The APETALA-2-Like Transcription Factor OsAP2-39 Controls Key Interactions between Abscisic Acid and Gibberellin in Rice
Bibliographic record
Abstract
The interaction between phytohormones is an important mechanism which controls growth and developmental processes in plants. Deciphering these interactions is a crucial step in helping to develop crops with enhanced yield and resistance to environmental stresses. Controlling the expression level of OsAP2-39 which includes an APETALA 2 (AP2) domain leads to phenotypic changes in rice. Overexpression of OsAP2-39 leads to a reduction in yield by decreasing the biomass and the number of seeds in the transgenic rice lines. Global transcriptome analysis of the OsAP2-39 overexpression transgenic rice revealed the upregulation of a key abscisic acid (ABA) biosynthetic gene OsNCED-I which codes for 9-cis-epoxycarotenoid dioxygenase and leads to an increase in the endogenous ABA level. In addition to OsNCED-1, the gene expression analysis revealed the upregulation of a gene that codes for the Elongation of Upper most Internode (EUI) protein, an enzyme that catalyzes 16α, 17-epoxidation of non-13-hydroxylated GAs, which has been shown to deactivate gibberellins (GAs) in rice. The exogenous application of GA restores the wild-type phenotype in the transgenic line and ABA application induces the expression of EUI and suppresses the expression of OsAP2-39 in the wild-type line. These observations clarify the antagonistic relationship between ABA and GA and illustrate a mechanism that leads to homeostasis of these hormones. In vivo and in vitro analysis showed that the expression of both OsNCED-1 and EUI are directly controlled by OsAP2-39. Together, these results reveal a novel mechanism for the control of the ABA/GA balance in rice which is regulated by OsAP2-39 that in turn regulates plant growth and seed production.
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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) | 0.001 | 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 | 0.001 |
| 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".