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 ProfilesDevin 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 Instituteof Environmental Health Sciences and a grant from the NIH/FDA Centerfor Tobacco Research.

We thank Drs. Michael Ziller and Alexander Meissner of the BroadInstitute of MIT and Harvard for their generosity in providing the R codeto 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

% D

NA

Me

thyla

tio

n(M

SP

-HR

M)

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

% D

NA

Me

thyla

tio

n(M

SP

-HR

M)

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

-HR

M (

%M

eth

yla

tion)

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