DNA-SCARS: distinct nuclear structures that sustain damage-induced senescence growth arrest and inflammatory cytokine secretion
Bibliographic record
Abstract
DNA damage can induce a tumor suppressive response termed cellular senescence. Damaged senescent cells permanently arrest growth, secrete inflammatory cytokines and other proteins and harbor persistent nuclear foci that contain DNA damage response (DDR) proteins. To understand how persistent damage foci differ from transient foci that mark repairable DNA lesions, we identify sequential events that differentiate transient foci from persistent foci, which we term 'DNA segments with chromatin alterations reinforcing senescence' (DNA-SCARS). Unlike transient foci, DNA-SCARS associate with PML nuclear bodies, lack the DNA repair proteins RPA and RAD51, lack single-stranded DNA and DNA synthesis and accumulate activated forms of the DDR mediators CHK2 and p53. DNA-SCARS form independently of p53, pRB and several other checkpoint and repair proteins but require p53 and pRb to trigger the senescence growth arrest. Importantly, depletion of the DNA-SCARS-stabilizing component histone H2AX did not deplete 53BP1 from DNA-SCARS but diminished the presence of MDC1 and activated CHK2. Furthermore, depletion of H2AX reduced both the p53-dependent senescence growth arrest and p53-independent cytokine secretion. DNA-SCARS were also observed following severe damage to multiple human cell types and mouse tissues, suggesting that they can be used in combination with other markers to identify senescent cells. Thus, DNA-SCARS are dynamically formed distinct structures that functionally regulate multiple aspects 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) | 0.001 | 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".