Epigenetic research in human subjects has been ongoing for decades but has primarily focused on alterations related to cancer. However, in order to properly understand aberrant epigenetic regulation that occurs during the course of disease, the normal methylome must be described. More specifically, there is a need to characterize the epigenetic state in non-pathologic tissues from healthy individuals to identify the variability in the overall profile of DNA methylation across individuals, and to clarify the relationship of that variability with aging or environmental exposures. Elucidation of the methylation patterns of CpG loci embedded in different genomic sequences or proximal to different features will critically inform our comprehension of alterations in epigenetic regulation that occur through pathologic processes and the mechanisms by which these alterations arise.
The study of the epigenetics of aging in healthy individuals is emerging as a novel discipline, seeking to discern the epigenomic changes that occur during the course of life. Early studies of this phenomenon examined candidate loci, such as individual gene promoters and “global” methylation markers, finding increased methylation of many of these specific gene promoters with aging,27–32
while methylation of the “global” markers (e.g., LINE-1, Alu, LUMA, CCGG
, etc.,) decreased,33–36
giving rise to the notion that we lose global methylation with age, while we gain localized promoter methylation.37
In accordance with these earlier reports, we present here evidence, based on a genome-wide approach, of an association between aging and DNA methylation, the magnitude and direction of which is dependent upon the genomic context of the sequence in which the CpG is embedded. This is demonstrated by our marginal model results, which show no effect of age on CGI methylation but a decrease in methylation, overall and for all other sequence features considered, including several repeat sequences, with varying effects. This is additionally corroborated by our previous results in reference 19
, which clustered 1,413 CpG loci into methylation classes using blood samples from 30 healthy adult subjects and finding the association of methylation with age to be CGI-context dependent. Furthermore, our present results indicate that inter- and intra-genomic differences in methylation acquired with age are more complex than just CGI vs. non-CGI, but rather vary according to biological differences in DNA sequence, as exemplified by the complex interactions observed in our bioinformatically-derived clustering approach.
We also undertook a more thorough examination of the relationship between methylation and environmental exposures experienced by the subjects studied. In doing so, we identified an association between hair dye use and methylation, where ever-use of hair dye was inversely associated with methylation among the more highly methylated unsupervised (RPMM-based) classes and positively associated with methylation in the classes with low methylation and higher CpG island contents. This finding was further supported by our marginal model estimates, which indicate an interaction between use of hair dye and methylation of CpGs in LINE-1
elements. However, using the bioinformatically-informed classification scheme, after adjusting for age and gender, we no longer observe a significant association between methylation and ever-use of hair dye by class. This may suggest that while the bioinformatically-derived classes are meaningful, they either do not fully explain the genomic context which accounts for differences in methylation between CpG loci or are over-parsing CpGs based on bioinformatic features, sacrificing statistical power for detection of associations. While several varieties of hair dyes exist, oxidative (permanent) dyes comprise 80% of the market share in the US.38
The main components of oxidative dyes include primary intermediates and couplers, composed of various forms of arylamines, oxidants and alkalinizing agents.39
A recent review concluded that there is no consistent evidence of genotoxicity from biomonitoring studies of hair dye exposure40
but there are some epidemiologic reports of increased risk of bladder41,42
and hematopoietic cancers43–45
among hair dye users, albeit the literature is conflicting.46
In light of our findings, further studies are indicated to examine the effect of hair dye use on epigenetic endpoints and the impact of these alterations on disease susceptibility.
We found no overall association of class methylation with ever-smoking but there was a borderline association among ever-smokers of pack-years with methylation using the RPMM-based approach, although the direction was contrary to what would be expected and thus further research is required to determine whether this effect is real or spurious. No association was observed for alcohol consumption, arsenic or selenium exposure (measured via toenail clippings). However, it is important to note that although the measured exposures may not be significantly associated with methylation in peripheral blood, they may be affecting methylation in other tissue types not measured in this study. Additionally, in response to evidence that ultraviolet (UV) exposure modifies the immune system,47,48
which could potentially result in altered methylation signatures in peripheral blood, we assessed measures of UV exposure, including ever-use of tanning lamps and lifetime number of painful sunburns. CpG class methylation was not associated with either UV measure, although we did observe an interaction between ever-use of tanning lamps and methylation of CpGs located within PcG target genes, the significance of which is unknown.
A key strength of this study is the employment of three complementary analytic strategies for evaluating the impact of aging and exposures on DNA methylation: (1) unsupervised clustering by recursively partitioned mixture modeling (RPMM), (2) a bioinformatically-informed clustering approach and (3) a marginal-model based analysis. Each of the 3 methodologies used bears its own set of strengths and weaknesses, with each making a positive contribution to the analysis and filling in for potential shortcomings of the others. The bioinformatically-derived clustering approach takes into account intricate interactions between DNA sequence features of the CpGs but is limited in scope to the sequence features that we considered and could potentially over-partition the data. Conversely, the unsupervised (RPMM-based) clustering approach has the capacity to capture variation in methylation due to unknown or poorly-understood features and interactions that would otherwise be unaccounted for since it clusters based on like-methylation patterns rather than specific DNA sequence attributes, although the source of variation may not be as easily interpreted. Additionally, the data-driven RPMM approach suffers from the weaknesses of all 2-stage latent variable approaches, i.e., “double-dipping” where the data are used twice (once to predict the latent variables and once again to assess their associations with other variables/phenotypes). In general, 2-stage approaches provide reasonably unbiased point estimates but can often underestimate standard errors.49
Finally, the addition of the marginal model-based (non-clustering) approach allows us to specifically analyze the interaction of each exposure of interest with each sequence feature. However, this assessment is limited to evaluation of 1st
order interactions, whereas the cluster analyses may better capture more complex relationships between aging/exposures, variation in the DNA sequence and methylation.
Our results clearly demonstrate that the genomic context of CpGs is important when assessing associations of methylation with aging or exposures. They also indicate that simple consideration of CpG island status is not sufficient with respect to methylation, but rather that other variations in DNA sequence should be taken into account. Moreover, we have provided additional evidence that DNA methylation is associated with age and novel evidence for an association with hair dye use, each operating in a CpG context-dependent manner. Proper careful analysis of CpG loci with respect to methylation patterns in response to aging and exposures in healthy individuals, such as we have described here, will help us to gain insight into the mechanics of DNA methylation and epigenetic control. Ultimately, such conception of normal epigenetic variation will help to guide future research of aberrant methylation that occurs during the course of disease, enhancing our understanding of pathologic processes.