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Inactive variants of death receptor p75NTR reduce Alzheimer's neuropathology by interfering with APP internalization

ABSTRACT: A prevalent model of Alzheimer's disease (AD) pathogenesis postulates the generation of neurotoxic fragments derived from the amyloid precursor protein (APP) after its internalization to endocytic compartments. The molecular pathways that regulate APP internalization and intracellular trafficking in neurons are incompletely understood. Here we report that 5xFAD mice, an animal model of AD, expressing signaling-deficient variants of the p75 neurotrophin receptor (p75NTR) show greater neuroprotection from AD neuropathology than animals lacking this receptor. p75NTR knock-in mice lacking the death domain or transmembrane Cys259 showed lower levels of A? species, amyloid plaque burden, gliosis, mitochondrial stress and neurite dystrophy than global knock-outs. Strikingly, long-term synaptic plasticity and memory, which are completely disrupted in 5xFAD mice, were fully recovered in the knock-in mice. Mechanistically, we found that p75NTR interacts with APP at the plasma membrane and regulates its internalization and intracellular trafficking in hippocampal neurons. Inactive p75NTR variants internalized considerably slower than wild type p75NTR and showed increased association with the recycling pathway, thereby reducing APP internalization and colocalization with BACE1, the critical protease for generation of of neurotoxic APP fragments, favoring non-amyloidogenic APP cleavage. These results reveal a novel pathway that directly and specifically regulates APP internalization, amyloidogenic processing and disease progression, and suggest that inhibitors targeting the p75NTR transmembrane domain may be an effective therapeutic strategy in AD

SUBMITTER: Prof. Chenju Yi

PROVIDER: S-SCDT-EMBOJ-2020-104450 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundComputer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data.ObjectiveTrain machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features.MethodsAD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women).ResultsOur models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status.ConclusionsOur models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.

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Inactive variants of death receptor p75NTR reduce Alzheimer's neuropathology by interfering with APP internalization

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