Toluene is a commonly abused inhalant that is easily accessible to adolescents. Despite the increasing incidence of use, our understanding of its long-term impact remains limited. Here, we used a range of techniques to examine the acute and chronic effects of toluene exposure on glutameteric and GABAergic function, and on indices of psychological function in adult rats after adolescent exposure. Metabolomics conducted on cortical tissue established that acute exposure to toluene produces alterations in cellular metabolism indicative of a glutamatergic and GABAergic profile. Similarly, invitro electrophysiology in Xenopus oocytes found that acute toluene exposure reduced NMDA receptor signalling. Finally, in an adolescent rodent model of chronic intermittent exposure to toluene (10000ppm), we found that, while toluene exposure did not affect initial learning, it induced a deficit in updating that learning when response-outcome relationships were reversed or degraded in an instrumental conditioning paradigm. There were also group differences when more effort was required to obtain the reward; toluene-exposed animals were less sensitive to progressive ratio schedules and to delayed discounting. These behavioural deficits were accompanied by changes in subunit expression of both NMDA and GABA receptors in adulthood, up to 10weeks after the final exposure to toluene in the hippocampus, prefrontal cortex and ventromedial striatum; regions with recognized roles in behavioural flexibility and decision-making. Collectively, our data suggest that exposure to toluene is sufficient to induce adaptive changes in glutamatergic and GABAergic systems and in adaptive behaviour that may underlie the deficits observed following adolescent inhalant abuse, including susceptibility to further drug-use.

NMR is a robust analytical technique that has been employed to investigate the properties of many substances of agricultural relevance. NMR was first used to investigate the properties of milk in the 1950s and has since been employed in a wide range of studies; including properties analysis of specific milk proteins to metabolomics techniques used to monitor the health of dairy cows. In this brief review, we highlight the different uses of NMR in the dairy industry.

Investigation of the metabolic status of the brain is problematic due to its inaccessibility. NMR-based metabolomics offers potential diagnostic capabilities via measurement of brain or related biofluid metabolic profiles. In this review we examine the extent to which NMR and MRS have been employed in studies of neurological disorders, the information obtainable, the strengths and limitations of each technology. The potential for wider application of these complementary technologies in metabolomics research is discussed, both in isolation and in combination. The current status of the statistical framework for dealing with these data sets is also examined and suggestions offered for future directions. Keywords: Blood plasma, cerebrospinal fluid, magnetic resonance spectroscopy.

A detailed understanding of the relationships between the distinct metabolic compartments of blood and milk would be of potential benefit to our understanding of the physiology of lactation, and potentially for development of biomarkers for health and commercially relevant traits in dairy cattle. NMR methods were used to measure metabolic profiles from blood and milk samples from Holstein cows. Data were analyzed using PLS regression to identify quantitative relationships between metabolic profiles and important traits. Statistical Heterospectroscopy (SHY), a powerful approach to recovering latent biological information in NMR spectroscopic data sets from multiple complementary samples, was employed to explore the metabolic relationships between blood and milk from these animals. The study confirms milk is a distinct metabolic compartment with a metabolite composition largely not influenced by plasma composition under normal circumstances. However, several significant relationships were identified, including a high correlation for trimethylamine (TMA) and dimethylsulfone (DMSO(2)) across plasma and milk compartments, and evidence plasma valine levels are linked to differences in amino acid catabolism in the mammary gland. The findings provide insights into the physiological mechanisms underlying lactation and identification of links between key metabolites and milk traits such as the protein and fat content of milk. The approach has the potential to enable measurement of health, metabolic status and other important phenotypes with milk sampling.

This study has undertaken a metabolomic assessment of Australian blue mussels, (Mytilus galloprovincialis). A comparison to the New Zealand green lipped mussel (Perna canaliculus) was also made. The aqueous metabolomes were investigated by NMR and the lipid metabolomes by NMR and GC/MS. There are clear metabolic differences between the species M. galloprovincialis and P. canaliculus though this may be due to environmental factors as well as genetic differences. There is also significant variation between the M. galloprovincialis samples collected from two different sites within Port Phillip Bay Victoria. These results indicate that metabolomics approaches could be useful for provenance determination for mussels and could be developed into a tool useful for fisheries and food agencies to trace mussel products.

