Obesity-induced alterations to the immunoproteasome: a potential link to intramuscular lipotoxicity
Bibliographic record
Abstract
Although the mechanisms are unclear, inflammation and/or lipotoxicity likely contribute to obese muscle pathology. The immunoproteasome is known to respond to inflammation and oxidative damage and may aid muscle regeneration. We sought to determine whether diet-induced obesity (DIO) influences the immunoproteasome subunits LMP7 and MECL-1 in mouse muscle with and without exercise-induced muscle damage (EIMD). Muscle mass, regeneration, macrophage content and lipid peroxidation (8-isoprostane) were also assessed. Sixty male, 4-week-old C57BL/6J mice were fed a high-fat (HFD) or low-fat diet for 12 weeks. Mice were then subdivided into EIMD or no muscle damage (NMD) groups. The gastrocnemius muscle was excised 1 or 5 days after EIMD, producing 6 groups (n = 10/group). Body mass was greater; however, relative gastrocnemius mass was lower in HFD-fed mice. Despite no macrophage or MECL-1 alterations, LMP7 and 8-isoprostane were increased in obese mice in the NMD and 1 day post-EIMD groups. However, 8-isoprostane was reduced in obese mice 5 days post-EIMD, and accompanied by increased muscle LMP7, MECL-1 and macrophage content. Consequently, DIO may impair the immunoproteasome’s ability to control muscle lipid peroxidation but is reversed with eccentric exercise. Although muscle regeneration was unchanged, immunoproteasome dysregulation occurs in obese muscle and may contribute to muscle pathology. Novelty: DIO may impair the intramuscular immunoproteasome response to lipid peroxidation. Acute eccentric exercise may protect obese individuals from muscle lipotoxicity via immunoproteasome upregulation.
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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) | 0.001 | 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".