EvoMPMI, effectoromics and AI-driven strategies to enhance durable disease resistance in potato

Potato (Solanum tuberosum) is threatened by numerous diseases each growing season, while wild tuber-bearing Solanum relatives rely on resistance traits that have evolved over time. Inspired by the Sun Tzu’s philosophy "know your enemy and yourself", we seek to understand the dynamic interactions between Solanum immune receptors and pathogen effectors. For Phytophthora infestans,effectoromics screens have led to the isolation of several intracellular immune receptors that target fast-evolving avirulence genes and therefore are quickly rendered ineffective. We expanded the effectoromics strategy to pattern recognition receptors (PRRs), which promise to provide a better durability by detecting conserved peptides. The PRR PERU recognizes transglutaminase-derived Pep13 peptides of Phytophthora and exhibits extensive genetic variation in wild Solanum species. We found remarkable recognition specificities to natural Pep13 alleles of P. infestans. Currently, we are integrating co-evolutionary analyses with artificial intelligence (AI)-guided protein structure prediction with the aim to enhance PERU-mediated immunity. In addition to late blight, our EvoMPMI studies have expanded to Verticillium dahliae, Alternaria solani, Candidatus Liberibacter solanacearum and Candidatus Phytoplasma solani, and diverse infection and defense strategies are being uncovered. Ultimately, we aim to leverage obtained knowledge to develop more effective disease resistance in potato.