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Record W2131974830 · doi:10.1091/mbc.e05-09-0858

DNA Damage Signaling and p53-dependent Senescence after Prolonged β-Interferon Stimulation

2006· article· en· W2131974830 on OpenAlexafffund
Olga Moiseeva, Frédérick A. Mallette, Utpal K. Mukhopadhyay, Adrian Moores, Gerardo Ferbeyre

Bibliographic record

VenueMolecular Biology of the Cell · 2006
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversité de Montréal
FundersCancer Research Society
KeywordsBiologyDNA damagePhosphorylationInterferonSenescenceCell biologyReactive oxygen speciesSerineSignal transductionDephosphorylationCell cycleMolecular biologyCellDNABiochemistryImmunologyPhosphatase

Abstract

fetched live from OpenAlex

Interferons are cytokines with potent antiviral and antiproliferative activities. We report that although a transient exposure to beta-interferon induces a reversible cell cycle arrest, a sustained treatment triggers a p53-dependent senescence program. Beta-interferon switched on p53 in two steps. First, it induced the acetylation of p53 at lysine 320 and its dephosphorylation at serine 392 but not p53 activity. Later on, it triggered a DNA signaling pathway, the phosphorylation of p53 at serine 15 and its transcriptional activity. In agreement, beta-interferon-treated cells accumulated gamma-H2AX foci and phosphorylated forms of ATM and CHK2. The DNA damage signaling pathway was activated by an increase in reactive oxygen species (ROS) induced by interferon and was inhibited by the antioxidant N-acetyl cysteine. More important, RNA interference against ATM inhibited p53 phosphorylation at serine 15, p53 activity and senescence in response to beta-interferon. Beta-interferon-induced senescence was more efficient in cells expressing either, p53, or constitutive allele of ERK2 or RasV12. Hence, beta-interferon-induced senescence targets preferentially cells with premalignant changes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.009
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.228
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations242
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueMolecular Biology of the CellSame topicTelomeres, Telomerase, and SenescenceFrench-language works237,207