Ai-Ping Li1, Sheng-Sheng He2, Wang-Ning Zhang1, Li-Chao Zhang3, Yue-Tao Liu1, Ke Li1, Xue-Mei Qin4. 1. Modern Research Center for Traditional Chinese Medicine of Shanxi University, No. 92, Wucheng Road, Taiyuan, 030006, Shanxi, People's Republic of China. 2. Modern Research Center for Traditional Chinese Medicine of Shanxi University, No. 92, Wucheng Road, Taiyuan, 030006, Shanxi, People's Republic of China; College of Chemistry and Chemical Engineering of Shanxi University, Taiyuan, 030006, People's Republic of China. 3. Institute of Biotechnology, Key Laboratory of Chemical Biology and Molecular Engineering of National Ministry of Education, Shanxi University, Taiyuan, 030006, People's Republic of China. 4. Modern Research Center for Traditional Chinese Medicine of Shanxi University, No. 92, Wucheng Road, Taiyuan, 030006, Shanxi, People's Republic of China; College of Chemistry and Chemical Engineering of Shanxi University, Taiyuan, 030006, People's Republic of China. Electronic address: qinxm@sxu.edu.cn.
Abstract
PURPOSE: This paper aimed to study the active compounds of Astragali Radix (AR) in the treatment of adriamycin nephropathy (AN) by a combination of network pharmacology and transcriptomics. METHODS: The chemical compounds of AR were screened out by text mining and database searching. Pharm Mapper was used to predict the targets of these chemical compounds. Potential targets of AN were screened by integrating the data from network pharmacology with known transcriptomics analysis results of kidney tissue. Compound-active target-potential target interactions networks were constructed so as to illustrate the relationship between compounds and targets, and obtain the chemical compounds directly related to potential targets of AN. The formula of compound contribution index (CI) based on algorithm was used to screen the active compounds of AR in the treatment of AN. In addition, we established an adriamycin-induced cell damage model with MPC5 cell, and used MTT assay, trypan blue dyeing and western blot analyses to validate the pharmacodynamic effect of the active compounds. RESULTS: 27 chemical compounds and 376 targets in AR were obtained by network pharmacology. Through Compound-active target-potential target interactions networks analysis, 22 compounds and 9 active targets as well as 130 potential targets were linked through 282 edges. The CI of every chemical compounds was further calculated by formula, the first four chemical compounds, including astragaloside IV, formononetin, quercetin and calycosin, whose cumulative contribution rate reached 87.28%, were considered to be active compounds. The results of MTT and trypan blue staining indicate that four active compounds had the significant protective effect on adriamycin-induced cell damage with MPC5 cell. Western blot result showed that four active compounds could significantly increase the expression of podocin protein in MPC5 cell. CONCLUSION: The active compounds of AR in the treatment of AN were successfully identified by using a network pharmacology and transcriptomics approach. This approach is expected to be beneficial to the study of the pharmacodynamic material basis of traditional Chinese medicine (TCM) in treating specific diseases.
PURPOSE: This paper aimed to study the active compounds of Astragali Radix (AR) in the treatment of adriamycinnephropathy (AN) by a combination of network pharmacology and transcriptomics. METHODS: The chemical compounds of AR were screened out by text mining and database searching. Pharm Mapper was used to predict the targets of these chemical compounds. Potential targets of AN were screened by integrating the data from network pharmacology with known transcriptomics analysis results of kidney tissue. Compound-active target-potential target interactions networks were constructed so as to illustrate the relationship between compounds and targets, and obtain the chemical compounds directly related to potential targets of AN. The formula of compound contribution index (CI) based on algorithm was used to screen the active compounds of AR in the treatment of AN. In addition, we established an adriamycin-induced cell damage model with MPC5 cell, and used MTT assay, trypan blue dyeing and western blot analyses to validate the pharmacodynamic effect of the active compounds. RESULTS: 27 chemical compounds and 376 targets in AR were obtained by network pharmacology. Through Compound-active target-potential target interactions networks analysis, 22 compounds and 9 active targets as well as 130 potential targets were linked through 282 edges. The CI of every chemical compounds was further calculated by formula, the first four chemical compounds, including astragaloside IV, formononetin, quercetin and calycosin, whose cumulative contribution rate reached 87.28%, were considered to be active compounds. The results of MTT and trypan blue staining indicate that four active compounds had the significant protective effect on adriamycin-induced cell damage with MPC5 cell. Western blot result showed that four active compounds could significantly increase the expression of podocin protein in MPC5 cell. CONCLUSION: The active compounds of AR in the treatment of AN were successfully identified by using a network pharmacology and transcriptomics approach. This approach is expected to be beneficial to the study of the pharmacodynamic material basis of traditional Chinese medicine (TCM) in treating specific diseases.