Enzymatic synthesis of valerena‐4,7(11)‐diene by a unique sesquiterpene synthase from the valerian plant (<i>Valeriana</i> <i>officinalis</i>)
Bibliographic record
Abstract
Valerian (Valeriana officinalis) is a popular medicinal plant in North America and Europe. Its root extract is commonly used as a mild sedative and anxiolytic. Among dozens of chemical constituents (e.g. alkaloids, iridoids, flavonoids, and terpenoids) found in valerian root, valerena-4,7(11)-diene and valerenic acid (C15 sesquiterpenoid) have been suggested as the active ingredients responsible for the sedative effect. However, the biosynthesis of the valerena-4,7(11)-diene hydrocarbon skeleton in valerian remains unknown to date. To identify the responsible terpene synthase, next-generation sequencing (Roche 454 pyrosequencing) was used to generate ∼ 1 million transcript reads from valerian root. From the assembled transcripts, two sesquiterpene synthases were identified (VoTPS1 and VoTPS2), both of which showed predominant expression patterns in root. Transgenic yeast expressing VoTPS1 and VoTPS2 produced germacrene C/germacrene D and valerena-4,7(11)-diene, respectively, as major terpene products. Purified VoTPS1 and VoTPS2 recombinant enzymes confirmed these activities in vitro, with competent kinetic properties (K(m) of ∼ 10 μm and k(cat) of 0.01 s(-1) for both enzymes). The structure of the valerena-4,7(11)-diene produced from the yeast expressing VoTPS2 was further substantiated by (13) C-NMR and GC-MS in comparison with the synthetic standard. This study demonstrates an integrative approach involving next-generation sequencing and metabolically engineered microbes to expand our knowledge of terpenoid diversity in medicinal plants.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".