Modeling apoptosis resistance in CHO cells with CRISPR‐mediated knockouts of Bak1, Bax, and Bok
Chinese hamster ovary (CHO) cells are the primary platform for the production of biopharmaceuticals. To increase yields, many CHO cell lines have been genetically engineered to resist cell death. However, the kinetics that governs cell fate in bioreactors are confounded by many variables associated...
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Published in | Biotechnology and bioengineering Vol. 119; no. 6; pp. 1380 - 1391 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
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01.06.2022
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Abstract | Chinese hamster ovary (CHO) cells are the primary platform for the production of biopharmaceuticals. To increase yields, many CHO cell lines have been genetically engineered to resist cell death. However, the kinetics that governs cell fate in bioreactors are confounded by many variables associated with batch processes. Here, we used CRISPR‐Cas9 to create combinatorial knockouts of the three known BCL‐2 family effector proteins: Bak1, Bax, and Bok. To assess the response to apoptotic stimuli, cell lines were cultured in the presence of four cytotoxic compounds with different mechanisms of action. A population‐based model was developed to describe the behavior of the resulting viable cell dynamics as a function of genotype and treatment. Our results validated the synergistic antiapoptotic nature of Bak1 and Bax, while the deletion of Bok had no significant impact. Importantly, the uniform application of apoptotic stresses permitted direct observation and quantification of a delay in the onset of cell death through Bayesian inference of meaningful model parameters. In addition to the classical death rate, a delay function was found to be essential in the accurate modeling of the cell death response. These findings represent an important bridge between cell line engineering strategies and biological modeling in a bioprocess context.
Mathematical modeling of the cellular stress resistance to characterize delayed death in BCL‐2 family protein knockout genotypes. Combinatorial Chinese hamster ovary cell variants of Δbak1, Δbax, and Δbok were generated and challenged with strong cytotoxic treatments. A population balance model was developed and fitted to time course cell density data using Bayesian inference. Model parameters elegantly capture phenotype survivability using an average time‐to‐death, allowing unambiguous comparison of genotype performance. |
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AbstractList | Chinese hamster ovary (CHO) cells are the primary platform for the production of biopharmaceuticals. To increase yields, many CHO cell lines have been genetically engineered to resist cell death. However, the kinetics that governs cell fate in bioreactors are confounded by many variables associated with batch processes. Here, we used CRISPR‐Cas9 to create combinatorial knockouts of the three known BCL‐2 family effector proteins: Bak1, Bax, and Bok. To assess the response to apoptotic stimuli, cell lines were cultured in the presence of four cytotoxic compounds with different mechanisms of action. A population‐based model was developed to describe the behavior of the resulting viable cell dynamics as a function of genotype and treatment. Our results validated the synergistic antiapoptotic nature of Bak1 and Bax, while the deletion of Bok had no significant impact. Importantly, the uniform application of apoptotic stresses permitted direct observation and quantification of a delay in the onset of cell death through Bayesian inference of meaningful model parameters. In addition to the classical death rate, a delay function was found to be essential in the accurate modeling of the cell death response. These findings represent an important bridge between cell line engineering strategies and biological modeling in a bioprocess context. Chinese hamster ovary (CHO) cells are the primary platform for the production of biopharmaceuticals. To increase yields, many CHO cell lines have been genetically engineered to resist cell death. However, the kinetics that governs cell fate in bioreactors are confounded by many variables associated with batch processes. Here, we used CRISPR‐Cas9 to create combinatorial knockouts of the three known BCL‐2 family effector proteins: Bak1, Bax, and Bok. To assess the response to apoptotic stimuli, cell lines were cultured in the presence of four cytotoxic