Senescent cells affect many physiological and pathophysiological processes. While select genetic and epigenetic elements for senescence induction have been identified, the dynamics, epigenetic mechanisms and regulatory networks defining senescence competence, induction and maintenance remain poorly understood, precluding the deliberate therapeutic targeting of senescence for health benefits. Here, we examined the possibility that the epigenetic state of enhancers determines senescent cell fate. We explored this by generating time-resolved transcriptomes and epigenome profiles during oncogenic RAS-induced senescence and validating central findings in different cell biology and disease models of senescence. Through integrative analysis and functional validation, we reveal links between enhancer chromatin, transcription factor recruitment and senescence competence. We demonstrate that activator protein 1 (AP-1) ‘pioneers’ the senescence enhancer landscape and defines the organizational principles of the transcription factor network that drives the transcriptional programme of senescent cells. Together, our findings enabled us to manipulate the senescence phenotype with potential therapeutic implications.
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All transcriptome data are hosted on the GEO site (GSE144397). ATAC-seq and ChIP-seq data (histone modification and transcription factor) are hosted on the Sequence Read Archive (BioProject no. PRJNA439280). Previously published data that were reanalysed here are available under accession codes GSE134751, GSE134753, GSE31099, GSE31312 and GSE98588. Source data for Figs. 3 and 8 and Extended Data Figs. 1, 4, 7 and 10 are presented with the paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.
Interactive maps, circos plots, workflows, scripts and software developed to preprocess raw data, perform statistical analyses as well as data mining and integration are available as .html and R Markdown files provided in Supplementary Data 1 and hosted on Zenodo (https://zenodo.org/record/3731264#.Xn4RXm5CcXo). This archive collapses all the material (including processed data) required to reproduce figures presented in the manuscript.
Martinez-Zamudio, R. I., Robinson, L., Roux, P. F. & Bischof, O. SnapShot: cellular senescence in pathophysiology. Cell 170, e1041 (2017).
Martinez-Zamudio, R. I., Robinson, L., Roux, P. F. & Bischof, O. SnapShot: cellular senescence pathways. Cell 170, e811 (2017).
Coppe, J. P., Desprez, P. Y., Krtolica, A. & Campisi, J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu. Rev. Pathol. 5, 99–118 (2010).
Schosserer, M., Grillari, J. & Breitenbach, M. The dual role of cellular senescence in developing tumors and their response to cancer therapy. Front. Oncol. 7, 278 (2017).
Milanovic, M. et al. Senescence-associated reprogramming promotes cancer stemness. Nature 553, 96–100 (2018).
Benhamed, M., Herbig, U., Ye, T., Dejean, A. & Bischof, O. Senescence is an endogenous trigger for microRNA-directed transcriptional gene silencing in human cells. Nat. Cell Biol. 14, 266–275 (2012).
Puvvula, P. K. et al. Long noncoding RNA PANDA and scaffold-attachment-factor SAFA control senescence entry and exit. Nat. Commun. 5, 5323 (2014).
Rai, T. S. et al. HIRA orchestrates a dynamic chromatin landscape in senescence and is required for suppression of neoplasia. Genes Dev. 28, 2712–2725 (2014).
Tasdemir, N. et al. BRD4 connects enhancer remodeling to senescence immune surveillance. Cancer Discov. 6, 612–629 (2016).
Sen, P. et al. Histone acetyltransferase p300 induces de novo super-enhancers to drive cellular senescence. Mol. Cell 73, e688 (2019).
Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).
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).
Ostuni, R. et al. Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171 (2013).
van Oevelen, C. et al. C/EBPα activates pre-existing and de novo macrophage enhancers during induced pre-B cell transdifferentiation and myelopoiesis. Stem Cell Rep. 5, 232–247 (2015).
Huggins, C. J. et al. C/EBPγ suppresses senescence and inflammatory gene expression by heterodimerizing with C/EBPβ. Mol. Cell Biol. 33, 3242–3258 (2013).
Soufi, A. et al. Pioneer transcription factors target partial DNA motifs on nucleosomes to initiate reprogramming. Cell 161, 555–568 (2015).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Loffler-Wirth, H., Kalcher, M. & Binder, H. oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor. Bioinformatics 31, 3225–3227 (2015).
Martinez, O. & Reyes-Valdes, M. H. Defining diversity, specialization, and gene specificity in transcriptomes through information theory. Proc. Natl Acad. Sci. USA 105, 9709–9714 (2008).
Sherwood, R. I. et al. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat. Biotechnol. 32, 171–178 (2014).
Thakore, P. I. et al. Highly specific epigenome editing by CRISPR–Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12, 1143–1149 (2015).
Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Kaikkonen, M. U. et al. Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol. Cell 51, 310–325 (2013).
Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).
Guo, Y. & Gifford, D. K. Modular combinatorial binding among human trans-acting factors reveals direct and indirect factor binding. BMC Genomics 18, 45 (2017).
Ren, X. & Kerppola, T. K. REST interacts with Cbx proteins and regulates polycomb repressive complex 1 occupancy at RE1 elements. Mol. Cell Biol. 31, 2100–2110 (2011).
Li, T. et al. CTCF regulates allelic expression of Igf2 by orchestrating a promoter-polycomb repressive complex 2 intrachromosomal loop. Mol. Cell Biol. 28, 6473–6482 (2008).
Weinmann, A. S., Bartley, S. M., Zhang, T., Zhang, M. Q. & Farnham, P. J. Use of chromatin immunoprecipitation to clone novel E2F target promoters. Mol. Cell Biol. 21, 6820–6832 (2001).
Garber, M. et al. A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol. Cell 47, 810–822 (2012).
Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011).
Drouin, J. Minireview: pioneer transcription factors in cell fate specification. Mol. Endocrinol. 28, 989–998 (2014).
Hoare, M. et al. NOTCH1 mediates a switch between two distinct secretomes during senescence. Nat. Cell Biol. 18, 979–992 (2016).
Nateri, A. S., Spencer-Dene, B. & Behrens, A. Interaction of phosphorylated c-Jun with TCF4 regulates intestinal cancer development. Nature 437, 281–285 (2005).
Angel, P., Hattori, K., Smeal, T. & Karin, M. The jun proto-oncogene is positively autoregulated by its product, Jun/AP-1. Cell 55, 875–885 (1988).
Weitzman, J. B., Fiette, L., Matsuo, K. & Yaniv, M. JunD protects cells from p53-dependent senescence and apoptosis. Mol. Cell 6, 1109–1119 (2000).
Tsankov, A. M. et al. Transcription factor binding dynamics during human ES cell differentiation. Nature 518, 344–349 (2015).
Goode, D. K. et al. Dynamic gene regulatory networks drive hematopoietic specification and differentiation. Dev. Cell 36, 572–587 (2016).
Xu, M. et al. Senolytics improve physical function and increase lifespan in old age. Nat. Med 24, 1246–1256 (2018).
Overman, J. et al. Pharmacological targeting of the transcription factor SOX18 delays breast cancer in mice. eLife 6, e21221 (2017).
Itahana, K., Campisi, J. & Dimri, G. P. Methods to detect biomarkers of cellular senescence: the senescence-associated beta-galactosidase assay. Methods Mol. Biol. 371, 21–31 (2007).
Georgilis, A. et al. PTBP1-mediated alternative splicing regulates the inflammatory secretome and the pro-tumorigenic effects of senescent cells. Cancer Cell 34, 85–102.e9 (2018).
Reimann, M. et al. Tumor stroma-derived TGF-β limits Myc-driven lymphomagenesis via Suv39h1-dependent senescence. Cancer Cell 17, 262–272 (2010).
Dorr, J. R. et al. Synthetic lethal metabolic targeting of cellular senescence in cancer therapy. Nature 501, 421–425 (2013).
Nateri, A. S., Riera-Sans, L., Da Costa, C. & Behrens, A. The ubiquitin ligase SCFFbw7 antagonizes apoptotic JNK signaling. Science 303, 1374–1378 (2004).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Chapuy, B. et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 24, 679–690 (2018).
Visco, C. et al. Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: a report from the International DLBCL Rituximab-CHOP Consortium Program Study. Leukemia 26, 2103–2113 (2012).
Monti, S. et al. Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood 105, 1851–1861 (2005).
