Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from “à la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.

We have identified and characterized a spontaneous Brown Norway from Janvier rat strain (BN-J

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The prokaryotic oxidation of reduced inorganic sulfur compounds (RISCs) is a topic of utmost importance from a biogeochemical and industrial perspective. Despite sulfur oxidizing bacterial activity is largely known, no quantitative approaches to biological RISCs oxidation have been made, gathering all the complex abiotic and enzymatic stoichiometry involved. Even though in the case of neutrophilic bacteria such as Paracoccus and Beggiatoa species the RISCs oxidation systems are well described, there is a lack of knowledge for acidophilic microorganisms. Here, we present the first experimentally validated stoichiometric model able to assess RISCs oxidation quantitatively in Acidithiobacillus thiooxidans (strain DSM 17318), the archetype of the sulfur oxidizing acidophilic chemolithoautotrophs. This model was built based on literature and genomic analysis, considering a widespread mix of formerly proposed RISCs oxidation models combined and evaluated experimentally. Thiosulfate partial oxidation by the Sox system (SoxABXYZ) was placed as central step of sulfur oxidation model, along with abiotic reactions. This model was coupled with a detailed stoichiometry of biomass production, providing accurate bacterial growth predictions. In silico deletion/inactivation highlights the role of sulfur dioxygenase as the main catalyzer and a moderate function of tetrathionate hydrolase in elemental sulfur catabolism, demonstrating that this model constitutes an advanced instrument for the optimization of At. thiooxidans biomass production with potential use in biohydrometallurgical and environmental applications.

Background Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities.ResultsWe present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols.ConclusionsiNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0441-1) contains supplementary material, which is available to authorized users.

Nonalcoholic fatty-liver disease (NAFLD) prevalence is increasing worldwide, with the affected US population estimated near 30%. Diet is a recognized risk factor in the NAFLD spectrum, which includes non-alcoholic steatohepatitis (NASH) and fibrosis. Low hepatic copper (Cu) was recently linked to clinical NAFLD/NASH severity. Simple sugar consumption including sucrose and fructose is implicated in NAFLD, while consumption of these macronutrients also decrease liver Cu levels. Though dietary sugar and low Cu are implicated in NAFLD, transcript-level responses that connect diet and pathology are not established. We have developed a mature rat model of NAFLD induced by dietary Cu deficiency, human-relevant high sucrose intake (30% w/w), or both factors in combination. Compared to the control diet with adequate Cu and 10% (w/w) sucrose, rats fed either high sucrose or low Cu diets had increased hepatic expression of genes involved in inflammation and fibrogenesis, including hepatic stellate cell activation, while the combination of diet factors also increased ATP citrate lyase (Acly) and fatty-acid synthase (Fasn) gene transcription (Fold change >2, p <0.02). Low dietary Cu decreased hepatic and serum Cu (p ≤0.05), promoted lipid peroxidation, and induced NAFLD-like histopathology, while the combined factors also induced fasting hepatic insulin resistance and liver damage. Neither low Cu nor 30% sucrose in the diet led to enhanced weight gain. Taken together, transcript profiles, histological and biochemical data indicate that low Cu and high sucrose promote hepatic gene expression and physiological responses associated with NAFLD and NASH, even in the absence of obesity or severe steatosis.

In this study, we present the first metabolic profiles for two bioleaching bacteria using capillary electrophoresis coupled with mass spectrometry. The bacteria, Acidithiobacillus ferrooxidans strain Wenelen (DSM 16786) and Acidithiobacillus thiooxidans strain Licanantay (DSM 17318), were sampled at different growth phases and on different substrates: the former was grown with iron and sulfur, and the latter with sulfur and chalcopyrite. Metabolic profiles were scored from planktonic and sessile states. Spermidine was detected in intra- and extracellular samples for both strains, suggesting it has an important role in biofilm formation in the presence of solid substrate. The canonical pathway for spermidine synthesis seems absent as its upstream precursor, putrescine, was not present in samples. Glutathione, a catalytic activator of elemental sulfur, was identified as one of the most abundant metabolites in the intracellular space in A. thiooxidans strain Licanantay, confirming its participation in the sulfur oxidation pathway. Amino acid profiles varied according to the growth conditions and bioleaching species. Glutamic and aspartic acid were highly abundant in intra- and extracellular extracts. Both are constituents of the extracellular matrix, and have a probable role in cell detoxification. This novel metabolomic information validates previous knowledge from in silico metabolic reconstructions based on genomic sequences, and reveals important biomining functions such as biofilm formation, energy management and stress responses.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-012-0443-3) contains supplementary material, which is available to authorized users.