Leading the Way in Life Science Technologies

GEN Exclusives

More »

Feature Articles

More »

Epigenetics and Reinventing Lamarck

Pioneer Evolutionary Scientist's Ideas Revisited in Light of Novel Research Findings

  • Microarray Platforms and OMICS

    During the last decade, the availability of microarray platforms and the upcoming wave of omics have revolutionized the concept of analyzing gene regulation (including that involved in epigenomics) in an unbiased manner. Genetic unmasking opened up a new line of investigation for DNA methylation or histone modifications. Different strategies were designed to fully exploit the possibilities of the new large-scale platforms.

    Genetic disruption of the major de novo DNMTs (DNMT1 and DNMT3) or truncation of mutated HDAC cells were used to identify differentially methylated or acetylated on a global genomic scale.20,21 Similarly, other strategies have taken advantage of the plasticity of epigenetic mechanisms by treating cells with pharmacological inhibitors of DNA methylation, such as the deocycytidine analogue 5-aza-2´-deocycytidine (5-aza) and histone deacetylase inhibitors, like valproic acid or trichostatin A. As a result, changes in gene expression due to aberrant methylation or histone deacetylation could be observed.

    Limitations of these strategies were the pleiotropic effects produced by the disruption of DNMTs and the epigenetic drugs, and the lack of specificity.

    To avoid these problems, new strategies based on methyl-cytosine enrichment were designed to study global epigenetic patterns at a whole-genome scale. The first method is the methylated–CpG island recovery assay (MIRA) that uses the high-binding affinity of the complex of GST-tagged-MBDs proteins to methylated DNA.22

    Following a similar approach, the MeDIP-on-chip (Methylated DNA immuno precipitation+microarray) is based on the direct immunoprecipitation of methylated DNA using an antibody that recognizes 5-methylcytosine.23

    The remaining epigenetic players were analyzed by ChIP-on-chip using antibodies that recognize histone modifications, chromatin modifying complexes, chromatin remodelers, or histone variants. Both MIRA and ChIP-on-chip need an amplification step for the enriched-methyl DNA and a subsequent hybridization in an array platform.

    Nevertheless, the drawbacks of these methodologies lie on the differential binding efficiency depending on the CG content, which can yield biased results toward CPG-rich sequences and on the lack of single-base resolution. In parallel, a methylation-sensitive restriction enzyme HpaII coupled with ligation-mediated PCR approach has been used for global methylation analysis.

    It is limited, however, by the recognition of a particular restriction DNA sequence and by the fact that LM-PCR can produce unspecific amplification.

    Finally, the use of bisulphite treatment of DNA with hybridization on an array platform has permitted the interrogation of methylated DNA in a large scale and single-base resolution analysis.

    There are different arrays commercially available for methylation profiling in bisulphite-converted samples, such as the GoldenGate assay, which interrogates the methylation state of up to 1,356 targeted CpG sites from 371 genes and the Infinium 27K, which analyzes 27,578 CpG sites from 14,475 genes.

    The latest platform to come to market is the Infinium 450K, which covers 485,764 sites, including CNG sites, from 21,233 genes and noncoding RNAs24. Technological improvement has so far been able to circumvent previous limitations. Researchers very likely will soon be able to cover the methylation status of the 28 million CpG dinucleotides of the human methylome.

    Genomic assays have experienced an enormous transformation due to the rapid technological development of next-generation sequencing. The possibility of sequencing millions of short DNA fragments in a single run is of crucial importance for the accomplishment of epigenomic studies.

    In 2007, the laboratory of Wold and Myers contributed to the progression of global genomic-scale analysis by combining chromatin immunoprecipitation and massively parallel sequencing (ChIP-seq) to identify mammalian DNA sequences bound by transcription factors in vivo.25 Soon after, different laboratories used ChIP-seq for large-scale profiling of histone modifications, chromatin modifying complexes, and chromatin remodelers.26,27

    As expected, whole-genome sequencing has been used for analyzing DNA methylation-enriched samples using different approaches: bisulphite conversion MethylC-seq, MeDIP-seq, MBD-seq, methylation-sensitive restriction enzyme sequencing (MRE-seq)28-32. ChIP-seq has become the method of choice for epigenomic analysis because of the higher resolution (single nucleotide), the low incidence of artifacts (since it avoids the noise generated by hybridization steps), larger dynamic range that circumvents saturation of intensity signals, and unlimited genomic coverage.

