© 2018. Published by The Company of Biologists Ltd.

Collaborative repressive action of the antagonistic ETS transcription factors Pointed and

Yan fine-tunes gene expression to confer robustness in Drosophila

Jemma L. Webber, Jie Zhang, Alex Massey, Nicelio Sanchez-Luege and Ilaria Rebay

Ben May Department for Cancer Research

University of Chicago

Chicago, Illinois 60637

Corresponding author:

Email: irebay@uchicago.edu

Key Words: gene regulatory network, cell fate specification, chromatin occupancy, mesoderm

development, RNA pol II pausing

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http://dev.biologists.org/lookup/doi/10.1242/dev.165985Access the most recent version at First posted online on 30 May 2018 as 10.1242/dev.165985

Abstract

The acquisition of cellular identity during development depends on precise spatiotemporal

regulation of gene expression, with combinatorial interactions between transcription factors,

accessory proteins and the basal transcription machinery together translating complex signaling

inputs into appropriate gene expression outputs. The Drosophila ETS family transcription factors

Yan and Pointed, whose opposing repressive and activating inputs orchestrate numerous cell

fate transitions downstream of receptor tyrosine kinase signaling, provide one of the premier

systems for studying this process. Current models describe the differentiative transition as a

switch from Yan-mediated repression to Pointed-mediated activation of common target genes.

We describe here a new layer of regulation whereby Yan and Pointed co-occupy regulatory

elements to coordinately repress gene expression, with Pointed unexpectedly required for the

genome-wide occupancy of both Yan and the corepressor Groucho. Using even-skipped as a

test-case, synergistic genetic interactions between Pointed, Groucho, Yan and components of

the RNA polymerase II pausing machinery suggest Pointed integrates multiple scales of

repressive regulation to confer robustness. We speculate that this mechanism may be used

broadly to fine-tune the expression of many developmentally critical genes.

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Introduction

Genetic and epigenetic mechanisms together produce the spatiotemporal gene expression

dynamics that drive accurate and robust developmental transitions. At the genetic level,

combinatorial codes of competing and collaborating transcriptional activators and repressors are

recruited to individual cis-regulatory enhancers to determine precise gene expression outputs

(Ma 2005; Bauer et al. 2010). Analogously at the epigenetic level, activating and repressive

marks facilitate open or closed chromatin states that respectively promote or preclude

expression, while more nuanced regulation can be achieved by the simultaneous presence of

activating and repressive marks (Reynolds et al. 2013; Lagha et al. 2012). For example, at

many developmentally important genes, specific combinations of inherently conflicting histone

modifications permit RNA pol II to initiate transcription but then stall, keeping gene expression

off yet poised for rapid activation (Schwartz et al. 2010; Gaertner et al. 2012). While chromatin

looping can physically coordinate the transcriptional complexes assembled at enhancers across

a locus with the promoter-proximal complexes that orchestrate RNA pol II pause and release,

the mechanisms by which these two layers of regulation are actually integrated to fine-tune

gene expression dynamics during development are just beginning to be elucidated (reviewed in

Gaertner and Zeitlinger 2014; Liu et al. 2015; Meng and Bartholomew 2017)

The Drosophila ETS transcriptional repressor Yan (also known as Anterior open (Aop),

Nüsslein-Volhard et al. 1984; Rogge et al. 1995) and activator Pointed (Pnt) provide a useful

model system for exploring how activator and repressor inputs are balanced to control

developmental gene expression. Genetic and biochemical analysis of several enhancer

elements, including the muscle heart enhancer (MHE) that drives the segmental pattern of

even-skipped (eve) expression in the cardiogenic mesoderm, has showcased competition

between Yan and Pnt for access to consensus ETS motifs as a mechanism for directing rapid

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off-on gene expression transitions in response to upstream signals. Thus prior to signaling, Yan

outcompetes Pnt to repress target gene expression, thereby stabilizing the uncommitted

precursor state. Following pathway activation, Yan is targeted for rapid degradation, allowing

Pnt access to sites previously occupied by Yan. This turns on formerly repressed gene

expression programs to promote a differentiative transition (Brunner et al. 1994; Klaes et al.

1994; O’Neill et al. 1994; Rebay and Rubin 1995; Hsu and Schulz 2000).

The results of several recent studies have motivated us to reconsider the universality of this

regulatory mechanism with respect to all Yan target genes and to ask whether more

complicated Yan-Pnt interactions might also contribute to regulation of well-studied targets like

eve. First, ChIP-seq studies have shown that Yan occupies chromatin in broad stretches of

clustered peaks, binding preferentially to enhancers associated with developmentally important

genes and signaling pathway effectors (Webber et al. 2013a). Simple binary off-on regulation of

all of these putative targets seems unlikely. Second, a comparative survey of Yan and Pnt

protein expression throughout development revealed extensive co-expression, particularly in

tissues in which RTK signaling levels are presumed low (Boisclair Lachance et al. 2014). This

raises the possibility of more complicated interactions than might be needed if their expression

were always mutually exclusive as it is in the embryonic midline. Indeed, two recent studies

focused on eve highlight the importance of properly balanced Yan and Pnt repressive and

activating inputs at the MHE before, during and after a cell fate transition, and emphasize the

use of long-range interactions between the MHE and other Yan-bound elements as a

mechanism for ensuring robust regulation (Webber et al. 2013b; Boisclair Lachance et al. 2018).

How Yan-Pnt-mediated regulatory mechanisms might be coordinated with epigenetic

mechanisms that influence gene expression is not known.

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In this study we report the discovery of a completely unexpected role for Pnt in recruiting or

stabilizing Yan occupancy and repression at regulatory elements across the genome. In wild

type embryos, we find that Yan and Pnt have virtually identical genome-wide occupancy

patterns and that the two actually co-occupy individual enhancers. While the majority of Pnt

occupancy is Yan-independent, the majority of Yan occupancy is Pnt-dependent, a finding that

positions Pnt as an anchor with respect to establishing Yan occupancy and repression. Further

challenging the model of exclusive Yan-Pnt regulatory antagonism, gene expression analyses

predict that in addition to the classic opposing Pnt and Yan inputs at select targets, Yan and Pnt

together negatively regulate many target genes. Pnt also facilitates chromatin binding of the

TLE corepressor protein Groucho, raising the possibility of context-specific roles for Pnt as a

repressor. Focusing on the target gene eve, synergistic interactions between Pnt, Yan, Groucho

and factors associated with RNA polymerase II pausing fine-tune Eve expression to ensure

robust cell fate specification. We propose that the collaborative action of an opposing activator-

repressor pair establishes repressive complexes that collaborate with the pol II pausing

machinery to create a locus-wide poised state that both prevents spurious gene activation and

ensures timely induction of expression following signaling cues.

Results

Yan and Pnt co-occupy regulatory regions

To investigate how regulatory inputs from Yan and Pnt are integrated across their target gene

loci we used ChIP-Seq to generate a genome-wide map of Pnt-bound regions in stage 11

embryos and then compared it to that of Yan. The two occupancy profiles were strikingly similar,

including at loci of the known Yan/Pointed targets argos (aos), even-skipped (eve) and mae

(Figure 1A and Supplemental Figure 1A,B). Using high confidence bound regions identified with

the Model-Based Analysis of ChIP-Seq (MACS) peak-calling tool (Zhang et al. 2008), 82% of

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Yan-bound peaks overlapped with Pnt-bound peaks. In the instances when a peak was called

only in the Pnt dataset, visual inspection of the tag density pileups often revealed a

subthreshold accumulation of reads in the Yan sample (Supplemental Figure 1A,B). Consistent

with the similar binding landscapes, central motif enrichment analysis showed that the

consensus sequences recognized by Mothers against dpp (Mad) and ETS transcription factors

were the two most enriched motifs in Pnt-bound peaks (Supplemental Figure 1C), exactly as in

Yan-bound peaks (Webber et al. 2013a). Assigning Pnt-bound regions to the nearest gene

produced a list of genes with significant overlap to a similarly generated Yan target list, and thus

near identical enrichment of gene ontology (GO) terms (Supplemental Table 1 and

Supplemental Figure 1D).

Although Yan and Pnt are coexpressed extensively in stage 11 embryos (Boisclair Lachance et

al. 2014), we expected the overlapping occupancy profiles would reflect mutually exclusive Yan

or Pnt binding to specific enhancers, consistent with current understanding of their antagonistic

relationship. To assess this we selected a subset of bound regions whose ability to respond

appropriately to Pnt and Yan activating and repressive inputs had been previously

demonstrated in S2 cell transcriptional reporter assays (Webber et al. 2013a). To our surprise,

sequential ChIP (ChIP-reChIP) followed by qPCR revealed Yan-Pnt co-occupancy at six of the

seven regions tested (Figure 1B). This suggested that the mechanisms that organize Yan and

Pnt chromatin occupancy and contributions to gene expression regulation are more complicated

than previously assumed.

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Pnt facilitates Yan recruitment across the genome

Yan-Pnt co-occupancy of an enhancer could result from either interdependent or independent

recruitment. Based on the accepted model of Yan and Pnt function in which Yan-mediated

repression maintains cells in an uncommitted progenitor-like state, we predicted that Yan would

be the more likely initiator, perhaps recruiting Pnt to poise bound target regions for subsequent

activation in response to signaling. We therefore first asked whether binding of Pnt to its target

regions depends upon Yan by examining Pnt chromatin occupancy in yan null mutant embryos.

In contrast to our predictions, the binding landscape of Pnt was broadly conserved in the

absence of Yan (Figure 1C,D and Supplemental Table 2), suggesting that Pnt recruitment

occurs primarily independently of Yan.

To determine if Yan recruitment was similarly independent of Pnt, we profiled Yan chromatin

occupancy in pnt null mutant embryos. In contrast to expectations, comparison of ChIP-seq

signal profiles revealed a global reduction in Yan occupancy (Figure 1E,F). This finding was

validated independently by ChIP-qPCR at all targets tested (Figure 1G). Indirect

immunofluorescence analysis confirmed comparable Yan protein levels in wildtype and pnt

mutant embryos (Supplemental Figure 2), ruling out the most trivial explanation for globally

reduced occupancy. In further support for a direct role for Pnt in facilitating Yan recruitment to

chromatin, Yan occupancy was preferentially reduced at regions identified as bound by both

Yan and Pnt versus regions identified as bound by Yan alone (Figure 1H). We conclude that Pnt

plays a critical and unexpected role in the recruitment and/or stabilization of Yan binding across

the genome.

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Pnt collaborates with Yan to mediate repressive function

Yan’s unanticipated dependency upon Pnt for proper occupancy motivated us to consider a

non-canonical role for Pnt as a repressor, and a collaborative rather than antagonistic

relationship with Yan in this capacity. The central prediction was that the expression of genes

subject to such Pnt-Yan cooperative repression should increase upon loss of either Pnt or Yan.

To test this, we utilized an unpublished analysis of mRNA expression changes in pnt or yan

mutant embryos that we had performed with a custom Agilent microarray made with probes

from Yan-bound genes identified by ChIP (for ChIP targets see Webber et al. 2013a). Using a

P-value cutoff of <0.05, we first identified probes whose expression was significantly changed in

pnt mutants versus wildtype (Figure 2A) and then selected a handful of up- or down-regulated

targets for qPCR validation. Comparison of array and qPCR results revealed broad agreement

between the two datasets, confirming the overall quality of the array data (Figure 2B). As a

second point of validation, we asked whether mRNA levels of the known Yan/Pnt targets, aos,

mae and eve, exhibited the expected opposite response to loss of Pnt or Yan. Consistent with

expectation, expression of aos and mae was reduced in pnt mutants and increased in yan

mutants. In contrast, while eve levels were elevated in the absence of Yan, they were not

significantly changed in pnt mutants, a finding perhaps in keeping with the stochastic and rather

modest loss of Eve expression that has been described in pnt mutant embryos (Halfon et al.

2000).

Although a handful of studies have uncovered roles for Pnt in negatively regulating the

expression of genes, including hid in the embryo, yan in the eye disc and asense in the larval

brain (Kurada and White 1998; Rohrbaugh et al. 2002; Zhu et al. 2011), Pnt has been

characterized exclusively as a transcriptional activator (Klämbt 1993; Scholz et al. 1993;

Brunner et al. 1994; O’Neill et al. 1994; Gabay et al. 1996; Schwartz et al. 2010). We were

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therefore intrigued by the set of genes upregulated in the pnt mutant. Although some of these

expression increases could reflect indirect regulation, because the genes used in the analysis

were selected based on chromatin occupancy, the approach should enrich for changes resulting

from loss of direct Pnt-mediated regulation. Focusing on genes with upregulated expression,

which would reflect loss of Pnt repressive inputs, we assessed their response to loss of Yan. If

Pnt’s ability to repress transcription depends on its ability to recruit Yan, then a similar set of

genes should be upregulated in both mutants; indeed, a strong positive correlation was

observed, with a R2 of 0.7 and P-value of <0.0001 (Figure 2C).

To gain insight into the developmental processes that might be regulated by coordinated Pnt-

Yan repression, we identified the ontologies of the upregulated genes using the PANTHER

classification system (Mi et al., 2013). Upregulated genes were enriched for categories

associated with muscle cell fate commitment and cardioblast differentiation. These GO terms

were absent from ontology analyses performed with downregulated genes (Supplemental Table

3). Considering these differences in light of the Yan and Pnt expression patterns in the st 11

embryo suggest that in the mesoderm, where Yan and Pnt are co-expressed and RTK signaling

levels are low (Gabay et al. 1997; Boisclair Lachance et al. 2014), the two collaborate as

repressors to stabilize the unspecified state.

Groucho is recruited to Yan and Pnt co-occupied regions

A second prediction of a model in which Pnt contributes repressive function to gene regulation is

that it should recruit co-repressor proteins, either directly or via its interaction with Yan. To

identify likely candidates, we examined the modENCODE database (Contrino et al. 2012;

wwww.modencode.org) to compare available corepressor genome-wide occupancy patterns to

those of Yan and Pnt. The binding landscape of the corepressor Groucho (Gro) immediately

stood out. Because the published datasets were not appropriately stage-matched to our work,

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we performed ChIP-seq analysis of Groucho in stage 11 wild type embryos. The results

confirmed the similarity of the Gro binding landscape to that of Yan and Pnt. Intersecting high

confidence peaks of Gro with the Yan and Pnt datasets revealed a 54 and 37 percent overlap

respectively, and heatmap analysis of Yan/Pnt co-bound regions suggested even greater

overlap (Figure 3A).

To ask if proper Gro occupancy requires Pnt, we performed ChIP-seq analysis of Gro in a pnt

mutant background. Western blot analysis revealed no significant change in Gro protein levels

in pnt mutant versus wildtype embryos (Supplemental Figure 3A,B) and Gro occupancy was

only moderately affected at regions of the genome without nearby Pnt binding (Figure 3B,C). In

contrast, analogous to our finding of reduced Yan occupancy in pnt null animals, Gro binding

was reduced in regions of the genome where Gro and Pnt profiles normally overlap (Figure 3C).

Comparison of the ChIP-seq peaks suggested that loss of Gro occurred at regions that also

displayed reduced Yan occupancy in pnt null embryos. For example, Gro occupancy was lost

across the neuralized locus (Figure 3D), in patterns similar to those observed for Yan loss, but

was barely reduced at the turtle locus that does not bind Yan (Figure 3E). Plotting the ratio of

Groucho occupancy at bound regions in pnt mutants relative to the wildtype control confirmed

that the reduction of Gro in the absence of pnt is more severe at Yan and Groucho co-occupied

sites, than at sites that are not bound by Yan (Figure 3F). Taken together, these data indicate

that Pnt recruits both Gro and Yan to common regulatory elements, raising the possibility of

coordinated Yan-Pnt-Gro occupancy and repression of the associated target gene.

We tested this prediction by correlating gene expression changes in pnt mutant embryos with

the changes in Yan and Gro occupancy described above. Of the 320 genes associated with Yan

and Groucho occupancy loss in pnt mutants, 129 were represented in the custom microarray.

Of these, 107 were differentially expressed in the absence in of pnt, with 72% displaying

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upregulated expression (Figure 3G). Using the converse approach, upregulated genes identified

in the microarray had reduced Yan and Groucho signal intensity in pnt mutants relative to WT;

this list included the validated Groucho target E(spl)mbeta-HLH (Supplemental Figure 3C and

4). We conclude that the loss of Yan and Groucho occupancy that occurs in pnt mutant embryos

reflects a novel mechanism by which Pnt recruits and collaborates with these two repressive

factors to negatively regulate expression at a significant subset of target genes.

Pnt mediates repressive inputs at eve

Having defined a novel role for Pnt in recruitment of Yan and Gro, we next asked how these

interactions influence expression at a specific locus. The heart identity gene eve provided an

ideal vantage point to do this because of the already deep mechanistic understanding of how

Yan repressive and Pnt activating inputs are organized at specific enhancers (Halfon et al.

2000; Webber et al. 2013b; Boisclair Lachance et al. 2018). In stage 11 embryos, Eve is

expressed in segmentally arrayed clusters of cells in the developing cardiogenic mesoderm.

Yan and Pnt exert antagonistic inputs at the level of a pattern-driving muscle heart enhancer

(MHE), such that in yan mutant embryos extra Eve+ cells are specified, while in pnt mutant

embryos, the number of Eve+ cells specified is reduced (Halfon et al. 2000). Additional

repressive input is provided via the D1, a Yan-responsive element whose deletion results in

elevated and more variable Eve expression (Webber et al. 2013b).

Matching the pattern of Yan occupancy (Webber et al. 2013a), tag density profiles of Pnt at the

eve locus revealed enrichment at both the D1 and MHE regulatory regions (Figure 4A); Pnt

occupancy at the D1, which genetically appears dedicated to dampening Eve expression

(Webber et al. 2013b), further supports the hypothesis of a role for Pnt in repressive regulation.

Analysis of the Yan genome-wide ChIP dataset in pnt mutant embryos suggested Pnt is

required for proper Yan occupancy at both the MHE and D1; ChIP-qPCR confirmed this

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dependency (Figure 4B). To test whether Yan and Pnt are co-bound, we performed sequential

ChIP. While we were unable to detect co-occupancy at the eve MHE, simultaneous occupancy

was detected at the eve D1 region (Figure 4C). One explanation for the negative results at the

MHE is that we are simply below the detection threshold. Indeed both the genome-wide ChIP

data sets and the ChIP-qPCR confirmation experiments always show low enrichment at the

MHE, perhaps indicating Yan/Pnt occupancy/co-occupancy of this pattern-driving enhancer

occurs in only the small subset of mesodermal cells from which Eve+ pericardial cells are

specified. Alternatively, Yan and Pnt might co-occupy the D1 but not the MHE, with 3D

interactions enabling D1-bound Pnt to recruit/stabilize Yan occupancy at both the D1 and the

MHE. While further testing will be required to distinguish between these possibilities, in support

of the latter, long-range interactions between the D1 and MHE stabilize Yan occupancy at the

two elements (Webber et al. 2013b; Boiclair Lachance et al. 2018).

Because complete loss of pnt results in reduced Eve expression, we devised an alternate

genetic strategy to assess repressive function. We reasoned that embryos heterozygous for

Yan might provide a suitably sensitized background to reveal a role for Pnt-mediated repressive

regulation. We first assessed Eve expression levels in animals heterozygous for either yan or

pnt and compared these to Eve levels in double heterozygotes. The yan and pnt loss of function

alleles were fully recessive, with no significant change in Eve expression detected relative to

wildtype control (Figure 4D). In contrast, in yan/+;pnt/+ embryos, Eve levels were significantly

elevated and extra Eve+ cells were specified (Figure 4D,E). We repeated the experiment using

a functional Eve-YFP BAC transgene (Webber et al. 2013b) and again measured elevated Eve

levels in doubly heterozygous animals compared to single heterozygotes (Supplemental Figure

5). Together these data suggest a cooperative function for Yan and Pnt in negative regulation of

eve.

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We extended the dose-sensitive genetic interaction analysis to assess involvement of the

corepressor Gro, whose occupancy at both the eve MHE and D1 is reduced in the absence of

pnt (Figure 4A). Consistent with previous studies of Gro repressive input at eve (Helman et al.

2011), we observed increased Eve levels and extra Eve+ cells in gro/+ embryos; both

phenotypes were enhanced in pnt/gro doubly heterozygous animals (Figure 4D,E). We

conclude that Yan, Pnt and Gro work collaboratively to negatively regulate Eve levels and Eve+

cell fate specification in the cardiogenic mesoderm.

Pnt integrates with pausing machinery to maintain a poised state

The inherent conflict of recruiting both active and repressive histone marks to a given bound

region is characteristic of a balanced chromatin state, whereby genes are held silent, but poised

for transcriptional activation (Schwartz et al. 2010; Gaertner et al. 2012). Analogously at the

transcription factor level, co-occupancy by the activator-repressor pair Pnt/Yan could both

prevent inappropriate activation of eve under sub-threshold signaling conditions and prime the

locus for rapid transcriptional activation following the onset of upstream signaling. Meta-analysis

of publically available ChIP-seq data for three different chromatin modifications from 4-8hr

embryos, which includes the stage 11 time-point used in our ChIP experiments, provided

circumstantial evidence that eve may be poised. Specifically, the combination of negligible

H3K27ac, which exclusively marks active enhancers, prominent H3K4me1, a mark of both

poised and active enhancers, and prominent H3K27me3, a mark that in the absence of

H3K27ac indicates a poised state, suggests that the eve locus may be poised (Rada-Iglesias et

al. 2011; Bonn et al. 2012; Koenecke et al. 2017; and Figure 5A).

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Poised genes commonly employ a RNA polymerase (pol) pausing strategy whereby RNA pol II

is recruited and initiates transcription, but then pauses downstream of the transcription start site

(TSS) until receiving the appropriate signaling cues for pause release (reviewed in Gaertner and

Zeitlinger 2014). Recent work from Drosophila cell lines implicates Groucho in RNA pol II

pausing (Kaul et al. 2014), an intriguing association given our discovery of a role for Pnt in

recruiting Gro, including to the eve MHE and D1 enhancers (Figs. 3D,E and 4A). Further, meta-

analysis of genome-wide ChIP datasets revealed frequent overlap between Yan and Pnt

occupancy patterns with those of components of the pausing machinery, including Trithorax-like

(Trl, also known as GAGA factor) and Bric à brac 1 (Bab1) (Figure 5B; Contrino et al. 2012; Tsai

et al. 2016). Focusing on eve, the locus bears hallmarks of pausing with high Pol II occupancy

near the TSS, together with low incidence of H3K4me3 and overlap with the pausing factor Trl (

Figure 5C; Contrino et al. 2012; Lee et al. 2008; Fuda et al. 2009 Gaertner et al. 2012; Tsai et

al. 2016).

Using eve as our model, we assessed genetic interactions between Pnt and members of the

pausing machinery. We first tested whether heterozygosity for either Trl or bab1 could influence

Eve expression and observed no significant change in Eve levels (Figure 6A). In contrast,

embryos doubly heterozygous for either pnt and Trl or pnt and bab1 displayed significantly

increased Eve levels with a corresponding increase in the number of Eve+ cells specified

(Figure 5A,B). Similar changes in Eve expression and number of Eve+ cells were observed in

embryos doubly heterozygous for either Trl and bab1 or yan/gro and Trl (Figure 6A,B and

Supplemental Figure 6). A trend towards increased Eve expression was also observed in

pnt/Nelf-E double heterozygotes, although the relative increase was not statistically different

from control, perhaps because of the maternal contribution of Nelf-E and/or the multi-subunit

nature of the NELF complex (Wang et al. 2010; Wu et al. 2005). Suggesting that interactions

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between Pnt and the pausing machinery may play a broader role in development beyond eve,

survival to adulthood of animals doubly heterozygous for either pnt and Trl or pnt and bab1 was

half that of any of the three single heterozygotes (Figure 6C).

Discussion

The precision with which multipotent cells commit to specialized fates relies on regulated

derepression of gene transcription. Thus in stem cells and in early embryos, concomitant with

the initial opening of chromatin domains by pioneer factors, conflicting epigenetic marks

deposited at the promoters of many developmentally important genes recruit yet stall RNA pol II,

thereby maintaining repression and multipotency. How such epigenetic-based repressive

poising is coordinated with the transcription factors that respond to inductive cues to direct

specific cell fate transitions as development proceeds is not well understood. Our study

positions the ETS1 homolog and transcriptional activator Pointed (Pnt) as a key integration point

between the transcriptional repressive complexes that assemble at regulatory elements across

a locus and the molecular complexes that establish, maintain and release RNAPII pausing. The

results not only redefine the classic Yan-Pnt cell fate switch paradigm in Drosophila, but more

broadly uncovers a novel strategy by which genetic and epigenetic regulation is coordinated to

confer robustness to developmental cell fate transitions.

The accepted model for Yan and Pnt function predicts mutually exclusive occupancy at

enhancers, with RTK signaling triggering the transition from an initial Yan-bound repressed state

in uncommitted progenitors to a subsequent Pnt-bound activated state that drives cell fate

acquisition. Our study paints a different picture in which Pnt plays a role in establishing and

stabilizing that initial Yan-bound repressed state, and in fact co-occupies many regulatory

elements with Yan. It is important to note that because the sequential ChIP analysis was not

performed genome-wide and because whole embryos rather than single cells were profiled, it is

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formally possible that Yan-Pnt co-occupancy only occurs at a small subset of targets and that

the broad similarity in overall occupancy patterns primarily reflects a mix of exclusively Yan or

exclusively Pnt binding at the same enhancers in different cells. Arguing against this, the

genome-wide loss of Yan occupancy in pnt mutants and normal Pointed occupancy in yan

mutants positions Pointed as the critical determinant and/or stabilizer of Yan binding and

repression, with co-occupancy likely relevant to the mechanism.

We therefore speculate that Pnt is required first to set up Yan occupancy and repression and

second to respond to RTK signaling by activating target gene expression; its activating function

is thus epistatic to its repressive role, explaining why predominantly loss of function phenotypes

have been described for pnt mutants. Use of the same transcription factor to dictate both the

repressive regulation that maintains the initial multipotent state and the subsequent activation

that changes it, enables a level of temporal coordination of gene expression dynamics that may

be critical to the robustness of differentiative transitions. We also note that although Yan/Pnt

function has been studied primarily in the context of RTK signaling, the two are co-expressed in

many tissues across development, including those presumed to have low RTK signaling input

(Gabay et al. 1997; Boisclair Lachance et al. 2014), and co-occupy regulatory elements across

a broad swath of signaling pathway genes and critical developmental regulators that are unlikely

all to be regulated downstream of RTK signaling. Thus Pnt-Yan-Gro enhancer co-occupancy

may provide a modular repressive mechanism that can be adapted to a variety of regulatory

situations.

How Pnt-Yan co-occupancy is organized/facilitated by the DNA sequence of each enhancer will

be interesting to explore. One possibility is that Pnt initially interacts with all ETS binding sites to

open up a regulatory element, but then gets displaced at a subset of sites upon recruitment of

Yan, perhaps remaining bound only at sites critical for subsequent activation. Alternatively,

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distinct sequence preferences rather than affinity differences might result in Pnt occupancy of

only a specific subset of ETS binding sites, leaving others free for Yan to bind. This model also

supports a variation in mechanism in which Pnt and Yan are recruited jointly, rather than

sequentially, to establish the initial repressed state, with co-occupancy essential to stabilize Yan

binding. Our recent work exploring how the cis-regulatory organization of the eve muscle heart

enhancer (MHE) organizes Yan and Pnt inputs supports the idea that distinct sequence

preferences enable simultaneous occupancy and hence complex integration of repressive and

activating inputs (Boisclair Lachance et al. 2018).

The mechanism by which Yan occupancy depends on Pnt also remains to be elucidated. One

possibility is that Pnt recruits Yan directly. However to date our efforts to detect Yan-Pnt protein-

protein interactions, either in vitro, in two-hybrid screens, or in standard co-immunoprecipitation

experiments, have yielded negative results. Indirect protein-level interaction mechanisms, such

as bridging the complex with Gro or with another transcription factor, may thus be more likely.

The strong overlap between Mad and ETS binding sites noted in Yan/Pnt-bound regions

genome-wide (Webber et al., 2013a) makes the Dpp effector Mad an intriguing candidate.

Alternatively, rather than nucleating specific protein complexes, Pnt may establish or interpret a

local chromatin state that permits Yan binding. Analogous pioneer-like activity has been

described for a few other ETS factors including PU.1 and ETV2 (reviewed in Iwafuchi-Doi and

Zaret 2014; Kanki et al. 2017). As pnt encodes two alternatively spliced products, Pnt-P1 and

Pnt-P2, that contain the same DNA binding domain but different amino-terminal activation

domains and exhibit different patterns of expression and signal responsiveness (Klämbt 1993;

Scholz et al. 1993; O’Neill et al. 1994; Brunner et al. 1994; Gabay et al. 1996; Shwartz et al.

2013), it will be important to re-evaluate the role of each isoform during cell fate transitions with

respect to the establishment of Yan/Gro binding, target gene repression and the subsequent

switch to activation.

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Independent of precise mechanism, Yan’s reliance on Pnt for its stable recruitment provides a

plausible explanation for a previous unexpected finding that doubling Yan dose does not lead to

increased or ectopic DNA occupancy (Webber et al. 2013a). More broadly, the strategy of the

activator recruiting the repressor could provide an occupancy feedback circuit that buffers

against fluctuations in activator or repressor concentrations. For example, the standard

competition model predicts that a multipotent cell with lower than normal Pnt will over-recruit

Yan; we speculate that such Yan-dominated repression would be sluggish in response to

inductive cues. In contrast, a system in which Yan occupancy depends on Pnt might be buffered

against such variation, since the consequence of lower Pnt levels would be less efficient Yan

recruitment, which should maintain the appropriate Yan-Pnt balance.

We speculate that this precisely poised Pnt-Yan-mediated repressive state is achieved through

close coordination with the RNA pol II pausing machinery (summarized in Figure 7). Several

pieces of evidence support this idea. For example, not only is Pnt essential for proper Yan

occupancy, but it also recruits the corepressor Gro to the same set of regulatory elements. Prior

genome-wide analyses of Groucho occupancy and function in embryos and cultured cells have

shown that Gro-regulated genes are enriched for epigenetic marks and promoter proximal

transcripts commonly associated with paused RNAPII (Kaul et al. 2014; Chambers et al. 2017).

Our demonstration of eve derepression in embryos doubly heterozygous for gro and Trl

provides the first genetic evidence of a possible direct mechanistic link between Gro repressive

complexes and the RNAPII pausing machinery. Our study also emphasizes the likely

importance of Pnt to the Gro-paused RNAPII connection. For example, included among the set

of genes showing coordinately disrupted Yan-Gro occupancy and derepression in pnt mutant

embryos is E(spl)mbeta-HLH, a target previously shown to be regulated by Gro-dependent

RNAPII pausing in cultured cells (Kaul et al. 2014 and Supplemental Figure 4).. The web of

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synergistic genetic interactions between pnt, yan, gro and mutants in RNAPII pausing factors

like Trl, Bab1 and NELF further supports such a model.

RNAP II pausing establishment and release is also closely linked to Polycomb (PcG) repressive

complexes in both Drosophila and mammals (Schwartz et al. 2010; Gaertner et al. 2012;

Bernstein et al. 2006; Ferrai et al. 2017) and PcG repression has in turn been connected to

Groucho (Abraham et al. 2015). For example, a recent study describes how recruitment of the

Hox family transcriptional activators AbdA and Ubx reduces PcG binding at RNAPII paused

genes to promote release and transcriptional activation (Zouaz et al. 2017). Similarly ETS1, the

mammalian ortholog of Pnt, promotes release of paused RNAPII to activate angiogenic gene

expression, although connections to PcG complexes were not investigated (Chen et al. 2017).

Trl, which our study connects genetically to Pnt-Gro-Yan repressive mechanisms, helps direct

PcG proteins to Polycomb response elements, or PREs, and thus contributes to PcG repressive

activity (Mahmoudi et al. 2003; Mishra et al. 2003; Mulholland et al. 2003). Given that eve is a

PcG target gene, with a validated PRE (Dura and Ingham 1988; Fujioka et al. 2008; Kim et al.

2011), it may provide an ideal context for elucidating the molecular levels of integration between

Yan-Pnt-Gro and PcG repressive complexes in relation to RNA Pol II pausing.

In conclusion, we propose that analogous to the use of conflicting epigenetic marks to poise

RNAPII, the inherent conflict of co-occupancy by an activator-repressor pair like Pnt-Yan

establishes an exquisitely sensitive and dynamic repressive mechanism that confers robustness

to developmental gene expression regulation. Because loss or misexpression of ETS

transcription factors contributes to many cancers and because oncogenic transformation relies

on dysregulated use of normal developmental pathways, exploration of these ideas in

mammalian systems may provide new insight into human disease.

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Materials and Methods

Chromatin immunoprecipitation

All ChIP was from Stage 11 embryos (5h20-7h20) processed as previously described (Webber

et al. 2013a) and summarized in supplementary methods. Sequential ChIP of Yan and Pnt was

performed by first immunoprecipitating Yan (guinea pig anti-Yan, (Webber et al. 2013a)). The

protein-DNA complex was eluted in reduced volume and diluted 10 times in ChIP lysis buffer

before performing the second round of ChIP with a rabbit GFP antibody (rabbit anti-GFP,

A6455, Invitrogen, Lot# 1603336). A no antibody control (mock-treated) processed identically to

experimental samples was included. For ChIP-seq, two biological replicates and an input

sample were sequenced on an Illumina Hi-seq instrument according to the Illumina protocols.

The raw sequence data was aligned to the April 2006 D. melanogaster genome using BWA (Li

and Durbin. 2009). Following standard practice in the field (Robertson et al. 2007; Rozowsky et

al. 2009; Zhong et al. 2010), having first confirmed consistency between replicates, the two IP

reads were combined and peak detection performed using MACS software with an mfold of 3,40

and otherwise default parameters. SPP was used to calculate genome-wide tag density profiles.

Default parameters were used, with the exception that scale.by.dataset.size=T option was used

to normalize tag density by the total dataset size to make it comparable across samples

(Kharchenko et al. 2008). ChIP-qPCR was performed as previously described (Webber et al.

2013a) and summarized in supplementary methods.

Microarray analysis

A custom expression array was designed on the Agilent GE 8x15K platform. The microarray

included 7080 probes for putative Yan target genes identified in Webber et al., 2013a (designed

using the Earray software by Agilent), 1894 probes for random genes and 536 control probes.

Total RNA was extracted from stage 11 wildtype, yan null or pnt null embryos with TRIzol

Reagent (Invitrogen) following manufacturer’s protocol, and purified using the RNeasy Mini Kit

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(Qiagen). Total RNA was labeled and hybridized to the microarray using the Quick Amp One-

Color Labeling Kit (Agilent) as described by the manufacturer. Triplicate experiments for each

genotype were performed. Expression array data were first analyzed by the Feature Extraction

Software (Agilent) using default parameter settings, and the processed signals for the probes

were then used for downstream analysis. Linear models were generated for each array using

only the probes in the random gene set, and then signals were normalized across all arrays.

After normalization, the average signal for each probe across triplicate experiments of each

genotype was calculated, and signal fold changes between wild-type and the pnt or yan mutants

were computed. T-tests were performed at the probe level, and probes with a p-value less than

0.05 were selected as significant (Supplemental Table 4).

Tag density analysis and generation of heatmaps

Sorted bed files were produced using the BEDOPS wig2bed script (Neph et al. 2012). To

manage the large number of calculations, a Visual Basic Application (VBA) for Microsoft Excel

was used to generate matrices of read density data for groups of given bound regions +/- 5kb

from the midpoint of each individual region. The matrices were used for a) calculating ratios of

TF occupancy in mutant vs. wildtype b) producing aggregate read density profiles by averaging

read density across all peaks and c) generating TF binding heatmaps.

Differential binding analysis

Bed files of MACS defined bound regions and sequence aligned reads for WT and mutant

datasets for each factor were generated. The differential binding analysis software, MAnorm

(Shao et al. 2012), was used to generate a merged set of bound regions for each factor with

quantitative values of differential binding and associated P-values for each peak. These

datasets were used to describe patterns of peak gain or peak loss, where peak loss

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corresponds to normalized M values of > 0.5 and peak gain of < -0.5 (Figure 1D,F,

Supplemental Tables 2 and 5).

Eve quantification

For quantification of Eve levels and numbers of Eve+ cells, embryos were stained as described

in Webber et al. 2013a with 1:10 mouse anti-Eve (3C10, Developmental Studies Hybridoma

Bank [DHSB]). Using a Zeiss 880 confocal microscope, serial 0.8µm z-sections were taken

through the Eve-positive mesodermal cells and maximum projections generated. Expression

intensity was calculated as the mean pixel intensity for each Eve+ cluster minus the mean

background pixel intensity, normalized to the average cluster intensity of the control imaged in

the same session. For cell counts, Eve+ cells were counted by going through Z-stack projections

of the relevant slices. For rescue experiments, stage 11 embryos of each genotype were hand-

selected, transferred to vials and incubated at 25C until adults emerged.

Drosophila strains and genetics

The following stocks were obtained from the Bloomington Drosophila Stock Center: w1118,

pnt∆88/TM3,Sb1, w1;Trl13C/TM6B,Sb1,Tb1 , Df(3L)babAR07,bab1AR07,bab2AR07/TM6B,Tb1 , y1,w67c23;

P{w+mC]y+mDint2=EPgy2}EY07065/TM3, Sb1,Ser1. Additional stocks used include: groMB36

(Jennings et al. 2008), pntAF397(Rebay et al. 2000), yanER443 and yanE833 (Karim et al. 1996), Pnt-

GFP (Boisclair Lachance et al. 2014) and Eve-YFP (Webber et al. 2013b). To allow genotyping

of stage 11 embryos, stocks were rebalanced over twist-Gal4>UAS-GFP marked 2nd and 3rd

chromosome balancers.

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Data Availability

ChIP-seq and microarray data from this study have been deposited at GEO with accession

numbers GSE114092 and GSE114209 respectively.

Statistical Analysis

Data are presented as mean +/- SEM except where otherwise described and minimum sample

sizes are reported in each figure. Data were plotted and analyzed for statistical significance

using Graphpad Prism software. Statistical significance was determined either by a two-tailed t-

test where appropriate, or alternatively with a one-way ANOVA in combination with Tukey’s

multiple comparison tests to compare two or more groups. P-values less than 0.05 were

considered to be statistically significant.

Acknowledgements

We thank Pieter Faber, Mikayka Marchuk and Abhilasha Cheruku in the University of Chicago

Genomics Facility for help with ChIP-seq and microarray. Jean-Francois Boisclair Lachance,

Kohta Ikegami, Rebecca Spokony and Matthew Slattery provided many helpful discussions and

comments on the manuscript. We acknowledge the Bloomington Drosophila Stock Center (NIH

P40OD018537) and the Developmental Studies Hybridoma Bank (created by the NICHD of the

NIH) for critical reagents. This work was supported by American Heart Association Grants

#12POST12040225/Jemma Webber/2012-2014 and #15POST22660028/Jemma Webber/2015

to J.L.W., by NIH R01 GM080372 to I.R. and by the Genomics Core Facility through a

University of Chicago Cancer Center Support Grant P30 CA014599. N.S.L was supported in

part by NIH T32 GM007281 and by NIH R01 EY12549 to I.R.

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Figures

Figure 1. Pnt recruits Yan to chromatin. A) Comparison of ChIP-seq read density for Pnt-

GFP (Pnt) and Yan across argos (aos), with RefGene gene track shown below profiles. * marks

region assessed by ChIP-qPCR. B) Sequential Yan-Pnt ChIP-qPCR analysis plotted as fold

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increase relative to mock-treated control, normalized to a negative control region (NC1, Webber

et al., 2013a), using mean +/- SEM from two (cv2 and lace) or more separate experiments. C,E)

ChIP-seq read density profiles for Pnt-GFP and Yan from wildtype (WT) and yan or pnt mutant

embryos at neur. Red shading highlights an example of statistically significant reduction in Yan

occupancy. D,F) Pie charts showing the proportion of Pnt or Yan peaks gained or lost in the

reciprocal mutant background. G) ChIP-qPCR analysis of Yan occupancy at candidate target

regions from either control (wildtype) or pnt mutant embryos. Data from at least three separate

experiments are plotted as mean +/- SEM values normalized to a negative control region. H)

Comparison of ratios of Yan read density in pnt mutants relative to the wildtype control. Yan

bound regions that do not intersect with a Pnt-bound peak were less affected by loss of pnt than

peaks that intersect Pnt. P-value of <0.01 depicted by ** (Students t-test).

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Figure 2. Yan and Pnt negatively regulate gene expression. A) Volcano plot depicting fold

change in gene expression in a pnt mutant relative to wildtype versus P-value. Probes that pass

a P-value threshold for either up- or down-regulated expression are depicted in red and blue,

respectively. B) qPCR confirmation of gene expression changes detected in the microarray. Bar

charts depict mean +/- SEM of at least 3 independent experiments. C) Scatterplot shows a

strong correlation between differential gene expression in the yan and pnt datasets.

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Figure 3. Pnt recruits the corepressor Gro to Yan bound regions. A) Heatmap analysis of

Yan, Pnt-GFP (Pnt) and Groucho occupancy at the top 250 Yan/Pnt bound regions. Each row

represents an individual peak that spans 2kb, inversely sorted by ChIP-signal and centered

around each peak midpoint. B) Ratios of Groucho occupancy in pnt mutants relative to the

wildtype control show that Groucho bound regions that do not intersect with a Pnt-bound peak

were relatively unaffected by loss of pnt, whereas Groucho binding was reduced at regions

normally bound by Pnt. P-value of <0.0001 depicted by **** (Students t-test) C) Average signal

intensity plots show reduced Groucho occupancy occurs predominantly at peaks with wildtype

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Pnt binding. D,E) Read density profiles for Pnt-GFP, Groucho and Yan from wildtype or mutant

embryos at the neur and turtle (tutl) loci. Red highlighted regions contrast the coordinate loss of

Yan and Gro occupancy at neur in the absence of pnt with the lack of change in Gro at tutl

where Yan is not normally bound. F) Groucho peaks not bound by Yan are less significantly

reduced in pnt mutants than Groucho peaks that overlap Yan in wildtype conditions. P-value of

<0.0001 depicted by **** (Students t-test) G) The set of genes associated with both Yan and

Groucho peak loss in the pnt mutant is enriched for genes with significantly elevated expression

in the microarray.

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Figure 4. Yan, Pnt and Groucho collaborate to fine-tune eve expression A) Read density

profiles for Pnt-GFP, Groucho and Yan from wildtype or mutant embryos at the eve locus. Red

highlighting shows that Pnt-GFP occupancy is broadly maintained at the MHE and D1 in the

absence of Yan, whereas Yan and Gro occupancy is reduced at both elements in the absence

of Pnt. B) ChIP-qPCR analysis of Yan occupancy at the D1 and MHE in stage 11 wildtype or pnt

mutant embryos. Data from at least five separate experiments are plotted as +/- SEM values

normalized to a negative control region. C) Sequential ChIP detects Yan-Pnt co-occupancy at

the D1 but not at the MHE. Fold increase relative to mock-treated control and normalized to a

negative control region is plotted. Bars represent +/- SEM of at least 6 independent

experiments. D) Quantification of average Eve levels per cluster in different genetic

backgrounds. Box plots depict measurements from at least 70 clusters. P-values of <0.0001

and <0.001 are depicted by **** and *** respectively (Anova, Tukey’s multiple comparison test).

E) Bar charts depicting the frequency of clusters with different numbers of Eve+ cells from at

least 7 embryos.

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Figure 5. Chromatin marks and TF occupancy associated with the eve locus predict a

poised chromatin state A) The eve locus is associated with the poised chromatin signature

comprising H3K27me3 and H3K4me1 and depletion of H3K27ac. ChIP-seq profiles of

modEncode datasets (H3K27me3: Stage 4-8hr embryos, modEncode 811; H3K4me3: Stage 4-

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8hr embryos, modEncode 778 and H3K27ac, Stage 4-8hr embryos, modEncode 835) were

visualized using IGB. Yan and Pnt ChIP datasets are shown for reference. The RefGene gene

track is shown below the profiles. Green boxes depict peaks called using IGB and a 97%

threshold. B) Trl and Bab1 are associated with Yan, Pnt and Gro bound regions. Aggregate

binding profiles of Trl (GAF: Stage 8-12 embryos, modEncode 3397) and Bab1 (Bab1: 0-12hr

embryos, modEncode 628) were generated for regions of the genome bound by Yan, Pnt and

Groucho. C) ChIP-seq profiles of Trl, H3K4me3 and RNApol II (GAF: Stage 8-12 embryos,

H3K4me3: Stage 4-8 hr embryos, modEncode 790; RNA pol II: Stage 4-8hr embryos,

modEncode 846) at the eve locus. Green boxes depict peaks called using a 97% threshold with

IGB.

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Figure 6. Pnt interacts with the pausing machinery to poise expression of eve A) Box plots

of relative Eve intensity measurements from at least 30 clusters. P-values of <0.0001 and

<0.001 are depicted by **** and *** respectively (Anova, Tukey’s multiple comparison test). E)

Bar charts depicting the frequency of clusters with different numbers of Eve+ cells from at least 3

embryos C) Adult survival rates of indicated genotypes, plotted as mean+/-SEM of at least three

independent experiments.

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Figure 7. Proposed model of Yan-Pnt repressive synergy Pnt promotes the recruitment and

stabilization of Yan and Gro and coordinates interactions with the RNA pol II pausing machinery

to maintain a poised state in progenitor cells. Following signaling cues, the disassembly of Yan,

Pnt and Gro complexes would release pol II pause and allow productive elongation. Such a

mechanism could act to sense the signaling status of the cell, ensuring that pol II pausing is only

released once a given signaling threshold is achieved, thereby conferring precise and perhaps

synchronous gene expression.

D

evel

opm

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Acc

epte

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crip

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Supplemental Figure 1) Yan and Pnt ChIP-seq bound regions are highly

overlapping. A,B) Comparison of ChIP-seq read density for Pnt-GFP (Pnt) and Yan

across the argos and neuralized loci. The RefGene gene track is shown below the

profiles. Green boxes depict peaks called using the Integrated Genome Browser (IGB;

Affymetrix) using a cut-off of the top 3% of bound regions (97% threshold) as previously

described in Webber et al. 2013a. Purple boxes depict peaks called by MACS. C)

Analysis of transcription factor PWMs revealed central enrichment for Mad and ETS

motifs in the top 100 sequences bound by Pnt. D) Venn Diagram depicting GO terms

significantly overrepresented in Yan and Pnt datasets.

naYPF

Gt nP

80

60

Chr3L:16,463,000-16,478,000

aos

2kb

Threshold (97%)MACS

A

2kb

neurhyx

Chr3R:4,846,000-4,868,000

Threshold (97%)MACS

B

Threshold (97%)MACS

Threshold (97%)MACS

0.00050.00100.00150.00200.00250.00300.00350.00400.00450.0050

0.0000-250-200-150-100-50 0 50 100 150200 250

Position of Best Site in Sequence

ytilibaborP

ETS96BYan Mad

C

886243 175

Yan and Pnt Biological GO termsD

YanPnt

Webber_SuppFig1

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Supplemental Figure 2) Yan protein levels in wildtype and pnt null embryos.

Embryos were fixed, stained and imaged in parallel with identical confocal settings.

Wildtype pnt mutant

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Supplemental Figure 3) Gro occupancy is reduced in pnt mutants, while Gro

protein levels do not change. A) Gro protein level does not change in pnt null

embryos. B) Pixel intensity ratios of pnt mutant to wildtype for Gro and Tubulin. C) The

proportion of genes associated with Gro occupancy loss (<0.5), no change (0.5-1.5) and

occupancy gain (>1.5) were determined for i) all bound genes and ii) bound genes

associated with upregulated expression in a pnt mutant. Genes that are upregulated in

pnt mutants are more frequently associated with Gro peak loss relative to all Gro bound

regions.

_-tubulin

Groucho

wildtyp

e

pnt -/

-A

<0.5 0.5-1 1-1.5 1.5-2Ratio of Groucho read density (pnt mutant/wildtype)

B

Gro_-t

ubuli

n0

50

100

150

Back

grou

nd s

ubtra

cted

pixe

l int

ensi

ty wildtypepnt -/-

CAll bound genes (2748) Genes that are upregulated in a pnt mutant (210)

51%35%

8%4%

16%

55%

20%

7%

Webber_SuppF

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Supplemental Figure 4) In the absence of pnt, Yan and Gro occupancy is reduced

at the E(spl)mbeta-HLH gene locus and unregulated expression is observed. A)

Comparison of ChIP-seq read density for Groucho and Yan across the E(spl)mbeta

locus. The RefGene track is shown below the profiles. B) Bar charts showing the

average upregulation of E(spl)mbeta-HLH probes from yan or pnt mutants relative to

WT. Error bars represent standard deviation of 4 probe sets.

AWebber_SuppFig4

Chr3R:21,828,000-21,838,000

250

250Groucho

WT

pnt

WT

pnt80

80

Yan

E(spl)mbeta-HLH

0

0.2

0.4

0.6

0.8

1

yan pnt

Log2

Fol

d C

hang

e (m

utan

t/wild

type

)

E(spl)mbeta-HLH

B

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Supplemental Figure 5) yan and pnt collaborate to fine-tune eve expression. For

each genotype, the average pixel intensity of Eve-YFP expression was measured per

cluster of Eve-positive cells and the background pixel intensity was subtracted.

Measurements were normalized to the average wildtype cluster intensity. Animals

doubly heterozygous for yan and pnt were associated with increased cluster intensity

relative to the wildtype control and single heterozygotes (n = at least 4 embryos per

genotype; P<0.0005 yan/+;eve-YFP/+ and P<0.0001 yan/+;YFP,pnt/+, ANOVA).

eve-Y

FP/+

eve-Y

FP,pnt/+

yan/+;

eve-Y

FP/+

yan/+;

eve-Y

FP,pnt/+

0

1

2

3

4

Rel

ativ

e In

tens

ity o

f Eve

-YFP

A

*** ****

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Supplemental Figure 6) yan and gro genetically interact with Trl. For each given

genotype, Eve expression was measured in each cluster of Eve-positive mesodermal

cells. An increase in Eve intensity was measured in animals heterozygous for both Trl

and either yan or gro relative to the single heterozygotes (n = at least 5 embryos; P

<0.05 yan/Trl; P<0.0005 gro/+ and P<0.0001 gro/Trl, ANOVA).

wtTrl

/+ya

n/+

yan/T

rlgro

/+gro

/Trl

0

1

2

3

4

Rel

ativ

e In

tens

ity o

f ev

e ex

pres

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A

**** ****

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Supplemental Table 1) List of Pnt and Yan bound regions and associated genes. UCSC

Table browser (Karolchik et al. 2004) was used to assign each Pnt-GFP and Yan bound region

to the nearest gene.

Supplemental Table 2) MAnorm comparison of Pnt datasets. M = log2 (Pnt-GFP read

density wildtype/Pnt-GFP read density yan mutant) and A = 0.5 x log2 (Pnt-GFP read density

wildtype/Pnt-GFP read density yan mutant). A linear regression model was applied using peaks

common to both wildtype and yan mutant datasets (Shao et al. 2012b). This model was then

used to output normalized M value (Column E) and A value (Column F) and associated -log10 P-

value (Column G). Normalized M values were used as a readout for differential binding with M

values of >0.5 or <-0.5 denoting peak loss or gain, respectively. Using this cut-off, all

differentially bound regions have P-values <0.05.

Supplemental Table 3) Panther enrichment analysis of genes that are upregulated or

downregulated in pnt mutant embryos. Significant GO terms describing each gene set

(upregulated or downregulated genes) are listed, along with the fold enrichment over

background and associated P-value. Significant GO terms for each dataset were compared to

identify overlapping GO terms or uniquely enriched GO terms.

Click here to Download Table S1

Click here to Download Table S2

Click here to Download Table S3

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Supplemental Table 5) MAnorm comparison of Yan datasets. M = log2 (Yan read density

wildtype/Yan read density pnt mutant) and A = 0.5 x log2 (Yan read density wildtype/Yan read

density pnt mutant). A linear regression model was applied using peaks common to both

wildtype and pnt mutant datasets (Shao et al. 2012b). This model was then used to output

normalized M value (Column E) and A value (Column F) and associated -log10 P-value (Column

G). Normalized M values were used as a readout for differential binding with M values of >0.5

or <-0.5 denoting peak loss or gain, respectively. Using this cut-off, all differentially bound

regions have P-values <0.05.

Supplemental Table 4) List of all probes with fold change (mutant/wildtype) and P-value.

Probes were assigned to genes using probe annotations from affymetrix and Flybase

(Gramates et al. 2017). A gene was considered as differentially expressed if it had an assigned

probe with a P-value <0.05 irrespective of fold change. Where two or more probes correspond

to the same gene, the highest absolute value (maximizing) was utilized for differential

expression analyses.

Click here to Download Table S4

Click here to Download Table S5

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Supplemental Methods

ChIP

Stage 11 embryos (5h20-7h20) were dechorionated in 50% bleach and cross-linked with

1.8% formaldehyde for 15 min. Cross-linking was stopped by washing embryos with

125mM Glycine. Fixed embryos were washed twice with PBS/T (PBS, 0.1% Triton X-

100) and Wash Solution (10mM HEPES, 10mM EDTA, 0.5mM EGTA, 0.25% Triton X-

100), and then homogenized in ChIP lysis buffer (50mM HEPES, 140mM NaCl, 1mM

EDTA, 1mM EGTA, 1% Triton X-100, 0.1% sodium deoxycholate, protease inhibitor

tablet (Roche)). Lysates were sonicated (11 cycles at 15% amplitude for 15 sec (0.9 sec

on/0.1 sec off)) using a Fisher Scientific Sonic Dismembrator sonicator. Clarified lysates

were incubated with the previously validated antibodies: guinea pig anti-Yan (1:500, in-

house, (Webber et al. 2013a)), rabbit anti-GFP (1:100, A6455, Invitrogen, Lot# 1603336,

(Yamaguchi et al. 2009)) or anti-Gro (1:200; (Nègre et al. 2011)) overnight at 4°C.

Gamma-bind sepharose beads (GE Healthcare) were added to the lysates and samples

were incubated for 4 hours, rotating at 4°C. The beads were washed thrice in ChIP lysis

buffer, once in high-salt ChIP lysis buffer and once in TE. ChIPed material was eluted by

a 15 min incubation at 65°C in TE/1% SDS with regular vortexing. Chromatin was

reverse cross-linked by incubation overnight at 65°C. DNA was purified using the

QIAquick PCR Purification Kit (Qiagen).

For ChIP experiments performed in a pnt mutant background, batches of 400 stage 11

GFP negative pnt null embryos were hand selected from embryo collections of either

pntAF397/TM3, twist-Gal4,UAS-GFP (TTG) or pnt∆88/TTG animals. Around 1600 pntAF397

embryos were used for each ChIP-seq replicate of Gro, and ~4000 pnt∆88 embryos for

each replicate of Yan.

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ChIP-qPCR was performed using the QuantiTech SYBR Green PCR Kit (Qiagen).

Briefly, the relative amounts of input and immunoprecipitated DNA were determined

based on standard curves generated for each primer pair (sequences previously

published in (Webber et al. 2013a, 2013b)), and the ChIP signals were calculated as %

input. ChIP signals were normalized to a negative control region (NC1) to account for

any differences in starting material amount.

RT-PCR

Total RNA was isolated using TRIzol (Invitrogen, 15596018) according to the standard

protocol (http://www.flychip.org.uk/protocols/gene_expression/standard_extraction.pdf).

Purified RNA was resuspended in 16µl of RNAse-free dH20 and subjected to a 1 hour

DNase I (Invitrogen, 18068015) treatment at 37°C. 2µg of RNA was reverse transcribed

using the Promega Reverse Transcription System (A3500) in 20µl using oligo-dT primer.

Real-time PCR was performed using a 1:5 -1:10 dilution of cDNA with the StepOnePlus

Real-Time PCR system (Applied Biosystems). The following primers were utilized: aos 5’

GCATCCTCTACCAAGTGGGG and 3’ GCGATTCGATTCAGGACAACG; mae 5’

TATCAAATGCTGGACAAGTG and 3’ TCAGTCGATTGTTATTGTCG; eve 5’

CCTCTTGGCCACCCAGTA and 3’ CGGACTGGATAGGCATTC; aop 5’

CCAGCAACGAGGACTGTTATCC and 3’ AAGCGGCTACCTGGTGTT; pnr 5’

AGAAAACGGGAAGTGGTTCG and 3’ CTGAGCGAGGGTTTGAGATC

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tinc 5’ ATCTGCATCTGAACTCGCTATG and 3’ TCCAGGGTATCAAAGAGCATCC; Adhr

5’ ACAAGAACGTGATTTTCGTTGC and 3’ ACGGTCACCTTTGGATTGATTG; IA2 5’

GCACTCCGAGGTCTGCTAC and 3’ CTTCTCAATGTCCTCAACGTC and RpS17 5’ 5'

CGAACCAAGACGGTGAAGAAG and 3’ CCTGCAACTTGATGGAGATACC.

ChIP-seq visualization and thresholding

Integrated genome browser was used to visualize wig files for each ChIP-seq dataset.

Thresholding of the top 3% of bound regions (97% threshold) was used to separate the

genome into bound and un-bound regions for each transcription factor.

Motif analysis

Centrimo analysis (Bailey and Machanick 2012) was performed on the top-scoring 100

Pnt ChIP-seq peaks identified by MACS. Each region was trimmed to 500bp around the

MACS defined summit. Sequences were scanned against a set of 1419 DNA motifs from

a combined database of Drosophila TF DNA binding sites (OnTheFly (Shazman et al.

2014), Fly Factor Survey (Zhu et al. 2011; http://pgfe.umassmed.edu/TFDBS), FLYREG

(Bergman et al. 2005), iDMMPMM and DMMPMM (Kulakovskiy and Makeev 2009;

Kulakovskiy et al. 2009)) with motif sites reported only when enriched.

GO analysis

MACS defined bound regions were assigned to the nearest TSS using the UCSC

genome browser. Genes were functionally classified with Gene Ontology terms using

GO (Ashburner et al. 2000; The Gene Ontology Consortium, 2017).

Assigning Yan/Gro peak loss to Pnt bound regions

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To assess whether Yan and Gro peak loss occurs preferentially at Pnt bound regions,

the read density of Pnt at the midpoint of Yan MACS defined peaks was first determined

(see materials and methods for description of read density analysis; Table S5). Using a

read density threshold of 97% (see Supplemental Figure 1), these peaks were

categorized according to the absence or presence of Pnt (Figure 1H; Yan only or Yan

and Pnt bound). The pnt mutant to wildtype Yan density ratio was calculated and plotted

for each “Yan only” bound region or each “Yan and Pnt” bound region. An identical

approach was taken to assess pnt mutant to wildtype Gro density ratios at “Gro only” or

“Pnt and Gro” bound regions in Figure 3B, and at “Gro only” or “Yan and Gro” bound

regions in Figure 3F.

Aggregate Profiles of Trl and Bab1

Intersectbed was used to overlay Yan, Pnt and Gro bound regions (called using our 97%

thresholded method). Sorted bed files for the Trl and Bab1 datasets were produced

using the BEDOPS wig2bed script (Neph et al. 2012). A Visual Basic Application (VBA)

for Microsoft Excel was then used to generate matrices of read density data spanning

2kb either side of the midpoint of peaks identified as Yan/Pnt and Gro bound (available

on request). These matrices were then aggregated to produce average read density

profiles.

Integrating ChIP-seq and expression array data

We have considered a probe to be differentially expressed if its expression changes

between two treatments, regardless of fold change. This approach was taken to

maximize the degree of overlap between the ChIP-seq and microarray datasets,

ensuring adequate sample size for the downstream analyses presented in Fig. 3G and

Supplemental Figure 3C. Thus, a P-value of the expression change between wildtype

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and yan or pnt mutants was calculated for each probe. Genes were considered to be

differentially expressed if they were associated with probes with P-values less than 0.05.

Determining whether Yan/Gro loss is associated with differential gene expression

For Figure 3G, MAnorm was used to first define regions of Yan loss. Regions with a

normalized M value >0.5 were selected as high-confidence peaks showing reduced Yan

occupancy. The read density of Gro was then determined at the midpoint of each of

these regions in WT and mutant datasets, and a ratio calculated. Regions were selected

as having reduced Yan and Gro occupancy if the M value was >0.5 and Gro ratio was

<0.5. These regions were then assigned to the nearest gene and cross-referenced to

genes in the microarray.

To assess Gro occupancy change at differentially expressed genes (Supplemental

Figure 3C), the genome coordinates of either i) genes associated with upregulated

probes (shaded red in Figure 2A) or ii) all genes bound by Groucho were determined

and cross-referenced with read densities of Groucho occupancy in both wildtype and pnt

mutant datasets. Ratios of Groucho occupancy in pnt mutants relative to the wildtype

control were calculated and used to bin genes into distinct categories of ratios of <0.5,

0.5-1, 1-1.5, or 1.5-2. For genes that were associated with multiple Gro peaks, the most

substantial wildtype peak was identified and used to assess the ratio of Gro read density

in the pnt mutant vs wildtype control.

Embryo staining and quantification

For Yan expression analysis, wildtype or pnt∆88 embryos were stained as described in

Boisclair-Lachance et al. 2014 with a guinea pig anti-Yan antibody (1:10,000 ; Webber et

al. 2013a). For Eve-YFP expression and quantification, w1118 females were crossed to

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the following males i) eve-YFP ii) eve-YFP, pntAF397/TTG iii) yan833/CTG; eve-YFP as

previously described in Webber et al. 2013b. Embryos were fixed and stained as

described here, using an anti-GFP rabbit antibody (1:1000, A6455, Invitrogen, Lot#

1603336). For determining whether yan and gro genetically interact with Trl, w1118 or

Trl13C/TTG females were crossed to i) yanER443/CTG or ii) groMB36/TTG males (Jennings

et al. 2008). Embryos were fixed and stained as described in Materials and Methods,

using an anti-Eve mouse antibody (3C10; 1:10, DHSB). Secondary antibodies are from

Jackson ImmunoResearch: donkey anti-guinea pig-Cy3 (1:2000) or donkey anti-mouse-

Cy3 (1:2000).

A Zeiss 880 confocal microscope was used to take serial 0.8µm z-sections through

layers of the embryo where Yan or Eve are normally expressed, and maximum

projections generated. For Eve quantification, the mean pixel intensity for each individual

Eve-positive cluster in the bilateral embryo was determined using Image J. After

subtracting the mean background pixel intensity for each cluster, measurements for

each genotype were normalized to the average cluster intensity of the control that was

fixed, stained and imaged in parallel.

Western blot analysis

Stage 11 wildtype or pnt∆88 embryos were dechorionated in 50% bleach and

homogenized in 50µl of SDS sample buffer (250mM Tris-Cl, pH 8, 10% SDS, 50%

glycerol, 50% β-mercaptoethanol, 0.04% bromophenol blue). Samples were passed

through a 27G needle 10 times and boiled for 10 min prior to running on an 8% SDS-

PAGE gel. After transfer to PVDF, blots were probed with mouse anti-Gro (1:100;

DHSB) and anti-tubulin (1:2000; Sigma), which served as a loading control.

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Bailey TL, Machanick P. 2012. Inferring direct DNA binding from ChIP-seq. Nucleic Acids Res 40: e128–e128.

Bergman CM, Carlson JW, Celniker SE. 2005. Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster. Bioinformatics 21: 1747–1749.

Hu Y, Sopko R, Foos M, Kelley C, Flockhart I, Ammeux N, Wang X, Perkins L, Perrimon N, Mohr SE. 2013. FlyPrimerBank: An Online Database for Drosophila melanogaster Gene Expression Analysis and Knockdown Evaluation of RNAi Reagents. G3 Genes Genomes Genet 3: 1607–1616.

Jennings BH, Wainwright SM, Ish‐Horowicz D. 2008. Differential in vivo requirements for oligomerization during Groucho‐mediated repression. EMBO Rep 9: 76–83.

Kulakovskiy IV, Favorov AV, Makeev VJ. 2009. Motif discovery and motif finding from genome-mapped DNase footprint data. Bioinformatics 25: 2318–2325.

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Nègre N, Brown CD, Ma L, Bristow CA, Miller SW, Wagner U, Kheradpour P, Eaton ML, Loriaux P, Sealfon R, et al. 2011. A Cis-Regulatory Map of the Drosophila Genome. Nature 471: 527–531.

Neph S, Kuehn MS, Reynolds AP, Haugen E, Thurman RE, Johnson AK, Rynes E, Maurano MT, Vierstra J, Thomas S, et al. 2012. BEDOPS: high-performance genomic feature operations. Bioinformatics 28: 1919–1920.

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Webber JL, Zhang J, Cote L, Vivekanand P, Ni X, Zhou J, Nègre N, Carthew RW, White KP, Rebay I. 2013a. The Relationship Between Long-Range Chromatin Occupancy and Polymerization of the Drosophila ETS Family Transcriptional Repressor Yan. Genetics 193: 633–649.

Webber JL, Zhang J, Cote L, Vivekanand P, Ni X, Zhou J, Nègre N, Carthew RW, White KP, Rebay I. 2013b. The Relationship Between Long-Range Chromatin Occupancy and Polymerization of the Drosophila ETS Family Transcriptional Repressor Yan. Genetics 193: 633–649.

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Development 145: doi:10.1242/dev.165985: Supplementary information

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