Glycosylphosphatidylinositol-anchored Proteins Regulate Transforming Growth Factor-β Signaling in Human Keratinocytes
Bibliographic record
Abstract
Glycosylphosphatidylinositol (GPI)-anchored proteins have been demonstrated to bind transforming growth factor-beta (TGF-beta) in certain cell lines. However, the identity of these GPI-anchored proteins and the role they may play in TGF-beta signaling remain unknown. We have previously reported the presence of GPI-anchored TGF-beta-binding proteins on human skin fibroblasts and keratinocytes (Tam, B. Y. Y., and Philip, A. (1998) J. Cell. Physiol. 176, 553-564; Tam, B. Y. Y., Germain, L., and Philip, A. (1998) J. Cell. Biochem. 70, 573-586). On human keratinocytes, we identified a 150-kDa GPI-anchored TGF-beta1-binding protein (r150) and demonstrated that it can form a heteromeric complex with the type I and II TGF-beta signaling receptors. To explore whether GPI-anchored proteins modulate TGF-beta signaling in keratinocytes, we created keratinocytes defective in GPI anchor biosynthesis (GPI mutant cells) by chemical mutagenesis of HaCaT cells. Mutant clones were selected by fluorescence-activated cell sorting analysis based on the loss of a CD59 marker. In comparison with parental HaCaT cells, GPI mutant cells demonstrated a significant loss of r150 expression. In contrast, the levels of the type I and II TGF-beta receptors and their ligand affinities, cell morphology, and doubling time remained unchanged. Importantly, GPI mutant cells displayed enhanced gene transcriptional activity and Smad2 and Smad3 activation in response to TGF-beta1 treatment in a dose-dependent manner. Taken together, our results indicate that GPI-anchored protein(s) inhibit TGF-beta signaling and implicate r150 as the GPI-anchored protein responsible for this inhibition in human keratinocytes.
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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 | 0.001 |
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".