Characteristics of host proteins interacting with known B. mallei virulence factors

B. mallei virulence factors are associated with multiple pathogenic mechanisms of action (Table 1) [3–7], but their direct molecular interactions are not well delineated. First, we applied functional enrichment analyses based on Gene Ontology (GO) annotation data [23] to assess the characteristics of the human proteins targeted by the nine virulence factors. Table 2 shows that these virulence factors interacted with a statistically significant number of human proteins that were associated with 1) protein ubiquitination and ubiquitin ligase activity, 2) vesicle organization, and 3) protein complexes located in the cytoskeleton, in lysosomes, and in the nuclear lumen. These results were consistent with the experimentally observed pathogen interference with host cytoskeleton organization and ubiquitination levels [2, 3, 19–21, 24].

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Table 2. Enrichment of Gene Ontology (GO) terms for human proteins interacting with B. mallei virulence factors.

https://doi.org/10.1371/journal.pcbi.1004088.t002

Next, we examined the gross topological properties of the network of interactions formed by the B. mallei-targeted host proteins and their interacting host partners, regardless of whether these proteins did or did not interact with the B. mallei proteins. We mapped the identified host proteins interacting with B. mallei onto a human PPI network [25] consisting of 76,043 physical PPIs among 11,688 proteins. Of the 663 human proteins interacting with the nine B. mallei virulence factors, approximately 75% (498) were present in our human PPI network. This set contained proteins that had, on average, a significantly larger number of interacting partners per protein (19.5 vs. 13.0) than would be expected from a corresponding random selection of proteins from the entire human PPI network (Table 3). Among the highest-interacting host proteins targeted by the virulence factors, we found the adapter protein YWHAG (14-3-3 protein gamma) with 376 interactions. This protein, an interacting partner of BimA, has been implicated in the regulation of a large spectrum of signaling pathways [26]. Further topological measures associated with the set of 498 proteins, such as their clustering coefficient (a measure of interactions among nearest neighbors), were not different from the random selection (Table 3). We observed small effects on the length of the shortest path between any two proteins in the set, but it was unclear how to associate these topological parameters with B. mallei virulence.

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Table 3. Topological properties of human proteins interacting with B. mallei.

We evaluated the following properties of the host proteins that interacted with B. mallei proteins based on the human protein-protein interaction (PPI) network [25]: the number of these host proteins in the human PPI network (N_p); the average number of interacting partners (in the human PPI network) of each host protein (D); the clustering coefficient, i.e., the number of interactions among the nearest neighbors (C); the average shortest path between any two proteins in the set (SP); the average number of interacting partners in the human PPI network where both partners interact with B. mallei proteins (D_i); and the number of host proteins in the largest connected component ( ). The top three rows show the results for the host proteins present in the PPI that interacted with the nine known virulence factors, whereas the three lower rows correspond to host proteins that interacted with all 21 tested B. mallei proteins from the yeast two-hybrid screening (known and putative virulence factors). The results for the randomly selected (498 or 619) human proteins from the entire human PPI network (All PPIs) were generated through 10³ random repetitions to create averages and standard deviations. The indicated p-values correspond to the probability of the observed properties being different from the randomly selected set from all PPIs.

https://doi.org/10.1371/journal.pcbi.1004088.t003

Next, we examined human protein interactions where both proteins individually interacted with one or more B. mallei proteins. For the 498-protein set, we found 202 unique proteins that participated in 325 human protein-protein interactions. In comparison with randomly selected proteins, the B. mallei-targeted proteins were engaged in a significantly larger number of these interactions (0.65 vs. 0.28 on a per-protein basis). A further examination of sets of connected human proteins that also interacted with the virulence factors, revealed the presence of a single, large connected component, i.e., a sub-network in which a path connects any two proteins to each other. This largest connected component was composed of 202 proteins and contained the majority (95%) of the 325 interactions between the human proteins interacting with B. mallei (Table 3). The other 11 connected components consisted of five or fewer proteins, an observation that was not statistically significant from a random selection of proteins (data not shown). We found a five-fold increase, from 0.28 to 1.53 (0.95*325/202), in the number of human PPIs for each protein in the largest connected component that were all targeted by the virulence factors, compared to a random selection of proteins. These results suggest that a property of the B. mallei virulence phenotype is to target well-connected host proteins in a unique set of interconnected host proteins. Next, we used this property to expand on our initial set of GO annotations to better characterize B. mallei infectivity and pathogenesis.

B. mallei virulence factors target interactions among host proteins

The analysis of the interactions between the virulence factors and host proteins showed that the targeted human proteins were highly likely to interact among themselves. We hypothesized that interactions among these host proteins are equally important targets as the proteins themselves and could be used to shed light on how virulence factors exert their influence. As detailed in Materials and Methods, we used the largest connected component identified above to detect 93 sets of human PPIs in which, in each set, all human proteins interacted with at least one of the nine known B. mallei virulence factors and had the same GO biological process annotations; we denoted these sets interaction modules.

