Activation of TIR signaling is required for pattern-triggered immunity
Bibliographic record
Abstract
Abstract Plant immune responses are mainly activated by two types of receptors. Plasma membrane-localized pattern recognition receptors (PRRs) recognize conserved features of microbes, and intracellular nucleotide-binding leucine rich repeat receptors (NLRs) recognize effector proteins from pathogens. NLRs possessing N-terminal Toll/interleukin-1 receptor (TIR) domains (TNLs) activate two parallel signaling pathways via the EDS1/PAD4/ADR1s and the EDS1/SAG101/NRG1s modules. The relationship between PRR-mediated pattern-triggered immunity (PTI) and TIR signaling is unclear. Here we report that activation of TIR signaling plays a key role in PTI. Blocking TIR signaling by knocking out components of the EDS1/PAD4/ADR1s and EDS1/SAG101/NRG1s modules results in attenuated PTI responses such as reduced salicylic acid (SA) levels and expression of defense genes, and compromised resistance against pathogens. Consistently, PTI is attenuated in transgenic plants that have reduced accumulation of NLRs. Upon treatment with PTI elicitors such as flg22 and nlp20, a large number of genes encoding TNLs or TIR domain-containing proteins are rapidly induced, likely responsible for activating TIR signaling during PTI. In support, overexpression of some of these genes results in activation of defense responses. Overall, our study reveals that TIR signaling activation is an important mechanism for boosting plant defense during PTI.
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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 | 0.002 |
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".