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Yan-Xia Lv

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Open access Aug 2026

Integrated metabolome and transcriptome analysis reveals the potential mechanism of anthocyanin biosynthesis in Paeonia lactiflora Pall. flowers

Paeonia lactiflora Pall. (Chinese peony), a perennial plant indigenous to northeastern China, exhibits extensive flower-color variation, but the metabolite-gene relationships underlying anthocyanin accumulation remain incompletely understood. We integrated metabolomic and transcriptomic of petals from three cultivars with pink (cv. ‘Exclusive Memory’, PF), pale pink (cv. ‘Salad’, PPF), and white (cv. ‘Duchess’, WF) flowers. Metabolomic profiling detected 689 metabolites, including 169 flavonoids and six anthocyanins. Pairwise comparisons identified 153, 199, and 137 differentially accumulated metabolites in PF vs. PPF, PF vs. WF, and PPF vs. WF, respectively, with 27 metabolites shared among the three comparisons. The total anthocyanin signal in PF petals was 3.25-fold that in PPF petals and 60.37-fold that in WF petals. By contrast, the abundance of the individual compound cyanidin-3,5-O-diglucoside (cyanin) was 76.85-fold higher in PF than in WF and 23.10-fold higher in PPF than in WF, these values therefore describe compound-specific rather than total-anthocyanin differences. RNA-seq identified 6,777, 8,030, and 6,794 differentially expressed genes in the three pairwise comparisons, including 779 shared genes. Correlation analysis prioritized 21 flavonoid-pathway structural genes and 53 candidate transcription factors associated with anthocyanin abundance. Weighted gene co-expression network analysis identified a turquoise module positively associated with anthocyanins and a DFR-centered subnetwork containing 24 candidate transcription factors, including MYB and bHLH genes. qRT-PCR of 12 selected genes reproduced the RNA-seq expression trends. These results elucidate the metabolic basis of peony petal coloration and identify candidate regulatory networks and key genes potentially involved in anthocyanin biosynthesis, providing a foundation for future functional validation.

Yang Cao, Pin Lv, Yong-Chuan Guo et al. · 0 citations

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