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      Methylation of recombinant mononucleosomes by DNMT3A demonstrates efficient linker DNA methylation and a role of H3K36me3

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          Abstract

          Recently, the structure of the DNMT3A2/3B3 heterotetramer complex bound to a mononucleosome was reported. Here, we investigate DNA methylation of recombinant unmodified, H3K C4me3 and H3K C36me3 containing mononucleosomes by DNMT3A2, DNMT3A catalytic domain (DNMT3AC) and the DNMT3AC/3B3C complex. We show strong protection of the nucleosomal bound DNA against methylation, but efficient linker-DNA methylation next to the nucleosome core. High and low methylation levels of two specific CpG sites next to the nucleosome core agree well with details of the DNMT3A2/3B3-nucleosome structure. Linker DNA methylation next to the nucleosome is increased in the absence of H3K4me3, likely caused by binding of the H3-tail to the ADD domain leading to relief of autoinhibition. Our data demonstrate a strong stimulatory effect of H3K36me3 on linker DNA methylation, which is independent of the DNMT3A-PWWP domain. This observation reveals a direct functional role of H3K36me3 on the stimulation of DNA methylation, which could be explained by hindering the interaction of the H3-tail and the linker DNA. We propose an evolutionary model in which the direct stimulatory effect of H3K36me3 on DNA methylation preceded its signaling function, which could explain the evolutionary origin of the widely distributed “active gene body-H3K36me3-DNA methylation” connection.

          Abstract

          DNA methyltransferase 3A (DNMT3A) is shown to efficiently methylate recombinant mononucleosome linker DNA and a role for H3K36me3 in the stimulation of DNA methylation is revealed that is independent of its interaction with the PWWP domain.

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          The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

          Abstract Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.
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            Functions of DNA methylation: islands, start sites, gene bodies and beyond.

            DNA methylation is frequently described as a 'silencing' epigenetic mark, and indeed this function of 5-methylcytosine was originally proposed in the 1970s. Now, thanks to improved genome-scale mapping of methylation, we can evaluate DNA methylation in different genomic contexts: transcriptional start sites with or without CpG islands, in gene bodies, at regulatory elements and at repeat sequences. The emerging picture is that the function of DNA methylation seems to vary with context, and the relationship between DNA methylation and transcription is more nuanced than we realized at first. Improving our understanding of the functions of DNA methylation is necessary for interpreting changes in this mark that are observed in diseases such as cancer.
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              PEAR: a fast and accurate Illumina Paired-End reAd mergeR

              Motivation: The Illumina paired-end sequencing technology can generate reads from both ends of target DNA fragments, which can subsequently be merged to increase the overall read length. There already exist tools for merging these paired-end reads when the target fragments are equally long. However, when fragment lengths vary and, in particular, when either the fragment size is shorter than a single-end read, or longer than twice the size of a single-end read, most state-of-the-art mergers fail to generate reliable results. Therefore, a robust tool is needed to merge paired-end reads that exhibit varying overlap lengths because of varying target fragment lengths. Results: We present the PEAR software for merging raw Illumina paired-end reads from target fragments of varying length. The program evaluates all possible paired-end read overlaps and does not require the target fragment size as input. It also implements a statistical test for minimizing false-positive results. Tests on simulated and empirical data show that PEAR consistently generates highly accurate merged paired-end reads. A highly optimized implementation allows for merging millions of paired-end reads within a few minutes on a standard desktop computer. On multi-core architectures, the parallel version of PEAR shows linear speedups compared with the sequential version of PEAR. Availability and implementation: PEAR is implemented in C and uses POSIX threads. It is freely available at http://www.exelixis-lab.org/web/software/pear. Contact: Tomas.Flouri@h-its.org
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                Author and article information

                Contributors
                albert.jeltsch@ibtb.uni-stuttgart.de
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                2 March 2022
                2 March 2022
                2022
                : 5
                : 192
                Affiliations
                GRID grid.5719.a, ISNI 0000 0004 1936 9713, Department of Biochemistry, Institute of Biochemistry and Technical Biochemistry, , University of Stuttgart, ; 70569 Stuttgart, Germany
                Author information
                http://orcid.org/0000-0001-6113-9290
                Article
                3119
                10.1038/s42003-022-03119-z
                8891314
                35236925
                59cc058d-99e8-44cb-9dce-458fdbeb061d
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 March 2021
                : 3 February 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: JE 252/10
                Award Recipient :
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                © The Author(s) 2022

                enzyme mechanisms,dna methylation,histone post-translational modifications,nucleosomes

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