Maternal high-fat diet associated with altered gene expression, DNA methylation, and obesity risk in mouse offspring

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Abstract

We investigated maternal obesity in inbred SM/J mice by assigning females to a high-fat diet or a low-fat diet at weaning, mating them to low-fat-fed males, cross-fostering the offspring to low-fat-fed SM/J nurses at birth, and weaning the offspring onto a high-fat or low-fat diet. A maternal high-fat diet exacerbated obesity in the high-fat-fed daughters, causing them to weigh more, have more fat, and have higher serum levels of leptin as adults, accompanied by dozens of gene expression changes and thousands of DNA methylation changes in their livers and hearts. Maternal diet particularly affected genes involved in RNA processing, immune response, and mitochondria. Between one-quarter and one-third of differentially expressed genes contained a differentially methylated region associated with maternal diet. An offspring high-fat diet reduced overall variation in DNA methylation, increased body weight and organ weights, increased long bone lengths and weights, decreased insulin sensitivity, and changed the expression of 3,908 genes in the liver. Although the offspring were more affected by their own diet, their maternal diet had epigenetic effects lasting through adulthood, and in the daughters these effects were accompanied by phenotypic changes relevant to obesity and diabetes.

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Pathview: an R/Bioconductor package for pathway-based data integration and visualization

Weijun Luo, Cory Brouwer (2013)

Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. Contact: luo_weijun@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.

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Effects of obesity on bone metabolism

Jay Cao (2011)

Obesity is traditionally viewed to be beneficial to bone health because of well-established positive effect of mechanical loading conferred by body weight on bone formation, despite being a risk factor for many other chronic health disorders. Although body mass has a positive effect on bone formation, whether the mass derived from an obesity condition or excessive fat accumulation is beneficial to bone remains controversial. The underline pathophysiological relationship between obesity and bone is complex and continues to be an active research area. Recent data from epidemiological and animal studies strongly support that fat accumulation is detrimental to bone mass. To our knowledge, obesity possibly affects bone metabolism through several mechanisms. Because both adipocytes and osteoblasts are derived from a common multipotential mesenchymal stem cell, obesity may increase adipocyte differentiation and fat accumulation while decrease osteoblast differentiation and bone formation. Obesity is associated with chronic inflammation. The increased circulating and tissue proinflammatory cytokines in obesity may promote osteoclast activity and bone resorption through modifying the receptor activator of NF-κB (RANK)/RANK ligand/osteoprotegerin pathway. Furthermore, the excessive secretion of leptin and/or decreased production of adiponectin by adipocytes in obesity may either directly affect bone formation or indirectly affect bone resorption through up-regulated proinflammatory cytokine production. Finally, high-fat intake may interfere with intestinal calcium absorption and therefore decrease calcium availability for bone formation. Unraveling the relationship between fat and bone metabolism at molecular level may help us to develop therapeutic agents to prevent or treat both obesity and osteoporosis. Obesity, defined as having a body mass index ≥ 30 kg/m2, is a condition in which excessive body fat accumulates to a degree that adversely affects health [1]. The rates of obesity rates have doubled since 1980 [2] and as of 2007, 33% of men and 35% of women in the US are obese [3]. Obesity is positively associated to many chronic disorders such as hypertension, dyslipidemia, type 2 diabetes mellitus, coronary heart disease, and certain cancers [4-6]. It is estimated that the direct medical cost associated with obesity in the United States is ~$100 billion per year [7]. Bone mass and strength decrease during adulthood, especially in women after menopause [8]. These changes can culminate in osteoporosis, a disease characterized by low bone mass and microarchitectural deterioration resulting in increased bone fracture risk. It is estimated that there are about 10 million Americans over the age of 50 who have osteoporosis while another 34 million people are at risk of developing the disease [9]. In 2001, osteoporosis alone accounted for some $17 billion in direct annual healthcare expenditure. Several lines of evidence suggest that obesity and bone metabolism are interrelated. First, both osteoblasts (bone forming cells) and adipocytes (energy storing cells) are derived from a common mesenchymal stem cell [10] and agents inhibiting adipogenesis stimulated osteoblast differentiation [11-13] and vice versa, those inhibiting osteoblastogenesis increased adipogenesis [14]. Second, decreased bone marrow osteoblastogenesis with aging is usually accompanied with increased marrow adipogenesis [15,16]. Third, chronic use of steroid hormone, such as glucocorticoid, results in obesity accompanied by rapid bone loss [17,18]. Fourth, both obesity and osteoporosis are associated with elevated oxidative stress and increased production of proinflammatory cytokines [19,20]. At present, the mechanisms for the effects of obesity on bone metabolism are not well defined and will be the focus of this review.

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A Six Months Exercise Intervention Influences the Genome-wide DNA Methylation Pattern in Human Adipose Tissue

Tina Rönn, Petr Volkov, Cajsa Davegårdh … (2013)

Introduction A sedentary lifestyle, a poor diet and new technologies that reduce physical activity cause health problems worldwide, as reduced energy expenditure together with increased energy intake lead to weight gain and increased cardiometabolic health risks [1]. Obesity is an important predictor for the development of both type 2 diabetes (T2D) and cardiovascular diseases, which suggests a central role for adipose tissue in the development of these conditions [2]. Adipose tissue is an endocrine organ affecting many metabolic pathways, contributing to total glucose homeostasis [2]. T2D is caused by a complex interplay of genetic and lifestyle factors [3], and a family history of T2D has been associated with reduced physical fitness and an increased risk of the disease [4]–[6]. Individuals with high risk of developing T2D strongly benefit from non-pharmacological interventions, involving diet and exercise [7], [8]. Exercise is important for physical health, including weight maintenance and its beneficial effects on triglycerides, cholesterol and blood pressure, suggestively by activating a complex program of transcriptional changes in target tissues. Epigenetic mechanisms such as DNA methylation are considered to be important in phenotype transmission and the development of different diseases [9]. The epigenetic pattern is mainly established early in life and thereafter maintained in differentiated cells, but age-dependent alterations still have the potential to modulate gene expression and translate environmental factors into phenotypic traits [10]–[13]. In differentiated mammalian cells, DNA methylation usually occurs in the context of CG dinucleotides (CpGs) and is associated with gene repression [14]. Changes in epigenetic profiles are more common than genetic mutations and may occur in response to environmental, behavioural, psychological and pathological stimuli [15]. Furthermore, genetic variation not associated with a phenotype could nonetheless affect the extent of variability of that phenotype through epigenetic mechanisms, such as DNA methylation. It is not known whether epigenetic modifications contribute to the cause or transmission of T2D between generations. Recent studies in human skeletal muscle and pancreatic islets point towards the involvement of epigenetic modifications in the regulation of genes important for glucose metabolism and the pathogenesis of T2D [11], [12], [16]–[21]. However, there is limited information about the regulation of the epigenome in human adipose tissue [22]. The mechanisms behind the long-lasting effects of regular exercise are not fully understood, and most studies have focused on cellular and molecular changes in skeletal muscle. Recently, a global study of DNA methylation in human skeletal muscle showed changes in the epigenetic pattern in response to long-term exercise [23]. The aims of this study were to: 1) explore genome-wide levels of DNA methylation before and after a six months exercise intervention in adipose tissue from healthy, but previously sedentary men; 2) investigate the differences in adipose tissue DNA methylation between individuals with or without a family history of T2D; 3) relate changes in DNA methylation to adipose tissue mRNA expression and metabolic phenotypes in vitro. Results Baseline characteristics of individuals with (FH+) or without (FH−) a family history of type 2 diabetes A total of 31 men, 15 FH+ and 16 FH−, had subcutaneous adipose tissue biopsies taken at baseline. The FH+ and FH− individuals were group-wise matched for age, gender, BMI and VO2max at inclusion, and there were no significant differences between FH+ and FH− individuals, respectively (Table S1). DNA methylation in the adipose tissue was analyzed using the Infinium HumanMethylation450 BeadChip array. After quality control (QC), DNA methylation data was obtained for a total number of 476,753 sites. No individual CpG site showed a significant difference in DNA methylation between FH+ and FH− men after false discovery rate (FDR) correction (q>0.05) [24]. Additionally, there were no global differences between the FH+ and FH− individuals when calculating the average DNA methylation based on genomic regions (Figure 1a) or CpG content (Figure 1b; q>0.05). 10.1371/journal.pgen.1003572.g001 Figure 1 Location of analyzed CpG sites and global DNA methylation in human adipose tissue. All CpG sites analyzed on the Infinium HumanMethylation450 BeadChip are mapped to gene regions based on functional genome distribution (A) and to CpG island regions based on CpG content and neighbourhood context (B). In the lower panels, global DNA methylation in human adipose tissue is shown for each gene region (C) and for CpG island regions (D). Global DNA methylation is calculated as average DNA methylation based on all CpG sites in each region on the chip, and presented separately for Infinium I and Infinium II assays, respectively. Data is presented as mean ± SD. TSS, proximal promoter, defined as 200 bp (basepairs) or 1500 bp upstream of the transcription start site; UTR, untranslated region; CpG island, 200 bp (or more) stretch of DNA with a C+G content of 50% and an observed CpG/expected CpG in excess of 0.6; Shelf, regions flanking island shores, i.e., covering 2000–4000 bp distant from the CpG island; Shore: the flanking region of CpG islands, 0–2000 bp. *Significant difference between average DNA methylation before versus after exercise, q 8%) in response to exercise are presented in Table 2–3 and included ITPR2 and TSTD1 for increased, and LTBP4 for decreased DNA methylation. We found 7 CpG sites in this list to be targeted by Infinium probes reported to cross-react to alternative genomic locations (47 or 48 bases) [27]. Additionally, to investigate the possibility that the changes we see in response to exercise is rather an effect of epigenetic drift over time, we compared our 1,009 differentially methylated CpG sites (q 5%) with three studies reporting aging-differentially methylated regions (a-DMRs) in a total of 597 unique positions [28]–[30]. Secondly we tested for association between age and the level of DNA methylation in the 31 individuals included at baseline in this study, representing a more valid age range (30–45 years) and