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NMR spectroscopy is a mainstay of metabolic profiling approaches to investigation of physiological and pathological processes. The one‐dimensional proton pulse sequences typically used in phenotyping large numbers of samples generate spectra that are rich in information but where metabolite identification is often compromised by peak overlap. Recently developed pure shift (PS) NMR spectroscopy, where all J‐coupling multiplicities are removed from the spectra, has the potential to simplify the complex proton NMR spectra that arise from biosamples and hence to aid metabolite identification. Here we have evaluated two complementary approaches to spectral simplification: the HOBS (band‐selective with real‐time acquisition) and the PSYCHE (broadband with pseudo‐2D interferogram acquisition) pulse sequences. We compare their relative sensitivities and robustness for deconvolving both urine and serum matrices. Both methods improve resolution of resonances ranging from doublets, triplets and quartets to more complex signals such as doublets of doublets and multiplets in highly overcrowded spectral regions. HOBS is the more sensitive method and takes less time to acquire in comparison with PSYCHE, but can introduce unavoidable artefacts from metabolites with strong couplings, whereas PSYCHE is more adaptable to these types of spin system, although at the expense of sensitivity. Both methods are robust and easy to implement. We also demonstrate that strong coupling artefacts contain latent connectivity information that can be used to enhance metabolite identification. Metabolite identification is a bottleneck in metabolic profiling studies. In the case of NMR, PS experiments can be included in metabolite identification workflows, providing additional capability for biomarker discovery. [ABSTRACT FROM AUTHOR]

1. The metabolic fate and urinary excretion of 2-bromophenol, a phenolic metabolite of bromobenzene, was investigated in male Sprague–Dawley rats following single intraperitoneal doses at either 0, 100, or 200 mg/kg. 2. Urine was collected for seven days and samples analysed using 1 H NMR spectroscopy, inductively coupled plasma (ICP)MS, and UPLC-MS. 3. 1 H NMR spectroscopy of the urine samples showed that, at these doses, 2-bromophenol had little effect on endogenous metabolite profiles, supporting histopathology and clinical chemistry data, which showed no changes associated with the administration of 2-bromophenol in this study. 4. The use of ICP-MS provided a means for the selective detection and quantification of bromine-containing species and showed that between 15 and 30% of the dose was excreted via the urine over 7 days of the study for both the 100 and 200 mg doses, respectively. 5. The bulk of the excretion of Br-containing material had occurred by 8 h post administration. UPLC-MS of urine revealed a number of metabolites of 2-bromophenol, with 2-bromophenol glucuronide and 2-bromophenol sulphate identified as the major species. A number of minor hydroxylated metabolites were also detected as their glucuronide, sulphate, or O-methyl conjugates. There was no evidence for the production of reactive metabolites. [ABSTRACT FROM AUTHOR]

The article looks at a study regarding urinary metabolic signatures of human adiposity and metabolic profiling by proton nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures of adiposity. It mentions reproducible patterns of metabolite excretion associated with adiposity and analysis of the urinary metabolome using NMR spectroscopy. It also mentions multiple associations between urinary metabolites and urinary glycoproteins.

Summary: MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. [ABSTRACT FROM AUTHOR]

2-Bromoethanamine (BEA) causes renal papillary necrosis (RPN) in rats after a single dose and has been widely used as a model compound for studying the lesion. Although the metabolism of BEA may be an important determinant of toxicity, the metabolic fate of the compound has not been fully elucidated. To date, the only identified BEA metabolites are aziridine, 2-oxazolidone and 5-hydroxy-2-oxazolidone. In this study, stable isotope labelling (SIL) of BEA analogs (13C and 2H) were used to differentiate generated BEA metabolites from endogenous molecules which enabled the accurate liquid chromatography mass spectrometry detection of more than 180 novel metabolites. BEA metabolism was evaluated in rats after acute administration of a non-toxic dose (50 mg/kg) and a toxic dose (250 mg/kg) that caused frank RPN and polyuria. Newly identified metabolites include three carbamoylation products, two mercapturic acids and a group of amino acid conjugates. Overall, the results indicate that BEA metabolism is very complex, suggest the potential formation of reactive intermediates and establish that BEA is subject to conjugation with glutathione. The results also demonstrate the utility and sensitivity of the SIL approach for identification of metabolites from small, reactive compounds. [ABSTRACT FROM AUTHOR]

