Top

Article

Split Viewer

Mol. Cells 2015; 38(5): 452-456

Published online May 31, 2015

https://doi.org/10.14348/molcells.2015.0005

© The Korean Society for Molecular and Cellular Biology

Increased Methylation of Interleukin 6 Gene Is Associated with Obesity in Korean Women

Yeon Kyung Na1, Hae Sook Hong1, Won Kee Lee2, Young Hun Kim3, and Dong Sun Kim3,*

1College of Nursing, School of Medicine, Kyungpook National University, Daegu 702-422, Korea, 2Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu 702-422, Korea, 3Department of Anatomy and BK21 Plus KNU Biomedical Convergence Program, School of Medicine, Kyungpook National University, Daegu 702-422, Korea

Correspondence to : *Correspondence: doskim@knu.ac.kr

Received: January 6, 2015; Revised: February 16, 2015; Accepted: February 17, 2015

Obesity is the fifth leading risk for death globally, and a significant challenge to global health. It is a common, complex, non-malignant disease and develops due to interactions between the genes and the environment. DNA methylation can act as a downstream effector of environmental signals; analysis of this process therefore holds substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. To assess the effects of excessive weight and obesity on gene-specific methylation levels of promoter regions, we determined the methylation status of four genes involved in inflammation and oxidative stress [interleukin 6 (IL6), tumor necrosis factor α (TNFα), mitochondrial transcription factor A (TFAM), and glucose transport 4 (GLUT4)] in blood cell-derived DNA from healthy women volunteers with a range of body mass indices (BMIs) by methylation-specific PCR. Interestingly, the samples from obese individuals (BMI ≥ 30 kg/m2) showed significantly increased hypermethylation for IL6 gene compared to normal weight (BMI < 23 kg/m2) and overweight samples (23 kg/m2 ≤ BMI < 30 kg/m2) (P = 0.034 and P = 0.026). However, there was no statistically significant difference in promoter methylation of the other 3 genes between each group. These findings suggest that aberrant DNA methylation of IL6 gene promoter may play an important role in the etiology and pathogenesis of obesity and IL6 methylation could be used as molecular biomarker for obesity risk assessment. Further studies are required to elucidate the potential mechanisms underlying this relationship.

Keywords BMI, GLUT4, IL6, Methylation, MSP, TFAM, TNFα

DNA methylation is an epigenetic modification that effectively regulates gene expression via gene silencing, and may significantly contribute to the risks of many complex diseases, including cancer, cardiovascular, and metabolic diseases (Esteller et al., 2001; Ozanne and Constancia, 2007; Rodenhister and Mann, 2006). In cancer and certain other diseases, DNA methylation alterations have mainly been observed at the tissue level (Esteller et al., 2001; Ozanne and Constancia, 2007; Rodenhister and Mann, 2006). Furthermore, an increasing number of studies have reported that cancer-related genes are hypermethylated or hypomethylated in the peripheral blood cells (PBCs) of cancer patients (Li et al., 2012; Terry et al., 2011). Data related to whether DNA methylation changes in PBCs can serve as useful, informative biomarkers for different health outcomes is rapidly emerging (Heyn and Esteller, 2012), but information in complex non-malignant disease is much more limited.

The prevalence of obesity (body mass index, BMI ≥ 30 kg/m2) has risen to epidemic proportions and continues to be a major health problem worldwide (Danaei et al., 2009; Mirsa and Khurana, 2008). Obesity as a common, complex, non-malignant disease is closely linked to the increased incidence of various diseases, including type 2 diabetes, hypertension, cardiovascular disease, and certain types of cancer (Anderson and Caswell, 2009; Kopelman, 2007). Obesity is the result of the interplay between external (environmental) and internal (genetic) factors (Catenacci et al., 2009). Recent genome-wide association studies (GWAS) have identified a large number of genetic variants contributing to obesity-related traits (Loos, 2012). However, a majority of these loci have only a small effect on obesity susceptibility and their accuracy in predicting obesity is poor (Loos, 2012). Importantly, DNA methylation is dynamic and plastic in response to cellular stress and environmental cues, controlling insulin sensitivity and metabolism in obesity (Foley et al., 2009). Emerging evidence suggests that gene-specific DNA methylation in blood DNA may play an important role in obesity etiology (Carless et al., 2013; Dick et al., 2014; Milagro et al., 2012; Relton et al., 2012; Su et al., 2014; Wang et al., 2010). Recently, epigenetic epidemiology is an area of great research interest and we have demonstrated a differential influence of BMI on global DNA methylation in healthy women (Na et al., 2014). Therein, to investigate the influence of obesity on methylation status of genes involved in inflammation and oxidative stress, we have determined the methylation levels of glucose transport 4 (GLUT4), interleukin 6 (IL6), tumor necrosis factor (TNF) α, and mitochondrial transcription factor A (TFAM) in blood DNA from 284 healthy women volunteers with a range of BMIs using methylation-specific PCR (MSP).

Study population

The study subjects included 284 apparently healthy volunteers aged between 16 and 60 years (mean 31.9 ± 7.8 years). This study was approved by the Institutional Review Board of Kyungpook National University Hospital. Additionally, informed written consent was obtained from all subjects before they participated in the study. Demographic information and lifestyle factors were determined for all participants by trained interviewers using a standardized questionnaire via face-to-face interviews. Height and bodyweight were measured using standard methods with participants wearing light clothes. BMI is calculated by weight divided by height squared [kg/m2] and is a convenient surrogate measure of total fat mass for defining overweight and obesity. BMI has also been shown to be directly related to health risks and mortality in many populations. Based on the current international standard (WHO 1998) and slight modifications for Asian populations (Low et al., 2009), we divided the participants into 3 categories based on BMI as previously described: normal weight (BMI < 23 kg/m2), over-weight (23 kg/m2 ≤ BMI < 30 kg/m2), and obese (BMI ≥ 30 kg/m2) (Na et al., 2014). Blood samples were obtained via venipuncture after overnight fasting, and serum samples were separated by centrifugation and transferred to sterile bottles with Teflon-coated caps. All samples were kept frozen at ?70°C until analyses were conducted. Clinical laboratory values were determined by standard biochemical automatic or semi-automatic methods.

