SULM – Schweizerische Union für Labormedizin | Union Suisse de Médecine de Laboratoire | Swiss Union of Laboratory Medicine

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P Buettner1 , S Mosig1 , H Funke1 , F.C. Mooren2

1Institute of Vascular Medicine, Dept. of Molecular Hemostaseology, Friedrich Schiller University Jena, Germany, 2Institute of Sports Medicine, University Münster, Germany

Gene expression analyses have been successfully used in classifying tumour diseases. Here we test, if changes in gene expression profiles (GEPs) of white blood cells can serve as surrogate markers for monitoring metabolic effects of physical training. For this purpose we analysed GEPs in four male probands immediately before and one hour after physical training. After determinations of the probands’ maximal O2 consumption (VO2max), they performed an exhaustive treadmill test (ET) at 80% of their VO2max, and a moderate treadmill test (MT) at 60% VO2max for exactly the same time one week later. White blood cells (WBCs) were isolated by the erythrocyte lysis method; peripheral blood mononuclear (PBMCs) cells were isolated with a density gradient method. Gene expression profiles were measured using the Affymetrix GeneChip® technology. After scaling, normalisation, and filtering groupwise and pairwise comparisons of gene expression intensities were performed. We found that more genes were at least twofold up-regulated after ET (79 genes 2-fold up; 36 p < 0.05 using non-parametric t-test) than after MT (18 [2x up]; 2 [p < 0.05]). All genes up-regulated (twofold or significant in t-test) after MT were also twofold up-regulated after the ET. Although sample preparation method and individual changes of WBC subpopulations influenced gene expression levels, there was only little impact on the list of prominently up-regulated genes. Genes whose expression was up-regulated by training included stress response genes (most prominently several heat shock proteins), and genes involved in calcium, fatty acid, and glucose metabolism. These results demonstrate that, in principle, it is possible to measure the effects of physical training by analyzing gene expression profiles in PBMCs. Further analyses will be needed to confirm our initial findings and to understand the observed changes in more detail. Eventually, gene expression profiling may become a useful tool to monitor training loads.

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