The Arabidopsis transcription factor LBD15 mediates ABA signaling and tolerance of water‐deficit stress by regulating <i>ABI4</i> expression
Bibliographic record
Abstract
To survive, sessile plants must adapt to grow and develop when facing water-deficit stress. However, the molecular mechanisms underlying fine-tuning of the antagonistic action between stress response and growth remain to be determined. Here, plants overexpressing Lateral Organ Boundaries Domain 15 (LBD15) showed abscisic acid (ABA) hypersensitivity and tolerance of water-deficit stress, whereas the loss-of-function mutant lbd15 presented decreased sensitivity to ABA and increased sensitivity to water-deficit stress. Further analysis revealed that LBD15 directly binds to the promoter of the ABA signaling pathway gene ABSCISIC ACID INSENSITIVE4 (ABI4) to activate its expression, thereby forming an LBD15-ABI4 cascade to optimally regulate ABA signaling-mediated plant growth and tolerance of water-deficit stress. In addition, drought stress-induced ABA signaling promoted LBD15 expression, which directly activates expression of ABI4 to close stomata. As a result, water loss is reduced, and then water-deficit stress tolerance is increased. The results of this study reveal a molecular mechanism by which LBD15 coordinates and balances plant growth and resistance to water-deficit stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".