Blood DNA extraction, bisulfite treatment and methylation analysis

Genomic DNA was extracted from whole-blood samples using the QIAamp DNA Blood Kit (Qiagen, USA). One microgram of DNA was bisulfite-modified using the EZ DNA Methylation-Gold Kit (Zymo Research, USA) according to manufacturer’s instructions. Final elution was performed with 30 μl of M-Elution Buffer (Zymo Research) and DNA was stored at ?70°C until analyzed. The methylation status of the each gene was determined by nested methylation-specific PCR (MSP). External PCR was performed with the flanking primers of target gene promoter, diluted at 1:200 and then subjected to the internal PCR that incorporated unmethylated or methylated primers. The respective primer sequences are listed in Table 1. All PCR amplifications were carried out using reagents supplied in the GeneAmp DNA Amplification Kit with AmpliTaq Gold as the polymerase on a PTC-100 thermal cycler (MJ Research, USA). CpGenome™ Universal methylated and unmethylated DNA (Chemicon, USA) was used as a positive control for the methylated and unmethylated genes, respectively. Negative control samples without DNA were included for each set of PCR. PCR products were analyzed on 2% agarose gel, stained with ethidium bromide, and visualized under UV light. Each MSP was repeated at least once to confirm the results.

Pyrosequencing

Methylation status of IL6 gene promoter was quantitatively confirmed by pyrosequencing (PS) method. Briefly, bisulfite-modified DNA was amplified using forward primer (5′-AGGGATAATTTAGTTTAGAGTTTATTTGT-3′) and reverse primer (biotin-5′-CTCCCTCTCCCTATAAATCTTAATT-3′) through PCR, enabling the conversion of the PCR product to a single-stranded DNA template suitable for PS. All samples were heated to 95°C for 5 min and then amplified for 45 cycles of 95°C 45 s, 54°C 45 s, and 72°C 45 s, followed by a final extension at 72°C for 5 min. The quality and lack of contamination of the PCR products were checked on 2% agarose gels with ethidium bromide staining. After purification of PCR product using Sepharose beads on PyroMark Vacuum Prep Workstation (Qiagen), PS was performed according to the manufacturer’s specifications with sequencing primer (5′-ATAAGAAATTTTTGGGTGT-3′) using the PyroMark Q96MD System (Qiagen). A mean methylation index (MI) was calculated from the mean of the methylation percentage for all observed CpG sites. To set the controls for PS, we used universal methylated and unmethylated DNA that were consistently positive or negative with stable levels of methylation.

Statistical analysis

Statistical analysis and plotting was performed using R version 3.1.0 (http://www.r-project.org). Mean age difference between BMI groups was compared by ANOVA. Comparisons of proportion of each BMI groups were conducted using Chi-square or Fisher’s exact test and pairwise comparison identified statistical difference following Bonferroni correction. P < 0.05 was considered statistically significant.

Growing evidence indicates that pro-inflammatory and oxidative stress molecules produced by adipose tissue have been implicated in obesity and its comorbidities (Fruhbeck, 2008). We thus analyzed the methylation profile of GLUT4, IL6, TNFα, and TRAM in the peripheral blood DNA of 284 healthy volunteers with a range of BMIs using MSP. Although the IL6, TFAM, and TNFα promoters do not contain classical CpG islands, recent reports have demonstrated the occurrence of CpG methylation in the control of their expression (Gemma et al., 2010; Gower et al., 2011; Niel et al., 2008). In addition, Yokomori et al. (1999) have shown that changes in DNA methylation of the GLUT4 promoter can account for the differences in gene expression. We thus designed MSP primers covering these CpG sites. Methylated alleles of representative samples are shown in Fig. 1A. Unmethylated bands were detected in most samples (data not shown), thus confirming the integrity of the DNA in those samples. Moreover, methylation status of IL6 gene was confirmed by pyrosequencing of representative PCR products showed that all cytosines at non-CpG sites were converted to thymine (Fig. 1B), ruling out the possibility of incomplete bisulfite conversion. Interestingly, the frequency of IL6 methylation was significantly higher in obese participants (88.2%) than in normal weight (75.3%) and overweight (74.1%) (P = 0.034 and P = 0.026, respectively) (Table 2 and Fig. 2), inconsistent with recent observation that levels of IL6 promoter methylation is not significantly correlated with BMI (Zhang et al. 2012). These divergent results may be due to different sub-classification of the experimental population. The Zhang et al. study (2010) divided subjects into 2 groups according to a BMI = 25. Alternatively, the specific racial/ethnic characteristics of the study population or the small sample size may account for these differences. Moreover, there was no statistically significant difference in promoter methylation of the other genes depending on BMI levels (Table 2 and Fig. 2). Furthermore, gene-specific methylation was not correlated with smoking status and alcohol consumption (data not shown). Therefore, these results suggest that IL6 promoter hypermethylation may be a BMI-associated event in obesity etiology.

Although the exact mechanism underlying elevated IL6 hypermethylation observed in the blood cells of obese people is not known, several possible explanations have been suggested. First, it is noteworthy that circulating levels of IL-6, mainly secreted by adipose tissue, are significantly correlated with BMI (Browning et al., 2008). Recently, the peripheral blood mononuclear cells (PBMCs) from obese subjects showed a decreased response of IL6 after stimulation with Toll-like receptor ligand compared with PBMCs from non-obese participants (Teran-Cabanillas et al., 2013). Thus, it is tempting to speculate that the higher basal levels of IL6 can induce IL6 hypermethylation via a negative feedback loop to contribute this poor response. Second, obesity is associated with a chronic inflammatory response, characterized by the abnormal production of adipokines and the activation of certain pro-inflammatory signaling pathways (Fruhbeck, 2008). Interestingly, persistent inflammation may cause global DNA hypermethylation in peripheral blood cells via IL6 signaling (Stenvinkel et al., 2007). Simultaneously, we recently demonstrated in the same cohort that a significantly higher level of Alu methylation is found in obese participants compared with normal weight or overweight participants (Na et al., 2014). Thus, it is likely that elevated IL6 methylation in people with high BMI is a consequence of a low but continuous inflammatory drive in obesity. Third, a plausible hypothesis is that sleeping disturbance might contribute to increases in IL6 methylation in obese people. Importantly, the levels of systemic inflammatory markers are higher in obstructive sleep apnea (OSA) patients compared to control subjects (Nadeem et al., 2013). Moreover, significant hypermethylation of inflammatory genes in blood DNA is linearly associated with the severity of OSA (Kim et al., 2012). Indeed, we found that scored sleep management was higher in normal and overweight subjects than in obese ones (data not shown), indicating that obese individuals may have poorer sleep quality than non-obese people. Studies with a larger sample size and quantitative analysis with pyrosequencing are therefore required to confirm these findings and make a clearer interpretation. Further studies to understand the biological mechanisms underlying observed relationship are also required.

