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High responders to resistance exercise training demonstrate differential regulation of skeletal muscle microRNA expression

2010· article· en· W2140154586 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Physiology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsSkeletal musclemicroRNAResistance trainingGene expressionBiologyMedicineInternal medicineEndocrinologyGeneGenetics

Abstract

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MicroRNAs (miRNA), small noncoding RNA molecules, may regulate protein synthesis, while resistance exercise training (RT) is an efficient strategy for stimulating muscle protein synthesis in vivo. However, RT increases muscle mass, with a very wide range of effectiveness in humans. We therefore determined the expression level of 21 abundant miRNAs to determine whether variation in these miRNAs was able to explain the variation in RT-induced gains in muscle mass. Vastus lateralis biopsies were obtained from the top and bottom ∼20% of responders from 56 young men who undertook a 5 day/wk RT program for 12 wk. Training-induced muscle mass gain was determined by dual-energy X-ray absorptiometry, and fiber size was evaluated by histochemistry. The expression level of each miRNA was quantified using TaqMan-based quantitative PCR, with the analysis carried out in a blinded manner. Gene ontology and target gene profiling were used to predict the potential biological implications. Of the 21 mature miRNAs examined, 17 were stable during RT in both groups. However, miR-378, miR-29a, miR-26a, and miR-451 were differentially expressed between low and high responders. miR-378, miR-29a, and miR-26a were downregulated in low responders and unchanged in high responders, while miR-451 was upregulated only in low responders. Interestingly, the training-induced change in miR-378 abundance was positively correlated with muscle mass gains in vivo. Gene ontology analysis of the target gene list of miR-378, miR-29a, miR-26a, and miR-451, from the weighted cumulative context ranking methodology, indicated that miRNA changes in the low responders may be compensatory, reflecting a failure to "activate" growth and remodeling genes. We report, for the first time, that RT-induced hypertrophy in human skeletal muscle is associated with selected changes in miRNA abundance. Our analysis indicates that miRNAs may play a role in the phenotypic change and pronounced intergroup variation in the RT response.

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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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