033 Regulation of TGF‐β Responses by a Novel Accessory TGF‐β Receptor in Human Keratinocytes
Bibliographic record
Abstract
The potential of TGF‐β isoforms to regulate wound healing and scarring is well documented in animal models. Since TGF‐β action is likely to be regulated at the level of its cell surface receptors, we analyzed TGF‐β receptor profiles and regulation of TGF‐β signaling in human keratinocytes. We identified a novel cell surface TGF‐β1 binding protein of 150 kDa (r150) on human keratinocytes that interacts with the TGF‐β signaling receptors. Further characterization of r150 demonstrated that it is a GPI‐anchored protein, that it can be released from the cell surface by an endogenous phospholipase C, and that the released form can bind to TGF‐β1. Recent cloning of r150 revealed it to be a novel protein of 1428 amino acids. Our objective was to determine the functional significance of r150 in regulating TGF‐β responses in keratinocytes. Affinity labeling of keratinocytes overexpressing r150 cDNA and immunoprecipitation stud‐ies using anti‐r150 antibodies show that the cloned cDNA represents r150. Importantly, over expression of r150 results in inhibition of TGF‐β1‐induced Smad 2 and Smad 3 phosphorylation, gene transcriptional activity, and keratinocyte migration. In contrast, loss of r150 function using antisense morpholino oligos of r150 leads to enhanced Smad 2 and Smad 3 phosphorylation, gene transcriptional activity and proliferation of keratinocytes. In summary, our results demonstrate that r150 is a potent inhibitor of TGF‐β signaling in keratinocytes, and that it may have potential therapeutic value in modulating TGF‐β action in human diseases where TGF‐β plays a pathophysiological role. As such, r150 may be of use in reducing hypertrophic scarring, and an r150 antagonist may promote wound healing.
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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".