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As mentioned above the genome organisation of these replicase polyprotein sequences seems to be very flexible. In order to analyse domain organisation the location of identified Pfam domains were plotted for a number of sequences, as shown in Figure 5 .

The results described above may indicate that the AlkB domains have been integrated into the replicase polyprotein relatively recently (see Discussion). In order to test for potential sources selected AlkB domains were compared to non-viral sequences. PSI-Blast was used to search the NCBI nr database, removing all viral hits in the final search report. Most of the remaining top-scoring hits were from bacteria. This included two different strains of Xanthomonas, X. axonopodis pv citri and X. campestris pv campestris. Xanthomonas attacks plants such as citrus, beans, grapevine, rice and cotton [13] . The search also returned high-scoring hits from another plant pathogen, Xylella fastidiosa. This bacterium infects a great variety of plants, including grapevine, citrus, periwinkle, almond, oleander and coffee [14] .

Pfam searches obviously will only identify known domain types in protein sequences. In order to identify potential similarities in regions that were not recognised by Pfam, systematic PSI-Blast searches were performed, using the polyprotein regions between the MT and HEL domains and searching against the NCBI database of reference sequences [15] , excluding all viral entries. A maximum of 5 PSI-Blast iterations were allowed, with an inclusion threshold of 0.005. The expected homologues of the AlkBdomain were found with high confidence, as most of the E-values were < 1 × 10 -50 . Homologues of typical viral domains like the viral peptidases were obviously not found, as all viral database entries were excluded. Very few Multiple alignment of sequence regions corresponding to the AlkB domains Figure 3 Multiple alignment of sequence regions corresponding to the AlkB domains. The alignment was generated with ClustalX. The residues involved in coordination of the essential Fe 2+ ion are completely conserved, except in one of the Vitivirus sequences. These residues are the HxD motif, a single H, and the first R in the RxxxxxR motif. The function of the remaining conserved residues is unclear, but at least some of them may be involved in coordination of the substrate [10] . Pairwise distances between sequence regions corresponding to methyltransferase (MT), RdRp and AlkB domains. Each data point corresponds to e.g. RP-RP and MT-MT distances for the same pair of sequences, and sequences showing similar evolutionary distance in these two regions will fall on the diagonal. The pairwise distances were estimated from multiple alignments using the Blosum50 score matrix [47] . Trend lines were estimated with Excel. The trend line for AlkB vs. RdRp is heavily influenced by the point at (675, 670). It represents two Foveavirus sequences (NCBI gi3702789 and gi9630738), they are 98% identical over the full polyprotein sequence. Alignment score (AlkB) Alignment score (RdRp) r 2 = 0.10 new similarities were found by these searches. Pepper ringspot virus (Tobravirus, NCBI gi20178599) showed significant similarity to site-specific DNA-methyltransferase from Nostoc sp (E = 1 × 10 -74 ), as well as other cytosine 5Cspecific DNA methylases. Bamboo mosaic virus (Potexvirus, NCBI gi9627984) showed similarity to aggregation substance Asa1 from Enterococcus faecalis (E = 6 × 10 -34 ). A small number of additional similarities seemed to be caused by biased sequence properties (e.g. proline-rich regions), and were probably not significant. This included matches against mucin and cadherin-like proteins from Homo sapiens and multidomain presynaptic cytomatrix protein (piccolo) from Rattus norvegicus. In general the variable regions seemed to be truly variable, with very little similarity to other proteins, except for the Pfam domains already identified.

As seen in Figures 2 and 5 , some closely related sequences are lacking specific domains in the sense that HMMER does not find a significant similarity to the Pfam entries for these domains. In order to understand the degree of sequence variation associated with this domain loss, as well as the general sequence variation in conserved vs. non-conserved regions of typical polyproteins, several dot plots were generated. The dot plot for two Carlavirus sequences, Potato virus M (NCBI gi9626090) and Aconitum latent virus (NCBI gi14251191), is shown in Figure 6 . The dot plot confirms that these two sequences are closely related in the MT, HEL and RdRp domains. However, there are significant differences in the region between MT and HEL. Potato virus M is lacking the AlkB domain whereas Aconitum latent virus is lacking the OTU domain. As seen from the dot plot, short regions of similarity close to the diagonal shows that both domains may have been present in an ancestral sequence. However, this region shows a high degree of sequence variation, and as indicated by the dot plot they are almost exclusively mutations. Non-essential or non-functional domains are probably rapidly lost. In this particular case, none of the typical AlkB motifs seem to be conserved in Potato virus M, indicating that this indeed is a non-functional AlkB domain.

The N-terminal domains of Flexiviridae and Tymoviridae are methyltransferases As described above the Pfam methyltransferase motif (Vmethyltransf) did not match any of the putative methyltransferase domains of Flexiviridae and Tymoviridae, despite the fact that they had been identified via PSI-Blast searches starting with known methyltransferases. Therefore an additional Pfam-type profile was generated. It is obviously a possibility that these domains in Flexiviridae and Tymoviridae are not methyltransferases, and that they are false positives from PSI-Blast. However, the essential residues of a typical viral methyltransferase motif are conserved in the alignment of these domains (data not shown) [16] . In Bamboo mosaic virus, which belongs to Flexiviridae, the residues H68, D122, R125 and Y213 have been identified as putative active site residues with similarity to the Sindbis virus-like methyltransferase [17] , and it has been demonstrated that this region of the Bamboo mosaic virus has methyltransferase activity, as it catalyses the transfer of a methyl group from S-adenosylmethionine (AdoMet) to GTP or guanylylimidodiphosphate (GIDP). The corresponding sequence positions are almost completely conserved in the alignment of Flexiviridae and Tymoviridae N-terminal domains. This is most likely significant, as only 7 positions in total are completely conserved in this alignment, which means that the majority of the conserved positions are known to be essential for methyltransferase activity. Work e.g. by Hataya et al. seems to support the assumption that this sequence region is a methyltransferase domain [18] . It therefore seems likely that all the sequences with AlkB domains also contain functional MT, HEL and RdRp domains. The MT Location of Pfam domains in the variable region of Flexiviridae 2 sequences Figure 5 Location of Pfam domains in the variable region of Flexiviridae 2 sequences. The regions have been extracted directly from Pfam output, and sequences and regions are drawn to scale. The black bar at each end of a motif indicates that a fulllength motif has been found, for partial motifs the bar at the truncated end would be missing. domains are probably involved in capping of genomic and subgenomic RNA [19] .

Based on the bioinformatic evidence generated here, it seems reasonable to assume that the viral AlkB domains identified by Pfam are functional. All the essential residues found in 2-oxoglutarate-and Fe(II)-dependent oxygenases are conserved, in particular the putative Fe 2+ coordinating H, D and H residues at alignment positions 19, 21 and 91 of Figure 3 , and the 2-oxoglutarate coordinating R at position 100. The conserved R at position 106 is also very characteristic of AlkB homologues [10] . The fact that all AlkB-like domains identified in these viral genomes are full-length, compared to the Pfam profile, also seems to support the hypothesis that these domains are functional.

The Pfam searches show that AlkB domains are found only in a subset of the viral genomes. This subset is phylogenetically consistent (see Figure 2 ), as it is mainly restricted to the Flexiviridae, and in particular to a subset of the Flexiviridae consisting of Viti, Capillo, Tricho, Fovea and Carlavirus. This subset is well separated from the remaining Flexiviridae in the phylogenetic analysis. The split seems to be robust from bootstrap analysis, therefore this family will be discussed here as two subfamilies, Flexiviridae 1 and 2. The same split was observed by Adams et al. in their recent analysis of the Flexiviridae family [20] . Most of the AlkB domains (15) are found in Flexiviridae 2. The remaining AlkB domains are found in Flexiviridae 1 (5) and Closteroviridae (2) . In general, all the Flexiviridae 2 sequences have at least one extra domain in addition to MT, HEL and RdRp: either AlkB, OTU-like cysteine protease or a peptidase. Most other plant viruses that are included in this survey do not have additional domains, except for Tymoviridae where a peptidase domain seems to be common. For the remaining plant virus families included here (excluding Tymoviridae and Flexiviridae 2), only 14% seem to have additional domains.

The observed distribution of AlkB domains could most easily be explained by assuming that an ancestral AlkB domain was integrated into the genome of the last common ancestor of the Flexiviridae 2 subfamily. Subsequent Figure 6 Dot plots for Potato virus M (NCBI gi9626090) and Aconitum latent virus (NCBI gi14251191). To the left the full sequences are shown, using the program default for similarity threshold, and to the right the region with AlkB, OTU and peptidase integration, using a slightly lower (more sensitive) threshold for sequence similarity. The Pfam regions corresponding to MT (magenta), AlkB (red), OTU (green), peptidase (blue), HEL (yellow) and RdRp (cyan) domains are indicated. virus generations derived from this common ancestor would then also contain an AlkB domain, except in those cases where the domain was lost again. This scenario could also include subsequent transfer to a small number of other virus families e.g. by recombination.

If this scenario was correct, then one would expect the different domains of the polyprotein to have a similar evolutionary history. From the phylogenetic analysis (Table 2) this seems to be confirmed for the MT, HEL and RdRp domains, but not for the AlkB domain. This indicates that the AlkB domain may not have co-evolved with the other domains, at least until relatively recently. This seems to be confirmed by looking at the degree of co-evolution, which was analysed by computing pairwise distances between alignment regions representing the relevant domains ( Figure 4 ). In the case of perfect co-evolution all points should fall on a diagonal. This seems to be the case for the MT, HEL and RdRp domains. However, the plot of the AlkB domain vs. these three domains for the same set of sequences does not show a similar correlation. Only some of the closely related sequence pairs in the upper right quadrant of the plot in Figure 4 show some degree of correlation for AlkB vs. RdRp. The most likely explanation seems to be that most of the AlkB domains have not coevolved with the other domains for any significant period of time. This seems to rule out the possibility of ancient integration of the AlkB domain, except if we assume that an ancient viral AlkB domain has frequently recombined with other AlkB domains. However, it is difficult to distinguish a scenario with frequent recombination of AlkB domains from de novo integration, and the net effect on the properties observed here would be the same.

