基于网络药理学葵花护肝片“多成分-多靶点-多通路”的作用机制研究
|
摘要
该研究通过网络药理学的方法,对葵花护肝片活性成分作用靶点预测,探讨其多成分-多靶点-多通路肝脏保护作用模式。首先,采用中药系统药理学技术平台(TCMSP)及TCM Database@Taiwan资料库,以口服利用度(OB),类药性(DL)和药物半衰期(HL)筛选葵花护肝片中活性成分。然后,依据反向药效团匹配(PharmMapper)方法预测葵花护肝片活性成分靶点;通过CooLGeN数据库文本挖掘及GeneCards数据库中与慢性肝炎及早期肝硬化相关靶基因比对筛选,借助生物信息学注释数据库(Metascape)分析靶点基因功能及信号通路,采用Cytoscape软件构建葵花护肝片化合物-靶点,化合物-靶点-通路。通过STRING数据库结合Cytoscape软件绘制蛋白相互作用(PPI)网络并进行网络拓扑学分析;最后,通过Systems Dock Web Site软件将葵花护肝片活性成分与枢纽靶点进行分子对接验证。实验筛选得到护肝片26个化合物和509个靶点;通过核心靶点蛋白互作网络分析确定了葵花护肝片活性成分与白蛋白(ALB)、胰岛素样生长因子1(IGF1)、基质金属蛋白酶-9 (MMP-9)、基质金属蛋白酶-2 (MMP-2)、非受体酪氨酸激酶原癌基因(SRC)及雌激素受体1(ESR1)等6个靶点及肿瘤-信号转导-炎症-药物代谢等相关生物过程和代谢通路紧密相关。结果表明,护肝片改善慢性肝炎及早期肝硬化作用体现了中药多成分-多靶点-多途径的作用特点,该研究为深入阐释葵花护肝片临床研究及进一步开发提供了思路。
To predict the targets of active ingredients of Kuihua Hugan Tablets by network pharmacology, and explore the "multi-component-multi-target-multi-pathway" hepatoprotective mechanism of action. First, through traditional Chinese medicine systems pharmacology(TCMSP) and TCM Database@Taiwan Database, main active ingredients of Kuihua Hugan Tablets were screened out based on oral bioavailability(OB), drug-likeness(DL) and effective half-lives(HL). The targets of active ingredients of Kuihua Hugan Tablets were predicted based on the PharmMapper method. Then, the prediction was conducted by screening the target genes associated with chronic hepatitis and early cirrhosis through CooLGeN and GeneCards databases. Target gene functions and signal pathways were analyzed by bioinformatics annotation database Metascape. Cytoscape software was used to construct the Kuihua Hugan Tablets ingredient-target and ingredient-target-pathway network. String database combined with Cytoscape software was used to construct the networks of component-target and component-target-pathway. STRING database was combined with Cytoscape software to draw protein-protein interaction(PPI) network and conduct network topology analysis. Finally, Systems Dock Web Site software was applied in verifying the molecular docking between active ingredients and potential protein targets. A total of 26 compounds and 509 potential targets were screened out from Kuihua Hugan Tablets in the experiment. The results of PPI network analysis indicated that albumin(ALB), insulin-like growth factor 1(IGF1), matrix metalloproteinase-9(MMP9), matrix metalloproteinase-2(MMP2), non-receptor tyrosine kinase proto-oncogene(SRC), estrogen receptor 1(ESR1) and cancer-signal transduction-inflammation-drugs metabolism-related biological processes and metabolic pathways were closely associated with the active ingredients in Kuihua Hugan Tablets. The effects of Kuihua Hugan Tablets in alleviating chronic hepatitis and early cirrhosis indicated the multi-component, multi-target, and multi-pathway characteristics of traditional Chinese medicines, providing new ideas for further research and development of Kuihua Hugan Tablets.
To predict the targets of active ingredients of Kuihua Hugan Tablets by network pharmacology, and explore the "multi-component-multi-target-multi-pathway" hepatoprotective mechanism of action. First, through traditional Chinese medicine systems pharmacology(TCMSP) and TCM Database@Taiwan Database, main active ingredients of Kuihua Hugan Tablets were screened out based on oral bioavailability(OB), drug-likeness(DL) and effective half-lives(HL). The targets of active ingredients of Kuihua Hugan Tablets were predicted based on the PharmMapper method. Then, the prediction was conducted by screening the target genes associated with chronic hepatitis and early cirrhosis through CooLGeN and GeneCards databases. Target gene functions and signal pathways were analyzed by bioinformatics annotation database Metascape. Cytoscape software was used to construct the Kuihua Hugan Tablets ingredient-target and ingredient-target-pathway network. String database combined with Cytoscape software was used to construct the networks of component-target and component-target-pathway. STRING database was combined with Cytoscape software to draw protein-protein interaction(PPI) network and conduct network topology analysis. Finally, Systems Dock Web Site software was applied in verifying the molecular docking between active ingredients and potential protein targets. A total of 26 compounds and 509 potential targets were screened out from Kuihua Hugan Tablets in the experiment. The results of PPI network analysis indicated that albumin(ALB), insulin-like growth factor 1(IGF1), matrix metalloproteinase-9(MMP9), matrix metalloproteinase-2(MMP2), non-receptor tyrosine kinase proto-oncogene(SRC), estrogen receptor 1(ESR1) and cancer-signal transduction-inflammation-drugs metabolism-related biological processes and metabolic pathways were closely associated with the active ingredients in Kuihua Hugan Tablets. The effects of Kuihua Hugan Tablets in alleviating chronic hepatitis and early cirrhosis indicated the multi-component, multi-target, and multi-pathway characteristics of traditional Chinese medicines, providing new ideas for further research and development of Kuihua Hugan Tablets.
引文
[1] 沈勇.护肝片在急慢性乙型病毒性肝炎中的应用和研究[J].光明中医,2017,32(4):525.
[2] 王少丽,姚乃礼,吕文良.中药治疗慢性乙型肝炎疗效优势的研究进展[J].中国中药杂志,2007,32(23):2468.
[3] 石志平,吴同玉.乙型肝炎肝硬化发病机制特点研究概况[J].辽宁中医药大学学报,2014,16(1):91.
[4] 贾云飞. 慢乙肝发生肝癌风险预测模型的建立及中西医结合治疗对乙肝肝硬化患者肝癌发病的影响[D]. 北京:北京中医药大学,2017.
[5] 崔艺馨.葵花护肝片联合阿托伐他汀钙片治疗冠心病的临床观察[J]. 中国现代药物应用,2016,10(1): 155.
[6] 朴美善,李宪.恩替卡韦联合护肝片对慢性乙型肝炎治疗效果观察[J].中国社区医师,2018,34(22):42.
[7] 官欣,陈希,罗丹.护肝片治疗药物性肝脏损害的临床研究[J].北方药学,2016,13(9):137.
[8] 梁增荣,龙样,陈少锐,等. 护肝片对四氯化碳致大鼠慢性肝损伤肝阴虚证的保护作用[J]. 中国实验方剂学杂志,2015,21(24):137.
[9] 李梢. 基于生物网络调控的方剂研究模式与实践[J].综合医学杂志, 2007, 5(5):489.
[10] 孙雨辉,陆景坤,王健,等.基于网络药理学的三味檀香散治疗冠心病的机制初探[J].中国新药与临床杂志,2018,37(5):272.
[11] Xu T,Li S,Sun Y,et al. Systematically characterize the absorbed effective substances of Wutou Decoction and their metabolic pathways in rat plasma using UHPLC-Q-TOF-MS combined with a target network pharmacological analysis[J]. J Pharm Biomed Anal,2017,141: 95.
[12] 李梢. 网络靶标:中药方剂网络药理学研究的一个切入点[J]. 中国中药杂志, 2011, 36(15):2017.
[13] Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application[J]. Chin J Nat Med, 2013, 11(2):110.
[14] Liu X, Ouyang S, Yu B, et al. PharmMapper server: a web server for potential drug target identification using pharma-cophore mapping approach [J]. Nucl Acid Res, 2010, 38:W609.
[15] Hou J B,Chen W,Lu H T, et al. Exploring the therapeutic mechanism of on oxalate crystal-induced kidney injuries using comprehensive approaches based on proteomics and network pharmacology[J].Front Pharmacol, 2018, 9: 620.
[16] 肖东,庄改改,李元杰,等.苦豆子中黄酮类成分的网络药理学研究[J].中国中药杂志,2018,43(16):3353.
[17] 梁经纬,孙琦,赵楠,等.金莲花碱的潜在作用靶标预测及分子对接研究[J].计算机与应用化学,2016,33(7):800.
[18] 吴丹,高耀,向欢,等.基于网络药理学的栀子豉汤抗抑郁作用机制研究[J].中草药,2018,49(7):1594.
[19] Ru Jinlong, Li Peng, Wang Jinan, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines[J]. J Cheminformatics, 2014, 16:13.
[20] Chen Y C. TCM Database@Taiwan: the world′s largest traditional Chinese medicine database for drug screening in silico[J]. PLoS ONE, 2011, 6:e15939.
[21] Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life[J]. Nucl Acid Res, 2015, 43:D447.
[22] Hsin K Y, Matsuoka Y, Asai Y, et al. SystemsDock: a web server for network pharmacology-based prediction and analysis [J]. Nucl Acid Res, 2016, 44(W1): W507.
[23] 刘楠,姜云耀,黄婷婷,等.基于网络药理学方法研究补阳还五汤治疗脑梗死的作用机制[J].中国中药杂志,2018,43(11):2190.
[24] 章亮,陈泽慧,陈韩英,等.基于网络药理学的白屈菜抗肿瘤分子机制研究[J].中草药,2018, 49(3):646.
[25] Hsin K Y, Ghosh S, Kitano H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology [J]. PLoS ONE, 2013, 8(12): e83922.
[26] 杨红丽.葵花护肝片联合酒石酸美托洛尔片治疗高血压的临床观察[J].中国医药指南,2018,16(1):177.
[27] 白剑,肖漓,韩永,等.护肝片对小鼠肝纤维化模型中SOD和MDA的影响[J].实用药物与临床,2015,18(8):906.
