Weighted least-squares deconvolution method for discovery of group differences between complex biofluid 1H NMR spectra.

Biomarker discovery through analysis of high-throughput NMR data is a challenging, time-consuming process due to the requirement of sophisticated, dataset specific preprocessing techniques and the inherent complexity of the data. Here, we demonstrate the use of weighted, constrained least-squares for fitting a linear mixture of reference standard data to complex urine NMR spectra as an automated way of utilizing current assignment knowledge and the ability to deconvolve confounded spectral regions. Following the least-squares fit, univariate statistics were used to identify metabolites associated with group differences. This method was evaluated through applications on simulated datasets and a murine diabetes dataset. Furthermore, we examined the differential ability of various weighting metrics to correctly identify discriminative markers. Our findings suggest that the weighted least-squares approach is effective for identifying biochemical discriminators of varying physiological states. Additionally, the superiority of specific weighting metrics is demonstrated in particular datasets. An additional strength of this methodology is the ability for individual investigators to couple this analysis with laboratory specific preprocessing techniques.

Optimisation of collection, storage and preparation of rat plasma for 1H NMR spectroscopic analysis in toxicology studies to determine inherent variation in biochemical profiles.

Biofluid 1H NMR spectroscopy has been assessed as a tool for toxicological investigations for almost two decades, with most studies focussing on urinary changes. This study has examined variations in the 1H NMR spectroscopy spectra of plasma collected from control rats at different times of the day. The collection, preparation and storage of samples were optimised and potential sources of variation in samples taken for toxicology studies identified. Plasma samples were collected into heparinised containers and analysed following a standard dilution with D(2)O. The value of deproteinising plasma with acetonitrile to look at low molecular weight metabolites has also been assessed. Variations in lactate and citrate levels in whole blood plasma were found and are consistent with the observation that lactate is one of the most variable metabolites in human plasma. Lipids levels also varied, in particular higher levels of lipids were found in spectra from male rats compared to female rats, and in samples collected in the morning following the feeding period. No significant changes were identified in samples which were snap-frozen and stored for up to 9 months at -80 degrees C. More changes were observed after storage at 4 degrees C or room temperature, including an increase in glycerol and choline levels, which may have resulted from lipid hydrolysis.

A metabolomic study of the CRND8 transgenic mouse model of Alzheimer's disease.

Alzheimer's disease is the most common neurodegenerative disease of the central nervous system characterized by a progressive loss in memory and deterioration of cognitive functions. In this study the transgenic mouse TgCRND8, which encodes a mutant form of the amyloid precursor protein 695 with both the Swedish and Indiana mutations and develops extracellular amyloid beta-peptide deposits as early as 2-3 months, was investigated. Extract from eight brain regions (cortex, frontal cortex, cerebellum, hippocampus, olfactory bulb, pons, midbrain and striatum) were studied using (1)H NMR spectroscopy. Analysis of the NMR spectra discriminated control from APP695 tissues in hippocampus, cortex, frontal cortex, midbrain and cerebellum, with hippocampal and cortical region being most affected. The analysis of the corresponding loading plots for these brain regions indicated a decrease in N-acetyl-L-aspartate, glutamate, glutamine, taurine (exception hippocampus), gamma-amino butyric acid, choline and phosphocholine (combined resonances), creatine, phosphocreatine and succinate in hippocampus, cortex, frontal cortex (exception gamma-amino butyric acid) and midbrain of affected animals. An increase in lactate, aspartate, glycine (except in midbrain) and other amino acids including alanine (exception frontal cortex), leucine, iso-leucine, valine and water soluble free fatty acids (0.8-0.9 and 1.2-1.3 ppm) were observed in the TgCRND8 mice. Our findings demonstrate that the perturbations in metabolism are more widespread and include the cerebellum and midbrain. Furthermore, metabolic perturbations are associated with a wide range of metabolites which could improve the diagnosis and monitoring of the progression of Alzheimer's disease.

A metabolomic study of brain tissues from aged mice with low expression of the vesicular monoamine transporter 2 (VMAT2) gene.

