Arabidopsis <i>ECERIFERUM9</i> Involvement in Cuticle Formation and Maintenance of Plant Water Status
Bibliographic record
Abstract
Mutation of the ECERIFERUM9 (CER9) gene in Arabidopsis (Arabidopsis thaliana) causes elevated amounts of 18-carbon-length cutin monomers and a dramatic shift in the cuticular wax profile (especially on leaves) toward the very-long-chain free fatty acids tetracosanoic acid (C₂₄) and hexacosanoic acid (C₂₆). Relative to the wild type, cer9 mutants exhibit elevated cuticle membrane thickness over epidermal cells and cuticular ledges with increased occlusion of the stomatal pore. The cuticular phenotypes of cer9 are associated with delayed onset of wilting in plants experiencing water deficit, lower transpiration rates, and improved water use efficiency measured as carbon isotope discrimination. The CER9 protein thus encodes a novel determinant of plant drought tolerance-associated traits, one whose deficiency elevates cutin synthesis, redistributes wax composition, and suppresses transpiration. Map-based cloning identified CER9, and sequence analysis predicted that it encodes an E3 ubiquitin ligase homologous to yeast Doa10 (previously shown to target endoplasmic reticulum proteins for proteasomal degradation). To further elucidate CER9 function, the impact of CER9 deficiency on interactions with other genes was examined using double mutant and transcriptome analyses. For both wax and cutin, cer9 showed mostly additive effects with cer6, long-chain acyl-CoA synthetase1 (lacs1), and lacs2 and revealed its role in early steps of both wax and cutin synthetic pathways. Transcriptome analysis revealed that the cer9 mutation affected diverse cellular processes, with primary impact on genes associated with diverse stress responses. The discovery of CER9 lays new groundwork for developing novel cuticle-based strategies for improving the drought tolerance and water use efficiency of crop plants.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".