Identification of molecular mechanisms of association amongst comorbidities and COVID-19: an interactome based systems biology approach

Introduction: Comorbidity has emerged as a major challenge in the last few decades that result from cascades of failures involving complex biochemical and physical interactions of genes, proteins, and metabolites responsible for cellular functions. Various epidemiological and demographic studies conducted since the emergence and global transmission of the SARS-CoV-2 virus have reported that patients with pre-existing medical conditions such as cardiovascular disease, diabetes, hepatitis, lung disease, and kidney disease are more prone to coronavirus infection. Objective: The present study was undertaken to elucidate the molecular mechanisms that are common amongst COVID-19-associated comorbidities using an interactome-based network biology approach to identify the shared genes/proteins and biological pathways. Material and methods: Genes of COVID-19-associated comorbidity diseases retrieved from disease databases were analyzed using in silico bioinformatics and systems network biology tools STRING and Cytoscape plug-ins CytoHubba and CytoCluster. Results: The shared hub proteins, namely IL1B, ACTB, IL6, MMP2, ALB, AKT1, MAPK3, FN1, TNF, CCL2, VEGFA, and TP53, among various pre-existing comorbidities, revealed their involvement in immunological pathways. All these proteins were also found to have significant associations with ACE2, TMPRSS2, and CD17/BSG, the entry receptors of the COVID-19 virus. Conclusion: The higher risk factor for COVID-19 in patients with pre-existing comorbidities is due to immune dysfunction that results in their higher susceptibility to infection by SARS-CoV-2 via its entry receptors on the host cells. This study provides novel insights into the association between host genetics and the consequences of viral infection that is responsible for the severity of COVID-19 in patients suffering from pre-existing comorbidities.