Genome-wide structural organization of proteins within human gene regulatory complexes

人类基因组的DNA序列告诉我们， 健康的细胞，但也改变了产生病变细胞的成分。如何控制蛋白质生产 通过调节编码它们的基因，对健康和患病细胞至关重要。 精确地知道基因调控蛋白结合的位置，并在整个基因组中组织，包括 它们之间的相互作用，告诉我们基因是如何调节和错误调节的。既然有 可能有数千种不同的调节蛋白和数千种不同的人类细胞 类型和环境反应是各种调节蛋白亚群的产物，整个 基因调控事件的“宇宙”是相当大的，因此，鉴定起来相当昂贵。的子集 这些事件将可能是疾病状态的信息或诊断。因此，一个重要的目标是定义 使用成本低廉、高准确度和稳健的全基因组测定进行信息交互。为此，委员会建议， ChIP-exo是一种近单碱基定位基因调控蛋白结合位点的方法 对分辨该测定将以高通量应用于确定全基因组位置， 在蛋白质-DNA复合物内的因子的组织，如增强体。通过广泛绘制各种 构成大部分受调控表观基因组的蛋白质类别，关于增强子和 阻遏物复合物的组织将被推断出来。目的1涉及收集全基因组ChIP-exo数据， 人细胞系中的蛋白质-DNA复合物。目标2将制定和实施 ChIP-exo数据集中模式识别和数据蒸馏的计算方法。结果 有望在基因组规模上提供对大分子蛋白质复合物组装的结构见解， 以及在各种细胞类型和条件下。

The DNA sequence of the human genome informs us as to the composition of proteins that make up healthy cells, but also altered compositions that create diseased cells. How protein production is controlled through the regulation of the genes that encode them is of critical importance for healthy and diseased cells. Knowing precisely where gene regulatory proteins bind, and are organized throughout the genome, including their interactions with each other, informs us as to how genes are regulated and mis-regulated. Since there are potentially thousands of different kinds of regulatory proteins and thousands of different kinds of human cell types and environmental responses that are a product of various subsets of regulatory proteins, the entire “universe” of gene regulatory events is quite substantial and consequently, quite costly to identify. A subset of these events will likely be informative or diagnostic of diseases states. Therefore, an important goal is to define informative interactions using cost-enabling, high accuracy, and robust genome-wide assays. To this end, ChIP-exo was developed to map the genomic binding locations of gene regulatory proteins at near-single base pair resolution. This assay will be applied, in high throughput, to determine the genome-wide positional organization of factors within protein-DNA complexes, like enhanceosomes. By broadly mapping the various classes of proteins that constitute much of the regulated epigenome, general rules about enhancer and repressor complex organization will be deduced. Aim 1 involves collecting genome-wide ChIP-exo data in human cell lines for a wide variety of protein-DNA complexes. Aim 2 will develop and implement computational approaches towards pattern recognition and data distillation in ChIP-exo datasets. The results are expected to provide structural insights into macromolecular protein complex assembly on a genomic scale, and in various cell types and conditions.