Quantitative and computational characterization of oxytocin receptor function

About This Grant

PROJECT SUMMARY Approximately half of the four million women who give birth in the United States each year receive oxytocin to induce or augment labor. However, the effective oxytocin dose varies by up to 20-fold, and there is no way to predict individual response. This unpredictability raises safety concerns since oxytocin use is associated with adverse maternal and neonatal outcomes. One way to decrease morbidity associated with oxytocin is to be able to predict individual response and personalize dosing regimens. In order to do this, we need a better understanding of oxytocin receptor (OXTR) function and dynamics. In the previous funding period, we addressed this issue by constructing an initial computational model of oxytocin-OXTR binding using available binding affinity data for the OXTR. We validated this model by showing that it recapitulates the experimentally established oxytocin dose response for oxytocin-OXTR binding in both a heterologous cell line and in a myometrial cell line. However, we were unable to accurately expand our model beyond the initial oxytocin-OXTR complex formation due to a lack of OXTR- and cell-specific kinetic data. In this next phase, our objective is to fill this gap by conducting experimental studies on OXTR to quantitatively define key steps in trafficking of the OXTR in uterine smooth muscle (myometrial) cells, including membrane insertion, internalization, and recycling dynamics. We have substantial published and preliminary data giving us confidence in our ability to achieve our objective. First, we identified OXTR genetic variants that exhibit reduced trafficking and localization to the cell surface and an attenuated response to oxytocin. Second, our rigorous quantitative analyses revealed that only 10% of endogenous OXTR is localized on the cell surface in myometrial cells. Third, we found that, after four hours of oxytocin exposure in vitro, OXTR is degraded rather than recycled to the cell surface, thereby depleting the pool of OXTR available for activation. Finally, we identified several small molecules that act as pharmacologic chaperones to enhance OXTR cell surface localization. Given these data, our central hypothesis is that enhancing OXTR cell-surface localization can improve the efficacy of oxytocin. We propose to test our central hypothesis while conducting rigorous kinetic experiments to discern fundamental principles of OXTR action and develop data-driven, computational models to predict oxytocin responsiveness. We will pursue the following three specific aims: Aim 1: Determine the kinetics of OXTR surface localization and activation in cells, 2) determine the dynamics of OXTR desensitization and recycling in cells, and 3) determine OXTR responsiveness in vivo in mice and ex vivo in human tissues. The research proposal here will obtain experimentally obtained quantitative kinetic data on the OXTR. We will use this to build a computational model to predict OXTR response, which will allow us to better understand the effects of oxytocin signaling on myometrial contractility. Ultimately, this will move us to our ultimate goal of creating a computational model that can be used to improve clinical oxytocin use and obstetric outcomes.