Back

Major effector loss reveals compensatory pathogenicity networks in a necrotrophic wheat pathogen

Morikawa, S.; Verdonk, C. J.; Lenzo, L. V.; Phan, H. T. T.; Furuki, E.; Gagalova, K.; Debler, J. W.; Turo, C. J.; Mousley, C.; John, E.; Henares, B.; Tan, K.-C.

2026-09-17 molecular biology
10.64898/2026.09.14.751338 bioRxiv
Show abstract

Necrotrophic effectors (NEs) are key determinants of virulence in the necrotrophic fungal pathogen Parastagonospora nodorum that causes septoria nodorum blotch of wheat. However, targeted removal of three important NEs SnToxA, SnTox1, and SnTox3 in the mutant{Delta} toxa13 previously revealed a redundancy mechanism is triggered, whereby pathogenicity on wheat is maintained. In this study, we investigated the gene regulatory profile underpinning this phenomenon and discover that virulence is not dependent on a fixed set of dominant effectors but instead arises from a flexible, epistatic compensatory network. Although host transcriptional responses to the P. nodorum wildtype SN15 and{Delta} toxa13 infection remained largely conserved, consistent with an overlapping disease-susceptibility pathway, a significant upregulation of candidate effector genes was observed in{Delta} toxa13. This included the recently characterised NE SnTox267, and several other candidate effectors able to induce necrosis in the non-host Nicotiana benthamiana, each carrying a predicted structural fold conserved across other pathogens. We therefore provide further direct evidence that virulence is maintained in P. nodorum lacking three NEs by an epistatic and compensatory effector network, underpinned by changes in pathogen gene expression. Targeting conserved effector-mediated virulence mechanisms rather than individual host-specific gene-for-gene interactions may provide a more tractable route to host resistance.

Matching journals

The top 3 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.