Global Methylation Profiles in Buccal Cells of Long-Term Smokers and Moist Snuff Consumers

Alternations in gene methylation and other epigenetic changes regulate normal development as well as drive disease progression. Chronic cigarette smoking causes hyper- and hypo-methylation of genes that could contribute to smoking-related diseases. It is unclear whether consumers of non-combustible tobacco, such as moist snuff, also exhibit such perturbations in their methylome. Here, we present global methylation changes relative to non-tobacco consumers in buccal cells collected from smokers (SMK) and moist snuff consumers (MSC). Generally healthy adult male study subjects were recruited into SMK, MSC and Non-Tobacco Consumer (NTC) cohorts (40 subjects/cohort). Global methylation profiling was performed on the Illumina 450K methylation array using buccal cell DNA. A total of 1,252 loci were found to be significantly differentially methylated in tobacco consumers relative to non-tobacco consumers. Overall, the SMK cohort exhibited larger qualitative and quantitative changes relative to MSC. Approximately half of the total number of gene loci, classified as Combustible Tobacco-Related signatures, and a third of the changes, termed Tobacco-Related signatures, were commonly detected in the tobacco consumers. Very few differences were detected between MSC and NTC, and hierarchical clustering of the top 50 significant gene loci suggested that MSC and NTC co-cluster. Consistent with physiological functions of AhR, combustible tobacco drives profound changes in buccal cell methylation status, principally impacting cell development and immune response pathways. These results aid in placing combustible and non-combustible tobacco products along a risk continuum and provide additional insights into the effects of tobacco consumption. DNA extraction and global methylation profiling was performed by Expression Analysis on Illumina Infinium HumanMethylation450 BeadChip arrays. All data were normalized using Illumina recommended normalization method to reduce the effects of experimental variation. This normalization procedure included background estimation using negative control probes and normalization of intensities to the housekeeping genes with no CpG sites. Beta values (β), which are used to estimate the methylation level of the CpG locus using the ratio of intensities between methylated and un-methylated alleles, were exported from Illumina Genome Studio software and all analyses were performed using the SAS® statistical software package. For normally distributed beta values, we analyzed the data by modeling each individual methylation locus by linear regression with age and race as independent variables.