Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment

Abstract

Emerging evidence suggests that host-microbe interaction in the cervicovaginal microenvironment contributes to cervical carcinogenesis, yet dissecting these complex interactions is challenging. Herein, we performed an integrated analysis of multiple “omics” datasets to develop predictive models of the cervicovaginal microenvironment and identify characteristic features of vaginal microbiome, genital inflammation and disease status. Microbiomes, vaginal pH, immunoproteomes and metabolomes were measured in cervicovaginal specimens collected from a cohort (n = 72) of Arizonan women with or without cervical neoplasm. Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive models. Our integrated analyses revealed that immune and cancer biomarker concentrations were reliably predicted by Random Forest regressors trained on microbial and metabolic features, suggesting close correspondence between the vaginal microbiome, metabolome, and genital inflammation involved in cervical carcinogenesis. Furthermore, we show that features of the microbiome and host microenvironment, including metabolites, microbial taxa, and immune biomarkers are predictive of genital inflammation status, but only weakly to moderately predictive of cervical neoplastic disease status. Different feature classes were important for prediction of different phenotypes. Lipids (e.g. sphingolipids and long-chain unsaturated fatty acids) were strong predictors of genital inflammation, whereas predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism. Finally, we identified key immune biomarkers associated with the vaginal microbiota composition and vaginal pH (MIF), as well as genital inflammation (IL-6, IL-10, MIP-1α).

Author summary

This work was undertaken to improve our understanding of interactions between microbes, metabolites and the host in the cervicovaginal microenvironment. We employed a multi-omics approach to investigate relationships between microbiome, vaginal pH, metabolome, immunoproteome in women with and without cervical neoplasm identifying a tight link to abundance of Lactobacillus spp. We established predictive models and identified key signatures related to vaginal microbiota, vaginal pH and genital inflammation. Integration of multiple different “omics” data types resulted in only modest increases in prediction accuracy compared to models trained on a single data type. Since the most predictive data type was not known a priori, this multi-omics approach yielded insights that would not have been possible with any single data type. Metabolomics data was predictive of different features of the cervicovaginal microenvironment and host response but integrating multi-omics data is likely to be essential for realizing the advances promised by microbiome research.