Control of cholesterol-induced adipocyte inflammation by the Nfe2l1-Atf3 pathway
Bibliographic record
Abstract
Abstract While adipocytes are critical pillars of energy metabolism, their dysfunction is linked to adipose tissue (AT) inflammation, insulin resistance, and ectopic lipotoxicity in cardiometabolic diseases. However, the mechanisms causing adipocyte inflammation and insulin resistance remain unclear. Here, we show that excess cholesterol induces adipocyte dysfunction, which is suppressed by the transcription factor Nfe2l1 (nuclear factor erythroid derived-2, like-1). Nfe2l1 is required to sustain proteasome function in adipocytes and proteotoxic stress induces adipocyte inflammation via the activation of Atf3. In humans, the Nfe2l1-proteasome pathway is inversely correlated to body mass index (BMI) in an adipose-depot specific manner. In mice, loss of adipocyte Nfe2l1 caused AT inflammation with a pronounced infiltration of macrophages and T cells. Mice lacking adipocyte Nfe2l1 displayed severe adipocyte dysfunction during diet-induced obesity (DIO), characterized by lower adipokine levels, steatosis, glucose intolerance and insulin resistance. Nfe2l1 ΔAT mice on an Apoe-deficient ( Apoe −/− ) background fed a cholesterol-rich Western Diet (WD), developed a lipoatrophy-like syndrome, dyslipidemia, and enhanced atherosclerosis. Our results reveal an important role for proteasome-mediated proteostasis in adipocytes and indicate that Nfe2l1 is linked to metabolic health in humans and preclinical mouse models. Promoting proteostasis in adipocytes may thus alleviate inflammation in obesity, potentially averting adverse cardiometabolic outcomes. Graphical abstract
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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 | 0.000 |
| 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".