Chemometrics in metabonomics

Metabolic alterations in the hamster co-infected with Schistosoma japonicum and Necator americanus

Co-infection with hookworm and schistosomes is a common phenomenon in sub-Saharan Africa, as well as in parts of South America and southeast Asia. As a first step towards understanding the metabolic response of a hookworm-schistosome co-infection in humans, we investigated the metabolic consequences of co-infection in an animal model, using a nuclear magnetic resonance (NMR)-based metabolic profiling technique, combined with multivariate statistical analysis. Urine and serum samples were obtained from hamsters experimentally infected with 250 Necator americanus infective L(3) and 100 Schistosoma japonicum cercariae simultaneously. In the co-infection model, similar worm burdens were observed as reported for single infection models, whereas metabolic profiles of co-infection represented a combination of the altered metabolite profiles induced by single infections with these two parasites. Consistent differences in metabolic profiles between the co-infected and non-infected control hamsters were observed from 4 weeks p.i. onwards. The predominant metabolic alterations in co-infected hamsters consisted of depletion of amino acids, tricarboxylic acid cycle intermediates (e.g. citrate and succinate) and glucose. Moreover, alterations of a series of gut microbial-related metabolites, such as decreased levels of hippurate, 3-hydroxyphenylpropionic acid, 4-hydroxyphenylpropionic acid and trimethylamine-N-oxide, and increased concentrations of 4-cresol glucuronide and phenylacetylglycine were associated with co-infection. Our results provide a first step towards understanding the metabolic response of an animal host to multiple parasitic infections.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Metabolic profiling of intracellular metabolites in fermentation broths from beta-lactam antibiotics production by liquid chromatography-tandem mass spectrometry methods

An analytical platform comprising three LC-ESI-MS/MS methods is presented for qualitative and quantitative profiling of more than 200 intracellular metabolites. Employing a silica based zwitterionic stationary phase in the HILIC mode, in total 223 hydrophilic metabolites can be determined. In particular, amino acids, organic acids as well as nucleotide sugars were found to be well separable and detectable under acidic mobile phase conditions, while in comparison especially phosphates such as nucleotides, coenzymes or sugar phosphates as well as sugars and sugar acids performed better at higher pH. Additionally, 21 less polar analytes turned out to be amenable for separation and analysis on a pentafluorophenyl modified silica stationary phase in RP mode. Solutes were detected by tandem mass spectrometry on a triple quadrupole instrument in the selected reaction monitoring (SRM) mode and specific SRM transitions for 258 metabolites are provided. All three methods were validated with respect to the limit of quantification, linear dynamic range, precision and accuracy. Applicability of the analytical platform was evaluated by analysis of the targeted metabolites in extracts of beta-lactam antibiotics fermentation broths. Thereby, 87 metabolites were determined qualitatively in penicillin fermentation broths, and 94 compounds were found in cephalosporin extracts. In addition, a number of selected metabolites that can be determined by at least two of the presented LC-MS/MS methods was analyzed quantitatively by both, external calibration using pure standards as well as by matrix-matched calibration performing standard addition. Quantitative results obtained with the different methods agreed well, however, for some analytes external calibration was found to be ill-suited due to matrix effects.

Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach.

MS-based Plant Metabolomic Approaches for Biomarker Discovery

Metabolomics, which aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples, is playing an increasingly important role in plant science. Various biological issues have been successfully studied by this holistic approach that includes global metabolite composition assessment, mutant characterization, taxonomy, developmental processes, stress response, interaction with environment, quality control assessment and mode of action of herbal medicine. This review summarizes the main mass spectrometry methods used for performing these studies and discusses the potential, but also the current limitations of the various approaches. The intention is not to cover exhaustively the field, which has considerably grown over about one decade, but to give a brief insight into the methods commonly employed and discuss some applications that reveal the potential of metabolomics in phytochemistry and systems biology.

