decoding epigenome with integrative “omics” data analysis

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Decoding epigenome with integrative “omics” data analysis Hehuang “David” Xie Sept, 2014

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Decoding epigenome with integrative “omics” data analysis. Hehuang “David” Xie Sept, 2014. Presentation Outline. Introduction to Epigenetic codes Genome-wide Hairpin Bisulfite Sequencing Application on embryonic stem cell renewal and differentiation - PowerPoint PPT Presentation

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Page 1: Decoding  epigenome with integrative “omics” data analysis

Decoding epigenome with integrative “omics” data analysis

 

Hehuang “David” XieSept, 2014

Page 2: Decoding  epigenome with integrative “omics” data analysis

Presentation Outline

– Introduction to Epigenetic codes

–Genome-wide Hairpin Bisulfite Sequencing• Application on embryonic stem cell renewal and

differentiation

– Decoding brain epigenome with integrative “omics” data analysis

Page 3: Decoding  epigenome with integrative “omics” data analysis

Epigenetics

Epigenetics is the study of heritable changes in gene activity that are NOT caused by changes in the DNA sequence.

http://en.wikipedia.org/wiki/

Page 4: Decoding  epigenome with integrative “omics” data analysis

Epigenetic codes DNA methylation

https://www.caymanchem.com

Histone modifications

http://cnx.org/content/m44536/latest/

Page 5: Decoding  epigenome with integrative “omics” data analysis

http://ips-cell.net/e/ips/index.html

Methylation & iPSC Reprogramming

Page 6: Decoding  epigenome with integrative “omics” data analysis

Presentation Outline

– Introduction to Epigenetic codes

–Genome-wide Hairpin Bisulfite Sequencing• Application on embryonic stem cell renewal and

differentiation

–Decoding brain epigenome with integrative “omics” data analysis

Page 7: Decoding  epigenome with integrative “omics” data analysis

Methylation Level vs Methylation Pattern

CGmCGmCG CGSequence read:

: methylated mCG: unmethylated CG

Sequence read:

Methylation level  = 50% 

Pattern 1:

Pattern 2:

Pattern 4:

Pattern 5:

Pattern 3:

Page 8: Decoding  epigenome with integrative “omics” data analysis

Epigenetic Heterogeneity

Diverse methylation patterns reflect distinct epigenetic events. Each line represents a sequence read or a DNA strand; open circle represents unmethylated cytosine; filled circle represents methylated cytosine; “M” and “P” indicate maternal and paternal alleles.

A) Homogenous B) Bipolar C) Heterogeneous

M

P

M

P

M

P

M

P

a) Asymmetric DNA methylation

b) Allele specific methylation (ASM)

c) Cell-subset specific methylation (CSM)

Locus 1 Locus 2 Locus 3 Locus 4

Page 9: Decoding  epigenome with integrative “omics” data analysis

mCGmCGmCG CGGCmGCGCmGCm

Sonication or enzyme digestion 

Adaptor Ligation

Pull down with dynabeads

Bisulfite PCR

mCGmCGmCG CGGCmGCGCm

mCGmCG TG mCG TG mCG

mCG: methylated CpG 

CG: unmethylated CpG

      : biotinylated hairpin adaptor

      : Illumina adaptor

GCm

Genome-wide Hairpin Bisulfite Sequencing

Zhao et al. The dynamics of DNA methylation fidelity during mouse embryonic stem cell self-renewal and differentiation.  Genome Research 2014

Page 10: Decoding  epigenome with integrative “omics” data analysis

SSM, CSM and Hairpin Bisulfite Sequencing

Cell-subset Specific Methylation

M

P

M

P

Asymmetric DNA Methylation

M

P

M

P

M

P

M

P

Completely Methylated

Completely Unmethylated

M

P

M

P

Page 11: Decoding  epigenome with integrative “omics” data analysis

Completely Unmethylated

Completely Methylated

Asymmetric DNA Methylation

Cell-subset Specific Methylation

1) Strand specific methylated CpG sites enriched in gene body with H3K36me3 marks2) Cell specific methylated CpG sites enriched in active enhancers with H3K27ac and

