SARS-CoV-2 Infections - Gene Expression Omnibus (GEO) Data Mining, Pathway Enrichment Analysis, and Prediction of Potentially Repurposable Drugs
Chaparala, Srilakshmi, Iwema, Carrie, Chattopadhyay, Ansuman · Figshare · 2020
The COVID-19 global pandemic has created dire consequences with an alarming rate of morbidity and mortality. There are not yet vaccine or efficacious treatment options to combat the causative SARS-CoV-2 infection. This project aimed to identify potentially repurposable drugs for COVID-19 treatment by conducting gene expression and pathway enrichment analysis on publicly available transcriptomic data provided by the NCBI Gene Expression Database (GEO). We first determined SARS-CoV-2 infection-induced Differentially Expressed (DE) genes in human cells from two GEO datasets - GSE147507 (bulk RNA-Seq data generated from various cell lines) and GSE152075 (RNA-seq data from nasopharyngeal swaps of SARS-CoV-2 positive and negative individuals). Next, statistically enriched pathways associated with the SARS-CoV-2-induced DE genes were determined using Ingenuity IPA and BaseSpace Correlation Engine (BSCE). Finally, we identified drugs or compounds that could target and counter virus-triggered cellular perturbations using BSCE. This Collection provides results from RNA-Seq data analysis, pathway enrichment analysis, and a list of predicted potentially repurposable drugs. Further, in vitro and in vivo studies are necessary to verify these results before clinical application.