Mitochondrial DNA damage induces apoptosis in senescent cells
Bibliographic record
Abstract
Senescence is a cellular response to damage and stress. The senescence response prevents cancer by suppressing the proliferation of cells with a compromised genome and contributes to optimal wound healing in normal tissues. Persistent senescent cells are also thought to drive aging and age-associated pathologies through their secretion of inflammatory factors that modify the tissue microenvironment and alter the function of nearby normal or transformed cells. Understanding how senescent cells alter the microenvironment would be aided by the ability to induce or eliminate senescent cells at will in vivo. Here, we combine the use of the synthetic nucleoside analog ganciclovir (GCV) with herpes simplex virus thymidine kinase (HSVtk) activity to create or eliminate senescent human cells. We show that low concentrations of GCV induce senescence through the accumulation of nuclear DNA damage while higher concentrations of GCV, similar to those used in vivo, kill non-dividing senescent cells via mitochondrial DNA (mtDNA) damage and caspase-dependent apoptosis. Using this system, we effectively eliminated xenografted normal human senescent fibroblasts or induced senescence in human breast cancer cells in vivo. Thus, cellular senescence and mtDNA damage are outcomes of synthetic nucleoside analog treatment, indicating that the GCV-HSVtk combination can be used effectively to promote the targeted formation or eradication of senescent cells.
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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".