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Protein Nutrition Following Endurance Exercise Regulates The Metabolic-mitochondrial Transcriptome In Skeletal Muscle

2011· article· en· W2329395012 on OpenAlexaff
David S. Rowlands, Jasmine Thomson, Brian W. Timmons, Frédéric Raymond, Andreas Fuerholz, Robert Mansourian, Marie-Camille Zawhlen, Sylviane Métairon, Trent Stellingwerff, Martin Kussman, Mark A. Tarnopolsky

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsPDK4Mitochondrial biogenesisTranscriptomePeroxisome proliferator-activated receptorCoactivatorPeroxisomeEndurance trainingBiologyGene expressionSkeletal muscleEndocrinologyInternal medicineGeneBiochemistryMedicineTranscription factor

Abstract

fetched live from OpenAlex

Peroxisome proliferator-activated receptor gamma (PPARγ) coactivator 1-alpha (PGC1α) regulates metabolic gene expression and mitochondrial biogenesis. Recently, supplementing the diet of exercising mice with branched-chain amino acids increased the expression of PGC1α and other mitochondrial biogenic regulators. PURPOSE: To examine the impact of adding protein to high-carbohydrate feeding following intense endurance exercise on the metabolic-mitochondrial transcriptome in well-trained men. METHODS: In a crossover, biopsies were obtained from 8 men at rest and 3H and 48H following 100 min of intense cycling. Isocaloric beverages containing 0.4/1.2/0.2 (PRO) or 0.0/1.6/0.2 (CON) g.kg-1 protein/carbohydrate/fat were ingested 0 and 1 h post exercise. Gene expression was assessed using Illumina bead arrays, with global error assessment used to identify differentially expressed genes. RESULTS: Bioinformatic analysis of the microarray revealed a temporally affected molecular programme governed by protein nutrition directing metabolism away from glucose and towards fatty-acid transport and oxidation. Genes for hexokinase, glycokinase, and PDK4 were up regulated in response to exercise, but hexokinase, glycokinase were down regulated with PRO at 48H, while PDK4 was up regulated. PPARγ was up regulated at 3H and 48H with exercise but relatively down/up regulated with PRO at 3H/48H. At 3H, PGC1α was up regulated by exercise, but not affected by PRO; but by 48H there was a moderate up regulation in PRO. This was complemented by increased expression of DNA-binding proteins active on the PGC1α promoter at 3H (ESRRγ) and 48H (CREB, MYOD1). Accordingly, the expression of several mitochondrial electron transport components were up, while DNA (cytosine-5-)-methyltransferase 3 (DNMT3B) was down with PRO at 48H. Genes involved in lipid transport, oxidation, and modification were differentially regulated with PRO at 48H, and included LPL, CPT2, CD36, ACSL1, SLC25A20, LPIN1, FABP5, SCD. Uncoupling protein 3 (UCP3) was down regulated at 48H with PRO. CONCLUSION: High protein-carbohydrate nutrition following intense endurance exercise may support the metabolic and mitochondrial adaptive response to endurance exercise training. Funding from SPARC, Massey University, Nestec/Nestlé Research Centre.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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 designBench or experimental
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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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