# Leveraging Natural Genetic Diversity and Systems Genetics to Elucidate the Complex Hierarchy of Gene Regulation Underlying Ground State Pluripotency, Cell Fate Decisions and Tissue Homeostasis

> **NIH NIH R35** · JACKSON LABORATORY · 2020 · $401,606

## Abstract

PROJECT SUMMARY
The Precision Medicine Initiative aims to leverage population-scale genome sequencing data to tailor treatment
strategies to each individual's specific disease etiology and genetic background. However, common disease is
increasingly understood to be both highly polygenic and pleiotropic, and many adult onset diseases likely stem
at least in part from insults to cell differentiation during early embryogenesis. This complexity presents a steep
challenge for achieving the goal of precision medicine, and points to the need for animal and cell models to
fully dissect the molecular hierarchy and temporal dynamics linking genetic lesions to proximal effects on gene
regulation and cell decisions, and to distal effects on disease. My research program takes advantage of
powerful mouse mapping populations – the Diversity Outbred (DO) and Collaborative Cross (CC) – and
embryonic stem cell lines derived from these populations, and integrates multi-scale genomics and advanced
statistical approaches to decode how segregating genetic variation perturbs gene regulatory networks and
influences ground state pluripotency, cell differentiation trajectories, and adult organ function. My published
studies have yielded important insights into post-transcriptional regulation of the liver proteome. In Project 1 of
this proposal, I will build on these previous and ongoing efforts to define the consequences of genetic variation
on quantitative measures of protein translation and phosphorylation in the liver. This multidimensional genomic
analysis will provide an unprecedented view of how genetic variation affects the molecular hierarchy of
transcriptional and post-transcriptional mechanisms that regulate protein abundance and function. In Project 2,
I will apply a similar systems genetic approach to characterize the genetic determinants and transcriptional
dynamics underlying ground state pluripotency and differentiation potential in genetically diverse mouse
embryonic stem cell (mESC) lines. This new research focus for my laboratory stems from an internal multi-
investigator collaboration and successful pilot project, and has already revealed how segregating genetic
variation influences chromatin accessibility, transcript abundance, and maintenance of the ground state.
Project 2 will extend this molecular characterization to include quantitative proteomics and temporal single-cell
transcriptomics, and will integrate statistical modeling tools to infer the molecular causal chain that links genetic
variation to the fate decisions of individual cells. Together, the proposed projects will yield important insights
into post-transcriptional regulation of the proteome, tissue homeostasis, and maintenance of ground state
pluripotency and differentiation potential, and the influence of segregating natural genetic variation on the
complex molecular hierarchy governing these processes. Future research will seek to further link this detailed
molecular characterization ...

## Key facts

- **NIH application ID:** 9994326
- **Project number:** 5R35GM133495-02
- **Recipient organization:** JACKSON LABORATORY
- **Principal Investigator:** Steven Carmen Munger
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $401,606
- **Award type:** 5
- **Project period:** 2019-08-12 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994326

## Citation

> US National Institutes of Health, RePORTER application 9994326, Leveraging Natural Genetic Diversity and Systems Genetics to Elucidate the Complex Hierarchy of Gene Regulation Underlying Ground State Pluripotency, Cell Fate Decisions and Tissue Homeostasis (5R35GM133495-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9994326. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*