The role of metabolites and metabolomics in clinically applicable biomarkers of disease

Maternal and fetal metabonomic alterations in prenatal nicotine exposure-induced rat intrauterine growth retardation

Prenatal nicotine exposure causes adverse birth outcome. However, the corresponding metabonomic alterations and underlying mechanisms of nicotine-induced developmental toxicity remain unclear. The aims of this study were to characterize the metabolic alterations in biofluids in nicotine-induced intrauterine growth retardation (IUGR) rat model. In the present study, pregnant Wistar rats were intragastrically administered with different doses of nicotine (0.5, 1.0 and 2.0 mg/kg d) from gestational day (GD) 11-20. The metabolic profiles of the biofluids, including maternal plasma, fetal plasma and amniotic fluid, were analyzed using (1)H nuclear magnetic resonance (NMR)-based metabonomic techniques. Prenatal nicotine exposure caused noticeably lower body weights, higher IUGR rates of fetal rats, and elevated maternal and fetal corticosterone (CORT) levels compared to the controls. The correlation analysis among maternal, fetal serum CORT levels and fetal bodyweight suggested that the levels of maternal and fetal serum CORT presented a positive correlation (r=0.356, n=32, P<0.05), while there was a negative correlation between fetal (r=-0.639, n=32, P<0.01) and maternal (r=-0.530, n=32, P<0.01) serum CORT level and fetal bodyweight. The fetal metabonome alterations included the stimulation of lipogenesis and the decreased levels of glucose and amino acids. The maternal metabonome alterations involved the enhanced blood glucose levels, fatty acid oxygenolysis, proteolysis and amino acid accumulation. These results suggested that prenatal nicotine exposure is associated with an altered maternal and fetal metabonome, which may be related to maternal increased glucocorticoid level induced by nicotine.

The utility of metabolomics in natural product and biomarker characterization

BACKGROUND

Metabolomics is a well-established rapidly developing research field involving quantitative and qualitative metabolite assessment within biological systems. Recent improvements in metabolomics technologies reveal the unequivocal value of metabolomics tools in natural products discovery, gene-function analysis, systems biology and diagnostic platforms.

SCOPE OF REVIEW

We review here some of the prominent metabolomics methodologies employed in data acquisition and analysis of natural products and disease-related biomarkers.

MAJOR CONCLUSIONS

This review demonstrates that metabolomics represents a highly adaptable technology with diverse applications ranging from environmental toxicology to disease diagnosis. Metabolomic analysis is shown to provide a unique snapshot of the functional genetic status of an organism by examining its biochemical profile, with relevance toward resolving phylogenetic associations involving horizontal gene transfer and distinguishing subgroups of genera possessing high genetic homology, as well as an increasing role in both elucidating biosynthetic transformations of natural products and detecting preclinical biomarkers of numerous disease states.

GENERAL SIGNIFICANCE

This review expands the interest in multiplatform combinatorial metabolomic analysis. The applications reviewed range from phylogenetic assignment, biosynthetic transformations of natural products, and the detection of preclinical biomarkers.

Text-mining-assisted biocuration workflows in Argo

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk.

Plasma free amino acid profiling of esophageal cancer using high-performance liquid chromatography spectroscopy

AIM: To perform plasma free amino acid (PFAA) profiling of esophageal squamous cell carcinoma (ESCC) patients at different pathological stages and healthy subjects.

METHODS: Plasma samples from ESCC patients (n = 51) and healthy control adults (n = 60) were analyzed by high-performance liquid chromatography (HPLC). The ESCC patients included moderate/poorly-differentiation (n = 24), lymph node metastasis (n = 17) and clinical stage > Ib2 (n = 36). Partial least squares discriminant analysis was performed to demonstrate that the PFAA metabolic patterns enabled discrimination between ESCC patients and controls, and the Student t test was applied to assess significant differences in PFAA concentrations between the two groups.

RESULTS: There were significant differences in the PFAA profiles between controls and ESCC patients. Compared with healthy controls, the levels of Asp, Glu, Gly, His, Thr, Tau, Ala, Met, Ile, Leu, and Phe were decreased in ESCC patients, but Cys was increased. There exists a strong correlation between PFAA profiles and clinicopathological characteristics in ESCC patients. The levels of many PFAAs (i.e., Glu, Asp, Ser, Gly, Tau, Ala, Tyr, Val, Ile, and Leu) were related to pathological grading, lymph node metastasis, and ESCC clinical stage. Very good discrimination between ESCC patients and control subjects was achieved by multivariate modeling of plasma profiles.

CONCLUSION: HPLC-based plasma profiling analysis was shown to be an effective approach to differentiate between ESCC patients and controls. PFAA profiles may have potential value for screening or diagnosing ESCC.

