Tooth root dentin mineralization defects in a mouse model of hypophosphatasia
Bibliographic record
Abstract
Tissue-nonspecific alkaline phosphatase (TNAP) is expressed in mineralizing tissues and functions to reduce pyrophosphate (PP(i) ), a potent inhibitor of mineralization. Loss of TNAP function causes hypophosphatasia (HPP), a heritable disorder marked by increased PP(i) , resulting in rickets and osteomalacia. Tooth root cementum defects are well described in both HPP patients and in Alpl(-/-) mice, a model for infantile HPP. In Alpl(-/-) mice, dentin mineralization is specifically delayed in the root; however, reports from human HPP patients are variable and inconsistent regarding dentin defects. In the current study, we aimed to define the molecular basis for changes in dentinogenesis observed in Alpl(-/-) mice. TNAP was found to be highly expressed by mature odontoblasts, and Alpl(-/-) molar and incisor roots featured defective dentin mineralization, ranging from a mild delay to severely disturbed root dentinogenesis. Lack of mantle dentin mineralization was associated with disordered and dysmorphic odontoblasts having disrupted expression of marker genes osteocalcin and dentin sialophosphoprotein. The formation of, initiation of mineralization within, and rupture of matrix vesicles in Alpl(-/-) dentin matrix was not affected. Osteopontin (OPN), an inhibitor of mineralization that contributes to the skeletal pathology in Alpl(-/-) mice, was present in the generally unmineralized Alpl(-/-) mantle dentin at ruptured mineralizing matrix vesicles, as detected by immunohistochemistry and by immunogold labeling. However, ablating the OPN-encoding Spp1 gene in Alpl(-/-) mice was insufficient to rescue the dentin mineralization defect. Administration of bioengineered mineral-targeting human TNAP (ENB-0040) to Alpl(-/-) mice corrected defective dentin mineralization in the molar roots. These studies reveal that TNAP participates in root dentin formation and confirm that reduction of PP(i) during dentinogenesis is necessary for odontoblast differentiation, dentin matrix secretion, and mineralization. Furthermore, these results elucidate developmental mechanisms underlying dentin pathology in HPP patients, and begin to explain the reported variability in the dentin/pulp complex pathology in these patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".