Gene Profiling on Mixed Embryonic Stem Cell Populations Reveals a Biphasic Role for β-Catenin in Osteogenic Differentiation
Bibliographic record
Abstract
The differentiation of embryonic stem cells (ESCs) into osteoblasts is enhanced to 60% when exposed to vitamin D3 (VD3) but leaves a remainder of one half of the cell population unidentified. To increase differentiation outcome, the known osteoinducers retinoic acid (RA) and bone morphogenetic protein-2 (BMP-2) were evaluated. Initial studies using RA and BMP-2 during early osteogenesis in addition to VD3 increased osteogenic yield in the case of RA, but surprisingly decreased osteogenesis when BMP-2 was administered together with VD3 or RA. This paper describes a comprehensive microarray study examining the gene expression profile of differentiating osteoblasts in these mixed ESC populations. In addition to five other families of signaling molecules (insulin growth factors, prostaglandin, follistatin, TGFbeta2, and Wnt molecules), we identified an endogenous expression pattern for BMPs and RA that differed from our previous exogenous administration of these molecules. By mimicking the change in expression of the RA and BMP-2 families with exogenous supplementation at the correct time, it was then possible to increase the number of ESC-derived osteoblasts to 90%. This effect was mediated through alteration in beta-catenin (CatnB) expression levels and nuclear CatnB activity, both of which are modulated by VD3, RA, and BMP-2. Our results suggest that blockage of CatnB activity by VD3 and RA is opposed by induction of CatnB activity through BMP-2 when administered together. Hence, osteoinduction, in vitro, is an intricate process involving both temporal and quantitative changes in gene expression and CatnB activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".