Gene expression profiling of SARS-CoV-2 infections reveal distinct primary lung cell and systemic immune infection responses that identify pathways relevant in COVID-19 disease. Issue 2 (18th December 2020)

• Record Type: Journal Article
• Authors: Moni, Mohammad Ali; Quinn, Julian M W; Sinmaz, Nese; Summers, Matthew A
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 2(2021)
• Page Start: 1324
• Page End: 1337
• Publication Date: 2020-12-18
• Subjects: SARS-CoV-2 -- SARS-CoV -- MERS-CoV -- coronaviruses -- transcriptomics -- RNAseq -- COVID-19; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa376
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: To identify key gene expression pathways altered with infection of the novel coronavirus SARS-CoV-2, we performed the largest comparative genomic and transcriptomic analysis to date. We compared the novel pandemic coronavirus SARS-CoV-2 with SARS-CoV and MERS-CoV, as well as influenza A strains H1N1, H3N2 and H5N1. Phylogenetic analysis confirms that SARS-CoV-2 is closely related to SARS-CoV at the level of the viral genome. RNAseq analyses demonstrate that human lung epithelial cell responses to SARS-CoV-2 infection are distinct. Extensive Gene Expression Omnibus literature screening and drug predictive analyses show that SARS-CoV-2 infection response pathways are closely related to those of SARS-CoV and respiratory syncytial virus infections. We validated SARS-CoV-2 infection response genes as disease-associated using Kaplan–Meier survival estimates in lung disease patient data. We also analysed COVID-19 patient peripheral blood samples, which identified signalling pathway concordance between the primary lung cell and blood cell infection responses. Graphical Abstract: GRAPHIC 1: SARS-CoV-2 graphical abstract depicting the study workflow and analysis pipeline. (A ) We first conducted phylogenetic and RNAseq analyses comparing SARS-CoV-2, SARS, MERS, and influenza strains, and the overlapping gene expression effects of infection with these viruses. (B –C ) Datasets from a study of transcriptional effects of SARS-CoV-2 infection in human lung epithelial cells (profiled using RNAseq) was analysed to determine differential gene expression. (D ) Gene ontology and curated cell signalling pathway databases were used to screen the differentially expressed gene list for functional enrichment. (E ) SARS-CoV-2 genes were used for drug prediction and survival analyses in disease datasets, results suggesting pathology associated genes and pathways for subsequent functional analysis. (F ) A targeted immune profile in SARS-CoV-2 infected patient blood samples was analysed to assess system wide immune effects of infection.