Expression of endogenous retroviruses reflects increased usage of atypical enhancers in T cells
2019; Springer Nature; Volume: 38; Issue: 12 Linguagem: Inglês
10.15252/embj.2018101107
ISSN1460-2075
AutoresSaliha Azébi, Éric Batsché, Frédérique Michel, Étienne Kornobis, Christian Muchardt,
Tópico(s)T-cell and B-cell Immunology
ResumoArticle8 May 2019free access Transparent process Expression of endogenous retroviruses reflects increased usage of atypical enhancers in T cells Saliha Azébi Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Ecole Doctorale "Complexité du Vivant" (ED515), Sorbonne Université, Paris, France Search for more papers by this author Eric Batsché Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Search for more papers by this author Frédérique Michel Unit of Cytokine Signaling, Department of Immunology, Institut Pasteur, Paris, France Search for more papers by this author Etienne Kornobis Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Search for more papers by this author Christian Muchardt Corresponding Author [email protected] orcid.org/0000-0003-0145-4023 Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Search for more papers by this author Saliha Azébi Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Ecole Doctorale "Complexité du Vivant" (ED515), Sorbonne Université, Paris, France Search for more papers by this author Eric Batsché Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Search for more papers by this author Frédérique Michel Unit of Cytokine Signaling, Department of Immunology, Institut Pasteur, Paris, France Search for more papers by this author Etienne Kornobis Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Search for more papers by this author Christian Muchardt Corresponding Author [email protected] orcid.org/0000-0003-0145-4023 Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France Search for more papers by this author Author Information Saliha Azébi1,2, Eric Batsché1, Frédérique Michel3, Etienne Kornobis1 and Christian Muchardt *,1 1Unité de Régulation Epigénétique, UMR3738, CNRS, Institut Pasteur, Paris, France 2Ecole Doctorale "Complexité du Vivant" (ED515), Sorbonne Université, Paris, France 3Unit of Cytokine Signaling, Department of Immunology, Institut Pasteur, Paris, France *Corresponding author. Tel: +33 145688525; E-mail: [email protected] EMBO J (2019)38:e101107https://doi.org/10.15252/embj.2018101107 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Several autoimmune diseases including multiple sclerosis (MS) cause increased transcription of endogenous retroviruses (HERVs) normally repressed by heterochromatin. In parallel, HERV-derived sequences were reported to drive gene expression. Here, we have examined a possible link between promoter and enhancer divergent transcription and the production of HERV transcripts. We find that HERV-derived sequences are in general counter-selected at regulatory regions, a counter-selection that is strongest in brain tissues while very moderate in stem cells. By exposing T cells to the pesticide dieldrin, we further found that a series of HERV-driven enhancers otherwise active only at stem cell stages can be reactivated by stress. This in part relies on peptidylarginine deiminase activity, possibly participating in the reawakening of silenced enhancers. Likewise, usage of HERV-driven enhancers was increased in myelin-reactive T cells from patients with MS, correlating with activation of nearby genes at several sites. Altogether, we propose that HERV-driven enhancers constitute a reservoir of auxiliary enhancers transiently induced by stress while chronically active in diseases like MS. Synopsis Human endogenous retroviruses (HERVs) derived from ancient exogenous viruses can act as cis-enhancers. This study shows that increased disease-related gene expression is in part due to a chronic increase of HERV cis-enhancer activity in the context of autoimmune diseases. Enhancers driven by HERVs are enriched in the neighborhood of immune genes. HERV-driven enhancers are mostly active in embryonic cells but silenced in mature adult tissues. Extensive stress causes transient reactivation of HERV transcription. In multiple sclerosis, some HERV-driven regulatory regions become chronically active. Introduction Several autoimmune diseases including multiple sclerosis (MS), type 1 diabetes mellitus, and rheumatoid arthritis are associated with increased transcription of sequences originating from ancient retroviral infections of the germ line. The possible role of these human endogenous retroviruses (HERVs) in the onset of autoimmune diseases has been extensively investigated, mostly with a focus on their protein products, that trigger immune reactions and occasionally assemble into virus-like particles (Christensen, 2016; Nexø et al, 2016). In contrast, the regulatory mechanisms at the root of the increased transcription of HERV-derived sequences in the