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Development Of High Throughput Epigenomic Profiling Technologies And
Their Application To Twin Based DNA Methylation Studies
By
Zachary Aaron Kaminsky
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Institute of Medical Science
University of Toronto
© Copyright by Zachary Aaron Kaminsky 2009
Development Of High Throughput Epigenomic Profiling Technologies And
Their Application To Twin Based DNA Methylation Studies
Zachary Aaron Kaminsky
Doctor of Philosophy
Institute of Medical Science
University of Toronto
2009
Thesis Abstract
Epigenetic studies hold the promise of addressing some of the fundamental questions of human
biology including development, cell differentiation, and the aetiological mechanisms of complex
disease. Over the last years, several new large scale high throughput technologies have been
developed to allow genome wide profiling of epigenetic signals such as DNA methylation and
histone modifications. Two of such technologies were developed in our laboratory enabling a
genome wide microarray based profiling of DNA methylation signatures and a high throughput
method for the site specific interrogation of the density of methylated cytosine. Using these
techniques, we identified a DNA methylation difference in the 3’UTR of the DLX1 gene with
potentially functional implications to discordance in risk taking behavior in a single pair of MZ
twins. We modeled a power analysis on the effect size of the detected difference and determined
that approximately 6~25 discordant twin pairs will be adequate to yield 80% power across the
entire 12 K CpG island microarray platform using our epigenomic microarray profiling
technique. We performed a DNA methylome analysis of MZ twins in white blood cells (WBC),
buccal epithelial cells, and gut (rectum) biopsies (N=57 pairs in total) using 12K CpG island
microarrays providing the basis for the first annotation of epigenetic metastability of ~6,000
unique genomic regions in MZ twins. We performed a classical twin study on DNA methylation
differences in WBC and buccal epithelial cells from 39 pairs of MZ twins to 40 pairs of DZ
twins. DZ co-twins exhibited significantly higher epigenetic difference compared to the MZ co-
twins in buccal cells (p=1.2x10-294). While such higher epigenetic discordance in DZ twins can
result from DNA sequence differences, our in silico SNP analyses and comparison of
methylomes in inbred vs. outbred mice favour the hypothesis that this is due to epigenomic
ii
differences in the zygotes. This study suggests that molecular mechanisms of heritability may not
be limited to DNA sequence differences.
iii
Table of Contents
Thesis Abstract................................................................................................................................ ii
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
List of Appendices .......................................................................................................................... x
List of Abbreviations ..................................................................................................................... xi
Acknowledgments........................................................................................................................ xiii
Thesis Introduction ......................................................................................................................... 1
Epigenetics .................................................................................................................................. 1
Epigenetic signals and biological development .......................................................................... 3
Epigenetic metastability .............................................................................................................. 4
Epigenomic profiling technologies ............................................................................................. 5
Site specific DNA methylation profiling .................................................................................... 6
Epigenomic microarray technologies.......................................................................................... 6
The classical twin design............................................................................................................. 8
Epigenetics and the classical twin design ................................................................................. 10
Epigenetic inheritance ............................................................................................................... 13
Thesis Objectives .......................................................................................................................... 18
Chapter 1......................................................................................................................................... 0
Microarray-based DNA Methylation Profiling:Technology and Applications .............................. 0
Summary ................................................................................................................................... 21
Introduction ............................................................................................................................... 22
Results ....................................................................................................................................... 23
Enrichment of the unmethylated fraction of genomic DNA ................................................. 23
Microarray design.................................................................................................................. 28
iv
Detection of confounding effects of DNA sequence variation.............................................. 29
Reproducibility ...................................................................................................................... 31
Sensitivity .............................................................................................................................. 34
Examples of DNA methylation profiles ................................................................................ 34
Verification of detected methylation differences .................................................................. 36
Chromosome-wide mapping of DNA methylation differences............................................. 37
Discussion ................................................................................................................................. 42
Material and Methods................................................................................................................ 45
Microarray fabrication and data processing .......................................................................... 45
Methylation -sensitive digestion of genomic DNA (gDNA)................................................. 47
Adaptor-Ligation ................................................................................................................... 47
PCR........................................................................................................................................ 49
Array hybridizations .............................................................................................................. 49
Whole genome amplification................................................................................................. 49
