Differential Regulatory Networks in Disease: Application to Macular Degeneration

疾病中的差异调节网络：在黄斑变性中的应用

• 批准号: 9132254
• 负责人: Jiang Qian
• 金额: $ 36.45万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9132254
• 关键词: Aberrant DNA Methylation; Address; Affect; Age related macular degeneration; Algorithms; American; Binding; Binding Sites; Biological Models; Biological Process; Blindness; Cell Line; Cells; Chromatin; Complex; Computational algorithm; Computer software; DNA; DNA Methylation; Data Set; Databases; Deoxyribonuclease I; Development; Disease; Enhancers; Epigenetic Process; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Gene Targeting; Genetic; Genetic Transcription; Genetic Variation; Goals; Health; Histones; Homology Modeling; Hypersensitivity; Laboratories; Link; Macular degeneration; Malignant Neoplasms; Maps; Measurement; Measures; Methylation; Modeling; Modification; Molecular; Normal tissue morphology; Nucleic Acid Regulatory Sequences; Play; Retina; Retinal; Role; Sampling; Site; Specificity; Techniques; Testing; Tissues; Transcriptional Regulation; Transfection; Untranslated RNA; Variant; aged; base; chromatin immunoprecipitation; computer framework; design; epigenetic variation; genetic association; genetic variant; genome wide association study; human disease; in vivo; insight; interest; methylome; new therapeutic target; normal aging; novel; programs; promoter; protein structure prediction; software development; therapeutic target; tool; trait; transcription factor; transcriptome; user-friendly

DESCRIPTION (provided by applicant): Defining the regulatory networks altered in the disease can provide not only the insights on the mechanisms underlying disease, but also the possible therapeutic targets. Several factors such as genetic variation and methylation sites can disrupt the interaction between transcription factors (TFs) and cis-regulatory regions (e.g. promoters and enhancers) and thus alter the regulatory networks. However, to identify the altered networks in disease is still challenging. First, to identify the genetic variation and methylation sites that play a role in gene regulation, we will need to map the genetic variation and methylation sites on the regulatory regions that is specific to the pathological tissues. While DNase I hypersensitivity sites (DHSs) and histone mark profiles are powerful to determine the regulatory regions, it is not feasible for every laboratory to be equipped to measure DHS and histone mark on the tissues of interest. Therefore, we need a computational algorithm that is accurate enough to differentiate the regulatory regions between diseased and normal samples. Second, although a large number of differentially methylated sites have been determined for different disease, their functional role remains largely unclear. DNA methylation has been generally considered as a potent epigenetic modification that prohibits TF recruitment, resulting in transcription suppression. Recent studies and our own preliminary results showed that some TFs preferentially bind to methylated DNA, an interaction that in some cases activates gene transcription. Therefore, we need to identify such TFs and incorporate these methylation- dependent TF-DNA interactions in the computational platform. Third, we need a unified computational framework to incorporate various and these diverse types of factors that could alter the regulatory networks. To address these challenges, we will develop a computational framework to incorporate the effects of genetic and epigenetic variations and identify the regulatory networks altered by these effects. In this framework, we will develop a computational approach to predict regulatory regions in tissue of interest by integrating various epigenetic datasets (Aim 1). Our approach is analogous to homology modeling for protein structure prediction, fully utilizing the existing epigenetic datasets from ENCODE project. We will then develop a model to provide quantitative measurement of interaction strength between TFs and DNA with consideration of genetic variation, DNA methylation and TF concentration (Aim 2). This model will incorporate our new discovery that some TFs preferentially bind to methylated DNA motifs. Our computational framework will then be applied to age-related macular degeneration (AMD), which is the leading cause of vision loss in Americans aged 60 and older. The altered regulatory networks in AMD will then be experimentally evaluated (Aim 3). Finally, we will make our software and the regulatory networks in AMD available through an interactive, user-friendly database (Aim 4).

描述（由申请人提供）：定义疾病中改变的调控网络不仅可以提供对疾病潜在机制的见解，还可以提供可能的治疗靶点。一些因素，如遗传变异和甲基化位点可以破坏转录因子（TF）和顺式调控区（如启动子和增强子）之间的相互作用，从而改变调控网络。然而，识别疾病中改变的网络仍然具有挑战性。首先，为了确定在基因调控中发挥作用的遗传变异和甲基化位点，我们需要绘制病理组织特异性调控区域上的遗传变异和甲基化位点。而 DNA酶I超敏位点（DHS）和组蛋白标记谱是确定调控区域的有力工具，但并非每个实验室都能检测感兴趣组织上的DHS和组蛋白标记。因此，我们需要一种计算算法，该算法足够精确以区分患病和正常样本之间的调节区域。第二，虽然大量的差异甲基化位点已被确定为不同的疾病，其功能作用仍然很大程度上不清楚。DNA甲基化通常被认为是一种有效的表观遗传修饰，它阻止了TF的募集，导致转录抑制。最近的研究和我们自己的初步结果表明，一些TF优先结合甲基化的DNA，在某些情况下激活基因转录的相互作用。因此，我们需要鉴定此类TF并将这些甲基化依赖性TF-DNA相互作用并入计算平台中。第三，我们需要一个统一的计算框架来整合各种不同类型的因素，这些因素可能会改变监管网络。为了应对这些挑战，我们将开发一个计算框架，将遗传和表观遗传变异的影响，并确定这些影响改变的调控网络。在这个框架中，我们将开发一种计算方法，通过整合各种表观遗传数据集来预测感兴趣组织中的调控区域（目标1）。我们的方法类似于蛋白质结构预测的同源建模，充分利用ENCODE项目现有的表观遗传数据集。然后，我们将开发一个模型，考虑遗传变异，DNA甲基化和TF浓度（目标2），提供定量测量TF和DNA之间的相互作用强度。这个模型将结合我们的新发现，一些TF优先结合甲基化的DNA基序。然后，我们的计算框架将应用于年龄相关性黄斑变性（AMD），这是导致60岁及以上美国人视力丧失的主要原因。然后将实验评估AMD中改变的调节网络（目标3）。最后，我们将通过一个交互式的、用户友好的数据库（目标4）提供我们的软件和AMD的监管网络。