Abstract
The functional engagement between an enhancer and its target promoter ensures precise gene transcription1. Understanding the basis of promoter choice by enhancers has important implications for health and disease. Here we report that functional loss of a preferred promoter can release its partner enhancer to loop to and activate an alternative promoter (or alternative promoters) in the neighbourhood. We refer to this target-switching process as ‘enhancer release and retargeting’. Genetic deletion, motif perturbation or mutation, and dCas9-mediated CTCF tethering reveal that promoter choice by an enhancer can be determined by the binding of CTCF at promoters, in a cohesin-dependent manner—consistent with a model of ‘enhancer scanning’ inside the contact domain. Promoter-associated CTCF shows a lower affinity than that at chromatin domain boundaries and often lacks a preferred motif orientation or a partnering CTCF at the cognate enhancer, suggesting properties distinct from boundary CTCF. Analyses of cancer mutations, data from the GTEx project and risk loci from genome-wide association studies, together with a focused CRISPR interference screen, reveal that enhancer release and retargeting represents an overlooked mechanism that underlies the activation of disease-susceptibility genes, as exemplified by a risk locus for Parkinson’s disease (NUCKS1–RAB7L1) and three loci associated with cancer (CLPTM1L–TERT, ZCCHC7–PAX5 and PVT1–MYC).
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The datasets generated by the current study are available in the GEO (GSE115604) and the Sequence Read Archive (PRJNA412021). For analyses of cancer mutations and GTEx data, codes are deposited in GitHub (https://github.com/wblilab-uth/ERR-project and https://github.com/zdz-lab/ERR). Other analyses of ChIP–seq peaks, 4C-seq or gene expression in the current study used standard bioinformatics tools and codes, which are available upon request. Source data are provided with this paper.
References
Schoenfelder, S. & Fraser, P. Long-range enhancer–promoter contacts in gene expression control. Nat. Rev. Genet. 20, 437–455 (2019).
Hsieh, T. S. et al. Resolving the 3D landscape of transcription-linked mammalian chromatin folding. Mol. Cell 78, 539–553 (2020).
Gasperini, M. et al. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell 176, 377–390 (2019).
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).
Li, W. et al. Functional roles of enhancer RNAs for oestrogen-dependent transcriptional activation. Nature 498, 516–520 (2013).
Arner, E. et al. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science 347, 1010–1014 (2015).
The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944 (2017).
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
Li, Y. et al. The structural basis for cohesin-CTCF-anchored loops. Nature 578, 472–476 (2020).
Lucas, J. S., Zhang, Y., Dudko, O. K. & Murre, C. 3D trajectories adopted by coding and regulatory DNA elements: first-passage times for genomic interactions. Cell 158, 339–352 (2014).
Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).
Jin, E. H. et al. Association between promoter polymorphisms of TFF1, TFF2, and TFF3 and the risk of gastric and diffuse gastric cancers in a Korean population. J. Korean Med. Sci. 30, 1035–1041 (2015).
Weinhold, N., Jacobsen, A., Schultz, N., Sander, C. & Lee, W. Genome-wide analysis of noncoding regulatory mutations in cancer. Nat. Genet. 46, 1160–1165 (2014).
Rheinbay, E. et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature 578, 102–111 (2020).
Cho, S. W. et al. Promoter of lncRNA gene PVT1 is a tumor-suppressor DNA boundary element. Cell 173, 1398–1412 (2018).
Zhang, W. et al. A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat. Genet. 50, 613–620 (2018).
Tate, J. G. et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 47, D941–D947 (2019).
Satake, W. et al. Genome-wide association study identifies common variants at four loci as genetic risk factors for Parkinson’s disease. Nat. Genet. 41, 1303–1307 (2009).
Liu, Z. et al. LRRK2 phosphorylates membrane-bound Rabs and is activated by GTP-bound Rab7L1 to promote recruitment to the trans-Golgi network. Hum. Mol. Genet. 27, 385–395 (2018).
Blackwood, E. M. & Kadonaga, J. T. Going the distance: a current view of enhancer action. Science 281, 60–63 (1998).
Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320 (2017).
Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).
Li, W. et al. Condensin I and II complexes license full estrogen receptor α-dependent enhancer activation. Mol. Cell 59, 188–202 (2015).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Stadhouders, R. et al. Multiplexed chromosome conformation capture sequencing for rapid genome-scale high-resolution detection of long-range chromatin interactions. Nat. Protocols 8, 509–524 (2013).
van de Werken, H. J. et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat. Methods 9, 969–972 (2012).
