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In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease

Abstract : Huntington's disease (HD) is an inherited neurodegenerative disease characterized by motor, cognitive and psychiatric symptoms. Atrophy of the striatum has been proposed for several years as a biomarker to assess disease progression in HD gene carriers. However, it does not provide any information about the biological mechanisms linked to HD pathogenesis. Changes in brain metabolites have been also consistently seen in HD patients and animal models using Magnetic Resonance Spectroscopy (MRS), but metabolite measurements are generally limited to a single voxel. In this study, we used Chemical Exchange Saturation Transfer imaging of glutamate (gluCEST) in order to map glutamate distribution in the brain of a knock-in mouse model (Ki140CAG) with a precise anatomical resolution. We demonstrated that both heterozygous and homozygous mice with pathological CAG repeat expansion in gene encoding huntingtin exhibited an atrophy of the striatum and a significant alteration of their metabolic profile in the striatum as compared to wild type littermate controls. The striatal decrease was then confirmed by gluCEST imaging. Surprisingly, CEST imaging also revealed that the corpus callosum was the most affected structure in both genotype groups, suggesting that this structure could be highly vulnerable in HD. We evaluated for the first time gluCEST imaging as a potential biomarker of HD and demonstrated its potential for characterizing metabolic defects in neurodegenerative diseases in specific regions.
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Contributor : Julien Flament Connect in order to contact the contributor
Submitted on : Friday, December 6, 2019 - 4:54:13 PM
Last modification on : Saturday, June 25, 2022 - 8:28:41 PM

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Jérémy Pépin, Laetitia Francelle, Maria-Angeles Carrillo-de Sauvage, Lucie de Longprez, Pauline Gipchtein, et al.. In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease. NeuroImage, Elsevier, 2016, 139, pp.53-64. ⟨10.1016/j.neuroimage.2016.06.023⟩. ⟨cea-02397809⟩



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