Bisulfite sequencing .............................................................................................................. 50
Genomic DNA....................................................................................................................... 50
Chapter 2....................................................................................................................................... 52
Single Nucleotide Extension Technology for Quantitative Site Specific Evaluation of metC/C in
GC-Rich Regions .......................................................................................................................... 52
Abstract ..................................................................................................................................... 52
Introduction ............................................................................................................................... 53
Materials and Methods .............................................................................................................. 55
DNA sequence targets for SNaPshot interrogation ............................................................... 55
SNaPshot ............................................................................................................................... 62
Mismatch bias........................................................................................................................ 64
Mismatch bias correction....................................................................................................... 65
Degenerative primer experiments on oligonucleotide templates .......................................... 68
v
Degenerative primer experiments on bisulfite modified DNA ............................................. 68
Discussion ................................................................................................................................. 71
Chapter 3....................................................................................................................................... 51
Epigenetics of Personality Traits: An Illustrative Study of Identical Twins Discordant for Risk
Taking Behavior............................................................................................................................ 51
Abstract ..................................................................................................................................... 77
Introduction ............................................................................................................................... 78
Methods..................................................................................................................................... 80
Psychometric assessment....................................................................................................... 80
Zygosity testing ..................................................................................................................... 83
Epigenetic testing .................................................................................................................. 83
Results ....................................................................................................................................... 85
Psychometric assessment....................................................................................................... 85
Genetics ................................................................................................................................. 87
Epigenetics............................................................................................................................. 88
Discussion ................................................................................................................................. 92
Chapter 4....................................................................................................................................... 76
DNA Methylation Profiles in Monozygotic and Dizygotic Twins ............................................... 76
Abstract ..................................................................................................................................... 98
Introduction ............................................................................................................................... 98
Results and Discussion.............................................................................................................. 99
Methods................................................................................................................................... 112
Twin sample ........................................................................................................................ 112
DNA methylation profiling ................................................................................................. 113
Animal studies ..................................................................................................................... 113
Data analysis........................................................................................................................ 114
Test for association of epigenetic difference with cellular heterogeneity ........................... 114
vi
Biological and technical variation ....................................................................................... 115
Spot-wise epigenetic variation............................................................................................. 116
Cross tissue comparison ...................................................................................................... 116
Investigation of genomic element class............................................................................... 116
Gene ontology analysis........................................................................................................ 117
Validation of the microarray findings ................................................................................. 117
In silico SNP analysis .......................................................................................................... 119
Thesis Discussion........................................................................................................................ 120
MZ co-twin epigenetic variation ............................................................................................. 121
Epigenomics of monochorionic and dichorionic twinning ..................................................... 122
Comparison of epigenetic profiles in MZ and DZ twins ........................................................ 123
Future Directions..................................................................................................................... 126
Appendix 1.................................................................................................................................. 128
References................................................................................................................................... 129
vii
List of Tables
Table 1.1. Enzymes that generate protruding ends in the restriction fragments,
which are complementary to the adaptors U-CG1, TA-1 and AATT-1
Table 1.2. Distribution of the detected unmethylated sites in respect to the known genes
as defined by the combined set of RefSeq and UCSC Known Genes for each
brain DNA sample (M17-M25) and the merged map.
