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Effective development of host cells for therapeutic protein production is hampered by the poor characterization of cellular transfection. Here, we employed a multi-omics-based systems biotechnology approach to elucidate the genotypic and phenotypic differences between a wild-type and recombinant antibody-producing Chinese hamster ovary (CHO) cell line. At the genomic level, we observed extensive rearrangements in specific targeted loci linked to transgene integration sites. Transcriptional re-wiring of DNA damage repair and cellular metabolism in the antibody producer, via changes in gene copy numbers, was also detected. Subsequent integration of transcriptomic data with a genome-scale metabolic model showed a substantial increase in energy metabolism in the antibody producer. Metabolomics, lipidomics, and glycomics analyses revealed an elevation in long-chain lipid species, potentially associated with protein transport and secretion requirements, and a surprising stability of N-glycosylation profiles between both cell lines. Overall, the proposed knowledge-based systems biotechnology framework can further accelerate mammalian cell-line engineering in a targeted manner.

The selection of suitable mammalian cell lines with high specific productivities is a crucial aspect of large-scale recombinant protein production. This study utilizes a metabolomics approach to elucidate the key characteristics of Chinese hamster ovary (CHO) cells with high monoclonal antibody productivities (q(mAb)). Liquid chromatography-mass spectrometry (LC-MS)-based intracellular metabolite profiles of eight single cell clones with high and low q(mAb) were obtained at the mid-exponential phase during shake flask batch cultures. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) subsequently revealed key differences between the high and low q(mAb) clones, as indicated by the variable importance for projection (VIP) scores. The mass peaks were further examined for their potential association with q(mAb) across all clones using Pearson's correlation analysis. Lastly, the identities of metabolites with high VIP and correlation scores were confirmed by comparison with standards through LC-MS-MS. A total of seven metabolites were identified-NADH, FAD, reduced and oxidized glutathione, and three activated sugar precursors. These metabolites are involved in key cellular pathways of citric acid cycle, oxidative phosphorylation, glutathione metabolism, and protein glycosylation. To our knowledge, this is the first study to identify metabolites that are associated closely with q(mAb). The results suggest that the high producers had elevated levels of specific metabolites to better regulate their redox status. This is likely to facilitate the generation of energy and activated sugar precursors to meet the demands of producing more glycosylated recombinant monoclonal antibodies.