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Highlights • Down Syndrome is manifested by overproduction of Aβ and hyperphosphorylated tau. • Anti-Aβ immunization reduces microglial and astroglial inflammatory phenotype. • Vaccination facilitates clearance of Aβ oligomers and small inclusion of Aβ from the neural tissue. • Vaccinated Ts65Dn performed better in spatial learning and short-term memory tasks. • DS individuals may benefit from active immunotherapy against Aβ from a young age. Abstract Down Syndrome (DS), the most common cause of genetic intellectual disability, is characterized by over-expression of the APP and DYRK1A genes, located on the triplicated chromosome 21. This chromosomal abnormality leads to a cognitive decline mediated by Amyloid-β (Aβ) overproduction and tau hyper-phosphorylation as early as the age of 40. In this study, we used the Ts65Dn mouse model of DS to evaluate the beneficial effect of a DNA vaccination against the Aβ 1-11 fragment, in ameliorating Aβ-related neuropathology and rescue of cognitive and behavioral abilities. Anti-Aβ 1-11 vaccination induced antibody production and facilitated clearance of soluble oligomers and small extracellular inclusions of Aβ from the hippocampus and cortex of Ts65Dn mice. This was correlated with reduced neurodegeneration and restoration of the homeostatic phenotype of microglial and astroglial cells. Vaccinated Ts65Dn mice performed better in spatial-learning tasks, exhibited reduced motor hyperactivity typical for this strain, and restored short-term memory abilities. Our findings support the hypothesis that DS individuals may benefit from active immunotherapy against Aβ from a young age by slowing the progression of dementia. [ABSTRACT FROM AUTHOR]

The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) – based, automated, M WM u nbiased st rategy c lassification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data. [ABSTRACT FROM AUTHOR]