Computational modeling of DNA methylation-mediated gene regulation

DNA甲基化介导的基因调控的计算模型

• 批准号: 10405488
• 负责人: John R Edwards
• 金额: $ 36.69万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405488
• 关键词: Affect; Binding; Binding Proteins; Binding Sites; Biological Assay; Brain; Cataloging; Catalogs; Cells; Characteristics; Chemicals; Clinic; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Computer Models; Computer software; Cytosine; DNA; DNA Methylation; DNA Sequence; DNA sequencing; Data; Data Set; Development; Diagnostic; Disease; Distal; Elements; Enhancers; Epigenetic Process; Etiology; Future; Gene Expression; Gene Expression Regulation; Generations; Genes; Genetic Transcription; Genetic Variation; Genome; Glioma; Goals; Individual; International; Label; Laboratories; Malignant Neoplasms; Maps; Measures; Mediating; Methods; Methylation; Modeling; Modification; Mus; Natural Language Processing; Neurons; Nucleic Acid Regulatory Sequences; Paper; Pathologic; Patients; Pattern; Play; Publishing; Recurrence; Regulation; Regulatory Element; Reporter; Resolution; Retrieval; Role; Sampling; Series; Signal Transduction; Site; Software Tools; Testing; The Cancer Genome Atlas; Training; Translating; base; cancer genome; cancer type; clinical sequencing; cofactor; demethylation; embryonic stem cell; epigenetic therapy; epigenome; epigenome editing; epigenomics; experimental study; genome sequencing; genome wide methylation; genome-wide; histone modification; human disease; in silico; individualized medicine; long short term memory network; machine learning model; methylation pattern; methylome; mutant; nanopore; network architecture; predictive modeling; predictive tools; prognostic; prognostic assays; promoter; recruit; relating to nervous system; success; tool; transcription factor; whole genome

Abstract Large numbers of complete methylomes are being acquired through clinical sequencing projects, such as through The Cancer Genome Atlas, Blueprint Epigenome Project, and International Cancer Genome Consortium. Furthermore, third-generation nanopore sequencers, which detect DNA methylation and genetic variation in a single experiment, are nearly ready for routine clinical sequencing and will provide complete methylomes for all patients where whole-genome sequencing is indicated. Current analysis tools however only perform preliminary methylome processing and catalogue differentially methylated regions (DMRs). In order to transform methylome analysis into a clinically useful diagnostic/prognostic test, we need to develop predictive tools to interpret the functional and pathological consequences of identified methylation changes. Towards this goal, we have published a series of papers demonstrating that machine-learning based models utilizing high- resolution signatures of all methylation changes around a promoter vastly outperform conventional DMR methods. Our models accurately predict expression states at genes potentially regulated by methylation and reveal predictive methylation signatures that facilitate mechanistic interpretation. Nonetheless, several challenges remain before we can achieve our goals of translating genome-wide methylation data for routine clinical use: (1) To our knowledge, no current models integrate distal enhancers, whose activation is affected by DNA methylation. Such integrative analysis is necessary to understand consequences of methylation changes in cancers, whose genomes frequently undergo wide-spread methylation changes. In addition, such modelling will be essential to understand the role of 5-hydroxymethylcytosine (5hmC), which may play both repressive and activating roles in neurons depending on whether it is found at promoters or enhancers. (2) Our current models (and conventional approaches) represent methylation data independent of DNA sequence despite mechanistic studies demonstrating that methylation changes can have different functional effects depending on which sequences change and depending on the context of the local regulatory grammar. In this proposal, we will meet these challenges by first developing a predictive model that incorporates 5-methylcytosine and 5hmC at promoters and enhancers to determine how these marks act in concert. In particular, we will examine the hypothesized dual role of 5hmC as a repressor at promoters and as an activator at enhancers in cortical neurons. We will then use new advances in natural language processing to model DNA sequence and methylation to predict expression states. Our results will reveal which regulatory elements and transcription factors binding sites are affected by DNA methylation and how changes at different sites collaborate to affect expression changes. We will experimentally validate our in silico predictions using a combination of reporter assays and CRISPR- based epigenome-editing tools. Thus, the software tools we develop will form an important toolkit for the analysis and mechanistic interpretation of whole-genome methylation studies, both in the laboratory and clinic.

摘要 通过临床测序项目获得了大量完整的甲基化组，例如 通过癌症基因组图谱，蓝图表观基因组计划和国际癌症基因组 财团此外，第三代纳米孔测序仪，检测DNA甲基化和遗传 在单一实验中的变异，几乎准备好常规临床测序，并将提供完整的 所有需要进行全基因组测序的患者的甲基化组。然而，目前的分析工具 进行初步的甲基化组处理和编目差异甲基化区域（DMR）。为了 将甲基化分析转化为临床上有用的诊断/预后测试，我们需要开发预测性 用于解释已鉴定的甲基化变化的功能和病理后果的工具。为实现这一 目标，我们已经发表了一系列论文，证明基于机器学习的模型利用高， 启动子周围所有甲基化变化的分辨率特征大大优于传统DMR 方法.我们的模型准确地预测了可能受甲基化调控的基因的表达状态， 揭示了促进机理解释的预测性甲基化特征。尽管如此， 在我们实现将全基因组甲基化数据翻译成常规DNA的目标之前， 临床用途：（1）据我们所知，目前没有模型整合远端增强子，其激活受 DNA甲基化这种综合分析对于了解甲基化变化的后果是必要的 在癌症中，其基因组经常发生广泛的甲基化变化。此外，这种模式 5-羟甲基胞嘧啶（5 hmC）的作用，它可能发挥抑制和 在神经元中的激活作用取决于它是否在启动子或增强子中被发现。(2)现有的模型来 (and常规方法）表示与DNA序列无关的甲基化数据， 研究表明，甲基化的变化可以有不同的功能影响，这取决于 序列改变并且取决于局部调节语法的上下文。在这个提案中，我们将见面 首先开发一个预测模型，将5-甲基胞嘧啶和5 hmC结合在一起， 启动子和增强子，以确定这些标记如何协同作用。特别是，我们将研究 假设5 hmC在皮质神经元中作为启动子的阻遏物和作为增强子的激活物的双重作用。 然后，我们将使用自然语言处理的新进展来模拟DNA序列和甲基化， 预测表达状态。我们的结果将揭示哪些调控元件和转录因子结合位点 受DNA甲基化的影响，以及不同位点的变化如何协同影响表达变化。 我们将使用报告基因测定和CRISPR的组合来实验验证我们的计算机预测。 基于表观基因组编辑工具。因此，我们开发的软件工具将成为分析的重要工具包 和全基因组甲基化研究的机制解释，无论是在实验室和临床。