Tools for Leveraging High-Resolution MS Detection of Stable Isotope Enrichments to Upgrade the Information Content of Metabolomics Datasets

项目摘要/摘要 高分辨率质谱仪(HRMS)仪器的最新进展尚未得到充分利用 提高从稳定同位素标记研究中获得的代谢组学数据集的信息含量。这 这主要是由于缺乏有效的软件工具来提取和解释来自HRMS的同位素丰度 数据集。当前应用的总体目标是开发能够实现代谢组学的工具 社区充分利用稳定的同位素来描述代谢网络的动态。将有两个新工具 在开源OpenMS软件库中实现，该库为快速 开发和推广质谱学软件。第一个工具将自动执行以下任务 从HRMS数据集中提取同位素浓缩信息，第二个工具将使用该信息 根据类似的同位素标记模式，将离子峰分组到相互作用网络中。这些工具将得到验证 使用来自动植物系统代谢通量研究的内部数据集，以及通过 来自代谢组学社区的反馈。研究的基本原理是，软件工具将使 代谢组学研究人员将解决有关途径动力学和调控的重要问题 在不使用稳定同位素的情况下得到回答。第一个目标是开发一种自动化数据的软件工具 从HRMS数据集中提取和量化同位素分布。该软件将提供几个 当前可用的代谢组学软件中未包含的主要功能：i)图形、交互式用户界面 这适用于非专家用户，ii)支持本机仪器文件格式，iii)支持 用多个稳定同位素标记，iv)串联质谱图的载体，以及v)多基团或 时间序列比较。第二个目标是开发一个配套的软件，应用机器学习和 基于相关性的算法，基于相似性将未知代谢物分组到模块和路径中 同位素标记。第三个目标是通过对稳定同位素标记的比较分析来验证这些工具 测试标准和来自动植物组织的样品，包括时间序列和双示踪实验。一个 将聘请各种合作者和专业工作组对软件进行测试和验证，以及 这些工具将根据他们的反馈进行改进。这项拟议的研究极具创新性，因为它 将提供靶向和非靶向同位素分析所需的高级软件功能 标签代谢物，但在灵活和用户友好的环境中。这项研究意义重大，因为它将 贡献软件工具，使提取和利用所需的数据处理步骤自动化和标准化 来自大规模代谢组学数据集的同位素浓缩信息。这项工作将具有重要的意义 对代谢组研究人员利用稳定同位素信息的能力产生积极影响 确定未知的代谢相互作用并量化代谢网络中的流量。此外，它还将使 研究生物系统内代谢动力学的全新方法。

PROJECT SUMMARY/ABSTRACT Recent advances in high-resolution mass spectrometry (HRMS) instrumentation have not been fully leveraged to upgrade the information content of metabolomics datasets obtained from stable isotope labeling studies. This is primarily due to lack of validated software tools for extracting and interpreting isotope enrichments from HRMS datasets. The overall objective of the current application is to develop tools that enable the metabolomics community to fully leverage stable isotopes to profile metabolic network dynamics. Two new tools will be implemented within the open-source OpenMS software library, which provides an infrastructure for rapid development and dissemination of mass spectrometry software. The first tool will automate tasks required for extracting isotope enrichment information from HRMS datasets, and the second tool will use this information to group ion peaks into interaction networks based on similar patterns of isotope labeling. The tools will be validated using in-house datasets derived from metabolic flux studies of animal and plant systems, as well as through feedback from the metabolomics community. The rationale for the research is that the software tools will enable metabolomics investigators to address important questions about pathway dynamics and regulation that cannot be answered without the use of stable isotopes. The first aim is to develop a software tool to automate data extraction and quantification of isotopologue distributions from HRMS datasets. The software will provide several key features not included in currently available metabolomics software: i) a graphical, interactive user interface that is appropriate for non-expert users, ii) support for native instrument file formats, iii) support for samples that are labeled with multiple stable isotopes, iv) support for tandem mass spectra, and v) support for multi-group or time-series comparisons. The second aim is to develop a companion software that applies machine learning and correlation-based algorithms to group unknown metabolites into modules and pathways based on similarities in isotope labeling. The third aim is to validate the tools through comparative analysis of stable isotope labeling in test standards and samples from animal and plant tissues, including time-series and dual-tracer experiments. A variety of collaborators and professional working groups will be engaged to test and validate the software, and the tools will be refined based on their feedback. The proposed research is exceptionally innovative because it will provide the advanced software capabilities required for both targeted and untargeted analysis of isotopically labeled metabolites, but in a flexible and user-friendly environment. The research is significant because it will contribute software tools that automate and standardize the data processing steps required to extract and utilize isotope enrichment information from large-scale metabolomics datasets. This work will have an important positive impact on the ability of metabolomics investigators to leverage information from stable isotopes to identify unknown metabolic interactions and quantify flux within metabolic networks. In addition, it will enable entirely new approaches to study metabolic dynamics within biological systems.