Dissecting the role of the human microbiome in COVID-19 via metagenome-assembled genomes

  1. Shanlin Ke
  2. Scott T. Weiss
  3. Yang-Yu Liu

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Abstract

Coronavirus disease 2019 (COVID-19), primarily a respiratory disease caused by infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is often accompanied by gastrointestinal symptoms. However, little is known about the relation between the human microbiome and COVID-19, largely due to the fact that most previous studies fail to provide high taxonomic resolution to identify microbes that likely interact with SARS-CoV-2 infection. Here we used whole-metagenome shotgun sequencing data together with assembly and binning strategies to reconstruct metagenome-assembled genomes (MAGs) from 514 COVID-19 related nasopharyngeal and fecal samples in six independent cohorts. We reconstructed a total of 11,584 medium-and high-quality microbial MAGs and obtained 5403 non-redundant MAGs (nrMAGs) with strain-level resolution. We found that there is a significant reduction of strain richness for many species in the gut microbiome of COVID-19 patients. The gut microbiome signatures can accurately distinguish COVID-19 cases from healthy controls and predict the progression of COVID-19. Moreover, we identified a set of nrMAGs with a putative causal role in the clinical manifestations of COVID-19 and revealed their functional pathways that potentially interact with SARS-CoV-2 infection. Finally, we demonstrated that the main findings of our study can be largely validated in three independent cohorts. The presented results highlight the importance of incorporating the human gut microbiome in our understanding of SARS-CoV-2 infection and disease progression.

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  1. Version published to 10.1038/s41467-022-32991-w
  2. ScreenIT

    SciScore for 10.1101/2022.03.09.483704: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collection: We identified COVID-19 metagenomic sequencing studies from keyword searches in PubMed and online repositories (i.e., NCBI, ENA, and GSA) and by following references in meta-analyses and related microbiome studies.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    According to the criteria of quality evaluation by CheckM (v1.0.12)75, 5403 nrMAGs were divided into medium-quality MAGs (50% ≤ completeness < 90% and ≤5% contamination) and high-quality MAGs (≥90% completeness and ≤5% contamination).
    CheckM
    suggested: (CheckM, RRID:SCR_016646)
    The phylogenetic tree of the nrMAGs was built using PhyloPhlAn (v3.0.58)80.
    PhyloPhlAn
    suggested: (PhyloPhlAn, RRID:SCR_013082)
    Genome annotation of MAGs: The genome annotation of MAGs was first performed with Prokka (v1.13)48 using the annotate_bins module of metaWRAP70.
    Prokka
    suggested: (Prokka, RRID:SCR_014732)
    Proteins without KO identifiers (or matches) are extracted and searched against other databases (e.g., Swissprot, curated RefSeq database or non-curated trEMBL database)49.
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    The KO identifiers associated with all proteins in each genome (or set of proteins) are extracted, and KEGG module completeness is calculated based on the total steps in a module, the proteins (KOs) required for each step, and the KOs present in each genome.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Differences in microbiome compositions across different groups were tested by the permutational multivariate analysis of variance (PERMANOVA) using the “adonis” function in R’s vegan package.
    R’s
    suggested: None
    vegan
    suggested: (vegan, RRID:SCR_011950)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The current study has several limitations. First, although we included a large number of shotgun metagenomic sequencing samples from the COVID-19 related human microbiome study (publicly available as of August 2021), most of the microbiome samples came from China. This limitation could be addressed by following this work with collection of more human microbiome samples from different populations and body sites to construct a more comprehensive genome catalog to reveal the full landscape of the human microbiome in COVID-19. Second, even though we adjusted for some potential confounders in our statistical models, we were unable to assess some covariates such as: medication, diet, and psychological stress that are not publicly available. Third, consistent with multiple MAG-related WMS studies25,65,66, we only recovered MAGs from bacteria and archaea. Given the fact that de novo discovery of non-bacterial genomes is quite challenging67, future study targets for other domains, including fungi and viruses, will give a more comprehensive view in the context of host-specific microbiotas and COVID-19. Although the majority of MAGs we reconstructed in this study have high quality, future investigations aiming for recovering the complete genome of microbes will further enhance our understanding of the interaction between human microbiome and SARS-CoV-2 infection68,69. Finally, additional experiments are needed to assess the casual role of candidate permissive and protective nrMAGs in CO...

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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

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  3. Version published to 10.1101/2022.03.09.483704v1 on bioRxiv