A comprehensive investigation of starch degradation process and identification of a transcriptional activator Mab<scp>HLH</scp>6 during banana fruit ripening
Bibliographic record
Abstract
Although starch degradation has been well studied in model systems such as Arabidopsis leaves and cereal seeds, this process in starchy fruits during ripening, especially in bananas, is largely unknown. In this study, 38 genes encoding starch degradation-related proteins were identified and characterized from banana fruit. Expression analysis revealed that 27 candidate genes were significantly induced during banana fruit ripening, with concomitant conversion of starch-to-sugars. Furthermore, iTRAQ-based proteomics experiments identified 18 starch degradation-associated enzymes bound to the surface of starch granules, of which 10 were markedly up-regulated during ripening. More importantly, a novel bHLH transcription factor, MabHLH6, was identified based on a yeast one-hybrid screening using MaGWD1 promoter as a bait. Transcript and protein levels of MabHLH6 were also increased during fruit ripening. Electrophoretic mobility shift assays, chromatin immunoprecipitation and transient expression experiments confirmed that MabHLH6 activates the promoters of 11 starch degradation-related genes, including MaGWD1, MaLSF2, MaBAM1, MaBAM2, MaBAM8, MaBAM10, MaAMY3, MaAMY3C, MaISA2, MaISA3 and MapGlcT2-2 by recognizing their E-box (CANNTG) motifs present in the promoters. Collectively, these findings suggest that starch degradation during banana fruit ripening may be attributed to the complex actions of numerous enzymes related to starch breakdown at transcriptional and translational levels, and that MabHLH6 may act as a positive regulator of this process via direct activation of a series of starch degradation-related genes.
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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".