MétaCan
Menu
Back to cohort
Record W4399296378 · doi:10.1002/1873-3468.14942

Nuclear receptor Nur77 and Yin‐Yang 1 synergistically increase mitochondrial abundance and activity in macrophages

2024· article· en· W4399296378 on OpenAlexaff
Duco S. Koenis, Inkie J. A. Evers‐van Gogh, Pieter B. van Loenen, Wilbert Zwart, Eric Kalkhoven, Carlie J.M. de Vries

Bibliographic record

VenueFEBS Letters · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNuclear Receptors and Signaling
Canadian institutionsInstitute of Infection and Immunity
FundersDiabetes Fonds
KeywordsNerve growth factor IBBiologyMitochondrial biogenesisCell biologyMitochondrionNuclear receptorGene expressionMitochondrial DNAMolecular biologyTranscription factorRegulation of gene expressionGeneGenetics

Abstract

fetched live from OpenAlex

Mitochondrial biogenesis requires precise regulation of both mitochondrial-encoded and nuclear-encoded genes. Nuclear receptor Nur77 is known to regulate mitochondrial metabolism in macrophages and skeletal muscle. Here, we compared genome-wide Nur77 binding site and target gene expression in these two cell types, which revealed conserved regulation of mitochondrial genes and enrichment of motifs for the transcription factor Yin-Yang 1 (YY1). We show that Nur77 and YY1 interact, that YY1 increases Nur77 activity, and that their binding sites are co-enriched at mitochondrial ribosomal protein gene loci in macrophages. Nur77 and YY1 co-expression synergistically increases Mrpl1 expression as well as mitochondrial abundance and activity in macrophages but not skeletal muscle. As such, we identify a macrophage-specific Nur77-YY1 interaction that enhances mitochondrial metabolism.

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 score0.286
Threshold uncertainty score0.619

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 head0.011
GPT teacher head0.235
Teacher spread0.223 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFEBS LettersSame topicNuclear Receptors and SignalingFrench-language works237,207