Our study has a few limitations. First, because of practical difficulties in obtaining tissues from living individuals, methylation levels were tested in PBCs, but not directly from the primary affected adipose tissues. Therefore, our results may not provide a direct index of DNA methylation in the system of adipose metabolism. In this respect, it is noteworthy that Dick and colleagues have addressed an association between BMI and HIF3A methylations in both blood and adipose tissue DNA (Dick et al., 2014). Second, DNA methylation usually influences disease risk through silencing gene expression. Unfortunately, IL6 expression was unable to be evaluated in this study because of the lack of fresh PBCs. Lastly, because our study was cross-sectional, we are unable to determine whether DNA methylation is likely to be a result of increased BMI or the part of causal pathways leading to an increased prevalence of obesity.

Nonetheless, this is the first study to demonstrate the association of aberrant DNA methylation in the promoter region of IL6 gene with obesity in Korean women, suggesting that the perturbation of IL6 pathways could have an important role in the etiology and pathogenesis of obesity. In addition, IL6 hypermethylation could become useful measure for overweight and obesity and for initial screening of other obesity-related diseases. Therefore, the present data are an excellent starting point for a deeper analysis of molecular diagnostics. Moreover, considering reversibility of DNA methylation, our result would suggest the potential for lifestyle or therapeutic interventions for obesity.

Fig. 1. Methylation analysis of GLUT4, IL6, TFAM, and TNFα genes in peripheral blood of healthy volunteers with normal weight (N), overweight (O), and obese (Ob) participants. (A) Representative data of the MSP analysis of 4 genes. Their methylation status was analyzed in whole blood DNA by using nested MSP. PCR amplification was carried out with methylation-specific (M) primers (M-MSP). CpGenome™ Universal methylated and unmethylated DNA (Chemicon) was used as positive controls for the M and U forms, respectively. (B) Representative pyrogram of IL6 gene. The letters on the axis represent the dispensation order; E, enzyme mix; S, substrate; A, G, C, and T, nucleotide. Shaded bars encompassing T/C pairs, indicate six interrogated CpG sites. The methylation of each CpG site was calculated as a percentage of C incorporation.
Fig. 2. Effect of body mass index on IL6 methylation. Methylation frequency of GLUT4, IL6, TFAM, and TNFα genes was expressed in normal weight, overweight, and obese subjects. Pairwise comparison identified statistical difference following Bonferroni correction.
Table. 1.

Table1.. Primer sequences for nested MSP

PrimerForward primerReverse primerSize (bp)
External PCR
??GLUT4GTTTTTGGTTTGTGGTTGTGCCTATCTATTAAAAACCCAAC188
??IL6GGTTTTTGAATTAGTTTGATTCCCTATAAATCTTGATTTAAAAT132
??TFAMGTTTTAGTTTTGGTTTGAATTCCAAAAAAATAATAAAAAAACC181
??TNFαGGGTTTTATATATAAATTAGTTAGTAATAAACCCTACACCTTCTA187
Internal PCR
??GLUT4
????U-MSPGGTTTGTTTTTGTATGTTATTTTCTAAACACACAAAAACAACA117
????M-MSPGGTTCGTTTTCGTACGTTATTTCCTAAACGCGCAAAAACGACG
??IL6
????U-MSPGAAATTTTTGGGTGTTGATGTAAAACTACAAACACAAACACA67
????M-MSPGAAATTTTTGGGTGTCGACGCAAAACTACGAACGCAAACACG
??TFAM
????U-MSPTTGAGATGTTTTGTTGGGTGTAAAAAAACCACAACAACAACC149
????M-MSPTTGAGACGTTTCGTTGGGCGCAAAAAAACCGCGACGACGACC
??TNFα
????U-MSPGTTTAGAAGATTTTTTTTGGAATTTCAATTTCTTCTCCATCACA138
????M-MSPGTTTAGAAGATTTTTTTCGGAATCTCGATTTCTTCTCCATCGCG

M-MSP, MSP for the methylated allele; U-MSP, MSP for the unmethylated allele


Table 2.

. Correlation between promoter methylation and BMI

Normal weight (n = 97)Overweight (n = 85)Obese (n = 102)F/X2P
Age, years (SD)31.23 (8.75)31.65 (5.50)32.86 (8.58)1.160.314 a
IL6 methylation, n (%)73 (75.3)63 (74.1)90 (88.2)7.380.025 b
TNF methylation, n (%)88 (90.7)77 (90.5)94 (92.2)0.180.912 b
TFAM methylation, n (%)1 (1.0)0 (0.0)2 (2.0)0.777 c
GLUT4 methylation, n (%)14 (14.4)17 (20.0)24 (23.5)2.670.264 b

aANOVA(Analysis of variance);

bChi-square test;

