Characterization of the wheat-<i>Stagonospora nodorum</i>disease system: what is the molecular basis of this quantitative necrotrophic disease interaction?<sup>†</sup>
Bibliographic record
Abstract
Abstract Stagonospora nodorum blotch (SNB) has long been a problem in wheat production areas by affecting both the leaves and glumes of susceptible bread and durum wheat. Resistance to both disease phases has been shown to be complexly inherited and although much effort has gone into the identification and introgression of disease resistance, less than satisfactory progress has been made in producing SNB resistant cultivars. A major pitfall in this process has been the lack of understanding of the underlying mechanism of disease resistance. Recently, we have shown the Stagonospora nodorum–wheat interaction to involve multiple effector proteins also known as host-selective toxins (HSTs) that interact either directly or indirectly with dominant wheat sensitivity/susceptibility gene products to induce disease. Therefore, we have referred to this system as an ‘inverse gene-for-gene’ interaction (i.e. effector-triggered susceptibility) because the recognition of an effector protein by the host leads to susceptibility rather than resistance as it does in classical gene-for-gene interactions currently referred to as effector-triggered immunity. To date, we have reported five HST–host gene interactions. In each case, toxin sensitivity and susceptibility is controlled by a single dominant gene and in all but one case the interaction is dependent on light. Using quantitative trait loci analysis, the toxin-host gene interactions have been shown to account for 18–95% of the disease variation, highlighting the importance of these interactions. Several unpublished interactions also exist making this a model system for the investigation of the molecular mechanism of necrotrophic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".