Differentially methylated regions

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).

The identification of tDMRs provides a comprehensive survey of epigenetic differences among human tissues. 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.

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

When one of the two parental chromosomes is methylated in a particular region, this results in an iDMR. 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.

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

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. 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.

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.

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.

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.

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 versus pre-B cells, germinal center B cell–like diffuse large B-cell lymphoma versus germinal center B cells and multiple myeloma 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.

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.

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.

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/. 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

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.