Impaired activation of transposable elements in SARS‐CoV ‐2 infection
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Abstract
Emerging evidence shows that transposable elements (TEs) are induced in response to viral infections. This TE induction is suggested to trigger a robust and durable interferon response, providing a host defense mechanism. Here, we analyze TE expression changes in response to SARS‐CoV‐2 infection in different human cellular models. Unlike other viruses, SARS‐CoV‐2 infection does not lead to global upregulation of TEs in primary cells. We report a correlation between TEs activation and induction of interferon‐related genes, suggesting that failure to activate TEs may account for the weak interferon response. Moreover, we identify two variables that explain most of the observed diverseness in immune responses: basal expression levels of TEs in the pre‐infected cells and the viral load. Finally, analyzing the SARS‐CoV‐2 interactome and the epigenetic landscape around the TEs activated following infection, we identify SARS‐CoV‐2 interacting proteins, which may regulate chromatin structure and TE transcription. This work provides a possible functional explanation for SARS‐CoV‐2 success in its fight against the host immune system and suggests that TEs could serve as potential drug targets for COVID‐19.
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SciScore for 10.1101/2021.02.25.432821: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources Datasets: The original sequencing datasets for IAV infection in A549 cells and the Blanco-Melo et al datasets can be found on the NCBI Gene Expression Omnibus (GEO) server under the accession numbers GSE133329 and GSE147507, respectively. A549suggested: NoneSoftware and Algorithms Sentences Resources Datasets: The original sequencing datasets for IAV infection in A549 cells and the Blanco-Melo et al datasets can be found on the NCBI Gene Expression Omnibus (GEO) server under the accession numbers GSE133329 and GSE147507, respectively. Gene Expression Omnibussuggested: (Gene Expression …SciScore for 10.1101/2021.02.25.432821: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources Datasets: The original sequencing datasets for IAV infection in A549 cells and the Blanco-Melo et al datasets can be found on the NCBI Gene Expression Omnibus (GEO) server under the accession numbers GSE133329 and GSE147507, respectively. A549suggested: NoneSoftware and Algorithms Sentences Resources Datasets: The original sequencing datasets for IAV infection in A549 cells and the Blanco-Melo et al datasets can be found on the NCBI Gene Expression Omnibus (GEO) server under the accession numbers GSE133329 and GSE147507, respectively. Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012), 2020): Preprocessing and alignment: Raw reads from GSE147507 and GSE133329 were trimmed to remove Illumina adapters using the Trimmomatic software (Bolger et al., 2014) version 0.39. Trimmomaticsuggested: (Trimmomatic, RRID:SCR_011848)We used the STAR aligner version 2.7.1a (Dobin et al., 2013) to align raw reads to human RefSeq reference genome (GRCh38). RefSeqsuggested: (RefSeq, RRID:SCR_003496)For ChIP-Seq data, alignment with STAR was followed by filtering out of non-uniquely mapped reads. STARsuggested: (STAR, RRID:SCR_015899)This was followed by differential expression analysis using DESeq2 (Love et al., 2014). DESeq2suggested: (DESeq, RRID:SCR_000154)To quantify gene expression and to determine the locations of individual TEs that change in expression we used featureCounts v2.0.0 (Jin et al., 2015; Liao et al., 2014) from the Subread package which uses only uniquely mapped reads. featureCountssuggested: (featureCounts, RRID:SCR_012919)Subreadsuggested: (Subread, RRID:SCR_009803)The list of epifactors was downloaded from https://epifactors.autosome.ru/ (Medvedeva et al., 2015) https://epifactors.autosome.ru/suggested: (EpiFactors , RRID:SCR_016956)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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