A multilayered regulatory mechanism for the autoinhibition and activation of a plant <scp>CC</scp>‐<scp>NB</scp>‐<scp>LRR</scp> resistance protein with an extra N‐terminal domain
Bibliographic record
Abstract
The tomato resistance protein Sw-5b differs from the classical coiled-coil nucleotide-binding leucine-rich repeat (CC-NB-LRR) resistance proteins by having an extra N-terminal domain (NTD). To understand how NTD, CC and NB-LRR regulate autoinhibition and activation of Sw-5b, we dissected the function(s) of each domain. When viral elicitor was absent, Sw-5b LRR suppressed the central NB-ARC to maintain autoinhibition of the NB-LRR segment. The CC and NTD domains independently and additively enhanced the autoinhibition of NB-LRR. When viral elicitor was present, the NB-LRR segment of Sw-5b was specifically activated to trigger a hypersensitive response. Surprisingly, Sw-5b CC suppressed the activation of NB-LRR, whereas the extra NTD of Sw-5b became a positive regulator and fully activated the resistance protein, probably by relieving the inhibitory effects of the CC. In infection assays of transgenic plants, the NB-LRR segment alone was insufficient to confer resistance against Tomato spotted wilt tospovirus; the layers of NTD and CC regulation on NB-LRR were required for Sw-5b to confer resistance. Based on these findings, we propose that, to counter the negative regulation of the CC on NB-LRR, Sw-5b evolved an extra NTD to coordinate with the CC, thus developing a multilayered regulatory mechanism to control autoinhibition and activation.
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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 | 0.001 |
| 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".