Dillard Lillian R, Glass Emma M, Kolling Glynis L, Thomas-White Krystal, Wever Fiorella, Markowitz Robert, Lyttle David, Papin Jason A
Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
Nat Commun. 2025 May 22;16(1):4768. doi: 10.1038/s41467-025-59965-y.
Bacterial vaginosis (BV) is the most prevalent vaginal condition among reproductive-age women presenting with vaginal complaints. Despite its significant impact on women's health, limited knowledge exists regarding the microbial community composition and metabolic interactions associated with BV. In this study, we analyze metagenomic data obtained from human vaginal swabs to generate in silico predictions of BV-associated bacterial metabolic interactions via genome-scale metabolic network reconstructions (GENREs). While most efforts to characterize symptomatic BV (and thus guide therapeutic intervention by identifying responders and non-responders to treatment) are based on genomic profiling, our in silico simulations reveal functional metabolic relatedness between species as quite distinct from genetic relatedness. We grow several of the most common co-occurring bacteria (Prevotella amnii, Prevotella buccalis, Hoylesella timonensis, Lactobacillus iners, Fannyhessea vaginae, and Aerrococcus christenssii) on the spent media of Gardnerella species and perform metabolomics to identify potential mechanisms of metabolic interaction. Through these analyses, we identify BV-associated bacteria that produce caffeate, a compound implicated in estrogen receptor binding, when grown in the spent media of other BV-associated bacteria. These findings underscore the complex and diverse nature of BV-associated bacterial community structures and several of these mechanisms are of potential significance in understanding host-microbiome relationships.
细菌性阴道病（BV）是有阴道不适症状的育龄妇女中最常见的阴道疾病。尽管它对女性健康有重大影响，但关于与BV相关的微生物群落组成和代谢相互作用的知识却很有限。在本研究中，我们分析了从人类阴道拭子获得的宏基因组数据，通过基因组规模的代谢网络重建（GENREs）对与BV相关的细菌代谢相互作用进行计算机模拟预测。虽然大多数表征有症状BV的努力（从而通过识别治疗的反应者和无反应者来指导治疗干预）是基于基因组分析，但我们的计算机模拟显示，物种之间的功能代谢相关性与遗传相关性截然不同。我们在加德纳菌属的用过的培养基上培养了几种最常见的共生细菌（羊普雷沃菌、颊普雷沃菌、蒂莫内斯霍伊尔斯菌、惰性乳杆菌、阴道芬尼希菌和克里斯滕西气球菌），并进行代谢组学分析以确定代谢相互作用的潜在机制。通过这些分析，我们发现当在其他与BV相关的细菌的用过的培养基中生长时，与BV相关的细菌会产生咖啡酸，这是一种与雌激素受体结合有关的化合物。这些发现强调了与BV相关的细菌群落结构的复杂和多样性，其中一些机制在理解宿主-微生物组关系方面具有潜在意义。
