Significance of the Expression of the CER6 Condensing Enzyme for Cuticular Wax Production in Arabidopsis
Bibliographic record
Abstract
To learn more about the role of the CER6 condensing enzyme in Arabidopsis surface wax production, we determined CER6 transcription domains and the timing of CER6 transcription in vegetative and reproductive structures from juvenile, mature, and senescing tissues. We found that CER6 is highly transcribed throughout development, exclusively in the epidermal cells in all tissues examined. The only exception to the epidermal expression was observed in anthers nearing maturity, in which CER6 mRNA was localized in the tapetum. To determine if environmental factors such as light and water deficit, which are known to stimulate wax accumulation, induce CER6 transcription, we examined the effects of these factors on CER6 transcript abundance. Our results demonstrate that light is essential for CER6 transcription, and that osmotic stress and the presence of abscisic acid enhance CER6 transcript accumulation. CER6 promoter-directed expression of the beta-glucuronidase reporter gene in transgenic plants demonstrated that the CER6 promoter was highly effective in directing epidermis-specific expression in Arabidopsis and tobacco (Nicotiana tabacum). Furthermore, CER6 promoter-driven CER6 overexpression resulted in increased wax deposition in Arabidopsis stems. These experiments indicate that the expression level of CER6 in the epidermis is one of the factors controlling wax accumulation on Arabidopsis stems.
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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".