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Record W2742001951 · doi:10.1093/nar/gkx684

Replication stress-induced endogenous DNA damage drives cellular senescence induced by a sub-lethal oxidative stress

2017· article· en· W2742001951 on OpenAlexfundno aff
Gireedhar Venkatachalam, Uttam Surana, Marie‐Véronique Clement

Bibliographic record

VenueNucleic Acids Research · 2017
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsSenescenceBiologyDNA damageOxidative stressCell biologyLaminChromatinEndogenyGeneticsDNABiochemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.105
GPT teacher head0.364
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations92
Published2017
Admission routes1
Has abstractyes

Explore more

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