A novel porcine model of ataxia telangiectasia reproduces neurological features and motor deficits of human disease.

Ataxia telangiectasia (AT) is a progressive multisystem disorder caused by mutations in the AT-mutated (ATM) gene. AT is a neurodegenerative disease primarily characterized by cerebellar degeneration in children leading to motor impairment. The disease progresses with other clinical manifestations including oculocutaneous telangiectasia, immune disorders, increased susceptibly to cancer and respiratory infections. Although genetic investigations and physiological models have established the linkage of ATM with AT onset, the mechanisms linking ATM to neurodegeneration remain undetermined, hindering therapeutic development. Several murine models of AT have been successfully generated showing some of the clinical manifestations of the disease, however they do not fully recapitulate the hallmark neurological phenotype, thus highlighting the need for a more suitable animal model. We engineered a novel porcine model of AT to better phenocopy the disease and bridge the gap between human and current animal models. The initial characterization of AT pigs revealed early cerebellar lesions including loss of Purkinje cells (PCs) and altered cytoarchitecture suggesting a developmental etiology for AT and could advocate for early therapies for AT patients. In addition, similar to patients, AT pigs show growth retardation and develop motor deficit phenotypes. By using the porcine system to model human AT, we established the first animal model showing PC loss and motor features of the human disease. The novel AT pig provides new opportunities to unmask functions and roles of ATM in AT disease and in physiological conditions.

Three-level rating of turns while walking.

Research concerning the assessment of turns during walking in healthy older adults is scarce. This study compared three independent assessments of entry and exit points of turns during walking; participant, clinical rater, and a computer algorithm. Nineteen non-demented and nondisabled older adults (mean age 75.40 ± 5.52 years) participated in the current study. Results revealed that overall the three assessment methods were consistent (68-100% agreement). However, participants determined their turn exit point before the algorithm, (-304.53 ± 326.67 ms), t(18) = -4.06, p = .001, 95% CI [-461.98, -147.08], and clinical rater, (-225.79 ± 303.79 ms), t(18) = -3.24, p = .005, 95% CI [-372.21, -79.37]. The differences in turn determination between the algorithm and rater were significant at turn entry points (131.24 ± 127.25 ms), t(18) = 4.50, p < .001, 95% CI [69.91, 192.58] but not at turn exit points (-78.74 ± 259.66 ms), t(18) = -1.32, p < .20, 95% CI [-203.89, -46.41]. Greater time discrepancies in assessing turn exit points between the participants and both the algorithm and clinical rater were associated with worse visuospatial performance. Despite the relatively small difference among the three assessments of turns, they were consistent and can be utilized interchangeably. Further studies are necessary to determine whether differences in the ability to accurately determine turns entry and exit points are related to fall risk in normal and disease populations.