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 responsible for many diseases on agricultural crops throughout the world. The strains have different lifestyles and host range. Here, we investigated whether specific metabolic pathways contribute to strain diversification. To this end, we carried out systematic comparisons 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. Results revealed that the metabolism is conserved between strains, with a core metabolism composed of 82% of the pan-reactome. The three species composing the species complex could be distinguished according to the presence/absence of some metabolic pathways, in particular, one involving 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, we generated mutants lacking the quorum-sensing-dependent regulator PhcA in four diverse strains, and we showed that the phcA-dependent trade-off between growth and production of virulence factors is conserved across the R. solanacearum species complex. IMPORTANCE Ralstonia 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 R. solanacearum name are hundreds of strains with different host range and lifestyle, classified into three species. Studying the differences between strains allows to better apprehend the biology of the pathogens 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 enzymes are overall conserved, with few variations between strains. However, more variations were observed when considering substrate usage. These variations probably result from regulation rather than the presence or absence of enzymes in the genome.

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

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 modeling of Chlorella sp. growing on fermentation wastes (blend of acetate, butyrate and other acids) in mixotrophic conditions, accounting also for the possible inhibitory substrates. This model extends previous works by modifying the metabolic network to include the consumption of glycerol and glucose by Chlorella sp., with the goal to test the addition of these substrates in order to overcome butyrate inhibition. The metabolic model was built using the DRUM framework and consists of 188 reactions and 173 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 valorization using microalgae.

MERRIN: MEtabolic regulation rule INference from time series data.

MOTIVATION: Many techniques have been developed to infer Boolean regulations from a prior knowledge network (PKN) and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks. RESULTS: We present a novel approach to infer Boolean rules for metabolic regulation from time-series data and a PKN. Our method is based on a combination of answer set programming and linear programming. By solving both combinatorial and linear arithmetic constraints, we generate candidate Boolean regulations that can reproduce the given data when coupled to the metabolic network. We evaluate our approach on a core regulated metabolic network and show how the quality of the predictions depends on the available kinetic, fluxomics or transcriptomics time-series data. AVAILABILITY AND IMPLEMENTATION: Software available at https://github.com/bioasp/merrin. SUPPLEMENTARY INFORMATION: Supplementary data are available at https://doi.org/10.5281/zenodo.6670164.

The paradoxes hidden behind the Droop model highlighted by a metabolic approach.

We propose metabolic models for the haptophyte microalgae Tisochrysis lutea with different possible organic carbon excretion mechanisms. These models-based on the DRUM (Dynamic Reduction of Unbalanced Metabolism) methodology-are calibrated with an experiment of nitrogen starvation under day/night cycles, and then validated with nitrogen-limited chemostat culture under continuous light. We show that models including exopolysaccharide excretion offer a better prediction capability. It also gives an alternative mechanistic interpretation to the Droop model for nitrogen limitation, which can be understood as an accumulation of carbon storage during nitrogen stress, rather than the common belief of a nitrogen pool driving growth. Excretion of organic carbon limits its accumulation, which leads to a maximal C/N ratio (corresponding to the minimum Droop N/C quota). Although others phenomena-including metabolic regulations and dissipation of energy-are possibly at stake, excretion appears as a key component in our metabolic model, that we propose to include in the Droop model.

The Fucoxanthin Chlorophyll a/c-Binding Protein in Tisochrysis lutea: Influence of Nitrogen and Light on Fucoxanthin and Chlorophyll a/c-Binding Protein Gene Expression and Fucoxanthin Synthesis.

We observed differences in lhc classification in Chromista. We proposed a classification of the lhcf family with two groups specific to haptophytes, one specific to diatoms, and one specific to seaweeds. Identification and characterization of the Fucoxanthin and Chlorophyll a/c-binding Protein (FCP) of the haptophyte microalgae Tisochrysis lutea were performed by similarity analysis. The FCP family contains 52 lhc genes in T. lutea. FCP pigment binding site candidates were characterized on Lhcf protein monomers of T. lutea, which possesses at least nine chlorophylls and five fucoxanthin molecules, on average, per monomer. The expression of T. lutea lhc genes was assessed during turbidostat and chemostat experiments, one with constant light (CL) and changing nitrogen phases, the second with a 12 h:12 h sinusoidal photoperiod and changing nitrogen phases. RNA-seq analysis revealed a dynamic decrease in the expression of lhc genes with nitrogen depletion. We observed that T. lutea lhcx2 was only expressed at night, suggesting that its role is to protect \cells from return of light after prolonged darkness exposure.

Trophic preferences of the pathogen Ralstonia solanacearum and consequences on its growth in xylem sap.