Table 4.1. GO analysis of loci with high MZ co-twin epigenetic similarity
Table 4.2. GO analysis of loci with low MZ co-twin epigenetic similarity
Table 4.3. Sodium bisulfite treated loci and primers
viii
List of Figures
Figure 1.1. Schematic outline of the microarray-based method for identification of DNA
methylation differences and DNA polymorphisms in genomic DNA
Figure 1.2. Selective enrichment of restriction fragments with the universal adaptor
U-CG1
Figure 1.3. Comt Microarray Design
Figure 1.4. Combined methylation- and SNP-analysis on a CpG island microarray
Figure 1.5. Reproducibility and sensitivity of the method
Figure 1.6. Applications of the epigenetic profiling technology
Figure 1.7. Examples of applications using a CpG island microarray
Figure 1.8. Profiles of unmethylated sites in three loci on human chromosomes 21 & 22
Figure 1.9. Genomic views showing unmethylated regions on chromosomes 21 and 22
Figure 2.1. Bisulfite modified COMT promoter region and SNaPshot primers
Figure 2.2. Single SNP oligonucleotide templates
Figure 2.3. Multiple SNP oligonucleotide templates
Figure 2.4. Galectin1 and Humanin SNaPshot primers
Figure 2.5. SNaPshot primers accurately measure DNA methylation
Figure 2.6. Multiplexed SNaPshot reaction output
Figure 2.7. Primer mismatch induced bias
Figure 2.8. Correction of mismatch induced bias
Figure 2.9. SNaPshot vs. cloning and sequencing data
Figure 2.10. Multiple peaks
Figure 3.1. The Toronto Gambling Task displays and contingencies
Figure 3.2. MMPI-2 scores for the “law” twin and the “war” twin
Figure 3.3. Gambling performance for the twins as well as control subjects (n = 11)
Figure 3.4. Relative DNA methylation profiles of the “war” twin vs. “law” twin
Figure 3.5. Power vs. technical replicate hybridization number and sample size (N)
Figure 3.6. Power vs. effect size and sample size (N)
Figure 4.1. Biological vs. technical variation
Figure 4.2. Correlations between microarray and sodium bisulfite sequencing data
Figure 4.3. Pyrosequencing correlations as a function of distance
Figure 4.4. Karyogram of MZ co-twin epigenetic similarity in WBCs
Figure 4.5. Raw binding intensities of MC and DC MZ twin hybridizations
Figure 4.6. MZ and DZ ICC distributions in buccal cells
Figure 4.7. Karyogram of MZICC-DZICC values in buccal cells of DC MZ twins
Figure 4.8. Technical variation volcano plots of HpaII and MspI based enrichments
Figure 4.9. Distributions of inbred and outbred epigenetic variation
Figure A.1. Karyogram of MZ co-twin epigenetic similarity in buccal cells
Figure A.2. Karyogram of MZ co-twin epigenetic similarity in gut
Figure A.3. Karyogram of MZICC-DZICC values in WBCs
Figure A.4. Karyogram of MZICC-DZICC values in buccal cells of MC MZ twins
ix
List of Appendices
Appendix 1. Supplementary Figures
x
List of Abbreviations
3’ untranslated region (3’ UTR)
Applied Biosystems (ABI)
Armadillo repeat gene deleted in VCFS (ARVCF)
C1q tumor necrosis factor-related protein 8 precursor (C1QTNF8)
Canadian Institutes for Health and Research (CIHR)
Catechol-o-methyltransferase gene (COMT)
Chromatin immunoprecipitation (ChIP)
Comprehensive high-throughput arrays for relative methylation (CHARM)
CpG island (CGI)
D4 receptor (D4DR)
Differentially methylated region (DMR)
Distal-less Homeobox 1 gene (DLX1)
Dizygotic (DZ)
DNA adenine methyltransferases (DAM)
DNA methyltransferases (DNMT)
Dopamine receptor (D4DR)
False discovery rate (FDR)
Family-wise error rate (FWER)
Gene Ontology (GO)
Genome wide association studies (GWAS)
Genomic DNA (gDNA)
Heterozygosity quotient (HQ)
Histone 3 (H3)
Histone 3-lysine 4 (H3-K4)
Histone acetyltransferases (HAT)
Histone deacetylases (HDAC)
Histone methyltransferases (HMT)
Hypothalamic pituitary adrenal axis (HPA)
In vitro fertilization (IVF)
Intraclass correlation coefficient (ICC)
