Journal article
Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion
University of California at San Diego1
Network Reconstruction in Silico Biology, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark2
Chalmers University of Technology3
Technical University of Denmark4
CHO Cell Line Engineering and Design, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark5
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark6
Turgut Illaclari A.S.7
Johns Hopkins University8
CHO Core, Translational Management, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark9
Big Data 2 Knowledge, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark10
...and 0 moreIn 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.
Language: | English |
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Publisher: | Nature Publishing Group UK |
Year: | 2020 |
Pages: | 68 |
ISSN: | 20411723 |
Types: | Journal article |
DOI: | 10.1038/s41467-019-13867-y |
ORCIDs: | 0000-0002-9876-7378 , Palsson, Bernhard O , 0000-0002-9955-6003 , 0000-0001-7700-3654 , Grav, Lise Marie , Ley, Daniel , Voldborg, Bjørn Gunnar and Kildegaard, Helene Faustrup |