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bioRxiv preprint
doi:
https://doi.org/10.1101/2021.03.04.433941
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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CC-BY-NC-ND 4.0 International license . Title: NKX2-1 controls lung cancer progression by inducing DUSP6 to dampen ERK activity
Kelley Ingram1, 2,*, Shiela C. Samson1, 2,*, Rediet Zewdu2,3, Rebecca G. Zitnay2,4, Eric L.
Snyder2, 3, and Michelle C. Mendoza1
1 Department of Oncological Sciences, University of Utah, Salt Lake City, Utah 84112
2 Huntsman Cancer Institute
3 Department of Pathology, University of Utah, Salt Lake City, Utah 84112
4 Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112
co-first author
Corresponding author: Michelle.Mendoza@hci.utah.edu
Key words: Lung adenocarcinoma, NKX2-1, DUSP6, ERK
bioRxiv preprint
doi:
https://doi.org/10.1101/2021.03.04.433941
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this version posted March 4, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . Abstract
Lung cancer remains a leading cause of cancer death, with unclear mechanisms driving
the transition to aggressive cancer with poor prognosis. The RAS®RAF®MEK®ERK pathway
is hyper-activated in ~50% of human lung adenocarcinoma (LUAD). An initial activating mutation
induces homeostatic feedback mechanisms that limit ERK activity. Additional, undefined genetic
hits overcome the feedback, leading to high ERK activity that drives malignant progression. At
detection, the majority of LUADs express the homeobox transcription factor NKX2-1, which also
limits malignant progression. NKX2-1 constrains LUAD, in part, by maintaining a well-
differentiated state with features of pulmonary identity. We asked if loss of NKX2-1 might also
contribute to the release of ERK activity that drives tumor progression. Using human tissue
samples and cell lines, xenografts, and genetic mouse models, we show that NKX2-1 induces the
ERK phosphatase DUSP6. In tumor cells from late-stage LUAD with silenced NKX2-1, re-
introduction of NKX2-1 induces DUSP6 and inhibits cell proliferation and migration and tumor
growth and metastasis. CRISPR knockout studies show that DUSP6 is necessary for NKX2-1-
mediated inhibition of tumor progression in vivo. Further, DUSP6 expression is sufficient to inhibit
RAS-driven LUAD. We conclude that NKX2-1 silencing, and thereby DUSP6 downregulation, is
a mechanism by which early LUAD can unleash ERK hyperactivation for tumor progression. bioRxiv preprint
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It is made
available under a
CC-BY-NC-ND 4.0 International license . Introduction
Lung cancer and specifically lung adenocarcinoma (LUAD) remains the leading cause of
cancer death world-wide. New therapies that molecularly target driver mutations have improved
patient outcome. However, the development of resistance mutations necessitate long-term,
dynamic, and sequential cycles of targeted therapeutics to combat resistance (1). Better
understanding of the molecular mechanisms of LUAD progression will enable the development
of new therapeutic strategies for long-term patient management. The mitogen-activated protein (MAP) kinase extracellular signal-regulated kinase (ERK)
is an established initiator and driver of lung adenocarcinoma (LUAD). ERK is activated
downstream of receptor tyrosine kinase signaling to the RAS®RAF®MEK®ERK pathway. Nearly 50% of LUADs harbor mutations in RAS genes, BRAF, or NF1, which encodes the RAS
GTPase activating protein (2, 3). An additional 13% harbor mutations upstream receptor tyrosine
kinases EGFR, ERBB2, MET, ALK, RET, and ROS1 (2, 3). Genetically-engineered mouse
models show that these mutations are sufficient to initiate pre-cancerous and low-grade lesions
(4, 5). Tumor initiation and maintenance requires ERK activity, as MEK inhibition or ERK deletion
blocks tumor development (4, 6, 7). Yet, the initial ERK activation is insufficient for progression
to metastatic cancer (5, 8). Active, phosphorylated ERK (phospho-ERK) is associated with more aggressive LUAD
(9, 10), suggesting that ERK promotes progression. This premise is also supported by
experiments in mouse models. When Kras mutation is combined with Trp53 or Lkb1 deletion and
given time for the spontaneous acquisition of additional mutations, high-grade metastatic cancer
develops (4, 8, 11-14). These aggressive cancers are associated with an increase in phospho-
ERK (11, 12). Increased ERK signaling is sufficient for progression. Paradoxical CRAF
activation, by inhibition of BRAFV600E in the context of KRAS activation, stimulates ERK activity
and accelerates tumor progression (15-17). How the initial low level of ERK activity transitions to
high activity sufficient for driving progression to metastasis is unclear. Plausible mechanisms
bioRxiv preprint
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https://doi.org/10.1101/2021.03.04.433941
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . include inactivation of the wildtype KRAS allele and loss of the ERK pathway negative feedback
loops (12, 18-20). LUAD presents with heterogeneous histopathology and differentiation states (21). In
addition to exhibiting high ERK activity, the most aggresive cancers are also poorly differentiated
(22, 23). Mouse models of LUAD suggest that de-differentiation involves transition through a
state of latent gastric differentiation, which is initiated by loss of the lineage transcription factor
NKX2-1/TTF-1 (24-26). |
In KRASG12D-driven tumors, NKX2-1 deletion increases phospho-ERK
and accelerates progression to invasive carcinoma and metastasis (8, 24, 27). Clinically, low
NKX2-1 expression portends a poor prognosis (28-30). We reasoned that in addition to beginning
the de-differentiation process, a repressed-NKX2-1 status might drive tumor progression by
releasing hyperactive ERK. We sought to identify the molecular mechanism that upregulates
ERK activity during NKX2-1 repression and the mechanism’s contribution to tumor growth and
invasion. ERK mediates negative feedback signaling to multiple upstream components of the RAS
pathway. In this way, cellular ERK activity is highly constrained, even in cells expressing mutant,
constitutively active KRAS (31). In vivo, release from negative feedback loops allows for elevated
ERK signaling that drives malignant progression (32-34). Negative feedbacks in LUAD include
ERK’s induction of Dual-specificity MAPK phosphatases (DUSPs) and SPROUTY proteins
(SPRYs) (33). The DUSP6 phosphatase specifically dephosphorylates the activation loop of ERK,
thereby inactivating ERK (35-37). DUSP6 is upregulated in early lung cancer lesions with
activating EGFR or RAS mutations (38) and is part of a five-gene signature that predicts relapse-
free and overall survival in patients with non-small cell lung cancer (NSCLC), of which LUAD is
the major subtype (39). DUSP6 expression is frequently lost in LUAD progression (40), which
suggests that reducing DUSP6 levels releases ERK activity for tumor-promoting signaling. Reduced DUSP6 can also promote resistance to ERK pathway inhibitors (41). SPRY proteins
inhibit ERK activity by inhibiting RAS and RAF activation (33, 42). Somatic KRASG12D mutation in
bioRxiv preprint
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https://doi.org/10.1101/2021.03.04.433941
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this version posted March 4, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . mice induces SPRY2 expression (18, 24). SPRY2 expression is reduced in human LUAD
compared to adjacent normal tissue (43). In mice, loss of Spry2 increases KRASG12D-driven tumor
burden (18). Thus, DUSP6 and SPRY2 are candidate RAS/ERK negative feedback loops that
must be overcome for progression to LUAD. KRAS mutations are inversely correlated with NKX2-1 expression (44, 45), suggesting
RAS-driven LUADs undergo selective pressure to lose NKX2-1. We previously found that
engineered deletion of NKX2-1 in KRASG12D-driven LUAD causes a 2-fold reduction in DUSP6
message (p=0.047) and a trend for a 2-fold reduction in SPRY2 message (p=0.103) (24). Further,
NKX2-1 and DUSP6 expression positively correlate in human LUAD tumor samples and cell lines
(46). Thus, we hypothesized that in RAS-driven LUAD, NKX2-1 silencing is a mechanism to
remove DUSP6 and/or SPRY2 expression and elevate ERK activity for tumor growth and
metastasis. |
We found that NKX2-1 loss in LUAD clinical samples and cell lines correlates with reduced
DUSP6. Further, NKX2-1 directly induces DUSP6 expression and limits tumor cell proliferation,
migration, invasion, and tumor growth metastasis. While DUSP6 knockout can be toxic, it
abrogates the effects of NKX2-1 on cell proliferation, migration and tumor growth. Inducing
DUSP6 in KRAS-driven tumors shows that the DUSP6-feedback loop is sufficient for NKX2-1
mediated tumor suppression. Thus, NKX2-1 tempers ERK activity and lung adenocarcinoma
progression through the induction of the ERK phosphatase DUSP6. Results
NKX2-1 transcriptionally induces DUSP6. We hypothesized that NKX2-1 promotes the DUSP6 or SPRY2 negative feedback loops
that temper ERK signaling in LUAD. In this model, NKX2-1 suppression would weaken the
negative feedback and release high RAS-driven ERK activity. Retention of NKX2-1 would
maintain low ERK activation and higher DUSP6 and SPRY2 expression. To test this, we first
bioRxiv preprint
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https://doi.org/10.1101/2021.03.04.433941
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this version posted March 4, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . compared the amounts of NKX2-1, DUSP6, and SPRY2 expression in human LUAD using RNA-
seq data from The Cancer Genome Atlas (2). We found that NKX2-1 mRNA levels positively
correlate with both DUSP6 and SPRY2 mRNA (Fig. S1A, DUSP6 r2=0.28, p=1.8 e-12, SPRY2
r2=0.33, p=1.2 e-16). The relatively low r2 values indicate that other factors also contribute to
DUSP6 and SPRY2 expression, which is expected given their regulation by multiple other
transcription factors including the ERK-activated ETS and CREB factors (20, 47-49). DUSP6 and
SPRY2 mRNA levels were highly correlated (Fig. S1B, r2=0.49, p=1.4 e-36), as they are both
similarly induced by ERK (33). We next tested if NKX2-1 protein expression correlates with
DUSP6 and/or SPRY2 in human samples of LUAD. We stained human tumor microarrays for
NKX2-1, DUSP6, and SPRY2 and scored their intensities on a scale of 0 (no staining) to 3+
(highest staining). Samples with NKX2-1 intensities of 2+ and 3+ contained significantly more
DUSP6 than samples lacking NKX2-1 (Fig. 1A, p=0.001 and 0.002). No association was found
between SPRY2 and NKX2-1 (Fig. 1A). Thus, NKX2-1 expression correlates with DUSP6 levels,
but not SPRY2 levels. We tested if NKX2-1 is sufficient to induce DUSP6 or SPRY2 mRNA and protein levels in
a panel of human LUAD cell lines that contain RAS oncogenes and silenced NKX2.1. Quantitative
reverse transcription PCR (qRT-PCR) showed that exogenous NKX2-1 expression increased
DUSP6 mRNA (A549 trend with p=0.069, H1299 p=0.003, H23 p=0.020, Fig. 1B). NKX2-1
increased SPRY2 mRNA in A549 and H1299 cells (p=0.026 and 0.034), but not in H23 cells
(p=0.414, Fig. 1C). Western blotting showed that re-expression of NKX2-1 induced DUSP6
protein levels (A549 p=0.033, H1299 p=0.007, H23 p=0.002, Fig. |
1D). NKX2-1 did not induce
SPRY2 protein in any cell line (A549 p=0.617, H1299 p=0.098, H23 p=0.493, Fig. 1D). We also tested if NKX2-1 induces DUSP6 or SPRY2 in a murine LUAD cell line derived
from KRASG12D; TP53Null; NKX2-1Null mouse tumors (3658 cells, (24)). Indeed, qRT-PCR again
showed that exogenous NKX2-1 increased Dusp6 mRNA (p=0.003), but not Spry2 mRNA
(p=0.238, Fig. 1E). Similarly, NKX2-1 increased DUSP6 protein levels (p=0.022), but not SPRY2
bioRxiv preprint
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https://doi.org/10.1101/2021.03.04.433941
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this version posted March 4, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . (p=0.140, Fig. 1F). Together, these data show that NKX2-1 induces DUSP6 consistently across
species and LUAD genotypes. Since NKX2-1 regulates DUSP6 expression, we sought to determine if NKX2-1 directly
controls DUSP6 transcription. The promoter region of DUSP6 is highly conserved in vertebrates
and located approximately 1000-250 bp upstream of the transcription start site (Mm, (48)). Our
previous chromatin immunoprecipitation-sequencing (ChIP-seq) of NKX2-1 in KRASG12D mouse
tumors showed NKX2-1 binds within the promoter region of DUSP6, with the greatest enrichment
between -600 and -400 (Fig. S1C, (24)). ChIP-seq of NKX2-1 in human LUAD lines showed
NKX2-1 binds promoters in areas proximal to AP-1 and Forkhead box (FOX) binding motifs (27,
46). We assayed in A549 cells the luciferase reporter activity of a control promoter region that
included the putative transcription start site at -463 and a portion of the putative NKX2-1 binding
region (191p, -550 to -359) and larger region that included the entire binding region for NKX2-1
(508p, -866 to -359). In the presence of NKX2-1, the 508p region exhibited 5 times more
transcriptional activity than the pGL3 vector (p=0.011, Fig. 1G). NKX2-1 did not induce the activity
of the shorter 191p region (p=0.275). Thus, in human lung adenocarcinoma, NKX2-1 appears to
directly induce DUSP6 gene expression and increase DUSP6 protein levels. NKX2-1 inhibits cell proliferation, migration, and invasion. If NKX2-1 inhibits tumor progression in part by inducing DUSP6 to suppress ERK activity,
then NKX2-1 should inhibit ERK-mediated and DUSP6-regulated cancer phenotypes, such as
cell proliferation, migration, and invasion, and tumor growth and dissemination. To test this
hypothesis, we assayed the proliferation of our NKX2-1-silenced LUAD cells complemented with
NKX2-1. NKX2-1 expression uniformly inhibited cell proliferation in A549, H1299, H23, and 3658
cells (Fig. 2A, S2A). We sought to test if NKX2-1 also inhibits cell migration and invasion. We previously found
that inhibition of MEK (and therefore ERK) reduces cell migration and persistence in multiple cell
bioRxiv preprint
doi:
https://doi.org/10.1101/2021.03.04.433941
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this version posted March 4, 2021. |
The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . lines, including A549 and 3658 cells (50). However, A549 and 3658 cells are poorly migratory
compared to other assayed lines (untransformed Cos7 and MDCK, and fibrosarcoma HT1080
cells (50)). Using a random walk migration assay and automated tracking, we found that of the
four KRAS mutant, NKX2-1 silenced LUAD cell lines, H1299 cells exhibit the fastest 2D motion,
with MSD velocity of 0.24 µm/min (Fig. S2B). We therefore used H1299 cells to test if NKX2-1
controls cell migration. NKX2-1 re-expression in H1299 cells slowed migration velocity from 0.33
µm/min in cells with empty vector to 0.19 µm/min in cells expressing NKX2-1 (p=1.8x10-9, Fig. 2B). Consistent with our prediction that NKX2-1 expression would phenocopy ERK inhibition,
NKX2-1’s inhibition of migration velocity was similar to treatment with the MEK inhibitor
Selumetinib, which reduced movement to 0.22 µm/min (p=7.1x10-6, Fig. 2B). We calculated the
persistence, or straightness of the trajectory, as a directionality ratio at each time point. A higher
score indicates more linear, productive motion. We found that NKX2-1 also reduces migration
directionality (Fig. 2C). We tested if NKX2-1 inhibits invasion in soft, 3D collagen matrices. We
embedded H1299 cells as spheroids in collagen I and tracked invasion velocity as the cells moved
out into the gel. We found that NKX2-1 re-expression slowed invasion velocity from 0.257 µm/min
to 0.177 µm/min (p=4x10-19, Fig. 2D, E). Thus, NKX2-1 inhibits migration on hard 2D surfaces
and invasion through a physiological 3D environment. We rationalized that if NKX2-1 induces DUSP6, then NKX2-1 expression would reduce
the amount of phospho- (p-) ERK. However, previous work in LUAD cell lines with mutant EGFR
or KRAS has shown that manipulation of DUSP6 has a more nuanced effect on p-ERK. After
DUSP6 knockdown, cells in culture show an initial, slight increase in p-ERK levels (1.5-fold within
24 hr), as expected from removing the negative regulator (38). However, the regulation is
transient. After 5 days of DUSP6 knockdown, cells in culture show decreased p-ERK and toxicity
(38). In normal human bronchial epithelial cells (NHBE), KRASV12 expression suppresses growth,
but surviving subpopulations with increased p-ERK emerge (40). When combined with dominant
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . negative DUSP6 mutant C293S, the surviving subpopulations appear to harbor even more p-ERK
(40). Thus, cells with RAS mutations adjust their feedback signaling to constrain p-ERK, and
long-term selective pressure can lead to genetic or epigenetic changes that allow cells to benefit
from reduced DUSP6 activity and increased p-ERK (31, 38, 40). |
In our LUAD cells with re-
introduction of NKX2-1, we found that the p-ERK induced by stimulation with RAS pathway
agonists EGF or PMA trended lower in cells expressing NKX2-1 compared to cells with empty
vector (Fig. S2C, S2D, not significant). The small change is consistent with the previous studies
on DUSP6. NKX2-1 induces DUSP6 and inhibits p-ERK during tumor progression. We further tested if NKX2-1’s inhibition of cancer phenotypes is associated with DUSP6
induction and ERK inhibition in vivo, in which more stringent biological pressures for growth and
survival model the pathway rewiring of tumorigenesis. Our and others’ previous studies in
transgenic mice showed that NKX2-1 limits KRASG12D-driven LUAD progression (24, 25, 27). We
also showed that Nkx2-1 deletion in KRAS-driven tumors reduces Spry2 expresion and induces
ERK activation (24). This experiment used KrasLA2; RosaCreERT2; Nkx2-1F/F mice, in which tumors
arise via spontaneous recombination of a latent KRasG12D allele and tamoxifen injection deletes
Nkx2-1 (5, 24). Since our qRT-PCR and Western data suggested that DUSP6 may be the more
significant ERK feedback effector in LUAD, we tested if DUSP6 is lost along with NKX2-1. We
used KRasfrtSfrt-G12D/+; Nkx2-1F/F; RosafrtSfrt-CreERT2 mice and intratracheal delivery of FlpO
recombinase to activate the KRasG12D oncogene and express CreERT2 Cre recombinase. Following 1 week of tumor initiation, mice were treated with Tamoxifen to activate Cre and delete
Nkx2-1. After 20 weeks of tumor initiation, lungs were harvested and NKX2-1 positive and
negative tumors were identified by immunohistochemistry (IHC) staining for NKX2-1 and its target
pro-surfactant protein B (pro-SPB, Fig. S3A). We found that indeed, NKX2-1 deletion reduces
DUSP6 along with reducing SPRY2 (Fig. S3A). In agreement with previous results, NKX2-1
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . deletion increased p-ERK and downstream effectors p-RSK and p-S6 and caused tumors to
transition to invasive mucinous adenocarcinoma (Fig. S3B). Thus, NKX2-1 is necessary for the
expression of DUSP6 and suppression of ERK during LUAD tumorigenesis. We note that NKX2-
1’s regulation of SPRY2 in the transgenic model contrasts with the LUAD cell lines. We next tested if NKX2-1 is sufficient for inhibition of tumor growth and metastasis,
induction of DUSP6, and inhibition of ERK. We generated subcutaneous tumors with the A549
cell line pairs and assessed primary tumor size and frequency of metastasis to the lung. Tumors
expressing NKX2-1 were significantly smaller by weight than tumors lacking NKX2-1 (A549-V
median tumor weight 0.64 g versus A549-NKX2-1 median 0.17 g, p=0.02, Fig. 3A). When we
harvested the lungs and manually examined sections for micrometastases, we found that NKX2-
1 reduces the occurrence of metastasis. |
5 out of 9 mice with A549-V tumors exhibited
micrometastases versus 0 out of 9 mice with A549-NKX2-1 tumors (Fig. 3B). IHC showed that
tumors lacking NKX2-1 exhibit low DUSP6 expression and high p-ERK (Fig. 3C). In contrast,
tumors with NKX2-1 reconstitution exhibited high DUSP6 expression and reduced p-ERK (Fig. 3C). SPRY2 was not regulated (Fig. 3C). We also tested the growth of H1299 cells transplanted orthotopically into mouse lungs. Our qRT-PCR and Western data showed that H1299 cells exhibit higher basal expression of
DUSP6 (Fig. 1). The cell line pairs with empty vector and NXK2-1 were infected for stable
expression of GFP-Luciferase, injected into the lung, and followed by bioluminescence imaging. After 5 weeks of growth, we found that tumors without NKX2-1 grew 18-fold (H1299-V normalized
median flux 1 photons/sec increased to 18.5 photons/sec, p=0.001, Fig. 3D, 3E). In contrast,
tumors expressing NKX2-1 did not exhibit significant growth (H1299-NKX2-1 normalized median
flux at 1 week versus 5 weeks, p=0.28, Fig. 3D, 3E). Rather, after 5 weeks H1299-NKX2-1 tumors
were 3 times smaller than H1299-V tumors (H1299-NKX2-1 average 6.1 photons/sec, p=0.04,
Fig. 3D, 3E). IHC showed a more complicated scenario of subpopulations with moderate and
high NKX2-1 expression (Fig. 3F). Cells with moderate NKX2-1 harbored high DUSP6
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . expression, but high p-ERK. In contrast, cells with high NKX2-1 harbored moderate increases in
DUSP6 and no p-ERK (Fig. 3F, arrows). SPRY2 levels were unchanged. We surmise that the
unexpected population with moderate NKX2-1, but high DUSP6 and p-ERK, is a resut of re-wiring
that strengthened the ERK®ETS®DUSP6 signal (20) relative to the NKX2-1®DUSP6 signal. In
sum, these A549 and H1299 transplant data are consistent with our hypothesis that NKX2-1-
mediated DUSP6 induction limits ERK activation to temper ERK-mediated tumor progression. NKX2-1 requires DUSP6 to inhibit cell proliferation and migration. We sought to directly test if NKX2-1 acts through DUSP6 or SPRY2 to control tumor cell
proliferation and migration. Previous work knocking down DUSP6 in NHBE and A549 cells did
not show regulation of cell growth, suggesting that knockdown efficiency was insufficient to
overcome KRAS-induced activation of ERK and expression of DUSP6 (40). Therefore, we
generated A549 and H1299 DUSP6 and SPRY2 CRISPR/Cas9 knockouts, confirmed by Western
(Fig. S4A, S4C). While DUSP6’s role as a negative feedback regulator of ERK suggests that
DUSP6 loss should result in faster cell proliferation and migration, complete loss of DUSP6 can
be toxic in LUAD cells that harbor KRAS and EGFR mutations (38). We found that in A549 cells,
DUSP6 and SPRY2 CRISPR/Cas9 knockout appeared to slow A549 cell proliferation by Day 5,
although the trend was not significant (Fig. |
S4B). In H1299 cells, DUSP6 loss did not change cell
proliferation (Day 6 p=0.38), but SPRY2 loss increased proliferation (Day 5 p=7.5x10-6, Fig. S4D). This suggests that in cell culture, A549 and H1299 cells engage compensatory signaling to limit
the effects of DUSP6 loss. However, under these unpressured growth conditions, SPRY2
tempers cell proliferation. We tested if NKX2-1 requires DUSP6 or SPRY2 to inhibit proliferation by introducing TRE-
NKX2-1 into DUSP6 and SPRY2 knockout clones of A549 and H1299 cells. Doxycycline induced
NKX2-1 expression and DUSP6 expression (Fig. 4A, B). As before, p-ERK was not significantly
changed in vitro (Fig. S5A, B). In A549 cells, NKX2-1 expression slowed the proliferation of the
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . control CRISPR cells 2.4-fold (Day 6, p=0.01), but not the already slow DUSP6 or SPRY2
knockouts (Fig. 4C). Similarly, in H1299 cells, NKX2-1 expression slowed the proliferation of the
control GFP CRISPR cells 1.6 fold (Day 6, p=0.03), but not the DUSP6 or SPRY2 knockouts (Fig. 4D). We conclude that NKX2-1 requires both DUSP6 and SPRY2 to fully suppress proliferation
