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RNA polymerase II dynamics shape enhancer–promoter interactions

Nature Genetics volume 55, pages 1370–1380 (2023)Cite this article

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Abstract

How enhancers control target gene expression over long genomic distances remains an important unsolved problem. Here we investigated enhancer–promoter communication by integrating data from nucleosome-resolution genomic contact maps, nascent transcription and perturbations affecting either RNA polymerase II (Pol II) dynamics or the activity of thousands of candidate enhancers. Integration of new Micro-C experiments with published CRISPRi data demonstrated that enhancers spend more time in close proximity to their target promoters in functional enhancer–promoter pairs compared to nonfunctional pairs, which can be attributed in part to factors unrelated to genomic position. Manipulation of the transcription cycle demonstrated a key role for Pol II in enhancer–promoter interactions. Notably, promoter-proximal paused Pol II itself partially stabilized interactions. We propose an updated model in which elements of transcriptional dynamics shape the duration or frequency of interactions to facilitate enhancer–promoter communication.

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Fig. 1: Micro-C contacts are enriched in functional enhancer–promoter pairs.
Fig. 2: Enhancer–promoter contacts depend on active transcription.
Fig. 3: Changes in Pol II pausing and gene body transcription correlate with enhancer–promoter contacts.
Fig. 4: NELFB depletion and recovery correlate with changes in enhancer–promoter contacts.
Fig. 5: Updated model integrating RNA Pol II dynamics into enhancer–promoter interactions.

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Data availability

Micro-C data generated in this study were deposited in the Gene Expression Omnibus (GEO) database under accession number GSE206133. H3K27ac and H3K4me2 ChIP–seq data from K562 cells were downloaded from GSE163043. K562 data for ATAC-seq (ENCSR868FGK), CTCF ChIP-–seq (ENCSR447BSF), MNase-seq (ENCSR000CXQ) and NELFE ChIP–seq (ENCSR000DOF) were downloaded from ENCODE. DMSO, TRP and FLV-treated mESCs Micro-C data28 were downloaded from GSE130275. PRO-seq data36,40 for Jurkat T cells were downloaded from GSE66031 and for K562 from GSE60455. PRO-seq data for mECSs harboring a homozygous endogenous NELFB-FKBP12F36V fusion protein, treated and untreated with dTAG-13 (ref. 85), were downloaded from GSE196653. GRO-seq data for mESCs58,99 were downloaded from GSE43390 and GSE48895. Positions for human (hg38) and mouse (mm10) CAGE peaks were downloaded from the FANTOM5 database (https://fantom.gsc.riken.jp/5/).

Code availability

All data normalization and visualization code are available at https://github.com/Danko-Lab/E-P_contacts ref. 105.

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Acknowledgements

We thank E. Apostolou and members of her lab for commenting on a manuscript draft as well as members of the Danko, Lis, and Yu Labs for valuable discussions and suggestions throughout the life of this project. Work in this publication was supported by R01-HG010346 and R01-HG009309 (NHGRI) to C.G.D. A.A. is supported by the NIH (T32GM007739 and F30HD103398). Work in AKH Lab is supported by the NIH (R01HD094868, R01DK127821, R01HD086478 and P30CA008748). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. Some of the figures in this manuscript were created using BioRender.

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Authors and Affiliations

  1. Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Gilad Barshad, James J. Lewis, Alexandra G. Chivu, Edward J. Rice & Charles G. Danko

  2. Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA

    James J. Lewis

  3. Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York City, NY, USA

    Abderhman Abuhashem & Anna-Katerina Hadjantonakis

  4. Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York City, NY, USA

    Abderhman Abuhashem

  5. Biochemistry Cell and Molecular Biology Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York City, NY, USA

    Abderhman Abuhashem & Anna-Katerina Hadjantonakis

  6. The Novo Nordisk Center for Protein Research (CPR), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Nils Krietenstein

  7. School of Life and Pharmaceutical Sciences, Dalian University of Technology, Dalian, China

    Yitian Ma & Zhong Wang

  8. Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA, USA

    Oliver J. Rando

  9. Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Charles G. Danko

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  1. Gilad Barshad
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Contributions

G.B., J.J.L. and C.G.D. designed the study. E.J.R. generated PRO-seq data. G.B. and A.G.C. analyzed PRO-seq data. A.A. and A.K.H. contributed NELFB-dTAG mESCs. A.A. performed all treatments and crosslinking for the time course of the NELFB degradation dTAG experiments. G.B. collected all Micro-C data. N.K. and O.J.R. assisted with the collection of Jurkat Micro-C and taught G.B. the protocol. J.J.L., Y.M., Z.W. and G.B. implemented contact normalization by local decay (Contact Caller). G.B., J.J.L., A.G.C., N.K., O.J.R., A.K.H. and C.G.D. analyzed and interpreted data. G.B. and C.G.D. drafted the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Charles G. Danko.

