RESUMEN

Alzheimer's disease (AD), characterized by cognitive decline, is increasingly recognized as a disorder marked by synaptic loss and dysfunction. Despite this understanding, the underlying pathophysiological mechanisms contributing to synaptic impairment remain largely unknown. In this study, we elucidate a previously undiscovered signaling pathway wherein the S-nitrosylation of the Cdk5 activator p39, a post-translational modification involving the addition of nitric oxide to protein cysteine residues, plays a crucial role in synaptic dysfunction associated with AD. Our investigation reveals heightened p39 S-nitrosylation in the brain of an amyloid precursor protein (APP)/presenilin 1 (PS1) transgenic mouse model of AD. Additionally, soluble amyloid-ß oligomers (Aß), implicated in synaptic loss in AD, induce p39 S-nitrosylation in cultured neurons. Notably, we uncover that p39 protein level is regulated by S-nitrosylation, with nitric oxide S-nitrosylating p39 at Cys265 and subsequently promoting its degradation. Furthermore, our study demonstrates that S-nitrosylation of p39 at Cys265 significantly contributes to amyloid-ß (Aß) peptide-induced dendrite retraction and spine loss. Collectively, our findings highlight S-nitrosylation of p39 as a novel aberrant redox protein modification involved in the pathogenesis of AD, suggesting its potential as a therapeutic target for the disease.

Asunto(s)

Enfermedad de Alzheimer , Péptidos beta-Amiloides , Ratones Transgénicos , Animales , Péptidos beta-Amiloides/metabolismo , Ratones , Enfermedad de Alzheimer/metabolismo , Enfermedad de Alzheimer/patología , Sinapsis/metabolismo , Óxido Nítrico/metabolismo , Procesamiento Proteico-Postraduccional , Proteolisis , Neuronas/metabolismo , Modelos Animales de Enfermedad , Precursor de Proteína beta-Amiloide/metabolismo , Precursor de Proteína beta-Amiloide/genética , Humanos , Presenilina-1/metabolismo , Presenilina-1/genética , Ratones Endogámicos C57BL , Fosfotransferasas

RESUMEN

Recent research suggests that in vitro neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. To develop a better artificial intelligent system, a hybrid bio-silicon computer is worth exploring, but its performance is still inferior to that of a silicon-based computer. One reason may be that a living neural network has many intrinsic properties, such as random network connectivity, high network sparsity, and large neural and synaptic variability. These properties may lead to new design considerations, and existing algorithms need to be adjusted for living neural network implementation. This work investigates the impact of neural variations and random connections on inference with learning algorithms. A two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints on the MNIST dataset. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity.