How can so many different cell types
arise from just one genome?
Brad Bernstein
MGH Pathology, Center for Cancer Research & Center for Systems Biology
MASSACHUSETTS
GENERAL HOSPITAL
HARVARD MEDICAL SCHOOL
Parsing 3 billion bases of the human genome
ctggaggtgcaatggctgtcttgtcctggccttggacatgg!
gctgaaatactgggttcacccatatctaggactctagacgg!
gtgggtaagcaagaactgaggagtggccccagaaataattg!
gcacacgaacattcaatggatgttttaggctctccagagga!
tggctgagtgggctgtaaggacaggccgagagggtgcagtg!
ccaacaggctttgtggtgcgatggggcatccgagcaactgg!
tttgtgaggtgtccggtgacccaaggcaggggtgagaggac!
cttgaaggttgaaaatgaaggcctcctggggtcccgtccta!
agggttgtcctgtccagacgtccccaacctccgtctggaag!
acacaggcagatagcgctcgcctcagtttctcccaccccca!
cagctctgctcctccacccacccagggggcggggccagagg!
tcaaggctagagggtgggattggggagggagaggtgaaacc!
gtccctaggtgagccgtctttccaccaggcccccggctcgg!
ggtgcccaccttccccatggctggacacctggcttca!
Parsing 3 billion bases of the human genome
ctggaggtgcaatggctgtcttgtcctggccttggacatgg!
gctgaaatactgggttcacccatatctaggactctagacgg!
gtgggtaagcaagaactgaggagtggccccagaaataattg!
gcacacgaacattcaatggatgttttaggctctccagagga!
tggctgagtgggctgtaaggacaggccgagagggtgcagtg!
ccaacaggctttgtggtgcgatggggcatccgagcaactgg!
tttgtgaggtgtccggtgacccaaggcaggggtgagaggac!
cttgaaggttgaaaatgaaggcctcctggggtcccgtccta!
agggttgtcctgtccagacgtccccaacctccgtctggaag!
acacaggcagatagcgctcgcctcagtttctcccaccccca!
cagctctgctcctccacccacccagggggcggggccagagg!
tcaaggctagagggtgggattggggagggagaggtgaaacc!
gtccctagGTGAGCCGTCTTTCCACCAGGCCCCCGGCTCGG!
GGTGCCCACCTTCCCCATGGCTGGACACCTGGCTTCA!
Protein coding genes account for
just 1% of the human genome
How do we find the genome’s other working parts?
How do these parts interact to give rise to >200
different cell types in the human?
One genome, many cell types
Diagram of stem cell division and differentiation.
A - stem cell
B - progenitor cell
C - differentiated cell
1 - symmetric stem cell division
2 - asymmetric stem cell division
3 - progenitor division
4 - terminal differentiation
http://en.wikipedia.org/wiki/File:Stem_cell_division_and_differentiation.svg
http://en.wikipedia.org/wiki/File:Stem_cells_diagram.png
Cell type-specific gene expression
Development &
Lineage-Specification
Cell Type-Specific Gene
Expression Programs
Cell Type A
X
X
Cell Type B
Gene X
X
Gene Y
Gene Z
.
.
X.
Chromatin Structure
& the Epigenome
Gene AA
Gene ZZ
X
.
.
.
http://en.wikipedia.org/wiki/File:Epigenetic_mechanisms.jpg
Cell type-specific gene expression
Cell differentiation can occur at many levels of gene expression
http://openi.nlm.nih.gov/detailedresult.php?img=3110863_ejn0033-1563-f1&req=4
Genomic DNA in the nucleus packaged into chromatin
Two meters of DNA in a
nucleus smaller than of the
head of a pin
http://en.wikipedia.org/wiki/File:DNA_to_Chromatin_Formation.jpg#file
Chromatin packaging affects accessibility of gene expression machinery
Nucleosome
DNA
Closed DNA, unreadable genes
DNA can be tightly wound and closely packed
around nucleosomes, blocking gene expression
Open DNA, readable genes
Other parts of the genome are less tightly wound,
allowing gene expression machinery more access to genes
A Variety of Chromatin Modifications Can Affect Gene Expression
Some modifications recruit factors to stop gene expression
while others open up chromatin to allow gene expression
Chromatin Binding Factors
Histone modifications
Chemical modifications (‘tags’) in chromatin
DNA methylation
Histone modifications
http://en.wikipedia.org/wiki/File:Epigenetic_mechanisms.jpg
Chemical tags added and removed by enzymes
Histone modifications
Acetyl groups
Chromatin compacts
Transcription repressed
Histone
acetylase
Histone
deacetylase
Chromatin decondenses
Transcription activated
Chemical tags recruit proteins that turn genes ‘on’ or ‘off’
“On”
“Off”
Polycomb compaction
(Francis et al, Science 2004)
The field of epigenetics seeks to understand
non-genetic changes that influence gene
activity and cell behavior
These changes may include modifications of
DNA, but also encompass many other entities
Changes remembered across a lifetime and
may rarely be inherited across generations
X-chromosome inactivation
Barr body (inactive Xi)
Calico Cat http://en.wikipedia.org/wiki/File:Sd4hi-unten-crop.jpg
http://en.wikipedia.org/wiki/File:Calico_cat_-_Phoebe.jpg
Epigenomics of human disease
•  Cancer is a genetic and epigenetic disease
•  Aberrant DNA methylation is a hallmark
•  Mutations of many genes with epigenetic functions
•  Neuropsychiatric, metabolic, developmental disorders
•  Long-term health consequences of early environmental
exposures may be mediated through epigenomic changes
Genome-wide maps of epigenomic features
Next-generation sequencing has transformed epigenomics research
RNA-seq
RNA
Whole Genome
Bisulfite Sequencing
Dnase-seq
ChIP-seq
http://en.wikipedia.org/wiki/File:DNA_to_Chromatin_Formation.jpg#file
Genomewide chromatin state maps
ChIP-­‐seq Fixed cells/tissue
Y
Enrich chromatin with
modified histones
Histone 3 lysine 4 methyl an0body Y
Deep sequence the
enriched DNA
Histone modification map
Mikkelsen et al, Nature 2007
Chromatin structure is dynamic in development
ES cells
HSCs
Hematopoeitic
B-cells
CD34
ES
ES cells
progenitors
‘poised’ ‘ac4ve’ PAX5
B-cells
CpG Island
PAX5
Bivalent chromatin domains
Some chromatin contains both activating and repressing epigenetic modifications in the
same areas, affecting accessibility of chromatin to RNA polymerase.
