Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host

  1. Verónica Lloréns-Rico
  2. Ann C. Gregory
  3. Johan Van Weyenbergh
  4. Sander Jansen
  5. Tina Van Buyten
  6. Junbin Qian
  7. Marcos Braz
  8. Soraya Maria Menezes
  9. Pierre Van Mol
  10. Lore Vanderbeke
  11. Christophe Dooms
  12. Jan Gunst
  13. Greet Hermans
  14. Philippe Meersseman
  15. CONTAGIOUS collaborators
  16. Yannick Van Herck
  17. Alexander Wilmer
  18. Michael Casaer
  19. Stephen Rex
  20. Nathalie Lorent
  21. Jonas Yserbyt
  22. Dries Testelmans
  23. Karin Thevissen
  24. Els Wauters
  25. Johan Neyts
  26. Diether Lambrechts
  27. Joost Wauters
  28. Jeroen Raes

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Abstract

Understanding the pathology of COVID-19 is a global research priority. Early evidence suggests that the respiratory microbiome may be playing a role in disease progression, yet current studies report contradictory results. Here, we examine potential confounders in COVID-19 respiratory microbiome studies by analyzing the upper ( n  = 58) and lower ( n  = 35) respiratory tract microbiome in well-phenotyped COVID-19 patients and controls combining microbiome sequencing, viral load determination, and immunoprofiling. We find that time in the intensive care unit and type of oxygen support, as well as associated treatments such as antibiotic usage, explain the most variation within the upper respiratory tract microbiome, while SARS-CoV-2 viral load has a reduced impact. Specifically, mechanical ventilation is linked to altered community structure and significant shifts in oral taxa previously associated with COVID-19. Single-cell transcriptomics of the lower respiratory tract of COVID-19 patients identifies specific oral bacteria in physical association with proinflammatory immune cells, which show higher levels of inflammatory markers. Overall, our findings suggest confounders are driving contradictory results in current COVID-19 microbiome studies and careful attention needs to be paid to ICU stay and type of oxygen support, as bacteria favored in these conditions may contribute to the inflammatory phenotypes observed in severe COVID-19 patients.

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  1. Version published to 10.1038/s41467-021-26500-8
  2. Version published to 10.1101/2020.12.23.20248425v3 on medRxiv
  3. Version published to 10.1101/2020.12.23.20248425v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.12.23.20248425: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study design and patient cohorts: All experimental protocols and data analyses were approved by the Ethics Commission from the UZ Leuven Hospital, under the COntAGIouS observational clinical trial (study number S63381).
    Consent: All participants gave their informed consent to participate in the study.
    RandomizationTo calculate strain-level diversity per sample, the number of strains of 5 detected OTU species were randomly selected and averaged.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In brief, raw nCounter data were processed using nSolver 4.0 software (Nanostring), sequentially correcting three factors for each individual sample: technical variation between samples (using spiked positive control RNA), background correction (using spiked negative control RNA) and RNA content variation (using 15 housekeeping genes).
    nSolver
    suggested: None
    In order to explore species-level and strain-level diversity, 16S sequences were first clustered into 97% nucleotide diversity operational taxonomic units (OTUs) using the R packages Biostrings and DECIPHER.
    Biostrings
    suggested: (Biostrings, RRID:SCR_016949)
    DECIPHER
    suggested: (DECIPHER, RRID:SCR_006552)
    Following quality control, reads from human and potential sequencing artifacts (phage phiX174) were mapped with STAR53 (v2.7.1) and discarded.
    STAR53
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    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
    NCT04327570RecruitingIn-depth Immunological Investigation of COVID-19.

    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.

  5. Version published to 10.1101/2020.12.23.20248425v1 on medRxiv