High glucose and low specific cell growth but not mild hypothermia improve specific r-protein productivity in chemostat culture of CHO cells.

In the biopharmaceutical sector, Chinese hamster ovary (CHO) cells have become the host of choice to produce recombinant proteins (r-proteins) due to their capacity for correct protein folding, assembly, and posttranslational modification. However, the production of therapeutic r-proteins in CHO cells is expensive and presents insufficient production yields for certain proteins. Effective culture strategies to increase productivity (qp) include a high glucose concentration in the medium and mild hypothermia (28-34 °C), but these changes lead to a reduced specific growth rate. To study the individual and combined impacts of glucose concentration, specific growth rate and mild hypothermia on culture performance and cell metabolism, we analyzed chemostat cultures of recombinant human tissue plasminogen activator (rh-tPA)-producing CHO cell lines fed with three glucose concentrations in feeding media (20, 30 and 40 mM), at two dilution rates (0.01 and 0.018 1/h) and two temperatures (33 and 37 °C). The results indicated significant changes in cell growth, cell cycle distribution, metabolism, and rh-tPA productivity in response to the varying environmental culture conditions. High glucose feed led to constrained cell growth, increased specific rh-tPA productivity and a higher number of cells in the G2/M phase. Low specific growth rate and temperature (33 °C) reduced glucose consumption and lactate production rates. Our findings indicated that a reduced specific growth rate coupled with high feed glucose significantly improves r-protein productivity in CHO cells. We also observed that low temperature significantly reduced qp, but not cell growth when dilution rate was manipulated, regardless of the glucose concentration or dilution rate. In contrast, we determined that feed glucose concentration and consumption rate were the dominant aspects of the growth and productivity in CHO cells by using multivariate analysis.