A physical model links structure and function in the plant immune system
Bibliographic record
Abstract
Effector-Triggered Immunity (ETI) is an important part of the plant immune system, allowing plants to sense and respond to harmful pathogen proteins known as "effectors." Effectors can be sensed directly or indirectly by NLR (Nucleotide-binding Leucine-rich Repeat) proteins, many of which "guard" the plant proteins targeted by effectors. Although a few effector-target-NLR interactions have been characterized, a general understanding of how these molecular interactions give rise to a functioning immune system is lacking. Here, we present a physics-based model of ETI based on protein-protein interactions. We show that the simplest physical model consistent with the biology gives rise to a robust immune sensor and explains the empirical phenomenon of effector interference as a generic consequence of molecules competing for binding partners. Using the evolutionarily conserved ZAR1 defense gene as a model, we explain how more complex interaction networks integrate multiple pathogen signals into a single response. We then examine alternatives to a guarding architecture, including direct sensing, decoys, and blended "integrated decoy" strategies, and reveal that these sensing architectures obey functional trade-offs between their sensitivity, target protection, and proteomic cost. This allows a quantitative analysis of the trade-offs between different forms of ETI. We discuss these findings in the context of the evolutionary forces shaping the plant immune system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".