tissue for the current hypothesis. We found no overlap between previously published a-DMRs or the age-associated CpG sites within our study (18 CpG sites; p 8%). Location in relation to DNA Methylation (%) Probe ID Chr Nearest Gene Gene CpG Island Before exercise After exercise Difference (>8%) p-value q-value Cross-reactive probes cg06550177 7 Intergenic S Shore 29.6±7.2 40.6±7.8 10.9 1.67×10−5 0.008 cg13906823 1 TSTD1 TSS200 CpG Island 39.2±12.5 50.1±15.6 10.9 4.03×10−5 0.011 cg23397147 17 Intergenic Open sea 48.1±11.0 58.9±7.5 10.8 4.75×10−4 0.028 cg24161057 1 TSTD1 TSS200 CpG Island 35.9±13.5 46.6±14.6 10.7 2.10×10−5 0.009 cg26155520 1 Intergenic Open sea 55.6±7.1 66.0±6.6 10.4 7.87×10−6 0.007 cg05874882 4 Intergenic N Shore 34.0±9.1 44.2±6.7 10.1 6.03×10−5 0.013 cg00257920 1 Intergenic S Shelf 47.5±9.7 57.5±7.7 10.0 1.53×10−4 0.018 cg03878654 16 ZFHX3 5′UTR N Shore 56.6±6.7 65.9±6.9 9.3 1.81×10−4 0.019 cg08360726 19 PLD3 5′UTR CpG Island 29.7±8.0 38.9±11.8 9.2 1.28×10−3 0.043 cg26682335 17 ABR Body Open sea 60.6±9.4 69.7±7.0 9.1 2.53×10−4 0.022 cg01425666 7 Intergenic CpG Island 33.3±6.8 42.3±5.7 9.0 2.62×10−5 0.010 cg01750221 12 Intergenic Open sea 52.3±7.5 61.1±6.4 8.8 8.49×10−4 0.036 cg05455393 X FHL1 TSS1500 N Shore 52.5±8.4 61.1±7.2 8.6 1.28×10−4 0.017 cg22828884 3 FOXP1 Body Open sea 62.6±4.4 71.2±4.3 8.6 1.67×10−5 0.008 cg11837417 19 CLDND2 TSS1500 S Shore 65.3±6.4 73.9±5.2 8.6 4.08×10−4 0.027 cg10323490 2 THNSL2 TSS1500 N Shore 64.1±8.1 72.6±6.2 8.5 9.76×10−4 0.038 cg03934443 10 Intergenic Open sea 67.4±11.8 75.8±5.6 8.4 9.76×10−4 0.038 cg01775802 14 RGS6 Body Open sea 63.2±10.1 71.4±10.9 8.2 9.76×10−4 0.038 cg24606240 1 NUCKS1 TSS1500 S Shore 55.4±7.9 63.6±5.9 8.2 7.38×10−4 0.034 cg23499846 17 KIAA0664 5′UTR S Shore 54.0±5.9 62.0±4.3 8.0 1.03×10−5 0.007 cg21821308 2 ASAP2 Body CpG Island 42.0±8.5 33.8±5.9 −8.1 3.49×10−4 0.025 cg19219423 10 PRKG1 Body Open sea 55.4±7.7 47.1±6.8 −8.3 1.81×10−4 0.019 cg03862437 3 TMEM44 Body N Shore 46.3±7.0 38.0±5.2 −8.3 5.96×10−6 0.006 cg08368520 7 FOXK1 Body Open sea 52.9±7.8 44.5±8.0 −8.4 9.76×10−4 0.038 cg01275887 7 FOXK1 Body Open sea 66.3±8.5 57.7±6.6 −8.5 7.38×10−4 0.034 cg06443678 17 Intergenic Open sea 51.7±8.2 43.0±6.7 −8.7 2.98×10−4 0.024 cg02514003 2 Intergenic Open sea 70.6±6.5 61.7±8.6 −8.9 2.53×10−4 0.022 cg26504110 19 LTBP4 Body CpG Island 36.9±8.7 27.4±5.1 −9.5 2.98×10−4 0.024 Data are presented as mean ± SD, based on paired non-parametric test and two-tailed p-values. Cross-reactive probes: Maximum number of bases (≥47) matched to cross-reactive target as reported by Chen et al. [27]. The genomic distribution of individual CpG sites with a significant change in DNA methylation ≥5% with exercise is shown in Figure 3c–d, in comparison to all probes located on the Infinium HumanMethylation450 BeadChip and passing QC. The distribution is based on location in relation to the functional genome distribution (Figure 3c) or CpG content and distance to CpG islands (Figure 3d). We found that the CpG sites with altered level of DNA methylation in response to exercise were enriched within the gene body and in intergenic regions, while the proximal promoter, in particular TSS200 and the 1st exon, had a low proportion of differentially methylated CpG sites (p = 7×10−20; Figure 3c). In relation to CpG content and distance to CpG islands, the region with the highest proportion of significant CpG sites compared to the distribution on the array was in the open sea, i.e., regions more distant from a CpG island than 4000 bp. In contrast, the number of significant CpG sites found within the CpG islands was only half of what would be expected (p = 2×10−31; Figure 3d). Exercise induces overlapping changes in DNA methylation and mRNA expression An increased level of DNA methylation has previously been associated with transcription repression [14]. We therefore related changes in adipose tissue DNA methylation of individual CpG-sites (q 0.05 cg07233933 CPEB4 Body 76.9±2.1 79.2±1.7 2.3 1×10−4 0.013 377.9±63.9 319.3±51.1 −58.7 0.05 cg22380033 GRB14 1stExon; 5′UTR; CpG Island 2.1±0.4 2.6±0.4 0.5 3×10−4 0.029 65.1±11.4 63.0±16.8 −2.1 >0.05 cg07645296 ITPR2 TSS1500; S Shore 56.2±4.5 60.1±4.6 3.9 1×10−4 0.013 421.5±63.1 401.6±91.0 −19.9 >0.05 cg13203394 ITPR2 Body 56.8±4.4 63.3±3.2 6.5 5×10−7 0.001 421.5±63.1 401.6±91.0 −19.9 >0.05 cg02212836 LY86 1stExon 40.0±2.4 42.7±2.5 2.7 9×10−5 0.013 44.2±17.0 57.8±41.0 13.6 >0.05 cg05021589 LY86 TSS200 39.7±2.9 43.5±3.0 3.7 3×10−5 0.013 44.2±17.0 57.8±41.0 13.6 >0.05 cg09249494 LY86 TSS200 31.4±3.9 35.2±3.4 3.9 1×10−4 0.013 44.2±17.0 57.8±41.0 13.6 >0.05 cg16681597 LYPLAL1 Body; CpG Island 9.6±1.6 11.1±2.1 1.6 5×10−4 0.038 146.7±34.7 165.0±28.7 18.3 0.019 0.08 cg01362115 MAP2K5 Body 76.3±2.4 78.2±2.1 2.0 7×10−4 0.043 205.2±23.8 195.0±24.6 −10.2 >0.05 cg02328326 MAP2K5 Body 79.6±5.8 84.8±3.8 5.2 6×10−4 0.043 205.2±23.8 195.0±24.6 −10.2 >0.05 cg20055861 MAP2K5 Body 67.4±3.9 71.9±2.9 4.5 1×10−4 0.013 205.2±23.8 195.0±24.6 −10.2 >0.05 cg27519910 MSRA Body; S Shelf 76.0±2.3 78.6±2.3 2.7 4×10−5 0.013 48 120.4±12.5 124.0±13.5 3.6 >0.05 cg20147645 MTIF3 5′UTR; N Shore 60.0±3.3 64.1±3.6 4.0 3×10−4 0.024 331.3±42.3 331.7±39.8 0.4 >0.05 cg16420308 NRXN3 Body; N Shore 87.3±2.4 89.5±1.7 2.3 7×10−4 0.043 34.4±4.1 34.7±5.4 0.3 >0.05 cg16592301 PRKD1 Body 84.0±2.7 86.3±2.4 2.3 8×10−4 0.048 182.1±20.5 177.4±28.0 −4.7 >0.05 cg16104450 SDCCAG8 Body; N Shore 40.5±3.9 44.2±3.6 3.7 1×10−4 0.013 258.8±22.1 234.9±32.9 −23.9 1×10−3 0.008 cg08222913 STAB1 Body; CpG Island 63.2±5.1 67.8±4.2 4.6 2×10−5 0.013 224.1±56.3 214.6±44.2 −9.6 >0.05 cg26104752 TBX15 5′UTR; S Shore 5.8±1.4 6.8±1.5 1.0 7×10−5 0.013 374.1±37.2 368.1±50.9 −6.0 >0.05 cg19694781 TMEM160 Body; N Shore 56.1±6.5 58.3±7.0 2.3 1×10−4 0.013 48 205.0±25.6 231.1±25.7 26.1 1×10−3 0.008 cg05003666 TUB TSS200;Body;CpG Island 21.8±2.5 18.4±2.3 −3.4 3×10−4 0.029 73.6±6.7 72.7±8.0 −0.9 >0.05 cg01610165 ZNF608 Body 10.5±2.3 13.1±2.3 2.6 7×10−4 0.043 171.2±22.9 162.7±25.1 −8.5 >0.05 cg12817840 ZNF608 Body 21.8±2.5 25.8±3.8 3.9 9×10−5 0.013 171.2±22.9 162.7±25.1 −8.5 >0.05 Data are presented as mean ± SD, based on paired non-parametric test (DNA methylation) or t-test (mRNA expression) and two-tailed p-values. Cross-reactive probes: Maximum number of bases (≥47) matched to cross-reactive target as reported by Chen et al. [27]. 