Enhancing the copy number of episomal plasmids in Saccharomyces cerevisiae for improved protein production
Bibliographic record
Abstract
2 μm-based episomal expression vectors are widely used in Saccharomyces cerevisiae for recombinant protein production and synthetic pathway optimization. In this study, we report a new approach to increase the plasmid copy number (PCN) and thus improve the expression of plasmid-encoded proteins. This was achieved by combining destabilization of the marker protein with decreasing the marker gene transcription level. Destabilization of the marker protein alone by fusing a ubiquitin/N-degron tag (ubi-tag) to the N-terminus of the Ura3 marker protein could increase the PCN and activity of LacZ expressed from the same vector. When arginine was exposed at the N-terminus of the marker protein after cleavage of ubiquitin, the PCN and LacZ activity were increased by 70-80%. Replacement of the native URA3 promoter with the HXT1, KEX2 or URA3-d promoter resulted in an increase in the PCN and LacZ activity by about 30-100%. Combining the ubi-tag and promoter modification of the marker gene, increased the PCN and LacZ activity by threefold. We also demonstrated that this new expression vectors can be used to increase enzyme activity by improving patchoulol production by threefold.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".