1. Epigenetic features, such as chromatin modifications and
DNA methylation can have profound effects on the
regulatory landscape of the human genome. These
epigenetic markers play important roles in modulating
tissue-specific and developmental-stage specific gene
expression and can be altered by environmental exposures
such as cigarette smoke. A deeper understanding of the
mechanisms by which cigarette smoke exerts toxic effects
within the cell has important implications for human health
and disease. We developed a novel methylation specific
high resolution melt assay (MS-HRM) to rapidly and
efficiently validate locus-specific DNA methylation. A
previous study used 450K methylation arrays to detect
changes in DNA methylation in newborn cord blood whose
mothers smoked during pregnancy and identified 26
significant differentially methylated CpG loci residing in the
aryl hydrocarbon receptor repressor (AHRR), myosin 1G
(MYO1G), and growth factor independent 1 (GFI1)
genes. Using 450K methylation arrays, we attempted to
detect the same differentially methylated regions in
mononuclear cells (MNCs) and monocytes (CD14+) of adult
smokers and non-smokers (n=261), and used this data to
compare with the MS-HRM assay. Using a small subset of
these samples (n=18), we were able to detect a significant
decrease in methylation in the AHRR gene (cg05575921)
using the MS-HRM assay (p=1.41x10-5) and 450K
methylation arrays (p=7.65x10-6). The monocyte fraction
showed a greater difference between smokers and non-
smokers, indicating that hypomethylation at cg0557521 is
more prominent in monocytes. This change in methylation
was found to have a non-linear exponential correlation with
the expression of the AHRR gene. Ultimately, MS-HRM
represents a useful and inexpensive tool to rapidly
determine the methylation status of specific genomic loci.
Abstract
A Novel High Resolution Melt Assay for Validating Locus-Specific DNA Methylation Profiles
Devin Porter, Ryan Gimple, Chris Crowl, Dan Su, Michelle Campbell, Gary Pittman, Kelly Adamski, Xuting Wang, Douglas Bell
National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
Gene Expression and DNA Methylation
Discussion
Funded in part by the Intramural Research Program of National Institute
of Environmental Health Sciences and a grant from the NIH/FDA Center
for Tobacco Research.
We thank Drs. Michael Ziller and Alexander Meissner of the Broad
Institute of MIT and Harvard for their generosity in providing the R code
to identify differentially methylated regions.
Acknowledgments
References
Future Directions
• Compare MS-HRM to BiSulfite Ampliction Sequencing (BSAS).
• Validation of other CpG sites that are differentially methylated in smokers
vs. non-smokers.
• Investigation of differentially methylated regions in other hematopoietic cell
types.
Hypomethylation of cg05575921 correlates with increased AHRR expression
Introduction
Epigenome-wide studies (EWAS) identified highly significant
and reproducible methylation changes associated with
prenatal smoke exposure
• This Manhattan plot shows differential methylation in response to prenatal
smoke exposure using 450K methylation array analysis. Findings are
reproducible in both MoBa (Norwegian Mother and Child Cohort Study)
and NEST (Newborn Epigenetics Study) cohorts.1
• cg05575921 was identified as the most significant differentially methylated
CpG site (p<10-27), and therefore we sought to detect methylation
changes at this site using our High Resolution Melt assay.
• MNCs (A and B) and CD14+ cells (C and D) are comparable between the two
methods, however MS-HRM has a higher dynamic range.
• CD14+ cells greatly contribute to the methylation differences seen between
smokers and non-smokers in MNCs, while the methylation differences in other
white blood cells may contribute to a lesser extent.
MS-HRM can be used to validate 450K results
• Hypomethylation of cg05575921 results in increased AHRR gene expression.
The data suggests that this phenomenon occurs exponentially and is more
prominent in CD14+ cells.
• Although hypomethylation of cg05575921 does not directly correlate with AHRR
gene expression, it play a prominent role. Other factors that regulate gene
expression may have a more profound effect, however DNA methylation should
be a central aspect of any detailed mechanism.
Aryl Hydrocarbon Receptor Pathway
How does DNA methylation affect gene expression?
What is the role of AHRR?
 CpG methylation changes in enhancer regions may impair transcription
factor binding.
 It is hypothesized that deregulation of vital cell processes are initiated by
these acquired differentially methylated regions.
 The Aryl Hydrocarbon Receptor
Repressor (AHRR) regulates AHR
in a negative feed-back loop by
heterodimerizing to ARNT, and
binding to AHR responsive
elements, thus suppressing
CYP1A1 expression.
 Dysregulation of this pathway has
been indicated to be involved in
tumorgenesis and interacts with
p53, hypoxia, and oxidative stress
pathways.
Conclusions
Using this novel methylation-specific high resolution melt assay, we were
able to successfully:
• Develop a method to analyze melt curve fluorescence signal data.
• Validate our method with Illumina’s 450K Methylation Array.
• Distinguish significant methylation differences between smokers
and non-smokers in mononuclear cells and CD14+ cells.
• Observe correlations between these methylation differences with
changes in gene expression.
• Create a cheaper alternative for standard curves using custom
designed oligonucleotides from IDT.
This method enables the user to cost-effectively and
rapidly analyze known differentially methylated regions in
the genome.
Custom Designed Oligonucleotide Standards
• To address the question of bisulfite conversion efficiency and the reliability of
interpolated methylation results using converted genomic DNA (Zymo), we
designed132 bp oligonucleotides representing the region of interest around
cg05575921 assuming 100% efficient bisulfite conversion (IDT).
• Standard curves using these DNA templates (IDT) were compared to
standard curves generated using converted genomic DNA (Zymo).
Methods Overview
(A) Bisulfite treatment deaminates
unmethylated Cytosine (C) to form
Uracil (U), while methylated cytosines
are unaffected. This process alters
the DNA sequence based on
methylation status. Figure from Penn
iGEM5.
(B) Methyl Primer Express Software
(Applied Biosystems) optimized
amplicon length and the number of CpG
sites in the amplicon and primer. These
factors affect the sensitivity of the
assay.2 Figure from PrimerDesign6.
Bisulfite ConversionA
Primer Design and
Optimization
B
High Resolution Melt Data Analysis
Fluorescence data is evaluated to yield a standard curve
(C) PCR amplified bisulfite converted
genomic DNA. Adjusting cycling
conditions and annealing
temperatures can affect the sensitivity
of the assay. Figure from Penn
iGEM5.
(D) Post-PCR products are melted
slowly, releasing the saturated
intercalating dye and causing the
fluorescent signal to dissipate. The
change in this signal over time can be
used to infer the starting DNA
methylation state2.
Polymerase Chain ReactionC
High Resolution MeltD
Figure from Applied
Biosystems Technical
Presentation 4
Color Key:
• Methylated CpG and non-methylated CpG
after conversion.
• Primer region.
AHRR cg05575921
Single stranded, synthesized, “pre-converted” oligonucleotide for 100% methylation
(+)5’TGTATTCGGTTGGGTTTTATTTGATACGTAGTTTTTTAGTTTTTTATTGTTCGAGGG
GTGGGTTTTGGGAGTGGTTTTGGTAGGGTTTTTTTTTGTAGAATTTGCGGGATTAGTAGGTC
GGGCGGTGGTTGG 3’
(-)5’CCAACCACCGCCCGACCTACTAATCCCGCAAATTCTACAAAAAAAAACCCTACCAAA
ACCACTCCCAAAACCCACCCCTCGAACAATAAAAAACTAAAAAACTACGTATCAAATAAAAC
CCAACCGAATACA 3’
Single stranded, synthesized, “pre-converted” oligonucleotide for 0% methylation
(+)5’TGTATTTGGTTGGGTTTTATTTGATATGTAGTTTTTTAGTTTTTTATTGTTTGAGGG
GTGGGTTTTGGGAGTGGTTTTGGTAGGGTTTTTTTTTGTAGAATTTGTGGGATTAGTAGGTT
GGGTGGTGGTTGG 3’
(-)5’CCAACCACCACCCAACCTACTAATCCCACAAATTCTACAAAAAAAAACCCTACCAAA
ACCACTCCCAAAACCCACCCCTCAAACAATAAAAAACTAAAAAACTACATATCAAATAAAAC
CCAACCAAATACA 3’
R² = 0.6316
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18 20
%DNAMethylation(MSP-HRM)
Gene Expression, FC (RT-PCR)
Non-smoker
Smoker
AHRR - cg05575921 Methylation Vs.
