There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at

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Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4-5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.

The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.

High-resolution 750 MHz 1H NMR spectra of control human blood plasma have been measured and assigned by the concerted use of a range of spin-echo, two-dimensional J-resolved, and homonuclear and heteronuclear (1H-13C) correlation methods. The increased spectral dispersion and sensitivity at 750 MHz enable the assignment of numerous 1H and 13C resonances from many molecular species that cannot be detected at lower frequencies. This work presents the most comprehensive assignment of the 1H NMR spectra of blood plasma yet achieved and includes the assignment of signals from 43 low M(r) metabolites, including many with complex or strongly coupled spin systems. New assignments are also provided from the 1H and 13C NMR signals from several important macromolecular species in whole blood plasma, i.e., very-low-density, low-density, and high-density lipoproteins, albumin, and alpha 1-acid glycoprotein. The temperature dependence of the one-dimensional and spin-echo 750 MHz 1H NMR spectra of plasma was investigated over the range 292-310 K. The 1H NMR signals from the fatty acyl side chains of the lipoproteins increased substantially with temperature (hence also molecular mobility), with a disproportionate increase from lipids in low-density lipoprotein. Two-dimensional 1H-13C heteronuclear multiple quantum coherence spectroscopy at 292 and 310 K allowed both the direct detection of cholesterol and choline species bound in high-density lipoprotein and the assignment of their signals and confirmed the assignment of most of the lipoprotein resonances.

We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.

Here, we study the intricate relationship between gut microbiota and host cometabolic phenotypes associated with dietary-induced impaired glucose homeostasis and nonalcoholic fatty liver disease (NAFLD) in a mouse strain (129S6) known to be susceptible to these disease traits, using plasma and urine metabotyping, achieved by 1 H NMR spectroscopy. Multivariate statistical modeling of the spectra shows that the genetic predisposition of the 129S6 mouse to impaired glucose homeostasis and NAFLD is associated with disruptions of choline metabolism, i.e., low circulating levels of plasma phosphatidylcholine and high urinary excretion of methylamines (dimethylamine, trimethylamine, and trimethylamine-Noxide), coprocessed by symbiotic gut microbiota and mammalian enzyme systems. Conversion of choline into methylamines by microbiota in strain 129S6 on a high-fat diet reduces the bioavailability of choline and mimics the effect of choline-deficient diets, causing NAFLD. These data also indicate that gut microbiota may play an active role in the development of insulin resistance.metabonomics ͉ NMR ͉ nonalcoholic fatty liver disease ͉ nutritional genomics ͉ metabolic syndrome H ighly complex animals such as mammals can be considered as ''superorganisms'' with a karyome, a chondriome, and a microbiome (1), resulting from a coevolutionary symbiotic ecosystem of diverse intestinal microbiota interacting metabolically with the host (2). Recent molecular analyses of human microbiota 16s ribosomal DNA sequences revealed a majority of uncultivated or unknown species with a strong degree of interindividual diversity (3, 4). Also, some of the molecular foundations of beneficial symbiotic host-bacteria relationships in the gut were revealed by colonization of germ-free mice with known microbes and by comparisons of the genomes of members of the intestinal microbiota (5). For instance, Bacteroides thetaiotaomicron, a dominant member of normal distal intestinal microbiota, hydrolyzes otherwise indigestible dietary polysaccharides, thus supplying the host with 10-15% of calorific requirement (6). Gut Lactobacillus spp. are also responsible for a significant proportion of bile acid deconjugation, a process that efficiently reduces lipid absorption in the gut (7). Such symbiotic relationships are the result of coevolution and operate at the genome, proteome, and metabolome levels (6,8).Insulin resistance (IR) is central to a cluster of frequent and increasingly prevalent pathologies, including type 2 diabetes mellitus, central obesity, hypertension hepatic steatosis, and dyslipidemia (9). IR contributes to major causes of morbidity and mortality worldwide (10). Epidemiological and genetic studies in human and animal models have demonstrated the importance of both genetic and environmental factors in the etiology of IR (9): Dietary variation and intervention, in particular, have a strong influence on the development of IR. Nonalcoholic fatty liver disease (NAFLD), is the most frequent liver condition associated with IR (11). It is associa...

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