Host‐induced silencing of essential genes in <i>Puccinia triticina</i> through transgenic expression of <scp>RNA</scp>i sequences reduces severity of leaf rust infection in wheat
Bibliographic record
Abstract
Summary Leaf rust, caused by the pathogenic fungus Puccinia triticina ( Pt ), is one of the most serious biotic threats to sustainable wheat production worldwide. This obligate biotrophic pathogen is prevalent worldwide and is known for rapid adaptive evolution to overcome resistant wheat varieties. Novel disease control approaches are therefore required to minimize the yield losses caused by Pt . Having shown previously the potential of host‐delivered RNA interference ( HD ‐ RNA i) in functional screening of Pt genes involved in pathogenesis, we here evaluated the use of this technology in transgenic wheat plants as a method to achieve protection against wheat leaf rust ( WLR ) infection. Stable expression of hairpin RNA i constructs with sequence homology to Pt MAP ‐kinase ( Pt MAPK 1 ) or a cyclophilin ( Pt CYC 1 ) encoding gene in susceptible wheat plants showed efficient silencing of the corresponding genes in the interacting fungus resulting in disease resistance throughout the T 2 generation. Inhibition of Pt proliferation in transgenic lines by in planta ‐induced RNA i was associated with significant reduction in target fungal transcript abundance and reduced fungal biomass accumulation in highly resistant plants. Disease protection was correlated with the presence of si RNA molecules specific to targeted fungal genes in the transgenic lines harbouring the complementary HD ‐ RNA i construct. This work demonstrates that generating transgenic wheat plants expressing RNA i‐inducing transgenes to silence essential genes in rust fungi can provide effective disease resistance, thus opening an alternative way for developing rust‐resistant crops.
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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".