We used a yeast two-hybrid pooled matrix approach 2122 to screen for binary interactions among the predicted C. jejuni NCTC11168 proteins. We generated two arrays of yeast strains that express full length C. jejuni open reading frames (ORFs) fused to the LexA DNA-binding domain (BD) or a transcription activation domain (AD), respectively (Materials and methods). Over 89% of the predicted C. jejuni ORFs are represented in the arrays (Table 1). To sample all possible binary interactions, each member of the BD array was mated with pools containing approximately 96 AD strains and the resulting diploids were assayed for reporter activity. Each BD strain was then tested with every AD strain comprising the pools with which it was positive to identify the specific interacting protein pair. The activities of the two yeast two-hybrid reporters were independently quantified based on growth on selective media and color on X-Gal plates, as previously described 21. Our screen initially detected a total of 16,022 putative interactions with above-threshold reporter scores (after subtraction of background activity for BD fusions capable of activating the reporters on their own). An additional 82 unique interactions were identified using a library screen (Materials and methods). We retested the combined 16,104 initial positives in individual one-on-one mating assays of BD strains and AD strains, and reproduced 11,687 of them. The majority of non-repeating interactions initially had shown low levels of reporter activity. The 11,687 repeated interactions were included in our final dataset (CampyYTH v3.1; Figure 1a).

The interaction map includes all of the major protein types and is not significantly enriched for any particular gene classification (Additional data file 2). As expected, however, integral membrane proteins are slightly depleted (Additional data file 3), which was likely due to failure to reach the nucleus or improper folding in the nuclear environment. The high coverage (80% of the predicted proteome) can be attributed in part to the number of proteins tested, to the systematic pooled matrix approach, and to the use of regulated promoters to detect interactions with toxic proteins or proteins that activated the reporters on their own. For example, proteins toxic or inhibitory to yeast were successfully assayed by expressing the fusion proteins with an inducible rather than constitutive promoter 23. Constitutive expression of inhibitory proteins can result in down regulation of the fusion proteins and loss of the ability to detect interactions 21. In this study we found that 114 (7%) of the proteins in our array were either toxic or inhibitory to yeast (Additional data file 4). Nevertheless, we were able to detect over 700 interactions that involved these proteins, including the well-known GroES-GroEL interaction.

To help distinguish true positives from false positives we applied a statistical method to generate confidence scores for each interaction 2425. We used logistic regression to assign weights to a set of experimental interaction attributes based on how well they correlated with biological significance. Sets of putative true positives and false positives were used to train the scoring system on biological significance (Materials and methods). One interaction attribute that strongly correlated with putative true positives, for example, was the level of reporter activity, an attribute not determined in most previous large-scale two-hybrid screens. An attribute that correlated with false positives was the number of interactions per protein. The weighted attributes were combined in a model that assigned probability scores between 0 and 1 to each interaction. Choosing 0.5 as the threshold between low and high confidence interactions resulted in 2,884 (25%) of the reproduced interactions falling into the higher confidence set (Figure 2a), which covered 67% of the C. jejuni proteins. As an independent test of the confidence-scoring system, we demonstrated that interactions with higher confidence scores were significantly more likely to involve pairs of proteins known to function in the same biological process, as would be expected for true positives, than do an equal number of randomly selected low scoring interactions (p < 3 × 10^-57; Figure 2b). For this analysis we used the biological role classifications that had been assigned previously 26, but which played no part in generating the confidence scores. Similarly, we found that the higher confidence interactions generally included more pairs of proteins that share more detailed Gene Ontology (GO) 27 functional annotations (Figure 2c). Combined, these analyses indicate that the confidence scores are a useful measure of biological significance to guide future studies.