Ethanol is a known neuromodulatory agent with reported actions at a range of neurotransmitter receptors. Here, we measured the effect of alcohol on metabolism of [3-¹³C]pyruvate in the adult Guinea pig brain cortical tissue slice and compared the outcomes to those from a library of ligands active in the GABAergic system as well as studying the metabolic fate of [1,2-¹³C]ethanol. Analyses of metabolic profile clusters suggest that the significant reductions in metabolism induced by ethanol (10, 30 and 60 mM) are via action at neurotransmitter receptors, particularly α4β3δ receptors, whereas very low concentrations of ethanol may produce metabolic responses owing to release of GABA via GABA transporter 1 (GAT1) and the subsequent interaction of this GABA with local α5- or α1-containing GABA(A)R. There was no measureable metabolism of [1,2-¹³C]ethanol with no significant incorporation of ¹³C from [1,2-¹³C]ethanol into any measured metabolite above natural abundance, although there were measurable effects on total metabolite sizes similar to those seen with unlabelled ethanol.

Osteoarthritis (OA) is a highly prevalent joint disease. Its slow progressive nature and the correlation between pathological changes and clinical symptoms mean that OA is often well advanced by the time of diagnosis. In the absence of any specific pharmacological treatments, there is a pressing need to develop robust biomarkers for OA. We have adopted a nuclear magnetic resonance (NMR)-based metabolomic strategy to identify molecular responses to surgically induced OA in an animal model. Sheep underwent one of three types of surgical procedure (sham (control), meniscal destabilization, MD or anterior cruciate ligament transaction, ACLT), and for every animal a serum sample was collected both pre- and postoperatively, thus, affording two types of "control" data for comparison. 1D 1H NMR spectra were acquired from each sample at 800 MHz and the digitized spectral data were analyzed using principal components analysis and partial least-squares regression discriminant analysis. Our approach, combined with the study design, allowed us to separate the metabolic responses to surgical intervention from those associated with OA. We were able to identify dimethyl sulfone (DMSO2) as being increased in MD after 4 weeks, while ACLT-induced OA exhibited increased 3-methylhistidine and decreased branched chain amino acids (BCAAs). The findings are discussed in the context of interpretation of metabolomic results in studies of human disease, and the selection of appropriate "control" data sets.

The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical total correlation spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSYS). STOCSYS exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSYS: the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSYS to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSYS is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.

Characterising the metabolic response to an intervention can be challenging due to the large inter-subject variation. Because traditional chemometric approaches such as OPLS -DA do not take into account the paired data structure in these studies, we have adopted multivariate paired data analysis (MVPDA) to enhance the recovery of metabolic biomarkers from sheep subjected to surgically-induced osteoarthritis (OA). Sheep underwent one of three types of surgical procedure (sham (control), meniscal destabilisation, MD or anterior cruciate ligament transaction, ACLT), and for every animal a serum sample was collected prior to being operated on and at sacrifice. 1D 1H NMR spectra were acquired from each sample at 800 MHz. Results from traditional chemometric techniques (PCA, OPLS-DA) were compared and contrasted with MVPDA, which displayed enhanced classification and interpretability. MVPDA showed all types of surgical procedure were associated with elevated lactate and deceased TMAO/betaine. Serum from sheep that underwent ACLT was additionally characterised by elevated branched-chain amino acids (BCAAs) and decreased histidine, consistent with previous findings. There was no observable change in BCAAs for the MD cohort indicating that different OA subtypes were associated with unique metabolic fingerprints. This study reinforces the utility of MVPDA for datasets with a paired structure, and offers new insights into the metabolic consequences of OA.

We have performed a metabolite quantitative trait locus (mQTL) study of the (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by (1)H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10(-11)

Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabonomics studies, but optimal recovery of latent biological information requires increasingly sophisticated statistical methods to identify quantitative relationships within these often highly complex data sets. Statistical heterospectroscopy (SHY) extracts latent relationships between NMR and mass spectrometry (MS) data from the same samples. Here we extend this concept to identify novel metabolic correlations between different biofluids and tissues from the same individuals. We acquired NMR data from blood plasma and cerebrospinal fluid (CSF) (N = 19) from HIV-1-infected individuals, who are known to be susceptible to neuropsychological dysfunction. We compared two computational approaches to SHY, namely the Pearson's product moment correlation and the Spearman's rank correlation. High correlations were observed for glutamine, valine, and polyethylene glycol, a drug delivery vehicle. Orthogonal projections to latent structures (OPLS) identified metabolites in blood plasma spectra that predicted the amounts of key CSF metabolites such as lactate, glutamine, and myo-inositol. Finally, brain metabolic data from magnetic resonance spectroscopy (MRS) measurements in vivo were integrated with CSF data to identify an association between 3-hydroxyvalerate and frontal white matter N-acetyl aspartate levels. The results underscore the utility of tools such as SHY and OPLS for coanalysis of high dimensional data sets to recover biological information unobtainable when such data are analyzed in isolation.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

