The challenge of linking intergenic mutations to target genes has limited molecular understanding of human diseases. Here we show that H3K27ac HiChIP generates high-resolution contact maps of active enhancers and target genes in rare primary human T cell subtypes and coronary artery smooth muscle cells. Differentiation of naive T cells into T helper 17 cells or regulatory T cells creates subtype-specific enhancer–promoter interactions, specifically at regions of shared DNA accessibility. These data provide a principled means of assigning molecular functions to autoimmune and cardiovascular disease risk variants, linking hundreds of noncoding variants to putative gene targets. Target genes identified with HiChIP are further supported by CRISPR interference and activation at linked enhancers, by the presence of expression quantitative trait loci, and by allele-specific enhancer loops in patient-derived primary cells. The majority of disease-associated enhancers contact genes beyond the nearest gene in the linear genome, leading to a fourfold increase in the number of potential target genes for autoimmune and cardiovascular diseases.
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.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Gene Expression Omnibus
Dixon, J.R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
van Arensbergen, J., van Steensel, B. & Bussemaker, H.J. In search of the determinants of enhancer–promoter interaction specificity. Trends Cell Biol. 24, 695–702 (2014).
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Rao, S.S.P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Fulco, C.P. et al. Systematic mapping of functional enhancer–promoter connections with CRISPR interference. Science 354, 769–773 (2016).
Altshuler, D., Daly, M.J. & Lander, E.S. Genetic mapping in human disease. Science 322, 881–888 (2008).
Kumar, V. et al. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet. 9, e1003201 (2013).
Edwards, S.L., Beesley, J., French, J.D. & Dunning, A.M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).
Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP–trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).
Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).
Grubert, F. et al. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162, 1051–1065 (2015).
Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).
Javierre, B.M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).
Sanjana, N.E. et al. High-resolution interrogation of functional elements in the noncoding genome. Science 353, 1545–1549 (2016).
Mumbach, M.R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).
Heintzman, N.D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311–318 (2007).
Creyghton, M.P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. USA 107, 21931–21936 (2010).
Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2011).
Ferraro, A. et al. Interindividual variation in human T regulatory cells. Proc. Natl. Acad. Sci. USA 111, E1111–E1120 (2014).
Arvey, A. et al. Genetic and epigenetic variation in the lineage specification of regulatory T cells. eLife 4, e07571 (2015).
Bettelli, E. et al. Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature 441, 235–238 (2006).
Acosta-Rodriguez, E.V. et al. Surface phenotype and antigenic specificity of human interleukin 17–producing T helper memory cells. Nat. Immunol. 8, 639–646 (2007).
Morita, R. et al. Human blood CXCR5+CD4+ T cells are counterparts of T follicular cells and contain specific subsets that differentially support antibody secretion. Immunity 34, 108–121 (2011).
Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Simeonov, D.R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017).
Engreitz, J.M. et al. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539, 452–455 (2016).
Dao, L.T.M. et al. Genome-wide characterization of mammalian promoters with distal enhancer functions. Nat. Genet. 49, 1073–1081 (2017).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
Sanborn, A.L. et al. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl. Acad. Sci. USA 112, E6456–E6465 (2015).
Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012).
Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Samstein, R.M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).
Ye, C.J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).
Petukhova, L. et al. Genome-wide association study in alopecia areata implicates both innate and adaptive immunity. Nature 466, 113–117 (2010).
Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).
Schiering, C. et al. The alarmin IL-33 promotes regulatory T-cell function in the intestine. Nature 513, 564–568 (2014).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).
O'Shea, J.J., Lahesmaa, R., Vahedi, G., Laurence, A. & Kanno, Y. Genomic views of STAT function in CD4+ T helper cell differentiation. Nat. Rev. Immunol. 11, 239–250 (2011).
Miller, C.L. et al. Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci. Nat. Commun. 7, 12092 (2016).
Nurnberg, S.T. et al. Coronary artery disease associated transcription factor TCF21 regulates smooth muscle precursor cells that contribute to the fibrous cap. PLoS Genet. 11, e1005155 (2015).
