A large scale investigation of the vaginal metagenome and metabolome and their role in spontaneous preterm birth

摘要 自发性早产（sPTB）是新生儿发病率和死亡率的主要原因。尽管进行了广泛 研究和医疗努力，我们既缺乏手段，以确定它的早期和治疗方案，以防止它 当识别。阴道微生物组被认为在很大一部分sPTB中具有因果作用， 多项研究表明，即使在怀孕早期进行测量，微生物组也与 不良妊娠结局。尽管有这一巨大的希望，但基于微生物组的sPTB预测指标仍然存在 尚未开发，我们缺乏对其参与的机制的详细了解， sPTB。我们将其归因于当前研究中的小样本量; sPTB作为二进制的集合定义 变量，忽略其异质性的介绍和病因;并侧重于微生物分类学，而不是 更注重功能性和机械性的数据。我们建议利用世界上最大的， 迄今为止收集的最佳表型妊娠队列，nuMoM 2b研究。这是一个全国性的多中心试验 该研究在三个时间点收集了来自不同地理和人口特征的女性的阴道拭子 背景这项研究分析了近500名患有sPTB的女性，几乎比任何一个数量级都多。 微生物学研究前我们组建了一个跨学科的团队， nuMoM 2b研究，在编译，分析和分析大规模微生物组方面拥有丰富的经验， 代谢组学队列，以及微生物组分析及其临床应用的创新记录。我们 将进行深度宏基因组测序，纵向样本测序，以及匹配的代谢组学 分析了来自近1,500名孕妇的近6,000份样本，产生了迄今为止最大的 数据集至今。该数据集将推动微生物群落的发展，推动该领域的发现，并使 美国和其他研究人员研究宿主-微生物组相互作用，一般情况下，特别是在 SPTB，前所未有的深度。我们提出的数据分析提供了一个细致入微的高分辨率视图， 临床和生物学数据。我们将研究sPTB，同时考虑其各种临床表现， 病因和重要的母体协变量。我们将从多个角度研究阴道生态系统， 研究微生物的基因组适应性，生态系统中代谢物的水平，以及 微生物组概况。我们将设计一个聚合计算框架，基于最先进的 方法和算法，其基于微生物组相关特征提供sPTB的早期预测。我们将 采用参数和非参数方法的组合，以获得主机的机械见解， 微生物组相互作用可能导致sPTB和其他不良妊娠结局。总之， 这项研究将改变我们对不良妊娠结局中阴道微生物组的理解。 以及宿主与微生物组之间的相互作用。

ABSTRACT Spontaneous preterm birth (sPTB) is a leading cause of neonatal morbidity and mortality. Despite extensive research and medical efforts, we lack both the means to identify it early and the therapeutic options to prevent it when identified. The vaginal microbiome is believed to have a causal role in a substantial fraction of sPTBs, and multiple studies have shown that even when measured early in pregnancy, the microbiome is associated with adverse pregnancy outcomes. Despite this great promise, robust microbiome-based predictors for sPTB have not been developed yet, and we lack a detailed understanding of the mechanisms underlying its involvement in sPTB. We attribute this to a small sample size in current studies; the aggregative definition of sPTB as a binary variable, ignoring its heterogeneous presentations and etiologies; and a focus on microbial taxonomy instead of data that is more functionally and mechanistically oriented. We propose to capitalize on one of the largest and best-phenotyped pregnancy cohorts collected to date, the nuMoM2b study. This was a national, multi-center trial that collected vaginal swabs at three time-points from women with diverse geographic and demographic backgrounds. This study profiled nearly 500 women with sPTB, almost an order of magnitude more than any previous microbiome study. We have compiled an interdisciplinary team with intimate knowledge of the nuMoM2b study, ample experience in compiling, profiling, and analyzing large-scale microbiome and metabolomic cohorts, and a track record of innovation in microbiome analysis and its clinical applications. We will perform deep metagenomic sequencing, longitudinal sample sequencing, and matched metabolomic analysis of a total of almost 6,000 samples from nearly 1,500 pregnant women, generating the largest such dataset to date. This dataset would advance the microbiome community, drive discovery in the field, and enable us and other researchers to study host-microbiome interactions, in general and specifically in the context of sPTB, with an unprecedented depth. Our proposed data analysis offers a nuanced and high-resolution view of both clinical and biological data. We will study sPTB while accounting for its various clinical presentations, etiologies, and important maternal covariates. We will study the vaginal ecosystem from multiple angles, investigating microbial genomic adaptations, levels of metabolites in the ecosystem, and dynamic changes to the microbiome profile. We will devise an aggregative computational framework, based on state-of-the-art methods and algorithms, that provides early prediction of sPTB based on microbiome-related features. We will employ a combination of parametric and non-parametric methods to obtain mechanistic insights into host- microbiome interactions that potentially contribute to sPTB and other adverse pregnancy outcomes. Altogether, this study will be transformative to our understanding of the vaginal microbiome in adverse pregnancy outcomes and of host-microbiome interactions in general.