Loss of the Osteogenic Differentiation Potential during Senescence Is Limited to Bone Progenitor Cells and Is Dependent on p53
Bibliographic record
Abstract
DNA damage can lead to the induction of cellular senescence. In particular, we showed that exposure to ionizing radiation (IR) leads to the senescence of bone marrow-derived multipotent stromal cells (MSC) and osteoblast-like stromal cells (OB-SC), a phenotype associated with bone loss. The mechanism by which IR leads to bone dysfunction is not fully understood. One possibility involves that DNA damage-induced senescence limits the regeneration of bone progenitor cells. Another possibility entails that bone dysfunction arises from the inability of accumulating senescent cells to fulfill their physiological function. Indeed, we show here that exposure to IR prevented the differentiation and mineralization functions of MSC, an effect we found was limited to this population as more differentiated OB-SC could still form mineralize nodules. This is in contrast to adipogenesis, which was inhibited in both IR-induced senescent MSC and 3T3-L1 pre-adipocytes. Furthermore, we demonstrate that IR-induced loss of osteogenic potential in MSC was p53-dependent, a phenotype that correlates with the inability to upregulate key osteogenic transcription factors. These results are the first to demonstrate that senescence impacts osteogenesis in a cell type dependent manner and suggest that the accumulation of senescent osteoblasts is unlikely to significantly contribute to bone dysfunction in a cell autonomous manner.
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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) | 0.001 | 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".