Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

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, Gyun Lee, Bernhard O. Palsson, Jens Nielsen and Nathan E. Lewis ()
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Jahir M. Gutierrez: University of California, San Diego
Amir Feizi: Kemivägen 10, Chalmers University of Technology
Shangzhong Li: University of California, San Diego
Thomas B. Kallehauge: Technical University of Denmark
Hooman Hefzi: University of California, San Diego
Lise M. Grav: Technical University of Denmark
Daniel Ley: Technical University of Denmark
Deniz Baycin Hizal: Pharmaceutical R&D Department, Turgut Illaclari A.S
Michael J. Betenbaugh: Johns Hopkins University
Bjorn Voldborg: Technical University of Denmark
Helene Kildegaard: Technical University of Denmark
Gyun Lee: Technical University of Denmark
Bernhard O. Palsson: University of California, San Diego
Jens Nielsen: Kemivägen 10, Chalmers University of Technology
Nathan E. Lewis: University of California, San Diego

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.

Date: 2020
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DOI: 10.1038/s41467-019-13867-y

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