An important goal of systems medicine is to study disease in the context of genetic and environmental perturbations to the human interactome network. For diseases with both genetic and infectious contributors, a key postulate is that similar perturbations of the human interactome by either disease mutations or pathogens can have similar disease consequences. This postulate has so far only been tested for a few viral species at the level of whole proteins. Here, we expand the scope of viral species examined, and test this postulate more rigorously at the higher resolution of protein domains. Focusing on diseases with both genetic and viral contributors, we found significant convergent perturbation of the human domain-resolved interactome by endogenous genetic mutations and exogenous viral proteins inducing similar disease phenotypes. Pan-cancer, pan-oncovirus analysis further revealed that domains of human oncoproteins either physically targeted or structurally mimicked by oncoviruses are enriched for cancer driver rather than passenger mutations, suggesting convergent targeting of cancer driver pathways by diverse oncoviruses. Our study provides a framework for high-resolution, network-based comparison of various disease factors, both genetic and environmental, in terms of their impacts on the human interactome.
Cellular function and behaviour are driven by highly coordinated biomolecular interaction networks. A prime example is the protein-protein interaction (PPI) network, also known as the protein “interactome” or interactome for short. A central focus of disease systems biology is to use interactome networks to study genotype-phenotype relationships in complex diseases [1]. The idea of using interactome networks to infer gene function and gene-disease association comes from the well-validated principle of “guilt by association”, which states that physically interacting proteins tend to share similar functions and, by extension, tend to be involved in similar disease processes [1–4]. Recent advances in systems biology have spawned the view of human disease as a manifestation of genetic and environmental perturbations to the human interactome, a key postulate being that similar perturbation patterns lead to similar disease phenotypes [5–8]. A corollary is that, for diseases with both genetic and infectious contributors, similar perturbations of the human interactome by either disease mutations or pathogens can have similar disease consequences. This corollary has been tested for several viral species at the level of whole proteins [9,10]. For example, Gulbahce et al. used yeast two-hybrid screens to map binary interactions between Epstein-Barr virus (EBV) and human papillomavirus (HPV) proteins and human proteins, and transcriptionally profiled human cell lines exogenously expressing HPV oncoproteins E6 and E7 [9]. They found that human genes associated with EBV- and HPV-implicated genetic diseases were often either directly targeted by the virus or transcriptionally regulated by viral targets. This finding led to the idea that oncoviral proteins may preferentially target host proto-oncogenes and tumour suppressors, which was experimentally validated in four families of DNA oncoviruses [10]. Despite insights from these studies on the etiology of virally-implicated genetic diseases, there has yet to be a systematic, structure-based comparison of mutation-induced and pathogen-induced perturbations of the human interactome. A high-resolution, structurally-resolved network biology approach is important for unravelling complex genotype-phenotype relationships, because mutations occurring in different PPI-mediating interfaces on the same protein often have distinct functional impacts and phenotypic consequences [5–8]. In this regard, structural systems biology has proved useful in uncovering evolutionary properties of single- and multi-interface PPI network hubs, systems-level principles governing human-virus interactions, and systems properties of disease variants [6,11,12]. For instance, by constructing atomic-resolution human-virus and within-human protein interactomes, Franzosa and Xia discovered that viral proteins tend to target existing endogenous PPI interfaces in the human interactome, rather than creating exogenous interfaces de novo, thereby efficiently perturbing multiple endogenous PPIs involved in cell regulation [12]. In a follow-up study, Garamszegi et al. expanded the coverage of the human-virus interactome using domain-resolved models of PPIs, and found that viral proteins tend to deploy short linear motifs to bind a variety of human protein domains [13]. The economical and pleiotropic nature of “host domain-viral motif” interactions reflects the efficiency with which viruses rewire the human interactome given limited