Long interspersed nuclear element (LINE)
Long tandem repeat (LTR)
Methylated cytosine (metC)
Methylation binding domain (MBD)
Methylation pattern error rates (MPER)
Methylation-sensitive single-nucleotide primer extension (Ms-SNuPE)
Methyl-CpG binding protein 2 (MECP2)
Methyl-DNA immunoprecipitation (MeDIP)
Methylene tetrahydrofolate reductase (MTHFR)
Monozygotic (MZ)
Multiphasic Personality Inventory-2 (MMPI-2)
Neuropeptide Y (NPY)
Online Mendelian Inheritance in Man (OMIM)
Ontario Mental Health Foundation (OMHF)
xi
Peptidylprolyl isomerase E-like (PPIEL)
Polycomb group complexes (PcG)
Polymerase chain reaction (PCR)
Position effect variegation (PEV)
S-adenosyl methionine (SAM)
SET and RING associated (SRA)
Short interfering RNA (siRNA)
Short interspersed nuclear element (SINE)
Single nucleotide polymorphism (SNP)
SWItch/Sucrose NonFermentable (SWI/SNF)
Thioredoxin reductase 2 gene (TXNRD2)
Wechsler Abbreviated Scale of Intelligence (WASI)
Wechsler Adult Intelligence Scale (WAIS-I)
White blood cells (WBC)
xii
Acknowledgments
I would like to thank all those who contributed to the completion of my PhD thesis. First of all, I
would like to thank my supervisor, Art Petronis, who inspired me to pursue my degree in the first
place and whose mentorship and faith in my abilities over the years has allowed me to succeed. I
would also like to thank John Vincent and Rosanna Weksberg, my advisory committee members
for their direction over the years. Additionally, I would like to thank Jim Kennedy, who always
made time for me despite his busy schedule. I would also like to thank James Flanagan and Jon
Mill for our numerous discussions that contributed to my scientific growth. Thanks to those who
contributed to the data and analysis presented within including Abbas Assasadazeh, Sigrid
Zeigler, Carolyn Ptak, Gabriel Oh, Jon Mill, Allan McRae, Peter Visscher, Grant Montgomery,
Carl Virtanen, Neil Winegarden, Jill Cheng, Thomas Gingeras, Thomas Tang, Axel Schumacher,
Philipp Kapranov, Sun-Chong Wang, Albert Wong, and Laura Feldcamp. I would also like to
thank our collaborators, Anthony Feinstein, Jonas Halfvarson, Curt Tysk, Darlene Floden, and
Nick Martin, for providing us with samples to study. I would like to acknowledge the Canadian
Institutes of Health Research who funded my work with a Canadian Graduate Scholarship,
Doctoral Award. Finally, I would like to thank my wife, Sharah Mar, for her support and
confidence in me over the years.
xiii
1
Thesis Introduction
Epigenetics
Epigenetics refers to regulation of various genomic functions controlled by partially stable
modifications of DNA and histones [1]. The epigenetic information is encoded in two types of
synergistically acting covalent modifications: DNA methylation and chromatin protein
modification [2]. In mammals, DNA methylation occurs most commonly on cytosines that are
directly followed by guanine, forming what is known as a CpG dinucleotide. Clusters of CpG
dinucleotides are referred to as CpG islands [3]. Methylated cytosine (metC) is often referred to as
the 5th base of the genetic code; however its function is related to transcriptional control as
opposed to DNA sequence based coding. There are numerous examples demonstrating that
transcription factor binding affinity is directly limited by the presence of methylation at the
binding sites [4, 5]. The density of metC in a gene regulatory region also contributes to gene
activity with a large number of genes exhibiting an inverse correlation between the degree of
methylation and the level of gene expression [6, 7]. DNA methylation can regulate genomic
functioning not only in terms of gene expression but also in the suppression of repetitive DNA
sequences [8], and the formation of architecturally functional chromatin structures such as
centromeric regions [9].
