Supplemental Figure 1 la avb san ra avn lv Tbx3 Tbx5 Scn5a Supplemental Figure 1 Cartoon depicting expression patterns of Tbx3, Tbx5 and Scn5a in the heart from fetal stages onwards. Tbx5 activates Scn5a in the heart. The pattern of Scn5a is broader than that of Tbx5, indicating other activators expressed in broader patterns are involved in Scn5a activation. Note the absence of expression of Scn5a in the Tbx3+ (red) and Tbx5+ (purple) region of the sinus node (san), internodal region, atrioventricular node (avn) and atrioventricular canal myocardium, indicating that the repressive function of Tbx3 is dominant in these parts of the heart. avb, atrioventricular bundle; l/ra, left/right atrium; l/rv, left/right ventricle. Supplemental Figure 2 20 kbp 30 Tbx3 3 25 Gata4 3 75 5 - Nkx2 9 50 p300 0 Tbx5* Hopx Gata4* Nkx2-5* P300# P300$ Pol2$ Supplemental Figure 2 Alignment of ChIP-seq datasets from mouse heart (Tbx3, red; Gata4, blue; Nkx2-5, green; p300, orange) described in this manuscript, with other published datasets in the vicinity of Hopx, a gene involved in conduction system function (1). Overlap in multiple datasets was used as selection criterion for enhancer activity screening (Figure 1C). Supplemental Figure 3 Tbx5 TRANSFAC GATA1 TRANSFAC NKX2-5 JASPAR T JASPAR GATA1 JASPAR NKX3-2 JASPAR HL1 Tbx5 ChIP-seq motif HL1 Gata4 ChIP-seq motif HL1 Nkx2-5 ChIP-seq motif TBX3 ChIP-seq motif Gata4 ChIP-seq motif Nkx2-5 ChIP-seq motif Supplemental Figure 3 Motif discovery analysis for ChIP-seq data. MEME motif discovery analysis for Tbx3 ChIP-seq data, Gata4 ChIP- seq data and Nkx2-5 ChIP-seq data (bottom row) compared to published consensus sequences available from TRANSFAC (2), JASPAR (3,4) and the recently published HL-1 ChIP-seq derived binding sites (5). Of note is the closer resemblance of the ChIP-seq experimentally derived Nkx2-5 sites to the NKX3-2 JASPAR consensus site than that of the NKX2-5 JASPAR consensus site. Supplemental Figure 4 A GOTERM: Biological Process Genes Count % P-value Cytoskeleton organization 8 8.9 1.4E-4 Actin cytoskeleton organization 5 5.6 1.0E-2 Actin filament-based process 5 5.6 1.3E-2 Heart development 5 5.6 2.8E-2 Actin filament organization 3 3.3 3.4E-2 Muscle contraction 3 3.3 3.8E-2 Intracellular signalling cascade 3 3.3 4.4E-2 Muscle system process 10 11.1 4.7E-2 Σn genes with Tbx3 peak Significance of B Σn genes (% of Σn genes) enrichment (Z test) Enriched in AVC 2580 1329 (51.5) p=6.7x10-10 Reduced in AVC 2627 1635 (62.2) p=3.9x10-43 non-enriched controls (N=6) 2545 x 1022 (40.2±2.5) Tbx3 induced 578 354 (61.2) p=2.3x10-12 Tbx3 reduced 524 347 (66.2) p=2.8x10-17 non-enriched controls (N=6) 525 197. 5(37.6±2.6) Supplemental Figure 4 Overlap between ChIP-seq datasets and expression arrays. (A) Molecular function GO-term analysis of genes containing an intragenic overlapping Tbx3/Nkx2-5/Gata4-ChIP-seq peak region. (B) Overlap between Tbx3 ChIP-seq dataset and Tbx3 microarray datasets. Intersection of Tbx3 ChIP-seq peaks, with genes found significantly (P < 0.05) enriched or reduced in the AVC myocardium, in the induced TBX3 expression system or in the control gene microarray sets. There are significantly more intersecting genes found in the reduced AVC array set compared the induced set (P < 0.001). The same holds for the reduced set of genes in the induced TBX3 expression model compared to the induced set of genes (P < 0.05). Supplemental Figure 5 Tbx3 embryonic heart 1000 Tbx5 adult heart 100 Hprt over over 10 Neg Enrichment 1 Krr1 Hprt Sbds Otos Eif3d Cog5 Gbp4 Sept9 Myh6 Chsy1 Grh13 Iqsec1 Isoc2a Frmd6 Tcmo7 Rbpms Wdr95 Ccrn41 Bcl2l11 Irf2bp2 Chchd6 Ppap2b Frmd4a Slc22a2 Gabarap Tead1 F2 0 Intergenic Supplemental Figure 5 ChIP-qPCR showing relative enrichment of randomly chosen ChIP-seq peaks over a negative control region in the Hprt gene for Tbx3 in embryonic heart (ED10.5, pink) and Tbx5 in adult heart (purple). Supplemental Figure 6 250% 200% ] 150% p < 0.05 mRNA [ control 100% Induced Tbx3 Relative 50% * 0% Gapdh Hprt Nkx2-5 Gata4 Gja1 Supplemental Figure 6 Quantitative PCR showing that housekeeping genes Gapdh and Hprt, used for normalization of the other genes, are not affected by the presence of Tbx3. Also transcription factors Nkx2-5 and Gata4, working in complex with Tbx3, are unaffected by the presence of Tbx3, while Gja1 (Cx43), a known target of Tbx3, is significantly down regulated by Tbx3. Error bars represent standard deviation. Supplemental Figure 7 Scn5a Scn10a A 0,2 0,4 Limb Brain 0,2 0,1 Atria Ventricle relative [mRNA] relative relative [mRNA] relative 0,0 0,0 E10.5 E14.5 E17.5 Adult E10.5 E14.5 E17.5 Adult Liver B 1,4 C 1,2 Input ChIP Target ] 1 PCR co Tbx3 co Tbx3 0,8 4 mRNA [ * 0,6 Control 9 0,4 a relative * Induced TBX3 0,2 b 0 Hprt Supplemental Figure 7 Expression and ChIP analysis of Scn5a / Scn10a locus (A) Relative mRNA expression levels of Scn5a and Scn10a in limb, brain, atria, ventricles and liver of E10.5, E14.5, E17.5 and adult mice. Both genes are primarily expressed in atria and ventricles. (B) qRT-PCR analysis shows down regulation of Scn5a and Scn10a in atria of induced TBX3 expressing mice (black bars) as compared to control mice (light grey bars). Both genes are reduced in the AV canal with a p-value < 0.05 and contain ChIP-seq peaks for Tbx3, Gata4 and Nkx2-5. Flanking genes Scn11a and Exog are unaffected by induced TBX3 expression and do not contain peaks for Nkx2-5, Gata4 and Tbx3. (C) ChIP-PCR on hearts of adult induced TBX3 expressing mice compared to control mice. Figure shows an enrichment of fragment 4 and 9, while there is no enrichment for control regions a and b (Figure 4A). Supplemental Figure 8 A F9 B 2 2 2 Tbx3 F1 * F2 F9 * 1 Gata4 1 1 RLU * RLU * RLU 5 * - * * * 0 0 0 Nkx2 - Tbx5 Tbx3 Tbx2 - Tbx2 Tbx5 Tbx3 Tbx3 - Tbx5 Tbx2 p300 C minP F9-A F9-B F9-C F9-D F9-E F9-F F9-G - F9-H Nkx2-5 + Gata4 F9-I Nkx2-5 + Gata4 + VP16-TBX2 RLU 0 50 100 150 200 250 Supplemental Figure 8 Transfection assays with Tbox factors on enhancer candidates. (A) Close-up of the alignment of ChIP-seq datasets Tbx3 (red), Gata4 (blue), Nkx2-5 (green) and p300 (orange) for fragment 9. (B) F1 and 2 are activated by Tbx5 and repressed by Tbx2 and Tbx3. F9 was not stimulated by Tbx5, but responds to repression with Tbx2 and Tbx3. (C) Shortening of 5' flanking sequences in fragment 9 results in an increased response to Gata4, Nkx2-5 and Tbx2-VP16 (activating fusion protein) and identifies a small, responsive element that harbors binding sites for all TFs. Experiments were performed in Cos7 cells. Supplemental Figure 9 1.2 p<0.001 1.0 p < 0.001 0.8 0.6 * 0.4 * * * 0.2 0.0 B Maj1 Min1 Mut Supplemental Figure 9 Quantitative analysis of EMSA data. Non-radioactive EMSA was performed 3 times and after chemilumiscent imaging (see Figure 6B for representative image), the results were quantified using AIDA software. Under these conditions, the allele containing SNP rs6801957 (Min1) shows a significant decrease of approximately 60% in T-box binding compared to the major allele (Maj1). B is average background signal, Mut shows signal of a probe in which the three base pairs of the T-box binding site were mutated. REFERENCES (1) Ismat FA et al. Homeobox protein Hop functions in the adult cardiac conduction system. Circ Res. 2005;96(8):898-903. (2) Matys V et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 2003;31(1):374-378. (3) Stormo GD. DNA binding sites: representation and discovery. Bioinformatics. 2000;16(1):16-23. (4) Wasserman WW, Sandelin A. Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet. 2004;5(4):276-287. (5) He A, Kong SW, Ma Q, Pu WT. Co-occupancy by multiple cardiac transcription factors identifies transcriptional enhancers active in heart. Proc Natl Acad Sci U S A. 2011;108:5632-5637. Supplemental Methods ChIP-peak calling For peak calling, a minimum binding site length was set to 15 bp and regions in closer proximity than 50 bp were concatenated into one region. For identification of regions with robust binding, a transcription factor-dependent (see below) minimum number of reads per peak was set. The rationale behind the transcription factor-dependent thresholds was the difference in signal to noise ratios between different transcription factors. The thresholds were calculated as follows: The log of the number of peaks called at different thresholds (y- axis) was plotted against the threshold used (x-axis). This plot was typically biphasic, reflecting a transition from random to genuine binding events at the point where the second derivative is maximal. For the Tbx3 data set, for example, this phase change was found at a tag-count of 5 (maximum of the second derivative) and for Nkx2-5 at a count of 15. To identify the most robust binding sites used in the analyses throughout the paper, a threshold of two times the number of reads found at the maximum of the second derivative was applied.