genomic resources at their disposal. Meanwhile, Wang et al. constructed a domain-resolution within-human interactome where protein domains are annotated with disease variant information [6]. They found that mutations occurring in different PPI-mediating domains within the same protein tend to be associated with different disorders (“gene pleiotropy”). By contrast, mutations occurring in the domains of two different but interacting proteins, where the interaction is mediated by said domains, tend to be associated with the same disorder (“locus heterogeneity”). These studies attest to the utility of structural systems biology in the study of infectious and genetic diseases. Here, we apply structural systems biology to the study of virally-implicated genetic diseases (VIDs), and rigorously test the postulate that endogenous genetic mutations and exogenous viral proteins give rise to similar disease phenotypes by inducing similar perturbations of the human interactome at the level of protein domains. Specifically, we constructed a domain-resolved human-virus protein interactome and characterized the distribution of genetic disease mutations with respect to human domains targeted by virus. Overall, we found that viral proteins and VID mutations induce similar perturbations of the human domain-resolved interactome, for individual viruses with clearly defined VIDs and sufficient numbers of host-virus PPIs (including EBV, HPV and HIV), for oncoviruses, as well as for all viruses combined. We first analyzed the disease associations of host proteins targeted by viral proteins and confirmed that virus-targeted proteins tend to be causally associated with VIDs rather than non-VIDs. We then analyzed the domain-level distribution of disease mutations in virus-targeted proteins and found that virus-targeted domains are significantly enriched for mutations causing VIDs rather than non-VIDs. Using a pooled analysis of all oncoviruses and all oncomutations, we found oncovirus-targeted domains to be significantly enriched for mutations causing cancer rather than other diseases. Furthermore, domains of oncoproteins either physically targeted or structurally mimicked by oncoviruses are significantly enriched for cancer driver mutations rather than passenger mutations, which implies convergent perturbation of cancer driver pathways by diverse oncoviruses. Finally, we also assessed the extent to which viral proteins and VID mutations perturb the same domain-domain interactions (DDIs) in the human interactome. We found that viruses preferentially target DDI partners of domains harbouring VID mutations, regardless of whether the DDI partners themselves are susceptible to known disease mutations. By correlating the equivalent pathogenicity of viral proteins and VID mutations with their convergent perturbation of the human domain-resolved interactome, we provide a framework for high-resolution, network-based comparison of the functional impacts of both genetic and environmental disease factors. On a broader note, our finding implies that similar perturbations of the human interactome at the domain level can have similar phenotypic consequences, regardless of the source of perturbation.
We first acquired human-endogenous and human-virus binary PPI data from IntAct, HPIDB 3. 0, and the HIV-1 Human Interaction Database [14–18]. Only PPIs supported by at least one PubMed ID were included in the whole-protein resolution human-virus interactome, which consists of 173830 PPIs between 15995 human proteins, and 28531 PPIs between 7761 human proteins and 624 viral proteins. 7211 human proteins participate in both endogenous and exogenous PPIs. To build homology models of PPIs, we collected high-confidence domain-domain interaction (DDI) and domain-motif interaction (DMI) templates derived from 3D structures of protein complexes in the Protein Data Bank, and scanned protein sequences for the occurrence of Pfam domains and domain-binding linear motifs [19–23]. Structural models were assigned to each PPI by extracting all DDIs and DMIs possibly mediating the PPI. The resulting domain-resolved human-virus structural interaction network (hvSIN) consists of 61041 PPIs between 11596 human proteins, and 4654 PPIs between 1590 human proteins and 405 viral proteins. 1517 human proteins participate in both endogenous and exogenous portions of hvSIN. We then obtained manually-curated disease variant data from UniProtKB and ClinVar [24,25], selecting missense variants located inside Pfam domains for our analyses. Overall, 19047 mutations associated with 5383 diseases were mapped to 3585 domains of 2622 proteins. 14720 mutations associated with 4185 diseases were mapped to 2642 domains of 1864 human proteins in hvSIN. Table 1 lists the number of mutations by the type of domain in which they occur. Incidentally, 1272 domains of 957 human proteins in hvSIN are susceptible to disease mutations, but lack interacting domains or motifs. 