Statistically Integrated Metabonomic−Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

Resistance to chemotherapy drugs in cancer sufferers is very common. Using a panel of 59 cell lines obtained from different types of cancer we study the links between the genes and metabolites measured in these cells and the resistance the cells show to common cancer drugs containing platinum. In order to combine the information given by the genes and metabolites we introduce a new pathway-based approach, which allows us to explore synergy between the different types of data. We then extend the procedure to look at a wider panel of drugs and show that the pathways we found were associated with platinum are not just the pathways which are frequently selected for a large number of drugs. Given the increasing use of multiple sets of measurements (genes, metabolites, proteins etc.) in biological studies, we demonstrate a powerful, yet straightforward method for dealing with the resulting large datasets and integrating their knowledge. We believe that this work could contribute to developing a personalised medicine approach to treating tumours, where the genetic and metabolic changes in the tumour are measured and then used for prediction of the optimal treatment regime.

NMR metabonomics for mammalian cell metabolism studies

The detailed knowledge of mammalian cell metabolism and its adjustments to different cell properties and perturbations, such as disease and drug exposure, is of enormous value in the deeper understanding of pathological processes and drug mechanisms, as well as in the development of new and improved methods for diagnosis, follow-up of disease progression and treatment response. This review covers recent developments in the use of NMR-based metabonomics to characterize cellular metabolomes and interpret them in terms of metabolic changes taking place in a wide range of situations. The analytical methodology available is briefly presented and the applications developed so far are reviewed. These include differences in cell properties (e.g., drug resistance, cell cycle stage, specific growth conditions and genetic characteristics) and changes induced in response to different perturbations (e.g., disease, drug exposure and irradiation).

A decade of advances in metabonomics

The metabonomic approach to biological analysis has demonstrated considerable success in obtaining and decoding metabolic signatures of health, disease and biological challenge. The rise of metabonomics to join the principal 'omics' streams in medical research has been enhanced in particular over the last 10 years by developments in modelling methods, rather than simply via advances in the supporting analytical platforms and biosampling modalities. Metabonomic analysis has been applied in a diverse range of areas from toxicology and dietary effects through to parasitology and molecular epidemiology, and promises yet further advances and wider future application. Some of the basis and methodology of this success is discussed, and some analytical sampling options, future modelling techniques and new targets, and 'blue skies' possibilities are presented in the context of personalised health and the delivery of optimised medical care to individuals. Metabonomics will continue to contribute significantly to improving our knowledge of a wide range of biological systems.

Pharmacometabonomics as an effector for personalized medicine

This article introduces and reviews the concept of pharmacometabonomics, with recent experimental exemplifications of the approach being described and discussed. Pharmacometabonomics seeks to predict the response of an individual to a stimulus (e.g., drug, toxin, surgery, nutrition and so on) prior to the stimulus or other perturbation. It is an integral part of top-down systems biology which aims to improve understanding of phenotypic differences and the impact of beneficial and pathological interventions. The pharmacometabonomic concept is also integral to the understanding of mammalian-gut microbiome cometabolic interactions and their consequences, including the impact on disease and therapy. Although the subject is only at an early stage and requires further exemplification and validation, the approach has major implications for improved efficiency in drug discovery efforts, for example, by enabling more careful selection of animals in preclinical studies, for better stratification of patients in drug clinical trials and for individualized therapies. It could also find application in population-wide large cohort studies and in studies of nutrition where it would allow the elucidation of health risk factors and provide easily measured surrogate biomarkers.

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.

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

The main factor of environmental contamination is the presence of the heavy metals lead, cadmium, and arsenic. The aim of serum protein profile analysis of people chronically exposed to heavy metals is to find protein markers of early pathological changes. The study was conducted in a group of 389 healthy men working in copper foundry and 45 age-matched non-exposed healthy men. Toxicological test samples included whole blood, serum, and urine. Thirty-seven clinical parameters were measured. Based on the parameters values of the healthy volunteers, the centroid in 37-dimensional space was calculated. The individuals in the metal-exposed and control groups were ordered based on the Euclidean distance from the centroid defined by the first component according to Principal Component Analysis (PCA). Serum samples of two individuals, one from the control and one from the metal-exposed group, were chosen for proteomic analysis. In optimized conditions of two-dimensional gel electrophoresis (2-DE), two protein maps were obtained representing both groups. Twenty-eight corresponding protein spots from both protein maps were chosen and identified based on PDQuest analysis and the SWISS-2DPAGE database. From a panel of six proteins with differences in expression greater than a factor of two, three potential markers with the highest differences were selected: hemoglobin-spot 26 (pI 7.05, Mw 10.53), unidentified protein-spot 27 (pI 6.73, Mw 10.17), and unidentified protein-spot 25 (pI 5.75, Mw 12.07). Further studies are required to prove so far obtained results. Identified proteins could serve as potential markers of preclinical changes and could be in the future included in biomonitoring of people exposed to heavy metals.

Metabolomics study of esophageal adenocarcinoma

OBJECTIVE

The objective of this study was to detect and evaluate reliable metabolite markers for screening and monitoring treatment of patients with esophageal adenocarcinoma (EAC) by studying metabolomics. The sensitivity and specificity of the study were evaluated not only for EAC but also for Barrett esophagus and high-grade dysplasia, which are widely regarded as precursors of EAC.

METHODS

Profiles of metabolites in blood serum were constructed using nuclear magnetic resonance spectroscopy and statistical analysis methods. The metabolite biomarkers discovered were selected to build a predictive model that was then used to test the classifications accuracies.

RESULTS

Eight metabolites showed significant differences in their levels in patients with cancer and in the control group on the basis of Student t test. A partial least-squares discriminant analysis model built on these metabolites provided excellent classifications of patients with cancer and the control group, with the area under the receiver operating in a characteristic curve of >0.85 for both training and validation sample sets. Evaluated by the same model, the Barrett esophagus samples were of mixed classification, and the high-grade dysplasia samples were classified primarily as cancer samples. A pathway study indicated that altered energy metabolism and changes in the trochloroacetic acid cycle were the dominant factors in the biochemistry of EAC.

CONCLUSIONS

1H nuclear magnetic resonance-based metabolite profiling analysis was shown to be an effective approach to differentiating between patients with EAC and healthy subjects. Good sensitivity and selectivity were shown by using the 8 metabolite markers discovered to predict the classification of samples from the healthy control group and the patients with the disease. Serum metabolic profiling may have potential for early diagnosis of EAC and may enhance our understanding of its mechanisms.

Metabolomic applications of HILIC-LC-MS

Hydrophilic interaction liquid chromatography (HILIC), although not a new technique, has enjoyed a recent renaissance with the introduction of robust and reproducible stationary phases. It is consequently finding application in metabolomics studies, which have traditionally relied on the stability of reversed phases (RPs), since the biofluids analyzed are predominantly aqueous and thus contain many polar analytes. HILIC's retention of those polar compounds and use of solvents readily compatible with mass spectrometry have seen its increasing adoption in studies of complex aqueous metabolomes. This review describes the stationary phases and their features, surveys HILIC-LC-MS's role in metabolomics experiments, discusses approaches to data extraction and analysis including multivariate analysis, and reviews the literature on HILIC-MS applications in metabolomics.

Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

Metabolic signatures of malignant progression in prostate epithelial cells

Prognostic markers that can distinguish indolent from aggressive prostate cancer could have substantial patient benefit, helping to target patients most in need of radical intervention, while avoiding overtreatment of a highly prevalent condition. The search for novel cancer biomarkers has been facilitated by the development of technologies for "global" biomolecular profiling, used in the sciences of transcriptomics, proteomics and metabolic profiling (metabonomics/metabolomics). Using an NMR-based approach we compared intracellular and extracellular metabolic profiles from the immortalised, non-tumourigenic prostate epithelial cell line, RWPE-1 and two tumourigenic sublines with increasing malignant phenotypes, WPE1-NB14 and WPE1-NB11, generated by N-methyl-N-nitrosourea (MNU) mutagenesis. Collectively, these cell lines present an in vitro model of prostate cancer progression and disease aggression. We observed progressive alterations to intracellular levels of multiple metabolites from choline and branched chain amino acid metabolic pathways from RWPE-1 to WPE1-NB14 to WPE1-NB11 cells. In addition specific perturbations to intracellular glycine and lactate and extracellular lactate and alanine were observed relative to the parent line. The pathways implicated by comparative metabolic profiling in this model are known to be altered in human prostate cancer, and potentially represent a source of biomarkers for prostate cancer aggression.

