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 i s 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 i s 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 i s 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.
The proposed research is relevant to public health because it will develop novel software tools to quantify and interpret data from stable isotope labeling experiments, which can be used to uncover relationships between metabolites and biochemical pathways. These tools have potential to accelerate progress toward identifying the causes and cures of many important diseases that impact metabolism.
