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Differentially methylated region

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Differentially methylated regions (DMRs) are stretches of DNA in an organism’s genome that have different DNA methylation patterns compared to other samples. These samples can be different cells or tissues within the same individual, the same cell at different times, or cells from different individuals. (Note: this is different from histone modification.)

DNA is mostly methylated at a CpG site, which is a cytosine followed by a guanine. The “p” refers to the phosphate linker between them. DMR usually involves adjacent sites or a group of sites close together that have different methylation patterns between samples. However, CpG islands appear to be excluded from this as they are normally unmethylated.[1]

Types of DMR

There are several different types of DMRs. These include tissue-specific DMR (tDMR), cancer-specific DMR (cDMR), reprogramming-specific DMR (rDMR), imprinting- specific DMR (iDMR), and aging-specific DMR (aDMR).[1]

The identification of tDMRs provides a comprehensive survey of epigenetic differences among human tissues.[2] An example of a tDMR would be different methylation patterns between an eye cell and a liver cell within the same individual.

DMRs between cancer and normal samples (cDMRs) demonstrate the aberrant methylation in cancers.[3]

It is well known that DNA methylation is associated with cell differentiation and proliferation.[4] Many DMRs have been found in the development stages (dDMRs) [5] and in the reprogrammed progress (rDMRs).[6]

When one of the two parental chromosomes is methylated in a particular region, this results in an iDMR.[7] This plays a role in epigenetics, which is discussed further in the following section.

In addition, there are aDMRs, also known as intra-individual DMRs (Intra-DMRs), which are changes in global DNA methylation as the age of a given individual increases.[8]

Finally, there are inter-individual DMRs (Inter-DMRs) with different methylation patterns among multiple individuals.[9]

Role in Epigenetics

DMRs are involved in genomic imprinting because they can be methylated in accordance with either the maternal or the paternal chromosome. The methylated allele is often, but not always, the silenced allele. Differences between the methylation pattern in the parental chromosome and the offspring’s chromosome are considered epigenetic lesions.[10] There has been some concern that artificial reproductive techniques increase the rate of abnormal methylation in DMRs, leading to an increased rate of disease. This appears to be dispelled by a study that found methylation differences between in vitro fertilized conceived twins and naturally conceived twins were statistically insignificant.[11]

Role in Disease

DMRs are implicated in a number of different diseases including various cancers, osteoarthritis and osteoporosis, Beckwith-Wiedemann syndrome (a developmental disorder), and [www.lupus.org/ lupus] (an autoimmune disease). The following discussion of specific diseases is but a small sampling of the body of research on DMRs and their potential roles in disease.

In a study published in 2005, Weber and colleagues studied the differences in methylation patterns between healthy colon cells and cancerous ones. In order to find the DMRs, they developed a new technique called methylated DNA immunoprecipitation (MeDIP). This new technique is easier and less biased than older techniques, and it is more suitable for large sections of the genome. It is based on antibodies specifically binding methylated DNA. The researchers found that the cancer cells experienced lower methylation rates than the healthy cells, but only in gene poor regions of the genome.[12]

In a study published in 2013, Delgado-Calle and colleagues compared genome-wide methylation patterns in women with osteoporosis and women with osteoarthritis. They found about 250 differentially methylated sites. These were primarily in regions coding for transcription factors that are involved in cell differentiation and skeletal formation, suggesting there may be a developmental predisposition for these disorders.[13]

In a study published in 2011, Jeffries and colleagues examined the differences in methylation patterns in CD4 T cells (a type of white blood cell) of healthy patients and patients with lupus, an autoimmune disease. They found 341 differentially methylated sites, of which 236 were less methylated and 105 were more methylated in lupus patients than in healthy patients. Many of the sites are in genes that have been previously shown to play a role in autoimmunity.[14]

In a study published in 2015, Kulis and colleagues, to evaluate the epigenetic link between normal B cell differentiation and neoplastic transformation, compared the DNA methylomes of B cell neoplasms with those of their normal cell counterparts. Acute lymphoblastic leukemia[15] versus pre-B cells, germinal center B cell–like diffuse large B-cell lymphoma versus germinal center B cells and multiple myeloma[16] versus plasma cells. A large fraction of the CpGs differentially methylated in cancers are dynamically methylated during normal B cell differentiation. Hypomethylation in acute lymphoblastic leukemia was enriched for CpGs in enhancers, whereas hypomethylation in diffuse large B-cell lymphoma and multiple myeloma predominantly affected CpGs in heterochromatin. Acute lymphoblastic leukemia cells are arrested at the pre-B cell stage, they acquired hypermethylation in Polycomb-repressed regions, which is characteristic of more mature differentiation stages. Multiple myeloma cells, in contrast, did not acquire hypermethylation of Polycomb-repressed regions, as their cell of origin already shows this feature, but, as they downregulate the B cell program, they acquire hypermethylation of CpGs in B cell–specific enhancers.[17]

