The Wnt antagonist secreted frizzled‐related protein‐1 controls osteoblast and osteocyte apoptosis
Bibliographic record
Abstract
Mechanisms controlling human bone formation remain to be fully elucidated. We have used differential display-polymerase chain reaction analysis to characterize osteogenic pathways in conditionally immortalized human osteoblasts (HOBs) representing distinct stages of differentiation. We identified 82 differentially expressed messages and found that the Wnt antagonist secreted frizzled-related protein (sFRP)-1 was the most highly regulated of these. Transient transfection of HOBs with sFRP-1 suppressed canonical Wnt signaling by 70% confirming its antagonistic function in these cells. Basal sFRP-1 mRNA levels increased 24-fold during HOB differentiation from pre-osteoblasts to pre-osteocytes, and then declined in mature osteocytes. This expression pattern correlated with levels of cellular viability such that the pre-osteocytes, which had the highest levels of sFRP-1 mRNA, also had the highest rate of cell death. Basal sFRP-1 mRNA levels also increased 29-fold when primary human mesenchymal stem cells were differentiated to osteoblasts supporting the developmental regulation of the gene. Expression of sFRP-1 mRNA was induced 38-fold following prostaglandin E2 (PGE2) treatment of pre-osteoblasts and mature osteoblasts that had low basal message levels. In contrast, sFRP-1 expression was down-regulated by as much as 80% following transforming growth factor (TGF)-beta1 treatment of pre-osteocytes that had high basal mRNA levels. Consistent with this, treatment of pre-osteoblasts and mature osteoblasts with PGE(2) increased apoptosis threefold, while treatment of pre-osteocytes with TGF-beta1 decreased cell death by 50%. Likewise, over-expression of sFRP-1 in HOBs accelerated the rate of cell death threefold. These results establish sFRP-1 as an important negative regulator of human osteoblast and osteocyte survival.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".