ESR1), ESR2, forkhead box A1 (FOXA1), ubiquitin interaction motif containing 1 (UIMC1) and cell division cycle 7 (CDC7). The functional polymorphism on CDC7, rs13447455, influences CDC7 transcriptional activity in an allele-specific manner and alters DNA–protein complex formation in breast cancer cell lines. Moreover, results from the Breast Cancer Association Consortium show a marginal association between rs13447455 and breast cancer risk (p = 9.3 × 10−5), thus warranting further investigation. Furthermore, our study has helped provide methodological solutions to some technical difficulties that were encountered with gene reporter assays, particularly regarding inter-clone variability and statistical consistency.Genetic variants affecting the regulation of gene expression are among the main causes of human diversity. The potential importance of regulatory polymorphisms is underscored by results from Genome Wide Association Studies, which have already implicated such polymorphisms in the susceptibility to complex diseases such as breast cancer. In this study, we re-sequenced the promoter regions of 24 genes involved in pathways related to breast cancer including sex steroid action, DNA repair, and cell cycle control in 60 unrelated Caucasian individuals. We constructed haplotypes and assessed the functional impact of promoter variants using gene reporter assays and electrophoretic mobility shift assays. We identified putative functional variants within the promoter regions of estrogen receptor 1 ( BRCA1) and BRCA2, an extensive body of literature has grown regarding the causes of hereditary and familial breast cancer , followed by 25 cycles of , then one cycle at 68 °C for 7 min.Following sequencing and characterization, rHap fragments were subcloned into the pGL3-Basic Firefly Luciferase reporter vector . The resulting constructs were verified by sequencing to confirm the presence of the expected haplotypes. After sequencing the resulting constructs, we observed spurious variants that appeared due to mistakes while copying DNA by the Polymerase enzyme. To overcome this problem, we optimized our PCR conditions by using a mix of two polymerases of high fidelity. In brief, PCR amplification of the studied promoters have been performed in a final volume of 50 µL , 3.5 uL of each primer (10 uM) 15 uL of Betaine, 5.75 uL HConstructs were then purified using a Sigma  Plasmid Purification kit prior to transfection. For several genes, a number of different clones (up to six) corresponding to each rHAPs were amplified, sequenced and subcloned into independent constructs.Using the MatInspector software  as a tra+ breast cancer adenocarcinoma) and Hela (cervix adenocarcinoma) were used for transient transfection experiments. MCF7 cells were grown in DMEM-F12 (1:1) medium  supplemented with 5% fetal bovine serum (FBS), penicillin/streptomycin , Hepes and 1 nM estradiol. HeLa cells were grown in Eagle’s Minimum Essential Medium  enriched with 5% FBS, 1% L-glutamine and penicillin/streptomycin . Both cell lines were grown at 37 °CC in a 5% CO2 atmosphere and 95% relative humidity.Two human cancer cell lines, MCF7 , whereas HeLa cells were transfected with polyethylenimine . Cells were plated in 24-well plates (6 × 104 cells/well) and incubated for 24 h to reach 50–70% confluence at the time of transfection. Both cell lines were co-transfected with 1 ug of each haplotype-specific construct and 10 ng of either CMV-driven Renilla luciferase pRL-CMV or pRL-null vectors (Promega) (ratio 100:1) to control for transfection efficiency. The choice of control vector to monitor transfection efficiency was based on optimal expression levels and the absence of cross-talk with the promoter under study. Similar experiments were performed with the empty promoterless pGL3-Basic plasmid (Promega) and the SV40-driven Firefly Luciferase pGL3-control plasmid  as negative and positive controls, respectively. Cells were harvested 24 h post-transfection and luciferase reporter gene activity was measured with the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer’s instructions, using an Infinite® 200 luminometer . The Renilla luciferase control reporter activity was used as an internal control to normalize results of the firefly luciferase activity. Results are expressed as the ratio of firefly luciferase activity divided by the Renilla activity and are presented as the mean relative luciferase activity from four independent replicates. The promoterless pGL3-Basic vector was used to measure basal (background) expression levels of the gene of interest. Each experiment was performed at least twice using one or more clones for the same rHaps, when available. MCF7 cells were transfected with Lipofectaminep < 0.01 relative to reference haplotype (H1) was considered statistically significant  according to the manufacturer’s instructions. Nuclear extracts (10 µg) were incubated with 35 fmol of the labeled probe in the binding buffer (50 mM Tris-HCl (pH 7.5), 5 mM MgCl2, 2.5 mM EDTA, 2.5 mM DTT, 250 mM NaCl, 0.25 ug/uL polydeoxyinosinate-polydeoxycytidylate and 20% glycerol) in a total volume of 10 µL for 20 min at room temperature. Prior to incubation with the radiolabeled probe, a 50-fold molar excess of the unlabeled target oligonucleotide probe, corresponding unlabeled mutant probe, or target-irrelevant oligonucleotide probe was added to minimize non-specific radiolabel binding. DNA-protein complexes were resolved on a 6% non-denaturing polyacrylamide gel (acrylamide:bisacrylamide 37.5:1) in 1× Tris-glycine-EDTA buffer (190 V at 4 °C), and analyzed using the Cyclone Plus Storage Phosphor System .Publicly available genomic data was used for functional annotation. The following regulatory features were obtained for breast cell types from ENCODE and NIH Roadmap Epigenomics data through the UCSC Genome Browser: Chromatin Hidden Markov Modelling (ChromHMM) states, DNase I hypersensitivity sites, histone modifications of epigenetic markers more specifically commonly used marks associated with enhancers (H3K4Me1 and H3K27Ac) and promoters (H3K4Me3 and H3K9Ac), and transcription factor chromosome immunoprecipitation-sequencing (ChiP-seq) data. Predicted enhancer-promoter determined interactions were obtained from the Integrated Method for Predicting Enhancer Targets (IM-PET) described in He et al. . OverlapMDC1) gene, only one major haplotype (95%) was estimated and therefore this gene was excluded from further functional analysis.Using a panel of 60 unrelated Caucasian individuals from families registered at the Centre d’Etude du Polymorphisme Humain (CEPH)/Utah, we amplified and sequenced a region of ~2.5 kb, upstream of the transcription start site of 24 genes involved in sex steroid action, DNA repair and cell cycle control . For eacTo assess the functional allelic differences between the various rHaps, the promoter activity for each gene was determined using luciferase reporter assays in two human tumor cell lines, namely MCF7 breast cancer cells and HeLa cervix adenocarcinoma cells. Despite nearly two decades of investigation, no consensus has been reached among the research community regarding the actual definition of a functionally significant difference in promoter activity for the expression of a given gene. In the present study, we used the criteria defined by Hoogendoorn et al. ,35 to asPALB2) gene promoter in MCF7 breast cancer cells are a case in point that illustrates the presence of substantial inter-clone variability between the three independent clones obtained for each of the three PALB2 rHaps , ESR2 and forkhead box A1 (FOXA1)) and six genes involved in cell cycle progression and in DNA repair (cell division cycle 7 (CDC7), serine/threonine kinase (ATR), MRE11 homolog, double strand break repair nuclease (MRE11A), PALB2, RAD51 paralog C (RAD51C) and ubiquitin interaction motif containing 1 (UIMC1)) displayed at least one regulatory haplotype with significant differential allelic expression in both cell lines. The progesterone receptor gene (PGR), as well as the BRISC and BRCA1 A complex member 1 (BABAM1), BRCA1/BRCA2-containing complex subunit 3 (BRCC3), BRISC and BRCA1 A complex member 2 (BRE), negative elongation factor complex member B (NELFB), nibrin (NBN), RAD51 recombinase (RAD51), ring finger protein 8 (RNF8) and ribosomal protein S6 kinase A2 (RPS6KA2) genes exhibited cell-specific expression in either MCF7 or HeLa cells only , BRCA1 interacting protein C-terminal helicase 1(BRIP1) and H2A histone family member X (H2AFX) genes, no significant variation in rHap expression was observed for these genes nor for ubiquitin conjugating enzyme E2 N (UBE2N) compared to the expression of their corresponding H1 haplotype. Moreover, the promoter activity of the androgen receptor gene (AR) was below the significance threshold of 1.5-fold increase in reporter activity compared to the promoterless control that was set as evidence of activity. For this reason, no additional experiments were performed for this gene. Despite the particularly high promoter activity observed for the Ataxia Telangiectasia Mutated serine/threonine kinase  in their transcriptional activity compared to H1  represented 92% of the observed haplotypes. H3 differs from H1 at the V4-rs1271572 position, whereas H6 differs from H1 at the V4-rs1271572 and V5-rs66615803 positions. Gene reporter analysis led to assign a significant increase of promoter activity to haplotypes carrying the minor allele of rs1271572 (H3 and H6) (ESR2 promoter activity may be due to either the binding of other transcription factors or via a different transcription regulatory mechanism.In the case of the  and H6) A. EMSA e and H6) . TFBS anFOXA1 promoter. Gene reporter assays showed that H3 and H5 were associated with a decrease of the FOXA1 expression level in both MCF7 and in HeLa cells . Supershift analysis using a HNF3-α antibody failed to show that rs35237183 affected the binding between the FOXA1 promoter and HNF3-α transcription factor  displayed at least one regulatory haplotype with significant differential allelic expression in both MCF-7 and HeLa cell lines. Among the 19 studied genes that were involved in either DNA repair or cell cycle control, six  (CDC7 promoter (data not shown), whereas rs13447455 displayed an intriguing functional effect. Indeed, EMSA showed a clear gel shift associated with its minor “G” allele  or Wilms Tumor 1(WT1) transcription factors were also predicted to be disrupted by the variant rs13447455; however, supershift assays did not provide any evidence of binding between antibodies to either of the two candidate transcription factors and the variant. It should be noted however that no data was available for the expression of these proteins in HeLa and MCF7 cells and thus we cannot refute the possibility that the absence of binding is due to the lack of expression of these proteins in these cell lines. Further functional annotation using VEXOR, a web application for the prioritization of functional variants , has shown that rs13447455 overlaps with several regulatory features in breast cell lines (HFM1), a gene located approximately 240 kb upstream CDC7. Taken together, these data, along with the functional results observed in the current study, seem to point towards rs13447455 as a likely regulatory variant in this region. Three different haplotypes were estimated for (r2 = 1) B. Furthe” allele . Using Tll lines . Indeed,UIMC1, also known as RAP80, encodes a protein that plays an essential role in the DNA repair pathway via its indirect interaction with BRCA1 .,12.CDC7  × 10r−5 . Our resproteins . It actsproteins  that regproteins . AdditioFOXA1 (rs35237183) represents a good candidate variant for breast cancer susceptibility; however further studies are needed to confirm this.On another note, it has been shown that several breast cancer associated SNPs are located in FOXA1 binding sites and alter gene expression by modulating the affinity of chromatin for FOXA1 . Thus, tBRCA1 and BRCA2 mutation carriers, in view of the known variability in the penetrance of the latter high-risk breast cancer genes [BRCA1 mutation carriers [BABAM1, ATM, CTIP (RBBP8), TOPBP1, BRIP1, BRE and RAD50 with the modification of breast cancer risk. They also showed that FAM175A and UIMC1 haplotypes might significantly modify the risk of ovarian cancer. Some of the SNPs in these genes, such as ATM rs228589 and BABAM1 rs2278256, were analyzed in the present study. We could not substantiate the observations previously made by Rebbeck et al. [In addition to their potential associations with breast cancer risk in the general population, polymorphisms in DNA repair genes are candidates for the modification of breast cancer risk in er genes . In facter genes ,50. Thuscarriers . They dek et al.  with a fIt is noteworthy that among the large number of polymorphisms tested in this study, only a few functional SNPs could be conclusively identified with supporting evidence. These genes, which encode proteins that are involved in steroid action, cell cycle control and DNA repair pathways, are highly conserved ,53, and Our study also had several limitations. Although the investigation of more distal SNPs would have been interesting, as it has been shown that distal SNPs can play an important role in the regulation of specific genes, this was beyond the scope of this study, which was not designed to provide an exhaustive analysis of the regulation of the selected genes but rather focused on proximal promoter SNPs. Moreover, this study was initiated in 2009. At the time of this study design, the information that is currently available in SNP databases and which helps guide the selection of SNPs for functional validation and association with disease, was rather limited.ESR1 rs75311867, ESR2 rs1271572, FOXA1 rs35237183, UIMC1 rs7726380, and CDC7 rs13447455 may indeed play a functional role in transcriptional activity. Further molecular and epidemiological analysis of these as well as other polymorphisms affecting these pathways will clearly be required to provide more detailed insight on the in vivo role between the estrogen- as well as DNA repair-and cell cycle related pathways and carcinogenesis. Furthermore, this study provides valuable insight on the experimental challenges and limitations faced in the functional assessment of the effect of a given SNP or haplotype. As poorly reproducible allelic expression has been reported in several studies [In conclusion, the approach proposed in the present report provides original evidence that certain polymorphisms of genes involved in sex steroid receptor expression, cell cycle and DNA repair, most notably  studies ,55, our


Embryonic stem cells (ESCs) maintain pluripotency through unique epigenetic states. When ESCs commit to a specific lineage, epigenetic changes in histones and DNA accompany the transition to specialized cell types. Investigating how epigenetic regulation controls lineage specification is critical in order to generate the required cell types for clinical applications. Uhrf1 is a widely known hemi-methylated DNA-binding protein, playing a role in DNA methylation through the recruitment of Dnmt1 and in heterochromatin formation alongside G9a, Trim28, and HDACs. Although Uhrf1 is not essential in ESC self-renewal, it remains elusive how Uhrf1 regulates cell specification. Here we report that Uhrf1 forms a complex with the active trithorax group, the Setd1a/COMPASS complex, to maintain bivalent histone marks, particularly those associated with neuroectoderm and mesoderm specification. Overall, our data demonstrate that Uhrf1 safeguards proper differentiation via bivalent histone modifications. Uhrf1 is a known regulator of heterochromatin and DNA methylation in embryonic stem cells (ESCs). Here, the authors demonstrate that Uhrf1 acts together with the Set1/COMPASS complex regulator of active transcription to promote H3K4 methylation at bivalent loci and Uhrf1 loss results in disruption of differentiation. Uhrf1 is essential in early embryogenesis4. Although Uhrf1 knock-out (KO) mouse embryonic stem cells (ESCs) are viable and able to self-renew, they display delayed cell cycle progression, a loss of DNA methylation, altered chromatin structure, and enhanced transcription of repetitive elements4. Uhrf1 is also highly expressed in neural stem cells (NSCs). Interestingly, loss of Uhrf1 in NSCs leads to the activation of retroviral elements, similar to that observed in Uhrf1 KO ESCs5. Recent studies showed that a reduction of Uhrf1 expression via Pramel7 (PRAME-like 7) is important in the conversion of primed ESCs to a naive state7.Uhrf1  is a multi-domain nuclear protein that faithfully regulates epigenetic modifications through two mechanisms: (i) by recognition of histone marks through subsequent interactions with chromatin modifying proteins and (ii) DNA methylation maintenance8. The plant homeo domain (PHD) and tandem Tudor domain (TTD) domains of Uhrf1 simultaneously recognize trimethylated H3 at lysine 9 (H3K9me3), which could potentially contribute to the interplay between histone modification and DNA methylation, and the localization of H3K9me3 to pericentric heterochromatin11. Uhrf1 also contains a really interesting new gene (RING) domain that ubiquitylates histone H3 at lysine 23 (H3K23ub) and is essential for the recruitment of Dnmt1 for the maintenance of DNA methylation12. Recent discoveries have demonstrated Uhrf1’s bipartite role as a DNA damage sensor and nuclease scaffold in DNA repair, as well as the importance of its SRA domain15. Although the biochemical function of Uhrf1 in DNA methylation and heterochromatin formation has been extensively investigated, its biological function in ESCs has yet to be explored.One of the major functions of Uhrf1 is the inheritance of DNA methylation during DNA replication. Uhrf1 binds to hemi-methylated DNA via its Set- and RING-Associated (SRA) domain, which facilitates the loading of DNA methyltransferase 1 (Dnmt1) onto the newly synthesized DNA strand during cell division16. When ESCs differentiate into a given lineage, active histone marks are maintained in genes that are expressed in that specific lineage, while the repressive histone marks in those genes are concomitantly removed16. The polycomb repressive complex 2 (PRC2) proteins mediate H3K27me3 modification to regulate gene repression18. In contrast, H3K4 methylation is catalyzed by the Set1 complex proteins. Metazoans have three subsets of this complex: the Set1/COMPASS, trithorax (Trx), and trithorax-related (Trr). These complexes share the same core protein components, but differ in their catalytic subunits. The Set1/COMPASS complex has Setd1a or Setd1b as its catalytic subunit, while Trx has myeloid/lymphoid or mixed-lineage leukemia 1 (MLL1) or MLL2, and Trr has MLL3 or MLL419. Set/MLL core subunits, such as WD repeat-containing protein 5 (Wdr5), Ash2l (Ash2-like), and retinoblastoma-binding protein 5 (Rbbp5), are required for full histone methyltransferases (HMT) activity of the Set complex, while Rbbp5 and Ash2l heterodimer participates in the HMT activity of MLL1 complex23. In spite of overwhelming evidence that Uhrf1 regulates repressive histone marks, it is still unclear whether Uhrf1 is involved in the regulation of active chromatin marks.Bivalent histone marks, represented by H3K4me3 and H3K27me3, are unique features of promoters associated with development and differentiation in ESCsHere, we investigate the function of Uhrf1 in its regulation of pluripotency and differentiation of ESCs. Surprisingly, our data show that Uhrf1 plays a critical role in lineage specification by controlling bivalent histone modifications. Its deletion in ESCs disrupts not only the repressive mark H3K27me3, but also the active histone mark H3K4me3 on bivalent loci, ultimately causing defects in differentiation. Furthermore, biochemical analysis demonstrates that Uhrf1 interacts with the Setd1a/COMPASS complex and positively regulates H3K4me3 modifications. Our findings reveal an essential function of Uhrf1 as a stabilizer of the epigenome by promoting H3K4me3 modifications necessary for faithful differentiation and the maintenance of bivalent histone modifications for pluripotency.p < 2.2e−16, Supplementary Fig. 24. Interestingly, Uhrf1 distribution was also highly correlated with H3K4me3 and H3K27me3 mark distribution   were dysregulated uniquely in Dnmt1 KO ESCs, while Uhrf1 KO cells exhibited dysregulation in several developmental genes (Hand1 and Sox15), and genes exclusively expressed at the early embryonic stage (e.g. Klf2 and Zscan4b)  to identify global targets of Uhrf1. 2784 Uhrf1-enriched regions (10.2%) were identified around promoters or gene bodies, while 10,860 were located in the intergenic regions (89.8%) Fig.. Comparaped Fig. 26. Pluri4b) Fig. . Gene onp < 2.2e−16, “E” in Fig. To determine Uhrf1’s contribution to chromatin organization, we compared various histone modifications  between wild type (WT) and Uhrf1 KO ESCs. We found that Uhrf1 deficiency disrupted H3K4me3, H3K27me3, and H3K9me3 modifications  showed a marked reduction of H3K4me3 and H3K27me3, while neuroectoderm (e.g. Tubb3 and Nes) and pluripotency genes  displayed a loss of H3K4me3   formation using WT and Uhrf1 KO ESC lines. Uhrf1 KO EBs presented with an irregular and inflated shape as early as day 7 when compared to the spherical EBs derived from WT ESCs Fig. . In addiion Fig. . In conters Fig. . Moreoveers Fig. . To elimTo further evaluate the role of Uhrf1 in ESC differentiation in vivo, we performed a teratoma assay by intramuscular injection of Uhrf1 KO and WT ESCs into immune-deficient mice. We were able to isolate teratomas derived from Uhrf1 KO ESCs that contained representative tissues for all the three germ layers Fig. . HoweverSox1 and TuJ, were rescued to WT levels  displayed a failure to gain of H3K4me3 marks on their promoter regions during Uhrf1 KO EB differentiation (FDR < 8.61e−3), while the muscle or heart developmental genes  showed a loss of the H3K27me3 modification on bivalent promoters  and mESCs, and then we examined H3K4me3, H3K27me3, and H3K9me3 modifications. We observed large changes in H3K9me3 and H3K4me3 that are strongly correlated with the expression levels of Uhrf1 30. It also has been reported that Dnmt1 mediates an interplay between DNA methylation and H3K27me3 at pericentric heterochromatin, and Uhrf1 KO ESCs can affect the kinetics of H3K27me3 modification through a Dnmt1-dependent DNA methylation mechanism31. However, to our knowledge, how Uhrf1 regulates H3K4me3 modification still remains elusive. Therefore, we focused on identifying the molecular mechanism of H3K4me3 regulation through Uhrf1.The way Uhrf1 recognizes and modifies histone H3 at lysine 9 is already well establishedFirst, we confirmed the interaction of Uhrf1 with Setd1a and its core complex proteins by co-immunoprecipitation (Co-IP) Fig. . Further22. Uhrf1 complex was immunoprecipitated from ESCs and used for subsequent HMT assays. The affinity-purified Uhrf1 complex demonstrated methyltransferase activity . The loci showing loss of Oct4 binding upon Uhrf1 KO were correlated with regions highly enriched with Uhrf1 . We also observed that H3K4me3 was significantly reduced with loss of Oct4 binding , indicating that Uhrf1 directly regulates Oct4 binding by mediating an active open chromatin conformation.To assess the effect of loss of Oct4 binding in Uhrf1 KO cells on chromatin structure, we performed assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq). Depletion of Uhrf1 significantly decreased chromatin accessibility in locations where Oct4 binding was lost, but not in locations replaced with either Sox2 or Nanog  embryos and essential for iPSC formation, was one of the identified targets of the loss of Oct4 binding  that are activated in the 2C stage and are included in top 20 down-regulated genes in Uhrf1KO ESCs   Fig. . The los04) Fig. . Further24. In this study, we have identified a novel function of Uhrf1 in ESCs, in the maintenance of bivalent chromatin structures through the interaction with the Setd1a/COMPASS complex protein.Previous studies have shown that Uhrf1 is critical in maintaining DNA methylation by regulating Dnmt1 and forming heterochromatin as a reader of H3K9me334, importantly we observed a number of unique features in this Uhrf1 KO study. Transcriptome analysis of Dnmt1 and Uhrf1 KO ESCs demonstrated distinct profiles that segregated the two KO phenotypes with regards to pluripotency and developmental gene expression. Global histone modification analyses by ChIP-seq also revealed that the deletion of Uhrf1 induced a significant disruption in both H3K4me3 and H3K27me3 histone modifications, mainly on bivalent promoters  overlapped with the Dnmt1 KO20. Interestingly, Mll2 KO ESCs showed delayed cardiomyocyte differentiation rather than neuroectodermal defect, further supporting that Uhrf1 regulates neuroectodermal specification with the Setd1a/COMPASS complex35. As stated, neuroectodermal genes are down-regulated in Uhrf1 KO EBs, but are upregulated in Dnmt1 KO EBs36. Another key interesting difference is that Dnmt1 KO cells successfully undergo differentiation to mature astrocytes37. Our data demonstrated that the disruptions of bivalent marks in Uhrf1 KO are not due to disruptions in H3K9me3 or Dnmt1, but partly the loss of Setd1a function, which results in the reduction of H3K4me3 modification on neuroectodermal gene promoters.We demonstrated that the interaction of Uhrf1 with the Setd1a complex proteins is critical for maintaining promoter bivalence and neuroectodermal specification in ESCs. Like Uhrf1 KO ESCs, Setd1a KO ESCs also present with defects in neuroectodermal differentiation38. Because of the accompaniment and crosstalk of repressive epigenetic modifications31, Uhrf1-mediated PRC2 recruitment may be more linked with H3K9me3 and Dnmt1. As observed in Uhrf1 KO EBs, Dnmt1 KO EBs activate mesodermal genes earlier than WT39. The detailed molecular mechanisms underlying skewed mesodermal lineage differentiation in Uhrf1 KO cells still require further investigation.In contrast to H3K4me3-mediated neuroectodermal lineage regulation, Uhrf1 KO cells showed a reduction of H3K27me3 on mesodermal lineage genes. One potential explanation is that the decrease of H3K27me3 in Uhrf1 KO may be due to the failure to recruit PRC2 at mesodermal gene promoters. This is further supported by our proteomics study showing the direct interaction of Uhrf1 with Rbbp4, which is one of components of PRC2. In addition, PRC2-deficient ESCs increased expression of mesodermal markers11. In our study we found that Uhrf1 interacts with Setd1a/COMPASS complex proteins and directs the catalytic activity of Setd1a for H3K4 methylation. In addition, the PHD and TTD domains of Uhrf1 are dispensable for binding Setd1a  with its PHD domain and is involved in activating target genesd1a Fig.  and alsod1a Fig. . These dd1a Fig. . It is k43. Dnmt3a was shown to be dispensable for reprogramming44, but inhibition of Dnmt1 accelerates reprogramming45. Proteins of the PcG and TrxG complexes have been tested for their roles in reprogramming46. Setd1a and Wdr5 are essential for reprogramming, while Ezh2, Suz12 and Eed were shown to be dispensable48. In this study, we observed a ∼100-fold increase in reprogramming efficiency of MEFs when Uhrf1 was included with a three-factor cocktail of Oct4, Sox2, and Klf4  on 0.1% gelatin or MEF-coated plates, incubated at 37 °C and passaged every second or third day. Uhrf1fl/fl mice were purchased from EMMA (B6N-Uhrf1tm1a(EUCOMM)Wtsi/Ieg). Animals were handled according to protocols approved by the Yale University Animal Care and Use Community. ESCs were isolated from E3.5 embryos and MEFs were prepared from E13.5 embryos and were cultured in mES medium and 10% FBS supplied DMEM, respectively. Retrovirus expressing Cre recombinase from pBABEpuro-Cre vector  was used to generate the Uhrf1 KO cell lines used in this study.E14 and Uhrf1 KO ES cells were obtained from the Koseki groupTo generate mouse BirA-ESC lines that stably expressed both FLAG and biotin-tagged Uhrf1, BirA-ESCs were transfected with Uhrf1 constructs using the Amaxa nucleofector (Lonza) according to the manufacturer’s protocol. Transfected cells were cultured for 10 days in growth medium supplemented with puromycin. Drug-resistant clones were subsequently selected based on Uhrf1 expression.27. Briefly 1 × 105 cells were plated and transfected the next day with reprograming factors using X-tremeGENE 9 according to manufacturer’s instructions. Two days after transfection, supernatants were collected and concentrated via ultracentrifuge. The titer of each viral supernatant was calculated. The virus mixture (MOI = 5 for each factor) was then applied to 1 × 105 MEFs. One day after virus infection, medium was replaced with DMEM medium supplemented with 10% FBS. After 3 days, cells were replated onto plates pre-seeded with irradiated MEFs. The cells were cultured with mESCs medium throughout the reprogramming. Successfully reprogramed cells were fixed with 4% formaldehyde in PBS and subsequently stained with the Alkaline Phosphatase Kit (Sigma-Aldrich) for analysis of reprogramming efficiency.Retroviruses containing the reprogramming factors  were generated using HEK293T cells2, 0.1 mM EDTA and 10% glycerol) supplemented with 100 μM cold (for immunoblotting) or 1 μCi 3H-labeled (for fluorography) S-adenosylmethionine (SAM). Proteins were resolved by SDS-PAGE and subjected to immunoblotting (cold SAM) or scintillation recording (3H-labeled SAM). Relative HMT activity was calculated against IgG-enriched or vector-enriched lysate.Wild type or mutant Uhrf1 protein complexes were purified using Uhrf1 antibody or FLAG antibody from mESCs. When preparing chromatin as substrate, HeLa core histones were assembled into chromatin using the chromatin assembly kit according to the manufactures instructions (Active motif). For each HMT assay reaction, 400 ng of assembled chromatin or histone H3 peptide were incubated with purified Uhrf1 complex in 20 μl reaction buffer  in binding buffer . Uhrf1 was pulled down using an Uhrf1 antibody and agarose beads. The bound proteins were separated on SDS-PAGE gel and used for the western blotting against a GST antibodyGST-tagged recombinant human partial SETD1A protein  was purchased from Creative BioMart Company (Cat #SETD1A-198H). In order to purify recombinant Uhrf1, Uhrf1 was cloned into pGEX6p1, and transformed into BL21(DE3) β-actin. Error bars represent mean ± SD of technical triplicates. All primers are shown in Supplementary Data Total RNA was isolated using an RNeasy mini kit (Qiagen), and 1 μg of RNA was transcribed into cDNA using the iScript cDNA Synthesis Kit (Bio-Rad). RT-qPCR was performed in triplicate using iQ SYBR Green Supermix (Bio-Rad). Delta values were normalized with Protein extracts for the detection of histone modifications were obtained by incubating cells of interest in RIPA buffer , followed by sonication. Total protein was separated on a 15% PAGE gel. For the immunoprecipitation, cells were lysed with GST–IP buffer  for 15 min on ice and used for immunoprecipitation. The information of primary antibodies and dilutions is described in Supplementary Data Cells were fixed with 4% formaldehyde/PBS fixation solution for 15 min and then washed with PBS three times. After permeablization with 0.1% Triton X-100/PBS solution, cells were subjected to blocking and then incubated with primary antibody in a 3% BSA/PBS solution. Alexafluor 488 and 555 were used for fluorescence detection, and DAPI was used for a counter-staining. All antibodies used in this study were listed in Supplementary Data 7 ESCs cells were cross-linked by the addition of formaldehyde  for 15 min on a rotator. The cross-linking process was stopped with 0.125 M glycine for 10 min, followed by three washes in PBS. The cells were resuspended in isotonic buffer supplemented with 1% NP-40 to isolate the nuclei. The isolated nuclei were then resuspended in ChIP buffer . Extracts were sonicated using for three runs of 10 cycles of 15 s “ON”, 15 s “OFF” at a high power setting. Cell lysate were centrifuged at 12,000×g for 10 min at 4 °C. The supernatant was diluted with ChIP dilution buffer  before the immunoprecipitation step. H3K4me3 , H3K27me3 , H3K9me3 , Oct4 , Sox2 , Nanog , or Uhrf1  antibodies were then incubated overnight at 4 °C on a rotator. All antibodies used in this study are listed in Supplementary Data Approximately 2 × 1052. LC–MS/MS sequencing and peptide identification were performed by Taplin Biological Mass spectrometry Facility at Harvard Medical School with five biological replicates. Proteins with less than two unique peptide sequences and common background proteins were removed from subsequent analysis. The background protein sets were used from a previously reported list52. The number of unique peptides in each protein was normalized to the total number of unique peptides from all sequenced proteins. To identify candidate interaction partners, we searched our data for proteins that had a two-fold higher normalized count of unique peptides in our biotinylated Uhrf1 SA or FLAG pull-down libraries when compared to a control BirA-SA or FLAG pull-down library in at least one replicate of our Uhrf1 pull-downs.One-step affinity purification with SA or M2 beads was performed as describedEB differentiation was initiated by forming hanging drops. Cells were suspended with EB differentiation medium . Multiple 200 cells/30 μL aliquots were pipetted onto bacterial Petri dishes that were inverted for 3 days at 37 °C, after which the EBs were flushed with EB differentiation medium and subsequently cultured as 3D spheroids.−1 random hexamers (Invitrogen), followed by second strand synthesis with DNA Polymerase I and RNase H. After purification, a sequencing library was generated from the double-stranded cDNA by using paired-end adaptors (Illumina) and the NEBNext DNA Sample Prep Reagent Set 1 (NEB). This library was sequenced using an Illumina Genome HiSeq2000.mRNA was isolated from 5 μg total RNA using Dynabeads mRNA DIRECT (Invitrogen) and was fragmented with RNA fragmentation reagent (Ambion). First strand cDNA synthesis was done using the SuperScript III First-Strand Synthesis System and 3 μg μl2, 0.1% IGEPAL CA-630)53. After spinning at 500×g for 5 min, the pellet was used for transposition reaction by Nextera® DNA Library Prep Kit. Following purification by Qiagen EinElute kit, ATAC-seq library was constructed and amplified by NEB High Fidelity 2xPCR master mix. Finally, the library was purified by AxyPrepTM Mag PCR Clean-up kit.50,000 cells were lysed in cold lysis buffer ) was calculated by Cufflinks (v1.1.0) using RefSeq genes as reference annotation by “--GTF” option55. Public transcriptome data from Dnmt1 KO ESCs (SRX695151 and SRX695157) and preimplantation embryos (SRP055882) was downloaded from the NCBI short read archive (SRA)56. Genes with more than a 1.5-fold change during at least one time point were used to identify transcriptional modules. Euclidean distances were calculated from Log2-transformed RPKM values between all gene pairs with the dist function and were subsequently used for Ward.D2 hierarchical clustering via the hclust function in R. Transcriptional modules were then identified by a 20 height cutoff. GOstats in the Bioconductor package was used to evaluate the overrepresentation of GO terms. The Benjamini and Hochberg (BH) method was used for multiple test correction by the p.adjust function. The enrichment of early developmental genes and stem cell functions was analyzed by GSEA (v2.1.0) software57. Log2-transformed RPKM values were used for GSEA with 100 permutations of gene sets, classic enrichment statistic and signal-to-noise metric. Gene sets for early development and stem cell functions were obtained from a previous publication27.Mouse genome sequences (version mm9) and the genomic coordinates of RefSeq genes and the repetitive element RepeatMasker were downloaded from UCSC genome browser. All RNA-seq reads were mapped into mm9 mouse genome using Tophat (v1.1.4), Samtools (v0.1.18), and Bowtie (v0.12.7) with default parametersp-value within 10k-bp sliding window. The p-value was estimated by Poisson distribution from the ChIP-seq read counts compared to the normalized input counts58. For histone modifications, peak regions were identified by the rseg-diff program in RSEG software (v0.4.8) with parameters “-i 20 –v –mode 2” and the 50bp-deadzone correction file59. Non-overlapped peak regions between WT and Uhrf1KO were defined as differentially modified regions. Promoter configuration was classified by H3K4me3 ChIP-seq reads within ±2kbp of the TSS and by H3K27me3 within ±5kbp. Combination of log2(H3K4me3/input) > 1 & FDR<0.05 by Poisson test, and log2(H3K27me3/input) > 1 & FDR<0.05 by Poisson test were used for classification, as follows. Active promoter: presence of H3K4me3 and absence of H3K27me3; bivalent: presence of H3K4me3 and presence of H3K27me3; repressive: absence of H3K4me3 and presence of H3K27me3; no mark: absence of H3K4me3 and absence of H3K27me3. The retention rate of promoter types was calculated by comparing KO to WT at each time point.ChIP-seq and ATAC-seq reads were mapped to mm9 genome by Bowtie2 (v2.1.0) with options “--local -D 15 -R 3 -N 1 -L 20 -i S,1,0.50 -k 1”. Read count was normalized as total number of mapped reads. Uhrf1 enriched and depleted regions were assigned when there was more than two-fold difference between ChIP and input and less than a 0.05 adjusted Oct4, Nanog, and Sox2 binding sites were identified by MACS peak caller (v1.4.2) with “-g mm” option. Loss and gain of their binding by Uhrf1 depletion was evaluated by comparing MACS peaks between WT and Uhrf1-/-. Peaks within 100-bp distance were considered as overlapping. Furthermore, WT-specific Oct4 peaks were compared with KO-specific Sox2 and Nanog peaks. We defined WT-specific Oct4 peaks overlapped with and without KO-specific Sox2 or Nanog peaks as “replacement” and “loss”, respectively. For example, “Sox2 + Nanog replacement” means that Oct4 alone peak in WT cells was changed into Sox2 and Nanog peak in KO cells. The closet genes to Oct4 peaks were used as target genes.T test.ATAC-seq reads, whose fragment size is lower than mono-nucleosome size (=150 bp), were used for subsequent analyses. Genomic coverage of ATAC-seq reads was calculated by genomecov function in Bedtools (v2.25.0). Read count was normalized by total number of mapped reads. ATAC-seq reads within Oct4 peaks were also counted. The difference of chromatin organization and histone modifications between WT and Uhrf1-/- ESCs was evaluated by comparing the normalized read count of ATAC-seq and ChIP-seq within Oct4 peaks with 25. Reads were mapped to mouse genome by Bowtie2 as described above.All high-throughput sequencing data produced by this study were listed in Supplementary Data iPSC line (iOSKU-13) derived from MEF cells (B6/DBA2 mouse) by expressing Oct4, Sox2, Klf4, and Uhrf1 were injected into fertilized CD-1 blastocyst to identify the coat-color chimerism. Injection was performed by Yale Genome Editing Center. In brief, super-ovulated CD1 female was mated with CD1 male, and fertilized embryos were isolated. CD1 blastocysts were injected with iOSKU-13, and transferred to uterus of a foster female. Newborn mice were analyzed for coat-color chimerism.t-test. p < 0.05 was defined as statistically significant.Data were represented as means ± standard errors. Statistical significance was determined using Student’s All data generated or analyzed during this study are included in this article or the Supplementary Information files. RNA-seq, ChIP-seq, and ATAG-seq (GSE113915) datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO).Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


Flowering Locus C. We propose that genome doubling modulates the transcription genome-wide by changed chromatin interactions and at the specific locus by altered chromatin loops and histone modifications.Autopolyploidy is widespread in higher plants and important for agricultural yield and quality. However, the effects of genome duplication on the chromatin organization and transcriptional regulation are largely unknown in plants. Using High-throughput Chromosome Conformation Capture (Hi-C), we showed that autotetraploid Arabidopsis presented more inter-chromosomal interactions and fewer short-range chromatin interactions compared with its diploid progenitor. In addition, genome duplication contributed to the switching of some loose and compact structure domains with altered H3K4me3 and H3K27me3 histone modification status. 539 genes were identified with altered transcriptions and chromatin interactions in autotetraploid Arabidopsis. Especially, we found that genome duplication changed chromatin looping and H3K27me3 histone modification in  Polyploidy plays a significant role in plant evolution and formation of the important agriculture traits such as high yield and stress resistances in crops . PolyploRecent studies have illustrated that nuclear architecture is important for gene expression and recombination , and theStudies on the three-dimensional (3D) genome organization have proposed that Arabidopsis exhibited no large local interactive domains . The chrTo address the spatial arrangement of the doubled genome and the relationship between chromatin structure and transcriptional regulation, we performed High-throughput Chromosome Conformation Capture (Hi-C) analysis of the autotetraploid (genome-duplicated) and diploid Arabidopsis. We showed that the genome duplication resulted in increased inter-chromosome interactions and decreased intra-chromosome arm interactions compared with its diploid progenitor. In conjunction with RNA-seq and ChIP-seq, we found that the transcription of a group of genes is specifically affected by the chromatin interactions with epigenome dynamics. These results shed light on the 3D genomic organization and transcriptional regulation of the autotetraploid plants.Arabidopsis thaliana accession Columbia (Col-0) was used as wild type. Autotetraploid Arabidopsis (4 × Col-0) generated by colchicine using Col-0 as the progenitor , 20 mM MOPS, 1% Triton-100, pH 7.0) for 3–5 min until the buffer turns green. Transfer the mixture into a 40 μm strainer. The filtrates were added DAPI solution with final concentration of 1 ng/ml, then incubate for 30 min in the dark on ice. The DAPI-treated filtrates were then loaded to the flow cytometer  to measure the ploidy levels.Ten-day-old Col-0 or 4 × Col-0 Arabidopsis seedlings were collected into a clean and pre-cooled plate, and then chopped with a new razor blade to release the nuclei in the sterile lysis buffer . Hi-C libraries were constructed with NEBNext Multiplex Oligos kit and KAPA Hyper Prep Kit. The final library was subject to sequencing on an Illumina HiSeq 2000 instrument with 2 × 150-bp reads.Hi-C experiments were performed essentially as described  with somArabidopsis reference genome (TAIR10). Following with HiC-Pro and Juicer software  were calculated  for chrop-value, 0.05 false discovery rate, and cutoff of 1 log-fold change. Gene ontology (GO) terms were extracted from clusterprofiler R package , and the libraries were constructed according to a standard protocol (Illumina). Sequencing reads were aligned against the reference genome (TAIR10) using Feature Counts and STAR with default parameters . The dif package . All of The ChIP assay was performed according to previously reported . BrieflyFLC  were calculated in CSD and LSD regions in 50 kb bins in Col-0 and 4 × Col-0. Because the SDs were identified with PCA1 , a relatively broad parameter which could not reflect the more complicated structures. To analyze the relationship between histone modification and SD transition more precisely, HTR12-GFP  of equal number randomly selected no-regulated genes were subjected to the same analysis to determine the percentage of those groups fallen in differentially interaction bins. The percentile of the test sample lie above the top 5 percentile of the control distribution was considered confidently.In the bootstrapping strategies , 1000 grThe 2 kb upstream of TSS was selected as the promoter. The distribution of promoters was then analyzed for each bin, and the bins that contained more than the median number of promoters was defined as promoter-enriched bins. The interaction frequency among these promoter-enriched bins was calculated as the genome-wide promoter-promoter interaction frequency.For analyzing the interactions of promoter-enhancers, the published candidate enhancer library  was usedBamHI and BglII at 37°C overnight. After inactivating the enzyme with SDS, the ligation reaction was performed by incubation at 16°C for at least 6 h with 100 U T4 DNA ligase. Reverse cross-link was performed at 65°C by treatment with Proteinase K overnight. Relative interaction frequency was assessed by real-time PCR (Two grams of 10-day-old seedlings were cross-linked and nuclei were isolated as previously reported . Digestitime PCR .Columbia) and autotetraploid Arabidopsis (4 × Col-0) were confirmed by flow cytometry  in autotetraploid Figure . Gene onHTR12-GFP, a centromere-specific histone H3 marker, using flower dipping , we obtained total 22 million and 32 million valid Hi-C reads from wild type and autotetraploid Arabidopsis, respectively , with a We then calculated the interaction difference matrix from the reads with the same sequencing depth between Col-0 and 4 × Col-0 to reveal the chromosome clustering traits caused by genomic doubling. Genome-wide chromatin interaction difference matrix revealed that autotetraploid Arabidopsis has increased inter-chromosomal interactions, and reduced intra-arm interactions compared to Col-0 Figure . The ratTo better understand the distribution of intra-chromosomal contacts, we used Interaction Decay Exponents (IDEs) with power-law curves to reveal the difference of chromatin fiber characters between autotetraploid and diploid. The IDE could characterize chromatin packing by calculating a linear fit to the average interaction frequencies observed at given genomic distances. The results displayed that interaction frequency decreases as the genomic distance increases both in autotetraploid and wild type Arabidopsis. The intra-chromosomal interaction in autotetraploid showed decreased contact probabilities compared with Col-0 as a function of increasing genomic distance Figure . Beside,To better understand the chromatin packing in autotetraploid Arabidopsis, we compared the interaction frequencies within an arm of a chromosome (intra-arm), between two arms of a chromosome (inter-arm) or among different chromosomes (inter-chromosome). We found that the intra-arm interactions in 4 × Col-0 tend to have lower frequency compared with Col-0 Figure , and intIn diploid Arabidopsis, a nuclear heterochromatic island structure was identified and known as KNOT ,30. We fIn order to understand the spatial chromosome arrangement of the duplicated genome in nucleus, we calculated the log-ratio of the observed inter-chromosome interaction frequencies and the expected interaction frequencies as described . The matPrincipal Component Analysis (PCA) of the distance-normalized and correlated intra-chromosomal interactions ,29 was pWe also analyzed the genome elements in the chromosome regions converted from CSD to LSD with RepeatMasker. The results showed that the CSD to LSD regions contain retroelements (1.89%), DNA transposons (1.36%), small RNA (0.08%), satellite DNA (0.06%), low complexity DNA (0.41%) and simple repeat sequence (1.29%). Then we annotated the genes in the CSD to LSD switched regions with ARAPORT . We found that there were 1749 coding genes (83%), 310 non-coding genes (14.7%) and 48 pseudogenes (2.3%).Similarly, we found that LSD to CSD regions contain retroelements (3.75%) and DNA transposons (2.51%), small RNAs (0.14%), low complexity DNA (0.38%) and simple repeat sequence (1.29%). The same gene annotation was also performed in these regions. The results showed that there were 939 coding genes (83.3%), 160 non-coding genes (14.2%) and 28 (2.5%) pseudogenes.The changes of chromatin structure domains in the autotetraploid prompted us to test whether histone modifications associate with these alterations. To examine the epigenomic dynamics between autotetraploid and diploid Arabidopsis, we analyzed H3K4me3 and H3K27me3 histone modifications by ChIP-seq. At the genome-wide scale, we found that the levels of H3K4me3 and H3K27me3 in 4 × Col-0 are only slightly lower than those in Col-0 Figure  and B wiTo gain insight into the correlation between histone modification and chromatin domain, we analyzed the distribution of histone modifications in the chromatin regions with converted and unconverted structure domains. Compared to the unconverted compacted structure domain  in the differentially expressed gene library localized in the differential interaction bins Figure . BootstrTo uncover the structure domains (SD) in the transcriptional regulation of the genome-doubled Arabidopsis, we analyzed the normalized RNA-seq RPKM in LSDs and CSDs, and found that genes in the LSD compartments display higher expression levels than those in CSD compartments for both Col-0 and 4 × Col-0 Figure . We thenTo gain more insight into the transcription regulation, we tested the genome-wide promoter-mediated interactions to address possible mechanisms through long-range interactions. We aligned all the promoters into the interaction bins and calculated the numbers of interactions among these bins. The results indicated that the promoter-promoter interaction frequency in the autotetraploid was higher than that in wild type Figure , implicaFLOWERING LOCUS C (FLC) was higher in autotetraploid than that in Col-0 in RNA-seq data  experiments. The results showed that the relative amount of FLC loop increased in autotetraploid plants  using primers covering promoter, exon and intron of FLC  57). We. WeFLC -GFP in 4 × Col-0 indicated that the homologous chromosomes do not merged together after genome duplication  is actively transcribed. In contrast, the short arm of chromosome 2 is excluded from nucleolus and NOR2 is inactive  and loose structure domains (LSDs) with ∼12% structural domains switched between CSD and LSD Figure , indicatinactive ,66. In aHistone modifications of H3K4me3 and H3K36me3 on euchromatin were known to correlate to LSDs, whereas the polycomb-associated H3K27me3 is connected with heterochromatin that contains CSDs ,25,29. ITo better understand the gene regulation underlying genome duplication in the autotetraploid, we addressed the relationship between chromosome structure and gene transcription. Our results showed that 72.5% of the differentially expressed genes localized in the differential interaction bins, which indicated that the chromatin interaction might play a role in the regulation of the doubled genome. In addition, we found that a large amount of these differentially expressed genes associate with and responses to stimuli, suggesting that the chromatin interaction might be important for the plant to acclimation Figure . AlthougFLC, we found that there are higher intensities of loops around FLC gene in autotetraploid Arabidopsis compared with diploid. This structure might help to recruit more RNA polymerase complexes to activate the FLC expression to repress flowering. The elevated promoter-promoter and enhancer-promoter interaction frequencies in autotetraploid confirmed the elevated level of long-range interactions in autotetraploid. We also found that the level of H3K27me3 modification but not H3K4me3 decreased on FLC gene in autotetraploid Arabidopsis, which promoted the expression of FLC in another way. Together, our results suggested that H3K4me3 and H3K27me3 modifications are not the major players in the global regulation of autotetraploid genomes. For some specific loci, they may synergistically regulate gene expression together with chromatin structure in autotetraploid. Our work thus revealed a new perspective regarding the genome organization and gene regulation in autotetraploid plants.At the specific gene locus of The raw and processed Hi-C, ChIP-seq and RNA-seq datasets have been submitted to NCBI Gene Expression Omnibus (GSE114950).gkz511_Supplemental_FilesClick here for additional data file.


Over 500 genetic loci have been associated with risk of cardiovascular diseases (CVDs); however, most loci are located in gene-distal non-coding regions and their target genes are not known. Here, we generated high-resolution promoter capture Hi-C (PCHi-C) maps in human induced pluripotent stem cells (iPSCs) and iPSC-derived cardiomyocytes (CMs) to provide a resource for identifying and prioritizing the functional targets of CVD associations. We validate these maps by demonstrating that promoters preferentially contact distal sequences enriched for tissue-specific transcription factor motifs and are enriched for chromatin marks that correlate with dynamic changes in gene expression. Using the CM PCHi-C map, we linked 1999 CVD-associated SNPs to 347 target genes. Remarkably, more than 90% of SNP-target gene interactions did not involve the nearest gene, while 40% of SNPs interacted with at least two genes, demonstrating the importance of considering long-range chromatin interactions when interpreting functional targets of disease loci. Our genomes contain around 20,000 different genes that code for instructions to create proteins and other important molecules. When changes, or mutations, occur within these genes, malfunctioning proteins that are damaging to the cell may be produced. Researchers of human genetics have tried to spot the genetic mutations that are associated with illnesses, for example heart diseases. However, they found that most of these mutations are actually located outside of genes, in the ‘non-coding’ areas that make up the majority of our genome. These mutations do not modify proteins directly, which makes it challenging to understand how they may be related to heart conditions.One possibility is that the genetic changes affect regions called enhancers, which control where, when and how much a gene is turned on by physically interacting with it. Mutations in enhancers could lead to a gene producing too much or too little of a protein, which might create problems in the cell. Yet, it is difficult to match an enhancer with the gene or genes it controls. One reason is that a non-coding region can influence a gene placed far away on the DNA strand. Indeed, the long DNA molecule precisely folds in on itself to fit inside its compartment in the cell, which can bring together distant sequences.Montefiori et al. take over 500 non-coding areas, which can carry mutations associated with heart diseases, and use a technique called Hi-C to try to identify which genes these regions may control. The tool can model the 3D organization of the genome, and it was further modified to capture only the regions of the genome that contain genes, and the DNA sequences that interact with them, in human heart cells.This helped to create a 3D map of 347 genes which come in contact with the non-coding areas that carry mutations associated with heart diseases. In fact, deleting those genes often causes heart disorders in mice.In addition, Montefiori et al. reveal that 90% of the non-coding regions examined were influencing genes that were far away. This shows that, despite a common assumption, enhancers often do not regulate the coding sequences they are nearest to on the DNA strand.Pinpointing the genes regulated by the non-coding regions involved in cardiovascular diseases could lead to new ways of treating or preventing these conditions. The 3D map created by Montefiori et al. may also help to visualize how the genetic information is organized in heart cells. This will contribute to the current effort to understand the role of the 3D structure of the genome, especially in different cell types. Mountinelements . Howeverllenging .Chromosome conformation capture techniques such as Hi-C  enable tMore recently, promoter capture Hi-C (PCHi-C) was developed which greatly increases the power to detect interactions involving promoter sequences . PCHi-C et al. generated promoter capture Hi-C data in 17 primary human blood cell types and identified 2604 potentially causal genes for immune- and blood-related disorders, including many genes with unannotated roles in those diseases , most of which map to non-coding genomic regions. To begin to dissect the molecular mechanisms by which genetic variants contribute to CVD risk, a comprehensive gene regulatory map of human cardiac cells is required. Here, we present high-resolution promoter interaction maps of human iPSCs and iPSC-derived cardiomyocytes (CMs). Using PCHi-C, we identified hundreds of thousands of promoter interactions in each cell type. We demonstrate the physiological relevance of these datasets by functionally interrogating the relationship between gene expression and long-range promoter interactions, and demonstrate the utility of long-range chromatin interaction data to resolve the functional targets of disease-associated loci.Cardiovascular diseases, including cardiac arrhythmia, heart failure, and myocardial infarction, continue to be the leading cause of death world-wide. Over 50 GWAS have been conducted for these specific cardiovascular phenotypes alone, with more than 500 loci implicated in cardiovascular disease risk  and differentiation products   with TopDom . TADs are organizational units of chromosomes defined by <1 megabase (Mb) genomic blocks that exhibit high self-interacting frequencies with a very low interaction frequency across TAD boundaries . Notablying TADs . We founing TADs . TAD-croing TADs . Inter-Ting TADs . Additioing TADs  and had ing TADs . These oing TADs .GATA4 locus in GATA4 gene is located in a TAD structure that is relatively stable between iPSCs and CMs . We identified CTCF as the most enriched motif in each case  between the degree of histone modifications and gene expression was first reported nearly 10 years ago ; in contrast, H3K27me3 negatively correlated with expression level in CMs ; however, this relationship was not present in iPSCs . Although moderate, these correlations could partially explain why higher expressed genes show stronger enrichment for promoter interactions overlapping histone peaks when using a genome-wide background model , and lends support to the notion that active genes are located in generally active genomic environments  or depleted (CM) for H3K27ac marks that were specific to the non-matched cell type . AdditioNPPA  in CMs; furthermore, functional characterization showed that this region corresponds to an in vivo enhancer recapitulating NPPA’s endogenous expression in the developing heart  for three important classes of CVDs: cardiac arrhythmias, heart failure, and myocardial infarction , increasing the number of putatively causal variants to 10,475 . We found that 1999 (19%) of the LD SNPs were located in promoter-distal MboI fragments that interacted with the promoters of 347 genes in CMs  using the CM interaction map. We compiled 524 lead SNPs from the NHGRI database  at 37°C for 5 min. After incubation, 4 mL lactate media was added to the TrypLE and a 1 mL pipet was used to dislodge cells. Cells were strained once with a 100 μM strainer and then once with a 40 μM strainer. Cells were pelleted at 500xg and then resuspended in PBS and counted. For each batch of differentiation, 5 million cells were taken for promoter-capture Hi-C and 1 million cells were taken for RNA-seq. To assess purity, 2 million cells were taken for flow cytometry analysis using an antibody for cardiac Troponin T (BD Biosciences 564767). All cells used in downstream experiments were at least 86% Troponin T positive . We carriPSCs or cardiomyocytes were harvested from tissue culture dishes and counted. Cells were resuspended in 1X PBS at a concentration of 1 million cells/mL and 37% formaldehyde was added to a final concentration of 1%. Crosslinking was carried out for 10 min at room temperature on a rocking platform. Glycine was added to a final concentration of 0.2 M to quench the reaction. The cells were pelleted, snap frozen in liquid nitrogen and stored at −80°C until ready for Hi-C processing.We prepared all promoter capture Hi-C libraries in one batch using three crosslinked pellets of 5 million cells for both iPSCs and iPSC-derived cardiomyocytes, representing three independent cardiomyocyte differentiations. The in situ Hi-C step was performed as in www.customarrayinc.com). Each oligo consisted of the sequence 5’-ATCGCACCAGCGTGTN120CACTGCGGCTCCTCA-3’ (120 represents the 120 nucleotides adjacent to the MboI cut site. The complete list of oligo probes and their corresponding gene name is provided in Hi-C capture probes were designed to target four MboI restriction fragment ends (120 bp) near the TSS of protein coding RefSeq genes  mapped tCCTCA-3’  where N1The oligos arrived as a pool containing 1000 ng of material. We used 16 ng of the oligo pool in a PCR reaction to make them double stranded using primers 5′-CTGGGAATCGCACCAGCGTGT-3′ (Primer A), and 5′-CGTGGATGAGGAGCCGCAGTG-3′ (Primer B) as in . The PCRTo isolate promoter-containing fragments from the whole-genome in situ Hi-C library, we hybridized the biotinylated RNA bait pool with the Hi-C library as follows. A mix containing 500 ng of the Hi-C library, 2.5 μg of human Cot-1 DNA (Invitrogen #15279–011), 2.5 μg of salmon sperm DNA (Invitrogen #15632–011), 0.5 μl blocking primer P5 (IDT #1016184), and 0.5 μl blocking primer P7 (IDT #1016186) was heated for 5 min. at 95°, held at 65° and mixed with 13 μl pre-warmed hybridization buffer  and a 6 μl pre-warmed mix of 500 ng of the biotinylated RNA bait and 20U SUPERase-In (Thermo Fisher #AM2694). The hybridization mix was incubated for 24 hr at 65°C. To isolate captured fragments, we prepared 500 ng of streptavidin-coated magnetic beads  in 200 μl of Binding buffer . The hybridization mix was added to the Streptavidin beads and rotated for 30 min at room temperature. The beads containing the captured Hi-C fragments were washed with 1X SSC, 0.1% SDS for 15 min at room temperature, followed by three washes (10 min each) at 65°C with 0.1X SSC/0.1% SDS. After the final wash, the beads were resuspended in 22 μl of water and proceeded to post-capture PCR. The PCR reaction was performed as before, with 11 μl of the ‘capture Hi-C beads’ and 8 cycles of amplification. An AMPure XP bead purification was used to clean the PCR reaction and DNA was quantified using the QuantiFluor dsDNA System (Promega #E2670) and a High Sensitivity Bioanalyzer. Final capture Hi-C libraries were subjected to 100 bp paired-end sequencing on an Illumina HiSeq 4000 machine. Read count summaries are provided in cis-interactions as the evidence that trans-chromosomal interactions contribute to gene expression regulation is limited. CHiCAGO reports interactions for each captured restriction fragment; to summarize interactions by gene, we considered the interval spanning all captured fragments (i.e. the set of probes spanning each TSS) as the promoter region (‘merged TSS’). This means the promoter regions created have variable lengths. In cases where multiple genes were annotated to the same promoter region, we report the interaction for each gene individually. This annotation allowed us to perform gene-level analyses, for example based on expression level. We removed this redundancy as necessary, for example in motif enrichment analyses of the promoter-interacting fragments. Using the ‘merged TSS’ interaction files, we filtered interactions to retain those that mapped within 1 kb of each other in at least two replicates. Specifically, we extended each promoter-interacting fragment by 1 kb on each end and then used BEDTools  can be found under the Human Hg19 browser.PCHi-C interactions, TADs, RNA-seq, publicly available ChIP-seq, and GWAS SNPs are hosted by the WashU EpiGenome Browser  as a pubTo generate the by-gene read counts displayed in the genome-browser figures, all read-pairs mapping to captured MboI fragments for a given promoter were summed across replicates. Specifically, we summed reads for each MboI fragment where the read was part of a paired-read that mapped to a bait for the given gene. The arcs that are displayed underneath the 4C-style plot represent significant interactions that were identified in at least two replicates as detailed above in ‘Interaction calling’.To identify TADs, we pooled reads across replicates for each cell type using the pre-capture Hi-C data (600M reads for iPSC and 733M reads for CM) and used HiCUP v0.5.9  to alignThe program runHiCpca.pl from the HOMER  v4.8.3 pTotal RNA was extracted from flash-frozen pellets of 1 million cells using TRI Reagent (Sigma #T9424) and a homogenizer followed by RNA isolation and clean-up using the Direct-zol RNA Kit (Zymo Research #11–331). RNA-seq libraries were generated with the Illumina TruSeq V2 kit  and 1 μg of RNA, following manufacturer’s instructions. Libraries were made from RNA isolated from three independent iPSC-CM differentiations (triplicates of iPSC and of cardiomyocytes). Libraries were sequenced on an Illumina HiSeq 4000.Gene counts were quantified with Salmon 0.7.2  and impoWe performed ChIP-seq on 2.5 million cells each for iPSCs and CMs using H3K27ac antibodies (Wako #306–34849). Briefly, cells were crosslinked with 1% formaldehyde for 10 min at room temperature, quenched with 0.2M glycine for 5 min, pelleted and snap-frozen in liquid nitrogen. Cells were lysed in Lysis Buffer 1 . Crosslinked chromatin was sheared to an average size of 300 bp using a Bioruptor with 30" on/30" off at high setting and then incubated overnight at 4°C with 1 μg antibody. Dynabeads M-280 Sheep Anti-Mouse IgG (ThermoFisher #11201D) were used to pull down chromatin and ChIP DNA was eluted and prepared for sequencing using the NEBNext Ultra II DNA Library prep kit (NEB #E7645S). ChIP-seq reads were aligned with Bowtie 2–2.2.3  and peakhttp://geneontology.org/gene-associations. GO terms were associated with RefSeq genes via gene symbols. Using the GO annotation graph, all parent terms were assigned to the terms annotated to a gene. A hypergeometric test was used to calculate the statistical significance of the difference of the number of genes associated with a given GO term in a particular gene set and the universe of all RefSeq genes (p<0.05). p-Values were corrected with the R package p.adjust function using the ‘fdr’ method.The human Gene Ontology (GO) associations of GO terms  to genesFor two of the GWAS disease groups , the list of genes looping to LD SNPs included many histone genes. This is because there is a tag SNP located in the middle of a histone gene cluster (containing >30 histone genes located close together) in each case. After expanding the tag SNP to all SNPs in LD, many of the histone genes in that cluster looped to the LD SNPs, resulting in a high representation of these genes in the final gene list. The resulting Gene Ontology enrichment analysis gave terms relating to nucleosome and chromatin organization because of this over-representation. We therefore chose to remove these genes from the final gene lists of heart failure and myocardial infarction target genes.The program findMotifsGenome.pl from the HOMER  v4.8.3 pWe obtained publicly available ChIP-seq data in the form of processed peak calls for H3K27ac, H3K4me1 and H3K27me3 from the Roadmap Epigenomics Project , and forTo calculate the correlation between expression and histone ChIP-seq peak density, we calculated the Spearman’s rank correlation between the expression value for each gene  and the number of peaks mapping within 300 kb of each gene TSS. We only considered genes with at least one significant interaction in the respective cell type to allow for generalizations to the enrichment analysis presented in http://www.ebi.ac.uk/gwas/); see 2 >0.9) using phase 3 data of the 1000 genomes project (2 >0.9) with the tag SNP and a minimum allele frequency of 0.01. We only included the populations primarily studied in the GWASs: CEU , ASW (African American) and JPT (Japanese). We assigned all SNPs in promoter-distal interactions to their interacting gene(s) (‘target genes’) using cardiomyocyte promoter capture Hi-C data. We did not require the SNP to map to regions associated with open chromatin or enhancer marks as these types of data are highly cell-type specific and we did not wish to exclude SNPs in regions that may be active in non-assayed cell types.We compiled genome-wide significant SNPs associated with GWAS for cardiac arrhythmia, heart failure, and myocardial infarction from the NHGRI-EBI database , we randomly selected 347 genes from the list of starting genes , and calculated the proportion that caused a cardiovascular phenotype in mice. We performed this randomized selection for 1000 iterations to generate the randomized (expected) values. Random genes were not required to be expressed, as the set of target genes contains genes that are not expressed. p-Value was calculated with a Z test.For eQTLs used in comparisons with GWAS variants and Hi-C interactions, we used the set of GTEx v7 eQTLs identified as significant in the left ventricle of the heart . eQTLs wTo calculate enrichment for eQTLs to loop to their associated gene, we used a background model whereby each promoter’s set of interactions were re-mapped to a different promoter, keeping the distance and strand orientation consistent. We performed this re-mapping of all promoter interactions 1000 times and calculated the proportion of all eQTLs that mapped to interactions for their eQTL-associated gene in each permutation. We either used the CM interactions or the iPSC interactions with the same set of left ventricle eQTLs to compare cell-type specificity of the promoter interaction data.Raw and processed sequencing data are provided at ArrayExpress through accession numbers E-MTAB-6014 (Hi-C) and E-MTAB-6013 (RNA-seq). In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Mark McCarthy as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Daan Noordermeer (Reviewer #3).Thank you for submitting your article "A promoter interaction map for cardiovascular disease genetics" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The manuscript presents PCHi-C data for iPSCs and CMs with the goal to identify target genes of non-coding loci identified in GWAS. They find that for 19% of LD SNPs identified in GWAS they find significant long-range interactions with distal genes that are likely candidates for genes mis-regulated in CVD. The manuscript presents two main datasets: capture data for promoters in iPSC and CM, and also RNAseq and H3K27ac ChIP. The bulk of the paper is dedicated to showing that the iPSCs and CM cells are indeed what they are supposed to be, and to show that the capture Hi-C data is good. The last part of the paper is focused on showing that a set of significant interactions connect GWAS SNPs to putative target genes. This can be a useful resource for the community.Essential revisions:1) Throughout the paper you have shown arcs indicating significant interactions. These should be replaced, or complemented with tracks showing the real underlying data, e.g. "4C-style" plots.2) Place significant interactions in the context of TADs: are these mostly within TADs or do they also connect loci in separate TADs? Do the significant interactions provide additional information over and above TAD-based enhancer-promoter predictions?3) The analysis in Figure 3 needs to be re-evaluated. Whereas the authors claim positive correlations, the reported Spearman correlations ranging from 0.09 – 0.2 suggest a very weak trend at best, with a large degree of variation within each category (as confirmed by the box-plots). Very significant P-values are reported to support the correlation, but the approach based on genome-wide randomization of H3K27ac-peaks is not appropriate. If anything, the current analysis confirms the tendency of active genes and their regulatory elements to cluster together in the genome  and Hi-C compartment A ). Further, the claim that number of H3K27ac and H3K4me1 peaks surrounding a promoter alone is a good predictor of gene activity does not take into account which peaks are contacted by promoters, which may constitute a large fraction. To improve the analysis, the authors should take a similar approach as Schoenfelder et al., , where for different interaction counts the distribution of expression groups is compared. Subsequently, more relevant p-values can be determined by randomizing the expression groups for all genes (without changing the position of genes). Taking this approach, the authors should be able to get similar correlations for interaction numbers as reported by Schoenfelder et al., (r = 0.98), whereas it will be interesting to see if similar correlations can be obtained by looking at the number of surrounding H3K27ac peaks, taking into account if they are contacted or not.4) The paper would be further strengthened by providing examples that some of the predicted long-range interactions are functional. Are any of the predicted target genes affected in disease, e.g. reduced or increased in expression in individuals carrying the distal variant? The manuscript presents PCHi-C data for iPSCs and CMs with the goal to identify target genes of non-coding loci identified in GWAS. They find that for 19% of LD-SNPs identified in GWAS they find significant long-range interactions with distal genes that are likely candidates for genes mis-regulated in CVD. The manuscript presents two main datasets: capture data for promoters in iPSC and CM, and also RNAseq and H3K27ac ChIP. The bulk of the paper is dedicated to showing that the iPSCs and CM cells are indeed what they are supposed to be, and to show that the capture Hi-C data is good. The last part of the paper is focused on showing that a set of significant interactions connect GWAS SNPs to putative target genes. This can be a useful resource for the community.We thank the reviewers for their positive assessment of our manuscript, and for providing helpful and critical feedback. We address all of the reviewer’s comments and concerns below. As we aim to provide the iPSC and CM maps as an accessible resource, we have hosted the full set of data presented in this study at the WashU EpiGenome Browser: http://epigenomegateway.wustl.edu/browser/?genome=hg19&session=X8F93vWJ7j&statusId=1128458586Essential revisions:1) Throughout the paper you have shown arcs indicating significant interactions. These should be replaced, or complemented with tracks showing the real underlying data, e.g. "4C-style" plots.We thank the reviewers for this suggestion. We have added a track that displays the read counts for all MboI fragments (summed across replicates) mapping to each promoter in the figures where we show interaction data. These counts represent the values CHiCAGO used to identify significant interactions (i.e. filtered reads).2) Place significant interactions in the context of TADs: are these mostly within TADs or do they also connect loci in separate TADs? Do the significant interactions provide additional information over and above TAD-based enhancer-promoter predictions?We analyzed our pre-capture Hi-C data in order to identify TADs and added a series of analyses that place promoter interactions in the context of TADs . We show that the majority of interactions are contained within TADs (73% in iPSCs and 77% in CMs), in line with what has been reported for other PCHi-C datasets . We characterized several differences between intra- and inter-TAD interactions, namely that inter-TAD interactions tend to be weaker as determined by the CHiCAGO score and are slightly more enriched for looping to CTCF sites compared to intra-TAD interactions. Furthermore, promoters with inter-TAD interactions tend to be located close to TAD boundaries and were generally higher expressed than promoters with intra-TAD interactions, particularly in cardiomyocytes. These observations are in line with known features of TAD boundaries, which are enriched for housekeeping genes .cis-regulatory blocks, it is well established that dynamic, cell type-specific cis-regulatory interactions occur within TADs to direct cell differentiation . Without high resolution promoter interaction data, which identifies precise sub-kilobase promoter contacts, or other similarly high-resolution approaches such as 5C, the within-TAD structure is obscured due to limitations in resolution of whole-genome Hi-C datasets. Even the most deeply sequenced Hi-C data  identified only ~10,000 loops which mostly represent structural interactions. It is within these structural (i.e. CTCF/cohesin-mediated) loops that enhancer-promoter (E-P) interactions are thought to occur. The more transient nature of E-P contacts necessitates a method that directly enriches for these interactions in Hi-C datasets.Although TADs represent relatively invariant and small (< 1Mb) units of IRX5 locus appear similar in iPSCs and CMs, however PCHi-C clearly identifies E-P interactions between IRX5 and a distal enhancer element specifically in CMs , non-promoter H3K27ac ChIP-seq peaks, and interactions that map fully within TADs. The average number of potential E-P interactions within TADs is 136 for iPSCs and 144 for CMs, compared to an observed average of only 5 E-P contacts per TAD in iPSCs and 7 in CMs from PCHi-C. While we acknowledge that PCHi-C data is still very sparse leading to many missed interactions, we believe that our data indicate that the number of realized functional interactions within TADs is likely restricted and necessitates direct assaying (through PCHi-C or other techniques) to identify E-P interactions in an otherwise complex landscape of many promoters and many enhancer elements.3) The analysis in Figure 3 needs to be re-evaluated. Whereas the authors claim positive correlations, the reported Spearman correlations ranging from 0.09 – 0.2 suggest a very weak trend at best, with a large degree of variation within each category (as confirmed by the box-plots). Very significant P-values are reported to support the correlation, but the approach based on genome-wide randomization of H3K27ac-peaks is not appropriate. If anything, the current analysis confirms the tendency of active genes and their regulatory elements to cluster together in the genome  and Hi-C compartment A ).We appreciate the reviewer’s criticism. We agree that our original presentation of the analysis in Figure 3 was suboptimal and we have fully revised the figure and the Results section to make the analysis and interpretation of the data more clear (see below).Further, the claim that number of H3K27ac and H3K4me1 peaks surrounding a promoter alone is a good predictor of gene activity does not take into account which peaks are contacted by promoters, which may constitute a large fraction. To improve the analysis, the authors should take a similar approach as Schoenfelder et al., , where for different interaction counts the distribution of expression groups is compared. Subsequently, more relevant p-values can be determined by randomizing the expression groups for all genes (without changing the position of genes). Taking this approach, the authors should be able to get similar correlations for interaction numbers as reported by Schoenfelder et al., (r = 0.98), whereas it will be interesting to see if similar correlations can be obtained by looking at the number of surrounding H3K27ac peaks, taking into account if they are contacted or not.In Figure 3H of Schoenfelder et al., 2015, the number of promoters looping to between 0 and >10 enhancer elements is depicted, with promoters grouped into 5 expression categories. The corresponding text that references this figure reports a Spearman correlation of 0.975 “between gene expression level and the number of interacting enhancer elements.” We confirmed with the authors that they obtained this value of 0.975 by correlating the median number of enhancer elements contacting promoters in each expression group with the expression group. We analyzed our data using the same approach and we also obtained very high Spearman’s correlations . These correlations are low, but positive, and this reflects that the number of enhancer contacts only partially captures the logic underlying expression levels. A correlation of nearly 1 would suggest, misleadingly, that this is the case.However, as can be seen from In our original Figure 3, we set out to explain why highly expressed genes are more enriched for looping to enhancer marks than lowly expressed genes. This trend has been reported previously . We reasoned this increasing enrichment reflects the fact that highly expressed genes are located in genomic regions with more ChIP-seq peaks of open chromatin marks; additionally, highly expressed genes have more long-range interactions in general (i.e. the number of ChIP-seq peaks and the number of interactions both correlate with expression level). The enrichment calculation used by us, as well as the previous studies listed above, used the same (genome-wide) background model for calculating enrichment for each of the five different expression groups. Thus, the difference in ChIP-seq peak density/interaction count was not accounted for between genes falling in different expression groups. When these differences were accounted for, the increase in enrichment level was reduced.Following the reviewer’s suggestion, we have now changed Figure 3 to focus less on this aspect of the data. As the reviewers pointed out, it is nevertheless the case that highly expressed genes contact enhancer regions with greater frequency than lowly expressed genes, and that this likely contributes to their increased expression levels. We have refocused Figure 3 to report that promoter interactions are enriched for histone marks that define enhancers and repressors, and that this enrichment correlates with expression level, as expected. Additionally, we re-calculated enrichment values by using the method reported in Schoenfelder et al., 2015, where the “expected” values were obtained by analyzing non-baited MboI fragments (i.e. not shuffling peaks). We also report that the correlation of histone ChIP-seq peaks with expression level is apparent even at large (300 kb) distances from promoters, an expansion of a phenomenon reported several years ago .We feel that the resulting Figure 3 is more intuitive and streamlined and thank the reviewers for their suggestions.4) The paper would be further strengthened by providing examples t some of the predicted long-range interactions are functional. Are any of the predicted target genes affected in disease, e.g. reduced or increased in expression in individuals carrying the distal variant?We have addressed this important point in several areas of our manuscript.GATA4 in Figure 1E and NPPA in Figure 3F) using the Vista enhancer database, the largest experimentally tested set of human enhancers in in vivoassays. We further assessed enrichment of the full repertoire of Vista enhancers in CM interactions and show here that heart enhancers are 3-fold enriched; importantly, the heart enhancer set is the most significantly enriched set of all 21 tissues characterized , supporting that target genes have biological roles in cardiovascular disease.Third, we show that human left ventricle eQTLs  are significantly enriched for looping to their eQTL-associated gene in CMs ; for this analysis, we used the stringent background of moving each promoter’s set of interactions to a new promoter, thereby retaining the overall clustering pattern of eQTLs (due to LD) and the often observed clustering of promoter interactions. Importantly, when we performed the same analysis using iPSC interactions, the observed proportion of eQTLs looping to their eQTL-associated gene was much less significant .Fourth, we report that the set of GWAS LD SNPs that falls within CM promoter interactions is enriched for eQTLs compared to the set of all GWAS LD SNPs . This result indicates that CM promoter interactions identify a subset of LD SNPs most likely to be functional, based on association with gene expression levels.SORT1 promoter contacts a myocardial infarction GWAS locus 120 kb away. This locus was experimentally dissected in Musunuru et al., 2010, where it was shown that the SNP rs12740374 introduces a binding site for the transcription factor C/EBP which resulted in increased enhancer activity of this region. Importantly, the allele associated with increased risk for myocardial infarction is the same allele that introduces the C/EBP binding site and is associated with increased expression of SORT1, consistent with the reporter assay results. The fact that the CM interaction map identifies multiple long-range interactions between the SORT1 promoter and this distal myocardial infarction locus  is an important level of evidence that this distal region is indeed a disease-associated regulatory element, and is supported by the functional characterization of the distal region in a previous study.Fifth, we show in Figure 7D that the Although we did not experimentally validate the function of promoter interactions as part of this study, the multiple tiers of existing experimental results and orthogonal data sets strongly support the function of several specific interactions , as well as large sets of interactions (e.g. interactions linking eQTLs to their eQTL-associated gene).