Regulation of Fibroblast Migration on Collagenous Matrix by a Cell Surface Peptidase Complex
Bibliographic record
Abstract
The invasion of migratory cells through connective tissues involves metallo- and serine types of cell surface proteases. We show that formation of a novel protease complex, consisting of the membrane-bound prolyl peptidases seprase and dipeptidyl peptidase IV (DPPIV), at invadopodia of migratory fibroblasts is a prerequisite for cell invasion and migration on a collagenous matrix. Seprase and DPPIV form a complex on the cell surface that elicits both gelatin binding and gelatinase activities localized at invadopodia of cells migrating on collagenous fibers. The protease complex participates in the binding to gelatin and localized gelatin degradation, cellular migration, and monolayer wound closure. Serine protease inhibitors can block the gelatinase activity and the localized gelatin degradation by cells. Antibodies to the gelatin-binding domain of DPPIV reduce the cellular abilities of the proteases to degrade gelatin but do not affect cellular adhesion or spreading on type I collagen. Furthermore, expression of the seprase-DPPIV complex is restricted to migratory cells involved in wound closure in vitro and in connective tissue cells during closure of gingival wounds but not in differentiated tissue cells. Thus, we have identified cell surface proteolytic activities, which are non-metalloproteases, seprase and DPPIV, that are responsible for the tissue-invasive phenotype.
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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".