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- Franks, P. W., et al.
(författare)
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Genomic variants at the PINK1 locus are associated with transcript abundance and plasma nonesterified fatty acid concentrations in European whites
- 2008
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Ingår i: The FASEB Journal. - : Wiley. - 0892-6638 .- 1530-6860. ; 22:9, s. 3135-3145
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of this study was to characterize associations between PINK1 genotypes, PINK1 transcript levels, and metabolic phenotypes in healthy adults and those with type 2 diabetes (T2D). We measured PINK1 skeletal muscle transcript levels and 8 independent PINK1 single nucleotide polymorphisms (SNPs) in a cohort of 208 Danish whites and in a cohort of 1701 British whites (SNPs and metabolic phenotypes only). Furthermore, we assessed the effects of PINK1 transcript ablation in primary adipocytes using RNA interference (RNAi). Six PINK1 SNPs were associated with PINK1 transcript levels (P < 0.04 to P < 0.0001). Obesity modified the association between PINK1 transcript levels and T2D risk (interaction P=0.005); transcript levels were inversely related with T2D in obese (n=105) [odds ratio (OR) per SD increase in expression levels=0.44; 95% confidence interval (CI): 0.23, 0.84; P=0.013] but not in nonobese (n=103) (OR=1.20; 95% CI: 0.82, 1.76; P=0.34) individuals. In the British cohort, several PINK1 SNPs were associated with plasma nonesterified fatty acid concentrations. Nominal genotype associations were also observed for fasting glucose, 2-h glucose, and maximal oxygen consumption, although these were not statistically significant after correcting for multiple testing. In primary adipocytes, Pink1 knockdown affected fatty acid binding protein 4 (Fabp4) expression, indicating that PINK1 may influence substrate metabolism. We demonstrate that PINK1 polymorphisms are associated with PINK1 transcript levels and measures of fatty acid metabolism in a concordant manner, whereas our RNAi data imply that PINK1 may indirectly influence lipid metabolism.
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| 2. |
- Timmons, James A., et al.
(författare)
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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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Ingår i: Journal of applied physiology. - : American Physiological Society. - 8750-7587 .- 1522-1601. ; 108:6, s. 1487-1496
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Tidskriftsartikel (refereegranskat)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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