Motivation: Metabolic profiles derived from high resolution 1H-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. [ABSTRACT FROM PUBLISHER]

Metabolic phenotypes are the products of interactions among a variety of factors—dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40–59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10-16), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk. [ABSTRACT FROM AUTHOR]

Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are nonlinear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data. [ABSTRACT FROM AUTHOR]

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

Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological, proteomic and transcriptomic profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants, are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome (microbiome) perturbations that affect disease processes through transgenomic effects may influence QTL detection. [ABSTRACT FROM AUTHOR]

This minireview is based on a lecture given at the First Maga Circe Conference on metabolomics held at Sabaudia, Italy, in March 2006 in which the analytical and statistical techniques used in metabonomics, efforts at standardization and some of the major applications to pharmaceutical research and development are reviewed. Metabonomics involves the determination of multiple metabolites simultaneously in biofluids, tissues and tissue extracts. Applications to preclinical drug safety studies are illustrated by the Consortium for Metabonomic Toxicology, a collaboration involving several major pharmaceutical companies. This consortium was able, through the measurement of a dataset of NMR spectra of rodent urine and serum samples, to build a predictive expert system for liver and kidney toxicity. A secondary benefit was the elucidation of the endogenous biochemicals responsible for the classification. The use of metabonomics in disease diagnosis and therapy monitoring is discussed with an exemplification from coronary artery disease, and the concept of pharmaco-metabonomics as a way of predicting an individual's response to treatment is exemplified. Finally, some advantages and perceived difficulties of the metabonomics approach are summarized. [ABSTRACT FROM AUTHOR]

Describes high-performance liquid chromatography linked to inductively coupled plasma mass spectrometry and orthogonal acceleration time-of-flight mass spectrometry for the simultaneous detection and identification of metabolites of 2-bromo-4-trifluoromethyl-[[sup 13]C]-acetanilide in rat urine. Separation of the metabolites.

Background Metabolic profiling of biofluid specimens is an established method for investigating disease states in clinical studies but is only recently being applied to large-scale human population studies. As part of protocol development for the UK Biobank study, a 1H nuclear magnetic resonance (NMR)-based metabonomic analysis of specimen storage effects and analytical reproducibility was carried out using urine and serum specimens from 40 volunteers.Methods Aliquots of each specimen were stored for t = 0 and t = 24 h at 4°C prior to freezing, and in the case of serum samples for a further 12 h (t = 36), to determine whether the storage times affected specimen composition and quality. A blinded split-specimen matching exercise was implemented to assign candidate spectral pairs stored for different times using multivariate statistical analysis of the NMR data.Results Using a chemometric strategy, split specimens at time t = 0 and t = 24 or 36 h after storage at 4°C were easily paired and the split-specimen matching task was reduced to a workable size. 1H NMR profiling established that the t = 24 h urine and serum groups showed no systematic metabolite changes, indicating biochemical stability. Some small differences in serum specimens stored for t = 36 h at 4°C were detectable only by multivariate analysis, and were attributed to generalized alterations in proteins and protein fragments, and possibly trimethylamine-N-oxide. No other specific metabolite was implicated.Conclusions For the purposes of NMR-based analysis, storage of urine and serum for up to t = 24 h at 4°C does not detectably affect the metabolic profile and the methodology is robust. Future application of multivariate methods to data-rich studies should substantially enhance information recovery from epidemiological studies. [ABSTRACT FROM AUTHOR]

We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r² value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set The variables are ranked according to r² values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r² values. The Q²X value for each PCA model is extracted. K-means clustering is then applied to the Q²X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q²X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q²X values is then further subclustered into two groups based on the i values. We considered the cluster with high Q²X but low r² values as nondiscriminatory, while the cluster with high Q²X and r² values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r² values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated ¹H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and J000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the sample size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini--Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data. [ABSTRACT FROM AUTHOR]

Analyzing metabolites (small molecules <1 kDa) in body fluids such as urine and plasma using various spectroscopic methods provides information on the metabotype (metabolic phenotype) of individuals or populations, information that can be applied to personalized medicine or public healthcare. [Copyright &y& Elsevier]

Focuses on recent advances and scope of metabonomics that involves quantitative measurement of the time-related multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification, as of September 2003. Significance of metabonomics in measurement of biological perturbations; Comparison between metabonomics and metabolomics; Use of nuclear magnetic resonance spectrometry to study complex molecular systems.

