TIF1γ Protein Regulates Epithelial-Mesenchymal Transition by Operating as a Small Ubiquitin-like Modifier (SUMO) E3 Ligase for the Transcriptional Regulator SnoN1
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) is a fundamental cellular process that contributes to epithelial tissue morphogenesis during normal development and in tumor invasiveness and metastasis. The transcriptional regulator SnoN robustly influences EMT in response to the cytokine TGFβ, but the mechanisms that regulate the fundamental role of SnoN in TGFβ-induced EMT are not completely understood. Here we employ interaction proteomics to uncover the signaling protein TIF1γ as a specific interactor of SnoN1 but not the closely related isoform SnoN2. A 16-amino acid peptide within a unique region of SnoN1 mediates the interaction of SnoN1 with TIF1γ. Strikingly, although TIF1γ is thought to act as a ubiquitin E3 ligase, we find that TIF1γ operates as a small ubiquitin-like modifier (SUMO) E3 ligase that promotes the sumoylation of SnoN1 at distinct lysine residues. Importantly, TIF1γ-induced sumoylation is required for the ability of SnoN1 to suppress TGFβ-induced EMT, as assayed by the disruption of the morphogenesis of acini in a physiologically relevant three-dimensional model of normal murine mammary gland (NMuMG) epithelial cells. Collectively, our findings define a novel TIF1γ-SnoN1 sumoylation pathway that plays a critical role in EMT and has important implications for our understanding of TGFβ signaling and diverse biological processes in normal development and cancer biology.
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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".