Bidirectional correlation of NMR and capillary electrophoresis fingerprints: a new approach to investigating Schistosoma mansoni infection in a mouse model

We demonstrate the statistical integration of nuclear magnetic resonance (NMR) spectroscopy and capillary electrophoresis (CE) data in order to describe a pathological state caused by Schistosoma mansoni infection in a mouse model based on urinary metabolite profiles. Urine samples from mice 53 days post infection with S. mansoni and matched controls were analyzed via NMR spectroscopy and CE. The two sets of metabolic profiles were first processed and analyzed independently and were subsequently integrated using statistical correlation methods in order to facilitate cross assignment of metabolites. Using this approach, metabolites such as 3-ureidopropionate, p-cresol glucuronide, phenylacetylglycine, indoxyl sulfate, isocitrate, and trimethylamine were identified as differentiating between infected and control animals. These correlation analyses facilitated structural elucidation using the identification power of one technique to enhance and validate the other, but also highlighted the enhanced ability to detect functional correlations between metabolites, thereby providing potential for achieving deeper mechanistic insight into the biological process.

Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology

BACKGROUND

Metabolic phenotyping of humans allows information to be captured on the interactions between dietary, xenobiotic, other lifestyle and environmental exposures, and genetic variation, which together influence the balance between health and disease risks at both individual and population levels.

OBJECTIVES

We describe here the main procedures in large-scale metabolic phenotyping and their application to metabolome-wide association (MWA) studies.

METHODS

By use of high-throughput technologies and advanced spectroscopic methods, application of metabolic profiling to large-scale epidemiologic sample collections, including metabolome-wide association (MWA) studies for biomarker discovery and identification.

DISCUSSION

Metabolic profiling at epidemiologic scale requires optimization of experimental protocol to maximize reproducibility, sensitivity, and quantitative reliability, and to reduce analytical drift. Customized multivariate statistical modeling approaches are needed for effective data visualization and biomarker discovery with control for false-positive associations since 100s or 1,000s of complex metabolic spectra are being processed.

CONCLUSION

Metabolic profiling is an exciting addition to the armamentarium of the epidemiologist for the discovery of new disease-risk biomarkers and diagnostics, and to provide novel insights into etiology, biological mechanisms, and pathways.

Serum S100A6 concentration predicts peritoneal tumor burden in mice with epithelial ovarian cancer and is associated with advanced stage in patients

BACKGROUND

Ovarian cancer is the 5th leading cause of cancer related deaths in women. Five-year survival rates for early stage disease are greater than 94%, however most women are diagnosed in advanced stage with 5 year survival less than 28%. Improved means for early detection and reliable patient monitoring are needed to increase survival.

METHODOLOGY AND PRINCIPAL FINDINGS

Applying mass spectrometry-based proteomics, we sought to elucidate an unanswered biomarker research question regarding ability to determine tumor burden detectable by an ovarian cancer biomarker protein emanating directly from the tumor cells. Since aggressive serous epithelial ovarian cancers account for most mortality, a xenograft model using human SKOV-3 serous ovarian cancer cells was established to model progression to disseminated carcinomatosis. Using a method for low molecular weight protein enrichment, followed by liquid chromatography and mass spectrometry analysis, a human-specific peptide sequence of S100A6 was identified in sera from mice with advanced-stage experimental ovarian carcinoma. S100A6 expression was documented in cancer xenografts as well as from ovarian cancer patient tissues. Longitudinal study revealed that serum S100A6 concentration is directly related to tumor burden predictions from an inverse regression calibration analysis of data obtained from a detergent-supplemented antigen capture immunoassay and whole-animal bioluminescent optical imaging. The result from the animal model was confirmed in human clinical material as S100A6 was found to be significantly elevated in the sera from women with advanced stage ovarian cancer compared to those with early stage disease.

CONCLUSIONS

S100A6 is expressed in ovarian and other cancer tissues, but has not been documented previously in ovarian cancer disease sera. S100A6 is found in serum in concentrations that correlate with experimental tumor burden and with clinical disease stage. The data signify that S100A6 may prove useful in detecting and/or monitoring ovarian cancer, when used in concert with other biomarkers.

1H NMR metabolomics study of age profiling in children

Metabolic profiling of urine provides a fingerprint of personalized endogenous metabolite markers that correlate to a number of factors such as gender, disease, diet, toxicity, medication, and age. It is important to study these factors individually, if possible to unravel their unique contributions. In this study, age-related metabolic changes in children of age 12 years and below were analyzed by (1)H NMR spectroscopy of urine. The effect of age on the urinary metabolite profile was observed as a distinct age-dependent clustering even from the unsupervised principal component analysis. Further analysis, using partial least squares with orthogonal signal correction regression with respect to age, resulted in the identification of an age-related metabolic profile. Metabolites that correlated with age included creatinine, creatine, glycine, betaine/TMAO, citrate, succinate, and acetone. Although creatinine increased with age, all the other metabolites decreased. These results may be potentially useful in assessing the biological age (as opposed to chronological) of young humans as well as in providing a deeper understanding of the confounding factors in the application of metabolomics.

Proteomics of rat prostate lobes treated with 2-N-hydroxylamino-1-methyl-6-phenylimidazo[4,5-b]pyridine, 5alpha-dihydrotestosterone, individually and in combination

Epidemiological and preclinical studies suggest that environmental factors, hormonal responses and lifestyle, including diet and physical inactivity, are likely contributors to the initiation and progression of prostate cancer in humans. Although the effects of the food derived carcinogen 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) and/or testosterone (T) in the development of prostate cancer in the rat have been reported, the extent to which such compounds impact cancer related proteins is not clear. Knowledge of cancer-related proteins impacted by PhIP and/or T is prerequisite to developing novel strategies to early-detect prostate cancer. Male F344 rats were sacrificed, the prostate tissue isolated and separated into dorsolateral, ventral, and anterior lobes. The lobes were cultured and treated with 10(-3) M NHPhIP and/or 10(-7) M DT for 24 h. NHPhIP is the genotoxic form of PhIP and DT is the more proliferative form of T. We used 2D-DIGE and LC/MS/MS technologies to study the proteome of the prostate lobes to determine if the compounds will trigger detectable changes in expression of cancer-related proteins. Analysis of the signals from 2D-DIGE revealed that about 10% of proteins were differentially expressed in the NHPhIP and/or DT treatments compared to controls. Eight candidate protein spots detected by 2D-DIGE in at least two out of three lobes showed > or =2-fold difference between treated and control samples. Five out of the eight spots contained single proteins; including, phospholipase Calpha (PLP-Calpha), Rab7, SAR1a, ribosomal protein S7 (RPS7), and nucleoside diphosphate kinase (NDPK). A survey of the literature shows that NDPK expression is altered in human cancers, including prostate cancer. Thus, we validated the altered expression of NDPK by Western blot analysis. The concordance between 2D-DIGE and Western blot analysis was 80%. The results of this study demonstrate, for the first time, that the combination of 2D-DIGE and LC/MS/MS is a powerful tool for identification of proteins in the prostate tissue that are altered by environmental carcinogens and/or hormones.

RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

Combining nuclear magnetic resonance spectroscopy and mass spectrometry in biomarker discovery

Metabolic profiling of biological specimens is emerging as a promising approach for discovering specific biomarkers in the diagnosis of a number of diseases. Amongst many analytical techniques, nuclear magnetic resonance spectroscopy and mass spectrometry are the most information-rich tools that enable high-throughput and global analysis of hundreds of metabolites in a single step. Although only one of the two techniques is utilized in a majority of metabolomics applications, there is a growing interest in combining the data from the two methods to effectively unravel the mammoth complexity of biological samples. In this article, current developments in nuclear magnetic resonance, mass spectrometry and multivariate statistical analysis methods are described. While some general applications that utilize the combination of the two analytical methods are presented briefly, the emphasis is laid on the recent applications of nuclear magnetic resonance and mass spectrometry methods in the studies of hepatopancreatobiliary and gastrointestinal malignancies.

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

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

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

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

Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen

Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

An integrated metabonomic method for profiling of metabolic changes in carbon tetrachloride induced rat urine

Carbon tetrachloride (CCl(4)) is a well-known model compound for inducing chemical hepatic injury. This work characterizes the metabolism disorders of hepatotoxicity induced by CCl(4) in a Wistar rat model with a single dosage of 1 ml/kg. A seven-day long continuous collection of urine was performed in male rats in this experiment. Blood biochemistry and histopathology were examined to identify specific changes of liver hepatotoxicity. At the same time, an integrated analytical approach based on liquid chromatography coupled with mass spectrometry (LC-MS) was developed to map the metabolic response in urine. The current metabonomic approach based on LC-MS indicated 23 endogenous metabolites as biomarkers in urine associated with the hepatotoxicity induced by CCl(4). The underlying regulations of CCl(4)-perturbed metabolic pathways were discussed according to the identified metabolites. The present study proves the great potential of LC-MS based metabonomics in mapping metabolic response for toxicology.