As seen in Figure 4 , the range of score values is generally smaller for the AlkB domains than e.g. the RdRp domains, particularly if we exclude a couple of very high-scoring cases (see figure caption) . On the other hand, the degree of sequence variation within the collection of AlkB domains is significant, average sequence identity for pairwise alignments is 38%, and only 10% of the positions are totally conserved. This can be consistent with a recent integration if we assume that several different AlkB-type vectors have been used for integration (see below for details). An increased mutation rate after integration could also have contributed to sequence diversity in this region. Moving the AlkB domain into a novel structural and functional context would have removed many of the original evolutionarily constraints, as well as introduced some new ones. This could have created a "punctuated equilibrium" type of situation, potentially leading to a very rapid evolution that could have introduced significant differences between the AlkB domains, independent of the evolution in the other domains. A high mutation rate seems to be the case for this region in general, as indi-cated in Figure 6 . Although the MT, HEL and RdRp domains seem to be well conserved from the dot plot, there are very large sequence variations in the intervening region. One sequence in Figure 6 has a well conserved AlkB domain, the other an OTU domain. The fact that there are very weak sequence similarities in these two domains in the dot plot indicates that both sequences originally had both domains. However, the fact that this similarity now is very weak and without any of the typical AlkB active site motifs also indicates a high mutation rate where non-essential domains are rapidly lost. Therefore the conservation of AlkB domains is a strong indication that they are functional, as already mentioned.

If we assume that AlkB domains have been integrated relatively recently, then either de novo integration or recombination (horizontal gene transfer) may have been the main driving force for spreading the AlkB domain to new genomes. In the first case a large number of individual integrations could have lead to the present situation. If horizontal gene transfer was the main driving force, the initial number of integrations might have been quite small. It is not easy to differentiate between these two situations.

The map of Pfam motifs in the variable region between the MT and HEL domains in Flexiviridae 2 polyproteins ( Figure 5) shows that they have a very similar domain organisation, basically an AlkB domain followed by an OTU domain and a peptidase domain, located towards the C-terminal part of the sub-sequence. The relatively constant domain organisation seems to be consistent with a small number of initial integrations that were subsequently diffused to related genomes e.g. by homologous recombination. However, this is not fully consistent with the fact that the viruses with AlkB domains have been collected from hosts at very different locations, e.g. Canada, USA, Russia, Italy, Germany, France, India, Taiwan, China and Japan. Although import of virus-infected species or transmission by insects may transport viruses over significant distances, it is not obvious that this is enough to explain the observed distribution of AlkB-like domains. Therefore several independent integrations, mainly from closely related hosts, have to be considered as an alternative explanation. This explanation seems to be supported by the apparent lack of any consistent evolutionary relationships between the various AlkB domains, as seen in Table 2 . It is not easy to see how this model can be consistent with the observed similarities in domain organisation in Flexiviridae. Assuming that this region has a high degree of variability, one would expect the variability to affect localisation of integrated domains as well. However, it is possible that conserved regions e.g. in the polyprotein play a significant role in integration of novel domains. It may be relevant in this context that preliminary simulations indicate that e.g. the AlkB domains tend to form independent folding domains in the folded RNA structure of the polyprotein RNA (F. Drabløs, unpublished data). This property may possibly facilitate the insertion of such domains into the viral genome.

There are many groups of organisms that can act as vectors and spread viruses, including bacteria, fungi, nematodes, arthropods and arachnids. The plant viruses may have acquired the AlkB domain either from the vector or from the host itself. As already mentioned, searching with viral AlkB domains in protein sequence databases resulted mainly in bacterial sequences, including the plant pathogens X. fastidiosa and campestris. It is therefore a reasonable possibility that AlkB domains in plant viruses have originated from bacterial mRNA. It is also possible that the mRNA originated from other vectors or from the host itself, but at the present time this is not easily verified or disproved because of the limited number of insect and plant genomes that have been sequenced.

It has previously been suggested that the viral AlkB domain may be involved in protecting the virus against the post-transcriptional gene silencing (PTGS) system of the host [1] . PTGS is known as one of a plant's intrinsic defence mechanisms against viruses [21] . Gene silencing can occur either through repression of transcription (transcriptional gene silencing -TGS) or through mRNA degradation, PTGS. The PTGS-mechanism in plants shows similarities to RNA interference (RNAi) in animals [22] . This mechanism results in the specific degradation of RNA. Degradation can be activated by introduction of transgenes, RNA viruses or DNA sequences homologous to expressed genes [23] . Many viruses have developed mechanisms to counteract PTGS in order to successfully infect plants [24] . Two of these suppressors of PTGS have been identified as Hc-Protease and the 2b protein of Cucumber mosaic virus [25] . Although both proteins suppress PTGS, it is likely that they do so via different mechanisms. Could the AlkB-like domain found in some of the plant viruses also be a suppressor of PTGS? Previously reported research indicates that methylation of transcribed sequences is somehow connected with PTGS, and the methylation can be mediated by a direct RNA-DNA interaction [26] . This RNA-directed DNA methylation has been described in plants, and leads to de novo methylation of nearly all cytosine residues within the region of sequence identity between RNA and DNA [27] . Both RNA methylation and methylation of host proteins that are essential for viral replication would be detrimental to the virus. It has already been mentioned that AlkB repairs 1methyladenine and 3-methylcytosine by oxidative demethylation. It is therefore possible that AlkB demethylates the nucleotides methylated by the PTGS mechanism, helping the virus to overcome one of the major defence mechanisms of the plant.

As shown here, only a subset of plant viruses have the AlkB domain. However, other viruses may be utilising naturally occurring AlkB proteins in the host. Viruses have to rely on a number of host proteins in order to replicate [28] . In some cases it is probably beneficial for the virus to integrate such genes into their own genome in order to ensure that they are accessible, although there will be a trade off between this advantage and the increased cost of maintaining a larger genome [29] .

However, there is an alternative hypothesis with respect to the AlkB integration that also has to be considered. As discussed above, the AlkB domain seems to have been integrated relatively recently in viruses found at very different geographical locations, and the only obvious connection seems to be that most viruses belong to a subset of the Flexiviridae. However, the source of these viruses points at another common feature. As seen from the table given in Additional file 1, AlkB domains are often found in viruses associated with grapevine, apple, cherry, citrus and blueberry -crops where the usage of pesticides is common. It is known that several common pesticides (e.g. methyl bromide and some organophosphorus compounds) may cause methylation of DNA and RNA [30] [31] [32] [33] . An integrated repair domain for methylation damage as part of the viral replication complex would therefore give the virus a competitive advantage in a highly methylating environment. The application of such pesticides would probably also stimulate AlkB production e.g. in co-infecting bacteria, giving these viruses easy access to AlkB mRNA for integration into their RNA genome.

It could be argued that a more active PTGS system in these plants would give a similar effect. However, in that case we would expect to see more ancient integrations of AlkB domains. It could also be argued that the presence of AlkB domains may be an artefact caused by promiscuous viral domains picking up available mRNA sequences during cultivation of viruses in the laboratory. However, given the large number of different laboratories involved, and the number of different hosts used (data not shown), this seems to be a very unlikely explanation.

The hypothesis that environmental compounds, in particular pesticides, may have provoked the integration of AlkB domains into the viral genomes depends upon a high mutation rate and frequent integrations of non-viral domains. The integrations have to be recent, not only in relative terms, compared to other domains in the same genome, but also in absolute terms, compared to the progress of modern agriculture. The integrations also have to be frequent, in the sense that it is likely that integration could have happened several times, in different biotopes.

It is difficult to estimate mutation rates in RNA viruses. They evolve very rapidly, and it is often difficult to assign reliable phylogenies. However, recent studies indicate that most ssRNA viruses have a mutation rate close to 10 -3 substitutions per site per year [34] , e.g. the SARS virus has 1.16-3.30 × 10 -3 non-synonymous substitutions per site per year, which is considered to be a "moderate" ssRNA mutation rate [34] . If we assume that most ssRNA viruses have effective mutation rates within the same order of magnitude, a realistic mutation rate for the viruses included here might be something like 2.0 × 10 -3 . In that case, the MT, HEL and RdRp trees shown in Additional file 2 represent approximately between 325 and 750 years of evolution. In general the NJ trees estimate a slightly shorter evolutionary history (between 325 and 450 years) compared to the ML trees (between 550 and 750 years). In this estimate the Ampelovirus sequences have not been included, as they seem to have diverged from the remaining AlkB-containing viruses at a much earlier stage. If we believe that the AlkB integrations happened after the divergence of most sequence included here, as indicated by the lack of co-evolution in Figure 4 , it does not seem unrealistic to assume that most of these integrations happened within the last 50 -100 years or so. This estimate is of course very approximate, in particular since we do not know the true mutation rate of these viruses. However, it shows that a likely time span for AlkB integration is compatible with the evolution of modern agriculture. Unfortunately, because of the lack of any robust phylogeny for the viral AlkB sequences it does not make sense to do a similar estimate for that domain.

Although it is generally accepted that viruses frequently use recombination to acquire functionality [35] , it is less well known how often this includes nonviral sequences. However, there are some well-documented examples, and in particular the properties of the ssRNA positive-strand Pestivirus may be relevant in this context. There are two biotopes of the pestiviruses, cytopathogenic (cp) and noncytopatogenic (noncp). The host is infected by the noncp form which is converted into the cp form by integration of a fragment of a cellular gene into the viral genome [36] . This introduces a protease cleavage site in the polyprotein. However, the important point here is that this happens as part of the normal infection process. It has been suggested that the integration is facilitated by the viral polymerase undergoing two subsequent template switches during minus-strand synthesis [37] , although nonreplicative RNA recombination also may be a possibility [38] . Inte-gration of cellular sequences have also been observed in other viruses, e.g. in influenza virus [39] . This shows that at least some viruses do have efficient mechanisms for recruitment of host genes into the viral genome. Therefore a recent and rapid integration of AlkB domains into selected plant virus genomes does not seem to be an unlikely scenario.

This study has focused on the AlkB domain, mainly as an attempt to get a better understanding of potential functions associated with this domain. However, it is likely that additional information about integration patterns and the relative importance of de novo integration vs. recombination can be achieved by a closer investigation of the other variable domains, e.g. by looking at how they correlate with the evolution of the AlkB domains.