patients remain essentially unexplored. This is an important issue as the understanding of transcriptional events associated with autoimmune diseases may help unraveling the mechanisms leading to their onset. Transposable elements are maintained under strict transcriptional control via a combination of DNA methylation, histone H3 lysine 9 methylation, and binding of KRAB domain-containing zinc-finger proteins (Karimi et al, 2011; Imbeault et al, 2017). It has been suggested that the function of the silencing is to restrain the mobility of the repeated elements and prevent them from damaging the genome. While mobility may be an issue for other transposable elements like LINES that maintain transposition capacity, there is currently no evidence for new somatic or germ line insertions of HERVs in humans, and nearly all have lost coding potential (Bannert & Kurth, 2006; Magiorkinis et al, 2015). Another possibility is that HERV sequences require silencing because of their regulatory potential. Indeed, HERVs are an important source of cis-regulatory elements initially necessary for the viral cycle, and several studies have established exaptation of HERV-derived sequences for the transcriptional regulation of host genes, mostly involved in stemness and development, but also in immunity and antiviral defense (Sundaram et al, 2014; Wang et al, 2014; Chuong et al, 2016; Hackett et al, 2017; Hummel et al, 2017; Imbeault et al, 2017). As an argument in favor of a regulatory role of HERVs, post-translational histone modifications characteristic of promoters and enhancers have been detected on retroviral sequences (Chuong et al, 2013; Xie et al, 2013). These histone modifications include the promoter-specific histone H3 lysine 4 tri-methylation and the enhancer-enriched histone H3 lysine 4 monomethylation and lysine 27 acetylation (Calo & Wysocka, 2013). In parallel, HERVs have been reported to contribute significantly more than expected by chance to DNase I accessible regions, characteristic of transcribed DNA sequences (Jacques et al, 2013). The implication of HERV-derived sequences in transcriptional regulation justifies their tight regulation, but it may also provide an explanation for their transcription. Indeed, promoters and enhancers are sites of (mostly) bidirectional transcription and the abundance of product of this transcription (uaRNAs for promoters and eRNAs for enhancers) reflects the activity of the regulatory sequences (Kim et al, 2010; Melgar et al, 2011; Hah et al, 2013). Thus, promoter or enhancer activity represents an opportunity for HERV-derived sequences to be transcribed, whether they are located at the site of transcription initiation or in its vicinity, and this could be the basis for HERV transcripts detected in autoimmune diseases. To investigate this possibility, we have here examined the position of HERV-derived sequences relative to enhancers and promoters. For this, we have taken advantage of the systematic mapping of functional domains by the NIH Roadmap Epigenomics Mapping and the Fantom5 consortia, respectively, relying on combinations of histone modifications detected by chromatin immunoprecipitation assays and on Cap Analysis of Gene Expression (CAGE), defining sites of transcription initiation. The approach showed that the bulk of HERV-containing sequences are counter-selected inside promoters and enhancers and even more so at the borders of these regulatory regions. Yet, it also confirmed that a subset of HERV sequences function as cis-regulatory elements active mostly in embryonic stem cells and frequently located in the neighborhood of genes involved in innate immune defense. To investigate whether transcriptional activation of such HERV-driven cis-regulatory elements could be a source of disease-related HERV transcripts, we identified a small molecule able to induce transcription of several MS-associated HERVs. This allowed us to show that production of disease-related HERV transcripts accompanies an acute stress-induced transcriptional response that involves awakening of cis-regulatory elements atypical for mature T cells and enriched in HERV-driven enhancers. Finally, examination of transcriptomic data from patients with MS revealed a chronic disease-associated increase in the usage of HERV-driven enhancers, resulting in the production of HERV transcripts, and correlating with transcriptional activation of nearby genes. Results HERV-driven enhancers and promoters are rare and tissue-specific To reach an overview of the participation of HERV-derived sequences in transcription initiation throughout human tissues, we extracted all regions annotated as active promoters or enhancers in the 127 tissues mapped by the NIH Roadmap Epigenomics Mapping Consortium (Roadmap Epigenomics Consortium et al, 2015). These regions will be referred to as PEs for "Promoter or Enhancer" regions. To estimate the similarity between each of the 127 sets of