A noncanonical TNL immune hub defines separable recognition and signaling modules for clubroot resistance
Bibliographic record
Abstract
Abstract Plant immunity mediated by nucleotide-binding leucine-rich repeat (NLR) receptors often relies on canonical EDS1 / NDR1 signaling, but alternative mechanisms are emerging. We uncover a novel modular, noncanonical immune hub orchestrated by Rcr1 , a TIR-NLR (TNL) gene conferring clubroot resistance in Brassica napus against the root-infecting protist Plasmodiophora brassicae . Unlike typical TNLs, Rcr1 engages non-NLR partners in two separable modules: a CP1 (cysteine protease)–WRKY-based recognition module, likely monitoring a pathogen virulence target, and an AP (ankyrin-repeat protein)–ERF-based signaling module, driving jasmonic acid/ethylene-mediated defense. This architecture functions without detectable EDS1 / NDR1 involvement, challenging salicylic acid-dominant models of biotrophic immunity and expanding current views of how TNLs can be wired in plant defense. Using high-throughput interactor screening and CRISPR/Cas9 knockouts, we validate these modules, while heat-inducible gene excision reveals Rcr1 ’s critical early role (0–14 days post-inoculation). Together, our findings position Rcr1 as an exemplar of modular TNL architecture, suggesting that separable recognition and signaling branches may represent a broader principle of immune flexibility in plants. This study redefines TNL flexibility, offering a blueprint for breeding durable disease-resistant crops via modular immune engineering, with clubroot resistance as a model.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".