Different SP1 binding dynamics at individual genomic loci in … · 2020. 12. 18. · SP1 from...

Different SP1 binding dynamics at individual genomic loci in human cells

Yuko Hasegawa and Kevin Struhl*

Dept. Biological Chemistry and Molecular Pharmacology

Harvard Medical School

Boston, MA 02115

(617) 432-2104

(617) 432-2529 (FAX)

[email protected]

*To whom correspondence should be addressed.

.CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted December 19, 2020. ; https://doi.org/10.1101/2020.12.18.423560doi: bioRxiv preprint

https://doi.org/10.1101/2020.12.18.423560

http://creativecommons.org/licenses/by/4.0/

2

ABSTRACT

Using a tamoxifen-inducible time-course ChIP-seq approach, we show that the

ubiquitous transcription factor SP1 has different binding dynamics at its target sites in

the human genome that are not correlated with SP1 occupancy levels at those sites.

While ~70% of SP1 binding sites are located in promoter regions, loci with slow SP1

binding turnover are enriched in enhancer and Polycomb-repressed regions.

Unexpectedly, SP1 sites with fast turnover times tend to have higher quality and more

copies of the SP1 sequence motif. Different co-binding factors associate near SP1

binding sites depending on their binding kinetics and on their location at promoters or

enhancers. For example, NFY and FOS are preferentially associated near promoter-

bound SP1 sites with fast turnover, whereas DNA motifs of ETS and homeodomain

proteins are preferentially observed at sites with slow turnover. At promoters but not

enhancers, proteins involved in sumoylation and PML bodies associate more strongly

with slow SP1 binding sites than with the fast-binding sites. The speed of SP1 binding

turnover is not associated with nucleosome occupancy, and it is not necessarily coupled

to higher transcriptional activity. These results with SP1 are in contrast from those of

human TBP, indicating that there is no common mechanism affecting transcription

factor binding kinetics.



https://doi.org/10.1101/2020.12.18.423560


3

INTRODUCTION

Transcription factor binding to target DNA sequences is the critical step for regulating

gene expression in response to environmental and developmental cues. As human cells

encode ~2000 specific DNA-binding transcription factors, combinatorial binding of these

proteins to enhancer and promoter-proximal elements is the basis of the extraordinary

diversity in gene expression patterns (Struhl 1991). A typical transcription factor binds

thousands of target sites in human cells (Cawley et al. 2004; Yang et al. 2006; Johnson et al.

2007; Robertson et al. 2007), and catalogs of such binding events for many transcription

factors have been described (ENCODE 2012; Kheradpour and Kellis 2014; ENCODE 2020).

As expected, regulatory elements are bound by multiple transcription factors, typically

localized to regions spanning several hundreds of base pairs.

The dynamic behavior of transcription factors is described by parameters such as

diffusion in the nucleus and kinetics of binding and dissociation from DNA (Berg et al. 1981;

Gorski et al. 2006). Microscopic observations using fluorescence recovery after

photobleaching (FRAP) reveal high mobility and rapid turnover of transcription factor

binding to the genome in a timescale of seconds (McNally et al. 2000; Bosisio et al. 2006).

Recent advances in imaging technologies enable tracking the behavior of transcription

factors in the nucleus, which includes target search and chromatin association at the single

molecule level (Gebhardt et al. 2013; Chen et al. 2014; Hansen et al. 2017; Liu and Tjian

2018). Binding kinetics of transcription factors can change in response to environmental

conditions (Nalley et al. 2006; Yao et al. 2006), implying a connection between the binding

dynamics and transcriptional regulation. However, these studies do not address binding

dynamics on individual target sites on a genomic scale and whether differences in binding

kinetics has transcriptional consequences.

The combination of high-throughput techniques and competitive chromatin

immunoprecipitation (ChIP) can capture genome-wide information about the binding



https://doi.org/10.1101/2020.12.18.423560


4

dynamics of a transcription factor at individual binding sites ( Dion et al. 2007; van Werven

et al. 2009; Lickwar et al. 2012; Hasegawa and Struhl 2019). In yeast, analyses of the

TATA-binding protein (TBP) and the DNA-binding repressor/activator protein Rap1 show

considerable variation in residence times among target sites that is poorly correlated with the

level of binding (van Werven et al. 2009; Lickwar et al. 2012). Longer residence time (i.e.

slower turnover) for Rap1 is coupled to higher levels of transcription (Lickwar et al. 2012).

In previous work, we developed an inducible time-course ChIP-seq approach and found

that slow exchange of human TBP binding at promoters is correlated with strong

transcriptional activity of the downstream gene (Hasegawa and Struhl 2019). As in yeast,

human TBP displays rapid turnover at RNA polymerase (Pol) II promoters, slow turnover at

Pol III promoters, and very slow turnover at the Pol I promoter (van Werven et al. 2009;

Hasegawa and Struhl 2019). In addition, yeast and human TBP show widely variable

turnover rates at Pol II promoters that are not correlated with binding levels. However,

promoters with slow TBP turnover in human cells frequently contain TATA consensus

motifs and have higher levels of transcription (Hasegawa and Struhl 2019), whereas the

opposite is the case for yeast promoters (van Werven et al. 2009).

TBP is a general transcription factor whose associations with Pol I, Pol II, and Pol III

promoters requires a large number of other general transcription factors. As such, TBP is a

very different from the large number of DNA-binding transcription factors that selectively

associate with promoter-proximal and promoter-distal sequences. Binding dynamics of any

DNA-binding transcription factor has not been addressed on the genomic scale in human

cells, so the similarities and differences with TBP binding dynamics are unknown.

SP1 (specificity protein 1), among the first identified transcription factors that bind

specific DNA sequences, is ubiquitously expressed and essential in mammalian cells (Dynan

and Tjian 1983; Briggs et al. 1986; Kadonaga et al. 1987; Saffer et al. 1991). It binds GC-box

sequences found in 27% of human promoter-proximal regions (-150 bp to +50 bp to TSS),

and it functions as a transcriptional activator (Gidoni et al. 1984; Courey and Tjian 1988).



https://doi.org/10.1101/2020.12.18.423560


5

SP1 motifs are strongly enriched in promoter-proximal regions, and some DNA-binding

proteins that show similar binding patterns or motif co-occurrences are known (Guo and

Gifford 2017; Ma et al. 2017; Zacher et al. 2017; Movva et al. 2019). Here, we use our

ERT2-based inducible time-course ChIP-seq method (Hasegawa and Struhl 2019) to analyze

SP1 binding dynamics at target sites throughout the human genome. The parameters that

influence binding dynamics of SP1 are very different from those of human TBP, indicating

that there is no common explanation for differential binding kinetics of transcription factors

to their target sites.



https://doi.org/10.1101/2020.12.18.423560


6

RESULTS

ERT2-based inducible time-course ChIP in human cells

To examine the dynamics of SP1 binding to its genomic target sites, we established a

cell line expressing SP1-ERT2-3HA, a derivative of SP1 containing the ligand binding

domain of estrogen receptor (ERT2) and 3 copies of the HA epitope fused to the C-terminus

of SP1, from the endogenous SP1 locus (Figure 1A). The strain also expresses endogenous

SP1 from another copy of the same chromosome, roughly at comparable levels (Figure 1B).

Upon the addition of tamoxifen and hence activation of the estrogen receptor (Figure 1C), the

fusion protein (green) rapidly translocates to the nucleus and competes with endogenous SP1

(purple) for its target sites. More than 50% of the fusion protein translocates to the nucleus

within 15 minutes of tamoxifen addition and virtually all of it translocates within 30 min

(Figure 1D), whereupon it binds to target sites (Figure 1E). In contrast, and as an internal

control for each sample, the of total SP1 binding (endogenous SP1 + SP1-ERT2-3HA) does

not change upon tamoxifen treatment (Figure 1F), indicating that endogenous SP1 molecules

initially associated with their target sites are replaced by the induced SP1-ERT2-3HA

protein. The magnitude of SP1-ERT2 binding throughout the time-course is consistent with

the amount of nuclear SP1-ERT2 (Figure 1G).

Different SP1 binding dynamics on target loci are not linked to SP1 occupancy levels

The binding kinetics at individual sites over the tamoxifen-induced time course

determines whether a given site is a slow- or fast-binding site (Figure 1C). To analyze SP1

binding dynamics on a genomic scale, we performed ChIP-seq throughout the time-course

after tamoxifen induction of SP1-ERT2 nuclear translocation. SP1-ERT2 binding levels

gradually increase after tamoxifen induction (Figure 2A), and the levels at each time point

(except for 0 min) are strongly correlated among samples (Figure S1A). The level of SP1-

ERT2 binding determined here is well correlated to binding of endogenous SP1 in the same



https://doi.org/10.1101/2020.12.18.423560


7

cell line determined elsewhere (ENCODE 2012) (Figure S1B), indicating the binding fidelity

of the fusion protein.

We next classified SP1 binding sites based on their binding dynamics. However, the

mathematical modeling we previously used to calculate turnover rate for TBP-ERT2

(Hasegawa and Struhl 2019) could not be applied to SP1-ERT2, because it assumes that the

protein level in the nucleus is saturated at the end of time-course, which is not the case

(Figure S1C, D). We therefore normalized the ChIP-seq signal at the later timepoints (360

and 1440 min) by the relative nuclear SP1-ERT2 protein amount to that at 90 min. With this

normalized data, we obtained binding turnover rates of SP1 by fitting to the model (Figure

S1E) and observed a broad distribution of rates that show almost no correlation with SP1

occupancy (Figure S1F).