The vesicular monoamine transporter 2 (VMAT2) sequesters monoamines into synaptic vesicles in preparation for neurotransmission. Samples of cerebellum, cortex, hippocampus, substantia nigra and striatum from VMAT2-deficient mice were compared to age-matched control mice. Multivariate statistical analyses of (1)H NMR spectral profiles separated VMAT2-deficient mice from controls for all five brain regions. Although the data show that metabolic alterations are region- and age-specific, in general, analyses indicated decreases in the concentrations of taurine and creatine/phosphocreatine and increases in glutamate and N-acetyl aspartate in VMAT2-deficient mouse brain tissues. This study demonstrates the efficacy of metabolomics as a functional genomics phenotyping tool for mouse models of neurological disorders, and indicates that mild reductions in the expression of VMAT2 affect normal brain metabolism.

Multi-platform investigation of the metabolome in a leptin receptor defective murine model of type 2 diabetes.

We describe a multi-platform ((1)H NMR, LC-MS, microarray) investigation of metabolic disturbances associated with the leptin receptor defective (db/db) mouse model of type 2 diabetes using novel assignment methodologies. For the first time, several urinary metabolites were found to be associated with diabetes and/or diabetes progression and confirmed in both NMR and LC-MS datasets. The confirmed metabolites were trimethylamine-n-oxide (TMAO), creatine, carnitine, and phenylalanine. TMAO and phenylalanine were both elevated in db/db mice and decreased in these mice with age. Levels of both creatine and carnitine increase in diabetic mice with age and creatine was also significantly decreased in db/db mice. Additionally, many metabolic markers were found by either NMR or LC-MS, but could not be found in both, due to instrumental limitations. This indicates that the combined use of NMR and LC-MS instrumentation provides complementary information that would be otherwise unattainable. Pathway analyses of urinary metabolites and liver, muscle, and adipose tissue transcripts from the db/db model were also performed to identify altered biochemical processes in the diabetic mice. Metabolite and liver transcript levels associated with the TCA cycle and steroid processes were altered in db/db mice. In addition, gene expression in muscle and liver associated with fatty acid processing was altered in the diabetic mice and similar evidence was observed in the LC-MS data. Our findings highlight the importance of a number of processes known to be associated with diabetes and reveal tissue specific responses to the condition. When studying metabolic disorders such as diabetes, multiple platform integrated profiling of metabolite alterations in biofluids can provide important insights into the processes underlying the disease.

Peak alignment of urine NMR spectra using fuzzy warping.

Proton nuclear magnetic resonance (1H NMR) spectroscopic analysis of mixtures has been used extensively for a variety of applications ranging from the analysis of plant extracts, wine, and food to the evaluation of toxicity in animals. For example, NMR analysis of urine samples has been used extensively for biomarker discovery and, more simply, for the construction of classification models of toxicity, disease, and biochemical phenotype. However, NMR spectra of complex mixtures typically show unwanted local peak shifts caused by matrix and instrument variability, which must be compensated for prior to statistical analysis and interpretation of the data. One approach is to align the spectral peaks across the data set. An efficient and fast warping algorithm is required as the signals typically contain ca. 32,000-64,000 data points and there can be several thousand spectra in a data set. As demonstrated in our study, the iterative fuzzy warping algorithm fulfills these requirements and can be used on-line for an alignment of the NMR spectra. Correlation coefficients between the aligned and target spectra are used as the evaluation function for the algorithm, and its performance is compared with those of other published warping methods.

A functional analysis of mouse models of cardiac disease through metabolic profiling.

Since the completion of the human and mouse genomes, the focus in mammalian biology has been on assessing gene function. Tools are needed for assessing the phenotypes of the many mouse models that are now being generated, where genes have been "knocked out," "knocked in," or mutated, so that gene expression can be understood in its biological context. Metabolic profiling of cardiac tissue through high resolution NMR spectroscopy in conjunction with multivariate statistics has been used to classify mouse models of cardiac disease. The data sets included metabolic profiles from mouse models of Duchenne muscular dystrophy, two models of cardiac arrhythmia, and one of cardiac hypertrophy. The metabolic profiles demonstrate that the strain background is an important component of the global metabolic phenotype of a mouse, providing insight into how a given gene deletion may result in very different responses in diverse populations. Despite these differences associated with strain, multivariate statistics were capable of separating each mouse model from its control strain, demonstrating that metabolic profiles could be generated for each disease. Thus, this approach is a rapid method of phenotyping mouse models of disease.

Tryptophan-NAD+ pathway metabolites as putative biomarkers and predictors of peroxisome proliferation.

