A Systems Approach to Uncover Novel Genes and Networks in Heart Failure

描述(申请人提供)：心力衰竭的发病机制是复杂的，涉及不同的遗传和环境因素。全基因组关联研究在治疗心力衰竭方面的成功有限，导致人类心力衰竭的遗传因素仍然知之甚少。该领域的一个主要挑战是充分了解疾病的异质性，以便开发更有效和个性化的治疗方法。在这项建议中，我们开发了一种新的方法，通过在明确定义的病理应激源下跨杂交小鼠多样性小组(HMDP)使用Gwas来系统地识别与心力衰竭有关的遗传因素和分子网络。我们相信这种新的方法有几个主要的优点。首先，HMDP由大约100个普通近交系和重组近交系(RI)菌株组成，这些菌株要么已经完全测序，要么已经密集分型[超过140,000个单核苷酸多态(SNPs)]。其次，从心力衰竭小鼠模型中学到的见解应该为未来的人类机械学、流行病学和遗传学研究提供相关指导。最后，慢性过度的肾上腺素能过度驱动是公认的人类心力衰竭的主要因素，了解β-AR信号的遗传调节器将产生重大影响。通过精确地长期给予异丙肾上腺素，一种非选择性的β-AR激动剂，我们将能够以相对高通量的方式定量地造成病理侮辱。因此，我们建议1)。通过定量分析HMDP小鼠对慢性异丙肾上腺素刺激的心脏功能和重塑，并使用高效的混合模型算法进行关联分析，以确定基因组区域和潜在的候选基因，以确定与慢性β肾上腺素能刺激的心脏反应的不同特征相关联的基因，从而确定小鼠对慢性β肾上腺素能刺激的心脏反应的遗传基因。2)。通过对异丙肾上腺素治疗前后心脏的全球表达阵列分析，绘制与慢性β-肾上腺素能反应相关的基因表达差异的基因座图，并构建共表达网络以识别与慢性β-肾上腺素能反应相关的子网络，从而建立有助于调节心脏功能和肥厚的模型通路。简而言之，本提案中设计的系统方法将为涉及β-AR信号和心力衰竭的基因及其相互作用网络带来新的见解。

DESCRIPTION (provided by applicant): The pathogenesis of heart failure is complex, involving heterogeneous genetic and environmental factors. Genome-wide association studies (GWAS) have had limited success for heart failure and the genetic factors contributing to human heart failure remain poorly understood. A major challenge in the field is to fully understand the heterogeneity of the disease in order to develop more effective and personalized therapies. In this proposal, we have developed a novel approach by using GWAS across a hybrid mouse diversity panel (HMDP) under a well defined pathological stressor to systematically identify genetic factors and molecular networks implicated in heart failure. We believe this novel approach has several major advantages. First, the HMDP consists of ~100 common inbred and recombinant inbred (RI) strains which have been either entirely sequenced or densely genotyped [over 140,000 single nucleotide polymorphisms (SNPs)]. Second, the insights learnt from mouse models of heart failure should provide relevant guidance for future mechanistic, epidemiological and genetic studies in human. Lastly, Chronic excessive adrenergic overdrive is a well recognized major contributor to human heart failure and understanding the genetic modulators to betaAR signaling would have a major impact. With precisely administered chronic treatment of isoproterenol, a non-selective betaAR agonist, we will be able to quantitatively inflict a pathological insult in a relatively high-throughput manner. Accordingly, we propose to 1). Identify genetic loci in mouse contributing to cardiac responses to chronic beta-adrenergic stimulation by quantitative analysis of cardiac function and remodeling in the HMDP mice in response to chronic isoproterenol stimulation, and association analysis with an efficient mixed model algorithm to identify regions of the genome and potential candidate genes linking to the different features of cardiac response to chronic beta-adrenergic stimulation. 2). Model pathways contributing to regulation of heart function and hypertrophy by global expression array analyses of hearts before and after isoproterenol treatment to map loci contributing to differences in gene expression that are associated with chronic beta-adrenergic response, and construction co-expression networks to identify subnetworks associated with chronic beta-adrenergic response. In short, the systems approach designed in this proposal will bring novel insights to genes and their interacting networks implicated in betaAR signaling and heart failure.