Transcriptome mining, functional characterization, and phylogeny of a large terpene synthase gene family in spruce (Piceaspp.)
Bibliographic record
Abstract
BACKGROUND: In conifers, terpene synthases (TPSs) of the gymnosperm-specific TPS-d subfamily form a diverse array of mono-, sesqui-, and diterpenoid compounds, which are components of the oleoresin secretions and volatile emissions. These compounds contribute to defence against herbivores and pathogens and perhaps also protect against abiotic stress. RESULTS: The availability of extensive transcriptome resources in the form of expressed sequence tags (ESTs) and full-length cDNAs in several spruce (Picea) species allowed us to estimate that a conifer genome contains at least 69 unique and transcriptionally active TPS genes. This number is comparable to the number of TPSs found in any of the sequenced and well-annotated angiosperm genomes. We functionally characterized a total of 21 spruce TPSs: 12 from Sitka spruce (P. sitchensis), 5 from white spruce (P. glauca), and 4 from hybrid white spruce (P. glauca × P. engelmannii), which included 15 monoterpene synthases, 4 sesquiterpene synthases, and 2 diterpene synthases. CONCLUSIONS: The functional diversity of these characterized TPSs parallels the diversity of terpenoids found in the oleoresin and volatile emissions of Sitka spruce and provides a context for understanding this chemical diversity at the molecular and mechanistic levels. The comparative characterization of Sitka spruce and Norway spruce diterpene synthases revealed the natural occurrence of TPS sequence variants between closely related spruce species, confirming a previous prediction from site-directed mutagenesis and modelling.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".