Bioinformatics analysis of single-cell mRNA-seq of SARS-CoV-1 and SARS-CoV-2 infection compared to MERS-CoV from Sequence Read Archive (SRA) database reveals novel targets for therapies [post]

Mengyao Wang, Peng Lu
2020 unpublished
The global pandemic of COVID-19 caused by SARS-CoV-2 is still threatening the world. By May 13, 2020, more than 40 million people have been infected by SARS-CoV-2 and almost 300 thousand deaths were reported. The discovery and development of anti-viral drugs and vaccines are being conducted worldwide and the understanding of the molecular responses of a single cell to SARS-CoV-2 is in urgent need. The comparative analysis of gene expression in SARS-CoVs and MERS-CoV infected Calu-3 cells
more » ... Calu-3 cells reveals that although the coronaviruses cause similar acute respiratory distress syndromes, the molecular responses of Calu-3 cells to SARS-CoVs infections showed a unique signature. A total of 64 correlated differentially expressed genes (DEGs) were identified in this study. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that the DEGs were significantly involved in biological process of 'Response to interferon-γ', 'Viral life cycle', 'Phagosome' and 'Epstein-Barr virus infection'. STRING analysis showed that the DEGs that were up-regulated after SARS-CoVs infections but down-regulated after MERS-CoV infections showed a strong interaction network. Molecular Complex Detection (MCODE) analysis further refined a unique network consisted of eight hub genes out of 64 DEGs, which are involved in cytokine response (CXCL8, CCL20, and CSF2), ISGylation (ISG15), macrophage activation (ITGAM), complement system (C3), and NFκB signaling pathway (TRAF1 and NFκB2). The unique network identified here will be a potential fingerprint for distinguishing SARS-CoVs infections from MERS-CoV infection. The identification of the eight hub genes will lead to the discovery of new possible therapeutic targets for fighting COVID-19.
doi:10.21203/ fatcat:dmqmzqr475d77hwjqx6e27boju