Metabolomics study of flavonoids in Coreopsis tinctoria of different origins by UPLC–MS/MS

Abstract

To analyze the flavonoids in Coreopsis tinctoria and compare the differences in flavonoids among C. tinctoria of different origins, the chemical composition of C. tinctoria capitulum was analyzed by ultra-high-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS), and the flavonoid metabolites were analyzed and identified based on their retention time, mass-to-charge ratio and fragment ions in the UPLC-QTOF-MS matrix. Capitulum samples of C. tinctoria were collected from three locations in the Xinjiang region at different altitudes. A total of 204 flavonoid compounds were identified, and 31 different flavonoid metabolites were then identified from flowers of C. tinctoria of different origins. Further analysis of these 31 significantly accumulated metabolites identified seven flavonoid metabolites, namely, homoplantaginin, kaempferol, quercetin, isorhamnetin, avicularin, quercetin 3-O-(6′-galloyl)-β-D-galactopyranoside and isorhamnetin 3-O-glucoside, with high accumulation only in sample collected from Tashkurgan Tajik (TX) and low expression in sample collected from Yutian County (YT) and Shaya County (SY). Moreover, 7,4′-dihydroxyflavone and 4,4′-dimethoxychalcone showed high accumulation only in SY, and afzelin was specifically highly accumulated in YT. In addition, the identified flavonoid metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and key pathways that might regulate the biosynthesis of these flavonoid compounds were analyzed. These findings provide key information for research on flavonoids and their biosynthesis in C. tinctoria and will provide a theoretical basis for studying the herbal quality and origin of C. tinctoria.

Metabolomic Study of Flavonoids in Camellia drupifera under Aluminum Stress by UPLC-MS/MS

“…The advantage of the OPLS-DA model over PCA analysis is that it allows for maximum differentiation between the groups, thus facilitating the detection of differential metabolites [ 17 ]. Q 2 represents the predictive power of the model, and Q 2 > 0.9 is usually considered to indicate an excellent model [ 36 ].…”

“…To accurately screen for differential flavonoid metabolites between the aluminum stress treatment and control groups, a combination of fold change and variable importance in projection (VIP) values in the OPLS-DA model was utilized in our study to screen for differential flavonoid metabolites, according to the method described by Wang et al [ 17 ]. Differential flavonoid metabolites were selected according to the following criteria: significant fold change of ≥2 or ≤0.5, and VIP ≥ 1.…”

“…Some substances can improve the stress resistance of plants [ 16 ]. Flavonoids are an important family of secondary metabolites involved in plant development and defense, with a variety of functions such as antioxidant and antibacterial activity as well as free radical scavenging, and they are also believed to enhance tolerance to abiotic stresses [ 17 , 18 ]. For example, flavonoids are involved in ultraviolet (UV-B) stress [ 18 , 19 ] and drought resistance [ 20 , 21 ], and may play a functional role in heat acclimation and cold tolerance in plants [ 22 , 23 ].…”

Cytogenetic Characterization and Metabolomic Differences of Full-Sib Progenies of Saccharum spp.

“…The abundance of 48 flavonoids, namely, 10 kaempferol, 1 apigenin and 3 catechin flavonoids, increased in Zhongzhe 1 sugarcane ( Figure 6 ). Previous studies have suggested that kaempferol [ 30 , 31 ], chalcone [ 32 ] and catechin [ 33 ] are bioactive substances that have antioxidant, antibacterial and antifungal properties. It has also been reported that endophytic fungi can cause the accumulation of flavonoids in the leaves of Cinnamomum longepaniculatum [ 34 ].…”

“…PCA and PLA-DA were conducted to identify the SCMs, criteria of which were a fold-change (FC) ≥ 2 or ≤0.5 and VIP ≥ 1. PCA and PLS-DA were implemented in R software ( , accessed on 8 May 2021) to investigate variety-specific accumulation of metabolites in the leaves [ 30 , 54 ]. Finally, the SCMs between Zhongzhe 1 sugarcane and G160 sugarcane were analyzed via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.…”