Even though SWEETs (Sugars Will Eventually be Exported Transporters) have been found in every sequenced plant genome, a comprehensive understanding of their functionality is lacking. In this study, we focused on the SWEET family of barley (Hordeum vulgare). A radiotracer assay revealed that expressing HvSWEET11b in African clawed frog (Xenopus laevis) oocytes facilitated the bidirectional transfer of not just sucrose and glucose, but also cytokinin. Barley plants harboring a loss-of-function mutation of HvSWEET11b could not set viable grains, while the distribution of sucrose and cytokinin was altered in developing grains of plants in which the gene was knocked down. Sucrose allocation within transgenic grains was disrupted, which is consistent with the changes to the cytokinin gradient across grains, as visualized by magnetic resonance imaging and Fourier transform infrared spectroscopy microimaging. Decreasing HvSWEET11b expression in developing grains reduced overall grain size, sink strength, the number of endopolyploid endosperm cells, and the contents of starch and protein. The control exerted by HvSWEET11b over sugars and cytokinins likely predetermines their synergy, resulting in adjustments to the grain’s biochemistry and transcriptome.

Elucidating the relationship between the sequences of non-coding regulatory elements and their target genes is key to understanding gene regulation and its variation between plant species and ecotypes. In this study, we developed deep learning models that link gene sequence data with mRNA copy number for the plant species Arabidopsis thaliana, Sorghum bicolor, Solanum lycopersicum and Zea mays, and predicted the regulatory effect of gene sequence variation. Our models achieved over 80% accuracy in the species-specific and multi-species prediction tasks and enabled predictive feature selection within the input regulatory sequences. Saliency scores of the model highlighted a set of expression-predictive sequence features and the profound importance of the UTR regions in determining the level of gene expression. Identified sequence features exhibited remarkable conservation across plant species and achieved more than 70% accuracy in cross-species expression prediction. We demonstrated the application of our model on 14 newly assembled tomato genomes, where the effect of structural genetic variation on gene expression is annotated. Finally, we showed that by providing an accurate prediction of differences in the expression of biosynthetic enzymes and their individual homologs, the model highlights known metabolic differences between related genotypes. This was demonstrated for biosynthetic pathways of stress-related compounds in Solanum lycopersicum and its wild drought-resistant relative Solanum pennellii.