Indirect recognition of pathogen effectors by NLRs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To perceive pathogen threats, plants utilize both plasma membrane-localized and intracellular receptors. Nucleotide-binding domain leucine-rich repeat containing (NLR) proteins are key receptors that can recognize pathogen-derived intracellularly delivered effectors and activate downstream defense. Exciting recent findings have propelled our understanding of the various recognition and activation mechanisms of plant NLRs. Some NLRs directly bind to effectors, but others can perceive effector-induced changes on targeted host proteins (guardees), or non-functional host protein mimics (decoys). Such guarding strategies are thought to afford the host more durable resistance to quick-evolving and diverse pathogens. Here, we review classic and recent examples of indirect effector recognition by NLRs and discuss strategies for the discovery and study of new NLR-decoy/guardee systems. We also provide a perspective on how executor NLRs and helper NLRs (hNLRs) provide recognition for a wider range of effectors through sensor NLRs and how this can be considered an expanded form of indirect recognition. Furthermore, we summarize recent structural findings on NLR activation and resistosome formation upon indirect recognition. Finally, we discuss existing and potential applications that harness NLR indirect recognition for plant disease resistance and crop resilience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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 it