Chemometrics in metabonomics

Gas chromatography/time-of-flight mass spectrometry based metabonomic approach to differentiating hypertension- and age-related metabolic variation in spontaneously hypertensive rats

Metabonomics is a systematic approach to the study of in vivo metabolic profiles and therefore allows deep insight into and a better understanding of the pathogenesis of disease. To characterize the development of hypertension, a hypertensive animal model, the spontaneously hypertensive rat (SHR), and its normotensive control, the Wistar Kyoto (WKY) rat, were investigated and their blood plasma analyzed using the high-throughput metabolomic tool, gas chromatography/time-of-flight mass spectrometry (GC/TOFMS). A total of 187 peaks were quantitatively determined after deconvolution, and 78 of them were identified. Principal components analysis (PCA) and projection to latent structure partial least-squares discriminant analysis (PLS-DA) were used to process the GC/TOFMS data. The resulting mathematical models were further validated by cross-validation. Plasma compositional differences of many identified compounds showed hypertension-related variation between SHR and WKY rats, and age-related changes from 10 to 18 weeks for both the SHR and WKY rats. These compositional changes involved compounds such as hexadecanoic acid, linoleic acid, oleic acid, stearic acid, 3-hydroxybutyric acid, citric acid, threonic acid, tyrosine, tryptophan, threonine, phenylalanine, serine, ornithine, methionine, 3-hydroxyproline, creatinine, erythrose, myo-inositol, D-methylglucopyranoside, tocopherol, sitosterol, and nonesterified cholesterol. Significantly elevated free fatty acids (FFA) were observed in SHR relative to those in WKY rats, and their levels increased as the SHR aged from 10 to 18 weeks. The close correlation between FFA and hypertension suggests that FFA are potential biomarker candidates for hypertension and they may play an important role in the development of hypertension in SHR. It is also indicated that GC/TOFMS-based metabonomics is a powerful approach to identifying potential biomarkers and investigating the pathological processes of hypertension and the physiological developments of aging.

Temporal metabonomic modeling of l-arginine-induced exocrine pancreatitis

The time-related metabolic responses to l-arginine (ARG)-induced exocrine pancreatic toxicity were investigated using single ip doses of 1,000 and 4,000 mg/kg body weight over a 7 day experimental period in male Sprague-Dawley rats. Sequential timed urine and plasma samples were analyzed using high resolution (1)H NMR spectroscopy together with complementary clinical chemistry and histopathology analyses. Principal components analysis (PCA) and orthogonal projection on latent structures discriminant analysis (O-PLS-DA) were utilized to analyze the (1)H NMR data and to extract and identify candidate biomarkers and to construct metabolic trajectories post ARG administration. Low doses of ARG resulted in virtually no histopathological damage and distinct reversible metabolic response trajectories. High doses of ARG caused pancreatic acinar degeneration and necrosis and characteristic metabolic trajectory profiles with several distinct phases. The initial trajectory phase (0-8 h) involved changes in the urea cycle and transamination indicating a homeostatic response to detoxify excess ammonia generated from ARG catabolism. By 48 h, there was a notable enhancement of the excretion of the gut microbial metabolites, phenylacetylglycine (PAG), 4-cresol-glucuronide and 4-cresol-sulfate, suggesting that compromised pancreatic function impacts on the activity of the gut microbiota giving potential rise to a novel class of surrogate extragenomic biomarkers of pancreatic injury. The implied compromise of microbiotal function may also contribute to secondary hepatic and pancreatic toxic responses. We show here for the first time the value of metabonomic studies in investigating metabolic disruption due to experimental pancreatitis. The variety of observed systemic responses suggests that this approach may be of general value in the assessment of other animal models or human pancreatitis.

