A new RNASeq-based reference transcriptome for sugar beet and its application in transcriptome-scale analysis of vernalization and gibberellin responses

Background: Sugar beet (Beta vulgaris sp. vulgaris) crops account for about 30% of world sugar. Sugar yield is compromised by reproductive growth hence crops must remain vegetative until harvest. Prolonged exposure to cold temperature (vernalization) in the range 6 degrees C to 12 degrees C induces reproductive growth, leading to bolting (rapid elongation of the main stem) and flowering. Spring cultivation of crops in cool temperate climates makes them vulnerable to vernalization and hence bolting, which is initiated in the apical shoot meristem in processes involving interaction between gibberellin (GA) hormones and vernalization. The underlying mechanisms are unknown and genome scale next generation sequencing approaches now offer comprehensive strategies to investigate them; enabling the identification of novel targets for bolting control in sugar beet crops. In this study, we demonstrate the application of an mRNA-Seq based strategy for this purpose. Results: There is no sugar beet reference genome, or public expression array platforms. We therefore used RNA-Seq to generate the first reference transcriptome. We next performed digital gene expression profiling using shoot apex mRNA from two sugar beet cultivars with and without applied GA, and also a vernalized cultivar with and without applied GA. Subsequent bioinformatics analyses identified transcriptional changes associated with genotypic difference and experimental treatments. Analysis of expression profiles in response to vernalization and GA treatment suggested previously unsuspected roles for a RAV1-like AP2/B3 domain protein in vernalization and efflux transporters in the GA response. Conclusions: Next generation RNA-Seq enabled the generation of the first reference transcriptome for sugar beet and the study of global transcriptional responses in the shoot apex to vernalization and GA treatment, without the need for a reference genome or established array platforms. Comprehensive bioinformatic analysis identified transcriptional programmes associated with different sugar beet genotypes as well as biological treatments; thus providing important new opportunities for basic scientists and sugar beet breeders. Transcriptome-scale identification of agronomically important traits as used in this study should be widely applicable to all crop plants where genomic resources are limiting.