Whole blood DNA methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection

  1. Guillermo Barturen
  2. Elena Carnero-Montoro
  3. Manuel Martínez-Bueno
  4. Silvia Rojo-Rello
  5. Beatriz Sobrino
  6. Óscar Porras-Perales
  7. Clara Alcántara-Domínguez
  8. David Bernardo
  9. Marta E. Alarcón-Riquelme

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

SARS-CoV-2 infection can cause an inflammatory syndrome (COVID-19) leading, in many cases, to bilateral pneumonia, severe dyspnea, and in ~5% of these, death. DNA methylation is known to play an important role in the regulation of the immune processes behind COVID-19 progression, however it has not been studied in depth. In this study, we aim to evaluate the implication of DNA methylation in COVID-19 progression by means of a genome-wide DNA methylation analysis combined with DNA genotyping. The results reveal the existence of epigenomic regulation of functional pathways associated with COVID-19 progression and mediated by genetic loci. We find an environmental trait-related signature that discriminates mild from severe cases and regulates, among other cytokines, IL-6 expression via the transcription factor CEBP. The analyses suggest that an interaction between environmental contribution, genetics, and epigenetics might be playing a role in triggering the cytokine storm described in the most severe cases.

Article activity feed

  1. Version published to 10.1038/s41467-022-32357-2
  2. ScreenIT

    SciScore for 10.1101/2021.11.03.21260184: (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

    Software and Algorithms
    SentencesResources
    Additionally, sexual chromosomes, cross-reactive probes and probes with overlapping SNPs from dbSNP v.
    dbSNP
    suggested: (dbSNP, RRID:SCR_002338)
    Statistical Analysis: Deconvolution of cell proportions: Iterative hierarchical procedure implemented in EpiDISH R package 37 was used to estimate the main blood cell type proportions from methylome information with the robust partial correlation method 38.
    EpiDISH
    suggested: (EpiDISH R package, RRID:SCR_018004)
    transcription factor binding site) motif enrichment analysis was performed with HOMER software 43 using a size of 200 nucleotides and including as background the CpGs interrogated with the EPIC array.
    HOMER
    suggested: (HOMER, RRID:SCR_010881)
    HLA and Immunoglobulin genes were removed from the Reactome pathways before activity calculation.
    Reactome
    suggested: (Reactome, RRID:SCR_003485)

    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.

  3. Version published to 10.1101/2021.11.03.21260184v1 on medRxiv