Neutrophil and monocyte dysfunctional effector response towards bacterial challenge in critically-ill COVID-19 patients

  1. Srikanth Mairpady Shambat
  2. Alejandro Gómez-Mejia
  3. Tiziano A. Schweizer
  4. Markus Huemer
  5. Chun-Chi Chang
  6. Claudio Acevedo
  7. Judith Bergada Pijuan
  8. Clement Vulin
  9. Nataliya Miroshnikova
  10. Daniel A. Hofmänner
  11. Pedro D. Wendel Garcia
  12. Matthias P Hilty
  13. Philipp Bühler Karl
  14. Reto A. Schüpbach
  15. Silvio D. Brugger
  16. Annelies S. Zinkernagel

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Abstract

COVID-19 displays diverse disease severities and symptoms. Elevated inflammation mediated by hypercytokinemia induces a detrimental dysregulation of immune cells. However, there is limited understanding of how SARS-CoV-2 pathogenesis impedes innate immune signaling and function against secondary bacterial infections. We assessed the influence of COVID-19 hypercytokinemia on the functional responses of neutrophils and monocytes upon bacterial challenges from acute and corresponding recovery COVID-19 ICU patients. We show that severe hypercytokinemia in COVID-19 patients correlated with bacterial superinfections. Neutrophils and monocytes from acute COVID-19 patients showed severely impaired microbicidal capacity, reflected by abrogated ROS and MPO production as well as reduced NETs upon bacterial challenges. We observed a distinct pattern of cell surface receptor expression on both neutrophils and monocytes leading to a suppressive autocrine and paracrine signaling during bacterial challenges. Our data provide insights into the innate immune status of COVID-19 patients mediated by their hypercytokinemia and its transient effect on immune dysregulation upon subsequent bacterial infections

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  1. ScreenIT

    SciScore for 10.1101/2020.12.01.406306: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the local ethics committee of the Canton of Zurich, Switzerland (Kantonale Ethikkommission Zurich BASEC ID 2020 - 00646).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antibodies included anti-CD15 eFluor450 (clone: HI98), anti-CD181 FITC (8F1-1-4), anti-CD182 PerCP-eFluor710 (5E8-C7-F10), anti-CD183 PE-eFluor610 (CEW33D), anti-CD66b APC (G10F5), anti-HLA-DR eFluor450 (LN3), anti-CD45 eFluor506 (HI30)
    anti-CD15
    suggested: (Thermo Fisher Scientific Cat# MA1-42216, RRID:AB_2537341)
    anti-CD181 FITC
    suggested: (Thermo Fisher Scientific Cat# 11-1819-42, RRID:AB_11218880)
    anti-CD182
    suggested: None
    anti-CD183 PE-eFluor610
    suggested: None
    anti-CD66b APC
    suggested: (Thermo Fisher Scientific Cat# 17-0666-41, RRID:AB_2573151)
    anti-HLA-DR
    suggested: None
    anti-CD45
    suggested: None
    HI30
    suggested: None
    Software and Algorithms
    SentencesResources
    Flow cytometry data were analyzed with FlowJo (v10.2).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    The obtained images were processed using Imaris 9.2.0 software (Bitplane) to obtain tifs for further analysis.
    Imaris
    suggested: (Imaris, RRID:SCR_007370)
    Images were processed using ImageJ software (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, https://imagej.nih.gov/ij/, 1997-2018) and Matlab R2020a (MathWorks).
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)
    Kruskal-Wallis test with Dunn’s multiple comparisons test was used to evaluate differences among the three groups in all the analyses (GraphPad).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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
    NCT04410263RecruitingMicrobiota in COVID-19 Patients for Future Therapeutic and P…

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.12.01.406306v1 on bioRxiv