chip-seq data processing - genes &...

Esnault et al

SUPPLEMENTARY MATERIALS

INVENTORY OF SUPPLEMENTARY MATERIALS

1. Supplementary Figures

Supplementary Figure S1 defines the effects of U0126, Latrunculin B and

Cytochalasin D on Ras-MAPK, Rho-actin and the Hippo signalling pathway. It

shows that U0126 specifically inhibits Ras-MAPK signalling, while LatB

inhibits Rho-actin signalling-dependent MRTF nuclear accumulation, and CD

specifically activates the MRTFs; and that the Hippo signalling pathway is

constitutively active under our assay conditions. Related to Figure 1.

Supplementary Figure S2 shows ChIP-seq control experiments including

inhibitor studies, validation by ChIP-qPCR, dimerisation of MRTFs, specificity

of MRTF and TCF binding, and motifs associated with SRF sites apparently

lacking cofactors. Related to Figures 1 and 2.

Supplementary Figure S3 displays examples of MRTF binding sites where

MRTF-B binds apparently in a SRF-independent manner. Related to Figure 2.

Supplementary Figure S4 shows that inducible SRF binding sites exhibit

MRTF-dependent nucleosome displacement in response to Rho-actin

signalling pathway. Related to Figure 3.

Supplementary Figure S5 shows the validation of the RNA-seq data by Q-

PCR and displays examples of potential ncRNA cis-regulatory effect on gene

activity. Related to Figure 4.

Esnault et al, Supplementary Materials

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Supplementary Figure S6 describes the role of SRF/MRTF in controlling

expression of genes regulating cytoskeleton remodelling, focal adhesion and

the extracellular matrix and shows that SRF deleted cells are impaired in both

actin and microtubule assembly. Related to Figure 6.

Supplementary Figure S7. Shows the validation of SRF, MRTF and TCF

association with selected genes involved in the circadian rhythm control and

confirms that MRTF can synchronise the circadian clock in response to actin

dynamics. Related to Figure 7.

2. Supplementary tables

Excel file of the supplementary tables summarising the genomic data. Related

to Figure 1, 2, 4, and 6.

Supplementary Table S1 summarises the ChIP-seq data. Related to Figures

1 and 2.

Supplementary Table S2 summarises the RNA-seq data of coding genes.

Related to Figure 4.

Supplementary Table S3 summarises the RNA-seq data of ncRNA. Related

to Figure 4.

Supplementary Table S4 summarises the relationship between SRF and

cofactor binding and the activity of associated genes. Related to Figure 4.

Supplementary Table S5 presents the SRF, MRTF and TCF signatures.

Related to figure 6.


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Supplementary Table S6 presents the gene ontology analysis. Related to

Figure 6

Supplementary Table S7 presents genes regulated by SRF/MRTF involved

in cytoskeleton control. Related to Figure 6.

Supplementary Table S8 shows a detailed gene signature enrichment

analysis. Related to Figure 6.

3. Extended Methods

4. Supplementary References


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SUPPLEMENTARY FIGURE LEGENDS

Supplementary Figure S1. Effects of inhibitors

Cells were maintained in 0.3% FCS and stimulated with 15% FCS with of

LatB (0.3µM), CD (2µM) or U0126 as indicated, or treated with drugs alone.

(A) U0126 (10µM), but not LatB (0.3µM), inhibits ERK activation following

serum stimulation, as assessed by immunoblot for phospho-ERK. As a

loading control the blot was stripped and reprobed with pan-ERK antibody.

(B) ERK inhibition by U0126 (10µM) does not prevent serum-induced nuclear

accumulation of MRTF-A, as assessed by immunofluorescence.

(C) Effects of actin-binding drugs on MRTF-A and YAP subcellular

localisation. Confocal immunofluorescence microscopy of MRTF-A (green; left

panels) or YAP1 (green; right panels) with nuclei and F-actin visualised by

DAPI (blue) and Phalloidin (red). MRTF-A is predominantly cytoplasmic in

unstimulated cells, and LatB inhibits its serum-induced nuclear accumulation;

CD alone is sufficient to induce MRTF-A nuclear localisation. YAP

localisation, which is predominantly nuclear, is unaffected by these

treatments.

(D) Quantitation of MRTF and YAP1 nuclear localisation and response to

signals in (C), with the corresponding signals for similarly-treated confluent

cell monolayers, where YAP is more cytoplasmic (Yu et al. 2012) are shown

for comparison. ArrayScan VTI and Compartmental analysis

(BioApplications). >3000 cells / condition. Mean signal is indicated by Red

bars.


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(E) Effect of serum stimulation on YAP phosphorylation, assessed by

immunoblot with Phospho-YAP (Ser127) antibody. Under our experimental

conditions - subconfluent cells grown on plastic - YAP phosphorylation is low

and unaffected by serum stimulation; in contrast, in confluent cells YAP

phosphorylation is high and decreases upon serum stimulation (see (Yu et al.

2012).

(F) Differential sensitivity of SRF target genes to Rho-actin and Ras-ERK

signalling. Endogenous MRTF targets (Acta2 and Srf) and TCF targets (Fos

and Egr1) were analysed by qRT-PCR. Expression following specific

activation of MRTF by Cytochalasin D (CD; 2µM) is also shown.

Supplementary Figure S2. ChIP-seq control and validation experiments.

(A) SRF ChIP-seq shows that LatB inhibits inducible (red) but not constitutive

(black) SRF binding, while U0126 has no effect.

(B) ChIP-qPCR validation of SRF ChIP-seq. 6 constitutive (black) and 49

inducible (red) ChIP-seq peaks. Data are means of 3 independent

experiments.

(C) MRTF and SRF signals correlate at inducible SRF sites. SRF peak

heights at the 2133 inducible SRF sites are compared to those of MRTF-A

(left panels) and MRTF-B (right panels), both expressed as percent of

maximum signal compared by scatter plot. Upper panels, high-confidence

MRTF sites (MACS p<10-5); lower panels, all MRTF sites.

(D) Serum stimulation and serum + U0126 treatment are pseudoreplicates for

MRTF ChIP-seq. Scatter-plot comparing high-confidence MRTF-A and MRTF-


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B ChIPseq signals (274 MRTF-A, 1176 MRTF-B; MACS p<10-5) from cells

stimulated with serum in the presence or absence of U0126.

(E) MRTF-A and MRTF-B dimerise. Left, NIH3T3 cell protein extracts were

immunoprecipitated with anti-MRTF-A, anti-MRTF-B, or no antibody, and

proteins detected by immunoblotting. Right, antibody specificity was

confirmed by immunoblotting of cells treated with SRF, MRTF-A or MRTF-B

specific siRNAs (Medjkane et al. 2009).

(F) MRTF-A and MRTF-B predominantly bind the same sites. Left, peak

heights for MRTF-A and MRTF-B at sites bound by both proteins, expressed

as percent of maximum, were compared by scatter plot (n=1320). Centre,

comparison of MRTF-A and MRTF-B raw readcounts at peaks called for

MRTF-B only (n=1021). Right, comparison of MRTF-A and MRTF-B raw

readcounts at peaks called for MRTF-A only (n=75).

(G) Comparison of SRF ChIP-qPCR data with MRTF-A and MRTF-B qPCR

data at 50 SRF ChIP-seq peaks and 3 negative control sites. Sites are ranked

in order of increasing SRF ChIP-seq signal. Vertical dashes indicate those

peaks called positive by ChIP-seq for SRF, MRTF-A and MRTF-B, and the

TCFs (see Figure S2H). Antibodies used for ChIP-qPCR assays are indicated

at the top. Note that at 4 peaks called as negative for MRTF-A by ChIP-seq

nevertheless validate as MRTF-A positive by ChIP-qPCR, as indicated by the

red arrows.

(H) Comparison of TCF ChIP-qPCR data with TCF peaks called by ChIP-seq.

14 SRF-positive ChIP-seq peaks which scored positive for TCF (9 SAP-1, 7

Elk-1, 6 Net; indicated in Figure S2G), and 3 negative control loci were

analysed by ChIP-qPCR. Assays are ranked in order of increasing SRF ChIP-


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seq signal. Antibodies used for ChIP-qPCR assays are indicated at the top.

Vertical dashes indicate peaks called as positive for SRF, and the different

TCFs by ChIP-seq. Red line marks the maximum negative control value.

(I) Sequence motifs associated "no-cofactor" SRF sites, classified according

to whether SRF binding was inducible and LatB-sensitive. Sequences within

100bp of each SRF summit were scanned. The spectrum of motifs associated

with each class suggests these sites may represent undetected MRTF- and

TCF-specific SRF sites. See Figure 2F.

Supplementary Figure S3. SRF-independent MRTF binding events

Apparently SRF-independent MRTF-B binding events at inducible SRF target

genes, at a constitutive gene, and in an intergenic region are shown in

orange. MRTF-A binding did not score at MACS p<10-5 at these sites.

Supplementary Figure S4. Inducible SRF binding is associated with

signal-regulated nucleosome displacement.

(A) SRF binding correlates with histone H3 displacement on the Fos and

Acta2 genes. Tracks show total H3 ChIP-seq signal in resting and serum-

stimulated cells, with inhibitors as indicated. Red bars show SRF binding

sites, black arrows highlight histone-depleted regions.

(B) SRF binding sites correlate with DNA'ase I sensitivity maxima.

Metaprofiles showing DNA'ase I cleavage per base across a 4 kb window

centred on the SRF peaks are shown. Inducible sites, red; constitutive sites,

black. DNA'ase I data at GEO accession number: GSM1003831.


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(C) Scatter plots displaying SRF-ChIP-seq readcounts against H3 ChIP-seq

signal (displayed by rank order of decreasing H3 signal; left panels,) or

DNA'ase I sensitivity (displayed by rank order of increasing cleavage signal;

right panels) in cycling NIH3T3 cells, as determined by others (right panels;

wgEncodeEM002916 (2011). Signals at inducible and constitutive SRF sites

are shown in red and black respectively. SRF ChIP-seq peak height in resting

cells correlates with DNA'ase I sensitivity maxima (left), but the correlation is

lost upon serum stimulation (right). Black line, Loess regression fitting curve

(20 value moving window). DNA'ase I data at GEO accession number:

GSM1003831.

Supplementary Figure S5. RNA-seq: functional validation and ncRNA

targets.

(A) RNA changes were assayed by qRT-PCR on 20 endogenous SRF target

genes defined by RNA-seq in the indicated culture conditions (Figure 1B).

Blue bars qRT-PCR ± SEM, analysis of 3 separate RNA preparations; red

bars, RNA-seq readcounts ± halfrange from analysis of 2 independent RNA

preparations.

(B) Comparison of RNA-seq and qRT-PCR data. Scatter plots comparing the

RNA changes quantified by qRT-PCR and RNA-seq are displayed in function

of the cell culture conditions. They show a good correlation between the two

methods of quantification (Spearman r, 0.79; p<0.0001).

(C) ncRNA targets in the vicinity of SRF target genes. Top, schematic view of

the genomic organisation around Gm15270, Gm13270, Neat1, GM10501,


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4930500J02rik and Gm1720 ncRNAs. Scale bars, 20kb. Red bars, ncRNA

transcription units; green bars, candidate SRF target genes; vertical lines,

SRF ChIP-seq peaks. Bottom, comparison of transcriptional responses of the

ncRNAs and adjacent SRF targets. ncRNA candidates for cis-regulation are

highlighted with gene expression changes indicated. For functional studies of

SRF ncRNA targets, see the following: miR-143 and miR145 (Xin et al. 2009);

miR-199a2 and miR214, (Park et al. 2011; Alexander et al. 2013); miR-21,

(Kumarswamy et al. 2011); miR-22 (Gurha et al. 2012); Tug1 and

Malat1/Neat2, (Gutschner et al. 2013).

Supplementary Figure S6. Cytoskeletal MRTF-SRF targets.

(A) MRTF-SRF signalling in cytoskeletal dynamics. MRTF-SRF target genes

(specific proteins or protein functional classes) are indicated by ovals. Left,

regulators of the actin dynamics and contractility; right, focal adhesion

components.

(B) SRF is required for assembly of F-actin and microtubules, and to maintain

nuclear morphology. F-actin and microtubules were visualised using Texas

Red-X Phalloidin and -Tubulin-Alexa488 in wildtype or SRF-deleted MEFs

under the indicated conditions. Data were quantified using ArrayScan VTI and

Compartmental analysis (BioApplications), >3000 cells / condition.

Supplementary Figure S7. SRF network and circadian clock regulation.

(A) ChIP-qPCR analysis of selected circadian SRF targets detected by ChIP-

seq.


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(B) CD treatment rapidly activates clock component gene transcription.

Analysis by qRT-PCR.

(C) Clock resetting by SRF activation. NIH3T3 cells were treated with 50%

FCS and transcripts were quantified by qRT-PCR over 36h.

(D) MRTF-induced clock resetting is SRF-dependent in MEFs. Wildtype and

SRF knockout MEFs (pooled cultures from each of 3 embryos) were treated

with 2 µM CD and transcripts quantified by qRT-PCR.


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SUPPLEMENTARY TABLES

Supplementary Table S1. SRF network ChIP-seq analysis

Summary of the ChIP-seq data from SRF, MRTF-A, MRTF-B, Sap-1, Elk-1

and Net experiments. Chromosomal location of each peak is given, together

with the identity of the nearest gene (or genes where the peak is located

within a gene feature). Each peak ID can be used on UCSC browser to

visualise the genomic area within the GEO database (instructions at

http://genome.ucsc.edu). The peak information, the summary of SRF peak

detection, the peak signal quantification for all factors in 0.3%FCS, 15%FCS,

LatB+15%FCS and U0126+15%FCS, the summary of the detection by MACS

of the factors and the relative MRTF and TCF scores are shown. The basis for

calling each peak by MACS score and/or coincidence with ChIP-seq signals

for other factors is indicated. Shading indicates SRF peaks called at MACS

p<10-5 (red) or MACS p<0.05 plus coincidence with a high-confidence MRTF

peak (green). Signals are quantified as the number of reads per 15 million.

The cofactor score normalises the cofactor signal across each condition to the

mean MRTF or TCF signals (MRTFs, mean of 15% FCS and 15%

FCS+U0126 signals; TCFs, mean across all conditions) to derive a measure

of the relative strength of MRTF and TCF signals at each SRF peak; the ratio

of the MRTF and TCF scores is also shown.

Supplementary Table S2. RNA-seq analysis: protein-coding genes

The table presents the RNA-seq analysis of NIH-3T3 cells maintained in 0.3%

FCS, stimulated with 15% FCS with or without LatB and/or U0126 treatment,


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or stimulated with CD, for all Refseq genes (release 47). For each gene,

expression was evaluated using all RNAseq reads within the gene feature, or

intronic reads only. The effect of serum or CD stimulation is summarised, and

serum-induced genes responsive to SRF-linked signals are indicated (serum-

induced, sensitive to LatB and/or U0126, or induced by CD; FDR<0.08.

Background shading indicates gene activity status: red, inducible; green,

repressed; yellow, constitutive; white, inactive. Active genes are defined as all

those which show detectable RNA-seq signal in any experimental condition.

ChIP-seq data are summarised according to whether the closest SRF site to

the gene is within the gene feature, within 70kb, or more distant. The genomic

coordinates of the SRF site closest to each gene, and its distance from the

TSS, are given (sites within a gene feature are indicated as zero). Cofactor

association is defined as those cofactors associated with the SRF peaks for

which the gene concerned is the closest; note that for genes associated with

multiple peaks, not all will be associated with that cofactor.

Supplementary Table S3. RNA-seq analysis: ncRNAs

The table presents the RNA-seq analysis of NIH-3T3 cells maintained in 0.3%

FCS, stimulated with 15% FCS with or without LatB and/or U0126 treatment,

or stimulated with CD, for all ncRNA (Ensembl release 69). The ncRNA

information section displays ncRNA coordinates, ncRNA biotype, database

source, and the identity and proximity of the nearest protein-coding gene. Red

shading indicates largest ncRNA in the database, green shading indicates

overlapping ncRNAs where multiple ncRNAs are transcribed from the same

locus. Expression and ChIP-seq peak data are displayed as in Table S2.


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Supplementary Table S4. Relationships between SRF and cofactor

binding and the activity of associated genes.

Genes are categorised according to their proximity to SRF binding events.

Direct genes exhibit SRF binding within a gene feature or within 2kb of 5'

flanking sequence. The Near (<70 kb) gene class has SRF sites within that

distance of the TSS; since SRF sites are significantly enriched within that

distance of active genes, these represent potential SRF targets. Genes in the

Far (>70 kb) class are more than 70kb from an SRF site; since there is no

significant enrichment of SRF sites at active genes in this set, we cannot infer

anything about the role of SRF in regulation of this gene class. (A) Relation

between gene expression status, SRF cofactor association, and regulatory

signal pathway. Genes are classified according to their response to serum

stimulation, sensitivity to inhibition by U0126 or LatB, induction by the MRTF

activator CD, and the cofactors associated with their nearest SRF binding

sites. See also Figure 4D. (B) Relationship between SRF peak type and gene

expression status. (i) Each SRF peak is classified according to cofactor

association, SRF binding inducibility, and the expression status of its nearest

gene; an accessory table below summarises properties of peaks that are

associated with more than one "Direct" gene. (ii) Peaks are classified

according to whether they are associated with gene regulatory events at

genes within the 70kb cutoff point. For example, 104 of the 720 peaks

associated with serum-inducible SRF-linked Direct genes are also the closest

peaks to a second serum-inducible "Near" gene (ie located within 70kb of the

site concerned).


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Supplementary Table S5. SRF, MRTF and TCF gene signatures.

The table presents SRF, MRTF, and TCF gene signatures comprising all

genes within 70kb of an SRF binding site which satisfy the criteria listed at the

top of each column. A list of all "Direct" genes satisfying the criteria is also

given for each signature. The stringent MRTF signature is defined as those

genes which both bind MRTF and are sensitive to direct inhibition or activation

of MRTF by LatB or CD.

Supplementary Table S6. Ontology analysis

Gene ontology analysis performed with DAVID on the various gene sets. P-

values below 10-3 are shown in bold red, and associated gene numbers and

enrichment factors in bold. The Near and Far serum inducible genes sets do

not exhibit significantly different ontology (Wilcoxon test p = 0.1).

Supplementary Table S7. SRF and MRTF in control of cytoskeletal gene

expression

The table displays MRTF-SRF cytoskeletal target genes, categorised

according to their functional roles. For functional studies of SRF targets

ncRNAs that may impact on the cytoskeleton, see the following: miR-143 and

miR145 (Xin et al. 2009); miR-199a2 and miR214, (Park et al. 2011;

Alexander et al. 2013); miR-21, (Kumarswamy et al. 2011); miR-22 (Gurha et

al. 2012) Tug1 and Malat1/Neat2 (Gutschner et al. 2013).


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Supplementary Table S8. Gene signature enrichment analysis

The SRF, MRTF and TCF gene signatures were compared with gene

expression databases. P-values from two-tailed Fisher test, number of genes

in common and article references are displayed. In several cases, the

inducible MRTF-SRF target gene set exhibited significant overlap with the

gene sets identified as up- and down-regulated between two experimental

conditions, possibly owing to normalisation procedures used for comparative

analysis of microarray datasets (for discussion see Loven et al. 2012). In

these cases only the upregulated set is included in the Table. For references

see (Iyer et al. 1999; Chang et al. 2004; Philippar et al. 2004; Wang et al.

2004; Kornmann et al. 2007a; Kornmann et al. 2007b; Wang et al. 2007;

Antipova et al. 2008; Hamilton and Kay 2008; Padua et al. 2008; Provenzano

et al. 2008; Boros et al. 2009a; Boros et al. 2009b; Descot et al. 2009;

Provenzano et al. 2009; Zhang et al. 2009; Costello et al. 2010; Cordenonsi et

al. 2011; Dupont et al. 2011; Bruna et al. 2012; Calvo et al. 2013; Jagannath

et al. 2013; Kwon et al. 2013; Park and Guan 2013).


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EXTENDED METHODS

Cell culture and immunofluorescence

NIH-3T3 or MEF cells at 50% confluence were serum-starved overnight (0.3%

FCS) and stimulated with 15% fetal calf serum (FCS), cytochalasin D

(Calbiochem, CD, 2M), or TPA (50ng/ml) for 30 min or as in figure legends.

For circadian clock entrainment analysis, cells were grown in 5% FCS and

stimulated with CD for 2h followed by washout or with 50% FCS. Signalling

pathways were inhibited by pretreating the cells for 30 minutes before

stimulation with latrunculin B (Calbiochem, LatB, 0.3 M) and/or U0126

(Promega, 10 μM). Immunofluorescence microscopy was performed as

described earlier (Vartiainen et al. 2007; Guettler et al. 2008). Cytoskeletal

changes were analysed using ArrayScan VTI and Compartmental analysis

(BioApplications).

Antibodies

Antibodies were: SRF (sc-335), SAP-1a (sc-13030), MRTF-A (sc-21558),

MRTF-B (sc-47282), PolII, CTD S2unP, 8WG16 (sc-56767) all from

SantaCruz; Elk-1 (Epitomics); Net, 1996 (Buchwalter et al. 2005), total H3

(ab1791, Abcam); PolII, CTD S2P (H5, covance); PolII, CTD S5P (H14,

covance); -Tubulin, Cell signalling #8058; YAP, Santa-Cruz H-125 #15407;

Phospho-YAP, Cell signalling #4911. F-actin was visualised with Texas

Red®-X Phalloidin (Invitrogen), and nuclei with DAPI.


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EMSA

Gel mobility shift probes comprised 120 bp centred on CArG motifs present in

specific SRF ChIP-seq peaks, generated by PCR and cloned using the

pENTR™⁄D-TOPO® cloning kit (Invitrogen). Binding assays were performed

as described previously (Marais et al. 1992; Murai and Treisman 2002). DNA

binding assays were in buffer D (1mM EDTA, 10mM Tris.HCl, pH 7.9, 50 mM

NaCl, 3mM DTT, 50µg/l BSA, 25µg/l poly(dIdC), 10% Ficoll 400, 5mM

Spermidine, 0.5% Bromophenol Blue with protease inhibitors (Roche)).

Reactions contained 60µg of whole cell extract from transfected NIH3T3 cells

transfected with SRF expression plasmid or pcDNA4 as a control, together

with 100-800 ng of recombinant MRTF-A123-1A (Vartiainen et al. 2007) prepared

using a recombinant baculovirus (from A.Mylona and S. Kjaer, unpublished).

Quantitation of binding assays was by phosphorimage analysis using

ImageQuant software. MRTF-A123-1A binding was estimated by quantification

material migrating more slowly than the SRF-DNA complex, which can be

supershifted using MRTF-A antibody, and compared to the yield of SRF-DNA

complex obtained in the absence of added MRTF-A123-1A.

Chromatin immunoprecipitation

ChIP was performed as described (Miralles et al. 2003), with the following

modifications: fixation was stopped by the addition of 250 mM glycine,

sonication was performed with a Bioruptor UCD 200 and recovery was

realised by using magnetic G-protein beads (Invitrogen). SYBR Green based

real-time PCR (Invitrogen) was performed using dilutions of genomic DNA

solution for calibration and to derive arbitrary abundance units. All data are


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from at least 3 independent experiments. For discussion of PolII antibody

specificity, see (Palancade and Bensaude 2003; Buratowski 2009).

Chip-Seq

DNA samples were end repaired, poly-A tailed and Illumina single end

adapters were ligated following the standard Illumina protocol with minor

adjustments. Agencourt AMPure XP beads at 0.8x ratio were used to size

select out adapter dimers after adapter ligation. The Illumina kit Phusion

enzyme was replaced by Kapa HiFi HotStart ready mix. Post PCR, AMPure

XP beads were used at a 1:1 ratio to maintain size integrity and to allow use

of the Invitrogen SizeSelect E-gel system. Samples were finally purified with

QIAquick gel extraction kit and quality controlled on the DNA 1000

BioAnalyser 2100 chip before clustering and subsequent 36bp single end

sequencing on the GAIIx analyser or on a Hi-seq 2000/1000.

ChIP-Seq Data Processing

All ChIP-Seq data sets were aligned using Eland (version pipeline 1.4) to

version NCBI37/mm9 of the mouse genome with the default settings. Raw

data and WIG files can be found online associated with the GEO Series ID

GSE45888.

Identifying ChIP-Seq Enriched Regions

We used MACS version 1.3.7.1 (Zhang et al. 2008) to identify regions

enriched over background (beads alone) in the aligned data. Default settings

were used apart from; model fold=8 and an effective genome size of


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2.43e+09. MACS threshold values of p<10-5 or p<0.05 were used as stated in

the text.

Calculating Read Density

All ChIP-Seq sample mapped read counts were normalised to 15 million total

reads and the normalised read density per base pair calculated for each

enriched region. Total H3 samples were normalised 300 million mapped

reads.

MRTF score

For all positive MRTF ChIP-seq peaks, the MRTF-A and MRTF-B signals from

the 15% FCS and 15% FCS+U0126 conditions, were each normalised using

the median MRTF-A and MRTF-B signal respectively. The MRTF score was

then defined as the mean of the normalised MRTF-A and MRTF-B signals at

each peak.

TCF score

For all positive TCF-SRF ChIP-seq peaks, the SAP-1, Elk-1, and Net signals

from each condition were normalised using the median SAP-1, Elk-1, and Net

signals respectively. The TCF score was then defined as the mean of the

normalised TCF signals at each peak.

Density plot

We calculated per nucleotide average read density profiles for the Pol II

datasets across gene loci ±5 kb. Gene lengths were standardised to 20 kb.

Samples were scaled to a total of 15 million reads. In the case of the H3 and

SRF datasets, we calculated average read density profiles centered on the

SRF ChIP binding loci (±2 kb around the SRF ChIP-seq signal summit). Here


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samples were scaled to a total of 300 million reads for total H3 and 15 million

for SRF. The same analysis was run on the imported DNase I Digital Genomic

Footprinting from GEO accession number GSM1003831 (2011). For MRTF-A

and MRTF-B datasets, we calculated average read density profiles centered

on the ChIP binding loci (±2 kb around the ChIP-seq peak summit). Here

samples were scaled to a total of 15 million reads.

Conservation plot

To visualise sequence conservation on ChIP-Seq loci, we used the Placental

phastCon30way conservation data from UCSC. We created per nucleotide

mean conservation profiles for binding loci sets. Profiles represent ±2 kb

centered on SRF ChIP-seq profile summit.

Identification of TF Binding Motifs

De novo motif discovery and known motif identification were performed using

Homer 3.10 (Heinz et al. 2010) on sequences ±100 bp from the peak summit

of enriched regions. This was performed for each of the TF datasets and also

for enriched regions that were represented in overlapping datasets. In addition

these datasets were interrogated with MEME 4.6.1 (Bailey and Elkan 1994),

which produced similar results. The SRF CArG binding consensus

(CC(AT)6GG), with mismatches as required, was used to search the selected

sequences by Fuzznuc, part of the EMBOSS suite (Rice et al. 2000).

Determination of RNA Pol II Enrichment

PolII ChIP-seq datasets were aligned to the mouse genome version

NCBI37/mm9 using Eland (version 1.4) and the number of mapped reads in

each sample was normalised to 15 million. For all transcripts annotated by

UCSC for mm9, we quantified the signal by counting the number of reads in


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500bp windows from -2Kb from the TSS to +70Kb or to the end of the gene, if

it was shorter.

RNA-Seq

Libraries were prepared using the Directional mRNA-Seq Library Prep. v1.0.

Pre-Release Protocol from Illumina with minor adjustments. To minimise the

ribosomal rRNA representation in the libraries, samples were processed

either using DSN (Evrogen JSC; serum- and CD-stimulation experiment) or

with the Ribo-zero rRNA removal kit (Epicentre; separate CD-stimulation

experiment). The Illumina kit Phusion enzyme was replaced by Kapa HiFi

HotStart ready mix which reduced the overall volume of the PCR and the ratio

for the Agencourt AMPure XP beads was adjusted accordingly. The standard

PCR cycling was also changed to match the concentration of the total RNA

from the initial QC. After passing the final QC, the libraries were subjected to

cluster formation and then 72bp single end sequencing on the GAIIx analyser.

RNA-Seq Data Processing

All RNA-Seq data were aligned to NCBI37/mm9 with BWA(version 0.5.6)

using the default settings. Raw data can be found online associated with the

GEO Series ID GSE45888.

Calculation of read density and gene expression analysis

After alignment read counts for the canonical gene features from RefSeq

gene and intron annotations, were calculated using coverageBed (bedtools

version 2.14.3), using the bam file generated by bwa as the input. The data

were normalised by using the average expression level of 6664 genes which


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do not significantly change in expression upon serum stimulation. The

differential gene and intron expression analysis was performed with DESeq

(Anders and Huber 2010) at p<0.2. Since gene changes in response to signal

were called in combination of several treatments, we simulated the false

discovery rate by using 5000 permutations and found an estimated fold

discovery rate of FDR<0.08. Figures present relative gene expression levels

across the conditions of cell growth from either all normalised gene feature

counts or normalised intronic counts.


22

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