We believe that the viral AlkB-like domains are conventional repair domains targeted towards the viral RNA. The integration of AlkB domains into viral genomes may have been provoked by environmental methylating agents, e.g. the introduction of DNA/RNA-methylating pesticides in farming. The hypothesis [1] that the domain interferes with the PTGS system of plants can not be excluded, but seems to be less consistent with observed features of the AlkB integration.

and Tymoviridae was generated from a ClustalX alignment, using hmmbuild and hmmcalibrate from the HMMER package. Visualisation of motif positions in viral sequences was generated directly from the HMMER output files using a local tool as an interface to the GNU [50] groff software. Systematic large scale searches with polyprotein subsequences were done locally with PSI-Blast and the NCBI reference sequence database [15] . Dot plots for comparison of viral protein sequences were generated with Dotter version 3.0 [51] . Managing emerging infectious diseases: Is a federal system an impediment to effective laws? In the 1980's and 1990's HIV/AIDS was the emerging infectious disease. In 2003–2004 we saw the emergence of SARS, Avian influenza and Anthrax in a man made form used for bioterrorism. Emergency powers legislation in Australia is a patchwork of Commonwealth quarantine laws and State and Territory based emergency powers in public health legislation. It is time for a review of such legislation and time for consideration of the efficacy of such legislation from a country wide perspective in an age when we have to consider the possibility of mass outbreaks of communicable diseases which ignore jurisdictional boundaries. The management of infectious diseases in an increasingly complex world of mass international travel, globalization and terrorism heightens challenges for Federal, State and Territory Governments in ensuring that Australia's laws are sufficiently flexible to address the types of problems that may emerge.

In the 1980's and 1990's HIV/AIDS was the latest "emerging infectious disease". Considerable thought was put into the legislative response by a number of Australian jurisdictions. Particular attention had to be given to the unique features of the disease such as the method of transmission, the kinds of people who were at risk, and the protections needed by the community and the infected population to best manage the care of those infected and to minimize new infections. Health workers and researchers began to find that "the most effective strategies that we have so far found to help promote reduction of the spread of HIV involve the adoption of laws and policies which protect the rights of people most at risk of infection" [1] . A good example of a legislative response which adopts this approach is found in section 119 and 120 of the Victorian Health Act 1958. These sections emphasize the need to protect the privacy of the infected individual and to undertake a staged response which is proportional to the risk presented by the infected individual. The legislation has been very effective with HIV and has been praised for its progressive approach [2] .

In 2003 the community has been faced with the emergence of two new infectious diseases, SARS and Anthrax. Whilst there were no cases of either disease in Australia, the threat of a possible outbreak had to be acknowledged and a response planned. Anthrax is not a new infectious disease. Humans can become infected with anthrax by handling products from infected animals or by breathing in anthrax spores from infected animal products (like wool, for example). People also can become infected with gastrointestinal anthrax by eating undercooked meat from infected animals. However, its manufacture and use as a weapon for bioterrorism forces us to rethink its management in a new context. These two infectious diseases have very different features from HIV which spreads only via transmission of infected bodily fluids such as blood or semen. SARS, by contrast is transmitted via droplets from infected cases which, as a result of coughing, carry the virus to close contacts [3] Thus, the infection profile of SARS requires planning for the possible overrun of Intensive Care Units and the likely infection of a number of ICU staff affecting both morale and capacity to cope. Anthrax raised different problems. These include the possible investigation of terrorist suspects alongside investigation of the outbreak of the infectious disease. Difficulties are also raised by likelihood of public panic, and the flooding of public health officials with reports of suspicious white powder.

In early 2004 the media reported the spread of avian influenza across South East Asia. This disease has different features from HIV/AIDS and SARS and an approach to an Australian outbreak would also be different. The main difference is in the source of transmission of the virus, that is, from infected birds to humans. There is very little difference [from ordinary influenza] in the symptoms (though these may vary in severity) or treatment of the virus [4] It is too early to predict whether this may be the next "emerging infectious disease", but its current spread has given rise to concern about such a possibility [5] Australia is a federal system. There are two parallel sets of laws in operation. The Commonwealth Constitution sets out the legislative powers of the Commonwealth. Specific powers are listed in the Commonwealth constitution but State constitutions have broad powers covering matters such as peace, order and good governance. As the Commonwealth has no specific power to legislate with respect to health, other than the quarantine power, national legislative schemes in public health which rely upon a cooperative approach from all States and Territories are cumbersome and difficult.

Without a specific head of power, the Commonwealth has limited ability to legislate with respect to health. "That is, the legislative powers of the Commonwealth are specified in the Constitution and do not include expressly most of the activities that together comprise the field of public health" [6] For this reason, there are no Commonwealth emergency health powers except quarantine powers. Quarantine powers are currently restricted to isolation at the border of the country of people, plants, and animals to prevent the spread of disease. There is a real possibility that quarantine laws could have a broader scope. It depends on how widely the High Court would interpret section 51(x) of the Commonwealth Constitution. A quarantine law could override state laws as long as it remained a law "with respect to quarantine". However, "the power is potentially a colossus so far as the expansion of legislative authority in the fields of public health is concerned". [6] The quarantine power would be the most likely candidate for a head of power on which to base development of commonwealth laws for the management of public health emergencies. Another possibility may be the external affairs power, if there was a relevant treaty or international agreement which could be given effect to in domestic law. However the legislation would have to be limited to laws giving effect to the treaty.

States and territories have a range of emergency powers available to them in their existing public health legislation. Some are relatively old. For example, the Health Act 1911 (WA), Public Health Act 1952 (NT) based on an 1898 Ordinance (Both these Acts are currently under review). Health emergency powers vary from one jurisdiction to another, but include powers to support disease surveillance, contact tracing and orders to restrict behavior or movement of individuals with an infectious disease in certain circumstances. There are also powers to recall food, search premises and seize property, close buildings and a range of other substantial and intrusive powers.

It is suggested that it is time to consider whether state and territory public health legislation contains sufficient measures to manage the outbreak of an infectious disease in a modern environment which includes mass travel, swift spread of infection and additional complexity raised by fears of bioterrorism.

Currently, in a public health emergency caused by the spread of an emerging infectious disease, Australia could need to rely on a patchwork of legislative measures to assist it to cope. Commonwealth quarantine laws and State and Territory powers in public health legislation may all be needed to address the problem. If an outbreak occurred on a border, or in some area where jurisdiction may be in doubt such as airspace or offshore and a state or territory response was required in addition to any quarantine measures, there could be confusion over jurisdiction for the application of State and Territory powers. State and Territory public health acts do not adequately provide for interjurisdictional communication and cooperation. There could also be difficulties if an infectious disease caused overseas deaths of people from more than one State or Territory in circumstances where an Australian coronial investigation was considered desirable. In such a situation, the jurisdiction of more than one Australian coroner would be triggered. Several State and Territory coronial laws could apply and there could be different inquests under different laws undertaken by different coroners into the same incident.

It is suggested that it is time to look at the efficiency of the emergency powers laws of Australia as a whole: to map the laws in each jurisdiction and the Commonwealth quarantine laws and to consider their effectiveness in the face of the outbreak of a fast moving, easily spread infectious disease. The efficacy of Australia's laws should also be considered in relation to bioterrorism. While there were no infections from anthrax in 2003 despite a great deal of media coverage and infections and deaths in the US, a responsible legislature ought to acknowledge the possibility and ensure that the law is ready to support a swift and effective response.

It is not enough to consider whether the individual pieces of legislation are up to the task of managing outbreaks of newly emerging infectious diseases. Indeed many of the jurisdictions are currently reviewing their public health legislation and will no doubt give proper consideration to this issue as part of the review. But who is thinking about how the legislation of all jurisdictions and the Commonwealth quarantine fits together? What powers enable communication and cooperation between jurisdictions about the outbreak of infectious disease? What kind of opportunity is there for a coordinated response? Can public health orders made in one jurisdiction travel to another jurisdiction when the infected individual travels? What arrangements can be made if an outbreak occurs on or close to a interstate border? What if there is an outbreak on a bus carrying passengers from Victoria, through South Australia to the Northern Territory?

It is encouraging to note that, even without specific legislation, there has been a mechanism to achieve communication and cooperation between jurisdictions through the Communicable Disease Network of Australia (CDNA). This Network has in fact been quite successful in fostering regular communication between the Communicable Disease Units across the country and has been involved in coordinated actions during a number of multistate outbreaks.

Despite the existence of this network and other good working relationships between government officials and various agencies in different jurisdictions, a serious outbreak of communicable disease would require the existence of legislative powers. Public health emergencies generate confusion, even panic. Clarity of powers and the way those powers interact with each other would be crucial in an emergency. It became apparent after the Bali tragedy in 2002 that coroner's jurisdiction was triggered differently in different jurisdictions and some acts did not support communication and cooperation when inquests might be needed for deaths of people ordinarily resident in several jurisdictions. The time to find the shortcomings in the legislation is well before the crisis.

A review of the efficacy of how these laws work together to protect the public health of all Australians should be undertaken. It has been possible to overcome the hangovers of federation for the betterment of all Australians in relation to corporations law. When doubts were recently raised about the constitutional basis of the corporations law scheme, the States and Territories were able to cooperate and refer the necessary powers to the Commonwealth to provide certainty about the laws which govern our corporations. Is our public health any less important than governance of our corporations? Could we cooperate to give ourselves certainty, flexibility and a consistent approach which protects the rights of those subject to some very broad powers?

The States and Territories are generally reluctant to refer powers to the Commonwealth. It may be time to seriously discuss referral of powers in the context of health emergency powers. At the very least, it is time that the Commonwealth, States and Territories recognised the need for the laws to work as a set of laws to protect the whole country, not simply individual laws to protect individual jurisdictions.

