Time-series bioinformatics analysis of SARS-CoV-infected cells to identify the biological processes associated with severe acute respiratory syndrome.

Abstract

The COVID-19 pandemic, caused by the new virus of the coronavirus family, SARS-CoV-2, could lead to acute respiratory syndrome. The molecular mechanisms related to this disorder are still debatable. In this study to understand the pathogenicity mechanism of SARS-CoV-2, using the bioinformatics approaches, we investigated the expression of involved genes, their regulatory, and main signaling pathways during the time on days1, 2, 3, and 4 of SARS-CoV infected cells. Here, our investigation shows the complex changes in gene expression on days 2 and 3post-infection. The functional analysis showed that especially related to immune response, response to other organisms, and defense response. IL6-AS1 is the predicted long non-coding RNA and is a key regulator during infection. In this study, for the first time has been reported the role of IL6-AS1. Also, the correlation of differential expression genes with the level of immune infiltration was shown in the relationship of Natural killer cells and T cell CD 4+ with DE genes. In the current study, identification of the altered expression pattern of genes in SARS-CoV-infected cells in time course also can help identify and link the molecular mechanisms and explore the holistic view of infection of SARS-CoV-2.