Optimized Metabolite Extraction, Separation, and Identification for Metabolomics

描述（由申请人提供）：本提案的总体目标是帮助定义肝脏代谢组中的关键代谢变化，以帮助研究各种疾病，包括肝脂肪变性、炎症和药物毒性。随着肥胖、II型糖尿病以及药物和酒精滥用的日益普遍，这些肝脏疾病对医疗保健系统造成越来越大的负担。代谢组学，或生物过程留下的化学指纹的研究，是一个快速发展的学科，有很大的希望，以提高我们对这些病理条件的理解。然而，在我们对提取方法和分离方法如何影响下游化学计量学分析中使用的数据的理解方面仍然存在重大差距。因此，我们设计了四个具体的目标，以解决任何基于液相色谱-质谱联用的代谢组学研究的基本方面：提取，分离和鉴定，特别是哺乳动物肝脏。我们假设可以开发出独立于疾病状态的可重复提取和分离方法。迄今为止，还没有一个有组织的，协调一致的努力，以优化和标准化这些最重要的步骤时，进行代谢组学研究。虽然在这里我们专注于肝脏组织，但这些方法将作为其他组织和生物流体以及包括核磁共振光谱和气相色谱与质谱联用的平台的基础。基于三个在代谢组学领域具有公认专业知识的独立实验室的努力（剑桥大学的格里芬实验室和英国医学研究理事会、国家癌症研究所的冈萨雷斯实验室和宾夕法尼亚州立大学的帕特森实验室）我们计划系统地解决和优化每一个步骤（代谢物提取、液相色谱分离和质谱鉴别），包括一系列代谢物类别、极性和代谢途径，从而确保将高质量数据传送到大量现有的代谢组学数据分析平台。此外，除了在同行评议的期刊上发表，我们还将向更广泛的科学界免费提供我们的方案和数据，包括学术界和制药界，通过使用社区主导的开源设施，如欧洲生物信息学研究所的MetaboLight，（实验代谢组学数据的中央存储库）和ISA-TAB倡议（用于组学实验的Meta数据描述的统一工具）。

DESCRIPTION (provided by applicant): The overall goal of this proposal is to help define key metabolic changes in the liver metabolome to aid research into a variety of diseases including hepatic steatosis, inflammation, and drug toxicity. These diseases of the liver are an increasing burden on the healthcare system with growing prevalence of obesity, type II diabetes, and drug and alcohol abuse. Metabolomics, or the study of chemical fingerprints that biological processes leave behind, is a rapidly advancing discipline that has great promise to improve our understanding of these pathological conditions. However, there remain significant gaps in our understanding of how extraction methodologies and separation approaches influence the data used in downstream chemometric analyses. Therefore, we have designed the four specific aims that address fundamentally essential aspects of any metabolomic study based on liquid chromatography coupled with mass spectrometry: extraction, separation, and identification with specific reference to the mammalian liver. We hypothesize that reproducible extraction and separation methodologies can be developed that are independent of disease state. To date, there has not been an organized, concerted effort to optimize and standardize these most essential steps when conducting a metabolomic study. While here we focus on liver tissue, these approaches will serve as a foundation for other tissues and biofluids as well as platforms including nuclear magnetic resonance spectroscopy and gas chromatography coupled with mass spectrometry. Based on the efforts of three independent laboratories with well-recognized expertise in metabolomics (Griffin Lab at the University of Cambridge and the UK Medical Research Council, Gonzalez Lab at the National Cancer Institute, and the Patterson Lab at Penn State University) we plan to systematically address and optimize each step (metabolite extraction, separation by liquid chromatography, and identification by mass spectrometry) across a range of metabolite classes, polarities and metabolic pathways, thus ensuring the delivery of high quality data to the vast array of already existing metabolomic data analysis platforms. Furthermore, in addition to publication in peer-reviewed journals, we will make our protocols and data freely available to the wider scientific community, including both academia and pharma, in an open format by making use of community-led open source facilities such as the European Bioinformatics Institute's MetaboLight (central repository for experimental metabolomics data) and the ISA-TAB initiative (a unified tool for meta data description of omic experiments).