SnRK1α1 Antagonizes Cell Death Induced by Transient Overexpression of Arabidopsis thaliana ABI5 Binding Protein 2 (AFP2)
Bibliographic record
Abstract
Plants are continuously exposed to environmental stressors. They have thus evolved complex signaling pathways to govern responses to a variety of stimuli. The hormone abscisic acid (ABA) has been implicated in modulating both abiotic and biotic stress responses in plants. ABI5 Binding Proteins (AFPs) are a family of negative regulators of bZIP transcription factors of the AREB/ABF family, which promote ABA responses. AFP2 interacts with Snf1-Related protein Kinase 1 (SnRK1), which belongs to a highly conserved heterotrimeric kinase complex that is activated to re-establish energy homeostasis following stress. However, the role of this interaction is currently unknown. Here, we show that transient overexpression of Arabidopsis thaliana AFP2 in Nicotiana benthamiana leaves induces cell death. Using truncated AFP2 constructs, we demonstrate that CD induction by AFP2 is dependent on the EAR domain. Co-expression of the catalytic subunit SnRK1⍺1, but not SnRK1⍺2, rescues AFP2-induced CD. Overexpression of SnRK1⍺1 has little effect on AFP2 protein level and does not affect AFP2 subcellular localization. Our results show that a high level of AFP2 is detrimental for cell function and that SnRK1 antagonizes AFP2-induced CD most likely through a mechanism that does not involve AFP2 protein degradation or a change in subcellular localization.
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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 | 0.000 |
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".