Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

Fast targeted multidimensional NMR metabolomics of colorectal cancer

The study of small molecules in body fluids has become an important tool to monitor the state of biological organisms. Applications range from model studies using cell lines to applications where human body fluids are used to monitor disease states or drug responses. NMR spectroscopy has been an important tool for metabolomics although severe overlap of signals has limited the number of compounds, which can be unambiguously identified and quantified. Therefore, deconvolution of NMR spectra is one of the greatest challenges for NMR-based metabolomics. This has commonly been achieved by using multidimensional spectra that have the disadvantage of requiring significantly longer acquisition times. Recently, a number of methods have been described to record NMR spectra much faster. Here, we explore the use of Hadamard-encoded TOCSY spectra to simultaneously select multiple lines from crowded NMR spectra of blood serum samples to acquire pseudo-two-dimensional spectra in minutes which would otherwise require many hours. The potential of this approach is demonstrated for the detection of a signature for colorectal cancer from human blood samples.

Ultra-high performance liquid chromatography-mass spectrometry for the metabolomic analysis of urine in colorectal cancer

We report here the results of a pilot study in which ultra-high performance liquid chromatography/time-of- flight-mass spectrometry (UPLC/TOF-MS) and multivariate statistical analysis (supervised partial least squares discriminant analysis, PLS-DA) were applied for urinary metabolite profiling and data interpretation. The results of the PLS-DA indicated that the metabolic pattern as a whole was significantly different between the groups of preoperative colorectal cancer (CRC) patients, postoperative CRC patients, and healthy volunteers, respectively. The preoperative group of patients showed significantly increased levels of low-molecular weight compounds (LMC) MW 283 and MW 234 in comparison to the group of healthy volunteers group. After the operation, the levels of these two LMC significantly decreased. These preliminary results suggest that the UPLC-MS-based method coupled with pattern recognition will likely lead to procedures with the potential to be clinically applicable for the diagnosis of CRC and, consequently, to an improvement in patient prognosis.

A sample preparation protocol for 1H nuclear magnetic resonance studies of water-soluble metabolites in blood and urine

We describe a general protocol for preparing protein-containing biofluids for (1)H nuclear magnetic resonance (NMR) metabolomic studies. In this protocol, untreated samples are diluted in deuterated solvents to precipitate proteins and recover metabolites quantitated relative to standard reference compounds such as 3-trimethylsilylpropionic acid (TSP) and 2,2-dimethyl-2-silapentane-5-sulfonic acid (DSS). The efficacy of this protocol was tested using a bovine serum albumin/metabolite mix and human serum samples. This sample preparation method can be readily applied to any protein-containing biofluid for (1)H NMR studies.

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.

Nontargeted urinary metabolite profiling of a mouse model of Crohn's disease

Crohn's disease is an inflammatory disorder of the bowel, believed to arise from the dysregulation of intestinal mucosal immunity. The interleukin-10-deficient (IL10-/-) mouse, which develops intestinal inflammation in the presence of gut microflora, serves as a mouse model of Crohn's disease. Nontargeted urinary metabolite profiling was carried out to identify systemic metabolic changes associated with the development of intestinal inflammation caused by IL10-deficiency. Spot urine samples, collected from IL10-/- and wildtype mice at ages 5.5, 7, 8.5, and 10.5 weeks old were analyzed by gas chromatography-mass spectrometry (GCMS). The data were analyzed using XCMS software, multiple t tests, and ANOVA. Among the key metabolic differences detected were elevated urinary levels of xanthurenic acid and fucose in IL10-/- mice relative to wildtype, indicating upregulation of tryptophan catabolism and perturbed fucosylation in IL10-/- mice. Three short-chain dicarboxylic acid metabolites were decreased in urine of IL10-/- mice relative to wildtype, suggesting the downregulation of fatty acid oxidation in IL10-/- mice. These metabolic differences were reproducible in an independent set of mice. This study demonstrates that nontargeted GCMS metabolite profiling of IL10-/- mice can provide insights into the metabolic effects of IL10-deficiency and identify potential markers of intestinal inflammation.

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.

