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）：定义疾病中发生的调节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标。诸如遗传变异和甲基化位点之类的几个因素会破坏转录因子（TFS）和顺式调节区域（例如启动子和增强子）之间的相互作用，从而改变了调节网络。但是，确定疾病中的改变网络仍然具有挑战性。首先，为了确定在基因调节中起作用的遗传变异和甲基化位点，我们将需要在调节区域的遗传变异和甲基化位点绘制特定于病理组织的遗传变异和甲基化位点。尽管 DNase I超敏反应位点（DHSS）和组蛋白Mark曲线具有强大的功能来确定调节区域，每个实验室都可以在感兴趣的组织上测量DHS和组蛋白标记。因此，我们需要一种计算算法，该算法足够准确，可以区分患病和正常样本之间的调节区域。其次，尽管已经确定了大量差异甲基化的位点，但其功能作用在很大程度上仍然不清楚。 DNA甲基化通常被认为是禁止TF募集的有效表观遗传修饰，从而导致转录抑制。最近的研究和我们自己的初步结果表明，某些TF优先结合甲基化的DNA，这种相互作用在某些情况下激活了基因转录。因此，我们需要确定此类TF，并将这些依赖性的TF-DNA相互作用纳入计算平台。第三，我们需要一个统一的计算框架来结合可能改变监管网络的各种不同类型的因素。为了应对这些挑战，我们将开发一个计算框架，以结合遗传和表观遗传变化的影响，并确定这些影响改变的调节网络。在此框架中，我们将通过整合各种表观遗传数据集（AIM 1）来开发一种计算方法来预测感兴趣组织中的调节区域。我们的方法类似于蛋白质结构预测的同源性建模，完全利用了编码项目的现有表观遗传数据集。然后，我们将开发一个模型，以考虑遗传变异，DNA甲基化和TF浓度的TFS和DNA之间的相互作用强度的定量测量（AIM 2）。该模型将结合我们的新发现，即某些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与年龄相关的黄斑变性（AMD），这是60岁及60岁以上美国人视力丧失的主要原因。然后，将对AMD中的监管网络改变，然后将对实验评估（AIM 3）。最后，我们将通过交互式，用户友好的数据库来制作AMD中的软件和监管网络（AIM 4）。