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P-86 The use of whole-genome expression to predict exercise training response in the gene smart study: preliminary results
  1. Antonia Karanikolou1,
  2. Guan Wang1,
  3. Ioannis D Papadimitriou2,
  4. Xu Yan2,
  5. Andrew Garnham2,
  6. David J Bishop2,
  7. Nir Eynon2,
  8. Yannis P Pitsiladis1
  1. 1FIMS Reference Collaborating Centre of Sports Medicine for Anti-Doping Research, University of Brighton, Eastbourne, UK
  2. 2Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Victoria, Australia


Differences in gene expression patterns may explain, at least partly, the inter-individual variability in response to similar exercise training [1, 2]. Studies such as the Gene Skeletal Muscle Adaptive Response to Training (SMART) study ( are necessary to elucidate the molecular mechanisms underlying those individual responses. Here we examine the individual differences in gene expression following exercise and training in the Gene SMART study.

Twenty-two moderately trained, healthy Caucasians participants (all males 20-45 y, BMI ≤ 30) completed a single session of High Intensity Interval Exercise (HIIE) on a cycle ergometer (8 × 2-min intervals at 85% of maximal power with 1 min of recovery between intervals), and a subset of those participants (n = 13) completed four weeks of High Intensity Interval Training (HIIT). Blood samples were collected before, immediately after HIIE, 3 h post HIIE and four weeks post HIIT. Total RNA extracted from whole blood was used for whole transcriptome analysis (GeneChip HTA 2.0 from Affymetrix UK Ltd, > 285,000 full-length transcripts). One-way repeated measures ANOVA analysis was used to identify differential expressed genes at those four time points. Changes considered significant at a 5% FDR and a fold change (FC) of 2.

Compared to baseline, 123 genes were differentially expressed immediately post HIIE whereas 204 genes were differentially expressed 3 hours post HIIE (n = 22). Specifically, 34 genes were upregulated and 89 genes were downregulated immediately post HIIE, whereas 23 genes were upregulated and 181 were downregulated 3 hours post HIIE. Four transcripts overlapped between those two time points; RUNX3 and CTSW expression was significantly increased immediately post HIIE (FC = 2.6, FDR adj.p=0.007 and FC = 2.4, FDR adj.p=0.005, respectively), and significantly decreased 3 hours post HIIE (FC = −2.2, FDR adj.p=0.002 and FC = −2.1, FDR adj.p=0.003, respectively), compared to baseline. Additionally, the gene expression levels of TC21000168.hg.1 and TC21000719.hg.1 were significantly decreased (FC = −2.1, FDR adj.p=0.005 and FC = −2.1, FDR adj.p=0.007) immediately post HIIE and significantly increased 3 hours post HIIE (FC = 2.7, FDR adj.p=0.0003 and FC = 2.7, FDR adj.p=0.0003). No significant changes in gene expression were found after 4 weeks of HIIT compared to baseline (n = 13).

Although a relatively small sample size, this preliminary whole-genome expression analysis from whole blood is encouraging and supports the idea of using molecular markers such as gene expression to predict individual response to exercise. More participants are currently being recruited to increase the sample size.


  1. Bouchard C, et al. Genomic predictors of the maximal O(2) uptake response to standardised exercise training programs. J Appl Physiol 1985, 2011.110(5):1160–70.

  2. Timmons JA, et al. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J Appl Physiol 1985, 2010;108(6):1487–96.

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