The cotton GhWIN2 gene activates the cuticle biosynthesis pathway and influences the salicylic and jasmonic acid biosynthesis pathways
Bibliographic record
Abstract
BACKGROUND: Metabolic pathways are interconnected and yet relatively independent. Genes involved in metabolic modules are required for the modules to run. Study of the relationships between genes and metabolic modules improves the understanding of metabolic pathways in plants. The WIN transcription factor activates the cuticle biosynthesis pathway and promotes cuticle biosynthesis. The relationship between the WIN transcription factor and other metabolic pathways is unknown. Our aim was to determine the relationships between the main genes involved in cuticle biosynthesis and those involved in other metabolic pathways. We did this by cloning a cotton WIN gene, GhWIN2, and studying its influence on other pathways. RESULTS: As with other WIN genes, GhWIN2 regulated expression of cuticle biosynthesis-related genes, and promoted cuticle formation. Silencing of GhWIN2 resulted in enhanced resistance to Verticillium dahliae, caused by increased content of salicylic acid (SA). Moreover, silencing of GhWIN2 suppressed expression of jasmonic acid (JA) biosynthesis-related genes and content. GhWIN2 positively regulated the fatty acid biosynthesis pathway upstream of the JA biosynthesis pathway. Silencing of GhWIN2 reduced the content of stearic acid, a JA biosynthesis precursor. CONCLUSIONS: GhWIN2 not only regulated the cuticle biosynthesis pathway, but also positively influenced JA biosynthesis and negatively influenced SA biosynthesis.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".