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. Torsten Birkholz
  9. Ixchel Castellanos
  10. Denis Trufa
  11. Horia Sirbu
  12. Marcel Vetter
  13. Andreas E. Kremer
  14. Kai Hildner
  15. Andreas Hecker
  16. Fabian Edinger
  17. Matthias Tenbusch
  18. Enrico Richter
  19. Hendrik Streeck
  20. Marc M. Berger
  21. Thorsten Brenner
  22. Markus A. Weigand
  23. Filip K. Swirski
  24. Georg Schett
  25. Robert Grützmann
  26. Georg F. Weber

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a worldwide health threat. Here, we report that low plasma interleukin-3 (IL-3) levels were associated with increased severity and mortality during SARS-CoV-2 infections. IL-3 promoted the recruitment of antiviral circulating plasmacytoid dendritic cells (pDCs) into the airways by stimulating CXCL12 secretion from pulmonary CD123 + epithelial cells. This study identifies IL-3 as a predictive disease marker and potential therapeutic target for SARS-CoV-2 infections.

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  1. 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.

  2. Version published to 10.1101/2020.07.02.184093 on bioRxiv