Nuclear Receptor Networks in Human Disease

描述(由申请人提供)：复杂疾病通常涉及多个因素和基因位点，并严重受环境条件的影响。代谢性疾病和影响心理健康的疾病尤其如此。就像这类疾病的常见情况一样，基因表达的调节以及它们的特定靶基因不仅在疾病的病因中发挥着重要作用，而且在疾病的治疗中也发挥着重要作用。与人类疾病相关的一些最具特征的调节因子是核受体超家族的成员--转录因子受配体的结合调节，而配体结合通常受环境条件的影响。然而，虽然人们对单个核受体调节单个靶基因表达的机制已经有了很多了解，但严重缺乏对它们在表达它们的所有组织中的所有潜在靶基因的全面看法。这不仅适用于研究核受体作用的代谢器官，也适用于对靶基因和核受体功能知之甚少的中枢神经系统。此外，几个核受体是非常有效的药物靶点，对代谢和精神障碍都是如此，使它们成为所有转录因子中最具临床相关性的转录因子之一。虽然最近在全基因组表达谱和全基因组定位分析(芯片、芯片序列)方面的进展使我们能够开始定义其中一些因子的完整转录组，但仍有更多工作要做。此外，这些方法可能在技术上具有挑战性，成本也很高，因此非常有限。一种尚未被充分利用的补充方法是基于DNA反应元件的计算方法，该DNA反应元件将核受体招募到其目标基因的调节区。然而，为了充分利用这一方法，必须首先拥有受体结合的结合基序的全面数据集。这项提议的目标有两个：1)使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)利用这些数据挖掘现有的数据集，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)来确定特定核受体在10‘S上1000’S独特序列的DNA结合特异性；然后利用这些数据开发高精度的计算模型来预测给定核受体与整个序列的结合；2)使用在目标1中产生的PBM数据和模型来计算识别人类基因组中每个核受体的所有潜在结合部位、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)进行交叉对照；3)将结果纳入核受体及其靶基因网络，特别强调代谢性疾病和精神健康障碍。所有的结合基序、潜在的靶基因、相关的SNPs和网络将被编目到在线资源Pazar、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)，和美国国立卫生研究院资助的核受体信号图集(NURSA)(http://www.nursa.org/).人类晚年出现的许多慢性疾病--如糖尿病、动脉粥样硬化和精神障碍--都是由多种因素造成的，既有遗传因素，也有环境因素。最近对人类基因组的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，通过检查基因的调节区和结合这些区域的蛋白质来帮助识别基因序列中可能与这些(和其他)疾病相关的变异。我们的工作将帮助我们更接近个性化医疗的时代，在这个时代，预防、诊断和治疗是针对个别患者量身定做的，使它们更有效，成本更低。

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.