Ralstonia solanacearum is one of the most destructive pathogens worldwide. In the last 30 years, the molecular mechanisms at the origin of R. solanacearum pathogenicity have been studied in depth. However, the nutrition status of the pathogen once inside the plant has been poorly investigated. Yet, the pathogen needs substrates to sustain a fast-enough growth, maintain its virulence and subvert the host immunity. This study aimed to explore in-depth the xylem environment where the pathogen is abundant, and its trophic preferences. First, we determined the composition of tomato xylem sap, where fast multiplication of the pathogen occurs. Then, kinetic growth on single and mixtures of carbon sources in relation to this environment was performed to fully quantify growth. Finally, we calculated the concentration of available metabolites in the xylem sap flux to assess how much it can support bacterial growth in planta. Overall, the study underlines the adaptation of R. solanacearum to the xylem environment and the fact that the pathogen assimilates several substrates at the same time in media composed of several carbon sources. It also provides metrics on key physiological parameters governing the growth of this major pathogen, which will be instrumental in the future to better understand its metabolic behavior during infection.

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

Predicting and understanding plant responses to perturbations require 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 (Solanum lycopersicum) plant during vegetative growth, named Virtual Young TOmato Plant (VYTOP) 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 6,689 reactions and 6,326 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 impacts exchanges between the different organs. The model was also able to predict how nitrogen limitation affects vegetative growth and the metabolic behavior of transgenic tomato lines with altered expression 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.

Polyamines: double agents in disease and plant immunity.

Polyamines (PAs) are ubiquitous amine molecules found in all living organisms. In plants, beside their role in signaling and protection against abiotic stresses, there is increasing evidence that PAs have a major role in the interaction between plants and pathogens. Plant PAs are involved in immunity against pathogens, notably by amplifying pattern-triggered immunity (PTI) responses through the production of reactive oxygen species (ROS). In response, pathogens use phytotoxins and effectors to manipulate the levels of PAs in the plant, most likely to their own benefit. It also appears that pathogenic microorganisms produce PAs during infection, sometimes in large quantities. This may reflect different infectious strategies based on the selective exploitation of these molecules and the functions they perform in the cell.

Unravelling physiological signatures of tomato bacterial wilt and xylem metabolites exploited by Ralstonia solanacearum.

The plant pathogen Ralstonia solanacearum uses plant resources to intensely proliferate in xylem vessels and provoke plant wilting. We combined automatic phenotyping and tissue/xylem quantitative metabolomics of infected tomato plants to decipher the dynamics of bacterial wilt. Daily acquisition of physiological parameters such as transpiration and growth were performed. Measurements allowed us to identify a tipping point in bacterial wilt dynamics. At this tipping point, the reached bacterial density brutally disrupts plant physiology and rapidly induces its death. We compared the metabolic and physiological signatures of the infection with drought stress, and found that similar changes occur. Quantitative dynamics of xylem content enabled us to identify glutamine (and asparagine) as primary resources R. solanacearum consumed during its colonization phase. An abundant production of putrescine was also observed during the infection process and was strongly correlated with in planta bacterial growth. Dynamic profiling of xylem metabolites confirmed that glutamine is the favoured substrate of R. solanacearum. On the other hand, a triple mutant strain unable to metabolize glucose, sucrose and fructose appears to be only weakly reduced for in planta growth and pathogenicity.

Genome-Scale Investigation of the Metabolic Determinants Generating Bacterial Fastidious Growth.

High proliferation rate and robustness are vital characteristics of bacterial pathogens that successfully colonize their hosts. The observation of drastically slow growth in some pathogens is thus paradoxical and remains unexplained. In this study, we sought to understand the slow (fastidious) growth of the plant pathogen Xylella fastidiosa Using genome-scale metabolic network reconstruction, modeling, and experimental validation, we explored its metabolic capabilities. Despite genome reduction and slow growth, the pathogen's metabolic network is complete but strikingly minimalist and lacking in robustness. Most alternative reactions were missing, especially those favoring fast growth, and were replaced by less efficient paths. We also found that the production of some virulence factors imposes a heavy burden on growth. Interestingly, some specific determinants of fastidious growth were also found in other slow-growing pathogens, enriching the view that these metabolic peculiarities are a pathogenicity strategy to remain at a low population level.IMPORTANCE Xylella fastidiosa is one of the most important threats to plant health worldwide, causing disease in the Americas on a range of agricultural crops and trees, and recently associated with a critical epidemic affecting olive trees in Europe. A main challenge for the detection of the pathogen and the development of physiological studies is its fastidious growth, as the generation time can vary from 10 to 100 h for some strains. This physiological peculiarity is shared with several human pathogens and is poorly understood. We performed an analysis of the metabolic capabilities of X. fastidiosa through a genome-scale metabolic model of the bacterium. This model was reconstructed and manually curated using experiments and bibliographical evidence. Our study revealed that fastidious growth most probably results from different metabolic specificities such as the absence of highly efficient enzymes or a global inefficiency in virulence factor production. These results support the idea that the fragility of the metabolic network may have been shaped during evolution to lead to the self-limiting behavior of X. fastidiosa.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

BACKGROUND: The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. RESULTS: We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. CONCLUSION: The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Dynamic metabolic modeling of heterotrophic and mixotrophic microalgal growth on fermentative wastes.

