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      DNA methylation signatures link prenatal famine exposure to growth and metabolism.

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          Abstract

          Periconceptional diet may persistently influence DNA methylation levels with phenotypic consequences. However, a comprehensive assessment of the characteristics of prenatal malnutrition-associated differentially methylated regions (P-DMRs) is lacking in humans. Here we report on a genome-scale analysis of differential DNA methylation in whole blood after periconceptional exposure to famine during the Dutch Hunger Winter. We show that P-DMRs preferentially occur at regulatory regions, are characterized by intermediate levels of DNA methylation and map to genes enriched for differential expression during early development. Validation and further exploratory analysis of six P-DMRs highlight the critical role of gestational timing. Interestingly, differential methylation of the P-DMRs extends along pathways related to growth and metabolism. P-DMRs located in INSR and CPT1A have enhancer activity in vitro and differential methylation is associated with birth weight and serum LDL cholesterol. Epigenetic modulation of pathways by prenatal malnutrition may promote an adverse metabolic phenotype in later life.

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          A unique chromatin signature uncovers early developmental enhancers in humans.

          Alvaro Rada-Iglesias, Ruchi Bajpai, Tomek Swigut … (2011)
          Cell-fate transitions involve the integration of genomic information encoded by regulatory elements, such as enhancers, with the cellular environment. However, identification of genomic sequences that control human embryonic development represents a formidable challenge. Here we show that in human embryonic stem cells (hESCs), unique chromatin signatures identify two distinct classes of genomic elements, both of which are marked by the presence of chromatin regulators p300 and BRG1, monomethylation of histone H3 at lysine 4 (H3K4me1), and low nucleosomal density. In addition, elements of the first class are distinguished by the acetylation of histone H3 at lysine 27 (H3K27ac), overlap with previously characterized hESC enhancers, and are located proximally to genes expressed in hESCs and the epiblast. In contrast, elements of the second class, which we term 'poised enhancers', are distinguished by the absence of H3K27ac, enrichment of histone H3 lysine 27 trimethylation (H3K27me3), and are linked to genes inactive in hESCs and instead are involved in orchestrating early steps in embryogenesis, such as gastrulation, mesoderm formation and neurulation. Consistent with the poised identity, during differentiation of hESCs to neuroepithelium, a neuroectoderm-specific subset of poised enhancers acquires a chromatin signature associated with active enhancers. When assayed in zebrafish embryos, poised enhancers are able to direct cell-type and stage-specific expression characteristic of their proximal developmental gene, even in the absence of sequence conservation in the fish genome. Our data demonstrate that early developmental enhancers are epigenetically pre-marked in hESCs and indicate an unappreciated role of H3K27me3 at distal regulatory elements. Moreover, the wealth of new regulatory sequences identified here provides an invaluable resource for studies and isolation of transient, rare cell populations representing early stages of human embryogenesis.
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            Analysing and interpreting DNA methylation data.

            Christoph Bock (2012)
            DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.
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              Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry.

              Mathias Ehrich, Matthew R. Nelson, Patrick Stanssens … (2005)
              Methylation is one of the major epigenetic processes pivotal to our understanding of carcinogenesis. It is now widely accepted that there is a relationship between DNA methylation, chromatin structure, and human malignancies. DNA methylation is potentially an important clinical marker in cancer molecular diagnostics. Understanding epigenetic modifications in their biological context involves several aspects of DNA methylation analysis. These aspects include the de novo discovery of differentially methylated genes, the analysis of methylation patterns, and the determination of differences in the degree of methylation. Here we present a previously uncharacterized method for high-throughput DNA methylation analysis that utilizes MALDI-TOF mass spectrometry (MS) analysis of base-specifically cleaved amplification products. We use the IGF2/H19 region to show that a single base-specific cleavage reaction is sufficient to discover methylation sites and to determine methylation ratios within a selected target region. A combination of cleavage reactions enables the complete evaluation of all relevant aspects of DNA methylation, with most CpGs represented in multiple reactions. We successfully applied this technology under high-throughput conditions to quantitatively assess methylation differences between normal and neoplastic lung cancer tissue samples from 48 patients in 47 genes and demonstrate that the quantitative methylation results allow accurate classification of samples according to their histopathology.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Nat Commun
                Title: Nature communications
                Publisher: Springer Nature
                ISSN (Electronic): 2041-1723
                ISSN (Print): 2041-1723
                Publication date (Electronic): Nov 26 2014
                Volume: 5
                Affiliations
                [1 ] Molecular Epidemiology, Leiden University Medical Center, 2300RC Leiden, The Netherlands.
                [2 ] Medical Statistics and Bioinformatics, Leiden University Medical Center, 2300RC Leiden, The Netherlands.
                [3 ] The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
                [4 ] 1] Molecular Epidemiology, Leiden University Medical Center, 2300RC Leiden, The Netherlands [2] Human Genetics, Leiden University Medical Center, 2300RC Leiden, The Netherlands.
                [5 ] Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.
                [6 ] 1] Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany [2] CeMM Research Center for Molecular Medicine, Austrian Academy of Sciences, 1090 Vienna, Austria [3] Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria.
                [7 ] 1] The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA [2] Department of Stem cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
                [8 ] 1] Molecular Epidemiology, Leiden University Medical Center, 2300RC Leiden, The Netherlands [2] Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York 10032, USA.
                Article
                Publisher Item ID: ncomms6592 Mid ID: NIHMS636242
                DOI: 10.1038/ncomms6592
                PMC ID: 4246417
                PubMed ID: 25424739
                SO-VID: b3f875f2-110a-4599-9511-dfee8c25eedf
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