# Machine learning integrates metabolomics and proteomics to identify key regulators of anthocyanin biosynthesis in edible rose petals

**Authors:** Dannuo Fu, Jian Chen, Shuang Liang, Hongwei Fu

PMC · DOI: 10.3389/fpls.2026.1751780 · Frontiers in Plant Science · 2026-03-13

## TL;DR

This study uses machine learning to uncover the proteins and genes that control anthocyanin production in edible rose petals, which could help improve their use in functional foods.

## Contribution

The integration of metabolomics, proteomics, and machine learning to identify key regulators of anthocyanin biosynthesis in edible roses.

## Key findings

- RC had the highest anthocyanin content, 6.9% higher than RD and fivefold greater than RA.
- The MEblue module was strongly correlated with anthocyanin accumulation through WGCNA.
- Ten structural proteins and several transcription factors were identified as central regulators of anthocyanin biosynthesis.

## Abstract

Edible rose petals represent a promising source of anthocyanins, natural pigments with health-promoting properties suitable for functional food applications. However, the molecular mechanisms regulating anthocyanin biosynthesis in roses remain incompletely understood. Here, we employed an integrated metabolomic and proteomic approach to investigate anthocyanin profiles and their regulatory networks in three edible rose varieties: Rosa alba (RA), Rosa damascena (RD), and Rosa centifolia (RC). We identified a total of 13 anthocyanins, with RC exhibiting the highest total anthocyanin content—6.9% higher than RD and fivefold greater than RA. Compositional analysis revealed variety-specific accumulation patterns: RD was rich in delphinidin and peonidin derivatives, while RA predominantly accumulated pelargonidin-3-O-rutinoside. Proteomic analysis identified 9,924 proteins, and weighted gene co-expression network analysis (WGCNA) highlighted the MEblue module as strongly correlated with anthocyanin accumulation. By integrating a KNN-based machine learning model, we identified ten key structural proteins (e.g., RhUFGT, F3H, DFR) and several transcription factors (e.g., NAC, bZIP_2, C3H) as central regulators. Our findings elucidate the molecular basis of anthocyanin biosynthesis in edible roses and provide potential targets for breeding strategies aimed at enhancing their value as functional food ingredients.

## Linked entities

- **Proteins:** F3H (flavanone 3-hydroxylase), DFR (dihydroflavonol 4-reductase), XK (X-linked Kx blood group antigen, Kell and VPS13A binding protein), bZIP2 (basic leucine-zipper 2), LOC123224750 (cytochrome P450 98A2)
- **Chemicals:** anthocyanins (PubChem CID 145858), delphinidin (PubChem CID 128853), peonidin (PubChem CID 164544), pelargonidin-3-O-rutinoside (PubChem CID 443917)

## Full-text entities

- **Genes:** XK (X-linked Kx blood group antigen, Kell and VPS13A binding protein) [NCBI Gene 7504] {aka KX, NA, NAC, X1k, XKR1}
- **Chemicals:** anthocyanin (MESH:D000872), pelargonidin-3-O-rutinoside (-), peonidin (MESH:C473205), delphinidin (MESH:C017185)
- **Species:** Rosa x damascena (damask rose, species) [taxon 3765], Rosa x alba (white rose-of-York, species) [taxon 267226], Rosa x centifolia (Burgundy rose, species) [taxon 396733]

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13021854/full.md

## References

37 references — full list in the complete paper: https://tomesphere.com/paper/PMC13021854/full.md

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Source: https://tomesphere.com/paper/PMC13021854