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

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.

Keywords: Corynebacterium; Dolosigranulum; COVID-19; generalized joint attribute modeling; pediatric microbiota.

Figure 1.

Nasopharyngeal microbiome alpha diversity by age. Shannon diversity (A) and the number of unique amplicon sequence variants (B) are shown by participant age. Each point represents an individual sample and lines correspond to the fit of the linear model between age and each alpha diversity measure. Abbreviation: ASV, amplicon sequence variant.

Figure 2.

Relative abundances of highly abundant bacterial genera by age. Each bar depicts the mean relative abundances of highly abundant genera in nasopharyngeal samples from participants in a specific age category. Only the 9 most highly abundant genera within nasopharyngeal samples from the entire study population are shown. Age is shown as a categorical variable only for graphical representation; all statistical analyses included age as a continuous variable.

Figure 3.

Nasopharyngeal microbiome profiles identified by unsupervised clustering. A, Principal coordinate (PC) plot of Euclidean distances demonstrating clustering of nasopharyngeal samples by microbiome profile. Each dot corresponds to a single nasopharyngeal sample. Centroids are shown as the confluence of the lines arising from individual points from each microbiome profile. Ellipses define the regions containing 95% of all samples that can be drawn from the underlying multivariate t distribution. B, Each bar depicts the mean relative abundances of highly abundant genera in nasopharyngeal samples assigned to specific microbiome profiles. Only the 9 most highly abundant genera within nasopharyngeal samples from the entire study population are shown.

Figure 4.

Interactive relationships between participant age, the relative abundances of specific bacterial ASVs in the nasopharyngeal microbiome, and SARS-CoV-2 status. A, Bar chart depicting differences in the mean relative abundance of ASV1163 (Corynebacterium propinquum) among SARS-CoV-2–infected participants relative to uninfected participants in different age categories. The line was constructed using the GJAM estimates for the association of SARS-CoV-2 infection with the relative abundance of ASV1163 (intercept) and the association of the interaction term between SARS-CoV-2 infection and age with the relative abundance of ASV1163 (slope). Higher relative abundances of ASV1163 were observed in SARS-CoV-2–infected compared with uninfected participants across all ages, but these differences were more pronounced in young children. B, Differences in mean relative abundance of ASV336 (Moraxella lincolnii) between SARS-CoV-2–infected participants with respiratory symptoms and SARS-CoV-2–infected participants without respiratory symptoms are depicted by age category. Dark (light) gray bars represent age categories in which ASV336 was more (less) abundant among SARS-CoV-2–infected participants with respiratory symptoms compared with SARS-CoV-2–infected participants without respiratory symptoms. The line was constructed using the GJAM estimates for the association of SARS-CoV-2–associated respiratory symptoms with the relative abundance of ASV336 (intercept) and the association of the interaction term between respiratory symptoms and age with the relative abundance of ASV336 (slope). The difference in the mean relative abundance of ASV336 between SARS-CoV-2–infected participants with and without respiratory symptoms differed by age, such that this ASV was less abundant in the context of SARS-CoV-2–associated respiratory symptoms among young children and more abundant in the context of SARS-CoV-2–associated respiratory symptoms in older age groups. Lines were fit using the regression coefficients generated using GJAM. Age is shown as a categorical variable only for graphical representation; all statistical analyses included age as a continuous variable. Abbreviations: ASV, amplicon sequence variant; GJAM, generalized joint attribute modeling; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.