Role of the stomatal development regulators FLP/MYB88 in abiotic stress responses
Bibliographic record
Abstract
Stomata are vital for the adaptation of plants to abiotic stress, and in turn stomatal density is modulated by environmental factors. Less clear, however, is whether regulators of stomatal development themselves participate in the sensing or response of stomata to abiotic stress. FOUR LIPS (FLP) and its paralog MYB88 encode MYB proteins that establish stomatal patterning by permitting only a single symmetric division before stomata differentiate. Hence, flp-1 myb88 double mutants have an excess of stomata, which are often misplaced in direct contact. Here, we investigate the consequences of loss of FLP/MYB88 function on the ability of Arabidopsis plants to respond to abiotic stress. While flp-1 myb88 double mutants are viable and display no obvious aerial phenotypes under normal greenhouse growth conditions, we show that flp-1 myb88 plants are significantly more susceptible to drought and high salt, and have increased rates of water loss. To determine whether flp-1 myb88 plants are already challenged under normal growth conditions, we compared genome-wide transcript levels between flp-1 myb88 and wild-type green tissues. Unexpectedly, uninduced flp-1 myb88 plants showed a reduced accumulation of many typical abiotic stress gene transcripts. Moreover, the induction of many of these stress genes under high-salt conditions was significantly lower in flp-1 myb88 plants. Our results provide evidence for a new function of FLP/MYB88 in sensing and/or transducing abiotic stress, which is severely compromised in flp-1 myb88 mutants.
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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".