Transcriptional control in large genomes often requires looping interactions between distal DNA elements, such as enhancers and target promoters. Current chromosome conformation capture techniques do not offer sufficiently high resolution to interrogate these regulatory interactions on a genomic scale. Here we use Capture Hi-C (CHi-C), an adapted genome conformation assay, to examine the long-range interactions of almost 22,000 promoters in 2 human blood cell types. We identify over 1.6 million shared and cell type–restricted interactions spanning hundreds of kilobases between promoters and distal loci. Transcriptionally active genes contact enhancer-like elements, whereas transcriptionally inactive genes interact with previously uncharacterized elements marked by repressive features that may act as long-range silencers. Finally, we show that interacting loci are enriched for disease-associated SNPs, suggesting how distal mutations may disrupt the regulation of relevant genes. This study provides new insights and accessible tools to dissect the regulatory interactions that underlie normal and aberrant gene regulation.
Your institute does not have access to this article
Open Access articles citing this article.
Genome Biology Open Access 30 May 2022
Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment
BMC Genomics Open Access 11 May 2022
Journal of Hematology & Oncology Open Access 04 May 2022
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Dekker, J., Marti-Renom, M.A. & Mirny, L.A. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14, 390–403 (2013).
Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012).
Sanyal, A., Lajoie, B.R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Dixon, J.R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).
Gnirke, A. et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat. Biotechnol. 27, 182–189 (2009).
Hughes, J.R. et al. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat. Genet. 46, 205–212 (2014).
Tan-Wong, S.M. et al. Gene loops enhance transcriptional directionality. Science 338, 671–675 (2012).
Phillips, J.E. & Corces, V.G. CTCF: master weaver of the genome. Cell 137, 1194–1211 (2009).
Parelho, V. et al. Cohesins functionally associate with CTCF on mammalian chromosome arms. Cell 132, 422–433 (2008).
Wendt, K.S. et al. Cohesin mediates transcriptional insulation by CCCTC-binding factor. Nature 451, 796–801 (2008).
Giresi, P.G., Kim, J., McDaniell, R.M., Iyer, V.R. & Lieb, J.D. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 17, 877–885 (2007).
Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).
Vakoc, C.R., Mandat, S.A., Olenchock, B.A. & Blobel, G.A. Histone H3 lysine 9 methylation and HP1γ are associated with transcription elongation through mammalian chromatin. Mol. Cell 19, 381–391 (2005).
Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).
Ma, W. et al. Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes. Nat. Methods 12, 71–78 (2015).
Pauler, F.M. et al. H3K27me3 forms BLOCs over silent genes and intergenic regions and specifies a histone banding pattern on a mouse autosomal chromosome. Genome Res. 19, 221–233 (2009).
Yano, K. et al. Identification and characterization of human ZNF274 cDNA, which encodes a novel kruppel-type zinc-finger protein having nucleolar targeting ability. Genomics 65, 75–80 (2000).
De Luca, A. et al. p300/cAMP-response-element-binding-protein (′CREB′)-binding protein (CBP) modulates co-operation between myocyte enhancer factor 2A (MEF2A) and thyroid hormone receptor–retinoid X receptor. Biochem. J. 369, 477–484 (2003).
Mink, S., Haenig, B. & Klempnauer, K.H. Interaction and functional collaboration of p300 and C/EBPβ. Mol. Cell. Biol. 17, 6609–6617 (1997).
Osborne, C.S. et al. Active genes dynamically colocalize to shared sites of ongoing transcription. Nat. Genet. 36, 1065–1071 (2004).
Osborne, C.S. et al. Myc dynamically and preferentially relocates to a transcription factory occupied by Igh. PLoS Biol. 5, e192 (2007).
Schoenfelder, S. et al. Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells. Nat. Genet. 42, 53–61 (2010).
Thompson, M., Haeusler, R.A., Good, P.D. & Engelke, D.R. Nucleolar clustering of dispersed tRNA genes. Science 302, 1399–1401 (2003).
Yochum, G.S. Multiple Wnt/β-catenin responsive enhancers align with the MYC promoter through long-range chromatin loops. PLoS ONE 6, e18966 (2011).
Sur, I.K. et al. Mice lacking a Myc enhancer that includes human SNP rs6983267 are resistant to intestinal tumors. Science 338, 1360–1363 (2012).
Shi, J. et al. Role of SWI/SNF in acute leukemia maintenance and enhancer-mediated Myc regulation. Genes Dev. 27, 2648–2662 (2013).
Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
Khor, B., Gardet, A. & Xavier, R.J. Genetics and pathogenesis of inflammatory bowel disease. Nature 474, 307–317 (2011).
Huang, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Nautiyal, J., Christian, M. & Parker, M.G. Distinct functions for RIP140 in development, inflammation, and metabolism. Trends Endocrinol. Metab. 24, 451–459 (2013).
Zhong, B. et al. Negative regulation of IL-17–mediated signaling and inflammation by the ubiquitin-specific protease USP25. Nat. Immunol. 13, 1110–1117 (2012).
Zhang, Y. et al. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 504, 306–310 (2013).
Neurath, M.F. Cytokines in inflammatory bowel disease. Nat. Rev. Immunol. 14, 329–342 (2014).
Dryden, N.H. et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res. 24, 1854–1868 (2014).
Cope, N.F. & Fraser, P. Chromosome conformation capture. Cold Spring Harb. Protoc. 2009, pdb prot5137 (2009).
Solovei, I. et al. in FISH: A Practical Approach (eds. Beatty, B., Mai, S. & Squire, J.) 119–157 (Oxford University Press, 2002).
We thank E. Darbo for assistance in data analysis and K. Tabbada for sequencing. This work was supported by a Leukaemia and Lymphoma Research Fellowship (C.S.O.), the Framework Programme 7 Epigenesys Network of Excellence (B.M., N.M.L.), Cancer Research UK (B.M., F.T.-C., N.M.L.), UCL (B.M., N.M.L.), OIST (F.T.-C., N.M.L.) and the Wellcome Trust (N.M.L.).
The Babraham Institute has filed a patent application relating to the content of this manuscript (PCT/GB2014/052664).
Integrated supplementary information
(a) Symmetric genome-wide contact matrix (log2 read count) from 45 million reads (chr. 1–chr. Y). Reads were grouped into 1-Mb bins. Chr. 1 is shown as a chromosome-wide example. (b) Distribution of di-tag read separation distances in cis, in Hi-C and CHi-C libraries from GM12878 and CD34+ cells. (c) Heat map of di-tag reads exhibiting conserved topological domain structure in Hi-C and CHi-C libraries. Reads were grouped into 40-kb bins. (d) Number of unique reads in Hi-C and CHi-C samples mapping to bait and non-bait fragments. The red dot denotes the mean.
(a–c) The sequencing reads pooled from three GM12878 CHi-C replicates were preprocessed by HiCUP and randomly downsampled to 10–90% of the total read counts. (a) The percentage of interactions identified using all reads also found in subsets of different sizes. (b) Box plots displaying the separation distances of the promoter interactions called in each subset of reads. (c) The percentage of interactions that are not present in the interaction list from all reads in each subset. (d) Overlap of the promoter interactions detected within three independently prepared GM12878 CHi-C libraries. (e) Overlap of the promoter interactions detected within two independently prepared CD34+ CHi-C libraries. (f) Overlap of the interaction lists using more stringent FDR thresholds.
(a) Overlap of the interactions to promoters detected by both CHi-C (this study) and 5C (Sanyal et al., 2012) in GM12878 cells. (b) Interaction profile between baited promoters and fragments containing a CTCF site (blue), enhancer (yellow) or non-baited promoter (red).
(a) Notch plot displaying the separation distances between active and inactive promoters in CD34+ cells and their interacting fragments. P values were derived by pairwise t test (Holm adjusted). (b) The separation distances of the interactions between two baited promoter fragments. (c) The interaction profiles up- and downstream of active and inactive promoters in CD34+ cells. The insets display a zoomed-in view of the interactions that are within 40 kb of the promoter, with light shading representing the 95% confidence interval. (d) The propensity of promoter interactions for the gene body relative to equally sized upstream sequences for active (red) and inactive (blue) genes. (e) Notch plot of the number of interactions of baited promoters in CD34+ cells with non-baited interacting fragments. P values were derived by pairwise t test (Holm adjusted). (f) Number of interactions of non-baited interacting fragments in CD34+ cells to baited promoters.
(a) Proportions of CD34+ interactions with an adjacent promoter and of those intervened by other promoters. (b) Examples of distal fragments (black) that interact with an active (green) or inactive (red) promoter in CD34+ cells, bridging across other genes. RNA-seq track indicates the gene activity. (c) Examples of promoter-other interactions near CTCF sites in GM12878 cells. Interactions originating data from an inactive KCNJ3 and active DOCK10 promoter bridge over CTCF sites (in red). (d) The proportion of CD34+ interactions between a baited fragment and a non-baited fragment that bridge CTCF sites. (e) The directionality of CD34+ interactions with respect to TAD boundary positions between baited promoter and non-baited fragments (brown) and between two baited promoters (green). (f) The interactions in GM12878 cells originating from an inactive ACB5 or active ZNF292 promoter are mainly directed away from the nearest TAD boundary. Green bars represent TADs, and vertical arrows highlight the boundaries.
(a) Percentage of fragments across the genome (black) and in promoter interacting fragments (yellow) that encompass a ChromHMM-annotated enhancer. (b) Relative enrichments of interacting fragments for histone modifications in CD34+ cells. Top, relative enrichments of interacting and non-interacting fragments. Below, segregation of interacting fragments into expression quartiles based upon the activity of the associated promoter. (c) Relative enrichments of interacting fragments for chromatin segments in CD34+ cells (left) and the emission parameters defining each state (right). (d) Relative enrichments of non-baited interacting fragments that do not contain canonical enhancers, in GM12878 (left) and CD34+ (right) samples. The enrichment of transcription-associated histone marks in fragments interacting with highly expressed promoters seen for all fragments is also observed in this subset. (e) Relative enrichments for histone modifications for CD34+ interactions between two baited promoters. (f) Relative enrichment of histone modifications for fragments that interact with H3K9me3- or H3K27me3-bound promoters.
Conserved interactions spanning less than 100 kb (top), 100–500 kb (middle) and more than 500 kb (bottom) for promoters that are active in both cell types (red), inactive in both cell types (blue) or have discordant expression in these cells (gray).
The promoter interaction map shows the promoter interaction profiles of (a) BCL6, (b) HOTTIP, (c) TMEM159, (d) SOX4 and (e) RFC3. The size and color of each interaction peak reflect the relative number of reads per fragment. ChromHMM functional annotation tracks are displayed below the interaction profiles. Graphs show the gel quantitation of interactions by 3C between the promoter fragment and non-interacting fragment (prom-neg; black columns) and between the promoter fragment and interacting fragment (prom-elem; gray columns). L, DNA ladder; Co, PCR control. Asterisks denote PCR primer positions. For BCL6, interactions were also assessed by three-dimensional DNA FISH, using BAC probes encompassing the BCL6 promoter (green, G) and non-interacting (purple, P) and +1.2-Mb interacting (red, R) regions, shown as bars below the map. Representative examples of signals in GM12878 and CD34+ cells are shown. The outline of DAPI counterstaining is shown as a dotted white line. Graphs show measurement of the distance between signals (green-red, GR; green-purple, GP; purple-red, PR); the number of loci analyzed is shown (n).
(a) The enhancer activity of interacting fragments that interact with active genes, relative to a minimal promoter plasmid. (b) The silencing activity of a fragment that interacts with the inactive BCL6 promoter, relative to a constitutively active promoter plasmid. Relative luciferase activity is displayed as the mean, with standard error of three independent experiments, each carried out with technical triplicates. Statistical significance was determined by t test (one-tailed).
Centered on the not-baited fragments that contain a GWAS SNP from the 8,808 SNPs analyzed. The graph shows the proportion of SNP-containing fragments and the 50 upstream and downstream fragments that interact with a baited promoter in GM12878 (left) and CD34+ (right) cells.
Supplementary Figure 11 Statistical significance of interacting and non-interacting GWAS SNP fragments.
The histogram shows the median P value of trait association for non-interacting GWAS SNPs divided by the median P value of trait association for interacting GWAS SNPs. A positive value indicates that interacting GWAS SNPs have a lower median P value than those that do not interact. A log10 scale is shown. Asterisks denote the statistical significance of the trend within each category (Mann-Whitney).
About this article
Cite this article
Mifsud, B., Tavares-Cadete, F., Young, A. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat Genet 47, 598–606 (2015). https://doi.org/10.1038/ng.3286
Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data
Genome Biology (2022)
Genome Biology (2022)
Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease
Alzheimer's Research & Therapy (2022)
Journal of Hematology & Oncology (2022)
Journal of Animal Science and Biotechnology (2022)