CD109‐mediated degradation of TGF‐β receptors and inhibition of TGF‐β responses involve regulation of SMAD7 and Smurf2 localization and function
Bibliographic record
Abstract
Transforming growth factor-β (TGF-β) is a multifunctional cytokine that regulates a wide variety of cellular processes including proliferation, differentiation, and extracellular matrix deposition. Dysregulation of TGF-β signaling is associated with several diseases such as cancer and tissue fibrosis. TGF-β signals through two transmembrane proteins known as the type I (TGFBR1) and type II (TGFBR2) receptors. The levels of these receptors at the cell surface are tightly regulated by several mechanisms, including degradation following recruitment of the E3 ubiquitin ligase SMAD ubiquitination regulatory factor (Smurf) 2 by SMAD7. In addition, TGF-β co-receptors can modulate TGF-β signaling receptor activity in a cell-specific manner. We have previously identified a novel TGF-β co-receptor, CD109, a glycosyl phosphatidylinositol (GPI)-anchored protein that negatively regulates TGF-β signaling. Despite CD109's potential relevance as a regulator of TGF-β action in vivo, the mechanisms by which CD109 regulates TGF-β signaling are still incompletely understood. Previously, we have shown that CD109 downregulates TGF-β signaling by promoting TGF-β receptor localization into the lipid raft/caveolae compartment and by enhancing TGF-β receptor degradation. Here, we demonstrate that CD109 enhances SMAD7/Smurf2-mediated degradation of TGFBR1 in a ligand-dependent manner. Moreover, we show that CD109 regulates the localization and the association of SMAD7/Smurf2 with TGFBR1. Finally, we demonstrate that CD109's inhibitory effect on TGF-β signaling and responses require SMAD7 expression and Smurf2 ubiquitin ligase activity. Taken together, these results suggest that CD109 is an important regulator of SMAD7/Smurf2-mediated degradation of TGFBR1.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".