Journal of Tea Science Research, 2024, Vol.14, No.6, 313-321 http://hortherbpublisher.com/index.php/jtsr 317 5.3 Epigenetic modifications affecting metabolic gene expression Alternative splicing and chromatin-level modifications add further complexity to the regulation of secondary metabolism. Full-length transcriptome analyses have revealed extensive alternative splicing events in key metabolic genes, suggesting that transcript diversity contributes to the fine-tuning of metabolite biosynthesis (Qiao et al., 2019). Although direct studies on DNA methylation and histone modifications in tea are limited, the presence of diverse transcript isoforms and regulatory lncRNAs points to a significant role for epigenetic mechanisms in metabolic gene expression (Qiao et al., 2019; Bordoloi et al., 2022). Figure 2 (A) Compare the category code generated by CuffCompare with the tea plants genome, and then calculate the percentage. “=”: complete, exact match of intron chain; “j”: multi-exon with at least one junction match; “k”: containment of reference (reverse containment); “m”: retained intron (s), all introns matched or retained; “n”: retained introns (s), not all introns matched/covered.; “u”: none of the above (unknown, intergene); “o”: other same strand overlap with reference exons; “i”: fully contained within a reference intron; “p”: possible polymerase run-on (no actual overlap). (b~c) Analysis of transcripts in LRs (lateral roots) of tea plant under three nitrogen treatments, LN, CK and HN, with three replicates for each. (B) Cluster heat map of DE-lncRNAs (differentially expressed lncRNAs) in three treatments. (C) Venn diagram of common DE-lncRNAs (Adopted from Hu et al., 2023) 5.4 Integration of hormone signaling in metabolic network regulation Plant hormones such as abscisic acid (ABA), jasmonic acid (JA), gibberellin (GA), and strigolactones (SLs) interact with transcriptional and post-transcriptional regulators to modulate secondary metabolism. Hormone treatments alter the expression of key TFs and metabolic genes, affecting the accumulation of catechins, theanine, and caffeine. For example, strigolactones can reprogram transcriptional networks, influencing both secondary metabolite biosynthesis and nutrient signaling, while ABA has been shown to regulate theanine metabolism during postharvest withering. 6 Application of Multi-Omics Technologies in Secondary Metabolism Research 6.1 Combined transcriptomic and metabolomic analysis Integrating transcriptomic and metabolomic data enables the mapping of gene expression to metabolite accumulation, revealing regulatory networks underlying secondary metabolite biosynthesis. This approach has clarified how polyphenol content changes during tea plant growth, development, and processing, and has identified candidate genes and pathways responsible for key metabolic traits (Li et al., 2022).
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