McPherson, R. et al. A common allele on chromosome 9 associated with coronary heart disease. Science 316, 1488–1491 (2007).
Helgadottir, A. et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316, 1491–1493 (2007).
Clarke, R. et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 361, 2518–2528 (2009).
CARDIoGRAMplusC4D Consortium. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45, 25–33 (2013).
Leung, D. et al. Integrative analysis of haplotype-resolved epigenomes across human tissues. Nature 518, 350–354 (2015).
Dixon, J.R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).
Franzén, O. et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 353, 827–830 (2016).
Sinha, R. et al. Index switching causes “spreading-of-signal” among multiplexed samples in Illumina HiSeq 4000 DNA sequencing. Preprint at bioRxiv http://dx.doi.org/10.1101/125724 (2017).
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).
Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature 518, 365–369 (2015).
Kagey, M.H. et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467, 430–435 (2010).
Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).
Bonnal, R.J.P. et al. De novo transcriptome profiling of highly purified human lymphocytes primary cells. Sci. Data 2, 150051 (2015).
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016).
Gilbert, L.A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Doench, J.G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Hsu, P.D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).
Horlbeck, M.A. et al. Nucleosomes impede Cas9 access to DNA in vivo and in vitro. eLife 5, e12677 (2016).
We thank members of the Chang and Greenleaf laboratories for helpful discussions and J. Tumey for artwork. We thank J. Engreitz, M. Pjanic, and C. Miller for assistance interpreting their published data sets. We thank X. Ji and J. Coller at the Stanford Functional Genomics Facility. We thank Agilent Technologies for generating oligonucleotide pools for cloning of the CRISPRa gRNAs. We thank the UC Berkeley High-Throughput Screening Facility and Flow Cytometry Facility. This work was supported by US National Institutes of Health (NIH) grants P50HG007735 (H.Y.C. and W.J.G.) U19AI057266 (W.J.G.), and 1UM1HG009436 (W.J.G.), the Human Frontier Science Program (W.J.G.), the Rita Allen Foundation (W.J.G.), and the Scleroderma Research Foundation (H.Y.C.). M.R.M. and E.A.B. acknowledge support from the National Science Foundation Graduate Research Fellowship. A.T.S. is a Cancer Research Institute Irvington Fellow supported by the Cancer Research Institute. B.G.G. was supported by an IGI-AstraZeneca Postdoctoral Fellowship. M.R.C. is supported by a grant from the Leukemia & Lymphoma Society Career Development Program. J.E.C. was supported by the Li Ka Shing Foundation and the Heritage Medical Research Institute. W.J.G. and A.M. are Chan Zuckerberg Biohub investigators. Sequencing was performed by the Stanford Functional Genomics Facility (NIH S10OD018220).
H.Y.C. and W.J.G. are co-founders of Epinomics and members of its scientific advisory board. A.K. is a member of the scientific advisory board of Epinomics. A.M. and J.E.C. are co-founders of Spotlight Therapeutics. J.E.C. serves as an advisor to Mission Therapeutics, and the Corn laboratory has received sponsored research support from AstraZeneca and Pfizer. A.M. serves as an advisor to Juno Therapeutics and PACT Therapeutics, and the Marson laboratory has received sponsored research support from Juno Therapeutics and Epinomics.
Integrated supplementary information
(a) Schematic of chromatin contacts captured in H3K27ac HiChIP. (b) Loop call overlap for cohesin HiChIP and H3K27ac HiChIP in mES cells. (c) Contact distance distribution for loops that are biased in cohesin versus H3K27ac HiChIP. (d) Proportion of cohesin- and H3K27ac-biased HiChIP loops that have cohesin, CTCF, H3K27ac, and RNA polymerase II binding in at least one loop anchor. (e) Virtual 4C interaction profile of an H3K27ac-biased loop focused at the Malat1 promoter. (f) Virtual 4C interaction profile of a cohesin-biased loop with low transcriptional activity associated.
