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

One approach for elucidating strain-to-strain metabolic differences is the use of genome-scale metabolic models (GSMMs). To date GSMMs have not focused on the industrially important area of flavor production and, as such; do not cover all the pathways relevant to flavor formation in yeast. Moreover, current models for Saccharomyces cerevisiae generally focus on carbon-limited and/or aerobic systems, which is not pertinent to enological conditions. Here, we curate a GSMM (iWS902) to expand on the existing Ehrlich pathway and ester formation pathways central to aroma formation in industrial winemaking, in addition to the existing sulfur metabolism and medium-chain fatty acid (MCFA) pathways that also contribute to production of sensory impact molecules. After validating the model using experimental data, we predict key differences in metabolism for a strain (EC 1118) in two distinct growth conditions, including differences for aroma impact molecules such as acetic acid, tryptophol, and hydrogen sulfide. Additionally, we propose novel targets for metabolic engineering for aroma profile modifications employing flux variability analysis with the expanded GSMM. The model provides mechanistic insights into the key metabolic pathways underlying aroma formation during alcoholic fermentation and provides a potential framework to contribute to new strategies to optimize the aroma of wines.

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

FEMS Yeast Research ◽

10.1093/femsyr/foaa006 ◽

2020 ◽

Vol 20 (2) ◽

Author(s):

S Tsouka ◽

V Hatzimanikatis

Keyword(s):

Lipid Metabolism ◽

Metabolic Networks ◽

Ad Hoc ◽

Mechanistic Model ◽

Model Organism ◽

Metabolic Model ◽

Lipid Biochemistry ◽

Large Size ◽

Scale Models ◽

Genome Scale

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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Reconstruction and Validation of Saccharomyces cerevisiae iND750, a Fully Compartmentalized Genome-Scale Metabolic Model

Genome Research ◽

10.1101/gr.2250904 ◽

2004 ◽

Vol 14 (7) ◽

pp. 1298-1309 ◽

Cited By ~ 417

Author(s):

N. C. Duarte

Keyword(s):

Saccharomyces Cerevisiae ◽

Metabolic Model ◽

Genome Scale

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Biotransformation Mechanism of Inorganic Selenium into Selenomethionine and Selenocysteine by Saccharomyces Boulardii: In Silico Study

10.21203/rs.3.rs-359033/v1 ◽

2021 ◽

Author(s):

Lourdes González-Salitre ◽

Alma Delia Román-Gutiérrez ◽

Gabriela Mariana Rodríguez-Serrano ◽

Judith Jaimez-Ordaz ◽

Mirandeli Bautista-Ávila ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

In Silico ◽

Metabolic Model ◽

Saccharomyces Boulardii ◽

Orthologous Genes ◽

Organic Species ◽

Inorganic Selenium ◽

In Silico Study ◽

Probiotic Yeast ◽

Genome Scale

Abstract The biosynthesis of inorganic selenium into seleno amino acids has been studied in recent years. Thus, it has been reported that Saccharomyces cerevisiae bioaccumulates selenium from the metabolism of inorganic selenium. Based on the studies conducted, several authors have proposed a biotransformation metabolism of selenate into selenomethionine or selenocysteine. However, the pathway in different yeast is unknown. Therefore, and given the relevance of Saccharomyces boulardii as probiotic yeast, this study aims to propose the pathway used by S. boulardii to biosynthesize inorganic selenium into organic species. A comparative in silico study was performed for Saccharomyces boulardii ASM141397V1 with the genome-scale metabolic model of Saccharomyces cerevisiae S288C. Orthologous genes were identified using BLASTp of NCBI. In addition, a circular representation was done using CIRCOS software. The metabolic pathway for the assimilation of selenium was proposed based on the results obtained

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SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework

10.1101/324863 ◽

2018 ◽

Cited By ~ 3

Author(s):

Benjamín J. Sánchez ◽

Feiran Li ◽

Eduard J. Kerkhoven ◽

Jens Nielsen

Keyword(s):

Experimental Data ◽

Saccharomyces Cerevisiae ◽

Lipid Metabolism ◽

Acyl Chain ◽

Energy Costs ◽

Detailed Data ◽

Modeling Framework ◽

Lipid Species ◽

Constraint Based Modeling ◽

Genome Scale

SummaryA recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can predict accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr.

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Genome-Scale Metabolic Model as a Virtual Platform to Reveal the Environmental Contribution of Methanogens

Current Biotechnology ◽

10.2174/2211550105666160901125353 ◽

2017 ◽

Vol 6 (2) ◽

pp. 149-160 ◽

Cited By ~ 5

Author(s):

P. Chellapandi ◽

M. Bharathi ◽

R. Prathiviraj ◽

R. Sasikala ◽

M. Vikraman

Keyword(s):

Metabolic Model ◽

Environmental Contribution ◽

Virtual Platform ◽

Genome Scale

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Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

Toxicology and Applied Pharmacology ◽

10.1016/j.taap.2020.115390 ◽

2021 ◽

Vol 412 ◽

pp. 115390

Author(s):

Kristopher D. Rawls ◽

Bonnie V. Dougherty ◽

Kalyan C. Vinnakota ◽

Venkat R. Pannala ◽

Anders Wallqvist ◽

...

Keyword(s):

Metabolic Model ◽

Renal Metabolism ◽

A Genome ◽

Compound Exposure ◽

Genome Scale

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Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Microbial Cell Factories ◽

10.1186/s12934-021-01614-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jingru Zhou ◽

Yingping Zhuang ◽

Jianye Xia

Keyword(s):

Aspergillus Niger ◽

Large Scale ◽

Measurement Techniques ◽

Metabolic Model ◽

System Level ◽

Metabolic Phenotype ◽

Omics Data ◽

Prediction Ability ◽

Phenotype Prediction ◽

Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

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Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Applied and Environmental Microbiology ◽

10.1128/aem.01795-12 ◽

2012 ◽

Vol 78 (24) ◽

pp. 8735-8742 ◽

Cited By ~ 12

Author(s):

Yilin Fang ◽

Michael J. Wilkins ◽

Steven B. Yabusaki ◽

Mary S. Lipton ◽

Philip E. Long

Keyword(s):

Proteomic Analysis ◽

In Silico ◽

Field Experiments ◽

Metabolic Model ◽

Geobacter Metallireducens ◽

Content Type ◽

Proteomic Data ◽

Subsurface Environment ◽

A Genome ◽

Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

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