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Record W3015885157 · doi:10.1101/2020.04.07.030148

Inflammation promotes adipocyte lipolysis via IRE1 kinase

2020· preprint· en· W3015885157 on OpenAlexafffund
Kevin P. Foley, Yong Chen, Nicole G. Barra, Mark Heal, Kieran Kwok, Akhilesh K. Tamrakar, Wendy Chi, Brittany M. Duggan, Brandyn D. Henriksbo, Yong Liu, Jonathan D. Schertzer

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaIndian Council of Medical Research
KeywordsLipolysisAdipocyteInternal medicineEndocrinologyAdipose tissueInsulin resistanceInflammationBiologyChemistryInsulinMedicine

Abstract

fetched live from OpenAlex

Abstract Obesity associates with inflammation, insulin resistance and higher blood lipids. It is unclear if immune responses facilitate lipolysis separate from hormone or adrenergic signals. We found that an ancient component of ER stress, inositol-requiring protein 1 (IRE1), discriminates inflammation-induced adipocyte lipolysis versus lipolysis regulated by adrenergic or hormonal stimuli. Inhibiting IRE1 kinase activity was sufficient to block adipocyte-autonomous lipolysis from multiple inflammatory ligands, including bacterial components, certain cytokines, and thapsigargin-induced ER stress. Adipocyte-specific deletion of IRE1 in mice prevented inflammatory ligand-induced lipolysis in adipose tissue. IRE1 kinase activity was dispensable for isoproterenol and cAMP-induced lipolysis in adipocytes and mouse adipose tissue. IRE1 RNase activity was not associated with inflammation-induced adipocyte lipolysis. We found no role for canonical unfolded protein responses (UPR) or ABL kinases in linking ER stress to lipolysis. Lipolysis was unchanged in adipose tissue from GRP78/BiP +/- compared to littermate mice. Tyrosine kinase inhibitors (TKIs) such as imatinib, which reduce ER stress and IRE1 RNase activity, did not alter lipolysis from inflammatory stimuli. Inhibiting IRE1 kinase activity blocked adipocyte NF-κB activation and Interleukin-6 (Il6) production due to inflammatory ligands. Inflammation-induced lipolysis mediated by IRE1 occurred independently from changes in insulin signalling in adipocytes. Therefore, inflammation can promote IRE1-mediated lipolysis independent of adipocyte insulin resistance. Our results show that IRE1 propagates an inflammation-specific lipolytic program independent from hormonal or adrenergic regulation, including insulin resistance. Targeting IRE1 kinase activity may benefit metabolic syndrome and inflammatory lipid disorders. Significance Adipocytes maintain metabolic homeostasis by storing nutrients and releasing lipids into the blood via lipolysis. Catecholamines stimulate adrenergic-mediated lipolysis, whereas insulin inhibits lipolysis. Obesity is associated with elevated blood lipids and inflammation, which can impair insulin-mediated suppression of lipolysis (i.e. insulin resistance). It is unclear if inflammatory triggers of lipolysis require insulin resistance or if specific lipolytic triggers engage distinct cell stress components. We found that a specific ER stress response was required for inflammation-mediated lipolysis, not adrenergic-mediated lipolysis. Bacterial and cytokine-induced lipolysis required adipocyte IRE1 kinase activity, but not IRE1 RNase activity typical of the ER stress-related unfolded protein response. We propose that inflammatory triggers of lipolysis engage IRE1 kinase independent of catecholamine and hormone responses, including insulin resistance. Graphical Abstract IRE1 kinase activity promotes an inflammation-specific adipocyte lipolytic program that is separate from hormonal or adrenergic regulation of lipolysis.

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 score0.007

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)0.0020.001

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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