850 of these 1272 domains harbour a total of 4154 mutations associated with 1381 diseases that are not accounted for by mutations occurring in PPI-mediating domains in hvSIN. Because the completeness of a domain’s PPI profile depends largely on the interactome search space and availability of 3D structures of protein complexes, and domains often have important biological functions besides mediating PPIs (e. g. enzymatic or nucleotide-binding activity), we included all domains of virus-targeted host proteins in a comprehensive analysis of the domain-level distribution of disease mutations. To relate the equivalent pathogenicity of viral proteins and VID mutations to their equivalent perturbation of the host interactome, we first characterized the mutational landscape of human proteins targeted by EBV, HPV and HIV, three viruses with clearly defined VIDs and sufficient numbers of host-virus PPIs. Since most oncoviruses are causally implicated in only a few site-specific malignancies (e. g. HBV/HCV in hepatocellular carcinoma, KSHV in Kaposi’s sarcoma, and HTLV in adult T-cell lymphoma), and various types of cancer share common molecular hallmarks [26,27], to increase the statistical power of our analysis and establish whether a general equivalence exists between endogenous and exogenous perturbagens of oncogenic pathways, we also performed a pooled analysis of host proteins targeted by diverse oncoviruses, by considering all types of cancer as interchangeable diseases, all oncomutations as interchangeable endogenous perturbagens, and all oncoviral proteins as interchangeable exogenous perturbagens. We found that for EBV, HIV, HPV and a broad spectrum of oncoviruses, virus-targeted host proteins tend to be causally associated with VIDs (Fig 1), and virus-targeted host domains tend to harbour mutations causally associated with VIDs (Fig 2). We discuss our findings for each type of virus below. A full list of VIDs and disease-associated proteins for EBV, HPV and HIV can be found in S1 Table. A main challenge in cancer research is to distinguish mutations which confer clonal growth advantage (i. e. drivers), from mutations that do not cause clonal expansion (i. e. passengers) [75]. Large-scale cancer genome sequencing projects have enabled systematic identification of cancer driver proteins and mutations [76]. Rozenblatt-Rosen et al. previously constructed an oncovirus-human interactome and demonstrated, at the whole-protein level, comparability between oncoviral perturbation and conventional functional genomics approaches to cancer gene discovery [10]. However, by representing proteins and PPIs as generic nodes and edges, their approach is not sensitive enough to distinguish driver mutations from passenger mutations occurring in the same oncoprotein. As we demonstrated earlier in the case of pleiotropic oncoproteins, the oncogenicity or “driver-ness” of a mutation is often correlated with its occurrence in oncovirus-targeted domains (OVTDs). To confirm that oncoviruses can help identify driver proteins, we first cross-classified human proteins in hvSIN by whether they are oncoviral targets, and whether they are curated by the Cancer Gene Census (CGC) as being causally implicated in cancer, i. e. driver proteins [76]. Out of 727 oncoviral targets, 93 (12. 8%) are in CGC, whereas out of 10897 remaining human proteins in hvSIN, 514 (4. 7%) are in CGC. In other words, there is a 3-fold enrichment of driver proteins among oncoviral targets (Fisher’s exact test, two-tailed P = 3 × 10−16) (Fig 5A). Next, to find out if oncoviruses can also help identify driver mutations, we cross-classified mutations in oncoproteins by whether they are drivers or passengers, and by whether they map to OVTDs. Oncogenic and resistance mutations with a ClinVar clinical significance value of “pathogenic” or “likely pathogenic” are considered drivers, while passengers include all other missense mutations in oncoproteins that are catalogued by ClinVar and COSMIC. Out of 194 oncoproteins with annotated driver mutations, we identified 30 oncoproteins as having at least one OVTD. Pooled analysis of all 30 oncoproteins mapped 340/398 (85. 4%) driver mutations and 3673/7177 (51. 2%) passenger mutations to OVTDs. In other words, the odds of finding a driver mutation in OVTDs is 5 times as high as that in non-OVTDs (Fisher’s exact test, two-tailed P < 2. 