10.1371/journal.pgen.1003572.t005 Table 5 Individual CpG sites located within/near candidate genes for T2D [3], with a significant change in DNA methylation in adipose tissue in response to exercise. Type 2 diabetes candidate genes DNA methylation (%) mRNA expression Probe ID Nearest Gene Location Before exercise After exercise Diffe-rence p-value q-value Cross-reactive probes Before exercise After exercise Diffe-rence p-value q-value cg05501868 ADAMTS9 Body 62.3±4.1 66.5±4.5 4.1 6×10−4 0.025 218.5±46.5 219.7±59.8 1.3 >0.05 cg21527616 ADAMTS9 Body 63.6±4.7 67.0±3.3 3.4 1×10−3 0.044 218.5±46.5 219.7±59.8 1.3 >0.05 cg14567877 ADCY5 Body 80.7±4.3 84.2±3.8 3.5 2×10−4 0.015 257.2±48.3 253.1±44.8 −4.1 >0.05 cg03720898 ARAP1 Body 73.7±3.7 77.1±2.3 3.4 9×10−5 0.013 48 209.4±35.0 202.8±36.4 −6.6 >0.05 cg06838038 ARAP1 Body 42.4±3.9 46.1±3.6 3.7 4×10−5 0.011 209.4±35.0 202.8±36.4 −6.6 >0.05 cg10495997 ARAP1 5′UTR; S Shore 61.8±3.2 64.0±2.9 2.2 2×10−4 0.015 209.4±35.0 202.8±36.4 −6.6 >0.05 cg15279866 ARAP1 5′UTR; Body 56.4±3.5 58.8±3.9 2.4 1×10−3 0.042 209.4±35.0 202.8±36.4 −6.6 >0.05 cg27058763 ARAP1 Body; S Shelf 57.1±4.0 60.7±3.3 3.5 1×10−3 0.041 209.4±35.0 202.8±36.4 −6.6 >0.05 cg01865786 BCL11A Body 64.7±4.3 67.7±2.8 3.0 8×10−4 0.034 19.4±2.1 21.3±2.7 1.8 0.009 0.04 cg03390300 CDKAL1 Body 85.2±2.3 87.5±1.8 2.4 2×10−5 0.011 263.1±24.7 268.3±25.1 5.2 >0.05 cg07562918 CDKN2A 1stExon; CpG Island 16.7±2.1 18.4±2.2 1.6 1×10−3 0.042 35.9±5.3 40.5±7.0 4.6 0.023 0.09 cg20836993 DGKB Body 68.3±1.8 70.0±1.7 1.7 6×10−4 0.025 48 16.8±3.5 17.8±3.9 0.9 >0.05 cg01602287 DUSP8 Body; CpG Island 75.4±4.5 79.5±3.4 4.2 1×10−3 0.042 97.7±13.9 94.9±13.5 −2.9 >0.05 cg26902557 DUSP8 Body 49.1±3.9 52.1±4.0 3.0 6×10−4 0.028 97.7±13.9 94.9±13.5 −2.9 >0.05 cg26580413 FTO Body 61.0±4.4 64.3±3.8 3.3 1×10−3 0.044 785.0±80.7 794.4±64.7 9.4 >0.05 cg20180364 HHEX TSS1500; N Shore 46.8±4.1 50.4±3.5 3.6 2×10−4 0.015 172.7±24.7 144.9±30.3 −27.8 5×10−5 0.05 cg03660952 KCNQ1 Body 51.8±3.5 55.0±2.6 3.2 1×10−5 0.011 67.0±7.0 66.1±7.4 −0.9 >0.05 cg04894537 KCNQ1 Body 40.5±3.5 44.6±4.3 4.2 3×10−4 0.021 67.0±7.0 66.1±7.4 −0.9 >0.05 cg06838584 KCNQ1 Body 46.8±3.7 44.0±3.4 −2.7 2×10−4 0.015 67.0±7.0 66.1±7.4 −0.9 >0.05 cg08160246 KCNQ1 Body 60.3±3.3 63.5±3.3 3.2 5×10−4 0.025 67.0±7.0 66.1±7.4 −0.9 >0.05 cg13577072 KCNQ1 Body 67.4±3.1 71.8±3.5 4.5 6×10−5 0.011 67.0±7.0 66.1±7.4 −0.9 >0.05 cg15910264 KCNQ1 Body 81.4±2.8 84.3±1.9 2.9 3×10−5 0.011 67.0±7.0 66.1±7.4 −0.9 >0.05 cg19672982 KCNQ1 Body 70.4±3.0 73.3±2.8 2.9 1×10−4 0.014 67.0±7.0 66.1±7.4 −0.9 >0.05 cg24725201 KCNQ1 Body 91.9±1.6 93.4±1.4 1.5 7×10−4 0.031 67.0±7.0 66.1±7.4 −0.9 >0.05 cg25786675 KCNQ1 Body 66.3±3.7 62.4±3.6 −3.9 6×10−5 0.011 67.0±7.0 66.1±7.4 −0.9 >0.05 cg04775232 PRC1 Body 82.1±2.4 84.0±2.2 1.9 2×10−3 0.048 64.3±12.0 59.8±15.8 −4.5 >0.05 cg01902845 PROX1 Body 73.5±5.0 77.7±3.4 4.2 6×10−4 0.025 21.2±6.2 21.2±7.0 0 >0.05 cg14545834 PTPRD Body; CpG Island 68.0±2.8 71.2±2.2 3.2 2×10−4 0.015 49 80.3±17.8 81.8±14.5 1.4 >0.05 cg00831931 TCF7L2 Body 82.4±2.7 84.8±2.5 2.4 2×10−4 0.015 529.9±58.9 474.0±74.7 −55.8 0.001 0.008 cg05923857 TCF7L2 Body 72.6±5.2 76.4±3.8 3.8 8×10−4 0.034 529.9±58.9 474.0±74.7 −55.8 0.001 0.008 cg06403317 TCF7L2 Body 92.1±2.6 94.2±1.7 2.1 1×10−3 0.037 529.9±58.9 474.0±74.7 −55.8 0.001 0.008 cg09022607 TCF7L2 Body; S Shore 25.5±4.5 21.2±3.1 −4.3 6×10−4 0.025 529.9±58.9 474.0±74.7 −55.8 0.001 0.008 cg19226647 TCF7L2 1stExon; N Shore 4.4±1.0 5.5±1.3 1.1 4×10−5 0.011 529.9±58.9 474.0±74.7 −55.8 0.001 0.008 cg23951816 TCF7L2 Body 63.8±3.8 68.4±3.5 4.6 5×10−4 0.025 529.9±58.9 474.0±74.7 −55.8 0.001 0.008 cg01649611 THADA Body 38.5±5.3 42.5±4.6 4.0 2×10−4 0.015 285.2±29.9 291.7±31.4 6.5 >0.05 cg12277798 THADA Body; S Shelf 77.2±4.4 81.6±3.3 4.5 5×10−4 0.025 285.2±29.9 291.7±31.4 6.5 >0.05 cg16417416 WFS1 Body 63.9±3.5 66.9±2.9 2.9 1×10−3 0.044 132.2±21.6 123.6±14.5 −8.6 0.036 0.13 cg22051204 ZBED3 5′UTR; S Shore 51.1±3.5 53.5±3.2 2.4 1×10−3 0.042 187.9±22.9 189.1±17.4 1.3 >0.05 Data are presented as mean ± SD, based on paired non-parametric test (DNA methylation) or t-test (mRNA expression) and two-tailed p-values. Cross-reactive probes: Maximum number of bases (≥47) matched to cross-reactive target as reported by Chen et al. [27]. Silencing of Hdac4 and Ncor2 in 3T3-L1 adipocytes is associated with increased lipogenesis To further understand if the genes that exhibit differential DNA methylation and mRNA expression in adipose tissue in vivo affect adipocyte metabolism, we silenced the expression of selected genes in 3T3-L1 adipocytes using siRNA and studied its effect on lipogenesis. Two of the genes where we found increased DNA methylation in parallel with decreased mRNA expression in human adipose tissue in response to exercise (Figure 5a–d and Table S3) were selected for functional studies in a 3T3-L1 adipocyte cell line. HDAC4 was further a strong candidate due to multiple affected CpG sites within the gene, and both HDAC4 and NCOR2 are biologically interesting candidates in adipose tissue and the pathogenesis of obesity and type 2 diabetes [33]–[35]. Silencing of Hdac4 and Ncor2 in the 3T3-L1 adipocytes resulted in 74% reduction in the Hdac4 protein level (1.00±0.50 vs. 0.26±0.20, p = 0.043; Figure 5e) while the Ncor2 mRNA level was reduced by 56% (1.00±0.19 vs. 0.44±0.08, p = 0.043; Figure 5f) of control after transfection with siRNA for 72 hours and 24 h, respectively. Lipogenesis was nominally increased in the basal state (1.00±0.26 vs. 1.44±0.42, p = 0.079) and significantly increased in response to 0.1 nM insulin (1.16±0.30 vs. 1.52±0.34, p = 0.043) in 3T3-L1 adipocytes with decreased Hdac4 levels (Figure 5g). Decreased Ncor2 levels also resulted in increased lipogenesis in the basal (1.00±0.19 vs. 1.19±0.19, p = 0.043) and insulin stimulated (1 nM; 1.38±0.17 vs. 1.73±0.32, p = 0.043) state (Figure 5h). 10.1371/journal.pgen.1003572.g005 Figure 5 Silencing of Hdac4 and Ncor2 in 3T3-L1 adipocytes results in increased lipogenesis. CpG sites in the promoter region of A) HDAC4 and B) NCOR2 showed increased DNA methylation in response to exercise as well as decreased mRNA expression (C–D). Knock-downs were verified either by E) Western blot analysis (for Hdac4) or F) by qRT-PCR (for Ncor2). Lipogenesis increased in 3T3-L1 adipocytes where G) Hdac4 (n = 5) or H) Ncor2 (n = 5) had been silenced. Data is presented as mean ± SEM. Technical validation of Infinium HumanMethylation450 BeadChip DNA methylation data To technically validate the DNA methylation data from the Infinium HumanMethylation450 BeadChips, we compared the genome-wide DNA methylation data from one adipose tissue sample analyzed at four different occasions. Technical reproducibility was observed between all samples, with Pearson's correlation coefficients >0.99 (p 0.05; LEP, PNPLA2, FAS, LIPE and PPARG as markers of adipocytes; SEBPA/B/D and DLK1 as markers of preadipocytes, PRDM16 and UCP1 as markers of brown adipocytes; ITGAX, EMR1, ITGAM as markers of macrophages; TNF and IL6 representing cytokines and finally CCL2 and CASP7 as markers for inflammation). Although this result suggests that there is no a major change in the cellular composition of the adipose tissue studied before compared with after the exercise intervention, future studies should investigate the methylome in isolated adipocytes. Additionally, in previous studies of DNA methylation in human pancreatic islets, the differences observed in the mixed-cell tissue were also detected in clonal beta cells exposed to hyperglycemia [20], [21], suggesting that in at least some tissues, the effects are transferable from the relevant cell type to the tissue of interest for human biology. The impact of this study is further strengthened by our results showing altered DNA methylation of genes or loci previously associated with obesity and T2D. Although there was no enrichment of differential DNA methylation in those genes compared to the whole dataset, this result may provide a link to the mechanisms for how the loci associated with common diseases exert their functions [18]. 18 obesity and 21 T2D candidate genes had one or more CpG sites which significantly changed in adipose tissue DNA methylation after exercise. 10 CpG sites were found to have altered DNA methylation in response to exercise within the gene body of KCNQ1, a gene encoding a potassium channel and known to be involved in the pathogenesis of T2D, and also subject to parental imprinting [45]. Moreover, exercise associated with changes in DNA methylation of six intragenic CpG sites in TCF7L2, the T2D candidate gene harbouring a common variant with the greatest described effect on the risk of T2D [3]. This is of particular interest considering that TCF7L2 is subject to alternative splicing [46], [47] and the fact that gene exons are more highly methylated than introns, with DNA methylation spikes at splice junctions, suggesting a possible role for differential DNA methylation in transcript splicing [42]. In addition to differential DNA methylation, we also observed an inverse change in adipose tissue mRNA expression for some of these candidate genes, including TCF7L2, HHEX, IGF2BP2, JAZF1, CPEB4 and SDCCAG8 in response to exercise. The understanding of the human methylome is incomplete although recently developed methods for genome-wide analysis of DNA methylation already have made, and are likely to continue to make, tremendous advances [48]. High coverage data describing differences in the levels of DNA methylation between certain human tissues or cell types [38], as well as differences observed during development [42], have started to emerge. Regardless, deeper knowledge about the epigenetic architecture and regulation in human adipose tissue has been missing until now. We found that the genetic region with the highest average level of DNA methylation in adipose tissue was the 3′UTR, followed by the gene body and intergenic regions, and those regions also increased the level of DNA methylation in response to exercise. This supports the view that the human methylome can dynamically respond to changes in the environment [14], [15]. One explanation for the low average levels of DNA methylation observed in the promoter region (TSS1500/200), 5′UTR and the first exon, may be that these regions often overlap with CpG islands, which are generally known to be unmethylated. Indeed, our results show a very low level of DNA methylation within the CpG islands, and how the level then increases with increasing distances to a CpG island. It has long been debated if increased DNA methylation precedes gene silencing, or if it is rather a consequence of altered gene activity [40]. The luciferase assay experiments from this study and others [21], [23] suggest that DNA methylation may have a causal role, as increased promoter DNA methylation leads to reduced transcriptional activity. Here we further related our findings of altered DNA methylation to mRNA expression, and we identified 197 genes where both DNA methylation and mRNA expression significantly changed in adipose tissue after exercise. Of these, 115 genes (58%) showed an inverse relation, 97% showing an increase in the level of DNA methylation and a decrease in mRNA expression. It should be noted that epigenetic processes are likely to influence more aspects of gene expression, including accessibility of the gene, posttranscriptional RNA processing and stability, splicing and also translation [49]. For example, DNA methylation within the gene body has previously been linked to active gene transcription, suggestively by improving transcription efficiency [42]. Two genes, HDAC4 and NCOR2, with biological relevance in adipose tissue metabolism were selected for functional validation. HDAC4 is a histone deacetylase regulated by phosphorylation, and known to repress GLUT4 transcription in adipocytes [35]. In skeletal muscle, HDAC4 has been found to be exported from the nucleus during exercise, suggesting that removal of the transcriptional repressive function could be a mechanism for exercise adaptation [50]. For HDAC4, we observed increased levels of DNA methylation and a simultaneous decrease in mRNA expression in adipose tissue in response to the exercise intervention. Additionally, the functional experiments in cultured adipocytes suggested increased lipogenesis when Hdac4 expression was reduced. This could be an indicator of reduced repressive activity on GLUT4, leading to an increase in adipocyte