PEs and regions annotated "LTR" in RepeatMasker, we used the Jaccard index defined as the size of the intersection divided by the size of the union of the sample sets. As the total number of PEs varies among the tissues, we also calculated the Jaccard index between the PEs from each tissue and randomly selected non-LTR regions (average of thousand iterations). The score shown for each tissue is the Jaccard index (PEs vs. LTRs) divided by the Jaccard index (PEs vs. random). The approach showed that in all tissues, the overlap between PEs and HERVs is smaller than that expected by chance (all ratios are below 1, Fig 1A, full list Appendix Fig S1A). It also showed that overlap of HERV sequences with PEs varied extensively from one tissue to the other, with several pluripotent stem and cancer cell lines harboring HERV sequences inside their PEs approximately fourfold more frequently than tissues at the bottom of the list. These last-mentioned tissues notably included pancreatic islets, several brain tissues, and tissues from the gastrointestinal tract. To further explore the tissue specificity of HERV-driven PEs, we also compared LTRs to either ubiquitous or tissue-specific PEs. Likewise, we compared LTRs to either evolutionary conserved or human-specific liver cis-regulatory elements (Appendix Fig S1B). In these tests, tissue-specific and human-specific elements reached the best similarity scores with LTRs, in agreement with earlier observations (Trizzino et al, 2017, 2018). Figure 1. Genome-wide localization of HERVs relative to promoters and enhancers Comparison of regions annotated "LTR" in RepeatMasker with the regions annotated TssA, TssAFlnk, Enh, EnhG, TssBiv, or EnhBiv in the 15 core marks model of the Epigenomic Roadmap consortium (referred to as PEs for Promoters and Enhancers). For each tissue, the Jaccard index comparing PEs to LTRs is divided by the average Jaccard index (1,000 iterations) obtained when comparing PEs to randomly selected genomic locations (of the same sizes as the LTRs). Only the 10 tissues with the highest and the lowest score are shown. Full list in Appendix Fig S1A. Profile reporting the position of regions annotated "LTR" in RepeatMasker relative to PEs from the indicated tissues. Red and blue horizontal lines materialize the level of LTR density expected in a random sequence from pancreatic islets and human embryonic stem cell line HUES6, respectively. A graphic interpretation of the positioning of PEs at the bottom of LTR valleys in panel (A). Example of a region annotated "LTR" in RepeatMasker and overlapping with a PE and with a CAGE peak from the FANTOM5 consortium data, indicative of a site of transcription initiation. Biological process GO terms associated with genes located 100 kb or less from a region annotated "LTR" in RepeatMasker and overlapping with a PE and with a CAGE peak. Download figure Download PowerPoint We next examined the position of HERV sequences relative to PEs. In all tissues, the PEs were located at the bottom of a HERV-depleted valley (Fig 1B). This indicates that the neighborhood of PEs is depleted of HERV sequences, possibly to avoid their serendipitous transcription (schematic in Fig 1C). In all tissues except pancreatic islets and fetal female brain, we also noted an upturn of variable size within the core of the PEs. In a vast majority of tissues, this upturn or "HERV-butte" remained below the basal level (considered 10 kb away from the PE—see, e.g., HUES6 and pancreatic islets). However, in a series of embryonic stem and iPS cells and in the K562 leukemia cell line, the HERV-butte reached at or above the baseline, indicative of extensive usage of HERV sequences in the PEs of these cells (Fig 1B and Appendix Fig S1C–F). We further tested whether the evolutionary age of the LTRs would affect their position relative to PEs (Appendix Fig S1G–I). Examinations of the profiles suggested that the ERV1 clade (approximately 170,000 copies) is better tolerated in the neighborhood of PEs than is the older ERVL clade (approximately 500,000 copies), in agreement with an earlier study showing that ERV1 is the most abundant LTR within cis-regulatory elements (Trizzino et al, 2017). The younger ERVKs were too few in number for the profiles to be meaningful (approximately 10,000 copies). The presence of a HERV-butte inside the boundaries of the PEs in most tissues prompted us to specifically examine HERVs likely to be the functional core of promoters or enhancers. To that end, we listed RepeatMasker "LTRs" overlapping with a PE in at least one of the 127 tissues while also matching a Fantom5 Cap Analysis of Gene Expression (CAGE) peak (example in Fig 1D and Appendix Fig S1M). These CAGE peaks define sites of transcriptional initiation active in at least one of the 975 CAGE libraries examined by the Fantom5 consortium (DGT, 2014). This approach selected approximately 1% of all HERV sequences (6684 "CAGE-LTRs" out of 