In silico approach for the identification of tRNA-derived small non-coding RNAs in SARS-CoV infection
© 2024. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:65 |
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| Enthalten in: |
Journal of applied genetics - 65(2024), 2 vom: 29. Apr., Seite 403-413 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ajmeriya, Swati [VerfasserIn] |
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| Links: |
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| Themen: |
9014-25-9 |
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| Anmerkungen: |
Date Completed 11.04.2024 Date Revised 11.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s13353-024-00853-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370041593 |
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| 245 | 1 | 0 | |a In silico approach for the identification of tRNA-derived small non-coding RNAs in SARS-CoV infection |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Coronavirus | |
| 650 | 4 | |a Deep sequencing | |
| 650 | 4 | |a SARS-CoV | |
| 650 | 4 | |a sncRNA | |
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| 700 | 1 | |a Kumar, Amit |e verfasserin |4 aut | |
| 700 | 1 | |a Rana, Shweta |e verfasserin |4 aut | |
| 700 | 1 | |a Singh, Harpreet |e verfasserin |4 aut | |
| 700 | 1 | |a Karmakar, Subhradip |e verfasserin |4 aut | |
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