Metabonomic analysis of serum of workers occupationally exposed to arsenic, cadmium and lead for biomarker research: a preliminary study

Environmental metabonomics is the application of metabonomics to characterize the interactions of organisms with their environment. Metabolic profiling is an exciting addition to the armory of the epidemiologist for the discovery of new disease risk biomarkers and diagnostics. This work is a continuation of research searching for preclinical serum markers in a group of 389 healthy smelter workers exposed to lead, cadmium and arsenic. Changes in the metabolic profiles were studied using Proton Nuclear Magnetic Resonance Spectroscopy on pooled serum samples from both the metal exposed and control groups. These multivariate metabonomic datasets were analyzed with Principal Component Analysis and Partial Least Squares Discriminant Analysis. Analysis of metabolic profiles of people exposed to heavy metals suggests energy metabolism disturbance induced by heavy metals. Changes in lipid fraction (very-low-density lipoprotein - VLDL, low-density lipoprotein - LDL), unsaturated lipids and in the level of amino acids suggest perturbation of the metabolism of lipids and amino acids. This study illustrated the high reliability of NMR-based metabonomic profiling on the study of the biochemical effects induced by the mixture of heavy metals. This approach is capable of identifying intermediate biomarkers of response to toxicants at environmental/occupational concentrations, paving the way to its use in a monitoring of smelter workers exposed to low doses of lead, cadmium and arsenic.

Global metabolite profiling of synovial fluid for the specific diagnosis of rheumatoid arthritis from other inflammatory arthritis

Currently, reliable biomarkers that can be used to distinguish rheumatoid arthritis (RA) from other inflammatory diseases are unavailable. To find possible distinctive metabolic patterns and biomarker candidates for RA, we performed global metabolite profiling of synovial fluid samples. Synovial fluid samples from 38 patients with RA, ankylosing spondylitis, Behçet's disease, and gout were analyzed by gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). Orthogonal partial least-squares discriminant and hierarchical clustering analyses were performed for the discrimination of RA and non-RA groups. Variable importance for projection values were determined, and the Wilcoxon-Mann-Whitney test and the breakdown and one-way analysis of variance were conducted to identify potential biomarkers for RA. A total of 105 metabolites were identified from synovial fluid samples. The score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between the RA and non-RA groups. The 20 metabolites, including citrulline, succinate, glutamine, octadecanol, isopalmitic acid, and glycerol, were identified as potential biomarkers for RA. These metabolites were found to be associated with the urea and TCA cycles as well as fatty acid and amino acid metabolism. The metabolomic analysis results demonstrated that global metabolite profiling by GC/TOF MS might be a useful tool for the effective diagnosis and further understanding of RA.

Validation of a dual LC-HRMS platform for clinical metabolic diagnosis in serum, bridging quantitative analysis and untargeted metabolomics

Mass spectrometry-based metabolomics is a rapidly growing field in both research and diagnosis. Generally, the methodologies and types of instruments used for clinical and other absolute quantification experiments are different from those used for biomarkers discovery and untargeted analysis, as the former requires optimal sensitivity and dynamic range, while the latter requires high resolution and high mass accuracy. We used a Q-TOF mass spectrometer with two different types of pentafluorophenyl (PFP) stationary phases, employing both positive and negative ionization, to develop and validate a hybrid quantification and discovery platform using LC-HRMS. This dual-PFP LC-MS platform quantifies over 50 clinically relevant metabolites in serum (using both MS and MS/MS acquisitions) while simultaneously collecting high resolution and high mass accuracy full scans to monitor all other co-eluting non-targeted analytes. We demonstrate that the linearity, accuracy, and precision results for the quantification of a number of metabolites, including amino acids, organic acids, acylcarnitines and purines/pyrimidines, meets or exceeds normal bioanalytical standards over their respective physiological ranges. The chromatography resolved highly polar as well as hydrophobic analytes under reverse-phase conditions, enabling analysis of a wide range of chemicals, necessary for untargeted metabolomics experiments. Though previous LC-HRMS methods have demonstrated quantification capabilities for various drug and small molecule compounds, the present study provides an HRMS quant/qual platform tailored to metabolic disease; and covers a multitude of different metabolites including compounds normally quantified by a combination of separate instrumentation.

Characterization of rational biomarkers accompanying fever in yeast-induced pyrexia rats using urine metabolic footprint analysis

Fever is a prominent feature of diseases and is an ongoing process that is always accompanied by metabolic changes in the body system. Despite the success of temperature regulation theory, the underlying biological process remains unclear. To truly understand the nature of the febrile response, it is crucial to confirm the biomarkers during the entire biological process. In the current study, a 73-h metabolic footprint analysis of the urine from yeast-induced pyrexia rats was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Potential biomarkers were selected using orthogonal partial least squares-discriminate analysis (OPLS-DA), the rational biomarkers were verified by Pearson correlation analysis, and the predictive power was evaluated using receiver operator characteristic (ROC) curves. A metabolic network constructed using traditional Chinese medicine (TCM) grammar systems was used to validate the rationality of the verified biomarkers. Finally, five biomarkers, including indoleacrylic acid, 3-methyluridine, tryptophan, nicotinuric acid and PI (37:3), were confirmed as rational biomarkers because their correlation coefficients were all greater than 0.87 and because all of the correlation coefficients between any pair of these biomarkers were higher than 0.75. The areas under the ROC curves were all greater than 0.84, and their combined predictive power was considered reliable because the greatest area under the ROC curve was 0.968. A metabolic network also demonstrated the rationality of these five biomarkers. Therefore, these five metabolites can be adopted as rational biomarkers to reflect the process of the febrile response in inflammation-induced pyrexia.

