Back

Sequential, Chromosome-Specific Glutamine Synthetase Double Knockout with Cas-CLOVER Establishes Enhanced CHO Platforms for Cell Line Development

Gomez Limia, C.; Cheng, H. C.; Machado, D. M.; Hart, T.; McHargue, M. C.; Cho, C.-C.; Brizzee, C.; Crawford, J.

2026-01-08 bioengineering
10.64898/2026.01.06.698029 bioRxiv
Show abstract

Cas-CLOVER is an emerging high-fidelity genome editing system that enables precise and efficient cell engineering. In this study, we applied Cas-CLOVER to establish a robust, gene-edited platform in suspension-adapted CHO-K1 cells supporting cell line development (CLD) for biopharmaceutical production. An attractive strategy for high yield clone selection is the use of glutamine synthetase (GS) knockout CHO cells. The primary GS gene resides on chromosome 5 (GS5), while a recently identified GS pseudogene is located on chromosome 1 (GS1). To compare editing efficiency, we evaluated Cas-CLOVER and Cas9 at both GS loci using the Neon Transfection System. Cas-CLOVER achieved 84% editing at GS5 and 74% at GS1, markedly higher than Cas 9. Leveraging Cas-CLOVERs dual-guide RNA design, we generated a GS5 single knockout (GS5-SKO) and subsequently a double knockout (GS-DKO) line at both the GS5 and GS1 loci, both with none detected off-target mutations analyzed in 40 predictably off-target sites. For functional validation, these cell lines were engineered with the proprietary Harbor-IN transposase system to stably express trastuzumab. Using an optimized protocol, the resulting GS-DKO platform, termed CleanCut GS CHO, enabled stringent selection and yielded high-producing clones with cell-specific productivity exceeding 100 pg/cell/day and antibody titers greater than 5 g/L in 24 deep well-plate fed-batch cultures after 14 days. The antibody titer stability analysis showed consistency over 60 generations. Collectively, these findings establish Cas-CLOVER as a versatile genome editing tool for developing high-yield CHO host platforms in CLD.

Published in Biotechnology Progress · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.