Transcriptome analysis of the <i>Brassica napus</i>–<i>Leptosphaeria maculans</i> pathosystem identifies receptor, signaling and structural genes underlying plant resistance
Bibliographic record
Abstract
The hemibiotrophic fungal pathogen Leptosphaeria maculans is the causal agent of blackleg disease in Brassica napus (canola, oilseed rape) and causes significant loss of yield worldwide. While genetic resistance has been used to mitigate the disease by means of traditional breeding strategies, there is little knowledge about the genes that contribute to blackleg resistance. RNA sequencing and a streamlined bioinformatics pipeline identified unique genes and plant defense pathways specific to plant resistance in the B. napus-L. maculans LepR1-AvrLepR1 interaction over time. We complemented our temporal analyses by monitoring gene activity directly at the infection site using laser microdissection coupled to quantitative PCR. Finally, we characterized genes involved in plant resistance to blackleg in the Arabidopsis-L. maculans model pathosystem. Data reveal an accelerated activation of the plant transcriptome in resistant host cotyledons associated with transcripts coding for extracellular receptors and phytohormone signaling molecules. Functional characterization provides direct support for transcriptome data and positively identifies resistance regulators in the Brassicaceae. Spatial gradients of gene activity were identified in response to L. maculans proximal to the site of infection. This dataset provides unprecedented spatial and temporal resolution of the genes required for blackleg resistance and serves as a valuable resource for those interested in host-pathogen interactions.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".