Identification of a metabolic syndrome transcriptome signature in the LH rat

描述(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)，是一组疾病的集合，包括肥胖、血脂异常、高血压和胰岛素抵抗，导致终末器官衰竭和死亡。虽然遗传学研究已经成功地识别了与肥胖、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)大鼠具有人类代谢综合征的几个共同特征-高体重、高胆固醇和甘油三酯，胰岛素和胰岛素/葡萄糖比率增加，以及高盐饮食加剧的高血压。里昂正常血压(LN)对照株在基因上与LH型非常相似，但表型非常不同。对黄体生成素和层粘连蛋白杂交F2代的作图研究确定了影响体重、脂肪水平、血压和胰岛素水平的数量性状基因座(QTL)。然而，遗传上相似的黄体生成素和肾小球肾炎大鼠之间的实质性表型差异背后的基因尚不清楚。通过比较疾病和控制模型的转录本，以及识别位置候选基因和受干扰的生物途径，可以方便地识别潜在的QTL基因。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，越来越重要的是发生了选择性的Pre-mRNA剪接。然而，传统的基因表达阵列不能检测不同的剪接模式。我们假设，黄体生成素大鼠的代谢综合征是由于复杂的基因调控网络导致基因表达和RNA加工的变化。由于常见疾病的内在复杂性，我们断言，对黄体生成素和层粘连蛋白大鼠品系的转录本进行深度测序将有助于发现S(音译)基因和参与黄体生成素代谢综合征的机制。我们建议进行高通量的RNA测序分析基因的表达和选择性剪接收集的组织从黄体生成素和LN株。具体地说，我们将1)在疾病相关组织中识别遗传相似的LH和LN菌株之间的全基因组基因表达差异；以及2)通过超深RNA测序来识别LH和LN菌株之间的选择性剪接差异。在动物模型中识别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。 公共卫生相关性：人类代谢综合征(肥胖、血脂异常、高血压和胰岛素抵抗)及其相关的终末器官衰竭影响着近25%的美国人口，并对美国的医疗保健成本产生重大影响，估计每年超过300亿美元。该项目将研究从人类代谢综合征大鼠模型收集的疾病相关组织中基因表达和RNA处理的全基因组模式。在动物模型中识别代谢综合征转录组特征将有助于更好地理解和治疗人类疾病。

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.