Decoding the epigenome to navigate human health

EPINOMICS

Decoding the epigenome to navigate

human health

Paul Giresi

EPA Workshop 9/2/2015

EPINOMICS 2

Epigenome is the instructions for using genome hardware

Hardware Software

Genome Epigenome

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Instructions encoded within non-coding sequence

Genes

98% 2%

Approximately 40% of genome encodes for the

regulation of gene expression

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Access to regulatory information controlled by epigenome

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Techniques for measuring epigenomic features

Output Direct measures

RNA DNA

Methylation

Protein-DNA

Mapping

Accessibility

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Landscape of technologies for measuring epigenome

TECHNOLOGY LANDSCAPE

Co

st

Sequencing

Microarray MeDIP

Bisulfite

conversionChIP

DNase

hypersenitivity

?

RNADNA

Methylation

Protein

Mapping

Open or active

regions

Scop

e

Static ~1% of genome Dynamic

Output of

epigenome

Assay only single

epigenetic factor

All bound

TFs

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Rapid and efficient technology for profiling the epigenome

ATAC-seq

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✔

✔

✔

Rapid and efficient technology for profiling the epigenome

ChIP-seq DNase-seq ATAC-seq

Input

requirement107 cells 107 cells 100-50,000 cells

Sample prep

time

2 days 4 days 2 hours

(30 min hands-on)

Outputs

• Measures single

factor only

• High skills required

to reproduce

• All proteins bound to

genome in 1 reaction

• Limited factors

can be probed

• Higher-order

chromatin compaction

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Types of epigenomic features measured using ATAC-seq

DNA-binding proteins

Chromatin compaction

Active regulatory elements

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Identification of active regulatory elements from limited number of cells

Closed Active

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Compatible with fresh and archived blood samples

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Landscape of hematopoietic development

HSC

MPP

CMP

GMP

LMP

MEP

CD8

B cells

Monos

CD4NK

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Active regulatory elements are fingerprint of cell identity

TET2 TET2 mRNA

HSC

CMP

GMP

MEP

CD8+ T cells

NK cells

100 kb0 5000

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Rapid and sensitive detection of epigenomic modulationC

D4

+ T

ce

ll a

cti

va

tio

n

PM

A/io

no

myc

in

0 hr

1 hr

2 hr

4 hr

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Rapid and efficient technology for profiling the epigenome

DNA-binding proteins

Chromatin compaction

Active regulatory elements

Implications

Dynamic readout of cell

state and functioning

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Binding locations of hundreds of factors read out at once

ATAC-seq

Ge

no

me

-wid

e C

TC

F m

oti

fs

Bound

Unbound

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Detection of therapeutic modulation of epigenomic factors

Resting

Distance from binding site (bp)

Bin

din

g s

co

re

NFAT

CD4+ T cell activation

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Detection of therapeutic modulation of epigenomic factors

Resting

Activated

Distance from binding site (bp)

Bin

din

g s

co

re

NFAT

CD4+ T cell activation

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Rapid and efficient technology for profiling the epigenome

DNA-binding proteins

Chromatin compaction

Dynamic

readout of all

protein-DNA

interactions

Active regulatory elements

Implications

Dynamic readout of cell

state and functioning

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Distribution of fragment lengths driven by chromatin

Paired-end sequencing

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Distribution of fragment lengths driven by chromatin

ATAC DNA

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Single basepair resolution nucleosome positioning

Detection of nucleosome

organization at transcription start

sites

Detection of nucleosome

positioning with loss of chromatin

remodeling complex

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Determination of functional epigenomic states

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Real time monitoring of epigenomic changes in patients

Week 0

Patient treatment

time course

Pati

en

t 20

Week 1

Week 2

Week 3 *Vo

rin

osta

t

* Treatment stopped

50 Fragment length (bp) 600

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Rapid and efficient technology for profiling the epigenome

Chromatin compaction

Implications

Determination of

functional epigenetic

states

DNA-binding proteins

Dynamic

readout of all

protein-DNA

interactions

Active regulatory elements

Implications

Dynamic readout of cell

state and functioning

Mapping epigenomic landscape to create purposeful navigation

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Reference Maps

• Genome variants (1000 Genomes)

• Many regulators (ENCODE)

• Many many regulatory elements

Navigation

• Fast: clinical time scale

• Sensitive: clinical samples

• Actionable: clinical decision criteria

Measuring impact of environmental factors using plant epigenomics

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• Continuously sample soil, air and water

• Material relative easy and inexpensive to obtain

• Able to sample from same plant over time

• Can perform follow-up experiments in controlled lab environment

Acknowledgements

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Team

Fergus Chan - Co-founder

Tracy Nance, PhD - Bioinformatics

Marie Brennan, MD/PhD - Physician

John Latham, PhD - Scientist

Matt Negulescu, Software engineer

Anupama Joshi, Software engineer

Advisers

Howard Chang, MD/PhD - Stanford

Will Greenleaf, PhD - Stanford

Mike Snyder, PhD - Stanford

Anshul Kundaje, PhD - Stanford

Robert Tibshirani, PhD - Stanford

Joseph Ecker, PhD - Salk

EPINOMICS 29