Leptosphaeria maculans-Brassica napus Battle: A Comparison of Incompatible vs. Compatible Interactions Using Dual RNASeq
Bibliographic record
Abstract
Leptosphaeria maculans causes blackleg disease, which is one of the most destructive diseases of canola (Brassica napus L.). Due to the erosion of the current resistance in B. napus, it is pivotal to introduce new resistant genotypes to the growers. This study evaluated the potential of Rlm7 gene as resistance to its corresponding avirulence AvrLm7 gene is abundant. The Rlm7 line was inoculated with L. maculans isolate with AvrLm7; UMAvr7; and the CRISPR/Cas9 knockout AvrLm7 mutant, umavr7, of the same isolate to cause incompatible and compatible interactions, respectively. Dual RNA-seq showed differential gene expressions in both interactions. High expressions of virulence-related pathogen genes-CAZymes, merops, and effector proteins after 7-dpi in compatible interactions but not in incompatible interaction—confirmed that the pathogen was actively virulent only in compatible interactions. Salicyclic and jasmonic acid biosynthesis and signaling-related genes, defense-related PR1 gene (GSBRNA2T00150001001), and GSBRNA2T00068522001 in the NLR gene family were upregulated starting as early as 1- and 3-dpi in the incompatible interaction and the high upregulation of those genes after 7-dpi in compatible interactions confirmed the early recognition of the pathogen by the host and control it by early activation of host defense mechanisms in the incompatible interaction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".