Molecular Networking-Guided Isolation of Cyclopentapeptides from the Hydrothermal Vent Sediment Derived Fungus Aspergillus pseudoviridinutans TW58-5 and Their Anti-inflammatory Effects.
Wenjuan DingDanmei TianMei ChenZixuan XiaXiyang TangSihao ZhangJihua WeiXunuo LiXin-Sheng YaoBin WuJinshan TangPublished in: Journal of natural products (2023)
Repetitive isolation of known compounds remains a major challenge in natural-product-based drug discovery. LC-MS/MS-based molecular networking has become a highly efficient strategy for the discovery of new natural products from complex mixtures. Herein, we report a molecular networking-guided isolation procedure, which resulted in the discovery of seven new cyclopentapeptides, namely, pseudoviridinutans A-F ( 1 - 7 ), from the marine-derived fungus Aspergillus pseudoviridinutans TW58-5. Compounds 1 - 7 feature a rare amino acid moiety, O ,β-dimethyltyrosine, observed for the first time from a marine-derived fungus. The planar structures of 1 - 7 were elucidated by detailed analyses of IR, UV, HR ESI-Q-TOF MS, and 1D and 2D NMR spectroscopic data. Meanwhile, their absolute configurations were determined through a combination of Marfey's method and X-ray diffraction. Subsequent bioassay revealed the anti-inflammation potential of 1 - 7 , especially 6 , which inhibited the production of nitric oxide (NO), a vital inflammatory mediator, in LPS-induced murine macrophage RAW264.7 cells by regulating the expression level of NLRP3 and iNOS.
Keyphrases
- highly efficient
- lps induced
- drug discovery
- nitric oxide
- high resolution
- small molecule
- inflammatory response
- oxidative stress
- induced apoptosis
- ms ms
- single molecule
- nitric oxide synthase
- poor prognosis
- adipose tissue
- high throughput
- machine learning
- magnetic resonance imaging
- high frequency
- molecular docking
- cell cycle arrest
- hydrogen peroxide
- single cell
- electronic health record
- cell death
- risk assessment
- signaling pathway
- minimally invasive
- cell wall
- endoplasmic reticulum stress
- deep learning
- human health
- dual energy