PTBP1-Mediated Alternative Splicing Regulates the Inflammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells

Athena Georgilis; Sabrina Klotz; Christopher J. Hanley; Nicolas Herranz; Benedikt Weirich; Beatriz Morancho; Ana Carolina Leote; Luana D'Artista; Suchira Gallage; Marco Seehawer; Thomas Carroll; Gopuraja Dharmalingam; Keng Boon Wee; Marco Mellone; Joaquim Pombo; Danijela Heide; Ernesto Guccione; Joaquín Arribas; Nuno L. Barbosa-Morais; Mathias Heikenwalder; Gareth J. Thomas; Lars Zender; Jesús Gil

doi:10.1016/j.ccell.2018.06.007

Georgilis, Athena;Klotz, Sabrina;Hanley, Christopher J.;Herranz, Nicolas;Weirich, Benedikt;Morancho, Beatriz;Leote, Ana Carolina;D'Artista, Luana;Gallage, Suchira;Seehawer, Marco;Carroll, Thomas;Dharmalingam, Gopuraja;Wee, Keng Boon;Mellone, Marco;Pombo, Joaquim;Heide, Danijela;Guccione, Ernesto;Arribas, Joaquín;Barbosa-Morais, Nuno L.;Heikenwalder, Mathias;Thomas, Gareth J.;Zender, Lars;Gil, Jesús;

2018-07-01 00:00:00

Article PTBP1-Mediated Alternative Splicing Regulates the Inﬂammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells Graphical Abstract Authors Athena Georgilis, Sabrina Klotz, Christopher J. Hanley, ..., Gareth J. Thomas, Lars Zender, Jesu´ s Gil Correspondence [email protected] In Brief By performing a genetic screen for regulators of the senescence-associated secretory phenotype (SASP), Georgilis et al. identify PTBP1, which controls SASP by regulating alternative splicing of genes involved in intracellular trafﬁcking such as EXOC7. PTBP1 knockdown blocks the tumor-promoting functions of SASP. Highlights d An RNAi screen identiﬁes 50 speciﬁc SASP regulators d The splicing factor PTBP1 regulates a pro-inﬂammatory SASP subset d PTBP1 regulates alternative splicing of EXOC7 to control the SASP d PTBP1 depletion safely inhibits inﬂammation-driven cancer Georgilis et al., 2018, Cancer Cell 34, 85–102 July 9, 2018 ª 2018 The Authors. Published by Elsevier Inc. https://doi.org/10.1016/j.ccell.2018.06.007 Cancer Cell Article PTBP1-Mediated Alternative Splicing Regulates the Inﬂammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells 1,2 3,4 5 1,2 6 7 Athena Georgilis, Sabrina Klotz, Christopher J. Hanley, Nicolas Herranz, Benedikt Weirich, Beatriz Morancho, 8 3,4 1,2,6 3,4 1,2 Ana Carolina Leote, Luana D’Artista, Suchira Gallage, Marco Seehawer, Thomas Carroll, 1,2 9,10 5 1,2 6 Gopuraja Dharmalingam, Keng Boon Wee, Marco Mellone, Joaquim Pombo, Danijela Heide, 11 7,12,13 8 6 5 Ernesto Guccione, Joaquı´n Arribas, Nuno L. Barbosa-Morais, Mathias Heikenwalder, Gareth J. Thomas, 3,4,14 1,2,15, Lars Zender, and Jesu´ s Gil * MRC London Institute of Medical Sciences (LMS), Du Cane Road, London W12 0NN, UK Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK € € Department of Internal Medicine VIII, University Hospital Tubingen, Tubingen 72076, Germany € € Department of Physiology I, Institute of Physiology, Eberhard Karls University Tubingen, Tubingen 72076, Germany Cancer Sciences Unit, Cancer Research UK Centre, University of Southampton, Somers Building, Southampton SO16 6YD, UK Division of Chronic Inﬂammation and Cancer, German Cancer Research Centre (DKFZ), Heidelberg 69121, Germany Preclinical Research Program, Vall d’Hebron Institute of Oncology (VHIO) and CIBERONC, Barcelona 08035, Spain Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal Institute of High Performance Computing, A STAR, 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore Bioinformatics Institute, A STAR, 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore Methyltransferases in Development and Disease Group, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A STAR), Singapore, Singapore Department of Biochemistry and Molecular Biology, Universitat Autonoma de Barcelona, Campus de la UAB, Bellaterra 08193, Spain Institucio´ Catalana de Recerca i Estudis Avanc¸ ats (ICREA), Barcelona 08010, Spain Translational Gastrointestinal Oncology Group, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.ccell.2018.06.007 SUMMARY Oncogene-induced senescence is a potent tumor-suppressive response. Paradoxically, senescence also induces an inﬂammatory secretome that promotes carcinogenesis and age-related pathologies. Consequently, the senescence-associated secretory phenotype (SASP) is a potential therapeutic target. Here, we describe an RNAi screen for SASP regulators. We identiﬁed 50 druggable targets whose knock- down suppresses the inﬂammatory secretome and differentially affects other SASP components. Among the screen candidates was PTBP1. PTBP1 regulates the alternative splicing of genes involved in intracel- lular trafﬁcking, such as EXOC7, to control the SASP. Inhibition of PTBP1 prevents the pro-tumorigenic effects of the SASP and impairs immune surveillance without increasing the risk of tumorigenesis. In conclusion, our study identiﬁes SASP inhibition as a powerful and safe therapy against inﬂamma- tion-driven cancer. Signiﬁcance Oncogene-induced senescence has opposing effects in cancer as it restrains tumor initiation but paradoxically can fuel the growth of advanced tumors via a pro-inﬂammatory secretome. Here, we identiﬁed multiple druggable targets whose inhi- bition suppresses inﬂammation without interfering with the senescence growth arrest. One of the identiﬁed candidates is PTBP1, a regulator of alternative splicing previously shown to promote cancer proliferation and metastasis. Knockdown of PTBP1 in vivo reduced the pro-tumorigenic effects of senescence without increasing the risk of tumor initiation. These ﬁndings suggest that targeting the pro-inﬂammatory SASP is a safe and effective therapeutic strategy that can be employed against inﬂammation-driven cancers such as advanced liver tumors. Cancer Cell 34, 85–102, July 9, 2018 ª 2018 The Authors. Published by Elsevier Inc. 85 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION We selected IL-8 and IL-6 as readouts for the screen due to their signiﬁcant induction during OIS and the relevance of these cyto- Senescence is a stress response that limits the replication of kines in mediating SASP-related phenotypes (Acosta et al., damaged or aging cells by implementing a stable growth arrest. 2008; Kuilman et al., 2008). After monitoring the kinetics of IL-8 Senescent cells display profound changes in nuclear and and IL-6 expression during OIS (Figures S1C and S1D), we chromatin organization, gene expression, and cell metabolism decided to carry out the screen 8 days after 4OHT induction. (Kuilman et al., 2010). Importantly, senescent cells also secrete Importantly, transfection of siRNAs targeting known SASP regu- a complex combination of mostly pro-inﬂammatory factors lators such as the RELA subunit of NF-kB, CEBPb, or MAPK14, collectively referred to as the senescence-associated secretory which encodes for p38a, decreased IL-8 and IL-6, as quantiﬁed phenotype (SASP). using an automated high-throughput microscopy system (Fig- During early tumorigenesis, the SASP adds to the cancer-pro- ures 1B, 1C, and S1E). We screened a ‘‘druggable genome’’ tective effects of senescence by reinforcing the growth arrest siRNA library targeting around 7,000 genes and identiﬁed 96 and by signaling to the immune system to clear incipient cancer genes whose knockdown increased IL-8 and IL-6, and 125 cells (Acosta et al., 2008, 2013; Kang et al., 2011). The SASP also genes whose knockdown downregulated IL-8 and IL-6 during contributes to tissue repair and normal development (Munoz- OIS (Figure 1D). We validated the siRNAs repressing the SASP Espin and Serrano, 2014). Conversely, the SASP can mediate in a secondary screen using a new library containing four many of the detrimental functions of senescent cells. The secre- siRNAs targeting each of the aforementioned 125 candidates tome of lingering senescent cells can promote malignancy of (Figure 1E). At least two independent siRNAs prevented the in- nearby cells (Coppe et al., 2010), chemoresistance (Kaur et al., duction of IL-8 and IL-6 during OIS for 84 of the 125 candidates 2016), and systemic inﬂammation associated with many age- tested (Figures 1E and 1F). related diseases (Franceschi and Campisi, 2014). Although the speciﬁc outcome depends on the context, it ap- Identifying SASP Regulators that Do Not Revert the pears that the net effect of the SASP in advanced cancer is to Senescence Growth Arrest promote tumorigenesis by enhancing the proliferative and meta- The siRNA screen described above identiﬁed 84 potential SASP static potential of neoplastic cells, among other mechanisms regulators. However, it was unclear whether knocking down (Coppe et al., 2010). The harmful inﬂammation imposed by the these genes reduced IL-8 and IL-6 by preventing SASP induction SASP suggests that eliminating senescent cells (Ovadya and speciﬁcally or by preventing senescence. Although genes Krizhanovsky, 2018) or suppressing the SASP can be advanta- belonging to either group are of biological interest, strategies geous in many pathologies and not just cancer. to repress the SASP while maintaining the senescence growth Several SASP regulators have been identiﬁed, most of which arrest are appealing to target inﬂammation-driven tumorigenesis drive inﬂammatory responses. These include nuclear factor kB (Coppe et al., 2010; Tchkonia et al., 2013). (NF-kB), CCAAT/enhancer-binding protein b (CEBPb), p38a To identify genes that regulate the SASP without reverting the MAPK (mitogen-activated protein kinase), mammalian target of senescence growth arrest, we assessed how the candidate rapamycin (mTOR), mixed-lineage leukemia (MLL), GATA4, siRNAs affected OIS-mediated induction of the senescence ef- INK4a CIP1 and Brd4 (Herranz and Gil, 2018). Many of the deﬁned pathways fectors p16 and p21 and of growth arrest, monitored that activate the SASP are by nature important senescence ef- by IF and assessing bromodeoxyuridine (BrdU) incorporation, fectors. Consequently, to devise coherent strategies to target respectively (Figure 2A). As controls, we transfected siRNAs tar- INK4a the SASP care must be taken not to negate the tumor-suppres- geting either p16 or p53 and conﬁrmed that these siRNAs INK4a CIP1 sive effects associated with the senescence growth arrest. caused downregulation of p16 or p21 , respectively, Preliminary evidence indicates that uncoupling cell arrest and and blunted the senescence growth arrest as assessed by the SASP is feasible (Herranz et al., 2015; Laberge et al., 2015; increased BrdU incorporation (Figures 2B [left panel] and S2A). Tasdemir et al., 2016; Wall et al., 2013). Here, we aimed to iden- By using K-means clustering, we classiﬁed the SASP-repressing tify genes that modulate the SASP without interfering with other siRNAs in four categories (Figures 2C and S2B). Clusters 2 and 4 senescence phenotypes and assess the therapeutic potential of encompassed siRNAs that prevented the senescence growth ar- INK4a CIP1 inhibiting the SASP against inﬂammation-driven cancer. rest as well as induction of p16 or p21 , whereas those in CIP1 cluster 3 further upregulated p21 exacerbating the arrest RESULTS (Figures S2B and S2C). Importantly, cluster 1 contained siRNAs that reduced IL-8 and IL-6 without reverting the senes- A Small Interfering RNA Screen Identiﬁes SASP cence response, satisfying our criteria for genes that are required Regulators for SASP but not growth arrest (Figures 2B [right panel] and 2C). To discover regulators of the SASP, we carried out a large-scale Fifty genes grouped in this cluster (Table S1). small interfering RNA (siRNA) screen (Figure 1A). We used IMR90 Although we used IL-8 and IL-6 as readouts of our screen, the ER:RAS, a well-characterized cellular system of oncogene- SASP comprises dozens if not hundreds of secreted factors induced senescence (OIS). Activation of RAS with 4-hydroxy- (Acosta et al., 2013; Coppe et al., 2010). To investigate how tamoxifen (4OHT) causes IMR90 ER:RAS cells to undergo the identiﬁed candidates regulate the SASP, we performed senescence (Acosta et al., 2013). IMR90 ER:RAS cells treated genome-wide transcriptome proﬁling. We targeted 38 out of with 4OHT become growth arrested and express interleukin-8 the 50 genes found in cluster 1 with two siRNAs against each. (IL-8), IL-6, and other SASP components, as analyzed by immu- To organize the transcriptome data, we used a gene set consist- noﬂuorescence (IF) or qRT-PCR (Figures 1B and S1A–S1D). ing of the SASP components induced in IMR90 cells undergoing 86 Cancer Cell 34, 85–102, July 9, 2018 Figure 1. An