Transcriptional Insight Into Brassica napus Resistance Genes LepR3 and Rlm2-Mediated Defense Response Against the Leptosphaeria maculans Infection
Bibliographic record
Abstract
The phytopathogenic fungus Leptosphaeria maculans causes the blackleg disease on Brassica napus, resulting in severe loss of rapeseed production. Breeding of resistant cultivars containing race-specific resistance genes is provably effective to combat this disease. While two allelic resistance genes LepR3 and Rlm2 recognizing L. maculans avirulence genes AvrLm1 and AvrLm2 at plant apoplastic space have been cloned in B. napus, the downstream gene expression network underlying the resistance remains elusive. In this study, transgenic lines expressing LepR3 and Rlm2 were created in the susceptible ‘Westar’ cultivar and inoculated with L. maculans isolates containing different sets of AvrLm1 and AvrLm2 for comparative transcriptomic analysis. Through grouping the RNA-seq data based on different levels of defense response, we find LepR3 and Rlm2 orchestrate a hierarchically regulated gene expression network, consisting of induced ABA acting independently of the disease reaction, activation of signal transduction pathways with gradually increasing intensity from compatible to incompatible interaction, and specifically induced enzymatic and chemical actions contributing to hypersensitive response with recognition of AvrLm1 and AvrLm2. This study provides an unconventional investigation into LepR3 and Rlm2-mediated plant defense machinery and adds novel insight into the interaction between surface-localized receptor-like proteins (RLPs) and apoplastic fungal pathogens.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".