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

使用纵向深度表型数据增强对常见产科疾病的个性化见解

• 批准号: 10723841
• 负责人: SAMANTHA PIEKOS
• 金额: $ 14.28万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-10 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723841
• 关键词: Aging; Artificial Intelligence; Automobile Driving; Behavioral; Bioinformatics; Biological; Birth; Blood; Cessation of life; Child; Clinical; Clinical Data; Collaborations; Communities; Complex; Data; Data Commons; Data Science; Data Set; Developed Countries; Development; Developmental Biology; Discipline of obstetrics; Disease; Doctor of Philosophy; Early Intervention; Epidemiology; Event; Fetal Development; Fetal Growth Retardation; Fetal health; Future; Genes; Grant; Health; Histopathology; Individual; Intervention; Investigation; Knowledge; Learning; Life Style; Maternal and Child Health; Measurement; Mentors; Molecular; Multiomic Data; Normal Range; Outcome; Perinatal; Persons; Phenotype; Physicians; Physiological; Physiological Processes; Physiology; Placenta; Placental Biology; Pre-Eclampsia; Pregnancy; Premature Birth; Process; Proteomics; Regulation; Research; Resources; Running; Structure; Surveys; System; Systems Biology; Time; Tissues; Training; Urine; Visualization software; Women' s Health; Work; adverse outcome; adverse pregnancy outcome; cohort; data integration; differential expression; distributed data; healthy pregnancy; improved; insight; knowledge graph; metabolomics; molecular dynamics; multiple omics; novel strategies; open data; open source; phenotypic data; post-doctoral training; precision medicine; prototype; skills; temporal measurement; transcription factor; transcription regulatory network; transcriptomics

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.

项目摘要 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， 以及其他终身健康问题。尽管如此，我们对 驱动这些疾病的机制突出了未满足的研究差距。在这里，我们合作 与Yoel Sadovsky博士一起编制了一个深度表型妊娠数据集， 提供纵向血液和尿液多组学数据的妇女整个怀孕期间的健康 与从200人（100 有不良后果的人）提供怀孕的全面观点。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调查 疾病包括早产、胎儿生长受限和先兆子痫。一是 使用胎盘评估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例常见产科疾病）。 我们将建立跨数据库和结果的组间胎盘网络， 了解胎盘生物学。我们还将确定分子网络的差异 结构和关键转录因子与不同的产科疾病。此外，本发明还提供了一种方法， 我们将使用深度表型妊娠数据来评估分子网络动力学， 定义怀孕的主要过渡状态。我们还将使用它来确定中断， 与常见产科疾病相关的分子网络。我们还将开发新的 在最早的偏离时间点识别个体中分析物离群值的方法 健康的怀孕轨迹，在以下背景下建立精准医疗方法的原型 怀孕最后，我们正在与Google Data Commons合作， 围产期特定的知识图谱，将本提案中的数据分发给更广泛的 围产期研究社区。总之，这将产生和优先考虑的假设， 常见产科疾病的分子机制，这将用于开发未来 临床干预措施，以促进母婴健康。最后，这项工作将为我提供 建立独立研究线所需的背景。