Quantitative 1H nuclear magnetic resonance metabolite profiling as a functional genomics platform to investigate alkaloid biosynthesis in opium poppy

Opium poppy (Papaver somniferum) produces a diverse array of bioactive benzylisoquinoline alkaloids and has emerged as a versatile model system to study plant alkaloid metabolism. The plant is widely cultivated as the only commercial source of the narcotic analgesics morphine and codeine. Variations in plant secondary metabolism as a result of genetic diversity are often associated with perturbations in other metabolic pathways. As part of a functional genomics platform, we used (1)H nuclear magnetic resonance (NMR) metabolite profiling for the analysis of primary and secondary metabolism in opium poppy. Aqueous and chloroform extracts of six different opium poppy cultivars were subjected to chemometric analysis. Principle component analysis of the (1)H NMR spectra for latex extracts clearly distinguished two varieties, including a low-alkaloid variety and a high-thebaine, low-morphine cultivar. Distinction was also made between pharmaceutical-grade opium poppy cultivars and a condiment variety. Such phenotypic differences were not observed in root extracts. Loading plots confirmed that morphinan alkaloids contributed predominantly to the variance in latex extracts. Quantification of 34 root and 21 latex metabolites, performed using Chenomx NMR Suite version 4.6, showed major differences in the accumulation of specific alkaloids in the latex of the low-alkaloid and high-thebaine, low-morphine varieties. Relatively few differences were found in the levels of other metabolites, indicating that the variation was specific for alkaloid metabolism. Exceptions in the low-alkaloid cultivar included an increased accumulation of the alkaloid precursor tyramine and reduced levels of sucrose, some amino acids, and malate. Real-time polymerase chain reaction analysis of 42 genes involved in primary and secondary metabolism showed differential gene expression mainly associated with alkaloid biosynthesis. Reduced alkaloid levels in the condiment variety were associated with the reduced abundance of transcripts encoding several alkaloid biosynthetic enzymes.

Metabolic profiling reveals disorder of amino acid metabolism in four brain regions from a rat model of chronic unpredictable mild stress

Chronic stress is closely linked to clinical depression, which could be assessed by a chronic unpredictable mild stress (CUMS) animal model. We present here a GC/MS-based metabolic profiling approach to investigate neurochemical changes in the cerebral cortex, hippocampus, thalamus, and remaining brain tissues. Multi-criteria assessment for multivariate statistics could identify differential metabolites between the CUMS-model rats versus the healthy controls. This study demonstrates that the significantly perturbed metabolites mainly involving amino acids play an indispensable role in regulating neural activity in the brain. Therefore, results obtained from such metabolic profiling strategy potentially provide a unique perspective on molecular mechanisms of chronic stress.

Urinary metabolic profiles of inflammatory bowel disease in interleukin-10 gene-deficient mice

Inflammatory bowel disease (IBD) is a chronic debilitating disorder that is thought to have both genetic and environmental contributors. Commensal microflora have been shown to play a key part in the disease process. Metabolomics, the study of large numbers of small molecule metabolites, has demonstrated that disease and/or changes in gut microbial composition modulate mammalian urine metabolite fingerprints. The aim of this project was to associate the development of IBD with specific changes in a mouse urinary metabolic fingerprint. Interleukin-10 (IL-10) gene-deficient mice were raised alongside age-matched 129/SvEv controls in conventional housing. Urine samples (22 h) were collected at ages 4, 6, 8, 12, 16, and 20 weeks. Metabolite concentrations were derived from analysis of nuclear magnetic resonance spectra, and both multivariate and two-way analysis of variance (ANOVA) statistical techniques were applied to the resulting data. Principal component analysis and partial least-squares-discriminant analysis of urine derived from the control and IL-10 gene-deficient mice revealed that while both groups initially had similar metabolic profiles, they diverged substantially with the onset of IBD as assessed through external phenotypic changes. Several metabolites, including trimethylamine (TMA) and fucose, changed dramatically in the IL-10 gene-deficient mice following 8 weeks of age, concomitant with the known timeline for development of severe histological injury. This study illustrates that metabolomics is effective at distinguishing IBD using urinary metabolite profiles.

A metabonomic characterization of CCl4-induced acute liver failure using partial least square regression based on the GC/MS metabolic profiles of plasma in mice

