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Tea plants, as essential health beverages, are widely recognized for their rich content of antioxidant metabolites. This study utilized metabolomics analysis techniques to compare the metabolic differences between Camellia tachangensis F. C. Zhang, Camellia gymnogyna Hung T. Chang, and Camellia sinensis (L.) O. Kunzte, aiming to reveal their potential antioxidant activities. By applying UPLC–MS/MS technology, we analyzed metabolites in the samples and identified 1056 metabolites. We utilized statistical methods such as orthogonal partial least squares discriminant analysis (OPLS‐DA) and principal component analysis to evaluate and compare the variations in the samples. An intersection analysis of metabolites from the three types of tea plants identified 29 major differential metabolites. Functional annotation and pathway analysis of these differential metabolites were conducted, ultimately discussing 24 antioxidant‐related metabolites, primarily belonging to flavonoids, phenolic acids, and other categories. Among the three types of tea, C. tachangensis has the highest content of procyanidin B4, which is the most abundant substance in this tea. In terms of the impact of this substance on tea, it is known to have antioxidant properties and contributes to the overall antioxidant capacity of tea. In both C. gymnogyna and C. sinensis, the top 5 antioxidant metabolites include substances such as apigenin‐6,8‐di‐C‐glucoside (vicenin‐2), epigallocatechin, and apigenin‐5‐O‐glucoside. These substances belong to the flavonoid class and contribute to the antioxidant properties of these teas. Beyond the flavonoids and phenolic acids, other classes of metabolites in tea plants also exhibited significant antioxidant properties. These different metabolites play a key role in the antioxidant function of tea, contributing to their potential health benefits. The metabolites provide crucial insights for exploring and developing high‐antioxidant products that occur naturally. [ABSTRACT FROM AUTHOR]

Among ornamental plants, the colour of the petals is an important feature. However, the reason for the colour differences of Rhododendron liliiflorum remains unclear. To reveal the differences in the colour of R. liliiflorum, high-efficiency liquid chromatographic collar (UPLC–MS/MS) technology was used to study the yellow and white parts of R. liliiflorum. A total of 1187 metabolites were identified in R. liliiflorum petals, including 339 flavonoid metabolites. Seventy-eight types of flavonoids in these metabolites were found in the yellow and white parts of R. liliiflorum petals, along with 11 other significantly enriched substances. Combining gene expression-related data with differential metabolite data demonstrated effects of enrichment in the flavanonols (fustin), flavonols (epiafzelechin and afzelechin), and flavanones (pinocembrin) of flavonoid biosynthesis; glyccitin, 6″-O-malonylgenistin, and 6-hydroxydaidzein of isoflavonoid biosynthesis; and anthocyanin biosynthesis of malvidin-3-O-galactoside (primulin), delphinidin-3-O-rutinoside, cyanidin-3-O-glucoside (kuromanin), and cyanidin-3-O-rutinoside (keracyanin), which are potentially the contributing factors responsible for the differences in petal colour in R. liliiflorum. This study establishes a connection between the differential metabolites underlying the color differences in the petals of R. liliiflorum and the gene expression in R. liliiflorum. This will provide a foundation for subsequent research on the regulation of flower color in R. liliiflorum and have profound implications for horticultural applications of R. liliiflorum. [ABSTRACT FROM AUTHOR]

13 C metabolic flux analysis ( 13 C-MFA) has emerged as a forceful tool for quantifying in vivo metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of 13 C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of 13 C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed., Competing Interests: BR was employed by company Eurofins Lancaster Laboratories Professional Scientific Services. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tian, Chen, Liu, Rui, Deng, Zhang and Shen.)