Methylation of DNA is mediated by proteins called DNA methyltransferases (DNMTs), four of
which have been identified thus far[10]. These include DNMT1, DNMT2, DNMT3a and
DNMT3b. DNMTL is believed to facilitate the activity of DNMT3a and 3b. DNMT1 is
primarily the maintenance DNA methyltransferase, responsible for methylating hemimethylated
daughter strands during DNA replication (ibid). DNMT3a and 3b are believed to be responsible
for de novo DNA methylation events [11]. The functions of DNMT2 are not well characterized.
Passive DNA demethylation can be achieved via the binding of transcription factors during DNA
replication[12] as well as through nuclear exclusion of oocyte specific DNA methyltransferase,
DNMT1o, during the demethylation that occurs post fertilization in mammals [13]. The
existence of a DNA demethylase capable of active DNA demethylation is a controversial subject
as none of the proposed mechanisms or implicated genes have been replicated or have affected
mouse viability in knock out experiments [12]. Recently, DNMT3a and 3b have been implicated
in DNA demethylase activity [14]. The authors observed periodic and strand specific
2
methylation and demethylation of the PS2 gene promoter in response to oestrogen activation in
cultured human breast cancer cells. Periods of demethylation coincided with chromatin
immunoprecipitation of DNMT3a and 3b. The authors proposed a mechanism whereby methyl-
cytosine is deaminated by the DNMTs in the absence of S-adenosyl methionine (SAM), resulting
in a mismatch that is subsequently excised and repaired with non-methylated cytosine by DNA
glycosylases (ibid). Unlike the other proposed DNA demethylases, knockout of DNMT3a
causes embryonic lethality in female mice and impairs spermatogenesis in males [15]; however,
the deleterious effects may be a reflection of this gene’s methyltransferase activity, not
demethylase activity. Like the other implicated DNA demethylases, further study will be
necessary to confirm its role in human development.
DNA modification acts in concert with alterations in chromatin structure that occur through
acetylation, methylation, phosphorylation, ubiquitination, and sumoylation of various histone
amino acid residues including lysine, arginine, and serine [16-21]. Histone acetylation and
methylation are the most heavily studied modifications of the ‘histone code’, as it has been aptly
named [2]. Histone acetylation mediated by histone acetyltransferases (HAT) and histone
deacetylases (HDAC) unravel and compact chromatin, respectively, through acetylation and
deacetylation of lysine residues on histone H4, respectively [22, 23]. Acetylated lysine residues
are recognized by proteins containing a conserved bromodomain that subsequently recruit
components of the basal transcription machinery [24]; however, alterations in histone acetylation
status are believed to be a short term mechanism [25]. Methylation occurs at lysine and arginine
residues via histone methyltransferases (HMT) [26]. Methylation of Histone 3-lysine 4 (H3-K4)
is associated with euchromatin (a loosely packed chromatin state) where as dimethylation of
lysine 9 (H3-K9) and trimethylation of lysine 27 (H3-K27) is associated with heterochromatin (a
tightly packed chromatin state) [24]. Histone methylation is not restricted to lysine residues and
may also occur at arginine residues at H3-R2, H3-R17, H3-R26, and H4-R3 [26]. The functions
of such modifications are only just beginning to be understood [27]. The epigenetic regulation of
the histone code is believed to be a dynamic process, switching between ‘on’ and ‘off’ states
during transcriptional regulation [28]. Conversely, DNA methylation is believed to represent a
more permanent mark denoting silenced genomic regions.