This work characterized the metabolism disorders of acute liver failure (ALF) induced by carbon tetrachloride (CCl(4)) in a mouse model with different dosage of intoxication (100, 500 and 1000 mg/kg). Metabolic profiles of mice plasma were detected by gas chromatography/mass spectrometry (GC/MS) after chemical derivatization. Here an effective information-extracting approach was implemented on the basis of partial least square regression analysis (PLS-RA). PLS modeling was achieved with two kinds of Y-vectors for the acquired metabonomics data and eight metabolites with different changing behaviors were selected. ALF of mice induced by CCl(4) was characterized by the elevation of glutamate, citrate, serine and threonine, as well as the decrease of alpha-glycerophosphate, docosahexaenoic acid, palmitic acid and oleic acid in plasma. The difference in the concentrations of serine, threonine, palmitic acid and oleic acid remained insignificant between the control and 100mg/kg groups, while significant distinction appeared when comparing the control and two higher dosed groups. The underlying regulation of CCl(4)-perturbed metabolic pathways was discussed according to the selected metabolites. The present study demonstrated a great potential of PLS-RA in exploiting a comprehensive metabolic effects of CCl(4) intoxication and its efficient capability to reveal the hepatotoxic mechanism of ALF induced by reactive oxygen species (ROS).

Mass spectrometry-based metabolomics: accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution

A strategy combining autocorrelation matrices and ultrahigh resolution mass spectrometry (MS) was developed to optimize the characterization of discriminating ions highlighted by metabolomics. As an example, urine samples from rats treated with phenobarbital (PB) were analyzed by ultrahigh-pressure chromatography with two different eluting conditions coupled to time-of-flight mass spectrometric detection in both the positive and negative electrospray ionization modes. Multivariate data analyses were performed to highlight discriminating variables from several thousand detected signals: a few hundred signals were found to be affected by PB, whereas a few tenths of them were linked to its metabolism. Autocorrelation matrices were then applied to eliminate adduct and fragment ions. Finally, the characterization of the ions of interest was performed with ultrahigh-resolution mass spectrometry and sequential MS(n) experiments, by using a LC-LTQ-Orbitrap system. The use of different eluting conditions was shown to drastically impact on the chromatographic retention and ionization of compounds, thus providing a way to obtain more exhaustive metabolic fingerprints, whereas autocorrelation matrices allowed one to focus the identification work on the most relevant ions. By using such an approach, 14 PB metabolites were characterized in rat urines, some of which have not been reported in the literature.

Technology insight: metabonomics in gastroenterology-basic principles and potential clinical applications

Metabonomics-the study of metabolic changes in an integrated biologic system-is an emerging field. This discipline joins the other 'omics' (genomics, transcriptomics and proteomics) to give rise to a comprehensive, systems-biology approach to the evaluation of holistic in vivo function. Metabonomics, especially when based on nuclear magnetic resonance spectroscopy, has the potential to identify biomarkers and prognostic factors, enhance clinical diagnosis, and expand hypothesis generation. As a consequence, the use of metabonomics has been extensively explored in the past decade, and applied successfully to the study of human diseases, toxicology, microbes, nutrition, and plant biology. This Review introduces the basic principles of nuclear magnetic resonance spectroscopy and commonly used tools for multivariate data analysis, before considering the applications and future potential of metabonomics in basic and clinical research, with emphasis on applications in the field of gastroenterology.

Critical evaluation of 1H NMR metabonomics of serum as a methodology for disease risk assessment and diagnostics

This review focuses on (i) the current status of 1H NMR spectroscopy to quantify lipoprotein subclasses directly from serum or plasma, and (ii) the applications of 1H NMR metabonomics of serum in biomedicine. Related to both themes, experimental and data analysis methodologies are discussed together with the biochemical rationales. Particular emphasis is placed on the concepts of risk assessment and diagnostics in relation to the potential clinical role of 1H NMR metabonomics; recent applications in the area of coronary heart disease and diabetes are addressed in more detail.

Piecewise multivariate modelling of sequential metabolic profiling data

Background

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints.

Results

A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted.

Conclusion

The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death

Subtle metabolic changes precede and accompany chronic vascular complications, which are the primary causes of premature death in diabetes. To obtain a multimetabolite characterization of these high-risk individuals, we measured proton nuclear magnetic resonance (1H NMR) data from the serum of 613 patients with type I diabetes and a diverse spread of complications. We developed a new metabonomics framework to visualize and interpret the data and to link the metabolic profiles to the underlying diagnostic and biochemical variables. Our results indicate complex interactions between diabetic kidney disease, insulin resistance and the metabolic syndrome. We illustrate how a single 1H NMR protocol is able to identify the polydiagnostic metabolite manifold of type I diabetes and how its alterations translate to clinical phenotypes, clustering of micro- and macrovascular complications, and mortality during several years of follow-up. This work demonstrates the diffuse nature of complex vascular diseases and the limitations of single diagnostic biomarkers. However, it also promises cost-effective solutions through high-throughput analytics and advanced computational methods, as applied here in a case that is representative of the real clinical situation.

