Genome-Scale Metabolic Modeling of Human Pancreas with Focus on Type 2 Diabetes

Type 2 diabetes (T2D) is characterized by relative insulin deficiency due to pancreatic beta cell dysfunction and insulin resistance in different tissues. Not only beta cells but also other islet cells (alpha, delta, and pancreatic polypeptide [PP]) are critical for maintaining glucose homeostasis in the body. In this overarching context and given that a deeper understanding of T2D pathophysiology and novel molecular targets is much needed, studies that integrate experimental and computational biology approaches offer veritable prospects for innovation. In this study, we report on single-cell RNA sequencing data integration with a generic Human1 model to generate context-specific genome-scale metabolic models for alpha, beta, delta, and PP cells for nondiabetic and T2D states and, importantly, at single-cell resolution. Moreover, flux balance analysis was performed for the investigation of metabolic activities in nondiabetic and T2D pancreatic cells. By altering glucose and oxygen uptakes to the metabolic networks, we documented the ways in which hypoglycemia, hyperglycemia, and hypoxia led to changes in metabolic activities in various cellular subsystems. Reporter metabolite analysis revealed significant transcriptional changes around several metabolites involved in sphingolipid and keratan sulfate metabolism in alpha cells, fatty acid metabolism in beta cells, and myoinositol phosphate metabolism in delta cells. Taken together, by leveraging genome-scale metabolic modeling, this research bridges the gap between metabolic theory and clinical practice, offering a comprehensive framework to advance our understanding of pancreatic metabolism in T2D, and contributes new knowledge toward the development of targeted precision medicine interventions.