MétaCan
Menu
← Back to cohort

Examining the Impact of Different Exercise Protocols on PGC‐1α and FNDC5 mRNA Expression in Human Skeletal Muscle

2017· article· en· W4389017205 on OpenAlexaffabout
Jacob T. Bonafiglia, Brittany A. Edgett, Trisha D. Scribbans, Jonathan P. Little, Brendon J. Gurd

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of ManitobaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsFNDC5Skeletal muscleEndocrinologyInternal medicineMessenger RNAInterval trainingMyokineEndurance trainingMedicineBiologyFibronectinGeneCell biologyBiochemistry

Abstract

fetched live from OpenAlex

A recent overexpression model demonstrated that peroxisome proliferator‐activated receptor coactivator 1‐alpha (PGC‐1α) regulates the expression of fibronectin type III domain containing protein 5 (FNDC5); a novel myokine with a potential role in stimulating brown‐fat‐like development in white adipose tissue. Although FNDC5 mRNA expression remains unaltered following an acute bout of endurance exercise (END) and high intensity interval training (HIIT), it is unclear whether FNDC5 mRNA expression increases following aerobic exercise protocols that demonstrate elevations in PGC‐1α mRNA expression. Therefore, the purpose of the present study was to test the hypothesis that aerobic exercise protocols that increase PGC‐1α mRNA expression will concomitantly increase FNDC5 mRNA expression in human skeletal muscle. Skeletal muscle biopsies were obtained from healthy men at rest (PRE) and three hours after one bout of moderate intensity interval exercise (MIIT; n = 10; 11 one‐minute intervals at 73% WR at VO 2 peak separated with one‐minute rest periods), HIIT (n = 10; 8 one‐minute intervals at 100% WR at VO 2 peak separated with one‐minute rest periods), or sprint interval training (SIT; n = 14; 8 20‐second intervals at 170% WR at VO 2 peak separated with 10 second rest periods). All samples and PGC‐1α mRNA expression data were obtained from previously published studies (1,2). PGC‐1α mRNA expression significantly ( p < 0.05) increased following MIIT (+393% ± 345%), HIIT (+720% ± 392%), and SIT (+284% ± 140%). However, FNDC5 mRNA expression did not change following acute MIIT (−8% ± 29%, p = 0.3), HIIT (+16% ± 32%, p = 0.3), or SIT (−5% ± 18%, p = 0.3). Furthermore, changes in PGC‐1α and FNDC5 mRNA expression did not significantly correlate following any exercise protocol. This study demonstrates that although PGC‐1α mRNA expression increased following all exercise protocols, FNDC5 mRNA did not change. Therefore, these results suggest that interval exercise protocols that induce increases in PGC‐1α mRNA expression do not increase FNDC5 mRNA expression. Support or Funding Information This study was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) to J.T.B., J. P. L., and B.J.G. B. A. E. and T. D. S. were supported by Ontario Graduate Scholarships. J. P. L. was also supported by a Canadian Institutes of Health Research (CIHR) New Investigator Award.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.059
GPT teacher head0.360
Teacher spread0.301 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicAdipose Tissue and Metabolism→French-language works237,207→