Impact of matrix metalloproteinases on inhibition of mineralization by fetuin
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Human subjects affected by inflammatory diseases, such as periodontitis, may be at increased risk for the development of cardiovascular diseases and calcification of atheromas; however, the potential mechanisms have not been defined. Alpha-2-Heremans Schmid glycoprotein (fetuin A) is an abundant serum glycoprotein of ~49 kDa that inhibits ectopic arterial calcification. We examined whether matrix metalloproteinases (MMPs), which are increased in inflammatory diseases, including periodontitis, bind and degrade fetuin and alter its ability to inhibit calcification in vitro. MATERIAL AND METHODS: Binding and cleavage of fetuin by MMPs were assessed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, in-silico analyses and mass spectrometry. The effects of intact and MMP-degraded human fetuin on mineralization were measured in a cell-free assay. RESULTS: From in-silico analyses and literature review, we found that only MMP-3 (stromelysin) and MMP-7 (matrilysin) were predicted to cleave human fetuin at levels that were physiologically relevant. In-vitro assays showed that MMP-7, and, to a lesser extent, MMP-3, degraded human fetuin in a time- and dose-dependent manner. Fetuin peptides generated by MMP-7 cleavage were identified and sequenced by mass spectrometry; novel cleavage sites were found. Hydroxyapatite mineralization in vitro was strongly inhibited by fetuin (> 1 μm), as was MMP-3-cleaved fetuin, while MMP-7-cleaved fetuin was threefold less effective in blocking mineralization. CONCLUSION: MMP-7 and, to a lesser extent, MMP-3, affect the ability of fetuin to inhibit the formation of hydroxyapatite in vitro. These data suggest that the MMPs increased in inflammatory diseases, such as periodontitis, could affect regulation of mineralization and potentially enhance the risk of calcified atheroma formation.
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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 | 0.001 | 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) | 0.000 | 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".