Plant metabolomics reveals conserved and divergent metabolic responses to salinity

New metabolic profiling technologies provide data on a wider range of metabolites than traditional targeted approaches. Metabolomic technologies currently facilitate acquisition of multivariate metabolic data using diverse, mostly hyphenated, chromatographic detection systems, such as GC-MS or liquid chromatography coupled to mass spectrometry, Fourier-transformed infrared spectroscopy or NMR-based methods. Analysis of the resulting data can be performed through a combination of non-supervised and supervised statistical methods, such as independent component analysis and analysis of variance, respectively. These methods reduce the complex data sets to information, which is relevant for the discovery of metabolic markers or for hypothesis-driven, pathway-based analysis. Plant responses to salinity involve changes in the activity of genes and proteins, which invariably lead to changes in plant metabolism. Here, we highlight a selection of recent publications in the salt stress field, and use gas chromatography time-of-flight mass spectrometry profiles of polar fractions from the plant models, Arabidopsis thaliana, Lotus japonicus and Oryza sativa to demonstrate the power of metabolite profiling. We present evidence for conserved and divergent metabolic responses among these three species and conclude that a change in the balance between amino acids and organic acids may be a conserved metabolic response of plants to salt stress.

Quantitative 1H NMR metabolomics reveals extensive metabolic reprogramming of primary and secondary metabolism in elicitor-treated opium poppy cell cultures

BACKGROUND

Opium poppy (Papaver somniferum) produces a diverse array of bioactive benzylisoquinoline alkaloids and has emerged as a model system to study plant alkaloid metabolism. The plant is cultivated as the only commercial source of the narcotic analgesics morphine and codeine, but also produces many other alkaloids including the antimicrobial agent sanguinarine. Modulations in plant secondary metabolism as a result of environmental perturbations are often associated with the altered regulation of other metabolic pathways. As a key component of our functional genomics platform for opium poppy we have used proton nuclear magnetic resonance (1H NMR) metabolomics to investigate the interplay between primary and secondary metabolism in cultured opium poppy cells treated with a fungal elicitor.

RESULTS

Metabolite fingerprinting and compound-specific profiling showed the extensive reprogramming of primary metabolic pathways in association with the induction of alkaloid biosynthesis in response to elicitor treatment. Using Chenomx NMR Suite v. 4.6, a software package capable of identifying and quantifying individual compounds based on their respective signature spectra, the levels of 42 diverse metabolites were monitored over a 100-hour time course in control and elicitor-treated opium poppy cell cultures. Overall, detectable and dynamic changes in the metabolome of elicitor-treated cells, especially in cellular pools of carbohydrates, organic acids and non-protein amino acids were detected within 5 hours after elicitor treatment. The metabolome of control cultures also showed substantial modulations 80 hours after the start of the time course, particularly in the levels of amino acids and phospholipid pathway intermediates. Specific flux modulations were detected throughout primary metabolism, including glycolysis, the tricarboxylic acid cycle, nitrogen assimilation, phospholipid/fatty acid synthesis and the shikimate pathway, all of which generate secondary metabolic precursors.

CONCLUSION

The response of cell cultures to elicitor treatment involves the extensive reprogramming of primary and secondary metabolism, and associated cofactor biosynthetic pathways. A high-resolution map of the extensive reprogramming of primary and secondary metabolism in elicitor-treated opium poppy cell cultures is provided.

NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology

We have reviewed the main contributions to the development of NMR-based metabonomic and metabolic profiling approaches for toxicological assessment, biomarker discovery, and studies on toxic mechanisms. The metabonomic approach, (defined as the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification) was originally developed to assist interpretation in NMR-based toxicological studies. However, in recent years there has been extensive fusion with metabolomic and other metabolic profiling approaches developed in plant biology, and there is much wider coverage of the biomedical and environmental fields. Specifically, metabonomics involves the use of spectroscopic techniques with statistical and mathematical tools to elucidate dominant patterns and trends directly correlated with time-related metabolic fluctuations within spectral data sets usually derived from biofluids or tissue samples. Temporal multivariate metabolic signatures can be used to discover biomarkers of toxic effect, as general toxicity screening aids, or to provide novel mechanistic information. This approach is complementary to proteomics and genomics and is applicable to a wide range of problems, including disease diagnosis, evaluation of xenobiotic toxicity, functional genomics, and nutritional studies. The use of biological fluids as a source of whole organism metabolic information enhances the use of this approach in minimally invasive longitudinal studies.

