Replication stress-induced endogenous DNA damage drives cellular senescence induced by a sub-lethal oxidative stress
Bibliographic record
Abstract
Although oxidative stress has been shown to induce senescence and replication stress independently, no study has implicated unresolved replication stress as the driver for cellular senescence in response to oxidative stress. Using cells exposed to increasing concentrations of hydrogen peroxide, we show that sub-lethal amount of exogenous hydrogen peroxide induces two waves of DNA damage. The first wave is rapid and transient while the second wave coincides with the cells transition from the S to the G2/M phases of cell cycle. Subsequently, cells enter growth arrest accompanied by the acquisition of senescence-associated characteristics. Furthermore, a p53-dependent decrease in Rad51, which is associated with the formation of DNA segments with chromatin alterations reinforcing senescence, and Lamin B1 that is involved in chromatin remodeling, is observed during the establishment of the senescent phenotype. On the other hand, increase in senescence associated-β-Gal activity, a classical marker of senescence and HMGA2, a marker of the senescence-associated heterochromatin foci, is shown to be independent of p53. Together, our findings implicate replication stress-induced endogenous DNA damage as the driver for the establishment of cellular senescence upon sub-lethal oxidative stress, and implicate the role of p53 in some but not all hallmarks of the senescent phenotype.
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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) | 0.002 | 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".