Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Chengyu Zhang; Benjamín J. Sánchez; Feiran Li; Cheng Wei Quan Eiden; William T. Scott; Ulf W. Liebal; Lars M. Blank; Hendrik G. Mengers; Mihail Anton; Albert Tafur Rangel; Sebastián N. Mendoza; Lixin Zhang; Jens Nielsen; Hongzhong Lu; Eduard J. Kerkhoven

doi:10.1038/s44320-024-00060-7

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Chengyu Zhang
, Benjamín J. Sánchez
, Feiran Li
, Cheng Wei Quan Eiden
, William T. Scott
, Ulf W. Liebal
, Lars M. Blank
, Hendrik G. Mengers
, Mihail Anton
, Albert Tafur Rangel
, Sebastián N. Mendoza
, Lixin Zhang
, Jens Nielsen
, Hongzhong Lu^*
, Eduard J. Kerkhoven^*

^*Corresponding author for this work

Systems Biology

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains’ growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Original language	English
Pages (from-to)	1134-1150
Number of pages	17
Journal	Molecular Systems Biology
Volume	20
Issue number	10
Early online date	12 Aug 2024
DOIs	https://doi.org/10.1038/s44320-024-00060-7
Publication status	Published - 3 Oct 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Funding

Funders	Funder number
Knut och Alice Wallenbergs Stiftelse
Deutsche Forschungsgemeinschaft
National Natural Science Foundation of China
Higher Education Discipline Innovation Project	B18022
Higher Education Discipline Innovation Project
Novo Nordisk Fonden	NNF20CC0035580
Novo Nordisk Fonden
Comisión Nacional de Investigación Científica y Tecnológica	72180373
Comisión Nacional de Investigación Científica y Tecnológica
Horizon 2020 Framework Programme	686070, 720824
Horizon 2020 Framework Programme
Centro de Modelamiento Matemático, Facultad de Ciencias Físicas y Matemáticas	FB210005, ACE210010
Centro de Modelamiento Matemático, Facultad de Ciencias Físicas y Matemáticas
ANID-Chile	ICN2021 044, 13220002
National Key Research and Development Program of China	2020YFA0907800
National Key Research and Development Program of China
Shanghai Pujiang Program	22378263, 22208211
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	184.035.007
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Keywords

Genome-scale Metabolic Models
Machine Learning
Multi-omics Integration
Saccharomyces cerevisiae

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10.1038/s44320-024-00060-7

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Cite this

Zhang, C., Sánchez, B. J., Li, F., Eiden, C. W. Q., Scott, W. T., Liebal, U. W., Blank, L. M., Mengers, H. G., Anton, M., Rangel, A. T., Mendoza, S. N., Zhang, L., Nielsen, J., Lu, H., & Kerkhoven, E. J. (2024). Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community. Molecular Systems Biology, 20(10), 1134-1150. https://doi.org/10.1038/s44320-024-00060-7

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title = "Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community",

abstract = "Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains{\textquoteright} growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.",

keywords = "Genome-scale Metabolic Models, Machine Learning, Multi-omics Integration, Saccharomyces cerevisiae",

author = "Chengyu Zhang and S{\'a}nchez, \{Benjam{\'i}n J.\} and Feiran Li and Eiden, \{Cheng Wei Quan\} and Scott, \{William T.\} and Liebal, \{Ulf W.\} and Blank, \{Lars M.\} and Mengers, \{Hendrik G.\} and Mihail Anton and Rangel, \{Albert Tafur\} and Mendoza, \{Sebasti{\'a}n N.\} and Lixin Zhang and Jens Nielsen and Hongzhong Lu and Kerkhoven, \{Eduard J.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = oct,

day = "3",

doi = "10.1038/s44320-024-00060-7",

language = "English",

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Zhang, C, Sánchez, BJ, Li, F, Eiden, CWQ, Scott, WT, Liebal, UW, Blank, LM, Mengers, HG, Anton, M, Rangel, AT, Mendoza, SN, Zhang, L, Nielsen, J, Lu, H & Kerkhoven, EJ 2024, 'Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community', Molecular Systems Biology, vol. 20, no. 10, pp. 1134-1150. https://doi.org/10.1038/s44320-024-00060-7

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T2 - a consensus genome-scale metabolic model for S. cerevisiae curated by the community

AU - Zhang, Chengyu

AU - Sánchez, Benjamín J.

AU - Li, Feiran

AU - Eiden, Cheng Wei Quan

AU - Scott, William T.

AU - Liebal, Ulf W.

AU - Blank, Lars M.

AU - Mengers, Hendrik G.

AU - Anton, Mihail

AU - Rangel, Albert Tafur

AU - Mendoza, Sebastián N.

AU - Zhang, Lixin

AU - Nielsen, Jens

AU - Lu, Hongzhong

AU - Kerkhoven, Eduard J.

PY - 2024/10/3

Y1 - 2024/10/3

N2 - Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains’ growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

AB - Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains’ growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

KW - Genome-scale Metabolic Models

KW - Machine Learning

KW - Multi-omics Integration

KW - Saccharomyces cerevisiae

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JF - Molecular Systems Biology

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Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Persistent URL (handle)

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