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Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

Author

Listed:
  • Iván Domenzain

    (Chalmers University of Technology
    Chalmers University of Technology)

  • Benjamín Sánchez

    (Technical University of Denmark
    Technical University of Denmark)

  • Mihail Anton

    (Chalmers University of Technology
    Chalmers University of Technology)

  • Eduard J. Kerkhoven

    (Chalmers University of Technology
    Chalmers University of Technology)

  • Aarón Millán-Oropeza

    (Université Paris-Saclay)

  • Céline Henry

    (Université Paris-Saclay)

  • Verena Siewers

    (Chalmers University of Technology
    Chalmers University of Technology)

  • John P. Morrissey

    (University College Cork)

  • Nikolaus Sonnenschein

    (Technical University of Denmark)

  • Jens Nielsen

    (Chalmers University of Technology
    Chalmers University of Technology
    BioInnovation Institute)

Abstract

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.

Suggested Citation

  • Iván Domenzain & Benjamín Sánchez & Mihail Anton & Eduard J. Kerkhoven & Aarón Millán-Oropeza & Céline Henry & Verena Siewers & John P. Morrissey & Nikolaus Sonnenschein & Jens Nielsen, 2022. "Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31421-1
    DOI: 10.1038/s41467-022-31421-1
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    References listed on IDEAS

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    5. Hongzhong Lu & Feiran Li & Benjamín J. Sánchez & Zhengming Zhu & Gang Li & Iván Domenzain & Simonas Marcišauskas & Petre Mihail Anton & Dimitra Lappa & Christian Lieven & Moritz Emanuel Beber & Nikola, 2019. "A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
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    Cited by:

    1. Matteo Mori & Chuankai Cheng & Brian R. Taylor & Hiroyuki Okano & Terence Hwa, 2023. "Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Marius Arend & David Zimmer & Rudan Xu & Frederik Sommer & Timo Mühlhaus & Zoran Nikoloski, 2023. "Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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