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Record W2298183149 · doi:10.1016/j.celrep.2016.02.076

α/β-Hydrolase Domain 6 Deletion Induces Adipose Browning and Prevents Obesity and Type 2 Diabetes

2016· article· en· W2298183149 on OpenAlexafffund
Shangang Zhao, Yves Mugabo, Gwynne Ballentine, Camille Attané, José Iglesias, Pegah Poursharifi, Dongwei Zhang, Thuy Anne Nguyen, Heidi Erb, Raphaël J. Prentki, Marie‐Line Peyot, Érik Joly, Stephanie W. Tobin, Stephanie Fulton, J. Mark Brown, S.R. Murthy Madiraju, Marc Prentki

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité de Montréal
FundersNational Heart, Lung, and Blood InstituteFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNational Institutes of HealthCanada Research Chairs
KeywordsAdipose tissueObesityType 2 diabetesBrowningHydrolaseDiabetes mellitusInternal medicineEndocrinologyChemistryMedicineBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Suppression of α/β-domain hydrolase-6 (ABHD6), a monoacylglycerol (MAG) hydrolase, promotes glucose-stimulated insulin secretion by pancreatic β cells. We report here that high-fat-diet-fed ABHD6-KO mice show modestly reduced food intake, decreased body weight gain and glycemia, improved glucose tolerance and insulin sensitivity, and enhanced locomotor activity. ABHD6-KO mice also show increased energy expenditure, cold-induced thermogenesis, brown adipose UCP1 expression, fatty acid oxidation, and white adipose browning. Adipose browning and cold-induced thermogenesis are replicated by the ABHD6 inhibitor WWL70 and by antisense oligonucleotides targeting ABHD6. Our evidence suggests that one mechanism by which the lipolysis derived 1-MAG signals intrinsic and cell-autonomous adipose browning is via PPARα and PPARγ activation, and that ABHD6 regulates adipose browning by controlling signal competent 1-MAG levels. Thus, ABHD6 regulates energy homeostasis, brown adipose function, and white adipose browning and is a potential therapeutic target for obesity and type 2 diabetes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.319

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 head0.010
GPT teacher head0.236
Teacher spread0.225 · 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

Citations85
Published2016
Admission routes2
Has abstractyes

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