Article

High Resolution 1H NMR-based Metabolomics Indicates a Neurotransmitter Cycling Deficit in Cerebral Tissue from a Mouse Model of Batten Disease

Department of Biochemistry, Tennis Court Road, University of Cambridge, Cambridge CB2 1GA, United Kingdom.
Journal of Biological Chemistry (Impact Factor: 4.57). 01/2006; 280(52):42508-14. DOI: 10.1074/jbc.M507380200
Source: PubMed

ABSTRACT The neuronal ceroid lipofuscinoses (NCLs) constitute a range of progressive neurological disorders primarily affecting children.
Although six of the causative genes have been characterized, the underlying disease pathogenesis for this family of disorders
is unknown. Using a metabolomics approach based on high resolution 1H NMR spectroscopy of the cortex, cerebellum, and remaining regions of the brain in conjunction with statistical pattern recognition,
we report metabolic deficits associated with juvenile NCL in a Cln3 knock-out mouse model. Tissue from Cln3 null mutant mice aged 1–6 months was characterized by an increased glutamate concentration and a decrease in γ-amino butyric
acid (GABA) concentration in aqueous extracts from the three regions of the brain. These changes are consistent with the reported
altered expression of genes involved in glutamate metabolism in older mice and imply a change in neurotransmitter cycling
between glutamate/glutamine and the production of GABA. Further variations in myo-inositol, creatine, and N-acetyl-aspartate were also identified. These metabolic changes were distinct from the normal aging/developmental process.
Together, these changes represent the first documented pre-symptomatic symptoms of the Cln3 mouse at 1 month of age and demonstrate the versatility of 1H NMR spectroscopy as a tool for phenotyping mouse models of disease.

