Mapping the gene regulatory architecture of pancreatic islet-specific cell types to diabetes

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

PROJECT SUMMARY/ABSTRACT Pancreatic islets are comprised of a plurality of diverse hormone-secreting cell populations that collectively contribute to the maintenance of glucose levels throughout the body over time. Dysfunction in the insulin secreting cells of pancreatic islets – beta cells – are a key determinant underlying liability to multiple forms of diabetes as well as disease complications that arise from glucose dysregulation over a patient's lifetime. The mechanistic programs that operate to coordinate gene regulation in islet cells which ultimately contribute to disease is not fully understood, limiting progress to understanding disease and its progression. Ongoing work by our team members, the Human Pancreas Analysis Program (HPAP), and other investigators are generating a wealth of single-cell data in the pancreas that provide the exquisite opportunity to address this gap in knowledge and improve care of patients with diabetes. To address this gap, we have assembled a multidisciplinary team with over 15 years of collaborative work dissecting the architecture of diabetes, who have also pioneered the creation of portals to aggregate and disseminate knowledge. In Aim 1, we will create the largest map of islet cell types by integrating single cell gene expression and accessible chromatin from publicly available and newly generated data. We will use these resources to map genetic determinants of these molecular phenotypes across islet cell types and identify causal variants, linking them to diabetes and glycemic traits. In Aim 2, we will use these aggregated data to generate regulatory circuitry in each cell type by linking cis-regulatory elements (cREs), transcription factors predicted to regulate them, and the target genes for cRE activity. We will use these circuits to predict pre-diabetes and type 2 diabetes, as well as to explain heritability in genetically determined diabetes subtypes. To prepare for increasing data generation, in Aim 3, we will implement pipelines used for our activities for robust integration with the Cardiometabolic disease Knowledge Portal (CMDKP) and will house summary data generated by the project in this resource, which will allow maximum benefit and use for exhaustive linkage of discoveries to diabetes and related traits. We will then apply our single-cell resources to expand into existing collection of bulk tissues, to improve power for discovery and characterization of the regulatory architecture of pancreatic islet specific cell types. The project will result in novel, large scale resources immediately applicable to understand the mechanisms underlying diabetes and disease progression.