ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters

Chromatin interactions connect distal regulatory elements to target gene promoters guiding stimulus- and lineage-specific transcription. Few factors securing chromatin interactions have so far been identified. Here, by integrating chromatin interaction maps with the large collection of transcription factor-binding profiles provided by the ENCODE project, we demonstrate that the zinc-finger protein ZNF143 preferentially occupies anchors of chromatin interactions connecting promoters with distal regulatory elements. It binds directly to promoters and associates with lineage-specific chromatin interactions and gene expression. Silencing ZNF143 or modulating its DNA-binding affinity using single-nucleotide polymorphisms (SNPs) as a surrogate of site-directed mutagenesis reveals the sequence dependency of chromatin interactions at gene promoters. We also find that chromatin interactions alone do not regulate gene expression. Together, our results identify ZNF143 as a novel chromatin-looping factor that contributes to the architectural foundation of the genome by providing sequence specificity at promoters connected with distal regulatory elements.

C ell fate determination relies on lineage-specific transcription programs set by master transcription factors acting on distal regulatory elements, such as enhancers, and proximal gene promoters 1 . Distal regulatory elements can be separated from their target promoter(s) over large genomic distances. They are brought in close proximity to one another through chromatin interactions/loops, defining the chromatin architecture of the genome 1 . Close to 60% of chromatin interactions are cell type specific 2,3 and significantly correlate with lineage-specific transcriptional programs 2 . These chromatin interactions form during cellular differentiation 4,5 and set the stage for stimulus-specific transcriptional responses 6 . Although a role for non-coding RNA was proposed, recent findings suggest that chromatin interactions rely on DNA sequences. For instance, a single-nucleotide polymorphism (SNP) associated with pigmentation modulates a chromatin interaction between a distal enhancer and the promoter of the oculocutaneous albinism II (OCA2) gene 7 . Similarly, mutations in the DNA recognition sequence for the CCCTC-binding factor (CTCF) impinge on the formation of chromatin interactions 8 .
CTCF is known to directly regulate the formation of chromatin interactions in partnership with the cohesin and/or mediator complexes 9 . It occupies distal regulatory elements located close to enhancers 5,10,11 and defines the boundaries of topological domains when paired with the cohesin complex 10,12 . Genomic regions bound by the mediator and cohesin complexes anchor interactions regulating lineage-specific gene expression found within topological domains 10 . Although the mediator and cohesin complexes lack DNA-binding domains, their recruitment to the chromatin commonly coincides with CTCF 13,14 or other transcription factors such as the oestrogen receptor alpha 15 . However, CTCF and oestrogen receptor alpha bind chromatin far from promoter regions 15,16 and cohesin-binding sites found at promoters relate to tissue-specific transcription 15 . This suggests the existence of a yet-to-be identified promoter-bound DNA recognition factor(s) capable of specifying the target gene promoter(s) of distal regulatory elements.
Here we report the enrichment of the zinc-finger protein ZNF143 at anchors of chromatin interactions connecting promoters with distal regulatory elements. Our results indicate that ZNF143 is directly recruited to the promoter of genes engaged in chromatin interactions, where it binds to its DNA recognition sequence. We also show that modulating ZNF143 binding by SNPs directly impacts chromatin interaction frequencies. This reveals the dependency of chromatin interactions on DNA sequence and implies that chromatin interactions can be affected by genetic alterations (genetic variants or mutations) associated with inherited traits and diseases. Overall, our results demonstrate that ZNF143 is a new factor controlling the formation of chromatin interactions.

Results
ZNF143 binds promoters and forms distal phantom events. CTCF and cohesin complex proteins form a cluster distinct from other transcription factors, especially those bound at gene promoters. To identify the transcription factor(s) involved in securing chromatin interactions between promoters and distal regulatory elements, we first looked for factors that bridge promoter factors with the CTCF-cohesin cluster. For this, we correlated the chromatin immunoprecipitation (ChIP)-seq signal intensities of more than 70 transcription factors profiled by the Encyclopedia of DNA elements (ENCODE) 17 project across all regions of open chromatin (see Methods) in GM12878 or K562 cells. In agreement with previous reports, we find that ZNF143 is unique because it associates with the 'CTCF-cohesin' cluster 18 in both cell lines ( Supplementary Fig. 1). However, we show that its genome-wide-binding profile is most similar to promoter-bound factors ( Supplementary Fig. 1). In agreement, ZNF143's correlation with the CTCF-cohesin cluster relies on its weakest binding sites (Fig. 1a), found primarily at distal regulatory elements defined by the 'CTCF-rich' chromatin state 19 (Fig. 1b). The strongest ZNF143-binding sites map to promoters (Fig. 1b) bound by RNA polymerase II (POL2; Fig. 1a) and other promoter-associated factors, such as the TATA-binding protein (TBP) and the TBP-associated protein, together forming a 'promoter' cluster ( Supplementary Fig. 1). This agrees with the reported enrichment of ZNF143's DNA recognition motif at promoters 20 . These same strongest ZNF143-binding sites associate with weak CTCF and cohesin binding (Fig. 1a). Of all the transcription factors profiled using ChIP-seq by the ENCODE project, ZNF143 is the only one correlated with the 'CTCF-cohesin' and the 'promoter' clusters in both GM12878 and K562 cells ( Supplementary Fig. 1) indicating its potential role in mediating chromatin interactions involving gene promoters.