Chemical structure informing statistical hypothesis testing in metabolomics

MOTIVATION

Metabolomics has been shown as an effective tool to study various biological and biomedical phenotypes, whereas interrogating the inherently noisy metabolite concentration data with limited sample size remains a major challenge. Accumulating evidence suggests that metabolites' structures are relevant to their bioactivities.

RESULTS

We present a new strategy to boost the statistical power of hypothesis testing in metabolomics by incorporating quantitative molecular descriptors for each metabolite. The strategy selects potentially informative summary molecular descriptors and outputs chemical structure-informed false discovery rates. The effectiveness of the proposed strategy is demonstrated by both simulation studies and a real application. In a metabolomic study on Alzheimer's disease, the posterior inclusion probability for summary molecular descriptors reaches 0.97. By incorporating the structure data, our approach uniquely identifies multiple Alzheimer's disease signatures, which are consistent with existing evidence. These results evidently suggest the value of the proposed approach for metabolomic hypothesis-testing problems.

AVAILABILITY AND IMPLEMENTATION

A code package implementing the strategy is freely available at https://github.com/HongjieZhu/CIMA.git.

Potential input from metabolomics for exploring and understanding the links between environment and health

Humans may be exposed via their environment to multiple chemicals as a consequence of human activities and use of synthetic products. Little knowledge is routinely generated on the hazards of these chemical mixtures. The metabolomic approach is widely used to identify metabolic pathways modified by diseases, drugs, or exposures to toxicants. This review, based on the state of the art of the current applications of metabolomics in environmental health, attempts to determine whether metabolomics might constitute an original approach to the study of associations between multiple, low-dose environmental exposures in humans. Studying the biochemical consequences of complex environmental exposures is a challenge demanding the development of careful experimental and epidemiological designs, in order to take into account possible confounders associated with the high level of interindividual variability induced by different lifestyles. The choices of populations studied, sampling and storage procedures, statistical tools used, and system biology need to be considered. Suggestions for improved experimental and epidemiological designs are described. Evidence indicates that metabolomics may be a powerful tool in environmental health in the identification of both complex exposure biomarkers directly in human populations and modified metabolic pathways, in an attempt to improve understanding the underlying environmental causes of diseases. Nevertheless, the validity of biomarkers and relevancy of animal-to-human extrapolation remain key challenges that need to be properly explored.

Metabolomics analysis reveals that bile acids and phospholipids contribute to variable responses to low-temperature-induced ascites syndrome

Hepatic metabolomic profiles of low-temperature induced ascites syndrome showed disturbances in the metabolism of bile acids and phospholipid.

Metabolomic identification of diagnostic plasma biomarkers in humans with chronic heart failure

Chronic heart failure (CHF), as a progressive clinical syndrome, is characterized by failure of enough blood supply from the heart to meet the body's metabolic demands, and there is intense interest in identifying novel biomarkers that could make contributions to the diagnosis of CHF. Metabolomics, compared with current diagnostic approaches, could investigate many metabolic perturbations within biological systems. The overarching goal of the work discussed here is to apply a high-throughput approach to identify metabolic signatures and plasma diagnostic biomarkers underlying CHF by 1H-NMR spectroscopy. Plasma samples from 39 patients with CHF and 15 controls were analyzed by NMR spectroscopy. After processing the data, orthogonal partial least square discriminant analysis (OPLS-DA) was performed. The statistical model revealed good explained variance and predictability, and the diagnostic performance assessed by leave-one-out analysis exhibited 92.31% sensitivity and 86.67% specificity. The OPLS-DA score plots of spectra revealed good separation between case and control on the level of metabolites, and multiple biochemical changes indicated hyperlipidemia, alteration of energy metabolism and other potential biological mechanisms underlying CHF. It was concluded that the NMR-based metabolomics approach demonstrated good performance to identify diagnostic plasma markers and provided new insights into metabolic process related to CHF.

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.

Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission

PURPOSE

Multiple myeloma remains an incurable disease. New approaches to develop better tools for improving patient prognostication and monitoring treatment efficacy are very much needed. In this study, we aimed to evaluate the potential of metabolomics by (1)H-NMR to provide information on metabolic profiles that could be useful in the management of multiple myeloma.

EXPERIMENTAL DESIGN

Serum samples were collected from multiple myeloma patients at the time of diagnosis and after achieving complete remission. A matched control set of samples was also included in the study. The (1)H-NMR measurements used to obtain the metabolic profile for each patient were followed by the application of univariate and multivariate statistical analyses to determine significant differences.

RESULTS

Metabolic profiles of multiple myeloma patients at diagnosis exhibited higher levels of isoleucine, arginine, acetate, phenylalanine, and tyrosine, and decreased levels of 3-hydroxybutyrate, lysine, glutamine, and some lipids compared with the control set. A similar analysis conducted in multiple myeloma patients after achieving complete remission indicated that some of the metabolic changes (i.e., glutamine, cholesterol, lysine) observed at diagnosis displayed a variation in the opposite direction upon responding to treatment, thus contributing to multiple myeloma patients having a closer metabolic profile to those of healthy individuals after the disappearance of major disease manifestations.

