基于生殖道微生物生态高通量测序及元数据预测不良妊娠结局研究

Prediction of host diseases using human microbiome data is a novel field, within this, the vaginal microbiome has some priorities, in terms of clear and easy to collect disease data, and to predict the adverse pregnancy outcomes. In addition, tons of results suggest the relationship between the vaginal microbiota dysbiosis with adverse pregnancy outcomes. In our previous experiment, we developed a cohort of 340 Chinese pregnant women and determined their vaginal microbiome with Illumina sequencing. While the community state types within individuals remained constant throughout pregnancy, random forest analysis showed that, in older parturient women, the existence of certain species was associated with a higher risk of the occurrence of premature rupture of membranes. In the present study, we plan to enroll 5000 volunteers and determine their vaignal microbiome using high throughput sequencing. The community data and the meta-data of hosts will be used to analyze the relationship between vaginal microbiome and adverse pregnancy outcomes, especially preterm delivery, and build a prediction model with machine learning methods including regression and classification. To validate the sensitivity and specificy of the model, 1000 more samples will be collected and analyzed. This project will provide new data to reveal the relationship between vaginal microbiome and adverse pregnancy outcomes. The model will be useful for future disease prediction and developing new diagnosis kits.

利用人体微生物生态数据预测宿主疾病是新兴研究方向。其中，利用孕妇生殖道微生物生态预测不良妊娠结局，其终点事件明确，观察周期短，疾病影响范围广，并且菌群与疾病发生具有流行病学基础。课题组前期建立340人前瞻性队列，高通量测序结果显示孕妇阴道菌群在孕期相对稳定，而随机森林法分析发现高年龄组别孕妇阴道菌群与胎膜早破等不良妊娠结局相关。本课题拟建立5000例前瞻性队列，收集首次孕检阴道拭子样品，扩增16S rRNA V4区，经Illumina测序分析菌群，通过回归分析和分类模型等数据挖掘技术，分析其阴道菌群与早产等不良妊娠结局的关联，建立预测模型；进一步，采集1000例新样品，依据模型预测其不良结局发生概率，并根据临床结局检验上述模型的敏感度与特异度。项目为揭示阴道菌群与不良妊娠结局关联提供新的数据支持；拟建立的模型有望为未来运用阴道微生态数据预测早产等不良妊娠结局、开发新的早期检测方法奠定基础。

研究按照课题计划书顺利执行，主要完成并取得进展如下：（1）完成了孕妇大规模队列招募及孕期生殖道和肠道菌群分析，发现孕期肠道菌群具有诊断子痫前期这一重大不良妊娠结局的潜能，提出肠道菌群紊乱是子痫前期的潜在致病因子，论文发表于Gut杂志（IF：17.943）；（2）发现阴道菌群菌型与临床体征（WBC、尿痛腰痛、宫颈肥大等）存在明显相关，提示阴道菌群可进一步作为评价临床生殖道疾病的潜在指标，文章发表于Infection, Genetics and Evolution杂志(IF：2.611)；（3）发现肠道菌群疾病诊断模型存在强烈的地域依赖性，提出菌群诊断模型研究新方法，论文发表于Nature Medicine（IF：30.641, 封面论文）；（4）发现菌群、生活方式及疾病之间的复杂关联，论文发表于Microbiome（IF：10.465）；（5）拓展菌群分析新技术新方法，论文发表于Frontiers in Microbiology（IF：4.259）；Radiotherapy and Oncology（IF：5.252）；EBioMedicine（IF：6.68）；（6）对孕妇队列研究，我们还发现:a) Sialidese作为一个评估细菌性阴道病的指标，除了既往已知的与阴道加德纳菌等微生物代谢相关之外，亦与不同的阴道CSTs相关，且潜在与不良的妊娠结局相关；b）高BMI人群可能存在更高的阴道多样性，且与部分疾病风险相关；c) GDM、PIH等疾病人群与正常孕妇的阴道菌群构成存在差异。上述论文在进一步整理投稿中；（7）申请人获得2019年度国家自然科学基金杰出青年基金资助，研究领域为“人体微生物组诊断方法学”;（8）开展多项学术活动，多次邀请国内外知名学者来院进行学术交流。同时课题组多次举办微生态分析方法培训班，并于2017年面向全国举办微生物组学研究培训班“微生物（组）精准诊断前沿基础与临床暨QIIME生物信息培训班”（项目编号：2017-11-00-243 (国)，大力推广微生物组研究相关技术的应用。