Table 4 shows that these interaction modules were associated with biological processes related to ligase activity, ubiquitination, protein modification, transcription and translation, immune response, signaling, cytoskeleton organization, development, and mRNA processing. Overall, the identified biological processes were similar to the ones identified when interactions among host proteins were not taken into account; however, they provided an improved annotation granularity. For example, the interaction modules allowed us to identify a biological process termed “positive regulation of protein ubiquitination” instead of just “protein ubiquitination.” Importantly, the analysis provided evidence of a much larger effort to target intracellular host signaling processes, in particular those related to the immune response. Fig. 2 shows the subset of 116 proteins and 163 interactions from the largest connected component that were part of the 93 identified interaction modules and the location of six interaction modules. Each of the interaction modules constituting ubiquitination and ligase activity, transcriptional regulation, immune response, cytoskeleton organization, and mRNA processing, consisted of proteins and interactions that were closely grouped together in the largest connected component (Fig. 2A–E). Fig. 2 also shows that some human proteins are a part of multiple interaction modules, suggesting that B. mallei interacts with multifunctional or “moonlighting” host proteins [27]. Multifunctional proteins have been associated with such neurological disorders as Alzheimer’s and Parkinson’s diseases [28], as well as with bacterial virulence in Helicobacter pylori, Mycobacterium tuberculosis, and Streptococcus pneumonia [29]. Given the multifaceted role of these proteins in enzymatic catalysis, signal transduction, transcriptional regulation, apoptosis, motility, and growth [30, 31], interactions with them suggest an avenue for B. mallei to simultaneously interfere with multiple host-cellular processes to facilitate invasion and survival. In particular, Fig. 2F shows the largest interaction module associated with biological processes linked to multifunctional proteins. This interaction module contained 54 interactions among 44 human proteins associated with various types of regulation (regulation of gene expression, cytokinesis, or apoptosis), signal transduction (GTPase mediated signal transduction and Janus kinase/signal transduction), and response triggering (immune response and response to stress). Additionally, this module contained host-interacting partners of eight out of the nine B. mallei virulence factors from our set, lacking only BopA. These results suggest that B. mallei virulence factors target multifunctional host proteins to simultaneously interfere with multiple host processes required for normal cellular function.

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Table 4. Enrichment of Gene Ontology (GO) biological processes in host subnetworks.

LCC represents the number of proteins in the largest connected component annotated with a given term; LIM represents the number of proteins in the largest interaction module for a given term; p_GO denotes the probability of the same number of proteins as the LCC being annotated with a given GO term solely through a random selection; p_Rp denotes the probability that a given number of proteins as the LIM are annotated with a given GO term solely through random selection; p_Rn represents the probability that a given number of proteins as the LIM are annotated with a given GO term solely through random selection in a random network that has the same degree distribution as our human network. All p-values were assessed using the Benjamini-Hochberg method to meet a maximum false discovery rate threshold of 5% [45]. The table contains only the lowest-level GO terms; the complete data are available in S1 Table.

https://doi.org/10.1371/journal.pcbi.1004088.t004

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Fig 2. Clustering of human proteins targeted by B. mallei known virulence factors.

The graphic shows 116 proteins of the largest connected component of the human PPI network that belong to one or more statistically significant interaction modules. Note that each of these human proteins also interacted with one or more known B. mallei virulence factors. As exemplified by the annotated interaction modules in A-F, the known virulence factors targeted human proteins that were highly interacting among themselves and belonged to the same biological process. For a list of host proteins that compose each interaction module, see S2 Table.

https://doi.org/10.1371/journal.pcbi.1004088.g002

Putative B. mallei virulence factors improve characterization of B. mallei targets

Given that our host-B. mallei interaction dataset contained a number of putative virulence factors, we also evaluated the effect of adding these virulence factors into our analysis to characterize host targets. Similarly to the above analyses, we first evaluated the prevalent characteristics of human proteins using GO annotation [23]. The identified molecular annotations largely matched those identified for known virulence factors only, but also included additional GO terms, such as terms related to RNA metabolic processes (S3 Table). Table 3 shows that the analysis of topological properties of host proteins interacting with known and putative virulence factors displayed the same trends observed in the analysis of the interacting partners of known virulence factors. Next, we evaluated the extent to which known and putative virulence factors also targeted connected subsets of host PPIs. We identified 75 statistically significant interaction modules whose GO biological process annotations largely overlapped with the ones identified for interacting partners of known virulence factors only. Although the number of statistically significant interaction modules was smaller than above (an increase in the number of host proteins dilutes the enrichment), the addition of new host proteins increased the size (in terms of proteins and interactions) of previously identified interaction modules (S4 Table). This suggests that with the increase of protein annotation or with the identification of additional host-B. mallei PPIs, we will be able to identify larger and more complete host interaction modules targeted by B. mallei virulence factors.

Consequently, we used all the interactions shown in Fig. 1 to identify biological pathways targeted by all tested B. mallei proteins using the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation database [32]. We identified two statistically significantly enriched host pathways: bacterial invasion of epithelial cells and focal adhesion. Fig. 3 shows that the proteins targeted in the focal adhesion pathway appeared to be coordinated for pathway activation and largely interacted with each other (yellow boxes). The majority of these molecular interactions belonged to a connected sub-pathway located at the beginning of the pathway (the probability of observing such connectivity at random is < 10^-6), and they provided a link between membrane receptors and signaling events that led to reorganization of the actin cytoskeleton.

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Fig 3. Focal adhesion pathway as a virulence factor target.

