The proteasome regulator PTRE1 contributes to the turnover of SNC1 immune receptor
Bibliographic record
Abstract
Plants have evolved a sophisticated immune system in order to recognize and respond to microbes in their environments. Nucleotide-binding leucine-rich repeat (NLR) proteins detect the presence of specific effector molecules delivered into host cells by pathogens and activate strong defence responses. However, as excessive accumulation of NLRs can result in inappropriate immune responses, their abundance must be tightly regulated. Targeted degradation of NLRs through the ubiquitin proteasome pathway is an important mechanism to limit NLR accumulation. Mutations that perturb NLR degradation can cause autoimmune phenotypes. In this study, we show that the proteasome regulator PTRE1 also contributes to NLR degradation. ptre1 mutant plants exhibit increased defence marker gene expression and enhanced disease resistance against virulent pathogens. The stability of the NLR, SUPPRESSOR OF npr1-1 CONSTITUTIVE 1 (SNC1) is also increased in the ptre1 mutant. Although the mouse homologue of PTRE1 was reported to interact with a Cell Division Control protein 48 (CDC48) homologue in vitro (Clemen et al., 2015), we only observed interaction between PTRE1 and AtCDC48A in a split luciferase assay, but not in co-immunoprecipitation. In addition, a related Arabidopsis protein PTRE1h shares partial redundancy with PTRE1. Together, PTRE1 acts as a negative regulator of plant immunity partly by facilitating the degradation of immune receptors such as SNC1.
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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) | 0.001 | 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 | 0.001 | 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".