代谢组
代谢组学
化学
暴露的
代谢物
体内
姜黄
计算生物学
药理学
化学成分
传统医学
生药学
初级代谢物
药用植物
作者
Xing Wang,Chenxi Shen,SHAO Si-min,Tian-Bo Ma,Jing Cui,Qing-Xuan Ren,Hua Yang,Wen Yuan Gao
标识
DOI:10.1021/acs.analchem.6c01533
摘要
Abstract The metabolomic comparison of closely related herbal species, both the intrinsic plant metabolome and the subsequent in vivo exposome, is vital for ensuring drug safety and elucidating therapeutic mechanisms. However, such a comparison is challenging because medicinal plants within the same genus share similar chemical profiles with minor chemical or abundance differences, and their in vivo exposure levels are typically low and are affected by complex biological matrices. In this study, Curcumae Radix (CR), a traditional Chinese medicine derived from the roots of four Curcuma species, was used as a case study. A pseudotargeted workflow based on scheduled high-resolution multiple reaction monitoring (sMRMHR) was established to compare the metabolites and plasma exposure profiles of these species, using a transition library constructed from the four plant species. The sMRMHR pseudotargeted method showed acceptable linearity, repeatability, and stability in QC samples and 12 reference standards, with performance comparable to that of SWATH and TOF MS1 while maintaining stable coefficients of variation (CVs) at low concentrations. Specifically, 95.2% of metabolomic features showed linearity with R2 ≥ 0.8, 98.16% showed a CV ≤ 30%. In exposomics, 83.14% of exposures had matrix effect factor (MF) CVs below 30%, and over 300 exposures showed MFs within 70%−130%. Finally, the application results of CR demonstrate its capability for chemome comparison of metabolites and exposures. In conclusion, this approach provides a practical HRMS-based strategy for the integrated comparison of plant metabolite profiles and plasma exposure-related profiles across closely related medicinal species, and may support subsequent studies of interspecies chemical differences and in vivo exposure characteristics after oral administration.
科研通智能强力驱动
Strongly Powered by AbleSci AI