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Page 1: Exploring Monoallelic Methylation Using High-throughput Sequencing

Exploring Monoallelic Methylation Using High-throughput Sequencing

Cristian CoarfaRonald Harris

Aleksandar MilosavljevicJoe Costello

Page 2: Exploring Monoallelic Methylation Using High-throughput Sequencing

Sequence-based profiling of DNA methylation: comparisons of methods and catalogue of allelic epigenetic modifications

Harris RA, Wang T, Coarfa C, Zhou X, Xi Y, Nagarajan RP, Hong C, Downey S, Johnson BE, Delaney A, Zhao Y, Olshen A, Ballinger T, Schillebeeckx M, Echipare L, O’Geen H, Lister R, Pelizzola M, Epstein C, Bernstein BE, Hawkins RD, Ren B, Chung WY, Gu H, Bock C, Gnirke A, Zhang MQ, Haussler D, Ecker J, Li W, Farnham PJ, Waterland RA, Meissner A, Marra MA, Hirst M, Milosavljevic A, Costello JF.

In press, Nature Biotechnology

Page 3: Exploring Monoallelic Methylation Using High-throughput Sequencing

1. Imprinting

2. Non-imprinted monoallelic methylation

3. Cell type-specific methylation

4. Sites of inter-individual variation in methylation level?

Biological importance of intermediate methylation levels

Page 4: Exploring Monoallelic Methylation Using High-throughput Sequencing

Methylated CpGsUnmethylated CpGs

methyl DNA immunoprecipitation

(MeDIP)

methylation-sensitive restriction digestion

(MRE)

~20 million reads/sample ~100 million reads/sampleIGAII sequencing

data visualization

Illumina library construction

5’ CpG islandsare unmethylated

3’ CpG island is partially methylated

Methylated

Unmethylated

combine parallel digests,ligate adapters,

size-select 100-300 bp

IP sonicated, adapter-ligatedDNA, size-select 100-300 bp

Page 5: Exploring Monoallelic Methylation Using High-throughput Sequencing

Unmethylated and Methylated patches within a CpG island

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high MeDIP, no or low MRE

high MRE, no or low MeDIP

1

2

high MRE and MeDIP (uniform)

high MRE and MeDIP(patch Methylation)

3

4

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Intermediate methylation levels at imprinted genes

Page 8: Exploring Monoallelic Methylation Using High-throughput Sequencing

Chr11 1533281 1536667 1.0342 91.9069 -205410 HCCA2

chr11 1946475 1948787 0.7769 58.5443 -18939 LOC100133545

chr11 1975141 1977439 1.2845 87.5516 0 H19

chr11 2245680 2250508 2.3451 99.4044 -29211 C11orf21

chr11 2420747 2423224 1.6565 29.5161 0 KCNQ1

Start Stop MRE MeDIP nearest gene Gene

Chr1. . . . . . . . . . . . . . . ...

. chr22 . . . . . . . . . . . . . . . .

Initial catalogue of Intermediate methylation sites

Ting Wang, Washington University

Page 9: Exploring Monoallelic Methylation Using High-throughput Sequencing

Using Genetic Variation to Detect Monoallelic Epigenomic and Transcription

States1. Monoallelic DNA methylation (MRE and MeDIP)

2. Monoallelic expression (MethylC-seq and RNA-seq)

3. Monoallelic Histone H3K4me3 (MethylC-seq and Chip-seq)

Page 10: Exploring Monoallelic Methylation Using High-throughput Sequencing

MethylC-seq +

ChIP-seq

MethylC-seq + RNA-seq

MRE-seq +

MeDIP-seq

Monoallelic Epigenomic Marks and Expression

3439

21

4

21

10

Page 11: Exploring Monoallelic Methylation Using High-throughput Sequencing

CpG islands

MRE-seq 1

MeDIP-seq 1

MRE-seq 2

MeDIP-seq 2Bisulfite

POTEB

Intermediate methylation levels in POTEB

chr15:19346666-19350003 G 9 A 30

Location Medip Allele Count MRE Allele Count

Page 12: Exploring Monoallelic Methylation Using High-throughput Sequencing

Validation of monoallelic DNA methylation in POTEB

Page 13: Exploring Monoallelic Methylation Using High-throughput Sequencing

Searching for Monoallelic Methlylation Using Shotgun Bisulfite Sequencing

• We expect streaks of 50%+/-delta methylation ratios

• Use 500bp windows tiling CpG Islands

• Compute average CpG methylation– CpG Islands– 1000 loci

• Infer distribution of methylation in 1000 loci

• Subselect 500bp windows tiling CpG Islands

• In the selected windows, look for allele specific methylation

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Questions

• How many of the 1000 loci can we rediscover ?

• How many of the 1000 loci show allele-specific methylation ?

• How many additional 500bp sites do we discover ?

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Average methylation over 500 bp window in CpG Islands and 1000 loci

Average Methylation Scores over 500bp windows in CpG Islands and 1000 putative intermediate methylation loci

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96

Percent methylation

% o

f w

ind

ow

s

% of CpG Islands w indow s

% w indow s in 1000 loci

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Parameter Search

• Experimented with various lower and upper bounds for methylation

• Guidelines

• Discover as many of the 1000 loci

• Reduce the number of additional 500bp windows

Lower Bound

Upper Bound

Number of 500bp windows

Number of 500bp windows overlapping 1000 loci

% of 500bp windows overlapping 1000 loci

1000 loci overlapped

10 70 24793 2851 0.114992135 950

10 80 28060 3877 0.138168211 989

10 90 36677 5512 0.15028492 999

20 70 14084 2345 0.166500994 926

20 80 17351 3371 0.19428275 977

20 90 25968 5006 0.192775724 990

30 70 9403 1912 0.20333936 884

30 80 12670 2938 0.231886346 958

30 90 21287 4573 0.21482595 979

30-80 rediscovers 958 of loci, at the highest specificity

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Incorporating Genetic Variation

• Search for allele-specific methylation• Look only into the 30-80% methylation loci overlapping

with CpG Islands• Use het SNPs• Check for those that separate reads into methylation

states in different directions• One allele >20%• Other allele <20%• Other thresholding methods possible

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Results• Found 6295 heterozygous sites • 586 sites have allele specific methylation• Overlap with 62 of the 1000 loci

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MethylC-seq +

ChIP-seq

MethylC-seq + RNA-seq

MRE-seq +

MeDIP-seq

Monoallelic Epigenomic Marks and ExpressionDistribution of the 62 SBS-ASM loci

79

1

4

16

00

Additional25 loci

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Acknowledgements NIEHS/NIDA: Joni Rutter, Tanya Barrett, Fred Tyson, Christine Colvis EDACC: R. Alan Harris, Cristian Coarfa, Xin Zhou, Yuanxin Xi, Wei Li, Robert A. Waterland,

Aleksandar Milosavljevic

UCSF/GSC REMC: Raman Nagarajan, Chibo Hong, Sara Downey, Brett E. Johnson, Allen Delaney, Yongjun Zhao, Marco Marra, Martin Hirst, Joseph Costello

– UCSC: Tracy Ballinger, David Haussler

– Washington University: Maximiliaan Schillebeeckx, Ting Wang

– UCD: Lorigail Echipare, Henriette O’Geen, Peggy J. Farnham

UCSD REMC: Ryan Lister, Mattia Pelizzola, Bing Ren, Joseph Ecker

– Cold Spring Harbor: Wen-Yu Chung, Michael Q. Zhang

Broad REMC: Hongcang Gu, Christoph Bock, Andreas Gnirke, Chuck Epstein, Brad Bernstein, Alexander Meissner


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