Enhancing personalized insights into common obstetric disorders using longitudinal deep-phenotyping data

项目总结 产科疾病在全球范围内很常见，是五岁以下儿童死亡的主要原因 以及其他终生健康问题。尽管如此，我们对这个问题的了解还是有限的 驱动这些疾病的机制突出了一个尚未填补的研究缺口。在这里，我们合作 与Yoel Sadovsky博士一起编制了一个深度表型怀孕数据集，评估 提供纵向血液和尿液多组学数据的孕期妇女健康 与从200人(100人)收集的临床、调查、行为和环境数据配对 有不良后果的人)提供了对怀孕的全面看法。我们假设 数据驱动的系统生物学方法将定义正常的胎盘和妊娠 系统生物学和促进常见产科疾病机制的研究 疾病包括早产、胎儿生长受限和先兆子痫。首先，我们将 用胎盘评价常见产科疾病的分子网络差异 多组学(代谢组学、蛋白质组学和转录组学)数据与临床和 胎盘组织病理学数据收集自342人(213人患有常见产科疾病)。 我们将建立跨数据类型和结果的内部胎盘网络，以提高我们的 了解胎盘生物学。我们还将确定分子网络中的差异 与不同产科疾病相关的结构和关键转录因子。此外, 我们将使用深度表型妊娠数据来评估分子网络动力学和 定义怀孕的主要过渡状态。我们还将使用它来识别中断 与常见产科疾病相关的分子网络。我们还将开发一种新的 在最早偏离的时间点识别个体中分析物异常值的方法 健康怀孕轨迹，在以下背景下建立精确医学方法的原型 怀孕了。最后，我们正在与Google Data Commons合作，构建一个开源的 专门针对围产期的知识图谱，将此建议中的数据分发给更广泛的 围产期研究社区。总而言之，这将产生并优先考虑以下假设 常见产科疾病的分子机制，将用于未来的研究 促进母婴健康的临床干预措施。最后，这项工作将为我提供 背景需要建立一条独立的研究路线。

PROJECT SUMMARY Obstetric disorders are common globally and a major driver for deaths of children under five as well as other lifelong health issues. Despite this we have a limited understanding of the mechanisms driving these disorders highlighting an unmet research gap. Here, we collaborate with Dr. Yoel Sadovsky to compile a deep-phenotyping pregnancy dataset that evaluates women’s health throughout pregnancy providing longitudinal blood and urine multiomics data paired with clinical, survey, behavioral, and environmental data collected from 200 people (100 people with adverse outcomes) providing a comprehensive view of pregnancy. We hypothesize that a data-driven systems biology approach will define normal placental and pregnancy systems biology and facilitate investigation of disease mechanisms in common obstetric disorders including preterm birth, fetal growth restriction and preeclampsia. First, we will evaluate molecular network differences in common obstetric disorders using placental multiomics (metabolomics, proteomics, and transcriptomics) data paired with clinical and placental histopathology data collected from 342 people (213 with common obstetric disorders). We will build inter-omic placental networks across datatypes and outcomes increasing our understanding of placental biology. We will also determine differences in molecular network structures and key transcription factors associated with distinct obstetric disorders. In addition, we will use the deep-phenotyping pregnancy data to evaluate molecular network dynamics and define major transition states of pregnancy. We will also use it to identify disruptions to molecular networks associated with common obstetric disorders. We will also develop a new approach to identify analyte outliers in individuals at the earliest time point of deviation from a healthy pregnancy trajectory, prototyping a precision medicine approach in the context of pregnancy. Finally, we are partnering with Google Data Commons to build an open-source perinatal-specific knowledge graph to distribute the data from this proposal to the broader perinatal research community. Altogether this will generate and prioritize hypotheses of the molecular mechanisms of common obstetric disorders, which will be used to develop future clinical interventions to promote maternal-fetal health. Finally, this work will provide me with the background needed to establish an independent line of research.