Temporal transcriptomes unravel the effects of heat stress on seed germination during wheat grain filling
Bibliographic record
Abstract
Abstract Promoting seed germination after short episodes of heat stress during the wheat grain filling stage is a serious problem that results in pre‐harvest sprouting. The plant hormones abscisic acid (ABA), gibberellins (GAs), and ethylene (ETH) are well known to be involved in germination control. However, the genes associated with the metabolism and responsiveness of these hormones to heat stress during wheat grain filling are not well understood. Transcriptomic analysis was carried out to explore the mechanisms controlling seed germination under five days (15–20 days after flowering, DAF) of heat stress (20, 24, 28, and 32°C) in wheat grains at 15–30 DAF using comparative RNA sequencing. A dataset of 2073 differentially regulated genes was used to help elucidate the molecular mechanisms that respond to heat stress and affect seed germination in wheat. Some genes related to ABA, GA, and ETH biosynthesis, transport, and signaling had significantly different expression levels under heat stress. Among these genes, the transcriptional alterations of plant hormone‐related genes, such as NCED9 , AAO3 , CYP707A2 , GA20ox , and SAM1 uncovered here, provide a foundation for identifying key players involved in determining seed dormancy and germination. The expression levels of many germination‐related genes did not linearly increase with increasing temperature. In this study, 28°C is a threshold of temperature tolerance during the grain filling stage. Heat stress, especially extremely high temperature (>28°C), represses ABA‐related gene expression and promotes seed germination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".