Boolean Network Modeling Identifies Cognitive Resilience in the First Murine Model of Asymptomatic Alzheimer's Disease

布尔网络建模在首个无症状阿尔茨海默病小鼠模型中识别出认知韧性

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Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder defined by amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs), yet approximately 20-30% of aged individuals exhibit these hallmark lesions without developing cognitive impairment-a clinically silent condition termed asymptomatic AD (AsymAD). The molecular basis of this cognitive resilience remains poorly understood due to a lack of mechanistic models. Here, we integrate systems-level Boolean network modeling with in vivo validation to define the transcriptomic logic of AsymAD and uncover a novel preclinical model. Using Boolean implication networks trained on large-scale human cortical RNA-seq datasets, we identified a robust and invariant AD gene signature that accurately stratifies disease states across independent datasets. Application of this signature to Chromogranin A-deficient PS19 mice (CgA-KO/PS19) revealed a unique resilience phenotype: male mice developed AD-like molecular and neuropathological profiles in the pre-frontal cortex yet retained intact learning and memory. Female CgA-KO/PS19 mice displayed even greater protection, including reduced Tau phosphorylation and preserved synaptic ultrastructure. These findings establish the first validated murine model of AsymAD and identify CgA as a modifiable node linking neuroendocrine signaling, Tauopathy, and cognitive preservation. This work provides a scalable platform to probe sex-specific resilience, uncover early-stage biomarkers, and accelerate preventive therapeutic development in AD.

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