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

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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 (proYeast8 ^DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

Abstract

Genome-scale metabolic models provide a platform to study metabolism through simulations and analysis of omics data. Here the authors introduce Yeast8 with its model ecosystem, a comprehensive computational resource for simulating the metabolism of Saccharomyces cerevisiae.

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Most cited references 46

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Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

B W Matthews (1975)

Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both beta-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140-142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.

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Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

Jan Schellenberger, Richard Que, Ronan M. T. Fleming … (2011)

Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.

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Genome evolution across 1,011 Saccharomyces cerevisiae isolates

Jackson Peter, Matteo De Chiara, Anne Friedrich … (2018)

Large-scale population genomic surveys are essential to explore the phenotypic diversity of natural populations. Here we report the whole-genome sequencing and phenotyping of 1,011 Saccharomyces cerevisiae isolates, which together provide an accurate evolutionary picture of the genomic variants that shape the species-wide phenotypic landscape of this yeast. Genomic analyses support a single ‘out-of-China’ origin for this species, followed by several independent domestication events. Although domesticated isolates exhibit high variation in ploidy, aneuploidy and genome content, genome evolution in wild isolates is mainly driven by the accumulation of single nucleotide polymorphisms. A common feature is the extensive loss of heterozygosity, which represents an essential source of inter-individual variation in this mainly asexual species. Most of the single nucleotide polymorphisms, including experimentally identified functional polymorphisms, are present at very low frequencies. The largest numbers of variants identified by genome-wide association are copy-number changes, which have a greater phenotypic effect than do single nucleotide polymorphisms. This resource will guide future population genomics and genotype–phenotype studies in this classic model system.

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Author and article information

Contributors

Jens Nielsen:

ORCID: http://orcid.org/0000-0002-9955-6003

nielsenj@chalmers.se

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 8 August 2019

Publication date PMC-release: 8 August 2019

Publication date Collection: 2019

Volume: 10

Electronic Location Identifier: 3586

Affiliations

[1 ]ISNI 0000 0001 0775 6028, GRID grid.5371.0, Department of Biology and Biological Engineering, , Chalmers University of Technology, ; Kemivägen 10, SE412 96 Gothenburg, Sweden

[2 ]ISNI 0000 0001 0708 1323, GRID grid.258151.a, School of Biotechnology, , Jiangnan University, ; 1800 Lihu Road, 214122 Wuxi, Jiangsu China

[3 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, The Novo Nordisk Foundation Center for Biosustainability, , Technical University of Denmark, ; DK-2800 Kgs Lyngby, Denmark

[4 ]BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen N, Denmark

Author information

Gang Li http://orcid.org/0000-0001-6778-2842

Iván Domenzain http://orcid.org/0000-0002-5322-2040

Simonas Marcišauskas http://orcid.org/0000-0003-0770-6383

Petre Mihail Anton http://orcid.org/0000-0002-7753-9042

Dimitra Lappa http://orcid.org/0000-0001-6336-2680

Christian Lieven http://orcid.org/0000-0001-5377-4091

Moritz Emanuel Beber http://orcid.org/0000-0003-2406-1978

Eduard J. Kerkhoven http://orcid.org/0000-0002-3593-5792

Jens Nielsen http://orcid.org/0000-0002-9955-6003

Article

Publisher ID: 11581

DOI: 10.1038/s41467-019-11581-3

PMC ID: 6687777

PubMed ID: 31395883

SO-VID: b590cc88-dae9-4b67-a3d1-8cb8eb3123e2

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 19 March 2019

Date accepted : 17 July 2019

Funding

Funded by: FundRef https://doi.org/10.13039/501100009708, Novo Nordisk Fonden (Novo Nordisk Foundation);

Award ID: NNF10CC1016517

Award Recipient : Jens Nielsen

Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);

Award ID: 686070

Award ID: 720824

Award Recipient : Jens Nielsen

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ScienceOpen disciplines: Uncategorized

Keywords: genomic analysis,biochemical networks,computational models

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ScienceOpen disciplines: Uncategorized

Keywords: genomic analysis, biochemical networks, computational models

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

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Cancer Metabolism

Most cited references 46

Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

Genome evolution across 1,011 Saccharomyces cerevisiae isolates

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Cited by 108

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