Quantitative and computational characterization of oxytocin receptor signaling

项目摘要 在美国，400万分娩的妇女中大约有一半使用了催产素 每年.对于提供者来说，一个重大挑战是引产或增强分娩所需的催产素剂量 变化高达20倍，他们没有办法预测如何，甚至是否，一个女人会回应给定的 次给药结束这种可预测性的缺乏引起了重要的安全问题，并成为催产素与 产妇不良事件和新生儿结局。因此，有必要建立一种预测催产素的方法 反应性，从而个性化给药方案。该提案迈出了解决 这需要通过测试中心假设，即子宫（子宫肌层）平滑的催产素反应性 肌肉细胞（MSMCs）可以通过催产素受体（OXTR）基因变体来预测。这种变体很常见; 外显子组聚集联盟在OXTR中鉴定了132个错义单核苷酸变体（mSNV）， 通过突变分析软件预测其约50%对OXTR功能有害。我们的假设是 两项研究证实了罕见的mSNV和常见的非编码单核苷酸多态性 OXTR中与催产素剂量需求相关的SNP。此外，几个OXTR编码和 非编码变异与不良的生殖结果有关，包括早产和长期 劳动时间。尽管这些研究提供了OXTR变异与临床重要的 表型，潜在的机制是未知的。这种知识的缺乏阻碍了我们翻译的能力 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， 具体目的：1）确定OXTR mSNV影响催产素信号传导的机制，2）确定 OXTR非编码SNP对MSMCs中OXTR mRNA和蛋白表达的影响，以及3）发育和 测试计算模型以预测OXTR变体对催产素信号传导功效的影响。工作 这里提出的将根据一个多PI计划，汇集莎拉英格兰博士，谁拥有专业知识， 生殖和子宫肌层平滑肌，和公主Imoukhuede博士，谁使用定量和 计算方法来定义疾病的细胞和分子基础，并具有特定的 受体定量分析的专业知识。这些目标的成功实现将提供重要的 关于OXTR变体对催产素反应性的影响的信息。

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.