in vitro. We tested if NKX2-1 regulates cell migration using the H1299 cells. DUSP6 knockout
slowed cell migration velocity (control CRISPR targeting GFP 0.34 µm/min versus DUSP6
knockout 0.19 µm/min, p=7.4x10-16) and reduced migration directionality (Fig. S4E, S4F). We
tested additional DUSP6 knockout clones and found they uniformly exhibited slower migration
velocity and reduced directionality (Fig. S4G, S4H). In contrast, SPRY2 knockout increased
migration velocity (0.52 µm/min, p=1.1x10-19, Fig. S4E). We then tested if NKX2-1 suppressed
migration in the absence of DUSP6 or SPRY2. Doxycycline-mediated NKX2-1 expression slowed
the migration of control CRISPR cells from 0.32 µm/min to 0.21 µm/min (p=1.5x10-13, Fig. 4E). NKX2-1 also slowed the migration of the SPRY2 knockouts (0.42 µm/min slowed to 0.23 µm/min,
p=8.5x10-17, Fig. 4E). However, NKX2-1 expression did not further slow the migration of the
DUSP6 knockouts. DUSP6 knockout migration of 0.14 µm/min was unchanged with NKX2-1,
p=0.67, Fig. 4E). Regulation of migration directionality followed a similar pattern (Fig. 4F). Thus,
NKX2-1 requires DUSP6, but not SPRY2, to inhibit cell migration. NKX2-1 drives tumor progression through DUSP6. While NKX2-1 required both DUSP6 and SPRY2 to control in vitro cell proliferation (Fig. 4C, 4D), NKX2-1 primarily induced DUSP6 expression in human samples and cell lines (Fig. 1)
and only DUSP6 was required for NKX2-1 suppression of migration (Fig. 4E). Thus, we
hypothesized that NKX2-1 acts through DUSP6 to promote LUAD progression in vivo. |
To test
this, we generated subcutaneous tumors with the A549 DUSP6 knockouts harboring inducible
TRE-NKX2-1 and GFP-luciferase. After the tumors reached 150-400 mm3 (5 weeks for control
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . tumors and 7 weeks for DUSP6 knockout tumors that initially grew more slowly), we induced
NKX2-1 expression with doxycycline. In the week before induction, control tumors grew 8.5-fold
(A549-GFP normalized median flux 1 photons/sec increased to 8.5 photons p=0.008) while
DUSP6 knockout tumors did not appreciably grow (Fig. 5A, B). After 3.5 weeks of NKX2-1
induction, control tumors had stopped growing, with final flux 3.3 photons. In contrast, DUSP6
knockout tumors exhibited the same slow growth trend (Fig. 5A, B). This indicates that NKX2-1
inhibition of tumor growth requires DUSP6. Lastly, we tested if DUSP6 or SPRY2 overexpression is sufficient to temper LUAD tumor
growth. We generated tumors in KrasLSL-G12D/+; Nkx2-1F/F control mice and KrasLSL-G12D/+; Nkx2-
1F/F CAG-rtTA3 mice (8, 51) using a dual promoter lentivirus that encodes constitutive Cre and
doxycycline-inducible Dusp6 or Spry2. The CAG-rtTA3 transgene drives ubiquitous expression
of the tetracycline-regulated transactivator gene. After 5 weeks of tumor growth, we used a
doxycycline diet to induce exogenous DUSP6 or SPRY2 in established KRASG12D, NKX2-1Null
tumors. After 1 week of treatment, we found that in control mice lacking rtTA3, the tumors
resembled those of our KRasfrtSfrt-G12D/+;Trp53frt/frt;Nkx2-1F/F mice: low DUSP6 expression and
high, uniform p-ERK (Fig. 5C wildtype, Fig. S3A, S3B NKX2-1Null). The tumors in mice with the
rtTA transgene showed heterogeneous HA-tagged DUSP6 and SPRY2 expression, likely due to
stochastic silencing of integrated lentivirus following tumor initiation (Fig. 5C, D). Individual
tumors that successfully induced DUSP6 with one week of doxycycline chow had regressed to
small lesions with low p-ERK that resemble the alveolar hyperplasia induced by Nkx2-1 deletion
alone (24). Interestingly, induction of DUSP6 caused some decrease in SPRY2, and induction of
SPRY2 caused some decrease in DUSP6 levels (Fig. S5C, D). This suggests that in vivo
pressures are actively constraining p-ERK. We quantified tumor burden in the lung by histological
analysis and found that DUSP6 expression reduced overall tumor burden (Fig. 5E). Consistent
with our previous finding that NKX2-1 deletion reduces SPRY2 (24) in mouse tumors, SPRY2
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It is made
available under a
CC-BY-NC-ND 4.0 International license . expression reduced tumor size and p-ERK in this system (Fig. 5F). In summary, DUSP6 and
SPRY2 are sufficient to limit tumorigenesis in the genetically engineered model of LUAD. Discussion
Activating mutations in EGFR, KRAS and BRAF initiate LUAD tumors by enhancing ERK
signaling to drive cell survival, proliferation, and migration (52). Mouse models of LUAD suggest
that an ERK pathway negative feedback must be weakened for early KRASG12D lesions to acquire
tumor-promoting ERK activity (5, 8). Discerning the mechanisms of negative feedback disruption
is a high priority, as they could be targeted to prevent tumor progression. One mechanism likely
involves KRAS allelic imbalance, in which mutant KRAS is amplified and the wildtype KRAS allele
is inactivated (12, 17). Allelic imbalance has been documented in the KRASG12D; p53Null tumors
in mice, in some human cancer cell lines (12, 53), and in 2% of human LUADs (19, 54). However,
how ERK activity is increased to levels that promote tumor progression in the remaining LUADs
and mechanisms specific to LUAD histopathologies and differentiation states are unknown. Here, we identify a mechanism of ERK feedback disruption specific to KRAS-driven
mucinous LUAD transitioning through dedifferentiation. NKX2-1 is expressed in alveolar type II
cells in the adult, the predominant cell of origin for LUAD (55-57). We show that NKX2-1 directly
induces DUSP6. 5-10% of LUADs are diagnosed as invasive mucinous (21) and loss of NKX2-1
leads to invasive mucinous adenocarcinoma with pulmonary to gastric transdifferentiation (24). We show in a genetically-engineered mouse model of LUAD that DUSP6 is downregulated
concomitant with NKX2-1. In LUAD cell lines and xenografts, NKX2-1 re-expression causes
DUSP6 upregulation. Further, DUSP6 is both necessary and sufficient for NKX2-1 to inhibit in
vivo ERK activity and tumor growth. These findings lead to the conclusion that in early KRAS
mutant lesions, NKX2-1’s induction of DUSP6 maintains the negative feedback signaling of the
RAS®RAF®MEK®ERK pathway and thereby limits ERK activity and tumor progression. NKX2-
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available under a
CC-BY-NC-ND 4.0 International license . 1 silencing and reduced DUSP6 levels would allow for increased ERK activity that promotes tumor
progrssion during the transdifferentiation process. In addition to DUSP6, SPRY2 also appears to play a role in ERK pathway feedback in
mouse LUAD. Previous analysis of human tumors in the TCGA found that DUSP6 was the only
ERK pathway negative-feedback regulatory gene expressed differently in tumors with KRAS or
EGFR mutations versus tumors without (38). Consistent with this human study, we found that
DUSP6, but not SPRY2 levels correlated with NKX2-1 in human LUAD samples. |
Further, DUSP6
but not SPRY2, protein was significantly upregulated upon introduction of NKX2-1 into human
LUAD cell lines, both in vitro and in xenografts. However, in the genetically engineered mouse
tumors, Nkx2-1 deletion caused a loss of SPRY2 along with a loss of DUSP6. This suggests that
NKX2-1 works through DUSP6 in the human disease, but in the KRASG12D mouse model NKX2-
1 may utilize both DUSP6 and SPRY2 to suppress tumor progression. Regardless of regulation
by NKX2-1 or not, SPRY2 contributes to the homeostatic regulation of proliferation. Loss of DUSP6 expression likely occurs more broadly across the LUAD histopathologies. In general, lower DUSP6 expression correlates with higher tumor grade and reduced overall
survival (58). In addition to being a transcriptional target of the ERK-induced ETS transcription
factors, and NKX2-1 shown here, DUSP6 is a target of p53 (59, 60). p53 loss occurs in 20-40%
of LUADs, except the recently defined proximal-proliferative subcluster (2, 44, 61). Thus, it will
be important for future studies to determine if p53 silencing participates in removing the DUSP6
negative feedback loop in non-mucinous LUADs. Different cell types in the lung have distinct thresholds for oncogenic versus toxic ERK
activity, suggesting that NKX2-1-regulated DUSP6 could contribute to a cell type-specific
transformation process. Alveolar type II cells transformed by dual KRas and BRaf mutations
suffer toxicity from excessively high p-ERK that occurs with loss of the wildtype BRaf allele (16). In contrast, club cells are transformed by the same high p-ERK and develop into intrabronchiolar
lesions (16). Our data suggest that in KRAS-transformed alveolar type II cells, silencing of NKX2-
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available under a
CC-BY-NC-ND 4.0 International license . 1 and the resulting reduction in DUSP6 levels increases p-ERK to a sweet spot of tumor-
promoting activity without toxic hyperactivity. However, complete knockout of DUSP6 in vitro had
the opposite effect: cell proliferation was unchanged and cell migration was slowed. These latter
data are consistent with previous in vitro findings that nearly complete knockdown of DUSP6 in
cells with RAS or EGFR mutations induces cell toxicity (38, 62, 63). Surprisingly, the same cells
showed tumor growth in vivo, suggesting that under selective pressure, the cells rewire to adopt
a p-ERK signal intensity that promotes growth and dissemination. NKX2-1’s regulation of DUSP6 to control ERK activation has therapeutics implications. We recently found that BRAFV600E; NKX2-1WT tumor cells exit the cell cycle when treated with
BRAF and MEK inhibitors, but NKX2-1-negative persister cells arrest within the cell cycle (64). Together, these data suggest that the NKX2-1-positive cells are addicted to the lower level of
ERK activity maintained in the presence of NKX2-1-induced DUSP6. |
Similarly, LUAD cells with
acquired resistance to EGFR inhibitors are addicted to the inhibitor-dampened ERK activity such
that inhibitor withdrawal induces toxic ERK hyperactivation (65). Lineage heterogeneity promotes
chemoresistance (26). Models of tumors with heterogeneous ERK activity show that ERK
heterogeneity contributes to differences in transcriptional states that promotes tumorigenesis
thorugh paracrine signaling (66). This suggests the acquisition of an NKX2-1-negative
subpopulation would contribute to both lineage and ERK activity heterogeneity, both of which
would complicate treatment. Therapeutic strategies to inhibit ERK activity need to attain a uniform
reduction in p-ERK, and upfront inhibition of MEK along with the upstream oncogene can forestall
resistance (41). Alternative strategies to inhibit DUSP6 and induce toxic ERK hyperactivation are
also under investigation (67, 68), but due to the heterogeneity and adaptability of the ERK
pathway system, will likely be most beneficial as agents that sensitize cells to chemotherapeutics
rather than stand-alone treatments. bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . Figure Legends
Figure 1. NKX2-1 transcriptionally induces DUSP6. A. Immunohistochemistry (IHC) intensity scores of DUSP6 and SPRY2 in human LUAD TMAs. Significance calculated with two-sided Dunnett test with 95% CI. B. and C. qRT-PCR of DUSP6
and SPRY2 upon NKX2-1 expression in human RAS mutant, NKX2-1-negative LUAD cell lines:
A549 KRASG12S n=3, H1299 NRASQ61K n=3, and H23 KRASG12C n=4 biological replicates. Values
are mean and SEM. D. Representative Westerns and quantification of DUSP6 and SPRY2 levels
upon exogenous NKX2-1 expression in LUAD cell lines. V = empty vector. Relative expression
compared to GAPDH. Means and SD from n=4 independent experiments for A549, n=3 for H1299
and H23. E. qRT-PCR of DUSP6 and SPRY2 upon NKX2-1 expression in mouse 3658 cells,
KRASG12D n=3. Mean and SEM. F. Representative Westerns of DUSP6 and SPRY2 levels upon
NKX2-1 expression in 3658 cells. Relative expression compared to GAPDH. Means and SD
from n=3 independent experiments. G. DUSP6 luciferase reporter assay using A549 cells. Mean
and SEM of normalized luciferase from n=3 independent experiments. Significance of
differences in means in RT-PCR, Western, and luciferase reporter data tested with one-way
ANOVA and Tukey-Kramer test, 95% CI. Figure S1. NKX2-1 expression correlates with DUSP6 and SPRY2 in human LUAD. A. and B. Correlation analyses of NKX2-1, DUSP6, and SPRY2 expression (TCGA LUAD). R
value is Pearson’s correlation coefficient. C. Representattive signal from ChIP-Seq data for
NKX2-1 at DUSP6 locus (MACS, (69)). Figure 2. NKX2-1 inhibits cell proliferation, migration, and invasion. |
A. Proliferation of A549 and H1299 cells. Significance from one-way ANOVA. B. and C.
Random-walk migration velocity and directionality of H1299 cells upon NKX2-1 expression and
MEK inhibition (Selumetinib 10 µM). Velocity is Mean Squared Displacement. Significance from
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available under a
CC-BY-NC-ND 4.0 International license . two-sample Kolmogorov-Smirnov (K-S) test. **** indicates p<0.0001. D. Representative images
from invasion assay. Central mass is spheroid and colored lines are tracks of invading cells. E.
Invasion velocity as distance/time from 4 independent experiments. Significance from K-S test. Figure S2. NKX2-1 inhibits cell proliferation, but p-ERK levels are normalized. A. Proliferation of H23 and 3658 cells with exogenous NKX2-1. Error bars are SEM. Significance one-way ANOVA, **** indicates p<0.0001. B. Random-walk migration velocity of
LUAD cell lines (A549 n=836, H1299 n=770, H23 n=515, 3658 n=916). Significance from two-
sample Kolmogorov-Smirnov (K-S) test, **** indicates p<0.0001. C. and D. Westerns and
quantification of active, phosphorylated (p-) ERK in LUAD cell lysates with exogenous NKX2-1. Relative expression compared to total ERK. V = empty vector. Error bars are SD. n=3 for each
cell line. Cells starved for 24 h. EGF 50 ng/ml stimulation for 10 min. Figure 3. NKX2-1 limits ERK activity and tumor growth and dissemination. A. Tumor weight from A549 cells transplanted subcutaneously in mice. p=0.02. Central line is
median. Lower and upper box limits are 1st and 3rd quartiles. Dashed bars span minimum and
maximum. Significance by Wilcox Mann-Whitney test. B. Number of mice (9 total per condition
of V or NKX2-1) with lung A549 micrometastases. Fisher’s exact test p=0.0294, Chi-square
p=0.0085. C. Representative images of hematoxylin and eosin (H&E) staining to show cells
and extracellular matrix, and IHC staining of NKX2-1, DUSP6, SPRY2, and p-ERK in primary
A549 tumors. Scale bar 100 µm. D. Tumor size in H1299 orthotopic transplants, indicated by
total luciferase flux and normalized to initial size at week 1. Blue tumors grew. Red tumors
shrank. Horizontal black lines show median. n=16 mice with V, 19 mice NKX2-1. Significance
by Wilcox Mann-Whitney test of medians. E. Representative H1299 IVIS. F. IHC of stable
H1299 cells transplanted orthotopically into mouse lungs. Representative images of H&E,
NKX2-1, DUSP6, SPRY2, and p-ERK stains. Scale bar 50 µm. bioRxiv preprint
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available under a
CC-BY-NC-ND 4.0 International license . |
Figure S3. NKX2-1 is required for DUSP6 expression and ERK activity in vivo. A. and B. Representative IHC images of KRASG12D;NKX2-1WT and KRASG12D;NKX2-1Null tumors:
NKX2-1 target pro-SPB, DUSP6, SPRY2, p-ERK and effector p-RSK and p-S6 levels. Scale bars:
100 µm. n=6 mice tamoxifen, n=6 mice corn oil injections. Figure 4. NKX2-1 requires DUSP6 to slow cell proliferation and migration. A. and B. Westerns and quantification of A549 and H1299 DUSP6 and SPRY2 knockouts (KOs)
with doxycycline (DOX) induction of TRE-NKX2-1. Relative expression compared to GAPDH. Means and SD from n=3 independent experiments. C. and D. Cell proliferation upon NKX2-1
induction in A549 and H1299 DUSP6 and SPRY2 KOs. Significance between uninduced and
DOX-induced NKX2-1 expression for each cell line calculated by one-way ANOVA. GFP and
DUSP6 KO significance at day 6. SPRY2 KO significance at day 5. n=3 experiments. E. and F.
H1299 cell migration velocity and directionality upon NKX2-1 induction. Significance from two-
sample Kolmogorov-Smirnov (K-S) test, **** indicates p<0.0001. Figure S4. DUSP6 and SPRY2 control LUAD cell proliferation and migration. A. Westerns and quantification of A549 DUSP6 and SPRY2 knockouts (KOs). B. Proliferation of
A549 DUSP6 and SPRY2 KOs. Error bars are SEM. Significance one-way ANOVA, n=3
experiments. C. Westerns and quantification of H1299 DUSP6 and SPRY2 KOs. D. Proliferation
of H1299 DUSP6 and SPRY2 KOs. Error bars are SEM. Significance one-way ANOVA, ****
indicates p<0.0001. n=3 experiments. E. and F. Random-walk migration velocity and directionality
of H1299 DUSP6 and SPRY2 KOs. Significance from two-sample Kolmogorov-Smirnov (K-S)
test, **** indicates p<0.0001. G. and H. Random-walk migration velocity and directionality of three
H1299 DUSP6 KO clones. Figure 5. NKX2-1 controls tumor progression through DUSP6. bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . A. Tumor size in A549 subcutaneous tumors, indicated by total luciferase flux and normalized to
initial size before induction of NKX2-1 with DOX. Horizontal black lines show median flux at 1
week before induction (week -1), at induction (week 0), and 4 weeks after induction (week 4). n=5
mice with control A549 cells with GFP CRISPR and 5 mice with A549 with DUSP6 knockout. Significance by Wilcox Mann-Whitney test of medians. B. IVIS images for time point in A. C. and
D. IHC for p-ERK in KN-rtTA3 mice infected with TRE-Dusp6 and TRE-Spry2, after 1 week of
doxycycline-mediated de-repression of rtTA3 and induction of DUSP6 or SPRY2. E. and F.
Tumor burden in mice with KRASG12D;NKX2-1Null; WT tumors and KRASG12D;NKX2-1Null; rtTA3
treated with TRE-Dusp6 and TRE-Spry2 for 1 week. Figure S5. A. and B. Westerns and quantification of p-ERK in A549 and H1299 DUSP6 and SPRY2 KOs
with DOX induction of TRE-NKX2-1. |
Relative expression compared to total ERK. Means and
SD from n=3 independent experiments. C. IHC for DUSP6 in TRE- Spry2 tumors and SPRY2 in
TRE-DUSP6 tumors. Methods
Histology and IHC
Human tissue microarrays were formalin fixed and paraffin embedded: US Biomax
BCS04017, BCS04017a, LC10014a. Arrays were stained for NKX2-1, DUSP6, and SPRY2 using
standard methods. Mouse lung lobes were fixed in 10% neutral buffered formalin, processed
through 70% ethanol, and paraffin embedded. Sectioning at 4 μm and slide processing, and H&E
staining was carried out by the HCI Research Histology Shared Resource. IHC was performed
manually using the Vectastain ABC Kit (Vector Laboratories) and Sequenza slide staining racks
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available under a
CC-BY-NC-ND 4.0 International license . (Electron Microscopy Sciences). Sections were treated with Bloxall (Vector labs) followed by
horse serum (Vector Labs, Burlingame, CA) or Rodent Block M (Biocare Medical, Pacheco, CA),
primary antibody, and HRP-polymer-conjugated secondary antibody (anti-rabbit, goat and rat
from Vector Labs; anti-mouse from Biocare. The slides were developed with Impact DAB (Vector)
and counterstained with hematoxylin. The following primary antibodies were used: NKX2-1
(EP1584Y, Abcam), DUSP6 (EPR129Y, Abcam), SPRY2 (EPR4318(2)(B), Abcam), pERK1/2
(D13.14.4E, CST), pRSK S380 (D3H11), pS6 S235/236 (D57.2.2E, CST), pS6 S240/244 (D68F8,
CST), and GFP (D5.1, CST). Images were acquired on a Nikon Eclipse Ni-U microscope with a
DS-Ri2 camera and NIS-Elements software or a 3D Histech Pannoramic Midi slide scanner with
Case Viewer Software. Tumor quantitation was performed on H&E-stained or IHC-stained slides
using NIS-Elements software. All histopathologic analysis was performed by a board-certified
anatomic pathologist (E.L.S.). Total tumor burden (tumor area/total area × 100%) and individual
tumor sizes were calculated using ImageJ. Plasmids
SPRY2 CRISPR
oligonucleotides
used
in H1299
cells
(gRNA
target
site:
GTACTCATTGGTGTTTCGGA) were cloned into pSpCas9(BB)-2A-Puro (Addgene plasmid
#62988, cloned by UofU HSC Mutation Generation and Detection Core). SPRY2 CRISPR
oligonucleotides used in A549 cells (gRNA target site: CGTACTGCTCCGCGACCCTG) were
cloned
into
lentiCRISPR v2 plasmid
(Addgene pasmid #52961). DUSP6 CRISPR
oligonucleotides used in H1299 cells were cloned into lentiCRISPR v2 plasmid (gRNA target site:
GGTATACATTCTGGTTG-GAA). pBabe H2B-mCherry-Ftractin-eGFP was cloned as follows: pBabePuro-Ftractin-eGFP
was first generated by amplifying Ftractin-eGFP from of Ftractin-C1-eGFP (Addgene #58473)
using the following primers: (For): AGGCGCGCCTGCCACCATGGCGCGACCACGG and (Rev):
GGAATTCCTTATTGTACAGCTCGTCCATGCCG. Amplified fragments were purified, digested
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The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . with AscI and EcoRI, and ligated into a modified pBabe-Puro retroviral backbone (Addgene
plasmid #1764 modified to insert FseI and AscI between BamHI and EcoRI within the MCS, using
the following primers (For): GATCCGGCCGGCCCCGCGGATCGATGGCGCGCCG and (Rev):
AATTCGGCGCGCCAT-CGATCCGCGGGGCCGGCCG, PMID: 21419341). Second, H2B-
mcherry was amplified from H2B mCherry-N2 (PMID 21098116) using the following primers:
(For): CCCAAGCTTGGGATGCCAGAGCCAGCGAAG and (Rev): CTAGCTAGCTAGCTTGT-
ACAGCTCGTCCATGCC. PCR products were purified and digested with HindIII and NheI and
ligated into the puromycin resistance cassette of pBabePuro-Ftractin-eGFP. Digested inserts and
vectors were purified and ligated using T4 DNA Ligase (NEB). For bacterial transformation, Stbl3
cells were used. Plasmid isolation was performed with the PureLink HiPure Plasmid Filter
Maxiprep Kit (Thermo Fisher Scientific). Insertion was confirmed by DNA sequencing. Lentiviral pCDH-TRE-DUSP6 and pCDH-TRE-SPRY2 were generated as follows:
Amplified Dusp6 and Spry2 fragments and empty pCDH-TRE-CRE vector were digested by PspXI
and NotI and ligated using T4 DNA Ligase (NEB). For bacterial transformation, Stbl3 cells were
used. Plasmid isolation was performed with the PureLink HiPure Plasmid Filter Maxiprep Kit
(Thermo Fisher Scientific). Insertion was checked by DNA sequencing. The pCDH-TRE-CRE
vector was generated by replacing the CMV promoter in pCDH-EF1-Cre (upstream of EF1-Cre,
PMID 25936644) with the TRE3G promoter so that cloned cDNAs can be expressed in an
rtTA/Doxycycline-dependent manner. The TRE3G promoter was amplified from the pTRE3G
vector (Clontech) and inserted into SpeI/EcoRI sites of pCDH-EF1-Cre. Key resources table:
Reagent type (species) or resource Genetic reagent (Mus musculus)
Designation
KRasfrtSfrt-G12D/+
Source or reference
Young et al. 2011 PMID 21512139
Identifiers
MGI 5007794
Additional information
Dr. Tyler Jacks (MIT,
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . Genetic reagent (Mus musculus)
Genetic reagent (Mus musculus)
Genetic reagent (Mus musculus)
Nkx2-1F/F
Kusakabe et al. 2006 PMID 16601074
Rosa26frtSfrt-CreERT2 Schonhuber et al. 2014 PMID 25326799
KRasLSL-G12D/+
Jackson et al. 2001 PMID 11751630
MGI 3653706
MGI 5616874
MGI 2429948
Cambridge, Massachusetts) Dr. Shioko Kimura (NCI, NIH, Bethseda, Maryland) Dr. Dieter Saur (Technische Universität München, München, Germany) Dr. Tyler Jacks (MIT, Cambridge, Massachusetts)
Genetic reagent (Mus musculus)
Tg(CAG-rtTA3)
Premsrirut et al. |
2011 PMID 21458673
MGI 4950589
Dr. Scott Lowe (MSK, New York, New York)
Cell line
293T
ATCC
# CRL-11268
Cell line Cell line
Phoenix-Ampho A549
ATCC ATCC
# CRL-3213 # CCL-185
Cell line
H1299
ATCC
# CRL-5803
Cell line Cell line
Virus
Plasmid
Plasmid
H23 3658
Ad5CMV-FlpO
MSCV-NKX2-1
pGL3-191p pGL3-508p
ATCC Snyder et al. 2013 PMID 23523371 University of Iowa Viral Vector Core Snyder et al. 2013 PMID 23523371 Ekerot et al. 2008 PMID 18321244
# CRL-5800
VVC-U of Iowa- 530HT
Dr. Eric Snyder (UofU, Salt Lake City, Utah)
Dr. Eric Snyder (UofU, Salt Lake City, Utah) Dr. Stephen Keyse, Jacqui Wood Cancer Centre, Dundee, Scotland, U.K.