glucose uptake and subsequent incorporation of glucose into triglycerides in the process of lipogenesis. NCOR2 also exhibited increased levels of DNA methylation and a simultaneous decrease in mRNA expression in adipose tissue in response to the exercise intervention, and furthermore we observed increased lipogenesis when Ncor2 expression was down regulated in the 3T3-L1 cell line. NCOR2 is a nuclear co-repressor, involved in the regulation of genes important for adipogenesis and lipid metabolism, and with the ability to recruit different histone deacetylase enzymes, including HDAC4 [51]. These results may be of clinical importance, since HDAC inhibitors have been suggested in the treatment of obesity and T2D [18], [52]. In summary, this study provides a detailed map of the human methylome in adipose tissue, which can be used as a reference for further studies. We have also found evidence for an association between differential DNA methylation and mRNA expression in response to exercise, as well as a connection to genes known to be involved in the pathogenesis of obesity and T2D. Finally, functional validation in adipocytes links DNA methylation via gene expression to altered metabolism, supporting the role of histone deacetylase enzymes as a potential candidate in clinical interventions. Materials and Methods Ethics statement Written informed consent was obtained from all participants and the research protocol was approved by the local human research ethics committee. Study participants This study included a total of 31 men from Malmö, Sweden, recruited for a six months exercise intervention study, as previously described [23], [53]. Fifteen of the individuals had a first-degree family history of T2D (FH+), whereas sixteen individuals had no family history of diabetes (FH−). They were all sedentary, but healthy, with a mean age of 37.4 years and a mean BMI of 27.8 kg/m2 at inclusion. All subjects underwent a physical examination, an oral glucose tolerance test and a submaximal exercise stress test. Bioimpedance was determined to estimate fat mass with a BIA 101 Body Impedance Analyzer (Akern Srl, Pontassieve, Italy). To directly assess the maximal oxygen uptake (VO2max), an ergometer bicycle (Ergomedic 828E, Monark, Sweden) was used together with heart rate monitoration (Polar T61, POLAR, Finland) [53]. FH+ and FH− men were group-wise matched for age, BMI and physical fitness (VO2max) at baseline. Subcutaneous biopsies of adipose tissue from the right thigh were obtained during the fasting state under local anaesthesia (1% Lidocaine) using a 6 mm Bergström needle (Stille AB, Sweden) from all participants before and from 23 participants after the six months exercise intervention (>48 hours after the last exercise session). The weekly group training program included one session of 1 hour spinning and two sessions of 1 hour aerobics and was led by a certified instructor. The participation level was on average 42.8±4.5 sessions, which equals to 1.8 sessions/week of this endurance exercise intervention. The study participants were requested to not change their diet and daily activity level during the intervention. Genome-wide DNA methylation analysis DNA methylation was analyzed in DNA extracted from adipose tissue, using the Infinium HumanMethylation450 BeadChip assay (Illumina, San Diego, CA, USA). This array contains 485,577 probes, which cover 21,231 (99%) RefSeq genes [25], [54]. Genomic DNA (500 ng) from adipose tissue was bisulfite treated using the EZ DNA methylation kit (Zymo Research, Orange, CA, USA). Analysis of DNA methylation with the Infinium assay was carried out on the total amount of bisulfite-converted DNA, with all other procedures following the standard Infinium HD Assay Methylation Protocol Guide (Part #15019519, Illumina). The BeadChips' images were captured using the Illumina iScan. The raw methylation score for each probe represented as methylation β-values was calculated using GenomeStudio Methylation module software (β = intensity of the Methylated allele (M)/intensity of the Unmethylated allele (U)+intensity of the Methylated allele (M)+100). All included samples showed a high quality bisulfite conversion efficiency (intensity signal >4000) [55], and also passed all GenomeStudio quality control steps based on built in control probes for staining, hybridization, extension and specificity. Individual probes were then filtered based on Illumina detection p-value and all CpG sites with a mean p 5%) were removed [27]. Due to different performance of Infinium I and Infinium II assays [25], the results based on average DNA methylation are calculated and presented separately for each probe type. To control for technical variability within the experiment, one adipose tissue sample was included and run on four different occasions (Figure S1a). As the β-value is easier to interpret biologically, M-values were reconverted to β-values when describing the results and creating the figures. mRNA expression analysis RNA extracted from the subcutaneous adipose tissue biopsies was used for a microarray analysis, performed using the GeneChip Human Gene 1.0 ST whole transcript based array (Affymetrix, Santa Clara, CA, USA), following the Affymetrix standard protocol. Basic Affymetrix chip and experimental quality analyses were performed using the Expression Console Software, and the robust multi-array average method (RMA) was used for background correction, data normalization and probe summarization [60]. Luciferase assay The human promoter fragment containing 1500 bp of DNA upstream of the transcription start site for RALBP1 (Chr18:9474030–9475529, GRCh37/hg19) was inserted into a CpG-free luciferase reporter vector (pCpGL-basic) as previously described [21]. The construct was methylated using two different DNA methyltransferases; SssI which methylates all cytosine residues within the double-stranded dinucleotide recognition sequence CG, and HhaI which methylates only the internal cytosine residue in the GCGC sequence (New England Biolabs, Frankfurt, Germany). INS-1 cells were co-transfected with 100 ng of the pCpGL-vector without (control) or with any of the three RALBP1 inserts (no DNA methyltransferase, SssI, HhaI) together with 2 ng of pRL renilla