Sumoylated SnoN Represses Transcription in a Promoter-specific Manner
Bibliographic record
Abstract
The transcriptional modulator SnoN controls a diverse set of biological processes, including cell proliferation and differentiation. The mechanisms by which SnoN regulates these processes remain incompletely understood. Recent studies have shown that SnoN exerts positive or negative regulatory effects on transcription. Because post-translational modification of proteins by small ubiquitin-like modifier (SUMO) represents an important mechanism in the control of the activity of transcriptional regulators, we asked if this modification regulates SnoN function. Here, we show that SnoN is sumoylated. Our data demonstrate that the SUMO-conjugating E2 enzyme Ubc9 is critical for SnoN sumoylation and that the SUMO E3 ligase PIAS1 selectively interacts with and enhances the sumoylation of SnoN. We identify lysine residues 50 and 383 as the SUMO acceptor sites in SnoN. Analyses of SUMO "loss-of-function" and "gain-of-function" SnoN mutants in transcriptional reporter assays reveal that sumoylation of SnoN contributes to the ability of SnoN to repress gene expression in a promoter-specific manner. Although this modification has little effect on SnoN repression of the plasminogen activator inhibitor-1 promoter and only modestly potentiates SnoN repression of the p21 promoter, SnoN sumoylation robustly augments the ability of SnoN to suppress transcription of the myogenesis master regulatory gene myogenin. In addition, we show that the SnoN SUMO E3 ligase, PIAS1, at its endogenous levels, suppresses myogenin transcription. Collectively, our findings suggest that SnoN is directly regulated by sumoylation leading to the enhancement of the ability of SnoN to repress transcription in a promoter-specific manner. Our study also points to a physiological role for SnoN sumoylation in the control of myogenin expression in differentiating muscle cells.
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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) | 0.002 | 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".