Diabetes is rapidly becoming a major public health concern, with 14.2 million cases reported in North America for the year 2000, 151 million cases worldwide, and a projected increase of 46% for worldwide incidence by the year 2010. In both Type 1 and Type 2 diabetes, the insulin-producing pancreatic beta cell plays a central role for the pathophysiology of the disease. Therefore, a better understanding of beta cell biology is crucial for the development of therapeutic strategies that reduce morbidity and premature mortality associated with this disease. The advent of genomics has considerably expanded the knowledge base on beta cells with the discovery that several thousand genes are specifically expressed in the endocrine pancreas. These findings bring up the question of how this tissue-specific expression arises. The prominent role of pancreatic transcription factors for insulin gene expression and pancreatic development is well characterized. Furthermore, mutations in transcription factor genes are linked to genetic forms of diabetes, again raising the question of gene control specifically in the beta cell. We will address this biological problem with a new strategy that allows identification of gene regulatory elements on a genome-wide level. Such a comprehensive identification of gene control elements has not been achieved to date and will make the beta cell the best understood cell type with regard to control of gene expression. We propose an integrated approach to genome-wide identification of beta cell-specific gene control elements that combines experimental investigation with bioinformatics and computational analysis. From a human genomic library, DNA fragments carrying beta cell-specific enhancers will be selected by virtue of their ability to drive expression of an antibiotic resistance gene after transfection into beta cells. A database of conserved non-coding regions for genes with specific expression in the pancreas will be generated through interspecies sequence comparisons. We will cross-reference the conserved regions on experimentally identified enhancers with this database, analyze such regions for transcription factor binding sites, incorporate public data on gene expression, and characterize mechanisms of co-regulation among genes. Together, our studies will show how speck gene expression is achieved in beta cells and provide a framework to integrate gene transcription and beta cell biology.
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