NLR-Associating Transcription Factor bHLH84 and Its Paralogs Function Redundantly in Plant Immunity
Bibliographic record
Abstract
In plants and animals, nucleotide-binding and leucine-rich repeat domain containing (NLR) immune receptors are utilized to detect the presence or activities of pathogen-derived molecules. However, the mechanisms by which NLR proteins induce defense responses remain unclear. Here, we report the characterization of one basic Helix-loop-Helix (bHLH) type transcription factor (TF), bHLH84, identified from a reverse genetic screen. It functions as a transcriptional activator that enhances the autoimmunity of NLR mutant snc1 (suppressor of npr1-1, constitutive 1) and confers enhanced immunity in wild-type backgrounds when overexpressed. Simultaneously knocking out three closely related bHLH paralogs attenuates RPS4-mediated immunity and partially suppresses the autoimmune phenotypes of snc1, while overexpression of the other two close paralogs also renders strong autoimmunity, suggesting functional redundancy in the gene family. Intriguingly, the autoimmunity conferred by bHLH84 overexpression can be largely suppressed by the loss-of-function snc1-r1 mutation, suggesting that SNC1 is required for its proper function. In planta co-immunoprecipitation revealed interactions between not only bHLH84 and SNC1, but also bHLH84 and RPS4, indicating that bHLH84 associates with these NLRs. Together with previous finding that SNC1 associates with repressor TPR1 to repress negative regulators, we hypothesize that nuclear NLR proteins may interact with both transcriptional repressors and activators during immune responses, enabling potentially faster and more robust transcriptional reprogramming upon pathogen recognition.
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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".