|Application:||Gene expression microarray analysis|
|Number of samples:||16|
|Release date:||Jan 4 2008|
|Last update date:||Jul 31 2017|
|Diseases:||Heart Diseases, Metabolic Diseases|
|Dataset link||Low and High Capacity Runners - Sedentary and Trained: Left Ventricle|
Animals 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). Endurance training The animals in the training groups were submitted to an aerobic interval training program previously described by Hoydal 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. Tissue collection 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 -80C for later genetic screening and protein analysis. Experimental protocols were approved by the respective Institutional Animal Research Ethics Councils. Ribonuclease (RNA) isolation Tissue samples (20 mg) were homogenized in 100 uL 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. 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). 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. Processing of Affymetrix data 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. Computing summary measures (RMA) 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. Statistical analysis for finding differentially expressed genes 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. 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. 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.