To further assess the quality of the C. jejuni interaction data we compared them to E. coli and H. pylori datasets presumed to be enriched for true positives. First, we considered a set of high-confidence E. coli protein interactions from literature-cited low-throughput experiments compiled within the Database of Interacting Proteins (DIP) 28. Reciprocal best-match C. jejuni orthologs of the E. coli proteins were used to predict 147 conserved C. jejuni interactions or interologs. The overlap between the two-hybrid data and the predictions from the E. coli reference set was 28 of 147, significantly (p = 2 × 10^-11) more than the overlap between the reference set and random maps with the same size and topology as the two-hybrid map (Figure 3a). The overlaps between our data and interologs predicted from H. pylori yeast two-hybrid data 11 or E. coli protein complexes 16 were also significantly greater than expected by chance (Figure 3b-d). Moreover, the fraction of C. jejuni data that overlaps with the reference set is similar to that for the E. coli and H. pylori high throughput datasets (Table 2). This analysis suggests that the C. jejuni yeast two-hybrid map has rates of true positives, false positives, and false negatives similar to the previous maps for E. coli and H. pylori.

The entire dataset of C. jejuni interactions and the subset of higher confidence interactions each assemble primarily into single large network components containing 99% and 95% of their interactions, respectively (Figure 1). Both networks have characteristics similar to those observed for other large-scale protein interaction datasets (Additional data file 5). Global analysis of the connectivity (k) of each protein, also known as a protein's degree, revealed a network in which most proteins have few connections, some (hubs) have many connections, and the distribution of interactions per protein is nonrandom (Figure 4a,b). A rank-degree plot of the CampyYTH v3.1 data is best modeled by an exponential curve rather than the power law expected for a scale-free network 29 (Figure 4c). In many studies, the process of selecting the higher confidence interactions has involved removal of the most highly connected proteins, or in our case, trimming interactions preferentially from those proteins. While this enriches for biologically relevant true positives, it may also change the topology of the network. Consistent with this, the higher confidence C. jejuni network appears to be scale-free (Figure 4d).

Several studies have shown that highly interconnected regions of experimentally derived protein interaction maps correspond to biologically relevant protein modules, such as complexes or pathways. Proteins with related functions, for example, tend to be clustered into highly interconnected subnetworks 253031. Moreover, interactions within more highly interconnected regions of protein networks tend to be enriched for true positives 3233. This suggests that clustering is a biological feature of a protein interaction map. The C. jejuni protein network has many groups of highly interconnected proteins, as indicated by its average clustering coefficient (0.10), which is high compared to other large-scale interaction maps (Additional data file 5). The C. jejuni higher confidence set, for example, is more highly clustered than the Drosophila interaction map (average clustering coefficient of 0.05 versus 0.02, respectively) even though the average number of interactions per protein in the two maps is similar. This could be explained by the fact that the C. jejuni map covers much more of the proteome than the Drosophila map. Indeed, among all the maps there is a general trend of increased clustering as the coverage increases (Additional data file 5).

We compared the C. jejuni protein interaction network to protein networks from E. coli, H. pylori, and S. cerevisiae using the NetworkBlast algorithm, which can identify subnetworks that are conserved among species (Materials and methods) 34. The algorithm identified 48 conserved subnetworks between C. jejuni and E. coli, and 19 between C. jejuni and S. cerevisiae. Representative conserved subnetworks are shown in Figure 5a. The subnetworks were found to be statistically significant compared to a random distribution generated by the NetworkBlast algorithm (Additional data file 6). Most of the conserved subnetworks were enriched for proteins with specific GO functions (Additional data file 6), suggesting that they represent important functional pathways or protein complexes. Surprisingly, comparison of C. jejuni and H. pylori, two organisms from the same order, resulted in no significant conserved subnetworks. This is possibly a result of low interactome coverage in the H. pylori protein-protein interaction network relative to the others (0.93 interactions per protein in H. pylori versus 1.47, 2.44, or 9.52 interactions per protein in the entire proteome of E. coli, S. cerevisiae, or C. jejuni, respectively). Furthermore, the fraction of the genome covered by the interaction networks differs markedly between species. Because the NetworkBlast algorithm identifies densely conserved regions of protein networks, sparse regions conserved between H. pylori and C. jejuni would not have been detected. Further analysis of the conserved subnetworks in this study allowed the prediction of a total of 379 new C. jejuni protein interactions (Additional data file 7). These interactions were not present in the experimental yeast two-hybrid analysis, but were derived from the significant conserved subnetworks based on the presence of the orthologous interactions in E. coli or S. cerevisiae (Materials and methods). Such predictions have become a powerful way to construct more complete interaction maps using incomplete experimental data 3435.