siRNA Screen Identiﬁes Regulators of the SASP (A) Workﬂow of the SASP siRNA screen. (B) Representative immunoﬂuorescence (IF) images of IL-8 and IL-6 following transfection of indicated siRNAs. Scale bar, 100 mm. (C) IF quantiﬁcation. Left panel shows single-cell intensity values of IL-8 in a representative sample well of a 96-well plate seeded with cells transfected with indicated siRNAs. Blue line denotes quantiﬁcation cutoff resulting in the IL-8 percentages shown in the right panel. (D) Screen results. Normalized IL-8 versus normalized IL-6 values for each replicate sample of the screen. Dotted lines indicate cutoffs of ±2 SD of negative scramble controls. siRNA pools were considered ‘‘hits’’ if they showed a B score of >2 or <3, in at least 2 out of 3 replicates for both IL-6 and IL-8. (E) Volcano plots of the secondary siRNA screen performed in IMR90 ER:RAS cells as per the workﬂow given in (A). Normalized percent inhibition (NPI) shown as mean of 3 replicates. Three replicate NPI values of each sample siRNA were compared with all scramble siRNA values by unpaired Student’s t test. Eighty-four genes met the selection criteria depicted by lines:R2 siRNAs with an IL-8 and IL-6 NPI <0.8 and a p value of%0.05. Only siRNAs targeting the 84 genes are color coded as ‘‘Hit siRNAs’’. (F) Summary of SASP screen. Venn diagrams (not to scale) show number of siRNA pools passing the ﬁlter and overlap between IL-8 and IL-6. See also Figure S1. Cancer Cell 34, 85–102, July 9, 2018 87 A (legend on next page) 88 Cancer Cell 34, 85–102, July 9, 2018 OIS (Acosta et al., 2013; Herranz et al., 2015). The analysis cate- correlates with tumor growth and poor prognosis (Wang et al., gorized the candidate genes from cluster 1 into four different 2017; Xue et al., 2009). subclusters (1.1, 1.2, 1.3, and 1.4) demonstrating differential To study the function of PTBP1 during OIS, we used two SASP regulation (Figures 2D, 2E, and S2D). All the candidates independent shRNAs targeting human PTBP1. Both shRNAs analyzed prevented IL-8 and IL-6 induction and the expression efﬁciently knocked down PTBP1 expression (Figures 3A and of most inﬂammatory SASP components clustering in group B S3A). In agreement with our data above, PTBP1 knockdown (in Figure 2E, compare row B with maximum SASP induction, did not prevent the growth arrest observed during OIS (Figures ‘‘P’’). However, differential effects were observed on the expres- 3B and 3C). In fact, knockdown of PTBP1 resulted in slightly sion of other SASP components such as transforming growth slower proliferation of normal cells (Figures 3C and 3D). A similar factor b and pro-ﬁbrotic SASP components (clustering in group induction of senescence markers, including SA-b-galactosidase C). The summary heatmap (Figure 2E) also reveals that the activity, the DNA damage response, and upregulation of INK4a CIP1 siRNAs grouped in subclusters 1.3 and 1.4 were more potent p16 , p21 , and p53 expression was observed in senes- inhibitors of the inﬂammatory subset of the SASP (see also Fig- cent cells irrespective of PTBP1 depletion (Figures 3E and ure 2D, top right). S3B). In contrast, PTBP1 knockdown prevented the induction To validate our candidates and verify their therapeutic poten- of a speciﬁc subset of SASP components that included inﬂam- tial, we utilized chemical inhibitors targeting ﬁve candidates from matory factors such as IL-8, IL-6, and IL-1a (Figure 3F). cluster 1 or close family members: BW-B (inhibiting ALOX5), FIPI The ability of PTBP1 depletion to dampen the SASP without (inhibiting phospholipase D), JQ1 (inhibiting BET family proteins, affecting other parts of the senescence program was not related to BRD8), Tautomycetin (inhibiting protein phosphatase restricted to RAS-induced senescence nor was it unique to 1), XE-991 (inhibiting Kv7 voltage-gated potassium channels), IMR90 cells. Knockdown of PTBP1 prevented SASP induction and Torin (mTOR, positive control) (Herranz et al., 2015). in response to doxorubicin in IMR90 cells (Figures 3G and Treatment with these inhibitors blunted the induction of IL-8 S3C), irradiation in human ﬁbroblasts (Figure S3D), and onco- and IL-6 without rescuing the senescence growth arrest (Figures genic HER2 expression in MCF7 breast cancer cells (Figure S3E), 2F and S2E). Similarly, pools of four short hairpin RNAs (shRNAs) without reverting the growth arrest (Figures 3G, S3D, and S3E). targeting six cluster 1 genes (BRD8, PP1A, PTBP1, PTPN14, Moreover, PTBP1 depletion also prevented RAS-dependent SKP1A, and TMEM219; Figure 2G, left panel) attenuated IL-8 induction of IL-8 and IL-6 in cells that were incapable of undergo- and IL-6 induction (Figure 2G, right panel and Figure S2F, left ing senescence due to p53 depletion (Figure S3F), suggesting panel) without reverting the growth arrest (Figures S2E that PTBP1 could control inﬂammation in settings other than and S2F, right panels). Thus, chemical inhibitors and shRNAs senescence. Finally, knocking down PTBP1 in already senes- phenocopied the corresponding siRNAs, conﬁrming that our cent cells also decreased IL-8 and IL-6 levels (Figure 3H). There- screen successfully identiﬁed regulators that differentially fore, PTBP1 reduction not only prevents SASP induction but can affect SASP composition without disturbing the senescence also inhibit the SASP once senescence has been established. growth arrest. Overall, these results reveal that depletion of PTBP1 represses the SASP without affecting other features of senescence. PTBP1 Regulates the SASP without Affecting Other Senescence Phenotypes PTBP1 Regulates a Pro-inﬂammatory SASP Subset Among the SASP regulators identiﬁed in the screen, knocking Affecting Its Paracrine Functions down the polypyrimidine tract binding protein 1 (PTBP1) had a To better understand the extent of PTBP1-mediated SASP regu- strong effect on the pro-inﬂammatory SASP. PTBP1 encodes lation, we analyzed the transcriptome of senescent cells upon for a regulator of alternative splicing whose expression positively PTBP1 knockdown (Figure 4A). While senescence induction Figure 2. A Subset of Screen Candidates Differentially Regulates the SASP without Affecting the Senescent Growth Arrest (A) Workﬂow for the categorization of SASP-repressing siRNAs. (B) B scores showing the effects of different siRNAs on the expression of p16 and p21 and incorporation of BrdU. Left: positive controls. Right: two independent siRNAs targeting a gene representative of cluster 1. Data represent mean ± SD (n = 3). (C) K-means clustering of the SASP-repressing siRNAs. Heatmap of cluster 1 showing B-score expression for each replicate experiment (column). Each row reﬂects the measures from one siRNA. (D) Heatmap showing the differential regulation of SASP components by siRNAs targeting cluster 1 candidates. IMR90 ER:RAS cells were independently transfected with two siRNAs targeting 38 of the cluster 1 candidates. RNA-seq was performed and samples clustered according to the expression of SASP components. G: growing cells including cells transfected with scramble siRNA but not treated with 4OHT (no SASP induction), N: grouping senescent cells transfected with siRNAs targeting CEBPb and RELA (preventing the induction of many SASP components), Pos: cluster grouping senescent cells transfected with scramble siRNA or a siRNA targeting p16 (showing a ‘‘maximum’’ SASP induction). Each column represents an average of two siRNAs per gene with three replicates each. Left: hierarchical cluster showing the siRNA subclusters (colored horizontally) and the clustering of SASP components in groups (colored vertically). Right: zoom-in showing the effect of siRNAs on indicated SASP groups. (E) Summary heatmap derived by averaging the heatmap presented in (D) showing differential regulation of SASP components. (F) IL-8 IF analysis 8 days after senescence induction of IMR90 ER:RAS cells treated with indicated drugs targeting cluster 1 genes. Torin 1 was included as control. Data represent mean ± SD (n = 3); ***p < 0.001. Comparisons with DMSO + 4OHT. (G) Expression levels of each gene or IL-8 measured by qRT-PCR 6 days after 4OHT induction of IMR90 ER:RAS cells stably infected with an empty pGIPZ vector (Vector) or pools of four pGIPZ-based shRNAs against the indicated candidate SASP regulators. An shRNA targeting mTOR (sh_mTOR) was included as control. Data represent mean ± SD (n = 3); *p < 0.05, **p < 0.01, ***p < 0.001. Comparisons with Vector + 4OHT. One-way ANOVA (Bonferroni’s test) was used in (F) and (G) to calculate statistical signiﬁcance. See also Figure S2 and Table S1. Cancer Cell 34, 85–102, July 9, 2018 89 Figure 3. The Splicing Factor PTBP1 Regulates the SASP without Affecting Growth Arrest (A) Immunoblot of protein extracts 6 days after 4OHT induction of IMR90 ER:RAS cells infected with indicated pGIPz shRNA vectors targeting PTBP1. Vec, empty vector. (legend continued on next page) 90 Cancer Cell 34, 85–102, July 9, 2018 resulted in substantial changes in gene expression, only a small model that monitors the effect of senescent ﬁbroblasts on tumor set of these changed upon PTBP1 knockdown (Figure 4B). growth (Herranz et al., 2015). We co-injected squamous cell Principal component analysis (PCA) substantiated this observa- carcinoma 5PT cells with normal or senescent (irradiated) ﬁbro- tion (Figure S4A), and gene set enrichment analysis (GSEA) blasts subcutaneously into nude mice and conﬁrmed that the further conﬁrmed that depletion of PTBP1 inhibited the SASP presence of senescent ﬁbroblasts enhanced tumor growth (Fig- without interfering with the growth arrest (Figures 4C and S4B). ure 5A). Depletion of PTBP1 impaired the ability of irradiated Analysis of the transcriptome data found NF-kB-dependent ﬁbroblasts to promote the growth of 5PT tumor cells in this signatures were downregulated upon PTBP1 knockdown (Fig- setting (Figures 5A, S5A, and S5B). These experiments suggest ures 4D [left panel] and S4B). We investigated whether that that knocking down PTBP1 suppresses the ability of senescent reﬂected direct regulation of NF-kB signaling by PTBP1 or was ﬁbroblasts to promote tumor growth. However, the use of an the result of reduced SASP disrupting the positive feedback immunocompromised mouse model does not fully capture the loop signaling needed to amplify SASP expression. To this complex interactions occurring in the tumor microenvironment. end, IMR90 cells bearing an NF-kB reporter (Natarajan et al., Senescent hepatocytes are present in damaged livers (Jurk 2014) were treated with tumor necrosis factor a (TNFa). While et al., 2014; Krizhanovsky et al., 2008), and during chronic liver knocking down RELA prevented NF-kB activation, knocking disease precancerous senescent hepatocytes co-exist with down PTBP1 did not affect it (Figure 4D). These experiments readily transformed cells. A mouse model recapitulating this suggest that the inhibition of NF-kB signaling observed upon interaction was recently published (Eggert et al., 2016)(Fig- PTBP1 knockdown is caused by the reduced SASP impairing ure 5B). In this model, senescence is induced in wild-type (WT) the NF-kB-dependent SASP autocrine loop. mouse livers by transposon-mediated transfer of oncogenic G12V Next, we cataloged the factors secreted by senescent cells NRAS (NRAS ) by hydrodynamic tail vein injection. Transduc- G12V, D38A using label-free mass spectrometry (Acosta et al., 2013; Herranz tion of a non-oncogenic NRAS (NRAS ) serves as a con- et al., 2015). We identiﬁed 60 SASP components enriched in the trol (Kang et al., 2011). After 4 days, hepatocellular carcinoma conditioned media (CM) of senescent cells, and the knockdown cells expressing ﬁreﬂy luciferase are seeded in the liver of synge- of PTBP1 reduced the levels of 32 (Figure 4E), including many neic mice, and tumor growth can be monitored by ex vivo tumor pro-inﬂammatory cytokines. We next explored how PTBP1 imaging (Figure 5B). Importantly, this model allows us to assess depletion affected different paracrine actions mediated by se- the growth of orthotopic liver tumors in fully immunocompetent nescent cells. First, we examined whether PTBP1 depletion mice, hence in a context more relevant to human disease. could affect paracrine senescence (Acosta et al., 2013). Naive To study the therapeutic value of targeting PTBP1, G12V, D38A IMR90 cells treated with CM from senescent cells undergo we expressed NRAS , an inactive NRAS mutant G12V, D38A senescence and express an SASP (Acosta et al., 2013). These (NRAS ) that does not induce