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    • "For example, 1 H-NMR has been used to uncover specific highly abundant metabolites (N-acetyl-aspartate, myo-inositol, glutamate , glutamine, creatine, choline, and GABA). Such studies have proven to be a key for the characterization of neurochemical and metabolic profiles relevant to brain health (Davidovic et al., 2011; Liu et al., 2013; Pears et al., 2005; Prabakaran et al., 2004; Tká c et al., 2004). Granting high reproducibility, the MR technologies are restricted by their low sensitivity, which hinders comprehensive pathologic assessments. "
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    ABSTRACT: Historically, studies of brain metabolism have been based on targeted analyses of a limited number of metabolites. Here we present an untargeted mass spectrometry-based metabolomic strategy that has successfully uncovered differences in a broad array of metabolites across anatomical regions of the mouse brain. The NSG immunodeficient mouse model was chosen because of its ability to undergo humanization leading to numerous applications in oncology and infectious disease research. Metabolic phenotyping by hydrophilic interaction liquid chromatography and nanostructure imaging mass spectrometry revealed both water-soluble and lipid metabolite patterns across brain regions. Neurochemical differences in metabolic phenotypes were mainly defined by various phospholipids and several intriguing metabolites including carnosine, cholesterol sulfate, lipoamino acids, uric acid, and sialic acid, whose physiological roles in brain metabolism are poorly understood. This study helps define regional homeostasis for the normal mouse brain to give context to the reaction to pathological events.
    Chemistry & Biology 11/2014; 21(11):1575-1584. DOI:10.1016/j.chembiol.2014.09.016 · 6.59 Impact Factor
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    • "quantitatively assay the entire range of metabolites present in a sample to characterize its overall biochemical state. Although MA sensitization has not yet been investigated using metabolomics, the platform has successfully identified biochemical signatures for other CNS pathologies including schizophrenia, Parkinson's and motor neuron disease (Bogdanov et al. 2008; Pears et al. 2005; Prabakaran et al. 2004). Thus, metabolomics offers the possibility of comprehensively mapping neurochemical profiles associated with MA-induced BSn, facilitating the identification of novel mechanisms , biomarkers and therapeutic targets. "
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    ABSTRACT: Behavioral sensitization has been widely studied in animal models and is theorized to reflect neural modifications associated with human psychostimulant addiction. While the mesolimbic dopaminergic pathway is known to play a role, the neurochemical mechanisms underlying behavioral sensitization remain incompletely understood. In the present study, we conducted the first metabolomics analysis to globally characterize neurochemical differences associated with behavioral sensitization. Methamphetamine-induced sensitization measures were generated by statistically modeling longitudinal activity data for eight inbred strains of mice. Subsequent to behavioral testing, nontargeted liquid and gas chromatography-mass spectrometry profiling was performed on 48 brain samples, yielding 301 metabolite levels per sample after quality control. Association testing between metabolite levels and three primary dimensions of behavioral sensitization (total distance, stereotypy and margin time) showed four robust, significant associations at a stringent metabolome-wide significance threshold (false discovery rate < 0.05). Results implicated homocarnosine, a dipeptide of GABA and histidine, in total distance sensitization, GABA metabolite 4-guanidinobutanoate and pantothenate in stereotypy sensitization, and myo-inositol in margin time sensitization. Secondary analyses indicated that these associations were independent of concurrent methamphetamine levels and, with the exception of the myo-inositol association, suggest a mechanism whereby strain-based genetic variation produces specific baseline neurochemical differences that substantially influence the magnitude of MA-induced sensitization. These findings demonstrate the utility of mouse metabolomics for identifying novel biomarkers, and developing more comprehensive neurochemical models, of psychostimulant sensitization.
    Genes Brain and Behavior 09/2013; 12(8). DOI:10.1111/gbb.12081 · 3.51 Impact Factor
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    • "The term 'metabolomics' is defined as the study of ''the complete set of metabolites/low-molecular-weight intermediates , which are context dependent, varying according to the physiological, developmental or pathological state of the cell, tissue, organ or organism'' (Oliver 2003). Often, the terms metabolomics and metabonomics are used similarly in the literature and it is thus important to search for both terms, separately, while conducting a full literature search (Brindle et al. 2002; Leo et al. 2006; Pears et al. 2005; Psihogios et al. 2008; Rahmioglu et al. 2011; Rochfort et al. 2009; Verwaest et al. 2011; Viant et al. 2003; Wen et al. 2011; Qi et al. 2012; Hasim et al. 2012; Nevedomskaya et al. 2012; Zhang et al. 2012). However, Nicholson has distinguished between the two terms such that metabolomics is ''the measurement of metabolite concentrations and fluxes and secretion in cells and tissues in which there is a direct connection between the genetic activity, protein activity and the metabolic activity itself'' (Nicholson and Wilson 2003), whereas metabonomics is ''the quantitative measurement of the multivariate metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification'' (Nicholson et al. 1999; Nicholson and Wilson 2003). "
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    ABSTRACT: Metabolomics is a dynamic and emerging research field, similar to proteomics, transcriptomics and genomics in affording global understanding of biological systems. It is particularly useful in functional genomic studies in which metabolism is thought to be perturbed. Metabolomics provides a snapshot of the metabolic dynamics that reflect the response of living systems to both pathophysiological stimuli and/or genetic modification. Because this approach makes possible the examination of interactions between an organism and its diet or environment, it is particularly useful for identifying biomarkers of disease processes that involve the environment. For example, the interaction of a high fat diet with cardiovascular disease can be studied via such a metabolomics approach by modeling the interaction between genes and diet. The high reproducibility of NMR-based techniques gives this method a number of advantages over other analytical techniques in large-scale and long-term metabolomic studies, such as epidemiological studies. This approach has been used to study a wide range of diseases, through the examination of biofluids, including blood plasma/serum, urine, blister fluid, saliva and semen, as well as tissue extracts and intact tissue biopsies. However, complicating the use of NMR spectroscopy in biomarker discovery is the fact that numerous variables can effect metabolic composition including, fasting, stress, drug administration, diet, gender, age, physical activity, life style and the subject’s health condition. To minimize the influence of these variations in the datasets, all experimental conditions including sample collection, storage, preparation as well as NMR spectroscopic parameters and data analysis should be optimized carefully and conducted in an identical manner as described by the local standard operating protocol . This review highlights the potential applications of NMR-based metabolomics studies and gives some recommendations to improve sample collection, sample preparation and data analysis in using this approach.
    Metabolomics 04/2013; 9(5). DOI:10.1007/s11306-013-0524-y · 3.97 Impact Factor
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