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      DNA methylation predicts age and provides insight into exceptional longevity of bats

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      1 , , 1 , 2 , 2 , 3 , 4 , 5 , 6 , 7 , 6 , 8 , 9 , 10 , 11 , 12 , 6 , 13 , 1 , 9 , 14 , 10 , 13 , 11 , 15 , 3 , 13 , 16 , 17 , 13 , 18 , 19 , 14 , 19 , 11 , 20 , 21 , 17 , 2 , 22 , 22 , 2 , 3 ,
      Nature Communications
      Nature Publishing Group UK
      Methylation analysis, Epigenomics, Ageing

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          Abstract

          Exceptionally long-lived species, including many bats, rarely show overt signs of aging, making it difficult to determine why species differ in lifespan. Here, we use DNA methylation (DNAm) profiles from 712 known-age bats, representing 26 species, to identify epigenetic changes associated with age and longevity. We demonstrate that DNAm accurately predicts chronological age. Across species, longevity is negatively associated with the rate of DNAm change at age-associated sites. Furthermore, analysis of several bat genomes reveals that hypermethylated age- and longevity-associated sites are disproportionately located in promoter regions of key transcription factors (TF) and enriched for histone and chromatin features associated with transcriptional regulation. Predicted TF binding site motifs and enrichment analyses indicate that age-related methylation change is influenced by developmental processes, while longevity-related DNAm change is associated with innate immunity or tumorigenesis genes, suggesting that bat longevity results from augmented immune response and cancer suppression.

          Abstract

          DNA methylation profiles from 26 bat species accurately predicts chronological age, while longevity-related methylation patterns across the genome suggest that bat longevity results from augmented immune response and cancer suppression.

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          Most cited references66

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          Regularization Paths for Generalized Linear Models via Coordinate Descent

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            DNA methylation age of human tissues and cell types

            Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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              Topological Domains in Mammalian Genomes Identified by Analysis of Chromatin Interactions

              The spatial organization of the genome is intimately linked to its biological function, yet our understanding of higher order genomic structure is coarse, fragmented and incomplete. In the nucleus of eukaryotic cells, interphase chromosomes occupy distinct chromosome territories (CT), and numerous models have been proposed for how chromosomes fold within CTs 1 . These models, however, provide only few mechanistic details about the relationship between higher order chromatin structure and genome function. Recent advances in genomic technologies have led to rapid revolutions in the study of 3D genome organization. In particular, Hi-C has been introduced as a method for identifying higher order chromatin interactions genome wide 2 . In the present study, we investigated the 3D organization of the human and mouse genomes in embryonic stem cells and terminally differentiated cell types at unprecedented resolution. We identify large, megabase-sized local chromatin interaction domains, which we term “topological domains”, as a pervasive structural feature of the genome organization. These domains correlate with regions of the genome that constrain the spread of heterochromatin. The domains are stable across different cell types and highly conserved across species, suggesting that topological domains are an inherent property of mammalian genomes. Lastly, we find that the boundaries of topological domains are enriched for the insulator binding protein CTCF, housekeeping genes, tRNAs, and SINE retrotransposons, suggesting that these factors may play a role in establishing the topological domain structure of the genome.
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                Author and article information

