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. 2023 Apr 12;13(1):6015.
doi: 10.1038/s41598-023-32435-5.

De novo full length transcriptome analysis of a naturally caffeine-free tea plant reveals specificity in secondary metabolic regulation

Xiaozeng Mi #  1 , Chun Yang #  1 , Dahe Qiao  1 , Mengsha Tang  1 , Yan Guo  1 , Sihui Liang  1 , Yan Li  1 , Zhengwu Chen  1 , Juan Chen  2
Affiliations

De novo full length transcriptome analysis of a naturally caffeine-free tea plant reveals specificity in secondary metabolic regulation

Xiaozeng Mi et al. Sci Rep. .

Abstract

Tea plants are crops with economic, health and cultural value. Catechin, caffeine and theanine are the main secondary metabolites of taste. In the process of germplasm collection, we found a resource in the Sandu Aquatic Autonomous County of Guizhou (SDT) that possessed significantly different characteristic metabolites compared with the cultivar 'Qiancha 1'. SDT is rich in theobromine and theophylline, possesses low levels of (-)-epicatechin-3-gallate, (-)-epigallocatechin-3-gallate, and theanine content, and is almost free of caffeine. However, research on this tea resource is limited. Full-length transcriptome analysis was performed to investigate the transcriptome and gene expression of these metabolites. In total, 78,809 unique transcripts were obtained, of which 65,263 were complete coding sequences. RNA-seq revealed 3415 differentially expressed transcripts in the tender leaves of 'Qiancha 1' and 'SDT'. Furthermore, 2665, 6231, and 2687 differentially expressed transcripts were found in different SDT tissues. These differentially expressed transcripts were enriched in flavonoid and amino acid metabolism processes. Co-expression network analysis identified five modules associated with metabolites and found that genes of caffeine synthase (TCS) may be responsible for the low caffeine content in SDT. Phenylalanine ammonia lyase (PAL), glutamine synthetase (GS), glutamate synthase (GOGAT), and arginine decarboxylase (ADC) play important roles in the synthesis of catechin and theanine. In addition, we identified that ethylene resposive factor (ERF) and WRKY transcription factors may be involved in theanine biosynthesis. Overall, our study provides candidate genes to improve understanding of the synthesis mechanisms of these metabolites and provides a basis for molecular breeding of tea plant.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Morphological characteristics of ‘Qiancha 1’ and SDT. (A) SDT tea plant contains leaves, stem, and flowers. (B) Mature leaves of QC1 (left) and SDT (right). (C) Flower diameter of QC1 (left) and SDT (right). (D) Style and ovary of QC1 (left) and SDT (right). The image was taken by the author of this article without copyright problem.
Figure 2
Figure 2
Contents of purine alkaloids, catechins and theanine in tea samples. Data represent mean ± SD of three biological replicates. The different lowercase letters on the bar graph indicate significant differences at p < 0.05.
Figure 3
Figure 3
Characteristics of full-length transcriptome sequencing. (A) Length distribution of the unigenes. (B) Number and percentage of annotated genes in different database. (C) Number of complete coding sequences.
Figure 4
Figure 4
Analysis of differentially expressed genes (DEGs) by RNA-seq. (A) Statistics of DEGs in different samples. (B) Common and specific DEGs identified by upset venn diagram analysis. (C) KEGG enrichment analysis of DEGs in SDT-TL vs. QC1-TL and SDT-TL vs. SDT-R groups (p < 0.01). The size and color of the circles represent the number of DEGs and p-values, respectively. The x-axis represents the rich factor.
Figure 5
Figure 5
Biosynthetic pathway and gene expression analysis of caffeine metabolism. The orange letters indicate enzymes associated with the metabolites. Heat map showing the relative expression level of genes in QC1-TL and SDT-TL. Data represent the mean values of three biological replicates.
Figure 6
Figure 6
WGCNA analysis of DEGs. The left box indicates the number of genes in each module. The color and number of each cell indicate the correlation coefficient between the module and metabolite content, respectively. Blue and red colors indicate the negative and positive correlation between modules and compound content.
Figure 7
Figure 7
Co-expression network of DEGs related to taste compound biosynthesis in tea plants. The size and color of the circle represent the number of edges and different genes, respectively. This image was generated by Cytoscape software (v3.6.0, https://cytoscape.org/).
See this image and copyright information in PMC

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