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. 2021 Mar 31:12:640141.
doi: 10.3389/fpls.2021.640141. eCollection 2021.

Cross-Species Metabolic Profiling of Floral Specialized Metabolism Facilitates Understanding of Evolutional Aspects of Metabolism Among Brassicaceae Species

Yuting Liu  1 , Mutsumi Watanabe  1 , Sayuri Yasukawa  1 , Yuriko Kawamura  1 , Chaiwat Aneklaphakij  1   2 , Alisdair R Fernie  3 , Takayuki Tohge  1
Affiliations

Cross-Species Metabolic Profiling of Floral Specialized Metabolism Facilitates Understanding of Evolutional Aspects of Metabolism Among Brassicaceae Species

Yuting Liu et al. Front Plant Sci. .

Abstract

Plants produce a variety of floral specialized (secondary) metabolites with roles in several physiological functions, including light-protection, attraction of pollinators, and protection against herbivores. Pigments and volatiles synthesized in the petal have been focused on and characterized as major chemical factors influencing pollination. Recent advances in plant metabolomics have revealed that the major floral specialized metabolites found in land plant species are hydroxycinnamates, phenolamides, and flavonoids albeit these are present in various quantities and encompass diverse chemical structures in different species. Here, we analyzed numerous floral specialized metabolites in 20 different Brassicaceae genotypes encompassing both different species and in the case of crop species different cultivars including self-compatible (SC) and self-incompatible (SI) species by liquid chromatography-mass spectrometry (LC-MS). Of the 228 metabolites detected in flowers among 20 Brassicaceae species, 15 metabolite peaks including one phenylacyl-flavonoids and five phenolamides were detected and annotated as key metabolites to distinguish SC and SI plant species, respectively. Our results provide a family-wide metabolic framework and delineate signatures for compatible and incompatible genotypes thereby providing insight into evolutionary aspects of floral metabolism in Brassicaceae species.

Keywords: Brassicaceae; chemodiversity; compatible and incompatible species; cross-species comparison; flavonoids; floral specialized metabolite; plant metabolomics.

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Conflict of interest statement

The 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.

Figures

Figure 1
Figure 1
Phylogenetic diagram of the relationships among selected Brassicaceae genotypes. The phylogenetic tree was constructed by MEGA X (Kumar et al., 2018) using the sequence of the internal transcribed spacer (ITS) gene of each genotype. Cleome serrulata was set as outgroup. The maximum likelihood (ML) method was used with the following parameters: General time reversible model, complete deletion and bootstrap (1,000 replicates). Values on the branches indicate bootstrap support in percentages. SC indicates self-compatible. SI indicates self-incompatible, incomplete SC indicates incomplete self-compatible. White square indicates white flower color, yellow square indicates yellow flower color, and white squares with purple stripes indicate white flower with purple stripes.
Figure 2
Figure 2
Relative proportion of metabolites in each genotype. (A) The proportion of 49 phenylpropanoid compounds in each genotype; (B) The proportion of 22 phenolamides in each genotype; (C) The proportion of 11 glucosinolate compounds in each genotype.
Figure 3
Figure 3
Relative abundance of 82 annotated metabolites in floral organ of 20 genotypes. Color indicates the level of log2 (mean/mean_average). Gray grids indicate no detection. Metabolites and genotypes in heatmap were clustered using hierarchical clustering method (HCL).
Figure 4
Figure 4
Results of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) of the metabolite data. (A) PCA scores plot between the principal component 1 (PC1) and PC2. The explained variances are shown in brackets, colors of circle indicate three replications of metabolite data of species; (B) PCA loading plots between the PC1 and PC2; (C) PLS-DA scores plot between the PC1 and PC2. The explained variances are shown in brackets; (D) Important features identified by variable importance in projection (VIP) scores. The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each clade.
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