Integrating epigenomics with DNA breathing dynamics for human non-coding disease variants

将表观基因组学与 DNA 呼吸动力学相结合，研究人类非编码疾病变异

• 批准号: 9908170
• 负责人: Boian Stoianov Alexandrov
• 金额: $ 59.24万
• 依托单位: NORTHSHORE UNIVERSITY HEALTHSYSTEM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-08 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9908170
• 关键词: ATAC-seq; Address; Adenine; Affect; Alleles; Base Pairing; Binding; Biological; Biological Assay; Biology; Brain; Breathing; Cell Line; ChIP-seq; Chromatin; Classification; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer Models; Computing Methodologies; DNA; DNA Binding; DNA-Binding Proteins; Data; Data Set; Development; Disease; Equilibrium; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Genetic Translation; Genetic Variation; Genomics; Human; Individual; Machine Learning; Methods; Modeling; Molecular; Neurodevelopmental Disorder; Neurons; Phenotype; Property; Rest; Risk; Sampling; Schizophrenia; Shapes; Software Tools; Structure; Tissues; Training; Untranslated RNA; Validation; Variant; Work; base; causal variant; chromatin modification; clinical translation; computer framework; computerized tools; cost effective; disorder risk; epigenomics; genetic variant; genome sequencing; genome wide association study; genome-wide; improved; induced pluripotent stem cell; novel; open source; predictive modeling; predictive tools; risk variant; single-cell RNA sequencing; support vector machine; targeted treatment; trait; transcription factor; whole genome

ABSTRACT Genome-wide association studies (GWAS) and whole genome sequencing of complex diseases have revealed a plethora of disease risk variants, most of which lie in noncoding regions of DNA without easily interpretable function. A main functional mechanism of noncoding variants is to alter chromatin accessibility to transcription factors (TFs), thereby influencing gene expression. Predicting the effects of noncoding variants on TF binding and gene expression on a large scale is thus important but remains challenging. Available computational tools for predicting regulatory variants largely rely on TF-binding motif models and/or local chromatin modification features. Here, we aim to develop a novel computational framework to address two major limitations of these methods. First, given that known disease causal noncoding variants often reside outside of TF binding motifs, how can we improve the prediction of TF binding variants outside of motifs? For this, we plan to integrate TF ChIP-seq data with features that are important for TF binding but have not been considered in previous methods, in particular the DNA breathing dynamics (AIM1). DNA breathing reflects local transient opening of the DNA double helix due to thermal fluctuations. We have shown that genetic variants can affect nearby (up to a few hundred base pairs) DNA breathing dynamics that affect TF binding. Using TF ChIP-seq data, we will train models that predict specific TF binding variants in or outside TF motifs, incorporating DNA breathing dynamics with other features such as DNA shapes and cooperative TF binding. Secondly, given that chromatin features only show modest (<2-fold) enrichment of genetic variants associated with complex diseases or traits, how can we improve the prediction of regulatory variants? For this, we will build a computation model, considering the allele-specific chromatin accessibility (ASCA; i.e., two alleles of a heterozygous individual show read imbalance in chromatin accessibility assays) as a functional readout of a regulatory variant (AIM2). We have shown that neuronal ASCA SNPs are highly enriched for those implicated by schizophrenia (SZ) GWAS. Using neuronal ASCA data, we will train models that predict variants with regulatory effects, taking advantage of our TF-specific classifiers (from AIM1). As a proof of concept, the models will be applied to a large SZ GWAS dataset to predict putative causal regulatory variants. We will validate the effects of the predicted top-ranking regulatory SZ variants on gene expression in a well-powered hiPSC sample by combining multiplex CRISPR-based SNP editing and single-cell RNA-seq analysis (AIM3). For SNPs showing the strongest regulatory effects, we will further use CRISPR editing to verify the SNP effect on gene expression and disease-relevant neuronal phenotypes. Accurately predicting TF-affecting noncoding variants will enable better understanding of the large number of noncoding variants implicated in complex disorders and help formulate testable biological hypotheses, ultimately facilitating the development of targeted therapeutics.

摘要 全基因组关联研究（GWAS）和复杂疾病的全基因组测序揭示了 大量的疾病风险变异，其中大多数位于DNA的非编码区， 功能非编码变体的一个主要功能机制是改变染色质对转录的可及性 转录因子（TF），从而影响基因表达。预测非编码变体对TF结合的影响 因此，大规模的基因表达是重要的，但仍然具有挑战性。可用的计算工具 用于预测调控变体的方法主要依赖于TF结合基序模型和/或局部染色质修饰 功能.在这里，我们的目标是开发一个新的计算框架，以解决这些两个主要的局限性， 方法.首先，鉴于已知的致病非编码变体通常位于TF结合之外， 基序，我们如何才能提高预测TF结合基序以外的变体？为此，我们计划 将TF ChIP-seq数据与对TF结合重要但在 以前的方法，特别是DNA呼吸动力学（AIM 1）。DNA呼吸反映了局部瞬态 DNA双螺旋因热波动而打开。我们已经证明，遗传变异可以影响 附近（多达几百个碱基对）的DNA呼吸动力学，影响TF结合。使用TF ChIP-seq 数据，我们将训练模型，预测特定的TF结合变体或TF基序外，纳入DNA 呼吸动力学与其他特征，如DNA形状和合作TF结合。其次，鉴于 染色质特征仅显示出与复杂的染色体相关的遗传变体的适度（<2倍）富集。 疾病或性状，我们如何提高对调控变异的预测？为此，我们将建立一个 计算模型，考虑等位基因特异性染色质可及性（ASCA;即，a的两个等位基因 杂合个体在染色质可及性测定中显示读数不平衡）作为 调节变体（AIM 2）。我们已经表明，神经元ASCA SNP高度富集了那些涉及 精神分裂症（SZ）GWAS。使用神经元ASCA数据，我们将训练预测变异的模型， 调节作用，利用我们的TF特异性分类器（来自AIM 1）。作为概念验证，模型 将被应用到一个大的SZ GWAS数据集，以预测推定的因果调节变异。我们将验证 预测的顶级调控SZ变体对功效良好的hiPSC样本中基因表达的影响 通过结合基于多重CRISPR的SNP编辑和单细胞RNA-seq分析（AIM 3）。对于显示 最强的调控作用，我们将进一步使用CRISPR编辑来验证SNP对基因表达的影响 和疾病相关的神经元表型。准确预测影响TF的非编码变异将使 更好地了解复杂疾病中涉及的大量非编码变异， 提出可验证的生物学假设，最终促进靶向治疗的发展。