Integrins αvβ5 and αvβ3 promote latent TGF-β1 activation by human cardiac fibroblast contraction
Bibliographic record
Abstract
AIMS: Pathological tissue remodelling by myofibroblast contraction is a hallmark of cardiac fibrosis. Myofibroblasts differentiate from cardiac fibroblasts under the action of transforming growth factor-β1 (TGF-β1), which is secreted into the extracellular matrix as a large latent complex. Integrin-mediated traction forces activate TGF-β1 by inducing a conformational change in the latent complex. The mesenchymal integrins αvβ5 and αvβ3 are expressed in the heart, but their role in the activation of TGF-β1 remains elusive. Here, we test whether targeting αvβ5 and αvβ3 integrins reduces latent TGF-β1 activation by cardiac fibroblasts with the goal to prevent the formation of α-smooth muscle actin (α-SMA)-expressing cardiac myofibroblasts and their contribution to fibrosis. METHODS AND RESULTS: Using a porcine model of induced right ventricular fibrosis and pro-fibrotic culture conditions, we show that integrins αvβ5 and αvβ3 are up-regulated in myofibroblast-enriched fibrotic lesions and differentiated cultured human cardiac myofibroblasts. Both integrins autonomously contribute to latent TGF-β1 activation and myofibroblast differentiation, as demonstrated by function-blocking peptides and antibodies. Acute blocking of both integrins leads to significantly reduced TGF-β1 activation by cardiac fibroblast contraction and loss of α-SMA expression, which is restored by adding active TGF-β1. Manipulating integrin protein levels in overexpression and shRNA experiments reveals that both integrins can compensate for each other with respect to TGF-β1 activation and induction of α-SMA expression. CONCLUSIONS: Integrins αvβ5 and αvβ3 both control myofibroblast differentiation by activating latent TGF-β1. Pharmacological targeting of mesenchymal integrins is a possible strategy to selectively block TGF-β1 activation by cardiac myofibroblasts and progression of fibrosis in the heart.
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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) | 0.003 | 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".