We thank all members, in particular N. Rozenblum, of O.B.’s laboratory for fruitful discussions and suggestions through the course of this work. We would like to thank the Transcriptome and Epigenome facility of Institut Pasteur. We thank C. Chica for expert advice on ChIP-seq data processing. We thank I. Amit and D. Winter for valuable discussion and technical support. We thank B. Schwikowski for key insights and technical advice. We also thank L. Zender, E. Gilson and H. Gronemeyer for valuable intellectual input. R.I.M.-Z. was supported by La Ligue Nationale Contre le Cancer and is a Mexican National Scientific and Technology Council (CONACYT) and Mexican National Researchers System (SNI) fellow. L.R. was supported by the Pasteur–Paris University (PPU) International Ph.D. Program and by the Fondation pour la Recherche Médicale (FRM). J.A.N.L.F.d.F. was supported by La Ligue Nationale Contre le Cancer. J.G. was supported by the Medical Research Council (MRC; MC_U120085810) and by a grant from Worldwide Cancer Research (WCR; 18-0215). O.B. was supported by the Pasteur–Weizmann Foundation, ANR–BMFT, the Fondation ARC pour la recherche sur le Cancer, La Ligue Nationale Contre le Cancer and INSERM–AGEMED. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number R01CA136533. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. O.B. is a CNRS Research Director DR2.
J.G. owns equity and has acted as a consultant for Unity Biotechnology and Geras Bio. Unity Biotechnology funded research on senolytics in J.G.’s laboratory. J.G. is a named inventor in an MRC patent related to senolytic therapies (PCT/GB2018/051437). All of these links are not directly related to the results presented in this paper.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Multi-state establishment of the senescence transcriptional program.
a-b, Representative DAPI, EdU, SABG indirect fluorescence and phase contrast (from left to right) microscopy images of WI-38 fibroblasts undergoing RAS-OIS or quiescence at indicated time-points. Insets, mean percentage of SABG positive cells ± SD and proliferative capacity expressed as percent EdU-positive staining cells ± SD (biologically independent time-series). Scale bar, 100 µm. c-l, Growth and EdU incorporation curves for RAS-OIS (c-d), RAF-OIS (e-f), RS (g-h) in W38-, and RAS-OIS in GM21 skin fibroblasts (i-j) and quiescence in W38 fibroblasts (k-l) at indicated time-points (c-h, k-l; data shown represent average from 2 biologically independent experiments). For i-j, average + /- s.e.m. of n = 3 biologically independent experiments. m-q, RT-qPCR profiles for select target genes for each condition as in (c-l) (the experiment has been performed once). Statistical source data are presented in Source Data Extended Data Fig. 1.
Extended Data Fig. 2 Multi-state establishment of the senescence transcriptional program.
a, Scatter plot depicting evolution of transcriptome diversity (Hj) vs. transcriptome specialization (σj) in cells undergoing Q or RAS-OIS in WI-38 fibroblasts. For each time-point and treatment, average Hj and σj values are given. T0 is start of time-course. b, Violin plots depicting gene expression profiles for each of RAS-OIS transcriptomic modules in WI-38 fibroblasts. Note the sharp transitions of modules I and V. Data are expressed as row Z-score. Data shown in (a) and (b) are from 2 biologically independent experiments. Single gene expression values are over-plotted. c-d, Heatmap of temporally co-expressed differentially regulated genes and associated functional overrepresentation of MSigDB hallmark gene sets for RAS-OIS in GM21 skin fibroblasts. Data shown represent 3 biologically independent experiments (c). N > 200 genes per transcriptomic module (d).
Extended Data Fig. 3 A dynamic enhancer program shapes the senescence transcriptome.
a, Genome Percentage covered by each chromatin state at indicated time-point. Bottom table assigns histone modification combinations (grey: presence, white: absence) to biologically meaningful mnemonics. Venn diagrams highlight specificities and overlaps in chromatin states associated with active (left) and poised enhancers (right) at indicated time points. Histogram depicts average state coverage across biological replicates collected from 2 biologically independent experiments. b-c, Arc plots visualizing dynamic chromatin state transitions for RAF-OIS and replicative senescence at indicated intervals. Edge width is proportional to number of transitions. Each arc plot depicts average transition landscape. d-i, Enriched sequence motifs in active enhancers for RAS-OIS in WI-38 fibroblasts (d) and (e-i) ATAC-seq peaks for RAS-OIS (WI-38) (e), RAF-OIS (WI-38) (f), RS (WI-38) (g), RAS-OIS (GM21 skin fibroblasts) (h) and quiescence (WI-38) (i) time-courses. Motif logos are shown. Black, dotted boxes highlight core AP-1 TF-motif. Note that transcriptional repressor BACH shares AP1-motif. Data in (b-i) are collected from 2 biologically independent experiments. j, ATAC-seq (grey lines-forward, black lines-reverse reads) and nucleosome density (red line) for AP-1 FOSL1 (pioneer), RELA (settler), and SREBF1 (migrant) in WI-38 fibroblasts undergoing RAS-OIS. Average footprints and nucleosome density collected from 3 biologically independent experiments are shown. k-m, Comparison between PIQ predictions and RELA (k), AP-1-JUN (l), and AP-1-FOSL2 (m) ChIP-seq profiles. Two density heatmaps at center of each panel illustrate ChIP-seq and ATAC-seq signals computed in 10 bp non-overlapping windows at selected bound- (25%) and unbound- (75%) predicted PWM hits ± 1 kb ranked according to ChIP-seq signal in the most central 100 bp. Stack histogram on left shows distribution of bound (red) and unbound (green) PWM hits as defined by PIQ along ranking. Curves on right depict evolution of enrichment score (ES) along ranking as defined with a Set Enrichment Analysis (SEA) comparing ChIP-seq signal and bound (red) and unbound (green) status of the PWM hit. 1,000 permutations were performed and the associated Benjamini–Hochberg adjusted p-value and ES score is provided (2 biologically independent experiments per condition).
