<scp>ABI</scp>4 mediates antagonistic effects of abscisic acid and gibberellins at transcript and protein levels
Bibliographic record
Abstract
Abscisic acid (ABA) and gibberellins (GAs) are plant hormones which antagonistically mediate numerous physiological processes, and their optimal balance is essential for normal plant development. However, the molecular mechanism underlying ABA and GA antagonism still needs to be determined. Here, we report that ABA-INSENSITIVE 4 (ABI4) is a central factor in GA/ABA homeostasis and antagonism in post-germination stages. ABI4 overexpression in Arabidopsis (OE-ABI4) leads to developmental defects including a decrease in plant height and poor seed production. The transcription of a key ABA biosynthetic gene, NCED6, and of a key GA catabolic gene, GA2ox7, is significantly enhanced by ABI4 overexpression. ABI4 activates NCED6 and GA2ox7 transcription by directly binding to the promoters, and genetic analysis revealed that mutation in these two genes partially rescues the dwarf phenotype of ABI4 overexpressing plants. Consistently, ABI4 overexpressing seedlings have a lower GA/ABA ratio than the wild type. We further show that ABA induces GA2ox7 transcription while GA represses NCED6 expression in an ABI4-dependent manner; and that ABA stabilizes the ABI4 protein whereas GA promotes its degradation. Taken together, these results suggest that ABA and GA antagonize each other by oppositely acting on ABI4 transcript and protein levels.
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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 | 0.001 |
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".