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首页> 美国卫生研究院文献>other >Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry
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Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry

机译：通过将3D NMR与傅立叶变换离子回旋共振共振串联质谱相结合准确鉴定未知和已知的代谢混合物成分

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摘要

Metabolite identification in metabolomics samples is a key step that critically impacts downstream analysis. We recently introduced the SUMMIT NMR/mass spectrometry (MS) hybrid approach for the identification of the molecular structure of unknown metabolites, based on the combination of NMR, MS, and combinatorial cheminformatics. Here, we demonstrate the feasibility of the approach for an untargeted analysis of both a model mixture and E. coli cell lysate, based on 2D/3D NMR experiments in combination with Fourier transform ion cyclotron resonance MS and MS/MS data. For 19 of the 25 model metabolites SUMMIT yielded complete structures that matched those in the mixture independent of database information. Of those, 7 top-ranked structures matched those in the mixture, and 4 of those were further validated by positive ion MS/MS. For 5 metabolites, not part of the 19 metabolites, correct molecular structural motifs could be identified. For E. coli, SUMMIT MS/NMR identified 20 previously known metabolites with 3 or more 1H spins independent of database information. Moreover, for 15 unknown metabolites, molecular structural fragments were determined consistent with their spin systems and chemical shifts. By providing structural information for entire metabolites or molecular fragments, SUMMIT MS/NMR greatly assists the targeted or untargeted analysis of complex mixtures of unknown compounds.
机译：代谢组学样品中的代谢物鉴定是关键影响下游分析的关键步骤。我们最近引入了SUMMIT NMR /质谱（MS）混合方法，用于基于NMR，MS和组合化学信息学的组合鉴定未知代谢物的分子结构。在这里，我们结合2D / 3D NMR实验以及傅里叶变换离子回旋共振MS和MS / MS数据，证明了该方法对模型混合物和大肠杆菌细胞裂解液进行非目标分析的可行性。对于25种模型代谢物中的19种，SUMMIT产生了与混合物中的结构完全匹配的完整结构，而与数据库信息无关。其中有7个排名最高的结构与混合物中的结构匹配，其中有4个通过阳离子MS / MS进一步验证。对于5种代谢物，而不是19种代谢物的一部分，可以确定正确的分子结构基序。对于大肠杆菌，SUMMIT MS / NMR识别了20种先前已知的具有3个或更多 1 H自旋的代谢物，而与数据库信息无关。此外，对于15种未知的代谢物，确定了分子结构片段与其自旋系统和化学位移一致。通过提供整个代谢物或分子片段的结构信息，SUMMIT MS / NMR极大地帮助了未知化合物复杂混合物的目标分析或非目标分析。

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