Krijger, P. H. L., Geeven, G., Bianchi, V., Hilvering, C. R. E. & de Laat, W. 4C-seq from beginning to end: a detailed protocol for sample preparation and data analysis. Methods 170, 17–32 (2020).
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).
Hayward, N. K. et al. Whole-genome landscapes of major melanoma subtypes. Nature 545, 175–180 (2017).
Schuijers, J. et al. Transcriptional dysregulation of MYC reveals common enhancer-docking mechanism. Cell Rep. 23, 349–360 (2018).
Tanenbaum, M. E., Gilbert, L. A., Qi, L. S., Weissman, J. S. & Vale, R. D. A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159, 635–646 (2014).
MacLeod, D. A. et al. RAB7L1 interacts with LRRK2 to modify intraneuronal protein sorting and Parkinson’s disease risk. Neuron 77, 425–439 (2013).
Latourelle, J. C. et al. Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson’s disease: a longitudinal cohort study and validation. Lancet Neurol. 16, 908–916 (2017).
Pierce, S. E., Tyson, T., Booms, A., Prahl, J. & Coetzee, G. A. Parkinson’s disease genetic risk in a midbrain neuronal cell line. Neurobiol. Dis. 114, 53–64 (2018).
DeBoever, C. et al. Large-scale profiling reveals the influence of genetic variation on gene expression in human induced pluripotent stem cells. Cell Stem Cell 20, 533–546 (2017).
Panopoulos, A. D. et al. The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Res. 22, 168–177 (2012).
Panopoulos, A. D. et al. iPSCORE: a resource of 222 iPSC lines enabling functional characterization of genetic variation across a variety of cell types. Stem Cell Rep. 8, 1086–1100 (2017).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).
van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).
Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).
Mayba, O. et al. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 15, 405 (2014).
Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).
Li, M. J. et al. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 44, D869–D876 (2016).
Nair, S. J. et al. Phase separation of ligand-activated enhancers licenses cooperative chromosomal enhancer assembly. Nat. Struct. Mol. Biol. 26, 193–203 (2019).
Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78, 554–565 (2020).
Fudenberg, G. et al. Formation of chromosomal domains by loop extrusion. Cell Rep. 15, 2038–2049 (2016).
Hatzis, P. & Talianidis, I. Dynamics of enhancer–promoter communication during differentiation-induced gene activation. Mol. Cell 10, 1467–1477 (2002).
Acknowledgements
This work is supported by the Howard Hughes Medical Institute and NIH grants (DK018477, DK039949, HL150521 and NS093066) to M.G.R.; by NIH grants (K22CA204468, R21GM132778, R01GM136922 and ‘4D nucleome’ U01HL156059), a University of Texas Rising STARs Award, the Welch Foundation (AU-2000-20190330), the Gulf Coast Consortium John S. Dunn Foundation and a Cancer Prevention and Research Institute of Texas (CPRIT) Award (RR160083) to W.L.; by NIH grants (HG008118, HL107442, DK105541, DK112155 and NSF-CMMI division award 1728497) to K.A.F.; and by NIH NHGRI grants (R01HG008153 and R01HG008153-S1) to Z.D.Z. W.L. is a CPRIT Scholar in Cancer Research. M.G.R. is an investigator with the Howard Hughes Medical Institute. We thank J. Hightower for assistance with figure preparation, and we acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin (http://www.tacc.utexas.edu) for providing high-performance computing resources for part of this work.
Author information
Authors and Affiliations
Contributions
W.L., S.O. and M.G.R. conceived the project. S.O. and W.L. conducted most experiments, with contributions from X.Z., J.-H.L., S.J.N., F.Y. and K.O. J.S., W.L. and S.O. conducted most of the bioinformatic analyses of this paper, except for the GTEx related analyses, which were done by J.M. and Z.D.Z. M.D. and K.A.F. generated and analysed the RNA-seq data for allelic gene expression in human iPS cells. The 4C-seq data were analysed by F.X., R.W. and Q.M. I.G.-B. contributed to the iPS-cell-derived NPC culture and RNA-seq. W.L., M.G.R. and S.O. wrote the manuscript, with input from all authors.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature thanks Peter Scacheri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Disrupted enhancer–promoter looping alters eRNA and mRNA transcription.