Plasmid
pMIG Luc-IRES-GFP
Addgene
# 75021
Plasmid
plentiCRISPR v2
Addgene
# 52961
Antibody
Rabbit monoclonal anti-NKX2-1
Abcam
EP1584Y, #ab76013
IHC mouse and human (1:2000)
Antibody
Rabbit monoclonal anti-NKX2-1
Abcam
EPR8190-6, #ab133638
WB (1:1000)
Antibody
Antibody
Rabbit monoclonal anti-DUSP6
Rabbit monoclonal anti-SPRY2
Abcam
Abcam
EPR129Y, #ab76310
EPR4318(2)(B), #ab180527
IHC mouse and human (1:400) WB (1:500) IHC mouse (1:1000) IHC human (1:50)
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . Antibody
Antibody
Antibody
Antibody
Antibody
Rabbit polyclonal anti-pro-SPB Rabbit monoclonal anti-p-ERK Rabbit monoclonal anti-pRSK S380 Rabbit monoclonal anti-pS6 235/236 Rabbit monoclonal anti-pS6 240/244
Abcam
Cell Signaling Transduction Cell Signaling Transduction Cell Signaling Transduction Cell Signaling Transduction
#ab40876
D13.14.4E, #4370 D3H11, #11989
D57.2.2E, #4858 D68F8, #5364
WB (1:1000) IHC mouse (1:1000)
IHC (1:600)
IHC (1:300)
IHC (1:1000)
IHC (1:1000)
Antibody
Antibody
Antibody
Tissue Microarray
TaqMan Gene Expression Assay (FAM) TaqMan Gene Expression Assay (FAM) TaqMan Gene Expression Assay (FAM)
Mouse monoclonal anti-GAPDH Rabbit monoclonal anti-GFP Rabbit monoclonal anti-HA Lung adenocarcinoma
DUSP6 Hs04329643_s1
SPRY2 Hs00183386_m1
GAPDH Hs00266705_g1
Ambion
Cell Signaling Transduction Cell Signaling Transduction US Biomax
Thermo-Fisher
Thermo-Fisher
Thermo-Fisher
6C5, #AM4300 WB (1:8000)
D5.1, #2956S
IHC (1:200)
C29F4, #3724S
IHC (1:400)
BCS04017 BCS04017a LC10014a 4331182
4331182
4331182
Gene expression analysis
TCGA data for LUAD was not filtered for any demographic or clinical information. Upper
quantile normalized FPKM values vs standard FPKM values were log transformed (natural
log). Pearson correlation (r values) were generated using the base R function “cor”. Significance
was determined using the base R function “cor.test”. Fitted lines were generated using the base
R function “lm”. Cell lines and cell culture
A549, Phoenix-Ampho, H1299, H23, and 293T cells (ATCC) were cultured in DMEM/High
Glucose medium (Gibco) with 5% FBS (Sigma). |
Cells were tested for mycoplasma every 3-4
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . months. Mycoplasma-negative cells were used for all experiments. 3658 cells were described
previously (24, 50). Cell line identity was authenticated using STR analysis at the University of
Utah DNA Sequencing Core. CRISPR/Cas9 knockout cells were generated by Cas9/CRISPR
transfection, 2-day puromycin selection, dilution cloning, and sequencing. Retro- and lentivirus production and cell line infection
Retrovirus MSCV and MSCV-NKX2-1 and were produced by transfection of HEK293T
cells with TransIT-293 (Mirus Bio, Madison, WI) with the retrovirus backbone as well as packaging
vectors gag/pol and CMV-VSV-G. Transfections were carried-out in Opti-mem (Thermo Fisher
Scientific) following Mirus’s TransIT-293 protocol. pBabe-H2B-mCherry-Ftractin-eGFP retrovirus
was produced by transfection of Phoenix-Ampho cells. 24 hours after transfection, the media in
the virus-producing cells was exchanged for DMEM with 30% FBS. Supernatant was collected
at 48, 60, and 72 hr after transfection, filtered through a 0.45 µm filter, and used with 8 µg/ml
polybrene to infect target cell lines once per day for three days. Following MSCV-NKX2-1
infection, cells were selected with 2.5 µg/ml puromycin for 1 week to achieve stable transduction. To generate H1299 cells expressing H2B-mCherry and NKX2-1, cells were first infected with
pBabe-H2B-mcherry-Ftractin-EGFP retrovirus and sorted for mCherry and eGFP co-expression
by flow cytometry. Positive cells were then infected with MSCV-EV or MSCV-NKX2.1 retrovirus
and selected with puromycin. To generate the H1299 cells expressing luciferase, cells were
infected with pMIG-luciferase-IRES-GFP and sorted for GFP expression. Cells infected with
MSCV-EV or -NKX2-1 were maintained in 0.5 µg/ml puromycin prior to use for experiments. Lentivirus pCW22-TRE-NKX2-1, pCDH-TRE-DUSP6, pCDH-TRE-SPRY2, and pMIG-
luciferase-IRES-GFP was produced by transfection of 293T cells with TransIT-293 (Mirus Bio,
Madison, WI), lentiviral backbone as well as packaging vectors Δ8.9 (gag/pol) packaging vector
and CMV-VSV-G envelope vector. Supernatant was collected at 36, 48, 60 and 72 hrs after
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . transfection. pCW22-TRE-NKX2-1 was used to infect A549 and H1299 DUSP6 and SPRY2
CRISPR knockout cells in the presence of 8 µg/ml polybrene for 24 hrs. |
Cells were selected with
blasticidin for 1 week. NKX2-1 expression was induced in cells with 1 µg/ml of doxycycline and
confirmed by Western blotting. Ultracentrifugation at 25,000 rpm for 2 hrs was necessary to
concentrate pCDH-TRE-DUSP6 and pCDH-TRE-SPRY2 virus for in vivo infection. Fluorescence-activated cell sorting (FACS)
Cells were sorted for GFP/mCherry double positivity (H2B-mCherry-Ftractin-eGFP) or
GFP positivity (Luciferase-IRES-GFP). Cells were suspended in DMEM, 5% FBS supplemented
with penicillin/streptomycin (Pen/Strep) and sorted using a FacsAria cell sorter (BD Biosciences)
with help from the UofU Flow Cytometry Core. Real-time PCR (qRT-PCR)
Total RNA was extracted using TRIzol with the PureLink RNA mini kit (Thermo Fisher
Scientific). RNA was treated with on-column RNase-Free DNAse I digestion (Invitrogen). cDNA
was synthesized from 500 ng of extracted RNA using the iScript reverse transcription Supermix
(Bio-Rad). Real-time quantitative PCR was performed in 4 technical replicates with the
SsoAdvanced Universal Probes Supermix (Bio-Rad) and detected with a Bio-Rad CFX Connect
Real-Time PCR detection system. Gene expression was calculated relative to GAPDH (loading
control) using the 2-(dCt.x-average(dCt.control)) method and normalized to the control group for graphing. TaqMan Assays used in this study are listed in table above. Immunoblot analysis (Western, WB)
Cells were washed once with cold PBS and then lysed on ice using RIPA buffer (10 mm
Tris-Cl, pH 8.0, 1 mm EDTA, 0.5 mm EGTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1%
SDS) supplemented with 1 mM sodium orthovanadate, 1 mM phenylmethylsulfonyl fluoride, 5
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . μg/ml leupeptin, 5 μg/ml aprotinin, and 5 μg/ml pepstatin A. Protein extracts were clarified by
centrifugation at 14,000 rpm for 10 min to remove cell debris and concentrations were measured
with Pierce Coomassie Plus Protein Assay reagent (Thermo Fisher Scientific). Lysates were then
subjected to SDS-PAGE gels (BioRad), and transferred to a Nitrocellulose membrane. Membranes were probed with primary antibodies against DUSP6 (EPR129Y, Abcam), SPRY2
(EPR4318(2)(B), Abcam), NKX2-1 (EPR8190-6, Abcam), and GAPDH and subsequently with
DyLight fluorochrome-conjugated secondary antibodies (Thermo Fisher Scientific). Signal was
visualized with the Odyssey CLx Imaging System (LI-COR). p-ERK Westerns were carried out in
cells starved of serum for 24 hours and stimulated with epidermal growth factor (EGF, 50 ng/ml)
or phorbal 12-myristate (PMA, 40 ng/ml). Luciferase assay
A549 control cells and cells re-expressing NKX2-1 were plated in a white 96-well
polypropylene assay plate (Corning) at a density of 10,000 cells per well, 3 replicate wells per
condition. |
Cells were transfected the next day with pRenilla (Addgene) and empty pGL3 vector
or pGL3 vector containg the DUSP6 promoter regions 191p and 508p using Lipofectamine 2000
(Invitrogen). After 24 hours the Dual-Glo Luciferase Assay System (Promega) was used and
luminescence was measured by the Synergy HT microplate reader (Biotek). Proliferation assay
Proliferation was assessed by either manual counting with trypan blue in triplicate or with
Janus Green B (Sigma Aldrich #2869-83-2) with four technical replicates read on an Epoch2
(Biotek) plate reader at 620 nm. 2D migration assay and analysis
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . Cells were plated on acid-etched glass-bottomed 12-well plates (Matek). Two days after
plating and just before imaging, medium was changed to Fluorobrite (Invitrogen) with 10% FBS
and 20 mm HEPES and stained with DRAQ5 (ThermoFisher, 2 µM) to label the nuclei for tracking. Cell migration was imaged by phase contrast microscopy, at 37 °C, 5% CO2 on a Nikon Ti inverted
microscope with a Plan Fluor ELWD 20× air objective and an environmental chamber, at the UofU
Cell Imaging Core. Images were acquired with a Zyla cMOS camera using Elements every 10
mins over 15 h. Cells were tracked automatically in MATLAB using custom software based off of
u-track multiple-particle tracking. The explored surface area explored by each tracked cell was
calculated as the Mean Squared Displacement (MSD), the average square displacement between
positions on a migration trajectory over increasing time intervals. Cell motion was tested for
persistent random walk, in which the mean squared displacement increases in a superdiffusive
manner: MSD(t) ∝ tα, where 1<α<2). Only cells exhibiting persistent random walk were included
in the velocity and directionality calculations. Significance was calculated using the two-sample
nonparametric Kolmogorov–Smirnov test at 5% significance, which compared the continuous,
one-dimensional probability distributions. 3D invasion assay and analysis
H1299 cells were stably infected with pBabe-H2B-mCherry-Ftractin-eGFP and then
infected with MSCV EV or NKX viral vectors. Spheroids were prepared with 3000 cells seeded
in a nucleon Sphera U bottom plate (Thermo Scientific). After 3-4 days, spheroids were
sandwiched between two 8 ul layers of a 2 mg/ml Collagen I gel in 15 well µ-slides (Ibidi, µ-slide
angiogenesis #81501) (70). Reagents, plates, and tips were chilled to prevent early gelation. Collagen pre-gel was prepared with 4mg/ml Rat-tail collagen (Advanced Biomatrix) mixed 1:10
with 10x DMEM (Gibco), neutralized with 1 M NaOH to a pH of 7.5, and diluted to 2 mg/ml with
1x DMEM (Gibco). The first layer of Collagen was polymerized at 37°C for 10 minutes. |
Spheroids
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . were suspended in 8 µl of collagen I pre-gel and incubated for 30 min at 37°C, media was added
to cover the spheroid (1:1 DMEM:F12 phenol red free media (Gibco), with 10% FBS, 20 mM
HEPES, and 1% Pen/Strep), and encapsulated spheroids were further incubated for 4-6 hours. Spheroids were imaged live on a Leica SP8 WLL confocal microscope equipped with live cell
imaging chamber and Plan Apo PL APO 10X/0.40 objective (150 µm Z-stacks for a total Z of 2
µm). CO2 was maintained at 5% and temperature at 37°C. Images were acquired every 20
minutes for 14-18 hours. Cell invasion away from the spheroid was tracked live. Individual Cell
tracks of cells that left the spheroid were analyzed using the Imaris “spots function” in Imaris,
which identifies moving cells from background noise in motion (est. particle diameter 22 µm,
quality > 1.5, autoregressive motion, max distance 20 µm, max Gap Size = 1, filtered for tracks
greater than 1000 sec and track length greater than 50 µm to reduce noise). Erroneous tracks
in the spheroid center were removed by including filters for Ar1Mean, Track Displacement Length,
and Distance from image border. Any remaining broken tracks were corrected manually. Tumor xenograft and analysis
With the University of Utah PRR Core, IACUC #18-11004, subcutaneous tumors were
generated and monitored as follows: 5 x106 A549 cells, expressing MSCV-NKX2-1 were mixed
with Matrigel and injected subcutaneously into the flank of NSG mice (both male and female
mice). Tumors were resected and weighed after 6 weeks. Lungs were harvested, sectioned,
stained by H&E (Core-ARUP look AP Histology Research), and examined manually by board-
certified pathologist (E.L.S.) for the presence of micrometastases. For CRISPR knockouts, A549
cells with CRISPR-knockout of Gfp (control) or Dusp6 were expressing both doxycycline (Dox)-
inducible TRE-NKX2-1 and luciferase-IRES-GFP. Tumor burden was monitored weekly by
caliper and bioluminescence measurements (IVIS). After reaching 150-400 mm3 tumor volumes,
the mice were fed Dox (Envigo) for 4 weeks. bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . For the orthotopic tumors, H1299 cells were expressing luciferase-IRES-GFP and MSCV-
NKX2-1, and 0.5x106 cells were mixed with Matrigel and transplanted into the lungs of NOD-
SCID mice by intrathoracic injection. Tumor burden was monitored weekly from bioluminescence
by IVIS Spectrum and reported as background-corrected total flux, which is average radiance
(flux per unit area and unit solid angle) integrated over the region of interest. |
Genetically engineered mice and tumor initiation
Under University of Utah IACUC #18-08005, mice harboring KRasfrtSfrt-G12D/+;Nkx2-1f/f;
RosafrtSfrt-CreERT2 alleles were infected intratracheally with 1 x 107 pfu/mouse Ad5CMV-FlpO
(University of Iowa Viral Vector Core). FlpO recombined Frt-STOP-Frt for simultaneous KRASG12D
and Cre-ER expression. After 1 weeks, Tamoxifen was used to activate CreERT2 in established
lesions and delete Nkx2-1. Tamoxifen was delivered by intraperitoneal injection of 120 mg/kg
tamoxifen (Sigma), dissolved in corn oil. Mice received a total of 6 injections over the course of
9 days. Lungs were harvested 20 weeks after tumor initiation. KrasLSL-G12D/+; Nkx2-1F/F mice and KrasLSL-G12D/+; Nkx2-1F/F ; CAG-rtTA3 mice were infected
intratracheally with 4x104 pfu/mouse lentivirus pCDH-TRE-DUSP6 or pCDH-TRE-SPRY2 that
encoded 1) doxycycline inducible Dusp6 or Spry2 and 2) constitutive Cre. The TRE promoter
requires the tetracycline-controlled transactivator for expression. In mice with the CAG-rtTA3
transgene, ubiquitous expression of the third-generation rtTA reverse tetracycline-regulated
transactivator gene repressed expression. The doxycycline diet (Envigo, 625 mg/kg dose) was
administered for 1 week, which released the rtTA repression and induced exogenous DUSP6 or
SPRY2 in established tumors. Tumor burden was quantified by histological analysis. Statistics
Significance’s calculated in MATLAB: two-sided Dunnett test, one way ANOVA with Tukey-Kramer
post-hoc test, two-sample Kolmogorov-Smirnov (K-S), or Wilcox Mann-Whitney as appropriate. bioRxiv preprint
doi:
https://doi.org/10.1101/2021.03.04.433941
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this version posted March 4, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . Correlation analyses and Pearson’s correlation coefficient calculated in R. Not significant (NS)
for p>0.05, * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. Acknowledgements
Thanks to Dr. Stephen Keyse for the gift of the DUSP6 promoter constructs and to Dr. Doug
Mackay for the gift of H2B-mCherry-N2. Flow cytometry work was supported by the University
of Utah Flow Cytometry Facility and the National Cancer Institute through 5P30CA042014-24
and by the National Center for Research Resources under 1S10RR026802-01. Cell imaging
was supported by the University of Utah Cell Imaging Core. Xenografts were supported by the
Huntsman Cancer Institute Preclinical Research Resource. M.C.M was supported by
K01CA168850, an American Lung Association Research Grant, American Cancer Society RSG
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CC-BY-NC-ND 4.0 International license
. A
B
C
DUSP6 Expression
SPRY2 Expression
DUSP6 SPRY2 ***
y t i
2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
V NKX2-1
V NKX2-1
s n e t n
**
40 30 20 10 0
ns
2.0
**
e g n a h C d o F
ns
ns
I
e g n a h C d o F
l
1.5
a n g S C H
1.0
i
0.5
2.0 1.5 1.0 0.5 0
l
l
0
I
A549
H1299
H23
n a e M
0
1+
2+
3+
A549
H1299
H23
NKX2-1 Intensity
F
n= 35 n= 33
26 17
23 28
13 18
3658
X 2 - 1
E
Expression DUSP6 SPRY2
K
N
V
V NKX2-1
40
ns
D
**
2.5
DUSP6
A549
H1299
H23
X 2 - 1
X 2 - 1
X 2 - 1
e g n a h C d o F
2.0 1.5
40
SPRY2
K
K
K
V N
V N
V N
NKX2-1
40
40
40
40
1.0 0.5
DUSP6
40
GAPDH
40
40
l
0
40
! SPRY2
2
3658
35 40
6 P S U D
40
1
NKX2-1
G
40
DUSP6 Promoter
0
40
GAPDH
35
35
ns
10 9 8 7 6 5 4 3 2 1 0
3 2
**
0
ns
2 Y R P S
**
**
ns
2
3 2
15
1 0
ns
6 P S U D
e s a r e f i c u L
10
1
V NKX2-1
V
NKX2-1
1 0
5
0
0
ns
ns
ns
2
2 Y R P S
2 1 0 0 0 0
10 5 0
2 Y R P S
1
0
V
NKX2-1
V
NKX2-1
V
NKX2-1
Figure 1, Ingram et al. pGL3
191p 508p
bioRxiv preprint
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
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. A
B
C o 1.0
A549
H1299
H1299 Migration
H1299
120
160 140 120 100 80 60 40 20
1.0
V NKX2-1
V NKX2-1
)
****
* * *
V NKX2-1 MEKi
n m m μ
i t a R y t i l
100
#
i
0.8
0.8
****
l l
* * *
80
e C e v i t a e R
/
0.6
0.6
60
a n o i t c e r i D
(
y t i c o e V
0.4
0.4
40
l
0.2
0.2
20
l
0.0
0
0
0.0 0 Time Interval (min)
1
2
4 3 Day
5
6
1
2
4 3 Day
5
6
V - 164
NKX2-1 - 152
V MEKi 159
200
400
600
trmt: n:
D
Time:
0 h
15 h
E
160 140
Invasion
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
)
****
n m m μ
V
i
100 6080 40
/
Time 0h 15h
(
70 μm
y t i c o e V
20 0
1 - 2 X K N
l
V 664
NKX2-1 892
n:
Figure 2, Ingram et al. bioRxiv preprint
doi:
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license
. A A549 Tumors *
B Lung Micrometastases
C
p-ERK
H&E
NKX2-1
DUSP6
SPRY2
2.0
p=0.02
+
1.6
'),
)
g
V
1.2
(
4
5
')%
V
t h g e W
0.8
1 - 2 X K N
()+
i
9
0
0.4
()*
1 - 2 X K N
0
V
NKX2-1
H1299 tumors ***
D
E
2500 F
1 wk
5 wk
100
H&E
p-ERK
DUSP6
NKX2-1
SPRY2
ns
2000 1500 1000 500 100
x u F d e z
l
V
10
V
i l
a m r o N
1 - 2 X K N
1
1 - 2 X K N
0.1
Week: 1
5
1 NKX2-1
5
V
Figure 3, Ingram et al. |
bioRxiv preprint
doi:
https://doi.org/10.1101/2021.03.04.433941
;
this version posted March 4, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license
. A
B
DUSP6 KO +
SPRY2 KO +
DUSP6 KO +
SPRY2 KO +
A549
H1299
GFP - +
GFP - +
DOX (NKX2-1):
DOX (NKX2-1):
40
NKX2-1
40
NKX2-1
40
40
DUSP6
DUSP6
35
SPRY2
SPRY2
GAPDH
35
35
***
****
GAPDH
ns
ns ns
6 P S U D
35 6 P S U D
+DOX (NKX2-1)
8 4 0
ns
**
4
***
ns ns
+DOX (NKX2-1)
2
ns
ns
ns ns **
ns
0 2
ns
ns
ns
2 Y R P S
ns
2
2 Y R P S
***
1
1
0
0
GFP4
DUSP6 KO
SPRY2 KO
GFP4
DUSP6 KO A549 Proliferation
SPRY2 KO
C
D
H1299 Proliferation GFP4 GFP4 + NKX2-1 DUSP6 KO DUSP6 KO + NKX2-1 SPRY2 KO SPRY2 KO + NKX2-1
e c n a b r o s b A e v i t a e R
e c n a b r o s b A e v i t a e R
GFP GFP + NKX2-1 DUSP6 KO DUSP6 KO + NKX2-1 SPRY2 KO SPRY2 KO + NKX2-1
11
15 13 11 9 7 5 3 1
ns
9
* * *
ns
7
5
ns ns
3
1
l
l
1
2
3 Days
4
5
6
1
2
3 Days
4
5
6
E
F
H1299 Migration ns
H1299 Migration
1.0
GFP GFP + NKX2-1 DUSP6 KO DUSP6 KO + NKX2-1 SPRY2 KO SPRY2 KO + NKX2-1
1.0
****
****
)
n m m μ
****
0.8
0.9
o i t a R y t i l
i
****
0.8
/
0.6
0.7
(
y t i c o e V
0.6
a n o i t c e r i D
0.4
0.5
l
0.2
0.4
0.3
0.0
0.2
GFP
DUSP6 KO SPRY2 KO
0
600 Time Interval (min)
200
400
NKX2-1: n:
+ 362
236
383
+ 209
377
+ 308
Figure 4, Ingram et al. bioRxiv preprint
doi:
https://doi.org/10.1101/2021.03.04.433941
;
this version posted March 4, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license
. A549 Tumors
A
B
GFP
DUSP6 KO
100
ns
ns
ns
**
x u F d e z
ns
ns
1 wk pre-treat
l
10
i l
a m r o N
1
start DOX
0.1
0 -1 DUSP6 KO
4
DOX week:-1
0 GFP
4
+ DOX (NKX2-1) 4 wk
C Virus: Transgene:
D
E
F
TRE-DUSP6 WT
TRE-SPRY2 WT
TRE-DUSP6 **
TRE-SPRY2
rtTA3
rtTA3
n e d r u B r o m u T %
n 30 e d 25 r u 20 B 15 r o 10 m u 5 T 0%
25
**
20
HA (SPRY2)
HA (DUSP6)
15
10 Lorem ipsum 5
0
WT rtTA3
WT rtTA3
p-ERK
p-ERK
Figure 5, Ingram et al. |
bioRxiv preprint
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Caspase-2 regulates S-phase cell cycle events to protect
from DNA damage
accumulation independent of apoptosis
Ashley Boice,1,2,3 Raj K Pandita,3 Karla Lopez, 1,2,3 Melissa J Parsons,1 Chloe I
Charendoff,1,2 Vijay Charaka,4 Alexandre F. Carisey,2 Tej K. Pandita3,4 and Lisa Bouchier-
Hayes1,2,3
1Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine,
Houston, TX 77030, USA. 2William T. Shearer Center for Human Immunobiology, Texas Children’s Hospital, Houston, TX
77030, USA. 3Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030,
USA. 4Department of Radiation Oncology, Houston Methodist Research Institute, Houston, TX 77030,
USA. Corresponding author:
Lisa Bouchier-Hayes
E-mail: bouchier@bcm.edu
1
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Caspase-2, cell cycle, apoptosis, DNA replication fork, S-phase
2
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Caspase-2 regulation during cell division
3
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In addition to its classical role in apoptosis, accumulating evidence suggests that caspase-2 has
non-apoptotic functions, including regulation of cell division. Loss of caspase-2 is known to
increase proliferation rates but how caspase-2 is regulating this process is currently unclear. We
show that caspase-2 is activated in dividing cells in G1- and early S-phase. In the absence of
caspase-2, cells exhibit numerous S-phase defects including delayed exit from S-phase, S-
phase-associated chromosomal aberrations, and increased DNA damage following S-phase
arrest. In addition, caspase-2-deficient cells have a higher frequency of stalled replication forks,
decreased DNA fiber length, and impeded progression of DNA replication tracts. This indicates
that caspase-2 reduces replication stress and promotes replication fork protection to maintain
genomic stability. These functions are independent of the pro-apoptotic function of caspase-2
because blocking caspase-2-induced cell death had no effect on cell division or DNA damage-
induced cell cycle arrest. |
Thus, our data supports a model where caspase-2 regulates cell cycle
events to protect from the accumulation of DNA damage independently of its pro-apoptotic
function. 4
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Caspase-2 is a member of the caspase family of proteases that have essential roles in the
initiation and execution of apoptosis.1 Caspase-2 has been implicated in apoptosis in response
TO a variety of cell stressors including: DNA damage, heat shock, metabolic stress, and
endoplasmic reticulum (ER) stress.2 However, the requirement for caspase-2 for cell death in
each of these contexts has been subject to debate.3 Despite this, caspase-2 has been shown to
function as a tumor suppressor in several murine models. Caspase-2-deficient mice show
accelerated
tumorigenesis
in murine models of hematological cancers (Eµ-Myc-driven
lymphoma4 and Atm knockout-associated lymphoma5), and solid tumors (Kras-driven lung
tumors6 and MMTV-c-neu-driven mammary tumors7). Caspase-2-deficient tumors from these
mice often showed increased features of genomic instability, including aneuploidy,5 and cell
cycle defects, such as bizarre mitoses and an increased mitotic index.7 Interestingly, caspase-2-
deficient tumors have shown minimal differences in apoptosis compared to wild type tumors5, 6
suggesting that, in addition to inducing apoptosis, caspase-2 may carry out its tumor
suppressive function by regulating other cellular functions such as cell cycle. Although it has been postulated that caspase-2 plays a role in cell cycle arrest or cell cycle
checkpoint regulation,4 the exact phase of the cell cycle where caspase-2 primarily functions
remains unclear. Caspase-2 knockout cells proliferate at higher rates.4, 8 In addition, caspase-2
deficiency is associated with impaired cell cycle arrest in response to DNA damage.4 The
activation platform for caspase-2 is a large molecular weight protein complex called the
PIDDosome, comprising of the proteins PIDD and RAIDD.9 PIDD overexpression induces
growth suppression that dependent on RAIDD and partially dependent on caspase-2.4, 10 PIDD
is a p53 target gene and PIDD can induce growth arrest in cells that are wild type for p53 but
not in cells where p53 is absent or mutated.11 Caspase-2 induces p53-dependent cell cycle
5
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stabilization. Because these studies place caspase-2 upstream of p53, it is possible that
caspase-2 can also regulate cell cycle in a p53-independent manner. |
This has been noted for
caspase-2 dependent apoptosis that can be either p53-dependent12-14 or p53-independent.15, 16
Caspase-2 has been shown to be activated by several different inducers of DNA damage
including etoposide, cisplatin, and camptothecin.13, 17-19 However, apoptosis can often proceed
in the absence of caspase-2 in response to these triggers and, when apoptosis is reduced, it is
rarely completely blocked by the absence of caspase-2.17, 18, 20 In particular, while caspase-2 is
efficiently activated by topoisomerase I inhibitors such as camptothecin, death in response to
camptothecin is only reduced by around 50% in caspase-2-deficient cells.17 Of note, these
drugs are also potent inducers of cell cycle arrest.21, 22 Inhibition of topoisomerase I triggers both
cell cycle arrest and apoptosis following stalling of DNA replication forks.23 Fork stalling and fork
collapse results in single strand DNA breaks that, in the absence of repair, are converted to
double-strand breaks (DSBs), serving as a trigger for cell death or cell cycle arrest.24
Here, we demonstrate that in response to replication stress caspase-2 plays a key role in
protecting from stalled replication forks and the subsequent DNA damage. Caspase-2 is
activated during G1 and early S-phase and loss of caspase-2 is associated with several
additional S-phase-related cell cycle defects
including S-phase specific chromosomal
aberrations and delayed exit from S-phase following arrest. In addition, we show that these
defects in cell cycle regulated events are independent of caspase-2’s ability to induce apoptosis. 6
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Chemicals and antibodies
The following antibodies were used: anti-Caspase-2 (clone 11B4 from Millipore); anti-phospho-
ATM (Ser1981) (clone 10H11 from Invitrogen and clone 10H11.E12 from Cell Signaling
Technology), anti-ATM (clone D2E2 from Cell Signaling Technology), anti-phospho-ATR
(Ser428) (polyclonal from Cell Signaling Technology), anti-phospho-ATR (Thr1989) (polyclonal
from Cell Signaling Technology), anti-ATR (clone E1S3S from Cell Signaling Technology), anti-
phospho-Chk1 (Ser317) (clone D12H3 from Cell Signaling Technology), anti-Chk1 (clone
2G1D5 from Cell Signaling Technology), anti-phospho-Chk2 (Thr68) (clone C13C1 from Cell
Signaling Technology), anti-Chk2 (polyclonal from Cell Signaling Technology), anti-caspase-3
(polyclonal from Cell Signaling Technology), anti-actin (C4 from MP Biomedicals),
γ
H2AX (EMD
Millipore). All cell culture media reagents were purchased from Thermo Fisher (Carlsbad, CA,
USA). Unless otherwise indicated, all other reagents were purchased from Sigma-Aldrich (St.
