<scp>TNL</scp>‐mediated immunity in <i><scp>A</scp>rabidopsis</i> requires complex regulation of the redundant <i><scp>ADR</scp>1</i> gene family
Bibliographic record
Abstract
Nucleotide-binding leucine-rich repeat proteins (NLRs) serve as intracellular immune receptors in animals and plants. Sensor NLRs perceive pathogen-derived effector molecules and trigger robust host defense. Recent studies revealed the role of three coiled-coil-type NLRs (CNLs) of the ADR1 family - ADR1, ADR1-L1 and ADR1-L2 - as redundant helper NLRs, whose function is required for defense mediated by multiple sensor NLRs. From a mutant snc1-enhancing (MUSE) forward genetic screen in Arabidopsis targeted to identify negative regulators of snc1 that encodes a TIR-type NLR (TNL), we isolated two alleles of muse15, both carrying mutations in ADR1-L1. Interestingly, loss of ADR1-L1 also enhances immunity-related phenotypes in other autoimmune mutants including cpr1, bal and lsd1. This immunity-enhancing effect is not mediated by increased SNC1 protein stability, nor is it fully dependent on the accumulation of the defense hormone salicylic acid (SA). Transcriptional analysis revealed an upregulation of ADR1 and ADR1-L2 in the adr1-L1 background, which may overcompensate the loss of ADR1-L1, resulting in enhanced immunity. Interestingly, autoimmunity of snc1 and chs2, which encode typical TNLs, is fully suppressed by the adr1 triple mutant, suggesting that the ADRs are required for TNL downstream signaling. This study extends our knowledge on the interplay among ADRs and reveals their complexity in defense regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".