TY - JOUR
T1 - Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion
AU - Gutierrez, Jahir M
AU - Feizi, Amir
AU - Li, Shangzhong
AU - Kallehauge, Thomas B
AU - Hefzi, Hooman
AU - Grav, Lise Marie
AU - Ley, Daniel
AU - Baycin Hizal, Deniz
AU - Betenbaugh, Michael J
AU - Voldborg, Bjørn Gunnar
AU - Kildegaard, Helene Faustrup
AU - Min Lee, Gyun
AU - Palsson, Bernhard O
AU - Nielsen, Jens
AU - Lewis, Nathan E
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.1038/s41467-019-13867-y
DO - 10.1038/s41467-019-13867-y
M3 - Journal article
C2 - 31896772
SN - 2041-1723
VL - 11
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 68
ER -