ITranscriptional Regulation and Genome Structure
By
Abraham S. Weintraub
B.S., University of California Santa Barbara (2009)
Submitted to the Department of Biology in Partial Fulfillment of the Requirements for the Degree
of Doctor of Philosophy at the Massachusetts Institute of Technology
June 2018
@ 2018 Massachusetts Institute of Technology. All rights reserved.
The author hereby grants to MIT permission to reproduce or distribute publicly paper and
electronic copies of this thesis document in whole or in part in any medium now known or
hereafter created.
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Department of Biology
February 150', 2018
Richard A. Young
Member, Whitehead Institute
Professor of Biology
Thesis Supervisor
Accepted by -1 z \ I
Amy Keating
Professor of Biology
Co-Chair, Biology Graduate Committee
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Transcriptional Regulation and Genome Structure
By
Abraham S. Weintraub
Submitted to the Department of Biology on March 1st, 2018 in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy at the Massachusetts Institute of
Technology
ABSTRACT
The regulation of gene expression is fundamental to the control of cell identity, development and
disease. The control of gene transcription is a major point in the regulation of gene expression.
Transcription is regulated by the binding of transcription factors to DNA regulatory elements
known as enhancers and promoters. This leads to the formation of a DNA loop connecting the
enhancer and the promoter resulting in the subsequent transcription of the gene. Thus the
structuring of the genome into DNA loops is important in the control of gene expression. This
thesis will focus on the role of genome structure in transcriptional regulation. Two key questions
in this area that I have attempted to address during my PhD are "how are enhancer-promoter
interactions constrained so that enhancers do not operate nonspecifically?" and "are there
proteins that facilitate enhancer-promoter looping?" I will describe the identification of DNA loop
structures formed by CTCF and cohesin that constrain enhancer-promoter interactions. These
structures-termed insulated neighborhoods-are perturbed in cancer and this perturbation
results in the inappropriate activation of oncogenes. Additionally, I will describe the identification
and characterization of the transcription factor YY1 as a factor that can structure enhancer-
promoter loops. Through a combination of genetics, genomics, and biochemistry, my studies
have helped to identify insulated neighborhood structures, shown the importance of these
structures in the control of gene expression, revealed that these structures are mutated in
cancer, and identified YY1 as a structural regulator of enhancer-promoter loops. I believe these
studies have produced a deeper understanding of the regulatory mechanisms that connect the
control of genome structure to the control of gene transcription.
Thesis supervisor: Richard A. Young
Title: Member, Whitehead Institute; Professor of Biology
3
ACKNOWLEDGEMENTS
There are a number of people who have had a tremendous impact on me that I would like to
thank. Starting first on the academic side, I am thankful to have encountered many brilliant
teachers and scientists that have always been willing to give advice and share knowledge. My
undergraduate mentors-Misty Riddle and Joel Rothman-deserve recognition for encouraging
me to ask questions and teaching me essential lab skills. I have also had a number of great
interactions with many colleagues at MIT.
I am extremely thankful to all of the members of the Young Lab, including the alumni that have
graciously provided scientific and career advice. Joining the Young Lab was a transformative
experience for me, where I really learned how one does science. Denes Hnisz took me under his
wing and taught me the value of doing the right experiment the right way, the first time. His logic
is unmatched and I can only hope that I have absorbed at least a little of it. Jurian Schuijers, with
his acerbic wit, has been a great collaborator and a better friend. I had the fortune of working
closely with Charlie Li on several projects and benefitted greatly from his clear and rational
thinking. Tony Lee has been a valuable resource and a true asset to the lab. Tom Volkert and his
staff at the Genome Core deserve thanks for being excellent. Of course there are a number of
former and current lab members (Alicia, Dan Day, Dan Dadon, ZPF, Ike, Jess, Eliot, Ale, Nick,
Brian, John, Ben, Eric, Ann, Alla, and others) who have helped to create a fun and productive
environment in the lab. Nancy Hannet and Katie Lazuk deserve immense thanks for managing to
keep the lights on despite me trying to spend all of Rick's money.
Rick deserves a special thanks for taking me into his lab. From him I have learned many
invaluable lessons about science and leadership. Rick has created a great training environment
and taught me how to identify interesting problems, attack them scientifically, and then
communicate the results in a clear way. Rick has always been willing to provide help and guidance
on any number of problems and has truly been a positive force in my life.
I am grateful to my thesis committee. Len Zon, Phil Sharp, and David Sabatini all have dedicated
significant amounts of time to provide help and advice. Phil Sharp is incredibly knowledgeable;
conversations with him are always insightful. David Sabatini, despite not being a transcription
expert, somehow still manages to ask all the right questions during committee meetings. Sabatini
has always stressed the importance of doing impactful science and I have benefitted greatly from
my interactions with him. The Sabatini lab also deserves special thanks for welcoming me into
their group and inviting me to events like NOLA.
None of this would have been possible without my loving family. My parents have always
encouraged me to be curious and didn't give up on me despite some rough patches during high
school. From them I learned to be resilient and that anything is possible, two skills that are
incredibly important for graduate school. My brother Max has always been supportive and loving
and for that I thank him.
I am thankful to my friends, both in California and Cambridge. David Cundiff, Bill Foster, Jake
Becraft, Ryan Stott, and David Canner have always been down to have fun.
Finally, I am very lucky to have met Megan Knoster in 2015. She has been a great joy and made
life more fun. It takes a special person to put up with the hours of a graduate student and Megan
has always been there for me, with a smile on her face.
4
STATEMENT ON WORK PRESENTED
Chapter 1
1 wrote Chapter 1 with input and minor edits from Rick Young, Charles Li, Tony Lee, and Denes
Hnisz. Several figure panels (1A, 1C, 1D, 4B, 4C, 4D) were previously used in reviews
published by our lab.
Chapter 2
I performed experiments and analyses in Figure 1B, 1C, 2A-C, 3A, 3B, 3C, 3G, 3H, 31, and
contributed to the design of the others. Denes Hnisz wrote the manuscript and created the
figures, with help and input from me and other authors.
Chapter 3
All experiments and analyses were either performed by or directly supervised by me. Rick
Young and I wrote the manuscript, I generated the figures with input from all other authors.
Chapter 4
I wrote Chapter 4 with input and minor edits from Rick Young, Charles Li, Tony Lee, and Denes
Hnisz.
5
TABLE OF CONTENTS
ABSTRACT .............................................................................................................................. 3
ACKNOW LEDGEM ENTS ...................................................................................................... 4
STATEM ENT ON W ORK PRESENTED ..................................................................................... 5
CHAPTER 1: INTRODUCTION ................................................................................................ 7
Overview..........................................................................................................................................7
Trans-acting factors control the recruitment and activity of RNA Polymerase ............................... 8
Structure and function of transcription factors..............................................................................9
Cis-acting regulatory elements ....................................................................................................... 10
Enhancers act at a distance ............................................................................................................ 13
Regulators of genome structure ..................................................................................................... 16
Genome structure constrains enhancers ................................................................................... 17
References ..................................................................................................................................... 20
CHAPTER 2: ACTIVATION OF PROTO-ONCOGENES BY DISRUPTION OF CHROMOSOME
NEIGHBORHOODS ................................................................................................................. 29
Abstract ......................................................................................................................................... 30
M ain text ....................................................................................................................................... 31
Acknowledgments..........................................................................................................................37
M aterials and methods .................................................................................................................. 38
Supplementary tables .................................................................................................................... 55
References and notes ..................................................................................................................... 56
Supplemental figures ..................................................................................................................... 58
CHAPTER 3: YY1 IS A STRUCTURAL REGULATOR OF ENHANCER-PROMOTER LOOPS............79
Summary........................................................................................................................................80
Introduction ................................................................................................................................... 81
Results ........................................................................................................................................... 82
Discussion ...................................................................................................................................... 94
Supplemental information ............................................................................................................. 96
References ..................................................................................................................................... 97
Supplemental figures ................................................................................................................... 105
CHAPTER 4: FUTURE DIRECTIONS AND DISCUSSION ............................................................ 149
Additional structural regulators....................................................................................................149
New models of transcriptional regulation.....................................................................................149
Pathological consequences of perturbations to genome structure................................................151
Concluding thoughts .................................................................................................................... 153
References ................................................................................................................................... 154
6
CHAPTER 1: INTRODUCTION
Overview
The regulation of gene expression is fundamental to the control of cell identity, development and
disease. Gene expression is the process of transcribing a gene-encoded by DNA-into an
RNA intermediary which can then be translated into a protein product. Cell identity is controlled
through the expression of different combinations of genes, and for development to proceed this
process must be highly coordinated. The misregulation of gene expression drives numerous
pathologies-including both cancer and non-cancer diseases(Lee and Young, 2013). Therefore,
studying gene regulation provides insights into the control of cell identity, development, and
disease. This thesis will cover the role of genome structure in transcriptional regulation. First in
the introduction I will detail some of our current understanding of the control of gene regulation
and genome structure. The rest of the overview will briefly outline the topics that will be
discussed in further detail in the introduction.
In 1961 Francois Jacob and Jacques Monod proposed the cis/trans model of gene
regulation(Jacob and Monod, 1961). In this model, transcription is controlled by the binding of
trans-acting regulatory factors to cis-acting regulatory sequences. We have since learned that
eukaryotic transcription is a complex multi-step process that requires a large number of
proteins(Lee and Young, 2000; Shilatifard et al., 2003; Young, 1991). Trans-acting factors
regulate different steps in this process by affecting the recruitment and activity of these
proteins(Ptashne and Gann, 1997). Thus although the process is complicated, the general
model put forward by Jacob and Monod still endures as a useful framework.
While on occasion RNA can act as a trans-regulatory factor(Bartel, 2009; Engreitz et al., 2016),
trans-regulatory factors are generally proteins. The predominant class of protein trans-regulatory
factors are transcription factors(Jones et al., 1988; Levine and Tjian, 2003; Vaquerizas et al.,
2009). Transcription factors stereotypically have two domains-a DNA-binding domain and a
transactivation domain(Ptashne, 1988). The DNA-binding domain selectively binds to cis-
regulatory elements giving the factor specificity whereas the transactivation domain provides
function through recruiting the transcription machinery and other enzymes that regulate
transcription(Spitz and Furlong, 2012; Weirauch and Hughes, 2011 a).
Cis-acting regulatory elements can be divided into three major classes(Maston et al., 2006). The
first class is the core promoter, which begins at the transcription start site and runs upstream
about 30 base pairs. The core promoter contains binding sites for general transcription factors
that orient the binding of RNA polymerase at the transcription start site(Kadonaga, 2012; Lenhard
et al., 2012). Both the second class-the promoter proximal region-and the third class-
enhancers-function to direct the proper spatiotemporal expression of the gene(Buecker and
Wysocka, 2012; Bulger and Groudine, 2011; Heinz et al., 2015). The primary difference between
the two is that the promoter-proximal region is several hundred base pairs upstream of the
transcription start site whereas enhancers are located distally (>1kb). The formation of a DNA
loop between an enhancer and promoter allows the enhancer to modulate the expression of the
gene that is being regulated. Because enhancers are located distally and act at a distance there
must be a mechanism to constrain enhancers to only activate their target genes.
The structure of the genome plays an essential role in constraining enhancers to activate only
specific genes. The structure of the genome is established largely through the formation of DNA
loops by the proteins CTCF and cohesin(Merkenschlager and Nora, 2016). One of the
fundamental units of genome structure is the insulated neighborhood(Hnisz et al., 2016a). An
7
insulated neighborhood is a DNA loop formed by the interaction between two DNA sites bound
by CTCF and cobound by the cohesin complex. Insulated neighborhoods are several hundred
kilobases in size and contain on average 2-3 genes and their associated regulatory
elements(Hnisz et al., 2016a). Insulated neighborhoods were shown to be functional by
experiments that perturbed the CTCF site that makes up the boundary. Perturbation resulted in
the ectopic activation of nearby genes because the enhancer that was previously constrained
within the neighborhood was able to loop out to contact nearby promoters(Dowen et al., 2014).
This and other similar functional experiments provide strong evidence that genome structure is
important for proper gene regulation.
Thus the regulation of eukaryotic gene expression is orchestrated by transcription factors which
act in trans through the binding of cis-acting regulatory elements. Transcription factors regulate
the recruitment and activity of the transcriptional machinery. Cis-acting regulatory elements
include the core promoter, promoter-proximal region, and enhancers. Enhancers are located
distally and form a DNA loop with the promoter of the gene they regulate. Insulated neighborhoods
constrain enhancer-promoter looping to prevent the ectopic activation of genes.
Trans-acting factors control the recruitment and activity of RNA Polymerase
In 1961 Francois Jacob and Jacques Monod proposed a model in which gene expression is
controlled by trans-acting factors that bind to cis-acting regulatory elements(Jacob and Monod,
1961). While mutants in the cis-acting regulatory elements had already been described it would
take several years before the trans-acting factors were found(Jacob and Monod, 1961). Both
repressors-ac(Gilbert and Muller-Hill, 1966) and Iambda(Ptashne, 1967)-as well as the arac
activator(Englesberg et al., 1965) were identified helping to confirm the model. It was found that
these early prokaryote transcription factors exerted their function by binding to cis-regulatory
elements and influencing the recruitment and activity of RNA polymerase(Eron and Block, 1971;
Ippen et al., 1968; Scaife and Beckwlth, 1966). The identification of eukaryotic transcription
factors and eukaryotic cis-acting regulatory elements reaffirmed the importance and relevance of
this model(Dynan and Tjian, 1983; Engelke et al., 1980; McKnight and Kingsbury, 1982; Payvar
et al., 1981). While eukaryotic transcription is considerably more complex, the general model of
gene control through the binding of activators and repressors to specific DNA sequences holds
true.
There are three different eukaryotic DNA-dependent RNA polymerases(Roeder and Rutter,
1969). Protein coding genes are transcribed into messenger RNA by RNA polymerase II
(RNAPII). The 25S ribosomal RNA and short untranslated RNAs (i.e. tRNAs) are transcribed by
RNA polymerases I and Ill, respectively. I will focus here on transcription by RNAPII; there are
many excellent reviews addressing transcription by RNAPI and RNAPIII (Geiduschek and
Kassavetis, 2001; Goodfellow and Zomerdijk, 2013; Russell and Zomerdijk, 2006; Vannini and
Cramer, 2012; White, 2011).
The formation of the RNAPII holoenzyme is required for site-specific transcription initiation that is
responsive to activators. The RNAPII holoenzyme is composed of three major components: core
RNA polymerase 11, general transcription factors, and the Mediator complex (reviewed in
(Greenblatt, 1997; Koleske and Young, 1995; Lee and Young, 2000; Myer and Young, 1998). The
core RNA polymerase 11 is composed of 12 subunits (RPB1-12) and can catalyze the synthesis
of RNA from a DNA template in vitro; however, the initiation of transcription is not
specific(reviewed in (Sawadogo and Sentenac, 1990; Young, 1991). The addition of the general
transcription factors results in specific transcription initiation in vitro; however, the reaction does
not respond to the addition of activators until the Mediator complex is added (Kim et al., 1994;
8
Koleske and Young, 1994; Matsui et al., 1980; Samuels et al., 1982; Sayre et al., 1992; Weil et
al., 1979). The Mediator complex is a large multi-subunit (-30 different polypeptides) complex
that interfaces with core RNAPII and DNA-bound transcription factors to integrate various signals.
The structure and function of the Mediator complex has been the subject of a number of
reviews(Allen and Taatjes, 2015; Conaway et al., 2005; Malik and Roeder, 2010; Roeder, 2005).
Thus the formation of the RNAPII holoenzyme consisting of the core RNA polymerase 11, general
transcription factors, and the Mediator complex allows for specific transcription that is responsive
to activators.
Gene transcription by RNAPII can be regulated at many levels. Eukaryotic DNA is packaged into
nucleosomes, and these are restrictive to polymerase binding(Kornberg and Lorch, 1991;
Workman and Buchman, 1993). Pioneer transcription factors can recruit nucleosome remodelers
such as the SWI/SNF complex which can mobilize nucleosomes and allow the recruitment of the
RNAPII(Zaret and Carroll, 2011). RNAPII then initiates transcription by melting the DNA, forming
an open complex by positioning the template strand of the DNA in the active cleft of the enzyme,
and transcribing -10 base pairs(Hahn, 2004; Sainsbury et al., 2015). After successful initiation
RNAPII transcribes about 30-60 base pairs and pauses(Adelman and Lis, 2012; Kwak and Lis,
2013). Pausing depends on the balance between pausing factors and activating factors. Upon
release by activating factors, productive elongation generally occurs to the completion of the
transcript(Jonkers and Lis, 2015; Shilatifard et al., 2003). While in theory any of these steps could
be regulated, genome-wide analysis indicates that regulation generally occurs at the levels of
recruitment and/or pause release(Core et al., 2008; Guenther et al., 2007; Kwak et al., 2013).
Thus transcription is a multi-step process and transcription factors regulate the passage through
the different steps by binding to cis-acting regulatory elements and affecting the recruitment and
activity of RNAPII.
Structure and function of transcription factors
Further study of transcription factors led to the emergence of several key concepts regarding their
structure and function. Structurally, transcription factors tend to be composed of two major
domains-a DNA binding domain and a transactivation domain(Ptashne, 1988). The DNA binding
domain serves to provide specificity and direct the binding of the transcription factor to the
genome(Hahn and Young, 2011; Weirauch and Hughes, 2011a). The transactivation domain
recruits different effector molecules such as RNAPII, coactivators, or corepressors(Triezenberg,
1995; Weirauch and Hughes, 2011 a). Thus a transcription factor controls gene expression by
binding to the genome via the DNA binding domain and then recruiting different effectors through
the transactivation domain.
The DNA binding domains are usually fairly structured whereas the transactivation domains are
enriched for regions predicted to lack structure in the absence of a partner molecule(Dyson and
Wright, 2016; Liu et al., 2006; Minezaki et al., 2006; Ptashne and Gann, 1997; Triezenberg, 1995).
DNA binding domains have been well-studied and x-ray crystallography has led to an
understanding of how the different classes of DNA binding domains interact with DNA(reviewed
in(Garvie and Wolberger, 2001; Luscombe et al., 2000; Pabo and Sauer, 1992)). The lack of
structure in transactivation domains has prevented the same type of study and the mechanism
by which transactivation domains recruit effectors is less well understood.
Attempts to understand transactivation domains have largely been focused on classifying the
domains based on the amino acids they are comprised of. The major groups include acidic,
glutamine-rich, proline-rich, or serine/threonine-rich(Triezenberg, 1995). However, mutagenesis
9
studies of the yeast transcription factor GCN4 have suggested that rare hydrophobic residues
within the transactivation domain are more critical for function than the abundant amino
acids(Drysdale et al., 1995; Hope and Struhl, 1986; Hope et al., 1988). This has resulted in several
different hypotheses to explain transactivation domain function: (1) Net negative charge governs
transactivation domain strength(Ma and Ptashne, 1987; Sigler, 1988). (2) Net hydrophobicity
governs transactivation domain strength(Cress and Triezenberg, 1991; Jackson et al., 1996). (3)
Structural flexibility governs transactivation domain strength(Dyson and Wright, 2016; Minezaki
et al., 2006). A recent preprint attempted to test these different hypotheses with a library of GCN4
mutants and concluded that acidity and structural flexibility function to keep hydrophobic residues
exposed for potential interactions with coactivators(Staller et al., 2017). This is an attractive model
but will need to be tested for additional transcription factors. Thus in general, transcription factors
are composed of a structured DNA binding domain and an unstructured transactivation domain.
The function of the transactivation domain is to recruit effectors yet the molecular mechanism is
still unclear.
Functional studies have shown that transcription factors are extremely important in controlling cell
identity. Classic experiments by Hal Weintraub and colleagues found that expression of a single
transcription factor-MyoD-is sufficient to convert fibroblasts to myoblasts(Davis et al., 1987;
Weintraub et al., 1989). A number of other so-called master transcription factors have been
identified that can reprogram cell identity (reviewed in(Buganim et al., 2013; Graf and Enver,
2009; Morris and Daley, 2013; Vierbuchen and Wernig, 2012). One particularly striking example
is the finding by Takahashi and Yamanaka that the ectopic expression of four factors; OCT4,
SOX2, MYC, and KLF4; is capable of inducing pluripotency in terminally differentiated
cells(Takahashi and Yamanaka, 2006)(Figure 1A). Master transcription factors establish and
regulate cell identity in two ways. First, they form an interconnected autoregulatory circuit by
binding together at their own promoters and positively regulating their own expression. Second,
they activate the expression of genes necessary to maintain cell identity while repressing the
expression of other lineage-specific transcription factors. Together this results in a gene
expression program that establishes cell identity.
The transcriptional control of cell identity is thought to involve both establishment of a cell-type-
specific gene expression program by master transcription factors, as well as a state of
responsiveness to the signaling environment. Transcription factors mediate the gene expression
response to many signaling pathways(Clevers and Nusse, 2012; Darnell et al., 1994; Hayden and
Ghosh, 2012; Jarriault et al., 1995) and are often the terminal component of signaling pathways.
The genome-wide binding of these signaling transcription factors is determined by the binding of
master transcription factors(Mullen et al., 2011). This results in a gene expression response that
is specific to that particular cell type. Response to the transforming growth factor beta (TGF-P)
pathway is mediated through the binding of the Smad transcription factors(Massague et al., 2005).
Smad3 cobinds with Oct4 in embryonic stem cells, Myod1 in myotubes, and PU.1 in pro-B
cells(Mullen et al., 2011). When Myod1 is ectopically expressed in nonmuscle cells, Myodi
recruits Smad3 to new sites(Mullen et al., 2011). Thus the binding of signaling transcription factors
is directed by cell-type specific master transcription factors, resulting in a specific gene expression
response.
Cis-acting regulatory elements
The core-promoter is one of the major classes of cis-acting regulatory elements. The core
promoter is an approximately 30 base pair region that begins at the transcription start site and
runs upstream. Sequence elements in the promoter such as the TATA box and Initiator elements
direct the correct positioning of RNAPII through the binding of general transcription
10
factors(reviewed in(Lenhard et al., 2012; Maston et al., 2006)). While there appear to be some
differences in the activity of different core promoters(Zabidi et al., 2014), the primary function is
to position RNAPII at the transcription start site.
The second and third class of cis-acting regulatory elements both function to direct the proper
spatiotemporal regulation of gene expression(Buecker and Wysocka, 2012; Bulger and
Groudine, 2011; de Laat and Duboule, 2013). The second class is the promoter-proximal region
which occurs adjacent to the core promoter and stretches several hundred base pairs upstream.
The third class is the enhancer which can be located kilobases away from the promoter of the
gene it regulates. Both these regions contain binding sites for transcription factors that can
activate or repress expression. Thus in contrast to the core promoter, the promoter-proximal
region and enhancers confer regulation on the timing and level of expression(Levine, 2010; Ong
and Corces, 2012; Ren and Yue, 2016). This is evident in the tissue-specific expression
patterns that are observed when one of these elements is attached to a reporter gene and
inserted into an animal(Visel et al., 2009).
The ability of cis-regulatory elements distal to the promoter to affect gene expression was first
described in the early 1980's. While performing promoter mutagenesis experiments, Benoist and
Chambon found that proper transcription of the SV40 early genes was dependent on two 72-bp
repeat elements located several hundred base pairs upstream of the transcription start
site(Benoist and Chambon, 1981). Schaffner and colleagues found that these 72-bp elements
could enhance the expression of the /-globin gene even when placed hundreds of bp away from
the transcription start site, leading these elements to be called enhancers(Banerji et al., 1981).
Rapidly other enhancers began to be discovered and studied(Banerji et al., 1983; Gillies et al.,
1983; Neuberger, 1983; Queen and Baltimore, 1983); the field of enhancer biology was opened.
Through the study of model enhancer loci, a number of important principles governing the
function of enhancers were established. These model loci included the #-globin LCR (Bulger et
al., 2002; Grosveld et al., 1993; Martin et al., 1996), the interferon-# enhanceosome(Maniatis et
al., 1998), and the Drosophila pair rule gene even-skipped (eve)(Small et al., 1992; Stanojevic
et al., 1991). It was found that enhancers are typically several hundred base pairs long and
comprised of many binding motifs for transcription factors. The combinatorial binding of
transcription factors leads to the recruitment of RNAPII, coactivators and the generation of a
transcriptional response(Levine and Tjian, 2003) (Figure 1B). As exemplified by eve, broad
expression domains of multiple morphogens can therefore lead to very precise spatiotemporal
expression patterns (Figure 1C). The advent of genome-wide localization techniques (first ChIP-
Chip(Ren et al., 2000), then ChIP-seq(Albert et al., 2007)) allowed the binding of transcription
factors to be mapped across the genome. This resulted in these principles being extended
genome-wide and lead to additional insights on how transcription factors create regulatory
circuits that define cell identity(Lee et al., 2002) (Figure 1 D).
11
Figure 1
A Fibroblast Fibroblast Ceve stripe 2
Anterior Posteror
OCT4 HcbK X2 1 Kr
MyoD KLF4 (repressor)
MYC
Fact"r
conotntstion Gnt Bod(represo) 10uvtr)
0 A Posto b
Mucl I --------- eve eve
Musle PRuripotent Bod Kr Bcd Kr GntHcb Bod Bd Kr Bd Kr GntHeob Bd
stem call eve stripe 2 enhancer = ON eve stripe 2 enhancer = OFF
B D
3'-ATTT C GA TCC-5'
TF1 TF2 TF3
' Enhancer
''sboundMediator
RNAPil
Core promoter
TFs bound at prornoter
proxdmal elements
Figure 1. Control of cell identity by transcription factors and cis-acting regulatory elements
(A) Model depicting the reprogramming of fibroblasts into muscle (left) or into pluripotent stem cells (right) through the
ectopic expression of master transcription factors.
(B) Schematic of an enhancer-promoter DNA loop. At the top a segment of the enhancer DNA is shown with the binding
sites for three transcription factors (TFs) highlighted. In the middle is an atomic model showing the binding of the
transcription factors to the DNA (PDB structure 1T2K). The transactivation domains were removed from the TFs in
order to generate the crystal. At the bottom is the enhancer-promoter loop, with Mediator, RNA Polymerase 1l (RNAPII)
and various transcription factors bound.
(C) Schematic showing how expression gradients can result in precise expression. At the top is a Drosophila embryo,
stripes indicate developing segmentation. The chart in the middle shows the concentration of different activators and
repressors across the embryo. The bottom shows a model of the eve stripe 2 enhancer at two different positions along
the embryo. Only at stripe 2 is the enhancer active.
(D) An example of a core regulatory circuit, shown here is one for an embryonic stem cell. The master transcription
factors OCT4, SOX2, and NANOG positively regulate their own expression.
12
Enhancers act at a distance
The identification of enhancers as DNA elements distal to the transcription start site but critical
for proper gene expression led to an immediate question: "If enhancers are located distally, how
do they regulate the expression of a gene?" As stated by Mark Ptashne in 1986 several models
seemed possible: twisting, sliding, oozing, and looping(Ptashne, 1986) (Figure 2A). Here again
simple prokaryotic systems proved to be exceptionally valuable in distinguishing amongst these
possibilities. I will summarize several of the most elegant and striking results.
Three orthogonal lines of evidence were built up supporting looping as the most likely mechanism.
(1) Dependence on periodicity. Schleif, and later Hochschild and Ptashne, found that regulation
was disrupted when a non-integral number of helical turns was introduced between the enhancer
and promoter. They argued that over short distances (<500 bp) the energy required for a loop to
form between proteins on the opposite face of the DNA helix was prohibitively high whereas if
there was oozing, twisting, or sliding there would not be this dependence(Hahn et al., 1984;
Hochschild and Ptashne, 1986) (Figure 2B). (2) Electron microscopy of DNA loops. Working in
the Ptashne lab, Jack Griffith was able to capture images of lambda repressor binding to two
distal operator sites, with the DNA looped out in between(Griffith et al., 1986) (Figure 2C). (3)
Concatemerized DNA molecules. DNA molecules were generated that separated the enhancer
from the promoter. These two molecules were then concatemerized either by interlinking of the
rings or through biotin-streptavidin. Only following concatemerization was the enhancer able to
activate gene expression(Dunaway and Dr6ge, 1989; MOller et al., 1989; Wedel et al., 1990)
(Figure 2D). This showed that enhancers activate gene expression by directly contacting the
promoter, rather than the propagation of a signal along the double helix. Together, these
experiments led to the general acceptance of looping as the means by which enhancers
communicate with promoters.
The study of enhancers was facilitated by the development of a technique to detect
chromosome interactions in vivo which allowed the mapping of enhancer-promoter contacts.
This assay, chromosome conformation capture (3C), was developed by Job Dekker in
2002(Dekker et al., 2002) (Figure 3A). Briefly, cells are treated with formaldehyde resulting in
the crosslinking of DNA that is in spatial proximity. The DNA is then digested with a restriction
enzyme and a ligation is performed. The ends of the DNA molecules that were in spatial
proximity are ligated together, creating a junction that represents a DNA interaction that was
occurring in vivo. The junctions are then quantified by a variety of means including qPCR (3C)
or high throughput sequencing (Hi-C) (Figure 3B). Additionally, a ChIP step can be performed to
detect interactions associated with a protein of interest (ChIA-PET, HiChIP). 3C was used to
determine that upon activation the #-globin enhancers loop to the gene promoter, providing in
vivo confirmation of the looping model(Tolhuis et al., 2002).
13
Figure 2
A
Twist Slide Ooze
Enhancer Promoter
B
Dependence on periodicity
C
Direct visualization
0
Concatemerization
Gene = ON
G
Gene = OFF
Figure 2. Enhancers regulate gene expression through DNA looping(A) Four different models put forward to explain how enhancers regulate gene expression from a distance. Twist model.
A protein binds at the enhancer and induces a twist in the DNA that is propagated to the promoter. Slide model. A
protein binds at the enhancer and slides along the DNA until it reaches the promoter. Ooze model. A protein binds at
the enhancer and recruits more proteins until the promoter is reached. Loop model. A protein binds at the enhancer
and the promoter and a DNA loop is formed.
(B) Experiments showed gene expression depended on the periodicity between regulatory elements, supporting DNA
looping as a means of connecting enhancers and promoters. The addition of a half turn disrupted expression by placing
proteins on the opposite side of the DNA face, making the energy barrier for forming a loop prohibitively high. No such
effect was seen with the addition of a full turn.
(C) Electron microscopy was used to visualize DNA loops, supporting DNA looping as the model by which enhancers
function.
(D) Concatemerization experiments supported the DNA looping model. An enhancer and promoter were placed on two
separate DNA molecules and either concatemerized or not. When transfected into cells only the concatemerized
molecules were expressed.
14
Loop
Gene = OFF
+ half turn
(5 bp)
+ full turnm Gene = ON
(10 bp)
Figure 3
A Chromosome conformation capture
Crosslink with
formaldehyde
Chromatin
interactions
occuring in vivo
Shear DNA Proxi
ChIA-PET/HiChIP:
Perko= Immunoprecipi-
tation to select for
interactions associated
with protein of interest
nity ligation Purify DNA
- 8 -
3C 4C-seq
PCR Inverse PCR(one-to-one) followed by
sequencing(one-to-many)
Hi-C/
ChIA-PET/
5C HiChIP
Multiplexed
PCR followed
by sequencing
(many-to-nany)
Sequencing(all-to-all)
Figure 3. Methods for detecting DNA interactions
(A) Workflow for chromosome conformation capture experiments. DNA is crosslinked with formaldehyde and then
sheared. An optional immunoprecipitation step can be performed to select for interactions associated with a protein of
interest. A proximity ligation is then performed, resulting in the formation of a ligation junction that represents a DNA
interaction.
(B) Various methods for quantifying ligation junctions; including PCR (3C), inverse PCR followed by sequencing (4C-
seq), multiplexed PCR followed by sequencing (5C), sequencing (Hi-C, ChIA-PET, HiChIP).
15
B
0
0 0--
0 0 6- ..-
Regulators of genome structure
Technological advances that allowed the in vivo mapping of DNA looping (3C) and protein binding
(ChIP) led to progress in identifying and characterizing the proteins that establish and regulate
DNA loops. CTCF and cohesin are two of the major mammalian structural proteins that have been
best characterized. Some additional proteins that have also been implicated in structuring the
genome and merit more study include the condensin complex(Hirano, 2012), histone H1(Hergeth
and Schneider, 2015), and TFIIIC(Bortle and Corces, 2012). CTCF and cohesin have been
subject of multiple reviews(Ghirlando and Felsenfeld, 2016; Merkenschlager and Nora, 2016;
Merkenschlager and Odom, 2013; Nasmyth and Haering, 2009; Ong and Corces, 2014; Peters
et al., 2008) and so in this section I will only summarize some of the key points.
CTCF is a zinc-finger transcription factor that has been suggested to have a number of different
roles in the control of gene expression; however, all of these roles can be explained by its function
as a structural protein. CTCF was originally described by several different groups as a repressor
of MYC expression(Baniahmad et al., 1990; Lobanenkov et al., 1990); however, subsequent work
also proposed that CTCF was capable of activation as well(Klenova et al., 1993). Work by
Felsenfeld and colleagues at the #-globin locus and at the H19/Igf2 imprinted locus found that
CTCF was also capable of insulation (the ability to prevent, or insulate, an enhancer from
activating a nearby promoter)(Bell and Felsenfeld, 2000; Bell et al., 1999; Hark et al., 2000;
Kanduri et al., 2000; Saitoh et al., 2000; Szab6 et al., 2000). Upon comparison of the binding of
CTCF with other transcription factors it was found that CTCF primarily binds at intergenic regions
rather than at enhancer or promoter sites(Kim et al., 2007). This led Jennifer Phillips-Cremins and
Victor Corces to propose a unifying model in which the disparate effects of CTCF can all be
explained by its major function as a structural regulator of DNA loops(Phillips and Corces, 2009).
Subsequent studies that integrated global binding analysis with chromosome conformation
capture confirmed that CTCF is frequently bound at the sites of DNA loops(Dixon et al., 2012a;
Dowen et al., 2014; Handoko et al., 2011; Phillips-Cremins et al., 2013; Rao et al., 2014a).
Perhaps more striking, global depletion of CTCF was found to perturb DNA looping(Nora et al.,
2017). The role of CTCF as a structural regulator led to the search of proteins analogous to CTCF
that function primarily at enhancers and promoters rather than insulators. The discovery of YY1
as a structural regulator of enhancer-promoter loops will be described in Chapter 3.
Cohesin was initially characterized for its role in chromosome segregation(Michaelis et al., 1997)
and was only later implicated in transcription(reviewed in(Merkenschlager and Nora, 2016;
Nasmyth and Haering, 2009)). Clues that cohesin was involved in DNA looping came from the
observation that cohesin was frequently cobound with CTCF(Parelho et al., 2008; Rubio et al.,
2008; Wendt et al., 2008). A screen for chromatin regulators required for pluripotency in murine
embryonic stem cells implicated cohesin and follow up work found that cohesin interacts with the
Mediator complex to establish DNA loops at enhancers and promoters(Kagey et al., 2010).
Conclusive evidence for the role of cohesin in DNA looping came from a pair of studies that
depleted cohesin and observed striking perturbation of DNA loops(Rao et al., 2017; Schwarzer et
al., 2017). Recently, it has been proposed that cohesin establishes DNA loops through loop
extrusion(Fudenberg et al., 2016; Nasmyth, 2001; Sanborn et al., 2015). The loop extrusion model
proposes that an "extruder complex" is loaded onto the DNA with two strands of DNA topologically
entrapped. The complex then extrudes a DNA loop by translocating along the DNA until a stopping
point is reached, i.e. a CTCF site. Intriguingly, loop extrusion can explain the observation that the
DNA motif of two CTCF sites that are contacting each other are found in a stereotypical
orientation. This is in contrast to the opposing model which suggests that DNA loops form by
random diffusion followed by affinity stabilized interactions. The extrusion model is primarily
supported by computational modeling but there have been several recent studies that provided
16
experimental support as well(Fudenberg et al., 2016; Sanborn et al., 2015; Wang et al., 2017).
Despite the relative newness of the proposal, it has already gained widespread acceptance.
Genome structure constrains enhancers
With the realization that enhancers can act over tens to hundreds of kilobases another central
question emerged: "How are enhancer-promoter interactions constrained so that enhancers do
not operate nonspecifically?" One attractive model involved the structuring of the genome into
domains. Indeed, in an incredibly prescient statement in the Discussion of their 1981 paper
describing enhancers Banerji, Rusconi, and Schaffner propose(Banerji et al., 1981):
"The DNA in chromosomes of higher organisms seems to be organized into loop
structures of 10-100 kb(Marsden and Laemmli, 1979; Razin et al., 1979), and it was
speculated that these chromosome loops constitute the domains of gene
activation(Bernards and Flavell, 1980; Bernards et al., 1979). Taken together it appears
possible that cellular 'enhancers' are activating the genes within each chromosome
domain, and that classes of different 'enhancers' are involved in the developmental, as
well as the tissue-specific expression of genes."
The development of techniques such as Hi-C that allowed the mapping chromatin interactions
genome-wide allowed the domain model to be tested. Upon mapping chromatin interactions, it
was found that the genome tends to organize into a hierarchy of domains. The development of
mammalian gene editing tools such as CRISPR/Cas9 allowed the functional interrogation of the
domains. First I will describe the general organization of mammalian chromosomes in the nucleus
and then I will describe some experiments that demonstrate this organization is functional.
Mammalian chromosomes are organized into a hierarchy of domains(Gibcus and Dekker, 2013).
Chromosomes tend to occupy distinct spaces in the nucleus termed chromosome
territories(Cremer and Cremer, 201 0)(Figure 4A). Chromosomes are then compartmentalized into
alternating intervals of active and inactive regions (typically 2-3 mb in size). The active regions
tend to interact more with other active regions than with inactive regions, and vice versa for
inactive regions. Chromosomes are then further subdivided into domains of high internal
interaction frequency termed topologically associating domains (TADs) (Figure 4A). TADs are
typically 1 mb in size, contain 5-10 genes, and are characterized by increased interaction between
the regions within the domain relative to outside(Dixon et al., 2012a, 2015; Nora et al., 2012).
Within TADs there are DNA loops that have been termed insulated neighborhoods(Dowen et al.,
2014; Hnisz et al., 2016b; Ji et al., 2016)(Figure 4A). Insulated neighborhoods are typically several
hundred kb in size and contain 2-3 genes on average. Insulated neighborhoods are formed by
the interaction of two DNA sites bound by the transcription factor CTCF and cobound by
cohesin(reviewed in(Hnisz et al., 2016a)). Thus chromosomes occupy distinct territories within
the nucleus and then are parsed into active and inactive compartments which contain 1-2 mb
sized TADs. Within TADs there are insulated neighborhoods which contain enhancer-promoter
loops.
At the onset of studying genome structure, it was not clear if the structures being detected were
actually contributing to gene regulation. Multiple lines of circumstantial and direct evidence argue
that they are indeed functional. (1) Reproducibility. Genome structure can be reproducibly
detected across multiple experiments and multiple cell types. This suggests that genome structure
is not random, circumstantially arguing for function. (2) Constraint. Chromatin interaction mapping
revealed that the majority (-90%) of enhancer-promoter loops are constrained within insulated
neighborhoods, which argues that insulated neighborhoods are involved in insulating enhancer-
17
promoter loops(Dowen et al., 2014; Hnisz et al., 2016a; Ji et al., 2016)(Figure 4B). (3) Genetics.
Direct evidence of functionality comes from the perturbation of boundaries. Our group used
CRISPR/Cas9 to genetically perturb the boundary of insulated neighborhoods containing super-
enhancers in mouse embryonic stem cells(Dowen et al., 2014) and human embryonic stem
cells(Ji et al., 2016). In all cases, we found that upon perturbation of the boundary, the super-
enhancer was no longer constrained and could loop out to activate nearby genes resulting in local
gene misregulation (Figure 4C). CTCF has a strong preference for binding to unmethylated DNA
and the Jaenisch Lab showed that epigenetic perturbation of the boundary through the use of a
fusion of dCas9 and a DNA methyltransferase results in local gene misregulation(Liu et al., 2016).
