Biology subjects

Baroukh, C.

Publications and source records attributed to Baroukh, C..

3 recordsLinked to original sources

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↗

bioRxiv2022

A universal dynamical metabolic model representing mixotrophic growth of Chlorella sp.

An emerging idea is to couple wastewater treatment and biofuel production using microalgae to achieve higher productivities and lower costs. This paper proposes a metabolic modelling of Chlorella sp. growing on wastes in mixotrophic conditions, accounting also for the possible inhibitory substrates. A metabolic model considering several possible carbon substrates was developed and run. The addition of several organic carbon substrates such as acetate, butyrate or glucose were tested, along with glycerol, a more realistic substrate from an economical point of view. The metabolic model was built using DRUM framework and consists of 188 reactions and 176 metabolites. After a calibration phase, the model was successfully challenged with data from 122 experiments collected from scientific literature in autotrophic, heterotrophic and mixotrophic conditions. The optimal feeding strategy estimated with the model reduces the time to consume the volatile fatty acids from 16 days to 2 days. The high prediction capability of this model opens new routes for enhancing design and operation in waste valorisation using microalgae. Author SummaryWaste valorisation is one of the current envisaged strategies to make renewable processes more economically advantageous. For example, wastewater treatment can be used to produce biohydrogen from bacteria, through a process called dark fermentation, and to cultivate microalgae for biofuel production. Dark fermentation has, as by-products, organic acids that have inhibitory effects on the growth of microalgae, increasing the time to completely treat the waste. Advances in metabolic knowledge and techniques allow for the deployment of new strategies to improve the efficiency of bioprocesses. In this work, we validate a mathematical model of the metabolism of the microalgae genus Chlorella using the DRUM framework for 122 experiments from the scientific literature. This model enables us to apply control and optimisation techniques to provide a strategy to treat wastes coming from dark fermentation processes, overcoming the inhibition of some organic acids. The strategy is able to reduce the time to treat the waste from 16 days to only 2 days. The high prediction capability of this model opens new routes for enhancing design and operation in waste valorisation using microalgae.

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↗