Search

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.

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.

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, in vitro 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 (10 000 ppm), 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 10 weeks 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.

Investigation of the metabolic status of the brain is problematic due to its inaccessibility. NMR-based me- tabolomics 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 di- rections.

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.

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.

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.

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.

Metabonomics has been defined as "quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification" and can provide information on disease processes, drug toxicity, and gene function. In this approach many samples of biological origin (biofluids such as urine or plasma) are analyzed using techniques that produce simultaneous detection. A variety of analytical metabolic profiling tools are used routinely, are also currently under development, and include proton nuclear magnetic resonance spectroscopy and mass spectrometry with a prior online separation step such as high-performance liquid chromatography, ultra-performance liquid chromatography, or gas chromatography. Data generated by these analytical techniques are often combined with multivariate data analysis, i.e., pattern recognition, for respectively generating and interpreting the metabolic profiles of the investigated samples. Metabonomics has gained great prominence in diabetes research within the last few years and has already been applied to understand the metabolism in a range of animal models and, more recently, attempts have been done to process complex metabolic data sets from clinical studies. A future hope for the metabonomic approach is the identification of biomarkers that are able to highlight individuals likely to suffer from diabetes and enable early diagnosis of the disease or the identification of those at risk. This review summarizes the technologies currently being used in metabonomics, as well as the studies reported related to diabetes prior to a description of the general objective of the research plan of the metabonomics part of the European Union project, Molecular Phenotyping to Accelerate Genomic Epidemiology.

(39)K nuclear magnetic resonance was used to measure the efflux of K(+) from suspensions of human erythrocytes [red blood cells (RBCs)], that occurred in response to the calcium ionophore, A23187 and calcium ions; the latter activate the Gárdos channel. Signals from the intra- and extracellular populations of (39)K(+) were selected on the basis of their longitudinal relaxation times, T (1), by using an inversion- recovery pulse sequence with the mixing time, tau(1), chosen to null one or other of the signals. Changes in RBC volume consequent upon efflux of the ions also changed the T (1) values so a new theory was implemented to obviate a potential artefact in the data analysis. The velocity of the K(+) efflux mediated by the Gárdos channel was 1.19+/-0.40 mmol (L RBC)(-1) min(-1) at 37 degrees C.

Ca(2+)-dependent K(+) efflux from human erythrocytes was first described in the 1950s. Subsequent studies revealed that a K(+)-specific membrane protein (the Gárdos channel) was responsible for this phenomenon (the Gárdos effect). In recent years several types of Ca-activated K(+) channel have been identified and studied in a wide range of cells, with the erythrocyte Gárdos channel serving as both a model for a broader physiological perspective, and an intriguing component of erythrocyte function. The existence of this channel has raised a number of questions. For example, what is its role in the establishment and maintenance of ionic distribution across the red cell membrane? What role might it play in erythrocyte development? To what extent is it active in circulating erythrocytes? What are the cell-physiological implications of its dysfunction?This review summarises current knowledge of this membrane protein with respect to its function and structure, its physiological roles (some putative) and its contribution to various disease states, and it provides an introduction to adaptable NMR methods, which is our own area of technical expertise, for such ion transport analysis.

We show how to incorporate the membrane potential and its effects on the kinetics of ion transport processes into kinetic models.

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.

Ethanol is a known neuromodulatory agent with reported actions at a range of neurotransmitter receptors. Here, we used an indirect approach, measuring the effect of alcohol on metabolism of [3-13C]pyruvate in the adult Guinea pig brain cortical tissue slice and comparing 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. Ethanol (10, 30 and 60 mM) significantly reduced metabolic flux into all measured isotopomers and reduced all metabolic pool sizes. The metabolic profiles of these three concentrations of ethanol were similar and clustered with that of the α4β3δ positive allosteric modulator DS2 (4-Chloro-N-[2-(2-thienyl)imidazo[1,2a]-pyridin-3-yl]benzamide). Ethanol at a very low concentration (0.1 mM) produced a metabolic profile which clustered with those from inhibitors of GABA uptake, and ligands showing affinity for α5, and to a lesser extent, 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 unlabeled 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 (STOCSY(S)). STOCSY(S) 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 STOCSY(S): 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 STOCSY(S) 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, STOCSY(S) 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.

