A signature of 12 microRNAs is robustly associated with growth rate in a variety of CHO cell lines.

As Chinese Hamster Ovary (CHO) cells are the cell line of choice for the production of human-like recombinant proteins, there is interest in genetic optimization of host cell lines to overcome certain limitations in their growth rate and protein secretion. At the same time, a detailed understanding of these processes could be used to advantage by identification of marker transcripts that characterize states of performance. In this context, microRNAs (miRNAs) that exhibit a robust correlation to the growth rate of CHO cells were determined by analyzing miRNA expression profiles in a comprehensive collection of 46 samples including CHO-K1, CHO-S and CHO-DUKXB11, which were adapted to various culture conditions, and analyzed in different growth stages using microarrays. By applying Spearman or Pearson correlation coefficient criteria of>|0.6|, miRNAs with high correlation to the overall growth, or growth rates observed in exponential, serum-free, and serum-free exponential phase were identified. An overlap of twelve miRNAs common for all sample sets was revealed, with nine positively and three negatively correlating miRNAs. The here identified panel of miRNAs can help to understand growth regulation in CHO cells and contains putative engineering targets as well as biomarkers for cell lines with advantageous growth characteristics.

Utilization and evaluation of CHO-specific sequence databases for mass spectrometry based proteomics.

Recently released sequence information on Chinese hamster ovary (CHO) cells promises to not only facilitate our understanding of these industrially important cell factories through direct analysis of the sequence, but also to enhance existing methodologies and allow new tools to be developed. In this article we demonstrate the utilization of CHO specific sequence information to improve mass spectrometry (MS) based proteomic identification. The use of various CHO specific databases enabled the identification of 282 additional proteins, thus increasing the total number of identified proteins by 40-50%, depending on the sample source and methods used. In addition, a considerable portion of those proteins that were identified previously based on inter-species sequence homology were now identified by a larger number of peptides matched, thus increasing the confidence of identification. The new sequence information offers improved interpretation of proteomic analyses and will, in the years to come, prove vital to unraveling the CHO proteome.