Medicinal Plant Research 2025, Vol.15, No.5, 197-205 http://hortherbpublisher.com/index.php/mpr 201 2021). In polysaccharides, similar regulatory networks with hormone- and stress-inducible TFs direct biosynthetic gene expression (Fang et al., 2022). 4.3 Epigenetic regulation Epigenetic pathways, particularly non-coding RNAs such as microRNAs (miRNAs), are also emerging as important regulators of ginsenoside biosynthesis. miRNAs can target and silence key biosynthetic genes, such as dammarenediol synthase and protopanaxatriol synthase, by regulating triterpenoid biosynthesis (Wei et al., 2024; Eom and Hyun, 2025). Although DNA methylation and histone modification research in ginseng is still limited, these epigenetic changes will definitely influence the chromatin state and transcriptional activity of biosynthetic genes. The integration of multi-omics approaches and genome editing technologies will keep revealing the active epigenetic regulation of ginsenoside and polysaccharide biosynthesis (Eom and Hyun, 2025). 5 Application of Multi-Omics in Biosynthetic Pathway Studies 5.1 Transcriptomics revealing key gene expression patterns Transcriptome analyses, including bulk and single-cell RNA sequencing, have made possible the discovery of genes and gene clusters involved in ginsenoside and polysaccharide biosynthesis. Coexpression network analysis associates gene expression profiles with specific biosynthetic steps, showing tissue-specific and developmental regulation of pathway genes. Time-series transcriptomics also enables determination of regulatory genes and dynamic variation upon environmental or developmental cues (Singh et al., 2022; Wang et al., 2024). 5.2 Proteomics in metabolic pathway elucidation Proteomics complements transcriptomics by confirming the presence, abundance, and post-translational modifications of enzymes in biosynthetic pathways. Quantitative proteomic profiling is used for the validation of candidate genes, the discovery of enzyme complexes, and the determination of the functional organization of metabolic networks. Integration with transcriptomic data enhances the accuracy of pathway reconstruction and functional annotation (Yang et al., 2021). 5.3 Metabolomics for tracing accumulation patterns of secondary metabolites in ginseng Metabolomics quantifies ginsenosides, polysaccharides, and intermediates directly, enabling the monitoring of metabolite accumulation in various tissues, developmental stages, and environmental conditions. The alignment of metabolite profiles with gene and protein expression datasets enables the identification of regulatory bottlenecks and key nodes in biosynthetic pathways (Singh et al., 2022). 5.4 Multi-omics integration and construction of regulatory network models Integrative multi-omics strategies combine transcriptomic, proteomic, and metabolomic data to construct large-scale models of gene-protein-metabolite interactions and regulatory modules. These models give insight into pathway behavior under varying conditions, identify regulatory modules, and enable predictions regarding gene-protein-metabolite interaction networks (GPMN). Advanced computational capabilities and unsupervised integration techniques such as correlation-based network analysis and machine learning facilitate the discovery of novel pathway components and regulatory interactions, giving a global view of specialized metabolism in ginseng (Singh et al., 2022; Wieder et al., 2024). 6 Advances in Synthetic Biology and Metabolic Engineering 6.1 Establishment of microbial heterologous expression systems Microbial hosts such as Saccharomyces cerevisiae (yeast) and Escherichia coli have also been genetically modified to express plant biosynthetic pathways, whereby complex isoprenoids and ginsenosides are synthesized. These systems are blessed with well-characterized genetics, ease of manipulation, and scalable fermentations and therefore most appropriate for industrial application. Non-conventional yeasts and bacteria are also being explored because they are metabolically flexible and capable of utilizing various substrates (Navale et al., 2021; Patra et al., 2021).
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