The dioxygenase GIM2 functions in seed germination by altering gibberellin production in <i>Arabidopsis</i>
Bibliographic record
Abstract
Abstract The phytohormones gibberellic acid (GA) and abscisic acid (ABA) antagonistically control seed germination. High levels of GA favor seed germination, whereas high levels of ABA hinder this process. The direct relationship between GA biosynthesis and seed germination ability need further investigation. Here, we identified the ABA‐insensitive gain‐of‐function mutant germination insensitive to ABA mutant 2 (gim2) by screening a population of XVE T‐DNA‐tagged mutant lines. Based on two loss‐of‐function gim2‐ko mutant lines, the disruption of GIM2 function caused a delay in seed germination. By contrast, upregulation of GIM2 accelerated seed germination, as observed in transgenic lines overexpressing GIM2 (OE). We detected a reduction in endogenous bioactive GA levels and an increase in endogenous ABA levels in the gim2‐ko mutants compared to wild type. Conversely, the OE lines had increased endogenous bioactive GA levels and decreased endogenous ABA levels. The expression levels of a set of GA‐ and/or ABA‐related genes were altered in both the gim2‐ko mutants and the OE lines. We confirmed that GIM2 has dioxygenase activity using an in vitro enzyme assay, observing that GIM2 can oxidize GA12. Hence, our characterization of GIM2 demonstrates that it plays a role in seed germination by affecting the GA metabolic pathway in Arabidopsis.
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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 | 0.000 |
| 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".