besides their beneficial adjuvant impact, on the one hand, glucocorticoids were suggested to prevent breast cancer by decreasing the levels of various mediators, such as oestrogens, pro-inflammatory cytokines, and eicosanoids, potentially involved in the pathophysiology of breast cancer [2,4,5]. On the other hand, glucocorticoids might promote breast cancer progression by facilitating tumour cells to escape from immune surveillance, promoting metabolic dysfunction or insulin resistance [6,7,8,9,10,11]. An increased circulating GC level has been associated with breast cancer progression [7,12]. Additionally, in vivo animal models have also demonstrated that rats exposed to chronic stress (accompanied by increased GC levels in the blood) developed more aggressive breast cancer compared to non-stressed animals [13]. While in ER+ breast cancer the presence of GR has been reported to have a favourable prognosis, probably due to crosstalk between the two nuclear receptors [14], in ER− (and triple-negative) breast cancer, GCs supported cancer growth and metastasis leading to enhanced aggressiveness [14,15,16,17] (Figure 1). Additionally, in a translational study, glucocorticoids resulted in the activation of the glucocorticoid receptor during breast cancer progression and increased colonization, and reduced survival [7]. Additionally, the authors indicated that the judicious adjuvant administration of corticosteroids could be considered when treating cancer-related complications [7]. Due to the finding that GR can be activated in the absence of the ligand as well [18], the effect of the presence of the ligand on GR activity has an important relevance. Additionally, the potential beneficial role of GR antagonism has been suggested to increase apoptosis during chemotherapy efficacy in ER-negative breast cancers, blocking metastatic spread [9]. The association between systemic GC use and breast cancer risk was evaluated in a prospective cohort study by Cairat et al., including 62,512 postmenopausal women [19]. Overall, it was observed that the use of systemic GC exposure was not associated with overall breast cancer risk; however, it was associated with a higher risk of in situ breast cancer and a lower risk of invasive breast cancer. GC exposure was also inversely associated with the risk of stage 1 or stage 2 tumours, while it positively associated with the risk of stage 3/4 breast cancers [19]. In addition, Shi et al., described that GR negatively correlated with survival, and ER+ patients showed similar results compared to TNBC and invasive subtypes [20]. However, the literature data indicate that GR was not an independent predictor of survival, and no association was found between GR expression and breast cancer-specific survival (BCSS) or distant metastasis-free interval (DMFI) [14]. Additionally, we hypothesized that the presence of GR isoforms could be another explanation for the heterogeneous findings. The human GR is encoded by the NR3C1 gene (nuclear receptor 3, group C, member 1). The gene itself is composed of nine exons, and different splice isoforms are generated by alternative splicing. GRα is considered to be the main and most abundant isoform in almost all tissues [21]. Besides GRα (“the classical receptor”), GRβ has been considered as the other main GR isoform differing in the splicing of exon 9. GRα and GRβ are identical up to amino acid 727. GRα consists of 777 amino acids, while in the GRβ protein, the 50 carboxy-terminal amino acids are replaced by 15 non-homologous amino acids, resulting in a protein of 742 amino acids [21]. As exon 9 encodes the ligand-binding domain, GRα and GRβ differ significantly in their ligand-binding abilities: GRβ is shorter, hence preventing GRβ from binding to the GCs. The GRβ isoform is also expressed ubiquitously among different tissues, but is detected at lower levels compared to GRα [22]. The relative expression levels of GRα and GRβ have been associated with GC sensitivity–insensitivity in various cell types. GRβ could induce GC resistance by forming a non-transactivating heterodimer with GRα, hence impairing GRα-mediated genomic actions, which is called the dominant-negative effect [22]. In addition to GRα-dependent mechanisms, GRβ has been also shown to have intrinsic activities, and it has been shown that it can regulate the activity of numerous genes related to the inflammatory process, cell communication, migration, and tumourigenesis in HeLa and U-2 OS, and in T24 bladder cancer cells [22] (Figure 1). While in the literature, several studies have reported significant associations between prognosis and GR expression [15,17,23,25], the detection of GR is still challenging. When investigating GR at the RNA level, in ER+ patients, high levels of GR expression in tumours have been found to be associated with a better prognosis compared to patients whose tumours harboured low levels of GR expression [15]. Additionally, high GR expression is associated with improved relapse-free survival in early-stage breast cancer patients [26]. In ER− patients, high levels of GR expression significantly correlated with shorter relapse-free survival independently of adjuvant chemotherapy [15]. Additionally, in ER− and triple-negative breast cancer patients, high GR expression was associated with a worse prognosis [16,17,25]. When GR is detected at the protein level, the findings are not so concordant. Shi et al. described that overall, GR negatively correlated with the survival rates in breast cancer patients, and ER+ patients showed similar results compared to TNBC and invasive subtypes [20]. Additionally, Adbuljabbar et al. observed that positive nuclear GR staining was associated with shorter breast cancer-specific survival in ER− and TNBC cases [14]. However, in this study, the authors indicated that GR was not an independent predictor of survival, and no association was found between GR expression and breast cancer-specific survival (BCSS) or distant metastasis-free interval (DMFI) in the whole series or the ER-positive group [14]. Additionally, Elkashif reported different outcomes in the context of anthracycline-based chemotherapy, depending on GR expression in ER− patients [25]. Based on all the ambiguous findings and the challenging detection of GR and GR isoforms, the roles of GCs and GR expression in breast cancer development and during progression are still diverse and context-dependent. Due to the unclear biological mechanism of action, we aimed to investigate the potential roles of (i.) different GR isoforms in breast cancer cell behaviour, since it is known that GRβ has an opposite effect compared to the most abundant isoform GRα [22,24], (ii.) the context of oestrogen receptor, and (iii.) the presence of receptor ligands as impacting the action of the glucocorticoid receptor. We investigated GR protein expression in 20 independent patients with breast cancer (9 triple-negative (TNBC) and 11 luminal A type ER+) through the Department of Pathology at the National Institute of Oncology, Hungary. Pathological assessment (histology and immunostaining for oestrogen, progesterone, Her2 receptor status, and Ki67 proliferation indices) were done as part of the routine diagnostics that gave the basis of breast cancer subtype classification according to [4]. Control samples were selected from an FFPE block of a surgical specimen of an ER+ breast cancer patient where no malignant tissue was identified by the pathologist in parts adjacent to the tumours. The study was approved by the Scientific and Research Committee of the Medical Research Council of the Ministry of Health, Hungary (BMEÜ/1774-1/2022/EKU). Histologic characteristics of samples used for GRtotal and GRβ immunohistochemistry can be found in Supplementary Table S1. Different validation sets of GR protein and the encoding NR3C1 gene expression were investigated in normal breast tissue, breast cancer, and other cancer types through the Protein Atlas database (https://www.proteinatlas.org/ (accessed on 26 October 2022)). GR protein expression was evaluated in 184 normal tissue samples (see details of Figure 2) by immunohistochemistry using GRtotal antibodies (Cat#HPA004248, Atlas Antibodies, RRID:AB_1078976; and Cat#sc-8992, Santa Cruz Biotechnology, RRID:AB_2155784). NR3C1 gene expression in normal breast specimens was also tested in 459 (168 females and 291 males) samples. In different types of cancer, NR3C1 expression was investigated in 7931 specimens (see details in Figure 3). The GR protein in breast cancer was examined in 16 breast cancer samples using the same antibodies as in normal tissues. Gene expression and mutational data of NR3C1, BRCA1, BRCA2, PTEN, and TP53 in 86 breast cancer cell lines of the Cancer Cell Line Encyclopedia (CCLE) were used to assess genetic dependency through the DepMap portal (https://depmap.org/portal/ (accessed on 26 October 2022)) For exhaustive gene correlation analysis, the bc-GenExMiner v4.8 database of published annotated breast cancer transcriptomic (DNA microarrays (n = 11,359) and RNA-seq (n = 4421)) data were used (PMID: 23325629) (accessed on 11 November 2022). Gene set enrichment analysis was applied for the functional annotation of selected gene sets. Common terms for gene ontology biological processes were further analyzed by MonaGO [27], redundancy was reduced and similar GO terms were clustered. Table 1 summarizes different sample cohorts used in this study. Two human triple-negative (MDA-MB231 (#92020424) and HS578T (#86082104)) and two oestrogen-positive breast cancer cell lines (T47D (#85102201) and ZR-75-1 (#87012601)) were purchased from European Collection of Cell Cultures (ECACC) General Cell Collection. Cell lines were propagated at 37 °C in a humidified atmosphere containing 5% CO2 and used until passage number 25. HS578T cells were grown in Dulbecco’s modified eagle’s medium (DMEM; #10-013-CV, Corning, Corning, NY, USA) supplemented with 10% fetal bovine serum (FBS; #P40-37500 PAN-Biotech, Aidenbach, Germany) and 1% penicillin/streptomycin (#10378016, Thermo Fisher Scientific, Waltham, MA, USA). MDA-MB-231, T47D, and ZR-75-1 breast mammary gland carcinoma cell lines were maintained in RPMI-1640 base medium (#BE12-702F, Lonza Biosciences; Basel, Switzerland) using fetal bovine serum (FBS; #P40-37500 PAN-Biotech, Aidenbach, Germany) in a final concentration of 10% and 1% penicillin/streptomycin (#10378016, Thermo Fisher Scientific, Waltham, MA, USA). Three times a week, the cell culture medium was replaced with a fresh complete medium. When cells reached 90% confluence, they were detached from the bottom of the flask using 0.05% Trypsin-EDTA (#25300062, Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). Microscopic control and imaging were done with an EVOS M7000 imaging system using ×10 objective. In experimental settings, cells were kept in complete media or steroid-free media for 48 h before plating. Steroid-free media was prepared using charcoal-stripped FBS as previously reported [28]. Then, the cells were plated on 6-well tissue culture plates (maintaining complete or steroid-free conditions) and after 24 h they were transfected as described below. All experiments were carried out three times. Cells were seeded in 6-well plates in antibiotic-free media 24 h before transfection. For transfections, pcDNA3.1(+) expression vectors containing cDNA of GR-α (pcDNA3.1-GR-α) and GR-β (pcDNA3.1-GR-β) were used as previously reported [29]. An empty vector (pcDNA3.1) was utilized as the control plasmid per transfection in the Western blot (WB). For transfections, Lipofectamine 3000 (#L3000001, Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) was used following the manufacturer’s instructions. Cells were harvested or fixed 24 to 48 h post-transfection. After transfection, the cells were lysed in M-PER™ Mammalian Protein Extraction Reagent (#7851, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with halt protease inhibitor cocktail (#87785, Thermo Fisher Scientific, Waltham, MA, USA). The total protein concentration was determined with the Bradford Protein Assay (#6916, Merck, Darmstadt, Germany) using bovine serum albumin as standard (#A9418, Merck, Darmstadt, Germany). In total, 20 µg samples of protein homogenates were loaded onto the polyacrylamide gel. Then, the proteins were transferred onto a polyvinylidene difluoride (PVDF) membrane (#88518, Thermo Fisher Scientific, Waltham, MA, USA). Membranes were blocked in blocking buffer (tris-buffered saline with Tween 20 solution (TBST) containing 5% non-fat dry milk) for 1 h at room temperature. The blots were then incubated with primary antibodies Rabbit Polyclonal Glucocorticoid Receptor antibody (#GTX101120, GeneTex, Irvine, CA, USA), 10G8 (ImmunoGenes Ltd., Budakeszi, Hungary), and beta-actin (#4967, Cell Signaling Technology, Danvers, MA, USA) in 1:1000 dilution overnight at 4 °C. The following day, the blots were incubated with goat anti-mouse immunoglobulins/HRP (#P0447, Dako, Santa Clara, CA, USA) and goat anti-rabbit immunoglobulins/HRP (#P0448, Dako, Santa Clara, CA, USA) secondary antibodies in 1:1000–1:2000 dilution for 1 h at room temperature. The proteins were visualized using the SuperSignal West Pico PLUS chemiluminescence detection kit (#34577, Thermo Fisher Scientific, Waltham, MA, USA). For immunohistochemistry analysis, paraffin-embedded sections were processed by the ABC technique to visualize antigens (ABC Elite Kits, Vector Laboratories, Burlingame, CA, USA), slightly modifying a previously described protocol [30]. For single immunohistochemistry, ABC immunoperoxidase staining and a DAB solution as the chromogen (Vector Laboratories, Burlingame, CA, USA, Impact® DAB Substrate, Peroxidase (HRP) were applied. To visualize the glucocorticoid β receptor a mouse monoclonal antibody produced and characterized by ImmunoGenes Ltd. (10G8) was used at a dilution of 1:4000. For localizing the total glucocorticoid receptor (α and β), a polyclonal antibody against human GR N terminal (GTX101120, GeneTex, Irvine, CS, USA) was applied at a dilution of 1:100. Briefly, the paraffin sections were dewaxed and rehydrated with 0.05 mol/L potassium phosphate-buffered saline (KPBS). The primary antibodies were applied for 1 h at room temperature, followed by 24 h at 4 °C (diluted in KPBS + 0.4% triton-X100). On the second day, the samples were incubated with biotinylated goat anti-mouse antibody (BA-9200 Vector Laboratories, Burlingame, CA, USA) or anti-rabbit (BA-1000, Vector Laboratories, Burlingame, CA, USA) for 1 h in room temperature at a 1:500 dilution in KPBS + 0.4% triton-X100. Then, sections were incubated with ABC solutions for 1 h at room temperature (45 mL each A and B in 10 mL of KPBS + 0.4% triton-X100, Vector Laboratories, Burlingame, CA, USA). The samples were then rinsed three times for 5 min each in KPBS and then exposed to DAB H2O2-containing chromogen solution. Staining was performed for 8 min in the case of the GR β antibody and 12 min for the GRtotal and was terminated by rinsing in KPBS. Sections were counterstained with hematoxylin (Novolink, Leica Biosystems Newcastle Ltd., Newcastle Upon Tyne, UK) and coverslipped with Glycergel aqueous mounting medium (Agilent, Santa Clara, CA, USA). Cells seeded on coverslips were washed twice with PBS and then fixed with 4% paraformaldehyde (PFA) in PBS. Coverslips were blocked in 5% bovine serum albumin in PBST at room temperature for 1 h, then incubated with primary GRβ antibody (ImmunoGenes Ltd., #10G8) at 1:100 dilution overnight at 4 °C. Goat anti-mouse IgG (H+L) highly cross-adsorbed the secondary antibody, and Alexa Fluor Plus 555 (Thermo Scientific #A32727) was applied as the secondary antibody (at 1:500 dilution) for 1 h at room temperature. The cells then were incubated with Hoechst 33342 to stain the nuclei. Images were obtained using a 10× objective. Cells were seeded on 6-well plates. Cell viability, proliferation, and dead cell ratio were investigated as we previously reported [31]. Briefly, for cell viability assessment, the metabolic Alamar Blue assay (#DAL1025, Invitrogen, Thermo Fisher Scientific, Grand Island, NY, USA) was used. Fluorescent signals were detected using a flash spectral scanning multimode reader (#5250040, Varioskan, Thermo Fisher Scientific, Waltham, MA, USA) with SkanIt Software 2.4.5 RE (ex: 560 nm, em: 590 nm). This metabolic assay is applied as a cell health indicator using the metabolic activity of living cells to quantitatively measure viability. Optical density (OD) data were presented as normalized values relative to monolayer cultures at each point in average ratio ± standard deviations. To assess cell proliferation cell numbers were determined using 0.4% Trypan Blue staining (#15250061, Gibco, Thermo Fisher Scientific, Waltham, MA, USA). Trypan Blue staining represents a cruder analysis to identify dead cells. Results from Trypan Blue assays (live cell number) have been defined as “proliferation”. All experiments were repeated at least three times (biological replicates) with one to three technical replicates in each experiment. Mean and standard deviation were calculated and are illustrated on graph bars. To assess the effects of GRα and GRβ on migration, wound-healing assays were performed on 24-well plates as previously reported [32]. Twenty-four hours after transient transfection the cell monolayer was wounded using a 200 μL pipette tip and floating cells were washed with phosphate-buffered saline (PBS) (#21-040-CV, Corning, Corning, NY, USA). Photos were taken after 0, 24, and 48 or 0, 6, and 12 h depending on cell type. Images were analyzed with ImageJ Software (https://imagej.nih.gov/ij/ (accessed on 7 January 2022), Bethesda, MD, USA) to calculate cell-free area (CFA %: [(CFA at target time/CFA 0 h) × 100]) [32]. For the comparison of multiple groups, analysis of variance was used to identify statistical significance among different groups, and the Dunnett test was used to correct for multiple comparisons. To compare the two groups unpaired t-test with Welch’s correction was applied. A p-value < 0.05 was considered statistically significant. For investigating the correlation between NR3C1 and other genes’ expression in RNAseq studies, Pearson’s correlation was used. As a first step, we examined the glucocorticoid receptor encoding NR3C1 expression at the RNA and GR protein levels across different normal tissues and cancer types, including normal and cancerous breast samples, using high-throughput data (Figure 2A,B). Expectedly, due to its general function, NR3C1 showed an overall broad expression among different tissue types, hence low tissue specificity. In normal breast tissue, glucocorticoid receptor protein expression was found to be medium and high compared to other tissue types (Figure 2A). Interestingly NR3C1 expression was higher in male breast tissue compared to female; however, it did not depend on age in either group (Figure 2B). Regarding breast cancer, NR3C1 expression was found around the average level compared to different tumour types at the RNA level (Figure 3A). Regarding glucocorticoid receptor protein immunohistochemistry in human breast cancer tissues, two different types of anti-GR antibodies (HPA004248, CAB010435) showed variant staining—some only nuclear, some cytoplasmic/membranous—and nuclear staining on the same samples of the Protein Atlas database (Figure 3B). In contrast to normal breast tissue, NR3C1 expression was decreased in male breast cancer tissue compared to females, reaching the level of significance (p = 0.055); however, it did not depend on age in either group (Figure 3C). As a further step, we also analyzed NR3C1 expression in 86 different breast cancer cell lines. NR3C1 did not show a significant expressional difference between primary and metastatic breast cancer cell lineages; however, its level was higher in ER− samples compared to ER+ cases (Figure 3D). NR3C1 expression was independent of BRCA1, BRCA2, PTEN, or TP53 mutational status (data not shown). GRα and β isoforms have important roles with opposite functions, hence we assessed the expression of the GR using an N-terminal specific antibody referred to as GRtotal, and a selective antibody specific against the GRβ isoform (GRβ) on an independent sample cohort of 9 TNBC and 11 luminal A type (ER+, PR+, negative for HER2) breast cancer tissues (see primary staining and negative controls omitting the primary antibody in Supplementary Figure S1, which also indicates the specificity of cytoplasmic localization). We found that both GRtotal and GRβ were detectable in normal and cancerous breast specimens (Figure 4). Staining was heterogeneous among samples irrespective of tumour type. Both GRα and GRβ exhibited mostly cytoplasmic localization in tumours, and in some samples with nuclear positivity (Figure 5). When dissecting different cell types, in control tissue, the mammary gland lactiferous duct epithelial cells showed less frequent immunostaining with GRβ than with GRtotal. Both isoforms could appear as nuclear and cytoplasmic as well. In almost every epithelial and myoepithelial cell, the nuclear staining of GRtotal could be observed. GRβ labeled a few of the epithelial and myoepithelial cells. Other connective tissue cells, such as fibrocytes, adipocytes, and endothelial cells, also showed GRtotal positivity and much less GRβ positivity. The oestrogen-positive and -negative cancer tissues showed both cytoplasmic, and less frequently, nuclear staining for GRtotal and GRβ. Generally, the infiltrating lymphocytes exhibited intensive GRtotal and GRβ staining, but not in all lymphocytes. The specificity of the GRβ antibody was tested by in vitro models using expression vectors encoding GRα and GRβ isoforms without any cross-reactivity of GRβ with GRtotal (Figure 6A). In line with our immunohistochemical findings, we found that GRβ localized mainly in the cytoplasm using immunocytochemistry in both control and transfected cells (Supplementary Figure S2). We found significantly higher GRtotal expression in TNBC cell lines compared to ER+ ones, which is in line with the finding at the RNA level. Additionally, compared to GRα, a low amount of GRβ was detected in both ER+ and ER− cell lines (Figure 6B,C) It has been previously reported that GR expression represented a worse prognostic factor for ER−, but not for ER+ patients, and that GRβ has an opposite effect compared to the main GR isoform (GRα). Therefore, we separately investigated the effects of GRα and GRβ on breast cancer cell behavior. In parallel, we assessed the effect of the presence of the receptor ligands on glucocorticoid action, as well as in the context of the oestrogen receptor. We found that GRα increased cell viability and cell proliferation in ER− cells independently of the presence of the ligand, while it had no or a mild effect on ER+ breast cancer cells regardless of the availability of steroid ligands (Figure 7A,B). GRβ expression did not alter cell viability or proliferation in either type of cell. Interestingly, GRβ increased the dead cell ratio in ER+ but not in ER− cells, and this effect was also independent of the presence of the ligand (Figure 7C). Both GRα and GRβ increased the cell migration of ER− breast cancer cell lines, and neither of them influenced the cell migration of ER+ cells (Figure 8A–C and Figure 9A–C). Based on the finding that GR signalling did not depend on the presence of the ligand, we screened genes that exhibited a significant positive and negative correlation with NR3C1 in breast cancer tissue samples (10,455 samples analyzed by 57 microarray studies and 4421 samples analyzed by three RNAseq studies). We assessed biological functions via GO biological process gene set enrichment analyses of both microarray and RNAseq experiments and focused on the common findings. We found that genes that positively correlated with NR3C1 were mainly implicated in the cell migration, angiogenesis, and intracellular steroid hormone receptor signaling pathways (Figure 10 and Table 2). Negatively correlated genes represented smaller gene sets compared to positively correlated genes: 15% and 3% of all correlating genes in microarray and RNAseq studies, respectively. Therefore, we investigated the union of the biological functions of the negatively correlated genes that were involved in cell division and ubiquitination (Table 3). These findings corroborate our in vitro results as well. The dual (tumour-suppressing and -promoting) role of GCs has been well-documented [6]. In animal models, GCs protected against cancer development, and studies have indicated the tumour-suppressive roles of GR in epithelial solid cancers [6,33]. However, GR action in cancer biology appears to be strongly cell type- and context-dependent [12,23]. Due to its essential function in homeostasis, GR is abundantly expressed among different tissue and cancer types. In normal breast tissue, the reasons for and relevance of our finding that GR showed higher expression in males compared to females need further investigation. In contrast to normal tissue, in breast cancer, NR3C1 showed the opposite—its expression was decreased in male compared to female patients. The lower expression of NR3C1 in male breast cancer seems to be in line with the finding that breast cancer in males is mostly oestrogen-positive, and it has a good prognosis [3,34]. In line with the findings of others, we detected GR protein expression in the majority of breast tissues. We also observed that NR3C1 gene expression was increased in ER− breast cancer cell lines compared to ER+ ones. On the protein level, both GRtotal and GRβ were detectable in normal and cancerous breast specimens. Staining was heterogeneous among samples irrespective of tumour subtype (i.e., presence of oestrogen receptor). Both GRα and GRβ exhibited mostly cytoplasmic localization in tumour samples, and in some samples with nuclear positivity. Our results regarding GRtotal are similar to the findings described in the Protein Atlas using the CAB010435 antibody (Cat#sc-8992, Santa Cruz Biotechnology) that also indicated both cytoplasmic and nuclear staining. However, when another antibody (HPA004248: Cat#HPA004248, Atlas Antibodies) was used, only nuclear staining was indicated. While nuclear positivity indicates GR activation and cytoplasmic positivity reflects the expression and non-genomic action of GR, the reliable detection of GR is considered crucial when correlating with clinicopathological parameters. The ambiguous results could be due to both technical and biological factors. In immunohistochemistry staining, various antibodies were used, and different staining patterns (purely nuclear vs. cytoplasmic/nuclear) have been observed, which could be traced back to the possibility of technical (e.g., antigen retrieval and antibody specificity) aspects, hence warranting caution in the interpretation of results. GR and ER coactivation enhanced GR binding to both glucocorticoid-responsive elements (GRE) and oestrogen-responsive elements (ERE), resulting in anti-tumourigenic effects, such as the increased expression of pro-differentiating genes and negative regulators of pro-oncogenic pathways, as well as the decreased expression of EMT-related genes [35]. As GR and ER co-occupy the same genomic nuclear receptor-responsive regions, GCs antagonized oestrogen-stimulated endogenous ER target gene expression and oestrogen-mediated cell proliferation [23,35,36,37]. On the other hand, oestrogen also influences GC action. Oestrogen could induce the dephosphorylation of GR, consequently decreasing its activity on the target genes involved in cell growth arrest [38]. Additionally, ER antagonists could lead to the enhanced proteasomal degradation of GR [39]. This GR–ER crosstalk manifested as the improved relapse-free survival of breast cancer patients with ER-positive tumours, and GR was related to a favourable prognosis, while low GR expression was associated with worse outcomes, such as high Ki67, p53, and CD71 expression [35,40]. According to the context of ER, we showed that GR expression was higher in ER− breast cancer cells compared to ER+ ones, which may indicate a potential reciprocal inhibitory action between GR and ER [41]. Additionally, our findings that the presence of GR itself increased cell proliferation in ER− breast cancer cells, while it had no impact on ER+ tumour cells, are in line with studies reporting an association between GR expression and prognosis/outcome [15,16,17,25]. The GR–ER crosstalk is additionally illustrated by feedback loops, where GR could back-regulate ER expression. While there is no GRE identified in the promoter of ER, the indirect regulation of ER by GR can be hypothesized as a feedback loop control. The promoters of ERα contain multiple predicted and validated transcription factor-binding sites [42,43]. Several of them are in indirect interaction with GR, such as ER itself, BRCA1, ZEB2, NF-κB, and circadian genes [42,43]. In addition, DNMT1 and ZEB1, as GR-regulated genes, can induce ERα promoter methylation and the down-regulation of ERα expression [42]. Similarly, histone acetylation and methylation also play a role in ER expression, while histone acetyltransferases and demethylase are also regulated by GR at the promoter level [43]. Additionally, another way in which GR and ER signaling interact is by decreasing levels of free oestrogen through the GR-mediated activation of oestrogen sulfotransferase [44]. In the complex interaction network of GR–ER, there are indirect processes in which ER is itself regulated by other receptors, which, in turn, could regulate GR expression. Signal transduction by Her2 and epidermal growth factor receptors (EGFR) was described to alter the phosphorylation of ER and ER-dependent signaling irrespective of the presence of ER ligands [45]. In addition, both oestrogen and growth factor signaling pathways regulate the secretion of vascular endothelial growth factors that stimulate tumour-associated angiogenesis [45]. Additionally, evidence has suggested that crosstalk between ER and growth factor receptor pathways contributed to the development of tamoxifen resistance in breast cancer. Signaling via the EGFR and Her2 could activate both ER and the ER coactivator AIB1. In turn, ER located in the cell membrane can activate the growth factor receptor pathways [46]. Besides interactions between nuclear receptors, crosstalk between GR and growth factors has also been reported [47,48]. EGFR, one of the most active growth factors exerting strong growth-promoting effects in the mammary epithelium [47], can interact with GR through both genomic and epigenomic processes [48]. Regarding crosstalk with other growth factors, GR has been shown to be a required effector of TGFβ1-induced p38 MAPK signaling [49], and it suppresses the transcription of the insulin receptor substrate 1 (IRS-1), which mediates insulin-like growth factor (IGF) signals [20]. Interestingly, we found a similar effect of GRα and GRβ following transfection regarding viability and proliferation. Our data suggest that in ER– breast cancer cells, even the increased relative expression of GRβ does not abolish the effect of GRα regarding tumour cell viability, proliferation and migration. This finding was somewhat surprising, as in allergic respiratory and inflammatory bowel diseases, increased GRβ has been associated with resistance against glucocorticoids [22]. However, GRβ increased the dead cell ratio in ER+ cells only, while it had no—or a mildly opposite—effect in cells lacking ER. While the crosstalk between GRα and ER is well-known [35], the explanation of the different effects of GRβ depending on the presence of ER needs further clarification. In the physiological GR action, in the presence of steroid ligand, the GR monomers are removed from their GRE half sites, and instead, GR-dimer formation and assembly on classical GREs in the DNA occurs [23]. Indeed, in breast cancer, unliganded GR has been described to play a protective role. In non-malignant mammary cells, GR has been shown to bind to the promoter region of the BRCA1 gene, up-regulating its expression [50]. GCs induced a loss of GR recruitment to the BRCA1 promoter with a concomitant decrease in BRCA1 expression [50,51]. Despite this interaction of GR with BRCA1 expression, we did not find any effect of BRCA1 (or other hereditary breast cancer predisposition genes) mutation status on GR expression. Interestingly, the effects of both GRα and GRβ on cell viability, proliferation, dead cell ratio, and cell migration were independent of the presence of the ligand, indicating that the receptor expression/the presence of the receptor itself may have an important prognostic role. The genomic effects of GRα include both transactivation and transrepression, which could be realized by the direct binding of GRα to GRE sequences. Several pieces of evidence have substantiated that GRα can also be activated in the absence of ligands [6,50,51,52,53]. Indeed, certain chemicals, elevated temperature, cellular pH, and shear stress were demonstrated to induce GRα nuclear translocation, hence its activation [21,52]. Additionally, posttranslational modifications of the receptor and the presence of TNFα were also shown to induce ligand-independent GRα activation [21,54]. Moreover, non-genomic GC action (e.g., cytoplasmic, membrane-bound, or mitochondrial GR action) could also occur independently of the ligand [23]. Furthermore, the GRβ negative–dominant effect on GRα can occur in a ligand-independent way. Upon GRE binding, GRβ competes with GRα, or it forms an inactive heterodimer; consequently, it does not induce transcription [6,47,52,55]. It is also suggested that GRβ can bind other ligands (e.g., synthetic GC antagonists, unknown molecules, or endogenous steroids) as well. Moreover, the intrinsic activity of the GRβ isoform (also in the absence of the ligand) has been proven by in vitro and in vivo experiments, where GRβ exerted transcriptional activity on several genes, including both GRE-containing promoters and non-GC-regulated genes [6,47,52,55]. Based on our findings, ligand-independent GR action (including both GRα and GRβ) may play an important role in breast cancer cell proliferation and migration. GR transactivates or transrepresses (in an either ligand-dependent or -independent way) numerous genes. Additionally, the GR activity signature (expressional changes) was demonstrated to have a stronger association with RFS than GR expression alone [56]. Therefore, we screened for genes positively and negatively correlating with GR in breast cancer specimens from 14,876 patients. We found that positively correlated (transactivated) genes were implicated mainly in cell migration, and also in the angiogenesis and intracellular steroid hormone receptor signaling pathways, while they were negatively correlated (transrepressed) with cell division and ubiquitination. These biological processes of GR action are fully reflected by our results, which were derived by in vitro functional assays. Previously, GR activation has been linked to apoptosis regulation and the modulation of the expression of apoptotic genes by interfering with p53 function in ER+ breast cancer [36,57,58]. Furthermore, GR activation was protective against apoptosis both in vitro and in vivo [59,60], with which our data—demonstrating GRβ’s effect on the dead cell ratio in ER+ breast cancer cell lines—are in complete agreement. Additionally, in TNBC, the GR activation signature was also related to epithelial–mesenchymal transition (EMT), cell adhesion, and inflammation pathways [56,61]. Recently, Obradovic et al., while investigating both patient-derived and TNBC cell line-derived xenograft models, demonstrated that GR activation increased breast cancer heterogeneity and metastasis. In this study, elevated GC levels during cancer progression augmented tumour cell colonization and reduced the survival of animal models of ER-negative breast cancer [7], reflecting our results on the cell migratory GR signature. GR’s role in cancer biology is still ambiguous. This is most probably a consequence of the strong context-dependent activity of GR. Indeed, the role of altered GR expression, different isoforms due to alternative splicing, posttranslational modifications, availability of the ligand and nuclear receptor crosstalk have been suggested to modulate GR action in steroid-sensitive tissues and diseases (e.g., asthma, inflammatory bowel diseases), and in cancer as well. Therefore, in this study, we aimed to unravel the context-dependent function of GR action in breast cancer, such as the presence of ER, the role of GRβ isoforms, and the availability of the ligand. We have reinforced the finding that the expression of ER is a main factor in GR action probably due to receptor crosstalk. We found that the main isoform, GRα, increased cell proliferation and viability in ER− (TNBC) cells. By dissecting the effects of GRα and GRβ, we have demonstrated that GRβ showed low/heterogenous abundance in breast cancer, and that it has a similar effect on breast cancer cell lines’ viability, proliferation, and migration. However, the GRβ isoform has an opposite effect depending on the presence of ER, increasing dead cell ratio in ER+ breast cancer cells compared to ER− ones. Interestingly, we found that GRα and GRβ’s effects on cell viability, proliferation, dead cell ratio, and cell migration did not depend on the presence of the ligand, suggesting the role of the “intrinsic”, ligand-independent action of GR in breast cancer. Our findings may add a new perspective regarding the previously suggested potential danger of adjuvant steroid therapy. Furthermore, different GR isoforms may have an important effect on the outcome of ER+ breast cancer patients by increasing dead cell ratio. These data add a further degree of complexity to the context-dependent effects of glucocorticoid and GR action in breast cancer.

PMC10000937

Qiyu Gan,Luning Mao,Rui Shi,Linlin Chang,Guozeng Wang,Jingxin Cheng,Rui Chen

Prognostic Value and Immune Infiltration of HPV-Related Genes in the Immune Microenvironment of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

23-02-2023

cervical squamous cell carcinoma and endocervical adenocarcinoma,TCGA datasets,immune microenvironment,HPV

Simple Summary Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) generally presents with HPV infection and is the second most common gynecological malignancy. However, the effect of immune infiltrate and immune microenvironment on the tumorigenesis and development of CESC remains unclear. In this study, we divided CESC cases into different immune subtypes and performed a differential gene expression analysis. The CESC cases (n = 303) were divided into five subtypes (C1–C5) based on their expression profiles. Subtype C4 demonstrated a downregulation of the immune profile, lower tumor immune/stroma scores, and worse prognosis, while subtype C1 showed the opposite characteristics. In addition, GSEA screened out some key genes associated with HPV infection pathways, among which high FOXO3 and low IGF-1 protein expression were closely correlated with decreased clinical prognosis. Our results may provide guidance for developing potential immunotherapeutic targets and biomarkers for CESC. Abstract Mounting evidence has highlighted the immune environment as a critical feature in the development of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). However, the relationship between the clinical characteristics of the immune environment and CESC remain unclear. Therefore, the aim of this study was to further characterize the relationship between the tumor and immune microenvironment and the clinical features of CESC using a variety of bioinformatic methods. Expression profiles (303 CESCs and three control samples) and relevant clinical data were obtained from The Cancer Genome Atlas. We divided CESC cases into different subtypes and performed a differential gene expression analysis. In addition, gene ontology (GO) and gene set enrichment analysis (GSEA) were performed to identify potential molecular mechanisms. Furthermore, data from 115 CESC patients from East Hospital were used to help identify the relationship between the protein expressions of key genes and disease-free survival using tissue microarray technology. Cases of CESC (n = 303) were divided into five subtypes (C1–C5) based on their expression profiles. A total of 69 cross-validated differentially expressed immune-related genes were identified. Subtype C4 demonstrated a downregulation of the immune profile, lower tumor immune/stroma scores, and worse prognosis. In contrast, the C1 subtype showed an upregulation of the immune profile, higher tumor immune/stroma scores, and better prognosis. A GO analysis suggested that changes in CESC were primarily enriched nuclear division, chromatin binding, and condensed chromosomes. In addition, GSEA demonstrated that cellular senescence, the p53 signaling pathway, and viral carcinogenesis are critical features of CESC. Moreover, high FOXO3 and low IGF-1 protein expression were closely correlated with decreased clinical prognosis. In summary, our findings provide novel insight into the relationship between the immune microenvironment and CESC. As such, our results may provide guidance for developing potential immunotherapeutic targets and biomarkers for CESC.

Prognostic Value and Immune Infiltration of HPV-Related Genes in the Immune Microenvironment of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) generally presents with HPV infection and is the second most common gynecological malignancy. However, the effect of immune infiltrate and immune microenvironment on the tumorigenesis and development of CESC remains unclear. In this study, we divided CESC cases into different immune subtypes and performed a differential gene expression analysis. The CESC cases (n = 303) were divided into five subtypes (C1–C5) based on their expression profiles. Subtype C4 demonstrated a downregulation of the immune profile, lower tumor immune/stroma scores, and worse prognosis, while subtype C1 showed the opposite characteristics. In addition, GSEA screened out some key genes associated with HPV infection pathways, among which high FOXO3 and low IGF-1 protein expression were closely correlated with decreased clinical prognosis. Our results may provide guidance for developing potential immunotherapeutic targets and biomarkers for CESC. Mounting evidence has highlighted the immune environment as a critical feature in the development of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). However, the relationship between the clinical characteristics of the immune environment and CESC remain unclear. Therefore, the aim of this study was to further characterize the relationship between the tumor and immune microenvironment and the clinical features of CESC using a variety of bioinformatic methods. Expression profiles (303 CESCs and three control samples) and relevant clinical data were obtained from The Cancer Genome Atlas. We divided CESC cases into different subtypes and performed a differential gene expression analysis. In addition, gene ontology (GO) and gene set enrichment analysis (GSEA) were performed to identify potential molecular mechanisms. Furthermore, data from 115 CESC patients from East Hospital were used to help identify the relationship between the protein expressions of key genes and disease-free survival using tissue microarray technology. Cases of CESC (n = 303) were divided into five subtypes (C1–C5) based on their expression profiles. A total of 69 cross-validated differentially expressed immune-related genes were identified. Subtype C4 demonstrated a downregulation of the immune profile, lower tumor immune/stroma scores, and worse prognosis. In contrast, the C1 subtype showed an upregulation of the immune profile, higher tumor immune/stroma scores, and better prognosis. A GO analysis suggested that changes in CESC were primarily enriched nuclear division, chromatin binding, and condensed chromosomes. In addition, GSEA demonstrated that cellular senescence, the p53 signaling pathway, and viral carcinogenesis are critical features of CESC. Moreover, high FOXO3 and low IGF-1 protein expression were closely correlated with decreased clinical prognosis. In summary, our findings provide novel insight into the relationship between the immune microenvironment and CESC. As such, our results may provide guidance for developing potential immunotherapeutic targets and biomarkers for CESC. Cervical squamous cell carcinoma and endocervical adenocarcinoma is a malignant tumor occurring in cervical epithelial cells, with more than 500,000 women diagnosed per year [1]. In addition, over 300,000 patients per year die as a result of the disease [1,2]. Among the majority of CESC cases, high-risk subtypes of the human papilloma virus (HPV) are considered to be the primary cause [1,3]. Despite a standardized treatment involving radical hysterectomy, chemoradiation, or a combination [4,5], the mortality and recurrence rates of CESC continue to increase [4]. Recent studies have demonstrated that tumor heterogeneity and immune microenvironment [6,7] act in carcinogenesis and the epithelial–mesenchymal transition (EMT). However, the molecular mechanisms underlying the role of the immune microenvironment in CESC remain unclear. CESC is primarily considered to be a typical immunogenic cancer as a result of HPV infection, and much attention has been paid to immunotherapy treatments for this disease. As such, monoclonal antibodies (mAb) that target various immune checkpoint pathways have been approved for the treatment of CESC by the U.S. Food and Drug Administration (US FDA) [8]. Indeed, CESC is characterized by several alterations in the immune system, indicating the potential for patients to benefit from immunotherapy. Recently, the U.S. FDA granted approval for the use of pembrolizumab in patients who experienced disease progression during or after chemotherapy in cases of programmed death-ligand 1 (PD-L1) positive expression [9]. However, the benefit of immunotherapy in primary CESC remains unknown. Previous studies have shown that various features of the tumor microenvironment in CESC are crucial in the immune process of carcinogenesis and progression. To illustrate the underlying immune gene expression profiles, the prognosis of CESC patients has been previously observed by quantitative PCR (qPCR) and immunohistochemistry (IHC) [10]. A gene expression profile analysis of 27 immune response genes and 15 vascularized marker genes in 56 CESC samples has been performed by weighted gene coexpression network analysis. However, further studies with a larger sample size would have increased the reliability of the conclusions. In another study, 27 survival-related immune response genes and 15 vascularized marker genes were elucidated from six tumor clusters (four immune response clusters and two vascularization clusters) in 52 CESC samples using qPCR and IHC techniques [11]. With these types of high-throughput approaches, comprehensive analysis of the tumor molecular types and global immune profiles are necessary. The transcriptome serves as a powerful tool for systematically exploring the tumor microenvironment, which is infiltrated with immune cells such as T cells, B cells, and dendritic cells (DCs), and involved in tumor initiation, growth, angiogenesis, metastasis, and tumor immunity [12,13]. In addition, several supporting studies have suggested RNA sequencing and gene expression data from underlying expression profiles may explain the correlations between heterogeneous clinical outcomes and coexistent tumor immune microenvironments. Using microarray analysis, qPCR, and Western blot techniques, an immune-related signature of the microenvironment has been identified as a potential therapeutic target of early-stage HPV infection in the cervical cancer tissue of CESC [14]. However, owing to the lack of external validation and cross-validation in the training set of this previous study, studies with a larger number of patients with CESC are required for the findings to be further validated. In this study, we present the analysis of patients with CESC from a cohort available from The Cancer Genome Atlas (TCGA) in order to explore relevant immune subtypes and their clinical associations with the immune microenvironment. RNA sequencing data were used to investigate the correlation between the immune score of CESC subtypes and clinical information. In addition, we analyzed the immune infiltration level of key genes that were differentially expressed in the immune microenvironment. Furthermore, the correlations between the expression of key genes and tumor progression/regression status was validated by tissue specimens from 115 CESC patients using tissue microarray technology. The results of our study should provide additional insight into the mechanism of immunotherapy in CESC. RNA-seq data of CESC were retrospectively retrieved from the TCGA (https://tcga-data.nci.nih.gov/tcga/ accessed on 9 January 2022) [15]. Corresponding clinical information, including age, pathological type, survival, and prognosis were also obtained from TCGA database and integrated with RNA-seq data. The criteria were as follows: (1) stage I, II, III, or IV; (2) with complete survival data; and (3) accompanied by the gene expression pattern. Gene expression data were preprocessed from limma (R package) using quantile normalization [16]. Stromal and immune scores were calculated using ESTIMATE algorithm for tumor purity [17]. No research was performed on patients, participants, or animals by any of the authors in this part of the current study. There was a total of 115 Chinese patients diagnosed with CESC from January 2010 to December 2011 in Shanghai East Hospital who were included in this study. The average age was 46.574 (range, from 29 to 70 years). The inclusive criteria were (1) a pathologic diagnosis of squamous cell cancer; (2) complete surgical section of the primary tumor and regional lymph node, with negative margins by histologic examination; (3) entire clinicopathologic testing; and (4) follow-up information. Exclusion criteria included the administration of neoadjuvant chemotherapy before surgery. All patients had signed the written informed consent forms before surgical resection. This clinical part of our study was approved by the Ethics Committee of the Shanghai East Hospital, which is affiliated with the Tongji University of Shanghai. Table 1 lists the detailed clinicopathologic features of the CESC patients. RNA-seq data were obtained using an Illumina® system, which is a next-generation sequencing platform [18]. We downloaded the fragments per kilobase of gene per million fragments mapped with upper quartile normalization (FPKM-uq) from the TCGA database, converted the gene annotation using the Ensemble database [19], and transformed the gene expression value into log2 for further analysis. The ESTIMATE algorithm was used to calculate tumor purity and the total immune component in each CESC sample using the downloaded gene expression data [20]. The scores of six immune infiltration subtypes were downloaded from the Timer database (https://cistrome.shinyapps.io/timer/ accessed on 1 March 2022), including those of B cells, T cells (CD8+ T cells, CD4+ T cells), macrophages, neutrophils, and DCs [21]. Using the ConsensusClusterPlus R package [22], we performed consensus clustering to identify the molecular subtypes of CESC underlying the immune gene expression profiles. Then, we selected immune-related genes from the Gene Ontology database [23] by searching immune-related GO terms. Gene expression data were median centered. Using the euclidean distance, we analyzed the similarity distance between all samples [24]. A clustering program based on K-means [25] was then carried out with 1000 iterations by extracting 80% of the samples at each iteration. Through CDF curves of the consensus score, the optimal cluster number was achieved [26]. In addition, SigClust analysis in pairwise comparisons was employed to illustrate the significance of clustering among the classified subtypes [27]. Bonferroni correction [28] was applied for multiple comparison testing. Characteristic genes were selected by the sample comparison in each subtype, and Student’s t-test was used for the remaining samples. Different molecular subtypes within each subtype were distinguished. The Benjamini–Hochberg procedure was applied to calculate the false discovery rate (FDR) [29]. A differential gene expression analysis was carried out using limma (R package) [30]. We corrected the DEGs using FDR adjustment for multiple comparisons. Limma [31] was also used to screen for DEGs based on each subtype. The cut-off criteria were set to fold change >−1 with an adjusted p-value <0.05. The results for the top 100 upregulated genes were used to distinguish the different molecular subtypes. Subsequently, the top 100 upregulated DEGs from each subgroup were intersected using a Venn diagram, and overlapped DEGs were obtained. GO and Kyoto encyclopedia of genes and genomes (KEGG) gene expression profiles of the DEGs were used to identify GO categories [32] and relevant pathways of protein networks, chemical information, and genomic information [33]. The GO term and KEGG pathway analyses were carried out using the clusterProfiler method (R package) [34]. An FDR < 0.05 was regarded to be significant. The Reactome pathways of the DEGs were explored by GSEA [35] using clusterProfiler (R package). The reference gene set of C2.all.v6.2.symbols.gmt was investigated in the study. The cut-off criteria were set as FDR < 0.1 and a p-value <0.01. The mean survival time of the samples was estimated by using the Kaplan–Meier method. Survival curves were assessed using the log-rank test. All tests were two-sided and were executed using the R Version 3.6.1 statistical software [36]. The cut-off criteria were set as p-value <0.05. Using tissue microarray, all samples were fixed in 4% paraformaldehyde at 4 °C overnight. Five micrometer-thick histological sections were processed using ethanol dehydration, xylene clearing, and paraffin embedding. Sections were incubated with primary antibodies (anti-PERP, -BAK1, -CDK2, -VDAC1, -MDM2, -DAC1, -FOXO3, -AKT3, and -IGF1; 1:100; Bioss, Beijing, China) at 4 °C overnight. The staining procedure was performed according to the instruction of the commercial kit (ZsBio, Beijing, China). The IHC analysis was performed by two independent pathology investigators at 400× magnification in five randomly selected representative fields, separately. A semiquantitative scoring system was applied to the assessment. The criteria were as follows: (1) staining intensity: 0, no staining; 1+, weak staining; 2+, moderate staining; 3+, strong staining; (2) percentage of stained cells: 0, <5%; 1, 5–25%; 2, 26–50%; 3, 51–75%; and 4, >75% [37]. Samples were stratified into high or low expression by the threshold of median value. Data were presented as the mean ± standard deviation. The possible differences of the key genes were analyzed using an independent samples t-test or one-way ANOVA. The clinicopathologic variables and expressions of FOXO3 and IGF1 were analyzed using either the Pearson χ2 test (the theoretical frequency was less than five) or the Fisher exact test (the theoretical frequency was less than five). DFS [38] was estimated between the date of surgery and that of cancer recurrence, death, or last follow-up. A censored event included the status of patient who was “alive” without recurrence or death at the last follow-up, whereas failure events were defined as patient death. Kaplan–Meier estimates were performed to illustrate the differences between the survival curves. The same thresholds for FOXO3 and IGF1 were utilized for subsequent analysis. Univariate analysis comparing patient survival between the high and low FOXO3 and IGF1 groups was applied while stratifying for individual clinicopathologic variables. Multivariate analysis with the Cox proportional hazards model was performed to estimate the DFS while adjusting for potential confounders. The hazard of individual factors was measured using the hazard rate with 95% confidence interval. All statistical data were analyzed using SPSS (Version 22.0) and GraphPad Prism (GraphPad Software, La Jolla, CA, USA) with p-value <0.05. We assembled a set of immune-associated genes (n = 1101) with gene expression profiles. Selected genes from the TCGA cohort were further analyzed in order to distinguish the CESC subtypes. We stratified all samples into k (k = 2–10) differential clusters using the ConsensusClusterPlus (R package). For the result k = 5, the optimal division was achieved, underlying the cumulative distribution function (CDF) curves of the consensus score (Figure 1A). The SigClust analysis elucidated that, among the paired comparisons, the consensus clusters were of significance in k = 5 (Figure 1B). Thus, a set of 303 CESC samples were achieved from the TCGA cohort and stratified into five subtypes (C1, n = 85; C2, n = 87; C3, n = 53; C4, n = 51; C5, n = 27) based on the whole immune gene expression dataset (Figure 1C). Selected TCGA cases with gene expression data and complete clinical files were analyzed, including age, TNM (tumor, node, metastasis) staging, FIGO stage, neoplasm histologic grade, lymphovascular invasion indicators, and the survival possibility of each subtype. We analyzed the gene expression data and observed the distribution of the clinical features. Using Kruskal–Wallis, the age-group distribution was of significance (p = 0.016) (Figure 1D). Among all the subtypes, the average age was the highest in C5 and the lowest in subtype C3. We explored and compared the average age and TNM stage in the molecular subtypes. As compared with the other subtypes, the proportions of T1 in subtype C4, and T3/T4 in subtype C1 were significantly higher, corresponding to the tumor category (p < 0.01) (Figure 1E). In addition, the proportions of N0 in subtype C4 and N1 in subtype C1 were significantly higher, corresponding to the node stage (p < 0.01) (Figure 1F). Similarly, the proportions of M0 in subtype C1 and M1/MX in subtype C4 were significantly higher, corresponding to metastasis (p < 0.01) (Figure 1G). Next, we explored the FIGO stage, the proportions of stage I in sybtype C4 and stage II in subtype C3 were higher, whereas stage III in subtype C4 was significantly lower (p < 0.01) (Figure 1H). In addition, the proportions of G1 in subtype C4 and G3 and G4 in subtype C3 were significantly higher (p < 0.01) (Figure 1I). The proportions of absence in subtype C4 and presence in subtype C5 were significantly higher (p < 0.01) (Figure 1J). Finally, we estimated the survival probability with these five subtypes using the Kaplan–Meier method. The survival probability in sybtype C4 was marginally higher than the other subtypes (Figure 1K, p = 0.062). The data indicated that subtype C4 was associated with decreased immune status, whereas subtype C1 was related to an enhanced immune status. Furthermore, the five molecular subtypes from the TCGA cohort showed marginally different prognostic results, with subtype C4 demonstrating the best prognosis. Underlying the ESTIMATE algorithm and immune scores, we calculated the immune, stromal, and ESTIMATE scores among the five CESC subtypes from the TCGA cohort. The immune scores (Figure 2A) and stromal scores (Figure 2B) were both significantly higher in subtype C1 and lower in subtype C4. Moreover, the ESTIMATE scores were also significantly higher in subtype C1 and lower in subtype C4 (Figure 2C). Therefore, the majority of the enhanced immune profiles were included in subtype C1, whereas the majority of the decreased immune profiles were included in subtype C4. In addition, we identified the potential relationship between overall survival (OS) and immune or stromal scores using the ESTIMATE algorithm and Kaplan–Meier method. The set of 303 CESC cases was stratified into a top half and a bottom half (high- vs. low-score groups), corresponding to median overall survival. On the one hand, Kaplan–Meier survival curves showed that cases with either high immune scores (Figure 2D, p = 0.021 in the log-rank test) or ESTIMATE scores (Figure 2E, p = 0.044) lived longer than those with low scores. On the other hand, cases with high or low stromal scores showed no statistically different median OS (Figure 2F, p = 0.14). Thus, subtype C4 demonstrated a downregulation of the immune profile, lower tumor immune/stromal scores, and worse prognosis, whereas subtype C1 showed an upregulation of the immune profile, higher tumor immune/stromal scores, and better prognosis. Next, we analyzed the prognostic potential and evaluated the distinct outcomes of the CESC patients on the basis of the immune infiltration of B cells, T cells (CD8+ and CD4+), macrophages, neutrophils, and dendritic cells (DCs), with or without the presence of a given mutation, via the Kaplan–Meier plotter database (Figure 3). Cases were stratified into a top half and a bottom half (high- vs. low-expression groups) according to the levels of immune infiltration. Kaplan–Meier curves showed that lower levels of immune infiltrates, including CD4+ T cells, demonstrated a worse cumulative survival (p = 0.027 in the log-rank test). However, there was no correlation between the distinct outcomes for other tumor-infiltrating lymphocytes (TILs). Next, we performed a functional enrichment analysis to further explore the differentially expressed genes (DEGs). Among a set of 224 immune-related genes, we selected the top 100 genes, based on the absolute value of logFC, that were significantly enhanced in each subtype, and 69 overlapped genes between each subtype were achieved. However, just a few of the overlapped genes could be distinguished in the subtype pairs (Figure 4A). Changes in the biological process (BP) were significantly enriched in chromosome segregation, nuclear division, organelle fission, nuclear chromosome segregation, mitotic nuclear division, and sister chromatid segregation (Figure 4B). Changes in molecular function (MF) were significantly enriched in chromatin binding, protein serine/threonine kinase activity, the catalytic activity of DNA, coupled ATPase activity, and ATPase activity (Figure 4C). Changes in cellular component (CC) were enriched mainly in the chromosomal region, spindle, the centromeric region of the chromosome, condensed chromosomes, and the kinetochore using GO analysis (Figure 4D). The GO analysis suggested that changes in CESC were enriched in nuclear division, chromatin binding, and condensed chromosomes. To explore the HPV-related biological pathways involved in CESC, we analyzed the biological pathways that significantly changed in the samples using a GSEA performed on the samples (GSEA v2.0, http://www.broad.mit.edu/gsea/ accessed on 16 April 2022). The GSEA on gene expression data was primarily related to cellular senescence, the p53 signaling pathway, and viral carcinogenesis which had important correlations with HPV infection (Figure 4E). the GSEA demonstrated that cellular senescence, the p53 signaling pathway, and viral carcinogenesis are critical features of CESC. Since TILs have an independent influence on sentinel lymph status and prognostic outcome in various types of cancer [39], we assessed the correlations between the immune infiltration level and the gene expression of the key genes, including PERP, BAK1, CDK2, VDAC1, MDM2, HDAC1, FOXO3, AKT3, and IGF1. PERP, BAK1, CDK2, VDAC1, MDM2, HDAC1, FOXO3, AKT3, and IGF expression were significantly negatively related to tumor purity and positively correlated with T cells (CD8 and CD4), macrophages, neutrophils, and DCs in CESC, but not significantly correlated with B cells (Figure 5A–I). To explore the clinical relevance of the key genes in the tumor immune subsets of CESC cases, we generated a Kaplan–Meier curve to compare the expressions of PERP (Figure 6A), BAK1 (Figure 6B), CDK2 (Figure 6C), VDAC1 (Figure 6D), MDM2 (Figure 6E), HDAC1 (Figure 6F), FOXO3 (Figure 6G), AKT3 (Figure 6H), and IGF1 (Figure 6I) at different expression levels of immune infiltration. High expression of VDAC1 (Figure 6D) and FOXO3 (Figure 6G) suggested a significantly poor outcome in the log-rank test (p = 0.045 and p = 0.048, respectively). However, low expression of IGF1 (Figure 6I) also showed a poor outcome (p = 0.047). In addition, high expression of PERP (Figure 6A), BAK1 (Figure 6B), CDK2 (Figure 6C), MDM2 (Figure 6E), HDAC1 (Figure 6F), and AKT3 (Figure 6H) predicted poor overall survival, but it was not of statistical significance (p > 0.05). VDAC1, FOXO3, and IGF1 might be the crucial differential expression genes in CESC. To investigate whether the mutation of the key genes, including PERP (Figure 7A), BAK1 (Figure 7B), CDK2 (Figure 7C), VDAC1 (Figure 7D), MDM2 (Figure 7E), HDAC1 (Figure 7F), FOXO3 (Figure 7G), AKT3 (Figure 7H), and IGF1 (Figure 7I) were involved in the immune infiltration portion of the infiltrates, we divided the key genes into six groups underlying different copy number variation (CNV) conditions to explore the different infiltration levels of six immune cells between six groups. Mutation in the key genes was negatively related to the infiltration level of DCs, and most of the other infiltrates, including T cells (CD8+ and CD4+), macrophages, and neutrophils, decreased significantly. p-value significance codes: p < 0.1, * 0.01 ≤ p < 0.05, ** 0.001 ≤ p < 0.01, **** 0 ≤ p < 0.001. We performed and verified the key genes of patients for further validation in the East Hospital (EH) cohort using tissue chip. Six key genes were located within malignant tumor cells and enriched predominantly in the cytoplasm of tumor cells, including PERP, BAK1, VDAC1, FOXO3, AKT3, and IGF1 (Figure 8A). Punctate staining was also identified in the membrane nuclei of some tumor cells. However, MDM2, HDAC1, and CDK2 were not expressed in tissue chip. Significant differences were identified for age (11.989 versus 13.123, p < 0.0001), T category (HR 14.746 versus 12.926, p < 0.0001), N stage (HR 15.670 versus 11.657, p < 0.0001), and recidivation (HR 52.145 versus 60.813, p < 0.0001), in light of the distinct FOXO3 and IGF1 expression levels. However, FOXO3 and IGF1 gene expressions were not statistically different in terms of pathology grade (HR 0.930 versus 0.894, p = 0.335 versus p = 0.344) or HPV infection (HR 0.393 versus 0.673, p = 0.531 versus p = 0.412). In addition, there was no significant difference between FOXO3 and IGF1 expression levels (HR 1.138 versus 1.700, p = 0.286 versus p = 0.192). We performed Kaplan–Meier survival curves to further analyze the associations between the disease-free survival (DFS) of patients in the East Hospital (EH) cohort and key genes, including PERP (Figure 8B), BAK1 (Figure 8C), VDAC1 (Figure 8D), FOXO3 (Figure 8E), AKT3 (Figure 8F), and IGF1 (Figure 8G) in CESC. Low expression of FOXO3 (p = 0.0423, Figure 8E) and high expression of IGF1 (p = 0.0438, Figure 8G) demonstrated significantly poor DFS in the log-rank test. In the univariate analysis, age (hazard rate (HR) 6.101, p < 0.001), T category (HR 12.73, p < 0.001), N stage (HR 4.658, p < 0.001), and FOXO3 (HR 2.473, p = 0.0423), and IGF1 expression (HR 0.454, p = 0.0438) were significantly correlated with DFS, whereas pathology grade (HR 1.149, p = 0.734), HPV infection (HR 1.016, p = 0.976), and BAK1 (HR 0.872, p = 0.6924), VDAC1 (HR 1.377, p = 0.3943), AKT3 (HR 1.435, p = 0.3282), and PERP expression (HR 0.855, 95%, p = 0.6892) were not related to DFS. In the multivariate analysis, only age (HR 7.959, p = 0.007), N stage (HR 6.892, p = 0.013), and FOXO3 expression (HR 11.611, p = 0.047) were independent prognostic factors for CESC (Table 1 and Table 2). CESC generally presents with HPV infection and is the second most common gynecological malignancy. However, its pathological mechanism remains unclear. Although advances in treatment strategies have improved overall patient prognosis, metastasis and recurrence still pose challenges in clinical setting. Therefore, understanding the molecular mechanisms of the progression of CESC, including metastasis, would aid in the development of effective diagnostic and targeted therapies. A number of studies have reported that the intricate microenvironment sustained by HPV infection increases the risk for CESC progression and participates in a variety of signaling pathways, including those related to cell adhesion and EMT [40]. In addition, partly based on the interaction between cancer cells and the tumor microenvironment [41], cancer is best defined as an ecosystem in which tumor cells interact with specific environments such as immune cells and interstitial cells, adapt to each other, and even coevolve in a multidimensional space-time manner [42]. Here, we present a case study of CESC patients for systematically analyzing coexisting CESC molecular subtypes and the clinical significance underlying global immune genes with downloaded data from TCGA. We used gene expression data to validate that the five molecular subtypes potentially underlie the pathological process of CESC. To further evaluate the immunological relationships between the CESC samples and the molecular subtypes, we determined the clinical characteristics, immune infiltration levels, and survival outcomes. In addition, we selected all DEGs and performed a functional enrichment analysis. Furthermore, correlations between key genes with immune infiltrates, gene copy number variation, and outcome were validated. Numerous studies have revealed several molecular subtypes of CESC according to genome-wide profiles [43]. In this study, we explored global gene expression data as part of a more comprehensive study of the immune landscape of CESC. Subtype C4 was associated with decreased immune status, whereas subtype C1 was related to an enhanced immune status, which exhibited a positive relationship with immune-related cell expression features. Furthermore, the five molecular subtypes from TCGA cohort showed marginally different prognostic results, with subtype C4 demonstrating the best prognosis. Therefore, we hypothesized that the immune-enhanced and immune-reduced subtypes coexist in the tumor microenvironment of CESC. These subtypes showed remarkably different expression data related to mutagens, immune component scores, and clinical prognosis. We further investigated the prognostic potential of the immune infiltration levels and the relationship with clinical prognosis in the five subtypes in the microenvironment of CESC. The clinical outcomes of the CESC patients were closely related with the immune infiltrating levels of CD4+ T cells. As expected, among all the subtypes, immune scores, stromal scores, and ESTIMATE scores were highest in subtype C1 and lowest in subtype C4. To identify the most effective diagnostic biomarkers, we performed a synthetic analysis and 224 DEGs were identified. To analyze the function enrichment of DEGs, we annotated CESC with GO terms and performed GSEA as represented by DEGs. A set of biological processes and pathways, including the mitotic cycle, chromosomes, and the catalytic activity of nuclear DNA, were suggested to be involved in CESC. As such, the DEGs may locate in the nucleus and participate in cell cycle processes in order to promote cellular proliferation by enhancing DNA catalytic activity in CESC. Biological processes, the mitotic cycle, chromosome segregation, nuclear division, organelle fission, nuclear chromosome segregation, mitotic nuclear division, and sister chromatid segregation were the most significant. Gene expression analysis, GO function enrichment, and pathway enrichment analysis indicated that the occurrence and progression of CESC are considerably complex in terms of gene expression, multi-cellular processes, and coexisting immune microenvironments. Factors in the immune microenvironment of CESC have been previously reported [40], and our results were consistent with previous findings. Previous studies have demonstrated that CESC presents with a positive regulation of transcriptional, DNA template, nucleoplasm, and intrinsic apoptotic signaling pathways [44]. The DEGs identified in the immune microenvironment, including IL1R2 [45], CDK1 [46], CXCL14 [47], and RANKL [48] have been further revealed as functional oncogenes involved in the cell cycle of CESC by PCR and Western blot analysis. Furthermore, we performed GSEA and investigated the biological functions of the DEGs in CESC. In CESC, dysregulation of signaling pathways involved in oocyte meiosis, cell cycle, p53 signaling, and progesterone-mediated oocyte maturation have been reported [46]. In addition, we identified cellular senescence, p53 signaling, and viral carcinogenesis to be implicated in CESC. It is well known that about 90% of CESC cases are caused by HPV infection, which plays an important role in the development of carcinogenesis [49]. After infection, disrupted p53-mediated regulation of the cell cycle and apoptosis has been shown to be inhibited by E6 and E7 viral oncogenic proteins [50]. E6/E7 oncogene is associated with the rapid regeneration of p53 [51] and pRb anti-proliferative protein [50], phenotypically resulting in cellular senescence [50]. The tumor suppressor p53 is a key gene with regard to cellular proliferation and apoptosis and exhibits a relatively high prognostic value in CESC. Some persistent viral oncogenes can inactivate p53 and pRb, leading to increased genomic instability, accumulation of somatic mutations and, in some cases, integration of HPV into the host genome [7]. In brief, the findings of our study suggested that HPV oncogenes induced mitotic processes and prevented the progression of the cell cycle through the p53 pathway. In addition, we performed an overall survival analysis of patients on the basis of the immune infiltration levels of infiltrates with or without the presence of a given mutation in CESC. Indeed, accumulating evidence has suggested that reversed CD4/CD8 ratios are closely correlated with clinical outcome in patients with CESC [52,53]. In recent studies, it has been observed that DEG expression was closely correlated with diverse immune infiltration levels in CESC. Moreover, there appears to be a strong positive relationship between the prognosis of key genes and immune infiltration levels of CD4+ T cells. TILs have been regarded as an independent factor of lymph status and prognosis in tumors. In this study, we investigated, in detail, the correlations between immune infiltration levels of key genes and clinical outcomes. We performed and verified the key genes of patients for further validation in the East Hospital (EH) cohort using tissue chip. Six key genes were located within malignant tumor cells and enriched predominantly in the cytoplasm of tumor cells, including PERP, BAK1, VDAC1, FOXO3, AKT3, and IGF1. Significant differences were identified for age, T category, N stage, and recidivation, in light of the distinct FOXO3 and IGF1 expression levels. However, FOXO3 and IGF1 gene expressions were not statistically different in terms of pathology grade or HPV infection. In addition, we performed Kaplan–Meier survival curves to further analyze the associations between the disease-free survival (DFS) of patients in the East Hospital (EH) cohort and key genes. We demonstrated high protein expression of FOXO3 and low expression of IGF1 suggested poor clinical outcome. Meanwhile, mutation of the key genes was negatively related to the infiltration levels of most of the other infiltrates. CD4+ T cells are crucial in immune response protection, and the memory subtype endows the host with increased secondary immune reactions [54,55]. HPV-related lesions have been shown to be removed by the anti-infective immune responses of T cells [56]. During the clearance of HPV, T cells (CD4+ and CD8+) appear to be the major anti-infective cells [57]. This conclusion has been well exemplified by other findings that have demonstrated that the density and distribution of immune T cells depend on the malignant potential of HPV-related lesions, with increases in circulating CD4+ T cell populations associated with the progression of CESC [58,59]. Therefore, CD4+ T cells may be essential regulators for controlling HPV-related cervical lesions and preventing carcinogenesis. Accumulating evidence has shown that FOXO increases antitumor activity by negatively regulating immunosuppressive protein expression, including that of PD-L1 and vascular endothelial growth factor (VEGF) [60,61]. Thus, FOXO3 has been proposed to be a regulator that is intrinsically involved in tumor immunity, homeostasis, and immunocytes growth, including T cells, NK cells, and DCs [62,63]. A recent study has indicated that HPV protein blocks the TGFβ/miR-182/BRCA1 and FOXO3 path in head and neck squamous cell carcinoma cell [64]. Moreover, FOXO3, which is considered to be a regulator for FOXM1, has been shown to participate in the development of cervical cancer and the lactate-rich microenvironment during HPV infection in cervical squamous carcinoma cell [40]. However, the role of FOXO3 in CESC and HPV infection in the immune microenvironment remains to be elucidated. The IGF family plays a significant role in cell differentiation, proliferation, carcinogenesis, and apoptosis [65,66], and it has become an attractive therapeutic target [67]. Preclinical studies have reported that the IGF1/IGF1 receptor axis participates in multiple cross-talk signaling pathways involved in the maintenance of keratinocytes [68] as a result of HPV infection. IGF1 has also been elucidated as a dictator CD4+ T cell by enhancing ERβ transcriptional activity [69] and an immuno editor that eradicates new emerging tumor cells in the immune system [70]. Accordingly, an important approach in cancer therapy participates in the inhibition of the IGF1 pathway [71]. In addition, it has been suggested that, in CESC, IGF1 facilitates the stabilization of direct or indirect interactions with E6 and E7 HPV proteins [72]. Other evidence has shown that Bak-1 is involved in the DHA-induced autophagy observed in HeLa cells [73] and the ERK-mediated CDK2/cyclin-A signaling pathway, and induces apoptosis and G1/S arrest in Hela and Caski cells [74]. Furthermore, VDAC1 has been shown to prevent the progression of HPV-induced cervical cancer [75] and DHX9–lncRNA–MDM2 interactions regulate tumor cell invasion and the angiogenesis of CESC [22]. HDAC1 increases during immune-editing and contributes to immune refractory cancers, including CESC. Silencing AKT3 in Hela/DDP cells may enhance their sensitivity to cisplatin [76]. Moreover, PERP inhibition has been associated with apoptosis and VEGF suppression in lung cancer [77]. Therefore, these key genes in the immune microenvironment may be potential prognostic factors and biomarkers for predicting the occurrence and metastasis of CESC. This study had several limitations, including the relatively low number of clinical characteristics available, including relatives and the pathological features of patients with CESC in the genetic subgroup study. Less data may also have contributed to unilateral results and high false positives. Future research should include a larger sample size and further validation of our findings to evaluate whether the deviation is caused by the difference in the sequencing race at home and abroad or the insufficient sample size. In conclusion, our findings indicated that immune-related genes in the tumor microenvironment can be stratified into five subtypes, with potential mechanisms of immunological evasion in CESC that can distinguish immunophenotype features, immune checkpoint molecules, and clinical prognosis indicators, including immune and stromal scores. In addition, specific functional pathways, including cellular senescence, p53 signaling, and viral carcinogenesis may be closely associated with HPV infection and the coexisting microenvironment subtypes. These findings may aid in promoting the improvement of CESC immunotherapy strategies.

PMC10000941

Alicja Rajtak,Arkadiusz Czerwonka,Michael Pitter,Jan Kotarski,Karolina Okła

Clinical Relevance of Mortalin in Ovarian Cancer Patients

23-02-2023

mortalin/mtHsp70/GRP75/PBP74/HSPA9/HSPA9B,ovarian cancer,biomarker,metastasis,recurrence,OXPHOS,EMT,stemness,RNAseq

Background: Ovarian cancer (OC) is the most lethal malignancy of the female reproductive tract. Consequently, a better understanding of the malignant features in OC is pertinent. Mortalin (mtHsp70/GRP75/PBP74/HSPA9/HSPA9B) promotes cancer development, progression, metastasis, and recurrence. Yet, there is no parallel evaluation and clinical relevance of mortalin in the peripheral and local tumor ecosystem in OC patients. Methods: A cohort of 92 pretreatment women was recruited, including 50 OC patients, 14 patients with benign ovarian tumors, and 28 healthy women. Blood plasma and ascites fluid-soluble mortalin concentrations were measured by ELISA. Mortalin protein levels in tissues and OC cells were analyzed using proteomic datasets. The gene expression profile of mortalin in ovarian tissues was evaluated through the analysis of RNAseq data. Kaplan–Meier analysis was used to demonstrate the prognostic relevance of mortalin. Results: First, we found upregulation of local mortalin in two different ecosystems, i.e., ascites and tumor tissues in human OC compared to control groups. Second, abundance expression of local tumor mortalin is associated with cancer-driven signaling pathways and worse clinical outcome. Third, high mortalin level in tumor tissues, but not in the blood plasma or ascites fluid, predicts worse patient prognosis. Conclusions: Our findings demonstrate a previously unknown mortalin profile in peripheral and local tumor ecosystem and its clinical relevance in OC. These novel findings may serve clinicians and investigators in the development of biomarker-based targeted therapeutics and immunotherapies.

Clinical Relevance of Mortalin in Ovarian Cancer Patients Background: Ovarian cancer (OC) is the most lethal malignancy of the female reproductive tract. Consequently, a better understanding of the malignant features in OC is pertinent. Mortalin (mtHsp70/GRP75/PBP74/HSPA9/HSPA9B) promotes cancer development, progression, metastasis, and recurrence. Yet, there is no parallel evaluation and clinical relevance of mortalin in the peripheral and local tumor ecosystem in OC patients. Methods: A cohort of 92 pretreatment women was recruited, including 50 OC patients, 14 patients with benign ovarian tumors, and 28 healthy women. Blood plasma and ascites fluid-soluble mortalin concentrations were measured by ELISA. Mortalin protein levels in tissues and OC cells were analyzed using proteomic datasets. The gene expression profile of mortalin in ovarian tissues was evaluated through the analysis of RNAseq data. Kaplan–Meier analysis was used to demonstrate the prognostic relevance of mortalin. Results: First, we found upregulation of local mortalin in two different ecosystems, i.e., ascites and tumor tissues in human OC compared to control groups. Second, abundance expression of local tumor mortalin is associated with cancer-driven signaling pathways and worse clinical outcome. Third, high mortalin level in tumor tissues, but not in the blood plasma or ascites fluid, predicts worse patient prognosis. Conclusions: Our findings demonstrate a previously unknown mortalin profile in peripheral and local tumor ecosystem and its clinical relevance in OC. These novel findings may serve clinicians and investigators in the development of biomarker-based targeted therapeutics and immunotherapies. Ovarian cancer (OC) is the most lethal of all gynecological malignancies [1]. Specifically, 75% of patients are diagnosed at advanced stages, and 75% of these patients die within 5 years. The majority of these mortalities are due to recurrence of disease, resistance to current therapies, significant heterogeneity of tumors, and immune suppression in tumor microenvironments (TMEs) [2,3,4,5]. Additionally, early-stage OC is usually asymptomatic; therefore, it is mainly diagnosed at an advanced stage, during spread of disease across the peritoneal cavity, usually accompanied by malignant ascites [6]. While in the initial phase of the disease, OC patients usually respond well to cytoreductive debulking surgery and chemotherapy, bur most women develop recurrence of a chemotherapy-resistant form of the disease within 12 to 18 months [7,8]. In recent years, immunotherapy has revolutionized cancer treatment; however, the results of immunotherapy are unsatisfactory in OC [9]. Although OC is an immunogenic tumor that can be recognized by the host immune system, spontaneous antitumor immune response has only been demonstrated in about 50% of patients, mainly due to tumor-favorable immunosuppressive TMEs [10]. The previous results of our and other research groups described the establishment of the immunosuppressive milieu in OC, including the presence of monocytic myeloid-derived suppressor cells (M-MDSCs) and others [4,11,12]. This provides a very rich “soil” in TMEs for immune escape of cancer cells. Currently, there are no approved immunotherapies for OC. However, many researchers are looking for ways to control OC and to enhance antitumor immunity as a means to increase the patient’s responsiveness to (immuno)therapy. Mortalin (mtHsp70/GRP75/PBP74/HSPA9/HSPA9B) is a member of the heat-shock protein (Hsp) 70 family of chaperone proteins which regulates physiological functions of cells such as controlling oxidative stress response of cells, mitochondrial function, and maintaining physiological balance [13]. Overexpression of mortalin can play an essential role in cancer including the regulation of cell proliferation, progression, metastasis, apoptosis, and phenotype of cancer stem cells. Additionally, mortalin has been found to promote chemotherapy resistance [14]. Interestingly, mortalin can also regulate proinflammatory cytokines and immune response. Indeed, immunoregulatory properties of these proteins have led to their classification as “chaperokines” [15]. Consequently, broad evidence related to mortalin-related promotion of carcinogenesis suggests this factor as a promising strategy for anticancer (immuno)therapy. Mortalin is constitutively expressed in eukaryotes at low levels in physiological conditions [16]; however, it is often upregulated in human cancers including breast, colon, lung, pancreatic, cancers, and hepatocellular carcinoma (HCC) [17,18,19,20,21]. Clinically, the abnormal expression of mortalin has been shown to be a poor prognostic biomarker in breast, lung, colorectal, and pancreatic cancers and glioblastoma [14]. Although previous studies reported mortalin within the tumor tissue of OC, a paucity of data on soluble mortalin level in the peripheral and local ecosystems of OC exist. Moreover, results derived from the expression of mortalin in tumor tissue are conflicting. Whereas some authors have shown elevated expression level of mortalin in OC [22], another study revealed no changes in the level of mortalin expression versus control [13]. Extracellular soluble blood mortalin was documented in colorectal cancers [17,23]; however, soluble mortalin data are lacking in OC. Fresh whole peripheral blood and ascites samples were obtained before or during surgery in First Department of Oncologic Gynecology and Gynecology, Medical University of Lublin. Whole blood and/or ascites samples were collected from patients suffering from 50 OC and 14 benign ovarian tumors. Blood samples were also collected from 28 healthy women. The inclusion criterion for patients was diagnosis of ovarian pathology (OC, benign). Exclusion criteria were an age <18, history of previous cancers, chemo- or radiotherapy prior to surgical procedure, and presence of allergic, autoimmune disorders, infections. Clinical data from ovarian pathology, i.e., benign and OC patients, are presented in Table 1. Kurman–Shih classification was used for determined of OC type [24]. The clinical data of patients, i.e., International Federation of Gynecology and Obstetrics (FIGO) stage, grade, histology, and treatment history, were obtained from a centralized database. Written consent was obtained from participants. Ethical approval was granted by our Institutional Ethics Committee. Pretreatment, fresh, venous whole peripheral blood (9 mL) was collected into heparinized tubes (Sarstedt, Nümbrecht, Germany) before surgery. Fresh ascites samples were obtained aseptically during the operation. Cell-free blood plasma and ascites fluid samples were obtained using centrifugation (2000 rpm/10 min). Blood plasma, ascites fluid, and tumor tissue samples were stored at −80 °C before testing. Blood plasma and ascites fluid samples were blinded for the contractor who performed experiments. Mortalin concentrations (pg/mL) were measured using an enzyme-linked immunosorbent assay (ELISA, Human Mortalin (75 kDa glucose-regulated protein) ELISA Kit, FineTest, Wuhan, China) according to the manufacturer’s protocol (detection range: 15.625–1000 pg/mL and sensitivity: <9.375 pg/mL). Samples were measured in duplicates, and the coefficient of variance (%CV) was <20%. Absorbances were measured using an ELX-800 plate reader and analyzed by KC Junior software (Bio-Tek, Instruments, Winooski, VT, USA). Mononuclear cells (MCs) were isolated from OC blood and ascites samples as we previously described [5]. In brief, blood and ascites specimens were centrifuged (1500 rpm/10 min), and MCs were isolated by density gradient centrifugation. Next, isolated MCs were collected, washed, and cryopreserved until analysis. To determine the frequency of blood-circulating and ascites-infiltrating HLA-DR−/lowCD14+ M-MDSCs among isolated MCs, cell suspensions were stained for 30 min using monoclonal antibodies including CD14-PE-Cy7 (clone: M5E2, Catalog No. 557742) and HLA-DR-PerCP-Cy5.5 (clone: G46-6, Catalog No. 560652) (all obtained from BD Biosciences, Franklin Lakes, NJ, USA). Flow cytometric data were collected using BD FACSCanto Flow Cytometer (BD Biosciences, Franklin Lakes, NJ, USA) and analyzed using FCS Express 6 Flow Cytometry (De Novo Software, Pasadena, CA, USA). An EasySep™ Human EpCAM Positive Selection Kit II (StemCell) was used for isolation of tumor cells. An EasySep™ Human CD14 Positive Selection Kit II (Stem cell) was used for isolation of tumor-infiltrating myeloid cells. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) [25] generated the mass spectrometry-based proteomic data used in this publication. Analysis was performed using the University of Alabama at Birmingham Cancer (UALCAN) data analysis portal with data of normal tissues (n = 25) and OC primary tumors (n = 100). For validation of mortalin protein expression, 10 normal tissue samples which were matched with tumor samples from the same HGSOC patient were used from proteomic dataset [26]. Mortalin expression of a matched cell-line series from three patients with high-grade serous OC (HGSOC) before and after development of clinical platinum resistance (PEA1/PEA2, PEO14/PEO23) was analyzed using the Ovarian Cancer Cell Line Data Portal [27]. HSPA9 polyclonal antibody (Invitrogen) was used for analysis of protein in tumor and myeloid cells from HGSOC tissues using Western blot analysis. Single-cell RNA-seq counts were obtained from the Gene Expression Omnibus database with the accession numbers GSE211956 (eight HGSOC patients) and GSE130000 (eight OC samples, including four primary tumors, two peritoneal metastases, and two relapse tumors). The UMI counts, gene information, and barcode matrix output from the Cell Ranger software pipeline provided by 10x Genomics were used for downstream analysis with the pipeline of Seurat (version 4.1.0, Satija Lab, Cambridge, MA, USA) R (version 4.2.0, Satija Lab, Cambridge, MA, USA). For data quality control, cells with fewer than 200 genes detected and cells with greater than 30% mitochondrial RNA content were excluded from analysis. Following this step, 20,096 cells passed these filters and were included in downstream analysis. Counts on the filtered matrix of each gene were then normalized with the total library size with the Seurat ‘NormalizeData’ function. To detect the biologically meaningful variation across cells, we used a subset of highly variable genes (2000) identified by the function of ‘FindVariableGenes’ from Seurat to perform unsupervised clustering. Next, using the ‘ScaleData’ function, we applied a linear transformation which shifts the expression of each gene so that the mean expression across cells is 0 and scales the expression of each gene so that the variance across cells is 1. This gives equal weight to each gene so that highly expressed genes do not dominate. Linear dimensionality reduction (PCA) was performed using the function ‘RunPCA’. To partition the cellular distance matrix into clusters, the graph-based ‘FindClusters’ function was used with the resolution set to 0.9. Next, UMAP projections were generated to visualize the clusters of cells localized in the graph-based clusters using the ‘RunUMAP’ function with the same principal components described above. Cluster markers were identified by finding differentially expressed genes between cells in a single cluster versus all cells in all other clusters using the ‘FindAllMarkers’ function (Seurat). To find statistical associations between human ovarian tumor-derived HSPA9 and other protumor gene expression, we calculated Pearson correlation coefficients (R) and p-values between HSPA9 and other key gene expression vectors each containing the expression values of the gene of interest per cell. Next, we separated the ovarian tumor cells according to low and high expression of HSPA9 on the basis of the normalized mean expression and then conducted gene set enrichment analysis (GSEA) on the ovarian tumor cells with high expression of HSPA9 using the ‘gseGO’ function of the clusterProfiler (3.18.1, Yu G, Guangzhou, China) R package. The GSEA determined whether genes associated with high expression of HSPA9 were together involved in key cellular pathways in cancer progression. Gene sets were obtained from MSigDB, Gene Ontology and Disease Ontology databases. The bulk RNA-seq dataset [28] for malignant fluids of serous OC, including tumor-derived organoids and malignant effusion cells (no cultured) paired with normal ovarian tissues, was used for the HSPA9 expression profile. For in silico analysis, the Kaplan–Meier plotter database was used to further analyze the prognostic relevance of mortalin gene expression using gene chip data in serous OC [29]. Only the optimal probe set (JetSet best probe set) for HSPA9 was used. The probabilities of OS of the study group were estimated using the Kaplan–Meier method (Mantel–Cox log-rank test). OS was defined as the time from the primary diagnosis until death. All data were performed using GraphPad Prism 8.0 (La Jolla, CA, USA) and analyzed using the Mann–Whitney U test, one-way analysis of variance (ANOVA), and t-test. Spearman correlation was used for calculation of correlation coefficient r between parameters. A p -value < 0.05 was considered significant (p < 0.05 *, p < 0.01 **, p < 0.001 ***, p < 0.0001 ****). For prognostic relevance of HSPA9 mRNA, the Kaplan–Meier plotter tool was used (http://kmplot.com/analysis/ (accessed on 15 December 2022)) [29]. To study the mortalin profile in the three different ecosystems, we analyzed its level in the blood plasma, ascites fluid, and tumor tissue. We determined the level of extracellular soluble mortalin in healthy women, benign ovarian tumors, and OC using ELISA. Due to current inability to obtain normal ascites fluid, we used ascites from patients with benign ovarian tumors to compare mortalin concentrations in benign and malignant ascites fluid (ELISA). When we analyzed the level of mortalin in the blood plasma, we found no significant change of this factor in OC patients compared to healthy women and benign ovarian tumors (p > 0.05; Figure 1a). In contrast, in the ascites fluid, we noted a significantly higher level of this protein in OC patients versus patients with benign ovarian tumors (p < 0.01; Figure 1b). In concordance with this, analysis of mortalin in CPTAC-generated mass spectrometry-based proteomic data revealed significantly higher levels of mortalin in OC primary tumors compared to nontumor tissues (p < 0.001) (Figure S1a). Similarly, data showed lower mortalin protein expression in normal tissues compared to adjacent tumor tissues in HGSOC (p < 0.01) (Figure S1b). Having shown high levels of soluble mortalin in the ascites fluid in OC patients, we comparatively analyzed mortalin concentrations using paired samples of blood and ascites and asked questions about the level of mortalin in these two ecosystems. As we surmised, we showed higher abundance of mortalin in the ascites fluid versus blood plasma in OC, but not in the benign tumors (p < 0.01; p > 0.05, respectively, Figure 2a). Similarly, when we compared mortalin level in the blood plasma and ascites fluid in patients with different clinicopathologic features, we revealed higher accumulation of ascites fluid mortalin in advanced stage (stage III and stage IV, p < 0.01; Figure 2b), both grades (GII and GIII, p < 0.05; Figure 2c), both Kurman–Shih types (type I and type II, p < 0.05; Figure 2d), and both endometrioid and serous types of OC (both p < 0.05; Figure 2e) compared to blood mortalin. Subsequently, the relationship of different clinicopathologic features of OC patients with levels of mortalin in the blood and ascites was investigated. The levels of blood-circulating mortalin were similar regardless of clinicopathological characteristic of ovarian tumors (p > 0.05, Figure 3a–d). In contrast, we observed higher abundance of mortalin in the ascites fluid in low (I/II) and advanced (III/IV) stage (p < 0.05 and p < 0.01, respectively, Figure 3e), grade II and III (p < 0.05 and p < 0.01, respectively, Figure 3f), type I and II (p < 0.05 and p < 0.01, respectively, Figure 3g), and endometrioid and serous histology types (p < 0.05 and p < 0.01, respectively, Figure 3h) of OC compared with benign ovarian tumors. It is well known that malignant ascites creates a cancer-driven immunosuppressive ecosystem which promotes OC metastases [30]. Our previous reports indicated M-MDSCs as the main population with immunosuppressive properties in OC patients [5,31]. Indeed, we observed significantly higher levels of immunosuppressive M-MDSCs in the ascites compared to blood in OC patients (p < 0.05, Figure S2a). Interestingly, when analyzing the relationship between local mortalin and myeloid cells, we showed that mortalin level had a positive correlation with M-MDSC in the ascites (R = 0.43, p = 0.04, Figure S2b). Next, we asked which cell types contribute more to the upregulation of mortalin. Using human serous OC scRNA-seq data (Figure S2c), we showed higher expression of HSPA9 in tumor cells compared to tumor-infiltrating immune cells (i.e., myeloid cells and T cells). In concordance with this, we observed higher expression of mortalin protein in malignant tumor cells compared to tumor-infiltrating myeloid cells in HGSOC patients. (Figure S2d). Overall, this indicates that malignant tumor cells can be the major producers of soluble mortalin in the local environment. Most importantly, mortalin protein analysis of a matched cell line series derived from ascites or pleural effusions from two patients with HGSOC before and after development of clinical platinum resistance (PEO1 sensitive/PEO4 resistance and PEO14 sensitive/PEO23 resistance) identified significantly higher mortalin expression in platinum-resistant cells compared to platinum-sensitive cells (p < 0.003 and p < 0.04, respectively, Figure S1c), indicating mortalin as an one of the factors that can promote drug resistance in OC. Next, we attempted to validate our observations that mortalin can promote OC pathogenesis. Using human serous OC scRNA-seq data, GSEA demonstrated enrichment of several cancer-related gene pathways in OC with high mortalin (HSPA9) expression (Figure 4a). These pathways included oxidative phosphorylation (OXPHOS) signaling (Figure 4b, EMT signaling (Figure 4c), and stemness maintenance (Figure 4d). In concordance with this, mortalin was correlated with OXPHOS-related genes (CYCS, COX5B, COX8A, SDHD, and ATP5F1B) (Figure S3a) and EMT stemness-like related genes (STK3, STK4, PAX2, PAX8, CD24, SNAI1, SNAI2, MYC, TWIST2, and BMP7) (Figure S3b). Therefore, mortalin expression can regulate functional potency and aggressiveness of OC cells. Indeed, mortalin gene expression was upregulated in the malignant fluid of serous OC, including tumor-derived organoids (cultured) and malignant effusion cells (no cultured) compared to normal ovarian tissues (p < 0.001, Figure 4e). Moreover, the mortalin gene was highly expressed in metastatic and relapse tumors compared to primary tumors in HGSOC patients (p < 0.0001, Figure 4f). Collectively, the results supported that mortalin can support worse clinical outcome. Lastly, we asked whether mortalin can be an independent prognostic factor in serous OC patients. As shown in Figure 5a, patients with a high tumor mortalin gene expression levels showed significantly decreased OS (p = 0.001). Moreover, higher expression of mortalin was associated with worse PFS (p = 0.004) in OC patients (Figure 5b). In contrast, there was no significant change in OS in patients with different levels of soluble mortalin in the blood and peritoneal fluid (p > 0.05, Figure S24a,b, respectively). Among all gynecological malignancies, OC is the most lethal disease. Therefore, a better understanding of the malignant features of this disease is relevant. To our knowledge, this is the first study to examine the profile of mortalin in the peripheral and local tumor ecosystem in OC patients. Although previous studies examined mortalin within OC tumor tissue, results were inconclusive. Additionally, data of extracellular soluble mortalin in the blood and ascites of OC are lacking. In the present study, we evaluated parallel analysis of mortalin in the peripheral (blood plasma) and local (ascites fluid, tumor tissue) ecosystem and its clinical relevance in OC pathology. In our study, we found significantly increased level of mortalin protein in ascites and tumor tissue in OC patients compared to control. These observations confirm a previous study which demonstrated elevated expression level of mortalin in OC [22]. In contrast, another research group revealed higher mortalin expression versus normal controls in 16/24 tumor tissues including bladder, brain, breast, colon, duodenum, fallopian tube, gallbladder, kidney, liver, pancreas, parotid, prostate, thymus, thyroid, ureter, and uterus neoplasms, and no significant change in mortalin level in 8/24 tumor tissues (including ovary, adrenal gland, lung, esophagus, rectum, stomach, testis, and lymphoma) [13]. Although we did not observe a higher level of soluble mortalin in the blood of OC patients compared to the control group, extracellular soluble blood mortalin was documented in colorectal cancer, and data showed a significantly higher level of mortalin in these patients compared to control [17,32]. Overall, this indicates that peripheral blood is not the primary source of soluble mortalin in OC patients. It is well known that mortalin drives cancer pathogenesis, while malignant ascites creates an immunosuppressive ecosystem, representing the main route of OC metastases. Indeed, we observed significantly higher levels of mortalin in local tumor ascites compared to peripheral blood in malignant OC. To evaluate the clinical relevance of soluble mortalin in the blood and ascites, we integrated the above data with clinicopathologic features of OC patients. We observed similar levels of blood mortalin regardless of clinicopathologic characteristics of patients. Previous studies also demonstrated stage and grade disease-independent accumulation of serum mortalin concentration in patients with colorectal cancer [17,32]. Interestingly, our findings imply a higher level of ascites mortalin in OC patients with advanced stage (III/IV), high grade (GII/III), type I/II Kurman–Shih, endometrioid, and serous histology type compared with benign ovarian tumors. Our data led us to the conviction that high mortalin level in the ascites can be a characteristic feature of malignant disease and can play significant role in its peritoneal dissemination. Function analysis of the conserved marker genes indicated that OC with a high level of tumor HSPA9 was mainly related to OXPHOS pathways (e.g., CYCS, COX5B, COX8A, SDHD, and ATP5F1B) and EMT-stemness-like related pathways (e.g., STK3, STK4, PAX2, PAX8, CD24, SNAI1, SNAI2, MYC, TWIST2, and BMP7). It has been shown that OXPHOS is characteristic of OC stem cells (OCSCs), suggesting that OCSCs favor OXPHOS over glycolysis. Moreover, chemosensitive cells rely mainly on glycolysis, while chemoresistant cells have the ability to switch between glycolysis and OXPHOS [33]. Therefore, mortalin may be one of the factors promoting OC chemoresistance. Indeed, analysis of mortalin protein in a matched cell line series from two patients with HGSOC before and after development of clinical platinum resistance (PEO1/PEO4, PEO14/PEO23) showed upregulation of this protein in resistant OC cells. It has been demonstrated that expression of the OXPHOS pathway was elevated in resistant cell lines [27], and mortalin upregulation can be associated with resistance of OC cells to cisplatin [14]. Indeed, a chemosensitive cancer cell line (PEO1) displayed a glycolytic phenotype, while its chemoresistant counterpart (PEO4) exhibited a high metabolically active phenotype with the ability to switch between OXPHOS and glycolysis [34]. Importantly, mortalin was highly expressed in metastatic and relapse HGSOC compared to primary tumors and correlated with immunosuppressive M-MDSCs. Overall, this may indicate that a certain subpopulation of tumor cells may exist and evade chemotherapy, perhaps with the assistance of mortalin and other immune cells (e.g., M-MDSCs), migrating out of the primary site to initiate relapse OC tumors. Further study will be needed to validate this concept. Nevertheless, previous data showed that downregulation of mortalin expression was associated with a reduction in OC cell proliferation, colony formation, and migration/invasion, which confirms that mortalin can promote the development and progression of OC [35]. In concordance with this, we revealed that high mortalin gene expression level is negatively correlated with OS and PFS in OC patients, indicating the prognostic value of this biomarker in OC disease. Of note, treatment of cancer cells with mortalin short hairpin (sh)RNA or inhibitors reverted the drug resistance of cells and suppressed their migration and invasion properties [21]. Intriguingly, extracellular Hsp70 forms an activation complex with different Hsps, including Hsp90α, Hsp70/Hsp90 organizing protein (Hop), and Hsp40, which together enhance the invasion and migration of the breast cancer cells via the upregulation of metalloproteinase-2 (MMP2). Furthermore, previous findings revealed that mortalin enhances the resistance of cancer cells to complement-dependent cytotoxicity (CDC) and can, thus, promote tumor escape and attenuate immunotherapy efficacy [18]. Taking into consideration that only a small percentage of OC patients (~15%) respond to immunotherapy [36], our findings should be taken into consideration in cancer immunomonitoring and design of future (immuno)therapeutic trials. Firstly, mortalin is highly upregulated in local ecosystems, i.e., ascites and tumor tissues in OC patients compared to control groups. Secondly, high expression of local tumor mortalin is associated with cancer-driven signaling pathways (i.e., OXPHOS and EMT/stemness-like signaling) and worse clinical outcome. Thirdly, high tumor mortalin level is associated with bad OS and PFS. To sum up, our current findings are important to investigators in the OC field and those who are working on development of new biomarkers and (immuno)therapies.

PMC10000962

Naresh Kumar Manda,Upendarrao Golla,Kishore Sesham,Parth Desai,Shrushti Joshi,Satyam Patel,Sharada Nalla,Susmitha Kondam,Lakhwinder Singh,Deepak Dewansh,Hemalatha Manda,Namita Rokana

Tuning between Nuclear Organization and Functionality in Health and Disease

23-02-2023

nuclear shape regulation,nuclear size regulation,nuclear envelope proteins,nucleophagy,nuclear lamins,nucleopathy,cancer,neurodegenerative disorders,signaling pathways,targeted therapy

The organization of eukaryotic genome in the nucleus, a double-membraned organelle separated from the cytoplasm, is highly complex and dynamic. The functional architecture of the nucleus is confined by the layers of internal and cytoplasmic elements, including chromatin organization, nuclear envelope associated proteome and transport, nuclear–cytoskeletal contacts, and the mechano-regulatory signaling cascades. The size and morphology of the nucleus could impose a significant impact on nuclear mechanics, chromatin organization, gene expression, cell functionality and disease development. The maintenance of nuclear organization during genetic or physical perturbation is crucial for the viability and lifespan of the cell. Abnormal nuclear envelope morphologies, such as invagination and blebbing, have functional implications in several human disorders, including cancer, accelerated aging, thyroid disorders, and different types of neuro-muscular diseases. Despite the evident interplay between nuclear structure and nuclear function, our knowledge about the underlying molecular mechanisms for regulation of nuclear morphology and cell functionality during health and illness is rather poor. This review highlights the essential nuclear, cellular, and extracellular components that govern the organization of nuclei and functional consequences associated with nuclear morphometric aberrations. Finally, we discuss the recent developments with diagnostic and therapeutic implications targeting nuclear morphology in health and disease.

Tuning between Nuclear Organization and Functionality in Health and Disease The organization of eukaryotic genome in the nucleus, a double-membraned organelle separated from the cytoplasm, is highly complex and dynamic. The functional architecture of the nucleus is confined by the layers of internal and cytoplasmic elements, including chromatin organization, nuclear envelope associated proteome and transport, nuclear–cytoskeletal contacts, and the mechano-regulatory signaling cascades. The size and morphology of the nucleus could impose a significant impact on nuclear mechanics, chromatin organization, gene expression, cell functionality and disease development. The maintenance of nuclear organization during genetic or physical perturbation is crucial for the viability and lifespan of the cell. Abnormal nuclear envelope morphologies, such as invagination and blebbing, have functional implications in several human disorders, including cancer, accelerated aging, thyroid disorders, and different types of neuro-muscular diseases. Despite the evident interplay between nuclear structure and nuclear function, our knowledge about the underlying molecular mechanisms for regulation of nuclear morphology and cell functionality during health and illness is rather poor. This review highlights the essential nuclear, cellular, and extracellular components that govern the organization of nuclei and functional consequences associated with nuclear morphometric aberrations. Finally, we discuss the recent developments with diagnostic and therapeutic implications targeting nuclear morphology in health and disease. The foundation of life is dependent on the functional stratification of specialized subcellular compartments. In a eukaryotic system, the nucleus forms a distinctive micro-terrain to conceal the genetic material from damaging cytoplasmic enzymes and metabolism and to provide a unique regulatory molecular framework for the genome. The spatial encapsulation of the nucleus by the lipid bilayer forms a physical and physiological intercept between cytoplasmic processes and the genome that regulates them. The construct of the nucleus is collectively furnished by a nuclear envelope along with the underlaying chromatin fiber, intermediate filaments of nucleoskeleton, nucleoplasmic subcompartments and nucleolus. These contractual components collectively impose their own effect on the rigidity, morphology and size of the nucleus [1,2,3]. The nuclear shape and size are also subjected to the layers of cellular regulatory mechanisms, including C/N volume regulators, mechanobiology activated signaling cascades, macro- and micronucleophagy, etc. [4,5,6,7]. Although the nuclear size and morphology varies widely among unicellular and multicellular eukaryotes, its extent is precisely maintained in the individual cell type [8]. However, the nucleus of same cell type may also differ among various growth phases and under different extracellular matrices. It is now understood that the nuclear, cellular or extracellular stimulants which mediate morphological alteration in the nucleus could also modulate gene expression, and therefore, the physiology of the cell [9,10]. The connection between nuclear structure and function has been outlined by many researchers who have categorized the nuclear pathophysiology into some broad groups, such as envelopathy, (a group of disease caused by mutation in genes encoding nuclear envelope proteins), laminopathy (diseases caused by mutations in LMNA gene) and tauopathy (a heterogeneous group of neurodegenerative diseases characterized by deposition of abnormal tau protein in the brain cells), and conferring the major responsibility for malfunctioning nuclear or cellular components to them. The structural aberrations are mostly compelled by abnormalities of nuclear envelope proteins and disorganized nucleoplasmic subcompartments, as well as hindered nucleo-cytoskeletal interactions, nuclear transport and repair mechanism. It is well-known that morphological deformations may alter cell cycle progression [11], chromatin accessibility [12], and the gene expression profile of a cell [13]. Consequently, the genetic rearrangement associated with nuclear aberration could be involved with different types of malignancy, progeria syndromes, neurodegenerative diseases, neuromuscular dystrophy and many other terminal illnesses, as discussed in the following sections. Nuclear aberrations may be either the cause of a disease or the consequences of cellular events related to the disease. In both of these situations, identifying the factors involved in the modifications could be used to pinpoint the onset of pathogenesis at an earlier stage. Moreover, understanding the connection between the nuclear morphology and the altered cellular and extracellular components could pave the way for designing targeted and effective treatment strategies for many related life-threatening diseases [14]. In this review, we examined the diverse cellular activities associated with regulating nuclear size and morphology. We investigated how the altering or malfunctioning of certain factors affects the shape, size and organization of the nucleus. We have also underlined the concepts involved in specific theranostic approaches for early and targeted diagnosis and treatment of nuclear deformation that accompanies pathogenesis. The structural components of a nucleus, such as chromosomes, nucleoplasmic compartments, nuclear envelope proteins and lipid bilayer, are the core elements involved in the regulation of nuclear morphology. Each component has a distinct functionality and approach by which they help to maintain the characteristic shape and size of the nucleus. Here, we will evaluate the mechanisms of individual nuclear constituents that collectively gather to fabricate the controlled morphology of the nucleus in normal and diseased condition. Nuclear envelopes are the structural and physiological interface between the central genomic material and cytoplasm of the cell. The double lipid membrane of the nuclear envelope originates from ER and remains in continuous contact with its network afterward. In contrast to the origin, both the outer nuclear membrane (ONM) and inner nuclear membrane (INM) of the nuclear envelope are enriched with a very distinguished set of proteomes (Figure 1) [15]. These subsets of proteins play key roles in bidirectional nucleoplasmic transportation, maintenance of nuclear architecture, cell cycle control, chromatin organization, gene regulation and DNA repair. The most complex macromolecular assemblies of the nuclear envelope are nuclear pore complexes (NPCs) [16,17,18,19]. NPCs encompass multiple subsets of more than 30 types of nuclear pore proteins called nucleoporins (Nups) [20,21]. The de novo assembly of Nups during interphase starts with the accumulation of Nups in both the outer and inner nuclear membranes, and the subsequent fusion these proteins forms the doughnut-shaped core (consisting of eight spokes arranged around a central channel) of NPC [22,23,24]. The fusion creates an energetically unfavorable and highly curved membrane that surrounds the NPC [25]. Some nuclear-basket-associated peripheral Nups reportedly conserve this membrane curvature by holding the membrane with their amphipathic α-helix domains [26]. Specifically, the synergistic participation of Nup1, Nup60 (yeast) and Nup153 (higher eukaryotes), along with other membrane curvature sensing proteins (Y complex, Nup145, Nup133, Pom34), equilibrate the membrane-shaping forces into the NPC assembly [26,27,28,29]. The colocalization of Sun1 protein with Nup153 and POM121, as well as lamin with the nucleoplasmic basket of NPC, have been discovered in different types of cellular models [30,31,32]. These establishments evidently link the roles of the NPCs in the nucleo-cytoskeletal coupling and mechanobiology of the nuclear envelope; at the same time, the assembly of NPCs could also regulate the nuclear morphology indirectly [33,34,35]. It has been observed that defects in postmitotic assembly of NPCs results in a smaller nuclear size in mammalian cells. The shortcoming of functional NPCs is subsequently reflected in the lower density of NPC at the nuclear envelope, which decreases the nuclear import and localization of lamin proteins, thereby reducing the nuclear size [36]. Furthermore, a study by Kittisopikul et al. on lamina knockout and NPCs knockdown in mouse embryo fibroblast cells confirms the interdependent effect of NPCs and lamins on their respective organization at the nuclear periphery. Knocking down the NPCs situated at close proximity to the lamina (i.e., ELYS, TPR) resulted in a spatial distribution of lamin isoforms and vice versa [37]. The codependent relation between NPCs and lamina suggests that the loss of NPCs’ integrity not only compromises the diffusion barrier but also the morphology of the nucleus, which is linked to the pathophysiology of a number of diseases. Another macromolecular assembly of NE that spans both INM and ONM is known as the linker of nucleoskeleton and cytoskeleton (LINC) complex. It physically connects the cytoskeletal framework to the nucleoplasmic filaments by forming a dynamic intermediate bridge between them. The elemental structure of LINC complex involves two transmembrane domains, ONM embedded KASH (Klarsicht, ANC-1 and SYNE homology protein) motif and INM anchored SUN (Sad1 and UNC-84 protein) domain protein. KASH motif interacts bidirectionally with SUN domain as well as with the actin filaments, microtubule and intermediate filaments network using different intermediate proteins, i.e., nesprin-1, nesprin-2, nesprin-3, dynein, kinesin and plectin, etc. In different species, at the nucleoplasmic front, various isoforms of SUN domain proteins (SUN1/2/3/4/5, Msp3, kalroid, etc.) also bind to the NPC, lamina and chromatin using several intermediate proteins [38]. The KASH motif is a connecting link between the SUN domainand cytoskeleton. The conserved SUN domain proteins interact with lumen to carry the force aroused between cytoskeletal and nucleoskeletal network [39]. Most importantly, the both components of LINC complex (i.e. KASH motif and SUN domain proteins) physically couple with the plasma membrane and nuclear envelope to provide a mechano-transduction signaling interface between the extracellular/cellular microenvironment and the genome [40]. We will see the molecular route of mechanobiology-mediated nuclear alterations in Section 4. Moreover, the membrane-spanning SUN and KASH motif also interacts with various nuclear envelope transmembrane proteins (NETs) and plays a vital role in maintaining the nuclear architecture [41,42]. Since the LINC complex provides a functional connection between cytoplasmic and nucleoplasmic compartments, any constitutional or compositional change in LINC-associated harnessing proteins could affect chromatin dynamics in the nucleoplasm [43] and cause morphological aberrations in the nuclear envelope [44,45]. The influence of LINC complex on nuclear stiffness could be apprehended by the example of granulocytes. The modified expression level of emerin and its allied network proteins, i.e., lamin A/C, B1 and lamina associated polypeptides 2 β (LAP2β), have been recorded in the nucleus of granulocytes [46]. In addition, the inner membrane anchoring protein Lamin B receptor protein/LBR both mediates the nuclear envelope distortion with underlying heterochromatin and influences the lobular shape of granulocyte nucleus [47]. The resulting cellular malleability provides additional advantage to the cells during migration through narrow intracellular channels [48]. Similar types of adaptations have also been observed in different metastatic cancer cells [49,50,51]. In higher eukaryotes, lamin A/C, lamin B and other associated proteins assemble around the inner nuclear membrane and play a remarkable role in the regulation of nuclear forms and functions. The subtype of lamin B (lamin B1) in particular forms an outer loose meshwork surrounding the tighter, nucleoplasm facing, lamin A/C meshwork, and both isoforms assemble into a distinct but interlinked filamentous network. Cells devoid of lamin isoforms develop an irregular nuclear shape and become susceptible to large scale DNA damage due to a ruptured nuclear membrane [52]. The rigidity of the nucleus is very reliant on lamina and co-localized INM anchoring proteins, also known as tethering proteins (i.e., LBR, Lamina associated polypeptide 2-Emarin-Man1 protein/LEM, Methyl CpG binding protein 2/MECP2, Proline rich protein 14/PRR14, Kugelkern, Kurzkern, etc.) [53]. The divergent expression of these tethering proteins in different cell types or during the cell division and development indicate their distinctive roles in shaping the nucleus [54,55,56,57]. The meshwork of A/C and B type lamins helps in the organization of the chromatin territories by binding to those co-localized tethering proteins that anchor at specific “lamina associated domains (LAD)” of the genome [58]. The study on viscoelastic properties of lamin-null mouse embryonic fibroblast cells revealed that both lamin A and B contribute to nuclear stiffness [59]. Briefly, a manometer-based micropipette aspiration system measured the nuclear resistance or mechanical stability to applied forces on different knockout models of mouse embryonic cells. The cell types with decondensed chromatin increased the viscosity of nuclei. Meanwhile, co-expression of lamin A and lamin B1 increases both elasticity and stiffness and stabilizes chromatin condensation. The lamin A/C predominantly bind to the peripheral heterochromatin via the complex formed with proteins LED, PRR14, etc. [53,60]. The second LBR dependent mechanism is also used to localize the heterochromatin to the peripheral nuclear interior during the cell development and differentiation [60]. The tether proteins contain a long neucleoplasmic, chromatin binding domain with an INM span and a short luminal domain between INM and ONM [61]. The tether between lamina and heterochromatin also provides a docking site for chromatin interacting proteins, including histone and histone modifiers (mostly histone methyltransferases and histone deacetylases) [58]. The INM proteins that have LEM domains bind with lamin and histone deacetylase 3 (HDAC3). The emarin domain anchor to chromatin through barrier to autointegration factor (BAF), a sequence independent DNA binding protein. The LAP2β domain binds to HDAC3 and cKrox (zinc finger transcription factor- Zbtb7b), a DNA binding protein that contain Lamina associating sequence (LAS element). On the other hand, LBR binds to H3/H4 and heterochromatin protein 1 (HP1) [60,62]. The MECP2 and PRR14 protein also connect HP1 with LBR and lamin A/C respectively (Figure 1) [53]. It is well-established that lamins and associated proteins not only form a structural element of the nucleus that maintains the nucleus’s stiffness and morphology, but they also play a crucial role in functional components by regulating gene’s radial position and expression [63,64]. The role of lamins has also been recognized in genome organization and stability, regulation the cell division, DNA replication, DNA repair and the transcription process [65,66]. The absence of these lamin and associated tethering proteins cause modification in organization of peripheral heterochromatin during the cell differentiation and development that may reflect via altered architecture of the nucleus. These facts corroborate the correlation between morphological aberrations of the nucleus and the altered pathophysiology of the cell. Nuclear envelopes are one of the most functional organelles of the cell and have many simultaneous operations, including signaling, transport, genome compartmentalization, gene regulation, lipid metabolism, DNA repair and cell division, etc. These functional assortments entirely rely on the composition and physicochemical properties of the lipid membrane. The regulation of fatty acid composition of phospholipids (PL) provides specific biophysical properties, such as fluidity, rigidity or curvature to the membrane, which are required for the maintenance of the integrity and morphology of the nucleus. Interestingly, INM itself could regulate lipid composition with the help of some membrane associated proteins. It was previously noted that INM might host the lipid metabolism to expand the membrane through localized stimulation of phospholipid biosynthesis [67]. Later, numerous proteins involved in the regulation of phospholipid biosynthesis, lipid storage and homeostasis were identified at NE [15]. The lipid homeostasis is a complex and multifactorial mechanism that oscillates between formation of phospholipids and storage lipid using a common precursor phosphatidic acid (PA). Based on the cellular demand, PA could be converted first to diacylglycerol (DAG) and then to the storage lipid triacylglycerol (TAG); in other situations, PA could be converted into cytidine diphosphate-DAG (CDP-DAG) to form structural phospholipids. Furthermore, in depth investigation outlines the contribution of specific INM associated proteins in lipid membrane biogenesis during morphological alteration of the nucleus. In response to the growth signals during stationary phase, a conserved PA-phosphatase Pah1 generates DAG from PA at nuclear membrane subdomain connected with storage lipid droplet. During NE growth, the activity of Pah1 is regulated by Nem1-Spo7 complex, which redirects PA towards phospholipid synthesis and membrane expansion [68]. Many advanced studies in this line also suggest that INM localized lipid modifying proteins could also modulate nuclear morphology by transcriptional regulation of lipid synthesis genes. An interesting study by Friederichs and co-workers revealed that the nuclear morphology in budding yeast can be altered by a monopolar spindle 3 (Mps3), which is lipid remodeling mechanism that uses the activity of SUN protein [69]. The previous knowledge describes Mps3 protein as an initiator of spindle pole body (SPB) duplication and a mediator for tethering SPB to the membrane. The depletion of this protein also causes overproliferation of the inner nuclear membrane due to accumulation of abnormal amounts of polar and neutral lipids; it also inhibits the biosynthesis of sterols into the membrane [69]. It was proposed that Mps3 promotes membrane rigidity by influencing the balance between diacyl glycerol (DAG) and phosphatidic acid (PA). Further exploration of the underlying mechanism by Ponce et al. explained that Mps3 is uniquely positioned at INM to perform along multiple pathways. Its N-terminal remains in the nucleoplasm to anchor the telomeres close to the nuclear periphery, whereas the C-terminal situated in the lumen could mediate lipid metabolism. The authors reasoned that a link between Msp3 and Scs2 (a phospholipid biosynthesis and lipid trafficking protein) could be a possible mechanism for this behavior [70]. Scs3 is localized at ONM and has the affinity to bind with a transcriptional corepressor of the phospholipid biosynthesis enzyme gene Opi1 [71]. Using the connection with Scs2, Msp3 could mediate transcriptional control of lipid synthesis at the nuclear periphery (Figure 1). Similarly, Romanauska and Kohler also postulated the role of storage lipid droplet associated INM protein in the Opi1 mediated transcriptional circuit regulation [72]. However, further validation of theory is needed before drawing concrete conclusions. The nuclear aberration during growth, division or stress that leads to membrane deformation could also be regulated by remodeling the membrane properties and recruiting specific lipid species in the nuclear envelope. For example, Hwang et al. noticed that the morphological abnormalities in the aneuploid yeast and human cell nucleus could be suppressed by accumulation of long-chain base fatty acids in the membrane [73]. The extra chromosome number in aneuploid yeast generates biophysical stress on the nuclear membrane. To release this stress, these single chain amphipathic molecules provide tight packaging and high curvature to the membrane [73]. Evidently, maintaining dynamic nuclear envelope during different physiological and environmental conditions requires recurrent remodeling of the membrane lipid profile. It is not yet understood how the nuclear membrane sensitizes these biophysical stresses and saves the nuclear integrity via alteration of phospholipid metabolism. The organization of the genome within the nucleus is a nonrandom process. The second level arrangement of the genome contains euchromatin and constitutive or facultative heterochromatin that gives rise to some advanced assemblies, such as chromosome loops, topological associated domains (TADs, fundamental units of three-dimensional (3D) nuclear organization), lamin associated domains (LADs, heterochromatin located adjacent to lamina), nucleolar associated domains (NADs, heterochromatin located adjacent to the nucleolus) and chromosome territories. It is also known that the nuclear arrangement of chromatin is somehow related to the morphology of the nucleus [58,74]. The role of chromatin in sizing and shaping the nucleus is very intricate and diverse. However, it is widely understood that chromatin contributes to nuclear morphological regulation by (i) interacting with nuclear envelope via the LAD/NAD binding domains of INM integrated proteins and (ii) altering the biophysical properties of heterochromatin. In addition to nuclear envelope assembly, the biophysical state of constitutive and facultative heterochromatin largely influences the rigidity, shape and size of the nucleus [2,75]. Numerous studies have explored the role of ‘chromatin packing’ in nuclear morphology. A direct investigation was completed by Stephens et al. on chromatin decompaction of mammalian cells using histone deacetylase and histone methyltransferase inhibitors [2]. The study showed that an increase in the ratio of euchromatin caused softer nuclei and nuclear blebbing, which was independent of the involvement of lamins. The deformation was reversed after treating the cells with histone demethylase inhibitors. It was suggested that decompacted euchromatin might be mechanically weaker than heterochromatin, or that the altered chromatin state could cause a loss of chromatin lamina connection and nuclear rigidity [2]. In search of mechanisms involved in the mediation of nuclear volume through chromatin compaction, Furusawa et al. investigated the interaction of heterochromatin and a nucleosome binding protein HMGN5 [76]. HMGN5 is found at the periphery of the nucleus and is bound to the underlying nucleosome without any sequence specificity. The overexpression of HMGN5 in transgenic mice decreased chromatin compaction by reducing the interaction between histone H1 and chromatin. Decompaction of chromatin leads to a decrease in nuclear rigidity and a subsequent increase in nuclear blebbing [76]. Hence, the structure of the nuclear envelope and the disseminated genetic material inside it are not at all independent from each other. Therefore, the constant mobile states of the genome have a significant impact on nuclear mechanics. For instance, Imbalzano et al. have reported the effect of the ATPase dependent chromatin remodeling enzyme BRG1 on nuclear structure. Inhibition of BRG1 activity resulted in irregular nuclear morphology [75]. Corresponding to this, Wang et al. found that increased activity of WDR5 (WD repeat domain 5), an epigenetic modulator of H3K4 methylation, resulted in less compacted euchromatin in acute lymphoblastic leukemia (ALL) cells [77]. The observations indicate that chromatin associated alternation of nuclear morphology in certain conditions could be induced by altered biophysical stress into the nucleoplasm. Nucleoplasms are among the very eventful and crowded niche of the cell that provides a common working platform to several types of heterogeneous components. It includes chromatin attached proteins and other nuclear bodies, such as nucleoli, Cajal bodies, promyelocytic leukemia (PML) bodies, speckles, paraspeckles, polycomb bodies and histone locus bodies. The consortia of nuclear bodies combine to make the nuclear matrix, which is responsible for organizing different domains of chromatin fiber into the nuclear volume [78]. The microenvironment created by the concentration of specific proteins is referred to as membrane-less nuclear subcompartments (Figure 1). Some components of nuclear subcompartments could also contribute to the structural organization of the nucleus. For example, Morelli and coworkers have observed that the aberrant expression of heat shock protein B2 (HSPB2), which is a nuclear subcompartment protein, in myoblast cells could cause impaired LMNA-SUN2 anchoring at the nuclear envelope, thereby disrupting NE integrity [79]. The findings also stimulated reasonable thoughts about the impact of the nucleolus on nuclear morphology. Nevertheless, a multiprotein mixed lineage leukemia 4 (MLL4)–complex of proteins that is involved in epigenetic modification was also found to play a crucial role in preserving the mechanical properties of the nucleus by maintaining the equilibrium between chromatin and associated biomolecular condensates [80]. In the congenital disorder Kabuki syndrome, a haploinsufficiency causes a loss of function of MLL4 that affects chromatin liquid–liquid phase separation and alters the assembly of transcriptional condensates and transcriptional regulation of cohesion and condensing genes. The mesenchymal stem cell-based Kabuki syndrome model showed that the impaired chromatin compartmentalization due to loss of function of MLL4 could increase mechanical stress through increasing the chromatin compaction and nuclear stiffness, followed by altering the nuclear architecture in the disease condition [80]. The nucleolus is the most prominent nuclear subcompartment and covers almost one third of the nucleoplasm’s peripheral space. Its size varies during growth, and both normal and cancer cells proliferate due to the increased demand for ribosome biogenesis [81,82,83]. Almost any type of cancer exhibits abnormalities in the number and shape of nucleoli due to overactivated ribosome biosynthetic core machinery. However, it would be interesting to know whether nucleolus could have any influence on nuclear morphology in any of these conditions. The little research conducted on this topic have shown that a nucleolus has the ability to sequester the nuclear envelope to avoid nuclear morphological disruption [84]. The direct interaction between the nuclear envelope and nucleolus was explored by some researchers. A study on breast cancer cells revealed that depletion of the nuclear envelope protein SUN1 induced nucleolus enlargement [85]. It is already known that INM anchored and associated proteins contribute to maintaining nuclear envelope integrity and morphology. Sharing nuclear envelope proteins to maintain nucleolar and nuclear morphology was also observed by Sen Gupta and Sengupta [55]. The authors reported the independent role of lamin B2 at the nucleolus and nuclear envelope. Collectively, the N-terminal head domain of lamin B2 interacts with the nucleolar proteins nucleolin and nucleophosmin, whereas the C-terminal tail domain makes contact with the nuclear envelope. Depletion of lamin B2 caused morphological abnormalities in both the nucleolus and the nuclear envelope [55]. These studies indicate the presence of common mechanisms which regulate both nucleolar and nuclear morphology. Indeed, there is not sufficient information to know where there is any substantial correlation between the regulation of the nucleolus’s morphology and the nucleus. If yes, then how and in which direction are these mechanisms induced (from nucleolus to nucleus or from nucleus to nucleolus) and what are the exact regulating factors between them? These queries need to be addressed to resolve the ambiguity and to present a clear picture. The nucleus is a largest organelle and the center of essential genetic and regulatory activities of the eukaryotic cell. Constant physiological communication among the nucleus and other cellular components, such as mitochondria, ER, vacuoles, peroxisomes, plasma membrane, lipid droplets and cytosol, maintains the cellular homeostasis [86,87]. Strikingly, the direct physical interconnection involving specific tethering contacts has also been recognized among the membrane-bound organelles [88]. In this context, the involvement of reticulon (Rtn), an ER membrane stabilizing protein, is reviewed by Mukherjee et al. [1]. The increased activity of Rtn is observed with a decreased nuclear size in many cell types [89,90,91]. Beside macro-organelles, the cytoskeleton makes up a significant portion of the cytoplasm and plays an important role in nuclear positioning and regulation of its morphology. Since the nucleus is the largest and most vigorous organelle of the cell, the organization or reorganization of the cytoskeleton quickly transmits the cellular stress to the nucleus. For example, findings of Monroy-Ramírez and coworkers established that aberrant binding of tau protein and tubulin alters the radial organization of cytoskeleton to the thick ring type arrangement at peripheral and perinuclear sites [92]. The rehabilitated nuclear–cytoskeleton assembly causes enlargement and lobulation of the nucleus followed by functional abnormalities into the cell. The externally applied tension transfers to the nucleus via the actin filament anchoring LINC complex. The direct connection between actin cytoskeleton and nuclear morphology was observed in human melanoma cells by Colón-Bolea et al. [93]. The nuclear shape alteration in invasive melanoma cells was orchestrated by alteration in the connection between the tubulin cytoskeleton and LINC complex using a RHO GTPase (RAC1)-mediated mechanism [93]. The concept is further corroborated by Lu et al., who demonstrated the consequence of disruption in connection between a KASH motif containing proteins and an actin network [94]. A multivariate KASH motif containing protein, Nesprin, interacts with the actin cytoskeleton covering the outer nuclear membrane. The study revealed that Nesprin 1/ Nesprin 2 consists of a specific N-terminal actin binding domain (ABD) which is involved in actin mediated nuclear shape regulation. The overexpression of Nesprin 2 ABD leads to increase in nuclear area, but replacing it with a mini-isoform of Nesprin 2 that lacks the long rod segment produces smaller nuclei [94]. The authors proposed that an interchain association of Nesprin produces a basket-like protein network which has a key role in effective transduction of nuclear and cytoplasmic forces. The nuclear shape is the net outcome of external (cytoskeleton) and internal (microfilaments, lamina, genome) generated forces from opposite sides of the nuclear envelope (Figure 2). Furthermore, research into isolated nuclei has also revealed that nuclei are able to resist force by adjusting their stiffness in the direction of the applied tension [95]. This acclimatization is completed by phosphorylation of tyrosine residues on the emerin protein followed by rearrangement in the LINC-lamin A/C connections. In addition to reinforcing its rigidity, nuclear membrane tension is sometimes lowered to dissipate the mechanical energy. Recently, Nava and coworkers found that Ca2+ influxes from ER to nucleoplasm are enhanced to induce nuclear softening during mechanical stretch conditions [74]. This is thought to be a defense mechanism designed to prevent the mechanical damage of genetic material by changes in the Ca2+ dependent chromatin rheology. Release of Ca2+ reduces the association between lamina and H3K9me3-marked heterochromatin, and subsequent nuclear softening is required to insulate the genetic material [74]. Hence, this untethering of chromatin from the INM under cytoskeletal forces could result in a highly deformable nucleus [96]. Further exploration of nuclear structural and physiological harmony under the influence of physical forces reveals that uneven deformation of the nucleus enhances the expression of some mechanosensitive genes [11,97,98,99]. These studies found that deformation of nucleus due to force transmission causes localization and activation of some mechanosensitive transcription activators (i.e., YAP, AP1, TEAD) in the nucleus. This connects the role of nuclear morphological aberrations in cell fate switch and the development of pathogenicity. Any morphological aberration of the nucleus could be rooted in functional abnormalities, including instability of genetic material, aneuploidy, micronuclei formation, altered gene expression and metabolic dysregulation. Nuclear pathophysiology is categorized into broad groups based on the major responsible malfunctioning component, such as envelopathy (nuclear envelope proteins that are involved in fundamental nuclear functions, such as gene transcription and DNA replication, cause human diseases through inherited or de novo mutated proteins cause human diseases, called “nuclear envelopathies”), laminopathy (diseases caused by mutations in LMNA gene, called “laminopathies”) and tauopathy (a heterogeneous group of neurodegenerative diseases characterized by abnormal metabolism of misfolded tau proteins (tau prions) which eventually results in massive loss of brain cells). Such structural aberrations affect the operational activities of the nucleus and causes devastating impact on human health, including oncogenesis, aging disorders, neuronal or muscular dystrophy or cardiomyopathy [100,101,102,103]. The pathophysiological significance of nuclear deformation has been studied exponentially in human and animal subjects, which is reflected by a tremendous number of publications in this field. Here, we will examine the cellular cause or consequences of nuclear deformation relating to physiological disorders (Figure 3). The relation between nuclear deformation and progression of physiological defects are widely studied in cancer cells. In contrast to normal cells, the tumorigenic cell’s nucleus shows an unusual size and a floppy and irregular appearance due to fragmented, lobulated or deep invading outline [104,105]. The altered structural mechanics provide plasticity and increased invasion properties to metastatic cells; they also induce chromatin remodeling and cell cycle regulation in primary oncogenic cells [100,106]. Mutations in a large range of NE proteins are frequently observed in different types of cancer cells. It has been noted that the deregulation of lamin or emerin proteins could predispose mechanical distress that compromises nuclear compartmentalization and nuclear envelope integrity in cancer cells [107,108] and causes DNA damage in skeletal muscle cells [109]. Most types of the cancers show aneuploidy during the progression of carcinomas. The chromosomal instability of cancer cells also found associated deformation of nuclear envelope. The mechanistic study on ovarian cancer cells has revealed a mechanism mediated by suppression of the GATA6 transcription factor followed by loss of the nuclear envelope protein emerin [110]. Furthermore, Nader et al. explored how nuclear deformation in cancer cells leads to chronic and sublethal damage of genomic DNA. The study recorded the presence of an ER membrane-associated exonuclease, TREX1, in the deformed nucleus of tumor cells. The TREX1-mediated DNA damage again promoted tumor growth and invasion by leading aberrant invasiveness in the tumor cells [111]; the nuclear instability caused by altered expression of NE proteins is required for tumor aggressiveness in different types of cancer [50,112]. The laminopathy-linked nuclear envelope fragility sometimes leads to abnormal nuclear division and formation of unstable micronuclei that have small genome fractions can cause aneuploidy, a common feature in oncogenic cells [113]. The rearrangement of transenvelope components, such as LINC complexes and NPCs, are required for the coordinated cell migration and attachment of invasive malignant cells [114]. Furthermore, the altered arrangement of these nuclear envelope proteins could also modulate the genome organization that changes the nuclear mechanophysics and gene expression profile. For example, atypical Nup98 protein contributes to morphological alteration by affecting the lamina and lamina-associated polypeptides 2α (LAP2α) in leukemia cells [115]. The formation of chimeric protein involving NUP98 and transcription factors, such as homeodomain (HD), were observed to induce morphological alterations of the NE in acute myeloid leukemia (AML) cells (Table 1) [115]. The aberrant NE phenotypes include lobulation due to altered chromatin organization, relocalization of A and B lamins and alteration in lamin A associated LAP2α protein. The LAP2α is a networking protein that interacts with nucleosome binding proteins, thereby affecting chromatin distribution and NE organization associated with malignant transformation. The similar protein has also been reported to be involved in epigenetic regulation of gene expression using histone modifying complex in yeast, drosophila and human leukemia cells [116,117]. Similarly, a nuclear importer family protein, karyopherin α7 (KPNA7), is expressed at higher level in cancer cells. The intensity of KPAN7 protein affects the organization of lamina and nuclear morphology. Interestingly, it also has a critical role in the organization of mitotic spindles and acts as an important element in cancer cell proliferation [118]. On the other hand, some of the nuclear proteins have been found to regulate cell growth, apoptosis, and differentiation in cancer cells using components of cell signaling pathways. For instance, Kong and colleagues observed a correlated change in the level of lamin A/C and PI3K/AKT/PTEN pathways in prostate cancer cells [119]. A large-scale study on primary lung cancers uncovered that larger distorted nuclei of tumor cells have significant association with the altered expression of cell cycle checkpoint protein p53 and DNA repair protein p16INK4A [120]. The spontaneous link between signaling proteins and nuclear deformities is recorded in numerous studies (reviewed in [114,121]). However, the precise connecting mechanisms by which cancer cells stimulate mechanotransduction signaling to maintain self-sustained proliferation remain elusive. Lamin A farnesylation, which is key to almost all cellular defects and nuclear deformations, is also a principal prognosis component of premature aging or progeroid syndromes. Progeroid syndromes are terminal genetic disorders characterized by an accelerated aging process due to a decline in physical and physiological function at early age [122]. Aging nucleus shows evident structural and molecular changes, including nuclear membrane lobulation and detachment, altered nuclear transport, altered genome compartmentalization and packing and an increase in transposable element transcripts and nuclear inclusions [123,124,125]. The nuclear defects in progeria syndrome are caused by mutations in the LAMA gene. For example, in Hutchinson–Gilford Progeria Syndrome (HGPS), mutations in exon 11 of the LMNA gene alters its splicing pattern and results in an in-frame deletion at C-terminus in prelamin A that produces a protein which is 50 amino acids shorter. “Progerin,” an altered prelamin A protein, interrupts the function of normal nuclear lamina at the nuclear periphery [126]. The progerin-induced irregularities include nuclear envelope blebbing, relaxation of peripheral heterochromatin, altered epigenetic modifications and, thus, gene expression [124,127]. Even after correct expression of the LMNA gene, the defects in post-transcription modification of prelamin A protein may cause several premature aging diseases, including HGPS, mandibuloacral dysplasia syndrome (MAD) and restrictive dermetopathy (RD). A membrane zinc metalloprotease, ZMPSTE24, is a crucial tool for biogenesis of the lamin A scaffold protein. For the prelamin A substrate, encoded by LMNA must be farnesylated and carboxymethylated at C-terminal CAAX motif [128]. Recessive LMNA and ZMPSTE24 mutations impede the prelamin A post-transcriptional modifications mediated by the ZMPSTE24 metalloprotease and cause cardinal nuclear morphological dysfunctions (Table 1). Moreover, similar nuclear disorders are recorded in multiple cancers, nucleopathies associated with muscular cells (Emery–Dreifuss muscular dystrophy, EDMD), neurons (Alzheimer’s disease and Parkinson’s disease), adipose (familial partial lipodystrophy), and myofibroblasts (Table 1). The dissimilar genesis of different types of nucleopathies provides inclusive information for disease prognosis. For example, mutations in a range of LINC complex components and LMNA alters the nuclear envelope plasticity in EDMD disease [101]. Another observation was recorded in a cardiomyopathy and muscular dystrophy mutant model of mice embryonic fibroblast. The study revealed that amino acid substitution in LMNA caused an increase in nuclear size and dilution of heterochromatin near the lamina without altering the nuclear morphology [129]. The proposed pathogenicity mechanism suggested that mutant lamin A/C variant leads to chromatin organization and gene expression, followed by altered cellular mechanotransduction. In myofibroblast emerinopathy, the altered emerin function causes failure of perinuclear actin fibers assembly [103]; in Alzheimer’s disease, however, tau protein-induced nuclear envelope invagination coupled with lamin B dysfunction causes neuronal death [102]. An age dependent aberrant inclusion of two RNA binding proteins, the Musashi and tau proteins, are also reported to cause nuclear transport, chromatin remodeling and nuclear lamina formation in Alzheimer’s disease [130]. The progression of Parkinson’s disease, the most common age-related movement disorder, is diagnosed by degradation of dopaminergic, nigrostriatal neurons, which is reportedly caused by multiple factors affecting cellular homeostasis. Among them, toxic accumulation of a presynaptic protein α-synuclein and the missense mutation of Leucine-rich repeat kinase 2 (LRRK2) reportedly contribute to PD related motor symptoms by causing dopamine transmission dysfunction among the neurons. The LRRK2 deficiency is also correlated with nuclear hypertrophy, nuclear invagination and dendritic atrophy during aging [131,132]. The nuclear morphological and functional alteration in brain neurons is also a hallmark of Huntington’s disease, another neurodegenerative disorder. The disease mechanism studies have established a relation between altered lamin B levels followed by altered nucleoplasmic transport, perturbation in nuclear lamina heterochromatin organization and altered nuclear morphology HD specific brain neurons [133]. Associations have been found between many important genetic or inherited diseases and an array of nuclear deformations. For instance, in Down syndrome, the extra copy of chromosome 21 affects the nuclear organization following epigenetic rearrangements that increase heterochromatin and reduces global transcription level, hinders the nucleoli fusion pattern that increases the number of nucleoli and influences the pre-mRNA splicing that reduces the number of Cajal Bodies [134] (Table 1). Hence, complete knowledge of molecular mechanisms activated by nuclear deformation in such physiologically challenging conditions will be instrumental for strategic management of the diseases. The shape of the nucleus impacts the functional status of the cell. Although the majority of cell types have either a spheroid or ovoid nucleus, different cell types can have different nuclear shapes, such as lobed, spindle shape, etc. These varied nuclear shapes have a definitive role in the transcriptional or functional activity of the cell. The human granulocytes are a good example of the need for varied nuclear shape to perform different functions. Mature neutrophils have multilobed segmented nuclei separated by thin filaments of nucleoplasm facilitating the flexibility necessary for them to pass through small gaps in the endothelium and extracellular matrix more easily. The bilobed circulating monocyte nuclei become more rounded following recruitment into tissues that further differentiate into macrophage. The assembly of the nucleus is dynamically organized to adjust its shape and size to maintain homeostasis during different phases and needs of the cell. It is a common phenomenon of cellular functionality in which alterations in morphology happen in response to a modification in the cell’s physiological or structural environment. These morphological alterations are vital to maintain optimal functioning of the nucleus during growth and the cell’s changing needs under stress. However, the same has also been correlated with the development of cancer and several other neuronal or muscular disorders (Table 1) [160]. Altered mechanical properties of nuclei are associated with altered cell behavior and disease. Here, we sought to determine the nuclear deformation-based pathogenesis and possible utility of such knowledge in the development of therapeutic approaches. Nuclear morphometry plays a significant role in the histopathological and cytological diagnosis of many diseases. For instance, a 35-month follow-up study on osteosarcoma patients revealed that nuclear morphological parameters, such as area and shape, could be applied to identify which patients had a good prognosis [161]. It was also recorded that patients with large and round tumor nuclei had better outcomes then patients with small and polymorphic nuclei. Nuclear morphological changes include alterations in size, shape, margins (grooves/molding/convolutions/thickening), shifts in chromatin pattern, enlargement of nucleoli and perinucleolar space. Morphometry and image analysis techniques are helpful to characterize the size and shape of nuclear substructures, such as nucleoli, nuclear membranes and chromatin granules. Intranuclear informatics have been developed by combined application of fluorescence microscopy, image processing and statistical analysis using specific computerized nuclear morphometric methods [162]. Irregularities in nuclear size, shape and chromatin texture are often correlated with altered gene organization and expression in tumor cells [11]. The remedy of such complications is completely dependent on early-stage diagnosis, when the disease is less destructive and treatment is more effective. Thereby, specific structural aberrations, including blebbing, development of nucleoplasmic reticulum, altered size and number of nucleoli and changes in nuclear rigidity have been used as important diagnostic standards to determine the type and stage of disease [104,163,164]. For instance, Antmen et al. identified differences in the mechanical properties of breast cancer cells at three different disease states, including benign, malignant noninvasive and malignant highly invasive breast cancer cells [165]. The three cell types showed nuclear deformability in order to progress their malignancies when observed using a scanning electron microscope (SEM) and fluorescence micrograph over a specific micropatterned substrate film. The increased nuclear deformation was also correlated at the molecular level with suppressed expression of Lamin A/C and Nisprin-2 genes in respective cells [165]. There are several quantitative imaging techniques that could identify the irregularities in nuclear shape (area, diameter and perimeter), nuclear contour ratio (circularity or lobulation), boundary curvature and elliptic Fourier coefficient ratio (deformation) with higher accuracy [166]. Along with imaging techniques, the presence of circulatory nuclear matrix proteins (e.g., NMP22, NuMA, lamin B1) in the body fluids (plasma, urine, saliva, etc.) is used as a biomarker for diagnosis and prognosis of many cancer types, including prostate, bladder, colorectal, hepatic, head and neck cancers [167,168,169,170,171]. Recently, Wu et al. reported that the nucleus morphology features measured in more than 30,000 single-cell-derived clones from the parental breast cancer cells exhibited distinct and yet heritable traits associated with genomic and transcriptomic phenotypes [172]. These findings highlight the significance of nuclear morphometric analysis through digital pathology combined with multiomics (i.e., single-cell genomics, transcriptomics) for improved diagnosis and prognosis of individual cancer patients [173]. In vitro analysis of morphological features could offer an effective and affordable method to reveal the intratumoral heterogeneity, thereby improving the overall disease prognosis and survival. The nuclear–structural abnormalities-based prognostic or diagnostic approach has been further extended for the development of targeted and personalized treatment strategies [174]. Moreover, the histological measurement of nuclear abnormalities may also be used as a marker to access the efficacy of those treatments. The study by Stephens et al. on lamin B1 and A mutant progeria model showed a similar concept [2]. The authors established that increases in heterochromatin level-based nuclear stiffness using histone demethylase inhibitors improved nuclear morphology by decreasing the number of blebbed nuclei in progeria cells [2]. Relatedly, Dou et al. have also suggested that inhibition of LC3-lamin B1 interaction protects cells from tumorigenesis by preventing lamin B1 loss and attenuating oncogene-induced senescence in primary human cells [175]. Targeting the signaling pathways regulating nuclear morphology has also been suggested by some researchers in a few disease models. For example, two centromere binding proteins namely transforming acidic coiled-coil (tACC) domain-containing protein and tuberous sclerosis 2 (tSC2) play an important role in nuclear morphology management [176]. Both proteins are regulated by Akt-mediated pathways which could be used as key therapeutic target in abnormal cellular growth [177]. tSC2 is a tumor suppressor and gatekeeper protein that functions as GTPase activating protein in association with tSC1 protein. Meanwhile, tACC is a centromere binding protein that also has a significant role in maintenance of nuclear membrane structure and cell division after binding with tSC2. The direct correlation between the lamins, NPCs and tumor suppressor protein p53 was elucidated by Panatta et al. very recently [178]. Their observation of p53 depleted mouse pancreatic ductal adenocarcinoma cell revealed that p53 regulates the expression of nuclear component genes, including Lmnb1, Tmpo, Nup205, Nup107, Nup85 and Nup35. The p53 protein indirectly represses these target genes using a cell cycle regulating protein complex [178]. This study provides a significant connection between nuclear architecture components and cancer progression. The morphology of the nucleus is also dependent on alteration in nucleoli architecture during tumorigenesis. Nucleolar component-targeted therapeutic drugs, namely Doxorubicin, Mitomycin [179], Cisplatin, Etoposide [180], Actinomycin D [181], are emerging for the treatment of various cancers, including breast, bladder, thyroid, hematological cancers, sarcomas, head and neck cancers. Doxorubicin, Etoposide and Mitomycin are RNA polymerase transcription targeting drugs that inhibit the tumor cells via selective inhibition at different interfaces in the transcription complex. Both Doxorubicin and Etoposide bind to topoisomerase II to arrest tumor growth. Actinomycin D is a DNA-binding drug that intercalates into GC rich DNA regions and inhibits the polymerase I transcription. Similarly, Cisplatin is a DNA-intercalating agent which forms an irreversible interstrand crosslink to guanine and adenine residues of the DNA strand. A new class of targeting rDNA, DNA aptamers and naphthalene diimides, have shown significant potency in inhibiting breast and lung carcinoma proliferation [182,183]. Such highly effective drugs restore the nuclear structure and could be also used to reveal the structural and functional connection of the nucleus. Advances in understanding the mechanism of nuclear structure-based pathophysiology will serve as powerful tool for increasing survival rate and reducing the treatment costs for many fatal diseases. The molecular mechanisms orchestrating nuclear morphology and their connection to disease development still need to be elucidated clearly. In this review, we have summarized various factors that are contributing to maintaining nuclear morphology and architecture in eukaryotic cells. In fact, the factors described above have profound effects on the structure and function of chromatin, showing correlations with the resulting gene expression and chromosome stability. Moreover, these factors act as a bridge between the cytoskeleton and nucleoskeleton, thus emerging as a promising signal transduction between the nucleus and cytoplasm. It was established that the abnormalities in nuclear morphology could be due to mutations, abnormal gene expression, altered signal transduction pathways and chromatin architecture as well as aneuploidy. In recent years, questions regarding the molecular mechanisms that regulate nuclear size and shape differently in normal and disease states remain largely unanswered. However, there is clear evidence that highlights the influence of abnormal nuclear morphology on different cellular functions, cell cycle, genomic stability, apoptosis and signal transduction pathways. The current literature supports the use of nuclear morphological abnormalities for the early diagnosis of diseases and is beginning to shed light on the use of theranostic approaches for the treatment of diseases. The identification of these nuclear morphological abnormalities-related targets for therapeutic intervention could be promising for personalized cancer treatment and eradication of life-threatening diseases.

PMC10000963

Giulia Cencelli,Laura Pacini,Anastasia De Luca,Ilenia Messia,Antonietta Gentile,Yunhee Kang,Veronica Nobile,Elisabetta Tabolacci,Peng Jin,Maria Giulia Farace,Claudia Bagni

Age-Dependent Dysregulation of APP in Neuronal and Skin Cells from Fragile X Individuals

27-02-2023

Fragile X syndrome,APP processing,protein synthesis,peptide therapy,iPSCs,ADAM10,SAP97

Fragile X syndrome (FXS) is the most common form of monogenic intellectual disability and autism, caused by the absence of the functional fragile X messenger ribonucleoprotein 1 (FMRP). FXS features include increased and dysregulated protein synthesis, observed in both murine and human cells. Altered processing of the amyloid precursor protein (APP), consisting of an excess of soluble APPα (sAPPα), may contribute to this molecular phenotype in mice and human fibroblasts. Here we show an age-dependent dysregulation of APP processing in fibroblasts from FXS individuals, human neural precursor cells derived from induced pluripotent stem cells (iPSCs), and forebrain organoids. Moreover, FXS fibroblasts treated with a cell-permeable peptide that decreases the generation of sAPPα show restored levels of protein synthesis. Our findings suggest the possibility of using cell-based permeable peptides as a future therapeutic approach for FXS during a defined developmental window.

Age-Dependent Dysregulation of APP in Neuronal and Skin Cells from Fragile X Individuals Fragile X syndrome (FXS) is the most common form of monogenic intellectual disability and autism, caused by the absence of the functional fragile X messenger ribonucleoprotein 1 (FMRP). FXS features include increased and dysregulated protein synthesis, observed in both murine and human cells. Altered processing of the amyloid precursor protein (APP), consisting of an excess of soluble APPα (sAPPα), may contribute to this molecular phenotype in mice and human fibroblasts. Here we show an age-dependent dysregulation of APP processing in fibroblasts from FXS individuals, human neural precursor cells derived from induced pluripotent stem cells (iPSCs), and forebrain organoids. Moreover, FXS fibroblasts treated with a cell-permeable peptide that decreases the generation of sAPPα show restored levels of protein synthesis. Our findings suggest the possibility of using cell-based permeable peptides as a future therapeutic approach for FXS during a defined developmental window. Fragile X syndrome (FXS), an X-linked condition, is the most frequent form of hereditary intellectual disability (ID) and monogenic cause of autism [1]. Individuals with FXS show physical and behavioral features, including intellectual disability, attention-deficit/hyperactivity disorder (ADHD), repetitive behaviors, and anxiety. Reduced social interactions have been reported in FXS individuals diagnosed with autism spectrum disorder (ASD) [2,3,4,5,6,7]. Indeed, around 40% of patients with FXS meet the criteria for ASD [8,9,10]. FXS occurs due to the absence or mutation of fragile X messenger ribonucleoprotein 1 (FMRP). FMRP is an RNA-binding protein involved in several aspects of mRNA metabolism, including the regulation of mRNA translation [11,12,13,14,15]. The absence of FMRP compromises the regulated expression of a variety of proteins critical for brain development, synaptic plasticity, and dendritic spine morphology, ultimately impinging on cognition and behavior [2,12,15]. Consistent with its key role in the brain, FMRP regulates a large subset of mRNAs [12,16,17], including the mRNA encoding amyloid precursor protein (APP) [12,18,19,20,21]. APP is a type I transmembrane protein central to the pathogenesis of Alzheimer’s disease (AD) [22,23]. Besides its well-established role in neurodegeneration, APP also exerts a key role in physiological functions, including synaptogenesis and synaptic plasticity [24,25,26,27,28,29]. APP undergoes a complex series of proteolytic processing events, which can be divided into amyloidogenic and non-amyloidogenic pathways. The amyloidogenic pathway is characterized by the production of Aβ peptides, which are associated with AD progression [23,28]. Under physiological conditions, the non-amyloidogenic pathway results in the release of the soluble amyloid precursor protein-alpha (sAPPα) [24,25,28,30] catalyzed by/driven by the α-secretase activity of the disintegrin and metalloprotease ADAM10 [31,32,33]. The sAPPα fragment, regulates several processes in brain development, including synaptic plasticity, spine density, and cognition [25,30,34,35]. Several points of evidence support the involvement of APP in the FXS phenotype [18,36,37,38,39,40,41]. FMRP mediates mGluR5-dependent translation of APP mRNA, and its absence leads to exaggerated APP expression in Fmr1 KO mice and individuals with FXS [18,20,36,40]. Genetic reduction of APP expression rescues synaptic deficits and behavioral phenotypes in the Fmr1 KO mice [20,40,41]. In addition, FMRP regulates Adam10 mRNA translation, and lack of FMRP in mice (Fmr1 KO) increases both APP and ADAM10 protein levels [18,20]. This dysregulation ultimately leads to excessive production of sAPPα during a specific developmental window in mice (analogous to childhood and early adolescence in human), corresponding to a critical stage of synaptogenesis [20]. Excessive release of sAPPα contributes to some of the main molecular features of FXS, namely increased protein synthesis, aberrant spine morphology, and altered synaptic function and behavior [2,20,42]. Of note, treatment of juvenile Fmr1 KO mice with a specific cell-permeable peptide (TAT-Pro ADAM10709−729) that interferes with ADAM10-mediated APP processing rescues mRNA translation, spine morphology and behavioral defects [20]. Despite the promising results obtained in Fmr1 KO mice, the use of a rodent model for FXS presents limitations that may hamper the translation of preclinical data to humans. Several clinical trials for FXS, based on findings generated in the mouse model, have not been very successful so far [4,5,43,44], suggesting that models using human patient-derived cells will be important for the development of new and personalized therapies. In the present study, we analyzed the processing of APP in FXS human fibroblasts, neurons derived from human induced pluripotent stem cells (iPSCs), and human forebrain organoids. We observed a specific age-dependent dysregulation of APP metabolism in human cells. In addition, treatment of FXS fibroblasts with the cell-permeable TAT-Pro ADAM10709−729 peptide reduces sAPPα release and normalizes the level of protein synthesis. These findings suggest that a subset of individuals with FXS, those with elevated protein synthesis, could benefit from a peptide therapy based on the reduction of excessive sAPPα. Fibroblasts. Control fibroblast cell lines (n = 19, age range 5–57 years) were purchased from the Coriell Cell Repositories. FXS fibroblast cell lines (n = 32, age range 6–69 years) were obtained from dermal biopsies with patient consent and under approval from multiple centers as listed in Table S1 (CHUV University Hospital of Lausanne; M.I.N.D. Institute in Sacramento; Erasmus Medical Center in Rotterdam and University Hospital A. Gemelli in Rome). The clinical assessment, inclusion criteria, study protocol, the FMR1 mRNA and FMRP levels, and all amendments for Switzerland, USA, and Netherlands cohorts have been previously described [42]. The samples collected at the University Hospital A. Gemelli in Rome were derived from 4 FXS individuals (ET001, ET002, ET003, and ET004). CGG sizing and methylation status were evaluated using AmplideX® PCR and AmplideX® mPCR assays (Asuragen, Austin, TX, USA) or by Southern blot analysis using HindIII restriction enzyme and/or the methylation-sensitive enzyme EagI (New England Biolabs, Ipswich, MA, USA). The study protocol was approved by the Ethics Committee of the University Hospital A. Gemelli in Rome (prot. N. 9917/15 and prot.cm 10/15). The level of FMR1 mRNA and FMRP for these lines are included in Figure S1. Fibroblasts were maintained in DMEM/F-12 (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (Gibco, Thermo Fisher Scientific), 1X GlutaMaxTM (Gibco, Thermo Fisher Scientific), 1X penicillin-streptomycin (Gibco, Thermo Fisher Scientific) and MycoZap reagent (Lonza, Basel, Switzerland). Induced pluripotent stem cells (iPSCs). iPSCs derived from fibroblasts of typically developing individuals (TDI) and FXS individuals were established at the Children’s Hospital of Orange County and kindly provided by Dr. Philip H. Schwartz [45]. iPSCs were cultured on Matrigel (BD Biosciences, Franklin Lakes, NJ, USA) in mTeSR medium (Stem Cell Technologies, Vancouver, BC, Canada). The clinical characteristics of iPSCs used in the present study are summarized in Table S2. The relative levels of FMR1 mRNA and FMRP are shown in Figure S1. Forebrain organoids. Human forebrain organoids used in this study were generated from TDI and FXS iPSCs at Emory University School of Medicine in Atlanta and previously characterized [46]. iPSCs were cultured on irradiated mouse embryonic fibroblasts (MEFs) in human iPSC medium consisting of DMEM/F-12 (Gibco, Thermo Fisher Scientific), 20% knockout serum replacement (KSR, Gibco, Thermo Fisher Scientific), 1X GlutaMAX (Gibco, Thermo Fisher Scientific), 1X MEM non-essential amino acids (Gibco, Thermo Fisher Scientific), 100 μM β-mercaptoethanol (Gibco, Thermo Fisher Scientific), and 10 ng/mL human basic FGF (PeproTech, London, UK). iPSCs were differentiated into neurons as previously described [47]. Briefly, iPSCs were maintained in culture in defined default media (DDM) consisting of DMEM/F-12 supplemented with 1X N-2 supplement (Gibco, Thermo Fisher Scientific), 1X B-27 supplement (Gibco, Thermo Fisher Scientific), bovine albumin fraction V 7.5% (Gibco, Thermo Fisher Scientific), 1X MEM non-essential amino acids (Gibco, Thermo Fisher Scientific), 1 mM sodium pyruvate (Gibco, Thermo Fisher Scientific), 100 μM β-mercaptoethanol (Gibco, Thermo Fisher Scientific), and 100 ng/mL human recombinant Noggin (Stem Cell Technologies) with a daily medium change [47,48]. After 16 days, the medium was changed to DDM, supplemented with B-27 supplement (Gibco, Thermo Fisher Scientific) without recombinant Noggin. After 24 days, cells were dissociated and plated into poly-ornithine/laminin-coated wells. Five to seven days after dissociation, half of the medium was replaced with neurobasal (Gibco, Thermo Fisher Scientific) supplemented with 1X B-27 supplement (Gibco, Thermo Fisher Scientific) and 2 mM glutamine (Gibco, Thermo Fisher Scientific). Forebrain-specific organoids were generated using established protocols as previously described [46,49]. Human iPSC colonies were detached from the MEF feeder layer with 1 mg/mL collagenase treatment for 1 h and suspended in embryonic body (EB) medium, consisting of FGF-2-free iPSC medium supplemented with 2 μM dorsomorphin (MilliporeSigma, Burlington, MA, USA) and 2 μM A-83 (Tocris Bioscience, Bristol, UK) in non-treated polystyrene plates for 4 days with a daily medium change. On days 5–6, half of the medium was replaced with induction medium consisting of DMEM/F-12, 1X N-2 supplement (Gibco, Thermo Fisher Scientific), 10 μg/mL heparin (MilliporeSigma) 1X penicillin/streptomycin, 1X MEM non-essential amino acids (Gibco, Thermo Fisher Scientific), 1X GlutaMAX (Gibco, Thermo Fisher Scientific), 4 ng/mL WNT-3A (R&D Systems, Minneapolis, MN, USA), 1 μM CHIR99021 (Tocris Bioscience), and 1 μM SB-431542 (Tocris Bioscience). On day 7, organoids were embedded in Matrigel (BD Biosciences) and grown in the induction medium for 6 more days. On day 14, embedded organoids were mechanically dissociated from Matrigel by pipetting onto the plate with a 5 mL pipette tip. Typically, 10–20 organoids were transferred to each well of a 12-well spinning bioreactor (SpinΩ) containing differentiation medium, consisting of DMEM/F-12, 1X N-2, and B-27 supplements (Gibco, Thermo Fisher Scientific), 1X penicillin/streptomycin, 100 μM β-mercaptoethanol (Gibco, Thermo Fisher Scientific), 1X MEM non-essential amino acids (Gibco, Thermo Fisher Scientific), and 2.5 μg/mL insulin (MilliporeSigma). Media was changed every other day. Protein synthesis assays were performed as previously described using the surface sensing of translation (SUnSET) technique [42,50]. Briefly, cells (80,000/well) were seeded on 12-multiwell plate wells and incubated with 5 µg/mL puromycin (Merck, Darmstadt, Germany) for 30 min, chased with fresh complete medium for 15 min, and then lysed. Cell lysates were analyzed for puromycin incorporation by Western blotting using a specific antibody against puromycin (PMY-2A4, DSHB, Iowa City, IA, USA). Coomassie staining of total proteins was used as a loading control. For protein precipitation, 1.5 volumes of saturated ammonium sulfate (according to [51]) was added to the cell media for protein extraction. Proteins were precipitated by centrifugation at max speed for 20 min, and the pellet was resuspended in Laemmli buffer. TAT-Pro ADAM10709−729 and TAT-Ala ADAM10709−729 peptides were produced by Peptide 2.0 Inc. (https://www.peptide2.com/) and resuspended in sterile H2O. Cells were treated with 20 µM TAT-Pro or TAT-Ala peptide, added to the medium. After 18 h, protein extracts were prepared from the collected cell medium. Standard methodologies were used. Protein extracts were separated by 10% or 8% SDS-PAGE and transferred to a PVDF membrane. Membranes were incubated using the following specific antibodies, including mouse anti-puromycin (1:500, DSHB), mouse anti-Vinculin (1:2000, Merck), mouse anti-GAPDH (1:2000, Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA), rabbit anti-APP (1:2000, Merck), rabbit anti-ADAM10 (1:500, Abcam, Cambridge, UK), mouse anti-sAPPα (1:500, IBL America, Minneapolis, MN, USA), rabbit anti-OCT3/4 (1:1000, Santa Cruz Biotechnology, Dallas, TX, USA), mouse anti-MAP2 (1:2000, Merck), mouse anti-Nestin (1:1000 Santa Cruz Biotechnology), mouse anti-SAP97 (1:1000, ENZO Life Sciences, Farmingdale, NY, USA) and rabbit anti-FMRP (1:1000, produced in house PZ1 [52]), HRP-conjugated anti-rabbit and anti-mouse secondary antibodies (1:5000, Cell Signaling Technology, Danvers, MA, USA). Proteins were revealed using an enhanced chemiluminescence kit (Bio-Rad, Hercules, CA, USA) and the imaging system LAS-4000 mini (GE Healthcare, Chicago, IL, USA). Quantification was performed using the IQ ImageQuant TL software (GE Healthcare). Detection of GAPDH, Vinculin, and Coomassie staining were used as normalizers. For all SDS-PAGE PageRuler™ Plus Prestained Protein Ladder (10 to 250 kDa, Thermo Fisher Scientific) was used. Total RNA was extracted with TRIzol according to the manufacturer’s protocol (Invitrogen, Thermo Fischer Scientific). For the synthesis of cDNA, 500 ng of total RNA was used. mRNAs were quantified by real-time PCR using SYBR® Green Master Mix (Bio-Rad) on StepOnePlus™ Real-Time PCR machine (Applied Biosystems, Thermo Fischer Scientific, Waltham, MA, USA) according to the manufacturer’s instructions using specific primers. mRNA levels were expressed as relative abundance compared to HPRT1 and GAPDH mRNAs using the (2−ΔΔCT) method. The primers used for the amplification of the selected human genes are: hFMR1 Forward 5′-TGT CAG ATT CCC ACC TCC TG-3′ hFMR1 Reverse 5′-TAA CCA CCA ACA GCA AGG CT-3′ hHPRT1 Forward 5′-TGC TGA GGA TTT GGA AAG GGT-3′ hHPRT1 Reverse 5′-TCG AGC AAG ACG TTC AGT CC-3′ hGAPDH Forward 5′-CTC AAC TAC ATG GTT TAC ATG-3′ hGAPDH Reverse 5′-CCA TTG ATG ACA AGC TTC CCG-3′ Sample size calculation was performed based on the level of sAPPα in fibroblasts measured in a preliminary study. We determined the need for a sample size of at least n = 16/TDI and n = 28/FXS with a power of 80%, alpha = 0.05, and effect size d = 0.92. The analysis was performed using G*Power 3 [53]. Statistical analysis was performed with Prism GraphPad version 5.0. Data distribution was tested for normality using the Kolmogorov–Smirnov test. Non-normally distributed data were analyzed through non-parametric tests. The significance level was established at p < 0.05. Differences between the two groups were analyzed using an unpaired Mann–Whitney test. The correlation was assessed by Spearman’s correlation test. Two-way ANOVA without repeated measures, followed by Sidak’s multiple comparisons test, was performed to examine the effect of genotype and treatment and their interaction. All data are expressed as mean ± SEM and as fold change relative to TDI. Previous work in mice revealed that impaired processing of APP leads to excessive production of sAPPα, at a critical developmental period (Post-natal day 21, P21), contributing to molecular, cellular, and behavioral FXS phenotypes [18,20,40]. An overall increase in APP, ADAM10, and sAPPα was previously reported in a small sample of FXS fibroblasts compared to controls [20]. Here we addressed the contribution of age and cell type specificity to the dysregulation of APP processing in human cells derived from FXS individuals. Specifically, we analyzed APP metabolism in cells derived from a large cohort of FXS subjects with different ages (n = 32; age range 6–69 years) and typically developing individuals (TDI n = 19; age range 5–57 years) (Figure 1). FXS individuals and TDI were subdivided in three different groups: group 1 with an age below 20 years (TDI n = 10; FXS n = 10); group 2 between 20 and 30 years (TDI n = 5; FXS n = 11); group 3 above 30 years (TDI n = 4; FXS n = 11). The expression of APP, the α-secretase ADAM10 and, as control, FMRP was analyzed in cellular extracts, while the release of sAPPα was assessed in cell media. Increased levels of sAPPα and ADAM10 were observed only in FXS individuals belonging to groups 1 and 2 (<30 years) (Figure 1A,B) compared to age-matched controls, whereas no significant differences were detected in group 3 (>30 years) (Figure 1C). The level of full-length APP remained elevated in all FXS conditions, independently of age as in [20]. In conclusion, the dysregulation of APP processing is age-dependent and in FXS primary somatic cells appears during the first three decades of postnatal life. Next, we investigated whether the dysregulation of APP processing was associated with disease severity. Based on Vineland adaptive behavioral scale (VABS) scores available for 18 FXS individuals [42] (see details in Material and Methods and Table S1) we analyzed correlations between the levels of sAPPα and the scores in four VABS main domains, namely daily living, communication, adaptive behavior, and socialization. Data on motor skills were available only for a few individuals and were not included in the analysis. We found that sAPPα levels negatively correlated with daily living score for the entire cohort, while no significant correlation was detected with other VABS domains (Figure 2). Previous work showed that the TAT-Pro ADAM10709–729 peptide (TAT-Pro) blocks the interaction of ADAM10 with the synapse-associated protein 97 (SAP97), thereby reducing ADAM10 localization at the cell surface [54,55,56] (Figure 3A). In addition, we have previously shown that the modulation of ADAM10 activity and APP processing with TAT-Pro peptide restored excessive protein synthesis and rescued key behavioral deficits in Fmr1 KO mice [20]. Here we found that SAP97 expression in human fibroblasts is significantly increased in FXS fibroblasts compared to TDI (TDI n = 8; FXS n = 8: Figure 3B), further supporting the relevance of the SAP97-ADAM10 interaction in FXS neuronal and non-neuronal cells. Next, we investigated the effects of the TAT-Pro peptide in FXS fibroblasts. To define optimal conditions for TAT-Pro peptide treatment, a FXS fibroblast cell line secreting a high level of sAPPα (ID: 94E0363 described in Table S1 and [42]) was treated with different concentrations of peptide (5, 10, and 20 µM) and the amount of sAPPα released in the cell media was measured by Western blot at different time points, i.e., 6, 12 and 18 h. Optimal reduction of released sAPPα was obtained after a treatment with 20 μM TAT-Pro peptide for 18 h (Figure S2). Fibroblasts derived from 10 FXS subjects and 6 TDI were treated with control (TAT-Ala) or specific (TAT-Pro) peptides. TAT-Pro peptide treatment caused an significant reduction of sAPPα release in FXS fibroblasts, while no differences were observed in the control group (Figure 3C), suggesting a specific effect on FXS cells with altered APP processing. Since sAPPα levels affect brain protein synthesis [20,57,58,59,60,61], we analyzed the level of mRNA translation in fibroblasts upon treatment with TAT-Pro peptide. Considering that not all FXS individuals exhibit increased protein synthesis and that such variability does not appear to be age-dependent [42,62], we stratified FXS fibroblasts according to their rate of protein synthesis. Fibroblast lines showing 50% more puromycin incorporation than the average in TDI cell lines were classified as “high protein synthesis”. Remarkably, the treatment of FXS cells with TAT-Pro peptide decreased protein synthesis to levels comparable to control cells—specifically in the subset of patients with a higher translation rate (n = 5) (Figure 3D). No significant effects were observed in the subgroup of FXS individuals with levels of mRNA translation comparable to controls (n = 5) or in the control (TDI) group itself (n = 6) (Figure 3D). Overall, these findings show the specificity of action of TAT-Pro peptide on a well-defined subgroup of FXS individuals with possible implications for therapy. Fibroblasts are well suited to age-dependent studies, since they retain the epigenetic imprinting of gene expression based on donor’s age [63,64,65]. However, addressing the role of sAPPα in patient-derived cellular models, such as neurons differentiated from iPSCs and forebrain organoids, represent a necessary step forward to validate the relevance of this pathway in FXS. Cortical neurons derived from FXS and control iPSCs were obtained using a well-established protocol [47]. Neuronal differentiation was monitored following the cellular morphology and evaluating the expression of specific pluripotency and neuronal markers (Figure 4A,B). During neural differentiation (day 0 iPSCs, day 6, day 24 neural precursor cells (NPCs) and day 60 neurons), a gradual reduction of the pluripotency marker OCT3/4 and a progressive appearance of cortical neural progenitor (Nestin) and neuronal (MAP2) markers were observed (Figure 4B). The release of sAPPα was analyzed in vitro on different days after neuronal differentiation in cells derived from 3 FXS and 3 TDI. Protein levels were measured at specific stages: iPSCs (day 0), neural precursors cells (NPCs) (day 19 and day 24), and neurons (day 60) (Figure 4C–F). A significant increase in sAPPα release was observed in the media of FXS NPCs at day 24 compared to control media, while no significant genotype-dependent difference was detected in iPSCs, NPCs at day 19, or mature neurons (Figure 4C–F). Finally, we evaluated APP processing in human forebrain organoids derived from TDI and FXS. APP and sAPPα expression were analyzed at two different developmental stages—day 30 and day 69—in both TDI and FXS human forebrain organoids. While APP expression was increased in FXS organoids regardless of the developmental stage, the levels of sAPPα were specifically upregulated in the early phase of forebrain development (Figure S3), consistent with the results obtained in the 2D stem cell model (Figure 4). Several FXS clinical and behavioral phenotypes, such as attention and social deficits, aggressive behavior, and brain structural abnormalities, undergo considerable changes during development [7,66,67,68,69,70,71,72,73]. Therefore, a better understanding of the time window during which the dysregulation of specific molecular pathways occurs might help to design more precise therapeutic interventions. Here, we demonstrated that the dysregulation of APP processing occurs in an age-dependent manner in three different human FXS cellular models and that such dysregulation can be targeted by using a specific cell-permeable peptide. Particularly, we observed increased levels of released sAPPα in fibroblasts derived from young FXS individuals, iPSC-derived NPCs, and early-stage forebrain organoids. Age-dependent dysregulation of APP processing in FXS fibroblasts and NPCs. sAPPα plays a key role in processes that are crucial for proper brain structure and function, such as synaptogenesis, synaptic plasticity, protein synthesis, and ultimately, memory formation [28,34,35,60,61,74,75]. The dysregulated release of sAPPα may affect critical neuronal functions and, ultimately, lead to neurodevelopmental defects. Consistent with this proposed mechanism, increased sAPPα levels have been found in the plasma of pediatric FXS subjects [38] and in juvenile Fmr1 KO mice [20]. In contrast, increased Aβ levels have been observed in plasma and post-mortem brain samples of adult FXS individuals and in adult Fmr1 KO mice [18,20,37,76], supporting the development-dependent dysregulation of APP processing in FXS. Of note, high levels of sAPPα have also been reported in the plasma of juvenile idiopathic autistic patients [37,38,39,77,78,79,80,81], in ASD individuals with severe clinical manifestations [78,79,80] and subjects with Angelman syndrome [82], suggesting that sAPPα may play a critical role in the pathogenesis of different neurodevelopmental disorders. The reprogramming and differentiation of patient-derived cells into neurons allows investigators to model/duplicate some of the cellular and molecular features of neurodevelopmental disorders [83,84,85]. Neurons and brain organoids derived from FXS iPSCs recapitulate several cellular pathological aspects reported in the murine model, such as deficits in neurite initiation and outgrowth, increased protein synthesis, neuronal hyperactivity, and deficits in action potential firing and spontaneous synaptic activity [46,86,87,88,89,90,91,92,93,94,95,96]. Moreover, FXS NPCs showed increased proliferation and protein synthesis [46,86,89,96], and several studies demonstrated the involvement of sAPPα in the proliferation and differentiation of murine NPCs [97,98,99,100]. In addition, a study performed on iPSCs derived from TDI reported that non-amyloidogenic processing of APP occurs predominantly at the early stages of neurogenesis [101]. sAPPα, therefore, plays an important role in the initial step of neuronal induction for proper brain development. Here, we observed an excess of sAPPα release in FXS NPCs derived by iPSCs in both 2D and 3D cellular models, which may underlie some of the pathological FXS phenotypes observed in NPCs [46,86,96] and the long-lasting defects reported in the mature neurons [90]. Contribution of sAPPα to FXS clinical manifestations. Although the number of individuals with FXS included in our analysis is limited, our findings showing a negative correlation between the levels of sAPPα and Vineland scale scores, -- specifically, the daily living score --suggest that the levels of sAPPα may be predictive of the clinical outcome. We based the present study on a heterogeneous group of FXS individuals from four cohorts from different countries (see Table S1) and used standardized methods for the molecular analyses. Additional multicentric studies that include a longitudinal follow-up will be valuable and necessary to further validate the presence of excessive sAPPα during a defined developmental window to consider sAPPα as a biomarker for FXS. Some FXS clinical features show changes in severity across ages; deficits in social behavior, for example, appear to improve during development [7,70,71,72]. It is tempting to hypothesize that age-dependent dysregulation of sAPPα could contribute to this clinical manifestation. In agreement with this, data generated in the mouse model of FXS showed that the age-dependent upregulation of sAPPα has an effect on a measure/aspect of social activity (nest building) that is rescued by decreasing the excess of sAPPα [20]. In addition, a clinical manifestation that tends to resolve over time is the presence of seizures [6,102]. Interestingly, seizures have also been reported in individuals with ASD [103,104] and with Angelman syndrome [105,106], which share with FXS the excessive sAPPα production [78,79,80,82]. TAT-Pro peptide treatment as a therapeutic strategy for FXS. Despite the efforts to understand the pathophysiology of FXS, to date, there is still no effective treatment for this disorder. Reduction of sAPPα levels by modulating its receptor activity and downstream-activated pathways might represent a valid approach to reducing the pathological effects of exaggerated sAPPα release. Nevertheless, no specific receptor for sAPPα has been currently identified. The postsynaptic α7 nicotinic acetylcholine receptor (nAChR) [107] and GABABR1a have been recently proposed as candidates for sAPPα receptors [108,109]. However, both receptors have different crucial functions in the brain and are not specific for sAPPα, therefore their modulation may have deleterious consequences in FXS [110]. The use of cell-permeable peptides represents a new promising therapeutic strategy for precise medicine, and several peptides are currently being tested in clinical trials [111,112,113,114,115]. ADAM10 activity can be modulated using a specific cell-permeable peptide, which is able to reduce ADAM10 localization to the membrane [55]. We previously demonstrated that modulation of ADAM10 activity using TAT-Pro peptide normalizes sAPPα levels in Fmr1 KO mice and ameliorates various molecular, synaptic, and behavioral defects, including exaggerated protein synthesis, enhanced mGluR-dependent long-term depression (LTD), maternal/social skills, memory, and hyperactivity [20]. In addition, the same TAT-Pro peptide treatment has been demonstrated effective in rescuing cognitive decline in a murine model for Huntington’s disease [56]. Here, we tested and demonstrated the validity of a peptide-based strategy in human FXS fibroblasts. Although the iPSC-derived NPCs and neurons represent a suitable tool for drug discovery [116,117], the reprogramming of adult somatic cells to the stem cell state seems to be independent of the age of the individuals [118]. In contrast, fibroblasts retain the epigenetic memory of the donor’s age [63,64,65], allowing the identification of subgroups of FXS that may benefit from the use of the TAT-Pro peptide. Aberrant mRNA translation represents one of the major hallmarks of FXS and, therefore, a putative therapeutic target [2,14,119]. While therapies aimed at rescuing protein synthesis have provided successful results in mice [120], similar approaches have failed in clinical trials, highlighting the difficulties of translating data obtained in murine models to the clinic [4,5,43,44,66,121]. Several factors may explain these failures, including the lack of patient stratification, varying ages of the enrolled FXS subjects, and the validity of the outcome measures [4,43,66]. In this context, we reported that the dysregulation of protein synthesis is observed only in a subset of patient-derived fibroblasts [42]. We observed a positive effect of the TAT-Pro peptide treatment on protein translation, specifically in FXS cells derived from children/adolescents with a high translation rate. Our findings supporting the hypothesis that targeting protein synthesis, based on patient’s stratification, may be a valid outcome measure in future clinical trials based on personalized medicine [4]. Nevertheless, the restoration of sAPPα levels upon peptide treatment should be carefully monitored over time to maintain sufficient sAPPα levels. Indeed, an excessive reduction of ADAM10 activity has been linked to learning deficits, altered spine morphology, defective synaptic functions, and increased formation of Aβ peptides in mice [122]. Furthermore, subchronic treatment of WT mice with the TAT-Pro peptide has been used to generate a model of sporadic AD that mimics the events occurring in the disease, including β-amyloid aggregate production [54]. Although TAT-Pro peptide is not currently used in clinical trials, our results in human cells, as well as its ability to cross the blood-brain barrier in vivo [20,56], support the possible application of TAT-Pro peptide as a new therapeutic approach for a subgroup of individuals with FXS. Overall, our study demonstrates an age-dependent regulation of APP metabolism in different FXS cellular models (fibroblasts, iPSCs, and brain organoids). Particularly, sAPPα is involved in the APP-mediated increase in protein synthesis in FXS, supporting the critical role of APP processing in the pathophysiology of FXS. This work identifies the early stages of childhood and adolescence in humans as the crucial time window for therapeutic intervention based on the restoration of protein homeostasis following the regulation of sAPPα release, with possible long-lasting effects. Our findings suggest that APP may therefore represent a new therapeutic target and/or biomarker for FXS and for other neurodevelopmental disorders and intellectual disabilities, such as ASD, that share with FXS the dysregulation of APP processing.

PMC10000966

Lina Che,Caixia Zhu,Lei Huang,Hui Xu,Xinmiao Ma,Xuegang Luo,Hongpeng He,Tongcun Zhang,Nan Wang

Ginsenoside Rg2 Promotes the Proliferation and Stemness Maintenance of Porcine Mesenchymal Stem Cells through Autophagy Induction

02-03-2023

mesenchymal stem cells,cultivated meat,ginsenoside Rg2,proliferation,replicative senescence,autophagy

Mesenchymal stem cells (MSCs) can be used as a cell source for cultivated meat production due to their adipose differentiation potential, but MSCs lose their stemness and undergo replicative senescence during expansion in vitro. Autophagy is an important mechanism for senescent cells to remove toxic substances. However, the role of autophagy in the replicative senescence of MSCs is controversial. Here, we evaluated the changes in autophagy in porcine MSCs (pMSCs) during long-term culture in vitro and identified a natural phytochemical, ginsenoside Rg2, that could stimulate pMSC proliferation. First, some typical senescence characteristics were observed in aged pMSCs, including decreased EdU-positive cells, increased senescence-associated beta-galactosidase activity, declined stemness-associated marker OCT4 expression, and enhanced P53 expression. Importantly, autophagic flux was impaired in aged pMSCs, suggesting deficient substrate clearance in aged pMSCs. Rg2 was found to promote the proliferation of pMSCs using MTT assay and EdU staining. In addition, Rg2 inhibited D-galactose-induced senescence and oxidative stress in pMSCs. Rg2 increased autophagic activity via the AMPK signaling pathway. Furthermore, long-term culture with Rg2 promoted the proliferation, inhibited the replicative senescence, and maintained the stemness of pMSCs. These results provide a potential strategy for porcine MSC expansion in vitro.

