New Tools for Metabolite Identification and Quantitation

项目摘要 靶向代谢组学提供了一个敏感和准确的平台， 生物样品中的代谢物。特别地，使用选择离子监测（SIM）和选择反应监测（SCR）可以是一种有效的方法。 监测（SRM）已成为一种理想的方法，用于定量分析代谢物的目标，使用气体 色谱-质谱法（GC-MS）和液相色谱-质谱法（LC-MS）， 分别尽管靶向代谢组学允许比靶向代谢组学更灵敏和更准确的测量， 非靶向代谢组学，即从非靶向代谢组学中确定大多数潜在靶点的能力。 研究以及缺乏设计目标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 （MetaboSearch）用于代谢物鉴定和R-包SIMAT用于设计靶向代谢组学 通过GC-SIM-MS进行实验并分析所得数据。在本申请中，我们提出开发：（1）一种 R-package metID，用于通过基于质量的搜索确定的推定代谢物ID的基于网络的优先级排序 工具，如MetaboSearch;和（2）一个R包SRMAT，选择最佳的过渡，为目标 与MetaboSearch一起，metID将使用户能够优先考虑 基于既往研究结果，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。对于高评分的推定代谢物ID，SIMAT和SRMAT将选择 分别通过GC-SIM-MS和LC-SRM-MS进行的目标实验的跃迁和保留时间/指数， 并对目标代谢物的水平进行定量。成功实施拟议的一套 这些工具将使研究人员能够利用靶向代谢组学的力量， 生物样品中有希望的代谢物的水平，以揭示代谢物与 样品的表型。

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.