Network pharmacology to unveil the mechanism of Moluodan in the treatment of chronic atrophic gastritis

系统药理学 计算生物学 机制(生物学) 细胞生长 细胞 细胞凋亡 炎症 系统生物学 药理学 生物 生物信息学 化学 生物化学 药品 免疫学 哲学 认识论
作者
Wuai Zhou,Huan Zhang,Xin Wang,Jun Kang,WuYan Guo,Lihua Zhou,Huiyun Liu,Menglei Wang,Ruikang Jia,Xinjun Du,Weihua Wang,Bo Zhang,Shao Li
出处
期刊:Phytomedicine [Elsevier BV]
卷期号:95: 153837-153837 被引量:156
标识
DOI:10.1016/j.phymed.2021.153837
摘要

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.Elucidate the MoA of MLD for treating CAG based on network pharmacology.Integrate computational prediction and experimental validation based on network pharmacology.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.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.The network pharmacology framework integrating computational prediction and experimental validation provides a novel way for exploring the MoA of MLD.
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