We identified the Kyoto Encyclopedia of Genes and Genomes (KEGG) focal adhesion pathway as enriched with multiple virulence factor targets. The majority (17 of 20) of host proteins interacting with B. mallei virulence factors in this pathway belong to a connected sub-pathway of proteins (yellow boxes and red lines), mainly grouped at the beginning of the pathway. This observation implied that receptors and signaling molecules were likely B. mallei targets, corroborating previous observations that pathogens tend to interfere with host processes related to cell communication and actin cytoskeleton organization. The pathway diagram for the focal adhesion pathway was adapted from the KEGG pathway map [32] with permission from the KEGG database administrators.

https://doi.org/10.1371/journal.pcbi.1004088.g003

Human-B. mallei interactions and their effect on the crosstalk between different biological processes

One of the most prominently recurring results across all of our analyses was the link between B. mallei pathogenicity and host cytoskeleton organization. It has been shown that a number of bacterial pathogens, including Yersinia, Salmonella, Shigella, Listeria, and Burkholderia, interfere with host signaling pathways to stimulate the host’s cytoskeleton rearrangement [2, 33]. These changes in signaling lead to changes in the host-cell shape and facilitate bacterial internalization and cell-to-cell spread [33]. Fig. 4 shows host proteins that interacted with known and putative B. mallei virulence factors that can be directly associated with cytoskeleton organization. The largest statistically significant interaction module, represented by red stars, contained proteins previously identified as bacterial targets vital for host actin cytoskeleton rearrangement, e.g., membrane-associated small GTPases (CDC42 and RALA), Filamin-A (FILA), and Rho GDP-dissociation inhibitor (ARHGDIB) [2, 33]. The remaining cytoskeleton-related host proteins, represented as dark red circles, participated in smaller cytoskeleton organization interaction modules that were, on average, less than two proteins (three interactions or edges) away from the largest module.

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Fig 4. Actin cytoskeleton organization as a virulence factor target.

Human proteins targeted by B. mallei proteins formed an interaction module that was primarily linked to cytoskeleton organization and focal adhesion. Twenty-five of these proteins were involved in cytoskeleton organization processes; 13 of them (red stars) interacted with each other (forming an interaction module), and the remaining 12 proteins (dark red circles) were on average < 2 nodes (< 3 edges) away from the interaction module. The figure also shows the overlap between the cytoskeleton organization interaction module and the focal adhesion pathway (shaded area), where connecting protein interactions from focal adhesion pathway proteins or other proteins appear as smaller circles and dashed lines. Note that all human proteins shown interacted with one or more B. mallei proteins.

https://doi.org/10.1371/journal.pcbi.1004088.g004

Given that connector proteins (represented as white and yellow circles in Fig. 4) between the cytoskeleton organization interaction modules were annotated with biological processes different from cytoskeleton organization, as well as the multifunctional nature of some cytoskeletal reorganization proteins, we examined the occurrence of shared proteins that interacted with multiple pathways, i.e., pathway crosstalk. Initially, out of all human proteins interacting with the examined B. mallei proteins, we evaluated the relationships among proteins involved in the focal adhesion pathway that participated in or interacted with human proteins associated with cytoskeleton organization. The shaded area in Fig. 4 shows that six of 12 proteins were present in both systems. Fig. 5 shows an extension of this analysis that includes B. mallei interacting host proteins that are components of eight other molecular pathways that shared proteins with the focal adhesion pathway. Fig. 5A (left) shows that among these nine pathways, the number of pathways that shared one or more proteins was low. However, the number of PPIs connecting the proteins from one pathway with proteins from another pathway was markedly higher [Fig. 5A (right)]. The large number of signaling pathways affected via the focal adhesion pathway magnified the effects of these cross-pathway interactions. Fig. 5B illustrates the propagation and number of cross-pathway interactions that were mediated via the focal adhesion pathway and shows the known virulence factors (Table 1) that can be associated with each pathway. Thus, virulence factors affected biological processes and molecular pathways associated with multiple interconnecting host processes, providing an explanation of how interference with the function of a single protein propagated to and influenced multiple host processes and pathways.

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Fig 5. Crosstalk between host pathways targeted by B. mallei virulence factors.

A) The number of shared proteins and shared pathway protein-protein interactions (PPIs) among human proteins interacting with B. mallei that appeared in the focal adhesion pathway and in up to eight other molecular pathways that shared proteins (partially overlapped) with this pathway. The number of shared proteins across pathways was smaller than the number of shared pathway PPIs. B) The location and number of crosstalk interactions affected by B. mallei centered around the focal adhesion pathway and appear as arrows with line thicknesses proportional to the number of shared PPIs. The identity and number of virulence factors that target each pathway are illustrated using a word cloud. By preferentially targeting signaling pathways, the effect of one protein modulated through interaction with a virulence factor could propagate and disproportionally influence a larger number of biological processes.

https://doi.org/10.1371/journal.pcbi.1004088.g005

Using multiple host-pathogen interaction networks to predict the role of pathogen proteins

Our statistical analyses show that the aggregated host-pathogen interaction data could identify host molecular mechanisms targeted by B. mallei. However, detecting specific mechanisms of action for each pathogen protein based on enrichment analysis of large-scale Y2H protein interaction data is not trivial. This partly stems from experimental, biological, and statistical considerations. For example, the Y2H methodology is biased for certain types of interactions in a non-native environment [34], binding events may or may not be biologically relevant, and statistical testing is hampered by small effect sizes and small statistical power. Conversely, this and previous studies have shown that multiple pathogens tend to target the same host proteins, biological processes, and pathways [2, 9, 10, 18]. Hence, one could potentially use common pathogenic mechanisms to more robustly characterize bacterial proteins and their host targets.