• CCS data were obtained for over 500 metabolites by direct infusion at two sites. • Excellent agreement was seen for the CCS data generated at both sites. • Good concordance was seen for these data with both external and predicted CCS values. • The metabolites were also analysed by RP-UHPLC-IM-MS to obtain tr data. • Some 63 metabolites in rat urine LC-IM-MS were identified using tr, CCS and MS. A comprehensive Collision Cross Section (CCS) library was obtained via Travelling Wave Ion Guide mobility measurements through direct infusion (DI). The library consists of CCS and Mass Spectral (MS) data in negative and positive ElectroSpray Ionisation (ESI) mode for 463 and 479 endogenous metabolites, respectively. For both ionisation modes combined, TWCCS N2 data were obtained for 542 non-redundant metabolites. These data were acquired on two different ion mobility enabled orthogonal acceleration QToF MS systems in two different laboratories, with the majority of the resulting TWCCS N2 values (from detected compounds) found to be within 1% of one another. Validation of these results against two independent, external TWCCS N2 data sources and predicted TWCCS N2 values indicated to be within 1–2% of these other values. The same metabolites were then analysed using a rapid reversed-phase ultra (high) performance liquid chromatographic (U(H)PLC) separation combined with IM and MS (IM-MS) thus providing retention time (t r), m/z and TWCCS N2 values (with the latter compared with the DI-IM-MS data). Analytes for which TWCCS N2 values were obtained by U(H)PLC-IM-MS showed good agreement with the results obtained from DI-IM-MS. The repeatability of the TWCCS N2 values obtained for these metabolites on the different ion mobility QToF systems, using either DI or LC, encouraged the further evaluation of the U(H)PLC-IM-MS approach via the analysis of samples of rat urine, from control and methotrexate-treated animals, in order to assess the potential of the approach for metabolite identification and profiling in metabolic phenotyping studies. Based on the database derived from the standards 63 metabolites were identified in rat urine, using positive ESI, based on the combination of t r , TWCCS N2 and MS data. [ABSTRACT FROM AUTHOR]

Metabolic profiling studies aim to achieve broad metabolome coverage in specific biological samples. However, wide metabolome coverage has proven difficult to achieve, mostly because of the diverse physicochemical properties of small molecules, obligating analysts to seek multiplatform and multimethod approaches. Challenges are even greater when it comes to applications to tissue samples, where tissue lysis and metabolite extraction can induce significant systematic variation in composition. We have developed a pipeline for obtaining the aqueous and organic compounds from diseased arterial tissue using two consecutive extractions, followed by a different untargeted UPLC-MS analysis method for each extract. Methods were rationally chosen and optimized to address the different physicochemical properties of each extract: hydrophilic interaction liquid chromatography (HILIC) for the aqueous extract and reversed-phase chromatography for the organic. This pipeline can be generic for tissue analysis as demonstrated by applications to different tissue types. The experimental setup and fast turnaround time of the two methods contributed toward obtaining highly reproducible features with exceptional chromatographic performance (CV % < 0.5%), making this pipeline suitable for metabolic profiling applications. We structurally assigned 226 metabolites from a range of chemical classes (e.g., carnitines, α-amino acids, purines, pyrimidines, phospholipids, sphingolipids, free fatty acids, and glycerolipids) which were mapped to their corresponding pathways, biological functions and known disease mechanisms. The combination of the two untargeted UPLC-MS methods showed high metabolite complementarity. We demonstrate the application of this pipeline to cardiovascular disease, where we show that the analyzed diseased groups (n = 120) of arterial tissue could be distinguished based on their metabolic profiles. [ABSTRACT FROM AUTHOR]

Statistical total correlation spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a ±H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations. [ABSTRACT FROM AUTHOR]

