Metabonomics in pharmaceutical R&D

Application of GC/MS-based metabonomic profiling in studying the therapeutic effects of Huangbai-Zhimu herb-pair (HZ) extract on streptozotocin-induced type 2 diabetes in mice

A protocol for metabolic profiling of mice urine was developed based on gas chromatograph-mass spectrometer (GC-MS) to explore metabolic state directly. The intra-day, inter-day, repeatability, and stability RSD for most endogenous compounds were less than 3%. Type 2 diabetic mellitus (T2DM) mice model was induced by high calorie diet combined with streptozocin. Urine from the control, T2DM and Huangbai-Zhimu herb-pair (HZ) treatment mice were enrolled in the subsequent study to show the usefulness of the method. OPLS-DA scores plots demonstrate that the cluster of T2DM mice is separated from that of control mice, while HZ-T2DM mice are located close to control mice, indicating that metabolic profiles of these HZ-T2DM mice are placed toward those of control group. The results illustrate that HZ treatment could lower the level of d-glucose, hexadecanoic acid, octadecanoic acid, propanoic acid, 3-hydroxybutyric acid, and 2,3-dihydroxybutanoic acid in urine of DM mice, meanwhile the results show that HZ treatment could ameliorate T2DM symptoms by intervening the fatty acid metabolism, starch and sucrose metabolism, and glyoxylate and dicarboxylate metabolism. This preliminary application indicated that the method is suitable and reliable for urine metabolic profiling. This study might explain the metabolic effects of T2DM and the mechanisms of action of HZ against T2DM.

Investigation of Host-Gut Microbiota Modulation of Therapeutic Outcome

A broader understanding of factors underlying interindividual variation in pharmacotherapy is important for our pursuit of "personalized medicine." Based on knowledge gleaned from the investigation of human genetics, drug-metabolizing enzymes, and transporters, clinicians and pharmacists are able to tailor pharmacotherapies according to the genotype of patients. However, human host factors only form part of the equation that accounts for heterogeneity in therapeutic outcome. Notably, the gut microbiota possesses wide-ranging metabolic activities that expand the metabolic functions of the human host beyond that encoded by the human genome. In this review, we first illustrate the mechanisms in which gut microbes modulate pharmacokinetics and therapeutic outcome. Second, we discuss the application of metabonomics in deciphering the complex host-gut microbiota interaction in pharmacotherapy. Third, we highlight an integrative approach with particular mention of the investigation of gut microbiota using culture-based and culture-independent techniques to complement the investigation of the host-gut microbiota axes in pharmaceutical research.

Ayurnutrigenomics: Ayurveda-inspired personalized nutrition from inception to evidence

Ayurveda proclaims food and drugs are intersecting concepts that are vital for human survival and for the prevention and mitigation of diseases. Food interferes with the molecular mechanisms of an organism's “physiome”. It is consumed in large amounts compared to any drug. Hence, research on its effect and interaction with genome is highly relevant toward understanding diseases and their therapies. Ayurgenomics presents a personalized approach in the predictive, preventive, and curative aspects of stratified medicine with molecular variability, which embodies the study of interindividual variability due to genetic variability in humans for assessing susceptibility, and establishing diagnosis and prognosis, mainly on the basis of the constitution type of a person's Prakriti. Ayurnutrigenomics is an emerging field of interest pervading Ayurveda systems biology, where the selection of a suitable dietary, therapeutic, and lifestyle regime is made on the basis of clinical assessment of an individual maintaining one's Prakriti. This Ayurveda-inspired concept of personalized nutrition is a novel concept of nutrigenomic research for developing personalized functional foods and nutraceuticals suitable for one's genetic makeup with the help of Ayurveda. Here, we propose and present this novel concept of Ayurnutrigenomics and its emerging areas of research, which may unfold future possibilities toward smart yet safe therapeutics.

Metabolomic identification of biochemical changes induced by fluoxetine and imipramine in a chronic mild stress mouse model of depression

Metabolomics was applied to a C57BL/6N mouse model of chronic unpredictable mild stress (CMS). Such mice were treated with two antidepressants from different categories: fluoxetine and imipramine. Metabolic profiling of the hippocampus was performed using gas chromatography-mass spectrometry analysis on samples prepared under optimized conditions, followed by principal component analysis, partial least squares-discriminant analysis, and pair-wise orthogonal projections to latent structures discriminant analyses. Body weight measurement and behavior tests including an open field test and the forced swimming test were completed with the mice as a measure of the phenotypes of depression and antidepressive effects. As a result, 23 metabolites that had been differentially expressed among the control, CMS, and antidepressant-treated groups demonstrated that amino acid metabolism, energy metabolism, adenosine receptors, and neurotransmitters are commonly perturbed by drug treatment. Potential predictive markers for treatment effect were identified: myo-inositol for fluoxetine and lysine and oleic acid for imipramine. Collectively, the current study provides insights into the molecular mechanisms of the antidepressant effects of two widely used medications.

Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease

There is an urgent need for the identification of Alzheimer's disease (AD) biomarkers. Studies have now suggested the promising use of associations with blood metabolites as functional intermediate phenotypes in biomedical and pharmaceutical research. The aim of this study was to use lipidomics to identify a battery of plasma metabolite molecules that could predict AD patients from controls. We performed a comprehensive untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from 35 AD patients, 40 elderly controls and 48 individuals with mild cognitive impairment (MCI) and used multivariate analysis methods to identify metabolites associated with AD status. A combination of 10 metabolites could discriminate AD patients from controls with 79.2% accuracy (81.8% sensitivity, 76.9% specificity and an area under curve of 0.792) in a novel test set. Six of the metabolites were identified as long chain cholesteryl esters (ChEs) and were reduced in AD (ChE 32:0, odds ratio (OR)=0.237, 95% confidence interval (CI)=0.10-0.48, P=4.19E-04; ChE 34:0, OR=0.152, 95% CI=0.05-0.37, P=2.90E-04; ChE 34:6, OR=0.126, 95% CI=0.03-0.35, P=5.40E-04; ChE 32:4, OR=0.056, 95% CI=0.01-0.24, P=6.56E-04 and ChE 33:6, OR=0.205, 95% CI=0.06-0.50, P=2.21E-03, per (log2) metabolite unit). The levels of these metabolites followed the trend control>MCI>AD. We, additionally, found no association between cholesterol, the precursor of ChE and AD. This study identified new ChE molecules, involved in cholesterol metabolism, implicated in AD, which may help identify new therapeutic targets; although, these findings need to be replicated in larger well-phenotyped cohorts.

Gas chromatography-mass spectrometry of biofluids and extracts

Gas chromatography-mass spectrometry (GC-MS) has been widely used in metabonomics analyses of biofluid samples. Biofluids provide a wealth of information about the metabolism of the whole body and from multiple regions of the body that can be used to study general health status and organ function. Blood serum and blood plasma, for example, can provide a comprehensive picture of the whole body, while urine can be used to monitor the function of the kidneys, and cerebrospinal fluid (CSF) will provide information about the status of the brain and central nervous system (CNS). Different methods have been developed for the extraction of metabolites from biofluids, these ranging from solvent extracts, acids, heat denaturation, and filtration. These methods vary widely in terms of efficiency of protein removal and in the number of metabolites extracted. Consequently, for all biofluid-based metabonomics studies, it is vital to optimize and standardize all steps of sample preparation, including initial extraction of metabolites. In this chapter, recommendations are made of the optimum experimental conditions for biofluid samples for GC-MS, with a particular focus on blood serum and plasma samples.

Metabolomics study of metabolic variations in enterotoxigenic Escherichia coli-infected piglets

This study aimed to explore the metabolic profiling in the serum of enterotoxigenic Escherichia coli (ETEC) infected piglets.

A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis

OBJECTIVE

We tested whether it is possible to differentiate relapsing-remitting (RR) from secondary progressive (SP) disease stages in patients with multiple sclerosis (MS) using a combination of nuclear magnetic resonance (NMR) metabolomics and partial least squares discriminant analysis (PLS-DA) of biofluids, which makes no assumptions on the underlying mechanisms of disease.

METHODS

Serum samples were obtained from patients with primary progressive MS (PPMS), SPMS, and RRMS; patients with other neurodegenerative conditions; and age-matched controls. Samples were analyzed by NMR and PLS-DA models were derived to separate disease groups.

RESULTS

The PLS-DA models for serum samples from patients with MS enabled reliable differentiation between RRMS and SPMS. This approach also identified significant differences between the metabolite profiles of each of the MS groups (PP, SP, and RR) and the healthy controls, as well as predicting disease group membership with high specificity and sensitivity.

CONCLUSIONS

NMR metabolomics analysis of serum is a sensitive and robust method for differentiating between different stages of MS, yielding diagnostic markers without a priori knowledge of disease pathogenesis. Critically, this study identified and validated a type II biomarker for the RR to SP transition in patients with MS. This approach may be of considerable benefit in categorizing patients for treatment and as an outcome measure in future clinical trials.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that serum metabolite profiles accurately distinguish patients with different subtypes and stages of MS.

Direct infusion ESI–MS analysis for metabolite profiling of human plasma using various fractionation techniques

Background: Metabolomics is a real challenge owing to the complexity and chemical heterogeneity of biological samples. In this study, a comparative analysis of metabolite profiling using ten metabolite extraction techniques were investigated on a pooled plasma sample followed by direct infusion ESI–MS analysis on both positive and negative modes. Methodology & Results: Metabolites from a pooled sample of 50 healthy volunteers were separated one- and two-dimensionally utilizing solvent precipitation, solid-phase extraction and molecular weight fractionation. Numbers of unique metabolites that are specific to a particular sample preparation approach were also identified by online available database, Metlin. The 1H NMR study of different extraction procedures were also recorded to elaborate the comparative profiling of the various fractionation procedures. Conclusion: The results of this study suggest that the metabolite extraction procedures based on 1- and 2-dimensions, followed by direct infusion ESI–MS analysis were able to detect endogenous metabolites belonging to different chemical classes.

Ultra-performance liquid chromatography-mass spectrometry as a sensitive and powerful technology in lipidomic applications

Lipidomics, the comprehensive illumination of lipid-based information in biology systems, involves in identifying lipids and profiling lipids and lipid-derived mediators. The development of lipidomics enables the characterization of lipid species and detailed lipid profiling in body fluid, tissue or cell, and allows for a wider understanding of the biological roles of lipid networks. Lipidomic research has been greatly facilitated by recent advances in ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and involved in lipid extraction, lipid identification and data analysis supporting applications from qualitative and quantitative assessment of multiple lipid species. UPLC technique, different mass spectrometry technique, lipid extraction and data analysis in lipidomics are reviewed. Afterwards, examples are provided on the use of UPLC-MS for finding lipid biomarkers in disease, drug, food, nutrition and plant fields. We also discuss the UPLC-MS-based lipidomics for the future perspectives and their potential problems.

