Resistance training rejuvenates the mitochondrial methylome in aged human skeletal muscle
Bibliographic record
Abstract
ABSTRACT Resistance training (RT) alters skeletal muscle nuclear DNA methylation patterns (or the methylome). However, no study has examined if RT affects the mitochondrial DNA (mtDNA) methylome. Herein, ten older untrained males (65±7 years old) performed six weeks of full-body RT (twice weekly). Body composition and knee extensor torque were assessed prior to and 72 hours following the last RT session. Vastus lateralis (VL) biopsies were also obtained. VL DNA was subjected to reduced representation bisulfite sequencing providing excellent coverage across the ~16-kilobase mtDNA methylome (254 CpG sites). Various biochemical assays were also performed, and older male data were compared to younger trained males (22±2 years old, n=7). RT increased whole-body lean tissue mass (p=0.017), VL thickness (p=0.012), and knee extensor torque (p=0.029) in older males. RT also profoundly affected the mtDNA methylome in older males, as 63% (159/254) of the CpG sites demonstrated reduced methylation (p<0.05). Notably, several mtDNA sites presented a more “youthful” signature after RT in older males when comparisons were made to younger males. The 1.12 kilobase D-loop/control region on mtDNA, which regulates mtDNA replication and transcription, possessed enriched hypomethylation in older males following RT. Enhanced expression of mitochondrial H- and L-strand genes and increases in mitochondrial complex III and IV protein levels were also observed (p<0.05). This is the first study to show RT alters the mtDNA methylome in skeletal muscle. Observed methylome alterations may enhance mitochondrial transcription, and RT remarkably evokes mitochondrial methylome profiles to mimic a more youthful signature in older males.
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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".