Ginsenoside Rg2 Promotes the Proliferation and Stemness Maintenance of Porcine Mesenchymal Stem Cells through Autophagy Induction Mesenchymal stem cells (MSCs) can be used as a cell source for cultivated meat production due to their adipose differentiation potential, but MSCs lose their stemness and undergo replicative senescence during expansion in vitro. Autophagy is an important mechanism for senescent cells to remove toxic substances. However, the role of autophagy in the replicative senescence of MSCs is controversial. Here, we evaluated the changes in autophagy in porcine MSCs (pMSCs) during long-term culture in vitro and identified a natural phytochemical, ginsenoside Rg2, that could stimulate pMSC proliferation. First, some typical senescence characteristics were observed in aged pMSCs, including decreased EdU-positive cells, increased senescence-associated beta-galactosidase activity, declined stemness-associated marker OCT4 expression, and enhanced P53 expression. Importantly, autophagic flux was impaired in aged pMSCs, suggesting deficient substrate clearance in aged pMSCs. Rg2 was found to promote the proliferation of pMSCs using MTT assay and EdU staining. In addition, Rg2 inhibited D-galactose-induced senescence and oxidative stress in pMSCs. Rg2 increased autophagic activity via the AMPK signaling pathway. Furthermore, long-term culture with Rg2 promoted the proliferation, inhibited the replicative senescence, and maintained the stemness of pMSCs. These results provide a potential strategy for porcine MSC expansion in vitro. With the growth of the world population and the economic development of developing countries, the demand for meat has increased rapidly [1,2,3]. It is estimated that by 2050, the global population will reach 9.5 billion [4]. To meet people’s demand for animal-based protein, the global meat production in 2050 is expected to increase to 169% of that in 2018 [5]. It is clear that traditional animal agriculture based on livestock and meat production methods cannot maintain the growth in the meat demand and will further exacerbate environmental stress. Recently, cultivated meat (CM), also known as in vitro meat, clean meat, cell-based meat, or cultured meat, was used as an alternative source of animal protein, providing a possible solution to these problems. In fact, meat is a set of complex muscle tissues, with a structure with specific characteristics and properties. Therefore, compared with the term “cultivated meat,” “food made with cultured animal cells” could describe this food more accurately at the current stage of development. Cultured animal cell food, as an important subfield of cellular agriculture, is produced in vitro using stem cells and tissue engineering, without sacrificing animals [6,7]. According to the ex ante life cycle assessment (LCA) of commercial-scale CM production in 2030 [8], compared to the traditional production of chicken, pork, and beef, it is estimated that industrialized CM production could reduce land use by 64%, 67%, and 55–90%, respectively. The carbon footprint of CM production is similar to that of chicken, which is significantly lower than that of pork and beef, and can be reduced by 43% and 67–92%, respectively. Food made with cultured animal cells is also beneficial to food security and animal welfare. The primary types of cell sources for cultured animal cell food production mainly include pluripotent stem cells, such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), and adult stem cells, such as myosatellite cells and mesenchymal stem cells (MSCs) [6,9,10]. MSCs from a variety of animals, including chicken, pig, and bovines, have been shown to differentiate into adipocytes [11] and myocytes [12,13], thus producing fat and muscle, respectively. MSCs can be obtained mainly from bone marrow and also from other tissues, including adipose, umbilical cord, and placental tissues. Recent studies have focused on the use of MSCs for food made with cultured animal cells [14,15,16]. Zagury et al. constructed a three-dimensional fat-rich, edible engineered tissue by loading bovine MSCs within alginate hydrogel [14]. Machour et al. reported a “print-and-grow” approach using κ-carrageenan-based microgels (CarGrow), which was expected to be used in the production of CM [15]. MSCs printed and grown within CarGrow exhibited higher viability and proliferation capability compared to the control group. Hanga et al. developed a strategy for the expansion of bovine MSCs based on microcarriers [16]. Stem cell harvesting is the basis of cultured animal cell food production, and therefore, long-term culture and amplification of cells in vitro are required to obtain enough cells. However, the proliferative capacity of MSCs in vitro is limited. Long-term culture of MSCs in vitro leads to loss of stemness [17], and the cells undergo replicative senescence [18,19], which is accompanied by a decline in the differentiation potential [20], DNA damage response [19], anti-oxidation ability [21], and immune regulation ability [22,23]. Similar to MSCs derived from humans and mice, MSCs derived from porcine bone marrow or adipose tissue also suffer from replicative senescence after long-term in vitro passaging [11,23,24]. Therefore, it is of positive significance to study the biological characteristics of porcine MSCs’ (pMSCs) in vitro expansion and explore possible effective strategies to promote proliferation, maintain stemness, and inhibit replicative senescence. Macroautophagy (hereafter referred to as autophagy) is a process that produces energy and macromolecular precursors for cellular renovation by degrading unnecessary or dysfunctional cell components, which is essential for maintaining cell, tissue, and organ homeostasis [25]. Activation of autophagy also helps to remove oxidized and damaged proteins and prevent the accumulation of toxic substances [26]. Aged MSCs are characterized by high levels of reactive oxygen species and accumulation of toxic or oxidized metabolites [27]. Recently, it has been reported that activation of autophagy can prevent radiation-induced ROS production and DNA damage in MSCs and therefore contributes to the preservation of stemness [28]. Additionally, blocking autophagy has been found to lead to ROS accumulation and stemness loss, suggesting that autophagy plays a crucial role in the maintenance of MSC stemness [28]. Similarly, Garcia-Prat et al. reported the important role of basal autophagy in preserving stemness in muscle satellite cells [29]. Compared to young quiescent satellite cells, autophagic activity in aged cells has been found to be impaired, while reactivation of autophagy could restore cellular stemness, rescue the proliferative defect, and reduce senescence [29]. However, the role of autophagy in the senescence of MSCs is still not fully understood, and results from the literature are controversial. For instance, autophagy has been reported to be activated in aged bone marrow MSCs (BMMSCs) due to the observed increase in autophagy-related gene expression [30]. Conversely, some recent investigations have stated that senescent BMMSCs have low or defective autophagy [31,32,33]. Therefore, it is imperative to explore the regulatory role of autophagy in porcine MSC stemness maintenance and senescence, which is critical for improving stem cell in vitro expansion. Ginsenoside Rg2 is a biactive natural component of ginseng. The contents of Rg2 in the root of red ginseng (RG) are reported to be from 0.6 mg/g [34] to 1.1 mg/g [35]. Fermentation of ginseng with Rhizopus oligosporus increases the contents of Rg2 from 0.85 mg/g to 2.05 mg/g [36]. Black ginseng fermented with Saccharomyces cerevisiae contains 2.86 μg/mL of Rg2 [37]. Rg2 has been shown to have a variety of pharmacological effects, including anti-oxidant [38], anti-inflammatory [38], anti-cancer [39], cardiovascular protection [40], and neuro-protection [41,42] activities. Our previous study confirmed that Rg2 can activate autophagy in multiple mouse tissues and effectively improve cognitive impairment in mice with Alzheimer disease [41]. Recently, the other two ginsenosides, Rg1 [43] and Rg3 [44], were found to increase human BMMSC proliferation and suppress senescence in vitro. Moreover, it is reported that Rg1 is also able to improve the proliferative capacity of hematopoietic stem cells [45] and neural stem cells [46]. However, the effect of ginsenoside Rg2 on the proliferation and senescence of MSCs is unclear. In this study, the senescence characteristics and autophagic activities of porcine MSCs during long-term culture in vitro were evaluated. Next, using a D-galactose (D-gal)-induced accelerated senescence model, we investigated the effect of ginsenoside Rg2 on the proliferation, senescence, and stemness of porcine MSCs and explored its potential mechanisms. Furthermore, whether Rg2 can stimulate the proliferation and maintain the stemness of porcine MSCs during long-term culture in vitro was also assessed. In this study, 1–3-day-old pigs were obtained from the Tianjin Fushengyuan livestock farm. Animal experiments were performed in accordance with the guidelines established by the Institutional Animal Care and Use Committee at Tianjin University of Science & Technology. pMSCs were isolated from the femur and tibia of pigs according to the reported method with minor modifications [47]. Briefly, the femur and tibia were retrieved and rinsed twice with phosphate-buffered saline (PBS) containing 3% penicillin/streptomycin. After both ends of the femur and tibia were cut, the marrow was flushed out by inserting a syringe needle into the cut surface and centrifuged for 5 min at 1000 rpm at room temperature. The cells were resuspended in Dulbecco’s modified Eagle’s medium/F12 (DMEM/F12; Gibco, New York, NY, USA) containing 10% fetal bovine serum (FBS; AusGeneX, Gold Coast, Australia) and cultured at 37 °C in a humidified atmosphere containing 5% CO2. Porcine MSCs were passaged with digestion with 0.25% trypsin containing 0.02% EDTA when they reached 80–90% confluence. Cellular morphology was observed and photographed using a phase-contrast microscope (Nikon Eclipse Ti, Nikon, Tokyo, Japan). To identify cellular surface immunophenotypes, porcine MSCs were digested and washed twice with PBS. The cells were labeled with antibodies against PerCP-CD45 (Cat.#: 642275; BD Biosciences, New York, NY, USA), APC-CD44 (Cat.#: 103011; BioLegend, San Diego, CA, USA), FITC-CD90 (Cat.#: 328107; BioLegend, CA, USA), and PE-CD34 (Cat.#: 343605; BioLegend, CA, USA) for 30 min. After washing twice with PBS, the labeled cells were analyzed using a flow cytometer (BD Biosciences, New York, NY, USA). To measure the intracellular reactive oxygen species (ROS) level, cells were incubated with 10 μM of DCFH-DA (Nanjing Jiancheng Biotechnology, Nanjing, China) at 37 °C for 1 h, followed by washing and resuspending with PBS. Fluorescence was analyzed via flow cytometry (BD Biosciences, New York, NY, USA) with excitation at 500 nm and emission at 525 nm. For adipogenic differentiation, porcine MSCs (1 × 105/well) at P4 were seeded in 6-well plates until they reached 70%–80% confluence. These cells were first cultured in adipogenic induction medium for 3 days and sequentially in maintenance medium for another 3 days. Next, the two media were replaced alternately until 21 days. Adipogenic induction medium is composed of DMEM/F-12 supplemented with 10% FBS, 10 μM of dexamethasone (Solarbio, Beijing, China), 200 μM of indomethacin (Solarbio, Beijing, China), and 10 μM of insulin (Solarbio, Beijing, China), while maintenance medium is composed of basal medium supplemented with 0.2 nM of insulin. After 21 days, the cells were fixed with 4% paraformaldehyde and then stained with oil red O (Solarbio, Beijing, China). For osteogenic differentiation, porcine MSCs (1 × 105/well) at P4 were seeded in 6-well plates until they reached 80–90% confluence. The medium was replaced with osteogenic induction medium. Osteogenic induction medium is composed of basal medium supplemented with 0.1 μM of dexamethasone (Solarbio, Beijing, China), 10 μM of β-glycerophosphate (Coolaber, Beijing, China), and 50 μM of vitamin C (Solarbio, Beijing, China). The media were changed every 2–3 days. After 21 days, the cells were fixed with 4% paraformaldehyde and then stained with alizarin red S (Solarbio, Beijing, China). To evaluate cellular senescence, β-gal activity was analyzed using a SA-β-Gal staining kit (Biyuntian, Beijing, China), following the manufacturer’s instructions. Briefly, pMSCs at P5, P10, and P15 were plated in a 6-well plate, fixed with fixative solution for 15 min at room temperature, and washed three times with PBS. The cells were incubated overnight with freshly prepared staining solution at 37 °C in the absence of CO2. After washing with 70% ethanol, the aging cells were dyed blue. The number of these blue cells was counted under a inverted phase-contrast microscope (Nikon, Tokyo, Japan). Cellular proliferation was detected according to the instructions of a Click-iT EdU (5-Ethynyl-2′-deoxyuridine) Cell Proliferation Kit (Meilunbio, Dalian, China). pMSCs at P5, P10, and P15 were plated in a 24-well plate and cultured overnight. For labeling cells with EdU, an equal volume of 2× EdU solution was added to the cells, and the cells were incubated at 37 °C for 2 h. The samples were then fixed and permeabilized. The nuclei were stained using the Hoechst 33342 (Meilunbio, Dalian, China) fluorescent stain. Digital images were acquired using a laser confocal microscope (OLYMPUS, Tokyo, Japan), and the number of EdU-positive cells were calculated using Image-Pro Plus 5.1 software (MEDIA CYBERNETICS, Silver Spring, MD, USA). EdU incorporation (the ratio of EdU-labeled cells to total cells) indicated the cellular proliferation rate. Total RNA was isolated from porcine MSCs using Trizol reagent (Invitrogen, Carlsbad, CA, USA), and reverse transcription of the RNA sample to cDNA was carried out using M-MLV reverse transcriptase (Promega, Madison, WI, USA). qRT-PCR was performed on a Applied Biosystems StepOneTM RT-PCR system (Applied Biosystems, Foster City, CA, USA) with the Fast SYBR1 Green Master Mix obtained from Applied Biosystems. Primers for each targeted mRNA were designed and are listed in Table 1. The 2−ΔΔCt method was used to calculate the relative expression levels of target genes, and GAPDH was used as an internal control. To monitor autophagic flux, pMSCs at P5, P10, and P15 were treated with 150 nM of bafilomycin A1 (Santa Cruz Biotechnology, Santa Cruz, CA, USA) for 2 h, and then, the protein samples were collected for LC3II detection. To assess the effect of Rg2 on cellular proliferation, Rg2 (Shanghai Yuanye Bio-Technology Co., Shanghai, China) was dissolved in DMSO and provided to the cells. pMSCs were treated with 25 μM, 50 μM, and 100 μM of Rg2 in DMEM/F-12 containing 1%, 5%, and 10% FBS for 24, 48, and 72 h, followed by MTT and EdU staining assays. To investigate the effect of Rg2 on D-gal-induced senescence, pMSCs were pre-treated for 24 h with 20 g/L of D-gal and then incubated with 100 μM of Rg2 in the presence/absence of D-gal for another 24 h, followed by MTT, EdU staining, SA-β-gal activity, and Western blot assays. pMSCs treated under different conditions were collected and then lysed with RIPA buffer along with PMSF protease inhibitor. The primary antibodies used for immunodetection included anti-OCT4 (Cat.#: AF0226; Affinity Biosciences, Changzhou, China), anti-p53 (Cat.#: AF0879; Affinity Biosciences, Changzhou, China; Cat.#: 10442-1-AP; Proteintech, Wuhan, China), anti-p62 (Cat.#: ab109012; Abcam, Cambridge, MA, USA), anti-LC3-I/II (Cat.#: NB100-2220; Novusbio, CO, USA), anti-p-AMPK (Cat.#: AF3423; Affinity Biosciences, Changzhou, China), anti-AMPK (Cat.#: sc74461; Santa Cruz Biotechnology, Santa Cruz, CA, USA), and anti-β-actin (Cat.#: sc8432; Signalway Antibody, Baltimore, MD, USA). The specific protein bands were visualized with the Odyssey Infrared Imaging System (LI-COR, Lincoln, Dearborn, MI, USA). The band density was analyzed using Image-Pro Plus 5.1 software (MEDIA CYBERNETICS, Silver Spring, MD, USA) using β-actin as an internal control and then normalized to the vehicle control. For the detection of cellular viability, pMSCs (5 × 103/well) were seeded in a 96-well plate with 100 μL of the medium, followed by MTT assay. The cells were treated with 5 mg/mL of 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyltetrazolium (MTT; Solarbio, Beijing, China) solution (10 μL per well) and then incubated for 4 h. The medium was then discarded, and 100 μL of dimethyl sulfoxide (DMSO) was added to each well. The absorbance of each well was measured using a Synergy 4 plate reader (Bioteck, Winooski, VT, USA) with a wavelength of 490 nm. Absorbance was directly proportional to the number of surviving cells. After pre-incubation with 20 g/L of D-gal for 24 h, pMSCs were incubated with 100 μM of Rg2 in the presence/absence of D-gal for another 24 h, and then, the cells were collected and lysed. The contents of MDA and the activities of SOD were detected using commercial available kits (Solarbio, Beijing, China) according to the manufacturer’s instructions. All data are shown as the mean ± SD, and all experiments were repeated at least three times. Statistical analysis was conducted using Microsoft Excel and GraphPad Prism 6. Two-tailed, unpaired Student’s t-tests were performed to determine statistical significance when comparing two groups, and one-way ANOVA followed by a Dunnett multiple-comparison test was used when comparing more than two groups. A p-value of <0.05 was considered statistically significant. The primary cells isolated from porcine bone marrow were cultured in basic medium for 12 h and adhered to the wall. After 3–5 days, the cells began to fuse, and the rate of cell fusion reached 65%–70% within 1 week. As shown in Figure 1A, porcine MSCs at P2 and P5 showed a spindle shape and strong proliferative capacity, while the cells after several passages gradually showed the characteristics of aging, such as a flat body, hypertrophy, and weak refraction. Almost all cells lost their ability of proliferation beyond passage 20. To identify the immunophenotypes of the primary cells isolated, cellular surface markers CD34, CD44, CD45, and CD90 were analyzed in the cells at P3 using flow cytometry. The isolated porcine MSCs were strongly positive for CD44 (96.27 ± 0.13%) and CD90 (98.79 ± 0.05%) but negative for the hematopoietic lineage markers CD34 (0.08 ± 0.02%) and CD45 (0.04 ± 0.01%); see Figure 1B. In addition, the multi-lineage differentiation ability of MSCs to adipocytes and osteoblasts was studied. Lipid droplets and positive oil red O staining were observed in the pMSCs exposed to adipogenic differentiation medium for 21 days (Figure 1C), while calcified nodules and positive alizarin red S staining appeared in the cells exposed to osteogenic differentiation medium (Figure 1D). These results indicated that the primary cells isolated from porcine bone marrow were mesenchymal stem cells. It is known that MSCs show reduced proliferation capacity and undergo replicative senescence with cellular expansion in vitro [48]. Here, some age-related changes were observed in pMSCs at P10 and P15 compared to the cells at P5. With an increase in the number of passages, the proportion of SA-β-gal-staining-positive cells significantly scaled up (Figure 2A,B), yet the proportion of EdU-positive cells decreased notably (Figure 2C,D). Next, we detected the mRNA levels of the stemness gene OCT4 and the proliferative marker Ki67 in pMSCs at P5, P10, and P15. The mRNA levels of OCT4 and Ki67 significantly decreased in pMSCs at higher passage numbers (Figure 2E,F). Consistent with the change in the OCT4 mRNA level, the protein level of OCT4 was downregulated in pMSCs at higher passage numbers (Figure 2G,H and Figure S1). Furthermore, a prominent increase in the protein level of the aging-related marker p53 was observed in pMSCs at P10 and P15 compared to the counterparts at P5 (Figure 2G,I and Figure S1). The relationship between MSC senescence and autophagy remains unclear and debatable [26]. To investigate the relationship between the autophagy and replicative senescence of pMSCs, the expression of microtubule-associated protein 1 light chain 3 (LC3) and cargo protein SQSTM1/p62 was tested in pMSCs at P5, P10, and P15 using Western blot. As shown in Figure 3A–C and Figure S2, the relative protein levels of LC3-II and P62 in pBMSC significantly increased in aged pMSCs (P10 and P15) compared to young cells (P5). The increase in LC3-II indicates the combined results of increased autophagosome synthesis (activated autophagy induction) or suppressed autophagosome degradation (blockage of autophagic flux), while the accumulation of P62 indicates suppressed autophagic flux. To further distinguish between these two possibilities, BafA1 was used to block autophagosome–lysosome fusion (Figure 3D). Treatment with BafA1 for 2 h resulted in a noticeable accumulation of LC3-II in young pMSCs at P5, suggesting activated autophagic flux. Compared with BafA1-treated cells at P5, BafA1-treated pMSCs at P10 exhibited a further increase in LC3-II levels (Figure 3E,F and Figure S2), suggesting that in the early stages of aging, pMSCs can promote autophagy induction to remove toxic substrates. Although increased autophagosome synthesis was observed in pMSCs at P10, the accumulation of P62 in these cells (Figure 3A and Figure S2) suggested a defect in the later stages of autophagy (a potential inhibition of autophagosome degradation). Combined with markedly enhanced P62 levels, these results illustrate that senescent pMSCs activate autophagy at an early stage in response to oxidative stress, but the weakened autophagic flux makes it insufficient for them to completely remove toxic substances. Importantly, there was no significant difference in LC3-II levels between P5 and P15 pMSCs along with BafA1, but a profound increase in LC3-II levels was observed in P15 pMSCs without BafA1, compared to P5 cells, indicating that in the late stages of aging, autophagic flux is further impaired in pMSCs (Figure 3E,F and Figure S2). Oxidative stress can cause oxidative damage to organelles and proteins, leading to cell senescence [49]. Correspondingly, we detected the ROS levels in pMSCs at P5, P10, and P15 using flow cytometry. As shown in Figure 3G,H, compared with young cells at P5, aged pMSCs at P10 and P15 showed a marked increase in ROS levels. These results further indicate the attenuated ability of senescent cells to scavenge ROS. To evaluate the stimulatory effect of ginsenoside Rg2 on the proliferation of porcine MSCs, cells at P6 were treated with different concentrations of Rg2 (25, 50, and 100 μM) in DMEM/F12 containing 1%, 5%, and 10% FBS for 24 h, 48 h, and 72 h, and then, MTT assay was carried out. Our data showed that ginsenoside Rg2 at a concentration of 25–100 μM exhibits no cytotoxicity and that cellular viability increased remarkably with increasing Rg2 concentration (Figure 4A–C). In addition, 100 μM of Rg2 showed the most significant proliferative effect under the condition of 1% serum (Figure 4A). Furthermore, the number of EdU-staining-positive cells markedly increased in pMSCs treated with 50 and 100 μM of Rg2 (Figure 4D,E). Our data showed that Rg2 can promote the proliferation of pMSCs in a concentration- and time-dependent manner. Next, the anti-senescence effect of Rg2 was assessed using a model of accelerated aging induced by D-gal. After pre-incubation with 20 g/L of D-gal for 24 h, pMSCs were treated with 100 μM of Rg2 in the presence/absence of D-gal for another 24 h and subsequently subjected to MTT, EdU staining, and SA-β-gal staining assays. Consistent with a previous study [50], we found that D-gal treatment significantly inhibited cell viability (Figure 5A), reduced the numbers of EdU-positive cells (Figure 5D,E) and increased the percentage of SA-β-gal-positive cells (Figure 5B,C). These changes mediated by D-gal were reversed by the administration of 100 μM of Rg2 (Figure 5A–E). Moreover, decreased OCT4 levels induced by D-gal were rescued by the addition of Rg2 (Figure 5F,G and Figure S3), suggesting that Rg2 can contribute to the maintenance of pMSC stemness. Similarly, Rg2 significantly inhibited the D-gal-caused increase in the protein expression of P53 in pMSCs (Figure 5F,H and Figure S3). These results indicated that treatment with Rg2 effectively prevents the pro-senescence effects of D-gal on pMSCs. To determine whether Rg2 can delay the senescence of pMSCs by reducing ROS levels, intracellular ROS levels were assessed in Rg2-treated pMSCs using flow cytometry. D-gal treatment markedly stimulated the production of ROS in pMSCs, whereas the effect was attenuated by the administration of Rg2 (Figure 6A,B). Furthermore, MDA contents and SOD activities were detected in Rg2-treated pMSCs. The addition of Rg2 dramatically inhibited the D-gal-stimulated increase in MDA contents (Figure 6C). SOD activities were significantly downregulated in D-gal-stimulated pMSCs, while Rg2 treatment partly reversed the D-gal-induced reduction in SOD activities (Figure 6D). These results indicated that Rg2 prevents the senescence of pMSCs by increasing SOD activities and reducing ROS and MDA levels. To demonstrate whether the positive effect of Rg2 on the anti-senescence and stemness maintenance of pMSCs is related to autophagy induction, the protein expression of LC3 and P62 was tested in Rg2-treated pMSCs with/without D-gal using Western blot. Compared with the D-gal group, Rg2-treated cells showed increased LC3II expression and reduced P62 levels, indicating the activation of autophagy (Figure 7A–C and Figure S4). Our previous study confirmed that Rg2 can activate autophagy in multiple types of cells via the AMPK signaling pathway [41], but it is unknown whether Rg2 can activate the AMPK signaling pathway in porcine MSCs. Thus, we detected the expression of p-AMPK and AMPK in Rg2-treated pMSCs with/without D-gal using Western blot. As shown in Figure 7D,E and Figure S4, the relative protein level of p-AMPK/AMPK significantly increased in the Rg2 group compared to the D-gal group. These results further confirmed that autophagy activated by Rg2 can play a critical role in the anti-senescence and stemness maintenance of pMSCs via the AMPK signaling pathway. As Rg2 could maintain the stemness of pMSCs and stimulate proliferation, we next checked the effects of Rg2 on pMSCs during long-term culture. First, we checked the protein expression of OCT4 and P53 in pMSCs at P5, P10, and P15 in the presence/absence of Rg2. The protein expression of OCT4 significantly decreased in pMSCs at higher passage numbers, whether in Rg2-treated pMSCs or in cells without Rg2 (Figure 8A,B and Figure S5). However, higher OCT4 protein expression was observed in Rg2-treated cells compared to the control group. Similarly, Rg2 treatment also resulted in low expression of P53 protein (Figure 8A,C and Figure S5). Consistent with these results, the percentage of EdU-positive cells remarkably decreased in pMSCs at higher passage numbers, whereas the administration of Rg2 upregulated a percentage of EdU-positive cells (Figure 8D,E). Meanwhile, we found that the administration of Rg2 downregulated the numbers of SA-β-gal-positive cells (Figure 8D,F). Taking together, long-term culture of pMSCs with Rg2 can help maintain stemness and promote proliferation, as well as inhibit aging. MSCs have the potential of self-renewal and multi-directional differentiation, including adipocytes and muscle cells [11,51,52,53,54], and thus are considered one of the most advantageous seed cells for cultured animal cell food [55]. However, the replicative senescence of porcine MSCs during in vitro expansion limits their application in the large-scale industrial production of cultured animal cell food [56]. Therefore, it is of great significance to explore an effective method to promote the proliferation and delay the senescence of porcine MSCs. An increasing amount of evidence indicates that basal autophagy serves as a key mechanism to regulate the proliferation, differentiation, and stemness maintenance of adult stem cells, including MSCs [57,58], muscle stem cells (MuSCs) [29], and hematopoietic stem cells (HSCs) [59]. Human MSCs have been demonstrated to possess constitutive autophagic flux due to the observed LC3 conversion (LC3-I to LC3-II) [57,58]. Accumulation of cellular damage during senescence activates stem cell autophagic flux to remove toxic material and maintain their stemness. Emerging evidence has revealed that the activation of autophagy can eliminate ROS and oxidative proteins in aged MSCs, thus keeping their stemness and genomic integrity [28,60,61]. The role of autophagy in MSC aging seems puzzling due to some contrary reports. Zheng et al. observed the increased expression of autophagy-related protein, including LC3-II, ATG7, and ATG12, in aging rat MSCs, thus considering that autophagy is activated during cellular senescence [30]. However, the increase in LC3-II is the result of the combination of autophagosome formation and blockage of autophagic degradation. Thus, it is necessary to analyze autophagic flux by blocking autophagy with bafilomycin A1. Contrary to activated autophagy in aged MSCs [30], more studies support that autophagy activity is impaired during aging [31,32,33]. Compared with young BMMSCs, aged cells showed reduced expression of Atg7, Beclin1, and LC3II/I and the accumulation of P62, as well as fewer autophagosomes [32]. After chloroquine (CQ) treatment, young BMMSCs possessed more LC3 dots compared to aged cells, indicating that aged BMMSCs might be characterized by impaired autophagy [32]. In addition, autophagy markedly decreased in aged BMMSCs under normoxic and hypoxic conditions [31]. Here, we found that although the expression of LC3II increased in aged pMSCs compared to young counterparts, p62 proteins accumulated, suggesting the potential blockage of autophagic flux. Accordingly, the number of autophagosomes first increased and then decreased during pMSC senescence, confirmed by the addition of bafilomycin A1. Autophagic flux is significantly impaired due to the blockage of autophagic degradation in P15 pMSCs compared with P5 cells. These results indicate that in the early stage of senescence, pMSCs need to activate autophagy in response to oxidative stress and damaged proteins, while in the late stage of senescence, cells display a decline in autophagy function, thus leading to reduced clearance ability. In line with the impaired ROS clearance during senescence, increased ROS levels were observed in aged pMSCs. Our data indicate that the ability of aged pMSCs to remove toxic substrates might be defective. The activation of autophagy could protect MSCs from oxidative stress, thus resisting aging and promoting proliferation. The autophagic agonist rapamycin has been reported to alleviate the senescent features of aged MSCs [32,62]. Hypoxia can promote the self-renewal and proliferation of MSCs by activating autophagy [63,64]. Contrarily, the inhibition of autophagy could promote aging in MSCs. The autophagic inhibitor 3-methyladenine (3-MA) aggravates the aging of MSCs [32,62]. It is reported that blocking autophagy with kynurenine accelerates senescence in mice BMMSCs via the aryl hydrocarbon receptor pathway [65]. Our previous study confirmed the positive effect of ginsenoside Rg2 on autophagy induction [41]. However, it is not clear whether Rg2 has a retarding effect on MSC aging. Here, we demonstrated that Rg2 promotes the proliferation of porcine MSCs and slows down senescence by activating autophagy. Similar to our results, a number of natural and synthetic compounds that can activate autophagy have been demonstrated to inhibit the senescence of MSCs and increase their proliferative potential [62,66,67]. Autophagy induced by curcumin protects canine BMMSCs against replicative senescence during in vitro expansion, defined by the increased colony-forming unit–fibroblastic (CFU-F) capacity and decreased SA-β-gal activities [62]. A combination of 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), an AMPK activator, and nicotinamide (NAM), an activator of sirtuin1 (SIRT1), showed the protective effect of anti-senescence and proliferation promotion in MSCs [61]. Camphorquinone [67] and licochalcone D [66] have been reported to be able to activate autophagy via the adenosine-monophosphate-activated protein kinase (AMPK) signal pathway, therefore alleviating the H2O2-induced senescence of human BMMSCs in vitro and also inhibiting D-gal-induced aging in mice in vivo. Reactive oxygen species are known to be important risk factors affecting the aging of mesenchymal stem cells [68,69,70]. It is known that ROS production increases with age, leading to oxidative DNA damage and decreased proliferation of stem cells [71]. D-gal induced accelerated senescence has been used as a conventional experimental model to study cell senescence [72,73]. Previous studies have also shown that D-gal can significantly induce senescence in MSCs by promoting ROS production [74]. Here, we also found that the percentage of SA-β-gal-positive cells significantly increased and the number of EdU-positive cells remarkably decreased in D-gal-treated pMSCs, whereas the changes were reversed with Rg2. Rg2 treatment also inhibited D-gal-induced upregulation of P53 expression and downregulation of OCT4 expression, suggesting that Rg2 prevents D-gal-mediated senescence in porcine MSCs. A recent report showed that Rg2 could delay D-gal-induced brain aging and recover impaired memory function in mice by increasing mitochondrial autophagy flux and relieving oxidative stress [42]. Similarly, ginsenoside Rg1 has been shown to have protective effects in multiple tissues of mice with D-gal-induced aging through attenuating oxidative stress [75,76,77]. Upregulating autophagy with by rapamycin has been shown to inhibit ROS generation and attenuate senescence caused by D-gal in rat BMMSCs [78]. Furthermore, our data demonstrated that Rg2 can protect porcine MSCs against the oxidative stress signal triggered by D-gal, as evidenced by the enhanced SOD activity and reduced MDA and ROS levels. This result was coincident with a previous finding that Rg2 effectively inhibits oleic acid and palmitic acid (OA&PA)-induced ROS generation in mouse primary hepatocytes [79]. The combined treatment of Rg2 and Rh1 has been found to significantly suppress LPS-induced excessive ROS accumulation in HepG2 cells [38]. One of the major regulators of autophagy is the adenosine-monophosphate-activated protein kinase (AMPK) signaling pathway [80]. AMPK can inhibit the activation of mammalian target of rapamycin (mTOR) through phosphorylating raptor, while mTOR functions as a critical negative regulator of autophagy by inhibiting Unc-51-like kinase 1 (ULK1) activation [81,82]. In addition, AMPK can trigger autophagy by directly phosphorylating ULK1 at multiple sites, such as S317, S467, and S777. [83,84]. The AMPK-mediated activation of autophagy has been reported to ameliorate D-gal-induced senescence in multiple tissues, including the heart [66,67], hippocampus [66,85,86], kidney [87], and skeletal muscle [88]. In human BMMSCs, licochalcone D or camphorquinone can induce autophagy and reduce H2O2-induced senescence via the AMPK signal pathway [66,67]. Rg2 has been reported to activate the AMPK signal in multiple cell lines, including 3T3-L1 preadipocytes [89], HepG2 cells [90], MCF-7 cells [39], Neuro2A cells [41], and PC12 cells [41]. Similarly, our data confirmed that pre-incubation with Rg2 significantly upregulates LC3-II expression and activates authophagy in D-gal-treated pMSCs via the AMPK signaling pathway. In addition, we found that 100 μM of Rg2 can significantly enhance the proliferative capacity of porcine MSCs and inhibit replicative senescence during long-term culture in vitro. A recent study focused on the positive effect of Rg2 on the proliferation of induced-pluripotent-stem-cell-derived endothelial cells (iPSC-ECs) for clinical application [91]. Similar to the concentration of Rg2 used in our study, 10–200 μM of Rg2 was found to remarkably upregulate the EdU-positive cellular number of iPSC-ECs after three passages [91]. Mechanically, the stimulatory effect of Rg2 on iPSC-EC proliferation depends on mTOR-independent AMPK/ULK1-mediated autophagy. Furthermore, two recent studies on the use of Rg2 in the development of functional foods reported that the working concentration of Rg2 in the cells is approximately 80 μM [35,89], which is similar to the concentration of Rg2 (25–100 μM) used in our study. Taken together, our findings suggest that in the early stage of senescence, pMSCs enhance autophagosome formation in respond to oxidative stress, while in the late stage, aged cells display impaired autophagic flux, thus leading to reduced clearance ability. Furthermore, ginsenoside Rg2 improves the longevity of porcine MSCs by inducing AMPK-mediated protective autophagy. Ginsenoside Rg2 may be an effective protector of MSC senescence induced by oxidative stress. These findings highlight the positive role of Rg2 in porcine MSC expansion in vitro.

PMC10000969

Jazmin Rivera,Laxman Gangwani,Subodh Kumar

Mitochondria Localized microRNAs: An Unexplored miRNA Niche in Alzheimer’s Disease and Aging

25-02-2023

mitochondrial miRNAs,Alzheimer’s disease,mitochondrial dysfunction,synaptic energy,aging

Mitochondria play several vital roles in the brain cells, especially in neurons to provide synaptic energy (ATP), Ca2+ homeostasis, Reactive Oxygen Species (ROS) production, apoptosis, mitophagy, axonal transport and neurotransmission. Mitochondrial dysfunction is a well-established phenomenon in the pathophysiology of many neurological diseases, including Alzheimer’s disease (AD). Amyloid-beta (Aβ) and Phosphorylated tau (p-tau) proteins cause the severe mitochondrial defects in AD. A newly discovered cellular niche of microRNAs (miRNAs), so-called mitochondrial-miRNAs (mito-miRs), has recently been explored in mitochondrial functions, cellular processes and in a few human diseases. The mitochondria localized miRNAs regulate local mitochondrial genes expression and are significantly involved in the modulation of mitochondrial proteins, and thereby in controlling mitochondrial function. Thus, mitochondrial miRNAs are crucial to maintaining mitochondrial integrity and for normal mitochondrial homeostasis. Mitochondrial dysfunction is well established in AD pathogenesis, but unfortunately mitochondria miRNAs and their precise roles have not yet been investigated in AD. Therefore, an urgent need exists to examine and decipher the critical roles of mitochondrial miRNAs in AD and in the aging process. The current perspective sheds light on the latest insights and future research directions on investigating the contribution of mitochondrial miRNAs in AD and aging.

Mitochondria Localized microRNAs: An Unexplored miRNA Niche in Alzheimer’s Disease and Aging Mitochondria play several vital roles in the brain cells, especially in neurons to provide synaptic energy (ATP), Ca2+ homeostasis, Reactive Oxygen Species (ROS) production, apoptosis, mitophagy, axonal transport and neurotransmission. Mitochondrial dysfunction is a well-established phenomenon in the pathophysiology of many neurological diseases, including Alzheimer’s disease (AD). Amyloid-beta (Aβ) and Phosphorylated tau (p-tau) proteins cause the severe mitochondrial defects in AD. A newly discovered cellular niche of microRNAs (miRNAs), so-called mitochondrial-miRNAs (mito-miRs), has recently been explored in mitochondrial functions, cellular processes and in a few human diseases. The mitochondria localized miRNAs regulate local mitochondrial genes expression and are significantly involved in the modulation of mitochondrial proteins, and thereby in controlling mitochondrial function. Thus, mitochondrial miRNAs are crucial to maintaining mitochondrial integrity and for normal mitochondrial homeostasis. Mitochondrial dysfunction is well established in AD pathogenesis, but unfortunately mitochondria miRNAs and their precise roles have not yet been investigated in AD. Therefore, an urgent need exists to examine and decipher the critical roles of mitochondrial miRNAs in AD and in the aging process. The current perspective sheds light on the latest insights and future research directions on investigating the contribution of mitochondrial miRNAs in AD and aging. Alzheimer’s disease (AD) is a progressive neurological disorder that affects approximately 50 million people worldwide [1]. Alzheimer’s disease is ranked as the seventh leading cause of death in the United States and is most associated with dementia among older adults (https://www.nia.nih.gov/health/alzheimers-disease-fact-sheet, accessed on 15 October 2022). Dementia refers to a loss of cognitive function, with a broad range of symptoms depending on the stage of diagnosis. Memory loss, an inability to carry out daily activities and a difficulty in organizing thoughts and thinking logically are symptoms associated with brain atrophy caused by AD. Researchers continue to unravel complex changes involving the AD brain. Alzheimer’s has been divided into familial and sporadic forms associated with the stages of the disease. According to studies, early-onset (familial) cases account for ~10% of all patients with AD, primarily affected individuals below 65 years of age. On the other hand, late-onset (sporadic) contributes up to 30% of all patients with AD developing before the age of 65 [2]. Certain AD cases are caused by inherited changes within genes. Early-onset AD has been linked to mutations on chromosome 19, entailing the apolipoprotein E (APOE) gene. However, a large body of research suggests that other genetic components may also be involved in the manifestation of AD, such as mutations in amyloid-beta precursor protein (AβPP), presenilin1, and presenilin 2 genes attributing to the overproduction of Aβ plaques. A group of proteins and peptides including transthyretin, calcitonin, gelsolin, amylin, atrial natriuretic peptide and amyloid-beta have been hallmarked as the fibrillar components of diseases that are characterized by amyloid deposits [2]. We recently conducted a meta-analysis study on the deregulated mitochondrial microRNAs (miRNAs) in AD [3]. We reviewed and proposed the potential roles of mitochondrial miRNAs in mitochondrial activities and synapse function. However, our meta-analysis study lacked information about brain mitochondria localized miRNAs and their relevance in AD. The main objective of the current article is to unveil the mitochondria localized and associated miRNAs and their critical role in maintaining the normal mitochondrial function and in AD pathogenesis. The mitochondrion is an important cellular component known for its role in bioenergetics, metabolism, signaling pathways and cell viability [4]. Mitochondria play several vital roles in the brain cells, especially in neurons to provide synaptic energy (ATP), Ca2+ handling, reactive oxygen species (ROS) production, apoptosis, mitophagy, axonal transport and neurotransmission [5,6,7]. It is established that a healthy pool of mitochondria provides necessary energy to the neurons for proper neuronal function and synaptic activity. The mitochondria also protect neurons from oxidative stress and free radical damages [5,6,7]. It is reported that mitochondrial dysfunction is a new hallmark of AD initiation and progression [8,9,10]. A number of mitochondrial abnormalities are reported in the AD brain, including disrupted mitochondrial bioenergetics, increased oxidative stress, mitochondrial genomic stress, abnormal mitochondrial fusion and fission, mitochondrial axonal trafficking deficits and abnormal mitochondrial distribution, impaired mitochondrial biogenesis, abnormal endoplasmic reticulum–mitochondrial interaction, impaired mitophagy and impaired mitochondrial proteostasis [5,6,7,8]. All these mitochondrial dysfunctions are caused by multiple biological, genetic, and environmental factors, including Aging, Aβ and p-tau toxicities, inflammation, miRNAs deregulation, gender differences and environmental toxins [3,8,9,10,11]. Mitochondrial dysfunction has a significant negative impact on synaptic activities in AD, such as impaired calcium signaling, reduced synaptic energy, defective neurotransmission, and synaptic dysfunction [5,6,7,8,9,10,11]. As mitochondrial genome disturbance is one of the contributing factors for mitochondrial dysfunction in AD, it is important to uncover the mitochondrial genome-associated miRNAs deregulation and their impact on mitochondrial and synaptic dysfunction in AD. MicroRNAs (miRNAs) are regulators of human genes, and their therapeutic relevance has been explored in human diseases, including AD [3]. MiRNAs are present throughout the cells and some miRNAs are localized to cellular organelles. Subcellular compartmentalization and localization of miRNAs, miRNA-induced silencing complex (miRISC) and target mRNA have been observed to localize in multiple subcellular compartments including mitochondria, nucleus, rough endoplasmic reticulum, processing (P)-bodies, early/late endosomes, multivesicular bodies, lysosomes and synaptosomes [3,12,13,14,15,16,17]. MiRNAs are small and noncoding RNAs that regulate gene expression through the process of binding to messenger RNA (mRNA) [3,16,17,18]. According to studies, miRNAs are attributed to the post-transcriptional regulation of mitochondrial gene expression and control mitochondrial activities [19]. MiRNAs are transcribed as double-stranded RNA, also known as pre-miRNA. On the other hand, mature miRNAs bind to Argonaute proteins that form the RNA-induced silencing complex ribonucleoprotein (RISC) [4]. Pre-miRNAs and mature miRNAs found in the mitochondria interfere with mitochondrial genome derived mRNA, which affects the mitochondria and cell function. Through complementary base pairing RISC binds to 3’-UTR, thus downregulating gene expression. The result of this process initiates mRNA degradation or translational repression affecting the production of protein levels [3,20]. MiRNAs found in the central nervous system play a role in the translation and degradation of genes. According to recent research, synaptic activity and function has been linked to miRNAs due to their ability to interact with mRNAs, resulting in physiological changes [16]. Specific mitochondrial miRNAs are miRNAs that are localized in the mitochondria. The presence of miRNA in mitochondria has only been discovered in the past decade in living organisms. Several studies have identified the presence of miRNAs in the mitochondria and their important roles in local mitochondrial protein synthesis, and in the regulation of mitochondrial functions [12,13,14,17]. Initially, in 2009, Kren et al., identified the rat-liver-derived mitochondrial miRNAs and unveiled their roles in apoptosis [12]. The five miRNAs—miR-130a, miR-130b, miR-140, miR-320 and miR-494—were identified as mitochondrial enriched miRNAs, and most of them are involved in the expression of genes associated with apoptosis, cell proliferation, and differentiation [12]. In 2011, Barrey et al. studied the miRNA localized in the mitochondria isolated from human skeletal primary muscular cells [17]. The three precursor miRNAs—pre-mir-302a, pre-let-7b and mir-365—were found to be localized in the mitochondria of human myoblasts [17]. In the same year, Bendiera et al. studied mitochondrial-enriched miRNAs in HeLa cells [14]. First, the author separated the mitochondrial and cytosolic fraction from the same samples of HeLa cells. Next, they performed the miRNAs analysis in both mitochondria and cytosolic fraction. A total of 57 miRNAs was found to be significantly deregulated in the mitochondria versus the cytosol. The three nuclear-encoded miRNAs signatures hsa-miR-494, hsa-miR-1275 and hsa-miR-1974 were found to be consistently upregulated in mitochondria [14]. Further, the author found the presence of Argonaute 2 protein in the mitochondria, which suggests that mitochondria miRNAs could modulate the expression of local mitochondrial proteins [14]. Therefore, the precise roles and therapeutic relevance of these mitochondria localized miRNAs are still unknown. Hence, despite all information and available research [3,20,21,22,23,24,25], mitochondrial miRNAs are still an unexplored niche in human diseases [22], neurological disorders. Alzheimer’s disease is tied to mitochondrial dysfunction, including overproduction of ROS, low ATP production and influx of calcium ion. The disruption of calcium homeostasis causes damage to the mitochondria, resulting in damage to synaptic dysfunction. Together, all these features contribute to the dysfunction of the mitochondria, leading to the progression of AD [2]. Furthermore, Aβ plaques and NFTs have been associated with impeding mitochondrial function increasing neuronal deficits, leading to neurodegeneration associated with neurological disorders such as AD [26]. Mitochondrial respiration defects are the characteristics found in the brains of patient with AD. Decreased neurological function, impairments and brain atrophy have been tied to the reduction in mitochondrial enzyme activity in the premorbid cognitive level [18]. MiR-338 is a brain-specific miRNA that has been shown to modulate the expression of cytochrome c oxidase IV (COXIV), a protein within the ETC which contributes to ATP production, in neuronal cells [27]. Moreover, the expression of miR-338 is correlated to the reduction in COXIV mRNA and reduction in protein levels. A study based on the control of mitochondrial activity by miRNAs revealed overexpression of miR-338 reduced mitochondrial oxygen consumption, metabolic activity, and ATP production [21]. MiRNAs mediated mitochondrial impairment decreases ATP production, alters calcium influx, and increases ROS production. A decrease in ROS concentration is essential for normal cell signaling; on the other hand, a high concentration of ROS damages macromolecules and increases the mutation rate of mitochondrial DNA (mtDNA). Studies have linked mitochondrial dysfunction with age and oxidative stress, contributing to neurodegenerative diseases [18]. Furthermore, quite a few specific miRNAs play important roles in mitochondrial function, as well as in various aspects of synaptic plasticity, including synaptotoxicity, synaptic activity and neurotransmission [28]. The miR-132, miR-34a, miR-484, miR-218, miR-455-3p, miR-34a and miR-212 are the potential miRNAs that were studied in mitochondria mediated synaptic functions (Table 1) [29]. For example, miR-132 downregulated in AD and involved in the regulation of PTEN, FOXO3a, P300, NOS1 and MMP- 9 genes thereby enhances neurotransmission and synaptic plasticity [30,31]. MiR-34a upregulated in AD causes synaptic plasticity dysfunction via the modulation of VAMP2, SYT1, HCN, NR2A and GLUR1 proteins [32]. Another study in 2019, Qian et al., provided evidence that the overexpression of miR-338 in mice is associated with neuropathology in AD. The results from in vitro cultured neurons showed an increase in NF-kB activity due to the regulation of miR-338 that might contribute to inflammatory states in patients with AD. Analysis of the lysates of hippocampal tissue from 6-month-old 5XFAD transgenic (TG) mice demonstrated that the transcription of miR-338 promoted the expression of BACE1, leading to an increase in Amyloid beta formation resulting in neuroinflammation and cognitive dysfunction [33]. Previous studies identified several miRNAs deregulated in AD brain, blood, serum, plasma, CSF and AD mouse model [3,15,16]. MiRNAs deregulation linked with AD in two ways; (i) the deregulation of miRNAs could be initiated by AD pathogenic factors such as Aβ, p-tau, inflammation, aging, oxidative stress, and mitochondrial DNA damage, and/or (ii) the genetic alteration of miRNAs could be a contributing factor in AD progression. In both ways, miRNAs significantly contribute to AD via the modulation of expression of disease associated genes/proteins and the regulation of cellular pathways either in a positive or negative way. Since normal mitochondrial function is crucial to control AD, we recently summarized the potential miRNAs; those are deregulated in AD and involved in several aspects of mitochondrial function such as mitochondrial biogenesis, dynamics, mitophagy, ATP production, oxidative stress and apoptosis, that ultimately lead to impaired synaptic function in AD [3]. Based on the miRNA’s location, association and pivotal roles, we categorized them as ‘mitochondrial localized miRNAs’ and ‘mitochondria associated miRNAs’ (Table 1). Mitochondria localized miRNAs are supposed to be present and expressed within mitochondria and regulate mitochondrial functions. While mitochondria associated miRNAs could be some common miRNAs, those significantly modulate mitochondrial function. Therefore, several miRNAs are identified in AD, those regulate the key mitochondrial functions; however, it is unclear if these miRNAs are expressed within the mitochondria and transcribed from mitochondrial genome, or what their impact is on the levels of local mitochondrial proteins. Important mitochondria localized miRNAs and mitochondria-associated miRNAs, their location and their cellular functions in human diseases are summarized in Table 1. Aging is a known factor that increases the progression of brain deterioration, causing epigenetic changes, protein damage and mitochondrial dysfunction, thus contributing to AD progression. As an individual begins to age the production of mitochondrial reactive oxygen species (ROS) is instigated, which alters the electron transport chain. The result of the disruption to the electron transport chain has been linked to apoptotic cell death. According to a research article published in 2021, mutations found in APP, PS1 and PS2 are associated with early onset-AD, whereas age related factors such as ROS production, mitochondrial DNA changes and epigenetic factors have been observed in sporadic AD [8]. Cognitive function depends on synaptic activity and ATP production. Elderly individuals with AD experience synaptic mitochondria interference through the accumulation of Aβ and p-tau proteins. The accumulation of these proteins at the nerve terminals and synapses results in defective and inactive mitochondria affecting the communication of neuronal cells. Recent evidence from postmortem brains of animals and clinical studies suggest that mitochondria play crucial roles in aging and neurodegenerative diseases [8]. Mitochondrial dysfunction was noticed in the postmortem brains of neurodegenerative disease expressing mutant proteins such as Aβ, mutant Htt, mutant SOD1 and mutant DJ1, among others. Furthermore, the abnormal expression of mitochondrial encoded genes has been observed in AD brains, suggesting that mitochondrial metabolism plays a crucial role in AD. Synapses are crucial for neuronal communication and cognitive function. Both chemical and electrical synapses compose the complexity of the synaptic network in the human brain. Chemical synapses receive signals through various presynaptic neurons to a postsynaptic neuron. Electrical synapses, on the other hand, form connections through gap junctions that result in the direct transfer of ions. Neuronal function and plasticity are correlated to the fluctuance of calcium ion concentration. Calcium ions result in the depolarization of neurons affecting synaptic activity. Calcium channels are triggered upon transport of calcium ions into the presynaptic terminal, which releases neurotransmitters through exocytosis. Mitochondria work to maintain homeostasis by regulating the concentration of calcium depending on ATP consumption. A high concentration of calcium triggers the activation of mitochondrial permeability transition pores (mPTPs), resulting in apoptosis [36]. Synaptic mitochondria play an essential role in synaptic activity through the fluctuation in levels of calcium ions. Synaptic stress is a known pathological hallmark for AD. As another example, Aβ and Aβ-associated cellular changes cause neuronal perturbations and synaptic changes in AD. Aβ in young AD mouse models displayed extracellular deposition. Synaptic mitochondria displayed an increase in Aβ levels and changes were noticed in the function of non-synaptic mitochondria in AD models. This suggests that mitochondria are more susceptible to Aβ damage and mitochondrial stress, causing symptoms associated with AD [37]. A recent study showed the initiation of apoptosis through the activation of caspase-3 in the hippocampal dendritic spines of mice models leading to early synaptic dysfunction and dendritic spine loss [37]. Synaptic activity is modulated by axonal transport, which is dependent on mitochondrial density [37]. Presynaptic sites are known to influence synaptic vesicle release, impairing axonal transport, and contributing to the pathogenesis of AD. Patients with AD were demonstrated to have axonal degeneration that accumulates in the mitochondria [37]. During a 2020 study, Naval Medical Research Institute (NMRI) mice were monitored during the aging process. Several changes in cognitive performance and mitochondrial metabolism were observed over a span of 24 months [38]. Additionally, studies show that impaired axonal transport reduces synaptic mitochondrial density and interferes with mitochondrial trafficking through the AD synapse. Together, altered axonal transport and fluctuations in calcium ions concentration cause impaired synaptic vesicle release and synaptic distress associated with AD pathogenesis. The modulation of mitochondrial and synaptic proteins via miRNAs is critical for the normal synapse function and very important to understand the pathophysiology of plasticity-related diseases. Since the synaptic activities are closely tied with healthy mitochondrial function and a consistent supply of ATP at synapse, therefore each mitochondrial component (here miRNAs) needs in-depth investigation. Mitochondrial miRNAs paved the way to further understand molecular mechanisms of translocation of miRNAs from the nucleus to the mitochondria. By mapping the nuclear genome of mitochondrial miRNAs, studies revealed a link between mitochondrial function and disease. Studies have shown how mitochondrial miRNAs can target mitochondrial genome and can harbor sequences [19]. The targeting of the mitochondrial genome could directly influence the energetic, oxidative, and inflammatory status of cells, which may cause changes in an organism. Through the effect of ATP synthesis, mitochondrial miRNAs can influence mitochondrial function. MiR-181c-5p is a known target of mitochondrial miRNAs that originates from the nuclear genome and translates to mitochondria. The over expression of miR-181c-5p causes the loss of mt-COX1 protein and results in an imbalance in ROS generation. Recently, a study of miR-181c-5p in rats showed that altered mitochondrial metabolism and ROS generation causes heart failure in animals [34]. In 2010, Bian et al. identified the mouse liver mitochondria-associated miRNAs and studied their potential biological functions [13]. A set of three miRNAs—miR-705, miR-494 and miR-202-5p—were identified as potential mitochondria-associated miRNAs [13]. These miRNAs have several putative targets related to mitochondria-specific functions, such as tryptophanyl-tRNA synthetase and transcription factor A, and may be involved in the modulation of mitochondria and cell-specific functions [13]. In this study, eight-week-old mice were treated with STZ (150 mg/kg) and harvested 14 days post-injection, and mitochondria were isolated from the liver. A Western blot analysis using antibodies against cytochrome c and AGO2 demonstrated the purity of liver mitochondria. These results suggested that mitochondria associated miRNAs were involved in mitochondrial dysfunction, causing progression in neurodegenerative diseases such as Alzheimer’s. The metabolic regulation of mitochondria is regulated by peroxisome proliferator-activated receptor y coactivator 1 (PGC-1), which interacts with many other transcriptional factors. Located in the first intron of the PGC-1 gene is the miR-378. Studies involving miR-378 in mice, which is controlled by PGC-1b, regulate mitochondrial metabolism and the homeostasis of the organism. Previous studies have found a link between miR-378’s targets and metabolic protein repression. However, several other miRNAs are involved in metabolic homeostasis based on mice studies. For example, miR-33 is an element binding protein gene that has been associated with cholesterol levels and lipid homeostasis by targeting adenosine triphosphate through the binding of cassette transporter A1. Other miRNAs have been linked to the regulation of glucose metabolism involving the silencing of miR-103/107, affecting glucose homeostasis and insulin sensitivity [39]. Deregulation of the mitochondria has been implicated in the onset and the progression of neurological disorders such as Alzheimer’s, Parkinson’s and Huntington diseases [40]. An increase in ROS generation is seen as people age and adds to the oxidative damage seen during mitochondrial respiration. Mitochondrial miRNAs are known for disrupting the respiratory chain complexes and increasing ROS production, resulting in mitochondrial damage. In 2014, Rippo et al. focused on investigating miRNAs expression in HUVEC cells, where miR-146a was compared against younger cells and regulated with specificity to Bcl-2. The results were examined using different cell models in in vivo which demonstrate the complexity of aging process [40]. For example, miR-155 and miR-146a are associated with inflammation in the nervous system by the activation of TLR7. The dysregulation of mitochondrial miRNAs affects the mitochondria causing immune responses in the brain. Mitochondrial damage and cell stress starts promoting inflammation at neuronal synapses that spread across the postsynaptic membrane to neighboring neurons. In 2015, Wang et al. provided evidence linking mitochondria-associated miRNA expression in relation to controlled cortical impact (CCI) injury in rats. The experiment confirmed that several mitochondrial-associated miRNAs such as miR-155 and miR-223 were elevated in rats subjected to TBI (traumatic brain injury) compared to uninjured rats. It was concluded that mitochondrial-associated miRNAs are crucial to regulating the response to TBI [41]. Another experiment conducted by Wang et al. presented new findings indicating that mitochondria-associated endoplasmic reticulum membranes (MAMs) played a role in neurodegenerative diseases. Analysis was made using subcellular fractionation and TaqMan RT-qPCR to quantify miRNA levels using rat and human brain samples. The results showed evidence of miR-223 causing inflammatory and immune response contributing to neurodegenerative diseases such as AD [42]. In essence, several mitochondrial miRNAs have been associated with controlling mitochondrial function by targeting and affecting different protein expressions. Their modulation, along with the changes in the mitochondria, induces the inflammatory response tied to age-related diseases. Moreover, the early detection and reduction of mitochondrial loss may slow/prevent the progression of neurodegenerative diseases such as Alzheimer’s [4]. To summarize, miRNAs are involved in cellular changes associated with neurodegenerative diseases and aging. Alterations in mitochondrial miRNAs expression continue to be an important topic of current research. Identifying mitochondrial miRNAs changes could help to understand the details of mitochondrial dysfunction in AD progression, could forward the invention of mitochondria based diagnostic tools and could allow the development of suitable preventive strategies against Alzheimer’s disease. Current studies have suggested that miRNAs significantly contribute to the development and progression of AD in either positive or negative ways. For this reason, investigating the role of mitochondrial miRNA and understanding their deregulation is necessary to better understand the mitochondrial dysfunction in AD. Several key questions are still unanswered in terms of mitochondrial miRNAs and AD pathogenesis: (1) Are mitochondrial genome encoded miRNAs levels altered in AD? (2) If yes, what is the impact of altered mitochondrial miRNA levels on mitochondrial function? (3) Are any mitochondrial miRNAs altered in response to Aβ and p-tau induced toxicities in AD? (4) Is mitochondrial miRNA deregulation responsible for deprived mitochondrial functions and synaptic activity in AD? and (5) What could be the possible miRNAs-based research strategy to improve the mitochondrial function and synaptic activity in AD? Therefore, further research is needed to evaluate the critical roles that mitochondrial miRNAs might play in mitochondrial and synaptic function in AD. Multi-Omics analysis of brain mitochondria is the best way to understand the genomic and proteomic changes in the mitochondrial genome in AD versus cognitively healthy control brains. The transcriptomic analysis of brain mitochondrial mRNAs, miRNAs, small RNAs and circular RNAs, and the proteomic analysis of mitochondrial proteins within the same sample, will unveil the local RNA-protein interaction in mitochondria. The multi-Omics analysis will provide insight into the potential mitochondrial Omics targets altered in the AD brain. Therefore, a high throughput multi-Omics analysis of the mitochondrial genome and proteome is needed to understand mitochondria-based synaptic dysfunction in AD. Further, a deeper understanding of mitochondrial miRNAs could help to develop mitochondrial biomarkers that could be used as a diagnostic tool to detect early stages of AD. Further, mitochondrial miRNAs research will help to understand mitochondria-based disease pathobiology and to develop novel therapeutic strategies against AD.

PMC10000974

Evan N. Cohen,Gitanjali Jayachandran,Hui Gao,Phillip Peabody,Heather B. McBride,Franklin D. Alvarez,Megumi Kai,Juhee Song,Yu Shen,Jie S. Willey,Bora Lim,Vicente Valero,Naoto T. Ueno,James M. Reuben

Phenotypic Plasticity in Circulating Tumor Cells Is Associated with Poor Response to Therapy in Metastatic Breast Cancer Patients

06-03-2023

circulating tumor cells (CTCs),neoplastic cells,circulating,neoplasms/diagnosis,circulating/pathology,biopsy,breast neoplasms/pathology,breast cancer,biomarkers,tumor,blood,liquid biopsy,metastatic process,EMT

Simple Summary Circulating tumor cells (CTCs) have served as an independent prognostic factor in the management of metastatic breast cancer (MBC). Through the enrichment of CTCs from peripheral blood, tumor cells can be acquired multiple times during therapy and provide a broad sample of tumor heterogeneity, thereby offering a complementary approach to tissue biopsy. Traditionally, CTCs have been enriched from blood based on the expression of epithelial-specific surface proteins. However, this approach might miss the migratory cells that lack epithelial features and favor the expression of more mesenchymal features. Therefore, enrichment of CTCs based on size and deformability may capture a wider range of tumor cells in circulation. Here we present a longitudinal study using a novel microcavity array to enrich CTCs and find that a shift from epithelial CTCs to those with a mesenchymal expression pattern is associated with inferior clinical outcomes. Abstract Circulating tumor cells (CTCs) are indicators of metastatic spread and progression. In a longitudinal, single-center trial of patients with metastatic breast cancer starting a new line of treatment, a microcavity array was used to enrich CTCs from 184 patients at up to 9 timepoints at 3-month intervals. CTCs were analyzed in parallel samples from the same blood draw by imaging and by gene expression profiling to capture CTC phenotypic plasticity. Enumeration of CTCs by image analysis relying primarily on epithelial markers from samples obtained before therapy or at 3-month follow-up identified the patients at the highest risk of progression. CTC counts decreased with therapy, and progressors had higher CTC counts than non-progressors. CTC count was prognostic primarily at the start of therapy in univariate and multivariate analyses but had less prognostic utility at 6 months to 1 year later. In contrast, gene expression, including both epithelial and mesenchymal markers, identified high-risk patients after 6–9 months of treatment, and progressors had a shift towards mesenchymal CTC gene expression on therapy. Cross-sectional analysis showed higher CTC-related gene expression in progressors 6–15 months after baseline. Furthermore, patients with higher CTC counts and CTC gene expression experienced more progression events. Longitudinal time-dependent multivariate analysis indicated that CTC count, triple-negative status, and CTC expression of FGFR1 significantly correlated with inferior progression-free survival while CTC count and triple-negative status correlated with inferior overall survival. This highlights the utility of protein-agnostic CTC enrichment and multimodality analysis to capture the heterogeneity of CTCs.

Phenotypic Plasticity in Circulating Tumor Cells Is Associated with Poor Response to Therapy in Metastatic Breast Cancer Patients Circulating tumor cells (CTCs) have served as an independent prognostic factor in the management of metastatic breast cancer (MBC). Through the enrichment of CTCs from peripheral blood, tumor cells can be acquired multiple times during therapy and provide a broad sample of tumor heterogeneity, thereby offering a complementary approach to tissue biopsy. Traditionally, CTCs have been enriched from blood based on the expression of epithelial-specific surface proteins. However, this approach might miss the migratory cells that lack epithelial features and favor the expression of more mesenchymal features. Therefore, enrichment of CTCs based on size and deformability may capture a wider range of tumor cells in circulation. Here we present a longitudinal study using a novel microcavity array to enrich CTCs and find that a shift from epithelial CTCs to those with a mesenchymal expression pattern is associated with inferior clinical outcomes. Circulating tumor cells (CTCs) are indicators of metastatic spread and progression. In a longitudinal, single-center trial of patients with metastatic breast cancer starting a new line of treatment, a microcavity array was used to enrich CTCs from 184 patients at up to 9 timepoints at 3-month intervals. CTCs were analyzed in parallel samples from the same blood draw by imaging and by gene expression profiling to capture CTC phenotypic plasticity. Enumeration of CTCs by image analysis relying primarily on epithelial markers from samples obtained before therapy or at 3-month follow-up identified the patients at the highest risk of progression. CTC counts decreased with therapy, and progressors had higher CTC counts than non-progressors. CTC count was prognostic primarily at the start of therapy in univariate and multivariate analyses but had less prognostic utility at 6 months to 1 year later. In contrast, gene expression, including both epithelial and mesenchymal markers, identified high-risk patients after 6–9 months of treatment, and progressors had a shift towards mesenchymal CTC gene expression on therapy. Cross-sectional analysis showed higher CTC-related gene expression in progressors 6–15 months after baseline. Furthermore, patients with higher CTC counts and CTC gene expression experienced more progression events. Longitudinal time-dependent multivariate analysis indicated that CTC count, triple-negative status, and CTC expression of FGFR1 significantly correlated with inferior progression-free survival while CTC count and triple-negative status correlated with inferior overall survival. This highlights the utility of protein-agnostic CTC enrichment and multimodality analysis to capture the heterogeneity of CTCs. Metastatic breast cancer (MBC) was the leading cause of death in women worldwide in 2018 WHO World Cancer Report, 2020–2021, https://iarc.who.int/biennial-report-2020–2021web/, accessed on 2 March 2023). The high mortality of MBC could be attributed to the extraordinary tenacity of cells that travel through the bloodstream and house themselves in conducive locations, generating distant metastasis. The voyage of circulating tumor cells (CTCs) through blood is a complex biological phenomenon that is still being unraveled. Not all CTCs survive the harsh blood environment to be successfully housed in a metastatic site [1]. Hence, these highly tenacious cells carry the molecular profile that plays a crucial role in metastasis, and understanding it not only sheds light on the disease biology but also provides a more clinically valid prognosis for the patient. CTCs also carry the potential for ex vivo exploration of therapeutic agents, especially the identification of actionable targets in a companion diagnostic setting for cancers [2]. Also, molecular characteristics of CTCs could reflect intratumor heterogeneity and may explain the discrepancy often seen in gene expression patterns between primary tumors and CTCs [3]. For a cancer cell to intravasate from the initial tumor site [4], survive in the blood, evade host immune defenses, and reestablish tumor growth in a pre-metastatic niche [5], it must maintain the ability to express several different phenotypic programs [6]. This CTC plasticity affects invasion, survival, and proliferation and to a certain extent is mirrored by the typical heterogeneity of cancer [7,8]. For example, established tumors are primarily epithelial while the migratory cells show mesenchymal features. Cells from a primary tumor must undergo epithelial-to-mesenchymal transition (EMT) as an early step in the metastatic cascade to enter the bloodstream. Multiple hybrid epithelial/mesenchymal phenotypes situated along an EMT spectrum account for intra-patient temporal and spatial heterogeneity. Furthermore, the acquisition of EMT is often associated with features that define cancer stem cells (CSCs)—namely, the enhanced potential for self-renewal, tumor initiation, invasiveness, motility, and heightened resistance to apoptosis—and is also instrumental for metastasis, suggesting that a subset of CTCs with high metastatic potential might be CSCs [9]. Since CSCs are often associated with EMT [10], it is critical to capture CTCs with mesenchymal features. However, the gold standard, FDA-cleared CELSEARCH platform enumerates EpCAM-expressing CTC and may not enrich cells in EMT [11]. Furthermore, the inter- as well as intra-tumor heterogeneity of antigen expression may limit the efficiency of single or even multiantigen immunoaffinity enrichment [12,13]. Therefore a label-free enrichment method may capture a broader sample of the CTC population. To test the relevance of the temporal heterogeneity of CTCs undergoing EMT in patient prognosis in MBC, we enriched CTCs using a microcavity array platform [14,15,16] that employs a biophysical strategy that is agnostic to cell surface protein expression and thereby can capture a wide variety of CTCs including cells with mesenchymal features. Two parameters, namely, enumeration and gene expression profiles, were used to explore the larger picture of prognostic implications of CTCs in a large clinical trial of MBC patients followed using longitudinal peripheral blood samples for up to 2 years after initiation of therapy. Temporal bulk gene expression profiles of the CTCs from these patients served as a surrogate for the phenotypic plasticity and heterogeneity that help drive metastasis and disease progression. This clinical study further demonstrates that CTC enumeration and gene expression analysis complement each other as liquid biopsy tools. This was a single-institution study designed to prospectively collect blood samples from patients with any subtype of newly diagnosed MBC at The University of Texas MD Anderson Cancer Center. For hormone receptor–positive (HR+) MBC, enrollment was allowed in patients undergoing first-line, second-line, or third-line treatment for MBC, while for HR-negative MBC, only patients undergoing first-line therapy for MBC were enrolled. In both cases, previous treatment for the pre-metastatic disease was allowed. Patients with metastatic disease of the brain, leptomeningeal disease, or concurrent malignancies were excluded. As there is a range of MBC subtypes, patients were treated with a range of therapeutic regimens. The clinical trial was designed to enroll 200 patients and obtain a baseline sample and up to 8 longitudinal follow-up samples for a total of up to 9 samples per patient. Patients were able to remain in the study regardless of the therapy used (standard of care or clinical trials and any changes in therapy) or disease progression during the study. All patients had blood drawn every 3 months (±1 month) during their routine blood draws per their treatment schedule and roughly concurrent with their imaging tests for 2 years, as available. The first baseline MBC patient sample was processed on 12 October 2016, and samples accrued until 31 December 2020, were processed and analyzed for this study. All study participants and healthy donors (HDs) provided written informed consent and were recruited under protocols approved by the Institutional Review Board (IRB) of MD Anderson Cancer Center. A total of 184 patients provided baseline and follow-up samples, totaling 733 samples that were collected according to IRB-approved protocol PA16–0507. HDs provided 118 blood samples for this study under IRB-approved protocol PA14–0063. Each HD self-reported being cancer free at the time of the study. The study was conducted in accordance with the Declaration of Helsinki. Study outcomes did not affect the clinical management of the enrolled patients. Two tubes of peripheral blood with EDTA anticoagulant were acquired from each patient at each timepoint with ~9.5 mL collected per tube. The two tubes were subjected to parallel CTC enhancement procedures, one for enumeration and the second for molecular characterization of the enriched CTCs. CTCs were agnostically enriched with a microcavity array (MCA) from Hitachi Chemical Co. (now Showa Denko Materials Co., Tokyo, Japan) [15]. For CTC enumeration, CTC-enriched fractions were stained in situ within the capture chip with a cocktail of antibodies and DAPI prior to image analysis. Chips were typically imaged within 2 days of the blood draw by confocal microscopy (Flow Cytometry and Cellular Imaging Core Facility at MD Anderson). CTCs were identified as pan-cytokeratin (CK)+ CD45− nucleated cells (DAPI+) based on guidelines published by Zeune and colleagues [17]. Leukocytes were depleted prior to CTC enrichment and the lysed CTCs were archived in Trizol (Thermo Fisher Scientific, Waltham, MA, USA) at −80 °C until further use. HD and patient samples were subjected to gene expression analysis by quantitative real-time polymerase chain reaction (qRT-PCR) in batches at the end of the study. HD samples were randomized with the patient samples and tested concurrently. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines was applied in the design of the primers [18] and wet lab validated by the manufacturer. A Qiagility liquid handler (Qiagen) was employed to set up PCR reactions. Additional details have been previously published [16]. For gene expression, custom-designed 384-well plates, including CTC-related genes, housekeeping control genes, and hematopoietic control genes were evaluated by qRT-PCR to characterize cells of epithelial, mesenchymal, and cancer stem cell lineage. The CTC panels included genes related to epithelial characteristics (CDH1, EGFR, EPCAM, KRT7, KRT18, MUC1), epithelial to mesenchymal transition (EMT) characteristics (AXL, CDH2, FN1, SNAI2, ZEB2), cancer stem-like lineage (ALDH1A1), and signaling pathways commonly perturbed in cancer (BCL2, CD274/PD-L1, ERBB2, FGFR1, and MET). Control genes included PTPRC (CD45) as a white blood cell control, GYPA as a nucleated red blood cell control, and housekeeping genes B2M, GAPDH and HPRT1 as positive controls (Table S1). From 184 patients (enrolled October 2016 through December 2020), 733 samples were subjected to gene expression analysis. Only 2 patients missed baseline samples. HD blood (118 samples) processed by MCA was used to establish a threshold for positive gene expression. Additionally, thresholds were set separately for samples processed with or without RBC (red blood cell) lysis based on the gene expression levels of 54 HD samples processed with RBC lysis and 55 without RBC lysis. A gene was considered positive if its expression was higher than 1 standard deviation above its mean expression in samples from HDs. An epithelial-mesenchymal score (EM score) was calculated based on the total expression of the target genes, resulting in a score of +1 if only mesenchymal genes and stem cell–related genes were detected and a score of –1 if only epithelial genes were detected. For the EM score, CDH1, EPCAM, KRT7, and KRT18 were considered epithelial (epi) while ALDH1A1, CDH2, FN1, and ZEB2 (mes) were considered mesenchymal. Genes with ambiguous epithelial/mesenchymal polarity characteristics such as FGFR1 were not considered here. CTC count and gene expression levels were measured at baseline and then at 2- to 3-month intervals, thereafter. The effect of baseline CTC count (<5 vs. ≥5 CTCs) and baseline gene expression (low: ≤mean +1 standard deviation of HD level vs. high: >mean +1 standard deviation) on progression and death were evaluated using Cox proportional hazards models. Time to progression and time to death was calculated from the study enrollment consent date. Patients who died without indication of progression were considered to have experienced progression on the date of death. The temporal effect of the CTC count (<5 vs. ≥5 CTCs) and gene expression groups (≤mean + 1SD of healthy donor controls as low vs. >mean + 1SD as high) on progression and the effect on death were evaluated using Cox proportional hazards models with time-dependent covariates. CTC counts greater than 40 were truncated as 40. For recurrent outcomes of progression events, the Prentice, Williams, and Peterson counting process model [19] and a sandwich estimate of the variance-covariance matrix was used to obtain standard errors accommodating the clustering of observations on patients. The last-value-carried-forward method was used to assign CTC count and gene expression levels for each patient and time interval. Incident rates (progression rate and death rate) by subgroup were calculated as the number of events per 100 person-years, with 95% confidence intervals. Mean cumulative number of progression events according to baseline CTC count (≥5 vs. <5 CTCs) was estimated using the Nelson estimator [20]. Analyses were conducted with SAS (9.4 SAS Institute INC, Cary, NC, USA) with figures generated with R version 4.1 (R Project for Statistical Computing, Vienna, Austria), using packages tidyr, survminer, survivalAnalysis, complexHeatmap, ggpubr, and swimplot. One hundred eighty-four (184) patients were enrolled following a new diagnosis of MBC prior to front-line therapy for current metastatic disease. Many had previously been treated for non-metastatic disease; however, 45 patients had de novo stage IV disease. During follow-up, 80 patients died, and 122 patients had disease progression (including 17 who died without indication of progression). Fourteen patients were reported disease free (no evidence of disease, NED) at the last follow-up. The median follow-up was 24.6 months (95% CI, 21.4–30.4 months) and the median overall survival (OS) was 43.6 months (95% CI, 33.4 months–not estimated). The median time to first progression (or death) was 10.4 months (95% CI, 8.1–15.3 months). Among 184 patients, the best responses included 7 patients with complete response, 58 with partial response, 50 with stable disease, 43 with progressive disease, and 26 not evaluated. Therefore, 115 patients had some clinical benefit from therapy. There was a range of breast cancer subtypes based on pathological analysis of metastatic tumors, but HER2+ disease was slightly under-represented with 24 patients (Table 1). The number of patients that provided samples at each timepoint is displayed in the study design summary (Figure S1). CTCs from patients with MBC were enumerated in real-time at the time of image acquisition. Across timepoints, 84% of the MBC patients had detectable CTCs from at least one blood draw over the course of the study. At baseline, 39% of patients had 0 CTCs, about 62% had 0 or 1 CTCs (≤1 CTC), and 84% had fewer than 5 CTCs (Figure 1a). The highest CTC count observed was about 5000 CTCs (visit 5). Enumerated CTCs generally decreased as time-on-study progressed. Median CTC counts were significantly lower than baseline from visit 4 (9 months) onward (Wilcoxon rank sum test p < 0.05, Figure S2). Archived images were subsequently analyzed by an independent observer as well as image analysis software, showing similar results (not shown). Patients who experienced progression at some point while on protocol had significantly higher CTC counts at baseline and 3 months (visit 2) (Figure S3a,b, Wilcoxon rank sum p < 0.05). The CTC counts and progression events for each patient at each timepoint are represented in a swimmer plot (Figure 1b). Looking at the effect of therapy (with no consideration of baseline measurement), several clear distinctions can be seen in CTC counts between patients who experienced disease progression and those who did not (Figure 1c). CTC counts ≥1 were concentrated in patients with the shortest study times. In contrast, patients without disease progression (Figure 1c, top) have generally low CTC counts (shades of blue). Several of these patients had high counts at earlier timepoints that decreased by later timepoints, suggesting a positive response to therapy. These results highlight the utility of CTC enumeration in patients with MBC, as has been extensively documented. Prior to further analysis, we first determined the optimal cutoff for CTC enumeration to stratify patients. Univariate Cox regression was used to analyze the effect of CTC enumeration using different cutoffs at first follow-up (3 months) on time to first progression (progression-free survival, PFS) including all 184 patients with MBC (Figure 1d) reaffirming ≥5 CTCs as the optimal cutoff with remarkably similar characteristics to the original FDA submission by CELLSEARCH. A similar cutoff was obtained with a similar analysis of the baseline blood sample or all samples combined (Figure S4). Using the cutoff of ≥5 CTCs per blood sample, established previously for other platforms and reaffirmed above, CTC counts were prognostic in patients with MBC starting a new therapy. Cox proportional hazard modeling showed that CTC counts were prognostic for PFS before therapy (baseline) and at early points in therapy (visit 2, about 3 months) (baseline HR for first progression = 1.97, p = 0.004; visit 2 HR for first progression = 2.6, p < 0.001). However, CTC counts were not significant prognostic factors of progression for the remainder of the first year (visits 3, 4, and 5). However, CTC count regained significance after the first year of therapy (visit 6 (15 months) and visit 7 (1.5 years)), when there is a likely survivor bias with an early drop out of patients whose therapy failed to clear high levels of CTCs (Figure 2a,b). Similarly, there was a trend for CTC counts to predict OS at baseline and significant effects early in therapy (visit 2 (3 months), visit 3 (6 months)), (baseline HR for first progression = 1.78, p = 0.053; visit 2 HR for first progression = 3.67, p < 0.001; visit 3 HR for first progression = 2.47, p = 0.041), with trends continuing at later timepoints. In a multivariate Cox analysis that included age and tumor subtype (as determined from a biopsy of metastatic tumor tissue), a CTC count of ≥5 CTCs remained an independent prognostic factor for PFS and OS at 3 months, 15 months, and 18 months but only for PFS at baseline (Figure S5). Repeated CTC enumeration using archived images by an independent observer, while not independent of the original count, was also an independent prognostic factor when substituted for the initial count (for example, in the baseline model of PFS, HR = 2.58, p < 0.001 for the independent repeated count). High CTC counts can predict progression before definitive manifestation by clinical imaging. To quantify the lead time provided by high CTC counts, we measured the time to first progression in patients with ≥5 CTCs. There were 28 patients with ≥5 CTCs at baseline, of whom 25 progressed with a median time to progression of 2.99 months. Several patients had disease progression at or after the second visit; of the 14 patients with ≥5 CTCs at the second visit who subsequently progressed, the average time to documented clinical progression by imaging was 6.34 months after ≥5 CTCs were detected. A similar pattern emerged at 6 months (visit 3) with 20.1 months lead time, albeit with only 3 patients with CTC ≥5. Since reductions in CTC counts, representing clearance of CTC during therapy, may be indicative of therapeutic response, we looked at changes in CTC counts over time. Dynamic longitudinal changes in CTC status with a cutoff of ≥5 CTCs offered similar prognostic value as static evaluation: decreases in CTC counts from baseline were significantly associated with improved prognosis only at the 3-month visit, but not at subsequent follow-ups (Figure S6). Patients with any CTCs detected (>0) that were cleared by therapy had good OS (Figure 2c), but clearance was not indicative of PFS (Figure 2c), and, anecdotally, the 5 patients who had ≥5 CTCs at baseline that resolved to <5 CTCs by the first follow-up (visit 2) were still alive as of this analysis. Baseline CTC count was also prognostic of the number of progression events in longitudinal modeling of the rates of progression and death (Figure 2d). Patients with ≥5 CTCs at baseline had a higher mean cumulative number of progressions than patients with <5 CTCs, and the difference broadened with increasing follow-up time. A CTC count of ≥5 was also associated with higher rates of progression and death than CTCs <5. For progression, the incidence rate ratio (IRR) of a CTC count of ≥5 to a CTC count of <5 was significantly higher than 1 (IRR = 2.03, 95% CI, 1.53–2.69). For OS, the IRR of ≥5 CTCs to <5 CTCs was again significantly higher than 1 (IRR = 4.58, 95% CI, 2.84–7.39). Together, these data reaffirm that enumeration of CTC can help stratify patients by risk and monitor response to therapy. There has been an unmet clinical need for further characterization of CTCs beyond enumeration. Since gene expression is especially relevant for deciphering EMT plasticity and also signaling pathways that are pertinent to therapeutic targets, we investigated gene expression as a prognostic factor in MBC using a pre-selected panel of CTC-related genes. The distribution of CTC-related gene expression is shown in Figure 3a. CTC-related genes were detected in the baseline samples of 129 patients (71%), and at least 1 cancer-related gene was detected in 486 of 677 longitudinal samples analyzed (72%). Across timepoints, 169 (93%) of the patients had at least 1 gene from the panel detectable in at least 1 sample. As with CTC count, the total expression of target genes tended to decrease with time on the protocol. As with CTC count, patients who did not experience progression on protocol had a decrease in total expression over time, including expression of both epithelial (CDH1, EPCAM, KRT18, KRT7, and MUC1) and mesenchymal/CSC-related genes (ALDH1A1, CDH2, FN1, ZEB2) (Figure 3b). Patients with disease progression had a significantly higher total expression of CTC-related target genes compared to patients without disease progression at visits 3, 4, 5, and 6 (Figure S7). Generally, at later timepoints, the CTC-enriched samples of patients who experienced tumor progression had higher mRNA expression of the CSC marker ALDH1A1, the anti-apoptotic gene BCL2, epithelial cell adhesion gene CDH1 (e-cadherin), the migratory adhesion gene FN1, and the immune checkpoint inhibitor CD274 (PD-L1) (Figure 3c). In the 9-month to 1-year range (visits 4 and 5), the CTC-enriched cells of patients with progression had higher mRNA levels of ALDH1A1, AXL, CD274, CDH1, and ZEB2 (Wilcoxon rank sum test p < 0.05, Figure S8). Longitudinal changes from baseline in the expression of these genes showed similar patterns (Figure S9): Patients who did not experience progression had significant decreases from baseline in ALDH1A1, BCL2, CD274, CDH1, and FN1, while patients who experienced progression had significant increases in ALDH1A1, BCL2, CD274, and CDH1 (Figure 3d and Figure S10). As noted above with the summed expression, there was a greater differential expression among mesenchymal genes (with the exception of CDH1), which highlights the benefit of a protein-agnostic enrichment approach. For a better-unified summary of gene expression, we counted the number of genes that were expressed at higher levels compared to HD blood samples subjected to the same processing as patient samples using a cut-off of ≥4 positive CTC genes (Figure S11a). In contrast to CTC enumeration, ≥4 positive CTC genes was not significantly prognostic of PFS or OS at baseline nor at the first follow-up (Figure 4), although several individual genes were prognostic in univariate analysis at 3 months (Figure S11b). Conversely, and also in contrast to enumeration, gene expression of ≥4 CTC genes was associated with significantly increased risks of progression as determined by samples collected at 6 months, 9 months, and 1 year (at 1 year, HR for PFS = 3.4, p = 0.003, Figure 4). Interestingly, although baseline gene expression was poorly prognostic of the first progression, patients with higher baseline expression of CTC-related genes had a higher number of subsequent progression events (Figure S12). To assess the risk associated with the detection of CTCs by either imaging or gene expression, multivariate Cox analysis including CTC count, the presence of at least 4 positive CTC genes, and the tumor subtype determined from the metastatic tumor tissue (and therefore the basis of the therapy administered at baseline in this study) was performed for each time point. As suggested above, CTCs enumerated by imaging at baseline, visit 2 at 3 months, visit 6 at 15 months, and visit 7 at 18 months were each an independent prognostic factor for PFS. In contrast to CTC enumeration, gene expression was an independent prognostic factor for PFS visit 3 at 6 months, visit 4 at 9 months, and visit 5 at 1 year (Figure S13). There were no timepoints where both CTC count and CTC gene expression were significant independent prognostic factors of PFS. In terms of OS, CTCs enumerated by imaging were an independent prognostic factor at visits 2, 3, 4, and 6. Gene expression and CTC enumeration were both independent predictors of OS only at visit 3. Although CTC gene expression at baseline showed marginal utility, longitudinal evaluation was a much more powerful prognostic tool. Univariate Cox regression analyses of the effects of time-dependent covariates on recurrent progression by the Prentice, Williams, and Peterson counting process model are summarized in Table S2, which shows the HR of experiencing progression for every one-unit increase in each covariate or each response of a covariate relative to a reference group. CTC count (continuous and categorical) and gene expression of CTC were considered time-dependent covariates, whereas others were baseline covariates. Covariates significantly associated with progression were number of lines of therapy, the subtype of the metastatic lesion (estrogen receptor (ER)/progesterone receptor (PR)/HER2 expression), CTC count with a threshold of ≥5 CTCs, and the CTC expression of FGFR1, KRT7, and MUC1; KRT18 expression was marginally associated with increased risk of progression. This analysis shows, for example, at any given timepoint, if an MBC patient has a CTC count of ≥5, her hazard rate of progression is higher than if she had a CTC count of <5 (HR = 2.104, p < 0.0001). Similarly, univariate Cox regression analyses of the effects of time-dependent covariates on death are summarized in Table S2. Covariates that were significantly associated with death were lines of therapy, primary and metastatic tumor markers (HR/HER2), CTC count ≥5, and a number of positive CTC genes. At any given timepoint, if a patient had a CTC count of ≥5, their hazard of death was higher than if they had a CTC count of <5 (HR = 4.827, p < 0.0001). In multivariate Cox regression models of the effects of time-dependent covariates on progression, the covariates of metastatic tumor subtype, CTC count ≥5, and CTC expression of FGFR1 remained significant as independent prognostic factors (Table 2 and Table S2), although in an alternate multivariate model including metastatic tumor subtype, CTC count, and KRT7 (without FGFR1), KRT7 was significant (p = 0.0227) but not as strong as FGFR1 (not shown). As expected, triple-negative breast cancer, which tends to be more aggressive and lacks targeted therapy, showed an increased HR in this analysis. Notably, CTC count and an independent recount subsequently performed on archival images by an independent observer were not independent of each other, as they were highly, but imperfectly, correlated, further suggesting the CTC counts are reasonably robust (not shown). Although the amalgamated gene expression measure of any 4 positive genes was prognostic at individual timepoints (as described above in Figure 4), in the time-dependent analysis, this variable did not remain independent of CTC count and was dropped from the final model. However, a lack of independence may not imply a lack of utility. At baseline, 26 MBC patients had at least 5 CTCs. Of these, 5 had no positive genes, and only 3 had at least 5 positive genes. In contrast, 38 MBC patients had at least 5 positive genes, of whom only 3 patients had CTC counts greater than 5. Therefore, although CTC counts and CTC gene expression are correlated and are not independent prognostic factors, additional information is gained by using both measures. At no timepoint were a CTC count ≥5 and expression of ≥4 CTC genes both significant in multivariate analysis for PFS (as shown above in Figure S13). As epithelial genes make up a large portion of the gene panel, it is intuitive that CTC count based on cytokeratin-based staining and gene expression including cytokeratins and other epithelial markers would not be independent. However, when combining all timepoints into multivariate Cox regression models of the effect of time-dependent covariates on recurrent progression, some individual genes, most prominently FGFR1, were independent prognostic factors along with CTC count. However, in similar models of OS, only CTC count and tumor subtype were independent prognostic factors, but no genes were included in the final model. In mixed effects modeling, FN1, MUC1, and standardized EPCAM were significantly associated with clinical benefit after Bonferroni-Holm correction for multiple tests (Table S3). Overall, these results suggest that CTC enumeration and gene expression are complementary. We have shown that the enumeration of CTCs is less prognostic than gene expression while patients are in therapy. However, in a disease-monitoring setting at these intermediate timepoints, gene expression including a panel of epithelial genes, mesenchymal genes, and cancer-related genes is better able to identify patients at high risk of disease progression. Since clusters of CTCs may have higher metastatic potential than individual cells, we compared the number of clusters observed in patients with and without progression. CTC clusters were observed in 27 samples. Between 6 months and 1 year of therapy (visits 3, 4, and 5), CTC clusters (Figure 5a) were observed only in patients who experienced progression. Furthermore, patients who experienced progression had significantly higher numbers of CTCs in clusters at 6 months (visit 3) and 1 year (visit 5) (Figure 5b), with an increased risk of disease progression (Figure 5c), and these patients exhibited an increase in the number of clusters compared to baseline. Since CTC clusters contain cells with both increased cell-cell adhesions and migratory properties that can promote metastatic seeding at distant sites, we compared the ratio of epithelial and mesenchymal gene expression (EM score) in samples with identified CTC clusters. The EM score significantly favored mesenchymal polarity in samples with CTC clusters, but only in patients with disease progression (Figure 5d). Furthermore, irrespective of the presence of clusters, CTCs from patients with disease progression had increased mesenchymal/CSC-like polarity after initiation of therapy, whereas CTCs from patients without disease progression (stable disease) shifted towards a more epithelial expression pattern (Figure 5e), with a significant difference in cohort expression patterns at the 1-year timepoint (visit 5, t-test p = 0.009). Patients with detectable CTC clusters had a greater risk of progression at 6 months (visit 3) and 1 year (visit 5) (Figure 5f). Overall, these results suggest patients with a shift towards a mesenchymal CTC phenotype, particularly within CTC clusters, are less likely to respond to therapy. (a) HER2: Among baseline samples, 21 MBC patients showed HER2+ CTCs, defined as ERBB2 gene expression higher than 1 standard deviation above the mean expression in the HDs. Of these, only 7 patients with MBC had HER2+ metastases by clinical evaluations (concordant with CTCs), but 11 patients with MBC had HER2− metastases, i.e., the metastatic tissue and CTC results were discordant. Of the patients with MBC with HER2-discordant CTC and metastases, 9 were evaluable for clinical benefit, of which only one received a benefit from first-line therapy for metastatic disease (Figure S14). Since these patients were HER2− by clinical evaluations, they did not receive HER2-targeted therapies. One of these patients (B152) subsequently tested positive for HER2 at a different metastatic site 4 months after the baseline CTC measurement. This patient had been placed on a HER2-targeted therapy at that time and is doing well as of this report. A second patient, B158, also subsequently tested positive for HER2. After switching to HER2-targeted therapy, this patient had a stable disease as of this report. (b) EGFR: Among patients with high EGFR expression by CTCs at baseline, only one received EGFR-targeted therapy and had a partial response to therapy. The other 9 patients were not treated with EGFR-targeted therapy; 7 of these patients have died, and 2 were alive at the last follow-up with progressive disease. Case studies have been described in the Supplementary Materials (Figures S15 and S16). Here, we report the results of the first longitudinal study of CTCs from patients with MBC using the protein-agnostic MCA CTC enrichment platform. In contrast to CELLSEARCH, the platform used here enriches CTCs based on size and deformability without a bias towards epithelial characteristics, potentially increasing the yield of clinically relevant CTCs. Although the MCA system enriches CTCs with both epithelial and mesenchymal features, the CELLSEARCH platform showed remarkably similar data in the initial FDA submission, and we re-affirmed the cutoff of ≥5 epithelial CTCs as the most relevant threshold for MBC prognosis. The enumeration of CTCs by imaging had prognostic value, but with limitations. In the full patient cohort, there was a general decrease in enumerated (primarily epithelial) CTCs as the time on study progressed; median counts were significantly lower than the baseline from visit 4 (9 months) onwards (p < 0.05, Figure S2). Importantly, survivorship bias may account for the decreasing counts at later timepoints. More critically, our results show that primarily epithelial CTC counts have significant prognostic utility at baseline and the first follow-up at 3 months after treatment initiation, but thereafter their utility is diminished (Figure 2). As a pure conjecture, it is possible that cytotoxic therapies distort the CTC morphology and make counts more difficult to obtain. In MBC, dynamics of CTC counts during treatment have been repeatedly shown to be related to poor prognosis [21,22,23]. The SWOG S0500 trial failed to demonstrate that CTC enumeration could be useful in selecting new lines of therapy [24]. However, larger, more recent studies have suggested that for ER+, ERBB2− MBC, CTC count can be useful for deciding between chemotherapy and endocrine therapy [25]. Therefore, there is still debate about the clinical utility of CTC enumeration per se, and further characterization of CTC phenotypes can fine-tune the understanding of CTC biology. In model systems, we have previously seen that gene expression increases with the number of CTCs [26]. Therefore, gene expression can also be a surrogate for enumerated cells. Furthermore, the increased multiplexing ability of molecular assays allows the interrogation of a broader range of phenotypes. Temporal total gene expression by our targeted panel was significantly higher in patients who developed progressive disease compared to those without progression at later timepoints but before or early in therapy (Figure S7). This is in stark contrast to CTC counts, which stratified patients by progression primarily at the earlier timepoints (Figure S3). Furthermore, as noted in multivariate survival analysis, CTC counts correlated with gene expression at some timepoints, but not at others. This may suggest that the phenotypic plasticity induced or enriched by therapy can be captured by gene expression analysis of CTC. We observed that high expression of CDH1, FGFR1, FN1, KRT7, KRT18, and MUC1 was correlated with poor prognosis. Prior to therapy, many of these genes are expected to be correlated with CTC counts. For example, the proteins for KRT8 and KRT7 are both targeted in the cocktail of antibodies used for image analysis, and most of the other genes were specifically chosen because they are associated with epithelial cells. CTCs that have undergone partial EMT express some mesenchymal markers and show downregulation of epithelial markers. These cells with mesenchymal features may be the population most responsible for the development of metastasis, have been related to higher stage [27] and inferior prognosis [28,29,30], and may change in response to therapy [31]. However, these CSC-like and migratory phenotypes may be both enriched and induced by therapy [32,33,34,35] or inflammation [36,37] and may be targeted by therapies such as eribulin [38]. Such cells may be more difficult to detect with imaging but are detected by analysis of the EMT-associated genes whose expression patterns may change over the course of therapy. CSCs, and by extension mesenchymal cells [10], have been shown to be resistant to therapy; however, completely mesenchymal CTCs are associated with a more favorable survival outcome in breast cancer [39]. In the current study, patients whose disease responded poorly to therapy had slightly fewer mesenchymal CTCs at the start of therapy but had a shift towards greater mesenchymal polarity during therapy (Figure 5e). We also observed greater mesenchymal polarity in samples with CTC clusters consistent with previous reports [40]. The metastatic process is very inefficient; only a tiny portion of CTCs establish metastases [6]. However, CTCs in clusters have several advantages over single CTCs, as they may be better protected from the stresses of the bloodstream [41] and have heterogeneous cells that increase the probability of metastatic seeding [42,43,44]. Detection of CTC clusters is frequently associated with poor prognosis [45,46,47,48,49]. Clustered CTCs have been extensively shown to play a crucial role in the metastatic spread of breast cancer in advanced stages [50,51] and have increased metastatic potential compared to single CTCs [44]. More recently, clustered CTCs have been reported in early breast cancer patients [52], and are potentially more prevalent than in metastatic disease [53] highlighting the significance of liquid biopsy in cancer care. Size-based enrichment such as the one used in the current study offers a straightforward way to enrich CTC since even a two-cell cluster is significantly larger than WBC [54]. We noted that patients with progression had more CTCs in clusters after the initiation of therapy. Intriguingly, we also observed significantly higher relative mesenchymal gene expression in patients with CTC clusters (Figure 5), with the highest mesenchymal polarity in patients with CTC clusters who experienced a disease progression. The current study is limited by the use of microscopy and gene expression analysis on parallel samples from the same blood draw such that the enumerated CTCs are not the cells subjected to EMT analysis and lacks single-cell resolution for EMT. Interestingly, it makes the correlation between clusters and EMT in parallel samples more remarkable. A true test of CTC plasticity would require following the changes in individual cells, which is beyond the scope of this project. We attempted to monitor plasticity by interrogating CTC gene expression changes over time, with the caveat that we cannot distinguish between selection and induction. However, the data lend credence to a model of heterogeneous clusters that maintain both epithelial cell junctions with associated paracrine signaling and migratory ability affording greater plasticity and adaptation to the multiple environments encountered during the metastatic cascade. Understanding the mechanisms underlying the various forms of cell plasticity may deliver new strategies for targeting the most lethal aspects of cancer: metastasis and resistance to therapy [55]. In time-dependent multivariate analysis, expression of FGFR1 offered the greatest independent prognostic utility among tested genes. As an expression of more traditionally epithelial genes was correlated with the CTC count in this study, it is expected that epithelial genes would not be independent of CTC count. FGFR1 is frequently overexpressed in breast cancer [56], leading to endocrine therapy resistance [57] as well as HER2 therapy resistance [58]. Several therapies targeting FGFR1 are currently in trials [59]. However, none of the patients enrolled began any of these therapies at baseline, so they could not be explored in this analysis. As such, the CTC expression of FGFR1 warrants further study. In terms of other biomarkers, the discordant HER2 expression between tumor and CTCs that we observed has been previously reported using other platforms [60,61,62,63]. HER2 expression has been shown to be highly variable within CTC [64] and may represent dynamic functional states with more rapidly growing HER2+ CTC and chemotherapy-resistant HER2- CTC [65] that can be exploited by HER2 targeting therapies [66]. Together, the EGFR and ERBB2 (HER2) gene expression data show that CTCs can be a valuable source of tumor information. The CTC data supplement the tumor tissue and may better capture tumor heterogeneity that may be prevalent in HER2+ CTC [51,52]. Of note, although numerous ERBB2 and EGFR mutations are clinically targetable, the data presented here rely on expression and not mutational status. As expression can be elevated without a mutation in the target gene, these examples may not show up on one of the now-ubiquitous cell-free DNA mutation panels. There have been a plethora of alternative approaches that attempt to overcome the information bottleneck and extract both count and extended phenotype data from a single sample [67]. On the enrichment side, we chose a size-based, protein-agnostic enrichment method, but there are several immunocapture methods that use a panel of cell surface antibodies to capture both epithelial and mesenchymal cells. For example, Adnatest is a commercially available positive selection platform for the enrichment of CTC from breast, prostate, ovarian and colon cancer [68,69]. Among others [70], cell-surface vimentin has been proposed as a surface antigen for magnetic enrichment from squamous cell sarcoma [71], pediatric sarcoma [72], neuroblastoma [73], prostate [74], gastric [75], pancreatic [76], lung [77], and breast cancer [78]. For analysis, a higher-plex immunostain with separate cocktails for epithelial and mesenchymal markers has been used to show mesenchymal and epithelial CTC, or imaging cytof can be used to stain for multiple markers. Workflows have been established for multimodality imaging that includes morphology and fluorescence imaging [79]. Some platforms forgo enrichment altogether such as the Epic Sciences platform [80,81]. For broader characterization, many groups have published intriguing data using single-cell RNA Seq [82,83,84,85,86,87], however, the expense and low recovery are not amendable to a clinical workflow. On a narrower scale, alternative gene expression platforms such as HTG [26] can be used on fixed samples following imaging if the CTC counts are high enough or spatial gene expression platforms such as NanoString’s GeoMX. The MCA microfluidic device employed in the study does present several limitations. Recent reports have suggested that EPCAM-negative CTC tends to be smaller and therefore may not be captured by size-based enrichment [88]. Most microfluidic devices (10-year review are relatively low-throughput [89]. The protocol used here required about 1 hour to enrich ~9.5 mL of blood (200 µL/min) for the gene expression assay and longer for imaging. The extended processing time may limit clinical utilization, but the automated system limits hands-on time. Although filtration devices may be susceptible to clogging, the enhanced elongated pores in the MCA design allow continuous flow after CTC capture to reduce clogging [14]. Furthermore, the extended dwell time may expose CTCs and CTC clusters to extended shear stress that can damage CTC and disaggregate CTC clusters [90]. Although the pressure drop is attenuated by the design of the MCA (no pubmed citation), it is possible CTC cluster recovery was suboptimal. However, the MCA system has been shown to isolate significantly more clusters than the CELLSEARCH [91]. Overall, this study suggests that CTC gene expression and enumeration by imaging are complementary. We have also shown that the enumeration of epithelial cells is less prognostic while patients are in therapy. We speculate that this may be due to cytotoxic and cytostatic therapies that alter the morphology of CTCs resistant to therapy and make the cells more difficult to enumerate. Interestingly, this plasticity can be observed in multiplexed gene expression. As such, the detection of CTCs with EMT characteristics was highly prognostic in MBC. Further studies exploring multidimensional data including digital pathology that can recognize complicated and subtle phenotypes are needed. This study reaffirmed the cut-off of ≥5 CTCs for inferior prognosis of patients with MBC using a technology that enriched epithelial and mesenchymal phenotypes. Epithelial CTC counts were prognostic before initiation of therapy and early in therapy, whereas a shift towards mesenchymal CTC phenotypes as detected by gene expression was associated with disease progression. Discordances between CTCs and tissue biopsy, as seen here in patients with HER2+ CTCs and EGFR+ CTCs, offer opportunities to explore alternative therapies as CTCs better represent the metastatic scenario than tumor tissue. Overall, this study suggests that with the use of a multimodal liquid biopsy and a protein-agnostic enrichment platform, enumeration and gene expression profiling of CTCs are complementary, offering a broader, more readily accessible picture of tumor response to therapy.

PMC10000982

Prapenpuksiri Rungsa,Htoo Tint San,Boonchoo Sritularak,Chotima Böttcher,Eakachai Prompetchara,Chatchai Chaotham,Kittisak Likhitwitayawuid

Inhibitory Effect of Isopanduratin A on Adipogenesis: A Study of Possible Mechanisms

27-02-2023

fingerroot,Boesenbergia rotunda,obesity,adipocyte,isopanduratin A,AKT/GSK3β,AMPK-ACC,MAPKs,MCE

The root of Boesenbergia rotunda, a culinary plant commonly known as fingerroot, has previously been reported to possess anti-obesity activity, with four flavonoids identified as active principles, including pinostrobin, panduratin A, cardamonin, and isopanduratin A. However, the molecular mechanisms underlying the antiadipogenic potential of isopanduratin A remain unknown. In this study, isopanduratin A at non-cytotoxic concentrations (1–10 μM) significantly suppressed lipid accumulation in murine (3T3-L1) and human (PCS-210-010) adipocytes in a dose-dependent manner. Downregulation of adipogenic effectors (FAS, PLIN1, LPL, and adiponectin) and adipogenic transcription factors (SREBP-1c, PPARγ, and C/EBPα) occurred in differentiated 3T3-L1 cells treated with varying concentrations of isopanduratin A. The compound deactivated the upstream regulatory signals of AKT/GSK3β and MAPKs (ERK, JNK, and p38) but stimulated the AMPK-ACC pathway. The inhibitory trend of isopanduratin A was also observed with the proliferation of 3T3-L1 cells. The compound also paused the passage of 3T3-L1 cells by inducing cell cycle arrest at the G0/G1 phase, supported by altered levels of cyclins D1 and D3 and CDK2. Impaired p-ERK/ERK signaling might be responsible for the delay in mitotic clonal expansion. These findings revealed that isopanduratin A is a strong adipogenic suppressor with multi-target mechanisms and contributes significantly to anti-obesogenic activity. These results suggest the potential of fingerroot as a functional food for weight control and obesity prevention.

Inhibitory Effect of Isopanduratin A on Adipogenesis: A Study of Possible Mechanisms The root of Boesenbergia rotunda, a culinary plant commonly known as fingerroot, has previously been reported to possess anti-obesity activity, with four flavonoids identified as active principles, including pinostrobin, panduratin A, cardamonin, and isopanduratin A. However, the molecular mechanisms underlying the antiadipogenic potential of isopanduratin A remain unknown. In this study, isopanduratin A at non-cytotoxic concentrations (1–10 μM) significantly suppressed lipid accumulation in murine (3T3-L1) and human (PCS-210-010) adipocytes in a dose-dependent manner. Downregulation of adipogenic effectors (FAS, PLIN1, LPL, and adiponectin) and adipogenic transcription factors (SREBP-1c, PPARγ, and C/EBPα) occurred in differentiated 3T3-L1 cells treated with varying concentrations of isopanduratin A. The compound deactivated the upstream regulatory signals of AKT/GSK3β and MAPKs (ERK, JNK, and p38) but stimulated the AMPK-ACC pathway. The inhibitory trend of isopanduratin A was also observed with the proliferation of 3T3-L1 cells. The compound also paused the passage of 3T3-L1 cells by inducing cell cycle arrest at the G0/G1 phase, supported by altered levels of cyclins D1 and D3 and CDK2. Impaired p-ERK/ERK signaling might be responsible for the delay in mitotic clonal expansion. These findings revealed that isopanduratin A is a strong adipogenic suppressor with multi-target mechanisms and contributes significantly to anti-obesogenic activity. These results suggest the potential of fingerroot as a functional food for weight control and obesity prevention. With the steady increase in the number of overweight and obese populations in recent years, obesity has been declared a pandemic disease by the World Health Organization (WHO) [1]. Obesity is the result of an energy imbalance, characterized by excessive fat accumulation in the body. This irregularity, though a non-communicable disorder, is closely associated with several metabolic conditions, such as hyperglycemia, hyperlipidemia, hypertension, cancer, and cardiovascular diseases, all of which have a high mortality rate and can cause a socioeconomic burden, particularly in countries where access to the healthcare system is limited [2]. Modulation of the excess mass of adipose tissues due to hyperplasia (excessive adipogenesis) and the hypertrophy of adipocytes is one of the reasonable strategies to regulate lipid homeostasis and obesity. Recently, inhibition of adipogenic differentiation and maturation has become a novel therapeutic approach to treating obesity [3]. Adipogenesis, a multistep process that converts undifferentiated preadipocytes into mature adipocytes, is modulated by a series of biochemical cascades that include coordinated changes in hormone sensitivity and gene expression, together with morphological alterations. Triggered by adipogenic stimulants, preadipocytes undergo mitotic clonal expansion (MCE) to re-enter the cell cycle. Concurrently, the upregulation of adipogenic regulating genes and adipogenic effector proteins leads to adipocyte differentiation and maturation [4,5,6,7]. Adipocyte differentiation and development are directed by lipogenesis-related transcription factors such as CCAAT/enhancer-binding protein alpha (C/EBPα), peroxisome proliferator-activated receptor gamma (PPARγ), sterol response element-binding protein-1c (SREBP-1c) [8,9], and the adenosine monophosphate-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC) enzymes [10]. AMPK, a serine/threonine kinase, forms a heterotrimeric complex with one catalytic α subunit and two regulatory β and γ subunits [11]. Its roles in cellular lipid metabolism involve the synthesis and degradation of fatty acids. Another upstream regulatory molecule in adipocyte differentiation is protein kinase B (AKT), as its activation strongly links to the upregulation of SREBP-1c and cellular lipogenesis [12]. Subsequent phosphorylation of glycogen synthase kinase 3β (GSK3β) by AKT upregulates C/EBPα and promotes adipocyte maturation [13]. Additionally, mitogen-activated protein kinases (MAPKs), including c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and stress-activated protein kinase (p38), mediate adipogenesis [14]. Suppression of these signaling molecules efficiently inhibits adipocyte differentiation [15,16]. For example, inhibition of p38 function can hamper adipocyte differentiation by suppressing PPARγ transcription. Modulation of these biomolecules during adipocyte differentiation proved to be a promising strategy to limit cellular lipogenesis and adipocyte differentiation and maturation [17]. Recently, a growing body of evidence has revealed medicinal and culinary plants as a rich source of phytochemicals that exert their anti-obesity potential through multi-target mechanisms [18,19,20]. Boesenbergia rotunda (L.) Mansf., also known as Boesenbergia pandurata (Roxb.) Schltr., is commonly called fingerroot. The plant is found in the wild and is widely cultivated in South Asia and Southeast Asia [21,22]. Traditionally, people use its roots as food and flavoring agents. In Thailand, they are the main ingredient in shrimp soup, which is popularly consumed by lactating women to help improve their breast milk supply. Various medicinal values for fingerroot were reported, including anti-inflammatory, antimicrobial, antiviral [21,22,23,24], anti-obesity [25], anti-osteoporosis [26], and anticancer activities [27], as well as aphrodisiac and vasorelaxant effects [28]. The bioactive constituents were characterized as several subclasses of flavonoids [29,30]. In a recent study, the anti-obesity activity of fingerroot was demonstrated in mice on a high-fat diet [31]. Our previous phytochemical study of the roots of this plant revealed the presence of several flavonoids, along with a monoterpene alcohol and a styrylpyrone [32]. In a preliminary Oil Red O assay, we found that the flavonoids pinostrobin, panduratin A, isopanduratin A, and cardamonin were strong adipogenic inhibitors, which may be responsible for the anti-obesity activity of fingerroot (see Section 3.1). In our previous study, pinostrobin was shown to inhibit adipogenesis in murine 3T3-L1 preadipocytes by lowering the levels of lipid-metabolism-mediating proteins, such as C/EBPα, PPARγ, and SREBP-1c, and suppressing the signals of MAPKs (p38 and JNK) and AKT (AKT/GSK3β and AKT/AMPKα-ACC) [33]. The other flavonoids, i.e., panduratin A and cardamonin, were previously investigated for the molecular mechanisms underlying their anti-adipogenic effects in 3T3-L1 cells [25,34,35]. In this study, we report the inhibitory effects of isopanduratin A, another fingerroot flavonoid, on adipogenesis in mouse 3T3-L1 and human PCS-210-010 preadipocytes. The relevant molecular mechanisms are also elucidated and addressed. Isopanduratin A and other phytochemicals were isolated and characterized from B. rotunda roots with a protocol described previously [32]. The purity of these phytochemicals was more than 98% (by NMR). Dimethyl sulfoxide (DMSO), Oil Red O, crystal violet, isobutylmethylxanthine (IBMX), dexamethasone, isopropanol, RNase A, and skim milk powder were purchased from Sigma-Aldrich (St. Louis, MO, USA). Ethanol, methanol, formaldehyde, and chloroform were ordered from Merck KgaA (Darmstadt, Germany). Dulbecco’s Modified Eagle Medium (DMEM), fetal bovine serum (FBS), penicillin/streptomycin solution, l-glutamine, and trypsin were bought from Gibco (Gaithersburg, MA, USA). Fibroblast basal medium (FBM) was purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). Insulin was ordered from Himedia (Mumbai, India). Bicinchoninic acid (BCA) protein assay kit, western chemiluminescent ECL substrate, and radio-immunoprecipitation assay (RIPA) buffer were acquired from Thermo-Fisher (Rockford, IL, USA). A protease inhibitor cocktail was obtained from Roche Applied Science (Indianapolis, IN, USA). Primary antibodies against β-actin (Cat. No. 4970; dilution 1:1000), Cyclin D1 (Cat. No. 2978; dilution 1:1000), Cyclin D3 (Cat. No. 2936; dilution 1:2000), CDK2 (Cat. No. 2546; dilution 1:1000), AKT (Cat. No. 4691; dilution 1:1000), p-AKT (Ser473) (Cat. No. 4060; dilution 1:2000), GSK3β (Cat. No. 12456; dilution 1:1000), p-GSK3β (Ser9) (Cat. No. 9322; dilution 1:1000), AMPKα (Cat. No. 5831; dilution 1:1000), p-AMPKα (Thr172) (Cat. No. 2535; dilution 1:1000), AMPKβ1/2 (Cat. No. 4150; dilution 1:1000), p-AMPKβ1 (Ser182) (Cat. No. 4186; dilution 1:1000), ACC (Cat. No. 3676; dilution 1:1000), p-ACC (Ser79) (Cat. No. 11818; dilution 1:1000), PPARγ (Cat. No. 2435; dilution 1:1000), C/EBPα (Cat. No. 8178; dilution 1:1000), FAS (Cat. No. 3180; dilution 1:1000), PLIN1 (Cat. No. 9349; dilution 1:1000), adiponectin (Cat. No. 2789; dilution 1:1000), ERK1/2 (Cat. No. 9102; dilution 1:1000), p-ERK1/2 (Thr202/Tyr204) (Cat. No. 4695; dilution 1:1000), JNK (Cat. No. 9252; dilution 1:1000), p-JNK (Thr183/Tyr185) (Cat. No. 9251; dilution 1:1000), p38 (Cat. No. 8690; dilution 1:1000), p-p38 (Thr180/Tyr182) (Cat. No. 4511; dilution 1:1000), and horseradish peroxidase (HRP)-linked secondary antibodies (Cat. No. 7074; dilution 1:2000) were purchased from Cell Signaling Technology (Danvers, MA, USA). Specific primary antibodies against SREBP-1c (Cat. No. PA1-337; dilution 1:1000) and LPL (Cat. No. PA5-85126; dilution 1:1000) were acquired from Invitrogen (Waltham, MA, USA). Human PCS-210-010 preadipocyte and mouse embryonic preadipocyte 3T3-L1 cells obtained from the American Type Culture Collection (ATCC; Manassas, VA, USA) were, respectively, cultured in FBM and DMEM containing 10% FBS, 100 units/mL of penicillin/streptomycin, and 2 mmol/L of l-glutamine under humidified conditions of 5% CO2 at 37 °C. For a differentiation program to convert preadipocytes to adipocytes, preadipocytes growing as monolayers up to 90% confluent for 2 days were exposed to a differentiation medium made of FBM or DMEM containing 10% FBS, 0.5 mM IBMX, 1 μM dexamethasone, and 5 μg/mL insulin for 2 days. At this stage, various concentrations of isopanduratin A were added, while 0.5% (v/v) DMSO was used as vehicle control. The differentiation medium was replaced with culture medium supplemented with 5 μg/mL of insulin. After further incubation for 2 days, cells were maintained in complete medium, which was changed every 2 days until lipid-droplet-containing adipocytes were observed under the microscope. Undifferentiated and differentiated cells were defined as negative control and positive control groups, respectively. Following the recommended course of action, the cytotoxicity of isopanduratin A was evaluated using a crystal violet colorimetric assay [33]. Cells were seeded in a 96-well plate at a density of 1 × 104 cells/well and incubated under humidified 5% CO2 at 37 °C overnight and then exposed for 48 h to isopanduratin A in a range of final concentrations (0–100 μM). A vehicle control (0.5% (v/v) DMSO) was also included. Dead detached cells were removed after washing twice with phosphate buffer saline (PBS; pH 7.4). The adherently viable cells were then stained with crystal violet solution (0.05% w/v) for 30 min at room temperature after being fixed with 10% w/v formic aldehyde for 30 min. The assayed plate was washed twice with deionized water to remove any excess crystal violet solution and then left to dry overnight. The stained cells were treated with 100 μL of methanol prior to absorbance measurement (570 nm) with a microplate reader (Anthros, Durham, NC, USA). The percentage of cell viability was calculated using the absorbance value of each treatment relative to that of the vehicle control. The ability of 3T3-L1 cells to proliferate in the presence of isopanduratin A at its non-cytotoxic doses for 24–72 h was investigated by crystal violet staining [33,36]. 3T3-L1 cells (3.5 × 103 cells/well in a 96-well plate) growing as a monolayer for 2 days were exposed to differentiation medium containing varying concentrations of isopanduratin A (0–10 μM) and incubated for 24, 48, and 72 h. A vehicle (0.5% (v/v) DMSO) was also included. At the end of each incubation period, the crystal violet staining assay was carried out as described previously, and the ability of cells to proliferate was calculated and reported as the percentage of cell proliferation in each treatment relative to that of the vehicle control measured at 24 h. The impact of isopanduratin A on the passage of 3T3-L1 cells through the cell cycle was analyzed by flow cytometry. Cells seeded in a 6-well plate and at 90% confluent of their growth were treated with non-cytotoxic doses of isopanduratin A for 18 h. Undifferentiated or differentiated control cells were established by exposure to 0.5% (v/v) DMSO. Cells in each treatment and control were harvested by centrifugation for 5 min at 2500× g and 4 °C and then fixed overnight in 1 mL of ice-cold 70% (v/v) ethanol at −20 °C. The fixed cells were washed with PBS (pH 7.4), stained with 50 μg/mL PI solution (400 μL) containing 5 μg/mL DNase-free RNase solution for 30 min at room temperature, and kept away from light. DNA content was analyzed by flow cytometry (EMD Millipore, Austin, TX, USA). The percentages of cells in the G0/G1, S, and G2/M phases were then calculated using the FlowJo V10 software trial version (Williamson Way, Ashland, OR, USA). The impact of isopanduratin A, at varying non-toxic doses, on the formation of lipid droplets in 3T3-L1 and PCS-210-010 adipocytes was evaluated by the Oil Red O staining assay. Both adipocytic cells undergoing the differentiation program, as described previously, were fixed with 10% formaldehyde for 30 min at room temperature, and then the fixed cells were stained with Oil Red O solution (at an Oil Red O:distilled water ratio of 6:4) for 1 h at room temperature. The stained cells were washed twice with 60% (v/v) isopropanol and randomly photographed under an inverted light microscope (Nikon Ts2, Tokyo, Japan). Intracellular Oil Red O-stained lipid droplets were eluted using 100% isopropanol, and their absorbance values at 500 nm wavelength were measured using a microplate reader (Anthros, Durham, NC, USA). The effects of isopanduratin A at varying non-cytotoxic doses on cellular triglyceride and released glycerol levels were also determined, respectively, using triglyceride and glycerol assay kits (Sigma Aldrich, St. Louis, MO, USA), in accordance with the instructions of the manufacturer. Undifferentiated or differentiated cells treated with DMSO (0.5% v/v) functioned as controls for each experiment. The effects of isopanduratin A (0–10 μM) on the expression of proteins related to adipogenesis after 48 h of incubation were tracked by western blot analysis. Undifferentiated and differentiated 3T3-L1 cells treated with DMSO (0.5% v/v) functioned as controls. Cells were collected and lysed on ice in RIPA buffer supplemented with a protease inhibitor cocktail for 45 min. Cell lysates were quantified for protein concentration using the BCA assay and stored at −80 °C until further use. Equal protein samples (30 μg) were loaded to separate on 10% SDS-PAGE and transferred onto a nitrocellulose membrane (BIO-RAD, Hercules, CA, USA). The membranes were blocked in 5% skim milk for 1 h at room temperature and incubated overnight with primary antibodies at 4 °C. The membranes were then washed (7 min × 3 times) with Tris-buffered saline with 0.1% Tween® 20 (TBST) before incubation with HRP-conjugated secondary antibody for 2 h at room temperature. The membranes were washed 3 times with TBST to remove excess antibodies and detected using western chemiluminescent ECL substrates. The protein expression level was calculated as the ratio of the band intensity of the target protein to that of β-actin—a housekeeping protein. The impact of isopanduratin A on the expression of some proteins involved in the differentiation of 3T3-L1 adipocytes was confirmed at the transcriptional level using the RT-qPCR technique. 3T3-L1 preadipocytes (5 × 104 cells/well in a 6-well plate) with up to 90% confluent were treated with varying non-cytotoxic doses of isopanduratin A for 2 days in differentiation medium. Undifferentiated and differentiated 3T3-L1 cells treated with DMSO (0.5% (v/v) functioned as controls for this study. The medium was removed, and the cells were rinsed thrice with ice-cold PBS (pH 7.4) and extracted for their RNA using the PureLink™ RNA Mini Kit (Invitrogen, Carisbad, CA, USA). An equal amount (1 μg) of total RNA was reverse-transcribed to complementary DNA with a RevertAid first-strand cDNA synthesis kit (Thermo Scientific Pierce, Rockford, IL, USA). The Bio-Rad Luna Universal qPCR master mix (Hercules, CA, USA) was used in the assay reaction, while amplification was performed with the Bio-Rad CFX96 Touch real-time PCR detection system (Hercules, CA, USA), in accordance with the instructions of the manufacturer. The RT-qPCR primers (Table 1) and conditions were previously described elsewhere [36]. The expression level of each target gene was normalized with that of Gapdh—a housekeeping gene. Relative mRNA expression levels were analyzed using the 2−(ave.∆∆CT) method, where CT is the threshold cycle. All experiments were carried out in triplicate, and the results are expressed as mean ± standard deviation (SD). Statistical comparison of means by one-way analysis of variance (ANOVA) with Tukey’s post hoc test was performed using GraphPad Prism 8.0.2 software (San Diego, CA, USA). A p-value of <0.05 was considered statistically significant. In this study, murine 3T3-L1 preadipocyte cells, which can differentiate into mature adipocytes under appropriate conditions [4,37], were used. Initially, the toxicity of each test compound was evaluated at 5 μM by a crystal violet assay, as previously described [33]. At this concentration, pinostrobin (1), panduratin A (3), isopanduratin A (4), and cardamonin (6) were all non-toxic and showed a significant reduction in intracellular lipid content in the Oil Red O staining assay (Table 2), suggesting their anti-adipogenic potential. Isopanduratin A showed a drop in the percentage of stained cells to approximately 60%, compared to the vehicle control. The cytotoxic effect of isopanduratin A was then further assessed in a wider range of concentrations (0–100 μM). The highest non-toxic dose was found to be 10 μM, and the half-maximum inhibitory concentration was 28.63 ± 0.70 μM. The dose-dependent effect of isopanduratin A on 3T3-L1 adipocyte differentiation was then further examined by measuring the accumulation of cellular lipid droplets stained with Oil Red O dye (Figure 1a). Figure 1b shows that isopanduratin A at 5 and 10 μM inhibited cell differentiation in a dose-dependent manner, as indicated by the lower percentage of stained lipid droplets. The intracellular triglyceride content in the cells exposed to 1–10 μM isopanduratin A for 48 h decreased significantly, compared to untreated control cells (Figure 1c), although a reduction in cellular lipid droplets by 1 μM isopanduratin A was not clearly observed. Similarly, isopanduratin A at 1–10 μM significantly increased the amount of extracellular glycerol released from differentiated cells (Figure 1d). The expression of proteins related to lipid metabolism as markers of mature adipocytes was further investigated in differentiated cells. Elevated expression levels of FAS, LPL, PLIN, and adiponectin, which play an important role in lipogenesis, were clearly observed in cells cultured with differentiation medium for 8 days (Figure 2a). Intriguingly, 5–10 μM of isopanduratin A significantly suppressed the expression of PLIN (Figure 2c) and adiponectin (Figure 2e) in differentiated cells, while lower levels of FAS (Figure 2b) and LPL (Figure 2d) were observed at as low as 1 μM of isopanduratin A. These results demonstrated that isopanduratin A at non-cytotoxic doses could efficiently limit lipogenesis during cell differentiation. Preadipocytes undergo mitotic clonal expansion (MCE) during the early stage of adipogenesis. Before the beginning of cell differentiation, these growth-arrested preadipocytes usually undergo a few rounds of mitosis. Concurrent reentry into the cell cycle caused by MCE leads to an increased number of adipocytes [7]. MCE is mediated by the activation of cyclin-dependent kinase (CDK) and cyclin family proteins. Following MCE, activated C/EBPβ stimulates C/EBPα, which in turn causes PPARγ to begin transcription [36,38]. As presented in Figure 3a (see Figure S1), isopanduratin A (1–10 μM) significantly inhibited the proliferation of 3T3-L1 preadipocytes after incubation for 24, 48, and 72 h, compared to differentiated control cells at each time point. The effect of isopanduratin A on cell cycle progression during MCE was further determined. The number of cells at different stages of the cell cycle was assessed after culture in differentiation medium for 18 h in the presence or absence of 1–10 μM of isopanduratin A. The histograms obtained from flow cytometry reveal the entry into the S phase of the cell cycle in differentiated 3T3-L1 cells (Figure 3b). Surprisingly, isopanduratin A significantly hindered the progression of the cell cycle, as indicated by the higher number of cells in the G0/G1 phase, compared to the differentiated control group (Figure 3c). Figure 4 shows that isopanduratin A markedly altered the expression of MCE-mediated proteins (cyclins D1 and D3 and CDK2) in differentiated 3T3-L1 cells after 18 h of incubation, as proven by western blot analysis. Cyclin D1 is known to be suppressed, while other cyclin proteins are upregulated, during the initial phase of adipogenesis [39]. Cyclin D1 inhibits adipogenesis by preventing the expression of C/EBPα [40]. In this study, a reduction in cyclin D1 levels was observed in differentiated 3T3-L1 cells, but this downregulation was effectively reversed by isopanduratin A (Figure 4a,b) (See Figure S2). Lower levels of CDK2 (Figure 4c) and cyclin D3 (Figure 4d) were found in cells treated with isopanduratin A (10 μM) in comparison with the differentiated control group. These observations indicate that isopanduratin A delayed cell passage in the cell cycle by modulating MCE-mediated protein expression. To further elucidate the molecular mechanisms underlying the suppressive effect of isopanduratin A on adipogenesis, the expression of various adipogenic transcription factors was determined at both the mRNA and protein expression levels. Preadipocyte 3T3-L1 cells were collected during the early differentiation stage after 48 h of incubation with or without differentiation medium with isopanduratin A at non-toxic concentrations. Upregulated levels of transcription factor mRNA, including PPARγ, SREBP-1C, and C/EBPα, were observed in cells cultured in differentiation medium for 48 h (Figure 5a) (see Figure S3). Nevertheless, isopanduratin A at 5 and 10 μM significantly decreased the levels of SREBP-1C and PPARγ mRNA, compared to those of the differentiated control cells. It should be noted that the decreased level of C/EBPα mRNA was observed only in the 3T3-L1 cells incubated with isopanduratin A at a high concentration (10 μM). Consistent with the mRNA levels detected by qRT-PCR, western blotting revealed lower expression levels of the SREBP-1C, PPARγ, and C/EBPα proteins in the differentiated 3T3-L1 cells cultured with 5–10 μM of isopanduratin A, compared to differentiated control groups (Figure 5b–d). Mitogen-activated protein kinases (MAPKs), including ERK, p38, and JNK, play an important role during adipogenesis, in which their regulating roles, such as cell proliferation and differentiation, are exerted [37]. Suppression of MAPK signaling molecules efficiently inhibits adipocyte development, and it has been demonstrated that altering these biomolecules during adipocyte differentiation is one of the promising strategies to slow adipogenesis and cellular lipid metabolism [16]. In the present investigation, a western blot analysis was performed to determine whether isopanduratin A modulates the signaling molecules in the MAPK pathway (Figure 6a). The decreased levels of p-JNK/JNK (Figure 6b) (see Figure S4) and p-ERK/ERK (Figure 6c) were clearly indicated in the 3T3-L1 cells cultured with differentiation medium containing 5–10 μM of isopanduratin A, compared to differentiated control cells. It is worth noting that isopanduratin A at a high concentration (10 μM) dramatically suppressed p-p38/p38 signaling (Figure 6d). Thus, these results indicated that isopanduratin A might attenuate adipogenesis by inhibiting the MAPK pathway. Several reports suggest that AMP-activated protein kinase (AMPK) regulates the cellular energy balance by inhibiting lipogenesis and promoting lipolysis [41,42]. In this study, isopanduratin A affected AMPK signaling molecules, as illustrated by Western blot analysis (Figure 7a) (see Figures S5 and S6). The activation of the AMPK pathway by this compound was indicated by the highly elevated levels of p-ACC/ACC (Figure 7b), p-AMPKα/AMPKα (Figure 7c), and p-AMPKβ/AMPKβ (Figure 7f) in the differentiated 3T3-L1 cells cultured with 10 μM of isopanduratin A for 48 h. Protein kinase B (AKT) is another upstream molecule that plays an important role in adipogenesis. Phosphorylated AKT (p-AKT) suppresses AMPK-ACC signals, resulting in the upregulation of adipogenic transcription factors and promotion of lipogenesis [43]. Additionally, p-GSK3β, which mediates the transcription of adipogenic transcription factors, is also modulated by p-AKT. The AKT/GSK3β cascade is required for the expression of C/EBPβ, C/EBPα, and PPARγ during cell differentiation [44]. Consistent with the elevated expression of AMPK-ACC signals and decreased levels of adipogenic transcription factors, the ratios of p-AKT/AKT (Figure 7d) and p-GSK3β/GSK3β (Figure 7e) were suppressed by isopanduratin A. These results suggest that isopanduratin A modulates the signaling pathways of AKT/GSK3β and AKT/AMPK-ACC to inhibit adipogenesis. The antiadipogenic potential of isopanduratin A was further studied in primary human PCS-210-010 preadipocytes. The lipid contents were analyzed by Oil Red O staining (Figure 8a). Treatment with isopanduratin A at 1, 5, and 10 μM decreased the number of cellular lipid droplets by 93.51%, 71.75%, and 49.79%, respectively (Figure 8b). These results suggested that isopanduratin A suppresses adipogenesis in human preadipocytes in a dose-dependent manner. Obesity is associated with the onset of metabolic syndrome and various degenerative diseases that can cause various chronic health problems and often lead to premature death. During the recent COVID-19 pandemic, obesity increased the risk of hospitalization and admission to intensive care units [45]. The unusual expansion of adipose tissue, a characteristic feature of obesity, depends on adipocyte hypertrophy (an increase in cell size) and/or hyperplasia (an increase in cell number) [46]. It is commonly acknowledged that a long-term regulated lifestyle that involves reducing food intake and increasing physical activity can effectively lower body weight. However, these diet and lifestyle modifications are challenging for many overweight patients. Currently, nutrition intervention is highlighted as an alternative strategy to treat obesity [47]. In this study, in the Oil Red O staining assay, isopanduratin A at non-toxic concentrations reduced the number of mature, lipid-containing adipocytes in both mouse 3T3-L1 (Figure 1a,b) and human PCS-210-010 (Figure 8) preadipocyte models. These results indicate its anti-adipogenic activity. It should be noted that isopanduratin A at 1 μM could reduce cellular fat accumulation in human preadipocytes more than in murine preadipocytes. Lipid metabolism plays a crucial role in adipocyte differentiation, and its dysregulation is a critical factor in the development of obesity [48]. The decrease in intracellular triglyceride content and the elevated levels of released glycerol (Figure 1c,d) demonstrated the lipolytic effect of isopaduratin A. The suppressive effect of isopanduratin A on 3T3-L1 adipogenesis is further evidenced by decreased expression levels of adipogenic effectors, including FAS, PLIN1, LPL, and adiponectin (Figure 2). These lipid-metabolism-modulating proteins are essential for maintaining cellular lipid homeostasis and are associated with various metabolic conditions such as hyperlipidemia, insulin resistance, atherosclerosis, and obesity [9,37,48,49,50,51,52]. Due to its ability to modulate cellular lipid accumulation and interact with these lipid metabolism proteins, isopanduratin A might be a potential nutraceutical candidate for the treatment of several metabolic diseases. Mitotic clonal expansion (MCE) is the process in which the number of premature adipocytes increases as a result of cell cycle re-entry and the repeated cycles (two–three cycles) of cell proliferation at the early stage of adipogenesis [7]. Several natural compounds that possess an anti-adipogenic potential exhibit cell cycle arrest in differentiated preadipocytes [37,38,39]. As mentioned above, growth-arrested preadipocytes undergo MCE, which is mediated by the activation of cyclin/CDK complexes [7]. Interestingly, treatment with 1–10 μM of isopanduratin A showed a significant decrease in the percentage of cell proliferation, compared to the differentiation control cells (Figure 3a). Increased cyclin D1 expression in preadipocytes treated with isopanduratin A, with concomitant lowered levels of cyclin D3 and CDK2 (Figure 4), indicated cell cycle arrest in the G0/G1 phase. Similar effects on cyclin D1 levels were reported earlier for other natural polyphenols such as delphinidin and curcumin, both of which are strong anti-adipogenic compounds [53,54]. The increase in cyclin D1 levels may suggest that isopanduratin A also inhibits adipogenesis by activating the Wnt/β-catenin signaling pathway. Consistent with the change in the DNA content analyzed by flow cytometry, the accumulation of G0/G1 cells and the decrease in S phase cells occurred in differentiated preadipocytes cultured with isopanduratin A at 1–10 μM (Figure 3b,c). These results suggested that isopanduratin A inhibited the generation of mature adipocytes from preadipocytes by triggering cell cycle arrest. After the MCE period, activation of C/EBPα triggers PPARγ transcription in association with the expression of adipogenesis-regulating proteins [36]. During adipocyte differentiation, transcription factors C/EBPα, PPARγ, and SREBP-1c cross-activate one another to exert their adipogenic functions [38,55]. Previous studies showed that C/EBPα controls the expression of SREBP-1c and that low C/EBPα levels lead to reduced PPARγ activity. In addition, gene expressions related to cellular lipid storage and insulin response are affected by C/EBPα [56,57]. Intriguingly, isopanduratin A suppressed adipogenesis in 3T3-L1 cells by downregulating these transcription factors at both the translation and transcription levels (Figure 5). The expression of adipogenic transcription factors is also governed by the opposite correlation between the AKT/GSK3β and the AMPK-ACC pathways. As these two pathways critically mediate the upstream machinery of adipocyte differentiation [58], the regulation of proteins involved in these processes could be another mechanism for suppressing adipogenesis. It is plausible that AMPK and AKT are competitively phosphorylated by an energy balance sensor that controls several metabolic pathways [59]. The AKT/GSK3β cascade is vital for the expressions of C/EBPβ, C/EBPα, and PPARγ during cell differentiation [60]. Moreover, the AMPK pathway influences the expression of FAS and FABP4, which participate in lipogenesis at the late stage of adipogenesis [57]. In mouse and human mesenchymal cells, upregulated levels of C/EBPα, PPARγ, and SREBP-1c are caused by the downregulation of AMPK, which also affects the activation of ACC [55]. Activation of AMPK (p-AMPK), in association with ACC initiation, hampers triglyceride and fatty acid production by suppressing SREBP-1c and FAS during adipogenesis [47,59]. Therefore, the good correlation between the suppressive effects of isopanduratin A on adipogenic proteins (Figure 4 and Figure 5) and the downregulated levels of p-AKT and p-GSK3β as well as the upregulated levels of p-AMPK and p-ACC (Figure 7) suggests that the compound inhibits adipogenesis and lipogenesis in mature adipocytes through the AKT/AMPK-ACC pathway. In general, extracellular stimuli can induce MAPK signaling, which, in turn, activates several intracellular responses through the phosphorylation of specific sites and components, including ERK, JNK, and p38. Studies showed that adipogenic transcription regulators are influenced by proteins in the MAPK family [61]. In this study, isopanduratin A decreased the phosphorylated forms of JNK, ERK, and p38 (Figure 6). Interestingly, isopanduratin A suppressed MAPK signaling concomitantly with a reduction in intracellular lipid accumulation (Figure 1). ERK phosphorylation is known to be essential for cell proliferation and cell cycle progression during the MCE process [62,63,64]. Isopanduratin A prevented MCE, in parallel with the downregulated levels of p-ERK/ERK (Figure 6c). On the other hand, in our previous report, pinostrobin did not suppress MCE, in agreement with its lack of activity on p-ERK/ERK [33]. Panduratin A and cardamonin, the other adipogenic suppressors obtained from fingerroot, have never been reported for MCE interference. It is worth noting that the non-theoretical alteration of the upstream regulating molecules (p-AKT, p-GSK3β, p-AMPK, p-ACC, and p-ERK) observed in this study could be the result of late-stage detection. However, isopanduratin A indeed restricts these signaling pathways during adipogenesis. Although more in-depth investigations are needed, the overall results suggest that isopanduratin A suppresses adipogenesis through multi-target mechanisms. Fingerroot (Bosenbergia rotunda) possesses pinostrobin, panduratin A, cardamonin, and idopanduratin A as adipogenic inhibitors. Isopanduratin A suppresses adipogenesis by modulating AKT/AMPK-ACC (AKT/GSK3β and AKT/AMPK-ACC) and MAPK (JNK/ERK/p38) signals that correspond to the downregulation of key adipogenic regulators (SREBP-1c, PPARγ, and C/EBPα) and adipogenic effectors (FAS, PLIN1, LPL, and adiponectin) (Figure 9). It is worth noting that isopanduratin A also inhibits MCE by preventing ERK phosphorylation at the early stage of adipogenesis. This property is absent in pinostrobin and has not yet been described for panduratin A or cardamonin. Taken together, our results shed light on the molecular mechanisms underlying the anti-adipogenic activity of isopanduratin A and provide further evidence for the potential use of fingerroot as a functional food against weight gain and obesity. Rigorous preclinical and clinical trials should be performed to establish this hypothesis. As a culinary plant, fingerroot might be consumed directly as a functional food or used as an ingredient in nutraceutical products for body weight control. However, the safety for long-term daily consumption, as well as the stability and bioavailability of the active principles, must be thoroughly investigated before any application can be realized.

PMC10000985

Paul T. Winnard,Laura Morsberger,Raluca Yonescu,Liqun Jiang,Ying S. Zou,Venu Raman

Isogenic Cell Lines Derived from Specific Organ Metastases Exhibit Divergent Cytogenomic Aberrations

23-02-2023

karyotypes,isogenic metastatic cell lines,inter- and intra-cytogenomic heterogeneity,comparisons

Simple Summary Normal human cells have 22 pairs of chromosomes as well as 2 sex chromosomes for a total of 46 chromosomes; this normal karyotype is called diploidy (euploidy). On the other hand, aberrant numbers of chromosomes, i.e., gains and/or losses of chromosomes, have been found in most human cancer cells. This condition is called aneuploidy. Within in a clinical context, aneuploidy has been shown to be a marker of poor prognosis and drug resistance. Importantly, the deadliest stage of a cancer occurs when the cancer has been found to have spread from a primary tumor site to other organ sites, which is called metastasis. Controlled comprehensive clinical studies of metastatic cancer, which require an interrogation of the affected site(s), such as lungs, or liver, brain, or bone, with the goal of developing better treatment are very challenging. Therefore, repeatable controlled studies of complex human metastatic disease are simulated in animal systems using human cancer cells in special mouse strains. We used such a model system to better understand the chromosomal changes and the processes that bring them about, along with a study of gene variants, chromosomal amplifications, gains, and losses in metastatic cancer cells. We compared these differences to their primary tumor cell counterparts. This information aids us in suggesting possible new therapeutic treatments that may have a potential to limit the growth of metastatic cancer. Abstract Aneuploidy, a deviation in chromosome numbers from the normal diploid set, is now recognized as a fundamental characteristic of all cancer types and is found in 70–90% of all solid tumors. The majority of aneuploidies are generated by chromosomal instability (CIN). CIN/aneuploidy is an independent prognostic marker of cancer survival and is a cause of drug resistance. Hence, ongoing research has been directed towards the development of therapeutics aimed at targeting CIN/aneuploidy. However, there are relatively limited reports on the evolution of CIN/aneuploidies within or across metastatic lesions. In this work, we built on our previous studies using a human xenograft model system of metastatic disease in mice that is based on isogenic cell lines derived from the primary tumor and specific metastatic organs (brain, liver, lung, and spine). As such, these studies were aimed at exploring distinctions and commonalities between the karyotypes; biological processes that have been implicated in CIN; single-nucleotide polymorphisms (SNPs); losses, gains, and amplifications of chromosomal regions; and gene mutation variants across these cell lines. Substantial amounts of inter- and intra-heterogeneity were found across karyotypes, along with distinctions between SNP frequencies across each chromosome of each metastatic cell line relative the primary tumor cell line. There were disconnects between chromosomal gains or amplifications and protein levels of the genes in those regions. However, commonalities across all cell lines provide opportunities to select biological processes as druggable targets that could have efficacy against the primary tumor, as well as metastases.

Isogenic Cell Lines Derived from Specific Organ Metastases Exhibit Divergent Cytogenomic Aberrations Normal human cells have 22 pairs of chromosomes as well as 2 sex chromosomes for a total of 46 chromosomes; this normal karyotype is called diploidy (euploidy). On the other hand, aberrant numbers of chromosomes, i.e., gains and/or losses of chromosomes, have been found in most human cancer cells. This condition is called aneuploidy. Within in a clinical context, aneuploidy has been shown to be a marker of poor prognosis and drug resistance. Importantly, the deadliest stage of a cancer occurs when the cancer has been found to have spread from a primary tumor site to other organ sites, which is called metastasis. Controlled comprehensive clinical studies of metastatic cancer, which require an interrogation of the affected site(s), such as lungs, or liver, brain, or bone, with the goal of developing better treatment are very challenging. Therefore, repeatable controlled studies of complex human metastatic disease are simulated in animal systems using human cancer cells in special mouse strains. We used such a model system to better understand the chromosomal changes and the processes that bring them about, along with a study of gene variants, chromosomal amplifications, gains, and losses in metastatic cancer cells. We compared these differences to their primary tumor cell counterparts. This information aids us in suggesting possible new therapeutic treatments that may have a potential to limit the growth of metastatic cancer. Aneuploidy, a deviation in chromosome numbers from the normal diploid set, is now recognized as a fundamental characteristic of all cancer types and is found in 70–90% of all solid tumors. The majority of aneuploidies are generated by chromosomal instability (CIN). CIN/aneuploidy is an independent prognostic marker of cancer survival and is a cause of drug resistance. Hence, ongoing research has been directed towards the development of therapeutics aimed at targeting CIN/aneuploidy. However, there are relatively limited reports on the evolution of CIN/aneuploidies within or across metastatic lesions. In this work, we built on our previous studies using a human xenograft model system of metastatic disease in mice that is based on isogenic cell lines derived from the primary tumor and specific metastatic organs (brain, liver, lung, and spine). As such, these studies were aimed at exploring distinctions and commonalities between the karyotypes; biological processes that have been implicated in CIN; single-nucleotide polymorphisms (SNPs); losses, gains, and amplifications of chromosomal regions; and gene mutation variants across these cell lines. Substantial amounts of inter- and intra-heterogeneity were found across karyotypes, along with distinctions between SNP frequencies across each chromosome of each metastatic cell line relative the primary tumor cell line. There were disconnects between chromosomal gains or amplifications and protein levels of the genes in those regions. However, commonalities across all cell lines provide opportunities to select biological processes as druggable targets that could have efficacy against the primary tumor, as well as metastases. Aneuploidy, a deviation in chromosome numbers from the normal diploid set, has a long history and was first described 130 years ago from observations in fresh human carcinoma specimens [1,2]. It is now recognized as a fundamental characteristic of all cancer types and is found in 70–90% of all solid tumors [3,4,5]. Consequently, cancer genomes exhibit massive aberrations in copy number changes due to losses or gains in whole chromosomes or chromosome arms that result in numerical and structural chromosomal changes. As such, aneuploidy reflects extensive genetic defects that exceed levels of any other genetic lesion [6]. The majority of aneuploidies are generated by chromosomal instability (CIN), which has been found to be generated by a variety of mechanisms [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. However, it has been noted that aneuploidy can arise independent of CIN [6]. Importantly, it has been repeatedly demonstrated that CIN/aneuploidy is an independent prognostic marker of cancer survival [4,24,25,26,27] and is a cause of drug resistance [28,29,30]. Hence, ongoing research has been directed towards the development of therapeutics aimed at targeting CIN/aneuploidy as a means of overcoming chemotherapy resistance and prolonging survival [6,29,31,32,33,34,35,36]. It is important to note that the bulk of research has been focused on primary tumor samples or their cell lines. Hence, there are relatively limited reports on the evolution of CIN/aneuploidies within metastatic lesions and how the resulting aneuploidies compare to the aneuploidies of their primary tumors [9,37,38,39], which has left gaps in our knowledge, particularly from the perspective of alternative treatment strategies for metastatic disease. In this work, we built on our previous omics studies on a human xenograft model system of metastatic disease in mice [40,41,42]. This model system generated isogenic cell lines derived from the primary tumor and specific metastatic organs (brain, liver, lung, and spine), which enabled a comparison of proteomes, transcriptomes, and metabolomes, as well as associated pathways across all isogenic cell lines. Those studies revealed commonalities, along with important tissue-specific divergencies in protein, mRNA, metabolites, pathways, and drug sensitivities [40]. The studies reported here were aimed at exploring distinctions and commonalities between the karyotypes; biological processes that have been implicated in CIN/aneuploidy; losses, gains, and amplifications of chromosomal regions, i.e., further indications of CIN; single-nucleotide polymorphisms (SNPs); and gene mutation variants that may reflect gene-level instabilities across these cell lines. Substantial amounts of inter- and intra-heterogeneity were found across karyotypes, along with distinctions between SNP frequencies across each chromosome of each metastatic cell line relative to the primary tumor cell line. There were disconnects between chromosomal gains or amplifications and protein levels of the genes in those regions. Overall, our analyses underscore the complexity of tissue-specific differential distinctions between all cell lines from the level of the genome (i.e., aberrant karyotypes) and gene (differences in SNP signatures and mutant variants) to transcript- and protein-level differences within the context of biological processes, which, if dysregulated, mediate CIN. However, commonalities across all cell lines provide opportunities to select biological processes or gains and amplifications as druggable targets that could have efficacy against the primary tumor, as well as metastases. Generation and characterization of the parental MDA-MB-435-tdTomato (435-tdT) fluorescent cell line and subsequent isogenic primary (1°) tumor and metastatic cell lines have been previously described [41,43]. Briefly, orthotopic 1° tumor xenografts were initiated by injection of 435-tdT (2 × 106) cells into the second thoracic mammary fat pad of 5 female NOD-SCID mice. After 13–15 weeks of tumor growth, the mice were sacrificed and, 1° tumor, brain, liver, lungs, and spine were immediately excised, dissected away from fat and muscle, and placed into sterile phosphate-buffered saline on ice. All organs/bones were inspected using fluorescence microscopy for any signs of metastatic burden, which was easily discerned as bright tdT red fluorescence. Areas of fluorescence, along with adjacent tissue, were cut away and placed into 100 mm cell culture plates in 10 mL sterile medium and then immediately minced within a sterile hood. All tissue explants were initially cultured in Roswell Park Memorial Institute (RPMI)-10% fetal bovine serum (FBS) medium supplemented with antibiotics (100 I.U./mL penicillin (pen), 100 μg/mL streptomycin (strep), 100 μg/mL ampicillin, and 100 μg/mL kanamycin) and, as necessary, Fungizone. Medium was refreshed every 2–3 days, and after 2 weeks of culture, the medium was changed to RPMI-10% FBS supplemented with pen/strep. Further studies resulted in optimal media selections: Dulbecco’s modified Eagle medium (DMEM-10% FBS) for the parental cell line and DMEM:Ham’s F12 (50:50)-5% FBS for the 1° tumor and all metastatic cell lines. Cells were cultured in standard humidified incubators at 37 °C and 5% CO2. Proteomics were performed from a single sampling of each cell line’s proteins in the Mass Spectroscopy and Proteomics Facility at the Johns Hopkins University Medical School using tandem mass tags (TMTs) for direct comparisons of all 10 samples in a single tandem MS experiment, as previously described [40]. RNA-seq was performed from a single sampling of each cell line’s RNA at a commercial facility (BGI Americas, San Jose, CA, USA), as previously described [40]; the exome single-nucleotide polymorphism (SNP) datasets from 2 biological replicates were part of the RNA-seq sequencing results. Conventional G-banded chromosome studies were performed using standard techniques. Cells in the exponential phase of growth were incubated with colchicine (Sigma-Aldrich, St. Louis, MO, USA) to a final concentration of 0.8 μg/mL for 4 h and harvested. Cells were then treated with a hypotonic solution of potassium chloride (0.075 mol/L) and incubated at 37 °C for 30 min, fixed in acetic acid:methanol (1:3, v:v), mounted on grease-free chilled 4 °C slides, and air-dried. Giemsa–trypsin banding was performed for chromosome examination. One hundred mitotic cells per sample were analyzed. The abnormal karyotypes were described using the International System for Human Cytogenomic Nomenclature (ISCN 2020). Aneuploidy and chromosomal instability (CIN) are tightly linked, with the former being generated during a loss of high-fidelity cell division, which is a function of the latter [27,44]. Therefore, to better understand and put into perspective any dysregulations of cell division processes that could participate in the generation of the patterns of differential aneuploidies observed across our isogenic cell line model system, we utilized our proteomic and RNA-seq datasets for comparison analyses of the expression levels of 469 proteins/genes from a recently complied list of 701 proteins, which were shown to function in biological processes that are necessary for passage through the synthesis (S), G2, and mitosis (M) periods of cell division [45]. Although the entire set of 701 proteins was validated with respect to functioning during S/G2-mitosis, a subset of 469 proteins was selected because they have well-characterized known functions in cell division processes, while the remaining proteins were described as more recent additions, with some being reported for the first time [45]. We compared the linear fold change (F.C.) of the expression levels of these proteins and their transcripts for the cases of the 1° tumor cell line vs. parental cell line and each of the metastatic cell lines vs. the 1° tumor cell line. We selected and catalogued proteins and transcripts across a broad F.C. range of ≤−1.25 ≥1.25 to be consistent with our previous analyses [40,41] but focused on and stressed moderate-to-high F.C.s, i.e., those ≤−1.5 (see Result Section 3.2 and Discussion). The biological processes with which these proteins were functionally associated were cell cycle regulation, centrosome regulation, cytokinesis, chromosome partition, DNA condensation, kinetochore formation, microtubule regulation, nuclear envelope regulation, spindle assembly and regulation, spindle checkpoint, DNA damage, DNA replication, DNA metabolism, and chromatin organization [45]. The targeted next-generation sequencing (NGS) assay has been previously described [46,47]. DNA was extracted from cell lines by conventional methods (Qiacube; Qiagen, Hilden, Germany), and DNA concentration was assessed using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Library preparation was performed using Kapa Roche HyperPrep reagents (Roche Diagnostics, Inc., Wilmington, MA, USA); hybrid capture used 40,670 probes with provided reagents (Integrated DNA Technologies, Inc., Coralville, IA, USA), and products were sequenced using a NovaSeq 6000 with NovaSeq Rapid Cluster and SBS v2 200-cycle reagents with Illumina paired-end technology (Illumina, Inc., San Diego, CA, USA). An in-house variant and copy number variant (CNV) caller software (MDL VC 10) and CNV kit software version 0.9.6 (https://cnvkit.readthedocs.io/en/stable, last accessed 30 June 2022) were used to generate variants (single-nucleotide variants, insertion–deletion variants, etc.) and genome-wide copy number discovery from the targeted NGS data. Only specimens with more than a 1000× unique sequencing read depth were processed through gene variant/mutation and CNV analysis pipelines. Copy number variants were determined using autosomal log2 ratio thresholds set at 1.3, 0.6–1.0, and −1.0 for the detection of amplification, gain, and loss, respectively. Analysis was performed using human reference sequence genome assembly hg19 (National Center for Biotechnology Information build GRCh37/hg19). Conventional cytogenomic analyses revealed complex karyotypes with multiple structural and numerical chromosome abnormalities across all cell lines. All cell lines were hyperdiploid with similar modal numbers of chromosomes of 56, 56, 56, 55, and 56 for the 1° tumor, brain, liver, lung, and spine metastatic cell lines, respectively. Nevertheless, comprehensive karyotyping analyses provided evidence of a vast amount of intra- and intercell line karyotype heterogeneities, with the overall numbers of chromosomes ranging from 54 to 58 (Figure 1, Figure 2 and Figure 3). As seen in Figure 1, the modal karyotype (outlined in red, panel Pa-1) for the parental cell line differed substantially from four (panels: Pa-2–Pa-5) representative examples of distinctly different karyotypes, i.e., intrakaryotype heterogeneity (red arrows), found in the same population of cells. Figure 1 also shows a pattern of intrakaryotype heterogeneity (blue arrows) in the 1° tumor cell line population (panels: Tu-1–Tu-5) when comparing its model karyotype (outlined in blue, panel: Tu-1) to four different representative karyotypes (panels: Tu-2–Tu-5), which indicates that all five karyotypes are different. Figure 1 also demonstrates a substantial intercell line heterogeneity between the modal parental karyotype and all five 1° tumor karyotypes (red arrows). The modal parental karyotype exhibited numerical chromosome abnormalities, such as gains of chromosomes 1, 2, 3, 4, 5, 7, 9, 11, and 15; heterozygous (one-copy) loss of chromosomes 8, 14, 19, and 21; and homozygous (two-copy) loss of chromosome 13, as well as structural chromosome abnormalities, including a derivative 1;7 chromosome; an isochromosome 7q; additions of genetic material to chromosomes 1q21, 3q12, 11p14, 15p11.2, 18p11.2, 19p13, and 20q13.2; a duplication of chromosome 6p, which leads to a net imbalance of four copies of the 6p21.3-p22 segment; a paracentric inversion of 9q; a terminal deletion of chromosome 12p; and a gain of seven unidentified marker chromosomes (M1–M7). The modal 1° tumor’s karyotype differed from the modal parental karyotype, with a homozygous loss of chromosome 8, heterozygous losses of chromosomes 6 and 22, and a gain of four unidentified marker chromosomes (M8–M11) (Figure 1). Figure 2 shows, in the upper left-hand panel, the modal karyotype of the 1° tumor cell line, which is outlined in blue and separated from the other karyotypes in the figure by a black border along its bottom and right sides. The modal karyotype of the metastatic brain cell line (panel: Br-1) to the right of the modal 1° tumor karyotype is outlined in orange. Compared to the modal 1° tumor karyotype, the modal brain karyotype had a gain of two abnormal number 6 chromosomes characterized by additional material added to the 6q13 arm, i.e., a gain of 6q11-6q13, a loss of five marker chromosomes (M5, M7–M9, and M11, blue type), and a gain of two novel marker chromosomes (M13 and M, blue arrows). Inter- and intrakaryotype heterogeneities between the modal karyotype of the 1° tumor cell line and the five brain cell line karyotypes, as well as between the model brain cell line karyotype and four (panels: Br-2–Br-5) additional representative brain cell line karyotypes, are indicated by blue and orange arrows, respectively, and it can be noted that the five brain cell lines are distinctly different. The five right-hand-side karyotypes in Figure 2 are the modal karyotypes of the metastatic liver cell line (panel: Li-1) outlined in violet, along with four (panels: Li-2–Li-5) other liver cell line karyotypes. In comparison to the modal 1° tumor karyotype, the modal liver cell line karyotype had a heterozygous loss of chromosome 10 (blue arrow), along with four marker chromosomes (M2, M5, M9, and M10 in blue type) and gained five novel marker chromosomes (M12, M13, and Ms, blue arrows). The distinctions between the liver cell line’s modal karyotype (outlined in violet, panel: Li-1) and the four (panels: Li-2–Li-5) other liver karyotypes are indicated by violet arrows, while the interkaryotype differences between these karyotypes and the modal 1° tumor karyotype are indicated by blue arrows. The variations between the brain and liver cell lines’ karyotypes are not indicated due to the complexity of the comparisons between 10 karyotypes; however, it can be noted that none of the representative brain and liver karyotypes are the same. In Figure 3, a comparison between the modal karyotype of the 1° tumor cell line (upper-left-hand side), the modal karyotypes of the lung cell line (panel: Lu-1, outlined in green), and the modal karyotype of the spine cell line (panel: Sp-1, outlined in dark red) again illustrate the intra- and interkaryotype distinctions between these cell lines. Thus, comparison between the modal karyotype of the 1° tumor and the modal karyotype of the lung cell line (panel: Lu-1) indicates that the latter loses abnormal chromosomes 7 and 15 (blue arrows) and two marker chromosomes (M9 and M10, blue type) and gains chromosomes 4 and 6 (blue arrows), along with a novel marker chromosome (M, blue arrow). Finally, relative to the modal 1° tumor cell line’s karyotype, the modal spine cell line karyotype (panel: Sp-1) gained two abnormal number 6 chromosomes (blue arrows), with additional material added to the 6q13 arm (blue arrows), in addition to a loss of abnormal chromosome 3 (blue arrow) and five marker chromosomes (M4, M5, M8, M9, and M11, blue type), as well as a gain of four novel marker chromosomes (M12, M13, and Ms, blue arrows). The two central karyotypes (panels: Lu-2 and Lu-3) in Figure 3 are additional lung cell line karyotypes, with interkaryotype distinctions between these and the modal 1° tumor karyotype indicated by blue arrows and losses of marker chromosomes shown in blue type. In these cases, intrakaryotype differences are indicated by green arrows. Surprisingly, a karyotype of the lung cell line (panel: Lu-2) exhibited an apparent gain of chromosome 8 that was not observed in any of the 1° tumor karyotypes (Figure 1), which indicates that this karyotype was derived from a rare, unobserved 1° tumor subclone that had retained chromosome 8 from the parental cell line (Figure 1). In the lower-right-hand side (panel: Sp-2) is a second spine cell line karyotype with an intrakaryotype distinction indicated with a dark red arrow and interkaryotype differences between spine and 1° tumor karyotypes indicated by blue arrows. It can be noted that all three lung (Lu-1–Lu-3) cell line karyotypes are different from each other, as well as from the two spine (Sp-1 and Sp-2) cell line karyotypes. In addition, the three lung cell karyotypes are distinct from all brain and liver cell line karyotypes, and the two spine cell line karyotypes differ from all the liver cell line karyotypes. However, the non-modal spine karyotype (panel: Sp-2) is identical to the modal brain cell line karyotype (Br-1, Figure 2), which may indicate at least a limited conserved adaptation to different tissue microenvironments. A summary of the differences between each cell line’s modal karyotype relative to the 1° tumor’s modal karyotype is presented in a karyogram (Figure 4), which indicates that although relative to the 1° tumor cell line, substantial amounts of genetic material were altered across several characteristic diploid chromosomes or chromosome regions in the metastatic cell lines, generally, several abnormal chromosomes from the 1° tumor were retained, and most of the divergent genetic changes in the metastatic karyotypes involved large changes in marker chromosome content. To gain a better understanding of factors that could compromise cellular processes of cell division (S/G2-mitosis) and, consequently, be involved in driving/maintaining chromosomal instability (CIN) and, subsequently, aneuploidy, we evaluated changes in the expression levels of the proteins (in our proteomic dataset) of these processes. Table 1 shows the linear fold change (F.C.) in the range ≤−1.25 ≥1.25 of 1° tumor cell line proteins relative to the parental cell line proteins in the cell division processes listed in the Methods section, except for the nuclear envelope regulation process, as no 1° tumor protein levels of this process were found to have changed relative to the parental cell line levels. Chromosomal locations are also given, and notably, despite not being observed in the karyotypes presented in Figure 1, three genes of the proteins (ESCO2, MTBP, and RAD54B) are located on chromosome 8. This is consistent with the finding of a chromosome 8 in one of the lung cell line karyotypes and supports the suggestion that the 1° tumor harbored a subclone that retained this chromosome from the parental cell line and/or that chromosome 8 genetic material was incorporated into one or more of the 1° tumor’s marker chromosomes. Sixty of the 1° tumor proteins were found to be associated with the various biological processes, and of these, 75% exhibited increased levels of expression (Table 1). At the same time, 83% of the 60 proteins exhibited no change in transcript (mRNA) levels. Nonetheless, as indicated in Table 1 (bold type and underlined F.C. values), twelve proteins were associated with their corresponding transcripts. Of these, three (TUBB3, PCLAF, and BLM) had elevated levels of expression, as did their matched proteins, while another three (MTBP, RAD54B, and TYMS) had diminished levels of expression, as did their matched protein counterparts; notably, in all of the six remaining matched transcripts/proteins (PRR11, ZW10, HIST1H3A, HIST1H3C, HIST1H3D, and HIST1H3G), we found that decreased transcript levels were matched to increased protein levels. As such, this mismatch of protein levels and their transcript levels is supporting evidence that a decrease in transcript levels does not necessarily reflect the status of their protein counterpart levels. Overall, the fact that only 17% of proteins could be matched to their transcripts reflects the established differential regulation of the levels of transcripts and the levels of their corresponding proteins, as discussed previously [40]. On balance, relative to the parental cell line, most of the 1° tumor proteins exhibited increased levels of expression in the indicated processes (particularly in responses to DNA damage), which participate in accurate error-free transversion through S/G2-mitosis. Consequently, these results can be interpreted as a measure of decreased CIN or increased stability in the 1° tumor’s accuracy of traversing S/G2 mitosis over that of the parental cell line. The analysis of these S/G2 mitosis-associated processes and proteins was extended to a comparison of these proteins in the metastatic cell lines relative to the 1° tumor cell line (Table 2 and Supplemental Tables S1 and S2). As in Table 1, in Table 2, Table S1 and S2, proteins with transcript counterparts are indicated by bold, underlined type and the percentage of these was 22.1% for brain, 23% for liver, 20% for lung, and 25% for spine, which again indicates that the bulk of these proteins were not matched to their transcript counterparts. However, in these cases, all but two (SMC in lung and HMGB1 in brain) were in the same direction of change (increased or decreased levels) as their associated proteins. In these tables, when considering all four metastatic cell lines, beige shading indicates that no transcript counterparts were observed for the proteins in these processes. Table S1 shows relatively moderate-to-low decreases in protein levels in all the biological processes listed in Methods. However, Table S2 shows augmented protein levels, which were found only in centrosome regulation, kinetochore formation, microtubule regulation, DNA damage, DNA replication, and chromosome organization, while no increases in protein levels were found in cell cycle regulation, cytokinesis, chromosome partition, chromosome condensation, nuclear envelope regulation, spindle assembly and regulation, spindle checkpoint, or DNA metabolism. Table 2 shows relatively high levels of diminished protein levels in all biological processes, except for cytokinesis, as well as nuclear envelope regulation, where no proteins were observed to have F.C.s. The total number of proteins for the metastatic cell lines was 136 for brain, 74 for liver, 60 for lung, and 28 for spine. Of these total proteins, the number that were decreased in each metastatic cell line was 104 (76.5%) for brain, 60 (81.1%) for liver, 50 (83.3%) for lung, and 16 (57.1%) for spine. Furthermore, when considering the total number of proteins found in each metastatic cell line, the percentages that were at the higher levels of decreased expression, i.e., those solely represented in Table 2, were similar between the brain and lung cell lines, at 36.8% and 40%, respectively, and lower in the liver and spine cell lines, at 17.6% and 10.7%, respectively. In summary, metastatic cell lines exhibited a decline in the majority of proteins associated with S/G2 mitosis processes, which can be interpreted as a measure of increased CIN of these cell lines’ genomes. Alternatively, although there was a likely decrease in the competence/efficiency of these biological processes in the metastatic cell lines, these cell lines may have acquired an increase in compensatory mechanisms to stabilize mitosis/cytokinesis so as to minimize increased aneuploidy, which could promote cellular survival. Along the lines of the analysis described above for Table 2, a protein abundance measure, i.e., percentage of the number of proteins from each metastatic cell line in each of the S/G2 mitosis processes relative to the total number of all proteins in the process, was calculated as an estimate of which of the biological processes may be most impacted by the observed F.C.s (≤−1.25) in protein levels. It was reasoned that higher percentages would likely reflect a higher impairment/dysregulation of the normal functioning of a given biological process. Table 3 indicates that DNA metabolism (yellow shading) was the most likely process to be dysregulated by proteins that are involved in modulating this process. This possible effect was seen across three cell lines (brain, liver, and lung) in instances in which percentages indicate that greater than 40–50% of the pathway would be compromised. We then used roughly 25–30% (green shading) as a minimal cutoff value to decide which other processes might be dysregulated within, as well as across, cell lines. Using these criteria, DNA condensation would likely be dysregulated across the brain, liver, and lung cell lines, with cytokinesis, chromosome partition, microtubule regulation, spindle checkpoint, and DNA replication likely dysregulated in the brain cell line. Using the same metric, we also analyzed the differential regulation of transcript levels for a comparison with protein levels in the S/G2 mitosis processes. Table 4 indicates that a much larger number of transcripts with F.C.s ≤ −1.25 were found in the liver, lung, and spine cell lines compared to the number of proteins (Table 3). Consistent with the findings of the percentage of proteins associated with DNA condensation (Table 3), a large number of transcripts linked to DNA condensation were decreased in liver, lung, and spine. A decrease in transcript levels associated with DNA metabolism in the brain cell line is consistent with the findings of decreased protein levels in this process (Table 3). Notably, the liver cell line exhibited the greatest numbers of decreased levels of transcripts, which could potentially impact all listed biological processes, except for DNA metabolism and chromatin organization (Table 4). Along with DNA condensation, relatively moderate numbers of diminished transcripts of the lung cell line could possibly affect centrosome regulation, chromosome partition, microtubule regulation, DNA damage, and DNA replication. Overall, Table 3 and Table 4 again indicate a differential regulation of protein levels relative to their mRNA counterparts across all cell lines, which was very striking in the liver cell line. To determine whether there were gene-level instability differences between the 1° tumor and parental cell lines, as well as between the 1° tumor cell line and each metastatic cell line and between metastatic cell lines, we analyzed the linear fold change in SNP frequencies in each chromosome in each cell line. Figure 5 shows plots of the resulting datasets where linear F.C.s in the frequencies of the 1° tumor cell line/parental cell line or metastatic cell line/1° tumor cell line SNP ratios are plotted and ratios between ≤−1.25 and ≥1.25 are bounded by red lines. Figure 5A indicates that the mean values of the SNP 1° tumor/parental ratios were ≥1.25 for 14 chromosomes (#s: 3, 5, 6, 9–16, 18, 21, and X; Table 5), which indicates that the frequencies of the occurrence of gene-specific SNPs for the majority the 1° tumor genome increased relative to the parental cell line’s genome. Based on this metric, at the gene level, the 1° tumor exhibited increased instability relative to the parental cell line. The notable exception was chromosome 4, where the number of SNPs in the 1° tumor cell line decreased (mean F.C = −1.64, Table 5) relative to the parental cell line, which indicates an increased stability against SNP events. However, there was no change in SNP frequencies in eight chromosomes (#s: 1, 2, 7, 8, 17, 19, 20, and 22; Table 5). Similarly, Figure 5B–E show the linear F.C. (metastatic cell line/1° tumor cell line ratio) in SNP frequencies in the genes of each chromosome for each of the metastatic cell lines, brain, liver, lung, and spine, respectively. The majority of the chromosomes in both the brain and liver cell lines exhibited mean increased SNP ratios (≥1.25, decreased stability), i.e., across 13 (#s: 1, 2–5, 8, 9, 12, 15, 17, 18, 21, and 22; Table 5) and 14 (#s: 2–4, 6, 8, 10, 12, 14–17, 19, 22, and X; Table 5) chromosomes, respectively (Figure 5B,C). No F.C.s in SNP frequencies, i.e., ratios of ~1, were observed for any of the remaining chromosomes in these two cell lines. For the lung cell line, only the genes on chromosome 15 showed an increase in instability (mean F.C. = 1.54 in the number of SNPs) relative to the 1° tumor cell line, while five chromosomes (#s: 7, 11, 20, 21, and X; Table 5) had gene-level increases in stability, i.e., mean linear F.C.s ≤ −1.25. For the spine cell line, five chromosomes (#s: 4–6, 8, and 22; Table 5) had increased mean linear F.C.s in SNPs, i.e., gene-level increases in instability relative to the 1° tumor, and three chromosomes (#s: 13, 18, and 21; Table 5) had decreases in instability relative to the 1° tumor cell line. All other chromosomes in the lung and spine cell lines showed no changes in F.C. ratios for SNPs relative the 1° tumor cell line. It must be emphasized that the F.C.s shown in Figure 5 represent two different sets of separate chromosomal ratios, i.e., 1° tumor cell line/parental cell line ratios and each metastatic cell line/1° tumor cell line ratios, which obscures the findings of the compounded increases in SNP frequencies in metastatic cell line chromosomes above those that occurred in the 1° tumor relative to the parental cell line. Thus, Table 5 shows that many of the increased SNP frequencies in chromosomes (#s: 3, 5, 6, 9, 10, 12, 14, 15, 16, 18, and 21) of the 1° tumor further increased in the metastatic cell lines. A compilation of the linear F.C. of SNP frequencies for each chromosome in the 1° tumor cell line relative to the parental cell line and metastatic cell lines relative to the 1° tumor cell line is shown in Figure 6, which can be regarded as reflecting the F.C.s of the collective exome SNP frequencies for each cell line and, hence, changes in genomic instability at the level of the exomes. Figure 6 indicates that, on average, relative to the parental cell line, the 1° tumor cell line cell line had gene-level instabilities, i.e., increased F.C. SNP frequencies across its exomes (mean F.C. = 1.36). This was also the case for the brain and liver cell lines’ average increases in F.C.s in exome-wide SNP frequencies (mean F.C.s = 1.38 and 1.36 for brain and liver, respectively) relative to 1° tumor cell line, while, on average, relative to the 1° tumor cell line, the lung and spine cell lines’ exome-wide F.C.s in SNP frequencies remained unchanged (mean F.C.s = 0.93 and 1 for lung and spine, respectively); nevertheless, as indicated above, notable increases, along with decreases in gene-level stabilities, were observed for specific chromosomes of the lung and spine cell lines. Consistent with these results, Table 6 indicates that linear F.C.s in SNP frequency comparisons between the individual chromosomes across cell lines were fewer in the brain and liver cell lines vs. the 1° tumor cell line than for the lung and spine cell line vs. the 1° tumor cell line, reflecting the results presented in Figure 6. Similarly, comparisons between the brain and liver cell lines show only two that were significantly different (Table 6), which is consistent with Figure 6. Table 6 also indicates that 15 of the 23 comparisons between the liver and lung cell lines were significantly different, as well as 10 significant differences in comparisons between the liver and spine cell lines, which is again consistent with Figure 6. However, there were fewer than expected significant differences between the brain and lung (only two differences), as well as between the brain and spine (only three differences); however, this was likely due to the relatively high amount of inherent variance between the biological replicates of the brain dataset. On the other hand, given the similarity between the lung and liver cell line plots in Figure 6, the finding that only a few of the comparisons in Table 6 were significantly different was to be expected. In the present analysis, it became apparent that large portions of the parental cell line’s karyotypes were retained in the1° tumor’s karyotypes, as well as across the metastatic cell line karyotypes. This led us to consider an analysis of a small fraction of conserved yet aberrant chromosomal regions that likely contribute to the successful growth of the 1° tumor, as well as dissemination and growth of metastasis, which could also provide insights into druggable targets across all manifestations of a metastatic disease. As such, Figure 7 shows three such large chromosomal alterations that were retained across all cell lines. An interstitial amplification within chromosome arm 7q increased the copy numbers of 33 genes (Figure 7A). Deviations from the normal diploid copies to three copies of MNX, XRCC2, KMT2C, CHPF2, and EZH2; to five copies of EPHB6, PRSS1, MGAM, BRAF, MET, RINT1, and EPHB4; and to four copies for the other 21 genes (Figure 7A). A search for reports (PubMed) of known activities of these genes in breast cancer showed that only three (CHPF2, KEL, and CCT6P1) have not yet been associated with this cancer. Figure 7B shows that a gain in the entire chromosome arm 20p increased the copy number of 17 genes to 3 copies for GNAS, CD40, PTPRT, and MAFB and to 4 copies for the remaining 13 genes. Only MAFB has not been reported in breast cancer. Figure 7C indicates the loss of 6sixgenes due to an interstitial loss of chromosome arm 12p. Consistent with these losses, PTPRO and CDKN1B have been reported to be tumor suppressor genes [48,49,50], which means that these losses may be advantageous for tumor growth and disease progression. However, the four other genes (ETNK1, ABCC9, RECQL, and ETV6) can be upregulated in breast cancer [51,52,53,54]. Given the latter discordant findings, we screened the combined set of genes from all three chromosomal sites against our transcriptomic and proteomic datasets to determine whether the genomic amplifications, gains, and losses were reflected in the transcriptomes and proteomes of these cell lines. We screened for F.C.s between ≤−1.25 and ≥1.25 of the 1° tumor and metastatic cell lines relative to the parental cell line, as well as the metastatic cell lines relative to the 1° tumor cell line. These analyses indicated that a large portion of the genes (amplified, gained, or lost) exhibited differential expression (tissue-context-specific) of transcript counterparts that diverged (increased or decreased) from their amplifications, gains, or losses relative to their genes (Table 7, Table 8 and Table 9). Similarly, we found tissue-specific differential divergences in the proteins of amplified or gained genes (Table 10 and Table 11), as well as low levels of concordance between changes found in transcript F.C.s relative to these genes and the changes found in F.C.s of their protein counterparts (compare Table 7 and Table 8 to Table 10 and Table 11). In addition, some of the amplified genes (Figure 7A), such as MNX1 (homeodomain family, i.e., developmental gene), PRSS1 (germline-associated gene), CCT6P1 (pseudogene), and GRM3, were not represented in our transcriptome dataset and therefore not recorded in Table 7. The lack of representation of these genes in the transcriptome dataset was largely reflected in our proteome dataset, where MNX1, GRM3, and CCT6P1 were also not found but PRSS1 was represented (Table 10). Moreover, several more amplified or gained genes (Figure 7A,B), such as XRCC2, KEL, MGAM, SMO, GRM8, PIK3CG, RELN, CD36, MAGI2, SEMA3A, RTEL1, PTPRT, MAFB, and ASXL1, were absent from the proteome dataset, regardless of transcript level. The interstitial loss of genes in chromosome arm 12p (Figure 7C) also exhibited tissue-type-dependent differential F.C.s in the expression of transcripts (Table 9). However, despite increases in some of the levels of expression of the transcripts of these genes, relative to the parental cell line, there was an apparent loss of expression of five of these genes at the protein level; ETNK1, ABBC9, PTPRO, ETV6, and CDN1B proteins were not found in our proteomic dataset, while RECQL was observed but with no changes in expression levels across all tissue types. Overall, these F.C. comparisons proved to be consistent with our previous findings that transcript and protein levels are not generally found to be correlated [40] and extend those results to differential changes in chromosome-level gene expression, regardless of a state of gene amplification, gain, or loss, which reflects compounded complexities due to changes influenced by tissue context. NGS was performed for the parental cell line, 1° tumor, and metastatic cell lines with mean unique sequencing reads ranging from 1241× to 1485×. A total of 143 variants were found among these cell lines. Of these, 125 variants (87%) were shared across all cell lines in this study, while 18 variants (13%) were presented either only in one cell line or shared in two to five cell lines (Table 12). The parental cell line had five variants: DDX41, GRIN2A, LILRB1, PLCG1, and PCLO, which were not detected in the 1° tumor or metastatic cell lines (Table 12). GRIN2A is a subunit of the NMDA glutamate receptor and is recurrently altered by mutation in various cancer types. The GRIN2A E1123* variant, as found in the parental cell line, is likely oncogenic with a likely loss of function. The lung metastatic cell line had NHS and PIK3R1 variants, and the liver metastatic cell line had an EIF4A1 variant (Table 12). Both liver and spine cell lines had EPHA2 and ERCC3 variants (Table 12). The 1° tumor had eight variants in MKI67, PRKN, PCLO, POLE, CDKN1C, IGSF3, and MED12 genes, which were also present in the brain and spine metastatic cell lines but not present in the parental cell line (Table 12). Among these eight variants, the two variants in IGSF3 and MED12 were present in the lung and the liver metastatic cell lines, and the three variants in PCLO, POLE, and CDKN1C were present in the liver metastatic cell line (Table 12). The focus of our previous multiomics-based studies was to characterize the transcriptomic, proteomic, and metabolomic distinctions of the isogenic cell lines that were generated from a human xenograft model system of metastatic disease in mice [40,41,42]. Our reasoning was that tissue-specific microenvironments drive altered phenotypes as metastatic cells adapt to each organ. A goal was to emphasize that the biological divergence of metastatic lesions from a 1° tumor needs to be considered for the development of more efficacious treatments against deadly metastasis. Here, by studying karyotypes; biological processes implicated in CIN; SNPs; losses, gains, and amplifications of chromosomal regions; and gene mutation variants across these cell lines, our focus was to expand our understanding of molecular and biological distinctions that exist between tissue-specific metastatic cell lines and their divergence from the 1° tumor cell line, as well as from each other. Cytogenomic studies in clinical settings have consistently demonstrated that copy number variations, ploidy, chromosomal aberrations, and heterogeneity are very often independent prognostic markers of survival and resistance to chemotherapies [4,25,28,30,55,56]. Moreover, an “aneuploidy score” was recently proposed; a high aneuploidy score is associated with a poor outcome in patients undergoing immunotherapy [57]. Nevertheless, most aneuploidy assessments have been performed on primary tumor samples. Although a few studies have reported a comparison of the cytogenomics of primary tumors and a metastatic site [9,58], very few studies have made comparisons across two or more metastatic sites [38]. Within this context, our human xenograft metastatic model system in mice provided us with the ability to assess the aneuploidies of four metastatic cell lines that were generated from specific organs (brain, liver, lung, and spine) and make comparisons of aneuploidies between these cell lines, as well as to aneuploidies of the 1° tumor cell line. Given that implanting parental cells into the mammary fat pad of a mouse drastically changes growth conditions relative to those of cell culture, we began with a cytogenomic comparison between the parental cell line grown in culture and the 1° tumor cell line (Figure 1). This revealed that both cell lines exhibited several different karyotypes and that the 1° tumor cell line had diverged from the parental cell line, with numerical aberrations in the form of gains and losses of entire chromosomes, along with structural aberrations, which, in sum, indicated changes to very large amounts of genetic material. It was also revealed that, although our karyotyping was comprehensive in scope, rare clones were missed, such as a 1° tumor karyotype with a chromosome 8. The latter finding highlights the fact that, due to the vast numbers of cells in a tumor, not every karyotype (clone) can be expected to be directly found and studied, which has implications for the development of therapies that are meant to be broadly effective against all of a tumor’s cells. The scope of this complexity increased when found that the processes involved in the progression of metastasis to brain, liver, lung, and spine caused further evolution, which resulted in a variety of organ-specific karyotypes that differed from the 1° tumor (Figure 2 and Figure 3), as well as between each metastatic cell line (Figure 2 and Supplementary Figures S1 and S2). Thus, in concordance with our earlier multiomic datasets, cytogenomic analyses showed that adaptations to different organ microenvironments resulted in substantial intra- and interkaryotype heterogeneity and metastatic karyotypes that diverged from the 1° tumor karyotypes. To better understand possible causes of this vast inter- and intrakaryotype heterogeneity, we studied CIN. CIN, the loss of the absolute fidelity of chromosomal replication and segregation during cell division, has been established as the principal cause of aneuploidy [6,10,15]. Several forms of CIN have been characterized, including the chromosome–fusion–bridge cycle [59], centrosome amplification [8,10], kinetochore–microtubule attachment errors [14], replicative instability [11], single “catastrophic events” or punctuated evolution [13,60,61], and chromothripsis [16,17,20,23]. Recognizing that CIN is manifested during cell division (mitosis), we sought to link biological processes associated with S/G2-M phases of mitosis through the proteins that participate in these processes [45]. Thus, we screened an established 469 genes in 14 biological processes [45] against our proteomic dataset and catalogued the proteins that had linear fold changes between ≤−1.25 and ≥1.25 relative to the parental cell line in the case of the 1° tumor or relative to the 1° tumor in the case of the metastatic cell lines. We focused on the proteins rather than the transcripts, as we reasoned that proteins are the functional components of these biological processes and would therefore best reflect their status. As described above, based on aneuploidy, it could be reasoned that the 1° tumor cell line had a robust CIN phenotype, yet at the protein level in the S/G2-M analysis, we found that there was an overall increase in proteins of the S/G2-M biological processes, which is an implicit indication of a decreased CIN (Table 1). Understanding this inconsistency will require future studies, but it can be stated that a change from parental cell culture growth to in vivo 1° tumor growth is a likely a reason (among others) for this disconnect. On the other hand, all the metastatic cell lines showed predominant decreases in F.C.s of proteins relative to the 1° tumor cell line across these biological processes (Table 2 and Supplementary Table S1). This could be interpreted as an indication of possible increases in CIN in the populations of the metastatic cell lines and relative to five 1° tumor cell line karyotypes. Nevertheless, without more definitive research, one should consider that conclusions from the S/G2-M analyses of increased or decreased CIN may not be accurately reflected in our karyotype datasets or may be biased due to the relatively limited subsets of protein changes identified among the 469 possible protein changes, i.e., a more comprehensive coverage of the proteins associated with these 14 biological processes could result in more balanced results with findings of either no change in S/G2-M stability or decreased or increased CIN. However, it must be noted that it has been shown that CIN can be experimentally generated by perturbing the expression of selected single proteins [59]. Along these lines, Table 2 indicates that two proteins (CNTROB [62] and NCAPG2 [63]) were ~3- and ~2-fold lower, respectively, across all metastatic cell lines relative to the 1° tumor cell line, which may be adequate to increase CIN across all metastatic cell lines. Moreover, five other proteins (TRIP13 [64], ZW10 [65], PRIM1 [66], CDC45 [67], and RFC3) were found to be decreased by ~1.5 to ~2-fold in brain, liver, and lung cell lines (Table 2). Consequently, overall, the cumulative effect of all the decreases in protein levels (Table 2 and Table S1) would likely substantially increase CIN in the metastatic cell lines relative to the 1° tumor cell line. Importantly, several reports are in concordance with the validity of these S/G2-mitosis/biological process results, i.e., disruptions of several of the biological processes of mitosis does define CIN, which drives aneuploidy, and prognostic, as well as therapeutic, strategies have been proposed based on such findings [10,31,32,33,63,66,67,68,69,70,71]. To gain further insights into the stability of the genomes of the 1° tumor and metastatic cell lines, we analyzed the fold change in SNP frequencies across all chromosomes and the cumulative changes for each cell line’s genome. In the case of the 1° tumor cell line, when considering cumulative changes across all chromosomes, these results indicate a significant average increase in SNP frequencies in the 1° tumor cell line relative to the parental cell line, which demonstrates that controlling factors/processes that modulate SNPs are decreased or compromised in the 1° tumor cell line relative to the parental cell line. Similarly, the brain and liver cell lines exhibited increased instabilities with respect to repairing SNP, causing events such as significant cumulative SNP frequencies exceeding those of the 1° tumor cell line and, by extension, the parental cell line as well. Cumulatively, the frequencies of SNPs in the lung and spine cell line did not change relative to the 1° tumor cell line. In addition, SNP data analyses provided evidence that individual chromosomes have varying degrees of stability toward SNP formation, with the numbers chromosomes and specific chromosomes involved, as well as the amount of change in stability, being a function of tissue type. Thus, we found higher numbers of chromosomes with SNP instability in the 1° tumor, brain, and liver cell lines, while in general, fewer chromosomes with SNP instabilities were found in the lung and spine cell lines. Notably, an increase in SNP stability was infrequently found, i.e., occurring in only one, five, and four chromosomes of the 1° tumor, lung, and spine cell lines, respectively. Our findings of differential tissue-specific distinctions in biological processes implicated in CIN and exome-specific SNP frequencies across cell lines are further indications that tissue-specific biochemical conditions modulate cancer cell evolution during their adaptations to each tissue’s microenvironment. Finally, DNA-based NGS revealed different gene variants among the parental cell line, i.e., the 1° tumor cell line, and metastatic cell lines with various gene-variant allele frequencies, which further supports the idea that selection pressures contribute to various organ-specific alterations to the genome populations of the metastases. Karyotyping revealed that our cell lines are not isogenic, i.e., they are instead populations of a variety of related yet distinctly different aberrant karyotypes. The inter- and intrakaryotype heterogeneities that we observed here starkly reflect the well known histological and genetic profiling descriptions of the complex heterogeneity of solid tumors and metastatic lesions (e.g., [72]). These findings indicate that a reason that aneuploidy is associated with poor prognosis and drug resistance is the large distinct subpopulations of cancer cells present in a primary tumor or metastatic lesion. To better understand the ongoing generation of aneuploidy within metastases, we studied changes in the levels of proteins involved in the biological processes of S/G2-M phases of mitosis as a measure of CIN. These results allow us to conclude that these processes are compromised in all the metastatic cell lines relative to the 1° tumor cell line and, in particular, in the brain and liver cell lines. The SNP analyses support this conclusion. Overall, our analyses underscore the complexity of tissue-specific differential distinctions between all our cell lines from the level of the genome (i.e., aberrant karyotypes) and gene (differences in SNP signatures) to the transcript and protein levels. This is important to note from the perspective of recent clinical practices aimed at developing targeted treatment regimens. This concept is generally aimed at finding a single or a few druggable targets in a patient’s primary tumor, as it is difficult to find targets that are common to a primary tumor and its metastases due to the divergence of the metastatic cells from their primary tumor, as emphasized here. Notably, our results indicate that even a comprehensive search for such dual lesion targets will miss rare clones, and a proportion of these may be resistant to treatment. Consequently, although it cannot completely solve this problem, our biological process results allow us to suggest some possible pan-metastatic therapeutic targets, i.e., the biological processes that were common to all four or at least three metastatic cell lines: CNTROB (centrosome regulation); NCAPG2 (DNA condensation); TRIP13 and ZW10 (spindle checkpoint); and PRIM1, CDC45, and RFC3 (DNA replication). Moreover, in the case of the brain metastatic cell line, the DNA damage process could be added to this list. Furthermore, the findings of our study of the interstitial amplification within chromosome arm 7q, which was retained across all cell lines (including the 1° tumor cell line), CUX1 and its associated pathways emerged as important therapeutic targets [73,74]. Finally, our biological process results indicate that the DNA damage response processes were generally compromised, which indicates that radiation therapy could represent a complementary component to a chemotherapeutic regime.

