Recent advances in metabolomics technologies, especially those that combine the complementarily of mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) enables rapid analysis of thousands of peaks representing hundreds of metabolites. Yet, key barriers remain for large-scale metabolomics applications, most notably: even higher throughput with robust metabolite assignment and quantification;identification of unknown metabolites;analysis of low abundance/labile metabolites;and reconstruction of metabolic networks and regulation. Here, we propose an integrated approach that uses chemoselective (CS) tagging of key metabolite functional groups to boost the speed and accuracy of metabolite identification and enhance detection. Such tagged metabolites are inherently amenable for multiplexed analysis and reliable relative quantification via stable isotope encoding. This approach will accelerate analytical throughput while widening the classes of analytes, including metabolically enriched isotopologues, far beyond current limits. We will achieve our goals via the following specific aims: SA1. To develop CS probes for tagging metabolites by targeting functional groups: Hydrophobic quaternary ammonium (QA)-based CS probes will be developed for tagging carbonyl, amino, thiol, &diol functional groups (FG) in metabolites, optimized for direct infusion FT- ICR-MS detection &assignment;SA2. To develop a set of isotope-encoded CS probes for metabolite identification and multiplexed quantification by FT-ICR-MS and NMR: Developing 13C-encoded QA-CS probes will facilitate integrated structural analysis of metabolites by 13C edited 2D NMR and FT-ICR-MS, &multiplexed analysis by FT-ICR-MS;SA3. To develop web-based software for large-scale CS-adducted metabolite assignment &pathway reconstruction: Algorithm for robust automated MS assignment of metabolite &labeled isotopologues will be developed based on isotopologue cliques, FG profile, &molecular formula (MF). NMR- derived substructure &MF will be combined with MS data for semi-automated assignment of metabolite isotopomers &unknowns. Atom-resolved human metabolic database will be refined and tools developed for pathway reconstruction based on labeled metabolite profiles;SA4. To demonstrate the integrated CS profiling and biochemo-informatic approach in three basic and translational projects: Metabolic reprogramming driven by oncogenic gene defects or enzyme deletion in human lung and kidney cancers will be mapped using Stable Isotope-Resolved Metabolomics (SIRM) enhanced by newly developed CS tagging chemistry and automated assignment tools. Our long-term goal is to decipher human disease metabolic networks for drug discovery &early diagnosis.

Public Health Relevance

The proposed chemoselective tagging of metabolites with integrated informatics tools will enhance both metabolite profiling and elucidation of human disease metabolic networks far beyond current limits. The information gained from these tools will help accelerate at an unprecedented rate our functional understanding of human disease development and progression while, improving our ability to diagnose, prognose, and treat intractable human diseases such as cancer.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Research Project (R01)
Project #
5R01ES022191-04
Application #
8687656
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Balshaw, David M
Project Start
2014-04-26
Project End
2017-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
4
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Kentucky
Department
Type
DUNS #
City
Lexington
State
KY
Country
United States
Zip Code
40506
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