Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in-creased production capacity

Abstract

Over the last three decades, product yields from CHO cells have increased dramatically, yet specific productivity (qP) remains a limiting factor. In a previous study, using repeated cell-sorting, we have established different host cell subclones that show superior transient qP over their respective parental cell lines (CHO-K1, CHO-S). The transcriptome of the resulting six cell lines in different biological states (untransfected, mock transfected, plasmid transfected) was first explored by hierarchical clustering and indicated that gene activity associated with increased qP did not stem from a certain cellular state but seemed to be inherent for a high qP host line. We then performed a novel gene regression analysis identifying drivers for an increase in qP. Genes significantly implicated were first systematically tested for enrichment of GO terms using a Bayesian approach incorporating the hierarchical structure of the GO term tree. Results indicated that specific cellular components such as nucleus, ER, and Golgi are relevant for cellular productivity. This was complemented by targeted GSA that tested functionally homogeneous, manually curated subsets of KEGG pathways known to be involved in transcription, translation, and protein processing. Significantly implicated pathways included mRNA surveillance, proteasome, protein processing in the ER and SNARE interactions in vesicular transport. Pre-selection of CHO host cell lines has been shown to be a valuable strategy for cell line engineering based on transient transfection experiments. Transcriptomic mRNA and miRNA data from the present study show that clonally derived daughter cell lines of CHO-K1 and CHO-S, that show a 3-fold increased transient productivity in comparison to the respective parent, have undergone substantial genetic diversification. A novel approach of correlating differentially expressed mRNAs to a high productivity phenotype followed by unsupervised and supervised methods of pathway analyses have identified the strong influence that mRNA processing, ER, Golgi and associated genetic pathways have on productivity.