Matrix metalloproteinase 9 is associated with peritoneal membrane solute transport and induces angiogenesis through β-catenin signaling
Bibliographic record
Abstract
Background: For patients using peritoneal dialysis (PD), the peritoneal membrane can develop fibrosis and angiogenesis, leading to ultrafiltration failure, chronic hypervolemia and increased risk of technique failure and mortality. Matrix metalloproteinases (MMPs), and specifically the gelatinases (MMP2 and MMP9), may be involved in peritoneal membrane injury. Methods: From stable PD patients, mesothelial cells were assayed for MMP gene expression. MMP9 was overexpressed in mouse peritoneum by adenovirus, and MMP9 -/- mice were subjected to transforming growth factor β (TGF-β)-induced peritoneal fibrosis. Results: MMP9 mRNA expression correlated with peritoneal membrane solute transport properties. Overexpression of MMP9 in the mouse peritoneum induced submesothelial thickening and angiogenesis. MMP9 induced mesothelial cell transition to a myofibroblast phenotype measured by increased alpha smooth muscle actin and decreased E-cadherin expression. Angiogenesis was markedly reduced in MMP9 -/- mice treated with an adenovirus expressing active TGF-β compared with wild-type mice. TGF-β-mediated E-cadherin cleavage was MMP9 dependent, and E-cadherin cleavage led to β-catenin-mediated signaling. A β-catenin inhibitor blocked the angiogenic response induced by AdMMP9. Conclusions: Our data suggest that MMP9 is involved in peritoneal membrane injury possibly through cleavage of E-cadherin and induction of β-catenin signaling. MMP9 is a potential biomarker for peritoneal membrane injury and is a therapeutic target to protect the peritoneal membrane in PD patients.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".