The putative kinase substrate MUSE7 negatively impacts the accumulation of <scp>NLR</scp> proteins
Bibliographic record
Abstract
Stringent modulation of immune signaling in plants is necessary to enable a rapid response to pathogen attack without spurious defense activation. To identify genes involved in plant immunity, a forward genetic screen for enhancers of the autoimmune snc1 (suppressor of npr1, constitutive 1) mutant was conducted. The snc1 mutant contains a gain-of-function mutation in a gene encoding a NOD-like receptor (NLR) protein. The isolated muse7 (mutant, snc1-enhancing, 7) mutant was shown to confer a reversion to autoimmune phenotypes in the wild-type-like mos4 (modifier of snc1, 4) snc1 background. Positional cloning revealed that MUSE7 encodes an evolutionarily conserved putative kinase substrate of unknown function. The muse7 single mutants display enhanced resistance to the bacterial pathogen Pseudomonas syringae pv. tomato DC3000. While transcription of SNC1 is not enhanced, elevated SNC1 protein accumulation is associated with mutations in muse7. Accumulation of two additional NLR proteins, RPS2 (RESISTANCE TO PSEUDOMONAS SYRINGAE 2) and RPM1 (RESISTANCE TO PSEUDOMONAS SYRINGAE pv. MACULICOLA 1), was also observed in muse7 plants. Although proteasome-mediated degradation of NLR proteins is a well studied event in plant immunity, no interactions were detected between MUSE7 and selected components of this pathway. This study has demonstrated a role for MUSE7 in modulating plant immune responses through negatively affecting NLR accumulation, and will benefit future studies of MUSE7 homologs in other species.
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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) | 0.002 | 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".