Potential of metabolomics in preclinical and clinical drug development

Metabolomics is an upcoming technology system which involves detailed experimental analysis of metabolic profiles. Due to its diverse applications in preclinical and clinical research, it became an useful tool for the drug discovery and drug development process. This review covers the brief outline about the instrumentation and interpretation of metabolic profiles. The applications of metabolomics have a considerable scope in the pharmaceutical industry, almost at each step from drug discovery to clinical development. These include finding drug target, potential safety and efficacy biomarkers and mechanisms of drug action, the validation of preclinical experimental models against human disease profiles, and the discovery of clinical safety and efficacy biomarkers. As we all know, nowadays the drug discovery and development process is a very expensive, and risky business. Failures at any stage of drug discovery and development process cost millions of dollars to the companies. Some of these failures or the associated risks could be prevented or minimized if there were better ways of drug screening, drug toxicity profiling and monitoring adverse drug reactions. Metabolomics potentially offers an effective route to address all the issues associated with the drug discovery and development.

Plasma metabolomic profiling to reveal antipyretic mechanism of Shuang-huang-lian injection on yeast-induced pyrexia rats

Shuang-huang-lian injection (SHLI) is a famous Chinese patent medicine, which has been wildly used in clinic for the treatment of acute respiratory tract infection, pneumonia, influenza, etc. The existing randomized controlled trial (RCT) studies suggested that SHLI could afford a certain anti-febrile action. However, seldom does research concern the pharmacological mechanisms of SHLI. In the current study, we explored plasma metabolomic profiling technique and selected potential metabolic markers to reveal the antipyretic mechanism of SHLI on yeast-induced pyrexia rat model using UPLC-Q-TOF/MS coupled with multivariate statistical analysis and pattern recognition techniques. We discovered a significant perturbance of metabolic profile in the plasma of fever rats and obvious reversion in SHLI-administered rats. Eight potential biomarkers, i.e. 1) 3-hydeoxybutyric acid, 2) leucine, 3) 16:0 LPC, 4) allocholic acid, 5) vitamin B2, 6) Cys-Lys-His, 7) 18:2 LPC, and 8) 3-hydroxychola-7, 22-dien-24-oic acid, were screened out by OPLS-DA approach. Five potential perturbed metabolic pathways, i.e. 1) valine, leucine, and isoleucine biosynthesis, 2) glycerophospholipid metabolism, 3) ketone bodies synthesis and degradation, 4) bile acid biosynthesis, and 5) riboflavin metabolism, were revealed to relate to the antipyretic mechanisms of SHLI. Overall, we investigated antipyretic mechanisms of SHLI at metabolomic level for the first time, and the obtained results highlights the necessity of adopting metabolomics as a reliable tool for understanding the holism and synergism of Chinese patent drug.

Towards the disease biomarker in an individual patient using statistical health monitoring

In metabolomics, identification of complex diseases is often based on application of (multivariate) statistical techniques to the data. Commonly, each disease requires its own specific diagnostic model, separating healthy and diseased individuals, which is not very practical in a diagnostic setting. Additionally, for orphan diseases such models cannot be constructed due to a lack of available data. An alternative approach adapted from industrial process control is proposed in this study: statistical health monitoring (SHM). In SHM the metabolic profile of an individual is compared to that of healthy people in a multivariate manner. Abnormal metabolite concentrations, or abnormal patterns of concentrations, are indicated by the method. Subsequently, this biomarker can be used for diagnosis. A tremendous advantage here is that only data of healthy people is required to construct the model. The method is applicable in current–population based –clinical practice as well as in personalized health applications. In this study, SHM was successfully applied for diagnosis of several orphan diseases as well as detection of metabotypic abnormalities related to diet and drug intake.

Multiplexed, quantitative, and targeted metabolite profiling by LC-MS/MRM

Targeted metabolomics, which focuses on a subset of known metabolites representative of biologically relevant metabolic pathways, is a valuable tool to discover biomarkers and link disease phenotypes to underlying mechanisms or therapeutic modes of action. A key advantage of targeted metabolomics, compared to discovery metabolomics, is its immediate readiness for extracting biological information derived from known metabolites and quantitative measurements. However, simultaneously analyzing hundreds of endogenous metabolites presents a challenge due to their diverse chemical structures and properties. Here we report a method which combines different chromatographic separation conditions, optimal ionization polarities, and the most sensitive triple-quadrupole MS-based data acquisition mode, multiple reaction monitoring (MRM), to quantitatively profile 205 endogenous metabolites in 10 min.

New tools and new biology: recent miniaturized systems for molecular and cellular biology

Recent advances in applied physics and chemistry have led to the development of novel microfluidic systems. Microfluidic systems allow minute amounts of reagents to be processed using μm-scale channels and offer several advantages over conventional analytical devices for use in biological sciences: faster, more accurate and more reproducible analytical performance, reduced cell and reagent consumption, portability, and integration of functional components in a single chip. In this review, we introduce how microfluidics has been applied to biological sciences. We first present an overview of the fabrication of microfluidic systems and describe the distinct technologies available for biological research. We then present examples of microsystems used in biological sciences, focusing on applications in molecular and cellular biology.

