The Banana Transcriptional Repressor MaDEAR1 Negatively Regulates Cell Wall-Modifying Genes Involved in Fruit Ripening
Bibliographic record
Abstract
Ethylene plays an essential role in many biological processes including fruit ripening via modulation of ethylene signaling pathway. Ethylene Response Factors (ERFs) are key transcription factors (TFs) involved in ethylene perception and are divided into AP2, RAV, ERF, and DREB sub-families. Although a number of studies have implicated the involvement of DREB sub-family genes in stress responses, little is known about their roles in fruit ripening. In this study, we identified a DREB TF with a EAR motif, designated as MaDEAR1, which is a nucleus-localized transcriptional repressor. Expression analysis indicated that MaDEAR1 expression was repressed by ethylene, with reduced levels of histone H3 and H4 acetylation at its regulatory regions during fruit ripening. In addition, MaDEAR1 promoter activity was also suppressed in response to ethylene treatment. More importantly, MaDEAR1 directly binds to the DRE/CRT motifs in promoters of several cell wall-modifying genes including MaEXP1/3, MaPG1, MaXTH10, MaPL3, and MaPME3 associated with fruit softening during ripening and represses their activities. These data suggest that MaDEAR1 acts as a transcriptional repressor of cell wall-modifying genes, and may be negatively involved in ethylene-mediated ripening of banana fruit. Our findings provide new insights into the involvement of DREB TFs in the regulation of fruit ripening.
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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".