<i>Arabidopsis</i> MADS-box transcription factor AGL21 acts as environmental surveillance for seed germination by regulating <i>ABI5</i>
Bibliographic record
Abstract
ABSTRACT Seed germination is a crucial checkpoint for plant survival under unfavorable environmental conditions. Abscisic acid (ABA) and its signaling play a vital role in integrating environmental information to regulate seed germination. MCM1/AGAMOUS/DEFICIENS/SRF (MADS)-box transcription factors are mainly known as key regulators of seed and flower development in Arabidopsis . However, their functions in seed germination are still poorly understood. Here we report that MADS-box transcription factor AGL21 negatively modulates seed germination and post-germination growth by controlling the expression of ABA-INSENSITIVE 5 ( ABI5 ) in Arabidopsis. AGL21 responds to multiple environmental stresses and plant hormones. The AGL21 -overexpressing plants are hypersensitive to ABA, salt and osmotic stresses during seed germination and early post-germination growth, whereas agl21 mutants are less sensitive. AGL21 positively regulates ABI5 expression in seeds. Genetic analyses reveal that AGL21 is epistatic to ABI5 in controlling seed germination. Chromatin immunoprecipitation assays further demonstrate that AGL21 could directly bind to the ABI5 promoter in plant cells. Taken together, our results suggest that AGL21 acts as a surveillance integrator that incorporates environmental cues and endogenous hormonal signals into ABA signaling to regulate seed germination and early post-germination growth.
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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) | 0.004 | 0.002 |
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".