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Record W4365512579 · doi:10.1016/j.celrep.2023.112399

Complementary gene regulation by NRF1 and NRF2 protects against hepatic cholesterol overload

2023· article· en· W4365512579 on OpenAlexafffund
May G. Akl, Lei Li, Raquel Baccetto, Sadhna Phanse, Qingzhou Zhang, Michael J. Trites, Sherin McDonald, Hiroyuki Aoki, Mohan Babu, Scott B. Widenmaier

Bibliographic record

VenueCell Reports · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of CanadaSaskatchewan Health Research Foundation
KeywordsNRF1CholesterolGeneCell biologyBiologyInternal medicineTranscription factorChemistryEndocrinologyMedicineGenetics

Abstract

fetched live from OpenAlex

Hepatic cholesterol overload promotes steatohepatitis. Insufficient understanding of liver stress defense impedes therapy development. Here, we elucidate the role of stress defense transcription factors, nuclear factor erythroid 2 related factor-1 (NRF1) and -2 (NRF2), in counteracting cholesterol-linked liver stress. Using a diet that increases liver cholesterol storage, expression profiles and phenotypes of liver from mice with hepatocyte deficiency of NRF1, NRF2, or both are compared with controls, and chromatin immunoprecipitation sequencing is undertaken to identify target genes. Results show NRF1 and NRF2 co-regulate genes that eliminate cholesterol and mitigate inflammation and oxidative damage. Combined deficiency, but not deficiency of either alone, results in severe steatohepatitis, hepatic cholesterol overload and crystallization, altered bile acid metabolism, and decreased biliary cholesterol. Moreover, therapeutic effects of NRF2-activating drug bardoxolone require NRF1 and are supplemented by NRF1 overexpression. Thus, we discover complementary gene programming by NRF1 and NRF2 that counteract cholesterol-associated fatty liver disease progression.

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 score0.010
Threshold uncertainty score0.650

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 head0.225
Teacher spread0.217 · 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

Citations23
Published2023
Admission routes2
Has abstractyes

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