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Record W2070622422 · doi:10.1371/journal.pgen.1001385

Inactivation of Chk2 and Mus81 Leads to Impaired Lymphocytes Development, Reduced Genomic Instability, and Suppression of Cancer

2011· article· en· W2070622422 on OpenAlexafffund
Samah El Ghamrasni, Ashwin Pamidi, Marie Jo Halaby, Miyuki Bohgaki, Renato Cardoso, Li Li, Shriram Venkatesan, Swaminathan Sethu, Atsushi Hirao, Tak W. Mak, M. Prakash Hande, Anne Hakem, Razqallah Hakem

Bibliographic record

VenuePLoS Genetics · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of TorontoOntario Institute for Cancer ResearchUniversity Health Network
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchDefence Science and Technology GroupMinistry of Education - SingaporeOntario Ministry of Health and Long-Term Care
KeywordsBiologyGenome instabilityDNA damageCell biologyEffectorSuppressorMitosisCell cycle checkpointMitotic catastropheProgrammed cell deathCell cycleCancer researchApoptosisGeneticsDNACancer

Abstract

fetched live from OpenAlex

Chk2 is an effector kinase important for the activation of cell cycle checkpoints, p53, and apoptosis in response to DNA damage. Mus81 is required for the restart of stalled replication forks and for genomic integrity. Mus81(Δex3-4/Δex3-4) mice have increased cancer susceptibility that is exacerbated by p53 inactivation. In this study, we demonstrate that Chk2 inactivation impairs the development of Mus81(Δex3-4/Δex3-4) lymphoid cells in a cell-autonomous manner. Importantly, in contrast to its predicted tumor suppressor function, loss of Chk2 promotes mitotic catastrophe and cell death, and it results in suppressed oncogenic transformation and tumor development in Mus81(Δex3-4/Δex3-4) background. Thus, our data indicate that an important role for Chk2 is maintaining lymphocyte development and that dual inactivation of Chk2 and Mus81 remarkably inhibits cancer.

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 score0.029
Threshold uncertainty score0.434

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 head0.029
GPT teacher head0.254
Teacher spread0.225 · 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

Citations19
Published2011
Admission routes2
Has abstractyes

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