2 × 10−16) (Fig 5B). Closer inspection identified 19 candidates for focused investigations into the common basis of viral and mutational oncogenesis (Table 2): (I) 7 oncoproteins where all domains are OVTDs, and the driver: passenger ratio is higher than the average ratio across all oncoproteins; (II) 8 oncoproteins where some domains are OVTDs, and driver mutations are exclusively found in OVTDs; and (III) 4 oncoproteins where some domains are OVTDs, and driver mutations are significantly enriched in OVTDs (Fisher’s exact test, two-tailed P < 0. 05). An example of each type of candidate is given in Fig 6. Viruses are known to encode structural homologues that mimic host domains in order to modulate the biological activities of host targets. Such viral homology domains (VHDs) play key roles in mediating immune response (e. g. PF00048 in CMV and KSHV), apoptosis (e. g. PF00452 in EBV and KSHV), cell differentiation (e. g. PF07684 in feline leukemia virus), and protein phosphorylation (e. g. PF06734 in CMV), among other cellular processes involved in virally-implicated diseases. VHDs often compete with cellular counterparts for interaction partners, thereby rewiring host signaling networks to the virus’s advantage. Table 3 lists instances of human proteins convergently targeted by human domains and oncoviral homology domains in hvSIN. The preceding section established that oncovirus-targeted host domains are enriched for cancer driver mutations. Here, we test the hypothesis that oncovirus-mimicked host domains are also enriched for cancer driver mutations, independent of whether they are physically targeted by the virus. To this end, we identified 21 oncoproteins having at least one oncovirus-targeted domain (OVTD) and at least one viral homology domain (VHD). We further classified viral homology domains (VHDs) into those enriched in oncogenic viruses (oncoviral homology domains, or OVHDs), versus those enriched in non-oncogenic, i. e. “generic” viruses (generic viral homology domains, or GVHDs) (Methods, S2 Table). We found that domains structurally mimicked by oncoviruses (OVHDs) are more likely to harbour driver mutations, compared to domains structurally mimicked by generic viruses (GVHDs), independent of whether the domain is physically targeted by oncoviruses (OVTD) (CMH test, common odds ratio = 2. 2, P = 5 × 10−5). We then analyzed the mutational landscape of 44 oncoproteins having at least one oncoviral homology domain (OVHD) but not physically targeted by the virus, i. e. having no OVTDs. Pooled analysis of all 44 oncoproteins mapped 245/298 (82. 2%) driver mutations and 5422/9554 (56. 8%) passenger mutations to OVHDs. In other words, the odds of finding a driver mutation in OVHDs is 3 times as high as that in non-OVHDs (Fisher’s exact test, two-tailed P < 2. 2 × 10−16) (Fig 5B). Closer inspection identified 23 candidates for focused investigations into the common basis of viral and mutational oncogenesis (Table 4): (I) 4 oncoproteins where all domains are OVHDs, and the driver: passenger ratio is higher than the average ratio across all oncoproteins; (II) 16 oncoproteins where some domains are OVHDs, and driver mutations are exclusively found in OVHDs; and (III) 3 oncoproteins where some domains are OVHDs, and driver mutations are significantly enriched in OVHDs (Fisher’s exact test, two-tailed P < 0. 05). An example of each type of candidate is given in Fig 7. In summary, oncovirus-mimicked host domains are enriched for cancer driver mutations, regardless of whether these domains are physically targeted by the virus. Gulbahce et al. previously hypothesized, and established at the whole-protein level, that viruses and VID mutations induce similar perturbations of the human interactome [9]. Here, we test the same hypothesis at the higher resolution of protein domains, by examining whether viruses and VID mutations perturb the same domain-domain interactions (DDIs) in the human interactome. In other words, do viruses tend to target DDI partners of domains harbouring VID mutations (viral disease domain-interacting domains, or VDDiDs), rather than DDI partners of domains harbouring non-VID mutations (non-viral disease domain-interacting domains, or nVDDiDs) (Fig 8A)? As some domains can interact with both VID domains and non-VID domains, we define VDDiDs as domains that interact with at least one VID domain, and nVDDiDs as domains that exclusively interact with non-VID domains. We found that EBV and HPV exhibit a slight preference for targeting VDDiDs, although the effect sizes are not statistically significant (42/62 VDDiDs vs. 58/104 nVDDiDs for EBV, and 20/29 VDDiDs vs. 41/69 nVDDiDs for HPV). HIV targets 218/309 (70. 6%) VDDiDs and 193/346 (55. 8%) nVDDiDs, representing a 1. 9-fold enrichment of VDDiDs among HIV-targeted domains (Fisher’s exact test, two-tailed P = 1 × 10−4). Similarly, oncoviruses target 204/285 (71. 6%) VDDiDs and 164/291 (56. 4%) nVDDiDs, i. e. a 1. 