Potential mechanisms of quantitative resistance to Leptosphaeria maculans (blackleg) on cotyledons of canola (Brassica napus)
Bibliographic record
Abstract
<title>Abstract</title> <bold>Background: </bold>Blackleg disease, caused by <italic>Leptosphaeria maculans</italic> (<italic>Lm</italic>), can lead to significant losses of canola/rapeseed crops. Growing resistant canola cultivars can be an effective and environmentally friendly way to manage blackleg. Major resistance genes may stop infection, but can also be rapidly overcome by shifts in pathogen population towards virulence. Thus, using race-nonspecific or quantitative resistance (QR) is of interest because it is potentially more durable. However, the mechanisms and genes underlying QR are mostly unknown. In this study, we explored QR in “74‑44 BL”, a Canadian canola cultivar carrying a moderate level of race nonspecific resistance, based on cotyledon inoculation (Supple. Fig.1) . The susceptible cultivar “Westar” was used as a control. Lesions developed more slowly on 74-44BL than on Westar. We used RNA-Seq to identify genes and gene functions putatively involved in the QR. <bold>Results:</bold> Relative to inoculated Westar, some of the <italic>B. napus</italic> genes that were differentially expressed strongly in inoculated 74-44 BL included those putatively involved in programmed cell death (PCD), reactive oxygen species (ROS) generation, signal transduction and/or intracellular endomembrane transport. Examples included genes annotated as a Bax inhibitor 1, a development/cell death (DCD) domain containing proteinases and peptidases, all of which could play a role in PCD and a zinc-finger Sec23/Sec24 and five small GTPases likely involved in endoplasmic reticulum (ER) to Golgi vesicle traffic and/or signal transduction. Further experiments, however, did not confirm changes in genomic DNA degradation, a potential marker for PCD, between the two cultivars. In addition, infection progression in cotyledons was not altered by applying protease inhibitors directly to cotyledons. Additional testing was done using green fluorescent protein (GFP)-tagged <italic>Lm </italic>for cotyledon colonization as well as ROS production, in relation to the lesion development. The results showed that ROS production occurred beyond the area colonized by <italic>Lm</italic> hyphae in 74-44 BL.<bold>Conclusions: </bold>ROS may also be involved in signal transduction and/or intracellular endomembrane transport. These results provide a starting point for a better understanding of the mechanisms behind QR against <italic>Lm</italic> in canola and developing new host-resistance strategies for management of blackleg.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".