Long-read 16S-seq reveals nasopharynx microbial dysbiosis and enrichment of Mycobacterium and Mycoplasma in COVID-19 patients: a potential source of co-infection

  1. Punit Prasad
  2. Soumendu Mahapatra
  3. Rasmita Mishra
  4. Krushna Chandra Murmu
  5. Shifu Aggarwal
  6. Manisha Sethi
  7. Priyanka Mohapatra
  8. Arup Ghosh
  9. Rina Yadav
  10. Hiren Dodia
  11. Shamima Azma Ansari
  12. Saikat De
  13. Deepak Singh
  14. Amol Suryawanshi
  15. Rupesh Dash
  16. Shantibhushan Senapati
  17. Tushar K. Beuria
  18. Soma Chattopadhyay
  19. Gulam Hussain Syed
  20. Rajeeb Swain
  21. Sunil K. Raghav
  22. Ajay Parida

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Abstract

Schematic representation of workflow to understand the nasal microbiome dysbiosis in COVID-19 patients. (Image created by Biorender.com).

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  1. Version published to 10.1039/d2mo00044j
  2. ScreenIT

    SciScore for 10.1101/2021.11.10.21266147: (What is this?)

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

    Table 1: Rigor

    EthicsIACUC: Ethical approval: Ethical permission for nasopharyngeal microbiome study and the biorepository was obtained from the Institutional Ethical Committee (IEC)/Institutional Review Board (IRB) of the Institute of Life Sciences [(102/HEC/2020) and (100/HEC/2020)].
    IRB: Ethical approval: Ethical permission for nasopharyngeal microbiome study and the biorepository was obtained from the Institutional Ethical Committee (IEC)/Institutional Review Board (IRB) of the Institute of Life Sciences [(102/HEC/2020) and (100/HEC/2020)].
    Field Sample Permit: Sample collection and reverse transcription-polymerase chain reaction (RT-PCR): In total, 60 NP samples were collected for 16S rDNA amplicon sequencing from the Institute of Life Science (ILS) COVID-19 sample biorepository unit.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The quality and quantity of DNA were determined using the Multiskan™GO spectrophotometer (Thermo Scientific)
    Multiskan™GO
    suggested: None
    Equimolar amounts of amplicon libraries were pooled and sequenced using the MinION OXFORD NANOPORE™ device at the ILS DNA sequencing facility.
    MinION
    suggested: (MinION, RRID:SCR_017985)
    Microbiome data processing: RAW fast5 files were generated using the MinKNOW™ tool for individual samples.
    MinKNOW™
    suggested: None
    Operational taxonomic units (OTUs) were generated using Kraken2 (https://ccb.jhu.edu/software/kraken2/index.shtml) (21) and the unclassified reads were filtered for downstream analysis using the ‘phyloseq’ ‘R” package to generate combined OTUs for all the samples and metadata (Supplemental Table 1).
    Kraken2
    suggested: None
    Normalization and differential OTU abundance were determined between control, and symptomatic and asymptomatic subjects using the DESeq2 function (cutoff of p-value ≤ 0.05).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    The accession ID in NCBI is PRJNA774098.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Linear discriminant analysis (LDA) effect size (LEfSe) analysis: The LEfSe was calculated using the online Galaxy web application with the Huttenhower lab’s tool (https://huttenhower.sph.harvard.edu/galaxy/).
    Galaxy
    suggested: (Galaxy, RRID:SCR_006281)
    Briefly, pairwise Spearman correlation between OTUs (which was generated from LefSe analysis) was calculated using the WGCNA function.
    LefSe
    suggested: (LEfSe, RRID:SCR_014609)
    Network metrics such as betweenness, closeness, Eigen centrality, and PageRank centrality of the resulting network were calculated and visualized using ‘Gephi’, (https://gephi.org/) (23).
    https://gephi.org/
    suggested: (Gephi, RRID:SCR_004293)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has certain limitations. The subject size is limited and a larger cohort would have strengthened our findings. The clinical manifestations are limited, and therefore, the larger picture is difficult to interpret. Future studies should include NP samples of vaccinated, asymptomatic, and hospitalized COVID-19 patients with detailed pathophysiology. Furthermore, blood biochemistry and metabolite studies from the serum would boost conclusions regarding functional aspects of the NP microbiome.

    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 10.1101/2021.11.10.21266147v1 on medRxiv