Enriched motif analysis reveals that more than 80% of the strongest ZNF143-binding sites harbour its DNA recognition motif, while it is found in less than 30% of weak binding sites (Fig. 1c). The presence of the motif suggests that ZNF143 is recruited directly to promoters where it binds next to POL2 (Fig. 1d). These results agree with its role as a promoter-bound transcriptional activator [20][21][22][23][24] . The fact that weak ZNF143-bound sites rarely harbour its DNA recognition motif and align with CTCF and the cohesin complex (Fig. 1b,d), suggests that ZNF143 indirectly binds distal regulatory elements. Although tethering mechanisms allow indirect protein binding to the chromatin 25 , phantom binding events 26,27 resulting from the use of crosslinked cells in ChIP-seq assays where chromatin interaction are stabilized was recently proposed to account for indirect transcription factor binding to the chromatin. Strong ZNF143 binding at sites deprived of its recognition motifs may arise from chromatin interactions from a single enhancer, such as the locus control region (LCR) at the b-globin gene cluster (see below), to multiple gene promoters. Together our results support the direct binding of ZNF143 at promoters and indirect binding to CTCF and the cohesin complex bound distal regulatory elements, which may arise due to chromatin interactions.
Considering that different cell types have distinct chromatin architectures, we assessed whether ZNF143-binding events correspond with cell type-specific chromatin interactions and gene expression. First, we compared the ZNF143-binding sites called in GM12878, K562 and HelaS3 cells. This revealed thousands of cell type-specific sites (Fig. 2b) and is similar to what is observed for CTCF and cohesin 31,32 . Comparing cell typespecific ZNF143-binding sites with chromatin interactions unique to GM12878, K562 or HelaS3 cells revealed that ZNF143 binding directly relates to cell type-specific chromatin interactions ( Fig. 2c and Supplementary Fig. 2b). Epigenetic modifications, such as the mono-and dimethylation of lysine 4 on histone 3 (H3K4me1 and H3K4me2, respectively) may contribute to the cell type specificity of ZNF143, since these modifications can assist transcription factors binding and relate to cell type-specific binding profiles [33][34][35] . In agreement, the strongest cell type-specific ZNF143-binding sites harbour epigenetic modifications typical of active chromatin 19,36 , namely histone 3 lysine 4 monomethylation (H3K4me1), H3K4me2, histone 3 lysine 27 acetylation (H3K27ac) and the histone variant H2A.Z (Fig. 2d). Focusing on genes uniquely expressed in GM12878, K562 or HelaS3 cells reveals that cell type-specific ZNF143 binding correlates with differential gene expression ( Fig. 2c and Supplementary Fig. 2c).
The cell type-specific association between ZNF143 binding, chromatin interactions and gene expression is exemplified by the LCR found B50 kb upstream of the b-globin gene cluster. The promoters of the b-globin genes (haemoglobin delta (HBD) and haemoglobin gamma A (HBG1)) are bound by ZNF143 only in K562 cells ( Supplementary Fig. 2e). The LCR harbours a single ZNF143-binding site shared between GM12878, K562 and HelaS3 cells ( Supplementary Fig. 2e). Using an intercellular feature correlation (IFC) tool (see Methods), we predicted interactions between the LCR and the promoter of the HBD and HBG1 genes in K562 but not in GM12878 or HelaS3 cells ( Supplementary Fig. 2d). Chromatin conformation capture (3C) assays confirmed that chromatin interactions connect the LCR and the promoter of the HBD and HBG1 genes only in K562 cells ( Supplementary Fig. 2d). This agrees with these genes being expressed exclusively in K562 cells (Supplementary Table 1 and Supplementary Data 1). Chromatin interactions predicted in all three cell lines for a ubiquitously expressed gene, such as the one connecting the TBL1XR1 promoter to an B160 kb upstream regulatory element, validate by 3C assays in all cell lines ( Supplementary Fig. 2e). These results support the preferential binding of ZNF143 at chromatin interaction anchors, including cell type-specific anchors related to lineage-specific transcriptional programs.
ZNF143 is required for chromatin interactions. To directly assess the requirement of ZNF143 for the formation of chromatin interactions between promoter and distal regulatory elements, we determined the impact of modulating ZNF143 binding to the chromatin on the frequency of chromatin interactions. We first focused on the chromatin interactions predicted by IFC in HelaS3 cells between distal regulatory elements and the promoter of the transducing beta-like 1 X-linked receptor (TBL1XR1) or the eukaryotic translation elongation factor 1-alpha (EEF1A1) genes (Fig. 3). Using 3C assays anchored at the promoters of the TBL1XR1 or EEF1A1 genes, we validated a series of predicted chromatin interactions (Fig. 3a,b,e,f). Depletion of ZNF143 using   small-interfering RNA (siRNA)-based silencing in HelaS3 cells significantly decreased the frequency of these chromatin interactions (Fig. 3b,f). Consistently, a reduction in ZNF143 binding at the distal regulatory elements and promoters of the TBL1XR1 and EEF1A1 genes was observed (Fig. 3c,g), as was a decrease in the expression of both the TBL1XR1 and EEF1A1 genes (Fig. 3d,h). Overall, these results support a role for ZNF143 in chromatin loop formation.   The global depletion of ZNF143 induced by silencing its expression using siRNAs can indirectly impact chromatin interactions. To bypass this limitation, we identified SNPs inducing allele-specific binding of ZNF143 to the chromatin and determined their impact on chromatin interactions. We first identified SNPs heterozygous in GM12878 cells found at ZNF143-bound sites using the genotype data provided by the 1,000 genomes project 37 . Using our allele-specific binding from ChIP-seq (ABC) tool (see Methods), we then identified 28 SNPs displaying an allele-specific bias in the ZNF143 ChIP-seq reads from GM12878 cells (Po0.005). Two SNPs, rs2232015 and rs13228237, located within the promoter of the protein arginine methyltransferase 6 (PRMT6) and the first intron of the zincfinger CCCH-type antiviral 1 (ZC3HAV1) genes, respectively (Fig. 4), were in close proximity (B300 bp) to restriction sites for HindIII (enzyme used in the 3C assay). The rs2232015 SNP maps   rs13228237   4  3  2  1  1516 17 18 19  14  13  12  11  10  9  8  7  6  5  4  3  to the fourth position of the ZNF143 DNA recognition sequence (motif 1; Fig. 4a) the most prominent motif found within B85% of the top 500 sites. The rs13228237 SNP changes the fourteenth position of a reported extension of a ZNF143 DNA recognition sequence 22,38 (motif 2; Fig. 4b), which is found within B25% of the top 500 sites. Consistent with the observation that the actual ZNF143-binding sites are located at gene promoters, B43% and B76% of gene promoters ( ± 2.5 kb of the transcription start site) bound by ZNF143 were found to contain motif 1 or motif 2 (motif P values o1 Â 10 À 4 ) in GM12878 cells, respectively. Interesting, motif 2 appears to be the most prominent ZNF143 motif found at gene promoters and most closely resembles the ZNF143 motif characterized using in vitro methods 22,39 . The imposed changes to the DNA sequence based on the positionweighted matrix predict preferential binding of ZNF143 to the reference A and the variant C allele of the rs2232015 and rs13228237 SNPs, respectively, compared with the other alleles (Fig. 4a,b). In agreement, 242 reads from the ZNF143 ChIP-seq data, mapping to the rs2232015 SNP, contain the reference A allele and 136 reads contain the variant T allele (P ¼ 5.47 Â 10 À 8 ; Fig. 4c). Likewise, of the 25 reads mapping to the rs13228237 SNP, five contain the reference G allele and 20 contain the variant C allele (P ¼ 4.08x10 À 3 ; Fig. 4d). Importantly, the signal intensity of the ZNF143-binding site containing the rs13228237 SNP is high (n ¼ 175) indicating that this SNP falls within the centre of the inferred ZNF143-binding site and between the positive and negative strand peaks of the unprocessed ChIP-Seq reads (Fig. 4d). Allele-specific ChIP-quantitative PCR (qPCR) assays against ZNF143 in GM12878 cells validated the predicted allelic imbalance for both SNPs (Fig. 4e,f and Supplementary Fig. 3). Consistent with ZNF143 being directly responsible for chromatin loop formation, the decreased binding of ZNF143 to the chromatin caused by the variant allele at the