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Seq Captures Enhancer Landscapes Immediately After T-Cell Stimulation

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Seq Captures Enhancer Landscapes Immediately After T-Cell Stimulation Published online: March 7, 2017 Article TT-seq captures enhancer landscapes immediately after T-cell stimulation Margaux Michel1,† , Carina Demel1,† , Benedikt Zacher2, Björn Schwalb1, Stefan Krebs2, Helmut Blum2, Julien Gagneur3,* & Patrick Cramer1,** Abstract transcription over long distances (Banerji et al, 1981). Active enhan- cers adopt an open chromatin structure (Calo & Wysocka, 2013) To monitor transcriptional regulation in human cells, rapid and recruit co-activators such as Mediator (Fan et al, 2006). Media- changes in enhancer and promoter activity must be captured with tor can apparently bridge between enhancers and promoters high sensitivity and temporal resolution. Here, we show that the because it binds both transcriptional activators and the RNA poly- recently established protocol TT-seq (“transient transcriptome merase II (Pol II) initiation complex at the promoter (Malik & sequencing”) can monitor rapid changes in transcription from Roeder, 2010; Liu et al, 2013). Promoter–enhancer interaction enhancers and promoters during the immediate response of T cells (“pairing”) increases initiation complex stability and promotes Pol II to ionomycin and phorbol 12-myristate 13-acetate (PMA). TT-seq escape from the promoter (Splinter et al, 2006; DeMare et al, 2013; maps eRNAs and mRNAs every 5 min after T-cell stimulation with Allen & Taatjes, 2015). Promoter–enhancer pairing requires DNA high sensitivity and identifies many new primary response genes. looping that is facilitated within insulated neighborhoods, which are TT-seq reveals that the synthesis of 1,601 eRNAs and 650 mRNAs genomic regions formed by looping of DNA between two CTCF- changes significantly within only 15 min after stimulation, when binding sites co-occupied by cohesin (Phillips-Cremins et al, 2013; standard RNA-seq does not detect differentially expressed genes. Dowen et al, 2014; Hnisz et al, 2016a). Transcription of enhancers that are primed for activation by nucle- The genomewide identification of enhancers is crucial for study- osome depletion can occur immediately and simultaneously with ing cellular regulation and differentiation, but remains technically transcription of target gene promoters. Our results indicate that challenging (Shlyueva et al, 2014). Enhancers may be distinguished enhancer transcription is a good proxy for enhancer regulatory from other genomic regions through a signature of histone modifi- activity in target gene activation, and establish TT-seq as a tool for cations that can be mapped by chromatin immunoprecipitation monitoring the dynamics of enhancer landscapes and transcription (ChIP; Heintzman et al, 2007; Schu¨beler, 2007; Visel et al, 2009) or programs during cellular responses and differentiation. DNA accessibility assays (Xi et al, 2007; Thurman et al, 2012; Shlyueva et al, 2014). Regulatory active enhancers may be identified Keywords enhancers; functional genomics; promoters; T-cell response; through their transcriptional activity, which is thought to be a good transcriptome analysis proxy for their function in promoter activation (Melgar et al, 2011; Subject Categories Chromatin, Epigenetics, Genomics & Functional Wu et al, 2014; Li et al, 2016). Transcribed enhancers produce Genomics; Genome-Scale & Integrative Biology; Transcription enhancer RNAs (eRNAs) (Kim et al, 2010; Djebali et al, 2012), DOI 10.15252/msb.20167507 | Received 16 December 2016 | Revised 13 which are difficult to detect because they are short-lived (Rabani February 2017 | Accepted 15 February 2017 et al, 2014; Schwalb et al, 2016), rapidly degraded by the exosome Mol Syst Biol. (2017) 13: 920 (Lubas et al, 2015), and generally not conserved over species (Andersson et al, 2014). The role of enhancer transcription and/or eRNAs remains Introduction unclear (Li et al, 2016). It is likely that the process of enhancer tran- scription has a functional role, maybe in recruiting chromatin In metazoan cells, the synthesis of mRNAs from protein-coding remodelers through their association with transcribing Pol II genes during transcription is driven from promoters and activated (Gribnau et al, 2000). Consistent with this model, enhancer tran- by enhancers (Lenhard et al, 2012; Levine et al, 2014). Enhancers scription can precede target gene transcription (De Santa et al, 2010; are regulatory units in the genome that contain binding sites for Kaikkonen et al, 2013; Schaukowitch et al, 2014; Arner et al, 2015). sequence-specific transcription factors and can activate mRNA It is also possible that eRNAs themselves have a function, because 1 Department of Molecular Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany 2 Gene