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Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints

Author

Listed:
  • Feiran Li

    (Chalmers University of Technology)

  • Yu Chen

    (Chalmers University of Technology)

  • Qi Qi

    (Chalmers University of Technology)

  • Yanyan Wang

    (Chalmers University of Technology)

  • Le Yuan

    (Chalmers University of Technology
    Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology)

  • Mingtao Huang

    (Chalmers University of Technology
    South China University of Technology)

  • Ibrahim E. Elsemman

    (Chalmers University of Technology
    Assiut University)

  • Amir Feizi

    (Chalmers University of Technology)

  • Eduard J. Kerkhoven

    (Chalmers University of Technology
    Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology)

  • Jens Nielsen

    (Chalmers University of Technology
    BioInnovation Institute)

Abstract

Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production.

Suggested Citation

  • Feiran Li & Yu Chen & Qi Qi & Yanyan Wang & Le Yuan & Mingtao Huang & Ibrahim E. Elsemman & Amir Feizi & Eduard J. Kerkhoven & Jens Nielsen, 2022. "Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30689-7
    DOI: 10.1038/s41467-022-30689-7
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    as
    1. 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.
    2. Omid Oftadeh & Pierre Salvy & Maria Masid & Maxime Curvat & Ljubisa Miskovic & Vassily Hatzimanikatis, 2021. "A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    3. Xin Chen & Boyang Ji & Xinxin Hao & Xiaowei Li & Frederik Eisele & Thomas Nyström & Dina Petranovic, 2020. "FMN reduces Amyloid-β toxicity in yeast by regulating redox status and cellular metabolism," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
    4. Ulrich Schubert & Luis C. Antón & James Gibbs & Christopher C. Norbury & Jonathan W. Yewdell & Jack R. Bennink, 2000. "Rapid degradation of a large fraction of newly synthesized proteins by proteasomes," Nature, Nature, vol. 404(6779), pages 770-774, April.
    5. Jahir M. Gutierrez & Amir Feizi & Shangzhong Li & Thomas B. Kallehauge & Hooman Hefzi & Lise M. Grav & Daniel Ley & Deniz Baycin Hizal & Michael J. Betenbaugh & Bjorn Voldborg & Helene Kildegaard & Gy, 2020. "Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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