Computational modeling of DNA methylation-mediated gene regulation

摘要 通过临床测序项目可以获得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可检测 DNA 甲基化和遗传 单个实验中的变化，几乎已准备好进行常规临床测序，并将提供完整的 对所有需要进行全基因组测序的患者进行甲基化组分析。然而，当前的分析工具仅 进行初步甲基化处理并对差异甲基化区域 (DMR) 进行编目。为了 将甲基化组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方法 解释已识别甲基化变化的功能和病理后果的工具。朝这个方向 目标，我们发表了一系列论文，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远远优于传统的 DMR 方法。我们的模型准确预测可能受甲基化调节的基因的表达状态 揭示有助于机制解释的预测甲基化特征。尽管如此，几个 在我们实现将全基因组甲基化数据转化为常规数据的目标之前，挑战仍然存在 临床应用：（1）据我们所知，目前的模型没有整合远端增强子，其激活受到以下因素的影响： 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。另外，这样的造型 对于了解 5-羟甲基胞嘧啶 (5hmC) 的作用至关重要，它可能同时发挥抑制作用和 神经元中的激活作用取决于它是否存在于启动子或增强子处。 (2) 我们现有的型号 （和传统方法）代表独立于 DNA 序列的甲基化数据，尽管机制不同 研究表明，甲基化变化可能会产生不同的功能影响，具体取决于哪些因素 序列会根据当地监管语法的上下文而变化。在这个提案中，我们将满足 首先开发一个包含 5-甲基胞嘧啶和 5hmC 的预测模型来应对这些挑战 启动子和增强子来确定这些标记如何协同作用。特别是，我们将检查 假设 5hmC 在皮层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，我们将利用自然语言处理的新进展来模拟 DNA 序列和甲基化，以 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 甲基化以及不同位点的变化如何协同影响表达变化的影响。 我们将结合报告分析和 CRISPR 来通过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。 以及实验室和临床全基因组甲基化研究的机制解释。

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.