Metabolomic signatures of inbreeding at benign and stressful temperatures in Drosophila melanogaster

While the population genetics of inbreeding is fairly well understood, the effects of inbreeding on the physiological and biochemical levels are not. Here we have investigated the effects of inbreeding on the Drosophila melanogaster metabolome. Metabolite fingerprints in males from five outbred and five inbred lines were studied by nuclear magnetic resonance spectroscopy after exposure to benign temperature, heat stress, or cold stress. In both the absence and the presence of temperature stress, metabolite levels were significantly different among inbred and outbred lines. The major effect of inbreeding was increased levels of maltose and decreased levels of 3-hydroxykynurenine and a galactoside [1-O-(4-O-(2-aminoethyl phosphate)-beta-d-galactopyranosyl)-x-glycerol] synthesized exclusively in the paragonial glands of Drosophila species, including D. melanogaster. The metabolomic effect of inbreeding at the benign temperature was related to gene expression data from the same inbred and outbred lines. Both gene expression and metabolite data indicate that fundamental metabolic processes are changed or modified by inbreeding. Apart from affecting mean metabolite levels, inbreeding led to an increased between-line variation in metabolite profiles compared to outbred lines. In contrast to previous observations revealing interactions between inbreeding and environmental stress on gene expression patterns and life-history traits, the effect of inbreeding on the metabolite profile was similar across the different temperature treatments.

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

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

Discovering oxysterols in plasma: a window on the metabolome

While the proteome defines the expressed gene products, the metabolome results from reactions controlled by such gene products. Plasma represents an accessible "window" to the metabolome both in regard of availability and content. The wide range of the plasma metabolome, in terms of molecular diversity and abundance, makes its comprehensive analysis challenging. Here we demonstrate an analytical method designed to target one region of the metabolome, that is, oxysterols. Since the discovery of their biological activity as ligands to nuclear receptors there has been a reawakening of interest in oxysterols and their analysis. In addition, the oxysterols, 24S- and 27-hydroxycholesterol, are currently under investigation as potential biomarkers associated with neurodegenerative disorders such as Alzheimer's disease and multiple sclerosis; widespread analysis of these lipids in clinical studies will require the development of robust, sensitive and rapid analytical techniques. In this communication we present results of an investigation of the oxysterols content of human plasma using a newly developed high-performance liquid chromatography-mass spectrometry (HPLC-MS) method incorporating charge-tagging and high-resolution MS. The method has allowed the identification in plasma of monohydroxylated cholesterol molecules, 7alpha-, 24S-, and 27-hydroxycholesterol; the cholestenetriol 7alpha,27-dihydroxycholesterol; and 3beta-hydroxycholest-5-en-27-oic acid and its metabolite 3beta,7alpha-dihydroxycholest-5-en-27-oic acid. The methodology described is also applicable for the analysis of other sterols in plasma, that is, cholesterol, 7-dehydrocholesterol, and desmosterol, as well as cholesterol 5,6- seco-sterols and steroid hormones. Although involving derivatization, sample preparation is straightforward and chromatographic analysis rapid (17 min), while the MS method offers high sensitivity (ng/mL of sterol in plasma, or pg on-column) and specificity. The methodology is suitable for targeted metabolomic analysis of sterols, oxysterols, and steroid hormones opening a "window" to view this region of the metabolome.

