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.