Hydrophilic interaction ultra performance liquid chromatography (HILIC-UPLC) permits the analysis of highly polar metabolites, providing complementary information to reversed-phase (RP) chromatography. HJLIC-UPLCTOF-MS was investigated for the global metabolic profiling of rat urine samples generated in an experimental hepatotoxicity study of galactosamine (gaIN) and the concomitant investigation of the protective effect of glycine. Withinrun repeatability and stability over a large sample batch (>200 samples, 60 h ron-time) was assessed through the repeat analysis of a quality control sample. Following system equilibration, excellent repeatability was observed in terms of retention time (CV < 1.7%), signal intensity (CV < 14%), and mass variability (<0.005 amu), providing a good measure of reproducibility. Classification of urinary metabolic profiles according to treatment was observed, with significant changes in specific metabolites after gaIN exposure, including increased urocanic acid, N-acetylglucosamine, and decreased 2-oxoglutarate. A novel finding from this HIUC-UPLC-MS approach was elevated unnazy tyramine in gaIN-treated rats, reflecting disturbed amino acid metabolism. These results show HIUC-UPLC-MS to be a promising method for global metabolic profiling, demonstrating high within-run repeatability, even over an extended run time. Retention of polar endogenous analytes and xenobiotic metabolites was improved compared with RP studies, including gaIN, N-acetylglucosamine, oxoglutarate, and urocanic acid, enhancing metabolome coverage and potentially improving biomarker discovery. [ABSTRACT FROM AUTHOR]

A series of six protocols were evaluated for UPLC-MS based untargeted metabolic profiling of liver extracts in terms of reproducibility and number of metabolite features obtained. These protocols, designed to extract both polar and nonpolar metabolites, were based on (i) a two stage extraction approach or (ii) a simultaneous extraction in a biphasic mixture, employing different volumes and combinations of extraction and resuspension solvents. A multivariate statistical strategy was developed to allow comparison of the multidimensional variation between the methods. The optimal protocol for profiling both polar and nonpolar metabolites was found to be an aqueous extraction with methanol/water followed by an organic extraction with dichloromethane/methanol, with resuspension of the dried extracts in methanol/water before UPLC-MS analysis. This protocol resulted in a median CV of feature intensities among experimental replicates of <20% for aqueous extracts and <30% for organic extracts. These data demonstrate the robustness of the proposed protocol for extracting metabolites from liver samples and make it well suited for untargeted liver profiling in studies exploring xenobiotic hepatotoxicity and clinical investigations of liver disease. The generic nature of this protocol facilitates its application to other tissues, for example, brain or lung, enhancing its utility in clinical and toxicological studies. [ABSTRACT FROM AUTHOR]

Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz 1H NMR spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assignment of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to determine the relationships between reaction rates and metabolite levels, revealing that choline, N-acetylglucosamine, and uridine are associated with higher peptidase activity. The novel approach adopted here has the potential to capture dynamic information in any complex mixture of reacting chemicals including other biofluids or cell extracts. [ABSTRACT FROM AUTHOR]

We present a new approach for analysis, information recovery, and display of biological 1H nuclear magnetic resonance (NMR) spectral data, cluster analysis statistical spectroscopy (CLASSY), which profiles qualitative and quantitative changes in biofluid metabolic composition by utilizing a novel local-global correlation clustering scheme to identify structurally related spectral peaks and arrange metabolites by similarity of temporal dynamic variation. Underlying spectral data sets are presented in a novel graphical format to represent high-dimensionality biochemical information conveying both statistical metabolite relationships and their responses to experimental perturbation simultaneously in a high-throughput and intuitive manner. The method is exemplified using multiple 600 MHz 1H NMR spectra of rat (n = 40) urine samples collected over 160 h following the development of experimental pancrealitis induced by L-arginine (ARG) and a wider range of model toxins including acetaminophen, galactosamine, and 2-bromoethanamine. The CLASSY approach deconvolutes complex biofluid mixture spectra into quantitative fold-change metabolic trajectories and clusters metabolites by commonalities of coexpression patterns. We demonstrate that the developing pathological processes cause coordinated changes in the levels of many compounds which share similar pathway connectivities. Variability in individual responses to toxin exposure is also readily detected and visualized allowing the assessment of interanimal variability. As an untargeted, unsupervised approach, CLASSY provides significant advantages in biological information recovery in terms of increased throughput, interpret- ability, and robustness and has wide potential metabonomic/metabolomic applications in clinical, toxicological, and nutritional studies of biofluids as well as in studies of cellular biochemistry, microbial fermentation monitoring, and functional genomics. [ABSTRACT FROM AUTHOR]