Center Munich, Ludwig-Maximilians-Universität München, Munich, Germany 3 Department of Informatics, Technische Universität München, Garching, Germany *Corresponding author. Tel: +49 89 289 19411; E-mail: [email protected] **Corresponding author. Tel: +49 551 201 2800; E-mail: [email protected] †These authors contributed equally to this work ª 2017 The Authors. Published under the terms of the CC BY 4.0 license Molecular Systems Biology 13: 920 | 2017 1 Published online: March 7, 2017 Molecular Systems Biology TT-seq captures enhancer landscapes Margaux Michel et al eRNA knockdown may impair target gene activation (Li et al, 2013; as a simple-to-use, very sensitive tool to investigate transcriptional Ilott et al, 2014; Schaukowitch et al, 2014). eRNA knockdown may responses at high temporal resolution, ideally suited to monitor also have negative effects on promoter–enhancer pairing (Li et al, rapid changes in enhancer landscapes and in transcriptional 2013), although some studies came to different conclusions (Hah programs during cellular differentiation and reprogramming. et al, 2013; Schaukowitch et al, 2014). Here, we investigate the relationship between transcription from enhancers and promoters during the human T-cell response. Upon Results T-cell stimulation, the T-cell receptor and the costimulatory receptor CD28 are activated, leading to a signaling cascade (Smith-Garvin Monitoring the immediate T-cell response et al, 2009). Phosphorylation of multiple factors at the plasma membrane leads to recruitment and activation of PLC-c that cleaves We monitored immediate changes in RNA synthesis in Jurkat T cells PI(4,5)P2 in DAG and IP3. First, DAG binds and activates PKCh, during the first 15 min after stimulation with ionomycin and PMA which leads to activation and nuclear translocation of the transcrip- using both TT-seq and RNA-seq (Fig 1A, Materials and Methods, tion factors NF-jB and AP-1. Second, IP3 diffuses away from the Appendix Figs S1 and S2). We selected time points before stimula- plasma membrane and activates calcium channel receptors on the tion (0 min), and 5, 10, and 15 min after stimulation. The TT-seq ER, increasing intracellular calcium ion concentration and leading to data revealed strong up- and downregulation of mRNA synthesis for activation of calmodulin and calcineurin. Calcineurin activates immediately responding genes (Fig 1B and C). In TT-seq data, we NFAT that translocates to the nucleus and drives gene activation. T- also observed a high coverage of intronic regions and regions down- cell stimulation via the T-cell receptor and CD28 can be mimicked stream of the poly-adenylation site (PAS, annotated by GENCODE; by addition of PMA and ionomycin because phorbol esters activate Harrow et al, 2012), demonstrating that TT-seq could trap short- PKC and calcium ionophores raise intracellular calcium levels lived RNA (Fig 1B and C). (Weiss & Imboden, 1987). We combined the TT-seq data to segment the genome into tran- The T-cell response involves rapid changes in gene expression scribed and non-transcribed regions using GenoSTAN (Zacher et al, (Marrack et al, 2000; Rogge et al, 2000; Feske et al, 2001; Diehn 2017). Then, we automatically annotated a total of 22,141 tran- et al, 2002; Raghavan et al, 2002; Cheadle et al, 2005). Responding scribed regions (“transcripts”) before and after T-cell stimulation genes were classified into immediate-early, early, and late response (RPK cutoff = 16.5, Materials and Methods, Fig EV1 and genes based on changes in RNA levels. Immediate-early response Table EV1). Comparison with the GENCODE annotation (Harrow genes are transiently activated within the first hour after stimulation et al, 2012) enabled us to classify our annotated transcripts into (Bahrami & Drablos, 2016). There are ~40 immediate-early genes 8,878 mRNAs, 590-long non-coding RNAs (lincRNAs), by requiring described, most of which code for transcription factors such as FOS, at least 20% of the transcribed region to overlap with GENCODE- FOSB, FRA1, JUNB, JUN, NFAT, NFKB, and EGR1 (Greenberg & Ziff, annotated “protein_coding” or “lincRNA” (long, intervening non- 1984; Sheng & Greenberg, 1990). Several hours after stimulation, coding RNA that can be found in evolutionarily conserved, inter- immediate-early factors activate early and late response genes, genic regions) transcripts (Materials and Methods). The 12,673 including cytokines such as IL-2, TGF-b, or IFN-c (Crabtree, 1989; remaining transcripts we categorized as non-coding RNAs (ncRNAs) Ellisen et al, 2001). Despite these studies, the immediate T-cell (Fig EV1C). These RNAs may contain additional long non-coding response and the primary events after T-cell stimulation remain RNAs that do not fall into GENCODE’s “lincRNA” definition (Materi- incompletely understood. als and Methods). The length distribution of RNAs
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