High Humidity Induces Abscisic Acid 8′-Hydroxylase in Stomata and Vasculature to Regulate Local and Systemic Abscisic Acid Responses in Arabidopsis
Bibliographic record
Abstract
Levels of endogenous abscisic acid (ABA) are changed dynamically in response to environmental conditions. The ABA 8'-hydroxylase is a key enzyme in ABA catabolism and is encoded by CYP707A genes. In this study, we examined physiological roles of Arabidopsis (Arabidopsis thaliana) CYP707As in the plant's response to changes in humidity. The cyp707a1 and cyp707a3 mutants displayed lower stomatal conductance under turgid conditions (relative humidity 60%) than the wild type. When wild-type plants were transferred to high-humidity conditions (relative humidity 90%), CYP707A1 and CYP707A3 transcript levels increased, followed by the reduction of ABA levels. The cyp707a3 mutant exhibited high ABA levels even after transferring to high-humidity conditions, whereas, under similar conditions, the cyp707a1 mutant exhibited low ABA levels comparable to the wild type. Analysis of spatial expression patterns by using transgenic plants harboring a promoterbeta-glucuronidase gene indicated that high-humidity-induced expression of CYP707A1 and CYP707A3 occurred primarily in guard cells and vascular tissues, respectively. Furthermore, stomatal closure of the cyp707a1 mutant, but not cyp707a3 mutant, was ABA hypersensitive when epidermal peel was treated with exogenous ABA, suggesting that CYP707A1 is essential for ABA catabolism inside the guard cells. These results implicate that CYP707A3 reduces the amount of mobile ABA in vascular tissues in response to high humidity, whereas CYP707A1 inactivates local ABA pools inside the guard cells. Taken together, ABA catabolism in both vascular tissues and guard cells participates in the systemic ABA action that controls stomatal movement in response to high humidity.
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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".