H3K4me1 marks

SSM, CSM and Histone Modifications

Active enhancer

Enhancer

Gene body orIntergenic regions

Promoter

Page 12: Decoding  epigenome with integrative “omics” data analysis

Completely Unmethylated

Completely Methylated

Asymmetric DNA Methylation

Cell-subset Specific Methylation

1) Strand specific methylated CpG sites depleted from TF binding sites2) Cell specific methylated CpG sites enriched in a number of TF binding sites

SSM, CSM and TF Bindings

Page 13: Decoding  epigenome with integrative “omics” data analysis

Presentation Outline

– Introduction to Epigenetic codes

–Genome-wide Hairpin Bisulfite Sequencing• Application on embryonic stem cell renewal and

differentiation

–Decoding brain epigenome with integrative “omics” data analysis

Page 14: Decoding  epigenome with integrative “omics” data analysis

Epigenetic dynamics of developing brain

Genomic DNA methylation pattern from a mixed cell population

Cell 1 A B DCCell 2 A B DCell 3 A B DCell 4 A B DCell 5 A B DCell 6 A B D

C

CC

CC

Cell n A B DC

.

.

.

Page 15: Decoding  epigenome with integrative “omics” data analysis

Ongoing Research

Brain Methylome Science 2013

Hairpin MethylomeGenome Research 2014

Enhancer mapsXie et al, Cell 2012Nord et al, Cell 2013Zhu et al, Cell 2013Andersson et al, Nature 2014

TF binding mapsEncode Project, Nature 2012

TranscriptomeALLEN Brain Altas, Cell 2013Mazi  et al, MSB 2013

Genetic mutationsKhurana et al, Science 2013Human Gene Mutation DatabaseGWAS catalogue

Page 16: Decoding  epigenome with integrative “omics” data analysis

CSM frequencies increase dramatically at early stages of brain development.

Epigenetic dynamics of developing brain

Page 17: Decoding  epigenome with integrative “omics” data analysis

The CSM associated genes are enriched for function related to brain development.

GO enrichment for CSM associated genes

Page 18: Decoding  epigenome with integrative “omics” data analysis

CSM and Histone Modifications

Page 19: Decoding  epigenome with integrative “omics” data analysis

TF binding enrichment in CSM regions

Page 20: Decoding  epigenome with integrative “omics” data analysis

                          Samples Diseases\Traits

Addiction

Alzheimer's disease

Attention deficit hyperactivity and conduct disorder

Bipolar disorder and schizophrenia

Eating disorders

Helicobacter pylori serologic status

Multiple sclerosis

Parkinson's disease

Stroke

Fetal 35d 2y 5y 12y 16y 25y 53G 53N 55G 55N

Color Key and Histogram

Disease-associated genetic variations in CSM regions

Page 21: Decoding  epigenome with integrative “omics” data analysis

Summary

• The combination of hairpin bisulfite sequencing and computational approach assist in the distinction of cell-subset specific methylation from allele-specific and asymmetric DNA methylation; • Mammalian brain development is accompanied with a dramatic increase in the number of cell-subset specific methylated loci; 

• Cell-subset specific methylation is highly correlated with histone marks for active enhancers and enriched in transcription factor binding; 

• Genetic variations associated with neurological disorders are enriched in brain cell-subset specific methylated regions 

Page 22: Decoding  epigenome with integrative “omics” data analysis

AcknowledgementsXie’s lab

• Dr. Lei Zhao

• Dr. Ming-an Sun

• Dr. Zhenqing “James” Xie

• Dr. Ni Li

• Zhixiong “Kevin” Sun

• Karthikraja Velmurugan 

• David Keimig

• Jessica Lim

• Sarah Sam

Collaborators

• Dr. Veena Rajaram (UTSW)

• Dr. Xiaowei Wu (VT)

• Dr. Liqing Zhang (VT)

• Dr. Jianjun Chen (UC)

• Dr. Chuan He (UC, HHMI)

• Dr. Henk Stunnenberg 

    (Netherlands, Radboud University)

• Dr. Wolf Reik (UK, Cambridge)

• Dr. Haruhiko Koseki (Riken Institute)

• Dr. Xuemei Lu (China, BIG)