cFisher’s exact test


  1. Anderson, AS, and Caswell, S (2009). Obesity management-an opportunity for cancer prevention. Surgeon. 7, 282-285.
    CrossRef
  2. Browning, LM, Krebs, JD, Magee, EC, Fruhbeck, G, and Jebb, SA (2008). Circulating markers of inflammation and their link to indices of adiposity. Obes. Facts. 1, 259-265.
    CrossRef
  3. Carless, MA, Kulkarni, H, Kos, MZ, Charlesworth, J, Peralta, JM, Goring, HH, Curran, JE, Almasy, L, Dyer, TD, and Comuzzie, AG (2013). Genetic effects on DNA methylation and its potential relevance for obesity in Mexican Americans. PLoS One. 8, e73950.
    Pubmed KoreaMed CrossRef
  4. Catenacci, VA, Hill, JO, and Wyatt, HR (2009). The obesity epidemic. Clin. Chest Med. 30, 415-444.
    Pubmed CrossRef
  5. Danaei, G, Ding, EL, Mozaffarian, D, Taylor, B, Rehm, J, Murray, CJ, and Ezzati, M (2009). The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 6, e1000058.
    Pubmed KoreaMed CrossRef
  6. Dick, KJ, Nelson, CP, Tsaprouni, L, Sandling, JK, A?ssi, D, Wahl, S, Meduri, E, Morange, PE, Gagnon, F, and Grallert, H (2014). DNA methylation and body-mass index: a genome-wide analysis. Lancet. 383, 1990-1998.
    CrossRef
  7. Esteller, M, Corn, PG, Baylin, SB, and Herman, JG (2001). A gene hypermethylation profile of human cancer. Cancer Res. 61, 3225-3229.
  8. Foley, DL, Craig, JM, Morley, R, Olsson, CJ, Dwyer, T, Smith, K, and Saffery, R (2009). Prospects for epigenetic epidemiology. Am. J. Epidemiol. 169, 389-400.
  9. Fruhbeck, G (2008). Overview of adipose tissue and its role in obesity and metabolic disorders. Methods Mol. Biol. 456, 1-22.
    Pubmed CrossRef
  10. Gemma, C, Sookoian, S, Dieuzeide, G, Garc?a, SI, Gianotti, TF, Gonz?lez, CD, and Pirola, CJ (2010). Methylation of TFAM gene promoter in peripheral white blood cells is associated with insulin resistance in adolescents. Mol. Genet. Metab. 100, 83-87.
    Pubmed CrossRef
  11. Gowers, IR, Walters, K, Kiss-Toth, E, Read, RC, Duff, GW, and Wilson, AG (2011). Age-related loss of CpG methylation in the tumour necrosis factor promoter. Cytokine. 56, 792-797.
    Pubmed CrossRef
  12. Heyn, H, and Esteller, M (2012). DNA methylation profiling in the clinic: applications and challenges. Nat. Rev. Genet. 13, 679-692.
    Pubmed CrossRef
  13. Kim, J, Bhattacharjee, R, Khalyfa, A, Kheirandish-Gozal, L, Capdevila, OS, Wang, Y, and Gozal, D (2012). DNA methyltion in inflammatory genes among children with obstructive sleep apnea. Am. J. Respir. Crit. Care Med. 185, 330-338.
    Pubmed KoreaMed CrossRef
  14. Kopelman, P (2007). Health risks associated with overweight and obesity. Obes. Rev. 8, 13-17.
    Pubmed CrossRef
  15. Li, L, Choi, JY, Lee, KM, Sung, H, Park, SK, Oze, I, Pan, KF, You, WC, Chen, YX, and Fang, JY (2012). DNA methylation in peripheral blood: a potential biomarker for cancer molecular epidemiology. J. Epidemiol. 22, 384-394.
    KoreaMed CrossRef
  16. Loos, RJ (2012). Genetic determinants of common obesity and their value in prediction. Best Pract. Res. Clin. Endocrinol. Metab. 26, 211-226.
    Pubmed CrossRef
  17. Low, S, Chin, MC, Ma, S, Heng, D, and Deurenberg-Yap, M (2009). Rationale for redifining obesity in Asians. Ann. Acad. Med. Singap. 38, 66-69.
  18. Milagro, FI, Gomez-Abellan, P, Campion, J, Martinez, JA, Ordovas, JM, and Garaulet, M (2012). CLOCK, PER2 and BMAL1 DNA methylation: association with obesity and metabolic syndrome characteristics and monounsaturated fat intake. Chronobiol. Int. 29, 1180-1194.
    Pubmed CrossRef
  19. Misra, A, and Khurana, L (2008). Obesity and the metabolic syndrome in developing countries. J. Clin. Endocrinol. Metab. 93, S9-30.
    CrossRef
  20. Na, YK, Hong, HS, Lee, DH, Lee, WK, and Kim, DS (2014). Effect of body mass index on global DNA methylation in healthy Korean women. Mol. Cells. 37, 467-472.
    Pubmed KoreaMed CrossRef
  21. Nadeem, R, Molnar, J, Madbouly, EM, Nida, M, Aggarwal, S, Sajid, H, Naseem, J, and Loomba, R (2013). Serum inflammatory markers in obstructive sleep apnea: a meta-analysis. J. Clin. Sleep Med. 9, 1003-1012.
    CrossRef
  22. Nile, CJ, Read, RC, Akil, M, Duff, GW, and Wilson, AG (2008). Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis. Arthritis Rheum. 58, 2686-2693.
    Pubmed CrossRef
  23. Ozanne, SE, and Constancia, M (2007). Mechanisms of disease: the developmental origins of disease and the role of the epigenotype. Nat. Clin. Pract. Endocrinol. Metab. 3, 539-546.
    Pubmed CrossRef
  24. Relton, CL, Groom, A, St Pourcain, B, Sayers, AE, Swan, DC, Embleton, ND, Pearce, MS, Ring, SM, Northstone, K, and Tobias, JH (2012). DNA methylation patterns in cord blood DNA and body size in childhood. PLoS One. 7, e31821.
    CrossRef
  25. Rodenhister, D, and Mann, M (2006). Epigenetics and human disease: translating basic biology into clinical application. CMAJ. 174, 341-348.
    Pubmed KoreaMed CrossRef
  26. Stenvinkel, P, Karimi, M, Johansson, S, Axelsson, J, Suliman, M, Lindholm, B, Heimb?rger, O, Barany, P, Alvestrand, A, and Nordfors, L (2007). Impact of inflammation on epigenetic DNA methylation-a novel risk factor for cardiovascular disease?. J. Intern. Med. 261, 488-499.
    Pubmed CrossRef
  27. Su, S, Zhu, H, Xu, X, Wang, X, Dong, Y, Kapuku, G, Treiber, F, Gutin, B, Harshfield, G, and Snieder, H (2014). DNA methylation of the LY86 gene is associated with obesity, insulin resistance, and inflammation. Twin Res. Hum. Genet. 17, 183-91.
    Pubmed KoreaMed CrossRef
  28. Teran-Cabanillas, E, Montalvo-Corral, M, Caire-Juvera, G, Moya-Camarena, SY, and Hernadez, J (2013). Decreased interferon-β and interferon-β production in obesity and expression of suppressor of cytokine signaling. Nutrition. 29, 207-212.
    Pubmed CrossRef
  29. Terry, MB, Delgado-Cruzatak, L, Vin-Raviv, N, Wu, HC, and Santella, RM (2011). DNA methylation in white blood cells: association with risk factors in epidemiologic studies. Epigenetics. 6, 828-837.
    KoreaMed CrossRef
  30. Wang, X, Zhu, H, Snieder, H, Su, S, Munn, D, Harshfield, G, Maria, B, Dong, Y, Treiber, F, and Gutin, B (2010). Obesity related methylation changes in DNA of peripheral blood leukocytes. BMC Med. 8, 87.
    CrossRef
  31. Yokomori, N, Tawata, M, and Onaya, T (1999). DNA demethylation during the differentiation of 3T3-L1 cells affects the expression of the mouse GLUT4 gene. Diabetes. 48, 685-690.
    CrossRef
  32. Zhang, FF, Santella, RM, Wolff, M, Kappil, MA, Markowitz, SB, and Morabia, A (2012). White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population. Epigenetics. 7, 606-614.
    Pubmed KoreaMed CrossRef

Article

Article

Mol. Cells 2015; 38(5): 452-456

Published online May 31, 2015 https://doi.org/10.14348/molcells.2015.0005

Copyright © The Korean Society for Molecular and Cellular Biology.

