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Background: Although exposure to cigarette smoking during pregnancy has been associated with alterations of DNA methylation in the cord blood or placental cells, whether such exposure before pregnancy could induce epigenetic alterations in the placenta of former smokers has never been investigated., Methods: Our approach combined the analysis of placenta epigenomic (ENCODE) data with newly generated DNA methylation data obtained from 568 pregnant women, the largest cohort to date, either actively smoking during their pregnancy or formerly exposed to tobacco smoking., Results: This strategy resulted in several major findings. First, among the 203 differentially methylated regions (DMRs) identified by the epigenome-wide association study, 152 showed "reversible" alterations of DNA methylation, only present in the placenta of current smokers, whereas 26 were also found altered in former smokers, whose placenta had not been exposed directly to cigarette smoking. Although the absolute methylation changes were smaller than those observed in other contexts, such as in some congenital diseases, the observed alterations were consistent within each DMR. This observation was further supported by a demethylation of LINE-1 sequences in the placentas of both current (beta-coefficient (β) (95% confidence interval (CI)), - 0.004 (- 0.008; 0.001)) and former smokers (β (95% CI), - 0.006 (- 0.011; - 0.001)) compared to nonsmokers. Second, the 203 DMRs were enriched in epigenetic marks corresponding to enhancer regions, including monomethylation of lysine 4 and acetylation of lysine 27 of histone H3 (respectively H3K4me1 and H3K27ac). Third, smoking-associated DMRs were also found near and/or overlapping 10 imprinted genes containing regions (corresponding to 16 genes), notably including the NNAT, SGCE/PEG10, and H19/MIR675 loci., Conclusions: Our results pointing towards genomic regions containing the imprinted genes as well as enhancers as preferential targets suggest mechanisms by which tobacco could directly impact the fetus and future child. The persistence of significant DNA methylation changes in the placenta of former smokers supports the hypothesis of an "epigenetic memory" of exposure to cigarette smoking before pregnancy. This observation not only is conceptually revolutionary, but these results also bring crucial information in terms of public health concerning potential long-term detrimental effects of smoking in women.

Background: Air pollution exposure represents a major health threat to the developing foetus. DNA methylation is one of the most well-known molecular determinants of the epigenetic status of cells. Blood DNA methylation has been proven sensitive to air pollutants, but the molecular impact of air pollution on new-borns has so far received little attention., Objectives: We investigated whether nitrogen dioxide (NO 2 ), particulate matter (PM 10 ), temperature and humidity during pregnancy are associated with differences in placental DNA methylation levels., Methods: Whole-genome DNA-methylation was measured using the Illumina's Infinium HumanMethylation450 BeadChip in the placenta of 668 newborns from the EDEN cohort. We designed an original strategy using a priori biological information to focus on candidate genes with a specific expression pattern in placenta (active or silent) combined with an agnostic epigenome-wide association study (EWAS). We used robust linear regression to identify CpGs and differentially methylated regions (DMR) associated with each exposure during short- and long-term time-windows., Results: The candidate genes approach identified nine CpGs mapping to 9 genes associated with prenatal NO 2 and PM 10 exposure [false discovery rate (FDR) p < 0.05]. Among these, the methylation level of 2 CpGs located in ADORA2B remained significantly associated with NO 2 exposure during the 2nd trimester and whole pregnancy in the EWAS (FDR p < 0.05). EWAS further revealed associations between the environmental exposures under study and variations of DNA methylation of 4 other CpGs. We further identified 27 DMRs significantly (FDR p < 0.05) associated with air pollutants exposure and 13 DMRs with meteorological conditions., Conclusions: The methylation of ADORA2B, a gene whose expression was previously associated with hypoxia and pre-eclampsia, was consistently found here sensitive to atmospheric pollutants. In addition, air pollutants were associated to DMRs pointing towards genes previously implicated in preeclampsia, hypertensive and metabolic disorders. These findings demonstrate that air pollutants exposure at levels commonly experienced in the European population are associated with placental gene methylation and provide some mechanistic insight into some of the reported effects of air pollutants on preeclampsia., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. Results: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30–35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Conclusion: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir. [ABSTRACT FROM AUTHOR]