Elastase and Tryptase Govern TNFα-Mediated Production of Active Chemerin by Adipocytes
Bibliographic record
Abstract
Chemerin is a leukocyte chemoattractant and adipokine with important immune and metabolic roles. Chemerin, secreted in an inactive form prochemerin, undergoes C-terminal proteolytic cleavage to generate active chemerin, a ligand for the chemokine-like receptor-1 (CMKLR1). We previously identified that adipocytes secrete and activate chemerin. Following treatment with the obesity-associated inflammatory mediator TNFα, unknown adipocyte mechanisms are altered resulting in an increased ratio of active to total chemerin production. Based on these findings we hypothesized adipocytes produce proteases capable of modifying chemerin and its ability to activate CMKRL1. 3T3-L1 adipocytes expressed mRNA of immunocyte and fibrinolytic proteases known to activate chemerin in vitro. Following treatment with a general protease inhibitor cocktail (PIC), the TNFα-stimulated increase in apparent active chemerin concentration in adipocyte media was amplified 10-fold, as measured by CMKLR1 activation. When the components of the PIC were investigated individually, aprotinin, a serine protease inhibitor, blocked 90% of the TNFα-associated increase in active chemerin. The serine proteases, elastase and tryptase were elevated in adipocyte media following treatment with TNFα and their targeted neutralization recapitulated the aprotinin-mediated effects. In contrast, bestatin, an aminopeptidase inhibitor, further elevated the TNFα-associated increase in active chemerin. Our results support that adipocytes regulate chemerin by serine protease-mediated activation pathways and aminopeptidase deactivation pathways. Following TNFα treatment, increased elastase and tryptase modify the balance between activation and deactivation, elevating active chemerin concentration in adipocyte media and subsequent CMKLR1 activation.
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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 | 0.001 |
| 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".