Spectroscopic profiling of biological samples is an integral part of metabolically driven top-down systems biology and can be used for identifying biomarkers of toxicity and disease. However, optimal biomarker information recovery and resonance assignment still pose significant challenges in NMR-based complex mixture analysis. The reduced signal overlap as achieved when projecting two-dimensional (2D) J-resolved (JRES) NMR spectra can be exploited to mitigate this problem and, here, full-resolution (1)H JRES projections have been evaluated as a tool for metabolic screening and biomarker identification. We show that the recoverable information content in JRES projections is intrinsically different from that in the conventional one-dimensional (1D) and Carr-Purcell-Meiboom-Gill (CPMG) spectra, because of the combined result of reduction of the over-representation of highly split multiplet peaks and relaxation editing. Principal component and correlation analyses of full-resolution JRES spectral data demonstrated that peak alignment is necessary. The application of statistical total correlation spectroscopy (STOCSY) to JRES projections improved the identification of previously overlapped small molecule resonances in JRES (1)H NMR spectra, compared to conventional 1D and CPMG spectra. These approaches are demonstrated using a galactosamine-induced hepatotoxicity study in rats and show that JRES projections have a useful and complementary role to standard one-dimensional experiments in complex mixture analysis for improved biomarker identification.

J. Neurochem. (2010) 115 , 58–67. Abstract Gamma‐hydroxybutyrate is found both naturally in the brain and self‐administered as a drug of abuse. It has been reported to act at endogenous γ‐hydroxybutyrate (GHB) receptors and GABA(B) receptors [GABA(B)R], and may also be metabolized to GABA. Here, the metabolic fingerprints of a range of concentrations of GHB were measured in brain cortical tissue slices and compared with those of ligands active at GHB and GABA‐R using principal components analysis (PCA) to identify sites of GHB activity. Low concentrations of GHB (1.0 μM) produced fingerprints similar to those of ligands active at GHB receptors and α4‐containing GABA(A)R. A total of 10 μM GHB clustered proximate to mainstream GABAergic synapse ligands, such as 1.0 μM baclofen, a GABA(B)R agonist. Higher concentrations of GHB (30 μM) clustered with GABA(C)R agonists and the metabolic responses induced by blockade of the GABA transporter‐1 (GAT1). The metabolic responses induced by 60 and 100 μM GHB were mimicked by simultaneous blockade of GAT1 and GAT3, addition of low concentrations of GABA(C)R antagonists, or increasing cytoplasmic GABA concentrations by incubation with the GABA transaminase inhibitor vigabatrin. These data suggest that at concentrations > 30 μM, GHB may be active via metabolism to GABA, which is then acting upon an unidentified GABAergic master switch receptor (possibly a high‐affinity extrasynaptic receptor), or GHB may itself be acting directly on an extrasynaptic GABA‐R, capable of turning off large numbers of cells. These results offer an explanation for the steep dose–response curve of GHB seen in vivo , and suggest potential target receptors for further investigation.

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 STOCSY-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.

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.

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.

Optimizing NMR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the total biochemical information obtainable from (1)H NMR data, which includes concentration and molecular dynamics information. Here we have applied two different types of mathematical transformation (calculation of the first derivative of the NMR spectrum and Gaussian shaping of the free-induction decay) to attenuate broad spectral features from macromolecules and enhance the signals of small molecules. By application of chemometric methods such as principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (O-PLS-DA) and statistical spectroscopic tools such as statistical total correlation spectroscopy (STOCSY), we show that these methods successfully identify the same potential biomarkers as spin-echo (1)H NMR spectra in which broad lines are suppressed via T2 relaxation editing. Finally, we applied these methods for identification of the metabolic phenotype of patients with type 2 diabetes. This "virtual" relaxation-edited spectroscopy (RESY) approach can be particularly useful for high-throughput screening of complex mixtures such as human plasma and may be useful for extraction of latent biochemical information from legacy or archived NMR data sets for which only standard 1D data sets exist.

The present invention relates generally to field of human male fertility, and in particular, to the use of the presence or absence or amount of a uridine marker (e.g., uridine) in a seminal fluid sample from a human male subject as a means of assessing the subject's fertility status.

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 H-1 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 (T-1) of individual resonances. T-1 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 delta 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.