The <scp>A</scp>rabidopsis immune adaptor <scp>SRFR</scp>1 interacts with <scp>TCP</scp> transcription factors that redundantly contribute to effector‐triggered immunity
Bibliographic record
Abstract
The plant immune system must be tightly controlled both positively and negatively to maintain normal plant growth and health. We previously identified SUPPRESSOR OF rps4-RLD1 (SRFR1) as a negative regulator specifically of effector-triggered immunity. SRFR1 is localized in both a cytoplasmic microsomal compartment and in the nucleus. Its TPR domain has sequence similarity to TPR domains of transcriptional repressors in other organisms, suggesting that SRFR1 may negatively regulate effector-triggered immunity via transcriptional control. We show here that excluding SRFR1 from the nucleus prevented complementation of the srfr1 phenotype. To identify transcription factors that interact with SRFR1, we screened an Arabidopsis transcription factor prey library by yeast two-hybrid assay and isolated six class I members of the TEOSINTE BRANCHED1/CYCLOIDEA/PCF (TCP) transcription factor family. Specific interactions were verified in planta. Although single or double T-DNA mutant tcp8, tcp14 or tcp15 lines were not more susceptible to bacteria expressing AvrRps4, the triple tcp8 tcp14 tcp15 mutant displayed decreased effector-triggered immunity mediated by the resistance genes RPS2, RPS4, RPS6 and RPM1. In addition, expression of PATHOGENESIS-RELATED PROTEIN2 was attenuated in srfr1-4 tcp8-1 tcp14-5 tcp15-3 plants compared to srfr1-4 plants. To date, TCP transcription factors have been implicated mostly in developmental processes. Our data indicate that one function of a subset of TCP proteins is to regulate defense gene expression in antagonism to SRFR1, and suggest a mechanism for an intimate connection between plant development and immunity.
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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 | 0.000 |
| 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".