Arabian Journal of Chemistry (Mar 2024)
A comprehensive strategy integrating metabolomics with DNA barcoding for discovery of combinatorial discriminatory quality markers: A case of Cimicifuga foetida and Cimicifuga dahurica
Abstract
The multiple species characteristics of traditional Chinese medicines (TCMs) are crucial for expanding TCMs sources, meeting the needs of the pharmaceutical industry and ensuring clinical requirements. It’s also one of the significant factors affecting the quality control of TCMs. Systematic differential analysis of original species in TCMs is an important link in achieving comprehensive quality control, ensuring the effectiveness and safety of clinical medication. The study aims to establish a reliable and efficient approach to screen combinatorial discriminatory quality markers for rapid differentiation of original species by metabolomics coupled with DNA barcoding as a case of Cimicifugae Rhizoma. DNA barcoding is used to identify the origin of Cimicifugae Rhizoma. The data-dependent acquisition mode integrated with the computerized intelligent filtering system was established for in-depth characterization of metabolites from Cimicifugae Rhizoma using ultra-high performance liquid chromatography to quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). The untargeted metabolomics combined with multivariate statistical analysis was performed to screen and identify the potential combinatorial discriminatory quality markers. Finally, quantitative analysis and predictive model of these markers were employed to validate the feasibility of this strategy to distinguish the original species. Based on the scores of variable importance in projection greater than 1.0 and t-test (p < 0.05) in chemometric analysis, caffeic acid, cimifugin, ferulic acid and isoferulic acid were authenticated as combinatorial discriminatory quality markers for the two original species of Cimicifugae Rhizoma. In addition, the Fisher discriminant model successfully classified 56 batches of Cimicifugae Rhizoma with an accuracy of 94.4 %, showcased the practicality and scientific validity of this method. This study has provided a comprehensive strategy for efficient discrimination of multiple species of medicinal materials.