Here we present a novel method for enhanced NMR spectral information recovery, utilizing a statistical total correlation spectroscopy editing (STOCSY-E) procedure for the identification of drug metabolite peaks in biofluids and for deconvolution of drug and endogenous metabolite signals. Structurally correlated peaks from drug metabolites and those from closely related drug metabolite pathways are first identified using STOCSY. Subsequently, this correlation information is utilized to scale the biofluid 1H NMR spectra across these identified regions, producing a modified set of spectra in which drug metabolite contributions are reduced and, thus, facilitating analysis by pattern recognition methods without drug metabolite interferences. The application of STOCSY-E is illustrated with two exemplar 1H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz 1H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz 1H NMR spectra of rat urine (n = 27) collected over 48 h following exposure to the renal papillary toxin 2-bromoethanamine (BEA). STOCSY-E efficiently identified and removed the major xenobiotic metabolite peaks in both data sets, providing enhanced visualization of endogenous changes via orthogonal to projection filtered partial least-squares discriminant analysis (OPLS-DA). OPLS-DA of the SFOCSY-E spectral data from the BEA-treated rats revealed the gut bacterial-mammalian co-metabolite phenylacetylglycine as a previously unidentified surrogate biomarker of toxicity. STOCSY-E has a wide range of potential applications in clinical, epidemiology, toxicology, and nutritional studies where multiple xenobiotic metabolic interferences may confound biological interpretation. Additionally, this tool could prove useful for applications outside of metabolic analysis, for example, in process chemistry for following chemical reactions and equilibria and detecting impurities. [ABSTRACT FROM AUTHOR]

The application of a 1H nuclear magnetic resonance (NMR) spectroscopy-based screening method for determining the use of two widely available analgesics (acetaminophen and ibuprofen) in epidemiologic studies has been investigated. We used samples and data from the cross-sectional INTERMAP Study involving participants from Japan (n = 1145), China (n = 839), U.K. (n = 501), and the U.S. (n = 2195). An orthogonal projection to latent structures discriminant analysis (OPLS-DA) algorithm with an incorporated Monte Carlo resampling function was applied to the NMR data set to determine which spectra contained analgesic metabolites. OPLS-DA preprocessing parameters (normalization, bin width, scaling, and input parameters) were assessed systematically to identify an optimal acetaminophen prediction model. Subsets of INTER- MAP spectra were examined to verify and validate the presence/absence of acetaminophen/ibuprofen based on known chemical shift and coupling patterns. The optimized and validated acetaminophen model correctly predicted 98.2%, and the ibuprofen model correctly predicted 99.0% of the urine specimens containing these drug metabolites. The acetaminophen and ibuprofen models were subsequently used to predict the presence/absence of these drug metabolites for the remaining INTERMAP specimens. The acetaminophen model identified 415 out of 8436 spectra as containing acetaminophen metabolite signals while the ibuprofen model identified 245 out of 8604 spectra as conferring ibuprofen metabolite signals from the global data set after samples used to construct the prediction models. The NMR-based metabolic screening strategy provides a new objective approach for evaluation of self-reported medication data and is extendable to other aspects of population xenometabolome profiling. [ABSTRACT FROM AUTHOR]

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 1H NMR-spectroscopy-based metabolic profiling of human urine samples have been assessed. Effects of various concentrations of borate (range 0-30 mM) on 1H 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 1H resonance metabolite patterns were observed for compounds such as mannitol, citrate, and α-hydroxylsobutyrate and confirmed by ESI-MS analysis. These included line-broadening, T1 and T2 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 T1 and T2 proton relaxation times respectively (T1 or T2 ordered projection spectroscopy, TOPSY) was also developed and applied. Addition of borate caused a general decrease in 1H T1 values, consistent with nonspecific effects such as solution viscosity changes. Minor changes in proton T2 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 1H NMR-based epidemiological studies, since the essential biochemical classification features of the samples are robustly maintained. [ABSTRACT FROM AUTHOR]

