Noor Fatima, Aslam Sidra, Piras Ignazio S, Tremblay Cecilia, Beach Thomas G, Serrano Geidy E
Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan.
Department of Pathology, Banner Sun Health Research Institute, Sun City, AZ, United States.
Front Immunol. 2025 Jun 26;16:1572468. doi: 10.3389/fimmu.2025.1572468. eCollection 2025.
Alzheimer's disease (AD), the most common form of dementia, has treatments that slow but do not stop cognitive decline. Additional treatments are based on its pathogenic mechanisms are needed. Evidence has long highlighted oxidative stress and impaired glucose metabolism as crucial factors in AD pathogenesis. Therefore, in this study we aimed to find key AD pathogenic pathways combining genes involved in oxidative stress and glucose metabolism as well as potential small-molecule therapeutic agents.
Using autopsy brain RNA sequencing data (GSE125583) derived from the Arizona Study of Aging and Brain and Body Donation Program, AD-related genes were identified via differential gene expression, pathway and coexpression analysis. Oxidative stress and glucose metabolism genes were correlated to pinpoint module genes. GSE173955 was used an independent dataset was used for validation, conducting molecular docking, assessing hub genes for AD, and integrating machine learning approaches.
We identified 13,982 differentially expressed genes (DEGs) in AD patients. Through WGCNA coexpression analysis, 1,068 genes were linked to AD-specific modules. Pearson's correlation analysis highlighted 99 genes involved in oxidative stress and glucose metabolism. Overlap analysis of DEGs, module genes, and these metabolic genes revealed 21 key overlapping targets. PPI network and receiving operating curve (ROC) curve analyses then identified AKT1 and PPARGC1A as diagnostic hub genes for AD. Machine learning-based virtual screening of small molecules identified various inhibitors and enhancers with drug-like potential targeting AKT1 (upregulated) and PPARGC1A (downregulated), respectively. Among others, the Random Forest model was the most reliable for predicting molecular activity. Molecular docking further validated the binding affinities of these small molecules (inhibitors/enhancers) to AKT1 and PPARGC1A.
This study identified AKT1 and PPARGC1A as potential therapeutic targets in AD. We discovered drug candidates with strong binding affinities, offering new avenues for effective AD treatment strategies.
阿尔茨海默病（AD）是最常见的痴呆形式，现有治疗方法虽能减缓但无法阻止认知能力下降。因此需要基于其致病机制的更多治疗方法。长期以来，证据表明氧化应激和葡萄糖代谢受损是AD发病机制中的关键因素。所以，在本研究中，我们旨在寻找结合参与氧化应激和葡萄糖代谢的基因以及潜在小分子治疗药物的关键AD致病途径。
利用来自亚利桑那衰老与脑体捐赠项目的尸检脑RNA测序数据（GSE125583），通过差异基因表达、通路和共表达分析鉴定AD相关基因。将氧化应激和葡萄糖代谢基因进行关联以确定模块基因。使用GSE173955作为独立数据集进行验证，进行分子对接，评估AD的枢纽基因，并整合机器学习方法。
我们在AD患者中鉴定出13982个差异表达基因（DEG）。通过加权基因共表达网络分析（WGCNA），1068个基因与AD特异性模块相关联。Pearson相关性分析突出了99个参与氧化应激和葡萄糖代谢的基因。对DEG、模块基因和这些代谢基因的重叠分析揭示了21个关键的重叠靶点。蛋白质 - 蛋白质相互作用（PPI）网络和接受操作曲线（ROC）曲线分析随后确定AKT1和PPARGC1A为AD的诊断枢纽基因。基于机器学习的小分子虚拟筛选分别鉴定出具有类药潜力的针对AKT1（上调）和PPARGC1A（下调）的各种抑制剂和增强剂。其中，随机森林模型在预测分子活性方面最可靠。分子对接进一步验证了这些小分子（抑制剂/增强剂）与AKT1和PPARGC1A的结合亲和力。
本研究确定AKT1和PPARGC1A为AD的潜在治疗靶点。我们发现了具有强结合亲和力的候选药物，为有效的AD治疗策略提供了新途径。
