A distinctive epigenetic ageing profile in human granulosa cells.

STUDY QUESTION: Does women's age affect the DNA methylation (DNAm) profile differently in mural granulosa cells (MGCs) from other somatic cells? SUMMARY ANSWER: Accumulation of epimutations by age and a higher number of age-related differentially methylated regions (DMR) in MGCs were found compared to leukocytes from the same woman, suggesting that the MGCs have a distinctive epigenetic profile. WHAT IS KNOWN ALREADY: The mechanisms underlying the decline in women's fertility from the mid-30s remain to be fully elucidated. The DNAm age of many healthy tissues changes predictably with and follows chronological age, but DNAm age in some reproductive tissues has been shown to depart from chronological age (older: endometrium; younger: cumulus cells, spermatozoa). STUDY DESIGN, SIZE, DURATION: This study is a multicenter cohort study based on retrospective analysis of prospectively collected data and material derived from healthy women undergoing IVF or ICSI treatment following ovarian stimulation with antagonist protocol. One hundred and nineteen women were included from September 2016 to June 2018 from four clinics in Denmark and Sweden. PARTICIPANTS/MATERIALS, SETTING, METHODS: Blood samples were obtained from 118 healthy women with varying ovarian reserve status. MGCs were collected from 63 of the 119 women by isolation from pooled follicles immediately after oocyte retrieval. DNA from leukocytes and MGCs was extracted and analysed with a genome-wide methylation array. Data from the methylation array were processed using the ENmix package. Subsequently, DNAm age was calculated using established and tailored age predictors and DMRs were analysed with the DMRcate package. MAIN RESULTS AND ROLE OF CHANCE: Using established age predictors, DNAm age in MGCs was found to be considerable younger and constant (average: 2.7 years) compared to chronological age (average: 33.9 years). A Granulosa Cell clock able to predict the age of both MGCs (average: 32.4 years) and leukocytes (average: 38.8 years) was successfully developed. MGCs differed from leukocytes in having a higher number of epimutations (P = 0.003) but predicted telomere lengths unaffected by age (Pearson's correlation coefficient = -0.1, P = 0.47). DMRs associated with age (age-DMRs) were identified in MGCs (n = 335) and in leukocytes (n = 1) with a significant enrichment in MGCs for genes involved in RNA processing (45 genes, P = 3.96 × 10-08) and gene expression (152 genes, P = 2.3 × 10-06). The top age-DMRs included the metastable epiallele VTRNA2-1, the DNAm regulator ZFP57 and the anti-Müllerian hormone (AMH) gene. The apparent discordance between different epigenetic measures of age in MGCs suggests that they reflect difference stages in the MGC life cycle. LARGE SCALE DATA: N/A. LIMITATIONS, REASONS FOR CAUTION: No gene expression data were available to associate with the epigenetic findings. The MGCs are collected during ovarian stimulation, which may influence DNAm; however, no correlation between FSH dose and number of epimutations was found. WIDER IMPLICATIONS OF THE FINDINGS: Our findings underline that the somatic compartment of the follicle follows a different methylation trajectory with age than other somatic cells. The higher number of epimutations and age-DMRs in MGCs suggest that their function is affected by age. STUDY FUNDING/COMPETING INTEREST(S): This project is part of ReproUnion collaborative study, co-financed by the European Union, Interreg V ÖKS, the Danish National Research Foundation and the European Research Council. The authors declare no conflict of interest.

Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects.

BACKGROUND: Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers. METHOD: A smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989-1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA). RESULTS: Altogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results. CONCLUSION: This study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.