Two N-Terminal Acetyltransferases Antagonistically Regulate the Stability of a Nod-Like Receptor in Arabidopsis
Bibliographic record
Abstract
Nod-like receptors (NLRs) serve as immune receptors in plants and animals. The stability of NLRs is tightly regulated, though its mechanism is not well understood. Here, we show the crucial impact of N-terminal acetylation on the turnover of one plant NLR, Suppressor of NPR1, Constitutive 1 (SNC1), in Arabidopsis thaliana. Genetic and biochemical analyses of SNC1 uncovered its multilayered regulation by different N-terminal acetyltransferase (Nat) complexes. SNC1 exhibits a few distinct N-terminal isoforms generated through alternative initiation and N-terminal acetylation. Its first Met is acetylated by N-terminal acetyltransferase complex A (NatA), while the second Met is acetylated by N-terminal acetyltransferase complex B (NatB). Unexpectedly, the NatA-mediated acetylation serves as a degradation signal, while NatB-mediated acetylation stabilizes the NLR protein, thus revealing antagonistic N-terminal acetylation of a single protein substrate. Moreover, NatA also contributes to the turnover of another NLR, RESISTANCE TO P. syringae pv maculicola 1. The intricate regulation of protein stability by Nats is speculated to provide flexibility for the target protein in maintaining its homeostasis.
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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 | 0.000 |
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".