Advertisement

A Rapid and Robust Protocol for Reduced Representation Bisulfite Sequencing in Multiple Myeloma

  • Samrat Roy Choudhury
  • Brian A. Walker
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1792)

Abstract

Reduced representation bisulfite sequencing (RRBS) is one of the most comprehensive yet economic ways of mapping whole genome DNA-methylation. Here, we have substantially modified the RRBS protocol by combining end-repair and A-tailing steps, and by introducing a bead-based method for rapid and easy size selection of the library molecules. The method has been optimized for myeloma clinical samples, where the input DNA concentration can be as low as 100 ng. The method developed can be accomplished in 3 days, including the initial overnight MspI enzyme digestion. Although the protocol has been optimized in myeloma samples, it is broadly applicable to any clinical sample, which is restricted by very low input DNA concentrations.

Key words

RRBS DNA methylation Bisulfite conversion Size selection Library preparation Next generation sequencing Multiple myeloma 

References

  1. 1.
    Esteller M (2008) Epigenetics in cancer. N Engl J Med 358(11):1148–1159. https://doi.org/10.1056/NEJMra072067 CrossRefGoogle Scholar
  2. 2.
    Egger G, Liang G, Aparicio A, Jones PA (2004) Epigenetics in human disease and prospects for epigenetic therapy. Nature 429(6990):457–463CrossRefGoogle Scholar
  3. 3.
    Laird PW, Jaenisch R (1996) The role of DNA methylation in cancer genetic and epigenetics. Annu Rev Genet 30:441–464. https://doi.org/10.1146/annurev.genet.30.1.441 CrossRefGoogle Scholar
  4. 4.
    Choudhury SR, Cui Y, Lubecka K, Stefanska B, Irudayaraj J (2016) CRISPR-dCas9 mediated TET1 targeting for selective DNA demethylation at BRCA1 promoter. Oncotarget 7(29):46545–46556. https://doi.org/10.18632/oncotarget.10234 CrossRefGoogle Scholar
  5. 5.
    Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo Q-M, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462(7271):315–322. http://www.nature.com/nature/journal/v462/n7271/suppinfo/nature08514_S1.html CrossRefGoogle Scholar
  6. 6.
    Bird A (2002) DNA methylation patterns and epigenetic memory. Genes Dev 16(1):6–21. https://doi.org/10.1101/gad.947102 CrossRefGoogle Scholar
  7. 7.
    Li E, Beard C, Jaenisch R (1993) Role for DNA methylation in genomic imprinting. Nature 366(6453):362–365. https://doi.org/10.1038/366362a0 CrossRefGoogle Scholar
  8. 8.
    Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, Schubeler D (2005) Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37(8):853–862. http://www.nature.com/ng/journal/v37/n8/suppinfo/ng1598_S1.html CrossRefGoogle Scholar
  9. 9.
    Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, Zhang X, Bernstein BE, Nusbaum C, Jaffe DB, Gnirke A, Jaenisch R, Lander ES (2008) Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454(7205):766–770. http://www.nature.com/nature/journal/v454/n7205/suppinfo/nature07107_S1.html CrossRefGoogle Scholar
  10. 10.
    Meissner A, Gnirke A, Bell GW, Ramsahoye B, Lander ES, Jaenisch R (2005) Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res 33(18):5868–5877. https://doi.org/10.1093/nar/gki901 CrossRefGoogle Scholar
  11. 11.
    Gu H, Bock C, Mikkelsen TS, Jager N, Smith ZD, Tomazou E, Gnirke A, Lander ES, Meissner A (2010) Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat Methods 7(2):133–136. https://doi.org/10.1038/nmeth.1414 CrossRefGoogle Scholar
  12. 12.
    Guo H, Zhu P, Yan L, Li R, Hu B, Lian Y, Yan J, Ren X, Lin S, Li J, Jin X, Shi X, Liu P, Wang X, Wang W, Wei Y, Li X, Guo F, Wu X, Fan X, Yong J, Wen L, Xie SX, Tang F, Qiao J (2014) The DNA methylation landscape of human early embryos. Nature 511(7511):606–610. https://doi.org/10.1038/nature13544. http://www.nature.com/nature/journal/v511/n7511/abs/nature13544.html#supplementary-information CrossRefGoogle Scholar
  13. 13.
    Morgan GJ, Walker BA, Davies FE (2012) The genetic architecture of multiple myeloma. Nat Rev Cancer 12(5):335–348. https://doi.org/10.1038/nrc3257 CrossRefGoogle Scholar
  14. 14.
    Walker BA, Wardell CP, Chiecchio L, Smith EM, Boyd KD, Neri A, Davies FE, Ross FM, Morgan GJ (2011) Aberrant global methylation patterns affect the molecular pathogenesis and prognosis of multiple myeloma. Blood 117(2):553–562. https://doi.org/10.1182/blood-2010-04-279539 CrossRefGoogle Scholar
  15. 15.
    Kaiser MF, Johnson DC, Wu P, Walker BA, Brioli A, Mirabella F, Wardell CP, Melchor L, Davies FE, Morgan GJ (2013) Global methylation analysis identifies prognostically important epigenetically inactivated tumor suppressor genes in multiple myeloma. Blood 122(2):219–226. https://doi.org/10.1182/blood-2013-03-487884 CrossRefGoogle Scholar
  16. 16.
    Salhia B, Baker A, Ahmann G, Auclair D, Fonsa R, Carpten J (2010) DNA methylation analysis determines the high frequency of genic hypomethylation and low frequency of hypermethylation events in plasma cell tumors. Cancer Res 70(17):6934–6944. https://doi.org/10.1158/0008-5472.can-10-0282 CrossRefGoogle Scholar
  17. 17.
    Bollati V, Fabris S, Pegoraro V, Ronchetti D, Mosca L, Deliliers GL, Motta V, Bertazzi PA, Baccarelli A, Neri A (2009) Differential repetitive DNA methylation in multiple myeloma molecular subgroups. Carcinogenesis 30(8):1330–1335. https://doi.org/10.1093/carcin/bgp149 CrossRefGoogle Scholar
  18. 18.
    Wang T, Liu Q, Li X, Wang X, Li J, Zhu X, Sun ZS, Wu J (2013) RRBS-analyser: a comprehensive web server for reduced representation bisulfite sequencing data analysis. Hum Mutat 34(12):1606–1610. https://doi.org/10.1002/humu.22444 CrossRefGoogle Scholar
  19. 19.
    Xi Y, Bock C, Muller F, Sun D, Meissner A, Li W (2012) RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing. Bioinformatics 28(3):430–432. https://doi.org/10.1093/bioinformatics/btr668 CrossRefGoogle Scholar
  20. 20.
    Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for bisulfite-Seq applications. Bioinformatics 27(11):1571–1572. https://doi.org/10.1093/bioinformatics/btr167 CrossRefGoogle Scholar
  21. 21.
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079. https://doi.org/10.1093/bioinformatics/btp352 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Internal Medicine, Myeloma InstituteUniversity of Arkansas for Medical SciencesLittle RockUSA

Personalised recommendations