Applying defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs
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ABSTRACT:
PROVIDER: PRJNA749002 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15215972.fastq.gz | Fastqsanger.gz | |||
| SRR15215973.fastq.gz | Fastqsanger.gz | |||
| SRR15215974.fastq.gz | Fastqsanger.gz |
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Project description:Coronavirus RNA-dependent RNA polymerases produce subgenomic RNAs (sgRNAs) that encode viral structural and accessory proteins. The kinetics and efficiency of sgRNAs production during viral replication in different cell types or sgRNA transcription by individual viral strains or variants are yet to be studied to shed light on fundamental mechanisms necessary for viral replication. User-friendly bioinformatic tools to detect and quantify sgRNA production are urgently needed to study a growing number of next-generation sequencing (NGS) data of SARS-CoV-2. Starting from DI-tector, a bioinformatic tool for the detection of viral defective interfering genomes, here we introduced sgDI-tector to identify and quantify sgRNA in SARS-CoV-2 NGS data. This new tool allowed detection of sgRNA without initial knowledge of the transcription-regulatory sequences. As a proof of principle, we analyzed new data sets and successfully detected the nested set of sgRNAs produced with the ranking M>ORF3a>N>ORF6>ORF7a>ORF8>S>E>ORF7b. Our study also compared, for the first time for SARS-CoV-2, the level of sgRNA production with other types of viral RNA products such as defective interfering viral genomes.
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Project description:Using next-generation sequencing (NGS) combined with bioinformatics tools, we characterized two major 5’copy-back defective interfering (5’cb DI) genomes generated during SAD replication. Furthermore, we identified a specific interaction of 5’cb DI genomes and RIG-I that correlated with a high stimulation of the type I IFN signaling
2024-03-22 | GSE196051 | GEO
Project description:Coronavirus RNA-dependent RNA polymerases produce subgenomic RNAs (sgRNAs) that encode viral structural and accessory proteins. User-friendly bioinformatic tools to detect and quantify sgRNA production are urgently needed to study the growing number of next-generation sequencing (NGS) data of SARS-CoV-2. We introduced sgDI-tector to identify and quantify sgRNA in SARS-CoV-2 NGS data. sgDI-tector allowed detection of sgRNA without initial knowledge of the transcription-regulatory sequences. We produced NGS data and successfully detected the nested set of sgRNAs with the ranking M > ORF3a > N>ORF6 > ORF7a > ORF8 > S > E>ORF7b. We also compared the level of sgRNA production with other types of viral RNA products such as defective interfering viral genomes.
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Project description:Influenza defective interfering (DI) viruses have long been considered promising antiviral candidates because of their ability to interfere with replication-competent viruses and to induce antiviral immunity. However, the mechanisms underlying DI-mediated antiviral immunity have not been extensively explored. Here, we demonstrated interferon (IFN) independent protection conferred by influenza DI virus against homologous virus infection in mice deficient in type I and III IFN signaling. By integrating transcriptional and post-transcriptional regulatory data we identified unique host signatures in response to DI co-infection. DI-treated mice exhibited reduced viral transcription, less intense inflammatory and innate immune responses, and primed multiciliated cell differentiation in their lungs at an early stage of infection, even in the absence of type I or III IFNs. This increased multiciliogenesis could also be detected at the protein level by immunofluorescence staining of lung tissue from DI-treated mice. Overall, our study provides mechanistic insight into the protection mediated by DIs, implying a unifying theme involving inflammation and multiciliogenesis in maintaining respiratory homeostasis, and reveals their IFN-independent antiviral activity.
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Project description:To clarify that the interfering effect in iPS induction was not because of extraordinary gene expression which was caused by overexpression of infected genes, transcriptional profile of the cells infected six genes were analysed using microarray analysis. As result, overexpression of each of the 6 interfering TFs in NSEB5-2C did not compromise transcriptional profile compared with the five non-interfering TFs.
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