Medicinal Plant Research 2025, Vol.15, No.6, 274-282 http://hortherbpublisher.com/index.php/mpr 279 Figure 2 Integrated analysis of DAMs and DEGs in S. miltiorrhiza. (A) The nine-quadrant map of metabolites and genes. Each point represents a pair of correlated metabolites and genes with |r| ≥ 0.85 and p value ≤ 0.05. The X-axis represents the log2 (fold change) of the gene, and the Y-axis represents the log2 (fold change) of the metabolite. (B) Number of DAMs and DEGs in each quadrant. Each row represents a quadrant, corresponding to Q1 to Q9 in (A) from bottom to top. Green represents DAMs and red represents DEGs. (C) The KEGG analysis of metabolites. Red represents metabolites in quadrant 3. Green represents metabolites in quadrant 7. The X-axis represents the proportion of metabolites in Q3 or Q7 to the total metabolites identified on the pathways (Adopted from Jiang et al., 2024) 7.2 Proteomics and metabolomics uncovering regulatory networks These mapped the accumulation of certain metabolites, such as anthocyanins, flavonoids, and tanshinones, to the expression of their corresponding biosynthetic enzymes and regulatory proteins through integrative proteomic and metabolomic studies. Such analyses have identified key enzymes and transcription factors whose abundance correlates with metabolite levels, and further revealed environmental and genetic factors driving ecotype-specific metabolite accumulation (Jiang et al., 2020; Yu et al., 2025). Proteomic data integrated with transcriptomics underlined the role of WRKY transcription factors, such as SmWRKY61, in promoting tanshinone content. 7.3 Signal transduction and metabolic pathway integration Multi-omics approaches unraveled the way in which hormone signaling, for example, ABA, JA, and auxin, is integrated with secondary metabolism. Examples include that transcriptome and metabolome analyses under stress conditions reveal a tight link between hormone signal transduction pathways and the activation of biosynthetic genes of secondary metabolites, where transcription factors act as central nodes that interlink these pathways (Jiang et al., 2024; Liu et al., 2025). This thus enables plants to coordinate their growth, defense, and metabolite production according to environmental cues. 7.4 Application of molecular markers and gene regulation in agronomic optimization Multi-omics have identified the key regulatory genes and molecular markers, thus providing targets for molecular breeding and optimization of agronomic practices. For instance, genes such as SmWRKY40 and SmWRKY61, among others, together with ABC transporters, are implicated in enhanced metabolite accumulation and improved stress tolerance, hence strategies toward improvement of cultivars and precision agriculture (Yu et al., 2025). These support the development of high-yield and high-quality varieties of S. miltiorrhiza.
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