CONCLUSIONS

The results highlight the potential of metabolic profiles obtained by 1H-NMR in identifying multiple myeloma biomarkers that may be useful to objectively discriminate individuals with and without multiple myeloma, and monitor response to treatment.

Human metabolomics: strategies to understand biology

Metabolomics provides a direct functional read-out of the physiological status of an organism and is in principle ideally suited to describe someone's health status. Whereas only a limited number of small metabolites are used in the clinics, in inborn errors of metabolism an extensive repertoire of metabolites are used as biomarkers. We discuss that the proper clinical phenotyping is crucial to find biomarkers and obtain biological insights for multifactorial diseases. This requires to study the phenotype dynamics including the concepts of homeostasis and allostasis, that is, the ability to adapt and cope with a challenge. We also elaborate that biology-driven metabolomics platforms (i.e. development of metabolomics technology driven by the need of studying and answering important biomedical questions) addressing clinically relevant pathways and at the same time providing absolute concentrations are key to allow discovery and validation of biomarkers across studies and labs. Following individuals over years will require high throughput metabolomics approaches, which are emerging for nuclear magnetic resonance spectroscopy and direct-infusion mass spectrometry, but should also include the biochemical networks needed for personalized health monitoring.

A review of applications of metabolomics in cancer

Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions.

Comparing metabolomic and pathologic biomarkers alone and in combination for discriminating Alzheimer's disease from normal cognitive aging

Background

A critical and as-yet unmet need in Alzheimer disease (AD) research is the development of novel markers that can identify individuals at risk for cognitive decline due to AD. This would aid intervention trials designed to slow the progression of AD by increasing diagnostic certainty, and provide new pathophysiologic clues and potential drug targets.

Results

We used two metabolomics platforms (gas chromatography-time of flight mass spectrometry [GC-TOF] and liquid chromatography LC-ECA array [LC-ECA]) to measure a number of metabolites in cerebrospinal fluid (CSF) from patients with AD dementia and from cognitively normal controls. We used stepwise logistic regression models with cross-validation to assess the ability of metabolite markers to discriminate between clinically diagnosed AD participants and cognitively normal controls and we compared these data with traditional CSF Luminex immunoassay amyloid-β and tau biomarkers. Aβ and tau biomarkers had high accuracy to discriminate cases and controls (testing area under the curve: 0.92). The accuracy of GC-TOF metabolites and LC-ECA metabolites by themselves to discriminate clinical AD participants from controls was high (testing area under the curve: 0.70 and 0.96, respectively).

Conclusions

Our study identified several CSF small-molecule metabolites that discriminated especially well between clinically diagnosed AD and control groups. They appear to be suitable for further confirmatory and validation studies, and show the potential to provide predictive performance for AD.

Identification of plasma metabolomic profiling for diagnosis of esophageal squamous-cell carcinoma using an UPLC/TOF/MS platform

Epidemiological studies indicated that esophageal squamous-cell carcinoma (ESCC) is still one of the most common causes of cancer incidence in the world. Searching for valuable markers including circulating endogenous metabolites associated with the risk of esophageal cancer, is extremely important A comparative metabolomics study was performed by using ultraperformance liquid chromatography-electrospray ionization-accurate mass time-of-flight mass spectrometry to analyze 53 pairs of plasma samples from ESCC patients and healthy controls recruited in Huaian, China. The result identified a metabolomic profiling of plasma including 25 upregulated metabolites and five downregulated metabolites, for early diagnosis of ESCC. With a database-based verification protocol, 11 molecules were identified, and six upregulated molecules of interest in ESCC were found to belong to phospholipids as follows: phosphatidylserine, phosphatidic acid, phosphatidyl choline, phosphatidylinositol, phosphatidyl ethanolamine, and sphinganine 1-phosphate. Clinical estimation of metabolic biomarkers through hierarchical cluster analysis in plasma samples from 17 ESCC patients and 29 healthy volunteers indicated that the present metabolite profile could distinguish ESCC patients from healthy individuals. The cluster of aberrant expression of these metabolites in ESCC indicates the critical role of phospholipid metabolism in the oncogenesis of ESCC and suggests its potential ability to assess the risk of ESCC development in addition to currently used risk factors.

Role of fatty acid-based functional lipidomics in the development of molecular diagnostic tools

Lipids are molecules with different structures which have the feature of water insolubility in common. They have very important biological roles within structural, functional and signaling activities that have recently received renewed attention from life science research. Lipidomics considers the structural and functional roles played by lipids, but also their in vivo changes due to metabolic or degradation pathways, as well as their biological consequences. In this context, the dynamic vision of phospholipid metabolism and, in particular, fatty acid transformations combine with nutritional aspects and health consequences, providing important information for molecular medicine. Fatty acid-based functional lipidomics can be successfully applied to the follow-up of human lipid profiles under normal and pathological conditions, and this review provides several examples of this powerful molecular diagnostic tool, which is expected to have a strong influence on biomedical research in the 21st century.

