Systems Approach to Unraveling the Genetic Basis of Heart Failure

描述（由申请人提供）：揭开常见多基因疾病（如高血压、糖尿病和心力衰竭）的遗传基础，将需要新的方法来观察基因如何在群体中而不是单个地共同工作。在这个建议中，我们研究基因网络分析作为一个有前途的新方法。我们的目标是确定基因模块的特定表达模式，而不是单个基因，预测心力衰竭（HF）的易感性。DNA微阵列数据的网络分析通常将20，000个基因分组为20-30个模块，每个模块包含10到100个基因，大大减少了进行基于基因网络的基因模块关联研究（GMAS）所需的可能候选者的数量，这将与GWAS互补。为了验证GMAS的概念，我们将使用系统遗传学方法，将DNA微阵列分析与生理学研究和计算建模相结合，以研究基因模块表达模式是否预测心脏应激引起的心力衰竭（HF）的易感性。为此，我们将使用 在UCLA开发的新资源，杂交小鼠多样性小组（HMDP），由102个近交系小鼠组成，从其构建了由20个基因模块组成的常见小鼠心脏模块基因网络。我们的初步研究结果表明，不同的HMDP菌株在基因模块表达模式和慢性心脏应激（异丙肾上腺素）的表型反应中表现出相当大的变异性。使用生物学和计算实验，我们将测试的假设，基因模块的表达模式之间的HMDP菌株代表不同的“足够好的解决方案”，所有这些都是足够的正常兴奋收缩代谢耦合，但有不同的能力，以适应慢性心脏应激。提出了三个具体目标，整合实验和计算生物学，并结合发现驱动，假设驱动和翻译元素，朝着HMDP结果直接与人类心力衰竭相关的目标。

DESCRIPTION (provided by applicant): Unraveling the genetic basis of common polygenic diseases, such as hypertension, diabetes and heart failure, will require fresh approaches to view how genes work together in groups rather than singly. In this proposal, we investigate gene network analysis as a promising new approach. Our goal is to identify specific expression patterns of gene modules, rather than single genes, which predict susceptibility to heart failure (HF). A network analysis of DNA microarray data typically groups 20,000 genes into 20-30 modules, each containing 10's to 100's of gene, drastically reducing number of possible candidates required to perform a gene network- based Gene Module Association Study (GMAS), which will be complementary to GWAS. To test the GMAS concept, we will use a systems genetics approach integrating DNA microarray analysis with physiological studies and computational modeling, to examine whether gene module expression patterns predict susceptibility to heart failure (HF) induced by cardiac stress. For this purpose, we will utilize a novel resource developed at UCLA, the Hybrid Mouse Diversity Panel (HMDP), consisting of 102 strains of inbred mice from which a common mouse cardiac modular gene network comprised of 20 gene modules has been constructed. Our preliminary findings reveal that different HMDP strains show considerable variability in both gene module expression patterns and phenotypic response to chronic cardiac stress (isoproterenol). Using biological and computational experiments, we will test the hypothesis that gene module expression patterns among HMDP strains represent different "good enough solutions," all of which are adequate for normal excitation-contraction- metabolism coupling, but have different abilities to adapt to chronic cardiac stress. Three Specific Aims integrating experimental and computational biology and combining discovery-driven, hypothesis-driven, and translational elements are proposed, towards the goal of relating HMDP results directly to human heart failure.