Untargeted metabotyping to study phenylpropanoid diversity in crop plants

Abstract

Plant genebanks constitute a key resource for breeding to ensure crop yield under changing environmental conditions. Because of their roles in a range of stress responses, phenylpropanoids are promising targets. Phenylpropanoids comprise a wide array of metabolites; however, studies regarding their diversity and the underlying genes are still limited for cereals. The assessment of barley diversity via genotyping-by-sequencing is in rapid progress. Exploring these resources by integrating genetic association studies to in-depth metabolomic profiling provides a valuable opportunity to study barley phenylpropanoid metabolism; but poses a challenge by demanding large-scale approaches. Here, we report an LC-PDA-MS workflow for barley high-throughput metabotyping. Without prior construction of a species-specific library, this method produced phenylpropanoid-enriched metabotypes with which the abundance of putative metabolic features was assessed across hundreds of samples in a single-processed data matrix. The robustness of the analytical performance was tested using a standard mix and extracts from two selected cultivars: Scarlett and Barke. The large-scale analysis of barley extracts showed (1) that barley flag leaf profiles were dominated by glycosylation derivatives of isovitexin, isoorientin, and isoscoparin; (2) proved the workflow's capability to discriminate within genotypes; (3) highlighted the role of glycosylation in barley phenylpropanoid diversity. Using the barley S42IL mapping population, the workflow proved useful for metabolic quantitative trait loci purposes. The protocol can be readily applied not only to explore the barley phenylpropanoid diversity represented in genebanks but also to study species whose profiles differ from those of cereals: the crop Helianthus annuus (sunflower) and the model plant Arabidopsis thaliana.