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Record W4405461772 · doi:10.26685/urncst.710

The Role of DNA Methylation in Regulating Skeletal Muscle Adaptation to Exercise: A Literature Review

2024· review· en· W4405461772 on OpenAlexaff
Rozhan Aali, Pardis Shirkani

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSkeletal muscleDNA methylationAdaptation (eye)MethylationBiologyComputational biologyDNABioinformaticsGeneticsNeuroscienceEndocrinologyGeneGene expression

Abstract

fetched live from OpenAlex

Introduction: The skeletal muscle tissue has a remarkable degree of plasticity. In the case of exercise training, the skeletal muscle adapts to match the specific stress imposed by the training modality. Aerobic exercise training promotes oxidative metabolism, angiogenesis, and fiber type switching from type IIb to IIa. Conversely, resistance exercise training promotes muscle protein synthesis and a hypertrophic response of the skeletal muscle. Regardless of training modality, the tissular adaptation of the skeletal muscle is a consequence of underlying changes in gene expression. Changes in promoter DNA methylation regulate genes' activation, or silencing, by influencing euchromatin and heterochromatin organization, respectively, ultimately determining chromatin accessibility to transcriptional machinery. It surmises that dynamic DNA methylation mechanisms would regulate acute exercise-responsive genes in skeletal muscle. In the context of exercise training, alterations in DNA methylation of gene promoters could support long-term changes in gene expression or restrict the responsiveness of acute exercise genes. Methods: This literature review will involve a comprehensive search of peer-reviewed articles published after the year 2000 in databases including UBC Library, PubMed, Google Scholar, and Web of Science. Articles selected for inclusion were screened based on relevance to the topic and quality of evidence. Data extraction was focused on identifying key findings related to DNA methylation changes in skeletal muscle following exercise interventions. Results: Resistance training alters DNA methylation in skeletal muscle, enhancing genes for muscle growth and strength. Aerobic training reduces DNA methylation, boosting genes for mitochondrial biogenesis, glucose metabolism, and muscle endurance. Discussion: The discussion highlights that both aerobic and resistance training induce long-term epigenetic modifications in skeletal muscle, creating a "memory" that enhances muscle adaptability and performance in future exercises. These findings suggest potential therapeutic applications for muscle-wasting diseases and metabolic disorders, emphasizing the need for personalized exercise regimens to maximize health benefits. Conclusion: This literature review emphasizes the pivotal role of DNA methylation in skeletal muscle adaptation to exercise, highlighting the distinct epigenetic modifications induced by aerobic and resistance training that enhance muscle function and metabolic health.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.009
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.045
GPT teacher head0.423
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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