    Minimal technical defects such as sequencing errors toward the end of each read, bias toward GC-rich content in fragment selection, or loss of sensitivity or specificity in detection of enriched regions with low number of reads have been reduced as technology has advanced. Nevertheless, the major drawback with ChIP-seq is the high cost and availability.

    In store for the near future are an unprecedented number of datasets, including epigenomic, transcriptomic, proteomic, genomic, and diverse technology platforms. Multiple challenges and issues are awaiting researchers integrating and processing the data.

    Regarding data analysis, it is necessary to standardize the storage, transfer, and compilation of such a huge quantity of data. In addition, user-friendly peak-finders and alignment algorithms need to be developed to minimize variability in the identification of enriched regions and between different sequencing platforms.

    However, a major challenge is to perform global integrative analysis of different omics datasets to understand the mechanisms of gene regulation and the biology of complex systems. In this sense, many approaches can be taken to analyze the data resulting in correlations between epigenetic marks with annotated functional features of genes (definition, structure, and ontology).

    Other approaches could be to compare epigenomics with transcriptomics, noncoding RNAs (ncRNAs) or genomics to identify imprinted genes or splicing RNA processing.33-35

    In conclusion, the ultimate goal of epigenomics is to map any epigenetic variant and with the integrated analysis of complementary omics data, being able to connect it to a specific phenotype, such as prognostic and predictive markers for cancer patients. One powerful proof-of-principle of how epigenomics can help in translational medicine is the demonstration that DNA methylomes identify the primary tumor type of the metastases of unknown origin.36

    Further basic research will impact the clinic and will allow us to design tailored therapies with higher effectiveness and minimal side effects. One patient, one treatment and at the precise time.

  • References


    1 Lamarck JBPA (1809). Philosophie zoologique: ou Exposition des considâerations relative áa l’histoire naturelle des animaux.

    2 Riggs AD (1975). Cytogenet. Cell Genet. 14:9–25.

    3 Holliday R, Pugh JE (1975). Science 187:226–232.

    4 Bird AP, Southern EM (1978). J. Mol. Biol. 118:27–47.

    5 Bestor TH, Ingram VM (1983). Proc Natl Acad Sci U S A. 80(18):5559-63.

    6 Li E., Bestor TH, Jaenisch R (1992). Cell 69:915–926.

    7 Stedman E. (1950). Nature 166(4227):780-1.

    8 Allfrey V, Faulkner RM, Mirsky AE (1964). Proc. Natl. Acad. Sci. U.S.A. 51:786–794.

    9 Kornberg RD, Thomas JO (1974). Science 184(139):865-8.

    10 Richmond TJ, Finch JT, Rushton B, Rhodes D, Klug A (1984). Nature 311(5986):532-7.

    11 Brownell JE, Zhou J, Ranalli T, Kobayashi R, Edmondson DG, Roth SY, Allis CD (1996). Cell 84(6):843-51.

    12 Taunton J, Hassig CA, Schreiber SL (1996). Science 272(5260):408-11.

    13 Peterson CL, Herskowitz I (1992). Cell 68(3):573-83.

    14 Jones PL, Veenstra GJ, Wade PA, Vermaak D, Kass SU, Landsberger N, Strouboulis J, Wolffe AP. (1998). Nat Genet. 19(2):187-91.

    15 Wade PA, Gegonne A, Jones PL, Ballestar E, Aubry F, Wolffe AP (1999). Nat Genet. 23(1):62-6.

    16 Hatada I, Hayashizaki Y, Hirotsune S, Komatsubara H, Mukai T (1991). Proc Natl Acad Sci U S A 88(21):9523-7.

    17 DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM (1996). Nat. Genet. 14: 457-460,.

    18 Frigola J, Ribas M, Risques RA, Peinado MA (2002). Nucleic Acids Res. 30(7):e28.

    19 Strahl BD, Allis CD (2000). Nature 403(6765):41-5.

    20 Rhee I, Bachman KE, Park BH, Jair KW, Yen RW, Schuebel KE, Cui H, Feinberg AP, Lengauer C, Kinzler KW, Baylin SB, Vogelstein B (2002). Nature 416(6880):552-6.

