SCF<sup>SNIPER4</sup> controls the turnover of two redundant <scp>TRAF</scp> proteins in plant immunity
Bibliographic record
Abstract
Summary In mammals, tumor necrosis factor receptor associated factors (TRAFs) are signaling adaptors that regulate diverse physiological processes, including immunity and stress responses. In Arabidopsis, MUSE13 and MUSE14 are redundant TRAF proteins serving as adaptors in the SCFCRP1 complex to facilitate the turnover of nucleotide‐binding domain and leucine‐rich repeats (NLR) immune receptors. Degradation of MUSE13 is inhibited by proteasome inhibitor, suggesting that the MUSE13 stability is controlled by the 26S proteasome. However, the E3 ligase that regulates MUSE13 level is unknown. Here we report the identification of an F‐box protein, SNIPER4 that regulates the turnover of MUSE13 and MUSE14. Protein levels of MUSE13 and MUSE14 are reduced by SNIPER4 overexpression, while higher accumulation of MUSE13 and MUSE14 is observed when dominant‐negative SNIPER4 is expressed. Furthermore, SNIPER4 associates with MUSE13 or MUSE14. Taken together, the SCFSNIPER4 complex controls the turnover of TRAF proteins for an optimum immune output.
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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) | 0.001 | 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".