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NET-CAGE characterizes the dynamics and topology of human transcribed cis-regulatory elements

Abstract

Promoters and enhancers are key cis-regulatory elements, but how they operate to generate cell type-specific transcriptomes is not fully understood. We developed a simple and robust method, native elongating transcript–cap analysis of gene expression (NET-CAGE), to sensitively detect 5′ ends of nascent RNAs in diverse cells and tissues, including unstable transcripts such as enhancer-derived RNAs. We studied RNA synthesis and degradation at the transcription start site level, characterizing the impact of differential promoter usage on transcript stability. We quantified transcription from cis-regulatory elements without the influence of RNA turnover, and show that enhancer–promoter pairs are generally activated simultaneously on stimulation. By integrating NET-CAGE data with chromatin interaction maps, we show that cis-regulatory elements are topologically connected according to their cell type specificity. We identified new enhancers with high sensitivity, and delineated primary locations of transcription within super-enhancers. Our NET-CAGE dataset derived from human and mouse cells expands the FANTOM5 atlas of transcribed enhancers, with broad applicability to biomedical research.

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Fig. 1: Development of NET-CAGE.
Fig. 2: NET-CAGE is applicable to cryopreserved cells and tissues.
Fig. 3: NET-CAGE can be used to study transcript stability at the promoter level.
Fig. 4: NET-CAGE shows accurate transcriptional dynamics of promoters and enhancers.
Fig. 5: De novo identification of transcribed enhancers and super-enhancers in MCF-7 cells.
Fig. 6: Connectivity of cis-regulatory elements identified by integrated analysis of NET-CAGE and ChIA-PET data.
Fig. 7: Expanding the atlas of transcribed enhancers: FANTOM-NET enhancers.

Data availability

All datasets generated in this study are summarized in Supplementary Table 5. Raw and processed data are available from the Gene Expression Omnibus under accession GSE118075. The enhancer data are also accessible from the FANTOM website: http://fantom.gsc.riken.jp/5/suppl/Hirabayashi_et_al_2019/.

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Acknowledgements

We are grateful to all of the members of the RIKEN Genome Network Analaysis Support Facility and the K.K. DNAFORM genetic analysis department for library preparation, sequencing and primary data processing. We thank E. Arner, R. Andersson, K. Vitting-Seerup and A. Sandelin for helpful discussions. We thank I. Yamaguchi, K. Goto, M. Furuno and T. Kasukawa for assistance. We also thank M. Okada-Hatakeyama for guidance on performing the time-course experiment using MCF-7 cells. This work was supported by: JSPS Grants-in-Aid for Scientific Research (KAKENHI) (16H06153 and 18H03992) and grants from the Kanae Foundation for the Promotion of Medical Science, Ono Medical Research Foundation, Takeda Science Foundation, Japan Foundation for Applied Enzymology and Mochida Memorial Foundation for Medical and Pharmaceutical Research (to Y. Murakawa); JSPS Grants-in-Aid for Scientific Research (KAKENHI) (16H02902) (to H.K.); AMED under grant number 18ek0109282h0002 (to Y.H.); the RIKEN Junior Research Associate Program (to S.H.); the International Program Associate program and Karolinska Institutet (to S.B.); Invitational Fellowships for Research in Japan (F1606103) (to J.K. and P.C.); the Knut and Alice Wallenberg Foundation (Sweden) and The Royal Society Wolfson Research Merit Award (UK) (to J.K.).

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Authors

Contributions

S.H., S.B., Y.Matsuki, H.K. and Y.Murakawa conceived and designed the study. Y. Matsuki, S.H. and A.K. performed the experiments under the supervision of Y. Murakawa, and with input from Y.T., M.I., K.S. and A.T.-K. T.U. and O.T. performed the experimental validation. S.B. and S.H. performed the bioinformatic data analysis under the supervision of S.K., J.K., Y.Murakawa and H.K. S.H., S.B., S.K., J.K., H.K. and Y.Murakawa interpreted the results. S.H. and S.B. made the figures, with input from S.K., J.K., H.K. and Y.Murakawa. S.B., S.H., H.K. and Y. Murakawa wrote the manuscript, with input from Y.Matsuki, Y.T., O.T., P.C., S.K., Y.H. and J.K.

Corresponding authors

Correspondence to Hideya Kawaji or Yasuhiro Murakawa.

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Competing interests

Y. Matsuki, Y.T. and A.K. are employees of K.K. DNAFORM. Y. Murakawa received grant funding from K.K. DNAFORM. Japan patent number WO2017130750A1 has been awarded to K.K. DNAFORM (Y. Murakawa and Y.T.; inventors) for the NET-CAGE technology described in this paper.

