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Unbiased discovery of in vivo enzyme activities by metabolomic profiling of gene‐deleted mice: proof‐of‐principle using xanthine oxidoreductase
Author(s) -
Gross Steven S,
Goligorsky Michael S,
Ratliff Brian,
Park HC,
Chen Qiuying
Publication year - 2009
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.23.1_supplement.679.2
Subject(s) - metabolomics , metabolite , purine , purine metabolism , biomedicine , computational biology , biology , biochemistry , chemistry , enzyme , bioinformatics
A lofty goal of metabolomics is untargeted discovery of disease‐associated and drug‐induced changes in the expression of diverse small molecules in biological samples. Despite the enormous potential of metabolic profiling for translational biomedicine, effective analytical and informatic platforms remain to be established. This study sought to determine whether an untargeted metabolomics approach, utilizing accurate mass LC‐MS, feature profiling and unsupervised learning could effectively "discover" a purine metabolism defect in mice where xanthine oxidoreductase (XOR) is genetically‐deleted or pharmacologically‐inhibited. Untargeted profiling of XOR deficient mice conclusively revealed a marked decrease in the XOR product ureate (as predicted) in plasma and urine, as well as increases in purine metabolites upstream of XOR. Statistical methods, including principal component analysis and categorical clustering, demonstrated a remarkably similar metabolite profile in XOR‐inhibited and xor‐/‐ mice. Further, these methods revealed clear differences between the metabolite profiles of XOR deficient mice vs. xor+/+ and xor+/‐ mice. Together, the present studies provide proof‐of‐principle for the efficacy of untargeted metabolite profiling to discover altered biochemical pathways in diseases and in response to drug treatments.