rs2232015 SNP leads to a corresponding allele-specific reduction of the chromatin interaction frequency measured by 3C assays between the PRMT6 promoter and a distal regulatory element B85 kb away (Fig. 4e and Supplementary Fig. 3). Interestingly, the rs2232015 SNP modulates a portion of the ZNF143 recognition motif that is shared with THAP11 and recently shown in vitro to be dispensable for ZNF143 binding 22 . These results, while revealing that ZNF143 is required, may indicate that a complex of factors specify chromatin interactions. Similarly, the increased binding of ZNF143 to the chromatin caused by the variant C allele of the rs13228237 SNP leads to an increase in the chromatin interaction frequency between the first intron of the ZC3HAV1 gene and two distal regulatory elements located B200 kb away ( Fig. 4f and Supplementary Fig. 3). Interestingly, this ZNF143binding site is located B14 kb from the transcription start site of the ZC3HAV1 gene and may represent an unknown isoform of ZC3HAV1 gene. Consistently, a transcription start site was predicted from 5 0 cap analysis of gene expression data 89 bp from the rs13228237 in GM12878 by the ENCODE project ( Supplementary Fig. 4). Expression quantitative trait loci (eQTL) analysis of the rs2232015 and rs13228237 SNPs using RNA-Seq data from lymphoblastoid cells (n ¼ 373) (ref. 40) genotyped as part of the 1,000 Genomes Project 41 reveals that the ZC3HAV1 expression is modulated by the rs13228237 SNP in lymphoblastoid cells (P ¼ 1.73 Â 10 À 3 ; Fig. 4f). However, the rs2232015 SNP is not significantly associated with the expression of the PRMT6 gene (P ¼ 0.063; Fig. 4e). This coincides with a repressed element and poised promoter chromatin state at the distal regulatory element looping to the PRMT6 promoter in the GM12878 cells ( Supplementary Fig. 5), which contrasts with the active state at regulatory elements looping to the ZC3HAV1 promoter ( Supplementary Fig. 5). Interestingly, the rs2232015 SNP is in strong linkage disequilibrium (r 2 Z0.95) with two reported eQTLs captured by the rs1762509 and rs9435441 SNPs 42,43 . The rs1762509 and rs9435441 SNPs lead to allelespecific expression of the PRMT6 gene within the liver cells and monocytes, respectively 42,43 . Consistently, the interacting distal regulatory element looping to the PRMT6 promoter is in an active state within liver cells ( Supplementary Fig. 5). This suggests that chromatin interactions are not sufficient to impact gene expression, as recently reported at the b-globin locus 44 and that ZNF143 role in loop formation is not dependent on gene transcription.

Discussion
Cellular identity is dependent on lineage-specific transcriptional programmes set by master transcription factors acting at regulatory elements that communicate with one another through chromatin interactions 1 . Recently, the ENCODE project 17 observed well-positioned and symmetrical nucleosomes flanking the binding sites of CTCF, RAD21 and SMC3, which contrasted the variability observed surrounding the binding sites of other transcription factors with the exception of ZNF143 (ref. 17). In agreement with this observation representing a unique feature of chromatin-looping factors, we demonstrate that ZNF143 is required at promoters to stimulate the formation of chromatin interactions with distal regulatory elements (Fig. 5). This aligns with its reported role favouring POL2 occupancy at gene promoters 22 and in the assembly of the pre-initiation complex 23 . The fact that ZNF143 is ubiquitously expressed 21 suggests that ZNF143 may be a regulator of the architectural foundations of cell identity. Although the mechanisms accounting for cell type-specific ZNF143-binding profiles are unknown, chromatin interactions were recently reported to be set early during lineage commitment 6 . In agreement, ZNF143 is required for zebrafish embryo development 45 , for stem cell identity and for ARTICLE the self-renewal ability of human embryonic stem cells 46,47 . Altogether, our results reveal that ZNF143 directly binds promoters to secure chromatin interactions with distal regulatory elements. ZNF143 provides a sequence-dependent mechanism for the formation of chromatin interactions that can be modulated by genetic variants underlying inherited traits and diseases.
The maximum signal intensity value of each transcription factor across all DHS sites created the vectors used for the Pearson correlation (r) calculation. Hierarchical clustering was then performed on the resulting correlation matrix using average linkage and 1 À r as the distance metric. The input control was included in the analysis as a control. All transcription factors with binding profiles that clustered with the control were dismissed from the final figure. Since we correlated the binding profiles across regions of open chromatin, this analysis not only removes failures but also factors that bind to heterochromatin. This analysis was performed using the first replicate for all transcription factors.
Transcription factor-binding sites across chromatin states. The chromHMM 51 -derived genomic annotations of chromatin states in GM12878 and K562 cell lines were downloaded from the UCSC genome browser website (http:// genome.ucsc.edu). The intersection between genomic annotations and the summit of the binding sites for transcription factors were performed using the BEDTools software package 52 .
The proportion of paired-end tags (PET) where both interacting anchors overlap transcription factor-binding sites (peak files) was determined using a custom Perl script. The significance of this overlap was compared with that of 1,000 simulated random-matched binding sets (RMBSs) for each transcription factor. Each simulated RMBS matched the experimental set in chromosome distribution, absolute number, and size of the binding sites. We randomly selected binding sites of equal or greater size, trimming larger sites, from the complete set of all possible binding sites defined by the union of all reported binding sites for all transcription factors in a given cell line provided by the ENCODE project. Therefore, the probability of selecting a given binding site was equal to its observed frequency in all of the profiled transcription factors. Two-tailed P values were calculated from z scores using the generated null distributions.
This analysis was performed using the first replicate for all transcription factors and when multiple groups profiled the same factor the first replicate from the larger data set was used.
Identification of uniquely expressed genes. RNA-Seq data for the three cell lines, in four replicates, were downloaded from NCBI gene Expression Omnibus (GEO accession numbers: GSM591661; GSM591673; GSM591664; GSM591664; GSM958728; GSM958730; GSM591670; GSM591671; GSM591682; GSM591659; GSM765402; GSM767848; GSM883635; GSM672833; GSM591666; GSM591668; GSM591679; GSM591660; GSM958729, Supplementary Table 1). Reads were aligned to the human genome hg19 using the TopHat software tool version 2 (ref. 53). To identify genes that are uniquely expressed in each of the three cell lines, we used the Cufflinks software tool version 2. 1.1 (ref. 53). First, we filtered all genes that have an FPKM (fragments per kilobase of exon per million fragments mapped) value equal to 0 (no expression) in all three cell lines. Next we identified genes that are unique to each cell line (expressed in one cell line and not in the others) and genes found to be expressed in more than one cell line (commonly expressed genes). To identify differentially expressed genes between the three cell lines first, we did a one per one comparison (K562-HelaS3, K562-GM12878 and HelaS3-GM12878). Then we performed one per two comparisons to identify genes differentially expressed in one specific cell line compared with the others.
Predicting chromatin interactions. We predicted chromatin interaction using an intercellular feature correlation (IFC) approach similar to PreSTIGE (http://prestige.case.edu) 54 and others 19,55,56 to calculate the Pearson correlation coefficient (r) between two DHS sites based on the DNaseI hypersensitivity signals generated by DNase-seq across all cell lines available by the ENCODE project (http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/ wgEncodeUwDnase/; GEO accession numbers: GSM736491-GSM736639). To provide cell type specificity to the correlation analysis, we calculated the correlation coefficient, using DNase-seq data sets from all available cell lines, only at DHS sites identified by the hotspots algorithm 48 for K562, GM12878 or HelaS3 independently. This provides for correlated DHS in K562, GM12878 or HelaS3 cells, respectively. We also restricted our analysis to ± 500 kb surrounding the DHS anchor site that contained our region of interest (promoter or ZNF143binding site).
