Dataset Information

DNA methylation variations between six sugar beet genotypes with distinct bolting tolerance levels

ABSTRACT: The aim of this study was to explore epigenetic variations between sugar beet genotypes with distinct bolting tolerance levels and to identify genes that could be involved in sugar beet bolting tolerance. We focused on shoot apical meristem, the site of floral transition, from six sugar beet genotypes that were submitted to 9 weeks of vernalization treatment. This duration of cold exposure allowed bolting in the 3 sensitive genotypes but not in the 3 resistant ones. Single-copy sequences of DNA, potentially enriched in coding sequences, were isolated by Cot analysis and sequenced using Roche's GS-FLX sequencing technology in order to complete public sugar beet databases. Oligonucleotide arrays based on our single-copy sequences (about 42 000 predicted Open Reading Frames (ORFs)) and public database sequences (30 235 ESTs) were designed and used for transcriptomic and methylation analyses. We identified 1580 differentially expressed sequences (DES) and 1526 differentially methylated sequences (DMS) between bolting resistant and bolting sensitive genotypes. Using higher stringency criteria, we focused on 169 DES (with 87 up-regulated in R genotypes and 82 up-regulated in S ones) and 111 DMS (all hyper-methylated in S genotypes). In addition, 14 sequences were found to be both differentially methylated and differentially expressed, exhibiting negative correlation between their methylation and expression. We showed that bolting tolerance involved an integrated network of genes from environment perception, phytohormone signaling to flowering induction.

ORGANISM(S): Beta vulgaris subsp. vulgaris Beta vulgaris

PROVIDER: GSE56413 | GEO | 2014/12/31

SECONDARY ACCESSION(S): PRJNA243270

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: Sugar beet is an important root crop, accounting for 30 % of the sugar production worldwide. The long growing season make sugar beets exposed to a range of plant pathogens for longer periods than most other crops. Here, contrasting sugar beet genotypes were used for transcriptome analysis to reveal differential responses and new defense genes to Rhizoctonia solani, a soilborn fungal pathogen. Results: After curation of primary RNA-sequencing reads, 16,768 genes deriving from 36 samples composed of two susceptible and two resistant sugar beet genotypes, three time-points (0, two and five days post inoculation), each in three replicates were subjected for analysis. Among the elevated 217 transcripts at 2 dpi, three resistance-like genes (Bv4_088600_cumk, Bv8u_204980_frqg, and Bv_44840_iifo) were activated. By employing edgeR package statistics, 660 genes were significantly different (false discovery rate < 0.05) between resistant and susceptible genotypes in their response to R. solani inoculation. A combination of eukaryotic orthologous group assignments and gene ontology enrichment analyses, revealed three Bet v I/Major latex protein homologous genes (Bv7_162510_pymu, Bv7_162520_etow, Bv_27270_xeas) in the resistant genotypes after five days of fungal challenge. Co-expression network analysis of differentially expressed sugar beet genes further identified a MYB46 transcription factor, a plant disease resistance response protein (DRR206) and a flavonoid o-methyltransferase protein. MYB46 has a key function in secondary cell wall formation and exist as a singleton in the sugar beet genome. The genome of R. solani is enriched in cell wall degrading enzyme encoding genes and it is anticipated that they represent important virulence factors. Compared to Arabidopsis thaliana, sugar beet has 2.4-fold more carbohydrate esterases and particularly large numbers (26-fold) of auxiliary activity encoding genes whose function in cell wall biosynthesis is largely unknown. Conclusions: Based on components identified in this sugar beet transcript data set we conclude that defense responses to R. solani are attributed to a wide range of gene categories but functional information is missing to a large extent. This calls for careful integration to avoid negative side effects to obtain optimal combinations of these traits in order to reach the long-term goal of improved resistance in sugar beet.

Dataset Information

DNA methylation variations between six sugar beet genotypes with distinct bolting tolerance levels

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets