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Record W3009044748 · doi:10.1093/jbcr/iraa024.050

46 Inhibition of Lipolysis with Acipimox Targets Post-burn White Adipose Browning by Altering Macrophage Polarity

2020· article· en· W3009044748 on OpenAlexaff
Dalia Barayan, Roohi Vinaik, Marc G. Jeschke

Bibliographic record

VenueJournal of Burn Care & Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsLipolysisAdipose tissueWhite adipose tissueInternal medicineEndocrinologyMedicineMacrophage polarizationBurn injuryInflammationAdipocyteAdipose tissue macrophagesMacrophageChemistrySurgeryBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Abstract Introduction Severe burns are accompanied by a detrimental hypermetabolic stress response that can persist for years post-injury. Our previous work revealed that, under prolonged stress, white adipose tissue (WAT) adopts brown adipose-like traits in a process termed ‘browning’. This switch, characterized by the presence of uncoupling protein 1 (UCP1), is driven by the polarization of macrophages towards an M2 phenotype. Recently, we demonstrated that inhibiting lipolysis with the clinically approved drug, Acipimox, represses the burn-induced thermogenic activation of WAT. These findings raise the possibility that elevated rates of lipolysis may play a role in regulating the macrophage polarization state after major burn. However, the interconnection between post-burn lipolysis and inflammation remains unclear. In the present study, we investigated the mechanism underlying Acipimox’s inhibitory effects on burn-induced browning. Using a mouse model of thermal injury, we determine the metabolic effects of reducing WAT lipolysis on burn-induced macrophage recruitment and M2-polarization. Methods Adult C57BL/6 mice received a 30% total body surface area scald burn. Mice were then given daily intraperitoneal injections of APX (50 mg/Kg). On day 7 post-burn, the inguinal adipose tissue depot (iWAT) was harvested for histological analyses. Flow cytometry and F4/80 staining were used to assess adipose macrophage distribution and profile, and gene expression was analyzed via qPCR. Results APX administration significantly increased mitochondrial coupling, reflected by the decrease in UCP-1 (p< 0.05) and PGC-1a (p< 0.01) levels relative to the iWAT of untreated burn mice. F4/80 immunostaining of iWAT demonstrated decreased macrophage recruitment in Acipimox treated mice (p< 0.05). Flow cytometric analysis indicated decreased macrophage infiltration at 7 days in Acipimox treated mice (p< 0.05). Furthermore, iWAT from Acipimox treated mice demonstrated a pro-inflammatory profile, indicated by a greater distribution of TLR4 positive macrophages (p< 0.05). Conclusions Previously, we showed that the administration of Acipimox effectively suppressed PKA-mediated lipolysis and improved mitochondrial coupling in adipose tissue post-burn. Here, we elucidate the mechanism underlying these metabolic changes. Importantly, we show Acipimox exerts its inhibitory effects on burn-induced WAT browning by directly modulating macrophage recruitment and the M2-polarization state. Applicability of Research to Practice Our study highlights the critical role of lipolysis in mediating the key post-burn metabolic phenomena browning and inflammation. The data presented herein validate the pharmacological inhibition of lipolysis as a potentially powerful therapeutic strategy to counteract the detrimental metabolic effects induced by burn.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.330
Teacher spread0.298 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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