Brusini, I. | bioRxiv Science

bioRxiv2022

Humanized CYP2C19 transgenic mouse as an animal model of cerebellar ataxia

BackgroundAnimal models are essential for understanding etiology and pathophysiology of movement disorders. Previously, we have found that mice transgenic for the human CYP2C19 gene, expressed in the liver and developing brain, exhibit altered neurodevelopment associated with impairments of their motor function and emotionality. ObjectivesTo characterize motoric phenotype of the CYP2C19 transgenic mice and validate its usefulness as an animal model of ataxia. MethodsThe rotarod and beam-walking tests were utilized to quantify the functional alterations induced by motoric phenotype. Dopaminergic system was assessed by tyrosine hydroxylase immunohistochemistry and by chromatographic quantification of the whole-brain dopamine levels. Beam-walking test was also repeated after the treatment with the dopamine receptor antagonists, ecopipam and raclopride. The volumes of 20 brain regions in the CYP2C19 transgenic mice and controls were quantified by 9.4T gadolinium-enhanced postmortem structural neuroimaging. ResultsCYP2C19 transgenic mice were found to exhibit abnormal, unilateral ataxia-like gait, clasping reflex and 5.6-fold more paw-slips using the beam-walking test (p<0.0001, n=89); the phenotype was more pronounced in younger animals. Hyperdopaminergism was observed in the CYP2C19 mice; however, the motoric impairment was not ameliorated by dopamine receptor antagonists and there was also no midbrain dopamine neuron loss in CYP2C19 mice. However, in these mice, cerebellar volume was drastically decreased (11.8% [95%CI: -14.7, -9.0], q<0.0001, n=59), whereas a moderate decrease in hippocampal volume was observed (-4.2% [95%CI: -6.4%, -1.9%], q=0.015, n=59). ConclusionsHumanized CYP2C19 transgenic mice exhibit altered motoric function and functional motoric impairments; this phenotype is likely caused by an aberrant cerebellar development.

neuroscience↗

bioRxiv2021

MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention

MRI data can be used as input to machine learning models to accurately predict brain age in healthy human subjects. A large difference between predicted and chronological brain age (the so-called BrainAGE score) has been associated with disease and neurodegeneration, indicating the potential utility of neuroimaging-based ageing biomarkers. So far, most brain age prediction studies have been carried out on humans. However, it is important for such a biomarker to be validated on laboratory animals too, in order to better account for specific environmental or genetic factors within a more controlled laboratory framework. In this work, we developed a new algorithm for rat brain age prediction based on the combination of Gaussian process regression and a logistic regression classifier. The algorithm was trained on a cohort of 31 normal rats. High prediction accuracy was achieved using leave-one-out cross-validation (mean absolute error = 4.87 weeks, correlation between predicted and chronological age r = 0.92), supporting the validity and potential of the method. Furthermore, the trained model was tested on two independent groups of 24 rats each: a new normal control group and a "healthy lifestyle" group that underwent long-term environmental enrichment and dietary restriction (EEDR) between 3 and 17 months of age. After fitting a linear mixed-effects model, the BrainAGE values were found to increase more slowly with chronological age in the EEDR group than in the controls (slope = 0.52 vs. 0.61; p = 0.015 for the interaction term). When survival analysis was performed with a Cox regression model, the BrainAGE score at 5 months of age had a significant prediction power (p = 0.03). Our results demonstrate that BrainAGE, as computed by the proposed approach, is significantly modulated by EEDR intervention, hence it is a sensitive marker of biological ageing. These findings also support the potential of lifestyle-related prevention approaches to slow down the brain ageing process. Moreover, the results of the survival analysis further demonstrate that BrainAGE is indeed a predictor of ageing outcome.

neuroscience↗