Inflammation promotes adipocyte lipolysis via IRE1 kinase
Bibliographic record
Abstract
Obesity associates with inflammation, insulin resistance, and higher blood lipids. It is unclear if immune responses facilitate lipid breakdown and release from adipocytes via lipolysis in a separate way from hormones or adrenergic signals. We found that an ancient component of ER stress, inositol-requiring protein 1 (IRE1), discriminates inflammation-induced adipocyte lipolysis versus lipolysis from adrenergic or hormonal stimuli. Our data show that 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. IRE1-mediated lipolysis was specific for inflammatory triggers since 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. Inhibiting IRE1 kinase activity blocked NF-κB activation, interleukin-6 secretion, and adipocyte-autonomous lipolysis from inflammatory ligands. Inflammation-induced lipolysis mediated by IRE1 occurred independently from changes in insulin signaling in adipocytes, suggesting that inflammation can promote IRE1-mediated lipolysis independent of adipocyte insulin resistance. We found no role for canonical unfolded protein responses or ABL kinases in linking ER stress to IRE1-mediated lipolysis. Adiponectin-Cre-mediated IRE1 knockout in mice showed that adipocyte IRE1 was required for inflammatory ligand-induced lipolysis in adipose tissue explants and that adipocyte IRE1 was required for approximately half of the increase in blood triglycerides after a bacterial endotoxin-mediated inflammatory stimulus in vivo. Together, our results show that IRE1 propagates an inflammation-specific lipolytic program independent from hormonal or adrenergic regulation. Targeting IRE1 kinase activity may benefit metabolic syndrome and inflammatory lipid disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".