A dual resistance gene system prevents infection by three distinct pathogens
Bibliographic record
Abstract
Colletotrichum higginsianum causes typical anthracnose lesions on the leaves, petioles, and stems of cruciferous plants. Inoculation of Arabidopsis thaliana ecotype Columbia leaves with C. higginsianum results in fungal growth and disease symptoms reminiscent of those induced in other cruciferous plants. We performed map-based cloning and natural variation analysis of 19 A. thaliana ecotypes to identify a dominant resistance locus against C. higginsianum. We found that the A. thaliana RCH2 (for recognition of C. higginsianum) locus encodes two NB-LRR proteins, both of which are required for resistance to C. higginsianum in the A. thaliana ecotype Ws-0. Both proteins are well-characterized R proteins involved in resistance against bacterial pathogens; RRS1 (resistance to Ralstonia solanacearum 1) confers resistance to strain Rs1000 of R. solanacearum and RPS4 to Pseudomonas syringae pv. tomato strain DC3000 expressing avrRps4 (Pst-avrRps4). Furthermore, we found that both RRS1-Ws and RPS4-Ws genes are required for resistance to Pst-avrRps4 and to Rs1002 R. solanacearum. We therefore demonstrate that a pair of neighboring genes, RRS1-Ws and RPS4-Ws, function cooperatively as a dual R-gene system against at least three distinct pathogens.
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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) | 0.002 | 0.001 |
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".