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

人类基因调控复合体中蛋白质的全基因组结构组织

• 批准号: 10078275
• 负责人: Shaun Aengus Mahony
• 金额: $ 45.31万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-19 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10078275
• 关键词: Antibodies; Bar Codes; Base Pairing; Binding; Binding Proteins; Binding Sites; Biological Assay; Cell Line; Cells; Chromatin; Chromatin Remodeling Factor; Collection; Complex; DNA; DNA Polymerase II; DNA Sequence; DNA-Binding Proteins; DNA-protein crosslink; Data; Data Collection; Data Set; Detection; Development; Diagnostic; Disease; Enhancers; Event; Formaldehyde; Gene Expression Regulation; Genes; Genome; Genomics; Goals; Grain; Health; HepG2; Human; Human Cell Line; Human Genome; Individual; K-562; Libraries; Location; Machine Learning; Maps; Mechanics; Modeling; Multiprotein Complexes; Natural Language Processing; Nucleosomes; Pattern; Pattern Recognition; Physiological; Process; Production; Proteins; Protocols documentation; Publishing; RNA; Regulator Genes; Repressor Proteins; Resolution; Robotics; Running; Sampling; Site; Structure; System; Tissues; Transcript; Uncertainty; billboard; cell type; chromatin immunoprecipitation; cost; cost efficient; crosslink; deep sequencing; design; epigenome; experimental study; genetic regulatory protein; genome-wide; human embryonic stem cell; human model; in vivo; insight; organizational structure; promoter; protein complex; recruit; response; spatial relationship; syntax; transcription factor

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.

人类基因组的 DNA 序列告诉我们构成蛋白质的组成 健康细胞，但也改变了产生患病细胞的成分。如何控制蛋白质生产 通过调节编码它们的基因对于健康和患病细胞至关重要。 准确了解基因调控蛋白的结合位置以及在整个基因组中的组织方式，包括 它们之间的相互作用，告诉我们基因是如何被调节和错误调节的。由于有 可能有数千种不同的调节蛋白和数千种不同的人类细胞 类型和环境反应是调节蛋白各种子集的产物，整个 基因调控事件的“宇宙”相当大，因此识别成本相当高。的一个子集 这些事件可能会为疾病状态提供信息或诊断。因此，一个重要的目标是定义 使用成本允许、高精度和强大的全基因组检测进行信息交互。为此， ChIP-exo 旨在绘制基因调控蛋白在近单碱基处的基因组结合位置 对分辨率。该测定将以高通量应用，以确定全基因组位置 蛋白质-DNA 复合物中因子的组织，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、增强子和增强子的一般规则 将推断出阻遏物复合体的组织。目标 1 涉及收集全基因组 ChIP-exo 数据 人类细胞系中含有多种蛋白质-DNA 复合物。目标 2 将制定并实施 ChIP-exo 数据集中模式识别和数据蒸馏的计算方法。结果 有望为基因组规模的大分子蛋白质复合物组装提供结构见解， 以及在各种细胞类型和条件下。