The mutual regulation between <scp>DcEBF1</scp>/2 and <scp>DcEIL3</scp>‐1 is involved in ethylene induced petal senescence in carnation (<i>Dianthus caryophyllus</i> L.)
Bibliographic record
Abstract
Carnation (Dianthus caryophyllus L.) is a respiratory climacteric flower, comprising one of the most important cut flowers that is extremely sensitive to plant hormone ethylene. Ethylene signaling core transcription factor DcEIL3-1 plays a key role in ethylene induced petal senescence in carnation. However, how the dose of DcEIL3-1 is regulated in the carnation petal senescence process is still not clear. Here, we screened out two EBF (EIN3 Binding F-box) genes, DcEBF1 and DcEBF2, which showed quick elevation by ethylene treatment according to the ethylene induced carnation petal senescence transcriptome. Silencing of DcEBF1 and DcEBF2 accelerated, whereas overexpression of DcEBF1 and DcEBF2 delayed, ethylene induced petal senescence in carnation by influencing DcEIL3-1 downstream target genes but not DcEIL3-1 itself. Furthermore, DcEBF1 and DcEBF2 interact with DcEIL3-1 to degrade DcEIL3-1 via an ubiquitination pathway in vitro and in vivo. Finally, DcEIL3-1 binds to the promoter regions of DcEBF1 and DcEBF2 to activate their expression. In conclusion, the present study reveals the mutual regulation between DcEBF1/2 and DcEIL3-1 during ethylene induced petal senescence in carnation, which not only expands our understanding about ethylene signal regulation network in the carnation petal senescence process, but also provides potential targets with respect to breeding a cultivar of long-lived cut carnation.
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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 | 0.001 |
| 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".