An exploratory NMR nutri-metabonomic investigation reveals dimethyl sulfone as a dietary biomarker for onion intake

The metabolome following intake of onion by-products is evaluated. Thirty-two rats were fed a diet containing an onion by-product or one of the two derived onion by-product fractions: an ethanol extract and the residue. A 24 hour urine sample was analyzed using (1)H NMR spectroscopy in order to investigate the effects of onion intake on the rat metabolism. Application of interval extended canonical variates analysis (ECVA) proved to be able to distinguish between the metabolomic profiles from rats consuming normal feed and rats fed with an onion diet. Two dietary biomarkers for onion intake were identified as dimethyl sulfone and 3-hydroxyphenylacetic acid. The same two dietary biomarkers were subsequently revealed by interval partial least squares regression (PLS) to be perfect quantitative markers for onion intake. The best PLS calibration model yielded a root mean square error of cross-validation (RMSECV) of 0.97% (w/w) with only 1 latent variable and a squared correlation coefficient of 0.94. This indicates that urine from rats on the by-product diet, the extract diet, and the residue diet all contain the same dietary biomarkers and it is concluded that dimethyl sulfone and 3-hydroxyphenylacetic acid are dietary biomarkers for onion intake. Being able to detect specific dietary biomarkers is highly beneficial in the control of nutritionally enhanced functional foods.

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.

Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis

Background

Cellular hypoxia is a component of many diseases, but mechanisms of global hypoxic adaptation and resistance are not completely understood. Previously, a population of Drosophila flies was experimentally selected over several generations to survive a chronically hypoxic environment. NMR-based metabolomics, combined with flux-balance simulations of genome-scale metabolic networks, can generate specific hypotheses for global reaction fluxes within the cell. We applied these techniques to compare metabolic activity during acute hypoxia in muscle tissue of adapted versus "naïve" control flies.

Results

Metabolic profiles were gathered for adapted and control flies after exposure to acute hypoxia using ¹H NMR spectroscopy. Principal Component Analysis suggested that the adapted flies are tuned to survive a specific oxygen level. Adapted flies better tolerate acute hypoxic stress, and we explored the mechanisms of this tolerance using a flux-balance model of central metabolism. In the model, adapted flies produced more ATP per glucose and created fewer protons than control flies, had lower pyruvate carboxylase flux, and had greater usage of Complex I over Complex II.

Conclusion

We suggest a network-level hypothesis of metabolic regulation in hypoxia-adapted flies, in which lower baseline rates of biosynthesis in adapted flies draws less anaplerotic flux, resulting in lower rates of glycolysis, less acidosis, and more efficient use of substrate during acute hypoxic stress. In addition we suggest new specific hypothesis, which were found to be consistent with existing data.

Development and performance of a gas chromatography-time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum

A method for the preparation and GC-TOF-MS analysis of human serum samples has been developed and evaluated for application in long-term metabolomic studies. Serum samples were deproteinized using 3:1 methanol/serum, dried in a vacuum concentrator, and chemically derivatized in a two-stage process. Samples were analyzed by GC-TOF-MS with a 25 min analysis time. In addition, quality control (QC) samples were used to quantify process variability. Optimization of chemical derivatization was performed. Products were found to be stable for 30 h after derivatization. An assessment of within-day repeatability and within-week reproducibility demonstrates that excellent performance is observed with our developed method. Analyses were consistent over a 5 month period. Additional method testing, using spiked serum samples, showed the ability to define metabolite differences between samples from a population and samples spiked with metabolites standards. This methodology allows the continuous acquisition and application of data acquired over many months in long-term metabolomic studies, including the HUSERMET project (http://www.husermet.org/).

Detection of metabolic alterations in non-tumor gastrointestinal tissue of the Apc(Min/+) mouse by (1)H MAS NMR spectroscopy