a, qRT–PCR results showing the temporal expression kinetics of eRNAs and mRNAs at two loci after treatment with 17-β-oestradiol (E2). mRNAs measured by intronic primers (n = 3; biological replicates). Shaded error band represents mean ± s.d. b, 4C-seq heat maps using TFF1e, P2RY2e or KCNK5e as the viewpoints showing the effects of RAD21 siRNA depletion on the E-P looping events; yellow and pink highlights depict enhancers and promoters, respectively. The quantified chromosome contact frequency between enhancer and promoter from two replicates of 4C-seq is shown in the bar graph (bottom). c, Expression of TFF1 mRNA (TFF1m) and TFF1 enhancer RNA (TFF1e) in wild-type (n = 6) and TFF1p-KO MCF-7 isogenic cell clones (n = 14) was checked by qRT–PCR. The FOXC1 gene that locates on another chromosome was examined as a control. The WT5 and KO6 lines were used for many subsequent experiments (for example, 4C-seq, GRO-seq and ChIP). Each bar represents data from an independent cell clone and was made from n = 3 data points of technical replicates. The data presented here represent three biological replicates. d, qRT–PCR results showing the expression of TFF1 eRNA and mRNA in wild-type MCF-7 cells versus TFF1p-KO cells with two different gRNA pairs (TFF1p-KO_gRNA1/2 and TFF1p-KO_gRNA3/4) (n = 3 data points of technical replicates; representative of three independent experiments). e, ChIP–qPCR data indicating the binding of RNA Pol II and p300 at the TFF1 enhancer or promoter in wild-type compared to TFF1p-KO cells (n = 3 data points of technical replicates; representative of two biological replicates); their binding at an enhancer region near the NRIP1 gene (that is, NRIP1e) is shown as a control. Data are mean ± s.d.; *P < 0.05; **P < 0.01, ***P < 0.001, two-tailed Student’s t-test.
Extended Data Fig. 2 Promoter deletion affects gene and eRNA transcription in its chromosomal neighbourhood at the P2RY2, KCNK5 and PGR loci.
a, b, qRT–PCR showing the mRNA and eRNA expression of P2RY2 (a) and the expression of neighbouring genes (FCHSD2 and P2RY6) (b) in wild-type (n = 2) versus P2RY2 promoter KO (P2RY2p-KO) (n = 2) independent isogenic clones of MCF-7 cells. Each bar represents an independent cell clone (n = 3 data points of technical replicates; representative of two independent experiments). c, Top, Hi-C contact matrix and ChIP–seq tracks of CTCF and p300 showing the topology of the chromosomal neighbourhood of P2RY2 locus. Bottom, UCSC browser screenshots showing the binding of CTCF, cohesin subunit (that is, RAD21) and ERα at P2RY2 and surrounding gene promoters. d, e, qRT–PCR showing the mRNA and eRNA expression of KCNK5 (d) and the expression of neighbouring genes (KCNK17 and SAYSD1) (e) in wild-type (n = 2) versus KCNK5 promoter KO (KCNK5p-KO) (n = 2) independent isogenic cell clones of MCF-7 cells. Each bar represents an independent cell clone (n = 3 data points of technical replicates; representative of two independent experiments). f, Top, Hi-C contact matrix and ChIP–seq tracks of CTCF and p300 showing the topology of the chromosomal neighbourhood of the KCNK5 locus. Bottom, UCSC browser screenshots showing the binding of CTCF, cohesin subunit (that is, RAD21) and ERα at KCNK5 and surrounding gene promoters. g, h, qRT–PCR showing the mRNA and eRNA expression of PGR (g) and the expression of neighbouring genes (ARHGAP42 and TRPC6) (n = 3) (h), in wild-type (n = 2) versus PGR promoter KO (PGRp-KO) (n = 2) independent isogenic cell clones of MCF-7 cells. Each bar represents an independent cell clone (n = 3 data points of technical replicates; representative of two independent experiments). i, Top, Hi-C contact matrix and ChIP–seq tracks of CTCF and p300 showing the topology of the chromosomal neighbourhood of the PGR locus. Bottom, UCSC browser screenshots showing CTCF, cohesin and ERα binding at PGR and surrounding gene promoters. Data are mean ± s.d.; *P < 0.05; **P < 0.01, ***P < 0.001, two-tailed Student’s t-test.