9-fold enrichment of VDDiDs among oncovirus-targeted domains (Fisher’s exact test, two-tailed P = 1 × 10−4). Finally, a meta-analysis on the common effect of all viral proteins and all mutations causing proliferative and immunological diseases found that viruses target 424/599 (70. 8%) VDDiDs and 350/551 (63. 5%) nVDDiDs, i. e. a 1. 4-fold enrichment of VDDiDs among virus-targeted domains (Fisher’s exact test, two-tailed P = 0. 01) (Fig 8B). Virus’s preferential targeting of VDDiDs may be confounded by the tendency for viruses to target VID domains (Fig 2), and the tendency for VID domains to interact among themselves. We therefore excluded domains susceptible to known disease mutations and examined the extent to which virus targets “non-disease” domains that interact with VID domains. We found that HIV targets 179/250 (71. 6%) VDDiDs and 164/285 (57. 5%) nVDDiDs that do not harbour any known disease mutation (Fisher’s exact test odds ratio = 1. 9, two-tailed P = 8 × 10−4). Similarly, oncoviruses target 165/230 (71. 7%) VDDiDs and 137/237 (57. 8%) nVDDiDs that do not harbour any known disease mutation (Fisher’s exact test odds ratio = 1. 8, two-tailed P = 2 × 10−3). Pooled analysis of all viruses found that overall, viruses target 345/481 (71. 7%) VDDiDs and 295/456 (64. 7%) nVDDiDs that do not harbour any known disease mutation (Fisher’s exact test odds ratio = 1. 4, two-tailed P = 0. 02). Virus’s preferential targeting of VDDiDs supports our hypothesis that viruses and VID mutations inducing similar disease phenotypes convergently perturb the host domain interactome, possibly unveiling core disease modules underlying clinically heterogeneous virally-implicated diseases (Fig 9).
Structural interaction networks serve as a valuable tool for understanding the molecular mechanisms of genetic diseases, as well as the fundamental differences between endogenous and exogenous PPI networks. As experimental determination of protein structure remains an arduous task, homology modelling offers an efficient alternative for the structural annotation of protein complexes. This is based on the observation that PPIs are often mediated by evolutionarily conserved structural modules, such as domains and short linear motifs [77]. Here, we reassess the role of viral proteins as surrogates for human disease variants in relating interactome network perturbation to disease phenotypes, using a domain-resolved human-virus protein interactome where human domains are annotated with disease variant information. Compared to previous work demonstrating general proximity between viral targets and VID proteins in the human interactome, our results provide a structural explanation for the equivalent pathogenicity of viral proteins and VID mutations. Whereas previous studies merely recognized the existence of viral homologues of cellular domains, we delve deeper into the functional implications of oncoviral domain homology. Our approach can readily identify domains convergently targeted or mimicked by diverse oncoviruses for focused screening of driver mutations across various types of cancer. Further characterization of cellular domains and motifs interacting with domains targeted or mimicked by viruses may uncover immune evasion strategies exploited in common by cancer cells and pathogens, and shed light on pathways dysregulated in other virally-implicated disorders. Although most of our findings are statistically significant, there are notable differences in the enrichment of VID mutations in virus-targeted domains, both among individual viruses (EBV, HPV and HIV), as well as between single-virus analysis and pooled analysis on multiple viruses. For single-virus analysis, enrichment effect size and significance are impacted by the number of virus-host protein-protein interactions and virus-specific diseases, which ultimately determine the statistical power. Pooled analysis on all oncoviruses detected trends in the same direction as analysis on single oncoviruses (EBV and HPV), but with higher statistical power. In addition to investigator bias resulting in some viruses having a higher number of mapped virus-host PPIs, it is also possible that certain viruses prefer to perturb host regulatory network, rather than host PPI network, which is beyond the scope of this work. Compared to direct targeting of VID domains (a “first-degree” effect), viral targeting of the interaction partners of VID domains is expected to have a weaker, “second-degree” effect on the VID domains. This partly explains why results of the “first-degree” analysis on EBV and HPV (Fig 2) are stronger than those of the “second-degree” analysis (Fig 8B). Our pooled analysis of all oncoviral targets and all oncomutations is motivated by the assumption of convergent evolution and mimicry of endogenous oncogenic mechanisms by diverse oncoviruses. There is compelling evidence of different oncoviruses complementing each