The Endothelial-mesenchymal Transition in Systemic Sclerosis Is Induced by Endothelin-1 and Transforming Growth Factor-β and May Be Blocked by Macitentan, a Dual Endothelin-1 Receptor Antagonist
Bibliographic record
Abstract
OBJECTIVE: High endothelin-1 (ET-1) and transforming growth factor-β (TGF-β) levels may induce in healthy endothelial cells (EC) an endothelial-to-mesenchymal transition (EndMT). The same cytokines are associated with fibrosis development in systemic sclerosis (SSc). Although EndMT has not been definitively shown in SSc, this process, potentially induced by a stimulatory loop involving these 2 cytokines, overexpressed in this disease might contribute to fibroblast accumulation in affected tissues. Macitentan (MAC), an ET-1 receptor antagonist interfering with this loop, might prevent EndMT and fibroblast accumulation. METHODS: EC, isolated from healthy controls (HC) and patients with SSc, were treated with ET-1 and TGF-β and successively analyzed for gene and protein expressions of endothelial and mesenchymal markers, and for Sma- and Mad-related (SMAD) phosphorylation. Further, in the supernatants, we evaluated ET-1 and TGF-β production by ELISA assay. In each assay we evaluated the ability of MAC to inhibit both the TGF-β and ET-1 effects. RESULTS: We showed that both TGF-β and ET-1 treatments induced an activation of the EndMT process in SSc-EC as reported in HC cells. The ELISA assays showed a mutual TGF-β and ET-1 induction in both SSc-EC and HC-EC. A statistically significant increase of SMAD phosphorylation after treatment was observed in SSc-EC. In each assay, MAC inhibited both TGF-β and ET-1 effects. CONCLUSION: Our work is the first demonstration in literature that SSc-EC, under the synergistic effect of TGF-β and ET-1, may transdifferentiate toward myofibroblasts, thus contributing to fibroblast accumulation. MAC, interfering with this process in vitro, may offer a new potential therapeutic strategy against fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".