A urinary metabonomics study on biochemical changes in yeast-induced pyrexia rats: a new approach to elucidating the biochemical basis of the febrile response

Fever is a prominent feature of many diseases, such as infection, inflammation and trauma. In the clinic, fever can be easily judged by measuring the body temperature; however, the pathogenesis of fever is still not fully understood. A febrile response is a systemic pathological process that can cause metabolic disorders. Metabonomics can provide powerful tools to reveal the pathological mechanisms for such a systemic disease. Thus, to reveal subtle metabolic changes under the condition of fever and to explore its mechanism, an ultra performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometry metabonomics approach was employed to investigate the urine biochemical characteristics of yeast-induced pyrexia rats. The acquired data were subjected to principal component analysis for differentiating the pyrexia rats from the control rats. Potential biomarkers were screened by using orthogonal partial least-squares-discriminant analysis and were identified by accurate mass, database, and MS/MS fragment information obtained from the MS(E) technique. Sixteen metabolites in rat urine were identified as potential biomarkers. The relative intensities of the 15 potential biomarkers were calculated. The thermoregulatory circuitry of "endogenous pyrogen (EP) ↑-hypothalamus Na⁺/Ca²⁺-cAMP↑" was partially confirmed in this study. The results suggested that UPLC/MS-based metabolic profiling of rat urine identifies impaired tryptophan metabolism as the mechanism of yeast-induced fever. This research provided informative data that the impaired tryptophan metabolism might be one of the important reasons in elucidating the biochemical basis of the febrile response.

A compendium of inborn errors of metabolism mapped onto the human metabolic network

Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.

Characterization of global metabolic responses of glucose-6-phosphate dehydrogenase-deficient hepatoma cells to diamide-induced oxidative stress

Glucose-6-phosphate dehydrogenase (G6PD) is crucial to NADPH generation and redox homeostasis. We have recently shown that G6PD deficiency predisposes cells to oxidant-induced cell death, and it is associated with the impairment of glutathione regeneration. It remains unclear what other metabolic pathways are affected by G6PD deficiency and whether the altered metabolism disturbs cellular redox homeostasis and underlies increased susceptibility to oxidants. In this study, we examined the effects of diamide on global metabolite profiles of SK-Hep1-derived SK-i-Gi and SK-i-Sc cells, which could inducibly express short hairpin RNA (shRNA) against G6PD (Gi) and control shRNA (Sc), respectively. There was no significant difference in their metabolite profiles under uninduced conditions. Doxycycline (Dox) addition resulted in over 70% decrease in G6PD activity in SK-i-Gi cells. This was accompanied by relatively minor changes in the metabolome of SK-i-Gi cells. Upon further diamide treatment, the metabolite profiles of both SK-i-Gi and SK-i-Sc cells changed in a time-dependent manner. A number of metabolic pathways, including those involved in energy metabolism and metabolism of amino acids and glutathione, were affected. However, the changes in the metabolite profile of Dox-treated SK-i-Gi cells were distinct from those of control cells (i.e., Dox-treated SK-i-Sc, SK-i-Gi, and SK-i-Sc cells). Cellular glutathione was depleted, whereas its disulfide form increased significantly in diamide, Dox-treated SK-i-Gi cells. Metabolites related to energy metabolism, such as AMP, ADP, and acetylcarnitine, increased to a greater extent in these cells than in diamide-treated control cells. In contrast, NAD and glutathione dropped to lower levels in SK-i-Gi cells than in control cells. The NAD(+) depletion in SK-i-Gi cells was accompanied by a significant increase in NAD kinase activity. Targeted analyses revealed that NADP(+) and NADPH increased significantly in diamide, Dox-treated SK-i-Gi cells compared with similarly treated control cells. Our results suggest that diamide induces oxidation and depletion of glutathione in SK-i-Gi cells under conditions of G6PD shRNA induction and subsequently induces conversion of NAD(+) to NADP(+) through enhanced NAD kinase activity. This may represent a compensatory mechanism to restore cellular NADPH reserve in G6PD-deficient cells. It is accompanied by alteration in pathways of cellular energy metabolism, such as glycolysis and β-oxidation.

Profile of Circulatory Metabolites in a Relapsing-remitting Animal Model of Multiple Sclerosis using Global Metabolomics

Multiple sclerosis (MS) is a chronic inflammatory and demyelinating disease of the CNS. Although, MS is well characterized in terms of the role played by immune cells, cytokines and CNS pathology, nothing is known about the metabolic alterations that occur during the disease process in circulation. Recently, metabolic aberrations have been defined in various disease processes either as contributing to the disease, as potential biomarkers, or as therapeutic targets. Thus in an attempt to define the metabolic alterations that may be associated with MS disease progression, we profiled the plasma metabolites at the chronic phase of disease utilizing relapsing remitting-experimental autoimmune encephalomyelitis (RR-EAE) model in SJL mice. At the chronic phase of the disease (day 45), untargeted global metabolomic profiling of plasma collected from EAE diseased SJL and healthy mice was performed, using a combination of high-throughput liquid-and-gas chromatography with mass spectrometry. A total of 282 metabolites were identified, with significant changes observed in 44 metabolites (32 up-regulated and 12 down-regulated), that mapped to lipid, amino acid, nucleotide and xenobiotic metabolism and distinguished EAE from healthy group (p<0.05, false discovery rate (FDR)<0.23). Mapping the differential metabolite signature to their respective biochemical pathways using the Kyoto Encyclopedia of Genes and Genomics (KEGG) database, we found six major pathways that were significantly altered (containing concerted alterations) or impacted (containing alteration in key junctions). These included bile acid biosynthesis, taurine metabolism, tryptophan and histidine metabolism, linoleic acid and D-arginine metabolism pathways. Overall, this study identified a 44 metabolite signature drawn from various metabolic pathways which correlated well with severity of the EAE disease, suggesting that these metabolic changes could be exploited as (1) biomarkers for EAE/MS progression and (2) to design new treatment paradigms where metabolic interventions could be combined with present and experimental therapeutics to achieve better treatment of MS.

