Biochemical Analysis of Matrix Metalloproteinase Activation of Chemokines CCL15 and CCL23 and Increased Glycosaminoglycan Binding of CCL16
Bibliographic record
Abstract
Leukocyte migration and activation is orchestrated by chemokines, the cleavage of which modulates their activity and glycosaminoglycan binding and thus their roles in inflammation and immunity. Early research identified proteolysis as a means of both activating or inactivating CXC chemokines and inactivating CC chemokines. Recent evidence has shown activating cleavages of the monocyte chemoattractants CCL15 and CCL23 by incubation with synovial fluid, although the responsible proteases could not be identified. Herein we show that CCL15 is processed in human synovial fluid by matrix metalloproteinases (MMPs) and serine proteases. Furthermore, a family-wide investigation of MMP processing of all 14 monocyte-directed CC chemokines revealed that each is precisely cleaved by one or more MMPs. By MALDI-TOF-MS, 149 cleavage sites were sequenced including the first reported instance of CCL1, CCL16, and CCL17 proteolysis. Full-length CCL15-(1-92) and CCL23-(1-99) were cleaved within their unique 31 and 32-amino acid residue extended amino termini, respectively. Unlike other CCL chemokines that lose activity and become receptor antagonists upon MMP cleavage, the prominent MMP-processed products CCL15-(25-92, 28-92) and CCL23-(26-99) are stronger agonists in calcium flux and Transwell CC receptor transfectant and monocytic THP-1 migration assays. MMP processing of CCL16-(1-97) in its extended carboxyl terminus yields two products, CCL16-(8-77) and CCL16-(8-85), with both showing unexpected enhanced glycosaminoglycan binding. Hence, our study reveals for the first time that MMPs activate the long amino-terminal chemokines CCL15 and CCL23 to potent forms that have potential to increase monocyte recruitment during inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".