Quantitative and computational characterization of oxytocin receptor signaling

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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.