Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Automated summary

The direct analysis of metabolic intermediates in biological samples has greatly advanced the understanding of metabolic processes. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have played a key role in these advancements. NMR, originally used for elucidating molecular structures, has now been extended to the analysis of complex mixtures in biological samples, known as NMR-based metabolomics. NMR is also powerful in other areas of small molecule biochemistry, including quantification of metabolites, determining metabolic fate, uncovering new metabolic pathways, studying metabolite-protein interactions, and understanding metabolic regulation and pharmacological effects. The development of computational tools and resources has contributed to a more detailed understanding of systems biochemistry.