West Coast Metabolomics Center, UC Davis , Davis, California 95616, United States.
Department of Biochemistry, King Abdulaziz University , Jeddah 21589, Saudi Arabia.
Anal Chem. 2017 Oct 3;89(19):10171-10180. doi: 10.1021/acs.analchem.7b01134. Epub 2017 Sep 22.
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.
基于质谱的非靶向代谢组学通常会检测到统计学上显著的代谢物，但这些代谢物不能轻易被识别。由于没有明确的化学结构，因此无法对其生化相关性进行解释。表代谢物是由经典代谢物通过特定的酶反应产生的，可能代表了结构未知代谢组的很大一部分。在这里，我们提出了一种使用高分辨气相色谱-精确质量质谱、多种离子化技术和稳定同位素标记衍生化方法来注释未知表代谢物的系统工作流程。我们首先确定元素组成式，然后使用这些组成式查询“代谢物计算机扩展”数据库（MINE DB），以获得可能的分子结构，这些结构是由经典途径中的酶多功能性预测的。精确质量的碎片规则与计算机谱图预测程序 CFM-ID 和 MS-FINDER 相结合，以得出最佳候选物。我们通过正确识别 10 个甲基化核苷和 6 个甲基化氨基酸验证了该工作流程的有效性。然后，我们将该策略应用于注释来自癌症研究和其他生物系统的 8 种未知化合物。
