Improving peppermint essential oil yield and composition by metabolic engineering
Bibliographic record
Abstract
Peppermint (Mentha × piperita L.) was transformed with various gene constructs to evaluate the utility of metabolic engineering for improving essential oil yield and composition. Oil yield increases were achieved by overexpressing genes involved in the supply of precursors through the 2C-methyl-D-erythritol 4-phosphate (MEP) pathway. Two-gene combinations to enhance both oil yield and composition in a single transgenic line were assessed as well. The most promising results were obtained by transforming plants expressing an antisense version of (+)-menthofuran synthase, which is critical for adjusting the levels of specific undesirable oil constituents, with a construct for the overexpression of the MEP pathway gene 1-deoxy-D-xylulose 5-phosphate reductoisomerase (up to 61% oil yield increase over wild-type controls with low levels of the undesirable side-product (+)-menthofuran and its intermediate (+)-pulegone). Elite transgenic lines were advanced to multiyear field trials, which demonstrated consistent oil yield increases of up to 78% over wild-type controls and desirable effects on oil composition under commercial growth conditions. The transgenic expression of a gene encoding (+)-limonene synthase was used to accumulate elevated levels of (+)-limonene, which allows oil derived from transgenic plants to be recognized during the processing of commercial formulations containing peppermint oil. Our study illustrates the utility of metabolic engineering for the sustainable agricultural production of high quality essential oils at a competitive cost.
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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 | 0.000 | 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".