To explore the potential relationships among conserved subnetworks, we used hierarchical clustering to group proteins by their subnetwork memberships (Figure 5b). These clusters support the idea, previously argued by Gavin et al. 3, that the network is composed of a set of functional 'cores' that interact with interchangeable 'modules' to constitute distinct cellular functions. Both cores and modules appear as groups of proteins with similar profiles of subnetwork membership; however, while core proteins appear in many subnetworks, modules appear in relatively few. Moreover, cores may appear in the presence or absence of multiple modules, whereas modules are generally found only in the presence of a particular core. These data suggest a higher level of organization amongst protein interactions within organism-wide interaction networks. Additionally, hierarchical clustering also reveals that the conserved portion of the C. jejuni protein-protein interaction network generated from the comparison of C. jejuni and E. coli is distinct from that generated by the comparison of C. jejuni and S. cerevisiae. This may reflect key differences in divergence between the prokaryotes C. jejuni and E. coli versus the eukaryote S. cerevisiae.

Examination of proteins in the C. jejuni map that have been assigned a function (for example, based on sequence similarity to characterized proteins) reveals that proteins involved in the same process tend to interact with each other more frequently than expected by chance (Additional data file 10). This is consistent with the idea that interacting proteins in the map often function in the same pathway or protein complex. The C. jejuni interaction map, therefore, can be used to predict the biological role of uncharacterized proteins based on the functions of interacting proteins, as demonstrated for eukaryotic protein networks 30. An analysis of proteins involved in flagellum biosynthesis provides a useful example. The C. jejuni interaction map includes an interaction between FliS, a putative flagellum assembly export chaperone, and FlaA and FlaB, the flagellin subunits comprising the flagellum. This is consistent with orthologous protein interactions detected in Salmonella typhimurium 36, and in the solved Aquifex aeolicus co-crystal structure of FliS in complex with a FliC (flagellin) fragment 37. Unique to our C. jejuni dataset, however, is the additional interaction detected between FliS and the secreted protein FlaC. Despite homology to FlaA and FlaB at the amino and carboxyl termini, FlaC is not a component of the flagellum, but rather may have a role in cell invasion 38. Experimental data indicate that the flagellar apparatus is required for secretion of FlaC 38. Our interaction data suggest that FliS may help mediate FlaC export. The map likewise connects 663 other poorly characterized proteins into networks that provide initial clues about their functions (Figure 1).

The C. jejuni protein interaction dataset can also serve as a framework for mapping functional pathways, such as the chemotaxis signaling pathway (Figure 6a,b). Although not well characterized in C. jejuni, orthologs have been identified for the prototypical chemotaxis proteins CheW, CheA, CheY, and FliM 2639. In the canonical pathway, chemoattractants bind chemoreceptors known as methyl-accepting chemotaxis proteins (MCPs), which then activate the histidine kinase CheA in a complex stabilized by CheW. CheA phosphorylates CheY, which then interacts with the FliM protein at the base of the flagellar motor, resulting in changes in the direction of flagellar rotation. A search of the C. jejuni map for interactions involving motility and chemotaxis-related proteins reveals a large connected subnetwork of proteins (Figure 6a). The subnetwork includes the expected interactions between a putative MCP (Cj0262c) and CheW, CheW and CheA, and CheA and CheY (Figure 6b). The interaction between CheY and FliM, however, was missed, most likely because it depends upon CheY phosphorylation on a specific aspartate residue 4041, a modification unlikely to be provided by yeast. We also identified interactions between the poorly characterized CheV protein, and three putative MCP proteins, Cj0262c, Cj0448c, and Cj1110c, supporting previous suggestions that CheV may function early in the signal transduction pathway, similar to CheW 3942. Lastly, we detected an interaction between CheA and Cj0643 (Figure 6a). This interaction was predicted previously 43 because Cj0643 contains the conserved CheY-like receiver domain. Cj0643 also contains a diguanylate-cyclase domain, indicating the potential for 3',5'-cyclic diguanylic acid (cdiGMP) biosynthetic activity 44. CdiGMP is a signaling molecule in some bacteria 44. Perhaps in C. jejuni the interaction between CheA and Cj0643 links cdi-GMP generation to conditions outside of the cell.

