A short SUMOylation tag modulates transcription factor activity
Bibliographic record
Abstract
SUMOylation is a posttranslational modification that regulates multiple aspects of protein biology, including the activity of transcription factors such as p53. Although strategies exist to decrease protein SUMOylation in a targeted manner, options are limited to increase SUMOylation in a protein-specific manner. Here, we developed a strategy to induce SUMOylation of a target protein relying on its genetic fusion to a 32-residue tag termed ZNF and composed of the SUMO E3 module of ZNF451. Through in vitro and cell-based assays, we establish that this SUMOylation tag promotes robust poly/multi-SUMOylation of p53, used as a model substrate, with a strong preference for SUMO2/3 as compared to SUMO1. Mass spectrometry experiments performed on transfected HEK293 cells stably expressing a modified form of SUMO3 indicate that lysine 386, the main SUMOylation acceptor site of p53, is the primary target of ZNF-mediated SUMOylation. Increased SUMOylation represses p53 transcriptional activity in luciferase reporter assays, a result compatible with the general repressive effects of SUMOylation on transcription factor activity. Finally, fusion of ZNF to HSF1 and DNMT3A also increase their SUMOylation level, showcasing that ZNF could potentially be used to promote the SUMOylation of a broad range of proteins implicated in DNA metabolism. Overall, this strategy will facilitate the investigation of the impact of increased SUMOylation on specific protein substrates.
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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 | 0.001 |
| 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".