Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jbiotec.2013.07.010
Title: Translatome analysis of CHO cells to identify key growth genes
Authors: Courtes, F.C.
Lin, J.
Lim, H.L.
Ng, S.W.
Wong, N.S.C.
Koh, G.
Vardy, L.
Yap, M.G.S. 
Loo, B.
Lee, D.-Y. 
Keywords: Cellular growth
CHO cells
Polysome profiling
Translatome
Issue Date: 10-Sep-2013
Source: Courtes, F.C., Lin, J., Lim, H.L., Ng, S.W., Wong, N.S.C., Koh, G., Vardy, L., Yap, M.G.S., Loo, B., Lee, D.-Y. (2013-09-10). Translatome analysis of CHO cells to identify key growth genes. Journal of Biotechnology 167 (3) : 215-224. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jbiotec.2013.07.010
Abstract: We report the first investigation of translational efficiency on a global scale, also known as translatome, of a Chinese hamster ovary (CHO) DG44 cell line producing monoclonal antibodies (mAb). The translatome data was generated via combined use of high resolution and streamlined polysome profiling technology and proprietary Nimblegen microarrays probing for more than 13. K annotated CHO-specific genes. The distribution of ribosome loading during the exponential growth phase revealed the translational activity corresponding to the maximal growth rate, thus allowing us to identify stably and highly translated genes encoding heterogeneous nuclear ribonucleoproteins (Hnrnpc and Hnrnpa2b1), protein regulator of cytokinesis 1 (Prc1), glucose-6-phosphate dehydrogenase (G6pdh), UTP6 small subunit processome (Utp6) and RuvB-like protein 1 (Ruvbl1) as potential key players for cellular growth. Moreover, correlation analysis between transcriptome and translatome data sets showed that transcript level and translation efficiency were uncoupled for 95% of investigated genes, suggesting the implication of translational control mechanisms such as the mTOR pathway. Thus, the current translatome analysis platform offers new insights into gene expression in CHO cell cultures by bridging the gap between transcriptome and proteome data, which will enable researchers of the bioprocessing field to prioritize in high-potential candidate genes and to devise optimal strategies for cell engineering toward improving culture performance. © 2013 Elsevier Ltd.V.
Source Title: Journal of Biotechnology
URI: http://scholarbank.nus.edu.sg/handle/10635/64747
ISSN: 01681656
DOI: 10.1016/j.jbiotec.2013.07.010
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