A vaccine grade of yeast Saccharomyces cerevisiae expressing mammalian myostatin
Bibliographic record
Abstract
BACKGROUND: Yeast Saccharomyces cerevisiae is a widely-used system for protein expression. We previously showed that heat-killed whole recombinant yeast vaccine expressing mammalian myostatin can modulate myostatin function in mice, resulting in increase of body weight and muscle composition in these animals. Foreign DNA introduced into yeast cells can be lost soon unless cells are continuously cultured in selection media, which usually contain antibiotics. For cost and safety concerns, it is essential to optimize conditions to produce quality food and pharmaceutical products. RESULTS: We developed a simple but effective method to engineer a yeast strain stably expressing mammalian myostatin. This method utilized high-copy-number integration of myostatin gene into the ribosomal DNA of Saccharomyces cerevisiae. In the final step, antibiotic selection marker was removed using the Cre-LoxP system to minimize any possible side-effects for animals. The resulting yeast strain can be maintained in rich culture media and stably express mammalian myostatin for two years. Oral administration of the recombinant yeast was able to induce immune response to myostatin and modulated the body weight of mice. CONCLUSIONS: Establishment of such yeast strain is a step further toward transformation of yeast cells into edible vaccine to improve meat production in farm animals and treat human muscle-wasting diseases in the future.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.007 | 0.007 |
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".