Comparison of the DNA methylation profiles of human peripheral blood cells and transformed B-lymphocytes

Abstract

Epidemiological studies of DNA methylation (DNAm) profiles may hold substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. Different cell types are likely to have different DNAm patterns. We investigate the DNAm differences between two types of biospecimens available in many genetic epidemiology studies. We compared DNAm patterns in two different DNA samples from each of 34 participants in the Genetic Epidemiology Network of Arteriopathy study (20 Caucasians and 14 African-Americans). One was extracted from peripheral blood cells (PBC) and the other from transformed B-lymphocytes (TBL). The genome-wide DNAm profiles were compared at over 27,000 genome-wide methylation sites. We found that 26 out of the 34 participants had correlation coefficients higher than 0.9 between methylation profiles of PBC and TBL. Although a high correlation was observed in the DNAm profile between PBC and TBL, we also observed variation across samples from different DNA resources and donors. Using principal component analysis of the DNAm profiles, the two sources of the DNA samples could be accurately predicted. We also identified 3,723 autosomal DNAm sites that had significantly different methylation statuses in PBC compared to TBL (Bonferroni corrected p value <0.05). Both PBC and TBL provide a rich resource for understanding the DNAm profiles in humans participating in epidemiologic studies. While the majority of DNAm findings in PBC and TBL may be consistent, caution must be used when interpreting results because of the possibility of cell type-specific methylation modification.

Methylome-wide comparison of human genomic DNA extracted from whole blood and from EBV-transformed lymphocyte cell lines

“…As reported previously, 7,8 the sample correlation of all probes (45 M) between DNA extracted from whole blood and LCL is high. This high correlation can be explained by that the majority of probes are in regions that are not at all methylated.…”

“…[1][2][3][4] Although LCL DNA has been used to investigate associations between methylation markers and phenotypes, 5 it is less clear whether it is equally suitable for methylation investigations. [6][7][8] A number of targeted studies indicate that EBV transformation affects the methylation pattern, 9,10 and a recent study comparing DNA from primary B-lymphocytes and their corresponding LCLs, using 427 000 markers mainly located in CpG-rich regions, has shown that gene regulation is changed during EBV transformation. 6 Other studies using similar protocols to investigate CpG-rich regions have shown that the correlations between the methylation patterns in DNA from WB and LCL are high (r40.9).…”

“…6 Other studies using similar protocols to investigate CpG-rich regions have shown that the correlations between the methylation patterns in DNA from WB and LCL are high (r40.9). 7,8 In this article, we focus specifically on methylation sites that show variation between individuals and, therefore, are potentially useful as biomarkers in disease studies. As such sites may not be in CpG-rich regions, 11 we use a genome-wide (tiling array) approach, which is not limited to pre-selected regions of interest.…”

A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects

“…However, cell type heterogeneity will be captured by the PCA because it causes differences across many methylation sites (67,68). Previous studies have shown that PCA could accurately distinguish and control for methylation profiles in peripheral blood cells and transformed B-lymphocytes (69) and PCA has been used previously to control for unmeasured confounders in methylation studies (70). Therefore, by regressing out PCs, we therefore control for unmeasured confounders, including for possible cell type heterogeneity.…”

Global remodeling of the mouse DNA methylome during aging and in response to calorie restriction

“…Several earlier studies investigated DNA methylation changes during aging in humans (Horvath et al., 2012; Johansson, Enroth, & Gyllensten, 2013; Jones, Goodman, & Kobor, 2015; McClay et al., 2014; Rakyan et al., 2010; Sun et al., 2010). Changes in DNA methylation with age are less clearly understood in mice; yet, mouse studies offer an opportunity to develop a deeper understanding of methylome remodeling due to the availability of genetic and dietary models and amenability for whole‐organism experiments.…”