A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments

• Published in: Computational and structural biotechnology journal Vol. 14; no. C; pp. 135 - 153
• Main Authors: Dona, Anthony C., Kyriakides, Michael, Scott, Flora, Shephard, Elizabeth A., Varshavi, Dorsa, Veselkov, Kirill, Everett, Jeremy R.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2016; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: biotechnology; liquid chromatography; mass spectrometry; Metabolite identification; metabolites; Metabolomics; Metabonomics; mice; Molecular structure; Nuclear magnetic resonance (NMR) spectroscopy; nuclear magnetic resonance spectroscopy; prediction; Metabolomics; Metabolite identification; Molecular structure; Nuclear magnetic resonance (NMR) spectroscopy; Metabonomics
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2016.02.005

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC–MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice. •Many simple metabolites can be identified in biofluids using 1D 1H NMR methods.•Metabolites at lower levels and with more complex structures will require 2D NMR approaches for identification.•Metabolite databases such as the HMDB are helpful for metabolite identification.•Users need to be aware of the possibility of errors in metabolite databases.•Metabolite identification carbon efficiency (MICE) helps measure known metabolite identification confidence.