ABSTRACT: Aerobic capacity is a strong predictor of cardiovascular mortality. To determine the relationship between aerobic capacity and cardiac gene expression we examined genome-wide gene expression in hearts of rats artificially selected for high- and low running capacity (HCR and LCR, respectively) over 16 generations. HCR were born with an athletic phenotype, whereas LCR exhibited features of the metabolic syndrome. Left ventricle gene expression of both sedentary and exercise trained HCR and LCR was characterized by microarray- and gene ontology analysis. Western immunoblots and immunohistochemistry were used to validate the results at protein level. Out of 28.0000 screened genes, 1540 were differentially expressed between HCR and LCR. HCR expressed higher amounts of genes involved in lipid metabolism, whereas LCR expressed higher amounts of the genes involved in glucose metabolism and transport. By simply selecting for running capacity, we created a difference in cardiac energy substrate utilisation from normal mitochondrial fatty acid ï¢-oxidation in HCR to carbohydrate metabolism in LCR. This event often occurs in diseased hearts. Differential expression of genes involved in cardiomyocyte growth was also found. LCR rats were associated with fetal gene expression, indicating the presence of pathological remodelling signaling. In addition, different expression of genes associated with cardiac contractility and cellular stress were detected. Also, hypoxia triggered transcription seemed to be involved in several of the functional differences between HCR and LCR. In conclusion, high- versus low aerobic capacity was associated with differences in genes regulating cardiac energy substrate, growth signalling, contractility and cellular stress. Experiment Overall Design: Animals Experiment Overall Design: The experimental rats in this study are the result of artificial selection for high and low aerobic capacity, starting from the N: NIH stock obtained from the National Institutes of Health (USA). The generation of the model has been previously described. Briefly, the rats in each generation were tested for exercise capacity by treadmill running at about 11 weeks of age. The individuals with the highest and lowest aerobic capacity were selected and each group served as the mating population for the next generation of high- and low-capacity runners; HCR and LCR, respectively. Rats from generation 16 were used in this study. The study includes four groups; LCR trained (n=4), LCR sedentary (n=4), HCR trained (n=4) and HCR sedentary (n=4). Experiment Overall Design: Endurance training Experiment Overall Design: The animals in the training groups were submitted to an aerobic interval training program previously described by Høydal et al. Briefly, after 10 minutes of warm-up, rats ran uphill (25°) on a treadmill for 1.5 hours, alternating between 8 minutes at an exercise intensity corresponding to 85-90% of maximal oxygen uptake (VO2max), and 2 minutes active recovery at 50 - 60%. Exercise was performed 5 days per week over 8 week; controls were age-matched rats that remained sedentary. In the exercising animals VO2max was measured every week to adjust band speed in order to maintain the intended intensity throughout the experimental period. The VO2max-measurements consisted of a 20 minutes warm-up at 50-60% of VO2max, whereupon treadmill velocity was increased by 0.03 m/s every 2 minute until VO2 plateau despite of increased workload. The apparatus and method were previously described and validated. Experiment Overall Design: Tissue collection Experiment Overall Design: At approximately 7 months of age the animals were sacrificed. A section of the left ventricle was formalin fixated for immunohistochemistry and morphological studies, whereas the rest was snap frozen in liquid nitrogen and stored at -80oC for later genetic screening and protein analysis. Experimental protocols were approved by the respective Institutional Animal Research Ethics Councils. Experiment Overall Design: Ribonuclease (RNA) isolation Experiment Overall Design: Tissue samples (20 mg) were homogenized in 100 µL TRIzol (Life Technologies, Gaithersburg, MD) using a Mixer Mill MM301 at 20-25 Hz. RNA clean-up was performed using RNA Mini kit (Qiagen, Germantown, MD). Total RNA was isolated and RNA clean-up was performed according to the manufacturer's instructions. Experiment Overall Design: RNA integrity, purity and quantity were assessed by Bioanalyzer (Agilent Technologies, Santa Clara, CA) and Nanodrop (NanoDrop Technologies, Baltimore, MD). The concentration of total RNA was measured by Nanodrop with ultraviolet spectrophotometry at 260/280 nm. RNA quality was assessed by electrophoresis on Bioanalyzer chips (Agilent Technologies). Experiment Overall Design: Experiment Overall Design: High quality RNA was classified as a 260/280 ratio above 1.8. Only samples with a 260/280 ratio between 1.8-2.2 and no signs of degradation were used for analysis. Experiment Overall Design: Processing of Affymetrix data Experiment Overall Design: Gene expression were analyzed on whole-genome RAE 230 2.0 chip from Affymetrix GeneChip (Affymetrix, Santa Clara, CA) comprised of 31,042 probe sets, analyzing over 30,000 transcripts and variants from over 28,000 substantiated rat genes. On the Affymetrix GeneChip arrays, each gene is represented by a set of 11-20 probe pairs consisting of a perfect match (PM) and a mismatch (MM) probe. The statistical analysis is based on summary expression measures for each probe set. Experiment Overall Design: Computing summary measures (RMA) Experiment Overall Design: The summary measure for each probeset is computed based on a linear statistical model for background-corrected, normalized and log-transformed PM values for each probe pair by use of the robust multiarray average (RMA) method. The PM values are normalized using the quantile normalization method, normalizing the arrays such that the empirical distribution of the expression measures is equal across arrays. Experiment Overall Design: Statistical analysis for finding differentially expressed genes Experiment Overall Design: For each gene (probeset), a linear regression model, including parameters representing the effect of aerobe capacity is specified. Based on the estimated effects, tests for significant differential expression are performed using moderated T-tests. Experiment Overall Design: To account for multiple testing, we calculate adjusted p-values controlling the False Discovery Rate (FDR), with the use of the Benjamini-Hochberg step-up procedure. Consequently, selecting differentially expressed genes based on a threshold of 0.05 on the adjusted FDR p-values means that the expected proportion of genes falsely classified as differential expressed should be below 0.05. Experiment Overall Design: All statistical analyses on the gene expression data are performed using the R language (R Development Core Team, 2004) and packages affy, affyPLM and limma from the Bioconductor project.
