Macrocyclic Diterpenoids from Euphorbia helioscopia and Their Potential Anti-inflammatory Activity.
Jun-Cheng SuWen ChengJian-Guo SongYuan-Lin ZhongXiao-Jun HuangRen-Wang JiangYao-Lan LiMan-Mei LiWen-Cai YeYing WangPublished in: Journal of natural products (2019)
Guided by 1H NMR spectroscopic experiments using the aromatic protons as probes, 11 macrocyclic diterpenes (1-11) were isolated from the aerial parts of Euphorbia helioscopia. Their full three-dimensional structures, including absolute configurations, were established unambiguously by spectroscopic analysis and single-crystal X-ray crystallographic experiments. Among the isolated compounds, compound 1 is the third member thus far of a rare class of Euphorbia diterpenes featuring an unusual 5/10 fused ring system, and 2-4 are new jatrophane diterpenes. Based on the NMR data of the jatrophane diterpenes obtained in this study as well as those with crystallographic structures reported in the literature, the correlations of the chemical shifts of the relevant carbons and the configurations of C-2, C-13, and C-14 of their flexible macrocyclic ring were considered. Moreover, the anti-inflammatory activities of 1-11 were investigated by monitoring their inhibitory effects on nitric oxide production in lipopolysaccharide-stimulated RAW 264.7 cells. Compound 1 showed an IC50 of 7.4 ± 0.6 μM, which might be related to the regulation of the NF-κB signaling pathway by suppressing the translocation of the p65 subunit and the consequent reduction of IL-6 and TNF-α secretions.
Keyphrases
- high resolution
- signaling pathway
- induced apoptosis
- solid state
- nitric oxide
- molecular docking
- pi k akt
- magnetic resonance
- anti inflammatory
- cell cycle arrest
- lps induced
- systematic review
- rheumatoid arthritis
- inflammatory response
- mass spectrometry
- oxidative stress
- toll like receptor
- small molecule
- hydrogen peroxide
- endoplasmic reticulum stress
- nitric oxide synthase
- big data
- cell proliferation
- atomic force microscopy
- computed tomography
- living cells
- risk assessment
- machine learning
- human health
- deep learning
- molecular dynamics simulations
- dual energy
- drug induced
- contrast enhanced