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Spatial learning impairments in PLB1Triple knock-in Alzheimer mice are task-specific and age-dependent
Authors:D. Ryan  D. Koss  E. Porcu  H. Woodcock  L. Robinson  B. Platt  G. Riedel
Affiliation:1. School of Medical Sciences, College of Life Sciences and Medicine, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK
Abstract:We recently generated an advanced mouse model of Alzheimer’s disease (AD) by targeted knock-in of single-copy mutated human amyloid precursor-protein (APP) and tau genes, crossed with a non-symptomatic presenilin (PS1A246E) over-expressing mouse line. These PLB1Triple mice presented with age-dependent and AD-relevant phenotypes. Homozygous PLB1Triple mice aged 4–12 months were assessed here in a battery of spatial learning tasks: Exp.1 radial-arm water maze (spatial reference and working memory) Exp.2 open-field water maze (spatial reference memory); Exp.3 home cage observation system with spatial learning (IntelliCage); Exp.4 spontaneous object recognition (SOR; novel object and spatial object shift). A separate test with high-expression transgenic APP mice matching the design of experiment 1 was also performed. Spatial deficits in PLB1Triple mice were confirmed at 12, but not 4 months in both water maze tasks. PSAPP mice, by contrast, presented with severe yet non-progressive spatial learning deficits already at 4 months. During tests of spatial learning in SOR and IntelliCage, PLB1Triple mice neither acquired the location of the water-rewarded corner, nor recognize novel or spatially shifted objects at 4 months, indicating these protocols to be more sensitive than the water maze. Collectively and in line with AD symptomatology, PLB1Triple mice present with a graded and progressive age-dependent loss of spatial memory that can be revealed by the use of a battery of tasks. With the emergence of subtle deficits progressively increasing in severity, PLB1Triple mice may offer a more patho-physiologically relevant model of dementia than aggressive expression models.
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