Engineering Yeast for <i>De Novo</i> Synthesis of the Insect Repellent Nepetalactone
Bibliographic record
Abstract
While nepetalactone, the active ingredient in catnip, is a potent insect repellent, its low in planta accumulation limits its commercial viability as an alternative repellent. Here we describe for the first time de novo nepetalactone synthesis in Saccharomyces cerevisiae, enabling sustainable and scalable production. Nepetalactone production required introducing eight exogenous genes including the cytochrome P450 geraniol-8-hydroxylase, the bottleneck of the heterologous pathway. Combinatorial assessment of geraniol-8-hydroxylase and cytochrome P450 reductase variants, and copy-number variations were used to overcome this bottleneck. We found that several reductases improved hydroxylation activity and increasing geraniol-8-hydroxylase gene copy number improved 8-hydroxygeraniol titers. The accumulation of an unwanted metabolite implied inefficient channeling of carbon through the pathway. With the native yeast old yellow enzymes previously shown to use monoterpene intermediates as substrates, both homologues were deleted. These deletions increased 8-hydroxygeraniol yield, resulting in 3.10 mg/L/OD 600 of nepetalactone from simple sugar in microtiter plates. This optimized pathway will benefit the development of high yielding strains for the scale up production of nepetalactone.
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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 | 0.001 |
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".