Identification of a metabolic syndrome transcriptome signature in the LH rat

描述（由申请人提供）：人类代谢综合征是一种典型的复杂疾病（涉及多基因和环境相互作用），是包括肥胖、血脂异常、高血压和胰岛素抵抗在内的一系列疾病的集合，导致终末期器官衰竭和死亡。虽然基因研究已经成功地确定了与肥胖、血脂异常和高血压相关的基因，但许多变异仍然未知。里昂高血压（LH）大鼠有几个与人类代谢综合征相同的特征——高体重、高胆固醇、高甘油三酯、胰岛素和胰岛素/葡萄糖比升高，高盐饮食加剧了高血压。里昂正常血压（LN）控制菌株在遗传上与LH非常相似，但在表型上却截然不同。在LH和LN之间F2交叉的定位研究中，发现了影响体重、脂质水平、血压和胰岛素水平的数量性状位点（QTL）。然而，在遗传相似的LH和LN大鼠之间造成显著表型差异的基因尚不清楚。通过比较疾病模型和控制模型的转录组，以及通过确定位置候选基因和受干扰的生物学途径，可以促进QTL基础基因的鉴定。虽然基因表达阵列允许在疾病相关组织中进行分析，但它们受到阵列上包含的特征以及基因组注释的当前状态的限制。在代谢综合征和其他复杂疾病中，选择性前mrna剪接的发生越来越重要。然而，传统的基因表达阵列不能检查备选剪接模式。我们假设LH大鼠的代谢综合征是由于一个复杂的基因调控网络，它有助于基因表达和RNA加工的变化。由于常见疾病固有的复杂性，我们认为对LH和LN大鼠菌株的转录组进行深度RNA测序将有助于鉴定LH大鼠代谢综合征相关的基因和机制。我们建议对LH和LN菌株收集的组织进行基因表达和选择性剪接的高通量RNA测序分析。具体来说，我们将1)鉴定遗传相似的LH和LN菌株在疾病相关组织中的全基因组基因表达差异；2)通过超深RNA测序鉴定LH和LN菌株的选择性剪接差异。在动物模型中鉴定与肥胖和血脂异常相关的转录组特征将导致新的疾病基因和途径，并最终更好地理解和治疗人类代谢综合征。

DESCRIPTION (provided by applicant): The human metabolic syndrome, an archetypical complex disease (involving multiple genes and environmental interactions), is a collection of disorders including obesity, dyslipidemia, hypertension, and insulin resistance, leading to end organ failure and death. While genetic studies have had success in identifying genes related to obesity, dyslipidemia, and hypertension, much of the variation remains unknown. The Lyon Hypertensive (LH) rat has several features common to the human metabolic syndrome - high body weight, cholesterol, and triglycerides, increased insulin and insulin/glucose ratio, and high blood pressure exacerbated by a high salt diet. The Lyon normotensive (LN) control strain is genetically quite similar to the LH, but phenotypically very distinct. Mapping studies in an F2 intercross between the LH and LN identified quantitative trait loci (QTL) contributing to body weight, lipid levels, blood pressure and insulin levels. However, the genes that underlie the substantial phenotypic differences between the genetically similar LH and LN rats are not yet known. Identification of the genes underlying QTL can be facilitated by comparing transcriptomes of the disease and control models and by identifying positional candidate genes and perturbed biological pathways. While gene expression arrays allow for analyses in disease related tissues, they are limited by the features contained on the array as well as the current state of genome annotation. Of increasing importance in the metabolic syndrome and other complex diseases is the occurrence of alternative pre-mRNA splicing. However, conventional gene expression arrays cannot examine alternative splicing patterns. We hypothesize that the metabolic syndrome in the LH rat is due to a complex gene regulatory network which contributes to changes in gene expression and RNA processing. Because of the inherent complexity in common disease, we assert that deep RNA sequencing of transcriptomes from LH and LN rat strains will lead to the identification of gene(s) and mechanisms involved in the metabolic syndrome in the LH rat. We propose to carry out a high throughput RNA sequencing analysis of gene expression and alternative splicing in tissues collected from LH and LN strains. Specifically we will 1) identify genomewide gene expression differences between genetically similar LH and LN strains in disease-associated tissues; and 2) identify alternative splicing differences between LH and LN strains by ultra deep RNA sequencing. Identification of transcriptome signatures associated with obesity and dyslipidemia in animal models will lead to novel disease genes and pathways, and ultimately a better understanding and treatment of the human metabolic syndrome. PUBLIC HEALTH RELEVANCE: The human metabolic syndrome (obesity, dyslipidemia, hypertension, and insulin resistance) and its related end organ failure affects nearly 25% of the US population and has a major impact on health care costs in the US, estimated at over $30 billion annually. This project will examine genomewide patterns of gene expression and RNA processing in disease-associated tissues collected from a rat model of the human metabolic syndrome. Identification of a metabolic syndrome transcriptome signature in animal models will lead to better understanding and treatment of the human disease.