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Nano-MeDIP-seq Methylome Analysis Using Low DNA Concentrations

  • Lee M. ButcherEmail author
  • Stephan BeckEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1589)

Abstract

DNA methylation is an epigenetic mark that is indispensable for mammalian development and occurs at cytosine residues throughout the genome (the “methylome”). Approximately 70 % of all CpG dinucleotides are affected by DNA methylation, which serve to “lock in” chromatin states and thus transcriptional programs. The systemic and pervasive occurrence of DNA methylation throughout the genome defines cellular identity and therefore requires genome-wide assays to fully appreciate and discern differential patterns of methylation that influence aspects of phenotypic plasticity including susceptibility to common complex disease.

One method that permits methylome analysis is methylated DNA immunoprecipitation (MeDIP) combined with next-generation sequencing (MeDIP-seq). MeDIP uses an antibody raised against 5-methylcytosine to capture methylated fragments of DNA, which are subsequently sequenced to envisage the methylome landscape. The advantageous cost versus coverage balance of MeDIP-seq has made it the method of choice to replace or complement array-based methods for population epigenetic studies. Here we detail nano-MeDIP-seq, which allows methylome analysis using nanogram quantities of starting material.

Keywords:

Epigenetics DNA methylation Whole genome Next-generation sequencing Low concentration Bioinformatics 

Notes

Acknowledgements

The authors wish to acknowledge funding support from IMI-JU OncoTrack (115234), EU-FP7 BLUEPRINT (282510), and a Royal Society Wolfson Research Merit Award (WM100023).

References

  1. 1.
    Goll MG, Bestor TH (2005) Eukaryotic cytosine methyltransferases. Annu Rev Biochem 74:481–514CrossRefPubMedGoogle Scholar
  2. 2.
    Ehrlich M, Lacey M (2013) DNA methylation and differentiation: silencing, upregulation and modulation of gene expression. Epigenomics 5:553–568CrossRefPubMedGoogle Scholar
  3. 3.
    Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM et al (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462:315–322CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Li Y, Zhu J, Tian G, Li N, Li Q, Ye M, Zheng H, Yu J, Wu H, Sun J et al (2010) The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol 8, e1000533CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lister R, Pelizzola M, Kida YS, Hawkins RD, Nery JR, Hon G, Antosiewicz-Bourget J, O’Malley R, Castanon R, Klugman S et al (2011) Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471:68–73CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Bock C (2012) Analysing and interpreting DNA methylation data. Nat Rev Genet 13:705–719CrossRefPubMedGoogle Scholar
  7. 7.
    Down TA, Rakyan VK, Turner DJ, Flicek P, Li H, Kulesha E, Graf S, Johnson N, Herrero J, Tomazou EM et al (2008) A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol 26:779–785CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Beck S (2010) Taking the measure of the methylome. Nat Biotechnol 28:1026–1028CrossRefPubMedGoogle Scholar
  9. 9.
    Eckhardt F, Lewin J, Cortese R, Rakyan VK, Attwood J, Burger M, Burton J, Cox TV, Davies R, Down TA et al (2006) DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet 38:1378–1385CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Pettersson E, Lundeberg J, Ahmadian A (2009) Generations of sequencing technologies. Genomics 93:105–111CrossRefPubMedGoogle Scholar
  11. 11.
    Taiwo O, Wilson GA, Morris T, Seisenberger S, Reik W, Pearce D, Beck S, Butcher LM (2012) Methylome analysis using MeDIP-seq with low DNA concentrations. Nat Protoc 7:617–636CrossRefPubMedGoogle Scholar
  12. 12.
    Wilson GA, Dhami P, Feber A, Cortazar D, Suzuki Y, Schulz R, Schar P, Beck S (2012) Resources for methylome analysis suitable for gene knockout studies of potential epigenome modifiers. Gigascience 1:3CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Rakyan VK, Down TA, Thorne NP, Flicek P, Kulesha E, Graf S, Tomazou EM, Backdahl L, Johnson N, Herberth M et al (2008) An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Res 18:1518–1529CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.UCL Cancer InstituteUniversity College LondonLondonUK

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