Nuclear Receptor Networks in Human Disease

人类疾病中的核受体网络

基本信息

批准号：
7892935
负责人：
FRANCES M. SLADEK
金额：
$ 38万
依托单位：
UNIVERSITY OF CALIFORNIA RIVERSIDE
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-07-13 至 2012-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7892935
关键词：
Affect Atherosclerosis Atlases Binding Binding Sites Cataloging Catalogs Chronic Code Collection Complex Computer Simulation Contracts DNA DNA Binding Data Data Set Databases Diabetes Mellitus Diagnosis Disease Drug Delivery Systems Etiology Funding Gene Expression Gene Expression Profile Gene Proteins Gene Targeting Generations Genes Genetic Genome Genomics Goals Human Human Genome Individual Internet Life Ligand Binding Ligands Link Location Machine Learning Medicine Mental Health Mental disorders Metabolic Metabolic Diseases Mining Modeling Molecular Molecular Profiling Nuclear Receptors Nucleic Acid Regulatory Sequences Organ Patients Play Population Prevention Protein Binding Proteins Receptor Signaling Recruitment Activity Regulator Genes Resources Response Elements Risk Role Single Nucleotide Polymorphism Specificity Techniques Terminator Codon Tissues Transcription Initiation Site Transcriptional Regulation Variant Work base clinically relevant data modeling data sharing gene function genetic association genome wide association study genome-wide high throughput technology human disease member network models protein function receptor binding tool trait transcription factor

项目摘要

DESCRIPTION (provided by applicant): Complex disorders typically involve multiple factors and gene loci and are heavily influenced by environmental conditions. This is particularly true for metabolic diseases and diseases affecting mental health. As is often the case with such illnesses, regulators of gene expression, as well as their specific target genes can play a major role in not just the etiology but also the treatment of the disease. Some of the best characterized regulators associated with human disease are members of the nuclear receptor superfamily -- transcription factors that are regulated by the binding of ligands that are often influenced by environmental conditions. However, while a great deal is known about the mechanism by which individual nuclear receptors regulate the expression of individual target genes, what is sorely lacking is a comprehensive view of all their potential target genes in all the tissues in which they are expressed. This is true not only for metabolic organs for which the role of nuclear receptors is fairly well studied, but also the CNS where much less is known about the target genes and the functions of the nuclear receptors. Furthermore, several nuclear receptors are very effective drug targets, for both metabolic and mental disorders, making them among the most clinically relevant of all the transcription factors. Whereas recent advances in whole genome expression profiling and genome-wide location analysis (ChIP-chip, ChIP-seq) have allowed us to begin to define the complete transcriptome for some of these factors, much more remains to be done. Furthermore, these approaches can be technically challenging and costly, and hence very limiting. A complementary approach that has not been fully exploited is a computational one based on the DNA response elements that recruit the nuclear receptors to the regulatory regions of their target genes. However, in order to take full advantage of this approach, one must first have a comprehensive dataset of the binding motifs to which the receptors bind. The goals of this proposal are two-fold: 1) to comprehensively define the DNA binding specificity of a critical group of nuclear receptors using high throughput technology; and 2) to use that data to mine existing datasets in order to associate those nuclear receptors with human disease. These goals will be accomplished by pursuing three specific aims: 1) Use protein binding microarrays (PBMs) to determine the DNA binding specificity of select nuclear receptors on 10's of 1000's of unique sequences; and then use that data to develop high accuracy computational models to predict the entire set of sequences to which a given nuclear receptor binds; 2) use the PBM data and models generated in Aim 1 to computationally identify all the potential binding sites, target genes and related SNPs in the human genome for each nuclear receptor; and cross reference the results with databases linking genes and SNPs to human diseases (i.e., GAD and HapMap); 3) incorporate the results into a network of nuclear receptors and their target genes, with particular emphasis on metabolic diseases and mental health disorders. All of the binding motifs, potential target genes, related SNPs and networks will be catalogued in the on-line resource PAZAR, a public database of transcription factor and regulatory sequence annotation (http://www.pazar.info/cgi-bin/index.pl), and the NIH-funded Nuclear Receptor Signaling Atlas (NURSA) (http://www.nursa.org/). Many chronic human diseases that arise later in life - such as diabetes, atherosclerosis and mental disorders - are due to multiple factors, both genetic and environmental. The recent sequencing of the human genome has allowed us to identify new genes associated with these diseases at an ever increasing rate. In this study we apply the latest high throughput technology to help identify variations in the genetic sequence that might be related to those (and other) diseases by examining the regulatory regions of genes and the proteins that bind those regions. Our work will help bring us closer to an era of personalized medicine in which prevention, diagnosis and treatment are tailored to the individual patient, making them more effective and less costly.

