Biosynthesis of Sandalwood Oil: Santalum album CYP76F Cytochromes P450 Produce Santalols and Bergamotol
Bibliographic record
Abstract
Sandalwood oil is one of the world's most highly prized essential oils, appearing in many high-end perfumes and fragrances. Extracted from the mature heartwood of several Santalum species, sandalwood oil is comprised mainly of sesquiterpene olefins and alcohols. Four sesquiterpenols, α-, β-, and epi-β-santalol and α-exo-bergamotol, make up approximately 90% of the oil of Santalum album. These compounds are the hydroxylated analogues of α-, β-, and epi-β-santalene and α-exo-bergamotene. By mining a transcriptome database of S. album for candidate cytochrome P450 genes, we cloned and characterized cDNAs encoding a small family of ten cytochrome P450-dependent monooxygenases annotated as SaCYP76F37v1, SaCYP76F37v2, SaCYP76F38v1, SaCYP76F38v2, SaCYP76F39v1, SaCYP76F39v2, SaCYP76F40, SaCYP76F41, SaCYP76F42, and SaCYP76F43. Nine of these genes were functionally characterized using in vitro assays and yeast in vivo assays to encode santalene/bergamotene oxidases and bergamotene oxidases. These results provide a foundation for production of sandalwood oil for the fragrance industry by means of metabolic engineering, as demonstrated with proof-of-concept formation of santalols and bergamotol in engineered yeast cells, simultaneously addressing conservation challenges by reducing pressure on supply of sandalwood from native forests.
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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".