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Record W4406027918 · doi:10.1126/sciadv.ads5963

Transcriptional regulation of adipocyte lipolysis by IRF2BP2

2025· article· en· W4406027918 on OpenAlexfundno aff
Yang Chen, Lin Liu, Ryan Calhoun, Lan Cheng, David Merrick, David J. Steger, Patrick Seale

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of OttawaAmerican Heart Association
KeywordsLipolysisAdipocyteLipaseBiologyChromatin immunoprecipitationInternal medicineAdipose tissueEndocrinologyCatabolismEnergy homeostasisHormone-sensitive lipaseChemistryBiochemistryEnzymeGene expressionMetabolismGeneMedicinePromoter

Abstract

fetched live from OpenAlex

Adipocyte lipolysis controls systemic energy levels and metabolic homeostasis. Lipolysis is regulated by posttranslational modifications of key lipolytic enzymes. However, less is known about the transcriptional mechanisms that regulate lipolysis. Here, we identify interferon regulatory factor–2 binding protein 2 (IRF2BP2) as a transcriptional repressor of adipocyte lipolysis. Deletion of IRF2BP2 in human adipocytes increases lipolysis without affecting glucose uptake, whereas IRF2BP2 overexpression decreases lipolysis. RNA sequencing, and chromatin immunoprecipitation sequencing analyses show that IRF2BP2 represses lipolysis-related genes, including LIPE , which encodes hormone sensitive lipase, the rate-limiting enzyme in lipolysis. Adipocyte-selective deletion of Irf2bp2 in mice increases Lipe expression and free fatty acid levels, resulting in adipose tissue inflammation and glucose intolerance. Together, these findings demonstrate that IRF2BP2 restrains adipocyte lipolysis and opens avenues to target lipolysis for the treatment of metabolic disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.246
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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