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Record W2096456661 · doi:10.1093/toxsci/kfq391

Inactivation of YAP1 Enhances Sensitivity of the Yeast RNR3-lacZ Genotoxicity Testing System to a Broad Range of DNA-Damaging Agents

2010· article· en· W2096456661 on OpenAlexaff
Min Zhang, Chao Zhang, Jia Li, Michelle Hanna, Xiaohua Zhang, Heping Dai, Wei Xiao

Bibliographic record

VenueToxicological Sciences · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGenotoxicityDNA damageYAP1Saccharomyces cerevisiaeDNA repairBiologyOxidative phosphorylationMutantDNAOxidative stressMolecular biologyBiochemistryGeneChemistryCell biologyToxicityTranscription factor

Abstract

fetched live from OpenAlex

Despite the great advances by using microorganism-based genotoxicity testing systems to assess environmental genotoxic compounds, most of them respond poorly, particularly to oxidative agents. In this study, we systematically examined the RNR3-lacZ reporter gene expression in Saccharomyces cerevisiae mutant strains defective in the protection against reactive oxygen species and found that only YAP1 deletion resulted in a significant enhancement in the detection of oxidative damage. To our surprise, YAP1 deletion also caused an increased cellular sensitivity to a variety of DNA damage. This altered sensitivity appears to be independent of oxidative damage because under conditions in which vitamin C treatment rescued oxidative damage, it failed to reverse the phenotypes caused by other types of DNA damage. Furthermore, although inactivation of cell permeability genes enhanced the RNR3-lacZ detection sensitivity particularly to large molecular weight compounds, their effects on small molecular oxidative agents are minimal. Taken together, this study helps to create a hypersensitive genotoxicity testing system to a broad range of DNA-damaging agents by deleting a single yeast gene.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.171
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

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