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Record W2999506129 · doi:10.3390/sports8010007

Skeletal Muscle Myofibrillar Protein Abundance Is Higher in Resistance-Trained Men, and Aging in the Absence of Training May Have an Opposite Effect

2020· article· en· W2999506129 on OpenAlexfundno aff
Christopher G. Vann, Paul A. Roberson, Shelby C. Osburn, Petey W. Mumford, Matthew A. Romero, Carlton D. Fox, Johnathon H. Moore, Cody T. Haun, Darren T. Beck, Jordan R. Moon, Andreas N. Kavazis, Kaelin C. Young, Veera L. D. Badisa, Benjamin Mwashote, Victor Ibeanusi, Rakesh K. Singh, Michael D. Roberts

Bibliographic record

VenueSports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersFlorida Agricultural and Mechanical UniversityMcMaster University
KeywordsMyofibrilMyosinSkeletal muscleSarcopeniaMuscle hypertrophySarcoplasmInternal medicineResistance trainingEndocrinologyAgeingBiologyChemistryAndrologyBiochemistryMedicineCalcium

Abstract

fetched live from OpenAlex

Resistance training generally increases skeletal muscle hypertrophy, whereas aging is associated with a loss in muscle mass. Interestingly, select studies suggest that aging, as well as resistance training, may lead to a reduction in the abundance of skeletal muscle myofibrillar (or contractile) protein (per mg tissue). Proteomic interrogations have also demonstrated that aging, as well as weeks to months of resistance training, lead to appreciable alterations in the muscle proteome. Given this evidence, the purpose of this small pilot study was to examine total myofibrillar as well as total sarcoplasmic protein concentrations (per mg wet muscle) from the vastus lateralis muscle of males who were younger and resistance-trained (denoted as YT, n = 6, 25 ± 4 years old, 10 ± 3 self-reported years of training), younger and untrained (denoted as YU, n = 6, 21 ± 1 years old), and older and untrained (denoted as OU, n = 6, 62 ± 8 years old). The relative abundances of actin and myosin heavy chain (per mg tissue) were also examined using SDS-PAGE and Coomassie staining, and shotgun proteomics was used to interrogate the abundances of individual sarcoplasmic and myofibrillar proteins between cohorts. Whole-body fat-free mass (YT > YU = OU), VL thickness (YT > YU = OU), and leg extensor peak torque (YT > YU = OU) differed between groups (p < 0.05). Total myofibrillar protein concentrations were greater in YT versus OU (p = 0.005), but were not different between YT versus YU (p = 0.325). The abundances of actin and myosin heavy chain were greater in YT versus YU (p < 0.05) and OU (p < 0.001). Total sarcoplasmic protein concentrations were not different between groups. While proteomics indicated that marginal differences existed for individual myofibrillar and sarcoplasmic proteins between YT versus other groups, age-related differences were more prominent for myofibrillar proteins (YT = YU > OU, p < 0.05: 7 proteins; OU > YT = YU, p < 0.05: 11 proteins) and sarcoplasmic proteins (YT = YU > OU, p < 0.05: 8 proteins; OU > YT&YU, p < 0.05: 29 proteins). In summary, our data suggest that modest (~9%) myofibrillar protein packing (on a per mg muscle basis) was evident in the YT group. This study also provides further evidence to suggest that notable skeletal muscle proteome differences exist between younger and older humans. However, given that our n-sizes are low, these results only provide a preliminary phenotyping of the reported protein and proteomic variables.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations34
Published2020
Admission routes1
Has abstractyes

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