The mechanism of galactosamine toxicity revisited; a metabonomic study

Assessment of animal experimental models of toxic liver injury in the context of their potential application as preclinical models for cell therapy

Preclinical animal models allow to study development and progression of several diseases, including liver disorders. These studies, for ethical reasons and medical limits, are impossible to carry out in human patients. At the same time, such experimental models constitute an important source of knowledge on pathomechanisms for drug- and virus-induced hepatotoxicity, both acute and chronic. Carbon tetrachloride, D-Galactosamine, and retrorsine are xenobiotics that can be used in immunocompetent animal models of hepatotoxicity, where chemical-intoxicated livers present histological features representative of human viruses-related infection. A prolonged derangement into liver architecture and functions commonly lead to cirrhosis, eventually resulting in hepatocellular carcinoma. In human, orthotopic liver transplantation commonly resolve most the problems related to cirrhosis. However, the shortage of donors does not allow all the patients in the waiting list to receive an organ on time. A promising alternative treatment for acute and chronic liver disease has been advised in liver cell transplantation, but the limited availability of hepatocytes for clinical approaches, in addition to the immunosuppressant regiment required to sustain cellular long-term engraftment have been encouraging the use of alternative cell sources. A recent effective source of stem cells have been recently identified in the human amnion membrane. Human amnion epithelial cells (hAEC) have been preclinically tested and proven sufficient to rescue immunocompetent rodents lethally intoxicated with drugs. The adoption of therapeutic procedures based on hAEC transplant in immunocompetent recipients affected by liver diseases, as well as patients with immune-related disorders, may constitute a successful new alternative therapy in regenerative medicine.

Understanding the effect of repeated administration of meloxicam on feline renal cortex and medulla: A lipidomics and metabolomics approach

Repeated administration of meloxicam can cause kidney damage in cats by mechanisms that remain unclear. Metabolomics and lipidomics are powerful, noninvasive approaches used to investigate tissue response to drug exposure. Thus, the objective of this study was to assess the effects of meloxicam on the feline kidney using untargeted metabolomics and lipidomics approaches. Female young-adult purpose-breed cats were allocated into the control (n = 4) and meloxicam (n = 4) groups. Cats in the control and meloxicam groups were treated daily with saline and meloxicam at 0.3 mg/kg subcutaneously for 17 days, respectively. Renal cortices and medullas were collected at the end of the treatment period. Random forest and metabolic pathway analyses were used to identify metabolites that discriminate meloxicam-treated from saline-treated cats and to identify disturbed metabolic pathways in renal tissue. Our results revealed that the repeated administration of meloxicam to cats altered the kidney metabolome and lipidome and suggest that at least 40 metabolic pathways were altered in the renal cortex and medulla. These metabolic pathways included lipid, amino acid, carbohydrate, nucleotide and energy metabolisms, and metabolism of cofactors and vitamins. This is the first study using a pharmacometabonomics approach for studying the molecular effects of meloxicam on feline kidneys.

Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses

We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r(2) value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r(2) values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r(2) values. The Q(2)X value for each PCA model is extracted. K-means clustering is then applied to the Q(2)X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q(2)X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q(2)X values is then further subclustered into two groups based on the r(2) values. We considered the cluster with high Q(2)X but low r(2) values as nondiscriminatory, while the cluster with high Q(2)X and r(2) values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r(2) values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated (1)H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the sample size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini-Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data.

Quantitative proteomic analysis of hepatocyte-secreted extracellular vesicles reveals candidate markers for liver toxicity

Extracellular vesicles have created great interest as possible source of biomarkers for different biological processes and diseases. Although the biological function of these vesicles is not fully understood, it is clear that they participate in the removal of unnecessary cellular material and act as carriers of various macromolecules and signals between the cells. In this report, we analyzed the proteome of extracellular vesicles secreted by primary hepatocytes. We used one- and two-dimensional liquid chromatography combined with data-independent mass spectrometry. Employing label-free quantitative proteomics, we detected significant changes in vesicle protein expression levels in this in vitro model after exposure to well-known liver toxins (galactosamine and Escherichia coli-derived lipopolysaccharide). The results allowed us to identify candidate markers for liver injury. We validated a number of these markers in vivo, providing the basis for the development of novel methods to evaluate drug toxicity. This report strongly supports the application of proteomics in the study of extracellular vesicles released by well-controlled in vitro cellular systems. Analysis of such systems should help to identify specific markers for various biological processes and pathological conditions.