As a model-independent approach, we employed k-medoids clustering algorithms to

classify SP1 binding sites based on their binding dynamics. We subdivided SP1 binding sites

into 4 classes (Figure 2B). These four classes represent the different increasing speed of SP1-

ERT2 ChIP-seq signals (Figure 2C) and are consistent with the turnover rates calculated by

the mathematical modeling (Figure 2D). We confirmed the robustness of the clustering result

by obtaining similar results without 1440 min sample (Figure S2A). For subsequent analysis,

we used target sites that fall into the same class in both datasets (Figure S2B, yellow,

n=7997).

Slow SP1 binding sites are enriched at enhancers and other non-promoter regions

Based on chromatin states (Zacher et al. 2017), we classified SP1 binding sites (peak

summit ±250 bp) based on their location in enhancers, promoters, gene bodies, Polycomb-

repressed, and other genomic regions. In line with previous reports, 68% of SP1 target sites

are in promoter regions, 23% are in enhancer regions, 3.3% are in gene bodies, 4.1% are in

polycomb-repressed (H3-K27me3 containing) regions, and 1.7% are in other regions (Figure

3A). Interestingly, the percentage of slow binding SP1 sites in enhancers and polycomb-



https://doi.org/10.1101/2020.12.18.423560


8

repressed regions is higher than the percentage in the faster classes (Figure 3B). In accord

with this observation, the histone modifications around the SP1 peak summits for the slow

class of target sites show an increased ratio of H3-K4me1:H3-K4me3 (a signature of

enhancers; Figure 3C) as well as increased levels of H3-K27me3 and EZH2 (signatures of

polycomb-repressed regions; Figure 3D). Higher EZH2 and H3-K27me3 signals were

observed when promoters and enhancers were analyzed separately (Figure S3).

Fast SP1 binding sites tend to contain multiple SP1 binding motifs

SP1 binds to a consensus motif known as the GC-box, and some genomic loci have

multiple copies of this motif. Interestingly, loci with faster SP1 binding dynamics tend to

have more copies of the SP1 consensus motif than Slow class (Figure 4A). For example,

18% of the Fast SP-1 binding sites have 4 or more motifs as compared to only 2% of the

Slow SP-1 sites. In addition, the quality of the SP1 motifs (determined by match to position

weight matrix) is lower in the Slow class of sites (Figure 4B, C). These results are similar

even when enhancer and promoter regions are analyzed separately (Figure S4). In addition,

the AT content around the peak summit is higher at the Slow SP1 sites as compared to Fast

sites (Figure 4D). This higher AT content at Slow SP1 sites is more pronounced at enhancers

than promoters. Unexpectedly, Slow SP1 binding sites have higher nucleosome occupancy

in enhancer (but not promoter) regions (Figure 4E).

Co-binding transcription factors are associated with SP1 binding dynamics

As SP1 often binds together with other transcription factors, we searched the entire set

of known sequence motifs that are overrepresented in the fast or slow SP1 binding classes in

promoter regions. As expected from the results in Figure 4A, the SP1 motif is

overrepresented in the Fast class (Figure 5A, Table S1). The consensus motif of NFY (A and

B subunits) is also enriched in Fast class, consistent with the co-occurrence of the SP1 and

NFY motifs in many human promoters (Reed et al. 2008; Suske 2016). In accord with these



https://doi.org/10.1101/2020.12.18.423560


9

results, occupancies of SP1 and the related SP2 and SP3 are higher in the Fast class than in

the Slow class (Figure 5B). FOS, a member of the AP-1 family, has a co-occurrence pattern

with SP1 and NFY (Fleming et al. 2015; Suske 2016), and it also shows enrichment in the

faster classes (Figure S5A). Another AP-1 family member, JUN, does not show enrichment

for the faster classes. Even when selected sites within each class are chosen to equalize SP1

binding levels, NFY and FOS binding levels are higher in the faster classes (Figure S5B, C).

Interestingly, although the KLF family motifs are similar to the SP1 motif and enriched in the

Fast class of SP1 sites (Figure 5A), binding of KLF5, KLF16, and KLF1 is higher in the

Slow class of SP-1 sites than in the Fast class (Figure 5B). Similarly, the MZF1 binding

signal is higher in the Slow class (Figure 5B).

On the other hand, the consensus motifs of ETS or homeobox family members are more

frequently observed in Slow class of SP1 sites (Figure 5C, Table S1), and most of these

proteins show higher occupancies in the Slow class (Figure 5D). In enhancer regions, a

different set of the transcription factor motifs are overrepresented in the Fast class or Slow

class, especially for the Slow class (Figure 6, Table S1). ChIP-seq data show that SP1, SP2,

SP3 and ZNF740 are favored at the Fast SP1 class (Figure 6B), whereas GATA3, GATA2

and FOXJ2 are favored at the Slow SP1 class (Figure 6D).

PML and SUMOylated proteins preferentially accumulate on slow SP1 binding sites in

promoter regions

SP1 undergoes sumoylation (Spengler and Brattain 2006), which is promoted by PML,

a member of TRIM/RBCC family proteins that binds to the SUMO E2 ligase UBC9 (Li et al.

2014). PML bodies are a subnuclear compartment that recruits many sumoylated proteins

including SP1 (Li et al. 2014; Lallemand-Breitenbach and de Thé 2018). At promoters, ChIP

signals of PML, SUMO1- or SUMO2-conjugated proteins, and UBC9 are significantly

higher in the slow SP1 binding sites than the faster binding sites (Figure 7A). This tendency

is not observed at SP1 binding sites in enhancer regions (Figure S6). Gene Ontology (GO)



https://doi.org/10.1101/2020.12.18.423560


10

analysis shows that different terms are overrepresented in each class (Figure 7B and Table

S2; the Fast class was excluded because of the small sample size). GO terms suggestive of

PML function (e.g. stress responses, a protein modification process such as ubiquitin or

ubiquitin-like protein, cellular response to DNA damage), frequently appear in a top list of

significantly enriched annotation in the Slow class, while different GO categories, such as

regulation of transcription, are overrepresented in Middle fast class (Figure 7B).

Promoters with fast SP1 binding dynamics and enhancers with slow dynamics have

lower transcriptional activity

Active histone (H3-K27Ac) modifications around SP1 binding sites in enhancer regions

tend to be lower in Slow SP1 sites, and conversely, repressive histone (H3-K27me3)

modifications are higher (Figure 8A). Consistent with this, levels of Pol II are lower at the

SP1 binding sites and the closest genes of Slow class sites in enhancer regions as compared

the other classes (Figure 8B). On the other hand, levels of both active and repressive histone

modifications around SP1 binding sites in promoter regions are higher in Slow class sites

(Figure 8C), and higher Pol II recruitment is observed (Figure 8D). These results indicate that

SP1 binding dynamics differently correlate with Pol II recruitment depending on whether the

binding sites locates in promoter or enhancer regions.



https://doi.org/10.1101/2020.12.18.423560


11

DISCUSSION

Molecular distinctions between SP1 target sites with fast or slow dynamics

Genome-scale analysis of binding dynamics of transcription factors at individual sites

has been performed for Rap1 and TBP in yeast cells and for TBP in human cells (van

Werven et al. 2009; Lickwar et al. 2012; Hasegawa and Struhl 2019). For each of these

transcription factors, binding dynamics differ among target sites in a manner that is very

poorly correlated with the overall level of binding. Here, we show that the sequence-specific

transcription factor SP1 also displays different binding dynamics among its target sites in

human cells, and the dynamics show almost no correlation with occupancy. However, the

parameters that govern differential binding dynamics among SP1 sites differ from those of

TBP and Rap1.

SP1 sites showing Fast or Slow binding dynamics have distinct molecular properties.

First, Slow SP1 binding sites are relatively enriched at enhancers and polycomb-repressed

regions, and they are relatively depleted at promoters. Second, Fast SP1 binding sites tend to

have more copies of the SP1 recognition motif than Slow sites. Third, Fast SP1 binding sites

tend to have higher motif strength. Fourth, Fast and Slow SP1 binding sites are associated

with different sets of other transcription factors bound to nearby locations. Thus, differential

binding dynamics of SP1 at its genomic target sites can arise for multiple reasons.

Transcription factors bind target sites with different affinities based on their match to a

consensus motif, best defined by a position-weight matrix. It is generally assumed that on-

rates do not vary much among different sites and hence that intrinsic affinity is determined

primarily by off-rates; i.e. strong binding sites have slower off-rates. In this regard, human

TBP binding dynamics is strongly influenced by the off-rate because the quality of the motif

at Pol II promoters (i.e. match to the TATA consensus) is associated with relatively slow

binding dynamics (Hasegawa and Struhl 2019). In contrast, multiple SP1 motifs and higher

motif strength are enriched at Fast SP1 binding sites in human cells. This unexpected result



https://doi.org/10.1101/2020.12.18.423560


12

suggests that SP1 binding dynamics in vivo is strongly influenced by the on-rate, not just the

strength of the intrinsic protein-DNA interaction. In this view, the induced SP1 goes more

rapidly to sites with chromatin features and/or co-binding proteins that enhance the

accessibility, thereby overriding the contributions of the intrinsic SP1-DNA interaction.

SP1 binding dynamics is affected by other factors bound near, but distinct from SP1

motifs. Fast SP1 binding sites in promoter regions are associated with strong NFY and FOS

binding at nearby locations, providing further evidence that SP1 binding dynamics are

favored by interactions that increase SP1 binding. KLF family proteins are highly related to

SP1 (Kaczynski et al. 2003), but KLF proteins (KLF5, KLF16 and KLF1) are enriched at

Slow, not Fast SP1 binding sites. Perhaps the KLF proteins and SP1 have different motif

preferences, such that weaker SP1 motifs in Slow SP1 binding sites are preferable for

binding by KLF proteins.

Alternatively, Slow SP1 binding sites often have ETS family transcription factors

bound nearby. SP1 and ETS proteins interact and can synergistically activate downstream

genes (Gegonne et al. 1993; Block et al. 1996; Zhang et al. 2009). Perhaps ETS factors,

unlike NFY, stabilize the interaction of SP1 with its target site to a sufficient extent such that

the off-rate becomes the key factor influencing the dynamics. In addition, EZH2, the

enzymatic component of PRC2 polycomb complex, and its corresponding H3-K27me3

histone modidfication are enriched at slow SP1 sites. Lastly, the stronger associations of

PML, SUMO, and the E2 SUMO conjugating enzyme UBC9 at Slow class sites imply a

connection between SP1 binding dynamics and nuclear sublocalization. In this regard,

assembly of PML nuclear bodies is modulated by stresses (Lallemand-Breitenbach and de

The 2010; Lallemand-Breitenbach and de Thé 2018), and genes associated with these

processes are enriched in the Slow class of SP1 binding sites.

Relationship of SP1 binding dynamics to transcription and biological processes



https://doi.org/10.1101/2020.12.18.423560


13

Binding dynamics among SP1 sites affect transcription in a different manner than

binding dynamics of TBP and Rap1. Slow binding dynamics (i.e. longer residence times) of

yeast Rap1 and yeast and human TBP are associated with lower nucleosome occupancy and

higher transcriptional activities of downstream genes (van Werven et al. 2009; Lickwar et al.

2012), supporting the idea that transcription factors compete with nucleosome to bind their

target sites. In contrast, slow SP1 binding sites in enhancer regions show higher nucleosome

occupancy and lower transcriptional activity (measured by Pol II recruitment) than observed

at Fast SP1 sites. This result might be explained by the ability of SP1 to bind to the target

sites occluded by nucleosomes (Li et al. 1994), such that nucleosome occupancy does not

affect SP1 binding dynamics. Whatever the molecular basis for this contrasting behavior of

SP1 at enhancers, these results indicate that there are no common rules for linking binding

dynamics of transcription factors to transcriptional activity.

Interestingly, the function of SP3, a protein related to SP1 with similar DNA-binding

specificity, depends on the number of SP1 binding motifs in the promoter. SP3 works as an

activator for promoters containing a single SP1 binding motif, but it represses transcription

from promoters containing multiple SP1 binding motifs by inhibiting SP1 dependent

transcription activation (Birnbaum et al. 1995). SP3 forms more stable complexes on

promoters with multiple SP1 motifs than those with a single SP1 motif, and this leads

efficient competition with SP1 for promoter binding (Yu et al. 2003). This property of SP3 at

sites with multiple SP1 motifs might result in the faster dynamics of SP1 at these promoters

as opposed to promoters with single SP1 motifs. Transcription activities of downstream

genes of Fast class promoters tend to be lower than that of the slower classes, which may

reflect the transcription repression by SP3 through the inhibition of the stable SP1 binding.

The entity of the SP1 binding sites in the non-promoter and non-enhancer regions is

unknown. It has been suggested that some transcription factors mark tissue-specific

enhancers in undifferentiated cells to prevent assembly of repressive chromatin environment

(Ram and Meshorer 2009; Smale 2010). In this view, SP1 associated with Slow binding sites



https://doi.org/10.1101/2020.12.18.423560


14

might mark tissue-specific genes or enhancers that are not active in the K562 cell line used

here. SP1 binds to the enhancer of thymocyte-specific gene Ptcra in mouse ES cells for

preventing DNA methylation (Xu et al. 2009). Whether this mechanism is involved in the

regulation of other enhancers remains to be elucidated.



https://doi.org/10.1101/2020.12.18.423560


15

MATERIALS AND METHODS

Plasmid construction

gRNA sequences (Table S3, SP1gRNA) were inserted between two BbsI sites of pX330

(Addgene #42230) to obtain pX330-SP1gRNA#1 and pX330-SP1gRNA#2 vectors. A PCR-

amplified fragment from pX330-SP1gRNA#2 with primers pX330XbaI and pX330EcoRV

was inserted between the XbaI and EcoRV sites of pX330-SP1gRNA#1 to express two

gRNAs from one vector. To construct a knock-in template, a PCR fragment amplified from

K562 genomic DNA with SP1 cloning primers was inserted between EcoRV and EcoRI sites

in pBluescript (pBlue-SP1genome), and genomic sequences corresponding to PAM were

mutated by site-directed mutagenesis. ERT2-3xHA was amplified from pCMV-TBP-ERT2-

3HA with ERT2_Fw and ERT2_Rv primers, and P2A-PuroR was amplified from

lentiCRISPR v2 (Addgene #52961) with PuroR_Fw and PuroR_Rv primers. ERT2-3xHA

and P2A-PuroR fragments were joined to PCR product amplified from pBlue-SP1genome

with SP1vector_Fw and SP1vector_Rv primers by In-Fusion. We used this construct as a

knock-in template for inserting ERT2-3xHA-PuroR after 3’ end of SP1 coding region.

Primers are listed in Table S3.

Cell line and culture

The K562 cell line was purchased from ATCC (ATCC CCL-243) and cultured with IMDM a

modification of Dulbecco’s Modified Eagle Medium, (GIBCO) with 10% FBS. For

establishing the SP1-ERT2-3HA expressing cell line, pX330 with gRNA sequences and the

knock-in template described above were transfected to K562 cells by Nucleofector (Amaxa).

Cells were selected by puromycin in TCS medium (STEMCELL technologies) and

individual colonies were picked and expanded. We detected the ERT2-3HA insertion by

PCR using Genotyping Fw and Rv primers (Table S3). To induce expression of SP1-ERT2-

3HA, 4-hydroxy tamoxifen (4OHT; Sigma, H7904) was added to culture medium to a final



https://doi.org/10.1101/2020.12.18.423560


16

concentration of 100 nM for the indicated times. For western blotting, cells were

immediately placed on ice, and subjected to the subcellular fractionation. For ChIP, culture

medium was exchanged with fixing solution (described below) at room temperature.

Nuclear fractionation

Cells were cultured in T25 flasks and nuclear fractionation was performed as described

previously (Hasegawa and Struhl 2019). Western blotting was performed to detect SP1-

ERT2-3HA and endogenous SP1 using anti-HA antibody (Abcam #ab51841) or anti-SP1

antibody (Millipore #07-645). Protein signals were detected by the LAS3000 imaging system

and analyzed by ImageJ software.

Chromatin immunoprecipitation (ChIP)

K562 cells in two T75 flasks were treated with 4OHT at 100 nM for indicated times, and

ChIP was performed as described previously (Hasegawa and Struhl 2019), except for a

change in the sonication program (Misonix 3000 sonicator, level 2, ON 30 sec, OFF 30 sec,

total sonication time 8 min). The protein concentration of the lysates was measured by BCA

Protein Assay Kit (Thermo Scientific) and 1.7 µg total protein was used for the

immunoprecipitation step. Yeast chromatin were prepared from HA-TBP expressing strain

as previously described (Hasegawa and Struhl 2019) and added to the lysates at a constant

ratio (3% of total protein amount in the K562 cell lysates) as a spike-in control. For

immunoprecipitation, 0.3 µg of anti-HA antibody (Santa Cruz #sc-7392) or 3 µg of anti-SP1

antibody (Millipore #07-645) were used. Primers for quantitative PCR are listed in Table S3.

Library preparation and sequencing

Sequencing library preparation was performed with NEBNext Ultra DNA library prep kit

(NEB #E7370S) and NEBNext Multiplex Oligos (NEB #E7335S) following the



https://doi.org/10.1101/2020.12.18.423560


17

manufacturer’s instructions. Libraries were sequenced on an Illumina HiSeq 2500 and

NextSeq 500 sequencing systems.

ChIP-seq data analysis

ChIP-seq data analysis was done as previously described (Hasegawa and Struhl 2019). In

brief, we mapped sequence reads to the custom reference genome built with human (hg38)

and yeast (sacCer3) genome by the default parameters of Bowtie2 (--sensitive). Peak calling

was performed using MACS2 with a p-value threshold of 0.01, and we used 14,436 peaks

that were detected at both the 360 and 1440 min samples. We calculated RRPM (reference

adjusted RPM) by dividing the number of the reads mapped to human genome loci by the

total reads number mapped to yeast genome. This RRPM value was used as the SP1-ERT2

ChIP-seq signal intensity. For k-medoids clustering, we determined the optimal number of

clusters by using factoextra package and performed k-medoids clustering with cluster

package in R. Mathematical modeling and fitting of experimental data as described

previously (Hasegawa and Struhl 2019). Other publicly available data used in this study are

listed in Table S4.

Defining categories of SP1 binding sites

We consolidated the chromatin states defined by GenoSTAN (Zacher et al. 2017) into five

classes: Promoters, Prom.11, PromWF.5, and PromF.3; Enhancers, Enh.15, EnhWF.2,

EnhF.10, ReprEnh.4; Gene bodies (GBs), Gen5’.13 and Elon’.14; Polycomb-repressed,

ReprD.17, Repr.7 and ReprW.9; Others, Low.1, Low.8, Low.12 and Low.16. We excluded

Elon.6 and Low.18 classes because no SP1 binding sites overlap these chromatin states.

Motif and Gene Ontology (GO) analysis

Motif enrichment analysis between Faster peaks and Slow peaks was performed using AME

(http://meme-suite.org/tools/ame) by setting each other as user-provided control sequences.



https://doi.org/10.1101/2020.12.18.423560


18

We combined Fast peaks and Middle fast peaks and used them as Faster peaks because the

number of Fast peaks was smaller than the other classes. The TF motifs from

JASPAR2018_CORE_vertebrates_non-redundant database and of which ChIP-seq data are

publicly available were chosen for the figures. For Gene Ontology analysis, we used

CistromeGO (http://go.cistrome.org/).

Data access

All raw and processed sequencing data generated in this paper have been submitted to the

NCBI Gene Expression Omnibus under accession number GSE162811.



https://doi.org/10.1101/2020.12.18.423560


19

ACKNOWLEDGEMENTS

We acknowledge the Bauer Core Facility at Harvard University for support in

sequencing. This work was supported by grants to K.S. from the National Institutes of

Health (GM30186, GM131801, and CA107486).



https://doi.org/10.1101/2020.12.18.423560


20

REFERENCES

Berg OG, Winter RB, Von Hippel PH. 1981. Diffusion-driven mechanisms of protein

translocation on nucleic acids. 1. Models and theory. Biochemistry 20: 6929-6948.

Birnbaum MJ, van Wijnen AJ, Odgren PR, Last TJ, Suske G, Stein GS, Stein JL. 1995.

Sp1 trans-activation of cell cycle regulated promoters is selectively repressed by

Sp3. Biochemistry 34: 16503-16508.

Block KL, Shou Y, Poncz M. 1996. An Ets/Sp1 interaction in the 5'-flanking region of

the megakaryocyte-specific alpha IIb gene appears to stabilize Sp1 binding and is

essential for expression of this TATA-less gene. Blood 88: 2071-2080.

Bosisio D, Marazzi I, Agresti A, Shimizu N, Bianchi ME, Natoli G. 2006. A hyper-

dynamic equilibrium between promoter-bound and nucleoplasmic dimers controls

NF-κB-dependent gene activity. EMBO J 25: 798-810.

Briggs M, Kadonaga J, Bell S, Tjian R. 1986. Purification and biochemical

characterization of the promoter-specific transcription factor, Sp1. Science 234:

47-52.

Cawley S, Bekiranov S, Ng HH, Kapranov P, Sekinger EA, Kampa D, Piccolboni A,

Smentchenko V, Cheng J, Williams AJ et al. 2004. Unbiased mapping of

transcription factor binding sites along human chromosomes 21 and 22 points to

widespread regulation of non-coding RNAs. Cell 116: 499-509.

Chen J, Zhang Z, Li L, Chen BC, Revyakin A, Hajj B, Legant W, Dahan M, Lionnet T,

Betzig E et al. 2014. Single-molecule dynamics of enhanceosome assembly in

embryonic stem cells. Cell 156: 1274-1285.

Courey AJ, Tjian R. 1988. Analysis of Sp1 in vivo reveals multiple transcriptional

domains, including a novel glutamine-rich activation motif. Cell 55: 887-898.



https://doi.org/10.1101/2020.12.18.423560


21

Dion MF, Kaplan T, Kim M, Buratowski S, Friedman N, Rando OJ. 2007. Dynamics of

replication-independent histone turnover in budding yeast. Science 315: 1405-

1408.

Dynan WS, Tjian R. 1983. Isolation of transcription factors that discriminate between

different promoters recognized by RNA polymerase II. Cell 32: 669-680.

ENCODE pc. 2012. An integrated encyclopedia of DNA elements in the human genome.

Nature 489: 57-74.

ENCODE pc. 2020. Expanded encyclopaedias of DNA elements in the human and mouse

genomes. Nature 583: 699-710.

Fleming JD, Giresi PG, Lindahl-Allen M, Krall EB, Lieb JD, Struhl K. 2015. STAT3 acts

through pre-existing nucleosome-depleted regions bound by FOS during an

epigenetic switch linking inflammation to cancer. Epigenetics Chromatin 8: 7.

Gebhardt JC, Suter DM, Roy R, Zhao ZW, Chapman AR, Basu S, Maniatis T, Xie XS.

2013. Single-molecule imaging of transcription factor binding to DNA in live

mammalian cells. Nat Methods 10: 421-426.

Gegonne A, Bosselut R, Bailly RA, Ghysdael J. 1993. Synergistic activation of the

HTLV1 LTR Ets-responsive region by transcription factors Ets1 and Sp1. EMBO

J 12: 1169-1178.

Gidoni D, Dynan WS, Tjian R. 1984. Multiple specific contacts between a mammalian

transcription factor and its cognate promoters. Nature 312: 409-413.

Gorski SA, Dundr M, Misteli T. 2006. The road much traveled: trafficking in the cell

nucleus. Curr Op Cell Biol 18: 284-290.

Guo Y, Gifford DK. 2017. Modular combinatorial binding among human trans-acting

factors reveals direct and indirect factor binding. BMC Genomics 18: 346.

Hansen AS, Pustova I, Cattoglio C, Tjian R, Darzacq X. 2017. CTCF and cohesin

regulate chromatin loop stability with distinct dynamics. eLife 6: e25776.



https://doi.org/10.1101/2020.12.18.423560


22

Hasegawa Y, Struhl K. 2019. Promoter-specific dynamics of TATA-binding protein

association with the human genome. Genome Res 29: 1939-1950.

Johnson DS, Mortazavi A, Myers RM, Wold B. 2007. Genome-wide mapping of in vivo

protein-DNA interactions. Science 316: 1497-1502.

Kaczynski J, Cook T, Urrutia R. 2003. Sp1- and Kruppel-like transcription factors.

Genome Biol 4: 206.

Kadonaga JT, Carner KR, Masiarz FR, Tjian R. 1987. Isolation of cDNA encoding

transcription factor Sp1 and functional analysis of the DNA binding domain. Cell

51: 1079-1090.

Kheradpour P, Kellis M. 2014. Systematic discovery and characterization of regulatory

motifs in ENCODE TF binding experiments. Nucl Acids Res 42: 2976-2987.

Lallemand-Breitenbach V, de The H. 2010. PML nuclear bodies. Cold Spring Harb

Perspect Biol 2: a000661.

Lallemand-Breitenbach V, de Thé H. 2018. PML nuclear bodies: from architecture to

function. Curr Opin Cell Biol 52: 154-161.

Li B, Adams CC, Workman JL. 1994. Nucleosome binding by the constitutive

transcription factor Sp1. J Biol Chem 269: 7756-7763.

Li J, Zou W-X, Chang K-S. 2014. Inhibition of Sp1 functions by its sequestration into

PML nuclear bodies. PLoS ONE 9: e94450-94410.

Lickwar CR, Mueller F, Hanlon SE, McNally JG, Lieb JD. 2012. Genome-wide protein-

DNA binding dynamics suggest a molecular clutch for transcription factor

function. Nature 484: 251-255.

Liu Z, Tjian R. 2018. Visualizing transcription factor dynamics in living cells. J Cell Biol

217: 1181-1191.

Ma S, Snyder M, Dinesh-Kumar SP. 2017. Discovery of novel human gene regulatory

modules from gene co-expression and promoter motif analysis. Sci Rep 7: 5557.



https://doi.org/10.1101/2020.12.18.423560


23

McNally JG, Muller WG, Walker D, Wolford R, Hager GL. 2000. The glucocorticoid

receptor: rapid exchange with regulatory sites in living cells. Science 287: 1262-

1265.

Movva R, Greenside P, Marinov GK, Nair S, Shrikumar Ai, Kundaje A. 2019.

Deciphering regulatory DNA sequences and noncoding genetic variants using

neural network models of massively parallel reporter assays. PLoS ONE 14:

e0218073-0218020.

Nalley K, Johnston SA, Kodadek T. 2006. Proteolytic turnover of the Gal4 transcription

factor is not required for function in vivo. Nature 442: 1054-1057.

Ram EV, Meshorer E. 2009. Transcriptional competence in pluripotency. Genes Dev 23:

2793-2798.

Reed BD, Charos AE, Szekely AM, Weissman SM, Snyder M. 2008. Genome-wide

occupancy of SREBP1 and its partners NFY and SP1 reveals novel functional

roles and combinatorial regulation of distinct classes of genes. PLoS Genetics 4:

e1000133-1000139.

Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G,

Bernier B, Varhol R, Delaney A et al. 2007. Genome-wide profiles of STAT1

DNA association using chromatin immunoprecipitation and massively parallel

sequencing. Nat Methods 4: 651-657.

Saffer JD, Jackson SP, Annarella MB. 1991. Developmental expression of Sp1 in the

mouse. Mol Cell Biol 11: 2189-2199.

Smale ST. 2010. Pioneer factors in embryonic stem cells and differentiation. Curr Opin

Genet Dev 20: 519-526.

Spengler ML, Brattain MG. 2006. Sumoylation inhibits cleavage of Sp1 N-terminal

negative regulatory domain and inhibits Sp1-dependent transcription. J Biol Chem

281: 5567-5574.



https://doi.org/10.1101/2020.12.18.423560


24

Struhl K. 1991. Mechanisms for diversity in gene expression patterns. Neuron 7: 177-

181.

Suske G. 2016. NF-Y and SP transcription factors — New insights in a long-standing

liaison. Biochim Biophys Acta Gene Regul Mech. 1860: 590-597

van Werven FJ, van Teeffelen HA, Holstege FC, Timmers HT. 2009. Distinct promoter

dynamics of the basal transcription factor TBP across the yeast genome. Nat

Struct Mol Biol 16: 1043-1048.

Xu J, Watts JA, Pope SD, Gadue P, Kamps M, Plath K, Zaret KS, Smale ST. 2009.

Transcriptional competence and the active marking of tissue-specific enhancers

by defined transcription factors in embryonic and induced pluripotent stem cells.

Genes Dev 23: 2824-2838.

Yang A, Zhu Z, Kapranov P, McKeon F, Church GM, Gingeras TR, Struhl K. 2006.

Relationships between p63 binding, DNA sequence, transcription activity, and

biological function in human cells. Mol Cell 24: 593-602.

Yao J, Munson KM, Webb WW, Lis JT. 2006. Dynamics of heat shock factor association

with native gene loci in living cells. Nature 442: 1050-1053.

Yu B, Datta PK, Bagchi S. 2003. Stability of the Sp3-DNA complex is promoter-specific:

Sp3 efficiently competes with Sp1 for binding to promoters containing multiple

Sp-sites. Nucl Acids Res 31: 5368-5376.

Yu P, Kodadek T. 2007. Dynamics of the Hypoxia-inducible Factor-1-Vascular

Endothelial Growth Factor Promoter Complex. J Biol Chem 282: 35035-35045.

Zacher B, Michel M, Schwalb B, Cramer P, Tresch A, Gagneur J. 2017. Accurate

promoter and enhancer identification in 127 ENCODE and roadmap epigenomics

cell types and tissues by GenoSTAN. PLoS One 12: e0169249.

Zhang Y, Hassan MQ, Xie RL, Hawse JR, Spelsberg TC, Montecino M, Stein JL, Lian

JB, van Wijnen AJ, Stein GS. 2009. Co-stimulation of the bone-related Runx2 P1

promoter in mesenchymal cells by SP1 and ETS transcription factors at



https://doi.org/10.1101/2020.12.18.423560


25

polymorphic purine-rich DNA sequences (Y-repeats). J Biol Chem 284: 3125-

3135.



https://doi.org/10.1101/2020.12.18.423560


26

FIGURE LEGENDS

Figure 1. Nuclear translocation of SP1-ERT2-3HA. (A) Electrophoretic separation of PCR

products corresponding to the endogenous (WT) and SP1-ERT2-3HA expressing (Insert)

alleles. (B) Western blotting (SP1 antibody) of K562 cells induced with tamoxifen that do or

do not express SP1-ERT2-3HA; fibrillarin is used as a control. (C) Schematic illustration of a

tamoxifen-inducible time-course ChIP analysis. Upon tamoxifen induction, the fusion

protein (purple) replaces the endogenous protein (green) at target sites; binding sites with

slow or fast kinetics are indicated. (D) Kinetics of SP1-ERT2-3HA nuclear translocation

(Cyt, cytoplasmic; Nuc, nuclear) analyzed by Western blotting of samples at the indicated

times after tamoxifen addition with tubulin as a cytoplasmic marker and histone H3 as a

nuclear marker. (E) ChIP-qPCR analysis of SP1-ERT2-3HA binding to target sites (RAB5B,

GHDC, and PRPF31) and negative control regions (chr7:int and chr8:int; int means

intergenic region) with anti-HA antibody. Error bars indicate SD (n=3). (F) ChIP-qPCR

analysis of total SP1 binding to target sites and negative control regions with anti-SP1

antibody. Error bars indicate SD (n=3). (G) Time-course analysis of SP1-ERT2-HA binding

to target sites and nuclear SP1-ERT2 protein amount. Error bars indicate SD (n=3).

Figure 2. Time-course ChIP-seq analysis of SP1-ERT2-3HA binding. (A) Examples of the

time-course ChIP-seq result at bound loci (green shaded area). (B) Heatmap of binding

levels (color coded) relative to the value of 1440-min sample. Four categories (vertical strip

on left with colors indicating the various classes) are shown from k-medoid clustering. (C)

Average binding level for each cluster and level of nuclear SP1-ERT2-3HA relative to the

value of 1440-min sample for each cluster. (D) Scatter plot representing the relationship

between SP1 occupancy and turnover rate and the distribution of the classes of SP1 binding

dynamics.



https://doi.org/10.1101/2020.12.18.423560


27

Figure 3. SP1-ERT2-3HA peak distribution and chromatin states. (A) Distribution of

binding sites according to the types of genomic regions. (B) Distribution of the SP1 binding

sites in the indicated genomic regions for each binding dynamics class; numbers are median

read coverage. (C) Ratio between H3K4me1 and H3K4me3 around SP1 binding sites in

each class. The area of which t-test p < 0.01 between Slow class and all the other three

classes is indicated by red shaded rectangle (-270 bp to +170 bp from SP1 peak summit). (D)

Comparison of EZH2 and H3K27me3 levels around SP1 binding sites in each class.

Figure 4. SP1 binding sites showing fast turnover tend to have multiple SP1 motifs with

stronger similarity to the consensus sequence. (A) Percentage of SP1 peaks that have strong

SP1 consensus motif (p < 0.0001), with colors indicating the number of SP1 motifs in each

peak. (B) Box plot represents the highest match score to the position weight matrix of SP1

consensus motif in each peak. (C) Cumulative frequency of the match score to the position

weight matrix of SP1 consensus motif. (D) AT-content around SP1 binding sites in enhancer

and promoter regions for each dynamic class. (E) MNase-seq signal around SP1 binding sites

in enhancer and promoter regions.

Figure 5. Co-binding transcription factors enriched at promoters in the Fast or Slow classes

of SP1 binding sites. (A) Top ranked examples of transcription factor binding motifs

enriched in Faster (Fast and Middle fast) class compared to the Slow class. (B) Means and

heatmap of binding of the indicated transcription factors corresponding to motifs

overrepresented in the Fast class; the center is the SP1-ERT2-3HA peak summit. (C) Top

ranked examples of transcription factor binding motifs enriched in Slow class compared to

the Faster classes. (D) Means and heatmap of binding of the indicated transcription factors

of which motifs are overrepresented in the Slow class.



https://doi.org/10.1101/2020.12.18.423560


28

Figure 6. Co-binding transcription factors enriched in the fast or slow SP1 binding classes at

enhancers. (A) Top ranked examples of transcription factor binding motifs enriched in the

Faster classes (Fast and Middle fast) compared to the Slow class. (B) Means and heatmap of

binding of the indicated transcription factors corresponding to motifs overrepresented in the

Faster classes; the center is the SP1-ERT2-3HA peak summit. (C) Examples of transcription

factor binding motifs enriched in Slow class comparing to Faster classes. (D) Means and

heatmap of binding of the indicated transcription factors of which motifs are overrepresented

in the Slow class.

Figure 7. PML and SUMOylated proteins are enriched at Slow SP1 binding sites. (A)

Means and heatmap of binding by the indicated factors; the center is the SP1-ERT2-3HA

peak summit. (B) Gene ontology (GO) terms (biological processes) of overrepresented genes

near different classes of SP1 binding sites. The fast class was excluded due to the small

sample size.

Figure 8. Chromatin structure and transcriptional activity of the nearest gene of SP1 binding

sites in each class. (A) H3K27Ac and H3K27me3 signals around SP1 binding sites in

enhancer regions. The center is the SP1-ERT2-3HA peak summit. (B) Profile and box plot

of Pol II association between -0.5 kb to +0.5 kb from SP1-ERT2 peak summit in enhancer

regions. Asterisk, p < 0.05; two asterisks, p < 0.001. (C) H3K27Ac and H3K27me3 signals

around SP1 binding sites in promoter regions. (D) Profile and box plot of Pol II association

between -0.5 kb to +0.5 kb from SP1-ERT2 peak summit in promoter regions.



https://doi.org/10.1101/2020.12.18.423560


RAB5BGHDC

PRPF31

chr7:

int

chr8:

int0.00

0.01

0.02

0.03

0.04

% o

f inp

ut

No treat (EtOH)4OHT

RAB5BGHDC

PRPF31

chr7:

int

chr8:

int0.00

0.05

0.10

0.15

% o

f inp

ut

No treat (EtOH)4OHT

0 10 20 30 40 50 60 70 80 900.0

0.5

1.0

1.5

2.0

Time after induction

Rela

tive

valu

e

RAB5BGHDC

Nuclear SP1-ERT2 proteinPRPF31

anti-HA (SP1-ERT2-3HA) anti-SP1 (endoSP1 + SP1-ERT2-3HA)

SP1-ERT2

Tubulin

H3

Cyt Nuc Cyt Nuc Cyt Nuc Cyt Nuc0 15 30 60 min

D E F

G

A B C

K562

K562SP1-ERT2-3HAK562

K562SP1-ERT2-3HA

Fibrillarin

SP1

SP1-ERT2-3HA 100kDa

140kDa

Endogenous SP1SP1-ERT2-3HA

NucCyto

t0 t2

SP1-

ERT2

ChI

P

tn

Time after tamoxifen induction

Slow Fast Slow Fast Slow Fast

K562

1kb3kb Insert

WT



Yuko Hasegawa

Figure 1

https://doi.org/10.1101/2020.12.18.423560


FastMiddle fastMiddle slowSlowNuclear SP1-ERT2

B

C

IP_0min

IP_2.5min

IP_5min

IP_10min

IP_15min

IP_20min

IP_30min

IP_60min

IP_90min

IP_360min

IP_1440min

cluster

0

0.2

0.4

0.6

0.8

1

0 15 30 45 60 75 90 360 720 1080 14400.0

0.5

1.0

1.5

2.0

Time after induction (min)

Rela

tive

valu

e

FastMiddle fasrMiddle slowSlow

-Log10(Turnover rate)

Log 2(O

ccup

ancy

)

D

2

4

6

8

0 1 2 3

PLXND1 TMCC1

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

[0 - 305]

70 kb

A .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made


Yuko Hasegawa

Figure 2

https://doi.org/10.1101/2020.12.18.423560


Distance from peak summit

H3K4

me1

/H3K

4me3

ratio

(a.u

.)

FastMiddle fast

Middle slowSlow

2.5

5.0

7.5

10.0

−3kb −2kb −1kb 0 1kb 2kb 3kb

-3.0 3.0Kb0.2

0.4

0.6

0.8

H3K27me3

Peak summit

-3.0 Peak summit 3.0Kb

6

7

8

9EZH2

A

C D

B

ChI

P si

gnal

ChI

P si

gnal

68.6%80.2% 77.4%

50.4%21.9%

4.0% 1.6% 1.5%

6.2%15.6% 18.1%

31.4%

3.2% 2.3% 2.3%0.4% 2.3%

0.7% 8.5% 3.6%

Fast (n=474) Middle fast (n=1714) Middle slow (n=3004) Slow (n=2805)

Total=7997

PromotersEnhancersGene bodyPolycomb-repressedOthers

68.0%

3.3%4.1% 1.7%

22.9%



Yuko Hasegawa

Figure 3

https://doi.org/10.1101/2020.12.18.423560


Fast Middle fast Middle slow Slow

Perc

enta

ge

Freq.

01234<


Hig

hest

mat

ch s

core

in a

pea

k0.00

0.25

0.50

0.75

1.00

0 500 1000 1500Match score

Cum

ulat

ive fr

eque

ncy

FastMiddle fastMiddle slowSlow

****

**

A B C

D

FastMiddle1Middle2Slow

E

Enhancer regionAll region Promoter region

MN

ase-

seq

sign

al

MN

ase-

seq

sign

al

MN

ase-

seq

sign

al

37.7%

27.6%

12.2%

4.6%

17.9%

35.3%

31.8%

16.6%

8.0%8.3%

38.2%

36.5%

14.9%

6.7%3.7%

41.4%

42.8%

10.6%3.4%1.8%0

25

50

75

100

0

500

1000

1500

0.3

0.4

0.5

-500 -250 0 +250 +500

AT c

onte

nts

0.3

0.4

0.5

-500 -250 0 +250 +500

AT c

onte

nts

0.3

0.4

0.5

-500 -250 0 +250 +500

AT c

onte

nts

FastMiddle fastMiddle slowSlow

All region

-1.0kb Peak summit 1.0kb

0.2

0.4

0.6

0.8

1.0Enhancer region

-1.0kb Peak summit 1.0kb0.4

0.6

0.8

1.0

1.2

Promoter region

-1.0kb Peak summit 1.0kb

0.2

0.4

0.6

0.8



Yuko Hasegawa

Figure 4

https://doi.org/10.1101/2020.12.18.423560


1 2 3 4 5 6 7 8 9 10

2.0

Bits

1.51.00.50.0

KLF5(MA0599.1) 5.62E-85

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2.0

Bits

1.51.00.50.0

SP2(MA0516.1) 3.58E-66

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

2.0

Bits

1.51.00.50.0

SP1(MA0079.3) 1.98E-97

2.0

Bits

1.51.00.50.0

SP3(MA0746.1) 1.17E-42

1 2 3 4 5 6 7 8 9 10 11

NFYA(MA0060.3) 8.01E-08

Bits

1.5

1.0

0.50.0

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

ETV5(MA0765.1) 9.06E-21

ETV4(MA0764.1) 4.75E-17 ETS1(MA0098.3) 2.42E-13

ISL2(MA0914.1) 7.62E-19

HOXA5(MA0158.1) 7.99E-19 DLX6(MA0882.1) 4.64E-15

ERF(MA0760.1) 1.07E-13

1 2 3 4 5 6 7 8 9 10

Bits

1.5

1.0

0.50.0

ZNF740(MA0753.1) 2.2E-31

A B

C D

1 2 3 4 5 6 7 8 9 10

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

ELK1(MA0028.2) 4.00E-16 GABPA（MA0062.2) 1.06E-12

NFAT5(MA0606.1) 8.37E-16

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

ETV1(MA0761.1) 2.76E-17

1 2 3 4 5 6 7 8 9 10 11

2.0

Bits

1.51.00.50.0

KLF16(MA0741.1) 3.64E-42

1 2 3 4 5 6

2.0

Bits

1.51.00.50.0

MZF1(MA0056.1) 1.63E-23

1 2 3 4 5 6 7 8 9 10 11

2.0

Bits

1.51.00.50.0

E2F4(MA0470.1) 1.23E-07

E2F6(MA0471.1) 1.39E-07

NFYB(MA0502.1) 3.31E-06

KLF1(MA0493.1) 4.93E-04

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

2.0

Bits

1.51.00.50.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

-2.0 Summit 2.0Kb

2.5

5.0

7.5

10.0

-2.0 Summit 2.0Kb

0 2 4 6 8 10

-2.0 Summit 2.0Kb1.0

1.5

2.0

2.5

3.0

0 1 2 3

-2.0 Summit 2.0Kb

-2.0 Summit 2.0Kb0

5

10

15

20

-2.0 Summit 2.0Kb

0 5 10 15 20 25

-2.0 Summit 2.0Kb

2468

10

-2.0 Summit 2.0Kb0 5 10

-2.0 Summit 2.0Kb

0.75

1.00

1.25

1.50

-2.0 Summit 2.0Kb

0.0

0.5

1.0

1.5

2.0

-2.0 Summit 2.0Kb

0.8

1.0

1.2

1.4

-2.0 Summit 2.0Kb

0.00.51.01.52.02.5

-2.0 center 2.0Kb0.8

1.0

1.2

1.4

1.6

-2.0 center 2.0Kb

0 1 2 3

-2.0 center 2.0Kb

1

2

3

4

-2.0 center 2.0Kb

0 1 2 3 4-2.0 center 2.0Kb

05

101520

-2.0 center 2.0Kb

0 2 4 6 8 10

-2.0 center 2.0Kb0

10

20

30

40

-2.0 center 2.0Kb

0 20 40 60

-2.0 center 2.0Kb0

5

10

15

-2.0 center 2.0Kb

0 5 10 15

-2.0 center 2.0Kb

1.0

1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3

-2.0 Summit 2.0Kb1.01.52.02.53.0

-2.0 Summit 2.0Kb

0 1 2 3 4

-2.0 Summit 2.0Kb1

2

3

-2.0 Summit 2.0Kb

0 1 2 3 4

-2.0 Summit 2.0Kb1.01.52.02.53.0

-2.0 Summit 2.0Kb

0 1 2 3

-2.0 Summit 2.0Kb

1.0

1.5

2.0

2.5

-2.0 Summit 2.0Kb

0 1 2 3 4

-2.0 Summit 2.0Kb

1

2

3

-2.0 Summit 2.0Kb

0 2 4

-2.0 Summit 2.0Kb1

2

3

4

-2.0 Summit 2.0Kb

0 1 2 3 4 5

-2.0 Summit 2.0Kb1.01.52.02.53.0

-2.0 Summit 2.0Kb

0 1 2 3 4 5

-2.0 Summit 2.0Kb

1.0

1.5

2.0

2.5

-2.0 Summit 2.0Kb

0 1 2 3

-2.0 Summit 2.0Kb1

2

3

4

-2.0 Summit 2.0Kb

0 2 4 6

1.0

1.5

2.0

2.5

-2.0 Summit 2.0Kb

-2.0 Summit 2.0Kb

0 1 2 3 4

SP1 KLF5 SP2 SP3 KLF16 ZNF740

ETV5 ISL2 HOXA5 ETV1 ETV4 ELK1

NFAT5 NFATC1 DLX6 ERF ETS1 GABPA

MZF1 NFYA E2F4 E2F6 NFYB KLF1

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8 9 10

2.0

Bits

1.51.00.50.0

1 2 3 4 5 6 7 8 9 10

2.0

Bits

1.51.00.50.0

NFATC1(MA0624.1) 2.36E-15

-2.0 Summit 2.0Kb12345

-2.0 Summit 2.0Kb0

5

10

15

20

-2.0 Summit 2.0Kb

0 2 4 6 8

-2.0 Summit 2.0Kb

0 10 20 30

Top motifs enriched in the fast class

Top motifs enriched in the slow class

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Mea

n si

gnal

Mea

n si

gnal

Mea

n si

gnal

Mea

n si

gnal



Yuko Hasegawa

Figure5

https://doi.org/10.1101/2020.12.18.423560


1 2 3 4 5 6 7 8 9 10

2.0

Bits

1.51.00.50.0

KLF5(MA0599.1) 8.22E-49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 2 3 4 5 6 7 8 9 10 11 12 13 14

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

SP2(MA0516.1) 1.62E-36 TFDP1(MA1122.1) 7.40E-12

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

SP1(MA0079.3) 1.84E-35 NR2C2(MA0504.1) 1.71E-05

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

SP3(MA0746.1) 1.35E-19 ZFX(MA0146.2) 3.44E-03

ZNF263(MA0528.1) 4.69E-03

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

Bits

1.5

1.0

0.50.0

ZNF740(MA0753.1) 2.29E-32

A B

C D

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

2.0

Bits

1.51.00.50.0

KLF16(MA0741.1) 3.30E-14

HLTF(MA0109.1) 2.36E-22 GATA2(MA0036.3) 1.11E-09

MNX1(MA0707.1) 1.14E-09

LBX2(MA0699.1) 1.56E-09

POU5F1(MA1115.1) 2.42E-16

FOXK2(MA1103.1) 2.54E-14

FOXK1(MA0852.2) 4.31E-13 STAT1(MA0137.3) 2.32E-08

GATA3(MA0037.3) 1.42E-11 FOXP1(MA0481.2) 4.31E-08

FOXJ2(MA0614.1) 4.65E-08MIXL1(MA0662.1) 1.03E-09

1 2 3 4 5 6

2.0

Bits

1.51.00.50.0

MZF1(MA0056.1) 1.05E-13

1 2 3 4 5 6 7 8 9 10 11

2.0

Bits

1.51.00.50.0

E2F4(MA0470.1) 1.23E-07

-2.0 Summit 2.0Kb

1.0

1.2

1.4

1.6

-2.0 Summit 2.0Kb

0 1 2 3

-2.0 Summit 2.0Kb

2.5

5.0

7.5

10.0

-2.0 Summit 2.0Kb

0 2 4 6 8

-2.0 Summit 2.0Kb1

2

3

4

-2.0 Summit 2.0Kb

0 1 2 3

-2.0 Summit 2.0Kb

0.81.01.21.41.6

-2.0 Summit 2.0Kb

0.0

0.5

1.0

1.5

2.0

-2.0 Summit 2.0Kb

2

4

6

-2.0 Summit 2.0Kb

0 2 4

-2.0 Summit 2.0Kb

1

2

3

4

-2.0 Summit 2.0Kb

0 1 2 3

-2.0 Summit 2.0Kb

0.8

1.0

1.2

-2.0 Summit 2.0Kb

0.0

0.5

1.0

1.5

2.0

-2.0 Summit 2.0Kb

0.751.001.251.501.75

-2.0 Summit 2.0Kb

0 1 2-2.0 Summit 2.0Kb

1.0

1.5

2.0

2.5

-2.0 Summit 2.0Kb

1 2 3 4 4

-2.0 Summit 2.0Kb

1.0

1.5

2.0

2.5

-2.0 Summit 2.0Kb

0 1 2 3

-2.0 Summit 2.0Kb

2

4

6

-2.0 Summit 2.0Kb

0.0

2.5

5.0

7.5

10.0

-2.0 Summit 2.0Kb

2468

10

0 5 10

-2.0 Summit 2.0Kb

-2.0 center 2.0Kb

1.01.21.41.61.8

-2.0 center 2.0Kb

1 2


1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3 4

-2.0 center 2.0Kb

1.0

1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3

-2.0 center 2.0Kb1

2

3

-2.0 center 2.0Kb

0 2 4

-2.0 center 2.0Kb

1.0

1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3

-2.0 center 2.0Kb

1.0

1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3 4

-2.0 center 2.0Kb

1.0

1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3


1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3 4

-2.0 center 2.0Kb

1.01.21.41.61.8

-2.0 center 2.0Kb

0 1 2 3

-2.0 center 2.0Kb

1.0

1.2

1.4

-2.0 center 2.0Kb

0 1 2

-2.0 center 2.0Kb

1.0

1.5

2.0

2.5

-2.0 center 2.0Kb

0 1 2 3 4


1.0

1.5

2.0

-2.0 center 2.0Kb

0 1 2KLF5 SP2 SP1 ZNF740 SP3 KLF16

HLTF POU5F1 FOXK2 FOXK1 GATA3 MIXL1

GATA2 MNX1 LBX2 STAT1 FOXP1 FOXJ2

MZF1 TFDP1 NR2C2 E2F4 ZFX ZNF26315

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Fast

Mid

dle

fast

Mid

dle

slow

Slow

Mea

n si

gnal

Mea

n si

gnal

Mea

n si

gnal

Mea

n si

gnal

Top motifs enriched in the fast classes

Top motifs enriched in the slow class



Yuko Hasegawa

Figure 6

https://doi.org/10.1101/2020.12.18.423560


A

B

-2.0 Summit 2.0Kb1

2

3

4

5

-2.0 Summit 2.0Kb

0 2 4 6 8

-2.0 Summit 2.0Kb

20

40

60

-2.0 Summit 2.0Kb

20 40 60 80

-2.0 Summit 2.0Kb

5.0

7.5

10.0

12.5

-2.0 Summit 2.0Kb

5 10 15 20

-2.0 Summit 2.0Kb

4

5

6

7


5.0

7.5

10.0

Fast

Mid

dle

fast

Mid

dle

slow

Slow

PML SUMO1 SUMO2 UBC9M

ean

sign

al

0 1 2 3 4

positive regulation of transcription, DNA-templated

negative regulation of RNA biosynthetic process

positive regulation of nucleobase-containingcompound metabolic process

negative regulation of gene expression

negative regulation of nucleobase-containing compound metabolic process

negative regulation of biosynthetic process

-Log10(adjusted p-value)

0 1 2 3 4 5

organophosphate biosynthetic process

cellular macromolecule catabolic process

macromolecule catabolic process

regulation of cell cycle

DNA metabolic process

mitotic cell cycle process

cellular response to stress

regulation of organelle organization

-Log10(adjusted p-value)0 2 4 6 8 10

mitochondrial transportmRNA metabolic process

regulation of chromosome segregation

regulation of mitotic spindle checkpoint

chromatin organization

Golgi vesicle transport

cellular response to DNA damage stimulus

protein modification by small protein conjugation or removal

RNA splicing, via transesterification reactions

establishment of localization in cell

RNA splicing

RNA processing

DNA repair

RNA biosynthetic process

cellular response to stress

organic cyclic compound biosynthetic process

cellular macromolecule biosynthetic process

nucleobase-containing compound biosynthetic process

macromolecule biosynthetic process

cellular nitrogen compound biosynthetic process

RNA metabolic process

-Log10(adjusted p-value)

GO enriched in Middle fast promoters GO enriched in Slow promoters

GO enriched in Middle slow promoters



Yuko Hasegawa

Figure 7

https://doi.org/10.1101/2020.12.18.423560


0

5

10

15

20

25

Fast Slow

Pol I

I ChI

P si

gnal

at

the

clos

est g

enes

Pol I

I ChI

P si

gnal

at

the

clos

est g

enes

Pol I

I ChI

P si

gnal

Pol I

I ChI

P si

gnal

H3K

27m

e3 C

hIP

sign

alH

3K27

me3

ChI

P si

gnal

H3K

27Ac

ChI

P si

gnal

H3K

27Ac

ChI

P si

gnal

Promoter region

Enhancer regionA B

**

**

****

0.0

2.5

5.0

7.5

10.0

Fast Middlefast

Middleslow

Middlefast

Middleslow

SlowFastMiddle fast

Middle slowSlow Fast

Middle fastMiddle slowSlow

FastMiddle fast

Middle slowSlow

FastMiddle fast

Middle slowSlow

C D


2.5

5.0

7.5

10.0

12.5


5

10

15


0.3

0.4

0.5


0.2

0.4

0.6


1.0

1.5

2.0

2.5

-2.0 Peak summit 2.0Kb1

2

3

4



Yuko Hasegawa

Figure 8

https://doi.org/10.1101/2020.12.18.423560


SP1-ERT2

TBP

0 2.5 5 10 15 20 30 60 90 360 1440 min

0 200 400 600 800 1000 1200 1400Time after induction (min)

0

0.2

0.4

0.6

0.8

1

1.2

Rel

ativ

e va

lue

Time after induction0

0.2

0.4

0.6

0.8

1

1.2

Rel

ativ

e va

lue

0 10 20 30 40 50 60 70 80 90

-Log10(Turnover rate)-0.500.511.522.533.5

C D

E F

-Log10(Turnover rate)

Log 2(O

ccup

ancy

)

2

4

6

8

0 1 2 3

Time after induction (min)

Rela

tive

valu

e

ChIP-seq signal (Average of all peaks)Nuclear SP1-ERT2 amount

0 10 20 30 40 50 60 70 80 90 100 360 720 1080 14400

1

2

3

4

1 0.34 0.24 0.14 0.09 0.11 0.14 0.14 0.16 0.17 0.16

0.34 1 0.59 0.47 0.41 0.43 0.41 0.43 0.43 0.45 0.45

0.24 0.59 1 0.81 0.74 0.76 0.73 0.75 0.73 0.76 0.76

0.14 0.47 0.81 1 0.89 0.92 0.89 0.9 0.87 0.88 0.87

0.09 0.41 0.74 0.89 1 0.91 0.88 0.88 0.85 0.86 0.84

0.11 0.43 0.76 0.92 0.91 1 0.91 0.91 0.88 0.89 0.88

0.14 0.41 0.73 0.89 0.88 0.91 1 0.93 0.9 0.9 0.87

0.14 0.43 0.75 0.9 0.88 0.91 0.93 1 0.9 0.9 0.88

0.16 0.43 0.73 0.87 0.85 0.88 0.9 0.9 1 0.91 0.89

0.17 0.45 0.76 0.88 0.86 0.89 0.9 0.9 0.91 1 0.96

0.16 0.45 0.76 0.87 0.84 0.88 0.87 0.88 0.89 0.96 1

0min

2.5min

5min

10min

15min

20min

30min

60min

90min

360min

1440min

0min

2.5min

5min

10min

15min

20min

30min

60min

90min

360min

1440min

0.250.500.751.00

Value

A

p = 0.51

102030

count

Log2(SP1-ERT2 ChIP signal at 360min)

Log 2(E

ndog

enou

s SP

1 Ch

IP s

igna

l) 7.5

5.0

2.5

0

-2.5

-5.04 5 6 7 8 9

B .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made


Yuko Hasegawa

Supplemental Figure S1. Nuclear translocation of SP1-ERT2-3HA and time-course ChIP-seq. (A) Spearman correlation coefficients between ChIP signal of each sample. (B) Correlation between SP1-ERT2 ChIP signal and endogenous SP1 ChIP signal. p means Spearman correlation coefficient (C) Time-course western blotting analysis of SP1-ERT2-3HA detected with anti-HA antibody, and TBP (internal control) detected with anti-TBP antibody. (D) Relative amount of nuclear SP1-ERT2-3HA (dashed black) and average ChIP-seq signal of SP1-ERT2-3HA (green). (E) Simulated time-course ChIP-seq data with various turnover rates. (F) Scatter plot representing the relationship between SP1 occupancy (Log2) and turnover rate (-Log10).

https://doi.org/10.1101/2020.12.18.423560


Endpoint 360 min

Endp

oint

144

0 m

in

A BRelativevalue

IP_0min

IP_2.5min

IP_5min

IP_10min

IP_15min

IP_20min

IP_30min

IP_60min

IP_90min

IP_360min

cluster

0

0.2

0.4

0.6

0.8

1

474 363 142 75

318 1714 1005 325

139 1118 3004 1188

37 299 1430 2805

Fast

Middle fast

Middle slow

Slow




Yuko Hasegawa

Supplemental Figure S2. Subdividing SP1 binding sites into categories based on kinetic properties. (A) Heatmap of ChIP signals relative to the value of 360 min sample. (B) Comparison between the classes of SP1 binding dynamics determined with dataset 1 (all time points) and dataset 2 (without 1440 min).

https://doi.org/10.1101/2020.12.18.423560


-2.0 Summit 2.0Kb

6

8

10

12

-2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb

-2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0 -2.0 Summit 2.0Kb5

6

7

8


-2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb

2

4

6

8

10

12

14


0.4

0.5

0.6


-2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb0.0

0.2

0.4

0.6

0.8

1.0-2.0 Summit 2.0Kb

0.2

0.4

0.6

0.8


-2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb -2.0 Summit 2.0Kb0.0

0.2

0.4

0.6

0.8

1.0

Fast Middle fast Middle slow Slow Fast Middle fast Middle slow Slow

Fast Middle fast Middle slow Slow Fast Middle fast Middle slow Slow

Enhancer region Promoter region

Mea

n si

gnal

EZH

2 at

SP1

ERT2

bin

ding

site

s

Mea

n si

gnal

Mea

n si

gnal

Mea

n si

gnal

H3K

27m

e3 a

t SP1

ERT2

bin

ding

site

s



Yuko Hasegawa

Supplemental Figure 3. EZH2 and H3K27me3 signal around SP1 binding sites.

https://doi.org/10.1101/2020.12.18.423560


Enhancer region Promoter regionH

ighe

st m

atch

sco

re in

a p

eak

Hig

hest

mat

ch s

core

in a

pea

k

A B

C D

54.7%

23.4%

10.9%1.6%9.4%

44.4%

30.4%

16.4%

4.7%4.1%

40.1%

39.7%

12.1%

5.5%2.6%

46.9%

43.1%

7.3%1.9%0.8%0

25

50

75

100


Perc

enta

ge

33.5%

29.4%

13.2%

4.8%

19.1%

33.8%

31.9%

16.7%

8.7%

8.9%

37.4%

36.1%

15.6%

7%3.9%

38.3%

43.8%

12.1%

3.5%2.2%0

25

50

75

100


Perc

enta

ge Frequency0

1

2

3

>4

0

500

1000

1500


0

500

1000

1500




Yuko Hasegawa

Supplemental Figure S4. SP1 consensus motif in SP1 binding sites in promoter and enhancer regions. (A) Percentage of SP1 peaks in enhancer region that have strong hit sequences (p < 0.0001) to SP1 consensus motif, with colors indicating the number of SP1 motifs in each peak. (B) Percentage of SP1 peaks in promoter region that have strong hit sequences (p < 0.0001) to SP1 consensus motif, with colors indicating the number of SP1 motifs in each peak. (C) Box plot represents the highest match score to the position weight matrix of SP1 consensus motif in each peak located in enhancer region. (D) Box plot represents the highest match score to the position weight matrix of SP1 consensus motif in each peak located in promoter region.

https://doi.org/10.1101/2020.12.18.423560


20406080

0

20

40

60

80

-1kb +1kb0 -1kb +1kb0 -1kb +1kb0 -1kb +1kb0

1

3

5

0

1

2

3

4

5


0

5

10

0

2

4

6

8

10

12

14


0

20

40

0

5

10

15

20

25

30


Fast Middle1 Middle2 Slow Fast Middle1 Middle2 Slow

Fast Middle1 Middle2 Slow Fast Middle1 Middle2 Slow

Mea

n si

gnal

Mea

n si

gnal

Mea

n si

gnal

SP1-

ERT2

at S

P1-E

RT2

peak

sM

ean

sign

al

NFY

A at

SP1

-ERT

2 pe

aks

Mea

n si

gnal

NFY

B at

SP1

-ERT

2 pe

aks

Mea

n si

gnal

FOS

at S

P1-E

RT2

peak

s

0.00

0.25

0.50

0.75

1.00

0 5 10 15 20NFYA ChIP signal

Cum

ulat

ive fr

eque

ncy

5

10

15

Fast Middle1 Middle2 Slow

NFY

A C

hIP

sign

al

****

**

0.00

0.25

0.50

0.75

1.00

0 20 40 60 80NFYB ChIP signal

Cum

ulat

ive fr

eque

ncy

Fast Middle1 Middle2 Slow0

20

40

60

80

NFY

B C

hIP

sign

al

****

*

0.00

0.25

0.50

0.75

1.00

0 100 200 300FOS ChIP signal

Cum

ulat

ive fr

eque

ncy

0

200

400

FOS

ChI

P si

gnal

Fast Middle1 Middle2 Slow

****

*

BA

C

-2.0 Summit 2.0Kb0

20

40

60

-2.0 Summit 2.0Kb

0 5 10 15 20 25

-2.0 Summit 2.0Kb

2

4

6

8

-2.0 Summit 2.0Kb

0.02.55.07.510.0

FOS JUN



Yuko Hasegawa

Supplemental Figure S5. NFY and FOS ChIP signals are higher in Fast class. (A) Means and heatmap of FOS and JUN binding level. (B) Means and heatmap of indicated transcription factors. (C) Box plots show ChIP signal intensity of the indicated factors between -0.5 kb to +0.5 kb from SP1-ERT2 peak summit in promoter region. Asterisk, p < 0.05; two asterisks, p < 0.001.

https://doi.org/10.1101/2020.12.18.423560


-2.0 Summit 2.0Kb1

2

3

4

-2.0 Summit 2.0Kb

0 2 4 6

-2.0 Summit 2.0Kb

20

30

40

-2.0 Summit 2.0Kb

20 40 60

-2.0 Summit 2.0Kb

4

6

8

10

-2.0 Summit 2.0Kb

5 10 15

-2.0 Summit 2.0Kb

3

4

5

6

-2.0 Summit 2.0Kb

2 4 6 8 10

Fast

Mid

dle

fast

Mid

dle

slow

Slow

PML SUMO1 SUMO2 UBC9

Mea

n si

gnal



Yuko Hasegawa

Supplemental Figure S6. PML, SUMO1, SUMO2, and UBC9 occupancy at SP1 binding sites in enhancer regions.

https://doi.org/10.1101/2020.12.18.423560


Different SP1 binding dynamics at individual genomic loci in … · 2020. 12. 18. · SP1 from...

Documents

Transcript of Different SP1 binding dynamics at individual genomic loci in … · 2020. 12. 18. · SP1 from...