Identification of metabolomic biomarkers for drug-induced acute kidney injury in rats

Nephrotoxicity is a common side effect observed during both nonclinical and clinical drug development investigations. The present study aimed to identify metabolomic biomarkers that could provide early and sensitive indication of nephrotoxicity in rats. Metabolomic analyses were performed using capillary electrophoresis-time-of-flight mass spectrometry on rat plasma collected at 9 and 24 h after a single dose of 2-bromoethylamine or n-phenylanthranilic acid and at 24 h after 7 days of repeated doses of gentamicin, cyclosporine A or cisplatin. Among a total of 169 metabolites identified, 3-methylhistidine (3-MH), 3-indoxyl sulfate (3-IS) and guanidoacetate (GAA) were selected as candidate biomarkers. The biological significance and reproducibility of the observed changes were monitored over time in acute nephrotoxicity model rats treated with a single dose of cisplatin, with the glomerular filtration rate monitored by determination of creatinine clearance. Increased plasma levels of 3-MH and 3-IS were related to a decline in glomerular filtration due to a renal failure. In contrast, the decrease in plasma GAA, which is synthesized from arginine and glycine in the kidneys, was considered to reflect decreased production due to renal malfunction. Although definitive validation studies are required to confirm their usefulness and reliability, 3-MH, 3-IS and GAA may prove to be valuable plasma biomarkers for monitoring nephrotoxicity in rats.

Metabolic profiling for studying chemotype variations in Withania somnifera (L.) Dunal fruits using GC-MS and NMR spectroscopy

Withania somnifera (L.) Dunal (Solanaceae), commonly known as Ashwagandha, is one of the most valued Indian medicinal plant with several pharmaceutical and nutraceutical applications. Metabolic profiling was performed by GC-MS and NMR spectroscopy on the fruits obtained from four chemotypes of W. somnifera. A combination of (1)H NMR spectroscopy and GC-MS identified 82 chemically diverse metabolites consisting of organic acids, fatty acids, aliphatic and aromatic amino acids, polyols, sugars, sterols, tocopherols, phenolic acids and withanamides in the fruits of W. somnifera. The range of metabolites identified by GC-MS and NMR of W. somnifera fruits showed various known and unknown metabolites. The primary and secondary metabolites observed in this study represent MVA, DOXP, shikimic acid and phenylpropanoid biosynthetic metabolic pathways. Squalene and tocopherol have been rated as the most potent naturally occurring compounds with antioxidant properties. These compounds have been identified by us for the first time in the fruits of W. somnifera. Multivariate principal component analysis (PCA) on GC-MS and NMR data revealed clear distinctions in the primary and secondary metabolites among the chemotypes. The variation in the metabolite concentration among different chemotypes of the fruits of W. somnifera suggest that specific chemovars can be used to obtain substantial amounts of bioactive ingredients for use as potential pharmacological and nutraceuticals agents.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Identification of differential responses to an oral glucose tolerance test in healthy adults

Background

In recent years an individual’s ability to respond to an acute dietary challenge has emerged as a measure of their biological flexibility. Analysis of such responses has been proposed to be an indicator of health status. However, for this to be fully realised further work on differential responses to nutritional challenge is needed. This study examined whether metabolic phenotyping could identify differential responders to an oral glucose tolerance test (OGTT) and examined the phenotypic basis of the response.

Methods and Results

A total of 214 individuals were recruited and underwent challenge tests in the form of an OGTT and an oral lipid tolerance test (OLTT). Detailed biochemical parameters, body composition and fitness tests were recorded. Mixed model clustering was employed to define 4 metabotypes consisting of 4 different responses to an OGTT. Cluster 1 was of particular interest, with this metabotype having the highest BMI, triacylglycerol, hsCRP, c-peptide, insulin and HOMA- IR score and lowest VO_2max. Cluster 1 had a reduced beta cell function and a differential response to insulin and c-peptide during an OGTT. Additionally, cluster 1 displayed a differential response to the OLTT.

Conclusions

This work demonstrated that there were four distinct metabolic responses to the OGTT. Classification of subjects based on their response curves revealed an “at risk” metabolic phenotype.

Inflammation and resolution are associated with upregulation of fatty acid β-oxidation in Zymosan-induced peritonitis

Inflammation is a fundamental defensive response to harmful stimuli. However, it can cause damage if it does not subside. To avoid such damage, organisms have developed a mechanism called resolution of inflammation. Here we applied an untargeted metabolomics approach to a sterile and self-resolving animal model of acute inflammation, namely zymosan-induced peritonitis in mice, to examine the effect of inflammation and resolution on the metabolomic profiles. Significant and time-dependent changes in metabolite profiles after zymosan administration were observed in both peritoneal wash fluid (PWF) and plasma. These metabolomic changes correlated well with inflammatory chemokine or cytokine production. In PWF, most of metabolites that could detected increased in zymosan-treated mice, which is suggestive of inflammation, oxidative stress and increased energy demands. In plasma, most metabolites in the central metabolic pathway (glycolysis and TCA cycle) were significantly downregulated after zymosan administration. The concentration of the ketone body 3-hydroxybutyric acid (3-HB) in plasma and PWF increased in zymosan-injected animals indicating upregulation of fatty acid β-oxidation. Increased 3-HB level was observed in the cells that infiltrated into the peritoneal cavity and these infiltrated cells might contribute, at least in part, to the production of 3-HB in the peritoneal cavity.

