Translatome analysis of CHO cells to identify key growth genes
Translatome analysis of CHO cells to identify key growth genes
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DOI：
10.1016/j.jbiotec.2013.07.010
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发表时间：
2013-09-10
影响因子：
4.1
通讯作者：
Lee, Dong-Yup
中科院分区：
文献类型：
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作者：
Courtes, Franck C.;Lin, Joyce;Lee, Dong-Yup
我们报告的第一次调查的翻译效率在全球范围内，也被称为translatome，中国仓鼠卵巢（CHO）DG 44细胞系生产单克隆抗体（mAb）。翻译组数据是通过结合使用高分辨率和流线型多核糖体分析技术和专有的Nimblegen微阵列探测超过13 K注释的CHO特异性基因而产生的。在指数生长期核糖体负载的分布揭示了对应于最大生长速率的翻译活性，从而使我们能够鉴定编码异质核核糖核蛋白的稳定和高度翻译的基因（Hnrnpc和Hnrnpa 2b 1），胞质分裂蛋白调节因子1（Prc 1），葡萄糖-6-磷酸脱氢酶（G6 pdh），UTP 6小亚基加工体（Utp 6）和RuvB样蛋白1（Ruvbl 1）作为细胞生长的潜在关键参与者。此外，转录组和翻译组数据集之间的相关性分析表明，95%的研究基因的转录水平和翻译效率是解偶联的，这表明翻译控制机制，如mTOR途径的含义。因此，目前的翻译组分析平台通过弥合转录组和蛋白质组数据之间的差距，为CHO细胞培养物中的基因表达提供了新的见解，这将使生物加工领域的研究人员能够优先考虑高潜力的候选基因，并为细胞工程设计最佳策略，以提高培养性能。(C)2013年由Elsevier B. V.出版
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. (C) 2013 Published by Elsevier B.V.