Puncta‐localized <scp>TRAF</scp> domain protein <scp>TC1b</scp> contributes to the autoimmunity of <i>snc1</i>
Bibliographic record
Abstract
Immune receptors play important roles in the perception of pathogens and initiation of immune responses in both plants and animals. Intracellular nucleotide-binding domain leucine-rich repeat (NLR)-type receptors constitute a major class of receptors in vascular plants. In the Arabidopsis thaliana mutant suppressor of npr1-1, constitutive 1 (snc1), a gain-of-function mutation in the NLR gene SNC1 leads to SNC1 overaccumulation and constitutive activation of defense responses. From a CRISPR/Cas9-based reverse genetics screen in the snc1 autoimmune background, we identified that mutations in TRAF CANDIDATE 1b (TC1b), a gene encoding a protein with four tumor necrosis factor receptor-associated factor (TRAF) domains, can suppress snc1 phenotypes. TC1b does not appear to be a general immune regulator as it is not required for defense mediated by other tested immune receptors. TC1b also does not physically associate with SNC1, affect SNC1 accumulation, or affect signaling of the downstream helper NLRs represented by ACTIVATED DISEASE RESISTANCE PROTEIN 1-L2 (ADR1-L2), suggesting that TC1b impacts snc1 autoimmunity in a unique way. TC1b can form oligomers and localizes to punctate structures of unknown function. The puncta localization of TC1b strictly requires its coiled-coil (CC) domain, whereas the functionality of TC1b requires the four TRAF domains in addition to the CC. Overall, we uncovered the TRAF domain protein TC1b as a novel positive contributor to plant 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 | 0.001 |
| 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".