Storage Root Developmental Milestones Resolved Using Complementary -Omics Approaches

The potential for a sugar beet storage root (Beta vulgaris subsp. vulgaris L.) to grow and yield well is programmed by its genetic makeup and influenced by its environment. To identify critical genetic factors responsible for development of the storage root, integrated   -omics approaches were applied to a time course of commercial sugar beets grown in UK fields and harvested regularly from seedling to flowering stage.  Phenotypic measurements taken at harvest were combined with weather data, metabolite profiles, and prior knowledge of individual gene expression patterns, to select the most diagnostic harvest points for RNA-seq analysis. A metabolomics approach used combined ¹H-NMR-MS to generate metabolite profiles via aqueous methanol extraction of lyophilised root material.  Total RNA from six harvest points, replicated over three growing seasons, was used for library construction (targeting the poly-adenylated RNA) and sequenced on the Illumina HiSeq2000 platform. The statistical significance of genes/transcripts for a harvest effect was determined using a Generalised Linear Model (GLM) following normalisation of the derived count data, after which clustering was applied to reveal coordinated gene expression patterns.  Candidate genes will be identified based on their predicted role in root vascular patterning, root expansion, sugar accumulation/assimilation, bolting, and flowering. Together with the transcriptomic data, population screening of Recombinant Inbred Lines (RILs) for variability in sucrose yield and cell wall composition will provide an unbiased approach to discovering novel genes of interest. Knowledge of these biological processes will reveal targets for the varietal improvement of sugar beet.