The influence of EDTA and citrate anticoagulant addition to human plasma on information recovery from NMR-based metabolic profiling studies

The widely-used blood anticoagulants citrate and EDTA give rise to prominent peaks in (1)H NMR spectra of plasma samples collected in epidemiological and clinical studies, and these cause varying levels of interference in recovering biochemical information on endogenous metabolites. To investigate both the potential metabolic information loss caused by these substances and any possible inter-molecular interactions between the anticoagulants and endogenous components, the (1)H NMR spectra of 40 split human plasma samples collected from 20 individuals into either citrate or EDTA have been analysed. Endogenous metabolite peaks were selectively obscured by large citrate peaks or those from free EDTA and its calcium and magnesium complexes. It is shown that the endogenous metabolites that give rise to peaks obscured by those from EDTA or citrate almost invariably also have other resonances that allow their identification and potential quantitation. Also, metabolic information recovery could be maximised by use of spectral editing techniques such as spin-echo, diffusion-editing and J-resolved experiments. The NMR spectral effects of any interactions between the added citrate or EDTA and endogenous components were found to be negligible. Finally, identification of split samples was feasible using simple multivariate statistical approaches such as principal components analysis. Thus even when legacy epidemiological plasma samples have been collected using the NMR-inappropriate citrate or EDTA anticoagulants, useful biochemical information can still be recovered effectively.

Analytical and statistical approaches to metabolomics research

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.

Quality assessment of ginseng by (1)H NMR metabolite fingerprinting and profiling analysis

Metabolite profiling and fingerprint analysis by (1)H NMR spectroscopy were used to identify potential biomarkers capable of distinguishing different ginseng species, varieties, and commercial products with the aim of establishing quality control code protocol based on biochemical phenotype. Principal component (PC) analyses of (1)H NMR spectra reliably discriminated between the various ginseng samples, demonstrating the potential utility of metabolomics in the natural health products industry. Four Asian ginseng varieties separated along the PC1 and PC2 axes, and four different Korean ginseng products were divided into two groups by PC1. A strong separation was also revealed between Asian ginseng (Panax ginseng) and American ginseng (Panax quinquefolius). Glutamine, arginine, sucrose, malate, and myo-inositol were the major metabolites in ginseng samples tested in this study. Combined metabolite fingerprinting and profiling suggested that several compounds including glucose, fumarate, and various amino acids could serve as biomarkers for quality assurance in ginseng.

Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines

Background

The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.

Results

In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.

Conclusion

Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.

Evaluating low-intensity unknown signals in quantitative proton NMR mixture analysis

Analytical analyses of highly complex mixtures, such as biofluids or liquid food products, often give rise to signals for unknown compounds, particularly for compounds at low concentration. Here we compare two conventional chemometric approaches for NMR spectral analysis ("spectral binning" and "high-resolution analysis") with a novel library-based method ("targeted profiling of unknowns", TPU). The three methods were applied to a proton NMR spectral data set of ultrafiltered mouse serum typical of those examined in metabolomics/metabonomics studies. The advantages of high-resolution analysis of typical NMR peaks have been well described previously, and as a result we examined low intensity unknowns peaks (LIUPs). A total of 25 LIUPs were assessed based on their significance to multivariate statistical analysis of the data set using the TPU method. The linearity of NMR signals at low incremental concentration changes (< 10 microM) was determined by titration of endogenously occurring metabolites into filtered mouse serum. Carbon-13 decoupling of the NMR spectra was used to ensure isotope-satellite peaks were eliminated. Four peaks were noted as significant to separation between arthritic and diseased animals. The conventional spectral methods were hampered by baseline noise or overlap with high concentration metabolites and were not able to identify these LIUPs reliably. In general, conventional methods, particularly high-resolution analysis, are recommended for peaks with moderate signal-to-noise. The TPU method is recommended for peaks with low signal-to-noise or when compression of spectral data with high fidelity is desirable, such as integration of NMR data into cross-platform studies.

Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism

We provide a demonstration in humans of the principle of pharmacometabonomics by showing a clear connection between an individual's metabolic phenotype, in the form of a predose urinary metabolite profile, and the metabolic fate of a standard dose of the widely used analgesic acetaminophen. Predose and postdose urinary metabolite profiles were determined by (1)H NMR spectroscopy. The predose spectra were statistically analyzed in relation to drug metabolite excretion to detect predose biomarkers of drug fate and a human-gut microbiome cometabolite predictor was identified. Thus, we found that individuals having high predose urinary levels of p-cresol sulfate had low postdose urinary ratios of acetaminophen sulfate to acetaminophen glucuronide. We conclude that, in individuals with high bacterially mediated p-cresol generation, competitive O-sulfonation of p-cresol reduces the effective systemic capacity to sulfonate acetaminophen. Given that acetaminophen is such a widely used and seemingly well-understood drug, this finding provides a clear demonstration of the immense potential and power of the pharmacometabonomic approach. However, we expect many other sulfonation reactions to be similarly affected by competition with p-cresol and our finding also has important implications for certain diseases as well as for the variable responses induced by many different drugs and xenobiotics. We propose that assessing the effects of microbiome activity should be an integral part of pharmaceutical development and of personalized health care. Furthermore, we envisage that gut bacterial populations might be deliberately manipulated to improve drug efficacy and to reduce adverse drug reactions.

Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry

The prognosis for oesophageal cancer is poor. Attempts have been made for the identification of biomarkers for early diagnosis. Metabolomic panel has been evaluated as potential candidate biomarkers. With gas chromatography/mass spectrometry (GC/MS) as a sensitive modality for metabolomics, various tissue metabolites can be detected and identified. We hypothesized that tissue metabolomic biomarkers may be identifiable and diagnostically useful for oesophageal cancer. We present a metabolomic method of chemical derivatization followed by GC/MS to analyze the metabolic difference in biopsied specimens between oesophageal cancer and corresponding normal mucosae obtained from 20 oesophageal cancer patients. The GC/MS data was analyzed using a two sample t-test to explore the potential metabolic biomarkers for oesophageal cancer. A diagnostic model was constructed to discriminate normal from malignant samples, using principal component analysis (PCA) and receiver-operating characteristic (ROC) curves. t-Test showed a total of 20 marker metabolites detected were found to be different with statistical significance (P<0.05). The multivariate logistic analysis yielded a complete distinction between the two groups. The diagnostic model could discriminate tumors from normal mucosae with an area under the curve (AUC) value of 1. Our findings suggest that this assay may potentially provide a new metabolomic biomarker for oesophageal cancer.

Panorganismal metabolic response modeling of an experimental Echinostoma caproni infection in the mouse

Metabolic profiling of host tissues and biofluids during parasitic infections can reveal new biomarker information and aid the elucidation of mechanisms of disease. The multicompartmental metabolic effects of an experimental Echinostoma caproni infection have been characterized in 12 outbred female mice infected orally with 30 E. caproni metacercariae each, using a further 12 uninfected animals as a control group. Mice were killed 36 days postinfection and brain, intestine (colon, ileum, jejeunum), kidney, liver, and spleen were removed. Metabolic profiles of tissue samples were measured using high-resolution magic angle spinning (1)H NMR spectroscopy and biofluids measured by applying conventional (1)H NMR spectroscopy. Spectral data were analyzed via principal component analysis, partial least-squares-derived methods and hierarchical projection analyses. Infection-induced metabolic changes in the tissues were correlated with altered metabolite concentrations in the biofluids (urine, plasma, fecal water) using hierarchical modeling and correlation analyses. Metabolic descriptors of infection were identified in liver, renal cortex, intestinal tissues but not in spleen, brain or renal medulla. The main physiological change observed in the mouse was malabsorption in the small intestine, which was evidenced by decreased levels of various amino acids in the ileum, for example, alanine, taurine, glutamine, and branched chain amino acids. Furthermore, altered gut microbial activity or composition was reflected by increased levels of trimethylamine in the colon. Our modeling approach facilitated in-depth appraisal of the covariation of the metabolic profiles of different biological matrices and found that urine and plasma most closely reflected changes in ileal compartments. In conclusion, an E. caproni infection not only results in direct localized (ileum and jejenum) effects, but also causes remote metabolic changes (colon and several peripheral organs), and therefore describes the panorganismal metabolic response of the infection.

Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling

Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in (1)H NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.

High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism

A high-throughput proton (1H) nuclear magnetic resonance (NMR) metabonomics approach is introduced to characterise systemic metabolic phenotypes. The methodology combines two molecular windows that contain the majority of the metabolic information available by 1H NMR from native serum, e.g. serum lipids, lipoprotein subclasses as well as various low-molecular-weight metabolites. The experimentation is robotics-controlled and fully automated with a capacity of about 150-180 samples in 24 h. To the best of our knowledge, the presented set-up is unique in the sense of experimental high-throughput, cost-effectiveness, and automated multi-metabolic data analyses. As an example, we demonstrate that the NMR data as such reveal associations between systemic metabolic phenotypes and the metabolic syndrome (n = 4407). The high-throughput of up to 50,000 serum samples per year is also paving the way for this technology in large-scale clinical and epidemiological studies. In contradiction to single 'biomarkers', the application of this holistic NMR approach and the integrated computational methods provides a data-driven systems biology approach to biomedical research.

Challenges in applying chemometrics to LC–MS-based global metabolite profile data

Metabolite profiling can provide insights into the metabolic status of complex living systems through the non-targeted analysis of metabolites in any biological sample. Metabolite profiling is complementary to genomics, transcriptomics and proteomics, and its applications span epidemiology, disease diagnosis, nutrition, pharmaceutical research, and toxicology. Metabolic phenotypes are a reflection of an organism’s environment, lifestyle, diet, gut microfloral composition and are also influenced by genetic factors, with important implications in genome-wide-association studies. Specialized analytical platforms, such as NMR spectroscopy and MS, are required to interrogate such metabolic complexity. The increased sophistication of such techniques has lead to a demand for improved data analysis approaches, including preprocessing and advanced chemometric techniques. This article discusses data generation, preprocessing, multivariate analysis and data interpretation for LC-MS-based metabolite profiling, focusing on challenges encountered and potential solutions.

Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometry

With the technique of metabolomics, gas chromatography/mass spectrometry (GC/MS), urine or serum metabolites can be assayed to explore disease biomarkers. In this work, we present a metabolomic method to investigate the urinary metabolic difference between hepatocellular carcinoma (HCC, n - 20) male patients and normal male subjects (n - 20). The urinary endogenous metabolome was assayed using chemical derivatization followed by GC/MS. After GC/MS analysis, 103 metabolites were detected, of which 66 were annotated as known compounds. By a two sample t-test statistics with p < 0.05, 18 metabolites were shown to be significantly different between the HCC and control groups. A diagnostic model was constructed with a combination of 18 marker metabolites or together with alphafetoprotein, using principal component analysis and receiver-operator characteristic curves. The multivariate statistics of the diagnostic model yielded a separation between the two groups with an area under the curve value of 0.9275. This non-invasive technique of identifying HCC biomarkers from urine may have clinical utility.

Large-scale human metabolic phenotyping and molecular epidemiological studies via 1H NMR spectroscopy of urine: investigation of borate preservation

Borate is an antibacterial preservative widely used in clinical and large-scale epidemiological studies involving urine sample analysis. Since it readily forms covalent adducts and reversible complexes with hydroxyl and carboxylate groups, the effects of borate preservation in (1)H NMR-spectroscopy-based metabolic profiling of human urine samples have been assessed. Effects of various concentrations of borate (range 0-30 mM) on (1)H NMR spectra of urine were observed at sequential time points over a 12 month period. Consistent with known borate chemistry, the principal alterations in the (1)H resonance metabolite patterns were observed for compounds such as mannitol, citrate, and alpha-hydroxyisobutyrate and confirmed by ESI-MS analysis. These included line-broadening, T(1) and T(2) relaxation, and chemical shift changes consistent with complex formation and chemical exchange processes. To further investigate complexation behavior in the urinary metabolite profiles, a new tool for visualization of multicomponent relaxation variations in which the spectra were color-coded according to the T(1) and T(2) proton relaxation times respectively (T(1) or T(2) ordered projection spectroscopy, TOPSY) was also developed and applied. Addition of borate caused a general decrease in (1)H T(1) values, consistent with nonspecific effects such as solution viscosity changes. Minor changes in proton T(2) relaxation rates were observed for the most strongly complexing metabolites. From a molecular phenotyping and epidemiologic viewpoint, typical interpersonal biological variation was shown to be vastly greater than any variation introduced by the borate complexation, which had a negligible effect on the metabolic mapping and classification of samples. While caution is indicated in the assignment of biomarker signals where metabolites have diol groupings or where there are adjacent hydroxyl and carboxylate functions, it is concluded that borate preservation is "fit-for-purpose" for (1)H NMR-based epidemiological studies, since the essential biochemical classification features of the samples are robustly maintained.

