Severity of SARS-CoV-2 infection as a function of the interferon landscape across the respiratory tract of COVID-19 patients

  1. Benedetta Sposito
  2. Achille Broggi
  3. Laura Pandolfi
  4. Stefania Crotta
  5. Roberto Ferrarese
  6. Sofia Sisti
  7. Nicola Clementi
  8. Alessandro Ambrosi
  9. Enju Liu
  10. Vanessa Frangipane
  11. Laura Saracino
  12. Laura Marongiu
  13. Fabio A Facchini
  14. Andrea Bottazzi
  15. Tommaso Fossali
  16. Riccardo Colombo
  17. Massimo Clementi
  18. Elena Tagliabue
  19. Antonio E Pontiroli
  20. Federica Meloni
  21. Andreas Wack
  22. Nicasio Mancini
  23. Ivan Zanoni

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Abstract

The COVID-19 outbreak driven by SARS-CoV-2 has caused more than 2.5 million deaths globally, with the most severe cases characterized by over-exuberant production of immune-mediators, the nature of which is not fully understood. Interferons of the type I (IFN-I) or type III (IFN-III) families are potent antivirals, but their role in COVID-19 remains debated. Our analysis of gene and protein expression along the respiratory tract shows that IFNs, especially IFN-III, are over-represented in the lower airways of patients with severe COVID-19, while high levels of IFN-III, and to a lesser extent IFN-I, characterize the upper airways of patients with high viral burden but reduced disease risk or severity; also, IFN expression varies with abundance of the cell types that produce them. Our data point to a dynamic process of inter- and intra-family production of IFNs in COVID-19, and suggest that IFNs play opposing roles at distinct anatomical sites.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.30.437173: (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 Ethics Committee of San Raffaele Hospital (protocol No. 34/int/2020) and was registered on ClinicalTrials.gov (NCT04318366).
    Consent: All patients signed an informed consent form.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Reagents and antibodies: For in vitro studies, we used LPS (ALX-581-013-L002) from ENZO, Poly (I:C) HMW (tlr-pic), R848 (tlr-r848), CpG(C) (tlrl-2395), 2’3’cGAMP (tlrl-nacga23-02) and 3p-hpRNA/LyoVec (tlrl-hprnalv) purchased from Invivogen.
    tlrl-nacga23-02
    suggested: None
    For flow cytometry we used PerCP/Cy5.5 CD14 (clone HCD14), APC/Cyanine7 HLA-DR (clone L243), PE/Cy7 CD11c (clone 3.9) and PE CD141 (clone M80) antibodies purchased from Biolegend.
    CD14
    suggested: (BD Biosciences Cat# 340827, RRID:AB_400137)
    APC/Cyanine7
    suggested: (BioLegend Cat# 307618, RRID:AB_493586)
    HLA-DR
    suggested: None
    Software and Algorithms
    SentencesResources
    Heatmaps and K-mean clustering were generated in R and visualized with the ComplexHeatmap package.
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)
    All statistical analyses were two-sided and performed using Prism9 (Graphpad) software or SAS version 9.4 (SAS Institute).
    Graphpad
    suggested: (GraphPad, RRID:SCR_000306)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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
    NCT04318366RecruitingCOVID-19 Patients Characterization, Biobank, Treatment Respo…

    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 10.1101/2021.03.30.437173v1 on bioRxiv