| 1. |
- Scheele, Camilla, et al.
(author)
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Altered regulation of the PINK1 locus: a link between Type 2 diabetes and neurodegeneration?
- 2007
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In: The FASEB Journal. - : Wiley. - 0892-6638 .- 1530-6860. ; 21:13, s. 3653-3665
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Journal article (peer-reviewed)abstract
- Mutations in PINK1 cause the mitochondrial-related neurodegenerative disease Parkinson’s. Here we investigate whether obesity, type 2 diabetes, or inactivity alters transcription from the PINK1 locus. We utilized a cDNA-array and quantitative real-time PCR for gene expression analysis of muscle from healthy volunteers following physical inactivity, and muscle and adipose tissue from nonobese or obese subjects with normal glucose tolerance or type 2 diabetes. Functional studies of PINK1 were performed utilizing RNA interference in cell culture models. Following inactivity, the PINK1 locus had an opposing regulation pattern (PINK1 was down-regulated while natural antisense PINK1 was up-regulated). In type 2 diabetes skeletal muscle, all transcripts from the PINK1 locus were suppressed and gene expression correlated with diabetes status. RNA interference of PINK1 in human neuronal cell lines impaired basal glucose uptake. In adipose tissue, mitochondrial gene expression correlated with PINK1 expression although remained unaltered following siRNA knockdown of Pink1 in primary cultures of brown preadipocytes. In conclusion, regulation of the PINK1 locus, previously linked to neurodegenerative disease, is altered in obesity, type 2 diabetes and inactivity, while the combination of RNAi experiments and clinical data suggests a role for PINK1 in cell energetics rather than in mitochondrial biogenesis.
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| 2. |
- Timmons, James A, et al.
(author)
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Expression profiling following local muscle inactivity in humans provides new perspective on diabetes-related genes
- 2006
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In: Genomics. - : Elsevier BV. - 0888-7543 .- 1089-8646. ; 87:1, s. 165-172
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Journal article (peer-reviewed)abstract
- Physical activity enhances muscle mitochondrial gene expression, while inactivity and mitochondrial dysfunction are both risk factors for developing diabetes. Defective activation of the transcriptional coactivator PGC-1 may contribute to the gene expression pattern observed in diabetic and insulin-resistant skeletal muscle. We proposed that greater insight into the mitochondrial component of skeletal muscle “diabetes” would be possible if the clinical transcriptome data were contrasted with local muscle inactivity-induced modulation of mitochondrial genes in otherwise healthy subjects. We studied PPARGC1A (PGC-1), PPARGC1B (PGC-1β), NRF1, and a variety of mitochondrial DNA (mtDNA) and nuclear-encoded mitochondrial genes critical for oxidative phosphorylation in soleus muscle biopsies obtained from six healthy men and women before and after 5 weeks of local muscle inactivity. Muscle inactivity resulted in a coordinated down-regulation of PGC-1 and genes involved with mitochondrial metabolism, including muscle substrate delivery genes. Decreased expression of the mtDNA helicase Twinkle was related to the decline in mitochondrial RNA polymerase (r = 0.83, p < 0.04), suggesting that mtDNA transcription and replication are coregulated in human muscle tissue. In contrast to the situation in diabetes, PGC-1β expression was not significantly altered, while NRF1 expression was actually up-regulated following muscle inactivity. We can conclude that reduced PGC-1 expression described in Type 2 diabetes may be partly explained by muscle inactivity. Further, although diabetes patients are typically inactive, our analysis indicates that local muscle inactivity may not be expected to contribute to the decreased NRF1 and PGC-1β expression noted in insulin-resistant and Type 2 diabetes patients, suggesting these changes may be more disease specific.
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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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