The Transactivation Function of the Pea3 Subfamily Ets Transcription Factors Is Regulated by Sumoylation
Bibliographic record
Abstract
Pea3, an Ets transcriptional factor, comprises multiple regulatory domains that affect its DNA binding and transcriptional activation. The aim of this work is to uncover the mechanism of action of negative regulatory regions flanking the transactivation domain. Mutagenesis of amino acid residues in the C-terminal negative regulatory region for transactivation revealed critical residues, including a lysine residue, K96, required for its function. Corresponding mutations in the closely related Pea3 subfamily members, Erm and Er81, also dramatically increased the transactivation capacity of their activation domains. Interestingly, all three proteins are sumoylated at this conserved lysine residue. Pea3 contains four other lysines, K222, K256, K318, and K437, embedded in a perfect SUMO consensus motif. The contribution of these lysine residues to the regulation of Pea3 activity and their sumoylation pattern was explored using a GAL4-PEA3 chimera devoid of the ETS DNA-binding domain and by analyzing the native protein. All four candidate SUMO sites included in the GAL4-PEA3 chimera were modified by sumoylation, and their simultaneous mutation dramatically increased the transactivation potential of Pea3. Similar analysis of full-length Pea3 confirmed K96, K222, and K256 as major SUMO modification sites. Collectively, these observations suggest that the activity of Pea3 and its paralogs, Erm and Er81, is negatively regulated by sumoylation.
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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 | 0.000 |
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".