Biology subjects

Gerlin, L.

Publications and source records attributed to Gerlin, L..

4 recordsLinked to original sources

bioRxiv2025

ArtSymbioCyc, a metabolic network database collection dedicated to arthropod symbioses: a case study, the tripartite cooperation in Sipha maydis

Most arthropods live in close association with bacteria. The genomes of associated partners have co-evolved creating situations of interdependence that are complex to decipher despite the availability of their complete sequences. We developed ArtSymbioCyc, a metabolism-oriented database collection gathering genomic resources for arthropods and their associated bacteria. ArtSymbioCyc uses the powerful tools of the BioCyc community to produce high quality annotations and to analyze and compare metabolic networks on a genome-wide scale. We used ArtSymbioCyc to study the case of the tripartite symbiosis of the cereal aphid Sipha maydis focusing on amino acid and vitamin metabolisms, as these compounds are known to be important in this strictly phloemophagous insect. We showed how the metabolic pathways of the insect host and its two obligate bacterial associates are interdependent and specialized in the exploitation of Poaceae phloem, for example for the biosynthesis of sulfur-containing amino acids and most vitamins. This demonstrates that ArtSymbioCyc does not only reveal the individual metabolic capacities of each partner and their respective contributions to the holobiont they constitute, but also allows to predict the essential inputs that must come from host nutrition. IMPORTANCEEvolution has driven the emergence of complex arthropod-microbe symbiotic systems, whose metabolic integration is difficult to unravel. With its user-friendly interface, ArtSymbioCyc (https://artsymbiocyc.cycadsys.org) eases and speeds up the analysis of metabolic networks by enabling precise inference of compound exchanges between associated partners, and helps unveil the adaptive potential of arthropods in contexts such as conservation or agricultural control.

bioinformatics↗

bioRxiv2024

Metabolic modeling of a plant-pathogen interaction quantifies the metabolic bottlenecks underlying bacterial wilt

During plant infection, complex metabolic interactions occurs between host and pathogen, including a genuine competition for resources. While the pathogen exploits host nutrients to support its growth and virulence, the plant attempts to restrict pathogen multiplication by limiting nutrient availability or producing antimicrobial compounds. To unravel these trophic interactions, we constructed a genome-scale metabolic model of a complete pathosystem by integrating a multi-organ metabolic model of the plant, a pathogen metabolic model, quantitative measurements, and a mathematical framework based on sequential flux balance analyses (FBAs). This strategy was applied to the Ralstonia pseudosolanacearum-tomato system. For the first time, quantitative fluxes of matter occurring during a plant infection were predicted. The model shows that (i) plant photosynthetic capacity is a stronger constraint than mineral availability for bacterial proliferation, (ii) infection-induced reduction of plant transpiration limits and ultimately halts first plant growth, then pathogen expansion, (iii) stem resource hijacking can enhance bacterial growth but remains secondary, and (iv) pathogen-excreted putrescine is likely reused for the plants needs. This study delivers the first holistic and quantitative representation of trophic interactions within a plant-pathogen system and highlights the central importance of water flow when the infectious agent is a fast-growing, xylem-colonizing bacterium.

plant biology↗

bioRxiv2024

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

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.

plant biology↗

bioRxiv2021

A multi-organ metabolic model of tomato predicts plant responses to nutritional and genetic perturbations

Predicting and understanding plant responses to perturbations requires integrating the interactions between nutritional sources, genes, cell metabolism and physiology in the same model. This can be achieved using metabolic modeling calibrated by experimental data. In this study, we developed a multi-organ metabolic model of a tomato plant during vegetative growth, named VYTOP (Virtual Young TOmato Plant) that combines genome-scale metabolic models of leaf, stem and root and integrates experimental data acquired from metabolomics and high-throughput phenotyping of tomato plants. It is composed of 6689 reactions and 6326 metabolites. We validated VYTOP predictions on five independent use cases. The model correctly predicted that glutamine is the main organic nutrient of xylem sap. The model estimated quantitatively how stem photosynthetic contribution impact exchanges between the different organs. The model was also able to predict how nitrogen limitation affects the plant vegetative growth, and to predict the metabolic behavior of transgenic tomato lines with altered expressions of core metabolic enzymes. The integration of different components such as a metabolic model, physiological constraints and experimental data generates a powerful predictive tool to study plant behavior, which will be useful for several other applications such as plant metabolic engineering or plant nutrition. One sentence summaryA multi-organ metabolic model of tomato gives biological insights into the functioning of a plant such as xylem composition, the role of the stem and the response to environmental or genetic perturbation.

systems biology↗