Coordinated actions of NLR-assembled and glutamate receptor–like calcium channels in plant effector-triggered immunity
Bibliographic record
Abstract
The plant immune system utilizes nucleotide-binding/leucine-rich repeat (NLR) proteins to detect pathogen virulence factors (effectors) inside host cells and transduce recognition to rapid defense. In dicotyledenous plants, pathogen activated Toll-like/interleukin-1 receptor-containing NLRs (TNLs) establish a signaling network of enhanced susceptibility 1 (EDS1)-family dimers with RPW8-type coiled-coil (CC R ) domain NLRs (RNLs) to stimulate transcriptional reprogramming leading to host cell death and pathogen restriction. Evidence suggests that TNL- and EDS1-activated RNLs function as oligomeric Ca 2+ permeable ion channels at the plasma membrane. However, the downstream processes for immunity execution are poorly understood. Here, we studied pathogen effector-triggered immunity conferred by Nicotiana benthamiana TNL (Roq1) which signals almost exclusively through the EDS1-senescence associated gene101 (SAG101)-N required gene 1 (NRG1) RNL module. We identify a pair of glutamate receptor–like Ca 2+ ion channels (GLR2.9a and GLR2.9b) which, unlike most other pathogen-induced GLRs, are highly up-regulated by the EDS1-SAG101-NRG1 module in the TNL immune response. We show that oligomeric NRG1 Ca 2+ channel activity is necessary for GLR2.9a and GLR2.9b induced expression. Consequently, GLR2.9a and GLR2.9b proteins contribute to NRG1 -dependent Ca 2+ accumulation in host cells, and to pathogen resistance and host cell death. We establish that GLR2.9a localizes mainly to the plasma membrane/cytoplasm whereas GLR2.9b accumulates preferentially at the nuclear envelope. The data show that transcriptionally up-regulated canonical Ca 2+ ion channels GLR2.9a and GLR2.9b are a functional output of the EDS1-SAG101-NRG1 module for TNL-triggered immunity.
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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) | 0.000 | 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".