Saare M.,Competence Center on Health Technologies Tartu |
Saare M.,University of Tartu |
Modhukur V.,University of Tartu |
Suhorutshenko M.,Competence Center on Health Technologies Tartu |
And 17 more authors.
Background: Alterations in endometrial DNA methylation profile have been proposed as one potential mechanism initiating the development of endometriosis. However, the normal endometrial methylome is influenced by the cyclic hormonal changes, and the menstrual cycle phase-dependent epigenetic signature should be considered when studying endometrial disorders. So far, no studies have been performed to evaluate the menstrual cycle influences and endometriosis-specific endometrial methylation pattern at the same time. Results: Infinium HumanMethylation 450K BeadChip arrays were used to explore DNA methylation profiles of endometrial tissues from various menstrual cycle phases from 31 patients with endometriosis and 24 healthy women. The DNA methylation profile of patients and controls was highly similar and only 28 differentially methylated regions (DMRs) between patients and controls were found. However, the overall magnitude of the methylation differences between patients and controls was rather small (Δβ ranging from –0.01 to –0.16 and from 0.01 to 0.08, respectively, for hypo- and hypermethylated CpGs). Unsupervised hierarchical clustering of the methylation data divided endometrial samples based on the menstrual cycle phase rather than diseased/non-diseased status. Further analysis revealed a number of menstrual cycle phase-specific epigenetic changes with largest changes occurring during the late-secretory and menstrual phases when substantial rearrangements of endometrial tissue take place. Comparison of cycle phase- and endometriosis-specific methylation profile changes revealed that 13 out of 28 endometriosis-specific DMRs were present in both datasets. Conclusions: The results of our study accentuate the importance of considering normal cyclic epigenetic changes in studies investigating endometrium-related disease-specific methylation patterns. © 2016, Saare et al. Source