Structural assignment of resonances is an important problem in NMR spectroscopy, and statistical total correlation spectroscopy (STOCSY) is a useful tool aiding this process for small molecules in complex mixture analysis and metabolic profiling studies. STOCSY delivers intramolecular information (delineating structural connectivity) and in metabolism studies can generate information on pathway-related correlations. To understand further the behavior of STOCSY for structural assignment, we analyze the statistical distribution of structural and nonstructural correlations from 1050 ¹H NMR spectra of normal rat urine samples. We find that the distributions of structural/nonstructural correlations are significantly different (p < 10-112). From the area under the curve of the receiver operating characteristic (ROC AUC) we show that structural correlations exceed nonstructural correlations with probability AUC = 0.98. Through a bootstrap resampling approach, we demonstrate that sample size has a surprisingly small effect (e.g, AUC 0.97 for a sample size of 50). We identify specific signatures in the correlation maps resulting from small matrix-derived variations in peak positions but find that their effect on discrimination of structural and nonstructural correlations is negligible for most metabolites. A correlation threshold of r> 0.89 is required to assign two peaks to the same metabolite with high probability (positive predictive value, PPV 0.9), whereas sensitivity and specificity are equal at 93% for r = 0.22. To assess the wider applicability of our results, we analyze ¹H NMR spectra of urine from rats treated with 115 model toxins or physiological stressors. Across the data sets, we find that the thresholds required to obtain PPV = 0.9 are not significantly different and the degree of overlap between the structural and nonstructural distributions is always small (median AUC = 0.97). The STOCSY method is effective for structural characterization under diverse biological conditions and sample sizes provided the degree of correlation resulting from nonstructural associations (e.g., from nonstationary processes) is small. This study validates the use of the STOCSY approach in the routine assignment of signals in NMR metabolic profiling studies and provides practical benchmarks against which researchers can interpret the results of a STOCSY analysis. [ABSTRACT FROM AUTHOR]

Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz `H NMR spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently. published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assigmnent of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to detennine the relationships between reaction rates and metabolite levels, revealing that choline, N-acetylglucosamine, and uridine are associated with higher peptidase activity. The novel approach adopted here has the potential to capture dynamic information in any complex mixture of reacting chemicals including other biofluids or cell extracts. [ABSTRACT FROM AUTHOR]

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 ¹H NMR and UPLC-TOF-MSE 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 MSE 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. [ABSTRACT FROM AUTHOR]

Metabolite profiling relies on optimal precision of the acquired data, which requires, among others, a high signal-to-noise ratio (S/N). In addition, increased S/N will increase the likelihood of identification of new biomarkers. Here we introduce, for the first time in metabolite profiling studies by ¹H NMR, an approach to enhance the precision of multivariate regression models by use of the FLIPSY (flip angle adjustable one-dimensional NOESY) pulse sequence, augmented by a homospoil pulse after the presaturation period to provide superior baseline quality. Unlike NOESYPRESAT, the standard one-dimensional (1D) sequence generally used in metabonomic studies, FLIPSY incorporates a variable flip angle, allowing use of the Ernst angle for excitation and thus optimization of S/N ratios according to spin lattice relaxation times (T1) of individual resonances. T1 values of metabolites present in human urine were determined by inversion--recovery experiments and subsequently used in calculations of optimal experimental conditions. Comparison of human urine analysis by the FLIPSY and NOESYPRESAT demonstrated an increase of S/N ratio in the former case that amounts to approximately 7% when measured for the hippurate doublet at δ 7.84. An orthogonal projection to latent structures discriminant analysis (O-PLS-DA) model exhibited superior discrimination between controls and simulated phenylketonuria urines when using data generated by the FLIPSY as compared to NOESYPRESAT. [ABSTRACT FROM AUTHOR]

