| dc.description.abstract | Type 2 diabetes is characterized by failure of pancreatic β-cells to secrete adequate insulin to meet the needs of the body. This β-cell failure is thought to be caused by increased metabolic load due to mounting insulin resistance, but the molecular mechanisms by which this dysfunction occurs are not fully understood. To better understand how β-cells fail, we took a whole-transcriptome approach, collecting RNA-sequencing datasets from purified β-cell populations from several mouse models of β-cell stress. First, we used mice lacking Abcc8, a key component of the β-cell KATP-channel, to analyze the effects of a sustained elevation in the intracellular Ca2+ concentration ([Ca2+]i) on β-cell identity and gene expression. We found that chronically elevated β-cell [Ca2+]i results in the dysregulation of over 4,200 genes, as well as modest loss of β-cell identity, characterized by decreased expression of key functional genes, increased expression of genes associated with β-cell dedifferentiation, increased β-cell transdifferentiation to PP-expressing cells, and decreased β-cell function. These studies prompted us to propose a model by which chronically elevated β-cell [Ca2+]i acts through a putative Ca2+-regulated transcription factor, ASCL1, to disrupt a network of genes, contributing to β-cell failure.
In addition to exploring the effects of chronically elevated [Ca2+]i on β-cell gene expression, we analyzed β-cells from mice ectopically expressing human growth hormone (hGH), mice made insulin resistant by feeding a high-fat diet (HFD), and mice of different sexes. We found that both ectopic hGH and HFD have beneficial effects (induction of β-cell proliferation genes) as well as deleterious effects (increased expression of ER stress genes) on β-cell function. Ultimately, the collection of 17 RNA-sequencing datasets allowed us to perform weighted gene correlation network analysis (WGCNA) to generate modules of similarly-expressed genes. Several of these initial modules have meaningful correlations to specific β-cell stresses. Overall, these studies highlight the power of using whole transcriptome datasets from highly-pure cell populations and have allowed us to elucidate how stress alters the β-cell gene regulatory network. | |