We explored a focused set of human-pathogen interactions derived from putative virulence factor proteins from S. enterica and Y. pestis; the majority of these proteins are associated with a Type 3 Secretion System (T3SS). These datasets contained 62 host-pathogen interactions for 21 S. enterica proteins [12] and 223 interactions for 69 Y. pestis proteins [13]. An initial orthology-based approach to retrieve annotations proved too restrictive and did not generate any novel insights into B. mallei virulence. Instead, we used an alternative network alignment-based methodology optimized for inter-species alignment, i.e., we differentiated between host and pathogen proteins and avoided mapping host proteins to pathogen proteins and vice versa. As detailed in Materials and Methods, we introduced a novel alignment algorithm (HPIA) designed specifically for the alignment of cross-species interactions. We used the HPIA algorithm to identify similarities between host-pathogen interactions using the B. mallei, S. enterica, and Y. pestis PPI datasets, based on a combined similarity measure that included topological similarity, sequence similarity, and functional similarity. Table 5 lists the B. mallei proteins, their aligned protein partners, and inferred function(s) derived from the alignment.

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Table 5. Putative role of B. mallei proteins inferred from host-pathogen interaction network alignment results.

https://doi.org/10.1371/journal.pcbi.1004088.t005

Table 5 shows B. mallei proteins with a known/assumed function in pathogenicity and the corresponding functionality, predicted based on the function of their aligned Y. pestis and S. enterica partners. We identified similarities between T3SS proteins involved in bacterial internalization for all three pathogens, including the orthologs BipB-SipB and BipC-SipC. Additionally, the B. mallei PilA protein was aligned to Y. pestis fimbrial protein FimA6, another cell adhesion protein. Furthermore, although the S. enterica and Y. pestis interaction datasets included mainly T3SS proteins, the two B. mallei Vgr proteins associated with bacterial survival and replication via Type 6 Secretion Systems (T6SS) were aligned to S. enterica and Y. pestis proteins also known to promote bacterial survival and replication. Thus, while the aligned proteins may have different roles within each pathogen, it is possible that they interact with a similar type of host proteins, causing the alignment algorithm to capture these similarities. Overall, the alignment-based inferred roles for six out of the 11 (55%) annotated B. mallei proteins matched their existing annotation and their corresponding secretion system assignment (Table 5, fourth and second columns, respectively). If we only consider matching functionality and ignore the Vgr association to T6SS, the inferred functions matched the existing annotation in eight of 11 (73%) cases.

For the B. mallei virulence factors without known functions in pathogenicity, listed in the lower part of Table 5, the functional mappings from S. enterica and Y. pestis provided an indication of their mechanistic role in virulence. Of special importance were the three novel virulence factors we identified from the Y2H data and experimentally verified in a B. mallei ATCC 23344 aerosol mouse infection model: BMAA1865, BMAA0728, and BMAA0553. The HPIA algorithm identified similarities between BMAA1865, S. enterica protein SopE2, and Y. pestis protein YopE, based on their interactions with host proteins involved in actin-cytoskeleton rearrangement processes. The alignment also identified similarities between B. mallei protein BMAA0728 and S. enterica protein SseL based on their interactions with host proteins involved in ubiquitination. Furthermore, the HPIA algorithm identified similarities between B. mallei protein BMAA0553 and S. enterica protein SseI associated with regulation of the host cytoskeleton and inhibition of cell motility. These results imply that the hypothetical protein BMAA1865 has a role in the host actin-cytoskeleton manipulation, that BMAA0728 has a role in (de)ubiquitination, and that serine/threonine phosphatase BMAA0553 has a role in cytoskeleton regulation. These are the same roles we previously proposed for these three proteins based on the literature review of pathogenic mechanisms [8].

The alignment also identified a putative role of another protein of interest from our previous study, cytidylate kinase BMA0429. The host-pathogen PPI data linked this protein to multiple processes related to pathogenicity. We were not able to test its pathogenicity in an animal model, because this protein appeared to be essential. However, the alignment results imply that this protein had a role in the regulation of T3SS secretion, as it is mapped to two T3SS regulators that are more likely to be localized in the bacterial cytoplasm than to be translocated into the infected cell: SpiC in S. enterica and YscN in Y. pestis [35–38].

Multiple B. mallei virulence factors target eukaryotic-specific host-cell processes

A key insight into the virulence mechanisms that we could derive from the Y2H interactions was that B. mallei targeted eukaryotic-specific cellular mechanisms, such as ubiquitination and focal adhesion. Thus, the specific virulence adaptations retained in the evolution of B. mallei as an obligate mammalian pathogen include targeting the ubiquitination degradation/signaling system and using the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections.

Based on our analysis of their host protein interactions and targeted pathways, the nine known virulence factors shared many common points of attack on the host cell’s physiology. Expanding on the cross-talk analysis shown in Fig. 5B, we created an interconnected host-pathway map. We inferred the connection between two pathways from the number of inter-pathway PPIs where both host proteins are interacting with at least one B. mallei protein. The larger the number of such host protein pairs, the larger the potential influence B. mallei have on the cross-talk between the involved pathways. Fig. 6 shows the extent of this influence between host pathways and the central role the focal adhesion pathway plays in propagating cell signaling and affecting key host cellular processes relevant to B. mallei pathogenesis. The pathways implicated in Fig. 5B directly related to focal adhesion are marked with stars in Fig. 6. These, in turn, are interconnected with a large number of signaling pathways (Fig. 6—grey background) that ultimately control cell cycle, morphology, and growth. A number of known disease processes, marked in red symbols, are also interconnected or directly connected to this signaling network. We hypothesized that this virulence factor host-pathogen network is central in controlling key cellular mechanisms that allow B. mallei to adapt the host cell environment and ensure robust infection.

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Fig 6. Focal adhesion as a central hub for targeting host cells.