Several groups have shown that in yeast, essential genes, which are genes required for growth or viability, are more likely to encode hubs in the protein network than nonessential genes 454647. To explore the relationship between essential genes and protein interactions in the C. jejuni network we generated a list of putative essential C. jejuni genes based on orthology to genes proposed to be essential in E. coli and Bacillus subtilis based on experimental evidence in those organisms (Materials and methods). We found higher percentages of putative essential genes amongst proteins with larger numbers of interactions (Figure 7; see also Materials and methods). It follows from this finding that, like in yeast, hub proteins are more likely to be essential than non-hub proteins. Thus, network topology may provide one way to estimate the potential importance of particular genes and may be useful in searches for new candidate drug targets.

Essential proteins often function together in pathways or processes that are important for cell growth or viability. Consistent with this, we found that the C. jejuni map contains interactions between putative essential proteins significantly more frequently than expected by chance (Additional data file 11). Similar results have been described for yeast protein interaction maps 46. One consequence of this enrichment for essential-essential interactions is that groups of essential proteins can form interconnected subnetworks within the interaction map. Additionally, the C. jejuni map may be used to predict that some of the previously uncharacterized proteins may be important for growth or viability based on their interactions with known essential proteins. To create a network enriched for important proteins we identified a subnetwork of interconnected proteins predicted to be essential in C. jejuni based on orthology to essential proteins in E. coli and B. subtilis (Figure 8, triangular, diamond, and rectangular nodes). To identify additional putative essential or important proteins, we added proteins that connect to two or more of the essential nodes through high confidence interactions (circular nodes). The resulting map (Figure 8) contains 264 proteins, many of which are of unknown function (yellow), and identifies potential connections amongst many proteins involved in processes known to be essential for viability, including ribosome function and DNA synthesis and repair. For example, Box A in Figure 8 highlights the interaction between RecJ and SSB. SSB is a single-stranded DNA (ssDNA) binding protein that resolves secondary structure in ssDNA (reviewed in 48), while RecJ is a conserved exonuclease that degrades ssDNA 49. Both proteins have roles in homologous recombination and mismatch repair 485051. A recent report has demonstrated that binding of ssDNA by SSB enhances RecJ binding and exonuclease activity 52, suggesting a functional relationship between the two proteins. This is further supported by the binary protein-protein interaction that we have detected in C. jejuni (this study) and the purification of an E. coli protein complex containing RecJ using affinity-tagged SSB 1.

The many uncharacterized proteins in the essential protein network are potentially biologically important and may include potential novel drug targets. For example, Box B in Figure 8 highlights a protein of unknown function, Cj0189c, which has interaction partners with five ribosomal proteins. Based on this and the fact that proteins with related functions tend to interact, it is reasonable to hypothesize that Cj0189c may also be involved in ribosome assembly or function. This is potentially significant given that the ribosome and protein synthesis are frequent targets of antibiotics 53. Box C in Figure 8 highlights the uncharacterized protein Cj0980, which is homologous to the dipeptidase, peptidase D. In E. coli, peptidase D is one of the enzymes that generates cysteine by cleaving cysteinylglycine 54. In our map, Cj0980 interacts with nine proteins predicted to be essential. One of these proteins, Cj0240c, is a homolog of IscS, a cysteine desulfurase required for the synthesis of all tRNA thiolated nucleosides in E. coli 55. Interestingly, four additional interactors of Cj0980 are tRNA synthetases. Whether or not their product tRNAs are modified in C. jejuni has not been determined, but this series of interactions suggests a possible pathway or protein complex that mediates the transfer of a thiol group originating from cysteinylglycine to specific tRNAs.