Population-specific metabolic phenotypes of Arabidopsis lyrata ssp. petraea

Plant populations growing at the margin of their range may exhibit traits that indicate genetic differentiation and adaptation to their local abiotic environment. Here, it was investigated whether geographically separated marginal populations of Arabidopsis lyrata ssp. petraea have distinct metabolic phenotypes within the plant foliage. Seeds of A. petraea were obtained from populations along a latitudinal gradient (49-64 N), namely Germany, Wales, Sweden and Iceland and grown in a controlled cabinet environment. Targeted metabolic profiles and fingerprints were obtained at the same initial developmental stage. The free amino acid compositions were population specific, with fold differences in arginine, aspartic acid, asparagines, glycine, phenylalanine, alanine, threonine, histidine, serine and gamma-aminobutyric acid (GABA) concentrations. Sucrose, mannose and fructose concentrations were also different between populations but polyhydric alcohol concentrations were not. Principal component analysis (PCA) of metabolite fingerprints revealed metabolic phenotypes for each population. It is suggested that glucosinolates were responsible for discriminating populations within the PCA. Metabolite fingerprinting and profiling has proved to be sufficiently sensitive to identify metabolic differences between plant populations. These findings show that there is significant natural variation in metabolism among populations of A. petraea.

Heteronuclear 1H-31P statistical total correlation NMR spectroscopy of intact liver for metabolic biomarker assignment: application to galactosamine-induced hepatotoxicity

As part of our ongoing development of methods for enhanced biomarker information recovery from spectroscopic data we present the first example of a new hetero-nuclear statistical total correlation spectroscopy (HET-STOCSY) approach applied to intact tissue samples collected as part of a toxicological study. One-dimensional 1H and 31P-{1H} magic angle spinning (MAS) NMR spectra of intact liver samples after galactosamine (galN) treatment to rats and after cotreatment of galN plus uridine were collected at 275 K. Individual samples were also followed by 1H and 31P-{1H} MAS NMR through time generating time dependent modulations in metabolite signatures relating to toxicity. High-resolution 1H NMR spectra of urine and plasma and clinical chemical data were also collected to establish a biological framework in which to place these novel statistical heterospectroscopic data. In HET-STOCSY, calculation of the covariance between the 31P-{1H} and 1H NMR signals of phosphorus containing metabolites allows their molecular connectivities to be established and the construction of virtual two-dimensional heteronuclear correlation spectra that connect all protons on the molecule to the heteroatom. We show how HET-STOCSY applied to MAS NMR spectra of liver samples can be used to augment biomarker detection. This approach is generic and can be applied to correlate the covarying signals for any spin-active nuclei where there is parallel or serial collection of data.

LC-MS-based metabonomics analysis

Metabonomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. It has shown particular promise in the areas of toxicology and drug development, functional genomics, systems biology, and clinical diagnosis. Comprehensive metabonomics investigations are primarily a challenge for analytical chemistry. High-performance liquid chromatography-mass spectrometry (HPLC-MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its main advantages include wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing HPLC-MS for metabonomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, the strategy and application of HPLC-MS-based metabonomics are reviewed.

LC-MS-based metabolomics in drug metabolism

Xenobiotic metabolism, a ubiquitous natural response to foreign compounds, elicits initiating signals for many pathophysiological events. Currently, most widely used techniques for identifying xenobiotic metabolites and metabolic pathways are empirical and largely based on in vitro incubation assays and in vivo radiotracing experiments. Recent work in our lab has shown that LC-MS-based metabolomic techniques are useful tools for xenobiotic metabolism research since multivariate data analysis in metabolomics can significantly rationalize the processes of xenobiotic metabolite identification and metabolic pathway analysis. In this review, the technological elements of LC-MS-based metabolomics for constructing high-quality datasets and conducting comprehensive data analysis are examined. Four novel approaches of using LC-MS-based metabolomic techniques in xenobiotic metabolism research are proposed and illustrated by case studies and proof-of-concept experiments, and the perspective on their application is further discussed.