The number of shared protein-protein interactions (PPIs) targeted by B. mallei virulence factors is shown as lines proportional to the number of PPIs (only connections with 10 or more interactions are illustrated). Physiological, cancer, and disease pathways were all interconnected via signaling pathways that could be affected through the focal adhesion pathway.

https://doi.org/10.1371/journal.pcbi.1004088.g006

Summary

Given the association of the selected pathogen proteins to secretion systems, the underlying Y2H methodology, and our analysis methodology, our detection capabilities were geared to finding host pathways and biological processes targeted by B. mallei via virulence factors. The limitation of this approach is that while a host-pathogen protein interaction may occur, as determined via Y2H experimentation, this type of data does not allow us to resolve when, where, or why such interactions are important. Furthermore, even though the strict statistical threshold at a false discovery rate (FDR) ≤ 5% minimizes the chances of identifying random data correlations, it does not test our hypothesis that the pathway is involved in B. mallei virulence. Conversely, the strength of our analysis is that the identified host interactions are dominated by known and validated virulence factors, allowing us to create new hypotheses around the biological interpretation of pathway interaction patterns.

Our results showed that host-pathogen PPIs represent a rich source of information about molecular mechanisms of pathogenicity, and that these interactions can be used to identify and characterize host molecular pathways and processes targeted by pathogens. Specifically, our topological analysis of human-B. mallei protein interactions showed that known and putative B. mallei virulence factors tend to target multifunctional host proteins, host proteins that interact with each other, and host proteins with a large number of interacting partners. Additionally, the analysis identified a number of host processes and pathways relevant to B. mallei pathogenicity, many of which have been linked to bacterial pathogenicity in previous experimental studies, e.g., signaling and communication, protein modification and regulation, cytoskeleton organization, and focal adhesion. Furthermore, the topological analysis suggested that B. mallei virulence factors target host molecular processes through interference with their direct and indirect host-interacting partners, implying that the process of pathogenic internalization and intracellular survival requires the modulation of multiple host cellular processes.

We further introduced the novel HPIA algorithm that can be used to identify common sets of host-pathogen interactions by aligning (mapping) host-to-host and pathogen-to-pathogen proteins from two interaction datasets. We used the HPIA algorithm to compare human-B. mallei interactions to those of human-Y. pestis and human-S. enterica and identified a statistically significant number of aligned interactions. We also showed that the resulting alignments could be used to predict roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners.

Finally, given that nine of 21 proteins in our dataset are known virulence factors, we could hypothesize on why and how B. mallei uses these proteins to overcome multiple defense systems and orchestrate a robust infection process in mammalian hosts. Ultimately, the bacterial host-virulence program is derived from a survival strategy developed in the rhizosphere, i.e., in a generally competitive environment containing multiple, diverse species. Using multiple virulence factors to target eukaryotic-specific mechanisms common to eukaryotic rhizosphere species, B. mallei broadly influences key processes in ubiquitination and cell signaling to modulate and adapt the host-cell environment for its benefit.

Human-B. mallei protein interaction set

To create a comprehensive set of human-B. mallei PPIs, we merged human-B. mallei and murine-B. mallei PPI datasets identified in [8]. These datasets contained 586 interactions between 409 human and 21 B. mallei proteins, and 797 interactions between 574 murine and 25 B. mallei proteins; 19 B. mallei proteins appeared in both sets, including nine known B. mallei virulence factors (Table 1). When creating the merged set, we considered only a subset of murine-B. mallei PPIs in which the B. mallei proteins also interacted with human proteins and, thus, had shown the ability to bind to human proteins. The merging procedure consisted of four steps. In the first step, we identified B. mallei proteins that interacted with both hosts (19 B. mallei proteins). Then, we found human orthologs for each of the 419 (73%) murine proteins that interacted with the B. mallei proteins identified in step 1. In the third step, we assessed whether the human orthologs constituted unique proteins, i.e., whether they were not a part of the experimentally detected human-B. mallei interactions. If not, we added this interaction into the orthologous human-B. mallei dataset. The resulting orthologous dataset consisted of 649 interactions between 419 human proteins and 19 B. mallei proteins, corresponding to 82% of the murine-B. mallei PPIs. Finally, we merged the experimental human-B. mallei dataset with the orthologous human-B. mallei dataset to create a merged set of human-B. mallei PPIs. The resulting merged dataset consisted of 1,235 unique interactions between 21 B. mallei and 828 human proteins (S1 Data and S6 Table). Approximately 72% (890) of these represent interactions among the nine B. mallei known virulence factors (Table 1) and 663 unique human proteins. All proteins were annotated by their official gene symbols as defined in the HUGO Gene Nomenclature Committee database [39].

We used the National Center for Biotechnology Information HomoloGene database of homologs (http://www.ncbi.nlm.nih.gov/homologene) to identify human-murine orthologs [40].

Topological properties of human proteins interacting with B. mallei in the human PPI network

We calculated the following topological properties for a set of human proteins interacting with B. mallei: 1) the number of human proteins interacting with B. mallei proteins (N_p); 2) the average number of their interacting partners in the human PPI network (D); 3) the clustering coefficient, i.e., the number of interactions among the nearest neighbors (C); the average shortest path between any two proteins in the set (SP); the average number of interacting partners in the human PPI network where both partners interact with B. mallei proteins (D_i); and the number of host proteins in the largest connected component ( ). All calculations were performed in R using the igraph package [41]. We evaluated whether the observed values for each of the five properties were statistically significant as follows. From the human interactome, we randomly selected the same number of proteins as the number of proteins interacting with B. mallei virulence factors. Next, we calculated each of the five topological properties for this random set of proteins, repeating the procedure 10³ times. This procedure yielded 10³ values for each property, which followed a Normal distribution (Normality was evaluated using the quantile-quantile plots and the Kolmogorov-Smirnov test [42], where we found that there was not enough evidence in the data to suggest that the distributions were not Normal). Then, for each property, we evaluated a relationship between the observed value for proteins interacting with B. mallei and the values obtained for random protein sets using a Z-score. Finally, we computed the p-values corresponding to the resulting Z-scores.