BIOLOGICAL SIGNIFICANCE

Identification of low invasive candidate marker for hepatotoxicity. Support to apply proteomics in the study of extracellular vesicles released by well-controlled in vitro cellular systems to identify low invasive markers for diseases.

UPLC-MS-based serum metabonomics for identifying acute liver injury biomarkers in Chinese miniature pigs

Metabonomics has emerged as an important technology for exploring the underlying mechanisms of diseases and screening for biomarkers. In this investigation, to comprehensively assess metabolite changes in D-galactosamine (GalN)-induced liver injury in Chinese miniature pigs and to increase our understanding of physiological changes in normal and pathological states, we used ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to analyze metabolites and identify biomarkers in serum. Blood samples were collected both from 18 h after GalN treatment group and control group pigs. We performed multivariate analyses on the metabolite profiles to identify potential biomarkers of acute liver injury, which were then confirmed by tandem MS. Based on "variable of importance in the project" (VIP) values and S-plots, four groups of biomarkers were identified--namely conjugated bile acids, lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs) and fatty acid amides (FAAs)--that were present at significantly different levels in the control and GalN-induced groups. LPCs, PCs, and FAAs showed marked decreases in the GalN-treated group, whereas conjugated bile acids in the treated group showed considerable increases. Taken together, our results suggested that obvious metabolic disturbances occur during acute liver injury, which provided novel insights into the molecular mechanism(s) of D-galactosamine (GalN)-induced liver injury, and will facilitate future research and management of liver injury.

Review: toxicometabolomics

Metabolomics use in toxicology is rapidly increasing, particularly owing to advances in mass spectroscopy, which is widely used in the life sciences for phenotyping disease states. Toxicology has the advantage of having the disease agent, the toxicant, available for experimental induction of metabolomics changes monitored over time and dose. This review summarizes the different technologies employed and gives examples of their use in various areas of toxicology. A prominent use of metabolomics is the identification of signatures of toxicity - patterns of metabolite changes predictive of a hazard manifestation. Increasingly, such signatures indicative of a certain hazard manifestation are identified, suggesting that certain modes of action result in specific derangements of the metabolism. This might enable the deduction of underlying pathways of toxicity, which, in their entirety, form the Human Toxome, a key concept for implementing the vision of Toxicity Testing for the 21st century. This review summarizes the current state of metabolomics technologies and principles, their uses in toxicology and gives a thorough overview on metabolomics bioinformatics, pathway identification and quality assurance. In addition, this review lays out the prospects for further metabolomics application also in a regulatory context.

Pharmacometabonomic investigation of dynamic metabolic phenotypes associated with variability in response to galactosamine hepatotoxicity

Galactosamine (galN) is widely used as an in vivo model of acute liver injury. We have applied an integrative approach, combining histopathology, clinical chemistry, cytokine analysis, and nuclear magnetic resonance (NMR) spectroscopic metabolic profiling of biofluids and tissues, to study variability in response to galactosamine following successive dosing. On re-challenge with galN, primary non-responders displayed galN-induced hepatotoxicity (induced response), whereas primary responders exhibited a less marked response (adaptive response). A systems-level metabonomic approach enabled simultaneous characterization of the xenobiotic and endogenous metabolic perturbations associated with the different response phenotypes. Elevated serum cytokines were identified and correlated with hepatic metabolic profiles to further investigate the inflammatory response to galN. The presence of urinary N-acetylglucosamine (glcNAc) correlated with toxicological outcome and reflected the dynamic shift from a resistant to a sensitive phenotype (induced response). In addition, the urinary level of glcNAc and hepatic level of UDP-N-acetylhexosamines reflected an adaptive response to galN. The unique observation of galN-pyrazines and altered gut microbial metabolites in fecal profiles of non-responders suggested that gut microfloral metabolism was associated with toxic outcome. Pharmacometabonomic modeling of predose urinary and fecal NMR spectroscopic profiles revealed a diverse panel of metabolites that classified the dynamic shift between a resistant and sensitive phenotype. This integrative pharmacometabonomic approach has been demonstrated for a model toxin; however, it is equally applicable to xenobiotic interventions that are associated with wide variation in efficacy or toxicity and, in particular, for prediction of susceptibility to toxicity.

