Tissue-Specific Regulation of Gibberellin Biosynthesis in Developing Pea Seeds
Bibliographic record
Abstract
Previous work suggests that gibberellins (GAs) play an important role in early seed development. To more fully understand the roles of GAs throughout seed development, tissue-specific transcription profiles of GA metabolism genes and quantitative profiles of key GAs were determined in pea (Pisum sativum) seeds during the seed-filling development period (8-20 d after anthesis [DAA]). These profiles were correlated with seed photoassimilate acquisition and storage as well as morphological development. Seed coat growth (8-12 DAA) and the subsequent dramatic expansion of branched parenchyma cells were correlated with both transcript abundance of GA biosynthesis genes and the concentration of the growth effector GA, GA(1). These results suggest GA(1) involvement in determining the rate of seed coat growth and sink strength. The endosperm's PsGA20ox transcript abundance and the concentration of GA(20) increased markedly as the endosperm reached its maximum volume (12 DAA), thus providing ample GA(20) substrate for the GA 3-oxidases present in both the embryo and seed coat. Furthermore, PsGA3ox transcript profiles and trends in GA(1) levels in embryos at 10 to 16 DAA and also in embryo axes at 18 DAA suggest localized GA(1)-induced growth in these tissues. A shift from synthesis of GA(1) to that of GA(8) occurred after 18 DAA in the embryo axis, suggesting that deactivation of GA(1) to GA(8) is a likely mechanism to limit embryo axis growth and allow embryo maturation to proceed. We hypothesize that GA biosynthesis and catabolism are tightly regulated to bring about the unique developmental events that occur during seed growth, development, and maturation.
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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".