Back

Spatial memory training reverses GirK channels modulation in the transgenic APPSw,Ind Alzheimer's disease mouse model

Temprano-Carazo, S.; Contreras, A.; Saura, C. A.; Navarro-Lopez, J. D.; Jimenez-Diaz, L.

2022-05-20 neuroscience
10.1101/2022.05.20.492780 bioRxiv
Show abstract

Alzheimers disease (AD) is a dementia characterized by progressive memory decline and neurodegeneration caused by the accumulation of amyloid-{beta} (A{beta}) peptides. Last findings point to an imbalance between excitatory and inhibitory neurotransmission as the initial impairment in early stages, and the hippocampus as one of the most susceptible brain areas. The G-protein-gated inwardly rectifying potassium (GirK) channel has been proposed as a potential target to restore excitatory/inhibitory balance in amyloidosis models. Moreover, cognitive training may counteract early AD symptoms, although its effect on GirK channels remains unknown. Here, the effect of genotype, age, and training in a hippocampal-dependent memory task on the protein expression of GirK subunits and modulators were studied using APPSw,Ind mice. Results showed a reduction of GirK2 expression as well as an increased expression of SNX27 in the hippocampus of 6-month-old APPSw,Ind mice. Training in a memory task restored GirK2 and SNX27 levels. Thus, the effect of A{beta} on GirK2 could account for the excitatory/inhibitory imbalance transmission found in AD models, and training in a cognitive hippocampal-dependent task reverses this effect and lessens early A{beta}-dependent AD deficits. Summary statementA{beta} decreases hippocampal GirK2 expression in APPSw,Ind mice, which could contribute to early hyperexcitability found in Alzheimers disease models. Training in spatial memory tasks has shown to counteract this reduction.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.