丹蛭降糖胶囊治疗糖尿病肾病的网络药理学、机器学习及体内验证研究
Network Pharmacology, Machine Learning, and In Vivo Validation of Danzhi Jiangtang Capsule in Diabetic Nephropathy.
摘要(中文译文)
尽管丹栀降糖胶囊（DJC）是一种临床上用于糖尿病的传统中药制剂，但其在糖尿病肾病（DN）中如何发挥肾脏保护作用尚未完全阐明。本研究旨在探讨DJC影响DN的潜在机制，重点关注NLR家族含pyrin结构域3（NLRP3）/Caspase-1/消皮素D（GSDMD）细胞焦亡相关信号级联。采用网络药理学与机器学习相结合的综合策略。利用中药系统药理学数据库与分析平台（TCMSP）和中药分子机制生物信息学分析工具（BATMAN-TCM）筛选DJC的生物活性成分及其对应的蛋白靶点。从基因表达综合数据库（GEO）、GeneCards和在线人类孟德尔遗传数据库（OMIM）中检索DN相关基因。使用多种机器学习算法筛选并排序关键候选靶点。通过分子对接评估DJC活性成分与核心靶点之间的结合亲和力。最后，在db/db糖尿病小鼠中评估治疗效果及预测机制。网络药理学分析共鉴定出599个DJC靶点和68个与DN共享的重叠基因。通过机器学习算法，C-C基序趋化因子配体2（CCL2）和CASP1被确定为优先候选靶点。分子对接预测DJC活性成分与这些核心蛋白之间可能存在较强的结合亲和力。功能富集分析（GO/KEGG）提示DJC的调控作用与炎症反应及MAPK通路相关。体内验证表明，DJC治疗减轻了肾脏损伤及纤维化标志物。这些发现提示DJC可能部分通过CCL2/C-C基序趋化因子受体2（CCR2）相关的细胞焦亡信号改变来减轻DN。
Abstract
Although Danzhi Jiangtang Capsule (DJC) is a traditional Chinese herbal preparation used clinically for diabetes, how it may protect the kidney during diabetic nephropathy (DN) has not been fully clarified. This investigation was designed to explore potential mechanisms by which DJC affects DN, with a focus on the NLR family pyrin domain-containing 3 (NLRP3)/Caspase-1/Gasdermin D (GSDMD) pyroptosis-related signaling cascade. An integrated strategy combining network pharmacology and machine learning was employed. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) were used to screen bioactive ingredients and their corresponding protein targets of DJC. DN-associated genes were retrieved from Gene Expression Omnibus (GEO), GeneCards, and Online Mendelian Inheritance in Man (OMIM). Key candidate targets were screened and ranked using multiple machine learning algorithms. The binding affinity between DJC's active ingredients and core targets was assessed via molecular docking. Finally, the therapeutic efficacy and predicted mechanisms were evaluated in db/db diabetic mice. Network pharmacology analysis identified 599 DJC targets and 68 overlapping genes shared with DN. Using machine learning algorithms, C-C motif chemokine ligand 2 (CCL2) and CASP1 were identified as prioritized candidate targets. Molecular docking predicted possible strong binding affinities between DJC active ingredients and these core proteins. Functional enrichment analyses (GO/KEGG) suggested that DJC modulation is associated with inflammatory responses and the MAPK pathway. In vivo validation showed that DJC treatment attenuated renal injury and fibrosis markers. These findings suggest that DJC may attenuate DN in part through CCL2/C-C motif chemokine receptor 2 (CCR2)-related pyroptosis signaling changes.
如何引用
AMA
Ziqiang Li, Ying Yuan, Yueyue Zhao, Yifang Deng, Gang Yin, Anlu Shen. Network Pharmacology, Machine Learning, and In Vivo Validation of Danzhi Jiangtang Capsule in Diabetic Nephropathy.. Journal of visualized experiments : JoVE. 2026; doi:10.3791/71328.
APA
Ziqiang Li, Ying Yuan, Yueyue Zhao, Yifang Deng, Gang Yin, Anlu Shen (2026). Network Pharmacology, Machine Learning, and In Vivo Validation of Danzhi Jiangtang Capsule in Diabetic Nephropathy.. Journal of visualized experiments : JoVE. https://doi.org/10.3791/71328
GB/T 7714
Ziqiang Li, Ying Yuan, Yueyue Zhao, Yifang Deng, Gang Yin, Anlu Shen. Network Pharmacology, Machine Learning, and In Vivo Validation of Danzhi Jiangtang Capsule in Diabetic Nephropathy.[J]. Journal of visualized experiments : JoVE, 2026 doi:10.3791/71328.
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