Impaired activation of Transposable Elements in SARS-CoV-2 infection
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Abstract
Transposable element (TE) transcription is induced in response to viral infections. TE induction triggers a robust and durable interferon (IFN) response, providing a host defense mechanism. Still, the connection between SARS-CoV-2 IFN response and TEs remains largely unknown. Here, we analyzed TE expression changes in response to SARS-CoV-2 infection in different human cellular models. We find that compared to other viruses, which cause global upregulation of TEs, SARS-CoV-2 infection results in a significantly milder TE response in both primary lung epithelial cells and in iPSC-derived lung alveolar type 2 cells. TE activation precedes, and correlates with, the induction of IFN-related genes, suggesting that the limited activation of TEs following SARS-CoV-2 infection may be the reason for the weak IFN response. Diminished TE activation was not observed in lung cancer cell lines with very high viral load. Moreover, we identify two variables which 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, as well as the epigenetic landscape around the TEs that are activated following infection, we identify SARS-CoV-2 interacting proteins, which may regulate chromatin structure and TE transcription in response to a high viral load. This work provides a functional explanation for SARS-CoV-2’s success in its fight against the host immune system, and suggests that TEs could be used as sensors and serve as potential drug targets for COVID-19.
Key points
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Unlike other viruses, SARS-CoV-2 invokes a weak and inefficient transposable element (TE) response
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TE induction precedes and predicts IFN response
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Basal TE expression and viral load explain immune responses
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Distinct chromatin and enhancer binding factors occupancy on TEs induced by SARS-CoV-2
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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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