MODELING DNA METHYLATION'S ROLE IN GENE REGULATION

模拟 DNA 甲基化在基因调控中的作用

• 批准号: 8759963
• 负责人: John R Edwards
• 金额: $ 34.31万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8759963
• 关键词: Address; Advanced Malignant Neoplasm; Affect; Bioinformatics; Biological Assay; Brain; Cells; Classification; Complication; Computational Technique; Computer Simulation; Computer Vision Systems; Computer software; Computing Methodologies; Conflict (Psychology); DNA; DNA Methylation; Data; Data Set; Development; Disease; Epigenetic Process; Event; Gene Activation; Gene Cluster; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Gene Silencing; Genes; Genetic Transcription; Genome; Genomics; Hypermethylation; Imagery; Individual; Knowledge; Lead; Link; Malignant Neoplasms; Mammary Neoplasms; Maps; Mediating; Methods; Methylation; Metric; Modeling; Mus; Neuraxis; Patients; Pattern; Play; Publishing; Regulation; Regulator Genes; Reporter; Research Personnel; Resolution; Retroelements; Retrotransposon; Role; Sampling; Shapes; Signal Transduction; Statistical Distributions; Techniques; Technology; Testing; Time; Tissues; Transcription Initiation Site; Transcriptional Regulation; Validation; Work; analytical method; anticancer research; base; cancer type; cell type; common rule; computerized tools; embryonic stem cell; genome-wide; human disease; improved; insight; malignant breast neoplasm; mouse model; promoter; public health relevance; tool

DESCRIPTION (provided by applicant): Changes in patterns of DNA methylation, a modulator of gene expression, play a key role in development and disease. There has been a flurry of development of new experimental methods to accurately map 5- methylcytosine and 5-hydroxymethylcytosine genome-wide. However, development of computational techniques to interpret these data has lagged behind. Current analytical methods are mostly concerned with visualization of genome-level correlations between DNA methylation and other epigenetic marks or with the identification of differentially methylated regions between samples. These tools have performed poorly when trying to link methylation and expression changes at specific loci, and they find quite weak genome-wide correlations between them. We hypothesize that such discrepancies are due to limitations of the current analysis methods, which tend to oversimplify local DNA methylation signals. We propose to develop a new suite of tools using methods from computer vision to associate spatially-similar methylation changes with corresponding changes in transcription. This approach allows us to make minimal assumptions about what these signals should look like. First, we use Dynamic Time Warping (DTW), a curve similarity metric, to cluster genes together based on their methylation signals. We then find clusters of genes with similar methylation patterns that have coordinated differential expression. We export the patterns defined by these curves for use as a classifier, enabling us to enumerate genes with associated methylation and expression changes. Our model considers the entire shape and course of the signal around the gene promoter, rather than averaging the signal across windows or modeling signals with idealized statistical distributions. We recently published how our method discovered a variety of DNA methylation patterns associated with gene silencing, and produced longer and markedly higher quality gene lists than those generated by other methods. In this proposal we build on these results to address three aims concerning DNA methylation's role in gene silencing: (1) We will build a classifier to model the methylation patterns we discover and use it to examine the role of DNA hypomethylation, which occurs primarily at alternative and retrotransposon promoters that are poorly annotated, in transcriptome regulation in breast cancer. (2) We will expand our model to address the controversial role of 5-hydroxymethylcytosine in gene regulation and its interplay with 5-methylcytosine in differentiated cells in the central nervous system. (3) We will develop general rules governing the methylation signatures we have identified and experimentally validate their functionality. This proposal will yield a set of experimentally validated models for how DNA methylation contributes to gene silencing as well as a set of computational tools other researchers can use to analyze the role of methylation in human disease.

描述（由申请人提供）：DNA甲基化模式的变化是基因表达的调节剂，在发育和疾病中起关键作用。近年来，人们开发了一系列新的实验方法来精确地绘制5-甲基胞嘧啶和5-羟甲基胞嘧啶的全基因组图谱。然而，解释这些数据的计算技术的发展落后了。目前的分析方法主要关注DNA甲基化与其他表观遗传标记之间基因组水平相关性的可视化或样品之间差异甲基化区域的识别。当试图将特定位点的甲基化和表达变化联系起来时，这些工具表现不佳，而且它们发现它们之间的全基因组相关性相当弱。我们假设这种差异是由于当前分析方法的局限性，这些方法往往过于简化局部DNA甲基化信号。我们建议开发一套新的工具，使用计算机视觉方法将空间相似的甲基化变化与相应的转录变化联系起来。这种方法允许我们对这些信号应该是什么样子做出最小的假设。首先，我们使用动态时间扭曲（DTW），一种曲线相似性度量，根据甲基化信号将基因聚类在一起。然后，我们发现具有相似甲基化模式的基因簇具有协调的差异表达。我们导出由这些曲线定义的模式作为分类器使用，使我们能够列举与甲基化和表达变化相关的基因。我们的模型考虑了基因启动子周围信号的整个形状和过程，而不是跨窗口平均信号或用理想化的统计分布建模信号。我们最近发表了我们的方法如何发现与基因沉默相关的各种DNA甲基化模式，并产生比其他方法产生的更长且明显更高质量的基因列表。在本提案中，我们将基于这些结果来解决关于DNA甲基化在基因沉默中的作用的三个目标：(1)我们将建立一个分类器来模拟我们发现的甲基化模式，并使用它来检查DNA低甲基化在乳腺癌转录组调节中的作用，DNA低甲基化主要发生在注释不充分的替代和反转录转座子启动子上。(2)我们将扩展我们的模型，以解决在中枢神经系统分化细胞中5-羟甲基胞嘧啶在基因调控中的有争议的作用及其与5-甲基胞嘧啶的相互作用。(3)我们将制定管理我们已经确定的甲基化特征的一般规则，并通过实验验证其功能。这一提议将产生一组实验验证的模型，用于DNA甲基化如何促进基因沉默，以及其他研究人员可以使用一组计算工具来分析甲基化在人类疾病中的作用。