Extended Data Fig. 4 AP-1 pioneer TF bookmarking of senescence enhancer landscape foreshadows the senescence transcriptional program.
a, Density heatmaps of normalized H3K27ac and H3K4me1 ChIP-seq signals computed in 10 bp non-overlapping windows at enhancers + /- 10 kb grouped by enhancer status (constitutive, de novo or remnant) at indicated time-points after RAS-OIS induction in W38 fibroblasts. Each heatmap depicts average profile across replicates from 2 biologically independent experiments per histone modification and time-point. b-c, Representative genome browser screenshots of normalized H3K4me1 (pink), H3K27ac (orange), H3K4me3 (blue) and H3K27me3 (green) ChIP-seq and ATAC-seq (light grey) profiles and chromatin states at IL1ß (b) and CDC6 (c) gene loci. Red boxes single-out IL1ß de novo and CDC6 remnant enhancers. Each track depicts the average profile across replicates from 2 biologically independent experiments (ChIP-seq and ATAC-seq). d, Boxplots depicting distribution of relative gene expression (row Z-score) through time for genes associated with constitutive (left), de novo (middle) and remnant (left) enhancer windows. Thick horizontal lines depict medians. Lower and upper hinges correspond to first and third quartiles. Upper whisker extends from hinge to largest value no further than 1.5 * IQR from hinge (where IQR is inter-quartile range, or distance between first and third quartiles). Lower whisker extends from hinge to smallest value at most 1.5 * IQR of hinge. Each box depicts average expression distribution across replicates from 2 biologically independent experiments per time-point. e, RAS-OIS WI-38 fibroblasts at day 14 infected with dCas9-KRAB and individual guides (g14, g15, g61, and g7) and analyzed by RT-qPCR for IL1α or IL1β expression as described in Fig. 3b. Data represent mean ± SD (n = 3 biologically independent experiments). **p = 0.0037 (g61); p = 0.0069 (g7), ****p < 0.0001. Comparison with ctrl 4OHT, one-way ANOVA (one-sided Dunnett’s test). f, RAS-OIS WI-38 fibroblasts were infected with dCas9-KRAB and individual guides (g2, g48 and g54) for non-enhancer regions (outside de novo enhancers) as described in Fig. 3b. 8 or 14 days after infection, cells were stained for IL1α or IL1β by indirect immunofluorescence and percentage positive cells were quantified (n = 3 biologically independent experiments for 8 days and 2 biologically independent experiments for 14 days. Data represent mean ± SD. *p = 0.0107, **p = 0.0021. Comparison with ctrl 4OHT, one-way ANOVA (one-sided Dunnett’s test). Statistical source data are presented in Source Data Extended Data Fig. 4.
Extended Data Fig. 5 AP-1 pioneer TF bookmarking of senescence enhancer landscape foreshadows the senescence transcriptional program.
