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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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/.
Murakawa, Y. et al. Enhanced identification of transcriptional enhancers provides mechanistic insights into diseases. Trends Genet. 32, 76–88 (2016).
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).
ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–329 (2015).
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).
Beagrie, R. A. et al. Complex multi-enhancer contacts captured by genome architecture mapping. Nature 543, 519–524 (2017).
Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012).
Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).
Core, L. J., Waterfall, J. J. & Lis, J. T. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322, 1845–1848 (2008).
Churchman, L. S. & Weissman, J. S. Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469, 368–373 (2011).
Core, L. J. et al. Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet. 46, 1311–1320 (2014).
Seila, A. C. et al. Divergent transcription from active promoters. Science 322, 1849–1851 (2008).
Schwalb, B. et al. TT-Seq maps the human transient transcriptome. Science 352, 1225–1228 (2016).
Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).
Tome, J. M., Tippens, N. D. & Lis, J. T. Single-molecule nascent RNA sequencing identifies regulatory domain architecture at promoters and enhancers. Nat. Genet. 50, 1533–1541 (2018).
Chu, T. et al. Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme. Nat. Genet. 50, 1553–1564 (2018).
Kim, T.-K. et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182–187 (2010).
Lam, M. T. Y., Li, W., Rosenfeld, M. G. & Glass, C. K. Enhancer RNAs and regulated transcriptional programs. Trends Biochem. Sci. 39, 170–182 (2014).
Michel, M. et al. TT-Seq captures enhancer landscapes immediately after T-cell stimulation. Mol. Syst. Biol. 13, 920 (2017).
Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
Forrest, A. R. R. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
Arner, E. et al. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science 347, 1010–1014 (2015).
Kanamori-Katayama, M. et al. Unamplified cap analysis of gene expression on a single molecule sequencer. Genome Res. 21, 1150–1159 (2011).
Adiconis, X. et al. Comprehensive comparative analysis of 5′-end RNA-sequencing methods. Nat. Methods 15, 505–511 (2018).
Murata, M. et al. Detecting expressed genes using CAGE. Methods Mol. Biol. 1164, 67–85 (2014).
Rabani, M. et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat. Biotechnol. 29, 436–442 (2011).
Kilchert, C., Wittmann, S. & Vasiljeva, L. The regulation and functions of the nuclear RNA exosome complex. Nat. Rev. Mol. Cell Biol. 17, 227–239 (2016).
Mayer, A. et al. Native elongating transcript sequencing reveals human transcriptional activity at nucleotide resolution. Cell 161, 541–554 (2015).
Kwak, H., Fuda, N. J., Core, L. J. & Lis, J. T. Precise maps of RNA polymerase reveal how promoters direct initiation and pausing. Science 339, 950–953 (2013).
Nojima, T. et al. Mammalian NET-Seq reveals genome-wide nascent transcription coupled to RNA processing. Cell 161, 526–540 (2015).
Henriques, T. et al. Widespread transcriptional pausing and elongation control at enhancers. Genes Dev. 32, 26–41 (2018).
Mayer, A. & Churchman, L. S. Genome-wide profiling of RNA polymerase transcription at nucleotide resolution in human cells with native elongating transcript sequencing. Nat. Protoc. 11, 813–833 (2016).
Bhatt, D. M. et al. Transcript dynamics of proinflammatory genes revealed by sequence analysis of subcellular RNA fractions. Cell 150, 279–290 (2012).
Carninci, P. et al. High-efficiency full-length cDNA cloning by biotinylated CAP trapper. Genomics 37, 327–336 (1996).
Yamazaki, S., Muta, T., Matsuo, S. & Takeshige, K. Stimulus-specific induction of a novel nuclear factor-κB regulator, IκB-ζ, via Toll/interleukin-1 receptor is mediated by mRNA stabilization. J. Biol. Chem. 280, 1678–1687 (2005).
Schueler, M. et al. Differential protein occupancy profiling of the mRNA transcriptome. Genome Biol. 15, R15 (2014).
Hon, C.-C. et al. An atlas of human long non-coding RNAs with accurate 5′ ends. Nature 543, 199–204 (2017).
Carninci, P. et al. The transcriptional landscape of the mammalian genome. Science 309, 1559–1563 (2005).