Increased Methylation of Interleukin 6 Gene Is Associated with Obesity in Korean Women

Yeon Kyung Na1, Hae Sook Hong1, Won Kee Lee2, Young Hun Kim3, and Dong Sun Kim3,*

1College of Nursing, School of Medicine, Kyungpook National University, Daegu 702-422, Korea, 2Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu 702-422, Korea, 3Department of Anatomy and BK21 Plus KNU Biomedical Convergence Program, School of Medicine, Kyungpook National University, Daegu 702-422, Korea

Correspondence to:*Correspondence: doskim@knu.ac.kr

Received: January 6, 2015; Revised: February 16, 2015; Accepted: February 17, 2015

Abstract

Obesity is the fifth leading risk for death globally, and a significant challenge to global health. It is a common, complex, non-malignant disease and develops due to interactions between the genes and the environment. DNA methylation can act as a downstream effector of environmental signals; analysis of this process therefore holds substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. To assess the effects of excessive weight and obesity on gene-specific methylation levels of promoter regions, we determined the methylation status of four genes involved in inflammation and oxidative stress [interleukin 6 (IL6), tumor necrosis factor α (TNFα), mitochondrial transcription factor A (TFAM), and glucose transport 4 (GLUT4)] in blood cell-derived DNA from healthy women volunteers with a range of body mass indices (BMIs) by methylation-specific PCR. Interestingly, the samples from obese individuals (BMI ≥ 30 kg/m2) showed significantly increased hypermethylation for IL6 gene compared to normal weight (BMI < 23 kg/m2) and overweight samples (23 kg/m2 ≤ BMI < 30 kg/m2) (P = 0.034 and P = 0.026). However, there was no statistically significant difference in promoter methylation of the other 3 genes between each group. These findings suggest that aberrant DNA methylation of IL6 gene promoter may play an important role in the etiology and pathogenesis of obesity and IL6 methylation could be used as molecular biomarker for obesity risk assessment. Further studies are required to elucidate the potential mechanisms underlying this relationship.

Keywords: BMI, GLUT4, IL6, Methylation, MSP, TFAM, TNFα

INTRODUCTION

DNA methylation is an epigenetic modification that effectively regulates gene expression via gene silencing, and may significantly contribute to the risks of many complex diseases, including cancer, cardiovascular, and metabolic diseases (Esteller et al., 2001; Ozanne and Constancia, 2007; Rodenhister and Mann, 2006). In cancer and certain other diseases, DNA methylation alterations have mainly been observed at the tissue level (Esteller et al., 2001; Ozanne and Constancia, 2007; Rodenhister and Mann, 2006). Furthermore, an increasing number of studies have reported that cancer-related genes are hypermethylated or hypomethylated in the peripheral blood cells (PBCs) of cancer patients (Li et al., 2012; Terry et al., 2011). Data related to whether DNA methylation changes in PBCs can serve as useful, informative biomarkers for different health outcomes is rapidly emerging (Heyn and Esteller, 2012), but information in complex non-malignant disease is much more limited.

The prevalence of obesity (body mass index, BMI ≥ 30 kg/m2) has risen to epidemic proportions and continues to be a major health problem worldwide (Danaei et al., 2009; Mirsa and Khurana, 2008). Obesity as a common, complex, non-malignant disease is closely linked to the increased incidence of various diseases, including type 2 diabetes, hypertension, cardiovascular disease, and certain types of cancer (Anderson and Caswell, 2009; Kopelman, 2007). Obesity is the result of the interplay between external (environmental) and internal (genetic) factors (Catenacci et al., 2009). Recent genome-wide association studies (GWAS) have identified a large number of genetic variants contributing to obesity-related traits (Loos, 2012). However, a majority of these loci have only a small effect on obesity susceptibility and their accuracy in predicting obesity is poor (Loos, 2012). Importantly, DNA methylation is dynamic and plastic in response to cellular stress and environmental cues, controlling insulin sensitivity and metabolism in obesity (Foley et al., 2009). Emerging evidence suggests that gene-specific DNA methylation in blood DNA may play an important role in obesity etiology (Carless et al., 2013; Dick et al., 2014; Milagro et al., 2012; Relton et al., 2012; Su et al., 2014; Wang et al., 2010). Recently, epigenetic epidemiology is an area of great research interest and we have demonstrated a differential influence of BMI on global DNA methylation in healthy women (Na et al., 2014). Therein, to investigate the influence of obesity on methylation status of genes involved in inflammation and oxidative stress, we have determined the methylation levels of glucose transport 4 (GLUT4), interleukin 6 (IL6), tumor necrosis factor (TNF) α, and mitochondrial transcription factor A (TFAM) in blood DNA from 284 healthy women volunteers with a range of BMIs using methylation-specific PCR (MSP).

MATERIALS AND METHODS

Study population

The study subjects included 284 apparently healthy volunteers aged between 16 and 60 years (mean 31.9 ± 7.8 years). This study was approved by the Institutional Review Board of Kyungpook National University Hospital. Additionally, informed written consent was obtained from all subjects before they participated in the study. Demographic information and lifestyle factors were determined for all participants by trained interviewers using a standardized questionnaire via face-to-face interviews. Height and bodyweight were measured using standard methods with participants wearing light clothes. BMI is calculated by weight divided by height squared [kg/m2] and is a convenient surrogate measure of total fat mass for defining overweight and obesity. BMI has also been shown to be directly related to health risks and mortality in many populations. Based on the current international standard (WHO 1998) and slight modifications for Asian populations (Low et al., 2009), we divided the participants into 3 categories based on BMI as previously described: normal weight (BMI < 23 kg/m2), over-weight (23 kg/m2 ≤ BMI < 30 kg/m2), and obese (BMI ≥ 30 kg/m2) (Na et al., 2014). Blood samples were obtained via venipuncture after overnight fasting, and serum samples were separated by centrifugation and transferred to sterile bottles with Teflon-coated caps. All samples were kept frozen at ?70°C until analyses were conducted. Clinical laboratory values were determined by standard biochemical automatic or semi-automatic methods.

Blood DNA extraction, bisulfite treatment and methylation analysis

Genomic DNA was extracted from whole-blood samples using the QIAamp DNA Blood Kit (Qiagen, USA). One microgram of DNA was bisulfite-modified using the EZ DNA Methylation-Gold Kit (Zymo Research, USA) according to manufacturer’s instructions. Final elution was performed with 30 μl of M-Elution Buffer (Zymo Research) and DNA was stored at ?70°C until analyzed. The methylation status of the each gene was determined by nested methylation-specific PCR (MSP). External PCR was performed with the flanking primers of target gene promoter, diluted at 1:200 and then subjected to the internal PCR that incorporated unmethylated or methylated primers. The respective primer sequences are listed in Table 1. All PCR amplifications were carried out using reagents supplied in the GeneAmp DNA Amplification Kit with AmpliTaq Gold as the polymerase on a PTC-100 thermal cycler (MJ Research, USA). CpGenome™ Universal methylated and unmethylated DNA (Chemicon, USA) was used as a positive control for the methylated and unmethylated genes, respectively. Negative control samples without DNA were included for each set of PCR. PCR products were analyzed on 2% agarose gel, stained with ethidium bromide, and visualized under UV light. Each MSP was repeated at least once to confirm the results.

