High resolution mapping of transcription factor DNA binding in vivo

项目总结/摘要 动物生物学中最大的挑战之一是了解基因序列信息是如何被 转录因子在调控网络中产生基因表达模式， 发育中的胚胎。该项目是一个更广泛的计划项目的一部分，将整合计算 建模和湿实验室方法来解决这一挑战，因为只有定量和 经过实验验证的预测数学模型可以提供严格的 对动物转录网络建模所需的理解。 该项目对整个计划的贡献将是获得序列特异性结合的数据。 转录因子转化为DNA。目前我们和其他研究小组的体内DNA结合数据有限 因为它定位于200 - 500 bp基因组区域的结合，该区域通常包括几个~6-12个碱基对（bp）， 研究中的因子的识别位点。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水平。因此，我们将在体内 使用Frank最近开发的Exo-ChIP方法获得的分辨率相当高的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之内 因子的同源识别位点。对于我们的第一个具体目标，数据将来自32转录 遗传学定义为果蝇胚盘网络的主要调节因子， 另外的普遍表达的转录因子，其可以提供进一步的特异性信息。为我们 第二个具体目标，DNA占有率的变化，导致关键核苷酸的突变， 还将使用项目2提供的转基因系测量识别位点。转录结合 将测定突变位点以及附近位点的转录因子， 影响体内DNA占有率的因素。项目1产生的所有数据都将是必不可少的， 测试我们目前的广义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，该项目将有助于 人类遗传疾病治疗方法的发展和对动物发育的理解。

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.