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

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.