Tools for Identification of Differentially Methylated Regions

QDMR (Quantitative Differentially Methylated Regions) is a quantitative approach to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy (http://bioinfo.hrbmu.edu.cn/qdmr). The platform-free and species-free nature of QDMR makes it potentially applicable to various methylation data. This approach provides an effective tool for the high-throughput identification of the functional regions involved in epigenetic regulation. QDMR can be used as an effective tool for the quantification of methylation difference and identification of DMRs across multiple samples.[18]

A Bioconductor computer software package, titled BiSeq, takes advantage of bisulfite sequencing data. It runs algorithms on supplied data in order to detect DMRs, and allows the researcher to focus on target regions and choose an acceptable error rate.[19]

The entropy-based Specific Methylation Analysis and Report Tool, termed "SMART”, which focus on integrating a large number of DNA methylomes for the de novo identification of cell type-specific MethyMarks, is available at http://fame.edbc.org/smart/.[20][21] SMART has been released as a Python package called “SMART-BS-Seq” and is freely available from the Python Package Index (https://pypi.python.org/pypi/SMART-BS-Seq).

Tools for Analysis of Differentially Methylated Regions

methyAnalysis is another computer software package provided by Bioconductor that is used to visualize and analyze DNA methylation data. This package includes a function that reduces noise in microarray data caused by variation in sequences of different samples and fixed probe designs. It can also detect DMRs using statistical tests. There is also a function designed to annotate the region. Finally, the data can be visualized either through exportation or using a heatmap function within the program[22]

Another tool for analyzing DMRs is an open software package called Bsmooth. It was developed by Kasper Daniel Hansen, Benjamin Langmead, and Rafael A. Irizarry at Johns Hopkins to be used with whole genome bisulfite sequencing data. It includes functions for aligning the data, quality control and identifying DMRs.[23]

