Splicing of Receptor-Like Kinase-Encoding SNC4 and CERK1 is Regulated by Two Conserved Splicing Factors that Are Required for Plant Immunity
Bibliographic record
Abstract
Plant immune receptors belonging to the receptor-like kinase (RLK) family play important roles in the recognition of microbial pathogens and activation of downstream defense responses. The Arabidopsis mutant snc4-1D contains a gain-of-function mutation in the RLK SNC4 (SUPPRESSOR OF NPR1-1, CONSTITUTIVE4), which leads to constitutive activation of defense responses. Analysis of suppressor mutants of snc4-1D identified two conserved splicing factors, SUA (SUPPRESSOR OF ABI3-5) and RSN2 (REQUIRED FOR SNC4-1D 2), that are required for the constitutive defense responses in snc4-1D. In sua and rsn2 mutants, SNC4 splicing is altered and the amount of SNC4 transcripts is reduced. Further analysis showed that SUA and RSN2 are also required for the proper splicing of CERK1 (CHITIN ELICITOR RECEPTOR KINASE1), which encodes another RLK that functions as a receptor for chitin. In sua and rsn2 mutants, induction of reactive oxygen species by chitin is reduced and the non-pathogenic bacteria Pseudomonas syringae pv. tomato DC3000hrcC grows to higher titers than in wild-type plants. Our study suggests that pre-mRNA splicing plays important roles in the regulation of plant immunity mediated by the RLKs SNC4 and CERK1.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".