compounds with different mechanisms of action. A population‐based model was developed to describe the behavior of the resulting viable cell dynamics as a function of genotype and treatment. Our results validated the synergistic antiapoptotic nature of Bak1 and Bax, while the deletion of Bok had no significant impact. Importantly, the uniform application of apoptotic stresses permitted direct observation and quantification of a delay in the onset of cell death through Bayesian inference of meaningful model parameters. In addition to the classical death rate, a delay function was found to be essential in the accurate modeling of the cell death response. These findings represent an important bridge between cell line engineering strategies and biological modeling in a bioprocess context. Mathematical modeling of the cellular stress resistance to characterize delayed death in BCL‐2 family protein knockout genotypes. Combinatorial Chinese hamster ovary cell variants of Δbak1, Δbax, and Δbok were generated and challenged with strong cytotoxic treatments. A population balance model was developed and fitted to time course cell density data using Bayesian inference. Model parameters elegantly capture phenotype survivability using an average time‐to‐death, allowing unambiguous comparison of genotype performance. |
Author | Barry, Craig Gray, Peter P. MacDonald, Michael A. Baker, Kym Munro, Trent Martínez, Verónica S. Mahler, Stephen Groves, Teddy Shave, Evan Nielsen, Lars K. Marcellin, Esteban |
AuthorAffiliation | 3 Patheon by Thermo Fisher Scientific Woolloongabba Queensland Australia 4 Metabolomics Australia The University of Queensland Brisbane Queensland Australia 2 The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Kgs. Lyngby Denmark 1 ARC Training Centre for Biopharmaceutical Innovation, Australian Institute for Bioengineering and Nanotechnology The University of Queensland St. Lucia Australia |
AuthorAffiliation_xml | – name: 2 The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Kgs. Lyngby Denmark – name: 3 Patheon by Thermo Fisher Scientific Woolloongabba Queensland Australia – name: 1 ARC Training Centre for Biopharmaceutical Innovation, Australian Institute for Bioengineering and Nanotechnology The University of Queensland St. Lucia Australia – name: 4 Metabolomics Australia The University of Queensland Brisbane Queensland Australia |
Author_xml | – sequence: 1 givenname: Michael A. surname: MacDonald fullname: MacDonald, Michael A. organization: The University of Queensland – sequence: 2 givenname: Craig orcidid: 0000-0001-8964-8118 surname: Barry fullname: Barry, Craig organization: The University of Queensland – sequence: 3 givenname: Teddy surname: Groves fullname: Groves, Teddy organization: Technical University of Denmark – sequence: 4 givenname: Verónica S. orcidid: 0000-0003-2729-5278 surname: Martínez fullname: Martínez, Verónica S. organization: The University of Queensland – sequence: 5 givenname: Peter P. surname: Gray fullname: Gray, Peter P. organization: The University of Queensland – sequence: 6 givenname: Kym surname: Baker fullname: Baker, Kym organization: Patheon by Thermo Fisher Scientific – sequence: 7 givenname: Evan surname: Shave fullname: Shave, Evan organization: Patheon by Thermo Fisher Scientific – sequence: 8 givenname: Stephen surname: Mahler fullname: Mahler, Stephen organization: The University of Queensland – sequence: 9 givenname: Trent surname: Munro fullname: Munro, Trent organization: The University of Queensland – sequence: 10 givenname: Esteban surname: Marcellin fullname: Marcellin, Esteban organization: The University of Queensland – sequence: 11 givenname: Lars K. orcidid: 0000-0001-8191-3511 surname: Nielsen fullname: Nielsen, Lars K. email: lars.nielsen@uq.edu.au organization: Technical University of Denmark |
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Snippet | Chinese hamster ovary (CHO) cells are the primary platform for the production of biopharmaceuticals. To increase yields, many CHO cell lines have been... |
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SubjectTerms | Apoptosis Batch processes Batch processing Bax protein Bayesian analysis Bayesian inference bioprocessing Bioreactors Cell death Cell fate Cell lines CHO cells Combinatorial analysis CRISPR Cytotoxicity Genetic engineering Genotypes Modelling Mortality population model Statistical inference |
Title | Modeling apoptosis resistance in CHO cells with CRISPR‐mediated knockouts of Bak1, Bax, and Bok |
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