redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast

ABSTRACT Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

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RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

10.1101/321067 ◽

2018 ◽

Cited By ~ 2

Author(s):

Hao Wang ◽

Simonas Marcišauskas ◽

Benjamín J Sánchez ◽

Iván Domenzain ◽

Daniel Hermansson ◽

...

Keyword(s):

Metabolic Networks ◽

De Novo ◽

Antibiotic Production ◽

Streptomyces Coelicolor ◽

Metabolic Model ◽

Primary And Secondary Metabolism ◽

Scale Models ◽

Improved Performance ◽

Metabolites Production ◽

Genome Scale

AbstractRAVEN 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.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.

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The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism

BMC Systems Biology ◽

10.1186/1752-0509-2-71 ◽

2008 ◽

Vol 2 (1) ◽

pp. 71 ◽

Cited By ~ 111

Author(s):

Intawat Nookaew ◽

Michael C Jewett ◽

Asawin Meechai ◽

Chinae Thammarongtham ◽

Kobkul Laoteng ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Lipid Metabolism ◽

Metabolic Model ◽

Genome Scale

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Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽

10.1099/mic.0.076091-0 ◽

2014 ◽

Vol 160 (6) ◽

pp. 1252-1266 ◽

Cited By ~ 28

Author(s):

Hassan B. Hartman ◽

David A. Fell ◽

Sergio Rossell ◽

Peter Ruhdal Jensen ◽

Martin J. Woodward ◽

...

Keyword(s):

Salmonella Enterica ◽

Energy Demand ◽

Drug Targets ◽

Minimal Medium ◽

Model Organism ◽

Metabolic Model ◽

Scale Model ◽

Glucose Minimal Medium ◽

A Genome ◽

Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

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Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

10.1101/2021.02.18.431926 ◽

2021 ◽

Author(s):

Christopher E. Lawson ◽

Aniela B. Mundinger ◽

Hanna Koch ◽

Tyler B. Jacobson ◽

Coty A. Weathersby ◽

...

Keyword(s):

Wastewater Treatment ◽

Metabolic Networks ◽

Metabolic Model ◽

Scale Model ◽

Nitrifying Bacteria ◽

Metabolomic Analysis ◽

A Genome ◽

Nitrite Oxidizing Bacteria ◽

Genome Scale ◽

Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

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A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Nature Communications ◽

10.1038/s41467-019-11581-3 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 24

Author(s):

Hongzhong Lu ◽

Feiran Li ◽

Benjamín J. Sánchez ◽

Zhengming Zhu ◽

Gang Li ◽

...

Keyword(s):

Model Organism ◽

Metabolic Model ◽

Phenotypic Traits ◽

Cell Factory ◽

Scale Level ◽

Single Nucleotide ◽

Multi Scale ◽

Yeast Strains ◽

Single Nucleotide Variations ◽

Genome Scale

Abstract 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.

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Genome Scale Constraint-Based Metabolic Reconstruction of Propionibacterium Freudenreichii DSM 20271

10.21203/rs.3.rs-847511/v1 ◽

2021 ◽

Author(s):

Mahsa Sadat Razavi Borghei ◽

Meysam Mobasheri ◽

Tabassom Sobati

Keyword(s):

Metabolic Networks ◽

Metabolic Model ◽

Physiological Data ◽

Metabolic Reconstruction ◽

Modeling And Analysis ◽

Microbial Cell Factories ◽

Culture Optimization ◽

Cell Factories ◽

Functional Features ◽

Genome Scale

Abstract Propionibacterium is an anaerobic bacterium with a history of use in the production of Swiss cheese and, more recently, several industrial bioproducts. While the use of this strain for the production of organic acids and secondary metabolites has gained growing interest, the industrial application of the strain requires further improvement in the yield and productivity of the target products. Systems modeling and analysis of metabolic networks are widely leveraged to gain holistic insights into the metabolic features of biotechnologically important strains and to devise metabolic engineering and culture optimization strategies for economically viable bioprocess development. In the present study, a high-quality genome-scale metabolic model of P. freudenreichii ssp. freudenreichii strain DSM 20271 was developed based on the strain’s genome annotation and biochemical and physiological data. The model covers the functions of 23% of the strain’s ORFs and accounts for 711 metabolic reactions and 647 unique metabolites. Literature-based reconstruction of the central metabolism and rigorous refinement of annotation data for establishing gene-protein-reaction associations renders the model a curated omic-scale knowledge base of the organism. Validation of the model against experimental data indicates that the reconstruction can capture the key structural and functional features of P. freudenreichii metabolism, including the growth rate, the pattern of flux distribution, the strain’s aerotolerance behavior, and the change in the mode of metabolic activity during the transition from an anaerobic to an aerobic growth regime. The model also includes an accurately curated pathway of cobalamin biosynthesis, which was used to examine the capacity of the strain to produce vitamin B12 precursors. Constraint-based reconstruction and analysis of the P. freudenreichii metabolic network also provided novel insights into the complexity and robustness of P. freudenreichii energy metabolism. The developed reconstruction, hence, may be used as a platform for the development of P. freudenreichii-based microbial cell factories and bioprocesses.