Louis, MO, USA). Cell culture and cell lines
HeLa cells were grown in Dulbecco’s Modified Essential Medium (DMEM) containing FCS (10%
(v/v)), L-glutamine (2 mM), and Penicillin/ Streptomycin (50 I.U./50 µg/ml). |
Mouse embryonic
fibroblasts (MEF) were grown in the same medium supplemented with sodium pyruvate (1 mM),
1X non-essential amino acids, and beta-mercaptoethanol (55 µM). Litter-matched Casp2+/+ and
Casp2-/- MEF were generated and transduced with E1A and Ras as previously described.19
Briefly, early passage MEF were simultaneously transduced with frozen supernatants of the
retroviral expression vectors pBabePuro.H-ras (G12V) and pWZLH.E1A (provided by S.W. Lowe and G. Hannon). After 48 hours, the cells were harvested by mild trypsinization, seeded at
1 x 105 cells/well in 6 well plates and cultured for 10 days in media containing 0.5 μg/ml of
7
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. puromycin and 40 μg/ml of hygromycin for selection of the transduced viruses. HeLa.C2-Pro
BiFC cells were generated as previously described.17 HeLa.C2-Pro BiFC cells expressing Am-
Cyan-Geminin and Casp2+/+ and Casp2-/- MEF stably expressing vector or BcLXL were
generated by retroviral
transduction. Gryphon-Ampho packaging cells were
transiently
transfected with Am-Cyan-Geminin (pRetroX-SG2M-Cyan Vector from Takara), pLZRS or
pLZRS.BCLxL using Lipofectamine 2000 transfection reagent (Invitrogen, Grand Island, NY,
USA) according to manufacturer’s instructions. After 48 hours, virus-containing supernatants
were cleared by centrifugation and incubated with Casp2+/+ and Casp2-/- MEF followed by
selection in neomycin for pRetroX or zeocin for pLZRS vectors. CRISPR/Cas9 gene editing
Casp2 was deleted from U2OS and HeLa cells using an adaptation of the protocol described in
ref. 25.25 Protospacer sequences for each target gene were identified using the CRISPRscan
scoring algorithm (www.crisprscan.org (Moreno-Mateos et al., 2015)). DNA templates for
sgRNAs were made by PCR using pX459 plasmid containing the sgRNA scaffold sequence and
using the following primers:
ΔCasp2(76) sequence: ttaatacgactcactataGGCGTGGGCAGTCTCATCTTgttttagagctagaaatagc
ΔCasp2(73) sequence: ttaatacgactcactataGGTGTGGAGGGCGCCATCTAgttttagagctagaaatagc
universal reverse primer: AGCACCGACTCGGTGCCACT. sgRNAs were generated by in vitro transcription using the Hiscribe T7 high yield RNA synthesis μ
μ
kit (New England Biolabs). Purified sgRNA (0.5
g) was incubated with Cas9 protein (1
g, PNA
Bio) for 10 min at room temperature. HeLa or U2OS cells were electroporated with the
sgRNA/Cas9 complex using the Neon transfection system (Thermo Fisher Scientific) at 1005 V,
35 ms, and two pulses. Knockout was confirmed by PCR and western blot. Microscopy
8
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Cells were imaged using one of two microscope systems. The first is a spinning disk confocal
microscope (Carl Zeiss MicroImaging, Thornwood, NY, USA), equipped with a CSU-X1A 5000
spinning disk unit (Yokogawa Electric Corporation, Japan), multi laser module with wavelengths
of 458 nm, 488 nm, 514 nm, and 561 nm, and an Axio Observer Z1 motorized inverted
microscope equipped with a precision motorized XY stage (Zeiss). Images were acquired with a
Zeiss Plan-Neofluar 40x 1.3 NA or 64x 1.4 NA objective on an Orca R2 CCD camera using Zen
2012 software (Zeiss). The second is a Leica SP8-based Gated Continuous Wave laser
scanning confocal microscope (Leica Microsystems) equipped with a white-light laser, operated
by Leica software. Cells were plated on dishes containing glass coverslips coated with
fibronectin (Mattek Corp. Ashland, MA, USA) 24 h prior to treatment. For time-lapse
experiments, media on
the cells was supplemented with HEPES
(20mM) and 2-
mercaptoethanol (55μM). Cells were allowed to equilibrate to 37°C in 5% CO2 prior to focusing
on the cells in an incubation chamber set at 37°C. For the Leica system, samples were
sequentially excited by a resonant scanner at 12,000Hz using a white light laser (excitation
wavelengths: 470nm, 514nm and 587nm) and emitted light was collected through a HC PL APO
63x/1.47 oil objective and through a pinhole of 1.5AU to be finally quantified by 3 HyD detectors
with the following spectral gates: 475-509nm / 520-581nm / 593-702nm and during an
acquisition window set from 0.7 to 6 ns using time gating. Immunofluorescence
Cells plated on dishes containing glass coverslips coated with fibronectin were washed in 3 x 2
ml PBS and fixed in 2% (w/v) paraformaldehyde in PBS pH 7.2 for 10 min. Cells were washed
for 3 x 5 min in PBS followed by permeabilization in PBS, 0.15% (v/v) Triton for 10 min. Cells
were blocked in FX image enhancer (Invitrogen) for 30 min and then stained with the anti- γ
H2AX antibody at a 1:100 dilution in PBS, 2% (w/v) BSA for 1 h. After washing in PBS, 2%
(w/v) BSA, the cells were incubated with anti-mouse Alexa Fluor 555-conjugated secondary
9
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stained with SYTOTM 13 green fluorescent nucleic acid stain (400 nM, Thermo Fisher) in PBS
and incubated at room temperature in the dark for 1 h prior to imaging
Image analysis
At least 30 individual images were acquired for each treatment. For quantitation of nuclei, cells
were stained with NucBlueTM Live ReadyProbesTM stain (Thermo Fisher) and individual nuclei
were counted using the python-based CellProfilerTM software (Massachusetts Institute of
Technology, Broad Institute of MIT and Harvard). Greyscale TIFF images were corrected for
illumination differences using an illumination function. |
Images were smoothed by Gaussian
method followed by separation using Otsu with an adaptive thresholding strategy. Cells were
then declumped by shape and automatically counted. For quantitation of
γ
H2AX foci, the
number of foci per cell was calculated from RGB TIFF images using FoCo, a graphical user
interface that uses Matlab and ImageJ.26 In the nuclei channel, Huang thresholding was used to
separate cells. A Top-Hat transformation was used on foci images for noise reduction. Foci
were segmented by using the threshold found by Otsu’s method as minimal peak height in H-
maxima transform. Foci per cell were then calculated automatically. For analysis of time-lapse imaging, cells expressing the BiFC components were identified by
fluorescence of the linked mCherry protein in stable cell lines. The raw signal from mCherry was
first improved using Noise2Void1 algorithm for denoising using deep learning approach. The
Noise2Void 2D model was trained from scratch for 100 epochs on 148480 image patches
(image dimensions: (520,520), patch size: (64,64)) with a batch size of 128 and a mse loss
function, using the Noise2Void 2D ZeroCostDL4Mic2 notebook (v 1.12) on Colab cloud services
(Google) and then applied to all images from the mCherry channel. Next, using a same platform
(Google Colab) and a beta version of ZeroCostDL4Mic notebook2, the CellPose algorithm3 was
10
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this version posted March 30, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. applied to this “enhanced” mCherry channel in order to create accurate masks of each cell
(Object diameter: 20um, Flow threshold 0.7 and Cell probability threshold -0.7). Raw data and
CellPose masks were imported into Aivia 9.8 (Leica Microsystems) and the mean fluorescence
intensity was measured in each of the Cyan and Venus channels using the CellPose-derived
masks as regions of interest. After manual verification and curation of the lineage attributions,
data was exported for further analysis in MATLAB (r2021a, MathWorks) where all the timelines
from the different cells were aligned according to their cytokinesis time point, and were scaled
by the following formula: scaled point = (Max − x)/MaxDifference, where Max equals the
maximum value in the series, x equals the point of interest, and MaxDifference equals the
maximum minus the minimum value in the series. Flow cytometry
For cell cycle analysis, cells were treated as indicated (see figure legends). Treatment media
was exchanged for media containing BrdU (10 µM). After 30 min cells were collected by
trypsinization, fixed, permeabilized, and stained with BrdU-FITC antibody and 7-AAD using the
BD PharmingenTM FITC BrdU flow kit according to the manufacturer’s protocol. Briefly, the cells
were fixed and permeabilized with BD Cytofix/Cytoperm buffer for 15 min at room temperature,
then with the secondary permeabilization buffer, BD Cytoperm Permeabilization Buffer Plus, for
10 min on ice and finally with BD Cytofix/Cytoperm buffer for 5 min at room temperature. |
Cells
were washed in BD Perm/Wash buffer and centrifuged at 300 x g between each step. To
uncover the BrdU epitope, the cells were treated with DNAse (30 µg per 106 cells) for 1 h at
37°C, washed in BD Perm/Wash buffer, and centrifuged at 300 x g. The cells were then
incubated in a 1:50 dilution of FITC-labeled anti-BrdU antibody in BD Perm/Wash buffer for 20
min at room temperature, washed and centrifuged at 300 x g. The cells were then resuspended
in Stain Buffer (FBS) containing 10 µl of 7-aminoactinomycin D (7-AAD)/106 cells. Cells were
11
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. quantitated for BrdU and 7-AAD positivity by flow cytometry. For annexin V binding, cells were
resuspended in 200 μl of annexin V binding buffer (10 mM HEPES, 150 mM NaCl, 5 mM KCl, 1
mM MgCl2, and 1.8 mM CaCl2) supplemented with 2 μl of annexin V-FITC (Caltag Laboratories,
Burlingame, CA). Annexin-V-positive cells were quantitated by flow cytometry. Immunoblotting
Cell lysates were resolved by SDS-PAGE. The proteins were transferred to nitrocellulose
(Thermo Fisher) and immunodetected using appropriate primary and peroxidase-coupled
secondary antibodies (Genesee Scientific). Proteins were visualized by West Pico and West
Dura chemiluminescence Substrate (Thermo Fisher). DNA fiber analysis
Exponentially growing cells were pulse-labeled with 50 mM 5-chlorodeoxyuridine (CldU) for 30
min, washed three times with PBS, treated with 2 mM hydroxyurea (HU) for 2 h, washed three
times with PBS, incubated again in fresh medium containing 50 mM 5-iododeoxyuridine (IdU)
for 60 min, and then washed three times in PBS. DNA fiber spreads were made by a
modification of a procedure described previously.27 Briefly, cells labeled with IdU and CldU were
mixed with unlabeled cells at a ratio of 1:10, and 2 µl cell suspensions were dropped onto a
glass slide
and then mixed with 20 µl hypotonic lysis solution (10 mM Tris-HCl [pH 7.4], 2.5 mM MgCl2, 1
mM phenylmethylsulfonyl fluoride [PMSF], and 0.5% Nonidet P-40) for 8 min. Air-dried slides
were fixed, washed with 1 x PBS, blocked with 5% bovine serum albumin (BSA) for 15 min, and
incubated with primary antibodies against IdU and CldU (rat anti-IdU monoclonal antibody [MAb]
[1:150 dilution; Abcam] and mouse anti-CldU MAb [1:150 dilution; BD]) and secondary
antibodies (anti-rat Alexa Fluor 488-conjugated [1:150 dilution] and anti-mouse Alexa Fluor 568-
12
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. conjugated [1:200 dilution] antibodies) for 1 h each. Slides were washed with 1x PBS with 0.1%
Triton X-100 and mounted with Vectashield mounting medium without 4’, 6-diamidino-2-
phenylindole (DAPI). |
ImageJ software was used to analyze the DNA fibers. For each data set,
about 300 fibers were counted for stalled forks, new origins, or elongated forks. Viability assay
Casp2+/+ and Casp2-/- MEF were plated in a 24-well plate at the indicated densities. After four
days colonies were visualized by staining overnight with methylene blue 0.1% w/v in
methanol/water 50% v/v at room temperature. Cells were washed in PBS. Assay for Chromosomal Aberrations at Metaphase. All three stage-specific chromosomal aberrations were analyzed at metaphase after exposure to
IR. G1-type chromosomal aberrations were assessed in cells exposed to 3 Gy of IR and
incubated for 9-10 h and metaphases were collected.28, 29 S-phase–specific chromosome
aberrations were assessed after exponentially growing cells irradiated with 2 Gy of IR. Metaphases were harvested following 4 h of irradiation, and S-phase types of chromosomal
aberrations were scored. For G2-specific aberrations, cells were irradiated with 1 Gy and
metaphases were collected 1-1.5 h post treatment. Chromosome spreads were prepared after
hypotonic treatment of cells, fixed in acetic acid·methanol (1:3), and stained with Giemsa. The
categories of G1-type asymmetrical chromosome aberrations that were scored include
dicentrics, centric rings, interstitial deletions/acentric rings, and terminal deletions. S-phase
chromosome aberrations were assessed by counting both the chromosome and chromatid
aberrations, including triradial and quadriradial exchanges per metaphase as previously
described.28, 29 G2-phase chromosomal aberrations were assessed by counting chromatid
breaks and gaps per metaphase as previously described.28, 29 Fifty metaphases were scored for
each post-irradiation time point. 13
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Statistical comparisons were performed using two-tailed Student’s t test calculated using Prism
6.0 (Graph Pad) software. 14
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Results
Loss of caspase-2 increases cell growth
Loss of caspase-2 has been previously shown to increase cellular proliferation rates.4, 8 To
confirm, we plated E1A/Ras-transformed
litter-matched Casp2+/+ and Casp2–/– mouse
embryonic fibroblasts (MEF) at low densities and stained for viable cells after 4 days. Casp2–/–
MEF consistently exhibited an increase in the number of colonies compared to Casp2+/+ cells
(Figure 1A). We compared the growth rate of Casp2+/+ and Casp2-/- cells and caspase-2-
deficient cells demonstrated increased proliferation 72 h following plating (Figure 1B, C). |
To
confirm these results, we used BrdU/7AAD staining to determine the cell cycle profiles of cycling
Casp2+/+ and Casp2-/- MEF and found that Casp2-/- cells had significantly more cells in S-phase
and less cells in G1-phase compared to the Casp2+/+ cells (Figure 1D, E). The increased
proportion of S-phase cells suggests more caspase-2-deficient cells are synthesizing DNA,
while the reduced G1 cells indicate that a decreased proportion of the Casp2-/- cells are in a
quiescent state compared to Casp2+/+ cells. This is consistent with the higher rate of
proliferation we observed for caspase-2 knockout cells. Together, these results indicate that
caspase-2 plays a role in limiting uncontrolled cell growth. Because caspase-2 appears to be involved in regulating cell proliferation, we hypothesized that
caspase-2 is activated in cells undergoing mitosis. To investigate this, we used the caspase-2
bimolecular fluorescence complementation (BiFC) technique.19 This technique relies on the fact
that caspase-2 is activated by proximity-induced dimerization following recruitment to its
activation platform.9, 30 Caspase-2 BiFC uses non-fluorescent fragments of the fluorescent
protein Venus fused to the prodomain of caspase-2.19 When caspase-2 is recruited to its
activation platform the subsequent induced proximity of caspase monomers enforces refolding
15
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which stably expresses the caspase-2 BiFC reporter comprising of the prodomain of caspase-2
(aa 1-147) fused to the Venus fragments, Venus N 1-173 (VN) or Venus C 155-249 (VC)
separated by the virally derived 2A self-cleaving peptide to ensure equal expression of the BiFC
components. 17 The C2-Pro BiFC sequence is also linked to an mCherry sequence to permit to
visualization of the cells. We used time-lapse confocal microscopy to track caspase-2 BiFC
relative to cell division in unstimulated cells. We used the mCherry signal, which fluoresces
throughout the cell, to visualize cell division (Figure 2A). We observed caspase-2 BiFC (Venus)
around the time of mitosis the majority of cells undergoing division (Figure 2A, 2B,
Supplemental Movie 1). In contrast, in cells that did not divide or in cells that died during the
course of the time-lapse, the proportion of cells inducing caspase-2 BiFC was equal to the
proportion of cells that remained BiFC-negative. This suggests that caspase-2 is activated in
dividing cells around the time of mitosis. To track cell division and the timing of caspase-2 BiFC relative to the cell cycle more accurately,
we monitored caspase-2 BiFC relative to a stably expressed fluorescent cell cycle marker,
AmCyan-hGeminin. hGeminin is only expressed in G2/M and S-phase of the cycle,31 therefore
fluorescence of AmCyan-hGeminin is most concentrated in G2 and is degraded in early G1.32
When we analyzed the cells that induced caspase-2 BiFC during division, we observed that the
peak of caspase-2 fluorescence came 160 min after the peak of the AmCyan-hGeminin signal
(Figure 2C, 2D, Supplemental Movie 2). |
This indicates that caspase-2 activation does not
coincide G2/M phase of the cell cycle, but is activated in G1 and early S-phase. Loss of caspase-2 results in S-phase specific chromosomal aberrations and delayed exit
16
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To determine which stage of the cell cycle is most impacted by caspase-2 activity, we examined
cell cycle-phase specific chromosomal aberrations at metaphase induced by ionizing irradiation
(IR) in the presence and absence of caspase-2. G1-specific chromosome aberrations were
analyzed in cells treated with 3 Gy and metaphases that were collected 10-12 h post irradiation. S-specific chromosome aberrations were analyzed in cells treated with 2 Gy and metaphases
were collected 4-6 h post irradiation and G2-specific chromosome aberrations were analyzed in
cells treated with 1 Gy and metaphases were collected 1-1.5 h post irradiation. The frequency of
G1-type chromosomal aberrations (mostly of the chromosomal type with frequent dicentric
chromosomes)33 and G2-type chromosomal aberrations (chromosomal and chromatids) was
similar for Casp2+/+ and Casp2–/– cells (Figure 3A). In contrast, S-type aberrations were
specifically increased in Casp2-/- cells. These aberrations consisted primarily of breaks and
radials (Figure 3B). The specific increase in S-phase specific chromosomal aberrations in the absence of caspase-2
suggests a role for caspase-2 in ensuring normal S-phase progression. Therefore, we
investigated the impact of loss of caspase-2 on cell cycle recovery after S-phase arrest. We
treated Casp2+/+ and Casp2–/– MEF with aphidicolin for 16 h to synchronize the cells in S-phase. Immediately after release from S-phase arrest (0 h), there was a greater proportion of Casp2–/–
MEF in S-phase compared to Casp2+/+ MEF. In addition, over four hours following release from
arrest the proportion of Casp2+/+ MEF in S-phase reduced as the cells moved into G2 and then
G1. In contrast, Casp2–/– cells exhibited a delay in exit from S-phase and concomitant entry into
G1 (Figure 3C, D). This suggests that the exit from S-phase was delayed in Casp2−/− MEF. 17
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The delayed recovery from S-phase arrest we observed in Casp2–/– cells could be due to DNA
replication stress. Therefore, we investigated the role of caspase-2 in replication fork dynamics
following transient genotoxic stress-induced replication blockage. We used a DNA fiber assay to
evaluate restart and recovery of replication forks after hydroxyurea (HU) treatment. |
HU induces
replication fork stalling and S-phase arrest by depleting the available nucleotide pool for DNA
polymerases.34 Cells were pulse-labeled with 5-iododeoxyuridine (IdU), treated with HU for 2 h
to induce replicative stress, and then washed and pulse-labeled with 5-chlorodeoxyuridine
(CldU). Individual DNA fibers, which incorporated the CldU and/or IdU pulses, were detected
with fluorescent antibodies against those analogs. We noted three types of DNA fiber tracts:
those with ongoing elongation forks (CIdU+IdU), stalled forks (CIdU), and newly initiated forks
(IdU) representing new origins of DNA replication (Figure 4A-F). Caspase-2-deficient MEF
demonstrated a significantly higher frequency of stalled replication forks (Figure 4B), new
origins of replication (Figure 4C), and a reduced frequency of CldU+IdU forks, indicating
ongoing replication (Figure 4D). This suggests that the loss of caspase-2 prevents or delays re-
initiation of stalled replication forks that can result in S-phase arrest, while also resulting in
excessive replication-origin firing. When replication is stalled, the length of DNA tract is
impacted.35 Therefore, to assess the impact of caspase-2 loss on DNA tract length, we measure
the length of the total tract of CldU + IdU and the length of CldU or IdU labeled DNA fibers in
Casp2+/+ and Casp2–/– cells. As expected, the length of the CldU fibers, which represents the
DNA prior to replication stress, were the same across the cell types. The length of IdU was
significantly reduced in Casp2–/– cells compared to wild type (Figure 4E, F). This reduced fiber
length indicates a reduced DNA fork speed and, thus, increased replication stress in the
absence of caspase-2. Thus caspase-2 promotes replication fork progression. 18
bioRxiv preprint
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Excessive replication fork stalling induced by HU treatment can lead to fork collapse and
breakage in the form of DNA double strand breaks (DSBs).36 To probe the effects of loss of
caspase-2 on DSBs induced by replication stress, we stained for phosphorylated H2AX (
γ -
H2AX). γ -H2AX forms foci at DSBs and therefore is a reliable and sensitive indicator of DSBs.37
We treated Casp2+/+ and Casp2–/– MEF with HU for 2 h and quantitated the percentage of cells
γ
γ
with
H2AX foci 24 h later. We observed a significantly higher level of cells with
H2AX foci in
Casp2–/– MEF compared to the wild type MEF (Figure 4G, H). These data indicate a higher level
of DNA damage following replication stress in the absence of caspase-2. This suggests that
caspase-2 may function to prevent DNA damage or to facilitate DNA repair. Loss of caspase-2 is not associated with impaired activation of known cell cycle
checkpoints
Stalled replication forks lead to activation of ATR and its substrate Chk1.38 Chk1 triggers a G2/M
checkpoint by inhibiting Cdc25C-mediated activation of Cyclin B39 and also triggers an intra S-
phase checkpoint through inhibition of Cdc25A-mediated activation of Cyclin A/E.40 Given the
increase in stalled forks and delayed exit from S-phase in the absence of caspase-2, we
measured the impact of loss of caspase-2 on ATR and Chk1 activation. |
We measured
checkpoint activation in Casp2+/+ and Casp2–/– MEF treated with HU (Figure 5A). Chk1 was
phosphorylated immediately after HU release to a similar extent with and without caspase-2 and
minimal differences in Chk1 phosphorylation between the two cell lines were noted over time. Interestingly, phosphorylation of ATR increased 2 h following HU release in Casp2+/+ MEF and
this was inhibited in Casp2–/– MEF. However, the phosphorylation that was detected was at
Serine 428. Phosphorylation of ATR at this site is not required for Chk1 phosphorylation and
therefore not associated with ATR activation.41 Because antibodies for the phospho-ATR site
that is associated with activation (threonine 1929) are not available for murine ATR, we used
19
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. CRISPR/Cas9 to generate human caspase-2-deficient U2OS cells to determine if caspase-2 is
required for ATR activation. As in the MEF, loss of caspase-2 had no effect on Chk1
phosphorylation induced by two different doses of HU or the DNA damage inducers: etoposide,
camptothecin or topotecan (Figure 5B). Phosphorylation of ATR at T1989 was not substantially
impacted by the loss of caspase-2 under any of the treatment conditions. We similarly
generated caspase-2-deficient HeLa cells using CRISPR/Cas9. Similar to the U2OS cells, no
difference in Chk1 phosphorylation was observed (Figure 5C). Finally, we examined Chk1
phosphorylation in response to S-phase arrest induced by aphidicolin in U2OS cells (Figure 5D). Following release from aphidicolin, we did not detect any difference in Chk1 phosphorylation. ATM regulates the intra-S phase checkpoint through Chk2 activation.42 Similar to ATR, we saw
some induction of ATM phosphorylation in MEF 2 h following HU release that was diminished in
the caspase-2 knockout cells (Figure 5A). However,
this was not accompanied by
phosphorylation of the ATM substrate, Chk2.43 In MEF, Chk2 phosphorylation was detected at
the later 4 h timepoint and this appeared caspase-2-dependent. ATM is activated primarily by
double strand breaks,44 therefore, the later Chk2 activation may be a result of DSBs resulting
from collapsed stalled replication forks. To determine the effect of loss of caspase-2 on ATM
activation in response to DSBs, we measured ATM and Chk2 phosphorylation in response to
etoposide compared to the single strand DNA break inducers, camptothecin and topotecan, and
to HU (Figure 5B). We observed strong phosphorylation of Chk2 immediately after release from
etoposide, camptothecin, or topotecan in the presence and absence of caspase-2. In contrast,
HU did not induce much Chk2 phosphorylation. ATM phosphorylation was detected at 2 h post
release in response to etoposide, camptothecin, or topotecan and this was diminished in
caspase-2 knockout cells. |
ATM phosphorylation was not induced by HU in the U2OS cells. In
HeLa cells and in U2OS cells, we detected strong Chk2 phosphorylation in response to HU and
20
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together, these results suggest that caspase-2 is either activated downstream of ATM and ATR
or in parallel to these checkpoints. Caspase-2 induced cell cycle regulation is independent from its ability to induce
apoptosis
Caspase-2 induces apoptosis through cleavage of the pro-apoptotic protein BID and
subsequent permeabilization of the outer mitochondrial membrane, cytochrome c release, and
downstream caspase activation.45, 46 However, it is unclear if the pathway engaged by caspase-
2 to regulate the cell cycle is the same pathway that induces apoptosis. To investigate this, we
overexpressed Bcl-XL in the Casp2+/+ and Casp2–/– MEF. Bcl-XL potently blocks caspase-2
induced apoptosis,19 and these cells were completely resistant to the apoptosis inducers
actinomycin D, staurosporine and etoposide (Figure 6A). Nevertheless, the cell cycle profile of
the Bcl-XL-overexpressing Casp2+/+ cells was unchanged compared to that of the vector-
transduced Casp2+/+ MEF (Figure 6B). This suggests that the association between the
increased frequency of S-phase defects and loss of caspase-2 is not due to apoptosis induced
by caspase-2, indicating that a distinct pathway is engaged to induce the observed cell cycle-
