ETICA AMBIENTAL

To investigate the effect of 16 weeks exercise training on changes in skeletal muscle gene expression (Chapter 1 section 1.3), mRNA expression profiling was completed using Illumina gene microarray. The steps involved extraction of mRNA from SM of SPIRIT study participants at 0 and 16 weeks (section 3.6.1), mRNA gene expression profiling (section 3.6.2) and then statistical and bioinformatic analysis (section 3.6.3). RNA extraction and mRNA profiling (section 3.6.2-3) was performed in Washington DC by research technicians

at the Children’s National Medical Research Center (CNMRC). The normalisation and

statistics were performed by the research center bioinfomaticians whilst the statistical and bioinformatics analysis for the genes related with lipid and energy metabolism by using ingenuity software was performed in Massey University under supervision of Dr David Rowlands.

3.6.1 RNA extraction

mRNA was isolated from muscle tissue samples (~10 mg) using mirVana™ miRNA Isolation

Kit (Applied Biosystems/Ambion, Austin, TX). Concentration of RNA was determined by NanoDrop® spectrophotometer ND-1000 (NanoDrop Technologies, Wilmington, DE) and RNA quality was determined with an Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA). The RNA isolated was used for mRNA gene expression profiling (Section 3.6.2).

3.6.2 mRNA Expression Profiling

200 ng mRNA (Section 3.6.1) from each sample was used for mRNA gene expression profiling using Illumina® bead arrays (Illumina, Inc., San Diego, CA) for all 20,000 genes in the human genome. Reverse transcription and in vitro transcription amplification incorporating biotin-labeled nucleotides was performed with Illumina® TotalPrep™ -96 RNA Amplification Kit (Ambion, Austin, TX). 750 ng of the biotin-labeled IVT product (cRNA) was hybridised to HumanHT-12_v4_BeadChip (Illumina) for 16 h followed by washing, blocking, and streptavidin-Cy3 staining according to the Whole-Genome Gene Expression

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Direct Hybridisation protocol (Illumina). Arrays were scanned using HiScanSQ System.

Decoded images were analysed by GenomeStudio™ Gene Expression Module (Illumina).

3.6.3 Statistical analysis and Bioinformatics analysis

Illumina signal intensity were normalised to controls and in GenomeStudio and subject to hierarchical clustering for further quality control integration (Partek 6.6, St Louis, MO); accordingly mRNA data was background adjusted. Average signal data and all annotation files were exported for statistical analysis and integration (Partek 6.6, St Louis, MO). The mRNA datasets were quantile normalised followed by log2 transformation. Principal components analysis plot revealed batch and sample bias in all arrays. Therefore, the effect of training was estimated using mixed model ANOVA with subject as the repeated-measures identifier and array chip as a random effect. A large number of methods for analysis of genomics data are available. To focus attention on outcomes with most likely biological relevance and to accommodate low sample size, we used Global Error Assessment (GEA) ANOVA adjustment to derive a robust p value (ROBP). We settled on a selection that included genes with a ROBP of <0.005. This data set is now ready for bioinformatics analysis that is described below.

Bioinformatics was conducted using Ingenuity pathway analysis to define molecular functions and physiological processes affected by training. The analysis comprised (a) analysis based on hypothesis (stated in chapter 1) on changes thought to affect metabolic fuel substrate handling and endomysium remodelling including vasculogenesis, and (b) an unbiased exploratory analysis driven by the statistical gene selection and Ingenuity Pathway Analysis (IPA; Ingenuity® Systems, www.ingenuity.com) filter criteria.

Bioinformatics analysis involving network construction and functions analysis was performed in IPA software. Core analyses were run on probe selections filtered specific for skeletal muscle tissue in humans only but unfiltered for prediction state and relaxed filter. The top ranked transcriptome networks were extracted and the involved molecular functions and physiological systems guided biological interpretation made and compared between conditions (IPA Comparison Analysis). Top ranked were reported, and upstream regulators identified. Further specific details regarding bioinformatics are discussed in chapter 6 section 6.2.

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3.7 References

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2. Evans, W., S. Phinney, and V. Young, Suction applied to a muscle biopsy maximises sample sise. Medicine and Science in Sports & Exercise, 1982. 14: p. 101-102.

3. Huber, K., et al., Prenatally induced changes in muscle structure and metabolic function facilitate exercise-induced obesity prevention. Endocrinology, 2009. 150((9)):

p. 4135-44.

4. Lanza, I.R. and K.S. Nair, Muscle mitochondrial changes with aging and exercise.

Am J Clin Nutr, 2009. 86(4): p. 67S-71S.

5. Gauthier, M.J., et al., Electrical stimulation-inducd changes in skeletal muscle enzymes of men and women. Medicine and Science in Sports and Exercise, 1992: p. 1252-1256.

6. Kim, H. J., Lee, J. S. & Kim, C. K. Effects of exercise training on muscle glucose transporter 4 protein and intramuscular lipid content in elderly men with impaired glucose tolerance. Eur J Appl Physiol, 2004. 93, 353-358.

7. Reed, G.F., Use of co-efficient of variation in Assessing Variability of quantitative Assays. Clinical and diagnostic Laboratory Immunology, 2002. 9: p. 1235-1239.

8. Tarnopolsky, M.A., et al., Influence of endurance exercise training and sex on intramyocellular lipid and mitochondrial ultrastructure, substrate use, and mitochondrial enzyme activity. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology, 2007. 292,: p. R1271-R1278.

9. Karnovsky , M., A formaldehyde-glutaraldehyde fixative of high osmolarity for use in electron microscopy. J Cell Biol 1965. 127: p. 137A.

10. Paul, A.C., P.W. Sheard, and M.J. Duxson, Development of a mammalian series- fibred muscle Anatomical Record, 2004. 287A: p. 571-578.

11. Hopkins, W.G., et al., Progressive statistics for studies in sports medicine and exercise science. Med Sci Sport Exercise, 2009. 41, : p. 3-13.

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