3D genomic capture of regulatory immuno-genetic profiles in COVID-19 patients for prognosis of severe COVID disease outcome

  1. Ewan Hunter
  2. Christina Koutsothanasi
  3. Adam Wilson
  4. Francisco C. Santos
  5. Matthew Salter
  6. Ryan Powell
  7. Ann Dring
  8. Paulina Brajer
  9. Benedict Egan
  10. Jurjen W. Westra
  11. Aroul Ramadass
  12. William Messer
  13. Amanda Brunton
  14. Zoe Lyski
  15. Rama Vancheeswaran
  16. Andrew Barlow
  17. Dmitri Pchejetski
  18. Peter A. Robbins
  19. Jane Mellor
  20. Alexandre Akoulitchev

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Human infection with the SARS-CoV-2 virus leads to coronavirus disease (COVID-19). A striking characteristic of COVID-19 infection in humans is the highly variable host response and the diverse clinical outcomes, ranging from clinically asymptomatic to severe immune reactions leading to hospitalization and death. Here we used a 3D genomic approach to analyse blood samples at the time of COVID diagnosis, from a global cohort of 80 COVID-19 patients, with different degrees of clinical disease outcomes. Using 3D whole genome EpiSwitch ® arrays to generate over 1 million data points per patient, we identified a distinct and measurable set of differences in genomic organization at immune-related loci that demonstrated prognostic power at baseline to stratify patients with mild forms of illness and those with severe forms that required hospitalization and intensive care unit (ICU) support. Further analysis revealed both well established and new COVID-related dysregulated pathways and loci, including innate and adaptive immunity; ACE2; olfactory, Gβψ, Ca 2+ and nitric oxide (NO) signalling; prostaglandin E2 (PGE2), the acute inflammatory cytokine CCL3, and the T-cell derived chemotactic cytokine CCL5. We identified potential therapeutic agents for mitigation of severe disease outcome, with several already being tested independently, including mTOR inhibitors (rapamycin and tacrolimus) and general immunosuppressants (dexamethasone and hydrocortisone). Machine learning algorithms based on established EpiSwitch ® methodology further identified a subset of 3D genomic changes that could be used as prognostic molecular biomarker leads for the development of a COVID-19 disease severity test.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.03.14.435295: (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
    Statistical analysis: The four COVID cohorts were normalised by background correction and quantile normalisation, using the EpiSwitch® R analytic package, which is built on the Limma and dplyr libraries.
    Limma
    suggested: (LIMMA, RRID:SCR_010943)
    Data was corrected for batch effects using ComBat R script.
    ComBat
    suggested: (ComBat, RRID:SCR_010974)
    Parametric (Limma R library, Linear Regression) and non-parametric (EpiSwitch® RankProd R library) statistical methods were performed to identify 3D genomic changes that demonstrated a difference in abundance between the Mild and Severe COVID-19 classes.
    RankProd
    suggested: (RankProd, RRID:SCR_013046)
    Mapping was carried out using Bedtools closest function for the 3 closest protein coding loci (Gencode v33).
    Bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    Gencode
    suggested: (GENCODE, RRID:SCR_014966)
    The top 100 3D genomic markers from this combined filter were then utilized for linear discriminant analysis (LDA) using the MASS library and visualized using the ggplot2 package in R.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Biological network and drug target analysis: Network analysis for functional/biological relevance of the 3D genomic markers was performed using the Hallmark Gene Sets and BioCarta and Reactome Canonical Pathway gene sets from the Molecular Signatures Database (MSigDB) [34].
    BioCarta
    suggested: (BioCarta Pathways, RRID:SCR_006917)
    Protein interaction networks were generated using the Search Tool for the Retrieval of Interacting proteins (STRING) database [35].
    STRING
    suggested: (STRING, RRID:SCR_005223)

    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.

    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.

  2. Version published to 10.1101/2021.03.14.435295v1 on bioRxiv