Microalgae are promising microorganisms for the production of numerous molecules of interest, such as pigments, proteins or triglycerides that can be turned into biofuels. Heterotrophic or mixotrophic growth on fermentative wastes represents an interesting approach to achieving higher biomass concentrations, while reducing cost and improving the environmental footprint. Fermentative wastes generally consist of a blend of diverse molecules and it is thus crucial to understand microalgal metabolism in such conditions, where switching between substrates might occur. Metabolic modeling has proven to be an efficient tool for understanding metabolism and guiding the optimization of biomass or target molecule production. Here, we focused on the metabolism of Chlorella sorokiniana growing heterotrophically and mixotrophically on acetate and butyrate. The metabolism was represented by 172 metabolic reactions. The DRUM modeling framework with a mildly relaxed quasi-steady-state assumption was used to account for the switching between substrates and the presence of light. Nine experiments were used to calibrate the model and nine experiments for the validation. The model efficiently predicted the experimental data, including the transient behavior during heterotrophic, autotrophic, mixotrophic and diauxic growth. It shows that an accurate model of metabolism can now be constructed, even in dynamic conditions, with the presence of several carbon substrates. It also opens new perspectives for the heterotrophic and mixotrophic use of microalgae, especially for biofuel production from wastes.

A state of the art of metabolic networks of unicellular microalgae and cyanobacteria for biofuel production.

The most promising and yet challenging application of microalgae and cyanobacteria is the production of renewable energy: biodiesel from microalgae triacylglycerols and bioethanol from cyanobacteria carbohydrates. A thorough understanding of microalgal and cyanobacterial metabolism is necessary to master and optimize biofuel production yields. To this end, systems biology and metabolic modeling have proven to be very efficient tools if supported by an accurate knowledge of the metabolic network. However, unlike heterotrophic microorganisms that utilize the same substrate for energy and as carbon source, microalgae and cyanobacteria require light for energy and inorganic carbon (CO2 or bicarbonate) as carbon source. This double specificity, together with the complex mechanisms of light capture, makes the representation of metabolic network nonstandard. Here, we review the existing metabolic networks of photoautotrophic microalgae and cyanobacteria. We highlight how these networks have been useful for gaining insight on photoautotrophic metabolism.

Mathematical modeling of unicellular microalgae and cyanobacteria metabolism for biofuel production.

The conversion of microalgae lipids and cyanobacteria carbohydrates into biofuels appears to be a promising source of renewable energy. This requires a thorough understanding of their carbon metabolism, supported by mathematical models, in order to optimize biofuel production. However, unlike heterotrophic microorganisms that utilize the same substrate as sources of energy and carbon, photoautotrophic microorganisms require light for energy and CO2 as carbon source. Furthermore, they are submitted to permanent fluctuating light environments due to outdoor cultivation or mixing inducing a flashing effect. Although, modeling these nonstandard organisms is a major challenge for which classical tools are often inadequate, this step remains a prerequisite towards efficient optimization of outdoor biofuel production at an industrial scale.

DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae.

Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells.

High content studies that profile mouse and human embryonic stem cells (m/hESCs) using various genome-wide technologies such as transcriptomics and proteomics are constantly being published. However, efforts to integrate such data to obtain a global view of the molecular circuitry in m/hESCs are lagging behind. Here, we present an m/hESC-centered database called Embryonic Stem Cell Atlas from Pluripotency Evidence integrating data from many recent diverse high-throughput studies including chromatin immunoprecipitation followed by deep sequencing, genome-wide inhibitory RNA screens, gene expression microarrays or RNA-seq after knockdown (KD) or overexpression of critical factors, immunoprecipitation followed by mass spectrometry proteomics and phosphoproteomics. The database provides web-based interactive search and visualization tools that can be used to build subnetworks and to identify known and novel regulatory interactions across various regulatory layers. The web-interface also includes tools to predict the effects of combinatorial KDs by additive effects controlled by sliders, or through simulation software implemented in MATLAB. Overall, the Embryonic Stem Cell Atlas from Pluripotency Evidence database is a comprehensive resource for the stem cell systems biology community. Database URL: http://www.maayanlab.net/ESCAPE

Genes2WordCloud: a quick way to identify biological themes from gene lists and free text.

BACKGROUND: Word-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications. RESULTS: Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice. METHODS: Genes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at http://www.maayanlab.net/G2W. CONCLUSIONS: Genes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications.