Molecular Cell, Volume 43

Supplemental Information

Transcription Factor AP1 Potentiates Chromatin Accessibility and Glucocorticoid Receptor Binding Simon C. Biddie, Sam John, Pete J. Sabo, Robert E. Thurman, Thomas A. Johnson, R. Louis Schiltz, Tina B. Miranda, Myong-Hee Sung, Saskia Trump, Stafford L. Lightman, Charles Vinson, John A. Stamatoyannopoulos, and Gordon L. Hager

Supplemental Item Descriptive Title Figure S1 Related to Figure 1: Co-occupancy of GR and AP1 binding genome-wide Figure S2 Related to Figure 2: Characterization of A-fos expression cell line; Functional analysis of A-fos on GR-mediated gene regulation Figure S3 Related to Figure 3: Genome-wide analysis of AP1 on GR binding and chromatin accessibility Figure S4 Related to Figure 4: Sequence motif features of GR and AP1 bound regulatory elements Table S1 Summary of sequenced datasetsSupplemental Experimental Procedures Details of experimental methods

Figure S1. GR binding significantly overlaps AP1 binding sites. A. Constitutive footprinting of AP1. To visualize AP1 occupancy at nucleotide resolution, we analyzed per nucleotide DNaseI cleavage patterns from deeply sequenced samples (>100M uniquely mapping reads) around consensus AP1 motifs (p-value matching threshold 10-4) within GR-plus-AP1 co-occupied DHSs. Panels show per-nucleotide DNaseI cleavage intensity (white) within a 200 bp window around GR and AP1 co-bound sites, centered on the AP1 motif (in the absence or presence of hormone). Each pixel row represents a single GR binding element centered on the AP1 motif (red bar) and sorted by the median DNaseI cleavage (left and middle panels). The right panel represents an equal sample size of random genomic elements centered around random 8 bp sequences. The lower panel shows averaged per-nucleotide DNaseI cleavage around AP1 motifs at GR-occupied sites before (blue) and after (red) hormone treatment. Evolutionary conservation scores (Phastcons) are superimposed (purple). B. GR and AP1 peaks are enriched over input. Shown are examples of sequenced input tracks (normalized for variable tag counts between libraries) before and after hormone, with GR and AP1 ChIP-Seq data [UCSC browser shots (Kent et al., 2002)]. The sequenced input was used as a control for peak calling for all GR and AP1 ChIP-Seq data. C-G. GR and AP1 simultaneously occupy chromatin. Sequential ChIP (re-ChIP) was performed by immunoprecipitating first with GR antibodies followed by AP1 (cJun) antibodies. A no-antibody control was also performed during the second ChIP. GR and AP1 co-occupy chromatin at sites that harbor both the GRE and AP1 motifs (composite elements, C - D) and the AP1 motif alone (non-composite site, E). A control site that has no AP1 binding shows no enrichment of AP1 in the re-ChIP (F). Similarly, a site in closed chromatin lacking both AP1 and GR binding shows no enrichment of either GR in the first ChIP or AP1 in the re-ChIP (G). GR ChIP and re-ChIP experiments are expressed as fold enrichment relative to the no hormone (no Dex) GR ChIP. H. Statistical analysis of GR and AP1 overlap. In silico simulations of the chance overlap between AP1 and GR were determined by the random sampling of genomic elements (DNaseI hotspots in 150 bp windows or the entire murine genome sampled in 150 bp windows). Genomic regions of interest are assigned unique identifiers. An equivalent number of experimentally derived ChIP-Seq peaks for GR and AP1 are randomly sampled from the list of unique identifiers. The intersection to compute random expectation of binding is determined where the identifier is present in both simulated random samplings

of GR and AP1 ChIP-Seq data sets. The simulation is repeated one thousand times for statistical power. I. DNA sequence motifs at GR binding sites in Hepa1c1c7 cells. De novo motif discovery (MEME) was performed for the top 350 GR binding sites in Hepa1c1c7 cells. Shown are the enriched motifs that match significantly to known sequence motifs (P < 10-4).

Figure S2A-S2D. Characterization of A-fos expression in a congenic mammary

epithelial cell line. A. Schematic for A-fos inhibition of AP1 binding at composite and non-composite

elements. The tet regulated expression of A-fos compromises endogenous AP1 binding at both composite and non-composite elements by forming DNA binding incompetent A-fos-c-Jun heterodimers. The dominant negative Acidic-Fos (A-fos), therefore, is a potent molecule that interferes with the binding of all members that comprise the AP1 family. B-C. Tight, conditional expression of A-fos. Flag-tagged A-fos was stably integrated in a mammary cell line under the control of a tet-regulated promoter. Western (B) or RNA (C) analysis of the parental murine mammary epithelial cell line containing the tTA regulator

(7110) and the congenic A-fos cell line following 48 hrs without tetracycline was performed. A-fos protein was detected using an anti-Flag antibody (generous gift from Anthony Imbalzano, UMass, Worcester). A-fos RNA was detected using Flag epitope specific primers. Histograms represent the mean of four biological replicates. Error bars represent the standard deviation of the mean. D. A-fos expression inhibits endogenous AP1 binding. ChIP-q-PCR of AP1 following tetracycline withdrawal at select sites. Cells were grown in the presence or absence (48 hrs) of tetracycline. Bars represent the mean of three biological replicates normalized to input DNA. Error bars represent the standard deviation of the mean. Greater than 90% loss of AP1 binding was observed at all sites tested. The loss of AP1 binding is not due to diminished AP1 protein levels (data not shown).

Figure S2E-S2J. Functional analysis of abrogating AP1 activity on GR-mediated gene

regulation.

E-H. Summary of AP1 regulated genes. Expression of A-fos alters the transcription of a minority of genes in the murine transcriptome (8%). However, of the 651 genes regulated by GR (407 induced and 244 repressed by hormone) in the murine mammary epithelial cell line (3134), a significant number of induced genes (46%, 187 genes) and repressed genes (51%, 124 genes) showed a compromised transcriptional response by at least 2-fold.

I – J . Validation of the transcriptional effects of A-fos on select GR-regulated genes. In the A-fos cell line, q-PCR analysis of nascent gene expression was performed using primer pairs designed to intron-exon junctions for Sorbs (E) and Suox (F) genes. Data represents the mean of expression microarrays and q-PCR (4 replicates). Error bars represent the standard deviation of the mean.

Figure S3A-S3D. Genome-wide analysis of AP1 on GR binding and chromatin

accessibility.

A. A-fos expression attenuates GR binding but not chromatin accessibility. Examples of loci showing AP1 ChIP-Seq in the absence and presence of hormone and DNaseI-Seq and GR ChIP-Seq in the presence and absence of A-fos [UCSC browser shots (Kent et al., 2002)]. GR binding is attenuated at sites of GR and AP1 binding (black arrows). However, DNaseI hypersensitivity does not change with A-fos expression at some regulatory elements (red

arrows), illustrating that a subset of accessible chromatin domains at co-bound regions are maintained independent of AP1 binding. B. AP1 binding regulates chromatin accessibility independent of GR binding. Shown are examples of AP1 peaks before and after hormone treatment that do not overlap GR peaks [UCSC browser shots (Kent et al., 2002). Abrogating AP1 binding with A-Fos attenuates chromatin accessibility at many sites, determined by DNase-Seq (black arrows). A site not affected by A-Fos is shown by the red arrow. C. Depletion of cJun attenuates GR binding and chromatin accessibility at GR and AP1

co-bound sites. To further assess the contribution of AP1 to GR binding and chromatin accessibility, siRNAs to cJun (the major protein of the AP1 heteromeric complex) were transfected into cell lines for 48h. Western blots and RT-qPCR experiments were performed to monitor protein and RNA levels. GR ChIP-qPCR and FAIRE-qPCR were performed to show that cJun depletion attenuated both GR binding and chromatin accessibility respectively, thereby validating the A-fos effect. The effects were observed at GR and AP1 co-bound sites, but not at sites where GR is found to bind independent of AP1. Bars represent the mean of two biological replicates. Error bars represent the standard deviation of the mean. D. Globally attenuated chromatin accessibility correlates with loss of GR binding at

GR and AP1 co-bound sites. Contribution of changes in DNaseI accessibility to GR binding upon A-fos expression. Plotted are GR and DNaseI tag density ratios: in the presence of A-fos over the absence of A-fos. The red line shows the linear model fit through the points on the scatter plot. The loss of GR binding and DNaseI accessibility are positively correlated. Statistical significance was determined using a linear model fit.

Figure S3E-S3G. Chromatin accessibility and GR binding are attenuated with A-Fos

expression at GR and AP1 co-bound sites. E. Heatmap of GR and DNaseI profiles. Distributions are presented as a function of A-fos expression (tag density ratios of no A-Fos over A-Fos). F-G. Distributions related to A-fos expression. Histograms show the frequency of DNaseI (B) and GR (C) tag density ratios as a function of A-fos expression. A majority of sites show loss of GR and DNaseI signal when A-Fos is expressed. Examples are shown for >75% loss (I), ~50% loss (II) and unaffected sites (III).

Figure S4. Sequence motif features of GR and AP1 bound regulatory elements.

A. Most GR and AP1 co-bound regions occur in pre-existing chromatin. Approximately 90% of GR and AP1 co-bound sites (3783 out of 4196 sites) are associated with regions open prior to the addition of hormone (constitutive chromatin). Only a small fraction (9% or 377 sites) of co-bound regions are in regions actively remodeled in a hormone-dependent fashion. A similar profile of transcription factor binding in constitutively open chromatin has been observed for GR (John et al., 2011). B. GR and AP1 co-bound regions at pre-existing chromatin contain an equal

proportion of composite and non-composite elements. A fraction (35%) of GR and AP1 co-bound regions contain both GRE and AP1 motifs (composite elements); similarly, 37% of GR and AP1co-bound regions contain the AP1 motf only (non-composite elements). In contrast, 86% of co-bound regions in inducible chromatin sites contain the GRE motif (see Figure 4B). Only motifs overlapping a peak by 1 bp were considered (MAST P < 10-3)

C. GR binds more robustly in inducible open chromatin. Box plots of of GR tag density at peaks associated with constitutively open chromatin or inducible chromatin. GR peaks are of a lower tag density in constitutively accessible chromatin suggestive of an indirect interaction of GR with chromatin at these sites. Statistical significance was determined using a two-sided KS-test. Boxplot shows the median and upper and lower quartiles. Whiskers show the minimum and maximum values. Notches denote the 95% confidence interval of the median. D. Inducible chromatin represent a large fraction of GR binding sites in the absence of

AP1. GR binding induces chromatin remodeling at 47% of GR only sites (no AP1) compared to 6% of GR and AP1 co-bound sites. A large fraction (94%) of GR and AP1 co-bound sites are found at constitutively open chromatin (compared to 53% at GR binding in the absence of AP1). E. Tag polarity localizes sequence-specific protein binding sites. The schematic (left panel) shows a polarity shift between positive and negative strands within a factor binding peak. The site of the polarity shift localizes a factor binding site and, therefore, the motif associated with that factor. Shown are tag densities (on positive and negative strands) for GR peaks containing a GRE motif (composite element, middle panel) or only an AP1 motif (non-composite element, right panel). F. A-fos expression compromises GR binding at both non-composite and composite

elements. The effect of A-fos on GR binding is expressed as a ratio of tag densities: minus

Tet (A-fos expressing) over plus Tet (no A-fos expression). Shown are effects of A-fos on GR binding at GR and AP1 co-bound sites containing the GRE motif alone, AP1 motif alone (non-composite) or both AP1 and GRE motifs (composite). GR binding is dependent on AP1 binding at both composite and non-composite elements. Boxplot shows the median and upper and lower quartiles. Whiskers show the minimum and maximum values. Notches denote the 95% confidence interval of the median. G. A-fos expression compromises chromatin accessibility at both non-composite and

composite elements. The effect of A-fos on chromatin accessibility is expressed as a ratio of tag densities: minus Tet (A-fos expressing) over plus Tet (no A-fos expression). Shown are effects of A-fos on chromatin accessibility at GR and AP1co- bound sites containing the GRE motif alone, AP1 motif alone (non-composite) or both AP1 and GRE motifs (composite). Chromatin accessibility is dependent on AP1 binding at both composite and non-composite elements. Boxplot shows the median and upper and lower quartiles. Whiskers show the minimum and maximum values. Notches denote the 95% confidence interval of the median.

Table S1. Summary of sequenced libraries.

Supplemental Experimental Procedures

Cell growth conditions and generation of congenic lines The 3134 murine mammary epithelial cell line is a subclone of 904.13 (Fragoso et al., 1998), originally derived from a mammary adenocarcinoma of the RIII mouse (Lowy et al., 1978). The A-fos cell line contains the DNA binding deficient, dominant-negative Fos variant (Olive et al., 1997) under tetracycline (tet) regulation (tet-off)(Gossen and Bujard, 1992), stably integrated into the 3134 cell line containing the tet regulator (tTa). The hepatocyte cell line (Hepa-1c1c7) was grown as described for 3134, with the use of alpha minimum essential medium (ATCC) instead of DMEM. Medium was supplemented with 10% fetal bovine serum (Invitrogen), sodium pyruvate, non-essential amino acids and 2 mM glutamine maintained in a humidifier at 37oC and 5% CO2. Tetracycline (1ug/ml) was supplemented to growth medium of relevant cell lines to prevent expression of recombinant proteins. Cells were plated for experiments in DMEM growth medium supplemented with 10% charcoal-dextran treated serum without tetracycline for 48 hours to induce expression of A-fos, prior to vehicle or hormone treatment. Gene expression profiling Total RNA from vehicle treated or 100nM dexamethasone-induced (4 hrs) cells were prepared via standard manufacturer protocols (Qiagen) using Trizol reagent (Invitrogen). Cells expressing A-fos were induced for 48 hrs prior to hormone stimulation. Two replicate samples, each a pool of two biological replicates, were prepared and hybridized to GeneChip mouse gene 1.0 sense target (ST) arrays as per manufacturer's protocols (Affymetrix). Data were normalized and annotated using Affymetrix expression console software and analyzed in Bioconductor (Wettenhall and Smyth, 2004), imposing a minimal fold change of log2 ≥0.5 for hormone effects. A-fos induced effects on hormone were calculated at a log2 difference of ≥1. q-PCR validation was performed by reverse transcription of total RNA using the BioRad cDNA Synthesis Kit as per manufacturer’s instructions and analyzed by q-PCR using SyBr green and a real-time detection system (iCycler IQ; Bio-Rad Laboratories).

Analysis of high-throughput sequencing data. Sequence reads (27 or 36 bp) from ChIP-Seq and DNaseI-Seq experiments were generated on the Illumina genome analyzer platform and quality-filtered reads were mapped uniquely to the mouse genome (UCSC mus musculus mm8 assembly). Non-uniquely mapping reads were not considered in subsequent analyses. Aligned tags were converted into tag density profiles by binning uniquely mapping reads in a 150bp sliding (step 20bp) window. Regions of significant sequence tag enrichment (‘hotspots’) were identified using a modified scan statistic algorithm, as described in (John et al., 2011). All sequencing experiments were performed on two independent biological replicates. Each replicate was independently analyzed, and a final dataset comprising replicate-concordant sites was constructed, as described in (John et al., 2011). Changes in GR binding or chromatin accessibility upon A-fos expression were calculated by an average ratio of tet-off (A-fos expression) over tet-on (no A-fos expression) tag densities for two replicates per condition. Analysis of deeply sequenced DNaseI samples were additionally processed by computation of per-nucleotide cleavage frequencies, determined from the 5’ end-sequences of DNaseI-released DNA fragments (Hesselberth et al., 2009). Identification of sequence tag hotspots. Regions of tag enrichment across the genome were determined using a binomial distribution of a small window (250bp) relative to a 50kb background window. Mapped tags are assigned a z-score, calculated for the small and background windows. The z-score is determined based on the total number of tags in the background 50kb window (N) relative to tags falling in the small window (n). The probability of mappability for each tag is p=250/50000, assuming each base is equal represented in the large window. Adjustments are further made to p to account for the number of uniquely mappable bases in the 50kb window. The expected number of tags in the small window is thus μ=Np, with the standard deviation σ= √ (Np(1-p)). The z-score for the tags in the small window is z = n-μ/σ. In addition to the 50kb window, a background model using the expected number of tags across the entire genome and z-score is computed with the lowest z-score reported as a conservative measure. ‘Hotspots’are determined by identifying successive neighboring tags within the small window with z-scores >2. Following identification of hotspots, each hotspot is additionally assigned a z-score relative to the 250bp small window and 50kb background

window. Analysis is treated to a two-pass procedure to address regions of high tag enrichment that inflate background estimates (reducing z-scores) of neighbouring hotspots. The two-pass procedure deletes tags in hotspots identified on the first pass to recompute for hotspots in the deleted background. Hotspots from both passes are combined and re-scored against the deleted 50kb background window. Calculation of false discovery rates (FDR). Hotspots are thresholded by a FDR z-score using a randomly generated dataset as a null model. Tags are generated computationally for the genome over uniquely mappable bases, equal to the number of experimental tag numbers. Random tags are enriched at genomic regions identified by the hotspot algorithm. The FDR for experimentally observed hotspots thresholded at a z-score (T) is given as: FDR (T) ≅ # of random hotspots with z ≥ T

# of random hotspots with z ≥ T We apply a 0% FDR for hotspots by thresholding at a z-score greater than the maximum z-score determined from the random data. Peaks are identified from thresholded hotspots using a peak detection algorithm that resolved hotspots to 150bp windows of high tag enrichment. ChIP-seq Analysis Parameters. ChIP-Seq and DNaseI-Seq data are analysed as previously described with the following additions to ChIP-Seq analysis. An additionally control model is applied using sequenced tags from ChIP input to estimate the background signal. Following two-pass hotspot detection, input tags are used to score for ChIP hotspots by subtracting from the hotspot the number of tags observed in the input dataset (following normalization of input by a numeric factor to account for differences in sequencing depth). Hotspot scoring is based on non-input subtracted background, as input tags are only subtracted from the hotspot, thus the scoring is conservative. Additionally, ChIP datasets that were sequenced to different depths were scaled to normalize for the different number of mapped tags where appropriate. Sequence artifact filtering. Artifacts of sequencing associated with element or region copy numbers emerge as high enrichment of tags in a small area. To address this we mask

satellite repeat regions, a source of sequence artifacts. Additionally, a scanning algorithm is applied to identify 50bp windows of ≥5 mapped tags that represent ≥80% of tags in a 250bp small window. The artifacts are masked from the hotspot and peak datasets.

DNA sequence motif analysis. The MEME algorithm was used to search for DNA sequence motifs enriched in ChIP peaks. For Hepa1c1c7 GR ChIP motif analysis, the top 350 sites were analyzed (using a width of 150 bp and settings of a minimum and maximum motif size of 8 bp and 40 bp respectively). The enriched motifs were searched against the Transfac database using TOMTOM in order to identify known motifs. For the AP1 non-composite motif analysis, we employed MEME-ChIP to analyze 600 random sites, 100 bp around peak centers, with masking of sites harboring the glucocorticoid response element (GRE). Statistical Analysis. P-values for overlap of genomic datasets use the binomial distribution R function pbinom. For boxplots, P-values were calculated using a Kolmogorov-Smirnov test (R function KS.test). The linear model fit and P-value were calculated using the R function lm. The heatmap was generated in R using the heatmap.2 function in the gplots bioconductor package. Sequential ChIP (Re-ChIP) Re-ChIP experiments were performing according to standard protocols (Shang et al., 2000). Re-ChIPs were performed by using GR antibodies in the first IP as described in experimental procedures with the exception of the elution step, instead re-ChIP experiments were eluted in 10mM DTT at 37oC for 30 minutes and diluted 1:60 in ChIP dilution buffer. Anti-cJun anitbodies were added for the second IP and processed as per the first IP ChIP protocol. siRNA Transfection Cells were cultured to 60% confluency and transfected with cJun Silencer Select siRNA Smartpool (Ambion ID s201552) or non-targeting pool (ThermoScientific D-001810-10-20) by electroporation and grown for 48 hours prior to harvesting for subsequent analysis.

Formaldehyde-assisted isolation of regulatory elements (FAIRE) FAIRE was performed by established procedures (Giresi and Lieb, 2009). Cells were fixed, lysed and sonicated as per ChIP protocol. After sonication, samples were subjected to 3 phenol/chloroform extractions. FAIRE and input samples were incubated overnight at 65̊C to reverse crosslinks followed by ProteinaseK and RNase treatment. Samples were phenol/chloroform extracted, ethanol precipitated and resuspended in water prior to qPCR analysis. Primer sequences: A-fos (Flag primer) Forward primer: TACAAGGACGACGATGACAA Reverse primer: AGTTCTGCCAGTTCCTGCTC Sorbs2 Forward primer: TGTGGGAGAATGCCTCACAG Reverse primer: ACGAATACGAGAAAGGATGG Suox Forward primer: CTAATGAGGGAGAGGTGACTGACCA Reverse primer: TGCAGAGCCTCAAGGGGGTT Supplemental References Fragoso,G., Pennie,W.D., John,S., and Hager,G.L. (1998). The position and length of the steroid-dependent hypersensitive region in the mouse mammary tumor virus long terminal repeat are invariant despite multiple nucleosome B frames. Mol. Cell Biol. 18, 3633-3644. Giresi,P.G. and Lieb,J.D. (2009). Isolation of active regulatory elements from eukaryotic chromatin using FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements). Methods 48, 233-239. Gossen,M. and Bujard,H. (1992). Tight control of gene expression in mammalian cells by tetracycline responsive promoters. Proc. Natl. Acad. Sci. USA 89, 5547-5551.

John,S., Sabo,P.J., Thurman,R.E., Sung,M.H., Biddie,S.C., Johnson,T.A., Hager,G.L., and Stamatoyannopoulos,J.A. (2011). Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat. Genet. 43, 264-268. Hesselberth,J.R., Zhang,Z., Sabo,P.J., Chen,X., Sandstrom,R., Reynolds.A.P., Thurman,R.E., Neph,S., Kuehn,M.S., Noble,W.S., Fields,S., and Stamatoyannopoulos,J.A. (2009). Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283-289. Lowy,D.R., Rands,E., and Scolnick,E.M. (1978). Helper-independent transformation by unintegrated Harvey sarcoma virus DNA. J. Virol. 26, 291-298. Olive,M., Krylov,D., Echlin,D.R., Gardner,K., Taparowsky,E., and Vinson,C. (1997). A dominant negative to activation protein-1 (AP1) that abolishes DNA binding and inhibits oncogenesis. J. Biol Chem. 272, 18586-18594. Shang,Y., Hu,X., DiRenzo,J., Lazar,M.A., and Brown,M. (2000). Cofactor dynamics and sufficiency in estrogen receptor-regulated transcription. Cell 103, 843-852. Wettenhall,J.M. and Smyth,G.K. (2004). limmaGUI: a graphical user interface for linear modeling of microarray data. Bioinformatics 20, 3705-3706.