other’s replication and persistence strategies, thus eliciting multiple cellular responses associated with the hallmarks of cancer. One example is primary effusion lymphoma, a disease causally linked to KSHV but also having an EBV component. While expression of KSHV lytic genes such as vIL-6 and K1 promote VEGF secretion and angiogenesis, concomitant expression of EBV latent genes confers additional anti-apoptotic properties to infected cells in the initial phase of lymphomagenesis [78,79]. Given the paucity of context-dependent (i. e. tissue- and disease-specific) host-endogenous and host-pathogen PPI data, here we focus on establishing viral proteins and genetic mutations that induce similar disease phenotypes as generally equivalent perturbagens of the human interactome. Future work will also consider the diversity of host range and tissue tropism among different viruses, and the potentially distinct functional impacts of the same mutation in different cell types and diseases. One potential caveat of our interactome perturbation model is its incompleteness, due to the following reasons. Firstly, current mapping of the host-virus protein interactome is far from exhaustive. Secondly, some bona fide host-virus PPIs cannot be modelled by existing domain-based interaction templates. Thirdly, virus may not interact with a host protein via PPI, but rather regulate its expression via transcriptional or epigenetic mechanisms. Lastly, our study only considers missense mutations, because domain-based analysis of interactome perturbation requires precise positioning of mutations with respect to protein domains. Missense mutations can be unambiguously mapped to individual domains, whereas other types of mutations (e. g. nonsense or frameshift) may cause more drastic changes in the protein structure and are more difficult to map to individual domains. We are aware, however, of literature suggesting that nonsense and frameshift mutations tend to occur more frequently in tumour suppressor genes than in oncogenes [80]. Effects of these mutations on the integrity of the human interactome warrant further investigation. Still, despite the incompleteness of our model, we observed significant convergent perturbation of the human domain-resolved interactome by viruses and mutations inducing similar disease phenotypes. The advent of high-throughput biotechnology has made it possible to comprehensively characterize genomic variations in and interspecies interactions between human and microbes, which play important roles in health and disease. As more data on pathogen-implicated diseases and host-pathogen interactions emerge, our approach may be extended to the study of bacterial diseases and co-infections involving multiple pathogenic species, such as the co-pathogenesis of HIV and Mycobacterium tuberculosis. By combining these data within the framework of structural systems biology, our work sets the stage for multi-scale, integrative investigations into endogenous and exogenous perturbagens of the human interactome, thus helping to elucidate the molecular mechanisms of infection and its possible connections to genetic diseases such as cancer, autoimmunity, and neurodegeneration.
Human-endogenous and human-virus binary PPI data were obtained from IntAct [14], HPIDB [15], and the HIV-1 Human Interaction Database [16–18]. Structural templates for domain-domain and domain-motif interactions were obtained from 3did [19], iPfam [21] and ELM [20]. Protein sequences were scanned for Pfam domains using InterProScan under default settings (version 5. 30–69. 0) [23,81], and for the occurrence of domain-binding motifs as defined by 3did and ELM. Domain-based interaction models were assigned to each PPI by extracting all DDIs and DMIs possibly mediating the PPI. Disease association and clinical significance of variants were obtained from UniProtKB, ClinVar, and COSMIC [24,25,76]. Ensembl Variant Effect Predictor (VEP v93. 0) was used for extracting variant genomic location, variation class, reference allele, HGVS notations, amino acid position, overlapping Pfam domains, among other features [82]. To facilitate counting of mutational events, variants are annotated with RefSNP IDs using VEP’s check_existing flag. Variants not co-located with any known variant are merged based on identical genomic location, variation class, and shared alleles, as per NCBI guidelines for merging submitted SNPs into RefSNP clusters (https: //www. ncbi. nlm. nih. gov/books/NBK44417/). Only missense mutations located inside Pfam domains were retained for analyses. Assignment of each virally-implicated disease (VID) to EBV, HPV and HIV was based on at least two literature sources (S1 Text). To minimize redundancy in disease annotation, UMLS and OMIM IDs given to subtypes of the same disease were merged into the more general Disease Ontology [83], Orphanet [84] and MeSH IDs. Oncoviruses are as classified by CDC, IARC, and MeSH (https: //www. ncbi. nlm. nih. gov/mesh/68009858). Cancer is defined as any disease whose parent terms include “DOID: 162”, “ORPHA: 250908”, or MeSH IDs beginning with “C04. 