The two-hybrid system used here is based on the version originally described by Brent and colleagues 63. C. jejuni ORFs were cloned into the yeast two-hybrid vector pJZ4-NRT for expression of AD fusions driven by the yeast GAL1 promoter 22, and pHZ5-NRT for expression of LexA DNA BD fusions driven by the yeast MAL62 promoter 23. Both vectors contain recombination tags for direct cloning of tagged inserts (see below). Yeast strain RFY231 (MATα trp1Δ::hisG his3 ura3-1 leu2::3LexAop-LEU2) contained the AD plasmids, while Y309 (MATa trp1Δ::hisG his3Δ200 leu2-3 lys2Δ201 ura3-52 mal- pSH18-34(URA3, lacZ)) contained the BD plasmids. The reporter genes include LEU2, facilitating growth on medium lacking leucine, and lacZ, expression of which turns yeast colonies blue when the substrate X-Gal is present.

PCR amplification of over 87% of the predicted ORFs from C. jejuni NCTC11168 genomic DNA was previously described 64. The amplification products included the 21 bp recombination tags 5RT1 and 3RT1 at their 5' and 3' ends, respectively, which match identical sites flanking the insertion site in the yeast two-hybrid vectors. PCR products were cloned into the vectors via homologous recombination in yeast as described previously 22. To validate the identity of the insert in each vector, the 5' ends of the inserted PCR products were sequenced. We generated 1,398 BD strains and 1,442 AD strains containing the two-hybrid vectors with inserts, of which 90% have been sequence verified. Most of the ORFs missing from the arrays failed PCR amplification prior to cloning.

We mated BD and AD strains using a two-phase pooling (pooled matrix) strategy as described previously 2122. Briefly, 15 pools of approximately 96 AD strains each were generated, along with one additional pool of 32 strains. Each pool was mated with individual BD strains arrayed on 96-well plates, and the resulting diploids were assayed for reporter activities. Positive BD strains were then mated with each member of the positive AD pool arrayed on 96-well plates to identify the interacting pairs. Reporter activities were scored using a custom program for image analysis 65 and at least one manual scoring. LacZ scores ranged from 0 (white) to 5 (dark blue) and Leu scores ranged from 0 (no growth) to 3 (heavy growth); combined scores ranged from 0 to 8. Many BDs have some level of background activity due to activation independent of the AD fusion or non-specific interactions. To correct for these we calculated the average interaction score for each BD based on at least 96 interaction assays and subtracted this background from the reporter scores for each of its interactions. Of these corrected scores, only those ≥ 1 were considered initial positives and were retested (see below). A small subset of BD strains (94 total) was also assayed using a library approach as described 2122. Briefly, BD strains were individually mated with a single pool containing almost all of the AD strains (except Cj1718c (leuB) and Cj1546, which activate reporters without a BD). Up to 30 diploids with reporter activity were picked for each BD. Their AD inserts were PCR amplified and restriction digested to identify strains carrying the same clones. Single representatives from each restriction fragment class (RFC) were then sequenced to identify the inserts. Of the 134 interactions detected, 52 (39%) were also identified in the two-phase matrix screen. Combined, 16,104 unique interactions were retested in one-on-one binary mating assays between individual AD and BD strains on 96-well plates. A total of 11,687 interactions proved repeatable (background-corrected combined activity score ≥ 1), including 73% of those from the two-phase matrix screen, 75% of those from the library screen, and 100% of those detected in both screens. The majority of interactions that failed to repeat had been low-scoring (less than 2) in the initial screen. The 11,687 interactions that repeated were combined with 325 non-repeated interactions that had high confidence scores (see below) to create a dataset containing 12,012 interactions, which we named CampyYTH v3.1. This version of the dataset was subsequently used for bioinformatics analysis as indicated. The interaction data can be visualized and downloaded at 17. The CampyYTH v3.1 data are also listed in Additional data file 13.