Extraction, derivatization, and determination of metabolome in human macrophages

A GC/TOF-MS was applied to the determination of metabolites in human macrophages. The extraction conditions and quenching conditions were investigated and optimized. The results indicated that 0.9% w/v sodium chloride at 4°C was the most favorable condition to quench macrophage, 1 mL 50% ACN for 2 min in ice bath was the optimal condition to extract 5 × 10(6) cells. Two hundred six peaks could be detectable with peak area over 50 using this method. Among these peaks, 45 peaks with the similarity over 700 were identified using standard compounds for endogenous metabolites. Thirty-seven out of 45 metabolites could be quantified directly by this method. Twenty metabolites were selected randomly, and 15 amino acids were used for method validation. The correlation coefficients (r) ranging from 0.9902 to 0.9977 were obtained for 15 amino acids in the range of 2.35-150.20 μg/mL. The intraday and interday precisions were lower than 19.90% for the randomly selected 20 endogenous metabolites. Using this development method and multivariate statistical technique, several potential biomarkers were found from human macrophages infected by different Mycobacterium tuberculosis (M. tuberculosis) strains. The results suggest that the method could be applied to the investigation of the pathogenicity of tuberculosis.

NMR-based metabolomics of urine in a mouse model of Alzheimer's disease: identification of oxidative stress biomarkers

Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia among elderly patients. A biomarker for the disease could make diagnosis easier and more accurate, and accelerate drug discovery. In this study, NMR-based metabolomics analysis in conjunction with multivariate statistics was applied to examine changes in urinary metabolites in transgenic AD mice expressing mutant tau and β-amyloid precursor protein. These mice showed significant changes in urinary metabolites throughout the progress of the disease. Levels of 3-hydroxykynurenine, homogentisate and allantoin were significantly higher compared to control mice in 4 months (prior to onset of AD symptoms) and reverted to control values by 10 months of age (early/middle stage of AD), which highlights the relevance of oxidative stress to this neurodegenerative disorder even prior the onset of dementia. The level of these changed metabolites at very early period may provide an indication of disease risk at asymptomatic stage.

Metabolomics in diabetes research

Diabetes represents one of the most important global health problems because it is associated with a large economic burden on the health systems of many countries. Whereas the diagnosis and treatment of manifest diabetes have been well investigated, the identification of novel pathways or early biomarkers indicative of metabolic alterations or insulin resistance related to the development of diabetes is still in progress. Over half of the type 2 diabetes patients show manifestations of diabetes-related diseases, which highlight the need for early screening markers of diabetes. During the last decade, the rapidly growing research field of metabolomics has introduced new insights into the pathology of diabetes as well as methods to predict disease onset and has revealed new biomarkers. Recent epidemiological studies first used metabolism to predict incident diabetes and revealed branched-chain and aromatic amino acids including isoleucine, leucine, valine, tyrosine and phenylalanine as highly significant predictors of future diabetes. This review summarises the current findings of metabolic research regarding diabetes in animal models and human investigations.

Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile

Targeted metabolomics provides an approach to quantify metabolites involved in specific molecular pathways. We applied an electrochemistry-based, targeted metabolomics platform to define changes in tryptophan, tyrosine, purine and related pathways in the depressed and remitted phases of major depressive disorder (MDD). Biochemical profiles in the cerebrospinal fluid of unmedicated depressed (n = 14; dMDD) or remitted MDD subjects (n = 14; rMDD) were compared against those in healthy controls (n = 18; HC). The rMDD group showed differences in tryptophan and tyrosine metabolism relative to the other groups. The rMDD group also had higher methionine levels and larger methionine-to-glutathione ratios than the other groups, implicating methylation and oxidative stress pathways. The dMDD sample showed nonsignificant differences in the same direction in several of the metabolic branches assessed. The reductions in metabolites associated with tryptophan and tyrosine pathways in rMDD may relate to the vulnerability this population shows for developing depressive symptoms under tryptophan or catecholamine depletion.

Metabolite profiles correlate closely with neurobehavioral function in experimental spinal cord injury in rats

Traumatic spinal cord injury (SCI) results in direct physical damage and the generation of local factors contributing to secondary pathogenesis. Untargeted metabolomic profiling was used to uncover metabolic changes and to identify relationships between metabolites and neurobehavioral functions in the spinal cord after injury in rats. In the early metabolic phase, neuronal signaling, stress, and inflammation-associated metabolites were strongly altered. A dynamic inflammatory response consisting of elevated levels of prostaglandin E2 and palmitoyl ethanolamide as well as pro- and anti-inflammatory polyunsaturated fatty acids was observed. N-acetyl-aspartyl-glutamate (NAAG) and N-acetyl-aspartate (NAA) were significantly decreased possibly reflecting neuronal cell death. A second metabolic phase was also seen, consistent with membrane remodeling and antioxidant defense response. These metabolomic changes were consistent with the pathology and progression of SCI. Several metabolites, including NAA, NAAG, and the ω-3 fatty acids docosapentaenoate and docosahexaenoate correlated greatly with the established Basso, Beattie and Bresnahan locomotive score (BBB score). Our findings suggest the possibility of a biochemical basis for BBB score and illustrate that metabolites may correlate with neurobehavior. In particular the NAA level in the spinal cord might provide a meaningful biomarker that could help to determine the degree of injury severity and prognosticate neurologic recovery.

