Composition and Physiological Function of the Wax Layers Coating Arabidopsis Leaves: β-Amyrin Negatively Affects the Intracuticular Water Barrier
Bibliographic record
Abstract
Plants prevent dehydration by coating their aerial, primary organs with waxes. Wax compositions frequently differ between species, organs, and developmental stages, probably to balance limiting nonstomatal water loss with various other ecophysiological roles of surface waxes. To establish structure-function relationships, we quantified the composition and transpiration barrier properties of the gl1 mutant leaf waxes of Arabidopsis (Arabidopsis thaliana) to the necessary spatial resolution. The waxes coating the upper and lower leaf surfaces had distinct compositions. Moreover, within the adaxial wax, the epicuticular layer contained more wax and a higher relative quantity of alkanes, whereas the intracuticular wax had a higher percentage of alcohols. The wax formed a barrier against nonstomatal water loss, where the outer layer contributed twice as much resistance as the inner layer. Based on this detailed description of Arabidopsis leaf waxes, structure-function relationships can now be established by manipulating one cuticle component and assessing the effect on cuticle functions. Next, we ectopically expressed the triterpenoid synthase gene AtLUP4 (for lupeol synthase4 or β-amyrin synthase) to compare water loss with and without added cuticular triterpenoids in Arabidopsis leaf waxes. β-Amyrin accumulated solely in the intracuticular wax, constituting up to 4% of this wax layer, without other concomitant changes of wax composition. This triterpenoid accumulation caused a significant reduction in the water barrier effectiveness of the intracuticular wax.
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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 | 0.001 |
| 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".