PLoS Genetics

Elucidating Transcriptional Regulation at
Multiple Scales Using High-Throughput
Sequencing, Data Integration, and
Computational Methods
Raymond Auerbach
PhD Candidate, Yale University
Gerstein and Snyder Labs
August 30, 2012
1

Outline
Background
Transcriptional Regulation, ENCODE, ChIP-Seq
Selected Projects from my PhD work
Understanding the technical aspects of ChIP-Seq scoring
and how choice of reference sample matters
Using high-throughput sequencing to gain a genome-wide
view of chromatin remodeling (SWI/SNF complex)
Understanding the effects of long-range interactions and
genome folding on transcription
CAPE: a tool to classify features by RNAPII binding and
gene expression
2

g
DNA folding
Transcriptional Regulation: A Cartoon View
TF combinations
DNA folding
Site-specific
binding
Holstege and Young, PNAS, 1999
Histone modifications
Chromatin remodeling
3 Credit: Adam Steinberg

ENCODE Data Description
The ENCODE
Project Consortium,
PLoS Biology, 2011
4

ChIP-Seq
5

Early ChIP-Seq Questions
How should peaks be identified
Which peaks are significant
The ChIP peaks seem obvious, so why not score
against randomized background
Are controls or references needed What biases are
present
Does the peak calling need to be tuned for different
factors and/or organisms
6

Highlights from Key Papers
Nature Biotechnology, 2009
PNAS, 2009
7

First surprise: Input DNA has structure
Input DNA profile shows peaks itself and is not “flat”
The Pol2 antibody is exceptional. For ChIP with a “typical” antibody,
the input DNA peaks could affect the ability to call significant peaks
8

Origin of Input DNA from Nuclear Lysate
1. Reverse cross-links
2. Phenol-chloroform
extract
3. Purify DNA
4. Size select DNA
5. Ligate Illumina
adapters
What happens if we change some of these
variables
9

Hypothesis and Strategy
Initial hypothesis: Input DNA peaks will be
highest in regions of open chromatin
Input DNA peaks also seen in other genomes
Strategy
Using ChIP-Seq experiments in HeLa S3 and
yeast, score all tracks and aggregate signal over
interesting features
10

Reference Types We Examined
Input DNA
ChIP DNA that is not IP’ed with an antibody
MNase-digested DNA
Use MNase to cleave DNA instead of sonication
IgG (non-specific antibody)
ChIP DNA IP’ed with a non-specific antibody
Naked DNA
Sonicated DNA. Not crosslinked or IP’ed. Proteins removed.
11

Both Size Selection and Crosslinking are
Necessary
Auerbach and Euskirchen et al., PNAS, 2009
12

Aggregation Plot
Expressed Genes (TSS)
Pol II
Input DNA 100-350 bp
Naked DNA
Input DNA 350-500 bp
IgG
Mappability
MNase
13
Input DNA enriched 4x
over background!

Regions Associated with Active Transcription
Input DNA 100-350 bp
Input DNA 350-500 bp
Auerbach and Euskirchen, et al. PNAS, 2009.
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Regions Associated with Transcriptional
Inactivity
Input DNA 100-350 bp
Input DNA 350-500 bp
Auerbach and Euskirchen, et al. PNAS, 2009.
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What are the peaks
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Summary and Bioinformatics Contributions
First comprehensive analysis of ChIP-Seq reference
DNAs on peak scoring
Led to the choice of a preferred reference by our lab
for ENCODE Consortium work (IgG)
Integration of various data sets with reference DNA
types to gain a greater understanding of scoring biases
Useful for detecting accessible chromatin regions,
particularly as a first pass
17

Generalized Peak Caller
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Considerations with Early Peak Callers
Usually designed around ChIP with an ideal
antibody
Also usually targeted toward one organism
Default parameters typically arise from choices of
the experimental collaborator
How do peak callers work with more typical
antibodies How about with members of a protein
complex
19

ChIP-Seq of a Large Chromatin
Remodeling Complex (SWI/SNF)
Paper: Euskirchen and Auerbach, et al., PLoS Genetics,
2011
20

Chromatin Remodeling: Why You Should Care
Can change whether a region is accessible to
TFs and other proteins
Quick way to regulate regions that are actively
transcribed
Zofall et al., Nature Structural & Molecular Biology, 2006
21

Chromatin Remodelers and Epigenetics
de la Serna et al., Nature Reviews Genetics, 2006
22

Role in Cancer
SWI/SNF subunit Cancer Mutation Type Reference
Ini1 malignant rhabdoid tumors truncating mutations
BAF250A/ARID1A
BAF250A/ARID1A
ovarian clear cell carcinomas
transitional cell carcinoma of the
bladder
somatically acquired, inactivating
mutations
(1998) Nature 394: 203; (2006)
Mod. Pathol. 19: 717
(2010) Science 330: 228; (2010) N.
Engl. J. Med. 363:1532
somatic, non-silent mutations (2011) Nat. Genet. 43: 875
BAF200
hepatitis C virus-associated
hepatocellular carcinomas
somatic, inactivating mutations (2011) Nat. Genet. 43: 828
BAF180 clear cell renal carcinomas somatic, inactivating mutations (2011) Nature 469: 539
Brg1 & Brm
Brg1
BAF250A/ARID1, Brg1 &
BAF180
non-small cell lung carcinomas
lung cancer cell lines, esp. nonsmall
cell lung cancers
pancreatic cancers
23
unknown; based on negative
staining of tissue
(2003) Cancer Res. 63: 560
inactivating mutations (2008) Hum. Mutat. 29: 617
various (nonsense, missense, indel,
frameshift, rearrangement, splice
site)
Brd7 breast cancer multi-gene deletion
(2012) PNAS 109: E252
(2010) Nature Cell Biol. 12,
380-389

SWI/SNF Has 288 Subunit Combinations!
ARID
(1a or 1b or 2)
* *
* *
24

Project Overview
Analysis Questions
Where does SWI/SNF bind and in what configurations
What other elements are associated with SWI/SNF binding
sites
Functional implications (pathway analysis, etc.)
Experimental Procedure
ChIP-Seq against Brg1, BAF155, BAF170, and Ini1 in HeLa
S3 cells
Mass spectrometry to inventory co-immunoprecipitating
proteins
25

Features We Integrated
Feature Platform Source
Ini1 Sequencing Euskirchen and Auerbach et al., 2011
Brg1 Sequencing Euskirchen and Auerbach et al., 2011
BAF155 Sequencing Euskirchen and Auerbach et al., 2011
BAF170 Sequencing Euskirchen and Auerbach et al., 2011
RNA Polymerase II Sequencing Rozowsky et al., 2009
IgG Control Sequencing Auerbach and Euskirchen et al., 2009
Lamin A/C Array Euskirchen and Auerbach et al., 2011
Lamin B Array Euskirchen and Auerbach et al., 2011
H3K27me3 Sequencing Cuddapah et al., 2009
CTCF Sequencing Cuddapah et al., 2009
Predicted enhancers Array Heintzman et al., 2009
RNA Polymerase III Sequencing Oler et al., 2010; Barski et al., 2010
RNA-Seq Sequencing Morin et al., 2008
Non-canonical small RNAs
Sequencing
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Affymetrix and CSHL ENCODE
Transcription Project, 2009
DNA replication origins Array Cadoret et al., 2008

How to Combine Data
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Subunit Breakdown from ChIP-Seq
Subunit
Number in
49,555 union
regions
Ini1 24,478 (49%)
BAF155 37,921 (77%)
BAF170 25,433 (51%)
Brg1 12,317 (25%)
SWI/SNF Subunit Combinations
Total Observed
SWI/SNF high-confidence union set 49,555
Two or more subunits 30,310
Three or more subunits 15,535
Core set: Ini1, BAF155, and BAF170
(may include Brg1)
9,760
Ini1, BAF155, BAF170, and Brg1 4,750
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SWI/SNF Co-occurrences
CTCF, Pol II
Enhancers, 5’ ends,
(any combination)
SWI/SNF Union Set
(49,555 regions)
SWI/SNF Core Set
(9,760 regions)
44,755 (90%) 8,968 (92%)
Unclassified 4,800 (10%) 792 (8%)
RNA Pol II Sites 19,669 (40%) 6,562 (67%)
Putative Enhancers 21,228 (43%) 3,431 (35%)
CTCF Sites 8,542 (17%) 1,692 (17%)
5’ ends of Ensembl
protein-coding genes
(within 2.5 kb)
14,291 (29%) 4,089 (42%)
29

Association of Subunit Combinations with
Transcription Levels
Euskirchen and Auerbach, et al.
PLoS Genetics, 2011.
30

Pathway Analysis
Euskirchen and
Auerbach, et al.
PLoS Genetics, 2011.
31

Overrepresented GO Categories
(Mass Spectrometry)
Euskirchen and Auerbach, et al.
PLoS Genetics, 2011.
32

Summary and Bioinformatics Contributions
Different peak scoring criteria for ubiquitous
factors
Inferring information about a complex given
ChIP-Seq from subunits
Overall, SWI/SNF binds very generally, but is
enriched at 5’ ends, genes associated with cell
cycle, DNA repair, and cancer.
33

SWI/SNF and DNA Looping
Euskirchen and Auerbach, et al.
PLoS Genetics, 2011.
CIITA locus (~150 kb)
34

Exploring Transcription, DNA Folding,
and Nuclear Organization in a
Multidimensional Context
Paper: Li, Ruan, Auerbach, and Sandhu, et al., Cell,
2012
35

ChIA-PET
Chromatin Interaction Analysis by Paired End diTag
Sequencing
Collaboration with Stanford and Genome Institute of
Singapore
In addition to ChIA-PET method, FISH, qPCR,
enhancer assays, and other methods used for validation.
Question: How does transcriptional regulation work in
3-D space on an intrachromosomal level
36

So How Does ChIA-PET Work
(Cliffs Notes Version)
ChIP-Seq
ChIA-PET
DNA 1
DNA 1
DN
Linker
DNA 2 DNA 2
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The Textbook Version
38

The Textbook Version
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First Goal - Transcription Factories
Sutherland and Bickmore.
Nature Reviews Genetics, 2009.
40

Second Goal - Formation of Protein
Complexes
PJ Farnham, Nature Reviews Genetics. 2009.
41

Models of Transcription
Li, Ruan, Auerbach, and Sandhu, et al. Cell, 2012.
42

Gene Expression Characteristics
Li, Ruan, Auerbach, and Sandhu, et al.
Cell, 2012.
43

Binding of Different TFs Across Models
Li, Ruan, Auerbach, and
Sandhu, et al. Cell, 2012.
44

IRS1 and T2D: Long Range Interactions
and Disease
Li, Ruan, Auerbach,
and Sandhu, et al.
Cell, 2012.
45

ChIA-PET Conclusions
Active regions are connected to other active regions
Some factors are present at promoters while others
are brought in by LRI
Most interactions follow the basal promoter model,
but most genes are involved in multigene complexes
Long range interactions and its role in disease
46

Bioinformatics Contributions
Integration of LRI data with ChIP-Seq, RNA-
Seq, etc., to look at transcription as a system
Basis for future studies in how various protein
complexes are formed in vivo
47

CAPE - Coupled Analysis of
Polymerase and Expression
48

Combining RNAPII ChIP-Seq with RNA-
Seq
A “natural experiment” for transcription analysis
Simple to generate, gain a lot of information
Many paired datasets available in public
repositories
Can identify transcripts with unexpected
relationships between binding and expression
compare to other organisms/samples/conditions
49

CAPE Summary
Publicly available tool designed to categorize features
based on expression & RNAPII binding
Open-source and multiplatform (Java)
Designed to work on diverse sets of genomes out of
the box, but also allows for parameter customization
Useful for comparative genomics (e.g. modENCODE)
Two modules: CAPE-analyze and CAPE-compare
50

CAPE: Coupled Analysis of Polymerase
Binding and Expression
Auerbach et al. In revision.
51

Sample CAPE-analyze Output
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Sample CAPE-compare Output (raw)
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Sample CAPE-compare Output (HTML)
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Sample CAPE-compare Output (Venn)
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Overall Summary
Technical implications of scoring ChIP-Seq data (PNAS)
Considerations when analyzing data from ChIP-Seq
experiments targeted to non-standard transcription
factors and protein complexes (PLoS Genetics)
How DNA folding affects how we view transcription and
ChIP-Seq data (Cell)
New, robust tool to quickly classify transcripts/genes
based on mRNA abundance and RNAPII binding levels
56

Other Work While at Yale
Co-author of 14 peer-reviewed papers while at
Yale (4 as primary or starred)
12 published
2 in press
One manuscript being revised for resubmission
57

Acknowledgements
58

Questions
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