A Cotton Laccase Confers Disease Resistance Against <i>Verticillium dahliae</i> by Promoting Cell Wall Lignification
Bibliographic record
Abstract
Verticillium wilt (VW), caused primarily by Verticillium dahliae, is a significant threat to cotton production. Lignification of the plant cell wall, a defence response triggered by pathogen invasion, is critical for plant resistance to numerous diseases. Laccases are known to participate in the lignification of secondary cell walls, but their role in cotton resistance to V. dahliae is not fully understood. In this study, we identified a cotton laccase gene, GhLAC14-3, that was significantly upregulated during early V. dahliae infection and was closely related to a gene previously reported to respond to V. dahliae infection in Arabidopsis. Silencing of GhLAC14-3 in cotton increased disease susceptibility and reduced lignin deposition and the expression of lignin-related genes. By contrast, overexpression of GhLAC14-3 in transgenic Arabidopsis increased lignin content and the expression of lignin-related genes, thereby enhancing VW resistance. We identified an interaction between GhLAC14-3 and the mitogen-activated protein kinase GhMAPKKK2 at the cell membrane. GhMAPKKK2 expression was also significantly induced by V. dahliae infection in cotton, and its overexpression in Arabidopsis activated multiple key resistance genes, thus improving V. dahliae resistance. Transient co-expression of GhMAPKKK2 and GhLAC14-3 in Nicotiana benthamiana leaves significantly increased lignin content. Conversely, silencing of AtMAPKKK2, the homologue of GhMAPKKK2, in GhLAC14-3-overexpressing Arabidopsis reduced both lignin levels and disease resistance. Our findings suggest that GhLAC14-3 is a promising target for enhancing VW resistance, as its interaction with GhMAPKKK2 at the cell membrane modulates defence-induced lignification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".