Highly Sprouting-Tolerant Wheat Grain Exhibits Extreme Dormancy and Cold Imbibition-Resistant Accumulation of Abscisic Acid
Bibliographic record
Abstract
Pre-harvest sprouting (PHS) of wheat (Triticum aestivum L.) grains induces hydrolyzing enzymes such as α-amylase, which considerably decreases wheat product quality. PHS occurs when cool and wet weather conditions before harvest break dormancy and induce grain germination. In this study, we used PHS-tolerant varieties, Gifu-komugi (Gifu) and OS38, to characterize the mechanisms of both dormancy breakage and dormancy maintenance at low temperatures. Physiologically mature Gifu grains exhibited dormancy after imbibition at 20°C, but germinated at 15°C. In contrast, OS38 grains remained dormant even at temperatures as low as 5°C. Embryo half-grains cut out from the dormant Gifu grains germinated by imbibition at 20°C, similar to conventional varieties worldwide. However, OS38 embryo half-grains were still dormant. Hormonome and pharmacological analyses suggested that ABA and gibberellin metabolism are important for temperature-dependent dormancy maintenance and breakage. Imbibition at 15°C decreased ABA levels but increased gibberellin levels in embryos of freshly harvested Gifu grains. Additionally, low temperatures induced expression of the ABA catabolism genes,TaABA8' OH1 and TaABA8' OH2, and the gibberellin biosynthesis gene,TaGA3ox2, in the embryos. However, in embryos of freshly harvested OS38 grains, ABA levels were increased while gibberellin levels were suppressed at 15°C. In these dormant embryos, low temperatures induced the TaNCED ABA biosynthesis genes, but suppressed TaABA8' OH2 and TaGA3ox2.These results show that the regulatory mechanism influencing the expression of ABA and gibberellin metabolism genes may be critical for dormancy maintenance and breakage at low temperatures. Our findings should help improve PHS-resistant wheat breeding programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".