Characterization of temporary metabolic changes following Cantonese herbal tea intervention

Cantonese herbal tea (CHT) has been consumed in South China to alleviate feelings of discomfort due to the heat and humidity in the body according to the theory of traditional Chinese medicine (TCM). To understand the in vivo mechanism of CHT, a ¹H-NMR-based metabonomic approach was used to investigate the global biological characterization of rat serum following the intake of CHT and to understand the mechanisms of action of CHT. Serum samples from rats with consecutive CHT intake after 10, 20 and 30 days and corresponding control rats were analysed by high-resolution ¹H-NMR spectroscopy. Principal component analysis (PCA) and orthogonal projection on latent structures discriminant analysis (OPLS-DA) were utilized for ¹H-NMR spectra analysis and temporal metabolic changes identification. For the 10-day CHT intake group, no significant metabolic response was detected, whereas the 20-day group showed elevation of glucogeneogenesis and a shift in energy metabolism from carbohydrate metabolism to lipid metabolism. In addition, a notable decrease in pyruvate content with a consistent increase in lactate content, and significant decrease in both lipoprotein and glucose contents was observed for the 30-day group, indicating potential metabolic dysfunction. The metabonomics technique combining metabolic profiles with multivariate analysis enhanced our current understanding of the host's metabolic response to CHT intake.

Implementation of molecular phenotyping approaches in the personalized surgical patient journey

OBJECTIVE

The present review describes commonly employed metabolic profiling platforms and discusses the current and likely future application of these technologies in surgery.

BACKGROUND

The metabolic adaptations that occur in response to surgical illness and trauma are incompletely understood. Evaluating these will be critical to the development of personalized surgical health solutions. Metabonomics is an advancing field in systems biology, which provides a means of interrogating these metabolic shifts.

METHODS

Recent literature regarding metabolic profiling technologies and their applications in surgical practice are discussed. Future strategies are proposed for the incorporation of these and next-generation technologies in the evaluation of all steps in the patient surgical pathway.

RESULTS

Metabolite-based profiling has provided valuable insights into the metabolic irregularities that occur in cancer development and progression across a variety of cancer subclasses including colorectal, breast, prostate, and lung cancers. In addition, metabolic modeling has shown considerable promise in other surgical conditions including trauma and sepsis and in the assessment of pharmacotherapeutic efficacy.

DISCUSSION

Metabonomics offers a posttranscriptional view of system activity providing functional information downstream of the genome and proteome. Information at this level will provide the surgeon with a novel means of evaluating major socioeconomic problems such as cancer and sepsis. In addition, the rapid nature of emerging next generation profiling platforms provides a viable means of "real-time" perioperative metabolic assessment and optimization.

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0350-z) contains supplementary material, which is available to authorized users.

Comparison of RP-HPLC columns used for determination of nucleoside metabolic patterns in urine of cancer patients

Background: Metabolic profiling allows the measurement of a large set of both known and unknown metabolites (such as nucleosides and nucleobases) present in a biological sample (e.g., urine). Results: Separation of the isolated urinary nucleosides was performed on two connected Gemini C18 columns – 3 µm pore size (50 cm total length) – at 55°C using mobile-phase gradient elution. The Mann–Whitney U test was used to distinguish differences in the concentration of compounds in urine from urogenital cancer patients and healthy controls. Comparison of mean concentration values from the healthy and cancer groups revealed statistically significant differences (p < 0.01) for most of the metabolites studied (excluding m7G, m3C and A). Observed elevated levels of nucleosides mean concentrated values in urine in the case of cancer patients are between 1.5 and 2.0.Conclusion: These results verify the usefulness of the RP-HPLC method to investigate the urinary pattern of normal and modified nucleosides.

Translational research in infectious disease: current paradigms and challenges ahead

In recent years, the biomedical community has witnessed a rapid scientific and technologic evolution after the development and refinement of high-throughput methodologies. Concurrently and consequentially, the scientific perspective has changed from the reductionist approach of meticulously analyzing the fine details of a single component of biology to the "holistic" approach of broadmindedly examining the globally interacting elements of biological systems. The emergence of this new way of thinking has brought about a scientific revolution in which genomics, proteomics, metabolomics, and other "omics" have become the predominant tools by which large amounts of data are amassed, analyzed, and applied to complex questions of biology that were previously unsolvable. This enormous transformation of basic science research and the ensuing plethora of promising data, especially in the realm of human health and disease, have unfortunately not been followed by a parallel increase in the clinical application of this information. On the contrary, the number of new potential drugs in development has been decreasing steadily, suggesting the existence of roadblocks that prevent the translation of promising research into medically relevant therapeutic or diagnostic application. In this article, we will review, in a noninclusive fashion, several recent scientific advancements in the field of translational research, with a specific focus on how they relate to infectious disease. We will also present a current picture of the limitations and challenges that exist for translational research, as well as ways that have been proposed by the National Institutes of Health to improve the state of this field.

Pharmacogenetics meets metabolomics: discovery of tryptophan as a new endogenous OCT2 substrate related to metformin disposition

Genetic polymorphisms of the organic cation transporter 2 (OCT2), encoded by SLC22A2, have been investigated in association with metformin disposition. A functional decrease in transport function has been shown to be associated with the OCT2 variants. Using metabolomics, our study aims at a comprehensive monitoring of primary metabolite changes in order to understand biochemical alteration associated with OCT2 polymorphisms and discovery of potential endogenous metabolites related to the genetic variation of OCT2. Using GC-TOF MS based metabolite profiling, clear clustering of samples was observed in Partial Least Square Discriminant Analysis, showing that metabolic profiles were linked to the genetic variants of OCT2. Tryptophan and uridine presented the most significant alteration in SLC22A2-808TT homozygous and the SLC22A2-808G>T heterozygous variants relative to the reference. Particularly tryptophan showed gene-dose effects of transporter activity according to OCT2 genotypes and the greatest linear association with the pharmacokinetic parameters (Cl_renal, Cl_sec, Cl/F/kg, and Vd/F/kg) of metformin. An inhibition assay demonstrated the inhibitory effect of tryptophan on the uptake of 1-methyl-4-phenyl pyrinidium in a concentration dependent manner and subsequent uptake experiment revealed differential tryptophan-uptake rate in the oocytes expressing OCT2 reference and variant (808G>T). Our results collectively indicate tryptophan can serve as one of the endogenous substrate for the OCT2 as well as a biomarker candidate indicating the variability of the transport activity of OCT2.

