Transcriptome and DNA methylome analysis of peripheral blood samples reveals incomplete restoration and transposable element activation after 3-months recovery of COVID-19

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Abstract

Comprehensive analyses showed that SARS-CoV-2 infection caused COVID-19 and induced strong immune responses and sometimes severe illnesses. However, cellular features of recovered patients and long-term health consequences remain largely unexplored. In this study, we collected peripheral blood samples from nine recovered COVID-19 patients (median age of 36 years old) from Hubei province, China, 3 months after discharge as well as 5 age- and gender-matched healthy controls; and carried out RNA-seq and whole-genome bisulfite sequencing to identify hallmarks of recovered COVID-19 patients. Our analyses showed significant changes both in transcript abundance and DNA methylation of genes and transposable elements (TEs) in recovered COVID-19 patients. We identified 425 upregulated genes, 214 downregulated genes, and 18,516 differentially methylated regions (DMRs) in total. Aberrantly expressed genes and DMRs were found to be associated with immune responses and other related biological processes, implicating prolonged overreaction of the immune system in response to SARS-CoV-2 infection. Notably, a significant amount of TEs was aberrantly activated and their activation was positively correlated with COVID-19 severity. Moreover, differentially methylated TEs may regulate adjacent gene expression as regulatory elements. Those identified transcriptomic and epigenomic signatures define and drive the features of recovered COVID-19 patients, helping determine the risks of long COVID-19, and guiding clinical intervention.

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  1. SciScore for 10.1101/2022.04.19.22274029: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: Ethical approval for the study was obtained by the Ethics Committee for Clinical Research of Reproductive Medicine Center, Tongji Medical College, Huazhong University of Science and Technology.
    Consent: All participants included in the study gave informed consent.
    Sex as a biological variable9 male patients at recovery stage and 5 male healthy controls were recruited for this study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: SAMtools (v1.3.1) was used to sort bam files by genomic coordination and make a bam file index.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To include as many non-uniquely mapped reads as possible, trimmed reads were firstly aligned to human genome (hg19) by STAR (v2.7.5b) (Dobin et al., 2013) with default settings including parameters ‘-- winAnchorMultimapmax 2000 --outFilterMultimapNmax 1000’
    STAR
    suggested: None
    UCSC genome browser was used for snapshots of transcriptome.
    UCSC genome browser
    suggested: (UCSC Genome Browser, RRID:SCR_005780)
    R package Deseq2 (v1.28.1) (Love et al., 2014) was used to obtain differentially expressed genes (DEGs) and differentially expressed TEs (DETEs).
    Deseq2
    suggested: None
    Principal component analysis (PCA) was performed using DESeq2 normalized counts for DEGs/DETEs. Metascape (Zhou et al., 2019) was used to visualize functional profiles of genes and gene clusters.
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    Images were organized by Adobe Illustrator.
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)
    WGBS data processing and quality control: Raw reads were processed with Trim Galore (v0.6.4) to remove adaptor sequences and poor quality bases with ‘--q 20 --phred33 --stringency 5 --length 20 --paired’.
    Trim Galore
    suggested: (Trim Galore, RRID:SCR_011847)
    SAMtools (v1.3.1) was used to sort bam files by genomic coordination and make a bam file index.
    SAMtools
    suggested: None
    PCR duplicates were removed using Picard (v2.23.3).
    Picard
    suggested: (Picard, RRID:SCR_006525)
    Methylation profiles were calculated by deeptools (v3.5.1) (Ramirez et al., 2014)
    deeptools
    suggested: (Deeptools, RRID:SCR_016366)
    Differentially methylated regions (DMRs) by methylKit: The R package methylKit (v1.14.2) (Akalin et al., 2012) was used to identify DMRs between healthy and recovery groups.
    methylKit
    suggested: (methylKit, RRID:SCR_005177)
    The bulk RNA-seq and WGBS data generated during this study is available in Genome Sequence Archive (GSA) for human data repository of National Genomics Data Center
    Genome Sequence Archive
    suggested: None
    r (BioProject No. PRJCA006301, https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA006301).
    BioProject
    suggested: None

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.