Matrix metalloproteinase processing of monocyte chemoattractant proteins generates CC chemokine receptor antagonists with anti-inflammatory properties in vivo
Bibliographic record
Abstract
Monocyte chemoattractant protein (MCP)-3 is inactivated upon cleavage by the matrix metalloproteinase (MMP) gelatinase A (MMP-2). We investigated the susceptibility to proteolytic processing of the 4 human MCPs by 8 recombinant MMPs to determine whether MCP-3 is an isolated example or represents a general susceptibility of chemokines to proteolytic inactivation by these important inflammatory proteases. In addition to MMP-2, MCP-3 is efficiently cleaved by membrane type 1 (MT1)-MMP, the cellular activator of MMP-2, and by collagenase-1 and collagenase-3 (MMP-1, MMP-13) and stromelysin-1 (MMP-3). Specificity was shown by absence of cleavage by matrilysin (MMP-7) and the leukocytic MMPs neutrophil collagenase (MMP-8) and gelatinase B (MMP-9). The closely related chemokines MCP-1, MCP-2, and MCP-4 were not cleaved by MMP-2 or MT1-MMP, but were cleaved by MMP-1 and MMP-3 with varying efficiency. MCPs were typically cleaved between residues 4 and 5, but MCP-4 was further processed at Val7-Pro8. Synthetic MCP analogs corresponding to the MMP-cleaved forms bound CC chemokine receptor (CCR)-2 and CCR-3, but lacked chemoattractant activity in pre-B cells transfected with CCR-2 and CCR-3 or in THP-1 monocytic cells, a transformed leukemic cell line. Moreover, the truncated products of MCP-2 and MCP-4, like MCP-3, were potent antagonists of their cognate CC chemokine receptors in transwell cell migration assays in vitro. When they were injected 24 hours after the initiation of carrageenan-induced inflammation in rat paws, their in vivo antagonist activities were revealed by a greater than 66% reduction in inflammatory edema progression after 12 hours. We propose that MMPs have an important role in modulating inflammatory and immune responses by processing chemokines in wound healing and in 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 | 0.001 | 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".