Impact of Phosphate Supplementation and PPi Reduction on Osteocyte Lacunar-Canalicular Network in XLH
Bibliographic record
Abstract
Proper phosphate homeostasis is critical for bone mineralization, with disruptions leading to skeletal abnormalities affecting growth and load-bearing capacities. X-linked hypophosphatemia (XLH), caused by mutations in PHEX gene, results in elevated fibroblast growth factor 23 (FGF23), a phosphaturic hormone that can cause impaired renal phosphate reabsorption and osteomalacia. High-phosphate diets improve mineralization in Hyp mice carrying inactivating Phex mutation but fail to restore osteocyte connectivity, possibly due to persistent mineralization defects around osteocyte lacunae. Excess inorganic pyrophosphate (PPi), a mineralization inhibitor, may contribute to these defects. This study investigates whether reducing PPi via the Enpp1asj/asj mutation, which impairs ENPP1-mediated PPi production, while supplementing phosphate in Hyp;Enpp1asj/asj mice, enhances osteocyte numbers and restores lacunar-canalicular connectivity. Hyp, Enpp1asj/asj, Hyp;Enpp1asj/asj, and wild-type (WT) mice (n=16, two per group) were assigned to either a standard chow or high-phosphate diet (2%P) for two weeks starting at six weeks of age. Dissected humeri were stained with rhodamine and embedded in epoxy for confocal microscopy to examine lacunar-canalicular connectivity, with 3D reconstruction performed using Dragonfly software. Osteocyte networks were severely disrupted in Hyp mice, with larger lacunae and reduced connectivity, showing minimal improvement with phosphate supplementation. Enpp1asj/asj mice had fewer osteocytes and reduced connectivity regardless of diet. Hyp;Enpp1asj/asj mice exhibited increased osteocyte numbers on a high-phosphate diet but retained irregular lacunae and disrupted connectivity, suggesting PPi reduction increases cell number but not full network restoration. Small sample sizes limit statistical significance; future work will expand sample sizes (e.g., n=80 mice, ten per group) and refine imaging analysis to quantify osteocyte morphology and connectivity.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".