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.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Anti-depressant effects of Xiaoyaosan on rat model of chronic unpredictable mild stress: a plasma metabonomics study based on NMR spectroscopy

OBJECTIVES

To investigate the antidepressant effects of Xiaoyaosan (XYS) in a chronic unpredictable mild stress (CUMS) depression model.

METHODS

The changes in behaviour and plasma metabolic profiles were investigated after four-week CUMS exposure and treatment. Drugs were administered during the four-week period of CUMS, with the healthy group serving as negative controls, and the fluoxetine and venlafaxine groups serving as positive controls. Plasma samples were collected at 28th day, and the plasma metabolic profiling was measured using NMR, followed by multivariate analysis.

KEY FINDINGS

Exposure to CUMS for four weeks caused depression-like behaviour in rats, as indicated by significant decreases in weight gain, sucrose consumption and locomotor activity. Eleven potential biomarkers, including seven in the Carr-Purcell-Meiboom-Gill spectra, five in the diffusion-edited spectra, and one in both were identified. It was found that trimethylamine-N-oxide, alanine, β-hydroxybutyrate, valine, leucine/isoleucine, low-density lipoprotein/very low-density lipoprotein and lipids were lower and phosphatidylcholine, high-density lipoprotein, choline and N-acetyl glycoproteins were higher in CUMS-treated rats, as compared with controls. XYS significantly suppressed behavioural changes and attenuated plasma metabolite changes.

CONCLUSIONS

XYS produced an obvious antidepressant effect, and the metabonomic approach benefits estimation of the pharmacodynamic action of traditional Chinese medicine prescriptions.

Enteric microbiome metabolites correlate with response to simvastatin treatment

Although statins are widely prescribed medications, there remains considerable variability in therapeutic response. Genetics can explain only part of this variability. Metabolomics is a global biochemical approach that provides powerful tools for mapping pathways implicated in disease and in response to treatment. Metabolomics captures net interactions between genome, microbiome and the environment. In this study, we used a targeted GC-MS metabolomics platform to measure a panel of metabolites within cholesterol synthesis, dietary sterol absorption, and bile acid formation to determine metabolite signatures that may predict variation in statin LDL-C lowering efficacy. Measurements were performed in two subsets of the total study population in the Cholesterol and Pharmacogenetics (CAP) study: Full Range of Response (FR), and Good and Poor Responders (GPR) were 100 individuals randomly selected from across the entire range of LDL-C responses in CAP. GPR were 48 individuals, 24 each from the top and bottom 10% of the LDL-C response distribution matched for body mass index, race, and gender. We identified three secondary, bacterial-derived bile acids that contribute to predicting the magnitude of statin-induced LDL-C lowering in good responders. Bile acids and statins share transporters in the liver and intestine; we observed that increased plasma concentration of simvastatin positively correlates with higher levels of several secondary bile acids. Genetic analysis of these subjects identified associations between levels of seven bile acids and a single nucleotide polymorphism (SNP), rs4149056, in the gene encoding the organic anion transporter SLCO1B1. These findings, along with recently published results that the gut microbiome plays an important role in cardiovascular disease, indicate that interactions between genome, gut microbiome and environmental influences should be considered in the study and management of cardiovascular disease. Metabolic profiles could provide valuable information about treatment outcomes and could contribute to a more personalized approach to therapy.

Metabonomic study on chronic unpredictable mild stress and intervention effects of Xiaoyaosan in rats using gas chromatography coupled with mass spectrometry

ETHNOPHARMACOLOGY

Xiaoyaosan (XYS), a famous Chinese prescription, composed of Radix Bupleuri (Bupleurum chinense DC.), Radix Angelicae Sinensis (Angelica sinensis (Oliv.) Diels), Radix Paeoniae Alba (Paeonia lactiflora Pall.), Rhizoma Atractylodis Macrocephalae (Atractylodes macrocephala Koidz.), Poria (Poria cocos (Schw.) Wolf), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.), Herba Menthae (Mentha haplocalyx Briq.), and Rhizoma Zingiberis Recens (Zingiber officinale Rosc.), has been widely used in the clinic for treating mental disorders. Behavior and biochemical analyses indicate XYS has obvious anti-depression activity. However, there is no report on the effects of XYS using a metabolomics approach.

AIM OF THE STUDY

Depression is a prevalent complex psychiatric disorder and its pathophysiological mechanism is not yet well understood. This paper was designed to study metabonomic characters of the depression induced by chronic unpredictable mild stress (CUMS) and the therapeutic effects of XYS, classic traditional Chinese medicine (TCM) in treating the depression.

MATERIAL AND METHODS

A plasma metabonomics method based on gas chromatography/mass spectrometry (GC/MS) was developed. Principal component analysis (PCA) was utilized to classify and reveal the differences between the model group and control group. In turns, the concentration of these differences was analyzed with t-test to determine whether XYS was possible to influence the metabolic pattern induced by CUMS.