Comparative analysis of sample preparation methods to handle the complexity of the blood fluid metabolome: when less is more

Blood sample preparation before LC-MS metabolomic fingerprinting is one of the most challenging and error-prone parts of the analytical procedure. Besides proteins, phospholipids contained in blood fluids are known to cause matrix effects and ion suppression phenomena, thus masking biological variation. Nevertheless, the commonly used sample preparation techniques do not consider their removal prior to analysis. Pooled plasma and serum samples were used as biological material, partly as raw samples and partly spiked with distinct concentrations of a metabolite mix (1-5 μg/mL). Prior to LC-ESI-qToF-MS-driven metabolomic analysis, samples were subjected to different preparation methods consisting of three extractions with organic solvents (acetonitrile, methanol, and methanol/ethanol), a membrane-based solvent-free technique, and a hybrid method combining solvent extraction and SPE-mediated removal of phospholipids. The comparative analysis among sample preparation procedures was based on the capacity to detect endogenous compounds in raw samples, differentiate raw versus spiked samples, and reveal real-life metabolomic changes, following a dietary intervention. Method speed, minimum sample handling, compatibility to automation, and applicability to large-scale metabolomic studies were also considered. The combination of solvent deproteinization and the selective removal of phospholipids was revealed to be the most suitable method, in terms of improvement of nonlipid metabolite coverage, extraction reproducibility, quickness, and compatibility with automation, the minimization of matrix effects being among the most probable causes for the good extraction performance associated with the removal of phospholipid species. The main advantage of conventional solvent extraction procedures was the metabolite information coverage for lipid low-molecular-weight species, and extraction with acetonitrile was generally the second choice for sample preparation. Ultrafiltration was the least effective method for plasma and serum preparation; thus, its use without a previous solvent extraction step of the samples should be discarded. According to the presented data, there is no apparent reason to believe that sacrificing information on lipid compounds is too high of a price to pay in order to gain more information on nonlipid LMW metabolites.

CE-MS for metabolomics: developments and applications in the period 2010-2012

CE-MS has emerged as a powerful technique for the profiling of (highly) polar and charged metabolites in biological samples. This review provides an update of the most recent developments in CE-MS for metabolomics covering the scientific literature from July 2010 to June 2012. The present paper is an update of two previous review papers covering the years 2000-2010 (Electrophoresis 2009, 30, 276-291; Electrophoresis 2011, 32, 52-65). Emerging technological developments used in CE-MS for metabolomics are discussed, such as the use of novel interfacing techniques for coupling CE to MS. Representative examples illustrate the applicability of CE-MS in the fields of biomedical, clinical, microbial, plant, environmental and food metabolomics. Concerning targeted and non-targeted approaches, a comprehensive overview of recent CE-MS-based metabolomics studies is given in a table. Information on sample type and pretreatment, capillary coatings and MS detection mode is provided. Finally, general conclusions and perspectives are provided.

Gas chromatography-mass spectrometry metabolite profiling of worker honey bee (Apis mellifera L.) hemolymph for the study of Nosema ceranae infection

Here, we are presenting a gas chromatography-mass spectrometry (GC/MS) approach for the study of infection of the worker honey bee (Apis mellifera L.) by the newly emerged obligate intracellular parasite Nosema ceranae based on metabolite profiling of hemolymph. Because of the severity of the disease, early detection is crucial for its efficient control. Results revealed that the parasite causes a general disturbance of the physiology of the honey bee affecting the mechanisms controlling the mobilization of energy reserves in infected individuals. The imposed nutritional and energetic stress to the host was depicted mainly in the decreased levels of the majority of carbohydrates and amino acids, including metabolites such as fructose, l-proline, and the cryoprotectants sorbitol and glycerol, which are implicated in various biochemical pathways. Interestingly, the level of glucose was detected at significantly higher levels in infected honey bees. Metabolomics analyses were in agreement with those of multiplex quantitative PCR analyses, indicating that it can be used as a complementary tool for the detection and the study of the physiology of the disease.