senescence (Kang G12V effects were impaired in naive cells treated with CM from et al., 2011), and co-expressed NRAS with either a neutral PTBP1-depleted senescent cells (Figure 4F). Conversely, treat- shRNA (shRen, targeting Renilla luciferase) or shRNAs targeting ment with senescent CM induced paracrine arrest on cells PTBP1 in the same vector (Figure S5C). To assess the effect depleted of PTBP1 but resulted in a reduced secondary SASP that depletion of PTBP1 in senescent hepatocytes has on (Figure S4C). tumor growth, we seeded the syngeneic tumor cells, and after 15 days evaluated the tumors macroscopically and by PTBP1 Knockdown Inhibits the Tumor-Promoting luciferase imaging. Knockdown of PTBP1 in senescent G12V Functions of the SASP NRAS -expressing hepatocytes prevented the acceleration The SASP can enhance the proliferative potential of cancer cells of tumor growth otherwise observed in senescent livers G12V to promote tumor progression (Coppe et al., 2010). To investi- (NRAS _shRen) compared with non-senescent livers G12V, D38A gate how PTBP1 depletion affects the tumor-promoting func- (NRAS )(Figure 5C). In accordance with what we tions of the SASP, we used an experimental xenograft mouse observed in cell culture, depleting PTBP1 expression did not (B) Quantiﬁcation of cells positive for BrdU incorporation at indicated days after 4OHT treatment. Data represent mean ± SD (n = 3). (C) Crystal violet-stained 6-well dishes of cells ﬁxed 12 days following 4OHT treatment. (D) Quantiﬁcation of BrdU incorporation 8 days after 4OHT treatment, 15 days after empty vector or PTBP1 shRNA infection. Data represent mean ± SD (n = 3); *p < 0.05, **p < 0.01, ***p < 0.001; ns, not signiﬁcant. Comparisons with Vec + 4OHT. One-way ANOVA (Dunnett’s test). (E) Quantiﬁcation of cells positive for the senescence markers p16, p21, p53, and gH2AX 6 days after 4OHT and b-galactosidase 8 days after 4OHT by IF analysis. Data represent mean ± SD (n = 3). ***p < 0.001; ns, not signiﬁcant. Comparisons with Vector + 4OHT, two-way ANOVA (Bonferroni’s test). (F) Expression levels of the indicated SASP genes assessed by qRT-PCR 6 days after 4OHT induction normalized and compared with Vector + 4OHT. Data represent mean ± SD (n = 3); ***p < 0.001, two-way ANOVA (Dunnett’s test). (G) IMR90 WT cells were infected with indicated pGIPZ empty vector or PTBP1 shRNAs and treated with doxorubicin to induce senescence. Left: IF analysisof the indicated senescence markers 6 days after doxorubicin induction. Right: mRNA analysis of the indicated genes by qRT-PCR (right) 8 days after doxorubicin induction normalized to the Vector + doxycycline (Doxo) condition. Data represent mean ± SD (n = 3). *p < 0.05, ***p < 0.001; ns, not signiﬁcant. Comparisons with Vector + Doxo, two-way ANOVA (Dunnett’s test). (H) IMR90 ER:RAS cells were transfected with two independent siRNAs targeting PTBP1 at day 5 after senescence induction as indicated in the scheme (left). Senescence establishment at day 6 was monitored by IF analysis (middle). Knockdown of PTBP1 and the effect on the indicated genes was assessed by qRT-PCR 5 days after siRNA transfection, and 10 days after senescence induction (right), normalized to the si_Scramble + 4OHT condition. Data represent mean ± SD (n = 3). ***p < 0.001; ns, not signiﬁcant. Comparisons with si_Scramble + 4OHT, two-way ANOVA (Dunnett’s test). See also Figure S3. Cancer Cell 34, 85–102, July 9, 2018 91 Figure 4. PTBP1 Regulates a Pro-inﬂammatory Subset of the SASP and Its Paracrine Functions (A) Experimental design for the global transcriptional proﬁling of IMR90 ER:RAS cells (6 days after 4OHT induction) presented in (B) to (D). (B) Subset of senescence-speciﬁc transcripts affected by PTBP1 knockdown. Mean expression (average of the normalized read counts for 3 replicates) in relation to log (FC) for the indicated comparison. Signiﬁcantly changing genes are highlighted in red. (C) SASP GSEA signature in PTBP1 depleted cells + 4OHT compared with cells expressing empty vector + 4OHT. (D) NF-kB GSEA signatures in PTBP1 depleted cells + 4OHT compared with cells expressing empty vector + 4OHT (left). GFP analysis atindicated time points following TNFa (50 ng/mL) treatment in IMR90 cells expressing a kB reporter (kB-GFP) and transfected with indicated siRNAs (right). Data represent mean ± SD (n = 3). (E) Mass spectrometry analysis of CM collected from IMR90 ER:RAS cells (empty vector or two PTBP1-targeting shRNAs) 6 days after senescence induction with 4OHT. Differential secretion of the listed SASP factors shown as mean (n = 3). (F) Experimental design to assess the effect of PTBP1 loss on secreted factors responsible for inducing paracrine senescence (left). IF analysis of the senescence markers in IMR90 cells treated for 3–4 days with CM from the indicated IMR90 ER:RAS cells. Data represent mean ± SD (n = 3); each replicate experiment corresponds to independent generation of CM. ***p < 0.001. Comparisons with cells treated with CM from Vector + 4OHT, two-way ANOVA (Dunnett’s test). See also Figure S4. 92 Cancer Cell 34, 85–102, July 9, 2018 A 600 *** ** Vector shPTBP1_53 shPTBP1_86 Vector shPTBP1_53 2000 IR shPTBP1_86 10 15 20 25 30 IR BC ** (legend on next page) Cancer Cell 34, 85–102, July 9, 2018 93 ββ vec sh53 sh86 vec sh53 sh86 inhibit senescence in the liver (Figure 5D) but signiﬁcantly icantly (Figures S6F and S6G). A corollary of the impaired im- reduced the expression of multiple SASP components (Fig- mune inﬁltration upon PTBP1 knockdown would be impaired ure 5E). In summary, these data provide functional proof in a senescent cell clearance. A reduced percentage of NRAS hepa- preclinical disease model of the feasibility of SASP modulation tocytes 6 days after transduction was observed when comparing G12V G12V, D38A as a strategy to attenuate tumor growth. NRAS and the senescent incapable NRAS control (Figure S6H). This reduction was impaired upon PTBP1 knock- PTBP1 Knockdown Affects Senescence Surveillance down, as noted by an increase in the percentage of NRAS cells but Does Not Increase the Risk of Tumorigenesis in those mice (Figure 6F). While the SASP is thought to mediate many of the detrimental Next, we evaluated whether the impaired immune surveillance effects attributed to senescent cells during aging and cancer, it caused by PTBP1 knockdown resulted in increased long-term also has protective functions. Notably, factors secreted by risk in tumorigenesis. To this end, mice were transduced G12V senescent cells are necessary to mount a protective immune with vectors co-expressing NRAS and an shRNA targeting surveillance response during tumor initiation or in response to PTBP1 (shPTBP1_43) or a neutral control shRNA (shRen; re-engagement of senescence in tumors (Kang et al., 2011). Figure 6G, left panel). Tumor formation was monitored periodi- Therefore, we decided to evaluate the effect of PTBP1 depletion cally by sonography (Figure S6I). Interestingly, knockdown of on the surveillance and elimination of incipient preneoplastic PTBP1 did not impart an increased risk of tumor formation, but G12V hepatocytes (Figure 6A). Mice injected with NRAS _shPTBP1 rather resulted in increased survival and decreased tumor forma- showed lower PTBP1 expression in NRAS hepatocytes tion (Figures 6G and S6J). Overall, these data suggest that G12V therapeutic SASP modulation to treat advanced cancers is not compared with mice injected with NRAS _shRen (Figures 6B and 6C). PTBP1 depletion did not signiﬁcantly alter only effective and feasible but can also be safe, without running the percentage of proliferating NRAS cells (Ki67 staining) and the risk of tumor initiation due to bypass of lingering senescent the SA-b-galactosidase activity was not signiﬁcantly different cells or dampened anti-tumor immunity. G12V G12V between NRAS _shPTBP1 and NRAS _shRen mice (Fig- ures 6B and 6C). Regulation of Alternative Splicing by PTBP1 Controls Since PTBP1 knockdown in senescent hepatocytes results in the SASP reduced SASP production (Figure 5E), we investigated how this PTBP1 is an RNA binding protein whose best-characterized affects immune cell recruitment. We ﬁrst carried out immunohis- function is to regulate alternative splicing (Xue et al., 2009). We tochemistry (IHC) staining and found that the formation of analyzed alternative splicing during OIS taking advantage of macrophage aggregates (F4/80 and MHC II staining) and the RNA sequencing (RNA-seq) and multivariate analysis of tran- inﬁltration of T cells (CD3 staining) was signiﬁcantly reduced in script splicing (Shen et al., 2014). We identiﬁed 434 splicing G12V the livers of NRAS _shPTBP1 (Figures 6D, S6A, and S6B). events signiﬁcantly altered (showing a splice change of R20%) 0 0 Next, we measured CD11b inﬁltrating immune cells using during OIS in IMR90 cells. These included alternative 5 or 3 ﬂow-cytometry analysis (Figures S6C and S6D). This is highly splice sites, mutually exclusive exons, skipped exons, and re- relevant since different populations of myeloid cells have been tained introns (Figures 7A and S7A; Table S2). PCA of exon inclu- implicated in both senescent immune surveillance (Kang et al., sion levels of previously published datasets (Tasdemir et al., 2011) and mediating the tumor-promoting effects of senescent 2016) suggested that alternative splicing occurring in RAS- cells in damaged livers (Eggert et al., 2016). Consistent with pre- induced senescence is likely due to both senescence induction vious observations (Eggert et al., 2016; Kang et al., 2011), and RAS activation, the latter being the strongest contributor G12V expression of oncogenic NRAS (NRAS ) resulted in signiﬁ- (Figure S7B). cant inﬁltration of CD11b cells and monocyte immature myeloid Next, we assessed how PTBP1 depletion affected alternative cells (Mo iMC), and a slight (not statistically signiﬁcant) increase splicing during OIS. Speciﬁcally, we examined exon-skipping in macrophage inﬁltration (Figure S6E). Depletion of PTBP1 events, since these are known to be regulated by PTBP1 (Xue decreased the inﬁltration of all these immune cells, although it et al., 2009) and are the most frequent type of alternative splicing. was not statistically signiﬁcant (Figure 6E). Macrophages can While increased exon skipping was observed when comparing be further subdivided in Kupffer cells and newly inﬁltrating mac- senescent and normal cells, knockdown of PTBP1 with two inde- rophages (Figure S6C). Although both subpopulations increased pendent shRNAs in senescent cells resulted in increased exon G12V in NRAS -injected livers, only the recruitment of new macro- inclusion (Figure S7C). This is in line with PTBP1 repressing phages was reduced by PTBP1 knockdown, although not signif- exon inclusion (Xue et al., 2009). In addition, we detected a Figure 5. PTBP1 Knockdown Inhibits the Tumor-Promoting Functions of the SASP (A) Tumor growth induced by senescent cells in a xenograft mouse model following PTBP1 knockdown. Left: experimental design. Middle: tumor growth monitored by measuring the volume at the indicated days. Graph symbols are mean volumes of all the mice in the indicated condition. Right: area under the curve (AUC) of the tumor growth for each mice. Data represent mean ± SD (n = 7 per group). **p < 0.01, ***p < 0.001, one-way ANOVA (Bonferroni’s test). (B–E) Tumor growth in an orthotopic model of advanced liver cancer following PTBP1 knockdown. (B) Experimental design. (C) Representative images of livers and luciferase imaging (left) and quantiﬁcation of luciferase intensity (right) shown as mean ± SD (n = 4 mice per group). *p < 0.05, one-way ANOVA (Bonferroni’s test). (D) Representative images and quantiﬁcation of SA-b-Galactosidase expression. Scale bar: 50 mm. Plots show median (line), upper and lower quartiles (boxes), and lines extending to highest and lowest observation (whiskers), **p < 0.01; ns, not signiﬁcant; one-way ANOVA (Bonferroni’s test). (E) qRT-PCR-based quantiﬁcation of SASP components shown as log (FC) between the conditions indicated at the top. See also Figure S5. 