Bibliographic record
Abstract
Verticillium wilt, caused by Verticillium dahliae , poses a significant threat to global cotton ( Gossypium hirsutum ) production, leading to substantial yield and quality losses. In this study, we employed a proteomic approach to investigate the molecular responses of cotton leaves to V. dahliae infection, aiming to elucidate defense mechanisms at the protein level. Using high-resolution mass spectrometry and bioinformatics analyses, we identified and quantified differentially expressed proteins (DEPs) in infected versus healthy cotton leaves, focusing on cultivar CRI 12 as a representative case. The identified DEPs were functionally categorized into defense and stress-related proteins, metabolic reprogramming factors, and signaling regulators, reflecting a complex reorganization of cellular processes in response to infection. Comparative proteomic analysis between susceptible and resistant cultivars revealed distinct defense protein profiles and metabolic adjustments associated with disease resistance. These findings provide insights into the molecular basis of cotton defense against V. dahliae and highlight candidate proteins for breeding and genetic engineering. This study underscores the value of integrative omics approaches in advancing our understanding of cotton-pathogen interactions and paves the way for the development of Verticillium wilt-resistant varieties through proteomic-guided breeding strategies.
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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".