NMR metabolomic approaches for plants, toxicology and medicine

NMR metabolomic approaches for plants, toxicology and medicine This article describes the principles as well as the analytical and chemometric tools that support the development of NMR metabolomic approaches. These analytical methods are illustrated through a range of selected examples in various domains of biology and medicine, concerning the study of genetically modified organisms, plant/environment interactions, environmental toxicology, pharmacology, cancerology, or molecular epidemiology

Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort

Background: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers. Methods: To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort. Results: A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis. Conclusion: Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.This work was supported by the French National Cancer Institute (L’Institut National du Cancer; INCA; grant number 2009-139; PI: M. Jenab). AF received financial support (BDI fellowship) from the Centre National de la Recherche Scientifique (CNRS) and Bruker Biospin. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, and Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum (DKFZ), and Federal Ministry of Education and Research (Germany); Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC), National Research Council, Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, and AIRE-ONLUS Ragusa, AVIS Ragusa, Sicilian Government (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), and Statistics Netherlands (the Netherlands); European Research Council (ERC; grant number ERC-2009-AdG 232997) and Nordforsk, and Nordic Center of Excellence Programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), Regional Governments of Andalucía, Asturias, Basque Country, Murcia (No. 6236) and Navarra, and ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council, and Regional Government of Skåne and Västerbotten (Sweden); Cancer Research UK, Medical Research Council, Stroke Association, British Heart Foundation, Department of Health, Food Standards Agency, and Wellcome Trust (UK)

Targeted projection NMR spectroscopy for unambiguous metabolic profiling of complex mixtures

Unambiguous identification of individual metabolites present in complex mixtures such as biofluids constitutes a crucial prerequisite for quantitative metabolomics, toward better understanding of biochemical processes in living systems. Increasing the dimensionality of a given NMR correlation experiment is the natural solution for resolving spectral overlap. However, in the context of metabolites, natural abundance acquisition of (1)H and (13)C NMR data virtually excludes the use of higher dimensional NMR experiments (3D, 4D, etc.) that would require unrealistically long acquisition times. Here, we introduce projection NMR techniques for studies of complex mixtures, and we show how discrete sets of projection spectra from higher dimensional NMR experiments are obtained in a reasonable time frame, in order to capture essential information necessary to resolve assignment ambiguities caused by signal overlap in conventional 2D NMR spectra. We determine optimal projection angles where given metabolite resonances will have the least overlap, to obtain distinct metabolite assignment in complex mixtures. The method is demonstrated for a model mixture composition made of ornithine, putrescine and arginine for which acquisition of a single 2D projection of a 3D (1)H-(13)C TOCSY-HSQC spectrum allows to disentangle the metabolite signals and to access to complete profiling of this model mixture in the targeted 2D projection plane. Copyright (C) 2010 John Wiley & Sons, Ltd

Ab initio simulation of proton spin diffusion

The many-body nature of the ubiquitous spin diffusion phenomenon makes it difficult to predict accurately from first principles. We show how the use of reduced Liouville spaces makes it possible to reproduce experimental proton spin diffusion measurements directly from crystalline geometry for powdered solids under magic-angle spinning

Homonuclear dipolar decoupling with very large scaling factors for high-resolution ultrafast magic angle spinning H-1 solid-state NMR spectroscopy

We present a new phase modulated radio-frequency pulse sequence for homonuclear dipolar decoupling in proton solid-state NMR spectroscopy, eDUMBO-PLUS-1, with a chemical shift scaling factor of 0.73. This sequence was determined by screening random sequences, and experimentally optimizing the best candidates directly on H-1 NMR spectra with 60 kHz magic angle spinning. It yields efficient decoupling with linewidths as little as 150 Hz for 1.3 mm MAS probes on different spectrometers. Experiments and calculations support the hypothesis of a radio-frequency and MAS joint averaging regime, in which the large scaling factor contributes significantly to the overall performance of the decoupling sequence. (C) 2010 Elsevier B. V. All rights reserved

Two-Dimensional Statistical Recoup ling for the Identification of Perturbed Metabolic Networks from NMR Spectroscopy

The development of Statistical Total Correlation Spectroscopy (STOCSY), a representation of the autocorrelation matrix of a spectral data set as a 2D pseudospectrum, has allowed more reliable assignment of one- and two-dimensional NMR spectra acquired from the complex mixtures that are usually used in metabolomics/metabonomics studies, thus, improving precise identification of candidate biomarkers contained in metabolic signatures computed by multivariate statistical analysis. However, the correlations obtained cannot always be interpreted in terms of connectivities between metabolites. In this study, we combine statistical recoupling of variables (SRV) and STOCSY to identify perturbed metabolite systems. The resulting Recoupled-STOCSY (R-STOCSY) method provides a 2D correlation landscape based on the SRV clusters representing physical, chemical, and biological entities. This enables the identification of correlations between distant clusters and extends the recoupling scheme of SRV, which was previously limited to the association of neighboring clusters. This allows the recovery of only meaningful correlations between metabolic signals and significantly enhances the interpretation of STOCSY. The method is validated through the measurement of the distances between the metabolites involved in these correlations, within the whole metabolic network, which shows that the average shortest path length is significantly shorter for the correlations detected in this new way compared to metabolite couples randomly selected from within the entire KEGG metabolic network. This enables the identification without any a priori knowledge of the perturbed metabolic network. The R-STOCSY completes the recoupling procedure between distant clusters, further reducing the high dimensionality of metabolomics/metabonomics data set and finally allows the identification of composite biomarkers, highlighting disruption of particular metabolic pathways within a global metabolic network. This allows the perturbed metabolic network to be extracted through NMR based metabolomics/metabonomics in an automated, and statistical manner

Computation and NMR crystallography of terbutaline sulfate

This article addresses, by means of computation and advanced experiments, one of the key challenges of NMR crystallography, namely the assignment of individual resonances to specific sites in a crystal structure. Moreover, it shows how NMR can be used for crystal structure validation. The case examined is form B of terbutaline sulfate. CPMAS (13)C and fast MAS (1)H spectra have been recorded and the peaks assigned as far as possible. Comparison of (13)C chemical shifts computed using the CASTEP program (incorporating the Gauge Including Projector Augmented Wave principle) with those obtained experimentally enable the accuracy of the two distinct single-crystal evaluations of the structure to be compared and an error in one of these is located. The computations have substantiallly aided in the assignments of both (13)C and (1)H resonances, as has a series of two-dimensional (2D) spectra (HETCOR, DQ-CRAMPS and proton-proton spin diffusion). The 2D spectra have enabled many of the proton chemical shifts to be pinpointed. The relationships of the NMR shifts to the specific nuclear sites in the crystal structure have therefore been established for most (13)C peaks and for some (1)H signals. Emphasis is placed on the effects of hydrogen bonding on the proton chemical shifts. Copyright (C) 2010 John Wiley & Sons, Ltd