High-throughput methods for elucidating the control of chromatin accessibility

阐明染色质可及性控制的高通量方法

• 批准号: 8861021
• 负责人: David K Gifford
• 金额: $ 76.29万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-13 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8861021
• 关键词: Access to Information; Base Pairing; Base Sequence; Binding; Binding Proteins; Cells; Chromatin; Chromatin Modeling; Code; Computer Simulation; Controlled Vocabulary; DNA; DNA Library; DNA Sequence; DNase I hypersensitive sites sequencing; Data; Ectopic Expression; Engineering; Enhancers; Etiology; Genome; Genomics; Genotype; Goals; Health; Human; Human Genome; Individual; Instruction; Knock-out; Learning; Libraries; Location; Machine Learning; Maps; Measures; Methods; Modeling; Oligonucleotides; Pancreas; Proteins; Regenerative Medicine; Regulation; Regulatory Element; Resolution; Role; Testing; Time; Variant; Vocabulary; Work; base; biological systems; cell injury; cell type; combinatorial; design; embryonic stem cell; gain of function; genetic regulatory protein; genome editing; genome sequencing; genome wide association study; genome-wide; improved; loss of function; novel; phrases; predictive modeling; programs; promoter; public health relevance; relating to nervous system; response; stem cell differentiation; syntax; synthetic construct; transcription factor

 DESCRIPTION (provided by applicant): We will develop the first validated predictive model of how transcription factors dynamically determine genome-wide chromatin accessibility that is generalizable across biological systems. We will accomplish this goal with three specific aims. We will develop novel Genome Syntax to Regulation (GSR) models that accurately learn a genomic regulatory vocabulary and predict how phrases in this vocabulary control chromatin accessibility (Aim 1). As part of this aim we will identify transcription factor binding motifs tha are in the discovered regulatory vocabulary. We will validate and refine the causality of these models by testing whether they accurately predict the chromatin accessibility of thousands of synthetic DNA "phrases" that have been engineered into specific genomic locations and measured in the context of transcription factor gain-of-function and loss-of-function studies. The phrases will be designed to elucidate both the factors and grammar that control chromatin opening in several distinct cellular states (Aim 2). We will use our predictive models to assign importance scores to individual genome bases and to predict how selected factors alter chromatin accessibility genome wide (Aim 3). We will test the ability of our importance scores to identify regulatory SNPs in the context of human genome-wide association study (GWAS) data, and we will validate model predictions of changes in whole genome chromatin accessibility in response to ectopic factor expression. Through computational modeling of the effect of such ectopic factor expression, we will develop a predictive understanding of how transcription factors alter chromatin state, laying the groundwork for a novel regenerative medicine paradigm of predictive cellular programming.

 描述（由申请人提供）：我们将开发第一个经过验证的预测模型，该模型可以预测转录因子如何动态地确定全基因组染色质可及性，该模型可在生物系统中推广。我们将通过三个具体目标来实现这一目标。我们将开发新的基因组调控（GSR）模型，准确地学习基因组调控词汇，并预测该词汇中的短语如何控制染色质可及性（目标1）。作为这一目标的一部分，我们将确定转录因子结合基序，这是在发现的监管词汇。我们将验证和完善这些模型的因果关系，通过测试它们是否准确预测了数千个合成DNA“短语”的染色质可及性，这些合成DNA“短语”已被工程化到特定的基因组位置，并在转录因子功能获得和功能丧失研究的背景下进行测量。这些短语旨在阐明在几种不同的细胞状态下控制染色质开放的因素和语法（目标2）。我们将使用我们的预测模型来为单个基因组碱基分配重要性分数，并预测所选因素如何改变全基因组染色质可及性（目标3）。我们将测试我们的重要性分数的能力，以确定在人类全基因组关联研究（GWAS）数据的背景下，调节单核苷酸多态性，我们将验证模型预测的变化，在全基因组染色质可及性响应异位因子表达。通过这种异位因子表达的影响的计算建模，我们将开发一个预测性的理解如何转录因子改变染色质状态，奠定了预测细胞编程的一种新的再生医学范式的基础。