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bioRxiv · 10.1101/2024.07.18.604046

Unraveling the in planta growth of the plant pathogen Ralstonia pseudosolanacearum by mathematical modeling

Baroukh, C.·Gerlin, L.·Escourrou, A.·Genin, S.
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Abstract

Ralstonia pseudosolanacearum, a plant pathogen responsible for bacterial wilt in numerous plant species, exhibits paradoxical growth in the host by achieving high bacterial densities in xylem sap, an environment traditionally considered nutrient-poor. This study combined in vitro experiments and mathematical modeling to elucidate the growth dynamics of R. pseudosolanacearum strain GMI1000 within plants. To simulate the xylem environment, a tomato xylem-mimicking medium containing amino acids and sugars was developed to monitor the growth kinetics of R. pseudosolanacearum. Results indicated that glutamine is the primary metabolite driving bacterial growth, while putrescine is abundantly excreted, and acetate is transiently produced and subsequently consumed. A mathematical model was constructed and calibrated using the in vitro data. This model was employed to simulate the evolution of bacterial density and xylem sap composition during plant infection. The model accurately reproduced in planta experimental observations, including high bacterial densities and the depletion of glutamine and asparagine. Additionally, the model estimated the minimal number of bacteria required to initiate infection, the timing of infection post-inoculation, the bacterial mortality rate within the plant, and the rate at which excreted putrescine is assimilated by the plant. The findings demonstrate that xylem sap is not as nutrient-poor and can sustain high bacterial densities. The study also provides an explanatory framework for the presence of acetate and putrescine in the sap of infected xylem and give clues as to the role of putrescine in the virulence of R. pseudosolanacearum.

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Baroukh, C., Gerlin, L., Escourrou, A., Genin, S.. 2024-07-22. Unraveling the in planta growth of the plant pathogen Ralstonia pseudosolanacearum by mathematical modeling. https://doi.org/10.1101/2024.07.18.604046

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