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.
