Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment
AffiliationDepartment of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona
Department of Obstetrics and Gynecology, College of Medicine-Phoenix
MetadataShow full item record
PublisherPublic Library of Science
CitationBokulich, N. A., Laniewski, P., Adamov, A., Chase, D. M., Gregory Caporaso, J., & Herbst-Kralovetz, M. M. (2022). Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment. PLoS Computational Biology.
JournalPLoS Computational Biology
RightsCopyright © 2022 Bokulich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
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AbstractEmerging 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). © 2022 Bokulich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Except where otherwise noted, this item's license is described as Copyright © 2022 Bokulich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
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