Development of a targeted integration Chinese hamster ovary host directly targeting either one or two vectors simultaneously to a single locus using the Cre/Lox recombinase-mediated cassette exchange system.
Domingos NgMeixia ZhouDejin ZhanShirley YipPeggy KoMandy YimZora ModrusanJohn JolyBrad SnedecorMichael W LairdAmy ShenPublished in: Biotechnology progress (2021)
Cell line development (CLD) by random integration (RI) can be labor intensive, inconsistent, and unpredictable due to uncontrolled gene integration after transfection. Unlike RI, targeted integration (TI) based CLD introduces the antibody-expressing cassette to a predetermined site by recombinase-mediated cassette exchange (RMCE). The key to success for the development of a TI host for therapeutic antibody production is to identify a transcriptionally active hotspot that enables highly efficient RMCE and antibody expression with good stability. In this study, a genome wide search for hotspots in the Chinese hamster ovary (CHO)-K1-M genome by either RI or PiggyBac (PB) transposase-based integration has been described. Two CHO-K1-M derived TI host cells were established with the Cre/Lox RMCE system and are described here. Both TI hosts contain a GFP-expressing landing pad flanked by two incompatible LoxP recombination sites (L3 and 2L). In addition, a third incompatible LoxP site (LoxFAS) is inserted in the GFP landing pad to enable an innovative two-plasmid based RMCE strategy, in which two separate vectors can be targeted to a single locus simultaneously. Cell lines generated by the TI system exhibit comparable or higher productivity, better stability and fewer sequence variant (SV) occurrences than the RI cell lines.
Keyphrases
- genome wide
- highly efficient
- cancer therapy
- dna methylation
- escherichia coli
- poor prognosis
- induced apoptosis
- copy number
- heavy metals
- binding protein
- climate change
- stress induced
- drug delivery
- risk assessment
- cell proliferation
- crispr cas
- cell death
- genome wide association study
- amino acid
- transcription factor
- neural network