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Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements

Abstract

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.

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Figure 1: HiChIP identifies high-resolution chromosome conformation in primary human T cells.
Figure 2: Validation of regulatory elements identified by H3K27ac HiChIP with CRISPR interference and activation.
Figure 3: Dynamic 3D enhancer landscapes in T cell differentiation.
Figure 4: HiChIP identifies cell type specificity and target genes of autoimmune disease–associated variants.
Figure 5: Fine-mapping of GWAS-identified variants using H3K27ac HiChIP.
Figure 6: HiChIP identifies allelic bias to target genes for cardiovascular disease risk variants.

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Acknowledgements

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).

Author information

Affiliations

Authors

Contributions

M.R.M., A.T.S., W.J.G., and H.Y.C. conceived the project. M.R.M., A.T.S., J.T., and R.L. performed all genomics assays with help from T.N., M.R.C., N.S., and R.A.F. A.T.S. performed all sorting for experiments. B.G.G., S.W.C., M.R.M., M.L.N., K.R.K., and D.R.S. performed all CRISPR validation experiments. E.A.B., C.D., M.R.M., and J.X. analyzed HiChIP data. J.G., A.T.S., and Y.W. analyzed ATAC–seq data. A.J.R. and P.G.G. analyzed GWAS SNPs in HiChIP data. A.K., P.A.K., A.M., J.E.C., T.Q., W.J.G., and H.Y.C. guided experiments and data analysis. M.R.M., A.T.S., E.A.B., C.D., W.J.G., and H.Y.C. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to William J Greenleaf or Howard Y Chang.

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Competing interests

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

Supplementary Figure 1 H3K27ac HiChIP enriches enhancer–promoter-associated chromatin contacts.

(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.

Supplementary Figure 6 Global enhancer connectome characterization in T cell differentiation.

(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.

Supplementary Figure 7 Positioning of differential HiChIP contacts in gene-dense chromosomes.

(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.

(ac) Virtual 4C interaction profiles anchored at the promoters of canonical naive (a), TH17 (b), and Treg (c) regulatory factors.

Supplementary Figure 9 Chromosome conformation dynamics of canonical T cell regulatory factors.

(ac) Delta interaction maps focused around known naive (a), TH17 (b), and Treg (c) regulatory factors.

Supplementary Figure 10 Contribution of H3K27ac ChIP and chromosome conformation to HiChIP EIS.

(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.

Supplementary Figure 12 T cell subtype HiChIP specificity of autoimmune disease–associated SNPs.

(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.

Supplementary Figure 13 Validation of HiChIP signal at SNP–eQTL contacts.

(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.

Supplementary Figure 14 H3K27ac HiChIP fine-mapping of GWAS variants in haplotype blocks.

(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 information

Supplementary Text and Figures

Supplementary Figures 1–15 and Supplementary Note (PDF 3774 kb)

Life Sciences Reporting Summary (PDF 129 kb)

Supplementary Table 1

HiChIP data processing metrics. (XLSX 58 kb)

Supplementary Table 2

HiCCUPS high-confidence loop calls. (XLSX 1444 kb)

Supplementary Table 3

HiCCUPS differential EIS in T cell subtypes by edgeR. (XLSX 6305 kb)

Supplementary Table 4

HiCCUPS differential EIS in T cell subtypes. (XLSX 336 kb)

Supplementary Table 5

HiChIP gene targets of autoimmune disease and CAD SNPs. (XLSX 7001 kb)

Supplementary Table 6

sgRNA and primer oligonucleotide sequences. (XLSX 50 kb)

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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

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