Supplementary Figure 2 H3K27ac HiChIP achieves high chromatin loop signal over background at low cell inputs.
(a) KR balanced interaction matrices focused around the Etv5 locus in mES cells with decreasing cellular starting material. (b) Read support reproducibility of loops in H3K27ac HiChIP libraries from 25 million cells as compared to HiChIP in lower cell input libraries. (c) Aggregate peak analysis of loops in mES H3K27ac HiChIP libraries.
Supplementary Figure 3 H3K27ac HiChIP generates reproducible chromatin loop signals at low cell inputs.
(a) Comparison of KR balanced interaction maps in H3K27ac HiChIP biological replicates. Each replicate corresponds to one side of the interaction map, separated by the diagonal. (b) Read support reproducibility of loops between H3K27ac HiChIP biological replicates. (c) HiCCUPS loop call overlap between H3K27ac HiChIP libraries from 25 million and 50,000 mES cells. (d) Preseq library complexity estimation of H3K27ac HiChIP libraries from 25 million and 50,000 mES cells.
Supplementary Figure 4 H3K27ac HiChIP biological replicates from primary sorted T cells are highly reproducible.
(a) FACS strategy for naive, TH17, and Treg cells starting from human peripheral blood. Post-sort validation was used to ensure purity of T cell subtypes. Number represents the percentage of cells within that gate. (b) KR balanced interaction map of T cell subtype biological replicates. Each replicate corresponds to one side of the interaction map, separated by the diagonal. (c) Read support reproducibility of loops between H3K27ac HiChIP biological replicates in naive, TH17, and Treg cells. (d) Aggregate peak analysis of loops in naive, TH17, and Treg H3K27ac HiChIP libraries.
Supplementary Figure 5 Validation of HiChIP-identified CD69 distal enhancers with CRISPR activation.
CRISPRa validation in Jurkat cells of CD69 distal enhancers. CD69 protein levels are shown for individual sgRNAs tiling H3K27ac HiChIP-identified distal CD69 enhancers relative to the KLRF2 promoter as a locus negative control and a non-targeting negative control.
(a) ChromHMM classification of union T cell loop anchors. (b) Contact distance distribution of union T cell loops. (c) Left, agreement in signal observed per loop between samples. The quantile–quantile plot shows modest enrichment above random pairings. Right, PCA on residual signal clusters samples first by naive versus memory cell types (PC1) and then by donor identity (PC2, PC3). (d) Overlap of differential interactions between naive, TH17, and Treg subtypes. Biased interactions were obtained by performing pairwise comparisons between T cell types and analyzing the top 5% enriched and top 5% depleted EIS in each T cell subtype. (e) ChromHMM annotation of total loops, differential loops, and shared loops in all three T cell subtypes. O corresponds to other loop anchors not classified as enhancer or promoter. (f) Number of connections for different classes of loop elements. (g) Quantification of promoters skipped before an enhancer reaches its loop target.
(a) Distribution of T cell subtype differential HiChIP contacts across different chromosomes in comparison to the distribution of all loops. (b) Correlation of differential loop density with gene density and GC content.
Supplementary Figure 8 Characterization of conformational landscapes surrounding key T cell regulatory factors.
(a–c) Virtual 4C interaction profiles anchored at the promoters of canonical naive (a), TH17 (b), and Treg (c) regulatory factors.
(a–c) Delta interaction maps focused around known naive (a), TH17 (b), and Treg (c) regulatory factors.
(a) Left, proportion of H3K27ac ChIP peaks within EIS differential loop anchors that are cell type specific (log2 (fold change) > 1) or shared across all three subtypes. Right, global correlation of EIS and H3K27ac ChIP fold change in different T cell subset pairwise comparisons. (b) Same as a, but using HiChIP 1D differential signal at EIS biased loop anchors. (c) Overlap of H3K27ac HiChIP and bins of Hi-C loops with increasing T cell subset and GM H3K27ac ChIP–seq signal. (d) Overlap of CD4+ capture Hi-C14 with total and differential T cell subset HiChIP loops. Naïve 4X and 16X corresponds to EIS fold-enrichment over TH17 and Treg, TH17 and Treg 4X and 16X corresponds to EIS fold-enrichment over Naïve. (e) Treg-specific loops at the LRRC32 promoter not observed in other H3K27ac HiChIP T cell subsets nor in CD4+ capture Hi-C data14.
