Exploring diterpene metabolism in non‐model species: transcriptome‐enabled discovery and functional characterization of labda‐7,13<i>E</i>‐dienyl diphosphate synthase from <i>Grindelia robusta</i>
Bibliographic record
Abstract
Grindelia robusta or gumweed, is a medicinal herb of the sunflower family that forms a diverse suite of diterpenoid natural products. Its major constituents, grindelic acid and related grindelane diterpenoids accumulate in a resinous exudate covering the plants' surfaces, most prominently the unopened composite flower. Recent studies demonstrated potential pharmaceutical applications for grindelic acid and its synthetic derivatives. Mining of the previously published transcriptome of G. robusta flower tissue identified two additional diterpene synthases (diTPSs). We report the in vitro and in vivo functional characterization of an ent-kaurene synthase of general metabolism (GrTPS4) and a class II diTPS (GrTPS2) of specialized metabolism that converts geranylgeranyl diphosphate (GGPP) into labda-7,13E-dienyl diphosphate as verified by nuclear magnetic resonance (NMR) analysis. Tissue-specific transcript abundance of GrTPS2 in leaves and flowers accompanied by the presence of an endocyclic 7,13 double bond in labda-7,13E-dienyl diphosphate suggest that GrTPS2 catalyzes the first committed reaction in the biosynthesis of grindelic acid and related grindelane metabolites. With the formation of labda-7,13E-dienyl diphosphate, GrTPS2 adds an additional function to the portfolio of monofunctional class II diTPSs, which catalytically most closely resembles the bifunctional labda-7,13E-dien-15-ol synthase of the lycopod Selaginella moellendorffii. Together with a recently identified functional diTPS pair of G. robusta producing manoyl oxide, GrTPS2 lays the biosynthetic foundation of the diverse array of labdane-related diterpenoids in the genus Grindelia. Knowledge of these natural diterpenoid metabolic pathways paves the way for developing biotechnology approaches toward producing grindelic acid and related bioproducts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".