Banana fruit VQ motif-containing protein5 represses cold-responsive transcription factor MaWRKY26 involved in the regulation of JA biosynthetic genes
Bibliographic record
Abstract
Most harvested fruits and vegetables are stored at low temperature but many of them are highly sensitive to chilling injury. Jasmonic acid (JA), a plant hormone associated with various stress responses, is known to reduce chilling injury in fruits. However, little is known about the transcriptional regulation of JA biosynthesis in relation to cold response of fruits. Here, we show the involvement of a Group I WRKY transcription factor (TF) from banana fruit, MaWRKY26, in regulating JA biosynthesis. MaWRKY26 was found to be nuclear-localized with transcriptional activation property. MaWRKY26 was induced by cold stress or by methyl jasmonate (MeJA), which enhances cold tolerance in banana fruit. More importantly, MaWRKY26 transactivated JA biosynthetic genes MaLOX2, MaAOS3 and MaOPR3 via binding to their promoters. Further, MaWRKY26 physically interacted with a VQ motif-containing protein MaVQ5, and the interaction attenuated MaWRKY26-induced transactivation of JA biosynthetic genes. These results strongly suggest that MaVQ5 might act as a repressor of MaWRKY26 in activating JA biosynthesis. Taken together, our findings provide new insights into the transcriptional regulation of JA biosynthesis in response to cold stress and a better understanding of the molecular aspects of chilling injury in banana fruit.
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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) | 0.002 | 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".