Characterization of respiratory microbial dysbiosis in hospitalized COVID-19 patients

  1. Huanzi Zhong
  2. Yanqun Wang
  3. Zhun Shi
  4. Lu Zhang
  5. Huahui Ren
  6. Weiqun He
  7. Zhaoyong Zhang
  8. Airu Zhu
  9. Jingxian Zhao
  10. Fei Xiao
  11. Fangming Yang
  12. Tianzhu Liang
  13. Feng Ye
  14. Bei Zhong
  15. Shicong Ruan
  16. Mian Gan
  17. Jiahui Zhu
  18. Fang Li
  19. Fuqiang Li
  20. Daxi Wang
  21. Jiandong Li
  22. Peidi Ren
  23. Shida Zhu
  24. Huanming Yang
  25. Jian Wang
  26. Karsten Kristiansen
  27. Hein Min Tun
  28. Weijun Chen
  29. Nanshan Zhong
  30. Xun Xu
  31. Yi-min Li
  32. Junhua Li
  33. Jincun Zhao

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of Coronavirus disease 2019 (COVID-19). However, the microbial composition of the respiratory tract and other infected tissues as well as their possible pathogenic contributions to varying degrees of disease severity in COVID-19 patients remain unclear. Between 27 January and 26 February 2020, serial clinical specimens (sputum, nasal and throat swab, anal swab and feces) were collected from a cohort of hospitalized COVID-19 patients, including 8 mildly and 15 severely ill patients in Guangdong province, China. Total RNA was extracted and ultra-deep metatranscriptomic sequencing was performed in combination with laboratory diagnostic assays. We identified distinct signatures of microbial dysbiosis among severely ill COVID-19 patients on broad spectrum antimicrobial therapy. Co-detection of other human respiratory viruses (including human alphaherpesvirus 1, rhinovirus B, and human orthopneumovirus) was demonstrated in 30.8% (4/13) of the severely ill patients, but not in any of the mildly affected patients. Notably, the predominant respiratory microbial taxa of severely ill patients were Burkholderia cepacia complex (BCC), Staphylococcus epidermidis , or Mycoplasma spp . (including M. hominis and M. orale ). The presence of the former two bacterial taxa was also confirmed by clinical cultures of respiratory specimens (expectorated sputum or nasal secretions) in 23.1% (3/13) of the severe cases. Finally, a time-dependent, secondary infection of B. cenocepacia with expressions of multiple virulence genes was demonstrated in one severely ill patient, which might accelerate his disease deterioration and death occurring one month after ICU admission. Our findings point to SARS-CoV-2-related microbial dysbiosis and various antibiotic-resistant respiratory microbes/pathogens in hospitalized COVID-19 patients in relation to disease severity. Detection and tracking strategies are needed to prevent the spread of antimicrobial resistance, improve the treatment regimen and clinical outcomes of hospitalized, severely ill COVID-19 patients.

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  1. Version published to 10.1038/s41421-021-00257-2
  2. Version published to 10.1101/2020.07.02.20143032v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.07.02.20143032: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics statement: The study was reviewed and approved by the ethics committees of all the four hospitals and the institutional review board of BGI-Shenzhen.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Identification and removal of human RNA reads from metatranscriptomic data: For each sample, the raw metatranscriptomic reads were processed using Fastp (v0.19.5, default settings) 17 to filter low-quality data and adapter contaminations and generate the clean reads for further analyses.
    Fastp
    suggested: (fastp, RRID:SCR_016962)
    Human-derived reads were identified with the following steps: 1) identification of human ribosomal RNA (rRNA) by aligning clean reads to human rRNA sequences (28S, 18S, 5.8S, 45S, 5S, U5 small nuclear RNA, as well as mitochondrial mt12S) using BWA-MEM 0.7.17-r1188 18; 2) identification of human transcripts by mapping reads to the hg19 reference genome using the RNA-seq aligner HISAT2 (
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)
    vertion 2.1.0, default settings) 19; and 3) a second-round identification of human reads by aligning remaining reads to hg 38 using Kraken2 (version 2.0.8-beta, default settings) 20
    Kraken2
    suggested: None
    Characterization of viral communities in hospitalized patients with SARS-CoV-2 infection: Before identification of virome and microbiota, SortMeRNA version 4.2.021 (default settings) was applied to filter microbial rRNA from non-human metatranscriptomic data.
    SortMeRNA
    suggested: (SortMeRNA, RRID:SCR_014402)
    The remaining non-human non-rRNA reads were processed by Kraken2X v2.08 beta (default parameters)20 with a self-built viral protein database by extracting protein sequences from all complete viral genomes deposited in the NCBI RefSeq database (8,872 genomes downloaded on March 1st, 2020 including the SARS-CoV-2 complete genome reference sequence, GCF_009858895.2).
    Kraken2X
    suggested: None
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    The number of reads annotated to each viral family was summarized based on the read alignment results of Kraken 2X, and all RNA reads annotated to family Coronaviridae were considered as SARS-CoV-2-like reads.
    Kraken
    suggested: (Kraken, RRID:SCR_005484)
    For each sample, reads were mapped against corresponding references by bowtie2 v2.3.0, and the sequencing depth and genome coverage were estimated by BEDTools coverage v2.27.1 as described above.
    bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    BEDTools
    suggested: (BEDTools, RRID:SCR_006646)

    Results from OddPub: Thank you for sharing your code and 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 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.
    • 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.

  4. Version published to 10.1101/2020.07.02.20143032v1 on medRxiv