related effects. To explore this further, we treated Casp2+/+ and Casp2–/– MEF expressing vector
or Bcl-XL with camptothecin for four hours and measured both cell cycle arrest at 4 h (Figure
6C) and apoptosis at 24 h (Figure 6D). When we challenged the cells with low doses of
camptothecin, Casp2+/+ cells accumulated in S-phase and this was increased in Casp2–/– cells. At higher doses of camptothecin, Casp2+/+ cells accumulated more in G2/M-phase. This was
slightly increased in the absence of caspase-2 but the effect was variable. The proportions of
G1 cells steadily decreased with increasing dose of camptothecin. The increases in S-phase
arrest at lower doses, G2-arrest at high doses and decreased G1 cells was not changed by the
21
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after 24 h that was potently blocked by overexpression of Bcl-XL (Figure 6D). Together these
results indicate that the S-phase defects associated with caspase-2-deficiency are independent
of the ability of caspase-2 to induce apoptosis. |
22
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Here we report that caspase-2 is activated during mitosis and loss of caspase-2 is associated
with S-phase specific defects, including DNA fork stalling, delayed exit from S-phase, and S-
phase specific chromosomal aberrations. These functions appear to be independent of
caspase-2’s ability to induce apoptosis and loss of caspase-2 has minimal impact on the intra S-
phase checkpoint. Altogether, our results demonstrate that caspase-2 plays a key non-apoptotic
role in the regulation of DNA replication that protects against genomic instability. Our data indicate that caspase-2 is activated during mitosis. Notably, this activation occured
during G1 and early S-phase of the cycle. The activation of caspase-2 in G1 or early S is
consistent with the S-phase associated defects. The higher rate of S-phase arrest in Casp2-/-
cells may suggest that the activation of caspase-2 in G1 leads to protection of the G1/S
transition. Caspase-2 is also required for timely S-phase progression. Following S-phase arrest,
cells progress through S-phase to G2 and G1 more quickly than when caspase-2 is absent. This
indicates that in the absence of caspase-2, cells struggle to exit S-phase. Thus, our data
indicates that caspase-2 has an important role in G1/S phase of the cell cycle. During S-phase
of the cell cycle, the genome of the cell is duplicated.47 Any errors sustained during this process
can manifest as replication stress and can result in stalled or collapsed replication forks.48 Our
data show that caspase-2 protects against stalled replication forks and against excessive
replication-origin firing induced by replication stress. Stalled replication forks produce single
strand DNA that, when collapsed, can be converted to DSBs, the accumulation of which
promotes genomic instability.49 Excessive replication origin firing can lead to the depletion of
necessary nutrients and metabolites required for correct genome replication, again contributing
to genomic instability.50, 51 Several groups, including our own, have provided evidence that
caspase-2 protects against genomic instability.5, 7, 52, 53 Loss of caspase-2 has been shown to be
23
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. associated with higher levels of aneuploidy,53, 54 polyploidy,52 and genome duplication.7 Our data
provides key evidence that the mechanism by which caspase-2 that protects against genomic
instability is by protecting DNA replication forks during S-phase of the cell cycle. The primary S-phase-associated cell cycle checkpoint is the intra-S-phase checkpoint that is
required to ensure genomic integrity and to prevent errors during DNA replication.55 ATR is a
critical mediator of the intra-S-phase checkpoint and is activated by ssDNA, DSBs, and it also
regulates origin firing in normal S-phase.56 Several studies indicate that the complex between
replication protein A and ssDNA is the convergence point for different types of lesions to
activate ATR.57, 58 In contrast, DSBs activate ATM.44, 59 Surprisingly, although loss of caspase-2
was associated with increased stalled replication forms and origin firing, it had minimal effects
on ATR or Chk1 phosphorylation. |
This would suggest that caspase-2 functions downstream of,
or independently of, ATR and Chk1 in response to stalled replication forks. Chk1 has been
proposed as an inhibitor of caspase-2 in response to IR-induced DNA damage.15 Inhibition of
Chk1 potentiates caspase-2 dependent apoptosis by derepressing ATM-mediated
phosphorylation of the upstream caspase-2 activator PIDD. However, this appears to be specific
to the IR pathway since inhibition of Chk1 alone is not sufficient to induce caspase-2.60
Therefore, it is unclear if this phenomenon could impact the response to replication stress. The
observed decrease in ATM autophosphorylation in the absence of caspase-2 suggests impaired
ATM activation that could also contribute to the intra S-phase checkpoint.61 However, this
decrease was not accompanied by a decrease in activation of its substrate Chk2. ATM
phosphorylates additional cell cycle proteins including p53,62 BRCA163 and NBS164 and it is
possible that loss of caspase-2 has an impact on a different ATM substrate to impact this or
other checkpoints. In addition, it has been demonstrated that Chk2 can be activated
independently of ATM in response to HU treatment.65-67 The fact that loss of caspase-2
24
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against any interdependence of these two pathways and suggests rather that caspase-2 and
ATM function in parallel to protect against genomic instability. During cell division, caspase-2 is subject to two separate phosphorylation events that are
reported to attenuate its activation downstream of activation platform assembly. These
phosphorylation events are induced by Cdk-cyclin B1 and Aurora B kinase at S340 and S384
respectively.69, 70 Cdk-cyclin B1 is required for G2/M transition,71 while Aurora B kinase is a
spindle checkpoint protein and is essential for the segregation of chromosomes.72 It has been
proposed that these inhibitory phosphorylation events are to prevent caspase-2 from inducing
apoptosis during cell division, a process referred to as mitotic catastrophe.69, 70 However, we
noted that caspase-2 activation in dividing cells was primarily in G1 phase rather than G2,
indicating that this is a separate event. In addition, while we noted a strong association between
caspase-2 activation and cell division, we did not observe a similar association with cell death. This demonstrates that apoptosis is not the primary outcome of this caspase-2 activation during
cell division. Therefore, it is possible that an alternative function of these phosphorylation events
is to fine tune caspase-2 activation during different stages of the cell cycle, allowing its
activation during G1- and S-phase and downregulating its activity during G2/M. |
Treatment with inducers of G2/M arrest including nocodazaole and Plk1 inhibition has been
shown to induce caspase-2-dependent apoptosis as a mechanism to remove aneuploidy cells.54
However, it is not clear whether caspase-2 regulates cell cycle during the DNA damage
response through activation of the same pathway components that are involved in caspase-2-
dependent apoptosis or if caspase-2-mediated cell cycle regulation is independent of its ability
25
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this version posted March 30, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. to induce apoptosis. Bcl-XL is an effective inhibitor of caspase-2 –induced apoptosis via its
ability to block the caspase-2 substrate BID and to inhibit mitochondrial outer membrane
permeabilization.73 Our evidence indicates that the cell cycle effects associated with the loss of
caspase-2 is not phenocopied by Bcl-XL overexpression. This indicates that the cell cycle
functions of caspase-2 leading to DNA fork protection and prevention of DNA damage are not
simply due to removal of damaged cells by caspase-2-dependent apoptosis. Our evidence
further suggests that the regulation of cell cycle by capsase-2 is mechanistically independent of
its ability to induce apoptosis. Caspase-2 has been shown to cleave MDM2 to increase p53
stabilization in response to Aurora B kinase inhibition.11 This has been shown to be important for
inducing cell cycle arrest in response to cytokinesis failure.52 It is possible that cleavage of
MDM2 rather than BID is responsible for protecting DNA replication forks. However, it remains
possible that these effects are due to an, as yet, unidentified caspase-2 target. Another group showed that PIDD-deficiency is associated with decreased recovery from stalled
DNA replication forks induced by UV.74 The similarity between the results of the latter study and
our results may suggest that the PIDDosome is responsible for the function of caspase-2 in
protecting
replication
forks. We
identified
the nucleolar PIDDosome comprising of
nucleophosmin (NPM1), PIDD, and RAIDD as a major caspase-2 activating complex in
response to topoisomerase I inhibitors that produce ssDNA.75 We showed that inhibition of
NPM1 overcomes caspase-2 associated growth arrest75. This may suggest that the nucleolar
PIDDosome drives the cell cycle events we report here. However, Lin et al concluded that PIDD
facilitates DNA-PKcs binding to ATR and, in contrast to our results, showed that the ATR
pathway was compromised in the absence of PIDD.74 PIDD has also been reported to bind
PCNA to facilitate trans-lesion synthesis following UV-induced damage.76 This complex was
shown to be independent of caspase-2 function. This may suggest that PIDD is acting
26
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this version posted March 30, 2021. |
The copyright holder for this preprint
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. independently of caspase-2 to activate ATR, but the nucleolar PIDDosome is required for
efficient resolution of stalled forks and prevention of DSBs. To fully understand the conditions
that lead to caspase-2 activation during cell cycle and how this leads to DNA replication fork
protection, further exploration of the complexes that lead activation platform in this process will
be essential. In conclusion, our data indicate an active role for caspase-2 in ensuring correct cell cycling that
does not simply lead to removal of damaged cells by apoptosis and that this pathway does not
overlap with caspase-2’s pro-apoptotic function. Importantly, our results provide strong evidence
that caspase-2 engages multiple cellular functions to safeguard against genomic instability and
tumor progression. 27
bioRxiv preprint
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Acknowledgements
We would like to thank Jennifer Martinez (NIEHS) for careful reading of this manuscript. Funding for this project includes NIH/NIGMS R01GM121389 and NIH/NCI R21CA256606
(LBH). This project was supported by the Cytometry and Cell Sorting Core at Baylor College of
Medicine with funding from the NIH (P30 AI036211, P30 CA125123, and S10 RR024574) and
the expert assistance of J. M. Sederstrom. We would like to acknowledge the Texas Children’s
Hospital William T. Shearer Center for Human Immunobiology for their generous support for this
research and the expert assistance of Rebecca Kairis. 28
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/- Figure 1. Caspase-2 limits cellular proliferation. (A) Litter-matched Casp2+/+ and Casp2-/
ell E1A/Ras transformed mouse embryonic fibroblasts (MEF) were plated at the indicated cell
29
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images of methylene blue-stained colonies are shown of three independent experiments. (B)
Litter-matched Casp2+/+ and Casp2-/- E1A/Ras transformed MEF, plated at low density, were
stained with DAPI and imaged at the indicated time points. Total cell number was determined by
counting DAPI-positive cells from at least 30 images per well. Results are shown as the percent
increase in cell growth compared to 0 h and are the average of three independent experiments
plus or minus standard deviation. |
*p<0.05. (C) Representative images are shown from the 0 h
and 72 h time points. Nuclei are shown in blue. Bar, 200 µm. (D) Untreated, cycling litter-
matched Casp2+/+ and Casp2-/- E1A/Ras MEF were stained with BrdU/7-AAD and analyzed by
flow cytometry. Representative flow plots are shown. (E) The percentage of cells in G1, S, or
G2/M phase for each cell line is shown. Results are the average of seven independent
experiments plus or minus standard deviation. **p<0.01. 30
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 2. Caspase-2 BiFC is induced in dividing cells. (A) HeLa cells stably expressing C2 2
Pro-VC-2A-C2 Pro-VN-2A-mCherry (HeLa.C2 Pro-BiFC) were imaged every 5 min for 16 h.
h.
ng Representative sequential images show the appearance of caspase-2 BiFC (green) in dividing
or cells (red). (B) The percentage of mCherry-positive cells that became Venus-positive or
31
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from at least 100 cells per experiment. Results are the average of three independent
experiments plus or minus standard deviation. ***p<0.001. (C) HeLa.C2 Pro-BiFC cells stably
expressing AmCyan-Geminin were imaged every 10 min for 24 h. Graph of the dividing cells
that became Venus-positive and Cyan-positive is shown. Each point on the Cyan graph (blue) is
scaled and aligned to each point on the caspase-2 BiFC graph (green) that represents the
average intensity of Cyan or Venus in the cell at 10 min intervals where time=0 is the time of cell
division. The peak of Cyan intensity represens G2-phase of the cell cycle. Error bars represent
SEM of 94 individual cell divisions. (D) Frames from the time-lapse show representative cells
undergoing BiFC (green) relative to geminin expression as measured by the Cyan fluorescence
(blue). Scale bars represent 10 µm
32
bioRxiv preprint
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this version posted March 30, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. d Figure 3. Loss of caspase-2 results in in S-phase specific chromosomal aberrations and
delayed exit from S-Phase
(A) Litter-matched Casp2+/+ and Casp2-/- E1A/Ras transformed MEF were treated with the he
indicated doses of
γ
for -irradiation. Metaphase spreads were prepared and analyzed for
A chromosomal aberrations that included chromosome and chromatid type breaks and fusions. A
ed total of 35 metaphases were analyzed from each sample and the experiment was repeated
33
bioRxiv preprint
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this version posted March 30, 2021. |
The copyright holder for this preprint
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. three times. **p<0.01. (B) Representative images of metaphase spread of untreated (-) or
irradiated Casp2+/+ and Casp2-/- MEF. Red arrows show breaks; blue arrows show exchanges. (C) Litter-matched Casp2+/+ and Casp2-/- E1A/Ras transformed MEF were either left untreated (-
) or treated with aphidicolin (1 µM). After 16 h, the treatment was removed and replaced with
fresh media. Cells were harvested following a 30 min BrdU (10 µM) pulse at the indicated time
points after aphidicolin release and stained with BrdU-FITC/7-AAD. The proportion of cells in S-,
G1- and G2-phase was determined by flow cytometry. Representative flow plots are shown. (D)
The percent of cells in each phase of the cell cycle was determined for each time point following
aphidicolin release. Results are the average of 3 independent experiments plus or minus
standard deviation. 34
bioRxiv preprint
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this version posted March 30, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ed Figure 4. Loss of caspase-2 is associated with stalled replication forks and increased
or DNA damage. (A) Scheme for dual labeling of DNA fibers to evaluate replication restart or
he recovery following HU-induced replication fork stalling. (B to E). Quantitative analysis of the
, DNA fiber replication restart assay after HU treatment shows the percentage of stalled forks (B),
35
bioRxiv preprint
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. the percentage of new origins (C), total tracts with both CldU and IdU labels at the fork
(CldU+IdU) (D), and tract lengths (E) in Casp2+/+ and Casp2-/- MEF. The experiment was
performed three times, and for each condition about 300 fibers were analyzed. Error bars
represent the standard deviation from three independent experiments. **p<0.01, ***p<0.001. (F)
Representative images of replication tracts from litter-matched Casp2+/+ and Casp2-/- E1A/Ras -
transformed MEF after HU treatment showing CIdU (5-chloro-2'-deoxyuridine, green) and IdU
(5-iodo-2-deoxyuridine, red) labeled tracts are shown. (G) Casp2+/+ and Casp2-/- MEF treated
with DMSO or HU (2 mM) for 2 h followed by recovery for 24 h were stained for
γ
H2AX. γ -
H2AX foci were counted per cell and the percentage of cells with
≥
10 foci/cell was calculated
from at least 30 images per treatment. Results are the average of three independent
experiments plus or minus standard deviation. *p<0.05. (H) Representative images from (G) are
shown as maximum intensity projections of 5 image Z stacks, with nuclei shown in green and γ
H2AX foci in red. |
Bar, 50 µm. 36
bioRxiv preprint
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ed Figure 5. Loss of caspase-2 does not impair ATR or ATM checkpoints. (A) Litter-matched
Casp2+/+ and Casp2-/- E1A/Ras-transformed MEF were either left untreated (-) or treated with ith
re hydroxyurea (HU, 2 mM) for 2 h followed by replacement with fresh media. Cells were
re harvested at the indicated time points following wash-out of HU. Cell lysates were
. immunoblotted for the indicated checkpoint proteins and their phosphorylated counterparts. Actin was used as a loading control. (B) U2OS cells or CRISPR/Cas9-generated caspase-2-
deficient U2OS cells were left untreated or treated with etoposide (20
μ
00 M), camptothecin (100
μ
M), topotecan (100
μ
nt M) for 4 h or with HU (2 mM or 20 mM) for 2 h followed by replacement
he with fresh media. Cells were harvested at the indicated time points following wash-out of the
drug and
lysates were
immunoblotted
for
the
or indicated proteins. (C) HeLa cells or
37
bioRxiv preprint
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. CRISPR/Cas9-generated caspase-2-deficient Hela cells were treated as in (A). Lysates were
immunoblotted for the indicated proteins. (D) Caspase-2 wild type or deficient U2OS cells were
μ
treated with aphidicolin (1
M) for 16 h followed by replacement with fresh media. Cells were
harvested at the indicated times and lysates were immunoblotted for the indicated proteins. Each experiment is representative of 2-6 independent experiments. 38
bioRxiv preprint
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this version posted March 30, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ce Figure 6. The role of caspase-2 in cell division is independent of its ability to induce
apoptosis. (A) Litter-matched Casp2+/+ and Casp2-/- E1A/Ras transformed MEF stably ly
or expressing vector or Bcl-XL were treated with actinomycin D (0.5 µM), etoposide (50 µM), or
+/+ staurosporine (1µM) for 24 h. Apoptosis was measured by Annexin V staining. Cycling Casp2+/+
and Casp2-/- MEF stably expressing vector or Bcl-XL were untreated (B) or were treated with the he
10 indicated doses of camptothecin for 4 h (C). Cells were harvested following a 30 min BrdU (10
39
bioRxiv preprint
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phase of the cell cycle. |
Results are an average of 3 independent experiments plus or minus
standard deviation. (D) Casp2+/+ and Casp2-/- MEF stably expressing vector or Bcl-XL were
treated with the indicated doses of camptothecin for 4 h or 24 h. Apoptosis was measured at 24
h by Annexin V staining. Results are the average of 3 independent experiments plus or minus
standard deviation. 40
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Title:
Genome-wide association study and functional validation implicates JADE1 in tauopathy
Authors:
Kurt Farrell1,2, SoongHo Kim1,2, Natalia Han1,2, Megan A. Iida1,2, Elias Gonzalez3, Marcos Otero-Garcia4, Jamie Walker5, Tim Richardson5, Alan E. Renton2,6, Shea J. Andrews2,6, Brian Fulton-Howard2,6, Jack Humphrey2,6, Ricardo A. Vialle2,6, Kathryn R. Bowles2,6, Kristen Whitney1,2, Diana K. Dangoor1,2, Edoardo Marcora2,6, Marco M. Hefti7, Alicia Casella1,2, Cheick Sissoko1,2, Manav Kapoor 2,6, Gloriia Novikova2,6, Evan Udine2,6, Garrett Wong2,6,Weijing Tang30, Tushar Bhangale8, Julie Hunkapiller8, Gai Ayalon9, Rob Graham10, Jonathan D. Cherry11, Etty Cortes1,2, Valeriy Borukov1,2, Ann C. McKee11, Thor D. Stein11, Jean-Paul Vonsattel12, Andy F. Teich12, Marla Gearing13, Jonathan Glass13, Juan C. Troncoso14, Matthew P. Frosch15, Bradley T. Hyman15, Dennis W. Dickson16, Melissa E. Murray16, Johannes Attems17, Margaret E. Flanagan18, Qinwen Mao18, M-Marsel Mesulam18, Sandra Weintraub18, Randy Woltjer19, Thao Pham19, Julia Kofler20, Julie A. Schneider21, Lei Yu21, Dushyant P. Purohit1,22, Vahram Haroutunian22, Patrick R. Hof2, Sam Gandy22,38, Mary Sano22, Thomas G. Beach23, Wayne Poon24, Claudia Kawas39, María Corrada24, Robert A. Rissman25, Jeff Metcalf25, Sara Shuldberg25, Bahar Salehi25, Peter T. Nelson26, John Q. Trojanowski27, Edward B. Lee27, David A. Wolk27, Corey T. McMillan28, Dirk C. Keene29, Thomas J. Montine29,30, Gabor G. Kovacs31,32,33, Mirjam I. Lutz33, Peter Fischer33, Richard J. Perrin34, Nigel Cairns37, Erin E. Franklin34, Herbert T. Cohen35, Maria Inmaculada Cobos Sillero30, Bess Frost3, Towfique Raj2,6, Alison Goate2,6, Charles L. White III36, John F. Crary1,2
Grants: MSSM: R01 AG054008, R01 NS095252, R01 AG060961, and R01 NS086736 Tau Consortium, Genentech/Roche, Alexander Saint-Amand Fellowship (JFC), F32 AG056098 and P30 AG066514 (KF), P50 AG005138 and P30 AG066514 (VH, JFC,MS, SG, AG, PH), 75N95019C00049 (VH) K99 AG070109 (SJA) BU / MSSM / MAYO: R01 AG062348 (AM JFC DD) CUMC: P50AG008702 (JPV AFT) BU: U54 NS115266 (AM) UPENN: P30 AG010124, P01 AG017586 and U19 AG062418 (JQT), P30 AG072979 and P01 AG066597 (EBL) PITT: R01 AG066152 P30 AG066468 (JK) Banner : U24 NS072026 and P30 AG019610 The Arizona Department of Health Services, and the Michael J. Fox Foundation for Parkinson’s Research (TB) Northwestern: P30 AG013854 (MEF) Emory: P30 NS055077 and P50 AG025688 (MG) OHSU: P30 AG08017 (RW) UTSW: Winspear Family Center for Research on the Neuropathology of Alzheimer Disease (CWIII) Vienna / Toronto: Rossy Foundation and by the Safra Foundation (GGK) MADRC: BT P50 AG05134 (BH) RUSH: ADC grant AG10161 and MAP grant (JS) UCI: R01AG021055 and P50AG016573 (CK) UCSD: P30 AG062429 01 and P50 AG005131 (RR) UK: P30 AG028383 (PN) U Washington: AG05136 AG006781 and the Nancy and Buster Alvord Endowment (DK) Washington U / Knight ADRC: P30 AG066444, P01 AG003991 and P01 AG026276 (DK) Other: J.M.R. |
Barker Foundation, The McCune Foundation
Acknowledgments: The authors would like to acknowledge the neuropathology core of the Massachusetts Alzheimer Disease Research Center, the Biosample Management Repository at Genentech/Roche, the brain repository at UCI, Knight Alzheimer Disease Research Center Neuropathology Core at Washington University School of Medicine, the neurodegenerative disease brain bank at the University of California San Francisco, the Neuropathology Brain Bank & Research Core at Mount Sinai, and the following people: Ryan Cassidy Bohannan, Chad Caraway, Allison Beller, Kim Howard, Suresh Selvaraj, Ward Ortmann, Ping Shang, Jeff Harris, and Chan Foong,
bioRxiv preprint
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Affiliations:
1
Department of Pathology, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York NY, USA
2
Nash Department of Neuroscience, Friedman Brain Institute, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York NY USA
3
Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, the Sam and Ann Barshop Institute for Longevity and Aging Studies, Department of Cell Systems and Anatomy, University of Texas Health, San Antonio, San Antonio, TX, USA
4
Department of Pathology and Laboratory Medicine, Division of Neuropathology, University of California, Los Angeles, CA, USA
5
Department of Pathology, UT Health San Antonio Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, San Antonio TX, USA
6
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York NY, USA
7
Department of Pathology, University of Iowa, Iowa City IA, USA
8
Department of Human Genetics, Genentech, San Francisco CA, USA
9
Ultragenyx Pharmaceuticals, Novato, CA, USA;
10
Maze Therapeutics, San Francisco CA, USA
11
Department of Pathology (Neuropathology), VA Medical Center & Boston University School of Medicine, Boston MA, USA
12
Department of Pathology & Cell Biology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York NY, USA
13
Department of Pathology and Laboratory Medicine (Neuropathology) and Neurology, Emory University School of Medicine, Atlanta GA USA
14
Department of Pathology, Division of Neuropathology, Johns Hopkins University School of Medicine, Baltimore MD, USA
15
Department of Neurology and Pathology, Harvard Medical School and Massachusetts General Hospital, Charlestown MA, USA
16
Department of Neuroscience, Mayo Clinic, Jacksonville FL, USA
17
Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK
18
Department of Pathology (Neuropathology), Northwestern Cognitive Neurology and Alzheimer Disease Center, Northwestern University Feinberg School of Medicine, Chicago IL USA
19
Department of Pathology, Oregon Health Sciences University, Portland OR, USA
20
Department of Pathology (Neuropathology), University of Pittsburgh Medical Center, Pittsburgh PA, USA
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Departments of Pathology (Neuropathology) and Neurological Sciences, Rush University Medical Center, Chicago IL, USA
22
Department of Psychiatry, Alzheimer's Disease Research Center, James J. Peters VA Medical Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
23
Neuropathology, Banner Sun Health Research Institute, Sun City AZ, USA
24
Department of Neurology, Department of Epidemiology, Institute for Memory Impairments and Neurological Disorders, UC Irvine, Irvine CA, USA
25
Department of Neurosciences University of California and the Veterans Affairs San Diego Healthcare System, San Diego, La Jolla, California, USA
26
Department of Pathology (Neuropathology) and Sanders-Brown Center on Aging, University of Kentucky, Lexington KY, USA
27
Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
28
Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
29
Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA (previous address for Thomas J. Montine)