Perspectives for metabolomics in testosterone replacement therapy

Testosterone is the major circulating androgen in men but exhibits an age-related decline in the ageing male. Late-onset hypogonadism or androgen deficiency syndrome (ADS) is a 'syndromic' disorder including both a persistent low testosterone serum concentration and major clinical symptoms, including erectile dysfunction, low libido, decreased muscle mass and strength, increased body fat, decreased vitality or depressed mood. Given its unspecific symptoms, treatment goals and monitoring parameters, this review will outline the various uncertainties concerning the diagnosis, therapy and monitoring of ADS to date. Literature was identified primarily through searches for specific investigators in the PubMed database. No date or language limits were applied in the literature search for the present review. The current state of research, showing that metabolomics is starting to have an impact not only on disease diagnosis and prognosis but also on drug treatment efficacy and safety monitoring, will be presented, and the application of metabolomics to improve the clinical management of ADS will be discussed. Finally, the scientific opportunities presented by metabolomics and other -omics as novel and promising tools for biomarker discovery and individualised testosterone replacement therapy in men will be explored.

Nutrimetabolomic strategies to develop new biomarkers of intake and health effects

Correctly assessing the metabolic status of subjects after consumption of specific diets is an important challenge for modern nutrition. Recently, metabolomics has been proposed as a powerful tool for exploring the complex relationship between nutrition and health. Nutritional metabolomics, through investigating the role that dietary components play in the maintenance of health and development of risk disease, aims to identify new biomarkers that allow the intake of these compounds to be monitored and related to their expected biological effects. This review offers an overview of the application of nutrimetabolomic strategies in the discovery of new biomarkers in human nutritional research, suggesting three main categories: (1) assessment of nutritional and dietary interventions; (2) diet exposure and food consumption monitoring; and (3) health phenotype and metabolic impact of diet. For this purpose, several examples of these applications will be used to provide evidence and to discuss the advantages and drawbacks of these nutrimetabolomic strategies.

Metabolomic markers for intestinal ischemia in a mouse model

BACKGROUND

Diagnosis of intestinal ischemia remains a clinical challenge. The aim of the present study was to use a metabolomic protocol to identify upregulated and downregulated small molecules (M(r) < 500) in the serum of mice with intestinal ischemia. Such molecules could have clinical utility when evaluated as biomarkers in human studies.

METHODS

A mouse model for intestinal ischemia was established and validated using histology and serum tumor necrosis factor α concentrations. A second mouse model of peritoneal sepsis was used as a positive control. Serial serum samples were collected from these and from sham-operated animals. Sera were analyzed by gas chromatography-mass spectrometry for 40 small molecules as their trimethylsilyl and O-methyloxime derivatives. Peak areas were normalized against an internal standard and resultant peak area ratios subjected to multivariate data analysis using unsupervised principal components analysis and supervised orthogonal projection to latent structures-discriminant analysis. Upregulated and downregulated serum molecules were identified from their correlation to the orthogonal projection to latent structures-discriminant analysis model.

RESULTS

Three highly significantly upregulated (fold-change) serum molecules in intestinal ischemia were inorganic phosphate (2.4), urea (4.3), and threonic acid (2.9). Five highly significantly downregulated (fold-change) serum molecules were stearic acid (1.7), arabinose (2.7), xylose (1.6), glucose (1.4), and ribose (2.2). Lactic acid remained unchanged in intestinal ischemia.

CONCLUSIONS

Distinct molecular changes are reported here for the first time in intestinal ischemia. They reveal impairments of gut microbiota metabolism, intestinal absorption, and renal function, together with increased oxidative stress. In contrast to other reports, lactic acid was not significantly changed. These molecular signatures may now be evaluated in clinical studies.

Separation technique for the determination of highly polar metabolites in biological samples

Metabolomics is a new approach that is based on the systematic study of the full complement of metabolites in a biological sample. Metabolomics has the potential to fundamentally change clinical chemistry and, by extension, the fields of nutrition, toxicology, and medicine. However, it can be difficult to separate highly polar compounds. Mass spectrometry (MS), in combination with capillary electrophoresis (CE), gas chromatography (GC), or high performance liquid chromatography (HPLC) is the key analytical technique on which emerging "omics" technologies, namely, proteomics, metabolomics, and lipidomics, are based. In this review, we introduce various methods for the separation of highly polar metabolites.

Serum amino acid profiles and their alterations in colorectal cancer

Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95% CI 0.815-0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls.

Exploring the human urine metabolomic potentialities by comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry

Metabolomics represents an emerging issue that can aid in the diagnosis and/or prognosis of different diseases. Metabolomic study of urine is particularly interesting as it can be on the base of the developing of new faster and non-invasive methodologies. In response to this actual trend, comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC×GC-ToFMS) combined with headspace solid phase microextraction (HS-SPME) is applied, for the first time to our knowledge, to the untargeted and comprehensive study of the volatile composition of human urine. From a total of ca. 700 compounds detected per sample, 294 were tentatively identified and distributed over the chemical families of hydrocarbons, amines, amides, esters, ketones, aldehydes, alcohols, carboxylic acids, ethers, nitriles, halides, sulfides, thiols, terpenoids, and heterocyclic compounds. To our knowledge, this is the most complete information available so far about whole human urine volatile composition, which represents a valuable data for future advanced studies in the clinical field based on urine fingerprinting. Relevant SPME and GC×GC parameters were considered. Complex sample characterization of human urine is significantly simplified due to the structured GC×GC chromatogram that produces distinct spaces for metabolite chemical families. Furthermore, the potential of this methodology in health related applications was explored by comparing the urinary volatile profiles between smoker (high-risk population for lung cancer) vs. non-smoker adults, focusing on metabolites related to oxidative stress (aliphatic alkanes and aldehydes). In spite of the small sample numbers considered, the results suggest that the urinary volatile profiles may be useful for differentiating subjects with different physiological conditions, thus making it worth to further explore its diagnostic potential.

