Peptides of Matrix Gla Protein Inhibit Nucleation and Growth of Hydroxyapatite and Calcium Oxalate Monohydrate Crystals
Bibliographic record
Abstract
Matrix Gla protein (MGP) is a phosphorylated and γ-carboxylated protein that has been shown to prevent the deposition of hydroxyapatite crystals in the walls of blood vessels. MGP is also expressed in kidney and may inhibit the formation of kidney stones, which mainly consist of another crystalline phase, calcium oxalate monohydrate. To determine the mechanism by which MGP prevents soft-tissue calcification, we have synthesized peptides corresponding to the phosphorylated and γ-carboxylated sequences of human MGP in both post-translationally modified and non-modified forms. The effects of these peptides on hydroxyapatite formation and calcium oxalate crystallization were quantified using dynamic light scattering and scanning electron microscopy, respectively. Peptides YGlapS (MGP1-14: YγEpSHEpSMEpSYELNP), YEpS (YEpSHEpSMEpSYELNP), YGlaS (YγESHESMESYELNP) and SK-Gla (MGP43-56: SKPVHγELNRγEACDD) inhibited formation of hydroxyapatite in order of potency YGlapS > YEpS > YGlaS > SK-Gla. The effects of YGlapS, YEpS and YGlaS on hydroxyapatite formation were on both crystal nucleation and growth; the effect of SK-Gla was on nucleation. YGlapS and YEpS significantly inhibited the growth of calcium oxalate monohydrate crystals, while simultaneously promoting the formation of calcium oxalate dihydrate. The effects of these phosphopeptides on calcium oxalate monohydrate formation were on growth of crystals rather than nucleation. We have shown that the use of dynamic light scattering allows inhibitors of hydroxyapatite nucleation and growth to be distinguished. We have also demonstrated for the first time that MGP peptides inhibit the formation of calcium oxalate monohydrate. Based on the latter finding, we propose that MGP function not only to prevent blood-vessel calcification but also to inhibit stone formation in kidney.
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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 | 0.000 |
| 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".