Global metabolic changes induced by plant-derived pyrrolizidine alkaloids following a human poisoning outbreak and in a mouse model.

Several hundred cases of Hirmi Valley Liver Disease (HVLD), an often fatal liver injury, occurred from 2001 to 2011 in a cluster of rural villages in Tigray, Ethiopia. HVLD is principally caused by contamination of the food supply with plant derived pyrrolizidine alkaloids (PAs), with high exposure to the pesticide DDT among villagers increasing their susceptibility. In an untargeted global approach we aimed to identify metabolic changes induced by PA exposure through 1H NMR spectroscopic based metabolic profiling. We analysed spectra acquired from urine collected from HVLD cases and controls and a murine model of PA exposure and PA/DDT co-exposure, using multivariate partial least squares discriminant analysis. In the human models we identified changes in urinary concentrations of tyrosine, pyruvate, bile acids, N-acetylglycoproteins, N-methylnicotinamide and formate, hippurate, p-cresol sulphate, p-hydroxybenzoate and 3-(3-hydroxyphenyl) propionic acid. Tyrosine and p-cresol sulphate were associated with both exposure and disease. Similar changes to tyrosine, one-carbon intermediates and microbial associated metabolites were observed in the mouse model, with tyrosine correlated with the extent of liver damage. These results provide mechanistic insight and implicate the gut microflora in the human response to challenge with toxins. Pathways identified here may be useful in translational research and as "exposome" signals.

Metabolic phenotyping for enhanced mechanistic stratification of chronic hepatitis C-induced liver fibrosis.

OBJECTIVES: The invasive nature of biopsy alongside issues with categorical staging and sampling error has driven research into noninvasive biomarkers for the assessment of liver fibrosis in order to stratify and personalize treatment of patients with liver disease. Here, we sought to determine whether a metabonomic approach could be used to identify signatures reflective of the dynamic, pathological metabolic perturbations associated with fibrosis in chronic hepatitis C (CHC) patients. METHODS: Plasma nuclear magnetic resonance (NMR) spectral profiles were generated for two independent cohorts of CHC patients and healthy controls (n=50 original and n=63 validation). Spectral data were analyzed and significant discriminant biomarkers associated with fibrosis (as graded by enhanced liver fibrosis (ELF) and METAVIR scores) identified using orthogonal projection to latent structures (O-PLS). RESULTS: Increased severity of fibrosis was associated with higher tyrosine, phenylalanine, methionine, citrate and, very-low-density lipoprotein (vLDL) and lower creatine, low-density lipoprotein (LDL), phosphatidylcholine, and N-Acetyl-α1-acid-glycoprotein. Although area under the receiver operator characteristic curve analysis revealed a high predictive performance for classification based on METAVIR-derived models, <40% of identified biomarkers were validated in the second cohort. In the ELF-derived models, however, over 80% of the biomarkers were validated. CONCLUSIONS: Our findings suggest that modeling against a continuous ELF-derived score of fibrosis provides a more robust assessment of the metabolic changes associated with fibrosis than modeling against the categorical METAVIR score. Plasma metabolic phenotypes reflective of CHC-induced fibrosis primarily define alterations in amino-acid and lipid metabolism, and hence identify mechanistically relevant pathways for further investigation as therapeutic targets.

Metabolic profiling framework for discovery of candidate diagnostic markers of malaria.

Despite immense efforts to combat malaria in tropical and sub-tropical regions, the potency of this vector-borne disease and its status as a major driver of morbidity and mortality remain undisputed. We develop an analytical pipeline for characterizing Plasmodium infection in a mouse model and identify candidate urinary biomarkers that may present alternatives to immune-based diagnostic tools. We employ (1)H nuclear magnetic resonance (NMR) profiling followed by multivariate modeling to discover diagnostic spectral regions. Identification of chemical structures is then made on the basis of statistical spectroscopy, multinuclear NMR, and entrapment of candidates by iterative liquid chromatography (LC) and mass spectrometry (MS). We identify two urinary metabolites (i) 4-amino-1-[3-hydroxy-5-(hydroxymethyl)-2,3-dihydrofuran-2-yl]pyrimidin-2(1H)-one, (ii) 2-amino-4-({[5-(4-amino-2-oxopyrimidin-1(2H)-yl)-4-hydroxy-4,5-dihydrofuran-2-yl]methyl}sulfanyl)butanoic acid that were detected only in Plasmodium berghei-infected mice. These metabolites have not been described in the mammalian or parasite metabolism to date. This analytical pipeline could be employed in prospecting for infection biomarkers in human populations.

