Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic - Hasankhani, Aliakbar; Bahrami, Abolfazl; Sheybani, Negin; Aria, Behzad; Hemati, Behzad; Fatehi, Farhang; Ghaem Maghami Farahani, Hamid; Javanmard, Ghazaleh; Rezaee, Mahsa; Kastelic, John P.; Barkema, Herman W. (Frontiers in Immunology, 2021)
Background: The recent emergence of COVID-19, rapid worldwide spread, and the lack of sufficient knowledge of the molecular mechanisms underlying infection have limited the development of therapeutic strategies. This study aimed to systematically investigate the molecular regulatory mechanisms of COVID-19 using a combination of different systems biology approaches. Methods: RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy and patients individuals were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis was used to identify co-expression modules in the healthy samples as a reference set. For differential co-expression network analysis, we utilized the module preservation approach to identify non-preserved modules whose network parameters were altered under infected condition. Then, protein-protein interaction networks based on the co-expressed hub genes were constructed to identify hub genes/TFs that had the highest information transfer (hub-high traffic genes) within the candidate modules. Results: The differential co-expression network analysis revealed that the connectivity patterns and network density of 72% (15 of 21) of the modules identified in the healthy samples were altered under SARS-CoV-2 infection. Functional enrichment analysis showed that among the 15 non-preserved modules, 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of the SARS-CoV-2 infection leads to the identification of signaling pathways and key genes/proteins associated with the main hallmarks of COVID-19, such as cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, such as asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) measure identified a total of 290 hub-high traffic genes, which are central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53 that play an important immunoregulatory role in the SARS-CoV-2 infection. Moreover, several hub-high traffic genes were identified that played a central role in the COVID-19 immunopathogenesis. Conclusion: This study provides comprehensive information on the molecular mechanisms of SARS-CoV-2-host interactions and suggests several hub-high traffic genes as promising therapeutic targets for COVID-19 pandemic.