New Tools for Metabolite Identification and Quantitation

项目总结 靶向代谢组学提供了一个灵敏而准确的平台来比较预先指定的 生物样品中的代谢物。特别是，选择离子监测(SIM)和选择反应的使用 监测(SRM)已成为利用GAS定量分析代谢物靶标的理想方法 色质联用(GC-MS)和液质联用(LC-MS)， 分别进行了分析。尽管靶向代谢组学允许比 非靶向代谢组学，即从非靶标中确定大多数潜在靶点的能力 研究以及缺乏设计有针对性的实验的工具一直是研究人员面临的挑战 旨在进行大规模的靶向代谢组学研究。我们的团队最近开发了一个软件工具 用于识别代谢物和设计靶向代谢组学的R-Package SIMAT 通过GC-SIM-MS进行实验，并对结果数据进行分析。在本申请中，我们建议开发：(1)一种 R-Package MetID，用于对基于质量的搜索识别的推定代谢物ID进行基于网络的优先排序 元搜索等工具；以及(2)为目标选择最佳过渡的R-Package SRMAT LC-SRM-MS代谢学实验与MetabSearch一起，metID将使用户能够区分优先级 根据以前的研究结果，可能与疾病相关的分析物的推定代谢物ID 非靶向代谢组学研究。对于得分较高的推定代谢物ID，SIMAT和SRMAT将选择 GC-SIM-MS和LC-SRM-MS分别用于靶向实验的转变和保留时间/指数， 并对目标代谢物的水平进行定量。成功实施拟议的套装 工具将使研究人员能够利用靶向代谢组学的力量来准确评估 生物样品中有希望的代谢物水平，以揭示代谢物与 样本的表型。

PROJECT SUMMARY Targeted metabolomics offers a sensitive and accurate platform to compare the levels of pre-specified metabolites in biological samples. In particular, the use of selected ion monitoring (SIM) and selected reaction monitoring (SRM) has become an ideal method for quantitative analysis of metabolite targets using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), respectively. Although targeted metabolomics allows more sensitive and accurate measurements than untargeted metabolomics, the ability to determine the identity of most of the potential targets from untargeted studies as well as the lack of tools for designing targeted experiments have been a challenge for researchers aiming to conduct large-scale targeted metabolomic studies. Our team recently developed a software tool (MetaboSearch) for metabolite identification and an R-package SIMAT for designing targeted metabolomics experiments by GC-SIM-MS and analyzing the resulting data. In this application, we propose to develop: (1) an R-package metID for a network-based prioritization of putative metabolite IDs identified by mass-based search tools such as MetaboSearch; and (2) an R-package SRMAT that selects optimal transitions for targeted metabolomic experiments by LC-SRM-MS. Together with MetaboSearch, metID will enable users to prioritize putative metabolite IDs for analytes that may be associated with disease based on findings from previous untargeted metabolomic studies. For highly-scored putative metabolite IDs, SIMAT and SRMAT will choose transitions and retention times/indices for targeted experiments by GC-SIM-MS and LC-SRM-MS, respectively, and perform quantitation of the levels of target metabolites. Successful implementation of the proposed suite of tools will enable researchers to capitalize on the power of targeted metabolomics for accurate evaluation of the levels of promising metabolites in biological samples to uncover the relationships of the metabolites with the phenotypes of the samples.