Identification of Genes in <i>Thuja plicata</i> Foliar Terpenoid Defenses
Bibliographic record
Abstract
Thuja plicata (western redcedar) is a long-lived conifer species whose foliage is rarely affected by disease or insect pests, but can be severely damaged by ungulate browsing. Deterrence to browsing correlates with high foliar levels of terpenoids, in particular the monoterpenoid α-thujone. Here, we set out to identify genes whose products may be involved in the production of α-thujone and other terpenoids in this species. First, we generated a foliar transcriptome database from which to draw candidate genes. Second, we mapped the storage of thujones and other terpenoids to foliar glands. Third, we used global expression profiling to identify more than 600 genes that are expressed at high levels in foliage with glands, but can either not be detected or are expressed at low levels in a natural variant lacking foliar glands. Fourth, we used in situ RNA hybridization to map the expression of a putative monoterpene synthase to the epithelium of glands and used enzyme assays with recombinant protein of the same gene to show that it produces sabinene, the monoterpene precursor of α-thujone. Finally, we identified candidate genes with predicted enzymatic functions for the conversion of sabinene to α-thujone. Taken together, this approach generated both general resources and detailed functional characterization in the identification of genes of foliar terpenoid biosynthesis in T. plicata.
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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".