94 Cancer Cell 34, 85–102, July 9, 2018 40 µm - 70 µm 30 µm - 40 µm >70 µm >40 µm µ * ns ns 10 600 0 0 shRen sh43 sh891 shRen sh43 sh891 shRen sh43 sh891 shRen sh43 sh891 0.1248 0.0867 shRen sh43 sh891 shRen sh43 sh891 0.138 20 * shRen sh43 sh891 (legend on next page) Cancer Cell 34, 85–102, July 9, 2018 95 β- signiﬁcant enrichment of PTBP1 RNA binding motifs around showed that PTBP1 affects the SASP by controlling the splicing the splice acceptor site upstream of exons whose inclusion of multiple targets, among them EXOC7. increased with PTBP1 depletion (Figures 7B and S7D), suggest- ing that those events were directly regulated by PTBP1. PTBP1 Regulates Alternative Splicing of EXOC7 to To investigate how alternative splicing mediated by PTBP1 Control the SASP affects SASP regulation, we selected the top 95 genes alterna- Extending the OIS observations, knockdown of PTBP1 also tively spliced in a PTBP1-dependent manner (Figure 7C and affected splicing of EXOC7 in other senescence types such as Table S3). To determine the alternative splicing events respon- doxorubicin-induced senescence (Figure S8A). Conversely, sible for the altered SASP caused by PTBP1 knockdown, we overexpression of PTBP1 resulted in preferential expression of devised a multi-step screening approach (Figure 7D). First, an the EXOC7-S isoform and SASP induction (Figures 8A and siRNA library targeting the 95 candidates regulated by PTBP1 S8B). To understand how EXOC7 isoform switching affects was screened for SASP regulators as previously (Figure 1). SASP regulation, we ectopically expressed either EXOC7-L The screen identiﬁed 13 genes whose knockdown affected (induced by depletion of PTBP1) or EXOC7-S in proliferating IL-8 and IL-6 expression during OIS (Figure 7E). Next, we and senescent IMR90 cells. In agreement with our results above, conﬁrmed by qRT-PCR that the splicing of 8 out of those 13 senescent cells overexpressing EXOC7-L displayed lower SASP genes depended on PTBP1 (Figures S7E and S7F). Interest- than cells overexpressing EXOC7-S (Figure 8B). Moreover, ingly, the alternative splicing of ﬁve of these genes changed expression of the SASP-promoting EXOC7-S isoform was sufﬁ- during OIS (e.g., MARK3), while this was not the case for the cient to partially rescue the inhibition of IL-8 and IL-6 expression others (e.g., EXOC7, Figures S7E and S7F). To verify that a caused by PTBP1 knockdown (Figures 8C and S8C). PTBP1-dependent switch in alternative splicing was respon- To examine how and to what extent EXOC7 regulates the sible for regulating the SASP, we designed steric hindrance SASP, we compared the transcriptome of senescent cells lack- antisense oligonucleotides (AONs) to target the splicing events, ing PTBP1 or EXOC7 (Figures 8D and S8D). PCA showed a sep- i.e., reverting the inclusion caused by PTBP1 depletion (Figures aration between normal and senescent cells, as deﬁned by PC1. 7F, S7E, and S7F). Most of the AONs tested partially rescued Interestingly, depletion of PTBP1 or, to a lesser extent, EXOC7 the downregulation of IL-8 and IL-6 caused by PTBP1 knock- separated the transcriptomes from those of senescent cells (Fig- down (Figure 7G). ure S8D, right). GSEA analysis showed that, similar to PTBP1 Two of the genes whose splicing most signiﬁcantly affected depletion, EXOC7 knockdown was associated with downregula- SASP regulation were EXOC7 and SNX14, both involved in regu- tion of SASP and NF-kB-dependent signatures (Figure S8E). lating different aspects of intracellular trafﬁcking. In particular, Moreover, there was a strong correlation between the effects EXOC7 is one of the eight core subunits of the exocyst complex of EXOC7 depletion and PTBP1 depletion on downregulating that tethers post-Golgi vesicles to the plasma membrane, speciﬁc components of the SASP (Figure 8E). mediating exocytosis (Wu and Guo, 2015). PTBP1 knockdown Since EXOC7 depletion resulted in a reduced SASP, the effect regulated the switching between the ‘‘long’’ EXOC7 isoform that EXOC7 splice switching has on SASP expression could be (EXOC7-L, including exon 7) and the ‘‘short’’ EXOC7 isoform due to differential activity. EXOC7 phosphorylation by ERK1/2 (EXOC7-S, lacking exon 7), and this could be partially prevented regulates exocyst assembly and activity at the plasma mem- using two different AONs (Figures 7H, S7F, and S7G). Restoring brane (Ren and Guo, 2012). Although ERK1/2 phosphorylation the levels of the EXOC7-S isoform using AONs resulted in a par- sites are not encoded within exon 7, we observed an increased tial rescue of IL-6 and IL-8 levels (Figures 7I and S7H). Moreover, ERK1/2-mediated phosphorylation of the EXOC7-S isoform (Fig- publicly available CLIP-seq (crosslinking immunoprecipitation ures 8F and S8F) that correlated with a distinct subcellular local- sequencing) data showed that PTBP1 binds near the splice ization of each isoform (Figure 8G). These results suggest that acceptor site upstream of EXOC7 exon 7 (Figure S7I). Although expression of the EXOC7-S isoform favors EXOC7 phosphoryla- this suggests that PTBP1 directly controls EXOC7 splicing, we tion and membrane localization, corresponding to increased cannot exclude an indirect effect. In summary, these results SASP production. Figure 6. PTBP1 Knockdown Impairs Senescence Surveillance without Increasing Tumorigenesis (A–F) Senescence surveillance following PTBP1 knockdown. (A) Experimental design. (B) Representative IF images of NRAS, PTBP1 and SA-b-galactosidase + + expression in livers. Scale bars, 50 mm. (C) Quantiﬁcation of high PTBP1-expressing or Ki67 among NRAS cells by IF. Quantiﬁcation of SA-b-gal expression is also shown (right). Plots show median (line), upper and lower quartiles (boxes), and lines extending to highest and lowest observation (whiskers). Data represent G12V mean ± SD (n = 4). *p < 0.05; ns, not signiﬁcant. Comparisons with NRAS -shRenilla, one-way ANOVA (Dunnett’s test). (D) Representative IHC images (left) and + + densitometric quantiﬁcation (right) of indicated immune cell markers. For MHCII and F4/80 areas, arrowheads indicate characteristic myeloid aggregate G12V formation that develops as a consequence of NRAS -driven senescence in the liver. Smaller aggregates are separated from larger aggregates based on diameter (comparison shown in Figures S6A and S6B) and are depicted as black and gray symbols, respectively. For CD3 staining, arrowheads indicate positive cells and are quantiﬁed as number of positive cells per counting area (10 mm ). Scale bar, 100 mm. Data represent mean ± SD (n = 4). *p < 0.05. Comparisons with G12V NRAS -shRenilla, one-way ANOVA (Bonferroni’s test). (E) Quantiﬁcation of indicated inﬁltrating immune cells by ﬂow cytometry. Gating strategy shown in G12V Figures S6C and S6D. Data represent mean ± SD (n = 4); *p < 0.05. Comparisons with NRAS -shRenilla, one-way ANOVA (Bonferroni’s test). (F) Quantiﬁcation + G12V of NRAS cells. Data represent mean ± SD (n = 4); *p < 0.05. Comparisons with NRAS -shRenilla, one-way ANOVA (Bonferroni’s test). (G) Long-term tumorigenesis in WT mice upon injection with indicated transposon-based plasmids. Left: experimental design. Middle: Kaplan-Meier survival G12V G12V curves. Right: NRAS _shRen (n = 10) and NRAS _shPTBP1 (n = 9). *p < 0.05 by log-rank (Mantel-Cox) test. Representative images of macroscopically visible GFP tumor nodules (>1 mm, black arrows) at endpoint. See also Figure S6. 96 Cancer Cell 34, 85–102, July 9, 2018 (legend on next page) Cancer Cell 34, 85–102, July 9, 2018 97 To investigate whether there is a relationship between senescence. With this in mind, we carried out a systematic PTBP1 expression and EXOC7 splicing in vivo, we analyzed search for SASP regulators and found 50 potential therapeutic available gene expression data of human tissues (GTEx) (GTEx targets whose inhibition blunts the SASP without posing a risk Consortium, 2015). PTBP1 expression levels inversely corre- of bypassing the tumor-suppressive growth arrest. The newly lated with inclusion of EXOC7 exon 7 across different tissues identiﬁed SASP regulators differentially modulate the various (Figure 8H). High PTBP1 expression and EXOC7 exon 7 skipping subsets of the SASP, providing us with a toolbox for ad hoc were associated with multiple inﬂammation-related signatures SASP regulation in future studies. as well as the epithelial-to-mesenchymal transition (Figures We focused on one of the screen candidates, the alternative 8I and S8G). Together these results suggest that PTBP1- splicing factor PTBP1. Expression of PTBP1 positively correlates driven alternative splicing of EXOC7 can regulate inﬂammation with growth of various cancers and poor prognosis (Wang et al., in high-turnover tissues in vivo. 2017) but had yet to be causally linked to the negative effect of Finally, we investigated whether EXOC7 can regulate SASP- inﬂammation on advanced cancer. Here, we showed that deple- mediated phenotypes in vivo. To this end, we transduced mice tion of PTBP1 inhibited a pro-inﬂammatory SASP subset without G12V with vectors co-expressing oncogenic NRAS (NRAS ) and a blunting growth arrest or other phenotypes associated with control shRNA or shRNAs targeting PTBP1 or EXOC7 (Figures senescence. Hence, PTBP1 presents a potentially powerful ther- 8J [top] and S8H). Knockdown of PTBP1 or EXOC7 did not apeutic target for inﬂammation-driven cancer. Future studies will affect senescence (Figure S8I) but resulted in increased evaluate whether it could also be used during therapy-induced numbers of NRAS hepatocytes (Figure 8J, bottom left). Similar senescence or age-related disease. to what we observed upon PTBP1 knockdown, EXOC7 deple- One of our key ﬁndings was that PTBP1 depletion prevented tion in senescent cells affected the SASP, as exempliﬁed by the tumor-promoting effects of the SASP. Knocking down G12V reduced expression of CXCL5 in NRAS -expressing hepato- PTBP1 prevented tumor growth caused by the presence of cytes (Figures 8J [second panel from left] and S8J). The forma- senescent cells in two tumor models. A potential caveat of tar- tion of macrophage aggregates (MHC II staining) and the inﬁl- geting the SASP as a tumor therapy is that it results in decreased tration of T cells (CD3 staining) were signiﬁcantly reduced in clearance of preneoplastic cells by the immune system. How- G12V the livers of mice co-expressing NRAS and an EXOC7-tar- ever, we did not observe an increased risk of tumorigenesis geting shRNA (Figures 8J [right panels] and S8K). In conclusion, upon PTBP1 depletion despite the reduced immune surveil- alternative splicing of EXOC7 contributes to PTBP1-mediated lance. This could be explained by PTBP1 depletion not inducing control of the SASP. cell cycle re-entry and, if anything, exacerbating the senescence growth arrest. This constitutes evidence in a preclinical model DISCUSSION that targeting the SASP can be a viable and safe therapeutic strategy in the context of chronic liver disease. Based on the Although senescence protects against tumor initiation and limits current understanding on how the SASP contributes to different ﬁbrosis, the aberrant presence of senescent cells can exacer- pathologies, PTBP1 inhibition or other anti-SASP therapies bate age-related pathologies and cancer progression. Conse- could also be used to ameliorate the detrimental effects of quently, there is growing interest in ﬁnding pharmacological chemotherapy or to treat age-related pathologies. agents that suppress the deleterious effects of senescent cells. The main role of PTBP1 is to regulate alternative splicing by Until now, studies have concentrated on identifying ‘‘senolytic’’ inducing exon skipping. A three-step screen approach sug- compounds: drugs that speciﬁcally kill senescent cells. Although gested that PTBP1 regulates the SASP by controlling splicing still ill deﬁned, the current view holds that the SASP is respon- of a number of genes, including EXOC7. EXOC7 is part of the sible for many of the detrimental effects caused by senescent exocyst complex and has been implicated in many cellular cells in disease. Thus, SASP inhibition has been proposed as features such as neurite outgrowth, epithelial cell polarity, cell an alternative to senolytics for targeting the harmful effects of motility, or cell morphogenesis (Wu and Guo, 2015). In this study, Figure 7. Regulation of Alternative Splicing by PTBP1 Controls the SASP (A) Distribution of the ﬁve types of AS events detected in senescent cells compared with proliferating cells by RNA-seq (see Figure 4A). (B) PTBP1 RNA binding motifs across alternative exons upon PTBP1 knockdown. Top: scheme. Motifs are mapped to potential regulatory sequences around the target alternatively spliced exon (dark-gray box). The yellow peak represents the area of predicted enrichment of PTBP1 binding responsible for exon splicing repression (red line), with no role known for PTBP1 in exon splicing enhancement (dashed blue line). Middle: motif density for exons with inclusion increasing (putatively repressed, red), decreasing (putatively enhanced, blue), or not altered (not regulated, gray) upon PTBP1 knockdown. Bottom: statistical signiﬁcance for local motif enrichment in putatively repressed (red) and enhanced (blue) exons. (C) Exon-skipping events and DPSI cutoffs used for shortlisting events changing due to loss of PTBP1. A stricter cutoff was used for events changing upon PTBP1 loss but not affected upon senescence. (D) Strategy to link PTBP1-driven alternative splicing and SASP regulation. (E) Ninety-ﬁve PTBP1-spliced genes were targeted with four siRNAs and screened for IL-8 and IL-6 regulators as described in Figure 1. NPI shown as mean of three replicates and cutoffs for hit selection (dotted lines). Hit siRNAs represent siRNAs targeting genes scoring with R2 siRNAs in both readouts. (F) Experimental design of (G). (G) IMR90 ER:RAS cells were transfected with AONs either not targeting (NC) or targeting the indicated exons. IF analysis of IL-6 (left) and IL-8 (right). Data represent mean ± SD (n = 4). *p < 0.05, **p < 0.01, ***p < 0.001. Comparisons with NC, si_PTBP1_5 + 4OHT, one-way ANOVA (Dunnett’s test). (H and I) Effect of AONs targeting EXOC7 exon 7 splicing on the SASP downregulation caused by PTBP1 knockdown. Timeline as in (F). (H) Immunoblot of protein extracts of IMR90 ER:RAS cells 5 days after 4OHT induction. (I) Representative IF images of IL-8 8 days after 4OHT induction. Scale bar, 100 mm. See also Figure S7 and Tables S2 and S3. 