Effect of aging, endurance training, and denervation on innate immune signaling in skeletal muscle
Bibliographic record
Abstract
Abstract Skeletal muscle function relies on mitochondria for energy and for mediating its unique adaptive plasticity. The NLRP3 inflammasome complex is an innate immune mechanism that responds to mitochondrial damage-associated molecular patterns (DAMPS), however its activity relative to mitochondrial dysfunction in muscle requires exploration. The purpose of this study was to characterize immune signaling and mitochondrial function in muscle during aging, endurance training, and disuse induced by denervation. Denervation led to decreases in muscle mass, mitochondrial content, and impaired respiration. Protein analyses revealed increases in NF-κB p65 and downstream inflammatory markers including NLRP3, caspase-1, GSDMD-N, STING and IL-1β, along with pro-apoptotic BAX and AIF. When assessing potential DAMPS, denervation led to increased ROS production but no changes in cytosolic mtDNA levels, relative to total mtDNA. Since we hypothesized that inflammasome activation would be increased with age, we studied young (6-8 months) and aged (21-22 months) mice that remained sedentary or underwent a 6-week voluntary running protocol. Aging resulted in marked increases in the expression of multiple pro-inflammatory and pro-apoptotic proteins. Remarkably, training uniformly attenuated age-related increases in BAX, NLRP3, caspase-1, STING, and GSDMD protein expression, and reduced the elevated level of cytosolic mtDNA evident in aged muscle. Training adaptations were evident also in the aged animals by the preservation of muscle mass and improvements in oxygen consumption and endurance performance and were achieved despite a lower training distance than in young animals. Our results strongly implicate endurance training as a promising therapeutic for combatting disuse and age-related inflammation in skeletal muscle.
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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) | 0.001 | 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".