Transcriptome and SARS-CoV-2 biological network directed analysis for better therapeutic development

Abstract

Background It has been almost 2 years since the first SARS-CoV-2 virus was first reported in the city of Wuhan. So far, 8 drugs are approved against Covid-19 by FDA, however, an active drug against multi-mutational Covid-19 is still missing. In this present study, we aim to identify the most crucial and highly correlated protein targets from the publicly available transcriptomics datasets. Methods Transcriptomics datasets were retrieved from Geo Omnibus (GEO). We use relevant datasets to identify the most significant and differentially expressed genes and integrated them into a Research graph called CovInt (a network of Covid-19) that includes all biological molecules associated in the network with their directionalities collected from publicly available and patient-derived multi-omics datasets from millions of unstructured and structured datasets such as publications, patents, grants, preclinical and clinical reports. CovInt utilizes powerful traversal, clustering and centrality algorithms to identify key connections in the pathophysiology of the disease and its treatments. Results Leveraging 3M + connections, important interactions among key 42 drugs, 962 biological processes and molecular functions, 926 pathways, 897 phenotypes, 7103 proteins, 61 tissues were identified. This narrowed interactome was explored further using PageRank, lovain detection & strongly connected components (SSC) algorithms. In our analysis 63, strongly connected communities were identified which gives us an understanding of hidden underlying mechanisms. We further explored this network to identify and triangulate the key proteins, metabolic pathways and associated risk factors that can regulate moderate to severe Covid-19 infections. Conclusions Our study suggests that CREB3L1, SOX2, UBR4, FLNC, ITPA, DLG3, ING4, TECR, NADH, SMAD, HUWE1, DDX17, CREBBP, RELA, HPSE, TRIM33, TNFSF13B are the key regulator proteins and involved in ER-stress, cytokine signaling, T-Cell activation, Activation of NLRP3 Inflammation by SARS-CoV-2, JAK-STAT, IL-4, IL-13 pathways, MAPK signaling pathways, Activation of NMDA receptor & postsynaptic events and TGF-β signaling pathways.