The SlWRKY42–SlMYC2 module synergistically enhances tomato saline–alkali tolerance by activating the jasmonic acid signaling and spermidine biosynthesis pathway
Bibliographic record
Abstract
Tomato (Solanum lycopersicum) is an important crop but frequently experiences saline-alkali stress. Our previous studies have shown that exogenous spermidine (Spd) could significantly enhance the saline-alkali resistance of tomato seedlings, in which a high concentration of Spd and jasmonic acid (JA) exerted important roles. However, the mechanism of Spd and JA accumulation remains unclear. Herein, SlWRKY42, a Group II WRKY transcription factor, was identified in response to saline-alkali stress. Overexpression of SlWRKY42 improved tomato saline-alkali tolerance. Meanwhile, SlWRKY42 knockout mutants, exhibited an opposite phenotype. RNA-sequencing data also indicated that SlWRKY42 regulated the expression of genes involved in JA signaling and Spd synthesis under saline-alkali stress. SlWRKY42 is directly bound to the promoters of SlSPDS2 and SlNHX4 to promote Spd accumulation and ionic balance, respectively. SlWRKY42 interacted with SlMYC2. Importantly, SlMYC2 is also bound to the promoter of SlSPDS2 to promote Spd accumulation and positively regulated saline-alkali tolerance. Furthermore, the interaction of SlMYC2 with SlWRKY42 boosted SlWRKY42's transcriptional activity on SlSPDS2, ultimately enhancing the tomato's saline-alkali tolerance. Overall, our findings indicated that SlWRKY42 and SlMYC2 promoted saline-alkali tolerance by the Spd biosynthesis pathway. Thus, this provides new insight into the mechanisms of plant saline-alkali tolerance responses triggered by polyamines (PAs).
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".