98 Cancer Cell 34, 85–102, July 9, 2018 A B C ns ns -6 -4 -2 2 -2 -4 si_PTBP1_5 F G Adipose Tissue Muscle Adrenal Gland Nerve Bladder Ovary HALLMARK_INTERFERON_GAMMA_RESPONSE Blood Pancreas HALLMARK_TNFA_SIGNALING_VIA_NFKB Blood Vessel Pituitary HALLMARK_ALLOGRAFT_REJECTION HALLMARK_INTERFERON_ALPHA_RESPONSE Brain Prostate HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION Breast Salivary Gland HALLMARK_IL6_JAK_STAT3_SIGNALING Cervix Uteri Skin HALLMARK_INFLAMMATORY_RESPONSE Colon Small Intestine 5 HALLMARK_APOPTOSIS Esophagus Spleen HALLMARK_IL2_STAT5_SIGNALING Fallopian Tube Stomach HALLMARK_CHOLESTEROL_HOMEOSTASIS Heart Testis HALLMARK_COMPLEMENT 0 Kidney Thyroid 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Liver Uterus Lung Vagina *** ** (legend on next page) Cancer Cell 34, 85–102, July 9, 2018 99 si_EXOC7_3 B Immunoﬂuorescence Staining of Cells we demonstrated how EXOC7 also regulates SASP induction. B Cytochemical SA-b-Galactosidase Assay Although the role of the exocyst complex in senescence is still B Staining of Tissue Sections unclear, we postulate that regulation of EXOC7 splicing can B High Content Analysis (HCA) affect exocyst activity and can be exploited as a strategy to B FACS repress the SASP. Targeting alternative splicing in disease is B Immunoblot and Immunoprecipitation gaining traction. Strategies employing CRISPR/Cas9 or adminis- B siRNA Screen Analysis tration of AONs to induce splice switching have been successful B Analysis of Proteomics Data in improving muscular dystrophy and spinal muscular atrophy B Analysis of RNA Sequencing Data both in mouse models and in clinical trials (Nelson et al., 2016; B Gene Set Enrichment Analysis (GSEA) of RNA- Wan and Dreyfuss, 2017). Seq Data In summary, we identiﬁed 50 genes whose knockdown specif- B RNA-Binding Motif Analysis ically inhibits the SASP without affecting the senescence growth B CLIP Data Visualization arrest. One of those genes was PTBP1, a regulator of alternative B Cross-Tissue Analysis of PTBP1 Expression and splicing. Validating the rationale of our screen, knockdown of EXOC7 Exon 7 Inclusion PTBP1 suppresses the tumor-promoting effects of the SASP B Cross-Tissue Gene Set Enrichment Analysis without reverting growth arrest in a preclinical model of d QUANTIFICATION AND STATISTICAL ANALYSES advanced liver cancer, suggesting that SASP modulation can d DATA AND SOFTWARE AVAILABILITY be a safe way to target inﬂammation-driven cancers. STAR+METHODS SUPPLEMENTAL INFORMATION Supplemental Information includes eight ﬁgures and ﬁve tables and can be Detailed methods are provided in the online version of this paper found with this article online at https://doi.org/10.1016/j.ccell.2018.06.007. and include the following: d KEY RESOURCES TABLE ACKNOWLEDGMENTS d CONTACT FOR REAGENT AND RESOURCE SHARING We are grateful to A.J. Innes and members of J.G.’s laboratory for reagents, d EXPERIMENTAL MODEL AND SUBJECT DETAILS comments, and other contributions to this project. We thank S. Vernia, J. B Cell Lines Ule, and R. Faraway for advice and members of the Proteomics (P. Faull B Mice and A. Montoya) and Genomics (L. Game, K. Rekopoulou, and A. Ivan) d METHOD DETAILS LMS facilities for help with the proteomics and RNA-seq, respectively. B Vector Construction We thank T.-W. Kang and C. Fellmeth for technical support and Life Science Editors for editorial assistance. J.A. is funded by the Breast Cancer B Growth Assays Research Foundation (BCRF, grant BCRF-17-008) and Instituto de Salud B Inhibitor Treatments Carlos III (PI16/00253). N.L.B.-M.’s laboratory is supported by EMBO (Instal- B Nucleic Acid Transfections lation grant 3057) and Fundac¸a˜oparaa Cieˆ ncia e a Tecnologia, Portugal B Conditioned Media (CM) Experiments (FCT Investigator Starting grant IF/00595/2014). M.H. was supported by B Total RNA Extraction an ERC consolidator grant (HepatoMetabopath). Core support from MRC B cDNA Synthesis and Quantitative RT-PCR (grants MC-A652-5PZ00 and MC_U120085810) funded the research in B RNA-Sequencing J.G.’s laboratory. Figure 8. PTBP1 Regulates Alternative Splicing of EXOC7 to Control the SASP (A) SASP expression and EXOC7 isoform switching following PTBP1 overexpression in IMR90 cells. Immunoblot of protein extracts (left) and mRNA analysis by qRT-PCR (right) 2 days after induction of PTBP1 expression with doxycycline (Dox). Normalized and compared with Vec Dox. Data represent mean ± SD (n = 5). ***p < 0.001; ns, not signiﬁcant; one-way ANOVA (Dunnett’s test). (B) Comparison of SASP production following overexpression of EXOC7-S (S) and EXOC7-L (L) 4 days after 4OHT and doxycycline treatment of IMR90 ER:RAS cells by immunoblot analysis. v, empty vector. (C) Effect of EXOC7-S on the SASP downregulation caused by PTBP1 knockdown. Representative IF images of IL-8 of IMR90 ER:RAS cells without () and with doxycycline treatment (EXOC7 S) 8 days after 4OHT induction. Scale bar, 100 mm. (D and E) Effect of EXOC7 depletion on the SASP (D). Left: experimental design. Right: mean expression (average of the normalized read counts for 3 replicates) in relation to log (FC) for the indicated comparison. Signiﬁcantly changing genes are highlighted in red. (E) Correlation between the expression of SASP genes upon PTBP1 and EXOC7 siRNA-mediated knockdown. (F) Comparison of EXOC7-S and EXOC7-L phosphorylation assessed by EXOC7 immunoprecipitation followed by immunoblotting. Experimental details as in (B). (G) Comparison of EXOC7-S and EXOC7-L localization to the plasma membrane in proliferating and senescent cells. Quantiﬁcation of cells showing the diffuse EXOC7 pattern. Data represent mean ± SD (n = 3). p < 0.01, comparing EXOC7-S + DMSO with either Vector + DMSO or EXOC7-L + DMSO; p < 0.05, comparing EXOC7-S + 4OHT with either Vector + 4OHT or EXOC7-L + 4OHT; two-way ANOVA (Bonferroni’s test). Experimental details as in (B). Scale bar, 100 mm. (H) PTBP1 expression versus EXOC7 exon 7 inclusion in data from the Genotype-Tissue Expression (GTEx) project. (I) Top 11 hallmarks with normalized enrichment score >2 and false discovery rate <0.05 in genes with expression positively correlating with EXOC7 exon 7 skipping in GTEx samples. (J) Effect of EXOC7 knockdown on the immune surveillance response. Top: experimental design. Bottom: quantiﬁcation of NRAS mouse hepatocytes, CXCL5 + + + G12V G12V expression in NRAS hepatocytes, and inﬁltrated MHC II and CD3 cells 6 days after transposon delivery of NRAS _shRenilla (n = 5), NRAS _shPTBP1 G12V G12V (n = 4), or NRAS _EXOC7 (n = 4). Data represent mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001. Comparisons with NRAS _shRenilla, one-way ANOVA (Bonferroni’s test). See also Figure S8. 100 Cancer Cell 34, 85–102, July 9, 2018 AUTHOR CONTRIBUTIONS GTEx Consortium (2015). Human genomics. the genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, A.G., S.K., C.J.H., B.M., N.H., M.M., S.G., D.H., M.S., L.D., J.P., and B.W. per- 648–660. formed and analyzed experiments. M.H. analyzed experiments. A.G., T.C., Herranz, N., and Gil, J. (2018). Mechanisms and functions of cellular senes- G.D., K.B.W., A.C.L., and N.L.B.-M. carried out bioinformatics analysis. L.Z., cence. J. Clin. Invest. 128, 1238–1246. G.J.T., and J.G. designed the in vivo experiments. A.G. and J.G. conceived Herranz, N., Gallage, S., Mellone, M., Wuestefeld, T., Klotz, S., Hanley, C.J., and designed the project and wrote the manuscript, with all authors providing Raguz, S., Acosta, J.C., Innes, A.J., Banito, A., et al. (2015). mTOR regulates feedback. J.G., E.G., J.A., G.J.T., and L.Z. secured funding. MAPKAPK2 translation to control the senescence-associated secretory phenotype. Nat. Cell Biol. 17, 1205–1217. DECLARATION OF INTERESTS Howe, E.A., Sinha, R., Schlauch, D., and Quackenbush, J. (2011). RNA-Seq analysis in MeV. Bioinformatics 27, 3209–3210. J.G. is a consultant to Unity Biotechnology, which funds research in his labo- ratory not directly related to this article. J.G. and N.H. are named inventors in a Jurk, D., Wilson, C., Passos, J.F., Oakley, F., Correia-Melo, C., Greaves, L., patent ﬁled related to senolytic therapies (GB1708456.7) that is not directly Saretzki, G., Fox, C., Lawless, C., Anderson, R., et al. (2014). Chronic inﬂam- relate to this article. mation induces telomere dysfunction and accelerates ageing in mice. Nat. Commun. 2, 4172. 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Genomics 16,51. 102 Cancer Cell 34, 85–102, July 9, 2018 STAR+METHODS KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Rabbit polyclonal anti-53BP1 Novus Biologicals Cat#NB100-304; RRID: AB_10003037 Mouse monoclonal anti-BrdU (clone: 3D4) BD Biosciences Cat#555627; RRID: AB_395993 Mouse monoclonal anti-CCL2/MCP-1 (clone: 24822) R&D Cat#MAB279; RRID: AB_2071645 Mouse monoclonal anti-CCL20/MIP-3 R&D Cat#MAB360; RRID: AB_2275415 alpha (clone 67310) Mouse monoclonal anti-Exoc7 (clone: D6) Santa Cruz Cat#sc-365825; RRID: AB_10843358 Rabbit polyclonal anti-Exoc7 Bethyl Cat#A303-365A; RRID: AB_10953161 Rabbit polyclonal anti-GAPDH Abcam Cat#ab22555; RRID: AB_447153 Puriﬁed goat polyclonal anti-CXCL1/GRO alpha R&D Cat#AF-275; RRID: AB_355288 Puriﬁed goat polyclonal anti-IL-6 R&D Cat#AB-206-NA; RRID: AB_354281 Mouse monoclonal anti-CXCL8/IL-8 (clone: 6217) R&D Cat#MAB208; RRID: AB_2249110 Mouse monoclonal anti-IL-1 alpha/IL-1F1 (clone: 4414) R&D Cat#MAB200; RRID: AB_2295862 Mouse monoclonal anti-IL-1 beta/IL-1F2 R&D Cat#MAB201; RRID: AB_358006 MAb (clone 8516) INK4a Mouse monoclonal anti-p16 (clone: JC-8) CRUK N/A Cip1 Rabbit polyclonal anti-p21 (M-19) Santa Cruz Cat#sc-471; RRID: AB_632123 Mouse monoclonal anti-p53 (clone DO-1) Santa Cruz Cat#sc-126; RRID: AB_628082 Rabbit monoclonal anti-phospho-MAPK/CDK Cell Signaling Technologies Cat#2325; RRID: AB_331820 Substrates (clone: 34B2) Goat polyclonal anti-PTBP1 Abcam Cat#ab5642; RRID: AB_305011 Mouse monoclonal anti-Vegfc (clone: 23410) R&D Cat#MAB2931; RRID: AB_2212835 Mouse monoclonal anti-phospho-histone Millipore Cat#05-636; RRID: AB_309864 H2A.X (clone: jbw301) Donkey anti-goat IgG (H+L), AlexaFluor 594, conjugated Thermo Fischer Scientiﬁc Cat#A11058; RRID: AB_142540 Goat anti-mouse IgG (H+L), AlexaFluor 488, conjugated Thermo Fischer Scientiﬁc Cat#A11029; RRID: AB_2534088 Rabbit anti-mouse IgG (H+L), AlexaFluor 488, conjugated Thermo Fischer Scientiﬁc Cat#A11059; RRID: AB_2534106 Goat anti-mouse IgG (H+L), AlexaFluor 594, conjugated Thermo Fischer Scientiﬁc Cat#A11032; RRID: AB_2534091 Goat anti-rabbit IgG (H+L), AlexaFluor 594, conjugated Thermo Fischer Scientiﬁc Cat#A11037; RRID: AB_2534095 Goat anti-rat IgG (H+L), AlexaFluor 488, conjugated Thermo Fischer Scientiﬁc Cat#A11006; RRID: AB_2534074 Donkey anti-goat IgG-HRP Santa Cruz Cat#sc-2020; RRID: AB_631728 Goat anti-mouse IgG-HRP Santa Cruz Cat#sc-2005; RRID: AB_631736 Goat anti-rabbit IgG-HRP Santa Cruz Cat#sc-2004; RRID: AB_631746 Rat monoclonal anti-MHC Class II (clone: M5/114.15.2) Novus Biologicals Cat#NBP1-43312; RRID: AB_10006677 Rabbit