Back

APP accumulates around dense-core amyloid plaques with presynaptic proteins in Alzheimer's disease brain

Jorda-Siquier, T.; Petrel, M.; Kouskoff, V.; Cordelieres, F.; Frykman, S.; Muller, U.; Mulle, C.; Barthet, G.

2020-10-16 neuroscience
10.1101/2020.10.16.342196 bioRxiv
Show abstract

In Alzheimers disease (AD), a central role is given to the extracellular deposition of A{beta} peptides, remotely produced by the proteolysis of the amyloid precursor protein (APP). This contrasts with other neurodegenerative diseases which are characterized by the intraneuronal aggregation of full-length proteins such as huntingtin, -synuclein or TDP-43. Importantly, the distribution of APP around amyloid plaques is poorly characterized. Here, we combined an extensive set of methodological and analytical tools to investigate neuropathological features of APP in the human AD hippocampus and in two mouse models of AD. We report that APP remarkably accumulates in the surrounding of dense-core amyloid plaques together with the secretases necessary to produce A{beta} peptides. In addition, the Nter domain, but not the Cter domain of APP is enriched in the core of amyloid plaques uncovering a potential pathological role of the secreted APP-Nter in dense-core plaques. To investigate the subcellular compartment in which APP accumulates, we labelled neuritic and synaptic markers and report an enrichment in presynaptic proteins (Syt1, VAMP2) and phosphorylated-Tau. Ultrastructural analysis of APP accumulations reveals abundant multivesicular bodies containing presynaptic vesicles proteins and autophagosomal built-up of APP. Altogether, our data supports a role of presynaptic APP in AD pathology and highlights APP accumulations as a potential source of A{beta} and Nter peptides to fuel amyloid plaques.

Matching journals

The top 2 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.