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.
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