Identifying metabolic features and engineering targets for productivity improvement in CHO cells by integrated transcriptomics and genome-scale metabolic model

Abstract

In this study, we presented an integrated systems biology approach to elucidate the key characteristics of cellular metabolism in Chinese hamster ovary (CHO) cells producing monoclonal antibodies (mAb). The cellular metabolism in high and low producers under batch conditions was interrogated dynamically both within and among cells. First, transcriptomics analysis was used to study the time-course change in the metabolic pathway within cells that was correlated with mAb productivity increase. Second, differentially regulated pathways between high and low producers were sought at each growth phase. Several up-regulated pathways were identified in the high producer at the late growth phase, including citrate cycle, oxidative phosphorylation, and pentose phosphate pathway. These activities were further analyzed by intracellular flux distributions estimated through a genome-scale CHO model. Our results revealed that these key pathways are identified to be characteristics of high mAb production, not only for the high-producing cell line but also a dynamic phenomenon in mAb-producing cell cultures. This study showed that the approach of integrating transcriptomics and flux analysis leads to a better understanding of cellular metabolism related to mAb productivity. In turn, this allows for the identification of metabolic bottlenecks and potential engineering targets for cell line development and process optimization.