Transcription Factor Networks in Cardiac Hypertrophy

描述（由申请人提供）：总体目标是开发心脏表达数据的分析策略，并使用它在没有任何先入为主的疾病相关基因知识的情况下识别关键基因，其靶点和调控途径，并在生物系统中验证这些结果。具体目标是：1：将肥大相关基因与转录调控网络联系起来。II：优化策略以应用于其他心脏基因表达数据集。健康相关性：更好地了解体内p300驱动网络的功能对于指导癌症和心脏病的药物开发至关重要。实验设计包括在小鼠肥大模型上运行微阵列，生成和应用新的微阵列数据统计方法来提取与肥大相关的基因，生成数学表示以3D方式可视化全局表达谱，在计算机上研究启动子组织以识别复杂的调控元件，在真核启动子数据库中搜索这些复杂元件以寻找共调控基因。利用ChIP实验在体内验证复杂元件，将微阵列和文献中的网络重叠到启动子驱动的网络中，以寻找未知的候选基因。

DESCRIPTION (provided by applicant): The broad objective is to develop an analysis strategy for heart expression data and use it to identify without any preconceived knowledge of disease-associated genes the key genes, their targets, and their regulatory pathway, and to validate these results in biological systems. Specific Aims are: I: Link hypertrophy associated genes to transcriptional regulatory networks. II: Optimize the strategy for application to other cardiac gene expression data sets. Health Relatedness: A better understanding of the function of a p300- driven network in vivo is essential to guiding drug development both in cancer and heart disease. The experimental design includes running microarrays on murine models of hypertrophy, generating and applying novel statistical approaches to microarray data to extract genes related to hypertrophy, generating mathematical representations to visualize global expression profiles in 3D, studying promoter organization in silico to identify complex regulatory elements, searching the eukaryotic promoter database for these complex elements to find co-regulated genes, validating the complex elements in vivo using ChIP assay, overlapping the network from microarrays and literature to promoter driven network to find unknown candidate genes.