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Defining a metabolic phenotype in the brain of a transgenic mouse model of spinocerebellar ataxia 3

¹ Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW
² Cell and Molecular Biology Section, Biomedical Sciences Division, Imperial College London, London SW7 2AZ
³ Department of Medical and Community Genetics, Imperial College London, Northwick Park Hospital, Harrow HA1 3UJ, United Kingdom

Many of the spinocerebellar ataxias (SCAs) are caused by expansions of CAG trinucleotide repeats encoding abnormal stretches of polyglutamine. SCA3 or Machado-Joseph disease (MJD) is the commonest dominant inherited ataxia disease, with pathological phenotypes apparent with a CAG triplet repeat length of 61–84. In this study a mouse model of SCA3 has been examined which was produced using a human yeast artificial chromosome containing the MJD gene with a CAG triplet expansion of 84 repeats. These mice have previously been shown to possess a mild progressive cerebellar deficit. NMR-based metabolomics/metabonomics in conjunction with multivariate pattern recognition identified a number of metabolic perturbations in SCA3 mice. These changes included a consistent increase in glutamine concentration in tissue extracts of the cerebellum and cerebrum and spectra obtained from intact tissue using magic angle spinning ¹H-NMR spectroscopy. Furthermore, these profiles demonstrated metabolic abnormalities were present in the cerebrum, a region not previously implicated in SCA3. As well as an increase in glutamine both brain regions demonstrated decreases in GABA, choline, phosphocholine and lactate (representing the summation of lactate in vivo, and postmortem glycolysis of glucose and glycogen). The metabolic changes are discussed in terms of the formation of neuronal intranuclear inclusions associated with SCA3. This study suggests high-resolution ¹H-NMR spectroscopy coupled with pattern recognition may provide a rapid method for assessing the phenotype of animal models of human disease.

Machado-Joseph disease; magic angle spinning; metabolomics; multivariate pattern recognition

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