a, Rank plot depicting the summed occurrences for TFs binding in proliferating cells (T0) in de novo enhancers (left) and after replicative senescence in remnant enhancers (right). Top ten TFs are highlighted. TF footprinting was performed on pooled ATAC-seq datasets from 2 biologically independent time experiments. b, Metaprofiles showing density in “active enhancer”-flagged genomic bins (top) and “constitutive enhancer”-flagged genomic bins (bottom) in vicinity (± 50 kb) of TF bookmarked de novo (left) and TF virgin de novo enhancers (right). Density in “active enhancer”-flagged genomic bins is provided for indicated time-points. Chromatin states were defined from 2 biologically independent ChIP-seq. c, Boxplot showing correlation between absolute leading log2 expression fold-change and number of genomic bins flagged as “de novo” enhancers per enhancer. ***: p-value < 0.001, Student’s unpaired bilateral t-test considering regions with 0 “de novo” enhancers bins as a control. Thick horizontal lines depict medians. Lower and upper hinges correspond to first and third quartiles. Upper whisker extends from hinge to largest value no further than 1.5 * IQR from hinge (where IQR is the inter-quartile range, or distance between the first and third quartiles). Lower whisker extends from hinge to smallest value at most 1.5 * IQR of hinge. Each box depicts average absolute leading log2 expression fold-change distribution across 2 biologically independent time series. Statistics was derived for N > 200 genes per box.
Extended Data Fig. 6 A hierarchical TF network defines the senescence transcriptional program.
a, Circos plots summarizing pairwise transcription factor co-binding at enhancers for transcriptomic modules IV and VI at indicated time-points. Co-interactions involving AP-1 are shown in black. Selected examples of gained (green) and lost (orange) interactions are highlighted. Pioneer TFs (blue), settler TFs (red), migrant TFs (green). See also dynamic Circos plot movies in Supplementary Data (see under Code availability in Material and Methods). TF footprinting was performed on pooled ATAC-seq datasets from 3 biologically independent experiments. Plots show the average co-binding profiles across replicates. b, Heatmap showing the overlap between TF lexicons (rows) and chromatin states, ChIP-seq and ATAC-seq peaks (columns) collected from 2 biologically independent experiments. Dendrograms were computed by applying hierarchical clustering on the fraction matrix with Pearson’s correlation and average linkage. c, Representative genome browser screenshots for lexicons 22 and 50 as described in Fig. 5. Chromatin states are color-coded as in Fig. 2. Transcription factor binding instances constituting each lexicon are highlighted in inset. Representative of two independent ChIP-seq and ATAC-seq time-series with similar results. d, Ratio of incoming edges based on classification of TF source node. Relative and absolute number of edges corresponding to all seven modules are displayed inside nodes, which are colored accordingly to TF classification as in previous panels. Thickness of links is proportional to relative number of TF hierarchy edges connecting nodes with corresponding classification. e, Dynamicity index and number of bound regions for each TF (rows) across all gene modules (columns). Left heatmap depicts dynamicity index scaled by column, middle heatmap depicts the square root of number of bound regions scaled by column, and right single-column heatmap illustrates TF classification.
Extended Data Fig. 7 A hierarchical TF network defines the senescence transcriptional program.
a, Chow-Ruskey diagram showing specificities and overlaps of TF interactions in each gene module. Each set corresponds to the TF-TF network edges identified for a given transcriptomic module. The global area of each set is proportional to the number of edges in its respective transcriptomic module and was calculated with the Chow-Ruskey algorithm. b-d, Chow-Ruskey diagrams for edges originating only from TFs at the top of hierarchy (b), connecting only TFs at the core layer (c), or reaching only TFs at the bottom (d). Note that edges at the top of the hierarchy are shared among the gene modules while edges towards the bottom of the hierarchy are module-specific. e-f, Knockdown efficiency for siRNAs against JUN (e), ETS1 (f) and RELA (g) as assessed by RT-qPCR for each time-point relative to non-targeting siRNA control (siC). One validation time series experiment per siRNA is shown (the experiment has been performed once). Statistical source data are presented in Source Data Extended Data Fig. 7.
Extended Data Fig. 8 Hierarchy Matters: Functional Perturbation of AP-1 pioneer TF, but no other TF, reverts the senescence clock.
a-c, Volcano plots depicting the -log10 p-value as a function of the log2 fold-change in gene expression defined by a differential analysis conducted with limma to highlight the effect of siRNA-mediated AP-1-cJUN (a), ETS1(b), and RELA (c) depletion in senescent RAS-OIS WI-38 fibroblasts at 144 h. Blue dots in respective plots indicate probes corresponding to AP-1-cJUN, ETS1 and RELA. Black outlined dots highlight direct targets of AP-1-cJUN, ETS1 and RELA. Data shown represent 2 biologically independent experiments per siRNA and target gene. d, Upset plot depicting specificities and overlaps in differentially expressed genes between siRNA-Control and siRNA-JUN silenced OIS WI-38 fibroblasts at indicated time-points. Yellow dots highlight gene sets specific to a single comparison set, while green dots highlight gene sets found in two different pair-wise comparisons.