Confidence scores were determined for each interaction based on methods described by Bader and colleagues 2425. We fit a generalized linear model 66 using experimental and topological attributes of yeast two-hybrid interactions, including the number of interactions for each protein in a pair and the Leu and lacZ reporter activities Fitting the model required both positive and negative training sets. Because a reference set of known interactions is not available for C. jejuni, we derived a set of positive training data (85 interactions total) by assuming that the conserved interactions (reciprocal best match interologs) in common with either the E. coli low-throughput interaction set 28, the H. pylori yeast two-hybrid set 11, or the E. coli protein complex set 1 are likely to be true positives. We derived a set of likely true negatives (111 total) for the negative training data by considering interactions between proteins whose orthologs in E. coli or H. pylori were separated in the respective interaction maps by greater than the average distance of all pairs (≥ 4). Positive and negative training cases were weighted inversely to the number of interactions in each set. When training sets are weighted this way, a confidence score greater than 0.5 means that available data and features support that a specific interaction has a better than random chance to be a true interaction; this allows 0.5 to be used as the threshold between high and low confidence interactions. Validation using protein features not used in the scoring system support the choice of 0.5 as a threshold for higher confidence interactions (discussed further in Additional data file 14; see also Figure 2c). Of the attributes tested, the numbers of interactions per protein were found to be negative predictors of biologically relevant interactions, while reporter activities were positive predictors. To evaluate the scoring model, we performed a stratified five-fold cross-validation. Cross-validation reported a precision of 91.4% and a recall of 78.9%, which gave us confidence that it is a reasonably well-fitted model. We then used the full sets of positives and negatives in training and obtained our final logistic model. The final model was used to compute confidence scores for 16,104 initial positive interactions prior to retesting. Of these, 3,209 scored higher than 0.5, which we define as the high confidence set. Of the interactions with high confidence scores (> 0.5), 90% corresponded to interactions that repeated when retested, while only 68% of the low confidence interactions repeated. Further discussion and details of the confidence scoring system are available in Additional data file 14.

Main role annotations 'mainrole' were downloaded from 67. Excluding self-interactions, out of the 3,209 high confidence interactions, 2,599 have 'mainrole' annotations, and 454 share at least one 'mainrole' annotation. We generated 5,000 groups of 2,599 randomly selected interactions that have 'mainrole' annotations and have a confidence score lower than 0.5. The number of pairs in each set that share 'mainrole' annotations was counted. The distribution was plotted in a histogram and compared with the high confidence set (Figure 2b). To examine whether high confidence interactions tend to share more detailed GO 27 annotations, we grouped interactions into confidence bins so that each bin contains only interactions with scores falling into a specific range. For each interaction, we determined the deepest level of GO biological process annotations shared by the pair of genes, and calculated the average depth of shared biological process for each group. Since GO for C. jejuni NCTC11168 was not available, we used annotations for best match orthologs of C. jejuni RM1221 genes 68. Figure 2c shows that there is a general pattern of increased depth of shared GO terms for interactions with confidence score higher than 0.5. This fact also suggests that our choice of 0.5 as a high confidence threshold is meaningful.

The frequency of each GO description from the iProClass database 69, amongst all of the proteins comprising the proteome was determined and compared to their frequency within the CampyYTH v3.1 dataset or the high confidence subset (Additional data file 3). A similar analysis was performed using the functional classifications assigned by the Sanger Institute 26 (Additional data file 2). We also looked for pairs of GO annotations that were enriched in the interaction data (Additional data file 10). To do this we counted the number of interactions having a specific pair of GO terms. We mapped the annotations to level 5; that is, for a protein with GO annotation A that is at a deeper level than 5, we mapped A to level 5 using 'parent' and 'part of' relationships in the ontologies, and we discarded A if it was above level 5. Self-interactions were excluded from the analysis. We did the same for all GO terms annotated to a protein. To compute the significance of finding specific GO pairs, we generated 2,000 random networks by randomly switching pairs of links while maintaining the degree distribution of the original map, and counted the number of times we found each GO pair in each randomized network. For each GO pair, a p value was computed based on the distribution of the 2,000 counts (assuming normal distribution) and the count in the original yeast two-hybrid map. The p value represents the probability of seeing such a pair in a random network. We listed only pairs with a p value less than 5%.