30
Department of Pathology, Stanford University, Stanford, USA
31
Laboratory Medicine Program & Krembil Brain Institute University Health Network Toronto Ontario Canada
32
Tanz Centre for Research in Neurodegenerative Disease and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
33
Institute of Neurology, Medical University of Vienna, Vienna, Austria (previous address for Gabor G. Kovacs, Mirjam I. Lutz, Peter Fischer)
34
Department of Pathology and Immunology, Washington University School of Medicine, St. Louis MO, USA
35
Department of Pathology, Boston University School of Medicine, Boston MA, USA
36
Department of Pathology (Neuropathology), University of Texas Southwestern Medical School, Dallas TX, USA
37
College of Medicine and Health, University of Exeter, Exeter, UK
38
Department of neurology, center for cognitive health, Icahn School of Medicine at Mount Sinai, New York NY, USA
39
Department of Neurology, Department of Neurobiology & Behavior, Institute for Memory Impairments and Neurological Disorders, UC Irvine, Irvine CA, USA
bioRxiv preprint
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract
Primary age-related tauopathy (PART) is a neurodegenerative tauopathy with features distinct from but also
overlapping with Alzheimer disease (AD). While both exhibit Alzheimer-type temporal lobe neurofibrillary
β
degeneration alongside amnestic cognitive impairment, PART develops independently of amyloid-
(A
deposition in plaques. |
The pathogenesis of PART is unknown, but evidence suggests it is associated with
β
genes that promote tau pathology as well as others that protect from A
toxicity. Here, we performed a
genetic association study in an autopsy cohort of individuals with PART (n=647) using Braak neurofibrillary
tangle stage as a quantitative trait adjusting for sex, age, genotyping platform, and principal components. We found significant associations with some candidate loci associated with AD and progressive
supranuclear palsy, a primary tauopathy (SLC24A4, MS4A6A, HS3ST1, MAPT and EIF2AK3). Genome-
wide association analysis revealed a novel significant association with a single nucleotide polymorphism on
chromosome 4 (rs56405341) in a locus containing three genes, including JADE1 which was significantly
upregulated in tangle-bearing neurons by single-soma RNA-seq. Immunohistochemical studies using
antisera targeting JADE1 protein revealed localization within tau aggregates in autopsy brain from
tauopathies containing isoforms with four microtubule-binding domain repeats (4R) and mixed 3R/4R, but
not with 3R exclusively. Co-immunoprecipitation revealed a direct and specific binding of JADE1 protein to
tau containing four (4R) and no N-terminal inserts (0N4R) in post-mortem human PART brain tissue. Finally,
knockdown of the Drosophila JADE1 homolog rhinoceros (rno) enhanced tau-induced toxicity and apoptosis
in vivo in a humanized 0N4R mutant tau knock-in model as quantified by rough eye phenotype and terminal
deoxynucleotidyl transferase dUTP nick end-labeling (TUNEL) in the fly brain. Together, these findings
indicate that PART has a genetic architecture that partially overlaps with AD and other tauopathies and
suggests a novel role for JADE1 as a mediator of neurofibrillary degeneration. β
)
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Primary age-related tauopathy (PART) is nearly ubiquitously observed with varying degrees of severity in
the brains of aged individuals characterized by the presence of neurofibrillary tangles (NFT) composed of
abnormal aggregates of tau protein. These NFTs are regionally, morphologically, biochemically and
ultrastructurally identical to those in early to moderate stage Alzheimer disease (AD), yet develops in the
absence of A
β plaques 1. A neuropathological diagnosis of PART is separate from a clinical diagnosis of
cognitive impairment; hence individuals with PART can be normal, mildly cognitively impaired (MCI) or have
dementia 2,3. Most individuals with PART remain cognitively normal, however some develop amnestic
cognitive changes 4-7. The similarities between PART and AD allows for a unique opportunity to focus
specifically on mechanisms of tau-mediated AD-type neurodegeneration. The neuropathological diagnosis
of PART is complicated by accompanying age-related comorbid dementing pathologies, therefore
discovering unique molecular drivers is challenging antemortem 8-13. |
Given the similarities between PART
and AD NFTs, PART as a diagnostic construct would have greater value if it were shown to arise
β independently 14,15. Alternatively, PART might be a component of the AD spectrum, and A
pathology might
have eventually developed in such individuals had they lived longer 15,16. Thus, the question remains as to
what extent a neuropathological diagnosis of PART diverges from or has similar risk factors to AD and other
dementias 17,18. Much of the mechanistic knowledge surrounding tauopathy stems from genetic studies 19. Autosomal
dominant mutations in the microtubule-associated protein tau gene (MAPT) in coding regions can interfere
with microtubule binding or promote transition to toxic forms. Also, mutations in splice sites disrupt
alternative pre-mRNA splicing of the tau mRNA transcript, modifying the ratio of three repeat (3R) and four
repeat (4R) tau isoforms leading to downstream pathological sequelae 20. Additionally, alternative splicing of
the N-terminal exons may also play a role in modulating toxic tau 21,22. However, MAPT mutations are rare,
whereas PART occurs sporadically and nearly ubiquitously with advanced age. Thus, PART provides a
unique opportunity to understand common genetic drivers of tauopathy and their association with
abnormalities in tau proteostasis and isoform expression. Prior research focusing on common genetic
variation has identified two distinct haplotypes in the MAPT 17q21.31 locus defined by a large ~900kb
inversion region that gives rise to two major haplotypes, H1 and H2. The more common H1 haplotype has
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been associated with increased risk for PART and several other sporadic tauopathies including APOE
negative AD, progressive supranuclear palsy, corticobasal degeneration, and even Parkinson disease
which is not classically considered a tauopathy 23-28. Research focusing on the genetics of PART have
consistently failed to show an association with the APOE
ε 4 allele, the strongest risk locus in AD 28-31. Outside of PART, one of the largest AD GWAS has identified 29 risk loci implicating 215 potential causative
genes 32. However, it has been suggested that the overwhelmingly strong signal on the APOE locus could
mask associations independently related to tauopathy 23. Another study examining candidate genes in aging
cohorts confirmed the lack of the association between APOE
ε 4 and PART, as well as a decreased
frequency of AD risk alleles in the BIN1, PTK2B, and CR1 loci 33. Taken together, these data suggest an
unexplored genetic risk driving tauopathy that might be revealed by conducting genome-wide association
studies in PART. While PART represents a compelling opportunity to focus on amyloid-independent mechanisms of
neurofibrillary degeneration, assembling an autopsy cohort large enough to assesses its genetics on a
genome-wide scale has not yet been attempted. |
In collaboration with twenty-one domestic and international
brain biorepositories, we performed the first genome-wide association study in PART and compared our
findings to known tauopathy risk loci. We then localized expression of candidate genes in our strongest risk
locus (chromosome 4q28.2) using single cell RNA-sequencing and immunohistochemistry. Finally, we
validated our findings biochemically in human postmortem brain and functionally using an in vivo Drosophila
model. The work presented here not only furthers the investigation of the genetics of PART, but also
suggests a novel role for JADE1 in tauopathy. 4-
bioRxiv preprint
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Cohort
Fresh-frozen brain tissue was obtained from the contributing centers (Supplementary Table 1). All tissue
was used in accordance with the relevant guidelines and regulations of the respective institutions. The
neuropathological assessments were performed at the respective centers using standardized criteria
outlined by the National Institutes on Aging-Alzheimer’s Association (NIA-AA) and National Alzheimer
Coordinating Center (NACC) which has a high degree of interrater reliability among centers 34,35. Inclusion
criteria were individuals with normal cognition, mild cognitive impairment (any type) and dementia. Cognitive
status was determined either premortem or postmortem by a clinical chart review, mini-mental score, or
clinical dementia rating 36,37. Both sexes were included and ages ranged from 51 to 108 years at death. Neuropathological inclusion criteria were Braak tangle stage of 0-IV and neuritic amyloid plaque severity
CERAD score of 0 38,39. In addition, tissue sections were obtained and reevaluated by the study
investigators to confirm the lack of amyloid and degree of PART tau pathology40. Clinical exclusion criteria
were motor neuron disease, parkinsonism, and frontotemporal dementia. Neuropathological exclusion
criteria were other degenerative diseases associated with NFTs (i.e., AD, progressive supranuclear palsy
[PSP], corticobasal degeneration [CBD], chronic traumatic encephalopathy [CTE], frontotemporal lobar
degeneration-tau [FTLD-tau], Pick disease, Guam amyotrophic lateral sclerosis/parkinsonism-dementia,
subacute sclerosing panencephalitis, globular glial
tauopathy). Individuals with aging-related
astrogliopathy (ARTAG) were not excluded 41. Genotyping
High-throughput isolation of DNA was performed using the MagMAX DNA Multi-Sample Ultra 2.0 Kit on a
KingFisher Flex robotic DNA isolation system (Thermofisher, Waltham, MA). Fresh frozen cortical brain
tissue (20-40 mg) was placed into a deep-well plate and treated with 480 µl of Proteinase K mix (Proteinase
K, phosphate buffered saline [pH 7.4], Binding Enhancer) and incubated overnight at 65°C at 800 rpm on a
shaking plate. |
Genomic DNA was isolated and purified using magnetic particles. DNA quality control was
performed using a nanodrop spectrophotometer (concentration > 50ng/µl, 260/280 ratio 1.7-2.2). Genotyping was performed using single nucleotide polymorphism (SNP) microarrays (Infinium Global
tau
bioRxiv preprint
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this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening Array v2.4. or Infinium OmniExpress-24; Illumina, San Diego, CA). Raw genotype files were
converted to PLINK-compatible files using GenomeStudio software (Illumina, San Diego, CA). Genetic analysis
PLINK v1.9 was used to perform quality control 42. SNP exclusion criteria included minor allele frequency
<1%, genotyping call-rate filter less then 95%, and Hardy-Weinberg threshold of 1×10-6 43. Individuals with
discordant sex, non-European ancestry, genotyping failure of >5%, or relatedness of >0.1 were excluded. A
principal component analysis (PCA) was performed to identify population substructure using EIGENSTRAT
and the 1000 genomes reference panel 44,45. Samples were excluded if they were five standard deviations
away from the European population cluster (Supplementary Fig. 4). PCA was performed again on the
genomic data with non-Europeans excluded to generate new PCs, of which the first four were used as
covariates in the regression model. All data was imputed on the University of Michigan server using
minimac3 and HRC reference panel 46,47. Imputed variants with MAF <0.01 and a dosage R2 <0.7 were
excluded. Downstream analyses were based on the most likely genotype. A quantitative trait association
test was run on 647 PART cases vs. Gaussian-normalized Braak stage using conditional linear regression
and age, sex, principal component 1-4 and SNP chip array as covariates. The analysis was run separately
on each genotyping array and a meta-analysis was performed using METAL 48. Regional genome-wide
association plots were created with LocusZoom, other plots were created using R 49. Single-cell mRNA profiling in tangle-containing neurons
Identification of differentially expressed genes in single neuronal somata with and without NFTs was
performed by analyzing a transcriptomic dataset of isolated neurons from post-mortem human brain from
individuals with and without AD reported by Otero-Garcia et. al. 2021 50. This dataset consists of single
neuronal soma transcriptomes from Brodmann area 9 subjected to fluorescence-activated cell sorting
(FACS) using p-tau (AT8) and MAP2 antisera to differentiate single NFT-positive and NFT-negative cells. Immunohistochemistry
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μ
Formalin-fixed paraffin-embedded tissue sections (5
m) on charged slides were baked at 70ºC and
immunohistochemistry (IHC) was performed on a Ventana Benchmark XT automatic stainer (Rouche,
Tucson, AZ). Antigen retrieval was done using citric acid buffer (CC1) for 1 hr followed by primary antibody
incubation for approximately 40 min. A secondary antibody, 3,3'-diaminobenzidine (DAB) was then applied. For slides that were doubled-labeled DAB and alkaline phosphatase were used for visualization. Slides
were stained with antibodies to JADE1 (1:100, Proteintech, Rosemont, IL) and hyperphosphorylated tau (p-
tau, AT8, 1:1000, Invitrogen, Waltham, MA). To ensure specificity of the JADE1 antisera, a peptide
competition was performed using a blocking peptide. The antisera and paired peptide were pre-incubated
for 24 hr. Whole slide images (WSI) were visualized and scanned using an Aperio CS2 digital slide scanner
(Leica Biosystems, Wetzlar Germany). In addition to PART cases, neuropathologically confirmed cases of
AD, PSP, CTE, CBD, argyrophilic grain disease (AGD) and Pick disease (PiD) (n=3 for each, Supplemental
table 6) were examined for convergent or divergent staining patterns. Biochemical analysis
Western blots were performed using homogenized fresh-frozen brain tissue. Samples were placed in a
micro-tube homogenizer (SP Bel-Art, Wayne, NJ) in 10 volumes (wt/vol) of ice-cold Pierce RIPA buffer
(Thermo Fisher Scientific, Waltham, MA) containing Halt protease and phosphatase inhibitor cocktail
μ
(Thermo Fisher Scientific), and incubated on ice for 30 min. For each sample, 20
g of proteins were boiled
in 1x Laemmli sample buffer (Bio-Rad, Hercules, CA) for 5 min, run on 10% Criterion TGX Precast Gels
(Bio-Rad, Hercules, CA), blotted to nitrocellulose membranes, and stained with JADE1 antisera (1:2000). Horseradish peroxidase-labeled secondary anti-rabbit antisera (1:20,000; Vector Labs, Burlingame, CA)
was detected by SuperSignal West Pico PLUS Chemiluminescent Substrate or Pierce ECL Western
Blotting Substrate (Thermo Fisher Scientific). To quantify and standardize protein levels, GAPDH was
detected with GAPDH antisera (6C5, 1:20,000; Abcam, Cambridge, MA) and total protein was detected with
Amido Black (Sigma-Aldrich, St. Louis, MO) as previously described 51. Chemiluminescence was measured
in a ChemiDoc Imaging System (Bio-Rad), and relative optical densities were determined by using
AlphaEaseFC software, version 4.0.1 (Alpha Innotech, San Jose, CA), normalized to GAPDH and total
protein loaded. bioRxiv preprint
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https://doi.org/10.1101/2021.06.30.450599
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Co-immunoprecipitation (IP) assays were performed using
fresh-frozen brain
tissue was
homogenized in a glass-Teflon homogenizer at 500 rpm in 10 volumes (wt/vol) of ice-cold lysis buffer
containing 50 mM Tris, pH 7.8, 0.5 % NP40, 150 mM NaCl, 1 mM EDTA, and Halt protease and
phosphatase inhibitor cocktail (Thermo Fisher Scientific). |
Samples were incubated on ice for 30 min,
centrifuged at 1000 x g for 10 min and the supernatant was collected as an input and used for
immunoprecipitation. In a microcentrifuge tube, 70 ul of supernatant, Lysis buffer (930 µl) and 2 µg of either
JADE1 antisera or anti-0N tau antisera (EPR21726, Abcam, Cambridge, MA) were combined and incubated
overnight at 4 °C. Two controls were also set up, one without the antisera and the other with 2 µg of IgG
isotype control antisera, either normal rabbit IgG (PeproTech, Rocky Hill, NJ) or Mouse IgG1 kappa, (clone:
P3.6.2.8.1, Thermo Fisher Scientific). Twenty µl of Pierce Protein A/G Agarose beads (Thermo Fisher
Scientific) was added to each reaction, and the mixture was incubated for 1 hr at 4 °C. Agarose beads were
pelleted at 1000 x g for 5 min at 4 °C, supernatant was removed, 1 ml of ice-cold lysis buffer was added,
and pellet was washed by inverting tube several times. Beads were washed 4 times, each time repeating
the centrifugation step above. After the final wash, pelleted beads were resuspended in 40 µl of 1x Laemmli
sample buffer (Bio-Rad, Hercules, CA) and boiled for 5 min. The samples were then centrifuged to pellet the
agarose beads followed by SDS-PAGE analysis of the supernatant. Fifteen µl of samples for JADE1
detection and 5 µl for tau with tau isoform ladders (rPeptide, Watkinsville, GA) were run on 10% PROTEAN
TGX Precast Gels (Bio-Rad, Hercules, CA), blotted to nitrocellulose membranes, and stained with JADE1
antisera (1:2000), total tau antisera (HT7, 1:3000; Thermo Fisher Scientific), three microtubule repeat
domain tau antisera (3R, 8E6/C11, 1:2000; MilliporeSigma, St. Louis, MO), four microtubule repeat domain
tau antisera (4R, 1:2000; Cosmo Bio, Carlsbad, CA), pThr231 tau antisera (RZ3, 1:200; a gift from Dr. Peter
Davies), pThr181 tau antisera (PHF1, 1:500; a gift from Dr. Peter Davies), pSer202 tau antisera (CP13,
1:500; a gift from Dr. Peter Davies), pSer202/pThr305 tau antisera (AT8, 1:1000; Thermo Fisher Scientific),
pSer214 tau antisera (S214; 44-742G, 1:1000; Thermo Fisher Scientific, Waltham, MA), or pSer409 tau
antisera (PG5, 1:200; a gift from Dr. Peter Davies). Horseradish peroxidase-labeled conformation-sensitive
secondary anti-mouse IgG for IP or anti-rabbit VeriBlot for IP Detection antibody (both at 1:20000; Abcam,
Cambridge, MA) was detected by SuperSignal West Femto Maximum Sensitivity substrate (Thermo Fisher
Scientific). bioRxiv preprint
doi:
https://doi.org/10.1101/2021.06.30.450599
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this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A dephosphorylation assay was performed using fresh-frozen brain tissue homogenized with a
glass-Teflon homogenizer at 500 rpm in 10 volumes (wt/vol) of ice-cold lysis buffer containing 50 mM Tris,
pH 7.8, 0.5 % NP40, 150 mM NaCl, and Halt protease inhibitor cocktail (Thermo Fisher Scientific, Waltham,
MA). |
Samples were incubated on ice for 30 min, centrifuged at 1000 x g for 10 min, and supernatant was
collected. Reaction mixtures (51 µul) consisted of 39 µl of supernatant, 1 µl of protease inhibitor cocktail, 5
µl of 10X NEBuffer for protein metallophosphatases, 5 µl of 10 mM MnCl2, 1 µl of lambda protein
phosphatase (New England BioLabs, Ipswich, MA). Each mixture was incubated at 30 °C for either 1, 2, 3,
or 4 h. Additional 1 µl of lambda protein phosphatase and 1 µl of protease inhibitor cocktail were added in
each mixture every hour. Proximity ligation assay
μ
A proximity ligation assay was performed on formalin-fixed paraffin embedded 5
m-thick hippocampal
sections mounted on charged slides using a Duolink kit (MilliporeSigma, St. Louis, MO). Sections were
deparaffinized and incubated in sodium citrate buffer (10 mM sodium citrate, 0.05 % Tween 20, pH 6.0) at
95 °C for 20 min, washed in running water, incubated in 0.2 % Tween 20 in PBS at room temperature for 20
min, and washed in PBS 3 times for 5 minutes.. From blocking, assays were performed using the in situ red
starter kit according to the manufacture’s protocol with JADE1 antisera (1:20) and 0N tau antisera (1:500;
BioLegend, San Diego, CA). Two control assays were also performed, one with JADE1 antisera only, and
′ ,6-diamidino-2-phenylindole the other with anti-0N tau antisera. All samples were counterstained with 4
(DAPI). Sections were imaged using an Axioview fluorescent microscope (Carl Zeiss, Oberkochen,
Germany) and processed using Zen Blue software. In vivo Drosophila model
Drosophila stocks, crosses, and aging were performed at 25 °C for the duration of the experiment and an
equal number of male and female flies were used for each experiment. The GAL4-UAS expression system
and the pan-neuronal elav-GAL4 driver were used to control transgenic human tau and rno expression. Analyses were run on four fly groups (Bloomington stock rnoRNAi line number 57774): elav-GAL4 driver in
the heterozygous state (control, elav-GAL4/+), elav-Gal4 positive plus rnoRNAi positive group (control +
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this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. rnoRNAi, elav-GAL4/+;UAS-rnoRNAi/+), elav-Gal4 positive transgenic human UAS-tauR406W tau group (0N4R
Tau, elav-GAL4/+;UAS-TauR406W), elav-Gal4 positive human transgenic UAS-tauR406W tau plus rnoRNAi
positive group (0N4R Tau + rnoRNAi, elav-GAL4/+;UAS-TauR406W/UAS-rnoRNAi). An additional rnoRNAi lines
were used but was did not produce progeny when crossed to tau transgenic Drosophila, as often occurs
with strong enhancers (Bloomington stock rnoRNAi line number 62880). Flies were aged to 10 days, at which
point terminal deoxynucleotidyl transferase dUTP nick end-labeling (TUNEL) assay was performed in the fly
brain (n=6 per genotype) and a blinded assessment of the fly eye phenotype was performed (n=16 total). |
μ
TUNEL was performed on 4
m formalin-fixed paraffin-embedded fly heads. Quantification of TUNEL-
positive nuclei was performed throughout the entire brain using DAB-based detection and bright field
microscopy. Fly eye phenotype scoring was performed blindly using light microscopy (n=16). The blinded
rater semi-quantitively evaluated the eye for four distinct qualities including roughness, size, shape and
conical shape on a 1 to 5 scale (five being the most severe phenotype) and an average summary score was
calculated. Statistical analysis
For GWAS, statistical analysis was performed in PLINK and our genome wide significance value was < 5 ×
10-8, which is Bonferroni-corrected for all the independent common SNPs across the human genome. Genome wide suggestive significant value was set at < 5 × 10-6. All other statistical analyses were
performed in R. For non-normally distributed data a Wilcox test was used to test for significance, and an
ANOVA was used for normally distributed data. bioRxiv preprint
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We assembled the first cohort of individuals with primary age-related tauopathy (PART) in wh
which all
individuals were neuropathologically confirmed to be devoid of any neuritic plaques. Autopsy brain
ain tissue
samples (n=647) were obtained from 21 brain banks. While each center had performed a compreh
rehensive
neurodegeneration workup, we restained and reassessed these cases for PART pathology as a com
omponent
of our ongoing histological studies40. Neurofibrillary tangles (NFTs) were assessed using the Braak
k staging
system which ranged from 0-IV with all stages well represented, as is consistent with PART (Table
le 1). The
average age of death was 83 years old, and the number of male and females in the stud
tudy was
approximately equal. Amongst the assigned Braak stages, stage II had the highest abundance (
(n=189),
relatively equal amounts in stages III (n=152) and I, (n=142), and the lowest in stages 0 (n=71)
1) and IV
(n=93). Sixty-six percent of the cases were cognitively normal, however amongst stages I-IV, there
re was an
equal number of cognitively impaired individuals. Braak staging performed at each center w
was not
disproportionally skewed (Supplementary Fig. 1a). Lastly, we investigated the effect of age of de
death on
Braak stage with respect to cognitive status and found a positive correlation that does not sign
gnificantly
change when cognitive status is accounted for in the model (Supplementary Fig. 1a,b). In summa
mary, our
cohort consists of primarily older individuals, with a range of clinical cognitive symptoms, as well as a
s a broad
spectrum of regional tau distribution, demonstrating the diversity of both the clinical and neuropatho
thological
features of the condition. Using this cohort, we ran a quantitative trait association analysis across the entire genome to
to identify
novel genetic risk loci in PART. |
Using Braak stage as a quantitative trait revealed a genom
me wide
β
associated signal on chromosome 4q28.2 (Fig. 1a, rs56405341; linear regression
=0.35, st
standard
error=0.06, p=4.82 × 10-8) and suggestive signals in 14 other loci (Table 2 and Supplementary Table
le 3). Our
λ
model, which adjusted for age, sex, and genotyping platform and principal component 1-4 produced
ed a
of
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1.04 (Supplementary Fig. 2a). The genome wide significant variant (rs56405341) has a mino
nor allele
frequency of 0.27. The locusZoom plot indicates our significant SNP is not directly in an intron
n of any
specific gene, but near C4orf33, SCLT1 and JADE1 (Figure 1b). We also observed in our regional p
l plot that
the lead SNP has a total of 22 other SNPs in Linkage Disequilibrium (r2 > 0.8, Supplementary ta
table 4). Further examination of the homozygous and heterozygous alleles using strip chart shows the sig
significant
relationship between higher Braak stage and homozygous minor allele carriers (Figure 1 c, AA-AG p
p=0.024,
AA-GG p=3.3 × 10-5, AG-GG p=7.2 × 10-5). Separate analyses two different genotyping chip valida
dated our
findings by showing replication of the signal on each genotyping platform, as well as comparable (cid:3)
le lambda
(cid:3)
β(cid:3)
(cid:3)
(cid:3)
(cid:3)
λ = 1.03,
1.11× 10-6,
0.05, p
0.27, SE λ
values (Infinium OmniExpress-24, n=440, (cid:3) (cid:3)
=
3, Global
=
=
(cid:3)
β(cid:3)
(cid:3)
(cid:3)
1.42× 10-2,
=
screening array
=
0.08, p
=
0.20, SE
= 1.01, Supplementary Table 2 and Fig. ig. 5 a-d). The individual summary statistics derived from the separate chip analysis were then combined to
to run a
meta-analysis, and the resulting p-value was similar to value on the combined analysis, as w
s well as
agreement
in
the
direction
of
effect
tested
allele
(p=
(cid:3)
5.61
×
10-8). Replication of our SNP in an independent cohort proved challenging given the lack of d
datasets
containing similarly neuropathologically ascertained individuals with PART. Nevertheless, we identif
tified two
other SNPs (rs4975209 and rs10009321) in the 4q28.2 locus in a prior Alzheimer disease (AD) GW
WAS that
also used Braak as a quantitative trait, however they were not in high D’ with rs56405341, our lead
d SNP 52.