Serum UPLC-MS/MS metabolic profiling in an experimental model for acute-liver injury reveals potential biomarkers for hepatotoxicity

A key interest in clinical diagnosis and pharmaceutical industry is to have a repertoire of noninvasive biomarkers to-individually or in combination-be able to infer or predict the degree of liver injury caused by pathological conditions or drugs. Metabolomics-a comprehensive study of global metabolites-has become a highly sensitive and powerful tool for biomarker discovery thanks to recent technological advances. An ultra-performance liquid chromatography/time-of-flight tandem mass spectrometry (UPLC/TOF MS/MS)-based metabolomics approach was employed to investigate sera from galactosamine-treated rats to find potential biomarkers for acute liver injury. Hepatic damage was quantified by determining serum transaminase activity and in situ liver histological lesions. Principal component analysis in combination with coefficient of correlation analysis was used for biomarker selection and identification. According to the data, serum levels of several metabolites including glucose, amino acids, and membrane lipids were significantly modified, some of them showing a high correlation with the degree of liver damage determined by histological examination of the livers. In conclusion, this study supports that UPLC-MS/MS based serum metabolomics in experimental animal models could be a powerful approach to search for biomarkers for drug- or disease-induced liver injury.

Non-linear modeling of 1H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing

Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.

Data-driven approach for metabolite relationship recovery in biological 1H NMR data sets using iterative statistical total correlation spectroscopy

Statistical total correlation spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a (1)H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations.

Integrated metabonomics analysis of the size-response relationship of silica nanoparticles-induced toxicity in mice

Understanding the underlying properties-dependent interactions of nanostructures with biological systems is essential to nanotoxicological research. This study investigates the relationship between particle size and toxicity, and further reveals the mechanism of injury, using silica particles (SP) with diameters of 30, 70, and 300 nm (SP30, SP70, and SP300) as model materials. The biochemical compositions of liver tissues and serum of mice treated with SP30, SP70, and SP300 were analyzed by integrated metabonomics analysis based on gas chromatography-mass spectrometry (GC-MS) and in combination with pattern recognition approaches. Histopathological examinations and serum biochemical analysis were simultaneously performed. The toxicity induced by three different sizes of SP mainly involved hepatocytic necrosis, increased serum aminotransferase, and inflammatory cytokines. Moreover, the toxic effects of SP were dose-dependent for each particle size. The doses of SP30, SP70, and SP300 that were toxic to the liver were 10, 40, and 200 mg kg(-1), respectively. In this study, surface area has a greater effect than particle number on the toxicity of SP30, SP70, and SP300 in the liver. The disturbances in energy metabolism, amino acid metabolism, lipid metabolism, and nucleotide metabolism may be attributable to the hepatotoxicity induced by SP. In addition, no major differences were found in the response of biological systems caused by the different SP sizes among the metabolite profiles. The results suggest that not only nano-sized but also submicro-sized SP can cause similar extents of liver injury, which is dependent on the exposure dose, and the mechanism of toxicity may be almost the same.

Glycosidic intermediates identified in 1H MR spectra of intact tumour cells may contribute to the clarification of aspects of glycosylation pathways

The glycosylation process, through the addition of carbohydrates, is a major post-translational modification of proteins and glycolipids. Proteins may be glycosylated in either the secretory pathway leading to N-linked or O-linked glycoproteins or as nucleocytoplasmic glycosylation that targets only single proteins involving a single β-linked N-acetylglucosamine. In both cases, the key precursors are the uridine diphospho-N-acetylhexosamines synthesised by the hexosamine biosynthetic pathway. Furthermore, uridine diphospho-N-acetylglucosamine participates in the biosynthesis of sialic acid. In this work, we propose MRS for the detection of uridine diphospho-N-acetylhexosamines visible in high-resolution MR spectra of intact cells from different human tumours. Signals from the nucleotide and amino sugar moieties, including amide signals observed for the first time in whole cells, are assigned, also taking advantage of spectral changes that follow cell treatment with ammonium chloride. Finally, parallel changes in uridine diphospho-N-acetylhexosamines and glutamine pools, observed after pH changes induced by ammonium chloride in the different tumour cell lines, may provide more details on the glycosylation processes.