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

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

Using extracellular biomarkers for monitoring efficacy of therapeutics in cancer patients: an update

Rapidly detectable and easily accessible markers of tumor cell death are needed for evaluating early therapeutic efficacy for immunotherapy and chemotherapy so that patients and their physicians can decide whether to remain with a given therapeutic strategy. Currently, image-based tests such as computed tomography scans and magnetic resonance imaging are used to visualize the response of a patient's tumor, but often these evaluations are not conducted for weeks to months after treatment begins. While serum levels of secreted proteins such as carcinoembryonic antigen and prostate specific antigen are commonly monitored to gauge tumor status during therapy and between image evaluations, the levels of these proteins do not always correlate well with the actual tumor response. In laboratory studies, it has been shown that tumor cells undergoing apoptosis can release cellular components into cell culture media such as cytochrome c, nucleosomes, cleaved cytokeratin-18 and E-cadherin. Studies of patient sera have found that these and other macromolecules can be found in circulation during cancer therapy, providing a potential source of material for monitoring treatment efficacy. In the future, analysis of biofluids from severe combined immunodeficiency mice bearing patient tumor specimens treated with a targeted therapy such as Apo2L/tumor necrosis factor-related apoptosis-inducing ligand will be useful in the preclinical identification of therapy response markers. In this review, the current status of the identification of serum markers of tumor cell apoptosis is provided, as well as a discussion of critical research questions that must be addressed and the considerations necessary when identifying a marker that reflects true clinical outcome.

Global expression profiling in leaves of free-growing aspen

BACKGROUND

Genomic studies are routinely performed on young plants in controlled environments which is very different from natural conditions. In reality plants in temperate countries are exposed to large fluctuations in environmental conditions, in the case of perennials over several years. We have studied gene expression in leaves of a free-growing aspen (Populus tremula) throughout multiple growing seasons

RESULTS

We show that gene expression during the first month of leaf development was largely determined by a developmental program although leaf expansion, chlorophyll accumulation and the speed of progression through this program was regulated by the temperature. We were also able to define "transcriptional signatures" for four different substages of leaf development. In mature leaves, weather factors were important for gene regulation.

CONCLUSION

This study shows that multivariate methods together with high throughput transcriptional methods in the field can provide additional, novel information as to plant status under changing environmental conditions that is impossible to mimic in laboratory conditions. We have generated a dataset that could be used to e.g. identify marker genes for certain developmental stages or treatments, as well as to assess natural variation in gene expression.

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

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

Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics

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

Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data

The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.

Nutrigenomic approaches for benefit-risk analysis of foods and food components: defining markers of health

To be able to perform a comprehensive and rigorous benefit-risk analysis of individual food components, and of foods, a number of fundamental questions need to be addressed first. These include whether it is feasible to detect all relevant biological effects of foods and individual food components, how such effects can confidently be categorised into benefits and risks in relation to health and, for that matter, how health can be quantified. This article examines the last of these issues, focusing upon concepts for the development of new biomarkers of health. Clearly, there is scope for refinement of classical biomarkers so that they may be used to detect even earlier signs of disease, but this approach defines health solely as the absence of detectable disease or disease risk. We suggest that the health of a biological system may better be reflected by its ability to withstand and manage relevant physiological challenges so that homeostasis is maintained. We discuss the potential for expanding the range of current challenge tests for use in conjunction with functional genomic technologies to develop new types of biomarkers of health.

High-precision frequency measurements: indispensable tools at the core of the molecular-level analysis of complex systems

This perspective article provides an assessment of the state-of-the-art in the molecular-resolution analysis of complex organic materials. These materials can be divided into biomolecules in complex mixtures (which are amenable to successful separation into unambiguously defined molecular fractions) and complex nonrepetitive materials (which cannot be purified in the conventional sense because they are even more intricate). Molecular-level analyses of these complex systems critically depend on the integrated use of high-performance separation, high-resolution organic structural spectroscopy and mathematical data treatment. At present, only high-precision frequency-derived data exhibit sufficient resolution to overcome the otherwise common and detrimental effects of intrinsic averaging, which deteriorate spectral resolution to the degree of bulk-level rather than molecular-resolution analysis. High-precision frequency measurements are integral to the two most influential organic structural spectroscopic methods for the investigation of complex materials-NMR spectroscopy (which provides unsurpassed detail on close-range molecular order) and FTICR mass spectrometry (which provides unrivalled resolution)-and they can be translated into isotope-specific molecular-resolution data of unprecedented significance and richness. The quality of this standalone de novo molecular-level resolution data is of unparalleled mechanistic relevance and is sufficient to fundamentally advance our understanding of the structures and functions of complex biomolecular mixtures and nonrepetitive complex materials, such as natural organic matter (NOM), aerosols, and soil, plant and microbial extracts, all of which are currently poorly amenable to meaningful target analysis. The discrete analytical volumetric pixel space that is presently available to describe complex systems (defined by NMR, FT mass spectrometry and separation technologies) is in the range of 10(8-14) voxels, and is therefore capable of providing the necessary detail for a meaningful molecular-level analysis of very complex mixtures. Nonrepetitive complex materials exhibit mass spectral signatures in which the signal intensity often follows the number of chemically feasible isomers. This suggests that even the most strongly resolved FTICR mass spectra of complex materials represent simplified (e.g. isomer-filtered) projections of structural space.

