U01HG012059
Cooperative Agreement
Overview
Grant Description
The impact of genomic variation on environment-induced changes in pancreatic beta cell states - 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.
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.
Funding Goals
NHGRI SUPPORTS THE DEVELOPMENT OF RESOURCES AND TECHNOLOGIES THAT WILL ACCELERATE GENOME RESEARCH AND ITS APPLICATION TO HUMAN HEALTH AND GENOMIC MEDICINE. A CRITICAL PART OF THE NHGRI MISSION CONTINUES TO BE THE STUDY OF THE ETHICAL, LEGAL AND SOCIAL IMPLICATIONS (ELSI) OF GENOME RESEARCH. NHGRI ALSO SUPPORTS THE TRAINING AND CAREER DEVELOPMENT OF INVESTIGATORS AND THE DISSEMINATION OF GENOME INFORMATION TO THE PUBLIC AND TO HEALTH PROFESSIONALS. THE SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM IS USED TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM IS USED TO FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
La Jolla,
California
920930688
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 396% from $1,280,000 to $6,348,795.
San Diego University Of California was awarded
Genomic Variation Impact on Pancreatic Beta Cell States
Cooperative Agreement U01HG012059
worth $6,348,795
from National Human Genome Research Institute in September 2021 with work to be completed primarily in La Jolla California United States.
The grant
has a duration of 4 years 8 months and
was awarded through assistance program 93.172 Human Genome Research.
The Cooperative Agreement was awarded through grant opportunity Defining Genomic Influence on Gene Network Regulation (U01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 8/6/25
Period of Performance
9/7/21
Start Date
5/31/26
End Date
Funding Split
$6.3M
Federal Obligation
$0.0
Non-Federal Obligation
$6.3M
Total Obligated
Activity Timeline
Transaction History
Modifications to U01HG012059
Additional Detail
Award ID FAIN
U01HG012059
SAI Number
U01HG012059-2832144192
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
75N400 NIH National Human Genome Research Institute
Funding Office
75N400 NIH National Human Genome Research Institute
Awardee UEI
UYTTZT6G9DT1
Awardee CAGE
50854
Performance District
CA-50
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Human Genome Research Institute, National Institutes of Health, Health and Human Services (075-0891) | Health research and training | Grants, subsidies, and contributions (41.0) | $2,560,000 | 100% |
Modified: 8/6/25