Enhanced Production of Monomeric Interferon-β by CHO Cells through the Control of Culture Conditions
Bibliographic record
Abstract
The enhancement of recombinant protein expression of a transfected cell line is essential for the development of an efficient large-scale bioprocess. The effect of various media additives and temperature conditions were studied in an attempt to optimize protein production, stability, and protein glycosylation from a Chinese hamster ovary (CHO) cell line producing human beta-interferon (Hu-beta-IFN). We observed a decrease in the ELISA response of the glycoprotein in the later stages of batch cultures, which was attributed to molecular aggregation. Cells were subjected to various concentrations of glycerol, dimethyl sulfoxide (DMSO), and sodium butyrate (NaBu) in a variety of culture systems and conditions. The addition of both NaBu and DMSO resulted in higher specific productivities but reduced growth rates that resulted in a net reduction of interferon produced. Glycerol appeared to stabilize the secreted beta-IFN, resulting in reduced aggregation, despite a decrease in cell growth rate. Glycosylation analysis of isolated beta-IFN showed a time-dependent decrease in sialylation in batch culture that was ameliorated by the presence of glycerol. Low-temperature conditions (30 degrees C) had the greatest effect on productivity with a significant increase in beta-IFN titer as well as a reduction in the degree of molecular aggregation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".