Regulatory Role of Promoter Variants in Cotton Stress-Responsive Genes
Bibliographic record
Abstract
Cotton, as one of the world's important economic crops, often faces abiotic stresses such as drought, saline-alkali conditions and extreme temperatures. These environmental pressures severely restrict its growth and development as well as the stability of its yield. In recent years, molecular genetic studies have shown that promoter variations play a key role in regulating the expression of stress response genes in cotton. This study systematically reviewed the structure and function of promoters, the classification and characteristics of cotton stress-response genes, as well as the types of promoter variations involved in regulation. It focused on analyzing how promoter polymorphisms lead to transcriptional regulatory differences under stress conditions and affect cotton's adaptability to environmental stress. The molecular mechanisms such as sequence variations in the promoter region, DNA methylation, histone modification, and transcription factor interactions were also deeply explored. Key stress response genes such as AtDREB1A , GhHSP70 , and GhERF1 were selected, and the specific functional manifestations of their promoter variations in salt tolerance, heat resistance, and drought resistance traits were introduced. This research is expected to provide new ideas for a deeper understanding of the regulatory network of plant environmental adaptability and promote the functional exploration and stress resistance improvement of cotton germplasm resources.
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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 | 0.001 | 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".