2018 — 2021 |
Gaulton, Kyle Jeffrie |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Diabetes Risk Variants Affecting Transcription Factor-Regulated Cellular Networks @ University of California, San Diego
Type 2 diabetes is a complex disease that affects 1 in 10 Americans and is influenced by many common genetic risk factors. Genetic association studies have identified over 100 loci that influence T2D risk, although how these loci mechanistically contribute to diabetes pathogenesis is largely unknown. The majority of these risk loci map to non-coding sequence, and likely alter gene regulatory processes in specific cell-types. Translating this breadth of diabetes regulatory variation into their molecular mechanisms can thus profoundly inform on diabetes pathophysiology, although remains challenging. In this study we propose a novel approach to identify T2D-relevant transcription factors and gene networks regulated by these factors by combining statistical human genetics, epigenomics, high-throughput assay and quantitative trait locus (QTL) mapping. In this approach we identify T2D risk variants that affect the cell-type expression of a transcription factor gene, characterize the genomic binding sites and target gene network regulated by these transcription factors, and broadly determine the effects of variants disrupting transcription factor-regulated networks on diabetes risk. In preliminary findings we have identified several diabetes risk variants that affect the cell-type expression level of a transcription factor gene, almost none of which have known involvement in diabetes- relevant pathways. In Aim 1 we will combine genetic fine-mapping with epigenomic annotation and eQTL data from diabetes-relevant cells to identify diabetes risk variants that affect the cell- type expression of a transcription factor. In Aim 2 we will perform ChIP-seq assays of five transcription factors in pancreatic islet samples combined with eQTL data to map the trans network of target genes affected by transcription factor regulatory variants. In Aim 3 we will combine allelic imbalance mapping and in silico motif prediction of islet ChIP-seq data to quantify the genome-wide effects of variants disrupting transcription factor-regulated networks on diabetes risk. The results of these studies will reveal specific transcription factors that are regulated by diabetes risk variants, and the gene networks regulated by these factors that in turn impact diabetes pathophysiology. Together these studies will provide insight into critical transcription factors and gene networks involved in diabetes pathogenesis.
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0.948 |
2019 — 2021 |
Gaulton, Kyle Jeffrie Sander, Maike (co-PI) [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Genetic Mechanisms of Type 1 Diabetes Risk in Stress-Induced Pancreatic Islets @ University of California, San Diego
PROJECT SUMMARY/ABSTRACT Type 1 diabetes (T1D) is characterized by autoimmune destruction of insulin-producing beta cells in pancreatic islets. While studies of T1D risk mechanisms have largely focused on immune cell function, recent evidence suggests the beta cells themselves actively contribute to the disease process. Beta cells are exposed to different environmental stimuli and stressors in the course of T1D development, such as pro-inflammatory cytokines and hyperglycemia which can contribute to beta cell stress and death. However, the extent to which T1D risk variants affect the beta cell epigenome and gene regulation in response to these external signals is unknown. To gain a deeper understanding of the variants, genes, and pathways that impact beta cell function and survival in T1D pathophysiology, it is critical to map changes in beta cell gene regulation the context of T1D-relevant immune and metabolic stressors. We have generated chromatin accessibility maps from primary pancreatic islet samples exposed to T1D-relevant cytokines and identified thousands of cytokine-responsive sites and transcription factors. Integrating these data with T1D genetic fine-mapping then revealed T1D risk variants with cytokine- dependent effects on islet chromatin accessibility. The proposed project will build on these findings in combining human genetics, islet epigenomics, and genome engineering to map T1D risk variants that affect beta cell chromatin upon in vitro exposure to multiple T1D-relevant stressors and identify target genes of stress-induced T1D variant effects that impact beta cell ER stress and survival. To accomplish this, in Aim 1 we will generate comprehensive maps of changes in beta cell chromatin accessibility and transcription factor binding upon exposure to multiple T1D-relevant stressors. Using these data, we will then fine-map T1D risk variants with stress-induced effects on beta cell chromatin using QTL mapping and validate their allelic effects using reporter assays. In Aim 2, we will identify target genes of stress-induced T1D variants by generating and analyzing changes in beta cell gene expression and 3D chromatin architecture upon exposure to the same stressors, and then validate target genes of stress-induced sites using a CRISPRi regulatory screen. Finally, in Aim 3 we will identify target genes of T1D risk variants that directly modulate beta cell ER stress and survival phenotypes using genome-wide CRISPR-mediated loss-of-function screens. The cellular phenotype of these genes will then be validated using CRISPR-mediated gene deletions in hiPSC-derived beta cells. Together our findings will provide novel insight into the intrinsic role of beta cells in T1D pathophysiology and inform therapeutic intervention through target discovery of T1D risk genes involved in beta cell stress response and survival.
