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      Potential energy landscapes identify the information-theoretic nature of the epigenome

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          Abstract

          Epigenetics studies genomic modifications carrying information independent of DNA sequence heritable through cell division. In 1940, Waddington coined the term “epigenetic landscape” as a metaphor for pluripotency and differentiation, but methylation landscapes have not yet been rigorously computed. By using principles of statistical physics and information theory, we derive epigenetic energy landscapes from whole-genome bisulfite sequencing data that allow us to quantify methylation stochasticity genome-wide using Shannon’s entropy and associate entropy with chromatin structure. Moreover, we consider the Jensen-Shannon distance between sample-specific energy landscapes as a measure of epigenetic dissimilarity and demonstrate its effectiveness for discerning epigenetic differences. By viewing methylation maintenance as a communications system, we introduce methylation channels and show that higher-order chromatin organization can be predicted from their informational properties. Our results provide a fundamental understanding of the information-theoretic nature of the epigenome that leads to a powerful approach for studying its role in disease and aging.

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          Most cited references 23

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          Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors.

          Cellular differentiation entails loss of pluripotency and gain of lineage- and cell-type-specific characteristics. Using a murine system that progresses from stem cells to lineage-committed progenitors to terminally differentiated neurons, we analyzed DNA methylation and Polycomb-mediated histone H3 methylation (H3K27me3). We show that several hundred promoters, including pluripotency and germline-specific genes, become DNA methylated in lineage-committed progenitor cells, suggesting that DNA methylation may already repress pluripotency in progenitor cells. Conversely, we detect loss and acquisition of H3K27me3 at additional targets in both progenitor and terminal states. Surprisingly, many neuron-specific genes that become activated upon terminal differentiation are Polycomb targets only in progenitor cells. Moreover, promoters marked by H3K27me3 in stem cells frequently become DNA methylated during differentiation, suggesting context-dependent crosstalk between Polycomb and DNA methylation. These data suggest a model how de novo DNA methylation and dynamic switches in Polycomb targets restrict pluripotency and define the developmental potential of progenitor cells.
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            Analysing and interpreting DNA methylation data.

            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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              A comparison of non-integrating reprogramming methods.

              Human induced pluripotent stem cells (hiPSCs) are useful in disease modeling and drug discovery, and they promise to provide a new generation of cell-based therapeutics. To date there has been no systematic evaluation of the most widely used techniques for generating integration-free hiPSCs. Here we compare Sendai-viral (SeV), episomal (Epi) and mRNA transfection mRNA methods using a number of criteria. All methods generated high-quality hiPSCs, but significant differences existed in aneuploidy rates, reprogramming efficiency, reliability and workload. We discuss the advantages and shortcomings of each approach, and present and review the results of a survey of a large number of human reprogramming laboratories on their independent experiences and preferences. Our analysis provides a valuable resource to inform the use of specific reprogramming methods for different laboratories and different applications, including clinical translation.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                28 July 2017
                27 March 2017
                May 2017
                27 September 2017
                : 49
                : 5
                : 719-729
                Affiliations
                [1 ]Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
                [2 ]Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, Maryland, USA
                [3 ]Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
                [4 ]Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
                Author notes
                Correspondence should be addressed to J.G. ( goutsias@ 123456jhu.edu ) or A.P.F. ( afeinberg@ 123456jhu.edu )
                Article
                NIHMS852360
                10.1038/ng.3811
                5565269
                28346445

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                Genetics

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