In this study, we have used metabolic profiling (metabolomics/metabonomics) via high resolution magic angle spinning (HRMAS) and solution state (1)H NMR spectroscopy to characterize small bowel and colon tissue from the Apc(Min/+) mouse model of early gastrointestinal (GI) tumorigenesis. Multivariate analysis indicated the presence of metabolic differences between the morphologically normal/non-tumor tissue from approximately 10 week-old Apc(Min/+) mice and their wild-type litter mates. The metabolic profile of isolated lamina propria and epithelial cells from the same groups could also be discriminated on the basis of genotype. Accounting for systematic variation in individual metabolite levels across different anatomical regions of the lower GI tract, the metabolic phenotype of Apc(Min/+) lamina propria tissue was defined by significant increases in the phosphocholine/glycerophosphocholine ratio (PC/GPC, +21%) and decreases in GPC (-25%) and the gut-microbial cometabolite dimethylamine (DMA, -40%) relative to wild type. In the whole tissue, elevated lactate (+15%) and myo-inositol (+19%) levels were detected. As the metabolic changes occurred in non-tumor tissue from animals of very low tumor burden (<2 polyps/animal), they are likely to represent the specific consequence of reduced Apc function and very early events in tumorigenesis. The observed increase in PC/GPC ratio has been previously reported with immortalisation and malignant transformation of cells and is consistent with the role of Apc as a tumor suppressor. Phospholipase A2, which hydrolyses phosphatidylcholine to Acyl-GPC, is a known modifier gene of the model phenotype (Mom1), and altered expression of choline phospholipid enzymes has been reported in gut tissue from Apc(Min/+) mice. These results indicate the presence of a metabolic phenotype associated with "field cancerization", highlighting potential biomarkers for monitoring disease progression, for early evaluation of response to chemoprevention, and for predicting the severity of the polyposis phenotype in the Apc(Min/+) model.

Exhaled nitric oxide and other major exhaled compounds for the diagnosis of metabolic diseases

BACKGROUND

Many metabolic diseases including obesity, cardiovascular disease and diabetes share common pathogenetic pathways, which may involve chronic oxidative stress and inflammation. There is increasing evidence that assessment of biomarkers on exhaled gases or exhaled breath condensate may serve as a non-invasive tool to detect abnormalities in metabolic diseases mirroring increased in oxidative stress, systemic inflammation and endothelial dysfunction.

METHODS

Among commonly used exhaled biomarkers, nitric oxide (NO) on exhaled air and some constituents of exhaled breath condensate in volatile or non-volatile form may represent suitable markers. Nasal, bronchial and alveolar NO could be analyzed separately, with implications in the assessment of systemic disease and endothelial dysfunction. Moreover, the profiles of several exhaled gases have a place in phenotyping diabetic patients and their risk of complications. Accordingly, metabolomics of the airway fluid using exhaled breath condensate has recently confirmed the value of this biological matrix for the evaluation of both volatile and non-volatile biomarkers.

CONCLUSION

Normative studies for reference values are, however, lacking, and the influence of preanalytical variables on the methodology warrants further studies.

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.

1H NMR-based metabonomics for investigating diabetes

Diabetes is characterized by hyperglycemia due to dysfunction of insulin secretion or action. The two most common forms are Type 1 diabetes, in which pancreatic β-cells are destroyed, and Type 2 diabetes, in which a combination of disordered insulin action and secretion results in abnormal carbohydrate, lipid and protein metabolism. Metabonomics employs analytical technologies to measure ‘global’ metabolic responses to a disease state. With the aid of statistical pattern recognition, this can reveal novel insights into the biochemical consequences of diabetes. The metabonomic method can be divided into four stages: sample collection; preparation; data acquisition and processing; and statistical analyses. In this review, we describe the most recent developments at each experimental stage in detail, and comment on specific precautions or improvements that should be taken into account when studying diabetes. Finally, we end with speculations as to where and how the field will develop in the future. Metabonomics provides a logical framework for understanding the global metabolic effects of diabetes. Continuing technological improvements will expand our knowledge of the causes and progression of this disease, and enhance treatment options for individuals.

Metabolic profiling strategy of Caenorhabditis elegans by whole-organism nuclear magnetic resonance

In this study, we present a methodology for metabotyping of C. elegans using 1H high resolution magic angle spinning (HRMAS) whole-organism nuclear magnetic resonance (NMR). We demonstrate and characterize the robustness of our metabolic phenotyping method, discriminating wild-type N2 from mutant sod-1(tm776) animals, with the latter being an otherwise silent mutation, and we identify and quantify several confounding effects to establish guidelines to ensure optimal quality of the raw data across time and space. We monitor the sample stability under experimental conditions and examine variations arising from effects that can potentially confuse the biological interpretation or prevent the automation of the protocol, including sample culture (breeding of the worms by two biologists), sample preparation (freezing), NMR acquisition (acquisition by different spectroscopists, acquisition in different facilities), and the effect of the age of the animals. When working with intact model organisms, some of these exogenous effects are shown to be significant and therefore require control through experimental design and sample randomization.