PMC10000987

Anna Stierschneider,Benjamin Neuditschko,Katrin Colleselli,Harald Hundsberger,Franz Herzog,Christoph Wiesner

Comparative and Temporal Characterization of LPS and Blue-Light-Induced TLR4 Signal Transduction and Gene Expression in Optogenetically Manipulated Endothelial Cells

22-02-2023

endothelial cells,Toll-like receptor 4,lipopolysaccharide,optogenetic control,pro-inflammatory proteins,quantitative mass-spectrometry,chemotaxis,transmigration

In endothelial cells (ECs), stimulation of Toll-like receptor 4 (TLR4) by the endotoxin lipopolysaccharide (LPS) induces the release of diverse pro-inflammatory mediators, beneficial in controlling bacterial infections. However, their systemic secretion is a main driver of sepsis and chronic inflammatory diseases. Since distinct and rapid induction of TLR4 signaling is difficult to achieve with LPS due to the specific and non-specific affinity to other surface molecules and receptors, we engineered new light-oxygen-voltage-sensing (LOV)-domain-based optogenetic endothelial cell lines (opto-TLR4-LOV LECs and opto-TLR4-LOV HUVECs) that allow fast, precise temporal, and reversible activation of TLR4 signaling pathways. Using quantitative mass-spectrometry, RT-qPCR, and Western blot analysis, we show that pro-inflammatory proteins were not only expressed differently, but also had a different time course when the cells were stimulated with light or LPS. Additional functional assays demonstrated that light induction promoted chemotaxis of THP-1 cells, disruption of the EC monolayer and transmigration. In contrast, ECs incorporating a truncated version of the TLR4 extracellular domain (opto-TLR4 ΔECD2-LOV LECs) revealed high basal activity with fast depletion of the cell signaling system upon illumination. We conclude that the established optogenetic cell lines are well suited to induce rapid and precise photoactivation of TLR4, allowing receptor-specific studies.

Comparative and Temporal Characterization of LPS and Blue-Light-Induced TLR4 Signal Transduction and Gene Expression in Optogenetically Manipulated Endothelial Cells In endothelial cells (ECs), stimulation of Toll-like receptor 4 (TLR4) by the endotoxin lipopolysaccharide (LPS) induces the release of diverse pro-inflammatory mediators, beneficial in controlling bacterial infections. However, their systemic secretion is a main driver of sepsis and chronic inflammatory diseases. Since distinct and rapid induction of TLR4 signaling is difficult to achieve with LPS due to the specific and non-specific affinity to other surface molecules and receptors, we engineered new light-oxygen-voltage-sensing (LOV)-domain-based optogenetic endothelial cell lines (opto-TLR4-LOV LECs and opto-TLR4-LOV HUVECs) that allow fast, precise temporal, and reversible activation of TLR4 signaling pathways. Using quantitative mass-spectrometry, RT-qPCR, and Western blot analysis, we show that pro-inflammatory proteins were not only expressed differently, but also had a different time course when the cells were stimulated with light or LPS. Additional functional assays demonstrated that light induction promoted chemotaxis of THP-1 cells, disruption of the EC monolayer and transmigration. In contrast, ECs incorporating a truncated version of the TLR4 extracellular domain (opto-TLR4 ΔECD2-LOV LECs) revealed high basal activity with fast depletion of the cell signaling system upon illumination. We conclude that the established optogenetic cell lines are well suited to induce rapid and precise photoactivation of TLR4, allowing receptor-specific studies. Endothelial cells (ECs) line the inner wall of blood vessels and are located at the interface between circulating blood and the surrounding tissue. Thus, endothelial cells are the first cells exposed to invading pathogens circulating in the blood stream. The endotoxin lipopolysaccharide (LPS) is derived from the outer cell wall of gram-negative bacteria and triggers endothelial activation through a receptor complex consisting of the Toll-like receptor 4 (TLR4), CD14, and MD2 [1]. Subsequent recruitment of the adaptor proteins Toll/interleukin 1-receptor (TIR)–domain-containing (TRIAP) and myeloid differentiation factor (MyD88) initializes the MyD88-dependent pathway leading to early activation of the nuclear factor κB (NF-κB) and the mitogen-activated protein kinases (MAPK) [2,3]. Sequential binding of the TIR-domain-containing adaptor-inducing interferon-β (TRIF) and the TRIF-related adaptor molecule (TRAM) to the TIR domain of TLR4, and the subsequent dynamin-controlled endosomal translocation of TLR4, characterize the MyD88-independent pathway, which culminates in a late-phase activation of NF-κB [4]. The final production of various pro-inflammatory mediators is beneficial in controlling bacterial infections; however, their systemic secretion is a main driver of sepsis and chronic inflammatory diseases [5,6]. The molecular and regulatory mechanisms of LPS/TLR4-induced signaling events have been extensively studied in recent years, accelerating the identification of negative regulators of LPS signaling cascades [7,8,9]. Common strategies for studying physiological processes in inflammation often rely on genetic manipulation of the proteins under study, or the treatment of cells with agonists or antagonists. However, these strategies often lead to irreversible phenotypes in the target cells, or to unintended cytotoxicity and signaling crosstalk due to off-target or pleiotropic effects. Using light to manipulate signaling processes in living cells is one of the most elegant techniques developed in recent years. It involves integrating light-sensitive protein domains of photoreceptors into effector proteins in order to direct them with light stimuli in a spatiotemporal and minimally invasive manner [10]. A multitude of optogenetic switches have already been designed to control the activation, inactivation, localization, stabilization, or destabilization of signaling pathways [11]. In this study, we fused the light-oxygen-voltage (LOV)-sensing domain, isolated from the yellow-green algae Vaucheria frigida aureochrome 1, C-terminally to the full-length TLR4, as well as to different versions in which the extracellular domain of TLR4 was deleted (ΔECD), in order to enable blue-light-induced homodimerization and subsequent activation of TLR4-related pro-inflammatory signaling pathways [12,13]. Since this photoreaction is reversible, endothelial cells with stable integrated TLR4-LOV constructs allow a very specific investigation of the underlying molecular and regulatory mechanisms of the receptor with spatial and temporal precision. The 293Ta (GeneCopoeiaTM, Rockville, MD, USA; LT008) (RRID: CVCL_BT05) were maintained in a DMEM growth medium (Thermo Fisher Scientific, Vienna, Austria; 31053044) supplemented with 100 U/mL penicillin/streptomycin (Thermo Fisher Scientific, Vienna, Austria; 15140-122), 2 mM L-glutamine (Thermo Fisher Scientific, Vienna, Austria; 25030-24) and 10% heat-inactivated fetal calf serum (Thermo Fisher Scientific, Vienna, Austria; A5256801). Human lymphatic endothelial cells (LECs) immortalized by ectopic expression of telomerase reverse transcriptase [14], human umbilical vein endothelial cells (HUVECs) (provided by the Medical University of Graz), and primary peripheral blood mononuclear cells (PBMCs) (ATCC®, Manassas, VD, USA; PCS-800-011™) (RRID:CVCL_WR41) were cultivated in a huMEC medium (InSCREENex, Braunschweig, Germany; INS-ME-1012) supplemented with 100 µg/mL Normocin (InvivoGen, Toulouse, France; ant-nr-1). LECs with stable integrated NF-κB-Gluc reporters and stable integrated TLR4-LOV or TLR4 ΔECD2-LOV constructs were cultivated with 100 µg/mL hygromycin B (Thermo Fisher Scientific, Vienna, Austria; 10687010) and 1 µg/mL puromycin dihydrochloride (Thermo Fisher Scientific, Vienna, Austria; A1113803), respectively. THP-1 (ATCC®, Manassas, VD, USA; TIB-202) (RRID: CVCL_0006) was cultivated in RPMI-1640 (Thermo Fisher Scientific, Vienna, Austria; 32404014), supplemented with 100 U/mL penicillin/streptomycin, 2 mM L-glutamine, and 10% fetal calf serum (Thermo Fisher Scientific, Vienna, Austria; 10270106). Monocytes (THP-1) were differentiated into monocyte-derived macrophages (THP-1 M0) by adding 4 ng/mL phorbol-12-myristat-13-acetate (PMA) (THP, Vienna, Austria; HY-18739) to a complete medium for 24 h and incubating for a further 24 h in a complete medium without PMA. Cells were passaged when a confluency of 90% was reached. For detachment of adherent cells, 0.25% Trypsin-EDTA (Thermo Fisher Scientific, Vienna, Austria; 25200-056) was used. Cell culture flasks (Sarstedt, Nümbrecht, Germany; 83.3911.002) for the maintenance of ECs were pre-coated with 0.5% gelatin (InSCREENex, Braunschweig, Germany; INS-SU-1015). The cloning strategy of the TLR4-LOV constructs and NF-κB-TRE-Gluc reporter used was performed as previously described [13]. TLR4 ΔECD2/15/21/36-LOV was engineered using the following primers: Forward: 5’-AATTTCGGATGGC-3’ (ΔECD2); 5’-TTCAATGGCATCTT-3’ (ΔECD15); 5’-TGGATCAAGGACCA-3’ (ΔECD21); 5’-ACACCTCAGATAAGC-3’ (ΔECD36); and reverse: 5’-[phos]CTTCTCAACC-3’ (ΔECD2/15/21/36). The 293Ta was transiently co-transfected with 7.5 µg of NF-κB-TRE-Gluc, Hygro reporter plasmid (THP, Vienna, Austria; CS-NF-κB-02) or AP-1 cis-Reporting System (Agilent Technologies, Santa Clara, CA, USA; 219073), and 7.5 µg of the engineered TLR4, TLR4-LOV, or TLR4 ΔECD2/15/21/36-LOV plasmid, respectively, in 10 cm Petri dishes using the calcium phosphate precipitation technique [15]. Then, 24 h later, cells were re-seeded and treated as described in Section 2.4. For stable transfection of engineered TLR4-LOV, TLR4 ΔECD2/15/21/36-LOV, and NF κB-TRE-Gluc, Hygro (THP, Vienna, Austria; CS-NF-κB-02), a lentiviral transfection system was performed as previously described [13]. The 293Ta and ECs were seeded into 96-well plates (Sarstedt, Nümbrecht, Germany; 83.3924300) (NF-κB/AP-1 reporter assay), black 96-well plates with an optical bottom (Thermo Fisher Scientific, Vienna Austria; 165305) (p65 localization), 6-well plates (Sarstedt, Nümbrecht, Germany; 83.3920300) (real-time quantitative PCR, Western blotting) or 8-well chambers (ibid, Gräfelfing, Germany; 80841) pre-coated with 0.5% gelatin (InSCREENex, Braunschweig, Germany; INS-SU-1015) (TLR4 localization), and they were grown in a complete medium until they were confluent. Cells were starved in a complete medium supplemented with 2% fetal calf serum for 2 h. Conventional TLR4 activation was induced with 100 ng/mL LPS from Escherichia coli 026:B6 (Thermo Fischer Scientific, Vienna, Austria; 00-4976-03). For light-induced TLR4-LOV activation, cells were exposed to blue light (470 nm, 8 V) using the Amuza LED assay system 10335 (San Diego, CA, USA). To block TLR4 signaling, cells were treated with 1, 10, or 100 µM TAK-242 (resatorvid; Merck, Darmstadt, Germany; 614316-5MG), whereas NF-κB inhibition was provoked by the addition of 0.3, 3, or 30 µM parthenolide (Abcam, Cambridge, United Kingdom; ab120849). NF-κB activation was quantified by measuring gaussia luciferase (Gluc) reporter using the Secrete-PairTM Gaussia Luciferase Dual Luminescence Assay Kit (THP, Vienna, Austria; LF062) according to the manufacturer’s instructions. AP-1 activation was determined by measuring firefly luciferase (Fluc) reporter using the Luc-PairTM Firefly Luciferase HT Assay Kit (THP, Vienna, Austria; LF018) according to the manufacturer’s instructions. Relative luminescence units (RLU) were measured in a plate reader (SpectraMaxi3x, Molecular Devices, LLC, San Jose, CA, USA; Luminescence Glow, Lum 384 cartridge), and normalized to the cell count generated with an imaging cytometer (Mini Max 300, Molecular Devices, LLC, San Jose, CA, USA). For TLR4 and p65 localization, EC monolayers were chemically fixed with 4% paraformaldehyde at room temperature for 30 min at the specified stimulation time points and permeabilized with 0.1% Triton-X-100 (Merck, Darmstadt, Germany; 11332481001) at room temperature for 15 min. ECs were gently washed with PBS and unspecific binding was blocked by incubation with 1% bovine serum albumin (Thermo Fisher Scientific, Vienna, Austria; 15260037) in PBS overnight at 4 °C. Subsequently, ECs were stained with primary antibodies anti-TLR4, h-Toll, CD284 (US Biological, Salem, MA, USA; 042879; 1 to 50 in PBS with 1% bovine serum albumin), or anti-NF-κB p65 antibody (Abcam, CA, USA; ab16502; 5 µg/mL in PBS with 1% bovine serum albumin) at room temperature for 2 h. After washing with PBS-T (0.05%), ECs were incubated with secondary antibody Alexa Fluor 488 goat anti-rabbit IgG (H+L) (Thermo Fisher Scientific, Vienna, Austria; A11008; 4 µg/mL in PBS-T (0.05%)) at room temperature for 1 h. Cell nuclei were stained with Hoechst 33342 (Thermo Fisher Scientific, Vienna, Austria; H1399; 2 µg/mL in PBS-T (0.05%)) at room temperature for 10 min before being washed with PBS-T (0.05%). For TLR4 localization, ECs were mounted with Roti®-Mount Fluor Care (Carl Roth, Karlsruhe, Germany; HP19.1) on high-precision microscope cover glasses (1.5H, Marienfeld, Lauda-Königshofen, Germany; 0107242). Fluorescent images were acquired with a confocal laser scanning microscope (TCS SP8, Leica, Mannheim, Germany) using a 63X glycerol objective (numerical aperture 1.3). Images were analyzed with the Leica Application Suite Version X 3.5.7.23225 software. For p65 localization, fluorescent images of ECs were directly taken from the 96-well plate with an inverted microscope (DMI6000 B, Leica, Mannheim, Germany) using a 63X objective and analyzed with the Leica Application Suite Version X 3.8.0 software. To quantify nuclear localization of NF-κB, the mean ratio of the fluorescence intensity of stained p65 in the nucleus to the cytoplasm was computed using ImageJ [16]. Total RNA was extracted and purified using the RNeasy® Mini Kit (Qiagen, Vienna, Austria; 74104) and 1 µg RNA was reverse transcribed using the Hight Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Vienna, Austria; 4368814) according to the manufacturers’ instructions. For target gene quantification following pre-designed TaqMan® Gene Expression Assays, a pair of unlabeled PCR primers, a TaqMan® probe with a FAM dye label on the 5’-end, a minor groove binder, and a nonfluorescent quencher on the 3’-end, were used: TLR4: Hs00152939_m1, IL-6: Hs00985639_m1, CXCL-8: Hs00174103_m1, CXCL-10: Hs00171042_m1, and IPO8: Hs00183533_m1 (control gene). The qPCR reaction mix contained a final volume of 10 µL of TaqMan® Gene Expression Master Mix (Thermo Fisher Scientific, Vienna, Austria; 4369016), 1 µL of TaqMan® Gene Expression assay, 5 µL of nuclease free water (Ambion, Austin, TX, USA; AM9937), and 4 µL of cDNA template diluted 1:10. All qPCRs were run on the Quant Studio 7 Flex (Applied Biosystems, Foster City, CA, USA; QSTUDIO7FLEX) using the following cycling conditions: 10 min at 95 °C for initial denaturation followed by 45 cycles of 95 °C for 20 s and 60 °C for 1 min. Data were analyzed using the Quant Studio Real-Time PCR Software v1.3 (Applied Biosystems, Foster City, CA, USA). Relative gene expression levels were calculated according to the comparative CT method (2-ΔΔCT) [17]. The mRNA target gene expression levels were computed relative to the endogenous control gene (IPO8). Cells were washed once with PBS and collected in 100 µL ice-cold lysis buffer (500 mM NaCl (Merck, Darmstadt, Germany; S5150), 50 mM Tris-HCl pH 7.4, (Thermo Fisher Scientific, Vienna, Austria; 15568-025), 0.1% SDS (Carl Roth, Karlsruhe, Germany; 1057.1), 1% NP-40 (VWR, Radnor, PA; USA; M158), 1U DNase I (Thermo Fisher Scientific, Vienna, Austria; 89836), 1 U protease, and a phosphatase inhibitor cocktail (Thermo Fisher Scientific, Vienna Austria; 1860932, 78428)). Cell lysates were shaken for 20 min on ice, followed by centrifugation at 12,000 rpm for 20 min at 4 °C. The total protein concentration of the supernatant was determined using a BCA protein assay (Thermo Fisher Scientific, Vienna, Austria; 23227) according to the manufacturer’s manual. A 4x Laemmli sample buffer (Bio-Rad, CA, USA; 1610747) containing 10% ß-mercaptoethanol (Merck, Darmstadt, Germany; M7522) was added to 1 µg protein and incubated at 70 °C for 10 min. For the investigation of transmembrane proteins (TLR4), lysates must not be heated. Protein extracts were subsequently loaded onto 7.5% Mini-PROTEAN TGX Precast Protein Gels (Bio-Rad, CA, USA; 456-1023) and run at 100 V before protein bands were electro-blotted onto nitrocellulose membranes (Bio-Rad, CA, USA; 1704155) using the Trans-Blot® TurboTM Blotting System (Standard, 35 min). Unspecific binding was blocked with 5% non-fat dry milk (New England Biolabs, Frankfurt, Germany; 9999) in PBS-T (0.1%) buffer on a shaker overnight at 4 °C. The primary and secondary antibodies used are specified in Table 1. Membranes were incubated with primary antibodies following secondary antibodies at the stated dilution and diluent at room temperature for 2 h each. Immunoblots were developed by applying the Clarity Western ECL Substrate (Bio-Rad, CA, USA; 1705060) according to the manufacturer’s instructions. Proteins were visualized with the chemiluminescence detector of the ChemiDoc MP platform (Bio-Rad, CA, USA; 17001402). Opto-TLR4-LOV LECs were seeded in T25 cell culture flasks (Sarstedt, Nümbrecht, Germany; 83.3910.002) and grown in a complete medium until confluent. EC monolayers were subsequently washed twice with PBS and stimulated with LPS or blue light, or left untreated as described in Section 2.4, in 3 mL huMEC medium (InSCREENex, Braunschweig, Germany; INS-ME-1012) without fetal calf serum. After incubation at 37 °C for 2 h, 6 h, or 16 h, the medium was collected and the proteins contained were precipitated using cold ethanol (ROTIPURAN® ≥99.8%, p.a., Carl Roth, Karlsruhe, Germany; 9065.1) and stored at −20 °C. After centrifugation at 3500 ×g, 4 °C for 30 min, the supernatant was discarded and the proteins were dried and dissolved in an 80 µL lysis buffer (8 M Urea, 50 mM NH₄HCO₃). After subsequent further centrifugation at 2000 ×g for 10 min, the supernatant was transferred and stored at −20 °C until further use. The protein concentration was determined using a BCA protein assay (Sigma-Aldrich, Vienna, Austria; 71285-3) according to the manufacturer’s instructions. A total of 20 µg of protein per sample was reduced and alkylated using TCEP and IAA and digested successively using Lys-C (FUJIFILM Wako Chemicals U.S.A. Corporation, Richmond, VA, USA; 125-05061) for one hour and Trypsin (Promega, Walldorf, Germany; V5113) for 16 h. Peptides were cleaned using Sep-Pak tC18 1 cc Vac Cartridges (Waters, Vienna, Austria; WAT054960), then dried and stored at −20 °C. For HPLC-MS/MS analysis, samples were analyzed using an Ultimate 3000 RSLCnano system coupled to a Orbitrap Eclipse Tribrid mass spectrometer (both Thermo Fisher Scientific, Vienna, Austria). The dried samples were dissolved in 40 µL mobile phase A (98% H2O, 2% ACN, 0.1% FA) and measured in duplicates. A total of 2 µL was injected onto a PepMap 100 (C18 0.3 × 5mm) TRAP column and analyzed using a PepMap RSLC EASY-Spray column (C18, 2 µm, 100 Å, 75 µm × 50 cm, Thermo Fisher Scientific, Vienna; ES903). Separation occurred at 300 nL min-1 with a flow gradient from 2% to 35% mobile Phase B (2% H2O, 98% ACN, 0.1% FA) within 60 min, resulting in a total method time of 80 min. The mass spectrometer was operated with the FAIMS Pro System in the positive ionization mode at CV-45. The scan range was 375–1500 m z-1 using a resolution of 120,000 at 200 m z-1 on MS1 level. Isolated peptides were fragmented using HCD at a collision energy of 30% NCE and fragments were analyzed in LIT using rapid scan mode. For protein identification and quantification, FragPipe (v18.0) was used selecting the LFQ-MBR workflow, and employing MSFragger (v3.5) [18] and IonQuant (v1.8.0) [19]. For statistical evaluation, Perseus (v2.0.6.0) [20] was used. Measurement duplicates were averaged, and protein groups were filtered according to their treatment, demanding at least three out of four values to be valid in at least one group. The remaining missing values were replaced with a down shift of 1.8 and a width of 0.3 to allow statistical testing for all remaining protein groups. ClueGO application [21] in Cytoscape [22] was used to group proteins according to GO terms. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [23] partner repository with the dataset identifier PXD038764. The chemotactic response of THP-1 cells to the medium of LPS or blue-light-stimulated opto-TLR4-LOV LECs was performed using Transwell® 96-well permeable supports (Merck, Darmstadt, Germany; CLS3388) and Transwell® 24-well permeable supports (Szabo-Scandic, Vienna, Austria; COS3421). Thereafter, opto-TLR4-LOV LECs were seeded in T25 cell culture flasks (Sarstedt, Nümbrecht, Germany; 83.3910.002) and grown in a complete medium until confluent. EC monolayers were subsequently washed twice with PBS and stimulated with LPS or blue light, or left untreated as described in Section 2.4, in 3 mL huMEC medium (InSCREENex, Braunschweig, Germany; INS-ME-1012) without fetal calf serum. After incubation at 37 °C for 6 h, the medium was collected and applied to the lower chamber of the transwell plates (100 µL to the 96-transwell plates and 600 µL to the 24-transwell plates). THP-1 cells were stained with Hoechst 33342 (Thermo Fisher Scientific, Vienna, Austria; H1399, 2 µg/mL) at 37 °C for 10 min and resuspended at a density of 1,000,000 c/mL in a huMEC medium without fetal calf serum, then applied to the upper chamber of the Transwell plates (50 µL to the 96 permeable supports and 100 µL to the 24 permeable supports). Post-incubation at 37 °C for 2 h, the remaining THP-1 cells of the upper chamber that did not migrate through the filters of the permeable supports were aspirated. THP-1 cells attached on the upper side of the filter were gently removed with a cotton swab. THP-1 cells that migrated through the filters of the 96-well permeable supports were quantified by measuring relative fluorescence units with a plate reader (SpectraMaxi3x, Molecular Devices, LLC, San Jose, CA, USA; Fluorescence Intensity cartridge; excitation 350 nm, emission 461 nm) in a well scan manner, whereas THP-1 cells that migrated through the filter of the 24-well permeable supports were visualized with an inverted microscope (DMI6000 B, Leica, Mannheim, Germany) using a 40X objective and analyzed with the Leica Application Suite Version X 3.8.0 software. An EC monolayer breakdown (transmigration) assay was performed using the electrical cell-substrate impedance sensing (ECIS) model 9600Z (Applied BioPhysics, Troy, NY, USA). Opto-TLR4-LOV LECs, opto-TLR4 ΔECD2-LOV LECs and opto-TLR4-LOV HUVECs were seeded at a density of 40,000 cells/100 µL of complete medium onto a 96-well plate containing 20 gold film electrodes per well (96W20idf PET; ibidi, Gräfelfing, Germany; 72098) pre-coated with 1 mg/mL neutralized rat tail collagen type I (Thermo Fisher Scientific, Vienna, Austria; A1048301) at room temperature for 10 min, and 2 µg/mL bovine plasma fibronectin (Thermo Fisher Scientific, Vienna, Austria; 33010018) at 37 °C for 45 min. A small-amplitude AC signal (4000 Hz) (I) was imposed across the electrodes, onto which cells were attached, resulting in a potential (V) across the electrodes that was measured using the ECIS instrument [24]. The impedance (Z) was determined by Ohm’s law Z=V/I. ECs were grown to confluent monolayers for 27 h before being treated with blue light, LPS (100 ng/mL), 50,000 c/well THP-1, 50,000 c/well THP-1 M0, 50,000 c/well PBMC, combinations of blue light or LPS, and the mentioned cell types, or left untreated. EC breakdown was assessed by continuous resistance measurement for 3 h. All experimental figures show data from at least two technical replicates, while n represents the number of biological replicates. The mean ± standard deviation of all data was computed and graphically displayed using GraphPad Prism version 9.3.0 for Windows (GraphPad Software, San Diego, California USA, www.graphpad.com). Post-ANOVA multiple comparisons relative to the control were performed using Dunnett’s or Šidák’s tests and they are specified in the figure legend. Probability values of p < 0.05 were considered statistically significant. To investigate signaling pathways and target gene expression, we developed different TLR4 (full-length, ΔECD) constructs as an optogenetic tool that allowed precise temporal and reversible stimulation of the TLR4 signaling pathway activation and target gene expression. We first fused the light-oxygen-voltage domain (LOV), a blue light-sensing protein domain isolated from the yellow-green algae Vaucheria frigida aureochrome 1 (VfAU1-LOV), to the C-terminus of TLR4 (TLR4-LOV). Next, we engineered TLR4-LOV constructs with different truncation variants of the extracellular domain. TLR4 ∆ECD2-LOV contains a deleted LPS/MD2 interaction domain and TLR4 ∆ECD15/21/36-LOV are constructs with different LPS/MD2 interaction domain and dimerization domain deletions (Figure 1a). Previously, the blue-light-sensitive TLR4-LOV chimeric gene stably integrated into a PANC-1 reporter cell line has been shown to be activated by blue light and turned off in the dark in a time- and dose-dependent manner [13] Another article demonstrated that the ectodomain of TLR4 served as an inhibitor to prevent spontaneous, ligand-independent dimerization [25]. To test blue-light-inducible dimerization and downstream signaling of these chimeras, 293Ta cells were transiently co-transfected with NF-κB-Gluc (gaussia luciferase) or AP-1 cis reporter and TLR4, TLR4-LOV, or TLR4 ∆ECD2/15/21/36-LOV constructs using the calcium phosphate precipitation technique (Figure S1a,b) and stimulated with 100 ng/mL LPS, blue light (470 nm), or left untreated for 6 h. Robust NF-κB and AP-1 activation upon blue light exposure could be seen in cells transfected with TLR4-LOV, but not in TLR4 transfected cells. In contrast, LPS stimulation provoked an upregulation of NF-κB and AP-1 in both TLR4-LOV and TLR4 transfected cell lines (Figure 1b,c). Importantly, cells co-transfected with different truncation variants of the TLR4 extracellular domain (TLR4 ∆ECD2/15/21/36-LOV) and reporter plasmids showed high basal NF-κB and AP-1 activation even without stimulation (Figure 1b,c). Additionally, LPS and blue light stimulation triggered an increase in p65 and ERK1/2 phosphorylation in cells transfected with TLR4-LOV, whereas constitutive NF-κB and ERK1/2 signaling could be observed in cells transfected with TLR4 ∆ECD2/15/21/36-LOV, reflected in their high basal activity of unstimulated cells (Figure 1d). No significant difference in NF-κB and ERK1/2 signaling activity could be detected between the different TLR4 ΔECD2/15/21/36-LOV constructs transfected in 293Ta cells (Figure 1b–d). Since the initial experiments suggested that this system was capable of activating TLR4-dependent signaling pathways (Figure 1), we next tested the spatiotemporal expression and localization of TLR4 and the ability of the TLR4-LOV chimeric constructs to induce NF-κB activity in endothelial cells. Therefore, the engineered full-length TLR4-LOV and TLR4 ΔECD2-LOV constructs were stably integrated into human lymphatic endothelial cells (LECs), immortalized by ectopic expression of telomerase reverse transcriptase [14], and into human umbilical vein endothelial cells (HUVECs), using a lentiviral transfection system. Stable integration of full-length TLR4-LOV and TLR4 ΔECD2-LOV was verified by an upregulation of TLR4 mRNA expression levels in engineered target cell lines compared to the wild type, and the change in molecular weight of the TLR4 due to the fusion of the LOV domain or additional truncation of the extracellular domain was verified via western blot analysis (Figure S1c,d,g,h). Temporal TLR4 mRNA expression level analysis of opto-TLR4-LOV LECs during 0-24 h of LPS and blue light treatment revealed a significant elevation after 6 h compared to unstimulated cells. Interestingly, after 24 h of blue light induction, a significant decline in TLR4 mRNA expression compared to unstimulated cells was found, which was not observed in cells treated with LPS. Here, no difference in TLR4 mRNA expression levels compared with the control was detected (Figure S1f). Figure S1e shows microscopic fluorescent images localizing stable integrated full-length TLR4-LOV in LEC. We next tested whether we could measure signaling activity downstream of the optogenetic TLR4 constructs, and sought to investigate the temporal kinetics of p65 and ERK after blue light or LPS induction. When endothelial cells with a stable integrated TLR4-LOV construct were exposed to 470 nm of blue light, a more than seven-fold p65 nuclear localization could be observed within 15 min, whereas nuclear localization could only be seen after 30 min when cells were stimulated with LPS, illustrating the fast activation of the receptor due to the optogenetic approach. After 15 (light) or 30 (LPS) min, a continuous decrease in the nuclear localization of p65 could be found in LPS and blue-light-induced optogenetic cells. In comparison, cells with the truncated version TLR4 ΔECD2-LOV showed a high nuclear translocation even without stimulation (two-fold compared to TLR4-LOV), which slightly decreased 15 min after stimulation, before returning to the basal level within one hour. (Figure 2a–d). To investigate whether the nuclear translocation of opto-TLR4-LOV LECs coincides with p65 and ERK1/2 phosphorylation, we next performed western blot analysis. After blue light or LPS stimulation, a peak phosphorylation of p65 was observed at 15 to 30 min, which could no longer be detected after 1 h. Interestingly, phosphorylation was stronger when cells were illuminated with blue light than when stimulated with LPS. In contrast, strong phosphorylation of ERK1/2 was detected 30 min after blue light or LPS treatment and attenuated after 1 h when cells were exposed to blue light, but not after LPS stimulation. Because we detected peak phosphorylation of p65 and ERK1/2 at 15 to 30 min after stimulation with LPS or blue light that leveled off after 1 h, we investigated longer exposure times and if there are differences when cells are illuminated with blue light continuously or only for short time. The opto-TLR4-LOV LECs (Figure 3a,c) and opto-TLR4-LOV HUVECs (Figure 3d) were continuously stimulated with 100 ng/mL LPS or blue light (470 nm), or were exposed to blue light for 30 min and incubated for a further 24 h in the dark (Figure 3b). Protein expression of phospho-p65 and phospho-ERK1/2 was analyzed 3 h, 6 h, and 24 h after the first stimulation with LPS or blue light, respectively. As is shown in Figure 3a–c, strong phosphorylation of p65 could be detected 3 h post-treatment when cells were illuminated with blue light, but not when they were stimulated with LPS. Similarly, ERK1/2 phosphorylation occurred much later when cells were treated with LPS compared to blue light, but they were still strongly activated after 24 h. Upon blue light exposure, peak phosphorylation was seen at 3 to 6 h, which then leveled off by 24 h. Interestingly, continuous blue light exposure caused decreased phosphorylation of p65 after 24 h, which could not be seen in cells that underwent short light or LPS stimulation (Figure 3a–d). In LECs with stable transfected TLR4 ΔECD2-LOV, both p65 and ERK1/2 were already strongly phosphorylated without stimulation. Continuous blue light exposure increased p65 phosphorylation after 24 h, but decreased ERK1/2 phosphorylation over time (Figure 3e). To investigate the time-dependent activation of the optogenetic receptor constructs, we next stably integrated the NF-κB-TRE-Gluc reporter in both opto-TLR4-LOV LEC full-length and ΔECD constructs using the lentiviral delivery system and selected reporter positive cells using hygromycin B. Next, opto-TLR4-LOV LECs (Figure 4a–c) and opto-TLR4 ΔECD2-LOV LECs (Figure 4d–f) were starved for 24 h with 2% serum overnight and stimulated with 100 ng/mL LPS, illuminated with blue light at 470 nm for 1–360 min or left untreated (wo); NF-κB signaling was quantified by measuring Gluc 6 h after the first stimulation (Figure 4a,d). As depicted in Figure 4a, NF-κB signaling in opto-TLR4-LOV LECs with the integrated full-length construct showed a time-dependent increase peaking (4-fold) at 6 h of continuous blue light induction (Figure 4a). In comparison, the truncated version (ΔECD2) showed no enhanced NF-κB activity compared to the control when cells were illuminated for 1-30 min and only a 1.5-fold increase after 6 h (Figure 4d). To investigate whether this blue-light-induced effect was TLR4-specific, cells were left untreated (dark), treated with LPS, or exposed to blue light for 24 h with 0–100 µM TAK-242 or 0–30 µM parthenolide. As depicted in Figure 4b,c, both the TLR4 inhibitor TAK-242, and the NF-κB inhibitor parthenolide, showed a concentration-dependent downregulation of NF-κB-induced Gluc expression in LPS and blue-light-stimulated opto-TLR4-LOV LECs. Interestingly, although NF-κB-Gluc activity was higher with blue light induction than with LPS, TAK-242 inhibited NF-κB activity more than two-fold when cells were illuminated with blue light, compared to after LPS stimulation, indicating a very specific TLR4 activation by blue light (Figure 4b). Even if the NF-κB reporter activity of the opto-TLR4 ΔECD2-LOV LECs showed only a four-fold elevation after blue light induction, a very specific and dose-dependent inhibition by TAK-242 and parthenolide could be detected (Figure 4e and f). For comparative time-curve and reactivation analyses, we next starved the opto-TLR4-LOV LECs with medium (2% serum) for 16 h, before cells were continuously stimulated (time point 0) with LPS or exposed to blue light for 96 h. The medium was changed every 24 h after the Gluc measurement. As is depicted in Figure 4g, LPS-stimulated cells showed the highest peak after 24 h, which continuously decreased over time, implying that reactivation decreased even after renewed LPS stimulation. Cells illuminated with blue light also peaked after 24 h but could not be reactivated thereafter. After 72 h, there was a slight continuous increase again, but after 96 h, it was half as strong as after 24 h, suggesting that the blue light stimulation exhausted the cell signaling system faster than LPS stimulation. A similar but lower NF-κB-Gluc activity could be observed when cells were only illuminated for 30 min every 24 h (Figure 4h). When cells were stimulated with LPS for 24 h, then incubated for a further 24 in a normal medium, no significant Gluc activity could be detected, even at the time point of 48 h (Figure 4i). In summary, we conclude that the established optogenetic cell lines—opto-TLR4-LOV LECs and opto-TLR4-LOV HUVEC—were very suitable to induce rapid and precise activation of the TLR4. In contrast, due to the truncated extracellular domain, the opto-TLR4 ΔECD2-LOV LECs had a higher basal activity, which also required longer blue light induction to obtain significant additional Gluc activity. Activation of TLR4 and distinct downstream signaling pathways such as NF-κB, AP-1, and IRF3 are known to mediate the transcription of pro-inflammatory cytokine and chemokine genes [26]. Therefore, the supernatant of opto-TLR4-LOV LECs were collected 2 h, 6 h and 16 h after LPS (100 ng/mL) or blue light (470 nm) stimulation, or no treatment (control), and processed for quantitative mass spectrometric analysis. The abundance of over 1000 proteins of four biological replicates at all three time points was determined using a label-free quantification (LFQ) approach (Table S1) and compared to the control (wo) (Figure 5a–f). A significant increase in early pro-inflammatory proteins secreted into the cell supernatant (CXCL-1, CCL-5, IL-6 and CXCL-8) was already found 2 h after LPS stimulation and remained elevated in abundance throughout the 16 h time course. A similar increase in the same pro-inflammatory proteins could be detected after blue light induction. However, protein expression was generally somewhat weaker and therefore not significant for all proteins except CCL-5 after 2 h. In contrast, other proteins such as the chemokine CXCL-10 were found to be highly significant in the supernatant just 6 h after blue light induction, which could not be seen when cells were treated with LPS. The adhesion molecule ICAM-1 was only slightly increased after 2 h of blue light or LPS stimulation, but reached significant elevation after 6 h and 16 h, respectively. Interestingly, after 16 h of continuous blue light illumination, the supernatant protein levels of IL-6 showed a detectable decrease compared to LPS-stimulated cells, whereas the protein levels of other pro-inflammatory mediators such as CXCL-1, CCL-5, CXCL-8, CXCL-10, and the adhesion molecule ICAM-1, remained significantly high after both blue light and LPS stimulation. The temporal accumulation of the pro-inflammatory mediator IL-6, CXCL-8 (IL-8) and CXCL-10 in the supernatant coincided with their augmented mRNA levels measured by RT-qPCR. Here, the target gene expression levels of stimulated opto-TLR4-LOV LECs had already peaked after 2 h (LPS: IL-6; blue light: IL-6, IL-8) or 6 h (LPS: IL-8, CXCL-10; blue light: CXCL-10), and decreased sequentially (Figure 5g–i). Importantly, augmented target gene expression of IL-6, IL-8, and CXCL-10 after 2 h of LPS/blue light stimulation could be confirmed in HUVECs with stable integrated TLR4-LOV (Figure S2a–c). Interestingly, upregulation of these pro-inflammatory genes was also observed in opto-TLR4 ΔECD2-LOV LECs after 2 h of LPS or blue light treatment, which then leveled off after 6 h and increased again after 24 h (Figure S2e–g). The adhesion molecule ICAM-1 is a main driver for leukocyte adhesion and trans-endothelial migration. It is expressed at a low level on the vascular endothelium and is increased by inflammatory stimuli [27]. Therefore, temporal ICAM-1 protein expression was analyzed upon LPS or blue light treatment during 0–24 h in engineered endothelial cell lines. Opto-TLR4-LOV LECs and opto-TLR4-LOV HUVECs were continuously stimulated with LPS (100 ng/mL) or blue light (470 nm), or opto-TLR4-LOV LECs were additionally exposed to blue light for 30 min and incubated for a further 24 h in the dark. Protein expression of ICAM-1 was analyzed 0–24 h after stimulation with LPS or blue light, respectively. Strong ICAM-1 expression could be detected 3–6 h post-LPS or blue light treatment, which then remained elevated throughout the 24 h time course (Figure 5j–m). In LECs with stable integrated TLR4 ΔECD2-LOV, high basal (0 h) ICAM-1 expression was found, which was slightly decreased after 24 h of continuous blue light treatment (Figure S2h). To monitor key cellular processes that play an important role during inflammation, different functional assays were performed to measure the chemoattraction of THP-1, barrier function, and the transmigration of leukocytes through the endothelial cell monolayer. To determine whether opto-TLR4-LOV LECs induced the chemoattraction of monocytic cells after blue light or LPS stimulation, THP-1 cells were stained with Hoechst 33342 and seeded into the upper chamber of Transwell® 96-well permeable supports and Transwell® 24-well permeable supports and allowed to migrate through the 5 µM pore-size filters for 2 h towards a medium of opto-TLR4-LOV LECs stimulated with LPS (100 ng/mL), blue light (470 nm), or left untreated for 6 h, in the lower chambers of the transwells. THP-1 cells that migrated through the filter of the 96-well permeable supports were quantified by measuring relative fluorescence units with a plate reader in a well scan manner, whereas THP-1 cells that migrated through the filter of 24-well permeable supports were visualized by fluorescence microscopy. As such, we found that the media of LPS- and blue-light-treated opto-TLR4-LOV LECs triggered a chemotactic response on THP-1 cells, with the highest migration detected with blue light (Figure 6a,b). To study whether LPS and blue light induce the breakdown of the EC monolayer and infiltration of monocytes in opto-TLR4-LOV LECs and opto-TLR4-LOV HUVEC, we applied the ECIS technology. Consequently, endothelial monolayer resistance, which is proportional to endothelial barrier function, can be monitored in real time by means of measuring the impedance over time. First, opto-TLR4-LOV LECs and opto-TLR4-LOV HUVECs were cultured onto ECIS arrays and allowed to grow to a monolayer before they were challenged with LPS (100 ng/mL), blue light (470 nm), and/or with different differentiated monocytic cell lines (THP-1 cells, THP-1 M0 cells) and PBMCs (50,000 cells/well). Endothelial barrier function was subsequently assessed by continuous resistance measurement. LPS and blue light treatment significantly enhanced the disruption of the opto-TLR4-LOV LEC and opto-TLR4-LOV HUVEC monolayers (Figure 6c and Figure S3a). Trans-endothelial migration of the monocytic cell lines or PBMCs through the opto-TLR4-LOV LEC and opto-TLR4-LOV HUVEC monolayers could be increased with additional LPS or blue light treatment. (Figure 6d–f and Figure S3b). Similar effects upon illumination were observed using opto-TLR4 ∆ECD2-LOV LECs (Figure S3c–f). These results clearly show that blue light, like LPS, promotes not only the chemotactic but also trans-endothelial migration of monocytic cell lines and PBMCs. This study aimed to examine how TLR4 activation is processed downstream in endothelial cells, focusing mainly on the temporal organization of signaling pathways and target gene expression. Carrying out these measurements requires fast and precise activation of the receptor, which is difficult to achieve with ligands such as LPS due to the often slow and poorly controllable specific and non-specific binding to receptors and other surface molecules [28,29]. Furthermore, there are hardly any processes in which LPS can be isolated with high purity, triggering various cell activations [30]. Therefore, we engineered new light-oxygen-voltage (LOV)-sensing domain-based optogenetic endothelial cell lines with full-length TLR4 (opto-TLR4-LOV LEC and HUVEC) to allow precise temporal and reversible activation of TLR4 signaling pathways and target gene expression. TLR4 homodimerization in the engineered ECs relies on the LOV domain isolated from the yellow-green algae Vaucheria frigida aureochrome 1 (VfAU1-LOV) fused C-terminally to TLR4. VfAU1-LOV noncovalently binds a flavin chromophore, which upon blue light (470 nm) absorption, initiates a photochemical reaction leading to the formation of a covalent adduct between the conserved cysteine and the flavin ring. The result is a conformational change that allows the dimerization of the LOV domains [31]. Since flavin nucleotides (FMN) are readily available in mammalian cells, no addition of exogenous molecules is required. Previously, we have described a technically similar engineered pancreatic adenocarcinoma cell line (opto-TLR4 PANC-1) that allows time- and voltage-dependent TLR4 activation by blue light, which can be switched off again in the dark. Low intensity blue light (8 V) was shown to be sufficient for the activation of the engineered TLR4-LOV construct and does not produce phototoxicity [13]. In this study, we demonstrated that 293Ta transiently transfected with TLR4-LOV but not with TLR4 lacking the LOV domain was able to activate NF-κB and AP-1 reporter activities upon blue light exposure. In contrast, NF-κB and AP-1 expression was significantly elevated after LPS stimulation in both TLR4 and TLR4-LOV transfected cells. Additionally, the newly engineered optogenetic endothelial cell lines were found to initiate TLR4-specific signaling events much faster and stronger upon illumination with blue light compared to the stimulation with LPS. It is well known that TLR4 signaling triggers the translocation of the pro-inflammatory transcription factor NF-κB from the cytoplasm into the nucleus to initiate gene transcription. In the nucleus, the newly synthesized protein IκBα (inhibitor of nuclear factor kappa B) deactivates NF-κB and facilitates its export back to the cytoplasm [32,33]. Oscillation of NF-κB in and out of the nucleus upon stimulation is reported to contribute considerably to the pattern of inflammatory gene expression [34]. Consistent with other studies [35,36], we found that LPS stimulation causes rapid (30 min) and transient translocation of p65 into the nucleus in opto-TLR4-LOV LECs. However, blue light illumination provoked an even faster and stronger p65 nuclear translocation (seven-fold and within 15 min) compared to LPS treatment, which also declined within an hour. Moreover, peak phosphorylation of p65 and ERK1/2 in opto-TLR4-LOV endothelial cells was measured 15 to 30 min after LPS or blue light stimulation, and again after 3 h and 6 h when cells were stimulated with light or LPS, respectively. On the other hand, we also found that the light-induced TLR4 signaling system was exhausted much quicker compared to LPS treatment. Studies have highlighted LPS as an agonist for receptors other than TLR4, including Toll-like receptor 2 (TLR2) [37], nucleotide-binding oligomerization domain (NOD)-like receptor 1 [38], and retinoic acid-inducible gene 1 (RIG-1)-like receptor [39], all of which are reported to induce NF-κB activity. TLR4–TLR2 cross talk was reported to be mediated by MyD88, resulting in the amplification and stable expression of NF-κB and ICAM-1 [40]. In addition, the cytosolic receptors NOD1 and RIG-1 are described to be activated upon internalization of LPS [41,42,43]. The resulting downstream signal transduction of the NF-κB and MAPK pathways amplifies the transcription of inflammatory cytokines and chemokines, which are also induced by TLR4 signaling [44,45]. Notably, RIG-1 was found to be a key factor in the autoloop cascade for late MyD88-independent activation of NF-κB, thereby maintaining sustained secretion of pro-inflammatory mediators [43,46]. Here, we show that continuous light exposure decreased phosphorylation of p65 after 24 h, which could not be seen when cells were permanently LPS stimulated, indicating a specific activation of TLR4 by light. Furthermore, by performing reporter analysis, we showed that NF-κB activation was much less repressed after TAK-242 (TLR4 inhibitor) treatment than after light induction. This is due to the binding of LPS to additional receptors other than TLR4 that amplify the NF-κB signal. In addition to the fast light-induced depletion, we were also able to demonstrate that the signaling pathways can be reactivated after a certain period, as demonstrated with the NF-κB-TRE-Gluc reporter assay. Here, exhaustion of the NF-κB signaling was observed between 48 and 72 h, with both continuous and recurrent illumination for 30 min every 24 h. Blue-light-induced TLR4 and NF-κB reactivation could be detected again after 72 h (continuous illumination) and 96 h (30 min every 24 h), respectively. In contrast, with continuous LPS treatment, NF-κB decreased slightly over the time course (4.5–2.5-fold), but remained relatively high even after 96 h. This phenomena has been studied extensively in monocytes and macrophages [44,47], but has also previously been reported in endothelial cells [48]. Panter and Jerala (2011) have clearly demonstrated that the ectodomain of TLR4 prevents constitutive receptor activity, since truncation of the TLR4 ectodomain from its N-terminus ultimately resulted in persistent active receptor variants [25]. They further showed that the ectodomain exhibits strong regulatory properties enabling a controlled ligand receptor activation by providing proper localization and inhibition of spontaneous, ligand-independent receptor dimerization. Consistent with this study, we found that transient transfection of different TLR4 ΔECD-LOV variants compared with the TLR4-full length-LOV into 293Ta were already relatively strongly activated prior to light stimulation. Here, no difference in NF-κB activity and p65 or ERK1/2 phosphorylation could be detected between TLR4 ΔECD2-LOV (with a deleted LPS/MD2 domain) and ΔECD15/21/36-LOV (with a deleted LPS/MD2 and dimerization domain). Stable integration of TLR4 ΔECD2-LOV into LECs revealed a high basal activity with fast depletion of the cell signaling system upon additional blue light stimuli, as evidenced by p65 and ERK1/2 phosphorylation, and nuclear translocation of NF-κB. NF-κB reporter activity and mRNA gene expression of pro-inflammatory genes were found to exert a slight activation potential with fast depletion of the TLR4 signaling system. Of note, it was also observed in TLR4 ΔECD2-LOV that the mRNA gene expression of pro-inflammatory genes (IL-6, IL-8, and CXCL-10) was elevated after 2 h of light induction, but it was depleted 6 h later and could be reactivated after 24 h. Because the endothelium forms a strong barrier that separates circulating blood from tissue, access to potential sites of infection requires the expression of different genes [49]. By performing functional assays using opto-TLR4-LOV LECs and HUVEC, we were able to show that TLR4 stimulation with blue light promoted chemotaxis of THP-1 cells, disruption of the EC monolayer, and transmigration that was faster and stronger compared to treatment with LPS. A higher EC-monolayer break-up and transmigration could also be seen in the TLR4 ΔECD2-LOV after light induction. Of note, proteomic analysis of opto-TLR4-LOV LECs revealed a temporal elevation of secreted disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) and the matrix metalloproteinase 10 (MMP10), both contributing to the degradation of the extracellular matrix and thus facilitating trans-endothelial migration [50,51,52,53]. Since LPS activates receptors other than TLR4, as already described, we assumed higher transcription rates of pro-inflammatory genes. As such, CXCL-1, CXCL-8, and IL-6 were found in higher concentration in the supernatant after 2 h of LPS stimulation compared to light induction. We further found that the protein level of IL-6 in the supernatant showed a detectable decrease after 16 h of continuous blue light illumination, which was not seen with LPS stimulation. However, a significant increase in known secreted pro-inflammatory mediators (CXCL-1, CCL5, IL-6, and CXCL-8) was observed 6 h after LPS or blue light stimulation, consistent with published studies on LPS-activated endothelial cells [54,55,56]. In addition, a strong and persistent expression of ICAM-1 as a transmembrane protein and secretion has been found in activated endothelial cells [48,57,58]. Time-course analysis of target mRNA expression levels of IL-6, IL-8 and CXCL-10 confirmed a generally stronger and more persistent expression of pro-inflammatory mediators when optogenetic cells were treated with LPS compared to light exposure. Interestingly, the chemokine CXCL-10 was found to be highly significant in the supernatant as early as 6 h after light induction, which was not seen when cells were treated with LPS. In addition, CXCL-10 mRNA expression in optogenetic endothelial cell lines was already higher when exposed to blue light for 2 h compared to LPS treatment. A recent temporal proteomic analysis of pro-inflammatory mediators in LPS-induced THP-1 cells revealed a significant upregulation of CXCL-10 6 h post-stimulation [59]. In summary, we conclude that the newly-engineered optogenetic cell lines elicit faster and more precise TLR4 activation under blue light illumination compared to LPS stimulation. Gene expression of pro-inflammatory target genes is, however, reduced and less persistent. In contrast, due to the truncated extracellular domain, opto-TLR4 ΔECD2-LOV LECs show a high basal activity with fast depletion of the cell signaling system upon additional blue light stimuli. Attaining deeper insights into the molecular and regulatory mechanisms of pro-inflammatory TLR4 signaling events in endothelial cells with spatiotemporal precision will expedite the establishment of novel therapeutic strategies beneficial for the treatment of sepsis and chronic inflammatory diseases.

PMC10001005

Jielin Deng,Yunqiu Jiang,Zhen Bouman Chen,June-Wha Rhee,Yingfeng Deng,Zhao V. Wang

Mitochondrial Dysfunction in Cardiac Arrhythmias

21-02-2023

mitochondrial dysfunction,arrhythmia,ATP supply,reactive oxygen species

Electrophysiological and structural disruptions in cardiac arrhythmias are closely related to mitochondrial dysfunction. Mitochondria are an organelle generating ATP, thereby satisfying the energy demand of the incessant electrical activity in the heart. In arrhythmias, the homeostatic supply–demand relationship is impaired, which is often accompanied by progressive mitochondrial dysfunction leading to reduced ATP production and elevated reactive oxidative species generation. Furthermore, ion homeostasis, membrane excitability, and cardiac structure can be disrupted through pathological changes in gap junctions and inflammatory signaling, which results in impaired cardiac electrical homeostasis. Herein, we review the electrical and molecular mechanisms of cardiac arrhythmias, with a particular focus on mitochondrial dysfunction in ionic regulation and gap junction action. We provide an update on inherited and acquired mitochondrial dysfunction to explore the pathophysiology of different types of arrhythmias. In addition, we highlight the role of mitochondria in bradyarrhythmia, including sinus node dysfunction and atrioventricular node dysfunction. Finally, we discuss how confounding factors, such as aging, gut microbiome, cardiac reperfusion injury, and electrical stimulation, modulate mitochondrial function and cause tachyarrhythmia.

Mitochondrial Dysfunction in Cardiac Arrhythmias Electrophysiological and structural disruptions in cardiac arrhythmias are closely related to mitochondrial dysfunction. Mitochondria are an organelle generating ATP, thereby satisfying the energy demand of the incessant electrical activity in the heart. In arrhythmias, the homeostatic supply–demand relationship is impaired, which is often accompanied by progressive mitochondrial dysfunction leading to reduced ATP production and elevated reactive oxidative species generation. Furthermore, ion homeostasis, membrane excitability, and cardiac structure can be disrupted through pathological changes in gap junctions and inflammatory signaling, which results in impaired cardiac electrical homeostasis. Herein, we review the electrical and molecular mechanisms of cardiac arrhythmias, with a particular focus on mitochondrial dysfunction in ionic regulation and gap junction action. We provide an update on inherited and acquired mitochondrial dysfunction to explore the pathophysiology of different types of arrhythmias. In addition, we highlight the role of mitochondria in bradyarrhythmia, including sinus node dysfunction and atrioventricular node dysfunction. Finally, we discuss how confounding factors, such as aging, gut microbiome, cardiac reperfusion injury, and electrical stimulation, modulate mitochondrial function and cause tachyarrhythmia. Cardiac arrhythmias are defined as disruption in the orderly electrical cycle of excitation and recovery through the myocardium. Arrhythmias can be broadly categorized into tachyarrhythmia and bradyarrhythmia based on the ventricular rate, although there are other classification methods based on the origin, means of propagation, associated symptoms, etc. Arrhythmias are highly heterogenous in pathophysiology and severity and cause substantial morbidity and mortality. Atrial fibrillation (AF) is the most frequent arrhythmia and is associated with an increased risk of stroke and mortality, as well as decreased quality of life [1]. A total of three to six million people in the US suffer from AF, leading to a major healthcare burden [2,3]. Ventricular tachyarrhythmias are the major causes of sudden cardiac death (SCD) in the US, accounting for 80% of cases [4,5]. Bradyarrhythmia and conduction abnormalities can cause syncope and SCD, and patients may also experience fatigue and decreased exercise capacity due to chronotropic incompetence [6]. Treatment of arrhythmias can be divided into medical therapies (e.g., anti-arrhythmic drugs) and electrophysiological interventions (e.g., ablations). The currently available treatment options, however, have limitations. For example, routine medical therapy and catheter AF ablation are often plagued by treatment failures, recurrences, and adverse events. Therefore, basic and translational research is critical to advance our understanding of pathophysiology and ultimately improve arrhythmia management through discovery of novel therapeutics [7]. Cumulative studies showed that mitochondrial dysfunction in cardiomyocytes plays an essential role in arrhythmogenesis in both humans and animal models. Mitochondria are an organelle responsible for the synthesis of adenosine 5′-triphosphate (ATP) via oxidative phosphorylation (OXPHOS) [8]. One-third of the cardiac ATP generated by mitochondria is used for the maintenance of ion channels and transporters, which are imperative for the rhythmic electrical activity of cardiomyocytes. Mitochondrial dysfunction adversely affects aerobic respiration and energy production, leading to impairment in cardiac rhythm. In addition, dysfunctional mitochondria may generate excessive reactive oxygen species (ROS), another factor contributing to ion channel and transporter abnormalities and membrane excitability disturbances, which are all crucial players in the pathogenesis of arrhythmias. Previous studies demonstrated that mitochondrial dysfunction is associated with both tachyarrhythmia and bradyarrhythmia. Additional evidence points to mitochondrial dysfunction as a causative factor of various arrhythmias. In this review, we summarize pathophysiological relevance of mitochondrial dysfunction in the initiation, development, and progression of arrhythmias, with a focus on underlying molecular mechanisms and potential therapeutic explorations. To better understand how mitochondrial dysfunction promotes cardiac arrhythmias, we first describe the physiologic contributions of ions (e.g., Ca2+) to cardiac action potential (AP). Several schemes have been proposed to classify arrhythmias, such as initiation and maintenance factors of arrhythmias [9], cellular or tissue origin of arrhythmias [10], and dynamics-based classification [11]. For example, based on the initiation and maintenance factors, arrhythmias can be categorized into abnormal impulse formation and conduction disturbances: Abnormal impulse formation covers automaticity disturbances and triggered activity, whereas conduction disturbances cover reentry tachycardia and conduction blocks [9] (Figure 1). Cardiac AP results from the sequential opening and closing of ion channel proteins that span the membrane of individual cardiomyocytes [12]. Cardiac AP consists of four phases. Phase zero stands for the rapid depolarization caused by the fast sodium ions (INa) diffusing down their electrochemical gradient from the extracellular space, across the membrane, and into the cell. Phase one of AP represents the early rapid repolarization resulting from activation of the fast and slow transient outward potassium currents (IK). This is followed by a prolonged plateau mediated by a dynamic balance between the inward currents by voltage-gated L-type calcium channel (ICaL) and Na+-Ca2+ exchanger (NCX) and the outward currents by the rapid and slow potassium currents (IKr and IKs, respectively) [13]. This plateau represents phase two of AP. As Ca2+ channels become inactivated, the outward potassium currents dominate, causing further repolarization, which is responsible for phase three of AP, and the time-dependent K+ current (IK1) may be the principal current responsible for the final repolarization [14]. In phase four, the Na+/K+ pump extrudes Na+ that has entered during depolarization and restores the K+ lost during repolarization. Automaticity is the spontaneous depolarization caused by a net inward current during phase four of AP. Automaticity is a property resulting from both voltage- and Ca2+-dependent mechanisms, which is intrinsic to the sinoatrial node (SAN), the atria, the atrioventricular node (AVN), the His bundle, and the Purkinje fiber network. The voltage-dependent mechanism involves the funny current (If), carried by both Na+ and K+, through hyperpolarization-activated and cyclic nucleotide-gated (HCN) channels located at the plasma membrane. The Ca2+-dependent mechanism (Ca2+ clock) involves the rhythmic release of Ca2+ from the sarcoplasmic reticulum (SR), with subsequent reuptake of Ca2+ by SR Ca2+-ATPase (SERCA) and extrusion via NCX. Normal automaticity allows cardiomyocytes to generate spontaneous AP, whereas abnormal automaticity includes both enhanced and decreased automaticity. Enhanced automaticity of pacemaker cells can increase the rate of AP discharge through steepening phase four which leads to tachyarrhythmia (e.g., sinus tachycardia, atrial tachycardia, accelerated AV junctional tachycardia), whereas decreased automaticity can lead to bradyarrhythmia (e.g., sinus bradycardia) [9]. If cells do not normally possess the automaticity to obtain this property, premature ectopic heartbeats may occur [9]. Alterations in HCN channel-mediated If and Ca2+ clock may induce abnormal impulse formation to facilitate associated arrhythmias. Triggered activity, including early afterdepolarizations (EADs) and delayed afterdepolarizations (DADs), is an impulse initiation disturbance that can evoke trains of APs. EADs are caused by net inward currents in phases two/three of AP, induced by ICaL and NCX [13]. EADs may also be caused by enhanced late sodium current [15]. EADs act as a repolarization interruption and can cause lethal ventricular arrhythmias in the context of action potential duration (APD) prolongation, such as long QT syndrome [16]. DADs usually occur in the context of Ca2+ overload, where Ca2+ is spontaneously released from SR after repolarization. Ca2+ efflux then exits the cell in exchange for Na+, generating a net inward depolarizing current [17,18]. The amplitude of DADs increases along with decreasing cycle length, thereby leading to triggered activity. Details in Ca2+ homeostasis will be discussed later in this review. Reentry is a self-sustaining cardiac rhythm abnormality in which AP propagates in a manner analogous to a closed-loop circuit. Reentry is a disorder of impulse conduction where a structural or functional obstacle around which an electrical activity can circulate is required, making reentry distinct from disorders of impulse generation [19]. The substrate of reentry is usually the areas of reduced conduction velocity and APD dispersion. Cardiac conduction velocity is largely determined by the maximum rate of membrane depolarization (dV/dt max) and physical properties of cardiomyocytes, along with their interconnections. Correspondingly, reduced conduction velocity has been attributed to alterations in Na+ channel and gap junction function, as well as fibrotic changes, providing substrates for reentry arrhythmias. Reentry is an electrophysiologic mechanism responsible for majority of clinically important arrhythmias [20]. Included among these arrhythmias are AF, atrial flutter, atrioventricular (AV) nodal reentry tachycardia (AVNRT), AV reentry tachycardia (AVRT) involving an accessory pathway, ventricular tachycardia (VT) involving ventricular scars, and ventricular fibrillation (VF). Liu et al. [16] showed that DADs could trigger premature ventricular complexes (PVCs) and cause reentry in vulnerable tissues with areas of unidirectional conduction block, called triggered activity with reentry, a classic example of reentry initiation. In addition to reentry, heart block also belongs to conduction abnormalities and can happen anywhere along the cardiac conduction system, including the SAN, the AVN, and the bundle branches. Herein, we mainly discuss ion alterations of conduction disturbance happening in the AVN, also called AV block. AV block is caused by alterations in the ion channel expression of the AVN [13]. As mentioned before, If mediated by HCN channels responsible for phase four depolarization, and Ca2+ clock is considered related to SAN pacemaking. Recent studies emphasized the importance of HCN channels [21] and If within the AVN [22] for AV conduction. In addition, knockout of voltage-dependent Ca2+ channels [23] was reported to slow or block AV conduction [21,24]. Although it is not well characterized, the Ca2+ clock is considered to control AV conduction [25]. Cardiomyocytes rely on OXPHOS in mitochondria to generate majority of cellular ATP (80–90%). Fatty acids are the main and preferred energetic substrates for ATP production in cardiac muscle. On the other hand, supply of ATP from glycolysis is restricted in the normal heart. When energetic demands increase, however, the relative contribution of glucose utilization increases for glycolytic ATP production. Glucose is converted into pyruvate in the glycolytic pathway, which is a substrate for ATP synthesis in mitochondria. Both fatty acids and glucose can produce acetyl-CoA to enter the TCA cycle, where nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FADH2) are produced [26]. NADH and FADH2 serve as electron donors for the mitochondrial electron transport chain (ETC), including Complexes I–IV, as well as the electron transporters ubiquinone and cytochrome c. There are two electron transport pathways in the ETC: Complex I/III/IV with NADH as the substrate; Complex II/III/IV with FADH2 as the substrate [27]. As electrons flow through the ETC, protons travel across the inner membrane from the mitochondrial matrix into the intermembrane space, establishing the proton gradient and the strongly negative mitochondrial membrane potential, ΔΨm. The energy accumulated in the proton gradient is used by Complex V (ATP synthase) to produce ATP [27]. Impaired OXPHOS leads to mitochondrial dysfunction primarily due to defects in ETC enzymes (Complexes I–V) [28]. In addition to producing ATP, mitochondria also generate ROS as a byproduct of OXPHOS: A small part of electrons do not follow the normal transfer order but instead leak out of the ETC and directly interact with O2 to generate ROS [29]. Since OXPHOS is not completely coupled, mitochondrial uncoupling is defined as the dissociation between ΔΨm generation and its use for mitochondria-dependent ATP synthesis. Mild uncoupling can be a feedback mechanism to prevent excessive ROS in mitochondria [30]. However, severe mitochondrial uncoupling may cause rapid cellular ATP depletion and excessive ROS production, leading to mitochondrial dysfunction [31]. Mitochondrial ETC proteins are encoded by both mitochondrial DNA (mtDNA) and nuclear DNA (nDNA). mtDNA encodes 13 major respiratory chain proteins with the rest of them encoded by nDNA, 2 ribosomal RNAs, and 22 transfer RNAs. In addition, mitochondria have other functions, including fatty acid oxidation, regulation of Ca2+ homeostasis, and cell death, as well as redox control, mostly carried out by nDNA-encoded proteins [32]. Mitochondrial dysfunction may be attributed to acquired factors, including aging, imbalance of gut microbiome, various diseases, adverse effects of drugs and infections, and inheritable factors such as mutations in mtDNA and nDNA (Table 1). All these adverse changes may lead to abnormalities inside mitochondria, with the ultimate outcome of mitochondrial dysfunction with diminished ATP production and excessive ROS generation. Elevated levels of ROS can inhibit the activities of ETC complexes, redox enzymes, and TCA cycle enzymes [32], which further exacerbates ROS production in a vicious circle (ROS-induced ROS-release, RIRR) [33]. Moreover, mitochondria-derived ROS can affect neighboring mitochondria and other organelles, finally propagating the surge of ROS to the whole cell, which is how mitochondrial function deteriorates from a pathophysiological perspective [34]. Mitochondria-derived ATP can be used by sarcolemmal and organellar ion channels and transporters, which are required for the electrical activity of cardiac cells. On the other hand, excessive ROS can impact ion currents by modulating the expression of these channels or altering their post-translational modifications. Therefore, mitochondrial dysfunction (decreased ATP and increased ROS) can deteriorate cardiac electrical function, impair intracellular ion homeostasis and membrane excitability, and elicit inflammatory signaling, thus facilitating arrhythmias. Moreover, other mitochondria-associated proteins such as uncoupling proteins (UCPs), mitochondrial connexin (Cx) proteins, mitochondrial renin–angiotensin system (RAS), mitochondria-derived peptides (MDPs), and mitochondrial GPCR kinases (GRKs) and β-arrestins, can regulate mitochondrial function and contribute to the development of arrhythmias (see below). A holistic overview of sarcolemmal and intracellular ion balance in cardiomyocytes and alterations under mitochondrial dysfunction can be found in Figure 2. Cycling of Ca2+ in cardiomyocytes begins with the entry of Ca2+ into cells through voltage-gated Ca2+ channels, including L-type (ICa-L) and T-type (ICa-T). ICa-L channel is the predominant Ca2+ channel in cardiomyocytes participating in myocardial contraction, whereas ICa-T channel is mainly expressed in pacemaker cells [46]. The main SR Ca2+ release channel in cardiomyocytes is ryanodine receptor 2 (RyR2). Its opening after a small initial amount of Ca2+ entry via Ca2+ channels results in sarcomere contraction. During the diastolic phase, around 70% of total cytosolic Ca2+ is taken up into SR by SERCA [47]. Ca2+ extrusion by NCX lowers intracellular Ca2+ and counterbalances the entry of Ca2+ through sarcolemmal Ca2+ channels. Furthermore, communication between SR and mitochondria impacts their functionality in a bidirectional manner [48]. Flux of Ca2+ in and out of mitochondria is essential for ATP generation during the constantly varying workloads of the heart by stimulating OXPHOS and increasing NADH production via activation of Ca2+-sensitive enzymes in the TCA cycle [49,50]. Mitochondrial Ca2+ influx is mainly mediated by the mitochondrial Ca2+ uniporter (MCU) complex on the inner mitochondrial membrane [51]. MCU complex is not the only Ca2+ transporter in mitochondria. Mitochondrial RyR1 [52], NCX, mitochondrial HCX leucine zipper EF hand-containing transmembrane protein 1 (LETM1) [53], transient receptor potential canonical 3 (TRPC3) [54], uncoupling proteins 2 and 3 (UCP2/3), and other Ca2+ transporters [55] are also present in the inner mitochondrial membrane. Mitochondrial Ca2+ efflux is primarily mediated by mitochondrial NCX [56,57]. In addition, mitochondrial HCX [53] and mPTP [58,59] are also implicated in mitochondrial Ca2+ efflux. Cardiomyocyte Ca2+ homeostasis is strongly influenced by cellular metabolism. Increased cellular ROS are known to cause a net increase in intracellular Ca2+ in cardiomyocytes [60]. However, it is still controversial whether the activity of NCX is promoted or inhibited by ROS [61,62,63]. The effects of ROS on ICa-L in cardiomyocytes are also under debate [64,65]. Besides sarcolemmal Ca2+ channels, excessive mitochondria-derived ROS lead to increased opening of RyR2, which triggers RyR2 Ca2+ sparks and increases Ca2+ leak from SR [66]. Increased RyR2 activity can, in turn, modulate mitochondrial Ca2+ handling, promote mitochondrial ROS emission, and alter channel activity in a pro-arrhythmic feedback cycle [67]. In contrast to RyR2, the activity of SERCA is inhibited by increased oxidative stress [68], which may be attributed to decreased ATP supply for SERCA, secondary to mitochondrial dysfunction [69]. High levels of ROS can also modulate the activity of mitochondrial Ca2+-related proteins [70], leading to mitochondrial Ca2+ overload, which favors opening of the mPTP and inner membrane anion channel (IMAC) to influence ΔΨm [71,72] and increases RIRR function and ROS production [73]. As mentioned before, ΔΨm depolarization reflects the decreased capacity of mitochondrial ATP production. In addition, excessive mitochondrial ROS can cause oxidative damage to ETC components, leading to impaired ATP production and increased ETC electron leak that further elevates ROS generation. All these effects can be defined as mitochondrial dysfunction, which is pro-arrhythmic and may cause Ca2+ alternans by affecting the capacity of mitochondria to handle Ca2+ on a beat-to-beat basis [69]. Cardiac voltage-gated Na+ (Nav) channels are critical in membrane excitability of cardiomyocytes by generating the rapid upstroke (phase 0) of AP. In addition, Nav channels, together with cardiac gap junctions, control impulse conduction velocity in the myocardium. In response to increased oxidative stress, the expression and function of Nav1.5 channel can be modified, causing an increase in the late component of sodium current (late INa) in cardiomyocytes, leading to APD prolongation and EADs. Increased intracellular Na+ caused by elevated late INa also reverses NCX activity and subsequently leads to intracellular Ca2+ overload [33,74]. In addition, increased late INa-induced repolarization defects promote transmural dispersion of repolarization arrhythmic substrate and spatiotemporal heterogeneity, all of which are arrhythmogenic [75,76]. Cardiac conduction velocity is largely determined by peak sodium current (peak INa) and conducted by Nav1.5 channel. Importantly, mitochondrial dysfunction can also lead to reduced peak INa, resulting in reduced conduction velocity and an increased propensity for reentry arrhythmias. Na+/K+ pump generates a transmembrane current by pumping three Na+ out and two K+ into the cell against their concentration gradients with the consumption of one ATP during AP. As a major ion transporter balancing the trans-sarcolemmal Na+ and K+ gradient and generating the resting membrane potential, Na+/K+ pump is prone to ATP insufficiency due to its high energy demand [77,78]. In addition, ATP depletion due to mitochondrial dysfunction may also affect Na+ clearance and detriment cellular excitability, which may lead to cardiovascular pathologies such as arrhythmias. On one hand, dysfunction of Na+/K+ pump represents prolonged APD90 and raises the AP plateau [79]. On the other hand, buildup of intracellular Na+ hinders the concentration gradient that usually drives NCX. Excessive Na+ buildup does not favor the extrusion of Ca2+ in exchange for Na+ entering [80]. This indirect inhibition of NCX further exacerbates Ca2+ overload and contributes to arrhythmia-triggering and other detrimental consequences. There are two main types of Kv channels: transient outward Kv (Ito) and delayed rectifier Kv (IK). Currents classified as Ito activate and inactivate rapidly upon membrane depolarization. Ito underlies the early (phase one) repolarization of AP, which is mostly attributed to Ito1 [81], whereas IK currents activate depolarization with variable kinetics and underlie the late (phases two and three) repolarization of AP [82]. Similar to Kv channels, multiple functionally distinct types of Kir channels have been identified. Among the Kir channels expressed in mammalian hearts, IK1 contributes to the terminal phase of repolarization and maintenance of resting membrane potentials in ventricular myocytes [83,84], whereas sarcolemmal ATP-sensitive potassium (sarcKATP) channels-mediated currents (IKATP) play an important role in regulating electrophysiological responses under stresses such as cardiac ischemia [85]. Opening of sarcKATP channels significantly promotes K+ efflux, shortens APD [86,87], and slows or blocks AV propagation [88], thereby promoting arrhythmias. In addition to the plasma membrane, sarcKATP channels are also present in the mitochondrial membrane. Transient opening of mitochondrial KATP may allow K+ to enter mitochondria and slow down the oscillation of ΔΨm. Under mitochondrial dysfunction-induced oxidative stress, multiple repolarizing potassium currents (Ito, IK, and IK1) are suppressed, which can cause delayed repolarization and prolonged APD. In addition, an altered intracellular ATP/ADP ratio, also a consequence of mitochondrial dysfunction, results in the opening of sarcKATP channels [89]. Further, excessive ROS may also cause mitochondrial Ca2+ overload and mitochondrial KATP channel activation, leading to increased K+ influx [90]. Taken together, these effects produce an inwardly rectifying repolarizing K+ current and Ca2+ alternans, which are capable of slowing or blocking cardiac electrical propagation, thereby fomenting arrhythmias [91,92]. An overview of mitochondrial proteins and their roles in the heart under physiological and pathophysiological conditions can be found in Figure 3. Several Ca2+ transport proteins have been described above. Here, we focus on the effects of MCU complex and UCPs in the regulation of mitochondrial Ca2+ uptake. Mitochondrial Ca2+ uptake is mostly driven by MCU. UCPs are shown to regulate MCU function and, for this reason, are suggested to influence mitochondrial Ca2+ handling. Under basal conditions, mitochondrial Ca2+ uptake can prevent arrhythmias, but under conditions of Ca2+ overload, this action may be pro-arrhythmic. MCU complex includes pore-forming subunit MCU and auxiliary regulatory proteins MICU1, MICU2, EMRE, MCUb, and MCUR1 [93]. MCU complex has been identified as a highly selective Ca2+ channel on the inner mitochondrial membrane [94]. Under pathological conditions, MCU is involved in EAD production [95]. MCU can also cause abnormal repolarization at the cellular level, which is partly dependent on the activation of CaMKII [96]. Knockdown of MCU was reported to inhibit Ca2+ uptake in mitochondria, decrease NCX currents, and suppress EADs, thereby reducing arrhythmic risk [95]. At the mechanistic level, ROS accumulation increases MCU activity, leading to mitochondrial Ca2+ overload, which enhances the production of mitochondrial ROS, forming a positive feedback loop [97]. On the contrary, another study reported that heart-specific loss of MCU caused defects in both mitochondrial Ca2+ uptake and Ca2+-induced activation of the TCA cycle, providing both trigger and substrate for arrhythmias [98]. Consistently, Liu et al. found that moderate overexpression of MCU inhibited SR Ca2+ leak and thus exerted an anti-arrhythmic effect [99]. The reason for this discrepancy is unclear. We speculate that decreased mitochondrial Ca2+ uptake may either promote or inhibit arrhythmias, depending on the severity of heart failure. The precise role of MCU in arrhythmias warrants further study and clarification. UCPs participate in the regulation of mitochondrial Ca2+ homeostasis [100] and mitochondrial ROS generation [101], which may be involved in arrhythmic pathophysiology. To date, five UCPs, including UCP1-5, have been identified in the form of dimers on the inner mitochondrial membrane in mammals [102]. UCP2 and UCP3 uncouple oxidative phosphorylation and reduce ROS production [101,103]. UCP2 and UCP3 also belong to a superfamily of mitochondrial ion transporters [104] and have been reported in Ca2+ regulation through MCU-related channel mCa1 and arrhythmia induction [105]. UCP2 overexpression markedly inhibits mitochondrial Ca2+ uptake, and UCP2 knockout mice were shown to have a higher susceptibility to arrhythmias with decreased APD after ICa-L activation and disturbed Ca2+ homeostasis [106]. However, another study suggests that UCP2 upregulation has a negative effect on mitochondrial Ca2+ uptake in excitable cells and disrupts excitation-contraction coupling, which potentially causes arrhythmic initiation [107]. These discrepancies may be attributed to different cardiomyocytes used, one being primary neonatal rat ventricular cardiomyocytes and the other being ventricular cardiomyocytes from young adult mice. On the other hand, UCP3 was shown to protect mitochondria from Ca2+ overload and propagation of arrhythmic initiation of calcium-induced calcium release (CICR) [105]. Impulse conduction through the heart depends on cell-to-cell electrical coupling mediated by gap junctions. Connexin is key to forming these gap junctions [108]. Connexin plays a crucial role in cardiac impulse conduction through the regulation of cardiac conduction velocity [109]. Connexin 43 (Cx43) is known to form gap junctions in ventricular myocytes at the sarcolemmal level, which may play a role in the crosstalk between mitochondrial dysfunction and arrhythmias. For example, mitochondrial ROS are suggested to affect the function of gap junctions by activating c-Src to replace Cx43 [110]. Several studies reported that downregulation of Cx43 results in abnormal conduction, impaired repolarization, prolonged APD, EADs, and DADs, and increased electrical heterogeneity to facilitate reentry arrhythmias [111]. Cx43 is also involved in mitochondrial function maintenance as hemichannels on the inner mitochondrial membrane of subsarcolemmal cardiomyocytes (mitochondrial Cx43) [112]. Mitochondrial Cx43 is an important Ca2+ regulator and has been shown to be involved in cardioprotection by ischemic preconditioning, likely involving decreased ROS formation [113]. Consistently, mitochondrial Cx43 deficiency depolarizes ΔΨm and increases Ca2+ within mitochondria, thereby augmenting Ca2+ spark frequency, ROS production, and arrhythmia susceptibility [114]. The underlying mechanism may be attributed to the modulation of mitochondrial KATP, of which the opening promotes ROS generation and downstream pathological signaling [115]. A growing amount of evidence suggests that intracellular RAS plays an important role in mammalian cell function and is involved in the pathogenesis of arrhythmias. Notably, Abadir et al. revealed the existence of functional mitochondrial RAS with colocalization of angiotensin II (Ang II) and Ang II type 2 receptor (AT2-R) on the inner mitochondrial membrane [116]. The presence of Ang II receptors, including AT1-R and AT2-R, in mitochondria was also identified in Percoll-purified samples [117]. In the presence of Ang II, mitochondrial AT1-R activation may directly affect ROS production. On the other hand, ROS may be generated through stimulation of mitochondrial respiratory chain activity secondary to AT1-R activation [118,119]. Moreover, mitochondrial AT2-R is functionally associated with nitric oxide (NO) production. AT2-R stimulation increased mitochondrial NO generation, which was mitigated by AT2-R antagonist in isolated mitochondria [116]. These studies indicate that the protective AT2-R-mediated NO generation balances AT1-R-mediated ROS generation. Therefore, mitochondrial AT1-R and AT2-R may play opposing roles in maintaining a balance of mitochondrial function and cell survival. An imbalance in mitochondrial RAS can increase AT1-R-mediated ROS, which may impair cardiac gap junction with further cardiac fibrosis (structural remodeling). In addition, ROS can reduce cardiac gap junction expression and impair repolarization, evidenced by prolonged APD, EADs, and DADs (electrical remodeling) [120]. These two types of remodeling may ultimately lead to cardiac arrhythmias. MDPs are a group of peptides encoded by open reading frames of mtDNA [121]. Recently, it has been demonstrated that MDPs play important roles in cardiovascular disease. However, studies exploring the effects of MDPs on arrhythmias so far are scarce. Humanin, one of the MDPs, is encoded in the 16S rRNA region of mtDNA. Humanin exists not only in the circulating body fluids but also in metabolically active organs, such as the heart. Thummasorn et al. found that humanin levels were decreased in the damaged myocardium at the end of cardiac ischemia/reperfusion (I/R). Importantly, administration of a humanin analog could increase humanin levels in the injured myocardium and reduce mitochondrial dysfunction in rats, as indicated by decreases in ROS production, mitochondrial membrane depolarization, mitochondrial swelling, and I/R-induced arrhythmias [122,123]. These results provided novel insights into MDP-mediated cardiac arrhythmia prevention through improving mitochondrial function. GRKs and GPCR adapter proteins such as GRK2 and β-arrestins are crucial regulators of GPCR signaling and mediate the functional crosstalk between mitochondria and other cellular structures [124]. These proteins may move across different cellular compartments, including mitochondria, and interact with various elements, thus affecting signaling transduction in a GPCR-independent manner. Previous studies have uncovered a key role for GRK2 as a regulator of mitochondrial function [124]. For example, ETC components were shown to be regulated by GRK2, particularly the ATP synthase barrel of Complex V, which is critical for ATP production [125]. Further, mitochondrial GRK2-mediated ATP synthesis may affect ROS production and fatty acid metabolism in failing hearts. In addition, GRK2 is involved in mitochondrial fusion and fission via phosphorylating and activating mitofusins [126]. Although several findings indicate that GRK2 is capable of controlling ATP and ROS generation, metabolic stress, and mitochondrial dynamics, the precise role of GRK2 in arrhythmias remains to be unraveled. β-arrestins can also interfere with key mitochondrial processes such as cell death, ROS production, and respiration [127]. Compared with GRK2, the involvement of β-arrestins in the regulation of mitochondrial function in cardiomyocytes requires more work. Although there have been no reports regarding the role of mitochondrial GRKs and GPCR adapters in cardiac arrhythmias, future studies may target these proteins to explore pro-arrhythmic or anti-arrhythmic effects through the regulation of mitochondrial function. In addition to mitochondria-associated proteins, nucleotide-binding domain and leucine-rich repeat pyrin 3 domain (NLRP3) inflammasome is also implicated in mitochondrial dysfunction to facilitate reentry arrhythmias. In cardiomyocytes, resting NLRP3 localizes to SR, whereas NLRP3 inflammasome activation redistributes NLRP3 to SR and mitochondria [128]. Mitochondrial dysfunction plays an important role in the instigation of NLRP3 inflammasome [129]. For example, excessive mitochondria-derived ROS fuel NLRP3 inflammasomal assembly [130]. Mitochondrial ROS also potentiate the release of oxidized mtDNA, which can trigger the assembly of NLRP3 inflammasome [130]. Consequently, NLRP3 inflammasome, via activation of Caspase 1 and generation of interleukin (IL)-1β/IL-18, may induce fibrosis and cause structural remodeling [131]. Moreover, NLRP3 inflammasome upregulation can produce reentry substrate for AF development and higher frequency of spontaneous SR Ca2+ releases, which may cause DADs and trigger ectopic activation [132,133,134]. Mitochondria are in close proximity to the ER, and MAMs are the contact sites of the membrane between mitochondria and the ER, which play an important role in organellar communication, such as transport of ions. In cardiomyocytes, MAM contacts are more specifically defined as SR-mitochondria contacts. MAM function depends on acetylated microtubules to support efficient mitochondrial Ca2+ uptake during cardiac contraction and relaxation. Mitochondrial Ca2+ is then able to boost activities of the TCA cycle and the ETC to promote ATP production [93,135]. Major Ca2+ regulatory proteins known to date include IP3R, GRP75, VDACs, Tespa1, Sig1R, SERCA, and RyRs [136]. Among them, IP3R, GRP75, and VDACs form a complex that facilitates the release of Ca2+ from SR, Ca2+ transport between the two organelles, and uptake of Ca2+ by mitochondria [137]. Tespa1 binds GRP75 to help maintain MAM integrity and affect IP3R/GRP75/VDAC complex function [138]. Sig1R forms complexes with another protein, BiP, to stabilize IP3R from the SR side [139]. The imbalance of MAMs is associated with disrupted microtubules, which may lead to abnormal mitochondrial Ca2+ uptake and ATP generation, thus facilitating arrhythmia. Several studies found the important effects of MAMs on the development of AF and SAN dysfunction. For example, Li et al. [140] identified a significant loss of MAMs in experimental and clinical AF, and SAN dysfunction has been reported to be associated with the loss of MAM contacts in SAN, which will be discussed in detail in the next section. After describing the molecular and ionic alterations linking mitochondrial dysfunction to cardiac arrhythmias, specific causes, including inherited factors, aging, gut microbiome, and various disease that contribute to mitochondrial dysfunction and related arrhythmias, are discussed below. Primary mitochondrial respiratory chain diseases (RCD) are systemic disorders caused by sporadic or inherited mutations in mtDNA or nDNA, which can affect genes encoding respiratory chain proteins, characterized by mitochondrial respiratory chain defects and subsequent energy-metabolism imbalance [141]. mtDNA mutations are the most common cause of RCD in adults, identified in ~70% of patients with impaired OXPHOS [142]. The clinical heterogeneity of mtDNA-based mitochondrial diseases is determined, in part, by the type of mutations (protein-coding genes vs. transfer-RNA vs. mtDNA rearrangement) [143]. Although the symptoms may involve nearly all organs, the most prone tissues are the ones that have a high energy demand, such as the heart and skeletal muscle [144]. Electrocardiogram (ECG) abnormalities are seen in up to 70% of RCD patients [145] who are manifested with conduction disturbances, ventricular pre-excitation, and tachyarrhythmias such as AF and ventricular tachycardia. Conduction disturbances are common in RCD (about 10%) [6,18,28], and their prevalence increases with age [5]. Kearns–Sayre syndrome (KSS) is a specific type of mitochondrial myopathy, with the most common abnormality being a 4.9 kb deletion from nucleotide positions 8469 to 13,447 of mtDNA. Conduction disturbances are the most common symptom occurring in KSS (84% prevalence), with AV block or bradycardia-related polymorphic ventricular tachycardia (PMVT) being part of the criteria for KSS diagnosis [146,147]. PMVT, principally torsade de pointes (TdP) in the setting of QT prolongation and progression to AV block and cardiac arrest [148], has been described as relatively rare [149,150,151]. KSS has also been reported with isolated, asymptomatic right bundle branch block (RBBB) [152]. Conduction disturbances occur less commonly in other forms of RCD with AV or intra-ventricular conduction disturbances. These patients are mainly reported in association with m.8344A > G and m.3243A > G mutations, which are responsible for most cases of myoclonic epilepsy with ragged-red fibers (MERRF), mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) [153,154]. Pre-excitation and Wolff–Parkinson–White syndrome (WPW), which can lead to reentry tachyarrhythmias, are present in 15–20% of RCD patients [6,21,28]. They were first observed in Leber’s hereditary optic neuropathy (LHON) patients [155] and may coexist with a type of cardiomyopathy, left ventricular non-compaction (LVNC) [156]. Supraventricular arrhythmias have also been reported in RCD patients, with AF being the most common type [148]. Different types of RCD as the response to specific mtDNA and nDNA mutations are summarized in Table 1. In line with the findings from RCD patients, acquired mtDNA and nDNA mutations have also been shown to cause mitochondrial dysfunction and associated arrhythmias in rodent models. For example, using the K320E-TwinkleMyo mouse model with an accelerated accumulation of mtDNA deletions in the heart, Baris et al. detected an increased rate of AV block and spontaneous PVC under stress conditions [157]. In addition, cardiac-specific deletion of mitochondrial transcription factor A (mtTFA), a nDNA-encoded key regulator of mtDNA transcription, induced dilated cardiomyopathy with AV block in mice [158]. Moreover, mice with mitochondrial Complex I subunit Ndufs4 deficiency (Ndufs4−/−) developed mitochondrial dysfunction and bradyarrhythmia resembling Leigh syndrome (LS). The underlying molecular mechanisms are related to a reduced NAD+/NADH ratio, leading to hyperacetylation of Nav1.5 and subsequent reduction of INa [159]. Since SAN cells are noncontractile and autorhythmic with a high density of mitochondria which are the fuel source for SAN automaticity, alterations in mitochondria or mitochondria-SR connectomics may contribute to SAN dysfunction and associated arrhythmias such as sick sinus syndrome. Heart blocks are variable and include prolonged intraventricular conduction time, bundle branch blocks, and complete AV block, which could cause deaths in 20% of patients [160]. Studies have also shown that heart blocks, especially AVN abnormalities, have been linked to mitochondrial dysfunction. HCN channel expression is positively related to If current and the slow component of IK current (IKs), which are involved in SAN automaticity and AVN function. Several studies showed that mitochondrial dysfunction might play an essential role in the regulation of cardiac automaticity and conduction by modulating HCN channels. Yang et al. found that mitochondrial Trx2 cardiac specific deletion mice decreased HCN4 expression and developed sinus bradycardia and AV block [161]. Since Trx2 counteracts oxidative stress by reducing oxidized proteins and indirectly scavenging ROS, Trx2 cardiac specific deletion increased mitochondria-derived ROS, which may lead to SAN and AVN abnormalities through the regulation of HCN channel expression. In addition, cumulative studies demonstrated that mitochondria regulate SAN’s automaticity through Ca2+ handling and energy production. Since mitochondria are in close proximity to SR, microdomains between mitochondria and SR in response to the beat-to-beat rise of intracellular Ca2+ may play a crucial role in modulating Ca2+ cycling in cardiomyocytes [162]. Mitochondria-SR connectomics in SAN ensures adequate ATP production, which is mediated by the Ca2+-regulated cAMP/PKA signaling [163]. A recent study reported that impaired mitochondrial connectomics, either through injury to mitochondria or disruption of their MAMs, can cause SAN dysfunction [164]. Moreover, mitochondria-derived ROS bursts rapidly induce cytosolic Ca2+ overload by stimulating RyR2 and inhibiting SERCA, which further exacerbates Ca2+ dysregulation and leads to AP triggered by abnormal automaticity [162]. Although aforementioned studies have not examined the role of mitochondria in regulating the Ca2+ clock in AVN cells, we speculate that mitochondrial dysfunction may also cause AV block through Ca2+ clock, similar to the mechanism in SAN cells. In addition to the studies showing mitochondrial dysfunction-associated ionic alterations in the promotion of SAN and AVN abnormalities, there are reports focusing on other mitochondrial abnormalities-induced bradycardia or heart block. For example, a recent study reported the construction of ACE8/8 transgenic mice with increased cardiac ACE and Ang II levels (mitochondrial RAS activation). These mice showed less severe bradycardia and conduction block through c-Src tyrosine kinase activation, Cx43 reduction, and the impairment of gap junction conduction [165,166,167]. Peroxisome proliferator-activated receptor γ coactivator-1 (PGC-1) is a crucial nuclear transcription co-activator, including PGC-1α, PGC-1β, and PGC-1 related co-activator [168]. They are the major factors in the transcriptional control of mitochondrial components. Whereas PGC-1α−/− or PGC-1β−/− mice presented mild cardiac dysfunction, double deletion of PGC-1α/β caused neonatal death with bradycardia, heart block, and cardiac dysfunction [169]. AF is the most common arrhythmia in clinics; however, our understanding of the initiation and maintenance of AF remains poor. In most AF patients, the reentry phenomenon is the main pathological presentation. AF severity usually depends on atrial enlargement and fibrosis (substrate), which are caused by systemic or cardiac disease [170]. Substrate abnormality, together with premature atrial beats (trigger), promotes reentry and initiates AF [171]. However, in rarer situations without significant cardiac morphological change, rapid focal activity from pulmonary veins may be more important as the underlying mechanism to induce lone AF either via electrical remodeling or genetic susceptibility. Here, arrhythmogenic foci may depend on SR Ca2+ leak due to RyR2 activation that promotes DADs to induce AF [172]. Moreover, recent genome-wide association studies demonstrated that relatively rare mutants in cardiac K+ and Na+ channels might be involved in AF pathophysiology [173,174,175,176]. The association between mitochondrial dysfunction and AF has been investigated over past decades. Energetic imbalance in AF patients may lead to mitochondrial dysfunction [177]: Frequent depolarization of the atrial myocardium increases ATP demands. In paroxysmal or short-lasting persistent AF, mitochondria can increase ATP synthesis, but over time the production of ATP decreases. As such, the reduced ATP/AMP ratio can activate adenosine monophosphate protein kinase (AMPK), which shifts the metabolic pathway toward glycolysis, affects sarcKATP, and slows inward Ca2+ channels to impair ion homeostasis and modify electrophysiological properties of cardiomyocytes [178,179]. In addition to ATP depletion, mitochondrial dysfunction-induced excessive ROS can oxidize RyR2 of SR, leading to aberrant Ca2+ sparks, thus facilitating AF development. The sarcolemmal inward Na+ channel can also be oxidized [180,181], which may directly alter cardiomyocytes’ excitability and intercellular coupling and establish the functional background to maintain reentry circuits. In addition to electrophysiological remodeling, mitochondrial dysfunction also leads to structural remodeling by promoting cytokine release, activating fibroblasts, and depositing connective tissues to facilitate the development of arrhythmias [182]. Therefore, a variety of factors can cause mitochondrial dysfunction to induce AF. Here, we mainly focus on the role of novel factors, including burst-pacing, aging, and gut microbiome-associated mitochondrial dysfunction, in the pathophysiology of AF. Burst pacing has been used to induce short episodes of AF in animals [183,184], which is non-physiological but the most used approach in creating AF models in vivo. A recent study has demonstrated that electrical stimulation regulates mitochondrial function through the increase in ROS production [185]. Bukowska et al. applied human atrial samples to rapid burst pacing to induce AF and found an increased number of swollen and completely disrupted mitochondria. It is demonstrated that Ca2+ inward current via ICa-L contributing to oxidative stress leads to mitochondrial ultrastructural changes [186]. In a rabbit model of pacing-induced AF, the expression of mtDNA-encoded proteins and transcription factors involved in mitochondrial biogenesis was decreased, and the atrial electrophysiological property APD was shortened [187]. Consistently, Shao et al. found that amelioration of mitochondrial dysfunction can reduce burst pacing-induced AF susceptibility through attenuation of ROS generation, systemic inflammation, and atrial fibrosis [188]. AF is the most prevalent aging-related arrhythmia affecting millions of people worldwide [189]. Alterations in mitochondrial function in senescent hearts have been documented. A clear link exists between aging and mitochondrial dysfunction in facilitating AF. There have been various mechanisms by which aging causes the increased incidence of AF, including mtDNA damage, clonal expansion of deleterious mutations in mtDNA, transcriptional downregulation of genes in mitochondrial energetics, and deficiencies in mitochondrial ETC enzymes [190,191], providing substrate for reduced energetic efficiency in senescent human hearts [192]. As such, aging-associated dysfunctional mitochondria result in reduced ATP production and high levels of ROS, which can facilitate AF through structural and electrical remodeling. As mentioned before, PGC-1 plays an important role in controlling the transcription of mitochondrial components. Studies showed that the shortening of telomeres by aging [193] might inhibit PGC-1 and cause mitochondrial dysfunction and a series of reactions, such as oxidative stress and intracellular Ca2+ overload, eventually inducing AF [194]. PGC-1α has been suggested as a key molecule of mitochondrial function through the regulation of mitochondrial biogenesis and energy metabolism. PGC-1α is also closely related to oxidative stress and inflammation [195,196]. Serum PGC-1α and ΔΨm were found to be reduced in aging-related AF patients [197]. PGC-1β has high sequence similarity to PGC-1α and is also believed to control mitochondrial oxidative energy metabolism and conduction velocity, revealed by reduced voltage-gated inward Na+ currents and gap junctions under PGC-1β deficiency. Young PGC-1β−/− hearts developed electrophysiological features resembling aging hearts, which may explain their increased propensity to AF. Moreover, PGC-1β−/− mice reflecting mitochondrial dysfunction showed reduced atrial Cx protein expression and increased cardiac fibrosis, associated with a pro-arrhythmic phenotype progressing with age [198]. Recent studies have reported that altered intestinal flora composition and fermentation metabolites are implicated in arrhythmias, especially AF. Mitochondria are suggested as the most responsive organelle to microbiotic signaling [199]. A growing amount of evidence shows that gut microbiota can interact with mitochondria in a variety of ways. Moreover, gut microbiome has emerged as a dynamic and central regulator of mitochondrial function in immune and epithelial cells located in the intestine and has been shown to regulate key transcriptional co-activators, transcription factors, and enzymes involved in mitochondrial biogenesis [200]. In addition, gut microbiome signaling to mitochondria has been shown to alter mitochondrial metabolism, which can induce inflammasome signaling [201]. For example, mitochondrial alterations such as increased mitochondrial ROS, oxidized mtDNA, extracellular ATP efflux, and ΔΨm loss are emerging as key activators of NLRP3 inflammasome to promote atrial inflammation and fibrosis [128,201]. Not only the gut microbiome itself but also its derived metabolites, including primary bile acids (BAs), TMAO, indoxyl sulfate, LPS, and choline, are implicated in mitochondrial dysfunction and AF development. For example, primary BAs can activate NADPH oxidase, promoting ROS production and inducing ATP release, which results in NLRP3 inflammasome activation. TMAO also leads to oxidative stress and activates NLRP3 inflammatory and TGFb1/Smad3 signaling pathways. Increased mitochondrial ROS are associated with mPTP opening followed by mitochondrial Ca2+ disturbances, which leads to electrical remodeling [202] or the release of pro-apoptotic cytochrome c, Apaf-1, Caspase 9, and Caspase 3, causing cardiac fibrosis [203]. The effects of these gut microbiota and their derived metabolites may increase the likelihood of AF-promoting ectopic firing and AF-maintaining reentry to enhance the susceptibility and maintenance of AF. Emerging studies showed that direct ion alteration-induced AF models present mitochondrial dysfunction. For example, Wan et al. found that expression of a gain-of-function mutant of Nav1.5 channel causing increased persistent Na+ current led to the development of spontaneous and long-lasting episodes of AF in mice, which also exhibited EADs and mitochondrial dysmorphology. All these pathologies could be attenuated by resolving mitochondrial oxidative stress [204]. In addition, Avula et al. showed that transgenic mice with increased persistent Na+ current caused both structural (atrial enlargement and fibrosis) and electrophysiological (EADs) remodeling in atria, leading to AF through modulating mitochondrial ROS [205]. SCD can often be the result of VAs, especially VT/VF, which remains one of the most important public health concerns worldwide [206]. As mentioned earlier, reentry, together with increased triggered activity, is the main mechanism of most tachyarrhythmias, including VAs. Cardiac mitochondrial dysfunction-induced reentry and triggering during VAs may share a similar reentry mechanism with AF. VA-associated mitochondrial dysfunction may reduce ATP and energy production and cause the accumulation of ROS, which further contributes to cardiomyocyte damage. Therefore, reentry circuit is maintained in promoting VAs under stress conditions, such as cardiac I/R injury and direct electrical stimulation. Ischemia-induced ionic alteration may directly facilitate VAs. In cardiac ischemia or during the ischemic period of I/R, impaired SERCA pump function was observed, indicating that Ca2+ waves can be induced by impaired SERCA and thus give rise to Ca2+ alternans. These alterations can lead to an increased propensity for cardiac arrhythmias such as ventricular reentry and VFs [207]. On the other hand, ischemia-induced ionic alteration can indirectly promote VAs through the regulation of mitochondrial function. Under ischemia conditions, depletion of oxygen and other substrates greatly limits aerobic respiration, causing the cytosol to become acidic. The increase in Na+/H+ exchange leads to a high level of intracellular Na+, which causes the NCX to work in a reverse mode to increase Ca2+ uptake and impair ATP synthesis [208]. These two mechanisms together cause a loss of ion homeostasis, stimulation of ROS, mPTP opening, matrix swelling, OMM rupture, and finally, cell death [209]. Upon reperfusion, intracellular pH is normalized, and OXPHOS resumes in reoxygenated mitochondria, resulting in an increase in ROS production [210]. Both in vitro and in vivo studies showed cellular and ionic alteration-induced mitochondrial dysfunction in the pathophysiology of VAs under cardiac I/R. For example, cardiac ischemia and tachypacing-induced VFs could lead to mitochondrial ΔΨm loss [211]. In addition, cardiac I/R decreased the expression of mitochondrial ETC components [212] and downregulated the ADP/oxygen ratio [213], which is related to impaired ion channel function and post-I/R ventricular arrhythmogenesis. Interestingly, cardiac I/R injury is suggested to induce VAs through the regulation of sarcKATP rather than mitochondrial KATP [214]. In addition to I/R, ischemia combined with aging, the latter showing a mosaic of normal cells and mitochondrial deficient cells in the heart, also contributes to a higher susceptibility for VAs through regulation of mitochondrial function. For example, Stöckigt et al. showed that aging-related cardiac mitochondrial dysfunction facilitated the occurrence of spontaneous and inducible VAs after cardiac ischemia, which was associated with the increase in phosphorylated Cx43 and slowing of electrical impulse propagation in the infarct area [215]. In line with I/R-induced VAs, electrically induced VAs have been shown to be related to mitochondrial ultrastructural alterations [216] and mitochondrial dysfunction, such as mPTP opening and downregulation of mitochondrial ETC components COXBIII and ATPS6 [212]. Furthermore, cardiomyocytes were observed to exhibit mitochondrial abnormalities of cytosolic Na+ and mitochondrial Ca2+ overload during the recovery of spontaneous circulation after electrical stimulation-induced VAs [217]. An in vitro study also demonstrated that electrical stimulation of cardiomyocytes could disturb CaMKII-dependent Ca2+ homeostasis and lead to mitochondrial stress, promoting both structural and electrophysiological remodeling and finally facilitating tachycardia-associated SCD [218]. Heart failure is accompanied by mitochondrial dysfunction. Similar to ischemia, heart failure can also induce ionic or Ca2+ transport protein alteration to facilitate VAs directly. In addition to prolonged APD, reduced Ca2+ transient, and elevated Na+ concentration, the lowered heart rate threshold for the onset of APD alternans is observed in heart failure [219,220]. For example, Pogwizd et al. found enhanced NCX activity-induced abnormal Ca2+ handling, DADs, and initiation of VTs in a heart failure model [221]. Heart failure-induced ionic and electrical remodeling can also indirectly promote VAs through the regulation of mitochondrial Ca2+ [95], which could be influenced by excessive ROS. Moreover, combined factors such as heart failure following myocardial infarction are also associated with a high incidence of arrhythmias through electrical remodeling. e.g., increasing the heterogeneity of AP repolarization [222]. Other mitochondrial abnormalities, such as mitochondrial RAS activation, can also promote VAs. Sovari et al. demonstrated that RAS activation in the ACE8/8 mouse model could increase mitochondrial ROS production and reduce conduction velocity via downregulation of Cx43 function and expression, which further leads to an increased risk of VAs [215]. Additional studies showed that both MCU alteration-associated insufficient and excessive mitochondrial Ca2+ uptake under the context of heart failure or high-fat diet feeding could lead to excessive ROS generation, which plays a major role in VA pathophysiology. For example, a recent study by Liu et al. showed that MCU overexpression in failing hearts reversed heat failure and prevented ectopic VAs by inhibiting mitochondrial ROS-induced SR Ca2+ leak [99]. However, another study by Joseph et al. reported that the presence of MCU promoted VAs during high-fat diet feeding, while cardiac-specific deletion of MCU could be protective in a rodent model [96]. Mitochondrial dysfunction also plays an important role in arrhythmia development in hereditary muscular dystrophies, in particular, Duchenne muscular dystrophy (DMD). Mitochondrial ROS production, as well as mitochondrial Ca2+ uptake, is believed to increase in the DMD mdx mouse model, which may contribute to the pathogenesis of cardiac remodeling and then arrhythmia induction [223,224]. The activity of cardiac ICaL, Cav1.2, determines Ca2+ entry in phase two of AP in cardiomyocytes. In mdx cardiomyocytes, Cav1.2 activation is significantly increased [225], which elevates Ca2+ influx during AP. In addition, RyR2-mediated Ca2+ leak was reported to contribute to VAs in mdx mice [226]. Excessive Ca2+ in the cytoplasm and MAMs also increase mitochondrial Ca2+ uptake. Dubinin et al. showed that the augmented mitochondrial Ca2+ uptake of mdx mice might be due to an increase in the ratio of MCU and MCUb subunits, whereas the elevation of Ca2+ efflux from mitochondria in mdx mice may be due to an increased NCLX level [223]. Moreover, cardiomyocyte mitochondria of mdx mice were more resistant to mPTP opening. All these Ca2+ overload effects may disturb cardiac electrophysiology, thereby causing arrhythmias in DMD. Mitochondrial dysfunction characterized by reduced ATP synthesis and increased ROS production can lead to cellular and ionic malfunction of the heart, including altered automaticity, triggered activity, reentry phenomenon, and conduction block, thereby causing arrhythmias. Mechanistically, mitochondrial dysfunction is closely related to the pathogenesis of arrhythmias through regulation of the activities of sarcolemmal and mitochondrial ion channels for Na+, K+, and Ca2+, thus leading to cardiac electrical remodeling. In addition, mitochondria-associated proteins and inflammasome signaling, including mitochondrial MCU complex, UCPs, Cx, RAS, MDPs, and NLRP3 inflammasome, are involved in mitochondrial dysfunction by triggering both electrical and structural remodeling. Moreover, mitochondrial dysfunction is implicated in the pathophysiology of specific types of arrhythmias, e.g., RCD-associated arrhythmia, SAN and AVN dysfunction, reentry arrhythmia embracing AF, and VAs. Future studies may focus on exploring mitochondria-related mechanisms in the onset and during progression and new treatments for arrhythmias. Taken together, mitochondrial dysfunction plays an essential role in the etiology of various arrhythmias, which may represent a unifying molecular mechanism and a promising target to ameliorate clinical arrhythmias.

PMC10001006

Federica Torricelli,Elisabetta Sauta,Veronica Manicardi,Vincenzo Dario Mandato,Andrea Palicelli,Alessia Ciarrocchi,Gloria Manzotti

An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization

03-03-2023

drug repurposing,endometrial cancer,gene expression,metastasis,PI3K pathway

Background: Endometrial cancer (EC) is the most common gynecologic tumor and the world’s fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC. Methods: Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach. Results: We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates. Conclusions: Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach.

An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization Background: Endometrial cancer (EC) is the most common gynecologic tumor and the world’s fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC. Methods: Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach. Results: We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates. Conclusions: Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach. Endometrial cancer (EC) is the most frequent gynecologic tumor [1]. Its incidence and associated mortality are increasing over the past years. The gold standard treatment for EC at an early stage is total hysterectomy with bilateral adnexectomy and sentinel lymph node biopsy or systematic pelvic/lumbar-aortic lymphadenectomy in case of increased risk of lymph node metastases. In advanced EC, chemotherapy is indicated and should precede debulking surgery if complete cytoreduction is achievable. Thus, a major limitation of a successful first-line treatment is the presence of metastasis. Despite many patients having a good prognosis, advanced or recurrent EC have a poor prognosis, and metastatic or recurrent tumors are partially or not responsive at all [2]. It emerges as an urgent need to identify new druggable targets to circumvent the tumor resistance mechanism and to target specifically metastatic tumors. In recent years, thanks to the acquired deep knowledge about genetic and molecular assets of many types of cancer, a growing number of targeted therapies have become available but, at present, few are approved for EC [3,4]. Drug repurposing is the application of old drugs to new indications. Repositioning already-approved drugs presents obvious advantages. Knowing the safety, toxicity, pharmacokinetic, pharmacodynamic, and metabolic properties of a compound significantly reduces risk, costs, and the time required to register the indication, as compared to a new chemical entity [5,6,7]. This approach could potentially provide new ready-to-use therapeutic options for aggressive and resistant cancers and has already been largely employed in different tumor settings [8,9,10,11,12,13,14,15,16,17]. Many different computational methodologies have been developed for this purpose, mainly data-driven [18]. One of the most used is based on the differential gene expression signature (dGES) and compares disease or clinical phenotypes against a reference condition [19,20]. The obtained signature could be used as input for various methods to make drug-disease associations, returning a list of compounds that could potentially revert the matched signature and the disease phenotype itself. Another increasingly used methodology is the pathway-based analysis which can help to identify the most deregulated processes, allowing to prioritize drug targets within the drug-gene association procedure [21,22,23]. In this work, we introduce a new robust integrated repurposing approach identifying new ready-to-use therapeutic options for metastatic EC. The drug repurposing integrative pipeline used in the present work is shown in Figure 1A. Briefly, we retrieved publicly available transcriptomics data of endometrial cancer samples and we stratified patients according to the FIGO Stage, classifying them as non-metastatic (NM) or metastatic (M). With the final aim to discover promising drug candidates, we compared these two groups with an integrated approach using two computational strategies, one signature-matching and one pathway-based, obtaining, respectively, a differential Gene Expression Signature (dGES) and a ranked list of the most deregulated pathways underlying metastatization. All Differential Expressed Genes (DEGs) participating in these pathways were identified as the most perturbed. Next, the dGES was used to query the Connectivity Map (cMap) database, while the perturbed genes associated with deregulated pathways were used to query the Drug-Gene Interaction database (DGIdb) [24], for identifying potential drug-gene pairs. Finally, the obtained drug lists were merged, and the remaining compounds were in-silico validated through the Genomics of Drug Sensitivity in Cancer (GDSC) database. Transcriptomics data used in this work belong to the Uterine Corpus Endometrial Carcinoma (UCEC) project of The Cancer Genome Atlas (TCGA) and were downloaded by the R package “UCSCXenaTools” [25]. Since our main purpose was to compare NM and M EC, we based on the FIGO (International Federation of Gynecology and Obstetrics) classification which divides EC into four stages, with an increasing level of aggressiveness and dissemination, based on histologic differentiation and intra-operative evaluation of abdominal and pelvic spreading [26,27,28]. Only cases described in the study by Cancer Genome Atlas Research Network, Kandoth C, Schultz N, et al. [29] were selected, since in this work Stages annotation uniformly referred to the FIGO stage nomenclature of 2009, without misinterpretations (as specified in the “Supplementary Method S1” of the cited work [29]). Moreover, we considered only Type 1 EC, which is the most common histotype accounting for 80–90% of total cases and includes only endometrioid tumors [30]. We also excluded cases with “unknown” histotypes. To properly define a sample as metastatic or not, we considered clinical and histopathological features and used, as a reference, the FIGO stage, considering both Stage 4 and Stage 3 as metastatic diseases (metastatic—M). Conversely, we grouped Stages 1 and 2 which never present metastasis (non-metastatic—NM) (Figure 1B). Thus, we obtained a final dataset composed of 246 samples for the NM group, and of 55 samples for the M one, for which RNA-sequencing, clinical, and histopathological data were available. The specimens used in this database were primary tumors, collected at diagnosis and only from patients with no prior treatment before surgery. All data were downloaded from TCGA, which is open to the public under certain restrictions, therefore no ethical approval was needed. Differential gene expression analysis was performed in the R environment (v3.6.3) (R Foundation for Statistical, Vienna, Austria) comparing the mean of the expression of each gene in M and NM groups by Kruskal Wallis statistical test and calculating the false discovery rate (FDR) using the Benjamini-Hochberg correction for multiple hypothesis testing to identify statistically significant deregulated genes (FDR < 0.05). For the definition of a consistent dGES, only protein-coding genes with a minimum mean expression higher than 0.5 log2 (FPKM + 1) in both groups were selected. The same statistical analysis was performed comparing Stage I vs. Stage II and Stage III vs. Stage IV. Enrichment pathways analyses were performed exploiting Gene Ontologies Biological Process (GO_BP) and Molecular Function (GO_MF) as references using the EnrichR R package [31]. Pathways were considered significantly enriched with a significance threshold of 0.05 (FDR < 0.05). The obtained dGES was used as input of the cMap (v1.1.1.43, dataset v1.1.1.2, accessed via https://clue.io) (accessed on 9 November 2020) [32,33]. cMap returns as output a list of perturbagenes ranked accordingly to a connectivity score which incorporates a p-value corrected for multiple hypothesis testing using the false discovery rate method [33,34]. Since none of the cell lines present on the cMap database at the time of our query were of endometrial cancer, we used the option “summary” which, given a set of connectivity scores for a particular perturbagen, summarizes those scores across all the cell lines tested. cMap output comprises four perturbagene types: gene over-expression and gene-knockdown (which are not considered in this study), compounds (which are our main interest), and Perturbagen Class (PCL) of compounds based on their strong connectivity to each other and shared mechanisms of action (MoA). We evaluated all compounds and PCL with a negative connectivity score, thus applying the reversal method [35,36,37,38,39], and we used a threshold of −60 to have a broader perspective of the molecules with potential reverse connection to our dGES. We applied the Pathifier algorithm [40] using the default parameters setting on our stratified expression dataset, comparing M and NM patients’ profiles. As prior information, we considered GO_BP (number of pathways = 5103) and GO_MF (number of pathways = 1151). Integrating these gene assignments to known pathways with expression data, Pathifier independently quantifies, for a given pathway and each sample, a pathway deregulation score (PDS), representing the deviation of a sample from the reference condition (NM samples). Graphically, this is represented by a cloud of points (each of which is a sample) whose variation is recapitulated by the calculation of a “principal curve”. In this dimensional space, PDS is the distance along the curve between a sample and a reference point, defined as the centroid of a reference set of samples (NM samples). Analyzed pathways were clustered using the assigned PDS obtained from the two Pathifier analyses using SPIN (Sorting Points Into Neighborhoods), an unsupervised sorting method [41] able to capture the gradual changes underlying heterogeneous expression data as a distance matrix to the cluster pathways that share a similar deregulation profile. The resulting matrixes were graphically represented as similarity heatmap using Matplotlib [42] within a Python environment. We then ranked all pathways using the median (PDSM) estimated on the PDS of M samples, assigning, to each pathway, a unique PDSM value. To further filter the obtained pathways, we then considered only those with PDSM > 0.6 and those that have, among interacting genes, differentially expressed genes derived from the dGES. For a graphical purpose, given the high number of perturbed pathways from each of the two Pathifier analyses, we grouped the resulting ranked pathways using a semantic similarity-based approach, which exploited the hierarchical nature of Gene Ontologies. To this aim, we used the Relevance information-content strategy implemented in the rrvgo R package [43], which depends on the frequencies of two GO terms and that of their closest common ancestor in a specific corpus of GO annotations. The resulting similarity matrices were then reduced by applying a threshold of 0.7 on estimated similarity scores. Similar terms were hierarchically clustered using a complete linkage method and the resulting tree was then cut at the corresponding level of 0.7. The obtained list of categories was then manually curated and combined in macro-categories for ease of graphical representation. The lists of DEGs mapping into the most altered biological processes and molecular functions were then merged for the following drug-target association analysis using the DGIdb database (release 4.2.0, www.dgidb.org) (accessed on 13 July 2021). The DGIdb was queried using Application Programming Interface (API) within the Python environment (v3.5.2, Python Software Foundation, Fredericksburg, VA, USA). Results are returned in JSON format, which was parsed to extract all DEGs associated with one or more drugs and the related type of interaction. To finally have a unique list of potential drug candidates, the obtained compounds set was then integrated with the cMap output. Since cMap molecules are identified with a Broad Institute (BRD) Identifier, to avoid loss of information during the merging step, we used the PubChem Identifier Exchange Service to convert compound names from DGIdb query in other identifiers. This mapping step was performed using an ad-hoc Python pipeline that returned a final dataset of drugs common to cMap and DGIdb query results. For each of our previously defined drugs, present in the GDSC database (release 8.3, https://www.cancerrxgene.org/, accessed on 1 July 2021) [24], IC50 results were filtered for “Tissue sub-type: endometrium” and “Disease: endometrial adenocarcinoma”. Since our samples only belonged to Type 1 EC, we excluded all cell lines for which it is not possible to certainly retrieve this information. The experimental information about the validated compounds were manually curated through the literature mining and querying several databases: DrugBank [44], clinicaltrials.gov, Harmonizome [45], The Drug Repurposing Hub (clue.io/repurposing-app), NCI Drug Dictionary, FDA (https://www.fda.gov/, accessed on 1 July 2021), Inxight Drugs (drugs.ncats.io, accessed on 1 July 2021). Differential gene expression analysis between metastatic (M) and non-metastatic (NM) EC identified a robust dGES composed of 212 genes, 29 UP-regulated and 183 DOWN-regulated (Figure 1C, Supplementary Table S1). Functional enrichment analysis revealed that, according to Gene Ontology-Biological Processes (GO_BP), positive regulation of protein kinase B (PKB) signaling is the top-scoring pathway, together with cilium assembly and movement, positive regulation of cell migration and cell mobility processes (Figure 1D). Coherently, among the enriched Gene Ontology-Molecular Functions (GO_MF), the top-scoring pathways mainly concern the phosphatidylinositol kinase activity (Figure 1E). All enriched processes describe a landscape that is connected to endometrial classical features, such as cilium and estrogen-related functions. In addition, a strong correlation between the PI3K/AKT/mTOR pathway and EC metastatization emerged from this analysis. This pathway is strongly associated with EC being altered in more than 93% of patients [29]. However, this is the first time that transcriptional deregulation of this pathway is linked to metastatic progression in EC. The connection between metastasis and the PI3K/AKT/mTOR pathway provided by our data could suggest a new and specific enrolling criterion for clinical trials involving these classes of drugs. To deeper investigate the molecular processes underlying metastatization, we expanded the information obtained from the dGES by comparing the transcriptomics profiles of M and NM patients, exploiting the Pathifier algorithm [40]. It translates the gene-level information into pathway-level information, incorporating prior knowledge about functional interactions of biological processes (GO_BP and GO_MF) thus, assigning to each pathway a Pathway Deregulation Score (PDS) which can be clustered based on similarity grade of perturbation (Figure 2A,B). We ranked the resulting deregulations by calculating for each pathway the median PDS on metastatic samples (PDSM) and considered as top scoring those with a PDSM > 0.6 (Figure 2C), thus obtaining a list of significantly deregulated pathways (2096 for GO_BP and 468 for GO_MF). Again, the PI3K/AKT/mTOR pathway was present in these lists, even if the majority of the identified perturbed pathways have never been linked to EC. Pathways from GO_BP vary mainly between nucleic acids regulation and biosynthesis, tissue differentiation and development, response to various external stimuli, vesicle trafficking, proteins and ions distribution, metabolic and biosynthetic processes, and protein translation and modification (Figure 2D). Similarly, the most represented GO_MF are associated with RNA/DNA regulation and transcription, enzyme activity, protein binding, and interaction (receptors activity and complex formation), and transmembrane transports, especially of ions (Figure 2E). Some of these processes are strictly related to cancer progression, while some others are non-obvious, such as “Cilium movement” and “Regulation of inositol phosphate biosynthetic process” (GO_BP), and “Phosphatidylinositol-3,5-bisphosphate binding” and “nuclear hormone receptor binding” (GO_MF) (Figure 2F–I). These results define a complex molecular landscape of biological alterations leading to EC metastatization and unveil perturbed pathways never considered before as EC vulnerability which can be exploited to target the most aggressive EC. Using the results of these analyses, we searched for already approved drugs that could be repurposed on EC by employing two different approaches: (i) querying the cMap to obtain matched drug signatures and, (ii) using the Drug Gene Interaction Database (DGIdb) to derive drug-gene associations. For the Signature-matching Approach, we used the dGES as input for the cMap which returned a list of perturbagens ranked by the Connectivity Score (CS). By applying the reversal method, we identified 115 compounds and six main PCL comprising most but not all the compounds (Figure 3A), potentially able to revert the metastatic phenotype. Most candidate drugs belong to mTOR, PI3K, SRC, MEK, Topoisomerase, and Growth factor receptors (such as IGF-1), inhibitor families, with a clear enrichment for molecules involved in interconnected pathways (MEK and PI3K/mTOR are parallel, and IGF-1 is one of their up-stream RTK). Other molecules that do not belong to the top-scoring PCL are also connected with the PI3K/AKT/mTOR pathway, for example, EGFR, RAF, CDK, and JNK inhibitors. Furthermore, in these interconnected families of drugs, it is interesting to note that some molecules, which are not part of the highly represented PCL, have a medium/high CS and are well-known drugs, used for a long time in clinical practice (methotrexate, mycophenolate-mofetil, dasatinib, tipifarnib, tamoxifen) (Figure 3B). Notably, one-third of the 115 compounds identified are launched, according to FDA approval status [46] (Figure 3C). For the Pathway-based approach, we used Pathifier to establish the most altered pathways. From these pathways, we obtained two lists of differentially expressed genes (DEGs), 113 genes for GO_BP, and 78 genes from GO_MF. Then, we combined them to define a unique list of 121 most perturbed DEGs to be considered as potential pharmacological targets. They were used to query the DGIdb, which integrates about 15 pharmacological databases with the purpose to identify, for a gene of interest, drugs able to interact with its functions. We obtained a dataset comprising 575 compounds (Supplementary Table S2), for which Figure 3D provides a brief example. As expected, not all the input genes have a drug pair, while others are associated with several compounds (Figure 3E). To get a robust prediction of drugs potentially able to restrain EC metastatization, we merged the results derived from cMap and DGIdb. Figure 4A, graphically represents how these compounds are distributed in 24 macro-categories related to their mechanism of action. The most represented are growth factors and hormone receptor inhibitors, estrogen receptor interactors, and kinase inhibitors. In particular, 18 compounds are common between the two lists (Figure 4B). Most of these drugs are inhibitors of the PI3K/AKT/mTOR pathway, cell cycle-related pathways (MEKs, CDKs, AURKs), or hormone/growth factor receptors (ESR1, RAR). Intriguingly, six compounds are launched (Dactinomycin, Palbociclib, Tamoxifen, Clotrimazole, GR-235, Retinol) and two of them are used for a specific disease, thus, suggesting that many of these drugs are yet defined as safe and effective, and are virtually ready to be used in clinical trials that specifically target metastatic EC. To validate the ability of our predicted drugs to restrain EC metastatization, we took advantage of the GDSC database (24) and searched for the predicted compounds. Eleven out of 18 were found. For each of them, we queried GDSC to evaluate their effect on a list of endometrial cancer cell lines considering only endometrial adenocarcinoma to best mimic EC samples used for our analysis (Supplementary Table S3). Interestingly, five out of eleven compounds reduce the proliferation of metastatic EC cells more efficiently than EC cell lines derived from primary tumors. The other three drugs were effective even if the behavior of the metastatic cell lines was not consistent (Figure 4C). Above all, four of these drugs (Selumetinib, Alisertinib, Dactynomicyn, and Palbociclib) are launched or at least used for treating specific diseases, thus suggesting them as a possible ready-to-use treatment for metastatic EC. To our knowledge, this is the first time that drug repurposing is applied to EC by integrating different approaches. We reasoned that reverting tumor phenotype to “normal” status is unlikely to be achieved in patients, but it would be more feasible to restrain tumor aggressiveness, by inhibiting its ability to metastasize, which is the deadliest feature of cancer cells. We identified a list of drug candidates using two drug repurposing approaches, one signature-matching and the other pathway-based, starting from transcriptomics data of EC patients. The combination of these two different computational approaches allowed us to exploit the strengths of both and minimize their weaknesses. Indeed, while the pathway-based method has a wider spectrum of candidate drugs, the signature-based method is more stringent. As a result, outputs overlap identified 18 drugs, pointing at these compounds as robust candidates to restrain EC metastatization and enhancing the reliability of our combined approach. In-silico validation highlighted eight of these compounds as very promising. Pictilisib is the most promising among the identified PI3K/AKT/mTOR pathway inhibitors. It has been tested in several Phase 2 trials on different types of tumors both hematopoietic and solid, but it was never specifically used to treat endometrial or other gynecological cancers. To date, clinical trials with these PI3K/AKT/mTOR pathway inhibitors have shown few encouraging results, but still, high expectations and efforts are put into this class of drugs [2]. Indeed, the most recent recommendations of the European Society for Medical Oncology about EC, suggest focusing on predictive factors identification to select patients most likely to benefit from this type of therapy [26]. In this context, our findings suggest focusing on the comparison between M and NM EC patients for future PI3K/AKT/mTOR pathway inhibitors trials assuming that this patient stratification would unveil better responses which were not achieved so far. Selumetinib, a MEK inhibitor, is registered in a total of 118 trials on different solid tumors, including one trial with EC patients (recurrent/resistant) [47]. The study results defined it as “tolerable”, even though it did not meet pre-trial specifications for clinical efficacy. It should be noted that it was used as a single-agent maintenance therapy in this trial. Theoretically, the combination of Selumetinib with other drugs and their use in not heavily treated patients could improve its efficiency. Moreover, it has been granted orphan drug status as a treatment for neurofibromatosis type 1 and as an adjuvant for thyroid cancer [48]. Alisertib is an AURKA inhibitor used in several clinical trials for many types of cancers, in particular hematopoietic tumors. Despite non-homogeneous results, its anti-tumor effect is compelling, and it remains under investigation both as monotherapy and in combination with chemotherapy [49,50]. Dactinomycin and Palbociclib are probably the most promising compounds since they are widely used in clinical practice. Dactinomycin is an RNA-polymerase inhibitor, thus a classical chemotherapy drug, but it has never been used for treating EC. Its extensive use in clinics ensures confidence in its scheduling and side effects management. Palbociclib is a CDK inhibitor, approved for metastatic breast cancer (ER+, HER2−). Notably, among the 174 currently active trials, one is testing Palbociclib in combination with Abexinostat and Fulvestrant in gynecological tumors (ER+, HER2−) (NCT04498520). Another interesting trial is evaluating the combination of Palbociclib with Letrezole in metastatic-ER+ EC (NCT02730429). Of interest, this second trial almost mimics part of our results, since we observed that ESR1 expression is significantly increased in M compared to NM EC. Thus, we expect that the vast majority of metastatic EC are ER+ and we suppose that these tumors are the best candidates for our predicted drugs, including Palbociclib. The results of this trial will be of great interest for the clinical validation of our research, and it will enhance the reliability of our repurposing approach. Our integrated approach enabled the identification of five drugs predicted to be highly efficient in reducing EC metastatization. Notably, they are already used in clinical practice which makes them excellent candidates to be tested as ready-to-use compounds to treat specifically metastatic EC. Moreover, our results suggest a new enrollment criterion for trials testing PI3K/AKT/mTOR pathway inhibitors, a class of drugs in which the medical community has high expectations, but that are still providing little results. Collectively, these results serve as proof of principle demonstrating the robustness and reliability of our new, integrated repurposing approach. Further studies on larger and more balanced cohorts of samples will be needed to challenge this method which can be successfully applied not only to EC but also to other cancer settings.

PMC10001010

Fei Liu,Yidan Gao,Jian Jiao,Yuyu Zhang,Jianda Li,Luogang Ding,Lin Zhang,Zhi Chen,Xiangbin Song,Guiwen Yang,Jiang Yu,Jiaqiang Wu

Upregulation of TLR4-Dependent ATP Production Is Critical for Glaesserella parasuis LPS-Mediated Inflammation

26-02-2023

G. parasuis LPS,inflammation,ATP,TLR4,P2X7R,acute inflammatory response,pharmacological target

Glaesserella parasuis (G. parasuis), an important pathogenic bacterium, cause Glässer’s disease, and has resulted in tremendous economic losses to the global swine industry. G. parasuis infection causes typical acute systemic inflammation. However, the molecular details of how the host modulates the acute inflammatory response induced by G. parasuis are largely unknown. In this study, we found that G. parasuis LZ and LPS both enhanced the mortality of PAM cells, and at the same time, the level of ATP was enhanced. LPS treatment significantly increased the expressions of IL-1β, P2X7R, NLRP3, NF-κB, p-NF-κB, and GSDMD, leading to pyroptosis. Furthermore, these proteins’ expression was enhanced following extracellular ATP further stimulation. When reduced the production of P2X7R, NF-κB-NLRP3-GSDMS inflammasome signaling pathway was inhibited, and the mortality of cells was reduced. MCC950 treatment repressed the formation of inflammasome and reduced mortality. Further exploration found that the knockdown of TLR4 significantly reduced ATP content and cell mortality, and inhibited the expression of p-NF-κB and NLRP3. These findings suggested upregulation of TLR4-dependent ATP production is critical for G. parasuis LPS-mediated inflammation, provided new insights into the molecular pathways underlying the inflammatory response induced by G. parasuis, and offered a fresh perspective on therapeutic strategies.

Upregulation of TLR4-Dependent ATP Production Is Critical for Glaesserella parasuis LPS-Mediated Inflammation Glaesserella parasuis (G. parasuis), an important pathogenic bacterium, cause Glässer’s disease, and has resulted in tremendous economic losses to the global swine industry. G. parasuis infection causes typical acute systemic inflammation. However, the molecular details of how the host modulates the acute inflammatory response induced by G. parasuis are largely unknown. In this study, we found that G. parasuis LZ and LPS both enhanced the mortality of PAM cells, and at the same time, the level of ATP was enhanced. LPS treatment significantly increased the expressions of IL-1β, P2X7R, NLRP3, NF-κB, p-NF-κB, and GSDMD, leading to pyroptosis. Furthermore, these proteins’ expression was enhanced following extracellular ATP further stimulation. When reduced the production of P2X7R, NF-κB-NLRP3-GSDMS inflammasome signaling pathway was inhibited, and the mortality of cells was reduced. MCC950 treatment repressed the formation of inflammasome and reduced mortality. Further exploration found that the knockdown of TLR4 significantly reduced ATP content and cell mortality, and inhibited the expression of p-NF-κB and NLRP3. These findings suggested upregulation of TLR4-dependent ATP production is critical for G. parasuis LPS-mediated inflammation, provided new insights into the molecular pathways underlying the inflammatory response induced by G. parasuis, and offered a fresh perspective on therapeutic strategies. Glaesserella (Haemophilus) parasuis (G. parasuis), a gram-negative bacterial species, is the etiologic agent of pigs Glässer’s disease which is characterized by fibrinous polyserositis, polyarthritis and meningitis in pigs [1,2]. In addition, it can be a contributor to swine respiratory disease and is found as a commensal bacterium in the nasal cavity of healthy swine [3]. Recently, G. parasuis has become one of the major causes of nursery morbidity and mortality in swine herds, resulting in significant economic losses in the pig industry [4]. So far, 15 serovars of G. parasuis have been identified, but >20% of isolates have not been isolated yet [5,6]. The serovar is thought to be an important virulence marker in G. parasuis [7]. G. parasuis serovars 4, 5, and 13 are the current epidemic strains in China, according to epidemiological studies, with serovar 5 of the organism being considered to be highly virulent and serovar 4 to be moderately virulent. [8,9]. Therefore, managing infection brought on by G. parasuis is essential since it is one of the most significant bacterial respiratory infections in pigs. Porcine alveolar macrophages (PAMs) are regarded as a crucial line of defense against G. parasuis infection in outbreaks of Glässer’s disease [10]. PAMs release pro-inflammatory and anti-inflammatory cytokines and chemokines to draw leucocytes to the infection site after recognizing the cell structures on the surface of the bacterium, phagocytosing, and lysing it [11,12,13]. However, the factors responsible for systemic infection and inflammatory responses of G. parasuis have not yet been fully clarified. Thus, the discovery of novel regulatory factors of G. parasuis-induced inflammatory responses may be an alternative strategy for the prevention and control of Glasser’s disease in swine production systems. Because of sickness, aging, or damage, many cells die at this certain point. Defects can impair cell development and ultimately result in a number of illnesses, such as autoimmune disorders, cancer, or infections [14]. Recently, the field of cell death has rapidly advanced, and multiple cell death pathways have been discovered, including apoptosis, necroptosis, pyroptosis, ferroptosis, and autophagy-dependent cell death. Studies have shown that a large number of effectors of cell death can regulate activation of the NOD-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasome, and NLRP3 inflammasome activation can lead to cell death [15,16]. At the moment, it is widely acknowledged that ligands for Toll-like receptors (TLRs), cytokine receptors (such as the IL-1 receptor and the TNF-α receptor), or NLRs can cause the activation of the transcription factor NF-κB and boost the production of NLRP3 and pro-IL-1β [17,18]. Lipopolysaccharide (LPS) is the most abundant component within the cell wall of Gram-negative bacteria, playing a vital role in the way bacteria interact with the environment and the host. LPS can lead to an acute inflammatory response toward pathogens [19,20]. Toll-like receptor 4 (TLR4), acting as a receptor for LPS, has a pivotal role in the regulation of immune responses to infection [21]. The binding of LPS to TLR4 leads to the activation of NF-κB which plays a crucial role in regulating the transcription of genes related to innate immunity and inflammation responses in the lungs and in monocytes [22]. Trimeric, non-selective cation channels P2X receptors are triggered by extracellular ATP. Because it plays a role in the pathways of apoptosis, inflammation, and tumor growth, the P2X7 receptor subtype is a therapeutic target [23,24]. Acute immobilization stress has been shown to activate P2X7 receptors in a significant quantity of extracellular ATP, which in turn activates NLRP3 and causes the production of inflammatory cytokines [25]. The P2X7R also activates intracellular pathways unrelated to the inflammasomes but frequently associated with them in order to increase inflammation. The activation of NF-κB, a transcription factor that regulates the production of various inflammatory genes such as TNFα, COX-2, and IL-1β, is perhaps one of the best characterized [26,27]. In this research, we explore the role of the ATP/P2X7 receptor axis on G. parasuis-induced Glässer’s disease, and the contribution of NLRP3 inflammasome to this pathological process. To further investigate the underlying causative processes of Glässer’s disease, we also explored the effects of various antagonist, agonists, and pathway inhibitors on P2X7 expression and activation. Collectively, these findings could provide a novel viewpoint on treatment options for Glässer’s disease. G. parasuis serovar 5 stain LZ was isolated in our lab. Bacteria were grown on Trypticase Soy Agar and in Trypticase Soy Broth, respectively (TSA and TSB; OXOID), at 37 °C with the addition of 0.01% nicotinamide adenine dinucleotide (NAD) and 5% (v/v) inactivated bovine serum. The RPMI1640 medium (Solarbio, Beijing, China) containing 10% fetal bovine serum (FBS) (10091148, Gibco, New Zealand) and 1% pen/strep solution (Solarbio, China) was used to maintain porcine alveolar macrophages (PAM) 3D4/2 cells (ATCC: CRL-2845) at 37 °C in a 5% CO2 incubator. To determine cell viability, the Cell Counting Kit-8 (CCK-8) assay (Beyotime, Shanghai, China) was used. Briefly, in a 96-well plate, PAM cells were planted and either received G. parasuis LZ/LPS treatment or not. After 24 h, 10 μL of CCK-8 solution was added to each well and incubated at 37°C for 2 h. The absorbance at a wavelength of 450 nm was read using a microplate reader (SpectraMax® M5, Molecular Devices, San Jose, CA, USA). LPS component of G. parasuis LZ was extracted using a Lipopolysaccharide Isolation Kit (Sigma, MAK339, St. Louis, MO, USA). LPS concentrations were determined with Pierce LAL Chromogenic Endotoxin Quantitation Kit (Thermo Fisher Scientific, New York, NY, USA) following the manufacturer’s instructions. In the RPMI1640 medium, LPS was diluted to a storage concentration of 1 mg/mL. The BeyoClickTM EdU Cell Prolifer-ation Kit with Alexa Fluor 555 (Beyotime Biotechnology, Haimen, China) was used to conduct cell proliferation tests in accordance with the manufacturer’s recommendations. PAM cells were treated, then incubated with 10 μm EdU for 2 h at 37 °C. Then cells were subjected to 4% para-formaldehyde fixation and 0.5% Triton X-100 permeabilization steps at room temperature. After the fixatives were removed, 2% BSA in PBS was used to wash the cells. PAM cells were stained with DAPI and treated in Click Additive Solution while being shielded from light. In the following step, a Leica SP8 confocal microscope was used to capture the fluorescence images of the EdU inclusion samples. The ATP levels of infected PAM cells were detected by an Enhanced ATP Assay Kit (S0027, Beyotime Biotechnology, Shanghai, China) based on the manufacturer’s instructions. Total ATP levels of PAM cells were quantified by firefly luciferase detection using a luminometer (Tecan Infinite 200pro) and calculated the ATP concentrations (nmol/μg) were based on ATP standard curve. The samples in the medium during cell culture were collected at 4 °C and then added to a 96-well ELISA plate. To measure releases of inflammation-related cytokines from the cells, IL-1β Porcine ELISA Kit (ESIL1B, Invitrogen, Carlsbad, CA, USA) was performed according to the instructions. The absorption value at 450 nm was read by a microplate reader (SpectraMax® M5, Molecular Devices). 24 h after cells were treated with G. parasuis LZ, total RNA was extracted using the TRIzol (Life Technologies, Grand Island, NY, USA) technique. After re-suspending whole RNA pellets in RNase-free water, RNA was measured using 260/280 UV spectrophotometry. Next, potentially contaminated DNA was removed by treating the samples with DNase I (Life Technologies). Then, in a 20 μL reaction mixture, 1 μg of total RNA from each sample was reverse transcribed using a ReverTra Ace qPCR RT Kit (TOYOBO, Osaka, Japan) to produce first-strand cDNA. The cDNA was then placed in a freezer before being used. qRT-PCR was performed to measure mRNA expression with the following primers (IL-1β-F: TCTGCCCTGTACCCCAACTG, IL-1β-R: CCCAGGAAGACGGGATTT; β-actin-F: TCTGGCACCACACCTTCT, β-actin-R: GATCTGGGTCATCTTCTCAC). qRT-PCR was performed with SYBR® Green Real-time PCR Master Mix (TOYOBO, Osaka, Japan). cDNA synthesized in 2.7 was used in this chapter. The following cycling circumstances existed: after a denaturation stage at 95 °C for 30 min, 40 cycles of conventional PCR are performed. Melting curve analysis was used to determine the amplified products’ specificity. The 2−ΔΔCt technique was used for quantification. The expression of β-actin mRNA, which was consistent across all samples, was used to standardize gene expression values. By lysing the cells with ice-cold RIPA buffer supplemented with a protease inhibitor cocktail, total cellular protein lysates were produced (Merck Millipore, Darmstadt, Germany). Following BCA protein quantification, samples were run through SDS-PAGE and then transferred to PVDF membranes. Membranes were incubated with the primary antibodies for an overnight period at 4 °C and with the secondary antibodies for an hour at room temperature following blocking with 5% skim milk. Then, the membrane was visualized with enhanced chemiluminescence and quantified by densitometry. All proteins were normalized to the level of β-actin. The main antibodies were mouse anti-β-actin antibodies and those against NF-κB, p-NF-κB, GSDMD, NLRP3, IL-1β, caspase1, and P2X7 receptor from Cell Signaling Technology in the United States. The secondary antibodies were goat anti-rabbit and goat anti-mouse antibodies (Beyotime, China). Image J software was used to quantify the gray values of protein bands. PAM were plated on a laser confocal Petri dish. Following the desired treatments, cells were fixed with 4% paraformaldehyde for 10 min and permeabilized with 0.25% Triton X-100 at room temperature for 15 min. Cells were blocked with 5% goat serum for 50 min at room temperature before being incubated with primary NF-κB antibodies (1:200) overnight at 4 °C. The cells were stained with secondary antibodies (1:400) for 1 h after being washed with PBS. All dishes were mounted after being DAPI stained to identify nuclei. All slides were then mounted with ProLongTM Gold Anti-fade mountant. A Leica SP8 confocal microscope was used to capture the immunofluorescence images. Plasmids, negative control (sense UUCUCCGAACGUGUCACGUTT, antisense ACGUGACACGUUCGGAGAATT) and TLR4-siRNA (sense CAG-GAAUCCUGGUCUAUAATT, antisense UUAUAGACCAGGAUUCCUGTT), are were synthesized by Sangon (China). LipofectamineTM 3000 (Invitrogen, Carlsbad, CA, USA), transfections were carried out in accordance with the manufacturer’s instructions. In a nutshell, PAM cells were plated in six wells and transfected with 1 mg of plasmid when they were 30–50% confluent. After 24 h of incubation, cells were treated with LPS for further expression. Statistical Analysis: The reported results were statistically evaluated using the paired Student’s t-test method and comparisons between more than two groups were obtained using ANOVA. The reported values are expressed as mean standard errors (SEM). The graphs were plotted using GraphPad Prism version 7.0 (GraphPad Software, La Jolla, CA, USA). Asterisks were used to denote significant values (* p < 0.05 and ** p < 0.001), whereas ns values (p > 0.05) were used to denote non-significant values. Each experiment included at least three replicates. We first examined the effect of G. parasuis on the viability of PAM cells. PAM cells were treated with G. parasuis LZ at MOI = 10 for 8 h. Compared with the mock group, the viability of PAM cells in the G. parasuis LZ group was lower (** p < 0.01) (Figure 1A). As well, the LPS of G. parasuis LZ also resulted in the cell viability decreases when compared with the mock group (** p < 0.01) (Figure 1B). To further investigate the effect of G. parasuis LZ and LPS on PAM proliferation, EdU staining was utilized. Results of the EdU staining showed that red fluorescence which represents proliferating PAM cells is significantly inhibited by G. parasuis LZ and LPS compared with the mock group (** p < 0.01) (Figure 1C,D). Extracellular ATP causes the cell membrane to become permeable and induces changes within the cell that could lead to apoptosis [27]. We test the level of extracellular ATP, and found that G. parasuis LZ and LPS significantly enhanced ATP levels (** p < 0.01) (Figure 1E,F). These results suggested that LPS-enhanced mortality may have a relationship with elevated extracellular ATP levels, and LPS may play a key role in the pathogenesis of G. parasuis. Although most of the ATP is located intracellularly, it is released into the extracellular space under specific conditions, where it is a relevant signaling molecule. It activates P2X7 and increases inflammatory cytokine levels [28]. So we hypothesized that LPS could induce cellular inflammation by releasing ATP. In order to test it, we regulated the concentration of extracellular ATP in different ways, then observed the effect on IL-1β. The expression of IL-1β in the ATP-added group was higher than G. parasuis LZ only group (** p < 0.01) (Figure 2A). Nigericin (similar to ATP) also enhanced the expression of IL-1β. While apyrase (a highly active ATP-diphosphohydrolase) reduced the enhanced IL-1β level (** p < 0.01) (Figure 2A). As well, in Figure 2B, similar results were shown. We also test the mRNA level of IL-1β, and the results were consistent with Figure 2B. As shown in Figure 2D, LPS accelerated the expressions of P2X7R and NLRP3, and Nigericin further increase the expressions (** p < 0.01). We also tested the expressions of NF-κB and p-NF-κB, and found that NF-κB was activated by LPS (** p < 0.01), and Nigericin enhanced the expression (* p < 0.05). These results revealed that LPS-induced release of ATP-activated inflammation. Physiological roles for GSDMD in both pyroptosis and IL-1β release during inflammasome signaling have been extensively characterized in macrophages and other mononuclear leukocytes. Assembly of N-GSDMD pores in the plasma membrane markedly increases its permeability to macromolecules, metabolites, ions, and major osmolytes, resulting in the rapid collapse of cellular integrity to facilitate pyroptosis [29]. As well, in this study, LPS treatment prominently increased the expression of N-GSDMD (** p < 0.01) (Figure 2D), Nigericin further increased the expression of N-GSDMD (* p < 0.05) which meant that pyroptosis was activated. All these results suggested that ATP-induced pyroptosis was through ATP/P2X7R pathway. To further explore the relationship between P2X7R and pyroptosis, we used 10 μM A740003 (P2X Receptor Antagonist) to treat PAM cells. First, we tested the expression of P2X7R, and found that LPS-enhanced P2X7R was inhabited by A740003. This result meant A740003 worked very well (* p < 0.05) (Figure 3A). Then the expression of NLRP3 was observed, A740003 also reduced NLRP3 level significantly (* p < 0.05) (Figure 3A), P2X7R was involved in LPS-induced pyroptosis. As well, A740003 inhibited the expression of NF-κB and p-NF-κB compared with cells infected with the LPS group (** p < 0.01), meaning that NF-κB may be downstream of P2X7R in this study. When treated with A740003, the level of N-GSDMD was reduced compared with LPS-only group (* p < 0.05) (Figure 3B). As well, the level of f IL-1β showed the same result (Figure 3C). We tested A740003 influence on the PAM cells’ survival rate, and found that LPS increases the mortality of PAM cells, when treated with A740003, the mortality decreased (* p < 0.05). According to the results of immunofluorescence, NF-κB p65 expression was elevated and more protein entered into the nucleus. These results indicated that the P2X7R pathway plays a central role in the pathogenesis of G. parasuis. To better verify the role of the formation of inflammation in cell death, MCC950 (a potent and specific inhibitor of the NLRP3 inflammasome) was utilized in this study. First, we treated cells with different concentrations of MCC950, then observed the expression of NLRP3. Compared with the LPS group, MCC950 markedly reduced the expression of NLRP3 in a concentration-dependent manner (** p < 0.01) (Figure 4A). We also detected the expression of caspase 1, showing the same rule (Figure 4A). Subsequently, we tested the level of GSDMD. Compared with the LPS group, MCC950 could significantly reduce the expression of GSDMD (** p < 0.01) (Figure 4B). Then we tested the content of IL-1β in the culture medium by ELISA, and found that MCC950 also significantly reduced the secretion of IL-1β (** p < 0.01) (Figure 4C). Finally, the cell survival rate was measured by CCK8, and data showed MCC950 could significantly reduce the cell mortality rate that was increased by LPS (** p < 0.01). These results suggested that the formation of inflammasome bodies plays a key role in G. parasuis infection. Toll-like receptor 4 (TLR4), acting as a receptor for LPS, has a pivotal role in the regulation of immune responses to infection [21]. The binding of LPS to TLR4 leads to the activation of NF-κB which plays a crucial role in regulating the transcription of genes related to innate immunity and inflammation responses in the lungs and in monocytes [22]. To prove that TLR4 plays an important role in G. parasuis infection, we used miRNA silencing technology to verify it. First, we tested the silence efficiency of siRNA and found that the siRNA significantly reduced the mRNA level of TLR4 (** p < 0.01), meaning that this siRNA worked well (Figure 5A). Then we observed the effect of TLR4 on ATP levels. Compared with the negative control group, we found that after silencing TLR4, ATP level decreased significantly (** p < 0.01) (Figure 5B). In addition, silencing TLR4 significantly restored cell death caused by LPS (** p < 0.01) (Figure 5C). Then, we detected the influence of TLR4 on the downstream inflammatory pathway, and found that the expressions of p-NF-κB and NLRP3 decreased, and TLR4 knockout decreased the activation of the NLRP3 inflammasome (** p < 0.01) (Figure 5D). These data evidently suggest that LPS induced inflammation in a TLR4-dependent manner. G. parasuis is the source of Glässer’s disease, which can lead to acute septicemia in non-immune high-health status pigs of all ages and cause instances of arthritis, fibrinous polyserositis, severe pneumonia, and meningitis in piglets worldwide [30]. In this research, we explored the role of the ATP/P2X7 receptor axis on G. parasuis-induced Glässer’s disease, and the contribution of NLRP3 inflammasome to this pathological process. Bacterial lipopolysaccharides (LPS) are the major outer surface membrane components present in almost all Gram-negative bacteria and act as extremely strong stimulators of innate or natural immunity in diverse eukaryotic species ranging from insects to humans [31,32]. No matter the kind of bacteria involved or the infection location, bacterial adaptation alterations, such as modification of LPS production and structure, are a common motif in infections [33,34]. Generally speaking, these modifications cause the immune system to evade detection, persistent inflammation, and enhanced antimicrobial resistance [35]. LPS derived from Escherichia coli (E. coli) is a well-characterized inducer of inflammatory response in vivo that activates cytokine expression via NF-κB and MAPK signaling pathway in a TLR4-dependent manner [36]. According to studies, pseudomonas aeruginosa (P. aeruginosa) LPS changes appear to be a key element in this pathogen’s ability to adapt to chronic infection. Over the duration of the chronic P. aeruginosa infection, decreased LPS immunostimulatory potential helps the immune system avoid detection and survive [37]. It has been reported that anti-LPS antibodies can protect against mortality caused by hematogenous Haemophilus influenzae type b meningitis infections in infant rats [38]. In this study, we found that G. parasuis LZ induced cells death and severe inflammation in PAM cells (Figure 1A and Figure 3A), and LPS derived from G. parasuis LZ treatment group also has similar phenomena, these suggested that G. parasuis LPS plays a key role in host-pathogen interactions with the innate immune system. Pyroptosis is an inflammatory form of cell death that is brought on by certain inflammasomes [39,40]. This kind of cell death causes the cleavage of gasdermin D (GSDMD) and the activation of dormant cytokines like IL-18 and IL-1β. Cell enlargement, lysis of the plasma membrane, fragmentation of the chromatin, and release of the pro-inflammatory substances inside the cell are all effects of pyroptosis [41]. The conventional inflammasome pathway, a noncanonical inflammasome pathway, and a newly discovered pathway are the pathways that cause pyroptosis [42,43]. Caspase-11 may selectively attach to the lipid A of intracellular LPS, which causes it to oligomerize, engage its proteolytic activity, and cleave the GSDMD to create a large number of holes in the cell membrane, ultimately causing membrane lysis and pyroptosis [44]. As well, the extracellular LPS stimulation of neutrophils can also activate the TLR4-P38-Cx43 pathway to autocrine ATP extracellularly [45]. The extracellular ATP could gather NLRP3 inflammasomes and subsequently activate the pro-caspase 1 through the P2X7 pathway, resulting in pyroptosis [46]. In this study, we found that G. parasuis LZ LPS induced cell death and promoted the increase of ATP content, thus activating the P2X7 pathway, promoting the development of IL-1β, and cleavage of GSDMD, leading to pyroptosis. This is consistent with the canonical inflammasome pathway. Luo et al. have reported that G. parasuis induces an inflammatory response in PAM cells through the activation of the NLRP3 inflammasome signaling pathway [30], which is consistent with our result. G. parasuis, an opportunistic pathogen of the lower respiratory tract of pigs, is also associated with pneumonia and is involved in the porcine respiratory disease complex [47]. Secondary G. parasuis infection enhances highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) infection-mediated inflammatory responses [48]. The polarization of LPS-stimulated PAMs toward M1 PAMs greatly reduces PRRSV replication [49], mainly because LPS reduced the level of CD163 expression to inhibit PRRSV infection via TLR4-NF-κB pathway [30]. In this study G. parasuis LPS activated inflammatory responses through TLR4-NF-κB pathway, and combined with the above reference, we got the hypothesis that G. parasuis infection can significantly inhibit PRRSV replication through downregulation of CD163 expression via TLR4-NF-κB pathway. However, this hypothesis needs further verification. In conclusion, G. parasuis induced PAM cell damage mainly through included pro-inflammatory and pro-pyroptosis events. The NLRP3 inflammasome in PAM cells plays a crucial role in G. parasuis-induced cells death and both TLR4- and P2X7R-dependent pathways are alternative signaling pathways required for NLRP3 inflammasome activation during the development of G. parasuis-induced Glässer’s disease. This work provides new insights into the molecular pathways underlying the inflammatory response induced by G. parasuis and a new perspective to inform the targeted treatment of G. parasuis-induced Glässer’s disease.

PMC10001020

Jiaqin Chen,Dong Feng,Yuanyuan Lu,Yanjun Zhang,Hanxiang Jiang,Man Yuan,Yifan Xu,Jianjun Zou,Yubing Zhu,Jingjing Zhang,Chun Ge,Ying Wang

A Novel Phenazine Analog, CPUL1, Suppresses Autophagic Flux and Proliferation in Hepatocellular Carcinoma: Insight from Integrated Transcriptomic and Metabolomic Analysis

05-03-2023

CPUL1,hepatocellular carcinoma,transcriptomics,metabolomics,autophagy

Simple Summary CPUL1 exhibits antitumor properties against hepatocellular carcinoma (HCC), although the underlying mechanisms remain unclear. Our study provides a comprehensive overview of the properties and molecular mechanisms of CPUL1 anti-HCC using transcriptomics and metabolomics and highlights the significance of progressive metabolic failure. This may be partially attributable to autophagy blockage, which is presumed to contribute to nutrient deprivation and increased cell susceptibility to stress. Therefore, the attractive properties of CPUL1 may endow this compound with the potential of becoming a promising anti-HCC agent. Abstract Background: CPUL1, a phenazine analog, has demonstrated potent antitumor properties against hepatocellular carcinoma (HCC) and indicates a promising prospect in pharmaceutical development. However, the underlying mechanisms remain largely obscure. Methods: Multiple HCC cell lines were used to investigate the in vitro effects of CPUL1. The antineoplastic properties of CPUL1 were assessed in vivo by establishing a xenograft nude mice model. After that, metabolomics, transcriptomics, and bioinformatics were integrated to elucidate the mechanisms underlying the therapeutic efficacy of CPUL1, highlighting an unanticipated involvement of autophagy dysregulation. Results: CPUL1 suppressed HCC cell proliferation in vitro and in vivo, thereby endorsing the potential as a leading agent for HCC therapy. Integrative omics characterized a deteriorating scenario of metabolic debilitation with CPUL1, presenting an issue in the autophagy contribution of autophagy. Subsequent observations indicated that CPUL1 treatment could impede autophagic flow by suppressing autophagosome degradation rather than its formation, which supposedly exacerbated cellular damage triggered by metabolic impairment. Moreover, the observed late autophagosome degradation may be attributed to lysosome dysfunction, which is essential for the final stage of autophagy and cargo disposal. Conclusions: Our study comprehensively profiled the anti-hepatoma characteristics and molecular mechanisms of CPUL1, highlighting the implications of progressive metabolic failure. This could partially be ascribed to autophagy blockage, which supposedly conveyed nutritional deprivation and intensified cellular vulnerability to stress.

