Developmental and Hormonal Regulation of Gibberellin Biosynthesis and Catabolism in Pea Fruit
Bibliographic record
Abstract
In pea (Pisum sativum), normal fruit growth requires the presence of the seeds. The coordination of growth between the seed and ovary tissues involves phytohormones; however, the specific mechanisms remain speculative. This study further explores the roles of the gibberellin (GA) biosynthesis and catabolism genes during pollination and fruit development and in seed and auxin regulation of pericarp growth. Pollination and fertilization events not only increase pericarp PsGA3ox1 message levels (codes for GA 3-oxidase that converts GA(20) to bioactive GA(1)) but also reduce pericarp PsGA2ox1 mRNA levels (codes for GA 2-oxidase that mainly catabolizes GA(20) to GA(29)), suggesting a concerted regulation to increase levels of bioactive GA(1) following these events. 4-Chloroindole-3-acetic acid (4-Cl-IAA) was found to mimic the seeds in the stimulation of PsGA3ox1 and the repression of PsGA2ox1 mRNA levels as well as the stimulation of PsGA2ox2 mRNA levels (codes for GA 2-oxidase that mainly catabolizes GA(1) to GA(8)) in pericarp at 2 to 3 d after anthesis, while the other endogenous pea auxin, IAA, did not. This GA gene expression profile suggests that both seeds and 4-Cl-IAA can stimulate the production, as well as modulate the half-life, of bioactive GA(1), leading to initial fruit set and subsequent growth and development of the ovary. Consistent with these gene expression profiles, deseeded pericarps converted [(14)C]GA(12) to [(14)C]GA(1) only if treated with 4-Cl-IAA. These data further support the hypothesis that 4-Cl-IAA produced in the seeds is transported to the pericarp, where it differentially regulates the expression of pericarp GA biosynthesis and catabolism genes to modulate the level of bioactive GA(1) required for initial fruit set and 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) | 0.001 | 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".