# Quantitative and computational characterization of oxytocin receptor signaling

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2022 · $484,629

## Abstract

PROJECT SUMMARY
Oxytocin is administered to approximately one-half of the four million women who give birth in the United States
each year. A significant challenge for providers is that the oxytocin dose required to induce or augment labor
varies by up to 20-fold, and they have no way to predict how, or even whether, a woman will respond to a given
dose. This lack of predictability raises important safety concerns and underlies oxytocin's association with
adverse maternal events and neonatal outcomes. Thus, it is essential to develop a method to predict oxytocin
responsiveness and thereby personalize the dosing regimens. This proposal takes the first step in addressing
this need by testing the central hypothesis that the oxytocin responsiveness of uterine (myometrial) smooth
muscle cells (MSMCs) can be predicted by oxytocin receptor (OXTR) gene variants. Such variants are common;
the Exome Aggregation Consortium identified 132 missense single nucleotide variants (mSNVs) in OXTR, of
which ~50% are predicted by mutation analysis software to be deleterious to OXTR function. Our hypothesis is
supported by two studies identifying rare mSNVs and common noncoding single nucleotide polymorphisms
(SNPs) in OXTR that are associated with oxytocin dose requirement. Additionally, several OXTR coding and
noncoding variants have been implicated in adverse reproductive outcomes including preterm birth and long
labor duration. Although these studies provide evidence that OXTR variants associate with clinically important
phenotypes, the underlying mechanisms are unknown. This lack of knowledge hampers our ability to translate
OXTR genetics to personalized labor management approaches. To fill this gap, we propose to determine the
effects of mSNVs and common SNPs on OXTR expression and function in MSMCs by pursuing the following
Specific Aims: 1) Determinw the mechanisms by which OXTR mSNVs affect oxytocin signaling, 2) Determine
the effect of OXTR noncoding SNPs on OXTR mRNA and protein expression in MSMCs, and 3) Developing and
test a computational model to predict the effect of OXTR variants on oxytocin signaling efficacy. The work
proposed here will be directed under a multi-PI plan bringing together Dr. Sarah England, who has expertise in
reproduction and myometrial smooth muscle, and Dr. Princess Imoukhuede, who uses quantitative and
computational approaches to define the cellular and molecular underpinnings of disease and has specific
expertise in quantitative analysis of receptors. Successful completion of these aims will provide important
information regarding the influence of OXTR variants on responsiveness to oxytocin.

## Key facts

- **NIH application ID:** 10428510
- **Project number:** 5R01HD096737-04
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Sarah K. England
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $484,629
- **Award type:** 5
- **Project period:** 2019-08-19 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10428510

## Citation

> US National Institutes of Health, RePORTER application 10428510, Quantitative and computational characterization of oxytocin receptor signaling (5R01HD096737-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10428510. Licensed CC0.

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