luciferase control reporter vector as a control for transfection efficiency (Promega, Madison, WI, USA). Firefly luciferase activity, as a value of expression, was measured for each construct and normalized against renilla luciferase activity using the TD-20/20 luminometer (Turner Designs, Sunnyvale, CA, USA). The results represent the mean of three independent experiments, and the values in each experiment are the mean of five replicates. Cells transfected with an empty pCpGL-vector were used as background control in each experiment. siRNA transfection of 3T3-L1 adipocytes and lipogenesis assay For detailed description of siRNA and lipogenesis experiments see Methods S1. Briefly, 3T3-L1 fibroblasts were cultured at sub-confluence in DMEM containing 10% (v/v) FCS, 100 U/ml penicillin and 100 µg/ml streptomycin at 37°C and 95% air/5% CO2. Two-day post-confluent cells were incubated for 72 h in DMEM supplemented with 0.5 mM IBMX, 10 µg/ml insulin and 1 µM dexamethasone, after which the cells were cultured in normal growth medium. Seven days post-differentiation, cells were transfected by electroporation with 2 nmol of each siRNA sequence/gene (Table S5). 0.2 nmol scrambled siRNA of each low GC-, medium GC- and high GC-complex were mixed as control. The cells were replated after transfection and incubated for 72 hours (siRNA against Hdac4) or 24 hours (siRNA against Ncor2). Cells harvested for western blot analysis were solubilized and homogenized, and 20 µg protein was subjected to SDS-PAGE (4–12% gradient) and subsequent transferred to nitrocellulose membranes. The primary antibody (rabbit polyclonal anti-hdac4; ab12172, Abcam, Cambridge, UK) was diluted in 5 ml 5% BSA/TBST and incubated overnight in 4°C. The secondary antibody (goat anti-rabbit IgG conjugated to horseradish peroxidase; ALI4404, BioSource, Life Technologies Ltd, Paisley, UK) was diluted 1∶20,000 in 5% milk/TBST. Protein was detected using Super Signal and ChemiDoc (BioRad, Hercules, CA, USA). Quantitative PCR (Q-PCR) analyses were performed in triplicate on an ABI7900 using Assays on demand with TaqMan technology (Mm00448796_m1, Applied Biosystems, Carlsbad, CA, USA). The mRNA expression was normalized to the expression of the endogenous control gene Hprt (Mm01545399_m1, Applied Biosystems). To measure lipogenesis, 10 µl tritium labelled ([3H]) glucose (Perkin Elmer, Waltham, MA, USA) was added followed by insulin of different concentrations; 0, 0.1, and 1 nM for Hdac4 siRNA and 0 and 1 nM for Ncor2 siRNA experiments, respectively. All concentrations were tested in duplicates. After 1 hour, incorporation of [3H] glucose into cellular lipids was measured by scintillation counting. Lipogenesis is expressed as fold of basal lipogenesis. DNA methylation analysis using PyroSequencing PyroSequencing (PyroMark Q96ID, Qiagen, Hilden, Germany) was used to technically validate data from the genome-wide DNA methylation analysis. PCR and sequencing primers were either designed using PyroMark Assay Design 2.0 or ordered as pre-designed methylation assays (Qiagen, Table S4), and all procedures were performed according to recommended protocols. Briefly, 100 ng genomic DNA from adipose tissue of 23 individuals both before and after the exercise intervention was bisulfite converted using Qiagen's EpiTect kit. With one primer biotinylated at its 5′ end, bisulfite-converted DNA was amplified by PCR using the PyroMark PCR Master Mix kit (Qiagen). Biotinylated PCR products were immobilized onto streptavidin-coated beads (GE Healthcare, Uppsala, Sweden) and DNA strands were separated using denaturation buffer. After washing and neutralizing using PyroMark Q96 Vacuum Workstation, the sequencing primer was annealed to the immobilized strand. PyroSequencing was performed with the PyroMark Gold Q96 reagents and data were analyzed using the PyroMark Q96 (version 2.5.8) software (Qiagen). Statistical analysis Clinical data is presented as mean ± SD, and comparisons based on a t-test and two-tailed p-values. Genome-wide DNA methylation data from the Infinium HumanMethylation450 BeadChip before vs. after the six month exercise intervention was analyzed using a paired non-parametric test, whereas a paired t-test was used to compare the mRNA expression. DNA methylation and mRNA expression data are expressed as mean ± SD. To account for multiple testing and reduce the number of false positives, we applied q-values to measure the false discovery rate (FDR) on our genome-wide analyses of DNA methylation and mRNA expression [24]. Luciferase activity was analyzed using the Friedman test (paired, non-parametric test on dependent samples) and presented as mean ± SEM. Data from 3T3-L1 adipocyte experiments showing protein, mRNA and lipogenesis levels are presented as mean ± SEM, and the results are based on Wilcoxon signed-rank test. Supporting Information Figure S1 Technical validation. A) Technical replicate of one adipose tissue DNA sample included in the study, analyzed using the Infinium HumanMethylation450 BeadChip on four different occasions. B–C) Data obtained from all adipose tissue samples for four CpG sites, from both the Infinium HumanMethylation450 BeadChip (x axis) and using Pyrosequencing (y axis). (TIF) Click here for additional data file. Methods S1 Detailed descriptions of small interfering RNA transfection, mRNA expression analysis, lipogenesis assay and statistical analysis. (DOC) Click here for additional data file. Table S1 Baseline clinical characteristics of individuals with (FH+) or without (FH−) a family history of type 2 diabetes. (DOC) Click here for additional data file. Table S2 Average DNA methylation for regions in relation to nearest gene or CpG islands, separately for Infinium I and II assays, respectively. (DOC) Click here for additional data file. Table S3 CpG sites with a change in DNA methylation (q<0.05 and difference in β≥5%) concurrent with an inverse change in mRNA expression (q<0.05) of the nearest gene, in response to the exercise intervention study. (DOC) Click here for additional data file. Table S4 Assay design for technical validation of DNA methylation data using PyroSequencing. (DOC) Click here for additional data file. Table S5 siRNA assays. (DOC) Click here for additional data file.

