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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/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读取的转录因子在调控网络的背景下产生基因表达模式正在发育的胚胎。该项目是更广泛的计划项目的一部分，该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理解动物转录网络建模所需的知识。该项目对整个计划的贡献将是获得有关序列特异性结合的数据胚胎中DNA的转录因子。我们和其他团队的体内 DNA 结合数据目前有限因为它定位结合到 200 - 500 bp 基因组区域，通常包括几个 ~6-12 碱基对 (bp) 所研究因素的识别位点。然而，机械计算模型必须描述每个识别位点转录因子占据的相对水平。因此，我们将推导出体内使用 Frank 最近开发的 Exo-ChIP 方法获得分辨率更高的 DNA 结合数据 Pugh 的实验室可以将蛋白质/DNA 交联的位置映射到转录的几个碱基对内因子的同源识别位点。对于我们的第一个具体目标，将导出 32 个转录的数据遗传学定义的因素是果蝇胚盘网络的主要调节因子以及其他普遍表达的转录因子可以提供进一步的特异性信息。为了我们的第二个具体目标，由于内部关键核苷酸的突变而导致的 DNA 占据的变化识别位点也将使用项目 2 提供的转基因系进行测量。转录结合将检测突变位点以及附近位点的因素，以测试转录之间是否存在相互作用影响体内 DNA 占据水平的因素。项目 1 产生的所有数据对于测试我们当前的广义 HMM 并开发更准确的第二代体内模型 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疾病疗法的发展和对动物发育的理解。