Pyrosequencing

Methylation status of IL6 gene promoter was quantitatively confirmed by pyrosequencing (PS) method. Briefly, bisulfite-modified DNA was amplified using forward primer (5′-AGGGATAATTTAGTTTAGAGTTTATTTGT-3′) and reverse primer (biotin-5′-CTCCCTCTCCCTATAAATCTTAATT-3′) through PCR, enabling the conversion of the PCR product to a single-stranded DNA template suitable for PS. All samples were heated to 95°C for 5 min and then amplified for 45 cycles of 95°C 45 s, 54°C 45 s, and 72°C 45 s, followed by a final extension at 72°C for 5 min. The quality and lack of contamination of the PCR products were checked on 2% agarose gels with ethidium bromide staining. After purification of PCR product using Sepharose beads on PyroMark Vacuum Prep Workstation (Qiagen), PS was performed according to the manufacturer’s specifications with sequencing primer (5′-ATAAGAAATTTTTGGGTGT-3′) using the PyroMark Q96MD System (Qiagen). A mean methylation index (MI) was calculated from the mean of the methylation percentage for all observed CpG sites. To set the controls for PS, we used universal methylated and unmethylated DNA that were consistently positive or negative with stable levels of methylation.

Statistical analysis

Statistical analysis and plotting was performed using R version 3.1.0 (http://www.r-project.org). Mean age difference between BMI groups was compared by ANOVA. Comparisons of proportion of each BMI groups were conducted using Chi-square or Fisher’s exact test and pairwise comparison identified statistical difference following Bonferroni correction. P < 0.05 was considered statistically significant.

RESULTS AND DISCUSSION

Growing evidence indicates that pro-inflammatory and oxidative stress molecules produced by adipose tissue have been implicated in obesity and its comorbidities (Fruhbeck, 2008). We thus analyzed the methylation profile of GLUT4, IL6, TNFα, and TRAM in the peripheral blood DNA of 284 healthy volunteers with a range of BMIs using MSP. Although the IL6, TFAM, and TNFα promoters do not contain classical CpG islands, recent reports have demonstrated the occurrence of CpG methylation in the control of their expression (Gemma et al., 2010; Gower et al., 2011; Niel et al., 2008). In addition, Yokomori et al. (1999) have shown that changes in DNA methylation of the GLUT4 promoter can account for the differences in gene expression. We thus designed MSP primers covering these CpG sites. Methylated alleles of representative samples are shown in Fig. 1A. Unmethylated bands were detected in most samples (data not shown), thus confirming the integrity of the DNA in those samples. Moreover, methylation status of IL6 gene was confirmed by pyrosequencing of representative PCR products showed that all cytosines at non-CpG sites were converted to thymine (Fig. 1B), ruling out the possibility of incomplete bisulfite conversion. Interestingly, the frequency of IL6 methylation was significantly higher in obese participants (88.2%) than in normal weight (75.3%) and overweight (74.1%) (P = 0.034 and P = 0.026, respectively) (Table 2 and Fig. 2), inconsistent with recent observation that levels of IL6 promoter methylation is not significantly correlated with BMI (Zhang et al. 2012). These divergent results may be due to different sub-classification of the experimental population. The Zhang et al. study (2010) divided subjects into 2 groups according to a BMI = 25. Alternatively, the specific racial/ethnic characteristics of the study population or the small sample size may account for these differences. Moreover, there was no statistically significant difference in promoter methylation of the other genes depending on BMI levels (Table 2 and Fig. 2). Furthermore, gene-specific methylation was not correlated with smoking status and alcohol consumption (data not shown). Therefore, these results suggest that IL6 promoter hypermethylation may be a BMI-associated event in obesity etiology.

Although the exact mechanism underlying elevated IL6 hypermethylation observed in the blood cells of obese people is not known, several possible explanations have been suggested. First, it is noteworthy that circulating levels of IL-6, mainly secreted by adipose tissue, are significantly correlated with BMI (Browning et al., 2008). Recently, the peripheral blood mononuclear cells (PBMCs) from obese subjects showed a decreased response of IL6 after stimulation with Toll-like receptor ligand compared with PBMCs from non-obese participants (Teran-Cabanillas et al., 2013). Thus, it is tempting to speculate that the higher basal levels of IL6 can induce IL6 hypermethylation via a negative feedback loop to contribute this poor response. Second, obesity is associated with a chronic inflammatory response, characterized by the abnormal production of adipokines and the activation of certain pro-inflammatory signaling pathways (Fruhbeck, 2008). Interestingly, persistent inflammation may cause global DNA hypermethylation in peripheral blood cells via IL6 signaling (Stenvinkel et al., 2007). Simultaneously, we recently demonstrated in the same cohort that a significantly higher level of Alu methylation is found in obese participants compared with normal weight or overweight participants (Na et al., 2014). Thus, it is likely that elevated IL6 methylation in people with high BMI is a consequence of a low but continuous inflammatory drive in obesity. Third, a plausible hypothesis is that sleeping disturbance might contribute to increases in IL6 methylation in obese people. Importantly, the levels of systemic inflammatory markers are higher in obstructive sleep apnea (OSA) patients compared to control subjects (Nadeem et al., 2013). Moreover, significant hypermethylation of inflammatory genes in blood DNA is linearly associated with the severity of OSA (Kim et al., 2012). Indeed, we found that scored sleep management was higher in normal and overweight subjects than in obese ones (data not shown), indicating that obese individuals may have poorer sleep quality than non-obese people. Studies with a larger sample size and quantitative analysis with pyrosequencing are therefore required to confirm these findings and make a clearer interpretation. Further studies to understand the biological mechanisms underlying observed relationship are also required.

Our study has a few limitations. First, because of practical difficulties in obtaining tissues from living individuals, methylation levels were tested in PBCs, but not directly from the primary affected adipose tissues. Therefore, our results may not provide a direct index of DNA methylation in the system of adipose metabolism. In this respect, it is noteworthy that Dick and colleagues have addressed an association between BMI and HIF3A methylations in both blood and adipose tissue DNA (Dick et al., 2014). Second, DNA methylation usually influences disease risk through silencing gene expression. Unfortunately, IL6 expression was unable to be evaluated in this study because of the lack of fresh PBCs. Lastly, because our study was cross-sectional, we are unable to determine whether DNA methylation is likely to be a result of increased BMI or the part of causal pathways leading to an increased prevalence of obesity.

