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Record W3137497684 · doi:10.3390/antiox10030437

Transcriptional Regulation of ROS Homeostasis by the ERR Subfamily of Nuclear Receptors

2021· review· en· W3137497684 on OpenAlexafffund
Charlotte Scholtes, Vincent Giguère

Bibliographic record

VenueAntioxidants · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsReactive oxygen speciesTranscription factorCell biologyBiologyTranscriptional regulationOxidative phosphorylationSignal transductionNuclear receptorHomeostasisBiochemistryGene

Abstract

fetched live from OpenAlex

) are generated endogenously by processes such as mitochondrial oxidative phosphorylation, or they may arise from exogenous sources like bacterial invasion. ROS can be beneficial (oxidative eustress) as signaling molecules but also harmful (oxidative distress) to cells when ROS levels become unregulated in response to physiological, pathological or pharmacological insults. Indeed, abnormal ROS levels have been shown to contribute to the etiology of a wide variety of diseases. Transcriptional control of metabolic genes is a crucial mechanism to coordinate ROS homeostasis. Therefore, a better understanding of how ROS metabolism is regulated by specific transcription factors can contribute to uncovering new therapeutic strategies. A large body of work has positioned the estrogen-related receptors (ERRs), transcription factors belonging to the nuclear receptor superfamily, as not only master regulators of cellular energy metabolism but, most recently, of ROS metabolism. Herein, we will review the role played by the ERRs as transcriptional regulators of ROS generation and antioxidant mechanisms and also as ROS sensors. We will assess how the control of ROS homeostasis by the ERRs can be linked to physiology and disease and the possible contribution of manipulating ERR activity in redox medicine.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.286
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2021
Admission routes2
Has abstractyes

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