DNA Methylation-Dependent Restriction of Tyrosine Hydroxylase Contributes to Pancreatic ß-Cell Heterogeneity.

The molecular and functional heterogeneity of pancreatic ß-cells is well recognized, but the underlying mechanisms remain unclear. Pancreatic islets harbor a subset of ß-cells that co-express tyrosine hydroxylase (TH), an enzyme involved in synthesis of catecholamines that repress insulin secretion. Restriction of the TH+ ß-cells within islets is essential for appropriate function in mice, such that a higher proportion of these cells corresponds to reduced insulin secretion. Here, we use these cells as a model to dissect the developmental control of ß-cell heterogeneity. We define the specific molecular and metabolic characteristics of TH+ ß-cells and show differences in their developmental restriction in mice and humans. We show that TH expression in ß-cells is restricted by DNA methylation during ß-cell differentiation. Ablation of de novo DNA methyltransferase Dnmt3a in the embryonic progenitors results in a dramatic increase in the proportion of TH+ ß-cells, whereas ß-cell-specific ablation of Dnmt3a does not. We demonstrate that maintenance of Th promoter methylation is essential for its continued restriction in postnatal ß-cells. Loss of Th promoter methylation in response to chronic overnutrition increases the number of TH+ ß-cells, corresponding to impaired ß-cell function. These results reveal a regulatory role of DNA methylation in determining ß-cell heterogeneity.

Systematic Bayesian posterior analysis guided by Kullback-Leibler divergence facilitates hypothesis formation.

Bayesian inference produces a posterior distribution for the parameters of a mathematical model that can be used to guide the formation of hypotheses; specifically, the posterior may be searched for evidence of alternative model hypotheses, which serves as a starting point for hypothesis formation and model refinement. Previous approaches to search for this evidence are largely qualitative and unsystematic; further, demonstrations of these approaches typically stop at hypothesis formation, leaving the questions they raise unanswered. Here, we introduce a Kullback-Leibler (KL) divergence-based ranking to expedite Bayesian hypothesis formation and investigate the hypotheses it generates, ultimately generating novel, biologically significant insights. Our approach uses KL divergence to rank parameters by how much information they gain from experimental data. Subsequently, rather than searching all model parameters at random, we use this ranking to prioritize examining the posteriors of the parameters that gained the most information from the data for evidence of alternative model hypotheses. We test our approach with two examples, which showcase the ability of our approach to systematically uncover different types of alternative hypothesis evidence. First, we test our KL divergence ranking on an established example of Bayesian hypothesis formation. Our top-ranked parameter matches the one previously identified to produce alternative hypotheses. In the second example, we apply our ranking in a novel study of a computational model of prolactin-induced JAK2-STAT5 signaling, a pathway that mediates beta cell proliferation. Within the top 3 ranked parameters (out of 33), we find a bimodal posterior revealing two possible ranges for the prolactin receptor degradation rate. We go on to refine the model, incorporating new data and determining which degradation rate is most plausible. Overall, while the effectiveness of our approach depends on having a properly formulated prior and on the form of the posterior distribution, we demonstrate that our approach offers a novel and generalizable quantitative framework for Bayesian hypothesis formation and use it to produce a novel, biologically-significant insight into beta cell signaling.

Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells.

Patients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin (PRL). Leveraging this proliferative response to PRL may be a strategy to restore endogenous insulin production for patients with diabetes. To investigate this potential treatment, we previously developed a computational model to represent the PRL-mediated JAK-STAT signaling pathway in pancreatic beta cells. Here, we applied the model to identify the importance of particular signaling proteins in shaping the response of a population of beta cells. We simulated a population of 10 000 heterogeneous cells with varying initial protein concentrations responding to PRL stimulation. We used partial least squares regression to analyze the significance and role of each of the varied protein concentrations in producing the response of the cell. Our regression models predict that the concentrations of the cytosolic and nuclear phosphatases strongly influence the response of the cell. The model also predicts that increasing PRL receptor strengthens negative feedback mediated by the inhibitor suppressor of cytokine signaling. These findings reveal biological targets that can potentially be used to modulate the proliferation of pancreatic beta cells to enhance insulin secretion and beta cell regeneration in the context of diabetes.

Dynamic Regulation of JAK-STAT Signaling Through the Prolactin Receptor Predicted by Computational Modeling.

INTRODUCTION: The expansion of insulin-producing beta cells during pregnancy is critical to maintain glucose homeostasis in the face of increasing insulin resistance. Prolactin receptor (PRLR) signaling is one of the primary mediators of beta cell expansion during pregnancy, and loss of PRLR signaling results in reduced beta cell mass and gestational diabetes. Harnessing the proliferative potential of prolactin signaling to expand beta cell mass outside of the context of pregnancy requires quantitative understanding of the signaling at the molecular level. METHODS: A mechanistic computational model was constructed to describe prolactin-mediated JAK-STAT signaling in pancreatic beta cells. The effect of different regulatory modules was explored through ensemble modeling. A Bayesian approach for likelihood estimation was used to fit the model to experimental data from the literature. RESULTS: Including receptor upregulation, with either inhibition by SOCS proteins, receptor internalization, or both, allowed the model to match experimental results for INS-1 cells treated with prolactin. The model predicts that faster dimerization and nuclear import rates of STAT5B compared to STAT5A can explain the higher STAT5B nuclear translocation. The model was used to predict the dose response of STAT5B translocation in rat primary beta cells treated with prolactin and reveal possible strategies to modulate STAT5 signaling. CONCLUSIONS: JAK-STAT signaling must be tightly controlled to obtain the biphasic response in STAT5 activation seen experimentally. Receptor up-regulation, combined with SOCS inhibition, receptor internalization, or both is required to match experimental data. Modulating reactions upstream in the signaling can enhance STAT5 activation to increase beta cell survival.

Neuropeptide Y expression marks partially differentiated ß cells in mice and humans.

ß Cells are formed in embryonic life by differentiation of endocrine progenitors and expand by replication during neonatal life, followed by transition into functional maturity. In this study, we addressed the potential contribution of neuropeptide Y (NPY) in pancreatic ß cell development and maturation. We show that NPY expression is restricted from the progenitor populations during pancreatic development and marks functionally immature ß cells in fetal and neonatal mice and humans. NPY expression is epigenetically downregulated in ß cells upon maturation. Neonatal ß cells that express NPY are more replicative, and knockdown of NPY expression in neonatal mouse islets reduces replication and enhances insulin secretion in response to high glucose. These data show that NPY expression likely promotes replication and contributes to impaired glucose responsiveness in neonatal ß cells. We show that NPY expression reemerges in ß cells in mice fed with high-fat diet as well as in diabetes in mice and humans, establishing a potential new mechanism to explain impaired ß cell maturity in diabetes. Together, these studies highlight the contribution of NPY in the regulation of ß cell differentiation and have potential applications for ß cell supplementation for diabetes therapy.