There has been work done internationally in this area. A model State Emergency Health Powers Act has been developed in the US in 2001 [7] In the preamble to this Act a rationale for its development is set out: "In the wake of the tragic events of September 11, 2001, our nation realizes that the Government's foremost responsibility is to protect the health, safety and wellbeing of its citizens. New and emerging dangers including emergent and resurgent infectious diseases and incidents of civilian mass casualties -pose serious and immediate threats to the population. A renewed focus on the prevention, detection, management and containment of public health emergencies is thus called for." The US, like Australia, is a Federal system. The model was intended to be taken up by those US states which wished to do so. To date, it has been passed in over half the US states. This bill would be an excellent starting point for development of an Australian model. There are a number of legislative mechanisms which could be used to support a nationally uniform approach to health emergency powers legislation in Australia.

The development and adoption of the model food legislation provides a useful model of a cooperative uniform approach. A model act was developed in consultation with all jurisdictions. It covered areas agreed to be core areas of the Act which ought to be the subject of a national approach and other provisions which were considered to be administrative and were to be adopted at the discretion of each jurisdiction. An intergovernmental agreement was signed as a mechanism to protect the uniformity of the legislation. The agreement sets up a Ministerial Council, supported by a Food Regulation Standing Committee. The Council has responsibility for deciding on proposals to amend the model [8] If a decision is made in favor of amendment, States and Territories will use their best endeavors to submit to their respective Parliaments, legislation which gives effect to the amendment.

The law is an important tool in supporting the management of the outbreak of infectious diseases. The existence of our Federal system has meant that we have a different approach in each State and Territory together with Commonwealth control of quarantine. Newly emerging infectious diseases creating real threats to public health in an era of easy mass travel, and the present threat of bioterrorism mean that it is time Australia examined all laws to contain and manage infectious disease outbreak. The laws should be examined both for their effectiveness in the areas they cover, and as part of a whole which ought enable a response which protects the health of all Australians, and crosses borders as easily as SARS or avian influenza. Protein secretion in Lactococcus lactis : an efficient way to increase the overall heterologous protein production Lactococcus lactis, the model lactic acid bacterium (LAB), is a food grade and well-characterized Gram positive bacterium. It is a good candidate for heterologous protein delivery in foodstuff or in the digestive tract. L. lactis can also be used as a protein producer in fermentor. Many heterologous proteins have already been produced in L. lactis but only few reports allow comparing production yields for a given protein either produced intracellularly or secreted in the medium. Here, we review several works evaluating the influence of the localization on the production yields of several heterologous proteins produced in L. lactis. The questions of size limits, conformation, and proteolysis are addressed and discussed with regard to protein yields. These data show that i) secretion is preferable to cytoplasmic production; ii) secretion enhancement (by signal peptide and propeptide optimization) results in increased production yield; iii) protein conformation rather than protein size can impair secretion and thus alter production yields; and iv) fusion of a stable protein can stabilize labile proteins. The role of intracellular proteolysis on heterologous cytoplasmic proteins and precursors is discussed. The new challenges now are the development of food grade systems and the identification and optimization of host factors affecting heterologous protein production not only in L. lactis, but also in other LAB species. Lactic Acid Bacteria (LAB) are anaerobic Gram positive bacteria with a GRAS (Generally Regarded As Safe) status. They are also food grade bacteria, and therefore, they can be used for the delivery of proteins of interest in foodstuff or in the digestive tract. A last advantage compared to other well-known protein producers is that L. lactis does not produce LPS or any proteases as Escherichia coli or Bacillus subtilis do, respectively.

In the last two decades, genetic tools for the model LAB, Lactococcus lactis, were developed: transformation protocols, cloning-or screening-vectors [1, 2] , and mutagenesis systems [3] are now available. Moreover L. lactis genome is entirely sequenced [4] . Many protein expression-and targeting-systems have also been designed for L. lactis [5] [6] [7] . These systems have been used to engineer L. lactis for the intra-or extra-cellular production of numerous proteins of viral, bacterial or eukaryotic origins (Table 1) . To produce a protein of interest in fermentors, secretion is generally preferred to cytoplasmic production because it allows continuous culture and simplifies purification. To use L. lactis as a protein delivery vehicle in the digestive tract of humans or animals, secretion is also preferable because it facilitates interaction between the protein (e.g. enzyme or antigen) and its target (substrate or immune system).

In LAB, like in other Gram positive bacteria, secreted proteins are synthesized as a precursor containing an N-terminal extension called the signal peptide (SP) and the mature moiety of the protein. Precursors are recognized by the host secretion machinery and translocated across the cytoplasmic membrane (early steps). The SP is then cleaved and degraded, and the mature protein is released in the culture supernatant (late steps). Sometimes, secreted proteins require subsequent folding and maturation steps to acquire their active conformation [8] .

In most of the works describing heterologous protein production by recombinant lactococci, only one cellularlocation (i.e. cytoplasm, external media or surface anchored) is described. Only a few works report the production of a given protein in different locations using the same backbone vector, the same induction level and or promoter strength, allowing thus a rigorous comparison of the production yields of cytoplasmic and secreted forms.

Here, six examples of different heterologous proteins produced in L. lactis in both secreted and cytoplasmic forms are reviewed and discussed. Our major conclusion is that the best production yields are observed in most of these cases with secretion (up to five-fold higher than with cytoplasmic production). Moreover, engineering the expres-sion cassette to enhance the secretion efficiency (SE, proportion of the total protein detected as mature form in the supernatant) resulted in increased overall amounts of the protein. L. lactis is able to secrete proteins ranging from low-(< 10 kDa) to high-(> 160 kDa) molecular mass through a Sec-dependant pathway. Altogether, these observations suggest that i) heterologous proteins produced in L. lactis are prone to intracellular degradation whereas secretion allows the precursor to escape proteolysis, and ii) conformation rather than protein size is the predominant feature that can impair SE. New perspectives are now opened in the studies of heterologous protein production in L. lactis. Indeed, there is a need for food grade systems and for a better understanding of the host factors influencing heterologous protein secretion in L. lactis . For example, HtrA-mediated proteolysis (HtrA is the unique housekeeping protease at the cell surface) is now well-characterized in L. lactis [9] and can be overcome by use of a htrA L. lactis strain designed for stable heterologous protein secretion [10] . However, intracellular proteolysis (involving Clp complex -the major cytoplasmic housekeeping protease-, and probably other cellular components) remains poorly understood and is also discussed here.

Genetic tools to target a given protein in different cellular compartments were developed using several reporter proteins [6, [11] [12] [13] (Table 1 ). The staphylococcal nuclease (Nuc) is a well-characterized secreted protein whose activity is readily detectable by petri plate assay and it has been used as a reporter protein for secretion studies in several Gram positive hosts [14] [15] [16] . In L. lactis, Nuc was used to develop protein targeting- [6] and SP screening-systems [1, 2] . Nuc was chosen to develop the pCYT and pSEC vectors for controlled production in L. lactis of cytoplasmic or secreted forms of a protein of interest, respectively ( Fig. 1 ) [5] . The pCYT and pSEC plasmids, where expression is controlled by a nisin inducible promoter, should be used in L. lactis NZ9000 (hereafter referred to as NZ) strain bearing a nisR,K chromosomal cassette, required for the nisin signal transduction [17] . In each case described below, protein sample concentration was adjusted to the cell density of the producing culture (for details see [18] ). At similar induction levels in lactococcal strains containing pCYT:Nuc and pSEC:Nuc vectors, the highest production yields were observed with the secreted Nuc form ( Table 2) . Similar results were obtained with constitutive nuc expression cassettes for cytoplasmic and secreted forms. Nuc was the first heterologous protein where highest protein yields were obtained with the secreted form.

Similar results were obtained for the production of a Brucella abortus ribosomal protein. B. abortus is a facultative intracellular Gram negative bacterial pathogen that infects Unpublished results Bacteriocins human and animals by entry through the digestive tract. The immunogenic B. abortus ribosomal protein L7/L12 is a promising candidate for the development of oral live vaccines against brucellosis using L. lactis as a delivery vector. L7/L12 was produced in L. lactis using pCYT and pSEC vectors [19] . Similarly to Nuc production, the production yield of secreted L7/L12 was reproducibly and significantly higher than that of the cytoplasmic form (Table 2) .

Another example of higher protein yields in secreted vs cytoplasmic form is the production the human papillomavirus type 16 (HPV-16) E7 antigen, a good candidate for the development of therapeutic vaccines against HPV-16 induced cervical cancer. The E7 protein is constitutively produced in cervical carcinomas and interacts with several cell compounds. E7 was produced in a cytoplasmic and a secreted form in L. lactis [20] . Using similar induction level in exponential phase cultures, E7 production 1: protein samples were adjusted to the cell density and protein quantification was performed as described in the references either by western blot or by ELISA. *: E7 was not quantified but ratio was calculated by scanning the western blot signals and comparing their intensity as described in the corresponding reference. nd: not determined was higher for the secreted form than for the cytoplasmic form (Table 2) . This difference was even higher when induction occurred in late-exponential phase, where intracellular E7 was detected at only trace amount whereas secreted E7 was accumulated in NZ(pSEC:E7) culture supernatant (see below). Thus, production of E7 clearly illustrates the fact that secretion results in higher yields in L. lactis.

Production of ovine interferon omega (IFN-ω) further illustrates this observation. In the case of poorly immunogenic antigens, co-delivery of an immuno-stimulator protein can enhance the immune response of the host. In order to optimize the use of lactococci as live vaccines, the production of cytokines was investigated in L. lactis [5, 21, 22] . IFN-ω is a cytokine able to confer resistance to enteric viruses in the digestive tract by reduction of viral penetration and by inhibition of intracellular multiplication of the viruses. Delivery of ovine IFN-ω in the digestive tract by recombinant L. lactis strains could therefore induce anti-viral resistance and could protect the enterocytes. Ovine IFN-ω cDNA was cloned into pCYT and pSEC plasmids for intracellular (pCYT:IFN) and secreted (pSEC:IFN) production respectively [5] . Induction of recombinant NZ(pCYT:IFN) and NZ(pSEC:IFN) strains were performed at equal level and IFN-ω production was measured. The levels of IFN-ω activity showed that i) an active form of IFN-ω was produced in both strains, and ii) the activity of IFN-ω found in the supernatant and cell fractions of NZ(pSEC:IFN) strain was about two-fold higher than that observed for the cytoplasmic form (Table 2) . Similarly to what was observed for Nuc and E7, secretion leads to higher heterologous protein yields.

