Systematic assessment of the human osteoblast transcriptome in resting and induced primary cells
Bibliographic record
Abstract
Osteoblasts are key players in bone remodeling. The accessibility of human primary osteoblast-like cells (HObs) from bone explants makes them a lucrative model for studying molecular physiology of bone turnover, for discovering novel anabolic therapeutics, and for mesenchymal cell biology in general. Relatively little is known about resting and dynamic expression profiles of HObs, and to date no studies have been conducted to systematically assess the osteoblast transcriptome. The aim of this study was to characterize HObs and investigate signaling cascades and gene networks with genomewide expression profiling in resting and bone morphogenic protein (BMP)-2- and dexamethasone-induced cells. In addition, we compared HOb gene expression with publicly available samples from the Gene Expression Omnibus. Our data show a vast number of genes and networks expressed predominantly in HObs compared with closely related cells such as fibroblasts or chondrocytes. For instance, genes in the insulin-like growth factor (IGF) signaling pathway were enriched in HObs (P = 0.003) and included the binding proteins (IGFBP-1, -2, -5) and IGF-II and its receptor. Another HOb-specific expression pattern included leptin and its receptor (P < 10(-8)). Furthermore, after stimulation of HObs with BMP-2 or dexamethasone, the expression of several interesting genes and pathways was observed. For instance, our data support the role of peripheral leptin signaling in bone cell function. In conclusion, we provide the landscape of tissue-specific and dynamic gene expression in HObs. This resource will allow utilization of osteoblasts as a model to study specific gene networks and gene families related to human bone physiology and diseases.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".