Gene Expression in MNC
p=2.35x10-4
R² = 0.6166
0
20
40
60
80
100
0 10 20 30 40 50
%DNAMethylation(MSP-HRM)
Gene Expression, FC (RT-PCR)
Non-smoker
Smoker
AHRR - cg05575921 Methylation Vs.
Gene Expression in CD14+ Cells
p=6.80x10-5
BA
cg05575921
B
C D
A
MS-HRM Vs. 450K in MNC and CD14+ Cells
MS-HRM: IDT Vs. Zymo
• The custom designed IDT standards (A) interpolated methylation 25% greater
than the Zymo standards (B). This is possibly due to bisulfite conversion
efficiency of the Zymo standards.
• Similar p-values were obtained for both plots.
• The two standards correlate with each other with an R2 of 0.999 and both
correlate similarly to 450K results (C).
Custom designed IDT controls are comparable to Zymo control
R² = 0.8894
R² = 0.9016
0
20
40
60
80
100
120
0 20 40 60 80 100
MS-HRM(%Methylation)
450K (% Methylation)
450K vs. MS-HRM AHRR-05575921
Smokers
Non-Smokers
Linear
(ZYMO)
100 %
75 %
50
%
25 %
10 %
0 %
A. Raw Melt Curves showing
dissipation of fluorescent signal
due to melting of DNA.
B. Negative derivative of melt curves
depicts the change in fluorescent
signal over time, with the peak
corresponding to the melting
temperature.
C. Melt curves are normalized and
aligned to visualize differences in
melting temperature, representing
differences in methylation
percentage.2 Higher percentages
of methylation lead to higher
melting temperatures because of
increased numbers of CG
nucleotides.
D. Difference plot shows melting
profile as fluorescence difference
from the 50% methylated
standard. For data analysis, a
single temperature is chosen
where standards can be most
easily resolved (orange line).
E. Fluorescence values at the
chosen temperature are used to
generate a standard curve.
Interpolation to this curve allows
for calculation of the methylation
percentage of unknown samples.
1. Joubert, B. R., S. E. Håberg, et al. (2012). "450K Epigenome-Wide Scan Identifies Differential
DNA Methylation in Newborns Related to Maternal Smoking During Pregnancy." Environmental
Health Perspectives.
2. Tobler, A., O’Donoghue, M., et al (2010). “Methylation Analysis using Methyation-Sensitive HRM
and DNA Sequencing.” Application Note: Applied Biosystems, Life Technologies.
3. Wojdacz, T. K., T. Borgbo, et al. (2009). "Primer design versus PCR bias in methylation
independent PCR amplifications." Epigenetics : official journal of the DNA Methylation Society
4(4): 231-234.
4. Bruno, A. “High Resolution Melt with MeltDoctor Reagents” Applied Biosystems Technical
Guide.
5. "Penn iGEM." University of Pennsylvania. 2013.
<http://2013.igem.org/Team:Penn/AssayOverview>.
6. "Primer Design." BioWeb. University of Wisconsin, 2008.
<https://bioweb.uwlax.edu/GenWeb/Molecular/seq_anal/primer_design/primer_design.htm>.
7. "Interpretation of Sequencing Chromatograms." University of Michigan DNA Sequencing
Core.<http://seqcore.brcf.med.umich.edu/doc/dnaseq/interpret.html>
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AHRR
RRBS: CD14+ Nonsmoker
RRBS: CD14+ Smoker
RRBS: Differential Methylation
H3K4Me1
ChiP-seq: TF-binding
H3K27Ac
Differentially Methylated Region