Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment.
Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment.
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多媒体数据整合揭示了代谢组是宫颈阴道微环境的主要预测因子。
DOI：
10.1371/journal.pcbi.1009876
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
Herbst-Kralovetz MM
中科院分区：
文献类型：
--
作者：
Bokulich NA;Łaniewski P;Adamov A;Chase DM;Caporaso JG;Herbst-Kralovetz MM
新出现的证据表明，宫颈阴道微环境中的宿主-微生物相互作用有助于宫颈癌的发生，但解剖这些复杂的相互作用具有挑战性。在本文中，我们对多个“组学”数据集进行了综合分析，以开发宫颈阴道微环境的预测模型，并确定阴道微生物组、生殖器炎症和疾病状态的特征。微生物组，阴道pH值，免疫蛋白质组和代谢组的宫颈阴道标本收集自一个队列（n = 72）的亚利桑那州妇女或没有宫颈肿瘤。多组学整合方法，包括神经网络（mmvec）和随机森林监督学习，被用来探索潜在的相互作用和开发预测模型。我们的综合分析显示，免疫和癌症生物标志物浓度通过对微生物和代谢特征进行训练的随机森林回归器可靠地预测，这表明阴道微生物组，代谢组和生殖器炎症之间存在密切的对应关系。此外，我们表明，微生物组和宿主微环境的特征，包括代谢物，微生物分类群和免疫生物标志物，可预测生殖器炎症状态，但只能弱至中度预测宫颈肿瘤疾病状态。不同的特征类对于不同表型的预测是重要的。脂质（例如鞘脂和长链不饱和脂肪酸）是生殖器炎症的强预测因子，而阴道微生物群和阴道pH值的预测主要依赖于氨基酸代谢的改变。最后，我们确定了与阴道微生物群组成和阴道pH值（MIF）以及生殖器炎症（IL-6，IL-10，MIP-1α）相关的关键免疫生物标志物。这项工作是为了提高我们对宫颈阴道微环境中微生物，代谢物和宿主之间相互作用的理解。我们采用了多组学方法来研究微生物组，阴道pH值，代谢组，免疫蛋白质组之间的关系，在妇女和没有宫颈肿瘤确定一个紧密的联系，丰富的乳酸杆菌属。我们建立了预测模型，并确定了与阴道微生物群、阴道pH值和生殖器炎症相关的关键特征。与在单一数据类型上训练的模型相比，集成多种不同的“组学”数据类型仅导致预测准确性的适度提高。由于最具预测性的数据类型事先并不知道，这种多组学方法产生了任何单一数据类型都不可能实现的见解。代谢组学数据可以预测宫颈阴道微环境和宿主反应的不同特征，但整合多组学数据可能对于实现微生物组研究所承诺的进展至关重要。
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). 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.
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影响因子：
3.7
作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
34.3
作者：
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通讯作者：
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