How to train your cell - Towards controlling phenotypes by harnessing the epigenome of Chinese hamster ovary production cell lines.

Recent advances in omics technologies and the broad availability of big datasets have revolutionized our understanding of Chinese hamster ovary cells in their role as the most prevalent host for production of complex biopharmaceuticals. In consequence, our perception of this "workhorse of the biopharmaceutical industry" has successively shifted from that of a nicely working, but unknown recombinant protein producing black box to a biological system governed by multiple complex regulatory layers that might possibly be harnessed and manipulated at will. Despite the tremendous progress that has been made to characterize CHO cells on various omics levels, our understanding is still far from complete. The well-known inherent genetic plasticity of any immortalized and rapidly dividing cell line also characterizes CHO cells and can lead to problematic instability of recombinant protein production. While the high mutational frequency has been a focus of CHO cell research for decades, the impact of epigenetics and its role in differential gene expression has only recently been addressed. In this review we provide an overview about the current understanding of epigenetic regulation in CHO cells and discuss its significance for shaping the cell's phenotype. We also look into current state-of-the-art technology that can be applied to harness and manipulate the epigenetic network so as to nudge CHO cells towards a specific phenotype. Here, we revise current strategies on site-directed integration and random as well as targeted epigenome modifications. Finally, we address open questions that need to be investigated to exploit the full repertoire of fine-tuned control of multiplexed gene expression using epigenetic and systems biology tools.

• Publication year

  2022

• Venue

  Biotechnology Advances

• Publication date

  2022-02-01

• Fields of study

  Biology, Medicine

• Identifiers
  DOI 10.1016/j.biotechadv.2022.107924PMID 35149147
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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