Biology subjects

Genin, S.

Publications and source records attributed to Genin, S..

5 recordsLinked to original sources

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↗

bioRxiv2023

Evidence for increased fitness of a plant pathogen conferred by epigenetic variation

Adaptation is usually explained by adaptive genetic mutations that are transmitted from parents to offspring and become fixed in the adapted population. However, more and more studies show that genetic mutation analysis alone is not sufficient to fully explain the processes of adaptive evolution and report the existence of non-genetic (or epigenetic) inheritance and its significant role in the generation of adapted phenotypes. In the present work, we tested the hypothesis of the role of DNA methylation, a form of epigenetic modification, in adaptation of the plant pathogen Ralstonia solanacearum to the host plant during an experimental evolution. Using SMRT-seq technology, we analyzed the methylomes of 31 experimentally evolved clones that were obtained after serial passages on a given host plant during 300 generations, either on susceptible or tolerant hosts. Comparison with the methylome of the ancestral clone revealed between 12 and 21 differential methylated sites (DMSs) at the GTWWAC motif in the evolved clones. Gene expression analysis of the 39 genes targeted by these DMSs revealed limited correlation between differential methylation and differential gene expression. Only one gene showed a correlation, the RSp0338 gene encoding the EpsR regulator protein. The MSRE-qPCR (Methylation Sensitive Restriction Enzyme - qPCR) technology was used as an alternative approach to assess the methylation state of the DMSs found by SMRT-seq between the ancestral and evolved clones. This approach also found the two DMSs upstream of RSp0338. Using site-directed mutagenesis, we demonstrated the contribution of these two DMSs in host adaptation. As these DMSs appeared very quickly in the experimental evolution, we hypothesize that such fast epigenetic changes can allow rapid adaptation to the plant stem environment. To our knowledge, this is the first study showing a link between epigenetic variation and evolutionary adaptation to new environment.

evolutionary biology↗

bioRxiv2023

Insights into the metabolic specificities of pathogenic strains from the Ralstonia solanacearum species complex

All the strains grouped under the species Ralstonia solanacearum represent a species complex which collectively constitute a devastating plant pathogen responsible of many diseases on agricultural crops throughout the world. The strains have different lifestyles and host range. Here we sought whether specific metabolic pathways contribute to strain diversification. To this end, we carried out systematic comparisons, followed by manual expertise on 11 strains representing the diversity of the species complex. We reconstructed the metabolic network of each strain from its genome sequence and looked for the metabolic pathways differentiating the different reconstructed networks and, by extension, the different strains. Finally, we conducted an experimental validation by determining the metabolic profile of each strain with the Biolog technology, also in a comparative approach. Results revealed that the metabolism is conserved between strains, with a core-metabolism composed of 82% of the pan-reactome. The 3 species composing the species complex could be distinguished according to the presence/absence of some metabolic pathways, in particular one implying salicylic acid degradation. Phenotypic assays revealed that the trophic preferences on organic acids and several amino acids such as glutamine, glutamate, aspartate and asparagine are conserved between strains. Finally, the generation and assessment of the transcription factor phcA regulating virulence in each specie showed that the faster growth compared to the WT strain was conserved across Ralstonia solanacearum species complex. Author summaryRalstonia solanacearum is one of the most important threats to plant health worldwide, causing disease on a very large range of agricultural crops such as tomato or potato. Behind the Ralstonia solanacearum name are hundreds of strains with different host range and lifestyle, classified into three species. Studying the differences between strain allows to better apprehend the biology of the pathogen and the specificity of some strains. None of the published genomic comparative studies have focused on the metabolism of the strains so far. We developed a new bioinformatic pipeline to build high-quality metabolic networks and used a combination of metabolic modeling and high-throughput phenotypic Biolog microplates to look for the metabolic differences between 11 strains across the three species. Our study revealed that genes encoding for enzymes are overall conserved, with few variations between strains. However, at the level of the phenotype, more variations were observed. These variations probably result from regulation rather than the presence or absence of enzymes in the genome.

systems 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↗