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Extended data

Extended Data Fig. 1 Details for the comparison between functional and nonfunctional enhancer-promoter pairs.

(a) Schematic representation of the LOWESS-based normalization for enhancer-promoter contacts. (b) Box and dot plots, similar to Fig. 1b, comparing the observed contact frequency relative to expected by a local distance-decay function of the validated functional enhancers in the MYC locus (functional pairs) compared to the rest of the dREG-detected TIRs in the TAD (nonfunctional pairs) with the MYC promoter. Here we divided the CRISPRi-tested TIRs to those that fall within the first 0.5 Mb (near, functional: n = 4, nonfunctional: n = 27) or beyond 1.5 Mb (far, functional: n = 3, nonfunctional: n = 12) within the TAD. Two-sided Mann-Whitney p-values are indicated. (c) Violin plots comparing contact levels relative to expected by local distance-decay function of functional versus the nonfunctional enhancer-promoter pairs in the genome, before matching for enhancer-promoter distance, accessibility or target gene expression. On the left column, the results are based on dREG CRISPRi-targeted TIRs33 either before (top) and after (bottom) excluding pairs that do not fall into the same mega-haplotype (MH) or fall within known structural variants (SVs) in K562 cells96. The middle violin plot shows the same as the top-left one, but using data from a different CRISPRi dataset97. The two violin plots on the right show the same as the two on the left, using the same CRISPRi dataset98, but centering on H3K27ac overlapping ATAC-peaks instead of TIRs. Two-sided Mann-Whitney p-values are indicated. (d) Venn diagram showing the overlap between H3K27ac+ ATAC-peaks (H3K27ac)- and dREG TIRs-defined enhancers tested by CRISPRi in33.

Extended Data Fig. 2 Matching possible confounders between CRISPRi functional and nonfunctional pairs.

Histograms demonstrating the distribution of functional and high-confidence nonfunctional enhancer-promoter pairs in terms of enhancer-promoter genomic distance (top), accessibility by mean ATAC-seq signal (middle) and PRO-seq target gene transcription signal in reads per kilobase per million reads (RPKM) (bottom), after matching for these possible confounding factors.

Extended Data Fig. 3 Functional constituent enhancers within super enhancers interact more with the target promoter.

a,b) Violin plots comparing contact levels relative to expected by local distance-decay function of functional versus the nonfunctional enhancer-promoter pairs in the genome, where the enhancers are mapped within (a) or outside (b) K562 defined super enhancers101. Two-sided Mann-Whitney p-values are indicated (c) Dot plot shows the log2 distribution of local genomic distance-normalized contact frequency between CRISPRi-defined functional constituent enhancers within 16 super enhancers compared to other constituent enhancers within these super enhancers. The dashed lines connect data points representing the median values of the same super enhancer. Two-sided Wilcoxon paired-test p-values are shown. (d) Dot plot shows the log2 distribution of local genomic distance-normalized contact frequency between CRISPRi-defined high-confidence nonfunctional constituent enhancers within 15 super enhancers compared to other constituent enhancers within these super enhancers. The dashed lines connect data points representing the median values of the same super enhancer. Two-sided Wilcoxon paired-test p-values is shown. (e) Violin plot shows the distribution of the ratios between the functional constituent enhancers to other constituent enhancers in the same super enhancer (yellow) and between high-confidence nonfunctional constituent enhancers to other constituent enhancers in the same super enhancer. Two-sided Mann-Whitney p-value is indicated.

Extended Data Fig. 4 Micro-C 1D signal near TSSs genome-wide.

One dimensional contact signal for intra-chromosomal contacts with both sides having mapping quality (mapq) ≥ 30. Total median signal was smoothed using a sliding window of 100 bp. Shown are signals around promoter TSSs (orange), enhancer TSSs (purple) and all TSSs genome-wide (black).

Extended Data Fig. 5 Elaborated schematic representation of the APA method used to calculate 1D background-normalized changes in contacts between samples.

