Functional Characterization of <i>ent</i>-Copalyl Diphosphate Synthase and Kaurene Synthase Genes from <i>Coffea arabica</i> L
Bibliographic record
Abstract
The biochemical profile of coffee beans translates directly into quality traits, nutraceutical and health promoting properties of the coffee beverage. Ent -kaurene is the ubiquitous precursor for gibberellin biosynthesis in plants, but it also serves as an intermediate in specialized (i.e., secondary) diterpenoid metabolism that leads to a diversity of more than 1,000 different metabolites. Nutraceutical effects on human health attributed to diterpenes include antioxidant, anticarcinogenic, and anti-inflammatory properties. Cafestol (CAF) and kahweol (KAH) are two diterpenes found exclusively in the Coffea genus. Our objective was to identify and functionally characterize genes involved in the central step of ent -kaurene production. We identified 17 putative terpene synthase genes in the transcriptome of Coffea arabica . Two ent -copalyl diphosphate synthase ( CaCPS ) and three kaurene synthase ( CaKS ) were selected and manually annotated. Transcript expression profiles of CaCPS1 and CaKS3 best matched the CAF and KAH metabolite profiles in different tissues. CaCPS1 and CaKS3 proteins were heterologously expressed and functionally characterized. CaCPS1 catalyzes the cyclization of geranylgeranyl diphosphate (GGPP) to ent -copalyl diphosphate ( ent -CPP), which is converted to ent -kaurene by CaKS3. Knowledge about the central steps of diterpene formation in coffee provides a foundation for future characterization of the subsequent enzymes involved in CAF and KAH biosynthesis.
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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 | 0.000 |
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".