Suppression of TGFβ-Induced Epithelial-Mesenchymal Transition Like Phenotype by a PIAS1 Regulated Sumoylation Pathway in NMuMG Epithelial Cells
Bibliographic record
Abstract
Epithelial-mesenchymal-transition (EMT) is a fundamental cellular process that is critical for normal development and tumor metastasis. The transforming growth factor beta (TGFβ) is a potent inducer of EMT like effects, but the mechanisms that regulate TGFβ-induced EMT remain incompletely understood. Using the widely employed NMuMG mammary epithelial cells as a model to study TGFβ-induced EMT, we report that TGFβ downregulates the levels of the SUMO E3 ligase PIAS1 in cells undergoing EMT. Gain and loss of function analyses indicate that PIAS1 acts in a SUMO ligase dependent manner to suppress the ability of TGFβ to induce EMT in these cells. We also find that TGFβ inhibits sumoylation of the PIAS1 substrate SnoN, a transcriptional regulator that antagonizes TGFβ-induced EMT. Accordingly, loss of function mutations of SnoN sumoylation impair the ability of SnoN to inhibit TGFβ-induced EMT in NMuMG cells. Collectively, our findings suggest that PIAS1 is a novel negative regulator of EMT and reveal that inhibition of the PIAS1-SnoN sumoylation pathway represents a key mechanism by which TGFβ induces EMT, with important implications in normal development and tumor metastasis.
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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".