References

  1. ^ a b Rakyan, Vardhman K.; Down, Thomas A.; Balding, David J.; Beck, Stephen (August 2011). "Epigenome-wide association studies for common human diseases". Nature Reviews Genetics. 12: 529–541. doi:10.1038/nrg3000.
  2. ^ Rakyan VK, Down TA, Thorne NP, Flicek P, Kulesha E, Gräf S, Tomazou EM, Bäckdahl L, Johnson N, Herberth M, Howe KL, Jackson DK, Miretti MM, Fiegler H, Marioni JC, Birney E, Hubbard TJ, Carter NP, Tavaré S, Beck S (September 2008). "An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs)". Genome Research. 18 (9): 1518–29. doi:10.1101/gr.077479.108. PMC 2527707. PMID 18577705.
  3. ^ Irizarry, RA; Ladd-Acosta, C; Wen, B; Wu, Z; Montano, C; Onyango, P; Cui, H; Gabo, K; Rongione, M; Webster, M; Ji, H; Potash, JB; Sabunciyan, S; Feinberg, AP (February 2009). "The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores". Nature Genetics. 41 (2): 178–86. doi:10.1038/ng.298. PMC 2729128. PMID 19151715.
  4. ^ Reik, W; Dean, W; Walter, J (Aug 10, 2001). "Epigenetic reprogramming in mammalian development". Science. 293 (5532): 1089–93. doi:10.1126/science.1063443. PMID 11498579.
  5. ^ 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 (Aug 7, 2008). "Genome-scale DNA methylation maps of pluripotent and differentiated cells". Nature. 454 (7205): 766–70. Bibcode:2008Natur.454..766M. doi:10.1038/nature07107. PMC 2896277. PMID 18600261.
  6. ^ Doi, A; Park, IH; Wen, B; Murakami, P; Aryee, MJ; Irizarry, R; Herb, B; Ladd-Acosta, C; Rho, J; Loewer, S; Miller, J; Schlaeger, T; Daley, GQ; Feinberg, AP (December 2009). "Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts". Nature Genetics. 41 (12): 1350–3. doi:10.1038/ng.471. PMC 2958040. PMID 19881528.
  7. ^ Tomizawa, Shin-ichi; Kobayashi, Hisato; Watanabe, Toshiaki; Andrews, Simon; Hata, Kenichiro; Kelsey, Gavin; Sasaki, Hiroyuki (March 1, 2011). "Dynamic stage-specific changes in imprinted differentially methylated regions during early mammalian development and prevalence of non-CpG methylation in oocytes". Development. 138 (5): 811–820. doi:10.1242/dev.061416.
  8. ^ Bjornsson HT, Sigurdsson MI, Fallin MD, Irizarry RA, Aspelund T, Cui H, Yu W, Rongione MA, Ekström TJ, Harris TB, Launer LJ, Eiriksdottir G, Leppert MF, Sapienza C, Gudnason V, Feinberg AP (Jun 25, 2008). "Intra-individual change over time in DNA methylation with familial clustering". JAMA: The Journal of the American Medical Association. 299 (24): 2877–83. doi:10.1001/jama.299.24.2877. PMC 2581898. PMID 18577732.
  9. ^ Bock, C; Walter, J; Paulsen, M; Lengauer, T (June 2008). "Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping". Nucleic Acids Research. 36 (10): e55. doi:10.1093/nar/gkn122. PMC 2425484. PMID 18413340.
  10. ^ Weksberg, Rosanna; Smith, Adam C.; Squire, Jeremy; Sadowski, Paul (January 2003). "Beckwith–Wiedemann syndrome demonstrates a role for epigenetic control of normal development". Human Molecular Genetics. 12 (Review Issue 1): R61–R68. doi:10.1093/hmg/ddg067.
  11. ^ Li, Lei; Wang, Liya; Le, Fang; Liu, Xiaozhen; Yu, Ping; Sheng, Jiangzhong; Huang, Hefeng; Jin, Fan (May 2011). "Evaluation of DNA methylation status at differentially methylated regions in IVF-conceived newborn twins". Fertility and Sterility. 95 (6): 1975–1979. doi:10.1016/j.fertnstert.2011.01.173.
  12. ^ Weber, M.; Davies, J.; Wittig, D.; Oakeley, E.; Haase, M.; Lam, W.; Schübeler, D. (2005). "Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells". Nature Genetics. 37 (8): 853–862. doi:10.1038/ng1598. PMID 16007088.
  13. ^ Delgado-Calle, Jesús; Fernández, Agustín F.; Sainz, Jesús; Zarrabeitia, María T.; Sañudo, Carolina; García-Renedo, Raúl; Pérez-Nu´n˜ez, María I.; García-Ibarbia, Carmen; Fraga, Mario F.; Riancho, José A. (January 2013). "Genome-Wide Profiling of Bone Reveals Differentially Methylated Regions in Osteoporosis and Osteoarthritis". Arthritis & Rheumatism. 65 (1): 197–205. doi:10.1002/art.37753.
  14. ^ Jeffries, Matlock A.; Dozmorov, Mikhail; Tang, Yuhong; Merrill, Joan T.; Wren, Jonathan D.; Sawalha, Amr H. (May 2011). "Genome-wide DNA methylation patterns in CD4+ T cells from patients with systemic lupus erythematosus". Epigenetics. 6 (5): 593–601. doi:10.4161/epi.6.5.15374.
  15. ^ Busche, Stephan; Ge, Bing; Vidal, Ramon; Spinella, Jean-François; Saillour, Virginie; Richer, Chantal; Healy, Jasmine; Chen, Shu-Huang; Droit, Arnaud (2013-07-15). "Integration of high-resolution methylome and transcriptome analyses to dissect epigenomic changes in childhood acute lymphoblastic leukemia". Cancer Research. 73 (14): 4323–4336. doi:10.1158/0008-5472.CAN-12-4367. ISSN 1538-7445. PMID 23722552.
  16. ^ Agirre, Xabier; Castellano, Giancarlo; Pascual, Marien; Heath, Simon; Kulis, Marta; Segura, Victor; Bergmann, Anke; Esteve, Anna; Merkel, Angelika (2015-04-01). "Whole-epigenome analysis in multiple myeloma reveals DNA hypermethylation of B cell-specific enhancers". Genome Research. 25 (4): 478–487. doi:10.1101/gr.180240.114. ISSN 1549-5469. PMC 4381520. PMID 25644835.
  17. ^ Kulis, Marta; Merkel, Angelika; Heath, Simon; Queirós, Ana C.; Schuyler, Ronald P.; Castellano, Giancarlo; Beekman, Renée; Raineri, Emanuele; Esteve, Anna (2015-07-01). "Whole-genome fingerprint of the DNA methylome during human B cell differentiation". Nature Genetics. 47 (7): 746–756. doi:10.1038/ng.3291. ISSN 1061-4036.
  18. ^ Zhang, Y; Liu, H; Lv, J; Xiao, X; Zhu, J; Liu, X; Su, J; Li, X; Wu, Q; Wang, F; Cui, Y (May 2011). "QDMR: a quantitative method for identification of differentially methylated regions by entropy". Nucleic Acids Research. 39 (9): e58. doi:10.1093/nar/gkr053. PMC 3089487. PMID 21306990.
  19. ^ Hebestreit, Katja; Dugas, Martin; Klein, Hans-Ulrich (May 8, 2013). "Detection of significantly differentially methylated regions in targeted bisulfite sequencing data". Bioinformatics. 29 (13): 1647–1653. doi:10.1093/bioinformatics/btt263.
  20. ^ Liu, Hongbo; Liu, Xiaojuan; Zhang, Shumei; Lv, Jie; Li, Song; Shang, Shipeng; Jia, Shanshan; Wei, Yanjun; Wang, Fang; Su, Jianzhong; Wu, Qiong; Zhang, Yan (8 January 2016). "Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes". Nucleic Acids Research. 44 (1): 75–94. doi:10.1093/nar/gkv1332.
  21. ^ Liu, Hongbo. "Specific Methylation Analysis and Report Tool". SMART.
  22. ^ Du, Pan; Bourgon, Richard. "methyAnalysis:an R package for DNA methylation data analysis and visualization" (PDF). Bioconductor. Retrieved September 14, 2014.
  23. ^ Hansen, Kasper D.; Langmead, Benjamin; Irizarry, Rafael A. (October 2012). "BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions". Genome Biology. 13 (10): R83. doi:10.1186/gb-2012-13-10-r83.{{cite journal}}: CS1 maint: unflagged free DOI (link)
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