Extended Data Fig. 3 Cohesin knockdown affects gene and eRNA transcription through coordinating looping, without changing histone methylation.
a, qRT–PCR results showing efficient knockdown of RAD21 by siRNA, which does not affect the mRNA level of ESR1 (encoding ERα) (n = 3 data points of technical replicates; representative of two independent experiments). b, c, qRT–PCR results showing that siRAD21 decreases the 17-β-oestradiol-induced activation of coding genes (b), but upregulates the eRNAs next to these genes (c) (n = 3 data points of technical replicates; representative of two independent experiments). d, MA plots showing the differential expression of RefSeq genes (left) or eRNAs (right) in cells with depletion of RAD21 versus cells transfected with control siRNA (that is, siCTL). Red dots represent significantly changed genes or eRNAs (fold change > 1.5; FDR < 0.05). The purple bars indicate twofold change. e, UCSC genome browser screen shoots of GRO-seq results in siRAD21- versus siCTL-transfected MCF-7 cells at KCNK5 (left) and P2RY2 (right) loci; p300 ChIP–seq serves to indicate active enhancers. Yellow highlights denote gene regions; pink highlights denote enhancer regions. f, Diagram showing an oppositely regulated transcription of many eRNAs and mRNAs caused by disruption of E-P looping (that is, siRAD21). g, The tag density plots showed no significant difference of histone marks for promoters (H3K4me3 ChIP–seq) or enhancers (H3K4me1 ChIP–seq). qPCR data represent mean ± s.d.; **P < 0.01, ***P < 0.001, two-tailed Student’s t-test.
Extended Data Fig. 4 Deletion of the TFF1 promoter alters the transcription of the cognate TFF1 enhancer and neighbouring genes.
a, Hi-C contact matrix in GM12878 cells (by Juicebox), CTCF-mediated chromatin loops (by ChIA-PET) and ChIP–seq tracks of CTCF and p300 in MCF-7 cells showing the topology (multiple contact domains) of the chromosomal neighbourhood of the TFF1 locus. Yellow triangles in the Hi-C map denote contact domains; domains are numbered for simplicity. The contact domains containing genes analysed in b are highlighted in yellow. b, Box plots of gene expression outside of the hosting contact domain of TFF1 generated by qRT–PCR in wild-type (n = 6) and TFF1p-KO (n = 14) isogenic clones of MCF-7 cells. The box plot centre lines represent medians; box limits indicate the 25th and 75th percentiles as determined by R software; and whiskers extend 1.5 × IQR from the 25th and 75th percentiles. c, qRT–PCR results showing the expression of TFF3 and RSPH1 mRNA in wild-type MCF-7 cells as compared to TFF1p-KO cells with two different gRNA pairs (TFF1p-KO_gRNA1/2 and TFF1p-KO_gRNA3/4) (n = 3 data points of technical replicates; representative of three independent experiments). d, ChIP–qPCR data indicating the binding of RNA Pol II and p300 at TFF3p in wild-type cells as compared to TFF1p-KO cells (n = 3 data points of technical replicates; representative of two independent experiments). e, qRT–PCR results showing the expression of TFF1e and TFF3m after knockdown of RAD21 by siRNA in wild-type MCF-7 cells versus TFF1p-KO cells (n = 3 data points of technical replicates; representative of two independent experiments). f, qRT–PCR results showing the expression TFF1e, TFF1m and TFF3m in wild-type MCF-7 cells versus cells with deletion of the TFF1 enhancer (TFF1e-KO), TFF1 promoter (TFF1p-KO) or both (TFF1e/p-DKO) (n = 3 data points of technical replicates; representative of three independent experiments). The diagram shows the TFF1e deletion in TFF1p-KO cells. Data are mean ± s.d.; *P < 0.05; **P < 0.01, ***P < 0.001, two-tailed Student’s t-test.