A metabolic system-wide characterisation of the pig: a model for human physiology.

The pig is a single-stomached omnivorous mammal and is an important model of human disease and nutrition. As such, it is necessary to establish a metabolic framework from which pathology-based variation can be compared. Here, a combination of one and two-dimensional (1)H and (13)C nuclear magnetic resonance spectroscopy (NMR) and high-resolution magic angle spinning (HR-MAS) NMR was used to provide a systems overview of porcine metabolism via characterisation of the urine, serum, liver and kidney metabolomes. The metabolites observed in each of these biological compartments were found to be qualitatively comparable to the metabolic signature of the same biological matrices in humans and rodents. The data were modelled using a combination of principal components analysis and Venn diagram mapping. Urine represented the most metabolically distinct biological compartment studied, with a relatively greater number of NMR detectable metabolites present, many of which are implicated in gut-microbial co-metabolic processes. The major inter-species differences observed were in the phase II conjugation of extra-genomic metabolites; the pig was observed to conjugate p-cresol, a gut microbial metabolite of tyrosine, with glucuronide rather than sulfate as seen in man. These observations are important to note when considering the translatability of experimental data derived from porcine models.

Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling.

Consumption of cruciferous vegetables (CVs) is inversely correlated to many human diseases including cancer (breast, lung, and bladder), diabetes, and cardiovascular and neurological disease. Presently, there are no readily measurable biomarkers of CV consumption and intake of CVs has relied on dietary recall. Here, biomarkers of CV intake were identified in the urine of 20 healthy Caucasian adult males using (1)H NMR spectroscopy with multivariate statistical modeling. The study was separated into three phases of 14 days: a run-in period with restricted CV consumption (phase I); a high CV phase where participants consumed 250 g/day of both broccoli and Brussels sprouts (phase II); a wash-out phase with a return to restricted CV consumption (phase III). Each study participant provided a complete cumulative urine collection over 48 h at the end of each phase; a spot urine (U0), 0-10 h (U0-10), 10-24 h (U10-24), and 24-48 h (U24-48) urine samples. Urine samples obtained after consumption of CVs were differentiated from low CV diet samples by four singlet (1)H NMR spectroscopic peaks, one of which was identified as S-methyl-l-cysteine sulfoxide (SMCSO) and the three other peaks were tentatively identified as other metabolites structurally related to SMCSO. These stable urinary biomarkers of CV consumption will facilitate future assessment of CVs in nutritional population screening and dietary intervention studies and may correlate to population health outcomes.

Systems parasitology: effects of Fasciola hepatica on the neurochemical profile in the rat brain.

We characterize the integrated response of a rat host to the liver fluke Fasciola hepatica using a combination of (1)H nuclear magnetic resonance spectroscopic profiles (liver, kidney, intestine, brain, spleen, plasma, urine, feces) and multiplex cytokine markers of systemic inflammation. Multivariate mathematical models were built to describe the main features of the infection at the systems level. In addition to the expected modulation of hepatic choline and energy metabolism, we found significant perturbations of the nucleotide balance in the brain, together with increased plasma IL-13, suggesting a shift toward modulation of immune reactions to minimize inflammatory damage, which may favor the co-existence of the parasite in the host. Subsequent analysis of brain extracts from other trematode infection models (i.e. Schistosoma mansoni, and Echinostoma caproni) did not elicit a change in neural nucleotide levels, indicating that the neural effects of F. hepatica infection are specific. We propose that the topographically extended response to invasion of the host as characterized by the modulated global metabolic phenotype is stratified across several bio-organizational levels and reflects the direct manipulation of host-nucleotide balance.

High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues.

Metabolic profiling, metabolomic and metabonomic studies require robust study protocols for any large-scale comparisons and evaluations. Detailed methods for solution-state NMR spectroscopy have been summarized in an earlier protocol. This protocol details the analysis of intact tissue samples by means of high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy and we provide a detailed description of sample collection, preparation and analysis. Described here are (1)H NMR spectroscopic techniques such as the standard one-dimensional, relaxation-edited, diffusion-edited and two-dimensional J-resolved pulse experiments, as well as one-dimensional (31)P NMR spectroscopy. These are used to monitor different groups of metabolites, e.g., sugars, amino acids and osmolytes as well as larger molecules such as lipids, non-invasively. Through the use of NMR-based diffusion coefficient and relaxation times measurements, information on molecular compartmentation and mobility can be gleaned. The NMR methods are often combined with statistical analysis for further metabonomics analysis and biomarker identification. The standard acquisition time per sample is 8-10 min for a simple one-dimensional (1)H NMR spectrum, giving access to metabolite information while retaining tissue integrity and hence allowing direct comparison with histopathology and MRI/MRS findings or the evaluation together with biofluid metabolic-profiling data.

