SummaryGenome-wide DNA methylation reprogramming occurs in mouse primordial germ cells (PGCs) and preimplantation embryos, but the precise dynamics and biological outcomes are largely unknown. We have carried out whole-genome bisulfite sequencing (BS-Seq) and RNA-Seq across key stages from E6.5 epiblast to E16.5 PGCs. Global loss of methylation takes place during PGC expansion and migration with evidence for passive demethylation, but sequences that carry long-term epigenetic memory (imprints, CpG islands on the X chromosome, germline-specific genes) only become demethylated upon entry of PGCs into the gonads. The transcriptional profile of PGCs is tightly controlled despite global hypomethylation, with transient expression of the pluripotency network, suggesting that reprogramming and pluripotency are inextricably linked. Our results provide a framework for the understanding of the epigenetic ground state of pluripotency in the germline.
The enzymatic control of the setting and maintenance of symmetric and non-symmetric DNA methylation patterns in a particular genome context is not well understood. Here, we describe a comprehensive analysis of DNA methylation patterns generated by high resolution sequencing of hairpin-bisulfite amplicons of selected single copy genes and repetitive elements (LINE1, B1, IAP-LTR-retrotransposons, and major satellites). The analysis unambiguously identifies a substantial amount of regional incomplete methylation maintenance, i.e. hemimethylated CpG positions, with variant degrees among cell types. Moreover, non-CpG cytosine methylation is confined to ESCs and exclusively catalysed by Dnmt3a and Dnmt3b. This sequence position–, cell type–, and region-dependent non-CpG methylation is strongly linked to neighboring CpG methylation and requires the presence of Dnmt3L. The generation of a comprehensive data set of 146,000 CpG dyads was used to apply and develop parameter estimated hidden Markov models (HMM) to calculate the relative contribution of DNA methyltransferases (Dnmts) for de novo and maintenance DNA methylation. The comparative modelling included wild-type ESCs and mutant ESCs deficient for Dnmt1, Dnmt3a, Dnmt3b, or Dnmt3a/3b, respectively. The HMM analysis identifies a considerable de novo methylation activity for Dnmt1 at certain repetitive elements and single copy sequences. Dnmt3a and Dnmt3b contribute de novo function. However, both enzymes are also essential to maintain symmetrical CpG methylation at distinct repetitive and single copy sequences in ESCs.
Imprinted genes are expressed from only one of the parental chromosomes and are marked epigenetically by DNA methylation and histone modifications. The imprinting center 2 (IC2) on mouse distal chromosome 7 is flanked by several paternally repressed genes, with the more distant ones imprinted exclusively in the placenta. We found that most of these genes lack parent-specific DNA methylation, and genetic ablation of methylation does not lead to loss of their imprinting in the trophoblast (placenta). The silent paternal alleles of the genes are marked in the trophoblast by repressive histone modifications (dimethylation at Lys9 of histone H3 and trimethylation at Lys27 of histone H3), which are disrupted when IC2 is deleted, leading to reactivation of the paternal alleles. Thus, repressive histone methylation is recruited by IC2 (potentially through a noncoding antisense RNA) to the paternal chromosome in a region of at least 700 kb and maintains imprinting in this cluster in the placenta, independently of DNA methylation. We propose that an evolutionarily older imprinting mechanism limited to extraembryonic tissues was based on histone modifications, and that this mechanism was subsequently made more stable for use in embryonic lineages by the recruitment of DNA methylation.
CpG island methylation plays an important role in epigenetic gene control during mammalian development and is frequently altered in disease situations such as cancer. The majority of CpG islands is normally unmethylated, but a sizeable fraction is prone to become methylated in various cell types and pathological situations. The goal of this study is to show that a computational epigenetics approach can discriminate between CpG islands that are prone to methylation from those that remain unmethylated. We develop a bioinformatics scoring and prediction method on the basis of a set of 1,184 DNA attributes, which refer to sequence, repeats, predicted structure, CpG islands, genes, predicted binding sites, conservation, and single nucleotide polymorphisms. These attributes are scored on 132 CpG islands across the entire human Chromosome 21, whose methylation status was previously established for normal human lymphocytes. Our results show that three groups of DNA attributes, namely certain sequence patterns, specific DNA repeats, and a particular DNA structure, are each highly correlated with CpG island methylation (correlation coefficients of 0.64, 0.66, and 0.49, respectively). We predicted, and subsequently experimentally examined 12 CpG islands from human Chromosome 21 with unknown methylation patterns and found more than 90% of our predictions to be correct. In addition, we applied our prediction method to analyzing Human Epigenome Project methylation data on human Chromosome 6 and again observed high prediction accuracy. In summary, our results suggest that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing CpG islands for DNA methylation. This finding may have a strong impact on our understanding of changes in CpG island methylation in development and disease.
BackgroundThe interplay of epigenetic processes and the intestinal microbiota may play an important role in intestinal development and homeostasis. Previous studies have established that the microbiota regulates a large proportion of the intestinal epithelial transcriptome in the adult host, but microbial effects on DNA methylation and gene expression during early postnatal development are still poorly understood. Here, we sought to investigate the microbial effects on DNA methylation and the transcriptome of intestinal epithelial cells (IECs) during postnatal development.MethodsWe collected IECs from the small intestine of each of five 1-, 4- and 12 to 16-week-old mice representing the infant, juvenile, and adult states, raised either in the presence or absence of a microbiota. The DNA methylation profile was determined using reduced representation bisulfite sequencing (RRBS) and the epithelial transcriptome by RNA sequencing using paired samples from each individual mouse to analyze the link between microbiota, gene expression, and DNA methylation.ResultsWe found that microbiota-dependent and -independent processes act together to shape the postnatal development of the transcriptome and DNA methylation signatures of IECs. The bacterial effect on the transcriptome increased over time, whereas most microbiota-dependent DNA methylation differences were detected already early after birth. Microbiota-responsive transcripts could be attributed to stage-specific cellular programs during postnatal development and regulated gene sets involved primarily immune pathways and metabolic processes. Integrated analysis of the methylome and transcriptome data identified 126 genomic loci at which coupled differential DNA methylation and RNA transcription were associated with the presence of intestinal microbiota. We validated a subset of differentially expressed and methylated genes in an independent mouse cohort, indicating the existence of microbiota-dependent “functional” methylation sites which may impact on long-term gene expression signatures in IECs.ConclusionsOur study represents the first genome-wide analysis of microbiota-mediated effects on maturation of DNA methylation signatures and the transcriptional program of IECs after birth. It indicates that the gut microbiota dynamically modulates large portions of the epithelial transcriptome during postnatal development, but targets only a subset of microbially responsive genes through their DNA methylation status.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0534-5) contains supplementary material, which is available to authorized users.
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