Modeling apoptosis resistance in CHO cells with CRISPR‐mediated knockouts of Bak1, Bax, and Bok. Issue 6 (6th March 2022) - MacDonald, Michael A.; Barry, Craig; Groves, Teddy; Martínez, Verónica S.; Gray, Peter P.; Baker, Kym; Shave, Evan; Mahler, Stephen; Munro, Trent; Marcellin, Esteban; Nielsen, Lars K.
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. Abstract : 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.