Meta‐Analysis of Publicly Available Chinese Hamster Ovary (CHO) Cell Transcriptomic Datasets for Identifying Engineering Targets to Enhance Recombinant Protein Yields

Abstract

Transcriptomics has been extensively applied to the investigation of the CHO cell platform for the production of recombinant biotherapeutic proteins to identify transcripts whose expression is regulated and correlated to (non)desirable CHO cell attributes. However, there have been few attempts to analyze the findings across these studies to identify conserved changes and generic targets for CHO cell platform engineering. Here, the authors have undertaken a meta-analysis of CHO cell transcriptomic data and report on those genes most frequently identified as differentially expressed with regard to cell growth (μ) and productivity (Qp). By aggregating differentially expressed genes from publicly available transcriptomic datasets associated with μ and Qp, using a pathway enrichment analysis and combining it with the concordance of gene expression values, the authors have identified a refined target gene and pathway list while determining the overlap across CHO transcriptomic studies. The authors find that only the cell cycle and lysosome pathways show good concordance. By mapping out the contributing genes the authors have constructed a transcriptomic "fingerprint" of a high-performing cell line. This study provides a starting resource for researchers who want to navigate the complex landscape of CHO transcriptomics and identify targets to undertake cell engineering for improved recombinant protein output.

Application of ER Stress Biomarkers to Predict Formulated Monoclonal Antibody Stability

“…A number of studies have assessed changes in gene expression profiles throughout culture of IgG‐producing CHO cell lines . Of particular relevance to the work reported here are those that report changes in the gene expression related to UPR and ERAD pathways.…”

Random epigenetic modulation of CHO cells by repeated knockdown of DNA methyltransferases increases population diversity and enables sorting of cells with higher production capacities

“…In addition, Vishwanathan et al (2014) showed that it is not only the increasing copy number of the gene of interest (GOI) during gene amplification that is responsible for higher productivities but also a stepwise change and adaptation of the cell's transcriptome pattern that occurs during the exposure to the selection agents and allows cells to handle higher production challenges. These studies and many others indicate that whether a cell is a high performer or not depends to a large degree on the precise regulation of many genes that contribute to productivity and the fine‐tuned combination of their expression levels (Harreither et al, 2015; Tamošaitis & Smales, 2018).…”

Defining lncRNAs Correlated with CHO Cell Growth and IgG Productivity by RNA-Seq

“…Despite advancements in the ability of CHO cells to reach higher cell concentrations and generate increasing amounts of target biotherapeutic proteins, particularly monoclonal antibodies (mAbs), there remains a desire to further understand the limitations upon CHO cell phenotypes and to engineer cells for the production of more difficult-to-express products (Godfrey et al, 2017;Jossé et al, 2018;Mead et al, 2009). One approach that has been applied toward improving our understanding of the limitations on CHO cell growth and recombinant protein production is the field of transcriptomics (Tamo saitis and Smales, 2018). Transcriptomic studies in particular could benefit from generating material from controlled miniature bioreactors that predict behavior at larger scale, as an issue of such previous studies is reproducibility and robustness across different transcriptomic datasets, given the high heterogeneity of CHO cell lines and their intrinsic genetic instability (Chen et al, 2017;Wurm, 2013;Wurm and Wurm, 2017).…”

“…The availability of CHO cell and Chinese hamster genome sequences (Lewis et al, 2013;Xu et al, 2011) has greatly enabled omics-based studies (Faustrup Kildegaard et al, 2013), and since there has been an increasing number of publicly available databases for different CHO cell lines. However, the focus of these studies has been on either coding genes or microRNAs (miRNAs), with few studies investigating other classes of RNAs and their impact on CHO cell behavior (Singh et al, 2018;Tamo saitis and Smales, 2018).…”