In silico model-based characterization of metabolic response to harsh sparging stress in fed-batch CHO cell cultures

•Explored the aeration effect on the metabolic states of CHO cell cultures by in silico analysis.•Profiled the CHO cell cultures under mild and harsh sparging conditions.•Observed that sparging stress leads to the decreased cell growth, viability and productivity.•Found that sparging stress rewires amino acid metabolism towards the enriched H2O2 turnover rate. Mammalian cell culture platform has been successfully implemented for industrial biopharmaceutical production through the advancements in early stage process development including cell-line engineering, media design and process optimization. However, late stage developments such as scale-up, scale-down and large-scale cell cultivation still face many industrial challenges to acquire comparable process performance between different culture scales. One of them is the sparging strategy which significantly affects productivity, quality and comparability. Currently, it is mainly relying on the empirical records due to the lack of theoretical framework and scarcity of available literatures to elucidate intracellular metabolic features. Therefore, it is highly required to characterize the underlying mechanism of physiological changes and metabolic states upon the aeration stress. To this end, initially we cultivated antibody producing CHO cells under mild and harsh sparging conditions and observed that sparging stress leads to the decreased cell growth rate, viability and productivity. Subsequent in silicomodel-driven flux analysis suggested that sparging stress rewires amino acid metabolism towards the enriched H2O2 turnover rate by up-regulated fluxes of amino acid oxidases. Interestingly, many of these H2O2-generating reactions were closely connected with the production of NADH, NADPH and GSH which are typical reducing equivalents. Thus, we can hypothesize that increased amino acid uptake caused by sparging stress contributes to restore redox homeostasis against oxidative stress. The current model-driven systematic data analysis allows us to quickly define distinct metabolic feature under stress condition by using basic cell cultivation datasets.