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. 2024 May 17;25(10):5462.
doi: 10.3390/ijms25105462.

Metabolite Profiling and Biological Activity Assessment of Paeonia ostii Anthers and Pollen Using UPLC-QTOF-MS

Fengfei Jiang  1   2 , Mengchen Li  1   2 , Linbo Huang  1   2 , Hui Wang  1   2 , Zhangzhen Bai  1   2 , Lixin Niu  1   2 , Yanlong Zhang  1   2
Affiliations

Metabolite Profiling and Biological Activity Assessment of Paeonia ostii Anthers and Pollen Using UPLC-QTOF-MS

Fengfei Jiang et al. Int J Mol Sci. .

Abstract

Paeonia ostii is an important economic oil and medicinal crop. Its anthers are often used to make tea in China with beneficial effects on human health. However, the metabolite profiles, as well as potential biological activities of P. ostii anthers and the pollen within anthers have not been systematically analyzed, which hinders the improvement of P. ostii utilization. With comprehensive untargeted metabolomic analysis using UPLC-QTOF-MS, we identified a total of 105 metabolites in anthers and pollen, mainly including phenylpropanoids, polyketides, organic acids, benzenoids, lipids, and organic oxygen compounds. Multivariate statistical analysis revealed the metabolite differences between anthers and pollen, with higher carbohydrates and flavonoids content in pollen and higher phenolic content in anthers. Meanwhile, both anthers and pollen extracts exhibited antioxidant activity, antibacterial activity, α-glucosidase and α-amylase inhibitory activity. In general, the anther stage of S4 showed the highest biological activity among all samples. This study illuminated the metabolites and biological activities of anthers and pollen of P. ostii, which supports the further utilization of them.

Keywords: Paeonia ostii; anther; biological activity; pollen; untargeted metabolomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Microscopic observation ((A1–A4): S1–S4, Bar = 2 mm) and paraffin section of anthers at different developmental stages ((B1–B4): S1–S4, Bar = 100 μm). Red arrows indicate pollen grains.
Figure 2
Figure 2
The contents of total phenolic (A) and total flavonoid (B) of anthers (S1, S2, S3, and S4) and pollen (P). Values are the means ± standard deviations, n = 3. Note: * corresponds the significant difference of the t-test * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns indicates there is no significant difference.
Figure 3
Figure 3
The results of DPPH assay (A), ABTS assay (B), FRAP assay (C) of anthers (S1, S2, S3, and S4) and pollen (P). Values are the means ± standard deviations, n = 3. Note: * corresponds the significant difference of the t-test * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns indicates there is no significant difference.
Figure 4
Figure 4
The results of α-glucosidase (A) and α-amylase (B) inhibitory activity of anthers (S1, S2, S3, and S4) and pollen (P), with acarbose as the positive control.
Figure 5
Figure 5
Data characterization of all detected ion peaks of anthers (S1, S2, S3, and S4) and pollen (P) based on untargeted metabolomics. (A) The PCA score scatter plot; (B) correlation analysis between samples; different colors indicate differences in correlation between samples, blue indicates positive correlation, and red indicates negative correlation.
Figure 6
Figure 6
Metabolites identification and classification. (A) Number of metabolites identified by each database; (B) compositional analysis of all identified metabolites at the superclass level.
Figure 7
Figure 7
Hierarchical analysis (HCA) heat map of all identified metabolites in different samples. Red and blue indicate higher and lower abundances, respectively.
Figure 8
Figure 8
Multivariate statistical analysis of samples. (A) The PCA score scatter plot of different samples and QC; (B) the PLS-DA score scatter plot of different samples.
Figure 9
Figure 9
Differentially accumulated metabolites (DAMs) between different samples. (A) Venn diagram of different comparison groups; (B) volcano plot of different comparison groups.
Figure 10
Figure 10
KEGG pathways of differentially accumulated metabolites (DAMs) identified among comparisons. (A) KEGG pathway analysis of S1 vs. P; (B) KEGG pathway analysis of S2 vs. P; (C) KEGG pathway analysis of S3 vs. P; (D) KEGG pathway analysis of S4 vs. P.
Figure 11
Figure 11
Pearson correlation network (|r| > 0.5, p ≤ 0.05) between metabolites and biological activities. AMY: α-amylase; GLU: α-glucosidase; SA: Staphylococcus aureus; EC: Escherichia coli; SE: Salmonella enterica subsp. Enterica; SH: Streptococcus hemolytis-β; LM: Listeria monocytogenes; PA: Pseudomonas aeruginosa; PSE: Pseudomonas aeruginosa.
Figure 12
Figure 12
Phenotypic variation in different samples. Bar = 2 cm.
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