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Madeline Rose Keleher:

ORCID: http://orcid.org/0000-0002-9735-6155

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: VisualizationRole: Writing – original draft

Rabab Zaidi: Role: Investigation

Shyam Shah: Role: Formal analysisRole: InvestigationRole: Software

M. Elsa Oakley: Role: Investigation

Cassondra Pavlatos: Role: Investigation

Samir El Idrissi: Role: Investigation

Xiaoyun Xing: Role: Investigation

Daofeng Li: Role: Formal analysisRole: Investigation

Ting Wang: Role: Writing – review & editing

James M. Cheverud: Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing

Cheryl S. Rosenfeld: Role: Editor

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Journal ID (nlm-ta): PLoS One

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Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 15 February 2018

Publication date Collection: 2018

Volume: 13

Issue: 2

Electronic Location Identifier: e0192606

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[1 ] Department of Evolution, Ecology, and Population Biology, Washington University in St. Louis, St. Louis, Missouri, United States of America

[2 ] Department of Biology, Loyola University, Chicago, Illinois, United States of America

[3 ] Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, United States of America

University of Missouri Columbia, UNITED STATES

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Competing Interests: The Thermo Fisher Scientific Antibody scholarship was awarded to MK. The company was not aware of the experimental design or results, and had no say over any part of the research. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

* E-mail: mkeleher@ 123456wustl.edu

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Madeline Rose Keleher http://orcid.org/0000-0002-9735-6155

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DOI: 10.1371/journal.pone.0192606

PMC ID: 5813940

PubMed ID: 29447215

SO-VID: bf9bb03f-f670-492c-8a06-4fd386e381f9

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Date received : 9 November 2017

Date accepted : 28 January 2018

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Figures: 8, Tables: 6, Pages: 28

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Funded by: funder-id http://dx.doi.org/10.13039/100000968, American Heart Association;

Award ID: 16PRE26420105

Award Recipient :

ORCID: http://orcid.org/0000-0002-9735-6155

Madeline Rose Keleher

Funded by: funder-id http://dx.doi.org/10.13039/100011033, Thermo Fisher Scientific;

Award ID: Thermo Fisher Scientific Antibody Scholarship Award

Award Recipient :

ORCID: http://orcid.org/0000-0002-9735-6155

Madeline Rose Keleher

This study was supported by the American Heart Association (#16PRE26420105 to MK), Thermo Fisher Scientific (Antibody Scholarship Award to MK), the Core Laboratory for Clinical Studies and the Diabetes Models Phenotyping Core (Washington University DRC, Grant #P30 DK020579), and the Genome Technology Access Center at Washington University School of Medicine (NCI Cancer Center Support Grant #P30 CA91842 and ICTS/CTSA Grant# UL1TR000448 from the National Center for Research Resources). This publication is solely the responsibility of the authors and does not necessarily represent the official view of NCRR or NIH. We received funding from the American Heart Association and Thermo Fisher Scientific. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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A Six Months Exercise Intervention Influences the Genome-wide DNA Methylation Pattern in Human Adipose Tissue

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