Triple concentrated tomato paste: discrimination between Italian and Chinese products

(1)H NMR spectroscopy was applied to discriminate triple concentrated tomato paste coming from two different countries. Notwithstanding different tomato cultivars and ripening stages employed to obtain the final product, significant discrimination between Italian and Chinese samples was obtained by combining NMR data and principal component analysis. Supervised orthogonal projection to latent structure discriminant analysis (OPLS-DA) technique was used to build robust classification models, while S-plot was employed to identify statistically significant variables responsible for class separation. Citrate content resulted in being the most relevant chemical compound for Chinese and Italian sample differentiation. In order to highlight other compounds able to contribute to sample differentiation, citrate content was excluded, and a new classification model was built. This latter model indicated aspartate, glutamine, and sugars as important variables in sample differentiation.

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.

Individual variation in macronutrient regulation measured by proton magnetic resonance spectroscopy of human plasma

Proton nuclear magnetic resonance ((1)H-NMR) spectroscopy of plasma provides a global metabolic profiling method that shows promise for clinical diagnostics. However, cross-sectional studies are complicated by a lack of understanding of intraindividual variation, and this limits experimental design and interpretation of data. The present study determined the diurnal variation detected by (1)H NMR spectroscopy of human plasma. Data reduction methods revealed three time-of-day metabolic patterns, which were associated with morning, afternoon, and night. Major discriminatory regions for these time-of-day patterns included the various kinds of lipid signals (-CH(2)- and -CH(2)OCOR), and the region between 3 and 4 ppm heavily overlapped with amino acids that had alpha-CH and alpha-CH(2). The phasing and duration of time-of-day patterns were variable among individuals, apparently because of individual difference in food processing/digestion and absorption and clearance of macronutrient energy sources (fat, protein, carbohydrate). The times of day that were most consistent among individuals, and therefore most useful for cross-sectional studies, were fasting morning (0830-0930), postprandial afternoon (1430-1630), and nighttime samples (0430-0530). Importantly, the integrated picture of metabolism provided by (1)H-NMR spectroscopy of plasma suggests that this approach is suitable to study complex regulatory processes, including eating patterns/eating disorders, upper gastrointestinal functions (gastric emptying, pancreatic, biliary functions), and absorption/clearance of macronutrients. Hence, (1)H-NMR spectroscopy of plasma could provide a global metabolic tolerance test to assess complex processes involved in disease, including eating disorders and the range of physiological processes causing dysregulation of energy homeostasis.

Metabolic footprinting study of white spruce somatic embryogenesis using NMR spectroscopy