L. lactis has been engineered to secrete of a wide variety of heterologous proteins from bacterial, viral or eukaryotic origins (Table 1) . There are reports about secretion bottlenecks and biotechnological tools for heterologous secretion in model bacteria such as Escherichia coli and Bacillus subtilis [23, 24] , but only few data are available concerning this aspect in L. lactis. Protein size, nature of the SP and presence of a propeptide are parameters that may interfere with protein secretion. Some data available about these features are compiled here.

To optimize secretion and thus production yields, the nature of the SP was the first parameter to modify on heterologous precursor as previously shown using Nuc as a reporter protein. The replacement of the native staphylococcal SP Nuc by the homologous lactococcal SP Usp45 to direct the secretion of Nuc in L. lactis led to an increased SE [25] (Table 3) . On the other hand, the replacement of SP Nuc by SP Usp45 did not enhance the SE of NucT (a truncated mature moiety of Nuc devoid of N-terminal propeptide) suggesting the importance of the propeptide in the SE for Nuc [25] (Table 3) . However, in several cases, the use of a homologous SP (and especially SP Usp45 ) allows a better SE compared to a heterologous one. Screening vectors were thus developed to search for new homologous secretion signals in L. lactis [1, 2] . These screening works offer now a panel of SPs that are suitable for heterologous secretion. However, when compared to SP Usp45, the newly described SPs were less efficient to direct secretion of Nuc [1] . Even after a direct mutagenesis on SP310, one of these new SPs identified using a screening strategy [1] , the enhanced SE was still lower than the one measured with SP Usp45 [26] . However, a recent study by Lindholm et al. showed that a Lactobacillus brevis SP (originated from a Slayer protein) drove the secretion of the E. coli FedF Schematic representation of Nuc cassettes for controlled and targeted production in L. lactis adhesin more efficiently than SP Usp45 [27] . High SE might thus result, at least in part, from good adequacy between the mature protein and the SP used to direct secretion.

The fusion of a short synthetic propeptide between the SP and the mature moiety is another innovative biotechnological tool to enhance protein secretion. One such propeptide (composed of nine amino acid residues, LEISSTCDA) was developed and was shown to enhance the SE of several heterologous proteins in L. lactis: NucB, NucT, (Table 3 ) [18] , the B. abortus L7/L12 antigen (Table 3 ) [19] , and the α-amylase of Geobacillus stearothermophilus (Table 3 ) [18] . Directed mutagenesis experiments demonstrated that the positive effect of LEISSTCDA on protein secretion was due to the insertion of negatively charged residues in the N-terminus of the mature moiety [25] . Furthermore, the enhancement effect does not depend on the nature of the SP, since the secretion of NucB fused to either SP Nuc or SP Usp45 was enhanced by LEISSTCDA insertion [25] . Strikingly, the enhancement of SE was reproducibly accompanied by an overall increase of protein yields as determined in Western blot experiments. This observation suggests that heterologous precursors are degraded by intracellular proteases when they are not efficiently secreted and that a higher secretion could be a way to escape proteolysis.

Proteins with molecular mass ranging from 165 kDa (size of DsrD, the Leuconostoc mesenteroides dextransucrase, [28] ) to 9.8 kDa (size of Afp1, a Streptomyces tendae antifungal protein; Freitas et al., submitted) have been successfully secreted in L. lactis. This suggests that protein size is not a serious bottleneck for heterologous protein secretion in L. lactis. In contrast to protein size, conformation may be a major problem for heterologous secretion in L.

lactis as illustrated by some recent examples. The first example is the production of the non-structural protein 4 (NSP4) of the bovine rotavirus, the major etiologic agent of severe diarrhea in young cattle. In order to develop live vaccines against this virus, the NSP4 antigen was successfully produced in L. lactis [29] . Derivatives of pCYT and pSEC plasmids were constructed to target NSP4 into cytoplasmic or extracellular location. The highest level of production was obtained with the secreted form. However, no secreted NSP4 was detected in the supernatant and both SP Usp45 -NSP4 precursor and NSP4 mature protein were detected in the cell fraction. Two degradation products were detected in addition to the NSP4 precursor and mature protein. These results suggest that the cytoplasmic form of NSP4 was probably totally degraded inside the cell whereas fusion to the SP Usp45 protected NSP4 protein against intracellular proteolysis.

Similar results were obtained when pCYT and pSEC vectors were used to produce the B. abortus GroEL chaperone protein: only pSEC:GroEL plasmid was obtained and subsequently the fusion SP Usp45 :GroEL was detected in Western blot experiments (V. Azevedo, unpublished data). In this case, B. abortus GroEL is likely to interact with lactococcal cytoplasmic proteins leading to severe cellular defects and thus to a lethal phenotype. On the other hand, fusion of SP Usp to GroEL might keep the chimeric protein in an unfolded and/or inactive state allowing thus its heterologous production.

Another example is the production of the bovine β-lactoglobulin (BLG) in L. lactis [30, 31] . BLG, a 162 amino acid residues globular protein, is the dominant allergen in cow's milk and was produced in L. lactis to test the immunomodulation of the allergenic response in mice when BLG is delivered by a bacterial vector [30] . Western blot and ELISA showed that BLG production was significantly higher when BLG was fused to SP Usp45 although the SE was very low, with no detectable BLG in the supernatant of pSEC:BLG strains [30] . Further studies revealed that a fusion between the LEISS propeptide and BLG could not enhance the SE of BLG above ~5%, as determined by ELISA [31] .

For rotavirus NSP4, B. abortus GroEL, and BLG (which are medium-sized compared to DsrD or Afp1), either very low secretion yields or absence of secretion was observed in L. lactis. In all cases, fusion to a SP stabilizes heterologous protein production even though they are not efficiently secreted. These results could be due either to the SP itself that reportedly acts as an intramolecular chaperone or to the protection of the chimeric precursor from intracellular proteolysis by the cytoplasmic chaperones of the Sec-machinery. GroEL (a cytoplasmic chaperone), NSP4 (a structural protein), and BLG (a globular protein) have dramatically different primary sequences. A higher affinity of intracellular housekeeping proteases for these particular sequences cannot be hypothesized since the fusion of a SP leads to the stabilization of the protein. Change of conformation is therefore the predominant criterion involved in the stabilization of the precursors and the higher yields observed. On the other hand, these proteins might undergo rapid folding right after their synthesis, which interferes with (or hampers) the secretion process. Such interferences between protein conformation and SE were previously shown in E. coli and B. subtilis [32, 33] . Altogether, these results suggest that protein conformation rather than protein size is a major problem for heterologous protein secretion in L. lactis as well.

It was clearly demonstrated that the secreted form of E7, a reportedly labile protein, can be stabilized by fusion to Nuc [20, 34] . Nuc is reportedly a stable protein and its use, as a fusion partner, does not affect its enzymatic activity. The production of the resulting chimerical protein is thus easy to follow. The cytoplasmic form of E7 was stabilized by the fusion to Nuc even when the production was induced in stationary phase ( Fig. 2A) , whereas cytoplasmic E7 alone was degraded (see below; Fig. 3 ). Thus, fusion to the stable Nuc could rescue E7 production in L. lactis and allowed higher protein yields compared to E7 alone [20] . Stabilization by fusion to Nuc was observed for several secreted proteins as well. First, a Nuc-E7 fusion on a pSEC backbone resulted in higher production yield although the SE was altered (Fig. 2B) . Fusion to the synthetic propeptide LEISSTCDA in a pSEC:LEISS:Nuc:E7 construction restored an efficient secretion yield [34] . Second, in an attempt to increase the protein yield of the secreted L7/L12, a fusion to Nuc (pSEC:Nuc:L7/L12) resulted in a 2.5-fold increase in production yield (Fig. 2B ) [19] . Recent results concerning the production of BLG provide a third example of yield enhancement by fusion to Nuc. A pSEC:Nuc:BLG construction allowed a 2-fold increase in BLG yields compared to pSEC:BLG [31] . These results show that Nuc is a stable carrier protein and has a protective effect on labile heterologous chimerical proteins by reducing its sensitivity to intracellular proteolysis. To our knowledge, Nuc is the fusion partner most commonly tested so far for stabilization in L. lactis. Bernasconi et al (2002) fused the Lactobacillus bulgaricus proteinase PrtB to BLG, which was subsequently stabilized by the PrtB carrier [13] . It is thus difficult to postulate any rule concerning the stabilization effect. Different results (i.e. no stabilization) could perhaps be observed with a different partner and thus could help to determine the mechanism of the stabilization effect. In biotechnological use of recombinant L. lactis strains for protein production, fusions can also facilitate purification (e.g. His-tag strategy). Protein fusion has also been successfully used to optimize the production of the two subunits of heterodimeric complexes as demonstrated with murine interleukin-12 in L. lactis [22] or with heterodimeric enzymes in E. coli [35] . In both cases, the resulting fusion had the expected properties. In other cases however, such fusions might dramatically interfere with the conformation of one or both of the proteins, which might be deleterious for the expected activity. Nevertheless, when L. lactis is used as an antigen delivery vector, fusions can be envisioned since it was demonstrated that both moieties of the chimerical protein are still recognized by the corresponding antiserum [10, 20, 34] and are immunogenic [36] .

Several of the results mentioned above suggest that secretion could be an efficient way to escape intracellular proteolysis. This hypothesis was particularly tested in E7 production [20] . E7 was indeed degraded when intracellular production was induced in late exponential or early stationary growth phase (Fig. 3) . E7 production was then tested in a clpP deficient strain (ClpP is reportedly the major house keeping protease in L. lactis; [37] ) and in a dnaK deficient strain (DnaK is an intracellular chaperone that may promote proteolysis by maintaining the protein in an unfolded state; [38] ). In exponential or stationary phase cultures, no significant difference in E7 patterns was observed between wild type and clpP - (Fig. 3 ) or dnaK -(not shown) strains: E7 was equally degraded in the cytoplasm and remained unchanged in supernatants samples. Altogether, these results indicate that E7 intracellular proteolysis is ClpP-and DnaK-independent. Until recently, only two cytoplasmic proteases, ClpP and FtsH [39] , have been identified in L. lactis. The existence of a third, as yet unidentified protease was postulated by studies of a clpP mutant suppressor [40] . E7 may thus be a useful screening target to identify a putative L. lactis protease that, as suggested by our data, is activated in stationary phase.

