The banana fruit Dof transcription factor MaDof23 acts as a repressor and interacts with MaERF9 in regulating ripening-related genes
Bibliographic record
Abstract
The DNA binding with one finger (Dof) proteins, a family of plant-specific transcription factors, are involved in a variety of plant biological processes. However, little information is available on their involvement in fruit ripening. We have characterized 25 MaDof genes from banana fruit (Musa acuminata), designated as MaDof1-MaDof25 Gene expression analysis in fruit subjected to different ripening conditions revealed that MaDofs were differentially expressed during different stages of ripening. MaDof10, 23, 24, and 25 were ethylene-inducible and nuclear-localized, and their transcript levels increased during fruit ripening. Moreover, yeast two-hybrid and bimolecular fluorescence complementation analyses demonstrated a physical interaction between MaDof23 and MaERF9, a potential regulator of fruit ripening reported in a previous study. We determined that MaDof23 is a transcriptional repressor, whereas MaERF9 is a transcriptional activator. We suggest that they might act antagonistically in regulating 10 ripening-related genes, including MaEXP1/2/3/5, MaXET7, MaPG1, MaPME3, MaPL2, MaCAT, and MaPDC, which are associated with cell wall degradation and aroma formation. Taken together, our findings provide new insight into the transcriptional regulation network controlling banana 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".