Gene set functional enrichment analyses

We performed GO and KEGG enrichment analyses in R using the Bioconductor packages BioMart and KEGGgraph, respectively [43, 44]. As the universe of human proteins, we used all constituent proteins from the human PPI network. As GO terms are specified at multiple levels of detail, we used a complete GO tree annotation, excluding the root and the top two levels of GO terms. GO annotation was obtained from BioMart [43]. For the KEGG enrichment analysis, as the universe of human proteins, we used the human proteins available in KEGGgraph that participated in at least one KEGG pathway [44]. All obtained p-values were assessed using the Benjamini and Hochberg multiple test correction [45]. We retained only annotations that were enriched at an FDR control level of 0.05, i.e., there is a less than 5% chance that the obtained p-values are not statistically significant.

We performed two types of enrichment analysis: standard enrichment analysis and network-based enrichment analysis. In the standard enrichment analysis, we computed the probability of observing the number of proteins annotated with a given term using the hypergeometric distribution. In the network-based enrichment analysis, we first identified the largest connected component in the human interactome that consisted of human proteins interacting with at least one B. mallei virulence factor (denoted as LCC). Then, we counted the number of proteins n_t in the LCC that were annotated with a GO biological process term t. Additionally, for each term t, we identified connected (sub)networks of LCC in which all proteins were annotated with t; these we termed interaction modules. We denoted such interaction modules as IM_t and the number of proteins in these modules as m_t.

We evaluated whether the observed IM_t interaction modules were statistically significant as follows. First, we evaluated whether the observed interacting proteins and their corresponding biological processes in the LCC were statistically significant compared to an equal number of random proteins. For each GO term t, we counted the number of proteins in the random set that were annotated with t (denoted as r_t). Additionally, for each t, we identified interaction modules in which all proteins from the random set were annotated with t. We denoted the number of proteins in such interaction modules as s_t. We repeated this procedure 10⁴ times. Given the obtained values, we defined the probability p_t of observing n_t proteins annotated with t as (1) and the probability p_rp of observing an interaction module IM_t, given a random set of host proteins, as (2)

Second, we evaluated whether the observed interacting proteins and their corresponding biological processes were statistically significant compared to random interactions. We randomly rewired the human protein interaction network, while preserving the same degree distribution as observed in the original network. Next, we mapped human proteins from the LCC on the rewired network and, for each term t, identified interaction modules in which all proteins were annotated with t. We denoted the number of proteins in such modules as w_t. We repeated this procedure 10⁴ times. Finally, we calculated the probability p_rw of observing an interaction module, IM_t, given a random set of host interactions, as (3) IM_t interaction modules with p_t ≤ 0.01, p_rp ≤ 0.01, and p_rw ≤ 0.01 contain a statistically significant number of human proteins and interactions among them, and are statistically significantly enriched in a biological process t.

Human-S. enterica and human-Y. pestis protein interactions set

The human-S. enterica subsp. enterica serovar Typhimurium dataset consisted of 62 interactions between 21 S. enterica virulence-associated proteins and 51 human proteins identified in several small-scale experiments [12]. The majority of S. enterica proteins from this set were associated with the bacterial T3SS. The human-Y. pestis PPI dataset consisted of a union of 204 interactions identified by Y2H screens and 23 interactions identified in several small-scale experiments [13]. The combined human-Y. pestis PPI dataset contained 223 unique interactions between 69 Y. pestis virulence-associated proteins and 125 human proteins. The majority of Y. pestis proteins were also associated with the bacterial T3SS. For the basic comparison of the human-B. mallei, human-S. enterica, and human-Y. pestis PPI networks’ characteristics, see S7 Table.

The HPIA algorithm

Network alignment algorithms [46–49] have been used previously to successfully identify both conserved PPIs [50–55] and phylogenetic relationships between species [51–53]. Although the existing network alignment algorithms can be applied to host-pathogen PPIs, these algorithms are not optimized for inter-species alignment, i.e., they cannot differentiate between different types of proteins, such as host and pathogen proteins, and consequently may map host proteins to pathogen proteins and vice versa. The primary motivation for designing a network alignment algorithm was to be able to use previous data and insights from other host-pathogen interaction studies for interpreting our B. mallei host interaction data. If conserved network motifs and interaction exist, we can use this information to infer/predict more complex roles for proteins than just transferring sequence-based annotation information [47, 56]. Thus, we designed the HPIA algorithm specifically for the alignment of host-pathogen interactions to augment the sparsely annotated B. mallei protein data.

We have taken a number of considerations into account in designing the algorithm based on the nature of the biological problem at hand. First, due to the non-exhaustive nature of Y2H experimentation, the underlying interaction data are not complete [8]. Second, the selected pathogen species are not identical to each other, i.e., proteins and biological processes have evolved differently between the species. Because of this sparse and diverse nature of the pathogen data at hand, we cannot a priori expect to obtain satisfactory alignments based on simply mapping human proteins to each other. In this sense, a “perfect” alignment is never attainable, and instead we must rely on approximate alignments with desirable properties, such as biological consistency and sequence similarity. Hence, we developed an alternate approach for aligning bipartite graphs for which one set of nodes (pathogen) are less-well characterized than the other nodes (human).

