Colocation and role of polyphosphates and alkaline phosphatase in apatite biomineralization of elasmobranch tesserae
Bibliographic record
Abstract
Elasmobranchs (e.g. sharks and rays), like all fishes, grow continuously throughout life. Unlike other vertebrates, their skeletons are primarily cartilaginous, comprising a hyaline cartilage-like core, stiffened by a thin outer array of mineralized, abutting and interconnected tiles called tesserae. Tesserae bear active mineralization fronts at all margins and the tesseral layer is thin enough to section without decalcifying, making this a tractable but largely unexamined system for investigating controlled apatite mineralization, while also offering a potential analog for endochondral ossification. The chemical mechanism for tesserae mineralization has not been described, but has been previously attributed to spherical precursors, and alkaline phosphatase (ALP) activity. Here, we use a variety of techniques to elucidate the involvement of phosphorus-containing precursors in the formation of tesserae at their mineralization fronts. Using Raman spectroscopy, fluorescence microscopy and histological methods, we demonstrate that ALP activity is located with inorganic phosphate polymers (polyP) at the tessera-uncalcified cartilage interface, suggesting a potential mechanism for regulated mineralization: inorganic phosphate (Pi) can be cleaved from polyP by ALP, thus making Pi locally available for apatite biomineralization. The application of exogenous ALP to tissue cross-sections resulted in the disappearance of polyP and the appearance of Pi in uncalcified cartilage adjacent to mineralization fronts. We propose that elasmobranch skeletal cells control apatite biomineralization by biochemically controlling polyP and ALP production, placement and activity. Previous identification of polyP and ALP shown previously in mammalian calcifying cartilage supports the hypothesis that this mechanism may be a general regulating feature in the mineralization of vertebrate skeletons.
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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 | 0.000 |
| 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".