Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
Mapping cross-domain drivers of Alzheimer's disease risk through integrated network analysis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Cary Gregory A, Keegan Stephen, Wiley Jesse C, Gockley Jake, Butler Robert R, Longo Frank M, Levey Allan I, Greenwood Anna K, Leal Karina, Carter Gregory W, Emory‐Sage‐SGC‐JAX TREAT‐AD Center
Аннотация
INTRODUCTION: Alzheimer's disease (AD) is a complex neurodegenerative disorder with numerous known risk factors. Identification of which genetic factors are causal drivers is difficult due to the long disease prodrome in an inaccessible organ. The application of integrative, systems-level approaches are crucial for addressing this complexity.
METHODS: Sixteen biological domain specific interaction networks were derived from the top AD risk-enriched proteins within each domain. Weighted key driver analysis (wKDA) identified influential hub nodes within each network.
RESULTS: Distinct processes and drivers were identified within each domain's network. Domains including structural stabilization, endolysosome, and lipid metabolism were especially influential. Integrating key drivers across domains identified consistent drivers such as CTNNB1, ACSL1, and ALDH3A2, suggesting fundamental roles contributing to AD risk.
DISCUSSION: This highly integrative network-based approach identified context-dependent drivers and enabled the inference of interactions between domains. The identified drivers suggest potential targets for future therapeutic development.
METHODS: Sixteen biological domain specific interaction networks were derived from the top AD risk-enriched proteins within each domain. Weighted key driver analysis (wKDA) identified influential hub nodes within each network.
RESULTS: Distinct processes and drivers were identified within each domain's network. Domains including structural stabilization, endolysosome, and lipid metabolism were especially influential. Integrating key drivers across domains identified consistent drivers such as CTNNB1, ACSL1, and ALDH3A2, suggesting fundamental roles contributing to AD risk.
DISCUSSION: This highly integrative network-based approach identified context-dependent drivers and enabled the inference of interactions between domains. The identified drivers suggest potential targets for future therapeutic development.