                Contributors
                wilkinso@umd.edu
                shorvath@mednet.ucla
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 March 2021
                12 March 2021
                2021
                : 12
                : 1615
                Affiliations
                [1 ]GRID grid.164295.d, ISNI 0000 0001 0941 7177, Department of Biology, , University of Maryland, ; College Park, MD USA
                [2 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Human Genetics, David Geffen School of Medicine, , University of California, ; Los Angeles, CA USA
                [3 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Biostatistics, Fielding School of Public Health, , University of California, ; Los Angeles, CA USA
                [4 ]GRID grid.488617.4, Altius Institute for Biomedical Sciences, ; Seattle, WA USA
                [5 ]GRID grid.428930.4, ISNI 0000 0001 0017 8712, Department of Biology, , Illinois College, ; Jacksonville, IL USA
                [6 ]GRID grid.261103.7, ISNI 0000 0004 0459 7529, Department of Anatomy and Neurobiology, , Northeast Ohio Medical University, ; Rootstown, OH USA
                [7 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Department of Evolution, Ecology and Organismal Biology, , The Ohio State University, ; Columbus, OH USA
                [8 ]GRID grid.507516.0, ISNI 0000 0004 7661 536X, Department of Migration, , Max Planck Institute of Animal Behavior, ; Radolfzell, Germany
                [9 ]GRID grid.9811.1, ISNI 0000 0001 0658 7699, Department of Biology, , University of Konstanz, ; Konstanz, Germany
                [10 ]GRID grid.438006.9, ISNI 0000 0001 2296 9689, Smithsonian Tropical Research Institute, ; Panama, FL USA
                [11 ]GRID grid.419550.c, ISNI 0000 0004 0501 3839, Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, ; Nijmegen, the Netherlands
                [12 ]GRID grid.9851.5, ISNI 0000 0001 2165 4204, Department of Ecology and Evolution, , University of Lausanne, ; Lausanne, Switzerland
                [13 ]GRID grid.422371.1, ISNI 0000 0001 2293 9957, Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, ; Berlin, Germany
                [14 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, School of Biological Sciences, , University of Bristol, ; Bristol, UK
                [15 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Department Biology II, , Ludwig Maximilians University Munich, ; München, Martinsried Germany
                [16 ]GRID grid.264269.d, ISNI 0000 0001 0151 0940, Department of Biology, , State University of New York, ; Geneseo, NY USA
                [17 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria, ; Mexico City, Mexico
                [18 ]Lubee Bat Conservancy, Gainesville, FL USA
                [19 ]GRID grid.7886.1, ISNI 0000 0001 0768 2743, School of Biology and Environmental Science, , University College Dublin, ; Belfield, Dublin 4 Ireland
                [20 ]GRID grid.5590.9, ISNI 0000000122931605, Donders Institute for Brain, Cognition and Behaviour, ; Nijmegen, the Netherlands
                [21 ]GRID grid.11914.3c, ISNI 0000 0001 0721 1626, School of Biology, , The University of St Andrews, ; Fife, UK
                [22 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Psychology, Neuroscience and Behaviour, , McMaster University, ; Hamilton, ON Canada
                Author information
                http://orcid.org/0000-0001-7799-8444
                http://orcid.org/0000-0002-2866-7961
                http://orcid.org/0000-0001-6309-0291
                http://orcid.org/0000-0002-5294-915X
                http://orcid.org/0000-0001-5589-8143
                http://orcid.org/0000-0002-1765-927X
                http://orcid.org/0000-0003-0043-8267
                http://orcid.org/0000-0002-2600-7652
                http://orcid.org/0000-0002-2936-2449
                http://orcid.org/0000-0002-1904-3735
                http://orcid.org/0000-0002-8928-8770
                http://orcid.org/0000-0002-3907-7983
                http://orcid.org/0000-0002-1345-2976
                http://orcid.org/0000-0002-4242-5344
                http://orcid.org/0000-0002-9768-3930
                http://orcid.org/0000-0002-8423-2145
                http://orcid.org/0000-0001-7402-3254
                http://orcid.org/0000-0002-4143-8186
                http://orcid.org/0000-0002-3309-1346
                http://orcid.org/0000-0003-0305-4584
                http://orcid.org/0000-0002-8531-2147
                http://orcid.org/0000-0001-6221-5718
                http://orcid.org/0000-0002-9921-2972
                http://orcid.org/0000-0002-4110-3589
                Article
                21900
                10.1038/s41467-021-21900-2
                7955057
                33712580
                397bed56-549e-4577-a64c-e0c4acf3926c
                © The Author(s) 2021

                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
                : 2 October 2020
                : 8 February 2021
                Funding
                Funded by: College of Computer, Mathematical and Natural Sciences, University of Maryland
                Funded by: Paul G. Allen Frontiers Group
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                © The Author(s) 2021

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                methylation analysis,epigenomics,ageing
                Uncategorized
                methylation analysis, epigenomics, ageing

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