polyclonal anti-CD3 Zytomed Cat#RBK024 Rat monoclonal anti-F4/80 (clone: BM8) BioLegend Cat#123105; RRID: AB_893499 Monoclonal anti-mouse Gr-1 (clone: RB6-8C5) Thermo Fischer Scientiﬁc Cat#11-5931-82; RRID:AB_465314 Monoclonal anti-mouse CD11b (clone: M1/70) Thermo Fischer Scientiﬁc Cat# 50-0112-82; RRID:AB_11218507 Monoclonal anti-mouse Ly6C (clone: HK1.4) Thermo Fischer Scientiﬁc Cat#17-5932-82; RRID:AB_1724153 Chemicals, Peptides, and Recombinant Proteins (+)-JQ1, BET bromodomain inhibitor Tocris Cat#4499; CAS: 1268524-70-4 BW-B 70C, 5-lipoxygenase inhibitor Tocris Cat#1304; CAS: 134470-38-5 FIPI, Phospholipase D inhibitor Tocris Cat#3600; CAS: 939055-18-2 PD98059, MEK inhibitor CALBIOCHEM Cat#513000; CAS: 167869-21-8 (Continued on next page) Cancer Cell 34, 85–102.e1–e9, July 9, 2018 e1 Continued REAGENT or RESOURCE SOURCE IDENTIFIER Tautomycetin, Protein phosphatase 1 inhibitor Tocris Cat#2305; CAS: 119757-73-2 Torin 1, mTOR inhibitor Tocris Cat#4247; CAS: 1222998-36-8 XE 991 dihydrochloride, Kv7 voltage-gated Tocris Cat#2000; CAS: 122955-13-9 potassium channels inhibitor Critical Commercial Assays Human IL-6 Quantikine ELISA Kit R&D D6050 Human IL-8/CXCL8 Quantikine ELISA Kit R&D D8000C Smart-seq 2 Picelli et al., 2014 N/A RNAscope 2.5 Assay HD Reagent Kit-BROWN Advanced Cell Diagnostics Cat#322300 RNAscope 2.5 LS Probe- Mm-Cxcl5 Advanced Cell Diagnostics Cat#467441 RNAscope Positive Control Probe - Mm-Ppib Advanced Cell Diagnostics Cat#322300 RNAscope Negative Control Probe - DapB Advanced Cell Diagnostics Cat#310043 Deposited Data Raw and analyzed data on RNA-Seq for PTBP1 This paper GSE101763 and EXOC7 depletion Raw and analyzed data on RNA-Seq for This paper GSE101766 differential regulation of SASP GTEx Portal GTEx Consortium, 2015 N/A DbGaP phs000424.v6.p1 N/A Experimentally determined list of SASP components Acosta et al., 2013 N/A PTBP1 CLiP-seq data on 293T cells Raj et al., 2014 GSE57278 PTBP1 CLiP-seq data on HeLa cells Coelho et al., 2016 and E-MTAB-3108 and Xue et al., 2009 GSE19323/GSE42701 Experimental Models: Cell Lines IMR90 (human) ATCC CCL-186 HFFF2 (human) ECACC 86031405 MCF-7Tet-Off (human) BD Biosciences 630907 MEFs (mouse) Tordella et al., 2016 N/A 5PT (human) Bauer et al., 2005 N/A RIL175 expressing luciferase (mouse) Eggert et al., 2016 N/A Experimental Models: Organisms/Strains Mouse: C57BL/6 Charles River, N/A Sulzfeld, Germany tm1Mom 1-/- Mouse: B6.129S7-RAG1 /J (Rag ) The Jackson Laboratory Stock#002216 Oligonucleotides ‘Human Druggabe Genome’ siRNA library QIAGEN HsDgV4, HsGpcrV4.1, HsKinV4.1 and HsPhosV4.1 si_RELA QIAGEN SI02663094 si_CEBPB QIAGEN SI02777292 si_MAPK14 QIAGEN SI03089989 si_p53 QIAGEN SI00011655 si_p16 QIAGEN SI02664403 si_PTBP1_2 QIAGEN SI00043631 si_PTBP1_5 QIAGEN SI00141638 si_PTBP1_18 QIAGEN SI02649206 si_EXOC7_3 QIAGEN SI00381787 si_EXOC7_4 QIAGEN SI00381794 (Continued on next page) e2 Cancer Cell 34, 85–102.e1–e9, July 9, 2018 Continued REAGENT or RESOURCE SOURCE IDENTIFIER AON Sequences, see Table S4 This paper N/A Primer sequences, see Table S5 This paper N/A Recombinant DNA V12 pLNC ER:RAS Acosta et al., 2008 and Plasmid #67844 Barradas et al., 2009 Pool of pGIPZ-based shRNAs targeting Brd8 Sigma-Aldrich and MRC LMS V3LHS_379314, V3LHS_392041, Genomics core facility V3LHS_379315 and V3LHS_379312 Pool of pGIPZ-based shRNAs targeting PP1A Sigma-Aldrich and MRC LMS V2LHS_262414, V3LHS_635633, Genomics core facility V3LHS_635634 and V3LHS_635636 Pool of pGIPZ-based shRNAs targeting PTBP1 Sigma-Aldrich and MRC LMS V3LHS_640391, V3LHS_645699, Genomics core facility V3LHS_362179 and V3LHS_362177 Pool of pGIPZ-based shRNAs targeting PTPN14 Sigma-Aldrich and MRC LMS V2LHS_70672, V2LHS_250104, Genomics core facility V3LHS_378143 and V3LHS_378142 Pool of pGIPZ-based shRNAs targeting SKP1A Sigma-Aldrich and MRC LMS V2LHS_153803, V2LHS_202391, Genomics core facility V2LHS_203112 and V2LHS_202737 Pool of pGIPZ-based shRNAs targeting TMEM219 Sigma-Aldrich and MRC LMS V2LHS_180141, V3LHS_392882, Genomics core facility V3LHS_392883 and V3LHS_392881 pGIPZ-based shRNA targeting p53 Sigma-Aldrich and MRC LMS V3LHS_333920 Genomics core facility pGIPZ-based shRNA targeting mTOR Sigma-Aldrich and MRC LMS V3LHS_312661 Genomics core facility pGIPZ-based shPTBP1_53 Sigma-Aldrich and MRC LMS V3LHS_640391 Genomics core facility pGIPZ-based shPTBP1_86 Sigma-Aldrich and MRC LMS V3LHS_645699 Genomics core facility pCANIG mirE-based shPtbp1.43 This paper N/A pCANIG mirE-based shPtbp1.891 This paper N/A pCANIG mirE-based shExoc7 This paper N/A pTR-mCMV-copGFP Natarajan et al., 2014 N/A pTR-NF-kB-mCMV-copGFP Natarajan et al., 2014 N/A p95HER2/611 Joaquin Arribas Laboratory; N/A Pedersen et al., 2009 Tet-ON/PTBP1 This paper N/A Tet-ON/EXOC7 This paper N/A pLenti-CMV rtTA3 Addgene w785-1 Software and Algorithms B-score Pelz et al., 2010 http://web-cellhts2.dkfz.de/ cellHTS-java/cellHTS2/ NPI Pelz et al., 2010 http://web-cellhts2.dkfz.de/ cellHTS-java/cellHTS2/ GraphPad PRISM 7 N/A https://www.graphpad.com/ scientiﬁc-software/prism/ TopHat version 2.0.11 Kim et al., 2013 R version 3.0.1 and 3.2.3 GSEA version 2.0.12 Liberzon et al., 2015 and http://software.broadinstitute.org/gsea/msigdb Subramanian et al., 2005 DESeq2 version 1.10.1 Love et al., 2014 https://bioconductor.org/packages/ release/bioc/html/DESeq2.html MATS Shen et al., 2014 http://rnaseq-mats.sourceforge.net Maxquant version 1.5.3.8 Cox and Mann, 2008 http://www.coxdocs.org/doku.php?id= maxquant:common:download_and_installation MeV Howe et al., 2011 http://mev.tm4.org/ (Continued on next page) Cancer Cell 34, 85–102.e1–e9, July 9, 2018 e3 Continued REAGENT or RESOURCE SOURCE IDENTIFIER CLIPdb Yang et al., 2015 N/A limma Ritchie et al., 2015 and N/A Phipson et al., 2016 IGV Browser Robinson et al., 2011 http://software.broadinstitute.org/ software/igv/download UCSC Genome Browser Kent et al., 2002 https://genome.ucsc.edu/cgi-bin/ hgGateway?redirect=manual&source= genome.ucsc.edu CONTACT FOR REAGENT AND RESOURCE SHARING Further information and requests for resources and reagents should be directed to and will be fulﬁlled by the Lead Contact, Jesu´ s Gil ([email protected]) EXPERIMENTAL MODEL AND SUBJECT DETAILS Cell Lines IMR90, MCF7 Tet-Off and HFFF2 were cultured in DMEM (Gibco) supplemented with 10% fetal bovine serum (Sigma) and 1% antibiotic-antimycotic solution (Gibco). MEFs were cultured in GMEM (Sigma) and 1% antibiotic-antimycotic solution (Gibco). HNSCC cell line 5PT was cultured in keratinocyte growth medium (KGM). G12V To generate IMR90 cells expressing ER:RAS , miR30-based shRNAs (pGIPz) or kB-GFP (pTR-mCMV-copGFP), and MEFs expressing miRE-based shRNAs, retroviral and lentiviral infections were carried out as previously described (Aarts G12V et al., 2017). To generate IMR90 ER:RAS cells expressing two miR30-based shRNAs simultaneously, viruses were mixed at a 1:1 ratio. To generate MCF7 Tet-Off cells expressing the carboxy-terminal fragment of HER2, known as ‘p95HER2’ or ‘611CTF’, under the control of a Tet-responsive element, MCF7 cells were infected with the appropriate viruses. To generate IMR90 PTBP1 Tet-ON or IMR90 ER:RAS EXOC7 Tet-ON, cells were infected with equal amounts of iCMV-tight and rtTA3 viruses. To select cells that efﬁciently integrated both constructs the cells were treated with 0.8 mg/ml Puromycin and 25 mg/ml Hygromycin. To induce OIS, IMR90 ER:RAS cells were treated with 100 nM 4-hydroxytamoxifen (4OHT; Sigma) reconstituted in DMSO or MCF7 Tet-Off/p95HER2 were depleted of 1 mg/ml doxycycline (dox; Sigma). To induce chemotherapy-induced senescence, IMR90 cells were treated with 0.4 mM doxorubicin (Sigma) for 24 hr which was subsequently removed by media change until analysis timepoint. To induce expression of PTBP1 or EXOC7, cells were treated with 100 and 12 ng/ml Doxycyline (Sigma), respectively, added on the day of 4OHT induction. Mice 6 6 For xenograft experiments, 1x10 5PT cells ± 3x10 IMR-90 were re-suspended in 150ml of serum free DMEM; 100ml of this mix was tm1Mom -/- injected subcutaneously into the ﬂank of partially immunocompromised B6.129S7-Rag1 /J (Rag1 ) female mice (5-6 months old) as previously described (Herranz et al., 2015). Tumor size was measured using an electronic calliper and calculated using the formula 4p/3 x r (radius (r) calculated from the average diameter, measured as the tumor width and length). Optical imaging was performed using the IVIS SpectrumCT system. All animal work followed institutional guidelines of the University of Southampton (UK) and has been approved by UK legal authorities. For hepatocyte senescence experiments, 4-6-week old female C57BL/6 mice were purchased from Charles River (Sulzfeld, G12V Germany). Intrahepatic delivery of a transposon-based plasmid pCaNIG-shRNA allowing co-expression of NRAS and miR30- based shRNAs together with an expression plasmid for sleeping beauty 13 was performed via hydrodynamic tail vein injection (HDTV) as described previously (Kang et al., 2011). For the short-term tumorigenesis study, seeding of luciferase-expressing hepatocellular carcinoma RIL175 cells in senescent livers 4 days post-HDTV was achieved by intrasplenic injection as previously described (Eggert et al., 2016). For immune surveillance analysis by immunohistochemistry, mice were sacriﬁced 6 days after HDTV and livers were collected and either 10% formalin-ﬁxed, parafﬁn embedded, or embedded in OCT compound (Tissue-Tek) and frozen. For tumorigenesis assessment 15 days after RIL175-cell seeding, livers were explanted and incubated with ﬁreﬂy luciferin (Biosynth) at a concentration of 0.381 mg/ml for 10 min at room temperature. Livers were consequently imaged with AEQUORIA MDS (Hamamatsu Photonics, Hamamatsu, Japan). In the long-term study, the livers were monitored by ultrasound and mice were sacriﬁced when termination criteria were fulﬁlled. All animal work followed institutional guidelines of Tubingen University and has been approved by German legal authorities. e4 Cancer Cell 34, 85–102.e1–e9, July 9, 2018 METHOD DETAILS Vector Construction pLNC-ER:RAS has been previously described (Acosta et al., 2008). pGIPZ-based shRNA vectors were obtained from Sigma. pTR-mCMV-kB-copGFP and empty vector were a gift from Venkatesh Natarajan. To generate the pCANIG-mirE-based shRNA targeting mouse PTBP1, ﬁrst the miR30-based shRNA from the pGIPZ vector V3LMM_509343 was converted to miRE-based shRNA by PCR ampliﬁcation using the primers miRE-Xho-short-fw and miRE-EcoPlasmid-Rev. For de novo generation of miRE-based shRNAs, the 97-mer oligonucleotides mousePTBP1.891 and mouseEXOC7.1546 were PCR ampliﬁed using the primers miRE- Xho-fw and miRE-EcoOligo-rev and cloned into the pRRL lentiviral backbone SGEP. The miRE-based shRNAs were then shuttled into the transposon plasmid pCaNIG-shRNA using XhoI and MluI fragments. 97-mers were as follows: PTBP1.891: TGCTGTTGACAGTGAGCGACAGTCTCAATGTCAAGTACAATAGTGAAGCCACAGATGTATTGTACTT GACATTGAGACTGGTGCCTACTGCCTCGGA EXOC7.1546: TGCTGTTGACAGTGAGCGACGCCATCTTCCTACACAACAATAGTGAAGCCACAGATGTATTGTTGTG TAGGAAGATGGCGCTGCCTACTGCCTCGGA To generate a Tet-ON/PTBP1 expressing vector, ﬁrst, full-length cDNA encoding PTBP1 (NM_002819.4) was PCR ampliﬁed from pBABE-PTBP1 vector custom-synthesised by GenScript, using primers CMVPTBP1F (5’-CGTTCGAAGCCACCATGGACGG CATTGTCCCA-3’) and CMVPTBP1R (5’-CCGGTTTAAACCTAGATGGTGGACTTGGAGAAG-3’), and the Platinum PCR SuperMix High Fidelity (Invitrogen) according to manufacturer’s instructions. The PTBP1 amplicon was then shuttled into the pLenti-CMV-tight inducible (iCMV-tight) vector using BstBI and PmeI fragments. A MCS linker had previously been introduced in place of the eGFP in the iCMV-tight vector. The iCMV-tight and the pLenti-CMV-rtTA3 (reverse tetracycline controlled transactivator) plasmids were purchased from Addgene (w771-1 and w785-1, respectively). To generate a Tet-ON/EXOC7 expressing vector, EXOC7 short and long isoforms were cloned in the iCMV-tight vector. First, EXOC7 short and long isoforms were prepared by PCR ampliﬁcation using the EXOC7cloningF (5’-CGTTCGAAGCCACCATGGAC TACAAGGACGACGATGACAAGATTCCCCCACAGGAG-3’) and EXOC7cloningR (5’-CCGCCTGCAGGTCAGGCAGAGGTGTCGA AAAGGC-3’) primers and as template the cDNA (produced with oligo-dT primers) from IMR90 cells or shPTBP1 IMR90 cells, respec- tively. The EXOC7 amplicons were then shuttled into the iCMV-tight vector using BstBI and SbfI fragments. Growth Assays BrdU incorporation and colony formation assays with crystal violet were performed as previously described (Herranz et al., 2015). Brieﬂy, for BrdU incorporation assays, the cells were incubated with 10 mM BrdU for 16-18 hr before being ﬁxed with 4% PFA (w/v). BrdU incorporation was assessed by Immunoﬂuorescence and High Content Analysis microscopy. For crystal violet staining, the cells were seeded at low density on 6-well dishes and maintained for 10-14 days in the absence or presence of 4OHT. Upon colony formation or conﬂuence of a control sample, the cells were ﬁxed with 0.5% glutaraldehyde (w/v). The plates were then stained with 0.2% crystal violet (w/v). Inhibitor Treatments The working concentrations of chemical compounds were determined after dose-response testing. Speciﬁcally, (+)-JQ1 at 40 nM, BW-B 70C at 2 mM, FIPI at 1 mM, PD98059 at 20 mM(Acosta et al., 2013), Tautomycetin at 150 nM, Torin 1 at 25 nM (Herranz et al., 2015) and XE 991 dihydrochloride at 20 mM. All chemical compounds were reconstituted in DMSO. Inhibitor treatment was initiated simultaneously to 4OHT-induction. Drug-containing media was refreshed every 2 days by media change to prevent additive effects. Nucleic Acid Transfections siRNAs were purchased from Qiagen lyophilised either in a Flexitube or spotted in Flexiplates . When available, the experimentally veriﬁed siRNA sequences were preferred. For immunoﬂuorescence analysis, IMR90, IMR90 ER:RAS cells, or senescent IMR90 ER:RAS cells in suspension (100 ml) were reverse transfected with siRNAs on a well of a 96-well plate. The suspension media was DMEM supplemented with 10% FBS only. The transfection mix for each sample well contained 0.1 mL DharmaFECT 1 (GE Health- care) in 17.5 mL plain DMEM mixed with 3.6 mL siRNA 30 min prior to cell seeding. 