The bioactivity and fractionation of peptide hydrolysates in cultures of CHO cells
Bibliographic record
Abstract
Peptide hydrolysate supplements in mammalian cell cultures provide enhanced growth and productivity. The objective of this study was to compare the bioactivity of ten different commercially available hydrolysates from plant, microbial, and animal sources. The peptide hydrolysates were tested as supplements to cultures of Chinese hamster ovary (CHO) cells that produce human beta interferon (β-IFN). A soy hydrolysate was shown to support high cell growth but not protein productivity compared to an animal component hydrolysate (Primatone RL). On the other hand, a yeast hydrolysate showed lower cell growth, but comparable productivity of the recombinant protein. Glycosylation analysis showed that the glycan profile of β-IFN produced in yeast hydrolysate supplemented cultures was equivalent to that from Primatone RL-supplemented cultures. Fractionation of the yeast hydrolysate and Primatone RL produced a similar protein-assayed pattern except for one extra peak at around 1 kDa in the Primatone RL profile. A fraction taken at a molecular weight range of 1.5-1.7 kDa showed the highest growth promoting activity in both samples. However, four other fractions in yeast hydrolysate and two in Primatone RL at lower molecular weights showed some growth promoting activity. In conclusion, the yeast hydrolysates provided a good alternative to the animal sourced Primatone RL for high productivity of β-IFN from CHO cells with equivalent glycosylation.
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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 | 0.000 |
| 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".