Handling the heterogeneity of genomic and metabolic networks data within flexible workflows with the PADMet toolbox

National audienceA main challenge of the era of fast and massive genome sequencing is to transform sequences into biological knowledge. The reconstruction of metabolic networks that include all biochemical reactions of a cell is a way to understand physiology interactions from genomic data. In 2010, Thiele and Palsson described a general protocol enabling the reconstruction of high-quality metabolic networks. Since then several approaches have been implemented for this purpose. They all rely mainly on drafting a first metabolic network from genome annotations and orthology information followed by a gap-filling step. More precisely, in the case of exotic species the lack of good annotations and poor biological information result in incomplete networks. Reference databases of metabolic reactions guide the filling process in order to check whether adding reactions to a network allows compounds of interest to be produced from a given growth media. As a final objective, as soon as the network is considered to be complete enough, functional studies are undergone, often relying on the constraint-based paradigm derived from the Flux Balance Analysis (FBA) framework (Orth et al., 2010). The high diversity of input files and tools required to run any metabolic networks reconstruction protocol represents an important drawback. In addition, most approaches require reference metabolic networks of a template organism. Dictionaries mapping the reference metabolic databases to the gene identifiers corresponding to the studied organism may be required. As a main issue, it appears very difficult to ensure that input files agree among them. Such a heterogeneity produces loss of information during the use of the protocols and generates uncertainty in the final metabolic model. Here we introduce the PADMet-toolbox which allows conciliating genomic and metabolic network information. The toolbox centralizes all this information in a new graph-based format: PADMet (PortAble Database for Metabolism) and provides methods to import, update and export information. For the sake of illustration, the toolbox was used to create a workflow, named AuReMe, aiming to produce high-quality genome-scale metabolic networks and eventually input files to feed most platforms involved in metabolic network analyses. We applied this approach to two exotic organisms and our results evidenced the need of combining approaches and reconciling information to obtain a functional metabolic network to produce biomass

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Autoantibodies against type I IFNs in patients with critical influenza pneumonia

In an international cohort of 279 patients with hypoxemic influenza pneumonia, we identified 13 patients (4.6%) with autoantibodies neutralizing IFN-alpha and/or -omega, which were previously reported to underlie 15% cases of life-threatening COVID-19 pneumonia and one third of severe adverse reactions to live-attenuated yellow fever vaccine. Autoantibodies neutralizing type I interferons (IFNs) can underlie critical COVID-19 pneumonia and yellow fever vaccine disease. We report here on 13 patients harboring autoantibodies neutralizing IFN-alpha 2 alone (five patients) or with IFN-omega (eight patients) from a cohort of 279 patients (4.7%) aged 6-73 yr with critical influenza pneumonia. Nine and four patients had antibodies neutralizing high and low concentrations, respectively, of IFN-alpha 2, and six and two patients had antibodies neutralizing high and low concentrations, respectively, of IFN-omega. The patients' autoantibodies increased influenza A virus replication in both A549 cells and reconstituted human airway epithelia. The prevalence of these antibodies was significantly higher than that in the general population for patients 70 yr of age (3.1 vs. 4.4%, P = 0.68). The risk of critical influenza was highest in patients with antibodies neutralizing high concentrations of both IFN-alpha 2 and IFN-omega (OR = 11.7, P = 1.3 x 10(-5)), especially those <70 yr old (OR = 139.9, P = 3.1 x 10(-10)). We also identified 10 patients in additional influenza patient cohorts. Autoantibodies neutralizing type I IFNs account for similar to 5% of cases of life-threatening influenza pneumonia in patients <70 yr old

Genome-scale network reconstruction of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85

Session 3: New approaches to study the gut microbiome :Communications P 105International audienceFibrobacter succinogenes is a cellulolytic predominant rumen bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. This bacterium converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate and formate. We reconstructed the genome scale metabolic network of the strain S85 of F. succinogenes using the Automatic Reconstruction of metabolic models (AuReMe) workspace. A first draft was reconstructed by annotation using Pathwaytools. The obtained draft model was enhanced using 5 external bacterial metabolic models by orthology-based reconstruction then gap-filled and curated manually for maximizing the biomass reaction flow. Adenosylcobalamin synthesis, glycogen metabolism, ammonium assimilation and the catabolism of glucose pathways were particularly investigated and completed with high-quality manual curation. The final network was achieved with a gene-reaction association for added reactions with high gene similarity based on BLASTP results.The F. succinogenes S85 metabolic network comprises 1314 genes, 1567 metabolic reactions, 1588 unique metabolites and 931 pathways. Genome annotation identified 623 reactions and manual curation, gap filling and orthology, respectively added 462, 105 and 174 reactions. The biomass reaction flow consisting of essential seeds and targets needed for F. succinogenes growth included 136 reactions.The resulting network is a useful resource for investigating the metabolic capabilities of F. succinogenes S85. We will further exploit the metabolic network to construct a dynamic model of microbial metabolism. Such an approach is a key step towards the integration of omic microbial information into predictive models of rumen metabolism

Genome-scale network reconstruction of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85

