Spectrum-effect relationship and mechanism of anti-inflammatory effects of different extracts of Corydalis yanhusuo
10.7501/j.issn.0253-2670.2019.10.023
- Author:
Ning-Ning MA
1
Author Information
1. Tianjin University of Traditional Chinese Medicine
- Publication Type:Journal Article
- Keywords:
Anti-inflammation;
Berberine;
Coptisine;
Corydalis yanhusuo W. T. Wang;
Dehydrocryptine;
Dihydrogenine;
Gray relational analysis;
Molecular docking;
Palmatine;
Partial least squares regression analysis;
Spectrum-effect relationship
- From:
Chinese Traditional and Herbal Drugs
2019;50(10):2413-2419
- CountryChina
- Language:Chinese
-
Abstract:
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, PI3K-α, PI3K-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 PI3K, JAK, PKC, ERK, IKKβ, and TNF-α signaling pathways.