描述（由申请人提供）：复杂疾病通常涉及多种因素和基因位点，并且深受环境条件的影响。对于代谢疾病和影响心理健康的疾病尤其如此。与此类疾病的常见情况一样，基因表达的调节因子及其特定靶基因不仅在病因学上发挥着重要作用，而且在疾病的治疗中也发挥着重要作用。一些与人类疾病相关的最有特征的调节因子是核受体超家族的成员——这些转录因子通过配体的结合进行调节，而配体的结合通常受到环境条件的影响。然而，虽然人们对单个核受体调节单个靶基因表达的机制了解很多，但最缺乏的是对其表达的所有组织中所有潜在靶基因的全面了解。这不仅适用于核受体作用已得到相当深入研究的代谢器官，也适用于对核受体的靶基因和功能知之甚少的中枢神经系统。此外，一些核受体对于代谢和精神疾病来说是非常有效的药物靶点，使它们成为所有转录因子中临床最相关的。尽管全基因组表达谱和全基因组定位分析（ChIP-chip、ChIP-seq）的最新进展使我们能够开始定义其中一些因子的完整转录组，但仍有许多工作要做。此外，这些方法在技术上具有挑战性且成本高昂，因此非常有限。一种尚未得到充分利用的补充方法是基于 DNA 反应元件的计算方法，该元件将核受体招募到其靶基因的调节区域。然而，为了充分利用这种方法，首先必须拥有受体结合的结合基序的综合数据集。该提案的目标有两个：1）利用高通量技术全面定义一组关键核受体的 DNA 结合特异性； 2) 使用该数据挖掘现有数据集，以便将这些核受体与人类疾病联系起来。这些目标将通过追求三个具体目标来实现：1）使用蛋白质结合微阵列（PBM）来确定选定核受体对 1000 个独特序列中的 10 个的 DNA 结合特异性；然后使用该数据开发高精度计算模型来预测给定核受体结合的整组序列； 2) 使用目标 1 中生成的 PBM 数据和模型，通过计算识别人类基因组中每个核受体的所有潜在结合位点、靶基因和相关 SNP；并将结果与将基因和 SNP 与人类疾病联系起来的数据库（即 GAD 和 HapMap）进行交叉参考； 3）将结果纳入核受体及其靶基因网络，特别强调代谢疾病和精神健康障碍。所有结合基序、潜在靶基因、相关 SNP 和网络都将在在线资源 PAZAR 中编目，这是一个转录因子和调控序列注释的公共数据库 (http://www.pazar.info/cgi-bin/ index.pl），以及 NIH 资助的核受体信号图谱 (NURSA) (http://www.nursa.org/)。许多晚年出现的人类慢性疾病，如糖尿病、动脉粥样硬化和精神障碍，都是由遗传和环境等多种因素造成的。最近的人类基因组测序使我们能够以越来越快的速度识别与这些疾病相关的新基因。在这项研究中，我们应用最新的高通量技术，通过检查基因的调控区域和结合这些区域的蛋白质，帮助识别可能与这些（和其他）疾病相关的基因序列变异。我们的工作将帮助我们更接近个性化医疗时代，在这个时代，预防、诊断和治疗都是针对个体患者量身定制的，从而使治疗更有效、成本更低。