The Transcriptional Activator Pti4 Is Required for the Recruitment of a Repressosome Nucleated by Repressor SEBF at the Potato<i>PR-10a</i>Gene
Bibliographic record
Abstract
Transcriptional reprogramming is critical for plant disease resistance responses. In potato (Solanum tuberosum), the marker gene PATHOGENESIS-RELATED-10a (PR-10a) is transcriptionally activated by pathogens, wounding, or elicitor treatment. Activation of PR-10a requires the recruitment of the activator Why1 to its promoter. In addition, PR-10a is negatively regulated by the repressor SEBF (for Silencer Element Binding Factor). Here, we show through a yeast two-hybrid screen that SEBF interacts with Pti4, which has been shown to be a transcriptional activator. SEBF recruits Pti4 via its consensus sequence-type RNA binding domain, while Pti4 is recruited to SEBF by means of its ethylene-response factor domain. In vivo plant transcription assays confirmed that SEBF interacts with Pti4 to form a repressosome, showing that Pti4 can also play a role in transcriptional repression. Chromatin immunoprecipitation revealed that both SEBF and Pti4 are recruited to the PR-10a promoter in uninduced conditions only and that the recruitment of Pti4 is dependent on the presence of SEBF, consistent with the fact that there is no Pti4 consensus binding site in PR-10a. Unexpectedly, we also demonstrated that recruitment of SEBF was dependent on the presence of Pti4, thereby explaining why SEBF, itself a repressor, requires Pti4 for its repressing function.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".