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. 2022 Oct 26:13:997851.
doi: 10.3389/fimmu.2022.997851. eCollection 2022.

A network view of human immune system and virus-human interaction

Kang Tang  1   2 , Jing Tang  1   2 , Jinfeng Zeng  1   2 , Wei Shen  2   3 , Min Zou  1   2 , Chi Zhang  1   2 , Qianru Sun  1   2 , Xiaoyan Ye  4 , Chunwei Li  4 , Caijun Sun  1   2 , Siyang Liu  1   2 , Guozhi Jiang  1   2 , Xiangjun Du  1   2   5
Affiliations

A network view of human immune system and virus-human interaction

Kang Tang et al. Front Immunol. .

Abstract

The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.

Keywords: gene module; immune-related gene; new gene; protein-protein interaction; virus-human interaction.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The construction of human protein-protein interaction network. (A) Acquisition of high-quality literature-curated PPIs and module statistics. HT, High-Throughput; LT, Low-Throughput. (B) Comparisons with interaction networks derived from HT approaches and literature-curation method with respect to number of protein-coding gene and interaction counts. Circle area is proportional to interaction counts, while shading denotes the experimental strategy. AP-MS, affinity-purification mass spectrometry; Y2H, yeast two-hybrid assay. (C) Power-law degree distribution of HUMPPI-2022. (D) The functional domains in HUMPPI-2022. All region-specific GO terms were combined into 19 domains based on the similarity of their enrichment landscapes. Different colors represent different functional domains.
Figure 2
Figure 2
Immune-related genes (IRGs) in PPI network. (A) The statistics of IRGs from four databases. (B-E) Network properties (degree, eigenvector centrality, clustering coefficient, and assortativity, respectively) of IRGs. (F) Number of enriched GO terms for neighborhoods of IRGs. (G) Network of 1,225 modules identified through MCL clustering of HUMPPI-2022. Nodes represent distinct modules and the size reflect the gene number in each. Nodes are connected with significant link (see Methods). Green nodes mean modules containing two or more IRGs and not enriched with IRGs; Blue nodes mean modules that are enriched with IRGs (1% FDR); and modules containing less than two IRGs are colored in grey. Nodes with red border represent modules enriched to IR (immune-related) process. (H) Relative fractions of 1,225 modules that contain specified numbers of IRGs. (I) Comparison of network connectivity (degree) for modules that contain specified numbers of IRGs. (J) Comparison of network connectivity for modules that enriched to IR and non-IR process.
Figure 3
Figure 3
Topological and functional characteristics of virally-targeted human genes. (A) Degree (upper) and eigenvector centrality (bottom) of VTGs. The red dots and the corresponding boxes represent the observed values and the simulated distributions using the bootstrapping method, respectively. DENV-2, Dengue virus 2; EBOV, Ebola virus; EBV, Epstein-Barr virus; HAdV, Human adenovirus C; HCMV, Human cytomegalovirus; HCV, Hepatitis C virus; HHV-1, Human herpesvirus 1; HIV-1, Human immunodeficiency virus 1; HPV-5, HPV-6b, HPV-8, HPV-9, HPV-11, HPV-16, HPV-18, HPV-31 and HPV-33, Human papillomavirus 5, 6b, 8, 9, 11, 16, 18, 31 and 33; HRSV, Human respiratory syncytial virus; H1N1, H3N2 and H5N1, Influenza A H1N1, H3N2 and H5N1 virus; KSHV, Kaposi’s sarcoma-associated herpesvirus; LCMV, Lymphocytic choriomeningitis virus; MV, Measles virus; MERS-CoV, Middle East respiratory syndrome coronavirus; SARS-CoV-1, severe acute respiratory syndrome coronavirus 1; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; VACV, Vaccinia virus; ZIKV, Zika virus. (B) Degree (left) and eigenvector centrality (right) distributions of specific- and pan-viral targets. (C) Functional enrichment analysis of pan-viral targets. (D) Number (left), degree (middle) and eigenvector centrality (right) of three coronaviruses targeted host genes. The red dots and the corresponding boxes represent the observed values and the simulated distributions using the bootstrapping method, respectively. Pan, pan-viral genes targeted by at least one coronavirus; Pan_excl_CoV, pan-viral genes not targeted by coronavirus; CoV & Others, host genes targeted by at least one coronavirus and at least one other virus; CoV-pan, host genes only targeted by all three coronaviruses; SARS-CoV-2 & SARS-CoV-1, SARS-CoV-2 & MERS-CoV and SARS-CoV-1 & MERS-CoV, three coronaviral pairs of targeted host genes; SARS-CoV-2, SARS-CoV-1 and MERS-CoV, specifically targeted host genes for each coronavirus. (E) Functional domain number of three coronaviruses targeted host genes. ***P-value < 0.001, **P-value < 0.01, *P-value < 0.05, ns, not significantly different.
Figure 4
Figure 4
Evolutionary pattern of genes and edges related to their divergence times. (A–C) Distribution of PPI network degree, eigenvector centrality and tissue expression specificity for four categories of genes from different phylogenetic branches. Virally-targeted immune-related genes (IRG&VTGs), immune-related genes (IRGs), Virally-targeted genes (VTGs) and other genes (Others) are highlighted in red, cyan, yellow and grey, respectively. (D) Evolutionary pattern of edges related to their divergence times. (E) Schematic diagram of four categories of edges. Virally-targeted immune-related edges (IRE&VTEs), immune-related edges (IREs), virally-targeted edges (VTEs) and other edges (Others) are highlighted in red, cyan, yellow and grey, respectively. (F) Evolutionary pattern of four categories of edges related to their divergence times. IRE&VTEs, IREs, VTEs and Others are highlighted in red, cyan, yellow and grey, respectively. The divergence time of each gene age group is assigned as the middle time point for each branch. The oldest branch (branch 0) is arbitrarily set as 500 Mya. *** P-value < 0.001, ** P-value < 0.01.
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