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- Nilsson, Emma A, et al.
(author)
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Altered DNA Methylation and Differential Expression of Genes Influencing Metabolism and Inflammation in Adipose Tissue From Subjects With Type 2 Diabetes
- 2014
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In: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 63:9, s. 2962-2976
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Journal article (peer-reviewed)abstract
- Genetics, epigenetics, and environment may together affect the susceptibility for type 2 diabetes (T2D). Our aim was to dissect molecular mechanisms underlying T2D using genome-wide expression and DNA methylation data in adipose tissue from monozygotic twin pairs discordant for T2D and independent case-control cohorts. In adipose tissue from diabetic twins, we found decreased expression of genes involved in oxidative phosphorylation; carbohydrate, amino acid, and lipid metabolism; and increased expression of genes involved in inflammation and glycan degradation. The most differentially expressed genes included ELOVL6, GYS2, FADS1, SPP1 (OPN), CCL18, and IL1RN. We replicated these results in adipose tissue from an independent case-control cohort. Several candidate genes for obesity and T2D (e.g., IRS1 and VEGFA) were differentially expressed in discordant twins. We found a heritable contribution to the genome-wide DNA methylation variability in twins. Differences in methylation between monozygotic twin pairs discordant for T2D were subsequently modest. However, 15,627 sites, representing 7,046 genes including PPARG, KCNQ1, TCF7L2, and IRS1, showed differential DNA methylation in adipose tissue from unrelated subjects with T2D compared with control subjects. A total of 1,410 of these sites also showed differential DNA methylation in the twins discordant for T2D. For the differentially methylated sites, the heritability estimate was 0.28. We also identified copy number variants (CNVs) in monozygotic twin pairs discordant for T2D. Taken together, subjects with T2D exhibit multiple transcriptional and epigenetic changes in adipose tissue relevant to the development of the disease.
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| 3. |
- Timmons, James A., et al.
(author)
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Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans
- 2010
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In: Journal of applied physiology. - : American Physiological Society. - 8750-7587 .- 1522-1601. ; 108:6, s. 1487-1496
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Journal article (peer-reviewed)abstract
- Timmons JA, Knudsen S, Rankinen T, Koch LG, Sarzynski M, Jensen T, Keller P, Scheele C, Vollaard NB, Nielsen S, Akerstrom T, MacDougald OA, Jansson E, Greenhaff PL, Tarnopolsky MA, van Loon LJ, Pedersen BK, Sundberg CJ, Wahlestedt C, Britton SL, Bouchard C. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J Appl Physiol 108: 1487-1496, 2010. First published February 4, 2010; doi:10.1152/japplphysiol.01295.2009.-A low maximal oxygen consumption ((V) over dotO(2max)) is a strong risk factor for premature mortality. Supervised endurance exercise training increases (V) over dotO(2max) with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts (V) over dotO(2max) training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous (V) over dotO(2max) response. Two independent preintervention RNA expression data sets were generated (n = 41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in (V) over dotO(2max) (""predictor"" genes). The HERITAGE Family Study (n = 473) was used for genotyping. We discovered a 29-RNA signature that predicted (V) over dotO(2max) training response on a continuous scale; these genes contained similar to 6 new single-nucleotide polymorphisms associated with gains in (V) over dotO(2max) in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., ""reciprocal"" RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in (V) over dotO(2max), corresponding to similar to 50% of the estimated genetic variance for (V) over dotO(2max). In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. (V) over dotO(2max) responses to endurance training can be predicted by measuring a similar to 30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
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