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.

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.

The cellular geometry of growth drives the amino acid economy of Caenorhabditis elegans

The nematode Caenorhabditis elegans grows largely by increases in cell size. As a consequence of this, the surface: volume ratio of its cells must decline in the course of postembryonic growth. Here we use transcriptomic and metabolomic data to show that this change in geometry can explain a variety of phenomena during growth, including: (i) changes in the relative expression levels of cytoplasmic and membrane proteins; (ii) changes in the relative usage of the twenty amino acids in expressed proteins, as estimated by changes in the transcriptome; and (iii) changes in metabolite pools of free amino acids. We expect these relations to be universal in single cells and in whole multicellular organisms that grow largely by increases in cell size, but not those that grow by cell proliferation.

Time-resolved metabolic footprinting for nonlinear modeling of bacterial substrate utilization

Untargeted profiling of small-molecule metabolites from microbial culture supernatants (metabolic footprinting) has great potential as a phenotyping tool. We used time-resolved metabolic footprinting to compare one Escherichia coli and three Pseudomonas aeruginosa strains growing on complex media and show that considering metabolite changes over the whole course of growth provides much more information than analyses based on data from a single time point. Most strikingly, there was pronounced selectivity in metabolite uptake, even when the bacteria were growing apparently exponentially, with certain groups of metabolites not taken up until others had been entirely depleted from the medium. In addition, metabolite excretion showed some complex patterns. Fitting nonlinear equations (four-parameter sigmoids) to individual metabolite data allowed us to model these changes for metabolite uptake and visualize them by back-projecting the curve-fit parameters onto the original growth curves. These "uptake window" plots clearly demonstrated strain differences, with the uptake of some compounds being reversed in order between different strains. Comparison of an undefined rich medium with a defined complex medium designed to mimic cystic fibrosis sputum showed many differences, both qualitative and quantitative, with a greater proportion of excreted to utilized metabolites in the defined medium. Extending the strain comparison to a more closely related set of isolates showed that it was possible to discriminate two species of the Burkholderia cepacia complex based on uptake dynamics alone. We believe time-resolved metabolic footprinting could be a valuable tool for many questions in bacteriology, including isolate comparisons, phenotyping deletion mutants, and as a functional complement to taxonomic classifications.

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.

Applications of metabolomics in drug discovery and development

Metabolomics is a relatively new field of 'omics' technology that is primarily concerned with the global or system-wide characterization of small molecule metabolites using technologies such as nuclear magnetic resonance, liquid chromatography and/or mass spectrometry. Its unique focus on small molecules and the physiological effects of small molecules aligns the field of metabolomics very closely with the aims and interests of many researchers in the pharmaceutical industry. Because of its conceptual and technical overlap with many aspects of pharmaceutical research, metabolomics is now finding applications that span almost the full length of the drug discovery and development pipeline, from lead compound discovery to post-approval drug surveillance. This review explores some of the most interesting or significant applications of metabolomics as they relate to pharmaceutical research and development. Specific examples are given that show how metabolomics can be used to facilitate lead compound discovery, to improve biomarker identification (for monitoring disease status and drug efficacy) and to monitor drug metabolism and toxicity. Other applications are also discussed, including the use of metabolomics to facilitate clinical trial testing and to improve post-approval drug monitoring. These examples show that metabolomics potentially offer drug researchers and drug regulators an effective, inexpensive route to addressing many of the riskier or more expensive issues associated with the discovery, development and monitoring of drug products.

