Pathogens generate and secrete effector proteins to the host plant cells during pathogenesis to promote virulence and colonization. If the plant carries resistance (R) proteins that recognize pathogen effectors, effector-triggered immunity (ETI) is activated, resulting in a robust immune response and hypersensitive response (HR). The bipartite effector AvrRps4 from Pseudomonas syringae pv. pisi has been well studied in terms of avirulence function. In planta, AvrRps4 is processed into two parts. The C-terminal fragment of AvrRps4 (AvrRps4C) induces HR in turnip and is recognized by the paired resistance proteins AtRRS1/AtRPS4 in Arabidopsis. Here, we show that AvrRps4C targets a group of Arabidopsis WRKY, including WRKY46, WRKY53, WRKY54, and WRKY70, to induce its virulence function. Indeed, AvrRps4C suppresses the general binding and transcriptional activities of immune-positive regulator WRKY54 and WRKY54-mediated resistance. AvrRps4C interferes with WRKY54\'s binding activity to target gene SARD1 in vitro, suggesting WRKY54 is sequestered from the SARD1 promoter by AvrRps4C. Through the interaction of AvrRps4C with four WRKYs, AvrRps4 enhances the formation of homo-/heterotypic complexes of four WRKYs and sequesters them in the cytoplasm, thus inhibiting their function in plant immunity. Together, our results provide a detailed virulence mechanism of AvrRps4 through its C-terminus.

Accurate quantification of hypersensitive response (HR) programmed cell death is imperative for understanding plant defense mechanisms and developing disease-resistant crop varieties. Here, a phenotyping platform for rapid, continuous-time, and quantitative assessment of HR is demonstrated: Parallel Automated Spectroscopy Tool for Electrolyte Leakage (PASTEL). Compared to traditional HR assays, PASTEL significantly improves temporal resolution and has high sensitivity, facilitating detection of microscopic levels of cell death. Validation is performed by transiently expressing the effector protein AVRblb2 in transgenic Nicotiana benthamiana (expressing the corresponding resistance protein Rpi-blb2) to reliably induce HR. Detection of cell death is achieved at microscopic intensities, where leaf tissue appears healthy to the naked eye one week after infiltration. PASTEL produces large amounts of frequency domain impedance data captured continuously. This data is used to develop supervised machine-learning (ML) models for classification of HR. Input data (inclusive of the entire tested concentration range) is classified as HR-positive or negative with 84.1% mean accuracy (F1 score = 0.75) at 1 h and with 87.8% mean accuracy (F1 score = 0.81) at 22 h. With PASTEL and the ML models produced in this work, it is possible to phenotype disease resistance in plants in hours instead of days to weeks.