557|C04. 588|C04. 619|C04. 626|C04. 651|C04. 666|C04. 682|C04. 692|C04. 697|C04. 700|C04. 730|C04. 834|C04. 850”. Diseases without Disease Ontology, Orphanet or MeSH IDs are manually labelled as “cancer” if their names match the following regular expression: “blastoma|cancer|carcino*|glioma|leukemia|leukaemia|lymphoma|melanoma|neoplas*|sarcoma|tumour|tumor”. Proliferative diseases have parent terms “DOID: 14566”, “ORPHA: 250908”, or MeSH IDs beginning with “C04”. Immunological diseases have parent terms “DOID: 2914”, “ORPHA: 98004”, or MeSH IDs beginning with “C20”. All statistical analyses were conducted in R [85]. Plots of domain-level distribution of disease mutations were created with Protter [86]. Pfam domain annotation for all human and viral proteins in UniProt was retrieved from InterPro (Release 69. 0) [87]. We define viral homology domains (VHDs) as Pfam domains conserved between human and viral proteins. For each VHD, the likelihood of it occurring in oncoviruses was calculated as the number of oncoviruses encoding the VHD, divided by the total number of unique oncoviral species in UniProt. Similarly, the likelihood of a VHD occurring in “generic” (i. e. non-oncogenic) viruses was calculated as the number of generic viruses encoding the VHD divided by the total number of unique generic viral species in UniProt. The observed likelihood ratio (LR) of an oncovirus vs. a generic virus encoding the VHD is then the ratio of the two likelihoods. We then permuted the label “oncovirus” and “generic virus” 10000 times among viruses encoding the VHD, thereby obtaining a null distribution for the LR. An empirical p-value for the enrichment or depletion of a VHD in oncoviral proteomes was calculated according to [88]. VHDs whose observed LR > 1 and Benjamini-Hochberg adjusted p-values (q-values) < 0. 1 are considered enriched in oncoviral proteomes. These VHDs and other VHDs exclusively occurring in oncoviruses are called oncoviral homology domains (OVHDs). Likewise, VHDs whose observed LR < 1 and q-values < 0. 1 are considered enriched in generic viral proteomes. These VHDs and other VHDs exclusively occurring in generic viruses are called generic viral homology domains (GVHDs).
Cellular function and behaviour are driven by highly coordinated biomolecular interaction networks. A prime example is the protein-protein interaction network, often simply referred to as the “interactome”. Recent advances in systems biology have spawned the view of human disease as a manifestation of genetic and environmental perturbations to the human interactome, a key postulate being that similar perturbation patterns lead to similar disease phenotypes. Here, we took a structural systems biology approach to compare mutation-induced and virus-induced perturbations of the human interactome in diseases with both genetic and viral contributors. Specifically, we constructed a domain-resolved human-virus protein interactome and characterized the distribution of genetic disease mutations with respect to human domains either physically targeted or structurally mimicked by virus. Overall, we found significant convergent perturbation of the human domain-resolved interactome by viruses and mutations inducing similar disease phenotypes. Structure-guided, integrated analysis of host genetic variation and host-pathogen protein interaction data may help elucidate the molecular mechanisms of infection and reveal its connections to genetic diseases such as cancer, autoimmunity, and neurodegeneration. On a broader note, our finding implies that similar perturbations of the human interactome at the domain level can have similar phenotypic consequences, regardless of the source of perturbation.
microbial mutation
interaction networks
medicine and health sciences
pathology and laboratory medicine
viruses and cancer
pathogens
cancer risk factors
microbiology
retroviruses
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immunodeficiency viruses
oncology
rna viruses
medical risk factors
proteins
medical microbiology
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epidemiology
microbial pathogens
pathogenesis
molecular biology
biochemistry
host-pathogen interactions
virology
viral pathogens
protein domains
genetics
biology and life sciences
human genetics
lentivirus
genetics of disease
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Convergent perturbation of the human domain-resolved interactome by viruses and mutations inducing similar disease phenotypes