Lagarde, J. et al. High-throughput annotation of full-length long noncoding RNAs with capture long-read sequencing. Nat. Genet. 49, 1731–1740 (2017).
Mina, M. et al. Promoter-level expression clustering identifies time development of transcriptional regulatory cascades initiated by ERBB receptors in breast cancer cells. Sci. Rep. 5, 11999 (2015).
Jin, Y., Eser, U., Struhl, K. & Churchman, L. S. The ground state and evolution of promoter region directionality. Cell 170, 889–898 (2017).
Scruggs, B. S. et al. Bidirectional transcription arises from two distinct hubs of transcription factor binding and active chromatin. Mol. Cell 58, 1101–1112 (2015).
Chen, K. et al. Broad H3K4me3 is associated with increased transcription elongation and enhancer activity at tumor-suppressor genes. Nat. Genet. 47, 1149–1157 (2015).
Pekowska, A. et al. H3K4 tri-methylation provides an epigenetic signature of active enhancers. EMBO J. 30, 4198–4210 (2011).
Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).
Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).
Khan, A. & Zhang, X. dbSUPER: a database of super-enhancers in mouse and human genome. Nucleic Acids Res. 44, D164–D171 (2016).
Hah, N. et al. Inflammation-sensitive super enhancers form domains of coordinately regulated enhancer RNAs. Proc. Natl Acad. Sci. USA 112, E297–E302 (2015).
Tang, Z. et al. CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell 163, 1611–1627 (2015).
Ing-Simmons, E. et al. Spatial enhancer clustering and regulation of enhancer-proximal genes by cohesin. Genome Res. 25, 504–513 (2015).
Short, P. J. et al. De novo mutations in regulatory elements in neurodevelopmental disorders. Nature 555, 611–616 (2018).
Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Zhang, W. et al. A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat. Genet. 50, 613–620 (2018).
Blinka, S. et al. Identification of transcribed enhancers by genome-wide chromatin immunoprecipitation sequencing. Methods Mol. Biol. 1468, 91–109 (2017).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Rackham, O. J. L. et al. A predictive computational framework for direct reprogramming between human cell types. Nat. Genet. 48, 331–335 (2016).
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).
Cao, Q. et al. Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines. Nat. Genet. 49, 1428–1436 (2017).
Chen, H. et al. A pan-cancer analysis of enhancer expression in nearly 9000 patient samples. Cell 173, 386–399 (2018).
Danko, C. G. et al. Identification of active transcriptional regulatory elements from GRO-Seq data. Nat. Methods 12, 433–438 (2015).
Wang, Z., Chu, T., Choate, L. A. & Danko, C. G. Identification of regulatory elements from nascent transcription using dREG. Genome Res. 29, 293–303 (2019).
Panigrahi, A. K. et al. SRC-3 coactivator governs dynamic estrogen-induced chromatin looping interactions during transcription. Mol. Cell 70, 679–694 (2018).
Zhang, Y. et al. Chromatin connectivity maps reveal dynamic promoter–enhancer long-range associations. Nature 504, 306–310 (2013).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Hasegawa, A., Daub, C., Carninci, P., Hayashizaki, Y. & Lassmann, T. MOIRAI: a compact workflow system for CAGE analysis. BMC Bioinformatics 15, 144 (2014).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-Seq aligner. Bioinformatics 29, 15–21 (2013).
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Mudge, J. M. & Harrow, J. Creating reference gene annotation for the mouse C57BL6/J genome assembly. Mamm. Genome 26, 366–378 (2015).
Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-Seq experiments. Bioinformatics 28, 2184–2185 (2012).
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).
Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32, D493–D496 (2004).
Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Kryuchkova-Mostacci, N. & Robinson-Rechavi, M. A benchmark of gene expression tissue-specificity metrics. Brief. Bioinform. 18, 205–214 (2017).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).
Warnes, G. R. et al. gplots Various R programming tools for plotting data. R package version 2.17.0 (2016).
Pohlert, T. trend: Non-parametric trend tests and change-point detection. R package version 1.1.0 (2018).
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.).
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.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
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.
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.
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.
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. d−f, 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 Figs. 1–8
Gene-level degradation index.
Promoter-level degradation index.
Transcriptional specificity for promoters.
Transcriptional specificity for enhancers.
Summary for high-throughput sequencing data.
Transcriptional levels for human enhancers.
Transcriptional levels for mouse enhancers.
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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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