β
We also observed in a separate AD GWAS using the cerebral spinal fluid A
42/40 ratio (dichot
otomized
normal and abnormal) another SNP in the region (rs13129839) in high D’ with our lead and sup
upporting
SNPs (>0.89), at a genome-wide suggestive significance level and with a positive (protective) odds
s ratio (p
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All rights reserved. No reuse allowed without permission. = 9.0 × 10-6, OR = 0.043) 53. Taken together, these two independent genetic studies in AD sugg
ggest the
signal in our GWAS might not be spurious. We also examined candidate SNPs previously found to be associated with AD and prog
ogressive
supranuclear palsy (PSP) in prior GWAS studies to explore convergent and divergent genetic risk (T
(Table 3,
Supplementary table 5). Of the 52 candidates investigated, we found five associated with PART (SLC
SLC24A4,
MS4A6A, HS3ST1, MAPT, and EIF2AK3). rs12590654, which is associated with SLC24A4, had the
e highest
significance level (p=0.001). rs1582763, rs2081545, rs7935829, were all associated with MS4A6A (
(p=0.01,
0.01, 0.02 respectively). The remaining AD SNP, rs7657553, was associated with HS3ST1 (p=0.0
0.02). We
found two variants that overlapped with PSP risk. rs242557 (p=0.02) in the MAPT locus, and rs7
s7571971
(p=0.03) in the EIF2AK3 locus. In summary, seven of the 52 probed risk AD and PSP SNPs
showed
significant associations (p<0.05) in PART. Next, we refocused on our strongest association at the 4q28.2 locus. Examination o
of RNA
expression quantitative trait loci (eQTL) using the Brain-eMeta eQTL summary data (derived fr
from six
datasets; Genotype-Tissue Expression v6, the CommonMind Consortium, Religious Orders Stu
tudy and
Memory and Aging Project, the Brain eQTL Almanac project, the Architecture of Gene Expressio
sion, and
eQTLGen) did not contain significant SNP associated eQTLs for any of the genes in the 4q28.2 lo
locus 54. Because our GWAS quantitative trait was specific to tau pathology, we then examined mRNA exp
xpression
levels of all the genes contained in the locus using a novel single-cell soma RNA sequencing datase
set which
measured transcriptomic changes specifically in tangle-bearing neurons and non-tangle-bearing n
neurons
(Fig. 2a-c). Using this dataset, we found that tangle-bearing excitatory neurons significantly differ
ferentially
expressed JADE1 compared to non-tangle-bearing excitatory neurons (p=1.04×10-61). Conversely C
C4orf33
and SCLT1 had low levels of expression regardless of cell type and tangle status. Additionally, we ob
observed
an upregulation of JADE1 expression in tangle-bearing inhibitory neurons however it was stat
tatistically
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of excitatory neurons in which JADE1 was significantly differentially expressed (adjusted p=4.55×10-15,
7.82×10-8). We then examined the relative average expression and percentage of cells expressing JADE1
and observed increases in both metrics in tangle-containing neuronal populations compared to non-tangle-
containing neuronal populations (Fig. |
2d-f). SCLT1 and C4orf33 had nominal expression levels in a
substantially smaller percentage of neurons. Taken together, these data suggest increased JADE1
expression is unique to specific populations of neurons which also contain NFTs. Because we did not
observe significant differences in C4orf33 or SCLT1 expression across the two groups, and the signals
were very low, these data suggest that JADE1 as the strongest candidate gene in the locus. Given our evidence that JADE1 is genetically and transcriptionally associated with NFT pathology,
we conducted an immunohistochemical study using specific antisera to JADE1 in our collection of post-
mortem tauopathy brain tissue (Fig. 3). We assessed tauopathies that are known to involve preferentially
tau isoforms with 3 microtubule-binding domain repeats (3R), 4 microtubule-binding domain repeats (4R) or
a mixture of the two. We found strong and specific JADE1 immunopositivity in structures morphologically
indicative of mature aggregate containing intracellular NFT in not only PART, but also the other mixed
3R/4R tauopathies (i.e., AD and chronic traumatic encephalopathy, Fig 3a-f). NFT pathology in PSP,
corticobasal degeneration (CBD), and argyrophilic grain disease (AGD) were also immunopositive for
JADE1 (Fig. 3g-l). Notably, gliofibrillary pathology in these diseases (i.e., aging-related tau astrogliopathy,
tufted astrocytes, and astroglial plaques) were also immunopositive. Surprisingly, no signal was detected in
NFT in Pick disease (PiD), a predominately 3R tauopathy (Fig. 3m,n). Double labeling experiments showed
that early pre-NFT were negative for JADE1 suggesting that this factor begins to coalesce into NFT at the
transition to the aggregate stage (Fig. 3o). To ensure the antibody was specifically targeting the JADE1
peptide and not binding non-specifically to NFT pathology, we blocked the JADE1 antibody and did not
observe any staining in neurons that had the morphological features of an NFT (Fig. 3p). These findings
indicate that JADE1 protein expression is localized specifically to cells with mature NFTs. Furthermore,
JADE1 upregulation does not occur in PiD, the only 3R tauopathy examined, suggesting isoform specificity
of expression. bioRxiv preprint
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. We then biochemically examined protein expression of JADE1 by western blotting using crude
lysates derived from the entorhinal cortex and hippocampus proper (cornu ammonis 1-4 and dentate gyrus)
of PART and AD individuals. JADE1 exists as 2 isoforms, JADE1 long (JADE1L) and JADE1 short
(JADE1S), both of which contain proline, glutamic acid, serine, threonine (PEST) domains and 2 PHD
fingers. However, the long form is 333 amino acid sequences greater in length and contains an additional
PEST domain as well as a nuclear localization signal (Fig 4a). |
A Western blot analysis revealed a strong
signal in both brain regions and diseases for JADE1S, but no bands were observed in the expected weight
for JADE1L, indicating the observed signal is specific to the short isoform of JADE1 (Fig. 4b). This signal is
in agreement with the immunohistochemical data given we did not observe a nuclear JADE1 signal which
would suggest JADE1L, the form containing the nuclear localization signal, was also expressed. Furthermore, cytoplasmic colocalization of JADE1 and NFTs immunohistochemically raises a possibility that
they form a functional complex in tauopathy brains. To examine this, co-immunoprecipitation (IP) was
performed using crude brain lysate from PART individuals as the input. We first IPed using JADE1 antisera
and observed a banding pattern that suggests JADE1 pulls down tau near the molecular weight of the 0N4R
isoform (Fig. 4 c). To confirm these results, we reverse co-IPed JADE1 with 0N tau antisera and observed a
banding pattern indicating the JADE1S isoform was pulled down (Fig. 4d). To investigate the observed
JADE1 immunoprecipitated lower banding pattern (Fig. 4c), we treated this form with protein phosphatase
over time and observed a shift over time to the expected 58 kDa weight (Fig. 4e), indicating JADE1 anti-
sera was not able to recognize the more abundant native phosphorylated JADE1S and instead a low
abundant species of the protein. This data suggests the predominant form of the JADE1S input is likely
being biologically modified (i.e., phosphorylation, acetylation, etc.). Staining with C-terminal isoform-specific
anti-tau antibodies (3R and 4R tau) revealed that the co-immunoprecipitated tau was predominantly 4R,
thus 0N4R tau (Fig. 4f,g). We then ran western blots for the IPed JADE1 using multiple phospho-tau site-
specific antibodies including RZ3 (pThr231), PHF1 (pThr181), CP13 (Ser202), AT8 (Ser202, pThr205),
S214 (Ser214) and PG5 (Ser409) of which multiple phosphor tau epitopes showed prominent reactivity (Fig. 4h). Lastly, to confirm protein-protein interactions of Jade1S and 0N tau, proximity-ligation assays (PLA)
were performed in fixed hippocampus from PART individuals. The PLA technique utilizes one pair of
oligonucleotide-labeled antibodies that detects different epitopes of the two proteins in close proximity
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indicates the close proximity of these two proteins (Fig. 4i), whereas control samples lacking one of the two
antisera did not show any signal (Supplementary Fig. 7e,f). In summary, the biochemical data strongly
indicates a JADE1S, 0N4R tau immunocomplex. Lastly, we asked whether JADE1 plays a functional role in tau-induced neurotoxicity. |
We used a
Drosophila model that overexpresses human mutant 0N4R tau55 as well as RNAi-mediated reduction of
rhinoceros (rno), the highest matched JADE1 human ortholog in Drosophila. We first blindly evaluated the
fly eye phenotype using a semi-quantitative assessment of eye size, roughness, overall shape, and conical
shape and saw a significant increase in eye severity between tau transgenic Drosophila and tau transgenic
Drosophila with rno knockdown (Fig. 5a-e, p=8.7 ×10-5). We did not observe significant differences between
rnornai and control group in the absence of transgenic tau. To directly quantify neurodegeneration in the
Drosophila brain, we quantified TUNEL-positive cells throughout the Drosophila brain. We find that rno
knockdown significantly enhances neurotoxicity in tau transgenic Drosophila but is not sufficient to induce
neurotoxicity in control flies based on TUNEL staining (p=0.008, Fig. 5f-i). These data provide in vivo
evidence that JADE1/rno loss plays a mechanistic role in promoting neurotoxicity in tauopathy, and suggest
that proper functioning of JADE1/rno is protective
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Genome-wide association studies (GWAS) have enabled advances in our understanding of sporadic
tauopathies 19,56-61. Yet, in the context of the growing numbers of genes associated with Alzheimer disease
(AD) 32, direct links with tau proteinopathy have been challenging to pinpoint as association signals show
minimal overlap with factors classically implicated in tauopathy (e.g., proteostasis, tau protein kinases, etc.). This is not surprising given the ubiquity and heterogeneity of tau and other pathological changes in the
aging human brain that are variably associated with cognitive impairment, which is the phenotype in most
genetic studies despite the fact that it is a non-specific trait. We performed an autopsy-based GWAS, which
minimizes classification errors and other issues with clinical studies of dementia, assembling the largest
cohort of post-mortem brain tissues from aged individuals devoid of amyloid pathology with a goal of
identifying factors independently associated with primary age-related tauopathy (PART). In doing so, we
sought to provide genetic evidence that might clarify the controversial relationship between PART and AD,
which it closely resembles neuropathologically. We failed to find an association with APOE ε4, the strongest
common risk allele for sporadic late-onset AD. We did, however, find associations with other candidate loci
in AD and progressive supranuclear palsy (SLC24A4, MS4A6A, HS3ST1, MAPT and EIF2AK3), as well as
a novel genome-wide significant association at the chromosome 4q28.2 locus. Our data indicate that among
genes in this locus, only the gene for apoptosis and differentiation in epithelia 1 (JADE1), a member of a
small protein family that serves as a multifunctional adaptor implicated in renal and other cancers62-64, is
upregulated in tangle-bearing neurons on both the mRNA and protein levels. |
This accumulation of JADE1
protein in NFT is not specific to PART but occurs in AD and all tauopathies with accumulation of 4R
isoforms, but not in Pick disease which is a 3R tauopathy, indicating that this is generally a shared feature. We also show that JADE1 binds 0N4R tau, an isoform proposed to be a critical driver of tau pathology65,66. Finally, experiments in Drosophila show that reducing expression of the JADE1 homolog rhinoceros (rno)
exacerbates tau-induced neurotoxicity in vivo. Together, these findings strongly argue that JADE1 is a
factor broadly capable of protecting neurons from neurofibrillary cell death that links PART to the tauopathic
component of AD. We confirm that the genetics of PART has a partial overlap with sporadic late-onset AD and
replicated the consistent finding showing the lack of a signal in the APOE locus despite its strong
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(cid:2)
allele frequency which distinguishes PART neuropathologically and genetically from AD 71-73. We have
reported the frequency of the APOE
ε 4 allele is lower in PART28 and other studies have revealed similar
findings in independent cohorts 6,33. It should be noted that these values can fail to reach significance when
cross comparing groups with varying degrees of neuritic plaque pathology 33,74. Recent studies in mice and
humans have indicated that the APOE
ε 4 allele may exacerbate tau pathology independently of Aβ
plaques75, however other human studies failed to show an interaction76,77. These results add to the strong ε 4 regardless of amyloid plaque pathology. evidence that PART is entirely independent of APOE
17q21.31 MAPT locus is the strongest genetic risk factor for PSP 26, which we and others had
previously reported is also associated with PART 28,31. The MAPT H1 haplotype has also been associated
with AD 78-80, however this region has a complex haplotype structure and may be more important in specific
AD subgroups given the modest signal and variable findings in these association studies81,82. Intriguingly, in
one AD GWAS using clinically ascertained individuals, removal of APOE
ε 4 carriers enhanced signals in the
17q21.31 locus 23. In the present study, there was only a modest association of MAPT with PART. This
result may stem from differences in cohort selection with previous studies focusing on extremes, while we
included a range of pathological severity, especially mildly affected individuals. Together, these data
highlight that further investigation of the role of 17q21.31 MAPT locus in PART is warranted. With regards to genes other than APOE and MAPT, we found four additional association signals in
PART that overlap with either AD or PSP. |
Eukaryotic translation initiation factor 2 alpha kinase 3 (EIF2AK3)
encodes an endoplasmic reticulum (ER) membrane protein critical for the unfolded protein response
(UPR)83,84. Activation of the UPR has been observed and positively correlated with tau pathology, but not
with A
β plaque burden, in the hippocampus of aged cognitively normal individuals 83. Solute carrier family
24 member 4 (SLC24A4), a gene in the locus most strongly associated with PART and AD, is a member of
the potassium-dependent sodium/calcium exchanger protein family and is involved in neural development,
however little is known about its possible function in AD 85,86. Additionally, we identified an association of
PART with the membrane spanning 4-domains A6A (MS4A6A) locus, which contains the binding regions for
the transcription factor PU.1 which is selectively expressed in brain microglia and macrophages 87. The last
overlapping genetic locus that contains heparan sulfate-glucosamine 3-sulfotransferase 1 (HS3ST1), has
2
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. been suggested to modulate heparan sulfate proteoglycans as receptors for the spreading of tau 88,89. Taken together, these data are compatible with the hypothesis that the genes we identified in our GWAS
are possible modulators of tau pathology. Our study highlighted a novel locus on chromosome 4q28.2, that previously gave a suggestive
signal in an independent autopsy GWAS in AD that also used Braak NFT stage as an endophenotype and a
second GWAS focusing on dichotomized CSF Aβ positivity53,90. This prompted us to focus on this locus for
further validation and functional studies. Because we failed to identify expression quantitative trait locus in
both blood and brain datasets, we hypothesized that given our trait was tangle-specific, modulation of
mRNA expression of genes in the locus might also be cell-type specific. This hypothesis was also motivated
by the increase in genetic to transcriptomic associations found in cell specific populations in other contexts
91-93. Our results indicate that of the genes in the 4q28.2 locus, only JADE1 mRNA was significantly and
differently expressed in tangle-bearing neurons. Our immunohistochemical studies also showed JADE1
protein accumulation in both neuronal and glial tangle containing cells, validating these findings. Thus,
JADE1 is most likely responsible for the GWAS signal at this locus. Our immunohistochemical studies indicate that JADE1 is potentially involved broadly in tauopathies. We observed immunopositivity not only in PART tangles, but also in tauopathies with aggregates containing
4R tau and mixed tauopathies with aggregates containing both 3R and 4R. The absence of staining in Pick
disease, the only tauopathy with 3R tau aggregates examined, was surprising. |
Our biochemical studies
suggest that JADE1 protein specifically interacts with 0N4R tau that is phosphorylated on epitopes known to
be hyperphosphorylated in NFT. Our proximity ligation assay confirms the direct interaction between JADE1
protein and tau. Studies using cryo-EM and mass spectrometry have shown the ultrastructure of tau
aggregates at unprecedented resolution, and it has been reported that 0N4R has a unique single
conformation for the fibril core 94-96. Intriguingly, recent mass spectrometry profiling studies of human
postmortem brain tissues have suggested that changes in 0N4R tau isoform specifically is an early event in
tauopathy 97. Double labeling experiments indicate that JADE1 increases shortly after the pre-tangle stage,
accumulating alongside insoluble tau aggregates during the transition to the intercellular tangle stage
perhaps reflecting a reactive/protective compensatory role 98.
β
sheet
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Our findings provide important clues as to how JADE1 may be functioning in tauopathy. JADE1 has
been previously implicated as a renal tumor suppressor involved in apoptosis, well as inhibition of Wnt
signaling by ubiquitylating
β -catenin, and interactions with cell-cycle regulators 62,99-103. Of the two JADE1
isoforms, only the short form which lacks a nuclear localization signal was observed, which was consistent
with the cytoplasmic localization of the protein we observed by immunohistochemistry. This prompted us to
hypothesize that JADE1S functions in the cytoplasm to mediate tauopathy. Our in vivo studies in which we
reduced rno (the closest JADE1 ortholog) levels in Drosophila overexpressing mutant human 0N4R tau
significantly enhanced the tau-induced rough eye phenotype as well as TUNEL-positive cells, a marker of
apoptotic DNA fragmentation. While JADE1 has been shown to promote apoptosis in some contexts, our
RNAi knockdown experiments suggest that proper functioning of JADE1 may be neuroprotective. Consistent with this, other studies have demonstrated that loss of rno function attenuates apoptosis in
Drosophila104. Because previous studies have found JADE1 to be stabilized by the von Hippel-Lindau tumor
suppressor which is a component of an E3 ubiquitin-protein ligase activity, JADE1 may be working with
0N4R tau through a similar mechanism to promote ubiquitin-mediated clearance of tau 105-107. Our study has several notable limitations. Our sample size is small for GWAS standards; however, it
should be noted that it is still the largest study of its kind. Given that PART is currently only reliably
diagnosed postmortem, the study is limited by the availability of tissue that meet our strict criteria. Our study
also relies on neuropathological assessments performed at multiple centers that may cause batch effects. |
While Braak staging is highly reproducible, with one report showing that across brains and raters the kappa
score was greater than 0.90 108, it is a semiquantitative (ordinal) variable. Further studies using a more
quantitative approach to measuring tau burden that we have shown more closely align with functional
clinical measures in PART may reveal additional candidates 109. Additional follow-up studies in experimental
models are necessary further to validate our findings. In conclusion, therapies for AD in clinical trials are moving towards targeting tau due to the lack of
clinical efficacy of A
β modulating therapeutic approaches 110. Here, by focusing on individuals with PART
β
who lack A
plaques, we enriched our cohort for signals related to tau proteinopathy. Our analysis provides
additional evidence
that PART overlaps with but has considerable differences
from AD. This
interdisciplinary approach led to the identification of JADE1, which interacts with 0N4R tau, and is protective
bioRxiv preprint
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. in vivo. Thus, JADE1 is a potential novel biomarker that differentiates tauopathies. Further understanding
the genetics of PART will provide pathways for rationally designed therapeutics for degenerative
tauopathies. bioRxiv preprint
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was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Fig. 1 | Genome-wide association study (GWAS) in primary age-related tauopathy. (a) A quantitat
tative trait
GWAS was performed using normalized Braak neurofibrillary tangle stage with age, sex, principle comp
(PCs) and genotyping SNP array as covariates (n=647). The threshold for genome-wide significance (p<5 is indicated by the solid grey line; the suggestive line (p<5 × 10−6) is indicated by the dotted line
mponents <5 × 10−8)
ne. (b) A
LocusZoom plot shows a strong signal with multiple SNPs in LD on chromosome 4q28.2. The x-axis is th
the base
pair position and the y axis is the –log10 of the p-value for the association with Braak stage. The b
blue line
represents the recombination rate. (c) Association between single-nucleotide polymorphism (SNP), rs56
56405341
and Braak tangle stage (adjusted for age and sex). Pairwise comparisons using Wilcoxon rank sum test,
st, AA-AG
p=0.024, AA-GG p=3.3E-05, AG-GG p=7.2E-05. bioRxiv preprint
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;
this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Fig. 2 | Single-cell sequencing of neurons with and without tau aggregates reveals JADE1 m
mRNA is
upregulated in tangle-bearing neurons. |
Neurons with and without neurofibrillary tangles from huma
an post-
mortem brains samples were separated using fluorescence-activated cell sorting and single-cel
ell RNA-
sequencing was performed. (a) In 2 unique excitatory neuronal populations JADE1 mRNA was sign differentially expressed in the tangle bearing neurons (adjusted p= 7.82×10-8, 4.55×10-15) and compar
gnificantly
aring the
overall population of tangle-bearing excitatory neurons to non-tangle bearing neurons the value is significant (adjusted p=1.04×10-61). (b,c) The other two genes in the locus, C4orf33 and SCLT1, were
is highly
re overall
nominally expressed in both excitatory neuronal groups, as well as subclusters (data not shown). (d) A
A dot plot
showing average relative expression and percent expression of the candidate genes in the locus. Both
th JADE1
relative average expression and percentage of cells expressed was higher than C4orf33 and SCLT1. (
(e,f) A t-
distributed stochastic neighbor embedding (tSNE) plot showing the populations of neurons, tangle bearing
ing status,
and relative expression of JADE1 in neuronal subpopulations. bioRxiv preprint
doi:
https://doi.org/10.1101/2021.06.30.450599
;
this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Fig. 3 | Selective immunolabeling of tau aggregates containing tau with four microtubule-binding d
g domain
repeats (4R), but not three (3R), in post-mortem human tauopathy brains with antisera targeting
g JADE1
protein. Immunohistochemical staining with phospho-tau (p-tau) specific antisera (AT8) and JADE1
1 specific
antisera demonstrates neurofibrillary tangles (NFT) formation marked by the presence of JADE1 is
is specific
populations of neurons and glia. (a,b) Primary age-related tauopathy (PART, n=3) NFTs contain JADE1
1 positive
staining in the soma and neurites in the entorhinal cortex. (c,d) Alzheimer disease (AD, n=3) individual wi
with beta-
amyloid AT8-positive neuritic plaques and NFTs in the subiculum also display JADE1 immunoposi
ositivity in
dystrophic neurites and NFTs. (e,f) Chronic traumatic encephalopathy (CTE, n=3) contains positive p-tau
u staining
around a blood vessel in the depth of a neocortical sulcus that is also immunopositive for JADE1. (g
g,h) AT8
positive tufted astrocytes, oligodendroglial coiled bodies, and NFTs are positive in the subthalamic nucle
cleus in a
individual with progressive supranuclear palsy (PSP, n=3) which are also in immunopositive for JADE
DE1. (i,j)
Astrocytic plaques in corticobasal degeneration (CBD, n=3) and extensive thread-like pathology positive f
e for p-tau
and JADE1 in the neocortex. (k,l) In the cornu ammonis 1 (CA1) sector in a individual with argyrophil
hilic grain
disease (AGD, n=3), abundant grains that are immunopositive for p-tau and JADE1 are evident. (m,n
,n) Pick
disease (PiD), a 3R tauopathy, with Pick bodies in the dentate gyrus that are immunopositive for p
p-tau but
negative for JADE1. |
(o) Double staining of a PART entorhinal cortex showing the absence of JADE1
1 (brown)
staining in early pre-tangles, but the presence of p-tau (pink, see inset). (p) peptide competition demon
onstrating
the antisera for JADE1 is specifically binding to the correct epitope give then absence of positive staining,
g, but the
presence of a tangle (inset). Scale bar 100 µm. bioRxiv preprint
doi:
https://doi.org/10.1101/2021.06.30.450599
;
this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Fig. 4. | Biochemical analysis of JADE1S and total tau validates the interaction with 4 micro
rotubule-
binding domain repeats (4R) but not 3R in post-mortem human brain tissue from tauopathy indi
dividuals
(a) Schematic of the two JADE1 isoforms, JADE1S and JADE1L (b) Representative immunoblot using a
g antisera
targeting JADE1 measured in entorhinal cortex and cornu ammonis in individuals with primary age
ge-related
tauopathy (PART) and Alzheimer disease (AD) shows a banding pattern with the JADE1S isoform at 58
8 kDa but
not the JADE1L at 95 kDA. GAPDH was used as a loading standard. (c) Co-immunoprecipitation (co-IP
IP) using
JADE1 antisera pulls down tau near the molecular weight of the 0N4R isoform. (d) Reverse co-IP with
ith 0N tau
antisera pulls down the JADE1S isoform. (e) The pulled down form of Jade1S molecular weight shifts do
downward
after treatment with lambda protein phosphatase over time to expected 58 kDa weight. (f,g) Co-IPed
tau with
JADE1 stained with C-terminal isoform specific anti-tau antisera are the 0N4R isoform and not the 3R isof
oform (h)
Co-immunoprecipitated tau was positively stained with multiple phospho-tau specific antibodies with the
he largest
signal coming from pSer396 pSer404 (PHF1), pSer214 (S214), and pSer409 (PG5). (i) Duo-link assay s
y showing
positive luorescence signal (red) around the nucleus of neurons (blue) indicating the potential inte
nteraction
between JADE1 and 0N terminus tau detected using the corresponding two primary antibodies in th
the soma
(inset). Scale bar, 20
μ
m
bioRxiv preprint
doi:
https://doi.org/10.1101/2021.06.30.450599
;
this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Fig. 5 | RNAi-mediated knockdown of rno enhances tau-induced rough eye and neurotoxicity in th Drosophila brain. (a-e) RnoRNAi significantly enhances the tau-induced rough eye phenotype based o roughness, shape, and conical shape (p=8.7 ×10-5). (f-j) RnoRNAi significantly enhances levels of
the adult
d on size,
f terminal
deoxynucleotidyl transferase dUTP nick end-labeling (TUNEL) in tau transgenic Drosophila compared
red to tau
expressed alone (p=0.008). TUNEL was performed at day 10 of adulthood. An equal number of male and
nd female
flies were used for each experiment. |
bioRxiv preprint
doi:
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this version posted July 1, 2021. The copyright holder for this preprint (which
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. References:
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CC-BY-NC-ND 4.0 International license . Low affinity integrin states have faster binding kinetics than the high affinity state
Running Title: Ligand-interaction kinetics of integrin
Jing Li1, 2, Jiabin Yan1, 2 and Timothy A. Springer1, 2 *
Affiliations