708210 regions annotated "LTR"). Interestingly, CAGE-LTRs had highest similarity (Jaccard index) with PEs from several hematopoietic tissues, while brain tissues segregated clearly at the bottom of the list (Appendix Fig S1N). We then identified genes located within 100 kb of the CAGE-LTRs using GREAT (McLean et al, 2010). GO term analysis of these genes showed a clear enrichment in genes involved in cytokine and inflammatory response (Fig 1E). As above, we next stratified the LTRs according to their evolutionary age. The ERV1 clade showed the highest similarity with CAGE peaks (Appendix Fig S1J) with approximately 2% of the ERV1 copies hosting a CAGE peak; the younger ERVKs showed the lowest similarity. GO term analysis of genes located within 100 kb of CAGE-containing ERV1s essentially recapitulated that reached when considering all CAGE-LTRs (Appendix Fig S1L). GO term analysis of genes located within 100 kb of CAGE-containing ERVLs also designated pathways related to innate immunity, yet with a focus on the interferon gamma response (Appendix Fig S1K). In contrast, ERVKs did not appear to have specialized in any specific pathway. We note however here that, due to the difficulty of their single-locus mapping, the impact of these relatively young HERVs on transcriptional regulation may be underestimated. Altogether, we concluded from this analysis that (i) most HERV sequences are located away from sites of transcriptional initiation and are therefore less likely to be transcribed accidentally upon activation of a neighboring promoter or enhancer, (ii) the usage of HERVs as cis-regulatory elements varies extensively from one tissue to the other, being highest in stem and cancer cells and lowest in brain tissues, (iii) cis-regulatory elements borne by HERV sequences, while associated with multiple regulatory pathways, play a distinct role in the regulation of immune genes, in line with earlier observations on the role of HERV sequences in cellular defense (Chuong et al, 2016; Grandi & Tramontano, 2018). Dieldrin as a tool to induce transcription of disease-related HERVs To challenge the bioinformatic analysis described above, we searched for a reliable tool to activate HERVs in a tissue culture cell line. In an initial approach, we chose to monitor some of the HERVs of the H, K, and W clades frequently described as activated in patients with autoimmune diseases (Appendix Fig S2A). Searching the literature for compounds potentially interfering with heterochromatin-mediated repression brought us to test dieldrin, an organochlorine pesticide described as an inducer of histone acetylation in dopaminergic neuronal N27 cells (Song et al, 2010). In our assays, we examined the effect of dieldrin on HERV transcription in the Jurkat CD4+ T-cell line in which we had previously observed inducible HERV activity (Sharma et al, 2012). The sequences of the primers we used to detect HERV transcription were obtained from the literature, yet they all detected more than one locus in both Jurkat and haploid HAP1 cells as determined by digital droplet PCR (Appendix Fig S2B). Titration experiments showed that Jurkat cells tolerated dieldrin at concentrations up to 100 μM for 1 h with no significant cell death (< 2%—Appendix Fig S2C). Exposure to that dosage resulted in a very transient increase in transcription for 6 of the 7 tested HERVs (twofold to fourfold) peaking at 30 min, while being undetectable at 60 min (Fig 2A). In comparison, activation of the Jurkat T cells with 100 μM of the phorbol ester PMA did not induce HERV expression (Fig 2B). The concentration of PMA was here intentionally higher than that usually used (40 nM) to ascertain that the absence of HERV stimulation was not a consequence of insufficient dosage. Cell responsiveness to PMA was verified by monitoring activation of two cytokine genes TNFα and IL8 (Appendix Fig S2D). As a negative control, we monitored TGFβ, a gene induced neither by PMA nor dieldrin (Fig 2A). Figure 2. Dieldrin is a strong T-cell activator causing transcription of several HERVs A–E. Jurkat T cells were treated with either DMSO (vehicle) or with 100 μM of either dieldrin or PMA for the indicated times. Abundance of mRNA for the indicated genes or HERVs was assessed by RT–qPCR. Data shown are means ± SEM from four independent experiments. Significance (P-value) was estimated using the two-sided Student's t-test appropriate for small sample numbers. ***P-value < 0.001. F. Jurkat cells were exposed to either DMSO, PMA in DMSO, or dieldrin in DMSO for 30 min in triplicates. RNA-seq was then performed on cDNA libraries prepared with poly(A) selection: principal component analysis on the 485 genes the most affected by DMSO, PMA, or dieldrin. G. Venn diagrams reporting genes up- or down-regulated by dieldrin or PMA as compared to DMSO. H, I. Gene ontology analysis on genes up- and down-regulated by either dieldrin or PMA as estimated from RNA-seq