ABA guides stomatal proliferation and patterning through the <i>EPF</i>-<i>SPCH</i> signaling pathway in <i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Adaptation to dehydration stress requires plants to coordinate environmental and endogenous signals to inhibit stomatal proliferation and modulate their patterning. The stress hormone abscisic acid (ABA) induces stomatal closure and restricts stomatal lineage to promote stress tolerance. Here, we report that mutants with reduced ABA levels, xer-1, xer-2 and aba2-2, developed stomatal clusters. Similarly, the ABA signaling mutant snrk2.2/2.3/2.6, which lacks core ABA signaling kinases, also displayed stomatal clusters. Exposure to ABA or inhibition of ABA catabolism rescued the increased stomatal density and spacing defects observed in xer and aba2-2, suggesting that basal ABA is required for correct stomatal density and spacing. xer-1 and aba2-2 displayed reduced expression of EPF1 and EPF2, and enhanced expression of SPCH and MUTE. Furthermore, ABA suppressed elevated SPCH and MUTE expression in epf2-1 and epf1-1, and partially rescued epf2-1 stomatal index and epf1-1 clustering defects. Genetic analysis demonstrated that XER acts upstream of the EPF2-SPCH pathway to suppress stomatal proliferation, and in parallel with EPF1 to ensure correct stomatal spacing. These results show that basal ABA and functional ABA signaling are required to fine-tune stomatal density and patterning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".