Understanding the role of USP11, a novel SMAD2-interacting protein, in TGF-B signalling
Bibliographic record
Abstract
TGF-β signalling regulates various cellular activities throughout development and adulthood. Disruptions in the TGF-β signalling cascade are associated with several human diseases. SMAD2 is one of the principal effectors of TGF-β signalling. It interacts with various transcription factors, and is extensively regulated by post-translational modifications, such as ubiquitination. However, much remains unknown about the regulation of SMAD2 function. Using a proteomic screen, I identified ubiquitin-specific peptidase 11 (USP11) as a novel SMAD2 interactor. I confirmed their mutual interaction, and showed that USP11 specifically interacts with the linker domain of SMAD2, but did not appear to regulate either its stability or ubiquitination pattern. USP11 knockdown decreased TGF-β-mediated gene promoter-reporter activity, whereas USP11 over-expression potentiated it. USP11 knockdown did not, however, affect endogenous gene expression after 2 h TGF-β treatment, as determined by microarray analysis and quantitative reverse transcription PCR. I conclude that although USP11 did not regulate the stability of SMAD2, it might be involved in the regulation of other, currently unresolved, aspects of TGF-β signalling.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".