Epigenetic Changes During Mechanically Induced Osteogenic Lineage Commitment
Bibliographic record
Abstract
Osteogenic lineage commitment is often evaluated by analyzing gene expression. However, many genes are transiently expressed during differentiation. The availability of genes for expression is influenced by epigenetic state, which affects the heterochromatin structure. DNA methylation, a form of epigenetic regulation, is stable and heritable. Therefore, analyzing methylation status may be less temporally dependent and more informative for evaluating lineage commitment. Here we analyzed the effect of mechanical stimulation on osteogenic differentiation by applying fluid shear stress for 24 hr to osteocytes and then applying the osteocyte-conditioned medium (CM) to progenitor cells. We analyzed gene expression and changes in DNA methylation after 24 hr of exposure to the CM using quantitative real-time polymerase chain reaction and bisulfite sequencing. With fluid shear stress stimulation, methylation decreased for both adipogenic and osteogenic markers, which typically increases availability of genes for expression. After only 24 hr of exposure to CM, we also observed increases in expression of later osteogenic markers that are typically observed to increase after seven days or more with biochemical induction. However, we observed a decrease or no change in early osteogenic markers and decreases in adipogenic gene expression. Treatment of a demethylating agent produced an increase in all genes. The results indicate that fluid shear stress stimulation rapidly promotes the availability of genes for expression, but also specifically increases gene expression of later osteogenic markers.
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) | 0.002 | 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".