There is still much that is unknown about the scope and function of all aspects of the histone
code and its synergistic interactions with DNA methylation; however, there is evidence for
interaction between these two epigenetic signatures. Methylation of DNA recruits proteins
3
containing a methylation binding domain (MBD), termed MBD proteins[29]. The most well
characterized MBD protein is methyl-CpG binding protein 2 (MECP2), a protein that binds
methylated DNA and recruits HDAC complexes and ATP dependent chromatin remodeling
complexes such as the SWItch/Sucrose NonFermentable (SWI/SNF) complex [30, 31]. These
complexes deacetylate histones and compact chromatin, respectively, conferring a silenced
chromatin state complementing the DNA methylation status [32].
Epigenetic information encoded in histone modifications may also direct DNA methylation
patterning through interaction with the polycomb group proteins (PcGs), a heavily studied
protein family implicated in maintaining the tissue specific epigenetic profiles as well as
maintaining the pluripotency of embryonic stem cells [33]. PCGs bind genes involved in
developmental pathways both in Drosophila and mice as evidenced by chromatin
immunoprecipitation microarray (Chip on Chip) experiments binding a high proportion of
developmental transcription factors upon differentiation in murine embryonic stem cells [34, 35].
PCGs in turn can direct DNMTs, modulating DNA methylation in specific genomic regions [36]
[37] [38].
Other mechanisms for directing and maintaining epigenetic silencing exist through recruitment
of HDAC and DNMT complexes by proteins recognizing and being directed by the sequences of
non-coding and short interfering RNAs (siRNAs) in yeast, Drosophila, and plants [39, 40].To
date, the extent of the roles of non-coding RNA in transcription and genome stability is not well
understood [41] although these mechanisms have been implicated in X chromosome
inactivation[42] and silencing of repetitive elements[43].
Epigenetic signals and biological development
Epigenetic signals are necessary for the proper regulation and functioning of the genome [1],
with epigenetic mutations, or epimutations, having the potential to be as harmful to an organism
as genetic mutations. Knockout mice with homozygous deletions of DNMT1 exhibit embryonic
lethality. In addition to regulation of gene activity [4, 44-48] epigenetic factors may affect DNA
mutability [49] and genetic recombination [50]. Epigenetic patterns are established in a tissue
specific manner and are believed responsible for establishing and maintaining the cellular
identity of the >200 cell types in the human body[51-54]. DNA methylation controls lineage
commitment in hematopoeitic cells, with aberrant methylation observed in B-cell lymphoma cell
4
lines[55]. These studies and such examples highlight the importance of the epigenetic code to a
properly functioning genome [44, 56].
Epigenetic metastability
The epigenetic status of genes and genomes is far more dynamic in comparison to the DNA
sequence and is subject to changes under the influence of developmental programs, in the
presence of internal or external environmental epigenetic modifiers, or simply as a result of
stochastic processes relating to maintenance of epigenetic factors.
Numerous lines of evidence suggest that DNA methylation undergoes a stochastic rearrangement
referred to as metastability. Cell culture models of higher eukaryotic systems have demonstrated
that metastability can result from the relatively low fidelity of the DNA methylation maintenance
enzymes, such as DNMT1, as compared to that of the DNA repair machinery. DNMT1 has a
preference for binding hemimethylated CpGs in a manner dependent on the sequence content.
Acting alone, DNMT1 has a 30-fold affinity for long CpG stretches and 5 fold for randomly
dispersed CpGs [57]. Futher protein interaction is required for a more stable transmission of
DNA methylation during replication. DNMT1 is recruited to replicated DNA by the
ubiquitinated NP95 protein [57], which preferentially binds hemimethylated DNA via a SET and
RING associated (SRA) domain. NP95 then sequesters the N-terminal domain of DNMT1,
which subsequently methylates the replicating daughter strand (ibid). Tissue culture experiments
have identified a range of maintenance DNA methylation fidelity from 97% to 99.9% as well as
an additional fluctuation of 3-5% per mitosis in the form of de novo methylation [58]. In mice,
DNMT1 was found to methylate hemimethylated double stranded DNA with a fidelity of ~95%
[59]. This translates to a difference of roughly 3 orders of magnitude lower mitotic fidelity of
epigenetic patterns as compared to the DNA sequence (10-6 and 10-3 for DNA sequences and
DNA modification, respectively)[58]. It is clear that a portion of DNA methylation signals will
be lost or gained through thousands of replication events resulting in a DNA sequence
independent drift of epigenetic signals.