1Program in Cellular and Molecular Medicine, Boston Children’s Hospital 2Department of Biological Chemistry and Molecular Pharmacology and Department of Pediatrics, Harvard Medical School, Boston, MA 02115 *Correspondence to: springer@crystal.harvard.edu
Key words: integrin α4β1/ integrin α5β1/ binding kinetics / cytoskeletal force
Abstract. Integrin conformational ensembles contain two low-affinity states, bent-closed and extended-closed, and an active, high-affinity, extended-open state. It is widely thought that integrins must be activated before they bind ligand; however, one model holds that activation follows ligand binding. As ligand-binding kinetics are not only rate limiting for cell adhesion but also have important implications for the mechanism of activation, we measure them here for integrins α4β1 and α5β1 and show that the low-affinity states bind substantially faster than the high-affinity state. On and off-rate measurements are similar for integrins on cell surfaces and ectodomain fragments. Although the extended-open conformation's on-rate is ~20-fold slower, its off-rate is ~25,000-fold slower, resulting in a large affinity increase. The tighter ligand-binding pocket in the open state may slow its on-rate. These kinetic measurements, together with previous equilibrium measurements of integrin conformational state affinity and relative free energy on intact cells, are key to a definitive understanding of the mechanism of integrin activation. INTRODUCTION
Integrins are a family of adhesion receptors that mechanically integrate the intracellular
and extracellular environments and facilitate cell migration. Their α and β-subunits associate noncovalently to form an extracellular ligand-binding head and then form multi-domain ‘legs’ that connect to single-pass transmembrane and cytoplasmic domains with binding sites for cytoskeletal adaptor or inhibitory proteins (Fig. 1A). Integrins populate a conformational ensemble with three overall conformational states: the low-affinity bent-closed (BC) and extended-closed (EC) conformations and the high-affinity extended-open (EO) conformation (Fig. 1A). The equilibrium between these conformational states is allosterically regulated by extracellular ligand binding, intracellular adaptor/inhibitor binding (Bouvard et al, 2013; Iwamoto & Calderwood, 2015) and tensile force applied by the actin cytoskeleton on the integrin β- subunit that is resisted by ligand embedded in the extracellular matrix or on cell surfaces (Kim et al, 2011; Legate & Fassler, 2009; Li & Springer, 2017; Nordenfelt et al, 2016; Park & Goda, 2016; Sun et al, 2016; Zhu et al, 2008) (Fig. |
1A). The EO conformation has ~1000-fold higher binding affinity for ligand than the two closed conformations and is the final competent state to mediate cell adhesion and migration (Li & Springer, 2018; Li et al, 2017; Schürpf & Springer, 2011). Many previous studies have emphasized the importance of force in regulating integrin adhesiveness (Alon & Dustin, 2007; Astrof et al, 2006; Li & Springer, 2017; Nordenfelt et al., 2016; Nordenfelt et al, 2017; Sun et al, 2019; Zhu et al., 2008). Recent measurements of the intrinsic ligand-binding affinity of each conformational state and the equilibria linking them enabled a thermodynamic comparison of integrin activation models (Li & Springer, 2017, 2018; Li et al., 2017). Remarkably, only the combination of adaptor binding and cytoskeletal force can activate integrins in an ultra-sensitive manner, with the switch between on and off occurring over
1
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was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . a narrow range of signal input {Kuriyan, 2012 #24442}; the large increase in length between the bent and extended conformations (Fig. 1A) is indispensable for switch-like integrin activation. Despite these advances, thermodynamics cannot describe the sequence of events in a
multi-step transition; furthermore, energy-driven processes such as cytoskeleton movements occur under non-equilibrium conditions. Ligand-binding on- and off- rates are key parameters that determine whether integrin encounter of ligand is timely and whether the ligand remains bound for a sufficiently long time for the integrin to exert its function in the presence of force. Previous representative measurements (Dong et al, 2018; Kokkoli et al, 2004; Mould et al, 2014; Takagi et al, 2003) on integrin interaction with ligand have yielded kinetics on mixtures of conformational states, i.e., apparent on- and off-rates averaged over conformational states (Fig. 1B left). However, the ligand-binding kinetics of individual integrin conformational states remain unknown. These kinetics must be determined before we can understand how integrin function is regulated and how integrins work in concert with the cytoskeleton to provide traction for cell migration and firm adhesion for tissue integrity (Fig. 1B right). Putting the question another way, what is the first step in inside-out integrin activation? In one view, talin binding inside the cell activates the integrin, presumably to the high affinity state, which then binds ligand. In another view, the first step is activation of the actin cytoskeleton, followed by binding of the integrin to ligand embedded in the extracellular environment and to talin incorporated in the actin cytoskeleton, which enables actin retrograde flow to elongate the lifetime of the high affinity integrin state. |
For two classes of force-regulated adhesion molecules, each of which have a single low- affinity state and a single high-affinity state, selectins (Phan et al, 2006) and FimH (Yakovenko, 2015), the low-affinity conformation has a faster on-rate for ligand than the high-affinity conformation. If subsequent conformational change to the high affinity state is rapid, fast ligand binding kinetics to the low-affinity state efficiently couples ligand binding to stabilization by applied force of the high-affinity state, which has a long lifetime (Yakovenko, 2015). Work from our group on integrin αVβ6 showed that removal of the hybrid domain in the αVβ6 head resulted in a 50-fold increase in affinity for ligand yet decreased the apparent on-rate of ligand binding (Dong et al., 2018) suggesting that the open conformation has a lower on-rate than the BC and EC states. However, the intrinsic ligand-binding kinetics for each state of integrin αVβ6 could not be determined due to the lack of tools to stabilize specific conformational states. In this study, we utilized well-characterized conformation-specific Fabs (Li & Springer, 2018; Li et al., 2017; Su et al, 2016) (Fig. 1C) to stabilize integrins α4β1 and α5β1 into defined ensembles containing only one or two of the three integrin conformational states and measured the ligand-binding kinetics of each defined ensemble. Together with previously determined intrinsic ligand-binding affinities and populations of conformational states (Li & Springer, 2018; Li et al., 2017), our measurements enable us to define ligand-binding kinetics intrinsic to each conformational state. For each integrin, the two closed states have indistinguishable on- and off- rates for soluble peptide and macromolecular fragment ligands. Remarkably, the on-rate for ligand of the low-affinity closed integrin conformations is ~40-fold (α4β1) or ~5-fold (α5β1) higher than for the high-affinity EO conformation. The ~1,000-fold higher affinity of the EO conformation than the closed conformation is achieved by the ~25,000-fold lower off-rate of the EO conformation for both α4β1 and α5β1 integrins. These findings show for two representative β1 integrins that most ligand binding occurs to the bent-closed and/or extended-closed states, followed by conformational change to the extended-open state. The rapidity of ligand binding measured here, if coupled with similarly rapid binding of actin cytoskeleton adaptors to integrins and conformational change among integrin states, could enable coincidence of these binding events, together with tensile force transmission if the ligand is embedded in an extracellular environment, to regulate integrin activation. 2
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RESULTS
Ligand-binding kinetics of intact α4β1 and α5β1 on cell surfaces. We measured
binding kinetics of intact α4β1 on Jurkat cells to two fluorescently labeled ligands, a phenylureide derivative of Leu-Asp-Val-Pro (FITC-LDVP) and a fragment of vascular cell adhesion molecule (VCAM) containing its first two domains (Alexa488-VCAM D1D2) (Fig. 2). Before adding ligands, cells were equilibrated with saturating concentrations of Fabs for 30 min at 22°C to stabilize specific conformational states (Li & Springer, 2018). Integrin extension, i.e. the EC and EO states, was stabilized with 4 µM 9EG7 Fab, which binds to the β1-subunit knee (Fig. 2B). The EO conformation was stabilized with a combination of 4 µM 9EG7 Fab and 2 µM HUTS4 Fab; the latter binds to the interface between the βI and hybrid domains and stabilizes the EO conformation (Fig. 2C). Ligand binding kinetics was monitored as mean fluorescence intensity (MFI) by flow cytometry without washing (Fig. 2). Beginning at about 10 minutes, a 500-fold higher concentration of unlabeled ligand was added to measure the kinetics of dissociation. Background MFI at each fluorescent ligand concentration, measured under identical conditions except in presence of 10 mM EDTA (Fig. S1), showed no significant difference at different time points during the association and dissociation measurements and was averaged across different time points and subtracted to obtain specific binding. Under basal conditions, with all three integrin states present in the ensemble, binding of
FITC-LDVP to Jurkat cells reached equilibrium within 3 min (Fig. 2A). Upon addition of a 500- fold excess of LDVP, dissociation of FITC-LDVP was rapid and was 99.7% complete by 5 min (Fig. 2A). In contrast, both binding and dissociation of FITC-LDVP were slower when only the extended conformations (EC and EO) were present on Jurkat cells (Fig. 2B). Reaching steady state required ~5 min after addition of 20 nM FITC-LDVP, ~10 min with 10 nM ligand, and was not reached after 10 min with 5 nM ligand. After 10 min of dissociation, only 19.4% of ligand had dissociated (Fig. 2B). Association and dissociation were even slower when only the EO conformation was present (Fig. 2C). After 15 min of association, much less ligand had bound (Fig. 2C) than when both EC and EO conformations were present (Fig. 2B). Dissociation was also slower, with only 1.2% of bound ligand dissociating after 10 min (Fig. 2C). VCAM D1D2 binds with ~100-fold lower affinity than LDVP to α4β1 (Li & Springer,
2018). As a result, binding to the basal ensemble was too low to measure over the noise from unbound ligand; however, we were able to measure binding kinetics to intact α4β1 stabilized in the extended (EC+EO) and EO states (Fig. 2D and E). When the two extended conformations (EC and EO) were present, binding of all three concentrations of Alexa488-VCAM D1D2 (10nM, 20nM and 30nM) reached equilibria within 2 min. Upon addition of a large excess of LDVP, dissociation of Alexa488-VCAM D1D2 was also fast; 100% dissociated by 5min (Fig. |
2D). Association and dissociation both became markedly slower when only the EO conformation of α4β1 was present (Fig. 2E). To address the generality of these results, we studied another integrin and cell type by
measuring binding of a fluorescently-labeled two-domain fragment of fibronectin (Alexa488- Fn39-10) to intact α5β1 integrin on K562 cells (Fig. 3). The BC conformation of α5β1 integrin on K562 cells) is more stable than that of α4β1 integrin on Jurkat cells (Li & Springer, 2018). Therefore, to assure that the extended states (EC+EO) were saturably populated, they were stabilized with a combination of two Fabs, 6 µM 9EG7 Fab and 2 µM SNAKA51 Fab (Fig. 3A left). The EO state of α5β1 (Fig. 3B left) was stabilized with the same combination of Fabs as used for α4β1. Although binding affinity was too low to measure kinetics of the basal ensemble (Li et al., 2017), we were able to measure Alexa488-Fn39-10 kinetics with the EC+EO and EO ensembles of intact α5β1 (Fig. 3). When α5β1 was stabilized in the EO conformation, Alexa488- Fn39-10 bound and dissociated significantly more slowly than when both the EC and EO states of α5β1 were present in the ensemble (Fig. 3A and B). Faster binding and dissociation of Alexa488-Fn39-10 from the EC+EO ensemble than EO showed that the EC state of α5β1 binds and dissociates faster than the EO state, just as found for α4β1. To quantify the binding kinetics of intact α4β1 and α5β1 under each condition, we
globally fit the traces of specific binding in both association and dissociation phases at each
3
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was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . concentration of fluorescently labeled ligand to the 1 vs. 1 Langmuir binding model to determine app (Fig. 2F and Fig. 3C). The ratio of the apparent off- the apparent on- and off-rates, kon and on- rates, koff previously determined by saturation binding (Figs. 2F and 3C) (Li & Springer, 2018; Li et al., 2017). These agreements suggest that the 1 vs. 1 Langmuir binding model can reasonably fit the kinetic data. Overall, these results show that ligand binds to and dissociates from the EO conformation more slowly than from the BC and EC conformations. The kinetics measured here for the basal and EC+EO ensembles are apparent, because they include contributions from distinct conformational states present in these ensembles. In contrast, EO state kinetics are measured exactly because EO is the only state present in the EO ensemble. In the final section of Results, we will use previous measurements of the populations of the states in each ensemble to calculate the on- and off-rates for conformations within mixtures of states. app and koff
app/kon
app, agrees reasonably well with the equilibrium dissociation constant, Kd,
Binding kinetics of soluble α5β1 ectodomain for Fn39-10. |
We utilized bio-layer
interferometry (BLI) (Wallner et al, 2013) to measure the kinetics of binding of an ectodomain fragment of α5β1 to the biotin-labeled Fn39-10 fragment of fibronectin immobilized on streptavidin biosensors (Fig. 4). The ectodomain was truncated just prior to the transmembrane domains of the α5 and β1 subunits and was expressed in a cell line containing a glycan processing mutation so that it had high-mannose rather than complex-type N-glycans. Truncation of α5β1 and high mannose glycoforms raise the free energy of the BC conformation relative to the EO conformation, so that the population of the EO state in the basal ensemble increased from 0.11% in intact α5β1 to 4.6% in the high-mannose ectodomain fragment (Li et al., 2017). The practical consequence of the increase in population of the EO state in the α5β1 ectodomain basal ensemble was that it raised basal ectodomain ensemble affinity and, in contrast to intact α5β1 on K562 cells, enabled us to measure basal ensemble Fn39-10 binding kinetics (Fig. 4A). Binding kinetics were measured by transferring Fn39-10 biosensors to wells containing
the α5β1 ectodomain in the absence or presence of conformation-stabilizing Fabs. Dissociation kinetics were measured by transfer of sensors to wells lacking the integrin but containing identical Fab concentrations (Fig. 4 A-D cartoons). Equilibrium Kd values were previously shown to be independent of the Fab used to stabilize a particular state (Li et al., 2017). However, we were concerned that binding of Fabs, particularly those that bind close to ligand binding sites, might slow kinetics and therefore tested this by varying the Fabs used to stabilize the EO state. The kinetic curves showed that the α5β1 ectodomain EO state associated more slowly
than the mixtures with the closed states and also dissociated more slowly (Fig. 4A-D) as confirmed in the tabulated results (Fig. 4F). Overall, these differences among ensembles resembled those found for the EC+EO ensemble and EO state of intact α5β1 on K562 cells and extended measurements to the basal α5β1 ensemble. The on and off-rates of the EO state for Fn39-10 determined in the presence of 12G10 Fab were 4-fold and 2-fold lower, respectively, than those determined in the presence of 9EG7&HUTS4 Fabs (Fig. 4C, 4D and 4F). As 12G10 Fab binds close to the ligand-binding site in the β1 domain (Fig. 1A), we use koff kinetics determined with the 9EG7, 8E3, SNAKA51 & HUTS4 Fabs, which bind far from the ligand-binding site, for calculating true (koff and kon) kinetic rates for each state in the final section of Results. app and kon app
The off-rate of the closed states. Due to the low affinities of the closed states there
was too little binding to directly measure kon or koff in presence of saturating closure-stabilizing Fabs. We therefore used another approach. We first allowed ligand binding to integrins to reach steady state in the absence of a closure-stabilizing Fab. We then added different concentrations of closure-stabilizing Fab mAb13 and measured dissociation kinetics (Figs. |
5-6). Dissociation of the ligand from the EO state is very slow as shown above and is negligible in our experimental time scale. At high Fab mAb13 concentrations, when the EO ligand-bound state (EOL) converts to either BCL or ECL (they are grouped together here as (CL), mAb13 Fab binds and prevents back-conversion to EOL (Fig. 5A, B). After saturating concentrations of Fab mAb13 are added to basal or EO+EC ensembles pre-equilibrated with ligand, the effective off-
4
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was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a
CC-BY-NC-ND 4.0 International license . rate is contributed by two steps, the conformational change from EOL to CL and the dissociation of ligand from mAb13-bound CL (mAb13CL) (Fig.5A, B). Thus, the observed off- rate at saturating concentration of mAb13 Fab is contributed by the rates of both steps and C . permits the determination of the lower limit of koff
We measured FITC-LDVP dissociation from basal or extended ensembles of α4β1 on
Jurkat cells after addition of a range of mAb13 Fab concentrations (Fig. 5A-B). Saturable binding of mAb13 Fab to nascent cell surface CL was evident from the approach to a plateau max values measured for LDVP dissociation from basal and extended of koff α4β1 ensembles on Jurkat cells were similar and within error of one another, with an average of ~120 *10-3 /s (Fig. 5C). max (Fig. 5A-C). The koff
Similarly, we measured Fn39-10 dissociation from basal or extended ensembles of the
α5β1 ectodomain (Fig. 6). The effect of mAb13 Fab on increasing koff was saturable, as shown by approach to a plateau (Fig.6A-C). The fit to a saturation dose response curve yielded koff values for the basal and extended ensembles of (1600 ± 100) *10-3 /s and (1900 ± 100) *10-3/s, respectively (Fig. 6C). max
Calculation of ligand-binding kinetics from ensemble measurements. We directly measured the ligand-binding and dissociation kinetics for the EO state of α4β1 and α5β1 (Fig. 2C, E, Fig. 3B, Fig. 4C). In contrast, kinetics for the BC and EC states were only measured within ensembles. Their kinetics are convoluted in two respects. First, measurements on ensembles contain kinetics contributed by all states within the ensemble. Second, apparent association and dissociation kinetics may each contain a contribution from the kinetics of conformational change (Fig. 1B). Fig. 1B left shows apparent on- and off-rates and Fig. 1B right shows all the actual pathways by which ligand binding and dissociation can occur, which include all known integrin conformational states and the kinetics of conformational change between them. Furthermore, after ligand binding to the closed states, rapid conformational change to the EO state occurs and is responsible for our ability to measure the kinetics of binding as a result of accumulation of ligand-bound integrin in the EO state. |
The underlying assumption for deconvoluting the kinetics of the closed states is that if
integrin conformational transition kinetics are sufficiently fast so that the populations of the three integrin states do not deviate significantly during our experiments from the equilibrium values of the populations, then measured kinetics will not be significantly limited by conformational transition kinetics. In this case, both free integrins and ligand-bound integrins can be considered as readily equilibrated among their conformational states, and ligand binding coupled with integrin conformational changes can be approximated by the apparent 1 vs. 1 reaction between integrin and ligand (this allows the double tildes in Eqs. 1-4 in Fig. 7A to be treated as equal signs). All on- and off- rates measured here were well fit with the 1 vs. 1 Langmuir binding model (Fig. 2A-E, Fig. 3A-B, Fig. 4A-D, Fig 5B-C, and Fig. 6A-B), supporting this assumption. Moreover, reasonable agreement between the ratios of the apparent off- and on- rates, app/kon koff 4F), validates the assumption that the apparent on- and off- rates (kon defined ensemble can be approximated by the on- and off- rates of each state weighted by its population in the ensemble (Fig. 7A, Eqs. 1-4). The population of the integrin states in absence of ligand (BC, EC, and EO) and in presence of saturating concentrations of ligand (BCL, ECL, and EOL) were calculated based on the previously determined population and ligand-binding affinity of each state (Fig. S3B, Eqs. S5-S10) in the respective integrin α4β1 and α5β1 preparations (Li & Springer, 2018; Li et al., 2017) and are shown in Fig. 7B. app, and previously determined equilibrium dissociation constants, Kd, (Figs. 2F, 3C and
app) for each
app and koff
On- and off- rates for each α4β1 and α5β1 integrin state on intact cells and for the purified
α5β1 ectodomain are summarized in Fig. 7C. Values are best determined, i.e. with the lowest errors, for the on-rate of EO state. Errors were higher for the BC and EC states, particularly for koff. Therefore, koff values for each state were also calculated from koff =Kd*kon, where Kd is from equilibrium measurements (Li & Springer, 2018; Li et al., 2017). The koff values of each state determined from these two strategies agree well with one another for each integrin-ligand pair. 5
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EC approached by
In addition, koff values were also comparable to the lower limit of koff measuring dissociation in presence of a closure-stabilizing Fab (Fig. 5 and Fig. 6). DISCUSSION
Intrinsic ligand-binding kinetics of integrin conformational states. Employing
conformation-specific Fabs against the integrin β1 subunit to stabilize integrin α4β1 and α5β1 into defined ensembles, we determined the on- and off-rates of each integrin conformational state. |
We found that despite ~1,000 lower affinity, the closed states, BC and EC, of two β1 distinct integrins have markedly higher on-rates than the EO state. These findings have important implications for the sequence of events that occur when integrins interact with ligands, as discussed in the Integrin Activation section below. Previously, we determined equilibrium Kd values for the three different conformational states of integrins α4β1 and α5β1 (Li & Springer, 2018; Li et al., 2017). We used integrins on intact cells and as different types of ectodomain fragments. These different preparations differed up to 320-fold in affinity of their basal ensembles. However, integrin affinity for ligand was essentially identical for each integrin state and all differences in ensemble affinity were ascribable to variation among the preparations in the relative free energies of the three states. Therefore, we concluded that integrin affinity was intrinsic to each state (Li & Springer, 2018; Li et al., 2017). There may be real differences between cell surface and soluble integrins imposed by orientation, cell surface charge, and the glycocalyx; nonetheless, our previous measurements of Kd values for the EC and EO states of α5β1 on the cell surface and as an ectodomain fragment are within 2-fold of one another (Li & Springer, 2018; Li et al., 2017). These results are consistent with the intrinsic affinity concept, i.e. that integrin conformational state is the primary determinant of affinity, even though the geometry of integrins on cell surfaces may cause some modifications to these values that are minor compared to the large differences between the closed and open states. Similar to intrinsic affinities, the results here on ligand-binding kinetics were consistent
with on-rates and off-rates that are intrinsic to integrin conformational states. On-rates for intact α5β1 on cell surfaces and the α5β1 ectodomain in the EO state for the same fibronectin fragment were identical, and off-rates differed by only 1.8-fold. Similarly, on- and off-rates for the EC state of the intact cell-surface and ectodomain forms of α5β1 differed only by 1.1-fold and 1.9-fold respectively. We were able to measure on- and off-rates for the BC state of intact α4β1 binding to LDVP and for the BC state of the α5β1 ectodomain binding to Fn39-10. In each case, the values of the BC state were within error of those for the EC state. The similar ligand-binding kinetics of the BC and EC states are in agreement with the essentially identical intrinsic affinities of the two closed states (Li & Springer, 2018; Li et al., 2017). In further agreement, crystal structures of the integrin αIIBβ3 ectodomain in the BC state and of the αIIBβ3 closed headpiece fragment, which has no interactions with the lower legs and thus serves as a model for the EC conformation (Zhu et al., 2008; Zhu et al, 2013), show essentially identical conformations of the ligand binding site. We checked whether kinetics might be influenced by bound Fabs. |
In our previous work,
we compared affinities measured with at least two Fabs specific for the closed, open, and extended states and for each state compared Fabs that bound to different domains. The results showed no significant differences between affinities measured with different Fabs. Here, we compared two Fabs used to stabilize the EO state and found slower association and dissociation kinetics with 12G10, which binds near the ligand binding site in the βI domain than HUTS4, which binds distally in the hybrid domain (Fig. 4F). As Fabs generally decrease dynamic protein motions in their epitopes (Wei et al, 2014) and may also sterically slow binding, the kinetics measured using HUTS4 Fab more likely approximate integrin kinetics in the absence of Fab and are repored in Fig. 7C. The kinetics of the EC and BC states were calculated from measurements on extended
or basal ensembles after correction for the kinetics in these ensembles contributed by the EO state. As a check on these measurements, we also measured koff in the presence of mAb13 Fab, which after conformational conversion of EOL to ECL+BCL trapped the closed states so
6
bioRxiv preprint
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https://doi.org/10.1101/2021.07.26.453735
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available under a
CC-BY-NC-ND 4.0 International license . C and koff
EC determined from these
that their dissociation could be measured. The lower limit of koff experiments (Fig.5C and 6C) are in good agreement with the calculated off-rate of the closed states (Fig. 7C). Typical protein-protein on-rates as found for antibody-antigen interactions are in the
range of 105 to 106 M-1 s-1 (Alsallaq & Zhou, 2008). The on-rates for the BC and EC states were in this range, e.g. 3.5×105 and 1.5×106 M-1 s-1 for α5β1 binding to Fn39-10 and α4β1 binding to VCAM, respectively. In contrast, the on-rates for EO states for the corresponding integrin-ligand pairs were 7.5×104 and 3.4×104 M-1 s-1, respectively. These rates suggest a hindrance to ligand binding. Ligand-bound crystal structures in both open and closed conformations are known for two RGD-binding integrins, αIIBβ3 (Xiao et al, 2004; Zhu et al., 2013) and αVβ6 (Dong et al, 2014; Dong et al, 2017). Additionally, high resolution structures show RGD peptides bound to both closed and intermediate (partially open) conformations of α5β1 (Nagae et al, 2012; Xia & Springer, 2014). The open conformation has a tighter ligand-binding pocket. Slower ligand- binding kinetics for the open conformation is consistent with its tighter ligand-binding pocket, especially around the key RGD Asp reside (Fig. 7D). Movement of the β1-α1 loop toward the ligand and the MIDAS Mg2+ ion upon βI domain opening partially buries the Mg2+ ion and is expected to slow binding of the Asp sidechain, which must fit into a tight pocket with a specific geometry dictated by partially covalent and highly directional Asp sidechain metal coordination and hydrogen bonds to the β1-α1 loop backbone amide nitrogens. |