Besides the features of the precursor itself, these results also rise that host factors are involved in protein stability and SE (Fig. 4) . Research efforts are now focusing on the analysis of host factors involved in protein production and secretion by either directed or random mutagenesis in L. lactis [41] .

Although L. lactis possesses a wide range of enzymes (peptidases, housekeeping proteases) dedicated to intracellular proteolysis, it possesses only one extracellular housekeeping protease (HtrA) [9] and its major extracel-lular scavenger protease, PrtP, is plasmid encoded [42] . Thus, a plasmidless strain does not present any protease activity in the medium. Better production yields could then be expected when secretion is used versus cytoplasmic production. These results give clues and provide the research workers with target proteins to study intracellular proteolysis and protein stability inside and outside the host strain. Such studies already led to the development of htrA deficient L. lactis strains. Heterologous protein secretion and anchoring in a htrA deficient strain allowed Fusion to Nuc rescue E7 in intracellular production and increase protein yields for the secreted forms of E7 and L7/L12

Native E7 production in wt L. lactis depends on growth phase Figure 3 Native E7 production in wt L. lactis depends on growth phase. E7 production and secretion were analyzed by Western blot from cultures induced at different times so that, 1 hour after nisin induction, the samples are harvested at exponential (OD 600 = 0.5-0.6, upper panels) or stationary phase (OD 600 = 1.5, lower panels). wt/pCYT-E7, NZ(pCYT-E7) strain (encoding native E7, cytoplasmic form). wt/pSEC-E7 NZ(pSEC-E7) strain (encoding the precursor preE7). Positions of E7 mature and precursor forms are given by arrows. C, cell lysates; S, supernatant fraction. ClpP is not involved in the intracellular degradation of E7 in L. lactis. Analysis by western blot shows that a strain of L. lactis deficient in the intracellular protease ClpP cannot rescue cytoplasmic E7 production. Induced cultures samples of wt L. lactis or L. lactis clpP mutant strain containing pCYT-E7 (clpP/pCYT-E7) or pSEC-E7 (clpP/pSEC-E7) taken at exponential-(upper panel) or stationary-(lower panel) phase.

Stationary-phase

higher protein stability at the cell surface for several heterologous proteins [10] .

Current research works are now focusing on other host factors that affect protein production and secretion in L. lactis. L. lactis complete genome sequence analysis revealed indeed that the Sec machinery comprises fewer components than the well-characterized B. subtilis Sec machinery. Notably, L. lactis does not possess any SecDF equivalent and complementation of the lactococcal Sec machinery with B. subtilis SecDF results in better secretion yields as determined for Nuc reporter protein (Nouaille et al., submitted) . Random mutagenesis approaches also revealed that features of some cell compartment, such as the cell wall, play an important role in the secretion process [41] . Similar approaches allowed the identification and characterization of genes of unknown functions specifically involved in production yields of the secreted proteins in L. lactis (Nouaille et al., in preparation) .

Many molecular tools are now available to direct heterologous protein secretion in L. lactis and the list of heterologous proteins produced in this bacterium is regularly increased. The reports where cytoplasmic and secretion production can be compared mostly show that secretion allows better protein yields compared to intracellular Schematic presentation of the molecular tools and the cellular events that can affect the production yields of heterologous pro-tein in L. lactis Figure 4 Schematic presentation of the molecular tools and the cellular events that can affect the production yields of heterologous protein in L. lactis. Thicknesses of the arrows are proportional to the final production yields. All the host factors involved in the cellular events are not identified and or characterized yet. SP, signal peptide (encoded in pSEC constructions), +Nuc, fusion between the protein of interest and the stable Nuc protein. production; and allow a better understanding of the protein production and secretion process in L. lactis.

Future works should investigate the L. lactis capacities for protein modifications. For example, we showed that proteins that require a disulfide bond (DSB) to acquire their native conformation can be efficiently produced and secreted in L. lactis [5, 22, 27] . However, no equivalent of E. coli dsb or B. subtilis bdb, the genes involved in DSB formation, was found by sequence comparison in L. lactis. Similarly, other folding elements (i.e. PPIases, so-called maturases...) are still to be identified and the L. lactis capacities for post-translational modifications are still to be investigated.

Altogether, these works will contribute to the development and the improvement of new food-grade systems for L. lactis [43] and should lead, in a near future, to the construction of lactococcal strains dedicated to high-level production of proteins of interest. The GRAS status of L. lactis and LAB in general, is a clear advantage for their use in production and secretion of therapeutic or vaccinal proteins. Detection and characterization of horizontal transfers in prokaryotes using genomic signature Horizontal DNA transfer is an important factor of evolution and participates in biological diversity. Unfortunately, the location and length of horizontal transfers (HTs) are known for very few species. The usage of short oligonucleotides in a sequence (the so-called genomic signature) has been shown to be species-specific even in DNA fragments as short as 1 kb. The genomic signature is therefore proposed as a tool to detect HTs. Since DNA transfers originate from species with a signature different from those of the recipient species, the analysis of local variations of signature along recipient genome may allow for detecting exogenous DNA. The strategy consists in (i) scanning the genome with a sliding window, and calculating the corresponding local signature (ii) evaluating its deviation from the signature of the whole genome and (iii) looking for similar signatures in a database of genomic signatures. A total of 22 prokaryote genomes are analyzed in this way. It has been observed that atypical regions make up ∼6% of each genome on the average. Most of the claimed HTs as well as new ones are detected. The origin of putative DNA transfers is looked for among ∼12 000 species. Donor species are proposed and sometimes strongly suggested, considering similarity of signatures. Among the species studied, Bacillus subtilis, Haemophilus Influenzae and Escherichia coli are investigated by many authors and give the opportunity to perform a thorough comparison of most of the bioinformatics methods used to detect HTs. It is now widely admitted that actual genomes have a common ancestor (LUCA, Last Universal Common Ancestor). Their current diversity results from events that have modified genomes during evolution. While some of these events happen at the nucleotide level (point mutation, indel of few nucleotides), others [strand inversion, duplications, repetitions, transpositions and horizontal transfers (HTs)] may concern significant parts of the genome. It has been postulated that HTs (exchange of genetic material between two different species) were very frequent during the first stages of evolution and are essentially subsisting nowadays in prokaryotes (1) (2) (3) (4) . As a consequence, the detection of HTs appears crucial to the understanding of the evolutionary processes and to the qualitative and quantitative evaluation of exchange rate between species (5) (6) (7) (8) (9) .

The recent complete sequencing of several genomes allows to systematically search for the presence of DNA transfers in species, especially in prokaryotes where the probability of occurrence is higher (10) (11) (12) (13) (14) . It has been reported in particular that (i) HTs in bacteria account for up to 25% of the genome (8, (14) (15) (16) ; (ii) archaebacteria and non-pathogenic bacteria are more prone to transfers than pathogenic bacteria (15, 16) ; and (iii) operational genes are more likely transferred than genes dealing with information management (15) (16) (17) .

The HT concept has been originally coined to explain the dramatic homologies between genes of unrelated species (18, 19) . An 'unusual' match is subsequently the criteria for the detection of HTs (20, 21) . While this approach allows detection of gene transfers with only a partial knowledge of genomes, it requires the sequencing of homologous genes in a number of species and consequently cannot be used for HT screening.

Genes from a given species are very similar to one another with respect to base composition, codon biases and short oligonucleotide composition (15, 16, (22) (23) (24) . As a general rule, usage of oligonucleotides varies less along genomes than among genomes (24) (25) (26) (27) . In addition, it has been observed that transferred DNA retains (at least for some time) characteristics from its species of origin (8, 14) . These particularities are used alone or in conjunction to detect DNA transfers between species (8, 12, 13) . Transferred DNA is consequently detected on the basis of some of its singularities with respect to the sequence characteristics of the recipient species. However, these techniques suffer several drawbacks and weaknesses (28) (29) (30) that led us to consider generalizing the above approach for the screening of atypical regions in sequences. In fact, the genomic signature that accounts for all possible biases in DNA sequences has been shown to be speciesspecific (26, 27, 31, 32) . The signature is approximately invariant along the genome in such a way that the species of origin of DNA segments as small as 1 kb could be identified with a surprisingly high efficiency by means of their signatures (25, 27) . As a consequence, the sequence signature may be most often (at least in bacteria) considered a valuable estimation of the genomic signature. Assuming that (i) transferred DNA fragments exhibit signature of the species they come from and (ii) recipient and donor signatures are different, the screening of local variations of signature along genomes is expected to reveal regions of interest where HTs might be located. In addition, the status of HT is strongly suggested if the signatures of these regions of interest are found close to the signature of other species.

The sequence signature is defined as the frequencies of the whole set of short oligonucleotides observed in a sequence (26, 31) . It can be easily obtained thanks to a very fast algorithm derived from the Chaos Game Representation (CGR) (33) , which allows coping with a 1 Mb sequence in a few seconds on a laptop computer. Signatures may be visualized as square images where the color (or gray level) of each pixel represents the frequency of a given oligonucleotide (called word thereafter) (31) (for examples of signatures, see Supplementary Materials 2, 4 and 6).

DNA sequences are gathered from GenBank. The genomes of 22 prokaryotes are scanned for HTs, B.subtilis, E.coli and H.influenzae genomes being given a special attention to illustrate our approach. In particular, B.subtilis and E.coli provide valuable benchmark thanks to the set of previous works addressing that very issue (12, 14, 16, (34) (35) (36) (37) . Signatures of about 12 000 species are obtained from genomic sequences longer than 1.5 kb. Sequences derived from the same species are concatenated for accuracy purposes. Species from the three domains of life, archaea ($260 species), bacteria ($3950 species) and eukarya ($6750 species) as well as viruses ($1300 species), are represented for a total amount of 1.0 Gb.