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Integrated supplementary information

Supplementary Figure 1 Experimental optimization of the NET-CAGE protocol.

a, Western blot analysis of GAPDH (cytoplasmic marker), SNRNP70 (nucleoplasmic marker), Histone H3 (chromatin marker) and PolII in the cytoplasmic, nuclear soluble, and nuclear insoluble fractions. The nuclei were treated with urea lysis buffer containing 2 M urea to isolate the nuclear soluble and insoluble fractions. SNRNP70 was enriched in the nuclear soluble fraction, whereas Histone H3 and PolII were enriched in the nuclear insoluble fraction, indicating successful subcellular fractionation. The western blot is a representative of three independent experiments. Cropped gel images are shown. See also Supplementary Fig. 8. b, Scatter plots comparing 2 CAGE and 14 NET-CAGE biologically independent samples. NET-CAGE samples were treated with urea lysis buffer containing different concentrations of urea (0.5–8 M). Log2 CPM values for 59,915 FANTOM5 promoters are plotted. Pearson’s correlation coefficients are shown above the diagonal. c, Percentage of reads mapped to FANTOM5 promoters and enhancers in two biologically independent samples of CAGE and two of NET-CAGE in MCF-7 cells. d, Scatter plots comparing 2 biologically independent samples of fresh MCF-7 cells and 6 biologically independent samples of frozen MCF-7 cells. Transcription levels determined in NET-CAGE for 57,435 FANTOM5 promoters are plotted. Pearson’s correlation coefficients are shown above the diagonal. F, fresh; P, flash-frozen pellet; D, cryopreserved with 10% dimethyl sulfoxide; C, cryopreserved with CELLBANKER 1 plus; 1, replicate 1; 2, replicate 2. e, Percentages of mapped reads for nascent and total RNA-seq in mouse kidney and brain tissues.

Supplementary Figure 2 Reproducibility of methods for estimation of RNA degradation rate.

a, Reproducibility between degradation indexes calculated as log2 NET-CAGE/CAGE ratios in two MCF-7 biologically independent samples. Promoter-level data were summarized into gene-level data and each dot represents a gene. b, Reproducibility between log2 half-lives measured by 4sU-seq36 in two MCF-7 biologically independent samples. c,d, Scatter plots comparing degradation indexes at (c) major and (d) minor promoters (biologically independent samples, CAGE: n = 10, NET-CAGE: n = 10). Pearson’s correlation coefficients are shown above the diagonal. Promoters with 0 CPM in any library and promoters with average expression < 0 log2 CPM across all samples were filtered out. cor, Pearson’s correlation.

Supplementary Figure 3 ZNHIT1 transcripts.

a, UCSC Genome Browser view of the ZNHIT1 gene and two ZNHIT1 mRNA isoforms (with short and long 5’ UTRs) and their expression profiles across five ENCODE cell lines. NET-CAGE and CAGE data for the major and minor promoters are shown. b, UCSC Genome Browser view of full-length transcripts in HeLa cells. The transcript models were determined by a combination of Pacific Biosciences third-generation long-read sequencing, Illumina HiSeq short-read sequencing, and CAGE. The data were obtained from GEO: GSE9384839 and converted from hg38 to hg19 using the UCSC LiftOver tool (https://genome.ucsc.edu/cgi-bin/hgLiftOver) (Speir, M. L. et al., Nucleic Acids Res. 44, D717–25 2016).

Supplementary Figure 4 Transcriptional dynamics of stable and unstable RNAs during cellular activation.

a, The time courses of activation of six genes implicated in the heregulin beta-1 signaling pathway in MCF-7 cells. Log2 CPM values for CAGE are plotted. Error bars, standard deviation (n = 3 biologically independent samples). b, Comparison of the time lag and degradation indexes of 404 promoters (q < 10−8) upregulated during the time course. Time lag = CAGE peak time point – NET-CAGE peak time point; degradation indexes are log2 NET-CAGE/CAGE ratios at time point 0. Boxplot shows the 25th, median and 75th percentiles. Whiskers, 1.5× interquartile range. c, Comparison of activation patterns between CAGE and NET-CAGE for the EGR1 promoter (upper panel) and EGR1 enhancer (lower panel). Delta log2 CPM = signal at each time point – signal at time point 0. Error bars, standard deviation (n = 3 biologically independent samples). d, Cumulative distribution function of directionality scores for uaRNAs calculated in CAGE and NET-CAGE experiments. e, f, Comparison of (e) uaRNA and (f) convRNA levels detected using CAGE and NET-CAGE in MCF-7 cells. g, Heat map showing three hierarchical clusters with distinct temporal patterns of uaRNA transcription levels in NET-CAGE data. Each row of the heat map represents a promoter and each column represents a time point. Scale bar, Z score. Line graphs on the right show average profiles for uaRNAs and mRNAs in each cluster. Cluster 1, downregulation of uaRNAs; Cluster 2, simultaneous synthesis of uaRNAs and mRNAs; Cluster 3, earlier synthesis of uaRNAs than mRNAs. The size of each cluster is indicated in parentheses. h, Analysis similar to that in (g) but using convRNAs.

Supplementary Figure 5 De novo identification of enhancers with higher sensitivity in NET-CAGE than in CAGE.

a, Scheme of bidirectional enhancer identification. DPI, decomposition peak identification; TPM, tags per million; TSS, transcription start site; F, forward; R, reverse. b, Enhancers identified de novo were classified into three categories: identified only in CAGE, identified in both CAGE and NET-CAGE (common), and identified only in NET-CAGE. Bar plots are shown for enhancers with (i) no threshold (top), (ii) transcription levels of at least 0.5 TPM in ≥ 1 sample (middle), and (iii) transcription levels of at least 0.5 TPM in ≥ 2 samples (bottom). Percentages of enhancers overlapping with DNase I hypersensitive sites (DHS) are indicated. c, UCSC Genome Browser view of a representative enhancer identified by de novo enhancer call in NET-CAGE but not in CAGE. Reads shown in red represent the plus strand, while those shown in blue represent the minus strand. d, Heat maps of enhancer regions (rows) for the three categories defined in (b). Each row of the heat maps shows either the average TSS signal (CAGE and NET-CAGE) or DHS, which were calculated in 5-bp windows. Heat maps were aligned at the enhancer midpoint extended to ±500 bp and ordered by ascending length of enhancer regions. e, Heat maps of TSS and H3K27ac, H3K4me1 and H3K4me3 histone modifications for enhancers identified in both CAGE and NET-CAGE (rows). Each category was further divided into four quartiles (Q1−Q4) on the basis of enhancer transcription levels (log2 TPM). Each row of the heat maps shows average transcription signal calculated in 5-bp windows or histone modification signal calculated in 50-bp windows. Heat maps are arranged by descending order of enhancer transcription and are centered at the enhancer midpoint extended to ±2 kb.

Supplementary Figure 6 Comparison of active promoters and enhancers across five ENCODE cell lines.

a,b, Scatter plots of transcription levels across five cell lines for 69,616 FANTOM5 promoters (a) and 10,737 NET-CAGE enhancers (b). c, UCSC Genome Browser view of a ubiquitously transcribed enhancer. The NET-CAGE profile shows the aggregated transcriptional signal from 30 biologically independent samples across five ENCODE cell lines and also symmetric enhancer RNA transcription from the edges of the open chromatin region in forward (red) and reverse (blue) directions. Profiles for H3K27ac and H3K4me1 ChIP-seq data and DNase-seq data for the five ENCODE cell lines are also shown. DNase I hypersensitivity clusters in 125 cell types from ENCODE (V3) are shown at the bottom. The numbers of cell types in which chromatin was open are also shown. df, Metagene plots of 530 ubiquitously transcribed enhancers (cell type specificity score ≤ 0.1) and 7,636 random CpG regions. The y axes show average signals calculated in 5-bp windows for (d) EP300, (e) MED1, and (f) H3K27ac. Plots are aligned to the midpoint of ubiquitous enhancers and CpG regions and are extended to ±2.0 kb.

Supplementary Figure 7 Connectivity of cis-regulatory elements according to their cell type specificity.

a, Boxplot showing cell type specificity scores of enhancers and their target promoters (from left to right, n = 3807, 5521, 5850, 3757 and 1654) in MCF-7 cells. Enhancer−promoter pairs were retrieved from RNAPII ChIA-PET data for MCF-7 cells7. b, Analysis similar to that in (a) for interacting enhancers (from left to right, n = 272, 494, 623, 424 and 201). In (a) and (b), outliers were removed, and boxplots show the 25th, median, and 75th percentiles. Whiskers, 1.5× interquartile range. Notches, 95% confidence intervals for the median.

Supplementary Figure 8

Full scans of western blots presented in Supplementary Figure 1.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8

Reporting Summary

Supplementary Table 1

Gene-level degradation index.

Supplementary Table 2

Promoter-level degradation index.

Supplementary Table 3

Transcriptional specificity for promoters.

Supplementary Table 4

Transcriptional specificity for enhancers.

Supplementary Table 5

Summary for high-throughput sequencing data.

Supplementary Table 6

Transcriptional levels for human enhancers.

Supplementary Table 7

Transcriptional levels for mouse enhancers.

Supplementary Data 1

Human_FANTOM-NET_enhancers.bed.

Supplementary Data 2

Mouse_FANTOM-NET_enhancers.bed.

Supplementary Data 3

Time-course_NET-CAGE_for_promoters.xlsx.

Supplementary Data 4

Time-course_NET-CAGE_for_enhancers.xlsx.

Supplementary Data 5

Time-course_NET-CAGE_for_uaRNAs.xlsx.

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Hirabayashi, S., Bhagat, S., Matsuki, Y. et al. NET-CAGE characterizes the dynamics and topology of human transcribed cis-regulatory elements. Nat Genet 51, 1369–1379 (2019). https://doi.org/10.1038/s41588-019-0485-9

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