Cellular Localization of Neprilysin in Mouse Bone Tissue and Putative Role in Hydrolysis of Osteogenic Peptides
Bibliographic record
Abstract
The regulation of osteoblast and osteoclast metabolism is mediated by both hormones and local bone peptide factors. Peptides and hormones are under control of membrane peptidases such as Neprilysin (NEP). NEP is a widely distributed cell-surface zinc-metallopeptidase that is involved in the regulation of several important physiological processes by controlling the half-life of bioactive peptides. Although NEP is known to be present in skeletal tissues, neither its cellular localization nor its function have been established. To address this question, we examined NEP distribution in bones of postnatal mouse. In situ hybridization (ISH) and immunohistochemistry showed that NEP messenger RNA (mRNA) and protein are associated with bone-forming cells including presumptive osteoblast precursors, preosteoblasts, osteoblasts, and osteocytes. NEP levels in newborn and adult mice bones also were compared by immunoblotting. Higher amounts of NEP immunoreactivity were observed in newborn as compared with adult bones, suggesting a relationship between NEP expression and bone growth. To further explore this hypothesis, we monitored in vitro NEP proteolytic activity using a series of synthetic osteogenic peptides such as parathyroid hormone-related peptide 1-43 (PTHrP1-34), osteostatin (PTHrP107-139), osteogenic growth peptide (OGP), calcitonin, alpha-calcitonin gene-related peptide (alpha-CGRP), and PTH1-34. Except for PTH1-34, all peptides were found to be NEP substrates.
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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 | 0.001 |
| 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".