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（涉及多个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疾病的集合，导致终末器官衰竭和死亡。虽然遗传学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）大鼠具有与人类代谢综合征共同的几个特征-高体重、高胆固醇和高甘油三酯、增加的胰岛素和胰岛素/葡萄糖比率以及由高盐饮食加重的高血压。里昂血压正常（LN）对照菌株在遗传上与LH非常相似，但表型非常不同。在LH和LN之间的F2互交中的定位研究确定了有助于体重、脂质水平、血压和胰岛素水平的数量性状基因座（QTL）。然而，遗传相似的LH和LN大鼠之间的实质性表型差异的基础基因尚不清楚。通过比较疾病和对照模型的转录组，以及通过鉴定位置候选基因和受干扰的生物学途径，可以促进QTL相关基因的鉴定。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选择性剪接模式。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，有助于基因表达和RNA加工的变化。由于常见疾病的固有复杂性，我们断言，LH和LN大鼠品系转录组的深度RNA测序将导致LH大鼠代谢综合征中涉及的基因和机制的鉴定。我们建议进行高通量RNA测序分析的基因表达和选择性剪接组织中收集的LH和LN株。具体而言，我们将1）确定疾病相关组织中遗传相似的LH和LN菌株之间的全基因组基因表达差异; 2）通过超深度RNA测序确定LH和LN菌株之间的可变剪接差异。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中收集的疾病相关组织中基因表达和RNA加工的全基因组模式。在动物模型中鉴定代谢综合征转录组特征将导致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.