[28] 孔华丽,闫亮,段惠娟.五味子醇提取物保肝作用成分分析[J].解放军药学学报,2010, 20(1):27.
[29] Hong M, Zhang Y, Li S, et al. A network pharmacology-based study on the hepatoprotective effect of Fructus Schisandrae[J]. Molecules, 2017, 22(10):16.
[2] 王少丽,姚乃礼,吕文良.中药治疗慢性乙型肝炎疗效优势的研究进展[J].中国中药杂志,2007,32(23):2468.
[3] 石志平,吴同玉.乙型肝炎肝硬化发病机制特点研究概况[J].辽宁中医药大学学报,2014,16(1):91.
[4] 贾云飞. 慢乙肝发生肝癌风险预测模型的建立及中西医结合治疗对乙肝肝硬化患者肝癌发病的影响[D]. 北京:北京中医药大学,2017.
[5] 崔艺馨.葵花护肝片联合阿托伐他汀钙片治疗冠心病的临床观察[J]. 中国现代药物应用,2016,10(1): 155.
[6] 朴美善,李宪.恩替卡韦联合护肝片对慢性乙型肝炎治疗效果观察[J].中国社区医师,2018,34(22):42.
[7] 官欣,陈希,罗丹.护肝片治疗药物性肝脏损害的临床研究[J].北方药学,2016,13(9):137.
[8] 梁增荣,龙样,陈少锐,等. 护肝片对四氯化碳致大鼠慢性肝损伤肝阴虚证的保护作用[J]. 中国实验方剂学杂志,2015,21(24):137.
[9] 李梢. 基于生物网络调控的方剂研究模式与实践[J].综合医学杂志, 2007, 5(5):489.
[10] 孙雨辉,陆景坤,王健,等.基于网络药理学的三味檀香散治疗冠心病的机制初探[J].中国新药与临床杂志,2018,37(5):272.
[11] Xu T,Li S,Sun Y,et al. Systematically characterize the absorbed effective substances of Wutou Decoction and their metabolic pathways in rat plasma using UHPLC-Q-TOF-MS combined with a target network pharmacological analysis[J]. J Pharm Biomed Anal,2017,141: 95.
[12] 李梢. 网络靶标:中药方剂网络药理学研究的一个切入点[J]. 中国中药杂志, 2011, 36(15):2017.
[13] Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application[J]. Chin J Nat Med, 2013, 11(2):110.
[14] Liu X, Ouyang S, Yu B, et al. PharmMapper server: a web server for potential drug target identification using pharma-cophore mapping approach [J]. Nucl Acid Res, 2010, 38:W609.
[15] Hou J B,Chen W,Lu H T, et al. Exploring the therapeutic mechanism of on oxalate crystal-induced kidney injuries using comprehensive approaches based on proteomics and network pharmacology[J].Front Pharmacol, 2018, 9: 620.
[16] 肖东,庄改改,李元杰,等.苦豆子中黄酮类成分的网络药理学研究[J].中国中药杂志,2018,43(16):3353.
[17] 梁经纬,孙琦,赵楠,等.金莲花碱的潜在作用靶标预测及分子对接研究[J].计算机与应用化学,2016,33(7):800.
[18] 吴丹,高耀,向欢,等.基于网络药理学的栀子豉汤抗抑郁作用机制研究[J].中草药,2018,49(7):1594.
[19] Ru Jinlong, Li Peng, Wang Jinan, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines[J]. J Cheminformatics, 2014, 16:13.
[20] Chen Y C. TCM Database@Taiwan: the world′s largest traditional Chinese medicine database for drug screening in silico[J]. PLoS ONE, 2011, 6:e15939.
[21] Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life[J]. Nucl Acid Res, 2015, 43:D447.
[22] Hsin K Y, Matsuoka Y, Asai Y, et al. SystemsDock: a web server for network pharmacology-based prediction and analysis [J]. Nucl Acid Res, 2016, 44(W1): W507.
[23] 刘楠,姜云耀,黄婷婷,等.基于网络药理学方法研究补阳还五汤治疗脑梗死的作用机制[J].中国中药杂志,2018,43(11):2190.
[24] 章亮,陈泽慧,陈韩英,等.基于网络药理学的白屈菜抗肿瘤分子机制研究[J].中草药,2018, 49(3):646.
[25] Hsin K Y, Ghosh S, Kitano H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology [J]. PLoS ONE, 2013, 8(12): e83922.
[26] 杨红丽.葵花护肝片联合酒石酸美托洛尔片治疗高血压的临床观察[J].中国医药指南,2018,16(1):177.
[27] 白剑,肖漓,韩永,等.护肝片对小鼠肝纤维化模型中SOD和MDA的影响[J].实用药物与临床,2015,18(8):906.
[28] 孔华丽,闫亮,段惠娟.五味子醇提取物保肝作用成分分析[J].解放军药学学报,2010, 20(1):27.
[29] Hong M, Zhang Y, Li S, et al. A network pharmacology-based study on the hepatoprotective effect of Fructus Schisandrae[J]. Molecules, 2017, 22(10):16.