Two small secreted proteins from<i>Puccinia triticina</i>induce reduction of ß-glucoronidase transient expression in wheat isolines containing<i>Lr9, Lr24</i>and<i>Lr26</i>
Bibliographic record
Abstract
Little is known about the molecular interaction of wheat and leaf rust (Puccinia triticina Eriks). However, genomic tools are now becoming available so that the host–pathogen interactions can be better understood. Significant efforts are being placed on understanding the secretomes of various pathogens as secreted peptides are believed to be the best candidates for avirulence effectors. In this work, a P. triticina haustorial cDNA library was evaluated for the presence of proteins containing secretion signals. Ten predicted proteins were found in the library, of which two were expressed in haustorial fractions. Three of the secreted proteins, Pt3, Pt12 and Pt27, were used in biolistic experiments to determine whether they could induce hypersensitive cell death, which is commonly observed in incompatible rust interactions with wheat leaf rust resistance genes. When Pt3 was co-bombarded with a β-glucoronidase (GUS)-expressing vector into wheat isolines with resistance genes Lr9 or Lr24, a significant reduction of GUS expression was observed, presumably due to hypersensitive cell death. In other co-bombardment experiments, Pt27 induced a significant reduction in GUS expression in the Lr26 isoline. These results suggest that Pt3 and Pt27 may function in avirulence against wheat leaf rust in resistant genotypes.
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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) | 0.000 | 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) | 0.001 | 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 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".