A high-throughput method for isolation of salicylic acid metabolic mutants

Salicylic acid (SA) is a key defense signal molecule against biotrophic pathogens in plants. Quantification of SA levels in plants is critical for dissecting the SA-mediated immune response. Although HPLC and GC/MS are routinely used to determine SA concentrations, they are expensive and time-consuming. We recently described a rapid method for a bacterial biosensor Acinetobacter sp. ADPWH_lux-based SA quantification, which enables high-throughput analysis. In this study we describe an improved method for fast sample preparation, and present a high-throughput strategy for isolation of SA metabolic mutants. On the basis of the previously described biosensor-based method, we simplified the tissue collection and the SA extraction procedure. Leaf discs were collected and boiled in Luria-Bertani (LB), and then the released SA was measured with the biosensor. The time-consuming steps of weighing samples, grinding tissues and centrifugation were avoided. The direct boiling protocol detected similar differences in SA levels among pathogen-infected wild-type, npr1 (nonexpressor of pathogenesis-related genes), and sid2 (SA induction-deficient) plants as did the previously described biosensor-based method and an HPLC-based approach, demonstrating the efficacy of the protocol presented here. We adapted this protocol to a high-throughput format and identified six npr1 suppressors that accumulated lower levels of SA than npr1 upon pathogen infection. Two of the suppressors were found to be allelic to the previously identified eds5 mutant. The other four are more susceptible than npr1 to the bacterial pathogen Pseudomonas syringae pv. maculicola ES4326 and their identity merits further investigation. The rapid SA extraction method by direct boiling of leaf discs further reduced the cost and time required for the biosensor Acinetobacter sp. ADPWH_lux-based SA estimation, and allowed the screening for npr1 suppressors that accumulated less SA than npr1 after pathogen infection in a high-throughput manner. The highly efficacious SA estimation protocol can be applied in genetic screen for SA metabolic mutants and characterization of enzymes involved in SA metabolism. The mutants isolated in this study may help identify new components in the SA-related signaling pathways.