A practical approach to detect unique metabolic patterns for personalized medicine

Information-rich technologies have advanced personalized medicine, yet obstacles limit measurement of large numbers of chemicals in human samples. Current laboratory tests measure hundreds of chemicals based upon existing knowledge of exposures, metabolism and disease mechanisms. Practical issues of cost and throughput preclude measurement of thousands of chemicals. Additionally, individuals are genetically diverse and have different exposures and response characteristics; some have disease mechanisms that have not yet been elucidated. Consequently, methods are needed to detect unique metabolic characteristics without presumption of known pathways, exposures or disease mechanisms, i.e., using a top-down approach. In this report, we describe profiling of human plasma with liquid chromatography (LC) coupled to Fourier-transform mass spectrometry (FTMS). FTMS is a high-resolution mass spectrometer providing mass accuracy and resolution to discriminate thousands of m/z features, which are peaks defined by m/z, retention time and intensity. We demonstrate that LC-FTMS detects 2000 m/z features in 10 min. These features include known and unidentified chemicals with m/z between 85 and 850, most with <10% coefficient of variation. Comparison of metabolic profiles for 4 healthy individuals showed that 62% of the m/z features were common while 10% were unique and 770 discriminated the individuals. Because the simple one-step extraction and automated analysis is rapid and cost-effective, the approach is practical for personalized medicine. This provides a basis to rapidly characterize novel metabolic patterns which can be linked to genetics, environment and/or lifestyle.

Metabolomic assessment of fermentative capability of soybean starter treated with high pressure

Meju, a brick of dried fermented soybean naturally inoculated with microorganisms, is a starter used for producing traditional Korean fermented soybean products such as soybean paste (doenjang) and soy sauce (ganjang). In order to reduce aging time during production of soybean paste and soy sauce, high pressure (HP) treatment was applied to the meju starter at 500 MPa of pressure for 10 min at 15 degrees C. Fermentative behaviors of normal and HP-treated meju were assessed and compared through physicochemical and (1)H NMR-based metabolomic analysis. All mejues were incubated for 3 weeks at 30 degrees C. At 1 week of incubation, total bacterial population decreased mainly due to a reduction of water content by spontaneous evaporation during the incubation period. As the incubation time increased, glutamate, proline, betain, choline, and phosphocholine levels increased in both normal and HP-treated mejues, indicating that microorganisms in the mejues synthesize these metabolites to endure intracellular hyperosmotic stress induced by the reduction in water content. Through 3 weeks of incubation, the amino-type nitrogen contents and neutral protease activities in HP-treated meju were significantly higher (p < 0.05) than in normal meju, even though total bacterial content in HP-treated meju was 2 or 3 times lower. Moreover, marked increases in glycerol, acetate, tyrosine, and choline levels were observed in HP-treated meju compared to normal meju. In particular, higher levels of tyrosine in HP-treated meju were consistent with the increased neutral protease activities compared to normal meju, indicating an improvement in enzyme stability with HP treatment. These findings highlight a new or better understanding of the influence of the HP or physical treatments on fermentative products in food processing, such as those associated with soybean paste and soy sauce, regarding metabolic behaviors in fermentative starter induced by HP treatment.

Genomic and metabolomic advances in the identification of disease and adverse event biomarkers

Incomplete knowledge of tissue pathogenesis is hampering the identification of biomarkers for the appropriate therapeutic targets to prevent or inhibit disease processes, and the prediction and diagnosis of injury due to disease and adverse events of drug therapy. The revolution in genomics and metabolomics, combined with advanced bioinformatics and computational methods for mining such large, complex data sets, are beginning to provide critical insights into tissue injury. Such results will move us closer to the promise of personalized medicine.

Chemical shift calibration of 1H MAS NMR liver tissue spectra exemplified using a study of glycine protection of galactosamine toxicity

High-resolution (1)H magic angle spinning (MAS) NMR spectroscopy is a useful tool for analysing intact tissues as a component of metabonomic studies. The effect of referencing MAS NMR spectra to the chemical shifts of glucose or to that or trimethylsilylpropionic acid on the resultant multivariate statistical models have been investigated. It is shown that referencing to known chemical shifts of either alpha-glucose or beta-glucose in (1)H MAS NMR-based metabolic data of intact liver tissues is preferred. This has been exemplified in studies of galactosamine toxicity in the rat where co-administration of glycine ameliorates the toxic response. This approach leads to better aligned sets of spectra and reduces the inter-sample variability in multivariate statistical models. If glucose is not present in the tissue under study, then a number of alternative internal reference chemical shifts are presented. Finally, the chemical shift difference between that of the anomeric H1 proton of alpha-glucose and residual water is confirmed as a suitable internal temperature calibration method.

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.

Metabolomic characterization of malolactic fermentation and fermentative behaviors of wine yeasts in grape wine

Wine contains a number of metabolites that are produced during alcoholic and malolactic fermentations (MLF) or aging, which are important compounds for determining wine quality. This study investigated changes in metabolites in wines to characterize malolactic fermentation (MLF) and to assess fermentative behaviors of wine yeast strains using (1)H nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistics. Principal component analysis (PCA) showed clear differentiation between non- and induced-malolactic fermented wines by wine lactic acid bacteria (LAB) and between wines fermented with various wine yeast strains. Metabolites such as glycerol, lactate, 2,3-butanediol, succinate, leucine, isoleucine, alanine, valine, proline, choline, gamma-aminobutyric acid (GABA), and polyphenols contributed to the differentiations. Decreased levels of malate and citrate along with increased levels of lactate were the metabolites most responsible for the differentiation of induced-MLF wines from non-MLF wines. In particular, high succinate levels provided evidence of an inhibitory effect of Saccharomyces bayanus against spontaneous MLF. Furthermore, dependence of metabolites on wine yeast strains was observed, demonstrating their different fermentative behaviors. This study demonstrates that wine fermentation by yeast and LAB can be characterized through global and multivariate statistical analysis of (1)H NMR spectral data.

[Recent development of metabonomics and its applications in clinical research]

In the post-genomic era, systems biology is central to the biological sciences. Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or other intervention. However, these changes can't be coupled directly to changes in biological function. As a result, metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past several years. Metabonomics is a rapidly growing research area and a system approach for comprehensive and quantitative analysis of the global metabolites in a biological matrix. Analytical chemistry approach is necessary for the development of comprehensive metabonomics investigations. Fundamentally, there are two types of metabonomics approaches: mass-spectrometry (MS) based and nuclear magnetic resonance (NMR) methodologies. Metabonomics measurements provide a wealth of data information and interpretation of these data relies mainly on chemometrics approaches to perform large-scale data analysis and data visualization, such as principal and independent component analysis, multidimensional scaling, a variety of clustering techniques, and discriminant function analysis, among many others. In this review, the recent development of analytical and statistical techniques used in metabonomics is summarized. Major applications of metabonomics relevant to clinical and preclinical study are then reviewed. The applications of metabonomics in study of liver diseases, cancers and other diseases have proved useful both as an experimental tool for pathogenesis mechanism re-search and ultimately a tool for diagnosis and monitoring treatment response of these diseases. Next, the applications of metabonomics in preclinical toxicology are discussed and the role that metabonomics might do in pharmaceutical research and development is explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology (COMET), and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Finally, the role of metabonomics in elucidating the function of the unknown or novel enzyme is mentioned.

(1)H NMR-based metabolomic approach for understanding the fermentation behaviors of wine yeast strains

(1)H NMR spectroscopy coupled with multivariate statistical analysis was used for the first time to investigate metabolic changes in musts during alcoholic fermentation and wines during aging. Three Saccharomyces cerevisiae yeast strains (RC-212, KIV-1116, and KUBY-501) were also evaluated for their impacts on the metabolic changes in must and wine. Pattern recognition (PR) methods, including PCA, PLS-DA, and OPLS-DA scores plots, showed clear differences for metabolites among musts or wines for each fermentation stage up to 6 months. Metabolites responsible for the differentiation were identified as valine, 2,3-butanediol (2,3-BD), pyruvate, succinate, proline, citrate, glycerol, malate, tartarate, glucose, N-methylnicotinic acid (NMNA), and polyphenol compounds. PCA scores plots showed continuous movements away from days 1 to 8 in all musts for all yeast strains, indicating continuous and active fermentation. During alcoholic fermentation, the highest levels of 2,3-BD, succinate, and glycerol were found in musts with the KIV-1116 strain, which showed the fastest fermentation or highest fermentative activity of the three strains, whereas the KUBY-501 strain showed the slowest fermentative activity. This study highlights the applicability of NMR-based metabolomics for monitoring wine fermentation and evaluating the fermentative characteristics of yeast strains.

