Dipeptidyl Peptidase-4–Mediated Fibronectin Processing Evokes a Profibrotic Extracellular Matrix
Bibliographic record
Abstract
Fibronectin serves as a platform to guide and facilitate deposition of collagen and fibrillin microfibrils. During development of fibrotic diseases, altered fibronectin deposition in the extracellular matrix (ECM) is generally an early event. After this, dysregulated organization of fibrillins and fibrillar collagens occurs. Because fibronectin is an essential orchestrator of healthy ECM, perturbation of its ECM-organizational capacity may be involved in development of fibrosis. To investigate this, we employed recessive dystrophic epidermolysis bullosa as a disease model with progressive, severe dermal fibrosis. Fibroblasts from donors with recessive dystrophic epidermolysis bullosa in 2-dimensional and 3-dimensional cultures displayed dysregulated fibronectin deposition. Our analyses revealed that increase of profibrotic dipeptidyl peptidase-4-positive fibroblasts coincides with altered fibronectin deposition. Dipeptidyl peptidase-4 inhibitors normalized deposition of fibronectin and subsequently of fibrillin microfibrils and collagen I. Intriguingly, proteomics and inhibitor and mutagenesis studies disclosed that dipeptidyl peptidase-4 modulates ECM deposition through the proteolysis of the fibronectin N-terminus. Our study provides mechanistic insights into the observed profibrotic activities of dipeptidyl peptidase-4 and extends the understanding of fibronectin-guided ECM assembly in health and disease.
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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) | 0.001 | 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".