Advertisement

CpG Islands pp 137-156 | Cite as

Methylation-Sensitive Amplification Length Polymorphism (MS-AFLP) Microarrays for Epigenetic Analysis of Human Genomes

  • Sergio AlonsoEmail author
  • Koichi Suzuki
  • Fumiichiro Yamamoto
  • Manuel Perucho
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1766)

Abstract

Somatic, and in a minor scale also germ line, epigenetic aberrations are fundamental to carcinogenesis, cancer progression, and tumor phenotype. DNA methylation is the most extensively studied and arguably the best understood epigenetic mechanisms that become altered in cancer. Both somatic loss of methylation (hypomethylation) and gain of methylation (hypermethylation) are found in the genome of malignant cells. In general, the cancer cell epigenome is globally hypomethylated, while some regions—typically gene-associated CpG islands—become hypermethylated. Given the profound impact that DNA methylation exerts on the transcriptional profile and genomic stability of cancer cells, its characterization is essential to fully understand the complexity of cancer biology, improve tumor classification, and ultimately advance cancer patient management and treatment.

A plethora of methods have been devised to analyze and quantify DNA methylation alterations. Several of the early-developed methods relied on the use of methylation-sensitive restriction enzymes, whose activity depends on the methylation status of their recognition sequences. Among these techniques, methylation-sensitive amplification length polymorphism (MS-AFLP) was developed in the early 2000s, and successfully adapted from its original gel electrophoresis fingerprinting format to a microarray format that notably increased its throughput and allowed the quantification of the methylation changes. This array-based platform interrogates over 9500 independent loci putatively amplified by the MS-AFLP technique, corresponding to the NotI sites mapped throughout the human genome.

Key words

DNA methylation Methylation-specific amplified fragment length polymorphism Methylation microarray Epigenomics 

Abbreviations

CGI

CpG island

MSRE

Methylation specific restriction enzyme

Notes

Acknowledgments

We are grateful to our colleagues at the Jichi Medical School, Saitama, for the essential collaboration during the development, testing, validation, and application of the MS-AFLP arrays. We also are thankful to Dr. Lauro Sumoy for productive discussion during the writing of this chapter. This work was supported in part by the Spanish Ministry of Health Plan Nacional de I + D + I, ISCIII, FEDER, FIS PI09/02444, PI12/00511, and PI15/01763 grants, and 2014-SGR-1269 from the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR).

Supplementary material

417596_1_En_8_MOESM1_ESM.tsv (2.7 mb)
Supplementary File 1 Tab-separated list of 9654 probes targeting the NotI sites at the human genome (2789 kb)
417596_1_En_8_MOESM2_ESM.pdf (2.7 mb)
Supplementary Fig. 1 Distance to nearest neighbor distribution of all possible 8 bp (4G + 4C) in the human genome (PDF 2797 kb)
417596_1_En_8_MOESM3_ESM.pdf (2.6 mb)
Supplementary Fig. 2 Distance to nearest neighbor distribution of the recognition sequences in the human genome of commercially available methylation-sensitive restriction enzymes (PDF 2662 kb)