A Novel Phenazine Analog, CPUL1, Suppresses Autophagic Flux and Proliferation in Hepatocellular Carcinoma: Insight from Integrated Transcriptomic and Metabolomic Analysis CPUL1 exhibits antitumor properties against hepatocellular carcinoma (HCC), although the underlying mechanisms remain unclear. Our study provides a comprehensive overview of the properties and molecular mechanisms of CPUL1 anti-HCC using transcriptomics and metabolomics and highlights the significance of progressive metabolic failure. This may be partially attributable to autophagy blockage, which is presumed to contribute to nutrient deprivation and increased cell susceptibility to stress. Therefore, the attractive properties of CPUL1 may endow this compound with the potential of becoming a promising anti-HCC agent. Background: CPUL1, a phenazine analog, has demonstrated potent antitumor properties against hepatocellular carcinoma (HCC) and indicates a promising prospect in pharmaceutical development. However, the underlying mechanisms remain largely obscure. Methods: Multiple HCC cell lines were used to investigate the in vitro effects of CPUL1. The antineoplastic properties of CPUL1 were assessed in vivo by establishing a xenograft nude mice model. After that, metabolomics, transcriptomics, and bioinformatics were integrated to elucidate the mechanisms underlying the therapeutic efficacy of CPUL1, highlighting an unanticipated involvement of autophagy dysregulation. Results: CPUL1 suppressed HCC cell proliferation in vitro and in vivo, thereby endorsing the potential as a leading agent for HCC therapy. Integrative omics characterized a deteriorating scenario of metabolic debilitation with CPUL1, presenting an issue in the autophagy contribution of autophagy. Subsequent observations indicated that CPUL1 treatment could impede autophagic flow by suppressing autophagosome degradation rather than its formation, which supposedly exacerbated cellular damage triggered by metabolic impairment. Moreover, the observed late autophagosome degradation may be attributed to lysosome dysfunction, which is essential for the final stage of autophagy and cargo disposal. Conclusions: Our study comprehensively profiled the anti-hepatoma characteristics and molecular mechanisms of CPUL1, highlighting the implications of progressive metabolic failure. This could partially be ascribed to autophagy blockage, which supposedly conveyed nutritional deprivation and intensified cellular vulnerability to stress. As a principal pathological type of liver cancer, hepatocellular carcinoma (HCC) is characterized by an increasing incidence and subsequent mortality [1]. High recurrence, phenotypic and genetic heterogeneity, and metastasis confer poor prognosis and disappointing survival rate in HCC, which has posed a substantial health care burden worldwide [2]. Currently, chemotherapy is one of the systemic therapies for HCC in the clinic, with sorafenib and lenvatinib being the most effective single-drug regimens [3]. In contrast, the efficacy of sorafenib, the canonic first-line treatment for advanced HCC, remains moderate, with a median survival duration of less than one year and a tumor response rate of less than 5% [4]. There remains a critical and unmet demand for innovative and effective therapeutic options against HCC. Chemically, phenazines are a class of nitrogen-containing aromatic compounds of natural or synthetic origin. Their redox properties confer broad biological functions including antimicrobial, insecticidal, antimalarial, antitumor, and antiplatelet properties [5,6,7,8,9]. Our previous research focused on the design and synthesis of phenazine derivatives for chemotherapeutic screening and optimization, which led to the discovery of CPUL1, a promising lead compound. The compound demonstrated potent alleviative effects against HCC, represented by the apoptosis of HepG2 cells in vitro and the regression of H22 xenograft tumor in vivo [10]. Earlier research has observed that CPUL1 treatment could suppress thioredoxin reductase I (TrxR1), a central component in the thioredoxin system that maintains cellular redox homeostasis and protects against oxidative stress [11]. Therefore, excessive reactive oxygen species (ROS) are generated and accumulated, contributing to lipid peroxidation, DNA damage, and cell apoptosis, as confirmed by cytology and morphology [10]. Nonetheless, the precise functions and biological mechanisms of CPUL1 in HCC remain unclear, which presumably involves a complicated pathophysiological process and necessitates further research. The advent of omics techniques, and their extensive implementation, provides an operational platform for profiling intricate biological systems [12]. Transcriptomics and metabolomics reflect the systematic alterations in the genotype and phenotype and demonstrate comprehensive information comprising genetic regulation, protein synthesis, metabolic pathways, and cellular functions [13]. Moreover, multi-omics data integration offers a more reliable methodology and a holistic perspective for illuminating the complex biological scenario [14], presumably facilitating accurate and rational insights for research practice, especially for complex diseases and therapeutic regimens. In this regard, we sequentially evaluated the therapeutic effects of CPUL1 in human HCC cells BEL-7402 in vitro and in vivo and integrated the transcriptomics, metabolomics, and bioinformatics analyses to illuminate the underlying multi-threading mechanisms. The investigations present a panorama of genetic and metabolic profiles altered by CPUL1, with a particular focus on autophagy restriction, which not only substantiates the translational potentials of this lead compound, but also sheds some light on the chemotherapeutic interventions toward HCC. The human HCC cell lines HepG2, BEL-7402 (KeyGen Biotech, Nanjing, China), and HUH-7 (FuHeng Cell Center, Shanghai, China) were cultured in DMEM or RPMI-1640 (KeyGEN Biotech, Nanjing, China) supplemented with 10% fetal bovine serum (Gibco/BRL, New York, NY, USA), penicillin (80 units/mL), and streptomycin (80 mg/L) at 37 °C under 5% CO2. Compound CPUL1 was synthesized as described previously (China Pharmaceutical University, Nanjing, China), with a purity of 98% determined by RP-HPLC. Other reagents and solvents purchased were of analytical grade and obtained from commercial companies. Chloroquine (CQ) was procured from KeyGEN Biotech Co., Ltd. (Nanjing, China), and 3-methyladenine (3-MA) was obtained from MedChemExpress (Middlesex, NJ, USA). According to the manufacturer’s instructions, the cell inhibition rate was determined using the Cell Counting Kit-8 (CCK-8; Beyotime, Shanghai, China). Briefly, 4 × 104 cells were seeded in 96-well plates and treated with various concentrations of CPUL1 for 48 h. The optical density of each sample was measured at 450 nm on an automatic plate reader (ALLSHENG, Hangzhou, China). The half-maximal inhibitory concentration (IC50) of CPUL1 for different cells was calculated according to the standard curve. A colony formation assay was used to evaluate the cell proliferation capacity. Approximately 1 × 103 cells were cultured in 6-well plates and treated with CPUL1 at 0, 1, 2, and 5 μM. After 14 days, the cells were fixed with 4% paraformaldehyde for 20 min, stained with crystal violet (Beyotime, Shanghai, China) for 10 min, and washed thrice with phosphate-buffered saline (PBS). To estimate the number of colonies formed, crystal violet was dissolved in 95% ethanol, transferred to a 96-well plate, and absorbance was measured at 590 nm. The subcellular distribution of CPUL1 was investigated in the BEL-7402 cells (≈1 × 107). The adherent cells were treated with CPUL1 at 8 μM for 6 h, 24 h, and 48 h, respectively. Following treatment, the cells were collected by centrifugation. After lysis, the mitochondria, nucleus, and cytosolic fractions were isolated using a mitochondria/nuclei isolation kit (KGA828, KeyGEN Biotechnology, Nanjing, China) according to the manufacturer’s instructions. LC-MS then determined the concentrations of CPUL1 in different organelles with a 4000 Q Trap (Applied Biosystem, Foster City, CA, USA). The antitumor activity of CPUL1 was evaluated in vivo in a BEL-7402 xenograft model. The male immunodeficient nude BALB/c-nu-nu mice (4~5 weeks, 18–20 g) were purchased from Lingchang Biotechnology Co., Ltd. (Shanghai, China). Mice were maintained in SPF facilities under a controlled environment (22 °C–24 °C, 50–60% humidity, 12 h light/12 h dark cycle) with ad libitum access to standard laboratory food and water. All animal care and experimental procedures were conducted in accordance with the Institutional Animal Care and Use guidelines (IACU, China Pharmaceutical University) and the 3R principles. To establish a xenograft model, the BEL-7402 cells (1 × 107) were subcutaneously injected into mice at the right axillae. When the tumor volume reached approximately 90 mm3, the mice were randomly divided into six groups (n ≥ 6): control, sorafenib (20 mg/kg); cyclophosphamide (CTX, 20 mg/kg); CPUL1(10, 20, and 40 mg/kg). Before treatment, the test drugs were dissolved in dimethyl sulfoxide (DMSO) and diluted by saline containing Tween-80 [final vehicle: DMSO/Tween/0.9% NaCl (1.5/1.5/97, v/v)]. All groups were administered with vehicle (control), positive agents (sorafenib or cyclophosphamide), or CPUL1 at a dosing volume of 10 mL/kg. Administrations were performed by intragastric gavage (i.g., sorafenib and cyclophosphamide groups) or tail vein injection (control and CPUL1 groups) once a day continuously for a week. During therapy, the tumor volume (length × width2/2; mm3) and body weight of the mice were measured every other day. At the end of the experiment, mice were sacrificed, and tumors were isolated and weighed. Sample preparation: For RNA-Seq analysis, the BEL-7402 cells treated with CPUL1 at 0 μM (DMSO) and 8 μM for 6 h and 48 h were used. In each group, the samples were tripled. The total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA quantity and quality were determined using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Fremont, CA, USA) and an Agilent 2100 instrument (Agilent Technologies, Santa Clara, CA, USA), respectively. The samples with an RNA integrity number of >7 and OD260/280 of >1.8 were used for library construction. Library construction and sequencing: The cDNA library was constructed using 2 μg of RNA with a NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) according to the manufacturer’s instructions. All libraries were loaded onto the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA), followed by 2 × 150 bp paired-end read sequencing. Data processing and functional analysis: The raw reads in fastq format were processed using in-house Perl scripts. Clean reads were obtained by removing low-quality reads and those with adapters or poly-N sequences. Q20, Q30, GC content, and sequence duplication level were calculated for the clean data. High-quality sequences were aligned to the reference genome of BEL-7402 (ENSEMBL, Homo sapiens. GRCh38.101) using Hisat2 (v2.0.1) with the default parameters. Cufflinks, HTSeq (v. 0.5.4 p3), and DESeq (v. 1.10.1) were used for assembly, mRNA expression level evaluation, and differentially expressed gene (DEG) identification, respectively. Genes with Log2|fold change (FC)| >1 and Q-value ≤ 0.05 were considered DEGs. Functional analysis: All DEGs were implemented into GOSeq (v. 1.34.1) to identify Gene Ontology (GO) terms that annotate a list of enriched genes with a p-value ≤ 0.05. Meanwhile, DEGs were also subjected to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for pathway enrichment with a Q-value ≤ 0.05. Construction of protein–protein interaction network and screening of hub genes: The protein–protein interaction (PPI) network was constructed among the screened DEGs by the online database STRING [15]. The retrieved interaction networks were visualized by Cytoscape (v3.6.1) and calculated in the cytoHubba control panel for hub node prediction. The genes with the top ten values ranked by the Maximal Clique Centrality (MCC) algorithm were considered hub genes [16]. Collection and preparation of samples: The samples used for RNA-Seq analysis were also used for the LC-QTOF/MS analysis. The cells were washed with PBS, freezing-thawed three times, and normalized by the BCA assay (KeyGEN Biotech, Nanjing, China). The cell pellets were resuspended in methanol containing the internal standard (L-Tryptophan(13C11)/L-Phenylalanine(13C6), followed by shaking for 5 min to lyse the cells and release cellular metabolites. After centrifugation at 18,000 rpm and 4 °C for 10 min, the supernatant was obtained and evaporated. The residues were re-dissolved in 100 μL ice-cold methanol/water (1:1, v/v), and the supernatant was collected for liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) analysis. Acquisition of LC-QTOF-MS data: The relative amounts of each metabolite were obtained by integrating peaks detected on a Sciex TripleTOF 5600 (AB SCIEX, Foster City, CA, USA). Chromatographic separation was performed on a Waters HSS T3 1.8 μm, 2.1 × 100 mm column. The mobile phase for chromatographic elution was an online mixing of phase A and phase B, where phase A was water containing 0.1% formic acid and phase B was acetonitrile. Compounds were identified by matching chromatographic retention time and mass spectral fragmentation signatures with the reference library data created from authentic standards. Each treatment group comprised eight biological replicates. LC-QTOF/MS data processing: The raw data were processed with MS-DIAL (v. 4.70). Statistical data analysis was performed using SIMCA-P 14.1. Both univariate and multivariate data analysis were hired to extract information from the metabolomics datasets. A multivariate statistical method was then conducted to classify samples with optimal variables, which was cross-validated by principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA) [17], and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) [18]. The differential abundance metabolites (DAMs) were screened as significantly affected metabolites using multiple statistical approaches. The screening criteria were as follows: the “variable importance in projection” (VIP) scores of the OPLS-DA model greater than 1, the p-values from a t-test < 0.05, and the fold change values greater than 2 (p < 0.05, OPLS-DA-VIP > 1.0 and |FC| > 2) [19]. Then, the DAMs were mapped in the KEGG database, whereas pathway enrichment analysis was performed using the built-in function available in the MetaboAnalyst 5.0 server (www.metaboanalyst.ca accessed on 1 November 2022) [20]. Based on transcriptome and metabolome analysis data, we obtained differential genes and differential metabolites. Pathway enrichment analysis was performed using the MetaboAnalyst 5.0 server (www.metaboanalyst.ca accessed on 1 November 2022) to describe in detail the relationship between gene regulation and metabolic changes, with a p-value < 0.05 considered to be significant. The cells were lysed with RIPA buffer (Beyotime, Shanghai, China) containing a mixture of PMSF (Beyotime, China). Then, the lysates were cleared by centrifugation, and the concentrations of proteins were measured by the BCA Protein Assay Kit (Beyotime, China). Each protein sample (20 µg) was separated on SDS-PAGE gels and transferred to PVDF membranes (Millipore, Milford, DE, USA). The membranes were placed in 5% skim milk for 1 h at room temperature and incubated with antibodies of AMPK (WL02254; 1:1000; Wanleibio Co., Ltd., Shanghai, China), p-AMPK (WL05103; 1:1000; Wanleibio Co., Ltd.), ATG5 (WL02411; 1:1000; Wanleibio Co., Ltd.), and ATG12 (WL03144; 1:1000; Wanleibio Co., Ltd.), LC3 (14600-1-AP; 1:1000; Proteintech Group, Inc., Wuhan, China), p62 (66184-1-Ig; 1:1000; Proteintech Group, Inc.), mTOR (66888-1-Ig; 1:1000; Proteintech Group, Inc.), p-mTOR (67778-1-Ig; 1:1000; Proteintech Group, Inc.), and Beclin-1(66665-1-Ig; 1:1000; Proteintech Group, Inc.), LAMP1(67300-1-Ig; 1:20,000; Proteintech Group, Inc.), LAMP2(WL02761; 1:500; Wanleibio Co., Ltd.), RAb7(ET1611-96; 1:1000; HUABIO, Woburn, MA, USA), and GAPDH (AF7021; 1:1000; Affinity, San Francisco, CA, USA) at 4 °C overnight. Then, the samples were incubated with the secondary goat anti-rabbit IgG-HRP (BL001A, Biosharp, Tallinn, Estonia), conjugated with horseradish peroxidase for 1 h at room temperature. The bands were visualized using an image analysis system (Tanon, Shanghai, China). Autophagosomes were observed using TEM. The cells were fixed in 2.5% glutaraldehyde (pH 7.3–7.4) at 4 °C overnight and treated with 1% osmium tetroxide for 2 h. Then, the samples were dehydrated in ethanol (30%, 50%, 70%, 80%, 90%, and 95%) and pure acetone, embedded, cut into 90 nm sections, and stained with 3% uranyl acetate and lead citrate. TEM visualizations were performed using a Hitachi H-9000 transmission electron microscope at 300 kV, and images were captured using a slow-scan CCD camera. The cells were fixed with 4% paraformaldehyde for 20 min and permeabilized with 0.1% Triton X-100 for 5 min. The cells were incubated overnight with LC3 (14600-1-AP; 1:400; Proteintech Group, Inc.), p62 (66184-1-Ig; 1:800; Proteintech Group, Inc.), or LAMP1 (67300-1-Ig; 1:800; Proteintech Group, Inc.), antibodies at 4 °C on a horizontal shaker. Then, the cells were incubated with a Cy3-labeled goat anti-rabbit IgG (H+L) secondary antibody (Beyotime, China) or Alexa Fluor 647-labeled goat anti-rabbit IgG (H+L) secondary antibody (Beyotime, China) for 1 h at room temperature, washed with PBS, and stained with DAPI (Beyotime, China) for 5 min. The photographs were taken under laser scanning confocal microscopy (CLSM, LSM800, Zeiss, Oberkochen, Germany). Following CPUL1 administration or not, cells were stained with Lyso-Tracker Red (50 nM), a cell-permeable red-fluorescent dye for lysosomes, for 1 h at 37 °C and then counterstained with Hoechst 33342 (1 μM) for 10 min at room temperature in the dark. After washing by PBS, the cells were observed under a fluorescence microscope [21]. All data are expressed as the mean ± standard deviation (SD) of at least three independent experiments, except for eight biological replicates of metabolomics. Two-tailed Student’s t-tests and one-way analysis of variance were employed for statistical analysis. Differences were considered significant at * p < 0.05, ** p < 0.01, *** p < 0.001. Statistical data analysis was performed using GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA). Three HCC cell lines (HUH-7, HepG2, and BEL-7402) were used to investigate the in vitro effects of CPUL1 (Figure 1A). The cell viabilities of HCC cells were assessed by the CCK-8 assay, following 48 h treatment of CPUL1 at the indicated concentrations, which consistently revealed a dose-dependent inhibition pattern. The cytotoxic potencies were evident in different HCC cells, represented by the approximately comparable IC50 values, respectively, at 4.39 μM (HUH-7; Figure 1B), 7.55 μM (HepG2; Figure 1C), and 6.86 μM (BEL-7402; Figure 1D). Hence, BEL-7402 cells were used in subsequent experiments. Morphological changes were then observed in the BEL-7402 cells treated with 8 μM CPUL1 at different time points (6 h and 48 h), which induced rounding, shrinking, and crushing, in a time-dependent manner (Figure 1E). The colony formation assay further confirmed the antiproliferative effect of CPUL1 as the colony-forming efficiency was significantly decreased, close to a depletion level at a 5 μM concentration (p < 0.001; Figure 1F). The suborganelle localization of CPUL1 in the HCC cells was identified by LC-MS-based concentration measurements, which showed that the compound was predominantly distributed in the cytoplasm and mitochondria. In contrast to the relatively steady concentration in the cytoplasm, significant accumulations occurred gradually within the mitochondria and nuclei (Figure 1G), presumably underpinning the mitochondrial dysfunction and DNA damage previously observed [10]. Correspondingly, the BEL-7402 xenograft model was constructed to further evaluate the therapeutic potential of CPUL1 in vivo (Figure 1H). As expected, CPUL1 significantly inhibited the tumor growth at various concentrations, with potencies comparable or even superior to those of the positive controls (sorafenib and CTX; 20 mg/kg) at higher dosages (40 and 20 mg/kg), in terms of tumor volume and weight (Figure 1I,K). In contrast, the body weight of mice increased slightly during therapy, without statistically significant differences among all groups (Figure 1J). Hematoxylin and eosin staining showed no tumor cell necrosis in the model control group and cyclophosphamide group (Figure 1L). In contrast, both the CPUL1 administration group and sorafenib group showed tumor tissue necrosis to a certain extent, and the necrotic area of the tumor tissue in the CPUL1 administration group increased with the increase in the administration concentration, indicating that CPUL1 had obvious toxic effects on tumor cells and tissues in an obvious dose-dependent manner. In vitro and in vivo investigations consistently demonstrated the promising potential of CPUL1 for HCC therapy, justifying further studies of the detailed mechanisms. RNA-Seq analysis was performed to profile the transcriptomic changes in BEL-7402 cells treated with 8 μM of CPUL1 for 6 h and 48 h. A total of 40.44 Mb reads were generated in the control group, 42.80 Mb reads in the 6 h-treated group, and 46.71 Mb reads in the 48 h-treated group. After screening, 36.30–49.24 Mb clean reads remained (Table S1). Sample Q30 was >92.22%, and the average GC content was 52.60–53.60%. By pairwise comparisons, a total of 989 (598 downregulated and 391 upregulated) DEGs were identified from the 6 h_treated_vs._Control groups; and 1207 DEGs including 333 upregulated and 874 downregulated genes were identified in the 48 h_treated_vs._Control comparisons. All DEGs from both groups are cataloged in Supplementary Files S1 and S2, with detailed information sketched in the volcano plots (Figure 2A). As the CPUL1 exposure was prolonged, the number of downregulated transcripts increased from 598 to 874, whereas the number of upregulated transcripts decreased from 391 to 333. Moreover, 416 DEGs maintained a consistent pattern at the indicated time points, either descending (335 DAGs) or ascending (81 DEGs) in comparison with the control (Figure 2B). The transcriptional alterations were also represented in the cluster heat map, potentially implying an intricacy and multiplicity of genetic interactions and functional integrations (Figure 2C). Transcriptome profiles were integrally analyzed on DEGs by pathway-enrichment analysis using the GO and KEGG databases (Figure 3). The GO term analysis on the DEGs from 6h_vs._Control highlighted the significantly enriched functions involving “sequestering of TGF-beta in the extracellular matrix”, “collagen type IV trimer”, and “insulin-like growth factor-activated receptor activity” (Figure 3B). In contrast, the 48 h-treated DEGs were mainly enriched in “cell–cell adhesion mediated by integrin”, “laminin-1 complex”, and “ATP-dependent 3′-5′ DNA helicase activity” (Figure 3C). Regarding functional annotation, DEGs in the 6 h_vs._Control and 48 h_vs._Control comparisons were enriched to the 290 and 301 KEGG pathways, with an overlap of 277 identical pathways (Figure 3D). Even among the top 30 significantly altered pathways, the similarity in functional mapping was also apparent, with the most significantly enriched pathways converged in “valine, leucine, and isoleucine biosynthesis”, “MAPK signaling pathway”, “PI3K-Akt signaling pathway”, “mitophagy-animal”, and “autophagy-animal” (Figure 3E,F). Notably, “biosynthesis of valine, leucine and isoleucine” was the most enriched pathway in both groups, which assumably implied a substantial perturbation in metabolism due to CPUL1 treatment. Using STRING and Cytoscape, the DEGs from pairwise comparison were assembled into biological networks at a high confidence cutoff (interaction score ≥ 0.9) to investigate the major events from a complex interactome aspect. As shown in Figure 4A, the predictive network among DEGs from 6 h_vs._Control, consisting of 86 nodes and 313 edges, clustered into biological schemes also concerning “arginine and proline metabolism”, “inositol phosphate metabolism”, “WNT signaling pathway”, TGF-beta signaling pathway”, and “ECM–receptor interaction”. Comparatively, DEGs in the 48 h_vs._Control group were enriched in “WNT signaling pathway”, “TGF-beta signaling pathway”, and “ECM–receptor interaction”, which constructed a complex network by 125 nodes and 268 edges (Figure 4B). Hub genes, defined as nodes with high degrees of connectivity and centrality within a module, confer heuristic insights into the essential mechanisms beneath the physiological and pathological processes [22]. According to the MCC sores, the hub genes were screened by group, and are shown in Figure S1. Specifically, hub genes in the 6 h-treated group included ITGB1, EGFR, LAMB1, LAMC1, HSPG2, COL4A1, COL4A2, ITGA1, ITGA5, and ITGA6, which appeared to be implicated in carcinogenesis [23]. As for the 48 h-treated group, a hub gene panel was identified, consisting of ITGA1, ITGA2, ITGB1, EP300, CREBBP, BRCA1, LAMB3, LAMC1, LAMA3, and LAMB1. Functional analysis was performed on the hub gene collection utilizing the DAVID database for GO terminology and KEGG pathway enrichment. GO analysis indicated that hub genes were enriched in the canonical cellular processes associated with tumor progression (e.g., “extracellular matrix organization”, “cell adhesion”, and “cell migration”). Consonantly, the hub genes were also overrepresented in the KEGG pathways involving cancer occurrence and progression including “ECM-receptor interaction”, “focal adhesion”, and “PI3K-Akt signaling” (Supplementary File S3) [24]. Intriguingly, for either time point of CPUL1 treatment, the panel of hub genes or annotated pathways exhibited subtle differences, albeit within a generally consistent perspective, which to some extent, presumably implied a temporal pattern of the signaling network. Both the functional annotations and pathway enrichments of DEGs proposed a correlative and progressive change in the signaling panels during CPUL1 exposure, orchestrating the shift from metabolic regulation to cell fate and highlighting a chronological accumulation of metabolic perturbations. Hence, untargeted global metabolomics analysis was performed to profile the metabolites altered by compound therapy. The initial LC-QTOF/MS data were applied to the multivariate principal component analysis to determine cluster separations among the experimental groups (PCA and PLS-DA plots shown in Figure S2). Pairwise comparison of the metabolite profiles respectively identified 229 (59 upregulated and 170 downregulated) and 222 (83 upregulated and 139 downregulated) putative DAMs from the 6 h_vs._Control or 48 h_vs._Control group (Supplementary Files S4 and S5). Notably, the DAMs exhibited temporal discrepancies in panel composition or abundance level (Figure 5A). As shown in Figure 5B,C, for a range of putative DAMs, the substantial abundances were exclusively changed at a particular time point (6 h or 48 h) after treatment: formononetin, glucose 6-phosphate, phosphoenolpyruvic acid (PEP), were exclusively decreased at the late-stage (48 h), with FC values of 0.05, 0.11, and 0.15, respectively; however, asperuloside was explicitly abundant in 6 h (FC value ≈ 8.25). Interestingly, α-ketoglutarate (α-KG) and oleic acid evidenced the opposite dynamic at separate time phases, with their abundances declining (6 h) or ascending (48 h) in comparison with the control group. For most DAMs, the variation tendency was undoubtedly consistent, albeit the magnitude fluctuated over time. Correspondingly, further functional analysis on DAMs at different time phases also demonstrated a real discernible distinction in biochemical pathways, presumably proposing the difference in global metabolic status induced by CPUL1 treatment maintenance. As KEGG enrichment profiling revealed, DAMs identified in the 6 h_vs._Control groups mainly involved in “arginine biosynthesis”, “D-glutamine and D-glutamate metabolism”, “purine metabolism”, “alanine, aspartate and glutamate metabolism”, “linoleic acid metabolism”, “pantothenate and CoA biosynthesis”, and “pyrimidine metabolism” (Figure 5D); in contrast, “purine metabolism”, “pyrimidine metabolism”, “glutathione metabolism”, “linoleic acid metabolism”, “pentose and glucuronate interconversions”, “pentose phosphate pathway”, and “arginine biosynthesis” were enriched by DAMs from the 48 h_vs._Control group (Figure 5E). The DAGs and DAMs were merged to construct the transcript–metabolite interaction network to depict the biological responses induced by CPUL1 therapy at the indicated times. During the integration of the transcriptomics and metabolomics data of the initial therapy (6 h), seven pathways were significantly enriched (p < 0.05) including “arginine biosynthesis”, “alanine, aspartate and glutamate metabolism”, “glycerolipid metabolism”, “histidine metabolism”, “purine metabolism”, “arginine and proline metabolism”, and “inositol phosphate metabolism” (Figure 6A); but four pathways, “purine metabolism”, “mucin type O-glycan biosynthesis”, “lysine degradation”, and “pyrimidine metabolism” were evidently perturbed during the late phase (48 h; Figure 6B). Integrated analysis also confirmed a temporal heterogeneity of the global cellular response to CPUL1 treatment, either transcriptional regulations or metabolic reactions (Figure 6C). As for the integration pathways in the respective dosing durations, the DAMs were significantly downregulated, except for guanosine diphosphate (GDP) and 3′,5′-cyclic GMP (cGMP), which demonstrated a substantial dysregulation in multiple biological processes, despite the transcriptional upregulation of a proportion of genes (e.g., GPT, TKFC, HDDC3, NME1, PYCR3, PIP5KL1, PLCB2, and GLYCTK) (Supplementary File S6). Given the prominence of amino acid metabolism in tumorigenesis and metastasis [25], the early restriction (6 h) of amino acids could presumably hamper metabolic flexibility and induce extensive dysfunctions. Hence, based on the relative quantification of metabolomics, the cellular-scale nutrients and metabolic intermediates were assessed, which profiled a scenario of metabolic disorder and nutritional restriction characterized by the depletion of multitudinous metabolites (Figure 7). For instance, the relative abundances of several glycolysis intermediates including glucose 6-phosphate (FC ≈ 0.11), 3-phospho-D-glycerate (FC ≈ 0.20), and PEP (FC ≈ 0.15) were continuously decreased under 48 h-exposure of CPUL1, which might represent a moderating glycolysis rate and is consistent with the lower ATP level at 48 h (FC ≈ 0.34) (Figure 5C). The comparable pattern also recurred in diverse biological processes such as the metabolism of nucleotides, arginine, and proline, histidine, glutathione biosynthesis, and energy production (Figure 7), collectively indicating a deteriorative status of metabolic dysfunction and nutritional deficiency. Since CPUL1 was presumed as a cytotoxic agent, this raised a question as to what mechanisms would be underlying the metabolic disruption and malnutrition. Hence, we systemically examined the temporal dynamics of biological responses in the context of transcriptomic and biochemical modifications, which highlighted the putative involvement of autophagy, as it is not only a process tightly interconnected with nutrient status or metabolic stress, but also one of the enriched reactions by any approaches of data analysis in this study (Figure 3, Figure 5, Figure 6 and Figure 8A). Hence, genes associated with autophagy were screened from the transcriptomic data of different CPUL1-exposing times. As indicated in Figure 8A, many autophagy-related and regulatory genes were transcriptionally downregulated including AMPK, AKT3, PERK, and ATGs. The expression levels of the ATG4A, ATG9A, ATG12, and ATG5 genes in the autophagy pathway were verified by qPCR (Figure 8B). The results showed that the expression levels of the above genes were downregulated, which was consistent with the transcriptomic results. However, as autophagy is a highly dynamic and complex process regulated by multiple steps [26], it was challenging to predict autophagy flux or the consequence altered by those genes. The expressions of several hallmark autophagy proteins (e.g., AMPK, LC3, mTOR, and ATG) were assessed to speculate the autophagic status in HCC cells. The results suggest that CPUL1 dramatically inhibited the pan-expression of AMPK and mTOR in a time-dependent manner. However, the relative activation appeared in the opposite pattern, represented by the descending ratio of p-AMPK/AMPK and the ascending ratio of p-mTOR/mTOR, which presumably indicated the repression of autophagy. In addition, the expression of downstream protein C/EBP-β was downregulated, which was consistent with the expression trend of p-mTOR/mTOR and p-AMPK/AMPK. There was a decline in Beclin1, ATG5, and ATG12, the critical mediators of autophagy initiation and autophagosome formation [27]; however, the conversion of LC3-I to LC3-II was surprisingly increased, which often reflects autophagosome biogenesis. Subsequently, the expression level of p62, an indicator of autophagic flux, was assessed by Western blotting and QPCR, for which the significant intracellular accumulation was observed to imply the restraint of autophagosome degradation [28] (Figure 8D,E). The simultaneous increase in LC3-II and p62 indicated the initiation of autophagosome assembly, but the suppression of autolysosome degradation, which allegedly connotes an interception of autophagy flux [29]. Accumulation of autophagosomes was then confirmed using TEM micromorphological observations. The results show that double-membrane vesicles were prominently deposited in the cytoplasm following 6 h or 48 h of CPUL1 treatment, even though these structures were supposed to be transient and sporadic during the autophagy process (Figure 8C). To exclude the possibility that the observed autophagy disruption induced by CPUL1 was stochastic or cell-specific, we performed supplementary experiments on additional HCC lines, HepG2, and HUH-7. Western blotting and immunofluorescence analyses demonstrated elevated expressions of LC3-II and p62 after 6 h and 48 h of exposure to CPUL1 (particularly after 48 h) in the HepG2 and HUH-7 cells (Figure S3), consequently indicating the suppression of autophagy (Figure 8F,H), which were consistent with those obtained from the BEL-7402 cells. In light of the dynamic nature of the autophagic process, especially in response to nutrient deprivation (as occurs with CPUL1 treatment), it is crucial to investigate the temporal effect of CPUL1 on autophagy in a compact frame (i.e., 1 h, 2 h, and 4 h) to obtain insights into its underlying mechanisms. As revealed in Figure 8G, the protein expression levels of LC3 and p62 remained substantially unaltered following short-term exposure to CPUL1 in the investigated cell lines. This was corroborated by immunofluorescence assays (Figure 8I) with comparable protein depositions of LC3 and p62, regardless of 2-h treatment of CPUL1 or not (Figure S3). These observations indicated that brief exposure to CPUL1 is unlikely to induce an immediate suppression on autophagy, but rather a cumulative effect, possibly due to its gradual and gentle intracellular accumulation, as indicated by the pharmacokinetic profiles [30]. The mechanism underlying the effect of CPUL1 on autophagy was subsequently investigated in BEL-7402 cells by measuring LC3-II turnover and p62 degradation, in combination with an autophagy inhibitor (CQ or 3-MA), to further validate the CPUL1 impairment on autophagic dynamics. Both immunoblotting and immunofluorescence staining (Figure 9A–C) revealed the considerable accumulations of LC3-II and p62 caused by CPUL1 treatment (Figure S3), comparable to that induced by CQ, a late-stage inhibitor by interrupting autophagosomal degradation [31]. Moreover, the co-incubation of CQ and CPUL1 slightly but significantly potentiated the trend, possibly implicating a parallel mode of action. In contrast, 3-MA, a typical early autophagy blocker [32], specifically increased the expression of p62 and LC3 proteins. Simultaneous treatment with CPUL1 partially intensified the 3-MA-induced effects, as demonstrated by a considerable augmentation in p62 abundance (Figure 9C). The aggregate of the above results suggest that the cellular autophagy flux in BEL-7402 was potently suppressed by CPUL1, presumably through an intervention on autophagosomal degradation in a manner analogous to CQ, which could conceivably exacerbate the metabolic disturbances and nutritional deficiencies. The fusion of autophagosomes with lysosomes is a crucial step in autophagy, facilitating the delivery of autophagosome cargo for degradation by the lysosome [33]. To investigate the impact of CPUL1 on lysosome function, endogenous LC3 colocalization with LAMP1, a well-established lysosomal marker [34], was examined using immunofluorescence. Results showed that CPUL1 treatment decreased LAMP1 distribution as well as LC3-LAMP1 colocalization (Figure 9D), indicating the inhibition of autophagosome–lysosome fusion. Lysosome pH was also evaluated using Lyso-Tracker Red dye, which revealed a significant quenching in red fluorescence in cells treated with CPUL1 relative to the control cells (Figure 9E), presumably implying that CPUL1 induced a pH-dependent lysosomal dysfunction. Additionally, Western blotting analysis revealed that the expression of RAb7, LAMP1, and LAMP2, which are involved in lysosome membrane stability [35], were reduced by CPUL1 in a time-dependent manner (Figure 9F), suggesting the interference on lysosomal membrane function and obstruction in autophagy flux. Given its distressing prevalence and lethality, HCC has constituted and will likely continue to pose a severe threat to human health and life, necessitating the desperate development of effective chemotherapeutic agents [36]. Herein, in accordance with our previous reports, the therapeutic effects of CPUL1, a synthetic phenazine derivative, were systematically investigated against in vitro and in vivo HCC [10]. To elucidate the therapeutic mechanisms of this compound, comparative transcriptomics and untargeted metabolomics were performed for different exposure times (6 h or 48 h) in the HCC cell line BEL-7402. Multiple omics were combined to illustrate the biological processes dynamically and comprehensively within a genetic and phenotypic context, which interestingly underscored an aggravating metabolism dysfunction and nutritional deficiency. Thereupon, this molecule’s interception on autophagy flux was confirmed, which might provide unexpected insights into its underlying mechanisms and shed light on validating chemotherapeutic approaches against HCC in the context of metabolic intervention. In vitro CPUL1 inhibited cell proliferation in different human-derived HCC cells (HepG2, BEL-7402, and HUH-7), as indicated by the low IC50 values (<10 μM). The spatiotemporal localization of CPUL1 was the first to reveal the predominant distribution in cytoplasm and mitochondria, accompanied by the gradual aggregations in mitochondria and nuclei, seemingly justified in part by the mitochondrial apoptosis and DNA damage observed in previous work [11]. The significant antineoplastic efficacy of the compound was confirmed in vivo on BEL-7402 xenografted nude mice, demonstrating an equivalent or even better effect than sorafenib or CTX (20 mg/kg) at the higher dosage (40 or 20 mg/kg). Given that the xenograft models were performed in immunocompromised animals, which eliminates the contribution of the host immune system, logically, the therapeutic effects of CPUL1 should attribute to its direct actions rather than immunological regulation. Hence, the transcriptomic and metabolomic data from CPUL1 short- (6 h) or long-exposure (48 h) stages in BEL-7402 were analyzed to comprehensively understand the mechanisms against HCC. As for RNA profiling, the panoramic composition of DAGs apparently varied at different phases, however, the most drastic decreases coincided principally in several genes including COL12A1, FAT1, FBN2, IGF2R, and LRP1 (Figure 2A). Of note, recent reports have underscored the pivotal roles of these genes in tumorigenesis, invasion, and prognosis (e.g., COL12A1 in gastric cancer [37,38], FAT1 [39,40], and FBN1/2 [41,42] in HCC). Given the considerable degree and duration, the transcriptional suppressions on these genes may entail the disruption of respective neoplastic signals, fragmentarily elaborating the chemotherapeutic actions of CPUL1. Interestingly, the GO term or KEGG enrichment analysis showed that these DAGs from different time stages were mapped into a range of biological processes that only partially overlapped, supposedly indicating a temporal dissimilarity in the global transcriptional response. Further analysis of the interaction networks and hub genes demonstrated a time variation in transcriptional regulations, which brought about changes in the signaling processes (e.g., diverse cytokine pathways: EGFR, TGF-β, wnt), stress responses (e.g., DNA repair, ribosomal function, and RNA transport), metabolic processing (e.g., lipid metabolism, and glycan biosynthesis), ECM organization (e.g., ITGA, ITGB, LAMA, LAMB, and LAMC), endocytosis and protein recycling (e.g., collagen family), and cell cycle (Figure S1). As an emerging approach, metabolomics could provide a direct and comprehensive assessment of phenotype through the functional readout of metabolic processes to various environmental or genetic changes, highlighting the potential to significantly impact the oncology and intervention core areas [43]. In the present study, metabolomics offered chronologic snapshots of phenotypic status concerning CPUL1-triggered changes, which could present a non-overlapping perspective to substantiate the consequence of transcriptional regulation. The overall composition of DAGs under 6 h- or 48 h-CPUL1 treatment was inconsistent, perceivably describing a discrepancy in metabolic status. A typical case was α-KG, a signaling metabolite in carbon metabolism, of which the variation pattern was only reversed at different dosing phases, with the FC values (vs. control) boosting from the early (FC ≈ 0.40) to the advanced stage (FC ≈ 3.51) in abundance. In the recent two decades, the crucial and multifactorial contribution of α-KG has hogged the limelight in oncogenesis and tumor suppression. As for fast-proliferating cells, α-KG, often derived from glutamine, tends to influx into the anaplerotic pathway to replenish the TCA cycle for macromolecules biosynthesis and energy production, which hence rewires cellular metabolism for oncogenesis and proliferation [44]. On the other hand, α-KG is also a pivotal regulator of hypoxic adaptations and epigenetic modifications, two of the most significant drivers of tumor transformation [45], which could even open a therapeutic window to impinge on tumor progression and metastasis [46]. Therefore, the variation in the α-KG abundance might represent the outcome of metabolic events and the dynamic adjustment of physiological status, as illustrated by the widely perturbed genes and metabolites. Global functional annotations reflected an inactive scenario given the pervasive suppression of multiple metabolic pathways and corresponding metabolites, despite exceptional increments in several lipid metabolites (Figure 7). More specifically, the upregulated genes by CPUL1 exposure were significantly enriched within the pathway of unsaturated fatty acid (UFA) metabolism; correspondingly, a variety of metabolites including oleic acid (C18:1; FC ≈ 0.50/7.35, 6 h/48 h), linoleic acid (C18:2; FC ≈ 5.39/9.26, 6 h/48 h), α-linolenate (C18:3; FC ≈ 4.31/8.72, 6 h/48 h), and arachidonate (C20:4; FC ≈ 2.42/9.11, 6 h/48 h) were significantly enhanced, with an accentuating drift in abundance (Figure 5B,C). Increasing evidence has proposed the contribution of lipid remodeling to hepatic carcinogenesis and addressed the positive correlation between the content of lipid desaturation with malignant degrees and poor prognosis, which is potentially attributable to hypoxia adaptation, stress rescue, and immune regulations [47,48,49,50]. In our study, the UFAs were characterized to accumulate in the presence of CPUL1, which presumably meant the promotion of cellular proliferation according to the findings above, interestingly, contradicting its cytotoxic effects. Such a seeming paradox might be ascribed to the reduction in utilization due to CPUL1 treatment, which could nudge homeostatic lipid metabolism to cellular deposition given the steady and capped supply of these UFAs as a consequence of exogenous uptake, especially for essential fatty acids (e.g., linoleic and linolenic acids) [51]. In contrast, the excessive de novo biosynthesis of lipids, a hallmark of oncogenesis, possibly hindered by CPUL1, speculated from the consistent drops in the content of sn-glycerol-3-phosphate (FC ≈ 0.48/0.40, 6 h/48 h), CDP-choline (FC ≈ 0.96/0.29, 6 h/48 h), and octadecanoic acid (C18:0; FC ≈ 0.38/0.49, 6 h/48 h). Numerous metabolomic investigations have confirmed the correlations between the aberrant activation of lipogenesis and hepatocarcinogenesis and development, which could ensure tumor cells with extra lipids for membrane formation, biofuel supply, or post-translational modifications [48]. In this regard, a range of fatty acids have been explicated to abnormally accumulate in HCC such as octadecanoic acid (C18:0) [52,53], triglycerides (TG) [54], and phosphatidylcholine (PC) [55], which were significantly associated with the malignant degrees and thus identified as potential biomarkers for risk and prognostic assessment [56]. Coincidentally, sn-glycerol-3-phosphate and CDP-choline, whose concentrations decreased significantly according to our data, are the precursor and key intermediate metabolites for triglyceride generation [57] and PC biosynthesis [58], respectively, implying severe disturbances in these lipid anabolisms. Therefore, within the context of lipid utilization, it is plausible to infer that CPUL1 therapy may impede the availability and accessibility of intercellular lipids, potentially and profoundly altering a range of pathophysiological processes. Moreover, the therapeutic efficacies of CPUL1 are also reflected in the reversal of the elevated metabolism of glucose, amino acids, and nucleotides, which are acknowledged to accommodate the enhanced demand for energy production and building blocks in hyperplastic cells [59]. For instance, as the exposure time extended, the abundance of several intermediate products in central carbohydrate metabolism (e.g., ribulose, D-erythrose 4-phosphate, 6-phospho-D-gluconate) remained low, resulting in dramatic content decreases of 3-phospho-D-glycerate (FC ≈ 0.20) and phosphoenolpyruvate (FC ≈ 0.15) after 48 h of treatment. In terms of the highly intertwined nature of metabolic networks, the recessions were evident in the pentose phosphate pathway, nucleotide synthesis, and amino acid metabolism, characterized by the continuous decreases in extensive end products such as adenine (FC ≈ 0.19), guanosine (FC ≈ 0.32), thymine (FC ≈ 0.35), ATP (FC ≈ 0.34), UDP (FC ≈ 0.26), CMP (FC ≈ 0.32), and glycine (FC ≈ 0.44), at 48 h of the administration of CPUL1. Of note, the relative content of γ-glutamylcysteine (GGC) and S-adenosyl-L-homocysteine (SAH), precursors to glutathione and cysteine, dropped substantially over the treated course, with FC values of 48 h approximately in 0.21 and 0.20, respectively. Accordingly, the accumulation of glutathione disulfide (GSSG; FC ≈ 2.67, 48 h) followed the rapid decline (FC ≈ 0.43, 6 h) in glutathione (GSH), apparently consistent with the oxidative damage induced by CPUL1 in previous reports, in light of the inclusive and evident deficiencies of GSH biosynthetic substrates [60]. Collectively, as far as transcriptional and metabolomic data are concerned, after CPUL1 treatment, the metabolic flux experienced a full range of decline (Figure 6 and Figure 7), which limited intracellular nutrition availability and potentially aggravated oxidative stress. Given the scenario of nutritional deprivation induced by CPUL1, the supposed cytotoxic agent, it is obvious to question the potential participation of autophagy in the process, assuming its nature as a cytoprotective mechanism in reaction to starvation [61]. Hence, we further investigated the effect of CPUL1 on autophagy and found that LC3 conversion was markedly stimulated, with an accumulation of the LC3-II protein, the marker for autophagosome formation, which could technically be ascribed to either an increase in autophagosome formation or a decrease in autophagic turnover. Next, we assessed the expression of the p62 protein, a classical cargo adaptor for the degradation of ubiquitinated substrates via autophagy, which was time-dependently elevated, presumably indicating an obstruction in autophagic flux. Additionally, the observation of the comparable evidence of the autophagy flow inhibition in the HepG2 and HUH-7 cell lines, as evidenced in Figure 8F, implies a non-specific effect on the autophagy of CPUL1. Subsequent morphological and fluorescent profiling validated the above presumption, demonstrating a late-stage suppression of autophagy by CPUL1, especially in light of the discrepant effects induced by the co-treatment with different types of inhibitors (CQ or 3-MA). Interestingly, neither inhibitor, CQ or 3-MA alone, could induce significant cell apoptosis or suppress proliferation [29], indicating the cytotoxicity of CPUL1 could not be primarily ascribed to autophagy inhibition. Nevertheless, the suppression of autophagic flux should be addressed due to the comprehensive but consistent modulation of molecular events by the CPUL1 challenge, from genetic to protein to phenotypic levels. In the case of transcriptomics, LRP1, a functional gene implicated in intracellular signaling and endocytosis [62], was highlighted among those drastically downregulated genes, in contrast to other counterparts with direct entanglement in carcinogenesis and development (e.g., COL12A1, FBN1/2 [63], MET [64], LAMB1/2 [65], FAT1 [66], and IGF2R [67]). The gene product, a multifunctional and ubiquitous member of the LDLR family, low-density lipoprotein receptor-related protein 1 (LRP1), has been implicated in numerous pathophysiological processes including lipid and lipoprotein metabolism, protease degradation, and cell migration [68]. Previous observations have established that the LRP1 deficiency exacerbated hepatic susceptibility to palmitate-induced lipotoxicity in vitro and in vivo due to impairment in autophagic flux, as evidenced by the increased p62 levels but unaltered LC3 lipidation [69,70]. A similar scenario was described in the etiology and pathogenesis of developmental dysplasia of the hip, underlining a critical role of LRP1 in triradiate cartilage development via autophagy activation [71]. In this context, the substantial decline in the LRP1 transcript corresponded to the enrichment in the autophagy-related pathways in transcriptomics and the consequences of extensive metabolic abnormality and conceivable nutritional deprivation in metabolomics. Regarding our current approach, multi-omics data integration highlighted the significance of autophagic inhibition in the antineoplastic effects of CPUL1, which presumably conferred a nutrient deprivation by tapping into tumor-specific metabolic vulnerability and thus exacerbated its cytotoxicity. Indeed, the underlying mechanisms comprised of intricate, multivariate, and intertwined biochemical events would be an issue for future investigations. Lysosomes are commonly considered as the primary location for intracellular degradation, where autophagosomes fuse with lysosomes to enable the degradation of their contents. Therefore, blocking lysosomal and autophagolysosomal activities significantly impairs autophagic flux [72]. Our results demonstrate that CPUL1 treatment disrupted lysosome–autophagosome fusion and resulted in the accumulation of immature forms (Figure 9D). Additionally, CPUL1 administration led to lysosomal dysfunction represented by lysosomal alkalization (Figure 9E) and the reduced expression of lysosomal membrane-associated proteins (LAMP1 and LAMP2) as well as the late endosomal/lysosomal regulator RAb7 (Figure 9F). Notably, CPUL1 had a comparable effect to CQ in terms of interference with the late stage of autophagy, inducing destructive autophagy and cytotoxic consequences [73]. Accumulating research has underscored the correlation between autophagy dysregulation and tumorigenesis and treatment regimens, which is proposed as a cytoprotective mechanism conferring resistance to stress including chemotherapy and radiation [74,75]. For instance, sorafenib, the first-line therapy for advanced HCC, resulted in tumor stabilization and cytostatic rather than regression. The relative resistance is presumably due to enhanced autophagy [76]. Hence, the strategies in combination with autophagy suppression have attracted significant interest in improving medication outcomes by restoring cellular sensitization to cancer therapeutics [77]. Therefore, the attractive properties of CPUL1, comprising of cytotoxicity and autophagic blockage, might endow the potential to translate this compound into a promising anti-HCC agent, necessitating additional studies. The present study systemically demonstrated the therapeutic efficiency of CPUL1, a phenazine derivative, by inhibiting proliferation and inducing the apoptosis of HCC cells in vitro and in vivo. Subcellular localization suggested that CPUL1 accumulated in the cytoplasm and mitochondria, supposedly plotting the spatiotemporal context for previous observations that CPUL1 triggers mitochondrial apoptosis and ROS stress. Subsequent multi-omics analysis collectively profiled a scenario of time-dependent deterioration in metabolic dysfunction and nutritional depletion, which might be ascribed, at least partially, to the concurrent autophagy inhibition. Our results illustrate that CPUL1, a putative TrxR1 inhibitor, might mediate apparent metabolic stress by inhibiting cellular autophagy, in addition to the anticipated oxidative stress and subsequent cellular lesion. This multifaceted mechanism of action endows the compound with intriguing biological properties to sensitize malignant cells to damage, and highlights its potential promise as a therapeutic agent against HCC as well as providing insights into the development of innovative strategies for combating cancer.

PMC10001022

Haijie Xiao,Haifeng Zhu,Oliver Bögler,Fabiola Zakia Mónica,Alexander Y. Kots,Ferid Murad,Ka Bian

Soluble Guanylate Cyclase β1 Subunit Represses Human Glioblastoma Growth

02-03-2023

glioblastoma,sGCβ1,nucleus,p53,CDK6,integrin α6,G0 arrest

Simple Summary A marked reduction in soluble guanylyl cyclase β1 (sGCβ1) transcript is characteristic for human glioma specimens. Restoring the expression of sGCβ1 inhibited the aggressive course of glioblastoma in an orthotopic xenograft mouse model. The present study is the first to reveal that sGCβ1 migrated into the nucleus and interacted with the promoter of the TP53 gene. sGCβ1 overexpression impacted signaling in glioblastoma multiforme, including the promotion of nuclear accumulation of p53, a marked reduction in cyclin-dependent kinase 6 (CDK6), and a significant decrease in integrin α6. Antitumor effect of sGCβ1 was not associated with enzymatic activity of sGC. Abstract Malignant glioma is the most common and deadly brain tumor. A marked reduction in the levels of sGC (soluble guanylyl cyclase) transcript in the human glioma specimens has been revealed in our previous studies. In the present study, restoring the expression of sGCβ1 alone repressed the aggressive course of glioma. The antitumor effect of sGCβ1 was not associated with enzymatic activity of sGC since overexpression of sGCβ1 alone did not influence the level of cyclic GMP. Additionally, sGCβ1-induced inhibition of the growth of glioma cells was not influenced by treatment with sGC stimulators or inhibitors. The present study is the first to reveal that sGCβ1 migrated into the nucleus and interacted with the promoter of the TP53 gene. Transcriptional responses induced by sGCβ1 caused the G0 cell cycle arrest of glioblastoma cells and inhibition of tumor aggressiveness. sGCβ1 overexpression impacted signaling in glioblastoma multiforme, including the promotion of nuclear accumulation of p53, a marked reduction in CDK6, and a significant decrease in integrin α6. These anticancer targets of sGCβ1 may represent clinically important regulatory pathways that contribute to the development of a therapeutic strategy for cancer treatment.

Soluble Guanylate Cyclase β1 Subunit Represses Human Glioblastoma Growth A marked reduction in soluble guanylyl cyclase β1 (sGCβ1) transcript is characteristic for human glioma specimens. Restoring the expression of sGCβ1 inhibited the aggressive course of glioblastoma in an orthotopic xenograft mouse model. The present study is the first to reveal that sGCβ1 migrated into the nucleus and interacted with the promoter of the TP53 gene. sGCβ1 overexpression impacted signaling in glioblastoma multiforme, including the promotion of nuclear accumulation of p53, a marked reduction in cyclin-dependent kinase 6 (CDK6), and a significant decrease in integrin α6. Antitumor effect of sGCβ1 was not associated with enzymatic activity of sGC. Malignant glioma is the most common and deadly brain tumor. A marked reduction in the levels of sGC (soluble guanylyl cyclase) transcript in the human glioma specimens has been revealed in our previous studies. In the present study, restoring the expression of sGCβ1 alone repressed the aggressive course of glioma. The antitumor effect of sGCβ1 was not associated with enzymatic activity of sGC since overexpression of sGCβ1 alone did not influence the level of cyclic GMP. Additionally, sGCβ1-induced inhibition of the growth of glioma cells was not influenced by treatment with sGC stimulators or inhibitors. The present study is the first to reveal that sGCβ1 migrated into the nucleus and interacted with the promoter of the TP53 gene. Transcriptional responses induced by sGCβ1 caused the G0 cell cycle arrest of glioblastoma cells and inhibition of tumor aggressiveness. sGCβ1 overexpression impacted signaling in glioblastoma multiforme, including the promotion of nuclear accumulation of p53, a marked reduction in CDK6, and a significant decrease in integrin α6. These anticancer targets of sGCβ1 may represent clinically important regulatory pathways that contribute to the development of a therapeutic strategy for cancer treatment. Glioblastoma is an aggressive brain cancer known to be resistant to treatment. Over 13,000 Americans were estimated to have been diagnosed with glioblastoma in 2022, and over 10,000 people in the United States are diagnosed with glioblastoma on a yearly basis. Glioblastoma patients are characterized by 6.8% five-year survival rate, and the average survival of these patients is estimated to be only 8 months. The rate of survival and overall mortality due to glioblastoma have been essentially unchanged for many years [1]. It was suggested that veterans who were deployed to Iraq and Afghanistan developed glioblastoma at a higher rate [2,3,4,5]. Thus, a new therapeutic strategy for glioblastoma is necessary. Our previous study analyzed the changes in the signaling molecules of the nitric oxide (NO)/soluble guanylyl cyclase (sGC)/cyclic guanosine monophosphate (cGMP) pathway in the human glioma tissues and cell lines and compared the levels of these molecules with normal controls. We demonstrated that the expression of sGC is significantly lower in glioma preparations. The restoration of sGC expression by genetic manipulations or elevation of the level of intracellular cGMP using pharmacological agents in glioblastoma cells was shown to significantly suppress the growth of tumor cells. Orthotropic implantation of human glioblastoma cells transfected with a constitutively active mutant form of sGC (sGCα1β1cys105) in athymic mice was demonstrated to induce a four-fold increase in survival time compared with that in the control group [6]. sGC functions as a heterodimer composed of the α and β subunits, and α1/β1 sGC is the most abundant heterodimer [7]. The α1 and β1 subunit genes are located in the same chromosome 4 in humans and are encoded by separate genes [8]. The heterodimer is required for enzyme function. However, the levels of the α1 and β1 subunits of sGC can be independently regulated in most human tissues [9]. sGC is gaining a rapidly growing interest as a therapeutic target. The first-in-class sGC stimulator riociguat was approved for pulmonary hypertension in 2013, and another stimulator, vericiguat, was recently approved in the USA for patients with heart failure. These sGC stimulators enhance sGC activity independently from NO [10,11]. Our previous analysis has shown that higher levels of sGCβ1 in cancer tissues are correlated with greater survival probability of patients compared with that in patients with lower levels of sGCβ1 [12]. The present study reported that restoration of sGCβ1 expression in sGC-deficient human glioblastoma cells blocked the aggressive course of malignant tumors independently of cGMP. In contrast to sGCα1, the sGCβ1 subunit migrated into the nucleus and bound to the chromatin complex. The data of the present study will help advance our understanding of the role of sGCβ1 in glioma proliferation. The protocol for the generation of the stable clones of human glioblastoma U87 cells by transfection was described in a previous study [6]. Briefly, full-length sGCβ1 and sGCβ1Cys105 were cloned into the pcDNA3.1D/V5-His TOPO vector (Invitrogen) according to the manufacturer’s instructions. For sGCβ1 knockdown, BE2 cells were transfected with nonsilencing control shRNA or sGCβ1 shRNA (Origene). The structures of all plasmids used in the present study were confirmed by sequencing. The changes in the expression of sGCβ1 were confirmed by Western blot analysis prior to the experiments [6]. Glioblastoma U87 and neuroblastoma BE2 cells were obtained from the American Type Culture Collection (Manassas, VA, USA) and maintained at 37 °C under a humidified atmosphere containing 5% CO2. U87 cells were grown in Dulbecco’s modified Eagle’s medium, and BE2 cells were grown in Eagle’s minimal essential medium. Medium was supplemented with 10% fetal bovine serum (FBS, HyClone, Logan, UT, USA) and 1% penicillin/streptomycin mixture. Cell viability and proliferation were measured by the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide) method. The cells with sGCβ1 overexpression or knockdown were seeded at 20,000 cells/mL in a 96-well plate for at least 24 h and incubated further as described in the text. Then, MTT was added to the culture at a final concentration of 0.5 mg/mL, and the samples were incubated for 3 h. Then, medium was aspirated, and 100 µL/well of anhydrous isopropanol containing 40 mM hydrochloric acid was added. Optical density was read at 570 nm to evaluate the proliferation of the cells. For pharmacological treatments, the cells were treated with DMSO (dimethyl sulfoxide) (0.1%; vehicle control), ODQ (1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one; 10 µM), Bay41-2272 (3-(4-amino-5-cyclopropylpyrimidine-2-yl)-1-(2-fluorobenzyl)-1H-pyrazolo[3,4-b]pyridine; 1 µM), or YC-1 (3-(5′-hydroxymethyl-2′-furyl)-1-benzylindazole; 10 µM) for 24 h prior to the MTT assay. Traditional soft agar assay for colony formation was used with some modifications. U87 glioblastoma cells (3000 cells) were seeded in an agar–agarose semisolid gel system in a 30 mm cell culture dish covered by culture medium containing FBS, and the medium was replaced every three days. For pharmacological treatments, the cells were treated with DMSO (0.1%; vehicle control), ODQ (10 µM), Bay41-2272 (1 µM), or YC-1 (10 µM). The medium containing the reagents was replaced every three days. After 21 days, the colonies were stained with 0.005% crystal violet for 1 h, washed with PBS, and imaged. The colonies were counted, and the size of the colonies was calculated. The animal protocol was approved by the Institutional Animal Care and Use Committee of The University of Texas MD Anderson Cancer Center (protocol no. 10-07-12131), and all experiments were performed according to the National Institutes of Health guidelines. A total of 14 female mice (8–10-week-old; nu/nu athymic; Charles River Laboratories) were used in the experiments. Human glioblastoma cell lines with or without stable transfection (at a concentration of 1 × 106 cells/5 µL) were resuspended in PBS and injected into the right frontal lobe brain of nude mice by using a guide-screw system as described previously [6]. The animals were anesthetized with xylazine-ketamine (10 mg/kg xylazine and 100 mg/kg ketamine). The animals were euthanized when they became moribund due to tumor progression. The brain was then removed for histological and molecular analyses. To assay the accumulation of cGMP in the tumor cells, the cells were preincubated in Dulbecco’s PBS containing 1 mM 3-isobutyl-1-methylxanthine for 10 min. The medium was aspirated, and 50 mM sodium acetate (pH 4.0; 0.3 mL per well) was added to extract cGMP by rapid freezing of the plates at −80 °C. The accumulation of cGMP was measured by enzyme-linked immunosorbent assay (ELISA) as described previously [13]. Total RNA was isolated using Trizol reagent (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. Complementary DNA was synthesized by using an iScript™ reverse transcription supermix kit (cat. no. 170-8840; Bio-Rad, Hercules, CA, USA) following the manufacturer’s protocol. qRT-PCR was performed using a standard protocol in a total reaction volume of 25 μL as described previously [14] and as follows: 2 min at 95 °C, followed by 40 cycles of 15 s each at 95 °C and 30 s at 60 °C. The data of qRT-PCR were normalized to the level of β-actin. The PCR primers used in the present study are listed in Supplementary Materials Table S1. The cells were harvested and lysed by sonication in ice-cold RIPA buffer containing a protease inhibitor cocktail. Isolation of the protein fractions from the cytoplasm and soluble nuclear and chromatin fractions is described in the Supplementary Materials. Equal amounts of the protein (50 or 100 µg/lane) were separated by 4–15% SDS-PAGE. Separated proteins were transferred to a polyvinilydene difluoride (PVDF) membrane, which was blocked with 5% nonfat dry milk in TBS-T (20 mM Tris-HCl, pH 7.6, 130 mM NaCl, and 0.1% Tween 20) and incubated at 4 °C overnight with specific primary antibodies. Secondary horseradish peroxidase-conjugated antibodies (Sigma-Aldrich, St. Louis, MO, USA) were used at 1:3000–1:10,000 dilutions, and the protein bands were visualized by enhanced chemiluminescence (ECL Plus; Amersham Biosciences, Buckinghamshire, UK). The intensity of the bands was quantified using ImageJ software (NIH). All antibodies used in the present study are listed in Supplementary Materials Table S2. The cells were collected by trypsinization and fixed with 70% ethanol overnight at 4 °C. After fixation, the cells were centrifuged and stained in 1 mL of propidium iodide solution (0.05% NP-40, 50 ng/mL propidium iodide, and 10 μg/mL RNase A). Labeled cells were analyzed using a BD flow cytometer and FlowJo software, Mississauga, ON, Canada. Double staining of U87 cells was performed according to Li et al. [15]. Briefly, 1 × 106 PBS-washed cells were incubated in 0.5 mL of nucleic acid-staining solution (NASS) containing 0.02% saponin and 10 µg/mL 7AAD (7-aminoactinomycin D) for 20 min at room temperature protected from light. After the addition of 1 mL of PBS to the samples, the cells were centrifuged at 250 g for 5 min. Cell pellet was resuspended in 0.5 mL of NASS containing 10 µg/mL actinomycin D. The mixture was incubated on ice for 5 min protected from light. Pyronin Y was added to the cells to a final concentration of 1 μg/mL, and the suspension was vortexed. The cells were incubated on ice protected from light for at least 10 min before the data were acquired using a flow cytometer. Nuclear localization of sGCβ1 was determined by indirect immunofluorescence. In brief, the cells were grown on sterile glass coverslips, fixed in 4% paraformaldehyde, permeabilized using 0.1% Triton X-100, and blocked with 10% normal goat serum in PBS. The cells were incubated with primary antibodies, washed three times in PBS, and incubated with goat antimouse or goat antirabbit secondary antibodies conjugated with fluorescein isothiocyanate (FITC; green; Molecular Probes, Eugene, OR, USA). The nuclei were stained with the blue DNA dye 4′,6-diamidino-2-phenylindole (DAPI; Molecular Probes). The images were acquired by an Olympus FV300 laser-scanning confocal microscope using sequential laser excitation to minimize fluorescence emission bleed-through. ChIP assay was performed by using a SimpleChIP enzymatic chromatin immunoprecipitation kit (cat. no. 9003; Cell Signaling, Danvers, MA, USA) according to the manufacturer’s instructions. Briefly, 4 × 107 cells were fixed with 1% formaldehyde for 10 min, and the reaction was quenched with 0.125 M glycine. The cells were washed with PBS, and the nuclei were isolated by incubating the cells in buffer A for 10 min on ice. Micrococcal nuclease was then used to digest the chromatin for 20 min at 37 °C. The samples were sonicated by a Qsonica sonicator at 40% amplitude for 20 s three times. An anti-sGCβ1 antibody (Sigma; cat. no. G4405; 3.5 µg) or control IgG was added to the chromatin samples and incubated overnight with rotation. Then, magnetic beads were added to the chromatin samples and incubated at 4 °C for 2 h with rotation. After three washes with a low-salt buffer and one wash with a high-salt buffer, DNA was eluted from the beads with elution buffer and purified using a column. qRT-PCR was performed to detect sGCβ1 binding to the chromatin regions. Luciferase assay was used to evaluate the activity of the TP53 promoter. U87 cells were grown to 50% confluence in 24-well plates and transfected by using Lipofectamine LTX reagent (Invitrogen, Waltham, MA, USA) according to the manufacturer’s instructions. The cells were transfected with 0.1 μg of the pGL3-TP53 (or a corresponding deletion mutation) vector in combination with 0.2 µg of the sGCβ1 overexpression plasmid (pCDNA3.1 empty vector was used as a control) and pRL-SV40 (Promega, Madison, WI, USA). Twenty-four hours after the transfection, the luciferase activity was determined by using a dual luciferase assay kit (Promega) according to the manufacturers’ protocol, and the signal was acquired using a Biotek Synergy H1 microplate reader (Agilent Technologies, Santa Clara, CA, USA). The results are expressed as the mean ± S.E.M. One-way analysis of variance (ANOVA) was used for multiple comparisons, and the results were Bonferroni-adjusted. Significance of the differences between the treatment groups was assessed by Student’s t test versus the control groups using Welch correction as appropriate; the p-values of less than 0.05 were considered statistically significant. The n values indicate the numbers of animals or independent biological replicates used in the experiments. The Kaplan-Meier survival curves and the mean survival values were compared using the log-rank test (SigmaPlot version 12.5 software) and were Bonferroni-adjusted for multiple comparisons. We have previously demonstrated that the expression levels of α1 and β1 subunits of sGC are significantly lower in human glioma preparations [6]. We analyzed the data of SAGE (serial analysis of gene expression; GEO databases GSE15309; n = 327) based on the mRNA sequencing output and demonstrated a statistically significant reduction in sGCβ1 transcript levels in human glioma specimens compared with that in normal adjacent tissues (Figure 1a) [16]. To examine the effect of genetically restored sGCβ1 expression on the growth of glioma cells, we generated two stable clones of U87 human glioblastoma cells overexpressing unmodified sGCβ1 or mutant sGCβ1Cys105. The sGCβ1Cys105 mutant was created by substituting Cys for His at position 105 [17]. The sGCα1β1Cys105 heterodimer is constitutively active and is not stimulated by nitric oxide [17]. The proliferation of U87 cells was significantly inhibited by overexpression of either sGCβ1 or sGCβ1Cys105 (Figure 1b). Our previous study has shown that the delivery of sGCα1 alone failed to suppress the proliferation of human glioma cells [6]. The growth of the cells within a three-dimensional (3D) support system is known to simulate a natural microenvironment for the proliferation, morphology, signaling, and responses to therapeutic agents [18]. Thus, we used a colony formation assay to evaluate the influence of sGCβ1 on the growth of glioblastoma cells. As shown in Figure 1c, the expression of sGCβ1 or sGCβ1Cys105 decreased the number and size of the colonies of glioblastoma cells. The colonies formed by the stable clones overexpressing sGCα1 were similar to the colonies formed by control cells [6]. Next, we performed an in vivo study by orthotopic xenotransplantation of U87 cells with or without prior transfection of sGCβ1Cys105. As shown in Figure 1d, the animals inoculated with sGCβ1Cys105-transfected cells had significantly extended survival time; the longest survival time of the animals inoculated with sGCβ1 subunit-transfected cells was over 125 days (four-fold increase over the control group). The average survival time of mice inoculated with the sGCβ1Cys105-expressing cells was increased from 29 ± 2 days to 69 ± 11 days (Figure 1e). However, the intracranial xenograft of sGCαl-transfected cells prolonged the average survival time from 31 days to only 40 days [6], indicating that sGCβ1 was significantly more potent in suppression of tumor growth in vivo. The antiproliferative effect of sGCβ1 suggested to test whether silencing of sGCβ1 enhances the growth of other tumor cells. BE2 human neuroblastoma cells normally express both α1 and β1 subunits of sGC similar to the expression pattern detected in a normal human cortex [6,19,20,21]. Thus, BE2 cells were selected to study the effect of gene silencing. sGCβ1 was consistently silenced after the transfection with short hairpin RNA (shRNA). As shown in Figure 1f, a reduction in sGCβ1 expression resulted in an increase in the proliferation of BE2 neuroblastoma cells. To determine if the repression of the proliferation is due to cGMP overproduction, we examined the cGMP levels in the cells with sGCβ1 overexpression. sGCβ1 or sGCβ1Cys105 overexpression in U87 cells did not significantly change the cGMP levels compared with that in untransfected or control vector-transfected glioblastoma cells (Figure 2a). To assess possible involvement of enzymatic activity of sGC in the effects, we examined the influence of sGC inhibitors or activators in sGCβ1-overexpressing cells. The proliferation rate of stable clones overexpressing sGCβ1 or sGCβ1Cys105 was not influenced by the presence of the sGC inhibitor ODQ or by activators Bay41-2272 and YC-1 (Figure 2b). The colony formation by the cells overexpressing sGCβ1 or sGCβ1Cys105 was not changed by treatment with ODQ, Bay41-2272, or YC-1 (Supplementary Materials Figure S1a,b). Thus, these results indicated that the growth repression by sGCβ1 was not associated with cGMP production. The cell cycle phases of sGCβ1-overexpressing cells were analyzed by flow cytometry after the cells were stained with propidium iodide. As shown in Figure 3a, the G0/G1 phase was prolonged in sGCβ1-overexpressing cells because the number of the cells in the G0/G1 phase was higher than that in the control cells by 14%. To distinguish between the G0 and G1 phases, the cells were double stained with 7AAD and Pyronin Y. As shown in Figure 3b, sGCβ1-overexpressing cells had a significantly higher population of the cells in the G0 phase (17.6% in sGCβ1-overexpressing cells versus 4.8% in control U87 cells). No significant changes in the sub-G1 phase population were detected in sGCβ1-overexpressing cells after staining with propidium iodide or 7AAD, suggesting that sGCβ1 overexpression had no influence on apoptosis. The α1 and β1 subunits of sGC have variable distribution in various tissues and can be regulated independently under certain conditions. Moreover, intracellular sGCα1 and β1 can localize to various intracellular compartments [22,23,24]. As shown in Figure 4a, a significant portion of sGCβ1 was detected in the nuclear fraction of U87 cells overexpressing sGCβ1 or coexpressing the α1 and β1 subunits. Subcellular sGCβ1 localization patterns were similar in U87 cells cotransfected with or without sGCα1 (Figure 4a), suggesting that the formation of the functional heterodimer is not required for nuclear localization of sGCβ1. In contrast, sGCα1 was not detected in the nucleus even in sGCα1-overexpressing U87 cells (Figure 4b). Confocal fluorescence imaging analysis confirmed nuclear localization of sGCβ1 (Figure 4c). p53 is a well-known tumor suppressor that primarily alters the expression of numerous genes involved in cell cycle arrest, apoptosis, stem cell differentiation, and cellular senescence. The p53 pathway is frequently deregulated in glioblastoma, and this deregulation is correlated with a more invasive, more proliferative, and more stem-like phenotype [25]. The data of qRT-PCR showed a significant upregulation of TP53 gene expression after sGCβ1 overexpression (Figure 5a). The level of p53 protein was increased by approximately 50% after overexpression of sGCβ1 (Figure 5b). Wild type p53 localizes in the cytoplasm and nucleus of human primary glioblastomas [26]. Wild type p53 and naturally occurring p53 mutants migrate into the nucleus, and nuclear localization of p53 plays essential roles in tumorigenesis and malignant transformation. Cytoplasmic p53 associates with microtubular cytoskeleton to localize to the mitochondria during nontranscriptional apoptotic response [27,28,29]. The expression of p53 can be detected in the cytoplasmic and nuclear fractions of human glioblastoma U87 cells (Figure 5b,c), which express wild type p53. Boosting the expression of sGCβ1 markedly elevated nuclear accumulation of p53 and had a less pronounced effect on cytosolic p53, suggesting that sGCβ1 overexpression may enhance the interaction of p53 with the nuclear components (Figure 5b,c). To further explore the role of sGCβ1 in the nucleus, we examined possible transcription activity of sGCβ1 targeting the TP53 promoter by ChIP. The binding site of sGCβ1 to the TP53 gene was enriched in the region 1039 bp downstream of the transcription start site (TSS), with the MACS (model-based analysis of ChIP-sequencing) p-value of 184.44 (Supplementary Materials Table S3). The data of ChIP obtained using a specifically designed set of primers (Figure 5d) clearly indicated that the binding of sGCβ1 was markedly enriched at approximately 1 kb downstream of the TSS of the TP53 promoter (Figure 5d). To confirm the binding of sGCβ1 to the TP53 promoter region, the promoter of the TP53 gene was cloned into the pGL3 vector, and site-directed mutagenesis was performed (Figure 5e). The plasmids containing the TP53 promoter with or without the deletions in combination with sGCβ1-overexpressing plasmid and a control reporter vector pRL-SV40 were transfected into U87 cells. As shown in Figure 5e, the results of a dual luciferase assay indicated that sGCβ1 overexpression activated the TP53 promoter. When the putative sGCβ1-binding site was deleted, the activity of the TP53 promoter was not influenced by sGCβ1 overexpression. Thus, we confirmed that sGCβ1 binds to and activates the TP53 promoter. Two highly conserved p53-responsive elements in the p21 promoter directly bind p53 to activate p21 transcription [30]. p53-mediated apoptosis is preceded by an elevation in the levels of p21 [31]. To verify the effect of sGCβ1 on the p53 signaling pathway, we examined the status of p21 expression. As demonstrated in Figure 6a, mRNA levels of p21 were increased by approximately 21%, and the protein levels of p21 were increased by approximately 48% in sGCβ1-overexpressing cells compared with those in control human glioblastoma cells (Figure 6b,c). The retinoblastoma tumor suppressor protein (Rb) and p53 pathways appear among the most frequently mutated pathways in malignant glioma. The retinoblastoma family of proteins, including Rb and p107, undergoes cell cycle-dependent phosphorylation during the G1 to S phase transition, and p130 is phosphorylated during the G0 and early G1 phases of the cell cycle [32]. We sought to determine the role of sGCβ1 in the regulation of the phosphorylation levels of p130, p107, and pRb. However, sGCβ1 overexpression did not influence the phosphorylation levels of the members of the retinoblastoma family (Supplementary Materials Figure S2). CDKs are the key regulators of the retinoblastoma family to promote cell cycle progression, which is crucial for pathological process of cancer. Glioblastoma is characterized by a high frequency of CDK4/CDK6 pathway dysregulation [33]. Pharmacological blockage of CDK4/6 has been recently investigated in clinic and demonstrated promising activity in patients with breast and other cancers [34]. The data of qRT-PCR and Western blotting obtained in the present study demonstrated that sGCβ1 overexpression markedly inhibited the expression of CDK6 and induced a trend of downregulation of CDK4 (Figure 6d–f). Glioblastoma displays with remarkable cellular heterogeneity. Glioblastoma stem-like cells are the key players among various cellular elements [35,36]. Integrin alpha 6 (ITGA6) has received considerable attention due to its role in the regulation of glioblastoma stem-like cells [37,38]. As shown in Figure 6g–i, mRNA and protein levels of ITGA6 were significantly downregulated in sGCβ1-overexpressing cells. The data of the present study demonstrated that sGCβ1 expression was significantly reduced in human glioma preparations (Figure 1a). The results of the proliferation and 3D colony formation assays demonstrated that restoration of sGCβ1 expression played a critical role in the blockade of the growth of human glioblastoma cells (Figure 1). The growth-repressing effect of sGCβ1 was cGMP-independent since we did not detect significant changes in the inhibitory effect of sGCβ1 after treatment with the activators or an inhibitor of sGC activity (Figure 2). Orthotopic xenograftment with an sGCβ1Cys105-expressing stable clone of glioblastoma cells in athymic mice increased the maximal survival time of the animals four-fold compared with that in the vector control group. The role of the NO and cGMP signaling pathway in biological properties of the tumors has been actively investigated during the past 30 years. However, this pathway may be beneficial or detrimental for cancer. Several reasons for this ambiguity can be considered. NO participates in normal signaling (e.g., vasodilation and neurotransmission); however, NO has cytotoxic or proapoptotic effects when produced at high concentrations by inducible nitric oxide synthase (iNOS or NOS-2). In addition, the levels of the cGMP-dependent (the NO/sGC/cGMP pathway) and cGMP-independent (the NO redox pathway) components vary between various tissues and cell types. Frequent deregulation of sGC expression at the levels of transcription [39], splicing [40,41], mRNA stability [39], and protein stability [42] have been investigated by us and recently reviewed [43]. Solid tumors include two compartments, the parenchyma (neoplastic cells) and stroma (nonmalignant supporting tissues including connective tissue, blood vessels, and inflammatory cells), and biological properties and signaling pathways influenced by NO are different in these compartments. Thus, specific features of the NO/sGC/cGMP signaling pathway should be further characterized in the tumor and surrounding tissues [6,44]. Our previous study provided evidence for two possible roles of NO/cGMP signaling in malignant tumors. First, NOS-2 expression and NO overproduction contribute to the formation of an inflammatory cancer microenvironment, which promotes tumor cell proliferation. Second, a deficiency in sGC/cGMP signaling diminishes the role of these molecules as antagonists of cancer cell growth [6,44]. The present study revealed that sGCβ1 alone can migrate into the nucleus, thus impacting malignant cellular signaling to change the course of tumor growth. Consistent with this finding, the studies of sGCβ1 in rat brain astrocytes and glioma cells demonstrated that sGCβ1 is localized in the nucleus and is associated with the chromosomes during mitosis, regulating chromatin condensation and cell cycle progression in a cGMP-independent manner [45]. The p53 pathway is frequently inactivated in glioblastomas (for review, see [46] and references therein). Genome-wide system analyses based on transcriptome profiles have stratified gliomas into four molecular signatures: proneural, neural, classic, and mesenchymal [47]. The inactivation of the p53 pathway is common for glioblastomas of all four subgroups. The restoration of the expression of sGCβ1 in human glioblastoma cells significantly promoted p53 expression at mRNA and protein levels and correlated with the growth-suppressing effect of this sGC subunit (Figure 4 and Figure 5). The effect of sGCβ1 over expression on the level of TP53 mRNA was somewhat lower than the effect on the level of the p53 protein, which is a relatively common phenomenon for p53 [48]. Numerous studies have investigated the roles of cell cycle arrest and apoptosis in tumor suppression by p53 because these processes are the most evident antitumor mechanisms [49]. The human glioblastoma U-87 cell line expresses wild type p53 [50,51]. Upregulation of p53 by sGCβ1 promoted p21 expression (Figure 6), and sGCβ1 activated p53 and p21 signaling associated with a notable G0 phase arrest (Figure 3a,b). p21 has been reported to play an antiapoptotic role [52,53]. Thus, increased expression of p21 may lead to G0 arrest and prevent apoptosis [54]. Importantly, evidence obtained in the present study supported a hypothesis that sGCβ1 is associated with nuclear p53 (Figure 5) and directly impacts G0 phase arrest. In contrast, extranuclear transcriptionally inactive p53, which acts in the cytosol and mitochondria to promote apoptosis, was less influenced by sGCβ1 (Figure 5b,c) [55,56]. Mutations of the TP53 gene are the most frequent in lower grade glioma; however, the impact of sGCβ1 on the expression of mutant p53 is an important subject for further exploration. The NO/sGC/cGMP signaling pathway undergoes variable changes in various tumors [6,44]. sGCα1 expression is elevated to a high level in prostate tumors, and the expression of sGCβ1 remains very low [57]. Similar to glioblastoma, sGCα1 is exclusively cytoplasmic in prostate cancer cells. Androgen upregulates direct binding of sGCα1 with cytosolic p53 in prostate cancer cells, diminishing p53 activity and exerting procarcinogenic effects [58]. Interestingly, similar to sGCβ1, sGCα1 induces cytoplasmic sequestration of p53 independently of NO signaling or guanylyl cyclase activity. Notably, sGCβ1 takes a distinct route to interact with the p53 pathway. sGCβ1 migrated into the nucleus and interacted with the TP53 promoter to induce transcription-dependent mechanisms that resulted in a reduction in tumor aggressiveness (Figure 5). The G0 phase comprises three states: quiescent, senescent, and differentiated. Each of these states can be entered from the G1 phase before the cells commit to the next round of the cell cycle [59,60]. Adult neuronal cells are fully differentiated and reside in the G0 phase. Neurons reside in this state as a part of their developmental program but not because of stochastic or limited nutrient supply [61]. Anaplasia is a state of the cells with poor cellular differentiation and is detected in most malignant neoplasms [62]. Malignant transformation includes a group of morphological changes, such as nuclear pleomorphism, altered nuclear–cytoplasmic ratio, presence of nucleoli, and high proliferation index. The concept of differentiation therapy has emerged from the fact that several therapeutic agents, such as hormones or cytokines, promote the ability of tumor cells to differentiate from an anaplastic status to irreversibly alter the phenotype of aggressive cancer cells [63]. The studies by our group previously reported low levels of sGCα1 and β1 expression in undifferentiated human and mouse embryonic stem cells. Embryonic stem cells regain sGC expression after entering the differentiation [64,65]. Many aspects related to tumorigenesis and organogenesis are similar, and many types of cancer (including brain tumors) contain cancer stem-like cells [66,67]. Our previous findings demonstrated that restoring sGC function inhibits the growth of glioma and normalizes cellular architecture [6], and the results of the present study suggest that a prodifferentiation mechanism is involved in sGC-targeted therapy and may be used as an approach alternative to various toxic treatments, such as chemotherapy and radiation. Deregulation of the retinoblastoma (Rb) and p53 proteins has been pinpointed as an obligatory event in the majority of glioblastoma tumors [47]. CDK4 and CDK6 are the key components of the cell cycle machinery, driving the G1 to S phase transition via the phosphorylation and inactivation of the retinoblastoma protein [34]. The results of the present study demonstrated that sGCβ1 overexpression exerted insignificant effects on the phosphorylation levels of the retinoblastoma family proteins, such as pRb, p107, and p130 (Supplementary Materials Figure S2). In contrast, sGCβ1 overexpression markedly inhibited the expression of CDK6 and induced a trend of downregulation of CDK4 (Figure 6d–f). CDK6 plays the role of a transcriptional regulator, and this role is not shared by CDK4. Tumors with low levels of CDK6 have a higher-than-anticipated frequency of mutations of TP53. Furthermore, CDK6 kinase induces a complex transcriptional program to block p53 in cancer cells [68]. Thus, CDK6 acts at the interface of p53 and Rb by driving cell cycle progression and counteracts the p53-induced responses [69]. CDK6 expression levels inversely correlate with the status of the p53 pathway in mouse and human tumors [68]. Notably, CDK6 regulates the key genes involved in the survival, proliferation, and angiogenesis, including vascular endothelial growth factor A (VEGFA) [70,71]. The present study revealed a significant reduction in ITGA6 (Figure 6g–i) induced by sGCβ1. Integrins play a crucial role in tumor invasion and survival [72,73]. Comparison with other integrin isoforms indicates that ITGA6 is expressed at a high level in embryonic, hematopoietic, and neural stem cells [74]. Examination of ITGA6 expression in biopsy samples from glioblastoma patients indicated that ITGA6 is coexpressed with conventional glioblastoma stem-like cell markers and that ITGA6 is enriched in the perivascular niche [75]. Clinical relevance of ITGA6 was demonstrated using an in silico glioblastoma patient database to demonstrate that ITGA6 expression inversely correlates with survival (p = 0.0129) [75]. Thus, ITGA6 expression is elevated in glioblastoma stem-like cells, and this protein may be a target for therapeutic development. The data of the present study showed a marked expression of ITGA6 in U87 glioblastoma cells. Elevated expression of sGCβ1 resulted in de novo synthesis of p53 and may result in a reduction in ITGA6 in glioblastoma cells (Figure 5a and Figure 6g–i). The specific pathway involved in the effect of sGCβ1-induced blockade of ITGA6 is unknown. Potential clinical significance of our findings is due to the ability to target ITGA6 in glioblastoma cells via newly identified action of sGCβ1. In vivo studies demonstrated that ITGA6 blockade increases tumor latency and survival [75], suggesting that ITGA6 plays a role in tumor propagation. Moreover, previous reports showed that cancer stem-like cell-specific therapies may reduce tumor growth without an absolute termination of tumor growth [76]. sGCβ1 induces G0 arrest (Figure 3), and sGC expression inhibits glioma growth associated with normalized cellular architecture [6]. Thus, a new concept of glioblastoma treatment based on sGC is expected to integrate conventional and cancer stem-like cell-targeted therapeutic approaches. The present study provides a rationale for the development of a novel concept of glioblastoma pathology and molecular therapeutic pathway. We revealed that the β1 subunit of sGC migrated into the nucleus and repressed the growth of human glioblastoma cells. Thus, transcriptional responses induced by sGCβ1 may cause the differentiation of cancer cells, which is important for a decrease in tumor aggressiveness. The sGCβ1 overexpression impacted signaling in glioblastoma multiforme, including the promotion of nuclear accumulation of p53, a marked reduction in CDK6, and significant inhibition of integrin α6. These anticancer targets of sGCβ1 have been validated by various clinical studies and by the development of therapeutic strategies for cancer treatment [77,78,79]. sGCβ1-based glioblastoma therapy is characterized by boosting normal endogenous signaling and promoting differentiation of glioma cells, which may transform the treatment by shifting the paradigm from the killing of cancer cells to differentiation-induced transformation of cancer cells. The present study reveals a new therapeutic approach for treatment of malignant cancer with lower general toxicity.

PMC10001026

Jaesung Lee,Seohyun Chung,Minkyu Hwang,Yeongkag Kwon,Seung Hyun Han,Sung Joong Lee

Estrogen Mediates the Sexual Dimorphism of GT1b-Induced Central Pain Sensitization

06-03-2023

GT1b,pain central sensitization,estrogen,sexual dimorphism,IL-1β

We have previously reported that the intrathecal (i.t.) administration of GT1b, a ganglioside, induces spinal cord microglia activation and central pain sensitization as an endogenous agonist of Toll-like receptor 2 on microglia. In this study, we investigated the sexual dimorphism of GT1b-induced central pain sensitization and the underlying mechanisms. GT1b administration induced central pain sensitization only in male but not in female mice. Spinal tissue transcriptomic comparison between male and female mice after GT1b injection suggested the putative involvement of estrogen (E2)-mediated signaling in the sexual dimorphism of GT1b-induced pain sensitization. Upon ovariectomy-reducing systemic E2, female mice became susceptible to GT1b-induced central pain sensitization, which was completely reversed by systemic E2 supplementation. Meanwhile, orchiectomy of male mice did not affect pain sensitization. As an underlying mechanism, we present evidence that E2 inhibits GT1b-induced inflammasome activation and subsequent IL-1β production. Our findings demonstrate that E2 is responsible for sexual dimorphism in GT1b-induced central pain sensitization.

Estrogen Mediates the Sexual Dimorphism of GT1b-Induced Central Pain Sensitization We have previously reported that the intrathecal (i.t.) administration of GT1b, a ganglioside, induces spinal cord microglia activation and central pain sensitization as an endogenous agonist of Toll-like receptor 2 on microglia. In this study, we investigated the sexual dimorphism of GT1b-induced central pain sensitization and the underlying mechanisms. GT1b administration induced central pain sensitization only in male but not in female mice. Spinal tissue transcriptomic comparison between male and female mice after GT1b injection suggested the putative involvement of estrogen (E2)-mediated signaling in the sexual dimorphism of GT1b-induced pain sensitization. Upon ovariectomy-reducing systemic E2, female mice became susceptible to GT1b-induced central pain sensitization, which was completely reversed by systemic E2 supplementation. Meanwhile, orchiectomy of male mice did not affect pain sensitization. As an underlying mechanism, we present evidence that E2 inhibits GT1b-induced inflammasome activation and subsequent IL-1β production. Our findings demonstrate that E2 is responsible for sexual dimorphism in GT1b-induced central pain sensitization. Neuropathic pain is a chronic pathological pain caused by damage or dysfunction in the nervous system [1]. The clinical symptoms of neuropathic pain include spontaneous pain, allodynia, and hyperalgesia [2], but its severity and prevalence vary depending on sex [3]. Increasing evidence based on animal studies shows that neuroimmune mechanisms underlie the sexual dimorphism of neuropathic pain [3,4]. For example, spinal cord microglia activation is required for central pain sensitization after peripheral nerve injury in male rodents [5]. However, it is dispensable for the induction of neuropathic pain in female rodents. Instead, adaptive-immune-cell infiltration, most likely T lymphocytes, into the spinal cord contributes to the development of nerve-injury-induced neuropathic pain in female rodents [6]. In addition, intrathecal (i.t) administration of LPS or CSF-1 induces spinal cord microglia activation and subsequent pain sensitization only in male mice, although it induced comparable levels of morphological microglia activation in the female spinal cord [7,8,9]. Therefore, spinal cord microglia play a distinct role in the development of central pain sensitization depending on sex; the activation of only male microglia, not female, renders central pain sensitization. Thus, it is not clear why female spinal cord microglia activation is unable to induce central pain sensitization. Studies suggest that spinal cord microglia display a distinct sex-specific molecular signature after peripheral nerve injury [10], and regulatory T-cells infiltrating into the spinal cord after nerve injury render differential microglia activation in female mice [8]. In another study, testosterone, a male sex hormone, was implicated in the pain-inducing signature of activated microglia in male rodents [6]. Therefore, the exact mechanisms of sexual dimorphism in spinal cord microglia activation and pain sensitization remain to be elucidated. GT1b is one of the four major gangliosides of the CNS that constitute neuronal membrane lipid raft. We previously reported that GT1b is upregulated in damaged sensory neurons and transported to the spinal cord dorsal horn [11], which is required for the nerve-injury-induced spinal cord microglia activation and central pain sensitization [12,13]. Likewise, i.t. GT1b administration induced central pain sensitization [11]. Yet, the pain-inducing effect of i.t. GT1b administration was observed only in male mice [9]. In this study, we investigated the sexual dimorphism of GT1b-induced pain sensitization and the underlying mechanisms. Our data reveal that estrogen, a female sex hormone, is responsible for the sexual dimorphism of GT1b-induced spinal cord microglia activation and subsequent central pain sensitization. The animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Seoul National University. Male and female C57BL/6J mice (8~10 weeks of age) were purchased from Daehan Biolink (DBL, Eumsung, Korea), and all animals were housed and maintained in a controlled environment at 22–24 °C and 55% humidity with a 12 h light/dark cycle in a specific pathogen-free (SPF) environment. They were provided with access to food and water ad libitum. All the protocols were performed in accordance with the guidelines from the International Association for the Study of Pain. The mice were anesthetized with isoflurane in an O2 carrier (induction at 2% and maintenance at 1.5%), and the GT1b (25 μg/5 μL; Matreya LLC, Cat # 1548, State College, PA, USA) in saline solution was administered using a 10-μL Hamilton syringe (Hamilton Company, Cat # 701LT, Reno, NV, USA) with a 30 g needle as previously described [14]. The success of intrathecal injection was assessed by monitoring a slight tail-flick when the needle penetrated the subarachnoid space. The mice were transcardially perfused with 0.1 M phosphate buffer (pH 7.4) followed by 4% paraformaldehyde, and the L5 spinal cord was removed and post-fixed in the same solution at 4 °C overnight. The spinal cord samples were transferred to 30% sucrose for at least 48 h and coronally cut into 16-μm-thick sections using a cryostat (Leica, Cat # CM1860, Wetzlar, Germany). The spinal cord sections were blocked in a solution containing 5% normal goat serum, 2.5% bovine serum albumin (BSA), and 0.2% Triton X-100 for 1.5 h at room temperature. Then, the spinal cord sections were incubated with rabbit anti-Iba-1 antibody (1:1000; Wako, Cat # 019-19741, Osaka, Japan). After rinsing 5 times with 0.1 M PBS, the samples were incubated with CY3-conjugated secondary antibodies (1:200; Jackson ImmunoResearch Laboratories, Cat # 111-165-003, West Grove, PA, USA) for 1.5 h at room temperature. The samples were mounted on glass slides with a Vectashield mounting medium (Vector Laboratories, Cat # H-1000-10, Burlingame, CA, USA). The images were captured using an LSM 800 confocal microscope (Carl Zeiss, Oberkochen, Germany). For the 3D reconstruction of the microglia, we took Z-stack images (6 μm depth, 460 μm steps) of the spinal dorsal horns using an LSM 800 (1024 1024 pixels, 16-bit depth, 0.624 mm pixel size). The raw image files (.czi) were converted and analyzed using IMARIS (Version 9.8.0, Oxford Instruments, Abingdon, UK). The morphology of the single microglia (from 4 to 6 mice/group) was analyzed using the Filament Tracer Tool with the following settings: Autopath algorithm; Dendrite starting point diameter, 16.3 μm; and Dendrite seed point diameter, 1 μm. The mechanical allodynia tests were performed as previously reported [11]. All the behavior tests occurred between 10:00 a.m. and 3:00 p.m., and the experimenter was blind to group assignments throughout the experiment. The mechanical sensitivity of the right hind paw was assessed using a calibrated series of von Frey hairs (0.02–6 g; Stoelting, Wood Dale, IL, USA), following an up-down method [15]. The thermal sensitivity was determined by measuring the paw withdrawal latencies in response to radiant heat [16]. Rapid paw withdrawal, licking, and flinching were interpreted as pain responses. The tests were performed after at least three habituations at 24 h intervals. The assessments were determined 1 day before surgery for baseline and 1, 3, and 7 days after surgery or injection. All the behavioral tests were performed blinded. The total RNA was extracted from mouse spinal cords using TRIzol reagent (Thermo Fisher Scientific, Cat # 15596026, Waltham, MS, USA). For transcriptome analysis, five spinal cords were pooled to synthesize the cDNA library for each group after 24 h of intrathecal injection of gt1b (25 μg/5 μL). The RNA sequencing was conducted by E-Biogen (Seoul, Korea). Briefly, the RNA quality and quantity were evaluated with the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) and NanoDrop 2000 (Thermo Fisher Scientific). The library construction was achieved following the QuantSeq 3′ mRNA-Seq library prep kit FWD (LEXOGEN, Cat # 015.96 Vienna, Austria) manufacturer’s protocol. The cDNA libraries were sequenced on the Illumina NextSeq500 platform (Illumina, San Diego, CA, USA). The gene ontology (GO) enrichment analysis of differentially expressed genes (DEGs) was performed using DAVID 2021 (https://david.ncifcrf.gov/, accessed on 21 December 2021) [17]. The heatmap of RNASeq transcriptome analysis was generated for 23,281 genes after 24 h of i.t. injection of GT1b, and hierarchical clustering analysis (HCL) was conducted using TM4/MeV software (version 4.9.0) to analyze the RNA sequencing data and compare the GT1b-induced transcriptional changes between sexes [18]. The analyzed DAVID GO terms were visualized using a GOcircle plot in MATLAB displaying the fold change of each gene. The eight-week-old female C57BL/6J mice received bilateral ovariectomy (OVX). The mice were anesthetized with isoflurane in an O2 carrier (induction at 2% and maintenance at 1.5%) and subsequently subjected to OVX or sham operation via a bilateral back incision. For the OVX group, we excised the anterior uterine horns to remove the ovaries and mitigated bleeding using the High Temp Cautery Kit (FST, Cat # 18010-00, Foster City, CA, USA). The incisions were closed using sterile sutures. The eight-week-old male C57BL/6J mice received bilateral orchiectomy (ORX). The mice were anesthetized with isoflurane in an O2 carrier (induction at 2% and maintenance at 1.5%). A midline scrotal incision was performed, and the bilateral spermatic cords were ligated. The testes along with the epididymal adipose were excised from the distal end of the ligature, and bleeding was mitigated using the High Temp Cautery Kit. The incisions were closed using sterile sutures. The mouse blood was collected via a cardiac puncture in microtainer K2E (BD, Cat # 365974, Franklin Lakes, NJ, USA) and centrifuged at 1600× g for 15 min at 4 °C to measure the plasma 17β-estradiol levels. The supernatant plasma was collected, and the levels of 17β-estradiol were assessed using the 17 beta Estradiol ELISA kit (Abcam, Cat # ab108667, Cambridge, UK) following the manufacturer’s protocol. To measure the IL-1β release from the GT1b-stimulated primary mixed glia, we treated ATP 30 min prior to supernatants collection to induce the secretion of IL-1β, and collected supernatants of mixed glia centrifuged at 500× g for 5 min to discard the cell debris. The IL-1β levels were measured using the mouse IL-1 beta Quantikine ELISA kit (R&D Systems Inc., Cat # MLB00C, Minneapolis, MN, USA) following the manufacturer’s protocol. The primary mixed glia cultures were prepared from 1–2-day-old mice, as previously described [19]. In brief, the brain glial cells were cultured in a DMEM, high-glucose formula supplemented with 10% FBS, 10 mM HEPES, 2 mM L-glutamine, 1× penicillin/streptomycin, and 1× nonessential amino acid mixture at 37 °C in a 5% CO2 incubator. The media were renewed every 5 days. After 15 days, the primary mixed glia were harvested with 0.25% Trypsin-EDTA and plated in 6-well plates at a density of 5 × 105 cells per well for real-time RT-PCR and ELISA. The real-time RT-PCR experiments were performed using the StepOnePlus Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) following the 2−∆∆Ct method [20]. The total RNA from the L5 spinal cord tissue and primary mixed glia was extracted using TRIzol (Invitrogen, Carlsbad, CA, USA) and reverse transcribed using TOPscript RT DryMIX (Enzynomics, Cat # RT200, Daejeon, Korea). All the ∆Ct values were normalized to the corresponding GAPDH values and were represented as the fold induction. The following PCR primers were used: Gapdh forward, 5′-AGT ATG ACT CCA CTC ACG GCA A-3′; Gapdh reverse, 5′-TCT CGC TCC TGG AAG ATG GT-5′; Il-1β forward, 5′-GTG CTG TCG GAC CCA TAT GA-3′; Il-1β reverse, 5′-TTG TCG TTG CTT GGT TCT CC-3′; Casp1 forward, 5′-CTG ACA AGA TCC TGA GGG CA-3′; Casp1 reverse, 5′-AAA GAT TTG GCT TGC CTG GG-3′; Nlrp3 forward 5′-CCA TCA ATG CTG CTT CGA CA-3′; Nlrp3 reverse 5′-GAG CTC AGA ACC AAT GCG AG-3′; Tlr2 forward, 5′- CTC CCA CTT CAG GCT CTT TG -3′; and Tlr2 reverse, 5′-ACC CAA AAC ACT TCC TGC TG-3′. The data were analyzed using the Student’s t-test for comparisons between two groups. The one- and two-way analysis of variance (ANOVA) with Bonferroni’s post hoc test were used for the statistical analysis of multiple comparisons. All the data are presented as mean ± standard error of the mean (SEM), and differences were considered statistically significant when the p-value was < 0.05. To test if GT1b-induced central pain sensitization is sexually dimorphic, we administered GT1b into the spinal cord of male and female mice and compared the mechanical threshold to the von Frey stimuli (Figure 1A). As previously reported, i.t. GT1b administration induced mechanical allodynia 1 and 3 days post-injection (dpi) in the male mice (Figure 1B). However, the female mice were completely resistant to GT1b-induced pain sensitization (Figure 1B), thus indicating sexual dimorphism in GT1b-induced central pain sensitization. We have reported that GT1b activates spinal cord microglia via TLR2, which leads to central pain sensitization [21]. Therefore, we tested TLR2 transcript expression in the spinal cords of female mice, and similar levels of TLR2 transcript were detected in the spinal cords of the female mice compared to the male mice (Figure 1C). To test if there is a gender difference in spinal cord microglia activation upon GT1b administration, we assessed spinal cord microglia activation using Iba-1 immunohistochemistry. The GT1b injection induced spinal cord microglia activation in female mice at 3 dpi, although the activation was not as significant as in male microglia at 1 dpi (Figure 1D,E). We then characterized the morphological features of the GT1b-activated microglia in the female mice by analyzing the soma size, process length, and the branch point of each activated microglia (Figure 1F). In the female mice, GT1b injection increased the microglia soma size (1 dpi) and reduced the process length and branch point number (Figure 1G), which are typical morphological features of the activated microglia [22]. Intriguingly, GT1b injection did not reduce the process length or branch number of spinal cord microglia in male mice; rather, it increased the process length at 1 dpi (Figure 1G). Regarded together, our data indicate that i.t. GT1b administration induces spinal cord microglia activation both in male and female mice, but with distinct kinetics and morphological activation features. To further compare the activation features between male and female mouse spinal cord microglia, we analyzed and compared the gene expression profiles in the spinal cord dorsal horn after GT1b administration in the male and female mice, respectively, using RNASeq and hierarchical clustering analysis (Figure 2A). In search of the genes associated with the sexual dimorphism, we screened for differentially expressed genes (DEGs) with expression levels more than two-fold different in the females compared to the males (Figure 2B). To account for differences in the basal gene expression profiles between male and female mice, we conducted additional transcriptome comparisons and annotations using DAVID Gene Ontology (GO) analysis [23,24]. We compared the transcriptome profile between male and female (M-GT1b/M-Veh vs. F-GT1b/F-Veh); however, we did not find any GO terms related to pain (data not shown). Therefore, we compared and annotated the transcriptomes of GT1b-administered males exhibiting pain behavior with that of GT1b-administered females not showing pain behavior. We identified the top ten enriched categories of biological processes (BP), cellular components (CP), and molecular functions (MF) (Figure 2C). Based on DAVID gene ontology analysis, the genes involved in estrogen-related signaling pathways were found to be the most significantly different between the male and female mice (Figure 2D). Meanwhile, the other genes putatively involved in pain sensitization at the spinal cord level (e.g., proinflammatory cytokines, chemokines, etc.) were comparably regulated by GT1b administration (Supplementary Figure S1). In this regard, we hypothesized that estrogen plays a role in the sexual dimorphism of GT1b-induced central pain sensitization. To test whether estrogen plays a role in the GT1b-induced central pain sensitization in female mice, we reduced the systemic estrogen levels in female mice using OVX. Two weeks after removing the ovaries of the 8-week-old adult female mice, uterine shrinkage and an increase in body weight were detected (Figure 3A–C). We also confirmed a decrease in the estrogen levels in the OVX females, and that this decrease was reversed by supplementation with estrogen (OVX−E2) (Figure 3D). While the female mice were resistant to the GT1b-induced central pain sensitization, the OVX rendered female mice susceptible to GT1b-induced mechanical allodynia (Figure 3E). Then, we tested whether exogenous estrogen supplementation could restore the resistance to GT-1b-induced central pain sensitization. When we supplemented these OVX female mice with 17β-estradiol (OVX-E2, 5 μg/kg, 50 μL daily), the OVX mice became resistant again to GT1b-induced mechanical allodynia (Figure 3F). These data indicate that the high estrogen levels in female mice are responsible for the sexual dimorphism of GT1b-induced central pain sensitization. We then tested whether male sex hormones affect GT1b-induced pain sensitization. To this end, we subjected male mice to ORX. Unlike OVX female mice, the ORX male mice exhibited comparable levels of mechanical sensitivity upon GT1b administration (Supplementary Figure S2), indicating that male sex hormones are not involved in sexual dimorphism. Previously, we showed that i.t. GT1b administration induced microglia activation and IL-1β expression in the spinal cord, which results in the development of central sensitization [11]. Since GT1b upregulated IL-1β transcripts not only in male mice but also in female mice (Figure 4A), we tested whether estrogen affects the post-translational modification of IL-1β. IL-1β is initially expressed in its pro-form (pro-IL-1β) and then released upon cleavage by NLRP3- and Caspase-1-containing inflammasomes [21]. Therefore, we compared the gene expression of NLRP3 and Caspase-1 in male and female mice upon GT1b stimulation. Though GT1b administration induced NLRP3 expression in both sexes, Caspase-1 was upregulated only in male mice (Figure 4B,C), indicating sexually dimorphic inflammasome activation. Then, we examined the estrogen effects on primary mixed glia in vitro (Figure 4D). We treated cells with GT1b (10 μg/mL) for 16 h, following pre-treatment with 17β-estradiol (100 nM) for either 16 h or 0.5 h. To measure the IL-1β secreted from mixed glia, we pretreated all groups, including the control, with ATP (5 mM) 30 min prior to collecting the supernatant. While 17β-estradiol pretreatment did not affect GT1b-stimulated Caspase-1 and IL-1β transcript expression, it significantly inhibited the IL-1β release from the primary mixed glia (Figure 4E–G). Regarded together, these results suggest that estrogen inhibits IL-1β release in the spinal cords of female mice, which underlies the sexual dimorphism of GT1b-induced central pain sensitization. In this study, we discovered that GT1b, a previously identified endogenous TLR2 agonist used in nerve-injury-induced neuropathic pain, induces sexually dimorphic central pain sensitization; it induced pain sensitization only in male but not in female mice. Sexual dimorphism in central pain sensitization has been reported in several other animal models. In LPS-induced pain, i.t. administration of LPS induced neuropathic pain only in male mice [7], while local LPS administration in the paw induced pain in both male and female mice [25]. Likewise, i.t. CSF1 administration induced pain sensitization only in male mice [8,9]. Our data are in line with these previous studies. Considering TLR2 and TLR4 share most of their downstream intracellular signaling pathways mediated by MyD88 [26], it is not surprising that the GT1b-mediated activation of microglial TLR2 induces central sensitization only in male mice, not female mice. Our data, along with previous reports, indicate that microglia activation in female mice is not sufficient to induce central pain sensitization [27]. Similarly, nerve injury-induced spinal cord microglia activation is critical for the development of neuropathic pain only in male mice. However, it is not required for nerve injury-induced neuropathic pain in female mice [28]. Instead, in female mice, central pain sensitization is mediated by T cells recruited to the spinal cord after nerve injury [6]. In this regard, it is conceivable that i.t. injection of GT1b activates spinal cord microglia but does not recruit T cells into the spinal cord, and, thus, it fails to induce central pain sensitization in female mice. In the search for mechanisms underlying sexually dimorphic functions of male spinal cord microglia vs. female microglia activation, we investigated sex-specific transcriptome profiles in the spinal cord upon GT1b injection. Previous transcriptome analyses to identify DEGs in microglia responsible for sexual dimorphism have provided minimal insight into the mechanisms [10]. According to this study, upon chronic constriction sciatic nerve injury, most genes involved in microglia activation and implicated in central pain sensitization, such as proinflammatory cytokines, were also upregulated in female microglia [10]. Though it failed to identify specific genes that were selectively upregulated in male microglia and rendered central pain sensitization, male microglia displayed more prominent global transcriptional shifts and increased phagocytic activity compared to female microglia [10]. Likewise, our RNAseq results revealed that GT1b regulates genes involved in neuropathic pain in both sexes. Meanwhile, our DAVID gene ontology analysis indicated that most enriched DEGs in biological processes are associated with the response to estrogen, including F7, Tph2, Cyp27b1, Krt19, Abcc2, Mstn, Dhh, Ghrl, Smad6, Agtr1a, Tshb, and Agtr1b, which suggests the putative involvement of estrogen in the sexual dimorphism of GT1b-induced central pain sensitization. Therefore, we focused on the estrogen response, and our study using OVX mice demonstrated that estrogen was indeed responsible for the lack of pain-sensitizing effects of i.t. GT1b administration in female mice. Estrogen is well-known for its anti-inflammatory and neuroprotective effects on the nervous system. In stroke and ischemic brain injury, estrogen exerts neuroprotective functions by inhibiting inflammasome activation and proinflammatory cytokine expression in the brain [29,30]. In addition, estrogen attenuates the spinal-cord-injury-induced inflammatory response by regulating inflammasomes [31]. Studies indicate that estrogen mediates its effects by affecting glia activation. For instance, estrogen inhibits global cerebral-ischemia-induced NLRP3 inflammasome activation and proinflammatory cytokine expression in glia [32]. It also inhibits spinal-cord-injury-induced microglial p38 and ERK activation, astrocyte JNK activation, and thereby mediates the anti-inflammatory effects in the spinal cord [33]. In line with these previous studies, our data revealed that estrogen inhibits GT1b-induced IL-1β production in primary mixed glia. It was well-known that IL-1β expression in the spinal cord renders central pain sensitization [34,35]. Of note, IL-1β transcripts in the spinal cords of female mice were comparable to those of male mice. Instead, the IL-1β released into the media was inhibited by estrogen, and the Caspase-1 induction by GT1b was blocked in the female mice. It must be noted that GT1b-stimulated caspase-1 expression was not inhibited by E2. We speculate that E2 may indirectly suppress glial caspase-1 transcript in vivo. Although we revealed the essential role of E2 in the sexual dimorphism of the GT1b-induced central sensitization, the functional and mechanistic link between the results obtained in primary glial cultures and in vivo remains weak, which is a limitation of this study. Therefore, further studies are needed on the regulation of GT1b-induced inflammasome activation by E2 to completely elucidate the mechanism of sexual dimorphism in the GT1b-induced central sensitization (Figure 5). While estrogen exerts anti-inflammatory and neuroprotective effects on the nervous system, it has also been shown to enhance pain via the modulation of gene expression [36] and neuronal activation in dorsal root ganglia (DRG) of females [37]. Therefore, further studies are needed to fully elucidate the mechanisms underlying the biphasic effects of estrogen on pain modulation in different contexts. Anyhow, our study used an i.t. GT1b-induced pain model, which primarily involves upper circuits and may not directly affect DRGs. The sexual dimorphic mechanism of GT1b-induced central pain sensitization that we suggest is distinct from that of TLR4-induced pain. According to a study by Sorge et al., TLR4-mediated pain is dependent on testosterone [7]. However, in our study, GT1b induced comparable pain in ORX mice, and, therefore, GT1b-induced pain sensitization was independent of testosterone. Thus far, it is not clear why there is such a difference. Although TLR2 and TLR4 share MyD88 as their intracellular signaling molecule, TLR4 induces an additional intracellular signal via IRF3 [26]. Therefore, it is speculated that IRF3-dependent microglia activation is affected by testosterone, which needs to be tested in the future. Recent studies indicate that morphological phenotypes of microglia, such as ramified, rod-like, activated, and amoeboid forms, represent the state of microglia and can be used as indicator of the CNS physiological environment [22,38]. Here, we report that GT1b-induced microglial activation occurs in both males and females, but with different kinetics and morphology. These features might be involved in the E2-mediated suppression of inflammasome activation and IL-1β secretion. In summary, our study revealed that i.t. GT1b administration induces central pain sensitization in a sexually dimorphic manner. We discovered that estrogen is responsible for sexual dimorphism, and that estrogen ameliorates GT1b-induced IL-1β production in the spinal cord by inhibiting inflammasome activation as an underlying mechanism. Our study may elucidate sex-specific therapeutic strategies to resolve central pain sensitization utilizing estrogen.

PMC10001027

Florian Weber,Susanne Schueler-Toprak,Christa Buechler,Olaf Ortmann,Oliver Treeck

Chemerin and Chemokine-like Receptor 1 Expression in Ovarian Cancer Associates with Proteins Involved in Estrogen Signaling

02-03-2023

chemerin,chemokine-like receptor 1,estrogen-related receptors,ovarian cancer,overall survival,progression-free survival

Chemerin, a pleiotropic adipokine coded by the RARRES2 gene, has been reported to affect the pathophysiology of various cancer entities. To further approach the role of this adipokine in ovarian cancer (OC), intratumoral protein levels of chemerin and its receptor chemokine-like receptor 1 (CMKLR1) were examined by immunohistochemistry analyzing tissue microarrays with tumor samples from 208 OC patients. Since chemerin has been reported to affect the female reproductive system, associations with proteins involved in steroid hormone signaling were analyzed. Additionally, correlations with ovarian cancer markers, cancer-related proteins, and survival of OC patients were examined. A positive correlation of chemerin and CMKLR1 protein levels in OC (Spearman’s rho = 0.6, p < 0.0001) was observed. Chemerin staining intensity was strongly associated with the expression of progesterone receptor (PR) (Spearman´s rho = 0.79, p < 0.0001). Both chemerin and CMKLR1 proteins positively correlated with estrogen receptor β (ERβ) and estrogen-related receptors. Neither chemerin nor the CMKLR1 protein level was associated with the survival of OC patients. At the mRNA level, in silico analysis revealed low RARRES2 and high CMKLR1 expression associated with longer overall survival. The results of our correlation analyses suggested the previously reported interaction of chemerin and estrogen signaling to be present in OC tissue. Further studies are needed to elucidate to which extent this interaction might affect OC development and progression.

Chemerin and Chemokine-like Receptor 1 Expression in Ovarian Cancer Associates with Proteins Involved in Estrogen Signaling Chemerin, a pleiotropic adipokine coded by the RARRES2 gene, has been reported to affect the pathophysiology of various cancer entities. To further approach the role of this adipokine in ovarian cancer (OC), intratumoral protein levels of chemerin and its receptor chemokine-like receptor 1 (CMKLR1) were examined by immunohistochemistry analyzing tissue microarrays with tumor samples from 208 OC patients. Since chemerin has been reported to affect the female reproductive system, associations with proteins involved in steroid hormone signaling were analyzed. Additionally, correlations with ovarian cancer markers, cancer-related proteins, and survival of OC patients were examined. A positive correlation of chemerin and CMKLR1 protein levels in OC (Spearman’s rho = 0.6, p < 0.0001) was observed. Chemerin staining intensity was strongly associated with the expression of progesterone receptor (PR) (Spearman´s rho = 0.79, p < 0.0001). Both chemerin and CMKLR1 proteins positively correlated with estrogen receptor β (ERβ) and estrogen-related receptors. Neither chemerin nor the CMKLR1 protein level was associated with the survival of OC patients. At the mRNA level, in silico analysis revealed low RARRES2 and high CMKLR1 expression associated with longer overall survival. The results of our correlation analyses suggested the previously reported interaction of chemerin and estrogen signaling to be present in OC tissue. Further studies are needed to elucidate to which extent this interaction might affect OC development and progression. Ovarian cancer (OC) is the leading cause of death by a gynecological malignancy in the developed world [1]. Due to missing screening methods and the aggressive behavior of the disease, the majority are diagnosed in advanced stages [2]. OC has a five-year survival rate of only 10% when the most common serous type spreads rapidly throughout the peritoneal cavity. Overall, this disease has a poor prognosis, with a five-year survival rate of approximately 50%. If diagnosed in earlier stages when the cancer is still confined to the ovary, this survival rate could rise to about 90%, but today this occurs in only 20% of patients [2,3]. Increasing evidence suggests that ovarian cancer, like tumors of different origins, is affected by adipokine chemerin [4,5,6]. Chemerin (RARRES2) is a well-described adipokine [7]. It was initially identified as a chemoattractant protein for immune cells that binds to chemokine-like receptor 1 (CMKLR1) expressed by these cells. In the meantime, diverse functions of chemerin have been defined, and chemerin was shown to regulate angiogenesis, adipogenesis, insulin response, and blood pressure [8,9,10,11,12,13]. Although with CCRL2 and GPR1, two further chemerin receptors have been identified, CMKLR1 has been considered to be the most important receptor of this adipokine since chemerin binding to CMKLR1 particularly leads to broad G-protein activation [14]. CMKLR1, located in the cell membrane, is internalized upon chemerin binding. Ligand binding initiates activation of G-proteins and β-arrestin pathways, inducing cellular responses via second messenger pathways such as intracellular calcium mobilization, phosphorylation of mitogen-activated protein kinase (MAPK)1/MAPK2 (ERK1/2), tyrosine-protein kinase receptor (TYRO) 3, MAPK14/p38 MAPK and phosphoinositid-3-kinase (PI3K) [14,15]. Emerging studies have proven the role of chemerin in tumorigenesis, whose expression often differs between tumor and non-tumor tissues [4,16]. In most tumor entities, chemerin/RARRES2 is down-regulated compared to normal tissue, e.g., in tumors of the breast, melanoma, lung, prostate, liver, adrenal, and in melanoma, and this decrease of chemerin expression has been suggested to be part of the tumor´s immune escape [4,17]. Estrogens are known to affect the progression of ovarian cancer [18], although to a much lesser extent than breast cancer. These effects are dependent on the expression of estrogen receptors (ERs) α and β. Estrogens activate the proliferation of ovarian cancer cells via ERα, often being overexpressed in this cancer entity [18,19]. Expression of ERβ, which is the predominant ER in the ovary [20], is often down-regulated in OC. ERβ is associated with an improved overall survival (OS) [21,22] in line with in vitro data demonstrating that its activation reduces ovarian cancer cell proliferation and activates apoptosis [21,23,24,25]. There is a relationship between estrogen-related receptors (ERRs) α, β, and γ with various cancer-related genes as well as ERα in ovarian cancer [26]. ERRs interact with ERα and several other nuclear receptors [27,28]. Thereby, among others, a vast number of different genes modulating metabolic processes are regulated, and several different pathways are controlled [29]. ERRα, which has attracted the greatest attention to date, acts as a master regulator of cellular metabolism, thereby also promoting tumor growth [30]. Chemerin was shown to decrease ovarian steroidogenesis via CMKLR1 [31,32] and thus may be protective in hormone-dependent cancers. A tumor-suppressive effect of chemerin was also reported by a recent in vitro study demonstrating chemerin to reduce the growth of ovarian cancer cell spheroids via activating the release of interferon (IFN)α, leading to induction of a broad, IRF9/ISGF3-mediated anti-tumoral transcriptome response [6]. However, a recent Chinese in vitro study reported a tumor-promoting role of chemerin in ovarian cancer cell lines in terms of proliferation via upregulation of programmed death ligand 1 (PD-L1) [5]. On the mRNA level, data on the expression of RARRES2 and CMKLR1 in ovarian cancer tissue have been extensively collected, e.g., by The Cancer Genome Atlas (TCGA) project https://www.cancer.gov/tcga). However, studies based on protein data of both genes in OC are rare. Thus, to further approach the possible role of chemerin and CMKLR1 in this cancer entity, analyses of their protein levels in OC cancer tissue and identification of correlated proteins are necessary. In the current study, protein levels of chemerin and CMKLR1 were assessed by immunohistochemistry of tissue microarrays (TMA), including tissues of 208 ovarian cancer patients. Furthermore, their association with patients´ survival and with the expression of ovarian cancer markers, cancer-related proteins, and components of estrogen signaling pathways was tested. In this study, ovarian cancer samples collected in the Department of Pathology of the University of Regensburg were examined. Generally, Caucasian women with sporadic ovarian cancer and available information on grading, stage, and histological subtype from 1995 to 2013 were included. Patients’ clinical data were available from tumor registry database information provided by the Tumor Center Regensburg (Bavaria, Germany). This high-quality population-based regional cancer registry was founded in 1991, and it covers a population of more than 2.2 million people in Upper Palatinate and Lower Bavaria. Information about the diagnosis, course of the disease, therapies, and long-term follow-up are documented. Patient data originate from the University Hospital Regensburg, 53 regional hospitals, and more than 1000 practicing doctors in the region. Based on medical reports, pathology, and follow up-records, these population-based data are routinely documented and fed into the cancer registry (Table 1). The tissue microarray (TMA) was created using standard procedures that have been previously described [33,34]. From all patients included in this study, an experienced pathologist (FW) evaluated H&E sections of tumor tissues, and representative areas were marked. From these areas, core biopsies on the corresponding paraffin blocks were removed and transferred into the grid of a recipient block according to a predesigned array of about 60 specimens in each of the five TMA paraffin blocks. For immunohistochemistry, 4 μm sections of the TMA blocks were incubated with the indicated antibodies according to the mentioned protocols in the given dilutions (Table 2), followed by incubation with a horseradish peroxidase (HRP) conjugated secondary antibody and another incubation with 3,3′-diaminobenzidine (DAB) as substrate, which resulted in a brown-colored precipitate at the antigen site. An experienced clinical pathologist (FW) evaluated immunohistochemical staining according to localization and specificity (Table 3). For the determination of the staining intensity of ERRα and ERRγ, a score from 0 (negative) to 3 (strongly positive) was used. Since staining intensities for ERRβ were generally lower, a score from 0 to 2 was used. For steroid hormone receptors ERα, nuclear ERβ, and PR, the immunoreactivity score, according to Remmele et al., was used [35]. Expression of proliferation marker Ki-67 using antibody clone MIB-1 was assessed in the percentage of tumor cells with positive nuclear staining. Her2/neu expression was scored according to the DAKO score routinely used for breast cancer cases. EGFR was scored according to Spaulding et al. on a 4-tiered scale from 0 to 3 [36]. For p53 and polyclonal CEA, the “quick score” was used, where results are scored by multiplying the percentage of positive cells (P) by the intensity (I) according to the formula: Q = P × I; maximum = 300 [37]. CA-125 and ERβ were described as positive or negative, irrespective of staining intensity. Chemerin and CMKLR1 cellular staining intensity (non-specific nuclear staining was not considered) was scored on a 3-tiered scale from 1 (weak) to 3 (strong intensity) (Figure 1). To compare the expression of RARRES2 and CMKLR1 in normal ovary, OC, and OC metastases at the mRNA level, the TNMplot webtool (https://tnmplot.com/analysis/) was used to analyze gene chip data from GEO datasets, including 744 OC patients, 46 samples from the normal ovary and 44 OC metastases [38]. The statistical significance of the comparison was determined using the nonparametric Kruskal–Wallis test. To test the association of RARRES2 and CMKLR1 mRNA levels in OC patients with overall survival by means of the webtool KMplot (https://kmplot.com/analysis/index.php?p=service&cancer=ovar (accessed on 2 February 2023)), gene chip data from TCGA and 14 GEO datasets were analyzed. Both mRNA and survival data were available from 2021 OC patients. The following parameters were used for this analysis: splitting of the patients’ collective in a high and a low expression group was performed by choosing the “auto select best cutoff” option; all patient subgroups and treatment groups were included, and biased arrays were excluded. For RARRES2, the Affymetrix ID 209496_at was indicated, and for CMKLR1, the Affy ID 210659_at [39]. Apart from multivariate survival analyses, statistical analysis was performed using GraphPad Prism 5® (GraphPad Software, Inc., La Jolla, CA, USA). The non-parametric Kruskal–Wallis rank-sum test was used for testing differences in the expression among three or more groups. For pairwise comparison, the non-parametric Mann–Whitney U rank-sum test was used. Correlation analysis was performed using the Spearman correlation. Univariate survival analyses were performed using the Kaplan–Meier method. The chi-squared statistic of the log rank was used to investigate differences between survival curves. Hazard ratios were calculated using the Mantel–Haenszel method. A p-value below 0.05 was considered significant. Multivariate Cox regression survival analysis was performed using IBM® SPSS® Statistics 25 (SPSS®, IBM® Corp., Armonk, NY, USA) using the Enter method. Given that a sufficient amount of normal ovarian tissues or metastatic tissues could not be obtained, it was decided to use the benefits of open-source gene chip expression data, and it was thereby possible to compare mRNA expression of RARRES2 (coding for chemerin) and CMKLR1 in 744 OC tissues, 46 samples from the normal ovary and 44 tissue samples of OC metastases. This analysis of open-source data using TNMplot webtool (https://tnmplot.com/analysis/) [38] accessed on 15. September 2022 revealed decreased RARRES2 mRNA levels in the OC (Dunn test p = 0.0002) and the metastasis group (Dunn test p = 0.0646) compared to normal ovarian tissue, interpreted as an attempt for evasion from the immune response. Regarding CMKLR1 mRNA levels, only the metastasis samples exhibited a reduced expression (Dunn test p < 0.0001) of this receptor (Figure 2). Both chemerin and CMKLR1 were shown to be widely detectable in OC tissues as assessed on the protein level by means of immunohistochemistry of tissue microarrays (TMAs). Positive staining of chemerin was found in all cases (32.7% with weak staining, 40.5% moderate, and 26.8% with strong staining). CMKLR1 was also detected in all tumors, among them 22.2% with weak staining, 38.0% with moderate, and 39.9% with strong staining. There was a strong correlation between chemerin and CMKLR1 levels in all tumors (rho = 0.5959, p < 0.0001), as well as the largest subgroup of serous OC (rho = 0.6285, p < 0.0001). No significant differences in protein levels of either chemerin or CMKLR1 between G2 and G3 graded tumors, different FIGO stages, or in patients with different nodal statuses were observed. Moreover, the invasion of lymph or blood vessels did not depend on the expression of either protein. Subsequently, mean protein levels of chemerin and CMKLR1 in ovarian cancer subgroups were compared with high vs. low expression of the ovarian cancer markers, cancer-related proteins, and components of estrogen signaling pathways that were analyzed in this study. First, results showed that mean levels of chemerin and CMKLR1 were elevated in ovarian cancers with higher cytoplasmic ERβ expression when compared to the lower expressing subgroup (p = 0.0143 and p = 0.0133, respectively) (Table 3). Mean protein levels of CMKLR1 were increased in ovarian cancer specimens with higher expression of the proliferation marker Ki67 (p = 0.0304). Protein levels of chemerin and CMKLR1 were elevated in the ERRα-high subgroup (p < 0.0001 and p < 0.0001, respectively). In ovarian cancers with higher expression of ERRβ, increased levels of chemerin and CMKRL1 (p = 0.0091 and p < 0.0001, respectively) were observed. CMKLR1 levels were found to be elevated in tumors with higher expression of ERRγ (p = 0.0031). Finally, the mean protein expression of chemerin was elevated in ovarian cancers with higher expression of CMKRL1 (p < 0.0001), and the mean protein levels of CMKRL1 was increased in ovarian cancer with higher expression of chemerin (p < 0.0001). No differences in chemerin and CMKLR1 expression levels could be observed between tumor subgroups with different levels of ERα, nuclear ERβ, PR, CEA, CA125, CA72-4, p53, Her2, or EGFR. Since chemerin is known to affect ovarian steroidogenesis and was reported to correlate with steroid hormone receptors in breast cancer, correlations of both proteins with protein expression of PR, ERα, ERβ, PR, ERRα, β, and γ were examined first. Furthermore, intratumoral chemerin and CMKLR1 levels were tested for correlation with ovarian cancer markers CA125 (MUC16), polyclonal CEA (CEACAM1,3,4,6,7 and 8), and CA72-4 and with the cancer-related genes EGFR, HER2, Ki-67 and p53. By means of Spearman’s rank correlation analysis, a strong association of chemerin with progesterone receptor (PR) levels (Spearman’s rho = 0.7952, p < 0.0001) was observed. Chemerin and CMKLR1 were found to be moderately associated with intratumoral protein expression of ERβ, particularly in the largest serous subgroup, which was true both for nuclear (chemerin: rho = 0.2127, p = 0.0213; CMKLR1: rho = 0.2630, p = 0.0039) and cytoplasmic (chemerin: rho = 0.2731, p = 0.0029; CMKLR1: rho = 0.27, p = 0.003) ERβ expression. Notably, a considerable positive correlation between both chemerin and CMKLR1 with the estrogen-related receptors (ERR)s α, β, and γ was observed. Chemerin positively correlated with ERRα (rho = 0.384, p < 0.0001), ERRβ (rho = 0.3343, p < 0.0001), and ERRγ (rho = 0.383, p < 0.0001). CMKLR1 was associated with the expression of ERRα (rho = 0.5207, p < 0.0001), ERRβ (rho = 0.4239, p < 0.0001), and ERRγ (rho = 0.4198, p < 0.0001). Additionally, a weak positive association with cancer marker CEACAM5 (rho = 0.1594, p < 0.0498) was observed. Expression of the other proteins mentioned above was not significantly associated with either chemerin or CMKLR1 (Table 4). In silico analyses on the mRNA level (using gene chip data from 744 ovarian cancer patients accessed on the platform https://tnmplot.com) [38] on 15 September 2022 corroborated the positive correlation between chemerin (RARRES2) and CMKLR1 that had been observed on the protein level (Spearman’s rho = 0.26, p < 0.0001). With regard to genes involved in estrogen signaling, this analysis also substantiated the positive correlation of CMKLR1 with ERβ (ESR2) (rho = 0.33, p < 0.0001) and of CMKLR1 with ERRα (ESRRA) (rho = 0.33, p < 0.0001), which was further corroborated using the GEPIA2 platform [40] analyzing datasets from 426 serous OC patients (CMKLR1/ESR2 rho = 0.35 and CMKLR1/ESRRA rho = 0.31, both p < 0.0001). Using the same platform and data, a positive, albeit weaker correlation of CMKLR1 with ERRβ (ESRRB) (rho = 0.2, p < 0.001) in serous OC, but not with ERRγ (ESRRG) was found. In contrast to the chemerin protein data from IHC, mRNA levels of the RARRES2 gene in ovarian cancer were not correlated with PGR, ESR2, ESRRA, ESRRB, ESRRG, nor CEACAM5 after analysis of both patient collectives on the mentioned platforms (p > 0.05 for all). Association of chemerin and CMKLR1 in ovarian cancer tissue with overall and progression-free survival. Analyzing the protein data assessed in this study by IHC of TMAs, when OC patients exhibiting different levels of intratumoral chemerin or CMKLR1 were compared with regard to OS by means of Kaplan–Meier analysis, no significant differences were found. Subsequently, the survival of patients with serous ovarian cancers was investigated. However, neither chemerin nor CMKLR1 levels did influence the OS of the patients in this cohort (Figure S1). The levels of these proteins also did not correlate with progression-free survival (PFS), neither when including all ovarian cancer cases nor when analyzing only serous ovarian cancers. Since a weakness of this study is the relatively low number of OC samples, it was speculated that the association between chemerin and CMKLR1 expression with survival could be visible using a larger patient collective. Thus, the online tool kmplot.com providing microarray mRNA and OS data of 2021 OC patients from the Gene Expression Omnibus and The Cancer Genome Atlas [39] was used and accessed on 1 September 2022. This analysis revealed high mRNA levels of RARRES2 in OC tissue to be significantly associated with a shorter OS (HR = 1.32, p = 5.8 × 10−5). In contrast, high mRNA expression of CMKLR1 was associated with longer OS (HR = 0.8, p = 0.0002) (Figure 3). In this study, possible associations between the adipokine chemerin and its receptor CMKLR1 with other proteins involved in steroid hormone signaling were examined in OC tissues and in silico, as the role of these proteins in cancer is yet mostly unclear.It was found that in serous ovarian cancer, both chemerin and CMKLR1 protein positively correlated with ERβ protein expression and with levels of ERRα, β, and γ; additionally, chemerin protein expression was notably associated with that of PR. On the mRNA level, CMKLR1, not RARRES2 mRNA, correlated with ERRβ and γ. These findings thus showed an association of chemerin/CMKLR1 with a nuclear estrogen receptor (ERβ), an important estrogen target gene (PR), and with modulators of estrogen signaling, which plays essential roles in OC. Chemerin has been shown to modulate steroidogenesis, especially secretion of progesterone, in the porcine ovary in both stimulatory and inhibitory ways [41], and it has been proposed that chemerin via CMKLR1 plays a role in the development of polycystic ovary syndrome via inhibition of progesterone secretion [42]. Since progesterone is known to be of importance in OC development, the association between chemerin/CMKLR1 and PR was investigated. In our cohort of 208 patients, a strong correlation between chemerin staining intensity and PR protein expression could be shown. PR expression in OC was found to be associated with a more favorable prognosis [43], and further studies may confirm the role of chemerin herein. It has long been demonstrated that estrogens, their different receptors (ERs), and related receptors (ERRs) are major players in the origin and development of OC in various ways, which led to an investigation of possible associations of chemerin and CMKLR1 with different ERs and ERRs, on which there are few data published to date. One study by Hoffmann et al. indicated an anti-proliferative effect of chemerin partly via ERs [44]. In our study, both chemerin and CMKLR1 levels in tumor tissues positively correlated with estrogen receptor β (ERβ), which could be confirmed on the mRNA level for CMKLR1 and ESR2 by in silico analysis. According to past publications, this could indicate a protective role of chemerin and CMKLR1 similar to ERβ [21,22,23,24]. Concerning ERRs, both chemerin and its receptor positively correlated with estrogen-related receptor α (ERRα), particularly in serous OC tissue, an association being also validated in silico on the mRNA level for CMKLR1. This is in line with a previous study [26], where ERRα was detected abundantly in OC tissues. Also, protein levels of chemerin and its receptor were associated with ERRβ and ERRγ, with a stronger correlation present in serous OC. As these two receptors are indicative of poorer survival [26], the exact mechanisms of chemerin interaction with ERRs and other modulatory factors are to be further elucidated since these findings are contradictory in their putative pro-tumoral effects to the association found with ERβ protein expression and ESR2 gene expression. In silico analyses comparing mRNA expression of the RARRES2 gene in normal ovary, OC, and OC metastases revealed a notable decrease of RARRES2 expression in OC and in metastatic tissue, whereas CMKLR1 RNA levels were considerably reduced in OC metastases only. Low expression of chemerin in tumor tissue is in accordance with findings from other cancer entities and was suggested to indicate a protective role of chemerin in cancer progression. Gao et al., however, described a higher expression of chemerin protein in OC compared to normal tissues. Intratumoral chemerin protein levels were not associated with the overall (OS) or progression-free survival (PFS) of OC patients. In line with our data, chemerin was found to be low-expressed in melanoma and liver cancer, but according to the Human Protein Atlas, it was not prognostic in these cancers [45]. Analysis of open-source mRNA and survival data from 2021 OC patients moreover identified a favorable effect of high CMKLR1 and low RARRES2 mRNA levels on patients’ survival. Taken together, the association of chemerin and CMKLR1 with ovarian cancer prognosis seems to be complex, and factors such as hormonal status or comorbidities such as adiposity, dyslipidemia, or hypertension must be considered. The fact that an association of chemerin or CMKLR1 protein levels with OC survival was not observed, but instead, a significant correlation on the mRNA level of a larger patients´ collective might be explained by the different collective size. Furthermore, mRNA levels do not always correlate with the level of the coded protein. During phases such as cell proliferation or differentiation, post-transcriptional mechanisms may cause deviations from this association. The sampling of tissues for RNA and protein analysis is a further source of variations [46]. Chemerin is a secreted protein and may be taken up by cancer cells. Thus, there are different explanations for why mRNA and protein analysis of chemerin in OC did not always reveal concordant results. The first two arguments also apply to the further proteins analyzed in this study. For CMKLR1, it is important to note that only tumor cell expressed protein was quantified. At the mRNA levels, tumor cells, as well as further cells such as immune cells of the respective tissues, are included and contribute to variations of mRNA and protein data. Differences in protein level assessment of chemerin via immunohistochemistry and RARRES2 gene expression on the mRNA level can be explained by the fact that chemerin is mainly produced by extratumoral tissues, e.g., adipocytes and hepatocytes [8]. Therefore, intratumoral protein levels measured by immunohistochemical staining are expectedly higher than mRNA levels when comparing normal and cancer tissues, and associations of intratumoral chemerin levels with OS and PFS are not mirrored by mRNA gene expression data. Tumors including OC are able to escape the intrinsic anti-tumor activity of the immune system by means of so-called immune evasion strategies [47,48] and cancer immunoediting, often attributed to the interaction of tumor cells with tumor-infiltrating lymphocytes as well as immunomodulatory factors such as PD-L1, CTLA-4, and CXCR4 [49,50]. This might be a possible explanation for the missing effect of different intratumoral chemerin levels on OS or PFS, as well as the decrease of RARRES2 on the mRNA level in the in silico analysis of OC, compared to normal ovarian tissue. In this context, it might be of interest to investigate the composition of tumor-infiltrating lymphocytes and their interaction with chemerin via CMKLR1 in further studies. Limitations of this study are the medium-sized cohort of OC patients and the lack of normal ovarian tissue in the immunohistochemical analysis, which has been compensated for in the additional in silico analyses on the mRNA level. As always in the case of adipokines and the like, it remains to be further determined how serum levels of chemerin must be taken into account, as serum chemerin levels were not available for our OC cohort. Chemerin protein and its receptor CMKLR1 were demonstrated to be abundantly detectable by immunohistochemistry in ovarian cancer tissues and to positively correlate with intratumoral expression of PR, ERβ and ERRs, corroborating interaction with estrogen signaling pathways as previously suggested. Analysis of publicly available gene expression data demonstrated a significant downregulation of RARRES2 mRNA expression in OC and metastatic tissue, whereas CMKLR1 expression was found to be reduced in metastases only. Tumoral chemerin and CMKLR1 protein levels were not related to OS, but lower RARRES2 and higher CMKLR1 mRNA levels were associated with longer OS. Our data are able to encourage further studies examining the role of the interactions suggested in this study for the development and progression of ovarian cancer.

PMC10001033

Ana Sofia Coroadinha

Host Cell Restriction Factors Blocking Efficient Vector Transduction: Challenges in Lentiviral and Adeno-Associated Vector Based Gene Therapies

24-02-2023

gene therapy,lentiviral vectors,adeno-associated virus vectors,innate-immune response,host restriction factors,transduction

Gene therapy relies on the delivery of genetic material to the patient’s cells in order to provide a therapeutic treatment. Two of the currently most used and efficient delivery systems are the lentiviral (LV) and adeno-associated virus (AAV) vectors. Gene therapy vectors must successfully attach, enter uncoated, and escape host restriction factors (RFs), before reaching the nucleus and effectively deliver the therapeutic genetic instructions to the cell. Some of these RFs are ubiquitously expressed in mammalian cells, while others are cell-specific, and others still are expressed only upon induction by danger signals as type I interferons. Cell restriction factors have evolved to protect the organism against infectious diseases and tissue damage. These restriction factors can be intrinsic, directly acting on the vector, or related with the innate immune response system, acting indirectly through the induction of interferons, but both are intertwined. The innate immunity is the first line of defense against pathogens and, as such cells derived from myeloid progenitors (but not only), are well equipped with RFs to detect pathogen-associated molecular patterns (PAMPs). In addition, some non-professional cells, such as epithelial cells, endothelial cells, and fibroblasts, play major roles in pathogen recognition. Unsurprisingly, foreign DNA and RNA molecules are among the most detected PAMPs. Here, we review and discuss identified RFs that block LV and AAV vector transduction, hindering their therapeutic efficacy.

Host Cell Restriction Factors Blocking Efficient Vector Transduction: Challenges in Lentiviral and Adeno-Associated Vector Based Gene Therapies Gene therapy relies on the delivery of genetic material to the patient’s cells in order to provide a therapeutic treatment. Two of the currently most used and efficient delivery systems are the lentiviral (LV) and adeno-associated virus (AAV) vectors. Gene therapy vectors must successfully attach, enter uncoated, and escape host restriction factors (RFs), before reaching the nucleus and effectively deliver the therapeutic genetic instructions to the cell. Some of these RFs are ubiquitously expressed in mammalian cells, while others are cell-specific, and others still are expressed only upon induction by danger signals as type I interferons. Cell restriction factors have evolved to protect the organism against infectious diseases and tissue damage. These restriction factors can be intrinsic, directly acting on the vector, or related with the innate immune response system, acting indirectly through the induction of interferons, but both are intertwined. The innate immunity is the first line of defense against pathogens and, as such cells derived from myeloid progenitors (but not only), are well equipped with RFs to detect pathogen-associated molecular patterns (PAMPs). In addition, some non-professional cells, such as epithelial cells, endothelial cells, and fibroblasts, play major roles in pathogen recognition. Unsurprisingly, foreign DNA and RNA molecules are among the most detected PAMPs. Here, we review and discuss identified RFs that block LV and AAV vector transduction, hindering their therapeutic efficacy. Scientific knowledge and technological progress in the last few decades have not only accelerated the discovery of the cellular and molecular mechanisms behind human diseases, but also driven the generation of advanced gene and cell therapies. Gene therapy arose as a promising approach to revolutionize medicine, enabling treatments that provide long-term effects for a wide variety of inherited and acquired diseases. The potential of gene therapy is currently consolidated into effective treatments. Indeed, in the last decade, several gene therapy-based products have been approved in Europe and in the United States of America. Strimvelis, used for treating severe combined immunodeficiency due to adenosine deaminase deficiency (ADA-SCID), a rare disease; Luxturna, used for treating patients with inherited retinal disease due to mutations in both copies of the retinal pigment epithelium-specific 65 kDa gene (RPE65); and Kymriah used to treat cancer patients who have acute lymphoblastic leukemia (ALL) are three examples of such products in [1]. Gene therapy medicinal products are based on the use and administration of an active substance that contains or consists of a recombinant nucleic acid, ultimately aiming to regulate, repair, replace, add, or delete a genetic sequence to attain a therapeutic, prophylactic, or diagnostic effect [2]. Thus, gene therapies require the transfer of DNA- or RNA-based molecules into the patient’s cells. Naked nucleic acids cannot efficiently enter cells, are often unstable, and are subject to nuclease degradation. Delivery systems are required. Different types of gene therapy vectors, either viral on non-viral, have and are being developed for this purpose. However, gene therapy vectors face several obstacles in executing their function, namely they must escape host cell RFs. The latter are inhibitory host cell factors, belonging to the cellular innate immune system, or to intrinsic antiviral immunity, which interfere with vector trafficking and expression at diverse steps, from cell entry to nucleic acid translation [3]. Host cell RFs have evolved to protect the organism against infectious diseases and tissue damage. The innate immune response is the first line of defense against pathogens and, as such, the cells involved in it are well equipped with RFs that detect PAMPs and damage-associated molecular patterns (DAMPs). These cells include macrophages, neutrophils, eosinophils, basophils, mast cells, and dendritic cells that are derived from myeloid progenitors, but also, natural killer cells derived from lymphoid stem cells. In addition, some nonprofessional cells, such as epithelial cells, endothelial cells, and fibroblasts, also play major roles in pathogen recognition during the innate immune response. The cells of the host recognize PAMPs via germ line-encoded pattern recognition receptors (PRRs) [4]. These cells, encoding PRRs, may be difficult to transduce when the target of therapy, as they are sources of danger signals to other cells, upregulating the expression of their non-constitutive RFs. Thus, in in vivo therapies, the off-targeting of cells from the innate immune system, may indirectly hamper transduction of the targeted cells. Innate immunity is an evolutionary conserved system where cells contain an arsenal of specialized receptors, PRRs. Nucleic acids, are major ligands detected by PRRs (Figure 1). Innate immunity evolved to detect the invasion of pathogens and initiate host antimicrobial responses. However, it may also detect gene therapy vectors, hindering its efficacy or even eliciting unwanted side effects, such as the production of type I interferons and pro-inflammatory cytokines which lead to inflammation and tissue damage [5]. Once the gene therapy vectors are detected as invaders, inhibitory host RFs are up-regulated by interferons (IFNs). RFs can directly target evolutionarily conserved structural features on the vectors or exert broad and more indirect actions, as limiting the availability of cellular resources, such as nucleotides, transcription factors, or other factors [3]. Cyclic GMP-AMP synthase (cGAS) is an example of one such RF that acts indirectly; detects cytosolic double-stranded DNA (dsDNA), and dimerizes and triggers a cascade leading to type I IFN induction. IFNs are potent pleiotropic cytokines that broadly alter cellular functions, including changes in protein synthesis, proliferation, membrane composition, and nutritional microenvironment [6]. Intrinsic antiviral immunity refers to a form of innate immunity that directly restricts viral replication and assembly by rendering a cell non-permissive to a specific virus. Intrinsic immunity is conferred by RFs that are typically pre-existent in certain cell types. Intrinsic RFs recognize specific viral components, but unlike PRRs which inhibit viral infection indirectly by inducing interferons and other antiviral molecules, intrinsic antiviral factors block viral replication immediately and directly [7]. For example, the cytidine deaminases apolipoprotein B mRNA-editing enzyme catalytic polypeptide (APOBEC) edits the lentivirus’s genome during reverse transcription, converting cytidines to uridines that generate hypermutated defective viral genomes [7]. PRRs that recognize nucleic acids in cells can be generally divided in two groups based on their subcellular localization and expression patterns (Figure 1). The first group includes endosomal members of the Toll-like receptor family (TLR) that are present typically in immune cells. TLRs localize in the endosomal membrane and detect several forms of nucleic acid. They can also be at the cell membrane and translocate to the endosome. The second group of receptors is localized in the cytosol of almost all cell types. These include DNA receptors and RIG-I-like receptor (RLR) family members detecting RNA [8]. PAMPs (e.g., nucleic acids, liposacharides, peptidoglycans, proteins) are conserved molecular patterns that can be present in viral and non-viral vectors. Indeed, innate immune responses were also observed when using non-viral vectors. For example, the systemic injection of plasmid DNA with cationic lipids has been shown to elicit immune responses with tissue damage [9,10,11,12]. The same was observed when administering cationic polymer/plasmid DNA complexes [13]. Immune responses elicited by viral vectors based on DNA viruses, as adenoviral and AAV vectors, have also been documented [5,14,15,16,17]. RNAs are also potent activators of the innate immune system either delivered via non-viral or viral vectors. Indeed, exogenous mRNA is considered immunostimulatory, thus being explored in the development of vaccines [18]. Many interference RNA studies have also reported innate immune stimulation, attributed to features in the siRNA structure, sequence, and delivery mode [19]. RNA can activate innate immunity through TLRs, blocking RNA translation, and promoting mRNA degradation. In non-immune cells, the cytoplasmic receptors sense RNA and mediate cytokine and chemokine production [20]. RFs are therefore major bottlenecks to overcome in both viral and non-viral gene transfer applications. Here, we review recent advances elucidating the mechanism by which RFs impact the transduction efficiency of AAV and lentiviral vectors. Lentiviral vectors are derived from lentiviruses that belong to the Retroviridae family of complex retroviruses. Retroviruses are RNA-enveloped viruses with the ability to “reverse transcribe” their genome from RNA to DNA, a step that occurs in the cytoplasm of host cells [21]. Lentiviruses are particles of approximately 80–120 nm and contain two identical copies of linear single-stranded, positive-sense RNA (Figure 2A). The genome is complexed with nucleocapsid proteins. The virus codes for three enzymes: reverse transcriptase, integrase, and a protease. The ssRNA is contained in the nucleocapsid, the inner portion of the virus. The nucleocapsid is enclosed by a protein shell formed by capsid proteins. Matrix proteins form a layer outside the capsid and interact with the envelope lipid bilayer surrounding the viral core. The envelope is derived from the cell host membrane and incorporates the viral envelope glycoproteins responsible for the viral particle binding to specific cell receptors [21,22]. In addition to the enzymes and structural proteins mentioned above, coded by the gag-pol and envelope genes, human lentiviruses also code for two regulatory proteins (Tat and Rev) and four accessory proteins: Vif, Nef, Vpr, and Vpu [21]. LV vectors can be derived from different lentivirus species (e.g., Simian immunodeficiency virus, Bovine immunodeficiency virus, Equine infectious anemia virus, and Feline immunodeficiency virus) but the most used are based on Human immunodeficiency virus 1 (HIV-1) that will be the primary focus of this review. HIV-1 LV vectors only code for the therapeutic gene of interest (GOI). Therefore, lentiviral vector RNA genome, in addition to a heterologous promotor and the GOI, only retains, from the wild-type viruses, the cis-acting sequences which are required for its packaging, its reverse transcription, transport into the cellular nucleus and, integration into the cell genome [22]. The LV vector particles contain only structural and enzymatic proteins. Accessory proteins are removed from the second LV vector generation onwards. Regulatory proteins are not incorporated into virion particles. The envelope protein most used to generate lentiviral vectors is the vesicular stomatitis virus G protein (VSV-G), due to its efficient pseudotyping and broad tropism, but others can be used [23]. The route of entry of LV vectors is determined by the envelope glycoprotein. VSV-G pseudotypes exhibit a pH-dependent entry mechanism. After receptor attachment vector particles enter the cell via endocytosis [24]. Other envelopes, such as those derived from gamma-retroviruses, use a pH-independent entry mechanism and, after receptor binding, enter the cell directly by fusion with the plasma membrane [24]. In both entry pathways, the capsid is liberated and disassembled into the cytoplasm. Currently, LV vectors are the vector of choice to perform ex vivo gene transfer to hematopoietic stem cells, to treat inherited genetic diseases, and T and natural killer (NK) cells for immunotherapies, namely T cell receptor (TCR) and chimeric antigen receptor (CAR) therapies [25,26]. The development of in vivo LV vector-based therapies extends from applications targeting the central nervous system (CNS), targeting neurons or glial cells (e.g., neurodegenerative or lysosomal storage diseases), to liver-directed gene therapy (e.g., correction of hemophilia) [27,28,29]. Pre-existing immunity to LV vectors in humans is low, an advantage of these vectors, but during the several steps of vector transduction, LV vectors nucleic acids and proteins can be recognized by RFs acting as innate immunity sensors [30]. Lentiviral vectors rely mostly on the same viral and cellular machinery as HIV-1 to reach the nucleus and integrate their genome on target cells. As such, they are susceptible to most RFs identified in the context of HIV-1 wild-type virus infection. RFs are host cellular proteins that recognize and interfere with specific steps of the replication cycle of viruses, thereby blocking infection. These can have constitutive expression in different cell types, or be IFNs or DAMPs inducible, but generally their inherent features, such as their self-sufficient activity and rapidity of action, confer early restriction to viruses [31]. Several restriction factors have been identified to target the retroviral life cycle. However, while some of those are ubiquitously expressed in mammalian cells, others are cell-specific or only expressed upon induction. This explains why some cell types are efficiently transduced, while others poorly, by LV vectors [32]. The mode of delivery and the envelope glycoprotein used in LV pseudotyping will also impact the immune response. Here, we review and discuss the innate immune response through PRRs and intrinsic antiviral restriction factors and how these hamper successful LV vector transduction and clinic efficacy. The cells where each RF expression was observed are indicated. Lentiviral vectors have two strands of genomic ssRNA encapsulated. These nucleic acids, as well as their transcripts, can potentially act as PAMPs recognizable by a PRRs and initiating a cascade of innate signaling, leading to the induction of the host’s anti-viral response. Both Toll-like receptors (TLRs) and RIG-I-like receptors, family members of PRRs, have been identified as sensors of HIV-1 RNAs (Figure 2B and Table 1) [33]. TLR 7 and 8 detect ssRNA and TLR3 dsRNA species from lentivirus. TLR7, and 8 can recognize ssRNA in incoming retroviruses in the endosomal compartment. This recognition does not require viral replication [34,35,36]. Further studies indicate that TLR7 preferentially recognizes the guanosine (G)- and uridine (U)-rich ssRNA oligonucleotides [37]. MyD88 is the main adaptor molecule responsible for the activation of the innate response pathway for all TLRs with the exception of the TLR3 [38]. The MyD88 pathway is predominantly involved in the production of inflammatory cytokines and IFN type I through the translocation of NF-kB to the nucleus. TL3 uses the TIR-domain-containing adapter-inducing interferon-β (TRIF), i.e., the TRIF adaptor molecule will act through interferon regulatory factor (IRF) 3 and 7, leading to the production of IFNs type I [38]. TLR7 is primarily expressed in plasmacytoid dendritic cells, while TLR8 on myeloid cells (including dendritic cells, monocytes, and macrophages). The activation of those TLR pathways in LV transduction have been mostly observed in dendritic cells (DCs) [39]. Some authors have suggested that since TLR3, 7, and 8 are endosomal, that some LV pseudotypes may escape from their detection in the endosomes, particularly, LV pseudotypes that can enter via direct fusion with the cytoplasmic membrane [32]. However, entry routes are not always completely elucidated and the multiple PRR sensors detecting infection at different stages of the viral cycle complicate the direct validation of this hypothesis. RIG-I and its homolog melanoma-differentiation-associated protein 5 (MDA5) are RNA helicases localized in the cytoplasm, and were reported to detect dimeric and monomeric RNAs of HIV-1 in infected macrophages [23,40]. RIG-I-like receptor family, initially identified as DExD/H-box-containing proteins, comprise RIG-I, MDA5, and LGP2 (laboratory of genetics and physiology 2) and share highly conserved domain structures: a central DExD/H box helicase core and a C-terminal domain (CTD) that confers part of the ligand specificity. Both RIG-I and MDA5 have caspase activation and recruitment domains (CARD) mediating signaling to downstream adaptor proteins [8]. RIG-I detects RNAs possessing an uncapped 5′-di/triphosphate end (5′ ppp ssRNA) and a short blunt-ended double-strand portion, two essential features facilitating the discrimination of viral from self-RNAs [41]. The preferential binding of RNAs to RIG-I and MDA5 is attributed to the fragment length, long fragments (˃ 4kb) are preferred by the latter, while shorter ones are preferred by the former [8]. After binding to stimulatory RNAs, RIG-I and MDA5 RLRs go through a ATP-dependent conformational change enabling binding to downstream adapter molecules through the oligomeric assembly of the CARD domain [42]. Mitochondrial antiviral signaling protein (MAVS), also containing a CARD domain, is activated by RIG-I and MDA5 oligomers. MAVS aggregation on the surface of mitochondria and the formation of protein filaments via CARDS initiates downstream signaling. Aggregated MAVS recruit and activate E3 ligases TRAF2,3,5 and 6 which synthesize polyubiquitin that are sensed by NEMO proteins that recruit IKK and TBK1 proteins. The latter two, phosphorylate IRF3, 7 and IkB, lead to the transcription of type I IFNs and pro-inflammatory cytokines (e.g., TNFα, IL-6, IL-8, IL-1B, INF). NfkB is also activated via the IKK complex. HIV-1 RNAs are, however, enclosed within the capsid core, likely shielded from the innate immune sensors. The uncoating of the capsid occurs upon the initiation of reverse transcription, resulting in viral RNA exposure, although minimal, to RIG-I [33,43,44]. Therefore, RIG-I mediated signaling may play a limited role in triggering antiviral immunity during the early steps of LV vector transduction [33]. HIV-1 reverse transcription intermediates (e.g., cDNA, ssDNA, and DNA/RNA hybrids) can be sensed by DNA PRRs as interferon gamma inducible protein 16 (IFI16), cyclic GMP-AMP synthase (cGAS), and helicase DDX41 [33]. IFI16 is a HIV-1 DNA species sensor in macrophages and tonsillar CD4+ T cells [40,45]. It detects incomplete HIV-1 DNA reverse transcripts accumulated in the cytoplasm of abortively infected tonsillar lymphoid cells. It is believed that upon binding to HIV-1 cDNA, IFI16 recruits a stimulator of interferon genes (STING) to activate the TANK-binding kinase 1 (TBK1) and IRF3 signaling axis, resulting in the transcription of antiviral genes in myeloid cells. In tonsillar lymphoid cells, IFI16 activates the inflammasome pathway through ASC and caspase-1, leading to IL-1β production [40,45]. This may indicate that IFI16 is cell-type dependent [33,46]. IFI16 recognizes abortive RT products to induce IFNs, as well as inflammasome activation by binding to the adapter molecule ASC (apoptosis-associated speck-like protein containing a CARD). This leads to the activation of caspase-1 and cytokine IL-1β, triggering pyroptosis. During reverse transcription, DNA/RNA hybrids are created. DDX41, an RNA helicase protein thought to function in RNA splicing, was recently identified as the sensor that primarily binds the short-lived murine leukemia virus (MLV) DNA/RNA hybrids. Its binding to DNA/RNA hybrids activates downstream signals in a STING-dependent manner in primary mouse macrophages and DCs [40]. cGAS is widely recognized as the major sensor to HIV-1 DNA [47]. cGAS preferentially recognizes the stem-loop structures of single-stranded DNA (ssDNA), derived from HIV-1 cDNA. The DNA damage induced by HIV-1 integration might also be linked to cGAS-mediated signaling activation. cGAS might be responsible for the innate immune sensing of HIV-1 integration by being drafted to the nucleus and recognizing self-DNA from damaged chromatin. cGas is thus recruited to the double-stranded breaks to suppress homologous recombination [48]. Notwithstanding, cGas is negatively regulated by cellular proteins to avoid auto-immune responses. cGAS responds to threshold levels of accumulated viral DNA, thus avoiding aberrant activation by self-DNA species. Sterile alpha motif and histidine-aspartate domain-containing protein 1 (SAMHD1) and TREX1 (three prime repair exonuclease 1), are host factors limiting the accumulation of DNA that can be sensed by cGAS in the cytoplasm [49,50]. SAMHD1 prevents the accumulation of ssDNA by promoting the degradation of nascent DNA at stalled replication forks, which may escape the nucleus during mitosis, thereby limiting innate immune sensing by cGAS [51]. It has also been reported that SAMHD1 interacts with the inhibitor-κB kinase ε (IKKε) and IRF7 to suppress the innate immune response by reducing IKKε-mediated IRF7 phosphorylation [52,53]. TREX1 is a ubiquitously expressed exonuclease that prevents the activation of the cGAS/STING/IRF3 signaling axis by eliminating cytoplasmic DNAs before their detection by cGAS [53,54]. cGas antiviral response is generated by the STING-TBK1-IRF3 axis, i.e., upon stimulation, cGas synthetizes cyclic-di-GMP-AMP (cGAMP), that binds and activates the endoplasmic reticulum adaptor STING. STING engages the TANK-binding kinase 1 (TBK1) complex, activating IRF3 by phosphorylation leading to the expression of IFN type 1 (Figure 2B). TLRs expressed on cell membranes can recognize virus PAMPs and activate innate immune responses. The cell surface-expressed TLR2 and TLR4 have been reported in sensing HIV-1 glycoprotein gp120, in particular, in mucosal epithelial cells, leading to the activation of NF-kB and production of inflammatory cytokines [48]. Soluble TLR2, i.e., sTLR2 was first detected in milk and later shown to act as an inhibitor of virus entry [55]. Henrick et al. showed that TLR2 binds to gp41, core protein p24, and matrix protein p17, resulting in the inhibition of NF-κB activation [56]. It was also observed that the HIV-1 co-receptor CCR5 is highly downregulated through the TLR2-dependent pathway arresting infection [57]. Recently, HIV-1 gp41 was identified to be recognized by the TLR10 ligand in MCF-10A and THP-1 cells, leading to the generation of IL-8 and NF-kB activation. However, the exact mechanism is not known, and it cannot be ruled out that spontaneous heterodimerization of TLR2 and TLR10 occurs due to HIV-1 gp41 (acting as a common ligand for both TLRs) [58]. Information on VSV-G glycoprotein recognition by PRRs is scarce. However, it has been proposed that VSV-G pseudotyped LV vectors form tubulovesicular structures with DNA fragments, promoting TLR9-signaling structures [59]. In addition to the PRRs that recognize envelope glycoproteins, RFs sensing the HIV-1 capsid were identified [33]. While some host factors are hijacked by the HIV-1 capsid to prevent the innate sensing of HIV-1 infection (e.g., cyclophilin A (CypA) and specificity factor subunit 6 (CPSF6)), others, such as tripartite motif-containing protein 5 (TRIM5) and NONO, activate innate immune signaling [33]. These factors are, however, in the category of intrinsic antiviral restriction factors and will be described in the following section. The antiviral actions exerted by intrinsic RFs are immediate and direct and are herein defined as ‘intrinsic antiviral immunity’ to distinguish from the ones requiring effectors induced by IFNs and cytokines through transcriptional activation. Intrinsic viral restriction factors are usually preexisting in certain cell types, rendering the cell non-permissive to certain viruses. Notwithstanding, intrinsic RFs are frequently IFN-inducible, i.e., are interferon stimulated genes (ISGs), linking intrinsic and innate immunity. The production of IFNs will therefore stimulate their expression activation and upregulation. Examples of intrinsic restriction factors are interferon-inducible transmembrane (IFITMS), tripartite motif containing 5 (TRIM5) proteins, the human apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like 3 (APOBEC3) family, myxovirus resistance protein 2 (Mx2 or MxB), SAMHD1, and bone marrow stromal antigen 2 BST/tetherin/BST-2. The latter are briefly described below. IFITMs are a family of small proteins of type II transmembrane proteins. IFITM1, IFITM2, and IFITM3 are expressed almost ubiquitously in humans, whereas IFITM5 is primarily expressed in osteoblasts [60]. IFITM1, IFITM2, IFITM3 genes are ISGs with an interferon-stimulated response element (ISRE) in their promoter region [61]. IFITM5 and IFITM10 are not induced by IFNs [60]. IFITMs restrict a great number of enveloped RNA viruses. IFITM-mediated antiviral activity spans from the inhibition of viral entry to the inhibition of viral protein synthesis [62]. IFITMs that localize at the plasma membrane, as well as at the membranes of endocytic vesicles and lysosomes, restrict viral infections by inhibiting virus entry [63]. IFITMs block viral entry by impairing the hemifusion process, most likely by reducing membrane fluidity. Although the first antiviral action of IFITMs is to protect target cells from incoming viruses, a second antiviral mechanism of action is the production of virions that package IFITMs. The latter virions display a reduced entry capacity; this was observed for HIV-1, thus the absence of IFITMs in LV packaging cells is essential. Yu and colleagues explain the impairment of lentivirus fusogenic capacity by the decrease in the number of envelope spikes in the particles [64]. IFITM2 and IFITM3 avert viral entry, and IFITM1, IFITM2, and IFITM3 prevent Gag production [65]. TRIM proteins constitute a large family of E3 ubiquitin ligases involved in many cellular functions, such as cell differentiation, apoptosis, autophagy, immunity, and antiviral functions [60]. The natural functions of TRIM5α are not totally known, but it is one of the best studied, species-specific barriers in lentiviral transduction because it restricts lentivirus by uncoating viral particles, while activating early innate responses [38]. Several TRIM proteins have antiretroviral activity, and TRIM5α was identified as the factor responsible for HIV-1 restriction [65]. TRIM5α is a cytoplasmic protein ubiquitously expressed in all tissues in the human body, and although expression levels may be constitutively low, they can be upregulated by IFN through a putative ISRE [66]. Restriction patterns are variable between species, e.g., TRIM5α from the rhesus macaque strongly inhibits HIV-1, but not SIV, whereas human TRIM5α restricts equine infectious anemia virus. Human TRIM5α also mediates a mild restriction of HIV-2, but not as high as to HIV-1 [66]. The species specificities are attributed to CA sequence differences between viruses and subsequently the TRIM5α ability to recognize and bind. Although Ribeiro and colleagues have shown that TRIM5α can inhibit HIV-1 infection in some DC subsets, this antiviral function depends on the route of HIV-1 internalization [67]. TRIM5 recognizes capsids when in combination with cyclophilin A (CypA) and degrades LV vectors by binding and destabilizing the capsid, preventing reverse transcription completion [68]. TRIM5α leads to the accelerated and disrupted uncoating of the virus [66]. The TRIM5α E3 ubiquitin ligase RING domain has an effector function, recruiting proteasomal machinery. Proteasomes inhibition has shown to prevent the premature disassembly of the capsid and restores HIV-1 reverse transcription [60]. Human CD34+ CD38- cells have high TRIM5 expression, which may explain the lower LV vector transduction efficiency of hematopoietic stem/progenitor cells (HSPCs), which also varies among individuals [69]. TRIM5α expression levels are negatively correlated with LV vector transduction efficiency in human T-lymphocyte cell lines and CD34+ cells [70]. The treatment of HeLa and HepG2 cells with IFN-I increases TRIM5α mRNA levels [71]. A systematic analysis of TRIM gene expression in human primary lymphocytes and monocyte-derived macrophages in response to IFN-I and IFN-II has revealed that several TRIM genes are upregulated by IFNs. Among the 72 human TRIM genes tested, 27 were sensitive to IFN, including TRIM5α [72]. The human APOBEC cytidine deaminases family is well described as potent and well-characterized HIV RFs. APOBEC3G was the first restriction factor identified for HIV-1. Vif mediates the proteasomal degradation of APOBEC3G, counteracting its antiviral function. The 2nd and 3rd generation of LV vectors do not have Vif making them susceptible to APOBEC3G. APOBEC3 proteins can co-package into the LV particles [73]. Thus, it is important to verify that no APOBEC3 protein is expressed during LV vector production to avoid mutations. APOBEC3G is incorporated into the core of Vif-deficient particles through interactions with the nucleocapsid and viral RNA [74]. Once the particles infect a new cell, APOBEC3G remains associated with the mature viral proteins and RNA. APOBEC3G enzymatic activity is capable of mutating DNA by cytidine deamination during reverse transcription. The catalyzed cytosine-to-uracil deamination in the nascent viral DNA lead to a high frequency of G-to-A hypermutations introducing amino acid substitutions and premature STOP codons [31,66]. Additionally, APOBEC3 can act without the deaminase activity for its antiviral activity. It was observed in several studies that it can block reverse transcription elongation in a deaminase-independent manner It was observed in several studies that it can block reverse transcription elongation in a deaminase-independent manner [75]. APOBEC3G genes are most abundant in monocytes, macro-phages, DCs, resting CD4+ T cells but not in activated CD4+ T cells. The activation of innate immunity, in particular by the I IFN response, increases APOBEC3G expression. The treatment of immature DCs or monocyte-derived macrophages, with synthetic TLR3 ligands, leads to APOBEC3G expression and induces antiviral activity against HIV-1 [76,77]. The stimulation of human monocyte-derived dendritic cells with Gag virus-like particles, which are recognized by PRRs, generates an increase in the mRNA and protein expression of APOBEC3G [78]. Furthermore, several cytokines involved in innate and adaptive immunity, such as IL-2, IL-7, and IL-15, induce APOBEC3G in peripheral blood lymphocytes [79]. MX2 is a protein belonging to the dynamin superfamily of large guanosine triphosphatases (GTPases) [80]. MX2 acts after the reverse transcription step. It localizes in the cytoplasmic side of nuclear pores and regulates the nucleocytoplasmic transport of viral DNA. Borsoti et al. hypothesize that MX2 activity impacts LV vector nuclear translocation in transduced cells [38]. The N-terminal 25 amino acids of MX2 interact with HIV-1 CA, leading to the abrogation of HIV-1 infection [62]. Some studies suggest that Mx2 can impair HIV uncoating by binding to the capsid [81]. Mx2 may affect nuclear entry or post-nuclear trafficking without destabilizing the inherent catalytic activity of viral pre-integration complexes. The interaction between Mx2 and the viral capsid is either direct or mediated via CypA [82,83]. Mx2 GTPase activity is required for oligomer assembly, required for its antiviral function; however, the activity is dispensable for its restriction activity [60]. Buffone and colleagues showed that Mx2 could be involved in HIV restriction through interaction with SAMHD1 [84]. Mx2 is part of the ISG family and is induced by IFN-α and IFN-β, as shown in human fibroblasts [85]. After IFN treatment, Mx2 proteins were detected in human primary monocytes, lymphocytes, and macrophages [60]. SAMHD1 is a deoxynucleoside triphosphate phosphohydrolase (dNTPase). It hydrolyzes all four dNTPs, impairing HIV reverse transcription by decreasing the pool of nucleotides available for reverse transcription [60]. Recent data shows that it also has RNase activity that seems to be important for HIV restriction. SAMHD1 can bind ssDNA and RNA being able to degrade RNA–DNA duplexes and HIV genomic RNA [86,87,88]. Vpx, encoded by HIV-2 and SIV variants, but not HIV-1, targets SAMHD1 for proteasomal degradation (observed in myeloid and resting CD4+ T cells) [38]. SAMHD1 is not an ISG; its expression is not induced by IFN-I in DCs and primary CD4+ T cells. However, in some human cells lines (HeLa, HEK293, and MARC-145 cells), the activations of TLR3 and RIG-I/MDA5 upregulated SAMHD1 expression through IRF3 [60,89]. As SAMHD1 is regulated at the post-transcriptional level, and in a cell-cycle-dependent manner, it has low antiviral efficiency in dividing cells. In liver cells, SAMDH1 expression is only observed upon IFN-α treatment via STAT1-, STAT2-, and IRF9-dependent pathway [90]. SAMDH1 constitutive expression is missing in T-cell lines [60]. The constitutive expression of SAMHD1 is observed in cells of the myeloid lineage, inhibiting HIV-1 replication in DCs, monocytes, and macrophages, as well as in CD4+ T cells. IL-12 and IL-18 treatments in monocyte-derived macrophages lead to SAMHD1 overexpression [90]. HIV-1 infection is restricted by SAMHD1 in resting CD4+ T cells but not in activated T cells [91,92]. The stimulation of resting CD4+ T cells with IL-7 induces T592 phosphorylation of SAMHD1 and abrogates its antiviral activity [93]. The high SAMHD1 expression in HSPCs explains the resistance of HSCs to LV vectors [38]. While in activated CD4+ T cells, the high expression of SAMHD1 corresponds to a high concentration of dNTPs and a high permissiveness to HIV-1-based transduction. In HSPCs, an entirely different situation was shown with a low concentration of dNTPs and a low permissiveness to HIV-1-based LVs [38,94,95]. SAMHD1 is also expressed in B cells [94] which are resistant to LV transduction. However, by the use of different envelope pseudotypes, it was possible to remove the block on both B cells and HSPCs [96]. Thus, LV vector envelope glycoprotein pseudotyping seems to impact vector permissiveness. Tetherin, or BST2, belongs to the ISGs and was identified as a RF in HIV-1 infection. HIV-1 can escape restriction imposed by tetherin through Vpu but LV vectors do not contain the latter [38]. Tetherin is a dimeric Type II membrane protein with an N-terminal cytoplasmic tail, a transmembrane region, and a C-terminal glycophosphatidyl inositol anchor. Within the short cytoplasmic tail, tetherin encodes a dual-tyrosine motif important for clathrin-mediated endocytosis. In vitro cell culture studies revealed that HIV-1 Gag co-localizes with tetherin in intracellular compartments, suggesting that the internalization of tetherin-bound virions could lead to its degradation through the endolysosomal pathway [97]. Thus, the most consensual mode of action attributed to tetherin is the entrapment of viral particles at the surface of host cells preventing their release. This was observed in vitro [98]. To note, however, that other modes of action have been attributed to tetherin, namely, an immunomodulatory role, which may have positive or negative influence on viral replication. In the context of LV vectors, tetherin seems to be a concern when expressed in the producer cells. Tetherin is expressed at different levels in several human tissues. It is highly expressed on blood vessels [60]. The promoter region of the tetherin gene has a binding site for the transcription factor STAT3, suggesting it can be upregulated by innate immune-signaling pathways, such as IFN-I. Tetherin expression was observed to be upregulated in human umbilical vein endothelial cells, by the three types of IFNs (IFN-α, IFN-γ, and IFN-λ), in hepatocytes by IFN-I, and in neurons via a STAT1-dependent pathway [99,100]. IFN-α and TLR3 or TLR4 engagement also upregulates tetherin in myeloid DCs, monocyte-derived DCs, and macrophages [77,101]. Altogether, this data supports tetherin gene-expression induction by innate immunity after the sensing of infection by TLRs. This is confirmed in vivo studies showing that HIV infection upregulates its expression in human peripheral blood mononuclear cells. The full extent of activity and regulation of the above-described RFs is still to be elucidated. Additionally, there are hundreds of other RF antiviral proteins. Examples of these are: protein kinase R (PKR), the 2′-5′-oligoadenylate synthetase 1 (OAS1) schlafen protein 11 (SLFN11), DEAD box helicases (DDX) 3X (DDX3X), cholesterol 25-hydroxylase (CH25H), interferon-stimulated gene 15 (ISG15), and non-POU domain-containing octamer-binding protein (NONO). Most of the intrinsic RFs are IFN-inducible, linking intrinsic and innate immunity and making their study complex. Adeno-associated virus (AAV) belongs to the Parvoviridae family and its life cycle is dependent on the presence of a helper virus. Thus, AAV are replication-defective. The current consensus is that AAV does not cause any human diseases [102]. The AAV viral particles are composed of an icosahedral protein capsid of approximately 26 nm in diameter and a single-stranded DNA genome of approximately 4.7 kb that can be either a plus (sense) or a minus (anti-sense) strand. [103]. The genome is flanked by two T-shaped inverted terminal repeats (ITRs) at the ends that serve as viral origins for replication and packaging signal [102]. The AAV genome encodes three genes. The Cap gene encodes the three VP proteins through alternative splicing and translation from different start codons. The Rep gene codes for the four proteins required for viral replication. These, named after their molecular masses, are Rep78, Rep68, Rep52, and Rep40. The third gene generates the assembly activating protein (AAP) and is encoded within the cap coding sequence in a different reading frame. AAP promotes virion assembly [104]. AAV2 can integrate into a genomic locus denominated AAVS1 in human cells to establish latency [105], a phenomenon mediated by Rep activity. AAV vectors used for gene therapy are composed of the same capsid sequence and structure as found in wild-type AAVs; however, the genomes are devoid of all viral protein-coding sequences and just express the therapeutic gene of interest (Figure 3A). The only sequences of viral origin that remain are the ITRs, which are needed to guide genome replication and packaging during vector production [106]. The complete removal of viral coding sequences, namely, Rep genes, renders genome integration greatly reduced. Thirteen natural serotypes of AAV have been isolated and more than 100 variants have been explored in the context of gene therapy. The AAV capsid can accommodate mutations and ligand insertions, enabling the generation of capsid-engineered variants [107]. The capsid is responsible for the vector tropism and tissue specificity. Currently, AAV vectors are the vector of choice to perform in vivo gene transfer to several tissues to treat inherited genetic diseases. The development of AAV vector-based therapies in the clinic extend from broad therapeutic areas as blood disorders (e.g., correction of hemophilia through liver-directed gene therapy), central nervous system diseases (e.g., Parkinson’s), eye disorders (e.g., Leber congenital amaurosis), lysosomal storage disorders (e.g., GM1 gangliosidosis), and muscular and neuromuscular conditions (e.g., Duchenne muscular dystrophy and spinal muscular atrophy). AAV are non-pathogenic, thus, most of the knowledge related to innate immune response were obtained in the context of gene therapy applications. Due to the inherent nature of the AAV vector, a non-enveloped particle with repetitive capsid motifs, one of the major concerns is the adaptive immune response directed at AAV antigens. It is currently known that AAV wild-type infection prevalence among the human population is between 35 to 80%, depending on the serotype, making the unwanted adaptive response an important concern and side effect in AAV vector-mediated therapies [108,109]. However, the innate immune response stimulates the adaptive immune responses. The converse also occurs. The adaptive immunity against AAV vectors enhances the protective mechanisms of innate immunity, thus making it difficult to elucidate the primary cause of the immune responses observed. Antibodies against the AAV capsid will hamper vector transduction, therapeutic gene expression and overall gene therapy efficacy. Here, we review and discuss the innate immune response through PRRs and intrinsic antiviral restriction factors. RFs are the first cell barrier against AAV transduction, stimulating the adaptive immunity, and altogether hampering gene therapy efficacy. First, PRR restriction factors recognizing the AAV vector nucleic acid genome, their nucleic acids intermediate species and, proteins are described (Figure 3B). Thereafter, recent molecules suggested as intrinsic RFs against AAV vectors are reviewed (Table 2). The cells where each RF expression was observed are indicated. AAV vectors have one genomic single-stranded (ss) DNA encapsulated. The AAV vector genome, as well as its transcripts can act as PAMPs recognizable by host PRRs to initiate a cascade of innate signaling, and the induction of the host anti-viral response. These PRRs, as discussed in the previous sections, are widely expressed in innate immune cells as dendritic cells, macrophages, B cells, and some T cells, but can also be found in some non-immune cells, such as fibroblasts and epithelial cells. Even when the primary target cell of therapy does not express high levels of PRRs the nature of in vivo delivery system and the high doses of vectors used in AAV-based therapies often lead to off-targeting of cells that express them. Ultimately, the release of IFNs will lead to the expression of ISG in the target cells. AAV vectors attach the cells by binding to specific receptors, which may differ according to the serotype, and enter through endocytosis. After endocytosis, AAV particles traffic through the endosome, where capsid in the acidic environment extrudes VP1 unique portion, exposing the phospholipase domain to facilitate endosomal escape [110,111]. The trafficking of the AAV vector genome to the nucleus is still not fully elucidated, namely, where the capsid disassembly occurs. It is known that the capsid contains pores at the fivefold axis of symmetry where potentially the ssDNA could exit [112]. It is also well known that many AAV vectors do not reach the nucleus and suffer proteasomal degradation that increases the availability of AAV genomes for intracellular recognition [113]. Independently of the mechanism, capsid disassembly, escape through pores or proteasomal degradations, when the genome becomes exposed, it can be recognized by PRRs. TLR9, which recognizes CpG DNA from AAV vectors, will signal through MyD88 leading to the activation of Nf-kB and ISG expression. Several studies support this pathway activation in AAV vector transduction. Zhu and colleagues showed that AAV activates mouse and human plasmacytoid DCs via TLR9 to produce type I IFNs. TLR9 knockout plasmacytoid DCs fail to secrete IFN-α [114]. The authors also show that the activation of the TLR9-MyD88 pathway by AAV is independent of the nature of the transgene or AAV serotype. Martino et al. (2011) studied the innate immune response to self-complementary AAV vectors (scAAV) on hepatic gene transfer in mice [115]. For single-stranded AAV vectors a rapid, milder, and transient immune response was observed, up-regulating type 1 IFN, TLR9, MyD88, TNF-α expression in the liver. When using scAAV vectors, higher increases of those transcripts were observed after administration. Simultaneously, an upregulation of additional pro-inflammatory genes, and increased circulating IL-6 was observed [115]. Some but not all, of these responses were Kupffer cell dependent. Independent of the capsid or expression cassette, scAAV vectors induced dose-dependent innate responses by signaling through TLR9 [115]. Other studies, as the removal of CpG sequences from the AAV genome or, the incorporation of TLR9 inhibitory sequences derived from telomeric DNA sequences, resulted in the attenuation of the immune responses [112,116,117]. In addition to TLRs, AAV genome, contains elements that can activate cytosolic DNA sensors, such as the ITR hairpin structures, that can be recognized by soluble PRRs in the cytoplasm, such as cGAS, IFI16, and AIM2 [17]. These bind DNA in a sequence-independent way but in a length- and structure-dependent manner. As mentioned in a previous section, cGAS binds double-stranded DNA (dsDNA) or DNA-RNA hybrids preferentially longer than 36 bp. Although it is a cytoplasmic sensor, it was shown to exist at endogenous levels in the nucleus [112]. It dimerizes upon binding to DNA and triggers a cascade, involving STING and TBK1, to induce the transcription of type I IFN genes (through IRF3) and antiviral cytokines, such as TNF-α and IL-6 [118]. A recent study showed that AAV vectors induce the expression of DNA sensors including cGAS and antiviral genes, such as TNF-α and IFN-γ [119]. In that study, AAV transduction was sixfold higher in cGAS–/– mouse embryonic fibroblasts than in WT fibroblasts. IFI16 exists in both the cytosol and the nucleus and is activated by both ssDNA and dsDNA [120]. It is a member of the pyrin and HIN (hematopoietic IFN-inducible nuclear) domain-containing (PYHIN) family of proteins and the preferential length of its ligand DNA is 70 bp [121]. In the nucleus, upon binding to viral DNA, IFI16 can move to the cytosol to activate the inflammasome pathway. IFI16 may cooperate with the cGAS/STING pathway in some contexts. IFI16 also silences viral gene expression by facilitating the heterochromatinization of the viral genome in the nucleus [122]. IFI16 expression has been shown in CD34+ cells from human bone marrow and monocytoid lineage cells [123]. IFI16 and cGas have additionally been shown to be able to induce cell cycle arrest and apoptosis or pyroptosis in certain cell types [112]. IFI16 was also described to negatively regulate p53 and p21 influencing p53-mediated cell cycle arrest [112]. AIM2, as IFI16 is a PYHIN family member, preferentially binds to DNA stretches of approximately 80 bp in length [121]. It can bind dsDNA in the cytoplasm, forming a NLRP3 inflammasome (a multiprotein complex that triggers caspase-1) and leads to the production of mature IL-1β and IL-18. AIM2 is known to sense DNA damage in the nucleus and induces inflammasome activation, whether AIM2 can sense viral DNA in the nucleus is unknown. Further studies are required to elucidate whether AAV infection activates the AIM2 inflammasome pathway [17]. AAV transduction also originates responses to DAMPs for which hematopoietic cells are particularly sensitive. The AAV DNA hairpin with a free DNA end, as the ITRs, can be sensed by DDR proteins in the nucleus [112]. Through co-localization experiments, it has been shown that a variety of DDR proteins co-localize with nascent vector genomes upon nuclear entry as they undergo second-strand synthesis in discrete nuclear foci. These proteins include NBS1, phosphorylated NBS1 (p-S343-Nbs1), Mre11, Rad50, and Mdc1 [112,124]. The Mre11–Rad50–Nbs1 complex is responsible for recognizing dsDNA nicks near the 5′end of a double-strand break. Exposure of cells to DNA-damaging agents, which upregulate DDR proteins has been shown to increase recombinant AAV vector transduction in the absence of adenovirus co-infection [125,126]. The innate immune and DNA-damage response pathways are indistinguishably linked. Cellular DNA sensors function to prevent cellular replication in response to, not only, viral infection, but also to DNA damage. The cGAS/STING pathway gets activated as a response to genotoxic stress due to DNA damage, and the magnitude determines whether cells will repair, go into senescence, or undergo cell death [112]. During the late stage of AAV transduction, dsRNA is generated as an AAV genome-derived replication intermediate that stimulates intracellular dsRNA sensors, including RIG-I, MDA5, and LGP2, resulting in the activation of NF-κΒ and IRF signaling, consequently promoting type I IFN production [127]. Both NF-κΒ and IRF signaling pathways stimulate the expression of numerous downstream genes that induce an anti-viral state and activate anti-viral adaptive immune responses. Blocking dsRNA activation pathways, including MDA5 and MAVS, was shown to inhibit IFN-β expression from AAV-transduced cells and increase transgene expression [128]. The AAV ITR has inherent promoter activity. The presence of 5′- and 3′-ITRs in AAV genomes can result in the production of both sense and antisense RNAs, forming dsRNA intermediates [17]. These dsRNA molecules are then the subject of recognition by cytosolic RLRs. Cytosolic RLRs are expressed in almost all mammalian cell types. Additionally, RIG-I and MDA5 were observed to be upregulated in the primate retina after long-term AAV transduction [128]. RIG-I recognizes 5′-triphosphorylated blunt-ended short dsRNA or ssRNA. MDA5 preferentially recognizes long dsRNA. LGP2 can regulate RIG-I and MDA5 signaling via its ability to bind RNA [17]. MDA5 and RIG-I have a common signaling adaptor, MAVS, known to induce type I IFN production. The AAV capsids are potential PAMPs for TLR recognition, such as TLR2 and TLR4, that can detect viral liposaccharides and glycoproteins. TLR2 has been reported as the cell surface sensing venue for AAV capsids in human liver cells [129]. In addition to TLR2, AAV capsids activate the unfolded protein response (UPR). However, capsid variants exhibit different levels of activation, likely due to the differences in cellular entry [130]. UPR activation may contribute to further enhance TLR signaling and activation of NF-κΒ pathways [127]. The efficient cell entry of AAV vectors is mediated by the particle binding to ubiquitously expressed surface receptors and co-receptors that are specific to the viral serotype. Restriction of permissiveness occurs at the post-entry level, where multiple barriers constrain vector transduction. Several steps limit AAV vector transduction: entrapment of virions inside the endosomal/lysosomal compartments, inefficient nuclear translocation and uncoating, ineffective single-stranded to double-stranded genome conversion, and the poor stabilization of newly formed viral dsDNA (as single or concatemeric circular episomes) [131]. In the wild-type AAV, cellular co-infection is required by helper viruses. The latter modify the cellular environment of target cells to render them permissive for viral replication. In AAV vector transduction, the contribution of those helper factors is missing. Efficient transduction is mostly limited to post-mitotic cells, in particular, cardiomyocytes, neurons, retinal cells, and skeletal muscle fibers, and requires the use of relatively high multiplicities of infection [131]. AAV host-intrinsic RFs are poorly understood. Most studies are performed in the context of AAV vector transduction under the conditions discussed above, i.e., in the absence of helper virus factors and using high MOIs. Here, we review host intrinsic restriction factors associated with AAV transduction. Most of those target the (1) AAV DNA genome, hampering its conversion to double-stranded form, or the (2) AAV capsid, directing it to proteasomal degradation. Although the pathways have been identified, only part of their intervenient molecules have been discovered. Among the few host proteins reported to block double-stranded DNA conversion are FKBP52 (and the U2 snRNP spliceosome complex), PHD finger-like domain protein 5A (PHF5A)), and U2 snRNP-associated protein. FKBP52, or FKBP prolyl isomerase, when phosphorylated binds specifically with the D-sequence within the inverted terminal repeat (ITR) of the AAV genome [132,133]. FKBP52 can be phosphorylated at both tyrosine and serine or threonine residues, and phosphorylated FKBP52 inhibits the viral second-strand DNA synthesis, leading to inefficient transgene expression. Zhao and colleagues showed that dephosphorylated FKBP52 can no longer bind to the D-sequence, thereby allowing viral second-strand DNA synthesis and efficient transgene expression [132,133,134,135,136]. This was observed in hematopoietic and liver primary cells, and in cell lines. However, previous studies, using FKBP52-knockout (FKBP52-KO) mice, documented that the transduction efficiency in hematopoietic stem/progenitor cells and hepatocytes was significantly less pronounced than in mice, in which FKBP52 is dephosphorylated at tyrosine residues [134,135,136]. The authors suggest that dephosphorylated FKBP52, through interaction with HSP90, mediates the cytoplasmic transport of viral proteins to the nucleus [132]. FKBP52 is a cellular chaperone protein, ~80% is localized in the nucleus and ~20% in the cytoplasm, where it co-localizes with microtubules and dynein, a retrograde motor protein [137,138]. PHF5A, a component of the U2 snRNP mRNA splicing factor, was found to block the expression from recombinant AAV vectors [139]. PHF5A was identified among several candidates in a siRNA library screen study. The disruption of PHF5A expression specifically enhanced AAV vector performance in a serotype and cell type-independent manner. U2 snRNP proteins recognize incoming AAV capsids, through the direct recognition of intact capsids, to mediate the cellular restriction at the step after second-strand synthesis. U2 snRNP spliceosome complex was identified as a novel host factor effectively restricting AAV vectors. The genetic disruption of U2 snRNP and associated proteins, as SF3B1 and U2AF1, resulted in the increased transduction of AAV vectors [139]. The proteasomal degradation of AAV vectors blocks efficient vector transduction. Indeed, proteasome inhibitors were extensively described as enhancers of AAV vector cell transduction in many cell types, both in vitro and in vivo [140]. AAV2 surface tyrosines are targets of the epidermal growth factor receptor protein tyrosine kinase. The phosphorylation of these tyrosines, and subsequent ubiquitination, reduces AAV2 transduction. This hypothesis was tested by mutating key tyrosine residues on the AAV2 surface. These mutations showed to dramatically increase the AAV2 transduction in vitro as well as in vivo [141,142]. Additional mutations of threonines and serines also showed further enhancement in the transduction efficiencies of AAV2 vectors [143]. Moreover, the mutations of capsid residue targets of phosphorylation are not limited to AAV2, and are extensive to other serotypes [144]. Post-translational modification of the AAV capsid, either during vector production or during viral entry, can dramatically affect transduction efficiencies. Protein kinases, phosphorylating AAV capsids, targeting to ubiquitin–proteasome degradation, can thus be viewed as intrinsic restriction factors to AAV vector transduction. Similar to proteasome inhibition, it has been shown that suppressing SUMOylation significantly increases AAV2 transduction [145]. Via a genome-wide siRNA screen, the proteins of the small ubiquitin-like modifier (SUMO) pathway were identified in Chen et al. to be critical in AAV restriction. The authors identified several members of the SUMOylation pathway, as putative genes interfering with AAV gene transduction. Ubc9 (the E2 conjugating enzyme), Sae1, and Sae2 were identified as factors involved in restricting the AAV vectors. Sae1 and Sae2 are the enzymes responsible for activating E1. The knockdown of those genes increased AAV transduction. In a following study, it is shown that the capsid protein VP2 can become SUMOylated [146]. The SUMOylation could be mediated by E3 proteins, as Trim33, or PIAS1, E3 ligases identified in the siRNA screen as putative AAV2 restriction factors [145]. Cellular stress responses, as DDR can trigger SUMOylation activity. AAV vector genomes are inducers of DDR. Several viruses can suppress SUMOylation activity by targeting the key components of the SUMO pathway, and among these are AAV helper viruses [147]. The production of AAV vectors in helper-virus-free HEK 293 have shown particle SUMOylation; however, the prevention of capsid SUMOylation does not rescue the restriction of AAV2 gene transduction. Thus, AAV vectors can become SUMOylated during infection. The incubation of AAV vectors within HeLa and A549 cells induced an elevated SUMOylation activity, which became visible as early as 8 hours post-transduction, and the effect increased over time. This effect, however, was only visible at MOIs of 105 or higher [146]. The authors attribute the restrictive effect of SUMOylation and its activation on AAV transduction via two processes: (1) capsid SUMOylation and the (2) activation of cellular protein SUMOylation by AAV infection [146]. One candidate for the latter is DAXX, a known antiviral factor [148]. In DAXX knockout cells, a high increase in transduction is observed. Mano and colleagues performed a genome-wide RNAi screening to identify AAV vector transduction host RFs [131]. This study identified a high number of cellular factors restricting AAV transduction, with many having functions related to cell cycle regulation and DNA recombination and repair. Five of those proteins were: SETD8 (a SET domain containing lysine methyltransferase), CASP8AP2 (a component of the apoptotic machinery), SOX15 (a developmental transcription factor), TROAP (a cell adhesion molecule reported to mediate blastocyst implantation), and NPAT (a cell cycle progression regulator encoded within the ATM gene). Those proteins, when silenced, in addition to improved transduction, directly induced cellular DNA damage and checkpoint activation. Thus, they might function indirectly by inducing stress cell responses known to improve AAV vector transduction. Several groups have made efforts to identify AAV transduction RFs. Madigan et al. (2019) identified in a CRISPR screening the apical polarity determinant Crumbs 3 (Crb3) as a key RF; and demonstrated that CRISPR knockout (KO) of Crb3 renders cultured hepatocytes more permissive to AAV [149]. They further demonstrated that Crb3 enables the sequestration of essential glycan attachment factors, but not the AAV receptor (AAVR) from the cell surface. Ablation of Crb3 disrupts tight junction integrity and cell polarity, resulting in the mislocalization of glycans to the cell surface, allowing viral attachment and entry. CRISPR screens have also been used to identify host factors improving vector transduction. Meisen et al. conducted two independent, genome-wide CRISPR screenings in the U2 OS cell line, identifying GPR108 and TM9SF2 as critical host factors facilitating transduction [150]. Their full functions were not yet been elucidated; neither their interactor host proteins nor their connection with the innate cellular responses. Nucleic acids carry essential genetic information in all living organisms. Therefore, it is natural that the innate immune system has evolved to recognize foreign nucleic acids as a form of defense. Indeed, nucleic acid structures are the most widely recognized PAMPs. Gene therapy, being based on the transfer of nucleic acids, is subjected to the surveillance and response of the human innate immune system. Gene therapy vectors, either from viral or non-viral vectors, are thus targets of host RFs. Viral vectors, in addition to DNA and RNA recognizing PRRs, may also present protein motifs targeted by PRRs. The activation of PRRs leads to the generation of IFNs and inflammatory response cytokines, upregulating the antiviral innate immune response, and restricting vector transduction. Additionally, intrinsic antiviral RFs also recognize viral nucleic acids and proteins and can act directly on the viral infection pathway abrogating vector transduction. Here, it was reviewed and discussed the host RFs that can block lentiviral and adeno-associated vectors transduction. Although, human cells are well equipped with RF sensors, several aspects should be taken into consideration, and the existing data should be carefully analyzed. Indeed, LV and AAV vectors often provide efficient transduction rates. Several parameters influence vector immunogenicity and cell permissiveness being some host-dependent, while the others are vector-dependent. Measures can also be taken to minimize the induction of an innate immune response. Some of the host-dependent factors influencing vector immunogenicity are, the pre-existing immunity (since adaptive and innate immune responses are intertwined), the delivery mode (in vivo vs. in vitro) administration route, the targeted tissue, the patient age, existence of concurrent infections, and genetic background, among others [151]. The cell and tissue targets of gene therapy will be of utmost importance in the context of gene therapy, since they are the primary cell sensors that the vector should encounter. While some cells constitutively express PRRs and intrinsic antiviral RFs, others only express them when stimulated by IFNs. Off-targeting cells of the innate immune system, in in vivo therapies, as those derived from myeloid progenitors (e.g., dendritic cells and macrophages), is a secondary mechanism that will indirectly restrict transduction efficiency. In addition to difficulties that may exist in the transduction of stems cells, the activation of the innate immune response can also impair their cell fate and function. Within the vector-dependent factors are: (1) the particle serotype/species/pseudotype, (2) promoters used, (3) CpG content, (4) vector purity, and (5) vector dose [151,152]. Vector dose and purity are interconnected since the use of higher doses may increase DNA or other contaminants carry over from the producer cells or manufacturing bioprocess (eliciting unspecific immune responses) [32]. In the particular case of AAV vectors, the use of high doses has been related to the vector immunogenicity found in clinical trials [151]. Higher vector doses also elicit DNA damage responses that will induce further innate immunity responses. Lentivirus infections are extensively studied. Adeno-associated viruses, being non-pathogenic, are mostly studied in the context of gene therapy. Thus, much more information and consolidated knowledge exists for lentivirus than for AAV. Notwithstanding, data from the wild-type viral infection cannot be directly translated to vector transduction in the context of gene therapy. In LV and AAV vector-based therapies, innate sensing could be exacerbated by the absence of viral accessory and helper proteins known to help the escape from antiviral restriction factors. Additionally, the high vector doses are significantly superior to those of a typical initial infection. In contrast, the viral cycle is incomplete, since LV and AAV vectors are replication incompetent; as such, the production of viral genomes and proteins prone to RF detection does not occur. Several strategies can be used to lower innate immunity responses against AAV and LV vectors, for example, CpG depletion, the addition of TLR- and cGAS-inhibitory sequences, the expression of RIG-I-inhibitory proteins, the use of Cyclosporine H, and the use of immunosuppressants [17,153,154]. However, efforts to improve AAV and LV vector transduction toward the use of lower doses are still needed and will greatly improve their therapeutic efficacy in the clinic.

