IL-13 is a driver of COVID-19 severity

  1. Alexandra N. Donlan
  2. Tara E. Sutherland
  3. Chelsea Marie
  4. Saskia Preissner
  5. Benjamin T. Bradley
  6. Rebecca M. Carpenter
  7. Jeffrey M. Sturek
  8. Jennie Z. Ma
  9. G. Brett Moreau
  10. Jeffrey R. Donowitz
  11. Gregory A. Buck
  12. Myrna G. Serrano
  13. Stacey L. Burgess
  14. Mayuresh M. Abhyankar
  15. Cameron Mura
  16. Philip E. Bourne
  17. Robert Preissner
  18. Mary K. Young
  19. Genevieve R. Lyons
  20. Johanna J. Loomba
  21. Sarah J. Ratcliffe
  22. Melinda D. Poulter
  23. Amy J. Mathers
  24. Anthony J. Day
  25. Barbara J. Mann
  26. Judith E. Allen
  27. William A. Petri

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Immune dysregulation is characteristic of the more severe stages of SARS-CoV-2 infection. Understanding the mechanisms by which the immune system contributes to COVID-19 severity may open new avenues to treatment. Here, we report that elevated IL-13 was associated with the need for mechanical ventilation in 2 independent patient cohorts. In addition, patients who acquired COVID-19 while prescribed Dupilumab, a mAb that blocks IL-13 and IL-4 signaling, had less severe disease. In SARS-CoV-2–infected mice, IL-13 neutralization reduced death and disease severity without affecting viral load, demonstrating an immunopathogenic role for this cytokine. Following anti–IL-13 treatment in infected mice, hyaluronan synthase 1 ( Has1 ) was the most downregulated gene, and accumulation of the hyaluronan (HA) polysaccharide was decreased in the lung. In patients with COVID-19, HA was increased in the lungs and plasma. Blockade of the HA receptor, CD44, reduced mortality in infected mice, supporting the importance of HA as a pathogenic mediator. Finally, HA was directly induced in the lungs of mice by administration of IL-13, indicating a new role for IL-13 in lung disease. Understanding the role of IL-13 and HA has important implications for therapy of COVID-19 and, potentially, other pulmonary diseases. IL-13 levels were elevated in patients with severe COVID-19. In a mouse model of the disease, IL-13 neutralization reduced the disease and decreased lung HA deposition. Administration of IL-13–induced HA in the lung. Blockade of the HA receptor CD44 prevented mortality, highlighting a potentially novel mechanism for IL-13–mediated HA synthesis in pulmonary pathology.

Article activity feed

  1. Version published to 10.1172/jci.insight.150107
  2. ScreenIT

    SciScore for 10.1101/2020.06.18.20134353: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The collection of biological specimens and de-identified patient information was approved by the University of Virginia Institutional Review Board (IRB-HSR #22231 and 200110).
    IACUC: In mice, neutralizing antibodies or isotype controls were used to assess the role of IL-13 during COVID-19 All mouse work was approved by the University of Virginia Institutional Animal Care and Use Committee, and all procedures were performed in the University certified animal Biosafety Level Three laboratory.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    These P1 stocks were then used to infect additional Vero E6 cells, generating passage 2 (P2) stocks, which were used for all experiments.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Drug use was identified via RxNorm codes for Dupilumab (1876376) and the lab value for C-reactive protein (9063).
    RxNorm
    suggested: (RxNorm, RRID:SCR_006645)
    Library preparation, sequencing, quality control, and read mapping was performed by the Genome Analysis and Technology Core, RRID:SCR_018883.
    by the
    detected: Virginia University School of Medicine Genome Analysis and Technology Core Facility ( RRID:SCR_018883)
    RNAseq data analysis: RNAseq reads were first processed using Cutadapt (Martin, 2011) to trim the adapter sequences and then the quality of the reads was assessed by FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and MultiQC (Ewels et al., 2016).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    After these processes the reads were aligned to the mouse Ensembl GRCh38.76 primary assembly using STAR v2.5.3a (Dobin and Gingeras, 2015) in a two-passing mode to generate a gene matrix for differential gene expression.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Differentially expressed genes were determined using the DESeq2 package (Love et al., 2014) in Rstudio (RStudio Team (2020).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    For hierarchical clustering, the pheatmap function in the pheatmap library was used (R-project.org).
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Statistical analyses were performed using GraphPad Prism and R.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    ROC curve was generated using pROC library in RStudio.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04640168RecruitingAdaptive COVID-19 Treatment Trial 4 (ACTT-4)

    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.

  3. Version published to 10.1101/2020.06.18.20134353 on medRxiv