Extended Data Fig. 5 Epigenomic features of the enhancers and promoters that are putative looping pairs, as revealed by their opposite regulation after siRAD21 treatment.
a, ChIP–seq profile plots were generated using published data in MCF-7 cells (Supplementary Table 6), showing the differential enrichment of chromatin-looping-related structural factors, including CTCF, cohesin subunit (that is, RAD21), cohesin loading factor (that is, NIPBL) and Mediator subunit (that is, MED1) on the promoters that are downregulated by siRAD21 treatment, as well as on enhancers for which eRNAs are increased by siRAD21. Also refer to Extended Data Fig. 3. b, ChIP–seq profile plots showing the differential enrichment of factors that are relevant to enhancer function in MCF-7 cells, including those of p300, ERα, FOXA1 and GATA3. c, Diagram showing the non-stoichiometric distribution of chromatin structural factors and other transcription-related factors on functional E-P pairs. These E-P pairs denote RAD21-regulated enhancers (super-activated by siRAD21) and RAD21-dependent promoters (downregulated by siRAD21).
Extended Data Fig. 6 Promoters exhibit a higher frequency and affinity of CTCF binding than enhancers.
a, Percentages of promoters (P), enhancers (E) or random genomic regions (R) that contain a CTCF motif (left) or undergo CTCF binding (right) in MCF-7 cells (based on ENCODE data). The cartoons at the bottom indicate that promoters were further analysed for their features in c, on the basis of their distance to an adjacent enhancer. b, Similar to a but using GM12878 data. c, Percentage of MCF-7 promoters that contain a CTCF motif (left) or undergo CTCF binding (right) for three categories of promoters: promoters that have a super-enhancer, a typical enhancer or no enhancer in their 50-kb genomic proximity (cartoon at bottom of a). d, Similar to c but using GM12878 data. All P values in a–e were generated by two-sided Fisher’s exact test. e, Top, diagram showing the design of constructs. The binding between GCN4 and ScFV will bring CTCF to the sites at which dCas9/gRNA binds. Bottom, western blots showing the expression of scFv-CTCF-V5 (wild type or Y226A/F228A mutant) and dCas9-10xGCN4 after lentiviral transduction (uncropped images in Supplementary Fig. 1; representative of two independent experiments). f, ChIP–qPCR using a V5 antibody indicating comparable binding of V5-tagged wild-type CTCF or CTCF(Y226A/F228A) on TFF3p (n = 3 data points of technical replicates; representative of two independent experiments). Control gRNAs did not bring the V5-tagged CTCF to TFF3p. ChIP was performed with MCF-7 cells expressing dCas9-10xGCN4 and scFV-CTCF (wild type or Y226A/F228A mutant) as indicated. qPCR data in f represent mean ± s.d.; *P < 0.05; **P < 0.01, two-tailed Student’s t-test. The P values were based on comparisons between the same-coloured bar (either wild-type or mutant CTCF) in TFF3p-gRNA1/gRNA2 versus CTL-gRNA1/gRNA2 conditions.
Extended Data Fig. 7 Enhancer scanning and alternative models to interpret ERR and enhancer–promoter functional engagement.
a, Model describing the process through which an activated enhancer scans its chromosomal neighbourhood inside a contact domain to functionally engage with its cognate promoter target. This enhancer scanning process involves promoter-bound CTCF (green ovals) and is compatible with cohesin-mediated extrusion (dashed lines) in wild-type cells, but it continues to operate in the absence of the cognate promoter (that is, P1) owing to deletion or disease mutation. A hypothetical ‘enhancer engagement’ time is depicted that reflects the relative amount of time in which the active enhancer engages with the neighbouring promoters, correlating with their expression levels. b, There are alternative, non-exclusive models that could be largely consistent with our results. For example, one of the other possibilities is a ‘positional effect’ model. In this model, an active enhancer may engage with its preferred promoter, and the two are retained in a transcription-associated E-P loop, quite probably in a nuclear environment such as an interchromatin granule50, transcription factory or other perhaps phase-separated structure (pink coloured area). Upon functional loss (deletion, mutation or CTCF loss) of the original promoter, this ‘positional’ effect will follow the enhancer but it will engage with the secondary choices (P3 in the model; for example, TFF3 as compared to TFF1, or PAX5 as compared to ZCCHC7, or TERT as compared to CLPTM1L). But at a lower frequency or in a small percentage of single cells among a population, the enhancer does engage with other target promoters (such as P2, dotted black line). When the secondary choice is also lost, the entire enhancer will now select the third choice—for example, P2 in the model—to activate the promoter as its newest hierarchical choice. Overall, a few specific results from our study are in better support of the enhancer scanning model. First, it is consistent with the observation