Genome-Scale Consequences of Cofactor Balancing in Engineered Pentose Utilization Pathways in Saccharomyces cerevisiae

AbstractEukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

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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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Genome-Scale Screening and Combinatorial Optimization of Gene Overexpression Targets to Improve Cadmium Tolerance in Saccharomyces cerevisiae

Frontiers in Microbiology ◽

10.3389/fmicb.2021.662512 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yongcan Chen ◽

Jun Liang ◽

Zhicong Chen ◽

Bo Wang ◽

Tong Si

Keyword(s):

Saccharomyces Cerevisiae ◽

Genetic Engineering ◽

Heavy Metal Contamination ◽

Cadmium Toxicity ◽

Biomass Accumulation ◽

Model Organisms ◽

Cadmium Tolerance ◽

Wild Type ◽

Cadmium Resistance ◽

Genome Scale

Heavy metal contamination is an environmental issue on a global scale. Particularly, cadmium poses substantial threats to crop and human health. Saccharomyces cerevisiae is one of the model organisms to study cadmium toxicity and was recently engineered as a cadmium hyperaccumulator. Therefore, it is desirable to overcome the cadmium sensitivity of S. cerevisiae via genetic engineering for bioremediation applications. Here we performed genome-scale overexpression screening for gene targets conferring cadmium resistance in CEN.PK2-1c, an industrial S. cerevisiae strain. Seven targets were identified, including CAD1 and CUP1 that are known to improve cadmium tolerance, as well as CRS5, NRG1, PPH21, BMH1, and QCR6 that are less studied. In the wild-type strain, cadmium exposure activated gene transcription of CAD1, CRS5, CUP1, and NRG1 and repressed PPH21, as revealed by real-time quantitative PCR analyses. Furthermore, yeast strains that contained two overexpression mutations out of the seven gene targets were constructed. Synergistic improvement in cadmium tolerance was observed with episomal co-expression of CRS5 and CUP1. In the presence of 200 μM cadmium, the most resistant strain overexpressing both CAD1 and NRG1 exhibited a 3.6-fold improvement in biomass accumulation relative to wild type. This work provided a new approach to discover and optimize genetic engineering targets for increasing cadmium resistance in yeast.

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Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization

Metabolic Engineering ◽

10.1016/j.ymben.2014.07.004 ◽

2014 ◽

Vol 25 ◽

pp. 159-173 ◽

Cited By ~ 18

Author(s):

Benjamín J. Sánchez ◽

José R. Pérez-Correa ◽

Eduardo Agosin

Keyword(s):

Saccharomyces Cerevisiae ◽

Metabolic Model ◽

Dynamic Genome ◽

Genome Scale ◽

Model Structures

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Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors

10.1101/2021.07.22.453334 ◽

2021 ◽

Author(s):

Sara Moreno-Paz ◽

Joep Schmitz ◽

Vitor A.P. Martins dos Santos ◽

Maria Suarez-Diez

Keyword(s):

Saccharomyces Cerevisiae ◽

Model Organism ◽

Growth Dynamics ◽

Operational Conditions ◽

Strain Design ◽

Balance Analysis ◽

Different Strains ◽

Batch Bioreactors ◽

Constrained Models ◽

Genome Scale

Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bio-process design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyze flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models

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Curation and Analysis of a Saccharomyces cerevisiae Genome-Scale Metabolic Model for Predicting Production of Sensory Impact Molecules under Enological Conditions

Processes ◽

10.3390/pr8091195 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1195

Author(s):

William T. Scott ◽

Eddy J. Smid ◽

Richard A. Notebaart ◽

David E. Block

Keyword(s):

Saccharomyces Cerevisiae ◽

Alcoholic Fermentation ◽

Sulfur Metabolism ◽

Metabolic Model ◽

Growth Conditions ◽

Ehrlich Pathway ◽

Flux Variability Analysis ◽

Ester Formation ◽

Genome Scale ◽

New Strategies

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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