Extended Data Fig. 9 Hierarchy Matters: Functional Perturbation of AP-1 pioneer TF, but no other TF, reverts the senescence clock.
a-d, Venn diagrams (top) and heatmaps (bottom) depicting E2F- (that is pro-proliferation genes) (a), NFκB-(that is late SASP genes) (b), p53-target genes (c) and genes belonging to N1ICD-induced senescence (NIS) gene signature (that is early SASP genes) (d) after siRNA-mediated AP-1-cJUN and siControl (Ctrl) knock-down in RAS-OIS WI-38 fibroblasts (at indicated time-points after RAS-induction). Venn diagrams show data for JUN-depletion at 144 h. Heatmap data are expressed as row Z-score. E2F targets and NFκB targets were defined according to Molecular Signature Database (MSigDB). Data shown represent 2 biologically independent experiments per siRNA and target genes. e-f, Network representation of the interaction between AP1 TFs and p53 family TFs at enhancers of genes in gene modules II (e) and VI (f) as described in Fig. 1e. p53 is highlighted by arrows.
Extended Data Fig. 10 Functional role of AP1 in therapy-induced senescence.
a-b, Cell cycle analysis using BrdU incorporation and propidium iodide staining flow cytometry for HCT116 (a) and SW480 (b) CRC cell lines under experimental conditions described in Fig. 8a,b. Insets, percentage of cells in respective cell cycle phase, calculated based on 3 biologically independent experiments. Gating strategy for S, G1, and G2 phases is indicated. c-d, cJUN gene expression determined by RT-qPCR in HCT116 (c) and SW480 (d) CRC cell lines under experimental conditions described in Fig. 6a and b. A representative experiment of 4 independent experiments in each cell line is shown. Statistical source data are presented in Source Data Extended Data Fig. 10.
Supplementary material and methods.
Supplementary Table 1
Microarray gene expression data for RAS-OIS and quiescence in WI-38 fibroblasts.
Supplementary Table 2
ChIP-seq peak coordinates and nearest gene in RAS-OIS WI-38 fibroblasts.
Supplementary Table 3
De novo, remnant and constitutive enhancers in RAS-OIS WI-38 fibroblasts.
Supplementary Table 4
TF classification by PIQ.
Supplementary Table 5
Supplementary Table 6
Microarray gene expression data for siRNA-mediated depletion of cJUN, RELA and ETS1 in RAS-OIS of WI-38 fibroblasts.
Supplementary Table 7
Module-specific p53 enhancer gene targets.
Supplementary Table 8
Taqman probes for gene expression profiling CRC cell lines.
Supplementary Table 9
AP1 gene expression signature.
Supplementary Data 1
Interactive TF lexicon heatmap.
Source Data Fig. 3
Statistical source data for Fig. 3b (RT–qPCR of dCAS9–KRAB (8 days) IL1A and IL1B).
Source Data Fig. 8
Statistical source data for Fig. 8a–f (SABG and RT–qPCR heatmap of CRC cell lines, SABG and RT–qPCR heatmap of lymphomas).
Source Data Extended Data Fig. 1
Statistical source data for Extended Data Fig. 1a–q (SABG, EdU and RT–qPCR of senescence target genes in RAS-OIS, RAF-OIS, RS and quiescence in WI-38 fibroblasts, and RAS-OIS in GM21 fibroblasts).
Source Data Extended Data Fig. 4
Statistical source data for Extended Data Fig. 4a–f (RT–qPCR of dCAS9-KRAB (14 days) IL1A and IL1B and % IL-1A- and IL-1B-positive cells (14 days)).
Source Data Extended Data Fig. 7
Statistical source data for Extended Data Fig. 7e–g (RT–qPCR validation of siRNA knockdown of cJUN, RELA and ETS1 in during RAS-OIS in WI-38 fibroblasts).
Source Data Extended Data Fig. 10
Statistical source data for Extended Data Fig. 10a–d (BrdU FACS quantification and RT–qPCR of cJUN in CRC cell lines).
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Martínez-Zamudio, R.I., Roux, PF., de Freitas, J.A.N. et al. AP-1 imprints a reversible transcriptional programme of senescent cells. Nat Cell Biol 22, 842–855 (2020). https://doi.org/10.1038/s41556-020-0529-5
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