Journal of Tea Science Research, 2024, Vol.14, No.5, 293-303 http://hortherbpublisher.com/index.php/jtsr 300 Figure 3 Key genes involved in the theanine biosynthesis pathway. (A) The proposed pathway for theanine biosynthesis and expression of key genes upon precursor ethylamine feeding. TS, GS, GOGAT, GDH, and ADC represent genes encoding theanine synthetase, glutamine synthetase, glutamate synthetase, glutamate dehydrogenase, and arginine decarboxylase, respectively. Tea seedlings grown hydroponically were fed ethylamine chloride for different numbers of days before being sampled for amino acid profiling and transcriptome analyses. (B) Phylogenetic tree of tea TS and GS candidate genes and the available GS genes from prokaryotes, fungi, and plants. The tea TS candidate gene (CsTSI) shows high similarity to known GSI-type genes, and other GS candidate genes exhibit high homology with previously reported GSII-type genes in plants. (C) Assay of theanine synthesis activity of CsTSI in Arabidopsis seedlings. The candidate tea TS gene (CsTSI) that shows high similarity to known GSI-type genes was cloned into a binary vector and overexpressed in Arabidopsis driven by a 35S promoter. CsTSI-OE indicates CsTSI-overexpression lines, while WT represents wild type (control). Seedlings were fed with or without 10 mM EA chloride solution (with water as control) for 3 d. Theanine synthesized by the seedlings was extracted and measured. Data are expressed as means ± SD from at least three independent transgenic lines with replicate experiments. FW, fresh weight (Adopted from Wei et al., 2018) 7 Technological Advances and Applications 7.1 Progress in SV detection technologies The introduction of third-generation sequencing technologies—especially the widespread application of long-read sequencing—has significantly improved the accuracy of structural variation (SV) detection in the tea genome (Qiao et al., 2024). These platforms allow for high-quality genome assembly and enable the identification of large insertions, deletions, and presence/absence variations (PAVs), many of which are difficult to detect using traditional short-read sequencing methods (Xia et al., 2020; Tariq et al., 2024). Long-read technologies also excel at resolving repetitive sequences and transposon insertions, which are abundant in the tea genome (Tariq et al., 2024).
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