Metabolic signatures of bacterial vaginosis

Abstract

Bacterial vaginosis (BV) is characterized by shifts in the vaginal microbiota from Lactobacillus dominant to a microbiota with diverse anaerobic bacteria. Few studies have linked specific metabolites with bacteria found in the human vagina. Here, we report dramatic differences in metabolite compositions and concentrations associated with BV using a global metabolomics approach. We further validated important metabolites using samples from a second cohort of women and a different platform to measure metabolites. In the primary study, we compared metabolite profiles in cervicovaginal lavage fluid from 40 women with BV and 20 women without BV. Vaginal bacterial representation was determined using broad-range PCR with pyrosequencing and concentrations of bacteria by quantitative PCR. We detected 279 named biochemicals; levels of 62% of metabolites were significantly different in women with BV. Unsupervised clustering of metabolites separated women with and without BV. Women with BV have metabolite profiles marked by lower concentrations of amino acids and dipeptides, concomitant with higher levels of amino acid catabolites and polyamines. Higher levels of the signaling eicosanoid 12-hydroxyeicosatetraenoic acid (12-HETE), a biomarker for inflammation, were noted in BV. Lactobacillus crispatus and Lactobacillus jensenii exhibited similar metabolite correlation patterns, which were distinct from correlation patterns exhibited by BV-associated bacteria. Several metabolites were significantly associated with clinical signs and symptoms (Amsel criteria) used to diagnose BV, and no metabolite was associated with all four clinical criteria. BV has strong metabolic signatures across multiple metabolic pathways, and these signatures are associated with the presence and concentrations of particular bacteria.

Importance: Bacterial vaginosis (BV) is a common but highly enigmatic condition that is associated with adverse outcomes for women and their neonates. Small molecule metabolites in the vagina may influence host physiology, affect microbial community composition, and impact risk of adverse health outcomes, but few studies have comprehensively studied the metabolomics profile of BV. Here, we used mass spectrometry to link specific metabolites with particular bacteria detected in the human vagina by PCR. BV was associated with strong metabolic signatures across multiple pathways affecting amino acid, carbohydrate, and lipid metabolism, highlighting the profound metabolic changes in BV. These signatures were associated with the presence and concentrations of particular vaginal bacteria, including some bacteria yet to be cultivated, thereby providing clues as to the microbial origin of many metabolites. Insights from this study provide opportunities for developing new diagnostic markers of BV and novel approaches for treatment or prevention of BV.

FIG 1

Hierarchical clustering of metabolites. A dendrogram shows associations of 30% of most variable metabolites with BV status, determined using the Amsel and Nugent criteria. Clustering of the metabolites resulted in four groups (clusters I to IV). The clustering algorithm was not informed by BV status. Study participant identification numbers (IDs) are provided on the x axis, and metabolites are listed on the y axis. The heat map depicts log-transformed concentrations of the most variable metabolites between the clusters. Values ranged from −6.956 (dark green) to 4.818 (dark red).

FIG 2

Association between metabolites and bacterial abundance. Hierarchically clustered Pearson correlation coefficients are displayed in a heat map to demonstrate associations of vaginal bacteria (relative abundance) detected using broad-range PCR and pyrosequencing (x axis) with the abundance of 30% most variable metabolites (y axis). Correlation values ranged from −0.7 (dark green) to 0.84 (dark red).

FIG 3

Association of specific vaginal bacteria with metabolites. Hierarchically clustered Pearson correlation coefficients are displayed in a heat map to demonstrate associations of key vaginal bacterial concentrations (x axis) measured using taxon-directed qPCR with the 30% most variable metabolites (y axis). Correlation values range from −0.71 (dark green) to 0.85 (dark red). Two subgroups of BV-associated bacteria were observed, and clustering patterns were similar to those noted in Fig. 2 using an untargeted approach for bacterial community analysis. L. crispatus and L. jensenii exhibited correlation patterns that were similar and opposite to those by BV-associated bacteria.

FIG 4

Association of metabolites with Amsel clinical criteria. Shown is a model depicting metabolites associated with individual clinical criteria used in BV diagnosis (21). Stars denote metabolites that were positively or negatively associated with BV status. BV status is indicated on the x axis of box plots. The y axis of box plots represents scaled concentrations of metabolites. The lines in box plots represent the mean, and whiskers denote 95% confidence intervals.

FIG 5

Shifts in concentrations of key vaginal bacteria and metabolites with treatment for BV. Longitudinal data are presented for four study participants (19, 21, 23, 25) who were cured of BV posttreatment with metronidazole at the 1-month follow-up visit. Bacterial concentrations are displayed as 16S rRNA copies per swab on the y axis of the x-y plots. Scaled metabolite concentrations (y axis) of 14 metabolites associated with individual clinical criteria from Fig. 4 (Table 3) have been classified as positively associated with BV and are higher in women with BV (red) and negatively associated with BV and are lower in women with BV (green). y axis scales are different for each subject reflecting differences in metabolite concentrations. All four participants had high concentrations of lactobacilli at their follow-up visits and increased concentrations of metabolites negatively associated with BV (green). Women with high concentrations of L. crispatus at follow-up had high concentrations of metabolites negatively associated with BV (green). Women with high concentrations of L. iners also showed increased concentrations of metabolites negatively associated with BV (green), but these shifts were not as dramatic as increases seen in women with L. crispatus dominant communities.

FIG 6

Model representing putative putrescine metabolism pathways in bacterial vaginosis. Box plots show selected metabolites that were detected in our study. BV status is indicated on the x axis of box plots. The y axis of box plots represent scaled concentrations of metabolites. The lines in box plots depict the mean, and whiskers denote 95% confidence intervals. Arginine typically serves as a precursor for the generation of polyamines putrescine, spermidine, and spermine. Levels of putrescine were higher in BV, while levels of spermine were lower. Higher levels of succinate are a hallmark in BV. Recently, a novel putrescine utilization pathway has been discovered in Escherichia coli via γ-aminobutyrate (GABA), resulting in succinate production. This pathway may play a role in BV. Higher levels of N-acetylputrescine were observed in BV, which can also lead to GABA formation. Red or green arrows next to metabolites indicate metabolites detected in our study and show whether concentrations were higher or lower in women with BV.