Perrotta Allison R, Borrelli Giuliano M, Martins Carlo O, Kallas Esper G, Sanabani Sabri S, Griffith Linda G, Alm Eric J, Abrao Mauricio S
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Endometriosis Section, Gynecologic Division. Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, São Paulo, Brazil.
Reprod Sci. 2020 Apr;27(4):1064-1073. doi: 10.1007/s43032-019-00113-5. Epub 2020 Jan 6.
Endometriosis remains a challenge to understand and to diagnose. This is an observational cross-sectional pilot study to characterize the gut and vaginal microbiome profiles among endometriosis patients and control subjects without the disease and to explore their potential use as a less-invasive diagnostic tool for endometriosis. Overall, 59 women were included, n = 35 with endometriosis and n = 24 controls. Rectal and vaginal samples were collected in two different periods of the menstrual cycle from all subjects. Gut and vaginal microbiomes from patients with different rASRM (revised American Society for Reproductive Medicine) endometriosis stages and controls were analyzed. Illumina sequencing libraries were constructed using a two-step 16S rRNA gene PCR amplicon approach. Correlations of 16S rRNA gene amplicon data with clinical metadata were conducted using a random forest-based machine-learning classification analysis. Distribution of vaginal CSTs (community state types) significantly differed between follicular and menstrual phases of the menstrual cycle (p = 0.021, Fisher's exact test). Vaginal and rectal microbiome profiles and their association to severity of endometriosis (according to rASRM stages) were evaluated. Classification models built with machine-learning methods on the microbiota composition during follicular and menstrual phases of the cycle were built, and it was possible to accurately predict rASRM stages 1-2 verses rASRM stages 3-4 endometriosis. The feature contributing the most to this prediction was an OTU (operational taxonomic unit) from the genus Anaerococcus. Gut and vaginal microbiomes of women with endometriosis have been investigated. Our findings suggest for the first time that vaginal microbiome may predict stage of disease when endometriosis is present.
子宫内膜异位症在理解和诊断方面仍然是一个挑战。这是一项观察性横断面试点研究，旨在描述子宫内膜异位症患者和无该疾病的对照受试者的肠道和阴道微生物群特征，并探索将其作为子宫内膜异位症侵入性较小的诊断工具的潜在用途。总体而言，共纳入59名女性，其中n = 35名患有子宫内膜异位症，n = 24名作为对照。在月经周期的两个不同阶段收集了所有受试者的直肠和阴道样本。分析了不同rASRM（美国生殖医学学会修订版）子宫内膜异位症阶段患者和对照的肠道和阴道微生物群。使用两步16S rRNA基因PCR扩增子方法构建了Illumina测序文库。使用基于随机森林的机器学习分类分析对16S rRNA基因扩增子数据与临床元数据进行相关性分析。阴道CSTs（群落状态类型）在月经周期的卵泡期和月经期之间的分布存在显著差异（p = 0.021，Fisher精确检验）。评估了阴道和直肠微生物群特征及其与子宫内膜异位症严重程度（根据rASRM阶段）的关联。构建了基于机器学习方法的周期卵泡期和月经期微生物群组成的分类模型，并且能够准确预测rASRM 1-2期与rASRM 3-4期的子宫内膜异位症。对这一预测贡献最大的特征是来自厌氧球菌属的一个OTU（操作分类单元）。对患有子宫内膜异位症女性的肠道和阴道微生物群进行了研究。我们的研究结果首次表明，阴道微生物群可能在存在子宫内膜异位症时预测疾病阶段。
