Uncover Novel Regulators of Beta Cell Differentiation Through Unbiased, Genome-Scale CRISPR Screening
Abstract
Diabetes is a serious global health problem characterized by high blood glucose levels due to insulin deficiency. Impairment of pancreatic insulin-secreting beta cells is a hallmark of diabetes. There are two main challenges in advancing diabetes research towards a cure. First, to develop targeted therapies, it is critical to develop efficient methods to identify potential risk factors through functional assays. However, to identify which of the ~20,000 protein-coding genes in the human genome confer diabetes risk is a daunting task. Second, the current exogenous insulin treatment for diabetic patients is effective in reducing blood sugar levels, but it is not a cure. Islet transplantation can offer a promising curative option, but several critical hurdles, including organ donor shortage, need to be surmounted. One way to overcome the donor shortage problem is to produce large quantities of “off-the-shelf” beta cells for transplantation by utilizing self-renewing human pluripotent stem cells (hPSCs). Substantial progress has been made, but hPSC-directed differentiation outcomes remain variable and require further optimization. Existing mouse models are not adequate to address these challenges due to two notable limitations: (i) The differences between human and mouse development and physiology prevent direct extrapolation of some findings from murine studies to human patients, thus posing a serious challenge to the translation from the bench to bedside. (ii) Mouse studies tend to focus on the actions of a single gene or a few related pathways in a specific biological context. It has been difficult to systematically interrogate disease-relevant phenotypes in an unbiased and quantitative manner as each genetic manipulation requires substantial investment of time and effort. An increasing number of disease-associated genetic variants have been identified in family studies of diabetes, and to understand the functional consequences of these variants requires an experimental platform with greater speed and capacity. To overcome these limitations, we use hPSC-based disease modeling to bridge the gap between non-human model organism studies and human genetics. hPSCs can proliferate indefinitely while maintaining the ability to differentiate into disease-relevant cell types, thus providing an excellent model to recapitulate disease “in-a-dish.” Perhaps more importantly, hPSCs offer an experimental system that supports high-throughput data-driven studies that go beyond the traditional one-gene-at-a-time approach. As such, hPSCs are uniquely suitable for identifying disease risk genes through unbiased, large-scale, CRISPR/Cas-based genetic screens. In the present study, we will focus on identifying novel genes that affect beta cell formation from hPSCs, as defective beta cells are considered a main driver of Type 2 diabetes based on genome-wide association studies. Importantly, the use of hPSCs for gene discovery would allow us to readily translate our findings into improved hPSC differentiation methods necessary for off-the-shelf beta cell production to be used in transplantation therapy, a potential curative option. It is highly innovative to conduct genome-scale genetic screens to quickly identify potential diabetes risk factors. No such screens have been performed, and the screening approach is perhaps the best method possible to screen through ~20,000 protein-coding genes to find genes that may contribute to diabetes risk. As such, this proposal is aligned with several Fiscal Year 2019 Peer Reviewed Medical Research Program Areas of Encouragement under the topic of Diabetes: in terms of understanding the heterogeneity of diabetes caused by genetic components, the implementation of hPSC-based disease-in-a-dish models to uncover the pathogenesis of diabetes, and ultimately the translation of our findings to a treatment option and a potential cure for diabetes through the production of hPSC-derived insulin-
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010298
Entities
People
- Danwei Huangfu
Organizations
- Sloan-Kettering Institute
- United States Army