Identification of core candidate genes responding to Verticillium wilt (Verticillium dahliae) in cotton via integrated methods
Bibliographic record
Abstract
Cotton is a vital natural fiber and oil crop, yet it is severely affected by verticillium wilt (VW), known as the 'cancer' of cotton, hindering the industry's sustainable development. Upland cotton, which is widely cultivated, lacks effective resistance to VW, while most sea island cotton shows strong resistance. In this study, an F2:3 population was constructed by hybridizing the verticillium wilt-resistant island cotton variety 'Hai7124' with the susceptible variety 'Xinhai14'. Using Bulked Segregant Analysis (BSA-seq), we identified 10 genetic intervals significantly associated with resistance. Additionally, two pathogenic strains of Verticillium dahliae, Vd592 (a strong pathogenic type) and VdKT (a weak pathogenic type), were used to infect the 'Hai7124' and 'Xinhai14' for RNA-seq analysis, focusing on differentially expressed genes and signaling pathways in samples treated with different resistant and susceptible materials and infected with different pathogens. By integrating BSA-seq and RNA-seq association analyses, the candidate gene range was further refined. Five genes (GBMYB102, GBWRKY65, GBRDA2, GBSOT16, and GBCWINV1) were validated through virus-induced gene silencing (VIGS). The results revealed that reduced expression of these genes significantly decreases plant disease resistance and leads to a reduction in the activity of defense-related enzymes (such as SOD, CAT or PAL) and secondary metabolites (including lignin or flavonoids). Based on the preliminary functional analysis of these candidate genes, we speculate that redox metabolism and secondary metabolites play crucial roles in the resistance of island cotton to Verticillium wilt, and that the resistance of island cotton to verticillium wilt is the result of multiple genes working together.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".