Back

The wheat multi-pathogen resistance gene, Lr67res, confers a novel gain-of-function phenotype involving anion fluxes

Milne, R. J.; Dibley, K. E.; Bose, J.; Riaz, A.; Zhang, J.; Schnippenkoetter, W. H.; Ashton, A. R.; Ryan, P. R.; Tyerman, S. D.; Lagudah, E.

2022-03-22 plant biology
10.1101/2022.03.22.485337 bioRxiv
Show abstract

Partial resistance to multiple biotrophic fungal pathogens in wheat (Triticum aestivum L.) is conferred by the Lr67 gene, which encodes a Sugar Transport Protein 13 (STP13) family hexose-proton symporter variant. Two mutations (G144R, V387L) differentiate the resistant and susceptible protein variants (Lr67res and Lr67sus). The molecular function of the Lr67res protein is not well understood. We functionally characterized the wheat Lr67res protein variant using two heterologous expression systems - Xenopus laevis oocytes and Saccharomyces cerevisiae yeast. Wheat and barley (Hordeum vulgare) were used to verify disease resistance capability of Lr67/STP13 variants. The Lr67res allele, but not Lr67sus, induced large sugar-independent, anion-dominated currents in oocytes and an increased sensitivity to ions in yeast, implicating a novel gain-of-function. We demonstrate that the single mutant variant, Lr67susG144R, confers disease resistance in wheat and that transgenic barley (Hordeum vulgare L.) plants expressing the orthologous HvSTP13 gene carrying the equivalent mutations present in Lr67res exhibited increased resistance to Puccinia hordei. NaCl treatment was found to induce leaf tip necrosis in Lr67res wheat. An Lr67res-like gain-of-function can be introduced into orthologous plant hexose transporters via single amino acid mutation, highlighting the possibility of generating disease resistance in other crop species, especially with gene editing. One sentence summaryThe wheat Lr67res protein responsible for multipathogen resistance has a gain-of-function over the Lr67sus protein, that is associated with anion fluxes and likely contributes to disease resistance.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.