SlMYC2 interacted with the SlTOR promoter and mediated JA signaling to regulate growth and fruit quality in tomato
Bibliographic record
Abstract
Tomato ( Solanum lycopersicum ) is a major vegetable crop cultivated worldwide. The regulation of tomato growth and fruit quality has long been a popular research topic. MYC2 is a key regulator of the interaction between jasmonic acid (JA) signaling and other signaling pathways, and MYC2 can integrate the interaction between JA signaling and other hormone signals to regulate plant growth and development. TOR signaling is also an essential regulator of plant growth and development. However, it is unclear whether MYC2 can integrate JA signaling and TOR signaling during growth and development in tomato. Here, MeJA treatment and SlMYC2 overexpression inhibited the growth and development of tomato seedlings and photosynthesis, but increased the sugar–acid ratio and the contents of lycopene, carotenoid, soluble sugar, total phenol and flavonoids, indicating that JA signaling inhibited the growth of tomato seedlings and altered fruit quality. When TOR signaling was inhibited by RAP, the JA content increased, and the growth and photosynthesis of tomato seedlings decreased, indicating that TOR signaling positively regulated the growth and development of tomato seedlings. Further yeast one-hybrid assays showed that SlMYC2 could bind directly to the SlTOR promoter. Based on GUS staining analysis, SlMYC2 regulated the transcription of SlTOR , indicating that SlMYC2 mediated the interaction between JA and TOR signaling by acting on the promoter of SlTOR . This study provides a new strategy and some theoretical basis for tomato breeding.
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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) | 0.001 | 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".