Sumoylated SnoN interacts with HDAC1 and p300/CBP to regulate EMT-associated phenotypes in mammary organoids
Bibliographic record
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 for cancer invasion and metastasis. The transcriptional coregulator SnoN suppresses TGFβ-induced EMT-associated responses 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-related events in three-dimensional multicellular organoids derived from mammary epithelial cells or carcinomas. These findings suggest that sumoylated SnoN acts via the regulation of histone acetylation to modulate EMT-related effects 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 | 0.000 |
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".