Characterization of a Foxtail Mosaic Virus Vector for Rust Fungus Avirulence Gene Expression in Wheat
Bibliographic record
Abstract
The wheat fungal pathogens Puccinia graminis f. sp. tritici ( Pgt), P. striiformis f. sp. tritici, and P. triticina, causing stem, stripe, and leaf rust, respectively, pose a threat to global wheat production. Genetic resistance in the form of resistance ( R) genes provides the best protection, but rust fungal populations change frequently by mutating avirulence (AVR) effectors matching specific R genes, thereby defeating resistance. Hence, characterization of AVR effectors is needed to understand the evolution of pathogen populations, provide insights for extending the effectiveness of R genes, and yield tools for the identification and isolation of R genes. Functional characterization of Avr genes in rust fungi is challenging in these biotrophic pathogens that lack a reliable and efficient transformation system. Studies indicate that the recently engineered foxtail mosaic virus (FoMV) shows promise as an expression system in cereals. In this study, we utilized two confirmed AVR effectors from Pgt, AVRSr35 and AVRSr50, to assess the applicability of FoMV for investigating rust fungus Avr genes. We showed that vector FoMV PV101 carrying PgtAvrSr35 induced a hypersensitive response (HR) in wheat having the corresponding Sr35 resistance gene. However, when carrying PgtAvrSr50, no HR or even mild viral symptoms were seen in a wheat line having Sr50, as this particular wheat line was not susceptible to FoMV. Several wheat cultivars did not support FoMV replication. The results here show that FoMV PV101 is effective as an expression vector for studying rust fungi AVR effectors, but its applicability relies on the susceptibility of wheat cultivars to FoMV. [Formula: see text] Copyright © 2024 His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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 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) | 0.000 | 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 | 0.001 |
| 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 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".