S100A10 regulates plasminogen-dependent macrophage invasion
Bibliographic record
Abstract
The plasminogen activation system plays an integral role in the migration of macrophages in response to an inflammatory stimulus, and the binding of plasminogen to its cell-surface receptor initiates this process. Although previous studies from our laboratory have shown the importance of the plasminogen receptor S100A10 in cancer cell plasmin production, the potential role of this protein in macrophage migration has not been investigated. Using thioglycollate to induce a peritoneal inflammatory response, we demonstrate, for the first time, that compared with wild-type (WT) mice, macrophage migration across the peritoneal membrane into the peritoneal cavity in S100A10-deficient (S100A10(-/-)) mice was decreased by up to 53% at 24, 48, and 72 hours. Furthermore, the number of S100A10-deficient macrophages that infiltrated Matrigel plugs was reduced by 8-fold compared with their WT counterpart in vivo. Compared with WT macrophages, macrophages from S100A10(-/-) mice demonstrated a 50% reduction in plasmin-dependent invasion across a Matrigel barrier and a 45% reduction in plasmin generation in vitro. This loss in plasmin-dependent invasion was in part the result of a decreased generation of plasmin and a decreased activation of pro-MMP-9 by S100A10-deficient macrophages. This study establishes a direct involvement of S100A10 in macrophage recruitment in response to inflammatory stimuli.
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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 | 0.001 |
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".