that for all single-cell colonies investigated (that is, 14 independent cell clones of TFF1p-KO in Extended Data Fig. 1), they consistently exhibited the highest expression of TFF3 as the new hierarchical promoter choice, precluding models suggesting stochastic promoter choice. A positional effect model may predict that the enhancer and the target promoter are engaged so that the inhibition of one will reduce the other. This is true in that deletion of TFF1e inhibited TFF1p (Extended Data Fig. 4f). However, CRISPRi of TFF1p resulted in higher activity of TFF1e and higher expression of TFF3 (Fig. 3b). This data better support an enhancer scanning model—a dynamic enhancer–promoter interaction process inside the contact domain, in which TFF1p inhibition makes the ‘TFF1e-in-action’ preferentially interact with the next target in the hierarchy. Second, TFF1e exhibited quite broad chromatin interaction throughout the contact domain (for example, Fig. 1e, by 4C-seq), which is in accord with its ‘scanning’ behaviour. Furthermore, high-resolution Micro-C2,51 data showed that many ‘stripe loops’ formed in between enhancer and promoter at a finer scale (<50 kb or sometimes <10 kb), consistent with the suggestion that at least a subset of enhancers and promoters are actively scanning or extruding. The positional effect model is fundamentally compatible with the enhancer scanning model. The enhancer scanning model reflects a dynamic process in which the enhancer initiates target searching and it finds a major target as well as many additional minor targets. Were we to snapshot this dynamic process, then at every time point that an enhancer engages with one of its potential targets (for example, TFF3p or TFF1p), the target promoter would be repositioned closer to the enhancer. Our data suggest that the scanning process of the enhancer requires the cohesin complex, as its depletion by RNA interference reproduced many of the phenomena that are seen in promoter-knockout cells (for example, Fig. 1b, Extended Data Figs. 3, 4e). This is consistent with a proposed loop-extrusion model, rather analogous to the mechanism by which cohesin facilitates the formation of the larger TAD structures23,24,52, requiring opposing CTCF motif orientation and high-affinity CTCF binding. However, it cannot be excluded that other chromatin remodellers, or Pol II itself, are the critical driver of enhancer scanning and E-P engagement. In support of this, inhibition of Pol II elongation partially reduced the promoter-centred stripe loops in mouse embryonic stem cells in a Micro-C study2. Finally, it is noteworthy that the scanning concept that enhancers (either the DNA–protein complex as an entirety or specific transcription apparatus such as Pol II) travel along chromatin to reach target genes has been extensively discussed as one of the classic models to interpret enhancer activity22,53.
Extended Data Fig. 8 CTCF binding on promoters, but not on enhancers. dictates enhancer–promoter choice.
a, Bar graph showing the numbers of RAD21-regulated enhancers and promoters (same set used in Fig. 1b, Extended Data Fig. 5) that engage in chromatin loops in MCF-7 cells as discovered by analysing MCF-7 ChIA-PET data from ENCODE, as compared to their low incidence of looping in K562 ChIA-PET data. b, Coloured map showing that, among the functionally looped E-P pairs discovered in a, only certain percentages are bound by ERα, FOXA1 or CTCF by ChIP–seq. c, The numbers of looped E-P pairs that display various motif directionality of CTCF and FOXA1. The four categories below the plot describe the existence of CTCF or FOXA1 motifs on enhancers (E) or promoters (P); the colours indicate the combinations of motif directionality. For example, ‘CTCF (E) + CTCF (P)’ denotes the coincidence of CTCF motifs on both promoters and enhancers. There was no obvious orientation preference of binding motifs for CTCF. FOXA1 binds to functionally looped enhancers more frequently than CTCF (Extended Data Figs. 5b, 8b), but it also exhibits no preferred motif orientation. d, Diagrams showing the position and directionality of CTCF-binding peaks and motifs in the four E-P pairs that we have extensively studied in this work. The colour motif sequence indicates directionality (black for left-pointing motifs, red for right-pointing motifs); the letters in each motif sequence are coloured to match the core CTCF motif nucleotides shown in the canonical CTCF motif by Jaspar (upper left logo); the motif scores after each motif sequence were calculated by the FIMO motif toolset. e, qRT–PCR results showing the expression of TFF1 eRNA and mRNA in wild-type MCF-7 cells versus cells with deletion of the TFF1 enhancer (TFF1e-KO), and MCF-7 cells with deletion of a CTCF peak inside the TFF1 enhancer (TFF1e/CTCF-KO; Supplementary Table 1, Supplementary Fig. 3) (n = 3 data points of technical replicates; representative of two independent experiments). Data are mean ± s.d.; *P < 0.05; **P < 0.01, ***P < 0.001, two-tailed Student’s t-test.
