Endogenous silencing of <i><scp>P</scp>uccinia triticina</i> pathogenicity genes through <i>in planta</i>‐expressed sequences leads to the suppression of rust diseases on wheat
Bibliographic record
Abstract
Rust fungi are destructive plant pathogens. The draft genomes of several wheat-infecting species have been released and potential pathogenicity genes identified through comparative analyses to fungal pathogens that are amenable to genetic manipulation. Functional gene analysis tools are needed to understand the infection process of these obligate parasites and to confirm whether predicted pathogenicity genes could become targets for disease control. We have modified an Agrobacterium tumefaciens-mediated in planta-induced transient gene silencing (PITGS) assay for use in Triticum spp. (wheat), and used this assay to target predicted wheat leaf rust fungus, Puccinia triticina (Pt) pathogenicity genes, a MAP kinase (PtMAPK1), a cyclophilin (PtCYC1) and calcineurin B (PtCNB), to analyze their roles in disease. Agroinfiltration effectively delivered hairpin silencing constructs in wheat, leading to the generation of fungal gene-specific siRNA molecules in infiltrated leaves, and resulting in up to 70% reduction in transcription of the endogenous target genes in superinfected Pt. In vivo silencing caused severe disease suppression, compromising fungal growth and sporulation, as viewed by confocal microscopy and measured by reductions in fungal biomass and emergence of uredinia. Interestingly, using the same gene constructs, suppression of infection by Puccinia graminis and Puccinia striiformis was also achieved. Our results show that A. tumefaciens-mediated PITGS can be used as a reverse-genetics tool to discover gene function in rust fungi. This proof-of-concept study indicates that the targeted fungal transcripts might be important in pathogenesis, and could potentially be used as promising targets for developing RNA interference-based resistance against rust fungi.
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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) | 0.000 | 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) | 0.001 | 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".