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使用PrediXcan和多模式深度学习集成GWAS和转录基因数据揭示了阿尔茨海默病的遗传基础和药物重新定位机会

Xuecong Tian, Ying Su, Sizhe Zhang

medRxiv : the preprint server for health sciences

|June 30, 2025

概括

这项研究使用人工智能和多omics数据来揭示阿尔茨海默病 (AD) 的分子机制,并确定个性化药物治疗. 研究结果揭示了关键基因,并验证了像格莱沙替尼和达沙替尼这样的药物用于阿尔茨海默病模型的认知改善.

科学领域:

神经科学是一个神经科学.
遗传学是一个遗传学.
人工智能的人工智能
药理学药理学是指药理学的学科.

背景情况:

阿尔茨海默病 (AD) 是导致痴呆的主要原因,其分子基础不清楚.
显著的社会和经济负担需要对AD病原体进行先进的研究.
个性化医疗方法对于有效的AD治疗策略至关重要.

研究的目的:

使用多omics数据和AI阐明AD表型调节的分子基础.
探索个性化药物重新定位策略,以适应个体遗传特征.
为了提高AD表型预测的准确性,并确定新的治疗点.

主要方法:

整合多omics数据 (基因型,基因表达) 与人工智能方法,包括自动编码器和图形自动编码器.
PrediXcan的应用用于从61个与大脑相关的特征中识别AD相关的候选基因.
使用ROSMAP数据库样本进行验证,并对AD模型小鼠进行体内实验.

主要成果:

人工智能驱动的多层信息融合 (AD-MIF) 将AD表型预测提高了10-20%的AUC.
确定了参与AD病原发生的关键基因 (例如,APOE,APOC1,SCIMP,KAT8,POLR2C,TRAPPC4).
通过减少神经炎症,陶酸化和Aβ沉积来改善认知缺陷的验证格莱沙替尼 (MGCD-265) 和达沙替尼.

结论:

生物信息学和人工智能多模式融合方法在理解AD方面提供了互补的优势.
这项研究为基于遗传特征的个性化AD治疗提供了基础.
确定了候选药物,并在临床前AD模型中验证了它们的疗效.

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