The detection of atypical regions is based on the observation of deviation of local signatures (i.e. signature of small fragments of DNA) from the genomic signature of the recipient species. Genomes are consequently sampled by means of a sliding window with an appropriate size. In fact, it would be interesting to have windows the smallest as possible for highest sampling accuracy. However, intra-genomic variability of signature increases for small windows. In addition, variability depends on species and word length. Base composition (1-letter word), 2-and 3-letter words are poorly speciesspecific: they do not allow a good discrimination between species (25, 27) . As a general rule, the longer the words (up to 9-letter long), the higher the specificity of the signature (25, 27, 31) . However, counts of long words in small windows are too low to allow a reliable estimation of the parameters. In our hands, the analysis of 4-letter words in a sliding window of 5 kb (with a 0.5 kb step) offers a good trade-off between reliability of count, file size and computational charge, whatever the species. In addition, a double-strand signature (called local signature thereafter) is computed for each window to get rid of variations induced by strand asymmetry (38) (39) (40) (41) (42) .

For illustration purposes, local signatures are developed as vertical vectors and stacked together in genome order to give an overall picture of word usage variations along each genome. In such plots, horizontal lines show the variation in frequency of words along the genome, whereas local changes in word usage appear as vertical breaks ( Figure 1 ). Figure 1 . Signatures (4-letter words and 5 kb windows) along genome for Clostridium acetobutylicum, Deinococcus radiodurans and Mycobacterium tuberculosis. In this kind of displays, lines represent the frequency of words along genome, columns represent signature of windows.

Considering that the greatest part of the genome is speciestypical, the signature of the recipient species might have been estimated from the analysis of the whole sequence. Although the vast majority of local signatures look mostly the same (believed to be instances of the recipient species signature), some of them may greatly differ. In order to avoid potential biases linked to these outliers, it has been subsequently decided to select typical local signatures on the basis of their similarities, observed after clustering. The underlying idea is that typical local signatures aggregate in few large groups, whereas outliers are found in small complementary groups at a great distance from the recipient genome signature. Groups were consequently determined with the K-means clustering tool, using every scheme of clusters between 3 and 8 for each species. Finally, the best scheme of clusters was obtained by a decision tree-based partition [CART algorithm (43) ]. The purpose of the CART algorithm is to predict values of a categorical dependent variable (clusters of local signatures in this work, each signature being characterized by its distance to the estimated genomic signature) from one or more continuous and/or categorical predictor variables [the different clustering schemes (3-8 clusters) in this work]. The CART algorithm thus provides an optimal split between groups collecting signatures close to the estimated recipient genome signature and the others groups. For each species, a clustering scheme is selected (e.g. the 5-group clustering) and a partition offered (continued example: group 2 and 3 on one side; 1, 4 and 5 on the other). The recipient species signature is subsequently calculated as the mean of the signatures of the groups belonging to the partition with the smallest distance to the estimated genomic signature.

Comparison of signatures is made possible, thanks to an Euclidian metric, accounting for differences in word usage. It must be pointed out that distances between signatures are calculated for high dimensional data (256 dimensions corresponding to the 256 different 4-letter words) and are consequently subjected to the so-called 'concentration of measure phenomenon' (44) . All distances in a high dimension space seem to be comparable since they increase with the square root of the dimension of the space, whereas the variance of their distribution remains unchanged. In fact, the radius of the hyper sphere holding 99% of the signatures of our database is only seven times the nearest neighbor distance (smallest distance between two species). Small differences in distance may consequently be considered highly significant.

For each species, a set of recipient-specific distances is obtained, every local signature belonging to the large clusters being given a distance to the host signature. In order to select outlying signatures, a cut-off distance is chosen on the basis of the distribution of distances observed for each species. It appears that the 99% percentile offered a good trade-off between sensibility and specificity for outlier detection (for impact of the threshold on detection of atypical regions, see Results). Most signatures from minority clusters are detected in this way. Isolated signatures are detected as well, while very few signatures from the recipient species clusters are selected (1%). Outliers together with the flanking regions on the genome are later on reanalyzed with smaller window and step (1/10 th of the original size typically) in order to more accurately determine their limits, when signal-to-noise ratio allows it.

Finally, the gene content of all detected regions is analyzed with the help of species dedicated databases [Genome Information Broker, http://gib.genes.nig.ac.jp/]. A BlastN search (GenBank, default settings) is carried out for each atypical region in order to identify the origin of potential HTs if homology is high enough.

Search for the origin of atypical regions About 12 000 species (including chromosomal, plasmidic, mitochondrial and chloroplastic DNA) from GenBank are found eligible for a genomic signature. Given the signature of an atypical DNA fragment, species with a close signature might be considered as potential donors. Such a screening is performed for every atypical region of the 22 species under consideration. The first five nearby species are retained when their distance to the outlier was donor-compatible.

A total of 22 genomes are screened for atypical regions (Table 1 and Supplementary Material 1). On the average, the 6-cluster scheme offers the best partition. However, in a single case (Aeropyrum pernix), nine clusters are required. In general, a single cluster is devoted to rRNA. The mean distance of windows to host varies over species from 121 to 145 (mean = 132, coefficient of variation = 3%). It is tightly correlated (P-value for the Pearson correlation coefficient <10 À4 ) with the cut-off distance that varies from 178 to 289 (mean = 234, coefficient of variation = 14%). Such large variations can hardly be explained on the mere basis of statistical fluctuations. As already observed (31, 45, 46) , variation of oligonucleotides usage along genome depends on species and can consequently be considered as a species property.

Segmentation quality of atypical regions can be tested using rRNA genes. About 94% of rRNA is detected as atypical ( Table 1) . Borders of rRNA genes are accurate to within 130 nt (0.5 kb window and 50 bp step, threshold 99%). Meanwhile, adjacent tRNAs are identified as well. As a general rule, it can be concluded that rRNA has a specific signature that is consistently at variance with the host signature. In this context, it is worth noticing that rRNA and the remaining outliers lie at comparable distances from the species they belong to, but they are clearly different from one another, rRNAs being consistently found in their own cluster.

The percentage of RNA-free outliers (at the nucleotide level) varies from 1.3 to 13% as a function of species (threshold 99%, Table 1 ). B.subtilis shows the highest percentage of atypical regions, whereas Pyrococcus abyssi has the lowest. Percentages among species are found correlated with the cut-off distance: the higher the cut-off distance, the lower the percentage of outliers (P = 0.007). In fact, a high cutoff distance takes place in species that display a high intragenomic variability, also expressed by a high mean distance to the host (Table 1) . Whether the actual percentage of atypical DNA is an intrinsic property of the species or a mere consequence of the resolution power of nucleotide biases-based methods remains consequently an open question. In addition, as already observed (13, 14) , the percentage of outliers is significantly higher for longer genomes (P = 0.004), whereas the cut-off distance is not related to the length of the genome (P = 0.69).

The mean cut-off distance for the 22 species is 234 (Table 1) . This value is chosen to select credible donors. About 50% of atypical regions are subsequently given credible donors (Supplementary Material 1). Each species has it own set of (Table 1) . Many plasmids and viruses are also found in agreement with the known molecular mechanisms of horizontal transfer (Table 1 and Supplementary Material 1).

A clustering with three classes allows assessing the signature of B.subtilis. The most populated class (collecting 84% of the segments) is chosen to represent B.subtilis. For this subpopulation, the mean distance (arbitrary unit) to the recipient (centroid of the class) and the cut-off distance are 126 and 204, respectively ( Table 1 ). Runs of contiguous outlying windows sharing the same cluster are considered as single transfer events. As a consequence, 58 regions (Figure 2a and Supplementary Material 2) fall beyond the cut-off distance and are thus potential candidates for hosting foreign DNA (for a segmentation of the B.Subtilis genome in terms of genes, see Supplementary Material 3). Figure 2b illustrates the accuracy of segmentation of an atypical region obtained by using a sliding window of 0.5 kb with a 50 bp step. rRNA genes make up $1.1% of B.subtilis genome ( Table 1) . All rRNA genes are found in the outlier population. In addition, all windows containing rRNA are assigned to a specific cluster. In fact, it is known that rRNA has its own signature, which is at variance from the host signature (12) . rRNA genes account for 7% of the outliers (tRNAs are not considered in this study, because their size is too small to generate a significant deviation from the host signature if they are isolated).

A total of 86% of the B.subtilis genome should be considered as B.subtilis typical (Table 1) . When looking for the origin of B.subtilis segments in the 12 000 signature database, B.subtilis appears in the 10 first potential donors for 84% of the whole set of 5 kb sequences that can be derived from its genome. This result confirms that segments having signatures belonging to the predominant clusters are good representatives of the recipient species signature.

The 49 rRNA-free atypical regions vary in size from 1.5 to 135 kb and make up 13% of the total genome (Table 1) . About 50% of atypical regions are less than (or around) 6 kb long. Distances of outlier from first potential donor often fall within the intra-genomic range ( However, in some instances, the outlier-to-donor distance is too great to consider the 'closest' species as potential donor. In contrast, unusual small values deserve a specific attention. In particular, the very small distance between bacteriophage SPBc2 and '2150751-2285750' atypical region (d = 2) allows to spot the part of B.subtilis genome where bacteriophage SPBc2 is incorporated (12, 47) . Other regions in the genome are also found similar (in terms of signature) to bacteriophage SPBc2. Most of them correspond to bacteriophages, imbedded in B.subtilis genome, whose free forms are not sequenced (12, 47) . Observed similarities with SPBC2 are, however, expected since signatures of phages usually share some characteristics with the species they infect (48) . The SPBc2 sequence is the only foreign sequence identified in B.subtilis, using homology as criterion (BlastN, with parameters set to default). In fact, Blast analysis of B.subtilis outliers leads to contrasted results. Besides SPBc2 and 7 out of 9 prophages imbedded in the genome, the only atypical regions identified are those containing the 30 rRNA genes coded in B.subtilis genome. The only few genes that are homologous to parts of atypical regions are found in species belonging to the Bacillus genus. It is interesting to note that no house-keeping genes (except rRNA) are detected in atypical regions. In fact, a great number of genes in atypical regions (except bacteriophage genes and rRNA) have no known function.

A clustering with five classes is required to determine the recipient species signature of H.influenzae. The three most populated classes (collecting 94% of the segments) are chosen to calculate the H.influenzae signature. Mean distance to host and cut-off distance is subsequently found equal to 130 and 239, respectively (Table 1) . Similarly to B.subtilis, one cluster (1.5% of H.influenzae genome) is devoted to the 18 rRNA gene copies (Table 1) . A total of 91% of rRNA is labeled atypical and account for 29% of the outliers.

