A multipronged deep omics-based investigation of COVID-19 plasma samples identifies key molecular networks of disease severity progression

Abstract

Prognosis and management of COVID-19 severity is a challenge even after months of the pandemic. We employed high resolution mass spectrometry-based deep proteomics and metabolomics based investigation to study a cohort of 186 patients plasma samples to understand the COVID-19 disease severity mechanism. A cohort of patients displayed COVID-19 like symptoms but were negative for the virus while the COVID-19 positive patients were classified as non-severe or severe groups based on the clinical manifestation of the disease. Of the 1200 proteins detected, 27 were differentially regulated in COVID positive group while 38 were found to be differentially expressed between non-severe and severe groups. GO enrichment analysis highlighted the involvement of regulation of peptidase activity, regulated exocytosis, extracellular structure organization, blood coagulation, fibrin clot formation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes to be dysregulated in severe SARS-CoV-2 infection. Further, metabolomics identified 15 significant metabolites indicating dysregulation in tryptophan, glycine, serine, threonine, arginine, proline, and porphyrin metabolism pathways. We validated the potential severity biomarkers such as angiotensinogen, apolipoprotein B, SERPINA3, SERPING1, and Fibrinogen gamma chain using targeted proteomics. Moreover, using our proteomics dataset and docking studies we found FDA-approved drugs Selinexor and Ponatinib having potential to be repurposed for the therapeutics intervention of COVID-19.