Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases

  1. M. Nazmul Hoque
  2. M. Shaminur Rahman
  3. Rasel Ahmed
  4. Md. Sabbir Hossain
  5. Md. Shahidul Islam
  6. Keith A Crandall
  7. Md Tofazzal Islam
  8. M. Anwar Hossain
  9. AMAM Zonaed Siddiki

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Abstract

The novel coronavirus disease 2019 (COVID-19) is a rapidly emerging and highly transmissible disease caused by the Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2). Understanding the microbiomes associated with the upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD) and COVID-19 diseases has clinical interest. We hypothesized that the diversity of microbiome compositions and their genomic features are associated with different pathological conditions of these human respiratory tract diseases (COVID-19 and non-COVID; URTI and COPD). To test this hypothesis, we analyzed 21 whole metagenome sequences (WMS) including eleven COVID-19 (BD = 6 and China = 5), six COPD (UK = 6) and four URTI (USA = 4) samples to unravel the diversity of microbiomes, their genomic features and relevant metabolic functions. The WMS data mapped to 534 bacterial, 60 archaeal and 61 viral genomes with distinct variation in the microbiome composition across the samples (COVID-19>COPD>URTI). Notably, 94.57%, 80.0% and 24.59% bacterial, archaeal and viral genera shared between the COVID-19 and non-COVID samples, respectively, however, the COVID-19 related samples had sole association with 16 viral genera other than SARS-CoV-2. Strain-level virome profiling revealed 660 and 729 strains in COVID-19 and non-COVID sequence data, respectively and of them 34.50% strains shared between the conditions. Functional annotation of metagenomics sequences of thevCOVID-19 and non-COVID groups identified the association of several biochemical pathways related to basic metabolism (amino acid and energy), ABC transporters, membrane transport, replication and repair, clustering-based subsystems, virulence, disease and defense, adhesion, regulation of virulence, programmed cell death, and primary immunodeficiency. We also detected 30 functional gene groups/classes associated with resistance to antibiotics and toxic compounds (RATC) in both COVID-19 and non-COVID microbiomes. Furthermore, a predominant higher abundance of cobalt-zinc-cadmium resistance (CZCR) and multidrug resistance to efflux pumps (MREP) genes were detected in COVID-19 metagenome. The profiles of microbiome diversity and associated microbial genomic features found in both COVID-19 and non-COVID (COPD and URTI) samples might be helpful for developing the microbiome-based diagnostics and therapeutics for COVID-19 and non-COVID respiratory diseases. However, future studies might be carried out to explore the microbiome dynamics and the cross-talk between host and microbiomes employing larger volume of samples from different ethnic groups and geoclimatic conditions.

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    SciScore for 10.1101/2020.10.19.345702: (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
    Sequence retrieval: In addition to our five COVID-19 metagenome sequences, we retrieved six (n=6) Chinese COVID-19 metagenome sequences from the NCBI (National Center for Biotechnology Information) database (https://www.ncbi.nlm.nih.gov/) (Accession numbers: SRX7705831-SRX7705836), four (n=4) shotgun metagenome sequences of human URTI belonged to CDC, USA from the NCBI (Accession numbers: SRR10252885, SRR10252888, SRR10252889 and SRR10252892 under bio-project: PRJNA573045), and six (n=6) metagenome sequences of human COPD from the European Nucleotide Archive, UK (Accession numbers: ERR2732537, ERR2732541, ERR2732559, ERR2732558, ERR2732551 and ERR2732550 under bio-project: PRJEB14074).
    https://www.ncbi.nlm.nih.gov/
    suggested: (GENSAT at NCBI - Gene Expression Nervous System Atlas, RRID:SCR_003923)
    Taxonomic abundance was determined by applying the ‘‘Best Hit Classification” option using the NCBI database as a reference with the following settings: maximum e-value of 1×10-30; minimum identity of 80% for bacteria, 60% for archaea and viruses, and a minimum alignment length of 20 as the set parameters.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    A ‘target’ genome library was constructed containing all viral sequences from the NCBI RefSeq Release 201 database (
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    https://en.wikipedia.org/wiki/National_Center_for_Biotechnology_Information) using the Kraken 2 (Wood et al., 2019), and the metagenomics reads were then aligned against the target library using the BWA algorithm (Jaillard et al., 2016).
    BWA
    suggested: (BWA, RRID:SCR_010910)
    (KEGG) database (Kanehisa et al., 2019), and SEED subsystem identifiers (Glass et al., 2010) on the MG-RAST server using the partially modified set parameters (e-value cutoff: 1×10-30, min. % identity cutoff: 60%, and min. alignment length cutoff: 20).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Comparative taxonomic and functional profiling was performed with the reference prokaryotic metagenomes available in MG-RAST database for statistical analyses.
    MG-RAST
    suggested: (MG-RAST, RRID:SCR_004814)
    To identify differentially abundant SEED or KEGG functions, and resistance to antibiotics and toxic compounds (RATCs) across the four sampling locations (BD, China, UK and USA), statistical tests were applied with non-parametric test Kruskal-Wallis rank sum test at different KEGG and SEED subsystems levels IBM SPSS (SPSS, Version 23.0, IBM Corp.,
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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 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: Please consider improving the rainbow (“jet”) colormap(s) used on page 53. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.10.19.345702 on bioRxiv