NMR-based metabolic profiling identifies biomarkers of liver regeneration following partial hepatectomy in the rat.

Tissue injury and repair are often overlapping consequences of disease or toxic exposure, but are not often considered as distinct processes in molecular studies. To establish the systemic metabolic response to liver regeneration, the partial hepatectomy (PH) model has been studied in the rat by an integrated metabonomics strategy, utilizing (1)H NMR spectroscopy of urine, liver and serum. Male Sprague-Dawley rats were subjected to either surgical removal of approximately two-thirds of the liver, sham operated (SO) surgery, or no treatment (n = 10/group) and samples collected over a 7 day period. A number of urinary metabolic perturbations were observed in PH rats compared with SO and control animals, including elevated levels of taurine, hypotaurine, creatine, guanidinoacetic acid, betaine, dimethylglycine and bile acids. Serum betaine and creatine were also elevated after PH, while levels of triglyceride were reduced. In the liver, triglycerides, cholesterol, alanine and betaine were elevated after PH, while choline and its derivatives were reduced. Upon examining the dynamic pattern of urinary response (the 'metabolic trajectory'), several metabolites could be categorized into groups likely to reflect perturbations to different processes such as dietary intake or hepatic 1-carbon metabolism. Several of the urinary perturbations observed during the regenerative phase of the PH model have also been observed after exposure to liver toxins, indicating that hepatic regeneration may make a contribution to the systemic alterations in metabolism associated with hepatotoxicity. The observed changes in 1-carbon and lipid metabolism are consistent with the proposed role of these pathways in the activation of a regenerative response and provide further evidence regarding the utility of urinary NMR profiles in the detection of liver-specific pathology. Biofluid (1)H NMR-based metabolic profiling provides new insight into the role of metabolism of liver regeneration, and suggests putative biomarkers for the noninvasive monitoring of the regeneration process.

Cluster analysis statistical spectroscopy using nuclear magnetic resonance generated metabolic data sets from perturbed biological systems.

We present a new approach for analysis, information recovery, and display of biological (1)H nuclear magnetic resonance (NMR) spectral data, cluster analysis statistical spectroscopy (CLASSY), which profiles qualitative and quantitative changes in biofluid metabolic composition by utilizing a novel local-global correlation clustering scheme to identify structurally related spectral peaks and arrange metabolites by similarity of temporal dynamic variation. Underlying spectral data sets are presented in a novel graphical format to represent high-dimensionality biochemical information conveying both statistical metabolite relationships and their responses to experimental perturbation simultaneously in a high-throughput and intuitive manner. The method is exemplified using multiple 600 MHz (1)H NMR spectra of rat (n = 40) urine samples collected over 160 h following the development of experimental pancreatitis induced by L-arginine (ARG) and a wider range of model toxins including acetaminophen, galactosamine, and 2-bromoethanamine. The CLASSY approach deconvolutes complex biofluid mixture spectra into quantitative fold-change metabolic trajectories and clusters metabolites by commonalities of coexpression patterns. We demonstrate that the developing pathological processes cause coordinated changes in the levels of many compounds which share similar pathway connectivities. Variability in individual responses to toxin exposure is also readily detected and visualized allowing the assessment of interanimal variability. As an untargeted, unsupervised approach, CLASSY provides significant advantages in biological information recovery in terms of increased throughput, interpretability, and robustness and has wide potential metabonomic/metabolomic applications in clinical, toxicological, and nutritional studies of biofluids as well as in studies of cellular biochemistry, microbial fermentation monitoring, and functional genomics.

Metabonomic investigations into the global biochemical sequelae of exposure to the pancreatic toxin 1-cyano-2-hydroxy-3-butene in the rat.

The time-related metabolic effects of 1-cyano-2-hydroxy-3-butene (CHB, crambene), a naturally occurring nitrile and experimental model toxin causing exocrine pancreatitis, have been investigated in rats using high-resolution NMR spectroscopy of urine and serum in combination with pattern recognition analysis. Rats were administered CHB subcutaneously in two doses, 15 mg/kg dose (n = 10) and 150 mg/kg (n = 10), and conventional histopathology and clinical chemistry assessments were performed. Urine samples were collected at - 16 and 0, 8, 24, 48, 72, 96, 120, 144 and 168 h postdosing and serum samples were collected at 48 and 168 h postdosing; these were analyzed using a range of 1D and 2D NMR spectroscopic methods. The metabolic profile perturbations seen throughout the time-course of the study are described, and the application of the spectral correlation technique Statistical TOtal Correlation SpectroscopY (STOCSY) to detect both structural and novel toxicological connectivities between xenobiotic and endogenous metabolite signals is illustrated for the first time. As a result, it is suggested that the STOCSY approach may be of wider application in the identification of toxic versus nontoxic metabolites in drug metabolism studies.

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.

Heteronuclear 19F-1H statistical total correlation spectroscopy as a tool in drug metabolism: study of flucloxacillin biotransformation.

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.

Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts.

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.

Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach.

Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The sensitivities to liver and kidney toxicity were 67 and 41%, respectively, whereas the corresponding specificities were 77 and 100%. In some cases, it was not possible to make predictions because of interference by drug-related metabolite signals (18%), an inconsistent histopathological or urinary response (11%), genuine class overlap (8%), or lack of similarity to any other treatment (2%). This study constitutes the largest validation to date of the metabonomic approach to preclinical toxicology assessment, confirming that the methodology offers practical utility for rapid in vivo drug toxicity screening.

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice.

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.

Gas chromatography/flame ionisation detection mass spectrometry for the detection of endogenous urine metabolites for metabonomic studies and its use as a complementary tool to nuclear magnetic resonance spectroscopy.

Metabonomics is a relatively new field of research in which the total pool of metabolites in body fluids or tissues from different patient groups is subjected to comparative analysis. Nuclear magnetic resonance (NMR) spectroscopy is the technology that is currently most widely used for the analysis of these highly complex metabolite mixtures, and hundreds of metabolites can be detected without any upfront separation. We have investigated in this study whether gas chromatography (GC) separation in combination with flame ionisation detection (FID) and mass spectrometry (MS) detection can be used for metabolite profiling from urine. We show that although GC sample preparation is much more involved than for NMR, hundreds of metabolites can reproducibly be detected and analysed by GC. We show that the data quality is sufficiently high--particularly if appropriate baseline correction and time-warping methods are applied--to allow for data comparison by chemometrics methods. A sample set of urines from eleven healthy human volunteers was analysed independently by GC and NMR, and subsequent chemometrics analysis of the two datasets showed some similar features. As judged by NIST database searches of the GC/MS data some of the major metabolites that are detected by NMR are also visible by GC/MS. Since in contrast to NMR every peak in GC corresponds to a single metabolite, the electron ionisation spectra can be used to quickly identify metabolites of interest if their reference spectra are present in a searchable database. In summary, we show that GC is a method that can be used as a complementary tool to NMR for metabolite profiling of urine samples.

Comparative metabonomics of differential hydrazine toxicity in the rat and mouse.

Interspecies variation between rats and mice has been studied for hydrazine toxicity using a novel metabonomics approach. Hydrazine hydrochloride was administered to male Sprague-Dawley rats (30 mg/kg, n = 10 and 90 mg/kg, n = 10) and male B6C3F mice (100 mg/kg, n = 8 and 250 mg/kg, n = 8) by oral gavage. In each species, the high dose was selected to produce the major histopathologic effect, hepatocellular lipid accumulation. Urine samples were collected at sequential time points up to 168 h post dose and analyzed by 1H NMR spectroscopy. The metabolites of hydrazine, namely diacetyl hydrazine and 1,4,5,6-tetrahydro-6-oxo-3-pyridazine carboxylic acid (THOPC), were detected in both the rat and mouse urine samples. Monoacetyl hydrazine was detected only in urine samples from the rat and its absence in the urine of the mouse was attributed to a higher activity of N-acetyl transferases in the mouse compared with the rat. Differential metabolic effects observed between the two species included elevated urinary beta-alanine, 3-D-hydroxybutyrate, citrulline, N-acetylcitrulline, and reduced trimethylamine-N-oxide excretion unique to the rat. Metabolic principal component (PC) trajectories highlighted the greater degree of toxic response in the rat. A data scaling method, scaled to maximum aligned and reduced trajectories (SMART) analysis, was used to remove the differences between the metabolic starting positions of the rat and mouse and varying magnitudes of effect, to facilitate comparison of the response geometries between the rat and mouse. Mice followed "biphasic" open PC trajectories, with incomplete recovery 7 days after dosing, whereas rats followed closed "hairpin" time profiles, indicating functional reversibility. The greater magnitude of metabolic effects observed in the rat was supported by the more pronounced effect on liver pathology in the rat when compared with the mouse.

Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles.

Metabonomics can be viewed as the process of defining multivariate metabolic trajectories that describe the systemic response of organisms to physiological perturbations through time. We have explored the hypothesis that the homothetic geometry of a metabolic trajectory, i.e., the metabolic response irrespective of baseline values and overall magnitude, defines the mode of response of the organism to treatment and is hence the key property when considering the similarity between two sets of measurements. A modeling strategy to test for homothetic geometry, called scaled-to-maximum, aligned, and reduced trajectories (SMART) analysis, is presented that together with principal components analysis (PCA) facilitates the visualization of multivariate response similarity and hence the interpretation of metabonomic data. Several examples of the utility of this approach from toxicological studies are presented as follows: interlaboratory variation in hydrazine response, CCl(4) dose-response relationships, and interspecies comparison of bromobenzene toxicity. In each case, the homothetic trajectories hypothesis is shown to be an important concept for the successful multivariate modeling and interpretation of systemic metabolic change. Overall, geometric trajectory analysis based on a homothetic modeling strategy like SMART facilitates the amalgamation and comparison of metabonomic data sets and can improve the accuracy and precision of classification models based on metabolic profile data. Because interlaboratory variation, normal physiological variation, dose-response relationships, and interspecies differences are also key areas of concern in genomic and proteomic as well as metabonomic studies, the methods presented here may also have an impact on many other multilaboratory efforts to produce screenable "-omics" databases useful for gauging toxicity in safety assessment and drug discovery.

Spectral editing and pattern recognition methods applied to high-resolution magic-angle spinning 1H nuclear magnetic resonance spectroscopy of liver tissues.

Principal component analysis (PCA) has been applied to three nuclear magnetic resonance (NMR) spectral editing methods, namely, the Carr-Purcell-Meiboom-Gill spin-echo, diffusion editing, and skyline projection of a two-dimensional J-resolved spectrum, obtained from high-resolution magic-angle spinning NMR spectroscopy of liver tissues, to distinguish between control and hydrazine-treated rats. The effects of the toxin on rat liver biochemistry were directly observed and characterized by depleted levels of liver glycogen, choline, taurine, trimethylamine N-oxide, and glucose and by elevated levels of lipids and alanine. The highly unsaturated omega-3-type fatty acid was observed for the first time in hydrazine-treated rat liver. The contributions of the metabolites to the separation of control from dosed liver tissues varied depending on the type of spectral editing method used. We have shown that subtle changes in the metabolic profiles can be selectively amplified using a metabonomics approach based on the different NMR spectral editing techniques in conjunction with PCA.

Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project.

The role that metabonomics has in the evaluation of xenobiotic toxicity studies is presented here together with a brief summary of published studies. To provide a comprehensive assessment of this approach, the Consortium for Metabonomic Toxicology (COMET) has been formed between six pharmaceutical companies and Imperial College of Science, Technology and Medicine (IC), London, UK. The objective of this group is to define methodologies and to apply metabonomic data generated using (1)H NMR spectroscopy of urine and blood serum for preclinical toxicological screening of candidate drugs. This is being achieved by generating databases of results for a wide range of model toxins which serve as the raw material for computer-based expert systems for toxicity prediction. The project progress on the generation of comprehensive metabonomic databases and multivariate statistical models for prediction of toxicity, initially for liver and kidney toxicity in the rat and mouse, is reported. Additionally, both the analytical and biological variation which might arise through the use of metabonomics has been evaluated. An evaluation of intersite NMR analytical reproducibility has revealed a high degree of robustness. Second, a detailed comparison has been made of the ability of the six companies to provide consistent urine and serum samples using a study of the toxicity of hydrazine at two doses in the male rat, this study showing a high degree of consistency between samples from the various companies in terms of spectral patterns and biochemical composition. Differences between samples from the various companies were small compared to the biochemical effects of the toxin. A metabonomic model has been constructed for urine from control rats, enabling identification of outlier samples and the metabolic reasons for the deviation. Building on this success, and with the completion of studies on approximately 80 model toxins, first expert systems for prediction of liver and kidney toxicity have been generated.