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Alzheimer’s disease (AD) is characterized by the deposition of aggregated species of amyloid beta (Aβ) in the brain, which leads to progressive cognitive deficits and dementia. Aβ is generated by the successive cleavage of the amyloid precursor protein (APP), first by β-site APP cleaving enzyme 1 (BACE1) and subsequently by the γ-secretase complex. Those conditions which enhace or reduce its clearance predispose to Aβ aggregation and the development of AD. In vitro studies have demonstrated that Aβ assemblies spark a feed-forward loop heightening Aβ production. However, the underlying mechanism remains unknown. Here, we show that oligomers and fibrils of Aβ enhance colocalization and physical interaction of APP and BACE1 in recycling endosomes of human neurons derived from induced pluripotent stem cells and other cell types, which leads to exacerbated amyloidogenic processing of APP and intracellular accumulation of Aβ42. In cells that are overexpressing the mutant forms of APP which are unable to bind Aβ or to activate Go protein, we have found that treatment with aggregated Aβ fails to increase colocalization of APP with BACE1 indicating that Aβ-APP/Go signaling is involved in this process. Moreover, inhibition of Gβγ subunit signaling with βARKct or gallein prevents Aβ-dependent interaction of APP and BACE1 in endosomes, β-processing of APP, and intracellular accumulation of Aβ42. Collectively, our findings uncover a signaling mechanism leading to a feed-forward loop of amyloidogenesis that might contribute to Aβ pathology in the early stages of AD and suggest that gallein could have therapeutic potential.
Glucose hypometabolism, mitochondrial dysfunction, and cholinergic deficits have been reported in early stages of Alzheimer’s disease (AD). Here, we examine these parameters in TgF344-AD rats, an Alzheimer model that carries amyloid precursor protein and presenilin-1 mutations, and of wild type F344 rats. In mitochondria isolated from rat hippocampi, we found reductions of complex I and oxidative phosphorylation in transgenic rats. Further impairments, also of complex II, were observed in aged (wild-type and transgenic) rats. Treatment with a “cocktail” containing magnesium orotate, benfotiamine, folic acid, cyanocobalamin, and cholecalciferol did not affect mitochondrial activities in wild-type rats but restored diminished activities in transgenic rats to wild-type levels. Glucose, lactate, and pyruvate levels were unchanged by age, genetic background, or treatment. Using microdialysis, we also investigated extracellular concentrations of acetylcholine that were strongly reduced in transgenic animals. Again, ACh levels in wild-type rats did not change upon treatment with nutrients, whereas the cocktail increased hippocampal acetylcholine levels under physiological stimulation. We conclude that TgF344-AD rats display a distinct mitochondrial and cholinergic dysfunction not unlike the findings in patients suffering from AD. This dysfunction can be partially corrected by the application of the “cocktail” which is particularly active in aged rats. We suggest that the TgF344-AD rat is a promising model to further investigate mitochondrial and cholinergic dysfunction and potential treatment approaches for AD.
The prevention of tau protein aggregations is a therapeutic goal for the treatment of Alzheimer's disease (AD), and hydromethylthionine (HMT) (also known as leucomethylthioninium-mesylate [LMTM]), is a potent inhibitor of tau aggregation in vitro and in vivo. In two Phase 3 clinical trials in AD, HMT had greater pharmacological activity on clinical endpoints in patients not receiving approved symptomatic treatments for AD (acetylcholinesterase (AChE) inhibitors and/or memantine) despite different mechanisms of action. To investigate this drug interaction in an animal model, we used tau-transgenic L1 and wild-type NMRI mice treated with rivastigmine or memantine prior to adding HMT, and measured changes in hippocampal acetylcholine (ACh) by microdialysis. HMT given alone doubled hippocampal ACh levels in both mouse lines and increased stimulated ACh release induced by exploration of the open field or by infusion of scopolamine. Rivastigmine increased ACh release in both mouse lines, whereas memantine was more active in tau-transgenic L1 mice. Importantly, our study revealed a negative interaction between HMT and symptomatic AD drugs: the HMT effect was completely eliminated in mice that had been pre-treated with either rivastigmine or memantine. Rivastigmine was found to inhibit AChE, whereas HMT and memantine had no effects on AChE or on choline acetyltransferase (ChAT). The interactions observed in this study demonstrate that HMT enhances cholinergic activity in mouse brain by a mechanism of action unrelated to AChE inhibition. Our findings establish that the drug interaction that was first observed clinically has a neuropharmacological basis and is not restricted to animals with tau aggregation pathology. Given the importance of the cholinergic system for memory function, the potential for commonly used AD drugs to interfere with the treatment effects of disease-modifying drugs needs to be taken into account in the design of clinical trials.
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.