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0.948 |
2019 — 2021 |
Gaulton, Kyle Jeffrie Sander, Maike [⬀] |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Single Cell Analysis of the Human Pancreas in Type 1 Diabetes @ University of California, San Diego
PROJECT SUMMARY/ABSTRACT Type 1 diabetes (T1D) is characterized by autoimmune destruction of insulin-producing beta cells in pancreatic islets. In T1D the interplay between immune, endothelial, and endocrine cells in the islet niche leads to beta cell dysfunction and/or destruction; however, there is limited knowledge of the molecular blueprint that initiates and drives immune-mediated beta cell destruction. Recent single cell RNA-seq (scRNA-seq) profiling studies of human pancreas and islets from non-diabetic donors lack the resolution to characterize immune cells. Furthermore, T1D-related changes in the islet cell repertoire have not been comprehensively analyzed, and cell type-resolved epigenomic maps of gene regulatory elements remain to be generated for T1D-relevant cell types. When intersected with genetic variants from genome-wide T1D association studies, such maps could help pinpoint cells and genes with causal roles in T1D. To fill these knowledge gaps, we have assembled a team of highly accomplished researchers in islet biology and diabetes (Sander), genetics and genomics of diabetes (Gaulton) and functional genomics (Ren, UCSD Center for Epigenomics). The proposed project will apply novel single nuclei (sn) technologies to characterize the epigenomic (Aim 1) and transcriptomic (Aim 2) profiles of individual T1D-relevant cells in the pancreas of non-diabetic and T1D individuals. To enrich cell types most relevant for T1D pathogenesis (i.e. endocrine, immune and endothelial cells), we will deplete acinar cells from whole pancreas preparations. From these enriched cell preparations, we will generate maps of accessible chromatin (snATAC-seq) and gene expression (snRNA-seq). First, we will generate reference maps using fresh pancreatic tissue from non-diabetic donors, and then employ our recent adaptions of snATAC-seq and snRNA- seq technology to profile frozen, archived pancreata from non-diabetic, T1D antibody-positive, and T1D donors in the Network for Pancreatic Organ Donors with Diabetes (nPOD) biorepository. In Aim 3, we will integrate snATAC-seq and snRNA-seq data generated in Aims 1 and 2 with T1D genetic association data to identify pancreatic cell types and regulatory programs involved in T1D pathogenesis. This analysis will 1) define cell types and subtypes and their regulatory programs in the non-diabetic pancreas, 2) identify T1D-dependent changes in the existence, composition, regulation and inter-connectivity of pancreatic cell types, and 3) identify cells, networks and genes with likely causal roles in T1D by integrating snATAC-seq and snRNA-seq with T1D genetic association data. By generating reference maps of chromatin and gene expression in pancreatic cells from non-diabetic and T1D individuals, this proposal will identify resident immune and other cells that arise and change during T1D that can serve as novel biomarkers of disease and which will inform strategies for early intervention. Further integration with genetic data will reveal cells, networks and genes that are on the causal pathway to disease, which will inform therapeutic target discovery. Together our findings will provide novel insight into the pathogenic processes of cells in the pancreatic micro-environment that lead to beta cells loss in T1D.
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0.948 |
2021 |
Carter, Hannah Kathryn (co-PI) [⬀] Gaulton, Kyle Jeffrie Ren, Bing (co-PI) [⬀] Sander, Maike [⬀] |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
The Impact of Genomic Variation On Environment-Induced Changes in Pancreatic Beta Cell States @ University of California, San Diego
PROJECT SUMMARY/ABSTRACT Pancreatic beta cells secrete insulin in order to maintain blood glucose homeostasis. Insulin secretion is tightly regulated by glucose and modulated by numerous environmental signals, including other nutrients, hormones, and inflammatory cytokines. Exposure of beta cells to environmental signals affects gene regulatory programs within hours, and these signal-dependent changes serve to adapt insulin secretion to changes in organismal states. Genetic variants associated with measures of insulin secretion are strongly enriched in genomic elements active in beta cells, and many of these variants are also associated with risk of diabetes. Beta cells therefore possess characteristics that make them an ideal cellular model for studying signal-dependent gene regulatory processes relevant to human health and disease. However, the specific genomic programs that drive signal- induced state changes in beta cells remain poorly characterized. Recent advances in the development of human pluripotent stem cell (hPSC)-derived multi-cellular islet organoid models by us and others provide a genetically tractable beta cell model for linking genomic activity to cellular phenotypes. Our group has further pioneered the development of numerous single cell assays, including chromatin accessibility, ultra-high-throughput paired chromatin accessibility and gene expression, and paired 3D chromatin interactions and DNA methylation; methods that we have successfully applied to both primary human islets and hPSC-islet organoids. We have further developed machine learning and network-based approaches for variant interpretation including from single cell RNA and epigenetic data. In this proposal we will develop novel gene regulatory network (GRN) models to predict network-level relationships among genomic elements, genes, and phenotypes derived from single cell multiomic maps charting signal- and time-dependent changes in hPSC-islet organoids. In Sections B and C we will measure genomic element activity, gene expression, and insulin secretion in hPSC-islet organoids exposed to ten different secretory signals each across four time points using paired single nucleus accessible chromatin and gene expression and paired single cell DNA methylation and 3D chromatin architecture assays. In Section D we will generate a GRN from these data, use machine learning to infer element-gene and element-phenotype relationships and use the trained models to refine the GRN. From the resulting GRN we will predict the effects of genetic variants in specific genomic elements on target gene expression, gene network activity, and cellular phenotype. In Section E we will validate and refine models by using medium-scale CRISPR interference of genomic elements individually and in combination as well as allele-specific gene editing of selected glucose-associated variants in hPSC-islet organoids and measuring gene expression changes in cis and trans. Together, the results, data, and methods from this project using a model of a cell type which both rapidly responds to environmental signals and has a quantifiable phenotypic output will be widely applicable to the community studying the dynamics of genomic regulation.
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0.948 |