Age-Related Changes in the Nasopharyngeal Microbiome Are Associated With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and Symptoms Among Children, Adolescents, and Young Adults

  1. Jillian H Hurst
  2. Alexander W McCumber
  3. Jhoanna N Aquino
  4. Javier Rodriguez
  5. Sarah M Heston
  6. Debra J Lugo
  7. Alexandre T Rotta
  8. Nicholas A Turner
  9. Trevor S Pfeiffer
  10. Thaddeus C Gurley
  11. M Anthony Moody
  12. Thomas N Denny
  13. John F Rawls
  14. James S Clark
  15. Christopher W Woods
  16. Matthew S Kelly

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Abstract

Background

Children are less susceptible to SARS-CoV-2 infection and typically have milder illness courses than adults, but the factors underlying these age-associated differences are not well understood. The upper respiratory microbiome undergoes substantial shifts during childhood and is increasingly recognized to influence host defense against respiratory pathogens. Thus, we sought to identify upper respiratory microbiome features associated with SARS-CoV-2 infection susceptibility and illness severity.

Methods

We collected clinical data and nasopharyngeal swabs from 285 children, adolescents, and young adults (<21 years) with documented SARS-CoV-2 exposure. We used 16S ribosomal RNA gene sequencing to characterize the nasopharyngeal microbiome and evaluated for age-adjusted associations between microbiome characteristics and SARS-CoV-2 infection status and respiratory symptoms.

Results

Nasopharyngeal microbiome composition varied with age (PERMANOVA, P < .001; R2 = 0.06) and between SARS-CoV-2–infected individuals with and without respiratory symptoms (PERMANOVA, P  = .002; R2 = 0.009). SARS-CoV-2–infected participants with Corynebacterium/Dolosigranulum-dominant microbiome profiles were less likely to have respiratory symptoms than infected participants with other nasopharyngeal microbiome profiles (OR: .38; 95% CI: .18–.81). Using generalized joint attributed modeling, we identified 9 bacterial taxa associated with SARS-CoV-2 infection and 6 taxa differentially abundant among SARS-CoV-2–infected participants with respiratory symptoms; the magnitude of these associations was strongly influenced by age.

Conclusions

We identified interactive relationships between age and specific nasopharyngeal microbiome features that are associated with SARS-CoV-2 infection susceptibility and symptoms in children, adolescents, and young adults. Our data suggest that the upper respiratory microbiome may be a mechanism by which age influences SARS-CoV-2 susceptibility and illness severity.

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  1. Version published to 10.1093/cid/ciac184
  2. ScreenIT

    SciScore for 10.1101/2021.03.20.21252680: (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
    We analyzed raw sequences through a DADA2 pipeline [23] and assigned taxonomy to amplicon sequence variants (ASVs) using Silva SSU version 138 [24].
    Silva
    suggested: (SILVA, RRID:SCR_006423)
    We performed standard nucleotide REFSEQ BLAST searches of ASV reference sequences using the National Center for Biotechnology Information’s Bacteria and Archaea 16S ribosomal RNA project database [26].
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    We calculated nasopharyngeal microbiome alpha (Shannon and Chao1 indices) and beta diversity (Bray-Curtis dissimilarity) using the phyloseq R package [27].
    phyloseq
    suggested: (phyloseq, RRID:SCR_013080)
    We then evaluated the association between age and nasopharyngeal microbiome composition with PERMANOVA using the adonis function within the vegan R package [28].
    vegan
    suggested: (vegan, RRID:SCR_011950)
    Analyses conducted in ANCOM-II and DESeq2 were limited to ASVs present in at least 5% of samples and adjusted for the false discovery rate using the Benjamini-Hochberg procedure.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Analyses were performed using R version 4.0.3 [31] and all visualizations were created using the ggplot2 R package [32]
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study had several limitations. First, nasopharyngeal samples were collected at a single time point, and we did not have nasopharyngeal samples prior to infection in SARS-CoV-2-infected participants. Therefore, we were unable to determine if the differences in nasopharyngeal microbiome composition observed by SARS-CoV-2 infection status preceded, or were the consequence of, SARS-CoV-2 infection. Secondly, all of the SARS-CoV-2-infected study participants had relatively mild symptoms, and no study participants required antiviral treatment or hospitalization. We therefore were unable to evaluate if the microbiome features that we identified as being associated with SARS-CoV-2 respiratory symptoms are additionally associated with severe COVID-19. In addition, our use of 16S rRNA gene amplicon sequencing prevented us from evaluating other components of the upper respiratory microbiome, including viruses and fungi. Additionally, 16S rRNA gene amplicon experiments have several well-documented biases [63], although we sought to minimize these biases in our study through inclusion of all samples in a single processing run and use of appropriate negative controls. Finally, although analyses adjusted for age in evaluating associations between the nasopharyngeal microbiome and SARS-CoV-2 susceptibility and illness severity, residual confounding by unmeasured factors remains possible. In conclusion, we found that age-associated changes in the nasopharyngeal microbiome are independentl...

    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.

    About SciScore

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  3. Version published to 10.1101/2021.03.20.21252680v1 on medRxiv