Genome-Wide Mapping of Enhancer Elements for Neuronal Differentiation Genes

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DESCRIPTION (provided by applicant): Cellular differentiation requires the precise orchestration of gene expression programs. Chromatin regulatory complexes coordinate this process by modulating the accessibility and activation state of gene regulatory elements. The end states of cellular differentiation can be readily visualized through the comparison of cell typ specific epigenome maps. Such analyses indicate that the differential regulation of distal enhancer elements is the primary determinant of cell-type specific programs of gene expression. However the dynamic chromatin regulatory events that sculpt the distribution of active enhancers and thus drive the differentiation of any single cell type have remained largely unknown. The goal of this proposal is to identify the chromatin regulatory events that control the differentiation of neurons in vivo. We will identify developmentally regulated chromatin changes that control contemporaneous changes in neuronal gene expression by comparing epigenomic profiles of chromatin harvested from discrete stages in the differentiation of a specific neuronal cell type. Cerebellar granule neurons (CGNs) provide an ideal in vivo model for this study because they represent a largely homogeneous neuronal population that can be obtained in very large numbers at discrete developmental stages directly from the postnatal mouse brain. Using the technique of DNaseI chromatin digestion followed by high-throughput sequencing (DNase-Seq) we have already identified substantial differences in the distribution of accessible chromatin over the course of CGN differentiation. Here we propose to characterize the developmental regulation of promoters and enhancers in these neurons by performing genome-wide chromatin immunoprecipitation (ChIP) for histone marks that denote the nature and activation state of these gene regulatory elements. We will then test the relationship between chromatin states and dynamic changes in gene expression during CGN differentiation through hidden Markov modeling of our combined DNase-Seq, ChIP-Seq, and RNA-Seq datasets. The outcome of this proposal will be the first identification of a comprehensive defined set of gene regulatory elements that control the dynamic changes in gene regulation that underlie neuronal differentiation.