Analysis of Table 1 shows that 95% of the H.influenzae genome should be considered as H.influenzae typical. In fact, H.influenzae is one of the 10 first potential donors for 92% of all 5 kb sequences that can be derived from its genome. As already observed for B.subtilis, the concordance of these two percentages corroborates the partition procedure used for the selection of typical/atypical fragments.

The 13 rRNA-free atypical regions vary in size from 1.5 to 19.5 kb and make up 3.3% of the genome (Table 1 , Annex 4 and Figure 3 , see Annex 5 for a segmentation of the H.influenzae genome in terms of genes). About 50% of atypical regions are less than (or around) 2.5 kb long. Numbers for H.influenzae are clearly at variance with those for B.subtilis: a smaller percentage of the genome qualifies as atypical and the average size of atypical regions is also smaller. This result is examined below in the context of intra-species signature variability (see Discussion).

A clustering with six classes is required to determine the recipient species signature of E.coli. The main features are summarized in Table 1 . The potential donors of the 84 RNAfree atypical regions are given in Annex 6 (for a segmentation of the E.coli genome in terms of genes, see Annex 7). It is worth noticing that 56% of E.coli potential donors belong to the Enterobacteriales family. Segmentation in terms of genes is displayed in Annex 7. The analysis of this genome is particularly useful for the comparison with literature (see below).

Numerous approaches for detecting horizontal gene transfers have been proposed in the last 2 decades. Phylogenetic trees of protein or DNA sequences, unusual distribution of genes, nucleotide composition (including codon biases) are some of the HT features that are considered within the framework of these models (16, 34) , Hidden Markov Models (HMMs) (12, 14, 35) and Factorial Correspondence Analysis (FCA) (37) are some criteria that are currently employed. Each of the resulting models has its own advantages and caveats (28) (29) (30) . As it has been recently pointed out by Ragan (49) and Lawrence and Ochman (50) , each approach deals with a particular subset of HTs, being for example more efficient for detecting recent transfers, or more effective for the detection of ancient HTs. Our approach, which is clearly based on oligonucleotide composition, assumes that different species have different signatures but does not rely on any other assumption. It is not surprising, therefore, that the genomic signature approach provides results (in terms of % of DNA transferred) in reasonable agreement with those proposed by Garcia-Vallve (16) and Nakamura et al. (14) for the 22 species that were analyzed in common. Correlations between percentages of HTs found by these three methods are highly significant Two species are extensively studied for HT content: B.subtilis (five methods including ours) and E.coli (six methods including ours). H.influenzae is also analyzed by Garcia-Vallve (16) and Nakamura (14) . Comparisons of methods are presented in Tables 2-4 and detailed in Supplementary Materials 3, 5 and 7. A voting procedure (majority rule) has been implemented to determine the status of genes with respect to atypicality. For that task, our initial analysis is converted in terms of genes (Supplementary Materials 3, 5 and 7). Degree of agreement between methods is subsequently observed using the statistical Kappa coefficient (51) . Kappa measures the degree of agreement on a scale from minus infinity to 1. A Kappa of one indicates full agreement, a Kappa of zero indicates that there is no more agreement than expected by chance and negative values are observed if agreement is weaker than expected by chance (a very rare situation). (14, 13, 11, 13 and 15%, respectively). The number of detected genes per method is close, ranging from 457 for Nakamura (14) to 599 for this work (median 537). Detailed votes are given in Table 2 . Among the 4100 genes of B.subtilis genome, 1011 genes are detected by at least one method (about 25% of B.subtilis genes). The number of 'single vote' genes ranges from 116 for Garcia-Vallve (16) to 47 for Nicolas (12) . A total of 470 genes make up the majority consensus set and we detected 453 of them, which is the best score of the five methods. The best agreement with the majority consensus (in terms of Kappas) is reached by Nicolas (12), followed by our method and Moszer (36) ( Table 2 ). Our method gets the best agreement with Nicolas (12) and the worst with the other HMM method used by Nakamura (14) (pairwise Kappa comparison, Table 2 and Supplementary Material 3). In fact, Nakamura approach is at variance with every other approach (14) . It gets the lowest Kappa with the Garcia-Vallve (16) Hayes (35) Lawrence (34) Nakamura (14) Medigue (49) This work majority consensus or with whatever other methods. From Table 2 , the probable number of HT genes in B.subtilis would range from 230 to 1011 with a 'reasonable' estimation around 470 corresponding to the majority consensus. It is to be noted that our method is unable to find two genes that are detected by every other methods (Supplementary Material 3) . These genes are 338 and 236 nt long, respectively, as compared with 2500 nt, the median size of atypical regions detected by our method (Table 1) . Clearly, our method is not appropriate for detecting short isolated atypical genes.

H.influenzae. Garcia-Vallve (16), Nakamura et al. (14) and we are the voters concerned with the analysis of the H.influenzae genome (Supplementary Material 5 and Table 3 , H.influenzae). The originality of results obtained by Nakamura (14) is the salient feature of this comparison. The number of detected HT genes is more than twice higher for Nakamura et al., whereas the part belonging to the majority consensus is the smallest ( Table 3) . Eleven genes are detected both by Garcia-Vallve and Nakamura (14, 16) but not by our method; however, the small number of voters precludes any specific comment in this respect. The probable number of HT genes in H.influenzae would range between 11 and 273, with a 'reasonable' estimation around 60 (majority consensus of 57) ( Table 4 ).

The results obtained by Hayes and Borodovsky (35) are clearly at variance with the others (Table 4 ). Although the proportion of claimed outliers is within the range of published numbers for E.coli (14, 16, 24, 34, 35, 37) , 37% of them are method-specific, and the agreement with other methods is weak (Table 4 ). Hayes and Borodovsky have obviously developed an approach based on HMM dealing with specific outliers. Lawrence and Ochman (34) also get a poor rating especially because they detect about twice as many genes as the other authors do (Table 4) .

It is worth noting that if the cut-off distance for our method is lowered, i.e. 95% instead of 99% for instance, some of the 'single vote' genes are dug out (for details about the impact of the cut-off distance, see Supplementary Material 7). Meanwhile, the percentage of outliers as reported by our approach rises to 20% and the percentage of 'single vote' genes reaches 24%. As expected, a high cut-off distance provides few single vote genes at the risk of missing some potentially transferred genes. Lowering the cut-off increases the proportion of single vote genes with the advantage of detecting most of the potential transfers (Supplementary Material 7) . There is obviously a continuous grading in gene 'atypicality'. It is suggested to first consider most 'consensual' genes as potential HTs and then apply amelioration models to explain the grading.

It is difficult to assess the relevancy of proposed donors, because genes detected as potential HT have generally undergone amelioration (8) . The comparison of recently diverged genomes (species or strains) provides the opportunity to find recent HTs, for which corresponding homologous genes in the donor species may be detected (52) . Such a study is performed for five E.coli strains (two K12 strains: E.coli MG1655, E.coli W3110, one uropathogenic strain: E.coli CFT073, two enterohaemorrhagic strains: E.coli O157-H7 RIMD 0509952, E.coli O157-H7_EDL933) and two Shigella flexneri strains (S.flexneri 2a 2457T, S.flexneri 2a 301). These seven strains/ species have recently diverged, genome sizes are different and the proportion of horizontally transferred genes varies from one strain/species to another (14, 52) . For instance, only $40% of the non-redundant set of proteins is common to E.coli strains CFT073, 0157-H7 EDL 9333 and MG1655 (53) . These strains/species can be clustered in four groups with respect to phylogeny (Table 5) .

Two criteria are used to searching for 'recent horizontally transferred genes': atypical regions (window size 1 kb, step 0.5 kb) (i) must have a signature that differs greatly from that of the host [distance to host must be at least >325, 2.5 times the E.coli intrinsic mean distance (Table 1) ] and (ii) must be present in a limited number of strains/species to ascertain their recentness. In fact, outliers meeting the first criterion generally aggregate into several heterogeneous clusters (K-means clustering) that usually include samples from each strain/species. In some instances, however, some strains/species were absent from the cluster. It was subsequently considered that the corresponding regions might have been recently acquired by the relevant strains/ species. Table 5 shows a selection of potential recently transferred genes. Each cluster of atypical regions contains genes present in a specific set of strains. Some atypical genes are strainspecific, some are only absent in the non-pathogenic K12 strains and intermediate situations are also encountered.

FASTA and Blast searches confirm that these genes are absent from some of the tested strains as already observed in the analysis complete genomes (53) (54) (55) . In a large number of cases, we are able to find a well-conserved homologous gene in another species (Table 5) . It is interesting to note that some of the suggested donors using our 12 000 signature database are in agreement with the species found by alignment methods. When no homologous gene is found, the proposed donors give credit to the known mechanisms of gene transfer (bacteriophages or plasmids) ( Table 5) .

It is worth noticing that most of the selected genes that are absent in K12 strains are involved in the pathogenicity of the other strains (52) . E.coli 0157-H7 is the strain exhibiting the greatest number of genes absent in K12 strains [about 1400 (54) ]. It has the greatest number of genes for which no homolog can be found (Table 5) . Moreover, we are unable to propose a donor for a great part of these genes (Table 5) . Many selected genes for E.coli 0157-H7 lie in the Ter region of the genome (between positions 2 000 000 and 2 500 000) in agreement with the published results (56).

We have observed that most genomic regions are typical of the genome they belong to, using the signature as endpoint.

Considering that the genomic signature is species-specific, atypicality of a region in terms of oligonucleotide usage has been promoted as a criterion for the detection of HTs.

However, atypicality-based methods suffer several caveats that reduce their effectiveness in such a way that only a part of HTs can be detected. In fact, transfers between species with close signatures cannot be detected: significant differences between characteristics of transferred DNA and recipient species DNA are required. For similar reasons, HTs that were drastically ameliorated following their introduction cannot be detected either (8, 14) . The most stringent constraint, however, results from the size of the screening window. On the one hand, ideally, the best signal-to-noise ratio would be obtained when windows and HTs have a comparable size.