High resolution mapping of transcription factor DNA binding in vivo

体内转录因子 DNA 结合的高分辨率图谱

• 批准号: 8262267
• 负责人: Mark D BIGGIN
• 金额: $ 53.77万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-13 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8262267
• 关键词: Address; Affect; Affinity; Animals; Asses; Base Pairing; Belief; Binding; Binding Sites; Biochemical; Biological; Biological Assay; Biology; Blastoderm; Chromatin; Code; Computer Simulation; DNA; DNA Binding; DNA Sequence; DNA-protein crosslink; Data; Development; Drosophila genome; Drosophila genus; Embryo; Engineering; Functional RNA; Future; Gene Expression Profile; Generations; Genes; Genetic; Genetic Transcription; Genome; Genomics; Goals; Hereditary Disease; Housekeeping Gene; Human Genetics; In Vitro; Individual; Laboratories; Learning; Letters; Location; Maps; Measures; Methods; Modeling; Mutate; Mutation; Nucleic Acid Regulatory Sequences; Nucleotides; Output; Pattern; Point Mutation; Protein Region; Proteins; Publishing; Reading; Relative (related person); Resolution; Series; Site; Specificity; Testing; Transgenic Organisms; abstracting; base; genome-wide; improved; in vivo; in vivo Model; mathematical model; programs; therapeutic development; transcription factor

Project Summary/Abstract One of the greatest challenges in animal biology is to learn how genonnic sequence information is read by transcription factors to produce patterns of gene expression within the context of regulatory networks in developing embryos. This Project is part of a broader Program Project that will integrate computational modeling and wet laboratory methods to address this challenge in the belief that only quantitative and predictive mathematical models that have been validated experimentally can provide the rigorous understanding required for modeling transcriptional networks of animals. This Project's contribution to the overall Program will be to derive data on the binding of sequence specific transcription factors to DNA in the embryo. Ours and other groups in vivo DNA binding data is limited currently in that it localizes binding to 200 - 500 bp genomic regions that generally include several ~6-12 base pair (bp) recognition sites for the factor under study. Mechanistic computational models, however, must describe the relative levels of occupancy of transcription factors at each recognition site. Therefore, we will derive in vivo DNA binding data at considerably higher resolution using an Exo-ChIP method recently developed by Frank Pugh's laboratory that can map the location of protein/DNA crosslinks to within a few base pairs of transcription factors' cognate recognition sites. For our first Specific Aim, data will be derived both for the 32 transcription factors defined by genetics as the principal regulators of the Drosophila blastoderm network as well as for additional ubiquitously expressed transcription factors that may provide further specificity information. For our second Specific Aim, the change in DNA occupancy that results from the mutation of key nucleotides within recognition sites will also be measured using transgenic lines provided by Project 2. Binding of transcription factors to the mutated sites and also at nearby sites will be assayed to test if interactions between transcription factors affect DNA occupancy levels in vivo or not. All of the data produced by Project 1 will be essential in testing our current generalized HMM and in developing more accurate second generation models of in vivo DNA binding and transcriptional activity (Projects 3 and 4). By helping to establishing how to read transcriptional information in animal genomes, this Project will aid both the development of therapeutics for human genetic diseases and the understanding of animal development.

项目摘要/摘要 动物生物学中最大的挑战之一是了解基因组序列信息是如何被 转录因子在调控网络的背景下产生基因表达模式 发育中的胚胎。该项目是一个更广泛的计划项目的一部分，该计划项目将整合计算 建模和湿实验室方法来应对这一挑战，相信只有定量和 经过实验验证的预测数学模型可以提供严格的 了解对动物转录网络进行建模所需的知识。 这个项目对整个计划的贡献将是获得关于序列特定结合的数据 将转录因子转化为胚胎中的DNA。我们和其他小组在体内的DNA结合数据目前有限 因为它定位于200-500个碱基对的基因组区域，这些区域通常包括几个~6-12个碱基对(BP) 对正在研究的因素的认可站点。然而，机械计算模型必须描述 转录因子在每个识别位点的相对占有率。因此，我们将在体内推导出 使用Frank最近开发的Exo-Chip方法以相当高的分辨率获得DNA结合数据 Pugh的实验室，可以将蛋白质/DNA交联链的位置映射到几个碱基对的转录范围内 因子的同源识别站点。为了我们的第一个特定目标，我们将推导出32个转录的数据 被遗传学定义为果蝇胚层网络的主要调节因素以及 其他普遍表达的转录因子，可能提供进一步的特异性信息。为我们的 第二个特定目标是DNA占有率的变化，这是由于 识别位点也将使用由项目2提供的转基因品系来测量。 将检测突变位点和邻近位点的因子，以测试转录之间的相互作用 影响体内DNA占有率的因素是否存在。项目1产生的所有数据将在 测试我们目前的广义隐马尔可夫模型，并开发更准确的体内第二代模型 DNA结合和转录活性(项目3和4)。 通过帮助建立如何读取动物基因组中的转录信息，该项目将帮助 人类遗传病治疗学的发展和对动物发育的理解。