PMC10001044

Albert Leng,Manuj Shah,Syed Ameen Ahmad,Lavienraj Premraj,Karin Wildi,Gianluigi Li Bassi,Carlos A. Pardo,Alex Choi,Sung-Min Cho

Pathogenesis Underlying Neurological Manifestations of Long COVID Syndrome and Potential Therapeutics

06-03-2023

COVID-19,SARS-CoV-2,long COVID,neurological manifestations,neurological complication,outcome,brain fog

The development of long-term symptoms of coronavirus disease 2019 (COVID-19) more than four weeks after primary infection, termed “long COVID” or post-acute sequela of COVID-19 (PASC), can implicate persistent neurological complications in up to one third of patients and present as fatigue, “brain fog”, headaches, cognitive impairment, dysautonomia, neuropsychiatric symptoms, anosmia, hypogeusia, and peripheral neuropathy. Pathogenic mechanisms of these symptoms of long COVID remain largely unclear; however, several hypotheses implicate both nervous system and systemic pathogenic mechanisms such as SARS-CoV2 viral persistence and neuroinvasion, abnormal immunological response, autoimmunity, coagulopathies, and endotheliopathy. Outside of the CNS, SARS-CoV-2 can invade the support and stem cells of the olfactory epithelium leading to persistent alterations to olfactory function. SARS-CoV-2 infection may induce abnormalities in innate and adaptive immunity including monocyte expansion, T-cell exhaustion, and prolonged cytokine release, which may cause neuroinflammatory responses and microglia activation, white matter abnormalities, and microvascular changes. Additionally, microvascular clot formation can occlude capillaries and endotheliopathy, due to SARS-CoV-2 protease activity and complement activation, can contribute to hypoxic neuronal injury and blood–brain barrier dysfunction, respectively. Current therapeutics target pathological mechanisms by employing antivirals, decreasing inflammation, and promoting olfactory epithelium regeneration. Thus, from laboratory evidence and clinical trials in the literature, we sought to synthesize the pathophysiological pathways underlying neurological symptoms of long COVID and potential therapeutics.

Pathogenesis Underlying Neurological Manifestations of Long COVID Syndrome and Potential Therapeutics The development of long-term symptoms of coronavirus disease 2019 (COVID-19) more than four weeks after primary infection, termed “long COVID” or post-acute sequela of COVID-19 (PASC), can implicate persistent neurological complications in up to one third of patients and present as fatigue, “brain fog”, headaches, cognitive impairment, dysautonomia, neuropsychiatric symptoms, anosmia, hypogeusia, and peripheral neuropathy. Pathogenic mechanisms of these symptoms of long COVID remain largely unclear; however, several hypotheses implicate both nervous system and systemic pathogenic mechanisms such as SARS-CoV2 viral persistence and neuroinvasion, abnormal immunological response, autoimmunity, coagulopathies, and endotheliopathy. Outside of the CNS, SARS-CoV-2 can invade the support and stem cells of the olfactory epithelium leading to persistent alterations to olfactory function. SARS-CoV-2 infection may induce abnormalities in innate and adaptive immunity including monocyte expansion, T-cell exhaustion, and prolonged cytokine release, which may cause neuroinflammatory responses and microglia activation, white matter abnormalities, and microvascular changes. Additionally, microvascular clot formation can occlude capillaries and endotheliopathy, due to SARS-CoV-2 protease activity and complement activation, can contribute to hypoxic neuronal injury and blood–brain barrier dysfunction, respectively. Current therapeutics target pathological mechanisms by employing antivirals, decreasing inflammation, and promoting olfactory epithelium regeneration. Thus, from laboratory evidence and clinical trials in the literature, we sought to synthesize the pathophysiological pathways underlying neurological symptoms of long COVID and potential therapeutics. Coronavirus disease 2019 (COVID-19) is a multi-system disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The Centers for Disease Control and Prevention (CDC) considers long COVID-19 to be present when symptoms last longer than four weeks after the initial infection. The collection of symptoms goes by many names, including but not limited to “long COVID,” “chronic COVID,” “post-acute sequelae of COVID-19,” and “post-COVID conditions.” Prior studies have reported different frequencies of long COVID, ranging from 13.3% to 54% of patients after initial SARS-CoV-2 infection [1,2]. Notably, one subtype of long COVID includes neurological sequelae, which some reports have identified as being present in one third of patients in the first six months following acute COVID-19 infection. These symptoms manifest as objective abnormalities on neurological examination, such as motor/sensory deficits, hyposmia, cognitive deficits, and postural tremor [3]. While prior studies have proposed potential mechanisms for the symptoms of long COVID, there are limited reports that synthesize and evaluate the pathophysiology of neurological manifestations of long COVID and its therapeutic options. In doing so, we connect basic science research, translational research, and the findings of epidemiological and clinical studies. In a meta-analysis of 257,348 COVID-19 patients, some of the most common long COVID symptoms at three to six months included fatigue (32%), dyspnea (25%), and concentration difficulty (22%), reflecting the multisystemic nature of long COVID [4]. In addition to these symptoms, there is a specific cluster of specific neurological symptoms and sequelae of long COVID. For instance, in a sample of 10,530 long COVID patients at a 12-week follow-up, some of the most common neurological symptoms included fatigue (37%), brain fog (32%), memory issues (28%), attention disorder (22%), myalgia (28%), anosmia (12%), dysgeusia (10%), and headaches (15%) [5]. Some of these symptoms continue to persist at longer follow-up periods—including six-month and one-year follow-ups after initial diagnosis [6,7,8]. Considering these studies, it is evident that cognitive symptoms, headaches, sleep disorders, neuropathies, and autonomic dysfunction are some of the most common neurological manifestations of long COVID. Other, less frequent, neurological sequelae include dysexecutive syndrome, ataxia, and motor disturbances [9,10]. In all, these symptoms can lead to significant dysfunction and disability, with around 30% of long COVID patients aged 30–59 indicating that their neurological symptoms made them severely unable to function at work [10]. Specific demographic risk factors for long COVID have been identified. Females were reported to have a higher risk of developing long COVID symptomatology [1,11,12,13,14,15]. Information on age is less unanimous. Several studies have reported that older patients (vs. younger) are at increased risk of developing long COVID [1,14,15,16,17]. However, other studies have shown that younger patients are at increased risk, while some studies have shown no association between age and the development of long COVID [11,12,16,18]. Regarding race/ethnicity, a study of 8325 patients with long COVID reported that non-Hispanic white patients were more likely to develop long COVID while non-Hispanic black patients were less likely to develop long COVID [14]. Alternatively, in a longitudinal analysis of 1038 patients, race/ethnicity had no significant association with long COVID occurrence [17]. However, there are sparse data on the specific risk factors for neurological manifestations of long COVID. In one study, female sex and older age were shown to be associated with the neurological manifestations of long COVID, while race/ethnicity, COVID-19 severity, and other comorbidities, such as hypertension, diabetes, and congestive heart failure, were not [19]. Additionally, it has been noted that an increased severity of neurological symptoms is associated with a diminished CD4+ T cell response against the spike protein, suggesting that the T cell response is necessary to counteract the severity of neurological long COVID. In this cohort, mRNA COVID-19 vaccination elevated the T cell response and helped diminish the severity of neurological symptoms in long COVID [20]. Yet overall, the impact of demographic factors such as race/ethnicity, as well as comorbidities on long COVID, needs more dedicated epidemiological studies as many of the previous studies are influenced by geographical and recruitment biases. Focusing on biological and medical factors influencing long COVID, studies have focused on the magnitude and severity derived from the acute phase of COVID-19. For patients who required intensive care unit (ICU) admission in the acute phase of COVID-19, long-term impairment following ICU discharge appears to be frequent. For example, in a study of 117 patients that required high-flow nasal cannulae, non-invasive mechanical ventilation, or invasive mechanical ventilation, 86% reported long COVID symptoms at a six-month follow-up. These included, but were not limited to, fatigue, muscle weakness, sleep difficulties, and smell/taste disorders [6,17]. Metabolic risk factors such as a high body mass index, the presence of insulin resistance, and diabetes mellitus have been associated with long COVID [14,15,16,21,22]. Not surprisingly, patients who were “hospitalized” in the acute phase of COVID-19 were more likely to develop long COVID symptoms [15,17]. Other risk factors include Epstein–Barr virus reactivation, history of smoking, exposure to air toxicants and pollutants, and the presence of chronic comorbid conditions [13,14,20,22,23]. An increased risk of mortality has been observed in COVID-19 patients with post-acute sequelae (defined as at least 365 days of follow-up time for long COVID symptoms). In a large study of 13,638 patients, an increased 12-month mortality risk after recovery from the initial infection was observed as compared with patients with suspected COVID-19 and who had a negative polymerase chain reaction (PCR) test [24]. Additionally, long COVID patients with more severe initial infections (defined by occurrence of hospitalization) had an increased 12-month mortality risk after recovery from the initial infection and subsequent development of post-acute sequelae in comparison with patients with moderate or mild initial COVID-19 infections [24]. Furthermore, age, male sex, unvaccinated status, and baseline comorbidities were associated with higher mortality in patients with long COVID when followed over time [25]. Regarding vaccination, a systematic review of 989,174 patients across different studies demonstrated that vaccination before acute COVID-19 infection was associated with a reduced risk (RR = 0.71) of developing non-neurological symptoms of long COVID [26]. Likewise, in a survey of long COVID patients who had not yet been vaccinated, most patients had an improved average symptom score, suggesting that vaccination may play a role in mitigating the symptoms of long COVID [27]. Much of the available literature focuses on mortality outcomes related to long COVID broadly. To our knowledge, there are no studies that report mortality outcomes on the neurological symptoms of long COVID specifically. The SARS-CoV-2 virus is known to invade human cells through engagement with specific membrane cell receptors which include angiotensin-converting enzyme 2 (ACE2) transmembrane receptor and activation of SARS-CoV-2 spike protein by transmembrane serine protease 2 (TMPRSS2) cleavage. Undoubtedly, polymorphisms that alter the ACE2 and spike protein interaction, the TMPRSS2 proteolytic cleavage site, and ACE2 expression correlate with the susceptibility and severity of COVID-19 with some ACE2 variants incurring up to a three-fold increase in the development of severe disease [28,29]. Since the severity of disease is associated with the incidence of long COVID symptoms [30], there is a possibility that ACE2 and TMPRSS2 polymorphisms could potentiate long COVID as well. To our knowledge, the only study to have investigated this relationship found no predisposition of formerly identified ACE2 and TMPRSS2 polymorphisms linked to disease severity for long COVID symptoms in patients who were previously hospitalized for COVID-19 [31]. Relating specifically to neurological symptoms of long COVID, receptors are expressed by endothelial and nervous system cells such as neurons, astrocytes, and oligodendrocytes [32,33,34,35,36]. However, the possibility for viral invasion of neural tissue remains highly debated. The presence of SARS-CoV-2 in cortical neurons from autopsy studies and replicative potential of SARS-CoV-2 in human brain organoids implicates the neurotropic effects of the virus in the pathogenesis of neurological symptoms to some extent [36], but the possibility and mechanism of direct viral infection of the central nervous system (CNS) still remain unclear. Current proposed pathways include transsynaptic invasion by transport along the olfactory tract [37], which is highly unlikely due to the lack of ACE2 receptors and TMPRSS2 on olfactory neurons [38,39,40], and hematogenous spread through invasion of choroid plexus cells and pericytes [41,42]. The latter has been shown to occur in human neural organoid models where ACE2 receptors are heavily expressed on the apical side of the choroid epithelium, allowing for SARS-CoV-2 invasion through the vasculature, subsequent ependymal cell death, and blood–CSF barrier (B-CSF-B) disruption [41]. Despite this potential for viral neuroinvasion through hematogenous means, there is overwhelming evidence showing a lack of SARS-CoV-2 RNA and protein in the cerebrospinal fluid (CSF) of COVID-19 patients with neurological symptoms [43,44], globally in the brain tissue from autopsy studies [45,46], and even within the choroid plexus of individuals with severe disease [47]. In contrast, persistent anosmia is a symptom of long COVID that likely results from lasting effects of direct viral damage of the olfactory epithelium. In acute COVID-19, SARS-CoV-2 can infect non-neural cell types that express ACE2 in the olfactory epithelium, specifically stem cells, perivascular cells, sustentacular cells, and Bowman’s gland cells (Figure 1), that leads to cell death and loss of uniformity demonstrated in humanized ACE2 mice [38,48]. Unlike with transient anosmia observed in other respiratory infections, imaging studies performed on patients with persistent COVID-19 anosmia demonstrated extensive damage to the olfactory epithelium; this manifests as thinning of olfactory filia and reduction in olfactory bulb volumes [39,49]. Additionally, biopsies of olfactory mucosa from patients with persistent anosmia of varying etiologies reinforce the connection between thinning of olfactory epithelium and enduring symptoms [50]. Thus, the loss of stem cells and support cells in the neuroepithelium causes failure of epithelial repair, resulting in the thinning and loss of olfactory dendrites likely accounting for long-standing anosmia [51]. In addition, persistent inflammation evidenced by elevated levels of interleukin 6 (IL-6), type I interferon (IFN), and C-X-C motif chemokine ligand 10 (CXCL10) within the olfactory epithelium, secondary to invasion, appears to contribute to long-standing anosmia [52]. Altogether, SARS-CoV-2 invasion of supportive cells and subsequent long-lasting local inflammation causes irreversible damage to the olfactory epithelium and are thus the main drivers of persistent hyposmia, anosmia, and dysgeusia. The invasion of the olfactory epithelium by SARS-CoV-2, however, does not necessarily serve as a window of opportunity for neuroinvasion. Although early in vitro and in vivo studies may suggest the possibility for neuroinvasion of the CNS in the pathogenesis of disease in long COVID, they are limited by neuropathological evidence for SARS-CoV-2 in the brain parenchyma or CSF of patients. Additionally, anatomical barriers to neuroinvasion, such as the perineurial olfactory nerve fibroblasts that wrap olfactory axon fascicles and the added absence of ACE2 receptors for entry on olfactory neurons [51], further call into question the feasibility for this mechanism of pathogenesis. Thus, to this date, there has not been a validated demonstration of SARS-CoV-2 invasion of, and replication in, the CNS. Following SARS-CoV-2 infection, viral shedding has been shown to persist in the upper respiratory and gastrointestinal (GI) epithelium for a median of 30.9 days and 32.5 days, respectively, in severe COVID-19 [53,54]. Due to the ability of SARS-CoV-2 to cause persistent symbiont depletion and gut dysbiosis in the GI tract [55], prolonged viral shedding maintains microbiome perturbations, likely resulting in brain–gut axis dysfunction [56]. Additional evidence of brain–gut axis alteration from a co-expression analysis in SARS-CoV-2-infected human intestinal organoids revealed ACE2 co-regulation of dopa-decarboxylase (DDC) and clusters of genes involved in the dopamine metabolic pathway and absorption of amino acid precursors to neurotransmitters (Figure 1) [57]. Thus, the involvement of gut dysbiosis and brain–gut axis alterations that persist due to continual viral shedding have been suggested as a possible mechanism in neurological manifestations of long COVID [7]. Aside from SARS-CoV-2 viral persistence, reactivation of viruses of the herpesviridae family, including Epstein–Barr virus (EBV) and Varicella-zoster virus (VZV), have also been well documented in long COVID patients. EBV and VZV, a lymphotropic gammaherpesvirus and neurotrophic alphaherpesvirus respectively, can independently affect more than 90% of people worldwide [58,59]. Both viruses can remain latent in host cells after primary infection (in memory B cells in EBV and the neurons of sensory ganglia in VZV) such that the onset of a stressor, such as another acute viral infection, can lead to the reactivation of these herpes viruses and cause inflammation and neurological symptoms. SARS-CoV-2 can act as that stressor and precipitate reactivation of other viruses in COVID-19 and long COVID symptomatology. According to an early retrospective study of acute COVID patients post-hospitalization, 25% of patients with severe disease had increased serological titers of early antigen IgG (EA-IgG) and viral capsid antigen IgG (VCA-IgG) which serve as proxy markers for reactivation of EBV [60]. More specific to long COVID, a survey study found that two thirds of patients with symptoms 90 days after primary SARS-CoV-2 infection were positive for EBV reactivation, which was also indicated by positive titers of VCA-IgG and early antigen-diffuse IgG (EA-D IgG) [61]. Higher frequency of long COVID symptoms experienced by patients were also significantly correlated with increased EA-IgG titers. Similarly, a longitudinal study of 309 patients tracked from primary infection to convalescence revealed EBV viremia to be one of the four main risk factors for developing long COVID symptoms, with the other three being type II diabetes, SARS-CoV-2 RNAemia, and autoantibodies formation [20]. EBV reactivation has been specifically associated with memory and fatigue in long COVID. Apart from COVID-19, the immune response to EBV reactivation has been shown to reflect that of myalgic encephalomyelitis (ME) or chronic fatigue syndrome (CFS) which could link EBV viremia to the development of ME/CFS-like symptoms in long COVID [62,63]. This immune profile has been identified in a cross-sectional study with 215 long COVID patients where there was an elevated antibody reactivity to EBV gp23, gp42, and EA-D which all were correlated with interleukin 4 (IL-4) and IL-6 producing CD4+ T cells [64]. The same study also identified significant levels of antibody reactivity to the VZV glycoprotein E which was similarly associated with the immune profile mentioned above. VZV manifestations are also common in COVID-19, occurring in about 17.9% of patients mostly and in the form of dermatome rashes, with rare instances of encephalitis-meningitis and vasculitis [65]. Although less prominent in long COVID pathogenesis than EBV reactivation, VZV reactivation can still contribute to neurological symptoms due to its involvement with the CNS. The persistence of SARS-CoV-2 through viral shedding provides an impetus to the consideration of antiviral drugs as potential long COVID therapies. Antiviral drugs used in the treatment of acute COVID-19, specifically remdesivir, molnupiravir, fluvoxamine and nirmatrelvir/ritonavir combination (Paxlovid), have demonstrated substantial reductions in mortality and hospitalization [66,67]. Unlike its counterparts, nirmatrelvir is a highly specific competitive inhibitor of the SARS-CoV-2-3CL protease and therefore of its viral replication. Ritonavir increases the bioavailability of nirmatrelvir by preventing its hepatic metabolism [68]. An RCT in high-risk non-hospitalized adults with acute COVID-19 showed that Paxlovid reduced viral load rapidly (by day five) as well as mortality and hospitalization (Table 1) [67]. Paxlovid may be vital in reducing neurological symptoms of long COVID, secondary to viral load/shedding, such as persistent cognitive impairment and insomnia. In a recent study (preprint), Xie et al. compared patients who received no antiviral or antibody treatment during acute COVID-19 infection (N = 47,123) with those treated with oral nirmatrelvir within five days of a positive COVID-19 test (N = 9217). Compared with the controls, patients acutely treated with nirmatrelvir had substantially lower risk of developing long COVID symptoms [69]. Their definition of long COVID symptomatology involved 12 outcomes, including myalgia and neurocognitive impairment [69]. While the exact indications for its use in long COVID patients remain to be defined by RCT protocols (Table 2, NCT05576662), Paxlovid demonstrates promise. We posit that Paxlovid may be useful in reducing persistent viral shedding from infected epithelium, and can therefore reduce mechanisms secondary to SARS-CoV-2 invasion such as the previously mentioned gut dysbiosis and inflammation of the olfactory mucosa. The use of antivirals to address the issue of viral reactivation in long COVID, including treatments for herpesviruses such as acyclovir and valacyclovir, has been documented but their efficacy in alleviating neurological symptoms of long COVID have yet to be assessed [65]. One retrospective study in Wuhan assessed 28-day mortality outcomes for 88 COVID-19 patients with EBV reactivation that were treated with ganciclovir compared with those of matched controls [60]. Ganciclovir treated patients had a significantly higher survival rate than controls, however, specific neurological symptoms were not assessed. Further studies are needed to demonstrate the effectiveness of antivirals specific to herpesvirus reactivation in long COVID patients. In addition to the specific use of antivirals, other agents, such as cannabidiol, may have some antiviral efficacy as a therapy for long COVID. It is known that the active metabolite in cannabidiol, 7-OH-CBD, can block SARS-CoV-2 replication by inhibiting viral gene expression, upregulating interferon expression, and promoting antiviral signaling pathways [76]. Notably, cannabidiol has been reported to downregulate ACE2 and TMPRSS2 [77]—key enzymes involved in the SARS-CoV-2 virus invasion process and the potential evolution to long COVID. A phase 2 clinical trial (NCT04997395) has begun looking into the feasibility of using cannabidiol as a treatment for long COVID (Table 2). Additionally, Cannabidiol has been shown to induce neuroprotective effects [78,79]. Taken together, this suggests that cannabidiol can help ameliorate the neurological symptoms of long COVID, although future clinical trials are needed to provide further evidence. Regeneration of olfactory mucosa occurred with administration of intranasal insulin in non-COVID-19 patients. Insulin, through its action as a phosphodiesterase enzyme inhibitor, can increase cyclic adenylate monophosphate (cAMP) and cyclic guanylate monophosphate (cGMP) levels by interacting with the nitric oxide cycle [80]. These growth factors are known to stimulate the olfactory epithelium and promote regeneration [80]. In their pragmatic randomized controlled trial (RCT) in a small population (N = 38), Rezaian et al. assessed the success of twice weekly intranasal protamine insulin gel foam therapy (vs. normal saline placebo) on patients with mild to severe post-infectious hyposmia (Table 1) [74]. They determined that olfaction (via Connecticut Chemosensory Clinical Research Centre score) at four weeks was notably better in the insulin treated groups (5.0 ± 0.7) versus the placebo group (3.8 ± 1.1, p < 0.05) [74]. A larger trial in COVID-19 patients is ongoing (Table 2, NCT05104424). More novel interventions have also demonstrated promise. Indeed, 40 patients with anosmia post COVID-19 infection were randomized to either intranasal insulin film vs. normal saline (placebo). At 30 min after administration, patients in the treatment group had significantly greater odor detection compared with both their baseline and the placebo group [73]. Although RCTs with larger sample sizes and longer follow-up are necessary, these findings are promising in treating persistent anosmia in long COVID. Due to the damaging effects of local inflammation on the olfactory epithelium, corticosteroids have been used in certain patients to hasten the recovery and repopulation of the olfactory epithelium [71,81]. In 2021, an RCT of mometasone nasal spray, which included one hundred COVID-19 patients with post-infection anosmia, assigned patients to two treatment branches: mometasone furoate nasal spray with olfactory training for three weeks (N = 50) or the control group with only olfactory training (N = 50). There was no significant difference in duration of smell loss, from anosmia onset to self-reported complete recovery, between groups (p = 0.31). However, a significant improvement in smell score was recorded in both groups by week three [71]. Despite this, Singh et al. were able to demonstrate significant improvements in smell (on day five) compared with baseline (day one) using fluticasone nasal spray compared with no intervention (Table 1) [70]. It should be noted that many agents for the treatment of postinfectious hyposmia have been studied previously for non-COVID-19 patients including pentoxifylline, caffeine, theophylline, statins, minocycline, zinc, intranasal vitamin A, omega-3, and melatonin. An in-depth evaluation of their use in non-COVID-19 anosmia is outside the scope of this review. However, in their detailed systematic review, Khani et al. posit that different combinations of the above agents may be of use in long COVID depending on the etiology (viral invasion vs. inflammatory damage) [81]. With a unique range of immune cell phenotypes, chemokine and cytokine production, and inflammatory molecules, the immunological response to SARS-CoV-2 infection has been widely investigated to rationalize some of the neurological symptoms of long COVID. Although autoantibody generation has been proposed, this inflammatory response has been better characterized with persistent systemic inflammation leading to expansion of monocyte subsets and T cell dysregulation, which in turn is associated with BBB dysfunction, neural glial cell reactivity, and subcortical white matter demyelination (Figure 2). After SARS-CoV-2 infection, a variety of systemic inflammatory processes are upregulated, and specific immune cell populations are expanded; this disturbance of the peripheral immune system can persist for many months after the infection, which can lead to neurological symptoms. Broadly speaking, when compared with healthy controls, recovered COVID-19 individuals exhibited differences in the populations of innate immune cells, such as natural killer cells, mast cells, and C-X-C motif chemokine receptor 3+ (CXCR3+) macrophages, as well as adaptive immune cells, such as T-helper cells and regulatory T cells ([82], p. 2). With non-naive phenotypes, these cells tend to secrete and be activated by increased levels of cytokines and inflammatory markers, including, but not limited, to interferon β (IFN-β), interferon λ1 (IFN-λ1), C-X-C motif chemokine ligand 8 (CXCL8), C-X-C motif chemokine ligand 9 (CXCL9), CXCL10, interleukin 2 (IL-2), IL-6, and interleukin 17 (IL-17) [83,84]. Several studies have drawn a striking similarity between the symptomatology of long COVID and mast cell activation syndrome (MCAS), wherein aberrant mast cell activation promotes excessive release of inflammatory mediators, such as type 1 IFNs, and cytokine activation of microglia [85,86]. Triggered by viral entry, these mast cells are commonly found at tissue–environment interfaces and may contribute to persistent systemic inflammation microvascular dysfunction with CNS disturbances in long COVID [85,86]. SARS-CoV-2 specific T cell responses have also been described to have increased breadth and magnitude. These signature T cell responses against SARS-CoV-2 increase with higher viral loads, indicated by significantly elevated levels of nucleocapsid-specific interferon gamma (IFN-γ) producing CD8+ cells in serum samples of patients with persistent SARS-CoV-2 PCR positivity [87,88]. Furthermore, a study using flow cytometry on the peripheral blood of long COVID patients detected elevated expansion of non-classical monocytes (CD14dimCD16+) and intermediate monocytes (CD14+CD16+) up to 15 months post infection when compared with healthy controls [89]. Physiologically, non-classical monocytes are involved in complement-mediated and antibody-dependent cellular phagocytosis against viral insults and are commonly found along the luminal side of vascular endothelium, thereby contributing to BBB integrity. For example, severe long COVID patients were found to have increased levels of macrophage scavenger receptor 1 (MSR1), signifying a high degree of peripheral macrophage activation that can in turn disrupt the BBB and cause tissue damage [90]. Alternatively, intermediate monocytes specialize in antigen presentation and simultaneous secretion of pro-inflammatory cytokines. Although this marked systemic hyperinflammatory state has not been shown to directly cause neuropsychiatric manifestations, it may contribute to disease progression via chronic activation of specific monocyte and T cell populations and neurovascular dysfunction of the BBB. These mechanisms can result in the spread of inflammatory molecules and immune cells from the periphery into the CNS, inducing a persistent neuroinflammatory response. With several parallels to the described systemic inflammation, the CNS exhibits persisting trends of monocyte expansion and T cell exhaustion after SARS-CoV-2 infection, the latter of which refers to T cells adopting a distinct cytokine profile with poor effector function. Similar to systemic trends, monocyte expansion in the CNS refers to an increase in the population of non-classical monocytes (CD14dimCD16+) and intermediate monocytes (CD14+CD16+) in CSF of long COVID patients [91,92]. Indeed, monocyte pools analyzed in long COVID patients exhibit a reduction of classical monocytes, indicated by lower levels of pan-monocytic markers and CNS border-associated macrophage phenotypes [91]. While the function of monocytes may be less understood in a chronic disease setting, the expansion of monocyte subsets with antiviral and antigen-presenting phenotypes may be implicated in long COVID symptoms due to its role in BBB disruption and neuroinflammation. As a result of chronic stimulation by antigens, T cells can assume a distinct cytokine profile with increased inhibitory transcription factor expression and decreased effector function, a process termed exhaustion. More common in CD8+ T cells, this signature process implicates phenotypic and functional defects that can limit T cell functional responsiveness in clearing infection in chronic settings [93]. Persisting for months post infection to prevent recurring illness, CD8+ memory T cells in serum samples of long COVID patients were found to increase in number with higher levels of cytolytic granule expression but with limited breadth and reduced antigen-specific activation [94]. Although the secretion of cytolytic granzymes, IL-6, and nucleocapsid-specific IFN-γ increase in long COVID, these T cells present with limited polyfunctionality and decreased diversity of effector expression; this altered profile was strongly associated with symptoms of depression and decreased executive function [94]. Regarding localization, this population of T cells producing higher granzyme levels can be seen in certain anatomic niches, such as in microglial nodules which are hotspots for immune response activation [95]. Cytotoxic CD8+ T cells also congregate near vasculature and produce a surge of cytokines that disrupts the BBB, causing vascular leakage and the propagation of inflammation [95,96]. Evidence against T cell expansion and exhaustion also exists, as one immunophenotyping study shows that persistent T cell changes and neurological deficits are associated with age rather than ongoing illness and fatigue [92]. In summary, though there is a degree of heterogeneity in respect to inflammatory molecules and immune cell populations, long COVID patients with neurological symptoms exhibit persistent systemic inflammation with pronounced differences in circulating myeloid and lymphocyte populations, including prominent peripheral B cell activation with a greater humoral response against SARS-CoV-2 [64]. Elevated levels of non-classical monocytes and intermediate monocytes can bring about altered vascular homeostasis and chronic inflammatory processes, which are largely mediated by Th1 cytokines. Increased amounts of exhausted CD4+ and CD8+ T cells with decreased central memory CD4+ T cells implicate a distinct immunological signature with decreased effector function and resulting aberrant immune engagement [64]. Autoantibody generation has been hypothesized to contribute to the persisting abnormal immunological response observed post infection. Rather than being caused by the virus directly, the autoimmune antibody reaction is suggested to be a product of the pronounced immune and inflammatory reaction [22,97]. The serologically detected autoantibodies can be categorized into antibodies against extracellular, cell surface and membrane, or intracellular targets, which include immunoglobulin G (IgG) and immunoglobulin A (IgA) antibodies against cytokines [98], angiotensin converting enzyme 2 (anti-ACE2) [99], and nuclear proteins (ANA), respectively [100]. Following activation of B cells in the periphery and cytokine abnormalities, these serologic IgG and IgA antibodies exhibit a polyclonal distribution, affect cytokine function and endothelial integrity, and can enter the CNS given the BBB disruption [90]. Although there are limited reports, these autoimmune responses have been proposed to be present with acute-onset encephalitis, seizures, meningitis, polyradiculitis, myelopathy, and neuropsychiatric symptoms [101,102,103,104,105]. Persistent ANA autoreactivity has been linked with long-term symptoms of dyspnea, fatigue, and brain fog seen in long COVID [106]. Anti-ACE2 antibodies, which are associated with fatigue and myelitis, can elicit an abnormal renin–angiotensin response, cause malignant hypertension-related ischemia and upregulate thrombo-inflammatory pathways [97]. While these antibodies have been associated with neurological manifestations after SARS-CoV-2 infection, they have also been limited to parainfection and acute post-infection time periods. Furthermore, small studies have reported the lack of autoantibodies in acute COVID-19 patients presenting with encephalitis [107]. More convincingly, a recent exploratory, cross sectional study illustrated that despite patients exhibiting an array of autoantigen reactivities, the total levels of autoantibodies were definitively not elevated in the extracellular proteome of patients with long COVID compared with convalescent controls [64]. Perhaps sparked by previous demonstrations of peripheral B cell activation, research supporting autoantibody generation in the neurological manifestations of long COVID have been mostly limited to various case reports and studies that, due to sample size and timescale constraints, have limited generalizability. Though autoantibodies can drive inflammation, neuronal dysfunction, and subsequent neurodegeneration, which are all observed in long COVID, this mechanism is not as well understood and warrants further investigations to implicate it in the pathogenesis of long COVID. Control of inflammation post-infection may attenuate persistent cytokine release, immune cell activation, and the pronounced neural immune response, thereby alleviating neurological symptoms of long COVID. Support for this rationale derives from the studies that showed lower prevalence of long COVID in those with less robust inflammatory and immune responses to acute infection, such as vaccinated patients [69,108] and those treated with antivirals [109,110]. Here, we review several promising anti-inflammatory therapies for those with long COVID. A currently recruiting double-blinded placebo-controlled RCT assessing the efficacy of oral lithium (10 mg daily) aims to determine if fatigue, brain fog, anxiety and cognitive outcome scores improve after three weeks of lithium therapy (NCT05618587). Despite the anti-inflammatory effects of lithium, good CNS penetrance, and efficacy in reducing inflammation in patients with acute COVID-19 [111], lithium’s efficacy and benefit–risk profile in patients with long COVID and neurological symptoms have not been proven. RSLV-132 is a novel RNase fusion protein that digests ribonucleic acid contained in autoantibodies and immune complexes generated by the humoral immune response. Therefore, RSLV-132 has applications in both autoimmune disease and post-viral syndromes caused by autoantibody generation, such as long COVID. When compared with the placebo in patients with Sjogren’s syndrome, RSLV-132 decreased fatigue, which was assessed using Functional Assessment of Chronic Illness Therapy (FACIT), Fatigue Visual Analogue Score, and Profile of Fatigue Score at week 14 [112]. The phase 2 clinical trial of RSLV-132 (NCT04944121) follows patients 10 weeks after the start of treatment and assesses fatigue using Patient-Reported Outcome Measurement Information System (PROMIS), FACIT scores and long COVID symptoms via questionnaires. The precise indication for RSLV-132 requires further study, as patients with long COVID may have sub-threshold or absent autoantibody levels as previously mentioned [107]. RCTs assessing the efficacy of conventional anti-inflammatory agents, such as steroids or IV immunoglobulin, are ongoing: one upcoming trial involves the randomized treatment of patients with either IV methylprednisolone, IV immunoglobulin or IV saline (NCT05350774). Depression, anxiety, and cognitive assessment scores will be compared at the end of the three-month (minimum) follow-up period. Ongoing RCTs of anti-inflammatory agents are summarized in Table 2. Additionally, brain fog and fatigue, which are the most prevalent neurological symptoms of long COVID [5], might arise from the prolonged neuroinflammation secondary to the innate immune activation (immune cell migration across the BBB and chemokine release) and humoral activation (autoantibody generation) described above. While current trials are investigating various agents that control the systemic inflammation discussed above, dextroamphetamine–amphetamine (NCT05597722) may be suitable for use in long COVID patients to improve brain fog, given its use in attention-deficit/hyperactivity disorder. In a similar vein, low-dose naltrexone has shown promise in improving brain fog and fatigue (self-reported via questionnaire) [72]; an upcoming placebo-controlled RCT (NCT05430152) will provide greater clarity on its efficacy. Along with other mast cell mediator blockers and stabilizers used in MCAS that target mast cell overactivation and subsequent inflammation, antihistamine treatment via a combined histamine H1/H2 receptor blockade has been associated with significant symptomatic improvement in long COVID patients according to a recent observational study [75]. However, further studies are required to determine the optimal patients for the above interventions. One of the most prominent hypothesized mechanisms of long COVID symptomatology involves the activation of the neuroimmune system through the interplay of neural cells and glial cells, namely astrocytes, microglia, and oligodendrocytes. Astrocytes are critical for CNS homeostasis as they play roles in neuron–glial cell interaction, synaptic function, and blood–brain barrier integrity. Microglia are fundamental for processes of innate immunity within the CNS and are central to synaptic function, maintaining neural networks, and supporting homeostatic repair mechanisms upon injury to the micro-environment. However, with altered cytokine activity and brain injury, glial cells can become overactivated. Evidenced by increased levels of ezrin (EZR) in long COVID patients, these reactive astrocytes upregulate NF-κB, which can cause endothelial cell death and increase extracellular glutamate, resulting in BBB disruption and hyperexcitability-induced neurodegeneration, respectively [90,113,114,115]. Similarly, it is suspected that reactive microglia lose their plasticity-promoting function and facilitate disruption of neural circuitry with the release of microglial cytokines. Patients with neurological symptoms of long COVID were also found to have increased levels of the C-C motif chemokine ligand 11 (CCL11), an immunoregulatory chemokine that can recruit eosinophils, cross the BBB, induce microglial migration, disrupt hippocampal neurogenesis, and cause cognitive dysfunction (e.g., brain fog) [45,116]. Decreased ramification of microglia is partially stimulated by CCL11, causing the release of microglial cytokines and the death of vulnerable neuroglial cells, such as the myelinating oligodendrocytes which assist in the tuning of neural circuitry and the provision of metabolic support to axons. Mouse models and brain tissue samples of long COVID patients have shown extensive white matter-selective microglial and astrocytic reactivity, with subsequent loss of oligodendrocytes and subcortical white matter demyelination (Figure 2). As a result, circuit integrity may be compromised, thereby leading to persisting neurological symptoms [116]. Moreover, novel brain organoid models have demonstrated marked microgliosis 72 h post infection with upregulation of IFN-stimulated genes and microglial phagocytosis leading to engulfment of nerve termini and synapse elimination [117]. This observed postsynaptic destruction may persist along with chronic microglial reactivity to further propagate neurodegeneration in long COVID. Lastly, the most severe long COVID patients exhibited increased levels of tumor necrosis factor receptor superfamily member 11b (TNFRSF11B), an osteoblast-secreted decoy receptor that has been implicated in neuroinflammatory processes and in contributing to microglia overstimulation [90]. Associated with a variety of symptoms, such as cognitive dysfunction, poor psychomotor coordination, and working memory deficits, this mechanism of neural cell reactivity is not specific to COVID-19 and is in fact, strikingly similar to cancer therapy-related cognitive impairment (CRCI) [116]. The pathogenesis and neurological manifestations of long COVID implicate disturbances of neuroglial cells with resulting glial cell reactivity that can be localized to specific brain regions, such as the olfactory bulb, brainstem, and basal ganglia [45]. With persistent cytokine abnormalities and brain injury, reactive neuroglia can influence vascular and endothelial function, compromising the integrity of the BBB, and cause neurodegeneration with marked increases in extracellular glutamate leading to toxic hyperexcitability. Reactive microglia respond to increased levels of CCL11 and release microglial cytokines that can damage neural circuitry. This overactive state of microgliosis leads to a decrease in hippocampal neurogenesis, which is linked with deficits in memory and cognitive function, as well as the death of myelinating oligodendrocytes alongside white-matter selective demyelination. In summary, the most consistent neuropathological observation in all autopsy-based studies of COVID-19 patients is the prominent astroglial and microglial over-reactivity. At present, with the exception of a few anecdotal case reports, there are no neuropathological studies of long COVID conditions. Nevertheless, the neuroglial disturbances and ensuing cytotoxicity appear to facilitate persistent inflammation and subsequent axonal dysfunction in the CNS environment, leading to attention deficits, brain fog, fatigue, and anosmia [45,95]. COVID-19 is known to increase the risk for hemorrhages, ischemic infarcts, and hypoxic changes in the CNS during the acute phase of infection, implicating endotheliopathy and coagulopathy as important mechanisms of pathogenesis [46]. Although these neurological symptoms are not observed in high frequency among long COVID patients, small vessel thromboses (microclots) and microvascular dysfunction due to persisting mechanisms of endotheliopathy and coagulopathy could account for the neurological symptoms of long COVID that are associated with cerebrovascular disease and hypoxic-neuronal injury (Figure 3) [63]. A major mechanism of thrombosis in long COVID involves a unique signature of fibrinolysis-resistant, large anomalous amyloid microclot formation present in the serum of patients with long COVID [118]. Thioflavin T staining and microscopy have determined the size of these microclots to reach upwards of 200 µm, which can adequately occlude microcapillaries, reducing cerebral blood flow and causing ischemic neuronal injury [118,119]. Microclot formation occurs due to the binding of the SARS-CoV-2 spike protein with fibrinogen, which causes increased clot density, spike-enhanced release of reactive oxygen species, fibrin-induced inflammation at sites of vascular damage, and delayed fibrinolysis [119,120,121]. Additionally, interaction of the nine-residue segment SK9 located on the SARS-CoV-2 envelope protein with serum amyloid A (SAA) increases fibril formation and stability, thus contributing to the amyloid nature of the microclots [122]. Proteomics pairwise analysis of digested microclot samples from long COVID patients revealed significantly elevated levels of fibrinogen alpha chains and SAA which both contribute to fibrinolysis resistance and subsequent microclot persistence (Figure 3) [118]. The same study also revealed that the inflammatory molecule α2-antiplasmin (α2AP), a potent inhibitor of plasmin, was significantly elevated in microclots from long COVID patients in comparison with patients with acute COVID; likely contributing to an aberrant fibrinolytic system in addition to anomalous microclot formation [118]. Hypercoagulability can also be precipitated by prothrombotic autoantibody formation in long COVID. Prothrombotic autoantibodies targeting phospholipids and phospholipid-binding proteins (aPLs), including anticardiolipin, anti-beta2 glycoprotein I, and anti-phosphatidylserine/prothrombin, were found to be present in 52% of serum samples of patients hospitalized with acute COVID-19 [123]. It is currently hypothesized that aPLs can form through molecular mimicry, neoepitope formation, or both [124]. The S1 and S2 subunits of the SARS-CoV-2 spike protein could form a phospholipid-like epitope as a mechanism of molecular mimicry or, alternatively, oxidative stress due to SARS-CoV-2 may lead to the conformational change of beta2-glycoprotein I as a way of neoepitope generation. These proposed mechanisms can both result in aPL formation; however, in-vitro experimentation is needed to verify these pathologies in long COVID [124,125]. Antiphospholipid antibodies are then able to cause thrombosis through either the induction of adhesion molecule and tissue factor expression or the upregulation of IL-6, interleukin 8 (IL-8), vascular endothelial growth factor (VEGF), and nitric oxide synthase [124]. However, studies specific to these mechanisms have not yet been undertaken in the setting of long COVID. Alternatively, COVID-19-specific mechanisms of aPL-induced thrombosis include elevated platelet counts and neutrophil hyperactivity [123]. Specifically, IgG was purified from serum of COVID-19 patients with high titers of aPL and added to cultured neutrophils, increasing neutrophil extracellular trap (NET) release (Figure 3) [123]. With the persistence of the S1 subunit of the spike protein within CD16+ monocytes for up to 15 months post-infection as an epitope for aPL generation [89], aPL levels can remain elevated in long COVID. It is still important, however, to take into account the previously mentioned insignificant autoantibody generation to the exoproteome in long COVID patients when considering the role of aPL in disease pathogenesis [64]. Thus, the interplay between anomalous microclot formation, fibrinolytic system dysfunction, and possible aPL formation likely contribute to persistent coagulopathies leading to ischemic neuronal injury in long COVID. Persistent endotheliopathy, independent of the acute COVID-19 response, has been implicated in BBB disruption and neurovascular injury. Levels of plasma markers for endotheliopathy, including von Willebrand factor (VWF) antigen, VWF propeptide, soluble thrombomodulin, and endothelial colony-forming cells, remained elevated in a cohort of patients assessed at a median of 68 days post-infection [126,127]. This prolonged endotheliopathy in long COVID can be attributed to the sustained effect by tumor necrosis factor α (TNF-α) and interleukin 1β (IL-1β) proinflammatory cytokines [128], complement activation by immunoglobulin complexes [129], oxidative stress evidenced by elevated malondialdehyde levels [130], or by direct viral invasion of endothelial cells [113]. With regards to the immune-mediated processes, although cytokines themselves can directly activate endothelial cells, immune complexes positive for IgG and immunoglobulin M (IgM) at the vascular wall of post-mortem tissue of patients with acute COVID-19 were co-localized with membrane attack complexes (MAC) composed of activated C5b-9 complement factors [129]. The presence of MAC, paired with the previously mentioned evidence of autoantibodies to ACE2 receptors on endothelial cell surfaces [99], could very well lead to endothelial cell death. Additionally, in regards to viral invasion of endothelial cells, in vitro and in vivo experiments have elucidated the ability of SARS-CoV-2 protease Mpro to cleave NF-κB essential modulator (NEMO) in endothelial cells (Figure 3), resulting in cell death, empty basement membrane tube formation (also known as string vessels), and BBB dysfunction in mice [130]. BBB dysfunction has been hypothesized to be at the center of the mechanisms of long-term COVID complications. BBB alterations in permeability after addition of extracted SARS-CoV-2 spike protein have been observed in microfluidic models, likely due to endotheliopathy from a pro-inflammatory response [131]. The resulting BBB permeability and microvascular injury have been indicated by perivascular leakage of fibrinogen and persistent capillary rarefaction in an autopsy and a sublingual video microscopy study of long COVID patients, respectively [129,132]. BBB dysfunction can then allow for infiltration of immune cells and cytokines from the systemic circulation that can then propagate neuroinflammation mechanisms in the CNS. Notably, the same autopsy study revealed perivascular invasion of CD68+ macrophages and CD8+ T cells (Figure 3) along with notable reactive astrogliosis which might serve a currently unidentified role in perpetuating BBB leakage [129]. This loss of BBB function is thought to be more pronounced in areas of the cerebellum and brainstem, where most pathological abnormalities have been found with prominent hypometabolism in the bilateral pons, medulla, and cerebellum of long COVID patients (Figure 1) [45,46,133]. The increased permeability at the blood and CNS interface could allow for microglial activation by systemic inflammation evidenced by the presence of microglial nodules associated with neuronophagia and neuronal loss in the hindbrain of patients with acute COVID-19 [129], likely accounting for the persisting hypometabolic pathology. Subsequent neuronal degeneration and brainstem dysfunction could explain the similarities between long COVID symptoms and ME/CFS since the association between severity of ME/CFS symptoms and brainstem dysfunction has been elucidated in previous imaging studies [134,135,136,137]. Together, all endothelial-associated mechanisms can lead to the spread of inflammatory cytokines and immune cells into the CNS, infected leukocyte extravasation across the BBB, and microhemorrhage, ultimately contributing to underlying neurological and cognitive symptoms in long COVID. Current interventions that aim to ameliorate risk of thrombotic complications are not COVID-19 specific, however, due to the implication of SARS-CoV-2 Mpro in endotheliopathy, the role of nirmatrelvir or Paxlovid as Mpro inhibitors could possibly lessen BBB dysfunction. Still, a pharmacological challenge remains in demonstrating the benefit of traditional anticoagulation in patients with long COVID. Neurological manifestations of long COVID exist as a major complication of COVID-19 post-infection, affecting up to one third of patients with COVID symptoms lasting longer than four weeks. Although SARS-CoV-2 neurotropism, viral-induced coagulopathy, endothelial disruption, systemic inflammation, cytokine overactivation and neuroglial dysfunction have been hypothesized as mechanisms associated with pathogenesis of long COVID condition, further clinical, neuropathological, and experimental models are needed to address many of the unknown questions about pathogenesis. Similarly, current and potential therapeutics to target these hypothesized pathogenic mechanisms using anti-inflammatory, anti-viral, and neuro-regenerative agents are potentially able to reverse neurological sequelae but still require well designed clinical trials studies to prove their efficacy.

PMC10001046

Nastaran Karimi,Seyed Javad Moghaddam

KRAS-Mutant Lung Cancer: Targeting Molecular and Immunologic Pathways, Therapeutic Advantages and Restrictions

26-02-2023

lung cancer,KRAS,molecular pathways,therapy

RAS mutations are among the most common oncogenic mutations in human cancers. Among RAS mutations, KRAS has the highest frequency and is present in almost 30% of non-small-cell lung cancer (NSCLC) patients. Lung cancer is the number one cause of mortality among cancers as a consequence of outrageous aggressiveness and late diagnosis. High mortality rates have been the reason behind numerous investigations and clinical trials to discover proper therapeutic agents targeting KRAS. These approaches include the following: direct KRAS targeting; synthetic lethality partner inhibitors; targeting of KRAS membrane association and associated metabolic rewiring; autophagy inhibitors; downstream inhibitors; and immunotherapies and other immune-modalities such as modulating inflammatory signaling transcription factors (e.g., STAT3). The majority of these have unfortunately encountered limited therapeutic outcomes due to multiple restrictive mechanisms including the presence of co-mutations. In this review we plan to summarize the past and most recent therapies under investigation, along with their therapeutic success rate and potential restrictions. This will provide useful information to improve the design of novel agents for treatment of this deadly disease.

KRAS-Mutant Lung Cancer: Targeting Molecular and Immunologic Pathways, Therapeutic Advantages and Restrictions RAS mutations are among the most common oncogenic mutations in human cancers. Among RAS mutations, KRAS has the highest frequency and is present in almost 30% of non-small-cell lung cancer (NSCLC) patients. Lung cancer is the number one cause of mortality among cancers as a consequence of outrageous aggressiveness and late diagnosis. High mortality rates have been the reason behind numerous investigations and clinical trials to discover proper therapeutic agents targeting KRAS. These approaches include the following: direct KRAS targeting; synthetic lethality partner inhibitors; targeting of KRAS membrane association and associated metabolic rewiring; autophagy inhibitors; downstream inhibitors; and immunotherapies and other immune-modalities such as modulating inflammatory signaling transcription factors (e.g., STAT3). The majority of these have unfortunately encountered limited therapeutic outcomes due to multiple restrictive mechanisms including the presence of co-mutations. In this review we plan to summarize the past and most recent therapies under investigation, along with their therapeutic success rate and potential restrictions. This will provide useful information to improve the design of novel agents for treatment of this deadly disease. Lung cancer is among the cancers with the highest mortality rate and an approximate 5-year survival rate of 20%. Around 80% of total lung cancer cases happen to be non-small-cell lung cancer (NSCLC) with adenocarcinoma as the most common histological form [1,2,3]. RAS mutations are the most common oncogenic mutations in human cancers and KRAS has the highest frequency among other members of the RAS family in NSCLC. Approximately 30% of NSCLC cases in western countries are KRAS mutated, while Asian patients with NSCLC are estimated to be 10% positive [4,5,6,7]. KRAS-mutant NCSLC frequently emerges with other common mutations including mutations of tumor suppressor genes via loss of function and deletions. TP53, STK11, KEAP/NFE2L2 gene mutations have been shown to be associated with KRAS-mutant lung cancers, and controversial prognostic values of the co-mutations have been declared along with different therapeutic outcomes in multiple clinical trials [8,9,10]. Various therapies have been conducted for the purpose of stopping the KRAS-mutant NSCLC in its primary and advanced phases, however, none of the therapies including chemotherapies have shown optimistic results. In this review, we plan to summarize the past and most recent therapies under investigation along with their success rate and potential restrictions. This will provide useful information to improve the design of novel agents for the treatment of this deadly disease. The most frequently evaluated forms of the human RAS gene family are Kirsten rat sarcoma viral oncogene homolog (KRAS), neuroblastoma rat sarcoma viral oncogene homolog (NRAS), and Harvey rat sarcoma viral oncogene homolog (HRAS) with 80%, 11%, and 3% contribution of all RAS isoform respectively [11]. KRAS encrypts a membrane-bound GTPase protein which transfers the mitogenic data from upstream receptors—such as the epidermal growth factor receptor (EGFR), fibroblast growth factor receptor (FGFR), and human epidermal growth factor receptors 2–4 (HER2–4/ERBB2–4)—to different intracellular downstream pathways including those which have the most impact on tumorigenesis, such as the mitogen activated protein kinase (MAPK) pathway (RAF/MEK/ERK) that regulates cell survival, proliferation, and differentiation [12]. The phosphatidylinositol-3-kinase (PI3K) pathway activates AKT leading to phosphorylation of several transcription factors including nuclear factor (NF)-κB, mammalian target of rapamycin (mTOR), and forkhead box O (FOXO), which in turn controls cell apoptosis, survival, metabolism, and cell-cycle [13]. Another pathway with an impact on cell survival and proliferation is the RAL guanine nucleotide dissociation stimulator (RALGDS-RA) [14]. These actions are consequences of an active RAS protein which is GTP-bound, and dephosphorylation of GTP to GDP inactivates this protein [12,15]. RAS activation is assisted via guanine nucleotide exchange factors (GEFs) through GTP-GDP substitution, whereas GTPase activating proteins (GAPs) aim to inactivate RAS through increasing the GTPase activity of RAS [16,17]. To participate in membrane-bound activity, the soluble RAS protein requires post-transitional modifications [18]. Spontaneous activation of the RAS protein (e.g., due to mutations) without upstream signaling leads to oncogenic cell proliferation and uncontrolled cell survival. Figure 1 illustrates the KRAS mechanism and downstream pathways. RAS mutation frequently occurs at exons 2 and 3, with the highest ratio identified at codons 12 and 13 of the KRAS gene in NSCLC, which are mainly gain-of-function alterations fostering oncogenic manners [19,20]. Among these alterations, G12D (the substitution of Aspartic acid for Glycine 12) is the most common mutation in lung cancer patients with no history of cigarette smoking, while G12C (the substitution of Cysteine for Glycine 12) and G12V (the substitution of Valine for Glycine) are the two most frequently identified variations in smokers with lung cancer [21,22]. In an in vitro study, it was shown that each amino acid substitution has a different affinity to downstream KRAS pathways; G12C- and G12V-mutated KRAS cell lines have higher RAL activity downstream and lower AKT phosphorylation, whereas G12D mutated ones have higher PI3K-AKT activity comparing to other alterations and wild type [23]. The distinctive prognostic features of different KRAS subtypes have become a controversial matter, as one study supports the role of these different KRAS mutations as an overall survival prediction, while larger studies do not support these findings [23,24]. These oncogenic KRAS mutations and the co-occurrence of other alterations (STK11 or KEAP1) encourage metabolic rewiring toward anabolism, furthering proliferation as well as redox management i.e., acquiring an adaptation to oxidative stress. In one of the latest studies on murine models, it was demonstrated that the lung cancer murine model with KRAS homozygosity had higher glycolytic activity which rewired the cell program towards increasing glutathione synthesis as an adaptation to antioxidation. Higher sensitivity to glycolytic inhibitor 2-deoxyglucose and the glutathione synthesis inhibitor buthionine sulfoximine, along with a lower survival rate, were reported among homozygote KRAS-mutant murine models compared to those with heterozygote alterations [8,25]. As mentioned above, KRAS-mutant lung cancer is commonly associated with co-mutations, which have been categorized into three expressing clusters in a recent cohort of early and advanced stage KRAS-mutant tumors via RNAseq analysis: co-mutations in serine/threonine kinase 11 (STK11) and tumor protein P53 (TP53), with worse overall survival as a co-mutation to KRAS in lung cancer; inactivation of cyclin-dependent kinase inhibitors 2A/B (CDKN2A/B), with enrichment in the expression of neoplastic gastrointestinal and wildtype P53 transcriptional activity; and low level of thyroid transcription factor-1 (TTF-1) expression [8]. The most frequent co-alteration associated with KRAS in 330 advanced NSCLC patients were TP53 (41%), STK11 (28%), KEAP1/NFE2L2 (27%), RBM10 (16%), and PTPRD (15%). In this study, the median overall survival (mOS) was 17 months for all KRAS-mutant patients. In the absence or presence of the TP53 co-mutation, the reported OS was similar (P:0.5). The presence of STK11 and KEAP1 in patients was associated with a shorter OS (P:0.002 and p < 0.001 respectively) [10]. Next Generation Sequencing (NGS) analysis of 121 Brazilian patients with advanced NSCLC in a retrospective study demonstrated that 20.86% (24/115 valid samples) had KRAS mutations and 1 (0.87%) had KRAS amplification. G12D (6/24), G12V (5/24), G12C/G12A (3/24), and G13D/Q61L (2/24) were among the most frequent mutation varieties presented. Co-mutations associated with KRAS were noticed in 33.3% (8/24), of which 3/8 and 2/8 co-occurred with G12D and G12V alterations respectively [26]. A few studies suggest that the co-occurrence of STK11 deletion is associated with worse overall survival prognosis, and that this co-mutation might characterize the therapeutic outcomes [8,27]. A study in China on 103 NSCLC patients reported that KRAS mutation was exclusively gender-related (p = 0.027) with high prevalence in male patients and having no association with age, smoking, and metastasis history. Moreover, this study mentioned that KRAS mutation occurrence in NSCLC patients in the Chinese population (5.8%) is similar to that of the same demographic of patients in East Asian countries and in the USA population, while this number was shown to be higher in Western countries. These findings assert an ethnicity dependence in the relevance of KRAS alteration in NSCLC patients [28]. Prior pulmonary disease history in patients, such as chronic obstructive pulmonary disease (COPD), was shown to be irrelevant to KRAS mutation in a study on 325 NSCLC patients, however there is insufficient evidence to support this claim [29]. A comprehensive genomic cohort study on 371 lung cancer patients revealed precise information regarding different genetic alterations and co-mutations in subtypes of lung cancer. CDKN2A, KEAP1, STK11 were reported to be associated with smoking, while CDKN2B was considered to be age-related (p value: 0.038) [30]. Thanks to revolutionary technologies in pharmacological laboratories, and advanced perspectives into KRAS biology, scientists have started to develop strategies that could directly target KRAS oncoproteins. These recent studies have led to the identification of two groups of KRAS inhibitors, particularly by modifying cysteine 12 and targeting the effector binding switch II region of KRAS [31] or the nucleotides pocket binding site [32]. The nucleotide pocket beneath the switch II region of KRAS, which substitutes glycine with cysteine, renders a solvent near the active site, permitting a novel target for the direct inhibition of KRAS G12C [31,33,34]. Currently, there are two direct KRAS G12C inhibitors that have been advanced to clinical trials. Sotorasib (AMG 510), also known as LUMAKRAS™, developed by Amgen, is a small molecule that irreversibly inhibits cysteine 12 in the vicinity of pocket 2 of the switch II region, thus resulting in an inactive GDP-bound state. This molecule inhibits KRAS G12C by reducing the interaction between KRAS G12C and SOS and therefore blocking SOS activity in nucleotide exchange. This suggests that the KRAS G12C inhibitor lowers the affinity of this unit with nucleotide exchange factors and therefore traps KRAS G12C in a GDP-bound state. Assuming this to be a fact, it was inferred that cellular factors which affect the affinity of nucleotide exchange factors may modulate the inhibition of KRAS G12C [34,35,36]. A phase I/II randomized study evaluated the safety and efficacy of sotorasib (Code Break 100: NCT03600883) in 126 patients with advanced stage KRAS G12C mutated NSCLC who were pre-treated with platinum-based therapy and inhibitors of programmed death 1 (PD-1). Sotorasib was administered orally once per day at a dosage of 960 mg. The objective response rate (ORR) was 37.1% with a median response duration of 11.1 months. The median overall survival was 12.5 months with a disease control rate (DCR) of 80.6% in participants. Treatment-related adverse events (TRAEs) were reported in 69.8% of patients, with 19.8% grade 3 events. The majority of side effects related to the gastrointestinal system, such as diarrhea (31.7%), nausea (19%), and slightly elevated alanine aminotransferase (ALT) and aspartate aminotransferase (AST) (15.1%), offering low-grade hepatic toxicity. The U.S. Food and Drug Administration (FDA) recently granted sotorasib designation for the treatment of advanced KRAS-G12C-mutated NSCLC in adult patients [37]. Currently, there are two other trials investigating sotorasib with the purpose of identifying patients for whom sotorasib can be a beneficial first-line therapy (NCT04303780 and NCT04185883). Adagrasib (MRTX849) is another direct KRAS G12C inhibitor that acts irreversibly and selectively. A phase I/II study called KRYSTAL-1 (NCT03785249), in patients with a similar profile to that mentioned for sotorasib, evaluated the ORR and DCR of adagrasib and the results showed that in 51 patients with NSCLC, with a dosage of 600 mg twice per day, the ORR and DCR were 45% and 96% respectively. TRAEs of grade 3 or 4 were reported in 30% of patients; those with the highest frequency were fatigue (6%) and increased AST/ALT (5%). In a subgroup of patients with the STK-11 co-mutation the ORR was 64%, which may indicate the beneficial role of this co-mutation in therapeutic progress [38]. Needless to say, multiple clinical trials are investigating the impact of adagrasib as a monotherapy or in combination with other therapeutic agents, aspiring to a novel approach in the treatment of advanced NSCLC (NCT04613596, NCT04685135, NCT05472623, NCT05375994). BI 1701963, a pan-KRAS SOS1 inhibitor, is the only SOS1 inhibitor that has entered a clinical trial. SOS1 is one of the GEFs mediators of RAS activation (all variations, including G12D/C/V), and suppressing the activity of this small protein leads to lower GTP-bound (i.e., active) KRAS levels, and further inhibition of MAPK pathway signaling. A phase I dose-escalating trial of BI 1701963 as a monotherapy or in combination with trametinib (a MEK inhibitor agent) in advanced or metastatic KRAS-mutant patients (NCT04111458), is currently active and investigating the maximum tolerated dose and safety of this agent; by 11 February 2020, it has reported three treated patients [39]. Another clinical trial is investigating the safety and tolerability of adagrasib and BI 1701963 as a combination therapy in advanced KRAS-mutant patients (NCT04975256). Table 1 summarizes other direct KRAS inhibitor clinical trials. A toxin-mediated therapy has been developed using RAS/RAP1-specific endopeptidase (RRSP) from Vibrio vulnificus, which bisects RAS and RAP1 in the switch I region among Y32 and D33 residues. RRSP targets three isoforms of RAS and mutated RAS oncogenes at positions 12, 13, and 61 in both GTP-bound and GDP-bound forms, resulting in the termination of downstream signaling of ERK and subsequently a reduction in proliferation [40,41] In an in vivo study performed on three mouse xenograft models (WT or mutant RAS) scientists developed RRSP as a pan-RAS biologic inhibitor conjugated to diphtheria toxin. They observed a reduced tumor burden in KRAS-missense-alteration-carrying cell lines in colon cancer and NSCLC [41]. Table 1 lists other direct KRAS inhibitor clinical trials along with their required information. KRAS is activated as a consequence of the phosphorylation of EGFR, however targeting ERBB RTK signaling using erlotinib and gefitinib showed no impact on disease progression in KRAS-mutant lung cancers [42,43,44]. On the other hand, a recent study demonstrated that knocking out EGRF in a mouse model of KRAS-G12D-mutant lung cancer resulted in tumor regression [45], although compensation by a non-ERBB family was seen in the later stage of the disease. This could give insight into the notion that combinational therapies could be successful, and an example of this includes neratinib (a pan-ERBB inhibitor) in combination with a MEK inhibitor, which has been approved by the FDA for adjuvant treatment [46]. The prognostic value of ERBB inhibitors in KRAS-mutant lung cancer patients needs further evaluation to confirm the variability of these inhibitors. RAS trafficking to the plasma membrane (PM) is a crucial step, as RAS family proteins can only be activated once in the PM. RAS proteins are globular hydrophilic proteins that gain their ability to attach to the membrane through a process called farnesylation. Adding farnesyl via farnesyl transferase is the rate-limiting step in the process. Once farnesylated, the proteins are transported to the endoplasmic reticulum (ER) membrane where CAAX processing takes place as an interaction with RAS-converting enzyme 1 (RCE1) and isoprenyl carboxyl methyltransferase (ICMT) enzymes. The KRAS protein is then transported to the PM via a process in which PDE6δ might be involved. Phosphorylation of the Serine 181 residue of the KRAS protein can lead to the detachment of the protein from the PM and its return to the ER membrane [47]. The first generation of drugs that inhibited farnesyl transferase, including tipifarnib and lonafarnib, did not show significant clinical improvement in NSCLC patients, which could be explained by the compensatory pathway of prenylation through geranylgeranyl transferase I (GGTase I) [48,49,50,51]. The evaluation of satirasib, a second-generation agent of farnesyl transferase inhibitors (FTIs), was ended due to lack of efficacy in KRAS-mutant lung adenocarcinoma patients [52]. In vitro and in vivo studies targeting other enzymes involved in the post-transitional modification of the RAS protein, such as RCE1 and ICMT, have been conducted and led to contrary results [53]. As mentioned above, different downstream pathways are activated via the activity of KRAS, leading to cell proliferation and survival (e.g., MAPK, PI3K, mTOR, FOXO, RALGDS-RA). Numerous clinical trials have investigated the effect of inhibition of these pathways in the progress of NSCLC patients [54]. RAF Inhibitors Sorafenib was one of the first drugs used in attempts to target RAF/MEK/ERK pathways in KRAS-mutant NSCLC, and was shown to be ineffective [55,56]. In a mouse study, it was demonstrated that C-RAF, rather than BRAF, is responsible for oncogenic signaling in KRAS-mutant NSCLC [57]. Numerous studies are researching the possible effects of different kinase inhibitors in single or combination therapies (NCT04620330), to assess the efficacy of VS-6766 (a dual RAF/MEK inhibitor) as a monotherapy or in combination with defactinib in NSCLC patients with recurrency [3,58]. Other clinical trials are recruiting to evaluate the feasibility of VS-6766 in combination with sotorasib (NCT05074810) and adagrasib (NCT05375994) in KRAS-G12C-mutant NSCLC subjects. MEK Inhibitors MEK inhibitors that target other intermediates in KRAS/RAF/MEK/ERK pathways were abrogated in NSCLC tumor ablation as a single agent therapy due to ERK signaling activation. Nevertheless, combinational therapies utilizing MEK inhibitors with other agents showed some evidence of efficacy [59]. However, adverse effects followed in a phase I study (NCT2450656) of afatinib and selumetinib in KRAS-mutant subjects, with insufficient anticancer impact leading to no further trials for this combination [60]. Other combinational trials of MEK inhibitors are underway to further assess their safety and efficacy (NCT03581487, NCT03170206, NCT04967079). PI3K, AKT, mTOR Inhibitors PI3K/mTOR/AKT inhibitors are another group of cancer therapeutic agents that are commonly being tested in trials in combination with MEK inhibitors in the BTTLE-2 program (NCT01248247), which showed a better 8-week disease control rate compared to erlotinib. However, further investigation of better treatment strategies was suggested [61]. In another study, in vitro and in vivo studies of the second-generation KRAS-G12C-inhibitor ARS1620 in combination with PI3K inhibitors resulted in tumor regression in KRAS-mutant NSCLC [62]. A phase I study carried out to test pictilisib, a pan-PI3K inhibitor, in combination with first-line treatment options for NSCLC, demonstrated safety and promising anticancer abilities [63]. FAK Inhibitors Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase with a crucial role in cancer cell proliferation, migration, and adhesion to the extracellular matrix [64]. An in vivo study showed that RHOA-FAK signaling could be a potential target in KRAS-mutant NSCLC with INK4a/ARF and TP53 deficiency [65]. Based on these findings, a phase II trial (NCT01951690) assessed the efficacy and safety of defactinib, a FAK inhibitor, in previously treated NSCLC patients, although the clinical outcome was moderate with no relation to INK4a/ARF and TP53 status [66]. HSP90 Inhibitors Heat shock protein 90 (HSP90) is a chaperone with a particular N-terminus ATP binding domain that assists other protein folding, stabilizing the conformational state, and helping them adjust to heat. HSP90 inhibition was shown to disrupt the protein function and therefore cancer cell growth [67]. Ganetespib (STA-9090) is a potent HSP90 inhibitor that was evaluated in a randomized phase III (GALAXY II) study with docetaxel in previously treated NSCLC patients. The trial was terminated due to the inefficacy of the gantespib and docetaxel combination [68]. Luminespib (AUY922) is another drug of this group which demonstrated efficacy and disease regression as a monotherapy in previously treated NSCLC with EGFR gene mutations and ALK rearrangement, and had no particular impact on KRAS-mutant tumors (NCT01124864) [69]. SNX-5422, a novel HSP90 inhibitor, was evaluated in a phase I study with carboplatin and paclitaxel (an anti-microtubule agent) with a dose maintenance of SNX-5422 in NSCLC subjects. The study demonstrated the agent’s anti-cancer activity and good tolerance [70]. The metabolic rewiring of tumor cells in order to compensate for the increased demand for energy as a result of tremendous tumor cell proliferation, particularly altered glucose metabolism, has been a topic of interest in cancer therapy studies [71]. In a study on fresh surgically removed NSCLC tumor specimens, an elevated level of lactate was illustrated, pointing toward increased glycolysis in tumor cells as well as increased levels of citrate and succinate, elements of the TCA cycle [72]. In various mutant KRAS model studies in vitro and in vivo, besides altered glucose metabolism, other metabolic reprogramming such as increased autophagy and macro-pinocytosis were shown to play a crucial role in the metabolic rewiring process [73,74]. The studies targeting metabolic rewiring in NSCLC in murine models have mainly been performed in three areas: increased amino acid utilization; lipid biosynthesis and beta oxidation (e.g., autophagy inhibitor); and glucose metabolism. Drugs that interfere with glucose metabolism such as metformin [75], an antihyperglycemic agent targeting mitochondrial glycerophosphate dehydrogenase, have been investigated in a different clinical trial to evaluate their possible therapeutic effect on NSCLC patients [76]. Autophagy is a mechanism that contributes to the dynamic recycling of intracellular compartments in order to help cell homeostasis. There are three main autophagy methods: macro autophagy, micro autophagy, and chaperone-mediated autophagy [77]. There are contradictory results concerning the role of autophagy in cancer, as it can work as a tumor suppressor or as a mechanism for tumor cell survival [78]. Macro autophagy is the mechanism that KRAS-mutant cells depend on to maintain cellular homeostasis [79,80]. ‘Autophagy addiction’ was the name used in an article that reported the dependency of HRAS and KRAS mutated cells on autophagy, as the absence of it leads to mitochondrial waste-product, and TCA cycle intermediates build up in cells which eventually leads to apoptosis [79]. Various clinical trials have been conducted investigating the validity of the therapeutic role of autophagy inhibitors. Hydroxychloroquine is a lysosomotropic agent for autophagy blockage which has been widely under investigation for its role in the treatment of cancer. Particularly, a new study is being conducted to investigate the role of hydroxyquinoline and binimetinib, a selective MEK inhibitor, in 29 patients with advanced KRAS-mutant NSCLC (NCT04735068). Further trials need to be run to reveal the possible tumor-cell-suppressing role of autophagy inhibitors. Synthetic lethality is defined as the co-occurrence of two genetic contents that exploit cellular death. The main events which contribute to synthetic lethality include loss-of-function mutations, the over-expression of genes, and environmental factors [81]. This concept has drawn the attention of cancer biologists as a potential targeted therapy since various phenomena, including proteotoxic stress, oxidative stress, DNA damage, and metabolic stress, follow cancer cell metabolism. These conditions have manifested novel therapeutic avenues for cancer therapy [82]. KRAS signaling harbors cellular conditions known as non-oncogenic addiction, which is the notion behind the KRAS-signaling synthetic therapies [82]. RNA interference (RNAi) and CRISPR/Cas9 genetic screening methods have been applied to determine the genetics contributing to synthetic lethality in KRAS. However, these findings face various limitations in their application to the human cancer cell [83]. Reactive oxygen species (ROS) and hyper-replication of DNA lead to genomic toxicity in RAS signaling. Cancer cells compensate for these phenomena through the activation of DNA damage repair (DDR) checkpoints and pathways. It was found that wild type (WT) HRAS and NRAS have a crucial role in the activation of ATR/CHK1-mediated DDR, which leads to genomic instability in KRAS-mutant lung cancer cells. WT H/NRAS inhibit MAPK and AKT signaling, which in turn maintains the inhibition of CHK1; therefore, loss of function leads to impaired CHK1 activation and disturbed checkpoints and increases genomic instability and susceptibility to chemotherapies. Inhibition of the CHK1 pathway can be a suitable target for synthetic lethality in patients under chemotherapy [84]. Another study found that inhibition of nuclear trafficking via XPO1 leads to a synthetic lethal effect on KRAS-mutant cancer cells through the inhibition of transcription factor NF-κB, introducing a targetable notion for NSCLC therapies [85]. KRAS-mutant cancer cell metabolism is followed by endoplasmic reticulum stress and proteotoxic activation of unfolded protein response (UPR) pathways, which result in cell death. Scientists found that inhibition of HSP90 with the IPI-504 adjuvant, together with mTOR inhibition via Rapamycin, results in cell death and tumor regression in murine models [86]. A new study found that inhibition of NOP56, a ribosomal protein that plays a role in ribosomal assembly, leads to impairment in the response to ROS, therefore KRAS-mutant cells shifted their metabolism toward the mTOR pathway, and co-inhibition of NOP56 and mTOR led to tumor regression in xenograft mouse models [87,88]. Various clinical trials have been conducted to evaluate the feasibility of different therapeutic agents as synthetic lethality partner inhibitors; the proteosome inhibitor bortezomib, which was the first proteasome inhibitor approved by the FDA for mantle cell myeloma patients, is being clinically tested for NSCLC [89]. In a phase II (NCT00346645) study, the effectiveness of bortezomib on NSCLC patients with no prior chemotherapy was evaluated, but was terminated due to lack of efficacy. CDK4/6 inhibitors are another group of drugs including abemaciclib, ralimetinib, and palbociclib, whose efficacy in NSCLC disease progression is currently being investigated, whether as monotherapies or in combination with chemotherapies [90] The over expression of co-inhibitory molecules of the immune system (e.g., CTLA4, PD-1) in tumor infiltrating lymphocytes (TILs), which leads to the suppression of T cell activity against cancer cells, has led to several studies investigating the immune checkpoint inhibitors (ICIs) as a potential agent in cessation of disease progression in multiple cancers, including NSCLC [91,92,93]. T cells are the immune system’s main weapon in the defense against cancer, and are regulated in response to antigen presenting cells (APCs) via major histocompatibility complex (MHC) [94]. There are co-inhibitory receptors that downregulate T cell response, functioning as ICIs, of which CTLA4 and PD-1 are the main ones [94]. CTLA4 exerts its inhibitory role by interacting with CD80 and CD86 on APCs while competing with the co-stimulatory receptor, CD28 [95]. High levels of CTLA4 and PD-1 expressions on TILs were reported in a study comparing NSCLC and healthy individuals [91], therefore monoclonal antibodies targeting these receptors are the main subject in immunotherapy against NSCLC. Pembrolizumab is a selective, fully humanized immunoglobulin (Ig) G4-kappa monoclonal antibody (mAb) targeting PD-1, which was initially approved by the FDA in 2011 for the treatment of melanoma [96]. In 2016, pembrolizumab was approved as a first-line therapy in NSCLC patients with PD-L1 >1% with no EGFR or ALK mutations [97]. Furthermore, in a phase III KEYNOTE-024 study (NCT02142738) pembrolizumab treatment in NSCLC patients with PD-L1 tumor proportion score of 50% or higher demonstrated higher progression-free survival compared to those treated with platinum-based chemotherapy [98]. In a randomized, placebo-controlled KEYNOTE-407 study (NCT02775435), pembrolizumab used as a first-line therapy in combination with carboplatin and paclitaxel/nab-paclitaxel improved the OS and progression-free survival [99]. Atezolizumab is another humanized Ig G4-kappa mAb interacting with PD-L1 which [100] in a randomized study, IMpower130 (NCT02367781), of previously untreated NSCLC patients with no EGFR or ALK mutations, demonstrated increased OS and progression-free survival in combination with chemotherapy compared to chemotherapy alone [101]. IMpower 110 (NCT02409342), a randomized phase III clinical trial on patients with stage IV NSCLC, which studied atezolizumab as monotherapy or in combination with chemotherapies that were selected based on tumor histology, showed an OS of 20.2 months compared to 13.1 months with chemotherapy as the only agent. IMpower 150 (NCT02366143) is another phase III trial that investigated chemotherapy with atezolizumab, chemotherapy with bevacizumab, and chemotherapy with both atezolizumab and bevacizumab in NSCLC patients as first-line therapy. This study indicated a higher OS in the chemotherapy plus atezolizumab arm compared to the other two [102]. Nivolumab is another PD-1 targeting mAb which has been investigated in combination with ipilimumab, a CTLA4 targeting agent, in two different studies called CHECKMATE-227 (NCT02477826) and CHECKMATE 9LA (NCT03215706). The results of CHECKMATE 9LA showed 15.6 months OS in previously untreated NSCLC patients treated with nivolumab and ipilimumab plus chemotherapy, compared to 10.9 months in patients who only received chemotherapy [102]. Cemiplimab, a PD-1 mAb, was recently approved by the FDA as studied in EMPOWER-Lung 3 trial (NCT03409614). It was a randomized double blind phase III study on NSCLC patients without EGFR, ALK, and ROS mutations, comparing cemiplimab in combination with chemotherapy against chemotherapy alone; the OS was 21.9 and 13.8 months respectively, indicating a clinically significant difference [103]. Besides atezolizumab, durvalumab is another PD-L1 mAb which was evaluated in a phase III PACIFIC study (NCT02125461) on patients with unresectable NSCLC without progression after chemotherapy. Recently, a three-year report of all patients’ OS has shown 57% OS in those receiving durvalumab versus 43.5% in the placebo group, which indicates a remarkable improvement in the treated group [104]. Ipilimumab is an IgG1 mAb against CTLA4, and it has been approved by the FDA as a first-line therapy in combination with nivolumab in two different studies as mentioned above [102]. Tremelimumab is another CTLA4 mAb recently approved by the FDA in combination with durvalumab and platinum-based chemotherapy in metastatic NSCLC without EGRF and ALK gene mutations. The efficacy of this regimen was evaluated in a phase III, randomized, open label (NCT03164616) POSEIDON study with three treatment arms: (1) durvalumab + tremelimumab combination therapy + chemotherapy; (2) durvalumab monotherapy + chemotherapy; and (3) chemotherapy alone. The OS of the first arm was 14 months compared to 11.7 months in third arm, which signifies a clinical improvement in tremelimumab combinational therapy [105]. Tumor-promoting inflammation is a new concept of immune modality intervention, as it was demonstrated that inflammation has a fundamental role in tumorigenesis and progression in KRAS-mutant NSCLC. Manipulation of the immune system by cancer cells leads to the remodeling of immune cells in the tumor microenvironment (TME) and renders them incompetent [71]. Multiple cytokines, inflammatory signaling pathways, and innate immune signals play a crucial role in reprogramming the TME [106]. Interleukin 1β (IL-1β) via its two receptors, IL-1RI andIL-1RII could send up-regulatory and down-regulatory signals, respectively. Multiple immune cells such as macrophages, natural killer cells (NK), neutrophils, and T cells secret IL-1β [106,107]. We found that blocking IL-1β with an IL-1β mAb in a KRAS-G12D-mutant mouse model of lung cancer led to tumor regression, which was associated with increased infiltration of CD8+ T cells, potentially due to a decrease in myeloid-cell-associated immunosuppression as well as inhibition of the NF-kB and STAT3 pathways [108]. Our findings were consistent with two other studies stating that an increased level of IL-1β is associated with NSCLC cell proliferation [109,110] and there is a negative correlation between IL-1β and progression-free survival of patients with NSCLC [109]. A canakinumab (anti-IL-1β) agent trial on atherosclerosis patients revealed decreased incidence of lung cancer in these patients compared to the placebo group [111]. Other trials are investigating the effect of canakinumab as a monotherapy (NCT03447769) or in combination with PD-1 mAbs such as pembrolizumab (NCT03968419) and durvalumab (NCT04905316) and chemotherapeutic agent, docetaxel (NCT03626545). Interleukin 6 (IL-6) transfers signals to the cell via multiple pathways including RAF/MEK/ERK, mTOR, AKT/PI3K, and particularly JAK/STAT which fundamentally contributes to cell proliferation and survival [112]. IL-6 assists tumor cells’ survival with TME manipulation as well as downregulation of CD8+ T cells, macrophage M1 to M2 phenotype changes and upregulation of immune-suppressing cells such as T helper 17 (Th17), Tregs, and myeloid-derived suppressive cells (MDSCs) [106]. Advanced stage NSCLC patients with poor prognoses were found to have high levels of IL-6 [113,114,115]. The inhibition of IL-6 in KRAS-mutant lung cancer mouse models revealed lower pro-tumor characteristics in the TME, leading to tumor suppression [116,117]. Currently, three clinical trials are evaluating the efficacy of tocilizumab, an anti-IL-6 mAb, in combination with atezolizumab in locally advanced or metastatic NSCLC (NCT04691817), in combination with ipilimumab and nivolumab in melanoma, NSCLC, and urothelial cancer (NCT04940299), and in a multi-immunotherapy two-phased therapy in combination with chemotherapy or PD-1 inhibitors in metastatic NSCLC subjects (NCT03337698). The innate immune signaling pathways are intermittent pathways operating as recognition strategies for the immune system, including stimulator of interferon gene (STING) signaling pathways, toll-like receptor pathways, and inflammasome. Targeting cells intrinsic STING signaling pathway is an emerging topic. STING is a fundamental element of the innate immune system that is located on the ER; it recognizes cytosolic DNA and further induces the synthesis of type I interferons (IFNs). Pathogenic dsDNA interacts with cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS) and further stimulates the production of 2′,3′-cyclic GMP-AMP (2′,3′-cGAMP). The interaction of 2′,3′-cGAMP and STING leads to the activation of TANK-binding kinase 1 (TBK1) and phosphorylation of interferon regulatory factor 3 (IRF3). IRF3 then moves to the nucleus and aids in the expression of IFN type I. Upregulation of the immune system via IFN type I is a well-known phenomenon, therefore STING agonist agents could be a potential novel therapy in cancer via the immune regulatory role of this pathway [118]. Currently, various clinical trials are evaluating the role of STING agonists in various cancer types, particularly solid tumors. An open-label, dose escalating phase I study (NCT04879849) of TAK-676 (STING agonist) and pembrolizumab after radiation therapy in different cancer patients, including NSCLCs, is currently recruiting. This could be a promising method which needs further research to assess the therapeutic potential of these agents as an anti-cancer therapy. Most the downstream pathways activated by KRAS eventually trigger two well-known transcription factors: STAT3 and NF-κB. STAT3, a member of signal transducer and growth factors (STAT), is one of the main transcription factors that transmit the signals of several cytokines, particularly IL-6 in KRAS-mutant tumor cells. The activation of JAK1/JAK2 signaling via IL-6 leads to the mobilization of STAT3 toward the nucleus, resulting in several gene translations responsible for angiogenesis and metastasis [119]. As a result of the administration of ruxolitinib (a JAK1/2 selective kinase inhibitor) in mouse models, a decline in tumor cell proliferation rate was reported, showing a positive correlation between JAK1/2 and disease progression in KRAS-mutant NSCLC [120]. However, in our recent in vivo study, we demonstrated a gender-specific correlation between STAT3 expression and KRAS-mutant NSCLC; female mice with tumor-cell-specific STAT3 deletion showed decreased tumorigenesis while males had the opposite outcome [121]. Several clinical trials (e.g., NCT02983578) are emerging to assess a novel therapeutic agent, AZD9150 (danvatirsen), an antisense oligonucleotide targeting STAT3 in NSCLC subjects. The inhibition of STAT3 with AZD9150 in xenograft models of lung cancer patients was associated with increased anti-tumor activity [122]. Another STAT3 inhibitor, TTI-101(C188-9) was shown to decrease tumor volume and growth in the A549 xenograft model of lung cancer [123] and it has entered a phase I trial as an oral STAT3 inhibitor in patients with advanced cancers, including NSCLC (NCT03195699). In a phase I study (NCT01184807) of OPB-51602 (STAT3 inhibitor) in NSCLC patients, antitumor activity was detected [124]. Another transcription factor that is activated as a result of the downstream pathways of KRAS is nuclear factor-κB (NF-κB), which is kept in its inactive state in naïve cells via proteins called inhibitor of κB (IκB). Activation of this pathway by cytokines or other inflammatory stimuli results in the phosphorylation of IκB by IκB kinase and releases the transcription factor into the nucleus, which will then stimulate several phenomena such as cell proliferation, survival, and inflammation [125]. Clinical studies found a positive correlation between high levels of IκB and tumor staging and metastasis state of lung cancer patients [126]. NF-κB activation has been observed in the TME and tumors of KRAS-mutant lung cancer mouse models [127]. However, targeting NF-κB can potentially render the immune system undefended, as it has an essential regulatory role in the innate and humoral response. Figure 2 demonstrates various therapeutic targets and the drug interventions which are directed at them. CAR T cells are emerging novel therapeutic immunological agents which are being widely investigated in cancer therapy research, and have been particularly successful in hematological malignancies. CAR T cells are genetically manufactured from patients’ isolated T cells to detect and bind to antigens on cancer cells. They consist of an intracellular domain, a transmembrane domain, and an extracellular antigen recognition domain which is highly receptive to tumor-associated antigens (TAA). To validate a therapy, the most crucial step is to define the particular TAA to the extent that it is only found on cancer cells. TAA heterogenicity in solid tumors has been a burden in contrast to hematological malignancies. Other challenges of CAR T cell therapy in solid tumors, in particular lung cancers, include the immunosuppressive TME, cytokine release syndrome, and neurological toxicity [128]. Some of the current targets for CAR T cells therapy in lung cancer are as follows: EGFR, HER2, mesothelin (MSLN), MUC1, CEA, PD-1, and CD80/CD86 [129]. Table 2 provides a comprehensive and detailed list of clinical trials mentioned, including the type of agents, study group, status, and phase of the studies. An issue that burdens the expansion of KRAS inhibitors is the resistances that ultimately emerge despite disease regression in response to therapy. These acquired resistance mechanisms, which are defined as disease progression after 12 weeks of disease stability or regression, consist of on-target and off-target means [130]. ‘On-target mechanism’ refers to those alteration in KRAS which hardens agent binding, and ‘off-target’ refers to those biological alterations which indirectly affect KRAS inhibition. Several studies have aimed to investigate the phenomena underlying resistance to KRAS inhibitors. An in vitro study employing KRAS-G12C-mutant Ba/F3 cells investigated the secondary KRAS mutations which can harbor resistance to sotorasib and adagrasib using N-ethyl-N-nitrosourea as a mutagenesis screen. Of the 142 clones which evolved resistance, 124 demonstrated multiple secondary KRAS mutations including Y96D and Y96S. Y96 plays a key role in hydrogen bond formation between KRAS and both sotorasib and adagrasib. Thus, alteration of this residue results in resistance to these inhibitors [131]. Another study on 38 clinical trial patients, including 27 with NSCLC, described an acquired resistance to adagrasib treatment in 17 (45%) individuals (including 10 (26%) NSCLC patients) via next-generation sequencing (NGS) sampling tissues or circulating tumor DNAs. These alterations were divided into three different classifications including secondary KRAS mutations (i.e., Y96C) on the switch II region, the binding site of KRAS for inhibitors, along with activating KRAS mutations in trans G12C, G12V, and G12W (on-target mechanisms). The second classification is those with MET amplification leading to RAS-ERK pathway reactivation and eventually higher level of GTP-bound RAS (off-target mechanism). The last group were those who had histological transformations to squamous cell carcinoma. It was noted that 7 (41%) out of 17 NSCLC patients displayed more than one coexisting resistance mechanism [132]. Multiple (10) acquired resistance mutations to adagrasib were found in a KRAS-G12C-mutant patient, which led to RAS-MAPK reactivation and Y96D mutation [133]. Note that other genomic alterations such as MAPK pathway genes or other genomic material affecting KRAS downstream pathways could potentially lead to resistance to KRAS inhibitors [130]. As mentioned above, co-mutations associated with KRAS have different therapeutic impacts on treatment outcomes. The mutation of TP53 or STK11 in an in vivo study of KRAS-mutant tumors resulted in a failed response to docetaxel [134]. Patients with recurrent or metastatic NSCLC who were initially treated with a platinum agent, pemetrexed, +/- bevacizumab were evaluated to assess their co-mutation status. A shorter duration of therapy was linked to the presence of the KEAP/NFE2L2 co-alteration (P: 0.008). However, TP53 or STK11 co-occurrence did not lead to significant variance in the duration of platinum-based treatment [10]. KRAS/STK11 mutated NSCLCs are assumed to be unresponsive to ICIs as a result of a lack of TILs and low PD-L1 expression [135]. Another KRAS/STK11 mouse model study established tumor regression following T cell infiltration subsequent to IL-6 antibody administration, while the same models were irresponsive to PD-1 mAb [136]. The presence of the KEAP1 co-mutation in KRAS-mutant NSCLC is considered to cause resistance as well as lower OS in response to ICIs [10]. In a study investigating the impact of six SWI/SNF genes, including SMARCA4, on immunotherapy (e.g., CTLA4 and PD-1 inhibitors) outcomes in KRAS-mutant NSCLC, the worst OS and prognosis was associated with SMARCA4 mutants, indicating the unfortunate outcome of this gene alteration with KRAS [137]. Despite these findings, co-mutations do not always correspond to the worse clinical scenario, as KRAS/TP53 NSCLC patients responded well to therapies and had improved OS as a result. TP53 co-alterations in these cases demonstrated an increased immune response and regulation, along with expression of the PD-1 immune checkpoint [135]. These findings indicate the need for a more individualized mutation-based therapy which needs to be further addressed. According to the recent findings regarding the therapeutic impact of co-mutations on the prognosis of KRAS-mutant NSCLC patients, in particular SMARCA4 and STK11 being associated with worse prognosis and TP53 being correlated with improved immune response and OS. Genetic evaluation of patients with a KRAS-mutant NSCLC diagnosis is a key approach in order to define the best possible therapy. Furthermore, immune modalities that target the TME, including cytokines (IL-1, IL-6) and the STING pathway, have a fundamental role in tumorigenesis and progression. Their combination with other therapies, such as the direct targeting of KRAS (particularly the newly-FDA-approved sotorasib), conventional chemotherapy, and immune checkpoint blockade, could be a potential regimen which requires further assessment. We foresee the necessity of further studies that examine the targeting both of tumor-intrinsic factors and proinflammatory and immunosuppressive TMEs to better cover the complexity of KRAS-mutant NSCLC and provide more personalized treatment based on the genetic assessment of each patient.

PMC10001068

Jen-Hsiang T. Hsiao,Onur Tanglay,Anne A. Li,Aysha Y. G. Strobbe,Woojin Scott Kim,Glenda M. Halliday,YuHong Fu

Role of Oligodendrocyte Lineage Cells in Multiple System Atrophy

25-02-2023

alpha-synuclein,multiple system atrophy,neurodegeneration,oligodendrocyte,oligodendrocyte progenitor cell

Multiple system atrophy (MSA) is a debilitating movement disorder with unknown etiology. Patients present characteristic parkinsonism and/or cerebellar dysfunction in the clinical phase, resulting from progressive deterioration in the nigrostriatal and olivopontocerebellar regions. MSA patients have a prodromal phase subsequent to the insidious onset of neuropathology. Therefore, understanding the early pathological events is important in determining the pathogenesis, which will assist with developing disease-modifying therapy. Although the definite diagnosis of MSA relies on the positive post-mortem finding of oligodendroglial inclusions composed of α-synuclein, only recently has MSA been verified as an oligodendrogliopathy with secondary neuronal degeneration. We review up-to-date knowledge of human oligodendrocyte lineage cells and their association with α-synuclein, and discuss the postulated mechanisms of how oligodendrogliopathy develops, oligodendrocyte progenitor cells as the potential origins of the toxic seeds of α-synuclein, and the possible networks through which oligodendrogliopathy induces neuronal loss. Our insights will shed new light on the research directions for future MSA studies.

Role of Oligodendrocyte Lineage Cells in Multiple System Atrophy Multiple system atrophy (MSA) is a debilitating movement disorder with unknown etiology. Patients present characteristic parkinsonism and/or cerebellar dysfunction in the clinical phase, resulting from progressive deterioration in the nigrostriatal and olivopontocerebellar regions. MSA patients have a prodromal phase subsequent to the insidious onset of neuropathology. Therefore, understanding the early pathological events is important in determining the pathogenesis, which will assist with developing disease-modifying therapy. Although the definite diagnosis of MSA relies on the positive post-mortem finding of oligodendroglial inclusions composed of α-synuclein, only recently has MSA been verified as an oligodendrogliopathy with secondary neuronal degeneration. We review up-to-date knowledge of human oligodendrocyte lineage cells and their association with α-synuclein, and discuss the postulated mechanisms of how oligodendrogliopathy develops, oligodendrocyte progenitor cells as the potential origins of the toxic seeds of α-synuclein, and the possible networks through which oligodendrogliopathy induces neuronal loss. Our insights will shed new light on the research directions for future MSA studies. Multiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease. Its symptomology includes parkinsonism, autonomic dysfunction, and ataxia, progressing rapidly during the disease duration, with an average of ten years [1]. Approximately 50% of patients with MSA will need aids to walk by the third year of motor symptoms, suggesting the necessity of developing early disease modification to enhance the quality of life [2]. The typical neuropathological findings in the post-mortem brains of MSA patients are the glial cytoplasmic inclusions (GCI) positive for α-synuclein and the loss of neurons. The brain regions impacted by these neuropathological changes determine the clinical phenotypes of parkinsonism (MSA-P) and/or cerebellar dysfunction (MSA-C) [3,4]. MSA is primarily a sporadic disease, with limited evidence for a genetic basis and risk genes. However, the prevalence of MSA-P and MSA-C appears to vary in global populations, which shows it to be associated with environments in different ethnic backgrounds [5,6,7]. Clinically probable and possible diagnoses of MSA depend on the manifestation of motor symptoms, levodopa response, and imaging changes [8]. Non-motor symptoms and physical signs indicative of autonomic dysfunction and REM-sleep behavior disorder are often observed in the prodromal stage of the disease [9]. However, currently, there are no reliable biomarkers for a definite diagnosis of MSA. Novel peripheral biomarkers to reflect the accumulation and spread of GCIs in the brain will be promising to assist with disease diagnosis and monitoring. MSA is typically categorized into clinical subtypes depending on the motor phenotypes (Table 1), i.e., MSA-P, MSA-C, and a mixed subtype of MSA composing features of both MSA-P and MSA-C [4,10]. Patients with olivopontocerebellar atrophy display cerebellar symptoms, including limb and gait ataxia, whereas patients with nigrostriatal atrophy exhibit parkinsonism characterized by bradykinesia, hypokinesia, and rigidity [1,10,11]. Due to the significant overlap in clinical presentation with other parkinsonian diseases, such as Parkinson’s disease (PD), the definite clinical diagnosis of MSA remains a challenge. MSA patients generally respond poorly to levodopa treatment, which is in contrast to patients with PD and is currently one criterion to differentiate the two diseases [12]. There are also MSA variants that do not fulfill these well-known categories. As such, the benign variant of MSA-P (Table 1) has a long disease duration of more than 15 years [13], whereas incidental MSA with GCIs limited to the pons and olivary nuclei [14,15] may be a preclinical phase of MSA similar to incidental Lewy body disease. The non-motor variant of MSA has a predominant autonomic failure (Table 1) and no typical motor symptoms [16], indicating the nigrostriatal regions are spared from GCIs. In contrast, the restricted variant of MSA-P has neuropathology strictly in the nigrostriatal regions [17]. Different from all these variants, the dementia variant of MSA (Table 1) has frontotemporal lobar degeneration and GCIs in the nigrostriatal and olivopontocerebellar regions [18]. The oligodendrocyte pathology is the dominant feature of all these subtypes and variants of MSA, highlighting the underpinning mechanisms these glia contribute to disease pathogenesis. Although different subtypes and variants of MSA vary in the anatomical regions affected by synucleinopathy and accompanied copathology, GCI is the general pathological hallmark [1,3,10,19]. GCIs are primarily composed of misfolded α-synuclein, a protein known to be located in the synaptic terminals of neurons [20]. Due to this neuropathological feature, MSA falls under the umbrella of primary synucleinopathy, a collective name including PD and dementia with Lewy bodies (DLB), both characterized by neuronal α-synuclein aggregations [4,20]. As MSA progresses, the pathological spread of GCIs across the broad brain regions and the increased local density of GCIs highly correlate with the severity of the neuronal loss [4]. Besides the salient GCIs in the MSA brain, α-synuclein aggregation is also observed in the oligodendrocyte nuclei (GNI), neuronal cytoplasm (NCI), and neuronal nuclei (NNI) [1,4]. Interestingly, the distribution of NCIs and NNIs throughout the brain in relation to neurodegeneration is much less striking than that of GCIs and GNIs [21]. This suggests oligodendrocytes play a critical role in neurotoxicity. Hence, oligodendrogliopathy is a primary pathological cause of MSA. The underpinning mechanisms of oligodendrocytes in the pathogenesis of MSA remain elusive. It is also unknown why nuclear aggregation of α-synuclein is specific to MSA but much less often observed in PD and DLB. Oligodendrocyte progenitor cells (OPCs) have been observed close to GCIs in the MSA brain; however, little is known about the role of OPCs in MSA. Despite their presence in a shared microenvironment, insoluble α-synuclein aggregation has not been observed in OPCs, which has led to the conclusion that GCIs only impact and develop in mature oligodendrocytes [3]. However, the density of OPCs appears to be highly correlated with the number of GCIs, suggesting an association between the presence of GCIs and the capacity to generate OPCs [3]. Parallel to the formation of GCIs, oligodendrocytes also undergo significant morphological changes, where cell enlargement can occur up to sixfold, alongside increased numbers of OPCs in affected brain regions [20]. While this may imply that oligodendrocyte dysfunction locally induces the generation of OPCs, the processes underlying the formation of α-synuclein accumulation in mature oligodendrocytes are still not understood. There has been evidence of endogenous sources of α-synuclein in rodent and human oligodendrocyte lineage cells, with the expression level of α-synuclein significantly decreasing during oligodendrocyte maturation [3,22]. Similarly, the mRNA level of SNCA (the encoding gene of α-synuclein) in the adult mouse cortex is 12:5:1 for neurons, OPCs, and oligodendrocytes, compared with 3:1 for neurons and oligodendrocytes in the adult human cortex, according to Barres Lab’s brain transcriptome database [23]. Since the endogenous cellular level of α-synuclein that contributes to its aggregation is lower in oligodendrocytes compared with neurons, the mechanism leading to enriched α-synuclein being the main component of GCIs is mysterious. This may indicate that delayed or impaired maturation of OPCs into myelinating oligodendrocytes plays a role in forming α-synuclein inclusions within GCIs [3,24]. This has also raised the hypothesis that OPCs contain an earlier pathologic species of α-synuclein, which is soluble but potentially more toxic and prone to transmission. Although there has been limited information on OPCs in human neurodegenerative disorders and there may be questions about the proliferative potential of these brain cells in the aged human brain, the mean age of MSA onset can be as early as 30 years in MSA-P and 40 years in MSA-C [25]. Accounting for the preclinical phase of MSA when GCIs initiate, the pathological events can start even earlier in life, which is younger than most aging-related neurodegenerative disorders. In addition, abundant OPCs have also been identified in human demyelinating diseases, such as multiple sclerosis, which has an average onset between 20 and 40 years, with some late onset in the 50s [26]. The earlier onset of these conditions, at a time when the proliferative potential of OPCs is perhaps greater, may therefore suggest a key role for OPCs in initiating pathology. This perhaps also explains the biological preference of forming GCIs in MSA rather than neuronal inclusions as observed in PD and DLB if more immature OPCs are indeed the origin of pathologic seeds of α-synuclein. Our earlier study has shown that MSA brains feature demyelination [20]. Alternatively, α-synuclein inclusions may delay and alter the maturation process of OPCs [3,27], impacting remyelination through downregulation of myelin-associated proteins [24,27]. Failure of OPCs to readily proliferate, migrate, or differentiate will consequently impair remyelination [28], which may further enhance the cellular level of α-synuclein at this stage and continue to signal the brain to generate more OPCs and recruit OPCs to the early-formed loci. Indeed, the impairment of OPC maturation has been suggested as a significant underlying mechanism contributing to the pathogenesis of MSA [27,29]. Although synucleinopathy is the predominant pathological hallmark of MSA, it is not the only neuropathological feature observed in MSA. Other pathological findings include inflammation, dysregulation of iron, and deficient neurotrophic support [30]. The role of inflammation and its timing in disease progression as a causative initiating event or a subsequent resultant reaction needs to be better understood within the context of MSA [31]. OPCs have been found to play a role in antigen presentation and are the cytotoxic targets in inflammatory demyelination [32]. Aberrant oligodendroglial-vascular interactions disrupt the blood-brain barrier (BBB), which can also facilitate central nervous system (CNS) inflammation [33]. In addition, oligodendrocytes are responsible for maintaining iron homeostasis and require iron for the myelination of axons [34]. The total concentration of iron is significantly elevated in areas of the brain affected by MSA, highlighting the impairment of oligodendrocyte function in MSA [21]. As there is a scarcity of knowledge on how OPCs are involved in MSA pathogenesis, it is important to review their biological functions and assess aspects relevant to disease conditions. In the CNS, oligodendrocytes are responsible for myelination, which allows signal transmission and provides neurotrophic support to axons [24,27]. During development, OPCs expressing proteoglycan nerve glial antigen (NG2) differentiate into myelinating oligodendrocytes [28]. However, a pool of OPCs, comprising 5–8% of total glial cells, remain undifferentiated in the adult CNS and retain the ability to generate mature oligodendrocytes [35,36,37,38]. Although myelination and remyelination by mature oligodendrocytes are efficient in young adults, the efficiency of myelination declines dramatically with aging [39]. Demyelination can arise as a primary or secondary pathology. While primary demyelination infers the loss of myelin from an intact axon, secondary demyelination results from the initial axonal loss [28]. Demyelination often occurs in the presence of traumatic or antibody-induced lesions but is also a common neuropathological hallmark found in several degenerative diseases impacting broad brain regions [28]. When demyelination occurs, whether during development, injury, or disease, OPCs readily respond by migrating, proliferating, and differentiating into mature oligodendrocytes to support the remyelination of those exposed axons [3,28,35]. It is worth noting that the remyelination of exposed axons is critical to the survival of the axon, as the exposed axons are otherwise more susceptible to damage, even when neuroinflammation is not present [28]. Several recent studies have conducted comprehensive quantitative proteomic analyses comparing OPCs at different ages [39,40,41,42]. Proteins shown to be markedly downregulated in aged OPCs include the non-erythroid alpha chain of spectrin (SPTAN1, involved in actin stabilization), aldehyde dehydrogenase 1 family member A1 (ALDH1A1, known to promote OPC differentiation during CNS remyelination), alkaline phosphatase (ALPL), folate receptor alpha (FOLR1, a gene mutation known to cause myelination deficits), and transcription factor 4 (TCF4, involved in stage-specific regulation of OPC differentiation). In contrast, aged OPCs upregulate enzymes involved in sphingolipid synthesis, proteins and proteases associated with lysosomal functions, and citrullination protein functions in insulating neurons. Although oligodendrocytes are mature OPCs, OPCs (also known as NG2+ glia) and oligodendrocytes have been considered independent populations of cells due to the additional characteristics and functions OPCs possess [43,44]. OPCs arise from the ventricular germinal zones within the embryonic neural tubes [45]. They are evenly distributed in the gray and white matter of the developing and adult brains [46]. OPCs are the most proliferative cell type during homeostasis and pathological conditions [36,37,38]. Morphologically, OPCs have small cell bodies that have filopodia and lamellipodia on the multiple-branched processes [47]. The filopodia allow these cells to survey the local environment and guide their migration [48,49,50]. These dynamic filopods also allow OPCs to achieve a state of homeostasis by detecting nearby OPC density, oligodendrocyte viability, and neuronal axon myelination status [49,50]. When they contact neighboring OPCs, the process with the filopodia retracts [49]. Using embryonic human brain tissue samples, Huang et al. (2020) highlighted that this self-repulsive feature allows OPCs to disperse across the brain [51]. OPCs can be categorized based on their location in the brain. The gray matter OPCs tend to have radial processes, and the white matter OPCs have processes that align with the nerve fibers [45,47]. As the location and morphology of OPCs vary, the function of OPC subtypes may also differ. The current literature on the subtypes of OPCs is minimal. Future studies will be needed to examine the roles of different subtypes of OPCs in brain regions with different disease vulnerabilities. The main role of OPCs is to produce mature myelinating oligodendrocytes throughout one’s lifetime to be able to remyelinate upon injury [44,49]. OPCs form surveillance networks for CNS injuries and tissue repair [49]. Recently, many other functions were revealed to be independent of myelination. For instance, fine-tune neural circuits and axon arbor size perhaps by engulfing and pruning axons [52,53]. OPCs also have an immunomodulatory capacity as they express cytokine receptors and can cross-present antigens to cytotoxic CD8+ T cells [54]. Furthermore, OPCs have synaptic signaling properties due to their connections to neurons. OPCs also synapse with glutamatergic neurons in the gray and white matter. A transgenic mouse model highlighted that the pattern of neuronal activity in the brain region influences glutamate release and the differentiation of OPCs [55]. This may elucidate the differences in the cell cycle of OPCs between white matter and gray matter, with a higher rate of proliferation and differentiation in the white matter compared with the gray matter [46]. Recent studies have revealed that different OPCs respond to neurons differently [56]. OPCs possess heterogeneous functions between brain regions, and the OPC-expressed ion channel density predicts the functional state of the OPC [57]. McKenzie and colleagues found that blocking OPC differentiation into oligodendrocytes impairs motor skill learning in mouse models [58]. Consistent with this, Lewis and colleagues showed an association between OPC differentiation and motor learning [59]. In addition, OPCs in multiple sclerosis patients have impaired differential ability to mature oligodendrocytes [60]. These findings suggest that OPC pathology could be associated with motor symptoms. In addition, ischemic stroke models in mice have revealed that OPCs play a role in maintaining BBB integrity [61]. Wang and colleagues found that OPC transplantation can be a potential treatment intervention for ischemic stroke. This is because OPCs can activate the Wnt/β-catenin pathway, which increases BBB tight junction proteins to help prevent BBB leakage [61]. Most of the literature categorizes oligodendrocytes based on their maturation stage [62] (Figure 1). Following proliferation, NG2+ OPCs will turn into pre-oligodendrocytes (A2B5+), immature oligodendrocytes (O4+), and, finally, mature myelinating oligodendrocytes that express myelin basic protein (MBP) [62,63,64]. The mature myelinating oligodendrocytes can be categorized differently based on their morphological characteristics, locations in the CNS, and functional differences. Based on morphological characteristics, del Río-Hortega classified oligodendrocytes into four types (Figure 1). Small and rounded Types I and II oligodendrocytes compose the main population in the white and gray matter [65]. Type I oligodendrocytes form myelin segments on small-diameter axons in different orientations, whereas Type II oligodendrocytes are exclusively in white matter, forming parallel myelin segments [66,67]. Type III oligodendrocytes have one or more processes that do not usually branch. These cells myelinate fewer large-diameter axons [65,67]. Type IV oligodendrocytes do not have processes and form a single long myelin sheath to myelinate only one axon with a large diameter [67]. Similar to OPCs, the location and function of these subtypes of oligodendrocytes have not been extensively studied. Future studies investigating different subtypes of oligodendrocytes would further elucidate the pathomechanism of oligodendrocyte dysfunction-related diseases. Location-based categories include white matter and gray matter oligodendrocytes (Figure 1). Oligodendrocytes are the dominant cell type in the white matter [69]. The lipid-rich myelin produced by oligodendrocytes contributes to the pale appearance of the white matter. The oligodendrocytes in the gray matter are mainly responsible for regulating the neuronal microenvironment rather than myelination [70]. Moreover, oligodendrocytes can be categorized based on their functions and ability to produce myelin. Myelin-forming oligodendrocytes produce myelin sheaths, whereas OPCs and non-myelinating ‘perineuronal’ or ‘satellite’ oligodendrocytes generate myelin-producing oligodendrocytes upon injury, development, or plasticity during one’s lifetime [65]. The main function of mature white matter oligodendrocytes is myelination, while gray matter oligodendrocytes wrap cells and their processes locally [71,72]. By generating plasma membranes with a large lipid content and wrapping them around the targeted parts of neurons, oligodendrocytes produce a myelin interface for optimal function of the entire neuron [54,71,72]. The myelin sheath is an extended membrane of the oligodendrocytes that provides electrical insulation and thereby allows rapid signal transduction [54]. Oligodendrocytes have also been shown to play a role in metabolic support [73], uptake of fatty acids and lipid metabolism [68], and production of neurotrophic factors [74]. In healthy individuals, new oligodendrocytes must be produced from the OPC pool in the CNS to remyelinate neurons, prevent axonal degeneration, and preserve normal functions [54]. Upon CNS injury, there will be an increase in local OPC proliferation [45] and recruitment of OPCs to the injury site [37]. The recruited cells do not exhibit self-repulsive characteristics in an attempt to restore normal oligodendrocyte density [49]. When the adjacent OPC is removed due to death or differentiation, the nearby OPC proliferates to replace the lost cell and achieve homeostasis [49]. This proliferation of OPCs occurs in the presence of CNS demyelination, traumatic injuries, and chronic neurodegenerative diseases. This has been demonstrated by in vivo studies in an amyotrophic lateral sclerosis mouse model, which revealed enhanced OPC proliferation and accelerated differentiation to oligodendrocytes at the injury site [75]. Demyelinating diseases, such as multiple sclerosis, schizophrenia, and Alzheimer’s disease, are all closely associated with reduced/dysfunctional OPCs and oligodendrocytes [54]. Chang and colleagues proposed a possible disease cause in multiple sclerosis, highlighting the reduced number of OPCs in lesions [76]. The maturation process of OPCs is a prerequisite for remyelination in demyelinating disorders, which is coordinated by complex intracellular transcription factors, extracellular signals, and epigenetic mechanisms [77,78]. MSA is a disease without a renowned genetic preference. OPCs’ proliferation and myelination of axons by mature oligodendrocytes are highly receptive and adaptive to environmental stimuli affecting neuronal activities. Epigenetic changes are known to be very important, through which environmental stimuli are carried out [77,78]. Here, we summarize the potential risk factors revealed by recent methylation studies. Recent studies have focused on the importance of epigenetic mechanisms and their non-genetic regulation of gene expression at the cellular level [77,79,80]. These studies revealed epigenetic regulators during development and regeneration in response to environmental changes, including covalent modifiers of DNA methylation, histone-modifying enzymes, chromatin modifiers, and non-coding RNA (ncRNA) regulators. However, it remains elusive about the epigenetic mechanism in terms of how environmental changes trigger OPC differentiation and oligodendrocyte myelination. DNA methylation has been the hub of human epidemiological epigenetic research. The first study linking DNA methylation to oligodendrocyte development was conducted in a neonatal rat model, showing hypomyelination and disrupted oligodendrocyte genesis [81]. In this model, the inhibitor of DNA methyltransferases (DNMT), 5-azacytidine, was applied, suggesting oligodendrocyte lineage cells are vulnerable to DNA methylation. Dnmt1 and Dnmt3a/b are the most distinct forms of DNMTs, which are responsible for maintaining DNA methylation by adding a methyl group to cytosine (5mC). Transgenic mice lacking Dnmt3a showed impaired oligodendrocyte differentiation and dysfunction in remyelination after injury [82]. In contrast, the ablation of Dnmt1 showed altered splicing events such as intron retention and exon skipping in genes involved in myelination, cell cycle, and lipid metabolism, indicating the crucial role of DNA methylation during neonatal oligodendrocyte development [83]. Histone modification includes post-translational changes of histone tails by deacetylation, ubiquitination, phosphorylation, and methylation. Histone deacetylation of the lysine residue is the most prevalent type of histone modification [82,83,84]. The process of acetylation is established by histone acetyltransferases (HATs), whereas deacetylation is maintained by histone deacetylases (HDACs). HDACs have been shown to be involved in oligodendrocyte development, as their inhibition can potentially decrease oligodendrocyte maturation and differentiation [85]. Treatment with HDAC inhibitors in vitro can suppress inhibitory transcription factors, preserving OPCs in a proliferative and undifferentiated state during the early onset of oligodendrocyte lineage progression [86]. A recent study has shown that N6-methyladenosin (m6A) modification on mRNA has regulated OPC differentiation, whereas the deletion of Prrc2a (m6A reader) and Mettl14 (m6A writer) decreased mature oligodendrocytes and induced hypomyelination by regulating oligodendrocyte transcription factor 2 (Olig2) expression in an m6A-dependent manner in vitro and in vivo [87]. The pathological hallmark of MSA includes GCIs [88], which feature α-synuclein aggregates. With little evidence of α-synuclein expression in the human OPCs, it is unclear how the toxic form of α-synuclein initiated in the oligodendrocyte milieu propagates to form insoluble GCIs and then broadly spreads to other brain cells. Previously, knowledge that the endogenous α-synuclein expression is exclusively in neurons has facilitated the neuron-centric dogma that pathologic α-synuclein released from surrounding neurons is taken up by oligodendrocytes as the source of GCIs [22,89]. A recent finding in a mouse model revealed the other potential route for oligodendrocytes to succeed neuronal α-synuclein by pruning α-synuclein-containing neurites [90]. However, there are also proofs supporting GCI’s oligodendrocyte lineage origin. For instance, mature oligodendrocytes normally do not take up extracellular α-synuclein preformed fibrils (PFF) as neurons do. Instead, OPCs can take up PFFs, leading to inclusion formation [24]. These inclusions remain even after the maturation of these OPCs, providing a potential origin of pathologic α-synuclein seeding in MSA [91]. Recent studies have shown that OPCs treated with PFFs had an upsurge and multimerization of their endogenous α-synuclein, which interfered with the expression of proteins associated with neuromodulation and myelination [21,24,92]. PFF-treated OPCs have shown reduced autophagic proteolysis, deficient differentiation efficiency, and newly differentiated mature oligodendrocytes containing α-synuclein accumulation but reduced myelin-associated proteins, such as MBP and tubulin polymerization promoting protein (TPPP/P25) [24,80]. Immunoelectron microscopy confirmed that PFF treatment induced α-synuclein expression on the cell membranes and upregulated endogenous levels in the cytosolic matrix of OPCs but not in mature oligodendrocytes, whereas exposure to monomers did not induce enhanced α-synuclein immunoreactivity in OPCs [24]. Despite the accumulation of α-synuclein in OPCs being confirmed to contribute to the formation of GCIs, immunoblotting and immunostaining showed that these aggregations were not composed of phosphorylated α-synuclein as typically seen in post-mortem brains of MSA patients [24]. This may suggest that both α-synuclein monomers and PFFs are incapable of initiating the phosphorylation. Other pathologic or microenvironmental factors might be required to coexist to propagate the pathologic modification. To date, in vitro and in vivo MSA models mainly focus on investigating the effects of modifying α-synuclein (either wild type or SNCA mutations) by targeted expression in oligodendrocytes and OPCs [22]. The (Plp)-α-Syn transgenic mouse has specific oligodendroglial overexpression of human α-synuclein under the control of the proteolipid promoter. The mouse line has proven useful in studying MSA-related pathogenic mechanisms and is the most extensively characterized preclinical/prodromal MSA model. The representative MSA features in this model include GCI-like pathology, microglial activation, loss of trophic support, nigrostriatal degeneration, a progressive motor phenotype, and early autonomic dysfunction [3,93]. Although overexpression of α-synuclein in oligodendrocytes may be a simplified model that cannot fully mimic the actual pathogenesis of MSA, the presence of α-synuclein aggregates forming GCIs is the critical event in MSA pathology linked to the presented central cardiovascular autonomic failure [22,94]. The model proves that oligodendrogliopathy is sufficient to induce MSA-like prodromal symptoms and that MSA is a primary oligodendrogliopathy disorder. Induced pluripotent stem cells (iPSCs) from MSA patients have shown great cell modeling potential via the application of four transcription factors: Oct3/4, Sox2, Klf4, and C-Myc. Subsequent differentiation of the iPSCs into OPCs after 60 days was performed on fibroblast cells from MSA familial patients and characterized by immunocytochemistry with OPC markers and the bipolar morphology of these immature cells [95]. The transcript level of SNCA was subsequently downregulated with the maturation of the oligodendrocytes [95,96]. MSA pathogenesis is also known to be associated with mitochondrial dysfunction and a reduction in respiratory chain complex I activity, as shown in the skeletal muscle of MSA patients [97]. Rat models targeting nigrostriatal mitochondria by striatal injection of succinate dehydrogenase inhibitor 3-nitropropionic acid or mitochondrial complex I inhibitor (MPP+) induce extensive neuronal loss in the substantia nigra and striatum as well as a motor deficit resembling MSA-P [98]. The iPSCs-derived neurons from the patient with a variant of COQ2 MSA have shown mitochondrial dysfunction [99,100]. In this study, Nakamoto and colleagues reprogrammed peripheral blood mononuclear cells into iPSCs, followed by differentiation into different midbrain and hindbrain neurons, including glutamatergic, GABAergic, dopaminergic, and glycinergic neurons. The COQ2 variant patient-derived neurons were shown to have reduced mitochondrial mass, COQ10, and oxygen consumption rate and presented an extracellular acidification [101]. Unfortunately, the mitochondrial model has not been applied to oligodendrocyte lineage cells; therefore, there is currently missing proof of whether mitochondrial deficiency or dysfunction is one of the underpinning mechanisms of oligodendrogliopathy involved in MSA. Much of the literature on gliogenesis in MSA comes from murine models. These studies have established at least three stages of oligodendrocyte lineage differentiation (Figure 1). OPCs are characterized by markers such as NG2, A2B5, PDGFRα, Olig1, Olig2, and Sox10. Pre-myelinating oligodendrocytes can be identified using BCAS1, O4, Olig2, Sox10, and Sox17. Mature myelinating oligodendrocytes express O1, MAG, MBP, MOG, PLP1, CNP, Olig2, and Sox17 [7,102]. Indeed, at least 12 clusters of oligodendrocytes have been described through transcriptomic analysis, though the morphology and function of each remain poorly understood [103,104,105,106]. In mice, OPC formation occurs in three waves through asymmetric division of radial glia. Interestingly, Li et al. recently found a common progenitor for astrocytes, oligodendrocytes, and olfactory bulb interneurons [107,108]. The development of the oligodendrocyte lineage has recently been reviewed by Kuhn, Gritti, Crooks, and Dombrowski [54]. Briefly, newborn OPCs express DM-20 mRNA, an isoform of PLP. Commitment to the oligodendrocyte lineage is characterized by the induction of Sox10 by Olig1 and Olig2. Sox10 subsequently induces Cspg4, which encodes NG2. PDGFRα is a receptor for PDGF-A, an OPC survival marker produced by neurons and astrocytes [109,110]. Olig1 and Olig2 are abundant transcription factors throughout the oligodendrocyte lineage. While the role of Olig1 is less known, Olig2 is essential for OPC differentiation and inducing functional gain to promote remyelination in mice [111]. A recent transcriptomic analysis of embryonic human OPCs found that in addition to a cluster of cells expressing the conventional OPC genes OLIG1, OLIG2, PDGFRA, NKX2-2, SOX10, S100B, and APOD, there was an additional cluster of pre-OPC cells expressing OLIG1, OLIG2, PDGFRA, in addition to EGFR [51]. These pre-OPC cells are also expressed in the outer radial glial cells (oRGs), suggesting that oRGs, which are rarely found in rodents and are thought to contribute to gray matter expansion in primates, may provide an additional source of oligodendrocytes in addition to the outer subventricular zone [51]. Other OPC differentiation factors that have been identified include triiodothyronine (T3) [112], adenosine receptor ligands [113,114,115], and AMPA receptors [102]. The involvement of OPCs in MSA has long been suspected [21]. In fact, a recent CSF analysis in 50 patients with MSA revealed higher levels of NG2 and neurofilament-L (NF-L) than controls [116]. While NF-L as a marker of axonal injury is well established, increased detection of NG2 may be due to increased OPCs in MSA. Certainly, increased numbers of OPCs in the brains of MSA patients have been reported [27]. Such evidence of the potentially increased activity of OPCs in MSA, along with the much higher α-synuclein expression in OPCs compared with oligodendrocytes, points to a contribution of OPCs in MSA pathogenesis. This overexpression of α-synuclein has been shown to delay OPC maturation by downregulating the myelin-gene regulatory factor and MBP [27,29]. Interestingly, a recent analysis of O4+ pre-myelinating oligodendrocytes generated from patient-derived iPSCs demonstrated that overexpression of α-synuclein induced the upregulation of several genes important for OPC maturation into O4+ oligodendrocytes, including MBP, MOG, MAG, CNPase, and NKX2.2, and downregulated astrocyte genes [117]. This suggests that α-synuclein may play a physiological role early in the maturation of OPCs, where increased levels of α-synuclein may be necessary for the commitment of cells to the oligodendrocyte lineage, whereas later lower levels are necessary for their complete maturation into myelinating oligodendrocytes. The same study also demonstrated that instead of maturing into myelinating oligodendrocytes, O4+ pre-myelinating oligodendrocytes treated with α-synuclein fibrils transformed into an antigen-presenting phenotype. Early immunoreactivity of OPCs may therefore be crucial in the pathogenesis of MSA. However, this also demonstrates the need to consider the heterogeneity of OPCs in MSA, as it is becoming increasingly recognized in multiple sclerosis [104,118]. Another consideration is the differences in the transcriptomic profile of embryonic and adult OPCs, with Lin et al. demonstrating that neonatal primate O4+ cells preferentially expressed genes involved in differentiation and proliferation, whereas adult O4+ cells had higher expression of genes involved in cell death and survival [119]. In order to gain further insight into the oligodendrocyte lineage-related pathways involved in MSA pathogenesis, gene-based pathway prediction was performed. Risk genes were extracted from the literature, DisGeNet [120] and the Harmonizome database [121], and subsequently used Brain RNA-Seq [23] to determine the oligodendrocyte-specific expression of these genes in humans, though data for OPCs were not available. All genes with an FPKM greater than one were used for pathway prediction with QIAGEN IPA (QIAGEN Inc., Hilden Germany, http://digitalinsights.qiagen.com/IPA (accessed on 5 December 2022) [122]). This process yielded seven distinct networks, broadly falling into protein aggregation, DNA methylation, iron homeostasis, neuroinflammation, myelin formation, cell maturation, and glutaminergic excitotoxicity (Table 2 and Figure 2). Apart from MBP, only a few OPC-associated MSA risk genes we identified have been discussed in the literature. Most recently, Piras and colleagues demonstrated the downregulation of QKI in MSA-C following RNA expression profiling of cerebellar white matter [123]. QKI is known to regulate oligodendrocyte differentiation and myelination, though it may also have a potential role in regulating RNA metabolism in these cells. It may also be a potential therapeutic target in MSA, as Zhou et al. found that PPARβ and RXR agonists were able to alleviate QKI deficiency-induced demyelination in QKI knockout mice [124]. ATXN2 gain-of-function mutations are observed in spinocerebellar ataxia and amyotrophic lateral sclerosis, though they have also been identified in MSA [125]. Functionally, ataxin-2 has been associated with lipid metabolism and may inhibit myelin formation by repressing mTORC1 signaling [126]. Fyn kinase has been implicated as an effector in Aβ-induced remyelination and OPC proliferation and differentiation [127]. While this may be important in Alzheimer’s disease pathology, Aβ can be an observed copathology in MSA cases surviving to older ages. In addition to these genes, a recent unpublished study performed single-nuclei sequencing of putamen oligodendrocytes in MSA, PD, and controls [128]. Their results demonstrated the downregulation of genes associated with regulating apoptosis and senescence in MSA OPCs, including EGFR, NFKB, STAT3, FOXO1, MDM2, CDKN2A, TNIK, TXNIP, and BCL9. This points to either an impact of α-synuclein or another disease factor dysregulating the OPCs, or the accumulation of α-synuclein may be a consequence of cell senescence. Mature oligodendrocytes show increased expression of proteins associated with neurogenesis, oligodendrocyte differentiation, and myelination, which may reflect compensatory processes to counteract pathology. Oligodendrocytes impacted by MSA are well established, but there have been limited attempts to enhance oligodendroglial differentiation and remyelination in MSA. In the context of multiple sclerosis, however, multiple studies have identified molecules that may be able to promote oligodendrocyte differentiation, and there are also several ongoing animal and human trials (recently reviewed by [129]). Some of these include anti-muscarinic agents, such as benztropine and clemastine, which have been shown to increase MBP expression and promote OPC differentiation in rats; and Opicinumab (BIIB033), a blocker of LINGO-1, which is normally expressed by oligodendrocytes as a negative regulator of their differentiation and myelination; and histamine H3 receptor antagonists. So far, none of these agents has demonstrated sufficient efficacy in multiple sclerosis, and none has reached clinical use. Indeed, this reflects the ubiquitous struggle to identify disease-modifying therapies for all neurodegenerative disorders in general. It is likely that this stems from an incomplete understanding of such complex disorders, an attempt to distill the several pathogenic processes associated with them into a single target, and the inability to identify patients amenable to therapy early in the disease course. In the context of MSA, future studies should establish the relationship between the genetic influences implicated in MSA and OPC differentiation in association with α-synuclein aggregation (Figure 2). Aside from pharmacotherapy, stem cell transplantation may be another therapeutic avenue for MSA. Previously, intra-arterial administration of autologous mesenchymal stem cells demonstrated some benefit in MSA [130], and an evaluation of its intrathecal administration is underway. The therapeutic success of stem cell implantation may, however, be improved by the direct implantation of OPCs, given the advances that have been made in differentiating human pluripotent stem cells into OPCs. While this has shown remyelination potential in mice [61], it has not yet been undertaken in humans and requires further refinement to ensure efficacy. In conclusion, oligodendrocyte lineage cells are key players in the pathogenesis of MSA. The knowledge of the oligodendrocyte pathways and mechanisms involved in the development of synucleinopathy is extremely limited. Future studies revealing the role of the oligodendrocyte lineage in MSA pathogenesis will provide insights into developing novel pharmacological targets and early biomarkers for the disease.

PMC10001073

Ziwei Liu,Hangyu Li,Qianqian Liu,Yangyang Feng,Daiyan Wu,Xinnan Zhang,Linzi Zhang,Sheng Li,Feng Tang,Qun Liu,Xiaonong Yang,Haibo Feng

Ultrasonic Treatment Enhances the Antioxidant and Immune-Stimulatory Properties of the Polysaccharide from Sinopodophyllum hexandrum Fruit

21-02-2023

ultrasonic,carbohydrate,structural characterization,antioxidant,immunomodulatory effect

We aimed to assess the potential of ultrasonic treatment on the processing of polysaccharides as functional foods or food additives. The polysaccharide from Sinopodophyllum hexandrum fruit (SHP, 52.46 kDa, 1.91 nm) was isolated and purified. SHP was treated with various levels of ultrasound (250 W and 500 W), resulting in the formation of two polysaccharides, SHP1 (29.37 kD, 1.40 nm) and SHP2 (36.91 kDa, 0.987 nm). Ultrasonic treatment was found to reduce the surface roughness and molecular weight of the polysaccharides, leading to thinning and fracturing. The effect of ultrasonic treatment on polysaccharide activity was evaluated in vitro and in vivo. In vivo experiments showed that ultrasonic treatment improved the organ index. Simultaneously, it enhanced the activity of superoxide dismutase, total antioxidant capacity, and decreased the content of malondialdehyde in the liver. In vitro experiments demonstrated that ultrasonic treatment also promoted proliferation, nitric oxide secretion, phagocytic efficiency, costimulatory factors (CD80+, CD86+) expression, and cytokine(IL-6, IL-1β) production of RAW264.7 macrophages.

Ultrasonic Treatment Enhances the Antioxidant and Immune-Stimulatory Properties of the Polysaccharide from Sinopodophyllum hexandrum Fruit We aimed to assess the potential of ultrasonic treatment on the processing of polysaccharides as functional foods or food additives. The polysaccharide from Sinopodophyllum hexandrum fruit (SHP, 52.46 kDa, 1.91 nm) was isolated and purified. SHP was treated with various levels of ultrasound (250 W and 500 W), resulting in the formation of two polysaccharides, SHP1 (29.37 kD, 1.40 nm) and SHP2 (36.91 kDa, 0.987 nm). Ultrasonic treatment was found to reduce the surface roughness and molecular weight of the polysaccharides, leading to thinning and fracturing. The effect of ultrasonic treatment on polysaccharide activity was evaluated in vitro and in vivo. In vivo experiments showed that ultrasonic treatment improved the organ index. Simultaneously, it enhanced the activity of superoxide dismutase, total antioxidant capacity, and decreased the content of malondialdehyde in the liver. In vitro experiments demonstrated that ultrasonic treatment also promoted proliferation, nitric oxide secretion, phagocytic efficiency, costimulatory factors (CD80+, CD86+) expression, and cytokine(IL-6, IL-1β) production of RAW264.7 macrophages. Plant polysaccharides are carbohydrates comprising many different kinds of monosaccharides. They perform biological activities such as immune regulation as well as anti–inflammatory and anti–viral actions. Currently, polysaccharides have become a hot spot in food science because of their immense potential and economic value [1]. Sinopodophyllum hexandrum is a plant that is mainly distributed in Yunnan, Sichuan, Tibet and other places in China [2]. Its fruit appears as a red oval when it matures. Local people usually pick and eat this fruit directly. At this time, research in China and abroad focuses on podophyllotoxin extracted from the root of this plant, and there is little research on its fruit [3]. The polysaccharide found in the fruit of SHP is a type of biological macromolecule with antioxidant and immune regulation properties, and it has great potential to become a food additive. Due to the high molecular weight of the extracted polysaccharide, it is difficult for it to exert its biological activity, and some special methods are required to degrade the molecular weight of the polysaccharide [4,5,6,7,8]. At present, there are three known methods of polysaccharide degradation, namely, biological degradation, chemical degradation, and physical degradation. Enzymatic degradation is the most attractive type of biodegradation. Enzymatic degradation of polysaccharides makes use of its specificity and non–specificity and will not damage the effective functional groups of the substrate and the structure of the oligosaccharide itself in the reaction process, so the activity of the product is relatively high. In addition, the molecular weight of the degradation products is easy to control, the reaction conditions are mild, and the pollution is relatively small. However, the cost of enzyme degradation is too high to be widely used in industry. The chemical degradation of polysaccharides mainly uses chemical reagents such as NaNO2, acid, and oxidant to degrade polysaccharides. The advantages of this treatment method are low cost and rapid reaction, but the homogeneity of degradation products is relatively poor, the activity of polysaccharides will be broken, and the residues of these reagents will also lead to environmental pollution [9]. Physical degradation is a green and efficient degradation method, including microwave, ion radiation and high–pressure micro jet. These methods are difficult to operate and difficult to promote in the food industry. Therefore, finding a new and harmless way to degrade the molecular weight of polysaccharides is of great significance for polymers such as polysaccharides to become functional foods or additives in the food industry. In the past 10 years, the ultrasonic–assisted method has been widely used in the extraction of polysaccharides. Some studies have shown that ultrasonic improves the extraction rate of polysaccharides. The ultrasonic–assisted method uses the cavitation, mechanical, and thermal effects of ultrasonic to extract the effective components from raw materials [10,11,12]. Ultrasonic treatment also significantly enhanced the efficiency of polysaccharide extraction compared with the use of reagents only. Studies have shown that ultrasound can also be used for polysaccharide degradation. Both the process and extent of ultrasonic degradation can be easily controlled, resulting in the production of structurally intact low molecular-weight polysaccharides without the necessity of introducing new substances. Selection of the specific ultrasonic treatment leads to effective cleavage of the macromolecular chain and polysaccharide degradation [9]. The activity of polysaccharides is determined by the active centres of several oligosaccharide fragments in the polysaccharide molecule. Through ultrasonic degradation, the polysaccharide molecule begins to decompose from the centre. In this process, the physical and chemical properties of polysaccharides change slowly, and their biological activities, such as antioxidant activity and immune activity, also change [13]. Several studies have shown that polysaccharides can enhance the activity of antioxidant enzymes and reduce the levels of oxidative stress. For example, polysaccharides from Taraxacum mongolicum and Cissus pteroclada Hayata can increase the activity of superoxide dismutase and reduce the malondialdehyde content in the body. In addition, macrophages play a major role in innate immunity. Polysaccharides, such as apple, can enhance the immune function of macrophages [14,15,16]. In order to investigate the effect of ultrasonic treatment on polysaccharide functions, we evaluated the effects of ultrasonic irradiation on the structure and antioxidant activity of Sinopodophyllum hexandrum polysaccharide in vivo. Additionally, we conducted in vitro experiments to initially assess the potential toxicity and immunological effects of the ultrasonically treated polysaccharide on cells. We believe that our study results will provide a theoretical basis for the application of ultrasonic in the processing of polysaccharides as functional foods and additives. Neutral red was obtained from Leagene Biotechnology Co., Ltd. (Beijing, China). SHP fruit was obtained from Cangxitang Biotechnology Co., Ltd. (Chengdu, China). HCSS was obtained from National Institutes for Food and Drug Control (Beijing, China). LPS was obtained from Solarbio Technology Co., Ltd. (Beijing, China). Fluorescein thiocyanate (FITC) and DAPI (4′,6–diamidino–2–phenylindole) were obtained from Yuanye Biotechnology Co., Ltd. (Shanghai, China); DID (DiIC18(5) (1,1′–dioctadecyl–3,3,3′,3′– tetramethylindodicarbocyanine, 4-chlorobenzenesulfonate salt) was obtained from C–reagent Biotechnology Co., Ltd. (Shanghai, China); and sodium sulphate and sodium azide were purchased from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). The Nitric Oxide Assay kit was purchased from Yuhengsheng Material Technology Co., Ltd. (Suzhou, China). Superoxide dismutase (SOD), total antioxidant capacity and malondialdehyde (MDA) were obtained from the Nanjing Jiancheng Bioengineering Research Institute (Nanjing, China). The Cell Counting Kit–8, Dulbecco’s Modified Eagle Medium (DMEM), and sterile phosphate-buffered saline (PBS) were provided by Boster Biological Technology Co., Ltd. (Wuhan, China). Response surface methodology was used to optimize the extraction process of polysaccharides, and this part is presented as Supplementary Material. According to the method described in the literature, polysaccharides were decolorised using macroporous resin and purified using cellulose column, freeze-dried, and named SHP [17]. The polysaccharide solution was treated with ultrasonic at two different power levels (250 W and 500 W) in a specific pulse mode (1 s on and 1 s off) with an ultrasonic cell breaker (IID, Scientz, Ningbo, China) for 1 h. The treated solution was lyophilised to obtain two degraded polysaccharides named SHP1 and SHP2. The molecular weight of polysaccharides was determined by the gel–permeation chromatography (Eleos system, Wyatt, MO, USA) method. We set the temperature of the chromatographic column to 40 °C and injected 500 μL of the sample at a flow rate of 1 mL/min, and the mobile phase was 0.05 mol/L Na2SO4 and 0.02% NaN3. We accurately weighed the 40 mg sample and dissolved it in D2O, followed by 1H spectra analysis on an AVII–600 MHz nuclear magnetic resonance spectrometer (Bruker, Aarau, Switzerland). We mixed and ground the polysaccharide with potassium bromide, and pressed the tablet and scanned it with a Fourier Transform Infrared Spectrometer (Cary660, Agilent Technologies, Inc., Beijing, China) in the range of 400–4000 cm. We used the CD spectrum (J-815, Jasco, Tokyo, Japan) to detect the configuration of polysaccharides in water. After the polysaccharide was fully dissolved in distilled water, it was scanned at 190–400 nm on the machine. AFM was used to study the morphology of polysaccharides. Three kinds of polysaccharides were dripped on the surface of fresh mica, respectively. After they were completely dried, they were placed on AFM (Dimension ICON, Bruker, Aarau, Switzerland) for observation [18]. Finally, the morphological characteristics of the three samples were analysed by the SEM (SU8220, Hitachi, Tokyo, Japan). Forty–eight–week–old female mice (29 ± 3 g) supplied by Dashuo Laboratory Animal Center (Chengdu, China) were housed at 26 ± 2 °C on a regular (12 h/12 h) light/dark cycle and were given free diets for 7 days. After that, the mice were randomly divided into four groups (10 mice in each group). One group was given normal saline by gavage, and the other three groups were given various polysaccharides (300 mg/kg, 0.5 mL/day) by gavage every other day for 1 week. On the 8th day, the thymus, spleen and liver were taken to calculate the organ index. Finally, the liver was ground into tissue homogenate, and the SOD activity, total antioxidant capacity, and MDA content in the tissue homogenate were measured using a kit. In this study, the procedures related to animal care were performed in accordance with the internationally accepted principles as listed in the Guidelines for Keeping Experimental Animals issued by the government of China. The organ index (‰) was calculated as follows: The polysaccharide was diluted into five gradients (1000 μg/mL, 500 μg/mL, 250 μg/mL, 125 μg/mL and 62.5 μg/mL) with DMEM and incubated with mouse macrophages (1 × 105/mL) in 96 well plates for 24 h. We added the reagent according to the instructions of CCK-8 kit, and half an hour later, we measured the absorbance at 450 nm with a microplate reader (iMark, BIO-RAD, Hercules, CA, USA). The cell viability was calculated as follows: where W1 is the absorbance of the experimental group, W2 is the absorbance of the control group, and W0 is the absorbance of the blank group [19]. We first prepared a density of 1 × 105 macrophage cell suspension. Then, according to the control group (DMEM + macrophages), the positive control group [lipopolysaccharide (LPS) + macrophages], the negative control group [hydrocortisone sodium succinate (HCSS) + macrophages], and the polysaccharide group (macrophages + polysaccharide solutions of different treatments (250 μg/mL).) were added to 24–well plates, 1 mL for each well, with 3 repetitions in each group. After the cells adhered to the wall, the polysaccharide was added for 24 h, the supernatant was collected, and the NO content was obtained according to the method on the NO determination kit. Briefly, after the cells adhered to the wall in the 96–well plates, we discarded the medium and added polysaccharides to continue to culture for 24 h, and then removed the medium and added 0.1% (mg/mL) neutral red solution to each well and continued to culture for 30 min. After 10 μL of cell lysate were added to each well, the absorbance was detected at 450 nm with a microplate reader, after 1 h. The phagocytosis rate of cells was calculated as follows: where W3 is the absorbance of the experimental group, and W4 is the absorbance of the control group. RT–qPCR was used to detect IL-1β and IL-6. After total RNA was extracted, cDNA was synthesized with a reverse transcription kit and was finally detected by RT–qPCR. The reaction system volume was 25 μL, including 2 μL cDNA, 12.5 μL TB Green® Premix Ex Taq™ II (Tli RNaseH Plus), 0.5 μL ROX Reference Dye, 8 μL sterile water, and 2 μL upstream and downstream primers, as shown in Table 1. Flow cytometry was used to determine macrophage surface costimulators (CD80+, CD86+). In short, three different polysaccharide samples (250 μg/mL) were co–cultured with macrophages for 12 h, cells were collected and stained with anti mouse CD80+ and CD86+ antibodies, and finally analyzed by flow cytometry (CyFlow Cube8, Sysmex Co., Ltd., Berlin, Germany). At 4 °C, FITC, polysaccharides with different treatments (SHP, SHP1 and SHP2) and ovalbumin (OVA) were dissolved in dimethyl sulfoxide for 13 h, dialyzed with PBS for 3 days, and four different samples (FITC–OVA, FITC–OVA–SHP, FITC–OVA–SHP1 and FITC–OVA–SHP2) were obtained after freeze-drying [20]. Mouse macrophages with a density of 1 × 105 were added to 24–well plates containing round coverslips until the cells were full on the round coverslips. Then, we added different dissolved samples to continue to culture for 12 h, took out the culture medium and fixed the cells with paraformaldehyde (4%); the content of OVA and FITC in each sample was 250 μg/mL. After washing twice with PBS, we performed DAPI staining, washed again with PBS after 10 min, and repeated the above steps for DID staining. Finally, the stained cells were observed using confocal laser scanning microscopy (TSC SP8, ICA, Weztlar, Germany). Analysis using the phenol–sulfuric acid method showed a 7.215% polysaccharide extraction rate after response surface optimization (shown in the Supplementary Material), while the sugar content in SHP could reach 82.1%. The comparative morphology of SHP and the ultrasonic–treated polysaccharides is shown in Figure 1A–C. Compared with SHP, SHP1 appeared fluffier with an absence of lamellar structure. As the ultrasonic frequency increased, SHP2 became gradually fibrotic while retaining a fluffy texture. In the 1H NMR spectrum, δ Values above 5.00 indicate α monosaccharide of configuration, but below 5.00 indicates the presence of β monosaccharides of configuration. As shown in Figure 2A–C, In the 1H-NMR spectrum, SHP had significant peaks at 4.00–6.00, which proved that SHP exists at the same time—six α and four β monosaccharides of configuration. After ultrasonic treatment with different powers, we found that each peak value had almost no change, which also means that the corresponding functional groups did not change. Therefore, ultrasonic treatment has no effect on the structure of polysaccharides [21]. As illustrated in Table 2 and Figure 3A–C, the molecular weight of polysaccharides began to decrease after ultrasonic treatment, which confirmed that ultrasonic had the effect of reducing molecular weight. However, in this experiment, the molecular weight of SHP2 was higher than that of SHP1, which might be because there was still a small amount of protein in the purified polysaccharide, the interval of ultrasonic treatment was too short, the metal probe produced an elevated temperature, and the polysaccharide underwent carbonation reaction in aqueous solution. The specific reasons still need to be further studied. The FT–IR spectroscopy of polysaccharides before and after ultrasonic treatment are exhibited in Figure 4A. The absorption peaks of O–H and C–H vibrations were at 3251.38 and 2927.40 cm−1, respectively [22]; 1388.49 cm−1 (1403.92 cm−1) represented the absorption peaks generated by the C–H bending vibration. In addition, the absorption peak at 1025.94 cm−1 was attributed to the presence of pyranoside [23]. The absorption peak at 890.95 cm−1 indicated the presence of an β–glycoside bond. After ultrasonic treatment, only a small number of values moved slightly to the left after 1500 cm−1, and no change was found in each optical energy group. The present results indicated that ultrasonic cannot change the structure of polysaccharides [24]. When polysaccharides are dissolved in water, the molecules present irregular forms such as folding and winding, due to the interaction between polysaccharides molecules, resulting in asymmetry and leading to the cotton effect. The CD spectra of ultrasonic-treated and untreated polysaccharides showed a typical cotton effect. From this phenomenon [25], it can be inferred that they all have stable helical tertiary structures. However, the ovality of the CD spectrum and the position of the highest absorption peak of polysaccharide SHP1 and SHP2 changed after ultrasonic treatment (Figure 4B–D). The ellipticity of SHP1 and SHP2 obviously decreased, and the highest absorption peak changed from 193.6 to 241.1 and 241.9, respectively. This may be attributed to the n → π* transition of the carboxyl group [26], and the optical activity of the carboxyl chromophore could be affected by intra- and inter–molecular interactions. After ultrasonic treatment, polysaccharides are degraded to expose more carboxyl groups, resulting in changes in the surrounding environment [27]. As illustrated in Figure 5A–C, under the scanning electron microscope, the SHP without ultrasonic showed irregular sheet structure, some of which were slightly thicker, and different size pores were observed on the surface and cross–section. Under the ultrasonic treatment of 250 W frequency, the polysaccharide (SHP1) began to thin and gradually became curly like silk. When the ultrasonic frequency reached 500 W, the sheet structure of SHP2 became clearer and thin and gradually began to fracture. The three–dimensional and planar AFM images of SHP are shown in Figure 5D–F. They appeared to consist of an irregular network of fibres arranged in random linear chains, with occasional spherical and non–spherical structures. However, ultrasonic treatment can reduce the surface roughness of polysaccharides. The average roughness of the untreated polysaccharide was 1.91 nm. After 250 W and 500 W ultrasonic treatment, the average roughness decreased to 1.40 nm (SHP1) and 0.987 nm (SHP2), respectively. The thymus and spleen are the two most important immune organs of the body and play significant roles in the functioning of the immune system. The liver is the principal metabolic organ in the body. It plays an important role in anti–oxidation, detoxification, protein synthesis, and other nutrient functions, and also plays a role in pathogens prevention. As depicted in Figure 6A–C, compared with the control group, the ultrasonic–treated polysaccharides significantly improved the organ indices of the thymus, spleen, and liver in mice (p < 0.05). In comparison, SHP1 also led to a marked enhancement in the indices, especially that of the spleen, differing significantly from the other treatment groups (p < 0.05). These results indicate the production of lower molecular weight polysaccharides after ultrasonic degradation, which can more easily promote cell proliferation, stimulate the growth of immune organs, and thus improve the immune capacity of the body [28]. As a common antioxidant enzyme, SOD has a good scavenging effect on reactive oxygen species. MDA is also an oxidation product, which accumulates too much in the body and causes body damage, which is of great significance in the detection of antioxidants [29]. In Figure 6D,E, SHP1 can significantly enhance the SOD activity. There was a significant difference between the SHP1 group and other groups (p < 0.05), whereas there was no significant difference between other groups (p > 0.05). After ultrasonic treatment, the two polysaccharides significantly reduced the content of MDA in the body, and the MDA content in the SHP1 treatment group was the lowest (p < 0.05). In addition, we also tested the total antioxidant capacity. As illustrated in Figure 6F, after intra-gastric administration of SHP1, the total antioxidant capacity of the liver was significantly improved, with an important difference compared with other groups (p < 0.05). These results demonstrated that ultrasonic treatment can promote the absorption of polysaccharides, thus enhancing the antioxidant capacity in vivo and improving the immune capacity of the body to a certain extent [30,31]. In the immune system, macrophages are the first line of defence, and they participate in almost all immune responses [32]. As shown in Figure 7A, ultrasonic–treated and untreated polysaccharides at concentrations below 1 mg/mL have no toxic effect on mouse macrophages, and both can promote the proliferation of mouse macrophages (p < 0.05). There was no significant difference between the concentrations of each polysaccharide group on the effect of cells (p > 0.05); thus, the intermediate concentration (250 μg/mL) was selected for the following test. Activated macrophages can inhibit the pathogens by releasing NO, which has a certain bactericidal effect. The results in Figure 7B show that after the addition of polysaccharides, the secretion of NO by mouse macrophages significantly increased, among which the secretion of SHP1 was the highest, with a significant difference compared with SHP (p < 0.05) and no significant difference compared with SHP2 (p > 0.05). Neutral red is a fluorescent reagent with a large molecular weight. It can only be ingested into macrophages through endocytosis. After destroying the cells with cell lysate, neutral red inside cells is released, which can be used to analyse the phagocytosis of macrophages. As depicted in Figure 7C, after the addition of polysaccharides, the uptake of neutral red by mouse macrophages increased significantly, with a meaningful difference compared with the control group. The highest phagocytosis rate appeared in the SHP1 group (p < 0.05); however, there was no significant difference between the polysaccharide groups (p > 0.05). IL-6 and IL-1β are important members of the cytokine network and play an important role in immune regulation. As illustrated in Figure 7D,E, SHP can enhance the mRNA expression of IL-6 and IL-1β in macrophages. After ultrasonic treatment, their expression level was further increased (p < 0.05), and the expression level of SHP1, which had the smallest molecular weight, was significantly higher than that of other groups (p < 0.05). It may be that after the molecular weight of SHP is reduced, it is easy to be used by macrophages. CD80+ and CD86+ are costimulators when activating T lymphocytes and play a significant part in humoral immune response, transplantation response, and autoimmune monitoring [33]. In addition, they are also typical markers of M1 polarization of macrophages and play an immunomodulatory role by promoting the inflammatory response [34,35]. After ultrasonic treatment, compared with SHP treated macrophages, the expression of CD80+ and CD86+ significantly increased in the macrophage treated by SHP1 and SHP2 (Figure 8A,B, p < 0.05). It may be that after the molecular weight of polysaccharide is reduced, it is more easily used by macrophages, more capable of activating cells and increasing the expression of surface costimulatory molecules. The present results indicated that ultrasonic treatment could promote the activity of polysaccharides in macrophage M1 polarization. CLSM was used to observe the uptake of OVA by mouse macrophages. As shown in Figure 9, the green fluorescence intensity was greatly increased after the addition of the polysaccharide and the green fluorescence was strongest after the addition of SHP1. Similarly, after the three fluorescence overlaps, we could see that the number of orange light spots in the polysaccharide groups was increased, which was significantly stronger than was t in the FITC–OVA group, indicating that the uptake of OVA by mouse macrophages was notably increased, and the internalised OVA was mainly distributed in the nucleus. These data demonstrated that both ultrasonic–treated and untreated polysaccharides can enhance the ability of macrophages to phagocytise OVA. Surprisingly, after ultrasonic treatment, the effect of the polysaccharide in promoting cell phagocytosis was significantly enhanced, particularly SHP1, which may be because its molecular weight was the smallest of the three groups of polysaccharides and was easier to be absorbed and utilised. After ultrasonic treatment of polysaccharides, the molecular weight is lower and can be fully utilized by macrophages. After the activation of polysaccharides, macrophages demonstrate stronger antigen phagocytosis ability. It was also confirmed that ultrasonic degradation of polysaccharide molecular weight enhances the biological activity of polysaccharides. This study showed that, compared with the naturally extracted polysaccharide, there was no significant change in the main structure of the polysaccharide after ultrasonic treatment. However, ultrasonic treatment led to the production of polysaccharides of lower molecular weight with improved antioxidant and immune–stimulatory capacities. It may be that ultrasonic treatment reduces the thickness of the polysaccharide, together with reduced surface roughness and lower molecular weight with a more extensive contact surface after dissolution, allowing more effective utilization of the polysaccharide. This is the first report of the extraction of this polysaccharide, as well as the first description of the effects of ultrasonic treatment on its activity, showing that ultrasonic treatment improved both immune function in vitro and antioxidant activity in vivo. In summary, ultrasonic treatment is an efficient and non–toxic method for the processing of functional foods or food additives. Nevertheless, more in–depth research is required to explore the mechanism responsible for the degradation of polysaccharides by ultrasonic treatment and whether it produces additional effects on polysaccharides.

PMC10001075

Wan Norizzati Wan Mohamad Zamri,Nazihah Mohd Yunus,Ahmad Aizat Abdul Aziz,Ninie Nadia Zulkipli,Sarina Sulong

Perspectives on the Application of Cytogenomic Approaches in Chronic Lymphocytic Leukaemia

03-03-2023

cytogenomics,chronic lymphocytic leukaemia,microarray

Chronic lymphocytic leukaemia (CLL) is a haematological malignancy characterised by the accumulation of monoclonal mature B lymphocytes (positive for CD5+ and CD23+) in peripheral blood, bone marrow, and lymph nodes. Although CLL is reported to be rare in Asian countries compared to Western countries, the disease course is more aggressive in Asian countries than in their Western counterparts. It has been postulated that this is due to genetic variants between populations. Various cytogenomic methods, either of the traditional type (conventional cytogenetics or fluorescence in situ hybridisation (FISH)) or using more advanced technology such as DNA microarrays, next generation sequencing (NGS), or genome wide association studies (GWAS), were used to detect chromosomal aberrations in CLL. Up until now, conventional cytogenetic analysis remained the gold standard in diagnosing chromosomal abnormality in haematological malignancy including CLL, even though it is tedious and time-consuming. In concordance with technological advancement, DNA microarrays are gaining popularity among clinicians as they are faster and better able to accurately diagnose the presence of chromosomal abnormalities. However, every technology has challenges to overcome. In this review, CLL and its genetic abnormalities will be discussed, as well as the application of microarray technology as a diagnostic platform.

