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Record W2016210407 · doi:10.4161/nucl.1.3.11739

SMARCAL1 and replication stress: An explanation for SIOD?

2010· article· en· W2016210407 on OpenAlexaff
Carol E. Bansbach, Cornelius F. Boerkoel, David Cortez

Bibliographic record

VenueNucleus · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
FundersNational Cancer Institute
KeywordsReplication (statistics)Stress (linguistics)BiologyVirologyPhilosophy

Abstract

fetched live from OpenAlex

The SNF2 family of ATPases acts in the context of chromatin to regulate transcription, replication, repair and recombination. Defects in SNF2 genes cause many human diseases. For example, mutations in SMARCAL1 (also named HARP) cause Schimke immuno-osseous dysplasia (SIOD); a multi-system disorder characterized by growth defects, immune deficiencies, renal failure and other complex phenotypes. Several groups including ours recently identified SMARCAL1 as a replication stress response protein. Importantly, SMARCAL1 localizes to stalled replication forks and this localization of SMARCAL1 activity prevents DNA damage accumulation during DNA replication. We determined that SIOD-related SMARCAL1 mutants could not prevent replication-associated DNA damage in cells in which endogenous SMARCAL1 was silenced, establishing the first link between SIOD and a defect in a specific biological activity. Here, we also report that cells from patients with SIOD exhibit elevated levels of DNA damage that can be rescued by re-introduction of wild-type SMARCAL1. Our data suggest that loss of SMARCAL1 function in patients may cause DNA replication-associated genome instability that contributes to the pleiotropic phenotypes of SIOD.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.256
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations42
Published2010
Admission routes1
Has abstractyes

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