目的 采用文献数据挖掘结合网络药理学方法，预测西洋参治疗糖尿病肾病（DN）的活性成分及作用机制，并建立DN斑马鱼模型进行实验验证。方法 检索文献数据结合网络药理学及分子对接技术，预测西洋参抗DN的有效成分及核心靶点；构建优化的葡萄糖波动式诱导斑马鱼DN模型，以二甲双胍为阳性药，以肾水肿面积为指标评估造模是否成功，并结合肾小球滤过率、血糖、糖化血清蛋白、胰岛素、肌酐、尿素氮水平及HE染色结果进行验证；选取经文献数据挖掘结合网络药理学预测的关键活性成分拟人参皂苷F₁₁（PF₁₁）为实验药物，利用DN斑马鱼模型验证其对DN的治疗作用，并进一步采用实时荧光定量PCR（qRT-PCR）验证其对主要基因的调控作用。结果 经4%葡萄糖波动式诱导4 d建立DN斑马鱼模型，与对照组相比，模型组各考察指标均发生显著性变化（P＜0.05、0.01），验证模型构建成功；通过文献数据挖掘共得到27个西洋参入血成分，经网络药理学筛选得到64个作用靶点，利用蛋白质-蛋白质相互作用（PPI）网络分析确定PF₁₁为主要活性成分，分子对接结果显示其与核心靶点均具有较强的结合力。DN斑马鱼模型验证结果表明，PF₁₁可显著提高DN模型斑马鱼胰岛素水平、抑制血糖升高，降低尿素氮及肌酐水平，改善肾水肿及肾小球滤过率（P＜0.05、0.01）； qRT-PCR结果进一步显示，PF₁₁能显著下调DN模型斑马鱼组织中信号传导及转录激活蛋白3（STAT3）、表皮生长因子受体（EGFR）的mRNA表达水平（P＜0.05）。结论 建立整合网络药理学预测-斑马鱼模型验证的抗DN活性成分筛选方法，证实西洋参中的PF₁₁具有显著的DN治疗作用。

Objective To predict the active components, therapeutic effects, and mechanisms of Panax quinquefolium in treating diabetic nephropathy (DN), by integrates literature mining and network pharmacology, and conduct experimental validation using an established zebrafish DN model. Methods Integrating bibliographic mining with network pharmacology and molecular docking, predict the active components and core targets of Panax quinquefolius(American ginseng) for combating diabetic nephropathy (DN). To establish an optimized glucose fluctuation-induced diabetic nephropathy (DN) model in zebrafish, metformin served as the positive control drug. Model establishment success was assessed based on renal edema area and further validated by evaluating glomerular filtration rate, blood glucose, glycated hemoglobin, insulin, creatinine, and blood urea nitrogen levels, in conjunction with HE staining results. Subsequently, the key active ingredient, pseudoginsenoside F₁₁ (PF₁₁), was selected as the experimental drug through literature data mining combined with network pharmacology prediction. The therapeutic effect of PF₁₁ on DN was verified using the DN zebrafish model, and its regulatory effect on major genes was further confirmed by real-time fluorescence quantitative PCR (qRT-PCR). Results Zebrafish DN model was established by subjecting fish to a 4% glucose fluctuation regimen for four days. Compared with the control group, all the examined indicators in the model group showed significant changes (P < 0.05, 0.01), which verified the successful construction of the model. Through literature data mining, a total of 27 components of Panax quinquefoliusthat enter the bloodstream were obtained. After screening by network pharmacology, 64 target sites were identified. Using protein-protein interaction (PPI) network analysis, PF₁₁ was determined to be the main active component. Molecular docking results showed that it had strong binding force with the core target sites. The results of the DN zebrafish model verification indicated that PF₁₁ could significantly increase the insulin level of DN model zebrafish, inhibit the increase of blood sugar, reduce the levels of urea nitrogen and creatinine, and improve renal edema and glomerular filtration rate (P < 0.05, 0.01). The qRT-PCR results further showed that PF₁₁ could significantly downregulate the mRNA expression levels of signal transduction and transcriptional activator protein 3 (STAT3) and epidermal growth factor receptor (EGFR) in the tissues of DN model zebrafish (P < 0.05). Conclusion Established an anti-DN active component screening method by integrating network pharmacology prediction and zebrafish model verification, and confirmed that PF₁₁ in Panax quinquefoliushas a significant therapeutic effect on DN.

R285.5

国家中医药管理局科技司-山东省卫生健康委员会共建中医药科技项目（GZY-KJS-SD-2023-088）；山东省自然科学基金资助项目（ZR202211080026）；山东第一医科大学学术提升计划（2019LJ003）