Wnt7a deficit is associated with dysfunctional angiogenesis in pulmonary arterial hypertension
Bibliographic record
Abstract
Introduction: Pulmonary arterial hypertension (PAH) is characterised by loss of microvessels. The Wnt pathways control pulmonary angiogenesis but their role in PAH is incompletely understood. We hypothesised that Wnt activation in pulmonary microvascular endothelial cells (PMVECs) is required for pulmonary angiogenesis, and its loss contributes to PAH. Methods: Lung tissue and PMVECs from healthy and PAH patients were screened for Wnt production. Global and endothelial-specificWnt7a−/–mice were generated and exposed to chronic hypoxia and Sugen-hypoxia (SuHx). Results: Healthy PMVECs demonstrated >6-fold Wnt7a expression during angiogenesis that was absent in PAH PMVECs and lungs. Wnt7a expression correlated with the formation of tip cells, a migratory endothelial phenotype critical for angiogenesis. PAH PMVECs demonstrated reduced vascular endothelial growth factor (VEGF)-induced tip cell formation as evidenced by reduced filopodia formation and motility, which was partially rescued by recombinant Wnt7a. We discovered that Wnt7a promotes VEGF signalling by facilitating Y1175 tyrosine phosphorylation in vascular endothelial growth factor receptor 2 (VEGFR2) through receptor tyrosine kinase-like orphan receptor 2 (ROR2), a Wnt-specific receptor. We found that ROR2 knockdown mimics Wnt7a insufficiency and prevents recovery of tip cell formation with Wnt7a stimulation. While there was no difference between wild-type and endothelial-specificWnt7a−/–mice under either chronic hypoxia or SuHx, globalWnt7a+/–mice in hypoxia demonstrated higher pulmonary pressures and severe right ventricular and lung vascular remodelling. Similar to PAH,Wnt7a+/–PMVECs exhibited an insufficient angiogenic response to VEGF-A that improved with Wnt7a. Conclusions: Wnt7a promotes VEGF signalling in lung PMVECs and its loss is associated with an insufficient VEGF-A angiogenic response. We propose that Wnt7a deficiency contributes to progressive small vessel loss in PAH.
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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) | 0.005 | 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".