Methionine oxidation‐regulated MaERF95L controls starch and sucrose metabolism in postharvest banana during ripening
Bibliographic record
Abstract
ABSTRACT The conversion of starch into sugar during postharvest banana ( Musa acuminata , AAA group) ripening significantly influences fruit quality. Ethylene response factors (ERFs) regulate fruit ripening through ethylene signaling, while redox modifications affect their activity by post‐translational changes. This study identifies MaERF95L, an EDLL‐domain ERF in banana, as a central regulator of starch‐to‐sugar metabolism during postharvest ripening. Using electrophoretic mobility shift and dual‐luciferase reporter assays, we demonstrate that MaERF95L directly binds to and activates the expression of six genes related to starch degradation and sucrose synthesis ( MaGWD1 , MaAMY3 , MaBAM1 , MaHK5 , MaPGI1 , and MaUPG3 ). MaERF95L overexpression accelerates starch degradation and sugar accumulation in both banana and tomato fruits during ripening. Notably, methionine (Met, M)‐based oxidation modifications (e.g., Met‐16 and Met‐77) suppress MaERF95L's transcriptional regulatory function. Simulating oxidation by Met→glutamine (Gln, Q) substitutions (MaERF95L M16Q/M77Q ) alters its subcellular localization and also impairs its DNA‐binding and transcriptional activation capabilities. In contrast, blocking oxidation by Met→Valine (Val, V) substitutions (MaERF95L M16V/M77V ) maintains its transcriptional activation activity. Furthermore, transient overexpression of MaERF95L M16Q/M77Q in bananas reduced MaERF95L's activation of genes related to starch degradation and sucrose synthesis, and starch‐to‐sugar conversion. However, the overexpression of MaERF95L M16V/M77V showed no effect on MaERF95L's activation function. These findings reveal a Met oxidation‐sensitive regulatory mechanism connecting reactive oxygen species signaling to carbohydrate metabolism, providing molecular insights into quality formation regulation during ripening and potential strategies for reducing postharvest losses in climacteric fruits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".