For the limited number of host-pathogen interactions we have available for comparisons, our algorithm attempts to identify interactions “conserved” on the functional level rather than at the exact protein level. Thus, we can exploit the fact that all three host-pathogen PPI networks contained interactions with human proteins that participated in similar biological processes. In effect, this allowed us to extend the known annotations from the other networks to the previously uncharacterized B. mallei virulence factors.

Furthermore, the algorithm guarantees that host proteins will be aligned only to host proteins and that pathogen proteins will be aligned only to pathogen proteins.

Notation. Let G₁(U₁, V₁, E₁) and G₂(U₂, V₂, E₂) be two bipartite graphs (networks), where U₁ and V₁ are two disjoint sets of nodes in G₁, U₂ and V₂ are two disjoint sets of nodes in G₂, and E₁ and E₂ are sets of edges of G₁ and G₂ such that every edge in G₁ connects a node in U₁ to one node in V₁, and every edge in G₂ connects a node in U₂ to one node in V₂ (i.e., no two nodes within the same set are adjacent). Without loss of generality, we can assume that |U₁| <|U₂| and |V₁| <|V₂| (hence, G₁ < G₂). The HPIA algorithm is a global network alignment algorithm that uniquely matches each node from U₁ to exactly one node in U₂, and each node from V₁ to exactly one node in V₂. Formally, the alignment of G₁ to G₂ can be represented as a set of two ordered pairs {(u₁, u₂), (v₁, v₂)}, where u₁ ϵ U₁, u₂ ϵ U₂, v₁ ϵ V₁, and v₂ ϵ V₂, and no two ordered pairs share a node.

For the host-pathogen interaction networks (G₁ and G₂), sets U₁ and U₂ correspond to pathogen proteins, sets V₁ and V₂ correspond to host proteins, and sets E₁ and E₂ correspond to interactions between host and pathogen proteins. Thus, our host-pathogen interaction set corresponds to the host-pathogen network, nodes correspond to proteins, and edges correspond to host-pathogen interactions.

Algorithm description. The HPIA algorithm is a seed-and-extend algorithm that consists of the following three steps: 1) pre-processing, 2) identification of local alignment, and 3) identification of global alignment. In this context, we referred to “local alignment” as a smaller area of local network similarity between two networks where not all nodes need to be included, and “global” where all nodes from the smaller network must be aligned to nodes from the larger network [28]. Both mappings are 1-to-1, i.e., one node from one network can be aligned to only one node of the other network. In the first step, the algorithm reads in host-pathogen networks and node annotations, and it calculates similarities between nodes based on either the provided annotation (for node sets with available annotation) or the default topological similarity (for node sets without available annotation). This step also includes handling user-specified seed nodes (nodes that should be mapped to each other). The HPIA algorithm allows a user to provide a set of node pairs (seeds) and/or to employ the algorithm’s feature to automatically search for seed pairs (this search is based on the sequence similarity or equivalent protein names). The HPIA algorithm can treat seed pairs in two ways: 1) as a suggestion for node alignment, i.e., nodes that should be aligned to each other if other alignment constraints are satisfied (see below), or 2) as a requirement for the alignment, i.e., seed pairs that have to be aligned to each other. The first step also includes initialization of the aligned pairs list as empty.

In the second step, the HPIA algorithm first identifies a pair of seed nodes (s₁, s₂), where s₁ ϵ G₁ and s₂ ϵ G₂, based on the node similarity measures from one of the following three sets (in order of preference): 1) a set of aligned protein pairs in which both proteins are adjacent to at least one unaligned protein, 2) a set of user-suggested seed pairs, and 3) a set of unaligned proteins. All three of these sets contain proteins from the host and pathogen sets and, thus, a pair of seed nodes can come from either the host or pathogen sets of proteins. However, if there are seed node candidates from both sets, the HPIA algorithm preferentially selects a pathogen set. Once selected, the seed pair (s₁, s₂) is added to the list of aligned pairs. Next, the HPIA algorithm expands around the seed nodes by greedily aligning their direct neighbors s_1i and s_2j(s_1i ϵ N[s₁] and s_2j ϵ N[s₁]), based on the given node similarity measure (see below). The HPIA algorithm repeats step two while there exists at least one unaligned pair of host proteins or pathogen proteins adjacent to at least one other unaligned protein. When there are no such pairs left, HPIA proceeds to the third step.

In the third step, the HPIA algorithm greedily aligns all of the remaining unaligned pathogen nodes in G₁ to unaligned pathogen nodes G₂ and all of the remaining unaligned host nodes in G₁ to unaligned host nodes G₂, solely based on node similarity measure. Each pair of nodes is aligned one at a time based on the given node similarity (see below); network connectivity information is not taken into account explicitly (only as a part of the node similarity measure). The results of the alignment are lists of aligned nodes and edges and the following alignment statistics: the total number and percentage of aligned nodes, the number and percentage of aligned pathogen and host nodes, and the number and percentage of aligned edges.

The HPIA algorithm allows a user to provide a set of node pairs (seeds) and to use an automatic search for seed pairs (this search is based on the sequence similarity or equivalent protein names) by specifying an optional parameter, “additionalSeeds.” The HPIA algorithm can treat these seed pairs in two ways. If the “relaxSeeds” option is given, the nodes will be aligned to each other only if other alignment constraints are satisfied as detailed below. If the “relaxSeeds” option is not given, the seed pairs are forced to be aligned to each other. We recommend that the “relaxSeeds” parameter be turned on if the “additionalSeeds” parameter is used.

All ties in the algorithm are broken randomly. The implementation of the HPIA algorithm is also presented as pseudocode in S1 Text. Fig. 7 shows a high-level description of the HPIA alignment algorithm.

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Fig 7. Host-Pathogen Interactions Alignment (HPIA) algorithm.

The HPIA algorithm is a seed-and extend algorithm that aligns two bipartite graphs, e.g., two different host-pathogen protein interaction networks. A) Given an initial pair of seed nodes (red nodes U₁ and u₁) from two graphs G₁ (left) and G₂ (right), the algorithm first aligns seed nodes to each other. Then, it aligns the neighbors of the seed nodes from the first graph (green nodes V₁-V₆) to the neighbors of the seed nodes in the second graph (green nodes v₁-v₅) based on the node similarity measure (as defined in Equations 6, 8, and 9). This procedure results in six aligned nodes and five aligned edges. B) The algorithm iteratively selects new seeds and extends around them, e.g., it selects nodes V₆ from G₁ and v₅ from G₂ as new seed nodes and, based on the node similarity measure, aligns their unaligned neighbors U₂ to u₂, creating an additional aligned edge (U₂-V₆ to u₂-v₅). C) When the algorithm cannot find any seed nodes of the same type that have unaligned neighbors, it greedily aligns all of the remaining unaligned nodes based on their type and the node similarity measure. Some nodes may remain unaligned if the graphs’ sizes vary, e.g., when there is no match for v₆ from G₂ in G₁. The HPIA algorithm generates a list of aligned nodes and a list of aligned edges inferred from the aligned nodes.

https://doi.org/10.1371/journal.pcbi.1004088.g007

Node similarity measures. The HPIA algorithm uses one or more topologically and biologically based protein similarity measures to identify conserved interactions. Similarity between two proteins can always be calculated based on at least one of the metrics and, thus, the algorithm always has a metric to match one protein to another. If no node annotation is provided, the HPIA algorithm uses the default topological similarity measure, S_DT(n₁, n₂), to calculate the similarity between nodes n₁ ϵ G₁ and n₂ ϵ G₂: (4) where deg(n) denotes the degree of node n and nd(n) denotes the neighborhood density of n, defined as (5) where N[n] = N(n)∪{n} represents the closed neighborhood of node n, i.e., the node n and the set of its adjacent nodes (for a PPI network, this corresponds to a protein and all of its interacting partners). α is a parameter in [0, 1] that controls the contribution of the degree of a node to its similarity function. We empirically selected α = 0.7, as we wanted to weight the number of direct host-pathogen interactions higher than the number of host-pathogen-host or pathogen-host-pathogen interactions.

If node annotation is provided, the HPIA algorithm defines a similarity between pathogen proteins u₁ ϵ U₁ and u₂ ϵ U₂ as (6) and a similarity between host proteins v₁ ϵ V₁ and v₂ ϵ V₂ as (7) where S_GDV-P and S_GDV-H denote the graphlet degree vector similarity [57] derived from the host-pathogen interaction network and the host-PPI network, respectively; S_EB represents the BLAST expected value (E-value) similarity [58], defined as 1– E-value for E-values ≤ 1 and 0 otherwise; and S_GO denotes the GO term annotation similarity calculated using the Jaccard similarity measure [59]. If a specific type of annotation is not provided, the HPIA algorithm assigns the similarity value 0 to the corresponding similarity parameters, e.g., if BLAST E-values are not provided, the value of S_EB for all pairs of nodes is set to 0. We did not add the graphlet degree vector similarity for the pathogen networks because only a few pathogen-PPI networks are available, none of which are for B. mallei.

Data. For the topological node annotation, we used graphlet degree vectors [57] of all host and pathogen proteins from the host-pathogen PPI networks. Host proteins found in the host PPI network [25] were additionally annotated with another set of graphlet degree vectors calculated based on the host PPI network topology. We used GO annotation [23] downloaded from UNIPROT [60] [the lowest (leaf) level] as the biological node annotation. Additionally, we used BLAST E-values of ≤ 0.01 to define similarities between proteins [58]. Protein sequences were downloaded from UNIPROT and aligned using BLAST pairwise sequence alignment.

Alignment quality. To assess the topological quality of the alignment, we used edge correctness (EC), defined as the percentage of edges in G₁ that were aligned to edges in G₂ [51]. To assess the biological quality of the alignment, we evaluated whether the number of aligned protein pairs that share one or more GO term(s) was statistically significant compared to the number we could expect at random using the standard model of sampling without replacement, as described in previous studies [51–53]. We used the same approach to assess the statistical significance of the alignment of two bipartite networks, G₁(U₁, V₁, E₁) and G₂(U₂, V₂, E₂), with the EC of x% (similar to the implementation described above [51–53]). We aligned each pair of host-pathogen interaction networks 30 times and reported the average and standard deviations of the alignment scores over all runs, as well as the best score (S8 Table). We ascertained the robustness of the alignments with respect to the E-value cutoff and observed no significant differences in the results when lowering the cutoff value from 10^-2 to 10^-3. To assess the biological quality of the alignment, we evaluated whether the number of aligned protein pairs that share one or more GO term(s) was statistically significant compared to the number we could expect from a random alignment. Given that all obtained alignments were of similar biological quality, we further refined our prediction by using the alignments that had the highest EC score, i.e., we used the alignment with the highest EC score to infer the role of B. mallei proteins.

Implementation and availability

All statistical analyses were performed in R. All networks were plotted using Cytoscape [61]. The cross-species network alignment algorithm was developed in C++. Executable files and examples for the HPIA algorithm are provided at http://www.bhsai.org/downloads/hpia/.