Resistance training rejuvenates the mitochondrial methylome in aged human skeletal muscle
Bibliographic record
Abstract
Resistance training (RT) dynamically alters the skeletal muscle nuclear DNA methylome. However, no study has examined if RT affects the mitochondrial DNA (mtDNA) methylome. Herein, ten older, Caucasian untrained males (65 ± 7 y.o.) performed six weeks of full-body RT (twice weekly). Body composition and knee extensor torque were assessed prior to and 72 h 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). Biochemical assays were also performed, and older male data were compared to younger trained males (22 ± 2 y.o., n = 7, n = 6 Caucasian & n = 1 African American). RT increased whole-body lean tissue mass (p = .017), VL thickness (p = .012), and knee extensor torque (p = .029) in older males. RT also affected the mtDNA methylome, as 63% (159/254) of the CpG sites demonstrated reduced methylation (p < .05). Several mtDNA sites presented a more "youthful" signature in older males after RT in comparison to younger males. The 1.12 kilobase mtDNA D-loop/control region, which regulates replication and transcription, possessed enriched hypomethylation in older males following RT. Enhanced expression of mitochondrial H- and L-strand genes and complex III/IV protein levels were also observed (p < .05). While limited to a shorter-term intervention, this is the first evidence showing that RT alters the mtDNA methylome in skeletal muscle. Observed methylome alterations may enhance mitochondrial transcription, and RT evokes mitochondrial methylome profiles to mimic younger men. The significance of these findings relative to broader RT-induced epigenetic changes needs to be elucidated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".