TGF-β signaling of human T cells is modulated by the ancillary TGF-β receptor endoglin
Bibliographic record
Abstract
Transforming growth factor beta (TGF-beta) inhibits T cell activation and alters differentiation of naive T cells into effector cells. Although four main cell-surface proteins can interact with TGF-beta, only the signaling receptors type I (TGF-betaR type I) and type II (TGF-betaR type II) have so far been described on T cells. The aim of the present study was to investigate the expression of the ancillary receptor endoglin (CD105) by T cells and its role in TGF-beta-mediated signal transduction and function. CD105 expression was analyzed on resting and activated human CD4(+) T cells by flow cytometry, western blot, immunoprecipitation, proliferation and SMAD-responsive reporter gene assays. CD4(+) T cells constitutively expressed CD105 in memory T cells and partially also in naive T cells; however, surface expression is regulated and is increased following TCR engagement, which induced serine/threonine phosphorylation of CD105. In contrast to the suppressive signal mediated by the TGF-beta, cross-linking of CD105 substantially enhanced T cell proliferation, indicating that CD105 by itself mediates signal transduction. Furthermore, CD105 cross-linking induced SMAD-independent signaling via ERK kinase phosphorylation. The present study demonstrates that CD105 is expressed on the surface by activated CD4(+) T cells and CD3 regulated by post-translational means. Furthermore, CD105 acts as a regulatory receptor, counteracting TGF-beta-mediated suppression.
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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".