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      Methylome evolution in plants

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          Abstract

          Despite major progress in dissecting the molecular pathways that control DNA methylation patterns in plants, little is known about the mechanisms that shape plant methylomes over evolutionary time. Drawing on recent intra- and interspecific epigenomic studies, we show that methylome evolution over long timescales is largely a byproduct of genomic changes. By contrast, methylome evolution over short timescales appears to be driven mainly by spontaneous epimutational events. We argue that novel methods based on analyses of the methylation site frequency spectrum (mSFS) of natural populations can provide deeper insights into the evolutionary forces that act at each timescale.

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          The online version of this article (doi:10.1186/s13059-016-1127-5) contains supplementary material, which is available to authorized users.

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          Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning.

          Shawn Cokus, Suhua Feng, Xiaoyu Zhang … (2008)
          Cytosine DNA methylation is important in regulating gene expression and in silencing transposons and other repetitive sequences. Recent genomic studies in Arabidopsis thaliana have revealed that many endogenous genes are methylated either within their promoters or within their transcribed regions, and that gene methylation is highly correlated with transcription levels. However, plants have different types of methylation controlled by different genetic pathways, and detailed information on the methylation status of each cytosine in any given genome is lacking. To this end, we generated a map at single-base-pair resolution of methylated cytosines for Arabidopsis, by combining bisulphite treatment of genomic DNA with ultra-high-throughput sequencing using the Illumina 1G Genome Analyser and Solexa sequencing technology. This approach, termed BS-Seq, unlike previous microarray-based methods, allows one to sensitively measure cytosine methylation on a genome-wide scale within specific sequence contexts. Here we describe methylation on previously inaccessible components of the genome and analyse the DNA methylation sequence composition and distribution. We also describe the effect of various DNA methylation mutants on genome-wide methylation patterns, and demonstrate that our newly developed library construction and computational methods can be applied to large genomes such as that of mouse.
          Article 0 comments Cited 793 times – based on 0 reviews     Review now
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            Principles and challenges of genomewide DNA methylation analysis.

            Peter Laird (2010)
            Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understanding the influence of epigenetics. There has been a revolution in DNA methylation analysis technology over the past decade: analyses that previously were restricted to specific loci can now be performed on a genome-scale and entire methylomes can be characterized at single-base-pair resolution. However, there is such a diversity of DNA methylation profiling techniques that it can be challenging to select one. This Review discusses the different approaches and their relative merits and introduces considerations for data analysis.
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              The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana.

              Stephan Ossowski, Korbinian Schneeberger, José Lucas-Lledó … (2010)
              To take complete advantage of information on within-species polymorphism and divergence from close relatives, one needs to know the rate and the molecular spectrum of spontaneous mutations. To this end, we have searched for de novo spontaneous mutations in the complete nuclear genomes of five Arabidopsis thaliana mutation accumulation lines that had been maintained by single-seed descent for 30 generations. We identified and validated 99 base substitutions and 17 small and large insertions and deletions. Our results imply a spontaneous mutation rate of 7 x 10(-9) base substitutions per site per generation, the majority of which are G:C-->A:T transitions. We explain this very biased spectrum of base substitution mutations as a result of two main processes: deamination of methylated cytosines and ultraviolet light-induced mutagenesis.
              Article 0 comments Cited 462 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Aurélien Tellier: tellier@wzw.tum.de
                Frank Johannes: frank@johanneslab.org
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1474-7596
                ISSN (Electronic): 1474-760X
                Publication date (Electronic): 20 December 2016
                Publication date PMC-release: 20 December 2016
                Publication date Collection: 2016
                Volume: 17
                Electronic Location Identifier: 264
                Affiliations
                [1 ]Population Epigenetics and Epigenomics, Technical University of Munich, Liesel-Beckman-Str. 2, 85354 Freising, Germany
                [2 ]Population Genetics, Technical University of Munich, Liesel-Beckman-Str. 2, 85354 Freising, Germany
                [3 ]Groningen Bioinformatics Centre, University of Groningen, 9747 AG Groningen, The Netherlands
                [4 ]Institute for Advanced Study, Technical University of Munich, Lichtenbergstr. 2a, 85748 Garching, Germany
                Article
                Publisher ID: 1127
                DOI: 10.1186/s13059-016-1127-5
                PMC ID: 5175322
                PubMed ID: 27998290
                SO-VID: 47624222-d288-42f0-8ba6-253f2e24ad02
                Copyright © © The Author(s). 2016
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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