The MKK3 MAPK cascade integrates temperature and after-ripening signals to modulate seed germination
Bibliographic record
Abstract
The timing of seed germination is controlled by the combination of internal dormancy and external factors. Temperature is a major environmental factor for seed germination. The permissive temperature range for germination is narrow in dormant seeds and expands during after-ripening (AR) (dormancy release). Quantitative trait loci analyses of preharvest sprouting in cereals have revealed that MKK3, a mitogen-activated protein kinase (MAPK) cascade protein, is a negative regulator of grain dormancy. Here, we show that the MAPKKK19/20-MKK3-MPK1/2/7/14 cascade modulates the germination temperature range in Arabidopsis seeds by elevating the germinability of the seeds at sub- and supraoptimal temperatures. The expression of MAPKKK19 and MAPKKK20 is induced around optimal temperature for germination in after-ripened seeds but repressed in dormant seeds. MPK7 activation depends on the expression levels of MAPKKK19/20 , with expression occurring under conditions permissive for germination. Abscisic acid (ABA) and gibberellin (GA) are two major phytohormones which are involved in germination control. Activation of the MKK3 cascade represses ABA biosynthesis enzyme gene expression and induces expression of ABA catabolic enzyme and GA biosynthesis enzyme genes, resulting in expansion of the germinable temperature range. Our data demonstrate that the MKK3 cascade integrates temperature and AR signals to phytohormone metabolism and seed germination.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".