International audienceFibrobacter succinogenes is a cellulolytic predominant rumen bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. This bacterium converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate and formate. We reconstructed the genome scale metabolic network of the strain S85 of F. succinogenes using the Automatic Reconstruction of metabolic models (AuReMe) workspace. A first draft was reconstructed by annotation using Pathwaytools. The obtained draft model was enhanced using 5 external bacterial metabolic models by orthology-based reconstruction then gap-filled and curated manually for maximizing the biomass reaction flow. Adenosylcobalamin synthesis, glycogen metabolism, ammonium assimilation and the catabolism of glucose pathways were particularly investigated and completed with high-quality manual curation. The final network was achieved with a gene-reaction association for added reactions with high gene similarity based on BLASTP results.The F. succinogenes S85 metabolic network comprises 1314 genes, 1567 metabolic reactions, 1588 unique metabolites and 931 pathways. Genome annotation identified 623 reactions and manual curation, gap filling and orthology, respectively added 462, 105 and 174 reactions. The biomass reaction flow consisting of essential seeds and targets needed for F. succinogenes growth included 136 reactions.The resulting network is a useful resource for investigating the metabolic capabilities of F. succinogenes S85. We will further exploit the metabolic network to construct a dynamic model of microbial metabolism. Such an approach is a key step towards the integration of omic microbial information into predictive models of rumen metabolism

Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium<i>Fibrobacter succinogenes</i>S85

Fibrobacter succinogenes is a cellulolytic predominant bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the Automatic Reconstruction of metabolic models (AuReMe) workspace. The reconstruction was based on genome annotation, 5 templates-based orthology methods, gap-filling and manual curation. The metabolic network of F. succinogenes S85 comprises 1565 reactions with 77% linked to 1317 genes, 1586 unique metabolites and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for computation of Elementary Flux Modes (EFMs). A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the Root mean squared error of 19%. Resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step towards the integration of omics microbial information into predictive models of the rumen metabolism

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

Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe

Comparative analysis of Genome-Scale Metabolic Networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe-a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three datasets, one bacterial, one fungal, and one algal, and demonstrated that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared metabolic traits and divergence points among evolutionarily distant species, such as algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life

Handling the heterogeneity of genomic and metabolic networks data within flexible workflows with the PADMet toolbox

National audienceA main challenge of the era of fast and massive genome sequencing is to transform sequences into biological knowledge. The reconstruction of metabolic networks that include all biochemical reactions of a cell is a way to understand physiology interactions from genomic data. In 2010, Thiele and Palsson described a general protocol enabling the reconstruction of high-quality metabolic networks. Since then several approaches have been implemented for this purpose. They all rely mainly on drafting a first metabolic network from genome annotations and orthology information followed by a gap-filling step. More precisely, in the case of exotic species the lack of good annotations and poor biological information result in incomplete networks. Reference databases of metabolic reactions guide the filling process in order to check whether adding reactions to a network allows compounds of interest to be produced from a given growth media. As a final objective, as soon as the network is considered to be complete enough, functional studies are undergone, often relying on the constraint-based paradigm derived from the Flux Balance Analysis (FBA) framework (Orth et al., 2010). The high diversity of input files and tools required to run any metabolic networks reconstruction protocol represents an important drawback. In addition, most approaches require reference metabolic networks of a template organism. Dictionaries mapping the reference metabolic databases to the gene identifiers corresponding to the studied organism may be required. As a main issue, it appears very difficult to ensure that input files agree among them. Such a heterogeneity produces loss of information during the use of the protocols and generates uncertainty in the final metabolic model. Here we introduce the PADMet-toolbox which allows conciliating genomic and metabolic network information. The toolbox centralizes all this information in a new graph-based format: PADMet (PortAble Database for Metabolism) and provides methods to import, update and export information. For the sake of illustration, the toolbox was used to create a workflow, named AuReMe, aiming to produce high-quality genome-scale metabolic networks and eventually input files to feed most platforms involved in metabolic network analyses. We applied this approach to two exotic organisms and our results evidenced the need of combining approaches and reconciling information to obtain a functional metabolic network to produce biomass

Semi-Quantitative Targeted Gas Chromatography-Mass Spectrometry Profiling Supports a Late Side-Chain Reductase Cycloartenol-to-Cholesterol Biosynthesis Pathway in Brown Algae

International audienceSterols are biologically important molecules that serve as membrane fluidity regulators and precursors of signaling molecules, either endogenous or involved in biotic interactions. There is currently no model of their biosynthesis pathways in brown algae. Here, we benefit from the availability of genome data and gas chromatography-mass spectrometry (GC-MS) sterol profiling using a database of internal standards to build such a model. We expand the set of identified sterols in 11 species of red, brown, and green macroalgae and integrate these new data with genomic data. Our analyses suggest that some metabolic reactions may be conserved despite the loss of canonical eukaryotic enzymes, like the sterol side-chain reductase (SSR). Our findings are consistent with the principle of metabolic pathway drift through enzymatic replacement and show that cholesterol synthesis from cycloartenol may be a widespread but variable pathway among chlorophyllian eukaryotes. Among the factors contributing to this variability, one could be the recruitment of cholesterol biosynthetic intermediates to make signaling molecules, such as the mozukulins. These compounds were found in some brown algae belonging to Ectocarpales, and we here provide a first mozukulin biosynthetic model. Our results demonstrate that integrative approaches can already be used to infer experimentally testable models, which will be useful to further investigate the biological roles of those newly identified algal pathways