Interleukin-3 is a predictive marker for severity and outcome during SARS-CoV-2 infections

  1. Alan Bénard
  2. Anne Jacobsen
  3. Maximilian Brunner
  4. Christian Krautz
  5. Bettina Klösch
  6. Izabela Swierzy
  7. Elisabeth Naschberger
  8. Malgorzata J. Podolska
  9. Dina Kouhestani
  10. Paul David
  11. Torsten Birkholz
  12. Ixchel Castellanos
  13. Denis Trufa
  14. Horia Sirbu
  15. Marcel Vetter
  16. Andreas E. Kremer
  17. Kai Hildner
  18. Andreas Hecker
  19. Fabian Edinger
  20. Matthias Tenbusch
  21. Petra Mühl-Zürbes
  22. Alexander Steinkasserer
  23. Enrico Richter
  24. Hendrik Streeck
  25. Marc M. Berger
  26. Thorsten Brenner
  27. Markus A. Weigand
  28. Filip K. Swirski
  29. Georg Schett
  30. Robert Grützmann
  31. Georg F. Weber

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a worldwide health threat. In a prospective multicentric study, we identify IL-3 as an independent prognostic marker for the outcome during SARS-CoV-2 infections. Specifically, low plasma IL-3 levels is associated with increased severity, viral load, and mortality during SARS-CoV-2 infections. Patients with severe COVID-19 exhibit also reduced circulating plasmacytoid dendritic cells (pDCs) and low plasma IFNα and IFNλ levels when compared to non-severe COVID-19 patients. In a mouse model of pulmonary HSV-1 infection, treatment with recombinant IL-3 reduces viral load and mortality. Mechanistically, IL-3 increases innate antiviral immunity by promoting the recruitment of circulating pDCs into the airways by stimulating CXCL12 secretion from pulmonary CD123 + epithelial cells, both, in mice and in COVID-19 negative patients exhibiting pulmonary diseases. This study identifies IL-3 as a predictive disease marker for SARS-CoV-2 infections and as a potential therapeutic target for pulmunory viral infections.

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  1. Version published to 10.1038/s41467-021-21310-4
  2. ScreenIT

    SciScore for 10.1101/2020.07.02.184093: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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.

    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 10.1101/2020.07.02.184093v1 on bioRxiv