Integrative transcriptomic and gene co-expression network analysis of host responses upon Verticillium dahliae infection in Gossypium hirsutum
Bibliographic record
Abstract
Worldwide, Verticillium wilt is among the major harmful diseases in cotton production, causing substantial reduction in yields. While this disease has been extensively researched at the molecular level of the pathogen, the molecular basis of V. dahliae host response association is yet to be thoroughly investigated. In this study, RNA-seq analysis was carried out on V. dahliae infected two Gossypium hirsutum L. cultivars, Xinluzao-36 (susceptible) and Zhongzhimian-2 (disease resistant) for 0 h, 24 h, 72 h and 120 h time intervals. Statistical analysis revealed that V. dahliae infection elicited differentially expressed gene responses in the two cotton varieties, but more intensely in the susceptible cultivar than in the resistant cultivars. Data analysis revealed 4241 differentially expressed genes (DEGs) in the LT variety across the three treatment timepoints whereas 7657 in differentially expressed genes (DEGs) in the Vd592 variety across the three treatment timepoints. Six genes were randomly selected for qPCR validation of the RNA-Seq data. Numerous genes encompassed in disease resistance and defense mechanisms were identified. Further, RNA-Seq dataset was utilized in construction of the weighted gene co-expression network and 11 hub genes were identified, that encode for different proteins associated with lignin and immune response, Auxin response factor, cell wall and vascular development, microtubule, Ascorbate transporter, Serine/threonine kinase and Immunity and drought were identified. This significant research will aid in advancing crucial knowledge on virus-host interactions and identify key genes intricate in G. hirsutum L. resistance to V. dahliae infection.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".