Glycosaminoglycans accelerate biomimetic collagen mineralization in a tissue-based in vitro model
Bibliographic record
Abstract
Mammalian teeth are attached to the jawbone through an exquisitely controlled mineralization process: unmineralized collagen fibers of the periodontal ligament anchor directly into the outer layer of adjoining mineralized tissues (cementum and bone). The sharp interface between mineralized and nonmineralized collagenous tissues makes this an excellent model to study the mechanisms by which extracellular matrix macromolecules control collagen mineralization. While acidic phosphoproteins, localized in the mineralized tissues, play key roles in control of mineralization, the role of glycosaminoglycans (GAGs) is less clear. As several proteoglycans are found only in the periodontal ligament, it has been hypothesized that these inhibit mineralization of collagen in this tissue. Here we used an in vitro model based on remineralization of mouse dental tissues to determine the role of matrix GAGs in control of mineralization. GAGs were selectively removed from demineralized mouse periodontal sections via enzymatic digestion. Proteomic analysis confirmed that enzymatic GAG removal does not significantly alter protein content. Analysis of remineralized tissue sections by transmission electron microscopy (TEM) shows that GAG removal reduced the rate of remineralization in mineralized tissues compared to the untreated control, while the ligament remained unmineralized. Protein removal with trypsin also reduced the rate of mineralization, but to a lesser extent than GAG removal, despite a much larger effect on protein content. These results indicate that GAGs promote mineralization in mineralized dental tissues rather than inhibiting mineral formation in the ligament, which may have broader implications for understanding control of collagen mineralization in connective tissues.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".