18 hr after transfection, allowing target cells to adhere, the media were replaced with fresh complete media, containing 4OHT when appropriate. The cells were ﬁxed at the speciﬁed time-point with 4% PFA (w/v). For mRNA analysis, the procedure was identical but scaled up 20 times to ﬁt a 6-well plate. The cells were harvested by scraping in 0.8 ml TRIzol RNA isolation reagent (Ambion) per well. AONs were rationally designed using a computational approach previously described (Pramono et al., 2012), which considers co-transcriptional binding accessibility and relevant putative splice motifs of target, and AON-to-target binding thermodynamics. Sequences of AONs used in this study are provided in Table S4. All the designed AONs were synthesized by IDT as single- stranded RNA bases each modiﬁed with 2’-O-methyl and phosphorothioate backbone. Forward transfection of AONs was carried out as per siRNA transfection with the only difference being the addition of transfection mix on adhered cultures plated 3 or 4 days in advance. Cancer Cell 34, 85–102.e1–e9, July 9, 2018 e5 Conditioned Media (CM) Experiments IMR90 ER:RAS cells were seeded in a 10 cm dish for each condition. The next day the media were replaced with induction media (4OHT addition or doxycycline removal). For paracrine senescence or SASP analysis by ELISA, the media were replaced 3 days later with DMEM supplemented with 0.5% (v/v) FBS and 1% antibiotic-antimycotic solution. 4 days later, ensuring each 10 cm dish con- tained conﬂuent but alive cells, the CM were collected and initially centrifuged at 2,500 rpm to remove cellular debris. The CM were then ﬁltered through a 0.2 mm pore size cellulose acetate membrane (Gilson). For paracrine senescence experiments, the resulting media was mixed in a 3 to 1 ratio with DMEM supplemented with 40% (v/v) FBS. For SASP analysis by ELISA, equal volumes of CM were used and assay performed as per manufacturer’s instructions (R&D Systems). The samples were diluted 1,000X and each diluted sample was represented twice on the plate. The absorbance reading was taken at 450 nm (A ) in a SpectraMAX340PC (Molecular Devices) microplate reader. Protein concentration was then estimated according to a calibration curve obtained from the absorbance values of a dilution series of the supplied standard protein control. For proteomic analysis of the secretome, before replacement to appropriate media on the 3rd day, the cells were washed 3 times with pre-warmed PBS. After that, the cells were cultured for another 3 days in high glucose, no glutamine, no phenol red DMEM supplemented with L-Glutamine (Gibco), no FBS and 4OHT where appropriate. On the 6th day, the CM were collected and processed as mentioned above. Then the CM were concentrated by ultracentrifugation using the Vivaspin 20 5 kDa MWCO columns (GE Healthcare) about 100 times (Herranz et al., 2015). At this point, the protein concentration was determined using the Pierce BCA Protein Assay Kit (Thermo Scientiﬁc). Mass spectrometry of the CM was performed as previously described (Herranz et al., 2015). Total RNA Extraction Cells were scraped and homogenized in 0.8 ml TRIzol RNA isolation reagent (Ambion), mixed with 150 ml of Chloroform (Sigma), vortexed for 15 s and centrifuged at 15,000 rpm at 4 C for 30 min. After the phase separation step, the top clear RNA-containing phase was puriﬁed using the RNAeasy Mini Kit (Qiagen) from step 2 onwards according to manufacturer’s protocol. For mouse liver RNA extraction, harvested liver tissues were mixed with Qiazol Lysis Reagent (Qiagen) and homogenized with an electric homogenizer. After adding chloroform samples were centrifuged and the upper phase was mixed with isopropanol to precipitate RNA. Remaining DNA was digested with DNAseI (NEB). RNA was then puriﬁed with RNAeasy Mini Kit (Qiagen). RNA concentration was measured using a NanoDrop ND-1000 UV-Vis spectrophotometer at an absorbance of 260 nm (A260). cDNA Synthesis and Quantitative RT-PCR cDNA synthesis from cell total RNA was carried out using random hexamers, unless otherwise speciﬁed, and the SuperScript II Reverse Transcriptase (RT) kit (Invitrogen) according to manufacturer’s instructions. cDNA synthesis from mouse total RNA was performed using PrimeScript RT Master Mix (Takara, RR036A). For gene expression analysis, PCR ampliﬁcation was performed using SYBR Green PCR Master Mix (Applied Biosystems) and the samples were run on CFX96 Real-Time PCR Detection system (Bio-Rad). The primers were designed using PrimerBank or Primer-BLAST to span exon-exon junctions, or to ﬂank an intron of > 1 kb in size, to anneal to all transcript variants of the gene of interest, and to generate a PCR product of no more than 150 bp. To calculate gene expression (‘mRNA levels’) the DDCt method was used with the Ribosomal protein S14 (RPS14) expression as a normalizer and an untreated sample as relative control. For alternative splicing analysis, PCR ampliﬁcation was performed using Platinum PCR SuperMix High Fidelity (Invitrogen) and the samples were run on a Dyad Peltier Thermal cycler (Bio-Rad). The primers were speciﬁcally designed to anneal to ﬂanking exons of the alternatively spliced exon. The PCR product was then size-resolved using Agilent 2100 Bioanalyzer and Agilent High Sensitivity DNA Analysis Kit following manufacturer’s instructions and the bands were visualized using the 2100 Expert Software. In any given sample, PSI (J) was calculated by the given concentration of the larger product (included exon) relative to the sum of concentrations of both products. Sequence of primers used in this study are provided in Table S5. For mouse SASP analysis, Real-Time-PCR was conducted using the SYBR Premix Ex Taq (Takara, RR420A) with the RT Proﬁler PCR Array Mouse Cytokines & Chemokines Kit (Qiagen, PAMM-150ZA-12) in a 7300 Real Time PCR machine (Applied Biosystems). Data was analyzed with 7500 software (Applied Biosystems) and Data Analysis Center webtool (Qiagen) with the DDCt method using the mean expression of Actb, Gapdh and Hsp90ab1 as normalizers. RNA-Sequencing The RNA-sequencing libraries for the shPTBP1 and siEXOC7 experiments were prepared as described previously (Tordella et al., 2016) and were run on a Hiseq2500. For alternative splicing analysis (shPTBP1), we obtained on average 71 million 100bp paired end sequencing reads for each replicate of each condition (average of 213 million reads per condition). For GSEA (siEXOC7) we obtained on average 40 million single-end 50 bp reads for each sample. The RNA-sequencing library for the siRNAs repressing the SASP (2 different siRNAs targeting each one of the 38 genes plus addi- tional controls) was prepared following the Smart-seq2 protocol Picelli et al. (2014). siRNA transfection onto 96-wells was performed as described above and on the day of collection the cells in each well were lysed in 50 ml of lysis buffer which was prepared according to Smart-seq2. 2 ml of lysate was used for the following steps described in detail by (Picelli et al., 2014). The RNA-seq library containing 272 samples was run on a Hiseq2500 using single-end 50-bp reads with a coverage depth of 2.5x10 reads per sample in over 80% of samples. e6 Cancer Cell 34, 85–102.e1–e9, July 9, 2018 Immunoﬂuorescence Staining of Cells Cells were grown on 96-well plates, ﬁxed with 4% PFA (w/v) and stained as follows. The cells were permeabilised in 0.2% Triton X-100 (v/v) (Sigma) diluted in PBS for 10 min, blocked with 1% BSA (w/v) (Sigma) and 0.4% ﬁsh gelatin (v/v) (Sigma) for 30 min, treated with primary antibody for 40 min, then with ﬂuorescence labelled secondary antibody (Alexa Fluor ) for 30 min and ﬁnally treated with 1 mg/ml DAPI (Sigma) for 10 min. Primary and secondary antibodies were suspended in blocking solution. All incubations were followed by 3 PBS washes. For SA-b-Galactosidase assessment, live cells were treated with 100 mM 9H-(1,3-Dichloro-9,9-Dimethylacridin-2-One-7-yl) b-D-Galactopyranoside (DDAOG, Molecular Probes ) for 2 hr prior to PFA ﬁxation and subsequently stained with 1 mg/ml DAPI (Sigma) for 10 min. Cytochemical SA-b-Galactosidase Assay Cells were grown on 6-well plates, ﬁxed with 0.5% glutaraldehyde (w/v) (Sigma) in PBS for 10-15 min, washed with 1 mM MgCl in PBS (pH 6.0) 2-3 times and then incubated with X-Gal staining solution (1 mg/mL X-Gal, Thermo Scientiﬁc, 5 mM K [Fe(CN) ] and 3 6 5mMK [Fe(CN) ]) for 16-18 hr at 37 C with gentle agitation. Bright ﬁeld images of cells were taken using the DP20 digital camera 4 6 attached to the Olympus CKX41 inverted light microscope. The percentage of SA-b-Gal positive cells was estimated by counting at least 200 cells per replicate sample facilitated by the ‘point picker’ tool of ImageJ software. Staining of Tissue Sections Sections (2-4 mm) of parafﬁn-embedded mouse liver were processed for IF analysis, IHC or RNA in situ hybridization. For IF, antigen retrieval was carried out with 10 mM sodium citrate (pH 6) for 20 min in a steamer, after which sections were treated with Protein Block (Dako) for 10 min, incubated with primary antibody O/N at 4 C, incubated with ﬂuorescence labeled secondary antibody (Alexa Fluor ) for 1 hr and ﬁnally mounted in FluoroMount-G . Primary and secondary antibodies were suspended in Antibody Diluent (Dako). For colorimetric-based IHC, antigen retrieval was carried out with EDTA or sodium citrate buffer, after which sections were incu- bated with antibodies against antigens in BONDTM primary antibody diluent (AR9352, Leica Biosystems). Primary antibody exposure was followed by secondary antibody (Leica Biosystems) and staining using the Novolink DAB (Polymer) kit (RE7230-K, Leica Biosystems). Brightﬁeld images were captured using a Leica DM 1000 LED microscope and processed using Leica Application Suite software (Leica Biosystems). RNA in situ hybridization in 5 mm liver sections was carried out using the RNAscope 2.5 Assay (FFPE and 2.5 HD Brown Assay) from Advanced Cell Diagnostics, according to manufacturer’s protocol. Probes for Cxcl5 (Cat#467448), the housekeeping gene Ppib (positive control, Cat#313911) and the bacterial gene dapB (negative control, Cat#310043) were purchased from Advanced Cell Diagnostics. Signal detection was carried out by DAB staining. Slides were counterstained with haematoxylin prior to mounting and then whole digital slides were acquired using the Leica SCN400 scanner (Leica) at X20 magniﬁcation. For SA-b-Galactosidase assessment, frozen sections (6 mm) were ﬁxed in ice-cold 0.5% glutaraldehyde (w/v) solution for 15 min, washed 2 times in 1 mM MgCl /PBS (pH 6.0) for 5 min, then incubated with X-Gal staining solution (1 mg/mL X-Gal, Thermo Scientiﬁc, 5 mM K3[Fe(CN) ] and 5 mM K4[Fe(CN) ]) for 16-18 hr at 37 C, washed 2 times in distilled water, counterstained with eosin for 30 s, 6 6 TM dehydrated and mounted