Bone Response to Fluoride Exposure Is Influenced by Genetics
Bibliographic record
Abstract
Genetic factors influence the effects of fluoride (F) on amelogenesis and bone homeostasis but the underlying molecular mechanisms remain undefined. A label-free proteomics approach was employed to identify and evaluate changes in bone protein expression in two mouse strains having different susceptibilities to develop dental fluorosis and to alter bone quality. In vivo bone formation and histomorphometry after F intake were also evaluated and related to the proteome. Resistant 129P3/J and susceptible A/J mice were assigned to three groups given low-F food and water containing 0, 10 or 50 ppmF for 8 weeks. Plasma was evaluated for alkaline phosphatase activity. Femurs, tibiae and lumbar vertebrae were evaluated using micro-CT analysis and mineral apposition rate (MAR) was measured in cortical bone. For quantitative proteomic analysis, bone proteins were extracted and analyzed using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS), followed by label-free semi-quantitative differential expression analysis. Alterations in several bone proteins were found among the F treatment groups within each mouse strain and between the strains for each F treatment group (ratio ≥1.5 or ≤0.5; p<0.05). Although F treatment had no significant effects on BMD or bone histomorphometry in either strain, MAR was higher in the 50 ppmF 129P3/J mice than in the 50 ppmF A/J mice treated with 50 ppmF showing that F increased bone formation in a strain-specific manner. Also, F exposure was associated with dose-specific and strain-specific alterations in expression of proteins involved in osteogenesis and osteoclastogenesis. In conclusion, our findings confirm a genetic influence in bone response to F exposure and point to several proteins that may act as targets for the differential F responses in this tissue.
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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 | 0.001 | 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".