Modeling pseudomonas syringae metabolism to interrogate in planta infection dynamics

Plants have a complex innate immune system that conveys strong resistance to most microbial organisms. To maintain vitality, plants can respond to a wide range of potential threats with increases in phytohormones, secondary metabolites, and anti-microbials that successfully inhibit growth of non-pathogenic bacteria and fungus. Dysregulation of any number of mechanisms within a plant’s defensive capabilities can lead to otherwise harmless bacteria becoming serious threats to plant health. Arabidopsis thaliana has been used for decades as a model plant due to its genetic tractability and simple lifestyle. It is related to many important agricultural species and thus serves as a model to understand defense mechanisms plant-kingdom-wide.
Pathogenic species of microbes evade or suppress defense responses of plants and produce infections, often leading to a loss of yield in important agricultural species. Pathogenic species like Pseudomonas syringae (Pst) utilize host-made metabolites to produce growth and infect other tissues. The exact metabolites Pst uses during infections remain relatively uncharacterized. Due to the complexity of studying a two-organism system, we have generated a metabolic model, iPst19, to predict how the pathogen Pst produces infections in A. thaliana and what plant-made metabolites Pst uses while invading the leaf. iPst19 highlighted the importance of branched-chain amino acid (BCAAs) catabolism as a part of Pst combatting A. thaliana defenses. The availability of BCAAs reduces the infective capabilities of Pst and prevents infections from proceeding normally. In media designed to induce virulence factor synthesis, BCAAs are still able to suppress genes related to virulence.
iPst19 helped identify BCAA metabolism bacterial genes that could play a role in helping Pst express virulence during infection. Modulating these genes in Pst caused reduced infectivity in A. thaliana and reduced normal growth capabilities, suggesting these genes could be potential targets for anti-microbial development. Taken together, iPst19’s predictive capabilities can be an effective tool for developing strategies to prevent yield-loss in important agricultural species.