in VectaMount . Quantiﬁcation was performed as previously described (Tordella et al., 2016). High Content Analysis (HCA) IF imaging was carried out using the automated high-throughput ﬂuorescent microscope IN Cell Analyzer 2000 (GE Healthcare) with a 20x objective with the exception of DNA damage foci analysis which required a 40x objective. Fluorescent images were acquired for each of the ﬂuorophores using built-in wavelength settings (‘DAPI’ for DAPI, ‘FITC’ for AlexaFluor 488 FITC, ‘Texas Red’ for AlexaFluor 594 and ‘Cy5’ for DDAOG). Multiple ﬁelds within a well were acquired in order to include a minimum of 1,000 cells per sample-well. HCA of the images were processed using the IN Cell Investigator 2.7.3 software as described previously (Herranz et al., 2015). Brieﬂy, DAPI served as a nuclear mask hence allowed for segmentation of cells with a Top-Hat method. To detect cyto- plasmic staining in cultured cells, a collar of 7-9 mm around DAPI was applied. To detect cytoplasmic staining in tissue sections, a multiscale top-hat parameter was set on the reference wavelength (typically NRAS staining). Nuclear IF in the reference wavelength, i.e. all the other wavelengths apart from DAPI, was quantitated as an average of pixel intensity (grey scale) within the speciﬁed nuclear area. Cytoplasmic IF in the reference wavelength was quantitated as a coefﬁcient of variance (CV) of the pixel intensities within the collar area. Nuclear foci IF in the reference wavelength was quantiﬁed as n number of foci per nucleus. In samples of cultured cells, a threshold for positive cells was assigned above the average intensity of unstained or negative control sample unless otherwise spec- iﬁed. In tissue sections, a threshold for positive cells was assigned above background staining using the built-in ‘cell to background ratio’ measurement. Immunohistochemistry imaging and quantiﬁcation was also automated. FACS The liver was chopped into small 1 mm3 pieces and then enzymatically digested in a medium composed of equal volume of DMEM and HBS supplemented with 0.5 mg/ml Collagenase (Serva Collagenase NB 4G) for 30 min at 37 C. The enzymatic reaction was stopped using cold medium and the liver suspension was meshed through a 70 mm nylon mesh (Falcon). After centrifugation eryth- rocytes were lysed using an ACK buffer (150 mM NH4Cl, 10 mM KHCO3, and 0.1 mM EDTA). 10 cells were resuspended in blocking solution (2% BSA in PBS) and stained with antibodies on ice for 30 min. Samples were immediately acquired using a FACSCanto ﬂow Cancer Cell 34, 85–102.e1–e9, July 9, 2018 e7 cytometer (BD Biosciences). Samples were gated on viable leukocytes by DAPI exclusion and doublets were excluded using height versus area dot plots. Gating strategies were described previously (Eggert et al., 2016) and depicted in Figure S6. Data analysis was performed using FlowJo software (Tree Star). Immunoblot and Immunoprecipitation Cells were lysed in RIPA buffer (80 mM Tris pH 8.0, 150 mM NaCl, 1% Triton X-100, 0.5% Na-Doc, 0.1% SDS, 1mM EDTA) supplemented with 1 tablet of phosphatase and 1 tablet of protease inhibitors (Roche) per 10 ml RIPA. Lysis was performed on ice for 20 min with occasional vortexing followed by centrifugation at 15,000 rpm at 4 C for 15 min to collect protein extracts. Immunobloting was carried out as previously described (Herranz et al., 2015). Immunoprecipitation was performed by incubating the lysate (equal amounts of protein) with antibody (EXOC7 sc-365825 or control IgG) for 2 hr at 4 C and Dynabeads Protein A for 1hrat 4 C, before being washed 3 times in RIPA buffer and eluted in Laemmli buffer for 10 min at 95 C. siRNA Screen Analysis The screen readouts were normalized by B-score and Normalized Percent Inhibition (NPI) using the freely available online software found at http://web-cellhts2.dkfz.de/cellHTS-java/cellHTS2/ (Pelz et al., 2010). For NPI calculation, each sample was normalized to the control siRNAs present in the same 96-well plate. The scramble siRNA transfected cells served as negative controls and the cells transfected with siRNAs targeting RELA and C/EBPb served as positive controls. K-means clustering was performed using R. Analysis of Proteomics Data Raw ﬁles were analysed using Maxquant (Cox and Mann, 2008). Files were searched against the Swissprot human database th (downloaded on August 10 2015). Protein sequences were reversed to provide a decoy database that enabled a protein and peptide false discovery rate of 1%. Fixed modiﬁcation of cysteine residues (carbamidomethylated) and variable modiﬁcation of methionine residues (oxidised) were included. Protein quantiﬁcation information was produced using the label-free quantiﬁcation (‘‘LFQ’’) algorithm to enable direct comparison of protein intensity between samples. The list of proteins was then ﬁltered down to SASP proteins according to a previously experimentally determined list (Acosta et al., 2013). Protein measurements were then mean normalized using the ‘scale’ function in R and visualized as heatmaps using the unsupervised hierarchical clustering option in MultiExperiment Viewer (MeV). Analysis of RNA Sequencing Data RNA-seq sequencing reads were aligned to hg19 genome with TopHat using parameters ‘‘–library-type fr-ﬁrststrand’’ and using known transcripts annotation from Ensembl gene version 70. Number of reads on exons were summarised using featureCounts function available in rsubread R package (Liao et al., 2013). Differential expression analysis was performed using DESeq2 (Love et al., 2014; Tordella et al., 2016; Trapnell et al., 2009). PCA plots were generated using plotPCA() function from DESeq2 Bioconductor package. To generate the heatmaps shown in Figures 2D, 2E, and S2D, we initially identiﬁed differentially expressed genes between non-senescent conditions (in total 15 replicates) and senescent conditions (in total 6 replicates) using DESeq2 with adjusted p value of 0.05. Differentially expressed genes were ﬁltered for and their normalized expression values (rlog) were obtained from DESeq2. Clustering was performed with heatmap.2 function available in gplots R package version 3.0.1 with Z-score transformed rlog values. Summary heatmap (Figure 2E) was created using average rlog values from gene and shRNA clusters. To determine differential alternative splicing between samples, Multivariate Analysis of Transcript Splicing (MATS) on R (Shen et al., 2014) was used. The source-code is freely available at http://rnaseq-mats.sourceforge.net. The user-deﬁned cut-off used for the likelihood-ratio statistical test was assigned to 20% splicing change. Shashimi plots were generated with IGV. Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data For each differential expression analysis comparison, genes were ranked using ‘‘wald statistics’’ obtained from DESeq2 and GSEA was carried out on these ranked lists on all curated gene sets available in MSigDB (http://software.broadinstitute.org/gsea/msigdb). DESeq2 independent ﬁltering is based on mean of normalised read counts and ﬁlters out genes with very low expression level. The SASP and OIS GSEA signatures were derived from (Acosta et al., 2013) as described before (Herranz et al., 2015; Tordella et al., 2016). RNA-Binding Motif Analysis FIMO (Grant et al., 2011) was used to scan the human gene sequences for the PTBP1 RNA-binding motifs inferred by (Ray et al., 2013). The thereby predicted occurrences were mapped to the analyzed splicing events. To generate the RNA-maps (Figures 7B and S7D), for each comparison alternative exons were divided into those with PSIs signiﬁcantly increasing upon PTBP1 knockdown (putatively repressed), those with PSIs signiﬁcantly decreasing upon PTBP1 knockdown (putatively enhanced), and those with PSIs not altered upon PTBP1 knockdown (putatively not regulated). Statistical signiﬁcance for local motif enrichment is associated with Fisher’s exact tests for differences in motif occurrences between groups of exons within 31 bp moving windows. e8 Cancer Cell 34, 85–102.e1–e9, July 9, 2018 CLIP Data Visualization Publically available pre-processed (i.e. genomically mapped in BED-formatted ﬁles) PTBP1 CLIP-Seq data were localized through CLIPdb (Yang et al., 2015). Those included iCLIP and PAR-iCLIP in different samples of 293T cells (GEO accession GSE57278; Raj et al., 2014), and HITS-CLIP on HeLa cells (GEO accessions GSE19323/GSE42701; Xue et al., 2009). A BedGraph formated ﬁle of PTBP1 iCLIP data from HeLa cells (ArrayExpress accession E-MTAB-3108; Coelho et al., 2016) was also retrieved. The UCSC Genome Browser (Kent et al., 2002) was used for the visualization (Figure S7I). Cross-Tissue Analysis of PTBP1 Expression and EXOC7 Exon 7 Inclusion The data used for these analyses were obtained from the GTEx Portal (GTEx Consortium, 2015) and dbGaP (Accession number phs000424.v6.p1 on 2017/09/03). Gene expression was quantiﬁed from gene read counts and only genes sufﬁciently expressed (sum of counts per million (CPM) across all samples > 10) were kept for further analyses. Quantile normalization of gene expression was performed using the normalizeBetweenArrays function in limma (Ritchie et al., 2015). Splicing of EXOC7 exon 7 was quantiﬁed from the respective exon junction reads, using the percent spliced-in (PSI) metric, as in Barbosa-Morais et al. (2012). For both gene expression and alternative splicing analyses, samples corresponding to patient-derived cell lines and two samples with missing values for EXOC7 exon 7 skipping quantiﬁcation were excluded. Cross-Tissue Gene Set Enrichment Analysis Samples were separated based on their PTBP1 expression levels (cut-off set as the mean PTBP1 expression level across samples, approximately 9005 normalized read counts) or EXOC7 exon 7 inclusion levels (cut-off set as the local density minimum separating the two peaks of the respective bimodal PSI distribution, 0.4). Gene expression was modelled as a function of sample separation according to PTBP1 expression or EXOC7 exon 7 inclusion using limma’s lmFit (Phipson et al., 2016). GSEA with Hallmark Gene Sets (Liberzon et al., 2015), was run on the list of genes ordered by descending t-statistic for the subsequent differential gene expression analysis. QUANTIFICATION AND STATISTICAL ANALYSES For the secondary validation siRNA screen, 3 replicate NPI values of each sample siRNA were compared to all scramble siRNA values by unpaired, Student’s t-test. For xenograft experiments, differences between tumor growth rates were tested for statistical signif- icance using a 2-tailed homoscedastic t-test comparing the area under the curve (AUC) for each individual tumor within a treatment group. For the proportions of increased exon skipping/inclusion (Figure S7B), one-sided Pearson’s chi-square tests against an expected proportion of 0.5 were used. For alternative splicing analysis by RMATS, splicing changes with a false discovery rate (FDR) less than 0.05 were considered statistically signiﬁcant. For the rest of cell culture experiments and for mouse experiments, signiﬁcant differences were determined by one-way ANOVA or two-way ANOVA (for grouped data). Asterisks (*) always indicate signiﬁcant differences as follows. ns = not signiﬁcant, * = p < 0.05, ** = p < 0.01, *** = p < 0.001. n=number of biological replicates, unless otherwise speciﬁed. DATA AND SOFTWARE AVAILABILITY RNA-seq data have been deposited at the Gene Expression Omnibus under the accession numbers GSE101763 (comprising GSE101750 and GSE101758) and GSE101766. Cancer Cell 34, 85–102.e1–e9, July 9, 2018 e9

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PTBP1-Mediated Alternative Splicing Regulates the Inflammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells

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Publisher: Unpaywall
ISSN: 1535-6108
DOI: 10.1016/j.ccell.2018.06.007
Publisher site: See Article on Publisher Site

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PTBP1-Mediated Alternative Splicing Regulates the Inflammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells

PTBP1-Mediated Alternative Splicing Regulates the Inflammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells

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PTBP1-Mediated Alternative Splicing Regulates the Inflammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells

PTBP1-Mediated Alternative Splicing Regulates the Inflammatory Secretome and the Pro-tumorigenic Effects of Senescent Cells

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