Effects of HIV viremia on the gastrointestinal microbiome of young MSM

Abstract

Objective: We employed a high-dimensional covariate adjustment method in microbiome analysis to better control for behavioural and clinical confounders, and in doing so examine the effects of HIV on the rectal microbiome.

Design: Three hundred and eighty-three MSM were grouped into four HIV viremia categories: HIV negative (n = 200), HIV-positive undetectable (HIV RNA < 20 copies/ml; n = 66), HIV-positive suppressed (RNA 20-200 copies/ml; n = 72) and HIV-positive viremic (RNA > 200 copies/ml; n = 45).

Methods: We performed 16S rRNA gene sequencing on rectal swab samples and used inverse probability of treatment-weighted marginal structural models to examine differences in microbial composition by HIV viremia category.

Results: HIV viremia explained a significant amount of variability in microbial composition in both unadjusted and covariate-adjusted analyses (R = 0.011, P = 0.02). Alterations in bacterial taxa were more apparent with increasing viremia. Relative to the HIV-negative group, HIV-positive undetectable participants showed depletions in Brachyspira, Campylobacter and Parasutterella, while suppressed participants demonstrated depletions in Barnesiella, Brachyspira and Helicobacter. The microbial signature of viremic men was most distinct, showing enrichment in inflammatory genera Peptoniphilus, Porphyromonas and Prevotella and depletion of Bacteroides, Brachyspira and Faecalibacterium, among others.

Conclusion: Our study shows that, after accounting for the influence of multiple confounding factors, HIV is associated with dysbiosis in the gastrointestinal microbiome in a dose-dependent manner. This analytic approach may allow for better identification of true microbial associations by limiting the effects of confounding, and thus improve comparability across future studies.

Figure 1.

Rectal microbial composition of study participants, N = 383. (A) Columns represent the relative composition of each subject’s microbiome at the genus level. HIV status of the subjects is indicated by a colored line below their microbial composition. Subjects are ordered by the first principal coordinate of a Bray-Curtis pairwise distance matrix. Genera representing less than 1% of the composition on average across samples were combined into “Other.” (B) Average microbial composition within each HIV viremia category. Unadjusted and inverse probability of treatment weighted compositions are shown. Bacterial genera representing less than 1% of the overall relative composition or present in less than 20% of the samples were grouped into “Other.”

Figure 2.

Associations between HIV viremia and overall microbial composition. (A) Ordination of the Bray-Curtis distance between samples using principal coordinates analysis. PCoA = Principal coordinate axis. Ellipses are 95% confidence regions for each group assuming points follow a multivariate t distribution. (B) Boxplots of richness metrics. Boxes represent the lower, median, and upper quartile of the data and whiskers are 1.5*interquartile range.

Figure 3.

Comparisons of individual bacterial genera between HIV viremia categories. Forest plots of results of zero-inflated negative binomial models comparing genus-level bacterial counts between HIV-negative and (A) HIV+ undetectable (HIV RNA <20 copies/ml), (B) HIV+ suppressed (HIV RNA >20 and ≤200 copies/ml) and (C) HIV+ viremic (HIV RNA >200 copies/ml) participants. Inverse probability of treatment-weighted effect sizes and 90% false coverage rate-adjusted confidence intervals (truncated at −4, 4) are plotted, with statistical significance (q < 0.1) indicated in color. Effect sizes are log ratios of normalized genera counts.

Figure 4.

Summary of zero-inflated negative binomial (ZINB) and least absolute shrinkage and selection operator (LASSO) model results. Enriched taxa are those with positive effect sizes (relative to HIV-), depleted are those with negative effect sizes. Genera with no effect in either analysis are not shown. UNW = unadjusted, WT = IPTW adjusted.