Guar (Cyamopsis tetragonoloba (L.) Taub.) is an annual legume crop native to India and Pakistan. Seeds of the plant serve as a source of galactomannan polysaccharide (guar gum) used in the food industry as a stabilizer (E412) and as a gelling agent in oil and gas fracturing fluids. There were several attempts to introduce this crop to countries of more northern latitudes. However, guar is a plant of a short photoperiod, therefore, its introduction, for example, to Russia is complicated by a long day length during the growing season. Breeding of new guar varieties insensitive to photoperiod slowed down due to the lack of information on functional molecular markers, which, in turn, requires information on guar genome. Modern breeding strategies, e.g., genomic predictions, benefit from integration of multi-omics approaches such as transcriptome, proteome and metabolome assays. Here we present an attempt to use transcriptome-metabolome integration to understand the genetic determination of flowering time variation among guar plants that differ in their photoperiod sensitivity. This study was performed on nine early- and six delayed-flowering guar varieties with the goal to find a connection between 63 metabolites and 1,067 differentially expressed transcripts using Shiny GAM approach. For the key biomarker of flowering in guar myo-inositol we also evaluated the KEGG biochemical pathway maps available for Arabidopsis thaliana. We found that the phosphatidylinositol signaling pathway is initiated in guar plants that are ready for flowering through the activation of the phospholipase C (PLC) gene, resulting in an exponential increase in the amount of myo-inositol in its free form observed on GC-MS chromatograms. The signaling pathway is performed by suppression of myo-inositol phosphate kinases (phosphorylation) and alternative overexpression of phosphatases (dephosphorylation). Our study suggests that metabolome and transcriptome information taken together, provide valuable information about biomarkers that can be used as a tool for marker-assisted breeding, metabolomics and functional genomics of this important legume crop.
Keywords: differentially expressed genes; gene network analysis; systems biology; transcriptome-metabolome integration.