Microbial Composition Predicts Genital Tract Inflammation and Persistent Bacterial Vaginosis in South African Adolescent Females

Abstract

Young African females are at an increased risk of HIV acquisition, and genital inflammation or the vaginal microbiome may contribute to this risk. We studied these factors in 168 HIV-negative South African adolescent females aged 16 to 22 years. Unsupervised clustering of 16S rRNA gene sequences revealed three clusters (subtypes), one of which was strongly associated with genital inflammation. In a multivariate model, the microbiome compositional subtype and hormonal contraception were significantly associated with genital inflammation. We identified 40 taxa significantly associated with inflammation, including those reported previously (Prevotella, Sneathia, Aerococcus, Fusobacterium, and Gemella) as well as several novel taxa (including increased frequencies of bacterial vaginosis-associated bacterium 1 [BVAB1], BVAB2, BVAB3, Prevotella amnii, Prevotella pallens, Parvimonas micra, Megasphaera, Gardnerella vaginalis, and Atopobium vaginae and decreased frequencies of Lactobacillus reuteri, Lactobacillus crispatus, Lactobacillus jensenii, and Lactobacillus iners). Women with inflammation-associated microbiomes had significantly higher body mass indices and lower levels of endogenous estradiol and luteinizing hormone. Community functional profiling revealed three distinct vaginal microbiome subtypes, one of which was characterized by extreme genital inflammation and persistent bacterial vaginosis (BV); this subtype could be predicted with high specificity and sensitivity based on the Nugent score (≥9) or BVAB1 abundance. We propose that women with this BVAB1-dominated subtype may have chronic genital inflammation due to persistent BV, which may place them at a particularly high risk for HIV infection.

Keywords: 16S RNA; HIV susceptibility; HIV target cells; female genital tract microbiome; inflammation; vaginal microbiome.

FIG 1

Nonmetric multidimensional scaling (NMDS) of microbial compositional subtypes established by using Fuzzy clustering with weighted UniFrac distances. Samples that did not meet the ≥60% minimum probability of belonging to any of these clusters were excluded from downstream analyses (referred to as “no.cluster” in the key).

FIG 2

Heat map of the most abundant taxa (rows) identified by 16S rRNA gene microbiome profiling using unsupervised hierarchical clustering with Bray-Curtis distances for all samples (columns). The compositional subtype was determined from 16S rRNA gene-based microbiome profiles; the functional subtype was determined from inferred functional data (described in Materials and Methods).

FIG 3

Taxa significantly altered between inflammation-high and inflammation-low cases (adjusted for any versus no HC use). (A) Taxa that were significantly differentially abundant and/or frequent by inflammation category using MetagenomeSeq (FDR of ≤0.05; coefficient of ≥1.25; taxa present in ≥20% of samples in at least one of the two groups being compared). Shown is the unsupervised clustering of samples (columns) by Bray-Curtis distances; the heat map scale is on log₂-transformed standardized counts. M.hominis, Mycoplasma hominis; U.parvum, Ureaplasma parvum; D.succinatiphilus, Dialister succinatiphilus; A.christensenii, Aerococcus christensenii; A.radingae, Actinomyces radingae; M.mulieris, Mobiluncus mulieris; G.asaccharolytica, Gemella asaccharolytica; V.dispar, Veillonella dispar. (B) The top 20 most influential taxa by random forest analysis. The x axis indicates the mean decrease in the Gini index (lengths of bars represent the predictive ability of each taxon).

FIG 4

Community functional subtypes. Shown is nonmetric multidimensional scaling of samples colored by functional subtype (A); samples colored by compositional subtype, with BV subtypes displayed as shapes (B); and samples colored by BV status, with inflammation-H/L displayed as shapes (C).

FIG 5

Taxa significantly different in F3 versus F2. (A) Top 20 most important taxa by random forest analysis. The x axis shows the mean decrease in the Gini index (lengths of bars represent the relative predictive ability of each taxon). A.prevotii, Anaerococcus prevotii; M.indolicus, Mageeibacillus indolicus; F.equinum, Fusobacterium equinum; F.prausnitzii, Faecalibacterium prausnitzii; G.elegans, Granulicatella elegans; P.harei, Peptinophilus harei. (B) Taxa that were significantly differentially abundant and/or frequent in F3 versus F2 (FDR of ≤0.05; coefficient of ≥1.25; taxa present in ≥20% of samples in at least one of the two groups being compared). Shown is hierarchical clustering (Bray-Curtis distance); the heat map scale shows log₂-transformed standardized counts. P.buccalis, Prevotella buccalis.

FIG 6

Heat map summary of cytokines by microbiome compositional (C1, C2, and C3) and functional (F1, F2, and F3) subtypes highlighting cytokines significantly different between F2 and F3. Results that were significant by a Kruskal-Wallis test were evaluated by Dunn's post hoc test.

FIG 7

Box plot summaries of (log₂) target cell activation frequencies by compositional subtype (C1, C2, and C3).