The role of WNT5A and Ror2 in peritoneal membrane injury
Bibliographic record
Abstract
Patients on peritoneal dialysis are at risk of developing peritoneal fibrosis and angiogenesis, which can lead to dysfunction of the peritoneal membrane. Recent evidence has identified cross-talk between transforming growth factor beta (TGFB) and the WNT/β-catenin pathway to induce fibrosis and angiogenesis. Limited evidence exists describing the role of non-canonical WNT signalling in peritoneal membrane injury. Non-canonical WNT5A is suggested to have different effects depending on the receptor environment. WNT5A has been implicated in antagonizing canonical WNT/β-catenin signalling in the presence of receptor tyrosine kinase-like orphan receptor (Ror2). We co-expressed TGFB and WNT5A using adenovirus and examined its role in the development of peritoneal fibrosis and angiogenesis. Treatment of mouse peritoneum with AdWNT5A decreased the submesothelial thickening and angiogenesis induced by AdTGFB. WNT5A appeared to block WNT/β-catenin signalling by inhibiting phosphorylation of glycogen synthase kinase 3 beta (GSK3B) and reducing levels of total β-catenin and target proteins. To examine the function of Ror2, we silenced Ror2 in a human mesothelial cell line. We treated cells with AdWNT5A and observed a significant increase in fibronectin compared with AdWNT5A alone. We also analysed fibronectin and vascular endothelial growth factor (VEGF) in a TGFB model of mesothelial cell injury. Both fibronectin and VEGF were significantly increased in response to Ror2 silencing when cells were exposed to TGFB. Our results suggest that WNT5A inhibits peritoneal injury and this is associated with a decrease in WNT/β-catenin signalling. In human mesothelial cells, Ror2 is involved in regulating levels of fibronectin and VEGF.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".