In Silico approach for the Identification of tRNA-derived Small Non-coding RNAs in SARS-CoV Infection

Abstract

Abstract tsRNAs(tRNA derived small non-coding RNAs), including tRNA halves (tiRNAs), and tRNA fragments (tRFs) have been implicated in some viral infections like respiratory viral infections. However, their involvement in SARS-CoV infection is completely unknown. The objective of this study is to determine tsRNAs level in a mouse infected with wild-type, attenuated, and mock-infected SARS-CoV strains. Gene Expression Omnibus (GEO) dataset at NCBI with accession ID GSE90624 was used for this study. After a count matrix formation of tRNAs, tRNA differential expression analysis was performed subsequently using DESeq2. tsRNAs were identified from the significantly expressed tRNAs. Differentially expressed tRNAs, followed by tsRNAs derived from each significant tRNAs at different conditions and time points between the two groups.; WT(SARS-CoV-MA15-WT) vs Mock and DE (SARS-CoV-MA15-ΔE) vs Mock were identified. We found significantly differentially expressed tRNAs at 2dpi but not at 4dpi. By quantifying tsRNAs from differentially expressed tRNAs in all the samples belonging to each condition (WT, DE, and Mock), tRFs(tRNA-derived RNA Fragments) were found to be higher in number compared to tiRNAs(tRNA-derived stress-induced RNAs) from tRNAs in all the samples belonging to the WT SARS-CoV strain, indicating non-random formation of tsRNAs, as well as possible involvement of tsRNAs in SARS-CoV viral infection.