Effects of cysteine, asparagine, or glutamine limitations in Chinese hamster ovary cell batch and fed‐batch cultures
Bibliographic record
Abstract
Amino acid availability is a key factor that can be controlled to optimize the productivity of fed-batch cultures. To study amino acid limitation effects, a serum-free chemically defined basal medium was formulated to exclude the amino acids that became depleted in batch culture. The effect of limiting glutamine, asparagine, and cysteine on the cell growth, metabolism, antibody productivity, and product glycosylation was investigated in three Chinese hamster ovary (CHO) cell lines (CHO-DXB11, CHO-K1SV, and CHO-S). Cysteine limitation was detrimental to both cell proliferation and productivity for all three CHO cell lines. Glutamine limitation reduced growth but not cell specific productivity, whereas asparagine limitation had no significant effect on either growth or cell specific productivity. Neither glutamine nor asparagine limitation significantly affected antibody glycosylation. Replenishing the CHO-DXB11 culture with cysteine after 1 day of cysteine limitation allowed the cells to partially recover their growth and productivity. This recovery was not observed after 2 days of cysteine limitation. Based on these findings, a fed-batch protocol was developed using single or mixed amino acid supplementation. Although cell density and antibody concentration were lower compared to a commercial feed, the feeds based on cysteine supplementation yielded comparable cell specific productivity. Overall, this study showed that different amino acid limitations have varied effects on the performance of CHO cell cultures and that maintaining cysteine availability is a critical process parameter for the three cell lines investigated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".