The CGA1-SNAT regulatory module potentially contributes to cytokinin-mediated melatonin biosynthesis and drought tolerance in wheat
Bibliographic record
Abstract
BACKGROUND: Melatonin plays a pivotal role in alleviating abiotic stresses, yet its biosynthesis regulation in crops, particularly wheat, remains unclear. This study explores regulatory components of melatonin biosynthesis under drought stress using bioinformatic, physiochemical, and molecular approaches in contrasting wheat genotypes. RESULTS: Bioinformatic analysis identified SNAT, a key melatonin biosynthesis gene, and 88 transcription factors (TFs) from 26 families as potential regulators. The regulatory network for SNAT highlighted CYTOKININ-RESPONSIVE GATA FACTOR 1 (CGA1) as a significant TF. Under drought stress, contrasting wheat genotypes exhibited distinct CGA1-SNAT module expression, melatonin and cytokinin levels, photosynthetic activity, and oxidative damage. Cytokinin treatments regulated the CGA1-SNAT module, altering melatonin content, SPAD values, and chloroplast numbers, particularly in drought-susceptible genotypes. CONCLUSIONS: This study uncovers the pivotal role of the CGA1-SNAT module and its interaction with the cytokinin pathway in regulating melatonin biosynthesis during drought stress. These findings enhance our understanding of the molecular mechanisms underpinning drought tolerance and offer promising targets for genetic and biochemical interventions to improve crop resilience.
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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) | 0.002 | 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".