Repressing expression of difficult‐to‐express recombinant proteins during the selection process increases productivity of CHO stable pools
Bibliographic record
Abstract
More than half of licensed therapeutic recombinant proteins (r-proteins) are manufactured using constitutively-expressing, stably-transfected Chinese hamster ovary (CHO) clones. While constitutive CHO expression systems have proven their efficacy for the manufacturing of monoclonal antibodies, many next-generation therapeutics such as cytokines and bispecific antibodies as well as biological targets such as ectodomains of transmembrane receptors remain intrinsically challenging to produce. Herein, we exploited a cumate-inducible CHO platform allowing reduced expression of various classes of r-proteins during selection of stable pools. Following stable pool generation, fed-batch productions showed that pools generated without cumate (OFF-pools) were significantly more productive than pools selected in the presence of cumate (ON-pools) for 8 out of the 10 r-proteins tested, including cytokines, G-protein coupled receptors (GPCRs), the HVEM membrane receptor ectodomain, the multifunctional protein High Mobility Group protein B1 (HMGB1), as well as monoclonal and bispecific T-cell engager antibodies. We showed that OFF-pools contain a significantly larger proportion of cells producing high levels of r-proteins and that these cells tend to proliferate faster when expression is turned off, suggesting that r-protein overexpression imposes a metabolic burden on the cells. Cell viability was lower and pool recovery was delayed during selection of ON-pools (mimicking constitutive expression), suggesting that high producers were likely lost or overgrown by faster-growing, low-producing cells. We also observed a correlation between the expression levels of the GPCRs with Binding immunoglobulin Protein, an endoplasmic reticulum (ER) stress marker. Taken together, these data suggest that using an inducible system to minimize r-protein expression during stable CHO pool selection reduces cellular stresses, including ER stress and metabolic burden, leading to pools with greater frequency of high-expressing cells, resulting in improved volumetric productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".