Project description:The few investigations on exercise-induced global gene expression responses in human skeletal muscle haves typically focused at one specific mode of exercise and few such studies have implemented control measures. However, interpretation on distinct phenotype regulation necessitate comparison between essentially different modes of exercise and the ability to identify true exercise effects, necessitate implementation of independent non-exercise control subjects. Furthermore, muscle transkriptometranscriptome data made available through previous exercise studies can be difficult to extract and interpret by individuals that are inexperienced with bioinformatic procedures. In a comparative study, we; (1) investigated the human skeletal muscle transcriptome response to differentiated exercise and non-exercise control intervention, and; (2) aimed to develop a straightforward search tool to allow for easy extraction and interpretation of our data. We provide a simple spreadsheet containing transcriptome data allowing other investigators to see how mRNA of their interest behave in skeletal muscle following exercise, both endurance, strength and non-exercise. Our approach, allow investigators easy access to information on genuine transcriptome effects of differentiated exercise, to better aid hyporthesis-driven question in this particular field of research.
Project description:The few investigations on exercise-induced global gene expression responses in human skeletal muscle haves typically focused at one specific mode of exercise and few such studies have implemented control measures. However, interpretation on distinct phenotype regulation necessitate comparison between essentially different modes of exercise and the ability to identify true exercise effects, necessitate implementation of independent non-exercise control subjects. Furthermore, muscle transkriptometranscriptome data made available through previous exercise studies can be difficult to extract and interpret by individuals that are inexperienced with bioinformatic procedures. In a comparative study, we; (1) investigated the human skeletal muscle transcriptome response to differentiated exercise and non-exercise control intervention, and; (2) aimed to develop a straightforward search tool to allow for easy extraction and interpretation of our data. We provide a simple spreadsheet containing transcriptome data allowing other investigators to see how mRNA of their interest behave in skeletal muscle following exercise, both endurance, strength and non-exercise. Our approach, allow investigators easy access to information on genuine transcriptome effects of differentiated exercise, to better aid hyporthesis-driven question in this particular field of research. 18 subjects were divided into 3 groups, performing 12 weeks of Endurance or Strength training or no training. Biopsies for microarray were take before (Pre) and 2½ and 5 hours after the last training session. Isolated RNA from these biopsies were then measured with the Affymetrix Human Gene 1.0 ST arrays.
Project description:To investigate microRNAs related to mitochondria biogenesis in skeletal muscle, microRNA expressions during skeletal muscle differentiation and exercise were analyzed in vivo and in vitro. Murine skeletal muscle cell (C2C12) were assigned to undifferentiated, differentiated, and passively stretched (exercise mimicked). C57BL/6S mice were assigned to resting, acute exercise (1day), and chronic exercise (7days). Low molecular weight RNA (< 200 nucleotides) was isolated from C2C12 cell or tibialis anterior muscle of mice and hybridized to Ncode microRNA microarrays. The experiment was performed using a loop design for the data analysis.
Project description:A single bout of exercise induces changes in gene expression in skeletal muscle. Regular exercise results in an adaptive response involving changes in muscle architecture and biochemistry, and is an effective way to manage and prevent common human diseases such as obesity, cardiovascular disorders and type II diabetes. Our study is a transcriptome-wide analysis of skeletal muscle tissue in a large cohort of untrained Thoroughbred horses before and after a bout of high-intensity exercise and again after an extended period of training. We hypothesized that regular high-intensity exercise training primes the transcriptome for the demands of high-intensity exercise.
Project description:Exercise stimulates systemic and tissue-specific adaptations that protect against lifestyle related diseases including obesity and type 2 diabetes. Exercise places high mechanical and energetic demands on contracting skeletal muscle, which require finely-tuned protein post-translational modifications involving signal transduction (e.g. phosphorylation) to elicit appropriate short- and long-term adaptive responses. To uncover the breadth of protein phosphorylation events underlying the adaptive responses to endurance exercise and skeletal muscle contraction, we performed global, unbiased mass spectrometry-based phosphoproteomic analyses of skeletal muscle from two rodent models, in situ muscle contraction in rats and treadmill-based endurance exercise in mice.
Project description:Phosphorylation of skeletal muscle proteins mediates cellular signaling and adaptive responses to exercise. Bioinformatic and machine learning approaches identified preclinical models that recapitulate human exercise responses. Feature selection showed that muscles from treadmill running mice and maximum intensity contractions shared the most differentially phosphorylated phosphosites (DPPS) with human exercise. Benefits of exercise in chronic diseases may be reduced by hyperammonemia, a consistent perturbation in chronic diseases and a muscle cytotoxin generated during contractile activity. Comparative analysis of experimentally validated molecules identified 63 DPPS on 265 differentially expressed phosphoproteins (DEpP) shared between hyperammonemia in myotubes and skeletal muscle from exercise models. Functional enrichment analyses revealed distinct temporal patterns of enrichment shared between hyperammonemia and exercise models including protein kinase A(PKA), calcium signaling, mitogen activated protein kinase(MAPK) signaling, and protein homeostasis. Our approach of feature extraction of comparative unbiased data allows for model selection and target identification to optimize responses to interventions.