Sumoylated SnoN interacts with HDAC1 and p300/CBP to regulate EMT in mammary organoids
Bibliographic record
Abstract
Abstract Protein post-translational modification by the small ubiquitin-like modifier (SUMO) regulates the stability, subcellular localization, and interactions of protein substrates with consequences on cellular responses including epithelial-mesenchymal transition (EMT). Transforming growth factor beta (TGFβ) is a potent inducer of EMT with implications in cancer invasion and metastasis. The transcriptional coregulator SnoN suppresses TGFβ-induced EMT in a sumoylation-dependent manner, but the underlying mechanisms have remained largely unknown. Here, we find that sumoylation promotes the interaction of SnoN with the epigenetic regulators histone deacetylase 1 (HDAC1) and histone acetylase p300 in epithelial cells. In gain and loss of function studies, HDAC1 suppresses, whereas p300 promotes, TGFβ-induced morphogenetic changes associated with EMT in three-dimensional multicellular structures derived from mammary epithelial cells or carcinomas. These findings suggest that sumoylated SnoN acts via regulation of histone acetylation to modulate EMT in breast cell organoids. Our study may facilitate the discovery of new biomarkers and therapeutics in breast cancer and other epithelial cell-derived cancers.
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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 | 0.001 |
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".