Zhu Tianshu, Wang Yue, Wang Han, Xu Zheyu, Zhang Peng-Fei, Lu Zhiming
Department of Clinical Laboratory, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China.
Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
J Mol Neurosci. 2025 Jul 16;75(3):90. doi: 10.1007/s12031-025-02373-0.
DNA methylation plays a crucial role in the onset and progression of Alzheimer's disease (AD). Genome-wide methylation analysis of multi-tissue data can provide insights into the pathology and diagnostic biomarkers of AD. Computational tools were employed to identify pathways associated with AD and to develop a poly-methylation score (PMS). Key genes within the identified pathways were determined through module analysis and protein-protein interaction networks followed by validation in β-amyloid 42-induced cellular models. Linear mixed-effects model was used to investigate the longitudinal relationship between PMS and changes in AD phenotypes. AD-related pathways exhibited tissue specificity. The key genes in blood, frontal cortex, neurons, and glial cells were THBS1, TGFB1, HIF1A, and KLF4, respectively. Furthermore, the expression alterations of these genes were validated in three cellular models (SH-SY5Y, HMC3, and THP-1). Notably, higher PMS was significantly correlated with accelerated declines in cerebral metabolic rate and cognitive function. Using machine learning to analyze methylation data and identify key genes in AD patients enhanced our understanding of AD pathogenesis. Further research is needed to validate the potential of these key genes as intervention targets for AD.
DNA甲基化在阿尔茨海默病（AD）的发病和进展中起着至关重要的作用。对多组织数据进行全基因组甲基化分析可以为AD的病理学和诊断生物标志物提供见解。使用计算工具来识别与AD相关的通路并开发多甲基化评分（PMS）。通过模块分析和蛋白质-蛋白质相互作用网络确定已识别通路中的关键基因，随后在β-淀粉样蛋白42诱导的细胞模型中进行验证。使用线性混合效应模型来研究PMS与AD表型变化之间的纵向关系。AD相关通路表现出组织特异性。血液、额叶皮质、神经元和神经胶质细胞中的关键基因分别为THBS1、TGFB1、HIF1A和KLF4。此外，这些基因的表达变化在三种细胞模型（SH-SY5Y、HMC3和THP-1）中得到了验证。值得注意的是，较高的PMS与脑代谢率和认知功能的加速下降显著相关。利用机器学习分析甲基化数据并识别AD患者中的关键基因，增强了我们对AD发病机制的理解。需要进一步研究来验证这些关键基因作为AD干预靶点的潜力。