Additional details are in Additional file 14. Protein-protein interactions from C. jejuni were compared with those from E. coli 1, H. pylori 11 and S. cerevisiae from DIP 28. Corresponding protein sequences were obtained from the following sources: C. jejuni NCTC11168 26; E. coli 70; H. pylori 71; and S. cerevisiae 72. We used NetworkBlast to identify significant conserved protein-protein interaction subnetworks 34. A stand-alone Java version of the program is available at 73. Briefly, the algorithm takes as input a pair of protein-protein interaction networks, one for each of two species, along with a set of homology relationships between the proteins of the two networks. We constructed the homology relationships from an all-versus-all BLAST of the complete set of protein sequences for each of the two species, taking the top 10 hits with E-value = 10^-10. Next, a network alignment graph was created where each node represents a homologous pair of proteins from species 1 and 2 (for example, a1 and a2) and each edge represents a conserved interaction (a1/a2 connects to b1/b2 if the a-b interaction is found in both species; interactions may be either direct (distance 1) or indirect (distance 2), in which a-b is connected through a common neighbor, that is, a-c-b). A greedy search is initiated from each node to identify conserved protein subnetworks, defined as dense subgraphs within the network alignment graph (of maximum size 15 proteins per species). When multiple subnetworks contain protein homologs that overlap by ≥ 50%, only the complex with the highest density was included in the final result. GO annotations 27 of proteins in each conserved complex were analyzed to identify significant functional enrichments (Additional data file 6). We calculated a hypergeometric p value of enrichment for each GO annotation in the three divisions of the GO hierarchy and constrained the annotations by requiring that at least half of the proteins in a complex ascribe to the enrichment. The most specific annotations with hypergeometric p value < 0.05 in each of the three divisions were then assigned to each complex. A complete list of conserved complexes between C. jejuni and E. coli or S. cerevisiae is available for download at 73. The significant conserved subnetworks provided predictions of 379 new C. jejuni protein-protein interactions not found in the two-hybrid screens (Additional data file 7). A protein pair (a, b) was predicted to interact directly if: first, both a and b were present in the same significant conserved complex; second, this pair was observed to interact indirectly in C. jejuni; and third, this pair corresponded to a direct interaction in the comparison species' network.

Since proteins can belong to more than one complex, we clustered the significant conserved subnetworks by protein membership, in effect 'superclustering' the interactions (Figure 5b). An n × m matrix was constructed, where n is the number of significant subnetworks and m is the number of unique proteins involved in any of the significant subnetworks. Using the open source tool ClustArray 74, we clustered the proteins hierarchically using the unweighted pair group method with arithmetic mean (UPGMA) and clustered the subnetworks with a combination k-means algorithm followed by UPGMA hierarchical clustering. The number of clusters k = 3 was chosen as the parameter that approximately minimized within-cluster variability and maximized between-cluster variability (data not shown). Identities of complexes and proteins are shown in the high resolution image of the hierarchical clustering in Additional data file 8. Lists of the proteins comprising complexes are available for download at 73.

We generated lists of putative C. jejuni NCTC11168 essential proteins by identifying reciprocal best match orthologs of likely essential proteins from B. subtilis 75 and E. coli 76. We removed genes from our putative essential list if viable null mutants have been reported (Dr. B. Wren, personal communication). To examine the relationship between essentiality and centrality in the interaction map, we computed the numbers of essential and non-essential proteins in groups having the same number of interactions (degree) in the higher confidence dataset (interactions with confidence scores > 0.5). The result is shown in Figure 7, where r values in the graphs represent Pearson correlation coefficients between the fractions and the degrees. Figure 7 shows that there is a correlation between degree of proteins and the likelihood of being essential. A similar result was obtained with the entire dataset CampyYTH v3.1 (not shown). Lastly, we computed the fraction of essential and non-essential neighbors of each essential protein and compared this to the fraction for random groups of proteins (of the same size as the set of essential proteins). The results shown in Additional data file 11 indicate that essential genes tend to have more neighbors that are also essential; p values indicate the probability of seeing the real fraction (the red dot) by chance.