延胡索不同提取物抗炎作用的谱效关系及机制研究

Spectrum-effect relationship and mechanism of anti-inflammatory effects of different extracts of Corydalis yanhusuo

目的建立延胡索提取物指纹图谱与抗炎作用的谱效关系,为研究延胡索的药效物质基础提供思路和依据。方法利用UPLC-Q-TOF/MS建立延胡索不同提取物的指纹图谱,以核转录因子-κB(NF-κB)荧光素酶为标志对人体支气管上皮细胞进行抗炎活性实验,通过灰色关联度法和偏最小二乘回归法联合分析特征峰与抗炎作用的谱效关系。通过谱效关系分析得到的抗炎作用成分,利用分子对接技术预测其作用靶点,初步研究其抗炎作用机制。结果延胡索95%乙醇提取物具有显著抗炎效果,其中5、8～11号特征峰代表的化合物具有显著抗炎活性。分子对接结果显示延胡索可能通过作用于蛋白激酶C(PKC)、细胞外调节蛋白激酶2(ERK2)、抑制蛋白激酶β(IKKβ)、Janus激酶1(JAK1)、磷脂酰肌醇-3-激酶α(PI3K-α)、磷脂酰肌醇-3-激酶γ(PI3K-γ)、肿瘤坏死因子-α(TNF-α)影响炎症信号的传递,发挥抗炎作用。结论延胡索抗炎作用是多种成分联合作用的结果,抗炎作用有效成分主要为黄连碱、小檗碱、巴马汀、二氢血根碱和去氢紫堇碱,主要通过影响PI3K、JAK、PKC、ERK、IKKβ、TNF-α所在信号通路发挥抗炎作用。

Objective To establish a spectrum-effect relationship betweent anti-inflammation effects and extracts of Corydalis yanhusuo, in order to provide ideas and methods for study of material basis of efficacy. Methods UPLC-Q-TOF/MS was used to establishe fingerprints of different extracts of C. yanhusuo, and the flurescent enzyme was used as a marker to perform the anti-inflammation activity test. Finally, the relationships between characteristic peaks and anti-inflammation activity was established by partial least squares regression analysis(PLSR) and gray relational analysis(GRA). The anti-inflammatory component obtained by spectral effect analysis was predicted by molecular docking technology, and its anti-inflammatory mechanism was preliminarily studied. Results The 95% ethanol extract had significant anti-inflammatory activity. The characteristic peaks of No. 5 and 8-11 were significantly affected in PLSR and GRA. Molecular docking results showed that C. yanhusuo exerted anti-inflammatory effects by acting on PKC, ERK2, IKKβ, JAK1, PI3 K-α, PI3 K-γ, TNF-α, affecting the transmission of inflammatory signals. Conclusion The anti-inflammatory effect of C. yanhusuo is the result of the combination of various components. The main anti-inflammatory components are coptisine, berberine, palmatine, dihydrogenine, and dehydrocryptine, which exert anti-inflammatory effects by affecting PI3 K, JAK, PKC, ERK, IKKβ, and TNF-α signaling pathways.