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 β-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 1H 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), γ-amino butyric acid, choline andphosphocholine (combined resonances), creatine, phosphocreatine and succinate in hippocampus, cortex, frontal cortex (exception γ-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 1.2–1.3ppm) 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.
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 âknocked
out,â âknocked in,â 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.
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.
Nuclear magnetic resonance (NMR)-based metabolic profiling of biofluids and tissues are of key interest to enhance biomarker discovery for disease, drug efficacy and toxicity studies. Urine and blood ...plasma/serum are the biofluids of most interest as they are the most accessible in both clinical and preclinical studies. However, proteinaceous fluids, such as blood serum or plasma, represent the greatest technical challenge since the chemical shift (δ) and line-width (ν₁/₂) of internal standards currently used for aqueous NMR samples are greatly affected by protein binding. We have therefore investigated the suitability of 4,4-dimethyl-4-silapentane-1-ammonium trifluoroacetate (DSA) as a universal internal standard for biofluids. Proton (¹H) NMR spectroscopy was used to determine the effect of serum pH (3, 7.4 and 10) and DSA concentration on the overall lineshape and position of the trimethylsilyl resonance of DSA. The results were compared to that of 3-(trimethylsilyl)propionic acid sodium salt (TSP). Both the chemical shift and line-width of the DSA peak were not significantly affected by pH or DSA concentration, whereas these parameters for TSP showed large variations due to protein binding. Furthermore, the peak area of DSA correlated linearly with its concentration under all pH conditions, whilst no linear correlation was observed with TSP. Overall, in contrast to TSP, these results support the use of DSA as an accurate universal internal chemical shift reference and concentration/normalisation standard for biofluids. In the case of proteinaceous biofluids such as serum, where no current standard is available, this offers a considerable saving in both operator and spectrometer time.
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.
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.
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.
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.
The InnoMed PredTox consortium was formed to evaluate whether conventional preclinical safety assessment can be significantly enhanced by incorporation of molecular profiling (“omics”) technologies. ...In short-term toxicological studies in rats, transcriptomics, proteomics and metabolomics data were collected and analyzed in relation to routine clinical chemistry and histopathology. Four of the sixteen hepato- and/or nephrotoxicants given to rats for 1, 3, or 14
days at two dose levels induced similar histopathological effects. These were characterized by bile duct necrosis and hyperplasia and/or increased bilirubin and cholestasis, in addition to hepatocyte necrosis and regeneration, hepatocyte hypertrophy, and hepatic inflammation. Combined analysis of liver transcriptomics data from these studies revealed common gene expression changes which allowed the development of a potential sequence of events on a mechanistic level in accordance with classical endpoint observations. This included genes implicated in early stress responses, regenerative processes, inflammation with inflammatory cell immigration, fibrotic processes, and cholestasis encompassing deregulation of certain membrane transporters. Furthermore, a preliminary classification analysis using transcriptomics data suggested that prediction of cholestasis may be possible based on gene expression changes seen at earlier time-points. Targeted bile acid analysis, based on LC-MS metabonomics data demonstrating increased levels of conjugated or unconjugated bile acids in response to individual compounds, did not provide earlier detection of toxicity as compared to conventional parameters, but may allow distinction of different types of hepatobiliary toxicity. Overall, liver transcriptomics data delivered mechanistic and molecular details in addition to the classical endpoint observations which were further enhanced by targeted bile acid analysis using LC/MS metabonomics.
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