Document Type : Original Article
Authors
1
Ph.D. Student of Agricultural Biotechnology, Faculty of Agriculture Science, University of Guilan, Rasht, Iran
2
Ph.D. Graduate of Agricultural Biotechnology, Faculty of Agriculture Science, University of Guilan, Rasht, Iran
3
Associate Professor of Agricultural Biotechnology, Faculty of Agriculture Science, University of Guilan, Rasht, Iran
4
Professor of Agricultural Biotechnology, Faculty of Agriculture Science, University of Guilan, Rasht, Iran
10.30470/jmpb.2026.2080597.1163
Abstract
This study aimed to elucidate the multilayered regulatory network controlling tanshinone biosynthesis in Salvia miltiorrhiza, focusing on microRNA–gene interactions, transcription factors, and cis-regulatory elements. mRNA sequences of three key pathway genes (CPS1, KSL1, and CYP) were retrieved from the NCBI database, and microRNA sequences were collected from miRBase. Target prediction was performed using psRNATarget with an Expectation threshold of 5. Network architecture were visualized in Cytoscape. Promoter sequences (~2000 bp upstream) were extracted and analyzed for transcription factor binding sites using PlantRegMap and PlantCARE, with results processed for graphical representation in TBtools. A total of 252 microRNAs from 28 families were identified as targeting these three genes (CPS1: 84, KSL1: 90, CYP: 79). Dominant families included miR781/miR319/miR482 for CPS1, miR156/miR902 for KSL1, and miR2630/miR399 for CYP. Several microRNAs (e.g., miR7492 and miR482) targeted multiple pathway genes simultaneously, indicating cooperative regulatory activity and the potential for enhanced post-transcriptional repression. Transcription factor binding site analysis revealed high-frequency interactions (KSL1: 387, CPS1: 240, CYP: 159) involving WRKY, ERF, MYB, bZIP, and Dof families. Promoters were enriched in cis-elements responsive to light, ABA, MeJA, and gibberellin. These findings demonstrate that tanshinone biosynthesis is governed by a coordinated transcriptional and post-transcriptional network, allowing dynamic modulation and co-regulation in response to environmental and hormonal signals. The identified genes and microRNAs represent promising targets for experimental validation (e.g., ChIP-qPCR, EMSA) and metabolic engineering strategies, including CRISPR and RNAi, to enhance tanshinone production.
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