Additionally, the Reinberg Lab identified an insulated neighborhood boundary site that separates
expression of rostral Hox genes from the caudal Hox genes. Upon perturbation of the boundary,
topological separation between the two domains is lost as well as precise control over Hox gene
expression. Notably there are also examples of perturbations in genome structure with
pathological consequences. One of these-the activation of proto-oncogenes by disruption of
chromosome neighborhoods-is the subject of Chapter 2 of this thesis (Figure 4D). Other
examples will be covered in Chapter 4. Thus, multiple lines of both circumstantial and direct
evidence argue that chromosome structure is important for gene expression.
In this introduction I have outlined how genome structure affects the control of transcription.
Transcription factors bind to cis-acting DNA regulatory regions such as enhancers and promoters
in order to regulate the recruitment and activity of RNAPII. Enhancers function through the
formation of a DNA loop with the promoter of the gene that is being regulated. Because enhancers
are located distally from the promoter, their action must be constrained to prevent ectopic gene
activation. This constraint occurs through DNA loops formed by CTCF and cohesin known as
insulated neighborhoods. In Chapter 2 of this thesis I will describe how mutations in insulated
neighborhood boundaries accumulate in cancer and how that drives the inappropriate activation
of oncogenes. In Chapter 3 I will describe the identification of the transcription factor YY1 as a
protein that is key for structuring enhancer-promoter loops. In Chapter 4 1 will conclude by
describing some of the interesting directions in which the field is headed. I will also describe some
of the pathological consequences of perturbations in genome structure and structural regulators.
18
BChromosome
territories
C
Enhancer 97%
Gene
Silent Gene
7.!U
Insulated
neighborhood
Enhancer-Promoter
Loop
Enhancer
Gene
D Normal gene
Enh ancer
Silent
proto-oncogene
Activated
gene
Deleted
anchor
Gone
Mutated
anchor
Activated
oncogene
Figure 4. Genome structure constrains enhancer-promoter interactions.(A) The hierarchy of genome structure. Chromosomes tend not to intermingle within the nucleus, giving rise to
chromosome territories. Individual chromosomes then fold into topologically associating domains, which are
composed of insulated neighborhoods. Insulated neighborhoods contain enhancer-promoter loops.(B) The majority (97%) of enhancer-promoter loops are constrained within insulated neighborhoods.(C) Deletion of the anchor of an insulated neighborhood results in loss of enhancer constraint. The previously
constrained enhancer can now loop out and activate nearby genes.(D) Disruption of insulated neighborhood boundaries is a method by which tumor cells activate proto-oncogenes.
Boundary perturbation of a neighborhood that contains a silent proto-oncogene can allow a nearby active regulatory
element to loop in and activate the proto-oncogene.
19
I
Figure 4
A
Topologically
associating
domain
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28
CHAPTER 2: ACTIVATION OF PROTO-ONCOGENES BY DISRUPTION OF CHROMOSOME
NEIGHBORHOODS
Originally published in Science (80). 351, 1454-1458. (2016).
Reprinted with permission from AAAS.
Denes Hnisz 't, Abraham S. Weintraub 1,2t, Daniel S. Day 1, Anne-Laure Valton 3, Rasmus 0.
Bak 4, Charles H. Li 1, Johanna Goldmann 1, Bryan R. Lajoie 3, Zi Peng Fan 1,5 Alla A. Sigova
1 Jessica Reddy 1, Diego Borges-Rivera 1, Tong Ihn Lee 1, Rudolf Jaenisch 1, Matthew H.
Porteus 4, Job Dekker 3,6, Richard A. Young 1,2*
Affiliations:
1 Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142,
USA
2 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
3 Program in Systems Biology, Department of Biochemistry and Molecular Pharmacology,
University of Massachusetts Medical School, Worcester, MA 01605-0103, USA
4 Department of Pediatrics, Stanford University, Stanford, California, USA
5 Computational and Systems Biology Program, Massachusetts Institute of Technology,
Cambridge, MA, 02139, USA
6 Howard Hughes Medical Institute
f These authors contributed equally
* Correspondence to: young@wi.mit.edu
29
Abstract
Oncogenes are activated through well-known chromosomal alterations, including gene fusion,
translocation and focal amplification. Recent evidence that the control of key genes depends on
chromosome structures called insulated neighborhoods led us to investigate whether proto-
oncogenes occur within these structures and if oncogene activation can occur via disruption of
insulated neighborhood boundaries in cancer cells. We mapped insulated neighborhoods in T-
cell acute lymphoblastic leukemia (T-ALL), and found that tumor cell genomes contain recurrent
microdeletions that eliminate the boundary sites of insulated neighborhoods containing prominent
T-ALL proto-oncogenes. Perturbation of such boundaries in non-malignant cells was sufficient to
activate proto-oncogenes. Mutations affecting chromosome neighborhood boundaries were found
in many types of cancer. Thus, oncogene activation can occur via genetic alterations that disrupt
insulated neighborhoods in malignant cells.
30
Main text
Tumor cell gene expression programs are typically driven by somatic mutations that alter the
coding sequence or expression of proto-oncogenes (1) (Fig. 1A), and identifying such mutations
in patient genomes is a major goal of cancer genomics (2, 3). Dysregulatlon of proto-oncogenes
frequently involves mutations that bring transcriptional enhancers into proximity of these genes(4). Transcriptional enhancers normally interact with their target genes through the formation of
DNA loops (5-7), which typically are constrained within larger CTCF-cohesin mediated loops
called insulated neighborhoods (8-10), which in turn can form clusters that contribute to
topologically associating domains (TADs) (11, 12) (Fig. S1A). This recent understanding of
chromosome structure led us to hypothesize that silent proto-oncogenes located within insulated
neighborhoods might be activated in cancer cells via loss of an insulated neighborhood boundary,
with consequent aberrant activation by enhancers that are normally located outside the
neighborhood (Fig. 1A, lowest panel).
To test this hypothesis, we first mapped neighborhoods and other cis-regulatory interactions in a
cancer cell genome using Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-
PET) (Fig. 1 B, Table S1). A T-cell acute lymphoblastic leukemia (T-ALL) cell (Jurkat) was selected
for these studies because key T-ALL oncogenes and genetic alterations are well-known (13, 14).
The ChIA-PET technique generates a high-resolution (-5kb) chromatin interaction map of sites
in the genome bound by a specific protein factor (8, 15, 16). Cohesin was selected as the target
protein because it is involved in both CTCF-CTCF interactions and enhancer-promoter
interactions (5-7), and has proven useful for identifying insulated neighborhoods (8, 10) (Fig S1A-
B). The cohesin ChIA-PET data were processed using multiple analytical approaches (Fig. S1-
4, Table S2) and identified 9,757 high-confidence interactions, including 9,038 CTCF-CTCF
interactions and 379 enhancer-promoter interactions (Fig. S4C). The CTCF-CTCF loops had a
median length of 270 kb, contained on average 2-3 genes and covered -52% of the genome(Table S2). Such CTCF-CTCF loops have been called insulated neighborhoods because
disruption of either CTCF boundary causes dysregulation of local genes due to inappropriate
enhancer-promoter interactions (8, 10). Consistent with this, the Jurkat chromosome structure
data showed that the majority of cohesin-associated enhancer-promoter interactions had
endpoints that occurred within the CTCF-CTCF loops (Fig. 1C, S2H). These results provide an
initial map of the 3D regulatory landscape of a tumor cell genome.
Figure 1
A 
__m._ B - '
ludonWD o c o - - AdkW li
Won Ow Pumseopw Fust 0 uW ThWRSW4RG
-~. N
Ondn Ci ni
Fi.L 3D relpaatmy bleiep~ of the T-ALL mos. (A) Mechanisms C
actdKgU Proto-nogne.(3) Hi-C kitraction m TADs defAinh in
emlnyon stem cells (HM). cosn ChWAPET Intedtons (Intensity of e
arc rupiesents Inteacton signifca-ce) CTCF aid H3K27Ac dromaotin inn-
I n"reiptation seciuenif (ChIP-seq) profiles mid peak~s, and RNA-seq
in Juklat cell at the CD3D locuis. ChiP-seq peaks am denote as bars above
ChiP-seq profiles. (C) CtVA-PET Inta.actlons at the RLMtXImcs cisplmwe
abiove mClVP-We prfles of CTCF cohesi (SMC1). and H3K27Ar_ FlIP.
talee discovery rate.
HI lADs -
ChP-a.W u I V i.~i~~i
RHA-aegl. I .
Gs-IUu ''r %T! 3Syvuqi I L~ IL V
*1 0
"Si jauci .tI I
I LM=-dSUMC
31
We next investigated the relationship between genes that have been implicated in T-ALL
pathogenesis and the insulated neighborhoods. The majority of genes (40/55) implicated in T-
ALL pathogenesis (curated from the Cancer Gene Census and individual studies)(Table S3) were
located within the insulated neighborhoods identified in Jurkat cells (Fig. 2A, S5); 27 of these
genes were transcriptionally active and 13 were silent based on RNA-Seq data (Fig. 2A, Table
S4). Active oncogenes are often associated with super-enhancers (17, 18), and we found that 13
of the 27 active T-ALL Pathogenesis Genes associated with super-enhancers (Fig. 2A-B, S5A).
Silent genes have also been shown to be protected by insulated neighborhoods from active
enhancers located outside the neighborhood, and we found multiple instances of silent proto-
oncogenes located within CTCF-CTCF loop structures in the Jurkat genome (Fig. 2A, 2C, Fig.
S5B). Thus, both active oncogenes and silent proto-oncogenes are located within insulated
neighborhoods in these T-ALL cells.
FIGURE 2
WX I ===
NW9FH1 Mu:
LMOV MM-M47 MMM
~2 M -
---W/ACs M MECLMO M M =MM Cta :: z=Mm=
MC=
OD2 mm==3
Mcm
TAL2 M C=
M-j 2 M MC
gK'1 M * M
m=3
WLNB ===m
SET C1M 3
W10 _m C
Of===
B
Insulated
neighborhood:
ChIA-PET
interactions:
92]
220
8]
40k
interaction
signiflcance
(FDR)
1 0
72 kb
10 kb
CTCF
SMC1 I
- LaA. H3K27Ac
jill IA RNA-Seq
Insulated
neighborhood
Cohesin
CTCF CTCF
Super-
TALI enhancer
TALI
C
Insulated
neighborhood:
115 kb
ChIA-PET 10 kbinteractions: -
) CTCF L
2721 SMVl
8- H3K27Ac
40k~~40k RNA-Seq
~LM02
Insulated
neighborhood
Cohesin
CTCF CTF
LMO2
Fig. 2. ActIve oncogenes and silent proto-ancogunes occur in Insulated neIghborhoods. (A) T-ALL
pathogenesis genes. Colored boxes indicate whether a gene is located within a neighborhood, expressed.
and associated with a superenhancer. (B) Insulated neighborhood at the active TALI locus. The cohesin
ChIA-PET interactions are displayed above the ChIP-seq profiles of CTCF. cohesin (SMC1) H3K27Ac. and
RNA-seq profile. A model of the insulated neighborhood is shown on the right. (C) Insulated neigh-
borhood at the silent LMO2 locus.
32
If some insulated neighborhoods function to prevent proto-oncogene activation, some T-ALL
tumor cells may have genetic alterations that perturb the CTCF boundaries of neighborhoods
containing T-ALL oncogenes. To investigate this possibility, we identified recurrent deletions in
T-ALL genomes that span insulated neighborhood boundaries using data from multiple studies
(Table S5A) and filtered for relatively short deletions (<500 kb) in order to minimize collection of
deletions that affect multiple genes (Fig. S6A). Among the 438 recurrent deletions identified with
this approach, 113 overlapped at least one boundary of insulated neighborhoods identified in T-
ALL, and 6 of these affected neighborhoods containing T-ALL Pathogenesis Genes (Fig. S6B,
Table S5B). Examples of two such genes, TALI and LMO2, are shown in Fig. 3A and Fig. 3G.
If deletions overlapping neighborhood boundaries can cause activation of proto-oncogenes within
the loops, then site-specific deletion of a loop boundary CTCF site at the TALI locus should be
sufficient to activate these proto-oncogenes in non-malignant cells. TALI encodes a transcription
factor that is overexpressed in -50% of T-ALL cases and is a key oncogenic driver of this cancer
(19, 20). TALI can be activated by deletions that fuse a promoter-less TALl gene to the promoter
of STIL (19) and this was observed in many patient deletions (Fig. 3A). Several patient deletions,
however, retained the TALI promoter (endpoint >5kb from promoter) but overlapped the CTCF
boundary site of the TALI neighborhood (Fig. 3A), and TALl was active in the samples harboring
these deletions (Fig. S7A-B). This suggests disruption of the insulated neighborhood, allowing
activation of TALI by regulatory elements outside of the loop. We tested this idea by
CRISPR/Cas9 mediated deletion of the TALI neighborhood boundary in human embryonic kidney
cells (HEK-293T) (Fig. 3B). In these cells, the TALI proto-oncogene is silent as evidenced by low
H3K27Ac occupancy and RNA-Seq (Fig. 3B). However, at least one active regulatory element
occurs -60kb upstream of TALI, adjacent to the CMPK1 promoter, as evidenced by high levels
of H3K27Ac and p300/CBP (Fig. 3B) and enhancer reporter assays (Fig. S8A-B). Deletion of a
-400 bp segment encompassing the boundary CTCF site, which abolished CTCF binding (Fig.
S8A), caused a 2.3-fold induction of the TALI transcript (Fig. 3C), suggesting that the integrity of
the neighborhood contributes to the silent state of TALI (Fig. 3D). Supporting this model, contacts
between DNA regions that are normally within and outside of the neighborhood were increased
(Fig. 3E-F, S10). Furthermore, deletion of the CTCF site in primary human T-cells also caused a
small but detectable activation of TALI (Fig. S8C-G). These results are consistent with the model
that the silent state of the TALl proto-oncogene is dependent on the integrity of the insulated
neighborhood (Fig. 3D).
We further tested the model that site-specific perturbation of a loop boundary is sufficient to
activate a proto-oncogene at the LMO2 locus. The LMO2 gene encodes a transcription factor that
is overexpressed and oncogenic in some forms of T-ALL (14, 20). The region upstream of the
LMO2 promoter is recurrently deleted in T-ALL and these deletions are linked to LMO2 activation
(Fig. 3G); a previous study proposed that deletion of cryptic repressors located in the deleted
region enable activation of LMO2 (21). Analysis of a T-ALL patient cohort (22) revealed deletions
that overlap the CTCF boundary site of the LMO2 neighborhood, and that patient cells harboring
these deletions had generally high levels of LMO2 expression (Fig. S9A-B). CRISPR/Cas9-
mediated deletion in HEK-293T cells of a -25 kb segment encompassing the insulated
neighborhood boundary CTCF site and two additional CTCF sites that could act as boundary
elements, caused a 2-fold increase in the LMO2 transcript (Fig. 3H-J), and a large-scale
rearrangement of interactions around LMO2 as evidenced by 5C analysis (Fig. 3K-L, S 10). These
results indicate that the deleted CTCF sites contribute to the silent state of the LMO2 proto-
oncogene (Fig. 3J).
33
FIGURE 3
Fig. 3. Dnupaon oafbulated
ndgha haed boundaries
Is E..d to proto-ancgene
actHiatlen. (A) Cohesii
ChIA-PET interactions and
CTCF and cohesin (SMC1)
binding profiles at the TALI
locus in Jurkat cells. Patient
deletions described in (22) are
shown as bars below the gene
models. The deletion on the
bottom indicates the minimally
deleted region identified in
(26). (B) ChIP-seq profiles of
CTCF. H3K27Ac, p300. CBP.
and RNA-seq at the TALI locus
in HEK-293T cells. The region
deleted using a CRISPR/Cas9-
based approach is highlighted
in a gray box. (C) Quantitative
reverse transcription polymer-
ase chain reaction (qRT-PCR)
analysis of TALI expression in
wild-type HEK-293T cells (wt)
and in cells where the neigh-
borhood boundary highlighted
in (B) was deleted. (D) Model
of the neighborhood and per-
turbation at the TALI locus.
(E) 5C contact matrices in
wild-type HEK-293T cells and
TALI neighborhood boundary-
deleted cells. An arrow indi-
cates the position of the region
removed in the mutant cells.
(F) Distance-adjusted z-score
difference (5C) maps at the
TALI locus (ACTCF - wild-type
HEK-293T). Note the increase
in the 5C signal adjacent to the
deleted region. CTCF and
H3K27Ac binding profiles in
wild-type cells are displayed for
orientation. (G) Cohesin ChIA-
PET interactions and CTCFand
cohesin (SMC1) binding pro-
files at the LMO2 locus. Patient
deletions described in (22) are
shown as bars below the gene
models. (H) ChIP-Seq binding
profile of CTCF, H3K27Ac.
p300, CBP, and RNA-seq at
the LMO2 locus in HEK-293T
cells. The region deleted by a
CRISPR/Cas9-based approach
is highlighted in a gray box.
(I) qRT-PCR analysis of LMO2
expression in wild-type HEK-
293T cells and in cells where
the neighborhood boundary
highlighted in (H) was deleted.
(J) Model of the neighborhood
and perturbation at the LMO2
A
Insulated
neighborhood:
ChIA-PET
interactions:
9 1
ISO
o"i~canoo
IFOIAI
20 kb
s CMPKI
Owwdp TALI
Patient poromoter
deletion Do rot overap[TAL I promote
B
Insulated
neighborhood boury it* 20 kb
19 - - - - - - -
HEK- 3 -
293T 3 --- -
1 RNA-Seq L I
TALle. e1 4eaee4+ *.
STIL CMPKI
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C TAL, D
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T
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is
7 +
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20
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LMO2-ACTCF - wild iype HEK-293T
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locus. (K) 5C contact matrices in wild-type HEK-293Tcells and LMO2 neighborhood boundary-deleted cells. An arrow indicates the position of the region
rernoved in the mutant cells. (L) Distance-adjusted z-score difference (5C) maps at the LMO2 locus (ACTCF - wild-type HEK-293T). Note the increase in the
5C signal adjacent to the deleted region. CTCF and H3K27Ac binding profiles in wild-type cells are displayed for orientation. In (C) and (I), data from n = 3
independent biological replicates are displayed as means SD: P < 0.01 between wild-type aid boundary-deleted cells (two-tailed t test).
34
"o LUA02 I
CRISPR/Cas9 X
J
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H
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so I
The boundaries of chromosome neighborhoods may be disrupted in other cancers. A recent study
noted that mutations in CTCF binding sites occur frequently in cancers (23), but it is unclear if
mutations in boundaries are common as only a subset of CTCF sites form insulated
neighborhoods (8, 10, 24). CTCF-cohesin bound loops are largely preserved across cell types (8,
9, 24), and a set of -10,000 constitutive CTCF-CTCF loops shared by GM 12878 lymphoblastoid,
Jurkat cells and K562 CML cells (24) were identified for comparison (Fig. 4A, S1 1, Table S8). The
boundaries of these neighborhoods were examined for somatic point mutations found in cancer
genomes using the ICGC database containing data for -50 cancer types, -2300 WGS samples,
and -13 million unique somatic mutations (Table S9). We found a striking enrichment of mutations
at the CTCF boundaries of constitutive neighborhoods (Fig. 4B, S12A, Table S10) compared to
regions flanking the boundary CTCF sites (+/-1 kb of the CTCF binding motif; P<1 0 4, permutation
test (Fig. S12B), and in many instances these created a significant change in the consensus
CTCF binding motif (Fig. S12C). Non-boundary CTCF sites did not show such enrichment (Fig.
4B, S12D, S14). The genomes of esophageal and liver carcinoma samples were particularly
enriched for boundary CTCF site mutations (Fig. 4C-D, S12D-E, S13, Table S10), and there was
no similar enrichment of mutations at the binding sites of other transcription factors (Fig. S15). In
these cancers, a considerable fraction of the mutated neighborhood boundary CTCF sites were
affected by multiple mutations ( 3 mutations per site) [280/1826 (15%) in esophageal carcinoma,
and 54/1030 (5%) in liver carcinoma](Table S10), and recurrent mutations occurred more
frequently in neighborhood boundary CTCF sites compared to non-boundary CTCF sites (Fig.
S16A-C). The genes located within the most frequently mutated neighborhoods included known
cellular proto-oncogenes annotated in the Cancer Gene Census and other genes that have not
been associated with these cancers (Fig 4E-F, Table S11-S12). Two examples of proto-oncogene
-containing neighborhoods where the activation of the gene located in the neighborhood has been
observed in the respective cancer type are shown in Fig 4G-H. These results suggest that somatic
mutations of insulated neighborhood boundaries occur in the genomes of many different cancers.
In summary, disruption of insulated neighborhood boundaries can cause oncogene activation in
cancer cells. With maps of 3D chromosome structure such as those described here, cancer
genome analysis can consider how recurrent perturbations of boundary elements may impact
expression of genes with roles in tumor biology. Our understanding of 3D chromosome structure
and its control is rapidly advancing and should be considered for potential diagnostic and
therapeutic purposes. Because control of 3D chromosome structure involves binding of specific
sites by CTCF and cohesin, which is affected by protein cofactors, DNA methylation and local
RNA synthesis (25), future advances in our understanding of these regulatory processes may
provide new approaches to therapeutics that impact aberrant chromosome structures.
35
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Acknowledgments
Supported by NIH grants HG002668 (R.A.Y.), CA109901 (R.A.Y.), HG003143 (J.D.), NS088538
(R.J.), MH104610 (R.J.) and A1120766 (M.H.P.); an Erwin Schr6dinger Fellowship (J3490) from
the Austrian Science Fund (FWF)(D.H.), Ludwig Graduate Fellowship funds (A.S.W.), the Laurie
Kraus Lacob Faculty Scholar Award in Pediatric Translational Research (M.H.P.), Hyundai Hope
on Wheels (M.H.P.), an Individual Postdoctoral grant (DFF-1333-00106B)(R.O.B.) and a Sapere
Aude Research Talent grant (DFF-1331-00735B)(R.O.B.) from the Danish Council for
Independent Research, Medical Sciences. We thank Rebecca Fitzgerald, Sean Grimmond and
the ICGC Genome Projects ESAD-UK and OV-AU for permission to use genome sequence data.
Datasets generated in this study have been deposited in the Gene Expression Omnibus under
the Accession number GSE68978. The Whitehead Institute filed a patent application based on
this paper. R.A.Y. is a founder of Syros Pharmaceuticals and R.J. is a founder of Fate
Therapeutics.
37
Materials and methods
Cell culture
Jurkat T-ALL cells were cultured in RPMI GlutaMAX (Invitrogen, 61870-127), supplemented
with 10% fetal bovine serum, 100 U/mI penicillin and 100 pg/ml streptomycin (Invitrogen, 15140-
122).
HEK-293T cells were cultured in DMEM (high glucose, pyruvate; Invitrogen, 11995-073)
supplemented with 10% fetal bovine serum, 100 U/mI penicillin and 100 pg/ml streptomycin
(Invitrogen, 15140-122).
CD3+ T-cells were isolated from buffy coats obtained from the Stanford School of Medicine
Blood Center using a human Pan T-cell Isolation Kit (Miltenyi Biotec, San Diego, CA, USA)
according to manufacturer's instructions. CD3+ cells were cultured in X-VIVO 15 (Lonza,
Walkersville, MD, USA) supplemented with 5% human serum (Sigma-Aldrich, St. Louis, MO,
USA), 100 IU/ml human rlL-2 (Peprotech, Rocky Hill, NJ, USA), and 10 ng/ml human rlL-7 (BD
Biosciences, San Jose, CA, USA). Directly after isolation, T-cells were activated for 3 days with
immobilized anti-CD3 antibody (clone: OKT3, Tonbo Biosciences, San Diego, CA, USA) and
soluble anti-CD28 antibody (clone: CD28.2, Tonbo Biosciences). T-cells were cultured at 370C,
5% CO2, and ambient oxygen levels.
ChIP-Seq
ChIP was performed as described in (27) with a few adaptations. Jurkat cells (-100 million
cells, grown to a density of -1 million cells/ml) or HEK-293T cells (-100 million cells at -80%
confluency) were crosslinked for 10 min at room temperature by the addition of one-tenth of the
volume of 11% formaldehyde solution (11% formaldehyde, 50 mM HEPES pH 7.3, 100 mM NaCl,
1 mM EDTA pH 8.0, 0.5 mM EGTA pH 8.0) to the growth media followed by 5 min quenching with
125 mM glycine. Cells were washed twice with PBS, then the supernatant was aspirated and the
cell pellet was flash frozen in liquid nitrogen. Frozen crosslinked cells were stored at -80*C. 1 00pl
of Protein G Dynabeads (Life Technologies) were blocked with 0.5% BSA (w/v) in PBS. Magnetic
beads were bound with 10 pg of anti-H3K27Ac antibody (Abcam ab4729), anti-CTCF antibody
(Millipore 07-729), anti-RUNX1 antibody (Abcam ab23980) or anti-GATA3 (Santa Cruz sc-
22206X) antibody. Nuclei were isolated as previously described (27), and sonicated in lysis buffer
(20 mM Tris-HCI pH 8.0, 150 mM NaCl, 2 mM EDTA pH 8.0, 0.1% SDS, and 1% Triton X-100)
on a Misonix 3000 sonicator for 10 cycles at 30s each on ice (18-21 W) with 60 s on ice between
cycles. Sonicated lysates were cleared once by centrifugation and incubated overnight at 40C
with magnetic beads bound with antibody to enrich for DNA fragments bound by the indicated
factor. Beads were washed with wash buffer A (50 mM HEPES-KOH pH7.9, 140 mM NaCl, 1 mM
EDTA pH 8.0, 0.1% Na-Deoxycholate, 1% Triton X-100, 0.1% SDS), B (50 mM HEPES-KOH
pH7.9, 500 mM NaCl, 1 mM EDTA pH 8.0, 0.1% Na-Deoxycholate, 1% Triton X-100, 0.1% SDS),
C (20 mM Tris-HCI pH8.0, 250 mM LiCl, 1 mM EDTA pH 8.0, 0.5% Na-Deoxycholate, 0.5%
IGEPAL C-630 0.1% SDS) and D (TE with 50 mM NaCI) sequentially. DNA was eluted in elution
buffer (50 mM Tris-HCL pH 8.0, 10 mM EDTA, 1% SDS). Cross-links were reversed overnight.
RNA and protein were digested using RNase A and Proteinase K, respectively and DNA was
purified with phenol chloroform extraction and ethanol precipitation. Purified ChIP DNA was used
to prepare Illumina multiplexed sequencing libraries. Libraries for Illumina sequencing were
prepared following the Illumina TruSeq DNA Sample Preparation v2 kit. Amplified libraries were
size-selected using a 2% gel cassette in the Pippin Prep system from Sage Science set to capture
fragments between 200 and 400 bp. Libraries were quantified by qPCR using the KAPA
Biosystems Illumina Library Quantification kit according to kit protocols. Libraries were sequenced
on the Illumina HiSeq 2500 for 40 bases in single read mode.
ChIP-seq data analysis
38
ChIP-Seq datasets were aligned using Bowtie (version 0.12.2) (28) to the human genome
(build hg19, GRCh37) with parameter -k 1 -m 1 -n 2. We used the MACS version 1.4.2 (model-
based analysis of ChlP-seq) (29) peak finding algorithm to identify regions of ChIP-seq
enrichment over input DNA control with the parameter "--no-model --keep-dup=1". A P-value
threshold of le-09 was used for both H3K27Ac and CTCF. UCSC Genome Browser tracks were
generated using MACS wiggle outputs with parameters "-w -S - space=50". The browser
snapshots of the ChIP-Seq binding profiles displayed throughout the study use the number of
reads per kilobase per million mapped reads dimension (rpm/bp) on the y-axis. The SMC1
(cohesin) binding profiles show the number of reads in the merged ChIA-PET dataset and were
generated with MACS2 (version 2.1.0.20150420) with the parameters "-q 0.05 -B" as part of the
SMC1 ChIA-PET data processing using Mango (see below).
ChIP-seq enrichment heatmap
ChIP-seq read density (rpm/bp) for SMC1, MYB, RUNX1, GATA3, TAL1, RNAPII, H3K27Ac
and CTCF at the SMC1-bound regions are displayed on Fig. S1B. The input-subtracted average
ChIP-seq read density in 50 bp bins was calculated +/- 5 kb around the center of the SMC1-
enriched regions exactly as previously described (8). In Fig. S1B, the summits of SMC1 peaks
(79,976) identified as part of the SMC1 ChIA-PET data processing using Mango were used. Note
that Mango merges the SMC1 peaks into 67,596 PET peaks (Fig. S1C) before identifying
interactions.
ChIA-PET
ChIA-PET was performed using a modified version (16) of a previously described protocol (8).
Jurkat cells (up to 500-800 million cells, grown to a density of -1 million cells /ml) were crosslinked
with 1% formaldehyde at room temperature for 10 min and then neutralized with 125mM glycine.
Crosslinked cells were washed three times with ice-cold PBS, snap-frozen in liquid nitrogen, and
stored at -80oC before further processing. Nuclei were isolated as previously described (27), and
chromatin was fragmented using a Misonix 3000 sonicator. The anti-SMC1 antibody (Bethyl,
A300-055A) was used to enrich SMC1-bound chromatin fragments exactly as described at the
ChIP-Seq section. A portion of ChIP DNA was eluted from antibody-coated beads for
concentration quantification and for enrichment analysis using quantitative PCR. For ChIA-PET
library construction ChIP DNA fragments were end-repaired using T4 DNA polymerase (NEB)
followed by A-tailing with Klenow (NEB). A biotinylated bridge linker (F:
/5Phos/CGCGATATC/iBiodT/TATCTGACT; R: /5Phos/GTCAGATAAGATATCGCGT) with T-
overhangs was added and the proximity ligation was performed overnight at 160C in 1.5 mL
volume. Unligated DNA was then digested with exonuclease and lambda nuclease (NEB). DNA
was eluted off the beads in elution buffer (50 mM Tris-HCL pH 8.0, 10 mM EDTA, 1% SDS)
followed by overnight crosslink reversal, RNAse A treatment, and proteinase K digestion. A
phenol:chloroform:isoamyl alcohol extraction was performed followed by an ethanol precipitation.
Precipitated DNA was resuspended in Nextera DNA resuspension buffer (Illumina). The DNA was
then tagmented with the Nextera Tagmentation kit (Illumina). The tagmented library was purified
with a Zymo column and was bound to Streptavidin beads to enrich for ligation junctions
(containing the biotinylated bridge linker). 12 cycles of the polymerase chain reaction were
performed to amplify the library. The amplified library was size-selected (350-500 bp) with a Pippin
prep machine and sequenced with either 100xlOO (Replicate 1) or 125x125 (Replicate 2) paired-
end sequencing on an Illumina Hi-Seq 2500 platform.
ChIA-PET data analysis using Mango
ChIA-PET analysis was carried out using a combination of in-house scripts and the Mango
pipeline (Version: 1.0.4, (30)). Image analysis and base calling was done using the Solexa
pipeline. Read pairs were examined for the presence of at least 10 base pairs of linker sequence
39
(see above). Read pairs that did not contain linker in either mate were not processed further.
Reads containing linker were trimmed using cutadapt (cutadapt -m 17 -a
forward=ACGCGATATCTTATCTGACT -a reverse=AGTCAGATAAGATATCGCGT --overlap 10)
(http://code.google.com/p/cutadapt/). The trimmed mate pairs were then input into the Mango
pipeline with the parameters (Rscript mango/mango.R --shortreads FALSE --peakslop 1500 --
stages 2:5 -- reportallpairs TRUE). The value of the peakslop parameter was adapted from a
previous report (24). All other parameters were left as default. The interactions identified by
Mango are found in Table S2A. In order to determine an appropriate FDR cutoff for the definition
of high confidence interactions, we first identified interactions that were previously detected in the
Jurkat T-ALL cells. A previous study has already found a looping interaction between two CTCF
sites around the TALI gene in Jurkat T-ALL cells using chromosome conformation capture (3C)
assay (31), and we found that this positive control interaction has an estimated FDR of 0.14 in
our ChIA-PET dataset (Fig. 2B, Table S2A). We therefore defined high confidence interactions
as having an FDRs0.2 for downstream analyses. We note the 0.2 FDR cutoff is likely an over-
estimate of the true FDR and that interactions at even higher FDR values likely contain bona fide
interactions. For example, the vast majority (>85%) of interactions with an 0.2 5 FDR 0.3 overlap
genomic features such as enhancers, promoters or CTCF sites on each of their ends (Fig. S2G).
Mango determines a lower bound distance cutoff to exclude PETs that potentially arose from
self-circularization of the same fragment during the proximity ligation step (30). Since self-
circularized PETs are formed from a single DNA fragment, the mate pairs always map to opposite
DNA strands. Mango uses the fraction of PETs that have reads on opposite strands to estimate
the fraction of self-circularized PETs at various distances to estimate the distance at there is
negligible bias to which DNA strand the mate pairs of the PETs map (30) (Fig. S2C).
The RAD21 (cohesin) ChIA-PET datasets in GM12878 and K562 were described in a previous
study (24), and were processed as described for the Jurkat ChIA-PET data above.
Throughout the entire study, the interactions generated by the Mango pipeline are used for
genome wide interaction analyses and visualization of interactions at individual loci unless
otherwise noted (on Fig. S3, S4, Table S2B).
ChIA-PET data analysis using the Dowen et al. pipeline
All ChIA-PET datasets were also processed with an in house method ("Dowen et al. pipeline")
adapted from previous computational pipeline (8, 32). Image analysis and base calling was done
using the Solexa pipeline. Reads were examined for the presence of at least 10 base pairs of
linker sequence. Reads that did not contain linker were not processed further. Reads containing
linker were trimmed using cutadapt (cutadapt -m 17 -a forward=ACGCGATATCTTATCTGACT -
a reverse=AGTCAGATAAGATATCGCGT --overlap 10) (http://code.google.com/p/cutadapt/).
Trimmed mate pairs were mapped independently to hg19 using Bowtie version 1.1.1 (bowtie -e
70 -k 1 -m 1 -v 2 -p 4 --best --strata -S) (28). Aligned reads were paired with mates with an in-
house script using read identifiers. To remove PCR bias artifacts, reads were filtered for
redundancy: PETs with identical genomic coordinates and strand information at both ends were
collapsed into a single PET. The PETs were further categorized into intrachromosomal PETs or
interchromosomal PETs. Regions of local enrichment (PET peaks) were called using MACS 1.4.2
(29) with the parameters "-p le-09 -no-lambda -no-model". To identify long-range chromatin
interactions, we first removed intrachromosomal PETs of length < 5 kb because these PETs may
originate from self-ligation of DNA ends from a single chromatin fragment in the ChIA-PET
procedure (8). We next identified PETs that overlapped with PET peaks at both ends by at least
lbp. These PETs were defined as putative interactions. A statistical model based upon the
hypergeometric distribution was applied to identify high-confidence interactions, representing
high-confidence physical contacts between the PET peaks. Specifically, the numbers of PET
sequences that overlapped with PET peaks at both ends as well as the number of PETs within
PET peaks at each end were counted. The PET count between two PET peaks represented the
40
frequency of the interaction between the two genomic locations. A hypergeometric distribution
was used to determine the probability of seeing at least the observed number of PETs linking the
two PET peaks. A background distribution of interaction frequencies was then obtained through
the random shuffling of the links between two ends of PETs, and a cutoff threshold for calling
significant interactions was set to the corresponding p-value of the most significant proportion of
shuffled interactions (at an FDR of 0.01). This method yielded similar number of interactions as
the correction of p-values by the Benjamini-Hochberg procedure (33) to control for multiple
hypothesis testing. Operationally, the pairs of interacting sites with three independent PETs were
defined as high-confidence interactions in the SMC1 ChIA-PET merged dataset and with two
independent PETs in the individual SMC1 ChIA-PET replicates (8). The steps of the data
processing are displayed on Fig. S3A at the RUNXI locus. The high confidence interactions
identified by the Dowen et. al pipeline are found in Table S2B.
Comparison of ChIA-PET analytical approaches
An important component of the analytical methods to identify interactions in chromatin contact
data is the estimation of a background contact frequency, and the comparison of the observed
contact frequencies to the estimated background using statistical models. There are currently
multiple approaches to estimate the background contact frequency. One major approach is based
on previous studies using chromosome conformation capture methodologies that suggested that
the genome has polymer-like properties such that the interaction frequency between two genomic
loci decreases as a function of their linear genomic distance (34, 35). Several computational
pipelines have thus been developed to estimate background contact frequencies based on this
"genomic proximity bias," both for chromosome conformation capture data (35, 36) and ChIA-PET
data (30, 37). At the same time, other ChIA-PET analytical methods estimate the background
contact frequency based on the ligation frequency of the immunoprecipitated chromatin fragments
using a hypergeometric test (8, 15, 32, 38, 39). Studies using ChIA-PET analytical methods that
estimate background contact frequencies based on the "genomic proximity bias," have mostly
revealed long-range CTCF-CTCF associated interactions that are consistent with Hi-C data (24,
30), whereas studies using ChIA-PET analytical methods that estimate the background contact
frequency based on the ligation frequency of the immunoprecipitated chromatin fragments using
a hypergeometric test have also identified short-range enhancer-promoter interactions that do not
involve CTCF (8, 15, 38, 39).
We have analyzed our ChIA-PET data using two analytical approaches: one that estimates the
background contact frequency based on the genomic proximity bias (Mango pipeline) adapted
from previous reports (24, 30, 40), and an analytical approach that estimates the background
contact frequency based on the ligation frequency of the immunoprecipitated chromatin fragments
using a hypergeometric test (Dowen et al. pipeline). The Dowen et al. pipeline was adapted from
a previous report (8) and was recently developed based on multiple previous studies (15, 32, 39).
We found that the two analytical methods yielded largely similar high confidence interactions (Fig.
S1C, S3A, S4A, S4B, Table S2A-B). As expected, the median length of the high confidence
interactions identified using the Mango pipeline (-263 kb) were greater than the median length of
the high confidence interactions identified using the using the Dowen et al. pipeline (-144 kb),
and the Dowen et al. pipeline detected -3x more enhancer-promoter interactions than the Mango
pipeline (Fig. S4C). Furthermore, the short range high-confidence interactions detected by the
Dowen et al. pipeline but not by the Mango pipeline included interactions that were previously
detected by locus-specific 3C and perturbation approaches, e.g. at the CD3 and GATA3 loci (41,
42) (Table S2A-B). Since the key model investigated in this study involves CTCF-CTCF
interactions, we have used the interactions identified by the Mango pipeline for the main analyses
(Table S2A), and also deposit the interactions identified by the Dowen et al. pipeline, because
these include -3x more enhancer-promoter interactions (Table S2B) that may be a valuable
resource for the scientific community.
41
ChIA-PET replicate comparison
For the comparison of ChlA-PET replicates displayed on Fig. S2A, we first binned the genome
into 50kb non-overlapping bins. All unique PETs from each replicate ChIA-PET dataset were
placed into the bins, and the number of reads in each bin was counted. The scatter plot on Fig.
S2A shows the values in each bin in each of the two replicate dataset. The values were then used
to calculate a Pearson correlation coefficient (r = 0.99).
To compare the interactions identified in the replicates we also performed the following
analysis. We ranked the interactions identified in one of the replicates according to their FDR. We
then calculated the cumulative percentage of interactions in one replicate ChIA-PET dataset that
overlapped with an interaction in the other replicate ChIA-PET dataset (regardless of the FDR
value of the interaction in the latter). This analysis revealed that virtually all (>99%) of interactions
characterized by a low FDR value (e.g. FDR 0.2 "high confidence interactions") overlapped with
an interaction in the other replicate, suggesting high similarity between the ChIA-PET replicate
datasets (Fig. S2B).
CTCF motif orientation analysis
The DNA binding site of CTCF is asymmetric, and a previous study has suggested that
interactions between CTCF sites occur primarily between sites with motifs in the convergent
orientation (36). Therefore, we investigated the motif orientation of the CTCF sites connected by
ChIA-PET interactions in our dataset, and indeed found that the majority (-80%) of interacting
CTCF-CTCF sites are in a convergent motif orientation (Fig. S2F, S3E) suggesting high quality
of the ChIA-PET data.
For this analysis, FIMO was first used to identify the location and orientation of the CTCF motifs
at CTCF ChIP-seq peaks at a default P-value threshold of 104 (43). In the analysis, the canonical
CTCF motif from the JASPAR CORE vertebrate motif database (ID. MA0139.1) (44) was used.
The information of CTCF motif orientation at CTCF ChIP-seq peaks was next overlaid with PET
peaks at two ends of CTCF-CTCF/cohesin ChIA-PET interactions. For simplicity, we only used
the CTCF ChIP-seq peaks having a single CTCF motif for the analysis, and only the CTCF-
CTCF/cohesin ChIA-PET interactions were used whose ends overlapped with only a single CTCF
ChIP-seq peak by at least 1 base-pair at each end. The pairs of CTCF motifs at the two ends of
CTCF-CTCF/cohesin ChIA-PET interactions were then classified into one of the four possible
classes of motif orientation: a convergent orientation (forward-reverse), a divergent orientation
(reverse-forward), the same direction on the forward strand (forward-forward) or the same
direction on the reverse strand (reverse-reverse) (Fig. S2F, S3E).
Visualization of ChIA-PET interactions on the WashU Genome Browser
ChIA-PET interactions were visualized on the WashU Genome Browser (45). The depth of the
color of the interactions on the Browser snapshots reflects the following value: (1-FDR)+0.1. The
(1-FDR) operation was performed so that the interactions characterized by the lowest significance
score are displayed as the darkest arcs, and to improve visualization, a pseudo-count of 0.1 was
added. In Fig. 1B, 4A, 4G, 4H only the high confidence interactions are displayed (FDR:0.2).
Comparison of the Jurkat T-ALL ChIA-PET interactions to interactions described in other cell
types
Interactions detected using Hi-C data in GM12878, HeLa, HMEC, HUVEC, IMR90, K562,
KBM7 and NHEK were downloaded from a previous study (36). For the overlap analysis displayed
In Fig. S2D, S3C, an adaptation of the approach described in (36) was used: interactions were
scored as overlapping across two cell types if they had a reciprocal overlap of at least 80% of the
length of the interaction. The high confidence interactions detected in Jurkat T-ALL cells, but not
in the other 8 cell types are listed in Table S2C.
42
Hi-C data analysis
Previously published Hi-C datasets in H1 human ESCs (11) were downloaded from GEO
(GSM1267196). The raw reads from these datasets were mapped to the human genome build
hg19 and filtered as previously described (46). Corrected contact probability matrices at 40-kb
resolution were obtained using the hiclib library (https://bitbucket.org/mirnylab/hiclib). The
corrected contact probability matrices displayed on the heatmap in Fig. 1 B were generated by the
image function in R. The topologically associating domain (TAD) coordinates in H1 hESC used in
Fig. 1B, Fig. S2E, S3D were downloaded from a previous study (11) and were converted to hg19
co-ordinates using the liftover tool on the UCSC Genome Browser.
Topologically Associating Domain (TADs) boundary overlap analysis
To test whether the cohesin ChIA-PET interactions were contained within TADs, a permutation
test was conducted. The percentage of cohesin ChIA-PET interactions contained within TADs
was calculated by overlapping the interactions with the H1 TAD boundaries using bedtools
intersect. An interaction was considered contained within a TAD if it intersected only with one
TAD. After this percentage was calculated, the positions of ChIA-PET interactions were randomly
shuffled across a chromosome 10,000 times using bedtools shuffle with the -chrom option (to
ensure that chromatin loops were only shuffled chromosome-wide to control for the rate of
chromatin loops observed per chromosome). For each randomly shuffled permutation, the
percentage of ChIA-PET interactions contained within only one TAD was re-estimated to generate
a random background. The p-value was estimated by calculating the frequency that a randomly
generated permutation had a higher fraction of ChIA-PET interactions contained within TADs than
the fraction observed in the actual data. (Fig. S2E, S3D).
Identification of enhancers and super-enhancers
Enhancers and super-enhancers in Jurkat cells were identified using H3K27Ac ChIP-Seq data
as previously described (17). Briefly, enhancers were defined as H3K27Ac ChIP-Seq peaks
identified using MACS. To identify super-enhancers, the H3K27Ac ChIP-Seq peaks (i.e.
enhancers) were stitched together if they were within 12.5 kb, and the stitched enhancers were
ranked by their ChIP-Seq read signal for H3K27Ac, using the ROSE algorithm
(https://bitbucket.org/young computation/rose) (18). ROSE separates super-enhancers from
typical enhancers by identifying an inflection point of H3K27ac signal vs. enhancer rank (17, 18).
Assignment of genes to enhancers
For the assignment of genes to enhancers, the typical enhancers and super-enhancers
generated by ROSE were used (see above). Typical enhancers and super-enhancers were
assigned to promoters in two ways. When available, the enhancer-promoter ChIA-PET
interactions were used to assign enhancers to their target genes. In the absence of a ChIA-PET
interaction, typical enhancers and super-enhancers were assigned to the nearest active gene.
Active genes were defined as having an at least 0.5 mean rpm/bp H3K27ac ChIP-Seq density in
a window 500 bases up- and downstream of the TSS, as previously described (8, 17).
Assignment of interactions to regulatory elements
To identify the association of long-range chromatin interactions with different regulatory
elements, we assigned the PET peaks of interactions to different regulatory elements, including
enhancers (H3K27Ac ChIP-Seq peaks), promoters (+/- 2 kb of the Refseq TSS), and CTCF
binding sites as previously described (8). For the analysis displayed in Fig. S1 D, S3B, if an anchor
site overlapped with multiple regulatory elements priority was assigned as: (1) promoters, (2)
enhancers, (3) CTCF sites. A minimum of 1 base-pair overlap was required. These anchor
classifications represent the nodes in Fig. S1 D, S3B. Next the edges were calculated by counting
43
the number of interactions between the classified PET anchors. Note that this analysis does not
include CTCF sites that overlap either enhancers or promoters in the "CTCF" node of the plot.
The total number of CTCF-CTCF interactions displayed in Fig. S4C includes interaction between
any two CTCF-bound sites, regardless whether they overlap enhancers or promoters or not.
Insulated neighborhoods
Candidate insulated neighborhoods were defined as two CTCF ChIP-Seq binding sites that
have an at least 1bp overlap each with two PET peaks connected by a cohesin ChIA-PET
interaction (8). A gene was considered to be inside an insulated neighborhood, if its transcription
start site (TSS) is located within the neighborhood boundaries. When multiple TSSs were
annotated for the same gene, the TSS of the longest transcript was used for further analysis.
Heatmap representation of ChIA-PET interactions in insulated neighborhoods
Heatmap representation of ChIA-PET interactions in Fig. S2H was created by mapping high-
confidence ChIA-PET interactions across insulated neighborhoods using a previously described
method (8). We created three types of regions: upstream, the insulated neighborhood, and
downstream. Upstream and downstream regions are 20% of the insulated neighborhood's length
each. The upstream and downstream regions were divided into 10 equally sized bins each, and
insulated neighborhoods were length normalized by dividing them into 50 equally sized bins. To
calculate interactions in each bin the interactions were filtered in two ways: (1) we required
interactions to have at least one end in the interrogated region. This removed interactions that are
anchored outside of our region of interest. (2) We removed interactions that represent nested
interactions (i.e. where one CTCF anchor site of two interactions are identical). The density of the
whole span of ChIA-PET interactions in each bin was next calculated in the units of number of
interactions per bin. The density of ChIA-PET interactions was row-normalized to the row
maximum for each domain.
RNA isolation and RNA-Seq
Jurkat RNA was isolated and sequenced as previously described (47). RNA-Seq reads were
aligned to the hgl9 (GRCh37) reference genome using Tophat2 (48) version 2.0.11, using Bowtie
(28) version 2.2.1.0 and Samtools version 0.1.19.0. RPKMs for each Refseq transcript were
calculated from aligned reads using RPKMcount.py from RSeQC (49). For a gene to be
considered expressed the cutoff of >1 RPKM was used (50). For the analysis displayed in Fig.
2A, if multiple TSSs were annotated for the same gene, the RPKM value of the longest transcript
was considered (this method of collapsing TSSs produced qualitatively identical results compared
to using the RPKM value of the highest-expressed transcript).
CRISPR/Cas9 mediated genome editing
Genome editing was performed using CRISPR/Cas9 essentially as described (51). Briefly,
target-specific oligonucleotides were cloned into a plasmid carrying a codon-optimized version of
Cas9 and either an mCherry or GFP expression cassette. SgRNA sequences were cloned into
the Bbsl recognition sites as described (http://www.genome-engineering.org/crispr/). The
genomic sequences complementary to guide RNAs are listed below. Around 500,000 HEK-293T
cells were transfected with two plasmids expressing Cas9 and sgRNA targeting regions around
200 basepairs up- and down-stream of the center of the targeted CTCF site at the TAL1 locus,
and 200 basepairs up- and down-stream of the first and third CTCF binding sites at the LMO2
locus, respectively. One of the two guide RNAs were cloned into the Cas9 expression vector
containing the mCherry, and the other into the Cas9 expression vector containing the GFP
expression cassette. Transfection was carried out with the Lipofectamine 2000 reagent
(Invitrogen) according to the manufacturer's instructions. For the LMO2 locus 1 pl of a 10 pM
repair template (160 bp ultramer with the desired deletion junction) was included in the
44
transfection. Two days after transfections, cells positive for mCherry and GFP were FACS sorted,
and replated at clonal density. Individual colonies were picked, expanded, and genotyped by
PCR, and the edited alleles were verified by Sanger sequencing. The cell lines used for the
expression analysis in Fig. 3 that carry a deletion allele at the TALl locus are homozygous, and
the cell lines that carry a deletion allele at the LMO2 locus are heterozygous for the modification.
SglTALl: ACATTTCAATTATATGTTAA
Sg2_TAL1: ATACTAGTTAAGCTTTTCCT
Sg1_LMO2: AAACCAGCATTGCCACCTGG
Sg2_LMO2: CCAGGTGGCAATGCTGGTTT
LMO2 Repair Template: AGC CCC ATA GTT GGT GCT CAA TAA ATG CTA GTA ATA TTT ACT
TGT GGC TTA CTG GTT CCT CAA GAT TCC TTA AAA TCT GAT GGC ATC AGA AGA GAC
TAT CTC ACT GTT ATC ATG ACA TGG ACA TCC CGT GCA TGC CTG TAT TTG AAC ACT
TGT CTC ATT G
CRISPR/Cas9-mediated genome editing in CD3+ T-cells
After three days of activation, T-cells were nucleofected using the Lonza Nucleofector 2b
(program U-014) and the Human T-cell Nucleofector Kit (VPA-1002, Lonza). Nucleofection
conditions were the following: 100 pl nucleofection solution, 106 cells, 2x5pg synthetic sgRNA and
15 pg Cas9 mRNA. Cas9 mRNA was purchased from TriLink BioTechnologies (San Diego, CA,
USA). All synthetic sgRNAs contained three terminal nucleotides at both the 5' and 3' ends with
2'0-Methyl 3' phosphorothioate modifications. Paired synthetic sgRNAs targeting CCR5 (named
'D' and 'Q', referred to as control deletion on Fig. S8D) were previously reported (52). TAL1-
targeting sgRNAs (#3 and #4) were synthesized by TriLink BioTechnologies (San Diego, CA,
USA). Paired synthetic sgRNAs were designed to flank the CTCF motif of the TALI insulated
neighborhood boundary (Fig. S8C).
sgRNA sequences:
sgRNA #3:
5' 2'OMe(A(ps)C(ps)A(ps)) UUU CAA UUA UAU GUU AAG UUU UAG AGC UAG AAA UAG CAA
GUU AAA AUA AGG CUA GUC CGU UAU CAA CUU GAA AAA GUG GCA CCG AGU CGG
UGC 2'OMe(U(ps)U(ps)U(ps)) U 3'
sgRNA #4:
5' 2'OMe(A(ps)U(ps)A(ps)) CUA GUU AAG CUU UUC CUG UUU UAG AGC UAG AAA UAG CAA
GUU AAA AUA AGG CUA GUC CGU UAU CAA CUU GAA AAA GUG GCA CCG AGU CGG
UGC 2'OMe(U(ps)U(ps)U(ps)) U 3'
Abbreviations:
2'OMe: 2' 0-Methyl
ps: phosphorothioate
Target-specific complementary nucleotides are underlined. Editing was confirmed by PCR
amplification of the targeted region (Fig. S8E).
RNA isolation and quantitative RT-PCR
Gene expression experiments displayed in Fig. 3C and Fig. 31 were performed on wild type
and CTCF-site deleted clonal lines of HEK-293T cells.
For the expression analysis in primary human cells (Fig. S8D), T-cells were isolated from 9
independent donors, and transfected in two independent reactions per donor per time point. T-
cells isolated from three donors were assayed for gene expression 4 days and 7 days post-
transfection, T-cells isolated from three donors were assayed for gene expression 7 days post-
transfection, and T-cells isolated from three donors were assayed for gene expression 10 days
45
post-transfection, resulting in a total of 24 gene expression experiments. The PCR-based
genotyping for three representative transfections is displayed on Fig. S8E.
RNA was isolated using the RNeasy Plus purification kit (Qiagen), and reverse transcribed
using oligo-dT primers and SuperScript Ill reverse transcriptase (Invitrogen) according to the
respective manufacturer's instructions. Quantitative real-time PCR was performed on a 7000 AB
Detection System using the following Taqman probes, according to the manufacturer's
instructions (Applied Biosystems):
GAPDH: hs02758991_g1
TALl: hs01097987_ml
LMO2: hs00277106_ml
For the expression analysis in primary human T-cells, the differences in the Ct-values of TALI
and GAPDH are plotted on Fig. S8D. Samples in which no TALI expression was detectable were
excluded form the analysis, and only the samples where TALI was above the detection limit are
included on Fig. S8D, (9/24 mock samples, 14/24 control deletion samples, and 23/24 TAL1
neighborhood boundary -deletion samples).
Single molecule mRNA fluorescence in situ hybridization (FISH)
RNA FISH was performed as previously described (53). CRISPR/Cas9-edited primary human
T-cells or Jurkat T-ALL cells were washed with PBS and subsequently fixed with
paraformaldehyde (PFA) at a final concentration of 4%. The cells were incubated in 4% PFA for
10 minutes while rotating to avoid clumping of cells. After 10 minutes the cells were spun down
for 2 min at 1000rpm. To permeabilize the cells, the cells were placed in 70% ethanol overnight.
Cells of three independent transfections were pooled for further analysis. Cells from three
independent transfections (described above) were pooled for the subsequent steps. The fixed
cells were attached to chambered cover slides (Nunc Lab-Tek) coated with 0.1% poly-l-lysine
(Sigma) prior to imaging. 20nt probes for RUNXI and TALl were manually designed (sequences
available upon request) and ordered through Biosearch Technologies, coupled with either A594-
flurophore (Invitrogen) or TMR-fluorophore (Invitrogen), respectively, and hybridized with
standard FISH hybridization buffer containing 40% formamide. For hybridization conditions 75ng
probes per pl of hybridization buffer were used. The probes were hybridized for 16 h at 300C
followed by two wash steps with wash buffer containing 40% formamide and 2x SSC. The cells
were counterstained with Hoechst 33342. During imaging the cells were kept in a solution
containing PBS, Glucose, Catalase and Trolox to avoid bleaching of fluorophores. All images
were taken with a Nikon Ti-E inverted fluorescence microscope equipped with a 10OX oil-
immersion objective and a Photometrics Pixis 1024 CCD camera using MetaMorph software
(Molecular Devices, Downington, PA). Cells that seemed fragmented or had an excessive amount
of background were excluded from quantification. Only transcripts that colocalized with the
Hoescht signal were counted. Cells with more than 1 transcript for either RUNXI or TALI were
counted as positive for the respective transcript.
Chromosome conformation capture carbon copy (5C)
For each 5C library, 50 million cells were resuspended in 40 mL DMEM (high glucose,
pyruvate; Invitrogen, 11995-073) followed by the addition of 4 mL 11% formaldehyde solution
(11% formaldehyde, 50 mM HEPES pH 7.3, 100 mM NaCl, 1 mM EDTA pH 8.0, 0.5 mM EGTA
pH 8.0). Cells were cross-linked for 10 minutes with gentle agitation every 2 minutes. Cross-
linking was quenched by the addition of 2 ml 2.5M glycine followed by two washes with ice cold
PBS. Crosslinked cells were snap-frozen in liquid nitrogen and stored at -800C until further
processing.
5C experimental design
46
5C was carried out as previously described (54, 55). We investigated two 2 Mb
regions centered on the TALI and LMO2 loci. The TALI 2Mb region is located on
Chromosome 1 (hg19 chri: 46740122-48740121) and the LMO2 2Mb region on Chromosome 11
(hg19 chrl 1: 33003550-35003549). Libraries were generated for three HEK-293T cell lines: wild
type (wt), TALl-deletion (TAL-ACTCF), and LMO2-deletion (LMO2-ACTCF) with two biological
replicates for each line. Replicate data were pooled for processing.
5C primer design
5C primers were designed at HindlIl restriction sites (AAGCTT) using 5C primer design tools
previously developed and made publicly available online at the My5C
website (http://my5C.umassmed.edu) (56). Primers were designed according to a new "double
alternating scheme" (Fig. S10A). In this design, each restriction fragment has two primers, one
primer designed on the 5' end of the restriction fragment, and one primer designed on the 3' end
of the restriction fragment. If one fragment has a right (5') forward (FOR) and a left (3') reverse
(LREV), the adjacent restriction fragment will have a left (3') forward (LFOR) and a
right (5') reverse (REV) and so on (Figure S1). This design allows interrogation of all fragment-
fragment interactions throughout the regions (with up to two independent interactions per pair of
fragments), whereas the previously developed "alternating design" (54) allowed interrogation of
interactions between even-numbered and odd-numbered fragments only. Primers settings were:
U-BLAST, 3; S-BLAST, 50; 15-MER, 800; MIN_FSIZE, 100; MAX_FSIZE, 50,000; OPT_TM, 65;
OPTPSIZE, 40. The 5C primer tails uses were: (FOR/LFOR) T7
sequence: 5'- TAATACGACTCACTATAGCC-3'; (REV/LREV) T3
sequence 5'- TCCCTTTAGTGAGGGTTAATA-3'. The length of the forward primers was
60 bp and the length of the reverse primers was 61 bp. For the TALl locus, we designed 270
forward 5C primers and 285 reverse 5C primers for a possible of 76,950 interactions. For
the LM02 locus, we designed 367 forward 5C primers and 347 reverse 5C primers for a possible
of 127,349 interactions. All primer information can be found in Table S6.
Generation of 5C libraries
3C was performed with Hindill restriction enzyme as previously described with some minor
modifications (34, 57). Briefly, after digestion the restriction enzyme was inactivated 15 min at
650C and immediately placed on ice. Ligation was performed in a final volume of 1,200 pL with 10
U of T4 ligase (Invitrogen) and 1% of Triton X-100. For 50 million cells, 10 ligation reactions
were performed, and pooled after DNA purification. The 3C libraries were then interrogated by 5C
(54, 58). 5C was performed as described (55) with the following changes. The multiplex annealing
reaction was performed overnight at 500C. Pairs of annealed 5C primers were ligated at the same
temperature using Taq DNA ligase for 1 h. 7 independent ligation reactions were performed for
each 5C library, each containing an amount of 3C template that represents 600,000 genome
equivalents and 0.3 fmol of each primer. Ligated 5C primer pairs, which represent a specific
ligation junction in the 3C library and thus a long-range interaction between the two corresponding
loci, were then amplified using 20 cycles of PCR with T7 and T3R universal tail primers that
recognize the common tails of the 5C forward and reverse primers. Four separate amplification
reactions were carried out for each of 7 annealing reactions described above and all the PCR
products were pooled together. This pool constitutes the 5C library. The libraries were
concentrated using Amicon Ultra Centrifugal filters - 0.5ml 30K (Millipore) and purified
with Qiaquick PCR purification kit. Index adaptors (Illumina) were ligated to the 5C library using
the Illumina protocol (TruSeq Nano DNA sample Prep kit). The linkered 5C libraries were then
amplified by PCR (6 cycles), using the Illumina PCR mix. The 5C libraries were gel purified and
sequenced on either the Illumina Miseq or the Illumina HiSeq 2000 platform, generating 50-bp
paired-end reads. The indexed adapter was AD005 for WT, AD006 for TALI deletion and AD015
for LMO2 deletion.
47
5C read mapping
Sequencing data were obtained from both an Illumina MiSeq and
an Illumina HiSeq 2000 machine and was processed by a custom pipeline to map and assemble
5C interactions, as previously described (55, 56). We used an updated version of
the Novoalign mapping algorithm (V3.02.00). Data from the two biological replicates were
pooled, producing a single interaction map for the wild type (wt), TAL1-deletion (TAL-ACTCF),
and LMO2-deletion (LMO2-ACTCF) samples. The summary statistics and the read depth of each
5C libraries can be found in Table S7.
5C filtering and analysis
5C matrices were filtered using previously described methods (55, 56). To summarize, first we
removed the diagonal from all matrices, which represent self-circularization of a restriction
fragment. Second, we detected and flagged all outlier (singleton) pixel/ interactions that are
defined by a Z-score greater than 21 in each dataset. We then took the union of all
outlier (singleton) pixel/interactions across the 3 5C matrices, and removed these
pixels/interactions from all 3 datasets. Third, we detected and flagged all outlier (anchor) row/cols
that are defined as having a having an aggregate (row/col) signal greater than or less than 1.5 *
IQR (of the distribution of all row/col signals). We then took the union of all flagged (anchor)
row/col outliers across the 3 5C matrices, and removed these (anchor) row/cols from all 3
datasets, excluding the regions that overlapped our LMO2 and TALI deletions in
our LMO2 and TALI deletion matrices respectively. Fourth, the matrices were
balanced according to the ICE method developed for Hi-C (46). Fifth, since each restriction
fragment has two primers (one on the 3' end and one on the 5' end), an interactive between any
two fragments can be represented by 2 possible primer ligations. We chose to calculate the mean
of these two possible interactions thus collapsing the interaction map down to a fragment x
fragment interaction map (as compared to a primer x primer interaction map). Sixth, to properly
scale the matrix/heatmaps relative to genomic coordinates, the matrices were binned using
a binsize=20 kb, binstep=2.5kb, binmode=median.
5C distance correction (deletions)
To rule out the possibility that the increased SC interaction signal observed in the deletion
samples was an artifact due to the fact that the regions are now closer in linear distance, we
performed a distance-adjustment prior to the z-score / z-score difference calculation(s). For the
LMO2-deletion 5C map and the TALl-deletion 5C map, we first removed the primers contained
in the deleted regions. We then adjusted for the 345bp and 26,628bp differences in the TAL1
and LMO2 regions respectively by shifting the coordinates of each restriction fragment
downstream of the deleted region. We then transformed the interaction counts into a distance
normalized z-score, using LOWESS to estimated signal per distance as previously described (55).
Next we merged primer interactions from the same fragments as described above in step 5, and
binned the data using a binsize=20 kb, binstep=2.5kb, binmode=median. Finally, we calculated
the difference of the z-score matrices to quantify the amount of the differential interactions
observed between the WT, control and deletion datasets.
The plots without distance correction ("uncorrected"), and the plots generated with distance-
adjustment ("distance adjusted") are displayed on Fig. S1OC and Fig. S1OE, respectively. Fig. 3F
and Fig 3L show the distance adjusted plots.
Luciferase reporter assay
Luciferase reporter assays were performed as previously described (51) with modifications.
The candidate enhancer regions (-600bp) around the STIL and CMPKI promoters were cloned
into a pGL3 (Promega) reporter vector (BamHl-Sall sites) that contains a Firefly luciferase gene
48
driven by a minimal c-MYC promoter (18). The candidate enhancer region around STIL was PCR-
amplified using the following primer sequences (5'-3'): ATGTTACCCACCAACCTTCCC and
AAACTGTTCTTCGGGTGTCCG. The candidate enhancer region around CMPKI was PCR-
amplified using the following primer sequences (5'-3'): GATTCTCCTCTGCTCTCCACG and
AAGACACGTTCGGTGACAGTG. HEK-293T genomic DNA was used as template DNA in the
PCR reactions.
1*105 HEK-293T cells were transfected with 490ng of the reporters using Lipofectamine 2000
(Invitrogen). lOng of a Renilla luciferase control plasmid (pRL-SV40; Promega) was co-
transfected as a normalization control. After 24 hours of incubation luciferase activity was
measured using the Dual-Luciferase Reporter Assay System (Promega). All luciferase reporter
assays were performed in triplicates. Luciferase activity was normalized to the activity measured
in cells transfected with a construct containing only the promoter (empty vector) (Fig. S8B).
2*105 Jurkat T-ALL cells were transfected with 475ng of the reporters using MOLT4 Avalanche
transfection reagent (EZ Biosystems). 25ng of a Renilla luciferase control plasmid (pRL-SV40;
Promega) was co-transfected as a normalization control. After 40 hours of incubation luciferase
activity was measured using the Dual-Luciferase Reporter Assay System (Promega). All
luciferase reporter assays were performed in triplicates. Luciferase activity was normalized to the
activity measured in cells transfected with a construct containing only the promoter (empty vector)
(Fig. S8B).
T-ALL deletion catalog and overlap analysis
Deletions in T-ALL genomes were compiled from multiple studies (22, 26, 59, 60). We filtered
for relatively short deletions (<500 kb, around half the size of an average TAD (11)) in order to
minimize deletions that affect multiple genes. The overlap with insulated neighborhood
boundaries was analyzed as follows. A neighborhood boundary CTCF site was scored as
overlapping a deletion, if the boundary site (i.e. the PET peak) overlapped at least one deletion
by 1bp. A deletion was scored as overlapping a neighborhood (i.e. the PET peak) boundary if it
overlapped a boundary site by at least lbp. The deletion co-ordinates (hgl9/GRCh37) and the
source study are listed in Table S5.
To estimate significance of the overlap we determined whether the observed frequency of
overlap is higher than expected (Fig. S6B). For this analysis, we did random shuffling of the 438
T-ALL deletions 10,000 times across the genome and calculated the overlap of deletions with the
boundaries of 1) all insulated neighborhood and 2) insulated neighborhoods containing T-ALL
Pathogenesis Genes. The P-value of each permutation test was estimated by calculating the
fraction of 10,000 permutations that had a higher number of overlap with neighborhood
boundaries than in the actual data.
T-ALL Pathogenesis Genes
To identify a set of genes whose mutations have been causally linked to T-ALL, we manually
curated a list of genes using the Cancer Gene Census and individual studies. First, we
downloaded the Cancer Gene Census on 2015.04.01 from www.cancer.sanger.ac.uk/cosmic.
The complete Gene Census was filtered for genes that had "T-ALL" annotated in the "Tumor type"
columns of the Gene Census. We added to the list the genes that were described as recurrently
altered in T-ALL in (14). We then converted gene symbols into Refseq IDs for further analysis
using the table described in the RNA-seq section. This resulted in a manually curated list of 55
genes (Table S3).
T-ALL clinical expression data
T-ALL clinical expression data (Fig. S7B, S9B) were downloaded from (22). The array probe
IDs used are: 206283_PM_s_at (TAL1) and 204249_PM_s_at (LMO2). To estimate the
significance of association between the chromosomal deletions that overlap the boundaries of the
49
TALI or LMO2 insulated neighborhoods and elevated expression of those genes a Fisher's exact
test was performed (Fig. S7B, S9B). This test revealed a significant association at TALI (P-
value=0.003276) but not at LMO2. We note that this analysis is likely limited by the data in the
patient cohort, and that both TALI and LMO2 can be activated by complex chromosomal
rearrangements not only deletions (13, 14). Previous studies have established an association with
chromosomal deletions upstream of LMO2 and LMO2 activation in T-ALL (13, 14).
Constitutive interactions across three cell types
First, CTCF binding sites, and cohesin binding sites were identified in Jurkat, GM12878 and
K562 cells (datasets listed in Table S13). Cohesin ChIA-PET in the three cell types were
processed with the Mango pipeline as described above, and two CTCF bound sites that are
connected by a cohesin ChIA-PET interaction were annotated as CTCF-CTCF/cohesin
interactions in each cell type (i.e. candidate insulated neighborhoods). For the overlap analysis
displayed in Fig. S11A-B, binding peaks in the respective datasets were considered shared is
they overlapped by at least 1bp. In Fig. S 11C, the CTCF-CTCF/cohesin interactions were scored
as constitutive across two cell types if they had a reciprocal overlap of at least 80% of the length
of the interaction. The ChIA-PET datasets are likely not saturated, suggesting that not every
interaction found within a cell will be potentially represented in the dataset. Therefore, we defined
candidate constitutive CTCF-CTCF/cohesin interactions as the set of CTCF-CTCF/cohesin
interactions that were found overlapping in at least two of the three cell types. This resulted in
10,624 constitutive CTCF-CTCF loops (i.e. "constitutive neighborhoods") (Fig. S11 C, Table S8).
ICGC mutations analysis
For the mutation analysis, we first downloaded the ICGC release 19 catalog of somatic
mutations. Data from Genome Projects under publication embargo were excluded from further
analysis. We have also obtained permission to analyze genome sequence data from the ESAD-
UK (61) and OV-AU (62) Genome Projects. The complete list of ICGC Genome Projects included
in the study are the following: ALL-US, BLCA-CN, BLCA-US, BOCA-UK, BRCA-UK, BRCA-US,
CCLE-ES, CESC-US, CMDI-UK, COAD-US, ESAD-UK, GACA-CN, GBM-US, KIRC-US, KRIP-
US, LGG-US, LICA-FR, LIHC-US, LINC-JP, LIRI-JP, LUSC-KR, LUSC-US, MALY-DE, NBL-US,
ORCA-IN, OV-AU, OV-US, PACA-AU, PACA-CA, PBCA-DE, PRAD-CA, PRAD-UK, PRAD-US,
READ-US, SKCM-US, STAD-US, UCEC-US (Table S9).
To compare mutation frequencies at CTCF binding sites in the genome, we defined the set of
constitutive neighborhood boundary CTCF sites, and two sets of control CTCF sites ("non-
boundary CTCF sites"). To define constitutive neighborhood boundary CTCF sites, we used the
ChIP-Seq peaks that form the boundaries of the constitutive neighborhoods described above.
This resulted in a set of 16,637 CTCF binding sites. We then defined two sets of non-boundary
CTCF sites. Ideally, an appropriate control set would include CTCF sites that do not, or are less
likely to be involved in looping interactions than the boundary sites of constitutive neighborhoods,
and we used two known features of CTCF-CTCF/cohesin looping interactions to define such
control sets. Previous studies have established that CTCF is bound at the anchor sites of
chromosome loop structures, but not every CTCF-bound site in the genome is involved in looping
interactions, and the CTCF-bound sites that are detected at the boundaries at looping interactions
tend to be co-bound by cohesin (8, 9, 11, 24, 40, 42, 63-66). Based on this, we first defined a set
of non-boundary CTCF sites ("control set 1") the following way: we identified the set of interactions
in Jurkat, GM12878 and K562 cohesin ChIA-PET data that had an FDR>0.9, which represent
interactions of very low statistical significance. We then identified a set of low-confidence
interactions that occur in all three cell types (with a reciprocal overlap of at least 80% of the length
of the interaction). Only the interactions that overlapped a CTCF ChIP-Seq peak at both ends
were used for the analysis. The CTCF ChIP-Seq peaks at the anchors of these interactions were
defined as "non-boundary CTCF sites (control set 1)". The control set 1 contains 44,461 CTCF
50
binding sites. This set of CTCF sites thus represents a set of sites that appear to be involved in
looping interactions at a much lower statistical significance than the constitutive neighborhood
boundary CTCF sites (the latter being defined using the high-confidence ChIA-PET interactions).
To define a second control set of non-boundary CTCF sites ("control set 2"), we used previous
evidence that the CTCF-bound sites that are detected at the boundaries at looping interactions
tend to be co-bound by cohesin (8, 9, 11, 24, 40, 42, 63-66). The corollary of this is that CTCF-
sites that are not bound by cohesin are less likely to be involved in looping interactions. Therefore,
we first identified the CTCF ChIP-Seq peaks in Jurkat, GM12878 and K562 cells, and the cohesin
binding sites in Jurkat, GM12878 and K562 cells, and created a union of CTCF binding sites and
a union of cohesin binding sites in the three cell types. We then identified the set of CTCF binding
sites in the union that did not overlap a cohesin (SMC1 in Jurkat; RAD21 in K562 and GM12878)
binding site in the union of cohesin binding sites, and these were defined as "non-boundary CTCF
sites (control set 2)". The control set 2 contains 4,699 CTCF binding sites. This set of CTCF sites
thus represents a set of sites that are not bound by cohesin in any of the three cell types. Analysis
of somatic mutations in constitutive neighborhood boundary CTCF sites, and the two sets non-
boundary CTCF sites (control set 1 and 2) revealed a striking enrichment of mutations in
constitutive neighborhood boundary CTCF sites, and a much more moderate enrichment of
mutations in the non-boundary CTCF sites (control set 1), and no enrichment of mutations in the
non-boundary CTCF sites (control set 2) (see analytical details below, and Fig. 4B, Fig. S12D,
S14). We note that the moderate enrichment of mutations in the non-boundary CTCF sites
(control set 1) is likely caused by the presence of CTCF sites in this control set that may be
involved in looping interactions. This is supported by the finding that a significant fraction of
interactions of very low statistical significance (FDR>0.9) that were used to define this set, connect
genomic features such as enhancers, promoters and CTCF sites, therefore potentially represent
bona fide looping interactions (Fig. S2G).
CTCF motifs under the CTCF binding sites were identified as follows. We used a position
weight matrix (PWM) for CTCF from JASPAR CORE vertebrates 2014 database (MA1 39.1) and
the Biostrings Bioconductor R package (Pages H, Aboyoun P, Gentleman R and DebRoy S.
Biostrings: String objects representing biological sequences, and matching algorithms. R package
version 2.38.1.) using the matchPWM function with a min.score parameter of 80%. Only the CTCF
sites that had at least one CTCF motif detected within 100 bp of the MACS peak call summit were
used for further analysis. If multiple motifs were detected at the same CTCF peak, then only the
strongest one was used based on the motif score assigned by matchPWM. As an alternative
method, we also identified CTCF motifs by an independent approach. For this analysis, we used
the CTCF motif PWM identified using HT-SELEX from a previous study (67) (Fig. S14B). Overall,
both approaches yielded similar results (Fig. S14). To rule out that the enrichment of mutations
was not dependent on particular motif calling tool, we also identified CTCF motifs within the CTCF
peaks using FIMO (43) with default parameters and the same JASPAR CTCF motif (Fig. S14C).
Overall, both motif identification tools yielded similar results (Fig. S14).
To determine the relative enrichment of somatic mutations at constitutive neighborhood
boundary CTCF sites, we adapted an approach used in a previous study (23); we counted the
number of somatic mutations +/-5bp around the CTCF motif identified under the constitutive
neighborhood boundary CTCF sites and the two non-boundary CTCF sites control sets. The
mutation count was normalized by the number of CTCF peaks that contained at least one CTCF
motif within each set. The mutations identified in the constitutive neighborhood boundary sites are
listed in Table S10.
To assess the significance of the observed enrichment of mutations at CTCF sites, we
performed permutation tests. We randomly permutated the mutations detected within +/-1 kb
around the CTCF binding motif of the CTCF sites, and scored the frequency at which the number
of mutations around (+/-5bp) the CTCF motifs was greater than or equal to the number of
51
mutations observed around (+/-5bp) the CTCF motifs in the experimental data. The permutation
was performed 10,000 times (Fig. 4B, Fig. S12A-B, S12D, S14, S15).
To calculate the frequency of recurrent mutations in constitutive neighborhood boundary CTCF
sites and non-boundary CTCF sites, we counted the number of recurrent somatic mutations +/-
5bp around the CTCF motif identified under the constitutive neighborhood boundary CTCF sites
and the two non-boundary CTCF site control sets. Recurrent mutations were defined as the same
base substitution that occurs in at least two patient samples of the same Genome Project (or Pan-
cancer set). The mutation count was normalized by the number of CTCF peaks that contained at
least one CTCF motif within each set (Fig. S16).
To calculate the enrichment of mutations in the constitutive neighborhood boundary CTCF
sites versus non-boundary CTCF sites, we calculated the ratio of mutations that occur in the
constitutive neighborhood boundary CTCF sites and the mutations that occur in each non-
boundary CTCF site control set (described above). This was performed using the mutations
annotated in individual cancer Genome Projects, and a "pan-cancer set" created by aggregating
all the mutations seen across all the analyzed Genome Projects in the ICGC datasets. Only
Genome Projects that contained at least 5 mutations for each of the three CTCF site sets were
included in this analysis (Fig. S12E). We then estimated the 95% confidence interval using a
bootstrap procedure. For each Genome Project, we recalculated the enrichment ratio by
resampling the number of mutations that fell within the CTCF motif and +/-1kb window around it
in CTCF site set, and then recalculated the enrichment ratio as described above. Resampling was
performed over 1,000 iterations. From this distribution of enrichment ratios, we took the .025 and
.975 quantiles as the low and high ends of the confidence interval (Fig. S12E).
To calculate the enrichment of mutations in transcription factor binding sites in liver cancer
(Fig. S15), we first identified the ChIP-Seq binding peaks of the transcription factors (CTCF, MAX,
MYC, FOXA1, FOSL2, JUND, NR2F2) in a liver cancer cell line (HepG2) using publicly available
ChIP-Seq data (Table S13). We used a position weight matrix (PWM) for each transcription factor
from the JASPAR CORE vertebrates 2014 database and use the Biostrings Bioconductor R
package as described above to identify the binding motifs under the ChIP-Seq peaks. PWMs
used: CTCF: MA0139.1, MAX: MA0058.2, cMYC: MA0147.2, FOXA1: MA0148.3, FOSL2:
MA0478.1, JUND: MA0491.1, and NR2F1/2: MA0017.1. Only the transcription factor ChIP-Seq
peaks that contained at least one motif detected within 100 bp of the MACS peak call summit
were used for further analysis. If multiple motifs were called at the same peak, then only the
strongest was used based on the motif score assigned by Bioconductor.
To compare the distribution of somatic mutations in constitutive neighborhood boundary CTCF
sites and coding regions we counted the number of mutations in the constitutive neighborhood
boundary CTCF sites and coding regions, respectively; and normalized the counts to the size of
genome covered by constitutive neighborhood boundary CTCF sites and coding regions,
respectively (Fig. S13).
To investigate the distribution of HapMap SNPs in constitutive neighborhood boundary CTCF
sites, we downloaded data from the GATK resource bundle that included the 1000 Genomes
Project data (ftp://gsapubftp-anonymous(c-ftp.broadinstitute.org/bundle). Simple and indel SNPs
were merged into one set for further analysis. The SNPs were then plotted within +/-1 kb around
the CTCF binding motif of the constitutive neighborhood boundary CTCF sites (Fig. S12A).
Estimating differences in CTCF motif strength caused by somatic mutations
To estimate the effect of the observed somatic mutations in the ICGC data on CTCF binding,
we calculated the score based on the position weight matrix (PWM) of the CTCF motif consisting
of the reference genome sequence before and after inserting the somatic mutation into the motif
sequence (Fig. S12C). The motifs in constitutive neighborhood boundary sites were included for
this analysis if they contained at least one somatic mutation in the "pan cancer" dataset. The PWM
scores were estimated using the PWMscoreStartingAt function from the R Bioconductor
52
Biostrings package. The CTCF consensus motif used was from the JASPAR motif database
(MA0139.1).
Notes on the data types in the ICGC dataset
The individual Genome Projects in the ICGC database contain varying numbers of whole
genome (WGS) and whole exome (WES) sequence datasets (Table S9). While we used the total
number of mutations annotated in each Genome Project regardless of whether the mutation
originated from WGS and WES data, we note that the WES data alone potentially limits our ability
to observe enrichment of mutations of constitutive neighborhood boundary CTCF sites, because
the vast majority of these are intergenic (Table S2), and thus are not captured in WES data.
Therefore, the enrichment of such mutations in Genome Projects that predominantly contain WES
data is considered an estimate of the genomic distribution that will be further refined in the future
as more WGS data is collected in ICGC. We note that the two Genome Projects in which we
observed the highest level of enrichment for mutations in constitutive neighborhood boundary
CTCF sites contain >100 WGS data each (ESAD-UK and LIRI-JP). Furthermore, a recent study
that used >200 WGS data in colorectal cancer genomes has also reported frequent somatic
mutations in CTCF bound sites (23).
Gene Ontology (GO) -term enrichment analysis
To characterize the genes located in constitutive neighborhoods whose boundaries are most
frequently mutated we performed Gene Ontology (GO) -term enrichment analysis as follows. We
identified all protein-coding genes within the GENCODE version 19 annotation, and the genes
whose TSS +/-500bp was contained within the constitutive neighborhoods were identified. We
then filtered for genes found within constitutive neighborhoods whose boundary CTCF sites
contained at least 3 mutations This step was performed using 1) the complete ICGC dataset (i.e.
Pan-cancer), 2) mutations in the ESAD-UK Genome Project, and 3) mutations in the LIRI-JP
Genome Project. Genes that appeared multiple times were listed only once. We then estimated
the enrichment of GO terms (biological process, complex component or molecular function) for
this set of genes using the Bioconductor topGO package. We filtered out all GO terms with less
than 10 genes assigned to that term, and we used the Fisher test to estimate the p-value per GO-
term. To correct for multiple hypothesis testing, we converted all the p-values into q-values via
pFDR (68) using the Bioconductor qvalue package. The list of GO-terms and q-values are listed
in Table S11A-C (Pan-cancer), Table S11D-F (ESAD-UK), and Table S11G-1 (LIRI-JP).
Candidate proto-oncogenes
Candidate proto-oncogenes were identified as follows. We first downloaded the genes listed
in the Cancer Gene Census on 2015.04.01 from www.cancer.sanger.ac.uk/cosmic. This list
contains the genes whose mutations have been causally linked to cancer (i.e. both candidate
proto-oncogenes and tumor suppressor genes). Proto-oncogenes are generally activated by
mutations that result in a dominant phenotype and tumor suppressor genes are de-activated by
mutations that have a recessive phenotype (69), so we filtered for the genes whose mutations are
annotated as dominant in the Cancer Gene Census. This resulted in 329 candidate proto-
oncogenes (Table S12).
For the example proto-oncogenes whose neighborhood boundary is disrupted by recurrent
somatic mutations (Fig. 4G, 4H) the following evidence indicated activation of the genes in the
cancer types the mutations were documented in: FGFRI was found overexpressed in liver
carcinoma in previous studies (70, 71). LMO1 was found overexpressed in esophageal
adenocarcinoma in the ESCA-US clinical cohort (7.15-fold over corresponding normal tissue;
http://firebrowse.org/viewGene.html?gene=lmol).
Abbreviations
53
Abbreviations in Fig. 1A include: NSCLC:
myelogenous leukemia; AML: acute myeloid
leukemia; GBM: glioblastoma multiforme; SCLC:
non-small cell lung cancer; CML: chronic
leukemia; T-ALL: T-cell acute lymphoblastic
small cell lung cancer
Accession numbers
Datasets generated in this study have been deposited in the Gene Expression Omnibus under
the Accession number GSE68978. The GEO Accession numbers of the datasets used in this
study are listed in Table S13.
54
Supplementary tables
The following tables can be found online at:
www.sciencemag.org/content/351/6280/1454/suppl/DC1
Table S1. Summary statistics of the Jurkat SMCI ChIA-PET data
Table S2. SMC1 ChIA-PET interactions
Table S3. T-ALL Pathogenesis Genes
Table S4. Gene expression (RPKM) values in Jurkat cells
Table S5. Deletions in T-ALL genomes overlapping insulated neighborhood boundaries
Table S6. 5C primers
Table S7. 5C summary statistics
Table S8. Constitutive neighborhoods across three cell types
Table S9. ICGC datasets
Table S10. Somatic mutations in constitutive neighborhood boundary sites
Table SI1. GO-analysis of genes with neighborhood boundary mutations
Table S12. Candidate proto-oncogenes extracted from the Cancer Gene Census
Table S13. GEO accession IDs of the datasets used in this study
55
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57
Supplemental figures
B
TAO TAD
T
asociating
~IN I
n. htorhoo. (
(IN)
Enhancer- Ocoei
promoter aS enTanCer
10013 a- CTCF
SI I II I I I EIIIIEI II I 1
100
SMCI MYB RUNXI GATA3
0 1.50 1.5 0 150 1.5M-~p
Readt
(-000,000,000)
Untque hntr.-
chromosomi
PEli
(<1MB)
(1.438,279)
II II PET peak (67.596)
TALI RNAPII H3K27Ac CTCF
3%Promoter
43% CTcv
22% Omer
0 1.5 0 1.5 0 1.5 0 1.5
D
igh-condenme ChIA-PET Interactons
(FDR s0.2)
E(h6n,3r-E65)nterIrd raw
Enhaner-CTPrmoter
(92)
Promoter-CTCF
(14,551)(21 )
(6,385)
ChIA-PET Intereclons
between PET peak.
(no sletloel fier)
(150,306)
ChilA-PET knterecone(FDR a02)
(9,757)
CTCF
SMC1
H3K27Ac
kb
muper-Oenner
. AL.ahi
RUNXI
Fig S1. Cohesin ChlA-PET processing and analysis using the Mango pipeline
(A) Model of the hierarchical organization of chromosome structures.
(B) The majority of cohesin-bound sites are co-bound by CTCF or H3K27Ac-marked enhancers in Jurkat cells.
Displayed is a heatmap representation of ChIP-seq data for SMC1 (cohesin), MYB, RUNX1, GATA3, TAL1, RNAPII,
H3K27Ac and CTCF at 79,976 SMC1-bound sites in Jurkat cells. The regions are centered on the summit of the binding
58
A
C
1
270
9
.I. . . ..
I
peak, and read density is displayed within a 10kb window. Color scale intensities are shown below the heatmaps in
rpm/bp units.
(C) Overview of the cohesin ChIA-PET data analysis using the Mango pipeline at the RUNX1 locus. The algorithm used
to identify paired-end tags (PETs) is described in detail in the Materials and Methods section. PETs and interactions
involving enhancers, promoters and CTCF-bound sites within the window are displayed at each step in the analysis
pipeline: unique PETs, PET peaks, interactions between PET peaks, and high confidence interactions (FDR 0.2).
(D) Summary of the major classes of interactions identified in the cohesin ChIA-PET data. Enhancers, promoters, and
CTCF sites where interactions occur are displayed as blue circles, and the size of the circle is proportional to the
number of regions. The interactions between two sites are displayed as gray lines, and the thickness of the gray line is
proportional to the number of interactions. Note that in this analysis the CTCF sites displayed include only the non-
enhancer, non-promoter CTCF sites.
59
4 bo
10
103
o
2
10
10 * W Pearson's
r 1 0.997
102
10" 10 102 103 104 0 10
Read density in Replicate 1
B I 100-
- FDRs0.05
20 - FDR4.2
0 o 0 -FDRsO.5
Highs LOW"
Confenoe Confidence
Interactions ranked by FDR (Rep. 2)
D
I :100
60. i
III
140-
FDRsO.05
20 - FDRsO.2
0 - FDRsO.5
Highest Lowest
Confidence Conidence
Interactions ranked by FDR (Rep. 1)
E
2 5 10 20 50 100
PET distance (kb)
Cell
type
Interactions Cell
type
Interactions Interactions in Jurkat
overlapping an Interaction
in the other cell type
GM12878 9,448 Jurkat 9,757 5,451
HeLa 3,094 Jurkat 9,757 2,652
HMEC 5,152 Jurkat 9,757 3,406
HUVEC 3,865 Jurkat 9,757 3,331
IMR90 8.040 Jurkat 9,757 4,710
K562 6,057 Jurkat 9,757 4,243
KBM7 2,634 Jurkat 9,757 2,656
NHEK 4,929 Jurkat 9,757 3,698
G
CTCF-CTCF Interactions (%)
0 20 40 80 so
04
CTCF-motf
Interactions
FDR range Number
Overlap
genomic feautures
on both ends
Do not overlap
genomic features
on both ends
0 s FDR sO.2 9,757 9,184 (94%) 573 (6%)
(i.e. high confidence
interactions)
0.2 % FDR s 0.3 1,352 1,166 (88%) 186(14%)
0.2 s FDR s 0.5 3,739 3,085 (83%) 654 (17%)
0.2 % FDR s 0.8 7,659 5,926(77%) 1733(23%)
0.2 s FDR 1.0 140,549 95,391 (68%) 45,158 (32%)
High confidence
ChIA-PET Interactions
Cross TAD boundary
14%o
Do not cross TAD boundary
86%
H
boundary bound
mtrachn
0
Fig. S2. Cohesin ChIA-PET interactions
(A) The ChIA-PET replicate datasets display high correlation. Scatter plot of the number of uniquely mapped PETs per
50kb bins of the genome in each replicate dataset.
(B) (left) Cumulative percentage of interactions in the Replicate 2 ChIA-PET dataset that are overlapped by an
interaction in the Replicate 1 ChIA-PET dataset (regardless of the FDR value of the interaction in Replicate 1). The
interactions in Replicate 2 were ranked by their FDR values on the x-axis. (right) Cumulative percentage of interactions
in the Replicate 1 ChIA-PET dataset that are overlapped by an interaction in the Replicate 2 ChIA-PET dataset
(regardless of the FDR value of the interaction in Replicate 2). The interactions in Replicate 2 were ranked by their FDR
values on the x-axis. The dashed lines indicate that the set of interactions that have an FDR of less than or equal to
the displayed values are found to the left of the line, and serve to aid visual orientation.
(C) Calculation of the frequency of self-circularization among the PETs, and estimation of a minimum distance cutoff
for the PETs used in the downstream analyses. Plotted is the log2 ratio of PETs whose mate pairs map to same DNA
strand versus PETs whose mate pairs map to opposite DNA strands, and the log2 ratio is plotted at various PET
distance cutoffs. When fragments self-circularize during the ChIA-PET processing, the mate pairs map to the opposite
DNA strand, and these do not represent PETs that arise from bona fide chromatin interactions (detailed in the Materials
and Methods). The minimum PET distance cutoff is estimated as the distance above which the ratio is -1. For this
analysis, the merged ChIA-PET dataset was used (Rep. 1 and 2).
(D) Number of interactions in Jurkat cells that are overlapped by an interaction identified using Hi-C in the indicated
eight human cell lines. The high confidence Jurkat interactions (FDRs0.2) were used for this analysis.
(E) Percentage of interactions that cross or do not cross TAD boundaries (defined in H1 human ESCs). The vast
majority of interactions do not cross TAD boundaries (P<10-3 , permutation test).
60
A
04
C
2
1
-3
L ~2w
Q. .
Distance
c4toff b
10.481lbp
F
(F) The orientation of CTCF motifs at pairs of CTCF sites connected by cohesin ChlA-PET interaction is mostly
convergent. The high confidence Jurkat interactions (FDR 0.2) that are overlapped by CTCF-bound sites on both ends
were used for this analysis.
(G) Number of interactions that overlap genomic features (enhancer, or promoter or CTCF-bound site) on both ends,
at different FDR cutoffs of the interactions. At FDR 0.2, 94% of ChlA-PET interactions overlap genomic features on
both ends.
(H) Heat map of the density of ChIA-PET interactions around the 9,038 CTCF-CTCF interactions. The CTCF-CTCF
interactions were length normalized.
61
Reads
(~400,000,000)
Unique PETs
(21,102,872)
NM I 11IN IE l I PET peaks (44,094)
High-contence
interactions between
PET peaks
(3 PET, > 5kb,
FDR s 0.01)
(18,240)
1. - CTCF
H3K27Ac
RUNXI
B
Hig-onidence iermdonw
(FDR cOO1, 3 PETe)
Enhancer-Enhancer
ktteractione
(1,448)
Enhancer Enhancer-Promoter
(3.037) interactions
(1,308)
PromoterEnhancer-CTCF (2,676)
interactions
(2,584)
Promoter-Promoter
Interactions
(475)
Promoter-CTCF
-N
interactons
(2,880)
C
Cell
type
Interactions Cell Interactions Interactions In Jurkat
oveappn an Interaction
In the othe cell type
GM12878 9,448 Jurkat 17,718 7,551
HeLa 3,094 Jurkat 17,718 3,585
HMEC 5,152 Jurkat 17,718 5,130
HUVEC 3,865 Jurkat 17,718 4,436IMR90 8,040 Jurkat 17,718 6,745
K562 8,057 Jurkat 17,718 5.976
KBM7 2,834 Jurkat 17,718 3,428
NHEK 4,929 Jurkat 17,718 4,617
D
High-confidence
ChIA-PET interactions
E
CTCF-CTCF Interactions (%)
0 20 40 OD OD
Cross TAD boundary 0
i 04
CTCF-noWf
Do not cross TAD boundary L
88% .... m. roews*
Fig. S3. Cohesin ChIA-PET processing and analysis using the Dowen at al. pipeline
(A) Cohesin ChIA-PET processing and analysis using the Dowen et al. pipeline shown at the RUNX1 locus. The
algorithm used to identify paired-end tags (PETs) is described in detail in the Materials and Methods section ("ChIA-
PET data processing and analysis using the Dowen et al. pipeline" section). PETs and interactions involving enhancers,
promoters and CTCF-bound sites within the window are displayed at each step in the analysis pipeline: unique PETs,
PET peaks, interactions between PET peaks supported by at least three independent PETs and with a false positive
likelihood of <1%.
(B) Summary of the major classes of interactions identified in the cohesin ChIA-PET data using the Dowen et al.
pipeline. Enhancers, promoters, and CTCF sites where interactions occur are displayed as blue circles, and the size of
the circle is proportional to the number of regions. The interactions between two sites are displayed as gray lines, and
the thickness of the gray line is proportional to the number of interactions. Note that in this analysis the CTCF sites
displayed include only the non-enhancer, non-promoter CTCF sites.
(C) Number of interactions in Jurkat cells that are overlapped by an interaction identified using Hi-C in the indicated
eight human cell lines. The high confidence Jurkat interactions (at least 3 PETs) were used for this analysis.
(D) Percentage of interactions that cross or do not cross TAD boundaries (defined in H1 human ESCs). The vast
majority of interactions do not cross TAD boundaries (P<10 3 , permutation test).
(E) The orientation of CTCF motifs at pairs of CTCF sites connected by cohesin ChIA-PET interaction is mostly
convergent.
62
A
CTCF
(12,961)
CTCF-CTCF
Intraction
(8,637)
I II Il
utoraction aom(Mnber of PETs)
0 t -
~1i
123
A
Mango
hIA-PET1 0
interacifons
7
270
9.
CTCF
50 kb
H3K27Ac Lp-nog rmance -
RUNX1
Dowen et al. pipeline
0
OhIA-PET
50 kb
71 CTCF 1 7.
9.
B
L SMC
H3K27Ac - -
RUNX1
C
Mango
ChIA-PET interactions
(9.757)
Ovrmlape an
interacdon in the
Dowen at al. pipeline
Dowen et al. pipeline
ChIA-PET Interactions
(17,718)
overlaps an
itsracuon in
Mango
Mango
Interactions (n)
379Enhance Promoler
236
Enhancer Enhancer
Dowen et al. pipeline
Interactions (n)
C F 
s 15,339
1,398
Enhencer Pmfo
- - - 1,453
Enhancer Enhancer
Fig. S4. Comparison of ChlA-PET interactions identified using Mango and the Dowen et al. pipelines
(A) Cohesin ChlA-PET interactions at the RUNXI locus. The interactions identified using Mango are shown on the left,
and the interactions identified using the Dowen et al. pipeline are shown on the right panel. Note that all Mango
interactions are displayed (the high confidence interactions (FDR50.2) are displayed separately on Fig. S1C), while the
high confidence interactions (at least 3 PETs) are displayed from the output of the Dowen et al. pipeline.
(B) Percentage of interactions that overlap between the interactions identified using the Mango and the Dowen et al.
pipelines. Only the respective high confidence interactions were used for this analysis.
(C) Summary of types of interactions in the Jurkat ChIA-PET data using (left) Mango and (right) the Dowen et al.
pipeline. The high confidence interactions of each pipeline were used for this analysis.
63
PET.)
19
A
Active T-ALL Pathogenesis Genes In neighborhoods
CIIIA-PET (FOR)
Interacnons 1 -
20 kb
121 cCF
1501 f SM1 IL
SH3K27AC
RNA-Seq AA
NOTCH,1
Ch~i
Int
A-PET (FOR)
ractions
1 0
sok
AAii. . CF~LL
9.
370k.
*c A H3K27Ac
Ia.A... - A -~ -. A .j
I I RNA-Seq
MYB I&ED-4
, Mean
ChIA-PET (FOR)
InteractIons
1 0
2941 A, so11M CT1 ki
294 
_. I_ - IIIh~.. f
101 M3K7Ac
128k 'i& I-- _,
1k k RNA-Seq
ETV6
B Silent T-ALL Pathogenesis Genes In neighborhoods
ChIA-PET
Interatos 
-
5kb
al-CTCF 
--
100 SMC1
] H3K27AC
20k RNA-Seq
TLX3 .
ChIA-PET
interact~on
ChIA
Int5r
ktmmatnan
(FDR)
1 0
10kb
8] H3K27Ac
10k RNA-Se
OL102 O1UG1
-PET (FDR)
Rewns 1 0
-- 20 kb
8 H3K27Ac
20k] RNA-Seq
TCL6 0 TCL1A
TCLIB
Fig. S5. Active oncogenes and silent proto-oncogenes in insulated neighborhoods in T-ALL
(A) Examples of insulated neighborhoods containing active oncogenes at the NOTCHI, MYB and ETV6 loci in Jurkat
cells. The cohesin ChIA-PET interactions are displayed above the binding profiles of CTCF, SMC1 (cohesin), H3K27Ac,
and RNA-Seq track. Gene models are displayed below the binding profiles.
(B) Examples of insulated neighborhoods containing silent proto-oncogenes at the TLX3, OLIG2 and TCL6 loci in Jurkat
cells. The cohesin ChIA-PET interactions are displayed above the binding profiles of CTCF, SMC1 (cohesin), H3K27Ac,
and RNA-Seq track. Gene models are displayed below the binding profiles.
64
B
Patient deletions in
T-ALL genomes
200-
150-I
1 n = 438
mean size: 99kb
100
501
0
0 100 200 300 400 500
Size (kb)
Patient deletions i
T-ALL genomes (n=4
Overlaps insulated
neighborhood boundary
. (113)
Overlaps boundary of insulated
neighborhood containing T-ALL
Pathogenesis gene
(6)
Fig. S6. Chromosomal microdeletions in T-ALL genomes
(A) Distribution plot of the lengths of recurrent genomic deletions found in T-ALL genomes. Only deletions <500 kb in
size are plotted.
(B) Number of deletions found in T-ALL genomes that overlap insulated neighborhood boundaries. Significance of the
observed overlaps is P=0.647 for all insulated neighborhoods, and P=0.0013 for insulated neighborhoods containing
T-ALL Pathogenesis Genes.
65
A
0
E
Z
A
insulated isacllon
neighborhood: (Flo)
ChIA-PET 1
Interactions: 20 kbK
I. CTCF 150 S0
TALI * 4  i E s-*4 It -STIL CMPKI[Overlap TAL I -
Patient promoter
deletions Do not overlap
TAL I promoter
B
TAL1 expression In the T-ALL clinical cohort
8~ Expression
6madian modian
4 --- Eprssion 3 0 sh ar b deletionnei ghborhooda tC e bi 0 long deletion
2bar 12 20 no deletion
0 P=0.003276
Patient nmples ranked by TAL expression
Fig. S7. Expression of TALI in patient samples harboring deletions that disrupt the TALe insulated
neighborhood
(A) Insulated neighborhood at the TALI locus in Jurkat T-ALL cells. Cohesin ChIA-PET interactions are displayed
above the ChIP-Seq binding profiles of CTCF and cohesin (SMVC1I). Patient deletions described in (22) are shown as
bars below the gene models.
(13) Expression level of TALI in all patient samples with matched gene expression and genotype information in the
clinical cohort described in (22). The expression data was downloaded from (22). The red bars correspond to the
samples harboring the deletions denoted as red bars on panel (A). The grey bars correspond to the samples harboring
the deletions denoted as grey bars on panel (A). The white bars correspond to the samples where deletion is not
detected in the genome. The right side of the panel is the contingency table used for statistical testing (Fisher's exact
test), which estimates a significance of association between the deletion event and the expression level of TALI higher
than the median value measured in the cohort (P=0.003276 two-sided t-test).
66
A
Insulated
neighborhood:
Art 1
HEK- 3 -
293T 3
ACTCF 3
HEK-2g3T 
ALL 
-
Region 1 Region 2
20 kb
CT CF
H3K27Ac
--- P300
CBP
STL CMPKI
E
control TAL neighborhood
mock deletion boundary deletion
5.000
3.000
2,000
1,500
1,000
donornumber: 1 2 3 1 2 3 1 2 3
B
Luciterase
10
Cloned
regions
HEK-293T
20
1.5
1.0
0.5
00
0
Jurkat
3 *
2.1
0J1
C
Insulated
neighborhood: boundary ste 20 kb
8
Pri- I.. i .C C
mary H3K27Ac
T-ceII 20
Lk_ ,_ RNA-Seqd
TAL 1 11TAL1STIL CMPKI
D
(mock T
-+qRT-PCR
Primary
T-cell
Cesg+gRNA (control
(& _ qRT-PCR
Primary
T-cel (TALI
Cas9+sRgNA nelborhaod
p q-q T-PCR
Primary
T-cell
n=9 n=14 n=23
_j
18
201
22
24j 0409
F
75
G
S S
S
gOe
S
TAL 1 RUNX1
3 n=100 n-100 n=100 n=100
2 
10
0 1 0
Fig. S8. Disruption of an insulated neighborhood boundary is linked to proto-oncogene activation at the TALIlocus
(A) ChIP-Seq binding profiles of CTCF, H3K27Ac, p300 and CBP, and RNA-Seq at the TAL1 locus in HEK-293T cells.Arrows point to two candidate enhancer regions occupied by H3K27Ac, p300 and CBP.(B) The region around the CMPK1 promoter has enhancer activity. Region 1 and 2 (displayed on panel (A)) were
cloned into luciferase reporter vectors, and the luciferase activity was measured in HEK-293T and Jurkat cells. Data
from n=3 replicate experiments are shown as mean+SD. Asterisk indicates P<0.05 (Student's t-test).(C) ChIP-Seq binding profiles of CTCF, H3K27Ac, and RNA-Seq at the TALI locus in primary human T-cells. The
region deleted using a CRISPR/Cas9-based approach is highlighted in a grey box.(D) (left) Experimental scheme of CRISPR/Cas9-mediated editing experiments in primary human T-cells. (right) qRT-
PCR analysis of TALl expression. A total of 24 experiments were performed, and the difference in Ct values between
TALI and GAPDH is displayed in the samples with detectable signal. P-value=0.04 between the control deletion and
TAL1-neighborhood boundary -deletion samples (two-tailed t-test).
(E) Genotyping of the T-cell transfections described on panel (D). Genomic DNA was extracted 4-10 days after
electroporation and the genomic loci containing the target sites for TALI were PCR-amplified.(F) Single molecule RNA FISH of TALI and RUNXI in primary human T-cells after CRISPR/Cas9-mediated editing.
Cells were transfected with sgRNAs targeting a control region (control deletion) or sgRNAs targeting the TALI
neighborhood CTCF boundary site (see C, D). Arrowheads point to the respective gene transcripts. RUNXI was
67
-VW
"M " " "''..WOeu--em MWS
aVIC,
wild type
altele
deletion
allle
1
included as a positive control for the hybridization, and Jurkat T-ALL cells were also imaged as a positive control (for
TAL1 and RUNX1 expression). Nuclei are counterstained with Hoechst 33342. Magnification is 40X.
(G) Quantification of the single molecule RNA FISH in the CRISPR-edited primary human T-cells described on panel
(F). TAL1 and RUNXI transcripts were counted in 100 cells in each condition, and the number of transcripts per cell is
plotted for the 100 cells. The P-values between the control deletion and TAL neighborhood boundary deletion cells
are: P<0.0001 (two-tailed t-test) for TALl, and P=0.6 (i.e. n.s.; two-tailed t-test) for RUNX1.
68
Insulated
neighborhood:
ChIA-PET
interaction
8
100
Patient
deletions
1 1 TF
1 1L SMC1
LMO2 CAPRINI NAT10
LMO2 expression in the T-ALL clinical cohort
12
10
Medan
8 ex n
4-
2-
0
Patient samples ranked by LMO2 expression
Expression
Above Beiow
medan medan
contains deletion
no deletion
P=1
(two-sldad)
Fig. S9. Expression of LMO2 in patient samples harboring deletions that disrupt the LMO2 insulated
neighborhood
(A) Insulated neighborhood at the LMO2 locus in Jurkat T-ALL cells. Cohesin ChIA-PET interactions are displayed
above the ChIP-Seq profiles of CTCF and cohesin (SMC 1). Patient deletions described in (22) are shown as bars below
the gene models.
(B) Expression level of LMO2 in the patient samples in the clinical cohort described in (22). The expression data was
downloaded from (22). The red bars correspond to the samples harboring the deletions denoted as red bars on panel(A). The right side of the panel is the contingency table used for statistical testing (Fisher's exact test), which estimates
a significance of association between the deletion event and the expression level of LMO2 higher than the median
value measured in the cohort (P=1 two-sided t-test).
69
A
kntsrmcon
(FOR)
1 0
50kb
s:
A B C Q
'I *
It~ 
-3 .-
D
wild typ HEK-293T LM02-ACTCF HEK-293T TALl ACTCF HEK-293T
m MW .
I~V
~~0
614 %
+ + 9E
Co
wild type HEK-293T TALl-ACTCF HEK-293T LMO2-ACTCF HEK-293T
w id type HEK-293T TALl ACTCF HEK-293T LM02-ACTCF HEK-293T
20
C
LMO2-ACTCF - wt HEK-293T TAL1-ACTCF - wt HEK-293T
E
TALI-ACTCF - wt HEK-293T LMO2-ACTCF - wt HEK-293T
uncorrected distance adjusted
MMiemI am 0 "a a mm El U m
I a A
uncorrected distance adjusted
41 4r 4N
Fig. S10. Disruption of insulated neighborhood boundaries leads to defects in chromosome structure
(A) 5C double alternating design. Four restriction fragments are represented with the double alternating primers (F for
Forward, R for Reverse, LF for Left Forward and LR for Left Reverse). The universal tails are represented in green.
Only the reverse primers are phosphorylated. All possible interactions for only fragment B are represented by arrows(for simplicity). The orange arrow represents the self-ligation of the restriction fragment and is removed during step1 of
the 5C processing.
(B) The neighborhood boundary deletion leads to an increase in contact frequencies across the deleted boundary at
the TALI locus. 5C contact matrices at the TALI locus (chrl:46,740,122-48,740,121) in wild type HEK-293T cells, in
cells where the region containing the candidate LMO2 neighborhood boundary sites was deleted (LMO2-ACTCF HEK-
293T), and in cells where the TALI neighborhood boundary site were deleted (TAL1-ACTCF HEK-293T). The left and
right panels are identical to Fig. 3E. Note that the LMO2-ACTCF HEK-293T cells have an intact TALI locus, and serve
as a control that CRISPR/Cas9 perturbation of an unrelated neighborhood boundary does not lead to changes in
chromosome structure at the TALI locus. The position of TALI neighborhood boundary site highlighted with an arrow.
See also Fig. 3E.
(C) z-score difference (5C) map at the TALl locus (LMO2-ACTCF HEK-293T - wild type HEK-293T or TAL1-ACTCF
HEK-293T - wild type HEK-293T). Increase in signal is colored red. Note the increase in the 5C signal adjacent to the
deleted region in the TAL1-ACTCF HEK-293T cells. The position of the region removed in the TAL1-boundary deletion
mutant cells is highlighted with an arrow. The right panel is identical to Fig. 3F.
(D) The neighborhood boundary deletion leads to an increase in contact frequencies across the deleted boundary at
the LMO2 locus. 5C contact matrices at the LMO2 locus (chrl 1:33,003,550-35,003,549) in wild type HEK-293T cells,
in cells where the TALI neighborhood boundary site were deleted (TAL1-ACTCF HEK-293T), and in cells where the
region containing the candidate LMO2 neighborhood boundary sites was deleted (LMO2-ACTCF HEK-293T). The left
and right panels are identical to Fig. 3K. Note that the TAL1-ACTCF HEK-293T cells have an intact LMO2 locus, and
serve as a control that CRISPR/Cas9 perturbation of an unrelated neighborhood boundary does not lead to changes
in chromosome structure at the LMO2 locus. The position of the region removed in the LMO2-boundary deletion mutant
cells is highlighted with an arrow.
(E) z-score difference (5C) map at the LMO2 locus (TAL1-ACTCF HEK-293T - wild type HEK-293T or LMO2-ACTCF
HEK-293T - wild type HEK-293T). Increase in signal is colored red. Note the increase in the 5C signal adjacent to the
70
A
3
B
0 3~
deleted region in the LMO2-ACTCF HEK-293T cells. The position of the region removed in the LMO2-boundary
deletion mutant cells is highlighted with an arrow. The right panel is identical to Fig. 3L.
71
A
CTCF binding sites
Jurkat
10,272
10 788
28,107
18414308 1742
K562 GM12878
Cohesin (SMC1/Rad2l)
binding sites
Jurkat
10,942
30 619
23,631
18,470 3304 15,890
K562 GM12878
CTCF-CTCF/cohesin interactions
Jurkat
1882
1660568
4928
4034 3468 4523
K562 GM12878
Fig. S11. Comparison of CTCF and SMCI binding and cohesin ChIA-PET interactions in Jurkat, GM12878 and
K562 cells
(A) Overlap analysis of CTCF ChIP-Seq binding peaks in Jurkat, GM12878 and K562 cells.
(B) Overlap analysis of Cohesin (SMC1 in Jurkat or RAD21 in GM12878 and K562) ChIP-Seq binding peaks in Jurkat,
GM12878 and K562 cells.
(C) Overlap analysis of CTCF-CTCF/cohesin ChIA-PET interactions in Jurkat, GM12878 and K562 cells. The CTCF-
CTCF/cohesin interactions that are found in at least two of the three cell types are classified as "constitutive
neighborhoods."
72
B C
B CConstitutive neighborhood boundary CTCF sites
Observed
0.06. Somatic 0 .06 HapMAp so number ofi mutationsmutations . SNPs from00 in ICGC 
.5 1OOP
0.0.040. .-
o~o<. ooor0.03- 10.03- 
.40
0.02- 1500
0-20.01 :o il 20]
-I CTCF ,,k .1k CTCF 0-
motif motif 2000 3000 4000
Mutations in CTCF motif
E
to
OIS-
-04Os
0.4
o.so o.s 0 so o.ss 1.00
PWM score of reference sequence motif
aU
IL
0t
III
Constitutive neighborhood
boundary CTCF sites
0.05-
0.05-
0.04-
0.03.
0.01.
0- -
OH1& TCF 1kb
motif
0.02-
iL
0.015.
0.01-
0 
-005-
-Ib CTCF +1kb
V 0.012-
.
Mutations In 0.006.
liver cancer
(LIRI-JP) 0.008-
0002.
ToC
-1kb CCF ,1kb
motif
non-boundary non-boundary
CTCF sites CTCF sites
o.os- (control set 1) 00. (control set 2)
0.05- 0.05.
0.04. 0.04.
0.03- 0.03-
0.02- I0.02-
0.01- 0.01- hlm ~ d
0.g 0 
-1kb CTCF 1kb -kb CTCF Ikb
motif motif
0.02- 0.02-
0.015 0.015-
0.01. 0.01-
0.00. 
0.006-0-
-1kb CTF .1kb A-1kb OTF .1kb
motif motif
0.012- 0.012-
0.010. 0.010.
0.006- 0.006
0.004- 0.004
0.002 0.002
0- o -
-1kb CTCF 1kb -1kb CTCF +1kb
motif motif
log2 (mutation ratio constitutive
neighborhood boundary CTCF sites /
non-boundary CTCF sites (control set 1))
LIRI-JPESAD-UK
OYV-AUPRAD-UKPanCancer
LINC-JP
PACA-CA U -PACA-AUPRAD-CA
KIRC-US
UICA-RSTAD-USSKCM-USBRCA-US -LUSC-US i- JUCEC-US -CoAD-US
GBM-USCESC-US -
log2 (mutation ratio constitutive
neighborhood boundary CTCF sites /
non-boundary CTCF sites (control set 2))
10 0 1? 4.
ESAD-UK
LIRI-JPOV-AUPanCencer
PRAD-UK
PACA-AJBRCA-UK Ir--
UNc-JPACA-CAPRAD-CA
UICA-FR
BRCA-USLUSC-USSKCM-USCESC-USSTAD-US fE
COAD-US -GBM-JS
LUSC-KR ' E
Fig. S12. Enrichment of mutations at the constitutive neighborhood boundary CTCF sites in many cancers(A) (left) Frequency of somatic mutations and (right) Hapmap SNPs at CTCFs sites that form constitutive neighborhood
boundaries. The plots are centered on the CTCF motif identified under the CTCF binding sites, and regions 1 kb up-
and downstream of the motif position are shown. The enrichment of mutations at the constitutive neighborhood
boundary sites compared to regions flanking the binding sites has a P-value <0.0001 (permutation test).(B) The observed number of mutations within constitutive neighborhood boundary CTCF sites (red line) is significantly
greater than the randomly permutated background mutations within +/-1 kb around the CTCF binding motif. Permutation
was performed 10,000 times, and the y-axis shows the number of permutations in which the number of mutations
around (+/-5bp) the CTCF motifs in the constitutive neighborhood boundary sites occurs at the number shown on the
x-axis. The red line indicates the observed mutation frequency.
(C) Position weight matrix (PWM) scores of the sequence motifs containing somatic mutations at the constitutive
neighborhood boundary CTCF sites plotted against the PWM score of the same motif consisting of the reference
genome sequence.
(D) Frequency of somatic mutations at CTCFs sites that (left) form constitutive neighborhood boundaries, and (middle
and right) do not form neighborhood boundaries. The number of mutations is normalized to the number of CTCF sites
found in each set. The plots are centered on the CTCF motif identified under the CTCF binding sites, and regions 1 kb
up- and downstream of the motif position are shown. The two different control sets (i.e. CTCF sites that do not form
neighborhood boundaries) are described in the Supplemental Materials and Methods. On the top panel, all mutations
73
A
U..
D
0elm
: ;e OW
Mutations In
all ICGC
(pan-cancer)
Mutations In
esophageal
cancer
(ESAD-UK)
in the ICGC database were used ("Pan-cancer"). On the middle panel, the mutations in the ESAD-UK Genome Project
were used. On the bottom panel, the mutations from the LIRI-JP Genome Project were used.
(E) Ratio of the normalized mutation rates at CTCFs sites that form constitutive neighborhood boundaries versus
CTCFs sites that do not form neighborhood boundaries (two different control sets). The ratios were calculated using
the mutations annotated in the indicated Genome Projects. "Pan-cancer" includes all the mutations in the ICGC
database. Whiskers mark the estimated 95% confidence interval which was calculated using a bootstrap procedure.
74
10,000 - 5.3x
2.6x i Mutations i constitutie neghborhood
boundary CTCF sites
1000 - Mutations in protein-coding regions
100-
I
10
Genome Projects
Fig GC1en Muatonsi sconstitutive neighborhood boundary CTCF sites and in protein coding regions in the
Tnmers tof tthions in constitutiverneighborhood boundar CTCF~ sites and in protein coding regions were
nomlzdtJ h ieo h eoecvrdb hs eoi lmns
75
Mutations in
all lOGO
(pan-cancer)
CTCF motif
used:
JASPAR
Method:
Bloconductor
Constitutive neighborhood
boundary CTCF sites
0 06.
0.06.
0.04-
0.03.
0.02-
0.01-
0-
-1kb CTCF .1kb
motif
non-boundary non-boundary
CTCF sites CTCF sites
0.06- (control set 1) 0.06- (control set 2)
0.05- 0.05-
0.04- 0.04
0.03 0.03
0.02 0.02
0.01 0.01
0 0
-1kb CTCF kb -1kb CTCF .1kb
motif mow
Constitutive neighborhood
Mutations in boundary CTCF sites
all ICGC .
(pan-cancer) 0.05-
CTCF motif 0- I
used: 0.03-
SELEX o.02-
Method: 0.01
Bioconductor 0
- k CTCF 1kb
motif
non-boundary
CTCF sites
0.06- (control set 1)
0.05-
0.04.
0.03.
0.02-
0
-1kb CTCF 1kb
motif
non-boundary
CTCF sites
0-06 (control set 2)
0.05
0.04-
0-03
0.02
-1kb CTCF .1kb
motif
Constitutive neighborhood
boundary CTCF sites
0.06-
0.05-
0.04-
0.03-
0.02-
0.01.
-1kb CTCF 1kb
motif
non-boundary non-boundary
CTCF sites CTCF sites
0-06 (control set 1) 0-06- (control set 2)
0.05. 0.05-
0.04- 0.04
0.03 0-03
0.02 0.02
0.01 0.01
00
-1kb CTCF kb -1kb CTCF 1kb
most mot
Fig. S14. The enrichment of mutations at the constitutive neighborhood boundary CTCF sites is observed when
using different sources and methods for the identification of CTCF motifs
(A) Frequency of somatic mutations at CTCFs sites that (left) form constitutive neighborhood boundaries, and (middle
and right) do not form neighborhood boundaries. The number of mutations is normalized to the number of CTCF sites
found in each set. The plots are centered on the CTCF motif identified under the CTCF binding sites, and regions 1 kb
up- and downstream of the motif position are shown. The two different control sets (i.e. CTCF sites that do not form
neighborhood boundaries) are described in the Supplemental Materials and Methods. The CTCF motif used was
downloaded from the JASPAR CORE 2014 vertebrate database (MA139.1), and Bioconductor was used for motif
discovery. The enrichment of mutations at the constitutive neighborhood boundary sites compared to regions flanking
the binding sites has a P-value <0.0001 (permutation test).
(B) Frequency of somatic mutations at CTCFs sites that (left) form constitutive neighborhood boundaries, and (middle
and right) do not form neighborhood boundaries. The analysis was done as in (A) except the CTCF motif used was
derived from genomic SELEX data . The enrichment of mutations at the constitutive neighborhood boundary sites
compared to regions flanking the binding sites has a P-value <0.0001 (permutation test).
(C) Frequency of somatic mutations at CTCFs sites that (left) form constitutive neighborhood boundaries, and (middle
and right) do not form neighborhood boundaries. The analysis was done as in (A) except FIMO was used for motif
discovery. The enrichment of mutations at the constitutive neighborhood boundary sites compared to regions flanking
the binding sites has a P-value <0.0001 (permutation test).
76
A
B
C
Mutations in
all ICGC
(pan-cancer)
CTCF motif
used:
JASPAR
Method:
FIMO
021
IL
I-
I
Constitutive neighborhood
boundary CTCF sites
0.012.
0.010
0.008-
.
E0.006
0.0046
0 2
-1kb CTCF +1kb
motif
FOXA1 binding sites
in liver cancer cells
0.006.
0.004
0.002]
0,
-1kb FOXA1 +1kb
motif
CTCF binding sites
In liver cancer cells
0.012-
0.010-
0.008-
0.006.
E0.004-
0.002-
0 ML
-1kb CTCF +1kb
motif
0.012-
0.010-
0.008 -
0.008 -
I
FOSL2 binding sites
in liver cancer cells
0.0041
0.002
-1kb FOSL2 +1kb
motif
MAX binding sites
in liver cancer cells
0.012-
0.010.
0.008-
' 0.006.
0004E
j.002-
-kb MAX +1kb
motif
JUND binding sites
In liver cancer cells
0.012-
0.010-
0.006-
0.006-
0.004-
0.002-
-1kb JUND +1kb
motif
a
EI
MYC binding sites
In liver cancer cells
0.012
0.010
0.00.
0.006
0.004
0.002
0
-1kb MYC +1
motif
0.012
0.010
0.006.
0.006.
0.004-
NR2F2 binding sites
In liver cancer cells
0.002
-1kb NR2F2 +1kb
motif
Fig. S15. Somatic mutations occur frequently at constitutive
binding sites of other transcription factors in liver cancer
neighborhood boundary CTCF sites, but not at
Frequency of somatic mutations at CTCF sites that form constitutive neighborhood boundaries, and at the binding sites
of CTCF, MAX, MYC, FOXA1, FOSL2, JUND and NRFR2 in liver cancer cells. The numbers of mutations are
normalized to the number of sites bound by the respective transcription factor in each set. The plots are centered onthe transcription factor binding motif identified under the binding sites, and regions 1 kb up- and downstream of the
motif position are shown. The enrichment of mutations at the constitutive neighborhood boundary sites, and at CTCF-bound sites compared to regions flanking the binding sites has a P-value <0.0001 (permutation test).
77
Mutations in
liver cancer
(LIRI-JP)
0.012-
0.010-
0.00 -
gI
I
A
o.030. 1
' 0.025-
Mutations in 0.020.
all ICGC go
(pan-cancer) E L0.015
0.010
0.005
0.000
B
Mutations in
esophageal
cancer
(ESAD,-UK)
0.025.
0.020,
0.015.
0.010.
0.005
0.000
C
0.0025
0.002D
Mutations in g
liver cancer 0.0015.
(LIRI-JP) L-00.0010.
0.0005.
0.0000.
Constitutive neighborhood boundary CTCF sites
Non-boundary CTCF sites (control set 1)
Non-boundary CTCF sites (control set 2)
'7-,
Constitutive neighborhood boundary CTCF sites
- O TOF sites (control set 1)
Non-boundary TCF sites (control set 2)
Constitutive neighborhood boundary CTCF sites
Non-boundary CTCF sites (control set 1)I __ Non-boundary CTCF sites (control set 2)
Fig. S16. Recurrent base substitutions occur frequently in constitutive neighborhood boundary CTCF sites.
(A-C) Frequencies of recurrent mutations (at least two occurrence of the same base substitution) in the ICGC dataset
("pan-cancer") in neighborhood boundary CTCF sites and sites that do not form neighborhood boundaries. The
numbers of recurrent mutations are normalized to the number of CTCF sites found in each set. The two control sets
(i.e. CTCF sites that do not form insulated neighborhood boundaries) are described in the Supplemental Materials and
Methods.
(A) The plot was generated using all mutations in the ICGC database. Corrected P-values (proportion test):
- constitutive neighborhood boundary CTCF sites vs. non-boundary
CTCF sites (control set 1): 2.11E-14
- constitutive neighborhood boundary CTCF sites vs. non-boundary
CTCF sites (control set 2): 2.09E-7
(B) The plot was generated using the mutations in the ESAD-UK Genome Project. Corrected P-values:
- constitutive neighborhood boundary CTCF sites vs. non-boundary
CTCF sites (control set 1): 1.16E-16
- constitutive neighborhood boundary CTCF sites vs. non-boundary
CTCF sites (control set 2): 6.05E-13
(C) The plot was generated using the mutations in the LIRI-JP Genome Project. Corrected P-values:
- constitutive neighborhood boundary CTCF sites vs. non-boundary
CTCF sites (control set 1): 6.21 E-3
- constitutive neighborhood boundary CTCF sites vs. non-boundary
CTCF sites (control set 2): not significant
78
I .
CHAPTER 3: YY1 IS A STRUCTURAL REGULATOR OF ENHANCER-PROMOTER LOOPS
Originally published in Cell, Volume 171, Issue 7,1573-1588. (2017).
Reprinted with permission from Elsevier.
Abraham S. Weintraub1' 2 ,9, Charles H. Li1,2 ,9, Alicia V. Zamudio' 2 , Alla A. Sigoval'', Nancy M.
Hannett, Daniel S. Day', Brian J. Abraham', Malkiel A. Cohen', Behnam Nabet3-5 , Dennis L.
Buckley3-4,7 , Yang Eric Guo1 , Denes Hnisz1 , Rudolf Jaenisch 1,2,9, James E. Bradner3-5 ,7,9
Nathanael S. Gray4'5'9 , and Richard A. Young1'2,9, 10 *
'Whitehead Institute for Biomedical Research, Cambridge, MA, 02142, USA.
2Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.
3Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.4Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115,
USA.
5Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School,
Boston, Massachusetts 02115, USA.
6 Present address: Marauder Therapeutics, Cambridge, MA 02139, USA
7Present address: Novartis Institutes for Biomedical Research, Cambridge, MA 02139, USA.
8These authors contributed equally
9Senior Author
1 Lead Contact
*Correspondence to: young@wi.mit.edu.
79
Summary
There is considerable evidence that chromosome structure plays important roles in gene
control, but we have limited understanding of the proteins that contribute to structural
interactions between gene promoters and their enhancer elements. Large DNA loops that
encompass genes and their regulatory elements depend on CTCF-CTCF interactions, but most
enhancer-promoter interactions do not employ this structural protein. Here we show that the
ubiquitously expressed transcription factor Yin Yang 1 (YY1) contributes to enhancer-promoter
structural interactions in a manner analogous to DNA interactions mediated by CTCF. YY1
binds to active enhancers and promoter-proximal elements, and forms dimers that facilitate the
interaction of these DNA elements. Deletion of YY1 binding sites or depletion of YY1 protein
disrupts enhancer-promoter looping and gene expression. We propose that YY1 -mediated
enhancer-promoter interactions are a general feature of mammalian gene control.
80
Introduction
Cell-type specific gene expression programs in humans are generally controlled by gene
regulatory elements called enhancers (Buecker and Wysocka, 2012; Bulger and Groudine,
2011; Levine et al., 2014; Ong and Corces, 2011; Ren and Yue, 2016). Transcription factors
(TFs) bind these enhancer elements and regulate transcription from the promoters of nearby or
distant genes through physical contacts that involve looping of DNA between enhancers and
promoters (Bonev and Cavalli, 2016; Fraser et al., 2015; Heard and Bickmore, 2007; de Laat
and Duboule, 2013; Pombo and Dillon, 2015; Spitz, 2016). Despite the fundamental importance
of proper gene control to cell identity and development, the proteins that contribute to structural
interactions between enhancers and promoters are poorly understood.
There is considerable evidence that enhancer-promoter interactions can be facilitated by
transcriptional cofactors such as Mediator, structural maintenance of chromosomes (SMC)
protein complexes such as cohesin, and DNA binding proteins such as CTCF. Mediator can
physically bridge enhancer-bound transcription factors (TFs) and the promoter-bound
transcription apparatus (Allen and Taatjes, 2015; Jeronimo et al., 2016; Kagey et al., 2010;
Malik and Roeder, 2010; Petrenko et al., 2016). Cohesin is loaded at active enhancers and
promoters by the Mediator-associated protein NIPBL, and may transiently stabilize enhancer-
promoter interactions (Kagey et al., 2010; Schmidt et al., 2010). CTCF proteins bound at
enhancers and promoters can interact with one another, and may thus facilitate enhancer-
promoter interactions (Guo et al., 2015; Splinter et al., 2006), but CTCF does not generally
occupy these interacting elements (Cuddapah et al., 2009; Kim et al., 2007; Phillips-Cremins et
al., 2013; Wendt et al., 2008).
Enhancer-promoter interactions generally occur within larger chromosomal loop structures
formed by the interaction of CTCF proteins bound to each of the loop anchors (Gibcus and
Dekker, 2013; Gorkin et al., 2014; Hnisz et al., 2016a; Merkenschlager and Nora, 2016). These
loop structures, variously called TADs, loop domains, CTCF contact domains and insulated
neighborhoods, tend to insulate enhancers and genes within the CTCF-CTCF loops from
elements outside those loops (Dixon et al., 2012b; Dowen et al., 2014; Hnisz et al., 2016b; Ji et
al., 2016; Lupie6ez et al., 2015; Narendra et al., 2015; Nora et al., 2012; Phillips-Cremins et al.,
2013; Rao et al., 2014a; Tang et al., 2015). Constraining DNA interactions within CTCF-CTCF
loop structures in this manner may facilitate proper enhancer-promoter contacts.
Evidence that CTCF-CTCF interactions play important global roles in chromosome loop
structures but are only occasionally directly involved in enhancer-promoter contacts (Phillips
and Corces, 2009), led us to consider the possibility that a bridging protein analogous to CTCF
might generally participate in enhancer-promoter interactions. We report here that Yin Yang 1
(YY1) contributes to enhancer-promoter interactions in a manner analogous to DNA looping
mediated by CTCF. YY1 and CTCF share many features: both are essential, ubiquitously
expressed, zinc-coordinating proteins that bind hypo-methylated DNA sequences, form
homodimers and thus facilitate loop formation. The two proteins differ in that YY1 preferentially
occupies interacting enhancers and promoters, whereas CTCF preferentially occupies sites
distal from these regulatory elements that tend to form larger loops and participate in insulation.
Deletion of YY1 binding sites or depletion of YY1 can disrupt enhancer-promoter contacts and
normal gene expression. Thus, YY1 -mediated structuring of enhancer-promoter loops is
analogous to CTCF-mediated structuring of TADs, CTCF contact domains, and insulated
neighborhoods. This model of YY1-mediated structuring of enhancer-promoter loops accounts
for diverse functions reported previously for YY1, including contributions to both gene activation
and repression and to gene dysregulation in cancer.
81
Results
A candidate enhancer-promoter structuring factor in ES cells
We sought to identify a protein factor that might contribute to enhancer-promoter interactions in
a manner analogous to that of CTCF at insulators. Such a protein would be expected to bind
active enhancers and promoters, be essential for cell viability, show ubiquitous expression, and
be capable of dimerization. To identify proteins that bind active enhancers and promoters, we
sought candidates from chromatin immunoprecipitation with mass spectrometry (ChIP-MS),
using antibodies directed towards histones with modifications characteristic of enhancer and
promoter chromatin (H3K27ac and H3K4me3, respectively) (Creyghton et al., 2010), conducted
previously in murine embryonic stem cells (mES cells) (Ji et al., 2015). Of 26 transcription
factors that occupy both enhancers and promoters (Figure 1A), four (CTCF, YY1, NRF1 and
ZBTB1 1) are essential based on a CRISPR cell-essentiality screen (Figure 1 B) (Wang et al.,
2015) and two (CTCF, YY1) are expressed in >90% of tissues examined (Figure 1C). YY1 and
CTCF share additional features: like CTCF, YY1 is a zinc-finger transcription factor (Klenova et
al., 1993; Shi et al., 1991), essential for embryonic and adult cell viability (Donohoe et al., 1999;
Heath et al., 2008) and capable of forming homodimers (Lopez-Perrote et al., 2014; Saldaha-
Meyer et al., 2014)(Table S1). YY1, however, tends to occupy active enhancers and promoters,
as well as some insulators, whereas CTCF preferentially occupies insulator elements (Figure
1D, Figure S1A-C).
If YY1 contributes to enhancer-promoter interactions, then chromatin interaction analysis by
paired-end tag sequencing (ChIA-PET) (Fullwood et al., 2009) for YY1 should show that YY1 is
preferentially associated with these interactions. CTCF ChIA-PET, in contrast, should show that
CTCF is preferentially associated with insulator DNA interactions. We generated ChIA-PET
data for YY1 and CTCF in mES cells and compared these two datasets. The results showed
that the majority of YY1-associated interactions connect active regulatory elements (enhancer-
enhancer, enhancer-promoter, and promoter-promoter, which we will henceforth call enhancer-
promoter interactions), whereas the majority of CTCF-associated interactions connect insulator
elements (Figure 1E, Figure S1D). Some YY1-YY1 interactions involved simple enhancer-
promoter contacts, as seen in the Rafi locus (Figure 1 F) and others involved more complex
contacts among super-enhancer constituents and their target promoters, as seen in the Klf9
locus (Figure S1 E). Super-enhancers were generally occupied by YY1 at relatively high
densities and exhibited relatively high YY1-YY1 interaction frequencies (Figure S1E-H). For
both YY1 and CTCF, there was also evidence of enhancer-insulator and promoter-insulator
interactions, but these were more pronounced for CTCF (Figure S1D).
Previous studies have reported that YY1 can form dimers (Lopez-Perrote et al., 2014). To
confirm that YY1 dimerization occurs, FLAG-tagged and HA-tagged versions of YY1 protein
were expressed in cells, nuclei were isolated and the tagged YY1 proteins in nuclear extracts
were immunoprecipitated with either anti-FLAG or anti-HA antibodies. The results show that the
FLAG-tagged and HA-tagged YY1 proteins interact (Figure 1G, H, Figure S11, J), consistent
with prior reports that YY1 proteins oligomerize (Lopez-Perrote et al., 2014). Other highly
expressed nuclear proteins such as OCT4 did not co-precipitate, indicating that the assay was
specific (Figure S1J). We previously reported that YY1 can bind both DNA and RNA
independently, and that YY1 binding of active regulatory DNA elements is enhanced by the
binding of RNA species that are transcribed at these loci (Sigova et al., 2015). It is therefore
possible that YY1-YY1 interactions may be enhanced by the ability of each of the YY1 proteins
to bind RNA species. Indeed, when we repeated the experiment described above with nuclear
extracts containing the tagged YY1 proteins, and a portion of the sample was treated with
RNase A prior to immunoprecipitation with anti-tag antibodies, there was a -60% reduction in
82
the amount of co-immunoprecipitated YY1 partner protein (Figure 1G, H). These results suggest
that stable YY1-YY1 interactions may be facilitated by RNA.
YY1 generally occupies enhancers and promoters in mammalian cells
YY1 is ubiquitously expressed in mammalian cells, so we investigated whether YY1 generally
occupies enhancers and promoters in a broad spectrum of mammalian cell types. Examination
of sites bound by YY1 across human cell types showed that YY1 does generally occupy
enhancers and promoters genome-wide and, as expected, enhancer occupancy tends to be
cell-type-specific (Figure 2A, B; Figure S2A-F). As with mES cells, YY1 was also found at a
subset of insulators in the human cells (Figure S2A-F). Examination of YY1 ChIP-seq data in
multiple murine cell types confirmed that YY1 generally occupies enhancers and promoters, and
is present at some insulators (Figure S2G-J). These results indicate that YY1 generally
occupies enhancer and promoter elements in mammalian cells.
To determine whether YY1 is associated with sites of enhancer-promoter interactions in human
cells, we conducted YY1 HiChIP experiments (Mumbach et al., 2016) in three different cell
types. These experiments revealed that YY1 is predominantly associated with enhancer-
promoter interactions (Figure 2C-K). YY1 was also associated with some insulator-enhancer
and insulator-promoter interactions, suggesting that the factor may also occasionally participate
in such interactions (Figure S2K-M). In summary, the HiChIP results indicate that YY1 generally
occupies sites involved in enhancer-promoter interactions, and occasionally occupies sites of
insulator interactions, in mammalian cells.
83
Figure 1
A
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Figure 1. YYI is a candidate enhancer-promoter structuring factor
(A) Model depicting an enhancer-promoter loop contained within a larger insulated neighborhood loop. Candidate
enhancer-promoter structuring transcription factors were identified by ChlP-MS of histones with modifications
characteristic of enhancer and promoter chromatin.
(B) CRISPR scores (CS) of all genes in KBM7 cells from Wang et al. (2015). Candidate enhancer-promoter
structuring factors identified by ChIP-MS are indicated as dots and those identified as cell-essential (CS < -1) are
shown in red.
(C) Histogram showing the number of tissues in which each candidate enhancer-promoter structuring factor is
expressed across 53 tissues surveyed by GTEx. Candidates that are both broadly expressed (expressed in greater
than 90% of tissues surveyed) and cell-essential are shown in red.
(D) Metagene analysis showing the occupancy of YY1 and CTCF at enhancers, promoters, and insulator elements in
mouse ESCs.
(E) Summary of the classes of high-confidence interactions identified by YY1 and CTCF ChIA-PET in mES cells.(F) Example of a YY1-YY1 enhancer-promoter interaction at the Rafi locus in mES cells.(G) Model depicting co-immunoprecipitation assay to detect YY1 dimerization and evaluate dependence on RNA for
YY1 dimerization.
(H) Western blot results showing co-immunoprecipitation of FLAG-tagged YY1 and HA-tagged YY1 protein from
nuclear lysates prepared from transfected cells. Quantification of the remaining signal normalized to input after
RNase A treatment for the co-immunoprecipitated tagged YY1 is displayed under the relevant bands.
84
B\#40
Ad lee ~ ~
-2 C +2
E -,
o I*-
0.a
F
YY1 HiChIP
2%
22% 23% 32%
-2 C +2
chrl0:112,135,000-112280,000
z 1-5 kb
6 YY1
18 H3K27ac
Super-enhancer
10 kbchr16:70,310,000-70,370,W00
3 YY1
LJ~
z8 H3K27ac
Enhancer
YY1 HiChIP <1%
27% 25% 29%
G
L-
chrl0:104,215,000-104,39
2
11 H3K
Enhancer
H
YY1 HiChIP
1%
18% 30% 32%
Ai~
C-
chr7:73,675,000-73,825,000
25kb
YY1
5 H3K27ac
Enhancer
CLIP2
J
5,000 chr10:11,180000-11,381,000
5kb 25 kb
YY1 YY1
27ac 22  H3K27ac
Super-enhancer
CEL1
K
chr6:37,050000-37,180,000
a. 25 kb
C YY1
4
9  
21 H3K27ac
Super-enhancer 
PM
r&M
Figure 2. YY1 generally occupies enhancers and promoters in mammalian cells(A-B) Heatmaps displaying the YY1 occupancy at enhancers (A) and active promoters (B) in six human cell types.(C-E) Summaries of the major classes of high-confidence interactions identified with YY1 HiChIP in three human cell
types.
(F-K) Examples of YY1-YY1 enhancer-promoter interactions in three human cell types: colorectal cancer (F and I), T
cell acute lymphoblastic leukemia (G and J), and chronic myeloid leukemia (H and K). Displayed examples show
YY1-YY1 enhancer-promoter interactions involving typical enhancers (F-H) and involving super-enhancers (I-K).
85
Figure 2
A
C
LU
13
D
-
E
E
E
IMMIN
I
YY1 can enhance DNA interactions in vitro
CTCF proteins can form homodimers and larger oligomers, and thus when bound to two
different DNA sites can form a loop with the intervening DNA (Saldaha-Meyer et al., 2014). The
observation that YY1 is bound to interacting enhancers and promoters, coupled with the
evidence that YY1-YY1 interactions can occur in vitro and in cell extracts, is consistent with the
idea that YY1-YY1 interactions can contribute to loop formation between enhancers and
promoters. To obtain evidence that YY1 can have a direct effect on DNA interactions, we used
an in vitro DNA circularization assay to determine if purified YY1 can enhance the rate of DNA
interaction in vitro. The rate of DNA circularization catalyzed by T4 DNA ligase has been used
previously to measure persistence length and other physical properties of DNA (Shore et al.,
1981). We reasoned that if YY1 bound to DNA is capable of dimerizing and thereby forming
DNA loops, then incubating a linear DNA template containing YY1 binding sites with purified
YY1 protein should bring the ends into proximity and increase the rate of circularization (Figure
3A, D). Recombinant YY1 protein was purified and shown to have DNA binding activity using a
mobility shift assay (Figure S3A, B). This recombinant YY1 was then tested in the DNA
circularization assay; the results showed that YY1 increased the rate of circularization and that
this depended on the presence of YY1 motifs in the DNA (Figure 3B, 3C). The addition of an
excess of a competing 200 base pair DNA fragment containing the YY1 consensus binding
sequence abrogated circularization of the larger DNA molecule (Figure 3D-F). The addition of
bovine serum albumin (BSA) did not increase the rate of DNA ligation (Figure 3C, F). These
results support the idea that YY1 can directly facilitate DNA interactions.
86
Figure 3
A
3.5kb YY
DNA motifs
D
No YY1
motifs
+BSA +BSA +YY1 +YY1 $
+Ligase4  +Ligase +Ligase +Ligase4
, I
Separate circular DNA from linear
with gel electrophoresis
B + Motif - Motif + Motif - Motif
Time + BSA + BSA + YY1 + YY1 Circular
(min) 0 1 3 5 1015 0 1 3 5 1015 0 1 3 5 1015 0 1 3 5 1015
Linear
C
15-
1o.
5-
0.
0-
3.5kb YY1
DNA motifs
+BSA'
I I
+YY1 +YY1 4
+ Competitor DNA
~0
+Ligase +Ligase +Ligase
Separate circular DNA from linear
with gel electrophoresis
E +yy1
+BSA +YY1 +Competitor DNA
(min) 0 1 3 5 1015 2030 0 1 3 5 1015 20300 1 3 5 1 C15 2030
Linear
F
20-
+ Motif
+YY1
- Motif
+ YY1
- Motif
+ BSA
+ Motif
+ BSA
0 5 10 i15
Z 10.
0.
0
Time (minutes)
10
+ YY1
+ Competitor
+ YY1
+ BSA
20 30
Time (minutes)
Figure 3. YYI can enhance DNA interactions in vitro
(A and D) Models depicting the in vitro DNA circularization assays used to detect the ability of YY1 to enhance DNA
looping interactions.
(B and E) Results of the in vitro DNA circularization assay visualized by gel electrophoresis. The dominant lower
band reflects the starting linear DNA template, while the upper band corresponds to the circularized DNA ligation
product.
(C and F) Quantifications of DNA template circularization as a function of incubation time with T4 DNA ligase. Values
correspond to the percent of DNA template that is circularized and represents the mean and standard deviation of
four experiments.
87
i ' I
Enhancer-promoter interactions depend on YY1 in living cells
To test whether enhancer-promoter interactions in living cells depend on YY1 binding sites in
these elements, a CRISPR/Cas9 system was used to generate a small deletion of a YY1
binding motif in the regulatory regions of two genes (Figure 4A). Deletion of the optimal DNA-
binding motif for YY1 in the promoter of the Rafi gene resulted in decreased YY1 binding at the
promoter, reduced contact frequency between the enhancer and promoter, and a decrease in
Rafi mRNA levels (Figure 4B, Figure S4A). Deletion of the optimal DNA-binding motif for YY1
in the promoter of the Etv4 gene also resulted in decreased YY1 binding and decreased
enhancer-promoter contact frequency, although it did not significantly affect the levels of Etv4
mRNA (Figure 4C, Figure S4B). These results suggest that the YY1 binding sites contribute to
YY1 binding and enhancer-promoter contact frequencies at both Rafi and Etv4, although the
reduction in looping frequencies at Etv4 was not sufficient to have a significant impact on Etv4
mRNA levels. The lack of an effect on Etv4 mRNA levels may be a consequence of the residual
YY1 that is bound to the Etv4 promoter region, where additional CCAT motifs are observed
(Figure 4C). Indeed when YY1 protein is depleted (see below; Figure S6E), the levels of both
Rafi and Etv4 mRNA decrease.
Previous studies have reported that YY1 is an activator of some genes and a repressor of
others but a global analysis of YY1 dependencies has not been described with a complete
depletion of YY1 in mES cells (Gordon et al., 2006; Shi et al., 1997; Thomas and Seto, 1999).
We used an inducible degradation system (Erb et al., 2017; Huang et al., 2017; Winter et al.,
2015) to fully deplete YY1 protein levels and measured the impact on gene expression in mES
cells genome-wide through RNA-seq analysis (Figure 5A, B). Depletion of YY1 led to significant
(adjusted p-value < 0.05) changes in expression of 8,234 genes, divided almost equally
between genes with increased expression and genes with decreased expression (Figure 5C,
Table S2, S3). The genes that experienced the greatest changes in expression with YY1
depletion were generally occupied by YY1 (Figure 5D).
Previous studies have shown that YY1 is required for normal embryonic development (Donohoe
et al., 1999). We therefore investigated whether the loss of YY1 leads to defects in embryonic
stem (ES) cell differentiation into the three germ layers (Figure 5E). Murine ES cells, and
isogenic cells that were subjected to inducible degradation of YY1, were stimulated to form
embryoid bodies (Figure 5F) and the cells in these bodies were subjected to
immunohistochemistry staining and single-cell RNA-seq to monitor expression of differentiation-
specific factors. The results showed that cells lacking YY1 showed pronounced defects in
expression of the master transcription factors that drive normal differentiation (Figure 5G, H;
Figure S5).
We next investigated whether changes in DNA looping occur upon global depletion of YY1 in
mES cells. HiChIP for H3K27ac, a histone modification present at both enhancers and
promoters, was performed before and after YY1 depletion to detect differences in enhancer-
promoter interaction frequencies. Prior to YY1 depletion, the results of the HiChIP experiment
showed interactions between the various elements that were similar to the earlier YY1 ChIA-
PET results (Figure S6A, B). After YY1 depletion, the interactions between YY1-occupied
enhancers and promoters decreased significantly (Figure 6A, B). The majority (60%) of genes
connected by YY1 enhancer-promoter loops showed significant changes in gene expression
(Figure 6C; Figure S6D). Examination of the HiChIP DNA interaction profiles at specific genes
confirmed these effects. For example, with YY1 depletion the Slc7a5 promoter and its enhancer
showed a -50% reduction in interaction frequency, and Sic7a5 expression levels were reduced
by -27% (Figure 6D). Similarly, after YY1 depletion the K1f9 promoter and its super-enhancer
88
showed a -40% reduction in interaction frequency and KIf9 expression levels were reduced by
-50% (Figure 6E).
Figure 4
A
nrYncEi Y1 moti
Gone mal
C
y kb
VY 1
IAT
k:j~4 rIIo, fill T
ROO 9J
VP.~ fl
U
-na NOW@4
4C-Oq ChEP.qPCR RT*#ICR
Pi~Ar W icoIAN PA" ~EE
1]1 Cp.VIPPA P*V;F ~ ~ - Laf ; FUTVfI
10kh
r
U
WI ~ VV moor T
MUT ... A c-r-rYMh...
I PW
WIT
4C4eq ChIP-PCR RT-qPCR
IWMWW EbPM00 EkuacNr
i P p0.01 L P 0j 01
WW LW I" 1 El MW
Figure 4. Deletion of YYI binding sites causes loss of enhancer-promoter interactions
(A) Model depicting CRISPR/Cas9-mediated deletion of a YY1 binding motif in the regulatory region of a gene.
(B and C) CRISPR/Cas9-mediated deletion of YY1 binding motifs in the regulatory regions of two genes, RafI (B)
and Etv4 (C), was performed and the effects on YY1 occupancy, enhancer-promoter looping, and mRNA levels were
measured. The positions of the targeted YY1 binding motifs, the genotype of the wildtype and mutant lines, and the
4C-seq viewpoint are indicated. The mean 4C-seq signal is represented as a line (individual replicates are shown in
Figure S4) and the shaded area represents the 95% confidence interval. Three biological replicates were assayed for
4C-seq and ChIP-qPCR experiments, and six biological replicates were assayed for RT-qPCR experiments. Error
bars represent the standard deviation. All p-values were determined using the Student's t test.
89
13
C-)
Figure 5
A B
Knock-in +dTAG compound Degradation
FKBP tag (24 hours)
YY1 YY1-FKBPgmne
Cereblon E3 ligase
poly-ubiquitination
C
4.
2-
S0-
-2
10 100 1000
Expression at 0 hr
(Noinralized cont)
E
YY1'
OR
YY1
10.000 100,000
WT Degron
O hr O hr 24 hr
anti-YYI - tagged
endogenous
anti-P-ACTIN
D
z
z
change in YY1 density
expression at promoter
1.5 1.5
-2 kb 0 +2 kb
Embryoid body fomation cole id e Embryaid bodies 0Ex2 Microscopy
24 dhistoneilstry
serum sm serum Single-cell
+LIF -LIF -LIF RNA-seq
Gat&4
F Microscopy
YY1 YY1-
CE
G Immunohistochemlstry
vv1i YY1
I Single-cel RNA-seq
25 -Ox2 Gbx2 2.0 - sMna 10- Gat4 Sax7
.1 I J i I I 2 -&20 - I &".5 - ..
, -5 C 4-
Figure 5. Depletion of YYI disrupts gene expression
(A) Model depicting dTAG system used to rapidly deplete YY1 protein.
(B) Western blot validation of knock-in of FKBP degron tag and ability to inducibly degrade YY1 protein.
(C) Change in gene expression (log2 fold-change) upon degradation of YY1 for all genes plotted against the
expression in untreated cells. Genes that displayed significant changes in expression (FDR adjusted p-value < 0.05)
are colored with upregulated genes plotted in red and downregulated genes plotted in blue.
(D) Heatmaps displaying the change in expression of each gene upon degradation of YY1 and wild type YY1 ChIP-
seq signal in a 2kb region centered on the TSS of each gene. Each row represents a single gene and genes are
ranked by their adjusted p-value for change in expression upon YY1 degradation.
(E) Model depicting experimental outline to test the effect of YY1 degradation on embryonic stem cell differentiation
into the three germ layers via embryoid body formation from untreated cells (YY1*) and cells treated with dTAG
compound to degrade YY1 (YY1 ).
(F) Microscopy images of embryoid bodies formed from YY1+ and YY1~ cells
(G) lmmunohistochemistry images of embryoid bodies formed from YY1 + and YY1~ cells. GATA4 is displayed in
green and DNA stained using DAPI is displayed in blue. The scale bar represents 50 pm.
(H) Quantification of single-cell RNA-seq results for embryoid bodies formed from YY1+ and YY1~ cells. The
percentage of cells expressing various differentiation-specific genes is displayed for YY1 + and YY1 - embryoid bodies.
90
24,444 genes
4.074 genes
significantly
upregulaed
(padj 0 05)
4,160 genes
significantly
downregulated
Inadi n 0051
_F
Rescue of enhancer-promoter interactions in cells
The ability of an artificially tethered YY1 protein to rescue defects associated with a YY1 binding
site mutation would be a strong test of the model that YY1 mediates enhancer-promoter
interactions (Figure 7A). We carried out such a test with a dCas9-YY1 fusion protein targeted to
a site adjacent to a YY1 binding site mutation in the promoter-proximal region of Etv4 (Figure
7B, C). We found that artificially tethering YY1 protein to the promoter led to increased contact
frequency between the Etv4 promoter and its enhancer and caused increased transcription from
the gene (Figure 7D). These results support the model that YY1 is directly involved in
structuring enhancer-promoter loops.
To more globally test if YY1 can rescue the loss of enhancer-promoter interactions after YY1
degradation, we subjected mES cells to YY1 degradation with the dTAG method and then
washed out the dTAG compound and allowed YY1 to be restored to normal levels (Figure 7E;
Figure S7A, B). Enhancer-promoter frequencies were monitored with H3K27ac HiChIP.
Consistent with our previous experiment (Figure 6), the loss of YY1 caused a loss in enhancer-
promoter interactions, but the recovery of YY1 levels was accompanied by a substantial
increase in enhancer-promoter interactions (Figure 7F). These results were comparable to the
effects observed with the rescue of CTCF-CTCF interactions in a similar experiment described
recently (Figure 7F; Figure S7C) (Nora et al., 2017), and support the model that YY1 contributes
to structuring of a large fraction of enhancer-promoter loops genome-wide.
91
Figure 6
A Y VYl enhancer-promoter Interactions B C Genes with YYl-YY1
enhancer-promoter Interactions
2- (4,514)
2 1.042 genes g ca y upregulated
(pad 00)
1- 1 _
-1 0 - --. 0-
'10 100 10000
~ .4=Expreaalon at ohr
- ___-_ (Nonnalzad ounts)Q 20 100 10000
Normalized interaction frequency
D chrS:124,435,000-124,370,000 E ch1923145 000-23,245,000
YY1 10 kb yy1 25 kb
12 H3K27ac 12 H3K27ac
Enhance Super Enhancr
HO 20 HIChIP N251
21RNA-seq N ma 9 RNA-seq d ,.Nonedm 5,-349
21 9i
HIChIP HIChIP 25
2 'n _ .1 1M0 c
21 RNA-stq Nwalazedmean= 7,332 9 RNA-seq 2AN
Slc7a5 1m1 Kff94~
HiChIP RNA-seq HIChIP RNA-seq
1.5 p 0.05 1.5 P 0.01 1.5 P < 0.05 p 0.01
1.0 1. 1.0 - 1.1 H-
0.5 -0.5 -0.5- 0.5 -
0.0 - 0.0. - 0.0 0.0 -
Degron 0 t24hr Degmon 0h24hr Dogron 0 4hr Degron 0 hrh
Figure 6. Depletion of YY1 disrupts enhancer-promoter looping
(A) Scatter plot displaying for all YY1-YY1 enhancer-promoter interactions the change in normalized interaction
frequency (10g2 fold change) upon degradation of YY1, as measured by H3K27ac HiChIP, and plotted against the
normalized interaction frequency in untreated cells.
(B) Change in normalized interaction frequency (1092 fold change) upon degradation of YY1 for three different classes
of interactions: all interactions, interactions not associated with YY1 ChIP-seq peaks, and YY1-YY1 enhancer-
promoter interactions.
(C) Scatter plot displaying for each gene associated with a YY1-YY1 enhancer-promoter interaction the change in
gene expression (10g2 fold-change) upon degradation of YY1 plotted against the expression in untreated cells. Genes
that showed significant changes in expression (FDR adjusted p-value < 0.05) are colored with upregulated genes
plotted in red and downregulated genes plotted in blue.
(D and E) Effect of YY1 degradation at the Slc7a5 locus (D) and K/f9 locus (E) on enhancer-promoter interactions
and gene expression. The top of each panel shows an arc representing an enhancer-promoter interaction detected in
the HiChIP data. Signal in the outlined pixels was used to quantify the change in normalized interaction frequency
upon YY1 degradation. Three biological replicates were assayed per condition for H3K27ac HiChIP and two
biological replicates were assayed for RNA-seq. Error bars represent the standard deviation. P-values for HiChIP
were determined using the Student's t test. P-values for RNA-seq were determined using a Wald test.
92
Figure 7
A
LeOpNg rgsc%"e
scmw1
WVI ma r,'E
-Ooh r ed
dCa.VWiE 
.]
T*IpD~fE
Jrsa ft
ADDOU DDD MkDh
C
_eD
I0-o
E
CTCF .-
Enhom 'YYI<
D
I0l .
16 HK2a I&a.._ Enhae-u
+v7I'll a,
dC-9
4C-..q cIIp-qPC RT-qpM
s - U4MmD s o
it I.IID -
WYI CTCFF dugm.lai t grbb2-
.YI CTVFCTCF
Figure 7. Rescue of enhancer-promoter interactions in cells
(A) Model depicting use of dCas9-YY1 to artificially tether YY1 to a site adjacent to the YY1 binding site mutation in
the promoter-proximal region of Etv4 in order to determine if artificially tethered YY1 can rescue enhancer-promoter
interactions.
(B) Model depicting dCas9-YY1 rescue experiments. Etv4 promoter-proximal YYI binding motif mutant cells were
transduced with lentivirus to stably express either dCas9 or dCas9-YY1, and two sgRNAs to direct their localization to
the sequences adjacent to the deleted YY1 binding motif in the Etv4 promoter-proximal region. The ability to rescue
enhancer-promoter looping was assayed by 4C-seq.
(C) Western blot results showing that Etv4 promoter-proximal YYI binding motif mutant cells transduced with
lentivirus to stably express either dCas9 or dCas9-YY1 successfully express dCas9 or dCas9-YY1.(D) Artificial tethering of YY1 using dCas9-YY1 was performed at sites adjacent to the YY1 binding site mutation in
the promoter-proximal region of Etv4. The effects of tethering YYI using dCas9-YY1 on enhancer-promoter looping
and expression of the Etv4 gene were measured and compared to dCas9 alone. The genotype of the Etv4 promoter-
proximal YY1 binding motif mutant cells and the 4C-seq viewpoint (VP) is shown. The 4C-seq signal is displayed as
the smoothed average reads per million per base pair. The mean 4C-seq signal is represented as a line and the
shaded area represents the 95% confidence interval. Three biological replicates were assayed for 4C-seq and CAS9
ChIP-qPCR experiments, and six biological replicates were assayed for RT-qPCR experiments. Error bars represent
the standard deviation. All p-values were determined using the Student's t test.
(E) Model depicting the loss of looping interactions after the inducible degradation of the structuring factors CTCF and
YY1 followed by restoration of looping upon washout of degradation compounds.
(F) Change in normalized interaction frequency (log2 fold change) after YYI and CTCF degradation (treated) and
recovery (washout) relative to untreated cells. For YY1 degradation, change in normalized interaction frequency is
plotted for YY1-YY1 enhancer-promoter interactions. For CTCF degradation, change in normalized interaction
frequency is plotted for CTCF-CTCF interactions.
93
B
b
Discussion
We describe here evidence that the transcription factor YY1 contributes to enhancer-promoter
structural interactions. For a broad spectrum of genes, YY1 binds to active enhancers and
promoters and is required for normal levels of enhancer-promoter interaction and gene
transcription. YY1 is ubiquitously expressed, occupies enhancers and promoters in all cell types
examined, is associated with sites of DNA looping in cells where such studies have been
conducted, and is essential for embryonic and adult cell viability, so it is likely that YY1-
mediated enhancer-promoter interactions are a general feature of mammalian gene control.
Evidence that CTCF-CTCF interactions play important roles in chromosome loop structures, but
are only occasionally involved in enhancer-promoter interactions, led us to consider the
possibility that a bridging protein analogous to CTCF might generally participate in enhancer-
promoter interactions. CTCF and YY1 share many features: they are DNA-binding zinc-finger
factors (Klenova et al., 1993; Shi et al., 1991) that selectively bind hypo-methylated DNA
sequences (Bell and Felsenfeld, 2000; Yin et al., 2017), are ubiquitously expressed (Fig 1C)
(Mele et al., 2015), essential for embryonic viability (Donohoe et al., 1999; Heath et al., 2008),
and capable of dimerization (Figure 1G, H, Figure S11, J) (Lopez-Perrote et al., 2014; Saldafa-
Meyer et al., 2014). The two proteins differ in several important ways. CTCF-CTCF interactions
occur predominantly between sites that can act as insulators and to a lesser degree between
enhancers and promoters (Figure 1E, Figure S1A-D). YY1-YY1 interactions occur
predominantly between enhancers and promoters and to a lesser extent between insulators
(Figure 1E, Figure S1A-D). At insulators, CTCF binds to a relatively large and conserved
sequence motif (when compared to those bound by other TFs); these same sites tend to be
bound in many different cell types, which may contribute to the observation that TAD boundaries
tend to be preserved across cell types. At enhancers and promoters, YY1 binds to a relatively
small and poorly conserved sequence motif within these regions, where RNA species are
produced that can facilitate stable YY1 DNA binding (Sigova et al., 2015). The cell-type-specific
activity of enhancers and promoters thus contributes to the observation that YY1-YY1
interactions tend to be cell-type-specific.
The model that YY1 contributes to structuring of enhancer-promoter loops can account for the
many diverse functions previously reported for YY1, including activation and repression,
differentiation, and cellular proliferation. For example, following its discovery in the early 1990's
(Hariharan et al., 1991; Park and Atchison, 1991; Shi et al., 1991), YY1 was intensely studied
and reported to act as a repressor for some genes and an activator for others; these context-
specific effects have been attributed to many different mechanisms (reviewed in (Gordon et al.,
2006; Shi et al., 1997; Thomas and Seto, 1999)). There are many similar reports of context-
specific activation and repression by CTCF (reviewed in (Ohlsson et al., 2001; Phillips and
Corces, 2009)). Although it is reasonable to assume that YY1 and CTCF can act directly as
activators or repressors at some genes, the evidence that these proteins contribute to
structuring of DNA loops makes it likely that the diverse active and repressive roles that have
been attributed to them are often a consequence of their roles in DNA structuring. In this model,
the loss of CTCF or YY1 could have positive or negative effects due to other regulators that
were no longer properly positioned to produce their regulatory activities.
Previous studies have hinted at a role for YY1 in long distance DNA interactions. CTCF, YY1
and cohesin have been implicated in the formation of DNA loops needed for V(D)J
rearrangement at the immunoglobulin locus during B cell development (Degner et al., 2011;
Guo et al., 2011; Liu et al., 2007). B cell-specific deletion of YY1 causes a decrease in the
contraction of the IgH locus, thought to be mediated by DNA loops, and a block in the
94
development of B cells (Liu et al., 2007). Knockdown of YY1 has also been shown to reduce
intrachromosomal interactions between the Th2 LCR and the 1L4 promoter (Hwang et al., 2013).
As this manuscript was completed, a paper appeared reporting that YY1 is present at the base
of interactions between neuronal precursor cell specific enhancers and genes and that YY1
knockdown causes a loss of these interactions (Beagan et al., 2017). The results described
here argue that YY1 is more of a general structural regulator of enhancer-promoter interactions
for a large population of genes, both cell-type specific and otherwise, in all cells. Thus, the
tendency of YY1 to be involved in cell-type specific loops is a reflection of the cell-type
specificity of enhancers and, consequently, their interactions with genes that can be expressed
in a cell-specific or a more general manner.
YY1 plays an important role in human disease; YY1 haploinsufficiency has been implicated in
an intellectual disability syndrome and YY1 overexpression occurs in many cancers. A cohort of
patients with various mutations in one allele and exhibiting intellectual disability have been
described as having a "YY1 Syndrome", and lymphoblastoid cell lines from these patients show
reduced occupancy of regulatory regions and small changes in gene expression at a subset of
genes associated with YY1 binding (Gabriele et al., 2017). These results are consistent with the
model we describe for YY1 in global enhancer-promoter structuring, and with the idea that
higher neurological functions are especially sensitive to such gene dysregulation. YY1 is over-
expressed in a broad spectrum of tumor cells, and this over-expression has been proposed to
cause unchecked cellular proliferation, tumorigenesis, metastatic potential, resistance to
immune-mediated apoptotic stimuli and resistance to chemotherapeutics (Gordon et al., 2006;
Zhang et al., 2011). The mechanisms that have been reported to mediate these effects include
YY1-mediated downregulation of p53 activity, interference with poly-ADP-ribose polymerase,
alteration in c-Myc and NF- B expression, regulation of death genes and gene products,
differential YY1 binding in the presence of inflammatory mediators and YY1 binding to the
oncogenic c-Myc transcription factor (Gordon et al., 2006; Zhang et al., 2011). Although it is
possible that YY1 carries out all these functions, it's role as a general enhancer-promoter
structuring factor is a more parsimonious explanation of these pleotropic phenotypes.
Many zinc-coordinating transcription factors are capable of homo- and hetero-dimerization
(Amoutzias et al., 2008; Lamb and McKnight, 1991) and because these comprise the largest
class of transcription factors in mammals (Weirauch and Hughes, 2011 b), we suggest that a
combination of cell-type-specific and cell-ubiquitous transcription factors make a substantial and
underappreciated contribution to enhancer-promoter loop structures. There are compelling
studies of bacterial and bacteriophage transcription factors that contribute to looping of
regulatory DNA elements through oligomerization (Adhya, 1989; Schleif, 1992), and reports of
several eukaryotic factors with similar capabilities (Matthews, 1992). Nonetheless, most recent
study of eukaryotic enhancer-promoter interactions has focused on cofactors that lack DNA
binding capabilities and bridge enhancer-bound transcription factors and promoter-bound
transcription apparatus (Allen and Taatjes, 2015; Deng et al., 2012; Jeronimo et al., 2016;
Kagey et al., 2010; Malik and Roeder, 2010; Petrenko et al., 2016), with the notable exception
of the proposals that some enhancer-promoter interactions are determined by the nature of
transcription factors bound at the two sites (Muerdter and Stark, 2016). We predict that future
studies will reveal additional transcription factors that belong in the class of DNA binding
proteins whose predominant role is to contribute to chromosome structure.
95
Supplemental information
Supplemental Information includes STAR methods, 7 Figures and 5 Tables
AUTHOR CONTRIBUTIONS
Conceptualization, A.S.W., C.H.L., A.S., R.A.Y.; Methodology, A.S.W, C.H.L.; Software, A.S.W.,
C.H.L., D.S.D, B.J.A.; Formal Analysis, A.S.W., B.J.A., D.S.D., Investigation, A.S.W., C.H.L.,
A.Z., A.S., N.H., M.A.C, D.H.; Resources, N.M.H., B.N., D.L.B, R.J., J.E.B., N.S.G., Y.E.G.;
Writing - Original Draft, A.S.W., C.H.L., R.A.Y.; Writing - Review & Editing, all authors;
Visualization, A.S.W.; Supervision, R.A.Y., A.S.W.; Funding Acquisition, R.A.Y.
ACKNOWLEDGEMENTS
We thank the Whitehead Institute Genome Technology Core, FACS facility, and Johanna
Goldmann for their assistance. This work is supported by NIH grants HG002668/GM123511
(R.A.Y.), Ludwig Graduate Fellowship funds (A.S.W.), NSF GRFP (A.V.Z.), ACS New England
Division Postdoctoral Fellowship PF-16-146-01-DMC (D.S.D.), Margaret and Herman Sokol
Postdoctoral Award (D.H.), ACS Postdoctoral Fellowship PF-17-010-01-CDD (B.N.), Merck
Fellow of the Damon Runyon Cancer Research Foundation DRG-2196-14 (D.L.B), Hope Funds
for Cancer Research Grillo-Marxuach Family Fellowship (B.J.A.), Cancer Research Institute
Irvington Fellowship (Y.E.G.). The Whitehead Institute filed a patent application based on this
study. R.A.Y., J.E.B., and N.S.G. are the founders of Syros Pharmaceuticals. B.J.A. is a
shareholder in Syros. R.A.Y. is a founder of Marauder Therapeutics. J.E.B. is a Scientific
Founder of SHAPE Pharmaceuticals, Acetylon Pharmaceuticals, Tensha Therapeutics (now
Roche) and C4 Therapeutics. J.E.B. is now an executive and shareholder in Novartis AG.
N.S.G. is a founder and member of the Scientific Advisory Board of C4 and Petra Therapeutics.
Datasets generated can be found in GEO (GSE99521). Accession numbers of datasets used
are listed in Supplemental Table S4. Oligos used can be found in Table S5.
96
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104
Figure S1
A
; ee
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C
Enhmaner Promotern hnuWoater OierYY1 pea(23,316) (29,901) (15,010) (2853) GRO-eeq H3K27ac
I.C. 44 48 35 47 36 10 60
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I
6
YY1 (2 ---
ChIA-PET
4%A 23% 27% 3.5% 10%(75) (446) (660) (519) (185)
Ineujator Enhancer Enhwcer Promotr Promoter Instdatwr
15% 4% 7% 4% 23%
CTCF (459) (115) (218) (115) (697
ChIA-PET
47%
(1,441)
Enhancers Prmoers hulats, Other CTCF peal.(23,316) (29,901) (15,010) (40.940) GRO-seq H3K27ac
10 47 4 89 27 20
4 0. 0.2O 0 .6
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8 Sense
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FLAG-YY1 0 + HA-YY1
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Immunopreciplate and blot
anti-FLAG
ant-HA
anti-OCT4
Figure S1. YY1 -associated interactions connect enhancers and promoters, related to Figure 1(A) Heatmap displaying YY1, H3K27ac, and CTCF ChIP-seq signal and GRO-seq signal at promoters, enhancers,
and insulators in mouse embryonic stem cells (mES cells). ChIP-seq and GRO-seq signal is plotted as reads per
million per base pair in a 2kb region centered on each promoter, enhancer, and insulator.
105
Supplemental figures
or-enhanoer
(B) Expanded metagene analysis showing the occupancy of YY1 and CTCF at enhancers, promoters, and insulator
elements in mES cells. In addition, occupancy of YY1 was plotted at YY1 peaks that were not classified as an
enhancer, promoter, or insulator, and occupancy of CTCF was plotted at CTCF peaks that were not classified as an
enhancer, promoter, or insulator. ChIP-seq profiles are shown as mean reads per million per base pair for elements
of each class in a 2kb region centered on each region. The number of enhancers, promoters, and insulators
surveyed are noted in parentheses. To facilitate comparisons of the same factor between different regions the total
ChIP-seq signal in the region was quantified and is displayed in the top right corner of the plot for each metagene
analysis.
(C) Metagene analysis showing GRO-seq signal and H3K27ac ChIP-seq signal at YY1 and CTCF peaks in mES cells
that were not classified as part of an enhancer, promoter, or insulator. ChIP-seq profiles are shown as mean reads
per million per base pair for elements of each class in a 2kb region centered on each region. The number of YY1
and CTCF peaks surveyed are noted in parentheses. To facilitate comparisons of the same factor between different
regions the total ChIP-seq signal in the region was quantified and is displayed in the top right corner of the plot for
each metagene analysis.
(D) Expanded summary of the major classes of high-confidence interactions identified in YY1 and CTCF ChIA-PET
datasets presented in Figure 1 E. Interactions are classified based on the presence of enhancer, promoter, and
insulator elements at the anchors of each interaction. Interactions are displayed as arcs between these elements and
the thickness of the arcs approximately reflects the percentage of interactions of that class relative to the total number
of interactions that were classified.
(E) An example of extensive YY1-associated enhancer-promoter interactions. The high-confidence YY1 interactions
are depicted as red arcs, while high-confidence CTCF interactions are depicted as blue arcs. ChIP-seq binding
profiles for YY1, CTCF, and H3K27ac, and stranded GRO-seq signal are displayed as reads per million per base pair
at the KIf9 locus in mES cells. The KIf9 gene is indicated in the gene model and the interacting super-enhancers are
labeled under the H3K27ac ChIP-seq track.
(F) Metagene analysis showing the occupancy of YY1 at typical enhancer constituents and super-enhancer
constituents. ChIP-seq profiles are shown in mean reads per million per base pair for elements of each class in a
2kb region centered on each region. To facilitate comparisons of the same factor between different regions the total
ChIP-seq signal in the region was quantified and is displayed in the top right corner of the plot for each metagene
analysis. The number of elements surveyed is listed at the top of the plot. Both plots are floored at the minimum
amount of typical enhancer constituent signal.
(G) Heatmaps displaying for each high-confidence YY1 interaction the number of PETs that support the interaction,
for interactions that have at least one anchor overlapping a super-enhancer (right) and for interactions that have no
ends overlapping a super-enhancer (left). Each row represents an interaction and the color intensity of each row
represents the PET count for that interaction. (H) Box plot displaying the PET counts of high confidence YY1 ChIA-
PET interactions that are either not associated with super-enhancers or associated with super-enhancers.
(1) Model depicting co-immunoprecipitation assay to detect YY1 dimerization.
(J) Western blot results showing co-immunoprecipitation of FLAG-tagged YY1 and HA-tagged YY1 protein from
nuclear lysates prepared from transfected cells. Interaction between FLAG-tagged YY1 and HA-tagged YY1 protein is
observed, while interaction with OCT4 protein is not observed.
The sources of the datasets used in this figure are listed in Table S4.
106
Figure S2
A B C
cc
Enhancers Promoters Insulators Enhancers Promoters Insulators E Enhancers Promoters Insulators(18,127) (39,996) (10,900) (9162) (39 () 12,066) (14,985) (39,996) (12,678)
- 1.5 
C.-15 \83
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2 
*C +2 -2C -2 C+2  -  W .2  +2
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E F
Enhancers Promoters Insulators Enhancers Promoters Insulators Enhancers Promoters Insulators(15,286) (39,996) (9,586) (15,367) (39,996) (12,011) (16,131) (39,996) (11,908)
0.- 2 - +2 -2C2+ 0 .02 C+2 -2 + -2C2 + . 2- 0+ 2C+
22 2 40- 00 0 23, 11*[: 0. 3 7 2
00.0 2
2 C +2 -2 C +2 -2 C +2 2 C +2 2C+2 -2 C +2 (.0 2 C +2 -2 C +2 2C +2
G
- c
C
K
H
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Et
L
J
M
Enhancers Promoters Insulators
(11,322) (29,901) (13,644)
- 2.5 -22A2
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) -2 C +2 -2 C +2 -2 C +2
Enhancers Promoters Insulators
.- (14,540 (29,901) (12,821)
.- 2 C +2 -2 Cr+2 -2 C +2
CIL 181I0
00t0
= 0.02 - - 2C
Q 0 -2 C +2 -2 C +2 -2 C +2
Colorectal 2% T cell acute (84 Chronic (%
cancer lymphoblastic myaloid
lekei 
leukemia
YY1 /YY1 yy1
HiChlP HiChIP HiChlP
4% 22% 23% 32% 17% 5% 27% 25% 29% 14% 4% 18% 30% 32% 15%(343) (1,758) (1,833) (2,531) (1,366) (688) (4,012) (3,793) (4,378) (2,045) (710) (3,093) (5,204) (5,482) (2,595)
\ - ,0 JprO r,~ W~ S,0 ""0 'es"' n0 oo wo.
107
D
I
Figure S2. YY1 is associated with enhancers and promoters in cancer cells, related to Figure 2
Metagene analysis showing the occupancy of YY1 and CTCF at enhancers, gene promoters, and insulator elements
in six human cell types. ChIP-seq profiles are shown in mean reads per million per base pair for elements of each
class in a 2kb region centered on each region. For each cell type the numbers enhancers, promoters, and insulators
surveyed are listed below the listed elements. To facilitate comparisons of the same factor between different regions
the total ChIP-seq signal in the region was quantified and is displayed in the top right corner of the plot for each
metagene analysis.
(A) Metagene analysis in human lymphoblastoid cells.
(B) Metagene analysis in human colorectal cancer cells.
(C) Metagene analysis in human hepatocellular carcinoma cells.
(D) Metagene analysis in human embryonic stem cells.
(E) Metagene analysis in human T cell acute lymphoblastic leukemia (T-ALL) cells.
(F) Metagene analysis in human chronic myeloid leukemia (CML) cells.
(G) Heatmaps displaying the YY1 occupancy at enhancers and promoters in three murine cell types. YY1 ChIP-seq
signal is plotted as reads per million per base pair in a 2kb region centered on each an enhancer and promoter.
Each column represents a different cell type. Each row represents an enhancer that was identified in at least one of
the displayed cell types
(H) Heatmaps displaying the YY1 occupancy at active promoters in three murine cell types. YY1 ChIP-seq signal is
plotted as reads per million per base pair in a 2kb region centered on each promoter. Each column represents a
different cell type. Each row represents a promoter that was identified as an active promoter in at least one of the
displayed cell types
Metagene analysis showing the occupancy of YY1 and CTCF at enhancers, gene promoters, and insulator elements
in two murine cell types. ChIP-seq profiles are shown in mean reads per million per base pair for elements of each
class in a 2kb region centered on each region. For each cell type the numbers of enhancers, promoters, and
insulators surveyed are listed below the listed elements. To facilitate comparisons of the same factor between
different regions the total ChIP-seq signal in the region was quantified and is displayed in the top right corner of the
plot for each metagene analysis.
(1) Metagene analysis in murine neuronal precursor cells (NPC).
(J) Metagene analysis in murine B cells.
(K-M) Expanded summary of the major classes of high-confidence interactions identified in YY1 HiChIP datasets
presented in Figure 2C-E. Interactions are classified based on the presence of enhancer, promoter, and insulator
elements at the anchors of each interaction. Interactions are displayed as arcs between these elements and the
thickness of the arcs approximately reflects the percentage of interactions of that class relative to the total number of
interactions that were classified.
The sources of the datasets used in this figure are listed in Table S4.
108
Figure S3
A
Recombinant His6-YYI
250 kDA
150 kDAi
100 kDA -
75 kDA -
50 kDA -
37 kDA -
25 kDA
20 kDA
15 kDA
10 kDA
Coomassie
blue
anti-YY1
B
Probe + + +
Recombinant YY1 - + +
competitor - - +
Bound probe + A
Free probe +*
Western
blot
Figure S3. YY1 can enhance DNA interactions in vitro, related to Figure 3
(A) Purity of recombinant His6-YY1 protein was validated by gel electrophoresis of the purified material followed by
Coomassie blue staining and western blot analysis with anti-YY1 antibody.
(B) Activity of purified recombinant YY1 protein was validated by EMSA. Purified YY1 was incubated with biotinylated
DNA probe in the presence or absence of a non-biotinylated competitor DNA. Activity of the recombinant protein was
assessed by the ability to bind DNA and was determined by resolution on a native gel. Unbound "free" biotinylated
probe is found at the bottom of the gel, while probe bound by YY1 migrates slower and appears as a higher band.
Addition of competitor DNA abrogates this effect indicating that the activity is specific.
109
d10 kia.@O rb g
IV
- beumin
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4U WTitU
-M Oi WT
- VP
toTLO4
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M toT
It T
Figure S4. Loss of YYI binding causes loss of enhancer-promoter interactions, related to Figure 4
(A and B) CRISPR/Cas9-mediated deletion of YY1 binding motifs in the regulatory regions of two genes, Rafi (A)
and Etv4 (B). The top of each panel shows a high-confidence YY1-YY1 enhancer-promoter interaction and ChIP-seq
binding profiles for YY1 and H3K27ac displayed as reads per million per base pair. Position of the targeted YYI DNA
binding motif and the genotype of the wildtype and mutant lines are shown. The bottom of each panel shows
chromatin interaction profiles in wildtype and mutant cells anchored on the indicated viewpoint (VP) for three
biological replicates. 4C-seq signal is displayed as smoothed reads per million per base pair.
The sources of the datasets used in this figure are listed in Table S4.
110
Figure S4
A B
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to, KII
II
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Figure S5
A
Gata4 Foxcl SOXI
Gafta Hand2 Gbx2
Sox17 Sne2 Glop, 7fth3
D
Gajih
YY1* YY1
I0l(s*p) 6.5
YY1* YY1
) 4.6
Foxcl
YY1+ yYl-
3
__ _ Otx2__ _
YY1 YYi1
10%(_) 4.9
B
VVI+
GFAP TUBB3 DAPI
Nan
C Arranged by PCA
YY -
0 YY1+
YY1G
GFAP TUBB3 DAPI
00
YYi+ YY -
101h(eUP) 4.3
Sox7
YYI+ YY-
W 1
- -
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Gbx2
YY1 YYI
Iag,(eW p) 3.7
Esn*
Iog,(e1p) 3.3.-
Soxl7
YYI+ YYI
IO9,(,oP) 3-2.
SnaI2
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YY +1 Wi1
Figure S5. Depletion of YY1 impairs ES cell differentiation, related to Figure 5
(A) Model depicting differentiation of pluripotent ES cells into cells of the three germ layers. Pluripotency and
differentiation specific markers that were examined are indicated.
(B) Immunohistochemistry images of embryoid bodies formed from untreated cells (YY1+) and cells treated with
dTAG compound to degrade YYI (YYI-). GFAP and TUBB3, which are expressed in cells belonging to the ectoderm
lineage, are displayed in green and red, respectively. DNA stained using DAPI is displayed as blue.
(C) Principle component analysis (PCA) based representation of single-cell RNA-seq data for embryoid bodies
formed from untreated cells (YY1+) and cells treated with dTAG compound to degrade YYI (YYI-). Each dot
represents a single-cell and dots are arranged based on PCA. Cells from YYI+ embryoid bodies are shown in beige
and cells from YY1- embryoid bodies are shown in blue.
(D) Expression of pluripotency and differentiation specific genes (Figure S5A) as measured by single-cell RNA-seq of
embryoid bodies formed from untreated cells (YY1+) and cells treated with dTAG compound to degrade YYI (YYI-).
Each dot represents a single-cell and dots are shaded based on their normalized expression value.
The sources of the datasets used in this figure are listed in Table S4.
111
(24) B
HMc27ac
4% 23% 27% 36% 10%,
075) (446) OM6 (513) (166)
41%
(23)
6% 2M% 22% 33% 12%
Ow (3.06 MM)m 43 (3
C
f.1I DA, w4mosoW wuuain' 1061h 26
.11, I 
le4l4f
An IO
E Rt4"44(fanf)
OMMS Ow 35
9i
-m
Nd
4
Dew..n gg, ha
Figure S6. Depletion of YY1 disrupts enhancer-promoter looping, related to Figure 6
(A and B) Summaries of the major classes of high-confidence interactions identified by YY1 ChIA-PET (A) and
H3K27ac HiChIP (B). Interactions are classified based on the presence of enhancer, promoter, and insulator
elements at the anchors of each interaction. Interactions are displayed as arcs between these elements and the
thickness of the arcs approximately reflects the percentage of interactions of that class relative to the total number of
interactions that were classified.
(C) Percent of YY1 ChIP-seq peaks in mES cells that are associated with enhancer-promoter interactions, associated
with non-enhancer-promoter interactions, and not associated with a detected interaction for high confidence
interactions identified by YY1 ChIA-PET and H3K27ac HiChIP.
(D) Percent of genes that significantly increase in expression, significantly decrease in expression, or are not
differentially expressed in response to YY1 degradation for three classes of genes: all genes, genes involved in
enhancer-promoter interactions that do not have YY1 peaks at both ends, and genes involved in YY1-YYI enhancer-
promoter interactions.
(E) Expression of RafI and Etv4 genes before (0 hr) and after YY1 degradation (24 hr) as measure by RNA-seq.
Error bars represent the standard deviation of two biological replicates. P-values were determined using a Wald test.
The sources of the datasets used in this figure are listed in Table S4.
112
Figure 86
A
YY1
C3aA-PET
I I I
Figure S7
A This study B
Knock-in + dTAG compound Degradation Washout
FKBP tag (24 hours) (5 days)
anti-YY1
YY1 YY1-FKB
Cereblon E3 ligase
poly-ubiquitination anti-P-ACTIN 
- 1pm
C Nora et al. 2017
Knock-in + Auxin Degradation Washout
AID tag (48 hours) (2 days)
CTCF CTCF-AID egron
TIR1 E3 ligase
poly-ubiquitination
Figure S7. Rescue of enhancer-promoter interactions in cells, related to Figure 7
(A) Model depicting dTAG system used to rapidly degrade YY1 protein. The FKBP degron tag was knocked-in to both
alleles of the endogenous Yyl gene locus. Addition of dTAG compound results in recruitment of the cereblon E3
ligase to FKBP degron-tagged YY1 protein, resulting in rapid proteasome-mediated degradation. The effects of YY1
degradation were examined 24 hours after treatment with dTAG compound. Washout of the dTAG compound for 5
days allowed recovery of YY1 protein.
(B) Western blot validation of YY1 degradation after 24 hour treatment with dTAG compound and YY1 recovery after
5 day washout of the dTAG compound.
(C) Model depicting AID degradation system used to rapidly degrade CTCF protein in Nora et al. (2017). The AID tag
was knocked-in at the endogenous Ctcf gene locus. Addition of auxin results in the recruitment of the TIR1 E3 ligase
to AID-tagged CTCF protein, resulting in proteasome-mediated degradation. The effects of CTCF degradation were
examined 48 hours after treatment with dTAG compound. Washout of auxin for 2 days allowed recovery of CTCF
protein.
113
STAR METHODS
CONTACT FOR REAGENT AND RESOURCE SHARING
Further information and requests for resources and reagents should be directed to and will be
fulfilled by the Lead Contact, Richard A. Young (younq(@_wi.mit.edu).
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Cell Lines
V6.5 murine embryonic stem were a gift from R. Jaenisch of the Whitehead Institute. V6.5 are
male cells derived from a C57BL/6(F) x 129/sv(M) cross. Cells were negative for mycoplasma
(tested every three months).
Cell Culture Conditions
V6.5 murine embryonic stem (mES) cells were grown in serum + LIF on irradiated murine
embryonic fibroblasts (MEFs) or in 2i + LIF conditions. For all experiments except for the
washout experiment (Figure 7) cells were grown in serum + LIF on irradiated MEFs and then
passaged twice off of MEFs before harvesting. Genome editing was done in 2i + LIF conditions.
Cells were always grown on 0.2% gelatinized (Sigma, G1890) tissue culture plates. For the
washout experiment (Figure 7) cells were grown on 2i + LIF.
The media used for general culturing in serum + LIF conditions is as follows: DMEM-KO
(Invitrogen, 10829-018) supplemented with 15% fetal bovine serum (Hyclone, characterized
SH3007103), 1,000 U/mI LIF (ESGRO, ESG1106), 100 mM nonessential amino acids
(Invitrogen, 11140-050), 2 mM L-glutamine (Invitrogen, 25030-081), 100 U/mL penicillin, 100
mg/mL streptomycin (Invitrogen, 15140-122), and 8 ul/mL of 2-mercaptoethanol (Sigma,
M7522).
The media used for 2i + LIF media conditions is as follows: 967.5 mL DMEM/F12 (Gibco
11320), 5 mL N2 supplement (Gibco 17502048), 10 mL B27 supplement (Gibco 17504044), 0.5
mM L-glutamine (Gibco 25030), 0.5X non-essential amino acids (Gibco 11140), 100 U/mL
Penicillin-Streptomycin (Gibco 15140), 0.1 mM B-mercaptoethanol (Sigma), 1 uM PD0325901
(Stemgent 04-0006), 3 uM CHIR99021 (Stemgent 04-0004), and 1000 U/mL recombinant LIF
(ESGRO ESG1107).
Prior to differentiation mESCs were cultured in serum + LIF media as follows: DMEM
(Invitrogen, 11965-092) supplemented with 15% fetal bovine serum (Hyclone, characterized
SH3007103), 100 mM nonessential amino acids (Invitrogen, 11140-050), 2 mM L-glutamine
(Invitrogen, 25030-081), 100 U/mL penicillin, 100 mg/mL streptomycin (Invitrogen, 15140-122),
0.1mM beta-mercaptoethanol (Sigma Aldrich) and 2x106 units of leukemia inhibitory factor (LIF)
The media used for embryoid body formation (serum - LIF) is as follows: DMEM (Invitrogen,
11965-092) supplemented with 15% fetal bovine serum (Hyclone, characterized SH3007103),
100 mM nonessential amino acids (Invitrogen, 11140-050), 2 mM L-glutamine (Invitrogen,
25030-081), 100 U/mL penicillin, 100 mg/mL streptomycin (Invitrogen, 15140-122).
HCT-1 16 (male) cells were purchased from ATCC (CCL-247) and cultured in DMEM, high
glucose, pyruvate (Gibco 11995-073) with 10% fetal bovine serum (Hyclone, characterized
114
SH3007103), 100 U/mL Penicillin-Streptomycin (Gibco 15140), 2 mM L-glutamine (Invitrogen,
25030-081). Cells were negative for mycoplasma (tested every 3 months).
Jurkat (male) cells were purchased from ATCC (TIB-152) and cultured in RPMI-1640 (Gibco
61870-127) with 10% fetal bovine serum (Hyclone, characterized SH3007103), 100 U/mL
Penicillin-Streptomycin (Gibco 15140). Cells were negative for mycoplasma (tested every 3
months).
K562 cells (female) were purchased from ATCC (CCL-243) and cultured in in RPMI-1640
(Gibco 61870-127) with 10% fetal bovine serum (Hyclone, characterized SH3007103), 100
U/mL Penicillin-Streptomycin (Gibco 15140). Cells were negative for mycoplasma (tested every
3 months).
HEK293T cells were purchased from ATCC (ATCC CRL-3216) and cultured in DMEM, high
glucose, pyruvate (Gibco 11995-073) with 10% fetal bovine serum (Hyclone, characterized
SH3007103), 100 U/mL Penicillin-Streptomycin (Gibco 15140), 2 mM L-glutamine (Invitrogen,
25030-081). Cells were negative for mycoplasma (tested every 3 months).
METHOD DETAILS
Experimental Design
All experiments were replicated. For the specific number of replicates done see either the figure
legends or the specific section below. No aspect of the study was done blinded. Sample size
was not predetermined and no outliers were excluded.
Recombinant YY1 purification and characterization
YY1 purification
YY1 protein was purified using methods established by the Lee Lab (Jeon and Lee, 2011) and
previously described in (Sigova et al., 2015). A plasmid containing N-terminal His6-tagged
human YY1 coding sequence (a gift from Dr. Yang Shi) was transformed into BL21-CodonPlus
(DE3)-RIL cells (Stratagene, 230245). A fresh bacterial colony was inoculated into LB media
containing ampicillin and chloramphenicol and grown overnight at 370C. These bacteria were
diluted 1:10 in 500 mL pre-warmed LB with ampicillin and chloramphenicol and grown for 1.5
hours at 370C. After induction of YY1 expression with 1mM IPTG, cells were grown for another
5 hours, collected, and stored frozen at -80*C until ready to use.
Pellets from 500mL cells were resuspended in 15mL of Buffer A (6M GuHCI, 25mM Tris,
100mM NaCl, pH8.0) containing 10mM imidazole, 5mM
2-mercaptoethanol, cOmplete protease inhibitors (Roche, 11873580001) and sonicated (ten
cycles of 15 seconds on, 60 seconds off). The lysate was cleared by centrifugation at 12,000g
for 30 minutes at 40C and added to 1mL of Ni-NTA agarose (Invitrogen, R901-15) pre-
equilibrated with 1OX volumes of Buffer A. Tubes containing this agarose lysate slurry were
rotated at room temperature for 1 hour. The slurry was poured into a column, and the packed
agarose washed with 15 volumes of Buffer A containing 10mM imidazole. Protein was eluted
with 4 X 2 mL Buffer A containing 500mM imidazole.
Fractions were run out by SDS-PAGE gel electrophoresis and stained with Coomassie Brilliant
Blue (data not shown). Fractions containing protein of the correct size and high purity were
115
combined and diluted 1:1 with elution buffer. DTT was added to a final concentration of 100mM
and incubated at 600 C for 30 minutes. The protein was refolded by dialysis against 2 changes
of 1 Liter of 25mM Tris-HCI pH 8.5, 100mM NaCl, 0.1mM ZnC 2, and 10mM DTT at 40 C followed
by 1 change of the same dialysis buffer with 10% glycerol. Protein was stored in aliquots at -
800C.
YY1 characterization
The purity of the recombinant YY1 was assessed by SDS-PAGE gel electrophoresis followed by
Coomassie Brilliant Blue staining and Western blotting (Figure S3A). The activity of the
recombinant protein was assessed by EMSA (Figure S3B).
EMSA was performed using the LightShift Chemiluminescent EMSA Kit (Thermo Scientific
#20148) following the manufacturer's recommendations. Briefly, recombinant protein was
incubated with a biotinylated probe in the presence or absence of a cold competitor. Reactions
were separated using a native gel and transferred to a membrane. Labeled DNA was detected
using chemiluminescence.
To generate the biotin labeled probe, 30-nucleotide-long 5' biotinylated single stranded
oligonucleotides (IDT) were annealed in 10 mM Tris pH 7.5, 50 mM NaCl, and 1mM EDTA at a
50 uM concentration. The same protocol was used to generate the cold competitor. The probe
was serially diluted to a concentration of 10 fmol/pL and cold competitor to a concentration of 2
pmol/pL. 2 pL of diluted probe and cold competitor were used for each binding reaction for a
final amount of 20 fmol labeled probe and 4 pmol cold competitor (200 fold excess) in each
reaction.
Binding reactions were set-up in a 20 pL volume containing 1x Binding Buffer (10 mM Tris, 50
mM KCl, 1 mM DTT; pH 7.5), 2.5% Glycerol, 5 mM MgC 2 , 50 ng/pL Poly dl dC, 0.05% Np-40,
0.1 mM ZnC 2 , 10 mM Hepes, and 2 ug of recombinant YY1 protein. Binding reactions were pre-
incubated for 20 mins at room temperature with or without the cold competitor. Labeled probe
was then added to binding reactions and incubated for 80 minutes at room temperature. After
the 80 min incubation 5x Loading Buffer (Thermo Scientific #20148) was added to the reaction
and run on a 4-12% TBE gel using 0.5x TBE at 40 mA for 2.5 hrs at 40C. The TBE gel was pre-
run for 1 hr at 4'C. DNA was then electrophoretically transferred to a Biodyne B Nylon
Membrane (pre-soaked in cold 0.5x TBE for 10 mins) at 380 mA for 30 mins at 40C. The DNA
was then crosslinked to the membrane by placing the membrane on a Dark Reader
Transilluminator for 15 mins. The membrane was allowed to air dry at room temperature
overnight and chemiluminescence detected the following day.
Detection of biotin-labeled DNA was done as follows. The membrane was blocked for 20 mins
using Blocking Buffer (Thermo Scientific #20148). The membrane was then incubated in
conjugate/blocking buffer (Thermo Scientific #20148) for 15 mins. The membrane was then
washed four times with 1x Wash Buffer (Thermo Scientific #20148) for 5 mins. The membrane
was then incubated in Substrate Equilibration Buffer (Thermo Scientific #20148) for 5 mins and
then incubated in Substrate Working Solution (Thermo Scientific #20148) for 5 mins. The
membrane was then imaged using a CCD camera using a 120 second exposure. All of these
steps were performed at room temperature.
Genome Editing
116
The CRISPR/Cas9 system was used to genetically engineer ESC lines. Target-specific
oligonucleotides were cloned into a plasmid carrying a codon-optimized version of Cas9 with
GFP (gift from R. Jaenisch). The oligos used for the cloning are included in Table S3. The
sequences of the DNA targeted (the protospacer adjacent motif is underlined) are listed below:
Locus Targeted DNA
Raf1_promoter 5'-ACTCCCGCCATCCAAGATGGCGG-3'
Etv4_promoter 5'-GAGCTACTTGAAAACAAATGGAGG-3'
YY1_stopcodon 5'-GTCTTCTCTCTTCTTTTCACTGG-3'
For the motif deletions, five hundred thousand mES cells were transfected with 2.5 pg plasmid
and sorted 48 hours later for the presence of GFP. Thirty thousand GFP-positive sorted cells
were plated in a six-well plate in a 1:2 serial dilution (first well 15,000 cells, second well 7,500
cells, etc.). The cells were grown for approximately one week in 2i + LIF. Individual colonies
were picked using a stereoscope into a 96-well plate. Cells were expanded and genotyped by
PCR and Sanger sequencing. Clones with deletions spanning the motif were further expanded
and used for experiments.
For the generation of the endogenously tagged lines, five hundred thousand mES cells were
transfected with 2.5 ug Cas9 plasmid and 1.25 ug non-linearized repair plasmid 1
(pAW62.YY1.FKBP.knock-in.mCherry) and 1.25 ug non-linearized repair plasmid 2
(pAW63.YY1.FKBP.knock-in.BFP). Cells were sorted after 48 hours for the presence of GFP.
Cells were expanded for five days and then sorted again for double positive mCherry and BFP
cells. Thirty thousand mCherry+/BFP+ sorted cells were plated in a six-well plate in a 1:2 serial
dilution (first well 15,000 cells, second well 7,500 cells, etc). The cells were grown for
approximately one week in 2i medium and then individual colonies were picked using a
stereoscope into a 96-well plate. Cells were expanded and genotyped by PCR
(YY1_gPCR_3F/3R, Table S3). Clones with a homozygous knock-in tag were further expanded
and used for experiments.
Chromatin Immunoprecipitation (ChIP)
ChIP was performed as described in (Lee et al., 2006) with a few adaptations. mES cells were
depleted of MEFs by splitting twice onto newly gelatinized plates without MEFs. Approximately
50 million mES cells were crosslinked for 15 minutes at room temperature by the addition of
one-tenth volume of fresh 11% formaldehyde solution (11% formaldehyde, 50 mM HEPES pH
7.3, 100 mM NaCl, 1 mM EDTA pH 8.0, 0.5 mM EGTA pH 8.0) to the growth media followed by
5 min quenching with 125 mM glycine. Cells were rinsed twice with 1X PBS and harvested
using a silicon scraper and flash frozen in liquid nitrogen. Jurkat cells were crosslinked for 10
minutes in media at a concentration of 1 million cells /mL. Frozen crosslinked cells were stored
at -80*C. 100pl of Protein G Dynabeads (Life Technologies #10009D) were washed 3X for 5
minutes with 0.5% BSA (w/v) in PBS. Magnetic beads were bound with 10 pg of anti-YY1
antibody (Santa Cruz, sc-281X) overnight at 40C, and then washed 3X with 0.5% BSA (w/v) in
PBS.
Cells were prepared for ChIP as follows. All buffers contained freshly prepared 1 x cOmplete
protease inhibitors (Roche, 11873580001). Frozen crosslinked cells were thawed on ice and
then resuspended in lysis buffer I (50 mM HEPES-KOH, pH 7.5, 140 mM NaCl, 1 mM EDTA,
10% glycerol, 0.5% NP-40, 0.25% Triton X-100, 1 x protease inhibitors) and rotated for 10
minutes at 40C, then spun at 1350 rcf for 5 minutes at 40C. The pellet was resuspended in lysis
117
buffer 1 (10 mM Tris-HCI, pH 8.0, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 1 x protease
inhibitors) and rotated for 10 minutes at 4*C and spun at 1350 rcf for 5 minutes at 40C. The
pellet was resuspended in sonication buffer (20 mM Tris-HCI pH 8.0, 150 mM NaCl, 2 mM
EDTA pH 8.0, 0.1% SDS, and 1% Triton X-100, 1x protease inhibitors) and then sonicated on a
Misonix 3000 sonicator for 10 cycles at 30 seconds each on ice (18-21 W) with 60 seconds on
ice between cycles. Sonicated lysates were cleared once by centrifugation at 16,000 rcf for 10
minutes at 40C. 50 pL was reserved for input, and then the remainder was incubated overnight
at 40C with magnetic beads bound with antibody to enrich for DNA fragments bound by the
indicated factor.
Beads were washed twice with each of the following buffers: wash buffer A (50 mM HEPES-
KOH pH 7.5, 140 mM NaCl, 1 mM EDTA pH 8.0, 0.1% Na-Deoxycholate, 1% Triton X-100,
0.1% SDS), wash buffer B (50 mM HEPES-KOH pH 7.9, 500 mM NaCl, 1 mM EDTA pH 8.0,
0.1% Na-Deoxycholate, 1% Triton X-100, 0.1% SDS), wash buffer C (20 mM Tris-HCI pH8.0,
250 mM LiCl, 1 mM EDTA pH 8.0, 0.5% Na-Deoxycholate, 0.5% IGEPAL C-630 0.1% SDS),
wash buffer D (TE with 0.2% Triton X-1 00), and TE buffer. DNA was eluted off the beads by
incubation at 650C for 1 hour with intermittent vortexing in 200 pL elution buffer (50 mM Tris-HCI
pH 8.0, 10 mM EDTA, 1% SDS). Cross-links were reversed overnight at 650C. To purify eluted
DNA, 200 pL TE was added and then RNA was degraded by the addition of 2.5 pL of 33 mg/mL
RNase A (Sigma, R4642) and incubation at 370C for 2 hours. Protein was degraded by the
addition of 10 pL of 20 mg/mL proteinase K (Invitrogen, 25530049) and incubation at 550C for 2
hours. A phenol:chloroform:isoamyl alcohol extraction was performed followed by an ethanol
precipitation. The DNA was then resuspended in 50 pL TE and used for either qPCR or
sequencing.
For ChIP-qPCR experiments, qPCR was performed using Power SYBR Green mix (Life
Technologies #4367659) on either a QuantStudio 5 or a QuantStudio 6 System (Life
Technologies). Values displayed in the figures were normalized to the input, a negative control
region, and wild-type values according to the following formulas:
Input norm = 2 (Ctinput-CtChP)
Neg norm =Fl dne
WT norm = Neg normmut
Neg normwT
qPCRs were performed in technical triplicate, and ChIPs were performed in biological triplicate.
Values were comparable across replicates. The average WT norm values and standard
deviation are displayed (Figure 4A, 4B). The primers used are listed in Table S3.
For ChIP-seq experiments, purified ChIP DNA was used to prepare Illumina multiplexed
sequencing libraries. Libraries for Illumina sequencing were prepared following the Illumina
TruSeq DNA Sample Preparation v2 kit. Amplified libraries were size-selected using a 2% gel
cassette in the Pippin Prep system from Sage Science set to capture fragments between 200
and 400 bp. Libraries were quantified by qPCR using the KAPA Biosystems Illumina Library
Quantification kit according to kit protocols. Libraries were sequenced on the Illumina HiSeq
2500 for 40 bases in single read mode.
ChIA-PET
ChIA-PET was performed using a modified version (Tang et al., 2015) of a previously described
118
protocol (Fullwood et al., 2009). mES cells (- 500 million cells, grown to -80% confluency) were
crosslinked with 1% formaldehyde at room temperature for 15 min and then neutralized with
125mM glycine. Crosslinked cells were washed three times with ice-cold PBS, snap-frozen in
liquid nitrogen, and stored at -80oC before further processing. Nuclei were isolated as previously
described above, and chromatin was fragmented using a Misonix 3000 sonicator. Either CTCF
or YY1 antibodies were used to enrich protein-bound chromatin fragments exactly as described
in the ChIP-seq section. A portion of ChIP DNA was eluted from antibody-coated beads for
concentration quantification and for enrichment analysis using qPCR. For ChIA-PET library
construction ChIP DNA fragments were end-repaired using T4 DNA polymerase (NEB # M0203)
followed by A-tailing with Klenow (NEB M0212). Bridge linker oligos (Table S5) were annealed
to generate a double stranded bridge linker with T-overhangs. 800 ng of bridge linker was
added and the proximity ligation was performed overnight at 160C in 1.5 mL volume. Unligated
DNA was then digested with exonuclease and lambda nuclease (NEB M0262S, M0293S). DNA
was eluted off the beads by incubation at 65*C for 1 hour with intermittent vortexing in 200 pL
elution buffer (50 mM Tris-HCI pH 8.0, 10 mM EDTA, 1% SDS). Cross-links were reversed
overnight at 65'C. To purify eluted DNA, 200 pL TE was added and then RNA was degraded by
the addition of 2.5 pL of 33 mg/mL RNase A (Sigma, R4642) and incubation at 370C for 2 hours.
Protein was degraded by the addition of 10 pL of 20 mg/mL proteinase K (Invitrogen, 25530049)
and incubation at 550C for 2 hours.
A phenol:chloroform:isoamyl alcohol extraction was performed followed by an ethanol
precipitation. Precipitated DNA was resuspended in Nextera DNA resuspension buffer (Illumina
FC-121-1030). The DNA was then tagmented with the Nextera Tagmentation kit (Illumina FC-
121-1030). 5 pL of transposon was used per 50 ng of DNA. The tagmented library was purified
with a Zymo DNA Clean & Concentrator (Zymo D4003) and bound to streptavidin beads (Life
Technologies #1 1205D) to enrich for ligation junctions (containing the biotinylated bridge linker).
12 cycles of the polymerase chain reaction were performed to amplify the library using standard
Nextera primers (Illumina FC-121-1030). The amplified library was size-selected (350-500 bp)
and sequenced using paired-end sequencing on an Illumina Hi-Seq 2500 platform
HiChIP
HiChIP was performed as described in (Mumbach et al., 2016) with a few modifications. Ten
million cells cross-linked for 10 min at room temperature with 1% formaldehyde in growth media
and quenched in 0.125 M glycine. After washing twice with ice-cold PBS, the supernatant was
aspirated and the cell pellet was flash frozen in liquid nitrogen and stored at -80 0 C.
Cross-linked cell pellets were thawed on ice, resuspended in 800 pL of ice-cold Hi-C lysis buffer
(10 mM Tris-HCI pH 8.0, 10 mM NaCl, and 0.2% IGEPAL CA-630 with 1x cOmplete protease
inhibitor (Roche, 11697498001)), and incubated at 40C for 30 minutes with rotation. Nuclei were
pelleted by centrifugation at 2500 rcf for 5 min at 4*C and washed once with 500 pL of ice-cold
Hi-C lysis buffer. After removing supernatant, nuclei were resuspended in 100 pL of 0.5% SDS
and incubated at 620C for 10 minutes. SDS was quenched by adding 335 pL of 1.5% Triton X-
100 and incubating for 15 minutes at 370C. After the addition of 50 pL of 1OX NEB Buffer 2
(NEB, B7002) and 375 U of Mbol restriction enzyme (NEB, R0147), chromatin was digested at
370C for 2 hours with rotation. Following digestion, Mbol enzyme was heat inactivated by
incubating the nuclei at 620C for 20 min.
To fill in the restriction fragment overhangs and mark the DNA ends with biotin, 52 pL of fill-in
master mix, containing 37.5 pL of 0.4 mM biotin-dATP (Invitrogen, 19524016), 1.5 pL of 10 mM
dCTP (Invitrogen, 18253013), 1.5 pL of 10 mM dGTP (Invitrogen, 18254011), 1.5 pL of 10 mM
119
dTTP (Invitrogen, 18255018), and 10 pL of 5 U/pL DNA Polymerase I, Large (Kienow)
Fragment (NEB, M0210), was added and the tubes were incubated at 370C for 1 hour with
rotation. Proximity ligation was performed by addition of 947 pL of ligation master mix,
containing 150 pL of 1oX NEB T4 DNA ligase buffer (NEB, B0202), 125 pL of 10% Triton X-100,
7.5 pL of 20 mg/mL BSA (NEB, B9000), 10 pL of 400 U/pL T4 DNA ligase (NEB, M0202), and
655.5 pL of water, and incubation at room temperature for 4 hours with rotation.
After proximity ligation, nuclei were pelleted by centrifugation at 2500 rcf for 5 minutes and
resuspended in 1 mL of ChIP sonication buffer (50 mM HEPES-KOH pH 7.5, 140 mM NaCl, 1
mM EDTA pH 8.0, 1 mM EGTA pH 8.0, 1% Triton X-100, 0.1% sodium deoxycholate, and 0.1%
SDS with protease inhibitor). Nuclei were sonicated using a Covaris S220 for 6 minutes with the
following settings: fill level 8, duty cycle 5, peak incidence power 140, cycles per burst 200.
Sonicated chromatin was clarified by centrifugation at 16,100 rcf for 15 min at 40C and
supernatant was transferred to a tube. 60 pL of protein G magnetic beads were washed three
times with sonication buffer, resuspended in 50 pL of sonication buffer. Washed beads were
then added to the sonicated chromatin and incubated for 1 hour at 4*C with rotation. Beads
were then separated on a magnetic stand and the supernatant was transferred to a new tube.
7.5 pg of H3K27ac antibody (Abcam, ab4729) or 7.5 ug of YY1 antibody (Abcam, ab109237)
was added to the tube and the tube was incubated overnight at 40C with rotation. For YY1 six
reactions were carried out and pooled prior to tagmentation. The next day, 60 pL of protein G
magnetic beads were washed three time in 0.5% BSA in PBS and washed once with sonication
buffer before being resuspended in 100 pL of sonication buffer and added to each sample tube.
Samples were incubated for 2 hours at 40C with rotation. Beads were then separated on a
magnetic stand and washed three times with 1 mL of high salt sonication buffer (50 mM
HEPES-KOH pH 7.5, 500 mM NaCl, 1 mM EDTA pH 8.0, 1 mM EGTA pH 8.0, 1% Triton X-100,
0.1% sodium deoxycholate, 0.1% SDS) followed by three times with 1 mL of LiCI wash buffer
(20 mM Tris-HCI pH 8.0, 1 mM EDTA pH 8.0, 250 mM LiCl, 0.5% IGEPAL CA-630, 0.5%
sodium deoxycholate, 0.1% SDS) and once with 1 mL of TE with salt (10 mM Tris-HCI pH 8.0, 1
mM EDTA pH 8.0, 50 mM NaCl). Beads were then resuspended in 200 pL of elution buffer (50
mM Tris-HCI pH 8.0, 10 mM EDTA pH 8.0, 1% SDS) and incubated at 650C for 15 minutes to
elute. To purify eluted DNA, RNA was degraded by the addition of 2.5 pL of 33 mg/mL RNase A
(Sigma, R4642) and incubation at 370C for 2 hours. Protein was degraded by the addition of 10
pL of 20 mg/mL proteinase K (Invitrogen, 25530049) and incubation at 550C for 45 minutes.
Samples were then incubated at 65*C for 5 hours to reverse cross-links. DNA was then purified
using Zymo DNA Clean and Concentrate 5 columns (Zymo, D4013) according to manufacturer's
protocol and eluted in 14 pL water. The amount of eluted DNA was quantified by Qubit dsDNA
HS kit (Invitrogen, Q32854).
Tagmentation of ChIP DNA was performed using the Illumina Nextera DNA Library Prep Kit
(Illumina, FC-121-1030). First, 5 pL of streptavidin C1 magnetic beads (Invitrogen, 65001) was
washed with 1 mL of tween wash buffer (5 mM Tris-HCI pH 7.5, 0.5 mM EDTA pH 8.0, 1 M
NaCl, 0.05% Tween-20) and resuspended in 10 pL of 2X biotin binding buffer (10 mM Tris-HCI
pH 7.5, 1 mM EDTA pH 8.0, 2 M NaCl). 54.19 ng purified DNA was added in a total volume of
10 pL of water to the beads and incubated at room temperature for 15 minutes with agitation
every 5 minutes. After capture, beads were separated with a magnet and the supernatant was
discarded. Beads were then washed twice with 500 pL of tween wash buffer, incubating at 55"C
for 2 minutes with shaking for each wash. Beads were resuspended in 25 pL of Nextera
Tagment DNA buffer. To tagment the captured DNA, 3.5 pL of Nextera Tagment DNA Enzyme
1 was added with 21.5 pL of Nextera Resuspension Buffer and samples were incubated at 55*C
for 10 minutes with shaking. Beads were separated on a magnet and supernatant was
discarded. Beads were washed with 500 pL of 50 mM EDTA at 50*C for 30 minutes, then
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washed three times with 500 pL of tween wash buffer at 550C for 2 minutes each, and finally
washed once with 500 pL of 10 mM Tris-HCI pH 7.5 for 1 minute at room temperature. Beads
were separated on a magnet and supernatant was discarded.
To generate the sequencing library, PCR amplification of the tagmented DNA was performed
while the DNA is still bound to the beads. Beads were resuspended in 15 pL of Nextera PCR
Master Mix, 5 pL of Nextera PCR Primer Cocktail, 5 pL of Nextera Index Primer 1, 5 pL of
Nextera Index Primer 2, and 20 pL of water. DNA was amplified with 8 cycles of PCR. After
PCR, beads were separated on a magnet and the supernatant containing the PCR amplified
library was transferred to a new tube, purified using the Zymo DNA Clean and Concentrate-5
(Zymo D4003T) kit according to manufacturer's protocol, and eluted in 14 pL water. Purified
HiChIP libraries were size selected to 300-700 bp using a Sage Science Pippin Prep instrument
according to manufacturer's protocol and subject to paired-end sequencing on an Illumina
HiSeq 2500. Libraries were initially sequenced with 100x100 bp paired-end sequencing. A
second round of sequencing was done on the same libraries with 50x50 bp paired-end
sequencing.
4C-seq
A modified version of 4C-seq (van de Werken et al., 2012; Van De Werken et al., 2012) was
developed. The major change was the proximity ligation is performed in intact nuclei (in situ).
This change was incorporated because previous work has noted that in situ ligation dramatically
decreases the rate of chimeric ligations and background interactions (Nagano et al., 2015; Rao
et al., 2014b).
Approximately 5 million mES cells were trypsinized and then resuspended in 5 mL 10%
FBS/PBS. 5 mL of 4% formaldehyde in 10% FBS/PBS was added and cells were crosslinked for
10 minutes. Glycine was added to a final concentration of 0.125 M and cells were centrifuged at
300 rcf for 5 minutes. Cells were washed twice with PBS, transferred to a 1.5 mL Eppendorf
tube, snap frozen and stored at -80.
Pellets were gently resuspended in Hi-C lysis buffer (10 mM Tris-HCI pH 8, 10 mM NaCl, 0.2%
Igepal) with 1x cOmplete protease inhibitors (Roche 11697498001). Cells were incubated on ice
for 30 minutes then washed once with 500 pL of ice-cold Hi-C lysis buffer with no protease
inhibitors. Pellets were resuspended in 50 pL of 0.5% SDS and incubated at 620 C for 7 minutes.
145 pL of H20 and 25 pL of 10% Triton X-100 were added and tubes incubated at 370C for 15
minutes. 25 pL of the appropriate 1oX New England Biolabs restriction enzyme buffer and 200
units of enzyme were added and the chromatin was incubated at 370C degrees in a
thermomixer at 500 RPM for four hours, 200 more units of enzyme was added and the reaction
was incubated overnight at 370C degrees in a thermomixer at 500 RPM, then 200 more units
were added and the reaction was incubated another four hours at 370 C degrees in a
thermomixer at 500 RPM. Dpnil (NEB) was used as the primary cutter for both Rafi and Etv4.
Restriction enzyme was inactivated by heating to 620C for 20 minutes while shaking at 500 rpm.
Proximity ligation was performed in a total of 1200 pL with 2000 units of T4 DNA ligase (NEB
M020) for six hours at room temperature. After ligation samples were spun down for 5 minutes
at 2500 rcf and resuspended in 300 pL 10 mM Tris-HCI, 1% SDS and 0.5 mM NaCl with 1000
units of Proteinase K. Crosslinks were reversed by incubation overnight at 65*C.
Samples were then phenol-chloroform extracted and ethanol precipitated and the second
digestion was performed overnight in 450 pL with 50 units of restriction enzyme. Bfal (NEB
R0568S) was used for Etv4 and CviQl (NEB R0639S) was used for Rafi. Samples were
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phenol-chloroform extracted and ethanol precipitated and the second ligation was performed in
14 mL total with 6700 units of T4 DNA ligase (NEB M020) at 160C overnight. Samples were
ethanol precipitated, resuspended in 500 pL Qiagen EB buffer, and purified with a Qiagen PCR
purification kit.
PCR amplification was performed with 16 50 pL PCR reactions using Roche Expand Long
Template polymerase (Roche 11759060001). Reaction conditions are as follows: 11.2 pL
Roche Expand Long Template Polymerase, 80 pL of 10 X Roche Buffer 1, 16 pL of 10 mM
dNTPs (Promega PAU1515), 112 pL of 10 uM forward primer, 112 pL of 10 uM reverse primer
(Table S5), 200 ng template, and milli-q water until 800 pL total. Reactions were mixed and then
distributed into 16 50 pL reactions for amplification. Cycling conditions were a "Touchdown
PCR" based on reports that this decreases non-specific amplification of 4C libraries (Ghavi-
Helm et al., 2014). The conditions are: 2' 94*C, 10" 940C, 1' 63*C, 3' 68 0C, repeat steps 2-4
but decrease annealing temperature by one degree, until 530C is reached at which point the
reaction is cycled an additional 15 times at 530C, after 25 total cycles are performed the reaction
is held for 5' at 680C and then 40C. Libraries were cleaned-up using a Roche PCR purification
kit (Roche 11732676001) using 4 columns per library. Reactions were then further purified with
Ampure XP beads (Agencourt A63882) with a 1:1 ratio of bead solution to library following the
manufactures instructions. Samples were then quantified with Qubit and the KAPA Biosystems
Illumina Library Quantification kit according to kit protocols. Libraries were sequenced on the
Illumina HiSeq 2500 for 40 bases in single read mode.
RNA-isolation, qRT-PCR and sequencing
RNA was isolated using the RNeasy Plus Mini Kit (QIAGEN, 74136) according to
manufacturer's instructions.
For RT-qPCR assays, reverse transcription was performed using SuperScript Ill Reverse
Transcriptase (Invitrogen, 18080093) with oligo-dT primers (Promega, C1101) according to
manufacturers' instructions. Quantitative real-time PCR was performed on Applied Biosystems
7000, QuantStudio 5, and QuantStudio 6 instruments using TaqMan probes for Rafi (Applied
Biosystems, Mm00466513 ml) and Etv4 (Applied Biosystems, Mm00476696_ml) in
conjunction with TaqMan Universal PCR Master Mix (Applied Biosystems, 4304437) according
to manufacturer's instructions.
For RNA-seq experiments, stranded polyA selected libraries were prepared using the TruSeq
Stranded mRNA Library Prep Kit (Illumina, RS-122-2101) according to manufacturer's standard
protocol. Libraries were subject to 40 bp single end sequencing on an Illumina HiSeq 2500
instrument.
YY1 degradation
A clonal homozygous knock-in line expressing FKBP tagged YY1 was used for the degradation
experiments. Cells were grown two passages off MEFs and then treated with dTAG-47 at a
concentration of 500 nM for 24 hours.
dTAG-47 Washout Experiments
The homozygous knock-in line expressing FKBP tagged YY1 was cultured on 2i + LIF media.
Cells were treated with dTAG-47 at a concentration of 500 nM for 24 hours. After 24 hours of
drug treatment, cells were washed three times with PBS and passaged onto a new plate. Cells
122
were then fed daily and passaged onto a new plate every 48 hours until YY1 protein levels were
restored (5 days after drug withdrawal). Cells were then harvested for protein or RNA extraction
or cross-linked for ChIP or HiChIP.
dTAG-47 synthesis
0
2-(2,6-dioxopiperidin-3-yl)-5-fluoroisoindoline-1,3-dione
4-fluorophthalic anhydride (3.32 g, 20 mmol, 1 eq) and 3-aminopiperidine-2,6-dione
hydrochloride salt (3.620 g, 22 mmol, 1.1 eq) were dissolved in AcOH (50 mL) followed by
potassium acetate (6.08 g, 62 mmol, 3.1 eq). The mixture was fitted with an air condenser and
heated to 90 *C. After 16 hours, the mixture was diluted with 200 mL water and cooled over ice.
The slurry was then centrifuged (4000 rpm, 20 minutes, 4 0C) and decanted. The remaining
solid was then resuspended in water, centrifuged and decanted again. The solid was then
dissolved in MeOH and filtered through a silica plug (that had been pre-wetted with MeOH),
washed with 50% MeOH/DCM and concentrated under reduced pressure to yield the desired
product as a grey solid (2.1883 g, 7.92 mmol, 40%).
1H NMR (500 MHz, DMSO-d6 ) 6 11.13 (s, 1H), 8.01 (dd, J= 8.3, 4.5 Hz, 1H), 7.85 (dd, J= 7.4,
2.2 Hz, 1H), 7.72 (ddd, J = 9.4, 8.4, 2.3 Hz, 1H), 5.16 (dd, J = 12.9, 5.4 Hz, 1H), 2.89 (ddd, J =
17.2, 13.9, 5.5 Hz, 1 H), 2.65 - 2.51 (m, 2H), 2.07 (dtd, J = 12.9, 5.3, 2.2 Hz, 1 H).
LCMS 277.22 (M+H).
H 0 0
BocHN N N N
0
tert-butyl (8-((2-(2,6-dioxopiperidin-3-y)-1,3-dioxoisoindolin-5-yl)amino)octyl)carbamate
2-(2,6-dioxopiperidin-3-y)-5-fluoroisoindoline-1,3-dione (294 mg, 1.06 mmol, 1 eq) and tert-butyl
(8-aminooctyl)carbamate (286 mg, 1.17 nmol, 1.1 eq) were dissolved in NMP (5.3 mL, 0.2M).
DIPEA (369 pL, 2.12 mmol, 2 eq) was added and the mixture was heated to 90 *C. After 19
hours, the mixture was diluted with ethyl acetate and washed with water and three times with
brine. The organic layer was dried over sodium sulfate, filtered and concentrated under reduced
pressure. Purification by column chromatography (ISCO, 12 g column, 0-10% MeOH/DCM, 30
minute gradient) gave the desired product as a brown solid (0.28 g, 0.668 mmol, 63%).
'H NMR (500 MHz, Chloroform-d) 6 8.12 (s, 1H), 7.62 (d, J = 8.3 Hz, 1H), 7.02 (s, 1H), 6.81 (d,
J= 7.2 Hz, 1H), 4.93 (dd, J= 12.3, 5.3 Hz, 1H), 4.51 (s, 1H), 3.21 (t, J= 7.2 Hz, 2H), 3.09 (d, J
= 6.4 Hz, 2H), 2.90 (dd, J = 18.3, 15.3 Hz, 1H), 2.82 - 2.68 (m, 2H), 2.16 - 2.08 (m, 1H), 1.66
(p, J = 7.2 Hz, 2H), 1.37 (d, J = 62.3 Hz, 20H).
LCMS 501.41 (M+H).
H O O
TFA-H 2N N N O
0
5-((8-aminooctyl)amino)-2-(2,6-dioxopiperidin-3-yl)isoindoline-1,3-dione trifluoroacetate
tert-butyl (8-((2-(2,6-dioxopiperidin-3-yl)-1,3-dioxoisoindolin-5-yl)amino)octyl)carbamate (334.5
g, 0.668 mmol, 1 eq) was dissolved in TFA (6.7 mL) and heated to 50 C. After 1 hour, the
mixture was cooled to room temperature, diluted with DCM and concentrated under reduced
pressure. The crude material was triturated with diethyl ether and dried under vacuum to give a
dark yellow foam (253.1 mg, 0.492 mmol, 74%).
123
'H NMR (500 MHz, Methanol-d4) 6 7.56 (d, J = 8.4 Hz, 1H), 6.97 (d, J = 2.1 Hz, 1H), 6.83 (dd, J
= 8.4, 2.2 Hz, 1H), 5.04 (dd, J = 12.6, 5.5 Hz, 1H), 3.22 (t, J = 7.1 Hz, 2H), 2.94 - 2.88 (m, 2H),
2.85 - 2.68 (m, 3H), 2.09 (ddd, J = 10.4, 5.4, 3.0 Hz, 1H), 1.70 - 1.61 (m, 4H), 1.43 (d, J = 19.0
Hz, 8H).
LCMS 401.36 (M+H).
MeO
MeO
MeOJ$~OMe
W~e
(2S)-(1 R)-3-(3,4-dimethoxyphenyl)-1 -(2-(2-((8-((2-(2,6-dioxopiperidin-3-yl)-1,3-dioxoisoindolin-5-
yl)amino)octyl)amino)-2-oxoethoxy)phenyl)propyl 1-((S)-2-(3,4,5-
trimethoxyphenyl)butanoyl)piperidine-2-carboxylate (dTAG47)
5-((8-aminooctyl)amino)-2-(2,6-dioxopiperidin-3-yl)isoindoline-1,3-dione trifluoroacetate salt
(10.3 mg, 0.020 mmol, 1 eq) was added to 2-(2-((R)-3-(3,4-dimethoxyphenyl)-1-(((S)-1-((S)-2-
(3,4,5-trimethoxyphenyl)butanoyl)piperidine-2-carbonyl)oxy)propyl)phenoxy)acetic acid (13.9
mg, 0.020 mmol, 1 eq) as a 0.1 M solution in DMF (200 microliters) at room temperature. DIPEA
(10.5 microliters, 0.060 mmol, 3 eq) and HATU (7.6 mg, 0.020 mmol, 1 eq) were then added.
After 29.5 hours, the mixture was diluted with EtOAc, and washed with 10% citric acid (aq),
brine, saturated sodium bicarbonate, water and brine. The organic layer was dried over sodium
sulfate, filtered and condensed. Purification by column chromatography (ISCO, 4 g silica
column, 0-10% MeOH/DCM, 25 minute gradient) gave the desired product as a yellow solid
(14.1 mg, 0.0131 mmol, 65%).
1H NMR (500 MHz, Methanol-d4) 6 7.55 (d, J = 8.4 Hz, 1 H), 7.26 - 7.20 (m, 1 H), 6.99 - 6.93 (m,
1 H), 6.89 (t, J = 7.7 Hz, 2H), 6.82 (dd, J = 8.4, 2.3 Hz, 2H), 6.77 (d, J = 7.5 Hz, 1 H), 6.74 (d, J =
1.9 Hz, 1H), 6.63 (d, J = 9.6 Hz, 2H), 6.12 (dd, J = 8.1, 6.0 Hz, 1H), 5.40 (d, J = 4.3 Hz, 1H),
5.03 (dd, J = 13.1, 5.5 Hz, 1H), 4.57 (d, J = 14.9 Hz, 1H), 4.46 - 4.39 (m, 1H), 4.11 (d, J = 13.6
Hz, 1H), 3.86 (t, J = 7.3 Hz, 1H), 3.80 - 3.76 (m, 7H), 3.71 - 3.65 (m, 8H), 3.14 (ddt, J = 17.2,
13.3, 7.1 Hz, 4H), 2.90 - 2.80 (m, 1H), 2.77 - 2.40 (m, 6H), 2.24 (d, J = 13.8 Hz, 1H), 2.12 -
1.97 (m, 3H), 1.92 (dq, J = 14.0, 7.8 Hz, 1H), 1.67 (ddt, J = 54.1, 14.7, 7.1 Hz, 5H), 1.50 (dd, J
46.1, 14.1 Hz, 3H), 1.38 (dt, J = 14.5, 7.1 Hz, 4H), 1.28 - 1.17 (m, 6H), 0.87 (t, J = 7.3 Hz, 3H).
13C NMR (126 MHz, MeOD) 6 174.78, 174.69,172.53, 171.71, 170.50, 169.66, 169.31, 156.22,
155.41, 154.62, 150.36, 148.83, 138.05, 136.90, 136.00, 134.93, 130.54, 128.40, 126.21,
123.14, 121.82, 117.94, 116.62, 113.58, 113.05, 112.73, 106.59, 70.69, 68.05, 61.06, 56.59,
56.51, 56.45, 53.42, 50.99, 50.31, 45.01, 44.09, 40.07, 37.44, 32.22, 32.17, 30.38, 30.32, 30.18,
29.84, 29.32, 28.05, 27.80, 27.58, 26.38, 23.87, 21.95, 12.57.
LCMS: 1077.35 (M+H)
In vitro DNA circularization assay
First, two plasmids (pAW49, pAW79) were generated. pAW49 contains YY1 binding sites
separated by -3.5 kb of intervening DNA. pAW79 is identical except it contains filler DNA
instead of the YY1 motifs. The intervening DNA was chosen based on looking at YY1 ChIP-seq
and motif distribution in mES cells to identify regions that lacked YY1 occupancy and YY1
binding motifs. The YY1 binding motifs were chosen based on successful EMSAs (Sigova et al.,
2015). Approximately 200 bp of sequence was added between the binding motifs and the
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termini in order to provide flexibility for the termini to ligate. The plasmid was built using Gibson
assembly.
Next, a PCR was run using plasmid as a template to generate a linear piece of DNA (Table S5).
This PCR product was PCR purified (Qiagen 28104) and then digested with BamHI (NEB
R3136) and PCR purified. The BamHl digested template was used in the ligation assay.
The ligation assay was carried out as follows. Reactions were prepared on ice in 66 pL with the
following components:
BSA control: 0.25 nM DNA, 1x T4 DNA ligase buffer (NEB B0202S), H 20, 0.12 pg/pL of BSA
YY1: 0.25 nM DNA, 1x T4 DNA ligase buffer (NEB B0202S), H 20, 0.12 pg/pL of YY1
YY1 + competitor: 0.25 nM DNA, 1x T4 DNA ligase buffer (NEB B0202S), H20, 0.12 pg/pL of
YY1, 100 nM competitor DNA (Table S5)
Assuming an extinction coefficient for YY1 of 19940 M 1 cm 1 and 75% purity, that gives an
approximate YY1 molar concentration of - 3 uM.
Reactions were incubated at 200C for 20 minutes to allow binding of YY1 to the DNA. For each
timepoint 6 pL of the reaction was withdrawn and quenched in a total volume of 9 pL with a final
concentration of 30 mM EDTA, 1x NEB loading dye (NEB, B7024S), 1 ug/pL of proteinase K,
and heated at 650C for 5 minutes. Timepoint 0 was taken and then 600 units of T4 DNA ligase
(NEB M0202) was added and the reaction was carried out at 200C. Indicated timepoints were
taken and then samples were run on a 4-20% TBE gradient gel for three hours at 120 V. The
gel was stained with SYBR Gold (Life Technologies S1 1494) and imaged with a CCD camera.
Quantification was done using Image Lab version 5.2.1 (Bio-Rad Laboratories). First, band
density of the starting product and ligation product were measured. Then the percent
circularized was calculated: (ligation product)/(ligation product + starting band)*100. In Figure 3
to facilitate visualization overexposed gels are shown. For the quantification exposures were
used that did not have any overexposed pixels.
Co-immunoprecipitation
V6.5 mESCs were transfected with pcDNA3_FLAGYY1 and pcDNA3_FLAGHA using
Lipofectamine 3000 (Life Technologies #L3000001) according to the manufacturer's
instructions. Briefly, cells were split and 8 million cells were plated onto a gelatinized 15 cm
plate. 7.5 pg of each plasmid was mixed with 30 pL P3000 reagent and 75 pL Lipofectamine
3000 reagent (Life Technologies #L3000001) in 1250 pL of DMEM (Life technologies #11995-
073). After -12-16 hours media was changed.
Cells were harvested 48 hours after transfection by washing twice with ice-cold PBS and
collected by scraping in ice-cold PBS. Harvested cells were centrifuged at 1,000 rcf for 3
minutes to pellet cells. Supernatant was discarded and cell pellets were flash frozen and stored
at -800C until ready to prepare nuclear extract. For each 15 cm plate of cells, frozen cell pellets
were resuspended in 5 mL of ice-cold hypotonic lysis buffer (20 mM HEPES-KOH pH 7.5, 20%
glycerol, 10 mM NaCl, 0.1% Triton X-100, 1.5 mM MgC 2, 0.5 mM DTT and protease inhibitor
(Roche, 11697498001)) and incubated on ice for 10 minutes to extract nuclei. Nuclei were
pelleted by centrifugation at 14,000 rcf for 10 minutes at 40C. Supernatant was discarded and
nuclei were resuspended in 0.5 mL of ice-cold nuclear extraction buffer (20 mM HEPES-KOH
pH 7.5, 20% glycerol, 250 mM NaCl, 0.1% Triton X-100, 1.5 mM MgCl 2 and protease inhibitor)
and incubated for 1 hour at 40C with rotation. Lysates were clarified by centrifugation at 14,000
125
rcf for 10 minutes at 40C. Nuclear extract, supernatant, was transferred to a new tube and
diluted with 1 mL of ice-cold dilution buffer (20 mM HEPES-KOH pH 7.5, 10% glycerol, 100 mM
NaCl, 0.1% Triton X-100, 1.5 mM MgC 2 , 0.2 mM EDTA, 0.5 mM DTT and protease inhibitor).
Protein concentration of extracts was quantified by BCA assay (Thermo Scientific, 23225) and
protein concentration was adjusted to 400 pg/mL by addition of appropriate volume of 1:2
nuclear extraction buffer:dilution buffer. For RNase A-treated nuclear extract experiments, 250
pL of nuclear extract (100 pg) was treated by addition of 7.5 pL of 33 mg/mL RNase A (Sigma,
R4642) or 18.75 pL of 20 U/pL SUPERase In RNase Inhibitor (Invitrogen, AM2696) followed by
incubation at 37*C for 10 minutes. For all experiments, an aliquot of extract was saved and
stored at -800C for use as an input sample after immunoprecipitation.
To prepare beads for immunoprecipitation of FLAG-tagged and HA-tagged YY1 from nuclear
extract, 50 pL of protein G magnetic beads per immunoprecipitation was washed three times
with 1 mL of blocking buffer (0.5% BSA in PBS), rotating for 5 minutes at 40C for each wash.
After separation on a magnet, beads were resuspended in 250 pL of blocking buffer. After
addition of 5 pg of anti-FLAG (Sigma, F7425)), anti-HA (Abcam, ab9l 10), or normal IgG
(Millipore, 12-370) antibody, beads were allowed to incubate for at least 1 hour at 40C with
rotation to bind antibody. After incubation, beads were washed three times with 1 mL of blocking
buffer, rotating for 5 minutes at 40C for each wash.
Washed beads were separated on a magnet and the supernatant was discarded before
resuspending in 250 pL of nuclear extract (100 pg). Beads were allowed to incubate with extract
overnight at 40C with rotation. The following morning, beads were washed five times with 1 mL
of ice-cold wash buffer, rotating for 5 minutes at 40C for each wash. Washed beads were
resuspended in 100 pL of 1X XT sample buffer (Biorad, 1610791) with 100 mM DTT and
incubated at 950C for 10 min. Beads were separated on a magnet and supernatant containing
immunoprecipitated material was transferred to a new tube.
To assay immunoprecipitation results by western blot, 10 pL of each samples was run on a 4-
20% Bis-Tris gel (Bio-rad, 3450124) using XT MOPS running buffer (Bio-rad, 1610788) at 80 V
for 20 minutes, followed by 150 V until dye front reached the end of the gel. Protein was then
wet transferred to a 0.45 pm PVDF membrane (Millipore, IPVH00010) in ice-cold transfer buffer
(25 mM Tris, 192 mM glycine, 20% methanol) at 250 mA for 2 hours at 4'C. After transfer the
membrane was blocked with 5% non-fat milk in TBS for 1 hour at room temperature, shaking.
Membrane was then incubated with 1:50,000 anti-FLAG-HRP (Sigma, A8592), 1:25:000 anti-
HA-HRP (Cell Signaling, 2999), or anti-OCT3/4 (C-10, Santa Cruz sc-5279) 1:2000 antibody
diluted in 5% non-fat milk in TBST and incubated overnight at 4*C, with shaking. In the morning,
the membrane was washed three times with TBST for 5 min at room temperature shaking for
each wash. Membranes were developed with ECL substrate (Thermo Scientific, 34080) and
imaged using a CCD camera or exposed using film.
Embryoid Body Formation
Prior to differentiation, YY1-FKBP tagged knock-in mESCs were cultured in serum + LIF on
irradiated MEFs. Starting 48 hours prior to the differentiation and continuing throughout the
entire experiment the YY1 condition were exposed to 500 nM dTAG-47. 4,000 cells (either YY1
or YY1') were then plated into each well of a 96-well plate (Nunclon Sphera, ThermoFisher) in
Embryoid Body formation media (serum - LIF). Three plates were generated for each condition.
The EBs were cultured in 96-well plates for 4 days and then pooled and cultured in ultra-low
attachment culture plates (Costar, Corning). After three days, cells were harvested for single-
cell RNA-seq (day 7 of differentiation). Cells were harvested for single-cell RNA-seq by
126
dissociation with Accutase for 30 minutes at 370C. The cells were then resuspended in PBS
with 0.04% BSA and then prepared for sequencing (see section on single-cell RNA-seq).
Immunohistochemistry was performed after four days (day 8 of differentiation).
Immunohistochemistry
Cells were fixed in 4% paraformaldehyde in PBS and embedded in paraffin. Cells were
sectioned and stained according to standard protocols using TUJI (Biolegend 801201, 1:1000),
GFAP (Dako Z0344, 1:200), and Gata-4 (Abcam ab84593 1:100) primary antibodies and
appropriate Alexa Fluor dye conjugated secondary antibodies (1:1000, ThermoFisher) and
DAPI. Slides were mounted with Fluoro-mount G (Electron Microscopy Science) and imaged
using a Zeiss LSM 710 laser scanning confocal microscope. In all images scale bars are 50 pm.
Single-cell RNA-seq library preparation
Single-cell RNA-seq libraries were prepared using the Chromium Controller (1OX Genomics).
Briefly, single cells in 0.04% BSA in PBS were separated into droplets and then reverse
transcription and library construction was performed according to the 1OX Chromium Single Cell
3' Reagent Kit User Guide and sequenced on an Illumina Hi-seq 2500.
dCas9-YYI tethering
First two lentiviral constructs were generated by modifying lenti dCAS-VP64_Blast (lenti dCAS-
VP64_Blast was a gift from Feng Zhang (Addgene plasmid # 61425),(Konermann et al., 2014)).
The VP64 was removed to generate dCas9 alone (pAW91) or the human YY1 cDNA was
inserted to the C-terminus to generate dCas9-YY1 (pAW90).
For virus production, HEK293T cells grown to 50-75% confluency on a 15 cm dish and then
transfected with 15 ug of pAW90 or pAW91, 11.25 pg psPAX (Addgene 12260), and 3.75 pg
pMD2.G (Addgene 12259). psPAX and pMD2.G were kind gifts of Didier Trono. After 12 hours,
media was replaced. Viral supernatant was collected 24 hours after media replacement (36 hrs
post transfection) and fresh media was added. Viral supernatant was collected again 48 hours
after the media replacement (60 hours post transfection). Viral supernatant was cleared of cells
by either centrifugation at 500 x g for 10 minutes. The virus was concentrated with Lenti-X
concentrator (Clontech 631231) per manufacturers' instructions. Concentrated virus was
resuspended in mES media (serum + LIF) and added to 5 million cells in the presence of
polybrene (Millipore TR-1003) at 8 ug/mL. After 24 hours, viral media was removed and fresh
media containing Blasticidin (Invitrogen ant-bl-1) at 10 ug/mL. Cells were selected until all cells
on non-transduced plates died.
Two additional lentiviral constructs were generated (pAW12.lentiguide-GFP, pAW13.lentiguide-
mCherry) by modifying lentiGuide-puro (lentiGuide-Puro was a gift from Feng Zhang (Addgene
plasmid # 52963) (Sanjana et al., 2014)) to remove the puromycin and replace it either GFP or
mCherry. The tethering guide RNAs (Table S5, etv4_p_sgTl F&R, etv4_p_sgT2_F&R) were
then cloned into pAW12 and pAW13. Virus was generated as described above and mES cells
were transduced. Double positive cells were identified and collected by flow cytometry and
expanded. These expanded cell lines were analyzed by 4C-seq, ChIP-qPCR (anti-Cas9, CST
14697), and RT-qPCR exactly as described elsewhere in the methods.
QUANTIFICATION AND STATISTICAL ANALYSIS
127
ChIP-MS data analysis
Previously published ChIP-ms data was downloaded (Ji et al., 2015). For each mark the 1092
ratio of the immunoprecipitation over the input and over IgG was calculated. Then a high
confidence set of proteins was identified by filtering out all proteins that had a log 2 fold change
less than or equal to one in either the input or IgG control. Then we filtered for transcription
factors using the annotation provided in the original table to end up with the 26 candidates
displayed in Figure 1.
Tissue specific expression analysis
In order to identify candidate structuring factors that are broadly expressed across many
tissues, tissue specific expression data from RNA-seq was downloaded from the Genotype-
Tissue Expression (GTEx) Project (release V6p). Genes were considered to be expressed in a
particular tissues if the median reads per million per kilobase for that tissue was greater than 5
(RPKM > 5). Broadly expressed genes were identified as genes that were expressed in greater
than 90% of the 53 tissues surveyed by GTEx.
Definition of regulatory regions
Throughout the manuscript multiple analyses rely on overlaps with different regulatory regions,
namely enhancers, promoters, and insulators. Here we explain how these regulatory regions
were defined.
Promoters
Promoters were defined as +/- 2 kilobases from the transcription start site.
Active Promoters
Active promoters were defined as +/- 2 kilobases from the transcription start site that overlapped
with a H3K27ac peak.
Enhancers
Enhancers were defined as H3K27ac peaks that did not overlap with a promoter.
Insulators
Insulators were defined by downloading the called insulated neighborhoods from (Hnisz et al.,
2016a) (available at: http://younglab.wi.mit.edu/insulatedneighborhoods.htm). Each row
represents an insulated neighborhood (defined as a SMC1 cohesin ChIA-PET interaction with
both anchors overlapping a CTCF peak). The file contains six columns, columns 1-3 contain the
coordinates for the left interaction anchors of the insulated neighborhoods, and columns 4-6
contain the coordinates for the right interaction anchors of the insulated neighborhoods.
Columns 1-3 and 4-6 were concatenated and then filtered to identify the unique anchors. The
unique loop anchors regions correspond to SMC1 ChIA-PET peaks. Insulators elements were
identified as the subset of CTCF ChIP-seq peaks that overlapped the unique anchors.
Super-enhancers
Oct4/Sox2/Nanog/Medl super-enhancers and constituents were downloaded from (Whyte et
al., 2013)
Typical-enhancer constituents
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Oct4/Sox2/Nanog/Medl typical-enhancer constituents were downloaded from (Whyte et al.,
2013)
ChIP-seq data analysis
Alignment
Reads from ChIP-seq experiments were aligned to the mm9 revision of the mouse reference
genome using only annotated chromosomes 1-19, chrX, chrY, and chrM or to the hg19 revision
of the human genome using only annotated chromosomes 1-22, chrX, chrY, and chrM.
Alignment was performed using bowtie (Langmead et al., 2009) with parameters -best -k 1 -m
1 -sam and -l set to read length.
Read pileup for display
Wiggle files representing counts of ChIP-Seq reads across the reference genome were created
using MACS (Zhang et al., 2008) with parameters -w -S -space=50 -nomodel -shiftsize=200.
Resulting wiggle files were normalized for sequencing depth by dividing the read counts in each
bin by the millions of mapped reads in each sample and were visualized in the UCSC genome
browser (Kent et al., 2002).
Gene list and promoter list
For mouse data analysis 36,796 RefSeq transcripts were downloaded in the GTF format from
the UCSC genome browser on February 1, 2017. For human data analysis, 39,967 RefSeq
transcripts were downloaded on December 7th, 2016 in the GTF format from the UCSC genome
browser on February 1, 2017. For each transcript, a promoter was created that is a 4,000 bp
window centered on the transcription start site.
Peak calling
Regions with an exceptionally high coverage of ChIP-Seq reads (i.e. peaks) were identified
using MACS with parameters -keep-dup=auto -ple-9 and with corresponding input control.
Heatmaps and Metagenes
Profiles of ChIP-seq and GRO-seq signal at individual regions of interest were created by
quantifying the signal in reads per million per base pair (rpm/bp) in bins that equally divide each
region of interest using bamToGFF (https://github.com/BradnerLab/pipeline) with parameters -
m 200 -r -d. Reads used for quantification were removed of presumed PCR duplicate reads
using samtools vO.1.19-44428cd rmdup (Li et al., 2009). Promoters with the same gene id,
chromosome, start, and end coordinates were collapsed into one instance.
Heatmaps of ChIP-seq profiles were used to display ChIP-seq signal at enhancer and active
promoters. Each row of a heatmap represents an individual region of interest with the ChIP-seq
signal profile at that region displayed in rpm/bp in a 2kb region centered on the region of
interest. For each heatmap the number of regions of interest are displayed in parentheses in the
figure panel. For murine ES cell heatmaps, ChIP-seq signal was quantified in 200 bins per
region of interest. For human tissues and non-ES cell murine tissues, heatmaps were generated
by quantifying ChIP-seq signal in 50 bins per region of interest.
Metagene plots were used to display the average ChIP-seq signal across related regions of
interest. Metagene plots were generated for enhancer, promoter, and insulator elements,
separately. The average profile (metagene) was calculated by calculating the mean ChIP-seq or
GRO-seq signal profiles across the related regions of interest. For each metagene plot, the
average profile is displayed in rpm/bp in a 2kb region centered on the regions of interest. The
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number of enhancers, promoters, and insulators surveyed are noted in parentheses. To
facilitate comparisons of the ChIP-seq signal from a single factor between different sets of
regions, the total ChIP-seq signal for each metagene analysis was quantified and is displayed in
the top right corner of each metagene plot. We note that different antibodies have different
immunoprecipitation efficiencies resulting in different signal intensities. Therefore, we believe
that quantitative comparisons should be made across different sites in the same ChIP rather
than across different ChlPs at the same site.
RNA-seq data analysis
RNA-seq Analysis
RNA-seq data was aligned and quantified using kallisto (version 0.43.0) (Bray et al., 2016) with
the following parameters: -b 100 --single -1 180 -s 20 using the mm9 RefSeq transcriptome
(downloaded on February 1, 2017). The output files represent the estimated transcript counts.
Differential gene expression analysis was performed using deseq2 (version 1.14.1) (Love et al.,
2014). Analysis was performed on the gene level. To calculate the gene-level read counts, the
estimated transcript counts were summed across all the isoforms of the gene. This was then
input into deseq2 and adjusted p values were calculated using the default settings. Log 2 fold
changes and adjusted p values are included in Table S2. An FDR value of 0.05 was used as a
cut off for significant differential expression. For Figure 5C, the values on the y axis are the
deseq2-calculated log 2 fold change values. The values on the x axis are the deseq2 calculated
baseMean values.
For Figure 5D, the absolute value of the deseq2 calculated log 2 fold change is plotted on the left
side. On the right side the YY1 density at the promoter is plotted. Because the analysis is done
on the gene level, the YY1 promoter signal for genes with multiple isoforms was averaged.
For the GO analysis the list of differentially expressed genes (Table S3) was input into the
PANTHER GO analysis web tool (http://pantherdb.org/, Version 11.1) (Mi et al., 2013, 2017)
and a statistical overrepresentation test was performed using the default settings.
RNA-seq Display
For displaying RNA-seq tracks, the RNA-seq data was mapped with Tophat to the mm9 RefSeq
transcriptome (downloaded on February 1, 2017) using the following parameters: -n 10 tophat -
p 10 --no-novel-juncs -o. Wiggle files representing counts of RNA-Seq reads across the
reference genome were created using MACS (Zhang et al., 2008) with parameters -w -S -
space=50 -nomodel -shiftsize=200. Resulting wiggle files were normalized for sequencing
depth by dividing the read counts in each bin by the millions of mapped reads in each sample
and were visualized in the UCSC genome browser (Kent et al., 2002).
Single-cell RNA-seq Analysis
Sequencing data was demultiplexed using the 1OX Genomics Cell Ranger software (version
2.0.0) and aligned to the mm10 transcriptome. Unique molecular identifiers were collapsed into
a gene-barcode matrix representing the counts of molecules per cell as determined and filtered
by Cell Ranger using default parameters. Normalized expression values were generated using
Cell Ranger using the default parameters. For Figure 5H the number of cells with a >1
normalized expression value for the specified transcript were counted. For Figure S5C the cells
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were arranged by principal component analysis using the default Cell Ranger parameters. In
Figure S5D cells were split into the two panels based on what condition they came from. The
arrangement is the same as in Figure S5C. Individual cells are then colored by normalized
expression level.
4C-seq data analysis
4C-seq Analysis
The 4C-seq samples were first processed by removing their associated read primer sequences
(Table S5) from the 5' end of each FASTQ read. To improve mapping efficiency of the trimmed
reads by making the read longer, the restriction enzyme digest site was kept on the trimmed
read. After trimming the reads, the reads were mapped using bowtie with options -k 1 -m 1
against the mm9 genome assembly. All unmapped or repetitively mapping reads were
discarded from further analysis. The mm9 genome was then "digested" in silico according to the
restriction enzyme pair used for that sample to identify all the fragments that could be generated
by a 4C experiment given a restriction enzyme pair. All mapped reads were assigned to their
corresponding fragment based on where they mapped to the genome. The digestion of a
sample in a 4C experiment creates a series of "blind" and "non-blind" fragments as described by
the Tanay and De Laat labs (van de Werken et al., 2012). In brief, "blind" fragments lack a
secondary restriction enzyme site whereas "non-blind" fragments contain a secondary restriction
enzyme site. Because of this we expect to only observe reads derived from non-blind
fragments. We therefore only used reads derived from non-blind fragments.
Experiments were conducted in biological triplicate and the mutant and WT samples were
quantile normalized with each other.
If no reads were detected at a non-blind fragment for a given sample when reads were detected
in at least one other sample, we assigned a "0" to that non-blind fragment for the sample(s)
missing reads.
4C-seq Display
To display 4C-seq genomic coverage tracks, we first smoothed the normalized 4C-seq signal
using a 5kb running mean at 50bp steps across the genome for each sample. Individual
replicates are displayed in Fig S4. Next, biological replicates of the same condition were
combined and the mean and 95% confidence interval of the 4C-seq signal for each bin across
the genome was calculated. In Fig 4 and Fig 7, the 4C-seq signal tracks display the mean 4C-
seq signal along the genome as a line and the 95% confidence interval as the shaded area
around the line. For each 4C-seq signal track, the viewpoint used in the 4C-seq experiment is
indicated as an arrow labeled VP.
To quantify the change in 4C-seq signal in a specific region of interest, the normalized 4C-seq
signal (non-smoothed) was counted for each sample and the mean and standard deviation of
the quantified signal was calculated for biological replicates of the same condition. The mean
and standard deviation of the quantified signal was normalized to the appropriate control
condition (either WT or dCas9) before plotting. Below each 4C-seq signal track, the quantified
region is indicated as a red bar labeled "Quantified region". The coordinates of the quantified
region for Rafi are chr6:115598005-115604631, and for Etv4 are chr11:101644625-101648624.
ChIA-PET data analysis
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ChIA-PET Read Processing
For each ChIA-PET dataset, raw reads were processed in order to identify a set of putative
interactions that connect interaction anchors for further statistical modeling and analysis. First,
paired-end tags (PETs), each containing two paired reads, were analyzed for the presence of
the bridge-linker sequence and trimmed to facilitate read mapping. PETs containing at least one
instance of the bridge-linker sequence in either of the two reads were kept for further processing
and reads containing the bridge-linker sequence were trimmed immediately before the linker
sequence using cutadapt with options "-n 3 -o 3 -m 15 -a
forward=ACGCGATATCTTATCTGACT -a reverse=AGTCAGATAAGATATCGCGT"
(http://cutadapt.readthedocs.io/en/stable/). PETs that did not contain an instance of the bridge-
linker sequence were not processed further. Trimmed read were mapped individually to the
mm9 mouse reference genome using Bowtie with options "-n 1 -m 1 -p 6" (Langmead et al.,
Genome Biology, 2009). After alignment, paired reads were re-linked with an in-house script
using read identifiers. To avoid potential artifacts arising from PCR bias, redundant PETs with
identical genomic mapping coordinates and strand information were collapsed into a single
PET. Potential interaction anchors were determined by identifying regions of local enrichment in
the individually mapped reads using MACS with options "-g mm -p le-9 --nolambda --nomodel -
-shiftsize=100" (Zhang et al., Genome Biology, 2008). PETs with two mapped reads that each
overlapped a different potential interaction anchor by at least 1 bp were used to identify putative
interactions between the overlapped interaction anchors. Each putative interaction represents a
connection between two interaction anchors and is supported by the number of PETs (PET
count) that connect the two interaction anchors.
ChIA-PET Statistical Analysis Overview
In processing our chromatin interaction data, we sought to identify the putative interactions that
represent structured chromatin contacts, defined as chromatin contacts that are structured by
forces other than the fiber dynamics resulting from the linear genomic distance between the two
contacting regions. In contrast, we sought to filter out putative interactions that likely result from
PETs arising from non-structured chromatin contacts, defined as contacts resulting from the
close linear genomic proximity of the two contacting regions, or from technical artifacts of the
ChIA-PET protocol. We expect that putative interactions that represent structured chromatin
contacts should be detected with greater frequency, or PET count, than expected given the
linear genomic distance between the two contacting regions, allowing us to distinguish between
these two classes of interactions.
To this end, we developed Origami, a statistical method to identify high confidence interactions
that are likely to represent structured chromatin contacts. Conceptually, Origami uses a semi-
Bayesian two-component mixture model to estimate the probability that a putative interaction
corresponds to one of two groups: structured chromatin contacts, or non-structured chromatin
contacts and technical artifacts. Origami estimates this as a probability score for each putative
interaction by modeling the relationship between PET count, linear genomic distance between
interaction anchors, and read depth at the interaction anchors. High confidence interactions are
then identified as the subset of putative interactions that are likely to represent structured
chromatin contacts, by requiring high confidence interactions to have a probability score > 0.9.
All the methods below were developed within the origami software that is available at
https://github.com/younglab/origami. The version used was version 1.1 (tagged on GitHub
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repository as v1.1). The software below was run with the following parameters: --
iterations= 10000 --burn-in=100 --prune=0 --min-dist=4000 --peak-count-filter=5.
Origami Statistical Model
We developed Origami, a method to analyze ChIA-PET data, in order to identify putative
interactions that likely represent structured chromatin contacts, and to filter out putative
interactions that likely represent non-structured chromatin contacts that occur as a result of the
close linear genomic proximity of contacting regions and interactions that represent technical
artifacts of the ChIA-PET protocol. This includes modeling of the relationship between the
number of PETs observed to support each interaction (Ii), linear genomic distance between
interaction anchors (di), and the sequencing depth at the interaction anchors, to estimate the
probability that each putative interaction (i) represents a structured chromatin contact given the
observed PET count (Ii).
We initially assume that putative interactions classify into one of two groups, j c {0,1}, such that
each putative interaction, i C { 1 .. N}, has a latent group identity Zi that corresponds to a value
of j. Group 1 is designated as the set of putative interactions resulting from structured chromatin
contacts that we expect to detect with greater frequencies than expected given the linear
genomic distance between the contacting regions. Group 0 is designated as the set of putative
interactions resulting from non-structured chromatin contacts due to close linear genomic
proximity of the contacting regions, or from technical artifacts of the ChIA-PET protocol.
We developed a semi-Bayesian two-component mixture model to estimate the probability that
each putative interaction represents a structured chromatin contact. For each group, we
modeled the likelihood to observe the PET count (ii) under that group as a Poisson process
with two underlying factors. These factors are the number of PETs observed as a result of being
part of the group (Gij), and the number of PETs observed as a result of the linear genomic
distance between the anchors given the group (Dij). We modeled the number of PETs observed
as a result of being part of the group (Gij) as a Poisson process with mean, A,. We modeled the
number of PETs observed as a result of the linear genomic distance between the anchors given
the group (Dij) as a Poisson process with mean, pij. Since these two factors are thought to be
independent (Phanstiel et al., Bioinformatics, 2015), the total Poisson process is the summation
of these two underlying factors.
We modeled the data variables under the following distributions:
Ii- wij * (Gij + Dij)
jE{O,1}1
Gi;- Poisson( a1 )
Dij~ Poisson[pi; .(di)]
Ij~ Poisson[ Aj + Yij(dj)]
We modeled our parameters with the following prior distributions:
Aj ~ Gamma(1,1)
wi- Reta(1 + a1 , 1 + bi)
Since wil is a binomial probability, wj0 = 1 - wil.
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From these priors and likelihood distributions, the posterior distributions of these parameters are
as follows:
-j ~ Gamma[1 + Zz=j Gij, 1 + #(Zi =1)]
wei ~ Beta[1 + ai + Zi, 1 + fti + (1 - Zi)]
Aside from Dij and pi, we estimated the parameters using the iterative process Markov Chain
Monte Carlo (MCMC) with Gibbs Sampling with the appropriate posterior to sample from
(Gelman et al., 2004).
To estimate pi, we modeled the function between Dij and the linear genomic distance (di) on
the loglO scale using a smoothed cubic spline (via smooth.spline in R), taking y to be the
expected number of PETs to be observed due to distance (Dij) given the linear genomic
distance (di), for each putative interaction (i).
The constants a and fti were set to be as minimally informative as possible. The constant a
was set equal to the number of putative interactions sharing one anchor with i that have PET
counts less than Ii. The constant fti was set equal to the number of putative interactions sharing
one anchor with i that have PET counts greater than Ii plus the ratio of the depth score (si) to
the median depth score with all values <1 floored to 0. The depth score (si) for each putative
interaction is defined as the product of the number of reads that map to its interaction anchors.
Origami Implementation
We implemented the model described above by Markov Chain Monte Carlo simulation. By
iteratively estimating the group identity (Zi) of each putative interaction, we sought to explore the
probability space for Zi and determined a probability score (pi) for each putative interaction that
reflects the probability that the interaction results from a structured chromatin contact (belongs
to group 1). The steps in our implementation are as follows.
For each putative interaction, we recorded the number of PETs observed that support the
interaction (Ii), the linear genomic distance of the interaction between the outermost basepairs
of the putative interaction's two anchors (di), and a depth score (si), which is defined as the
product of the number of the reads in the dataset that map to each anchor of the putative
interaction.
To seed the parameters of the model for the first iteration, the following was performed. The
mixing weights (wij) were set to be equal at 0.5 for each interaction. The group process means
(A) were assigned values of 5 and 1 for group 1 and 0, respectively. The distance process
mean (pij) was initially set to 0 for all interactions.
Additionally values of ai and fti were computed for each interaction, but not used in the first
iteration. In all subsequent iterations, ai and ft1, are used in updating the values of the mixing
weights (wij). The parameter a was set equal to the number of putative interactions sharing
one anchor with i that have PET counts less than Ii. The parameter fti was set equal to the
number of putative interactions sharing one anchor with i that have PET counts greater than Ii
plus the ratio of the depth score (si) over the median depth score for all putative interactions,
where when this ratio is less than 1 it is floored to 0.
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For each putative interaction, we estimated the likelihood (lij) that the putative interaction is
observed with PET count (Ii), given that the putative interaction belongs to group 1 and group 0,
as follows.
lij = dPoisson(l; A; + puj)
Where dPoisson is the density function of the Poisson distribution for the mean Aj + pjj and
evaluated on Ii.
We calculated the relative weighted likelihood (ri) of each putative interaction belonging to
group 1. To do this we multiplied each of the two likelihoods calculated for each putative
interaction by their respective mixing weights (wij) and evaluated as follows.
wil * Lil
(wil * Lij) + (wj0 * Leo)
We update the group identity (Zi) of each interaction by drawing from the binomial distribution
with a probability of ri as follows.
Zi = rBinomial(1, ri)
Where rBinomial means we randomly draw 1 or 0 with the probability of r, for drawing 1.
We update the mixing weights (wij) using our newly updated group identies (Zi), by drawing
from the Beta distribution in the following way.
wil = rBeta[1 + ai + Zi, 1 + fli + (1 - ZI)]
Where rBeta means we randomly draw from the beta distribution with the above parameters.
Since w1i is a binomial probability, wj0 = 1 - wil.
In order to estimate the PET counts for Gij and Di, we randomly sampled the number of PETs
for Gij and Dij by taking advantage of the fact that when two Poisson variables are known to
sum to a given count, then the distribution of either variable follows a binomial distribution with
probability A/(Aj + pij). Accordingly, we estimated the PET counts for Gij and Dij in the
following way:
Gtj = rBinomial(Li,)
Aj + Pij
Dij = Ii - Gij
Where rBinomial means we randomly draw up to I, PETs with the probabilty Aj/(A + Pij) of
drawing each PET.
We update the group process mean (,2) using the following identity, requiring that A, > AO in
order to maintain identifiability of the two groups (although during our runs this constraint was
not necessary).
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Ai = rGamma(l + Zz,=j Gi, 1 + #(Zi = j))
Where rGamma means we randomly draw from the Gamma distribution with the above
parameters.
To update the distance process means (pij), we calculated the function between Dij and the
logl0 (di + 1), using a smoothed cubic spline (via smooth.spline in R). To simplify estimation of
pi, we chose to take the maximum likelihood estimate of this process.
We iterated steps 4-10 in the following way. We performed an initial 1,000 iterations as a burn-
in, which were discarded. Then we performed 10,000 iterations.
We estimated the probability that each putative interaction belongs to group 1 by calculating a
probability score (pi) for each putative interaction that equals the mean value of Zi across the
10,000 iterations. High confidence interactions were identified as putative interactions with pi >
0.9.
1
pi = #(iterations) Zi ~ P(Zi = 1)
HiChIP data analysis
HiChIP Processing
The HiChIP samples were processed by first identifying reads with a restriction fragment
junction (i.e. a site where ligation occurred). Reads containing the restriction fragment junction
were trimmed such that the information 5' to the junction was kept. Reads without restriction
fragment junctions were left untrimmed. Reads were then mapped using bowtie with options -k
1 -m 1 against the mm9 genome assembly. All unmapped or repetitively mapping reads were
discarded from further analysis. Reads were joined back together in pairs by their read identifier.
The genome was binned and for every pair of bins the number of PETs joining them was
calculated. These data were then used as input into the Origami pipeline described above to
identify significant bin to bin interaction pairs.
HiChIP Analysis
Quantitative analysis of HiChIP and Hi-C data (Figure 6,7) was done as follows. High
confidence interactions were identified by Origami. A union of high confidence interactions was
then created for each experiment.
Experiment Figure Condition Replicate
Degron 6, S6 noDrug 1
Degron 6, S6 noDrug 2
Degron 6, S6 noDrug 3
Degron 6, S6 yesDrug 1
Degron 6, S6 yesDrug 2
Degron 6, S6 yesDrug 3
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Washout 7 Untreated (UT) 1
Washout 7 Untreated (UT) 2
Washout 7 Untreated (UT) 3
Washout 7 Treated (TR) 1
Washout 7 Treated (TR) 2
Washout 7 Washout (WO) 1
Washout 7 Washout (WO) 2
Washout 7 Washout (WO) 3
CTCF Washout 7 Untreated (UT) 1
CTCF Washout 7 Untreated (UT) 2
CTCF Washout 7 Treated (TR) 1
CTCF Washout 7 Treated (TR) 2
CTCF Washout 7 Washout (WO) 1
CTCF Washout 7 Washout (WO) 2
For example, the degron high confidence set would consist of the union of the 6 degron
samples listed above. The PET counts were then normalized to each other using deseq2 (Love
et al., 2014). The mean of each group was then calculated and then the fold change was then
calculated by taking the ratio of the perturbed condition to the non-perturbed condition (i.e.
yesDrug to noDrug or TR/UT;WO/UT) with a pseudocount of 0.5 added to both. This complete
set of significant interactions is what is displayed in Figure 6B as "All Interactions."
For subset analysis the anchor of each interaction was classified by overlapping with known
genomic features as defined earlier. This resulted in a binary score for whether an anchor
overlapped with an enhancer, promoter, insulator, YY1, or CTCF. The interactions were then
subset to identify the following groups:
YY1 not present (Fig 6): no YY1 at either end of the interaction.
YY1 enhancer-promoter interactions (Fig 6, Fig 7): YY1 at both ends AND an enhancer or
promoter at both ends.
CTCF-CTCF interaction: CTCF at both ends.
The log2 fold change for these groups is plotted in Figure 6B, 7F.
The analysis in Figure 6C was done by identifying the gene at the end of YY1 enhancer-
promoter loops. This was done by intersecting promoters (as defined above) with the significant
loop anchors. Genes with multiple promoters were collapsed after the intersection to generate a
list of genes at the end of YY1 enhancer-promoter loops. The deseq2 calculated log2 fold
change for these genes is then plotted in Figure 6C. Genes are colored based on the deseq2
calculated adjusted p value (as in Figure 5).
HiCh/P Display
HiChIP interaction matrices displayed in Figure 6D and 6E. For these interaction matrices, all
putative interactions are displayed and the intensity of each pixel represents the mean of the
deseq2 normalized interaction frequency of all biological replicates of that condition. In Figure
6D & 6E the outlined pixel, which reflects the frequency of interaction between sites at the base
137
of the diagonals, was used to quantify the change in normalized interaction frequency upon YY1
degradation.
In Figure 2, high-confidence HiChIP interactions are displayed as arcs. For display, the
interactions displayed were filtered to remove bin to adjacent bin contacts and non-enhancer-
promoter interactions. Arcs were centered on the relevant genomic feature within the bin (for
example a ChIP-seq peak summit or transcription start site).
Interaction classification
High-confidence ChIA-PET and HiChIP interactions were classified based on the presence of
enhancer, promoter, and insulator elements at the anchors of each interaction as defined
above. In the case where an interaction anchor overlapped both an enhancer and an insulator
or a promoter and an insulator a hierarchy where anchors were considered first as promoters,
then enhancers, then insulators. For example, if there is an interaction where the left anchor is
insulator/promoter and the right anchor is enhancer/insulator it would be counted as an
enhancer-promoter interaction and not an insulator-insulator interaction.
To display summaries of the classes of high-confidence interactions, each class of interactions
is displayed as an arc between the relevant enhancer, promoter, and insulator elements. The
thickness of the arcs approximately reflects the percentage of interactions of that class relative
to the total number of interactions that were classified. In the main figures, enhancer-enhancer,
enhancer-promoter, promoter-promoter, and insulator-insulator interaction classes are
displayed. Extended summaries that additionally include enhancer-insulator and promoter-
insulator interactions are displayed in the supplemental figures.
Figure Display
In certain figure panels displaying genome tracks, enhancer elements are indicated as red
boxes labelled "Enhancer". These regions represent the authors' interpretation of the ChIP-seq
data and are distinct from the algorithmically defined enhancers used in the quantitative
genome-wide analysis.
Statistical Analysis
In order to use the unpaired t-test we made two assumptions.
1) Populations are distributed according to a Gaussian distribution. For most experiments three
replicates were used, and so sample sizes were too small to reliably calculate departure from
normality (i.e. with a D'Agostino test).
2) The two populations have the same variance. A test for variance was not carried out.
Full p values are listed here*:
Biological P value
Figure Sub panel Test Replicates
4B 4C-seq Student's T-Test 3 0.011
ChIP- 0.0066
4B qPCR Student's T-Test 3
4B RT-qPCR Student's T-Test 6 <0.0001
4C 4C-seq Student's T-Test 3 0.0013
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ChIP- 0.0048
4C qPCR Student's T-Test 3
4C RT-qPCR Student's T-Test 6 0.0394
Welch Two Sample T- < 2.2e-16
6B Test 3
6D HiChIP Student's T-Test 3 0.0162
6D RNA-seq Wald 2 7.22E-13
6E HiChIP Student's T-Test 3 0.0446
6E RNA-seq Wald 2 1.25E-58
7D 4c-seq Student's T-Test 3 0.004717003
7D RT-qPCR Student's T-Test 6 <0.0001
S6D Rafi Wald 2 1.63E-53
S6D Etv4 Wald 2 2.88E-34
*note that the Student's T-test was conducted using GraphPad Prism which sets a lower limit at
0.0001, the Welch Two Sample T-test was conducted using R which sets a lower limit at 2.2e-
16, Wald test was conducted using deseq2 in R which does not have a lower limit on the p
value.
DATA AND SOFTWARE AVAILABILITY
All datasets used are summarized in Table S4.
Origami: https://qithub.com/younglab/origami using version v1.1-alpha-2.
The data associated with this study have been deposited in the Gene Expression Omnibus
(GEO) under ID code GSE99521.
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SUPPLEMENTAL TABLES
Supplemental tables are available online at:
http://www.cell.com/cell/fulltext/SO092-8674(17)31317-X
Table SI: Comparison of YY1 and CTCF, related to Figure 1
Table S2: RNA-seq data, related to Figure 5
Table S3: GO Analysis, related to Figure 5
Table S4: Datasets used in the study, related to Figure 1, Figure 2, and Figures 4-7
Table S5: Oligos used in the study, related to STAR methods
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CHAPTER 4: FUTURE DIRECTIONS AND DISCUSSION
While the research into the control of gene expression has proceeded rapidly over the past several
decades there are still many open questions. In the first part of this chapter I will lay out some
open questions that will hopefully catalyze additional steps forward. These relate to the
identification of structural regulators of enhancer-promoter looping and developing new models
of transcriptional regulation.
In the second part of this chapter I will describe some of the pathological consequences upon
perturbations to transcriptional control. Part of the appeal to studying the control of gene
expression comes from it being an interesting scientific problem-understanding how an
organism coordinates precise patterns of gene expression throughout its life is a fundamental
biological question. This fundamental appeal is underscored by the array of pathological
consequences that arise upon perturbations to transcriptional control.
Additional structural regulators
Enhancer-promoter looping is important for gene expression and so understanding the proteins
involved in establishing enhancer-promoter loops will lead to a better understanding of the control
of gene expression. In Chapter 3 1 detailed the identification of the transcription factor YY1 as a
structural regulator of enhancer promoter loops; however, YY1 is likely not the only transcription
factor that is acting as a structural regulator. Upon perturbation of YY1 levels, enhancer-promoter
looping was decreased but not abolished. This suggests that there are additional proteins involved
in structuring enhancer-promoter loops (Figure 5A). Transcription factors comprise the most
abundant class of proteins in the human proteome, there are hundreds of transcription factors
that have not been studied, and regulatory regions are often bound by tens if not hundreds of
factors(Liu et al., 2017; Weirauch and Hughes, 2011a). It is likely that some of these transcription
factors have an underappreciated role in structuring enhancer promoter loops.
A close reading of the literature leads to multiple promising candidate regulators that merit
additional study. A number of proteins including E2(Knight et al., 1991), SP1(Su et al., 1991), T
antigen(Schiedner et al., 1990), TFIIIC(Schultz et al., 1989), P53(Stenger et al., 1994), and
progesterone receptor(Th6veny et al., 1987) have been shown be able to form DNA loops in vitro
via electron microscopy. A few other intriguing candidates that have been implicated in structuring
DNA loops via other techniques are ZNF143(Bailey et al., 2015), GATA3(Chen et al., 2012),
KLF1(Schoenfelder et al., 2010), and EKLF(Drissen et al., 2004). The further development of
mass spectrometry based proteomics and large scale CRISPR-based screens represent another
approach to generate candidates. Once a candidate has been chosen then in vitro experiments,
perturbation experiments and gain-of-function experiments can be used to establish the case that
the factor is a bona fide structural regulator, a la YY1(Weintraub et al., 2017).
New models of transcriptional regulation
Another interesting new avenue of research is the idea that liquid-liquid phase separated (LLPS)
droplets are forming at sites of active transcription. A number of cellular bodies have been
observed within eukaryotic cells including nucleoli, P granules, Cajal bodies, stress granules, and
others (reviewed in (Banani et al., 2017)). These cellular bodies exhibit the properties of liquid
droplets such as viscosity, fission and fusion(Banani et al., 2017; Brangwynne et al., 2015; Hyman
et al., 2014; Zhu and Brangwynne, 2015). Thus it is thought that these bodies are the product of
a liquid-liquid phase separation.
149
An intriguing possibility then is that liquid-liquid phase separation is occurring at sites of active
transcription (Figure 5B). This is in contrast to the more traditional model of transcriptional control
that favors a lock-and-key assembly of the transcriptional machinery(Buratowski, 1994; Liu et al.,
2013). A number of recent unpublished observations in our lab suggest that LLPS may indeed be
occurring at sites of active transcription. (1) Purified transcriptional cofactors form liquid droplets
in vitro. (2) Imaging of transcriptional cofactors such as BRD4 and Mediator that are known to be
associated with transcription results in the detection of puncta. Puncta indicate a high density of
factors, as would be expected in a LLPS droplet. (3) Fluorescent recovery after photobleaching
experiments indicate that these puncta exhibit liquid-like characteristics. Together these
experiments suggest that LLPS is occurring at sites of active transcription.
If one accepts that liquid-liquid phase separation is occurring at sites of active transcription, then
the immediate next question is "what is the impact on transcription of liquid-liquid phase
separation?" Of course, one possibility is that LLPS does not influence transcription, and is
instead a byproduct of assembly of the transcription apparatus. However, LLPS droplets have
interesting physical properties which are likely to be exploited by the cell. For example, LLPS
droplets allow for molecules to be highly concentrated, yet still undergo rapid diffusion; which is
an attractive way to organize a biochemical reaction.(Hyman et al., 2014). Furthermore, LLPS
could provide insight into the mechanism by which transactivation domains of transcription factors
recruit activators. As mentioned in the introduction, transactivation domains are often unstructured
and enriched for disorder(Dyson and Wright, 2016; Liu et al., 2006; Minezaki et al., 2006). Many
of the proteins known to form other types of LLPS droplets are enriched for disordered
regions(Das et al., 2015; Wright and Dyson, 2014; Zhu and Brangwynne, 2015), suggesting that
transactivation domains may have a propensity to form a LLPS droplet. Disordered regions are
thought to form LLPS droplets through a large number of weak interactions, which allows a
change in valency to have a dramatic effect on the droplet(Hnisz et al., 2017). Signals are often
transduced in the form of post-translational modifications (i.e. acetylation or methylation), which
have the effect of increasing or decreasing valency. Thus, the formation of LLPS droplets could
enable the rapid response to signals that is a hallmark of transcriptional control. New research
into the function of LLPS in transcriptional regulation is sure to be illuminating amongst these
possibilities.
150
Figure 5
A
z
B
Liquid-Liquid Phase Seperation
Germ
Enhancer ....
Insulated Neighborhood
Cohes
-L Other Enhancer-Promoter
- Structuring Factors?
Figure 5. Open questions
(A) Are there other enhancer-promoter structuring factors? Many other transcription factors bind at enhancers and
promoters, are some of these acting similar to YY1 and contributing to enhancer-promoter looping?
(B) Preliminary evidence indicates that liquid-liquid phase separation is occurring at sites of active transcription.
Diagrammed here are some of the interactions that could be formed by the various factors at enhancers and
promoters. Factors include cofactors (blue, red), TFs (purple, dark green), RNAPII (light green), histone modifications
(red flags), RNA (blue squiggles). It is not clear how liquid-liquid phase separation is contributing to transcriptional
regulation.
Pathological consequences of perturbations to genome structure
Outside of the interest in studying transcriptional control to understand basic biology there are a
number of implications for human health. Several recent reviews highlight the high number of
pathogenic mutations in different components of the transcriptional machinery(Bradner et al.,
2016; Easwaran et al., 2014; Flavahan et al., 2017; Garraway and Lander, 2013; Vogelstein et
al., 2013). Furthermore, there are a number of companies that have developed or are developing
compounds to target transcriptional control(Ahuja et al., 2016; Tanaka et al., 2015). As these
compounds proceed through clinical trials we will get an idea of the therapeutic value of this
approach. In this section I will describe some of the pathological consequences of perturbations
in genome structure. I will cover both mutations in the genome that disrupt structure as well as
mutations in the proteins that structure the genome.
Variants in the genomic sequence occur more frequently in noncoding regions of the genome
than coding regions of the genome. Recent estimates by the 1000 Genomes Project estimate that
the typical genome has -10,000 variants that alter peptide sequence versus -500,000 variants
that overlap regulatory sites(Altshuler et al., 2012). Interpreting these mutations is thus a major
challenge; which is made more difficult by the fact that the function of noncoding regions is not as
readily apparent as coding regions. Knowledge of genome structure has aided in the interpretation
of these variants. In Chapter 2 1 detailed the finding that somatic mutations accumulate at
insulated neighborhood anchor sites in cancer(Hnisz et al., 2016b). These mutations can result
in the disruption of insulated neighborhoods which can cause the inappropriate activation of proto-
oncogenes. A concurrent study from the Bernstein lab noted a very similar phenomenon in
glioblastoma(Flavahan et al., 2016). Many gliomas are defined by gain-of-function mutations in
the IDH gene resulting in the production of a new onco-metabolite,2-hydroxyglutarate, which
151
inhibits the TET family of enzymes. TET enzymes catalyze the removal of DNA methylation
through the hydroxylation of 5'-methylcytosine. This results in the accumulation of DNA
methylation. CTCF is unable to bind methylated DNA and the Bernstein lab found that the hyper-
methylation results in reduced CTCF binding and loss of insulation(Flavahan et al., 2016). This
causes the oncogene PDGFRA to be inappropriately activated. Thus perturbation of genome
structure through both genetic and epigenetic means can result in the inappropriate activation of
oncogenes.
In addition, there are examples of non-cancer phenotypes that are driven by mutations that
perturb genome structure. The Mundlos Lab identified limb malformations that result from
perturbations to an insulated neighborhood boundary that contains the limb specific EPHA4
gene(Lupieiiez et al., 2015). On the centromeric side the EPHA4 insulated neighborhood is
adjoined by an insulated neighborhood containing the genes WNT6 and IHH and on the telomeric
side an insulated neighborhood containing the gene PAX3. Deletion of the telomeric boundary of
the EPHA4 insulated neighborhood results in inappropriate activation PAX3 and brachydactyly
(short digit formation). Deletion of the centromeric boundary results in the inappropriate activation
of IHH and polydactyly (extra digit formation). Inversion of the centromeric boundary results in
WNT6 activation and F-syndrome (syndactyly of the first and second fingers). In a later study the
Mundlos lab found that perturbations to the boundary of the insulated neighborhood containing
SOX9 can result in male to female sex reversal and Cooks syndrome (aplasia of nails)(Franke et
al., 2016). Together, these results show the profound consequences on human physiology that
disruptions to genome structure can have.
In addition to pathogenic mutations that disrupt genome structure by disrupting insulated
neighborhood boundaries, a number of mutations have been observed in proteins that are
involved in structuring the genome. The molecular mechanism behind the pathological
consequence of these mutations is less clear.
YY1 is overexpressed in many cancers(Gordon et al., 2006) and it was recently reported that a
loss-of-function mutation in one copy of YY1 causes severe intellectual disability in
humans(Gabriele et al., 2017). While it is not yet clear how alterations in the levels of YY1 result
in these disparate phenotypes it is likely related to the role of YY1 in structuring enhancer-
promoter loops. We found that there was little morphological phenotype of YY1 depletion in
murine embryonic stern cells but that the cells were unable to differentiate(Weintraub et al., 2017).
One possibility is that YY1 is critical for the establishment of DNA loops during changes in cell
state and that neurodevelopment is highly sensitive to even a 50% loss of YY1. In cancer, it is
possible that the over-expression of YY1 leads to the establishment of new DNA loops that
support an oncogenic transcriptional program. Supporting this possibility is the finding that a
mutation in YY1 that alters its DNA binding specificity results in the establishment of a new
transcriptional program that drives insulin-producing adenomas(Cromer et al., 2015). New studies
are needed to generate a molecular understanding of how perturbation of YY1 results in the
observed pathologies.
Mutations in CTCF have been reported in a number of different types of cancers including
prostate(Filippova et al., 1998), Wilms'(Filippova et al., 2002), endometrial(Lawrence et al., 2014),
and head and neck(Lawrence et al., 2014). These include both predicted loss-of-function
mutations and altered or gain-of-function mutations(Filippova et al., 2002; Lawrence et al., 2014).
In addition, a mouse model showed that loss of a single allele of CTCF rendered the animals
more susceptible to various types of radiation and chemically induced tumors(Kemp et al., 2014).
Interestingly, germline mutations in CTCF have been reported to cause intellectual
152
disability(Gregor et al., 2013). Similar to YY1, the molecular mechanism linking the pathology with
perturbations in CTCF is not yet established.
Germline mutations that disrupt the structure and/or function of the cohesin complex have been
linked to a set of human developmental diseases. Cornelia de Lange syndrome is a congenital
malformation disorder which can be caused by mutations in NIPBL (the protein that loads cohesin
onto the genome)(Gillis et al., 2004; Tonkin et al., 2004), SMC1 (component of the cohesin
ring)(Musio et al., 2006), SMC3 (component of the cohesin ring)(Deardorff et al., 2007), and
HDAC8 (a deacetylase that modifies the residency time of cohesin on the DNA)(Deardorff et al.,
2012). Roberts-SC Phocomelia Syndrome is caused by mutations in the gene ESCO2 (an
acetylase that modifies the residency time of cohesin on the DNA)(Schole et al., 2005; Vega et
al., 2005). Interestingly, these mutations do not seem to disrupt cell division as cells have a normal
karyotype suggesting that the mutations disrupt the transcriptional function of cohesin(Remeseiro
et al., 2013; Skibbens et al., 2013; Zakari et al., 2015). However, the link between perturbations
in cohesin function and pathology is not yet understood.
Somatic mutations affecting cohesin have also been observed in a number of different cancer
types(Hnisz et al., 2018). These cancer types include acute myeloid leukemia, bladder cancer,
breast cancer, colorectal cancer, and Ewings Sarcoma(Hnisz et al., 2018). Most of the mutations
have been predicted to cause a loss-of-function in cohesin (Lawrence et al., 2014). Similar to the
autosomal cohesin mutants described above, tumors with defective cohesin generally have high
genomic integrity(Crompton et al., 2014). Again, the molecular mechanism is not clear; however,
several recent studies have suggested that cohesin loss in hematopoietic cells enforces a stem
cell program and prevents differentiation. Our lab is currently engaged in several studies to map
chromosome structure before and after cohesin mutation. Hopefully these and other studies will
shed light on why cohesin mutations accumulate in multiple different cancer types.
Concluding thoughts
The past half century of research has provided great insight into the control of gene expression.
Transcription factors bind to regulatory regions in the genome to form enhancers that regulate
gene transcription by forming a DNA loop with a gene promoter. Enhancer activity is constrained
by the genome structure of the genome and particularly insulated neighborhoods. Mutations
accumulate in insulated neighborhood boundaries in cancer, and that is one mechanism by which
oncogenes can be activated (Chapter 2). Enhancer promoter looping is in part controlled by the
actions of the transcription factor YY1, which oligomerizes with itself in order to structure
enhancer-promoter loops (Chapter 3). In the next decade we may see more structural regulators
identified. We may also see the classic lock-and-key model that describes the assembly of
transcriptional components replaced by a model that incorporates principles from soft matter
physics such as liquid-liquid phase separation. We may also start to understand the mechanism
by which mutations in components that structure the genome result in pathological consequences.
Regardless of the direction that the field ends up going, the next decade of research into
transcriptional control is sure to provide insight into both fundamental biology and into human
pathology.
153
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