http://openi.nlm.nih.gov/imgs/rescaled512/2634711_6604771f1.png
Systema4c detec4on of DNA elements and their cell-­‐type-­‐specifici4es Determinants of cellular state
Cell type
DNA
TF
ES cell
Silenced locus
Endoderm
Mesoderm
Ectoderm
Liver, Lung,
Pancreas
Blood, Heart
Skeletal Muscle
CNS, Skin
Enhancers and their regulators mediate
tissue-specific gene expression programs
E
P
Gene
e.g., Sonic hedgehog
Transcription factor (TF)
Yet conventional approaches have yielded few examples in
human. How do we parse 3 Gb of DNA to identify enhancers
systematically?
Chromatin maps identify genome regulatory elements
MLL
Bivalent domains
EZH2
Regulatory elements
Meissner et al, Nature 2008
Incorporating enhancers into regulatory networks
à Human genome contains ~1 million enhancers
TF2
-
TF1
+
E
TF3
Enhancers elements exhibit high cell type-specificity
Enhancer clusters Proximal genes Lymphoblas
t cell-­‐
specific Hepa0c cell-­‐specific Endothelial cell-­‐specific Func4onal annota4ons and regulatory predic4ons for GWAS Most disease-associated SNPs are non-coding
>900 GWAS studies for 165 phenotypes
(>3400 associated SNPs)
GWAS variants:
Coding
Promoter
Intron
Intergenic
Hindorff et al: A Catalog of Published Genome-Wide Association Studies.
Disease variants (SNPs) are enriched within enhancer states
http://www.genome.gov/gwastudies/
7%
2%
47%
44%
GWAS SNPs enriched within enhancers
that are active in related cell types
Erythrocyte phenotypes
Erythroid (K562)
Blood lipids
Hepatoma cells
Rheumatoid arthritis
Lymphoblastoids
Primary biliary cirrhosis
Lymphoblastoids
Systemic lupus erythematosus
Lymphoblastoids
Lipoprotein cholesterol/triglycer.
Hepatoma cells
Haematological traits
Erythroid (K562)
Haematological parameters
Erythroid (K562)
Colorectal cancer
Blood pressure
Hepatoma cells
Erythroid (K562)
Gene activity and human disease
TF2
-
TF1
+
E
TF3
•  Human genome contains ~1 million enhancers that act like switches to control the ac4vity of individual genes •  Human diseases such as diabetes are complex traits influenced by many sites in our genome (unlike Mendelian disorders) •  Gene4c studies have iden4fied thousands of DNA sites that vary between individuals and influence disease risk •  These sites of disease risk coincide with enhancers. Suggests complex diseases may be caused by defects in many switches… Determinants of cellular state
Cell type
DNA
TF
ES cell
Silenced locus
Endoderm
Mesoderm
Ectoderm
Liver, Lung,
Pancreas
Blood, Heart
Skeletal Muscle
CNS, Skin
Chromatin state transition upon developmental
specification
ES cells: Repressive H3K27me3 confined to ‘poised’
promoters
Differentiated cells: ~70% of the genome
sequestered by compact/repressive
chromatin
Polycomb compaction
(Francis et al, Science 2004)
The global organization of DNA is dynamic
Acknowledgements
MGH lab
Oren Ram
Alon Goren
Esther Rheinbay
Mario Suva
Mazhar Adli
Shawn Gillespie
Birgit Knoechel
Richard Koche
Manching Ku
Eric Mendenhall
Rusty Ryan
Kaylyn Williamson
Vicky Zhou
Jiang Zhu
James Zou
Broad Institute
Chuck Epstein
Noam Shoresh
Tim Durham
Robbyn Issner
Xiaolan Zhang
Nir Yosef
Ido Amit
Aviv Regev
MIT Computer Science
Manolis Kellis
Jason Ernst
Pouya Kheradpour
Luke Ward
MGH collaborators
Miguel Rivera
Hiro Wakimoto
Samuel Rabkin
David Louis
Andrew Chi