Subject characteristics (N = 12).

<p>Values are mean ± standard deviation. HR  =  heart rate, BMI  =  body mass index, VO2MAX  =  maximum oxygen uptake, VO2Peak  =  peak oxygen uptake, Wmax  =  maximum work load.</p

Induction of transcription factor pathways by exercise.

<p>Transcription factor pathways related to growth, stress response, cAMP signalling and hypoxia were induced by exercise. Transcription factor pathways were identified for the exercising leg using IPA and are displayed in a bar diagram. Genes induced by exercise for the different transcription factors can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051066#pone.0051066.s005" target="_blank">table S1</a>. Transcription factors with a z-score above 1.5 (or under −1.5) are considered as biologically relevant.</p

Top 20 of most highly induced genes in exercising and non-exercising leg.

<p>A) Left panel shows the top 20 of upregulated genes in the exercising leg (N = 9), right panel the corresponding genes in the non-exercising leg. B) Left panel shows the top 20 of upregulated genes in the non-exercising leg (N = 7), right panel the corresponding genes in the exercising leg. Green is a signal log ratio of −3, red a signal log ratio of 3. Values are displayed per subject to visualize inter-individual differences. FC = fold change, *  = p<0.05, ^#  = p<0.1 between exercising and non-exercising leg.</p

Exercise increases heart rate and plasma levels of FFA, insulin, cortisol and noradrenaline.

<p>Heart rate reserve (%) was calculated based on the heart rate measured during the exercise (N = 12). Plasma glucose, triglyceride, free fatty acids, lactate, insulin, cortisol, adrenaline and noradrenaline were measured before and after exercise (T0 and T1; N = 12) and after 2 hours of recovery (T3; N = 12). a = p<0.05 compared to T0, b = p<0.5 compared to T3, c = p<0.1 compared to T0, p<0.1 compared to T3, repeated measures ANOVA. Depicted is mean ± SEM.</p

Exercise mainly causes upregulation of gene expression in both the exercising and non-exercising leg.

<p>(A) Venn diagram of significantly regulated genes and their overlap. (B) Flowchart of microarray analysis. Heatmaps of all significant genes in the non-exercising (C) and exercising leg (D) N = 9, IQR  =  interquartile range.</p

Primer sequences used for qPCR.

<p>Primer sequences used for qPCR.</p

ClueGO network analysis.

<p>Analysis shows significant regulation of several GO categories involved in skeletal muscle development, angiogenesis, inflammation and MAPK cascade in the exercising leg (A; N = 9) and basal metabolism and signalling in the non-exercising leg (B; N = 7). The nodes represent significantly changed GO categories. Lines represent the overlap between different categories. All nodes with a large overlap have a similar colour.</p

The effects of polyphenol supplementation on adipose tissue morphology and gene expression in overweight and obese humans

<p>Dietary polyphenols have beneficial effects on adipose tissue mass and function in rodents, but human studies are scarce. In a randomized, placebo-controlled study, 25 (10 women) overweight and obese humans received a combination of the polyphenols epigallocatechin-gallate and resveratrol (282 mg/d, 80 mg/d, respectively, EGCG+RES, n = 11) or placebo (PLA, n = 14) supplementation for 12 weeks. Abdominal subcutaneous adipose tissue (SAT) biopsies were collected for assessment of adipocyte morphology and micro-array analysis. EGCG+RES had no effects on adipocyte size and distribution compared with PLA. However, we identified pathways contributing to adipogenesis, cell cycle and apoptosis were significantly downregulated by EGCG+RES <i>versus</i> PLA. Furthermore, EGCG+RES significantly decreased expression of pathways related to energy metabolism, oxidative stress, inflammation, and immune defense as compared with PLA. In conclusion, the SAT gene expression profile indicates a reduced cell turnover after 12-week EGCG+RES in overweight-obese subjects. It remains to be elucidated whether these alterations translate into long-term metabolic effects.</p

Minor allele frequency of common genetic variants related to iron metabolism in four African ancestry populations in comparison to the African and European population in the 1000 Genomes Project database.

<p>MAF 1000 A, Minor allele frequency for Africans-1000 Genomes Project; MAF 1000 E, Minor allele frequency for Europeans- 1000 Genomes Project.</p

General characteristics of the study participants in the four cohorts.

<p>General characteristics of the study participants in the four cohorts.</p