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accession-icon GSE106865
A longitudinal gene expression microarray analysis of skeletal muscle resistance training
  • organism-icon Homo sapiens
  • sample-icon 40 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

Skeletal muscle adapts to resistance exercise (RE) performance acutely and chronically. An important regulatory step of muscle adaptation to RE is gene expression. Microarray analysis can be used as an exploratory method to investigate how genes and gene clusters are modulated acutely and chronically by RE. The purpose of the present study was to investigate the effect of training status in the basal (rested) and pre- to 24h post-RE on the global transcriptome in vastus lateralis muscle biopsies of young men. Muscle biopsies of nine young men who undertook RE training for 10-wks were collected pre and 24h post-RE at the first (W1) and last (W10) weeks of training and analysed using microarray. An unaccustomed RE bout (at W1) up-regulated muscle gene transcripts related to stress (e.g., heat shock proteins), damage and inflammation, structural remodelling, protein turnover and increased translational capacity. Trained muscles (at W10) became more efficient metabolically, as training favoured a more oxidative metabolism, refined response to stress, showed by genes suppression related to RE-induced stress and inflammation, and up-regulated genes indicating greater muscle contractile efficiency and contribution to promote muscle growth and development. These data highlight that chronic repetition of RE increases muscle efficiency and adapt muscles to respond more specifically and accurately to RE-induced stress.

Publication Title

Resistance training in young men induces muscle transcriptome-wide changes associated with muscle structure and metabolism refining the response to exercise-induced stress.

Sample Metadata Fields

Sex, Specimen part

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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