Integrative Metabolomics of Asthma Severity

说明(申请人提供)：哮喘恶化是最常见的与健康有关的原因，导致学校和工作日的损失，并对每年用于哮喘的500多亿美元有很大贡献。1因此，任何系统地减少哮喘恶化都将对公众健康产生重大影响。哮喘是一种可遗传的疾病2，3虽然已经确定了一些分子决定因素，但关于这些变异如何影响疾病的严重程度，仍有许多有待了解。代谢谱是对所有代谢物的系统分析，已成功地用于确定几种疾病的新生物标记物。与转录、翻译或翻译后的变化相比，代谢物具有更接近疾病过程的标志的明显优势。代谢谱还能够捕捉过去相关暴露的历史，如高甲基化和对低氧的反应，这两者与哮喘高度相关。到目前为止，代谢组研究对哮喘的规模和范围有限。5，6代谢组仍然是一种未开发的资源，有可能全面表征哮喘的许多方面，包括疾病的严重程度。7，8这一提议的总体假设是，关键代谢物将通过使用代谢组谱和与其他形式的分子数据的整合来阐明我们对哮喘严重程度的理解。我们将1)在非靶向和候选方法中识别与哮喘严重程度相关的单个代谢物和代谢谱；2)将代谢组学数据与全基因组遗传(即SNP)和基因组(即基因表达)数据集成；3)通过整合环境、临床、遗传、基因组和代谢组学数据，识别准确预测哮喘恶化并区分哮喘严重程度的代谢组特征。这将是迄今为止对哮喘患者进行的最大规模的代谢组学研究，并能够确定哮喘严重程度不同的个体之间的重要区别，有可能激发针对哮喘恶化的具体治疗和初级预防方法。

DESCRIPTION (provided by applicant): Asthma exacerbations are the most common health-related cause of lost school and work days and contribute substantially to the more than $50 billion dollars spent on asthma annually.1 Therefore any systematic reduction in asthma exacerbations will have a large public health impact. Asthma is a heritable disease2,3 and although a number of molecular determinants have been identified4, much remains to be understood about how these variants impact the severity of disease. Metabolic profiling, the systematic analysis of all metabolites, has been used successfully to identify new biomarkers for several diseases. Metabolites have the distinct advantage of being more proximal markers of disease processes than are transcriptional, translational or post-translational changes. Metabolic profiling is also able to capture the history of relevant past exposures such as hypermethylation and response to hypoxia, both of which are highly relevant for asthma. To date, metabolomics studies have been limited in size and scope for asthma.5,6 The metabolome remains an untapped resource and has the potential to comprehensively characterize many aspects of asthma, including the severity of disease.7,8 The over arching hypothesis of this proposal is that key metabolites will elucidate our understanding of asthma severity through the use of metabolomic profiling and the integration with other forms of molecular data. We will 1) identify individual metabolites and metabolic profiles associated with asthma severity in both untargeted and candidate approaches; 2) Integrate metabolomics data with genome-wide genetic (i.e. SNP) and genomic (i.e. gene expression) data 3) identify metabolomic signatures that accurately predict asthma exacerbations and differentiate asthma severity through the integration of environmental, clinical, genetic, genomic, and metabolomics data. This will represent the largest metabolomics study in asthmatic patients to date and enable the identification of important distinctions between individuals with varying asthma severity, potentially motivating specific therapeutic and primary prevention approaches for exacerbations.