Current Alzheimer research · 2026
Proteomic Basis of Polypharmacological Cognitive Recovery in Down Syndrome and Alzheimer's Disease.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kulan Handan
Аннотация
INTRODUCTION/OBJECTIVE: Down Syndrome (DS) is a genetic disorder caused by trisomy of human chromosome 21 and represents the most common genetic cause of intellectual disability. It is also associated with an increased risk of developing Alzheimer's Disease (AD). Although various pharmacological treatments have been shown to improve learning and memory in DS models, the underlying mechanisms of cognitive improvement remain poorly understood. This study aims to identify molecular signatures associated with pharmacological cognitive rescue across different brain regions using a machine learning-guided targeted proteomics approach.
METHODS: Gradient Boosting Tree (GBT)-based feature selection combined with Principal Component Analysis (PCA) was applied to identify reproducible proteomic signatures in cortical samples from memantine-treated mice and in hippocampal samples from RO4938581-treated mice.
RESULTS: GBT models achieved classification accuracies exceeding 80% across experimental groups, and PCA showed distinct group separation, with PC1 and PC2 accounting for more than 60% of the total variance. The consistently identified proteins across datasets include APP, RCAN1, S6/pS6, IL1B, BAX, TAU, AMPKA, BRAF, ERK, and ADARB1.
DISCUSSION: The identified proteins converge on interconnected networks linking synaptic signaling, metabolic regulation, and neuroinflammation, reflecting pathways involved in Excitation/İnhibition (E/I) imbalance and neurodegeneration. Their consistency across datasets implies that coordinated regulation of these networks, rather than isolated pathway effects, is associated with cognitive improvement. Specifically, the MAPK-ERK and AMPK-mTOR signaling pathways emerge as key integrative nodes connecting synaptic function, energy balance, and cellular stress responses. These results point to possible mechanistic overlap with Alzheimer's disease-related pathology and support a network-based, multi-target model of cognitive improvement in DS.
CONCLUSION: These results demonstrate that different pharmacological treatments converge on shared protein signatures associated with cognitive improvement in DS. This convergence supports a network- based, multi-target therapeutic approach, in which modulation of key regulatory nodes rather than single targets may underlie effective treatment strategies.
METHODS: Gradient Boosting Tree (GBT)-based feature selection combined with Principal Component Analysis (PCA) was applied to identify reproducible proteomic signatures in cortical samples from memantine-treated mice and in hippocampal samples from RO4938581-treated mice.
RESULTS: GBT models achieved classification accuracies exceeding 80% across experimental groups, and PCA showed distinct group separation, with PC1 and PC2 accounting for more than 60% of the total variance. The consistently identified proteins across datasets include APP, RCAN1, S6/pS6, IL1B, BAX, TAU, AMPKA, BRAF, ERK, and ADARB1.
DISCUSSION: The identified proteins converge on interconnected networks linking synaptic signaling, metabolic regulation, and neuroinflammation, reflecting pathways involved in Excitation/İnhibition (E/I) imbalance and neurodegeneration. Their consistency across datasets implies that coordinated regulation of these networks, rather than isolated pathway effects, is associated with cognitive improvement. Specifically, the MAPK-ERK and AMPK-mTOR signaling pathways emerge as key integrative nodes connecting synaptic function, energy balance, and cellular stress responses. These results point to possible mechanistic overlap with Alzheimer's disease-related pathology and support a network-based, multi-target model of cognitive improvement in DS.
CONCLUSION: These results demonstrate that different pharmacological treatments converge on shared protein signatures associated with cognitive improvement in DS. This convergence supports a network- based, multi-target therapeutic approach, in which modulation of key regulatory nodes rather than single targets may underlie effective treatment strategies.