data. Download figure Download PowerPoint To gain insight into the global effect of dieldrin on gene expression, we used next-generation sequencing of a poly(A)-enriched random-primed cDNA library after a 30-min exposure. The transcriptome was compared to that of untreated cells or cells activated with PMA. Dieldrin caused exceptionally strong activation of multiple markers of T-cell activation, including CD69, SERPINE1, and the immediate early genes JUN, FOS, FOSB, EGR2/3/4, and NR4A1/2/3 (Table EV1, Fig 2C–E, and MA plots Appendix Fig S2E–F). Yet, principal component analysis positioned the effect of dieldrin away from that of PMA (Fig 2F). In particular, the effect of dieldrin was more extensive than that of PMA with 246 genes significantly upregulated by dieldrin, while only 93 genes were upregulated by PMA (false discovery rate, FDR < 0.05). KEGG pathway analysis further suggested that dieldrin was a particularly efficient activator of the MAPK pathway (Fig 2H and I). Finally, dieldrin caused moderate (less than twofold) but significant (FDR < 0.05) transcriptional repression of a large set of genes (197 genes—Fig 2G and Table EV1) along with moderate (less than twofold) activation of heat-shock proteins HSPA6, HSPA1A/B, and HSPH1. This was suggestive of an early phase of a heat-shock response antagonizing protein misfolding in the cytosol (Mahat et al, 2016). Dieldrin did not seem to induce other types of stress as confirmed by RT–qPCR analysis of a series of gene markers for environmental, oxidative, osmotic, cytotoxic, hypoxic, endoplasmic reticulum, or metal stress (Appendix Fig S2G and H). Atypical T-cell enhancers are enriched in HERVs We next questioned whether the transcription of HERV-derived sequences induced by dieldrin could be an indicator of increased usage of HERV-driven enhancers. HERV transcription induced by dieldrin was poorly detected in the poly(A)-enriched data (see example of an ERV1 downstream of the RIMS2 gene, Fig 3A, top lanes). We therefore repeated the RNA-seq on one set of samples using cDNA libraries constructed from ribo-depleted total RNA. This protocol allowed us to detect and quantify non-coding RNAs, including those resulting from transcription of HERV-derived sequences (see example Fig 3A, bottom). The inadequacy of the poly(A)-enriched data for the detection of HERV transcription was confirmed by examining a series of 4686 HERVs transcribed twofold or more in the ribo-depleted data. On average, the read count at these sequences was not affected by dieldrin in the data from the poly(A)-enriched transcripts in any of the replicates (Fig 3B). These observations suggested that HERV transcripts in their majority are not stably polyadenylated and therefore unlikely to be messenger RNAs (mRNAs). Figure 3. Dieldrin causes transcription of HERV-containing PEs atypical for activated T cellsJurkat cells were exposed to either DMSO or dieldrin in DMSO for 30 min. RNA-seq was then performed on cDNA libraries prepared after depletion of ribosomal RNAs. A. Screen captures from IGV showing an example of a HERV becoming a site of divergent transcription upon exposure of the Jurkat cells to dieldrin. Top tracks show coverage from sequencing of poly(A)-enriched libraries for comparison. All coverage tracks are at the same scale, and all the sequencing result files are of comparable size. Blue and red reads (rds) are in opposite orientation. CAGE peaks use the same color code. B. Reads mapping inside a series of 4,686 HERVs located away (30 kb) from protein-coding genes were quantified both in the poly(A)-enriched and the ribo-depleted data. Read counts from each replicate were then summed and plotted. C. Distribution of regions annotated "LTR" in RepeatMasker relative to sites of divergent transcription in Jurkat cells treated with either DMSO or dieldrin in DMSO. D. Schematic showing that only a fraction of the APEs (sites of divergent transcription activated twofold or more by dieldrin) match promoters and enhancers annotated as active in T cells (APETs). The remaining APEs are designated APEDs (specific to the dieldrin treatment). E. Comparison of regions annotated "LTR" in RepeatMasker with the APEs, APETs, and APEDs. The graph provides the ratio between the Jaccard index of the indicated category over the average of Jaccard indexes calculated for 1,000 series of randomly selected PEs. F, G. Comparison of either APEs or APEDs as indicated with the regions annotated TssA, TssAFlnk, Enh, EnhG, TssBiv, or EnhBiv in the 15 core marks model of the Epigenomic Roadmap consortium as in Fig 1A. H. Region upstream of IL2 as a screen capture from the genome browser of the Epigenomic Roadmap consortium. Promoters and enhancers are annotated in red and yellow, respectively. Green indicates transcription. Insert is a detail from Appendix Fig S3A showing the site of divergent transcription induced by dieldrin. I. Jurkat T cells were treated with either DMSO (vehicle) or 100 μM dieldrin for the indicated times. Abundance of IL2 mRNA was assessed by RT–qPCR. Data shown are means ± SEM from four independent experiments. Significance (P-value) was estimated using the two-sided Student's t-test appropriate for small sample numbers. ***P-value < 0.001. J. Screen captures from IGV showing increased divergent transcription inside a HERV sequence upon exposure of the Jurkat cells to dieldrin in the intergenic region between MUC21 and MUC22. Color code as in (A). Download figure Download PowerPoint We next mapped putative promoters and enhancers based on detection of sites of divergent transcription in both DMSO- (solvent) and dieldrin-treated cells. To increase robustness, we retained only positions previously annotated as sites of transcription initiation in at least one of the 975 CAGE libraries of the Fantom5 consortium. Examination of the distribution of RepeatMasker LTRs relative to the sites of divergent transcription revealed that dieldrin did not allow overcoming the counter-selection of HERV sequences in PEs, yet it increased the usage of HERV-containing PEs by approximately 25% (Fig 3C, compare blue and green lines). We next concentrated on sites of divergent transcription activated twofold or more in the presence of dieldrin (referred to as APEs for activated promoters and enhancers—Table EV2). The Jaccard similarity index between APEs and RepeatMasker LTRs was fivefold higher than expected for randomly selected sites of divergent transcription, indicating a further enrichment in HERV sequences among sites of divergent transcription strongly activated by dieldrin (Fig 3E, APE). We next separated APEs annotated as active in T-cell epigenomes (APETs) from those atypical for T cells and more likely to be dieldrin-specific (termed APED for dieldrin-specific, Fig 3D). This latter group was threefold richer in HERV sequences than the APETs as shown by the Jaccard similarity index (Fig 3E, compare APET and APED). Furthermore, comparing APEs and APEDs with the PEs from the 127 epigenomes (Jaccard index) showed that filtering away T-cell PEs from the APEs, generated a list of regulatory sites sharing similarity with PEs from the cancer cell line Dnd41, from fetal thymus, and from several hematopoietic stem cell lines (Fig 3F and G). This suggested that APEDs included regulatory sequences active in less differentiated hematopoietic cells. Taken together, this analysis suggested that dieldrin causes activation of regulatory sequences required for T-cell activation, but also of more atypical regulatory regions enriched in HERV sequences. To identify examples of such atypical regulatory regions, we examined individually some of the sites of divergent transcription overlapping with HERV sequences and activated by dieldrin. We particularly noted the presence of a site located upstream of IL2, annotated as an active enhancer in stem cells and in the K562 leukemia-derived cell line, but not in primary hematopoietic cells (Fig 3H and Appendix Fig S3A). At the time point examined by RNA-seq (30 min of exposure to dieldrin), the IL2 gene is still in a phase of progressive activation (Fig 3I). Remarkably, at this time, the HERV-driven enhancer had a transcriptional activity stronger than that observed at the two enhancers annotated as active in T cells (arrows, Appendix Fig S3A). This observation suggested that activation of the IL2 gene by dieldrin largely relied on the activity of a HERV-driven enhancer atypical for T cells. The atypical nature of enhancers overlapping with HERV sequences was also well illustrated by a site located upstream of the gene encoding the Fc receptor-like protein 4 (FCRL4) on chromosome 1, annotated as an enhancer in T cells from cord blood and in the Dnd41 T-cell leukemia cell line, while annotated as Polycomb-repressed, heterochromatinized, or quiescent in all other T-cell epigenomes (Appendix Fig S3B). Finally, we noted two unannotated HERV-based enhancers in the neighborhood of mucin genes MUC21 and MUC22, further suggesting that HERV sequences provide alternative enhancers for genes involved in cellular defense (Fig 3J and Appendix Fig S3C). Atypical activation of multiple pathways by dieldrin is required for HERV transcription To gain a better insight into how dieldrin may drive Jurkat cells into using atypical enhancers, we next investigated the signal transduction pathways stimulated by this molecule in comparison with PMA. A human phospho-kinase array showed that both dieldrin and PMA treatments activated the MAP kinase pathway as demonstrated by the phosphorylation of the TXY motif in ERK1/2 proteins and of the SRF kinases RSK1/2/3 (Fig 4A and B, and Appendix Fig S4A and B). In this context, we also examined phosphorylation of PKC, the primary target of PMA. Surprisingly, dieldrin, unlike PMA, did not cause any detectable change in
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