Gut microbiome dysbiosis during COVID-19 is associated with increased risk for bacteremia and microbial translocation

  1. Mericien Venzon
  2. Lucie Bernard-Raichon
  3. Jon Klein
  4. Jordan E. Axelrad
  5. Chenzhen Zhang
  6. Grant A. Hussey
  7. Alexis P. Sullivan
  8. Arnau Casanovas-Massana
  9. Maria G. Noval
  10. Ana M. Valero-Jimenez
  11. Juan Gago
  12. Gregory Putzel
  13. Alejandro Pironti
  14. Evan Wilder
  15. Yale IMPACT Research Team
  16. Lorna E. Thorpe
  17. Dan R. Littman
  18. Meike Dittmann
  19. Kenneth A. Stapleford
  20. Bo Shopsin
  21. Victor J. Torres
  22. Albert I. Ko
  23. Akiko Iwasaki
  24. Ken Cadwell
  25. Jonas Schluter

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Abstract

The microbial populations in the gut microbiome have recently been associated with COVID-19 disease severity. However, a causal impact of the gut microbiome on COVID-19 patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. Antibiotics and other treatments during COVID-19 can potentially confound microbiome associations. We therefore first demonstrate in a mouse model that SARS-CoV-2 infection can induce gut microbiome dysbiosis, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Comparison with stool samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, paralleling our observations in the animal model. Specifically, we observed blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species in hospitalized COVID-19 patients. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data obtained from these patients indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19.

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  1. Version published to 10.1101/2021.07.15.452246v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.07.15.452246: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: Bioethics statement: The collection of COVID-19 human biospecimens for research has been approved by the NYUSOM Institutional Review Board under
    IRB: Bioethics statement: The collection of COVID-19 human biospecimens for research has been approved by the NYUSOM Institutional Review Board under
    Consent: Informed consent was obtained from all enrolled patients.
    IACUC: All animal studies were performed according to protocols approved by the NYU School of Medicine Institutional Animal Care and Use Committee (IACUC n°170209). 24-week-old K18-hACE2 males were administered either 10PFU SARS-CoV-2 (low dose, LD), 104PFU SARS-CoV-2 (high dose, HD) diluted in 50μL PBS (Corning) or 50μL PBS (non-infected, CTRL) via intranasal administration under xylazine-ketamine anesthesia (AnaSedR AKORN
    Sex as a biological variableAll animal studies were performed according to protocols approved by the NYU School of Medicine Institutional Animal Care and Use Committee (IACUC n°170209). 24-week-old K18-hACE2 males were administered either 10PFU SARS-CoV-2 (low dose, LD), 104PFU SARS-CoV-2 (high dose, HD) diluted in 50μL PBS (Corning) or 50μL PBS (non-infected, CTRL) via intranasal administration under xylazine-ketamine anesthesia (AnaSedR AKORN
    Randomizationnot detected.
    BlindingAt the time of sample acquisition and processing, investigators were blinded to patient clinical status.
    Power AnalysisNo statistical methods were used to predetermine sample size for this cohort.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Viral titer in the inoculum was verified by plaque assay in Vero E6 cells.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Experimental Models: Organisms/Strains
    SentencesResources
    Mice: Heterozygous K18-hACE2 C57BL/6J mice (strain: 2B6.Cg-Tg(K18-ACE2)2Prlmn/J) were obtained from The Jackson Laboratory.
    C57BL/6J
    suggested: RRID:MGI:3589388)
    All animal studies were performed according to protocols approved by the NYU School of Medicine Institutional Animal Care and Use Committee (IACUC n°170209). 24-week-old K18-hACE2 males were administered either 10PFU SARS-CoV-2 (low dose, LD), 104PFU SARS-CoV-2 (high dose, HD) diluted in 50μL PBS (Corning) or 50μL PBS (non-infected, CTRL) via intranasal administration under xylazine-ketamine anesthesia (AnaSedR AKORN
    K18-hACE2
    suggested: RRID:IMSR_GPT:T037657)
    Software and Algorithms
    SentencesResources
    The data presented in this study were also approved by Yale Human Research Protection Program Institutional Review Boards (FWA00002571, protocol ID 2000027690).
    Yale Human Research Protection Program
    suggested: None
    The clinical data were collected using EPIC EHR and REDCap 9.3.6 software.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    All PCR products were analyzed with the Agilent TapeStation for quality control and then pooled equimolar and sequenced directly in the Illumina MiSeq platform using the 2×250 bp protocol.
    Agilent TapeStation
    suggested: (Agilent TapeStation Laptop, RRID:SCR_019547)
    Bioinformatic processing and taxonomic assignment: Amplicon sequence variants (ASVs) were generated via dada2 v1.16.0 using post-QC FASTQ files.
    Amplicon
    suggested: (Amplicon, RRID:SCR_003294)
    ASV taxonomy was assigned up to genus level using the SILVAv.138 database with the method described in 45 and a minimum boostrapping support of 50%.
    SILVAv.138
    suggested: None
    Principal Coordinate Analyses: Bray-Curtis distances were calculated from the filtered ASV table using QIIME 1.9.1 and principal components of the resulting distance matrix were calculated using the scikit-learn package for the Python programming language, used to embed sample compositions in the first two principal coordinates (see published code for the implementation in the Python programming language).
    QIIME
    suggested: (QIIME, RRID:SCR_008249)
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: 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.
    • 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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.07.15.452246v1 on bioRxiv