References

  1. 1.
    Koizumi K, Alonso S, Miyaki Y et al (2012) Array-based identification of common DNA methylation alterations in ulcerative colitis. Int J Oncol 40(4):983–994PubMedCrossRefGoogle Scholar
  2. 2.
    Muto Y, Maeda T, Suzuki K et al (2014) DNA methylation alterations of AXIN2 in serrated adenomas and colon carcinomas with microsatellite instability. BMC Cancer 14:466PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Yamamoto F, Yamamoto M, Soto JL et al (2001) Notl-Msell methylation-sensitive amplied fragment length polymorhism for DNA methylation analysis of human cancers. Electrophoresis 22(10):1946–1956PubMedCrossRefGoogle Scholar
  4. 4.
    Samuelsson JK, Alonso S, Yamamoto F et al (2010) DNA fingerprinting techniques for the analysis of genetic and epigenetic alterations in colorectal cancer. Mutat Res 693(1–2):61–76PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Yamashita K, Dai T, Dai Y et al (2003) Genetics supersedes epigenetics in colon cancer phenotype. Cancer Cell 4(2):121–131PubMedCrossRefGoogle Scholar
  6. 6.
    Suzuki K, Suzuki I, Leodolter A et al (2006) Global DNA demethylation in gastrointestinal cancer is age dependent and precedes genomic damage. Cancer Cell 9(3):199–207PubMedCrossRefGoogle Scholar
  7. 7.
    Bird AP (1980) DNA methylation and the frequency of CpG in animal DNA. Nucleic Acids Res 8(7):1499–1504PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Deaton AM, Bird A (2011) CpG islands and the regulation of transcription. Genes Dev 25(10):1010–1022PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Caiafa P, Zampieri M (2005) DNA methylation and chromatin structure: the puzzling CpG islands. J Cell Biochem 94(2):257–265PubMedCrossRefGoogle Scholar
  10. 10.
    Illingworth RS, Bird AP (2009) CpG islands—a rough guide. FEBS Lett 583(11):1713–1720PubMedCrossRefGoogle Scholar
  11. 11.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674PubMedCrossRefGoogle Scholar
  12. 12.
    Baylin SB, Jones PA (2011) A decade of exploring the cancer epigenome – biological and translational implications. Nat Rev Cancer 11(10):726–734PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Gama-Sosa MA, Slagel VA, Trewyn RW et al (1983) The 5-methylcytosine content of DNA from human tumors. Nucleic Acids Res 11(19):6883–6894PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Feinberg AP, Gehrke CW, Kuo KC et al (1988) Reduced genomic 5-methylcytosine content in human colonic neoplasia. Cancer Res 48(5):1159–1161PubMedGoogle Scholar
  15. 15.
    Ehrlich M (2002) DNA methylation in cancer: too much, but also too little. Oncogene 21(35):5400–5413PubMedCrossRefGoogle Scholar
  16. 16.
    Fraga MF, Esteller M (2002) DNA methylation: a profile of methods and applications. Biotechniques 33(3):632, 4, 6–49CrossRefGoogle Scholar
  17. 17.
    Jorda M, Peinado MA (2010) Methods for DNA methylation analysis and applications in colon cancer. Mutat Res 693(1–2):84–93PubMedCrossRefGoogle Scholar
  18. 18.
    Frommer M, McDonald LE, Millar DS et al (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 89(5):1827–1831PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Clark SJ, Harrison J, Paul CL et al (1994) High sensitivity mapping of methylated cytosines. Nucleic Acids Res 22(15):2990–2997PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Bibikova M, Le J, Barnes B et al (2009) Genome-wide DNA methylation profiling using Infinium(R) assay. Epigenomics 1(1):177–200PubMedCrossRefGoogle Scholar
  21. 21.
    Bibikova M, Barnes B, Tsan C et al (2011) High density DNA methylation array with single CpG site resolution. Genomics 98(4):288–295PubMedCrossRefGoogle Scholar
  22. 22.
    Sandoval J, Heyn H, Moran S et al (2011) Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6(6):692–702PubMedCrossRefGoogle Scholar
  23. 23.
    Moran S, Arribas C, Esteller M (2016) Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics 8(3):389–399PubMedCrossRefGoogle Scholar
  24. 24.
    Lister R, Pelizzola M, Dowen RH et al (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462(7271):315–322PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Stirzaker C, Taberlay PC, Statham AL et al (2014) Mining cancer methylomes: prospects and challenges. Trends Genet 30(2):75–84PubMedCrossRefGoogle Scholar
  26. 26.
    Frigola J, Ribas M, Risques RA et al (2002) Methylome profiling of cancer cells by amplification of inter-methylated sites (AIMS). Nucleic Acids Res 30(7):e28PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Jorda M, Rodriguez J, Frigola J et al (2009) Analysis of DNA methylation by amplification of intermethylated sites (AIMS). Methods Mol Biol 507:107–116PubMedCrossRefGoogle Scholar
  28. 28.
    Grunau C, Clark SJ, Rosenthal A (2001) Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res 29(13):E65–E65PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Kageyama S, Shinmura K, Yamamoto H et al (2008) Fluorescence-labeled methylation-sensitive amplified fragment length polymorphism (FL-MS-AFLP) analysis for quantitative determination of DNA methylation and demethylation status. Jpn J Clin Oncol 38(4):317–322PubMedCrossRefGoogle Scholar
  30. 30.
    Alonso S, Gonzalez B, Ruiz-Larroya T et al (2015) Epigenetic inactivation of the extracellular matrix metallopeptidase ADAMTS19 gene and the metastatic spread in colorectal cancer. Clin Epigenetics 7:124PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Samuelsson J, Alonso S, Ruiz-Larroya T et al (2011) Frequent somatic demethylation of RAPGEF1/C3G intronic sequences in gastrointestinal and gynecological cancer. Int J Oncol 38(6):1575–1577PubMedGoogle Scholar
  32. 32.
    Samuelsson JK, Dumbovic G, Polo C et al (2016) Helicase lymphoid-specific enzyme contributes to the maintenance of methylation of SST1 pericentromeric repeats that are frequently demethylated in colon cancer and associate with genomic damage. Epigenomes 1(1):2–18CrossRefGoogle Scholar
  33. 33.
    Yamamoto F, Yamamoto M (2004) A DNA microarray-based methylation-sensitive (MS)-AFLP hybridization method for genetic and epigenetic analyses. Mol Gen Genomics 271(6):678–686CrossRefGoogle Scholar
  34. 34.
    Wolber PK, Collins PJ, Lucas AB et al (2006) The agilent in situ-synthesized microarray platform. Methods Enzymol 410:28–57PubMedCrossRefGoogle Scholar
  35. 35.
    Ritchie ME, Phipson B, Wu D et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Huber W, Carey VJ, Gentleman R et al (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12(2):115–121PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Rouillard JM, Zuker M, Gulari E (2003) OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res 31(12):3057–3062PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Sergio Alonso
    • 1
    Email author
  • Koichi Suzuki
    • 2
  • Fumiichiro Yamamoto
    • 3
  • Manuel Perucho
    • 1
  1. 1.Program of Predictive and Personalized Medicine of Cancer (PMPPC)Germans Trias i Pujol Research Institute (IGTP)Badalona, BarcelonaSpain
  2. 2.Department of Surgery, Saitama Medical CenterJichi Medical UniversitySaitamaJapan
  3. 3.Program of Predictive and Personalized Medicine of Cancer (PMPPC)Josep Carreras Leukaemia Research Institute (IJC)Badalona, BarcelonaSpain

Personalised recommendations