Bioprocessing of Recombinant CHO-K1, CHO-DG44, and CHO-S: CHO Expression Hosts Favor Either mAb Production or Biomass Synthesis

Abstract

Chinese hamster ovary (CHO) cells comprise a variety of lineages including CHO-DXB11, CHO-K1, CHO-DG44, and CHO-S. Despite all CHO cell lines sharing a common ancestor, extensive mutagenesis, and clonal selection has resulted in substantial genetic heterogeneity among them. Data from sequencing show that different genes are missing in individual CHO cell lines and each cell line harbors a unique set of mutations with relevance to the bioprocess. However, not much literature is available about the influence of genetic differences of CHO on the performance of bioprocess operations. In this study, the host cell-specific differences among three widely used CHO cell lines (CHO-K1, CHO-S, and CHO-DG44) and recombinantly expressed the same monoclonal antibody (mAb) in an isogenic format by using bacterial artificial chromosomes (BACs) as transfer vector in all cell lines is examined. Cell-specific growth and product formation are studied in batch, fed-batch, and semi-continuous perfusion cultures. Further, two different cell culture media are used to investigate their effects. The authors find CHO cell line-specific preferences for mAb production or biomass synthesis that are determined by the host cell line. Additionally, quality attributes of the expressed mAb are influenced by the host cell line and media.

Multi‐omics profiling of CHO parental hosts reveals cell line‐specific variations in bioprocessing traits

“…For example, DXB11 has high amounts of less processed structures, particularly terminal GlcNAc glycans, and K1 shows much more sialylated structures in both N ‐ and O ‐glycosylation profiles, which is possibly attributed to high expression of relevant biosynthetic enzymes as well as high levels of precursor availability. Notably, these observations are consistent with an earlier study which compared the glycosylation profile of an antibody produced from DG44 and K1 cells and observed that K1 produced much higher sialylated structures (Reinhart et al, ). While we observed no gene copy number difference in the glycosylation biosynthetic pathway, all the cell lines have their own expression preferences in each step of the glycan processing as different isoforms were upregulated/downregulated.…”

“…Due to the presence of such “quasispecies” within the CHO parental cell lines (Wurm, ), there exist considerable dissimilarities in their phenotypes and possibly even variations in the critical quality attributes such as N ‐ and O ‐glycosylation profiles of the recombinant proteins produced from them. For example, when the same antibody was produced from different CHO cells, the viable cell densities and final titer across several bioprocess conditions varied significantly, depending on the parental cell line used (Hu et al, ; Reinhart et al, ). Furthermore, there are appreciable variations among the N ‐glycosylation patterns and other quality attributes of the recombinant proteins produced (Könitzer et al, ; Reinhart et al, ).…”

“…For example, when the same antibody was produced from different CHO cells, the viable cell densities and final titer across several bioprocess conditions varied significantly, depending on the parental cell line used (Hu et al, ; Reinhart et al, ). Furthermore, there are appreciable variations among the N ‐glycosylation patterns and other quality attributes of the recombinant proteins produced (Könitzer et al, ; Reinhart et al, ). Therefore, it is required to understand the diversity in the phenotypes of CHO cells and associate it with known genetic heterogeneity for the rational selection of suitable parental cells.…”

Oxidative stress‐alleviating strategies to improve recombinant protein production in CHO cells

“…Due to the variability factors associated with supplementation, the use of empirical statistical analysis and high throughput assays is recommended. In bioproduction, it is generally assumed that the cell lines have different historical backgrounds (origin, clone, selection procedure) and, as a direct consequence, display differences in metabolism and sensitivity to oxidative stress (Reinhart et al, ). A better understanding of these differences can be obtained through the use of omics techniques and can help experimenters highlight reactions leading to oxidative stress and adapt antioxidant supplementation to specific cell lines.…”

“…Due to the variability factors associated with supplementation, the use of empirical statistical analysis and high throughput assays is recommended. In bioproduction, it is generally assumed that the cell lines have different historical backgrounds (origin, clone, selection procedure) and, as a direct consequence, display differences in metabolism and sensitivity to oxidative stress (Reinhart et al, 2018).…”