Nonetheless, this is the first study to demonstrate the association of aberrant DNA methylation in the promoter region of IL6 gene with obesity in Korean women, suggesting that the perturbation of IL6 pathways could have an important role in the etiology and pathogenesis of obesity. In addition, IL6 hypermethylation could become useful measure for overweight and obesity and for initial screening of other obesity-related diseases. Therefore, the present data are an excellent starting point for a deeper analysis of molecular diagnostics. Moreover, considering reversibility of DNA methylation, our result would suggest the potential for lifestyle or therapeutic interventions for obesity.

Fig 1.

Figure 1.Methylation analysis of GLUT4, IL6, TFAM, and TNFα genes in peripheral blood of healthy volunteers with normal weight (N), overweight (O), and obese (Ob) participants. (A) Representative data of the MSP analysis of 4 genes. Their methylation status was analyzed in whole blood DNA by using nested MSP. PCR amplification was carried out with methylation-specific (M) primers (M-MSP). CpGenome™ Universal methylated and unmethylated DNA (Chemicon) was used as positive controls for the M and U forms, respectively. (B) Representative pyrogram of IL6 gene. The letters on the axis represent the dispensation order; E, enzyme mix; S, substrate; A, G, C, and T, nucleotide. Shaded bars encompassing T/C pairs, indicate six interrogated CpG sites. The methylation of each CpG site was calculated as a percentage of C incorporation.
Molecules and Cells 2015; 38: 452-456https://doi.org/10.14348/molcells.2015.0005

Fig 2.

Figure 2.Effect of body mass index on IL6 methylation. Methylation frequency of GLUT4, IL6, TFAM, and TNFα genes was expressed in normal weight, overweight, and obese subjects. Pairwise comparison identified statistical difference following Bonferroni correction.
Molecules and Cells 2015; 38: 452-456https://doi.org/10.14348/molcells.2015.0005

Table1.. Primer sequences for nested MSP.

PrimerForward primerReverse primerSize (bp)
External PCR
??GLUT4GTTTTTGGTTTGTGGTTGTGCCTATCTATTAAAAACCCAAC188
??IL6GGTTTTTGAATTAGTTTGATTCCCTATAAATCTTGATTTAAAAT132
??TFAMGTTTTAGTTTTGGTTTGAATTCCAAAAAAATAATAAAAAAACC181
??TNFαGGGTTTTATATATAAATTAGTTAGTAATAAACCCTACACCTTCTA187
Internal PCR
??GLUT4
????U-MSPGGTTTGTTTTTGTATGTTATTTTCTAAACACACAAAAACAACA117
????M-MSPGGTTCGTTTTCGTACGTTATTTCCTAAACGCGCAAAAACGACG
??IL6
????U-MSPGAAATTTTTGGGTGTTGATGTAAAACTACAAACACAAACACA67
????M-MSPGAAATTTTTGGGTGTCGACGCAAAACTACGAACGCAAACACG
??TFAM
????U-MSPTTGAGATGTTTTGTTGGGTGTAAAAAAACCACAACAACAACC149
????M-MSPTTGAGACGTTTCGTTGGGCGCAAAAAAACCGCGACGACGACC
??TNFα
????U-MSPGTTTAGAAGATTTTTTTTGGAATTTCAATTTCTTCTCCATCACA138
????M-MSPGTTTAGAAGATTTTTTTCGGAATCTCGATTTCTTCTCCATCGCG

M-MSP, MSP for the methylated allele; U-MSP, MSP for the unmethylated allele.


. Correlation between promoter methylation and BMI.

Normal weight (n = 97)Overweight (n = 85)Obese (n = 102)F/X2P
Age, years (SD)31.23 (8.75)31.65 (5.50)32.86 (8.58)1.160.314 a
IL6 methylation, n (%)73 (75.3)63 (74.1)90 (88.2)7.380.025 b
TNF methylation, n (%)88 (90.7)77 (90.5)94 (92.2)0.180.912 b
TFAM methylation, n (%)1 (1.0)0 (0.0)2 (2.0)0.777 c
GLUT4 methylation, n (%)14 (14.4)17 (20.0)24 (23.5)2.670.264 b

aANOVA(Analysis of variance);

bChi-square test;