Allele-specific transcription factor binding. To call SNPs displaying an allelespecific bias in transcription factor binding, we developed a software tool, which we refer to as allele-specific binding from ChIP-seq (ABC). ABC directly compares differences in read abundance between reference and variant alleles using a binomial probability test at heterozygous SNPs identified by genotyping. The genotype information for the GM12878 (NA12878) cell line was downloaded from the 1000 Genomes Project's website (www.1000genomes.org). The ABC approach relies on the number of aligned reads and contains the highest power to detect an allelic imbalance on the edges of an identified binding site, or the maxima of each strand-specific peak, obtained from single-end reads based on technical biases created by short-read sequencing of the ends of ChIP fragments. Thus, ABC also aims to determine the location of a particular genetic variant within a given binding site by assessing the strand distribution of reads containing the two alleles, not to be confused with a strand bias test applied for genotyping algorithms 57 , since unlike genomic DNA the null expectation of equal coverage of a particular genetic variant by reads in both orientations is not held for reads derived from ChIP-seq assays. In addition, a position bias where the alleles of a genetic variant are not equally distributed along the length of the reads spanning it can be used to identify potential false-positive allele-specific binding or potential transcription factor repositioning events. ABC currently applies the Mann-Whitney U-test to assess a potential position bias. SNPs violating the position test are dismissed.
We restricted our analysis to heterozygous SNPs reported in GM12878 by the 1000 Genomes Project 58 . We prioritized SNPs mapping to the ZNF143 DNA recognition sequence. ABC was then employed to identify heterozygous SNPs leading to observable allele-specific biases in the sequencing reads obtained from ChIP-seq assays against ZNF143 within GM12878 cells. Finally, we filtered out SNPs found within repetitive regions and known segmental duplications because these variables can confound allele-specific analyses. The ABC code used to identify SNPs causing allele-specific binding can be accessed via GitHub (https:// github.com/mlupien/ABC).
Chromatin conformation capture (3C) assay. Chromosome Conformation Capture (3C) assays were performed as we previously described 60 . In brief, cells were counted and balanced to the same number (six million) before the 3C experiments to allow for comparison between different cell types or treatments. Cells were crosslinked and lysed. Chromatin was digested using 400 units of HindIII, followed by ligation with 4,000 units of T4 DNA ligase (NEB M0202S). Crosslinks were reversed by Qiagen proteinase K digestion. 3C products were purified by phenol-chloroform extraction, followed by qPCR. To control for random digestion, ligation and different primer efficiencies, randomly ligated DNA fragments within the tested loci were generated as previously described [61][62][63][64][65][66][67][68] . A standard curve for the Ct value of each 3C primer pair, anchor and bait, were generated from these randomly ligated DNA fragments. The 3C frequency of each primer pair was normalized to their corresponding standard curves and was further normalized to a loading control, primers hybridized to the genomic region of the RHO gene. Primers used are listed in Supplementary Table 2.
Chromatin immunoprecipitation (ChIP). ChIP followed by qPCR was performed as we previously described 33 . In brief, cells were crosslinked and lysed. Chromatin was sonicated and immunoprecipitated with anti-ZNF143 (Novus Biologicals H00007702-M01), followed by reverse crosslinking and DNA extraction. Four mg of anti-ZNF143 was used per five million cells in each experiment. For ChIP assays after siRNA treatment, cells were harvested 72 h after transfection. The number of cells was counted and balanced before ChIP. Primers used are listed in Supplementary Table 2.
Gene expression. RNA was isolated from HelaS3 cells using the QIAGEN RNeasy mini kit according to manufacturer's recommendations. The purified RNA was treated with DNaseI to remove any possible DNA contamination. Reverse transcription PCR (RT) was performed to convert RNA into cDNA using an ABI high-capacity cDNA reverse transcription kit. The expression level of the queried genes was quantified by qPCR (RT-qPCR), as previously described 60 . Primers used are listed in Supplementary Table 2.
In vivo allele-specific ChIP assay. In vivo allele-specific ChIP assays were performed as we previously described 69 . In brief, anti-ZNF143 immunoprecipitated and genomic input DNA was qPCR amplified using allele-specific mismatch amplification mutation assays primers 70 to reveal the relative level of enrichment for each allele. To confirm the allele specificity, the PCR product from anti-ZNF143 immunoprecipitated and genomic input DNA were sequenced by Sanger sequencing. Primers used are listed in Supplementary Table 2.
In vivo allele-specific 3C assay. In vivo allele-specific 3C assays were performed as we previously described 69 . A forward primer hybridizing to a sequence outside of each SNP and its closest HindIII restriction enzyme site was used to target each SNP region. A reverse primer hybridizing to a sequence close to the HindIII site from the distal site was used to target the distal interacting region. Each primer pair was used to amplify the HindIII 3C product from GM12878 cells. The amplified 3C products were assessed by qPCR, using allele-specific mismatch amplification mutation assay primers, to determine the relative level of each allele of the SNP involved in the chromatin loop. Allele specificity was further demonstrated through Sanger sequencing of the amplified 3C products.