Abstract
tsRNAs (tRNA-derived small non-coding RNAs), including tRNA halves (tiRNAs) and tRNA fragments (tRFs), have been implicated in some viral infections, such as respiratory viral infections. However, their involvement in SARS-CoV infection is completely unknown. A comprehensive analysis was performed to determine tsRNA populations in a mouse model of SARS-CoV-infected samples containing the wild-type and attenuated viruses. Data from the Gene Expression Omnibus (GEO) dataset at NCBI (accession ID GSE90624) was used for this study. A count matrix was generated for the 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 ΔE (SARS-CoV-MA15-ΔE) vs Mock were identified. Notably, significantly differentially expressed tRNAs at 2dpi but not at 4dpi. The tsRNAs originating from differentially expressed tRNAs across all the samples belonging to each condition (WT, ΔE, and Mock) were identified. Intriguingly, tRFs (tRNA-derived RNA fragments) exhibited higher levels compared to tiRNAs (tRNA-derived stress-induced RNAs) across all samples associated with WT SARS-CoV strain compared to ΔE and mock-infected samples. This discrepancy suggests a non-random formation of tsRNAs, hinting at a possible involvement of tsRNAs in SARS-CoV viral infection.
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Data availability
A publicly available dataset was analyzed in this study. This data can be found in the link https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90624
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Acknowledgements
The authors acknowledge the support extended by the Indian Council of Medical Research (ICMR), New Delhi, and All India Institute of Medical Sciences (AIIMS), New Delhi, for providing computational facilities.
Funding
Research reported in this study was supported by the Indian Council of Medical Research (ICMR) under award number BMI/11(36)/2022 to Swati Ajmeriya.
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S.A. designed the analysis, applied methods, and wrote the main manuscript; D.B. applied methods, and reviewed the manuscript; A.K. applied methods and edited and reviewed the manuscript; H.S. reviewed and edited the manuscript; S.R. edited the manuscript. S.K. supervised and reviewed the manuscript. All authors read and approved the final manuscript.
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Communicated by: Agnieszka Szalewska-Palasz
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Ajmeriya, S., Bharti, D.R., Kumar, A. et al. In silico approach for the identification of tRNA-derived small non-coding RNAs in SARS-CoV infection. J Appl Genetics 65, 403–413 (2024). https://doi.org/10.1007/s13353-024-00853-4
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DOI: https://doi.org/10.1007/s13353-024-00853-4