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

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

Metabolomic and flux-balance analysis of age-related decline of hypoxia tolerance in Drosophila muscle tissue

The fruitfly Drosophila melanogaster is increasingly used as a model organism for studying acute hypoxia tolerance and for studying aging, but the interactions between these two factors are not well known. Here we show that hypoxia tolerance degrades with age in post-hypoxic recovery of whole-body movement, heart rate and ATP content. We previously used (1)H NMR metabolomics and a constraint-based model of ATP-generating metabolism to discover the end products of hypoxic metabolism in flies and generate hypotheses for the biological mechanisms. We expand the reactions in the model using tissue- and age-specific microarray data from the literature, and then examine metabolomic profiles of thoraxes after 4 h at 0.5% O(2) and after 5 min of recovery in 40- versus 3-day-old flies. Model simulations were constrained to fluxes calculated from these data. Simulations suggest that the decreased ATP production during reoxygenation seen in aging flies can be attributed to reduced recovery of mitochondrial respiration pathways and concomitant overdependence on the acetate production pathway as an energy source.

Metabolic profiling reveals distinct variations linked to nicotine consumption in humans--first results from the KORA study

Exposure to nicotine during smoking causes a multitude of metabolic changes that are poorly understood. We quantified and analyzed 198 metabolites in 283 serum samples from the human cohort KORA (Cooperative Health Research in the Region of Augsburg). Multivariate analysis of metabolic profiles revealed that the group of smokers could be clearly differentiated from the groups of former smokers and non-smokers. Moreover, 23 lipid metabolites were identified as nicotine-dependent biomarkers. The levels of these biomarkers are all up-regulated in smokers compared to those in former and non-smokers, except for three acyl-alkyl-phosphatidylcholines (e.g. plasmalogens). Consistently significant results were further found for the ratios of plasmalogens to diacyl-phosphatidylcolines, which are reduced in smokers and regulated by the enzyme alkylglycerone phosphate synthase (alkyl-DHAP) in both ether lipid and glycerophospholipid pathways. Notably, our metabolite profiles are consistent with the strong down-regulation of the gene for alkyl-DHAP (AGPS) in smokers that has been found in a study analyzing gene expression in human lung tissues. Our data suggest that smoking is associated with plasmalogen-deficiency disorders, caused by reduced or lack of activity of the peroxisomal enzyme alkyl-DHAP. Our findings provide new insight into the pathophysiology of smoking addiction. Activation of the enzyme alkyl-DHAP by small molecules may provide novel routes for therapy.

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.

Genetic algorithms for simultaneous variable and sample selection in metabonomics

MOTIVATION

Metabolic profiles derived from high resolution (1)H-NMR data are complex, therefore statistical and machine learning approaches are vital for extracting useful information and biological insights. Focused modelling on targeted subsets of metabolites and samples can improve the predictive ability of models, and techniques such as genetic algorithms (GAs) have a proven utility in feature selection problems. The Consortium for Metabonomic Toxicology (COMET) obtained temporal NMR spectra of urine from rats treated with model toxins and stressors. Here, we develop a GA approach which simultaneously selects sets of samples and spectral regions from the COMET database to build robust, predictive classifiers of liver and kidney toxicity.

RESULTS

The results indicate that using simultaneous sample and variable selection improved performance by over 9% compared with either method alone. Simultaneous selection also halved computation time. Successful classifiers repeatedly selected particular variables indicating that this approach can aid defining biomarkers of toxicity. Novel visualizations of the results from multiple computations were developed to aid the interpretability of which samples and variables were frequently selected. This method provides an efficient way to determine the most discriminatory variables and samples for any post-genomic dataset.

AVAILABILITY

GA code available from http://www1.imperial.ac.uk/medicine/people/r.cavill/

Metabonomic analysis of exhaled breath condensate in adults by nuclear magnetic resonance spectroscopy

Exhaled breath condensate (EBC) is a noninvasive method for the study of airway lining fluid. Nuclear magnetic resonance (NMR) spectroscopy can provide biochemical profiles of metabolites in biological samples. The aim of the present study was to validate the NMR metabonomic analysis of EBC in adults, assessing the role of pre-analytical variables (saliva and disinfectant contamination) and the potential clinical feasibility. In total, 36 paired EBC and saliva samples, obtained from healthy subjects, laryngectomised patients and chronic obstructive pulmonary disease patients, were analysed by means of (1)H-NMR spectroscopy followed by principal component analysis. The effect on EBC of disinfectant, used for reusable parts of the condenser, was assessed after different washing procedures. To evaluate intra-day repeatability, eight subjects were asked to collect EBC and saliva twice within the same day. All NMR saliva spectra were significantly different from corresponding EBC samples. EBC taken from condensers washed with recommended procedures invariably showed spectra perturbed by disinfectant. Each EBC sample clustered with corresponding samples of the same group, while presenting intergroup qualitative and quantitative signal differences (94% of the total variance within the data). In conclusion, the nuclear magnetic resonance metabonomic approach could identify the metabolic fingerprint of exhaled breath condensate in different clinical sets of data. Moreover, metabonomics of exhaled breath condensate in adults can discriminate potential perturbations induced by pre-analytical variables.