Supplementary Figure 11 Enrichment of autoimmune disease–associated SNPs in T cell HiChIP loop anchors.
(a) Enrichment of specific PICS autoimmune disease and non-immune SNPs in anchors of loops called by Juicer and Fit-Hi-C in comparison to a background shuffled loop set. (b) Enrichment of all PICS autoimmune disease and non-immune SNPs in T cell subset–biased loop anchors and all anchors in comparison to a background shuffled loop set.
(a) H3K27ac HiChIP signal bias in T cell subsets for PICS SNP–TSS pairs grouped by each SNP's presence in cell-type-specific or shared H3K27ac ChIP peaks up to 8 kb away. (b) H3K27ac HiChIP signal bias in T cell subsets for PICS SNP–TSS pairs grouped by each SNP's presence in cell-type-specific or shared H3K27ac ChIP peaks up to 2.5 kb away. (c) H3K27ac HiChIP signal bias in GM, K562, and My-La cell lines for PICS SNP–TSS pairs grouped by each SNP's presence in T cell subset-specific or shared H3K27ac ChIP peaks up to 8 kb away. (d) H3K27ac HiChIP signal bias in T cell subsets for GRASP SNP–TSS pairs grouped by each SNP's presence in cell-type-specific or shared H3K27ac ChIP peaks up to 2.5 kb away. (e) Left, average number of HiChIP gene targets for non-genic autoimmune disease and non-immune SNPs. Middle, quantification of SNP HiChIP gene targets in autoimmune disease. Right, quantification of promoters skipped before a SNP reaches its gene target.
(a) Validation of HiChIP signal at SNP–TSS pairs using interaction profiles of eQTL SNPs to ensure they contact their associated target gene promoter. (b) Interaction profiles of CRISPRi-validated loci in My-La cells.
(a) Global validation of HiChIP signal at putatively causal SNPs versus corresponding SNPs in LD (r2 ≥ 0.8) for naive and Treg cell subtypes. SNP–TSS pairs were generated from published fine-mapped data sets, in comparison to a distance-matched SNP–TSS pair set in the same LD block. (b) Interaction profile of the SATB1 promoter, and a 1-kb resolution visualization of the SNP-containing enhancer of interest. Differences in EIS bias between 5-kb and 1-kb resolution reflect high signal in a single 1 kb bin in TH17 cells around the SATB1 TSS and specific SNPs within the enhancer. LD SNPs (r2 ≥ 0.8) correspond to GRASP SNPs (genome-wide significance P < 10−8). The highlighted SNP is a PICS closest to focal EIS to SATB1.
Supplementary Figure 15 Chromatin interaction landscape of the 9p21.3 cardiovascular disease risk locus.
HCASMC v4C interaction profiles focused around the promoters of CDKN2A, CDKN2B, and ANRIL within the 9p21.3 locus.
Supplementary Figures 1–15 and Supplementary Note (PDF 3774 kb)
HiChIP data processing metrics. (XLSX 58 kb)
HiCCUPS high-confidence loop calls. (XLSX 1444 kb)
HiCCUPS differential EIS in T cell subtypes by edgeR. (XLSX 6305 kb)
HiCCUPS differential EIS in T cell subtypes. (XLSX 336 kb)
HiChIP gene targets of autoimmune disease and CAD SNPs. (XLSX 7001 kb)
sgRNA and primer oligonucleotide sequences. (XLSX 50 kb)
About this article
Cite this article
Mumbach, M., Satpathy, A., Boyle, E. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat Genet 49, 1602–1612 (2017). https://doi.org/10.1038/ng.3963
International Journal of Molecular Sciences (2021)
Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases
International Journal of Molecular Sciences (2021)
Cell Systems (2021)