Modulation in the ratio of abscisic acid to gibberellin level determines genetic variation of seed dormancy in barley (Hordeum vulgare L.)
Bibliographic record
Abstract
Abscisic acid (ABA) and gibberellin (GA) are major regulators of seed dormancy, an adaptive trait closely associated with preharvest sprouting. This study examined transcriptional regulation of ABA and GA metabolism genes and modulation of ABA and GA levels in seeds of barley genotypes exhibiting a range of dormancy phenotype. We observed a very strong negative correlation between genetic variation in seed germination and embryonic ABA level (r = 0.85), which is regulated by transcriptional modulation of HvNCED1 and/or HvCYP707A genes. A strong positive correlation was evident between variation in seed germination and GA level (r = 0.64), mediated via transcriptional regulation of GA biosynthesis genes, HvGA20ox2 and/or HvGA3oxs, and GA catabolism genes, HvGA2ox3 and/or HvGA3ox6. Modulation of the ABA and GA levels in the genotypes led to the prevalence of ABA to GA level ratio that exhibited a very strong negative correlation (r = 0.84) with seed germination, highlighting the importance of a shift in ABA/GA ratio in determining genetic variation of dormancy in barley seeds. Our results overall show that transcriptional regulation of specific ABA and GA metabolism genes underlies genetic variation in ABA/GA ratio and seed dormancy, reflecting the potential use of these genes as molecular tools for enhancing preharvest sprouting resistance in barley.
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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".