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.

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 concentrations30 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 (1)H 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 (1)H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz (1)H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz (1)H 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.

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 (1)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 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.

Background: Compromised sexual health is a major rehabilitative barrier for men with lower–spinal cord injury (SCI). Although studies have revealed decreased sperm motility, the quantitative biochemical changes that underlie the infertility mechanism remain poorly understood. Methods: We employed a nontargeted approach combining 800 MHz hydrogen nuclear magnetic resonance (1H NMR) spectroscopy and ultra-performance liquid chromatography–mass spectrometry (UPLC-MS) with pattern recognition methods to analyze seminal fluid metabolite profiles in 10 men with and 8 without SCI above thoracic vertebra 10 (T10). Results: The metabolic phenotype for SCI could be predicted from the 1H NMR data. The median concentration of uridine in fertile controls was 1.55 mmol/L (range 1.0–5.0 mmol/L), but was undetectable by both NMR and MS in all but 2 individuals from the SCI group, one who later fathered a child without assisted fertility techniques. Conclusions: We hypothesize that uridine is likely to be an essential precursor to metabolites required for capacitation and is a potential marker for the prognosis of post-SCI functional fertility recovery. We derived the term “seminal oligouridinosis” to describe this newly identified condition.

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 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.

The renal Fanconi syndrome is a defect of proximal tubular function causing aminoaciduria and low-molecular-weight proteinuria. Dent's disease and Lowe syndrome are defined X-linked forms of Fanconi syndrome; there is also an autosomal dominant idiopathic form (ADIF), phenotypically similar to Dent's disease though its gene defect is still unknown. To assess whether their respective gene products are ultimately involved in a common reabsorptive pathway for proteins and low-molecular-mass endogenous metabolites, we compared renal Fanconi urinary proteomes and metabonomes with normal (control) urine using mass spectrometry and1H-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.

1H NMR spectroscopy potentially provides a robust approach for high-throughput metabolic screening of biofluids such as urine and plasma, but sample handling and preparation need careful optimization to ensure that spectra accurately report biological status or disease state. We have investigated the effects of storage temperature and time on the 1H NMR spectral profiles of human urine from two participants, collected three times a day on four different days. These were analyzed using modern chemometric methods. Analytical and preparation variation (tested between -40 degrees C and room temperature) and time of storage (to 24 h) were found to be much less influential than biological variation in sample classification. Statistical total correlation spectroscopy and discriminant function methods were used to identify the specific metabolites that were hypervariable due to preparation and biology. Significant intraindividual variation in metabolite profiles were observed even for urine collected on the same day and after at least 6 h fasting. The effect of long-term storage at different temperatures was also investigated, showing urine is stable if frozen for at least 3 months and that storage at room temperature for long periods (1-3 months) results in a metabolic profile explained by bacterial activity. Presampling (e.g., previous day) intake of food and medicine can also strongly influence the urinary metabolic profiles indicating that collective detailed participant historical meta data are important for interpretation of metabolic phenotypes and for avoiding false biomarker discovery.

Metabolite channelling, the process in which consecutive enzymes have confined substrate transfer in metabolic pathways, has been proposed as a biochemical mechanism that has evolved because it enhances catalytic rates and protects unstable intermediates. Results from experiments on the synthesis of radioactive urea [Cheung, C., Cohen, N.S. & Raijman, L (1989) J. Biol. Chem.264, 4038–4044] have been interpreted as implying channelling of arginine between argininosuccinate lyase and arginase in permeabilized hepatocytes. To investigate this interpretation further, a mathematical model of the urea cycle was written, using Mathematica it simulates time courses of the reactions. The model includes all relevant intermediates, peripheral metabolites, and subcellular compartmentalization. Analysis of the output from the simulations supports the argument for a high degree of, but not absolute, channelling and offers insights for future experiments that could shed more light on the quantitative aspects of this phenomenon in the urea cycle and other pathways.

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 (8.6x10(-11)