Genome-wide analysis identifies hormone-responsive genes affecting phenolic acid accumulation in Salvia miltiorrhiza, suggesting regulatory insights.
APETALA2/ethylene-responsive factor (AP2/ERF) transcription factors integrate phytohormone signalling with developmental programmes and specialised metabolism, yet their family-wide features and potential contributions to phenolic-acid biosynthesis remain to be systematically clarified in Salvia miltiorrhiza. In this study, we conducted a comprehensive genome-wide analysis and identified 169 SmAP2/ERF genes, which were classified into five subfamilies (AP2, ERF, DREB, RAV and Soloist). SmAP2/ERFs were unevenly distributed across chromosomes and expanded predominantly through tandem and segmental duplication, and Ka/Ks analysis indicated that tandem-duplicated pairs have mainly undergone purifying selection. Promoter analysis revealed abundant cis-acting elements related to light, phytohormones and stress responses, indicating extensive regulatory potential. Comparative phylogenetic analysis with Arabidopsis thaliana prioritised four candidates (SmAP2/ERF88, SmAP2/ERF110, SmAP2/ERF121 and SmAP2/ERF122) closely associated with specialised-metabolism regulators. These genes exhibited distinct tissue-preferential expression patterns and divergent hormone responsiveness: SmAP2/ERF88/110 were broadly inducible, whereas SmAP2/ERF121/122 responded mainly to abscisic acid and were repressed by brassinosteroids. Confocal imaging of GFP fusions confirmed nuclear localisation of all four proteins. Phytohormone treatments differentially regulated key phenolic-acid pathway genes (PAL, C4H, 4CL, TAT, HPPR, RAS and CYP98A14) and altered rosmarinic acid and salvianolic acid B accumulation. These results broaden the genome-wide understanding of the SmAP2/ERF family in Salvia miltiorrhiza. Hormone-responsive SmAP2/ERFs show expression patterns associated with hormone-dependent transcriptional changes in phenolic-acid pathway genes and with RA and SAB accumulation, providing candidates for future functional validation and metabolic engineering.
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Chen et al. (2026) studied this question.
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