Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19

Abstract

The coronavirus disease, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a global health crisis that is being endured with an increased alarm of transmission each day. Though the pandemic has activated innumerable research attention to decipher an antidote, fundamental understanding of the molecular mechanisms is necessary to halt the disease progression. The study focused on comparison of the COVID-19 infected lung tissue gene expression datasets -GSE155241 and GSE150316 with the GEO2R-limma package. The significant up- and downregulated genes were annotated. Further evaluation of the enriched pathways, transcription factors, kinases, noncoding RNAs and drug perturbations revealed the significant molecular mechanisms of the host response. The results revealed a surge in mitochondrial respiration, cytokines, neurodegenerative mechanisms and deprived oxygen, iron, copper, and glucose transport. Hijack of ubiquitination by SARS-CoV-2, hox gene differentiation, histone modification, and miRNA biogenesis were the notable molecular mechanisms inferred. Long non-coding RNAs such as C058791.1, TTTY15 and TPTEP1 were predicted to be efficient in regulating the disease mechanisms. Drugs-F-1566-0341, Digoxin, Proscillaridin and Linifanib that reverse the gene expression signatures were predicted from drug perturbations analysis. The binding efficiency and interaction of proscillaridin and digoxin as obtained from the molecular docking studies confirmed their therapeutic potential. Two overlapping upregulated genes MDH1, SGCE and one downregulated gene PFKFB3 were appraised as potential biomarkers candidates. The upregulation of PGM5, ISLR and ANK2 as measured from their expressions in normal lungs affirmed their possible prognostic biomarker competence. The study explored significant insights for better diagnosis, and therapeutic options for COVID-19.

Communicated by Ramaswamy H. Sarma

Keywords:

• COVID-19
• bioconductor
• differential gene expression
• miRNAs
• lncRNAs
• drug perturbations

Acknowledgements

The authors are obligated to the management of Stella Maris College Chennai, Tamil Nadu, India. The facility provided by seed research grant is immensely accredited. The Schrodinger software used in the study was procured from the grant of Department of Science and Technology (DST), New Delhi, India, through FIST Programme (Level -0, No: SR/FIST/College-252-C.Dy.No.3555/IFD/2016-2017), to Stella Maris College, Chennai, India. DST-FIST grant is greatly acknowledged. The support extended by the CAS in Crystallography and Biophysics, University of Madras, Chennai, India and Bishop Heber College, Tiruchirappalli is extensively gratified.

Disclosure statement

No potential conflict of interest was reported by the authors.