Evaluation of Tubulointerstitial Lesions' Severity in Patients with Glomerulonephritides: An NMR-Based Metabonomic Study

An 1H NMR-based metabonomic approach was used to investigate the correlation of histopathologically assessed tubulointerstitial lesions with the urinary metabolite profile in 77 patients with glomerulonephritides submitted to renal biopsy. The presence of renal damage was predicted with a sensitivity of 96% and a specificity of 99%. Patients with mild, moderate, and severe tubulointerstitial lesions were progressively differentiated from the healthy individuals in the Orthogonal Signal Correction Partial Least-Squares-Discriminant Analysis (OSC/PLS-DA) models with a statistically significant separation between those with mild and with severe lesions. The onset of the tubulointerstitial lesions is characterized by decreased excretion of citrate, hippurate, glycine, and creatinine, whereas further deterioration is followed by glycosuria, selective aminoaciduria, total depletion of citrate and hippurate, and gradual increase in the excretion of lactate, acetate, and trimethylamine-N-oxide. NMR-based metabonomic urinalysis could contribute to the early evaluation of the severity of the renal damage and possibly to the monitoring of kidney function.

A multiway approach to analyze metabonomic data: a study of maize seeds development

The aim of this research was to show that the application of multiway partial least square-discriminant analysis to nuclear magnetic resonance spectra is a valuable tool to analyze metabonomic data of transgenic maize. We evaluated the effects, on the development of seeds, of the introduction of the antisense-mediated downregulation and overexpression of the Rpd3 gene (ZmRpd3) in the genome of a maize inbred line, we identified the metabolites involved in the differentiation between classes of samples, directly integrating the evolution of each metabolic perturbation over time in the model. Major differences were found at the beginning of development, confirming the results obtained by transcript analysis: ZmRpd3 transcripts and proteins accumulate during the initial stage of development, suggesting a role for this gene in cell cycle control.

The mechanism of galactosamine toxicity revisited; a metabonomic study

1H NMR spectroscopy was used to investigate the metabolic effects of the hepatotoxin galactosamine (galN) and the mechanism by which glycine protects against such toxicity. Rats were acclimatized to a 0 or 5% glycine diet for 6 days and subsequently administered vehicle, galN (500 mg/kg), glycine (5% via the diet), or both galN and glycine. Urine was collected over 12 days prior to administration of galN and for 24 hours thereafter. Serum and liver tissue were sampled on termination, 24 hours post-dosing. The metabolic profiles of biofluids and tissues were determined using high-field 1H NMR spectroscopy. Orthogonal-projection to latent structures discriminant analysis (O-PLS-DA) was applied to model the spectral data and enabled the hepatic, urinary, and serum metabolites that discriminated between control and treated animals to be determined. Histopathological data and clinical chemistry measurements confirmed the protective effect of glycine. The level of N-acetylglucosamine (glcNAc) in the post-dose urine was found to correlate strongly with the degree of galN-induced liver damage, and the urinary level of glcNAc was not significantly elevated in rats treated with both galN and glycine. Treatment with glycine alone was found to significantly increase hepatic levels of uridine, UDP-glucose, and UDP-galactose, and in view of the known effects of galactosamine, this suggests that the protective role of glycine against galN toxicity might be mediated by changes in the uridine nucleotide pool rather than by preventing Kupffer cell activation. Thus, we present a novel hypothesis: that administration of glycine increases the hepatic uridine nucleotide pool which counteracts the galN-induced depletion of these pools and facilitates complete metabolism of galN. These novel data highlight the applicability of NMR-based metabonomics in elucidating multicompartmental metabolic consequences of toxicity and toxic salvage.

Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry

Sources of analytical variation in high-performance liquid chromatography/mass spectrometry (HPLC/MS), such as changes in retention, mass accuracy or signal intensity, have been investigated to assess their importance as a variable in the metabonomic analysis of human urine. In this study chromatographic retention and mass accuracy were found to be quite reproducible with the most significant source of analytical variation in the data sets obtained being the result of changes in detector response. Depending on the signal intensity threshold used to define the presence of a peak a sample component could be present in some replicate injections and absent in others within the same run. The implementation of a more sophisticated data software analysis package was found to greatly reduce the impact of detector response variability resulting in improved data analysis.