Developing a Yeast Platform Strain for an Enhanced Taxadiene Biosynthesis by CRISPR/Cas9
Bibliographic record
Abstract
Paclitaxel is an important diterpenoid commonly used as an anticancer drug. Although the paclitaxel biosynthetic pathway has been mostly revealed, some steps remain to be elucidated. The difficulties in plant transformations and the scarcity of the precursor of paclitaxel, (+)-taxa-4(5), 11(12)-diene (taxadiene), have hindered the full comprehension of paclitaxel biochemistry and, therefore, its production by biotechnological approaches. One solution is to use the budding yeast, Saccharomyces cerevisiae, as a platform to elucidate the paclitaxel biosynthesis. As taxadiene is a diterpenoid, its common precursor, geranylgeranyl pyrophosphate (GGPP), needs to be increased in yeast. In this study, we screened various GGPP synthases (GGPPS) to find the most suitable GGPPS for taxadiene production in yeast. We also optimized the taxadiene production by increasing the flux toward the terpenoid pathway. Finally, to remove selection markers, we integrated the required genes using a CRISPR/Cas9 system in the yeast genome. Our result showed that a titer of 2.02 ± 0.40 mg/L (plasmid) and 0.41 ± 0.06 mg/L (integrated) can be achieved using these strategies. This platform strain can be used to readily test the gene candidates for microbial paclitaxel biosynthesis 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".