Genome-Wide Mapping of Enhancer Elements for Neuronal Differentiation Genes
Genome-Wide Mapping of Enhancer Elements for Neuronal Differentiation Genes
批准号:
8570126
负责人:
GREGORY E CRAWFORD
金额:
$23.29万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-07-01 至 2015-06-30
关键词:
AccountingBerylliumBiochemicalBiological AssayBiological ModelsBrainCell CycleCellsCerebellar cortex structureCerebellumChromatinCommitComplexCytoplasmic GranulesDataData SetDeoxyribonucleasesDevelopmentDigestionDiseaseDistalDistal Enhancer ElementsElementsEnhancersEventFoundationsGene ExpressionGene Expression RegulationGenesGenetic Enhancer ElementGenomeGenomicsGoalsGrantHarvestHeterogeneityHistonesIndiumLeadLifeLocationMalignant NeoplasmsMapsMediatingMolecularMolecular ProfilingMusMutationNatureNeurodevelopmental DisorderNeuronal DifferentiationNeuronsOutcomePatternPopulationProcessProteinsRegulationRegulator GenesRegulatory ElementStagingTechniquesTestingTimeTissue-Specific Gene ExpressionTranscription Regulatory ProteinValidationcell typechromatin immunoprecipitationepigenomeepigenomicsgenetic manipulationgenetic regulatory proteingenome wide association studygenome-widehistone modificationin vivoin vivo Modelmarkov modelnervous system disorderneuropsychiatrynovelpostnatalprogramspromoterpublic health relevancetranscriptome sequencing
中文摘要
描述(由申请人提供):细胞分化需要基因表达程序的精确编排。染色质调控复合物通过调节基因调控元件的可及性和激活状态来协调这一过程。通过比较细胞类型特异性表观基因组图谱,可以很容易地观察到细胞分化的最终状态。这些分析表明,远端增强子元件的差异调节是基因表达的细胞类型特异性程序的主要决定因素。然而,塑造活性增强子分布并因此驱动任何单细胞类型分化的动态染色质调节事件在很大程度上仍然未知。本研究的目的是确定体内控制神经元分化的染色质调控事件。我们将通过比较从特定神经元细胞类型分化的离散阶段收获的染色质的表观基因组图谱,来确定控制神经元基因表达的同期变化的发育调节的染色质变化。小脑颗粒神经元(CGN)为这项研究提供了一个理想的体内模型,因为它们代表了一个很大程度上同质的神经元群体,可以在离散的发育阶段直接从出生后的小鼠大脑中获得非常大量的。使用DNaseI染色质消化技术,然后进行高通量测序(DNase-Seq),我们已经确定了CGN分化过程中可接近染色质分布的实质性差异。在这里,我们提出的特点,在这些神经元的启动子和增强子的发育调控进行全基因组染色质免疫沉淀(ChIP)的组蛋白标记,表示这些基因调控元件的性质和激活状态。然后,我们将通过对我们组合的DNase-Seq、ChIP-Seq和RNA-Seq数据集进行隐马尔可夫建模,测试CGN分化过程中染色质状态与基因表达动态变化之间的关系。这项建议的结果将是第一次确定一套全面定义的基因调控元件,控制基因调控的动态变化,神经元分化的基础。
英文摘要
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.
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