Transcriptomics of temperature-sensitive R gene-mediated resistance identifies a WAKL10 protein interaction network
Bibliographic record
Abstract
Understanding temperature-sensitivity of R gene-mediated resistance against apoplastic pathogens is important for sustainable food production in the face of global warming. Here, we show that resistance of Brassica napus cotyledons against Leptosphaeria maculans was temperature-sensitive in introgression line Topas-Rlm7 but temperature-resilient in Topas-Rlm4. A set of 1,646 host genes was differentially expressed in Topas-Rlm4 and Topas-Rlm7 in response to temperature. Amongst these were three WAKL10 genes, including BnaA07g20220D, representing the temperature-sensitive Rlm7-1 allele and Rlm4. Network analysis identified a WAKL10 protein interaction cluster specifically for Topas-Rlm7 at 25 °C. Diffusion analysis of the Topas-Rlm4 network identified WRKY22 as a putative regulatory target of the ESCRT-III complex-associated protein VPS60.1, which belongs to the WAKL10 protein interaction community. Combined enrichment analysis of gene ontology terms considering gene expression and network data linked vesicle-mediated transport to defence. Thus, dysregulation of effector-triggered defence in Topas-Rlm7 disrupts vesicle-associated resistance against the apoplastic pathogen L. maculans.
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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 | 0.001 | 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".