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Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds
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) pro...
Autores principales: | , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3056800/ https://www.ncbi.nlm.nih.gov/pubmed/21356090 http://dx.doi.org/10.1186/1471-2164-12-131 |
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author | Shi, Cheng-Ying Yang, Hua Wei, Chao-Ling Yu, Oliver Zhang, Zheng-Zhu Jiang, Chang-Jun Sun, Jun Li, Ye-Yun Chen, Qi Xia, Tao Wan, Xiao-Chun |
author_facet | Shi, Cheng-Ying Yang, Hua Wei, Chao-Ling Yu, Oliver Zhang, Zheng-Zhu Jiang, Chang-Jun Sun, Jun Li, Ye-Yun Chen, Qi Xia, Tao Wan, Xiao-Chun |
author_sort | Shi, Cheng-Ying |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-3056800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30568002011-03-15 Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds Shi, Cheng-Ying Yang, Hua Wei, Chao-Ling Yu, Oliver Zhang, Zheng-Zhu Jiang, Chang-Jun Sun, Jun Li, Ye-Yun Chen, Qi Xia, Tao Wan, Xiao-Chun BMC Genomics Research Article 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. BioMed Central 2011-02-28 /pmc/articles/PMC3056800/ /pubmed/21356090 http://dx.doi.org/10.1186/1471-2164-12-131 Text en Copyright ©2011 Shi et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Shi, Cheng-Ying Yang, Hua Wei, Chao-Ling Yu, Oliver Zhang, Zheng-Zhu Jiang, Chang-Jun Sun, Jun Li, Ye-Yun Chen, Qi Xia, Tao Wan, Xiao-Chun Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds |
title | Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds |
title_full | Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds |
title_fullStr | Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds |
title_full_unstemmed | Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds |
title_short | Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds |
title_sort | deep sequencing of the camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3056800/ https://www.ncbi.nlm.nih.gov/pubmed/21356090 http://dx.doi.org/10.1186/1471-2164-12-131 |
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