Soluble CD109 binds TGF-β and antagonizes TGF-β signalling and responses
Bibliographic record
Abstract
Transforming growth factor-β (TGF-β) is a multifunctional cytokine implicated in many diseases, including tissue fibrosis and cancer. TGF-β mediates diverse biological responses by signalling through type I and II TGF-β receptors (TβRI and TβRII). We have previously identified CD109, a glycosylphosphatidylinositol (GPI)-anchored protein, as a novel TGF-β co-receptor that negatively regulates TGF-β signalling and responses and demonstrated that membrane-anchored CD109 promotes TGF-β receptor degradation via a SMAD7/Smurf2-mediated mechanism. To determine whether CD109 released from the cell surface (soluble CD109 or sCD109) also acts as a TGF-β antagonist, we determined the efficacy of recombinant sCD109 to interact with TGF-β and inhibit TGF-β signalling and responses. Our results demonstrate that sCD109 binds TGF-β with high affinity as determined by surface plasmon resonance (SPR) and cell-based radioligand binding and affinity labelling competition assays. SPR detected slow dissociation kinetics between sCD109 and TGF-β at low concentrations, indicating a stable and effective interaction. In addition, sCD109 antagonizes TGF-β-induced Smad2/3 phosphorylation, transcription and cell migration. Together, our results suggest that sCD109 can bind TGF-β, inhibit TGF-β binding to its receptors and decrease TGF-β signalling and TGF-β-induced cellular responses.
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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) | 0.002 | 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".