SCFAtPP2-B11 modulates ABA signaling by facilitating SnRK2.3 degradation in Arabidopsis thaliana
Bibliographic record
Abstract
The phytohormone abscisic acid (ABA) is an essential part of the plant response to abiotic stressors such as drought. Upon the perception of ABA, pyrabactin resistance (PYR)/PYR1-like (PYL)/regulatory components of ABA receptor (RCAR) proteins interact with co-receptor protein phosphatase type 2Cs to permit activation Snf1-related protein kinase2 (SnRK2) kinases, which switch on ABA signaling by phosphorylating various target proteins. Thus, SnRK2 kinases are central regulators of ABA signaling. However, the mechanisms that regulate SnRK2 degradation remain elusive. Here, we show that SnRK2.3 is degradated by 26S proteasome system and ABA promotes its degradation. We found that SnRK2.3 interacts with AtPP2-B11 directly. AtPP2-B11 is an F-box protein that is part of a SKP1/Cullin/F-box E3 ubiquitin ligase complex that negatively regulates plant responses to ABA by specifically promoting the degradation of SnRK2.3. AtPP2-B11 was induced by ABA, and the knockdown of AtPP2-B11 expression markedly increased the ABA sensitivity of plants during seed germination and postgerminative development. Overexpression of AtPP2-B11 does not affect ABA sensitivity, but inhibits the ABA hypersensitive phenotypes of SnRK2.3 overexpression lines. These results reveal a novel mechanism through which AtPP2-B11 specifically degrades SnRK2.3 to attenuate ABA signaling and the abiotic stress response in Arabidopsis.
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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 | 0.001 |
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".