Perspectives on the Application of Cytogenomic Approaches in Chronic Lymphocytic Leukaemia Chronic lymphocytic leukaemia (CLL) is a haematological malignancy characterised by the accumulation of monoclonal mature B lymphocytes (positive for CD5+ and CD23+) in peripheral blood, bone marrow, and lymph nodes. Although CLL is reported to be rare in Asian countries compared to Western countries, the disease course is more aggressive in Asian countries than in their Western counterparts. It has been postulated that this is due to genetic variants between populations. Various cytogenomic methods, either of the traditional type (conventional cytogenetics or fluorescence in situ hybridisation (FISH)) or using more advanced technology such as DNA microarrays, next generation sequencing (NGS), or genome wide association studies (GWAS), were used to detect chromosomal aberrations in CLL. Up until now, conventional cytogenetic analysis remained the gold standard in diagnosing chromosomal abnormality in haematological malignancy including CLL, even though it is tedious and time-consuming. In concordance with technological advancement, DNA microarrays are gaining popularity among clinicians as they are faster and better able to accurately diagnose the presence of chromosomal abnormalities. However, every technology has challenges to overcome. In this review, CLL and its genetic abnormalities will be discussed, as well as the application of microarray technology as a diagnostic platform. Chronic lymphocytic leukaemia (CLL) is a chronic lymphoproliferative disorder characterised by accumulation of mature monoclonal B lymphocytes, more than 5000 per microlitre in peripheral blood, positive for immunophenotype marker (CD5+ and CD23+) and/or the involvement of lymph nodes [1]. It is a common type of leukaemia in adults, especially in Western countries. The estimated incidence of this disease in the Western population (USA and Europe) is approximately 5 new cases per 100,000 individuals, regardless of gender [2]. In the USA itself, the estimated number of newly diagnosed cases for 2020 was 21,040 cases, which was around 1.2% of all cancer cases. The median age at diagnosis of this disease is 72 years old [3,4,5]; there is male predominance with a male-to-female ratio of approximately 2:1 [6,7,8]. It accounts for about 1% to 3% from total non-Hodgkin lymphoma cases reported. In contrast, the CLL cases reported in Asian countries as well as East Asia (0.1–0.2/100,000) [9,10], Africa (0.66/100,000) [11], and South America (Hispanic descendants) (1.17/100,000) [12] are relatively low compared to their Western counterparts [13,14,15,16,17,18,19,20]. Further, in Japan, CLL is classified as a rare disease, with the reported incidence rate being far below 0.5 per 100,000 person-years [21,22,23,24]. It is challenging to diagnose CLL in Japan due to the disease’s high degree of morphological and immunological variability [25]. For Australia and New Zealand, CLL is considered a common type of leukaemia to be diagnosed with, having an incidence rate of 2.99 per 100,000 [26]. In contrast, CLL is a rare disease in Africa [27,28]. Just 40 patients, with an average age of 61, were diagnosed with CLL over the course of 3 years across many centres in Senegal [29]. Meanwhile, in 2019, the reported incidence rate of CLL in Central Latin America was 0.41 per 100,000 individuals. Since the incidence rate of CLL in 1990 was 0.28 per 100,000, the incidence rate reported in 2019 has grown [26]. According to the Malaysia National Cancer Registry Report 2007–2011, the total number of new cases of CLL in Malaysia from 2007 to 2011 was 124 patients, implying that there were 24.8 newly diagnosed CLL cases per year on average [30]. This disparity in cases reported suggests that Asian CLL has different biological characteristics and, in some cases, has different chromosomal abnormalities when compared to Western CLL [31,32,33,34,35]. This disparity in disease incidence is postulated as being related to genetic differences between races. Although most of the CLL cases are asymptomatic and usually managed with watching-and-waiting until development of symptoms occurs—such as cytopenia, lymphadenopathy, and splenomegaly—in some patients, it will transform into aggressive form of B lymphocyte malignancy such as diffuse large B cell lymphoma (DLBCL) or, rarely, transform into Hodgkin lymphoma or another type of aggressive lymphoma [1]. CLL is a heterogeneous disease. Its pathogenesis can be viewed as cooperation between a patient’s risk factors and genetic aberrations. There have been several studies performed to identify risk factors for CLL development; however, to date, there is still no specific acquired factor that has been identified for disease development. However, there is strong evidence that genetic predisposition can lead to CLL [36,37,38,39]. Host factors including family history with haematological malignancy (CLL and/or non-Hodgkin lymphoma (NHL)) are among the strong evidence that has been studied. The study performed by Slager et al. revealed that relatives of CLL patients have a 2- to 8-fold increase in the risk of developing CLL and a 2-fold increased risk of getting NHL compared to the general population [40]. This finding was also supported by those of Goldin et. al, which state that familial CLL was diagnosed at an earlier age compared to sporadic CLL [41]. There are also case reports involving familial CLL where two or more individuals were affected by CLL in the same family. Histone modifications, such as those linked to active enhancer and promoter elements and regions of the genome that were actively transcribed, have been shown to play a role in the epigenetics of CLL. Additionally, it has been discovered that single-nucleotide polymorphisms (SNP), which increase the risk of CLL, overexpress transcription factor binding [42]. The latest studies using genome-wide association studies (GWAS) revealed more than 40 susceptibility loci which were important in B lymphocytes and apoptotic pathways [42,43]. The common chromosomal aberrations associated with CLL are del 13q14, trisomy 12, del 11p, and del 17p [44,45,46,47]. Other chromosomal aberrations observed in CLL are deletions in 6q, 9p21, and 10q23, total or partial trisomy of chromosomes 3, 8, 18, or 19, and duplications in 2p24 [47,48,49,50]. The most common genetic lesions in CLL are deletions of 13q14 (del 13q14), generally monoallelic in 50~60% patients (Figure 1; Table 1) [18], and involve the deletion of regions containing two long non-coding RNA genes (DLEU2 and DLEU1) which later develop clonal lymphoproliferation, recapitulating the different steps of CLL initiation and progression. Deletion 13q14 causes dysregulation of microRNAs, i.e., miR15A and miRNA16A, which are encoded in the deleted region. Both microRNAs have critical roles in controlling the production of proteins essential for cell apoptosis and normal cell cycle progression [51]. Consequently, cells are unable to respond to stress signals in a way that promotes apoptosis and leads to disease progression when these microRNA regions are absent [52]. Besides that, deletion of miR16A and miR15A causes upregulation of the BCL2 gene in CD5+ cells, which activates the BCL-2 proto-oncogene aberrant signalling pathway and assist in the development of the disease [53]. Deletion 13q14 is associated with good prognosis as well as prolonged time to first treatment (TTFT) and prolonged overall survival compared to other genetic abnormalities [44,51]. Trisomy 12 is a chromosomal aberration in CLL found in 10–20% of cases and often appears as a unique cytogenetic alteration (40–60% of cases with trisomy 12) (Figure 1; Table 1). In addition, it can be associated with other chromosomal aberrations, including trisomy 18 and 19, recurrent CLL deletions (e.g., 14q, 13q, 11q, or 17p), and IGH translocations [54]. Trisomy 12 is also associated with an atypical morphology of the lymphocytes. Although trisomy 12 is considered an intermediate-risk genetic lesion in CLL, the co-occurrence with NOTCH1 mutations are associated with poor survival outcome [55]. This finding is also in line with the increased frequency of trisomy 12 in Richter syndrome patients. Deletion of the long arm of chromosome 11 is detected in 5–20% of CLL patients (Figure 1; Table 1) [45,56,57]. This deleted region of chromosome 11 usually harbours ATM gene in almost all cases, as well as other genes including RDX, FRDX1, RAB39, CUL5, ACAT, NPAT, KDELC2, EXPH2, MRE11, H2AX, and BIRC3. ATM gene mutations have been largely studied in CLL patients with del(11q); however, they have been found in only 8–30% of 11q- patients [58,59], indicating that other genes could play a role in the pathogenesis of 11q deletions in CLL. One of these genes is BIRC3, which is located near to the ATM gene, at 11q22. BIRC3-disrupting mutations and deletions have been rarely detected in CLL at diagnosis (4%) but are detected in 24% of fludarabine-refractory CLL patients, suggesting that BIRC3 genetic lesions are specifically associated with a chemo-refractory CLL phenotype [60,61]. CLL patients with del(11q) are characterised by large and multiple lymphadenopathies and have been associated with progressive disease and poor prognostic factors, such as unmutated IGHV genes. It has been associated with shorter TTFT, shorter remission durations, and shorter OS following standard chemotherapy compared to non-deleted 11q (and non-deleted 17p) cases [62]. Deletion of 17p, especially at the region 17p13 chromosomal region (del17p), can be found at different frequencies depending on clinical stages of CLL disease, ranging from 1–3% during initial diagnosis to 20% in chemo-refractory disease (Figure 1; Table 1) [48,54]. Deletion 17p is associated with TP53 inactivation, thus causing genomic instability. This deletion is also linked to resistance to DNA-damaging agents (radiotherapy or chemotherapy) and presence at diagnosis usually indicates unfavourable OS and decreased TTFT. In addition to the common chromosome aberrations detected by FISH, Table 2 displays various chromosome abnormalities in CLL patients revealed by other platforms. Robbe et al. (2022) identified 74 regions of the genome that were currently affected by copy number alterations (CNAs), including 14 well-known CNAs such as del13q14.2, del11q22.3, and del17p13.1, through microarray. Another 60 regions—of which, 27 were previously not recognised and the remaining 33 CNAs—could be refined to a smaller minimal overlapping region. The author also demonstrated the most likely target gene for nine known regions, includingTP53/del17p13.1, and seven additional regions, including PCM1/del8p, IRF2BP2/del1q42.2q42.3, and SMCHD1/del18p11.32-p11.31 [72]. Certain gene mutations, in addition to chromosomal abnormalities, are critical to CLL pathogenesis, and multiple subpopulations of evolving malignant cells have been identified. These modifications have an impact on intracellular or microenvironment-dependent signalling pathways [58]. Over 5% of CLL patients have mutations in NOTCH1, ATM, SF3B1, and TP53. Notch proteins regulate the development of haematopoietic cells by acting as cell transmembrane receptors. Mutations in NOTCH1 at proto-oncogenes’ coding and non-coding regions can worsen disease through splicing events and increase their overall activity [73]. ATM, as previously stated, is a gene that detects damaged DNA and induces cell apoptosis, and its mutation will lead to dysregulation of the cell cycle [74]. SF3B1 is the gene that produces nuclear ribonucleoproteins, which are required for messenger RNA splicing and, thus, affect the cell cycle [75]. As previously stated, TP53 is essential for responding to DNA damage and inducing cell apoptosis. Aberrant signalling pathways also play important roles in the pathophysiology of CLL. The three main pathways involved are antigen-independent BCR signalling, BCL2 proto-oncogene upregulation, and impaired DNA damage response. Through antigen-independent or antigen-dependent autonomous signalling of CLL cells, the antigen-independent BCR signalling pathway directly affects cell survival, growth, differentiation, and cellular adhesion or migration. It is influenced by low miR150 levels as well as high FOXP1 and GAB1 expression [76]. BCR activation causes the kinases such as PI3K, SYN, BTK, and LYN to be activated, which results in cytoplasmic domain integrin activation and conformational changes that allow more ligand to bind to integrin’s extracellular activity, affecting cell proliferation, migration, differentiation, and survival [77]. Somatic mutations in immunoglobulin heavy chain variable region (IGHV) genes also affect the antigen-independent BCR signalling pathway. Mutated IGHV has weaker BCR signalling due to narrower antigen specificity, resulting in a higher mutation burden and a lower frequency of driver mutations. As a result, mutated IGHV CLL cells proliferate more slowly, making the disease process more benign and less clinically aggressive. Unmutated IGHV CLL cells, on the other hand, have sustained BCR signalling by binding to multiple epitopes, resulting in a lower mutation burden and a higher driver mutation frequency. This process eventually leads to faster clonal expansion and more clinically aggressive disease [75,78]. Table 3 highlights the gene mutations that contribute to the prognosis of CLL. Even though the incidence of CLL in Western countries is higher than in Asian countries, the disease progression in Asian patients has been reported to be more aggressive and with a shorter time to treatment compared to its counterpart. This event was postulated to happen due to different biomarkers and susceptibility in Asian populations. Based on a prospective study conducted in Senegal by Sall et al., CLL was found to be more aggressive and had a poorer prognosis at a younger age than in developed nations [29]. To depict the exact pattern of disease progression in African countries, however, it was necessary to conduct large-scale epidemiological research in African countries, as this study only represents a small-scale African study [13]. There were several case reports showing Asian CLL had reported a few different chromosomal aberrations than Western CLL. Western and Asian CLL shared the major copy number changes, which are del13q14, trisomy 12, deletion 17p, and deletion 11q [79]. Kawamata et al. also reported that Asian CLL patients more frequently have either trisomy/duplication of 3q or trisomy 18/dup18q; none of these chromosomal aberrations were reported in Western CLL patients [80]. Another study performed by Wu and his team members revealed Asian CLL patient had high frequency of TP53 mutation compared to Western CLL [81,82]. Prior to the last two decades, it was reported that the common chromosome abnormalities of CLL in South Africa are comparable to those of the rest of the world [83]. In 2016, Sall and colleagues found that CLL patients in Senegal exhibited the same clinical presentation as individuals globally. The epidemiology of haematologic malignancies, particularly CLL, is less understood in Latin America (Central and South America) [84]. A study performed by Hahn and his colleagues discovered two gene candidates, PRPF8 and SAMHD1, in Australian familial CLL [85]. Even though African CLL is considered rare, their patients usually have a younger median age of onset (59 years old), higher frequency of adverse prognostic factors, and poor clinical outcome. It also found that TP53, SF3B1, and NFKBIE mutations in African CLL is higher than in Western CLL [86]. For almost 40 years, the Rai and Binet clinical staging systems, which base their evaluations on a patient’s physical examination as well as their blood counts, have served as the foundation for determining a patient’s prognosis in CLL [87,88]. Rai and modified Rai classification stress the lymphocytes count and nodal and organ (spleen) involvement more, while Binet classification looks more at haemoglobin level, platelet count, and number of nodal areas involved. However, the information gained from these classifications during diagnosis of CLL in patients will not be able to predict the progression of disease in each individual [1]. Recently, an international team of researchers reviewed data from patients participating in eight randomised clinical trials in Europe and the United States in order to construct a prognostic score that contains widely available clinical, biochemical, and genetic prognostic characteristics. The CLL International Prognostic Index (CLL-IPI) was developed as a result of this international effort, and it is a reasonably straightforward prognostic tool. This prognostic model divides patients into four distinct categories, each of which has a significantly different overall survival rate, based on five parameters such as age, clinical stage, TP53 status (normal vs. del(17p), and/or TP53 mutation), IGHV mutational status, and serum β2-microglobulin. Subsequently, the prognostic utility of the CLL-IPI was validated in two separate cohorts of newly diagnosed patients, one from the Mayo Clinic and the other from the Swedish CLL registry [89]. Despite the fact that CLL-IPI was initially established to predict overall survival, it was found that the index could also predict TTFT in newly diagnosed patients with CLL. Only 20% of the original dataset consisted of patients with early illness, and no effort has been made to optimise the CLL-IPI risk score to stratify TTFT among early-stage patients. It is important to emphasise that TTFT is a disease-specific goal that is more relevant than overall survival for patients who have recently been identified with early-stage disease [90,91,92]. The CLL-IPI is used as a supplement for the existing methods of risk stratification for CLL [93]. For decades, diagnosis of CLL was performed using a full blood picture with the presence of lymphocytes more than 5 × 109/µL, examination of marrow morphology, marrow immunophenotyping, marrow cytogenetics, and clinical examination to detect nodal involvement. However, for the past 10 years, rapidly developed technology has made the detection of genetic aberrations in haematological malignancies, especially in CLL, become more comprehensive and elaborate. Genetic aberration detection plays a pivotal role in diagnosis, disease prognosis determination, risk stratification, and survival outcome. It is also essential in specific targeted therapy selection that is tailored to a patient’s genetic aberrations in order to achieve a better outcome [37]. Various methods of cytogenomic testing can help clinicians to detect the presence of genetic aberrations in patients. Cytogenomics can be defined as the study of the numerical and structural variation of the genome at the chromosomal and subchromosomal level as well as at a molecular resolution using methods that cover the entire genome or specific DNA sequences [94,95]. It evaluates chromosomes and their relation to disease [96]. The term “cytogenomics,” also called “chromosomics,” was proposed by Uwe Claussen to highlight the three-dimensional morphological changes that occur in chromosomes and which are crucial aspects in the regulation of genes [97]. Cytogenomic testing is not limited to conventional cytogenetic analysis (CCA) and molecular cytogenomics methods, i.e., fluorescence in situ hybridisation (FISH), polymerase chain reaction (PCR), or Multiplex Ligation-dependent Probe Amplification (MLPA); it also comprises high-throughput cytogenomics technologies which include applications of whole-genome Copy Number Variation (CNV) analysis such as DNA microarray, next-generation sequencing (NGS), and, more recently, GWAS as a diagnostic method [98,99]. These fancy, sophisticated, and typically very costly methods are only possible in conjunction with high-tech apparatuses and/or bioinformatics. In return, they are competent for achieving a high-resolution view of genomes as well as the generation of massive data sets in a time-effective manner [100]. Furthermore, cytogenomics exemplify the understanding of genomic instability and its association with normal and abnormal aging throughout ontogeny which later may contribute to cancer development [101]. Until now, CCA still remains the gold standard to diagnose chromosomal aberrations in CLL, especially in detecting the presence of complex karyotypes or balanced chromosomal translocations [54,102,103]. However, CCA is time-consuming, unable to assess non-dividing cancer cells, and sometimes yields poor morphology or inadequate cells for analysis [104,105]. It also can only detect chromosomal aberrations around 30% of CLL cases [106,107]. In developed countries, this method has become the last choice as array-based testing is more favoured and CCA only acts as last resort in detecting balanced chromosomal abnormalities. Based on a number of prospective clinical trials, the latest International Workshop on Chronic Lymphocytic Leukemia (iwCLL) guidelines for the management of CLL recommend performing FISH analysis as well as analysis of the TP53 gene in all patients with CLL, in both general practice and clinical trials. The use of CCA is recommended only in the context of clinical trials rather than routine clinical settings. This recommendation is mostly based on recent reports highlighting the prognostic significance of complex karyotype (CK) which, presently, can be detected only through CCA [94,108]. In CLL, CK is classically defined as the presence of ≥3 clonal structural or numerical abnormalities. Although present in 8% of monoclonal B lymphocytosis cases, 26 CK ≥3 is associated with advanced-stage disease, cases harbouring unmutated IGHV genes (U-CLL), del(11q), TP53 aberrations [del(17p) and/or TP53 mutation], and telomere dysfunction [109,110]. Combining FISH with NGS, as well as FISH and long-range sequencing methods, has led to significant advances in the field of cytogenomics in the 2010s [111,112]. FISH techniques are the most effective for researching genomes’ repetitive sections [113], and as a result, numerous probes targeting heterochromatic and euchromatic areas of the human genome have been created [111]. In early 2010, FISH and MLPA were becoming more popular as tools to diagnose chromosomal aberrations in CLL. However, despite the high sensitivity test for both methods, they are limited to specific known genomic loci [114,115]. FISH and MLPA act as the supplementary test to CCA. Both can be used in diagnosing genetic aberrations in non-dividing cells with high specificity and sensitivity. FISH is also able to detect low levels of mosaicism and mosaics of mono- and biallelic deletions [116,117]. However, FISH testing needs to be performed separately with specific probes for each genomic abnormality, making this method relatively expensive and time-consuming. It also unable to detect any other chromosomal abnormalities aside from the known genomic loci that have been specified by probes [47]. FISH is more sensitive than karyotyping; nevertheless, it is only effective for analysing specified loci, and it requires an assay for each targeted aberration [118]. While MLPA testing is able to detect copy number alterations, methylation pattern changes, and/or even point mutations simultaneously in multiple target regions [114,119,120,121], it has its own disadvantages. This method cannot detect copy-neutral loss of heterozygosity and has problems with mosaicism, i.e., unable to be obtained, tumour heterogeneity, or sometimes can cross contaminate with normal cells [116]. This finding proves that FISH and MLPA cannot be a stand-alone test and only able to act as complementary test for CCA. The emergence of microarray-based comparative genome hybridisation (array-CGH) and high-density single-nucleotide polymorphism (SNP) arrays has led to deeper understanding of the CLL genomic landscape. By delivering a genome-wide, high-resolution analysis that does not require cell culturing or viable cells for testing, chromosomal microarray analysis fills the void between genome-wide low-resolution chromosome studies and region-limiting disease-specific targeted FISH panels [122,123]. However, array-CGH has a several shortcomings, including its inability to detect low-level mosaics, its insensitivity to heterochromatin, and its inability to detect balanced aberrations. Only copy number variations were able to be identified between the years 2000 and the 2010s [124,125,126]. Initially, microarray-based detection of copy number alterations (CNAs) is the standard of care for the diagnosis of most constitutional chromosomal imbalances in children with developmental disability abnormalities [123], but recently it has become more popular for diagnosing haematological malignancies. Microarray technology, especially that using CNA+SNP chip technology, is the best at diagnosing aneuploidies, microdeletions, especially cryptic loci deletion and duplications, as well as amplification in CLL. It also can detect additional confirmation of CNAs and the ability to detect copy-neutral loss of heterozygosity (CN-LOH) and some polyploidies. The integration of microarray analysis into the cytogenetic diagnosis of haematological malignancies improves patient management by providing clinicians with additional information about potentially clinically actionable genomic alterations [123]. However, every technology has its own limitation. Microarray limitation include the inability to detect balanced rearrangements, decreased performance at low levels of tumour [50], the need for well-trained laboratory technologists, and high operation costs, even though this method is far superior compared to CCA, FISH, and MLPA [127]. Examples of commonly used microarray platforms in haematological malignancies are the CytoScan HD array platform (Affymetrix) and the HumanOmniExpress Array (Illumina). Both platforms use CNA+SNP chip technology in detecting cytogenomic alterations. Data obtained by the CytoScan HD array platform supplied by Affymetrix were analysed using the Chromosome Analysis Suite software while HumanOmniExpress platform data were analysed using Nexus copy number software (Biodiscovery Inc.) using annotations of genome version GRCh37 (hg19). In a study done in the Netherlands by Steven-Kroef et. al, both platforms show a high limit of resolution and detection of clinically relevant genomic aberrations which were unable to be detected by CCA and FISH [127]. For the past few years, optical genome mapping (OGM) has emerged as a promising new approach that may be able to circumvent all of the aforementioned testing hurdles with a single, comprehensive analysis. OGM is based on high-throughput imaging of long DNA molecules (>250 Kb) that have been fluorescently labelled at a specific 6 bp sequence motif found about 15 times per 100 Kb in the human genome [128]. The unique labelling pattern throughout the genome allows for the unambiguous identification of every imaged molecule’s genomic location, resulting in a local consensus map that can be compared to a reference genome to detect structural variants (SVs). The so-called rare variant pipeline is used for this study; it targets mosaic samples and can discover SVs from single molecules across the genome, beginning at 5 Kb and falling to a fraction of 1% in allele frequency. In addition, information on the depth of the genome’s coverage is utilised in order to recognise copy number variants (CNVs) and whole-chromosome aneuploidies [129]. Several recent studies have shown that OGM performs well in the cytogenomic assessment of various haematological malignancies, with a particular emphasis on myeloid neoplasms (acute myeloid leukaemia and myelodysplastic syndromes) and acute lymphoblastic leukaemia cases. In these studies, OGM was able to efficiently detect the bulk of clinically relevant abnormalities reported by standard approaches, while at the same time revealing new cytogenomic information in some situations [130,131]. A cohort study done by Puiggros and her team on 46 CLL patients found that the usage of OGM in CLL enabled them to achieve better characterisation of these patients’ genomic complexity in comparison to current approaches, and also showed increasing detection of cytogenomic abnormalities via the OGM approach which can contribute to adverse disease progression in those CLL patients [103]. NGS genomic oncology profiling assays and GWAS brought into play an unpreceded analytical depth to accommodate the characterisation of the highly complicated genetic landscape of haematological cancers, especially CLL [132], and can become a key driver of personalised cancer care [133]. NGS is able to detect single-nucleotide variants (SNV), small structural changes, and balanced translocations, as well as to confirm CNV detected by array, by providing a base-to-base view of the genome [134] while GWAS is able to identify multiple low-risk variants that together explain about 16% of the familial risk of CLL other than detection of higher-risk SNPs or CNVs associated with disease risk in those families [135]. NGS is also to detect gene mutation in TP53, ATM, NOTCH1, SF3B1, MYD88, and BIRC3; all the aforementioned genes are related to increased susceptibility of patients to develop CLL [58,136,137]. The commonly used NGS platforms are Illumina HiSeq and Illumina MiSeq as well as Ion Torrent from Life Technologies. Data provided by array CGH and NGS technologies has significantly enhanced the knowledge of cancer biology and its underlying driver genes for pharmacogenetics and has guided targeted therapy development and drug-resistance prediction [61]. However, NGS and GWAS has its own pitfalls that need to be addressed. First, the massive amount of data that is obtained from the NGS and GWAS may not be relevant for a diagnostic setting. Second, high cost can be incurred from procurement of NGS equipment, software, and consumables. Third, NGS needs a specialised high-power computer and technician to analyse and store all the data obtained [62]. Increased sensitivity is one of the main benefits of NGS methods for genetic diagnostics; however, so far, this method has only been applied to the detection of single-nucleotide variants (SNVs). Although some chromosomal fusions can be detected using NGS-based approaches with prior knowledge of translocation/fusion partners, a large portion of the genome is still unavailable for structural variant detection due to technical restrictions [138]. A major advantage of using whole-genome sequencing (WGS) is it can identify chromosome inversions and translocation. A study conducted by Robbe et al. (2022) using WGS identified 1248 inversions with frequent breakpoints involving either immunoglobulin light chain kappa (IGK), immunoglobulin heavy chain (IGH) locus, or ch13q14.2 and 993 translocations with no previously documented role in CLL, including t(14;22) with a breakpoint within WDHD1 and t(5;6) (CTNND2-ARHGAP18). Moreover, authors also identified STED2/del3p.21.31, del9p21.3, and gain of chr17q21.31 are associated with relapsed/refractory (R/R) disease and TP53 disruption, whereas MED12 and DDX3X mutations are associated with unmutated IGH CLL [73]. This technology has been reported successfully as not only capturing SNVs with a high level of accuracy but also working well for the detection of disease-causing CNVs. In addition, WGS has the capability of identifying chromosomal rearrangements, as well as STRs and ROH. It is interesting to note that the diagnosis rate of WGS in this study was 27%, which was much higher than the diagnostic rate of clinical microarray (12%) [139]. NGS and arrays are appropriate for cytogenomic studies across a variety of constitutional and cancer research applications, as NGS provides complementary detection capabilities. On a single piece of equipment referred to as the NextSeq 550 System, the researchers are able to carry out both NGS and array scanning. Genome visualisation is possible with the conventional molecular cytogenomic methods for evaluating chromosomal aberrations, such as FISH and karyotyping. However, these approaches often produce a low-resolution image of the genome. As a consequence, the results of such procedures are not always comprehensive [140]. Cytogenomic microarrays provide not only a simple tool but also a reliable method for analysing chromosomal abnormalities at a higher resolution. High-quality microarrays from Illumina are available for the purpose of detecting chromosomal abnormalities while also providing precise and dependable cytogenomic data [140]. In Malaysia, there are a few centres that offers genetic testing in cancer, especially for haematological malignancies. Commonly, most centres will offer CCA and FISH for known chromosomal abnormalities in certain types of haematological malignancies as a tool for diagnosis. They also offer molecular testing (PCR) to detect common fusion genes that are involved in haematological malignancies, such as the BCR-ABL fusion gene in chronic myeloid leukaemia (CML), BCR-ABL fusion gene, TEL-AML1 fusion gene, and E2A-PBX1 fusion gene and MLL gene rearrangement in acute lymphocytic leukaemia (ALL), and PML-RARA gene in acute promyelocytic leukaemia. For array-based technologies such as DNA microarray and NGS, there are not many centres to choose from except for private companies. Furthermore, the array-based technologies are too costly (around ~MYR 2000-MYR 2500 per test) and the need for well-trained staff and experts to interpret the results make them not suitable to be the first-line diagnostic tool in haematological malignancies. However, as the Western countries and other Asian countries such as Korea, China, and Taiwan already used array-based technologies as first diagnostic tools, we need to improve our diagnostic tools so that we are in line with the current diagnosis developments, thus later contributing to better and more precise treatments [79]. This study is a pioneer in Malaysia for performing CLL profiling using a microarray platform using Affymetrix CytoScan 750K array chip; it hopefully will illustrate the genetic aberrations that are involved in CLL pathogenesis. The findings in this study are crucial, as many studies done previously by other populations have already acknowledged the difference of genomic aberrations between Asian CLL and Western CLL [80,141]. Therefore, databases for CLL patients in Malaysia can be created based on these data. The current assay, called, the “CytoTerraTM Platform”, elevates cytogenetics to new heights. This assay combines the genome-wide structural variation detection capability of conventional cytogenetics with the molecular-level precision of chromosomal microarrays (CMA) and FISH in a single, cost-effective manner with an NGS-based assay. The CytoTerra Platform uses ultra-long-range genome sequencing to assess the breadth of chromosome aberrations with greater resolution than conventional cytogenetic analysis, CMA, and FISH combined. The CytoTerraTM Platform possesses unique features such as genome-wide detection, the ability to detect complex rearrangements, the ability to identify unbalanced chromosomal alterations (deletion, duplication, and amplification), and the ability to examine balanced rearrangements (inversion, insertion, reciprocal, and Robertsonian translocation), and does not require specialised instrumentation [142,143]. Table 4 highlights the advantages and disadvantages of each cytogenomics approach used to diagnose CLL. All genetic aberration data obtained from CCA, FISH, DNA microarray and whole-genome sequencing in CLL patients will help the clinician to tailor treatment according to patients’ needs, reduce the complication of treatment, and improve survival outcomes [144,145]. Moreover, according to [113], the most recent applications of cytogenomic techniques include conducting research on topologically associated domains, studying interchromosomal interactions, and chromoanagenesis, characterising the 3D structure of chromosomes in various tissue types and shedding light on the multilayer arrangement of chromosomes and the function of repetitive repeats and noncoding RNAs in the human genome. The landscape of CLL genomics will become more thorough and precise with the help of technological evolution. Together with data collected from the DNA microarray technology as well as conventional cytogenetic, FISH, and other advanced technology, whole-genome sequencing may create a new pathway for creating potential therapeutic agents that are more focused on targeted therapy. Despite the fact that there were many methods to detect genomic aberration in CLL, microarray-based technology was deemed to be superior to others (CCA, FISH, MLPA, and PCR) and cost-effective compared to NGS and GWAS. Thus, laboratory technologists should be well-trained and well-versed with microarray technology to keep up with the latest technology. It also helps the clinicians to obtain more detailed data on the disease as well as to determine and quantify disease-associated genetic profiles and improve clinical diagnosis/prognosis, tumour classification, and ultimately, cancer therapy.

PMC10001080

Vladimir O. Sigin,Alexey I. Kalinkin,Alexandra F. Nikolaeva,Ekaterina O. Ignatova,Ekaterina B. Kuznetsova,Galina G. Chesnokova,Nikolai V. Litviakov,Matvey M. Tsyganov,Marina K. Ibragimova,Ilya I. Vinogradov,Maxim I. Vinogradov,Igor Y. Vinogradov,Dmitry V. Zaletaev,Marina V. Nemtsova,Sergey I. Kutsev,Alexander S. Tanas,Vladimir V. Strelnikov

DNA Methylation and Prospects for Predicting the Therapeutic Effect of Neoadjuvant Chemotherapy for Triple-Negative and Luminal B Breast Cancer

06-03-2023

breast cancer,DNA methylation,RRBS,MSRE-qPCR,predictive markers,neoadjuvant chemotherapy

Simple Summary Breast cancer (BC) is a group of diseases heterogeneous in morphology, progression, survival, and response to therapy. Although BC is among the most exhaustively studied cancers, there is still a lack of molecular markers to predict its response to neoadjuvant chemotherapy (NACT). Tumor development is determined by alterations not only of its genome, but of its epigenome as well. In order to identify epigenomic markers of BC NACT effectiveness, we have applied genome-wide DNA methylation screening of tumors in cohorts of NACT responders and nonresponders. Combining several of the most informative DNA methylation markers, we suggest tiny diagnostic panels that may be readily implemented in diagnostic laboratories. We also demonstrate that clinical characteristics predictive of NACT response, such as the clinical stage and lymph node status, are independently additive to the epigenetic classifiers and in combination improve prediction. Abstract Despite advances in the diagnosis and treatment of breast cancer (BC), the main cause of deaths is resistance to existing therapies. An approach to improve the effectiveness of therapy in patients with aggressive BC subtypes is neoadjuvant chemotherapy (NACT). Yet, the response to NACT for aggressive subtypes is less than 65% according to large clinical trials. An obvious fact is the lack of biomarkers predicting the therapeutic effect of NACT. In a search for epigenetic markers, we performed genome-wide differential methylation screening by XmaI-RRBS in cohorts of NACT responders and nonresponders, for triple-negative (TN) and luminal B tumors. The predictive potential of the most discriminative loci was further assessed in independent cohorts by methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR), a promising method for the implementation of DNA methylation markers in diagnostic laboratories. The selected most informative individual markers were combined into panels demonstrating cvAUC = 0.83 (TMEM132D and MYO15B markers panel) for TN tumors and cvAUC = 0.76 (TTC34, LTBR and CLEC14A) for luminal B tumors. The combination of methylation markers with clinical features that correlate with NACT effect (clinical stage for TN and lymph node status for luminal B tumors) produces better classifiers, with cvAUC = 0.87 for TN tumors and cvAUC = 0.83 for luminal B tumors. Thus, clinical characteristics predictive of NACT response are independently additive to the epigenetic classifier and in combination improve prediction.

DNA Methylation and Prospects for Predicting the Therapeutic Effect of Neoadjuvant Chemotherapy for Triple-Negative and Luminal B Breast Cancer Breast cancer (BC) is a group of diseases heterogeneous in morphology, progression, survival, and response to therapy. Although BC is among the most exhaustively studied cancers, there is still a lack of molecular markers to predict its response to neoadjuvant chemotherapy (NACT). Tumor development is determined by alterations not only of its genome, but of its epigenome as well. In order to identify epigenomic markers of BC NACT effectiveness, we have applied genome-wide DNA methylation screening of tumors in cohorts of NACT responders and nonresponders. Combining several of the most informative DNA methylation markers, we suggest tiny diagnostic panels that may be readily implemented in diagnostic laboratories. We also demonstrate that clinical characteristics predictive of NACT response, such as the clinical stage and lymph node status, are independently additive to the epigenetic classifiers and in combination improve prediction. Despite advances in the diagnosis and treatment of breast cancer (BC), the main cause of deaths is resistance to existing therapies. An approach to improve the effectiveness of therapy in patients with aggressive BC subtypes is neoadjuvant chemotherapy (NACT). Yet, the response to NACT for aggressive subtypes is less than 65% according to large clinical trials. An obvious fact is the lack of biomarkers predicting the therapeutic effect of NACT. In a search for epigenetic markers, we performed genome-wide differential methylation screening by XmaI-RRBS in cohorts of NACT responders and nonresponders, for triple-negative (TN) and luminal B tumors. The predictive potential of the most discriminative loci was further assessed in independent cohorts by methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR), a promising method for the implementation of DNA methylation markers in diagnostic laboratories. The selected most informative individual markers were combined into panels demonstrating cvAUC = 0.83 (TMEM132D and MYO15B markers panel) for TN tumors and cvAUC = 0.76 (TTC34, LTBR and CLEC14A) for luminal B tumors. The combination of methylation markers with clinical features that correlate with NACT effect (clinical stage for TN and lymph node status for luminal B tumors) produces better classifiers, with cvAUC = 0.87 for TN tumors and cvAUC = 0.83 for luminal B tumors. Thus, clinical characteristics predictive of NACT response are independently additive to the epigenetic classifier and in combination improve prediction. According to GLOBOCAN statistics (185 countries), 19.3 million new cases of cancer were diagnosed in 2020, of which 11.7% (2,261,419 of cases) were breast cancer (BC), and the death rate from BC was 684,996 people per year, which is more than in previous years. In women, BC occupies a leading position in both new cancer cases and fatalities [1]. Despite advances in diagnosis, surgical treatment, and systemic therapy, the main cause of death is resistance to existing therapies [2]. An approach to improve the effectiveness of therapy in patients with aggressive subtypes of breast cancer, such as luminal B or triple-negative BC (TNBC), is the administration of neoadjuvant (preoperative) chemotherapy (NACT). According to large clinical studies, when a pathomorphological complete response (pCR) is achieved because of NACT, the survival of patients with aggressive subtypes of breast cancer is close to the survival of patients with more favorable subtypes, compared with patients in this group with a residual tumor [3]. In addition, in patients with locally advanced breast cancer, NACT is a mandatory component of treatment of all breast cancer subtypes; it allows for a reduction in the volume of the primary tumor and increases the frequency of organ-preserving operations. The response to neoadjuvant chemotherapy for aggressive subtypes is less than 65% according to large breast cancer clinical trials [4]. At the same time, it has been shown that DNA methylation is an early and frequent event in carcinogenesis [5]. DNA methylation (5-methylcytosine, 5-mC) is one of the best-studied epigenetic modifications that acts directly on genomic DNA, where a CH3- group is added at the C5 position of the cytosine ring in the palindromic CpG dinucleotide. Methylation is known to affect gene expression through the regulation of gene transcription [6]. Back in 2003, Peter Laird described how recent advances in understanding the role of methylation in cancer could one day lead to many powerful biomarkers based on DNA methylation, especially for use as diagnostic markers in oncology [7]. This belief was based on a number of characteristics of aberrant DNA methylation that make it a promising source of biomarkers: abnormal DNA methylation is an early and frequent event in carcinogenesis, is easy to detect using well-established methods, and is stable in fixed samples over time, and the DNA molecule is stable double-stranded nucleic acid and can be detected in various body fluids [5]. Although luminal B tumors are highly proliferative, they are less likely to respond to NACT, as treatment resistance is common in this subtype. Therefore, luminal B is one of the breast cancer subtypes that needs new markers to personalize the prescription of preoperative chemotherapy and identify those patients who can get the maximum benefit from treatment with a minimum effect of chemotherapy drug toxicity. Neoadjuvant chemotherapy remains the gold standard of care for patients with TNBC, but is characterized by limited efficacy, a narrow response time, and significantly toxic profiles. TNBC is the most aggressive subtype with a higher metastasis rate, early recurrence, and poor overall survival, accounting for about 15–20% of all breast cancer cases. Unfortunately, only one in three patients respond successfully to treatment [8], which makes it urgent to find alternative methods for predicting the response of each patient in order to provide patients with more individualized medical care. The task of forming epigenetic diagnostic panels for predicting the effectiveness of NACT in breast cancer patients can be effectively solved using a modern method of genome-wide analysis of DNA methylation, reduced representation bisulfite sequencing (RRBS). RRBS increases the relative informational value of DNA methylation analysis compared to whole-genome bisulfite sequencing (WGBS): each RRBS sequence read includes at least one informative CpG position. Theoretically, RRBS is better than WGBS and is applicable to large-scale studies of DNA methylation and the search for markers of epigenetic processes in health and disease, since RRBS focuses on functionally significant methylation in CpG islands, ignoring less clinically significant regions [9]. This study included 156 patients with early (T1-2N0-1M0) and locally advanced (T2-4N2-3M0) TNBC or with luminal B breast cancer of the IIA-IIIC clinical stages. Genome-wide bisulfite sequencing was performed on 73 breast cancer biopsy specimens (discovery cohort) obtained before neoadjuvant chemotherapy to select genome regions for the further formation of DNA methylation panels of a limited number of the most informative markers. On an independent cohort of 83 core biopsy specimens taken before NACT, we performed an evaluation of the diagnostic significance of DNA methylation panels by quantitative multilocus methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR). The discovery cohort consisted of 29 TNBC samples and 44 samples of luminal B immunohistochemical subtypes (Table 1). The NACT scheme for a group of patients with TNBC was eight courses of doxorubicin, cisplatin, and paclitaxel. Patients with luminal B subtype tumors received 2 to 4 courses of NACT with CAX (cyclophosphamide + doxorubicin + xeloda) or FAC (fluorouracil + doxorubicin + cyclophosphamide). The independent cohort consisted of 48 TNBC samples and 35 samples of the luminal B subtype (Table 1). In this cohort, the NACT regimen for TNBC patients was 8 courses of doxorubicin, cisplatin, and paclitaxel. Patients with luminal B tumors received 4–6 courses of adriamycin (doxorubicin) and cyclophosphamide. A statistical comparison of clinical features between the discovery and independent cohorts determined significant differences in patient’s ages (p = 0.02) and NACT response (p = 0.03) for TN breast cancer and luminal B BC (p < 0.001). No other statistical differences were found. The effect of neoadjuvant chemotherapy was assessed based on the results of clinical examination, ultrasound, and mammography. For luminal B tumors of a discovery cohort, the response to NACT was classified according to the RECIST criteria for evaluating the response in solid tumors (version 1.1) [10]. A complete regression was defined as the complete disappearance of the primary tumor and lymph node metastases. Such tumors were classified as sensitive to NACT. A partial response was defined as tumor reduction by ≥30% and such tumors were also referred to as sensitive. NACT-resistant tumors included those with post-NACT stabilization (<30% tumor reduction or <20% tumor size increase) and progression (≥20% tumor size increase). For TNBC, the evaluation was performed according to the Lavnikova scale. Samples with a pathological complete response (pCR) were assigned to the group of tumors sensitive to NACT and all the rest to the group of resistant tumors. The response to NACT of tumors from an independent cohort of both triple-negative and luminal B subtypes was assessed using the Residual Cancer Burden (RCB) scale [11]. The sensitive group included samples with RCB0-RCB2; RCB3 tumors were classified as resistant. DNA was isolated by the classical phenol–chloroform method. After the complete dissolution of the precipitate, DNA concentration was measured on a Qubit 4 fluorimeter (Thermo Fisher Scientific, Waltham, MA, USA) using Qubit DNA BR Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA). Genome-wide bisulfite DNA sequencing was performed according to the previously described XmaI-RRBS technology [12] on an Ion Torrent PGM sequencer (Thermo Fisher Scientific, USA). Briefly, the DNA was treated with restriction endonuclease XmaI, and then the sticky ends were partially blunted with methylated cytosines using a 3′-5′ Klenow exo- and ligated with adapters containing methylated cytosines (presented in Supplementary Table S1). The libraries obtained were selected by length to obtain a fraction of fragments with an insert size of 100–200 bp, with subsequent bisulfite conversion using the Qiagen EpiTect Bisulfite Kit (Qiagen, Hilden, Germany). To avoid the nonspecific priming of the 3′ ends of DNA fragments in the polymerase reaction to follow, those were blocked with chain-terminating dideoxynucleotides using the SNaPshot Multiplex Kit (Thermo Fisher Scientific, USA). Then, RNase A (Sigma-Aldrich, St. Louis, MO, USA) and alkaline phosphatase (SibEnzyme, Novosibirsk, Russia) were used to remove the carrier RNA used in the EpiTect Bisulfite Kit protocol and dephosphorylate residual ddNTPs, respectively. The final libraries were amplified by PCR, the number of cycles of which was determined based on preliminary measurements by quantitative PCR. The resulting libraries were quantified on a Qubit 4.0 fluorimeter (Thermo Fisher Scientific, USA). Emulsion PCR was performed on an Ion OneTouch 2 instrument using Ion OneTouch 200 kits (Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. The resulting Ion Sphere Particles (ISPs) were enriched at 37 °C using an Ion OneTouch ES system (Thermo Fisher Scientific, USA). The sequencing results were processed using standard Ion Torrent Suite software. Bismark software [13], which was used to align the obtained reads on the GRCh37/hg19 human genome sequence using the Bowtie 2 aligner [14]. From the RRBS results, CpG dinucleotides were selected, for which no more than 20% of missing data on the methylation level were observed in the entire set of studied samples. Hierarchical clustering was performed for thus obtained CpG dinucleotides using the normalized pairwise Manhattan distance and the ward D2 agglomerative method [15]. To form a list of candidate markers, we selected loci whose differential methylation significantly differed (nominal p < 0.05) in groups of tumors resistant and sensitive to neoadjuvant chemotherapy after applying the Mann–Whitney test and for which there was information on the methylation status of 4 or more consecutive CpG pairs in the region. The difference in TERT and DPYS methylation levels between the groups did not pass the threshold of statistical significance; however, these loci were included in the further study due to their biological role. To select genome regions for inclusion in MSRE-qPCR-based assays, the following criteria were used: location in the region of the XmaI-RRBS library fragment (between two XmaI restriction enzyme sites), the presence of at least 3 recognition sites for methylation-sensitive restriction enzyme BstHHI; length of potential PCR product of no more than 400 bp; and flanking areas of a target locus not containing BstHHI recognition sites of at least 50 bp. Alongside target loci (DNA methylation markers), each multiplex MSRE-qPCR assay was designed to include a positive internal control (PC) and a digestion efficacy control (DC). Loci for the PC were selected from 4 regions of the genome not containing recognition sites for the restriction enzyme used (regions of the CpG islands of the SBNO2, LINC00493, and CLDND1 genes and the intergenic CpG-island on chromosome 18q21.33). The DCs were selected from 5 genome regions, each containing 2 restriction enzyme recognition sites and nonmethylated both in normal tissues and in tumors (CpG islands of the TMEM158, ANO10, and ABHD5 genes) (Supplementary Table S2). DC scores were not used for target methylation level calculation. They were only used for DNA hydrolysis quality control. Samples that demonstrated ΔCt < 6 for the DCs in digested versus nondigested aliquots were excluded from the final analysis. Primers and TaqMan probes were designed using MPprimer 1.4 software [16]. All candidate markers were combined into 11 pools according to the compatibility matrix in multilocus PCR. Each pool contained 1 to 3 target loci and 2 internal controls, a PC and DC (Supplementary Table S3). As a template for qPCR, we used DNA samples hydrolyzed with BstHHI (SibEnzyme, Russia) restriction enzyme (GCG/C recognition site), as well as intact DNA, without the addition of BstHHI but maintaining the reaction conditions and the composition of the reaction mixture. Enzymatic hydrolysis was carried out in a total volume of 35 μL, containing 10 units of restriction endonuclease, 3.5 μL of 10× SE-buffer Y (SibEnzyme, Russia), and 60 ng of DNA for 12 h at 50 °C. PCR was performed on a QuantStudio 5 (Thermo Fisher Scientific, USA) in GenPak PCR Core kit (Isogen, Moscow, Russia) plates, in 20 µL reaction volumes containing 10 µL of PCR Diluent (supplied with GenPak PCR Core kit), 300 nM of each primer, 200 nM of each TaqMan probe, and 10 ng of the input DNA template. The PCR program for all pools was unified to simplify the use of the laboratory protocol: the reaction was heated at 95 °C for 5 min and 50 PCR cycles were performed as follows: primary template denaturation at 95 °C for 30 s and annealing combined with elongation for 2 min with signal detection at this stage. Primer annealing temperatures were selected empirically for each pool. The determination of the methylation level according to MSRE-qPCR results was performed using the ΔΔCy0 method. The methylation level of the target locus (tgt) relative to the PC locus, which does not contain restriction enzyme recognition sites, was determined by the following formulas in pairs of samples, MS (treated with methylation-sensitive restriction enzyme) and mock (without enzyme treatment): where is the Cy0 value for the PC locus when treated with a methylation-sensitive restriction enzyme, and is the Cy0 value for the PC locus without treatment with a methyl-sensitive restriction enzyme; where is the Cy0 value for the target locus when treated with a methylation-sensitive restriction enzyme, and is the Cy0 value for the target locus without treatment with a methyl-sensitive restriction enzyme; where the is the methylation level of the target locus and is the PCR efficiency. To combine individual DNA methylation markers into panels, markers were selected that demonstrated cvAUC > 0.5 according to the results of MSRE-qPCR. From the resulting panels, the best ones were selected in terms of cvAUC. Statistical data processing and visualization of the results were carried out using the programming language for statistical data processing, R. Clinical features between the discovery and independent cohorts were compared using the Wilcoxon test for patient’s age and Chi-squared test for other features. Over-representation analysis (ORA) was carried out using g:Profiler portal (https://biit.cs.ut.ee/gprofiler/gost (accessed on 23 February 2023)). DNA methylation markers were selected from XmaI-RRBS data using the Wilcoxon–Mann–Whitney test. Point-biserial correlation was used to test the independence between DNA methylation markers and the clinical parameters of patients. Differences were considered significant at p < 0.05. To determine the correlations between the methylation level of epigenetic markers and clinical/morphological features, the coefficient ρ was used. Higher ρ coefficients denote a stronger magnitude of relationship between variables. Smaller ρ coefficients denote weaker relationships. Positive correlations denote a relationship that travels at the same trajectory. As one value goes up, the other value goes up. When assessing the strength of correlations, the Chaddock scale was used. An FDR correction for multiple hypothesis testing was applied. A Chi-square test was used to assess the significance of the association of clinical features and the response to NACT. Differences were considered significant at p < 0.05. To determine the relationship between the response to therapy and clinical/morphological features, the coefficient φ was used. The φ coefficient is similar to the correlation coefficient in its interpretation. The φ coefficient value can be between 0 and 1. A coefficient of zero (0) indicates that the variables are perfectly independent. The larger the coefficient, the closer the variables are to forming a pattern that is perfectly dependent, which is 1. When assessing the strength of the relationship of the correlation coefficient, the Chaddock scale was used. As a classification algorithm, logistic regression with a standard threshold of 0.5 was used to determine if a sample belongs to the group of sensitive or resistant to NACT. For assessing goodness-of-fit epigenetic and combined marker panels logistic regression models, a likelihood ratio test was used. Classification quality was analyzed using ROC curves (ROC analysis). Due to the moderate number of patients in the cohorts tested in our study, cross-validation was used to characterize individual markers and their combinations. The procedure of the cross-validation of models was carried out by the 100-fold randomized division of an independent cohort of breast cancer samples into training and a test in the ratio of 75% and 25%, respectively. A cross-validated AUC (cvAUC) R package was used to calculate ROCcurves (https://github.com/ledell/cvAUC (accessed on 15 October 2022)). The most favorable sensitivity and specificity points for classifiers were obtained using the Youden index. The NACT response score (NRS) was determined by the following formula: where PRS is a patient’s risk score: where β0 is an intercept of logistic regression model, βmarker is a regression coefficient, marker is a DNA methylation marker’s methylation value measured by MSRE-qPCR, and n is a number of elements in the diagnostic panels. In this study, genome-wide bisulfite sequencing was performed on 73 breast cancer biopsy specimens obtained prior to neoadjuvant chemotherapy. The samples constituted a discovery cohort for a genome-wide NACT response markers search from 29 samples of triple-negative breast cancer and 44 samples of luminal B subtypes. Based on the results of XmaI-RRBS sequencing, we obtained data on average of 118 million base pairs in 750,000 reads per sample with an average depth of 50. The sequencing data obtained were placed in the GEO database, under the numbers GSE123712 and GSE123828, and can be used by other research teams to conduct comparative studies and explore breast cancer epigenetics. Upon the results of XmaI-RRBS, 290 differentially methylated genes marking NACT-resistant and -sensitive triple-negative (Figure 1) tumors and 202 genes marking luminal B (Figure 2) tumors were identified in the discovery cohort. Gene lists are presented in the Supplementary Materials and Supplementary Table S10. Candidate markers enrichment for specific biological processes, molecular functions, and KEGG pathways was assessed using an over-representation analysis (Supplementary Figures S3–S5 and Table S11). Based on the presented lists of differentially methylated genes and taking into account the criteria for selecting genome regions for inclusion in panels, the set of candidate DNA methylation markers was determined to further design panels of a limited number of markers of TNBC sensitivity to NACT: CDO1, CLEC14A, DLEU2, BNC1, PRKCB, GMDS, TERT, TTC34, TMEM132D, VGLL4, ABCA3, DPYS, IRF4, TMEM132C, SFRP2, SOX21, and MYO15B (17 markers) and for luminal B: LTBR, NRN1, TERT, TTC34, TMEM132D, VGLL4, ABCA3, DPYS, IRF4, TMEM132C, SFRP2, and MYO15B (12 markers). In the generated list, 11 markers (TERT, TTC34, TMEM132D, VGLL4, ABCA3, DPYS, IRF4, TMEM132C, SFRP2, SOX21, and MYO15B) potentially mark sensitivity to NACT of tumors of both BC subtypes under study. The predictive value of the above candidate differential methylation markers was further assessed by MSRE-qPCR, as far as we deem it an optimal method for the practical implementation of differential DNA methylation assays. Considering all the requirements described in the Materials and Methods section, 11 multiplex MSRE-qPCR assays (pools of primers/probes) were designed (G1–G5, TN1–TN4, TN6, and LB1). The G (general) pools include markers that discriminate both TN and luminal B tumors in terms of response to NACT; the TN (triple-negative) pools include markers exclusive for TNBC and the LB (luminal B) pools for the luminal B subtype. Primers and probes for each of the pools are presented in Supplementary Table S3, and related functions of the marker genes are listed in Table 2. Using the MSRE-qPCR method, we measured the methylation level of individual candidate markers (for vast differentially methylated regions, several loci were assessed, within the same gene) of tumor sensitivity to neoadjuvant chemotherapy in an independent cohort (n = 83) of TN and luminal B tumors. The predictive value of individual markers was characterized in terms of cvAUC (cross-validated area under the ROC curve), sensitivity, and specificity. The results in TN and luminal B tumor groups are shown in Supplementary Tables S4 and S5, respectively. The highest predictive values in terms of predicting the sensitivity to NACT of triple-negative tumors, were shown by methylation markers of the TMEM132D, ABCA3, DPYS, MYO15B, GMDS, CDO1, SFRP2, DLEU2, IRF4, TMEM132C, and VGLL4 genes (cvAUC values are in order decreasing from 0.72 to 0.59). All markers demonstrate hypomethylated status in the group of NACT-sensitive tumors compared to the group of resistant ones. For the luminal B group of tumors, most informative were the methylation markers of the LTBR, VGLL4, DPYS, CLEC14A, and TTC34 genes (cvAUC in descending order from 0.69 to 0.56). The markers of LTBR, VGLL4, and CLEC14A showed a hypomethylated status in the group of sensitive tumors compared with the resistant group, while the DPYS marker showed a hypermethylated status in sensitive tumors. The accuracy of diagnostics can be significantly improved by combining several markers into panels [17]. To achieve this, we combined the most informative individual DNA methylation markers in panels (Supplementary Tables S6 and S7). When forming combinations of markers, no more than four markers per panel were taken, as far as the qPCR format usually allows no more than six detection channels (two channels are occupied by PC and DC controls in MSRE-qPCR assays). Combining individual markers into panels improved the quality of NACT response classifiers for triple-negative tumors up to cvAUC = 0.83 (TMEM132D and MYO15B markers panel, with sensitivity and specificity both equal 0.76) and up to cvAUC = 0.76 (TTC34, LTBR, and CLEC14A markers panel, with sensitivity of 0.7 and specificity of 0.79) for luminal B tumors. The same classifiers validated using the RRBS results demonstrated similar performance: cvAUC = 0.83 with sensitivity of 0.87 and specificity of 0.67 for TMEM132D and MYO15B; cvAUC = 0.67 with sensitivity of 0.6 and specificity 0.75 for TTC34, LTBR, CLEC14A (Supplementary Figure S6). Statistical analysis has revealed no significant associations between the level of methylation of the studied markers and clinical and morphological characteristics of breast cancer, such as the patient’s age, tumor size (T), regional lymph nodes status (N), or stage, for both TN and luminal B tumor groups, after applying multiple-testing correction (Supplementary Figures S1 and S2). This observation allows us to suggest that clinical and morphological characteristics predictive of the NACT response, if any, would be independently additive to the epigenetic classifier, and combining epigenetic, clinical, and morphological markers would further improve prediction of NACT response. Correlations between different clinical features and the response to NACT in our cohorts are presented in Figure 3 and Figure 4. In the TN subtype, a significant correlation (p < 0.05) was found between the tumor response to NACT and clinical stage (φ = 0.4365), the clinical stage and tumor size (φ = 0.5789), and the clinical stage and lymph node status (φ = 0.6395). There is a moderate correlation between the NACT response and lymph node status in the luminal B group of tumors (p < 0.05; φ = 0.573). In addition, a high correlation was found in the luminal B group between the clinical stage of the tumor and its size (p < 0.05; φ = 0.7976). The association of the clinical stage with the tumor size and the lymph node status may be explained by the derivation of the clinical stage from the TNM characterization of the tumor. The results of the correlation analysis suggest that the addition of the variable “clinical stage, S” for TN breast cancer, and “lymph node status, N” for luminal B tumors might add to the quality of the developed molecular epigenetic classifiers. We have added S and N predictors to DNA methylation markers for TN and luminal B tumors, respectively, and reevaluated the diagnostic characteristics of the resulting panels (Supplementary Tables S8 and S9 and Figure 5). Using classifiers, consisting not only of epigenetic markers, but also of clinical ones, makes it possible to develop a classifier with significantly better characteristics than using only DNA methylation markers or clinical features (Figure 6). For lum.B tumors, a DNA methylation panel including “LTBR, CLEC14A, N” shows cvAUC = 0.83; 95% CI = 0.82–0.85, with a sensitivity of 0.89 and a specificity of 0.71. For TN breast cancer, the inclusion of the clinical stage in the epigenetic classifier made it possible to achieve an area under the curve cvAUC = 0.87; 95% CI = 0.86–0.88 with a sensitivity of 0.71 and a specificity of 0.80 for the “TMEM132D, TMEM132C, MYO15B, S” panel. We assessed epigenetic and combined marker panels using a likelihood ratio test. As a result, the combined model for TN breast cancer (TMEM132D, TMEM132C, MYO15B, S) demonstrated statistical significance (p = 0.0004) vs. the epigenetic model (TMEM132D, TMEM132C, MYO15B). The luminal B breast cancer subtype combined (LTBR, CLEC14A, N) vs. the epigenetic model (LTBR, CLEC14A) also showed statistical significance (p = 0.006). To investigate the potential predictive value of combined epigenetic and clinical panels, we calculated the NACT response score for epigenetic panels developed within this study and combined it with independent clinical features, such as the tumor size, lymph node involvement, clinical stage, and patient’s age (Figure 7). The association between the NRS and NACT response retained statistical significance when using clinical features, such as the clinical stage for TN breast cancer subtype (OR = −0.47 (95% CI = −0.76; −0.20) p = 0.002) and lymph node involvement for luminal B breast cancer (OR = −0.55 (95% CI = −0.86; −0.24), p = 0.001). Breast cancer is a group of diseases that is extremely heterogeneous in terms of clinical and morphological characteristics, progression, survival, response to therapy, and molecular profiling [18]. For BC, molecular genetic classifiers are being actively developed with the prospect of using them as sets of prognostic and predictive markers [19]. Although BC is among the most exhaustively studied cancers, there is still a lack of molecular markers to predict its response to neoadjuvant chemotherapy (NACT) [20]. Tumor development is determined by alterations not only of its genome, but of its epigenome as well. In order to identify epigenomic markers of BC NACT effectiveness, we have applied genome-wide DNA methylation screening of tumors in cohorts of NACT responders and nonresponders. As a result, we demonstrate the predictive potential of DNA methylation markers in assessing the BC response to NACT. To characterize DNA methylation markers in an independent cohort, the MSRE-qPCR method was used, which has competitive analytical sensitivity and allows a reduction in analysis time and expenses, as well as the volume of input tissue material, which is critical in biopsy analysis. We have performed genome-wide bisulfite sequencing on 73 BC biopsy samples (discovery cohort) obtained prior to NACT to select genome regions to further develop DNA methylation panels of a limited number of markers. The discovery cohort consisted of 29 samples of TNBC and 44 samples of luminal B tumors. Other research groups have developed sets of genome-wide DNA methylation data, some on larger collections of BC samples [21,22,23]; however, the most common way to obtain data nowadays is probe hybridization that allows for determining the status of the methylation of only a predesigned selection of CpG dinucleotides. The fundamental difference between the methods based on microarray hybridization and bisulfite sequencing is that the latter allows an assessment of each and every CpG pair in a locus, thus presenting the full picture of methylation at a genome regulatory region of interest. To perform genome-wide bisulfite sequencing, we have used our version of reduced representation bisulfite sequencing, XmaI-RRBS [12]. In general, compared to the whole-genome bisulfite sequencing (WGBS), RRBS approaches increase the relative informational value of DNA methylation analysis: each RRBS sequence read includes at least one informative CpG position. Theoretically, RRBS is better applicable to large-scale studies of DNA methylation and in the search for markers of epigenetic processes in health and disease than WGBS, since RRBS focuses on functionally significant methylation in CpG islands, which constitute a small portion of the genome, and ignores the major portion that is less significant for clinical research [9]. To our knowledge, by now only one panel of DNA methylation markers to predict the BC response to NACT have been published [24]. Begona Pineda et al. have proposed a DNA methylation assay based on the levels of methylation of the FERD3L and TRIP10 genes, with AUC = 0.905 (78.6% accuracy), for predicting pathological complete response (pCRs) in TNBC patients treated with anthracyclines and taxanes [24]. In their study, exploratory analysis for a genome-wide search for DNA methylation markers was performed using Infinium Human Methylation 450 K microarrays on a cohort of 24 patients, twice smaller than ours. However, it is valuable that their study was the first to demonstrate the potential of DNA methylation markers in assessing the BC response to NACT. We previously published a study [9] in which we examined 25 luminal B breast cancer biopsies obtained prior to neoadjuvant chemotherapy. In the current study, these 25 samples were included in a bigger discovery cohort and the fundamental approach to data analysis and the formation of panels from a limited number of DNA methylation markers was changed. For example, we selected markers among loci for which methylation information was obtained for at least four consecutive CpG pairs. The change in approach to the analysis of XmaI-RRBS results does not allow us to combine the results of the two studies; however, the expanded discovery sample provides a broader view of differential methylation in the luminal B breast cancer. Some of the predictive factors used to make decisions regarding BC systemic treatment are the tumor size, status of lymph node involvement, and clinical stage [25]. Moreover, in addition to the widely used surrogate molecular markers ER, PR, HER2, and Ki-67, attempts are being made to search for both new potential markers and a more detailed study of markers that are not common in clinical practice, such as proliferating cell nuclear antigen PCNA [26], caveolin [27], and chemokine receptor CXCR4 [28]. Since there is still no gold standard, it is important to continue attempts to search for new predictive markers and, if possible, to pay attention to combined panels of markers of various natures (genetic, epigenetic, transcriptomic, proteomic, clinical, morphological). Some of the epigenetic markers identified in our study, TMEM132D, MYO15B, TTC34, LTBR, and CLEC14A genes, whose methylation levels showed the best classification of BC sensitivity to NACT, were previously partially studied in terms of involvement in the etiopathogenesis of malignant neoplasms and potential use as diagnostic markers in oncology. Mutations in the TMEM132D gene were found in pancreatic cancer [29] and small cell lung cancer [30]. The gene itself codes for transport receptors in the brain. Specific studies of its methylation in breast cancer, or associations with chemotherapy, have not been performed, but it was published that its overexpression correlates with cytotoxic T-lymphocytes infiltration and better survival in patients with early-stage ovarian cancer [31]. MYO15B genetic variants were found to be associated with an increased risk of depressive disorder in females [32], and the function of the protein is unknown, since it lacks a motor domain according to the GeneCards database. TTC34 is a paralog of the TMTC4 gene responsible for protein–protein interactions. The function of TTC34 is unknown. TTC34 was upregulated in luminal and triple-negative BC subtypes upon LINC01087 overexpression [33]. LTBR (lymphotoxin beta receptor) is probably the most interesting of the list. It is a member of the tumor necrosis factor family and is closely associated with carcinogenesis [34], but gene methylation has not previously been studied as a predictive marker in oncology. CLEC14A is involved in regulating the growth of cancer cells, maintaining body hemostasis, and facilitating cell communication [35]. It is found in the tumor endothelium and is a tumor endothelial marker [36]. Gene methylation is also associated with carcinogenesis [37] but has not been studied as a predictive marker in breast cancer. Finally, some limitations and perspectives of this study are to be discussed. Many of the markers selected in the discovery cohort are not significantly associated with the NACT response in the independent cohort. This can be explained both by the use of two fundamentally different platforms for the analysis of cohorts and by the use of a nominal p-value when selecting markers from the results of the genome-wide RRBS screening, which could lead to false positive hits included in the list of candidate markers identified in the discovery cohort. Taking into account such limitations, we formed a deliberately large number of panels for validating RRBS findings and formed both subtype-specific test systems and universal ones (for TN and lum.B subtypes of breast cancer). In order to support our results, larger independent cohorts should be tested. This will increase precision and allow us to better understand the meaning of DNA methylation in terms of the responses of different subtypes of BC to NACT. If our findings are well confirmed on independent validation cohorts and lead to the clinical implementation of DNA methylation markers for treatment prediction, an improvement in BC NACT may be anticipated. One of the intriguing research perspectives is the assessment of the dynamics of methylation levels of the markers as a result of NACT. Studying methylation changes associated with chemotherapy may shed more light on the biology of the tumor response to treatment and on the nature of tumor chemoresistance. As a result of this study, we confirmed our hypothesis about the high predictive potential of DNA methylation markers in assessing the response to neoadjuvant chemotherapy in breast cancer. The use of combined predictors, including not only epigenetic markers, but also data obtained in the course of clinical and morphological examinations, allows us to approach the issue of personalized chemotherapy prescription for patients with breast cancer. Since there is still no gold standard, it is necessary to continue attempts to search for new predictive markers and, possibly, pay attention to complex panels of markers of various natures (genetic, epigenetic, proteomic, clinical, morphological).

PMC10001084

Andressa Roehrig Volpe-Fix,Elias de França,Jean Carlos Silvestre,Ronaldo Vagner Thomatieli-Santos

The Use of Some Polyphenols in the Modulation of Muscle Damage and Inflammation Induced by Physical Exercise: A Review

21-02-2023

flavonoids,physical exercise,inflammation,muscle damage

Food bioactive compounds (FBC) comprise a vast class of substances, including polyphenols, with different chemical structures, and they exert physiological effects on individuals who consume them, such as antioxidant and anti-inflammatory action. The primary food sources of the compounds are fruits, vegetables, wines, teas, seasonings, and spices, and there are still no daily recommendations for their intake. Depending on the intensity and volume, physical exercise can stimulate oxidative stress and muscle inflammation to generate muscle recovery. However, little is known about the role that polyphenols may have in the process of injury, inflammation, and muscle regeneration. This review aimed to relate the effects of supplementation with mentation with some polyphenols in oxidative stress and post-exercise inflammatory markers. The consulted papers suggest that supplementation with 74 to 900 mg of cocoa, 250 to 1000 mg of green tea extract for around 4 weeks, and 90 mg for up to 5 days of curcumin can attenuate cell damage and inflammation of stress markers of oxidative stress during and after exercise. However, regarding anthocyanins, quercetins, and resveratrol, the results are conflicting. Based on these findings, the new reflection that was made is the possible impact of supplementation associating several FBCs simultaneously. Finally, the benefits discussed here do not consider the existing divergences in the literature. Some contradictions are inherent in the few studies carried out so far. Methodological limitations, such as supplementation time, doses used, forms of supplementation, different exercise protocols, and collection times, create barriers to knowledge consolidation and must be overcome.

The Use of Some Polyphenols in the Modulation of Muscle Damage and Inflammation Induced by Physical Exercise: A Review Food bioactive compounds (FBC) comprise a vast class of substances, including polyphenols, with different chemical structures, and they exert physiological effects on individuals who consume them, such as antioxidant and anti-inflammatory action. The primary food sources of the compounds are fruits, vegetables, wines, teas, seasonings, and spices, and there are still no daily recommendations for their intake. Depending on the intensity and volume, physical exercise can stimulate oxidative stress and muscle inflammation to generate muscle recovery. However, little is known about the role that polyphenols may have in the process of injury, inflammation, and muscle regeneration. This review aimed to relate the effects of supplementation with mentation with some polyphenols in oxidative stress and post-exercise inflammatory markers. The consulted papers suggest that supplementation with 74 to 900 mg of cocoa, 250 to 1000 mg of green tea extract for around 4 weeks, and 90 mg for up to 5 days of curcumin can attenuate cell damage and inflammation of stress markers of oxidative stress during and after exercise. However, regarding anthocyanins, quercetins, and resveratrol, the results are conflicting. Based on these findings, the new reflection that was made is the possible impact of supplementation associating several FBCs simultaneously. Finally, the benefits discussed here do not consider the existing divergences in the literature. Some contradictions are inherent in the few studies carried out so far. Methodological limitations, such as supplementation time, doses used, forms of supplementation, different exercise protocols, and collection times, create barriers to knowledge consolidation and must be overcome. The use of plant-based or natural supplements has been growing, as has the research on their properties [1]. The benefits of these supplements seem to be linked to the presence of food bioactive compounds (FBC) in the composition of leaves, roots, seeds, fungi, or seaweed [2]. FBCs are substances related to the secondary metabolism of plants, and its function is to protect against environmental aggressions [3]. There are several types of FBCs, with a huge variety of functions, with polyphenols being the most abundant [4]. It is known that FBCs bring benefits to the organism that consumes them [5], and due to the anti-inflammatory, antioxidant, and immunoregulatory [6] capacities demonstrated by some of these compounds, they have been investigated in the context of physical exercise. The practice of physical exercise is considered a potent immunomodulator. Beyond protecting the body against pathogens, the immune system also plays a crucial role in tissue remodeling after injury [7]. Physical exercise can induce significant injuries in the skeletal muscle tissue, leading to a consequent drop in performance. For the immune system to rescue muscle performance, improvement in pro-anti/inflammatory balance must occur to allow for muscle regeneration. However, muscle regeneration takes time [8], and hypothetically, the magnitude of cell injury (during exercise and the inflammatory phase) can be too much and make the recovery phase difficult [8]. Immunonutrition strategies to counter these deleterious effects on the immune system have been proposed [6]. Here, we discuss the role of some polyphenol supplementation strategies in muscle damage, inflammatory profile, and recovery, followed by exhaustive physical exercise. FBCs comprise an immense class of substances with different chemical structures, and they exert physiological effects on the individuals who consume them [5]. They are secondary metabolites synthesized as a defense mechanism against environmental aggressions and are present mostly in plants and vegetables but also in bacteria, fungi, and, in lower concentration, animal products [3]. The positive physiological effects of the compounds are related to protection against chronic non-communicable diseases (NCDs), especially cardiovascular diseases and cancer. The negative effects are related to toxicity or allergenic potential, depending on the dose and bioavailability of the substance [5,9]. The main food sources of these compounds are fruits, vegetables, wines, teas, seasonings, and spices, and there are still no daily recommendations for their intake [4,10]. There are 11 major groups of compounds, classified according to their chemical structure and functionality, which may vary according to the origin of the organism. The groups are: (1) polyunsaturated fatty acids; (2) alkaloids; (3) peptides; (4) polyphenols; (5) polysaccharides; (6) triterpenes; (7) terpenoids all from plants, bacteria, fungi, and animals; (8) capsaicinoids and (9) phytosterols, produced exclusively by plants; (10) carotenoids and tocopherols, available in plants and fungi; and finally, (11) glucosinolates, synthesized by plants and bacteria [4]. Polyphenols constitute a comprehensive class of compounds with more than 8000 compounds identified [11]. Its chemical structure corresponds to one or more phenolic rings linked to hydroxyl groups [12] and is divided into flavonoids and non-flavonoids [4,10,13], as we can see in Figure 1. The direct antioxidant action (neutralization of free radicals) or indirect action (improvement of antioxidant capacity) are some highlights of the physiological effects of polyphenols [14]. In vitro studies also demonstrate anti-inflammatory activity and immunoregulatory properties [15,16,17]. As the absorption of polyphenols is conditioned on the health constitution of the intestinal microbiota and the bioavailability of these compounds varies widely, the effects demonstrated in vitro are questioned in humans [6]. Flavonoids are the most abundant and studied class [18]. They are glycosylated from polyphenols, and their best-known physiological effect is antioxidant, due to their chemical structure and to the degree of glycosylation of the molecule [19]. This class of compounds is responsible for the colorings red, blue, orange, and purplish [20], and its functions within the plant kingdom involve protection against pathogens and ultraviolet radiation [10]. The general chemical structure of flavonoids can be observed in Figure 2 and corresponds to a skeleton of 15 carbons containing two aromatic rings and a heterocyclic ring [10,21]. From this basic structure, there have derived some variations that differentiate the flavonoids in the following subclasses: flavanols, flavonols, anthocyanins, flavones, and isoflavones [22]. The non-flavonoid polyphenols are phenolic acids, stilbenes, and lignans [4,13]. Regarding stilbenes, resveratrol is studied for functions involving the immune system, neural protection, antitumor and antitumoral effects, and especially anti-inflammatory and antioxidant effects [23,24,25]. The main food sources of resveratrol are grapes, purple fruits and vegetables, and peanuts [13]. Although their best-known positive effects are related to NCDs, the different kinds of compounds that are studied within the scope of physical exercise involve sports performance [26,27,28], fatigue [29,30], muscle recovery [31,32,33], and immunomodulatory, antioxidant [5,6,34], and anti-inflammatory actions [32,34]. The results of studies associating polyphenol (mainly flavonoid) supplementation with exercise, especially those involving outcomes related to the immune system, are still controversial. This review relates the effects of some polyphenol supplementation—cocoa flavonols, anthocyanins, green tea catechins, curcumin, quercetin, and resveratrol—on muscle damage, inflammatory profile, and recovery, followed by exhaustive physical exercise or training. In this review, we do not address phenolic acid compounds and their role in performance and recovery because they were brilliantly addressed in the article by Gonçalves et al. [35]. During physical exercise, both in trained and sedentary individuals, it is possible to observe a brief increase in the number of circulating leukocytes, which are mobilized from the lymphatic system, vessel walls, and spleen, indicating the ability of exercise to influence different cell compartments [36]. In healthy people, moderate training seems to be associated with improved immunity from the point of view of antigen recognition, presentation, and elimination mechanisms, in addition to the organization of the immune response, protecting or attenuating the symptoms of infections and reducing the days with symptoms in case of illness [37,38]. On the other hand, strenuous exercise for prolonged periods has the opposite effect. After running a marathon, a picture of immunosuppression is generated, characterized by a marked decrease in the number of T cells, circulating NK cells, and neutrophils, a decrease in their activities and functions, in addition to a decrease in the salivary concentration of IgA [36]. In the above context, the theory of the ‘Open Window’ period after performing strenuous exercise was postulated. The ‘Open Window’ period is related to the moment after exhausting exercises that can last from 3 h to 72 h depending on the parameters analyzed, during which a lower functionality of the immune system is observed, increasing the risk and probability of opportunistic infections, mainly upper respiratory tract infections (URTIs) [39]. Performing several acute exercise sessions with strenuous characteristics without adequate recovery time can result in chronic immunosuppression. The modulatory effect that exercise poses on the immune system can be explained by an S-curve graphic model. Therefore, people undergoing moderate training are less likely to develop infections, especially URTIs, while amateur athletes are more likely to develop infections than people who train moderately or are sedentary. On the other hand, professional athletes, despite the high training overload, are less likely than amateur athletes to develop these infections [40,41]. The intensity and duration of physical exercise are determining factors for different changes in the immune system. Studies show that moderate aerobic training (30 to 60 min, 3 to 5 days a week at an intensity between 60 and 80% of VO2 max), as opposed to other intensities and volumes, results in an improvement of the immune system in the face of inflammation, in the capacity of phagocytic activity of neutrophils and monocytes [36,39,42] and a lower risk of URTIs [40]. The guiding mechanisms of exercise immunology are still discussed; however, hormonal and metabolic changes induced by exercise seem to play a relevant role [43,44,45,46,47]. The immune system plays a crucial role in tissue remodeling after injury. The adaptation of skeletal muscle tissue in response to physical exercise depends on the immune system’s function. It has been postulated that muscle adaptation/regeneration depends on the inflammatory response in a coordinated (five waves/phases) and time-dependent process [7]. The practice of physical exercise can induce significant injuries in the skeletal muscle tissue, leading to a consequent drop in performance, i.e., loss of muscle functionality. Resolving the muscle injury is paramount to restoring muscle performance. For this, during and after physical exercise, metabolites are released from damaged muscle tissue (such as CK, LDH, troponin, and complement C4) that act as damage-associated molecular patterns (DAMPs). DAMPs (the first wave) trigger an inflammatory response (i.e., recruiting immune system cells such as neutrophils, monocytes, and CD8+ T cells from the other sites to remove myofiber debris in the injured areas) [48]. In the second wave, neutrophils secrete IL-1 and IL-8 (which activate M1 macrophages to the lesioned region). M1 macrophages infiltrate skeletal muscle tissue to phagocytose cellular debris and secrete significant amounts of pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β) and nitric oxide for a proper inflammatory response [48]. During the second wave, infiltrated CD8+ T cells also secrete TNF-α, IFN-γ, IL-1α, and IL-13. Pro-inflammatory cytokines such as IL1-β and TNF-α stimulate IL-6 and COX-2, which are essential to induce myoblast proliferation and differentiation (i.e., the stimulation of the myogenesis process mediated mainly by prostaglandins (PGEs and PGDs)) [49]. In the third wave, Treg cells (in response to elevated IL-6) secrete IL-33, and IL-10 stimulates the phenotypic shift from inflammatory M1 macrophages to anti-inflammatory M2 macrophages (which secrete IGF-1, IL-4, and IL-13) [48]. Anti-inflammatory cytokines (such as IL-4, IL-10, IL-13, and IL-33) provide a favorable environment for growth factors (such as IGF-1 and TGF-β) to promote the recruitment of satellite cells (to injury areas) and the differentiation, growth, and maturation of new muscle fibers [49]. Complete muscle recovery (and possible overcompensation) occurs with the maturation (fourth wave) of new muscle fibers [7]. The muscle injury/regeneration process is summarized in Figure 3. The first wave (DAMPS) appears not to be necessary to induce better muscle adaptation [50]. On the other hand, CK secretion above normal levels has been associated with poor physical performance in athletes [46] and clinical patients [51]. Elevated CK in blood plasma occurs mainly after unaccustomed exercises or exercise protocols with weightlifting, eccentric exercises, downhill running, and prolonged exercise (e.g., ultramarathons). Well-trained individuals or those accustomed to repeated exercise to induce muscle damage showed lower muscle CK release into the bloodstream and a lower performance decrement than untrained or unaccustomed individuals [52]. Interestingly, supplementation strategies (e.g., mainly with FBC antioxidant and anti-inflammatory characteristics) decrease muscle damage after physical exercise that induces muscle damage [53]. Additionally, it is well known that FBCs increase athletic performance, such as creatine, taurine, citrulline, and nitrate, and also decrease muscle damage through antioxidant and anti-inflammatory mechanisms [53]. Therefore, preventing muscle damage with FBCs is used in sports nutrition to avoid declining athletic performance. As described earlier, the second wave (inflammation) responds to the first wave (muscle damage) to promote the removal of damaged cell debris and to induce tissue regeneration. As indicated in Figure 2, muscle damage associated with the inflammatory process (second wave) has also been associated with decreased performance in athletes [54] and clinical patients [51]. For instance, in an experimental study, an acute IL-6 injection impaired endurance performance in healthy subjects and increased fatigue sensation [55]. Additionally, evidence has shown that the acute use of paracetamol (an inhibitor of COX-1, COX-2, and IL-6) increases athletic performance [56]. Therefore, managing inflammation-related processes might be the main reason for using sports-related pharmacological anti-inflammatory drugs because the major reason for using pharmacological anti-inflammatory drugs in athletes is to treat pain or injury, to treat illness, and to enhance performance [57]. However, the chronic and indiscriminate use of pharmacological anti-inflammatories (which interfere with the second wave described in Figure 3) can hinder the adaptation to training such as muscle strength and hypertrophy [58]. Physical exercise breaks body homeostasis, and restoring the broken balance depends on the ability of different physiological systems and cellular biochemistry to act in coordination. In this context, for exercise immunology, there is an ambiguous scenario in which metabolic, hormonal, and cytokine changes alter cell traffic directing immune system cells to skeletal muscle, despite the risk of increasing the likelihood of opportunistic infections [3,59,60]. Probably, the greater the muscle damage, the longer the cell mobilization time in the skeletal muscle and the greater the vulnerability in other body sites [8]. Therefore, strategies that can accelerate the muscle regeneration process and maintain greater control of the pro/anti-inflammatory balance after exercise can contribute to the faster restoration of muscle and immunological homeostasis. Immunonutrition strategies to counter these deleterious effects on the immune system have been proposed [6]. Following these, we report several derived FBC supplements that can influence the process of tissue repair and muscle recovery after exercise. Cocoa is rich in flavanols and receives particular attention both for its palatability with good oral acceptance [61] and for its chemical composition that meets a good proportion of catechins, epicatechins, and gallocatechin [62], giving this food an interesting antioxidant potential [63]. The studies selected in this review used cocoa “in natura” supplementation, either beverage or chocolate. Physical exercise leads to an increase in the production of ROS, and the antioxidant system, whether endogenously represented by antioxidant enzymes or exogenously represented by the consumption of antioxidants, is responsible for maintaining the balance between the production and neutralization of ROS. Excess ROS leads to oxidative stress and inflammation, compromising performance and hindering the adaptation to exercise. Regarding performance, Patel, Brouner, and Spendiff [64] found a 13% increase in distance traveled in a time trial test after 14 days of commercial dark chocolate supplementation (rich in (-) epicatechin) when compared to the placebo, white chocolate, both 40 g in trained men [64]. Using 7 days of supplementation (beverage enriched with flavonols—308 mg, from cocoa powder or a placebo—0 mg of flavonols) and undergoing muscle injury on day 5, trained men were evaluated for performance through vertical jump and yo-yo tests, and no differences were observed between groups [65]. The studies that evaluated the acute supplementation of cocoa flavonols, in different doses and forms of presentation, did not demonstrate differences in terms of performance. Davison et al. used 100 g of commercial 70% cocoa chocolate 2 h prior cycling for 2.5 h at ~60%VO2 max or a control bar in healthy men without differences in the distance [66]. Using a beverage fortified with cocoa (350 mg of flavonols, coming from cocoa powder) × a placebo drink (0 mg of flavonols), in trained men, also acutely, Peschek and Pritchett [67] did not verify differences in the performance of the time trial test nor in the strength isometric between groups. In this study, participants underwent a muscle injury induction protocol and were evaluated in a time trial test and muscle function test 48 h after injury induction [67]. In the study by Decroix and Tonoli [68], they also used a beverage as a form of cocoa supplementation (900 mg flavonols from food × 15 mg in placebo). Participants underwent a time trial test 1.5 h and 3 h after consuming the supplementation or placebo, and no performance differences were observed between groups [68]. Some studies, as seen in Table 1, evaluated the effect of CF supplementation or a placebo on the antioxidant capacity of individuals submitted to exercise. Acute supplementation (1.5 h or 3 h before exercise) of 900 mg CF from cocoa powder in a drink versus 15 mg CF in the placebo drink in trained men improved the total antioxidant capacity of plasma after a time trial test [68]. This result was also observed with a lower dose of CF (247 mg) in untrained men, who consumed 100 g of dark chocolate 2 h before low-intensity aerobic exercise [66]. The improvement of the total antioxidant capacity of plasma after physical exercise was also observed after the supplementation of 40 g of dark chocolate containing 85% cocoa for 30 days in trained men who did not receive the chocolate [69]. On the other hand, a longer time of supplementation seemed to have an opposite effect. The use of 5 g of cocoa dissolved in semi-skimmed milk (425 mg CF) for 10 weeks compared to 5 g of maltodextrin inhibited the formation of ROS (evaluated indirectly by decreasing lipid peroxidation and increasing the SOD activity), which led to lower mitochondrial biogenesis; that is, it negatively impacted the adaptation inherent to exercise [70]. The ROS and reactive nitrogen species (RNS) production, as an effect of physical exercise, is required during the inflammatory process for optimal muscle adaptation [7,28,71]. Decreasing the production of ROS with chronic CF seems to inhibit the aerobic exercise adaptation process [70]. Inflammatory processes resulting from the practice of physical exercise can lead to the development of muscle pain, compromising the recovery of athletes [95]. This process also depends on the magnitude of the damage caused by training [96]. Thus, modulating inflammatory cytokines and contributing to the reduction of muscle damage are important strategies for post-exercise recovery. Decreased IL-6 release (inflammatory characteristic) was observed after aerobic exercise in trained men who received 425 mg of CF (5 g of cocoa powder to be dissolved in semi-skimmed milk) for 10 weeks compared to the placebo (5 g of maltodextrin) [70]. For untrained men who received a lower dose of CF (74 mg) from cocoa juice for 7 days and underwent resistance exercises on day 8, no differences were observed in IL-6 and hsPCR levels compared to the placebo that received a drink that did not contain flavonoids [72]. Thirty days supplementation with 40 g of dark chocolate containing 85% cocoa (799 µg GAE/mL CF) in elite soccer players had a protective effect on muscle damage. Compared to the placebo group that did not received chocolate, lower levels of creatine kinase (CK) and lactate dehydrogenase (LDH) were observed [69]. Additionally, in trained individuals, the acute supplementation of a beverage containing 350 mg of CF used by Peschek and Pritchett [67] showed no changes in CK levels and no difference in muscle pain after aerobic exercise compared to the placebo, which consumed a similar beverage without CF [67]. Using another presentation (cocoa juice containing 74 mg of CF) for 10 days in untrained individuals, Morgan and Wollman [72] observed an improvement in the recovery of explosive strength after exercise compared to the placebo. As a food, cocoa contains several FBCs (such as fatty acids, vitamins, minerals, fiber and alkaloids) [95], and the synergy between these compounds may be responsible for the observed effects. We must be careful in claiming that the results are just due to the cocoa flavonols, as these were not supplemented isolated. Within the flavonoid group, anthocyanins are the most abundant class [97]. They are the blue, purple, red, and orange pigments that can be found in several fruits and vegetables, among which the most studied are strawberry, cherry, and blackberry [98]. The main physiological effect of anthocyanins is the improvement of endothelial function and oxidative stress, inhibiting COX-1 and COX-2 enzymes [99]. For this reason, the application of anthocyanins has been studied in diseases such as hypertension and dyslipidemia [100,101]. From the point of view of physical exercise, this effect of anthocyanins could increase performance as verified in the following works, which can be found in Table 1. The chronic use (3.3 weeks) of 300 mg anthocyanins by trained women in the form of blackberry juice—300 mg—resulted in a decrease of the time trial test time compared to the placebo [102]. In this same public, also using chronic supplementation (6 weeks) of 100 mg anthocyanins pills, an increase in VO2 maximum was observed in relation to placebo lactose pills [103]. A shorter time trial test was observed in trained men using 257 mg of anthocyanins, from Montmorency cherry capsules for 7 days compared to the placebo [104]. The supplementation of 116 mg of anthocyanins from a commercial juice for 9 days was able to improve the VO2 max of a trained man in a downhill exercise (−15%) for 30 min compared to an isocaloric maltodextrin solution [73]. Regarding muscle pain, the work of Lima et al. [73], captured in Table 1, was conducted with trained men who consumed 240 mL of an antioxidant juice twice a day (116 mg anthocyanins) for 9 days, including the day of the test, resulting in a lower perception of muscle pain using a visual scale, as well as lower CK levels evaluated 48 h and 96 h after exercise compared to the placebo group. The work of Drummer et al. [74] that evaluated muscle pain in this same public did not identify differences in this sense. In this study, participants consumed 320 mg of anthocyanins from Montmorency cherry juice or an anthocyanin-free beverage and performed an acute bout of resistance training. No differences were observed in muscle pain assessed by a visual analog scale (VAS) and pressure pain threshold (PPT). Additionally, no differences were observed in IL-6 secretion between groups [74]. Previously, the anti-inflammatory effect of anthocyanins was demonstrated in vitro. Strawberry and mulberry alcoholic extracts were added to rat splenocyte culture and the ratio of pro-inflammatory (IFN-g, IL-2 e IL-12) and anti-inflammatory (IL-10) cytokine secretion was decreased both in the presence and absence of lipopolysaccharides (LPS). In the presence of LPS, there was also a decrease in the ratio TNF-a/IL-10 [15]. It is important to highlight that the use of anthocyanins, as well as other flavonoids, is dependent on the intestinal microbiota, and therefore, there is a low bioavailability of these compounds in the human body (less than 2%). The proportion of anthocyanins eliminated is much higher than the levels of anthocyanins consumed [105,106]. This fact may explain why in vitro results do not reproduce in vivo. In another in vitro analysis, treatment with strawberry extract decreased the ROS formation by stimulating the antioxidant enzyme activity in a RAW.7 macrophage culture LPS-challenged [17]. The effect of improving the antioxidant capacity was also observed in untrained men who consumed 500 g of strawberries in their usual diet for 14 days [16]. Green tea is a very popular drink, especially in eastern countries. Therefore, the studies selected in this review used the Cammelia sinensis extract supplementation and not the green tea isolated FBC. Its use is widespread in sports due to its benefits in body composition, performance, recovery, and antioxidant action [107]. Catechins are the main flavonoids of green tea. The main catechins of green tea are: epigallocatechin gallate (EGCG, 60%), epigallocatechin (EG, 20%), epicatechin-3- gallate (ECG, 14%), and epicatechin (EC, 6%) [108]. This chemical structure favors the antioxidant action of green tea [109]. Although catechins play a major role in this function, green tea contains other FBCs (such as caffeine and carotenoids), chlorophyll, amino acids, and lipids [108] that may contribute to the observed effects of exercise. The interaction between these FBCs must be considered in the final analysis of the results. In Table 1, we can see that the improvement of the total antioxidant capacity of plasma was verified in some studies. Trained men who used 250 mg of green tea extract in capsules for 4 weeks (200 mg catechins), or placebo, performed an aerobic test and observed an improvement of the total antioxidant capacity and a decrease in malondialdehyde (MDA) levels [77]. The decrease in MDA levels after the use of green tea was observed in well-trained men (elite football athletes) who maintained their usual physical activities for 6 weeks and used 450 mg of extract of green tea supplementation compared to a placebo [76]. The same authors investigated the CK levels of the athletes after the supplementation use and did not observe significant differences in comparison with the placebo. The chronic use of green tea extract in trained men did not have benefits for muscle damage indexes compared to the placebo [76] (see Table 1). In untrained men, the results are conflicting. Jajtner et al. [75] used 1 g of green tea extract supplementation (containing 50 to 80 mg of epigallocatechin gallate (EGCG)) for 28 days (or a placebo) and verified an increase in CK levels 24 h, 48 h, and 96 h after a strength training session. The use of 250 mg of green tea extract for 4 weeks following an aerobic test [77] or a 15-day supplementation of 500 mg of green tea extract following a strength exercise protocol until exhaustion [79] led to a decrease in CK concentration after exercise. Da Silva et al. [79] observed that supplementation with green tea did not alter the feeling of muscle pain reported by the participants. The anti-inflammatory effect of green tea after physical exercise was demonstrated in overweight women who consumed 500 mg of green tea extract (225 mg EGCG) or a placebo for 8 weeks and followed an aerobic training program for the duration of the study. In this case, a decrease in hsPCR levels and an increase in plasma adiponectin levels were observed [80]. The anti-inflammatory effect was also demonstrated in untrained men who consumed 1 g of green tea extract supplementation (50–80 mg EGCG) for 28 days or a placebo and performed a strength exercise session. Intramuscular IL-8 levels were lower in the group that used supplementation compared to the placebo [75]. Turmeric, discovered about two centuries ago, is a tuberous herbaceous plant with yellow flowers and broad leaves that grows in tropical climates. Curcumin is an FBC derived from polyphenols and is present in turmeric; this phytochemical is responsible for the yellowish color [110,111]. Over time, studies initially demonstrated antibacterial properties [112]; however, other studies in the coming years demonstrated that curcumin has antioxidant and anti-inflammatory properties [113,114]. Thus, the body of work was growing, demonstrating the beneficial properties of this FBC in inflammatory diseases such as Alzheimer’s, Parkinson’s, rheumatoid arthritis, and diabetes [115,116,117,118]. According to the current state of the art, curcumin can contribute with its antioxidant property through phenolic groups (OH−) linked to the aromatic hydrocarbon groups in its chemical structure. These phenolic groups are essential for ROS scavenging activity [110,111]. Still, other works demonstrate that curcumin can act in the modulation of the anti-inflammatory response through different cytokines mediated by ROS, and this has been demonstrated by the reduction of the expression of the nuclear factor kappa B (NF-kB) induced by cyclooxygenase-2 (COX-2) through the inflammatory process, as well as by increasing the enzymatic antioxidant capacity through the increased activity of erythroid-derived nuclear factor 2 (NrF2) [84,119]. It is important to remember that acute physical exercise increases the NF-kB, NrF2, and COX-2 downstream canonical pathway activity, and all these molecule pathways are necessary to induce beneficial muscular adaptations [49,71]. Therefore, identifying the benefit of curcumin supplementation concomitant with physical training remains an open debate. In the study of McFarlin et al. [81], the authors found a lower increase in CK (−69%), as well as TNF-α (−23%) and IL-8 (−23%), after the supplementation of capsules containing 400 mg of curcumin (1 ×/day) for six days in resistance training subjects compared to the placebo (see Table 1) [81]. Interestingly, using the acute supplementation of similar doses (450 mg pre-exercise) from a commercial powder dissolved in water, Mallard et al. [85] identified lower concentrations of blood lactate immediately after resistance exercise in healthy men, bringing a possible role, still poorly understood, of curcumin as an BC buffer, acting in the modulation of muscular acidosis. However, it is noteworthy that the authors did not find significant responses to other physiological markers, such as CK, LDH, and TNF-α [85]. Other studies demonstrated the benefit of curcumin supplementation at lower CK concentrations (~30% lower) after an exercise protocol to induce muscle damage in men through a 28-day supplementation but with higher doses (1.5 g of curcumin extract per day divided into three 500 mg capsules per day) [120]. According to some authors, curcumin supplementation seems not only to decrease the course of symptoms related to inflammatory responses, such as DOMS, but also contributes to attenuating the magnitude of cellular damage induced by resistance exercise. Tanabe et al. [83] identified that DOMS decreased after the supplementation of 180 mg of curcumin extract daily in 3 to 6 days after resistance exercise (see Table 1). Mallard et al. [85] observed that the acute supplementation of 450 mg of curcumin extract (30 min before resistance exercise) was able to decrease DOMS 48 h and 72 h after exercise, despite an IL-6 increase. This could be explained by an IL-10 increase and a modulation of inflammation. Authors also observed a reduction in thigh circumference in the curcumin group 24 and 48 h post exercise. This finding could suggest that a return to training may be possible earlier in the supplementation group, improving training adaptations and exercise performance [85]. A supplementation with 150 mg of a commercial curcumin extract 1 h after and 12 h before the exercise that induces loss of muscle strength could attenuate cell injuries in untrained individuals. Lower CK concentrations were observed 72 h and 96 h after exercise in the curcumin group in comparison to the placebo group. No differences were observed in neither TNF-alfa and IL-6 concentrations nor in muscle soreness (VAS) [82]. At the same time, other studies worked to demonstrate the possible antioxidant and anti-inflammatory response of curcumin supplementation (see Table 1). Takahashi et al. [86] investigated the antioxidant effects of a commercial curcumin extract in two different models: (1) a single dose 2 h before an aerobic exercise and (2) a double supplementation, 90 mg 2 h before exercise and 90 mg immediately after the same test. The single and double supplementation of curcumin could attenuate serum concentrations of derivatives of reactive oxygen metabolites (d-ROMS) and plasma thioredoxin-1 (TRX-1) and could improve the biological antioxidant potential (BAP) after exercise. Single supplementation increased the reduced glutathione (GSH) concentration after exercise. Taken together, these findings suggest that curcumin supplementation may attenuate exercise-induced oxidative stress markers by increasing antioxidant capacity [86]. A higher dose of curcumin in capsules (5 g/day) was also used by untrained men undergoing a training session that induced muscle damage. Capsules were consumed 2.5 days before and 2.5 days after exercise. Curcumin supplementation led to a decrease in DOMS assessed by VAS 24 h and 48 h after exercise. The IL-6 concentrations, which could contribute to muscle pain, had a peculiar course; they increased immediately and 48 h after exercise, but they were lower in the 24 h after the test in the supplementation group. CK activity was also lower 24 and 48 h after exercise in the curcumin group, suggesting that supplementation may attenuate muscle damage [87]. However, these findings on curcumin and the clinical trials seem to be inconclusive due to the discrepancy of studies related to its low availability and chemical instability. Apparently, higher doses of supplementation allow better effects related to polyphenol activity [121]. This low availability is due, for example, to the fact that curcumin is lipophilic, and when administered without a specific active ingredient, its absorption and, consequently, the results found may be compromised. Interestingly, in the study by Mallard et al. [85], the authors paid attention to this detail and offered curcumin extract supplementation together with 50 mg of the delivery system, LipiSperse, an active lipophilic ingredient. Quercetin is a flavonol found in foods such as apples, onions, peppers, kale, pears, and spinach, among others. Quercetin can help the body with antioxidant and immunoprotective properties, modulating antioxidant responses as well as the production of cytokines [88,89,91]. Thus, in conjunction with current work, quercetin supplementation is considered a strategy to modulate the immune response of athletes by the International Olympic Committee (IOC) consensus statement [122] and can be an additional strategy to improve muscle recovery. One of the recent studies that investigated the effects of quercetin supplementation on muscle recovery was the study by Bazzucchi et al. [89], where the authors offered 1 g of quercetin in capsules (or a placebo) for 14 days to physically active adult men and induced muscle damage via a resistance exercise bout. The authors identified decreases in CK and LDH in the quercetin group compared to the placebo immediately after and 72 h after rest [89]. Using the same supplementation and exercise protocol, now in untrained men, the same authors also identified, in 2020, that the decrease in CK and LDH seemed to remain lower for a longer period (~96 h post-exercise) (see Table 1) [90]. Furthermore, more recent studies have shown that the same amount of quercetin mentioned above, for the same period and using the same exercise protocol, seems to modulate cell injury markers for a longer period. The CK concentrations are lower in the quercetin group 72 h and 96 h post-exercise in comparison to the placebo group. LDH levels are lower in the quercetin group 48 h, 72 h, 96 h, and 7 days post exercise. The quercetin supplementation also modulates the inflammation generated by physical exercise, demonstrated by the decrease in IL-6 concentrations 48 h and 72 h post-exercise (see Table 1). All these findings indicate interesting responses to quercetin supplementation and suggest that quercetin may be used to promote a fast recovery after eccentric exercise [91]. Quercetin supplementation is also studied because its possible antioxidant effect can modulate the increase in ROS production and, therefore, prevent damage to lipid membranes, as well as protein disruption and DNA changes [123]. This antioxidant effect of quercetin supplementation was investigated by Duranti et al. [88]. In a double-blind, placebo-controlled, randomized crossover design, the authors used 1 g/d of quercetin supplementation or a placebo for 14 days in adult male volunteers, who performed maximal lengthening contractions of the upper limb at the isokinetic dynamometer following supplementation. Blood samples were collected immediately before the supplementation (0 wk), after the quercetin/placebo supplementation (2 wks), and immediately after exercise (2 wks post). In comparison with the placebo after exercise, quercetin supplementation improved the ratio of reduced/oxidized glutathione (GSH/GSSG) and decreased thiobarbituric acid (TBARS) levels, both in erythrocytes and plasma, suggesting that the chronic use of quercetin may improve redox status after a single bout of exercise [88]. On the other hand, other studies do not seem to generate scientific support of the real capacity of quercetin supplementation to beneficially modulate cell injury responses, as well as immunomodulatory responses. Nieman et al. [124] found no significant differences for IL-8 and IL-10 with 1 g of quercetin supplementation for 24 days on 40 trained male cyclists after cycling for 3 h at a 57% maximal work rate (in Watts). Additionally, O’Fallon et al. [125] demonstrated that eccentric exercise of the elbow flexors induces a small increase in plasma IL-6 ~8 h after exercise that returns to the baseline by 24 h post-exercise and that no significant differences were identified for CK, IL-6, and C-reactive protein (CRP) after supplementation with 1 g of quercetin for 7 days in 30 recreationally active subjects (15 women and 15 men). It still seems quite complex to understand the real role of quercetin supplementation in physical exercise with the aim of modulating muscle recovery. According to studies, for immunomodulatory and antioxidant responses to occur, quercetin supplementation needs a more chronic strategy (>2 weeks) and should preferably be used in conjunction with other FBCs concomitantly [126,127]. In this sense, studies that explore the use of various FBCs such as cocoa, quercetin, and curcumin, among others, may bring better responses related to the antioxidant and anti-inflammatory modulatory capacity in practitioners of physical exercise and athletes [128,129]. Resveratrol is a stilbene often found in grapes, both internally and externally, and has been studied over the years as an interesting strategy for FBCs that brings health benefits to human bodies. Studies point out that the properties of resveratrol can help modulate chronic non-transmissible diseases by improving insulin sensitivity, altering the intestinal microbiota, as well as acting in other functions, such as modulating oxidative stress, inflammation, neurodegeneration processes, and several others [128,129]. Some recent works have investigated the use of resveratrol, mainly through the ingestion of grape juice, on the effects of physical exercise. Martins et al. [94] identified that 400 mL/day of grape juice for 14 days in 12 male volleyball players could help reduce lipid peroxidation and damage caused to DNA compared to a physical exercise session capable of inducing cellular damage to the muscles (see Table 1). Similar responses were found in another study, with 20 young Judo athletes, who consumed the same 400 mL/day of grape juice for 14 days; the athletes performed a Kimono Grip Strength Test (maximum number of repetitions while holding the judogi), and after the tests, lower lipid peroxidation, lower DNA damage, and lower activity of the enzyme superoxide dismutase (SOD) were identified for the group that consumed grape juice (see Table 1) [93]. Interestingly, the works cited above indicate a potential antioxidant effect of resveratrol ingestion through grape juice, which may contribute to the prevention of oxidation of lipids, proteins, and DNA (demonstrated through some blood indicators, such as malondialdehyde (MDA) and carbonyls). However, it is important to emphasize that this beneficial antioxidant effect of resveratrol on physical exercise is still much discussed and controversial since the works also indicate harmful effects of resveratrol intake, since this load of antioxidants can impair the adaptations that physical exercise of long duration or high intensity is capable of generating in the organism, for example, in the production of enzymatic antioxidants (catalase, superoxide dismutase, glutathione peroxidase). This impairment in adaptations was identified in the same work by Goulart et al. [93]; the authors found a lower catalase enzyme activity after the ingestion of 400 mL of grape juice. Previously, Scribbans et al. [92] also identified a lower SOD2 gene transcription in 16 recreationally active men who consumed 150 mg of resveratrol supplementation for 28 days (see Table 1). In addition, it is important to emphasize that, in grape juice, we also have the presence of phenolic acids, proanthocyanidins, anthocyanins, and flavonols, in addition to resveratrol, bringing a future discussion about studies that do not cite these compounds and their relevance in the antioxidant or anti-inflammatory system [55]. Still, several other studies have not found significant differences with resveratrol intake, indicating that the path to nutritional prescription is still quite controversial and uncertain [130,131,132]. It is classic information that exercise can result in microlesions in the exercised muscle. These injuries are the basis for post-exercise inflammation and muscle recovery, including the activation of satellite cells and the emergence of new myotubules and new cells. In this context, the muscles are more adapted to exercise. Despite the importance of post-exercise inflammation, a high level of microdamage during exercise can be detrimental to performance, as it reflects worsening muscle function. Furthermore, a more intense and prolonged inflammatory process can partially delay the complete process of muscle regeneration. Therefore, strategies that can attenuate the injury induced by exercise and the post-exercise inflammatory process can be beneficial to accelerate recovery and help adapt to exercise. In this context, polyphenols can have a significant nutritional impact thanks to their possible immunometabolic actions. However, discussions about the impact of polyphenols on muscle injury and post-exercise recovery are still incipient. Due to the limited number of studies, the range of studies that used an acute exercise session to induce muscle damage and studies in which participants underwent a training period was opened. Future research should clearly distinguish effects on performance such as time trial tests or on muscle recovery, including measures of muscle regeneration and the action of inflammatory cells involved in muscle regeneration. However, this narrative review sheds light on the role of FBCs, especially polyphenols, in post-exercise oxidative stress, injury, and inflammation. Several consulted studies show that supplementation with cocoa, anthocyanins, and quercetin can increase total antioxidant capacity and attenuate markers of injury, oxidative stress, and cytokines such as IL-6. Studies with green tea and resveratrol supplementation showed an increase in total antioxidant capacity, decreased CK and oxidative stress markers, increased total antioxidant capacity, and reduced MDA, but it could not alter cytokines. Curcumin mitigated the increase in CK and LDH and reduced TNF-α. On the other hand, there are intriguing results and questions to be answered. For example, resveratrol supplementation reduced SOD and lipid peroxidation. However, no studies showed the impact on markers of cell damage and inflammation, despite the power that ROS has to promote the damage and modulation of inflammatory pathways, such as those regulated by NFKB. Future research should point to the role of supplementation of different types of polyphenols together and isolated since the results are impacted if the FBC supplementation is isolated (as observed in curcumin, quercetin, and resveratrol) or considering the synergy between all the FBC present in the food (as observed in cocoa, green tea, grape juice, or the antioxidant juices used in the studies discussed in the anthocyanins section). Considering the existing variety of polyphenols, as well as their different functions, it is known that other compounds from this class not addressed in this review can affect muscle recovery [35]. Future studies could be conducted in this sense to contribute to a broad knowledge of this class of FBC in the muscle recovery process. Some phenolic acids may have an anti-inflammatory role and consequently play a role in post-exercise inflammation and muscle recovery, including the modulatory role in intestinal microbiota and the regulation of the immune system. The relationship of the intestinal microbiota and immune system cells is based on cell regulation mediated by short-chain fatty acids and metabolites produced by the microbiota, such as butyrate. Consequently, the pro/anti-inflammatory balance may undergo adjustments based on the production of cytokines by cells in the intestinal tissue. Furthermore, phenolic acids could contribute to maintaining the integrity of the barrier function and lower LPS extravasation into the bloodstream after exhaustive exercise and recovery. A scheme with the possible impact of joint supplements is shown in Figure 4. Collective action may more efficiently reduce oxidative stress and consequent cell damage caused by exercise. The consequence, in this case, would be less disruption of the pro/anti-inflammatory balance and less post-exercise inflammation. The benefits discussed here do not consider the existing divergences in the literature. Some contradictions are inherent in the few studies carried out so far. Methodological limitations, such as supplementation time, doses used, forms of supplementation, different exercise protocols, and collection times, create barriers to knowledge consolidation and must be overcome.

PMC10001085

Albano Toska,Nikita Modi,Lin-Feng Chen

RUNX3 Meets the Ubiquitin-Proteasome System in Cancer

24-02-2023

E3 ligase,proteasomal degradation,RUNX3,tumor suppressor,ubiquitination

RUNX3 is a transcription factor with regulatory roles in cell proliferation and development. While largely characterized as a tumor suppressor, RUNX3 can also be oncogenic in certain cancers. Many factors account for the tumor suppressor function of RUNX3, which is reflected by its ability to suppress cancer cell proliferation after expression-restoration, and its inactivation in cancer cells. Ubiquitination and proteasomal degradation represent a major mechanism for the inactivation of RUNX3 and the suppression of cancer cell proliferation. On the one hand, RUNX3 has been shown to facilitate the ubiquitination and proteasomal degradation of oncogenic proteins. On the other hand, RUNX3 can be inactivated through the ubiquitin–proteasome system. This review encapsulates two facets of RUNX3 in cancer: how RUNX3 suppresses cell proliferation by facilitating the ubiquitination and proteasomal degradation of oncogenic proteins, and how RUNX3 is degraded itself through interacting RNA-, protein-, and pathogen-mediated ubiquitination and proteasomal degradation.

RUNX3 Meets the Ubiquitin-Proteasome System in Cancer RUNX3 is a transcription factor with regulatory roles in cell proliferation and development. While largely characterized as a tumor suppressor, RUNX3 can also be oncogenic in certain cancers. Many factors account for the tumor suppressor function of RUNX3, which is reflected by its ability to suppress cancer cell proliferation after expression-restoration, and its inactivation in cancer cells. Ubiquitination and proteasomal degradation represent a major mechanism for the inactivation of RUNX3 and the suppression of cancer cell proliferation. On the one hand, RUNX3 has been shown to facilitate the ubiquitination and proteasomal degradation of oncogenic proteins. On the other hand, RUNX3 can be inactivated through the ubiquitin–proteasome system. This review encapsulates two facets of RUNX3 in cancer: how RUNX3 suppresses cell proliferation by facilitating the ubiquitination and proteasomal degradation of oncogenic proteins, and how RUNX3 is degraded itself through interacting RNA-, protein-, and pathogen-mediated ubiquitination and proteasomal degradation. Runt-related transcription factor 3 (RUNX3) is a member of the Runt domain family of nuclear transcriptional regulators in diverse biological processes, including development, cell proliferation and differentiation, senescence, DNA repair, and inflammation [1,2]. RUNX3, also called PEBP2αC, CBFA3 and AML2, contains an evolutionarily conserved Runt DNA-binding domain and a C-terminal transactivating domain [3,4,5]. RUNX3 is predominantly characterized as a tumor suppressor and is frequently inactivated in many cancers, including gastric, lung, and breast cancer [6]. However, the role of RUNX3 in cancer is more nuanced and context-dependent [7], and there is emerging evidence of the oncogenic capacity of RUNX3 in various cancers [6,8,9,10,11]. The tumor suppressor function of RUNX3 is highlighted by its transcriptional silencing from hemizygous deletion of the Runx3 gene or promoter hypermethylation [12]. In addition, post-translational modifications play an important role in RUNX3 inactivation. RUNX3 is subject to various post-translational modifications, including acetylation, phosphorylation, methylation, and SUMOylation, that converge on the ubiquitin–proteasome system (UPS) to regulate the stability and activity of RUNX3 [13,14,15,16]. At the same time, RUNX3 exerts its tumor suppressor function by targeting oncogenic proteins for degradation via the UPS [17]. The UPS contains various components responsible for two distinct and successive steps: ubiquitination and proteasomal degradation. Ubiquitin is a small, evolutionarily conserved protein, of which the C-terminal glycine is conjugated via an isopeptide bond to distinct lysine residues on target proteins, to signal for further modification, transport, or destruction [18]. This process is usually carried out by three different enzymes, including ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2), and ubiquitin-ligating enzyme (E3), respectively. The UPS represents one fate of poly- and sometimes monoubiquitination, whereby ubiquitinated proteins are marked for degradation in the 26S proteasome [18]. Because aberrant UPS activation is implicated in cancer development [19], establishing the interactions between ubiquitination and RUNX3 in cancer could further explain the ambivalent nature of RUNX3 in carcinogenesis. In this review, we explore two prominent interactions between RUNX3 and the UPS. First, we examine the role of RUNX3 in mediating the ubiquitination and proteasomal degradation of oncogenic proteins in cancer. Given the nature of proteins targeted by RUNX3-mediated degradation in the literature, the subsequent agency of RUNX3 as tumor suppressive is established in a cancer-specific manner. Next, we examine the cellular mechanisms and pathways that facilitate ubiquitination and proteasomal degradation of RUNX3 in cancer. We cover RNA-, protein-, and pathogen-mediated RUNX3 ubiquitination, as well as various post-translational modifications of RUNX3 that may affect its degradation in the UPS. Together, these perspectives provide a comprehensive look at RUNX3–UPS interactions, from which to gain new insights into cancer physiology. Estrogen receptor α (ERα) is a transcription factor chiefly expressed in cells of reproductive tissue and is activated by its ligand, estrogen (E2), to promote cellular proliferation [20]. Abnormal estrogen signaling through Erα is associated with initiation and progression of breast cancer [21]. Overexpressed Erα is found in nearly two-thirds of breast cancers, in part due to increased protein stability [22]. As such, understanding the tight control of Erα levels would provide unique insights for the treatment approach to Erα+ breast cancer. RUNX3 functions as a tumor suppressor in breast cancer and is frequently inactivated by promoter hypermethylation and protein mislocalization, and expression of RUNX3 has been suggested as a promising prognostic biomarker in breast cancer patients [17,23]. Importantly, increased levels of RUNX3 promoter hypermethylation have been directly linked to increased Erα protein expression in Erα+ breast cancer [24], implying a potential role of RUNX3 in downregulating Erα protein levels. Expression of RUNX3 is inversely correlated with Erα expression in breast cancer cells and human breast cancer samples (25). Consistently, about 20% of female Runx3+/− mice spontaneously developed ductal carcinoma, with an enhanced expression of ERα and proliferation marker Ki-67 [25]. The inverse correlation between RUNX3 and ERα expression is partially due to the ability of RUNX3 to destabilize ERα through UPS recruitment [25]. RUNX3 directly interacts with ERα, and restoration of RUNX3 in MCF-7 breast cancer cells triggers the ubiquitination and degradation of ERα [25]. However, the detailed mechanism remains uncharacterized. RUNX3 binds to the hinge region of ERα, which is heavily post-translationally modified and crucial in stabilizing ERα [17]. It has been suggested that the binding of RUNX3 to the hinge domain alters its post-translational modifications, thus changing its stability [17]. It is also possible that RUNX3 facilitates the recruitment of an E3 ligase for ERα. For example, the E3 ligase, MDM2, has been shown to ubiquitinate both ERα and RUNX3 for degradation [26,27]. The binding of RUNX3 to Erα could facilitate MDM2 recruitment for Erα ubiquitination and proteasomal degradation. Hedgehog (Hh) is a conserved signaling pathway implicated in embryonic development and deregulated in certain cancers [28]. Central targets of the Hh signaling cascade are Patched receptor proteins (Ptch1 and Ptch2) and glioma-associated oncogene (Gli) transcription factors, which include GLI1, an integral transcriptional activator of downstream Hh genes [29]. A study in colorectal cancer demonstrated that increased RUNX3 protein expression resulted in both increased ubiquitination and proteasomal degradation of the oncogene GLI1, which is enhanced in the presence of suppressor of fused (SUFU), a negative regulator of GLI1 [30]. After establishing RUNX3–GLI1 interactions, it was discovered that RUNX3 recruits SKP1-CUL1-F-box (SCF), an E3 ligase super-assembly evolved to ubiquitinate F-box protein-bound substrates [31]. Specifically, the F-box protein β-TrCP was shown to ubiquitinate GLI1 [30], suggesting that the RUNX3–GLI1–SUFU trimeric complex recruits the SCFβ-TrCP complex to ubiquitinate GLI1 for proteasomal degradation. MYCN (N-Myc; MYCN hereafter) is a protein from a family of regulatory proto-oncogenes that include MYC (c-Myc) and MYCL1 (L-Myc) [32]. MYC family proteins regulate expression levels of nearly 15% of human genes, including those facilitating cell cycle, differentiation, and apoptosis [33]. MYCN is commonly, but not exclusively, overexpressed in neural-origin cancers, such as neuroblastomas [34]. MYCN mRNA expression has been found to increase with later stages of neuroblastoma in patient samples, while RUNX3 expression, comparatively, shows a decrease. Higher RUNX3 expression in these samples indicated a higher survival probability [35]. RUNX3 was shown not only to bind to MYCN, but also facilitate its ubiquitination and proteasomal degradation. MYCN ubiquitination increased in a dose-dependent fashion with increasing RUNX3 protein levels, suggesting that RUNX3–MYCN interaction facilitates proteasomal degradation of MYCN [35]. No E3 ligase has been directly implicated in RUNX3-mediated ubiquitination of MYCN, which is known to be ubiquitinated for proteasomal degradation by SCFFbw7 [36]. In neuroblastoma, MYCN maintains stability via binding to Aurora kinase A (AURKA), which inhibits association of Fbw7 to ubiquitinate MYCN [37]. Fbw7 is a well-characterized tumor suppressor responsible for targeting many oncoproteins, such as c-MYC, Cyclin E, and mTOR, for proteasomal degradation, and it is the most commonly deregulated E3 ligase in cancer [38]. It remains to be determined whether RUNX3 similarly recruits an E3 ligase to Fbw7, to ubiquitinate MYCN. Hypoxia-inducible factor 1 (HIF-1) is a transcription factor upregulated in hypoxic conditions that regulates genes for cell survivability functions, such as angiogenesis and metabolism, by binding to the hypoxia response element (HRE) on the promoters or enhancers of its target genes [39]. Cancer cells, because of their high rate of proliferation, often have increased expression of HIF-1, due to the lack of oxygen in the tumor microenvironment (TME) from poor tumor vascularity [40]. In the presence of O2, the alpha subunit of HIF-1 (HIF-1α) is subject to asparaginyl hydroxylation in its C-terminal transactivation domain (CTAD) by factor-inhibiting HIF (FIH) and prolyl hydroxylation by prolyl hydroxylases (PHDs) [41,42] These post-translational modifications both downregulate the transcriptional activity of HIF-1α, and facilitate its proteasomal degradation by binding to the E3 ligase von Hippel-Lindau (pVHL; hereafter VHL) [42]. Hydroxylation of Proline 564 by PHDs in the CTAD of HIF-1α is a key recognition marker for VHL to promote its ubiquitination of HIF-1α for proteasomal degradation [42]. In gastric carcinoma, RUNX3 binds to HIF-1α and PHD2, resulting in the hydroxylation of HIF-1α by PHD2, and subsequent ubiquitination of HIF-1α by VHL [43]. RUNX3 binds to PHD2, which hydroxylates proline 564 and proline 402 of HIF-1α. At the same time, RUNX3 binds to the CTAD of HIF-1α in the nucleus and facilitates the export of HIF-1α into the cytoplasm, where hydroxylated HIF-1α is recognized by VHL for ubiquitination [43]. Through this mechanism, RUNX3 may work to maintain normoxic cellular functions by facilitating O2-dependent hydroxylation of HIF-1α by PHD2, and subsequent UPS degradation by VHL. Recently, overexpression of miR-290 in human lung adenocarcinoma was linked to increased HIF-1α and decreased RUNX3 and PHD2 protein levels [44]. Intriguingly, HIF-1α also binds to the promoter region of miR-290 and regulates its expression, raising a possibility that there is a positive feedback loop between HIF-1α and miR-290 that might inhibit RUNX3-medated degradation of HIF-1α [43,44]. It is also interesting to note that under hypoxia, RUNX3 is silenced by G9a-mediated histone methylation and HDAC-mediated histone deacetylation on the promoters of Runx3 in gastric cancer cells [45]. LncRNAs, generally 200 or more base pairs in length, regulate gene expression as epigenetic and translational regulators by their influence on chromatin state or by acting as a sponge for miRNA inhibition [46,47]. In addition, a large number of lncRNAs exert their oncogenic function by affecting protein stability through direct interaction with proteins or protein complexes [47]. For example, HOX antisense intergenic RNA (HOTAIR) is a lncRNA with oncogenic properties in a variety of cancers. HOTAIR could serve as a scaffold for the Mex3b RNA-binding protein (RBP), an E3 ligase, to optimize the ubiquitination and degradation of target proteins [48]. It has been shown that HOTAIR interacts with RUNX3 via a fragment of HOTAIR spanning 1951–2100 bp and decreases the expression of RUNX3 [49]. Mechanistically, HOTAIR complexes with Mex3b to ubiquitinate and degrade RUNX3 in gastric cancer cells, leading to increased cancer invasiveness [49]. RUNX3, as a transcriptional activator of tight-junction protein Claudin1, can prevent the epithelial–mesenchymal transition (EMT) [50]. HOTAIR, through promoting Mex3b-dependent ubiquitination and degradation of RUNX3, suppresses Claudin1 expression to foster gastric cancer cell invasion and EMT [49]. In this regard, HOTAIR/Mex3b promotes gastric cancer tumorigenesis by inhibiting the RUNX3–Claudin1 tumor suppressive axis. Recently, the Homeobox D gene cluster antisense growth-associated lncRNA (HAGLR) has been demonstrated to destabilize RUNX3 in Treg cells and regulate Treg cell differentiation [51]. Expression levels of HAGLR are directly correlated with ubiquitination of RUNX3 and inversely correlated with RUNX3 protein stability [51]. Considering that increased expression of HAGLR is linked to the progression of colon, hepatocellular, and triple negative breast cancer, where RUNX3 is often downregulated [52,53,54], it would be of great interest to determine whether a direct interaction between HAGLR and RUNX3 exists and whether HAGLR could serve as scaffold for an E3 ligase, similar to HOTAIR/Mex3b, to degrade RUNX3 via the UPS in cancer cells. PIN1 is a peptidyl-prolyl cis-trans isomerase (PPIase), and specifically recognizes phosphoserine or phosphothreonine residues preceding proline (pSer/Thr–Pro) motifs, and induces protein conformational changes by isomerization [55]. After binding to the pSer/Thr–Pro motif on a target protein via its N-terminal WW domain, PIN1 catalyzes cis/trans isomerization of the peptide bond via its C-terminal PPIase domain [55]. PIN1 is overexpressed in cancer and regulates numerous cancer-driving pathways by controlling the stability of oncogenes and tumor suppressors [55]. RUNX3 is one of the tumor suppressors of which the stability is regulated by PIN1 (59). The PIN1 WW domain binds to four pSer/Thr-Pro residues (T209, T212, T231 and S214) on RUNX3, resulting in the ubiquitination and degradation of RUNX3 in breast cancer [56]. The four PIN1 binding motifs are located immediately C-terminal of the Runt domain, which has been shown to be important for RUNX3 stability [57]. The binding of PIN1 to these phosphorylated motifs might induce the cis-trans isomerization and isomerization-mediated conformational change of RUNX3, leading to an increased accessibility of a RUNX3 E3 ligase. Further investigation is warranted to determine the E3 ligase involved in the PIN1-mediated degradation of RUNX3. PIN1 downregulates Smad2/3 proteins in the TGF-β pathway, in part through recruitment of the Smurf2 E3 ligase, to degrade these proteins [58]. RUNX3, also a target of Smurf2 in the UPS, acts synergistically with Smad proteins to activate downstream genes in the TGF-β pathway [57,59]. It has been speculated that the ubiquitination and degradation of RUNX3 by PIN1 may be carried out by Smurf E3 ligases, which also contain WW domains and degrade RUNX3 [56,57]. Furthermore, some of the known oncoprotein targets of RUNX3-mediated ubiquitination and degradation, including HIF-1α, ERα, and GLI1, are PIN1 substrates [55]. However, it remains unclear whether PIN1 is a factor of their regulation. It is possible that PIN1 might be directly or indirectly involved in the tumor suppressor function of RUNX3, and the regulation could be cell-type- or cancer-type-specific. c-Jun activation domain-binding protein-1 (JAB1), also known as subunit 5 of the COP9 signalosome (CSN), is a multifunctional protein that modulates signal transduction, gene transcription, and protein stability in cells [60]. Jab1/CSN5 is overexpressed in different types of cancer, and its overexpression has been implicated in the initiation and progression of many cancers [61]. Jab1/CSN5 could regulate cell proliferation by promoting the nuclear export and the degradation of several tumor suppressor proteins, including p53, p27kip1 and Smad4 [61]. RUNX3 is another tumor suppressor of which the activity is regulated by JAB1. JAB1 has been shown to facilitate CSN-mediated proteasomal degradation of RUNX3 in gastric cancer cells [62]. This process is accomplished by two integral interactions: the Mpr1/Pad1 N-terminal (MPN) domain of JAB1 binds to the Runt domain of RUNX3, while the nuclear export signal (NES) of JAB1 recruits the exportin, CRM1, to facilitate the export of RUNX3-JAB1 into the cytoplasm. Subsequently, the JAB1–RUNX3 complex is recruited to CSN, where the CSN-associated kinase, CK2α, phosphorylates RUNX3 and triggers phosphorylation-dependent proteasomal degradation of RUNX3 [62]. The CSN complex generally functions to reverse neddylation (rubylation) by removing NEDD8 (RUB1) from the cullin subunit of the cullin-RING-type E3 ligases (CRLs). This deneddylation stops the polyubiquitination process and allows the subsequent degradation of polyubiquitinated substrates by the proteasome [63]. JAB1 might utilize a similar mechanism for the polyubiquitination of RUNX3, but the relevant CRLs for RUNX3 remain to be determined. Further investigation may also determine whether JAB1 could directly regulate the neddylation and ubiquitination status of RUNX3 in the CSN for its proteasomal degradation. Helicobacter pylori is a Gram-negative bacterial pathogen that infects the gastric epithelium and causes gastritis in humans [64]. Infection by H. pylori presents the highest risk factor for the development of gastric cancer [64]. The carcinogenic nature of H. pylori is attributed to its virulence factors, notably cytotoxin-associated gene A (CagA) located in the cag pathogenicity island (cagPAI) [64,65]. H. pylori uses a type IV secretion system (T4SS), encoded by the cagPAI, to inject CagA directly into epithelial cells [64,65]. CagA contributes to oncogenesis by disrupting signaling pathways involved in cell shape and adhesion [64,65]. Infections with CagA+ H. pylori are linked both to increased inflammation and risk of gastric cancer [64,65]. H. pylori infection contributes to reduced RUNX3 expression by increasing Runx3 promoter methylation independent of CagA, or activating the oncogenic Ras GTPase by SRCK-phosphorylated CagA [12,65]. In addition to transcriptional level regulation, H. pylori could also regulate RUNX3 activity via protein stability. H. pylori CagA binds directly to RUNX3 and promotes its ubiquitination and proteasomal degradation in gastric cancer cells [66]. While CagA relies on two N-terminal WW domains (WW1-2) to degrade RUNX3, only WW2 is required for successful binding to RUNX3 on its PPxY (PY) motif [66]. The WW1 domain could recruit an E3 ligase for the ubiquitination of RUNX3 with CagA as a scaffold protein. While the E3 ligase involved in CagA-mediated ubiquitination and degradation of RUNX3 remains uncharacterized, several candidate E3 ligases emerge. Smurfs canonically ubiquitinate Smads in the TGF-β pathway for degradation, while CagA induces inflammation via binding and inactivating Smads [57,67]. Conversely, RUNX3 binds and stabilizes Smads to promote activation of TGF-β downstream genes [57]. An interplay between CagA, Smurf1/2, and RUNX3 could suggest that CagA recruits Smurfs to ubiquitinate RUNX3 in gastric cancer. Furthermore, CagA-activated AKT1 in gastric cancer can also phosphorylate HDM2 (MDM2) to ubiquitinate p53 for degradation [68,69]. In addition, complex formation of CagA and CD44 in gastric cancer induces AKT-dependent activation of the Wnt pathway, from which Wnt2 is an inhibitor of p14ARF [70]. Therefore, CagA–AKT signaling may induce both the activity of MDM2 and prevent its inactivation by p14ARF, an inhibitor of MDM2 [71]. Since RUNX3 can be shuttled into the cytoplasm and degraded by MDM2 [27], the CagA-mediated degradation of RUNX3 in the UPS may also occur through MDM2. Several post-translational modifications can regulate the stability of RUNX3. Acetylation is one such post-translational modification that has been demonstrated to promote the stability of RUNX3. The acetyltransferase p300 binds to RUNX3 and protects RUNX3 from ubiquitination-mediated degradation via acetylation [57]. RUNX3 is acetylated by p300 on three lysine residues, K148, K186, and K192 [57]. Of note, K148 is a known target of both MDM2-mediated ubiquitination and PIAS-1 mediated SUMOylation [16,27]. Conversely, histone deacetylase 1 (HDAC1) can remove these protective modifications and facilitate nuclear export and cytoplasmic degradation of RUNX3 [15,57]. The reversible acetylation of RUNX3 by p300 and HDAC1 may serve to maintain RUNX3 stability and transcriptional activity at optimal levels. In gastric cancer cells, the histone methyltransferase (HMT) G9a methylates lysines of the Runt domain of RUNX3 (K129 and K171) to promote nuclear export and cytoplasmic degradation [15]. G9a-mediated methylation of RUNX3 inhibits the RUNX3 transactivation activity by preventing its association with CBFβ/PEBP2β subunits and p300 [15]. Thus, RUNX3 can be destabilized via Runt domain lysine post-translational modification in two ways: reversal of p300 acetylation by HDAC1 [57] and methylation by G9a [15]. Besides acetylation and methylation, phosphorylation also mediates the stability and subcellular localization of RUNX3. Phosphorylation of JAB1-exported RUNX3 in the CSN by CSKα is a necessary step for JAB1-mediated degradation of RUNX3 [62]. While export of RUNX3 into the cytoplasm is well-described in JAB1-medated degradation of RUNX3 and RUNX3-mediated degradation of HIF-1α [43,62], the way RUNX3 is re-localized to the cytoplasm preceding UPS degradation is poorly understood. SRC family kinases (SRCKs) have been shown to bind to the Runt domain and phosphorylate tyrosine residues on RUNX3 [72]. Furthermore, phosphorylation of four Ser/Thr residues (S149, T151, T153, and T155) by the PIM1 kinase on the Runt domain of RUNX3 in cancer has also been demonstrated [73]. Phosphorylation of RUNX3 may be a prerequisite for the cytoplasmic re-localization of RUNX3 and the subsequent ubiquitination and proteasomal degradation in the cytoplasm. Additionally, phosphorylation may provide unique docking sites for the recruitment of proteins involved in the ubiquitination and degradation of RUNX3. For example, the phosphorylated PY motif of RUNX3 is recognized by PIN1 [56], CagA [66], and the E3 ligases, Smurf1/2 [57]. Similarly, CDK4-mediated phosphorylation of S356 could also provide a binding motif for some UPS proteins for the ubiquitination and degradation of RUNX3 [74]. This suggests that cancer cells may overexpress kinases to phosphorylate RUNX3 at PY motifs or other target residues that, after cytoplasmic localization, could be recognized by WW domain-containing E3 ligases, such as Smurf1/2, for ubiquitination and proteasomal degradation. SUMOylation by small ubiquitin-like modifiers (SUMOs) is a class of post-translational modification, with roles in altering protein activity, stability, and cellular localization. [75]. Much crosstalk exists between ubiquitination and SUMOylation, as SUMO ligases are related to RING domain-containing ubiquitin ligases and may even share substrates [76]. The SUMO E3 ligase, PIAS1, has been shown to bind to the Runt domain and SUMOylate K148 of RUNX3, leading to the decreased transcriptional activity of RUNX3 [16]. As K94/K148 residues are also targets for MDM2-mediated degradation of RUNX3, SUMOylation may prevent the binding and degradation of RUNX3 by MDM2 [27]. However, whether the reduced activity of RUNX3 is related to altered protein stability is still unclear. Taken together, RUNX3 stability appears to depend upon several post-translational modifications that can either promote RUNX3 stability or destabilize it for cytoplasmic export and degradation by the UPS. It remains an interesting topic whether these different modifications occur sequentially or antagonistically to regulate the stability and tumor suppressor function of RUNX3. RUNX3 has been shown to be a prognostic biomarker for a variety of cancers, including breast, gastric, colorectal, and ovarian cancer, and there is a negative correlation of RUNX3 inactivation and patient survival [23,77,78]. Loss of RUNX3 expression in patients, either through hypermethylation or cytoplasmic mislocalization, correlates with poor outcomes [79,80,81,82]. RUNX3 has been suggested as a potential therapeutic target for certain cancers, since restoration of RUNX3 expression by reactivation of Runx3 transcription or by overexpression, suppresses the proliferation of cancer cells [25,83,84]. For example, treatment of cancer cells with small molecules targeting HDACs or DNMTs can restore the expression of RUNX3 (79,80). However, therapeutic reactivation of RUNX3 expression through epigenetic means may be insufficient to sustain RUNX3 expression as cancer progresses, as many UPS components, including E3 ligases such as MDM2 and Smurfs, are hyperactivated in cancer cells. Thus, targeting the UPS may be effective as an adjuvant therapy for alleviating RUNX3 destabilization in cancer. Small molecules targeting various subunits of the 26S proteasome, such as bortezomib and carfilzomib, are already in clinical use to treat multiple myeloma (MM). These proteasome inhibitors are thought to shift the cellular protein equilibrium in favor of tumor suppressive and pro-apoptotic protein stability, resulting in the reduction of cancer cell proliferation [85]. Proteasomal inhibition may rescue RUNX3 from degradation, but it may also rescue oncoproteins targeted for RUNX3-mediated UPS degradation. Since the proteasome regulates the fast turnover of both p53 and p27kip1 [86], increased p53 stability from proteasomal inhibition may also lead to increased RUNX3 stability through p53-mediated inhibition of MDM2 [87]. High p53 levels are correlated with increased expression of p300, which facilitates acetylation-dependent stabilization of both p53 and RUNX3 [57,88]. Inhibitors of MDM2 and MDM2-p53 binding are in active development [89]. The therapeutic potential of maintaining MDM2-p53 homeostasis may be bifold in sustaining RUNX3 stability. In addition to blocking MDM2-mediated degradation of RUNX3, the higher resulting levels of p53 may also sustain RUNX3 in a tumor suppressive state, as p53 dysregulation is likely a switch for RUNX3 to become oncogenic [10,11]. JAB1/CSN5 inhibition may present another approach for targeting the UPS to restore RUNX3 protein expression. Several reports show a correlation between poor prognosis and an increase in JAB1/CSN5 in cancers such as pancreatic cancer, oral squamous cell cancer, and breast cancer [90,91,92]. Targeting JAB1 and the CSN may increase RUNX3 expression by preventing mislocalization and degradation of RUNX3. Recently, the NEDD8-activating enzyme (NAE) inhibitor, MLN4924, caused inactivation of CRLs and suppression of renal cell carcinoma proliferation in vitro [93]. CRL inactivation through NAE inhibitors could provide a treatment avenue for inhibiting CSN-mediated degradation of RUNX3. However, CRLs are also present in SCF complex E3 ligases, which are recruited by RUNX3 to degrade oncoproteins GLI1, and likely MYCN [30,35], so potential cancer treatments may require a more personalized approach. To achieve the best outcome in cancer therapy, a combination of small molecules targeting both epigenetic pathways and the UPS may be ideal to maintain the optimal levels of RUNX3 to sustain its tumor suppressor function. As mentioned above, post-translational modifications of RUNX3 also play a critical role in the stability of RUNX3. Directly targeting the modifications or indirectly targeting the enzymes could also affect the stability of RUNX3 with therapeutic outcomes. For example, a recent study demonstrated that a small peptide, RMR, inhibits phosphorylation of RUNX3 at T209 by PAK1, effectively suppressing cancer cell proliferation and cancer formation [94]. Since pT209 is one of the sites recognized by PIN1 for the degradation of RUNX3 [56], it would be interesting to evaluate whether the therapeutic effect of the peptides would be partially due to the prevention of PIN1-mediated degradation of RUNX3. In a different study, G9a inhibitor, UNC0638, was found to increase apoptosis in MYCN-amplified neuroblastoma cells [85]. Given that G9a is responsible for methylating and destabilizing RUNX3 [15,45], and that RUNX3 mediates UPS degradation of MYCN [35], the anticancer properties of UNC0638 may be in part due to the increasing stability of RUNX3 for MYCN degradation. RUNX3, a transcription factor with tumor suppressor activity, regulates multiple cellular responses, including transcription, signal transduction and cell proliferation [1,2]. The UPS is also well known to play an important role in cancer development by regulating protein function, signal transduction, transcription and apoptosis via proteolysis [18,19]. RUNX3 utilizes the UPS to exert its tumor suppressor function by inducing the degradation of oncogenic proteins (Figure 1). Additionally, RUNX3 could be inactivated via proteasomal degradation by the UPS in cancer cells (Table 1). While there is a clear interplay between RUNX3 and the UPS in cancer, which factors or signals determine whether RUNX3 is a UPS target or an activator of UPS to target other proteins is still unknown. Nevertheless, this switch likely occurs at the early stage of cancer development, when cells express normal levels of RUNX3 to target oncogenic proteins [17]. As cancer progresses, the UPS is activated with increased activity or accessibility to RUNX3, leading to the degradation and removal of RUNX3 (Figure 2). During this process, post-translational modifications of RUNX3 might be a key factor to switch RUNX3 from a UPS activator to a UPS target, as many enzymes modifying RUNX3 are overexpressed or hyperactive in cancer. Inactivation of RUNX3 by the UPS is likely an important step prior to the permanent epigenetic silencing of RUNX3 (Figure 2). Ubiquitination and proteasomal degradation of RUNX3 represent one of the mechanisms for the inactivation of RUNX3 in the early stages of cancer development; however, the identities of a majority of these RUNX3 E3 ligases are unknown (Table 1). There are three major types of E3 ligases: the RING (really interesting new gene) family, the HECT (homologous to the E6-AP carboxyl terminus) family, and the RBR (ring between ring fingers) family [95]. E3 ligases from the RING and HECT family, such as MDM2 and Smurf1/2, have been reported to ubiquitinate RUNX3 [27,57]. More efforts are needed to identify these RUNX3 E3 ligases. Since many E3 ligases are dysregulated in cancer with altered activity and expression level [96], an inverse expression correlation between RUNX3 E3 ligases and RUNX3 itself might exist in certain cancers. Due to the differential expression patterns of E3 ligases in cancers [96], it is likely that E3 ligases involved in the ubiquitination of RUNX3 varies in different cancers. RUNX3 undergoes a variety of post-translational modifications, including phosphorylation, acetylation and ubiquitination, and these different modifications contribute to regulating the stability of RUNX3 to some extent, as discussed above. Importantly, post-translational modifications also play a key role in RUNX3-mediated ubiquitination and degradation of oncogenic proteins and the activation of certain E3 ligases [96]. Since many of these post-translational modifications are catalyzed by similar enzymes, it remains an important question to determine how the tumor suppressor function of RUNX3 is coordinately regulated by the same type of modification on RUNX3 itself, its targeted proteins, and the UPS. Crosstalk between different post-translational modifications could also be critical for this regulation. Targeted therapy and immunotherapy are two advanced strategies for cancer treatment. While restoration of RUNX3 is able to suppress proliferation of various cancer cells in vitro, selectively targeting RUNX3 for its reactivation in human cancer cells remains a great challenge. Furthermore, the UPS has also been considered a potential target for cancer immunotherapy. For example, MDM2 inhibitors have been shown to sensitize cancer cells to T-cell-mediated killing, or synergies with PD1 blockade in a mouse model of immunotherapy [97,98]. Considering that MDM2 is an E3 ligase for RUNX3, it would be of great interest and importance to investigate whether reactivation of RUNX3 in cancer cells would increase the efficacy of immunotherapy in the future.

PMC10001097

Jiahao Li,Lansi Chen,Jingjing Pang,Chunxiu Yang,Wen Xie,Guoyan Shen,Hongshan Chen,Xiaoyi Li,Shu-Yuan Xiao,Yueying Li

Autophagy-Related Gene WD Repeat Domain 45B Promotes Tumor Proliferation and Migration of Hepatocellular Carcinoma through the Akt/mTOR Signaling Pathway

27-02-2023

hepatocellular carcinoma,autophagy,prognosis,WDR45B,Akt/mTOR signaling pathway

Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor. It has been found that autophagy plays a role both as a tumor promoter and inhibitor in HCC carcinogenesis. However, the mechanism behind is still unveiled. This study aims to explore the functions and mechanism of the key autophagy-related proteins, to shed light on novel clinical diagnoses and treatment targets of HCC. Bioinformation analyses were performed by using data from public databases including TCGA, ICGC, and UCSC Xena. The upregulated autophagy-related gene WDR45B was identified and validated in human liver cell line LO2, human HCC cell line HepG2 and Huh-7. Immunohistochemical assay (IHC) was also performed on formalin-fixed paraffin-embedded (FFPE) tissues of 56 HCC patients from our pathology archives. By using qRT-PCR and Western blots we found that high expression of WDR45B influenced the Akt/mTOR signaling pathway. Autophagy marker LC3- II/LC3-I was downregulated, and p62/SQSTM1 was upregulated after knockdown of WDR45B. The effects of WDR45B knockdown on autophagy and Akt/mTOR signaling pathways can be reversed by the autophagy inducer rapamycin. Moreover, proliferation and migration of HCC can be inhibited after the knockdown of WDR45B through the CCK8 assay, wound-healing assay and Transwell cell migration and invasion assay. Therefore, WDR45B may become a novel biomarker for HCC prognosis assessment and potential target for molecular therapy.

Autophagy-Related Gene WD Repeat Domain 45B Promotes Tumor Proliferation and Migration of Hepatocellular Carcinoma through the Akt/mTOR Signaling Pathway Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor. It has been found that autophagy plays a role both as a tumor promoter and inhibitor in HCC carcinogenesis. However, the mechanism behind is still unveiled. This study aims to explore the functions and mechanism of the key autophagy-related proteins, to shed light on novel clinical diagnoses and treatment targets of HCC. Bioinformation analyses were performed by using data from public databases including TCGA, ICGC, and UCSC Xena. The upregulated autophagy-related gene WDR45B was identified and validated in human liver cell line LO2, human HCC cell line HepG2 and Huh-7. Immunohistochemical assay (IHC) was also performed on formalin-fixed paraffin-embedded (FFPE) tissues of 56 HCC patients from our pathology archives. By using qRT-PCR and Western blots we found that high expression of WDR45B influenced the Akt/mTOR signaling pathway. Autophagy marker LC3- II/LC3-I was downregulated, and p62/SQSTM1 was upregulated after knockdown of WDR45B. The effects of WDR45B knockdown on autophagy and Akt/mTOR signaling pathways can be reversed by the autophagy inducer rapamycin. Moreover, proliferation and migration of HCC can be inhibited after the knockdown of WDR45B through the CCK8 assay, wound-healing assay and Transwell cell migration and invasion assay. Therefore, WDR45B may become a novel biomarker for HCC prognosis assessment and potential target for molecular therapy. Liver cancer has been one of the most common and malignant tumors in the digestive system. Over the world, liver cancer ranked the 6th in the morbidity and the 3rd in the mortality of cancers, and the 5-year survival rate was only about 18% [1,2,3]. Hepatocellular carcinoma (HCC) accounts for more than 90% of primary liver cancer. It is mainly developed from chronic fibrous liver disease caused by hepatitis B virus (HBV) or hepatitis C virus (HCV) infection, alcohol consumption, and metabolic syndrome. In recent years, with the rising incidence of obesity, diabetes, and non-alcoholic fatty liver disease/non-alcoholic steatohepatitis, chronic hepatitis has been thought to be the major driver of HCC increased morbidity globally [4]. At present, conventional treatments for HCC principally involved surgical resection, radiofrequency ablation (RFA), percutaneous ethanol injection, trans-arterial chemoembolization, liver transplantation, chemotherapy, molecular targeted therapy, and immunotherapy. Unfortunately, HCC was usually diagnosed at an advanced stage and was prone to metastasis and recurrence, so the effectiveness of the conventional treatments was fairly limited. Even if receiving thorough surgical excision or RFA, the carcinogenic tissue microenvironment in the remnant liver can still cause the recurrence in 70% of patients within 5 years [5]. Sorafenib, the only first-line multi-target tyrosine kinase inhibitor for advanced HCC, can only increase the median overall survival of most advanced patients to around 1 year [6]. Moreover, drug resistance was also one of the reasons for poor prognosis in HCC patients, which always occurred in patients receiving anti-cancer drug treatments for over 6 months [7]. Therefore, it is of great significance to elucidate the underlying pathophysiological process of occurrence and development and explore the complex mechanism behind HCC metastasis, which might be helpful for the early diagnosis of HCC and identifying effective prognostic biomarkers. Autophagy, a highly conserved catabolic process, plays a critical part in the quality control of organelles or cellular proteins, adjustment of nutrient balance, and pathogen defense. Based on the difference in morphology and mechanism, autophagy can be categorized into 3 subtypes, namely macro-autophagy, micro-autophagy, and chaperone-mediated autophagy [8,9]. The “autophagy” is usually referred to as “macro-autophagy”. In the process of autophagy, cytoplasmic macromolecules, aggregative proteins, damaged organelles, and pathogens are delivered to lysosomes through autophagosomes, which are digested by lysosomal hydrolases to generate nucleotides, amino acids, fatty acids, glucose, and ATP, which are eventually recycled into the cytoplasmic matrix. Nucleation of autophagic separation membrane, formation of autophagosome, expansion, and extension of autophagosome membrane, docking and fusion with lysosome membrane, as well as degradation and recycling of products in vesicles are the key steps in autophagy, in which there are lots of autophagy-related genes (ATGs) involved [10]. ULK1 complex consisting of unc-51-like autophagy activating kinase 1 (ULK1), ATG13, AG101, FAK family interacting protein of 200 kDa (FIP200), can all induce the nucleation of autophagic separation membrane. VPS34, beclin1, and ATG14L assembled by AMBRA1 promote the formation of autophagosomes. Later, the microtubule-associated protein light chain 3 (LC3) is recruited to help the autophagosome elongation. Lastly, under the regulation of Rabs, SNAREs and hydrolysis enzymes located in lysosomes could degrade the cargo on arrival [11,12]. Autophagy was considered controlled by a series of signal transduction, involving Serine/Threonine protein kinase (Akt)/mammalian target of rapamycin (mTOR) pathway, Liver kinase B1 (LKB1)/AMP-activated protein kinase (AMPK) pathway and Beclin complex [13]. Currently, it is widely recognized that autophagy appears to be a “double-edged sword” in HCC. On the one hand, autophagy can play a tumor suppressor role in the early stage of tumor, remove oncogenic protein aggregates and damaged organelles, alleviate oxidative stress, reduce DNA damage and genomic instability, as well as prevent the conversion of histologically normal cells into early premalignant lesions. On the other hand, under the stimulation of starvation, hypoxia, growth factor depletion, and anti-cancer treatment, autophagy can be strongly activated by removing toxic oxygen-free radicals, relieving stress, and maintaining mitochondrial function to enhance proliferation and survival of tumor cells [14]. It has been confirmed that autophagy is highly correlated with the tumor progression, tumor immune microenvironment, and drug resistance of HCC [15]. Inhibiting mTOR signaling pathway-inducing autophagy resulted in an obvious antitumor effect along with improved overall survival rates in HCC patients. The suppression of the autophagy could also benefit the HCC treatment by enhancing the response to anti-cancer agents. The autophagy inhibitor hydroxychloroquine (HCQ) combined with sorafenib has improved efficacy in HCC treatment when compared with sorafenib alone [16]. Moreover, ATGs have been defined as predictive signatures for anti-PD-L1 immunotherapy [17]. Targeting the NF-κB signaling pathway of tumor-associated macrophages, affecting the level of p62 to activate the autophagy, enabled the tumor-center T cells to restore their sensitivity to anti-PD-L1 therapy [18,19]. Therefore, a more in-depth investigation of complex and potential mechanism of autophagy in HCC would be essential for the treatment. Given the above situation, this study aimed to search for a potential biomarker and therapeutic target for HCC that is highly related to autophagy, in the hope of providing informative clues for effective management of HCC and related research. RNA-Seq transcription data, survival data, and clinical information were obtained from the HCC patients in TCGA (The Cancer Genome Atlas, https://portal.gdc.cancer.gov/, accessed on 30 April 2021), UCSC Xena (http://xena.ucsc.edu/, accessed on 30 April 2021) and ICGC (International Cancer Genome Consortium, https://dcc.icgc.org/, accessed on 30 April 2021). A total of 286 autophagy-related genes were collected from the HADb (Human Autophagy Database, http://www.autophagy.lu/, accessed on 4 May 2021). The raw microarray data were preprocessed using background correction and robust multi-array analysis (RMA) normalization with “Affy” R package in R version 4.2.0. software. “Limma” R package was used to screen out the differentially expressed autophagy-related genes by a cut-off of |LogFC| > 1 (fold change), p < 0.05. Then, univariate Cox regression analysis was performed based on the RNA-Seq transcription data and matched survival data to obtain differentially expressed autophagy-related genes highly correlated with survival. Insertion of the differentially expressed and prognostic autophagy-related genes in TCGA and ICGC datasets were taken. The protein–protein interaction network between these genes was visualized by “igraph” R package based on data obtained via “STRINGdb” R package. Filtering out those genes that have already been studied universally in HCC by retrieval in PubMed (https://pubmed.ncbi.nlm.nih.gov/, accessed on 15 June 2021), WDR45B was determined to be the target gene in this study. Later, the Kaplan–Meier survival curve and clinical traits related to the expression of WDR45B were plotted by “ggplot2” R package. Moreover, genomics characteristics of WDR45B involved somatic mutation, copy number variation, methylation level, and co-expression genes were analyzed based on the TCGA-HCC cohorts from cBioPortal for Cancer Genomics (http://www.cbioportal.org/, accessed on 2 May 2022). MethSurv (https://biit.cs.ut.ee/methsurv/, accessed on 21 August 2021) was used to compare the survival differences between high and low methylation levels. GSEA (Gene set enrichment analysis) was conducted with GSEA 4.1.0 software. WDR45B enriched KEGG pathways were picked out according to the |NES| > 1 (normalized enrichment score) and NOM (norminal) p-value < 0.05. The HCC tissue microarray was made by formalin-fixed paraffin-embedded (FFPE) specimens originating from 56 patients with primary HCC date from January 2021 to December 2021. The FFPE specimens of 3 patients with hepatic contusion were used as normal control. All the paraffin-embedded specimens were provided by the Pathology Department of Zhongnan Hospital of Wuhan University. The paraffin sections were dried at 60 °C for 1 h and dewaxed in xylene and ethanol. Citrate buffer was used to repair antigen, and then the sections were blocked by 3% hydrogen peroxide for 10 min and 5% goat serum (ZSGB-Bio, Beijing, China) for 1 h. Afterward, sections were incubated at 4 °C overnight with the WDR45B primary antibody (1:200, Signalway Antibody). On the following day, the sections were incubated with HRP-conjugated Goat Anti-Rabbit IgG H&L secondary antibody (abcam, Cambridge, MA, USA), and the DAB (Dako Diagnostics AG, Baar, Switzerland) was stained in appropriate time for microscopic examination. Washing with PBS three times was included between all processes. For imaging, the sections were scanned by an Olympus BX51 microscope equipped with a DP74 digital camera. Both the staining intensity and degree were assessed in semi-quantitative analysis by ImagePro Plus 6.0. HCC cell lines HepG2 and Huh-7 cells were purchased from Shanghai cell bank of the Chinese Academy of Science. The normal human hepatocytes LO2 cells were provided by Wuhan University Medical Science Research Center. These three types of adherent cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin/streptomycin at 37 °C in a humidified atmosphere of 5% CO2. Cells were grown to 75% to 80% confluency and collected by trypsinization with 0.25% trypsin-EDTA. For lentivirus infection, the HitransG (Genechem, Shanghai, China) was used according to the manufacturer’s instructions. HepG2 cells were infected at a high multiplicity of infection (MOI = 20) with LV-WDR45B-RNAi and blank lentivirus. When lentiviral infection efficacy reached around 80%, 2.0 μg/mL puromycin was used to screen the stable cells. A qRT-PCR was performed to verify the efficiency of the WDR45B knockdown. The RNA of cells was extracted with RNA extraction kit (TIANGEN), then RNA reverse transcription PCR was carried out using reverse transcription kits (Toyobo) to obtain cDNA. The cDNA, primers, and SYBER Green Master Mix (Toyobo) were mixed for a final volume of 12 μL to perform quantitative RT-PCR analysis with the CFX connect Real-Time PCR Detection System (Bio-Rad). The primers used were as follows: WDR45B Forward primer, 5′-CGAGAAAGGGACGCTTATAAGA-3′; Reverse primer, 5′-TGATGCAGTAAATATTGGCTGC-3′; ACTB Forward primer, 5′-TCGAGTCGCGTCCACC-3′; Reverse primer, 5′-GGGAGCATCGTCGCCC-3′. Relative expression levels were calculated using 2-ΔΔCt method. Firstly, total proteins of cells were extracted using RIPA lysis buffer and then subjected to centrifugation at 12,000× g rpm and 4 °C for 30 min. Subsequently, cell proteins were quantitated by BCA Protein Assay Kit (Biosharp, Tallinn, Estonia) following the manufacturer’s guidance. The protein samples were separated by using 7.5%, 10%, and 12.5% SDS polyacrylamide separating gel (EpiZyme, Cambridge, MA, USA) and electro-blotted onto PVDF membranes (Millipore, Burlington, MA, USA). After that, the membranes were blocked with 5% non-fat skimmed milk in TBST buffer for 1 h, followed by incubation with primary antibodies in TBST buffer overnight at 4 °C. Next day, the membrane was incubated with HRP-conjugated goat anti-rabbit secondary antibody (1:5000, Abcam, Cambridge, MA, USA) for 1 h at room temperature. Finally, the Electrochemiluminescence (ECL) substrate developer (Millipore) was added to membranes, exposed, and imaged by ChemiDoc Imaging System (Biorad, Hercules, CA, USA). Primary antibodies used for Western blotting were: WDR45B (Novus Biologicals (Cambridge, UK), 1:1000, Rabbit); LC3 (proteintech, 1:2500, Rabbit); p62 (CST, 1:1000, Rabbit); Akt (CST, 1:1000, Rabbit); p-Akt (Ser473) (CST, 1:2000, Rabbit); mTOR (CST, 1:1000, Rabbit); p-mTOR (Ser2481) (CST, 1:1000, Rabbit); and β-actin (CST, 1:5000, Rabbit). Approximately 3 × 103 cells suspended in 100 μL DMEM medium were seeded into a 96-well plate. A 10 μL CCK8 solution (Biosharp) was added to each well at 0 h, 24 h, 48 h, and 72 h after the cells adhered to the wall. Continued culture for 2 h was performed at 37 °C. Then, cell viability was measured using the CCK8 and microplate reader at a wavelength of 450 nm. The cell viability curve was plotted according to the absorbance values. Before the experiment, the backs of the 6-well plates were labeled with horizontal lines using marker pens. Each well was seeded with about 5 × 105 cells and cultured in an incubator with 5% CO2 at 37 °C. The monolayer was scratched with a 200 μL micropipette tip, washed with PBS to remove floating cells, and cultured with fresh serum-free medium. Images were captured at 0, 24, and 48 h after the initial scratch, and the widths of scratches were measured by ImageJ software. Cell suspension (200 μL; 2 × 104 cells/well) was added into the upper chamber of a 24-well Transwell plate with 8μm pore size (Corning, NY, USA) in migration assay or into chambers coated with Matrigel for the invasion assay. Then, 500 μL of DMEM with 20% FBS was added into the lower chamber. After 24 h or 48 h of incubation in migration assay or invasion assay, respectively, the residual cells attached to the upper surface of the membrane were gently wiped away with a cotton swab. Cells on the lower surface of the membrane were fixed with 4% paraformaldehyde for 30 min followed by staining using 0.1% (w/v) crystal violet (Sigma) for 20 min. After rinsing with PBS the stained samples were observed with an optical microscope, and the number of stained cells penetrating the pores was measured by ImageJ software (NIH, Bethesda, MD, USA). All statistical analysis was performed by using R version 4.2.0 software, IBM SPSS Statistics 23 software, and GraphPad Prism 8 software. The correlation between the staining intensity of HCC tissue microarray and matched pathological characteristics was analyzed by Chi-squared test. Comparisons between groups were performed using a two-tailed Student t-test. All experiments in this work were repeated for three times at least. The p < 0.05 was considered as statistically significant (* p < 0.05, ** p < 0.01, *** p < 0.001, # p < 0.0001). The workflow of our study is shown in Figure 1. It has been reported that 81 differentially expressed autophagy-related genes were screened out based on the RNA-seq transcription data of 424 HCC patients from TCGA cohort [20,21]. Among these, Univariate Cox regression analysis was performed with p < 0.01 as the cut-off value. It identified 29 autophagy-related genes highly correlated with survival, while 17 genes were identified in ICGC (Figure 2A), Complementary datasets from ICGC cohorts were also involved in the analysis. It turned out that there were 17 genes related to prognosis. All those genes were considered risk factors (Hazard ratio > 1) (Figure 2B). Taking the Insertion of prognostic autophagy-related genes from TCGA and ICGC cohorts, 14 key prognostic autophagy-related genes were selected (SPNS1, WDR45B, NPC1, NRAS, HSP90AB1, RHEB, GAPDH, HDAC1, BAK1, ATIC, FKBP1A, RRAGD, CDKN2A, BIRC5) (Figure 2C). The protein–protein interaction network was shown in Figure 2E. According to the retrieval from PubMed directed at these 14 genes, we chose WD repeat domain 45B (WDR45B), which has hardly been studied in HCC, as our target gene. The expression levels of WDR45B in tumor tissues and adjacent normal tissues from TCGA were compared in total and in pairs (Figure 3A). It suggested that WDR45B was overexpressed in tumor tissues. The somatic mutation of WDR45B in HCC was VUS (variant of uncertain significance) at S216 site (Figure 3B). The gain of WDR45B was the main copy number variations in WDR45B in HCC (Figure 3C). GRB2 (growth factor receptor bound protein 2) (R = 0.61, p < 0.05) and TRIM37 (tripartite motif containing 37) (R = 0.58, p < 0.05) were strongly associated with WDR45B in HCC (Figure 3D). Moreover, methylation level of WDR45B in HCC was also analyzed and presented in a violin plot (Figure 3E). It demonstrated that the methylation level of WDR45B in HCC was high, almost all methylation sites were completely methylated, and only a few were unmethylated. The β ≥ 0.6 was considered completely methylated, β ≤ 0.2 was considered completely unmethylated, and 0.2 < β < 0.6 was considered partially methylated. Based on the survival data of these HCC patients, Kaplan–Meier survival analysis of WDR45B methylation level was conducted (Figure 3F), which revealed the correlation between methylation level of 9 methylation sites and related survival situations. High methylation levels of 5 methylation sites (cg05554594, cg06938133, cg1165294, cg23713156, cg03247412) suggested shorter survival time, while low methylation levels of 4 methylation sites (cg01155404, cg12167135, cg14060471, cg25363258) could predict poor prognosis. Based on the detailed clinical traits of HCC patients in TCGA cohorts involving age, sex, cirrhosis scores, grade, TNM stage, vascular invasion, and adjacent inflammation from UCSC Xena (Table S1), the correlation between expression level of WDR45B and clinical traits was analyzed. It turned out that advanced grade and T stage was associated with high expression level of WDR45B (Figure 4A). In addition, Kaplan–Meier survival curve revealed that high expression level of WDR45B predicted poor prognosis (Figure 4B). The top 50 KEGG pathways that upregulated WDR45B were associated with were obtained through GSEA with criteria of NES > 1 and p < 0.05. Among these KEGG pathways, tumor-related pathways included ubiquitin-mediated proteolysis, insulin signaling pathway, cell cycle, regulation of autophagy, mTOR signaling pathway, ERBB signaling pathway, WNT signaling pathway, and Notch signaling pathway (Figure 4C). IHC staining of WDR45B was performed using 56 FFPE tissues of primary HCC patients and 3 normal liver FFPE tissues from patients with hepatic blunt trauma. Representative histological images of 4×, 10×, and 20× magnifications were exhibited to visualize the WDR45B expression in tumor and normal liver tissues (Figure 5A). More images are showed in the Figure S1. Moderate to strong cytoplasmic staining was visible in tumor cells, and weak positive staining can be observed in normal hepatocytes. Next, IHC staining Integrated Optical Density (IOD) values were measured and analyzed by ImagePro Plus software as well (Figure 5B). According to the median value of IHC staining scores, we divided the patients into high and low WDR45B expression groups. The correlation between WDR45B expression level and clinical pathological characteristics including age, sex, T stage, macroscope vein invasion, microvascular invasion, cirrhosis, tumor involvement, and Alpha-fetoprotein (AFP) IHC staining was analyzed by chi-square test (Table 1). It concluded that there were significant differences between grade and WDR45B IHC staining intensity in HCC patients (χ2 = 6.842, p < 0.05). Quantitative real-time PCR and Western blotting were executed to detect the WDR45B expression in HCC cell lines (HepG2 and Huh7 cell) and normal hepatocyte cells (LO2 cell). The results indicated that WDR45B expression was upregulated in HepG2 and Huh7 cells by contrast to that in LO2 cells, and more highly expressed in HepG2 cells compared with Huh7 cells (Figure 5C,D). For the further study of WDR45B in HCC, we generated HepG2 cells with stable knockdown of WDR45B using lentiviral transduction of shRNA (Figure 6A). We measured protein expression of autophagy marker LC3 and p62/SQSTM1 in WDR45B-knockdown HepG2 cells (Figure 6B). LC3-II distinctly downregulated and the ratio of LC3-II/LC3-I decreased, while p62/SQSTM1 upregulated. All these data indicated that the process of LC3-I transforming into LC3-II was blocked and p62 was accumulated, which demonstrated that autophagy was inhibited. In order to investigate the potential pathway mechanism of WDR45B in HCC, we analyzed the expression of Akt, mTOR, p-Akt (Ser473), mTOR, and p-mTOR (Ser2481) after the knockdown of WDR45B (Figure 6B). There was no significant difference in the protein translation level of Akt and mTOR, while the phosphorylation level of these two proteins increased. For validating WDR45B leading to the suppression of autophagy and Akt/mTOR signaling pathway, we used 20 nM autophagy inducer rapamycin that was also a kind of mTOR inhibitor. Detecting the expression level of protein above, we found that rapamycin could reverse the expression changes partially resulting from the knockdown of WDR45B (Figure 6C). Therefore, we speculated that WDR45B might promote autophagy by activating Akt/mTOR signaling pathway. To explore the effects of WDR45B in HCC tumorigenesis, some in vitro analyses were performed. Cell viability in the WDR45B-knockdown group was significantly lower than the control group at 48 h and 72 h (Figure 7A). As for migration ability, the wound width in the WDR45B-knockdown group was markedly higher than control group at 24 h and 48 h (Figure 7B). There were some cells that obviously migrated to the wound region at 48 h in control group while there was no similar situation in the WDR45B-knockdown group (Figure 7C). In Transwell cell migration and invasion assay, less-stained cells were found in the WDR45B-knockdown group than in after 24 h or 48 h of incubation, indicating the slower migration and weaker invasion (Figure 7D). The number of cells that migrated from the WDR45B-knockdown group was significantly smaller than that in the control group through statistical analysis. Meanwhile, fewer staining cells were observed in the WDR45B-knockdown group comparing to the control group in invasion assay. All results above suggested that the migration ability of HCC cell line declined after the knockdown of WDR45B expression. WD repeat domain 45B (WDR45B) is a member of the WD-repeat protein interacting with the phosphoinositides (WIPIs) family, alias WIPI3 or WDR45L. WDR45B contains seven WD-repeat sequences, which are believed to fold into β-propeller structures to mediate protein–protein interactions and to constitute a conserved motif for interaction with phospholipids. The WIPIs protein family consists of four members: WIPI1, WIPI2, WDR45B/WIPI3, and WDR45/WIPI4. All of them are considered to be crucial in the autophagy process, serving as the effectors of autophagy-specific phosphatidylinositol 3-phosphate (Ptdlns3P), playing an important role in the recognition and decoding of Ptdlns3P signals in newborn autophagosomes [22]. Moreover, the WIPIs can also function as the scaffold for recruiting vital proteins or complexes, contributing to the nucleation and amplification of autophagosome membranes. The WIPIs are localized to the endoplasmic reticulum associated intima and newborn autophagosome membrane [23]. Mutations in WIPIs could seriously impact autophagy, and are closely correlated with cancers, neuronal degeneration, and intellectual disability. WIPI1 is thought to be associated with osteosarcoma, nasopharyngeal carcinoma, melanoma, and other diseases [24,25,26]. WIPI2 participates in the genesis and extension of isolated membrane, being considered to be a key downstream substrate for mTOR regulating autophagy [27,28]. Mutations in WDR45B and WDR45 lead to β-propeller protein-associated neurodegeneration and intellectual disability, respectively [29]. Autophagy starts when ATG12 conjugates with ATG5 with the assistance of ATG7 and ATG10, which is then stabilized by ATG16L and further forms an ATG12-ATG5-ATG16L complex about 800 kDa located on the outer surface of the autophagosome membrane, thus promoting the formation of LC3 conjugation system. In response to LKB1 mediated AMPK stimulation, WDR45-ATG2 can be released from the WDR45-ATG2/AMPK-ULK1 complex and transferred to the nascent autophagosome to control the size of autophagosome. The complex of WDR45B and FIP200 is also involved in this process. WDR45B receives the regulations from AMPK, which binds to the activated tuberous sclerosis complex (TSC) to control the activity of mTOR in lysosomal compartment and combines with RB1CC1/FIP200 on the nascent autophagosome to promote the nucleation and extension of autophagosome. WDR45B deficiency has been proven to obstruct the generation of newborn autophagosomes in the downstream of LC3 [29]. Related human genetic studies have revealed that the autophagy defects caused by WDR45B deletion can lead to the accumulation of SQSTM1, ubiquitin aggregates, and autophagosomes in the damaged neurons, mainly in the cerebral cortex, corpus callosum, inner sac, thalamus, and other extensive regions, destroying the neuronal and axonal homeostasis. WDR45B-knockout mice showed dyskinesia, learning, and memory deficits [30]. Recently, WDR45B was found to improve airway remodeling and reduce collagen deposition and airway hyperreactivity in mouse asthma models, by means of combining with glucocorticoid induced 1 (GLCCI1) to inhibit the autophagy activation effect of GLCCI1 [31]. At present, the majority of studies about WDR45B almost all focused on neuronal degeneration diseases, while very few on the aspect of cancer. Therefore, this study was dedicated to the potential cancer-promoting mechanism of WDR45B in HCC, hoping to bring new strategies for the diagnosis and treatment of HCC. In our study, we found that the genomics of WDR45B was mainly characterized by the gain of copy number and abundant DNA methylation sites. The methylation level also had a certain predictive effect on the prognosis of HCC patients. In research on thioacetamide mouse liver cancer model, CpG island hypermethylation of WDR45B and Yin Yang 1 (YY1) was observed in the pretumor liver disease foci of mice, which was involved in the regulation of cell cycle and apoptosis, promoting tumor formation [32]. Furthermore, genomic analysis revealed that WDR45B was significantly co-expressed with GRB2 and TRIM37. GRB2 is considered to be a susceptibility gene for Alzheimer’s disease, and overexpression of GRB2 in prostate cancer suggests poor prognosis [33]. The SH2 domain of GRB2 binds to the insulin receptor substrates and activates tyrosine kinase, which is critical for the activation of RAS/MAPK signaling pathways [34]. In HCC, the deubiquitination enzyme PSMD14 can promote the growth and metastasis of HCC by stabilizing GRB2 [35]. As for TRIM37, it is a member of the tripartite motif containing (TRIM) family with E3 ubiquitin ligase activity. Ubiquitin mediated by TRIM37 can stabilize PEX5 and enhance the activity of peroxisome. The amplification of the TRIM37 genome contributes to the tumor progression in colorectal cancer, HCC, lung cancer, neuroblastoma, breast cancer, pancreatic cancer, and osteosarcoma [36,37,38]. It was confirmed that the overexpression of TRIM37 in HCC could promote the invasion and strengthen sorafenib resistance by activating the AKT signaling pathway [39]. According to the outcomes of GSEA the upregulation of WDR45B significantly enriched the mTOR signaling pathway, which was validated by experiments in our study. Autophagy marker LC3 is a light chain protein, mainly involved in the formation of autophagosomes. LC3 precursor molecules are cleaved by ATG4B at the carboxyl-terminal to form LC3-I, which is then conjugated with lipoylethanolamine to generate lipidized LC3-II, attaching to the autophagosome membrane as the structural skeleton [40]. The LC3-II/LC3-I ratio is extensively applied to monitor autophagy levels. P62/SQSTM1 as a pivotal protein taking part in autophagy-lysosome and ubiquitin protease systems can be degraded by proteolytic enzymes during lysosomal hydrolysis with the interaction with LC3 [41]. Autophagy defects are a common upregulation mechanism of P62/SQSTM1 in human tumors [42]. In our study, we confirmed in the HCC cell line that WDR45B knockdown could significantly inhibit autophagy by reducing LC3-II, increasing the accumulation of P62/SQSTM1, and raising the phosphorylation levels of Akt and mTOR. It indicated that WDR45B might promote autophagy by inhibiting the Akt/mTOR signaling pathway in HCC. At present, a large volume of research has shown that autophagy can maintain the survival of tumor cells by providing nutrition and energy under metabolic and oxidative stress originating from anti-cancer therapy in established metastatic tumors, which is a crucial mechanism of drug resistance to anti-cancer therapy in patients with advanced HCC. In HCC, the emergence of intrinsic acquired drug resistance to sorafenib is still a huge challenge for the prognosis of patients with advanced HCC, where only about 30% of patients respond to sorafenib treatment [43]. Autophagy activation caused by the inhibition of Akt/mTOR signaling pathway is an important mechanism of sorafenib drug resistance [44]. Cell surface molecules such as CD24, BEZ235, and SIRT1 have been found to suppress mTOR and promote autophagy in HCC, leading to resistance to sorafenib [45,46,47]. In addition to sorafenib, increased autophagy flux has also been observed in other anti-cancer drugs for HCC. Inhibition of autophagy has been shown to enhance the sensitivity of sorafenib and other anticancer drugs to a certain extent, and bring substantial survival benefits to patients. Currently, autophagy inhibitors chloroquine and hydroxychloroquine have been used clinically, as they can deacid lysosomes and block the fusion between autophagosomes and lysosomes preventing cargo degradation. Chloroquine can also sensitize tumor cells to chemotherapy drugs independent of autophagy approaches. Other autophagy modulators, such as VPS34, ULK1, and ATG4B inhibitors have been confirmed to exert tumor suppressor effect in clinical mouse models [48]. In conclusion, our study demonstrates that WDR45B is upregulated in HCC, suggesting less clear differentiation and poor prognosis. In vitro experiments showed that the knockdown of WDR45B can suppress autophagy by upregulating the Akt/mTOR signaling pathway and reduce tumor proliferation and migration. Our study provides a new insight to the mechanism of Akt/mTOR signaling pathway, suggesting that selective Akt/mTOR signaling pathway inhibitors may help to overcome drug resistance and improve the efficacy of anti-cancer treatment.

PMC10001101

Jessica Bamba-Funck,Emmanuelle E. Fabre,Marianne Kambouchner,Olivier Schischmanoff

Performance Characteristics of Oncomine Focus Assay for Theranostic Analysis of Solid Tumors, A (21-Months) Real-Life Study

01-03-2023

next generation sequencing (NGS),solid tumors,single nucleotide variation (SNV),copy number variation (CNV),fusion transcript,long-term follow-up

Next generation sequencing analysis is crucial for therapeutic decision in various solid tumor contexts. The sequencing method must remain accurate and robust throughout the instrument lifespan allowing the biological validation of patients’ results. This study aims to evaluate the long-term sequencing performances of the Oncomine Focus assay kit allowing theranostic DNA and RNA variants detection on the Ion S5XL instrument. We evaluated the sequencing performances of 73 consecutive chips over a 21-month period and detailed the sequencing data obtained from both quality controls and clinical samples. The metrics describing sequencing quality remained stable throughout the study. We showed that an average of 11 × 106 (±0.3 × 106) reads were obtained using a 520 chip leading to an average of 6.0 × 105 (±2.6 × 105) mapped reads per sample. Of 400 consecutive samples, 95.8 ± 16% of amplicons reached the depth threshold of 500X. Slight modifications of the bioinformatics workflow improved DNA analytical sensitivity and allowed the systematic detection of expected SNV, indel, CNV, and RNA alterations in quality controls samples. The low inter-run variability of DNA and RNA—even at low variant allelic fraction, amplification factor, or reads counts—indicated that our method was adapted to clinical practice. The analysis of 429 clinical DNA samples indicated that the modified bioinformatics workflow allowed detection of 353 DNA variants and 88 gene amplifications. RNA analysis of 55 clinical samples revealed 7 alterations. This is the first study showing the long-term robustness of the Oncomine Focus assay in clinical routine practice.

Performance Characteristics of Oncomine Focus Assay for Theranostic Analysis of Solid Tumors, A (21-Months) Real-Life Study Next generation sequencing analysis is crucial for therapeutic decision in various solid tumor contexts. The sequencing method must remain accurate and robust throughout the instrument lifespan allowing the biological validation of patients’ results. This study aims to evaluate the long-term sequencing performances of the Oncomine Focus assay kit allowing theranostic DNA and RNA variants detection on the Ion S5XL instrument. We evaluated the sequencing performances of 73 consecutive chips over a 21-month period and detailed the sequencing data obtained from both quality controls and clinical samples. The metrics describing sequencing quality remained stable throughout the study. We showed that an average of 11 × 106 (±0.3 × 106) reads were obtained using a 520 chip leading to an average of 6.0 × 105 (±2.6 × 105) mapped reads per sample. Of 400 consecutive samples, 95.8 ± 16% of amplicons reached the depth threshold of 500X. Slight modifications of the bioinformatics workflow improved DNA analytical sensitivity and allowed the systematic detection of expected SNV, indel, CNV, and RNA alterations in quality controls samples. The low inter-run variability of DNA and RNA—even at low variant allelic fraction, amplification factor, or reads counts—indicated that our method was adapted to clinical practice. The analysis of 429 clinical DNA samples indicated that the modified bioinformatics workflow allowed detection of 353 DNA variants and 88 gene amplifications. RNA analysis of 55 clinical samples revealed 7 alterations. This is the first study showing the long-term robustness of the Oncomine Focus assay in clinical routine practice. Theranostic analysis of solid tumors gains continuous complexity requiring the search for point mutations, short insertions or deletions, copy number variations, or gene fusions in always larger panels of genes [1,2]. Next generation sequencing (NGS) allows to keep up with the search for a growing number of genetic alterations in limited amounts of biologic material with a temporality compatible with clinical needs. NGS may allow reporting results to clinicians in a minimum of five days from the acquiring of formalin-fixed, paraffin-embedded (FFPE) samples. Implementation of a new NGS method follows well-defined procedures that are usually defined by academic societies or by state medical agencies [3,4]. Following the new NGS method set up, identification of factors that contribute to alter data quality over time is of critical importance. Indeed, several environmental, material, or human factors may impact NGS long-term performances (i.e., analytical sensitivity), possibly affecting the choice of clinical treatments [2,5]. Indeed, the initial validation does not ensure that the method will be robust enough to produce accurate results throughout the instrument lifespan. Two major NGS methods are commonly used in diagnostic laboratories. Although both methods are based on sequencing by synthesis, Thermo Fisher and Illumina platforms use different principles for sequencing. Thermo Fisher Ion TorrentTM technology directly converts nucleotide sequences into digital information using a semiconductor chip measuring variation of pH [6]. Illumina “bridge amplification” technology is based on incorporation and detection of fluorescent nucleotides in DNA fragments immobilized on a glass slide [7]. A recent study reported that both technologies achieved comparable NGS performances (i.e., mean read coverage, mean coverage uniformity, and variant allele frequency) and are suitable for diagnosis purpose [8]. Our laboratory implemented the OncomineTM Focus Assay (OFA) on an Ion S5XL (ThermoFisher, Les Ulis, France). This assay is dedicated to the identification of all types of theranostic variations in FFPE tissues and enables the concurrent analysis of DNA and RNA to detect variations in 52 genes in solid tumors [9,10]. To our knowledge, no study has been dedicated to investigate the evolution of the performances of OFA over a long period of time. The aim of this study was to evaluate retrospectively the evolution of the performances of the OFA during a 21-month period in order to verify whether it is suitable for hospital routine practice. This study was performed at Hospital Avicenne, Molecular and Biochemistry Laboratory (93000-Bobigny-France), from January 2020 to September 2021 and approved by the Local Ethic Comity (Avicenne Hospital). Formalin-fixed, paraffin-embedded (FFPE) tissues originating from surgical biopsies were systematically reviewed by a skilled pathologist who determined the tumor cellularity. Commercial quality samples were used for DNA assay validation (Quantitative Multiplex Reference Standard (cat. no. HD200, Horizon Diagnostics, Waterbeach, Cambridge, UK), EGFR Gene-Specific Multiplex Reference Standard (FFPE) 5% Variant Allelic Fraction (VAF) (cat. no. HD300, Horizon Diagnostics), Structural Multiplex Reference Standard (FFPE) (cat. no. HD789, Horizon Diagnostics). RNA assay characteristics performances were assessed using ALK-RET-ROS1 Fusion FFPE RNA (cat. no. HD784, Horizon Diagnostics) and Seraseq FFPE Tumor Fusion RNA Reference Material v4 (cat. no. SER0710-0496, Seracare, Ozyme, Saint Cyr l’Ecole, France). Anonymized FFPE patient samples or quality control (QC) samples originating from 73 consecutive sequencing runs were used for quality metrics assessment. DNA alterations were analyzed from 420 patient samples originating from various tumors including 277 lung, 64 colon, 34 skin, and 45 other tissues (18 pancreas, 16 bile ducts, 5 breast, 3 thyroids, and 3 spleens) over a 21-month period. RNA fusions and exon skipping were analyzed from 55 tissue samples (45 lung, 8 bile duct, 1 pancreas, and 1 thyroid) over a 12-month period. DNA and RNA were isolated using the Maxwell 16 Instrument using Maxwell® RSC DNA FFPE Kit and Maxwell® RSC RNA FFPE Kit, respectively, according to the manufacturer’s protocols (Promega, Madison, WI, USA). DNA and RNA quantification were performed on a Qubit 2.0 Fluorometer using Qubit dsDNA and Qubit HS RNA assay, respectively (Thermo Fisher, Les Ulis, France). For RNA assay, prior to library preparation, cDNA were synthetized using SuperScript VILO cDNA synthesis kit (catalog no. 11756050, Thermo Fisher). Library preparations were carried out using Oncomine Focus Assay, Chef-Ready Library (Thermo Fisher) using an Ion Chef Instrument (Thermo Fisher) following the manufacturer’s instructions using a total of 10ng of DNA or 2ng of RNA. The DNA panel is designed to identify hotspot mutations of 35 genes (AKT1, ALK, AR, BRAF, CDK4, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, ERBB4, ESR1, FGFR2 FGFR3, GNA11, GNAQ, HRAS, IDH1, IDH2, JAK1, JAK2, JAK3, KIT, KRAS, MAP2K1, MET, MTOR, NRAS, PDGFRA, PIK3CA, RAF1, RET, ROS, and SMO), copy number variations of 19 genes (ALK, AR, BRAF, CCND1, CDK4, CDK6, EGFR, ERBB2, FGFR1, FGFR2, FGFR3, FGFR4, KIT, KRAS, MET, MYC, MYCN, PDGFRA, and PIK3CA), fusions drivers of 21 genes (ABL1, ALK, AKT1, AXL, BRAF, ERBB2, ERG, ETV1, ETV4, ETV5, FGFR1, FGFR2, FGFR3, NTRK1, NTRK2, NTRK3, PDGFRA, PPARG, RAF1, RET, and ROS1), and exon skipping of 2 genes (EGFR and MET). RNA and DNA libraries were equalized at 100pM using the Ion Chef instrument and pooled before templating (Ion 510™ & Ion 520™ & Ion 530™ Kit—Chef, cat. no. A34461, Thermo Fisher). Eight DNA samples or both eight DNA samples and eight RNA samples were loaded on a 520 chip (Ion 520™ Chip Kit, catalog no. A27762 Thermo Fisher). Sequencing was performed on an Ion S5XL instrument (Thermo Fisher). The Tumor Hot Spot assay (THS, Multiplicom, Les Ulis, France), on a MiSeq sequencer (Illumina, Paris, France), was used for OFA inter-run variability comparison. For each run, quality metrics were assessed including chip loading density, number of total reads, percentage of clonality, percentage of adapter dimer, percentage of low quality, read length, and alignment of the reads to the hg19 human reference genome (Torrent server, version 5.12, Thermo Fisher). The “coverage Analysis” plugging was applied to assess the quality of sample sequencing. Each sample must reach strict validation criteria, i.e., yield a minimum of 400,000 reads, 98% of the amplicons with a minimal sequencing depth of 500X, 90% of the reads located within the target region boundaries, 80% of the amplicons being read from end-to-end, and 90% of the amplicons being read without strand bias. For DNA variant annotation, the Ion Reporter software was carried out (version 5.10). The following default parameters were modified from the “Oncomine Focus w2.4—DNA—Single Sample” workflow: variants were reported with allele view, complex variants were allowed, down-sampled to coverage was set to 5000 reads, and the variants were generated at a minimum VAF of 0.03. The hotspot file was also modified: some hotspot mutations were added to report minor variants within BRAF, KRAS, and NRAS genes (Appendix A). The minimal value for VAF detection was decreased to 0.02 for positions of theranostic interest within BRAF, EGFR, KRAS, and NRAS genes. CNV were reported by the pipeline if the MAPD (median absolute pairwise difference) was below 0.4 and if the amplification factor was above 4. For RNA variant annotation, the “Oncomine Focus—520—w2.4—Fusions—Single Sample” workflow was used with default parameters. The validation criteria for RNA samples were as follows: each sample must generate at least 20,000 reads and have a minimum mean read length of 50 pb. Additionally, at least three of the five RNA internal controls (TBP, LRP1, ITGB7, MYC, and HMBS) must be called. Finally, RNA alterations were reported only if a minimum number of reads was reached: 20 for targeted fusions, 250 for non-targeted fusions, and 120 for exon skipping. For the inter-run variability comparison study, BAM files generated by the MiSeq instrument were analyzed using a tailored Sophia Genetics pipeline (Sophia Genetics, Lausanne, Switzerland). Variant calls were confirmed using the Integrative Genomics Viewer tool (Broad Institute, Cambridge, MA, USA) when necessary. Immunohistochemistry for ROS1, ALK, and ERBB2 was performed as previously described [11,12]. Tissues were incubated with rabbit anti-ALK monoclonal antibody prediluted at 1:2 (clone K52076; Dako, Agilent, les Ulis, France) and anti-ROS1 monoclonal antibody (clone 1A4; Origene, Clinisciences, Nanterre, France). ERBB2 status was assessed using the HercepTest™ (kit K5207, Dako-Agilent, Les Ulis, France). The quality metrics of the OFA DNA panel were assessed using 73 consecutive runs on 520 chips including QC and clinical samples. An average of 11 × 106 (±0.3 × 106; CV = 3.4%) reads were initially generated per run. The polyclonal ion sphere particles (ISP), primer dimers, low quality reads, and test quality sequences were then filtered out (5.8 × 106 ± 0.8 × 106 reads). Hence, 49 ± 7% (CV = 13%) of the amplicons presented the required quality for further bioinformatics analysis (Figure 1a). Among an average number of sequenced bases of 77 × 106 (±2.5 × 106), 72 × 106 (±2.4 × 106) bases were sequenced with a quality score of 20 (Q20). The mean sequencing depth for a given on-target base was 2246X ± 939X. All the metrics, independent of DNA quality, were measured at each run during the 21-month period and did not reveal any loss of performance over time, as assessed by the low coefficients of variation. For each sample, an average of 6.0 × 105 (±2.6 × 105; CV = 44%) reads were mapped to hg19 genome reference and 98.2 ± 7.1% of the reads were aligned over a target region. The mean read length was 114 bp (± 4 bp). The uniformity of base coverage, defined as the percentage of on-target bases covered by at least 20% of mean coverage depth, was 98.5 ± 8.4%. Analysis of 400 consecutive samples revealed an average of 95.8 ± 16% (CV = 16.7%) of amplicons with at least 500X, 98.4 ± 2.0% (CV = 2.0%) of amplicons with no strand bias, and 97.3 ± 5.4% (CV = 5.6%) of amplicons with end-to-end sequencing (Figure 1b). A total of 64 of these 400 samples (16%) failed to reach Thermo Fisher quality requirements due to insufficient depth coverage (n = 58), strand bias (n = 2), or default of end-to-end sequencing (n = 4) (Figure 1b). A more detailed analysis of 136 consecutive samples indicated that 97.8% (133/136) of all samples reached an average depth of 500X with a mean sequencing depth of 2695X ± 432X across all amplicons. Some amplicons reached 5066X ± 2535X (OCP1_MET_8). The amplicons more likely to be covered with less than 500X were OCP1_DCUN1D1_9: 845X ± 386X, OCP1_KRAS_10: 892X ± 403X, and OCP1_AR_8: 938X ± 506X (Figure 1c). Inter-run variability for SNVs and indels analysis was performed using both QC materials and patient samples. To assess the inter-run performance of the OFA using S5XL instrument, different QC materials were analyzed (HD300: n = 42 runs; HD200: n = 35 runs). All expected mutations in the reference material were called at expected VAFs. Coefficient of variation of measured VAF ranged from 5.6% (p.His1047Arg, expected VAF = 17.5%) to 37.7% (p.Thr790Met, expected VAF = 1%) (Table 1; Figure 2 and Figure S1). The follow-up of the VAF QC material by Levey–Jennings charts assessed the low inter-run variability for VAF ranging from 1% to 11%. Furthermore, no drift of VAF values was observed. Indeed, during the 21-month period, for the lowest expected VAF (EGFR p.Thr790Met), only one value (2.3%) overpassed the limit of 3SD (Figure 2). These data were also compared to a similar inter-run variability study performed using the THS on the Miseq instrument (HD300: n = 42 runs; HD200: n = 43 runs). The comparison of the VAF obtained for expected variants indicated a strong correlation between both methods (Pearson’s r = 0.990) (Table 1). To further evaluate inter-run variability, clinical samples from colorectal and lung tissues were analyzed within six different runs. Indeed, PIK3CA p.His1047Arg, EGFR p.Leu747_Ala750del, AKT1 p.Gly17Leu, BRAF p.Val600Glu, and KRAS p.Gly12Val mutations previously detected using the THS assay were called at each run using the OFA (Figure 3). The VAF coefficients of variation ranged from 6.7% (BRAF p.Val600Glu) to 33.1% (KRAS p.Glu12Val). During the 21-month study of the OFA, the pipeline allowed to detect a large variety of mutations at a broad range of VAF. Among 420 consecutive DNA clinical samples, the pipeline reported SNV or indel in 294 samples (70%). The distribution of the most frequently mutated genes was KRAS 42.2% (n = 158), EGFR 13.9% (n = 52), PIK3CA 10.2% (n = 38), BRAF 9.1% (n = 34), NRAS 4.0% (n = 15), and CTNNB1 2.9% (n = 11) (Figure 4). During our study, 353 DNA variants were detected. Sixty-four mutations had a VAF comprised between 2 and 10%. The original pipeline was designed to detect hotspot mutations with a VAF above 3%. The modification of the pipeline allowed the detection of 10 (3.4%) mutations with VAF below 3% with potential theranostic impact: EGFR p.Leu747_Thr751del (2.9%), p.Leu858Arg (2.6%); KRAS p.Gly12Ala (2.4% and 2.4%) p.Gly12Asp (2.1% and 2.9%), p.Gly12Cys (2.7%), p.Gly13Cys (2.9%), p.Lys117Asn (2.3%); and NRAS (2.7%). To assess inter-run variability, 20 QC samples were analyzed in different runs. The MAPD scores ranged from 0.272 to 0.356, assessing a low read coverage noise (Table 2). Furthermore, the inter-run variability of MAPD was low (mean: 0.305 ± 0.032), indicating the long-term performance stability of CNV determination. MET and MYCN gene amplifications were systematically reported close to expected levels. In addition, the variation of inter-run CNV quantification remained low as assessed by the low coefficient of variation for each CNV level. No drift of the measured CNV value was observed, indicating that the performance of the method remained stable over time. This study was extended to three clinical samples, which were sequenced twice. KRAS, EGFR, FGFR2, CCND1, CDK4, and ERBB2 genes amplifications were reported in the two runs with equivalent amplification rates. Furthermore, we confirmed CNV detection of ERBB2 gene by studying protein expression level using immunochemistry assay in four lung tumors samples (Table 3; Figure 5). Nuclei were stained in blue (hematoxylin), cytoplasm of positives cells for ERBB2 were stained in brown, while negative cells remained unstained. A large view of bone tissue section showing negative staining for ERBB2 is shown in Figure S2. The pipeline revealed 88 (20.7%) gene amplifications among 420 clinical samples. These amplifications were detected in 56 lung, 6 colon, 3 bile duct, 3 pancreas, 2 skin, and 2 breast samples. The genes exhibiting most frequently CNV were CDK4 (n = 20), EGFR and MYC (n = 13 each), CCND1 (n = 7), ERBB2 (n = 6), and KRAS and MET (n = 5 each) (Figure 6a). The amplification factors reported by the OFA pipeline ranged from 5 to 75. Amplification factors remained below 6x in eight cases and were not reported to clinicians (7%) (Figure 6b). RNA samples from HD784 and Seracare QC materials were sequenced in 29 and 7 runs, respectively. Quality criteria were satisfactory for each sample, allowing further data analysis. Expected RNA fusions and exon skipping variations were systematically detected. Fusion reads represented 1.6 to 17.8% of total reads counts (Table 4). The inter-run CV of the percentage of total reads counts ranged from 7.9 to 38.4%. No drift in the detection of both fusions and exon skipping was observed, indicating that the performance of the method remained stable over time. To complete the validation of MET exon 14 skipping detection, we analyzed RNA material from two patients with MET alterations previously detected at DNA level with the THS assay (MET c.3082 +1G>C and MET c.2888-2A>C). These mutations were detected at the RNA level with the OFA in two independent experimentsIn order to validate ALK and ROS detection, two gene fusions determined by OFA [(EML4(6)—ALK(20) (reads counts: 2024, total mapped fusion reads: 49,617) and CD74(6)—ROS1(34) (reads counts: 1027, total mapped fusion reads: 31,980)] were confirmed by immunochemistry (Figure 7). Nuclei were stained in blue (hematoxylin), cytoplasm of positives cells for ALK and ROS1 were stained in brown, while negative cells remained unstained. Negative ROS1 staining of non-tumoral lung tissue is shown in Figure S3. Among 55 RNA clinical samples, 11 samples were not suitable for analysis because of poor sequencing quality and no RNA modification was detected for 37 samples. The OFA pipeline detected three MET exon skipping alterations, two ROS fusion transcripts in lung tissues (CD74(6)—ROS1(34) and SLC34A2(13)—ROS1(34)), one ALK fusion transcript in lung tissue (EML4(6)—ALK(20)), and one PAX8 fusion transcript in thyroid tissue (PAX8(9)—PPARG(2)). Theranostic analysis of tumors becomes continuously more complex requiring the study of ever more larger panels of genes in search of point mutations, short insertions and deletions, CNV, or gene fusions. In addition, the development of less invasive sampling techniques such as needle biopsy lead to the decrease of available material. Despite these limitations, genetic analysis must remain compatible with a rapid determination of therapeutic strategy. NGS is the method of choice to fulfill these challenges, assuming that optimal analytical performances are maintained over time. The aim of this study was to determine whether the analytical performances of the Oncomine Focus Assay remain suitable for clinical practice over a 21-month period. For this purpose, we evaluated the sequencing results obtained from both QC and clinical samples along 73 consecutive runs on an Ion S5 XL sequencer. During this period, we observed no technical failure and the inter-runs variability of critical parameters was adapted to clinical practice. We first observed that the template generation was reproducible over time. Indeed, the coefficient of variation of the chips loading was 3.4% (mean: 11 × 106 amplicons). Subsequent bioinformatic processing indicated that 49% (CV = 13%) of these amplicons had a sufficient quality for further processing. Additionally, we showed that 6.0 × 105 (CV = 44%) reads per sample were mapped to hg19 reference genome. These CV reflected also the variation in DNA quality of samples. However, our results were consistent with other studies using the same technique. Indeed, Bartlett et al. showed that intra-laboratory variability of mapped reads generation may range from 0.18 × 106 to 1.6 × 106 (10). In our study, the quality of mapped reads within both QC and clinical samples was maintained over time, assessed by the low variability of percentage of end-to-end reads (97.3%; CV = 5.6%) and reads without strand bias (98.4%; CV 2.0%). The main point of variation, influenced by DNA quantity, was the percentage of amplicons reaching 500X per sample [2]. In our routine practice, although a depth below 500X may remain informative when mutations are at a high allelic frequency, we consider that the depth should reach 500X to validate a negative result. We showed that, for a given sample, 95.8% (CV = 16.7%) of the amplicons reached a depth of 500X. Moreover, all amplicons were individually covered with at least 845X (Figure 1). These results are comparable with those of Williams et al.’s study reporting 89.7% of samples with an average amplicon coverage above 500X [9]. In our analysis, all hotspot regions were sequenced with at least 1574X. Of note, the amplicons sequenced with a lower depth targeted only CNV positions (Figure 1c; Appendix B). We successfully sequenced 98% of clinical samples, a proportion comparable to the 95% described by Williams et al. [9]. These data suggest (i) the tissular heterogeneity of samples did not affect the quality of the sequencing and (ii) the performance of sequencing did not decline over time. Considering that numerous samples were tissue biopsies, we show that the OFA is adapted to this limited amount of material. The call of the mutations with the Ion Reporter software could fail because of inadequacy of the bioinformatic pipeline. Therefore, we modify some parameters of the workflow. Clinical purpose led us to use samples with a very low acid nucleic concentration, potentially causing limited sequencing depth. In order to improve robustness of our analysis, we modified the hotspot file (Appendix A). We set the detection threshold down to 3% of AF for SNV and indel, and down to 2% for driver variations in BRAF, ERBB2, EGFR, KRAS, and NRAS genes. In addition, we increased the down sampled coverage up to 5000 reads (the minimum reads randomly considered), allowing to improve the inter-run variability. Finally, we and others observed incorrect nomenclature calls [9]. To attribute the mutation position to the correct variant annotation, the “allele view” and the “allow complex” parameters were selected. In order to validate our modifications of the bioinformatic pipeline, we analyzed the inter-run variability of two QC materials. We observed similar inter-run variabilities between the OFA and THS kits (Table 1). As expected, the variability was higher for low VAFs. However, the low coefficients of variation allowed the detection of mutations down to 1% VAF at each run. Furthermore, mutations from 3 to 5% expected VAF were systematically called (Table 1; Figure 2 and Figure S1). This long-term follow-up of VAF values highlighted that there were no loss of performance and no drift over time. This low variability was appropriate for clinical sample analysis. An inter-laboratory study involving six teams and analyzing the same QC materials obtained similar VAF for most mutations. However, we observed differences only for mutations with VAF < 5%. Five of the six laboratories did not report the EGFR p.Leu858Arg SNP with a VAF of 4% and none reported the EGFR p.Thr790Met and p.Glu746_Ala750del variations at VAF 1% and 2%, respectively [10]. This increased sensibility may be due to our optimization of the bioinformatic pipeline. Using FFPE clinical samples, the observed inter-run variability was higher (Figure 3). These results, which were expected, were possibly due to lower DNA quality. Nevertheless, this larger variability remained sufficient to systematically report each expected mutation. In our routine practice, we include a QC sample at each library preparation for quality purpose. Considering clinical results interpretation, to qualify a variant as both authentic and relevant, we take into consideration the number of reads at the corresponding genomic position, the tumor cellularity, and the clinical context. In the case of patient follow-up or for investigation of therapeutic resistance mechanism, the communication to clinicians of variants with VAF below the threshold of 5% may have a significant impact on prognosis [13,14]. During the 21-month period of DNA analysis, we detected 353 mutations among tissue samples. The increased sensitivity of the bioinformatic analysis allowed the detection of 10 mutations with VAF below 3%. The mutation with the lowest VAF (KRAS p.Gly12Asp; 2%) was detected in a colon sample. When such low VAF mutation are detected, the biologist has to consider also detailed metrics and genomic alignment visualization to appreciate its theranostic relevance [13,14,15]. Considering gene amplification, the CNV value is interpreted according to the MAPD score. The analysis of inter-run variability using QC material revealed that both expected gene amplifications were detected and associated MAPD scores were below the threshold of 0.4, giving confidence to these results. The coefficients of variation were notably low for MET and MYCN amplification (5.2% and 3.7%, respectively), attesting the inter-run stability over time. In four clinical samples, ERBB2 gene amplifications were confirmed by immunocytochemistry (Table 3; Figure 5). This limited comparison suggests that, using OFA, the CNV represented more a confidence level of positivity than an absolute value [16]. In order to avoid false positive, we choose to report only CNV with a “5% confidence lower limit” above 6 [17]. Among the 88 amplifications called by the pipeline, we reported 79 (91%) results to the clinicians. The scarcity of patients presenting RNA alteration imposed a validation protocol using two different QC materials in order to increase the diversity of fusion transcripts. The expected fusion transcripts were systematically detected throughout the 12-month period. Despite a high heterogeneity of determined inter-run variability CV, fusions reads counts were always above the manufacturer recommended thresholds (i.e., 20 for targeted fusions, 250 for non-targeted fusions, and 120 for exon skipping). Like others, we chose to determine the ratio of fusion reads counts to total mapped reads counts (Table 4) [10]. This ratio, similar to VAF, was useful to interpret clinical samples with low total RNA quantity. In our QC study, a fusion transcript could be reported with a percentage as low as 1% of total mapped reads. In contrast, in samples with a high amount of total RNA mapped reads, a ratio below 10% may indicate a false positive result. Regarding HD784 QC material, we obtained similar fusion reads and fusion reads ratios to those reported by Bartlett et al.’s study [10]. In our clinical samples study, we reported seven fusions or exon skipping variants; among them, two were validated by immunochemistry (Figure 7). Our results demonstrated that the sensitivity and the accuracy of OFA were suitable for molecular analysis of low quality and/or limited amounts of nucleic acids obtained from FFPE tumor samples in clinical routine practice. Slight modifications of the bioinformatic pipeline allowed to improve detection of low VAF variants, which may be useful to early detect genetic alterations involved in resistance mechanisms. This long-term study conducted in real-life conditions demonstrated that the performances of the OFA remained stable over time and ensured the reliability of results with theranostic impact.

