Myxoma viral serpin, Serp-1, inhibits human monocyte adhesion through regulation of actin-binding protein filamin B
Bibliographic record
Abstract
Serp-1 is a secreted myxoma viral serine protease inhibitor (serpin) with proven, highly effective, anti-inflammatory defensive activity during host cell infection, as well as potent immunomodulatory activity in a wide range of animal disease models. Serp-1 binds urokinase-type plasminogen activator (uPA) and the tissue-type PA, plasmin, and factor Xa, requiring uPA receptor (uPAR) for anti-inflammatory activity. To define Serp-1-mediated effects on inflammatory cell activation, we examined the association of Serp-1 with monocytes and T cells, effects on cellular migration, and the role of uPAR-linked integrins and actin-binding proteins in Serp-1 cellular responses. Our results show that Serp-1 associates directly with activated monocytes and T lymphocytes, in part through interaction with uPAR (P<0.001). Serp-1, but not mammalian serpin PA inhibitor-1 (PAI-1), attenuated cellular adhesion to the extracellular matrix. Serp-1 and PAI-1 reduced human monocyte and T cell adhesion (P<0.001) and migration across endothelial monolayers in vitro (P<0.001) and into mouse ascites in vivo (P<0.001). Serp-1 and an inactive Serp-1 mutant Serp-1(SAA) bound equally to human monocytes and T cells, but a highly proinflammatory mutant, Serp-1(Ala(6)), bound less well to monocytes. Serp-1 treatment of monocytes increased expression of filamin B actin-binding protein and reduced CD18 (beta-integrin) expression (P<0.001) in a uPAR-dependent response. Filamin colocalized and co-immunoprecipitated with uPAR, and short interference RNA knock-down of filamin blocked Serp-1 inhibition of monocyte adhesion. We report here that the highly potent, anti-inflammatory activity of Serp-1 is mediated through modification of uPAR-linked beta-integrin and filamin in monocytes, identifying this interaction as a central regulatory axis for inflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".