Abscisic Acid Increases <i>Arabidopsis</i> ABI5 Transcription Factor Levels by Promoting KEG E3 Ligase Self-Ubiquitination and Proteasomal Degradation
Bibliographic record
Abstract
The Arabidopsis thaliana RING-type E3 ligase KEEP ON GOING (KEG) is a negative regulator of abscisic acid (ABA) signaling. Seedlings homozygous for T-DNA insertions in KEG accumulate high levels of the ABA-responsive transcription factor ABSCISIC ACID-INSENSITIVE5 (ABI5). Here, we demonstrate that KEG E3 ligase activity is required for the regulation of ABI5 abundance. KEG ubiquitinates ABI5 in vitro, and a functional KEG RING domain is required to restore the levels of ABI5 in keg-1 to that of the wild type. Overexpression of KEG leads to ABA insensitivity, which correlates with KEG protein levels. In the presence of ABA, ABI5 levels increase drastically via a decrease in ubiquitin-meditated proteasomal degradation. Our results indicate that ABA promotes ABI5 accumulation by inducing the ubiquitination and proteasomal degradation of KEG. A functional RING domain is required for the ABA-induced degradation of KEG, suggesting that the loss is due to self-ubiquitination. Mutations within KEG's kinase domain or treatments with kinase inhibitors prohibit the ABA-induced ubiquitination and degradation of KEG, indicating that phosphorylation, possibly self-phosphorylation, is involved in the ABA regulation of KEG protein levels. We discuss a model for how ABA may negatively regulate KEG protein abundance, leading to accumulation of ABI5 and ABA-dependent cellular responses.
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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) | 0.002 | 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".