Insulin resistance (IR) is a sine qua non of Type 2 diabetes and a pathogenic factor in many other disease states. The molecular basis of IR is complex, and associated with many interweaving pathways. We have focused on nuclear mechanisms of IR, comprising the combined actions of transcription factors (TFs) and epigenomic modifiers. In the prior funding cycle we have identified several transcriptional regulators of cellular and tissue IR using advanced epigenomic strategies. For the upcoming cycle, we propose to shift to the study of human IR; specifically, the identification of transcriptional mechanisms that drive the development of IR in human adipocytes. Toward this end, we have generated chromatin state maps of primary adipocytes from insulin resistant and sensitive subjects, leading to the identification of thousands of enhancers with differential activity in IR. First, we will link these enhancers to their target genes using a novel mathematical model, and then validate a number of these predictions using CRISPRi. Next, we will assess which of these enhancers and genes show evidence of allelic imbalance, thus implying a genetic basis for their differential enrichment and predicting SNPs responsible for this effect. A massively parallel reporter assay will further implicate individual SNPs and TF motifs as candidates for drivers of IR, thus enabling the prediction of upstream regulators that bind and activate the enhancers. Finally, we will validate causal SNPs using base editing in cultured adipocytes testing their effects on target gene expression and insulin sensitivity. We will also validate candidate upstream regulators using a combination of gain- and loss-of-function approaches in vitro and in vivo. The key deliverable of this proposal will be the elucidation of the detailed transcriptional mechanisms underlying noncoding variation leading to human IR.
It is known that transcriptional mechanisms contribute to human insulin resistance, but it is unclear how genetic and epigenetic variation affect processes that promote disease risk. We have accumulated epigenomic, transcriptomic, and genetic data from the isolated adipocytes of multiple insulin sensitive and resistant human subjects. In this proposal we will identify specific noncoding variants that predispose to insulin resistance and will decipher the genes and pathways that promote Type 2 diabetes.
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