Prolactin receptor localization and dynamics: Insights from quantitative imaging and mathematical modeling

Abstract

Signal transduction through the prolactin receptor (PRLR) is crucial in pancreatic {beta}-cell pro-liferation, impacting pancreatic homeostasis. PRLR-induced JAK/STAT signaling is dynamic, involving changes in spatial organization of signaling molecules. Thus, the spatial organization of PRLR could have strong implications on signaling output. Internalization has been shown and modeled in other signaling pathways but has not been considered in a mathematical model of PRLR signaling. Here, we use live-cell fluorescence imaging, reconstitution approaches, and fluorescence correlation spectroscopy (FCS) to inform a mathematical model of PRLR signaling. Internal PRLR localization is observed in primary pancreatic tissue and in an engineered PRLR expression system. Our imaging data indicate the presence of intracellular and plasma membrane-bound receptor populations. We use FCS to resolve the membrane-bound PRLR population. Based on our data, we include internalization dynamics within an ordinary differential equation (ODE) model of PRLR signaling. We employ the model to explore how the spatial heterogeneity of PRLR affects downstream signaling. We show that the model is more sensitive to PRLR trafficking rates and ability to promote signaling than to its initial spatial distribution. Our data underscore the versatility of a modeling-imaging framework to quantitatively understand signal transduction in and beyond {beta}-cells. Significance StatementO_LIProlactin receptor (PRLR) signal transduction impacts the growth and survival of insulin-secreting cells, making this pathway a target for building our understanding of pancreatic homeostasis and exploring potential diabetes therapeutics. C_LIO_LILive fluorescence imaging techniques applied within an engineered PRLR expression platform indicate PRLR localization patterns consistent with primary pancreatic tissue and the presence of two spatially distinct PRLR populations. These observations inform a predictive mathematical model of PRLR signaling. C_LIO_LIIntegrating experimental data tailored to computational approaches shapes our understanding of complex, multiscale systems such as signal transduction. A generalizable modeling-imaging framework enables the study of molecular dynamics beyond {beta}-cells. C_LI