The present study was designed to provide further information about the relevance of raised urinary levels of N-methylnicotinamide (NMN), and/or its metabolites N-methyl-4-pyridone-3-carboxamide (4PY) and N-methyl-2-pyridone-3-carboxamide (2PY), to peroxisome proliferation by dosing rats with known peroxisome proliferator-activated receptor alpha (PPARalpha) ligands [fenofibrate, diethylhexylphthalate (DEHP) and long-chain fatty acids (LCFA)] and other compounds believed to modulate lipid metabolism via PPARalpha-independent mechanisms (simvastatin, hydrazine and chlorpromazine). Urinary NMN was correlated with standard markers of peroxisome proliferation and serum lipid parameters with the aim of establishing whether urinary NMN could be used as a biomarker for peroxisome proliferation in the rat. Data from this study were also used to validate a previously constructed multivariate statistical model of peroxisome proliferation (PP) in the rat. The predictive model, based on 1H nuclear magnetic resonance (NMR) spectroscopy of urine, uses spectral patterns of NMN, 4PY and other endogenous metabolites to predict hepatocellular peroxisome count. Each treatment induced pharmacological (serum lipid) effects characteristic of their class, but only fenofibrate, DEHP and simvastatin increased peroxisome number and raised urinary NMN, 2PY and 4PY, with simvastatin having only a transient effect on the latter. These compounds also reduced mRNA expression for aminocarboxymuconate-semialdehyde decarboxylase (ACMSDase, EC 4.1.1.45), the enzyme believed to be involved in modulating the flux of tryptophan through this pathway, with decreasing order of potency, fenofibrate (-10.39-fold) >DEHP (-3.09-fold) >simvastatin (-1.84-fold). Of the other treatments, only LCFA influenced mRNA expression of ACMSDase (-3.62-fold reduction) and quinolinate phosphoribosyltransferase (QAPRTase, EC 2.4.2.19) (-2.42-fold) without any change in urinary NMN excretion. Although there were no correlations between urinary NMN concentration and serum lipid parameters, NMN did correlate with peroxisome count (r2=0.63) and acyl-CoA oxidase activity (r2=0.61). These correlations were biased by the large response to fenofibrate compared to the other treatments; nevertheless the data do indicate a relationship between the tryptophan-NAD+ pathway and PPARalpha-dependent pathways, making this metabolite a potentially useful biomarker to detect PP. In order to strengthen the observed link between the metabolites associated with the tryptophan-NAD+ pathway and more accurately predict PP, other urinary metabolites were included in a predictive statistical model. This statistical model was found to predict the observed PP in 26/27 instances using a pre-determined threshold of 2-fold mean control peroxisome count. The model also predicted a time-dependent increase in peroxisome count for the fenofibrate group, which is important when considering the use of such modelling to predict the onset and progression of PP prior to its observation in samples taken at autopsy.

Effects of feeding and body weight loss on the 1H-NMR-based urine metabolic profiles of male Wistar Han rats: implications for biomarker discovery.

For almost two decades, 1H-NMR spectroscopy has been used as an 'open' system to study the temporal changes in the biochemical composition of biofluids, including urine, in response to adverse toxic events. Many of these in vivo studies have reported changes in individual metabolites and patterns of metabolites that correlated with toxicological changes. However, many of the proposed novel biomarkers are common to a number of different types of toxicity. These may therefore reflect non-specific effects of toxicity, such as weight loss, rather than a specific pathology. A study was carried out to investigate the non-specific effects on urinary metabolite profiles by administering four hepatotoxic compounds, as a single dose, to rats at two dose levels: hydrazine hydrate (0.06 or 0.08 g kg (1)), 1,2-dimethylhydrazine (0.1 or 0.3 g kg (-1)), alpha-napthylisothiocyanate (0.1 or 0.15 g kg(-1)) and carbon tetrachloride (1.58 or 3.16 g kg(-1)). The study included weight-matched control animals along with those that were dosed, which were then 'pair-fed' with the treated animals so they achieved a similar weight loss. The urinary metabolite profiles were investigated over time using 1H-NMR spectroscopy and compared with the pathology from the same animals. The temporal changes were analysed statistically using multivariate statistical data analysis including principal component analysis, partial least squares, parallel factor analysis and Fisher's criteria. A number of metabolites associated with energy metabolism or which are partially dietary in origin, such as creatine, creatinine, tricarboxylic acid (TCA) cycle intermediates, phenylacetylglycine, fumarate, glucose, taurine, fatty acids and N-methylnicotinamide, showed altered levels in the urine of treated and pair-fed animals. Many of these changes correlated well with weight loss. Interestingly, there was no increase in ketone bodies (acetate and beta-hydroxybutyrate), which might be expected if energy metabolism was switched from glycolysis to fatty acid beta-oxidation. In some instances, the metabolites that changed were considered to be non-specific markers of toxicity, but were also identified as markers of a specific type of toxicity. For example, taurine was raised significantly in carbon tetrachloride-treated animals but reduced in the pair-fed group. However, raised urinary bile acid levels were only seen after alpha-napthylisothiocyanate treatment. The methodology, statistical analysis used and the data generated will help improve the identification of specific markers or patterns of urinary markers of specific toxic effects.