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FALCONET: an R package to accelerate automatic visualisation of genome scale metabolic models

10.1101/662056 ◽

2019 ◽

Author(s):

Hongzhong Lu ◽

Zhengming Zhu ◽

Eduard J Kerkhoven ◽

Jens Nielsen

Keyword(s):

Metabolic Networks ◽

R Package ◽

Network Size ◽

Research Community ◽

Supplementary Information ◽

Large Network ◽

Strain Design ◽

Scale Models ◽

Genome Scale ◽

Integrative Omics

AbstractSummaryFALCONET (FAst visuaLisation of COmputational NETworks) enables the automatic for-mation and visualisation of metabolic maps from genome-scale models with R and CellDesigner, readily facilitating the visualisation of multi-layers omics datasets in the context of metabolic networks.MotivationUntil now, numerous GEMs have been reconstructed and used as scaffolds to conduct integrative omics analysis and in silico strain design. Due to the large network size of GEMs, it is challenging to produce and visualize these networks as metabolic maps for further in-depth analyses.ResultsHere, we presented the R package - FALCONET, which facilitates drawing and visualizing metabolic maps in an automatic manner. This package will benefit the research community by allowing a wider use of GEMs in systems biology.Availability and implementationFALCONET is available on https://github.com/SysBioChalmers/FALCONET and released under the MIT [email protected] informationSupplementary data are available online.

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Nature lessons: the whitefly bacterial endosymbiont is a minimal amino acid factory with unusual energetics

10.1101/043349 ◽

2016 ◽

Author(s):

Jorge Calle-Espinosa ◽

Miguel Ponce-de-Leon ◽

Diego Santos-Garcia ◽

Francisco J. Silva ◽

Francisco Montero ◽

...

Keyword(s):

Amino Acid ◽

Energy Production ◽

Metabolic Networks ◽

Metabolic Model ◽

Atp Production ◽

Symbiotic Associations ◽

Bacterial Endosymbiont ◽

Metabolic Functions ◽

A Genome ◽

Genome Scale

Bacterial lineages that establish obligate symbiotic associations with insect hosts are known to possess highly reduced genomes with streamlined metabolic functions that are commonly focused on amino acid and vitamin synthesis. We constructed a genome-scale metabolic model of the whitefly bacterial endosymbiont Candidatus Portiera aleyrodidarum to study the energy production capabilities using stoichiometric analysis. Strikingly, the results suggest that the energetic metabolism of the bacterial endosymbiont relies on the use of pathways related to the synthesis of amino acids and carotenoids. A deeper insight showed that the ATP production via carotenoid synthesis may also have a potential role in the regulation of amino acid production. The coupling of energy production to anabolism suggest that minimization of metabolic networks as a consequence of genome size reduction does not necessarily limit the biosynthetic potential of obligate endosymbionts.

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Integrating Genome-Scale and Superstructure Optimization Models in Techno-Economic Studies of Biorefineries

Processes ◽

10.3390/pr7050286 ◽

2019 ◽

Vol 7 (5) ◽

pp. 286

Author(s):

Amir Akbari ◽

Paul I. Barton

Keyword(s):

Net Present Value ◽

Metabolic Model ◽

Computational Method ◽

Cellular Growth ◽

Mixed Integer ◽

Design Parameters ◽

Nonlinear Program ◽

A Genome ◽

Scale Models ◽

Genome Scale

Genome-scale models have become indispensable tools for the study of cellular growth. These models have been progressively improving over the past two decades, enabling accurate predictions of metabolic fluxes and key phenotypes under a variety of growth conditions. In this work, an efficient computational method is proposed to incorporate genome-scale models into superstructure optimization settings, introducing them as viable growth models to simulate the cultivation section of biorefinaries. We perform techno-economic and life-cycle analyses of an algal biorefinery with five processing sections to determine optimal processing pathways and technologies. Formulation of this problem results in a mixed-integer nonlinear program, in which the net present value is maximized with respect to mass flowrates and design parameters. We use a genome-scale metabolic model of Chlamydomonas reinhardtii to predict growth rates in the cultivation section. We study algae cultivation in open ponds, in which exchange fluxes of biomass and carbon dioxide are directly determined by the metabolic model. This formulation enables the coupling of flowrates and design parameters, leading to more accurate cultivation productivity estimates with respect to substrate concentration and light intensity.

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Gsmodutils: A python based framework for test-driven genome scale metabolic model development

10.1101/430116 ◽

2018 ◽

Author(s):

James P Gilbert ◽

Nicole Pearcy ◽

Rupert Norman ◽

Thomas Millat ◽

Klaus Winzer ◽

...

Keyword(s):

Open Source ◽

Large Scale ◽

Model Development ◽

Metabolic Model ◽

Validation Data ◽

Engineering Research ◽

Modelling Framework ◽

Wide Range ◽

Scale Models ◽

Genome Scale

AbstractMotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for the continued management of curated large scale models. For example, when genome annotations are updated or new understanding regarding behaviour of is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build and test cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed thegsmodutilsmodelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.AvailabilityThe software framework described within this paper is open source and freely available fromhttp://github.com/SBRCNottingham/gsmodutils

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