PMC10001105

Yohann Dabi,Amélia Favier,Léo Razakamanantsoa,Léa Delbos,Mathieu Poilblanc,Philippe Descamps,Francois Golfier,Cyril Touboul,Sofiane Bendifallah,Emile Daraï

Insight on Non-Coding RNAs from Biofluids in Ovarian Tumors

28-02-2023

ovarian tumor,non-coding RNA,borderline ovarian tumor,ovarian cancer

Simple Summary Ovarian cancer is the most lethal gynecologic cancer since it is often diagnosed at advanced stages. Current tools for diagnosis are currently insufficient and include physical examination, ultrasound and pelvic magnetic resonance imaging, as well as algorithms combining thoraco-abdomino-pelvic scans and blood markers. In this context, there is a need for new tools not only to assess the diagnosis but also to predict the response to chemotherapy and to detect recurrences. Previous studies have highlighted the potential value of non-coding RNAs (ncRNA) in tissue samples, but rarely in biofluids. In this review, we aim to summarize the existing literature on ncRNAs and ovarian tumors in biofluids. Most studies are focused on serum and blood with no data on other biofluids and with few ncRNAs investigated using qRT-PCR or microarray, which does not reflect the heterogeneity of ovarian cancers. Abstract Ovarian tumors are the most frequent adnexal mass, raising diagnostic and therapeutic issues linked to a large spectrum of tumors, with a continuum from benign to malignant. Thus far, none of the available diagnostic tools have proven efficient in deciding strategy, and no consensus exists on the best strategy between “single test”, “dual testing”, “sequential testing”, “multiple testing options” and “no testing”. In addition, there is a need for prognostic tools such as biological markers of recurrence and theragnostic tools to detect women not responding to chemotherapy in order to adapt therapies. Non-coding RNAs are classified as small or long based on their nucleotide count. Non-coding RNAs have multiple biological functions such as a role in tumorigenesis, gene regulation and genome protection. These ncRNAs emerge as new potential tools to differentiate benign from malignant tumors and to evaluate prognostic and theragnostic factors. In the specific setting of ovarian tumors, the goal of the present work is to offer an insight into the contribution of biofluid non-coding RNAs (ncRNA) expression.

Insight on Non-Coding RNAs from Biofluids in Ovarian Tumors Ovarian cancer is the most lethal gynecologic cancer since it is often diagnosed at advanced stages. Current tools for diagnosis are currently insufficient and include physical examination, ultrasound and pelvic magnetic resonance imaging, as well as algorithms combining thoraco-abdomino-pelvic scans and blood markers. In this context, there is a need for new tools not only to assess the diagnosis but also to predict the response to chemotherapy and to detect recurrences. Previous studies have highlighted the potential value of non-coding RNAs (ncRNA) in tissue samples, but rarely in biofluids. In this review, we aim to summarize the existing literature on ncRNAs and ovarian tumors in biofluids. Most studies are focused on serum and blood with no data on other biofluids and with few ncRNAs investigated using qRT-PCR or microarray, which does not reflect the heterogeneity of ovarian cancers. Ovarian tumors are the most frequent adnexal mass, raising diagnostic and therapeutic issues linked to a large spectrum of tumors, with a continuum from benign to malignant. Thus far, none of the available diagnostic tools have proven efficient in deciding strategy, and no consensus exists on the best strategy between “single test”, “dual testing”, “sequential testing”, “multiple testing options” and “no testing”. In addition, there is a need for prognostic tools such as biological markers of recurrence and theragnostic tools to detect women not responding to chemotherapy in order to adapt therapies. Non-coding RNAs are classified as small or long based on their nucleotide count. Non-coding RNAs have multiple biological functions such as a role in tumorigenesis, gene regulation and genome protection. These ncRNAs emerge as new potential tools to differentiate benign from malignant tumors and to evaluate prognostic and theragnostic factors. In the specific setting of ovarian tumors, the goal of the present work is to offer an insight into the contribution of biofluid non-coding RNAs (ncRNA) expression. Adnexal masses represent a wide spectrum of tumors of various origins (ovary, fallopian tube, and pelvic organs), among them ovarian tumors are the most frequent, raising diagnostic and therapeutic issues linked to a large spectrum of tumors with a continuum from benign to malignant. The true incidence of ovarian tumors in the general population is unknown as most of them are asymptomatic and hence undiagnosed [1]. It is estimated that 10% of women will undergo surgery for an ovarian mass in their lifetime [2]. Ovarian tumors are generally detected at physical examination or at pelvic imaging for various reasons in asymptomatic patients. Less frequently, an ovarian tumor can be the source of symptoms, acute pain (torsion of the adnexa), or chronic pelvic pain related to compression of neighboring organs [1]. Occasionally, the diagnosis can be made in the context of ovarian cancer (OC) often diagnosed at an advanced stage alongside ascites, bloating, weight loss and peritoneal carcinomatosis [3,4]. OC is the fifth most common cancer and the most lethal gynecologic malignancy, with 313,959 new cases per year and 207,252 deaths per year in 2020 [5]. Epithelial ovarian cancer (EOC) represents more than 95% of all OC [6]. During their lifetime, approximately one in seventy women will develop the disease. The median age at diagnosis is 68 years with a maximum incidence in women in their 70s. The disease behaves as a chronic condition with relapses, and iterative chemotherapies can therefore lengthen survival [7]. The disease survival remains poor at 40% at five years, and 32% at ten years, since more than 75% of patients are diagnosed at advanced stage disease, while the five-year survival rate for women diagnosed at an early stage reaches 90%, underlining the potential benefit of biomarkers of early stages as well as for detecting the transition of a borderline tumor into invasive cancer [5]. Except for patients with deleterious mutation, for whom risk-reducing surgery is recommended, no screening for OC in the general population has proved its relevance [8]. In routine practice, first-line transvaginal ultrasound is used to differentiate benign, borderline and malignant ovarian tumors [9]. Van Calster et al. evaluated the clinical utility of six prediction models for ovarian malignancy [10], and found that the ADNEX models with and without cancer antigen 125 (CA125) determination and SRRisk were the best calibrated. However, previous studies have underlined that 18% to 31% of ovarian tumors remain indeterminate after ultrasonography using International Ovarian Tumor Analysis (IOTA) Simple Rules or other ultrasonography scoring systems [9,11,12]. MRI is the second-line imaging technique for characterizing ovarian tumors. Ovarian-Adnexal Reporting Data System Magnetic Resonance Imaging (O-RADS MRI) score consists of five categories according to the positive likelihood ratio for a malignant neoplasm [13] with a sensitivity of 0.93 and a specificity of 0.91. However, these results are based on an observational study without randomization, and the score was not integrated into clinical decision-making, limiting its utility. CA125 is the most used biomarker for determining the nature of ovarian tumors [14,15], although normal levels have been reported in as many as 50% of early stage ovarian cancers [16,17,18]. A recent Cochrane review evaluated several algorithms to assess the risk of malignancy of ovarian tumors, including biological markers and imaging [19]. However, none of these scoring systems had sufficient relevance to characterize ovarian tumors [19]. Finally, a recent review by Funston et al. analyzing 18 documents from 11 countries showed that transabdominal/transvaginal ultrasound and the CA125 were the most widely advocated initial tests [20]. However, no consensus exists on the best strategy to improve diagnostic performance: “single test”, “dual testing”, “sequential testing”, “multiple testing options” and “no testing”. This further underlines the need for new biological tools to diagnose ovarian cancer in the general population as early as possible. In addition, there is a need for prognostic tools such as biological markers of recurrence and theragnostic tools to detect women not responding to chemotherapy in order to adapt therapies. Among ncRNAs, those with less than 50 nucleotides are defined as small RNAs (sncRNAs) and those with more than 200 nucleotides are defined as long non-coding RNAs (lncRNAs). SncRNAs are further classified into microRNAs (miRNAs), Piwi interacting RNAs (piRNAs), transfer RNAs (tRNAs), small nuclear RNAs (snRNAs), and small interfering RNAs (siRNAs) [21,22]. LncRNAs are classified into intergenic ncRNAs (lincRNAs), some circular RNAs (circRNAs), and ribosomal RNAs (rRNAs) [23,24]. Numerous studies indicate that ncRNAs, representing 98% of the transcriptome, are essential for tumorigenesis by regulating the expression of tumor-related genes [25,26,27,28,29,30,31,32]. These ncRNAs emerge as new potential tools to differentiate benign from malignant tumors and to evaluate prognostic and theragnostic factors. In the specific setting of ovarian tumors, the goal of the present work is to offer an insight into the contribution of biofluid non-coding RNAs (ncRNA) expression. miRNAs are small intracellular RNAs, 22 nucleotides long, capable of inducing the silencing of gene expression by post-transcriptional regulatory mechanisms [33] or alternatively by binding miRNAs to the 5′UTR regions, inducing either activation or repression of translation. The miRNAs synthesis is presented in Figure 1. Numerous studies focusing on tissue samples have demonstrated the role of miRNA in OC, characterized by a wide-scale deregulation of miRNAs and aberrant expression of miRNAs correlated with histotype, histological grade, lymphovascular space involvement, lymph node and distant metastasis as well as FIGO stages [34,35,36,37]. Dhar Dwivedi et al. reported 53 miRNAs upregulated and 68 miRNAs downregulated in OC. In the upregulated miRNA group, a total of 7605 gene targets were found. Among them, miRNA-20a-5p and miRNA 106a-5p regulate 14.1% and 9.4% of target genes, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis enrichment was performed for these upregulated miRNA target genes, identifying 67 and 24 pathways as enriched. Similarly, for downregulated miRNAs, a total of 9287 gene targets were identified. miR-26b-5p, miR-519d, miR-15a, and miR-15b regulated respectively 20.8%, 11.3%, 8.6%, and 8.9% of the target genes, with 41, 95, and 38 enriched pathways [38]. In contrast to the miRNA expression extensively analyzed in tissue samples, relatively little data are available on their expression in biofluids. This point is particularly important as it could allow preoperative tumor assessment, improving therapeutic strategy and decision making. There is published evidence that serum miR-221 [39], mir-205 [40], mir-375 [41], mir-210 [42], mir-34a-5p [43], mir-92 [44], mir-93 [43], mir-141 [45] mir-7 [46] and mir-429 [47] are upregulated in the biofluids of patients with different types of ovarian cancers. On the other hand, expression of microRNAs let-7f [48], mir-93 [45], mir-199a [49] and mir-148a [50] is downregulated in the biofluids of patients with ovarian tumors. From the diagnostic point of view, Oliveira et al. [51] evaluated the profile of plasma miRNAs on a panel of 46 candidates, finding four upregulated miRNAs (miR-200c-3p, miR-221-3p, miR-21-5p and miR-484) and two downregulated (miRNA-195-5p and miRNA-451a). However, only two miRNAs (miRNA-200c-3p and miRNA-221-3p) were confirmed in a validation cohort. Savolainen et al. [52], on a short series of nine patients, found that miRNA-200a, miRNA-200b and miRNA-200c, in both tumor tissue and plasma, allowed discrimination between malignant and benign samples. In addition, a correlation was found between the expression of miRNA-200 in urine and plasma with the malignant status of tumors. Another study reported a higher level of miR-590-3p in OC plasma compared to a control group [53]. Chang et al. [54] observed for germ cell tumors of the ovary (OGCT) and sex cord (SCST)-specific expression profiles of miRNAs in nine OGCTs (two malignant and seven benign) and three SCST. Overexpression of miRNA-373-3p, miRNA-372-3p and miRNA-302c-3p and underexpression of miRNA-199a-5p, miRNA-214-5p and miRNA-202-3p were reproducibly observed in malignant OGCT versus benign OGCT or SCST. Yokoi et al. reported a plasma signature composed of six miRNAs selected after RT-qPCR (miRNA-200a-3p, miRNA-766-3p, miRNA-26a-5p, miRNA-142-3p, let-7d-5p and miRNA-328 -3p) able to successfully distinguish patients with ovarian cancer from healthy controls (AUC: 0.97; sensitivity, 0.92; and specificity, 0.91), paving the way for screening ovarian cancer [55]. Among five miRNAs, Zhu et al. observed that only serum miRNA-125b could distinguish benign controls and EOC patients [56]. Moreover, among the miR-200 family, Meng et al. [57] identified that serum levels of miRNA-200a (p = 0.0001), miRNA-200b (p = 0.0001), and miRNA-200c (p = 0.019) could distinguish benign from malignant ovarian tumors. Resnick et al. [58], confirmed that miRNAs in serum could be used as a marker for ovarian cancer in a series of 28 patients, based on over-expression of miRNA-21, miRNA-92, miRNA -29a, miRNA-93 and miRNA-126, and underexpression of miRNA-99b, miRNA-127 and miRNA-155. In a meta-analysis on the diagnostic value of serum miRNA-21 expression, including six studies with limited sample size, Qiu & Weng reported a pooled respective sensitivity, specificity, and AUC of 0.81 (95%CI: 0.73–0.88), 0.82 (95%CI: 0.75–0.87), and 0.89 (95%CI: 0.85–0.91) imposing further validation [59]. Recently, Wenyu Wang developed a plasma signature for malignant tumors using extracellular vesicles, and identified a panel of eight miRNAs (miR-1246, miR-1290, miR-483, miR-429, miR-34b-3p, miR-34c-5p, miR-145–5p, miR-449a). Their model had a respective AUC of 0.9762 and 0.9375 in the training and the validation set [60]. In contrast to several studies focusing on serum miRNAs, Kai Berner et al. focused on urinary expression of twelve microRNAs. In their experience, miR-15a was upregulated whereas let-7a was downregulated in ovarian cancer patients [61]. From the prognostic point of view, Zhu et al. [56] found that elevated serum miRNA-125b levels were higher in patients with early OC stages (FIGO stages I-II), and with no residual tumor after surgery. In addition, elevated serum miR-125b were correlated with progression-free survival (p = 0.035). Meng et al. reported that high levels of miRNA-200b and miR-200c were associated with poorer overall survival (p = 0.007, p = 0.017, respectively) [57]. Gao et al. [62], in a study including 74 serum samples of OC patients, 19 of borderline tumors and 50 of healthy controls, found that elevated serum miRNA-200c was correlated with improved two-year survival, while decreased serum miR-145 levels was associated with disease progression [63]. Finally, Zuberi et al. observed in OC an association between high expression of serum miR-125b and lymph node and distant metastases [64]. From the theragnostic point of view, most studies are based on cell cultures [65,66]. Yang et al. [67] found that protein kinase B (AKT) pathway activation was regulated by miRNA-214 and miRNA-150. Moreover, Echevarria-Vargas et al. reported a cisplatin resistance associated with miR-21 expression [68]. Lu et al. [69] observed that let-7a expression was significantly lower in OC patients sensitive to platinum and paclitaxel compared to those resistant to these agents. Langhe et al. reported that a panel of four miRNAs (let-7i-5p, miRNA-122, miRNA-152-5p and miRNA-25-3p) significantly downregulated in OC with potential contribution to drug resistance [70]. Finally, in a recent review, Saburi et al. [71] noted a relation between miRNA-30a-5p, miRNA-34a, miRNA-34a-5p, miRNA-98-5p, miRNA-142-5p, miRNA-338-3p, miRNA-708 and cisplatin resistance. Similarly, a relation was noted between miRNA -136, miRNA-338-5p, miRNA-503-5p, miRNA-1246, miRNA-1307 and paclitaxel resistance. Finally, a relation was noted between miRNA509-3p and platinum resistance and between miRNA-744-5p and carboplatin resistance. However, it is important to note that none of these miRNAs have been evaluated in biofluids, which could be a major contributor to adapting chemotherapy. From the analysis of the literature on miRNAs in biofluids, it appears that there are arguments to suggest their role in physiopathology, in the differential diagnosis between benign and malignant tumors and to a lesser degree with borderline tumors, as well as to support their diagnostic, prognostic and theragnostic values. However, these analyses were mainly performed by microarray with validation by RT-qPCR, with potential biases linked to the methodology as proven in the context of endometriosis [72]. Moreover, the small number of studies with limited sample size focusing on the specific evaluation of miRNAs expression in biofluids of patients with ovarian tumors limit their potential clinical utility. Indeed, Langhe et al. [70] point out that miRNAs are abundant in tissues but are often rare in plasma and serum. For the quantification of miRNAs in plasma, the authors stressed that it was essential to use a high-sensitivity platform such as Next Generation Sequencing (NGS). However, no studies using NGS and bioinformatic tools to analyze miRNA content in large blood, serum, urine or saliva series are available to determine their role in routine practice. piRNAs are small ncRNAs of 24–32 nucleotides [73]. piRNA biosynthesis is summarized in Figure 2 [73,74]. Dysregulation of piRNAs and proteins (e.g., PIWI family proteins) has been observed in various cancers including OC [74,75]. The main function of piRNAs is to protect the genome from transposons. Giulio Ferrero et al. analyzing piRNA expression in urine, plasma exosomes, and stool observed that urine samples exhibited the highest piRNAs expression [76]. The piRNAs production and function are presented in Figure 2. Singh et al. [77] found that piRNAs distinguish endometrioid from serous OC with 159 and 143 piRNAs differentially expressed, respectively. Among these piRNAs, 74 were upregulated and 77 downregulated in endometrioid OC, and 56 upregulated and 81 downregulated in serous OC. piR-52,207 was found to be upregulated in endometrioid OC, and both piR-52,207 and piR-33,733 in serous. Interestingly, among 20 biofluids evaluated by Hulstaert et al. [78], saliva has the highest fraction of piRNAs. Thus far, in the specific setting of OC, little data are available on piRNA expression in blood samples allowing for the evaluation of their potential diagnostic and prognostic values, and no study has evaluated saliva piRNA expression. Transfer RNAs (tRNAs) are a source of small regulatory RNAs (tsRNAs) acting on protein translation [38,79]. Based on the cleavage site, tsRNAs are divided into transfer RNA-derived RNA fragments (tRFs) and tiRNAs [80] with tumorigenesis functions [81,82,83,84,85,86,87]. tRFs are also involved in gene expression, oncogene activation and ovarian cancer progression through association with Ago and PIWI proteins [88]. Dhar Dwivedi et al. [38] observed that tsRNAs can predict abnormal cell proliferation with high accuracy in serum samples from a cohort of patients, healthy controls and benign and malignant tumors [89]. Eric Y. Peng observed that four tsncRNAs differentially expressed in serum samples had a high diagnostic accuracy for malignancy with an AUC of 0.95. Similarly, serum tRF-03357 and tRF-03358 levels are increased in patients with high-grade OC [90,91]. In addition, i-tRF-GlyGCC is linked to advanced FIGO stages, suboptimal debulking and, most importantly, with early progression and poor overall survival in EOC patients [91]. Despite a relatively abundant literature on tRNA, no study has focused on their diagnostic, prognostic and theragnostic relevance in the specific setting of OC. CircRNAs are a large class of ncRNA with more than 70,000 specimen identified in human tissues [92,93,94,95,96,97,98] and in many cancerous cell types including OC [94,99,100,101]. CircRNAs Biosynthesis see Figure 3. circRNAs have a high prevalence, specificity [102,103], stability [104] and conservation [105], conferring a particular value as biomarkers. Moreover, using 20 biofluids, Hulstaert et al. demonstrated that circRNAs are enriched in biofluids compared to tissues [78]. Indeed, the median circRNA read fraction in biofluids was 84.4% vs. 17.5% in tissues. circRNAs act as specific miRNA reservoirs or sponges, as protein or peptide translators, and as regulators of gene transcription and expression, and interact with RNA-binding proteins (RBPs) impacting on the transcription and translation of genes. From the diagnostic point of view, in the serum of OC patients, Wang et al. identified five circRNAs (circ-0002711; Chr5:170610175-170632616+; circ-0001756; Chr4:147227078-147230127-; and Chr16:53,175091-53191453+) with diagnostic value [106]. From the prognostic point of view, a previous study demonstrated that serum circ-0049116 released from a cell surface protein (mucin 16) could have value [107]. Increased expression of the circ-MUC16/miR-199a-5p axis positively correlates with the aggressiveness of OC. Expressions of circ-FAM35b, circ-051239, circ-ABCB10, circ-0072995, circ-EEF2, circ-RAB11FIP1, circ-FGFR3, circ-NOLC1 and circ-PGAM1 were correlated with metastasis of EOC [108,109,110,111,112,113,114,115]. Moreover, circ-0015756, circ-0002711, hsa-circ-0015326, circ-0001068, circ-0025033 and circ-KIF4A exhibited prognostic value in OC [116,117,118,119]. Serum circ-SETDB1 is positively correlated with lymph node metastasis and advanced stages of serous OC [120]. From the theragnostic point of view, circ-0002711/miR-1244/ROCK1 and has-circ-0015326/miR-127-3p/MYB pathways could be potential therapeutic targets [117]. CircRNAs, has-circ-0000714, circ-TNPO3 and circ-NRIP1 are expressed in OC with Paclitaxel resistance [121,122,123]. You et al. also reported that Circ-0063804 promoted OC cell proliferation and resistance to cisplatin by enhancing CLU expression via sponging miR-1276 [124]. In a recent review, Min Liu et al. [125] reported the different implications of circ-RNAs in the pathogenesis of OC, and underlined that circRNA-Cdr1as inhibited OC cell proliferation and promoted cisplatin-induced cell apoptosis, while circRNA-TNPO3 enhanced paclitaxel resistance. Despite the potential contribution of these circ-RNAs to the diagnostic, prognostic and theragnostic value in patients with OC, no study has focused on these biomarkers in biofluids in this specific setting. Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs with 60–300 nucleotides, and are mainly divided into two classes: C/D box SnoRNAs and H/ACA box SnoRNAs [126]. Most snoRNAs act as guide RNAs for the post-transcriptional modification of ribosomal RNAs, by modifying 2′-O-ribose methylation and pseudo-uridylation of ribosomal RNAs (rRNAs) [125]. Cumulative evidence demonstrates that snoRNAs play a role in the tumorigenesis of various cancers [127,128,129,130]. Using microarray on 197 EOC (162 serous, 15 endometrioid, 11 mucinous, and 9 clear cell), Oliveira et al. found that SNORA68 and SNORD74 were associated with decreased overall survival (OS) and poor clinicopathological features [131]. In an in vitro study Huilong Lin et al. observed that SNHG5 enhanced the sensitivity of ovarian cancer cells to paclitaxel by sponging miR-23a [132]. Wenjing Zhu et al. developed a signature based on nine snoRNAs (SNORD126, SNORA70J, SNORD3C, SNORA75B, SNORA58, SNORA11B, SNORA36C, SNORD105B, SNORD89,) to predict the prognosis of OC patients [133]. Finally, Peng-Fei Zhang et al. reported that SNHG22 overexpression is associated with poor prognosis and induces chemotherapy resistance to cisplatin and paclitaxel via the miR-2467/Gal-1 signaling pathway in EOC [134]. Although the number of dysregulated snoRNAs in ovarian cancer is up to 462 [127], one preliminary study investigated the role of snoRNA RNU2-1f in ovarian cancer. In this study, snoRNA abundance was investigated in serum (n = 10) by microarray analysis and validated in a serum set (n = 119) by reverse-transcription quantitative PCR. They reported that abundance of U2-1 snoRNA fragment (RNU2-1f) was significantly increased in sera of ovarian cancer patients (p < 0.0001) and paralleled International Federation of Gynecology and Obstetrics stage as well as residual tumor burden after surgery (p < 0.0001 and p = 0.011, respectively). lncRNAs act via various pathways to regulate gene expression at different levels [135] with a biogenesis similar to mRNAs (Figure 3). Arbitrarily, lncRNAs are defined as being composed of more than 200 nucleotides mainly between 1000 and 10,000. Four different archetypes of lncRNA functions have been described (Figure 4) [136]. Since the discovery of lncRNAs, more than a thousand publications have been listed in PubMed in the specific setting of OC, although only about one percent focused on their expression in biofluids. Numerous studies about lncRNAs, mainly based on OC tissue samples, have demonstrated an association between clinicopathological characteristics such as histological type and grade, FIGO stages, lymph node and distant metastasis and some lncRNAs [137]. In a review including 34 studies involving more than 4000 women with OC, Hosseini and al observed an association between lncRNAs expression and PFS (HR: 1.88, 95% CI: (1.35–2.62)) and DFS (HR: 6.07, 95% CI: 1.28–28.78)). However, among the 34 studies, only one evaluating lncRNA in plasma [138] was included. They concluded that their work supported the robust prognostic significance of altered lncRNAs in ovarian cancer, but that more extensive studies are required. From the diagnostic point of view, numerous studies have reported a relation between some lncRNAs and clinicopathological characteristics using OC samples analyzed by RT-qPCR, microarray and hybridization, and fluorescence in situ hybridization (FISH) from cancer tissue compared with adjacent normal tissue or samples from healthy patients (Salamini-Montemurri). In a recent review, Salamini-Montemurri et al. listed the various lncRNAs with clinicopathological value [137]. Among them, only E2F4AS [139], FLVCR1-AS1 [140], LINK-A [141], MLK7-AS1 [142] and aHIF [143] were evaluated in blood serum, but none of them exhibited a sufficient diagnostic value. Chun-Na Liu et al., in 185 EOC patients and 43 healthy volunteers, evaluated by RT-qPCR the expression of LOXL1-AS1 showing a higher expression in EOC patients with an AUC of 0.843 but a sensitivity and specificity of only 65.3% and 68.2%, respectively [144]. Using RT-qPCR, Jiezhi Ma & Min Xue investigated the expression of LINK-A in the plasma of 68 patients with OC and 34 healthy females, showing a higher level in OC patients. Recently, for the diagnosis of OC, Barwal and al found that blood lncRNA RP5-837J1.2 had a sensitivity, specificity and AUC of 97.3%, 94.6% and 0.99, respectively, but without external validation [145]. From the prognostic point of view, some studies have evaluated the value of lncRNAs in blood or serum to predict survival. In the meta-analysis using RT-qPCR, Chen et al. evaluated plasma levels of MALAT1 in 47 patients with EOC with metastasis (EOC/DM), 47 patients without metastasis (EOC/NDM), and 47 healthy controls (HC) [146]. Plasma MALAT1 allowed to distinguish EOC/DM and HC with an AUC of 0.884 (95% CI, 0.820–0.949; p < 0.001) with respective sensitivity and specificity of 89.4% and 72.3%. Jianming Gong et al., analyzing plasma samples from 66 patients with OC and 54 healthy controls, showed that lncRNA MIR4435-2HG was higher in patients with stage I-II FIGO stages OC, but with a high overlap of the value between the groups [147]. From the theragnostic point of view, Weiwei Xie et al. reviewed the contribution of lncRNAs in response to chemotherapy [148]. The main lncRNAs involved in the cisplatin resistance were HOTAIR, H19, MALAT1, MEG3, XIST, DNM3OS and ANRIL. Other lncRNAs have been proven to be associated with drug resistance, such as LSINCT5, NEAT1 for paclitaxel resistance, UCA1 for paclitaxel–cisplatin resistance, and GAS5 for platinum resistance. Until now, most of these lncRNA have merely been evaluated on very small series of plasma [149,150], not allowing to draw conclusions on their relevance. Despite abundant literature on ncRNAs in OC, mainly based on cell culture and tissue samples, relatively few data are yet available on biofluid. Meanwhile, previous studies [78,151,152] have demonstrated the possibility of quantifying ncRNAs in various biofluids such as plasma, serum, urine and saliva. Moreover, it is important to note some limitations of the previously published studies, such as the small sample size, the absence of external validation, and the use in most studies of RT-qPCR and microarrays allowing ncRNA quantification of a predefined set of target sequences, while NGS and bioinformatics, representing an unbiased biomarker discovery method, is rarely used. To improve the preoperative diagnosis of ovarian cancer, studies evaluating the expression of ncRNAs in easily accessible biofluids should be promoted, imposing the use of new sequencing technologies. Thus far, to our knowledge, only two studies, the clinical trial NCT03738319 focusing on ncRNA profile in exosomes of OC patients, ref. [153] and the clinical trial NCT [154] evaluating the saliva expression of ncRNA in ovarian tumors including benign, borderline and ovarian cancer, are ongoing.

PMC10001109

Kholoud Almaabdi,Zareen Ahmad,Sindhu R. Johnson

Advanced Autoantibody Testing in Systemic Sclerosis

23-02-2023

systemic sclerosis,scleroderma,antibodies

Systemic sclerosis is a systemic autoimmune rheumatic disease characterized by immune abnormalities, leading to vasculopathy and fibrosis. Autoantibody testing has become an increasingly important part of diagnosis and prognostication. Clinicians have been limited to antinuclear antibody (ANA), antitopoisomerase I (also known as anti-Scl-70) antibody, and anticentromere antibody testing. Many clinicians now have improved access to an expanded profile of autoantibody testing. In this narrative review article, we review the epidemiology, clinical associations, and prognostic value of advanced autoantibody testing in people with systemic sclerosis.

Advanced Autoantibody Testing in Systemic Sclerosis Systemic sclerosis is a systemic autoimmune rheumatic disease characterized by immune abnormalities, leading to vasculopathy and fibrosis. Autoantibody testing has become an increasingly important part of diagnosis and prognostication. Clinicians have been limited to antinuclear antibody (ANA), antitopoisomerase I (also known as anti-Scl-70) antibody, and anticentromere antibody testing. Many clinicians now have improved access to an expanded profile of autoantibody testing. In this narrative review article, we review the epidemiology, clinical associations, and prognostic value of advanced autoantibody testing in people with systemic sclerosis. Systemic sclerosis (SSc) is a systemic autoimmune rheumatic disease (SARD) characterized by endothelial dysfunction, leading to small vessel vasculopathy, immune dysregulation, fibroblast dysfunction, and subsequent fibrosis of the skin and viscera [1,2,3,4,5]. It is a rare disease with an estimated global prevalence of 17.6 per 100,000 and an incidence rate of 1.4 per 100,000 persons per year [6]. SSc presents and evolves differently across patients, leading to progressive disability, diminished quality of life, and mortality [7]. SSc is distinguished by the presence of serum autoantibodies that target various intracellular antigens. These autoantibodies, which are present in more than 95% of patients, can be useful diagnostic indicators for SSc [8]. SSc-specific antibodies target structures in the nucleoli, nucleoplasm, or chromatin of cells that are important for cell transcription and division. The autoantibody profile of a patient with SSc does not change over time and is not affected by immunosuppressive therapy [8,9,10]. Historically, clinicians have been limited to antinuclear antibody (ANA) measured either by immunofluorescence or enzyme-linked immunosorbent assay, anti-topoisomerase (ATA, also known as anti-Scl-70) antibody, and anticentromere antibody (CENP). These autoantibodies are associated with specific clinical features and organ involvement and can inform prognosis [11]. Anti-topoisomerase I, anticentromere, and anti-RNA polymerase III antibodies were deemed vitally important in the concept of SSc [12] due to their ability to distinguish SSc from other SARD, such that they were included in the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for SSc [13]. Clinicians now have access to a larger selection of SSc-specific autoantibodies. An understanding of the epidemiology of SSc-specific antibodies, clinical associations, and prognostic value will assist clinicians in their interpretation and inform the care of SSc patients. ANA are common in the general population, occurring in up to 20% of women. The presence of an ANA is not necessarily suggestive of a pathologic process, particularly at low titers [14]. Rather, low-titer ANA are thought to reflect a state of benign autoimmunity. However, a subset (5–8%) of these individuals will progress to develop a SARD, such as SSc, Sjogren’s syndrome, or systemic lupus erythematosus [14]. ANA-positive individuals that subsequently develop a SARD have significantly increased T and B cell activation and increased LAG3+ T regulatory cells and TGF-ß1 [15,16,17,18]. Immunoregulation usually prevents development of rheumatic disease in ANA-positive individuals. In contrast, immunoregulation becomes impaired in individuals who progress to develop a SARD, resulting in an imbalance favoring inflammation and fibrosis. Since the 1960s, it has been recognized that ANA are common in individuals with SSc [19,20]. ANA have been reported to occur in 75–95% of patients with SSc, with a sensitivity of 85% and specificity of 54% on immunofluorescence [21]. The antigen substrate that is utilized for the assay affects the specificity and sensitivity of ANA differently. An indirect immunofluorescence assay using HEp-2 cells (HEp-2 IFA) is the gold standard technique. The presence of ANA as a result of HEp-2 IFA is reported as a titer and a pattern. A clinically relevant ANA titer is 1:80 or more [22]. The staining pattern reported with ANA testing by HEp-2 IFA can also be informative. The presence of anti-Scl-70 and anti-U1-RNP antibodies in the sera creates a speckled pattern, while anti-Th/To, anti-fibrillarin (anti-U3RNP) and anti-PM/Scl antibodies create a nucleolar staining pattern. Anti-RNAP I antibodies result in nucleolar staining, while antibodies against RNAP II and III give a speckled appearance or no fluorescence [21]. With the identification of over 30 staining patterns that span many diseases, an international consensus on antinuclear antibody patterns (ICAP) has proposed a classification system to standardize the interpretation and reporting of staining patterns [23] (Table 1). While the presence of ANA and staining patterns is helpful, their absence should be interpreted with caution. For example, the anti-RNAP antibodies demonstrate nucleolar staining only 30–44% of the time [24,25]. Thus, ANA staining patterns should not be used as the sole screening test for SSc-specific antibodies. ANA-negative SSc patients exist and may reflect a subset of SSc who have delayed progression of nailfold microangiopathy, defined by an early nailfold capillary NVC pattern [10]. In the following section, we describe individual autoantibodies observed in SSc, their clinical associations, and their predictive value. In Table 2, we provide an overview of the sensitivity and specificity of these antibodies in people with SSc. In Table 3, we provide an overview of their clinical associations, prognostic value, and prevalence. Since anti-topoisomerase I antibodies (ATA) respond to immunoblots with a 70 kDa protein, they were originally known as anti-Scl-70 antibodies. Further research revealed that Scl-70 was a breakdown product of the full-length 100 kDa protein; thus, it was found that the name Scl-70 was misleading. ATA was detected in 15–42% of SSc patients, with 90–100% specificity [8,44]. ATA has sensitivity of 34% [21]. Table 2. ATA has a poor prognosis and is highly associated with diffuse cutaneous SSc (dcSSc). Patients with limited cutaneous SSc (lcSSc) and other SARD have also been noted to have ATA. The risk of severe pulmonary fibrosis and cardiac involvement is increased in SSc patients with ATA. Additionally, tendon friction rubs, the development of digital ulcers, and joint involvement have all been associated with ATA [8,29,44] (Table 3). An association with scleroderma renal crisis was reported but was not found consistently across all SSc cohorts. Furthermore, the presence of ATA in patients with Raynaud’s phenomenon is associated with a higher risk of developing SSc [29]. Anti-CENP antibodies, also known as anticentromere antibodies, were first reported in 1980. Several CENP proteins have been identified (CENP-A, CENP-B, CENP-C, and others), but CENP-B is thought to be the primary target of the anti-CENP B cell response in SSc [5]. Anti-CENP is the most commonly detected autoantibody in SSc cohorts, with a detection frequency of 20 to 38% [8,44,45]. Anti-CENP antibodies are specific to SSc and are reported to have specificity of 99.9% and sensitivity of 33% [26,27]. They occur less frequently in individuals of Afro-Caribbean descent compared to Caucasians [46]. Additionally, primary biliary cirrhosis, Sjogren’s syndrome, Raynaud’s phenomenon, and systemic lupus erythematosus have all been linked to anticentromere antibodies [47]. Patients with Raynaud’s phenomenon are at high risk of developing SSc if they have anti-CENP antibodies. [5,22]. When compared to other SSc-related antibodies, anti-CENP antibodies are typically associated with limited cutaneous SSc and have a better prognosis [48,49]. In this clinical subgroup of individuals, anti-CENP is associated with a higher risk of pulmonary arterial hypertension, peripheral neuropathy, and mortality [29,50] Anti-ribonucleic acid polymerase (anti-RNAP) antibodies were first described in the 1990s. Anti-RNAP I and III antibodies almost always coexist and are considered to be highly specific to SSc [8]. Anti-RNAP II antibodies are not only seen in SSc but also in systemic lupus erythematosus and overlap syndromes. Since ELISA and LIA are now more frequently used for their detection, the nucleolar speckled immunofluorescence pattern normally associated with anti-RNAP is not a sensitive tool for detecting these autoantibodies. [8,51]. The frequency of anti-RNAP I and III varies between 5% and 31% of SSc patients. In a recent meta-analysis, the pooled overall prevalence of anti-RNAP III was 11% [51]. RNAP antibodies consist of two subunits: the largest RP-155 and RP-11 [45]. RP-155 is associated with dcSSc and a higher risk of renal crisis. These patients may also be at higher risk of tendon friction rubs, synovitis, myositis, joint contractures, and the risk of developing malignancies. Despite the prevalence of renal involvement, survival is better in patients with anti-RNAP than in those with ATA or anti-U3RNP [29]. Although they have 100% specificity, autoantibodies against the RP-11 subunit of RNAP III are less sensitive than anti-RP-155 antibodies and do not seem to improve the diagnostic utility of anti-RP-155. Bernstein et al. published the first report on anti-fibrillarin (anti-U3RNP) antibodies in 1982. Anti-U3-RNP antibodies specifically target a 34 kDa component of the small nucleolar ribonucleoprotein, which is located in the fibrillar area of the nucleolus and is implicated in pre-RNA processing [52]. Anti-U3RNP antibodies are detected in 41% of SSc patients. It is considered relatively specific to SSc and is mutually exclusive from CENP, ATA, and anti-RNAP [8,53]. They are found more frequently in African American than in Caucasian patients [54]. Anti-U3 RNP is associated with male sex, Afro-Caribbean descent, younger age at diagnosis, and higher risk of developing PAH and gastrointestinal involvement [55]. Regardless of demographics or disease type, anti-fibrillarin antibody positivity is associated with poorer survival. [31]. Anti-Th/To ribonucleoprotein antibodies (anti-Th/To) mainly bind to two mitochondrial RNA processing (MRP) proteins and the ribonuclease P complexes. They are present in 1–13% of SSc patients [12]. Anti-Th/To antibodies have high specificity (99%) for SSc. However, anti-Th/To antibodies have been observed in patients with rheumatoid arthritis, systemic lupus erythematosus, polymyositis, and Sjogren’s syndrome. Despite reports of up to 21% of anti-Th/To positive patients having dcSSc, the majority of these patients have lcSSc [56]. Anti-Th/To-positive SSc patients often develop pulmonary hypertension and interstitial lung disease but experience less involvement of joints and muscles [57]. Anti-Th/To antibody is a predictor for a worse prognosis [53]. Low concentrations of macromolecular U11/U12 RNP complexes are present in eukaryotic cells, where they function as spliceosome components and catalyze the splicing of pre-messenger RNA into pre-mRNA introns [58]. The prevalence of anti-U11/U12 -RNP antibodies is 3.2% [59]. Anti-U11/U12 RNP antibodies have been associated with gastrointestinal manifestations, Raynaud’s phenomenon, pulmonary fibrosis, and an increased risk of mortality [41,60]. The presence of anti-U11/U12 RNP autoantibodies may indicate a subset of patients who are more likely to develop cancer when SSc first appears [61]. Anti-SSA/Ro52 antibodies occur with a prevalence of 20% in SSc patients [62]. Anti-Ro52 antibody is a risk factor for a serious pulmonary outcome [63]. While one study found no correlation between the presence of anti-Ro52 antibodies and Raynaud’s phenomenon, sclerodactyly, digital ulcers, gangrene, calcinosis cutis, telangiectasia, or esophageal dysmotility [63], anti-Ro52 antibody is predictor of poor survival in SSc [64]. Initially discovered in individuals with scleroderma–polymyositis overlap syndrome, anti-Ku antibodies were first reported in 1981 by Mimori et al. Ku is a DNA-binding protein involved in DNA repair, which is important for the non-homologous end-joining pathway’s ability to repair double-stranded DNA breaks [36]. In a recent international cohort, anti-Ku antibodies were rarely found in only 1.1% of SSc patients. Anti-Ku is more commonly detected in limited SSc patients with overlap disorders (myositis or lupus) [43,65,66]. Anti-Ku positivity is associated with myositis and interstitial lung disease (ILD), while vascular involvement is less prevalent [67]. With regard to prognosis, no survival difference has been associated with this autoantibody [67]. Anti-PM/Scl antibodies are a heterogeneous group of autoantibodies directed to several proteins of the nucleolar PM/Scl macromolecular complex. The two main autoantigenic protein components were identified and named PM/Scl-75 and PM/Scl-100, based on their molecular weights [68]. Anti-PM/Scl have sensitivity of 12.5% and specificity of 96.9% for SSc [69]. Anti-PM/Scl 75 antibodies occur with a prevalence of 10.4% [69]. Anti-PM75 antibodies are associated with high rates of calcinosis cutis and gastrointestinal manifestations, including gastroesophageal reflux disease, dysphagia, small intestinal bacterial overgrowth, and fecal incontinence. ILD was also prevalent in SSc patients with anti-PM75, second only to ATA-positive patients. Pulmonary hypertension is reported to be the clinical feature most commonly associated with the anti-PM75 antibody [70]. Anti-PM/Scl 100 antibodies have a prevalence of 7.1% [69]. Anti-PM100 is more associated with calcinosis rather than gastrointestinal manifestation. ILD was also less frequent compared to the anti-PM75 [70]. Patients with anti-PM100 antibodies had higher survival rates [70]. The anti-NOR90 antibody, a nucleolar type of ANA, is found in 6.1% of SSc patients [71]. However, this antibody tends to be less specific for SSc and is reported in other SARD, such as systemic lupus erythematosus, Sjogren’s syndrome, and rheumatoid arthritis [39]. Anti-NOR90 antibodies may be a biomarker for idiopathic interstitial pneumonia with features of systemic sclerosis. Anti-NOR90 antibodies are associated with the occurrence of arthritis/arthralgia, sicca symptoms, and Raynaud’s phenomenon [72,73]. Systemic sclerosis with anti-NOR90 antibodies can be complicated by interstitial lung disease and cancer [40]. Anti-NOR-90 antibodies may be associated with a favorable prognosis [71]. RuvBL1/2 is an important modulator of transcriptional activation and protein assembly and is essential for cell proliferation. It is located in the nucleus but can also be present in the cytoplasm [74]. Although only 1–2% of patients have anti-RuvBL1/2, it is highly specific to SSc. The relationship of anti-RuvBL1 and RuvBL2 with older onset age, more frequent diffuse skin and skeletal muscle involvement, male sex, and overlap myositis are its distinguishing features [75]. Platelet-derived growth factor stimulatory (PDGF) antibodies are the primary mitogens for cells of mesenchymal and neuroectodermal origin. PDGF, first described in the 1970s as a serum factor that stimulates smooth muscle cell proliferation, is now one of the best-characterized growth factor receptor systems [76]. SSc appears to have a distinctive signature that stimulates autoantibodies against PDGFR. Their biological effect on fibroblasts may contribute to the pathogenesis of the disease. [77]. Myositis autoantibodies have traditionally been divided into subgroups of myositis-specific and myositis-associated antibodies. Myositis-specific antibodies are predominantly found in patients with polymyositis or dermatomyositis, while myositis-associated antibodies are usually found in patients with overlapping features of myositis and other SARDs [78]. Anti-synthetase antibodies target aminoacyl tRNA synthetases. The tRNA synthetases are a family of cytoplasmic enzymes that load specific amino acids onto their cognate tRNA to form an aminoacyl tRNA. There are eight anti-tRNA synthetase autoantibodies. Anti-Jo-1 antibody (directed against histidyl-tRNA synthetase) is the most common myositis-specific antibody in adults with idiopathic inflammatory myopathy, with a frequency of 9–24%. Anti-PL-12 and antibodies to the OJ, EJ, PL-7, PL-12, KS, Zo, and Ha antigens are less common [79]. The anti-SRP antibody is directed at the signal recognition particle (SRP), which participates in the translocation of newly synthesized proteins into the endoplasmic reticulum [80] The anti-MDA5 antibody is directed against RNA helicase encoded by the melanoma differentiation-associated gene 5 (MDA5). The anti-NXP-2 antibody is directed against nuclear matrix protein 2 (NXP-2) involved in transcriptional regulation. The anti-Mi-2 antibody is directed against a helicase involved in transcriptional activation. The anti-SAE antibody is directed against the small ubiquitin-like modifier activating enzyme (SAE) that regulates gene transcription. Myositis-specific autoantibodies are only positive in 20 to 40 percent of myositis patients; therefore, a negative test does not rule out a diagnosis. Rather, a positive antibody informs the type of myositis and disease trajectory. While these antibodies have been associated with polymyositis and antisynthetase syndrome, their prevalence, clinical associations, and prognostic value in SSc is uncertain. Similarly, many antibodies have been associated with malignancy. In contrast, myositis-associated autoantibodies are present in patients with SARD that can be associated with myositis. The presence of anti-Ro/SSA, anti-La/SSB, anti-Sm, or anti-ribonucleoprotein (RNP) antibodies in a patient with myositis suggests an association or overlap with another SARD. Anti-Ro52 antibodies are common in patients with antisynthetase antibodies, and anti-Ro60 and anti-La/SSB may be seen with other myositis-specific antibodies. Myositis autoantibodies have rarely been described in SSc [81,82]. A recent study conducted in France showed that the prevalence of myositis-specific antibodies was 8.0% and that of myositis-associated autoantibodies was 9.7%. However, the prevalence of each antibody was low, at less than 5%. Myositis-associated autoantibodies positivity was associated with ILD and myositis, but this study showed no clinical associations with myositis-specific antibodies positivity [38,83]. It is important to remember that the sensitivity and specificity of these antibodies are affected by their prevalence and the choice of the comparator group. For example, anti-U3 RNP/fibrillarin, anti-Th/To, anti-PM/Scl, and anti-U11/U12 RNP are infrequent in the general population and other SARDs [8]. In SSc cohorts, the sensitivity and specificity of these antibodies may differ depending on several factors, such as race, area of origin, immunogenic markers, and the autoantigen immunoassay they were detected with [8,45] (Table 2). A recent study from the Netherlands compared the detection of SSc-specific autoantibodies through various diagnostic tests. Seventy-nine percent of patients tested positive for SSc autoantibodies in at least one diagnostic test. SSc-specific autoantibodies included in the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) criteria showed a high degree of concordance (antitopoisomerase, anticentromere antibody, and anti-RNA polymerase III). PM/Scl, Ku, fibrillarin, and Th-To antibodies demonstrated less concordance. A minority of patients were ATA and ARA positive [2]. The SSc-specific autoantibodies are thought to be mutually exclusive [26], but there is a small body of literature on their coexistence. In a recent study of 2799 SSc patients conducted in England, 5% had more than one SSc-specific autoantibody [84]. ATA and ACA expression are not completely mutually exclusive, but their coexistence is rare (<1% of patients with SSc). Patients with both autoantibodies often have diffuse cutaneous disease and display immunogenetic features of both antibody-defined subsets of SSc [85]. In another study of 4687 patients from the EUSTAR database, 29 patients (0.6%) were documented as double positive for both ATA and CENP antibodies. Sera from 14 patients were available for central reanalysis by immunofluorescence, enzyme immunoassay, and immunoblot to confirm antibody status. Eight patients were confirmed to contain both autoantibodies. The prevalence of cutaneous and visceral manifestations in double-positive antibody patients was similar to single-positive antibody patients [86]. In an Italian cohort of 210 SSc patients, in which a commercially available LIA was used for the simultaneous detection of 13 SSc-associated autoantibodies, except for anti-Ro52/TRIM21 (specificity of 50%), all autoantibodies were very specific (from 93.3% anti-PM/Scl-75 to 100% anti-PDGFR, AFA, and anti-RP-11) for SSc. Anti-Ku was associated with another autoantibody in 0.4% of positive patients, and anti-PM/Scl-110 was associated with another autoantibody in 0.42% of patients [45]. Given the association of specific organ involvement, outcomes, and antibodies, there are international initiatives to develop classification criteria to identify subsets of SSc patients [86,87,88]. The Very Early Diagnosis of Systemic Sclerosis (VEDOSS) criteria identify a subset of patients based on the presence of RP, puffy fingers, antinuclear antibodies, and capillaroscopy or SSc-specific antibodies [89]. Using the Australian Scleroderma Interest Group and the Canadian Scleroderma Research Group cohort data, three subsets of SSc were proposed [90]. Subset 1 is characterized by digital ulcers, pitting scars, and anti-topoisomerase I antibodies. Subset 2 is characterized by diffuse skin involvement, tendon friction rubs, and anti-RNA polymerase III antibodies. Subset 3 is characterized by limited or no skin involvement and anticentromere antibodies [91]. Other researchers have proposed a simplified system combining the extent of skin involvement and SSc-specific antibodies [87]. A reliable, responsive, and valid SSc subset system using an antibody profile could be used to identify patients most likely to derive a therapeutic benefit, and as a cohort enrichment strategy for trials of therapeutic agents [90,91]. Clinicians have increasing access to advanced autoantibody profiling in the assessment of patients with SSc. The staining pattern on ANA testing by indirect immunofluorescence assay on Hep-2 cells can provide a clue to the SSc-specific antibodies that may be present and that warrant further investigation. This narrative review provides up-to-date, practical information for the practicing clinician who cares for people with SSc. The presence of SSc-specific antibodies can inform the prediction of patients with Raynaud’s phenomenon who have an increased probability of developing SSc and warrant close follow-up. The presence of SSc-specific antibodies can assist with making a clinical diagnosis, can contribute to classification of SSc for research purposes, can guide monitoring for specific internal organ manifestations, and can inform prognosis.

PMC10001116

Giovanna Barbero,Roberta Zuntini,Pamela Magini,Laura Desiderio,Michela Bonaguro,Anna Myriam Perrone,Daniela Rubino,Mina Grippa,Antonio De Leo,Claudio Ceccarelli,Lea Godino,Sara Miccoli,Simona Ferrari,Donatella Santini,Pierandrea De Iaco,Claudio Zamagni,Giovanni Innella,Daniela Turchetti

Characterization of BRCA Deficiency in Ovarian Cancer

28-02-2023

BRCA1,BRCA2,ovarian cancer

Simple Summary Ovarian cancer (OC) is a highly lethal malignancy. Major improvements in treatment are expected from the identification of molecular features that may predict outcome or be used as therapeutic targets. Among genetic defects relevant for OC are those of BRCA1 and BRCA2 genes. Indeed, at least 20% of OC patients carry inherited or acquired BRCA1/2 pathogenic variants, the identification of which is important for treatment and prevention. A comprehensive study of 30 OC patients revealed that 7 (23%) had BRCA alterations (6 inherited and 1 acquired) detectable by usual clinical testing, while another 5 patients (17%) showed epigenetic silencing of BRCA1 in the tumor, which would have escaped standard sequencing analysis, and one had an inherited variant in another gene: RAD51C, involved in the same DNA repair mechanism as BRCA1 and BRCA2. Patients with BRCA deficit showed greater genomic instability, but better survival, than those with no evidence of BRCA deficit. Abstract BRCA testing is recommended in all Ovarian Cancer (OC) patients, but the optimal approach is debated. The landscape of BRCA alterations was explored in 30 consecutive OC patients: 6 (20.0%) carried germline pathogenic variants, 1 (3.3%) a somatic mutation of BRCA2, 2 (6.7%) unclassified germline variants in BRCA1, and 5 (16.7%) hypermethylation of the BRCA1 promoter. Overall, 12 patients (40.0%) showed BRCA deficit (BD), due to inactivation of both alleles of either BRCA1 or BRCA2, while 18 (60.0%) had undetected/unclear BRCA deficit (BU). Regarding sequence changes, analysis performed on Formalin-Fixed-Paraffin-Embedded tissue through a validated diagnostic protocol showed 100% accuracy, compared with 96.3% for Snap-Frozen tissue and 77.8% for the pre-diagnostic Formalin-Fixed-Paraffin-Embedded protocol. BD tumors, compared to BU, showed a significantly higher rate of small genomic rearrangements. After a median follow-up of 60.3 months, the mean PFS was 54.9 ± 27.2 months in BD patients and 34.6 ± 26.7 months in BU patients (p = 0.055). The analysis of other cancer genes in BU patients identified a carrier of a pathogenic germline variant in RAD51C. Thus, BRCA sequencing alone may miss tumors potentially responsive to specific treatments (due to BRCA1 promoter methylation or mutations in other genes) while unvalidated FFPE approaches may yield false-positive results.

Characterization of BRCA Deficiency in Ovarian Cancer Ovarian cancer (OC) is a highly lethal malignancy. Major improvements in treatment are expected from the identification of molecular features that may predict outcome or be used as therapeutic targets. Among genetic defects relevant for OC are those of BRCA1 and BRCA2 genes. Indeed, at least 20% of OC patients carry inherited or acquired BRCA1/2 pathogenic variants, the identification of which is important for treatment and prevention. A comprehensive study of 30 OC patients revealed that 7 (23%) had BRCA alterations (6 inherited and 1 acquired) detectable by usual clinical testing, while another 5 patients (17%) showed epigenetic silencing of BRCA1 in the tumor, which would have escaped standard sequencing analysis, and one had an inherited variant in another gene: RAD51C, involved in the same DNA repair mechanism as BRCA1 and BRCA2. Patients with BRCA deficit showed greater genomic instability, but better survival, than those with no evidence of BRCA deficit. BRCA testing is recommended in all Ovarian Cancer (OC) patients, but the optimal approach is debated. The landscape of BRCA alterations was explored in 30 consecutive OC patients: 6 (20.0%) carried germline pathogenic variants, 1 (3.3%) a somatic mutation of BRCA2, 2 (6.7%) unclassified germline variants in BRCA1, and 5 (16.7%) hypermethylation of the BRCA1 promoter. Overall, 12 patients (40.0%) showed BRCA deficit (BD), due to inactivation of both alleles of either BRCA1 or BRCA2, while 18 (60.0%) had undetected/unclear BRCA deficit (BU). Regarding sequence changes, analysis performed on Formalin-Fixed-Paraffin-Embedded tissue through a validated diagnostic protocol showed 100% accuracy, compared with 96.3% for Snap-Frozen tissue and 77.8% for the pre-diagnostic Formalin-Fixed-Paraffin-Embedded protocol. BD tumors, compared to BU, showed a significantly higher rate of small genomic rearrangements. After a median follow-up of 60.3 months, the mean PFS was 54.9 ± 27.2 months in BD patients and 34.6 ± 26.7 months in BU patients (p = 0.055). The analysis of other cancer genes in BU patients identified a carrier of a pathogenic germline variant in RAD51C. Thus, BRCA sequencing alone may miss tumors potentially responsive to specific treatments (due to BRCA1 promoter methylation or mutations in other genes) while unvalidated FFPE approaches may yield false-positive results. Ovarian cancer (OC) is the most lethal gynecological neoplasm, with an average overall survival of about 40% at 5 years from diagnosis [1,2]. The search for molecular defects which can affect disease outcomes and constitute therapeutic targets is therefore a priority to improve the management of OC patients. Among those, BRCA1/2 germline variants have been reported in about 14% of cases [3], while the fraction of OC with somatic BRCA mutations is generally reported to be between 3% and 9% [4,5]. In particular, several studies have shown that germline or somatic BRCA1/2 pathogenic variants predict greater sensitivity to standard platinum- and taxane-based therapies [6,7,8] and to maintenance treatments with Poly (ADP-ribose) Polymerase (PARP) inhibitors [9]. The therapeutic efficacy of the latter, which intervene in single-stranded DNA repair, is achieved through a mechanism of “synthetic lethality” in the presence of a concomitant loss of function of the double-stranded DNA repair mechanisms by homologous recombination (HR), in which BRCA1/2 proteins play an essential role [4,10,11,12]. BRCA genetic testing usually implies sequencing the coding portion and searching for deletion/duplications of the BRCA1/2 genes [13,14,15]. The traditional approach relies on the analysis of DNA extracted from the peripheral blood of the patients, which allows the detection of “constitutional” or “germline” variants. Recently, the evidence that about 1/3 of BRCA1/2 pathogenic variants in OC patients are confined to the tumor tissue [4,10], has led to a recommendation that BRCA analysis be performed on DNA extracted from cancer tissue, in order to detect both the constitutional and the somatic variants [16]. However, cancer tissue testing poses some critical issues, such as: differences between the types of samples, including formalin-fixed and paraffin-embedded (FFPE) tissues and snap-frozen (SF) tissues, the choice between primary tumor and relapse, the assessment of large rearrangements and the predictive value of specific variants for drug response [17,18,19,20]. Furthermore, a non-negligible fraction of ovarian tumors (11–16%) present BRCA deficiency due to epigenetic inactivation of BRCA1, not identifiable with routine somatic tests, and some tumors may present homologous recombination deficiency (HRD) due to alterations in other genes of the pathway [5,21,22,23]. In this work, we have performed a comprehensive assessment of BRCA defects in tissues from 30 clinically characterized OC patients in order to explore the landscape of genetic alterations and evaluate the accuracy of standard diagnostic testing. The primary aim of the study was to characterize OC samples of newly diagnosed patients for the presence of mutations, rearrangements, or epimutations of the BRCA1/2 genes and to validate tissue testing strategies. Secondary aims were to further dissect the molecular features of the samples, by assessing genomic rearrangements in BRCA-defective tumors and mutations in genes other than BRCA1/2 in tumors with no BRCA defects detected, and to assess clinical outcome according to BRCA status. The GeCO (Genetic Characterization of Ovarian cancer) study protocol was approved by the Ethical Board of S.Orsola-Malpighi Hospital, Bologna, Italy (Prot. 81/2014/U/Tess) and was conforming to the ethical guidelines of the WMA Declaration of Helsinki. Patients were considered eligible for the study if the following inclusion criteria were fulfilled: newly diagnosed OC; major age; informed consent. The exclusion criteria were: borderline, stromal, and/or mucinous type OCs; unavailability of tumor tissue samples suitable for molecular analysis. Thirty-nine consecutive newly diagnosed OC patients admitted to the Gynecological Oncology unit of S.Orsola-Malpighi Hospital in the first semester of 2015 to undergo surgical procedures were proposed for the study and 38 were accepted to be enrolled. Before surgery, patients underwent a genetic counseling session during which, after accurate collection of family history, they were informed in detail about the aims and implications of the study. Upon informed consent, a venous blood sample was drawn; then, immediately after surgery, tumor tissue was dissected by the pathologist, and a sample was snap-frozen. After the exclusion of 8 patients (5 because the histologic types were different from those eligible and 3 because tissue samples were not adequate for the analysis), 30 were included in the study. For included patients, 10 µm slides of FFPE tissue with a percentage of tumor cells greater than 70% were also prepared for genetic analysis. Clinicopathological data, including age at diagnosis, tumor location, histologic type, grade, stage, and type of surgery and therapy were collected from medical records and pathology reports. Follow-up data were updated on a regular basis until December 2022 by checking on clinical charts the situation of each patient at their last access to the Oncology Unit. DNA was extracted from peripheral blood, frozen tissue, and (FFPE) tissue using QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. RNA was isolated from frozen tissue stabilized in RNAlater (Qiagen, Hilden, Germany) using Rneasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNase treatment was performed using an RNase-Free DNase set (Qiagen, Hilden, Germany). DNA and RNA were quantified using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). For the first 23 patients, the analysis of DNA extracted from SF tumor was performed using either Sanger sequencing or next-generation sequencing (NGS), to allow comparison between the two sequencing methods and increase accuracy, while germline DNA analysis was carried out through NGS only. Sanger sequencing was performed on coding exons and splice site junction of BRCA1 and BRCA2 genes (NM_007294.3 and NM_000059.3 respectively) using “BigDye Terminator v1.1 Cycle Sequencing Kit” and analyzed on an automatic genetic sequencer (ABI3730 DNA Analyzer, Thermofisher); NGS analysis was performed using an Ion AmpliSeq BRCA1/2 Panel (Thermo Fisher Scientific, Waltham, MA, USA) under standard conditions. Briefly, 30 ng of DNA was used to set manually libraries with Ion AmpliSeq Library Kit v.2.0 and IonXpress Barcode Adapter Kit. A template was prepared with Ion PGM TM 510TM & 520 TM & 530 TM kit—Chef using the Ion OneTouch 2 InstrumentChef System. Sequencing was performed on an Ion PGM System using Ion 318 chip and Ion PGM Sequencing 200 Kit v2. NGS data were analyzed with Torrent suite and Ion Reporter Software, version 5.6 and later. For the last seven patients, both SF tumor tissue and constitutional DNA were analyzed by NGS using Oncomine BRCA Research Assay (Thermo Fisher Scientific, Waltham, MA, USA), made available at our center in the meantime, under standard conditions. Briefly, 20 ng of DNA was used to prepare manually libraries with an Ion AmpliSeq Library Kit Plus and IonXpress Barcode Adapter Kit. A template was prepared with Ion 520 & 530 Kit OT2 using an Ion OneTouch 2 Instrument and an Ion OneTouch ES Instrument. Sequencing was performed on an Ion S5 System using Ion 520 chip. NGS data were analyzed with Torrent suite and Ion Reporter Software 5.10. DNA extracted from all FFPE tumor samples was analyzed using Oncomine BRCA Research Assay (Thermo Fisher Scientific, Waltham, MA, USA) as described above. In more detail, in the first assay (Research FFPE, 2017), 20 ng of DNA, extracted from not-deparaffinized FFPE samples, was used to prepare manually libraries with Ion AmpliSeq Library Kit Plus and IonXpress Barcode Adapter Kit. A template was prepared with Ion PGM TM 510TM & 520 TM & 530 TM kit—Chef using the Ion OneTouch ES InstrumentChef System. Sequencing was performed on an Ion PGM System using Ion 318 chip and Ion PGM Sequencing 200 Kit v2. NGS data were analyzed with Torrent suite and Ion Reporter Software version 5.6 and later. In the second analysis (Diagnostic FFPE, 2020) 20 ng of DNA, extracted from deparaffinized FFPE samples, were used to prepare Chef-Ready libraries with Ion AmpliSeq TM Kit for Chef DL8 and IonXpress Barcode Adapter Kit. A template was prepared with Ion 510 TM & 520 TM & 530 TM Kit—Chef using an Ion Chef TM Instrument. Sequencing was performed on an Ion S5 System using Ion 520 chip. NGS data were analyzed with Torrent suite and Ion Reporter Software version 5.10 and later. Targeted sanger sequencing was performed to check C3 (VUS), C4 (likely pathogenic), and C5 (pathogenic) variants in respective constitutional DNA. Deletion and duplication of BRCA1/2 genes were analyzed in frozen tissue and blood samples using MLPA techniques (P002-D1 BRCA1 and P045-C1 BRCA2/CHEK2—MRC-Holland, Amsterdam, the Netherlands) under the manufacturer’s protocol. Fragments were separated on an ABI3730 DNA Analyzer and analyzed with Coffalyzer.net Software. Methylation analysis of the BRCA1/2-gene promoter was performed using MLPA ME053 probemix kit (MRC-Holland, Amsterdam, The Netherlands). This kit contains specific probes for CpG islands: three in the BRCA1 gene and four in the BRCA2 gene. In addition, there are four probes for copy number variation (CNV) detection of the BRCA1 gene (targeting exons 3, 13, 20, 23) and four probes for CNV detection of the BRCA2 gene (targeting exons 3-13-17-21). Fragments were separated on an ABI3730 DNA Analyzer and analyzed with Coffalyzer.net Software. Heterozygosity status was assessed through the analysis of 16 microsatellites mapping on chromosomes 17 and 13 (panels 23, 24, and 19 respectively; Thermo Fisher Scientific, Waltham, MA, USA). For chromosome 17 we selected 11 markers: D17S849, D17S831, D17S938, D17S1852, D17S799, D17S798, D17S1868, D17S949, D17S785, D17S784, D17S928; and five markers for chromosome 13: D13S171, D13S153, D13S265, D13S159, D13S158. Polimeration chain reaction (PCR) was performed using Kapa Taq HotStart DNA Polymerase (KAPA Biosystems, Wilmington, MA, USA) under standard conditions and run on an ABI3730 DNA Analyzer. Data were analyzed using GeneMapper Software (Thermo Fisher Scientific, Waltham, MA, USA). Reverse transcription was performed using 500 ng of RNA using iScript Reverse Transcription Supermix for RT-qPCR (BioRad Laboratories, Hercules, CA, USA). Two multiplex reactions were performed including both target and reference genes and using validated assays for BRCA1 (qHsaCEP0041326), BRCA2 (qHsaCEP0052184) (FAM probes), and reference gene PPIA (qHsa CEP0041342) (Hex probe) (BioRad Laboratories, Hercules, CA, USA). Briefly, PCR reactions were conducted using 1 ng of cDNA and ddPCR Supermix for Probes according to the manufacturer’s instructions. Droplets were generated by loading reaction mixtures and Droplet Generation Oil for Probes into a DG8 Cartridge using a QX200 Droplet Generator (BioRad Laboratories, Hercules, CA, USA). Samples were carefully transferred in-plate, sealed, and run on a thermocycler. Finally, the plates were transferred in the QX200 Droplet Reader and data were acquired and analyzed using QuantaSoft software. Tumor samples with no evidence of BRCA deficiency were subjected to sequencing of other candidate genes in order to identify any different molecular mechanisms underlying carcinogenesis. To this aim, a custom Ion AmpliSeq On-Demand panel (Thermo Fisher Scientific, Waltham, MA, USA) was used, designed to detect SNV and small indel variants in 21 genes associated with cancer predisposition: APC, ATM, BMPR1A, BRIP1, CHEK2, EPCAM, MLH1, MSH2, MSH3, MSH6, MUTYH, PALB2, RAD51C, RAD51D, PTEN, PMS2, POLD1, POLE, SMAD4, STK11, TP53. DNA analysis of the FFPE tumor samples was performed under standard conditions. Briefly, 20 ng of DNA was used to prepare manually libraries with Ion AmpliSeq Library Kit Plus and IonXpress Barcode Adapter Kit. A template was prepared with 510TM &520TM &530TM kit Chef using the Chef System. Sequencing was performed on an Ion S5 System using Ion 530 chip. NGS data were analyzed with Torrent suite and Ion Reporter Software 5.16. Class 4 or 5 variants (according to ClinVar classification (https://www.ncbi.nlm.nih.gov/clinvar/ accessed on 12 December 2022) in genes other than TP53 (which is expected to be somatically mutated in a substantial proportion of ovarian carcinomas) were searched for in the patient’s blood sample. For 13 SF DNA samples, CNV and LOH analysis was performed using GenetiSure Cancer Research CGH+SNP Microarray, 2 × 400 K (Agilent Technologies, Santa Clara, CA, USA), according to the manufacturer’s protocol, with appropriate Agilent reference DNAs (Euro female). The microarray contains approximately 300,000 in situ synthesized 60-mer oligonucleotides with a medium resolution of 30 kb (higher resolution in cancer-associated genes) and 103,000 SNP probes. The array data extraction and analysis were performed using CytoGenomics v.5.2 (Agilent Technologies, Santa Clara, CA, USA). Aberrations were detected using the ADM-2 algorithm with a threshold of 6.0. Due to the low quality of the DNA samples, some modifications were made to the protocol to improve the quality of the experiment and subsequent analysis: (1) a dye-swap design was used. DNA samples were labeled with cyanine 3 which has greater stability than cyanine 5; (2) different amounts of DNA for samples and reference were used in order to obtain a better yield and increase specific activity. Digestion, labeling, and hybridization were performed using 1500 ng of test DNA and 1000 ng of reference DNA. The analysis was performed simultaneously for CNVs and LOH detection. Only CNVs larger than 1 Mb and with a threshold of log2ratio > 0.2 for gain and <−0.2 for loss were considered. CNVs were initially classified based on the type of aberration (copy loss and copy gain) and then divided into “simple” and “complex” loss or gains. “Simple” CNVs were defined by a single aberrant mean log2ratio, while “complex” CNVs were split in two or more regions with different log2ratios, possibly indicating distinct cellular clones with differences in CNV length and/or copy number in that chromosomal location. CNV features (number and average size) were compared between BRCA defective and intact patients and their distributions were compared through the Kolmogorov-Smirnov test. The clinical-pathological data were organized into nominal variables and were analyzed using the “Statistical Package for Social Science (SPSS)” software, version 25.0 (SPSS, Chicago, IL, USA). Two-tailed p-values less than 0.05 were considered statistically significant. Mean, standard deviation (SD), ranges, and frequencies were used as descriptive statistics. Progression-free Survival (PFS) is defined as the elapsed time between the date of initial diagnosis and either the date of recurrence or the last follow-up. Overall Survival (OS) is defined as an estimate from the date of initial diagnosis to the date of death or the last follow-up (if death was not observed during the follow-up period). OS and PFS were estimated using the Kaplan–Meier method with STATA software, version 13.0. A Cox regression model was used to estimate the hazard ratio and its 95% CI. Follow-up times were described as medians. BRCA1/2 sequence analysis on SF OC tissues identified seven (23.3%) pathogenic variants (three in BRCA1 and four in BRCA2) and three (10.0%) variants of uncertain significance (two in BRCA1 and one in BRCA2). Among BRCA2 variants, p.Asn1784Lys (C3) and p.Ser2148Leufs*20 (C5) presented with an allele load consistent with the heterozygous status and were found to be exclusively somatic after a targeted search in peripheral blood. Conversely, all the six pathogenic variants that were subsequently found to be germline had a frequency consistent with the homozygous status in tumor tissue (VAF: 80–100%). The variants detected in BRCA genes are reported in Table 1. Variants were reported according to HGVS nomenclature using as reference sequence NM_007294.3 for BRCA1 and NM_000059.3 for BRCA2. Rearrangements revealed by MLPA affected BRCA1 in 23 (87%) samples and BRCA2 in 14 (57%); among those, 20 BRCA1 and 14 BRCA2 rearrangements involved the deletion/duplication of the whole allele, while partial BRCA1 rearrangements were observed in GECO 15 (deletion from exon 1 to exon 11), GECO 22 (duplication from exon 11 to the end) and GECO 31 (duplication of exons 1 and 2). Microsatellite analysis showed loss of heterozygosity (LOH) at both BRCA1 and BRCA2 regions in 16 (53%) samples. All six patients carrying germline pathogenic variants displayed LOH; this was caused by the deletion of the wild-type allele in four cases; of the other two, GECO 29 showed a copy neutral LOH (CN-LOH), GECO 31 a partial duplication associated with LOH of the entire chromosome. Instead, samples of the two patients carrying germline variants of uncertain significance in BRCA1 displayed the loss of the allele harboring the variant in the tumor, due to allele deletion (GECO 2) or to CN-LOH (GECO 27). Moreover, microsatellite analysis showed that in three cases with duplication of the BRCA2 gene, the entire chromosome was duplicated (Examples of microsatellite analysis are shown in Figure S1). MS-MLPA analysis performed on tumor tissue samples showed that BRCA1 promoter hypermethylation was present in five samples (17%), while no samples showedBRCA2 promoter hypermethylation. In four cases, BRCA1 promoter hypermethylation co-existed with a somatic BRCA1 deletion, while in the remaining case, both alleles presented hypermethylation, and CN-LOH was shown (Supplementary Materials, Figure S2). BRCA1/2 gene expression was evaluated by Digital PCR in 24 OC samples (for the remaining six, RNA was inadequate for the analysis). Gene expression levels were defined as increased, reduced, or normal by comparing gene expression in each tumor sample with alterations to gene expression levels in samples without gene alterations: variations greater than two-fold SD were considered reliable variations. BRCA1 expression was shown as decreased in three samples with BRCA1 promoter hypermethylation (GECO 3, GECO 5, and GECO 34), and in one sample harboring a pathogenic variant (GECO 31). Two samples (GECO 24 and GECO 30) showed a reduction in both BRCA1 and BRCA2 gene expression, which was associated with LOH in both genes and, in GECO 24, with a pathogenic variant in BRCA2. Gene expression increased in three samples, two with BRCA1 (GECO 14 and GECO 23), and one with BRCA2 increase (GECO 15). Tumors showing evidence of structural or functional loss of both the alleles of either BRCA1 or BRCA2 (for carrying a pathogenic germline or somatic BRCA1/2 variant and lacking the wild-type allele, or presenting with deletion of one BRCA1/2 copy and promoter methylation of the other), were classified as “BRCA-deficient” (BD), while tumors in which evidence of BRCA deficit was absent or inconclusive were defined as “BRCA deficit undetected/unclear” (BU). Overall, twelve patient samples (40%) were classified as BD: four (33.3%) because of germline pathogenic variant of one allele and partial/complete deletion of the other (one BRCA1 and three BRCA2), four (33.3%) because of partial/complete BRCA1 deletion and promoter methylation of the other allele, two (16.7%) because of a pathogenic variant of one allele (one BRCA1 germline variant and one BRCA2 somatic variant) and CN-LOH, one (8.3%) because of germline BRCA1 pathogenic variant and BRCA1 partial duplication and one (8.3%) because of promoter methylation of both BRCA1 alleles (CN-LOH was present). These results are summarized in Figure 1. The NGS analysis of a multigene panel of other cancer-predisposing genes was performed on tumor samples of the 18 BU patients and detected TP53 mutations in 7 samples (38.9%). In addition, two C4/C5 variants were detected in two patients: RAD51C c.904 + 5G > T was found in patient GECO 14 and was shown to be germline, while PTEN c.388C > T;p.Arg130Ter, found in GECO 22, was excluded in the germline. Panel results are detailed in Supplementary Materials (Table S1). Array-CGH + SNP analysis was performed to identify CNVs and LOH in six BU and seven BD patients. Table 2 summarizes the main results and shows the comparison between BD and BU sample sets, with p-values from a Kolmogorov–Smirnov test. The number and average size of global CNVs and separately of duplications and deletions were evaluated, and further divided into “simple” and “complex” from array-CGH profiles. The analysis revealed a great complexity of unbalanced chromosomal rearrangements in OC samples with about half of the genome involved (Table 2), as expected for high-grade cancers. The identification of CNVs composed of multiple segments with different log2ratios further supported the chromosomal heterogeneity of the analyzed samples. Statistically significant differences emerged in the number of total CNVs and duplications, especially “simple” gains, which are more numerous in BD samples. Deletions tended to be larger in BU samples, although without statistical significance. Interestingly, BD GECO 7 and 27, with less advanced OC (IIb and Ic, respectively) are the patients with the highest number of CNVs, showing that the chromosomal picture has evolved more rapidly and earlier than the biological features of the tumoral tissues, probably fostered by the deficiency of BRCA-related repair mechanisms. Conversely, GECO 18 has a very preserved genome despite its advanced stage (IIIc). NGS-based BRCA analysis of FFPE samples from 29 patients was performed in order to assess whether the results obtained on this type of sample were consistent with those found in SF from the same surgical specimen. Sequencing analysis provided results satisfying quality assessment in 27 cases (on target > 85%, Mean depth > 500, Uniformity > 85%), while two cases (GECO 16 and GECO 27) did not present with adequate quality. FFPE analysis was first carried out in 2017 to assess the accuracy of BRCA analysis on FFPE, according to the study design (Research FFPE analysis: “R-FFPE”); all the variants identified on SF samples were confirmed except one (GECO 3): the absence was confirmed in a different FFPE block from the same surgery. However, additional mutations were found in 24 out of 27 samples analyzed (88.9%); particularly, setting the allelic load threshold at 5%, C4–C5 mutations were retrieved in 14 samples with no pathogenic/likely pathogenic variants previously detected. Setting the threshold at 20%, 33 additional C3–5 variants were found in nine patients, as reported in Table 3. The analysis was repeated in 2020 (excluding samples with clear pathogenic variants and those with no additional mutations detected in tumor tissue), when the diagnostic analysis of FFPE had been implemented and used for three years in the clinical setting (Diagnostic FFPE analysis: “D-FFPE”); all the variants identified by testing SF tissues were confirmed with the exception, again, of the GECO 3 variant, but, unlike in R-FFPE, no additional variants were detected, suggesting that the additional mutations detected by R-FFPE were false findings. The comparison between the results of the sequence analyses performed on SF tissues and FFPE tissues at the two time points is shown in Table 3. Assuming as true the findings replicated in at least two assays and considering as a positive result the presence of at least one C3–C5 variant in a sample and as a negative result the absence of any variant, sensitivity was estimated to be 100% for all the approaches (SF, R-FFPE, and D-FFPE), while specificity was 95% for SF, 70% for R-FFPE and 100% for D-FFPE, with an accuracy of 96.3%, 77.8%, and 100%, respectively. The clinical characteristics and outcomes of the 30 OC patients enrolled in the study are summarized in Table 4. Patients were subdivided into two groups based on the presence (BD) or absence (BU) of BRCA deficiency revealed by the analysis performed on tumor tissue samples. As shown in Table 5, baseline clinical characteristics of the two groups were similar: median age at diagnosis was 50.8 (±12.9) years in the BD group and 58.9 (±7.5) years in the BU group (p = 0.070); the majority of tumors were high-grade papillary-serous carcinomas (only one case of endometrioid carcinoma in BD group); 10 patients of the BD group (83.3%) and 15 of the BU group (88.2%) presented with advanced FIGO (International Federation of Gynecology and Obstetrics stages III and IV) stage disease (p = 1.000); considering that the type of surgery performed was the same in both groups (Hysterectomy with Bilateral Salpingo-Oophorectomy), four patients in the BD group (33.3%) and one in the BU group (5.9%) had macroresidual post-surgery (R) > 0, (p = 0.130). Most patients (22) underwent adjuvant chemotherapy with carboplatin and paclitaxel, three with only carboplatin, and one patient did not undergo chemotherapy because of poor clinical conditions at diagnosis. After a median follow-up of 60.5 months, 18 (60.0%) patients had relapsed and 10 (33.3%) had died. For each patient, interval to disease progression and interval to death (in months) were evaluated to reveal any differences in PFS and OS between patients with BRCA deficiency and patients without deficit (GECO 2 was excluded from PFS calculation because she was never free from disease and died a few months after diagnosis): as reported in Table 5 and Figure 2, mean PFS was 54.9 ± 27.2 months in BD patients and 34.6 ± 26.7 months in BU patients (p = 0.055), while mean OS was 69.1 ± 20.0 months in BD patients and 58.4 ± 23.5 months in BU patients (p = 0.077). Impairment of BRCA function in OC has proven to predict response to platinum-based chemotherapy and PARP-inhibitors; consequently, BRCA1/2 analysis is being routinely used to inform the medical treatment of OC patients [24,25,26]; the advantage of identifying also somatic BRCA1/2 mutations has led to the recommendation that BRCA sequencing be performed on tumor tissue. However, heterogeneity in diagnostic approaches and result interpretation raises uncertainties regarding the clinical meaning of somatic findings [16,17]. To contribute to elucidating the landscape of BRCA defects and evaluating the ability of clinical testing to correctly identify them, we extensively analyzed BRCA alterations in a consecutive series of 30 well-characterized OCs. Consistently with previous evidence [27,28,29,30], we found that a substantial fraction of OCs (40.0%) presented with BRCA deficit, here defined by the presence of alterations predicted to result in the complete absence of functional copies of either BRCA1 or BRCA2 gene, due to sequence variants, rearrangements or epigenetic silencing. As only half of BD cases harbored germline BRCA1/2 variants, our results support the superiority of testing approaches involving tumor tissue analysis in detecting potentially actionable alterations. However, 16.7% of samples (41.7% of those classified as BD) displayed BRCA1 promoter hypermethylation, which would be missed by standard somatic tests that are based on gene sequencing [4,5,24,31]. Conversely, it has been suggested that promoter hypermethylation, if compared to gene mutations, may be more easily removed under the selective pressure induced by treatment, leading to a higher chance of drug resistance development [32]. All the samples with BRCA1 promoter hypermethylation showed LOH at the BRCA1 locus, suggesting the absence of unmethylated alleles, and were therefore classified as BD. Gene-expression analysis, however, failed to show a reduction in two out of three methylated samples, as it did in four of six samples with germline pathogenic variants associated with LOH in cancer. Although it is possible that an allele carrying a pathogenic variant is expressed, taken together, these findings suggest that the expression assay was not able to provide reliable information on BRCA deficiency. After assessing BRCA status through such a combined approach, we aimed at exploring the performance of FFPE-based BRCA sequencing, that is the BRCA test mainly used for predictive purposes in OC, in correctly classifying BRCA-deficient tumors. The first analysis, carried out in 2017 for research purposes, showed a plethora of additional mutations, including potentially significant (C3–5, allelic load > 20%) variants in nine patients if compared to SF analysis. In a total of 14 patients the detection of C4/C5 mutations (allele load > 20% in two, 5–20% in 12) would have changed the treatment based on current practice. A second analysis, performed in 2020 according to diagnostic standards, did not confirm those findings, since no variants were detected in addition to those found in SF samples, with the specificity raising from 70% to 100%. This increased accuracy can be explained by technical improvements made in the analytical approach before moving to diagnostic routine, which included prior de-paraffinization of samples and instrumental upgrades. Nevertheless, the high rate of false-positive results in the first analysis should alert about the reliability of somatic testing performed by inexperienced laboratories, and underlines the need for proper validation and adherence to verified protocols and quality controls [16,17,33]. In any case, the variant load in tissue appears to be a crucial issue for clinical interpretation. Indeed, all the validated variants predicted to lead to BRCA deficiency showed an allelic load in tumor tissue of 50% or higher. Regarding these as “predictive” mutations with a frequency lower than 20% in tumor DNA (provided that the proportion of tumor cells in the sample is adequate) may pose serious risks of misinterpretation, with implications for therapeutic choices. First, the lower the allele frequency, the higher the chance of a false positive result due to artifacts, as suggested by our findings; second, even if true, a low-load alteration is consistent with a normal copy of the gene being retained, leading to BRCA proficiency in the cell, that is the reason why we made the conservative choice to regard as BRCA-deficient only samples with evidence of no functional copies of either the BRCA1 or BRCA2 gene. Interestingly, two germline C3 variants were found at low frequency in tumor DNA, suggesting the loss of the allele harboring the variant in cancer cells: such finding provides evidence against pathogenicity that can eventually contribute to variant classification and supports the usefulness of combining germline and somatic testing. Another C3 variant was found in SF tissue, though not confirmed in the other samples from the same patients; as artifacts are less common in SF tissue, it can be hypothesized that the mutation occurred in a subpopulation of tumor cells, which underlines that tumor heterogeneity and the chance of passenger mutations should be taken into account when interpreting somatic test results for clinical purpose. In addition to gene mutations, CNVs were found in the majority of samples, confirming the frequency of high genomic instability in OC, irrespective of BRCA status. In patients with pathogenic BRCA variants or epigenetic silencing, rearrangements at BRCA loci accounted for LOH, according to the expected two-hit mechanism; however, in other patients, CNV at BRCA loci were not associated with alterations of the other allele, and could be viewed as an aspecific manifestation of genomic instability, not supporting the usefulness of including the analysis of somatic CNV in predictive BRCA testing. Indeed, genomic scars, including CNVs and LOH, are signs of genomic instability due to HRD. SNP arrays and more recently NGS have been used to detect these chromosomal anomalies to calculate an HRD score, predicting patients that might be responsive to PARP inhibitors [34,35]. Genomic rearrangements were assessed in a subgroup of samples in order to compare BRCA-deficient with non-deficient tumors. The analysis of deletions and duplications did not lead to statistically significant differences between BRCA intact and defective cancers; as already reported for serous OC [36], highly rearranged genomes were found, both in BRCA intact and defective cancers, with a complex heterogeneity of cellular clones with different chromosomal anomalies that cannot be fully appreciated and precisely defined by array-CGH. However, when the analysis was extended to small CNVs (>1 Mb and <10 Mb), the frequency, especially of duplications, was significantly higher in OC with BRCA deficiency, which is consistent with a previous report [37]. Considering the total number of CNVs and the average size of deletions, GECO 6 showed values more similar to those of patients presenting BRCA deficiency, suggesting a possible HRD, that, however, was not explained by sequencing other cancer genes. As for clinical outcome, although there were no significant differences by baseline prognostic factors and by treatment, BD, if compared to BU, patients showed a tendency to a better survival (not reaching, however, statistical significance); since at the time of the study, PARP inhibitors were not used as maintenance treatment after first-line chemotherapy, the prolonged PFS is likely the result of a better response to platinum-based chemotherapy, as previously reported in the literature [6,7,8,23,38]. Finally, the multigene panel analysis performed in samples without evidence of BRCA deficiency allowed the identification of a patient with inherited ovarian cancer predisposition due to a RAD51C germline variant, with clinical and familial implications, which support the appropriateness of extending genetic testing to clinically meaningful genes other than BRCA1/2 in OC patients [23]. The main limitation of the study is the small sample size, further reduced in specific sub-analyses due to the unavailability of suitable material for a fraction of cases, which impairs the solidity and statistical significance of figures obtained, though providing further support to existing evidence. However, the comprehensive assessment performed using combined molecular approaches allowed the in-depth characterization of BRCA status and associated genomic features and the provision of meaningful insights into the use and interpretation of predictive BRCA testing. The assessment of BRCA status in OC patients provides meaningful prognostic and predictive information. However, analysis of Formalin-Fixed-Paraffin-Embedded samples is prone to false results if not properly developed and validated. Moreover, the implementation of strategies able to detect also epigenetic changes and alterations of other cancer genes may improve the diagnosis of cancers defective for homologous recombination repair mechanisms.

PMC10001118

Yinuo Wang,Gergana Dobreva

Epigenetics in LMNA-Related Cardiomyopathy

01-03-2023

nuclear lamina,lamin A/C,LMNA,cardiomyopathy,epigenetics,chromatin architecture,stem cells

Mutations in the gene for lamin A/C (LMNA) cause a diverse range of diseases known as laminopathies. LMNA-related cardiomyopathy is a common inherited heart disease and is highly penetrant with a poor prognosis. In the past years, numerous investigations using mouse models, stem cell technologies, and patient samples have characterized the phenotypic diversity caused by specific LMNA variants and contributed to understanding the molecular mechanisms underlying the pathogenesis of heart disease. As a component of the nuclear envelope, LMNA regulates nuclear mechanostability and function, chromatin organization, and gene transcription. This review will focus on the different cardiomyopathies caused by LMNA mutations, address the role of LMNA in chromatin organization and gene regulation, and discuss how these processes go awry in heart disease.

Epigenetics in LMNA-Related Cardiomyopathy Mutations in the gene for lamin A/C (LMNA) cause a diverse range of diseases known as laminopathies. LMNA-related cardiomyopathy is a common inherited heart disease and is highly penetrant with a poor prognosis. In the past years, numerous investigations using mouse models, stem cell technologies, and patient samples have characterized the phenotypic diversity caused by specific LMNA variants and contributed to understanding the molecular mechanisms underlying the pathogenesis of heart disease. As a component of the nuclear envelope, LMNA regulates nuclear mechanostability and function, chromatin organization, and gene transcription. This review will focus on the different cardiomyopathies caused by LMNA mutations, address the role of LMNA in chromatin organization and gene regulation, and discuss how these processes go awry in heart disease. Mutations in genes encoding proteins of the nuclear lamina result in wide-ranging clinical phenotypes collectively referred to as laminopathies [1]. Many of these diseases are caused by mutations in the gene for lamin A/C (LMNA) and affect primarily the muscles, the peripheral nerves, and the adipose tissue or cause systemic diseases such as premature aging syndromes [2]. The LMNA gene encodes A-type lamins, generated by alternative splicing, of which lamins A and C are the main splicing products [3,4]. In addition to the A-type lamins, the nuclear lamina is composed of B-type lamins, i.e., lamins B1 and B2, encoded by LMNB1 and LMNB2 genes, respectively [5,6,7,8]. LMNB2 also encodes the germ-line-specific lamin B3, produced by alternative splicing [9]. A- and B-type lamins have a common structural organization: a short “head” domain at the N-terminus followed by a central helical rod domain and a C-terminal “tail” domain. The central rod domain is composed of four coiled-coil regions that allow lamins to form parallel coiled-coil dimers and higher-order meshworks [10,11,12]. The “tail” consists of a globular region, which adopts an immunoglobulin (Ig)-like β-fold involved in protein–protein interactions. Pre-lamin A- and B-type lamins also have a CaaX motif at the C-terminus which guides protein farnesylation and carboxyl methylation, important for targeting to the nuclear envelope [10,11,12] (Figure 1). Both A- and B-type lamins form separate but interconnected filamentous meshworks located between the inner nuclear membrane and the peripheral heterochromatin, which on the one hand provide structural support to the nucleus and on the other hand anchor chromatin at the nuclear periphery, thereby shaping the higher-order chromatin structure [13,14,15]. In contrast to lamins B1 and B2, which are localized at the periphery and associate mainly with transcriptionally inactive chromatin [16,17], lamins A and C are also found in the nuclear interior and associate with both heterochromatin and euchromatin [18]. In addition, lamins interact with the LINC complex, which couples the nucleoskeleton with the cytoskeleton [19,20], and thereby can directly translate mechanical cues and changes in extracellular matrix mechanics into alterations in chromatin structure and transcriptional activity [21]. In the last years, a large number of studies identified distinct molecular pathways dysregulated in patients with pathogenic LMNA mutations, mouse models, or stem cells carrying LMNA mutations. Here, we summarize the current knowledge on the role of lamin A/C in diseases of the heart muscle and specifically focus on how changes in lamin-A/C-dependent chromatin architecture could be involved in the pathogenesis of cardiomyopathies. Dilated cardiomyopathy (DCM) is characterized by enlargement and dilatation of one or both ventricles of the heart, which occurs together with impaired contractility and heart function [22]. The LMNA gene is the second most commonly mutated gene in familial dilated cardiomyopathy (DCM), accounting for 5% to 8% of cases [23]. Patients carrying pathogenic LMNA mutations have a poor prognosis due to the high rate of sudden cardiac death resulting from malignant arrhythmias. Atrial fibrillation (AF), atrioventricular (AV) conduction block, ventricular tachycardia, and sudden cardiac death often precede the development of systolic dysfunction [24,25,26]. Although LMNA-related DCM is an adult-onset disease, it cannot be excluded that structural changes and arrhythmias may be present in early asymptomatic individuals [27]. To date, around 500 mutations and 300 protein variants have been reported for LMNA; detailed information on the different mutations is available through the UMD-LMNA mutation database (www.umd.be/LMNA, accessed on 3 January 2023) (Table 1). Most of the mutations associated with cardiomyopathies are located in the head and rod domains and are mostly truncation or missense mutations [28]. Heterozygous truncation mutations often result in lamin A/C haploinsufficiency due to a premature termination codon generated by insertions or deletions resulting in a frameshift, aberrant splice site, or nonsense mutations. A homozygous LMNA nonsense mutation (Y259X) has also been reported, resulting in a lethal phenotype [29]. LMNA missense mutations, on the other hand, are thought to mostly act through a dominant negative mechanism [28]. Patients carrying heterozygous mutations in LMNA in combination with mutations within other genes such as TTN, DES, SUN1/2, etc., display a particularly severe clinical cardiac phenotype [30,31,32,33,34]. Since a number of LMNA mutations result in a loss of function, lamin A/C haploinsufficient (Lmna+/−) and Lmna knockout mice (Lmna-/-) have been extensively used to study the molecular mechanisms underlying LMNA loss-of-function (LOF) cardiomyopathy (Table 2). Lmna−/− mice develop DCM two weeks after birth and die within one month [62,63]. Lmna+/− mice are viable and fertile but already at ten weeks of age show AV conduction defects and atrial and ventricular arrhythmias, characteristic for patients with LMNA LOF mutations [64]. Cellular characterization revealed that Lmna haploinsufficiency results in AV node cardiomyocyte death and altered electrophysiological properties [64]. Furthermore, Lmna−/− and Lmna+/− cardiomyocytes (CMs) show premature binucleation, cell cycle withdrawal, and abnormal contractility [63,65]. Another mutation often used for modeling the LMNA LOF mutation is the p.R225X mutation, a nonsense mutation causing premature truncation of both lamin A and lamin C splice isoforms. Patients carrying this pathogenic mutation show early onset of AF, secondary AV block, and DCM [87]. Like Lmna−/− mice, homozygote Lmna R225X mice also exhibit retarded postnatal growth, conduction disorders, and DCM [36]. Other LOF mutations, e.g., K117fs and 28insA, also lead to a DCM phenotype. LMNA p. K117fs mutation is a frameshift mutation that leads to a premature translation-termination codon [38], whereas 28insA is an adenosine insertion mutation in exon 1 resulting similarly in a premature stop codon [40]. Messenger RNAs (mRNAs) that contain a premature stop codon often undergo degradation through the nonsense-mediated mRNA decay (NMD) surveillance mechanism and thus can cause haploinsufficiency. Consistent with this, a significant decrease in lamin A/C protein levels is observed in K117fs iPSC-CMs as a result of NMD-mediated degradation of LMNA mRNA [38]. In addition to truncation mutations, which can result in LMNA haploinsufficiency, mutations such as N195K, T10I, R541S, and R337H also show reduced lamin A/C protein levels [35,41]. Patients carrying these pathogenic mutations also develop DCM [26,51,88]. It is still unclear why these mutations lead to decreased lamin A/C levels. Possible reasons could be that protein translation or the stability of lamin A/C are affected in mutant CMs. For example, although Lmna mRNA does not change, both lamin A and lamin C levels are decreased in CMs and MEFs derived from Lmna N195K/N195K mutant mice [35]. Interestingly, patients carrying different LMNA missense mutations resulting in DCM also exhibit lower protein levels [89]. To what extent the decrease in lamin A/C levels or changes in protein function result in disease pathogenesis is still largely unknown and needs further investigation. Although it may seem that DCM is predominantly caused by LMNA haploinsufficiency, missense mutations in LMNA, which do not lead to changes in lamin A/C protein levels, also result in DCM. For example, LMNA K219T missense mutation causing severe DCM and heart failure with conduction system disease [52] does not lead to obvious changes in lamin A/C levels in K219T iPSC-CMs [53]. LMNA H222P missense mutation has been shown to cause Emery–Dreifuss muscular dystrophy (EDMD) and DCM in patients. Homozygous mice with the H222P mutation display muscular dystrophy, left ventricular dilatation, and conduction defects and die by 9 months of age [76]. Similarly to the K219T mutation, Western blot analysis of cardiac and skeletal muscle samples shows no obvious difference in lamin A/C protein levels between wild-type and Lmna H222P/H222P mice [74]. Interestingly, recent studies suggested a developmental origin of LMNA-related cardiac laminopathy. Lmna H222P/H222P embryonic hearts showed noncompaction, dilatation, and decreased heart function already at E13.5[75], while Lmna+/− and Lmna−/− embryonic hearts showed noncompaction cardiomyopathy with no decrease in ejection fraction [63]. Differentiation of mouse embryonic stem cells (ESCs) harboring the Lmna p.H222P mutation revealed decreased expression of cardiac mesoderm marker genes, such as Eomes and Mesp1 as well as cardiac progenitor (CP) markers and impaired CM differentiation. This is in stark contrast to Lmna+/− and Lmna−/− mESCs, which showed premature CM differentiation [63,75], suggesting different mechanisms behind the heart phenotype caused by lamin A/C haploinsufficiency or changes in protein functionality. Among laminopathy-associated missense mutations, the addition of proline is the most common. Proline addition can significantly alter protein structure. For example, LMNA S143P missense mutation causes DCM and disturbs the coiled-coil domain, thus affecting lamin A/C assembly into the nuclear lamina. This results in nuclear fragility and reduced cellular stress tolerance [49]. The addition of proline might also affect protein phosphorylation through proline-directed kinases, such as the mitogen-activated protein (MAP) kinases, cyclin-dependent protein kinase 5 (CDK5), glycogen synthase 3, etc. Mutations resulting in the addition of proline often result in striated muscle disease, suggesting a common underlying mechanism [90]. Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disorder that predominantly affects the right ventricle [91]. A progressive loss of myocytes and fibro-fatty replacement associated with arrhythmias in the right ventricular myocardium is a hallmark of the disease [92]. Mutations in desmosomal genes, such as Plakophilin 2 (PKP2), Desmoplakin (DSP), Desmoglein 2 (DSG2), Desmocollin 2 (DSC2), and junction plakoglobin (JUP), are the main cause of ARVC [93,94,95,96,97,98]. In addition, mutations in the calcium-handling protein Ryanodine Receptor 2 (RYR2) [99], Phospholamban (PLN) [100], the adherens junction protein Cadherin 2 (CDH2) [101], Integrin-Linked Kinase (ILK) [102], the signaling molecule Transforming Growth Factor-β3 (TGFB3) [103], the cytoskeletal structure protein Titin (TTN) [104], Desmin (DES) [105], transmembrane protein 43 (TMEM43), and lamin A/C (LMNA) have also been reported in ARVC [24,106,107,108]. In 2011, Quarta et al. first reported ARVC caused by mutations in LMNA. Four LMNA variants were identified: R190W, R644C, R72C, and G382V [24]. The R190W and R644C variants also cause DCM and left ventricular noncompaction (LVNC). In addition, R644C can also lead to lipodystrophy and atypical progeria, thus showing an extreme phenotypic diversity. ARVC patients with these four mutations all exhibit RV dilatation and systolic dysfunction. Histological examination of the right ventricular myocardium from R190W and G382V patients showed a loss of more than 50% of myocytes and extensive interstitial fibrosis and fatty replacement [24]. Interestingly, immunohistochemical staining showed significantly reduced plakoglobin expression at the intercalated discs in the myocardium, which could contribute to the development of ARVC [24]. M1K, W514X, and M384I mutations in LMNA have also been identified in ARVC. Patients with M1K and W514X mutations show RV dilatation, non-sustained ventricular tachycardia, and complete atrioventricular block [108]. A patient with the M384I variant not only developed ARVC but also peripheral neuropathy and peroneal muscular atrophy [109]. So far, it remains unknown how LMNA mutations result in ARVC. Since LMNA is a ubiquitously expressed protein, its mechanoprotective function in cardiomyocytes, which can limit the progressive loss of myocytes, its role in the regulation of genes involved in cardiac contractility, and its important role in regulating cell fate choices, which may result in an excess of fibroblasts and adipocytes, might be involved. Tracing back the origins of fat tissue in a mouse model of ARVC, Lombardi et al. suggested that second heart field (SHF)-derived progenitor cells switch to an adipogenic fate through nuclear plakoglobin (JUP)-mediated Wnt signaling inhibition [110]. A subset of resident cardiac fibro-adipocyte progenitor cells characterized by PDGFRAposLinnegTHY1negDDR2neg expression signatures have been shown to be a major source of adipocytes in ARVC caused by Desmoplakin (DSP) haploinsufficiency [111]. Furthermore, the endocardium, epicardium, and cardiac mesenchymal stromal cells also serve as a source of adipocytes in the heart [112,113,114]. Because the endocardium and epicardium give rise to diverse cardiac cell lineages, including mesenchyme and adipocytes [115], via endothelial-to-mesenchymal transition (EndMT) and epithelial-to-mesenchymal transition (EMT), lamin A/C function in regulating EMT [75] might also be a key mechanism driving ARVC pathogenesis. Left ventricular noncompaction (LVNC) cardiomyopathy is a rare congenital heart disease resulting from abnormal development of the endocardium and myocardium. Patients with LVNC exhibit a thin compact myocardium and excessive trabeculation and can eventually develop progressive cardiac dysfunction followed by heart failure. LVNC can manifest together with other cardiomyopathies and congenital heart disease [116]. Studies have identified various genes associated with LVNC, such as TTN, MYH7, TNNT2, LDB3, MYBPC3, ACTC1, DSP, CASQ2, RBM20, and the intermediate filaments DES [117] and LMNA [118], with the two most affected genes being TTN and LMNA [119]. The first reported LMNA mutant variant causing LVNC is R190W, which is also associated with familial DCM and ARVC [56]. Another pathogenic LMNA variant causing LVNC is LMNA R644C. R644C mutation carriers show an extreme phenotypic diversity, ranging from DCM and LVNC to lipodystrophy and atypical progeria [59]. Parents and colleagues reported four family members with the LMNA R644C mutation, three of whom developed left ventricular noncompaction cardiomyopathy with normal LV dimensions and function and without evidence of dysrhythmias [60]. Other mutations such as LMNA V74fs, R572C, and V445E have also been associated with LVNC. Patients with the V445E missense mutation are characterized by an arrhythmogenic form of LVNC, suggested to be due to dysfunctional SCN5A [58,119]. How LMNA mutations result in LVNC and the mechanisms underlying the high phenotypic diversity are largely unknown. Two recent studies demonstrated that Lmna H222P/H222P as well as Lmna−/− and Lmna+/− embryonic hearts exhibit noncompaction, suggesting these mouse models as important tools to study the developmental origin and the mechanisms behind LMNA-mediated noncompaction cardiomyopathy [63,75]. Interestingly, our own study revealed that Lmna LOF results in abnormal cell fate choices during cardiogenesis, i.e., promotes CM and represses endothelial cell fate. Since the crosstalk between CMs and endothelial cells is instrumental for proper cardiac development and myocardial compaction [120], abnormal cardiovascular cell fate choices and dysfunctional endothelium might also contribute to LVNC. Thus, understanding the link between alternative cell fate choices, changes in cell behavior, and tissue-specific phenotypes caused by pathogenic LMNA mutations would be an important question to address in further studies. Restrictive cardiomyopathy (RCM) is a rare cardiac disease characterized by increased myocardial stiffness resulting in impaired ventricular filling. Patients with RCM show enlarged atria and diastolic dysfunction, while systolic function and ventricular wall thicknesses are often normal until the later stages of the disease [121,122,123]. Although most causes of RCM are acquired, several gene mutations have also been identified in patients with RCM [121,122,123,124]. The most common mutated genes found in RCM are sarcomere-related genes such as TTN [125], TNNI3 [126], MYH7 [127], ACTC1 [128], etc. Mutations in non-sarcomere genes such as DES [129], TMEM87B [130], FLNC [131], etc., have also been reported. Recently, Paller et al. reported a truncation mutation of LMNA (c.835 delG:p.Glu279ArgfsX201) in an RCM patient who had a significant biatrial enlargement, atrial fibrillation, and skeletal muscle weakness. Both right and left ventricular size and function were normal, and histological analysis revealed cardiac hypertrophy and focal interstitial fibrosis in the endomyocardial tissue [61]. How Lmna mutations cause RCM is not known; a plausible mechanism could be the activation of profibrotic signaling, as discussed below. Since LMNA-related cardiomyopathies caused by distinct point mutations show phenotypic diversity, the precise molecular mechanisms resulting in disease pathogenesis are also distinct and complex. Taking into account the variety of different functions of the nuclear lamina, three central mechanisms have been suggested to drive disease pathogenesis. The “mechanical hypothesis” proposes that disruption of the nuclear lamina causes increased nuclear fragility and increased susceptibility to mechanical stress [132]. This hypothesis is supported by observations that CMs from patients or mouse models with lamin A/C mutations exhibit nuclear rupture, DNA damage, and cell cycle arrest [63,65,88,133]. Interestingly, Lmna−/− non-CMs subjected to stretch show significantly increased DNA damage, further supporting the notion that the elevated cell death could be due to the inability of Lmna−/− CMs to respond adequately to mechanical stress [63]. Importantly, a recent study revealed that disrupting the LINC complex and thereby decoupling the nucleus/nucleoskeleton from the mechanical forces transduced by the cytoskeleton increases more than fivefold the lifespan of LMNA-deficient mice [134], pointing to therapeutic opportunities for patients carrying mutations resulting in nuclear fragility. Myriad studies have demonstrated a role of lamins in regulating MAPK, TGF-β, Wnt–β-catenin, and Notch signaling cascades [135,136] and suggested that altered signaling is a key driver of LMNA-related dilated cardiomyopathy. For instance, LMNA-related cardiomyopathy shows a significant increase in myocardial fibrosis which contributes to left ventricular dysfunction and heart failure [24,35,137,138]. Profibrotic signaling, such as TGF-β, MAPK, and ERK signaling, is activated in Lmna H222P/H222P mice, and the partial inhibition of ERK and JNK signaling before the onset of cardiomyopathy in Lmna H222P/H222P mice significantly reduces cardiac fibrosis and prevents the development of left ventricle dilatation and decreased cardiac ejection fraction [138,139,140,141]. Indeed, therapies targeting intracellular signaling alterations are being developed in a preclinical setting [142]. Since nuclear lamins anchor chromatin at the nuclear periphery, the “chromatin hypothesis” suggests that chromatin alterations as a result of LMNA haploinsufficiency or mutation result in abnormal gene expression programs responsible for the disease phenotype [132]. In the last years, a number of studies using iPSC-CMs or mESC-CMs uncovered changes in chromatin architecture coupled to transcriptional changes in different ion channels such as SCN5A, CACNA1A/C/D, HCN4, SCN3b, and SCN4b, as well as Pdgfb pathway activation, which might explain the arrhythmogenic conduction defects in LMNA patients [38,53,63,143]. Lamina-associated domain reorganization and changes in chromatin architecture in LMNA-related cardiomyopathy. As already mentioned, the nuclear lamina shapes higher-order chromatin structure by anchoring large heterochromatic regions (~ 0.1–10 Mb stretches) at the nuclear periphery, termed lamina-associated domains (LADs). LADs are enriched in the repressive histone marks H3K9me2/3 and H3K27me3, and genes associated with LADs are mostly inactive [15]. Although most LADs are conserved between cell types (constitutive LADs (cLADs)), some chromatin nuclear lamina interactions are detected in specific cell types (facultative LADs (fLADs)) (Figure 2) [144,145]. Indeed, genome–nuclear lamina dynamics have been proposed to play a key role in cell fate decisions by “locking” or “unlocking” genes conferring cell identity at the nuclear periphery [145,146]. For example, during mESC differentiation into astrocytes (ACs), specific AC genes detach from ESC LADs resulting in gene activation. A substantial number of genes are not immediately activated upon detachment from the nuclear lamina but rather become primed for activation at a later stage [145]. Similar mechanisms also occur during CM differentiation. HDAC3 directly represses cardiac differentiation by tethering CM genes to the nuclear lamina. The loss of HDAC3 in cardiac progenitor cells releases these genomic regions from the nuclear periphery, leading to early cardiac gene expression and differentiation [147]. Our own study further showed that lamin A/C and not B-type lamins is responsible for the early activation of a transcriptional program promoting CM versus endothelial cell fate and differentiation [63]. Interestingly, lineage shifts upon LMNA loss or mutation have been reported in other tissues, suggesting that aberrant activation of genes driving an unscheduled differentiation could be a common feature of laminopathic cells [148,149,150,151]. Similar to ACs, we found two modes of regulation: (i) Lamin A/C keeps cell differentiation and cardiac morphogenesis genes silent, such as Gata4/6, Bmps, Wnts, Myl4, etc. Upon lamin A/C LOF, these genes are ectopically expressed in mESCs. (ii) Lamin A/C restricts transcriptional permissiveness at cardiac structural and contraction genes, such as Ryr2, Mybpc3, Adrb2, etc. Upon lamin A/C LOF, chromatin becomes more accessible, but this is not sufficient to elicit gene transcription in ESCs. However, during cardiac differentiation, these primed loci are readily accessible to cardiac transcription factors (TFs), resulting in aberrant cardiovascular cell fate choices, premature CM maturation, cell cycle withdrawal, and abnormal contractility. In contrast, Lmna H222P/H222P mESCs, or mESCs harboring the G609G mutation causing accelerated aging, did not show similar changes in chromatin accessibility nor in expression patterns, supporting the view that the molecular mechanisms underlying the distinct phenotypes upon lamin A/C LOF and missense mutations are different [63]. Many recent studies have focused on the role of lamin A/C in chromatin organization in human induced pluripotent stem cell (hiPSC)-derived CMs (hiPSC-CMs) to pinpoint the molecular mechanisms associated with LMNA cardiomyopathy. For instance, in hiPSC-CMs harboring the frameshift mutation K117fs that leads to lamin A/C haploinsufficiency, chromatin accessibility is increased at lamin A/C LADs, leading to transcriptional activation. Among others, the PDGF pathway was highly activated in K117fs iPSC-CMs and its inhibition rescued the arrhythmic phenotype, suggesting that PDGF inhibitors could be beneficial in preventing fatal arrhythmias often manifested in patients with LMNA-related cardiomyopathy [38]. Notably, the authors found that many genes located in non-LAD regions are also highly upregulated in K117fs iPSC-CMs compared to controls, suggesting that mutations in lamin A/C might also result in maladaptive epigenetic remodeling at non-LAD regions. This might be mediated through changes in B-type lamin function, upregulation of pioneer transcription factors, loss of binding of repressive complexes, or other mechanisms. Indeed, although B-type lamins form distinct meshworks, the loss of A-type lamins results in alterations in B-type meshworks, suggesting that their activity might be interconnected [152]. Thus, mutation-mediated changes in lamin A/C activity might also affect lamin B1/B2 function. Interestingly, lamin B2 plays an essential function in regulating CM karyokinesis, and Lmnb2 ablation resulted in polyploid CM nuclei in neonatal mice [153]. Lmna ablation also results in increased numbers of binucleated CMs in neonatal mice [63], suggesting that lamin A/C loss might affect lamin B2 function. The activation of pioneer transcription factors, which can engage developmentally silenced genes embedded in “closed” chromatin [154,155,156,157] and induce chromatin opening, might also play a role in LMNA-related cardiomyopathies. Indeed, the pioneer cardiac TF GATA4 is activated by lamin A/C loss, and Gata4 silencing or haploinsufficiency rescues the abnormal cardiovascular cell function induced by lamin A/C deficiency [63]. Another pioneer TF, FoxO1 [158], also shows increased binding to chromatin upon Lmna LOF. FoxO TFs play key functions in stress response, cell proliferation, and apoptosis, and the longevity and suppression of FoxO activity in CMs partially rescues the cardiac phenotype and prolongs survival [159]. Additionally, the nuclear lamina may serve as a binding platform for chromatin remodelers, such as the Polycomb Group Proteins, which can initiate large-scale epigenetic alterations. This will be discussed in the following section (Figure 3). Another study using an iPSC model harboring the T10I mutation in LMNA suggested a role of the nuclear lamina in safeguarding cellular identity [41]. In T10I iPSC-CMs, the peripheral heterochromatin enriched for non-myocyte lineage genes was disrupted, resulting in the activation of alternative cell fate genes. Upregulation of non-cardiac genes was also observed in iPSC-CMs carrying the R225X mutation in lamin A/C (Figure 2B). Importantly, CACNA1A, encoding a neuronal P/Q-type calcium channel, was upregulated, and pharmacological inhibition partially rescued the altered electrophysiological properties of R225X iPSC-CMs [143]. In this context, it is important to note that in contrast to mouse/human blastocysts and naïve mouse mESCs, hiPSCs cultured in standard conditions represent a primed state and do not express detectable levels of lamin A/C protein [63]. Since lamin A/C plays an important role in chromatin organization in naïve pluripotent stem cells, which is essential for normal cardiogenesis, some important aspects of lamin A/C function cannot be modeled using hiPSCs and requires studies using naïve hiPSCs carrying LMNA mutations. In addition, chromatin and expression analysis of CMs from patients with different LMNA-related DCM mutations revealed extensive rearrangement of LMNA–chromatin interactions in DCM patients [89]. The reorganization of lamin A/C LADs is associated with altered CpG methylation and dysregulated expression of a large number of genes involved in cell metabolism, the cell cycle, and cell death. Most of the LMNA-related DCM patients’ samples used in this study showed a decrease in lamin A/C protein levels, suggesting that LMNA LOF might account for the observed DNA, chromatin, and expression changes [89]. It is still poorly understood how cell-type-specific tethering at the nuclear lamina is achieved and how mutations in lamins affect the tethering of key cell fate determinants in stem cells and in cells already committed to a certain lineage. Lamins interact with chromatin either directly [160] or indirectly through chromatin-binding proteins. Consistent with its association with both hetero- and euchromatin, lamin A/C interacts with proteins associated with both hetero- and euchromatin, e.g., LAP2α, Emerin, and BANF1 [161,162], while B-type lamins interact with the lamin B receptor (LBR), which mediates the attachment to the inner nuclear membrane, and Heterochromatin Protein 1 (HP1α) associated with heterochromatin [163]. However, all these proteins are broadly expressed and cannot account for the cell-type-specific tethering of LADs. Thus, identifying cell-type-specific interacting partners for nuclear lamins and the effect of lamin mutations on these interactions will be key in understanding the molecular mechanisms underlying the wide-ranging clinical phenotypes and may pinpoint druggable protein–protein interfaces for therapeutic applications. Moreover, how mutations in lamin A/C affect the separation into relatively active and inactive chromatin regions is still debatable [164]. The genome is organized into higher-order structural domains referred to as topologically associated domains (TADs). TADs tend to interact based on their epigenetic status and transcriptional activity, thus dividing chromosomes into two types of large-scale compartments generally called A compartments (active) and B compartments (inactive) (Figure 3A) [165]. An analysis of A/B compartment changes revealed only ∼1.2% compartment switches in R225X iPSC-CMs with only a minimal correlation with highly dysregulated genes [143]. In contrast, during cardiac differentiation, ∼20% of the genome undergoes A/B compartment reorganization, while many others appear constitutively associated with the nuclear lamina. Interestingly, in Lmna−/− mESC, around 8% of the chromatin compartments switched from A to B and vice versa as a result of lamin A/C loss. These compartment switches highly overlap with lamin A LADs. Genes within the B/A compartment switches (inactive to active) were linked to calcium ion transmembrane transport, muscle cell differentiation, and relaxation of cardiac muscle, including genes such as Myl4, Atp2a3, Ryr2, and Camk2d, which were either activated or primed upon lamin A/C loss. Lamin A/C is expressed in naïve pluripotent stem cells, absent after the loss of pluripotency and during early differentiation, and re-expressed in CMs. This dynamic expression pattern may provide a window of opportunity for LAD and chromatin compartment reorganization, and the activation of transcriptional programs driving important developmental decisions and cell identity. The role of Polycomb Group Proteins in LMNA-related cardiomyopathy. As we discussed before, LADs are enriched for H3K27me3. The downregulation of lamin A/C remodels the repressive H3K27me3 and the permissive H3K4me3 histone marks, thereby enhancing transcriptional permissiveness [166]. Indeed, lamin A/C interacts with the Polycomb repressive complex 2 (PRC2) complex, which catalyzes H3K27me3 [167], and lamin A/C loss in myoblasts results in PcG protein foci disassembly, ectopic expression of Polycomb targets, and premature myogenic differentiation [167]. Polycomb Group (PcG) proteins are key epigenetic repressors during development and differentiation. The Polycomb repressive complex 2 (PRC2)-mediated deposition of H3K27me3 recruits the canonical Polycomb repressive complex 1 (PRC1) that monoubiquitinates lysine 119 of histone H2A (H2AK119ub1) and induces chromatin compaction. The core PRC2 is formed by EED, SUZ12, and the catalytic components EZH2 or EZH1 (Figure 3B) [168,169]. Both PRC1 and PRC2 play an important role in cardiac development and differentiation. EZH2 is essential for CM proliferation, survival, and postnatal cardiac homeostasis. The inactivation of Ezh2 specifically in cardiac progenitors results in ectopic transcriptional programs and lethal heart defects [170,171]. PRC2 function also ensures proper cardiac growth, and Eed ablation by TnT-Cre leads to myocardial hypoplasia and embryonic lethality [170,171]. In a mouse model of EDMD, lamin A/C loss results in PcG repositioning and de-repression of non-muscle genes in muscle satellite stem cells together with the activation of p16INK4a that induces cell cycle arrest. This aberrant transcriptional program causes impairment in self-renewal, loss of cell identity, and premature exhaustion of the quiescent satellite cell pool [172]. In a recent study using iPSC-CMs carrying the cardiac-laminopathy-associated K219T mutation, it was shown that the binding of lamin A/C together with PRC2 at the SCN5A promoter represses its expression, resulting in decreased conduction velocity [53]. Together, aberrant PRC activity upon LMNA mutation might play an important role in LMNA-related cardiomyopathies (Figure 3B). The clinical management of LMNA-related DCM includes pharmacological treatment with ACE inhibitors and beta blockers and implantable cardiac defibrillators (ICDs) [173,174]. Heart transplantation or ventricular assist devices may also be required for patients in the end stages of heart failure [173,174]. The inhibition of mTOR, MAPK, and LSD1 significantly rescues the LMNA-related DCM phenotype in mice [75,138,175], and a novel and selective p38 MAPK inhibitor is now in a phase 3 clinical trial in LMNA-related DCM [176]. In addition, CRISPR/Cas9-based genome editing strategies have been used in LMNA-caused Hutchinson–Gilford Progeria Syndrome (HGPS) and show promising results [177,178,179]. By using guide RNAs (gRNAs) that target LMNA exon 11 to specifically interfere with lamin A/progerin expression, both Santiago-Fernández et al. and Beyret et al. show a reduced progerin expression and improvement in the progeria phenotype in an HGPS mouse model [177,178]. However, off-target effects, e.g., resulting from insertion and deletions during non-homologous end joining (NHEJ), are a major concern. To overcome these limitations, CRISPR/Cas9-mediated base pair editing systems have been used in HGPS mice [179]. Base pair editing systems could modify the genome without the need of double-strand DNA breaks or donor DNA templates [180]. Two classes of DNA base editors have been reported: cytosine base editors (CBEs), which convert C:G to T:A, and adenine base editors (ABEs) which convert A:T to G:C [181,182]. Systemic injection of a single dose of dual AAV9 encoding ABE and sgRNA into an HGPS mouse model significantly extends the median lifespan of the mice, improves aortic health, and fully rescues VSMC counts as well as adventitial fibrosis [179]. Despite the power of the base pair editing technology, a major limitation is the inability to edit the genome beyond four transition mutations. Prime editing represents a novel approach which is not only suitable for all transition and transversion mutations but also for small insertion and deletion mutations [183]. Similar to base pair editing, prime editing does not require double-strand DNA breaks or donor DNA templates [183] and could be used in the correction of genetic cardiomyopathies. Accumulating evidence shows that epigenetic alterations play a crucial role in LMNA-related cardiomyopathies. Mutations in LMNA affect 3D genome architecture and chromatin accessibility, thereby altering gene expression programs. Several prospective target genes, such as PDGFRB, Gata4, SCN5A, and CACNA1A, have been identified using experimental models harboring different LMNA mutations, which may serve as potential therapeutic targets. As reviewed above, specific LMNA variants can cause extreme phenotypic diversity, which makes it challenging to understand the primary changes underlying disease pathogenesis and thus to design specific treatment strategies for patients. Therefore, an important question remains: how do different and specific LMNA mutations result in phenotypic diversity? Environmental factors, such as diet, exercise, and stress, as well as age, sex, and other comorbidities, might also contribute to the phenotypic variability in patients with pathogenic LMNA mutations. Identifying cell-type-specific interacting partners for nuclear lamins and the effect of lamin mutations on these interactions would also be important in understanding the wide-ranging clinical phenotypes and may pinpoint druggable protein–protein interfaces for therapeutic applications. Given the important role of lamin A/C in heart development and CM differentiation, developmental changes in asymptomatic-at-birth LMNA patients might result in late changes in heart structure and function, warranting further investigation.

PMC10001120

Diane Frankel,Isabelle Nanni,L’Houcine Ouafik,Laurent Greillier,Hervé Dutau,Philippe Astoul,Laurent Daniel,Elise Kaspi,Patrice Roll

Cytological Samples: An Asset for the Diagnosis and Therapeutic Management of Patients with Lung Cancer

27-02-2023

cytological sample,cytopathology,lung cancer,adenocarcinoma,PD-L1,molecular testing,NGS,immunocytochemistry

Background: Lung cancer has become the leading cause of cancer death for men and women. Most patients are diagnosed at an advanced stage when surgery is no longer a therapeutic option. At this stage, cytological samples are often the less invasive source for diagnosis and the determination of predictive markers. We assessed the ability of cytological samples to perform diagnosis, and to establish molecular profile and PD-L1 expression, which are essential for the therapeutic management of patients. Methods: We included 259 cytological samples with suspected tumor cells and assessed the ability to confirm the type of malignancy by immunocytochemistry. We summarized results of molecular testing by next generation sequencing (NGS) and PD-L1 expression from these samples. Finally, we analyzed the impact of these results in the patient management. Results: Among the 259 cytological samples, 189 concerned lung cancers. Of these, immunocytochemistry confirmed the diagnosis in 95%. Molecular testing by NGS was obtained in 93% of lung adenocarcinomas and non-small cell lung cancer. PD-L1 results were obtained in 75% of patients tested. The results obtained with cytological samples led to a therapeutic decision in 87% of patients. Conclusion: Cytological samples are obtained by minimally invasive procedures and can provide enough material for the diagnosis and therapeutic management in lung cancer patients.

Cytological Samples: An Asset for the Diagnosis and Therapeutic Management of Patients with Lung Cancer Background: Lung cancer has become the leading cause of cancer death for men and women. Most patients are diagnosed at an advanced stage when surgery is no longer a therapeutic option. At this stage, cytological samples are often the less invasive source for diagnosis and the determination of predictive markers. We assessed the ability of cytological samples to perform diagnosis, and to establish molecular profile and PD-L1 expression, which are essential for the therapeutic management of patients. Methods: We included 259 cytological samples with suspected tumor cells and assessed the ability to confirm the type of malignancy by immunocytochemistry. We summarized results of molecular testing by next generation sequencing (NGS) and PD-L1 expression from these samples. Finally, we analyzed the impact of these results in the patient management. Results: Among the 259 cytological samples, 189 concerned lung cancers. Of these, immunocytochemistry confirmed the diagnosis in 95%. Molecular testing by NGS was obtained in 93% of lung adenocarcinomas and non-small cell lung cancer. PD-L1 results were obtained in 75% of patients tested. The results obtained with cytological samples led to a therapeutic decision in 87% of patients. Conclusion: Cytological samples are obtained by minimally invasive procedures and can provide enough material for the diagnosis and therapeutic management in lung cancer patients. Lung cancer has become the leading cause of cancer death for men and women [1]. The management of this cancer has evolved over the last decade with the emergence of new therapies, such as tyrosine kinase inhibitors and immunotherapy. Therefore, patient samples must allow for both the diagnosis and molecular testing, as well as PD-L1 quantification. As patients are often diagnosed at an advanced stage [2], pathologists must use samples carefully and appropriately, as the diagnosis is no longer the only result needed for patient management. Cytological samples have a place in the management of patients with pulmonary mass, especially those with an advanced disease for whom surgery is not a therapeutic option. These patients with advanced diseases account for 45% of patients diagnosed with lung cancer [2]. The last version of the World Health Organization (WHO) classification granted a section entirely dedicated to cytology in lung cancer, showing the importance of these samples in this context [3]. In this article, we report the ability of cytological samples to perform the diagnosis of lung cancer and to obtain critical results, such as molecular profile and PD-L1 expression, which are essential for the therapeutic management. This study included cytological samples in which suspicious cells were observed and where immunocytochemistry was performed to characterize these cells. The samples were collected between January 2021 and September 2022 in the Cell Biology Laboratory (Timone Hospital, Assistance Publique des Hôpitaux de Marseille, Marseille, France). The different types of these cytological samples were pleural, pericardial, and peritoneal effusions, bronchoalveolar lavage fluids, endobronchial ultrasound guided transbronchial needle aspiration (EBUS-TBNA) lymph nodes, EBUS-TBNA mediastinal or pulmonary mass, cerebrospinal fluid, and bone marrow aspiration. Samples were not initially fixed and were kept at 4 °C until slide preparation (smears or cytospin). Slides were stained with Papanicolaou and May-Grünwald–Giemsa stains. The conventional cytological diagnosis was performed by the Cell Biology Laboratory. PD-L1 testing was performed by the Anatomopathology Laboratory (Assistance Publique des Hôpitaux de Marseille, Marseille, France). The next generation sequencing (NGS) was performed by the Oncobiology Laboratory (Assistance Publique des Hôpitaux de Marseille, France). Samples included in this study were obtained from patients attending the Assistance Publique des Hôpitaux de Marseille for diagnosis and treatment. Results of the molecular testing and clinical data were retrospectively analyzed. This project was approved by the local ethics committee (PADS22-389). Samples were prepared on cytospins as previously described in [4], following the manufacturer’s instructions. As a minimum, one wash was performed between each step. Slides were fixed with paraformaldehyde (PAF) 4% for 10 min, and then incubated with the peroxidase-blocking solution for 30 min. After being washed, slides were incubated with SensiTEK HRP kit (ScyTek Laboratories, Logan, UT, USA) for 10 min. Primary antibodies were incubated for 30 min (see Table S1 for the list of primary antibodies). Then, the biotinylated secondary antibody was incubated for 15 min, followed by Streptavidin/HRP for 20 min and DAB Quanto chromogen (Diagomics, Blagnac, France) for 5 min. Nuclei were counterstained with Mayer’s hemalun solution. Slides were mounted with Aquatex® (Merck Millipore, Darmstadt, Germany). Slides were observed under optical microscope (Leica, Wetzlar, Germany). Mouse isotype IgG and rabbit polyclonal antibodies were used as negative controls as part of best practice method. Cytoblocks were prepared to perform PD-L1 testing. Cytological samples were fixed with formalin 4% for 6 h then centrifugated for 5 min at 670 g. The supernatant was discarded. The cytoblockTM kit (Epredia, Kalamazoo, MI, USA) was used to prepare cytoblocks following the manufacturer’s instructions. A slide stained with H&E was systematically performed before PD-L1 immunostaining to confirm the cytoblock quality and evaluate the adequate number of tumor cells. PD-L1 immunostaining (QR001, Quartett, Germany) was performed with the optiview DAB detection Kit on Benchmarck Ultra (Ventana, Roche, Bale, Switzerland). A positive control was systematically performed as part of best practice method. NGS was performed from frozen cell pellets as previously described [5]. In short, total nucleic acids were extracted with the Maxwell RSC Cell DNA Kit (Promega, Madison, WI, USA) and RNAs were extracted with the Maxwell RSC Simply RNA Blood Kit (Promega). The detection of mutations and fusions were performed by NGS on the Ion Torrent S5XL (ThermoFisher, Waltham, MA, USA) with a custom panel Oncomine Solid Tumor and Oncomine Solid Tumor+ (OST/OST+) and Oncomine Focus RNA assay kit (ThermoFisher, Waltham, MA, USA) (see Table S2 for the fusion transcript panel and the mutation transcript panel). Ion Torrent Suite, Ion Reporter software (ThermoFisher, Waltham, MA, USA) and a pipeline developed in our laboratory were used for the interpretation of the results. Between January 2021 and September 2022, 259 cytological samples containing cells suspected of malignancy were analyzed by immunocytochemistry to characterize the type and origin of cancer. Immunocytochemistry allowed for the characterization of the type of cancer in 248 cases (95.7%). In 59 samples (mostly pleural and peritoneal effusions), the immunocytochemistry confirmed the malignancy but with another origin than lung (for example, ovarian, breast, colorectal or pancreatic carcinoma, mesothelioma, neuroblastoma, lymphoma, or melanoma). Concerning the 189 samples with lung cancer, lung adenocarcinoma was diagnosed in 106 cases, followed by non-small cell lung cancer not-otherwise specified (NSCLC NOS) (44 cases), squamous cell carcinoma (20 cases) and neuroendocrine tumors (19 cases), including large cell neuroendocrine carcinoma (3 cases), small cell lung cancer (15 cases), and 1 case of carcinoid tumor (see Figure 1 and Table 1). In 11 cases, samples contained cells that were suspected to be malignant, but the immunocytochemistry did not confirm the malignancy, either because the sample was too necrotic or because the samples contained a low number of tumor cells (<1% of total cells). Among the 189 samples diagnosed with lung cancer, 72 (38.1%) were pleural effusions, 71 (37.5%) were lymph nodes collected by EBUS-TBNA, 29 (15.3%) were mediastinal or pulmonary masses collected by EBUS-TBNA, 6 (3.2%) were bronchoalveolar lavage fluids (BAL), 6 (3.2%) were pericardial effusions, 2 (1.1%) were cerebrospinal fluids (CSF), 2 (1.1%) were peritoneal effusion, and one (0.5%) was bone marrow. Patients were mostly diagnosed at stage IV (75.1%) and were current or former smokers (73%) (Table 1). PD-L1 was performed on cytoblocks from 115 cytological samples. No results could be reported in 29 cases (25.2%) because there were less than 50 tumor cells. Of the 86 samples that could be analyzed, PD-L1 expression was found negative (<1%) in 22 cases (25.6%), between 1 and 49% in 34 cases (39.5%) and ≥50% in 30 cases (34.9%). Examples of these are shown in Figure 2. For 35 patients, the cytoblock was not performed because no sample remained after immunocytochemistry and NGS testing, or because the sample was too necrotic. For 20 patients, the cytoblock was available but PD-L1 was not performed on the cytoblock as it had already been done on a biopsy or on the resected tumor. Next generation sequencing (NGS) was performed on 140 out of 150 cases (93%) diagnosed with lung adenocarcinoma or NSCLC NOS. EGFR mutation was found in 20 cases (14.2%), of which 12 received a tyrosine kinase inhibitor. The other 8 patients were not at stage IV, or were treated with the best supportive care, or the treatment was not known. ALK fusions were found in 4 (2.8%) cases and ROS1 fusions in 2 (1.4%) cases. All these patients with ALK or ROS1 fusions were treated with appropriate tyrosine kinase inhibitors. A mutation in TP53 was found in 67 cases (47.8%), 46 of which had another associated mutation. KRAS mutation was found in 48 cases (34.3%), of which 29 had another associated mutation. Other mutations were found as HER2, PIK3CA, STK11, RET, BRAF, DDR2, CTNNB1, SMAD4, PTEN and POLE (Figure 3). The absence of mutation or fusion was found in 19 cases (13.6%). We analyzed the impact of cytology results on the management of 189 patients with lung cancer: For 124 (65.6%) cases, the result was conducive to the diagnosis and allowed for therapeutic management. Among them, 37 cases had a biopsy and a cytological sample during the same procedure: the same diagnosis was obtained on biopsy and cytology for 24 patients, while for 13 patients, the biopsy was free of tumor cells and the diagnosis was only performed on cytology. In 3 cases, a biopsy was recommended after cytology because they were necrotic or because there was not enough material to perform the NGS. For 40 (21.2%) patients already known to have lung cancer, a cytological sample was performed in the context of suspected lung cancer progression (demonstrated by imaging). The presence of tumor cells in the cytological sample confirmed the progression and led to a change of therapeutic line. For 19 (10.0%) cases, cytology results had no impact on the therapeutic management. In most of these cases, the metastatic site was already known (pleural, peritoneal, or pericardial) and the cytological analysis was performed because the effusion had to be drained. In other cases, the clinical status deteriorated rapidly, and the patient died within a few days. In 6 (3.2%) cases, no information was available. According to our data, the results obtained from cytological samples allow a therapeutic management for 87% of patients with lung cancer. In this study, we used cytospin specimens for staining and immunocytochemistry and cytoblocks for PD-L1 testing. The methods of cytological preparation each have their advantages and disadvantages. For example, cytoblocks can be compared to biopsies, while cytospin preparation provides good morphology that can easily be used for immunocytochemistry. Regardless of the method selected, rigorous quality controls are essential [6]. Studies report that small biopsies and cytological samples can account for up to 70% of specimens for the diagnosis of lung cancer [7]. In our study, immunocytochemistry on cytospin allowed to determine the type of cancer in 248 of the 259 cytological samples in which suspected tumor cells were observed. For the remaining 11 samples, the immunocytochemistry did not allow to characterize the cells either because of the quality (necrotic or too much altered cells), the lack of volume (e.g., cerebrospinal fluid), or the low number of suspected tumor cells (<1% of total cells). Despite this, the classification of tumor cells was successful in over 95% of cases. This rate shows the value of cytological samples for identifying tumor cells. Our results are consistent with other studies evaluating the ability of cytological samples to diagnose lung cancer [8,9,10]. For example, Rekhtman et al. [9] compared 192 pre-operative cytology specimens with histology and found a concordance of 96%. Proietti et al. [8] assessed the efficacy of lung cancer subtyping in cytology and biopsy samples from 941 patients and found a concordance in 92.8% of cases. Arnold et al. [11] conducted a prospective study to investigate the role of cytology in pleural effusions. They included 921 pleural effusions with 166 lung cancer and 100 lung adenocarcinomas. The sensitivity for the diagnosis of lung cancer was 56% and 82% for adenocarcinoma [11]. Others studies found similar results for the detection and the characterization of tumor cells in pleural effusion [12,13,14]. In the last decade, the emergence of immune checkpoint inhibitors and targeted therapies have modified the way in which cytological samples are managed. In addition to the diagnosis, the sample must allow for the assessment of PD-L1 expression and molecular testing. PD-L1 expression is a biomarker that predicts which patients are more likely to respond to immunotherapy. Immunotherapy can be prescribed in first line monotherapy for patients with advanced NSCLC and with ≥50% PD-L1 expression, and in second line therapy for metastatic NSCLC patients with ≥1% PD-L1 expression [15,16,17]. In this context, evaluation of PD-L1 expression is essential on cytological samples [18,19]. This test can be challenging; it needs an adequate protocol and quality controls [20]. For example, macrophages and mesothelial cells must be properly recognized to avoid counting them in the percentage of PD-L1 expressing cells. Cytoblocks are the most commonly used material for analysis and provide concordant results compared to biopsies, even if smears can also be used [21,22]. A recent multicenter study including 264 patients concluded that PD-L1 expression on cytological samples correctly predicts the efficacy of immunotherapy [23]. In our study, PD-L1 expression was tested only on 115 cytoblocks. When PD-L1 status has already been determined for a patient on biopsy or surgical specimen, the analysis was not performed again on cytological sample. Molecular testing must be performed for patients with advanced NSCLC as several oncogenic drivers are targetable. The International Association for the Study of Lung Cancer (IASLC) recommends to test EGFR mutations and ALK and ROS1 fusions. HER2, RET, MET, BRAF, and KRAS are not indicated as a routine stand-alone assay but may be included in a large molecular testing panel [24,25]. In accordance, the European Society for Medical Oncology (ESMO) recommends the use of NGS that includes at least EGFR common mutations, ALK fusions, MET mutations, BRAF mutations, and ROS1 fusions [24,26]. The absence of formalin in cytological samples facilitates molecular testing to be applied as NGS or polymerase chain reaction (PCR) [27]. Molecular testing on cytological samples has the advantage of providing results even if the sample volume or the number of tumor cells is low [28]. In our study, 93% of patients with lung adenocarcinoma or NSCLC had NGS results. Among them, only 13.5% did not show a molecular alteration in genes included in the tested panel. We have previously demonstrated the feasibility of detecting ALK and ROS1 fusions from cytological samples either by immunocytochemistry completed by fluorescence in situ hybridization (FISH) if positive, or by NGS, with high concordance of both techniques [4,5]. ALK and ROS1 fusions are now routinely performed by immunocytochemistry on cytological samples as part of diagnosis results [29]. Rekhtman et al. showed the feasibility of testing for EGFR and KRAS mutations in thoracic cytology [9]. In our study, the NGS is performed on the frozen cell pellets. The supernatant from post-centrifuge liquid based cytology can also be used for NGS [30]. The two main criteria that prevent all techniques (i.e immunocytochemistry, molecular testing and PD-L1) from being performed are low sample volume or the low representativeness of tumor cells. Regarding sample volume, pathologists should inform clinicians that the larger the volume sent to the laboratory, the more adequate the sample will be for diagnosis and additional testing. Particularly for effusions (pericardial, pleural, and peritoneal) where the puncture can evacuate up to several liters, the pathologist can receive only one or two milliliters. Adequacy of a standardized volume vary depending on the cellularity and the percentage of tumor cells. Currently, no standardized volume requirements exists, but several studies recommend 50 mL of fluid [31,32,33]. Dalvi et al. recommend at least 20 mL but demonstrated that the tumor cell proportion is critical for assessing diagnosis and molecular analysis [34]. The low number of tumor cells is a reason to perform a biopsy and obtain an accurate material for a new test. With a low number of tumor cells detected (1–5%), immunocytochemistry and NGS can potentially be interpreted [28]. But PD-L1 interpretation requires at least 100 tumor cells, otherwise pathologists are unable to obtain a result. Over the last decade, major therapeutic advances in the treatment of lung cancer, with the introduction of targeted therapies and immune checkpoint inhibitors, have forced pathologists to change their practice and use cytological samples differently. Diagnosis alone is no longer enough and the pathologist must keep a portion of the sample to perform PD-L1 analysis and molecular testing. Cytological samples are obtained by minimally invasive procedures and can provide enough material for the diagnosis and the therapeutic management in patients with lung cancer.

PMC10001121

Mariana de Moura e Dias,Vinícius da Silva Duarte,Lúcio Flávio Macedo Mota,Gabriela de Cássia Ávila Alpino,Sandra Aparecida dos Reis Louzano,Lisiane Lopes da Conceição,Hilário Cuquetto Mantovanie,Solange Silveira Pereira,Leandro Licursi Oliveira,Tiago Antônio de Oliveira Mendes,Davide Porcellato,Maria do Carmo Gouveia Peluzio

Lactobacillus gasseri LG-G12 Restores Gut Microbiota and Intestinal Health in Obesity Mice on Ceftriaxone Therapy

03-03-2023

Lactobacillus gasseri LG-G12,intestinal health,ceftriaxone,gut microbiota,obesity

Gut microbiota imbalance is associated with the occurrence of metabolic diseases such as obesity. Thus, its modulation is a promising strategy to restore gut microbiota and improve intestinal health in the obese. This paper examines the role of probiotics, antimicrobials, and diet in modulating gut microbiota and improving intestinal health. Accordingly, obesity was induced in C57BL/6J mice, after which they were redistributed and fed with an obesogenic diet (intervention A) or standard AIN-93 diet (intervention B). Concomitantly, all the groups underwent a treatment phase with Lactobacillus gasseri LG-G12, ceftriaxone, or ceftriaxone followed by L. gasseri LG-G12. At the end of the experimental period, the following analysis was conducted: metataxonomic analysis, functional profiling of gut microbiota, intestinal permeability, and caecal concentration of short-chain fatty acids. High-fat diet impaired bacterial diversity/richness, which was counteracted in association with L. gasseri LG-G12 and the AIN-93 diet. Additionally, SCFA-producing bacteria were negatively correlated with high intestinal permeability parameters, which was further confirmed via functional profile prediction of the gut microbiota. A novel perspective on anti-obesity probiotics is presented by these findings based on the improvement of intestinal health irrespective of undergoing antimicrobial therapy or not.

Lactobacillus gasseri LG-G12 Restores Gut Microbiota and Intestinal Health in Obesity Mice on Ceftriaxone Therapy Gut microbiota imbalance is associated with the occurrence of metabolic diseases such as obesity. Thus, its modulation is a promising strategy to restore gut microbiota and improve intestinal health in the obese. This paper examines the role of probiotics, antimicrobials, and diet in modulating gut microbiota and improving intestinal health. Accordingly, obesity was induced in C57BL/6J mice, after which they were redistributed and fed with an obesogenic diet (intervention A) or standard AIN-93 diet (intervention B). Concomitantly, all the groups underwent a treatment phase with Lactobacillus gasseri LG-G12, ceftriaxone, or ceftriaxone followed by L. gasseri LG-G12. At the end of the experimental period, the following analysis was conducted: metataxonomic analysis, functional profiling of gut microbiota, intestinal permeability, and caecal concentration of short-chain fatty acids. High-fat diet impaired bacterial diversity/richness, which was counteracted in association with L. gasseri LG-G12 and the AIN-93 diet. Additionally, SCFA-producing bacteria were negatively correlated with high intestinal permeability parameters, which was further confirmed via functional profile prediction of the gut microbiota. A novel perspective on anti-obesity probiotics is presented by these findings based on the improvement of intestinal health irrespective of undergoing antimicrobial therapy or not. The gut is home to approximately 70% of the microbiota detected in humans, including bacteria, fungi, viruses, and protozoa [1,2]. Modulation of gut microbiota composition and metabolic functions have been proposed as key factors that control obesity development [2,3]. External modulators of gut microbiota include probiotics, antimicrobials, and diet, which acts with speed and high precision in order to impact obesity [4,5]. Probiotics are live microorganisms which provide health benefits to host when consumed in sufficient amounts. They modulate the composition and metabolic functions of the gut microbiota, and contribute to immunological functions through the regulation of cytokines, promotion of oral tolerance to food antigens, and improvement of intestinal barrier functions [5]. In this context, several probiotics, used alone or as synbiotic mixtures, have shown antiobesity effects. For example, Lactobacillus gasseri has beneficial effects on weight loss and body fat reduction in overweight humans and animals [6,7,8]. Alternatively, antimicrobials can contribute to the loss of microbial diversity in the gut over time, impairing metabolic function and leading to impaired metabolism. Therefore, they potentially reduce the colonization resistance against invading pathogens, resulting in dysbiosis [9]. Antimicrobial’s ability to alter the microbiota of the gut varies based on diet, lifestyle, and drug spectrum of action as well as its absorption capacity, which indicates that broader spectrum antimicrobials such as ceftriaxone result in intestinal dysbiosis more frequently [10,11]. Similarly, a high-fat diet (HFD) is capable of promoting intestinal dysbiosis, thus contributing to intestinal barrier dysfunction, immune intolerance to food antigens, activation of pro-inflammatory routes, and circadian cycle disruption that leads to weight gain, abnormal glucose fluxes, and inflammatory response [12]. In contrast, balanced and diversified diets, such as the Mediterranean standard diet, rich in fruits, vegetables, whole grains, and seafood, promotes a diverse gut microbiota, thereby stimulating intestinal barrier function and immunity [13]. Moreover, the production of short-chain fatty acids (SCFAs), metabolites of gut microbiota, and intestinal permeability are also indicative of intestinal and systemic health [14,15]. SCFAs have numerous beneficial effects on the host from increasing mucus and tight junction expressions in the intestinal epithelium to metabolic and appetite modulation [16]. Intestinal permeability is influenced by gut microbiota imbalance and is one of the main factors for low-grade inflammation, making the microbiome a central player as regards inflammatory diseases such as obesity [12]. Given the above, there is a need for insights into mechanisms used to regulate dysbiosis in obese mice. Therefore, this study evaluated how a potential probiotic Lactobacillus gasseri LG-G12 (LG-G12), antimicrobial ceftriaxone, and diet [7,8] act to modulate the intestinal microbiota and subsequently impact intestinal health parameters. Animal Ethics Committee of Universidade Federal de Viçosa approved the experiment according to the protocols numbers 09/2017 and 33/2018, and the principles established by the National Animal Experimentation Control Council [17]. An experiment was conducted using 72 male C57BL/6J mice [7,8] from the Central Vivarium of the Center for Biological and Health Sciences of Universidade Federal de Viçosa (UFV). The mice were kept in collective cages (two animals per cage) and were submitted to a 12 h light/dark cycle and an average temperature of 22 ± 2 °C. A pair-feeding scheme was used to administer fructose solution and a diet to the animals throughout the experiment. All animal experiments were conducted at UFV’s Experimental Nutrition Laboratory. At 5 weeks, the animals underwent an obesity-induced protocol that lasted 3 months (induction phase). During this initial period, the mice were fed with an HFD where 60% of total calories were derived from lipids (nutritional composition based on diet D12492 of the Research Diets, Inc., New Brunswick, NJ, USA) [18], and a 10% fructose solution (Synth®, Diadema, Brazil) instead of drinking water [19]. The negative control group (G1, n = 8) received an AIN-93 diet, with 10% of total calories derived from lipids [20] and water from the induction phase until the end of the experiment. After this period, the treatment phase began and the mice that were fed with an HFD were randomly divided into two intervention groups (A and B) with a subset of four experimental groups each (Figure 1): high-fat diet (HFD) (G2, n = 7), LG-G12 HFD (G3, n = 7), cefriatoxane HFD (G4, n = 7), cefriatoxane + LG-G12 HFD (G5, n = 7), standard fat diet (SFD) (G6, n = 8), LG-G12 SFD (G7, n = 7), cefriatoxane SFD (G8, n = 7), and cefriatoxane + LG-G12 SFD (G9, n = 8). Intervention A groups continually received an obesogenic diet during the treatment phase. The obesogenic diet was confirmed by Dias et al. [7]. In contrast, intervention B groups initiated an AIN-93 standard diet and water instead of fructose solution during the treatment phase. A gavage treatment was administered every evening at the same time. The antimicrobial group was treated with 500 mg of ceftriaxone per kg of body mass (Triaxton, Blau Farmacêutica S/A®, Cotia, Brazil) [21]. The potential probiotic group received 109 colony-forming units (CFU) of LG-G12 (Lemma Supply Solutions®, São Paulo, Brazil). L. gasseri LG-G12 was lyophilized and diluted into 200ul of water. Both treatment groups received 500 mg of ceftriaxone per kg of body weight in the first two weeks of the treatment phase and, in the following two weeks, 109 CFU of L. gasseri LG-G12. At the conclusion of the treatment phase, a total exsanguination was carried out after the animals had been anesthetized with 3% isoflurane (Cristália®, Belo Horizonte, Brazil) and euthanized. This form of euthanasia is recommended for rodents by CONSEA [17]. For future analyses, tissue samples were collected and stored. More information about the experimental design is available at Dias et al. [7]. As previously described by Dias et al. [7], after the treatment phase, a lactulose (Daiichi Sankyo®, Barueri, Brazil) and mannitol solution (Synth®, Diadema, Brazil) were supplied to the animals. Subsequently, the 24 h urine of the animals was collected. These sugars were measured by high-performance liquid chromatography (detector model RID 10A, Shimadzu®, Tokyo, Japan). By Siegfried et al. [22] method, the acetic, propionic, and butyric acids, of the cecal content, were determined, as reported by Dias et al. [7], and analyzed by high-performance liquid chromatography (Ultimate 3000, Dionex, Thermo Fisher Scientific®, Waltham, MA, USA). A pool of stool samples was made as described by Dias et al. [7]. This methodology was adopted because the animals are isogenic and live in a controlled environment, and can therefore be considered biological replicates. Metagenomic DNA was extracted from 200 mg of feces using the method adapted from Zhang et al. [23]. Afterward, the quantity of the extracted DNA was evaluated utilizing Qubit (Invitrogen, Thermo Fisher, USA), whereas its integrity and quality were verified through electrophoresis in 1.8% agarose gel. The V3 and V4 regions of 16S rRNA genes were PCR amplified utilizing specific primers (Bakt 341F and Bakt 805R) and sequenced using an Illumina MiSeq desktop sequencer (Illumina, San Diego, CA, USA) at the Macrogen Company (Macrogen Inc®, Seoul, South Korea). Microbiota data were processed and analyzed with QIIME2 (version 2020.2) [24]. In brief, raw sequence data obtained across the C57BL/6J mice stool samples from group G1 to G9 were imported via the Casava1.8 paired-end pipeline followed by denoising with DADA2 [25] (via q2-dada2). Subsequently, an amplicon sequence variants (ASV) table was constructed to generate a phylogenetic tree by using the align-to-tree-might-fast tree pipeline from the q2-phylogeny plugin [26,27]. When appropriate, samples were rarefied to a sampling depth of 120,326 sequences. Taxonomy was assigned to the 16S data using a Naïve Bayes pre-trained Greengenes 13_8 99% OTUs classifier [28]. For the functional prediction of the gut microbiota, ASVs (read sequences and read counts) were used as inputs for the PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) pipeline [29]. In brief, ASVs were inserted and aligned into a reference tree composed of 20,000 full 16S rRNA genes from bacterial and archaeal genomes using, respectively, the HMMER (http://www.hmmer.org, accessed on 1 June 2021) and EPA-ng/GAPPA tools [30,31]. Subsequently, the castor R package [32] was used to predict the missing gene families (Enzyme Commission numbers) for each ASV, as well as their respective copy number of 16S rRNA gene sequences, by using the output tree generated in the previous step. Finally, MinPath [33] was adopted to infer MetaCyc pathways based on EC number abundances. Under accession numbers PRJNA705760 and PRJNA745938, the raw fastq data have been submitted to the Sequence Read Archive (SRA) at NCBI. The principal component analysis (PCA) was performed using the relative abundance of the most abundant genera (greater than 0.1% in at least one sample). OTU abundance was scaled and then the PCA analysis was performed using the prcomp function of the R program (R Core Team 3.6.2, 2019). Normalization was performed to assure that the PCA results are mathematically independent of the overlap measure. The factorial analysis for OTU information aimed at obtaining the variables which contribute to the highest differentiation across the treatment groups using the FactoMineR R package [34]. Shapiro-Wilk test was used to test the normality of the variables (i.e., Shannon, Chao1, acetate, propionate, butyrate, total SCFA, and lactulose/mannitol ratio). One-way analysis of variance (ANOVA) followed by Tukey’s post hoc test was adopted for parametric data, and Kruskal Wallis complemented by Dunn’s multiple-comparison test was used for non-parametric data. The results were expressed as mean ± standard error of the mean (SEM). Differentially abundant taxa after the treatment phase that most likely explain the differences among the groups (i.e., fecal biomarkers) were assessed using the linear discriminant analysis (LDA) effect size (LEfSe) [35] tool and setting an alpha value of 0.05 and Log LDA threshold of 2.0. Beta diversity was assessed using the Unweighted and Weighted UniFrac metrics to evaluate bacterial community dissimilarities between the groups. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was used to test whether distances between samples within a certain group are more similar to each other or not. Correlations between continuous variables were determined by Pearson’s (parametric data) or Spearman’s (non-parametric data) correlation with the Paleontological statistics software package for education and data analysis (PAST, v4.06b) [36]. Lastly, the Statistical Analysis of Metagenomic Profile (STAMP) software [37] was used to explore and compare the metabolic potential of the predicted microbial communities across the groups. Functional profiling was built based on the MetaCyc Metabolic Pathway Database [38]. Welch’s t-test (two-sided) was adopted as a statistical hypothesis test. For both analyses, a p-value less than 0.05 was considered a significant difference. MDS analysis based on intestinal microbiota abundance explains around 38.9% of the variation between the groups (G1 to G9) (Figure 2). In intervention “B” (groups G1, G6, G7, G8, and G9) there is microbial homogeneity between G7 and G9. Then it is believed that LG-G12 acted as a positive modulator of the intestinal microbiota (Figure 2A). Moreover, distinct bacterial genera contributed differently to the total intestinal microbial load across the different experimental groups, which suggests different biological and/or metabolic capabilities (Figure 2B). The absence of differences in alpha diversity between interventions “A” and “B” was observed (Table 1, Figure S1). However, in the groups that consumed an antimicrobial and high-fat diet (G4 and G5), a tendency towards a reduction in the Shannon and Chao1 indices could be observed, when compared to those who received ceftriaxone and a standard diet (G8 and G9). This result shows a synergy between the antibiotic and high-fat diet which can impair bacterial diversity/richness. As previously described [39,40], the use of antibiotics can harm the intestinal epithelium, as well as favor the growth of specific microbial taxa. When associated with high-calorie diets, both factors can contribute negatively to the development of the gut microbiota [40,41], which justifies our findings on cefriatoxane with the high-fat diet group (G4). Finally, switching to a standard diet (intervention “B”) was capable of increasing intestinal diversity in all groups, which justifies the lack of statistical difference. Regarding the beta-diversity analysis, there was no difference in terms of gut microbiota dispersion based on UniFrac distance metrics (weighted UniFrac: F-value = 0.77; p = 0.49; unweighted UniFrac: F-value = 1.66; p-value = 0.15). According to PERMANOVA results, a statistically significant difference was observed regarding community dissimilarity considering both UniFrac indices (weighted UniFrac: F-value = 2.68; p-value = 0.003; unweighted UniFrac: F-value = 1.75; p-value = 0.002). Moreover, pairwise comparisons using Qiime beta-group-significance command revealed that the gut composition of the group that received ceftriaxone and a high-fat diet (G4) showed the greatest distance to other groups, especially to G1 (Figure S2). This result indicates that significant differences in gut microbial composition were due to the intervention and not to dispersion effects. Taken together, alpha- and beta-diversity indices evidenced that a synergy between antibiotics and a high-fat diet can impair bacterial community structure and diversity both qualitatively and quantitatively. Overall, 127 significantly discriminative features (LDA > 2, p < 0.05) were identified in the LEfSE analysis. The phyla Proteobacteria (LDA > 5) and Spirochaetes (LDA > 2.0) were enriched in group G3, whereas the phylum Tenericutes (LDA > 2.0) can be considered a biomarker for group G4 (Figure 3). The consumption of a processed diet, commonly rich in emulsifiers and artificial sweeteners, can explain the expansion of Proteobacteria, which can increase intestinal permeability and reduce local mucus production [42,43]. Regarding the phylum Spirochaetes, although its identification in fecal samples has not been associated with obesity, Jabbar et al. [44] reported the association between Brachyspira and irritable bowel syndrome. Our results indicate a limited impact of the LG-G12 in controlling the two aforementioned taxa. Since a single strain was used in this study, its inclusion in a multiple strain formulation, as suggested by the World Gastroenterology Organisation [5], might represent a more effective strategy in limiting the growth of such undesired phyla, which must be evaluated in further studies. Nevertheless, Firmicutes and Bacteroidetes have not been identified as biomarkers in any of the interventions, group G4 showed a much higher F/B ratio (Table 2), which is common in the obese population [42]. This means that the combined use of ceftriaxone and a high-fat diet disrupts the intestinal bacterial balance, favoring the growth of a few phyla at the expense of others, which is common in intestinal dysbiosis [45]. Interestingly, following LG-G12 administration, the F/B ratio decreased in the cefriatoxane + LG-G12 A group (G5). This outcome suggests a mechanism of counteraction of LG-G12 to the damage caused by ceftriaxone administration favoring the restoration of intestinal homeostasis [45]. The Gram-positive and Gram-negative genera ratio was also evaluated in each group (Table 2). As expected, the G4 group presented the least proportion of Gram-negative among all groups (23.50%) and the greatest G+/G− ratio (3.24), indicating the selectivity of the antimicrobial ceftriaxone against this specific group of bacteria. Ceftriaxone is a third-generation cephalosporin that targets most Gram-negative bacteria inducing changes in gut microbiota [46]. Our results also reveal that the intervention with LG-G12 alleviated the effects of the synergism between the antimicrobial and the diet offered, restoring intestinal Gram-negative taxa at levels similar to the control groups. Crovesy et al. [6], in a systematic review of randomized controlled clinical trials, suggested that the modulatory effect of Lactobacillus in weight loss is strain-dependent and can require its association with calorie restriction, phenolic compounds, or other bacterial strains. Amongst the biomarkers identified by LEfSE analysis, the genera Oscillospira (LDA > 4), Sporosarcina (LDA > 4), Allobaculum (LDA > 3), Jeotgalicoccus (LDA > 3), Bifidobacterium (LDA > 3), and Yaniella (LDA > 3) were assigned as biomarkers for group G1 (Figure 3). The enrichment of the genera Bifidobacterium is in agreement with the literature, where an inverse relationship was shown between this genus and obesity. Bifidobacteria deconjugate bile acids, decreasing fat absorption [47]. The higher abundance of Oscillospira in the gut of lean subjects has been addressed in several studies and is positively associated with lower body mass index (BMI) in both children and adults [48]. It is also well reported that a high-fat diet can significantly reduce the intestinal abundance of Allobaculum, a relevant SCFA-producing bacteria, which may display an anti-obesogenic role by reducing intestinal inflammation and improving insulin resistance [49,50,51]. The increase in the relative abundance of the genera Jeotgalicoccus and Sporosarcina, although less described in the literature, are associated with beneficial outcomes in animal models fed high-fat diets [52,53]. To the best of our knowledge, there is no available information regarding the role of Yaniella, a high salt-tolerant microorganism, in healthy or obese subjects. Overall, our results show that low-calorie diets are beneficial to the maintenance of taxa that are negatively associated with obesity. The genera Lactobacillus (LDA > 4), Dehalobacterium (LDA > 2), and Erysipelotrichaceae cc_115 (LDA > 3) were identified as biomarkers in the obese control group (G2). Although most lactobacilli strains can have beneficial and auxiliary effects on weight loss in overweight adults [6], some species, such as Limosilactobacillus reuteri have been associated with weight gain in humans and animals [54,55]. Regarding the genera Dehalobacterium and Erysipelotrichaceae cc-115, little information is available regarding their abundance and role within the intestinal community of obese or overweight subjects. It is reported that the genus Dehalobacterium comprehends microorganisms strictly anaerobic and capable of degrading dichloromethane and was found enriched in both obese and non-obese asthmatic patients [56], whereas Erysipelotrichaceae cc-115 was found depleted in the gut microbiota of community-dwelling physically active older men [57]. Six different genera were enriched in LG-G12 A (G3) after the end of the experimental period: Ruminococcus (LDA > 4), Anaerotruncus (LDA > 4), Bilophila (LDA > 3), Desulfovibrio (LDA > 3), Brachyspira (LDA > 2), and Coprococcus (LDA > 2). We observed that when LG-G12 was offered to animals that were fed a high-fat diet, there was a remarkable enrichment of SCFA-producing bacteria such as Ruminococcus, Anaerotruncus, and Coprococcus which are commonly found in the microbiota of overweight or obese patients [58,59,60]. Intriguingly, we also detected the enrichment of taxa involved in mucus degradation and hydrogen sulfide production such as Brachyspira, Bilophila, and Desulfovibrio, which may indicate a limited action of LG-G12 against these taxa. The genera Enterococcus (LDA > 5), Salinispora (LDA > 3), and Akkermansia (LDA > 4) were identified as biomarkers of the ceftriaxone A group (G4), whereas only Clostridium (LDA > 4) was significantly enriched in the ceftriaxone + LG-G12 A group (G5). Interestingly, Akkermansia, which is a Gram-negative, obligate anaerobe, non-motile, non-spore-forming bacterium, seems to be resilient to the adverse effects of the antimicrobial ceftriaxone. This genus has attracted great interest due to its capability to enhance mucus formation, activate the innate immune system, and promote intestinal homeostasis [61]. As reported by Vesić & Kristich [62], the genus Enterococcus is intrinsically resistant to cephalosporins, antibiotics that act on cell wall biosynthesis, which may explain its identification as a biomarker of this group. Additionally, Mishra & Ghosh [63] reports that the E. faecalis AG5 strain mitigates HFD-induced obesity through several mechanisms such as activation of adipocyte apoptosis and the improvement of glucose, insulin, and leptin sensitivity. The genus Corynebacterium (LDA > 3) was identified as a biomarker of group G1, while the genera Blautia (LDA > 3), Clostridium (LDA > 4), and Akkermansia (LDA > 5) appear enriched in the control group G6 (AIN-93 intake during the treatment phase). The enrichment of the SCFA-producing bacteria Akkermansia, Blautia, and Clostridium may contribute to restoring intestinal integrity and the development of intestinal homeostasis. During calorie-restricted diet therapy for overweight or obese individuals, insulin resistance improves and is correlated with an increased abundance of Akkermansia in the gut [61]. Finally, an enrichment of the Bifidobacterium genus (LDA > 4) was observed following LG-G12 with a standard diet in the treatment phase (G7). This result indicates a synergism between LG-G12 and endogenous bifidobacteria, which might be strain-specific. Following probiotic intervention with Latilactobacillus curvatus and Lactiplantibacillus plantarum, Park et al. [64] observed enrichment of Bifidobacterium pseudolongum species in HFD-probiotic mice when compared to the HFD-placebo group. Differentially abundant genera were not identified in the ceftriaxone (G8) and ceftriaxone + LG-G12 groups (G9). The genera Enterococcus (r = −0.59, p = 6.27 × 10−3) and Bifidobacterium (r = −0.54, p = 1.32 × 10−2) were negatively correlated with high Lactulose/Mannitol (L/M) ratio (Table 3), in LG-G12 treated groups (G3 and G7) (Figure 4A). Species of the genus Enterococcus can interact with mucosal immune cells, thus activating intestinal immune response [64]. Wu et al. [65] report that the probiotic E. faecium NCIMB 11181 can ameliorate necrotic enteritis by improving intestinal mucosal barrier function and modulating gut microbiota. Bifidobacterium, which is indicative of microbial diversity [66], can protect against obesity and diabetes, as well as improve intestinal integrity and control metabolic endotoxemia, important parameters for the assessment of intestinal balance and health [41,67]. In terms of SCFA production (Figure 4B, Table 3), the genera Enterococcus (r = 0.48, p = 3.12 × 10−2), Allobaculum (r = 0.76, p = 8.96 × 10−5), Sporosarcina (r = 0.83, p = 4.73 × 10−6), Jeotgalicoccus (r = 0.87, p = 7.67 × 10−7), Staphylococcus (r = 0.91, p = 2.50 × 10−8), Bifidobacterium (r = 0.60, p = 4.79 × 10−3), Blautia (r = 0.59, p = 6.11 × 10−3) were positively correlated with the total amount of SCFA, whereas Prevotella (r = 0.50, p = 2.33 × 10−2) was only positively correlated with the production of butyrate. Kong et al. [41] reported that the consumption of high-fat and high-sucrose diets reduces the abundance of Prevotella and, consequently, butyrate levels. In addition, the probiotic administration (Lactobacillus acidophilus, Bifidobacterium longum, and Enterococcus faecalis) can restore the intestinal microbiota, increasing microorganisms such as Lactobacillus, Bifidobacterium, and Akkermansia. Based on our results, we believe that LG-G12 positively modulated SCFA-producing bacteria such as Allobaculum, Bifidobacterium, and Prevotella. Overall, the bacterial genera that negatively correlated with the L/M ratio were positively correlated with the production of SCFA, indicating a relevant role of such organic acids with the integrity of the intestinal barrier, which is in line with previous studies [41,68,69]. The correlation analysis performed considering groups G4, and G8 (Figure S3A, Table 3) revealed that Staphylococcus (r = −0.81, p = 7.49 × 10−4) was negatively correlated with lactulose, whereas Clostridium (r = 0.48, p = 3.79 × 10−2) was positively correlated with L/M ratio. Zeng et al. [70] reported that lactulose inhibited the effect of Staphylococcus aureus due to the production of sialyllactulose, an antimicrobial enzyme capable to cause damage to the S. aureus cell membrane, which can be good for intestinal health. The positive correlation between Clostridium and a high L/M ratio is not news as some species such as Clostridium difficile can secrete toxins with cytotoxic effects on the intestinal epithelium [71]. In terms of SCFA (Figure S3B, Table 3), the genera Allobaculum (r = 0.67, p = 1.59 × 10−3) and Bifidobacterium (r = 0.54, p = 1.72 × 10−2), were positively correlated with total SCFA production, whereas only the genus Stenotrophomonas (r = −0.48, p = 3.76 × 10−2) showed a negative correlation. Even following antimicrobial treatment, the genera Allobaculum and Bifidobacterium were negatively correlated with the L/M ratio and positively correlated with the production of SCFA, which indicate that these genera may act in the maintenance of intestinal integrity and homeostasis as previously described by Kong et al. [41]. Regarding the genus Stenotrophomonas, some species belonging to this genus, such as Stenotrophomonas maltophilia, are considered pathogenic bacteria [72] and highly resistant to antibiotics [73], which may justify its presence among the ceftriaxone-treated groups. Correlation analyses encompassing groups G5 and G9 (Figure S4A) revealed that the genus Desulfovibrio (r = 0.48, p = 3.29 × 10−2) was positively correlated with L/M ratio and, consequently, loss of intestinal integrity. Desulfovibrio members are frequently elevated in intestinal dysbiosis, causing intestinal permeability and inflammation [74], which is consistent with our findings. Concerning SCFAs (Figure S4B), the genera Prevotella (r = 0.49, p = 2.98 × 10−2) and Faecalibacterium (r = 0.50, p = 2.44 × 10−2) were positively correlated with their total amount (represented here by the sum of acetate, propionate, and butyrate), whereas some genera were positively correlated with only certain compounds, but not with total production, as follows: Allobaculum (Acetic: r = 0.88, p = 2.95 × 10−3; Propionic, r = 0.73, p = 2.70 × 10−4; Butyric, r = 0.64, p = 2.62 × 10−3) and Bifidobacterium (Acetic, r = 0.81, p = 1.71 × 10−2; Propionic, r = 0.67, p = 1.11 × 10−3; Butyric, r = 0.74, p = 1.87 × 10−4). Acetate and lactate are among the SCFAs produced by Bifidobacterium [75], whereas butyrate production in particular is more related to prebiotics, as discussed previously [68]. The identification of Faecalibacterium and Allobaculum, both genera described as producers of SCFA [41,69]. This association may also improve intestinal health by promoting SCFA-producing genera and, consequently, enhancing the gut microbiota. During the different interventions evaluated in the current study, we aimed to identify whether microbial metabolic pathways were enriched in the gut microbiota, which could be associated with the effects of a high-fat diet or not. In addition, we focused on identifying metabolic pathways related to SCFA production that can directly impact intestinal health. Taking into account the comparison between groups G7 and G3, 58 functional pathways differed significantly between the groups (Table S1), and the great majority (approximately 69.0%) were enriched in group G7. Regarding the MetaCyc pathways associated with SCFA production, six metabolic pathways were identified (Figure 5), and four of them were present in group G7 (Bifidobacterium shunt, heterolactic fermentation, hexitol fermentation to lactate, formate, ethanol, and acetate). The enrichment of Bifidobacterium shunt, which is a classic pathway of carbohydrate metabolisms such as fructose and glucose, can generate compounds serving as an energy source for intestinal epithelial cells [68,75], which explains the greater intestinal integrity noticed in this group. Only the acetyl-Coa fermentation to butanoate II and L-lysine fermentation to acetate and butanoate pathways were enriched in the G3 group. In the groups that underwent ceftriaxone treatment followed by LG-G12 (groups G5 and G9), only 14 functional pathways differed significantly between both groups (Table S2) with approximately 64.0% of the features enriched in the G9 group. The enrichment of the super pathway of D-glucarate and D-galactarate degradation in group G5 also demonstrates the use of alternative carbon sources for growth. The use of dicarboxylic acid sugars as growth substrate occurs in many different bacteria but is especially found in Gram-negative bacteria such as members of the Enterobacteriaceae, Moraxellaceae, and Pseudomonadaceae families [76]. Metabolic pathways, Bifidobacterium shunt, and heterolactic fermentation, associated with SCFA production were enriched only in group G9 (Figure S5). Similarly, in the G9 group, we also observed the enrichment of the Bifidobacterium shunt and heterolactic fermentation pathways, both associated with carbohydrate metabolism [77], which evidence an important role of low-calorie diets in the enrichment of these functions. Interestingly, the super pathway of D-glucarate and D-galactarate degradation and the pathway of purine nucleotide degradation II (aerobic) were enriched in the G5 group. This indicates that this group of microbes is using alternative carbon sources. Finally, 12 functional pathways differed significantly between groups G4 and G8 (ceftriaxone administration only) (Table S3), with the vast majority of pathways (approximately 66.0%) being enriched in the G4 group. The main metabolic pathways enriched in the G4 group were associated with menaquinol biosynthesis and de novo nucleotide biosynthesis. The enrichment of menaquinol biosynthesis might be related to the energy processes of bacteria since menaquinones are relevant growth factors for gut microbiota [78,79] Traditionally, the gut microbiota is an important source of purines, which are used in different functions related to the intestinal barrier and innate immunity, being necessary for intestinal protection and health [80]. Since dysbiosis was observed in group G4, it is believed that the enrichment of nucleotide biosynthesis pathways confirms an expansion of specific microbial taxa in this group. Consumption of a high-fat diet associated with ceftriaxone was able to reduce microbial diversity. It was observed that LG-G12 had the best effects when it was combined with a low-calorie diet in restoring gut homeostasis. Higher caecal SCFA contributed to increased intestinal integrity. Also, the genera that presented a negative correlation with a high L/M ratio were similar to those that had a positive correlation with total SCFA production. This trend was further confirmed by metagenomic predictions of the gut microbiota. LG-G12 is presented in this study as a novel adjuvant treatment for overweight or obese individuals through gut microbiota modulation and improvement of intestinal health in models undergoing antimicrobial therapy or not.

