[Gly²]-GLP-2, But Not Glucagon or [D-Ala²]-GLP-1, Controls Collagen Crosslinking in Murine Osteoblast Cultures
Bibliographic record
Abstract
Bone tissue is organized at the molecular level to resist fracture with the minimum of bone material. This implies that several modifications of the extracellular matrix, including enzymatic collagen crosslinking, take place. We previously highlighted the role of several gut hormones in enhancing collagen maturity and bone strength. The present study investigated the effect of proglucagon-derived peptides on osteoblast-mediated collagen post-processing. Briefly, MC3T3-E1 murine osteoblasts were cultured in the presence of glucagon (GCG), [D-Ala²]-glucagon-like peptide-1 ([D-Ala²]-GLP-1), and [Gly²]-glucagon-like peptide-2 ([Gly²]-GLP-2). Gut hormone receptor expression at the mRNA and protein levels were investigated by qPCR and Western blot. Extent of collagen postprocessing was examined by Fourier transform infrared microspectroscopy. GCG and GLP-1 receptors were not evidenced in osteoblast cells at the mRNA and protein levels. However, it is not clear whether the known GLP-2 receptor is expressed. Nevertheless, administration of [Gly²]-GLP-2, but not GCG or [D-Ala²]-GLP-1, led to a dose-dependent increase in collagen maturity and an acceleration of collagen post-processing. This mechanism was dependent on adenylyl cyclase activation. In conclusion, the present study highlighted a direct effect of [Gly²]-GLP-2 to enhance collagen post-processing and crosslinking maturation in murine osteoblast cultures. Whether this effect is translatable to human osteoblasts remains to be elucidated.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".