INIA: Robust Systems Genetics of Alcohol and Stress Effects on CNS

描述（由申请人提供）：环境因素与基因变异相互作用，影响酒精使用，滥用，适应，成瘾，戒断和复发的模式。乙醇是一种高度可溶的小分子，对CNS中的许多分子过程具有微妙的变构作用，并且并非所有的靶点都是已知的。人类对乙醇反应的个体差异也没有得到很好的理解。该项目利用INIA的广泛专业知识来探索和测试酒精消费的遗传，分子，突触，细胞和行为原因以及压力源后脆弱性的增加。我们的重点是利用新的高分辨率基因组资源（序列数据，SNP，mRNA微阵列）来模拟在酒精中毒中起重要作用的前脑区域的分子和突触相互作用网络。为了确保稳健的结果，我们使用了两个大型的啮齿动物遗传参考群体（BXD小鼠和HXB大鼠），并应用复杂的统计方法来生成假设，然后进行严格的实验测试。我们结合联合收割机的数据，从我们自己的工作与数据，从许多其他已发表的研究，使用系统遗传学的方法。这种结合的方法是通过我们使用经过充分研究的遗传参考群体而实现的。我们将定义共同的和独特的遗传和突触因素，调节乙醇的使用和收敛的压力对乙醇成瘾和复发的影响。在目标1（数据生成）中，我们从具有不同遗传结构的互补遗传参考群体（近交R1和杂交RIX系）生成四个关键前脑区域（内侧前额叶皮质、中脑核、终纹床核和基底外侧杏仁核）的规范表达数据和网络。我们正在获取C57 BU 6 J和DBA/2 J系中48个脑区域的基因表达数据。我们的目标是提取经过交叉验证的强大网络，并且具有推广到混合人群的强大前景。数据将在GeneNetwork（GN）网站（www.genenetwork.org）上公开提供。在目标2（模型构建）中，我们开发开源程序和标准操作程序，以产生定义明确且可测试的假设。INIA模型工作组将负责开发、测试和使用INIA基因网络（GN）和该软件来构建明确的过程图和统计模型。我们将把这些模型集成到GN中，供INIA和NIAAA研究人员使用和评论。在目标3（预测验证）中，INIA的成员将通过实验操作一组等基因系（BXD和RIX），使用INIA标准操作方案预测它们是高应答者还是低应答者。遗传，转录组，电生理和实验研究和数据集之间的协同作用将使我们能够测试复杂的相互作用中的mesocorticolimbic系统，有助于酗酒和适应不良的应激反应的作用。

DESCRIPTION (provided by applicant): Environmental factors interact with gene variants to influence patterns of alcohol use, abuse, adaptation, addiction, withdrawal, and relapse. Ethanol is a highly soluble small molecule that has subtle allosteric effects on many molecular processes in the CNS and not all of its many targets are known. Individual differences in how humans respond to ethanol are also not well understood. This project leverages the broad expertise of INIA to explore and test genetic, molecular, synaptic, cellular, and behavioral causes of alcohol consumption and the increased vulnerability following stressors. Our focus is on exploiting new high-resolution genomic resources (sequence data, SNPs, mRNA microarrays) to model networks of molecular and synaptic interactions in forebrain regions that have important roles in alcoholism. To ensure robust results we use two large genetic reference populations of rodents (BXD mice and HXB rats) and apply sophisticated statistical methods to generate hypotheses that are then subjected to rigorous experimental testing. We combine data from our own work with data from numerous other published studies using a systems genetics approach. This combined approach is made possible by our use of well studied genetic reference populations. We will define shared and unique genetic and synaptic factors that modulate ethanol use and the convergent effects of stress on ethanol addiction and relapse. In Aim 1 (Data Generation) we generate normative expression data and networks for four key forebrain regions (medial prefrontal cortex, nucleus accumbens, bed nucleus of the stria terminalis, and the basolateral amygdala) from complementary genetic reference populations with different genetic structures (inbred Rl and hybrid RIX lines). We are acquiring gene expression data for 48 brain regions in C57BU6J and DBA/2J lines. Our goal is to extract robust networks that are cross-validated and that have strong prospects of generalizing to admixed human populations. Data will be made publicly available on the GeneNetwork (GN) site (www.genenetwork.org). In Aim 2 (Model Construction) we develop open source programs and standard operating procedures to produce well defined and testable hypotheses. The INIA Models Work Group will be responsible for developing, testing, and using the INIA GeneNetwork (GN) and this software to construct explicit process diagrams and statistical models. We will integrate these models into GN for use and critique by INIA and NIAAA researchers. In Aim 3 (Predictive Validation), members of INIA will experimentally manipulate sets of isogenic lines (BXD and RIX), predicting whether they will be high or low responders using INIA standard operating protocols. The synergy among genetic, transcriptome, electrophysiological, and experimental studies and data sets will allow us to test the role of complex interactions in the mesocorticolimbic system that contribute to alcoholism and maladaptive stress response.