Meng Qingqi, Liu Qing, Mi Yan, Xu Libin, Wang Feng, Mu Danyang, Liu Yueyang, Yang Yuxin, Huang Yongye, He Dakuo, Hou Yue
Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China.
College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China.
Theranostics. 2025 Jun 23;15(15):7653-7676. doi: 10.7150/thno.112608. eCollection 2025.
The complexity of biological systems and misconceptions about neuroprotection have hindered the development of neuroprotective drugs for ischemic stroke. This study aims to identify new neuroprotective agents by integrating ischemic stroke transcriptomics with neuronal protection data using a Multidimensional Data-Driven Computational Drug Repositioning strategy (MDCDR). : Three microarray datasets related to ischemic stroke (GSE16561, GSE58294, and GSE22255) were obtained from the GEO dataset and pre - processed to analyze differentially expressed genes (DEGs). The Connectivity Map (CMap) database was used to predict potential drugs. A neuroprotection activity prediction model was constructed by combining six molecular fingerprints with three machine learning algorithms (Random Forest RF, Support Vector Machine SVM, Gradient Boosting Decision Tree GBDT) to screen for potential neuroprotective agents. The efficacy of the screened compounds was evaluated through experiments on SH-SY5Y cells treated with oxygen-glucose deprivation/reperfusion (OGD/R) and experiments on middle cerebral artery occlusion/reperfusion (MCAO/R) rat models. Multiple experimental techniques (such as RNA sequencing, DARTS, CETSA, etc.) were used to explore their potential mechanisms of action. : The MDCDR strategy screened out 19 potential neuroprotective agents, among which sulbutiamine (SUL) stood out. SUL significantly increased the survival rate, reduced neurological deficit scores, and decreased neuronal loss in MCAO/R rat models, and inhibited cell death in OGD/R - induced cell models. Mechanistic studies revealed that SUL inhibited pyruvate dehydrogenase kinase 2 (PDK2), enhanced mitochondrial function, reduced reactive oxygen species (ROS) levels, thereby suppressing the MAPK signaling pathway and reducing neuronal apoptosis. Silencing PDK2 abolished the protective effect of SUL on OGD/R - treated SH - SY5Y cells. : This study successfully developed the MDCDR strategy for screening neuroprotective agents for ischemic stroke. SUL was identified as a promising neuroprotective agent, and PDK2 was a crucial target. This research provides new directions and a theoretical basis for the development of neuroprotective agents against ischemic stroke.
生物系统的复杂性以及对神经保护的误解阻碍了缺血性中风神经保护药物的开发。本研究旨在通过使用多维数据驱动的计算药物重新定位策略（MDCDR），将缺血性中风转录组学与神经元保护数据相结合，来识别新的神经保护剂。从基因表达综合数据库（GEO）数据集获得了三个与缺血性中风相关的微阵列数据集（GSE16561、GSE58294和GSE22255），并进行预处理以分析差异表达基因（DEG）。使用连通图（CMap）数据库预测潜在药物。通过将六种分子指纹与三种机器学习算法（随机森林RF、支持向量机SVM、梯度提升决策树GBDT）相结合，构建神经保护活性预测模型，以筛选潜在的神经保护剂。通过对氧糖剥夺/再灌注（OGD/R）处理的SH-SY5Y细胞进行实验以及对大脑中动脉闭塞/再灌注（MCAO/R）大鼠模型进行实验，评估筛选出的化合物的疗效。使用多种实验技术（如RNA测序、DARTS、CETSA等）来探索其潜在的作用机制。MDCDR策略筛选出19种潜在的神经保护剂，其中舒必利（SUL）脱颖而出。在MCAO/R大鼠模型中，SUL显著提高了存活率，降低了神经功能缺损评分，并减少了神经元损失，且在OGD/R诱导的细胞模型中抑制了细胞死亡。机制研究表明，SUL抑制丙酮酸脱氢酶激酶2（PDK2），增强线粒体功能，降低活性氧（ROS）水平，从而抑制丝裂原活化蛋白激酶（MAPK）信号通路并减少神经元凋亡。沉默PDK2消除了SUL对OGD/R处理的SH-SY5Y细胞的保护作用。本研究成功开发了用于筛选缺血性中风神经保护剂的MDCDR策略。SUL被确定为一种有前景的神经保护剂，且PDK2是关键靶点。本研究为抗缺血性中风神经保护剂的开发提供了新方向和理论依据。