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

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

Statistical Correlation and Projection Methods for Improved Information Recovery from Diffusion-Edited NMR Spectra of Biological Samples

Although NMR spectroscopic techniques coupled with multivariate statistics can yield much useful information for classifying biological samples based on metabolic profiles, biomarker identification remains a time-consuming and complex procedure involving separation methods, two-dimensional NMR, and other spectroscopic tools. We present a new approach to aid complex biomixture analysis that combines diffusion ordered (DO) NMR spectroscopy with statistical total correlation spectroscopy (STOCSY) and demonstrate its application in the characterization of urinary biomarkers and enhanced information recovery from plasma NMR spectra. This method relies on calculation and display of the covariance of signal intensities from the various nuclei on the same molecule across a series of spectra collected under different pulsed field gradient conditions that differentially attenuate the signal intensities according to translational molecular diffusion rates. We term this statistical diffusion-ordered spectroscopy (S-DOSY). We also have developed a new visualization tool in which the apparent diffusion coefficients from DO spectra are projected onto a 1D NMR spectrum (diffusion-ordered projection spectroscopy, DOPY). Both methods either alone or in combination have the potential for general applications to any complex mixture analysis where the sample contains compounds with a range of diffusion coefficients.

Application of metabonomics in drug development

Metabolic profiling (metabonomics/metabolomics) is the untargeted analysis of metabolic composition in a biological sample, and is principally aimed at biomarker discovery. The frequent use of noninvasive biofluid analysis in metabonomics is suited to the clinic and facilitates dynamic monitoring. Analytical protocols for metabolic biomarkers are potentially robust because a metabolite is the same chemical entity irrespective of its origin, facilitating ‘bench-to-bedside’ translational research. Metabonomics can make an impact at several points in the drug-development process: target identification; lead discovery and optimization; preclinical efficacy and safety assessment; mode-of-action and mechanistic toxicology; patient stratification; and clinical pharmacological monitoring. This review describes and exemplifies the latest developments in each of these areas, including the impact of new data and chemical analytical techniques. The future goals for metabonomics are the validation of existing biomarkers, in terms of mechanism and translation to man, together with a focus on characterizing the individual (‘personalized healthcare’).

Combined proteomic and metabonomic studies in three genetic forms of the renal Fanconi syndrome

The renal Fanconi syndrome is a defect of proximal tubular function causing aminoaciduria and low-molecular-weight proteinuria. Dent's disease and Lowe syndrome are defined X-linked forms of Fanconi syndrome; there is also an autosomal dominant idiopathic form (ADIF), phenotypically similar to Dent's disease though its gene defect is still unknown. To assess whether their respective gene products are ultimately involved in a common reabsorptive pathway for proteins and low-molecular-mass endogenous metabolites, we compared renal Fanconi urinary proteomes and metabonomes with normal (control) urine using mass spectrometry and (1)H-NMR spectroscopy, respectively. Urine from patients with low-molecular-weight proteinuria secondary to ifosfamide treatment (tubular proteinuria; TP) was also analyzed for comparison. All four of the disorders studied had characteristic proteomic and metabonomic profiles. Uromodulin was the most abundant protein in normal urine, whereas Fanconi urine was dominated by albumin. (1)H-NMR spectroscopic data showed differences in the metabolic profiles of Fanconi urine vs. normal urine, due mainly to aminoaciduria. There were differences in the urinary metabolite and protein compositions between the three genetic forms of Fanconi syndrome: cluster analysis grouped the Lowe and Dent's urinary proteomes and metabonomes together, whereas ADIF and TP clustered together separately. Our findings demonstrate a distinctive "polypeptide and metabolite fingerprint" that can characterize the renal Fanconi syndrome; they also suggest that more subtle and cause-specific differences may exist between the different forms of Fanconi syndrome that might provide novel insights into the underlying mechanisms and cellular pathways affected.

Chemometrics in metabonomics

We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature.