Supporting InformationWang et al. 10.1073/pnas.1315023111SI Materials and MethodsChIP-Seq Antibodies. Antibodies used for ChIP were as followed:ETS1 (sc-350), GABPA (sc-22810), RUNX1 (Abcam ab23980),H3K27ac (ab4729), p300 (sc-585), BRD4 (Bethyl A301-985A),and MED1 (Bethyl A300-793A).

ChIP-Seq Data Processing. Sequence reads from ChIP-seq experi-ments were aligned (1) to human genome build hg19 and uniquelymapped, nonredundant reads were retained. After peak calling bymodel-based analysis for ChIP-Seq (MACS) 1.4, genomic wiggletraces were normalized by the total nonredundant read count ofeach dataset and displayed using innovative genomics viewer (IGV)Browser (2). Sequence motif analyses were performed usingMDSeqPos (3). Heat maps (Figs. 1A and 2C) were generated usingthe Cistrome analysis pipeline (4).

Quantitative Identification of Dynamic Binding Sites. Binding strengthof NOTCH1 peaks was measured by the normalized read countlocated in a 600-bp region centered on the peak summit. Tocompare the Notch-on and Notch-off states, a metric to define foldchange was calculated as described (5) using a linear model

M = kA+C;

where M = log2(xt) – log2(xc), and A = 1/2(log2(xt) + log2(xc)),with xt and xc referring to the normalized read counts in thetreated (Notch-on) and control (Notch-off) conditions, respec-tively. Here, k and C are the parameters of the linear model, andC is used as the metric for dynamism, termed “dynamic foldchange” as plotted in Fig. 1B and Fig. S2A. Dynamic sites wereidentified using an empirical false discovery rate (FDR) of 0.05that was estimated assuming that the background distribution ofthe adjusted fold change is symmetric to 0 and decreasedNOTCH1 binding to sites in the Notch-on state represents a falsepositive signal (Fig. S2A).

Composite Profiles of Chromatin Immunoprecipitation (ChIP)-SeqSignal on a Set of Genomic Loci. ChIP-Seq signals were measuredby normalized read density on identified peak regions. Forprofiles around transcription-factor binding sites (e.g., Fig. 4Band Figs. S2E and S5 D and F), all binding sites were aligned bytheir summits and nonredundant reads were counted in non-overlapping 100-bp bins from 5 kb upstream to 5 kb downstreamflanking the summits. For profiles around a set of broad peakregions (e.g., Fig. 4E and Fig. S5G), nonredundant reads wereassigned to nonoverlapping bins equal to 2% of the peak lengthwithin the peak region and 100-bp bins outside the peak regionfrom 5 kb upstream to 5 kb downstream. For both cases, the readcounts in each bin were then normalized by the total non-redundant read count of the given ChIP-seq dataset and the totalbase pairs of the bins to obtain the normalized density.

Identification of CTCF Domains in the Human Genome.ChIP-Seq datafor CTCF in 69 human cell lines were downloaded from the En-cyclopedia of DNA elements (ENCODE) consortium. CTCF-binding sites in each cell line were identified using MACS1.4with default parameters. Constitutive CTCF-binding sites were

defined as sites present in >90% of cell lines and that lie outsideof gene promoters. The entire human genome was then parti-tioned into nonoverlapping CTCF domains using these 11,326constitutive CTCF-binding sites.

Calculation of Average Regulatory Potential in Bins of Genes. Theregulatory potential of each differentially expressed gene betweenNotch-off and Notch-on states was calculated as described in theMaterials and Methods. All differentially expressed genes (340 upand 187 down, FDR < 0.05) were then ranked according to theirdifferential expression t score and binned by every 20 genes. Thecolor bar in Fig. 3E is a schematic of the differential expressionof genes in all bins. Within each bin (n = 20), the average reg-ulatory potential was calculated by

P=1n

Xn

i=1

Pi

and plotted against the rank of bins.

Retroviral Expression of cDNAs.AMigRI retrovirus vector encodingthe dominant negative RUNT domain of RUNX1was the kind giftof Dr. Andrew Weng (BC Cancer Agency, Vancouver, Canada).MigR1 and MigR1–DN-MAML retroviral constructs been de-scribed (6, 7). Pseudotyped retrovirus was produced by trans-fection of 293T cells as described (8). GFP+ cells transduced byMigR1 and MigR1–RUNT were isolated by cell-sorting.

Real-Time RT-PCR. Total RNA was prepared with RNeasy Mini kit(Qiagen) and cDNA was synthesized with the iScript cDNAsynthesis kit (Bio-Rad). Real-time PCR was performed in trip-licates using the SYBRgreen supermix and primers for IL7R,GAPDH, and 18s rRNA with the CFX96 Touch Real-Time PCRDetection System (Bio-Rad). Data were analyzed with CFXmanager software (BioRad).

Primer Sequences Used for DTX1 Superenhancer ChIP and IL7R RT-PCR. DTX1 Pair1: forward 5′-CGGGAGGGTTGAAGAGTG-AC -3′, reverse 5′- CCCCTCTGTGAAATGGGACC -3′; DTX1Pair2: forward 5′- CCAGCTGGGTGACCTTAGTG -3′, reverse:5′-GCCTTACCGCTAAGCCAAGA-3′; DTX1 Pair3 (dynamicNotch site): forward 5′-AGGGGTGAAGGCTGGCTCCC-3′,reverse 5′- GCAGGCCTCCTGCGAGAACC-3′; DTX1 Pair4:forward 5′- ATCCCTGCGCCAAATGATGA-3′, reverse 5′- AA-TGAACACAGCTCCCGAGG-3′; DTX1 Pair5: forward 5′-CTTTCTGGTGCCAACTGTGC-3′, reverse 5′-GACAGCTGG-AGAAGGAGCTG-3′; DNA negative control region: forward 5′-AATGCTGGGCTTCCAAGGA-3′, reverse 5′-GACCTTGGT-GACTGTTGAGGAAAC-3′; IL7R mRNA: forward 5′-TGT-CGTCTATCGGGAAGGAG-3′, reverse 5′-CGGTAAGCTAC-ATCGTGCATTA-3′; 18s rRNA: forward 5′-TAGAGTGTTC-AAAGCAGGCCC-3′, reverse 5′-CCAACAAAATAGAACCG-CGGT-3′; GAPDH mRNA: forward 5′- GAAGGTGAAGG-TCGGAGTCAAC-3′, reverse 5′-TGGAAGATGGTGATGGGATTTC-3′.

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2. Robinson JT, et al. (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26.

3. Liu XS, Brutlag DL, Liu JS (2002) An algorithm for finding protein-DNA binding siteswith applications to chromatin-immunoprecipitation microarray experiments. NatBiotechnol 20(8):835–839.

4. Liu T, et al. (2011) Cistrome: An integrative platform for transcriptional regulationstudies. Genome Biol 12(8):R83.

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Fig. S1. Overlap of NOTCH1–RBPJ steady-state genomic binding sites with ETS1, GABPA, and RUNX1 sites in T-LL cells. (A) Heat map of genomic NOTCH1genomic-binding sites, ranked by ChIP-Seq signal intensity, and associated signals of the indicated transcription factors and histone marks across a 1-kb windowcentered on NOTCH1-binding peak summits. (B) Distribution of genomic-binding sites of NOTCH1, RBPJ, and other transcription factors. Promoter binding isdefined as a site <2 kb from a mapped transcriptional start site (TSS). (C) Pairwise comparison of transcription-factor binding sites. The central diagonal in thegrid shows the total number of binding sites for each factor, and the pairwise comparisons indicate the number of overlapping sites for each pair of factors.The intensity of the red coloring of each factor pair corresponds to the significance of the overlap, as measured by Jaccard index (shown above). (D) Tran-scription-factor–binding sites and associated chromatin marks in the NOTCH3 locus. An arrow indicates the direction of transcription.

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Fig. S2. Identification and characteristics of dynamic and nondynamic NOTCH1-binding sites. (A) Distribution of dynamic fold change from Notch-off toNotch-on for all NOTCH1-binding sites. Red lines indicate the cutoffs with FDR = 0.05. (B) Distribution of distances separating NOTCH1-binding sites and thenearest TSSs. (C) Distribution of dynamic NOTCH1 sites in the genome. (D) DNA sequence motifs enriched near dynamic NOTCH1-binding sites. MDSeqPos Zscore indicates the significance of motif enrichment. (E) Composite H3K4me1 and H3K4me3 ChIP-seq signals aligned around the summits of dynamic andnondynamic NOTCH1-binding sites.

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Fig. S3. Notch and Runx are required for T-LL cell proliferation and expression of IL7R. (A) Effects of GSI (compound E, 1 μM), shRNA-mediated knockdown ofRUNX1, and both treatments in combination on the growth of CUTLL1 cells. Cells were harvested 6 d posttransduction of shRNA after 4 d of puromycin (1 ug/mL) selection. Growth was measured using Cell Titer Blue. (B, C) Effects of dominant negative inhibitors of Notch (DN-MAML) and Runx factors (RUNT) onCUTLL1 cell growth. Cells were transduced with empty MigRI virus or MigRI viruses expressing DN-MAML or RUNT. Cells were assessed for proliferation 2 dposttransduction. (B) Growth of GFP+ transduced cells relative to nontransduced cells in the same culture. (C) Absolute fold expansion of GFP+ transducedcells. To assess the effects of coinhibition of Notch and Runx, RUNT transduced cells were treated with GSI (compound E, 1 μM). (D) Endogenous IL7R expressionin T-LL cells requires Notch and Runx. Notch activity in CUTLL1 T-LL cells was inhibited by treatment with GSI (1 μM compound E, overnight) or by transductionof DN-MAML1, and Runx factor activity was inhibited by transduction of the Runt domain of RUNX1. DN-MAML1- and Runt-expressing cells were analyzed onday 5 posttransduction. IL7R expression was analyzed by qRT-PCR in triplicate. (E) Effects of Notch and Runx inhibition on IL7R surface expression in CUTLL1cells. IL7R was measured flow cytometry. Left shows IL7R staining distributions in various treatment groups. The embedded table shows the geometric meanfluorescent intensity of IL7R staining in each treatment group. IL7R surface staining was measured on day 6 posttransduction with MigR1, DN-MAML, or RUNT,and/or following 4 d of GSI treatment (compound E, 1 μM). (F) Effects of shRNA knockdown of RUNX1 on IL7R expression in CUTLL1 cells. Cells were harvested10 d posttransduction; IL7R expression was assessed by RT-PCR.

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Fig. S4. Numbers of dynamic NOTCH1 sites and NOTCH1 target genes in CTCF domains with at least one dynamic NOTCH1 site and at least one NOTCH1 targetgene. (A) Genomewide summary. The size of the circles is proportional to the number of constitutive CTCF domains in the genome that contain the indicatedcombinations of dynamic NOTCH1 sites and NOTCH1 target genes. The smallest circles correspond to combinations that occur only once in the genome, such asthe CTCF domain harboring the GIMAP gene cluster, which contains three dynamic NOTCH1 sites and five NOTCH1 target genes. The number of CTCF domainscontaining other combinations of target genes and dynamic binding sites is given. (B) Chromatin landscapes around the GIMAP gene cluster. Three dynamicNOTCH1–RBPJ sites lie close together within a central element with high levels of the H3K4me1-enhancer mark. GIMAP1, GIMAP2, GIMAP5-7 are all up-regulated when NOTCH1 is activated by GSI washout. *****P < 10−5; ***P < 10−3.

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Fig. S5. Effect of Notch activation status on chromatin landscapes around dynamic and nondynamic NOTCH1 sites. (A) NOTCH1 reactivation and loading ofNOTCH1–RBPJ complexes on a superenhancer located 5′ of HES5 increases H3K27ac levels across a region of >10 kb in width. (B) Changes in HES5 expressionassociated with NOTCH1 reactivation as assessed by qRT-PCR. All data points were obtained in triplicate. (C) H3K27ac levels of H3K27ac peaks associated withdynamic NOTCH1 sites in steady-state, Notch-off, and Notch-on states. (D) Composite profiles of H3K27ac aligned around the summits of nondynamic pro-moter NOTCH1 sites (Upper) and nondynamic nonpromoter sites (Lower) in the steady-state, Notch-off, and Notch-on states. (E) Genomewide width distri-bution of H3K27ac peaks. All 35,244 H3K27ac peaks were ranked by peak width and binned as described in Fig. 4C. Median width of the peaks in each bin wasplotted on the y axis. Only 9% of H3K27ac peaks are >5 kb in width. (F) Composite profiles of P300 signal aligned around the summits of dynamic NOTCH1-binding sites. (G) Composite profiles of BRD4 and MED1 aligned on dynamic NOTCH1-associated H3K27ac peaks. H3K27ac peaks were scaled to the same width.BRD4 and MED1 signals associated with scaled H3K27ac peaks were plotted across the H3K27ac peaks as well as flanking 5-kb regions.

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Fig. S6. Chromatin landscapes around the Notch-sensitive NRARP, HES5, DTX1, and IGF1R superenhancers.

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Fig. S7. DTX1 superenhancer H3K27 acetylation is Notch-dependent in multiple T-LL cell lines. (A) Chromatin landscapes of the DTX1 superenhancer inCUTLL1 and DND41 T-LL cell lines. CUTLL1 data are from this report, and DND41 data are from the resource paper of Hnisz et al. (9). The vertical lines denotefive regions analyzed by local ChIP. (B–D) Results of local ChIP for NOTCH1 (region 3, antibody designated Tc) and H3K27ac (regions 1–5) in KOPT-K1, DND41,and HPB-ALL T-LL cells, respectively. Controls for local ChIP were nonimmune rabbit Ig (rIgG) and a region in the MYC gene desert that lacks NOTCH1 binding(negative control). Individual data points were obtained in triplicate. Error bars represent 1 sd from the mean.

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Table S1. Summary of ChIP-Seq data sets

Factor/histone mark Uniquely mapped reads Nonredundant tags Peaks/binding sites

H3K4me1 29,904,653 29,585,328 71,325H3K4me3 19,084,592 17,021,424 27,910H3K27me3 26,401,660 26,320,910 76,313H3K27ac 44,883,204 30,921,421 42,843ZNF143 22,946,868 19,226,402 9,449ETS1 98,844,473 95,541,903 62,401GABPA 130,011,244 101,428,669 39,353RUNX1 30,624,572 30,422,702 28,156NOTCH1 GSI 24,144,774 23,147,328 8,918NOTCH1 w4h 18,373,896 18,110,018 13,986RBPJ GSI 26,012,893 25,262,387 6,600RBPJ w4h 22,889,385 22,544,520 13,429H3K27ac GSI 42,059,683 31,297,366 31,847H3K27ac w4h 44,677,046 31,307,801 33,162BRD4 37,393,634 29,015,587 28,289MED1 31,491,572 26,107,721 24,076p300 25,488,514 20,099,007 25,843

GSI, γ-secretase inhibitor treated cells; w4h, cells harvested 4 h after GSI washout.

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Table S2. Genes with high dynamic regulatory potential that are also up-regulated by GSIwashout

RefSeq ID Gene symbol Regulatory potential t score FDR Rank mean*

NM_001025197 CHI3L2 1.433 19.733 4.50E-12 2NM_001004354 NRARP 1.303 40.777 1.00E-18 2.5NM_005524 HES1 1.182 16.148 1.50E-10 6.5NM_001010926 HES5 1.318 13.621 3.30E-09 7NM_001040708 HEY1 0.914 21.453 1.30E-12 9NM_001163446 CPA4 0.882 17.203 4.70E-11 11.5NM_007261 CD300A 1.256 11.775 4.10E-08 11.5NM_001017971 ATP6AP1L 1 11.047 1.20E-07 16NM_020311 CXCR7 0.74 8.619 6.70E-06 31.5NM_018130 SHQ1 0.595 14.001 2.20E-09 39.5NM_001142467 HES4 0.606 7.785 2.80E-05 46NM_000435 NOTCH3 1.025 6.225 5.50E-04 49.5NM_002334 LRP4 0.629 6.464 3.60E-04 57.5NM_001003674 C18orf1 0.698 6.15 6.40E-04 59.5NM_001002926 TWISTNB 0.606 5.964 8.90E-04 70NM_015660 GIMAP2 0.56 8.427 9.20E-06 71.5NM_024901 DENND2D 0.807 5.492 2.30E-03 72.5NM_020909 EPB41L5 0.59 6.773 1.90E-04 77.5NM_001006658 CR2 0.497 8.258 1.20E-05 89.5NM_015895 GMNN 0.502 8.151 1.50E-05 90NM_014746 RNF144A 1.056 4.759 9.20E-03 96NM_020742 NLGN4X 0.573 5.871 1.10E-03 101NM_002185 IL7R 0.411 15.679 2.60E-10 103.5NM_000695 ALDH3B2 0.393 10.401 3.30E-07 117.5NM_001078174 SLC29A1 0.599 4.712 1.00E-02 126NM_198841 FAM120AOS 0.403 7.446 5.40E-05 130NM_001077365 POMT1 0.55 5.05 5.30E-03 131NM_006352 ZNF238 0.494 5.42 2.60E-03 133NM_000589 IL4 0.343 12.974 7.40E-09 135.5NM_001146029 SEMA7A 0.413 6.469 3.60E-04 138.5NM_001164211 LRCH1 0.54 4.839 8.00E-03 143.5NM_024980 GPR157 0.525 4.837 8.00E-03 146.5NM_015382 HECTD1 0.587 4.585 1.20E-02 148.5NM_006320 PGRMC2 0.459 5.307 3.30E-03 150.5NM_001037172 ACPL2 0.39 5.995 8.40E-04 155.5NM_020639 RIPK4 0.313 8.992 3.40E-06 159NM_001018053 PFKFB2 0.292 8.064 1.80E-05 177NM_018566 YOD1 0.267 9.944 6.70E-07 182NM_002976 SCN7A 0.502 4.414 1.70E-02 182.5NM_024711 GIMAP6 0.317 6.301 4.80E-04 182.5NM_032735 BEST3 0.253 11.231 9.50E-08 183NM_003325 HIRA 0.378 5.132 4.50E-03 184NM_004694 SLC16A6 0.358 5.059 5.20E-03 199.5NM_020979 SH2B2 0.251 6.812 1.80E-04 210.5NM_001166663 CD244 0.234 6.878 1.60E-04 221NM_012388 BLOC1S6 0.258 5.717 1.40E-03 226.5NM_014612 FAM120A 0.376 4.292 2.10E-02 232.5NM_001029884 PLEKHG1 0.237 5.516 2.10E-03 246NM_002165 ID1 0.303 4.633 1.20E-02 248.5NM_006577 B3GNT2 0.301 4.644 1.10E-02 249NM_022720 DGCR8 0.441 3.903 4.30E-02 251NM_004416 DTX1 0.17 12.415 1.60E-08 251.5NM_032373 PCGF5 0.158 17.429 4.10E-11 253NM_000299 PKP1 0.197 6.004 8.40E-04 261NM_001098614 PUS7L 0.196 5.565 2.00E-03 274NM_024093 C2orf49 0.235 4.672 1.10E-02 283NM_080605 B3GALT6 0.288 4.013 3.50E-02 303NM_153000 APCDD1 0.111 13.829 2.50E-09 303.5NM_130759 GIMAP1 0.163 5.689 1.50E-03 305NM_001031680 RUNX3 0.161 5.716 1.40E-03 305.5NR_002779 NUDT9P1 0.156 5.748 1.40E-03 308NM_018092 NETO2 0.163 5.354 3.00E-03 312.5

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Table S2. Cont.

RefSeq ID Gene symbol Regulatory potential t score FDR Rank mean*

NM_005907 MAN1A1 0.146 5.613 1.80E-03 322NM_004658 RASAL1 0.137 5.918 9.90E-04 324NM_006235 POU2AF1 0.149 5.149 4.40E-03 330.5NM_014754 PTDSS1 0.195 4.289 2.20E-02 335.5NM_017714 TASP1 0.096 7.872 2.40E-05 341NM_007054 KIF3A 0.084 10.507 2.70E-07 341.5NM_018384 GIMAP5 0.071 15.082 5.10E-10 350NM_001080527 MYO7B 0.078 9.694 1.00E-06 351NM_001135565 HDHD1 0.092 6.771 1.90E-04 360NM_001130690 PDE10A 0.066 11.639 4.90E-08 362NM_194255 SLC19A1 0.15 4.46 1.60E-02 367.5NM_001166175 NKX2-5 0.09 5.561 2.00E-03 383.5NM_001924 GADD45A 0.089 5.22 3.90E-03 393.5NM_002357 MXD1 0.122 4.541 1.40E-02 396.5NM_004831 MED26 0.118 4.364 1.90E-02 410NM_001040876 ABCE1 0.049 7.434 5.40E-05 410.5NM_018214 LRRC1 0.08 4.999 5.90E-03 412.5NM_015002 FBXO21 0.135 4.037 3.30E-02 417NM_001202 BMP4 0.031 10.926 1.50E-07 439NM_000055 BCHE 0.039 6.314 4.70E-04 442.5NM_017544 NKRF 0.104 3.969 3.80E-02 458.5NM_003749 IRS2 0.025 8.232 1.30E-05 464NM_001017397 TRIM36 0.026 8.124 1.60E-05 464.5NM_020182 PMEPA1 0.027 7.459 5.30E-05 465NM_006037 HDAC4 0.034 5.891 1.00E-03 469.5

*Rank mean represents the arithmetic mean of the gene ranks in regulatory potential and differential expression.

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