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RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes
BACKGROUND: Stevia (Stevia rebaudiana) is an important medicinal plant that yields diterpenoid steviol glycosides (SGs). SGs are currently used in the preparation of medicines, food products and neutraceuticals because of its sweetening property (zero calories and about 300 times sweeter than sugar)...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4108789/ https://www.ncbi.nlm.nih.gov/pubmed/25001368 http://dx.doi.org/10.1186/1471-2164-15-571 |
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author | Chen, Junwen Hou, Kai Qin, Peng Liu, Hongchang Yi, Bin Yang, Wenting Wu, Wei |
author_facet | Chen, Junwen Hou, Kai Qin, Peng Liu, Hongchang Yi, Bin Yang, Wenting Wu, Wei |
author_sort | Chen, Junwen |
collection | PubMed |
description | BACKGROUND: Stevia (Stevia rebaudiana) is an important medicinal plant that yields diterpenoid steviol glycosides (SGs). SGs are currently used in the preparation of medicines, food products and neutraceuticals because of its sweetening property (zero calories and about 300 times sweeter than sugar). Recently, some progress has been made in understanding the biosynthesis of SGs in Stevia, but little is known about the molecular mechanisms underlying this process. Additionally, the genomics of Stevia, a non-model species, remains uncharacterized. The recent advent of RNA-Seq, a next generation sequencing technology, provides an opportunity to expand the identification of Stevia genes through in-depth transcript profiling. RESULTS: We present a comprehensive landscape of the transcriptome profiles of three genotypes of Stevia with divergent SG compositions characterized using RNA-seq. 191,590,282 high-quality reads were generated and then assembled into 171,837 transcripts with an average sequence length of 969 base pairs. A total of 80,160 unigenes were annotated, and 14,211 of the unique sequences were assigned to specific metabolic pathways by the Kyoto Encyclopedia of Genes and Genomes. Gene sequences of all enzymes known to be involved in SG synthesis were examined. A total of 143 UDP-glucosyltransferase (UGT) unigenes were identified, some of which might be involved in SG biosynthesis. The expression patterns of eight of these genes were further confirmed by RT-QPCR. CONCLUSION: RNA-seq analysis identified candidate genes encoding enzymes responsible for the biosynthesis of SGs in Stevia, a non-model plant without a reference genome. The transcriptome data from this study yielded new insights into the process of SG accumulation in Stevia. Our results demonstrate that RNA-Seq can be successfully used for gene identification and transcript profiling in a non-model species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-571) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4108789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41087892014-08-04 RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes Chen, Junwen Hou, Kai Qin, Peng Liu, Hongchang Yi, Bin Yang, Wenting Wu, Wei BMC Genomics Research Article BACKGROUND: Stevia (Stevia rebaudiana) is an important medicinal plant that yields diterpenoid steviol glycosides (SGs). SGs are currently used in the preparation of medicines, food products and neutraceuticals because of its sweetening property (zero calories and about 300 times sweeter than sugar). Recently, some progress has been made in understanding the biosynthesis of SGs in Stevia, but little is known about the molecular mechanisms underlying this process. Additionally, the genomics of Stevia, a non-model species, remains uncharacterized. The recent advent of RNA-Seq, a next generation sequencing technology, provides an opportunity to expand the identification of Stevia genes through in-depth transcript profiling. RESULTS: We present a comprehensive landscape of the transcriptome profiles of three genotypes of Stevia with divergent SG compositions characterized using RNA-seq. 191,590,282 high-quality reads were generated and then assembled into 171,837 transcripts with an average sequence length of 969 base pairs. A total of 80,160 unigenes were annotated, and 14,211 of the unique sequences were assigned to specific metabolic pathways by the Kyoto Encyclopedia of Genes and Genomes. Gene sequences of all enzymes known to be involved in SG synthesis were examined. A total of 143 UDP-glucosyltransferase (UGT) unigenes were identified, some of which might be involved in SG biosynthesis. The expression patterns of eight of these genes were further confirmed by RT-QPCR. CONCLUSION: RNA-seq analysis identified candidate genes encoding enzymes responsible for the biosynthesis of SGs in Stevia, a non-model plant without a reference genome. The transcriptome data from this study yielded new insights into the process of SG accumulation in Stevia. Our results demonstrate that RNA-Seq can be successfully used for gene identification and transcript profiling in a non-model species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-571) contains supplementary material, which is available to authorized users. BioMed Central 2014-07-07 /pmc/articles/PMC4108789/ /pubmed/25001368 http://dx.doi.org/10.1186/1471-2164-15-571 Text en © Chen et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. 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 credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Chen, Junwen Hou, Kai Qin, Peng Liu, Hongchang Yi, Bin Yang, Wenting Wu, Wei RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes |
title | RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes |
title_full | RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes |
title_fullStr | RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes |
title_full_unstemmed | RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes |
title_short | RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes |
title_sort | rna-seq for gene identification and transcript profiling of three stevia rebaudiana genotypes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4108789/ https://www.ncbi.nlm.nih.gov/pubmed/25001368 http://dx.doi.org/10.1186/1471-2164-15-571 |
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