A Highly Characterized Synthetic Landing Pad System for Precise Multicopy Gene Integration in Yeast
Bibliographic record
Abstract
A fundamental undertaking of metabolic engineering involves identifying and troubleshooting metabolic bottlenecks that arise from imbalances in pathway flux. To expedite the systematic screening of enzyme orthologs in conjunction with DNA copy number tuning, here we develop a simple and highly characterized CRISPR-Cas9 integration system in Saccharomyces cerevisiae. Our engineering strategy introduces a series of synthetic DNA landing pads (LP) into the S. cerevisiae genome to act as sites for high-level gene integration. LPs facilitate multicopy gene integration of one, two, three, or four DNA copies in a single transformation, thus providing precise control of DNA copy number. We applied our LP system to norcoclaurine synthase (NCS), an enzyme with poor kinetic properties involved in the first committed step of the production of high-value benzylisoquinoline alkaloids. The platform enabled rapid construction of a 40-strain NCS library by integrating ten NCS orthologs in four gene copies each. Six active NCS variants were identified, whereby production of ( S)-norcoclaurine could be further enhanced by increasing NCS copy number. We anticipate the LP system will aid in metabolic engineering efforts by providing strict control of gene copy number and expediting strain and pathway engineering campaigns.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".