Previously we have demonstrated the use of 1H magic angle spinning (MAS) NMR spectroscopy for the topographical variations in functional metabolic signatures of intact human intestinal biopsy samples. Here we have analyzed a series of MAS 1H NMR spectra (spin-echo, one-dimensional, and diffusion-edited) and 31P-{1H} spectra and focused on analyzing the enhancement of infonnation recovery by use of the statistical total correlation spectroscopy (STOCSY) method. We have applied a heterospectroscopic cross-examination performed on the same samples and between 1H and 31P-{1H} spectra (heteronuclear STOCSY) to recover latent metabolic information. We show that heterospectroscopic correlation can give new information on the molecular compartmentation of metabolites in intact tissues, including the statistical "isolation" of a phosphlipid/triglyceride vesicle pool in intact tissue. The application of 31P-1H HET-STOCSY allowed the cross-assignment of major 31P signals to their equivalent 1H NMR spectra, e.g., for phosphorylcholine and phosphorylethanolainine. We also show pathway correlations, e.g., the ascorbate-glutathione pathway, in the STOCSY analysis of intact tissue spectra. These 31P-1H HFT-STOCSY spectra also showed different topographical regions, particular for minor signals in different tissue microenvironments. This approach could be extended to allow the detection of altered distributions within metabolic subcompartments as well as conventional metabonomics concentration.based diagnostics. [ABSTRACT FROM AUTHOR]

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

Carboxylic acid-containing drugs are often metabolized to 1-β-O-acyi glucuronides (AGs). These can undergo an internal chemical rearrangement, and the resulting reactive positional isomers can bind to endogenous proteins, with clear potential for adverse effects. Additionally any 1-β-O-acyl-glucuronidated phase I metabolite of the drug can also show this propensity, and investigation of the adverse effect potential of a drug also needs to consider such metabolites. Here the transacylation of the common drug ibuprofen and two of its metabolites is investigated in vitro. 1-β-O-Acyl (S)-ibuprofen glucuronide was isolated from human urine and also synthesized by selective acylation. Urine was also used as a source of the (R)-ibuprofen, (S)-2-hydroxyibuprofen, and (S,S)-carboxyibuprofen AGs. The degradation rates (a combination of transacylation and hydrolysis) were measured using 1H NMR spectroscopy, and the measured decrease in the 1-β anomer over time was used to derive half-lives for the glucuronides. The biosynthetic and chemically synthesized (S)-ibuprofen AGs had half-lives of 3.68 and 3.76 h, respectively. (R)-Ibuprofen AG had a half-life of 1.79 h, a value approximately half that of the (S)-diastereoisomer, consistent with results from other 2-aryl propionic acid drug AGs. The 2-hydroxyibuprofen and carboxyibuprofen AGs gave half-lives of 5.03 and 4.80 h, considerably longer than that of either of the parent drug glucuronides. In addition, two (S)-ibuprofen glucuronides were synthesized with the glucuronide carboxyl function esterified with either ethyl or allyl groups. The (S)-ibuprofen AG ethyl ester and (S)-ibuprofen AG allyl esters were determined to have half-lives of 7.24 and 9.35 h, respectively. In order to construct useful structure-reactivity relationships, it is necessary to evaluate transacylation and hydrolysis separately, and here it is shown that the (R)- and (S)-ibuprofen AGs have different transacylation properties. The implications of these findings are discussed in terms of structure-activity relationships. [ABSTRACT FROM AUTHOR]

A new approach to enhancing information recovery from cryogenic probe ‘on-flow’ LC-NMR spectroscopic analyses of complex biological mixtures is demonstrated using a variation on the statistical total correlation spectroscopy (STOCSY) method. Cryoflow probe technology enables sensitive and efficient NMR detection of metabolites on-flow, and the rapid spectral scanning allows multiple spectra to be collected over chromatographic peaks containing several species with similar, but nonidentical, retention times. This enables ¹H NMR signal connectivities between close-eluting metabolites to be identified resulting in a ‘virtual’ chromatographic resolution enhancement visualized directly in the NMR spectral projection. We demonstrate the applicability of the approach for structure assignment of drug and endogenous metabolites in urine. This approach is of wide general applicability to any complex mixture analysis problem involving chromatographic peak overlap and with particular application in metabolomics and metabonomics. [ABSTRACT FROM AUTHOR]

