Engineering of a Nepetalactol-Producing Platform Strain of <i>Saccharomyces cerevisiae</i> for the Production of Plant Seco-Iridoids
Bibliographic record
Abstract
The monoterpene indole alkaloids (MIAs) are a valuable family of chemicals that include the anticancer drugs vinblastine and vincristine. These compounds are of global significance-appearing on the World Health Organization's list of model essential medicines-but remain exorbitantly priced due to low in planta levels. Chemical synthesis and genetic manipulation of MIA producing plants such as Catharanthus roseus have so far failed to find a solution to this problem. Synthetic biology holds a potential answer, by building the pathway into more tractable organisms such as Saccharomyces cerevisiae. Recent work has taken the first steps in this direction by producing small amounts of the intermediate strictosidine in yeast. In order to help improve on these titers, we aimed to optimize the early biosynthetic steps of the MIA pathway to the metabolite nepetalactol. We combined a number of strategies to create a base strain producing 11.4 mg/L of the precursor geraniol. We also show production of the critical intermediate 10-hydroxygeraniol and demonstrate nepetalactol production in vitro. Lastly we demonstrate that activity of the iridoid synthase toward the intermediates geraniol and 10-hydroxygeraniol results in the synthesis of the nonproductive intermediates citronellol and 10-hydroxycitronellol. This discovery has serious implications for the reconstruction of the MIA in heterologous organisms.
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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) | 0.000 | 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".