White spruce is an important commercial species for reforestation. The success in its propagation through somatic embryogenesis is well documented; however the physiological processes involved are poorly understood and remain unoptimized. The variable quality embryos generated in vitro from the same genotype suggest control at the protein and metabolite level. In order to probe metabolic changes, we have conducted a "metabolic footprinting" study, whereby culture media from growing cells was quantitatively analyzed to determine which metabolites were consumed and excreted. Such experiments are advantageous in that there is no need to quench cellular metabolism or extract intracellular metabolites through time-consuming protocols. In this paper we demonstrate the application of the footprinting assay to somatic embryo cells of white spruce (Picea glauca) using 1D (1)H NMR spectroscopy. We have surveyed embryogenesis metabolism in two types of media, maintenance (MN) and maturation (MT). MN medium does not result in shoot apical meristem (SAM) formation, while MT medium induces the necessary changes leading to fully developed somatic embryos. The two types of media were easily distinguished using metabolomics analysis, namely multivariate pattern recognition statistics (orthogonal partial least squares discriminatory analysis). From this analysis, we have identified numerous compounds involved with branched chain amino acid pathways such as valine and isoleucine. These results are explained on the basis of known metabolic pathways implicated in plant and animal developmental processes, and ultimately implicate altered CoA biosynthesis.

[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.

A multi-gene analysis strategy identifies metabolic pathways targeted by trans-10, cis-12-conjugated linoleic acid in the liver of hamsters

In mice, hepatic functions can be greatly affected by dietary trans-10, cis-12-conjugated linoleic acid (CLA). However, this phenomenon has been less documented in hamsters. In the present study, male hamsters were fed two doses of the trans-10, cis-12-CLA (0.5 and 1%, w/w diet) or linoleic acid (0.5%) for 6 weeks. The effects on the liver were examined by measuring the expression of thirty-six genes representing key metabolic pathways. CLA-responsive genes and their relationships with physiological outcomes were examined by a multivariate analysis procedure. Compared with control hamsters, those receiving either 0.5 or 1% CLA exhibited similar fat loss (15-24%; P < or = 0.05) and liver enlargement (21-28%; P < or = 0.05), with no signs of steatosis. We also observed a dose-dependent increase in the transcription of genes involved in lipid breakdown and lipid harvesting from blood, and in genes related to the oxidative stress and inflammatory responses. These responsive genes varied in parallel with cell membrane lipids (R2 0.31-0.42) and to a lesser extent with liver enlargement (R2 0.22) (all P < 0.05). We conclude that in hamsters, liver enlargement induced by trans-10, cis-12-CLA is accompanied by an increased metabolic potential to process fatty acids from mobilised adipose stores. This elevated metabolic activity, comprised of anabolic pathways and their catabolic counterparts, can trigger inflammation and the oxidant stress defence pathways in a dose-dependent manner. These results provide novel insights into the mechanisms by which trans-10, cis-12-CLA affects pathways related to liver function.

Automated molecular formula determination by tandem mass spectrometry (MS/MS)

Automated software was developed to analyze the molecular formula of organic molecules and peptides based on high-resolution MS/MS spectroscopic data. The software was validated with 96 compounds including a few small peptides in the mass range of 138-1569 Da containing the elements carbon, hydrogen, nitrogen and oxygen. A Micromass Waters Q-TOF Ultima Global mass spectrometer was used to measure the molecular masses of precursor and fragment ions. Our software assigned correct molecular formulas for 91 compounds, incorrect molecular formulas for 3 compounds, and no molecular formula for 2 compounds. The obtained 95% success rate indicates high reliability of the software. The mass accuracy of the precursor ion and the fragment ions, which is critical for the success of the analysis, was high, i.e. the accuracy and the precision of 850 data were 0.0012 Da and 0.0016 Da, respectively. For the precursor and fragment ions below 500 Da, 60% and 90% of the data showed accuracy within < or = 0.001 Da and < or = 0.002 Da, respectively. The precursor and fragment ions above 500 Da showed slightly lower accuracy, i.e. 40% and 70% of them showed accuracy within < or = 0.001 Da and < or = 0.002 Da, respectively. The molecular formulas of the precursor and the fragments were further used to analyze possible mass spectrometric fragmentation pathways, which would be a powerful tool in structural analysis and identification of small molecules. The method is valuable in the rapid screening and identification of small molecules such as the dereplication of natural products, characterization of drug metabolites, and identification of small peptide fragments in proteomics. The analysis was also extended to compounds that contain a chlorine or bromine atom.