(a) To calculate the observed change in contacts, a matrix of contacts between each enhancer-promoter pair within the limited defined genomic distance range was calculated and then all of these matrices were summed to obtain the observed aggregated matrix. To get the obs the sequencing depth-normalized aggregated matrices were divided by the control matrix. Shown are also the depth-normalized aggregated matrices for the DMSO control, TRP- and FLV-treated mESCs, as well as the obs matrices for both treatments. (b) To calculate the 1D signal background matrices we calculated the average of 1D Micro-C signal vectors across cells around enhancers and promoters. Line plot representations of these vectors at 20 kb windows around enhancers and promoters, across cells of 200 bp are shown for both treatment conditions and DMSO control. The 1D background change matrix, B, was calculated by dividing the 1D signal background matrix of each treatment by the control. The 1D background matrices for both treatments and control samples as well as the matrices B for both treatments in mESCs are shown.

Extended Data Fig. 6 Genomic distance has little effect on the shape of enhancer-promoter contacts fold change.

Matrices showing the observed fold-change (a) the 1D background signal fold change (b) and the 1D background-normalized enhancer-promoter fold change (c) following TRP and FLV treatment compared to the DMSO control at 25kb-wide distance ranges starting at 25–50 kb (leftmost column) and ending at 125–150 kb (rightmost column). (d) Matrix showing the 1D background-normalized fold change of contacts between CTCF bound motifs following TRP and FLV treatments, compared with the DMSO control.

Extended Data Fig. 7 Changes in enhancer-promoter contacts at the Pou5f1 locus following transcriptional inhibition.

Virtual 4 C signal showing Micro-C signal associated with Pou5f1 promoter from a ~ 1.3 billion contacts library of untreated mESC, as well as the FLV and TRP treated mESCs (~400 million contacts each). Shown are also GRO-seq and ATAC-seq signals. Two regulatory elements shown to induce Pou5f1 gene expression55 are shown in green and the relative contacts between these regulatory elements and the Pou5f1 promoter, relative to the untreated control, in each treatment are shown in the associated bar plots. The position of the anchor for the virtual 4 C is shown.

Extended Data Fig. 8 Distribution of fold change in gene body transcription for K562 and Jurkat upregulated genes.

(a) Scatterplots where each dot represents a single enhancer-promoter pair where the promoter was associated with higher gene body transcription (top, n = 4,071) and pausing signal (bottom, n = 502) at Jurkat T cells compared to K562. The dots are colored based on the density of dots relative to their coordinates. The associated boxplots show the distribution of enhancer-promoter contacts relative to local background in both cell types, relative to the median ratio in K562. (*** Two-sided Wilcoxon signed-rank test p-value < 1 × 10−100). (b) Boxplots showing the distributions of fold change in gene body signal in genes with no associated paused Pol II change (NPC, K562 > Jurkat: n = 173, Jurkat > K562: n = 64) and associated significant paused Pol II change (PC, K562 > Jurkat: n = 167, Jurkat > K562: n = 43) (‘ns’ - Two-sided Mann-Whitney p-value > 0.5). (c) Boxplot depicting the relative increase of enhancer-promoter contacts associated with promoters of genes with upregulated gene body transcription in Jurkat T-cells with a corresponding significant increase in pausing signal (pause change—PC, n = 43) and without a change in pausing signal (no pause change—NPC, n = 64) (** Two-sided Mann-Whitney p-value < 1 × 10−10).

Extended Data Fig. 9 Changes in enhance-promoter contacts architecture following NELFB depletion.

(a) APA heatmaps of the 1D change-normalized contact change (log2) between enhancer and promoter regions at 20 kb around TSSs. Pixel size is 200 bp square. The APA heatmaps are oriented such that the gene TSS points to the right and the dominant TSS of the enhancer points upwards. (b) Line plot of the median fold changes at the dot (blue), stripes (red) and edges (gray) relative to T = 0 at the different time points of dTAG treatments and following dTAG washout.

Extended Data Fig. 10 Changes in ZRS-Shh contacts following NELFB depletion.

Micro-C contact maps in 10 kb resolution along with the associated virtual 4 C signal and PRO-seq signal in mESCs not treated (top) or treated (bottom) with the dTAG ligand for 30 minutes to degrade NELFB. The positions of the ZRS enhancer and the Shh promoter are indicated in red rectangles.

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Barshad, G., Lewis, J.J., Chivu, A.G. et al. RNA polymerase II dynamics shape enhancer–promoter interactions. Nat Genet 55, 1370–1380 (2023). https://doi.org/10.1038/s41588-023-01442-7

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