Extended Data Fig. 9 Cancer mutations in gene promoter regions, and their link to oncogene activation through ERR.
a, Plots showing the distribution of ICGC cancer whole-genome sequencing (WGS) mutations in the ±5 kb regions near gene TSSs. The two numbers in each plot indicate the mutations in the ±5kb of TSSs, as well as the total numbers of mutations in that cancer type, respectively. Cancer type abbreviations can be found in the ICGC web portal (http://icgc.org/), and in the Methods. b, The percentage of genes containing ICGC annotated promoter mutations or deletions among all RefSeq genes (all gene promoters; total 27,502), oncogene promoters (OPs, from cosmic database; total 315) and neighbouring gene promoters within ±200 kb of cosmic oncogenes (oncogene neighbouring promoters (ONPs); total 1,693). Hypergeometric tests were performed to compare the statistical significance of the enrichment of bearing a mutation or deletion for OPs or ONPs as compared to all RefSeq gene promoters (upon different frequency or size of mutations as indicated in the x axis). For each test, for example, comparing OPs to all RefGene promoters, p[X > k] was calculated, which denotes a one-sided probability that a higher percentage of oncogene promoters bears mutations or deletions than the percentage of RefSeq promoters. The random variable X follows a hypergeometric distribution with parameters N (number of total RefSeq genes), K (number of total RefSeq genes bearing promoter mutations or deletions), n (number of total oncogenes or oncogene neighbours), k (number of total oncogenes or oncogene neighbours that bear promoter mutations or deletions). The five significant P values shown in the plot (from left to right) are 9.2 × 10−4, 3.3 × 10−3, 5.2 × 10−5, 5.3 × 10−3 and 5.6 × 10−4, respectively. mut> = 1: any mutations being identified in ICGC; mut > 1: recurrent promoter mutations identified in more than 1 donors in ICGC release 28; del < 100 kb: genetic deletions with length smaller than 100 kb. c, Occurrence of somatic mutations and deletions at a selected list of ONPs that were included in our CRISPRi screening (see Fig. 3c). In this plot, the cancer cohorts (x axis) were ranked by the names of original cancer sites, and the y axis shows the ONP–OP gene pairs. Gene names before ‘–>’ are those of ONPs, whereas those after ‘–>’ are oncogenes. For example, ‘mutation: CLPTM1L –> TERT’ (the fourth row) indicates that TERT is an oncogene listed in COSMIC, and the gene promoter of CLPTM1L was identified as an ONP, which contains somatic mutations in several cancer types. The dot size was scaled by the percentage of affected donors in each of that cancer type or cohort (SamplePerc), and dots of red colour show those samples with mutations, whereas blue dots show those samples with deletion (<100 kb) covering an ONP. The cancer type abbreviations can be found in the ICGC web portal (http://icgc.org/) and in the Methods. d, qRT–PCR results showing the expression of gene pairs, PAX5–ZCCHC7, MYC–PVT1 and SS18L1–MTG2. Each pair consisted of an oncogene (PAX5, MYC and SS18L1) and its adjacent ONP genes (ZCCHC7, PVT1 and MTG2). The ONP promoters were inhibited with specific sgRNAs in MCF-7 cells together with dCas9-KRAB (see the diagrams at the top) (n = 3 data points of technical replicates; representative of two independent experiments). e, f, Similar to Fig. 3d, these are results for two other ONP–OP loci after ONP deletion. The Hi-C contact matrix at the top shows the relative location of PAX5–ZCCHC7 and MYC–PVT1 gene pairs in a shared contact domain. qRT–PCR results below show the expression of ONP and OP gene mRNAs (two independent clones each) (n = 3 biological replicates). g–i, Oncoplots showing the landscapes of ICGC mutations located in three pairs of OPs as well as their ONPs at the TERT–CLPTM1L locus (g), the PAX5–ZCCHC7 locus (h) and the MYC–PVT1 locus (i). The percentages of mutations between 0.5–1% are denoted as 1%, and these between 0–0.5% are denoted as 0%. Each column in the oncoplot represents a cancer sample. The bar graphs with numbers (for example, 187, 101, 98) in the right side indicate the numbers of donor samples that contain mutations for that specific gene promoter. The labels on top of each panel indicate the total numbers of donors that contain mutations in any of the promoters in that specific locus, for example, 386 (6.27%) is the sum number of donors that contain mutations for any of the promoters shown in g. It is noteworthy that it remains a challenge to directly compare oncogene expression changes in tumour samples that carry rare noncoding mutations, because the rarity of matched RNA-seq and genotype (WGS) precludes a robust statistical analysis. qPCR data in d–f represent mean ± s.d.; *P < 0.05; **P < 0.01, ***P < 0.001, two-tailed Student’s t-test.
