Wuai Zhou1, Huan Zhang2, Xin Wang1, Jun Kang3, Wuyan Guo2, Lihua Zhou4, Huiyun Liu5, Menglei Wang5, Ruikang Jia5, Xinjun Du6, Weihua Wang7, Bo Zhang8, Shao Li9. 1. Institute of TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China. 2. TCM Network Pharmacology Department, Tianjin Key Laboratory of Early Druggability Evaluation of Innovative Drugs, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China. 3. School of Life Sciences, Tianjin University, Tianjin 300072, China. 4. TCM Network Pharmacology Department, Tianjin Key Laboratory of Early Druggability Evaluation of Innovative Drugs, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China; College of Pharmacy, Nankai University, Tianjin 300350, China. 5. Hebei (Handan) TCM Industrial Technology Research Institute, Handan Pharmaceutical Co., Ltd., Handan 056000, China. 6. Key Laboratory of Food Nutrition and Safety, Ministry of Education, Tianjin University of Science and Technology, Tianjin 300457, China. 7. Center of Pharmaceutical Technology, Tsinghua University, China. 8. TCM Network Pharmacology Department, Tianjin Key Laboratory of Early Druggability Evaluation of Innovative Drugs, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China. Electronic address: zhangbo@tjab.org. 9. Institute of TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address: shaoli@mail.tsinghua.edu.cn.
Abstract
BACKGROUND: Moluodan (MLD) is a traditional Chinese patent medicine for the treatment of chronic atrophic gastritis (CAG). However, the mechanism of action (MoA) of MLD for treating CAG still remain unclear. PURPOSE: Elucidate the MoA of MLD for treating CAG based on network pharmacology. STUDY DESIGN: Integrate computational prediction and experimental validation based on network pharmacology. METHODS: Computationally, compounds of MLD were scanned by LC-MS/MS and the target profiles of compounds were identified based on network-based target prediction method. Compounds in MLD were compared with western drugs used for gastritis by hierarchical clustering of target profile. Key biological functional modules of MLD were analyzed, and herb-biological functional module network was constructed to elucidate combinatorial rules of MLD herbs for CAG. Experimentally, MLD's effect on different biological functional modules were validated from both phenotypic level and molecular level in 1- Methyl-3-nitro-1-nitrosoguanidine (MNNG)-induced GES-1 cells. RESULTS: Computational results show that the target profiles of compounds in MLD can cover most of the biomolecules reported in literature. The MoA of MLD can cover most types of MoA of western drugs for CAG. The treatment of CAG by MLD involved the regulation of various biological functional modules, e.g., inflammation/immune, cell proliferation, cell apoptosis, cell differentiation, digestion and metabolism. Experimental results show that MLD can inhibit cell proliferation, promote cell apoptosis and differentiation, reduce the inflammation level and promote lipid droplet accumulation in MNNG-induced GES-1 cells. CONCLUSION: The network pharmacology framework integrating computational prediction and experimental validation provides a novel way for exploring the MoA of MLD.
BACKGROUND: Moluodan (MLD) is a traditional Chinese patent medicine for the treatment of chronic atrophic gastritis (CAG). However, the mechanism of action (MoA) of MLD for treating CAG still remain unclear. PURPOSE: Elucidate the MoA of MLD for treating CAG based on network pharmacology. STUDY DESIGN: Integrate computational prediction and experimental validation based on network pharmacology. METHODS: Computationally, compounds of MLD were scanned by LC-MS/MS and the target profiles of compounds were identified based on network-based target prediction method. Compounds in MLD were compared with western drugs used for gastritis by hierarchical clustering of target profile. Key biological functional modules of MLD were analyzed, and herb-biological functional module network was constructed to elucidate combinatorial rules of MLD herbs for CAG. Experimentally, MLD's effect on different biological functional modules were validated from both phenotypic level and molecular level in 1- Methyl-3-nitro-1-nitrosoguanidine (MNNG)-induced GES-1 cells. RESULTS: Computational results show that the target profiles of compounds in MLD can cover most of the biomolecules reported in literature. The MoA of MLD can cover most types of MoA of western drugs for CAG. The treatment of CAG by MLD involved the regulation of various biological functional modules, e.g., inflammation/immune, cell proliferation, cell apoptosis, cell differentiation, digestion and metabolism. Experimental results show that MLD can inhibit cell proliferation, promote cell apoptosis and differentiation, reduce the inflammation level and promote lipid droplet accumulation in MNNG-induced GES-1 cells. CONCLUSION: The network pharmacology framework integrating computational prediction and experimental validation provides a novel way for exploring the MoA of MLD.