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

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

Temporal metabonomic modeling of l-arginine-induced exocrine pancreatitis

The time-related metabolic responses to l-arginine (ARG)-induced exocrine pancreatic toxicity were investigated using single ip doses of 1,000 and 4,000 mg/kg body weight over a 7 day experimental period in male Sprague-Dawley rats. Sequential timed urine and plasma samples were analyzed using high resolution (1)H NMR spectroscopy together with complementary clinical chemistry and histopathology analyses. Principal components analysis (PCA) and orthogonal projection on latent structures discriminant analysis (O-PLS-DA) were utilized to analyze the (1)H NMR data and to extract and identify candidate biomarkers and to construct metabolic trajectories post ARG administration. Low doses of ARG resulted in virtually no histopathological damage and distinct reversible metabolic response trajectories. High doses of ARG caused pancreatic acinar degeneration and necrosis and characteristic metabolic trajectory profiles with several distinct phases. The initial trajectory phase (0-8 h) involved changes in the urea cycle and transamination indicating a homeostatic response to detoxify excess ammonia generated from ARG catabolism. By 48 h, there was a notable enhancement of the excretion of the gut microbial metabolites, phenylacetylglycine (PAG), 4-cresol-glucuronide and 4-cresol-sulfate, suggesting that compromised pancreatic function impacts on the activity of the gut microbiota giving potential rise to a novel class of surrogate extragenomic biomarkers of pancreatic injury. The implied compromise of microbiotal function may also contribute to secondary hepatic and pancreatic toxic responses. We show here for the first time the value of metabonomic studies in investigating metabolic disruption due to experimental pancreatitis. The variety of observed systemic responses suggests that this approach may be of general value in the assessment of other animal models or human pancreatitis.

1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies

Statistical HeterospectroscopY (SHY) is a statistical strategy for the coanalysis of multiple spectroscopic data sets acquired in parallel on the same samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecular fingerprints measured by multiple spectroscopic techniques across cohorts of samples. Here, the method is applied to 600-MHz (1)H NMR and UPLC-TOF-MS (E) data obtained from human urine samples ( n = 86) from a subset of an epidemiological population unselected for any relevant phenotype or disease factor. We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/ z data from MS, together with fragment analysis from MS (E) scans, leads not only to the detection of numerous endogenous urinary metabolites but also the identification of drug metabolites that are part of the latent use of drugs by the population. We show previously unreported positive mode ions of ibuprofen metabolites with their NMR correlates and suggest the detection of new metabolites of disopyramide in the population samples. This approach is of great potential value in the description of population xenometabolomes and in population pharmacology studies, and indeed for drug metabolism studies in general.

Alzheimer's disease beyond the genomic era: nuclear magnetic resonance (NMR) spectroscopy-based metabolomics

Alzheimer's disease (AD) is a complex disease, with no definitive biomarkers available that allow clinical diagnosis; this represents a major problem for the advance of efficient drug discovery programs. A successful approach towards the understanding and treatment of AD should take into consideration this complex nature. In this sense, metabolic networks are subject to severe stoichiometric restrictions. Metabolomics amplifies changes both in the proteome and the genome, and represents a more accurate approximation to the phenotype of an organism in health and disease. In this article, we will examine the current rationale for metabolomics in AD, its basic methodology and the available data in animal models and human studies. The discussed topics will highlight the importance of being able to use the metabolomic information in order to understand disease mechanisms from a systems biology perspective as a non-invasive approach to diagnose and grade AD. This could allow the assessment of new therapies during clinical trials, the identification of patients at risk to develop adverse effects during treatment and the final implementation of new tools towards a more personalized medicine.