Obese patients need therapies that improve glycemic control to prevent the deadly consequences of diabetes. Obesity places metabolic stress on pancreatic ?-cells, leading to ?-cell dysfunction, loss of insulin production, and hyperglycemia. Efforts to improve ?-cell function are hindered by ?-cell heterogeneity, where cells differ in proliferative potential, insulin secretion, and stress resistance. Understanding the genetic networks underlying these ?-cell subpopulations will lead to the identification of pathways that can be exploited to increase insulin secretion in diabetes. Single cell RNA sequencing allows for identification of gene expression signatures of subpopulations, but has limited capacity to reveal genetic networks due to low read coverage. Bulk islet RNA sequencing is highly powered to detected lowly expressed genes, but is confounded by cell population structure within the islet. Both strategies need to be integrated to gain in-depth understanding of how ?-cell subpopulations behave in health and diabetic obesity. The Lawson utilizes a novel mouse model to understand how of glycemic control can be improved in obesity. Fed a high fat diet until 20 weeks of age, SM/J mice display characteristics of diabetic-obesity, including elevated adiposity, hyperglycemia, glucose intolerance, and deficient insulin production. By 30 weeks of age, their hyperglycemia naturally resolves, characterized by normoglycemia, improved glucose tolerance, and increased insulin levels all while remaining obese. Over this 10-week span, islets from SM/J mice grow in size and dramatically improve insulin secreting capacity. I hypothesize the improvement in insulin secretion seen in SM/J mice is driven by proliferation and maturation of immature ?-cells. To test this hypothesis, I will employ a novel RNA sequencing analysis strategy that incorporates single cell and bulk RNA sequencing technology on islets isolated before and after the resolution of hyperglycemia in SM/J mice. I will identify subpopulations of ?-cells based on expression signatures, assess how they change during the resolution of hyperglycemia using pseudotime analysis, and identify differentially expressed genes within each subpopulation. I will then de-convolute the bulk RNA sequencing data using the single cell analysis to control for gene expression changes caused by differences in cell population structure, identify gene expression networks associated with the unique subpopulation expression signatures, assess how the networks change during the resolution of hyperglycemia, and correlate the networks with diabetic traits using RNA sequencing analysis pipelines developed by the Lawson lab. Through my proposed training activities, I will generate a comprehensive assessment of ?-cell subpopulations in obese SM/J mice, how they change during the resolution of hyperglycemia, and how gene expression networks within each subtype relate to diabetic phenotypes. Results will provide insight into the plasticity and heterogeneity of ?-cells in diabetes, and reveal novel genes and pathways with therapeutic potential for improving glycemic control in obesity.
Diabetic obesity has rapidly grown into a public health crisis in the United States, demanding development of therapies that restore pancreatic ?-cell function and improve glycemic control. The goal of this project is to connect changes in ?-cell subpopulation composition and gene expression with the improvements in insulin secretion seen in a diabetic mouse model that naturally resolves hyperglycemia with age. Results from the project will reveal gene expression networks and cell types that correlate with improved ?-cell function, revealing pathways with potential therapeutic applications for treating diabetes.