    21 Ropero S, Fraga MF, Ballestar E, Hamelin R, Yamamoto H, Boix-Chornet M, Caballero R, Alaminos M, Setien F, Paz MF, Herranz M, Palacios J, Arango D, Orntoft TF, Aaltonen LA, Schwartz S Jr, Esteller M (2006). Nat Genet. 38(5):566-9.

    22 Jiang CL, Jin SG, Pfeifer GP (2004). J Biol Chem. 279(50):52456-64.

    23 Jacinto FV, Ballestar E, Ropero S, Esteller M (2007). Cancer Res. 67(24):11481-6.

    24 Sandoval J, Heyn HA, Moran S, Serra-Musach J, Pujana MA, Bibikova M, Esteller M (2011). Epigenetics, Vol. 6, Issue 6 .

    25 Johnson DS, Mortazavi A, Myers RM, Wold B (2007). Science. 316(5830):1497-502.

    26 Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K (2007). Cell 129(4):823-37.

    27 Robertson AG, Bilenky M, Tam A, Zhao Y, Zeng T, Thiessen N, Cezard T, Fejes AP, Wederell ED, Cullum R, Euskirchen G, Krzywinski M, Birol I, Snyder M, Hoodless PA, Hirst M, Marra MA, Jones SJ (2008). Genome Res. 18(12):1906-17.

    28 Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, Ecker JR (2008). Cell 133(3):523-36.

    29 Down TA, Rakyan VK, Turner DJ, Flicek P, Li H, Kulesha E, Gräf S, Johnson N, Herrero J, Tomazou EM, Thorne NP, Bäckdahl L, Herberth M, Howe KL, Jackson DK, Miretti MM, Marioni JC, Birney E, Hubbard TJ, Durbin R, Tavaré S, Beck S (2008). Nat Biotechnol. 26(7):779-85.

    30 Serre D, Lee BH, Ting AH (2010). Nucleic Acids Res. 38(2):391-9.

    31 Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, Pradhan S, Nelson SF, Pellegrini M, Jacobsen SE ( 2008). Nature 452(7184):215-9.

    32 Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR (2009). Nature 462(7271):315-22.

    33 Edwards CA, Ferguson-Smith AC (2007). Curr Opin Cell Biol. 19(3):281-9.

    34 Marson A, Levine SS, Cole MF, Frampton GM, Brambrink T, Johnstone S, Guenther MG, Johnston WK, Wernig M, Newman J, Calabrese JM, Dennis LM, Volkert TL, Gupta S, Love J, Hannett N, Sharp PA, Bartel DP, Jaenisch R, Young RA (2008). Cell. 134(3):521-33.

    35 Khalil AM, Guttman M, Huarte M, Garber M, Raj A, Rivea Morales D, Thomas K, Presser A, Bernstein BE, van Oudenaarden A, Regev A, Lander ES, Rinn JL (2009). Proc Natl Acad Sci U S A. 106(28):11667-72.

    36 Fernandez AF, Assenov Y, Martin-Subero JI, Balint B, Siebert R, Taniguchi H, Yamamoto H, Hidalgo M, Tan AC, Galm O, Ferrer I, Sanchez-Cespedes M, Villanueva A, Carmona FJ, Sanchez-Mut JV, Berdasco M, Moreno V, Capella G, Monk D, Ballestar E, Ropero S, Martinez R, Sanchez-Carbayo M, Prosper F, Agirre X, Fraga MF, Graña O, Perez-Jurado L, Mora J, Puig S, Prat J, Badimon L, Puca AA, Meltzer SJ, Lengauer T, Bridgewater J, Bock C, Esteller M. (2011). Genome Research, PMID:21613409, 2011.

Related content

Be sure to take the GEN Poll

Drug Price Hikes


More »