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Record W7074584633

The Caenorhabditis elegans Oxidative Stress Response Requires the NHR-49 Transcription Factor

2018· article· en· W7074584633 on OpenAlexfundno aff

Bibliographic record

VenueThe Journal of the American Medical Association (JAMA) Network (American Medical Association) · 2018
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
FundersOffice of Research Infrastructure Programs, National Institutes of HealthNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsTranscription factorCaenorhabditis elegansGene knockdownDetoxification (alternative medicine)RNA interferenceReactive oxygen speciesRegulatorOxidative stressMediatorRegulation of gene expression
DOInot available

Abstract

fetched live from OpenAlex

The overproduction of reactive oxygen species (ROS) in cells can lead to the development of diseases associated with aging. We have previously shown that C. elegans BRAP-2 (Brca1 associated binding protein 2) regulates phase II detoxification genes such as gst-4, by increasing SKN-1 activity. Previously, a transcription factor (TF) RNAi screen was conducted to identify potential activators that are required to induce gst-4 expression in brap-2(ok1492) mutants. The lipid metabolism regulator NHR-49/HNF4 was among 18 TFs identified. Here, we show that knockdown of nhr-49 suppresses the activation of gst-4 caused by brap-2 inactivation and that gain-of-function alleles of nhr-49 promote gst-4 expression. We also demonstrate that nhr-49 and its cofactor mdt-15 are required to express phase II detoxification enzymes upon exposure to chemicals that induce oxidative stress. Furthermore, we show that NHR-49 and MDT-15 enhance expression of skn-1a/c These findings identify a novel role for NHR-49 in ROS detoxification by regulating expression of SKN-1C and phase II detoxification genes.

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)0.020
metaresearch head score (Gemma)0.102
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.102
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.272
Teacher spread0.264 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2018
Admission routes1
Has abstractyes

Explore more

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