Multiplexed effector screening for recognition by endogenous resistance genes using positive defense reporters in wheat protoplasts.

Plant resistance (R) and pathogen avirulence (Avr) gene interactions play a vital role in pathogen resistance. Efficient molecular screening tools for crops lack far behind their model organism counterparts, yet they are essential to rapidly identify agriculturally important molecular interactions that trigger host resistance. Here, we have developed a novel wheat protoplast assay that enables efficient screening of Avr/R interactions at scale. Our assay allows access to the extensive gene pool of phenotypically described R genes because it does not require the overexpression of cloned R genes. It is suitable for multiplexed Avr screening, with interactions tested in pools of up to 50 Avr candidates. We identified Avr/R-induced defense genes to create a promoter-luciferase reporter. Then, we combined this with a dual-color ratiometric reporter system that normalizes read-outs accounting for experimental variability and Avr/R-induced cell death. Moreover, we introduced a self-replicative plasmid reducing the amount of plasmid used in the assay. Our assay increases the throughput of Avr candidate screening, accelerating the study of cellular defense signaling and resistance gene identification in wheat. We anticipate that our assay will significantly accelerate Avr identification for many wheat pathogens, leading to improved genome-guided pathogen surveillance and breeding of disease-resistant crops.

Blurred lines: integrating emerging technologies to advance plant biosecurity.

Plant diseases threaten global food security and biodiversity. Rapid dispersal of pathogens particularly via human means has accelerated in recent years. Timely detection of plant pathogens is essential to limit their spread. At the same time, international regulations must keep abreast of advances in plant disease diagnostics. In this review we describe recent progress in developing modern plant disease diagnostics based on detection of pathogen components, high-throughput image analysis, remote sensing, and machine learning. We discuss how different diagnostic approaches can be integrated in detection frameworks that can work at different scales and account for sampling biases. Lastly, we briefly discuss the requirements to apply these advances under regulatory settings to improve biosecurity measures globally.