Using Integrative Multiomics Approaches to Dissect Type 2 Diabetes Genetic Risk in Pancreatic Islets
Rai, Vivek
2022
Abstract
Type 2 Diabetes (T2D) is a complex disease characterized by pancreatic β-cell dysfunction and dysregulation of blood glucose levels. Genome-wide association studies for diabetes and related traits suggest a complex genetic architecture of the disease and identify >400 independent signals throughout the genome. However, more than 90 percent of the signals map to the non-protein-coding regions suggesting a strong regulatory component to the disease. It is hypothesized that these non-coding variants affect disease susceptibility by modulating the transcription factor (TF) binding in a tissue- and context-specific manner. As such, understanding the genetic architecture of the disease involves a careful assessment of the complexity across all layers of the genetic organization. While existing studies have used high-throughput sequencing ('omics) approaches to dissect the disease at different layers, they have either been limited to a bulk-sample view or have focused on a specific layer (modality) — thereby limiting our ability to map biological mechanisms and the consequences of their dysregulation comprehensively. In this work, I utilize high-throughput molecular profiling data-driven approaches, “multiomics,” in human pancreatic islets to characterize the tissue heterogeneity (complex interplay of cell types and their organization) and gene regulatory interactions (linking genetic variation to target genes and functions) to discover mechanistic insights relevant to the disease pathophysiology. In chapter 1, I discuss the genetic architecture of T2D and emphasize how multiomic approaches driven by high-throughput sequencing technologies can help us link variants to genes and genes to their function. I emphasize the need of understanding the epigenomic landscape of different constituent cell types within the pancreatic islets, and how we can use that to complement our understanding of gene expression and regulation from transcriptomic and genetic studies. In chapter 2, I use single-cell ATAC-seq to profile chromatin accessibility in pancreatic islets and identify molecular signatures unique to constituent cell types — one of the first published studies in this domain with a novel dataset. I show that major cell types can be easily identified from their epigenomic profiles and can be used to dissect genetic-risk associations across different cell types. We identify the pancreatic islet β cells to be the most enriched cell type for T2D genetic risk; and within each cell type, we use co-accessibility approaches to link variants to genes. In chapter 3, I build upon the findings from the previous chapter, where we identify β cell chromatin accessibility peaks to be highly enriched for T2D genetic risk and investigate how β cell function is impacted in early-stage T2D. Using integrative approaches combining data from RNA-seq, ATAC-seq, secretion assays, imaging, and mRNA knockdown experiments, we discover Regulatory Factor X 6 (RFX6) as the key transcription factor implicated in dysregulation of insulin response in β cells. Finally, in chapter 4, I discuss how my work establishes a framework for investigating complex diseases, where starting from genetic associations, which are non-specific and do not provide any mechanistic insight, we can integrate information across layers of the genetic organization using 'omics-driven approaches to build a mechanistic understanding in a stepwise manner. Applied to T2D, we show the strength of this approach and dissect the genetic heterogeneity to identify context-specific molecular signatures. Identification of such signatures will provide a higher-resolution map of our existing knowledge and enable the discovery of novel targets and approaches to prevent, monitor, and treat T2D.Deep Blue DOI
Subjects
integrative multiomics approaches pancreatic islets from healthy and diabetic donors understanding genetic risk of type 2 diabetes single-nuclei joint epigenome and transcriptome profiling in islets
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