Untargeted plasma metabolite profiling reveals the broad systemic consequences of xanthine oxidoreductase inactivation in mice

A major challenge in systems biology is integration of molecular findings for individual enzyme activities into a cohesive high-level understanding of cellular metabolism and physiology/pathophysiology. However, meaningful prediction for how a perturbed enzyme activity will globally impact metabolism in a cell, tissue or intact organisms is precluded by multiple unknowns, including in vivo enzymatic rates, subcellular distribution and pathway interactions. To address this challenge, metabolomics offers the potential to simultaneously survey changes in thousands of structurally diverse metabolites within complex biological matrices. The present study assessed the capability of untargeted plasma metabolite profiling to discover systemic changes arising from inactivation of xanthine oxidoreductase (XOR), an enzyme that catalyzes the final steps in purine degradation. Using LC-MS coupled with a multivariate statistical data analysis platform, we confidently surveyed >3,700 plasma metabolites (50-1,000 Da) for differential expression in XOR wildtype vs. mice with inactivated XOR, arising from gene deletion or pharmacological inhibition. Results confirmed the predicted derangements in purine metabolism, but also revealed unanticipated perturbations in metabolism of pyrimidines, nicotinamides, tryptophan, phospholipids, Krebs and urea cycles, and revealed kidney dysfunction biomarkers. Histochemical studies confirmed and characterized kidney failure in xor-nullizygous mice. These findings provide new insight into XOR functions and demonstrate the power of untargeted metabolite profiling for systemic discovery of direct and indirect consequences of gene mutations and drug treatments.

A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography-mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ∼12 h from metabolite extraction to peak integration for a data set containing 15 total samples (∼6 h for a single sample).

Advanced molecular biologic techniques in toxicologic disease

The advancement of molecular biologic techniques and their capabilities to answer questions pertaining to mechanisms of pathophysiologic events have greatly expanded over the past few years. In particular, these opportunities have provided researchers and clinicians alike the framework from with which to answer clinical questions not amenable for elucidation using previous, more antiquated methods. Utilizing extremely small molecules, namely microRNA, DNA, protein, and nanoparticles, we discuss the background and utility of these approaches to the progressive, practicing physician. Finally, we consider the application of these tools employed as future bedside point of care tests, aiding in the ultimate goal of unsurpassed patient care.

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.

Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers

BACKGROUND

In metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity.

FINDINGS

In this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise.

CONCLUSIONS

We propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/.

Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling

There has been limited analysis of the effects of hepatocellular carcinoma (HCC) on liver metabolism and circulating endogenous metabolites. Here, we report the findings of a plasma metabolomic investigation of HCC patients by ultraperformance liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS), random forests machine learning algorithm, and multivariate data analysis. Control subjects included healthy individuals as well as patients with liver cirrhosis or acute myeloid leukemia. We found that HCC was associated with increased plasma levels of glycodeoxycholate, deoxycholate 3-sulfate, and bilirubin. Accurate mass measurement also indicated upregulation of biliverdin and the fetal bile acids 7α-hydroxy-3-oxochol-4-en-24-oic acid and 3-oxochol-4,6-dien-24-oic acid in HCC patients. A quantitative lipid profiling of patient plasma was also conducted by ultraperformance liquid chromatography-electrospray ionization-triple quadrupole mass spectrometry (UPLC-ESI-TQMS). By this method, we found that HCC was also associated with reduced levels of lysophosphocholines and in 4 of 20 patients with increased levels of lysophosphatidic acid [LPA(16:0)], where it correlated with plasma α-fetoprotein levels. Interestingly, when fatty acids were quantitatively profiled by gas chromatography-mass spectrometry (GC-MS), we found that lignoceric acid (24:0) and nervonic acid (24:1) were virtually absent from HCC plasma. Overall, this investigation illustrates the power of the new discovery technologies represented in the UPLC-ESI-QTOFMS platform combined with the targeted, quantitative platforms of UPLC-ESI-TQMS and GC-MS for conducting metabolomic investigations that can engender new insights into cancer pathobiology.

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.

Application of metabolomics to prostate cancer

The prostate has long been known to exhibit unique metabolite profiles. In the last decade, advances in nuclear magnetic resonance spectroscopy and mass spectrometry have been applied toward identifying metabolic alterations in prostate cancer that may provide clinically useful biomarkers. As with genomics and proteomics, advances in technology and bioinformatics have led to the application of metabolomic profiling to prostate cancer-the high throughput evaluation of a large complement of metabolites in the prostate and how they are altered by disease perturbations. Recently, high profile publications have drawn attention to the potential of metabolomic analysis to identify biomarkers for early detection or disease progression from readily accessible body fluids as well as tissue specimens from biopsy and surgery. This review will examine applications of metabolomics to prostate cancer and highlight clinical associations and potential challenges.