cFisher’s exact test


References

  1. Anderson, AS, and Caswell, S (2009). Obesity management-an opportunity for cancer prevention. Surgeon. 7, 282-285.
    CrossRef
  2. Browning, LM, Krebs, JD, Magee, EC, Fruhbeck, G, and Jebb, SA (2008). Circulating markers of inflammation and their link to indices of adiposity. Obes. Facts. 1, 259-265.
    CrossRef
  3. Carless, MA, Kulkarni, H, Kos, MZ, Charlesworth, J, Peralta, JM, Goring, HH, Curran, JE, Almasy, L, Dyer, TD, and Comuzzie, AG (2013). Genetic effects on DNA methylation and its potential relevance for obesity in Mexican Americans. PLoS One. 8, e73950.
    Pubmed KoreaMed CrossRef
  4. Catenacci, VA, Hill, JO, and Wyatt, HR (2009). The obesity epidemic. Clin. Chest Med. 30, 415-444.
    Pubmed CrossRef
  5. Danaei, G, Ding, EL, Mozaffarian, D, Taylor, B, Rehm, J, Murray, CJ, and Ezzati, M (2009). The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 6, e1000058.
    Pubmed KoreaMed CrossRef
  6. Dick, KJ, Nelson, CP, Tsaprouni, L, Sandling, JK, A?ssi, D, Wahl, S, Meduri, E, Morange, PE, Gagnon, F, and Grallert, H (2014). DNA methylation and body-mass index: a genome-wide analysis. Lancet. 383, 1990-1998.
    CrossRef
  7. Esteller, M, Corn, PG, Baylin, SB, and Herman, JG (2001). A gene hypermethylation profile of human cancer. Cancer Res. 61, 3225-3229.
  8. Foley, DL, Craig, JM, Morley, R, Olsson, CJ, Dwyer, T, Smith, K, and Saffery, R (2009). Prospects for epigenetic epidemiology. Am. J. Epidemiol. 169, 389-400.
  9. Fruhbeck, G (2008). Overview of adipose tissue and its role in obesity and metabolic disorders. Methods Mol. Biol. 456, 1-22.
    Pubmed CrossRef
  10. Gemma, C, Sookoian, S, Dieuzeide, G, Garc?a, SI, Gianotti, TF, Gonz?lez, CD, and Pirola, CJ (2010). Methylation of TFAM gene promoter in peripheral white blood cells is associated with insulin resistance in adolescents. Mol. Genet. Metab. 100, 83-87.
    Pubmed CrossRef
  11. Gowers, IR, Walters, K, Kiss-Toth, E, Read, RC, Duff, GW, and Wilson, AG (2011). Age-related loss of CpG methylation in the tumour necrosis factor promoter. Cytokine. 56, 792-797.
    Pubmed CrossRef
  12. Heyn, H, and Esteller, M (2012). DNA methylation profiling in the clinic: applications and challenges. Nat. Rev. Genet. 13, 679-692.
    Pubmed CrossRef
  13. Kim, J, Bhattacharjee, R, Khalyfa, A, Kheirandish-Gozal, L, Capdevila, OS, Wang, Y, and Gozal, D (2012). DNA methyltion in inflammatory genes among children with obstructive sleep apnea. Am. J. Respir. Crit. Care Med. 185, 330-338.
    Pubmed KoreaMed CrossRef
  14. Kopelman, P (2007). Health risks associated with overweight and obesity. Obes. Rev. 8, 13-17.
    Pubmed CrossRef
  15. Li, L, Choi, JY, Lee, KM, Sung, H, Park, SK, Oze, I, Pan, KF, You, WC, Chen, YX, and Fang, JY (2012). DNA methylation in peripheral blood: a potential biomarker for cancer molecular epidemiology. J. Epidemiol. 22, 384-394.
    KoreaMed CrossRef
  16. Loos, RJ (2012). Genetic determinants of common obesity and their value in prediction. Best Pract. Res. Clin. Endocrinol. Metab. 26, 211-226.
    Pubmed CrossRef
  17. Low, S, Chin, MC, Ma, S, Heng, D, and Deurenberg-Yap, M (2009). Rationale for redifining obesity in Asians. Ann. Acad. Med. Singap. 38, 66-69.
  18. Milagro, FI, Gomez-Abellan, P, Campion, J, Martinez, JA, Ordovas, JM, and Garaulet, M (2012). CLOCK, PER2 and BMAL1 DNA methylation: association with obesity and metabolic syndrome characteristics and monounsaturated fat intake. Chronobiol. Int. 29, 1180-1194.
    Pubmed CrossRef
  19. Misra, A, and Khurana, L (2008). Obesity and the metabolic syndrome in developing countries. J. Clin. Endocrinol. Metab. 93, S9-30.
    CrossRef
  20. Na, YK, Hong, HS, Lee, DH, Lee, WK, and Kim, DS (2014). Effect of body mass index on global DNA methylation in healthy Korean women. Mol. Cells. 37, 467-472.
    Pubmed KoreaMed CrossRef
  21. Nadeem, R, Molnar, J, Madbouly, EM, Nida, M, Aggarwal, S, Sajid, H, Naseem, J, and Loomba, R (2013). Serum inflammatory markers in obstructive sleep apnea: a meta-analysis. J. Clin. Sleep Med. 9, 1003-1012.
    CrossRef
  22. Nile, CJ, Read, RC, Akil, M, Duff, GW, and Wilson, AG (2008). Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis. Arthritis Rheum. 58, 2686-2693.
    Pubmed CrossRef
  23. Ozanne, SE, and Constancia, M (2007). Mechanisms of disease: the developmental origins of disease and the role of the epigenotype. Nat. Clin. Pract. Endocrinol. Metab. 3, 539-546.
    Pubmed CrossRef
  24. Relton, CL, Groom, A, St Pourcain, B, Sayers, AE, Swan, DC, Embleton, ND, Pearce, MS, Ring, SM, Northstone, K, and Tobias, JH (2012). DNA methylation patterns in cord blood DNA and body size in childhood. PLoS One. 7, e31821.
    CrossRef
  25. Rodenhister, D, and Mann, M (2006). Epigenetics and human disease: translating basic biology into clinical application. CMAJ. 174, 341-348.
    Pubmed KoreaMed CrossRef
  26. Stenvinkel, P, Karimi, M, Johansson, S, Axelsson, J, Suliman, M, Lindholm, B, Heimb?rger, O, Barany, P, Alvestrand, A, and Nordfors, L (2007). Impact of inflammation on epigenetic DNA methylation-a novel risk factor for cardiovascular disease?. J. Intern. Med. 261, 488-499.
    Pubmed CrossRef
  27. Su, S, Zhu, H, Xu, X, Wang, X, Dong, Y, Kapuku, G, Treiber, F, Gutin, B, Harshfield, G, and Snieder, H (2014). DNA methylation of the LY86 gene is associated with obesity, insulin resistance, and inflammation. Twin Res. Hum. Genet. 17, 183-91.
    Pubmed KoreaMed CrossRef
  28. Teran-Cabanillas, E, Montalvo-Corral, M, Caire-Juvera, G, Moya-Camarena, SY, and Hernadez, J (2013). Decreased interferon-β and interferon-β production in obesity and expression of suppressor of cytokine signaling. Nutrition. 29, 207-212.
    Pubmed CrossRef
  29. Terry, MB, Delgado-Cruzatak, L, Vin-Raviv, N, Wu, HC, and Santella, RM (2011). DNA methylation in white blood cells: association with risk factors in epidemiologic studies. Epigenetics. 6, 828-837.
    KoreaMed CrossRef
  30. Wang, X, Zhu, H, Snieder, H, Su, S, Munn, D, Harshfield, G, Maria, B, Dong, Y, Treiber, F, and Gutin, B (2010). Obesity related methylation changes in DNA of peripheral blood leukocytes. BMC Med. 8, 87.
    CrossRef
  31. Yokomori, N, Tawata, M, and Onaya, T (1999). DNA demethylation during the differentiation of 3T3-L1 cells affects the expression of the mouse GLUT4 gene. Diabetes. 48, 685-690.
    CrossRef
  32. Zhang, FF, Santella, RM, Wolff, M, Kappil, MA, Markowitz, SB, and Morabia, A (2012). White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population. Epigenetics. 7, 606-614.
    Pubmed KoreaMed CrossRef
Mol. Cells
Jul 31, 2022 Vol.45 No.7, pp. 435~512
COVER PICTURE
Mesenchymal stem cells (MSCs) are multipotent stem cells capable of differentiating into mesodermal lineages like adipogenic, osteogenic, and chondrogenic. Alcian blue-positive extracellular matrix secreted by chondrocytes in the lacuna confirmed the chondrogenic differentiation of MSCs (Bashyal et al., pp. 479-494).

Share this article on

  • line
  • mail

Related articles in Mol. Cells

Molecules and Cells

eISSN 0219-1032
qr-code Download