(B-80) Characterizing Vaginal Microbiome Regulation of Progesterone Receptor Expression via Secondary Analysis of Host and Microbiome Multi-omics Data

Introduction::
Progesterone is a hormone essential for reproductive homeostasis. Dysfunctions in progesterone signaling are linked to many gynecological diseases. The vaginal microbiome, comprised primarily of aerobic Lactobacillus species, can become dysbiotic via colonization by anaerobes such as Gardnerella and Prevotella species. The mechanisms by which the vaginal microbiome regulates female sex hormones, such as progesterone, are still poorly understood. This study seeks to understand how the vaginal microbiome regulates progesterone receptor signaling via secondary analysis of host and microbiome multi-omics data.




Materials and Methods::
Data from HIV-negative participants in the Partners PrEP clinical trial, a study on antiretroviral prophylaxis for HIV prevention, with paired vaginal transcriptomics, metabolomics, metaproteomics, and microbiome composition information was used to investigate the relationship between vaginal microbiome factors and progesterone receptor (PGR) expression. Partial Least Squares Regression (PLSR) models were created for each biological data type to assess how these factors regulate PGR expression. Significant factors regulating PGR abundance were identified through variable importance of projection (VIP) analysis and Spearman correlation analysis. Partial correlation analysis and follow-up PLSR incorporating clinical and demographic variables were performed to assess the robustness of the vaginal microbiome-PGR associations.




Results, Conclusions, and Discussions::
The PLSR models indicated that lower PGR expression was associated with G. vaginalis, while higher PGR expression was associated with Lactobacillus species. Many metabolites were positively associated with PGR expression, including guanine, arginine, and cytosine. Metabolites including citrulline and succinate were negatively associated with PGR expression. Some pathways that were positively associated with PGR expression include the glucagon signaling pathway, RNA degradation, HIF-1 signaling pathway, and glycolysis and gluconeogenesis. The only significantly negatively correlated pathway was the amino sugar and nucleotide sugar metabolism pathway. The metabolomics model captured the most variance in PGR expression compared to the other data types. The role of G. vaginalis in bacterial vaginosis has been well-established, but our models suggest it could play a role in other progesterone-linked gynecological diseases. The HIF-1 signaling pathway regulates homeostasis under various oxygen conditions. This pathway was positively correlated with PGR expression as well as with glycolysis and gluconeogenesis pathways. Citrulline, a metabolite that is negatively correlated with PGR expression, was related in the network between G. vaginalis and amino sugar and nucleotide metabolism. The interplay between these variables suggests that oxygen metabolism may be indicative of PGR expression. We show that vaginal microbiome factors have significant statistical relationships with PGR expression. Clinical phenotypes did not significantly alter the association between the explanatory variables and PGR expression. Future experimental work is needed to validate the results of these models and support their use as predictive tools to understand the role of the vaginal microbiome.






Acknowledgements (Optional): :

References (Optional): :