Development of a multivariate statistical model to predict peroxisome proliferation in the rat, based on urinary 1H-NMR spectral patterns.

A previous report of this work (Ringeissen et al. 2003) described the use of nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical data analysis (MVDA) to identify novel biomarkers of peroxisome proliferation (PP) in Wistar Han rats. Two potential biomarkers of peroxisome proliferation in the rat were described, N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY). The inference from these results was that the tryptophan-nicotinamide adenine dinucleotide (NAD(+)) pathway was altered in correlation with peroxisome proliferation, a hypothesis subsequently confirmed by TaqMan analysis of the relevant genes encoding two key enzymes in the pathway, aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) and quinolinate phosphoribosyltransferase (EC 2.4.2.19). The objective of the present study was to investigate these data further and identify other metabolites in the NMR spectrum correlating equally with PP. MVDA Partial Least Squares (PLS) models were constructed that provided a better prediction of PP in Wistar Han rats than levels of 4PY and NMN alone. The resulting Wistar Han rat predictive models were then used to predict PP in a test group of Sprague Dawley rats following administration of fenofibrate. The models predicted the presence or absence of PP (above on arbitrary threshold of >2-fold mean control) in all Sprague Dawley rats in the test group.

Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development.

The application of liquid chromatography/mass spectrometry (LC/MS) followed by principal components analysis (PCA) has been successfully applied to the screening of rat urine following the administration of three candidate pharmaceuticals. With this methodology it was possible to differentiate the control samples from the dosed samples and to identify the components of the mass spectrum responsible for the separation. These data clearly show that LC/MS is a viable alternative, or complementary, technique to proton NMR for metabonomics applications in drug discovery and development.

Potential urinary and plasma biomarkers of peroxisome proliferation in the rat: identification of N-methylnicotinamide and N-methyl-4-pyridone-3-carboxamide by 1H nuclear magnetic resonance and high performance liquid chromatography.

This study identified two potential novel biomarkers of peroxisome proliferation in the rat. Three peroxisome proliferator-activated receptor (PPAR) ligands, chosen for their high selectivity towards the PPARalpha, -delta and -gamma subtypes, were given to rats twice daily for 7 days at doses known to cause a pharmacological effect or peroxisome proliferation. Fenofibrate was used as a positive control. Daily treatment with the PPARalpha and -delta agonists produced peroxisome proliferation and liver hypertrophy. 1H nuclear magnetic resonance spectroscopy and multivariate statistical data analysis of urinary spectra from animals given the PPARalpha and -delta agonists identified two new potential biomarkers of peroxisome proliferation--N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY)--both endproducts of the tryptophan-nicotinamide adenine dinucleotide (NAD+) pathway. After 7 days, excretion of NMN and 4PY increased 24- and three-fold, respectively, following high doses of fenofibrate. The correlation between total NMN excretion over 7 days and the peroxisome count was r=0.87 (r2=0.76). Plasma NMN, measured using a sensitive high performance liquid chromatography method, was increased up to 61-fold after 7 days' treatment with high doses of fenofibrate. Hepatic gene expression of aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) was downregulated following treatment with the PPARalpha and -delta agonists. The decrease was up to 11-fold compared with controls in the groups treated with high doses of fenofibrate. This supports the link between increased NMN and 4PY excretion and regulation of the tryptophan-NAD+ pathway in the liver. In conclusion, NMN, and possibly other metabolites in the pathway, are potential non-invasive surrogate biomarkers of peroxisome proliferation in the rat.