Another classic example of stochastic epigenetic regulation is position effect variegation (PEV).
PEV was first observed in 1930 in Drosophila when the expression of the White gene
responsible for white or red eye color manifested a mosaic pattern [60]. Such mosaic expression
was dependent on chromosomal rearrangements placing the White gene proximal to
epigenetically silenced heterochromatic regions, resulting in variable silencing [60, 61]. Models
5
for epigenetic silencing include a heterochromatic spreading in cis as well as effects mediated in
trans, possibly through spatial interactions and chromatin folding [60]. Importantly, maintenance
of the heterochromatic state of silenced genes is transmitted in a metastable state through
multiple cell divisions [60]. PEV highlights that the borders of epigenetically silenced regions of
the chromatin are not fixed and can affect the silenced status of neighboring regions.
Effects similar to PEV may be caused by aberrant regulation of repetitive elements, repetitive
DNA code of retroviral origin that comprises approximately 45% of the human genome [62].
Repetitive elements are often classified as long and short interspersed nuclear elements (LINEs
and SINEs) and long tandem repeats (LTRs) [63]. Epigenetic silencing via DNA and histone
methylation occurs at repetitive elements, which serves to silence their expression and inhibit
their retrotranspoable capability[62, 64]. Imperfect silencing of retrotransposons during
embryonic epigenetic reprogramming has been suggested to result in mosaic expression of
proximal genes [65].
Epigenomic profiling technologies
The past two decades have seen a dramatic increase in the available technologies for epigenetic
profiling, both at individual loci and at the genome wide level, have led to a promising beginning
to the profiling of the epigenome. Many of the new methods reflect an assimilation of existing
high throughput genome scanning technologies such as microarrays following a refitting to meet
the complexities of epigenetic studies. The primary epigenetic technologies can be broken down
into two categories. The first method is specific to DNA methylation and involves the chemical
treatment with sodium bisulfite, which deaminates all un-methylated cytosines to uracil while
methyl cytosine remains protected. This procedure produces sequence polymorphisms through
subsequent PCR amplification that can be detected with a variety of new techniques developed in
the last years, including the manuscript in chapter 2. These techniques allow a site specific
quantification of DNA methylation levels to within 5% with the resolution of 1 bp, allowing for
the identification of epimutations of individual CpGs with possible functional relevance. Such
techniques are invaluable for understanding the functional consequences of DNA methylation
changes at individual genes. The resolution of sodium bisulfite modification makes it the ‘gold
standard’ method of DNA methylation quantification; however, its applications lack the scope of
the second method, the genome wide technologies.
6
The second involves the segregation of the desired components of the genome, either with
antibodies specific to a chromatin modification or DNA methylation or through the selective
cutting of methylation sensitive restriction enzymes that will only cut at specific non-methylated
consensus sequences. This is then followed by the identification of the isolated sequences
through hybridization to microarrays or sequencing techniques. Such techniques allow a great
increase in the scope of epigenetic studies, often achieving a genome wide interrogation but
usually lacking in resolution.
Site specific DNA methylation profiling
The use of epigenomic microarray technology may identify regions of DNA methylation
difference of interest in the studies in which it is employed; however, in order to approach a
functional understanding of such differences, a more detailed investigation of DNA methylation
status at individual CpGs is necessary. While mi


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