Metabolomic investigation into variation of endogenous metabolites in professional athletes subject to strength-endurance training

Strength-endurance type of sport can lead to modification of human beings' physiological status. The present study aimed to investigate the alteration of metabolic phenotype or biochemical compositions in professional athletes induced by long-term training by means of a novel systematic tool, metabolomics. Resting venous blood samples of junior and senior male rowers were obtained before and after 1-wk and 2-wk training. Venous blood from healthy male volunteers as control was also sampled at rest. Endogenous metabolites in serum were profiled by GC/TOF-MS and multivariate statistical technique, i.e., principal component analysis (PCA), and partial least squares projection to latent structures and discriminant analysis (PLS-DA) were used to process the data. Significant metabolomic difference was observed between the professional athletes and control subjects. Long-term strength and endurance training induced distinct separation between athletes of different exercise seniority, and training stage-related trajectory of the two groups of athletes was clearly shown along with training time. However, most of these variations were not observed by common biochemical parameters, such as hemoglobin, testosterone, and creatine kinase. The identified metabolites contributing to the classification included alanine, lactate, β-d-methylglucopyranoside, pyroglutamic acid, cysteine, glutamic acid, citric acid, free fatty acids, valine, glutamine, phenylalanine, tyrosine, and so on, which were involved in glucose metabolism, oxidative stress, energy metabolism, lipid metabolism, amino acid metabolism. These findings suggest that metabolomics is a promising and potential tool to profile serum of professional athletes, make a deep insight into physiological states, and clarify the disorders induced by strength-endurance physical exercise.

Integrated analysis of serum and liver metabonome in liver transplanted rats by gas chromatography coupled with mass spectrometry

In this paper, we present a metabonomic method for the investigation of abnormal metabolic process in both serum and liver tissue of liver transplanted rats. Syngeneic transplantation was performed on male Lewis rats. The serum and grafted liver on day 1, 3, and 7 post-transplant were collected to analyze endogenous metabolites using gas chromatography coupled with mass spectrometry (GC-MS). The method was validated with acceptable linearity, precision, and repeatability. Thirty-four metabolites in serum and 29 metabolites in liver were identified. Results of correlation analysis illustrated metabolites with similar function exhibited similar variations in liver and serum. The data processed by principle component analysis (PCA) showed time-dependent biochemical variations. As a consequence, the present study may offer specific putative pathways in the pathophysiological mechanism of orthotopic liver transplantation.

Metabolic profiling studies on the toxicological effects of realgar in rats by (1)H NMR spectroscopy

The toxicological effects of realgar after intragastrical administration (1 g/kg body weight) were investigated over a 21 day period in male Wistar rats using metabonomic analysis of (1)H NMR spectra of urine, serum and liver tissue aqueous extracts. Liver and kidney histopathology examination and serum clinical chemistry analyses were also performed. (1)H NMR spectra and pattern recognition analyses from realgar treated animals showed increased excretion of urinary Kreb's cycle intermediates, increased levels of ketone bodies in urine and serum, and decreased levels of hepatic glucose and glycogen, as well as hypoglycemia and hyperlipoidemia, suggesting the perturbation of energy metabolism. Elevated levels of choline containing metabolites and betaine in serum and liver tissue aqueous extracts and increased serum creatine indicated altered transmethylation. Decreased urinary levels of trimethylamine-N-oxide, phenylacetylglycine and hippurate suggested the effects on the gut microflora environment by realgar. Signs of impairment of amino acid metabolism were supported by increased hepatic glutamate levels, increased methionine and decreased alanine levels in serum, and hypertaurinuria. The observed increase in glutathione in liver tissue aqueous extracts could be a biomarker of realgar induced oxidative injury. Serum clinical chemistry analyses showed increased levels of lactate dehydrogenase, aspartate aminotransferase, and alkaline phosphatase as well as increased levels of blood urea nitrogen and creatinine, indicating slight liver and kidney injury. The time-dependent biochemical variations induced by realgar were achieved using pattern recognition methods. This work illustrated the high reliability of NMR-based metabonomic approach on the study of the biochemical effects induced by traditional Chinese medicine.

Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites

This paper presents a simple and reliable gas chromatography/mass spectrometry (GC/MS) method for the metabonomic analysis of human urine samples. The sample preparation involved the depletion of excess urea via treatment with urease and subsequent protein precipitation using ice-cold ethanol. An aliquot of the mixture was separated, dried, trimethylsilyl (TMS)-derivatized and 1.0 microL of the derivatized extract was injected into the GC/MS system via split injection (1:10). Approximately 150 putative metabolites belonging to different chemical classes were identified from the pooled human urine samples. All the identified metabolites were selected to evaluate precision and stability of the GC/MS assay. More than 95% of the metabolites demonstrated good reproducibility, with intra-day and inter-day precision values below 15%. Metabolic profiling of 53 healthy male and female urine samples in combination with pattern recognition techniques was performed to further validate the GC/MS metabolite profiling assay. Principal component analysis (PCA) followed by orthogonal partial least squares analysis (OPLS) revealed differences between urinary metabolite profiles of healthy male and female subjects. This validated GC/MS metabolic profiling method may be further applied to the metabonomic screening of urinary biomarkers in clinical studies.

1H NMR metabolite fingerprinting and metabolomic analysis of perchloric acid extracts from plant tissues

Metabolite fingerprinting provides a powerful method for discriminating between biological samples on the basis of differences in metabolism caused by such factors as growth conditions, developmental stage or genotype. This protocol describes a technique for acquiring metabolite fingerprints from samples of plant origin. The preferred method involves freezing the tissue rapidly to stop metabolism, extracting soluble metabolites using perchloric acid (HClO4) and then obtaining a fingerprint of the metabolic composition of the sample using 1D 1H NMR spectroscopy. The spectral fingerprints of multiple samples may be analyzed using either unsupervised or supervised multivariate statistical methods, and these approaches are illustrated with data obtained from the developing seeds of two genotypes of sunflower (Helianthus annuus). Preparation of plant extracts for analysis takes 2-3 d, but multiple samples can be processed in parallel and subsequent acquisition of NMR spectra takes approximately 30 min per sample, allowing 24-48 samples to be analyzed in a week.

Gas chromatography/time-of-flight mass spectrometry based metabonomic approach to differentiating hypertension- and age-related metabolic variation in spontaneously hypertensive rats

Metabonomics is a systematic approach to the study of in vivo metabolic profiles and therefore allows deep insight into and a better understanding of the pathogenesis of disease. To characterize the development of hypertension, a hypertensive animal model, the spontaneously hypertensive rat (SHR), and its normotensive control, the Wistar Kyoto (WKY) rat, were investigated and their blood plasma analyzed using the high-throughput metabolomic tool, gas chromatography/time-of-flight mass spectrometry (GC/TOFMS). A total of 187 peaks were quantitatively determined after deconvolution, and 78 of them were identified. Principal components analysis (PCA) and projection to latent structure partial least-squares discriminant analysis (PLS-DA) were used to process the GC/TOFMS data. The resulting mathematical models were further validated by cross-validation. Plasma compositional differences of many identified compounds showed hypertension-related variation between SHR and WKY rats, and age-related changes from 10 to 18 weeks for both the SHR and WKY rats. These compositional changes involved compounds such as hexadecanoic acid, linoleic acid, oleic acid, stearic acid, 3-hydroxybutyric acid, citric acid, threonic acid, tyrosine, tryptophan, threonine, phenylalanine, serine, ornithine, methionine, 3-hydroxyproline, creatinine, erythrose, myo-inositol, D-methylglucopyranoside, tocopherol, sitosterol, and nonesterified cholesterol. Significantly elevated free fatty acids (FFA) were observed in SHR relative to those in WKY rats, and their levels increased as the SHR aged from 10 to 18 weeks. The close correlation between FFA and hypertension suggests that FFA are potential biomarker candidates for hypertension and they may play an important role in the development of hypertension in SHR. It is also indicated that GC/TOFMS-based metabonomics is a powerful approach to identifying potential biomarkers and investigating the pathological processes of hypertension and the physiological developments of aging.