A metabonomic study on the biochemical effects of doxorubicin in rats using (1)H-NMR spectroscopy

Metabonomic investigation of doxorubicin (adriamycin) was carried out in male Sprague-Dawley rats using high-resolution (1)H nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistics. Urine samples (d -1 to 7) from rats treated with doxorubicin at two dose levels (5 or 15 mg/kg body weight) were collected at each time point and doxorubicin-induced biomarkers were examined. Of metabolites, early elevated biochemical changes were observed in trimethylamine N-oxide (TMAO) levels suggesting renal dysfunction. Perturbation in TMAO was maximal in the low-dose group at 48 h post dose (p.d.) and returned to control at 168 h p.d., indicating recovery from renal toxicity induced by doxorubicin. After doxorubicin administration, the high-dose group was divided into low and high responders at 48 h and further divided into high, moderate, and no recovery animals at 96 h, indicating individual susceptible response to drug-induced toxicity. Urinary increases in glucose, lactate, alanine, and valine suggested progression of renal damage resulting in glycosuria, lactic aciduria, and aminoaciduria up to 168 h in the high-dose group. Urinary elevation of creatine and phenylacetylglycine (PAG) together with reduction of N-methylnicotinic acid (NMNA) and hippurate levels was suggestive of liver injury in the high-dose group. Impairment of energy metabolism was also indicated by decreased levels of tricarboxylic acid cycle intermediates in urine of rats treated with high-dose doxorubicin. This study highlights the applicability of NMR-based metabonomics with multivariate statistics for monitoring biomarkers produced by doxorubicin treatments.

1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas

(1)H NMR spectroscopy was used to investigate the metabolic differences in wines produced from different grape varieties and different regions. A significant separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was observed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The metabolites contributing to the separation were assigned to be 2,3-butanediol, lactate, acetate, proline, succinate, malate, glycerol, tartarate, glucose, and phenolic compounds by PCA and PLS-DA loading plots. Wines produced from Cabernet Sauvignon grapes harvested in the continental areas of Australia, France, and California were also separated. PLS-DA loading plots revealed that the level of proline in Californian Cabernet Sauvignon wines was higher than that in Australian and French Cabernet Sauvignon, Australian Shiraz, and Korean Campbell Early wines, showing that the chemical composition of the grape berries varies with the variety and growing area. This study highlights the applicability of NMR-based metabolomics with multivariate statistical data sets in determining wine quality and product origin.

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.

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).

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.

Defining personal nutrition and metabolic health through metabonomics

A major charter for modern nutrition is to provide a molecular basis for health outcome resulting from different food choices and how this could be designed to maintain individual health free of disease. Nutrigenomic techniques have been developed to generate information at various levels of biological organization, i.e. genes, proteins, and metabolites. Within this frame, metabonomics targets the molecular characterization of a living system through metabolic profiling. The metabolic profiles are explored with sophisticated data mining techniques mainly based on multivariate statistics, which can recover key metabolic information to be further linked to biochemical processes and physiological events. The power of metabonomics relies on its unique ability to assess functional changes in the metabolism of complex organisms stemming from multiple influences such as lifestyle and environmental factors. In particular, metabolic profiles encapsulate information on the metabolic activity of symbiotic partners, i.e. gut microflora, in complex organisms, which represent major determinant in nutrition and health. Therefore, applications of metabonomics to nutrition sciences led to the nutrimetabonomics approach for the classification of dietary responses in populations and the possibility of optimized or personalized nutritional management.

Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics

Methods for generating and interpreting metabolic profiles based on nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and chemometric analysis methods are summarized and the relative strengths and weaknesses of NMR and chromatography-coupled MS approaches are discussed. Given that all data sets measured to date only probe subsets of complex metabolic profiles, we describe recent developments for enhanced information recovery from the resulting complex data sets, including integration of NMR- and MS-based metabonomic results and combination of metabonomic data with data from proteomics, transcriptomics, and genomics. We summarize the breadth of applications, highlight some current activities, discuss the issues relating to metabonomics, and identify future trends.

Biomarker discovery for drug development and translational medicine using metabonomics

There exists at present an urgent desire for better biomarkers, especially in the context of pharmaceutical drug development and in the detection and management of disease. Many researchers in the area of biomarker discovery and development have turned to the "-omics" sciences as a way of addressing these needs. Metabolic profiling, or metabonomics, defines the metabolic phenotype and offers a source of novel biomarkers that have better potential to translate effectively. This review will discuss the broad philosophy and motivations behind metabonomics, and illustrate the case with applications relevant to pharmaceutical development and patient management. Particular focus will be paid to the potential of metabonomics to contribute to biomarker discovery in toxicology and cancer research.

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.