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Record W1810350704 · doi:10.1080/15384101.2015.1053671

Mutant lamin A links prophase to a p53 independent senescence program

2015· article· en· W1810350704 on OpenAlexaff
Olga Moiseeva, Frédéric Lessard, Mariana Acevedo-Aquino, Mathieu Vernier, Youla S. Tsantrizos, Gerardo Ferbeyre

Bibliographic record

VenueCell Cycle · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsBiologyLaminNuclear laminaCell biologyProphaseMitosisCytokinesisSenescenceMutantMolecular biologyGeneticsNuclear proteinCell divisionGeneCellTranscription factorMeiosis

Abstract

fetched live from OpenAlex

Expression of oncogenes or short telomeres can trigger an anticancer response known as cellular senescence activating the p53 and RB tumor suppressor pathways. This mechanism is switched off in most tumor cells by mutations in p53 and RB signaling pathways. Surprisingly, p53 disabled tumor cells could be forced into senescence by expression of a mutant allele of the nuclear envelope protein lamin A. The pro-senescence lamin A mutant contains a deletion in the sequence required for processing by the protease ZMPSTE24 leading to accumulation of farnesylated lamin A in the nuclear envelope. In addition, the serine at position 22, a target for CDK1-dependent phosphorylation, was mutated to alanine, preventing CDK1-catalyzed nuclear envelope disassembly. The accumulation of this mutant lamin A compromised prophase to prometaphase transition leading to invaginations of the nuclear lamina, nuclear fragmentation and impaired chromosome condensation. Cells exited this impaired mitosis without cytokinesis and re-replicated their DNA ultimately arresting in interphase as polyploid cells with features of cellular senescence including increased expression of inflammatory gene products and a significant reduction of tumorigenicity in vivo.

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 score0.277
Threshold uncertainty score0.322

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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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