New perspectives into the vaginal microbiome with systems biology

The vaginal microbiome and HIV transmission dynamics

PURPOSE OF REVIEW

Among women, having a nonoptimal, highly diverse vaginal microbiome dominated by bacteria other than optimal Lactobacillus species such as L. crispatus or L. jensenii predicts HIV transmission. Reducing HIV acquisition among women requires a better understanding of the mechanisms through which the vaginal microbiome impacts HIV transmission dynamics and how to more effectively treat and intervene. Technological advancements are improving the ability of researchers to fully characterize interacting host-bacteria mechanisms. Consequently, the purpose of this review was to summarize the most innovative research on the vaginal microbiome and its role in HIV transmission in the past year.

RECENT FINDINGS

Studies combining multiomics, experimental, and translational approaches highlight the associations of a nonoptimal microbiome with maladaptive alterations in immune cell functioning, vaginal metabolites, host cell transcription, mucosal immunity, and epithelial barrier integrity. While there are multiple mechanisms proposed to increase HIV acquisition risk, there are virtually zero acceptable and effective treatments to improve the vaginal microbiome and immunity.

SUMMARY

Women-centered solutions to modify the vaginal microbiome and bacterial metabolites should continue to be explored as a mechanism to reduce HIV acquisition.

Gut microbiome-metabolome interactions predict host condition

BACKGROUND

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

RESULTS

We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).

CONCLUSION

These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

Metabolic signature for a dysbiotic microbiome in the female genital tract: A systematic review and meta-analysis

BACKGROUND

The vaginal microbiome (VMB) is a critical determinant of reproductive health, where a microbial shift towards a dysbiotic environment has implications for susceptibility to, and clinical presentation of sexually transmitted infections (STIs). Metabolomic profiling of the vaginal microenvironment has led to the identification of metabolic responses to clinical conditions of dysbiosis. However, no studies have examined metabolic markers that are common across conditions and can serve as a signature for vaginal dysbiosis.

METHOD OF STUDY

We have conducted a comprehensive systematic review and meta-analysis to identify consistently deregulated metabolites along with their impact on host and microbial metabolism during dysbiosis. We employed two complementary approaches including a vote counting analysis for all eligible studies identified in the systematic review, in addition to a meta-analysis for a subset of studies with sufficient available data. Significantly deregulated metabolites were then selected for pathway enrichment analysis.

RESULTS

Our results revealed a total of 502 altered metabolites reported across 10 dysbiotic conditions from 16 studies. Following a rigorous, collective analysis, six metabolites which were consistently downregulated and could be generalized to all dysbiotic conditions were identified. In addition, five downregulated and one upregulated metabolite was identified from a bacterial vaginosis (BV) focused sub-analysis. These metabolites have the potential to serve as a metabolic signature for vaginal dysbiosis. Their role in eight altered metabolic pathways indicates a disruption of amino acid, carbohydrate, and energy metabolism during dysbiosis.

CONCLUSION

Based on this analysis, we propose a schematic model outlining the common metabolic perturbations associated with vaginal dysbiosis, which can be potential targets for therapeutics and prophylaxis.