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Open Access Publications from the University of California

Strategies to improve protein production of Chinese hamster ovary cell lines and genome annotation under the guidance of high throughput omics technology

  • Author(s): Li, Shangzhong
  • Advisor(s): Lewis, Nathan
  • Zhang, Kun
  • et al.

For 60 years, Chinese hamster ovary (CHO) cells have been invaluable for biomedical research and fundamental to the study of several biological processes, such as glycosylation and DNA repair. In addition, for >30 years, they have been the host cell of choice for the production of most biotherapeutics because of its easiness to overexpress target genes and its similarity to the human cell system. Drug production in CHO-based cell lines has been improved by over 100-fold during the past 3 decades. However, due to the absence of genomic resources, efforts in improving the production predominantly rely on media and process optimization. With the decrease in the price of high throughput sequencing technology and some CHO and hamster genome assemblies published after 2011, new opportunities of optimizing CHO cell lines are arising rapidly. However, the draft nature of these genome sequences and therefore the non-perfect genome annotations still pose challenges for many applications. The new Chinese hamster genome assembled using Pacbio and illumina hybrid strategy in 2018 removes large number of obstacles for applying cutting-edge technologies for cell line development and engineering. In this doctoral dissertation, high throughput sequencing guided cell line development strategy and high quality genomics information of CHO was provided to boost the development of the CHO field. First, Ribosome Profiling, a next generatiion sequencing (NGS) technology which provides systematic view of protein translation was applied to CHO cell. Using the information we identified the unnecessary highly translated gene, knocking it down improves the production and growth rate. Second, we quantified the improvements in the new Chinese hamster genome compared to the RefSeq one. And found the genes and mutations that would be missed if we use the old genome. Finally, proteogenomics method was utilized to generate a high quality genome annotation through combining RNA-Seq and proteomics data from multiple hamster tissues.

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