Background: Tea is one of the most popular non-alcoholic beverages worldwide. However, the tea plant, Camellia sinensis, is difficult to culture in vitro, to transform, and has a large genome, rendering little genomic information available. Recent advances in large-scale RNA sequencing (RNA-seq) provide a fast, cost-effective, and reliable approach to generate large expression datasets for functional genomic analysis, which is especially suitable for non-model species with un-sequenced genomes. Results: Using high-throughput Illumina RNA-seq, the transcriptome from poly (A)⁺ RNA of C. sinensis was analyzed at an unprecedented depth (2.59 gigabase pairs). Approximate 34.5 million reads were obtained, trimmed, and assembled into 127,094 unigenes, with an average length of 355 bp and an N50 of 506 bp, which consisted of 788 contig clusters and 126,306 singletons. This number of unigenes was 10-fold higher than existing C. sinensis sequences deposited in GenBank (as of August 2010). Sequence similarity analyses against six public databases (Uniprot, NR and COGs at NCBI, Pfam, InterPro and KEGG) found 55,088 unigenes that could be annotated with gene descriptions, conserved protein domains, or gene ontology terms. Some of the unigenes were assigned to putative metabolic pathways. Targeted searches using these annotations identified the majority of genes associated with several primary metabolic pathways and natural product pathways that are important to tea quality, such as flavonoid, theanine and caffeine biosynthesis pathways. Novel candidate genes of these secondary pathways were discovered. Comparisons with four previously prepared cDNA libraries revealed that this transcriptome dataset has both a high degree of consistency with previous EST data and an approximate 20 times increase in coverage. Thirteen unigenes related to theanine and flavonoid synthesis were validated. Their expression patterns in different organs of the tea plant were analyzed by RT-PCR and quantitative real time PCR (qRT-PCR). Conclusions: An extensive transcriptome dataset has been obtained from the deep sequencing of tea plant. The coverage of the transcriptome is comprehensive enough to discover all known genes of several major metabolic pathways. This transcriptome dataset can serve as an important public information platform for gene expression, genomics, and functional genomic studies in C. sinensis.

Tea plants (Camellia sinensis) are used to produce one of the most important beverages worldwide. The nutritional value and healthful properties of tea are closely related to the large amounts of three major characteristic constituents including polyphenols (mainly catechins), theanine and caffeine. Although oil tea (Camellia oleifera) belongs to the genus Camellia, this plant lacks these three characteristic constituents. Comparative analysis of tea and oil tea via RNA-Seq would help uncover the genetic components underlying the biosynthesis of characteristic metabolites in tea. We found that 3,787 and 3,359 bud genes, as well as 4,042 and 3,302 leaf genes, were up-regulated in tea and oil tea, respectively. High-performance liquid chromatography (HPLC) analysis revealed high levels of all types of catechins, theanine and caffeine in tea compared to those in oil tea. Activation of the genes involved in the biosynthesis of these characteristic compounds was detected by RNA-Seq analysis. In particular, genes encoding enzymes involved in flavonoid, theanine and caffeine pathways exhibited considerably different expression levels in tea compared to oil tea, which were also confirmed by quantitative RT-PCR (qRT-PCR). We assembled 81,826 and 78,863 unigenes for tea and oil tea, respectively, based on their differences at the transcriptomic level. A potential connection was observed between gene expression and content variation for catechins, theanine and caffeine in tea and oil tea. The results demonstrated that the metabolism was activated during the accumulation of characteristic metabolites in tea, which were present at low levels in oil tea. From the molecular biological perspective, our comparison of the transcriptomes and related metabolites revealed differential regulatory mechanisms underlying secondary metabolic pathways in tea versus oil tea.

The leaves of tea plants (Camellia sinensis) are used to produce tea, which is one of the most popular beverages consumed worldwide. The nutritional value and health benefits of tea are mainly related to three abundant characteristic metabolites; catechins, theanine and caffeine. Weighted gene co-expression network analysis (WGCNA) is a powerful system for investigating correlations between genes, identifying modules among highly correlated genes, and relating modules to phenotypic traits based on gene expression profiling. Currently, relatively little is known about the regulatory mechanisms and correlations between these three secondary metabolic pathways at the omics level in tea. In this study, levels of the three secondary metabolites in ten different tissues of tea plants were determined, 87,319 high-quality unigenes were assembled, and 55,607 differentially expressed genes (DEGs) were identified by pairwise comparison. The resultant co-expression network included 35 co-expression modules, of which 20 modules were significantly associated with the biosynthesis of catechins, theanine and caffeine. Furthermore, we identified several hub genes related to these three metabolic pathways, and analysed their regulatory relationships using RNA-Seq data. The results showed that these hub genes are regulated by genes involved in all three metabolic pathways, and they regulate the biosynthesis of all three metabolites. It is notable that light was identified as an important regulator for the biosynthesis of catechins. Our integrated omics-level WGCNA analysis provides novel insights into the potential regulatory mechanisms of catechins, theanine and caffeine metabolism, and the identified hub genes provide an important reference for further research on the molecular biology of tea plants.

Background: Pear (Pyrus spp) is an important fruit species worldwide; however, its genetics and genomic information is limited. Combining the Solexa/Illumina RNA-seq high-throughput sequencing approach (RNA-seq) with Digital Gene Expression (DGE) analysis would be a powerful tool for transcriptomic study. This paper reports the transcriptome profiling analysis of Chinese white pear (P. bretschneideri) using RNA-seq and DGE to better understand the molecular mechanisms in fruit development and maturation of Chinese white pear.Results: De novotranscriptome assembly and gene expression analysis of Chinese white pear were performed in an unprecedented depth (5.47 gigabase pairs) using high-throughput Illumina RNA-seq combined with a tag-based Digital Gene Expression (DGE) system. Approximately, 60.77 million reads were sequenced, trimmed, and assembled into 90,227 unigenes. These unigenes comprised 17,619 contigs and 72,608 singletons with an average length of 508 bp and had an N₅₀of 635 bp. Sequence similarity analyses against six public databases (Uniprot, NR, and COGs at NCBI, Pfam, InterPro, and KEGG) found that 61,636 unigenes can be annotated with gene descriptions, conserved protein domains, or gene ontology terms. By BLASTing all 61,636 unigenes in KEGG, a total of 31,215 unigenes were annotated into 121 known metabolic or signaling pathways in which a few primary, intermediate, and secondary metabolic pathways are directly related to pear fruit quality. DGE libraries were constructed for each of the five fruit developmental stages. Variations in gene expression among all developmental stages of pear fruit were significantly different in a large amount of unigenes.Conclusion: Extensive transcriptome and DGE profiling data at five fruit developmental stages of Chinese white pear have been obtained from a deep sequencing, which provides comprehensive gene expression information at the transcriptional level. This could facilitate understanding of the molecular mechanisms in fruit development and maturation. Such a database can also be used as a public information platform for research on molecular biology and functional genomics in pear and other related species.