PMC10001133

Łukasz Witucki,Hieronim Jakubowski

Depletion of Paraoxonase 1 (Pon1) Dysregulates mTOR, Autophagy, and Accelerates Amyloid Beta Accumulation in Mice

26-02-2023

APP,amyloid beta,Pon1−/−5xFAD mouse model,N2a-APPswe cells,Pon1,homocysteine thiolactone,Phf8,H4K20me1,mTOR,autophagy

Paraoxonase 1 (PON1), a homocysteine (Hcy)-thiolactone detoxifying enzyme, has been associated with Alzheimer’s disease (AD), suggesting that PON1 plays an important protective role in the brain. To study the involvement of PON1 in the development of AD and to elucidate the mechanism involved, we generated a new mouse model of AD, the Pon1−/−xFAD mouse, and examined how Pon1 depletion affects mTOR signaling, autophagy, and amyloid beta (Aβ) accumulation. To elucidate the mechanism involved, we examined these processes in N2a-APPswe cells. We found that Pon1 depletion significantly downregulated Phf8 and upregulated H4K20me1; mTOR, phospho-mTOR, and App were upregulated while autophagy markers Bcln1, Atg5, and Atg7 were downregulated at the protein and mRNA levels in the brains of Pon1─/─5xFAD vs. Pon1+/+5xFAD mice. Pon1 depletion in N2a-APPswe cells by RNA interference led to downregulation of Phf8 and upregulation of mTOR due to increased H4K20me1-mTOR promoter binding. This led to autophagy downregulation and significantly increased APP and Aβ levels. Phf8 depletion by RNA interference or treatments with Hcy-thiolactone or N-Hcy-protein metabolites similarly increased Aβ levels in N2a-APPswe cells. Taken together, our findings define a neuroprotective mechanism by which Pon1 prevents Aβ generation.

Depletion of Paraoxonase 1 (Pon1) Dysregulates mTOR, Autophagy, and Accelerates Amyloid Beta Accumulation in Mice Paraoxonase 1 (PON1), a homocysteine (Hcy)-thiolactone detoxifying enzyme, has been associated with Alzheimer’s disease (AD), suggesting that PON1 plays an important protective role in the brain. To study the involvement of PON1 in the development of AD and to elucidate the mechanism involved, we generated a new mouse model of AD, the Pon1−/−xFAD mouse, and examined how Pon1 depletion affects mTOR signaling, autophagy, and amyloid beta (Aβ) accumulation. To elucidate the mechanism involved, we examined these processes in N2a-APPswe cells. We found that Pon1 depletion significantly downregulated Phf8 and upregulated H4K20me1; mTOR, phospho-mTOR, and App were upregulated while autophagy markers Bcln1, Atg5, and Atg7 were downregulated at the protein and mRNA levels in the brains of Pon1─/─5xFAD vs. Pon1+/+5xFAD mice. Pon1 depletion in N2a-APPswe cells by RNA interference led to downregulation of Phf8 and upregulation of mTOR due to increased H4K20me1-mTOR promoter binding. This led to autophagy downregulation and significantly increased APP and Aβ levels. Phf8 depletion by RNA interference or treatments with Hcy-thiolactone or N-Hcy-protein metabolites similarly increased Aβ levels in N2a-APPswe cells. Taken together, our findings define a neuroprotective mechanism by which Pon1 prevents Aβ generation. Paraoxonase 1 (PON1), named for its ability to hydrolyze and inactivate the organophosphate paraoxon, is synthesized exclusively in the liver, circulates in the blood as a component of high-density lipoproteins (HDL) [1], and is present in many organs, including the brain [2]. In addition to protecting from organophosphate toxicity [3], PON1 protects against atherosclerosis induced by a high-fat diet [4] or ApoE depletion [5] in mice. Large-scale human studies showed that high arylesterase activity of PON1 protects from cardiovascular disease (CVD) in patients with coronary artery disease undergoing elective diagnostic coronary angiography [6,7] and in patients with chronic kidney disease [8], while low homocysteine thiolactonase activity of PON1 was associated with worse long-term mortality [9]. In the PREVEND prospective study involving 6902 participants, PON1 activity predicted CVD events [10]. The cardioprotective function of PON1 can be due both to its antioxidative function [4,6,11] and the ability to detoxify homocysteine (Hcy)-thiolactone [12,13,14,15], thereby attenuating lipid peroxidation, oxidative protein modification, and protein N-homocysteinylation. PON1 has also been implicated in Alzheimer’s disease (AD) [16,17], which can be expected given that AD has a significant vascular component [18]. For example, PON1 activity is lower in AD and dementia patients compared with healthy controls [19,20,21,22] and correlates with the severity of AD-related cognitive decline [23]. In patients with mild cognitive impairment, PON1 activity predicted global cognition, verbal episodic memory, and attention/processing speed [24]. In mice, ApoE−/−Pon1−/− animals, which have severe carotid atherosclerosis [5], showed AD markers and impaired vasculature in their brains at 14 months, although it was not clear whether brain pathology was caused by ApoE−/−, Pon1─/─, or both knockouts [25]. In a mouse model of AD (Tg2576), immunohistochemical fluorescence signals for Pon1 protein in various regions of the brain were found to surround Aβ plaques but could not be colocalized to any brain cell type [26]. Deletion of the Pon1 gene in mice impairs the metabolic conversion of Hcy-thiolactone to Hcy, increases brain Hcy-thiolactone levels, and makes the animals overly sensitive to the neurotoxicity of Hcy-thiolactone injections [12]. Studies of Pon1−/− mouse brain proteome demonstrated that Pon1 interacts with diverse cellular processes, such as energy metabolism, anti-oxidative defenses, cell cycle, cytoskeleton dynamics, and synaptic plasticity, that are essential for brain homeostasis [27]. Clusterin (CLU or APOJ), involved in the transport of amyloid beta (Aβ) from plasma to brain in humans (reviewed in [28]), is carried on a distinct HDL subspecies that contains three major proteins: PON1, CLU, and APOA1 [29]. Notably, levels of Clu (ApoJ) are significantly elevated in the plasma of Pon1−/− vs. Pon1+/+ mice [30]. These findings suggest that Pon1 plays a key role in brain homeostasis, possibly protecting from Aβ accumulation. The present work was undertaken to examine the effects of Pon1 depletion on Aβ levels in a novel model of AD, the Pon1─/─5xFAD mouse, generated in the present study and to elucidate the mechanism involved. Because dysregulated mTOR signaling and autophagy have been implicated in Aβ accumulation in Alzheimer’s disease [31,32], and H4K20me1 demethylation by PHF8 is important for maintaining homeostasis of mTOR signaling [33], we studied how these processes are affected by Pon1 depletion in the mouse neuroblastoma N2a-APPswe cells and Pon1─/─5xFAD mice. We also examined how changes in these processes affect the behavioral performance of Pon1─/─5xFAD mice. Pon1−/− [4] mice (kindly provided by Diane M. Shih) and 5xFAD mice [34] (The Jackson Laboratory, Bar Harbor, Maine, USA) on the C57BL/6J background were housed and bred at the New Jersey Medical School Animal Facility. 5xFAD mice overexpress the K670N/M671L (Swedish), I716V (Florida), and V717I (London) mutations in human APP (695), and M146L and L286V mutations in human PS1 and accumulate high levels of Aβ42 beginning around 2 months of age [35] (https://www.alzforum.org/research-models/5xfad-b6sjl) (accessed 27 December 2022). The Pon1−/− mice were crossed with 5xFAD animals to generate Pon1−/−5xFAD mice and their Pon1+/+5xFAD sibling controls. Mouse Pon1 genotype was established by PCR of tail clips DNA using the Pon1 forward primer p1 (5′-TGGGCTGCAGGTCTCAGGACTGA-3′), Pon1 exon 1 reverse primer p2 (5′-ATAGGAAGACCGATGGTTCT-3′), and neomycin cassette reverse primer p3 (5′-TCCTCGTGCTTTACGGTATCG-3′) [4]. The Pon1 genotype was also confirmed by RT-qPCR assays, which did not detect any Pon1 mRNA in the brains of Pon1−/−5xFAD mice but showed robust expression of Pon1 mRNA in the brains of their Pon1+/+5xFAD siblings. The 5xFAD genotype was established using human APP and PS1 primers (hAPP forward 5′-AGAGTACCAACTTGCATGACTACG-3′ and reverse 5′-ATGCTGGATAACTGCCTTCTTATC-3′; hPS1 forward 5′-GCTTTTTCCAGCTCTCATTTACTC-3′ and reverse 5′-AAAATTGATGGAATGCTAATTGGT-3′). The mice were fed a standard rodent chow diet (LabDiet 5010, Purina Mills International, St. Louis, MO, USA). Water supplemented with 1% methionine was used to induce hyperhomocysteinemia [12,27]. The high Met diet increases plasma total Hcy levels 5.6- and 10.4-fold in Pon1−/− (from 8.5 to 48 μM) and Pon1+/+ mice (from 7.4 to 77 μM) [27]. Animal procedures were approved by the Institutional Animal Care and Use Committee at the New Jersey Medical School. Mice were euthanized by CO2 inhalation; the brains were collected and frozen on dry ice. Frozen brains were pulverized with dry ice using a mortar and pestle and stored at −80 °C. Proteins were extracted from the pulverized brains (50 ± 5 mg; 30 ± 3 mg brain was used for Aβ analyses) using RIPA buffer (4 v/w, containing protease and phosphatase inhibitors) with sonication (Bandelin SONOPLUS HD 2070) on wet ice (three sets of five 1-s strokes with 1 min cooling interval between strokes). Brain extracts were clarified by centrifugation (15,000× g, 30 min, 4 °C) and clear supernatants containing 8–12 mg protein/mL were collected (RIPA-soluble fraction). Protein concentrations were measured with BCA kit (Thermo Fisher Scientific, Waltham, MA, USA). For Aβ analyses, pellets remaining after protein extraction with RIPA buffer were re-extracted by brief sonication in 2% SDS, centrifuged (15,000× g, 15 min, room temperature), and the supernatants collected again (SDS-soluble fraction). The SDS-extracted pellets were then extracted by sonication in 70% formic acid (FA), centrifuged, and the supernatants were collected (the FA-soluble fraction) [35]. Aβ was quantified using a dot blot assay [36]. Briefly, brain protein extracts (1 µL) were spotted onto the nitrocellulose membranes and dried (37 °C, 1 h). The membranes were washed with TBST buffer (RT, 15 min) and blocked with 5% BSA in TBST buffer (RT, 1 h). Blocked membranes were washed three times with TBST buffer (10 min each) and incubated with monoclonal anti-Aβ antibody (CS #8243; 4 °C, 16 h). Membranes were then washed three times with TBST buffer (10 min each) and incubated with goat horseradish peroxidase-conjugated anti-rabbit IgG secondary antibody. Positive signals were detected using Western Bright Quantum-Advansta K12042-D20 and GeneGnome XRQ NPC chemiluminescence detection system. Signal intensity was assessed using the Gene Tools program from Syngene. Mouse neuroblastoma N2a-APPswe cells, harboring a human APP transgene with the K670N and M671L Swedish mutations [37] were grown (37 °C, 5% CO2) in DMEM/F12 medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 5% FBS, non-essential amino acids, and antibiotics (MilliporeSigma, Saint Louis, MO, USA). After cells reached 70–80% confluency, the monolayers were washed twice with PBS and overlaid with DMEM medium without methionine (Thermo Scientific), supplemented with 5% dialyzed fetal bovine serum (FBS) (MilliporeSigma) and non-essential amino acids. L-Hcy-thiolactone (20 and 200 μM) (MilliporeSigma) or N-Hcy-protein (10 and 20 μM), prepared as described in ref. [38], were added, and the cultures were incubated at 37 °C in a 5% CO2 atmosphere for 24 h. For gene silencing, siRNAs targeting the Pon1 (Cat. # s71950 and s71951) or Phf8 gene (Cat. # s115808, and s115809) (Thermo Scientific) were transfected into cells maintained in Opti-MEM medium by 48-h Lipofectamine RNAiMax (Thermo Scientific) treatments. Cellular RNA for RT-qPCR analysis was isolated as described in Section 2.5. For protein extraction, RIPA buffer (MilliporeSigma) was used according to the manufacturer’s protocol. Proteins were separated by SDS-PAGE on 10% gels (20 µg protein/lane) and transferred to a PVDF membrane (Bio-Rad) for 20 min at 0.1 A, 25 V using the Trans Blot Turbo Transfer System (Bio-Rad). After blocking with 5% bovine serum albumin in TBST buffer (1 h, room temperature), the membranes were incubated with monoclonal anti-Pon1 (ab126597, Abcam, Cambridge, MA, USA), anti-Phf8 (Abcam, ab36068), anti-H4K20me1 (Abcam ab177188), anti-mTOR (Cell Signaling Technology, Davnvers, MA, USA, CS #2983), anti-pmTOR Ser2448 (CS, #5536), anti-Atg5 (CS, #12994), anti-Atg7 (CS, #8558), anti-Beclin-1 (CS, #3495), anti-Gapdh (CS, #5174), or anti-App (Abcam, ab126732) overnight at 4 °C. Membranes were washed three times with TBST buffer, for 10 min each, and incubated with goat anti-rabbit IgG secondary antibody conjugated with horseradish peroxidase. Positive signals were detected using Western Bright Quantum-Advansta K12042-D20 and GeneGnome XRQ NPC chemiluminescence detection system. Band intensity was calculated using the Gene Tools program from Syngene. Total RNA was isolated using Trizol reagent (MilliporeSigma). cDNA synthesis was conducted using Revert Aid First cDNA Synthesis Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. Nucleic acid concentration was measured using NanoDrop (Thermo Fisher Scientific). RT-qPCR was performed with SYBR Green Mix and CFX96 thermocycler (Bio-Rad, Hercules, CA, USA). The 2(−ΔΔCt) method was used to calculate the relative expression levels [39]. Data analysis was performed with the CFX Manager™ Software, Microsoft Excel, and Statistica. RT-qPCR primer sequences are listed in Table S1. For CHIP assays we used CUT&RUN Assay Kit #86652 (Cell Signaling Technology, Danvers, MA, USA) following the manufacturer’s protocol. Each ChIP assay was repeated three times. Briefly, for each reaction, we used 100,000 cells. Cells were trypsinized and harvested, washed 3× in ice-cold PBS, and bound to concanavalin A-coated magnetic beads for 5 min, at RT. Cells were then incubated (4 h, 4 °C) with 2.5 µg of anti-PHF8 antibody (Abcam, ab36068) or anti-H4K20me1 antibody (Abcam, ab177188) in the antibody-binding buffer plus digitonin that permeabilizes cells. Next, cells were treated with pAG-MNase (1 h, 4 °C), washed, and treated with CaCl2 to activate DNA digestion (0.5 h, 4°C). Cells were then treated with the stop buffer and spike-in DNA was added for each reaction for signal normalization, and incubated (10–30 min, 37 °C). Released DNA fragments were purified using DNA Purification Buffers and Spin Columns (CS #14209) and quantified by RT-qPCR using primers targeting the promoter, upstream, and downstream regions of the mTOR gene (Table S1). Rabbit (DA1E) mAb IgG XP® Isotype Control included in the CUT&RUN kit did not afford any signals in the RT-qPCR assays targeting mTOR. Mouse neuroblastoma N2a-APPswe cells were cultured in Millicell EZ SLIDE 8-well glass slides (Merck, Darmstadt, Germany). After treatments, cells were washed 3 times with PBS for 10 min. Cells were fixed with 4% PFA (MilliporeSigma) (37 °C, 15 min), washed 3 times with PBS buffer, permeabilized with 0.1% Triton X-100 solution (RT, 20 min), blocked with 0.1% BSA (RT, 1h), and incubated with anti-Aβ antibody (CS #8243; 4 °C, 16 h). Cells were then washed 3 times with PBS and stained with secondary antibody Goat Anti-Rabbit IgG H&L (Alexa Fluor® 488) (Abcam, ab150077; RT, 1 h) to visualize and quantify Aβ. DAPI (Vector Laboratories, Newark, CA, USA) was used to visualize nuclei. Fluorescence signals were monitored by using a Zeiss LSM 880 confocal microscope with a 488 nm filter for the Alexa Fluor® 488 (Aβ) and 420–480 nm filter for DAPI, taking a z stack of 20–30 sections with an interval of 0.66 μm and a range of 15 μm. Zeiss Plan-Apochromat X40/1.2 Oil differential interference contrast objective were used for imaging. Images were quantified with the ImageJ Fiji 2.9.0 software (NIH, Bethesda, MD, USA). The hindlimb clasping test is used to assess neurodegeneration in mouse models [40]. For this test, mice were suspended by the base of the tail and videotaped for 10 s. Three separate trials were taken over three consecutive days. Hindlimb clasping was scored from 0 to 3: 0 = hindlimbs splayed outward and away from the abdomen; 1 = one hindlimb retracted inwards towards the abdomen for at least 50% of the observation period; 2 = both hindlimbs partially retracted inwards towards the abdomen for at least 50% of the observation period; and 3 = both hindlimbs completely retracted inwards towards the abdomen for at least 50% of the observation period. Hindlimb clasping scores were added together for the three separate trials. The ledge test is used to assess motor deficits in rodent models of CNS disorders [41]. Typically, mice walk along the ledge of a cage and try to descend back into the cage. Three separate trials were taken for each mouse. The ledge test was scored from 0 to 3 points: 0 = a mouse walked along the ledge without slipping and lowered itself back into the cage using paws; 1 = the mouse lost its footing during walking along the ledge but otherwise appeared coordinated; 2 = the mouse did not effectively use its hind legs and landed on its head rather than paws when descending into the cage; and 3 = the mouse fell of the ledge or was shaking and/or barely moving. The cylinder test is used to assess sensorimotor function in rodent models of CNS disorders. A mouse is placed in a transparent 500 mL plastic cylinder. The number of times the mouse rears up and touches the cylinder wall during a period of 3 min is counted. A rear is defined as a vertical movement with both forelimbs off the floor so that the mouse is standing only on its hindlimbs. At the end of 3 min, the mouse was removed and placed back into its home cage. Because spontaneous activity in the cylinder is affected by repeated testing, resulting in reduced activity over time, mice were tested only once in their lifetime. The results were calculated as mean ± standard deviation. A two-sided unpaired t test was used for comparisons between two groups of variables; p < 0.05 was considered significant. Statistical analysis was performed using Statistica, Version 13 (TIBCO Software Inc., Palo Alto, CA, USA, http://statistica.io) (accessed 2 November 2022). To determine if Pon1 interacts with Phf8, we quantified Phf8 protein in the brains of Pon1−/−5xFAD mice and their Pon1+/+5xFAD sibling controls by Western blotting. We also examined the effects of hyperhomocysteinemia (HHcy), induced by providing 1% methionine in drinking water, on the Pon1–Phf8 interaction. Pictures of Western blots are shown in Figure 1I and Figure 2B, while quantification of individual proteins is illustrated by corresponding bar graphs in Figure 1 and Figure 2 for 5-month-old and 12-month-old mice, respectively. We found that Phf8 protein was significantly downregulated in the brains of Pon1−/−5xFAD mice vs. Pon1+/+5xFAD sibling controls in animals fed with a standard chow diet (5-month-old: from 1.0 ± 0.1 to 0.68 ± 0.15, Pgenotype = 2 × 10−5, Figure 1A; 12-month-old: from 1.0 ± 0.2 to 0.65 ± 0.12, Pgenotype = 1 × 10−4, Figure 2A). Reduced expression of Phf8 in Pon1−/−5xFAD vs. Pon1+/+5xFAD brains was also observed in mice fed with the HHcy diet (from 0.76 ± 0.11 to 0.60 ± 0.10, Pgenotype = 0.001; Figure 1A). HHcy diet significantly downregulated Phf8 expression in the brains of Pon1+/+5xFAD mice (to 0.76 ± 0.11, Pdiet = 6 × 10−5). In contrast, Phf8 expression in the brains of Pon1−/−5xFAD mice was essentially not affected by the HHcy diet (0.60 ± 0.19 vs. 0.68 ± 0.15, Pdiet = 0.099) (Figure 1A). The histone H4K20me1 epigenetic mark was significantly upregulated in Pon1−/−5xFAD vs. Pon1+/+5xFAD brains (5-month-old: 1.74-fold, Pgenotype = 1 × 10−7, Figure 1B; 12-month-old: 1.41-fold, Pgenotype = 1 × 10−4, Figure 2). Upregulated expression of H4K20me1 in 5-month-old Pon1−/−5xFAD vs. Pon1+/+5xFAD brains was also observed in mice fed with the HHcy diet (from 1.58 ± 0.27 to 1.87 ± 0.24, Pgenotype = 0.030; Figure 1B). HHcy diet significantly upregulated H4K20me1 levels in 5-month-old Pon1+/+ mice (1.6-fold, Pdiet = 6 × 10−6) but not in Pon1─/─ animals (1.74- vs. 1.87-fold, Pdiet = 0.275; Figure 1B). Because Phf8/H4K20me1 regulate mTOR signaling, we next examined the effects of Pon1 depletion on levels of mTOR and its active form, phosphorylated at Ser2448 (pmTOR). We found that mTOR protein was significantly upregulated in the brains of Pon1─/─5xFAD vs. Pon1+/+5xFAD mice (5-month-old: 1.69-fold, Pgenotype = 2 × 10−10, Figure 1C; 12-month-old: 1.39-fold, Pgenotype = 4 × 10−5, Figure 2A). Upregulated expression of mTOR in Pon1─/─5xFAD vs. Pon1+/+5xFAD brains was also observed in mice fed with the HHcy diet (from 1.43 ± 0.18 to 1.97 ± 0.19, Pgenotype = 2 × 10−5; Figure 1C). HHcy diet significantly upregulated mTOR protein expression in Pon1─/─5xFAD mice (1.97 ± 0.19 vs. 1.69 ± 0.12, Pdiet = 0.003) and Pon1+/+5xFAD animals (1.43 ± 0.18 vs. 1.00 ± 0.09, Pdiet = 5 × 10−6) (Figure 1C). Because mTOR is activated by phosphorylation, we quantified mTOR phosphorylated at Ser2448 (pmTOR). We found that pmTOR was also significantly upregulated in the brains of Pon1─/─5xFAD vs. Pon1+/+5xFAD mice (5-month-old: 1.69-fold, Pgenotype = 2 × 10−10, Figure 1C; 12-month-old: 1.86-fold, Pgenotype = 3 × 10−8, Figure 2A). Upregulated expression of pmTOR in Pon1─/─5xFAD vs. Pon1+/+5xFAD brains was also observed in mice fed with the HHcy diet (1.95 ± 0.17 vs. 1.56 ± 0.26, Pgenotype = 0.002) (Figure 1D). HHcy diet significantly elevated pmTOR levels in Pon1+/+5xFAD mice (1.56 ± 0.26 vs. 1.00 ± 0.18, Pdiet = 4 × 10−5) but not in Pon1−/−5xFAD mice (1.95 ± 0.17 vs. 1.87 ± 0.30, Pdiet = 0.528 (Figure 1D). Overall, the effects of the Pon1−/− genotype on mTOR and pmTOR levels were attenuated by the HHcy diet (Figure 1C,D). These findings indicate that Pon1 depletion upregulated pmTOR to a similar extent as mTOR, suggesting that the Pon1─/─ genotype affects mTOR signaling mostly at the level of mTOR protein expression. Because mTOR is a major regulator of autophagy, we quantified autophagy-related proteins in Pon1─/─5xFAD mice. We found that the regulators of autophagosome assembly, Bcln1, Atg5, and Atg7, were significantly downregulated in the brains of Pon1─/─5xFAD vs. Pon1+/+5xFAD sibling controls (by 22–35%, Pgenotype = 1 × 10−7 to 1 × 10−4, Figure 1E–G; 12-month-old: by 24–37%, Pgenotype = 2 × 10−5 to 3 × 10−4, Figure 2A). The HHcy diet significantly decreased Bcln1, Atg5, and Atg7 expression in 5-month-old Pon1+/+5xFAD mice (by 23–28%, Pdiet = 2 × 10−5 to 2 × 10−9). In 5-month-old Pon1─/─5xFAD mice, the HHcy diet also significantly decreased Bcln1 (0.68 vs. 0.80, Pdiet = 0.003) and Atg5 levels (0.66 vs. 0.74, Pdiet = 0.008); however, Atg7 levels were essentially not affected by the HHcy diet in Pon1─/─5xFAD mice (0.63 ± 0.05 vs. 0.65 ± 0.05, Pdiet = 0.714). Overall, the effects of the Pon1−/− genotype on the brain Bcln1, Atg5, and Atg7 levels were attenuated by the HHcy diet (Figure 1E–G). These findings indicate that autophagy was impaired by the Pon1─/─genotype. We found that APP protein was significantly elevated in the brains of Pon1−/−5xFAD mice vs. Pon1+/+5xFAD sibling controls in mice fed with a standard diet (5-month-old: 1.42-fold, Pgenotype = 2 × 10−8; Figure 1H; 12-month-old: 1.39-fold, Pgenotype = 3 × 10−6, Figure 2A). Upregulated expression of APP protein in 5-month-old Pon1−/−5xFAD vs. Pon1+/+5xFAD brains was also observed in mice fed with the HHcy diet (from 1.76 ± 0.08 to 1.92 ± 0.10, Pgenotype = 0.005; Figure 1H). Met diet increased APP protein levels in the brains of 5-month-old Pon1+/+5xFAD mice (1.76-fold, Pdiet = 1 × 10−13) and, to a lesser extent, in Pon1─/─5xFAD animals (1.35-fold, from 1.42 to 1.92, Pdiet = 4 × 10−7) (Figure 1H). To determine if the observed changes in the protein levels of Phf8, mTOR, autophagy-related proteins, and APP are caused by the transcriptional effects of the Pon1─/─ genotype, we quantified the corresponding mRNAs by RT-qPCR. We found that Phf8 mRNA was significantly downregulated in the brains of Pon1−/−5xFAD mice vs. Pon1+/+5xFAD sibling controls in animals fed with a standard chow diet (5-month-old: from 1.00 ± 0.15 to 0.66 ± 0.09, Pgenotype = 1 × 10−4, Figure S1A; 12-month-old: from 1.00 ± 0.16 to 0.76 ± 0.13, Pgenotype = 4 × 10−4, Figure 2C). HHcy did not affect the effects of the Pon1 genotype on Phf8 mRNA: reduced expression of Phf8 in the brains of 5-month-old Pon1−/−5xFAD vs. Pon1+/+5xFAD mice was observed in mice fed with the Met diet (from 0.63 ± 0.27 to 0.37 ± 0.23, Pgenotype = 0.048; Figure S1A). HHcy significantly downregulated Phf8 mRNA expression in the brains of 5-month-old 5xFAD mice, regardless of Pon1 genotype: from 1.00 ± 0.15 in mice fed with a standard diet to 0.63 ± 0.27 in animals fed with the Met diet, Pdiet = 0.002 in Pon1+/+5xFAD mice, and from 0.66 ± 0.09 (std diet) to 0.37 ± 0.23 (Met diet), Pdiet = 0.002 in Pon1─/─5xFAD animals (Figure S1A). We found that mTOR mRNA was significantly upregulated in the brains of Pon1─/─5xFAD vs. Pon1+/+5xFAD mice (5-month-old: 1.55-fold, Pgenotype = 0.006, Figure S1B; 12-month-old: 1.32-fold, Pgenotype = 4 × 10−5, Figure 2C). However, HHcy abrogated the effects of the Pon1 genotype on mTOR mRNA expression: similar levels of mTOR mRNA were found in Pon1−/−5xFAD and Pon1+/+5xFAD mice fed with the high Met diet (1.79 ± 0.55 and 1.46 ± 0.61, respectively, Pgenotype = 0.258; Figure S1B). HHcy diet significantly upregulated mTOR mRNA in Pon1+/+5xFAD mice (1.46 ± 0.61 vs. 1.00 ± 0.15, Pdiet = 0.044) but not in Pon1─/─5xFAD animals (1.79 ± 0.55 vs. 1.55 ± 0.46, Pdiet = 0.352) (Figure S1B). We also found that mRNA for the regulators of autophagosome assembly, Bcln1, Atg5, and Atg7, were downregulated in the brains of Pon1─/─5xFAD vs. Pon1+/+5xFAD sibling controls (Bcln1 and Atg7 mRNA by 31% and 22%, Pgenotype = 0.005 and 0.008, respectively, Figure S1C,E; 12-month-old: by 13–36%, Pgenotype = 2 × 10−5 to 0.040, Figure 2C). Met diet significantly decreased Bcln1 and Atg7 mRNA expression in 5-month-old Pon1+/+5xFAD mice (by 18–20%, Pdiet = 0.044 and 0.008, respectively) but not in Pon1─/─5xFAD animals. The Atg5 mRNA level was not affected by the Met diet regardless of Pon1 genotype. However, Atg5 mRNA was significantly reduced by the Pon1─/─ genotype in mice fed with the Met diet but in Pon1−/−5xFAD mice. Overall, the effects of the Pon1─/─ genotype on the brain Bcln1 and Atg7 levels were attenuated by the HHcy diet (Figure S1C,E). We found that APP mRNA was significantly elevated in the brains of Pon1─/─5xFAD mice vs. Pon1+/+5xFAD sibling controls in mice fed with a standard diet (5-month-old: 1.52-fold, Pgenotype = 0.002; Figure S1F; 12-month-old: 1.59-fold, Pgenotype = 0.003, Figure 2C). Upregulated expression of APP mRNA in 5-month-old Pon1─/─5xFAD vs. Pon1+/+5xFAD brains was also observed in mice fed with the HHcy diet (from 1.76 ± 0.08 to 1.92 ± 0.10, Pgenotype = 0.005; Figure S1F). Met diet increased APP mRNA levels in the brains of 5-month-old Pon1+/+5xFAD mice (1.75-fold, Pdiet = 0.010) but not in Pon1─/─5xFAD animals (Pdiet = 0.482) and abrogated the effects of the Pon1─/─ genotype on APP mRNA (Figure S1F). As expected, Pon1 mRNA was absent in Pon1─/─5xFAD brains (Figure S1G). Met diet did not affect Pon1 mRNA in Pon1+/+5xFAD mice brains (Figure S1G). These findings indicate that the Pon1 gene exerts transcriptional control over the expression of Phf8, mTOR, autophagy-related proteins, and APP in the mouse brain. To elucidate the mechanism by which Pon1 depletion impacts Phf8 and its downstream effects on mTOR, autophagy, and APP, we first examined whether the findings in Pon1−/− mice can be recapitulated in cultured mouse neuroblastoma N2a-APPswe cells that overproduce Aβ from a mutated human APP transgene [38]. We silenced the Pon1 gene in these cells by RNA interference using Pon1-targeting siRNA and studied how the silencing impacts Phf8 and its downstream effects. Changes in specific protein levels in Pon1-silenced and control cells were analyzed by Western blotting using Gapdh protein as a reference. We found that the Pon1 protein level was reduced by 71% in Pon1-silenced cells (p = 1 × 10─5; Figure 3A). We also found that the histone demethylase Phf8 protein level was significantly downregulated (by 33%, p = 4 × 10─4; Figure 3B), while the histone H4K20me1 level was significantly upregulated (1.70–1.76-fold, p = 0.001; Figure 3C) in Pon1-silenced N2a-APPswe cells. At the same time, the mTOR protein was significantly upregulated in Pon1-silenced N2a-APPswe cells (1.7-fold, p = 0.001; Figure 3D), as were pmTOR (1.6-fold, p = 2 × 10−5; Figure 3E) and APP (1.6-fold, p = 1 × 10−4; Figure 3I), while autophagy-related proteins Bcln1, Atg5, and Atg7 (Figure 3F–H, respectively) were significantly downregulated (by 33–45%, p = 2 × 10−4 to 0.003). The Western blot results show that the changes in Phf8, H4K20m31, mTOR signaling, autophagy, and APP induced by Pon1 gene silencing in N2a-APPswe cells (Figure 3) recapitulate the in vivo findings in the Pon1−/−5xFAD mouse brain (Figure 1 and Figure 2). To determine whether increased levels of the histone H4K20me1 mark can promote mTOR gene expression by binding to its promoter in Pon1-depleted cells, we carried out ChIP experiments using anti-H4K20me1 antibody (Figure 4). The Pon1 gene was silenced by transfecting N2a-APPswe cells using two different Pon1-targeting siRNAs. The cells were permeabilized and treated with anti-H4K20me1 antibody and a recombinant micrococcal nuclease-protein A/G. DNA fragments released form N2a-APPswe cells were quantified by RT-qPCR using primers targeting the transcription start site (TSS) of the mTOR gene as well as upstream (UP) and downstream (DOWN) regions. We found that in Pon1-silenced N2a-APPswe cells, the binding of H4K20me1 was significantly increased at the mTOR TSS (1.8 to 2.3-fold, p = 4 × 10−5), mTOR UP (2.0 to 2.2-fold, p = 2 × 10−5), and mTOR DOWN sites (1.4 to 1.6-fold, p = 1 × 10−4) (Figure 4A). Importantly, in Pon1-silenced cells there were significantly more DNA fragments from the mTOR TSS (2.3 ± 0.2 and 1.8 ± 0.2 for siRNA Pon1 #1 and #2, respectively) than from the DOWN site (1.4 ± 0.2 and 1.6 ± 0.1 for siRNA Pon1 #1 and #2, respectively; p = 0.004). There were also more DNA fragments from the UP site than from the DOWN site (2.2 ± 0.2 and 2.1 ± 0.2 for siRNA Pon1 #1 and #2 vs. 1.4 ± 0.2 and 1.6 ± 0.1 for siRNA Pon1 #1 and #2; p = 0.0004). Numbers of DNA fragments from the TSS and UP sites were similar (p = 0.713) (Figure 4A). Control experiments showed that the binding of H3K4me3 to RPL30 intron was not affected by Pon1 gene silencing (Figure 4B). These findings indicate that Pon1 gene silencing induces H4K20me1 binding at the mTOR gene, significantly higher at the mTOR TSS and UP site than at the DOWN site in Pon1-silenced cells. CHIP experiments using anti-Phf8 antibody showed that Pon1 gene silencing or treatments with Hcy-thiolactone or N-Hcy-protein did not affect binding of Phf8 to the mTOR gene. To determine whether Pon1 depletion affects Aβ accumulation, we silenced the Pon1 gene by RNA interference and quantified Aβ in N2a-APPswe cells by fluorescence confocal microscopy using anti-Aβ antibody. The Pon1 gene was silenced by transfection with two different siRNAs targeting Pon1; the cells were permeabilized, treated with anti-Aβ antibody, and Aβ was visualized with fluorescent secondary antibody and quantified. Representative confocal microscopy images are shown in Figure 5A. We found that Pon1 gene silencing led to increased Aβ generation manifested by significantly increased area (from 173 ± 27 and 162 ± 22 μm2 for -siRNA and siRNAscr controls, respectively, to 225 ± 28 μm2 for siRNA Pon1 #1, p = 0.013) and average size (from 0.63 ± 0.02 and 0.61 ± 0.06 for -siRNA and siRNAscr controls, respectively, to 1.29 ± 0.17 μm2 and 0.99 ± 0.09 μm2 for siRNA Pon1 #1 and #2, respectively; p = 1 × 10−4) of fluorescent Aβ puncta in Pon1 siRNA-treated N2a-APPswe cells compared with siRNAscr or -siRNA controls (Figure 5B). Signal intensity increased from 1.00 ± 0.16 and 0.86 ± 0.28 for -siRNA and siRNAscr controls, respectively, to 2.08 ± 0.27 and 2.01 ± 0.23 for siRNA Pon1 #1 and #2, respectively; p = 3 × 10−4) (Figure 5B). Because Pon1 depletion elevates Hcy-thiolactone and N-Hcy-protein in mice [12], we examined whether any of these metabolites can induce Aβ accumulation in N2a-APPswe cells. In cells treated with Hcy-thiolactone (20–200 μM) or N-Hcy-protein (10–20 μM), there was significantly more Aβ, manifested by significantly increased area of fluorescent Aβ puncta in confocal immunofluorescence images compared with control-siRNA and siRNAscr (Figure 5C,D). However, while treatments with Hcy-thiolactone led to increased size and signal intensity of the fluorescent Aβ puncta, treatments with N-Hcy-protein did not (Figure 5D), suggesting different effects of Hcy-thiolactone and N-Hcy-protein on the structure of Aβ deposits. These findings suggest that Hcy-thiolactone and N-Hcy-protein contribute to elevated Aβ levels induced by Pon1 gene silencing. Aβ was extracted from brains of 5- and 12-month-old mice fed with a standard chow diet, and from 5-month-old mice with the HHcy diet (1% Met in drinking water) since weaning at the age of 1 month. SDS-soluble and formic acid (FA)-soluble Aβ fractions, which contain the bulk of Aβ [36], as well as a minor RIPA-soluble Aβ fraction were obtained. Aβ was quantified in these fractions by a dot blot assay with a monoclonal anti-Aβ antibody [37]. We found that RIPA- and SDS-soluble Aβ was significantly elevated (Pgenotype = 2 × 10−5 and 1 × 10−8, respectively), and FA-soluble Aβ tended to be elevated (Pgenotype = 0.058) in the brains of 12-month-old Pon1−/−5xFAD mice vs. Pon1+/+5xFAD sibling controls fed with a standard diet (Figure 6A). Similarly, elevated Aβ was found in 5-month-old Pon1−/−5xFAD vs. Pon1+/+5xFAD mice fed with a standard diet (Figure 6B) or the HHcy diet (Figure 6C). This indicates that neither age nor HHcy influenced the effects of the Pon1−/− genotype on Aβ levels. However, the HHcy diet significantly elevated RIPA-, SDS-, and FA-soluble Aβ in 5-month-old Pon1−/−5xFAD mice (from 1.21 to 1.97, Pdiet = 1 × 10−4; 2.62 to 3.09, Pdiet = 0.034; 1.88 to 3.33, Pdiet = 1 × 10−6, respectively) and in 5-month-old Pon1+/+5xFAD mice, (from 1.00 to 1.95, Pdiet = 0.002; 1.00 to 1.91, Pdiet = 4 × 10−4; 1.00 to 1.45, Pdiet = 5 × 10−4, respectively) (Figure 6C). This indicates that HHcy and Pon1−/− genotype exert similar effects on Aβ levels. To examine the effects of Pon1 depletion on neurodegeneration and sensorimotor activity, 12-month-old Pon1−/−5xFAD mice and their Pon1+/+5xFAD sibling controls were assessed in the hindlimb clasping, ledge, and cylinder tests. The hindlimb test showed a similar degree of clasping (scores) in Pon1−/−5xFAD mice vs. their Pon1+/+5xFAD littermates (2.24 ± 0.44 vs. 2.08 ± 0.43, p = 0.335; Figure S2A). These findings indicate that the Pon1−/− genotype did not induce neurodegeneration in Pon1−/−5xFAD mice relative to Pon1+/+5xFAD animals. The ledge test showed similar performances (scores) in Pon1−/−5xFAD mice vs. their Pon1+/+5xFAD littermates (2.07 ± 0.43 vs. 1.97 ± 0.38, p = 0.589; Figure S2B). The cylinder test also showed similar performances (number of rears) in Pon1−/−5xFAD mice vs. their Pon1+/+5xFAD littermates (8.5 ± 6.0 vs. 10.4 ± 5.1, p = 0.307; Figure S2C). These findings indicate that the Pon1−/− genotype did not induce sensorimotor deficits in Pon1−/−5xFAD mice relative to Pon1+/+5xFAD animals. In previous studies, we found that Pon1 is a Hcy-thiolactone-hydrolyzing enzyme [13] and that Pon1 depletion in mice elevated brain Hcy-thiolactone and N-Hcy-protein [12], increased the animals’ susceptibility to Hcy-thiolactone-induced seizures [12], and resulted in pro-neurodegenerative changes in brain proteome [27], suggesting that Pon1 plays an important protective role in brain homeostasis. Our present findings show that Pon1 protects from amyloidogenic APP processing to Aβ in mice brains (Figure 6) and unravel the mechanistic basis of the protective role of Pon1 in the CNS. Specifically, we found that Pon1 depletion downregulated histone demethylase Phf8 both at the protein and mRNA level, increased H4K20me1 binding at the mTOR promotor (Figure 4A), and upregulated mTOR expression and phosphorylation in the mouse brain (Figure 1C,D) and neuroblastoma N2a-APPswe cells (Figure 3D,E). Treatments with Hcy-thiolactone and N-Hcy-protein, metabolites that are elevated in Pon1−/− mice, also increased H4K20me1 binding at the mTOR promotor in N2a-APPswe cells (Figure 4C). This suggests that Pon1 is a negative regulator of mTOR signaling by controlling levels of Hcy metabolites that affect binding of H4K20me1 at the mTOR promotor. The effects of Hcy-thiolactone and N-Hcy-protein on mTOR are explained by findings that Phf8, the regulator of mTOR expression, was downregulated by Pon1 depletion (Figure 1A and Figure 2A), whereas H4K20me1 was upregulated (Figure 1B and Figure 2A). These findings provide direct mechanistic evidence linking Hcy-thiolactone and N-Hcy-protein with dysregulated mTOR signaling and its downstream consequences such as downregulation of autophagy and upregulation of Aβ. This mechanism is further supported by our findings that Phf8 depletion by RNA interference affected mTOR, autophagy, APP, and Aβ, similar to treatments with Hcy-thiolactone or N-Hcy-protein [42] In the present study, we found that depletion of Pon1 upregulated APP in the Pon1−/−5xFAD mouse brain (Figure 1H, Figure 2 and Figure S1F) and in mouse neuroblastoma N2a-APPswe cells (Figure 3I). In contrast, depletion of Phf8 did not affect APP expression [42]. These findings suggest that Pon1 interacts with APP in the Pon1−/−5xFAD mouse brain while Phf8 does not. However, whether the Pon1–APP interaction is direct or indirect remains to be determined. Although Pon1 depletion in mouse neuroblastoma N2a-APPswe cells downregulated Phf8 (Figure 1A and Figure 2A) and upregulated APP (Figure 1H and Figure 2A) and Aβ (Figure 5), depletion of Phf8 upregulated Aβ but not APP [42]. These findings suggest that two pathways can lead to increased Aβ generation in Pon1-depleted brains and neural cells. One pathway involves Hcy metabolites, which upregulate APP, while another pathway involves impaired Aβ clearance due to downregulated autophagy. Notably, Pon1 depletion caused changes in the Phf8- > H4K20me1- > mTOR- > autophagy pathway akin to the changes induced by HHcy in the mouse brain (Figure 1) and neuroblastoma cells (Figure 3). Pon1 depletion or HHcy similarly increased accumulation of Aβ in the brain (Figure 6). Our previous work showed that a common primary biochemical outcome of Pon1 depletion or of HHcy was essentially the same: HHcy caused elevation of Hcy-thiolactone and N-Hcy-protein [43] as did Pon1 depletion [12,14]. In the present work, Pon1 depletion by RNA interference or treatments with Hcy-thiolactone or N-Hcy-protein similarly increased the accumulation of Aβ in mouse neuroblastoma cells (Figure 5). Taken together, these findings suggest that increased accumulation of Aβ in Pon1-depleted brains is mediated by the effects of Hcy metabolites on mTOR signaling and autophagy. 5xFAD mice develop sensorimotor deficits beginning at about 9 months of age (https://www.alzforum.org/research-models/5xfad-b6sjl) (accessed 27 December 2022). For example, 5xFAD mice perform worse than the wild-type animals in the hindlimb and balance beam tests [44,45]. We found that depletion of Pon1 did not aggravate these deficits: there was no difference in sensorimotor performance between Pon1−/−5xFAD mice vs. Pon1+/+5xFAD animals in the hindlimb, ledge, and cylinder tests (Figure S2). These findings suggest that upregulated Aβ accumulation may not be causing sensorimotor impairment. However, other aspects of sensorimotor abilities may be affected by Pon1, which remains to be assessed in future studies, as are the effects of Pon1 on various domains of cognition [24]. In conclusion, our findings define a mechanism by which Pon1 prevents Aβ generation in a mouse model of AD and neural cells.

PMC10001145

Maik Luu,Burkhard Schütz,Matthias Lauth,Alexander Visekruna

The Impact of Gut Microbiota-Derived Metabolites on the Tumor Immune Microenvironment

03-03-2023

tumor microenvironment (TME),commensal bacteria,intratumoral microbiota,oncobiome,microbiota-derived metabolites,cancer immunotherapy

Simple Summary The tumor microenvironment (TME) comprises various non-malignant cells and soluble factors that surround cancer cells and which have mostly a pro-tumorigenic role. Growing evidence indicates that commensal bacteria are involved in the pathogenesis and progression but also in the suppression of various human cancers. Recently, bacterial communities that populate solid tumors have been described. This review provides insights into the complex interaction between gut-microbiota-derived metabolites and the cells of the TME. Novel studies indicate that some microbial molecules can be therapeutically exploited to enhance intratumoral immune responses and to improve the efficacy of cancer immunotherapies. Abstract Prevention of the effectiveness of anti-tumor immune responses is one of the canonical cancer hallmarks. The competition for crucial nutrients within the tumor microenvironment (TME) between cancer cells and immune cells creates a complex interplay characterized by metabolic deprivation. Extensive efforts have recently been made to understand better the dynamic interactions between cancer cells and surrounding immune cells. Paradoxically, both cancer cells and activated T cells are metabolically dependent on glycolysis, even in the presence of oxygen, a metabolic process known as the Warburg effect. The intestinal microbial community delivers various types of small molecules that can potentially augment the functional capabilities of the host immune system. Currently, several studies are trying to explore the complex functional relationship between the metabolites secreted by the human microbiome and anti-tumor immunity. Recently, it has been shown that a diverse array of commensal bacteria synthetizes bioactive molecules that enhance the efficacy of cancer immunotherapy, including immune checkpoint inhibitor (ICI) treatment and adoptive cell therapy with chimeric antigen receptor (CAR) T cells. In this review, we highlight the importance of commensal bacteria, particularly of the gut microbiota-derived metabolites that are capable of shaping metabolic, transcriptional and epigenetic processes within the TME in a therapeutically meaningful way.

The Impact of Gut Microbiota-Derived Metabolites on the Tumor Immune Microenvironment The tumor microenvironment (TME) comprises various non-malignant cells and soluble factors that surround cancer cells and which have mostly a pro-tumorigenic role. Growing evidence indicates that commensal bacteria are involved in the pathogenesis and progression but also in the suppression of various human cancers. Recently, bacterial communities that populate solid tumors have been described. This review provides insights into the complex interaction between gut-microbiota-derived metabolites and the cells of the TME. Novel studies indicate that some microbial molecules can be therapeutically exploited to enhance intratumoral immune responses and to improve the efficacy of cancer immunotherapies. Prevention of the effectiveness of anti-tumor immune responses is one of the canonical cancer hallmarks. The competition for crucial nutrients within the tumor microenvironment (TME) between cancer cells and immune cells creates a complex interplay characterized by metabolic deprivation. Extensive efforts have recently been made to understand better the dynamic interactions between cancer cells and surrounding immune cells. Paradoxically, both cancer cells and activated T cells are metabolically dependent on glycolysis, even in the presence of oxygen, a metabolic process known as the Warburg effect. The intestinal microbial community delivers various types of small molecules that can potentially augment the functional capabilities of the host immune system. Currently, several studies are trying to explore the complex functional relationship between the metabolites secreted by the human microbiome and anti-tumor immunity. Recently, it has been shown that a diverse array of commensal bacteria synthetizes bioactive molecules that enhance the efficacy of cancer immunotherapy, including immune checkpoint inhibitor (ICI) treatment and adoptive cell therapy with chimeric antigen receptor (CAR) T cells. In this review, we highlight the importance of commensal bacteria, particularly of the gut microbiota-derived metabolites that are capable of shaping metabolic, transcriptional and epigenetic processes within the TME in a therapeutically meaningful way. Multiple lines of evidence suggest an essential role for the mutualistic interaction between intestinal microbiota and the host for the maturation of the immune system and maintenance of human health [1]. Long-lasting and parallel co-evolutionary processes have led to the establishment of a stable gut microbial ecology that exhibits reciprocal communication with the host [2]. The development of a protective immune system coincides with the expansion and alterations of the intestinal microbiota that, during the short weaning period, imprints the resistance or susceptibility to inflammatory processes later in life. This so-called “weaning reaction” is a central factor for the induction of Foxp3+ regulatory T cells (Tregs) in the gut and protection against diverse inflammatory and autoimmune diseases later in life [3]. Over the past decade, a number of studies have shown that the gut microbiota is not only essential for the mucosal tissue-associated development of the local immune system, but it also modulates the course of carcinogenesis and impacts treatment response [4,5], which may offer novel opportunities for the development of microbiota-based therapeutic strategies in the coming years. Emerging data demonstrate a complex interplay of bacterial and fungal molecules with cells of the tumor microenvironment (TME) across diverse cancer types [6,7]. There is evidence now that specific members of gut microbiota influence the treatment approaches, such as immune checkpoint inhibitors (ICI) and chimeric antigen receptor (CAR) T cell therapies [8,9,10,11]. The TME comprises various non-malignant cellular populations, such as tumor-infiltrating immune cells, fibroblasts and endothelial cells. Metabolic and transcriptomic alterations, induced by intercellular interactions, soluble factors and metabolites, frequently promote an immunosuppressive phenotype of immune cells, e.g., tumor-associated macrophages (TAMs), infiltrating myeloid-derived suppressor cells (MDSCs) and Tregs, which ultimately supports tumor progression and metastases [12]. Cancer and stroma cells commonly induce the expression of programmed cell death ligand 1 (PD-L1) that binds to programmed cell death 1 (PD-1) on T cells and leads to their exhaustion, a known phenomenon during cancer development and in chronic viral infections [13,14]. Recently, the antibodies targeting PD-L1, or its receptor PD-1, have revolutionized therapeutic options for the treatment of cancer patients [15,16]. Although ICI-based immunotherapy has greatly improved the overall survival among patients with metastatic melanoma, in other cancer types, only a small subset of patients responds to this treatment [17]. Remarkably, some commensal bacteria, such as Akkermansia muciniphila and Bifidobacterium longum, seem to augment anti-tumor immunity and enhance the effectiveness of ICI therapy [4,18,19,20,21]. Novel data suggest that the high diversity and richness of commensal bacteria synergize with ICI treatment and that exposure to antibiotics may result in worse outcomes among cancer patients [22,23]. Of note, the most commonly used laboratory mouse strain C57BL/6, reconstituted with natural microbiota of a wild population of mice (trapped in Maryland, USA), exhibited reduced tumor numbers in mutagen- and inflammation-induced colorectal tumorigenesis as compared to specific-pathogen-free (SPF) control mice [24,25], suggesting for yet uncharacterized, protective mechanisms due to natural host-microbiota crosstalk, which is absent in laboratory mice. By contrast, in some cancer types such as pancreatic cancer, host microbiota seems to have a pro-tumorigenic function by supporting the activity of immunosuppressive cells within the TME, such as TAMs and Tregs [26,27]. Thus, on one side, the commensal bacteria intimately linked to several human cancers are able to promote the course of carcinogenesis. On the other side, a beneficial microbial signature is associated with an increased response to ICI therapy and a better survival of patients. These findings highlight the importance of microbiota as a novel and still partially therapeutically unexploited factor, being potentially able to modulate cancer therapy and anti-cancer immunity. This review will focus on the emerging evidence of the functional impact of diverse microbiome-derived molecules on the cells of the tumor immune microenvironment. Progress in both basic cancer research in experimental animal models and translational oncology has essentially contributed to the current understanding of how gut commensal bacteria impact cancer development and targeted therapy for cancer. Mutual interactions between intestinal microbiota and host T cells seem to be a key factor that contributes substantially to a bacteria-primed immune reaction and the trafficking of intestinal and circulating T cells to tumor tissue that supports cancer therapy [28]. There is a growing awareness of the role of a “favorable” microbiota composition that correlates with an efficient response to ICI treatment in humans and mice [29]. Using a murine model of ICI therapy (anti-cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) blockade), Vetizou et al. found that enhanced anti-cancer immunotherapy relies on the presence of Bacteroides fragilis or Bacteroides thetaiotaomicron within the gut microbiome [30]. Another study suggested a strong impact of the Bifidobacterium species on the infiltration of intratumoral CD8+ T cells, which resulted in enhanced efficacy of anti-PD-L1 immunotherapy. A subsequent report demonstrating the abundance of eight different commensal species with a better response to ICI therapy confirmed the association of Bifidobacterium longum and an augmented anti-PD-1 efficacy [31]. Importantly, the fecal microbiota transplantation (FMT) from human responders to ICI therapy led to reduced tumor growth, an increasing number of intratumoral CD8+ T cells and the enhanced efficacy of PD-1/PD-L1 blockade in mice [32,33,34]. Similarly, a recent study has revealed that a defined commensal consortium comprising 11 human bacteria that were derived from the feces of healthy human donors elicits CD8+ T cell responses and promotes anti-tumor effects in murine subcutaneous tumor models [35,36]. Interestingly, also “non-favorable” members of gut microbiota, such as Roseburia intestinalis and Ruminococcus obeum, have been recently identified [31]. Collectively, the composition of gut microbiota influences anti-cancer immune responses, tumor microenvironments and the clinical benefits of ICI therapy. Although commensal bacteria are capable of reshaping the functionality of cells surrounding the tumors and even of enhancing the efficacy of anti-tumor immunity, our understanding of the impact of specific microbiota-derived species and their molecules on the tumor immune microenvironment is still limited. Several mechanisms have been suggested, potentially explaining how gut bacteria may influence anti-cancer immune surveillance and TMEs. The system effects of gut microbes can be mediated via the ligands of pattern recognition receptors that deliver adjuvant signals for the cells of innate immunity, such as dendritic cells and macrophages [37]. Additionally, cross-reactive anti-tumor T cell responses can be generated by specific T cells that recognize microbial antigens with high similarity in their structure to tumor neoantigens [38,39]. Finally, the host/microbiota interactions can be mediated through small molecules produced by commensal bacteria that can leave the bacterial community in the intestine and reach the TME via circulation [40,41,42,43]. Recent studies have demonstrated that gut microbiota-derived metabolites are capable of eliciting and strengthening T cell-mediated anti-tumor immunity [44,45]. Reduced diversity or altered composition of the intestinal microbiome has been found to correlate with many chronic disorders, such as metabolic dysfunctions and cardiovascular, inflammatory and autoimmune diseases [46]. Generally, a more diverse gut microbiome has a positive effect on the functional diversity of the immune system, likely lowering the risk of developing cancer. For example, the diversity of the microbial community is an independent predictor of survival in cervical cancer [47]. It was observed that cancer patients with a high diversity of gut microbiota had increased tumor infiltration of Th1 and CTLs in various cancer types. Surprisingly, a novel study investigating the human tumor microbiome uncovered that intratumoral bacteria are present in various solid tumors, such as breast and ovarian cancer, lung and pancreatic tumor tissues, and even in tumors that have no direct communication with the external environment (e.g., glioblastoma or bone tumors) [6,48]. Diverse intracellular bacteria have been detected mostly in both cancer and the neighboring immune cells. The characterization of the tumor microbiome revealed that different tumor types have distinct bacterial compositions. Interestingly, at the phylum level, only two phyla (Firmicutes and Proteobacteria) have been mostly observed in the TME; however, the Proteobacteria to Firmicutes ratio seems to vary between cancer types. Furthermore, a high diversity was found for bacterial families, genera and species among various cancers [6]. Several mechanisms may be involved in the translocation and transport of bacteria to the TME during tumor development. A leaky and flexible vasculature may allow the entry of circulating bacteria and immune cells, such as macrophages, engulfing and transporting bacteria to tumor tissue. Currently, it is difficult to speculate whether intratumoral bacteria actively modulate the development of cancer or if bacteria appear at later stages in established tumors, where they can persist in certain niches. A very recent study suggests that the distribution of bacteria in the TME does not occur randomly. Instead, the presence of tumor-associated bacteria in immunosuppressive microniches points to a highly organized colonization of tumor tissues that affect the behavior of tumor and immune cells [49]. Intriguingly, it was postulated that the cell-associated members of the intratumoral microbiota could drive the migration of cancer cells and impact the cellular heterogeneity of the TME. Interestingly, the total bacterial load in tumors was negatively regulated with the expression of tumor suppression protein p53 [49]. Our better understanding of these effects may contribute to the development of alternative approaches to enhance the current cancer treatment efficacy by modulating the composition of the so-called oncobiome [50]. The presence of tumor-associated bacteria in colorectal carcinoma is probably easier to explain than in cancers that are not in close proximity to the intestinal microbiome. The processes that damage the integrity and function of the epithelial barriers in our body might compromise mucosal homeostasis, leading to microbial dysbiosis. Interestingly, intestinal bacteria and some oral bacteria have been found in colorectal cancer (CRC) samples. It was reported that Fusobacterium nucleatum, a common oral bacterium, can migrate to the colon, where it enriches in tumor tissue and impairs the therapeutic outcome and prognosis of radiotherapy and promotes colorectal carcinogenesis [51,52,53,54]. Transcriptional modification, induced by this invasive bacterium, has been related to the upregulation of signaling cascades triggered through the growth factor receptors, such as epidermal growth factor receptor (EGFR) and platelet-derived growth factor (PDGF), as well as NF-kB signaling, while pathways linked to the cell cycle, DNA damage repair and p53 signaling were downregulated. In cultured cancer cell spheroids treated with F. nucleatum, intestinal epithelial cells detached from the spheroid mass and infiltrated the surrounding collagen [49]. Notably, this member of the oral microbiota was also abundantly detected in breast and pancreatic tumor patient cohorts [55,56]. Furthermore, using advanced high-throughput 16S rRNA sequencing techniques, several studies have demonstrated that pancreatic tumor cohorts are enriched in Proteobacteria, which are normally found in duodenum tissues [57,58]. These findings suggest a retrograde bacterial translocation from the duodenum to the pancreatic duct. Of note, in both cancer types with high frequencies of K-Ras mutations, pancreatic adenocarcinoma (PDAC) and lung adenocarcinoma, the intratumoral microbiota promotes the development of cancer due to local microbiota-immune crosstalk and by modulating the tumor immune microenvironment [59,60,61]. Interestingly, not only bacteria but also pancreatic fungal mycobiome seem to promote oncogenesis. Mechanistically, the binding of glycans of the fungal wall to the mannose-binding lectin (MBL) accelerates oncogenic progression [62]. Following diagnosis, the actual five-year survival of PDAC patients is very low (approximately 9%). A recent study focusing on the tumor-associated microbiota in short-term survivors and long-term survivors offered new insights into a complex interaction between bacterial communities and the cells of the TME in PDAC. In the tumor tissue of long-term survivors, particularly three genera (Saccharopolyspora, Pseudoxanthomonas and Streptomyces) were enriched that were marginally present in short-term survivors. A strong correlation between these top-three genera and CD8+ and granzyme B+ densities was found for long-term survivors [63], suggesting that infiltration of the TME with CTLs, but also higher activity of these cells might be connected to a specific microbial signature within tumor tissue. Collectively, although it is premature to interpret the functional influence of the local microbiome composition within tumors, the targeted modulation of tumor-associated bacteria may affect the effectiveness of cancer treatment. It might be important to define a specific fraction of bacteria that belong to a “favorable oncobiome” with the potential to reshape tumor immune responses and “re-educate” the cells of the TME. In the future, such therapeutic approaches could be combined with established types of cancer immunotherapies, such as CAR-T cell or ICI therapy. The discovery of specific tumor-associated microbiome signatures in various human cancer types may also lead to the development of novel diagnostic tools to predict the effectiveness of cancer immunotherapies. An important functional aspect of the host-microbiome crosstalk is determined by a variety of bacterial enzymatic systems that are capable of synthetizing a plethora of small molecules, potentially being able to exert direct effects not only in the intestine but also to modulate the function of cells in remote organs [64]. In contrast to commensal bacteria that are predominantly located in the luminal compartment of the large intestine and caecum, small molecules derived from the microbiome can easily cross the epithelial layer and diffuse through the lamina propria to enter the systemic circulation. Several studies have detected plenty of microbial molecules in the human bloodstream, estimating that between 5 and 10% of all plasma metabolites are derived from gut microbiota [65]. For a long time, the products generated by gut bacteria were considered merely dead-end by-products of their metabolic pathways [66]. However, in the past decade, small molecules produced by commensals have received increased attention in cancer research. Novel findings have challenged the long-held “metabolic waste dogma”, indicating a crucial role for microbiota-derived metabolites in communication with host cells [41,67], thus also potentially being able to influence the TME. The microbial signals mediated via the secretion of small metabolites and bacterial membrane-associated factors are thought to play a central role in the functional shaping of the immune system [68]. With a better understanding of complex intestinal microbial communities in our gut in the last decade, it becomes clear that various molecular families synthetized by luminal microbes are involved in the communication with host T cells. Commensal bacteria-derived metabolites, such as short-chain fatty acids (SCFAs) and secondary bile acids, are unique bioactive compounds that play an important role in the regulation of the differentiation of T cells into various specialized subsets, including the Th17 cells and Tregs that are essential for intestinal immune homeostasis [69,70,71]. Th17-derived cytokines, IL-22 and IL-17A, reinforce barrier function at the steady state by promoting epithelial regeneration and the expression of antimicrobial peptides, while cytokine IL-10, secreted from Tregs and other immune cells, prevents intestinal inflammation [72]. Immune imbalances caused by disrupting the epithelial barrier and intestinal homeostasis lead to pathological outcomes, such as inflammatory bowel disease (IBD) and colitis-associated colorectal cancer [73]. Although there is increasing evidence to suggest an influence of bacterial metabolites on tumor development, mechanisms underlying a direct interaction between the microbial molecules and cells of the tumor microenvironment are still poorly understood. Intestinal commensal bacteria have an enormous genetic and chemical diversity, outnumbering their host genome by more than 25-fold regarding genetic composition [74]. Anaerobic fermentation of dietary fiber in the gut lumen by commensal bacteria leads to the generation of SCFAs, the most abundant class of microbial metabolites comprising carboxylic acids with aliphatic tails of 1–5 carbons [75]. Although microbial fermentation of dietary indigestible carbohydrates is the largest source of SCFAs, some branched SCFAs (BCFAs), such as isobutyrate and isovalerate, can be generated from amino acids by bacterial utilization of valine and leucine [75]. Bacterial SCFAs, such as acetate (C2), propionate (C3), butyrate (C4) and valerate (C5), are potent signaling molecules that promote the induction of mucosal protective IgA responses and the epithelial barrier function [76,77]. Moreover, SCFAs are the first important example of how microbiota-derived molecules can regulate anti-cancer immunity and cancer immune surveillance [78]. Recently, we demonstrated that butyrate and valerate enhanced the cytotoxic capacity of murine and human CTLs by increasing the activity of the mTOR complex and by inducing the expression of granzyme B, which is the key death-inducing effector molecule for a potent anti-cancer immunity. Notably, both SCFAs and the valerate-producing bacterium Megasphaera massiliensis (a low-abundant commensal isolated from human gut) substantially increased CTL-mediated anti-tumor immunity in vivo, which resulted in reduced tumor growth in experimental models of melanoma and pancreatic cancer [45]. By acting as a potent physiological histone deacetylase (HDAC) inhibitor of class I HDACs and by enhancing the metabolism and functional activity of CTLs, SCFAs might be a potential therapeutic candidate to improve the adoptive T cell transfer in various tumors. Novel data from our laboratory suggest that the treatment of human CAR-T cells with SCFAs enhances their efficacy and ability to kill cancer cells in an in vitro killing assay by increasing their secretion of effector cytokines TNFα and IFN-γ (Figure 1). While the effects of SCFAs on anti-tumor immunity, either by directly impacting the T cells or indirectly influencing antigen-presenting cells, are well documented [79], much less is known about the potential influence of other microbiome-derived metabolites. Apart from SCFAs, various bacterial molecules, such as secondary bile acids, various oligosaccharides, peptidoglycan fragments, tryptophan catabolites, inosine and polyamines are capable of modulating the cells of the immune system [40]. Polysaccharide A (PSA) of Bacteroides fragilis was previously shown to interact directly with dendritic cells and to promote immune regulation of the T cells via TLR2 [80,81]. Recently, one study investigated the impact of microbiota-derived inosine on the outcome of ICI therapy. In this study, inosine strongly enhanced the efficacy of ICI therapy in several experimental tumor models by modulating T cells via adenosine A2A receptors [82]. The oral administration of inosine or treatment of mice with the inosine-producing bacterium Bifidobacterium pseudolongum, together with anti-CTLA blockade, resulted in a significantly reduced tumor mass and an increase in the frequency of IFN-γ-producing Th1 cells. Remarkably, some dietary compounds, particularly polyphenols, have been suggested to modulate the composition of intestinal microbiota, which has a significant influence on anti-tumor immunity. It was shown that castalagin, an ellagitannin derived from the polyphenol-rich berry camu-camu (Myrciaria dubia), supported the anti-PD-1 activity by expanding the commensal bacteria associated with strong immunotherapy responses, such as Ruminococcaceae and Alistipes [83]. Of note, not only beneficial effects of gut microbiota-derived molecules on the tumor microenvironment have been described. A very recent paper by Hezaveh et al., investigated the influence of dietary tryptophan on the development of PDAC [84]. This essential amino acid, tryptophan, serves as a substrate for several enzymes within the gut microbiota community. Various commensals can convert dietary tryptophan into multiple derivatives that may impact T cells and macrophages via the aryl hydrocarbon receptor (AhR) [41]. Interestingly, the AhR activity in the TAMs of the PDAC microenvironment was dependent on the metabolization of dietary tryptophan to indoles by Lactobacillus species in the gut lumen [84]. Removing tryptophan from the diet resulted in reduced TAM-associated AhR activity and increased the infiltration of TNFα+IFNγ+CD8+ T cells into the TME. In addition, increasing evidence suggests that secondary bile acids, which are produced solely by intestinal bacteria, can induce DNA damage and modulate the tumor’s immune microenvironment in CRC [43]. Moreover, colibactin-producing Escherichia coli strains, which are frequently found to colonize CRC lesions, can also induce DNA damage in epithelial cells [85]. Finally, novel preclinical reports indicate dual effects for SCFAs in tumor biology. By investigating the gut metabolite changes associated with the progression of CRC, the SCFA formate and the BCFA isovalerate were identified as oncometabolites that contribute to the invasion of cancer cells and metastasis [86,87]. A comprehensive overview of the known interactions between small microbial molecules and the TME, which either might act as a target of cancer therapy, or can promote tumor growth, is summarized in Table 1. Collectively, although the novel results suggest that microbial metabolites have a potential to directly influence complex and dynamic processes that dictate the progression and invasion of tumors, a substantial amount of exploratory work will be required in the future to better understand how microbiota-derived molecules promote their effects on the cells in the tumor immune microenvironment. Tumors develop gradually in a complex interaction with various cellular components surrounding the tumor mass, such as stroma cells, endothelial cells, adipocytes and immune cells, most of which exhibit an immunosuppressive capacity and collaborate with cancer cells to evade immune surveillance. Currently, several therapeutic strategies have been employed to disrupt the crosstalk of tumors with cancer-associated fibroblasts and other cells in the TME. Recently, the enhancing effects of the SCFAs butyrate and valerate on anti-tumor immunity and the microenvironmental architecture of solid tumors have been described [45]. These findings pave the way for the translational progression of laboratory studies to novel therapeutic interventions. It is tempting to speculate that many other microbiota-derived components and metabolites might be able to influence the anti-cancer activity of immune cells by modulating the TME. However, several questions still remain open, and particularly more refined delivery strategies to exploit the therapeutic potential of gut microbiota-derived molecules are needed. Oral administration of SCFAs is only moderately efficacious and is associated with an unpleasant odor and rapid absorption and oxidation. In order to address this problem, several novel approaches have been developed. Oral supplementation of butyrate in a starch-conjugated form was shown to have beneficial effects in suppressing type 1 diabetes [93]. In this study, high amylose maize starch (HAMS), which resists digestion in the upper gastrointestinal tract (GI), was used. Chemical modification, such as propionylated and butyrylated HAMS, allowed an effective delivery of esterified propionate or butyrate to the colon and other organs of mice. In a second approach, to overcome the existing limitations, water-soluble micelles carrying butyrate in their core were applied to deliver high amounts of butyrate to the lower GI to protect mice from colonic inflammation [94]. Such novel techniques could soon be tested in experimental tumor models to try to achieve therapeutic effects and the efficient biodistribution of SCFAs in the body. Interestingly, some bioactive molecules produced by commensals may have opposing roles in regulating important physiological aspects of the host. The small bacterial molecule trimethylamine (TMA), which is produced by the gut microbiome, is rapidly absorbed into the circulatory system and thereafter oxidized to trimethylamine N-oxide (TMAO) in the liver. Increased blood levels of TMAO were found to be associated with an increased risk for atherosclerosis [95]. A novel study suggested a potential role for this molecule in the tumor immune microenvironment of triple-negative breast cancer (TNBC). TMAO was abundant in tumors with an activated immune microenvironment and promoted anti-tumor immunity in TNBC [90], providing a further example of microbial metabolite-immune crosstalk, which can be exploited for therapeutic strategies to enhance the efficacy of cancer immunotherapy. Targeted delivery of such drug-like molecules to tumors may be achieved by using nanoparticle-based technologies while minimizing possible adverse systemic effects. Paradoxically, lactate, a key metabolite produced by glycolysis and highly abundant in the TME, in which it induces the M2-like polarization of TAMs and supports tumor growth [96], appears to increase the stemness of CD8+ T cells and augment anti-tumor immunity [97]. Several commensal bacteria are able to generate D-lactate, which is the stereoisomer of L-lactate and not produced by eukaryotic cells. In the future, it would be important to test the role of both lactate isomers in influencing cellular therapy and the anti-cancer capacity of CAR-T cells. Such small molecules might be ideal drug candidates for the in vitro treatment of CAR-T cells or CTLs before introducing them into patients by intravenous infusion to potentially enhance their ability to attack cancer cells. Taken together, while characterizing novel molecules derived from human gut microbiota is a promising area for discovering novel drug candidates, more fundamental laboratory research will be needed to expand the current cancer treatment options. A large heterogeneity within the TME, with regard to the composition of various cell types surrounding the tumor cells and their spatial distribution, is one of the major obstacles compromising cancer treatment outcomes. Different immune cell types are involved in pathological and immunosuppressive processes in the TME. In addition, a continually emerging body of evidence supports the role of various commensal bacteria and their metabolic products in either promoting tumor development or augmenting cancer immunotherapy. Novel studies suggest that microbial SCFAs are capable of modulating the cellular architecture of the TME by triggering anti-tumor T cell responses and that bacterial molecules, such as inosine or TMAO, enhance the efficacy of targeted immunotherapies, such as ICI therapy. In contrast, some microbial tryptophan derivatives synthetized by intestinal bacteria rather support the pro-tumorigenic function of TAMs in PDAC. Further research is required to characterize novel, still unknown microbiota-derived molecules that may be able to act on the cells of the tumor immune microenvironment, which could be a central translational step for the development of novel microbiota-based interventional strategies. Although many challenges exist, which must be addressed to achieve these goals, an innovative strategy could focus on the design of patient-tailored cancer therapeutics by exploiting diverse microbiota-derived molecules. Various interdisciplinary approaches, ranging from microbiology, high-throughput sequencing techniques and comprehensive functional analysis of the whole gut bacterial genome to biotechnology, offer new insights into the transcriptional, metabolic and epigenetic networks within the human microbiome. Currently, many studies are attempting to translate these novel findings to the clinic to achieve optimal and targeted manipulation of the immunosuppressive cellular networks within the TME by small microbial molecules, which is probably one of the most promising therapeutic strategies to extend the current options for tumor therapy.

PMC10001158

Ayse Sedef Köseer,Simona Di Gaetano,Claudia Arndt,Michael Bachmann,Anna Dubrovska

Immunotargeting of Cancer Stem Cells

05-03-2023

cancer stem cells,CSC,bsAB,CAR-T cells,cancer vaccines,immunotherapy

Simple Summary Tumor cells from the same specimen are functionally heterogeneous. Cancer stem cells (CSCs) are populations of tumor cells with self-renewal and differentiation properties. CSCs are found in nearly all solid and hematological tumors and are characterized by various surface or intracellular markers. These markers can be used to develop tumor-specific antibodies, cytotoxic immune cells, vaccines, and direct immune responses to the tumor cells, including CSC populations. This review discusses the emerging CSC-directed immunotherapies, the current state of their clinical development, the approaches to improve their safety and efficacy, and future strategies to strengthen anti-CSC immunotherapy. Abstract The generally accepted view is that CSCs hijack the signaling pathways attributed to normal stem cells that regulate the self-renewal and differentiation processes. Therefore, the development of selective targeting strategies for CSC, although clinically meaningful, is associated with significant challenges because CSC and normal stem cells share many important signaling mechanisms for their maintenance and survival. Furthermore, the efficacy of this therapy is opposed by tumor heterogeneity and CSC plasticity. While there have been considerable efforts to target CSC populations by the chemical inhibition of the developmental pathways such as Notch, Hedgehog (Hh), and Wnt/β-catenin, noticeably fewer attempts were focused on the stimulation of the immune response by CSC-specific antigens, including cell-surface targets. Cancer immunotherapies are based on triggering the anti-tumor immune response by specific activation and targeted redirecting of immune cells toward tumor cells. This review is focused on CSC-directed immunotherapeutic approaches such as bispecific antibodies and antibody-drug candidates, CSC-targeted cellular immunotherapies, and immune-based vaccines. We discuss the strategies to improve the safety and efficacy of the different immunotherapeutic approaches and describe the current state of their clinical development.

Immunotargeting of Cancer Stem Cells Tumor cells from the same specimen are functionally heterogeneous. Cancer stem cells (CSCs) are populations of tumor cells with self-renewal and differentiation properties. CSCs are found in nearly all solid and hematological tumors and are characterized by various surface or intracellular markers. These markers can be used to develop tumor-specific antibodies, cytotoxic immune cells, vaccines, and direct immune responses to the tumor cells, including CSC populations. This review discusses the emerging CSC-directed immunotherapies, the current state of their clinical development, the approaches to improve their safety and efficacy, and future strategies to strengthen anti-CSC immunotherapy. The generally accepted view is that CSCs hijack the signaling pathways attributed to normal stem cells that regulate the self-renewal and differentiation processes. Therefore, the development of selective targeting strategies for CSC, although clinically meaningful, is associated with significant challenges because CSC and normal stem cells share many important signaling mechanisms for their maintenance and survival. Furthermore, the efficacy of this therapy is opposed by tumor heterogeneity and CSC plasticity. While there have been considerable efforts to target CSC populations by the chemical inhibition of the developmental pathways such as Notch, Hedgehog (Hh), and Wnt/β-catenin, noticeably fewer attempts were focused on the stimulation of the immune response by CSC-specific antigens, including cell-surface targets. Cancer immunotherapies are based on triggering the anti-tumor immune response by specific activation and targeted redirecting of immune cells toward tumor cells. This review is focused on CSC-directed immunotherapeutic approaches such as bispecific antibodies and antibody-drug candidates, CSC-targeted cellular immunotherapies, and immune-based vaccines. We discuss the strategies to improve the safety and efficacy of the different immunotherapeutic approaches and describe the current state of their clinical development. Cancer remains a leading cause of death worldwide, despite advancements in its treatment [1]. Conventional cancer treatments such as surgery, chemotherapy, and radiotherapy may be the most effective during an earlier stage of tumor development. However, treatment efficacy might be limited by tumor genetic and epigenetic heterogeneity. Each tumor is composed of cells with different features, including therapy resistance, metastatic dissemination, differentiation potential, and potency to maintain tumor growth [2]. Cancer stem cells (CSC) are a population of tumor cells that sustain tumor growth and heterogeneity [3]. CSCs were first characterized by Dick and colleagues for acute myeloid leukemia (AML) and proven to possess two fundamental properties, such as the capacity of self-renewal (e.g., an asymmetrical division that produces an identical copy and more differentiated progeny cells) and differentiation into multiple cellular subtypes observed within tumors [4,5,6]. Because of their self-renewal and differentiation capabilities, it has been shown that leukemia-initiating stem cells could repopulate and induce AML in severe combined immunodeficient hosts (SCID) after transplantation [3]. Different studies supported the tumor-initiating and tumor-maintaining properties of CSCs in various tumor entities [7,8,9]. CSCs have been characterized by many surface or intracellular markers in solid and hematological tumors. The most used indicators for CSC identification are surface markers such as CD133, CD44, and CD123, as well as the activity of some intracellular proteins such as aldehyde dehydrogenase (ALDH), as recently reviewed [10,11,12,13,14]. As the tumor develops, the tumor microenvironment (TME) becomes progressively more crucial to maintaining the growth and functions of CSCs through the interplay with cellular components and modification of the extracellular matrix (ECM). The cellular components of TME, such as endothelial cells (ECs), mesenchymal cells (MSCs), immune cells, and cancer-associated fibroblasts (CAFs), play a role in therapeutic resistance by activating CSC-related signaling pathways such as Wnt, Notch, and nuclear factor kappa B (NF-κB) pathways [15,16,17,18]. In turn, CSCs, by secreting several signaling factors, including pro-inflammatory cytokines and chemokines, recruit and alter the functions of stromal and immune cells to facilitate tumor growth and progression, especially during and after anticancer treatments, hence compromising treatment outcomes [19]. The exosomes released from CSCs can form the premetastatic niche via upregulation of vascular endothelial growth factor (VEGF) and matrix metalloproteinase-2 (MMP-2), resulting in the activation of angiogenesis and promotion of metastatic growth [20]. Some CSCs can withstand conventional treatments such as chemo- and radiotherapy, which effectively destroys a large portion of the tumor bulk, causing tumor shrinkage. However, standard treatment often fails to prevent disease recurrence if the CSCs are not completely eradicated [21,22,23]. Some CSCs can resist the direct or indirect damages induced by ionizing irradiation. The described mechanisms of the CSCs radioresistance include the activation of DNA damage response mechanisms (e.g., ATM, ATR, and Chk1/2), the scavenging of reactive oxygen species (ROS), protection from oxidative stress, activation of the anti-apoptotic pathways, and residing in protective microenvironmental niches [6,23,24]. The chemotherapy-resistant CSCs might also exhibit enhanced expression and activity of the membrane transporters of the ATP-binding cassette (ABC) family, which are linked to multidrug resistance [17,22,23,24,25,26,27,28]. Due to their self-renewal and differentiation properties, some subpopulations of CSCs, termed metastasis-initiating cells, are capable of dissemination through the bloodstream and metastasis initiation in lymph nodes and distant organs [6,29]. Therefore, CSCs might serve as biomarkers for tumor diagnosis, prognosis, and therapy response prediction, whereas CSC-related markers can be utilized to develop more efficient targeted therapies. Targeted therapies against CSCs are promising strategies to prevent cancer development and reduce the risk of recurrence [30]. Therefore, the signaling pathways regulating CSC maintenance and therapy resistance can be utilized as potential treatment targets. These pathways include, e.g., Hedgehog (Hh), Notch, JAK-STAT, PI3K/AKT/mTOR, Wnt/β-catenin, NF-κB, TGF-β, and FGF signaling [31,32,33,34,35,36,37,38,39,40,41]. Deregulation of these signaling pathways has been observed in various cancers [42,43,44,45,46]. Furthermore, these signaling axes interplay to regulate the self-renewal and differentiation properties of the CSCs, TME, and tumor development. Downstream transcription and pluripotency factors such as β-catenin, signal transducer, and activator of transcription 3 (STAT3), OCT4, Sox2, Nanog, KLF4, and MYC were also characterized as potential clinical targets [12,33,36,47,48,49,50,51]. A wide variety of inhibitors have been developed to specifically target these mechanisms, and there have been many clinical trials to test their anti-tumor activities [40,52]. However, CSC heterogeneity often impedes the efficacy of the therapeutic approaches against a single molecular target. The high dependency of normal stem cells on the pathways mentioned above might also explain normal tissue toxicity and side effects in some of these trials [53]. While conventional cancer therapies, such as radio- or chemotherapy, may eliminate the tumor bulk, treatment resistance of CSCs is suspected to be responsible for recurrence. Thus, it is critical to specifically target and destroy these cells to prevent or significantly delay tumor relapse. Immune cells infiltrating the tumor are a powerful natural mechanism to target and eradicate cancer cells. However, CSCs can create an immunosuppressive microenvironment through intrinsic and extrinsic mechanisms [54]. The immunosuppressive TME is produced by CSCs and other tumor and non-cancerous cells, such as CAFs and pro-tumor immune cells (i.e., regulatory T cells (Tregs), tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), and myeloid-derived suppressor cells (MDCSs)) [55,56,57,58]. In addition, other extracellular physical and chemical factors of TME, such as pH and hypoxia, play a role in tumor immune evasion [59,60]. Tumor cells, including CSCs, can escape immune surveillance and immune-mediated cell killing by downregulating tumor-associated antigens (TAAs), increasing the expression of immune checkpoints such as programmed death-ligand 1 (PD-L1) and, therefore, inhibiting CD8+ cytotoxic T cells, and reducing the expression levels of major histocompatibility complex class I (MHC-I) and the transporter associated with antigen processing (TAP) molecules, which play vital roles in antigen processing and presentation processes [61,62,63,64,65,66,67,68,69,70,71,72,73]. Recently, it has been shown that AML stem cells suppress cytokine secretion and impair oxidative phosphorylation in T cells by overexpression of CD200 receptor [74]. It has also been demonstrated that PD-L1 was highly expressed on CD44+ CSCs compared to CD44− non-CSCs in head and neck squamous cell carcinoma (HNSCC) [65] and regulates stemness in breast cancer [71]. CSCs, due to their plasticity, can also evade immunosurveillance by entering a dormant state or converting into quiescent cells, and by selective reduction of their immunogenic properties, whereas circulating tumor cells (CTCs), which share many properties with CSCs [6], are shielded from the cytotoxic activity of natural killer (NK) cells by TANs [75,76]. A high expression of MHC-I molecules on the normal autologous cells inhibits NK cell activation and function. Tumor cells often downregulate MHC-I and therefore reduce their recognition by CD8+ cytotoxic T cells. The MHC-I downregulation in tumors has been associated with unfavorable clinical prognoses [77]. However, some CSCs, e.g., in ovarian and renal cell carcinoma, upregulate MHC-I molecules on their surface that can potentially contribute to the NK cell regulation through MHC-I specific inhibitory receptors and CSC escaping NK cell-mediated cytotoxicity [78,79]. Therefore, activating the immune response targeting of CSCs via cancer vaccines, adoptive T and NK cell therapies, monoclonal antibodies, bispecific antibodies (bsABs), and immune checkpoint inhibitors (ICIs) can be a promising approach for achieving clinical success in the treatment of different cancer types. The generally accepted view is that CSCs hijack the signaling pathways, attributed to normal stem cells, that regulate the self-renewal and differentiation processes. Therefore, the development of selective targeting strategies for CSCs, although clinically meaningful, is associated with significant challenges because CSCs and normal stem cells share many important signaling mechanisms for their maintenance and survival. Many chemical inhibitors used to target the CSC regulating signaling pathways described earlier, such as Notch, Hh, and Wnt/β-catenin signalings, also have a toxic effect on normal stem cells [80,81,82]. While there have been tremendous preclinical efforts to target CSC populations from inside the cell, considerably less effort has focused on the cell-surface targets. Cancer immunotherapies are based on inducing the anti-tumor immune response by specific activation and targeted redirecting of immune cells toward tumor cells. Since the first characterization of CSC phenotype in AML by Dick and colleagues in the late 1990s, various surface markers of CSC in hematopoietic malignancies and solid tumors have been identified, including CD44, CD133, CD117, CD123, CD47, CD98hc, and others [4,5,6]. These surface proteins play a pivotal role in the CSC interaction with their niche, cell–cell communication, nutrient uptake, and regulation of the immune system, and provide specific targets for CSC-directed immunotherapies (Figure 1). In 1993, Seiter and colleagues published a seminal study describing the anti-tumor effect of anti-CD44v antibody using preclinical syngeneic rat xenograft models [83]. The first monoclonal antibody (mAb), rituximab, a chimeric IgG1 against the B-cell-specific antigen CD20 highly expressed on non-Hodgkin’s lymphoma (NHL) cells, was approved for treatment of NHL in 1997 [84] paving the way for immunotherapeutic applications in oncological diseases. Shortly after, the clinical studies in patients with solid tumors showed the therapeutic potential and safety of radioimmunotherapy with the (186)Re-labeled humanized mAb bivatuzumab, which as directed against a CSC-related protein, the CD44 isoform variant 6 (CD44v6) (Table 1). The transmembrane glycoproteins of the CD44 family are highly expressed in the different types of malignant cells, including CSCs, and contribute to tumorigenesis by regulating various cell surface receptors [85]. This family includes several variant isoforms, and CD44v6 is one of the best-studied isoforms serving as a co-receptor for the receptor tyrosine kinases (RTKs), such as vascular endothelial growth factor receptor-2 (VEGFR-2), epidermal growth factor receptor (EGFR), and mesenchymal-epithelial transition factor (c-Met) [85,86]. Unfortunately, early clinical testing of bivatuzumab merstansine, a CD44v6-specific antibody conjugated with an antimicrotubule agent, in 2006 led to a fatal outcome related to the binding of the anti-CD44v6 antibody to skin keratinocytes and associated severe skin toxicity [87]. Therefore, further development of the CD44-targeted drug conjugate was discontinued. A newer anti-CD44 recombinant humanized antibody, RG7356, showed an acceptable safety profile in a clinical trial for patients with advanced solid tumors; however the clinical efficacy was modest. The trial was terminated early as no dose–response relationship was reported [88]. However, in patients with AML, RG7356 induced differentiation of CD34+ leukemic stem-like cells and accumulation of CD68+ macrophages [89]. Integrin associated protein (IAP), or CD47, is another promising target overexpressed on cancer cells, including CSCs, in different types of hematological and solid tumors. CD47 plays a critical role in regulating the homeostasis of immune cells (e.g., T cells, macrophages, and dendritic cells (DCs)), including their activation, differentiation, migration, and death. It is also crucial for tumor growth, as CD47 expression on solid tumors results in evasion of the innate immune response [90,91]. CD47 exerts these activities by interacting with integrin receptors, e.g., αvβ3, and activating integrin-dependent signaling molecules such as focal adhesion kinase (FAK) [92]. CD47 also binds to a transmembrane glycoprotein, signal regulatory protein α (SIRPα), therefore activating protein tyrosine phosphatases such as Src homology 2 (SH2) domain-containing phosphatase-1 (SHP-1) and SHP-2 [91]. In addition, CD47 also acts as a receptor for thrombospondin 1 (TSP-1) [93]. Of importance, previous studies using syngeneic prostate tumor models in CD47 deficient mice showed that inhibition of CD47 might have opposite consequences for tumor growth depending on the target cells: CD47/TSP-1 inhibition in tumor stromal ECs induces angiogenesis and tumor progression, and decreased TSP1 production in tumors from CD47 deficient mice reduced macrophage recruitments, whereas blocking the binding of CD47 on tumor cells with SIRPα on macrophages and DCs might, in contrast, induce an antitumor immune response and reduce tumor growth [94]. Breast cancer progression is associated with the development of intratumoral hypoxia and activation of hypoxia-inducible transcriptional factors (HIFs). HIF1 was shown to stimulate the CD47 expression in breast CSCs, enabling them to avoid macrophage-mediated phagocytosis. In addition, CD47 positively regulates breast CSC phenotypes and properties [95]. Furthermore, inhibition of CD47 by anti-CD47 antibodies was shown to effectively target pancreatic CSCs by increasing macrophage-mediated immunity and tumor cell apoptosis [96]. Of importance, CD47 expression and, therefore, immune evasion in ovarian CSCs has been shown to be induced by surrounding bulk tumor cells [97]. Recent studies also demonstrated that CD47 transcription is regulated via HER2–NF-κB pathway, and antibody-mediated blockage of both CD47 and HER2 synergized with radiotherapy for the treatment of syngeneic mouse breast tumors. Notably, radiotherapy increased the rate of macrophage-mediated phagocytosis in the tumors treated with anti-CD47 antibodies in combination with Herceptin or single anti-CD47 antibodies compared to radiotherapy applied alone in the orthotopic breast tumor models. This study showed synergistic tumor inhibition by a combination of radiotherapy plus anti-CD47 and anti-HER2 immunotherapy [98]. IBI188, also known as Letaplimab, is a recombinant human anti-CD47 mAb. IBI188, in combination with azacytidine (AZA), a DNA methyltransferase inhibitor, showed promising efficacy and a manageable toxicity profile in patients with newly diagnosed higher risk myelodysplastic syndrome (MDS) (NCT04485065) [99]. IBI188 is currently being tested in several early-phase clinical trials in combination with AZA in patients with AML (NCT04485052) and as a single treatment in patients with advanced solid cancers and lymphomas (NCT03717103 and NCT03763149). Another attractive target is the interleukin-3 receptor (IL-3R) alpha chain, or CD123, which is highly expressed in the undifferentiated precursor (blast) cells, but present at a low level on normal hematopoietic stem cells, and is associated with the development of AML, acute lymphoblastic leukemia (ALL), B-lymphoid leukemia (BLL), hairy cell leukemia (HCL), Hodgkin lymphoma, blastic plasmacytoid dendritic neoplasms (BPDCN), and MDS [100,101]. The early clinical trials for the recombinant IL-3 fused with diphtheria toxin (SL-401, or tagraxofusp) targeting CD123 showed clinical effectiveness, especially in patients with BPDCN [102,103]. Based on these clinical studies, tagraxofusp was approved by the Food and Drug Administration (FDA) for the treatment of patients with BPDCN cancer [102]. These clinical investigations fueled the further development of the anti-CD123 treatment using immunological approaches. In particular, a humanized anti-CD123 mAb Talacotuzumab (JNJ-56022473, CSL362) was engineered to have increased affinity for CD16 on NK cells and target CD123 positive cells through NK cell-mediated antibody-dependent cellular cytotoxicity (ADCC) [104]. The preclinical studies demonstrated an efficient depletion of CD123+ blasts in samples from AML patients [104] and in AML xenograft models that was mediated by both allogeneic and autologous NK cells [105,106]. However, in the clinical analysis as a single agent in elderly high-risk MDS or AML patients, Talacotuzumab demonstrated significant toxicities that resulted in early treatment discontinuation as well as in limited clinical efficacy, explained by the alteration in the NK- and T cell repertoire in these patients before treatment start [105]. Furthermore, Talacotuzumab was tested for its safety and efficacy in combination with decitabine, a chemotherapy drug, and in comparison with decitabine alone in patients with AML. These clinical studies showed no improvement of clinical efficacy for combination treatment compared to the single treatment with decitabine [107]. To improve the clinical effectiveness of the CD123-targeted antibody therapy, an antibody-drug conjugate was developed. To this end, a humanized high-affinity anti-CD123 antibody was linked to the DNA-alkylating cytotoxic compound from the class of indolinobenzodiazepine pseudodimer (IGN) [108]. This anti-CD123-targeting antibody-drug conjugate, called IMGN632, demonstrated robust antitumor efficacy in preclinical models for different hematological malignancies, including AML [108] and BPDCN [109] xenograft models, while sparing normal bone marrow cells, which express low levels of CD123. The early clinical study of IMGN632, given as monotherapy or in combination with AZA and venetoclax (VEN), a Bcl-2 inhibitor, in patients with CD123-positive AML, showed a manageable safety profile. The administration of IMGN632 was associated with a high objective response rate (ORR) of 75% and composite complete remission rate (CCR) of 40% in the high-intensity cohort of patients with AML, whereas ORR/CCR rates were even higher in the cohort of VEN-naïve patients (100%/60%, respectively) [110]. In contrast to the conventional monospecific antibody, bsAB are artificially engineered antibodies with dual specificity for different epitopes attributed to the same or different antigens, therefore offering a variety of therapeutic opportunities, including retargeting of immune cells and modulations of the ligand and receptor action with an efficacy that is hard to achieve for single antibodies [111]. The application of bsAB in cancer treatment is a fast growing area of clinical research. The first clinically approved bsAB, Catumaxomab, targeting the T cell antigen CD3 and human epithelial cell adhesion molecule (EpCAM), was designed to recruit T cells to tumors [112]. Since then, bsABs are one of the most promising tools for targeting tumor malignancies through the recruitment of immune cells such as T cells or NK cells (cell-bridging bsAB) or by antigen cross-linking [113]. In the laboratory, a bsAB is generated by genetic engineering, chemical conjugation of two purified monoclonal antibodies, or by quadromas, in which fused hybridoma cells produce bsABs along with non-functional by-products [114]. Recombinant bsABs might have different designs described in detail elsewhere [111,115,116]. The T cell engaging approach with cell-bridging bsAB was used to develop several CSC-related bsABs, which bind to the patient’s T cells through CD3/T cell receptor (TCR), CD28, or other surface molecules mediating T cell activation and proliferation. On the other hand, they bind to target antigens on tumor cells. Thus, T cell engaging bsABs specifically redirect T cells to target-positive tumor cells. The antigen cross-linking with bsAB can be used for simultaneous blocking and inhibition of two targets on the surface of tumor cells, including CSCs, thereby preventing ligand-induced activation and tumor growth [113]. Simultaneous bsAB binding to the checkpoint regulators on the surface of T cells e.g., PD-L1 and CTLA-4, might potentiate anti-tumor immune response. The design of novel bsAB enables binding to multiple targets, therefore making them a multi-specific antibody (MsAb) [115,117]. Table 1 includes several antibody-based CSC-targeted therapies that have already entered clinical trials. These therapies, in particular, include CD123 × CD3 bsAB, whose commercial name is Flotetuzumab (MGD006). Flotetuzumab has been evaluated in phase I/II clinical trials for refractory AML, and has shown encouraging anti-leukemic activity and acceptable safety. In particular, complete remission was observed in 26.7% of patients with refractory AML [118]. Several CD47-targeted bsABs are currently being tested in early-phase clinical trials, including HX009, PD-1 × CD47 bsAB, tested in patients with relapsed or refractory lymphoma (NCT05189093). HX009 binds to PD-1 expressed on T cells and CD47 on tumor cells. By this, HX009 blocks the binding of CD47 on tumor cells with SIRPα on macrophages and DCs and, therefore, activates macrophage-mediated phagocytosis of the CD47-expressing tumor cells. On the other hand, the binding of HX009 to PD-1 prevents the interaction between PD-1 and its ligands, PD-L1 and PD-L2, and inhibits the downstream signaling pathways. This signal inhibition recovers effector T cell functions and activates cytotoxic T cell-mediated antitumor immunity [119]. Preliminary results suggest that HX009 is well-tolerated and shows strong antitumor activity [120,121]. Another CD47 targeting bsAB, IBI322, with a dual specificity for CD47 and PD-L1 [122], is also being tested in several clinical trials in patients with advanced malignant tumors (NCT04328831, NCT04912466, NCT04338659) and hematologic malignancies (NCT04795128), but no clinical data has been reported yet. In addition, bsAB targeting CD47 and the B-lymphocyte antigens CD20 (IMM0306) or CD19 (TG-1801) also entered clinical testing for patients with B-cell lymphoma or chronic lymphocytic leukemia (CLL), (NCT04806035, NCT03804996) and B-cell NHL (NCT04746131). Another example is Amivantamab, a bsAB targeting EGFR and c-Met driving tumor growth in patients with non-small cell lung cancer (NSCLC). NSCLC progression is frequently associated with activating mutations in the kinase domain of EGFR. Some of these mutations, such as in-frame base pair insertions in exon 20 (ex20-ins), result in tumor resistance to conventional EGFR tyrosine kinase inhibitors [123]. EGFR and another receptor tyrosine-protein kinase, c-Met, cooperatively regulate tumor cell proliferation, migration, and activation of the downstream signaling pathways. Because of the synergy between the EGFR and c-Met pathways, their dual inhibition is critical for the treatment of NSCLC [124]. Amivantamab bsAB inhibits the activation of both receptors by binding to their extracellular domains, preventing ligand-induced activation and triggering receptor degradation. In addition, it activates tumor destruction by effector immune cells through Fc-mediated mechanisms, such as ADCC [125]. Amivantamab was demonstrated to be efficient against NSCLC with a resistance mutation in EGFR and c-Met activation [125]. Clinical trials (e.g., CHRYSALIS; NCT02609776; and others) have shown acceptable toxicity and anti-tumor efficacy of Amivantamab in patients with locally advanced or metastatic NSCLC and EGFR Ex20ins mutations based on the ORR of about 40% and duration of response [126]. In 2021, Amivantamab was approved by the US FDA for the treatment of patients with advanced or metastatic NSCLC with EGFR ex20-ins mutations, whose disease had progressed during or after platinum-based chemotherapy [127]. Of importance, c-Met has been characterized as a regulator of CSC populations in different types of solid tumors, including pancreatic cancer [128,129], prostate cancer [130], and colorectal cancer [131]. A novel bsAB c-Met × CTLA-4 targeting c-Met and CTLA-4, a negative regulator of T cell activation, showed significant anti-tumor activity in lung cancer models in vitro and in vivo. This anti-tumor effect was at least partially mediated by the inhibition of the CD166+ positive lung CSC populations [131]. EpCAM is another marker highly expressed in tumor cells, including CSCs [132]. Catumaxomab (Removab) is a EpCAM × CD3 bsAB approved in the European Union in April 2009 for the treatment of malignant ascites, a condition developing in patients with different types of epithelial cancers [112]. It is called a trifunctional antibody (trAb) due to its ability to bind tumor cells, T cells, and accessory cells (e.g., macrophages, DCs, and NK cells) through its intact Fc region [133]. The results from clinical studies (NCT00836654) demonstrated that the application of catumaxomab is associated with the depletion of CD133+/EpCAM+ CSCs from malignant ascites in patients with the ovarian, pancreatic, and gastric cancer [134]. Antibody-based cancer immunotherapy, particularly monospecific and bsAB therapy, is a promising strategy for cancer treatment [113], and most bsABs are still in the early phase of clinical trials. However, some biological and clinical factors might compromise the efficacy of bsAB and limit their clinical translation. In particular, the application of bsAB, like some other types of immunotherapy, e.g., chimeric antigen receptor (CAR) T cells, might cause potentially fatal adverse effects such as cytokine release syndrome (CRS)—systemic inflammatory response associated with high cytokine levels in peripheral blood [135]. There is a hope that many bsABs, currently being tested in more than 300 clinical trials, will be introduced in clinical practice [136]. However, only one bsAB for treating solid malignancies, Amivantamab, an EGFR/c-Met specific bsAB for the treatment of patients with NSCLC, has been clinically approved [137]. Limitations that might impact the efficacy of the therapeutic antibodies for the treatment of solid tumors include poor tumor penetration, unequal distribution, and endocytic clearance in tumor cells [138]. In addition, some tumor cell populations, such as CSCs, can occupy hypoxic niches where antibody delivery is complicated due to the sparse presence of blood vessels [23,139]. Notably, a robust assay to measure CSC functions in tumor samples, besides surface marker expression, is yet missing. Given the heterogeneity and plasticity of CSC, developing these assays would be essential for the reliable evaluation of CSC-directed immunotherapy in clinical trials [40]. The loss of the tumor antigen, which serves as bsAB's target and consequent tumor resistance and immune escape, is an additional complication for the clinical application of bsAB [140,141]. Furthermore, no single surface marker is currently available to define the entire CSC population in a given tumor entity, or even in one individual tumor [142]. There is evidence of high variability of the CSC phenotype between patients with a given type of cancer and a high heterogeneity of CSC within one individual tumor. Mutation changes, epigenetic reprogramming, and microenvironmental stimuli induce CSC plasticity during the treatment, cancer progression, and upon relapse [142,143,144]. An essential challenge is the scarcity of tumor-specific antigens that are not present in normal tissues: more than 70% of known CSC surface markers appear on normal adult and embryonic stem cells [145]. Even low epitope expression on normal cells can be associated with severe normal tissue toxicities from immune therapy [146]. Therefore, improving treatment specificity by inventing new cancer-specific treatment targets and sequential or combinational targeting of two or more tumor antigens during the course of treatment could reduce normal tissue toxicity and overcome tumor antigen escape. Thus, developing multispecific antibodies [147] and multispecific CAR T cell immunotherapy (as described in the next chapter) could be a promising strategy to improve treatment options for patients with malignant tumors. In contrast to the antibody-based immunotherapy available off-the-shelf, adoptive cell therapy (ACT) has been developed as more individualized anti-cancer immunotherapy when cytotoxic lymphocytes, such as T cells or NK cells, are customized for each patient. The cytotoxic activities of T cells and NK cells are mediated by releasing the pore-forming protein perforin, granzyme serine protease, and pro-inflammatory cytokines, making them attractive candidates for the ACT [155,156,157]. The objective regression of cancer after ACT was first documented in 1988 for patients with metastatic melanoma treated with autologous tumor-infiltrating lymphocytes [158]. The promising results of the ACT applications stimulated the genetic engineering of immune cells to improve tumor-specific response and to broaden ACT application to other types of cancer. For this, T cells were genetically modified using viral vectors to overexpress either conventional TCRs or artificial CAR. T cells equipped with TCRs recognize the antigens presented by MHC molecules on the surface of tumor cells, whereas T cells with CARs recognize tumor-specific cell surface antigens that do not need to be restricted by MHC [159]. The idea for CAR design was coined by Gross and colleagues in 1989 [160]. The CAR structure contains four major modules: (I) the extracellular domain responsible for the antigen binding and made by the variable domains of the heavy (VH) and light (VL) chains of tumor-specific immunoglobulins, (II) the spacer domain connecting the extracellular domain to the transmembrane domain, (III) the transmembrane domain, and (IV) the cytoplasmic domain derived from activating immune receptors, e.g., TCR. The initial design of CARs included a single intracellular CD3ζ motif. These CARs were able to efficiently trigger the signal for the activation and effector function of T cells against target antigen-expressing cells [160]. However, the early phase clinical trials for ovarian cancer patients showed the lack of patient antitumor response associated with a short-term persistence of genetically modified T cells in the blood of patients and poor T cell trafficking to the tumor sites [161]. The next generations of CARs included additional activation motives from costimulatory molecules, such as CD28 [162,163] and 4-1BB/CD137 [164,165], in the intracellular part of CAR molecules. This design is associated with robust T cell proliferation and full activation, better persistence in vivo, and amelioration of T cell exhaustion. The selection of the optimal CAR design is a fast-expanding field of immunology, including specificity, affinity and avidity of antigen binding regions, spacer length, and transmembrane domain interactions, as well as type, number, and order of the costimulatory domains [166]. The design of CAR constructs for NK cells includes similar components. However, the unique characteristics of NK cells motivated researchers to develop CARs containing NKG2D immunoreceptor elements and additional signaling subunits such as DAP10, DAP12 [167,168,169]. The manufacturing of CAR-T cells consists of multiple steps, including the collection of peripheral blood mononuclear cells (PBMCs) from the patient, T cell isolation, activation, genetic manipulation for CAR expression, expansion, and quality checks for further applications [170]. CAR-T cell-based immunotherapy is currently being investigated in more than 800 clinical trials [171]. Due to impressive clinical success rates, several autologous CAR-T based anti-cancer treatments have been clinically approved [172]. Kymriah (Tisagenlecleucel) was the first anti-CD19 CAR-T therapy approved by the FDA in 2017 for relapsed or refractory pediatric and young-adult B-cell ALL, and then for adult relapsed or refractory diffuse large B-cell lymphoma [173]. Since then, four additional anti-CD19 CAR-T-based anti-cancer therapies and one anti-B-cell maturation antigen (BCMA) CAR T product have been approved for the treatment of B-cell lymphomas/leukemias and multiple myeloma [172,174,175], respectively. Analysis of the ongoing clinical trials evaluating CAR-T cells revealed that CD19 and BCMA are the most frequently used antigens for CAR-T therapies against hematological malignancies, whereas, for solid tumors, CAR-T cells are directed against, e.g., mesothelin, carcinoembryonic antigen (CEA), Mucin 1 (MUC1), HER2, EGFR, and glypican-3 (GPC3) [171]. Among these targets, MUC1 was characterized as a stemness driver in colorectal cancer where MUC1 forms a complex with MYC transcriptional factor and activates the expression of leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5) gene, a marker of the intestine stem cells and CSCs [176]. In addition, MUC1 also activates the expression of other CSC markers, such as ALDH1, BMI1, and the pluripotency factors Oct4, Nanog, and Sox2171 [177]. MUC1 induces tumor cell plasticity and epigenetic reprogramming by coupling MYC activation with activation of other transcription factors such as STAT3, NF-κB, and E2F [178]. Furthermore, several CSC-targeted CAR-based therapies entered early-stage clinical trials. These clinical trials include CAR-T cells targeting CD44v6 in stomach cancer lymphosarcoma, AML, and multiple myeloma; CD133 in relapsed and/or chemotherapy refractory advanced malignancies; c-Met in patients with melanoma and breast carcinoma [179], EpCAM in nasopharyngeal carcinoma, breast cancer, gastric cancer and other EpCAM positive solid tumors (Table 2). Although several CAR-based therapies for blood cancers have been clinically successful, CAR-modified immune cells still face several hurdles in clinical application for solid tumors. Different biological factors might decrease CAR-T cell efficacy, including a loss of tumor antigen (antigen escape) associated with the development of therapy resistance, lack of antigen specificity resulting in the “on-target off-tumor” toxicity against normal tissues, CAR-T exhaustion [180], immunosuppressive tumor microenvironment, and poor CAR-T cell trafficking and tumor infiltration [181]. Furthermore, like other immunotherapy types, such as antibody-based cancer treatment, CAR-T cell-based therapy can cause severe side effects, including the above-described CRS and immune effector cell-associated neurotoxicity syndrome (ICANS) [182]. In addition, the ex vivo manipulation of T cells for the autologous CAR-T cell production is mainly performed as a manual or semi-automated process resulting in considerable variability and high acquisition costs [183]. The growing awareness of these limitations has driven the development of new generations of CAR T cells, and various strategies are being pursued to overcome the existing difficulties. Bispecific CARs, multi-CARs, and logic-gated CAR T cells are being developed to enhance the specificity as well as efficiency, and thereby reduce off-tumor effects of CAR T cell products [184,185,186]. The incorporation of suicide switches [187,188] and “biodegradable” CAR T cells [189,190] are further attempts to particularly address the safety concerns of CAR T cell therapy. Besides these and many other strategies, modular adapter CAR platforms currently represent a rapidly and steadily growing field to create safer and more efficient CAR T cell retargeting strategies [191]. The main concept of this technology is to separate the effector and targeting functions of conventional CARs (Figure 2A). Thus, adaptor CAR T cells are designed to be redirected for tumor cell killing only in combination with a second tumor-specific component. Unlike conventional CAR-T cells, they do not recognize any surface antigen and are, therefore, switched off by default. For cross-linking with target cells, and thus activation, a so-called adapter molecule is required. In principle, it consists of a tumor-specific binding site and an interaction site for CAR-T cell recruitment. The modular concept of adapter CAR approaches allows (I) control of therapy-related side effects by adaptor molecule dosing, (II) highly flexible targeting of different tumor-associated antigens, either simultaneously or sequentially, thereby increasing treatment specificity/efficacy and lowering the risk of tumor escape and off-tumor effects, and (III) co-delivery of payloads to locally enhance anti-tumor effects [191] (Figure 2B). The interaction of adapter CAR T cells and the adapter molecules is based on different connection systems following two major concepts: (I) adapter CARs recognizing diverse tags incorporated into the adapter molecule, e.g., peptide tags [192,193,194], FITC [195], biotin [196], dinitrophenyl [197], and (II) adapter CARs redirected to tumor cells via bispecific antibodies [198,199,200,201,202]. The interaction between human La/SS-B peptide epitopes (E5B9, E7B6) [203,204] and the corresponding anti-La antibody binding domains [205], for example, led to the development of both the peptide-binding adaptor CAR “UniCAR” [206] (Figure 2C) and the corresponding bsAB-binding adaptor CAR “RevCAR” [201] (Figure 2D). Under physiological conditions, naturally occurring human La/SS-B resides in the nucleus and is inaccessible to unwanted interactions with UniCAR/RevCAR components, rendering the corresponding adapter CAR-T cells inactive. The UniCAR is a second-generation CAR constructed by fusing an anti-La single-chain fragment variable (scFv) as an extracellular binding domain to the transmembrane and intracellular domain of CD28, as well as the signaling domains of CD3zeta (Figure 2C). UniCAR T cells can be cross-linked with tumor cells and induce tumor cell lysis only in the presence of a tumor-specific target module (TM). Such TMs are designed by connecting a tumor-specific binding moiety, e.g., peptide ligands [207], nanobodies [208], or antibody-derived fragments [209,210,211,212], to the La-epitopes. Vice versa, in the RevCAR system, La-peptide epitopes are used as the extracellular domain of the RevCARs (Figure 2D) [201,213]. Thus, bsABs (termed RevTMs) simultaneously target the La epitope and a tumor-associated antigen. They are utilized to bridge CAR T cells and tumor cells and, thereby engaging RevCAR T cells for efficient tumor cell lysis. Other recent designs have further focused on developing adaptor CARs that recognize common features of already approved drugs, e.g., the P329G Fc mutation of therapeutic antibodies [214] or a binding pocket within the Fab arm of monoclonal antibodies [215]. In preclinical in vitro and in vivo studies, adapter CAR T cells have been successfully redirected against various CSC-related antigens, such as CD123 [213,216,217], EpCAM [196,200,218] and CD98hc [219]. By modifying T cells with two different RevCARs and fine-tuning the selected adapter molecules, the RevCAR system was successfully applied for dual “AND”-gate targeting of CD33 and CD123 on AML blasts, highlighting the versatility of the platform technology (Figure 2B) [213]. Such logic-gated approaches will allow more specific targeting of tumor cells, including CSCs, thereby reducing unwanted toxicities against healthy tissues. More recently, CD123-directed UniCAR T cells showed the first proof-of-concept for functionality and controllability in a phase I clinical trial with AML patients [220,221]. So far, treatment was completed in 12 patients and proved to be tolerable, with overall mild adverse effects and only one dose-limiting toxicity. The observed treatment-related side effects, e.g., myelosuppression, disappeared rapidly when the infusion of the adapter molecules was interrupted. After the patients had recovered, therapy could be resumed by repeated TM administration. Ten patients treated with UniCAR-T-CD123 therapy have shown a clinical response, including two complete remissions with incomplete count recovery and four partial responses [220,221]. In contrast to CAR-T cells, CAR-NK cells are less likely to induce off-tumor toxicities and adverse side effects, such as CRS and neurotoxicity, that could be partially attributed to their short lifespan in the bloodstream and different types of secreted cytokines [222]. The preclinical and clinical studies demonstrated that allogenic NK cells have a low risk for graft versus host disease (GVHD) [223,224,225], can be prepared from different sources (e.g., cord blood, haploidentical donors, induced pluripotent stem cells, iPSC) [224,225,226,227] and expanded ex vivo for “off-the-shelf” allogeneic applications [228]. Unlike CAR-T cells, CAR-NK cells kill tumor cells through CAR-mediated and CAR-independent mechanisms. Due to these unique properties, several clinical trials are exploiting CAR-NK cells’ cytotoxic activity targeting specific tumor antigens in patients with hematopoietic malignancies and solid tumors [222]. These clinical trials include targeting the CSC-related antigens, such as CAR-NK cells, against CD123 in AML and MUC1 in solid tumors (Table 2). Although CAR-NK therapy emerged as a more cost-efficient and safer immunotherapy than CAR-T cells, it is challenged by the short in vivo lifespan and limited proliferation capacity of NK cells that compromise long-lasting therapeutic responses. Similar to CAR-T cells, CAR-NK anti-cancer efficiency is reduced by tumor heterogeneity, immune suppressive microenvironment, off-tumor cell killing, and poor CAR-NK infiltration into solid tumors [229]. An additional promising approach to boost immune responses against CSCs is to use CSCs as a source of antigens to pulse antigen-presenting DCs and develop anti-CSC DC vaccines. DCs were first discovered in 1973 by Ralph Steinmann and Zanvil A. Cohn as a phagocytic cell population in the murine spleen [232]. DCs are an immune cell population bridging innate and adaptive immune responses. They present the processed epitopes to CD4+ T cells and CD8+ T cells through MHC II and MHC I, respectively, and secrete cytokines critical for the survival and proliferation of T cells, NK cells, and T cell tumor infiltration [233]. DC vaccination is a promising form of immunotherapy, and many DC vaccines have been developed in the past years and tested in clinical trials. The DC vaccines are most commonly prepared by ex vivo differentiation of the autologous precursor cells into immature DCs, the maturation of DCs by addition of a cytokine cocktail, and then pulsing them with cancer antigens in the form of antigen peptides, tumor cell lysates, exosomes, or mRNAs. Afterwards, mature DCs are administered back into the patients where they activate antigen-specific T cells [231] (Figure 3). Numerous clinical studies have shown that DC vaccinations are both safe and efficient anti-cancer therapies capable of inducing an immunological response, increasing tumor-infiltrating lymphocytes, and improving overall survival (OS) [231,234]. Dendritic CSC vaccination (CSC-DC) is a promising form of DC-mediated immunotherapy. The administration of DCs loaded with MUC1-derived peptide, alone or in combination with other tumor-specific antigens, has been tested in several clinical trials for patients with refractory NSCLC [235], pancreatic cancer [236,237,238], biliary cancer [238], and castration-resistant prostate cancer (CRPC) [239]. These studies showed that the vaccine was well tolerated and associated with clinical responses. Another approach employing the peptide-loaded DCs is the ALDH peptide-based DC vaccine [240,241]. ALDH is a family of metabolic enzymes responsible for the detoxification of intracellular aldehydes through their oxidation to the carboxylic acids [242]. A high level of ALDH activity measured by the ALDEFLUOR analysis is used as a marker to isolate CSC populations in a wide variety of solid malignancies, such as breast cancer, prostate cancer, lung cancer, colon cancer, sarcoma, and HNSCC [240,242,243]. The ALDH family includes 19 genes. Out of them, several ALDH isoforms are highly expressed in CSCs, including ALDH1A1 and ALDH1A3 [242]. In addition to being markers for CSC, both genes play critical functional roles in the regulation of the activation of the retinoic acid signaling, PI3K/AKT pathway, ethanol and amino acid metabolism, and cell defense against ROS [242,243,244]. The critical role of ALDH proteins in tumor development and therapy resistance makes them promising therapeutic targets. In the first adoptive therapy experiments for targeting ALDH-positive CSCs, ALDH1A1-specific CD8+ T cells were induced in vitro by the DCs pulsed with ALDH1 [88,89,90,91,92,93,94,95,96] peptide and injected intravenously into the xenograft-bearing immunodeficient mice [241]. This study demonstrated that ALDH1 peptide-specific CD8+ T cells inhibited primary tumors growing subcutaneously and lung metastases [241]. Similar to other immunotherapy directed against a single antigen, the efficacy of the peptide-based DC vaccines could be compromised by heterogeneity and plasticity of antigen expression in tumor cells, including CSCs. A pulsing of DCs with the entire tumor cell lysate, potentially including the whole repertoire of the tumor antigens, is another promising strategy for developing CSC-specific DC vaccines. The tumor cells with high ALDH activity (ALDHhigh) can be identified and isolated quickly by fluorescence-activated cell sorting (FACS), providing a source of antigens for developing CSC-targeted therapeutic approaches. Ning and coauthors first confirmed high tumorigenicity of ALDHhigh cell populations compared to ALDHnegative cells using murine melanoma D5 and squamous cell carcinoma SCC7 syngeneic xenograft tumor models in the immunocompetent C3H and C57BL/6 mice. Next, they evaluated the antitumor immunity induced by vaccination with murine bone-marrow-derived DCs pulsed with the lysate of ALDHhigh cells (CSC-tumor pulsed DC, CSC-TPDC) compared with the lysate of whole unsorted heterogeneous tumor cells (H-TPDC). They demonstrated that the vaccination of DCs pulsed with the lysate of ALDHhigh cells induced significantly higher protective immunity against tumors than H-TPDCs, as well as DCs pulsed with the lysate of ALDHnegative cells [245]. An additional approach to increase anti-tumor immunogenicity by targeting multiple epitopes is a pulsing of DCs with mRNA derived from CSCs. A recent in vitro study by Sumransub et al. demonstrated a preclinical efficacy of an mRNA-based DC vaccine using patient-derived breast cancer cells. In this study, DCs were differentiated from PBMCs of a healthy donor and pulsed with mRNA isolated from CD44+/CD24− CSC population. This finding revealed that CSC mRNA induces a more potent cytotoxic T cell response as compared to mRNA isolated from the entire tumor cell population [72]. The DC vaccine based on the CSC lysates was also recently tested in a clinical trial for newly diagnosed or recurrent glioblastoma (GBM). In particular, the phase I clinical trial assessed the safety and tolerability of the autologous DC vaccine pulsed with lysate derived from allogeneic GBM stem-like cells. The glioblastoma cells used for the vaccine preparation were isolated from a single patient and propagated as neurospheres in serum-free conditions for CSC enrichment. For the autologous vaccine preparation, PBMCs were collected by leukapheresis and used to isolate monocytes, which were differentiated to DCs and pulsed with GBM CSC lysates. DC vaccines were administered intradermally. The therapy was safe and well tolerated. A subset of the patients (9 of the assessed 25 patients) developed a cytotoxic T cell immune response. The study was not powered to evaluate treatment efficacy, although a comparison of the progression-free survival (PFS) and OS with historical control suggested that CSC-targeted immunotherapy can be a treatment of patients with GBM [246]. Results of the clinical studies for DC-based vaccines demonstrated that their combination with therapies that stimulate immune responses and inhibit immune suppression might be more effective for cancer treatment than vaccine administration alone [238]. ICI represents one of the most used treatments in the last decade. It targets immune checkpoint molecules, including CTLA-4, PD-1, and PD-L1, and thereby enhances the immune response to cancer [234]. As discussed in chapter 1.2, CSCs can contribute to tumor immune evasion. Recent studies revealed that PD-L1 promotes the expression of stemness markers [65,70,71,72] and CSC populations with a high level of PD-L1 expression might be associated with tumor immune evasion. To overcome these therapeutic challenges, several studies combined CSC-DC vaccines and ICIs. For example, Hassani Najafabadi et al. developed a nanoparticle vaccine system to deliver ALDH1A1 and ALDH1A3 epitope peptides to antigen-presenting cells (APCs) in vivo and induce T cell responses against ALDH-high CSCs [247]. According to the study, vaccination with high-density lipoprotein nanodisks (ND) loaded with ALDH epitopes reduced the frequency of ALDHhigh CSCs in tumor tissue when combined with anti-PD-L1 therapy, and it exerted strong inhibitory effects on tumor growth in the syngeneic D5 melanoma and 4T1 breast cancer models [247]. Liao et al. used the synthetic ALDH1A1, and ALDHA1A3 peptides and DCs derived from murine bone marrow for the preparation of the CSC-targeted DC vaccines. DCs were administered subcutaneously into a C57BL/6 mice tumor model bearing syngeneic D5 murine melanoma. This study demonstrated that the DC vaccine induced T cell proliferation, humoral immune response, and T cell cytotoxicity against ALDHhigh tumor cells, and inhibited D5 tumor growth in vivo. The dual ALDH1A1 and ALDH1A3 peptide DC vaccine possessed significantly higher antitumor activity as compared to the single ALDH1A1 or ALDH1A3 peptide vaccines [240]. Of importance, this study also proved that anti-PD-L1 treatment significantly enhanced the number of CD3+ tumor-infiltrating lymphocytes and antitumor effect of this vaccine [240]. Zheng et al. investigated the CSC targeting effect of the CSC-DC vaccine combined with a dual blockade of immune checkpoints, such as PD-L1 and CTLA-4. This study confirmed the efficacy of the ALDHhigh-DC vaccines for the treatment of the syngeneic melanoma B16-F10 tumors growing in the immunocompetent C57BL/6 mice. Importantly, this study revealed that animals treated with the dual blockade of PD-L1 and CTLA-4 and CSC-DC vaccine conferred significantly more tumor regression than the CSC-DC vaccine alone. They also showed that the combination of the CSC-DC vaccine and immune checkpoint blockade significantly induced CD8+ T cell proliferation and CSC-specific cytotoxic T cell activity compared with the CSC-DC vaccine alone. This study provided the scientific basis for a clinical trial involving the combination of the CSC-DC vaccine and simultaneous PD-L1 and CTLA-4 blockades for improved tumor control in patients with cancer [248]. A combination of CSC-targeted DC vaccines with conventional therapy is a promising approach to target both CSC and non-CSC cell subsets and to prevent the interconversion between the cell populations. Lu et al. evaluated the therapeutic efficiency of ALDHhigh-CSC lysate-pulsed DCs in combination with local tumor radiation therapy (RT) given in 6 doses of 8.5 Gy. They employed syngeneic D5 melanoma and squamous sarcoma SCC7 tumor model in immunocompetent C57BL/6 and C3H mice. This study confirmed the therapeutic efficacy of the CSC-DC vaccine and demonstrated that it has a higher anti-cancer efficiency in the adjuvant setting when administered after RT. The studies conducted by Hu et al. also assessed the therapeutic potential of CSC-DC vaccination in adjuvant setting [249]. They revealed that ALDHhigh-DC treatment after surgical tumor resection significantly reduced local recurrence, prevented lung metastases, and reduced tumor ALDHhigh CSC populations in the immunocompetent C3H mice bearing syngeneic squamous carcinoma SCC7 tumors. The study of El-Ashmawy et al. suggested that the efficacy of the cisplatin-based chemotherapy for the treatment of murine Ehrlich carcinoma can be improved by its combination with the DC vaccine developed against the CD44+/CD24− CSC-like cell population [250]. Taken together, the immune targeting of CSCs represents a promising approach to cancer treatment. Furthermore, its combination with ICIs and conventional therapy such as surgery and chemo- and radiotherapy could be a strategy to optimize its therapeutic effectiveness. Conventional treatment strategies for patients with malignant diseases include surgery, chemotherapy, and radiotherapy, referred to as the traditional “three pillars” of cancer treatment. Combining immunotherapies with conventional treatments has recently become a cornerstone of cancer therapy (Table 3). It has been reported that many conventional cancer treatments, such as radiotherapy and chemotherapy, have additional immune activation mechanisms of action, including the depletion of immunosuppressive Tregs and MDSCs. The therapy-induced cell death releases tumor antigens recognized, processed, and presented to T lymphocytes by APCs [251,252,253,254,255]. Many studies have depicted the significance of combination therapy for the treatment of CSCs. In preclinical studies using an AML model, a combination of a DNA methylation inhibitor AZA and CD47 blockade via 5F9 mAb have resulted in increased macrophage-mediated phagocytosis in vitro compared to single treatments, inhibited AML growth, and prolonged survival in xenograft mice models [251]. Following these findings, a phase 1b study has demonstrated that the combination of magrolimab, a well-tolerated humanized anti-CD47 antibody, with AZA showed a greater ORR compared to AZA treatment alone, with a more rapid response time in AML patients ([252], NCT03248479). Bone marrow analysis has depicted significantly lower levels of leukemic stem cells in responding patients treated with the combination treatment. In another preclinical study, the combination of targeting receptor tyrosine kinase-like orphan receptor 1 (ROR1)-dependent signaling, which is associated with CSC maintenance and self-renewal, via cirmtuzumab and ibrutinib, which blocks B-cell receptor signaling, was more effective than single-agent treatments in reducing the number of CLL cells in the spleens of immunodeficient mice [253]. These preclinical findings have provided a rationale for clinical studies. A phase 1/2 trial is currently testing this combination in CLL patients, showing encouraging complete response results compared to ibrutinib treatment alone, suggesting a synergistic effect of the therapy ([254], NCT03088878). There are also preclinical studies that test the combination of immunotherapy with radiotherapies. Sequential treatment with fractionated photon irradiation followed by CD98hc-directed UniCAR treatment has exhibited a synergistic cytotoxic effect on radioresistant HNSCC spheroids compared to single treatments, underlining an improved antitumor effect [246]. A phase 2 clinical trial composed of induction treatment with FOLFOXIRI (folinic acid, 5-fluorouracil, oxaliplatin, and irinotecan) drug combination and bevacizumab, a mAb targeting angiogenic factor VEGF, followed by chemoradiotherapy and bevacizumab treatment, has been conducted in patients with advanced and resectable rectal adenocarcinoma. Still, no results have been posted on the ClinicalTrials.gov website yet (NCT03085992). Of importance, small-molecule inhibitors of fat mass and obesity-associated protein (FTO) demonstrated to suppress AML stem cell self-renewal as well as inhibit immune checkpoint expression, and immune evasion, hinting at the broad potential of anti-CSC therapy [255]. The CSC model predicts that therapies targeting the tumor bulk may induce tumor shrinkage; however, the responses will not be durable. Accumulating evidence suggests that stemness is rather a transient feature, and the de-differentiation of the non-CSC populations can replenish the pool of CSCs. Therefore, combining conventional therapies and CSC-targeting treatment could be a more efficient therapeutic strategy to improve therapeutic efficacy. The therapeutic success of anti-cancer immunotherapy depends on the ability of the immune system to detect and destroy tumors as foreign tissue. However, the efficiency of cancer immunotherapy can be limited by immune evasion. CSCs possess multiple mechanisms to escape immune surveillance and create an immunosuppressive microenvironment. Results of the preclinical studies demonstrated that a combination of CSC-targeted immunotherapies with an immune checkpoint blockade stimulates anti-tumor immune responses and might be a more promising cancer therapy for further clinical studies. Tumor cell plasticity and heterogeneity also reduce the efficacy of anti-CSC therapy. Developing immunotherapy against more than one CSC antigen might lower the risk of tumor escape and non-specific toxicity. A combination of CSC-specific immunotherapy with conventional treatments, such as radio- and chemotherapy targeting the bulk tumor cells and ICI enhancing the anti-tumor immune response, could be a strategy to prevent CSC replenishment by non-CSC de-differentiation. Furthermore, conventional therapy such as radiotherapy and chemotherapy can activate multiple immune-stimulating mechanisms, highlighting the consideration for their combination with anti-CSC immunotherapy [266,267,268,269,270]. The development of preclinical models that can recapitulate human immunity and heterogeneous CSC populations in human tumors is another challenge for developing efficient and clinically relevant CSC-targeted treatment. These demands for improving the translational potential of preclinical immunology models are currently addressed using humanized [271], naturalized [272], syngeneic [273], and genetically engineered mice models [274]. These models better recapitulate the human immune system compared to conventional human tumor xenografts and can be used for precise monitoring of immune–tumor interaction [275] and for hypothesis-driven experimentation [274]. In addition, different alternative strategies to modeling immunity are being further refined, including ex vivo cultures preserving the human tissue structures, microfluidic devices, and 3D engineered tissues providing physiologically relevant microenvironments [276]. The development of robust CSC analysis in the patient-derived specimens during treatment is essential to assess the efficacy of the CSC-targeted immunotherapy [40]. It is important to notice that most of the above-described clinical trials are developed to target the bulk tumor cells and do not specifically aim to eliminate CSCs. Although several above-described immunotherapies are showing promising clinical efficacy, including considerable CRR, for example, for the patients with AML treated with IMGN632 and Flotetuzumab, the analyses of bulk tumor response for evaluating the treatment efficacy might not be suitable indicators for its specificity against CSCs. Therefore, CSC-related assays are crucial to assessing the clinical response in these studies, including analyses of CSC frequency and characterization of their self-renewal and tumor-initiating properties. The preclinical efforts to improve the effectiveness of CSC-targeting approaches, including bsAB, CARs, and vaccines, and the data obtained from ongoing clinical trials, might pave the road for CSC-directed treatments to become a clinical reality.

PMC10001161

Tristram A. J. Ryan,Luke A. J. O’Neill

An Emerging Role for Type I Interferons as Critical Regulators of Blood Coagulation

28-02-2023

type I interferons,blood coagulation,IFN-α,IFN-β,thrombin,PARs,haemostasis,thrombosis,tissue factor,SLE,APS,COVID-19,cGAS-STING,neutrophil extracellular traps,FXII

Type I interferons (IFNs) are central mediators of anti-viral and anti-bacterial host defence. Detection of microbes by innate immune cells via pattern recognition receptors (PRRs), including Toll-like receptors (TLRs) and cGAS-STING, induces the expression of type I IFN-stimulated genes. Primarily comprising the cytokines IFN-α and IFN-β, type I IFNs act via the type I IFN receptor in an autocrine or exocrine manner to orchestrate rapid and diverse innate immune responses. Growing evidence pinpoints type I IFN signalling as a fulcrum that not only induces blood coagulation as a core feature of the inflammatory response but is also activated by components of the coagulation cascade. In this review, we describe in detail recent studies identifying the type I IFN pathway as a modulator of vascular function and thrombosis. In addition, we profile discoveries showing that thrombin signalling via protease-activated receptors (PARs), which can synergize with TLRs, regulates the host response to infection via induction of type I IFN signalling. Thus, type I IFNs can have both protective (via maintenance of haemostasis) and pathological (facilitating thrombosis) effects on inflammation and coagulation signalling. These can manifest as an increased risk of thrombotic complications in infection and in type I interferonopathies such as systemic lupus erythematosus (SLE) and STING-associated vasculopathy with onset in infancy (SAVI). We also consider the effects on coagulation of recombinant type I IFN therapies in the clinic and discuss pharmacological regulation of type I IFN signalling as a potential mechanism by which aberrant coagulation and thrombosis may be treated therapeutically.

An Emerging Role for Type I Interferons as Critical Regulators of Blood Coagulation Type I interferons (IFNs) are central mediators of anti-viral and anti-bacterial host defence. Detection of microbes by innate immune cells via pattern recognition receptors (PRRs), including Toll-like receptors (TLRs) and cGAS-STING, induces the expression of type I IFN-stimulated genes. Primarily comprising the cytokines IFN-α and IFN-β, type I IFNs act via the type I IFN receptor in an autocrine or exocrine manner to orchestrate rapid and diverse innate immune responses. Growing evidence pinpoints type I IFN signalling as a fulcrum that not only induces blood coagulation as a core feature of the inflammatory response but is also activated by components of the coagulation cascade. In this review, we describe in detail recent studies identifying the type I IFN pathway as a modulator of vascular function and thrombosis. In addition, we profile discoveries showing that thrombin signalling via protease-activated receptors (PARs), which can synergize with TLRs, regulates the host response to infection via induction of type I IFN signalling. Thus, type I IFNs can have both protective (via maintenance of haemostasis) and pathological (facilitating thrombosis) effects on inflammation and coagulation signalling. These can manifest as an increased risk of thrombotic complications in infection and in type I interferonopathies such as systemic lupus erythematosus (SLE) and STING-associated vasculopathy with onset in infancy (SAVI). We also consider the effects on coagulation of recombinant type I IFN therapies in the clinic and discuss pharmacological regulation of type I IFN signalling as a potential mechanism by which aberrant coagulation and thrombosis may be treated therapeutically. The type I interferon (IFN) family is expressed by most cells in humans and mice and is a critical host defence mechanism for mounting anti-viral responses. The best characterized members are IFN-α (of which there are 13 subtypes in humans and 14 in mice) and IFN-β. Type I IFNs are induced upon detection of infiltrating microbes in the bloodstream via a diverse range of interactions between pattern recognition receptors (PRRs) and pathogen-associated molecular patterns (PAMPs) or danger-associated molecular patterns (DAMPs) [1], depending on the stimulus. PRRs that induce type I IFNs include Toll-like receptors (TLRs), retinoic acid-inducible gene I (RIG-I)-like receptors, and cyclic GMP-AMP synthase (cGAS) [2]. In particular, type I IFN induction can be induced via detection of pathogen-derived dsRNA by endosomal TLR3; ssRNA by TLR7, RIG-I, and MDA-5; and cytosolic dsDNA by cGAS-STING. These are the main receptors that drive type I IFNs in response to viruses, attesting to the importance of type I IFNs in anti-viral immunity. In addition to viral-mediated type I IFN induction, exposure to bacteria can also trigger type I IFN signalling. One well-described system of type I IFN induction in myeloid cells occurs via activation of the TLR4-mediated immune signalling pathways upon detection of endotoxin, also called lipopolysaccharide (LPS), which is found in the outer membrane of Gram-negative bacteria. Host recognition of LPS by TLR4 induces increased expression of proinflammatory cytokines in a process regulated by the nuclear factor (NF)-κB and interferon-regulatory factor (IRF) transcription factors [3]. This occurs via recruitment of myeloid differentiation primary-response gene 88 (MyD88) by MyD88-adaptor-like protein (MAL), with MyD88 forming a complex with members of the IL-1R-associated kinase (IRAK) family, in particular IRAK4, to activate NF-κB. A second signalling cascade triggered by the LPS-TLR4 interaction involves TRIF-related adaptor molecule (TRAM), which recruits TIR-domain-containing adaptor-inducing interferon-β (TRIF) to induce transcriptional upregulation of IRFs, including IRF3/7 [4]. IRFs in turn stimulate the expression of type I IFNs. This leads to IFN-α and IFN-β release, which act in an autocrine or paracrine manner via their ubiquitously expressed heterodimeric IFN-α/β receptor (IFNAR), which comprises two subunits, IFNAR1 and IFNAR2. IFNs drive the transcription of hundreds of IFN-stimulated genes (ISGs) via the transcription factors signal transducer and activator of transcription (STAT)1, STAT2, and IRF9 of the Janus kinase (JAK)-STAT signalling pathway [5]. The precise context-dependent regulation of type I IFN induction has been reviewed in great detail previously [6,7,8,9]. Whilst type I IFNs are required early in the mounting of a host response to infection and the maintenance of homeostasis in human health and disease, overamplification of this response, or prolonged type I IFN signalling, can be detrimental [10,11]. Emerging evidence indicates that dysregulated type I IFN signalling can manifest as being a critical mediator of pathological blood coagulation in both viral and bacterial infection. Activation of coagulation may also feed-forward to amplify type I IFN production, which can have detrimental consequences. We will describe these key studies in this review and speculate on the future therapeutic implications of treating dysregulated type I IFN signalling for the management of coagulopathy. Blood coagulation maintains physiological haemostasis following blood vessel injury or infection via formation of a haemostatic plug primarily comprising platelets, before activation of coagulation upon exposure of tissue factor (TF), the initiator of the extrinsic pathway of coagulation, from leukocytes or sub-endothelial tissue. TF forms a complex with coagulation factor (F)VIIa, initiating thrombin generation. Thrombin generation is then amplified and propagated in concert with activated platelets and leukocytes [12], as well as via formation of the tenase FVIIIa:FIXa and prothrombinase FXa:FVa complexes, which feed-forward to further generate thrombin. Endogenous anticoagulants such as antithrombin and tissue factor pathway inhibitor (TFPI) maintain haemostasis by rapidly inhibiting further thrombin generation, before the blood clot is dissolved via fibrinolysis [13]. Sepsis, a lethal inflammatory condition accompanied by multi-organ dysfunction, is often amplified by inflammation-induced blood vessel injury which exposes TF to be released into the bloodstream. In addition, activation of innate immune signalling pathways, such as the NLRP3 inflammasome, can also trigger TF release on extracellular vesicles from innate immune cells such as macrophages. This can occur during pyroptotic cell death during sepsis [14]. This can result in excessive thrombin generation and blood clot formation during sepsis. As a result, sepsis is often associated with coagulopathy. Recently, numerous studies have directly implicated excessive type I IFN induction and signalling as a critical driver of blood coagulation, particularly in the context of Gram-negative bacterial-induced sepsis. This has been demonstrated using genetic mouse models lacking in core components of the type I IFN pathway. A recent study showed that Trif−/− mice are protected from the thrombotic complications associated with LPS-induced sepsis. Following intraperitoneal LPS administration, Trif−/− mice exhibited decreased thrombin generation and fibrin deposition in the livers and lungs compared with wild-type mice [15]. As a result, Trif−/− mice were protected from LPS-induced lethality. Deletion of TRIF also reduced Ifnb1 expression in the liver, spleen, and gut [15]. In the same study, investigators found that Ifnar−/− mice, which do not express the IFN-α/β receptor, displayed decreased D-dimer levels as well as thrombin–antithrombin complex formation compared with wild-type mice following intraperitoneal LPS injection [15]. Ifnar−/− mice also had reduced fibrin deposition in their livers and lungs compared with wild-type mice. After cecal ligation and puncture (CLP), a clinically relevant model of polymicrobial sepsis, both Trif−/− and Ifnar−/− mice were protected from excessive thrombin generation and coagulopathy [15]. This indicates that the TLR4-TRIF-type I IFN pathway is a critical mediator of thrombosis following Gram-negative bacterial infection. In another study, Dejager et al. showed that serum IFN-α is significantly elevated in mice as late as 48 h post CLP-induced sepsis [16]. Treatment of wild-type mice with an anti-IFNAR1 antibody, or deletion of IFNAR, protected mice from LPS-induced lethality and CLP-induced sepsis. In addition, this also significantly reduced serum interleukin (IL)-6 levels [16]. This is notable as IL-6 (formerly known as Ifnb2) contributes to coagulation via the rapid induction and synthesis of fibrinogen in the liver [17]. In another model of CLP-induced sepsis in mice, investigators found reduced endothelial damage in Ifnar−/− mice, with an associated suppression of inflammation-related gene expression in endothelial cells [18]. Consistent with a previous study [15], Ifnar−/− mice also displayed decreased aortic mRNA expression of Pai-1, an inhibitor of fibrinolysis [18], indicating that type I IFNs drive dysregulated fibrinolysis, which can lead to a prothrombotic state. This evidence of IFNAR playing a critical role in type I IFN-mediated sepsis and coagulation is intriguing, as there is a significant similarity between IFNAR and TF, which is itself a membrane glycoprotein receptor. Global alignment of the sequence and structural patterns of IFN receptors indicates that transmembrane TF is structurally homologous to IFNAR [19]. In particular, both TF and IFNAR share evolutionarily conserved fibronectin type III domains comprising antiparallel β-sheets [20] which, in the case of TF, are proposed to be essential for the post-translational activation of TF. This process, termed decryption, occurs in the extracellular domain of TF and leads to a significant increase in TF procoagulant activity [21,22,23]. One intriguing possibility therefore is that TF might bind type I IFNs, acting as a decoy receptor or possibly a facilitator of IFNAR signalling. Activation of type I IFN signalling can induce the ISG caspase-11 (in mice; caspase-4 and -5 in humans) [24], which is essential for the formation of a non-canonical inflammasome [25]. Type I IFN-mediated induction of caspase-11 occurs in response to Gram-negative—but not Gram-positive—bacteria [26]. Detection of cytosolic LPS in macrophages activates caspase-11 [27,28], which can then cleave and activate gasdermin D (GSDMD) to form pyroptotic pores in the cell membrane [29,30], through which proinflammatory and prothrombotic mediators are released. During Gram-negative bacterial sepsis, macrophage pyroptosis leads to TF release into the circulation on extracellular vesicles [31,32]. This results in excessive thrombin generation, leading to disseminated intravascular coagulation in mice [31,32]. Deletion of caspase-11 or GSDMD, or administration of an anti-TF antibody, protects mice from LPS-induced coagulation and lethality [31,32]. Caspase-11-mediated GSDMD cleavage also triggers the exposure of phosphatidylserine onto the outer membrane of macrophages [32], and this potentiates TF procoagulant activity [21,22]. Furthermore, CASPASE-5 expression is significantly increased in primary human macrophages from sepsis patients [33]. At the transcriptional level, induction of Gsdmd at the mRNA level is governed by the transcription factor IRF2 [34], which lies downstream of STAT1 and STAT2. IRF2 is also essential for induction of caspase-11 in macrophages [35], and thus IRF2 is a critical mediator of non-canonical inflammasome-induced pyroptosis. This indicates a critical role for type I IFN induction in the regulation of two key mediators of pyroptosis, which drives aberrant coagulation, and suggests that therapeutic targeting of IRF2 or the downstream non-canonical inflammasome may be a prospect for the inhibition of type I IFN-induced thrombosis. Another key player in IFN-related coagulation is the high-mobility group box (HMGB) protein family, which is critical for host defence during both sterile and infectious injury such as sepsis, and there is an intricate interplay between type I IFN and HMGB signalling. One of these mechanisms is via cytosolic detection of DNA or RNA. In particular, HMGB1 binds to the multivalent receptor for advanced glycation end-products (RAGE) to activate the endosomal TLRs 3, 7, and 9, and thus HMGB1 is essential for cytosolic nucleic acid-mediated induction of type I IFNs [36]. As such, elevated serum HMGB1 is a biomarker of inflammatory diseases [37]. Upon innate immune cell activation, HMGB1 translocates from the nucleus to the cytosol. This occurs upon activation of JAK/STAT1 via type I IFN signalling [37]. HMGB1 is then released from immune cells via inflammasome-mediated pyroptosis, which is induced upon autophosphorylation of the dsRNA-dependent protein kinase R (PKR), which is itself an ISG [38]. This results in physical interaction between PKR and the inflammasomes (NLRPs 1 and 3, NLRC4, and AIM2) [39]. Extracellular HMGB1 can then feed-back to further amplify the type I IFN signalling cascade in macrophages via activation of TLR4 [40]. In addition, HMGB1 physically binds extracellular LPS and is internalized into macrophage lysosomes via RAGE [41]. Destabilization of the lysosomal membrane by HMGB1 then releases LPS into the cytosol of macrophages, where it cleaves and activates caspase-11 to mediate pyroptosis, resulting in the release of proinflammatory and prothrombotic cytokines including IL-1β, IL-18, TF, and further HMGB1, amplifying and exacerbating the resulting inflammation [41]. Therefore, HMGB1 activity is critical for feed-forward inflammation and aberrant blood coagulation, possibly via type I IFNs. In addition, HMGB1 may contribute to blood coagulation by inducing F3 (TF) mRNA and TF protein, as well as TF procoagulant activity, in macrophages and endothelial cells [42]. This is likely due in part to HMGB1 being able to induce phosphatidylserine exposure from macrophages in a pyroptosis-dependent manner [15]. Induction of F3 by HMGB1 may also occur via activation of the transcription factor NF-κB [42], which is a transcriptional regulator of F3 [43]. Furthermore, platelet-derived HMGB1 has been implicated as an important contributor to neutrophil extracellular trap (NET) formation and subsequent deep vein thrombosis in mice [44]. These studies describing type I IFNs as drivers of blood coagulation are depicted in Figure 1. A recent study also implicated interferon-inducible transmembrane (IFITM) proteins, which block the early stages of viral replication, as drivers of platelet activation in bacterial sepsis. In vitro stimulation of megakaryocytes with IFN-α induced IFITM3 protein expression via STAT1 phosphorylation, as well as mTOR [45]. This increased fibrinogen endocytosis in megakaryocytes via localization of the integrin αIIbβ3 and clathrin into lipid rafts. This translated to an increase in platelet aggregation in mice in vivo. Furthermore, IFITM3 expression and fibrinogen endocytosis were increased in platelets from humans with non-viral sepsis [45]. Therefore, IFITMs are required for IFN-α-induced thrombosis. Mounting evidence indicates a critical role for cGAS-STING signalling in the regulation of blood coagulation following infection. cGAS senses cytosolic dsDNA, generating the second messenger cyclic guanosine monophosphate–adenosine monophosphate (cGAMP), which activates STING. This promotes activation of TANK-binding kinase 1 (TBK1), which in turn phosphorylates IRF3 [46], resulting in type I IFN production [47]. The severe autoinflammatory syndrome, STING-associated vasculopathy with onset in infancy (SAVI), is a rare clinical condition where patients with gain-of-function mutations in TMEM173 (the gene that encodes for STING) present with cutaneous vasculopathy and vasculitis [48,49]. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can also induce cGAS-STING activation. This occurs via the release of mitochondrial DNA (mtDNA) into the cytosol, which is detected by cGAS [50]. Following SARS-CoV-2 infection, cGAS-STING activates NF-κB-mediated inflammatory cytokine production as well as TBK1-IRF3-mediated type I IFN induction [51] to sustain the anti-viral response. In the context of blood coagulation, SARS-CoV-2-infected human endothelial cells exhibit increased IFNB1 and F3 mRNA and decreased TFPI mRNA expression, which are restored to basal levels upon treatment with the STING inhibitor H-151 [50]. H-151 also significantly reduced the SARS-CoV-2-induced expression of IL6 [50], which is prothrombotic [17]. Therefore, cGAS-STING likely contributes to type I IFN induction and COVID-associated pathology, including coagulopathy. However, the potent induction of type I IFNs by cGAS-STING can be harnessed to promote the host anti-viral response to SARS-CoV-2 infection. Humphries et al. found that a pharmacological STING activator, diABZI-4, potently induced type I IFNs and suppressed SARS-CoV-2 replication in human A549 lung epithelial cells [52]. Therapeutic administration of the STING agonist protected mice from SARS-CoV-2-induced pulmonary damage and mortality, with direct STING activation proving more effective at eliminating weight loss and reducing mortality than the administration of IFNs in mice after SARS-CoV-2 infection [52]. This raises the possibility of targeting STING for the treatment of COVID-19-associated inflammation and coagulopathy. STING may also drive blood coagulation following infection in a type I IFN-independent manner. In one study, STING increased GSDMD-mediated TF release in monocytes and macrophages [53]. The authors, however, noted that this occurred in a type I IFN-independent manner as there were no differences in disseminated intravascular coagulation (DIC) markers, including fibrinogen, D-dimer, fibrin, and TF release in the plasma of Ifnar−/− mice compared with wild-type mice 48 h after CLP-induced sepsis. The acute nature of type I IFN production in the innate immune response and in driving blood coagulation might explain why the authors attributed the role of STING in these models to be type I IFN-independent. It may also be because cGAS-STING can trigger pyroptosis in a type I IFN-independent manner. Gaidt et al. showed that the detection of cytosolic DNA results in pyroptosis via activation of TBK1 and IKKε to induce type I IFN-independent NLRP3 activation, as well as simultaneous type I IFN-dependent induction of caspase-11 [54]. Mechanistically, STING promoted DIC via binding to ITPR1 on the endoplasmic reticulum (ER) to promote ER calcium release, which decrypts TF [22,55] and triggers TF release via GSDMD-induced pyroptotic pores [53]. Therefore, the therapeutic targeting of STING may have beneficial effects for the treatment of inflammation-associated coagulopathy, regardless of the extent of the contribution of type I IFNs in those conditions. The host IFN response to viral infection has been the focal point of much research in the past three years, as it is an essential defence mechanism following SARS-CoV-2 infection. In particular, rapid type I IFN induction is necessary for the mounting of an effective anti-viral response against COVID-19. However, aberrant type I IFN signalling in COVID-19 is detrimental either due to overactivation of type I IFNs, or via SARS-CoV-2-mediated disruption of cellular RNA splicing and translation, as well as degradation of host mRNAs, which limits ISG production and enables further propagation of SARS-CoV-2 [56,57]. Thus, COVID-19 pathology is associated with dysregulated type I IFN signalling [58,59,60]. COVID-19 is also characterized by systemic coagulopathy [61]. This includes elevated plasma levels of D-dimer in patients [62] and increased pulmonary deposition of the thrombotic marker fibrinogen/fibrin [63], as well as elevated pulmonary von Willebrand Factor (vWF) deposition, which is a clinical marker of both acute and sustained endothelial cell activation, following SARS-CoV-2 infection [64]. Innate immune cells are core contributors to this pathology, with a recent report identifying a transcriptional shift in monocytes to a more prothrombotic genotype following SARS-CoV-2 infection [65]. Expression of F3 and TF-positive microvesicles are also increased in monocytes, macrophages, and platelets [66], as well as in endothelial cells and epithelial cells from patients with severe COVID-19 [50,67], propagating the coagulopathy associated with COVID-19. TF-positive microvesicles then drive excessive thrombin generation and coagulopathy following infection with SARS-CoV-2 [68,69]. In addition, thrombin and FXa have recently been shown to directly cleave the SARS-CoV-2 spike protein, augmenting viral entry into human airway epithelial cells and human pluripotent stem cell-derived lung organoids [70]. This suggests a broader anti-viral effect of deploying anticoagulants, in particular direct thrombin inhibitors and direct FXa inhibitors, as well as inhibitors of type I IFNs, in the treatment of COVID-19, as this will likely suppress pathological type I IFN production (as a result of reduced viral uptake) as well as thromboinflammation. Assessment of whole blood from COVID-19 patients found a correlative relationship between defective type I IFN signalling, elevated coagulation markers, and increasing disease severity [60]. In addition, macrophage type I IFN- and caspase-11-dependent pyroptosis have been implicated in mediating TF-dependent coagulopathy in a mouse model of COVID-19 [71]. Therefore, investigators have attempted to target this type I IFN-caspase-11–TF axis as a means of limiting inflammation and coagulopathy in COVID-19. Heparin, the clinically approved antithrombin activator, can also block caspase-11-mediated pyroptosis at concentrations lower than those required for heparin to activate antithrombin [72]. Notably, anticoagulation treatment with heparin has been shown to decrease mortality in non-severe COVID-19 patients [73] and patients with elevated D-dimer levels [74]. However, a caveat of using heparin for the treatment of COVID-19 is that it is not effective in severe patients when administered therapeutically versus standard care pharmacologic thromboprophylaxis [75]. This indicates a correlation between the rapid type I IFN response and the efficacy of heparin therapy following SARS-CoV-2 infection, with heparin proving most effective when administered prior to the onset of severe pathology. Therefore, inhibition of type I IFNs and the non-canonical inflammasome is an attractive target for anticoagulation treatment in COVID-19 associated coagulopathy, as well as during bacterial infections. Emerging evidence indicates that the presence of persistent fibrin amyloid microclots may provide a mechanistic basis for the long-term effects of long COVID (also termed post-acute sequelae of COVID-19), which include fatigue and vertigo, as fibrin amyloid microclots block capillaries and the transport of oxygen to tissues [76,77]. Type I IFNs may contribute to the development of microclots in long COVID as elevated type I IFN expression has been detected for at least 8 months after infection [78]. Thus, new studies are urgently required to study the effect of IFN-mediated coagulation in long COVID. Type I interferonopathies, an umbrella term first coined in 2011 by Yanick Crow [79], describes a group of clinical conditions associated with sustained, elevated type I IFN production. These related syndromes include systemic lupus erythematosus (SLE), an autoimmune disease characterized by persistent type I IFN upregulation [10]. Patients with SLE are at significantly greater risk of developing atherothrombotic cardiovascular disease [80]. PBMCs from patients with SLE have elevated mRNA expression of F3 [81]. SLE patients also have a greater number of activated platelets compared with healthy controls, and these platelets exhibit an elevated type I IFN mRNA and protein signature [82]. In addition, SLE patients with a history of vascular disease have increased type I IFN-regulated protein levels compared with SLE patients without a history of vascular disease [82]. Furthermore, in SLE patients, IFN-α rapidly triggers apoptosis of endothelial progenitor cells (EPCs) and myelomonocytic circulating angiogenic cells (CACs), which are required for blood vessel repair [83]. SLE EPCs and CACs exhibit increased IFN-α expression and an elevated type I IFN signature [83]. This indicates that sustained type I IFN production in SLE is closely intertwined with vascular damage and coagulopathy. This was demonstrated in one study whereby deletion of IFNAR in lupus-prone mice improved endothelial function and decreased atherosclerosis severity [80]. One primary mechanism of type I IFN induction in lupus occurs via the release of oxidized mtDNA, which is elevated in skin lesions from lupus patients and is associated with increased type I IFN production [84]. As cGAS-STING detects dsDNA and is a potent inducer of type I IFNs, cGAS-STING likely plays a key role in the pathogenesis of SLE. This is demonstrated by elevated cGAS expression in PBMCs from SLE patients [85]. Furthermore, expression of apoptosis-derived membrane vesicles, which are associated with elevated dsDNA levels, have been shown to activate cGAS-STING to induce type I IFNs in serum from SLE patients [86]. In addition to the thrombotic complications associated with SLE and SAVI, the importance of a functional type I IFN signalling pathway for regulating innate immune pathways and control of blood coagulation is demonstrated by the mutation of core signalling components which can predispose to increased risk of mortality. For example, a mutation in JAK2 first identified in 2005, JAK2V617F, is present in >80% patients with polycythaemia vera, a myeloproliferative leukaemia whereby excessive erythrocyte production can lead to excessive blood clotting and is associated with increased mortality [87]. In addition, the JAK2V617F mutation can also lead to essential thrombocythemia, a neoplasm whereby increased platelet production may further propagate the risk of excessive blood clotting [88]. Although the exact mechanisms underlying the contribution of type I IFNs to the increased thrombosis risk in these clinical conditions remains to be elucidated, this further indicates that dysregulated type I IFN signalling is a critical signal that drives aberrant blood coagulation. The association between dysregulated type I IFN signalling and coagulopathy was highlighted during the COVID-19 pandemic, with pharmacological inhibition of excessive type I IFN production being associated with a concomitant reduction in SARS-CoV-2-induced coagulopathy. A number of studies reported the beneficial effects of inhibition of type I IFN signalling, particularly using JAK inhibitors, which are clinically approved for the treatment of conditions such as rheumatoid arthritis and myeloproliferative neoplasms (MPNs). For example, the JAK1/3 inhibitor tofacitinib lowered the SARS-CoV-2-induced risk of death or respiratory failure over 28 days versus placebo [89]. Another clinical trial found a decrease in D-dimer and C-reactive protein levels in the blood of hospitalized SARS-CoV-2 patients when treated with the JAK1/2 inhibitor baricitinib plus corticosteroids, versus treatment with corticosteroids alone [90]. JAK inhibitors have been especially effective in COVID-19 patients receiving high-flow oxygen or non-invasive ventilation [91], indicating that pharmacological inhibition of type I IFN signalling is most beneficial prior to the onset of severe disease. JAK inhibition will also block IL-6 signalling, further limiting inflammation and coagulation. Increased TYK2 expression is also associated with mortality in COVID-19 patients [92]. Therefore, targeting JAKs with specific inhibitors and therefore downstream JAK-TYK signalling may confer protection on COVID-19 patients. In addition, JAK inhibition may also suppress thromboinflammation in COVID-19. There is also a growing body of evidence which suggests that the key mediators of blood clotting, the coagulation factors themselves, can act on innate immune cells to induce proinflammatory cytokines and type I IFNs. This can amplify IFN production to combat bacterial or viral infection and restore haemostasis via the rapid resolution of inflammation, but it can also trigger a detrimental, pathological inflammatory cycle, as we will describe below. The heterotrimeric GTP-binding protein-coupled protease-activated receptors (PARs) are expressed by a range of immune cells, and whilst PAR signalling is essential for the maintenance of haemostasis, it can also lead to the induction of proinflammatory cytokines [93]. PARs are the main substrate for thrombin and therefore numerous studies have assessed the induction of type I IFNs via thrombin-PAR signalling. The core procoagulant role of thrombin is the cleavage of fibrinogen into fibrin to generate a thrombus by forming a mesh at the sites of infection and vascular damage, in conjunction with activated platelets and neutrophils which expel their DNA, histones, and granule-derived enzymes during NETosis. Thrombin generation can occur as the endpoint of the intrinsic/contact (FXII-mediated) or extrinsic (TF-mediated) pathways of the coagulation cascade. Moreover, it has recently emerged that extracellular vesicles on erythrocytes may also contribute to the generation of thrombin [94,95]. However, excess thrombin generation can be pathological in a range of clinical conditions, resulting in tissue ischaemia by microvascular and macrovascular thrombosis. Thrombin can trigger inflammatory signalling through PARs, which can result in a process termed thromboinflammation. PARs 1, 3, and 4 recognize and are cleaved and activated by thrombin [93]. In addition, a recent study suggests that thrombin, when bound to the endogenous anticoagulant thrombomodulin, can also cleave PAR2 [96]. Thrombin-PAR signalling is critical for the interplay between inflammation and coagulation, boosting proinflammatory cytokine secretion, as PARs can physically interact and therefore synergize with TLRs on innate immune cells. For example, Subramaniam et al. found that although stimulation of human umbilical vein endothelial cells (HUVECs) with thrombin did not directly induce the mRNA expression of ISGs or F3, co-stimulation of HUVECs with both thrombin and the dsRNA polyinosinic:polycytidylic acid (poly(I:C)) resulted in significantly increased expression of F3 and TF procoagulant activity compared with poly(I:C) stimulation alone [97]. This provides evidence of PAR1/2 and TLR3 synergy and suggests that in the context of an innate immune response, thrombin may positively feed-back to amplify its own production. Recent studies have employed PAR knockout mice to examine further the effects of thrombin signalling on the innate immune system. Macrophages and splenocytes from Par1−/− mice exhibited decreased type I IFN signalling after administration of poly(I:C) [98]. Furthermore, mRNA expression of Ifnb1, Irf7, and Cxcl10 were reduced in Par1−/− mice after infection with Coxsackievirus group B (CVB), suggesting that PAR1, and therefore thrombin signalling, contributes to type I IFN-mediated anti-viral responses [98]. Furthermore, after stimulation of mouse cardiac fibroblasts with poly(I:C), PAR1 and TLR3 synergized to drive an anti-viral but proinflammatory response via induction of IFN-β and CXCL10 via increased phosphorylation of the MAPK p38 [99]. In vivo, poly(I:C) administration increased expression of F3 in the heart and liver as well as thrombin generation in mouse plasma. Administration of an anti-TF monoclonal antibody or the thrombin inhibitor dabigatran etexilate significantly increased CVB3-induced myocarditis [99], indicating that haemostatic coagulation contributes to the innate, anti-viral response. Moreover, the chemotherapeutic drug, doxorubicin, has recently been found to increase thrombin generation in a TF-dependent manner, driving thromboinflammation in mice via PAR1 activation in cardiomyocytes and cardiac fibroblasts [100]. This prothrombotic phenotype may explain the underlying basis for the cardiotoxicity associated with doxorubicin. In addition, PAR2 has also been shown to synergize and physically interact with TLRs during inflammation [101]. PAR2 synergized with TLRs 2, 3, and 4 in mucosal epithelial cells following poly(I:C) stimulation, which activated NF-κB via degradation of IκBα and phosphorylation of p65 [102]. However, in contrast to PAR1, PAR2 negatively regulated the TLR3 signalling pathway, resulting in decreased phosphorylation of IRF3 & STAT1, and therefore suppressing the type I IFN-mediated anti-viral response. Thus, Par2−/− and Tlr4−/− mice were protected from lethality induced by infection with H1N1 influenza A virus [102]. Additionally, in LPS-stimulated bone marrow-derived macrophages, PAR2 activation resulted in increased IL-10 secretion, possibly via increased STAT3 phosphorylation, but decreased secretion of the proinflammatory cytokines IL-6, TNF, and IL-12p40 [103]. This suggests that PAR2 counteracts LPS-induced proinflammatory cytokine production. Furthermore, PAR2 restrained type I IFN signalling in fibroblasts via the binding of TLR3 in a mouse model of CVB3-induced myocarditis [104]. Higher cardiac PAR2 mRNA expression correlated with low IFNB1 expression in patients with non-ischaemic cardiomyopathy, resulting in increased expression of inflammatory markers, including CD3+ and CD45+ T cells [104]. Thus, modulation of the host response by PAR2 differs in response to activation of different TLRs. Therefore, synergy of individual PARs with TLRs can have contrasting effects on downstream innate immune signalling, particularly with regards to type I IFN signalling. In addition to the induction of type I IFNs by thrombin-PAR-TLR signalling, thrombin can also induce the proinflammatory cytokines IL-6, IL-1β, and TNF in human monocytes [105] and vascular smooth muscle cells [106]. Intraperitoneal injection of thrombin in mice increased IL-6 secretion from peritoneal macrophages into the peritoneum in a fibrinogen-dependent manner [107]. Thrombin can also cleave and activate pro-IL-1α (p33) into its active form (p18) when expressed on the surface of macrophages, platelets, and keratinocytes [108]. Thrombin-activated IL-1α was found to be important for rapid thrombopoiesis and wound healing. Furthermore, thrombin-cleaved IL-1α is elevated in the plasma of ARDS patients versus healthy controls [108], and thus can be considered a biomarker of thromboinflammatory conditions. Furthermore, the formation of TF:FVIIa rapidly induces FXa to drive cytokine production via PAR2 [109]. TF:FVIIa:FXa can also form a complex with EPCR to trigger TLR4/PAR2-mediated type I IFN signalling. This occurs via induction of pellino-1, the TLR3/4 adaptor protein, in addition to IRF8 [110]. Deficiency of EPCR, PAR2, or TF in mice attenuates LPS-induced expression of IRF8 and subsequent type I IFN induction in vitro and in vivo [110]. This indicates a role for TF:FVIIa as a DAMP by activating innate immune signalling pathways. The role of PARs in the induction of type I IFNs and downstream blood coagulation is summarized in Figure 2. In the clinic, type I IFNs are administered therapeutically. Recombinant IFN-α is approved for the treatment of chronic hepatitis B and C viral infections as well as various cancers, including MPNs, whilst recombinant IFN-β is approved for multiple sclerosis (MS) treatment to regulate the persistent inflammation associated with the condition. Although these therapies are effective, there have been indications that IFN therapy can lead to an increased risk of thrombosis. Elevated vWF antigen expression and activity has been detected in the plasma of MPN patients receiving IFN-α compared with healthy controls [111]. Plasma from IFN-α-treated MPN patients also displayed significantly increased activity of fibrinogen and the coagulation factor FVIII, as well as reduced protein S activity, indicating a shift in MPN patients to a more procoagulant phenotype. Functionally, this resulted in elevated thrombin generation in MPN patient plasma. The investigators tracked the patients for 6 months and found that haemostasis was restored in patients when IFN-α treatment was discontinued, as demonstrated by a significant reduction in vWF and fibrinogen levels, as well as increased protein S activity [111]. Thus, IFN-α therapy increases prothrombotic biomarkers in the plasma of MPN patients. Recombinant type I IFN therapies have also been linked with a dose-dependent increase in the risk of thrombotic microangiopathies in MS patients [112]. A study by Jia et al. suggested a potential mechanism by which type I IFNs might drive thrombotic microangiopathies. They compared the effects of recombinant IFN-α and IFN-β on endothelial cell function and found that IFN-β suppressed proliferation and survival of HUVECs, but IFN-α did not affect these parameters [113]. Meanwhile, both IFN-α and IFN-β blocked angiogenesis via activation of IFN-inducible CXCL10 when HUVECs and human dermal fibroblasts were co-incubated in vitro. Endothelial cell activity was impaired by IFN-α and IFN-β via inhibition of endothelial cell-produced nitric oxide and prostacyclin. Intriguingly, IFN-β significantly increased PAI-1 and downregulated uPA in HUVECs, which is indicative of decreased fibrinolysis [113]. Thus, these studies indicate that type I interferonopathies or administration of type I IFNs may be associated with increased risk of pathological blood clotting. In addition to the emerging evidence linking aberrant type I IFN signalling with coagulopathy in conditions such as Gram-negative bacterial infection and COVID-19, type I interferonopathies such as SLE have been associated with an increased risk of thrombosis, as discussed above. Emerging evidence indicates a key role for excessive neutrophil activation and NET formation in the pathogenesis of SLE. Activation of neutrophils with ribonucleoprotein immune complexes, which are highly expressed in lupus, induce hyperpolarization of mitochondria, followed by the translocation of mitochondria to the cell surface and subsequent release of mtDNA [114]. Oxidized mtDNA drives NETosis in SLE and lupus-like diseases [114], leading to increased deposition of dsDNA, IL-17, HMGB1, and the anti-microbial peptide LL-37 in NETs from SLE patients [115,116]. LL-37 induces type I IFNs in plasmacytoid dendritic cells (pDCs) by binding extracellular dsDNA and transporting it into endosomal compartments of pDCs, triggering TLR9-mediated IFN production [117]. LL-37 may also transport dsDNA into monocytes to induce type I IFN signalling via cytosolic STING-TBK1 activation [118]. Interestingly, this occurs independently of TLR9 in monocytes [118]. This is supported by the fact that stimulation of human PBMCs with NETs induces IFNA1 and IFNB1 expression [119]. Thus, NETs are interferogenic. However, NETs are also prothrombotic, particularly in COVID-19 [120], in part by capturing TF and TF-positive extracellular vesicles from the circulation [121,122], thereby facilitating activation of the extrinsic pathway of coagulation. Furthermore, whilst neutrophil-released DNA and histones are prothrombotic, intact NETs are not directly thrombogenic themselves [123]. This is notable as DNA released from NETs can drive thrombin generation in a FXII-dependent manner [124], with a FXII-NETs cross-talk implicated in the pathogenesis of COVID-19 [125] as well as deep vein thrombosis [126]. In addition, in a feed-forward manner, FXII has been shown to drive NETs in a baboon model of Escherichia coli-induced sepsis [127]. Increased levels of autoantibodies to FXII have also been associated with thrombosis in SLE [128], suggesting the possibility that FXII might drive type I IFN signalling in SLE via NET formation. Future studies should assess this hypothesis to potentially unravel FXII-mediated thrombosis as a novel target for SLE therapy. The autoimmune disease antiphospholipid syndrome (APS) is also associated with an increased risk of aberrant type I IFN signalling and thromboinflammation. In APS, endocytosis of antiphospholipid antibodies (aPLs) into pDCs induces TLR7/8-dependent type I IFN production [129], as characterized by increased expression of TLR7 [130] and TLR8 [131] in PBMCs from patients with APS. This leads to IFN-α release from pDCs, which stimulates the production of B1a cells, a type of B cell associated with autoimmune diseases such as APS and SLE. B1a cells then produce further lipid-reactive aPLs, propagating APS [129]. aPLs also hijack haemostatic control of coagulation by inhibiting TFPI [132]. This results in increased thrombosis via TF:FVIIa-dependent thrombin generation, highlighted by elevated F3 expression in monocytes and PBMCs in patients with APS [133,134], demonstrating the prothrombotic genotype associated with APS. Furthermore, monocytes from patients with APS and SLE exhibit increased expression of PLSCR1 [135], which is involved in monocyte phosphatidylserine externalization and TF decryption [23]. In the presence of an aPL, stimulation of macrophages with IFN-α significantly increased F3 expression [135], indicating that type I IFN signalling drives TF-dependent thrombin generation in APS. Soluble TF levels are elevated in plasma from patients with APS [136], and there is an increase in TF-dependent procoagulant activity in the PBMCs of patients with APS [137] as well as in the carotid artery homogenates of mice injected intraperitoneally with serum IgG isolated from APS patients [138]. This suggests a mechanism (and potential therapeutic target) for the innate immune-mediated microthrombosis associated with APS. In addition, identifying the molecular mechanisms underlying a prothrombotic type I IFN–TF axis in APS would contribute greater understanding of the complex processes driving thrombosis in APS. Furthermore, excessive type I IFN production via cGAS-STING activation has been implicated in further models of inflammation-associated coagulopathy, including cerebral venous sinus thrombosis [139] and acute lung injury [140], indicating a broad spectrum of conditions where there is a strong correlation between dysregulated type I IFN production and aberrant coagulation. It would be intriguing to hypothesize that these two events are interrelated, and thus future studies should assess the extent of the cross-talk between type I IFN and thrombosis in the pathogenesis of these clinical conditions. Mounting evidence indicates that dysregulated host type I IFN production can manifest as being a critical driver of pathological blood coagulation during infection but also in such conditions as SLE and SAVI, and possibly during IFN therapy. Type I IFNs can trigger pathological coagulation via both the intrinsic/contact and extrinsic pathways of blood clotting. Coagulation factors can feed-forward to induce proinflammatory cytokines and type I IFNs, for example via thrombin-PAR-TLR signalling. Therefore, type I IFNs may be critical drivers of thrombosis. With this review, we hope to inspire greater focus on the precise mechanisms by which type I IFNs mediate blood coagulation and thrombosis, and vice versa, which may prove that type I IFNs lie at the fulcrum of inflammation and coagulation. Targeting IFNs may therefore present therapeutic opportunities for the treatment of aberrant coagulation in infection and inflammatory diseases, as well as providing new avenues for the prevention or treatment of DIC in Gram-negative bacterial sepsis.