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Record W4412486943 · doi:10.1016/j.dnarep.2025.103871

Synthetic cytotoxicity profiling of cohesin mutants highlights recombination-based dependencies

2025· article· en· W4412486943 on OpenAlexafffund
Rafaela Horbach Marodin, Ecaterina Cozma, Sivan Reytan-Miron, Nigel J. O’Neil, Peter C. Stirling, Philip Hieter

Bibliographic record

VenueDNA repair · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of British ColumbiaCanada's Michael Smith Genome Sciences Centre
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCanadian Cancer Society
KeywordsBiologyCohesinProfiling (computer programming)MutantRecombinationComputational biologyGeneticsHomologous recombinationCytotoxicityDNAGeneComputer scienceChromatinIn vitro

Abstract

fetched live from OpenAlex

Cohesin maintains genome integrity through its ability to bind and link DNA molecules via a Structural Maintenance of Chromatin (SMC) activity. These effects are manifested through its major function in sister chromatid cohesion, but also through activities during DNA replication, repair, and transcription. The array of cohesin functions can make interpreting cellular effects of cohesin loss difficult to interpret mechanistically. This is particularly important in cancer where cohesin subunit mutations are common, and where the identification of genetic dependencies would be useful for predicting the response of cohesin-mutated cells to genotoxic challenges. Here we performed a series of synthetic cytotoxicity screens with hypomorphic cohesin alleles in yeast to identify cellular pathways whose loss sensitizes cohesin-mutants to sublethal levels of genotoxic DNA damage. This dataset reveals important roles for cohesin in replication stress and homologous recombination regulation that can leave cohesin mutated cells with a dependency on translesion synthesis for survival.

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 score0.003
Threshold uncertainty score0.654

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 head0.008
GPT teacher head0.243
Teacher spread0.234 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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