CD109 release from the cell surface in human keratinocytes regulates TGF-β receptor expression, TGF-β signalling and STAT3 activation: relevance to psoriasis
Bibliographic record
Abstract
Transforming growth factor (TGF)-β is an important cytokine that negatively regulates keratinocyte proliferation. Deregulation of TGF-β signalling has been reported in psoriasis, where despite increased expression of TGF-β, psoriatic keratinocytes continue to hyperproliferate. Recently, we have identified CD109, a glycosyl phosphatidylinositol (GPI)-anchored protein, as a novel co-receptor and negative regulator of TGF-β signalling. In the current work, we demonstrate that release of CD109 from the cell surface or the addition of CD109 protein results in downregulation of TGF-β signalling and TGF-β receptor expression in human keratinocytes. Moreover, these effects are associated with an increase in phospho-STAT3 levels, enhanced total STAT3 and Bcl-2 expression and an increase in cell growth and survival, suggesting that released/soluble CD109 is able to induce molecular changes that are known to occur in psoriasis. Analysis of CD109 expression in psoriasis patients reveals that CD109 protein expression is markedly decreased in psoriatic epidermis as compared to adjacent uninvolved skin. In contrast, CD109 mRNA expression is unchanged in psoriatic plaques in comparison with normal skin. This raises a possibility that CD109 protein release is enhanced in psoriatic keratinocytes. Furthermore, psoriatic epidermis displays decreased expression of TGF-β receptors, consistent with the results obtained in vitro in keratinocytes with CD109 release or addition of CD109 recombinant protein. Together our findings suggest that aberrant CD109 release from the cell surface in human keratinocytes may induce molecular changes that are usually observed in psoriasis and may explain TGF-β receptor downregulation and decrease in TGF-β signalling in psoriasis.
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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".