Western populations use prescription and nonprescription drugs extensively, but large-scale population usage is rarely assessed objectively in epidemiological studies. Here we apply statistical methods to characterize structural pathway connectivities of metabolites of commonly used drugs detected routinely in ¹H NMR spectra of urine in a human population study. ¹H NMR spectra were measured for two groups of urine samples obtained from U.S. participants in a known population study. The novel application of a statistical total correlation spectroscopy (STOCSY) approach enabled rapid identification of the major and certain minor drug metabolites in common use in the population, in particular, from acetaminophen and ibuprofen metabolites. This work shows that statistical connectivities between drug metabolites can be established in routine "high-throughput" NMR screening of human samples from participants who have randomly self-administered drugs. This approach should be of value in considering interpopulation patterns of drug metabolism in epidemiological and pharmacogenetic studies. [ABSTRACT FROM AUTHOR]

The use of a combination of ultraperformance liquid chromatography at ~11 000 psi on sub 2-µm particles combined with reversed-phase gradient chromatography at a temperature of 90 °C is described as applied to the analysis of endogenous and drug metabolites in human and animal urine. By using elevated temperatures, back pressures can be reduced while maintaining high flow rates and chromatographic efficiency, with peaks 1-3 s wide at the base. Application to urine samples provided a peak capacity of ~700 for a 10-mm analysis and greater than ~1000 in 1 h. Despite the narrow nature of the peaks, good quality mass spectra were also obtained, allowing the identification of typical drug and endogenous metabolites. These ultra-high-resolution chromatograms should be ideal for the analysis of complex samples in, for example, metabolite identification, impurity identification, and metabonomic/metabolomic studies. Applications in natural product analysis and proteomics can also be envisaged. [ABSTRACT FROM AUTHOR]

Several essential and non-essential metals (typically those from periods 4, 5 and 6 in groups 11–15 in the periodic table) are commonly detoxified in higher plants by complexation with phytochelatin. The genetic and gross metabolic basis of metal tolerance in plants is, however, poorly understood. Here, we have analyzed plant cell extracts using 1H NMR spectroscopy combined with multivariate statistical analysis of the data to investigate the biochemical consequences of Cd2+ exposure in Silene cucubalus cell cultures. Principal components analysis of 1H NMR spectra showed clear discrimination between control and Cd2+ dosed groups, demonstrating the metabolic effects of Cd2+ and thus allowing the identification of increases in malic acid and acetate, and decreases in glutamine and branched chain amino acids as consequences of Cd2+ exposure. This work shows the value of NMR-based metabolomic approaches to the determination of biochemical effects of pollutants in naturally selected populations. [Copyright &y& Elsevier]

• A rapid microbore RP-UHPLC-MS assay for tyrosine and 11 metabolites is described. • A "fit-for-purpose" approach was applied to method validation. • The assay was applied to mouse urine and plasma after feeding a high tyrosine diet. • Antibiotics revealed the contribution of the gut microbiota to tyrosine metabolism. Tyrosine plays a key role in mammalian biochemistry and defects in its metabolism (e.g. , tyrosinemia, alkaptonuria etc.) have significant adverse consequences for those affected if left untreated. In addition, gut bacterially-derived p -cresol and its metabolites are of interest as a result of various effects on host xenobiotic metabolism. A fit-for-purpose quantitative ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) assay was developed to target and quantify tyrosine and eleven metabolites in urine and plasma. Dansylation, using dansyl chloride, was used to improve chromatographic and mass spectral properties for tyrosine and nine phenolic metabolites, with detection using positive electrospray ionisation (ESI). The sulfate and glucuronide conjugates of p -cresol, where the phenol group was blocked, were quantified intact, using negative ESI via polarity switching during the same run. Sample preparation for urine and plasma involved deproteinization by solvent precipitation (of acetonitrile:isopropyl alcohol (1:1 v/v)) followed by in situ dansylation in 96 well plates. To minimize sample and solvent usage, and maximize sensitivity, analysis was performed using microbore reversed-phase gradient UPLC on a C8 phase with a 7.5 min. cycle time. The coefficients of variation obtained were <15%, with lower limits of quantification ranging from 5 to 250 nM depending upon the analyte. The method was applied to plasma and urine samples obtained from mice placed on a high tyrosine diet with one subgroup of animals subsequently receiving antibiotics to suppress the gut microbiota. Whilst plasma profiles were largely unaffected by antibiotic treatment clear reductions in the amount of p -cresol sulfate and p -cresol glucuronide excreted in the urine were observed for these mice. [ABSTRACT FROM AUTHOR]