The active lung microbiota landscape of COVID-19 patients through the metatranscriptome data analysis

  1. Yang Han
  2. Zhilong Jia
  3. Jinlong Shi
  4. Weidong Wang
  5. Kunlun He

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Abstract

Introduction: With the outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the interaction between the host and SARS-CoV-2 was widely studied. However, it is unclear whether and how SARS-CoV-2 infection affects lung microflora, which contribute to COVID-19 complications. Methods: Here, we analyzed the metatranscriptomic data of bronchoalveolar lavage fluid (BALF) of 19 COVID-19 patients and 23 healthy controls from 6 independent projects and detailed the active microbiota landscape in both healthy individuals and COVID-19 patients. Results: The infection of SARS-CoV-2 could deeply change the lung microbiota, evidenced by the α-diversity, β-diversity, and species composition analysis based on bacterial microbiota and virome. Pathogens (e.g., Klebsiella oxytoca causing pneumonia as well), immunomodulatory probiotics (e.g., lactic acid bacteria and Faecalibacterium prausnitzii, a butyrate producer), and Tobacco mosaic virus (TMV) were enriched in the COVID-19 group, suggesting a severe microbiota dysbiosis. The significant correlation between Rothia mucilaginosa, TMV, and SARS-CoV-2 revealed drastic inflammatory battles between the host, SARS-CoV-2, and other microbes in the lungs. Notably, TMV only existed in the COVID-19 group, while human respirovirus 3 (HRV 3) only existed in the healthy group. Our study provides insights into the active microbiota in the lungs of COVID-19 patients and would contribute to the understanding of the infection mechanism of SARS-CoV-2 and the treatment of the disease and complications. Conclusion: SARS-COV-2 infection deeply altered the lung microbiota of COVID-19 patients. The enrichment of several other pathogens, immunomodulatory probiotics (lactic acid or butyrate producers), and TMV in the COVID-19 group suggests a complex and active lung microbiota disorder.

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  1. Version published to 10.34172/bi.2021.23378
  2. ScreenIT

    SciScore for 10.1101/2020.08.20.20144014: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collection and preprocessing: The raw metatranscriptomic fastq data of for 42 BALF samples, consist of 19 COVID-19 patients and 23 healthy controls, and two negative controls (NC) were downloaded from Sequence Read Archive, EMBL-EBI ArrayExpress and National Genomics Data Center, China with project IDs, PRJCA002202, PRJNA601736, PRJCA002326, PRJNA605983, PRJNA615032 and PRJNA434133.
    Sequence Read Archive
    suggested: (DDBJ Sequence Read Archive, RRID:SCR_001370)
    ArrayExpress
    suggested: (ArrayExpress, RRID:SCR_002964)
    Briefly, the raw reads underwent removal of host reads (Hg38 reference genome) via STAR (Spliced Transcripts Alignment to a Reference), a series of quality control, consist of removal of low-quality, low-complexity and redundant sequences via Paired-Read Iterative Contig Extension (PRICE), CD-HIT-DUP and Lempel-Ziv-Welch (LZW) compression score, respectively(15).
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Differential species were analyzed by DEseq2 (v1.24.0).
    DEseq2
    suggested: (DESeq2, RRID:SCR_015687)
    Correlation network was visualized by Cytoscape 3.7.2(16).
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    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: 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.

    About SciScore

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  3. Version published to 10.1101/2020.08.20.20144014v1 on medRxiv