Using Accurate Mass Gas Chromatography-Mass Spectrometry with the MINE Database for Epimetabolite Annotation

Mass spectrometry-based untargeted metabolomics often detects statistically significant metabolites that cannot be readily identified. Without defined chemical structure, interpretation of the biochemical relevance is not feasible. Epimetabolites are produced from canonical metabolites by defined enzymatic reactions and may represent a large fraction of the structurally unidentified metabolome. We here present a systematic workflow for annotating unknown epimetabolites using high resolution gas chromatography-accurate mass spectrometry with multiple ionization techniques and stable isotope labeled derivatization methods. We first determine elemental formulas, which are then used to query the metabolic in-silico expansion database (MINE DB) to obtain possible molecular structures that are predicted by enzyme promiscuity from canonical pathways. Accurate mass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FINDER to derive the best candidates. We validated the workflow by correctly identifying 10 methylated nucleosides and 6 methylated amino acids. We then employed this strategy to annotate eight unknown compounds from cancer studies and other biological systems.