与基于特征的分子网络的LC-HRMS合以有效地注释Alstonia scholaris的单烯醇化物
Ying-Jie He1,2,3, Yan Qin1, Xiao-Dong Luo1,4
1Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, China.
Plants (Basel, Switzerland)
|July 30, 2025
概括
这项研究提出了一种新的策略,用于识别使用LC-HRMS的草药中的单烯醇化物 (MIA). 该方法在Alstonia scholaris中高效地注释了229个MIA,有助于呼吸系统疾病研究.
科学领域:
- 自然产品化学 自然产品化学
- 分析化学 分析化学
- 药理学是指药理学,即药理学是指药理学.
背景情况:
- 单烯醇类化合物 (MIAs) 具有多样化的结构和药理活动.
- 使用LC-HRMS在复杂的草本矩阵中准确地注释MIA,这带来了重大挑战.
- 阿尔斯托尼亚学士 (ALAS) 是用于呼吸系统疾病的植物药物,需要详细的化学分析.
研究的目的:
- 开发和验证一种有效的策略,用于草药中MIA的结构性注释.
- 将这一策略应用于阿尔斯托尼亚学士的叶子化物中全面的MIA识别.
- 为进一步对ALAS进行临床研究奠定基础.
主要方法:
- 利用多重碰撞能量 (MCEs/MS2) 的液体染色学高分辨率质谱 (LC-HRMS) 进行增强的光谱数据采集.
- 采用基于特征的分子网络 (FBMN) 来可视化检测到的MIA之间的结构关系.
- 对诊断产品离子 (DPI),特征分离 (CCs),中性/激素损失 (NLs/RLs),生物合成途径 (BPs) 和结构特征进行综合分析.
主要成果:
- 在Alstonia scholaris叶类提取物中成功注释了总共229个MIA.
- MCEs/MS2-FBMN/BPs工作流程在捕获MIA识别的关键MS2特征方面表现出高效率.
- 根据生物合成途径和结构特征实现了注释MIA的系统分类.
结论:
- 开发的MCEs/MS2-FBMN/BPs策略显著提高了复杂草药中MIAs的结构注释.
- 这种方法提供了一种可靠的方法,用于分析植物药物如ALAS中的生物活性化合物.
- ALAS的全面MIA注释支持其未来的呼吸系统疾病临床研究和开发.
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