RESULTS

The significant difference in metabolic profiling was observed from model group compared with drug-dose group by using the PCA, indicating the recovery effect of XYS on CUMS rats. Some significantly changed metabolites like glycine, glucose and hexadecanoic acid have been identified. These biochemical changes are related to the disturbance in amino acid metabolism, energy metabolism and glycometabolism, which are helpful to further understand the CUMS and the therapeutic mechanism of XYS.

CONCLUSIONS

Metabonomic approach is helpful to further understanding the pathophysiology of depression and assisting in clinical diagnosis of depression and is also a valuable tool for studying the essence of Chinese medicine's syndrome theory and therapeutic effect mechanism of TCM.

Metabolomics in early Alzheimer's disease: identification of altered plasma sphingolipidome using shotgun lipidomics

BACKGROUND

The development of plasma biomarkers could facilitate early detection, risk assessment and therapeutic monitoring in Alzheimer's disease (AD). Alterations in ceramides and sphingomyelins have been postulated to play a role in amyloidogensis and inflammatory stress related neuronal apoptosis; however few studies have conducted a comprehensive analysis of the sphingolipidome in AD plasma using analytical platforms with accuracy, sensitivity and reproducibility.

METHODS AND FINDINGS

We prospectively analyzed plasma from 26 AD patients (mean MMSE 21) and 26 cognitively normal controls in a non-targeted approach using multi-dimensional mass spectrometry-based shotgun lipidomics to determine the levels of over 800 molecular species of lipids. These data were then correlated with diagnosis, apolipoprotein E4 genotype and cognitive performance. Plasma levels of species of sphingolipids were significantly altered in AD. Of the 33 sphingomyelin species tested, 8 molecular species, particularly those containing long aliphatic chains such as 22 and 24 carbon atoms, were significantly lower (p<0.05) in AD compared to controls. Levels of 2 ceramide species (N16:0 and N21:0) were significantly higher in AD (p<0.05) with a similar, but weaker, trend for 5 other species. Ratios of ceramide to sphingomyelin species containing identical fatty acyl chains differed significantly between AD patients and controls. MMSE scores were correlated with altered mass levels of both N20:2 SM and OH-N25:0 ceramides (p<0.004) though lipid abnormalities were observed in mild and moderate AD. Within AD subjects, there were also genotype specific differences.

CONCLUSIONS

In this prospective study, we used a sensitive multimodality platform to identify and characterize an essentially uniform but opposite pattern of disruption in sphingomyelin and ceramide mass levels in AD plasma. Given the role of brain sphingolipids in neuronal function, our findings provide new insights into the AD sphingolipidome and the potential use of metabolomic signatures as peripheral biomarkers.

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

BACKGROUND

The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data.

RESULTS

We present and justify a new technique for comparing pairs of networks--in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD), which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC) of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). This provides us with a means to quantify which predefined metabolic pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite pathway database) were altered in the PFC of PCP-treated rats. Several significant changes were discovered, notably: 1) neuroactive ligands active at glutamate and GABA receptors are disrupted in the PFC of PCP-treated animals, 2) glutamate dysfunction in these animals was not limited to compromised glutamatergic neurotransmission but also involves the disruption of metabolic pathways linked to glutamate; and 3) a specific series of purine reactions Xanthine ← Hypoxyanthine ↔ Inosine ← IMP → adenylosuccinate is also disrupted in the PFC of PCP-treated animals.

CONCLUSIONS

Network reordering via the GSVD provides a means to discover statistically validated differences in clustering between a pair of networks. In practice this analytical approach, when applied to metabolomic data, allows us to quantify the alterations in metabolic pathways between two experimental groups. With this new computational technique we identified metabolic pathway alterations that are consistent with known results. Furthermore, we discovered disruption in a novel series of purine reactions that may contribute to the PFC dysfunction and cognitive deficits seen in schizophrenia.

Current strategies in the discovery of small-molecule biomarkers for Alzheimer’s disease

With the number of patients suffering from Alzheimer’s disease rapidly increasing, there is a major requirement for an accurate biomarker capable of diagnosing the disease early. Much of the research is focused on protein and genetic approaches; however, small molecules may provide viable marker molecules. Examples that support this approach include known abnormalities in lipid metabolism, glucose utilization and oxidative stress, which have been demonstrated in patients suffering from the disease. Therefore, by-products of this irregular metabolism may provide accurate biomarkers. In this review we present the current approaches previously published in the literature used to investigate potential small-molecule and metabolite markers, and report their findings. A wide range of techniques are discussed, including separation approaches (LC, GC and CE), magnetic resonance technologies (NMR and magnetic resonance spectroscopy), and immunoassays.

Profiling the reactive metabolites of xenobiotics using metabolomic technologies

A predominant pathway of xenobiotic-induced toxicity is initiated by bioactivation. Characterizing reactive intermediates will provide information on the structure of reactive species, thereby defining a potential bioactivation mechanism. Because most reactive metabolites are not stable, it is difficult to detect them directly. Reactive metabolites can form adducts with trapping reagents, such as glutathione, which makes the reactive metabolites detectable. However, it is challenging to "fish" these adducts out from a complex biological matrix, especially for adducts generated via uncommon metabolic pathways. In this regard, we developed a novel approach based upon metabolomic technologies to screen trapped reactive metabolites. The bioactivation of pulegone, acetaminophen, and clozapine were reexamined by using this metabolomic approach. In all these cases, a large number of trapped reactive metabolites were readily identified. These data indicate that this metabolomic approach is an efficient tool to profile xenobiotic bioactivation.