Yue Zhong, Yiyang Ren, Jiayin Zhang, Jialei Liu, Yuguang Zheng, Dan Zhang, Lei Wang, Long Guo
HS-GC-MS enabled annotation of 44 volatile metabolites, comprising 36 monoterpenoids, 3 sesquiterpenoids, 3 alcohols, and 2 aromatic hydrocarbons. Pre-column derivatization GC-MS analysis allowed detection of 88 metabolites, including 38 sugar derivatives, 21 amino acids, 16 organic acids, and 13 miscellaneous metabolites. UHPLC-Q-TOF-MS further enabled the detection of additional of 44 metabolites, including 26 flavonoids, 12 phenolic acids, 5 coumarins, and 1 terpenoid. Most monoterpenoids and phenolic acids displayed significantly higher (P < 0.05) relative abundances at the early growth stage. Most flavonoids reached peak abundance at the vigorous growth stage. Sugar derivatives remained at low levels in AAL during the early growth stage, yet accumulated markedly at the vigorous and senescence stages. A proposed metabolic network encompassing pathways of carbohydrate, amino acid, flavonoid, terpenoid, and organic acid was constructed to further explore metabolism regulatory during AAL development.
INTRODUCTION: Artemisia argyi Levl. et Vant is a traditional Chinese medicinal herb with considerable medicinal value. A. argyi Levl. et Vant. leaves (AAL) have exhibited diverse biological activities that are tightly associated with their chemical components. During growth, the dynamic accumulation trends of bioactive metabolites were varied along with AAL developing. However, systematic studies on the dynamic variation patterns and regulatory mechanisms of major chemical constituents throughout their growth cycle remain scarce. The present study aims to clarify metabolic dynamic patterns of AAL during development.
METHODS: Mass spectrometry-based metabolomic platforms were applied for chemical profiling of AAL samples from 8 distinct growth stages. HS-GC-MS, pre-column derivatization GC-MS and UHPLC-Q-TOF-MS was applied for both volatile and non-volatile metabolite analysis. Orthogonal partial least squares-discriminant analysis and clustering analysis were further carried out for screening of differential components. A dynamic metabolism network of AAL was constructed by mapping annotated metabolites on their biosynthetic pathways.
RESULTS: HS-GC-MS enabled annotation of 44 volatile metabolites, comprising 36 monoterpenoids, 3 sesquiterpenoids, 3 alcohols, and 2 aromatic hydrocarbons. Pre-column derivatization GC-MS analysis allowed detection of 88 metabolites, including 38 sugar derivatives, 21 amino acids, 16 organic acids, and 13 miscellaneous metabolites. UHPLC-Q-TOF-MS further enabled the detection of additional of 44 metabolites, including 26 flavonoids, 12 phenolic acids, 5 coumarins, and 1 terpenoid. Most monoterpenoids and phenolic acids displayed significantly higher (P < 0.05) relative abundances at the early growth stage. Most flavonoids reached peak abundance at the vigorous growth stage. Sugar derivatives remained at low levels in AAL during the early growth stage, yet accumulated markedly at the vigorous and senescence stages. A proposed metabolic network encompassing pathways of carbohydrate, amino acid, flavonoid, terpenoid, and organic acid was constructed to further explore metabolism regulatory during AAL development.
DISCUSSION: The comprehensive profile of major metabolites in AAL across growth stages provides a scientific basis for the quality evaluation and sustainable exploitation of AAL medicinal resources.