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MICROMouse Program Application Abstract
Development of a targeted DNA methylation sequencing assay for the assessment of metabolic disease progression
Daniel Vera   (Boston, MA)
DNA methylation (DNAm) plays a crucial role in regulating chromatin architecture and gene expression1. DNAm is essential for normal vertebrate development and is misregulated in various disease contexts such as cancer, aging, and obesity. Numerous studies on DNAm in humans have identified differentially-methylated regions (DMRs) of the genome in patients with metabolic disease. Recently, DNAm signatures of metabolic disease have been found in human blood. DNAm-based signatures of diabetes have the potential of a clinical biomarker, but the high cost of measuring DNAm, lack of large-scale studies, and challenges associated with genetic and environmental variation across human populations have hindered its utility. In mouse studies where genetic and environmental conditions are tightly controlled, a DNAm-based biomarker of metabolic disease would be an invaluable tool for researchers. Because DNAm is a surrogate for cellular response to disease states, a blood-based DNAm biomarker may represent a highly-accurate indicator of ongoing or impending metabolic disease. However, due to the large size of mammalian genomes, measuring genome-wide DNAm costs thousands of dollars for each sample, and cost is a barrier to its use in biomarker development. The overall goal of this proposal is to develop a cost-effective and scalable DNAm-based blood biomarker assay. Once established, this assay will allow for efficient characterization of DNAm changes associated with metabolic disease, and allow for the low-cost testing disease interventions on DNAm dynamics in mouse models of metabolic disease. Aim 1. Develop a low-cost approach for targeted methylation sequencing (selectSeq). Aim 2. Train new machine learning models to predict metabolic disease states. Aim 3. Test the effect of a known diabetes drug on predicted metabolic state. The resulting methods and models developed in this work will be invaluable for characterizing epigenetic changes in metabolic disease, and enable the screening of genes and compounds that target their molecular mechanisms.
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