Browse > Article
http://dx.doi.org/10.5808/GI.2017.15.1.28

DNA Methylation Profiles of Blood Cells Are Distinct between Early-Onset Obese and Control Individuals  

Rhee, Je-Keun (Department of Medical Informatics, College of Medicine, The Catholic University of Korea)
Lee, Jin-Hee (Catholic Institute of U-Healthcare, Institute of Biomedical Industry, The Catholic University of Korea)
Yang, Hae Kyung (Department of Internal Medicine, College of Medicine, The Catholic University of Korea)
Kim, Tae-Min (Department of Medical Informatics, College of Medicine, The Catholic University of Korea)
Yoon, Kun-Ho (Department of Medical Informatics, College of Medicine, The Catholic University of Korea)
Abstract
Obesity is a highly prevalent, chronic disorder that has been increasing in incidence in young patients. Both epigenetic and genetic aberrations may play a role in the pathogenesis of obesity. Therefore, in-depth epigenomic and genomic analyses will advance our understanding of the detailed molecular mechanisms underlying obesity and aid in the selection of potential biomarkers for obesity in youth. Here, we performed microarray-based DNA methylation and gene expression profiling of peripheral white blood cells obtained from six young, obese individuals and six healthy controls. We observed that the hierarchical clustering of DNA methylation, but not gene expression, clearly segregates the obese individuals from the controls, suggesting that the metabolic disturbance that occurs as a result of obesity at a young age may affect the DNA methylation of peripheral blood cells without accompanying transcriptional changes. To examine the genome-wide differences in the DNA methylation profiles of young obese and control individuals, we identified differentially methylated CpG sites and investigated their genomic and epigenomic contexts. The aberrant DNA methylation patterns in obese individuals can be summarized as relative gains and losses of DNA methylation in gene promoters and gene bodies, respectively. We also observed that the CpG islands of obese individuals are more susceptible to DNA methylation compared to controls. Our pilot study suggests that the genome-wide aberrant DNA methylation patterns of obese individuals may advance not only our understanding of the epigenomic pathogenesis but also early screening of obesity in youth.
Keywords
DNA methylation; genome-wide DNA methylation profiling; genome-wide gene expression profiling; obese children;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond) 2008;32:1431-1437.   DOI
2 Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010;42:937-948.   DOI
3 Waalen J. The genetics of human obesity. Transl Res 2014;164:293-301.   DOI
4 Choquet H, Meyre D. Genetics of obesity: what have we learned? Curr Genomics 2011;12:169-179.   DOI
5 Perez-Pastor EM, Metcalf BS, Hosking J, Jeffery AN, Voss LD, Wilkin TJ. Assortative weight gain in mother-daughter and father-son pairs: an emerging source of childhood obesity. Longitudinal study of trios (EarlyBird 43). Int J Obes (Lond) 2009;33:727-735.   DOI
6 Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med 2008;359:61-73.   DOI
7 van Dijk SJ, Molloy PL, Varinli H, Morrison JL, Muhlhausler BS; Members of EpiSCOPE. Epigenetics and human obesity. Int J Obes (Lond) 2015;39:85-97.   DOI
8 Wang X, Zhu H, Snieder H, Su S, Munn D, Harshfield G, et al. Obesity related methylation changes in DNA of peripheral blood leukocytes. BMC Med 2010;8:87.   DOI
9 Almen MS, Nilsson EK, Jacobsson JA, Kalnina I, Klovins J, Fredriksson R, et al. Genome-wide analysis reveals DNA methylation markers that vary with both age and obesity. Gene 2014;548:61-67.   DOI
10 Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 2013;29:189-196.   DOI
11 Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 2010;11:587.   DOI
12 Marabita F, Almgren M, Lindholm ME, Ruhrmann S, Fagerstrom-Billai F, Jagodic M, et al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Epigenetics 2013;8:333-346.   DOI
13 McLachlan GJ, Peel D. Finite Mixture Models. New York: Wiley, 2000.
14 Benaglia T, Chauveau D, Hunter DR, Young DS. Mixtools: an r package for analyzing finite mixture models. J Stat Softw 2009;32:1-29.
15 Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:Article3.
16 Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 2012;9:215-216.   DOI
17 Wei S, Zhang L, Zhou X, Du M, Jiang Z, Hausman GJ, et al. Emerging roles of zinc finger proteins in regulating adipogenesis. Cell Mol Life Sci 2013;70:4569-4584.   DOI
18 Bonhoure N, Byrnes A, Moir RD, Hodroj W, Preitner F, Praz V, et al. Loss of the RNA polymerase III repressor MAF1 confers obesity resistance. Genes Dev 2015;29:934-947.   DOI
19 Al-Aubaidy HA, Jelinek HF. Oxidative DNA damage and obesity in type 2 diabetes mellitus. Eur J Endocrinol 2011;164:899-904.   DOI
20 Miller MA, Cappuccio FP. Cellular adhesion molecules and their relationship with measures of obesity and metabolic syndrome in a multiethnic population. Int J Obes (Lond) 2006;30:1176-1182.   DOI
21 Bjenning C, Al-Shamma H, Thomsen W, Leonard J, Behan D. G protein-coupled receptors as therapeutic targets for obesity and type 2 diabetes. Curr Opin Investig Drugs 2004;5:1051-1062.
22 Speidel D, Salehi A, Obermueller S, Lundquist I, Brose N, Renstrom E, et al. CAPS1 and CAPS2 regulate stability and recruitment of insulin granules in mouse pancreatic beta cells. Cell Metab 2008;7:57-67.   DOI
23 Kobayashi H, Yamazaki S, Takashima S, Liu W, Okuda H, Yan J, et al. Ablation of Rnf213 retards progression of diabetes in the Akita mouse. Biochem Biophys Res Commun 2013;432:519-525.   DOI
24 Talukdar S, Oh DY, Bandyopadhyay G, Li D, Xu J, McNelis J, et al. Neutrophils mediate insulin resistance in mice fed a high-fat diet through secreted elastase. Nat Med 2012;18:1407-1412.   DOI
25 Herman JG, Baylin SB. Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med 2003;349:2042-2054.   DOI
26 Yang X, Han H, De Carvalho DD, Lay FD, Jones PA, Liang G. Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell 2014;26:577-590.   DOI
27 Rountree MR, Bachman KE, Baylin SB. DNMT1 binds HDAC2 and a new co-repressor, DMAP1, to form a complex at replication foci. Nat Genet 2000;25:269-277.   DOI
28 Lynch LA, O'Connell JM, Kwasnik AK, Cawood TJ, O'Farrelly C, O'Shea DB. Are natural killer cells protecting the metabolically healthy obese patient? Obesity (Silver Spring) 2009;17:601-605.   DOI
29 Simar D, Versteyhe S, Donkin I, Liu J, Hesson L, Nylander V, et al. DNA methylation is altered in B and NK lymphocytes in obese and type 2 diabetic human. Metabolism 2014;63:1188-1197.   DOI
30 Anderson EK, Gutierrez DA, Hasty AH. Adipose tissue recruitment of leukocytes. Curr Opin Lipidol 2010;21:172-177.   DOI
31 Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012;13:86.   DOI