PGE2 and Thrombin Induce Myofibroblast Transdifferentiation via Activin A and CTGF in Endometrial Stromal Cells
Bibliographic record
Abstract
Endometriosis is characterized by inflammation and fibrotic changes. Our previous study using a mouse model showed that proinflammatory factors present in peritoneal hemorrhage exacerbated inflammation in endometriosis-like grafts, at least in part through the activation of prostaglandin (PG) E2 receptor and protease-activated receptor (PAR). In addition, menstruation-related factors, PGE2 and thrombin (P/T), a PAR1 agonist induced epithelial-mesenchymal transition (EMT) of endometrial cells under hypoxia. However, the molecular mechanisms by which P/T induce development of endometriosis have not been fully characterized. To investigate the effects of P/T, RNA extracted from endometrial stromal cells (ESCs) treated with P/T were subjected to RNA sequence analysis, and identified activin A, FOS, and GATA2 as upregulated genes. Activin A increased the expression of connective tissue growth factor (CTGF) and mesenchymal marker genes in ESCs. CTGF induced the expression of fibrosis marker type I collagen, fibronectin, and α-smooth muscle actin (αSMA), indicating fibroblast to myofibroblast transdifferentiation (FMT) of ESCs. In addition, activin A, FOS, GATA2, CTGF, and αSMA were localized in endometriosis lesions. Taken together, our data show that P/T induces changes resembling EMT and FMT in ectopic ESCs derived from retrograde menstruation, and that these are associated with fibrotic changes in the lesions. Pharmacological means that block P/T-induced activin A and CTGF signaling may be strategies to inhibit fibrosis in endometriotic lesions.
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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".