Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S.coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-todate databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community. Author summaryCellular metabolism is a large and complex network. Hence, investigations of metabolic networks are aided by in silico modelling and simulations. Metabolic networks can be derived from whole-genome sequences, through identifying what enzymes are present and connecting these to formalized chemical reactions. To facilitate the reconstruction of genome-scale models of metabolism (GEMs), we have developed RAVEN 2.0. This versatile toolbox can reconstruct GEMs fast, through either metabolic pathway databases KEGG and MetaCyc, or from homology with an existing GEM. We demonstrate RAVEN's functionality through generation of a metabolic model of Streptomyces coelicolor, an antibiotic-producing bacterium. Comparison of this de novo generated GEM with a previously manually curated model demonstrates that RAVEN captures most of the previous model, and we subsequently reconstructed an updated model of S. coelicolor: Sco4. Following, we used Sco4 to predict promising targets for genetic engineering, which can be used to increase antibiotic production.

Background In the biochemical milieu of human colon, bile acids act as signaling mediators between the host and its gut microbiota. Biotransformation of primary to secondary bile acids have been known to be involved in the immune regulation of human physiology. Several 16S amplicon-based studies with inflammatory bowel disease (IBD) subjects were found to have an association with the level of fecal bile acids. However, a detailed investigation of all the bile salt biotransformation genes in the gut microbiome of healthy and IBD subjects has not been performed. Results Here, we report a comprehensive analysis of the bile salt biotransformation genes and their distribution at the phyla level. Based on the analysis of shotgun metagenomes, we found that the IBD subjects harbored a significantly lower abundance of these genes compared to the healthy controls. Majority of these genes originated from Firmicutes in comparison to other phyla. From metabolomics data, we found that the IBD subjects were measured with a significantly low level of secondary bile acids and high levels of primary bile acids compared to that of the healthy controls. Conclusions Our bioinformatics-driven approach of identifying bile salt biotransformation genes predicts the bile salt biotransformation potential in the gut microbiota of IBD subjects. The functional level of dysbiosis likely contributes to the variation in the bile acid pool. This study sets the stage to envisage potential solutions to modulate the gut microbiome with the objective to restore the bile acid pool in the gut. Electronic supplementary material The online version of this article (10.1186/s12864-019-5899-3) contains supplementary material, which is available to authorized users.

BackgroundKluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45 °C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus.ResultsHere, we reported the first genome-scale metabolic model of Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include the missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures.ConclusionsK. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provides a new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement.

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Background: In the biochemical milieu of human colon, bile acids act as signaling mediators between the host and its gut microbiota. Biotransformation of primary to secondary bile acids have been known to be involved in the immune regulation of human physiology. Several 16S amplicon-based studies with inflammatory bowel disease (IBD) subjects were found to have an association with the level of fecal bile acids. However, a detailed investigation of all the bile salt biotransformation genes in the gut microbiome of healthy and IBD subjects has not been performed. Results: Here, we report a comprehensive analysis of the bile salt biotransformation genes and their distribution at the phyla level. Based on the analysis of shotgun metagenomes, we found that the IBD subjects harbored a significantly lower abundance of these genes compared to the healthy controls. Majority of these genes originated from Firmicutes in comparison to other phyla. From metabolomics data, we found that the IBD subjects were measured with a significantly low level of secondary bile acids and high levels of primary bile acids compared to that of the healthy controls. Conclusions: Our bioinformatics-driven approach of identifying bile salt biotransformation genes predicts the bile salt biotransformation potential in the gut microbiota of IBD subjects. The functional level of dysbiosis likely contributes to the variation in the bile acid pool. This study sets the stage to envisage potential solutions to modulate the gut microbiome with the objective to restore the bile acid pool in the gut.

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