分子网络引导下从[来源未提及]中分离环阿屯烷型三萜类化合物及其对一氧化氮生成的抑制作用。
Molecular Networking-Guided Isolation of Cycloartane-type Triterpenoids from and Their Inhibitory Effect on Nitric Oxide Production.
作者信息
Kim Jun Gu, Le Thi Phuong Linh, Han Jae Sang, Cho Yong Beom, Lee Dongho, Lee Mi Kyeong, Hwang Bang Yeon
机构信息
College of Pharmacy, Chungbuk National University, Cheongju 28160, South Korea.
Department of Plant Biotechnology, College of Life Sciences and Biotechnology, South Korea University, Seoul 02841, South Korea.
出版信息
ACS Omega. 2022 Jul 18;7(30):26853-26862. doi: 10.1021/acsomega.2c03243. eCollection 2022 Aug 2.
The MolNetEnhancer workflow was applied to molecular networking analysis of the CHCl-soluble fraction of the rhizomes of , which showed a potent inhibitory effect on the lipopolysaccharide (LPS)-induced nitric oxide production. Among the molecular network, clusters of cycloartane-type triterpenoids were classified using the ClassyFire module of MolNetEnhancer, and their structures were predicted by the in silico fragment analysis tool, Network Annotation Propagation (NAP). Using mass spectrometry (MS)-guided isolation methods, six cycloartane-type triterpenoids (-) were isolated, and their structures were elucidated based on the interpretation of NMR, HRESIMS, and single-crystal X-ray diffraction. Among the isolates, compounds and , which have an α,β-unsaturated carbonyl moiety on the A-ring, exhibited significant inhibitory effects on LPS-induced nitric oxide production in RAW264.7 cells with IC values of 12.4 and 11.8 μM, respectively.
MolNetEnhancer工作流程应用于某根茎氯仿可溶部分的分子网络分析，该部分对脂多糖（LPS）诱导的一氧化氮产生具有显著抑制作用。在分子网络中，使用MolNetEnhancer的ClassyFire模块对环阿尔廷型三萜类化合物簇进行分类，并通过计算机片段分析工具网络注释传播（NAP）预测其结构。采用质谱（MS）引导的分离方法，分离出6种环阿尔廷型三萜类化合物（-），并基于核磁共振（NMR）、高分辨电喷雾电离质谱（HRESIMS）和单晶X射线衍射解析其结构。在分离出的化合物中，化合物 和 在A环上具有α,β-不饱和羰基部分，对RAW264.7细胞中LPS诱导的一氧化氮产生表现出显著抑制作用，IC值分别为12.4和11.8 μM。
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