Quantitative and computational characterization of oxytocin receptor signaling

项目总结 在美国400万分娩的妇女中，大约有一半的人使用催产素 每年。提供者面临的一个重大挑战是，引产或增加分娩所需的催产素剂量 差异高达20倍，他们无法预测女性将如何反应，甚至是否会对给定的 剂量。这种缺乏可预测性引发了重要的安全问题，并奠定了催产素与 不良的母体事件和新生儿结局。因此，有必要开发一种预测催产素的方法。 反应速度快，从而使给药方案个性化。该提案迈出了解决问题的第一步 这需要通过检验中枢假说，即子宫(肌层)对催产素的反应是平稳的 肌肉细胞(MSMC)可以通过催产素受体(OXTR)基因变异来预测。这样的变种很常见； Exome Aggregation Consortium在OXTR中发现了132个错义单核苷酸变体(MSNV)， 突变分析软件预测其中50%的突变对OXTR功能有害。我们的假设是 由识别罕见mSNV和常见非编码单核苷酸多态的两项研究支持 (SNPs)与催产素剂量需求相关。此外，几种OXTR编码和 非编码变异与不良生殖结局有关，包括早产和长时间 劳动时间。尽管这些研究提供的证据表明，OXTR变异体与临床上重要的 表型，潜在的机制尚不清楚。这种知识的缺乏妨碍了我们的翻译能力 从OXTR遗传学到个性化的劳动管理方法。为了填补这一空白，我们建议确定 MSNV和常见SNPs对MSMCs中OXTR表达和功能的影响 具体目标：1)确定OXTRmSNV影响催产素信号转导的机制；2)确定 OXTR非编码区SNPs对MSMCs OXTRmRNA和蛋白表达的影响 测试一个计算模型，以预测OXTR变体对催产素信号有效性的影响。这项工作 在这里提出的建议将在一个多PI计划下进行指导，该计划汇集了Sarah England博士，她拥有 生殖和子宫肌层平滑肌，以及伊穆库德公主，他使用定量和 定义疾病的细胞和分子基础的计算方法，并具有特定的 在受体的定量分析方面的专业知识。成功完成这些目标将提供重要的 有关OXTR变异体对催产素反应的影响的信息。

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.