Cadherin-11–mediated adhesion of macrophages to myofibroblasts establishes a profibrotic niche of active TGF-β
Bibliographic record
Abstract
Macrophages contribute to the activation of fibroblastic cells into myofibroblasts, which secrete collagen and contract the collagen matrix to acutely repair injured tissue. Persistent myofibroblast activation leads to the accumulation of fibrotic scar tissue that impairs organ function. We investigated the key processes that turn acute beneficial repair into destructive progressive fibrosis. We showed that homotypic cadherin-11 interactions promoted the specific binding of macrophages to and persistent activation of profibrotic myofibroblasts. Cadherin-11 was highly abundant at contacts between macrophages and myofibroblasts in mouse and human fibrotic lung tissues. In attachment assays, cadherin-11 junctions mediated specific recognition and strong adhesion between macrophages and myofibroblasts. One functional outcome of cadherin-11-mediated adhesion was locally restricted activation of latent transforming growth factor-β (TGF-β) between macrophage-myofibroblast pairs that was not observed in cocultures of macrophages and myofibroblasts that were not in contact with one another. Our data suggest that cadherin-11 junctions maintain latent TGF-β-producing macrophages and TGF-β-activating myofibroblasts in close proximity to one another. Inhibition of homotypic cadherin-11 interactions could be used to cause macrophage-myofibroblast separation, thereby destabilizing the profibrotic niche.
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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) | 0.002 | 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".