Extended Data Fig. 10 Chromosomal interaction in three pairs of oncogene and oncogene-neighbourhood genes exhibiting an ERR-like phenotype.
a–c, UCSC and WashU genome browser screenshots of ChIA-PET and GRO-seq results at the PAX5–ZCCHC7 locus (a), MYC–PVT1 locus (b) and TERT–CLPTM1L locus (c). The putative enhancers and promoters are highlighted in pink and in yellow, respectively. These cells show strong interactions between the eRNA (enhancer) and the original promoters (that is, ZCCHC7p in a, PVT1p in b and CLPTM1Lp in c). d, 4C-seq contact matrix and heat map plots showing the chromatin contacts with the PAX5 promoter as viewpoint, and its change in 293T cells with ZCCHC7 promoter knockout. The viewpoint PAX5 promoter is highlighted in yellow, and the neighbouring enhancer in pink. e, 4C-seq contact matrix and heat map plots showing the chromatin contacts with the TERT promoter as viewpoint, and its change in 293T cells with CLPTM1L promoter knockout. The viewpoint TERT promoter is highlighted in yellow, and the neighbouring enhancer in pink. f, The numbers of ICGC cancer mutations that reside in the motifs of CTCF, ERα, FOXA1 and GATA3, and random genomic regions near gene TSSs.
Extended Data Fig. 11 GTEx data analysis identifies potential ERR gene pairs acting in human populations.
a, Schematic overview of GTEx data-processing workflow to identify potential ERR events. Also see Supplementary Figs. 8–10 and associated notes. b, Histogram showing the number of potential ERR gene pairs that appear in one tissue or more than one. c, Histogram showing the number of potential ERR events distributed in each tissue. d, qRT–PCR results of CRISPRi showing the expression of NUCKS1 and RAB7L1 mRNAs with sgRNA-control (iCTL) or sgRNA specific for the putative enhancer (iEnhancer) in two clones of NPCs expressing dCas9-KRAB (see enhancer location in Fig. 4c, d) (n = 3 data points of technical replicates; representative of two independent experiments). e, Published CTCF ChIP–seq screen shots in human-iPS-cell-derived neurons or neuronal cell lines (SK-N-SH) in the NUCKS1 and RAB7L1 promoters. The ChIP–seq data tracks were generated by the Cistrome data browser (http://cistrome.org/). The location of the three SNPs is shown. f, Sanger sequencing results of the NUCKS1 promoter DNA, showing the allele bias of CTCF binding (comparison between the input DNA and ChIP DNA). Data are mean ± s.d.; *P < 0.05; **P < 0.01, ***P < 0.001, two-tailed Student’s t-test.
Supplementary information
Supplementary Information
This file contains Supplementary Figures 1-11, Supplementary Notes and Supplementary References.
Supplementary Tables
This file contains Supplementary Tables 1-7 – see the Table Guide worksheet within the file for details.
Source data
Rights and permissions
About this article
Cite this article
Oh, S., Shao, J., Mitra, J. et al. Enhancer release and retargeting activates disease-susceptibility genes. Nature 595, 735–740 (2021). https://doi.org/10.1038/s41586-021-03577-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-021-03577-1
This article is cited by
-
Enhancer selectivity in space and time: from enhancer–promoter interactions to promoter activation
Nature Reviews Molecular Cell Biology (2024)
-
A distal enhancer guides the negative selection of toxic glycoalkaloids during tomato domestication
Nature Communications (2024)
-
Topological reorganization and functional alteration of distinct genomic components in gallbladder cancer
Frontiers of Medicine (2024)
-
Etiology of super-enhancer reprogramming and activation in cancer
Epigenetics & Chromatin (2023)
-
Methylation across the central dogma in health and diseases: new therapeutic strategies
Signal Transduction and Targeted Therapy (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
