Dataset Information

RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes.

ABSTRACT: 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.

SUBMITTER: Chen J

PROVIDER: S-EPMC4108789 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes.

Chen Junwen J Hou Kai K Qin Peng P Liu Hongchang H Yi Bin B Yang Wenting W Wu Wei W

BMC genomics 20140707

<h4>Background</h4>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 genomic ...[more]

PMID: 25001368

Similar Datasets

Project description:BACKGROUND: Alfalfa, [Medicago sativa (L.) sativa], a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. The recent advent of RNA-Seq, a massively parallel sequencing method for transcriptome analysis, provides an opportunity to expand the identification of alfalfa genes and polymorphisms, and conduct in-depth transcript profiling. RESULTS: Cell walls in stems of alfalfa genotype 708 have higher cellulose and lower lignin concentrations compared to cell walls in stems of genotype 773. Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. In addition, 341,984 ESTs were generated from ES and PES internodes of genotype 773 using the GS FLX Titanium platform. The first alfalfa (Medicago sativa) gene index (MSGI 1.0) was assembled using the Sanger ESTs available from GenBank, the GS FLX Titanium EST sequences, and the de novo assembled Illumina sequences. MSGI 1.0 contains 124,025 unique sequences including 22,729 tentative consensus sequences (TCs), 22,315 singletons and 78,981 pseudo-singletons. We identified a total of 1,294 simple sequence repeats (SSR) among the sequences in MSGI 1.0. In addition, a total of 10,826 single nucleotide polymorphisms (SNPs) were predicted between the two genotypes. Out of 55 SNPs randomly selected for experimental validation, 47 (85%) were polymorphic between the two genotypes. We also identified numerous allelic variations within each genotype. Digital gene expression analysis identified numerous candidate genes that may play a role in stem development as well as candidate genes that may contribute to the differences in cell wall composition in stems of the two genotypes. CONCLUSIONS: Our results demonstrate that RNA-Seq can be successfully used for gene identification, polymorphism detection and transcript profiling in alfalfa, a non-model, allogamous, autotetraploid species. The alfalfa gene index assembled in this study, and the SNPs, SSRs and candidate genes identified can be used to improve alfalfa as a forage crop and cellulosic feedstock.

Project description:BACKGROUND: MicroRNAs (miRNAs) constitute a family of small RNA (sRNA) population that regulates the gene expression and plays an important role in plant development, metabolism, signal transduction and stress response. Extensive studies on miRNAs have been performed in different plants such as Arabidopsis thaliana, Oryza sativa etc. and volume of the miRNA database, mirBASE, has been increasing on day to day basis. Stevia rebaudiana Bertoni is an important perennial herb which accumulates high concentrations of diterpene steviol glycosides which contributes to its high indexed sweetening property with no calorific value. Several studies have been carried out for understanding molecular mechanism involved in biosynthesis of these glycosides, however, information about miRNAs has been lacking in S. rebaudiana. Deep sequencing of small RNAs combined with transcriptomic data is a powerful tool for identifying conserved and novel miRNAs irrespective of availability of genome sequence data. RESULTS: To identify miRNAs in S. rebaudiana, sRNA library was constructed and sequenced using Illumina genome analyzer II. A total of 30,472,534 reads representing 2,509,190 distinct sequences were obtained from sRNA library. Based on sequence similarity, we identified 100 miRNAs belonging to 34 highly conserved families. Also, we identified 12 novel miRNAs whose precursors were potentially generated from stevia EST and nucleotide sequences. All novel sequences have not been earlier described in other plant species. Putative target genes were predicted for most conserved and novel miRNAs. The predicted targets are mainly mRNA encoding enzymes regulating essential plant metabolic and signaling pathways. CONCLUSIONS: This study led to the identification of 34 highly conserved miRNA families and 12 novel potential miRNAs indicating that specific miRNAs exist in stevia species. Our results provided information on stevia miRNAs and their targets building a foundation for future studies to understand their roles in key stevia traits.

Dataset Information

RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes.

Publications

RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets