Molecular Response Mechanism of Cotton to Verticillium Wilt and Fusarium Wilt
Bibliographic record
Abstract
Cotton is an important fiber crop worldwide and is threatened by soil-borne fungal pathogens such as Verticillium dahliae and Fusarium oxysporum , which cause Verticillium wilt and Fusarium wilt, respectively. This study systematically explored the molecular responses of cotton to these two devastating diseases, aiming to lay the foundation for improved disease resistance strategies. We first outlined the biological characteristics, infection mechanisms, and global distribution of the pathogens, and then discussed in detail the innate immune response of cotton, including pattern recognition receptors, phytohormone-mediated pathways, and effector-triggered immune responses. We further emphasized the changes in the transcriptome and proteome during infection, as well as the functional roles of resistance genes, transcription factors, and secondary metabolites. We also discussed the recent progress in functional genomics and gene editing tools such as CRISPR/Cas in the discovery and validation of resistance genes, as well as the overlapping molecular responses triggered by the two pathogens. Using disease-resistant Xinjiang cotton varieties as an example, this study will provide a practical reference for regional breeding programs. This comprehensive study highlights the complexity of cotton-pathogen interactions and anticipates that integrating multi-omics data will be key to cultivating durable resistance through molecular breeding and precision agriculture.
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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) | 0.001 | 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".