Mesenchymal Bmp7 Controls Onset of Tooth Mineralization: A Novel Way to Regulate Molar Cusp Shape
Bibliographic record
Abstract
Investigating the molecular basis for tooth shape variation provides an important glimpse into the evolution of tooth function. We recently showed that loss of mesenchymal BMP7 is sufficient to alter morphology and function of the toothrow. Here we report on the underlying mechanism. Expression of mesenchymal Bmp7 is observed at sites where mineralization is initiated, in tooth cusps of developing molars. Neural crest-specific deletion of Bmp7 (Bmp7ncko) resulted in a complete lack of dentin/enamel formation at birth, the time when mineralization is normally initiated in the upper molars, similar to what was observed in Bmp2ncko mice. Unlike loss of Bmp2, loss of Bmp7 did not affect odontoblast polarization, did not significantly alter levels of pSmad1/5/8, but almost completely abolished canonical Wnt signalling in (pre)-ameloblasts. Tooth mineralization resumed with a 48-hour delay allowing for additional mesenchymal proliferation. Enamel volume was still reduced at P4 and P8, but was comparable in erupted teeth, which were broader and had altered cusp shapes. Tooth eruption was also delayed. Overall, enamel appeared inconspicuous, although some structural changes along with reduced mineral density could be observed. Loss of BMP7 led to an increase in mesenchymal Bmp6 suggesting an interplay between Bmp6 and Bmp7 in the regulation of mineralization initiation. Our findings show that regulation of the onset of tooth mineralization is a hitherto unsuspected mechanism controlling tooth shape variation. Initiation of tooth mineralization is regulated by a complex epithelial-mesenchymal Bmp/Wnt-signalling network to which Bmp7 contributes. This network is separate and independent of the Bmp2-signaling network regulating odontoblast cell polarization. From an evolutionary perspective, addition of Bmp7 as initiator of tooth mineralization might be akin to an upgrade of an existing computer operating system. While not essential, it provides obviously sufficient advantage warranting its evolutionary incorporation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".