Cell Surface-bound Collagenase-1 and Focal Substrate Degradation Stimulate the Rear Release of Motile Vascular Smooth Muscle Cells
Bibliographic record
Abstract
To migrate in the vessel wall, smooth muscle cells (SMCs) must contend with abundant type I collagen. We investigated the mechanisms used by human SMCs to efficiently migrate on type I collagen, following stimulation with fibroblast growth factor-2 (FGF-2). FGF-2-stimulated migration was inhibited by a hydroxamic acid inhibitor of matrix metalloproteinases and by a neutralizing anti-collagenase-1 antibody. Moreover, migration speed of SMCs plated on mutant collagenase-resistant type I collagen was not increased by FGF-2. Time-lapse video analysis of unstimulated SMCs migrating on collagen revealed discrete phases of leading edge membrane extension and rear retraction, the latter often after rupture of an elongated tail. FGF-2 stimulation yielded a more synchronous, gliding motion with a collagenase-1-mediated decrease in tail ripping. Surface labeling of SMCs with biotin followed by immunoprecipitation revealed that a proportion of active collagenase-1, expressed in response to FGF-2, was bound to the plasma membrane. Pericellular collagen substrate cleavage was verified by immunostaining for neoepitopes generated by collagenase-1 action and was localized to discrete zones beneath the cell tail and the leading edge. These results identify a novel mechanism by which SMC migration on collagen is enhanced, whereby rear release from the substrate is orchestrated by the localized actions of membrane-bound collagenase-1.
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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".