HP1γ binding pre‐mRNA intronic repeats modulates RNA splicing decisions
2021; Springer Nature; Volume: 22; Issue: 9 Linguagem: Inglês
10.15252/embr.202052320
ISSN1469-3178
AutoresChristophe Rachez, Rachel Legendre, Mickaël Costallat, Hugo Varet, Jia Yi, Étienne Kornobis, Christian Muchardt,
Tópico(s)RNA modifications and cancer
ResumoArticle27 July 2021free access Source DataTransparent process HP1γ binding pre-mRNA intronic repeats modulates RNA splicing decisions Christophe Rachez Corresponding Author Christophe Rachez [email protected] orcid.org/0000-0001-8502-4738 Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France CNRS UMR 8256, Biological Adaptation and Aging, Paris, France Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Rachel Legendre Rachel Legendre orcid.org/0000-0002-5196-9431 Bioinformatics and Biostatistics Hub, Department of Computational Biology, Institut Pasteur, USR 3756 CNRS, Paris, France Biomics Technological Platform, Center for Technological Resources and Research, Institut Pasteur, Paris, France Search for more papers by this author Mickaël Costallat Mickaël Costallat orcid.org/0000-0002-3048-7038 Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France CNRS UMR 8256, Biological Adaptation and Aging, Paris, France Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Hugo Varet Hugo Varet orcid.org/0000-0003-3980-4463 Bioinformatics and Biostatistics Hub, Department of Computational Biology, Institut Pasteur, USR 3756 CNRS, Paris, France Biomics Technological Platform, Center for Technological Resources and Research, Institut Pasteur, Paris, France Search for more papers by this author Jia Yi Jia Yi Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Sorbonne Université, Ecole Doctorale Complexité du Vivant (ED515), Paris, France Search for more papers by this author Etienne Kornobis Etienne Kornobis Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Christian Muchardt Corresponding Author Christian Muchardt [email protected] orcid.org/0000-0003-0145-4023 Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France CNRS UMR 8256, Biological Adaptation and Aging, Paris, France Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Christophe Rachez Corresponding Author Christophe Rachez [email protected] orcid.org/0000-0001-8502-4738 Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France CNRS UMR 8256, Biological Adaptation and Aging, Paris, France Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Rachel Legendre Rachel Legendre orcid.org/0000-0002-5196-9431 Bioinformatics and Biostatistics Hub, Department of Computational Biology, Institut Pasteur, USR 3756 CNRS, Paris, France Biomics Technological Platform, Center for Technological Resources and Research, Institut Pasteur, Paris, France Search for more papers by this author Mickaël Costallat Mickaël Costallat orcid.org/0000-0002-3048-7038 Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France CNRS UMR 8256, Biological Adaptation and Aging, Paris, France Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Hugo Varet Hugo Varet orcid.org/0000-0003-3980-4463 Bioinformatics and Biostatistics Hub, Department of Computational Biology, Institut Pasteur, USR 3756 CNRS, Paris, France Biomics Technological Platform, Center for Technological Resources and Research, Institut Pasteur, Paris, France Search for more papers by this author Jia Yi Jia Yi Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Sorbonne Université, Ecole Doctorale Complexité du Vivant (ED515), Paris, France Search for more papers by this author Etienne Kornobis Etienne Kornobis Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Christian Muchardt Corresponding Author Christian Muchardt [email protected] orcid.org/0000-0003-0145-4023 Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France CNRS UMR 8256, Biological Adaptation and Aging, Paris, France Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France Search for more papers by this author Author Information Christophe Rachez *,1,2,3, Rachel Legendre4,5, Mickaël Costallat1,2,3, Hugo Varet4,5, Jia Yi3,6, Etienne Kornobis3,7 and Christian Muchardt *,1,2,3 1Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France 2CNRS UMR 8256, Biological Adaptation and Aging, Paris, France 3Epigenetic Regulation Unit, Institut Pasteur, CNRS UMR 3738, Paris, France 4Bioinformatics and Biostatistics Hub, Department of Computational Biology, Institut Pasteur, USR 3756 CNRS, Paris, France 5Biomics Technological Platform, Center for Technological Resources and Research, Institut Pasteur, Paris, France 6Sorbonne Université, Ecole Doctorale Complexité du Vivant (ED515), Paris, France 7Present address: Biomics Technological Platform, Center for Technological Resources and Research (C2RT), and Bioinformatics and Biostatistics Hub, Department of Computational Biology, Institut Pasteur, Paris, France **Corresponding author. Tel: +33 1 4427 3477; E-mail: [email protected] ***Corresponding author. Tel: +33 6 7609 8437; E-mail: [email protected] EMBO Reports (2021)22:e52320https://doi.org/10.15252/embr.202052320 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 HP1 proteins are best known as markers of heterochromatin and gene silencing. Yet, they are also RNA-binding proteins and the HP1γ/CBX3 family member is present on transcribed genes together with RNA polymerase II, where it regulates co-transcriptional processes such as alternative splicing. To gain insight in the role of the RNA-binding activity of HP1γ in transcriptionally active chromatin, we have captured and analysed RNAs associated with this protein. We find that HP1γ is specifically targeted to hexameric RNA motifs and coincidentally transposable elements of the SINE family. As these elements are abundant in introns, while essentially absent from exons, the HP1γ RNA association tethers unspliced pre-mRNA to chromatin via the intronic regions and limits the usage of intronic cryptic splice sites. Thus, our data unveil novel determinants in the relationship between chromatin and co-transcriptional splicing. Synopsis HP1γ associates with pre-mRNA preferentially at specific repeat motifs enriched in introns, and this association affects co-transcriptional processes such as alternative splicing. HP1γ is associated genome-wide with introns of chromatin-enriched pre-messenger RNA. HP1γ-associated RNA is enriched in CACACA and GAGAGA repeat motifs. A subset of intragenic sense SINE repeats containing CACACA motifs are targeted by HP1γ. Association of HP1γ with intronic RNA affects alternative splicing and the occurrence of cryptic splice junctions. Introduction Maintenance and propagation of the transcriptionally inactive heterochromatin extensively relies on Heterochromatin Protein 1 (HP1), a family of proteins identified in a large variety of species, ranging from fission yeast to man (Almouzni & Probst, 2011). Mammals typically express three isoforms of HP1, namely HP1α/CBX5, HP1β/CBX1 and HP1γ/CBX3, each with unique subnuclear localization patterns (Minc et al, 2000; Dialynas et al, 2007). HP1 proteins bind histone H3 trimethylated at Lysines 9 (H3K9me3) via their N-terminal chromodomain (Bannister et al, 2001; Lachner et al, 2001). At their C-terminus, a chromoshadow domain ensures dimerization and mediates interaction with numerous molecular partners characterized by the presence of a PXVXL motif (Smothers & Henikoff, 2000). In-between these two structurally very similar globular domains, an unstructured region known as the Hinge harbours both DNA and RNA-binding activity (Hiragami-Hamada et al, 2016). The RNA-binding activity of HP1 proteins seems very important for their molecular function. In the fission yeast Schizosaccharomyces pombe, HP1/Swi6 associates with noncoding transcripts expressed in centromeric heterochromatin, and its silencing activity relies on a mechanism involving RNAi-dependent degradation of these transcripts (Motamedi et al, 2008). This process was later shown to involve a dynamic trafficking of HP1/Swi6 between its free, H3K9me-bound and RNA-bound forms, leading to the repression of heterochromatin (Keller et al, 2012). For murine and human HP1α, the RNA-binding activity of the Hinge is essential for the targeting of this protein to heterochromatin, possibly even more so than the H3K9me3 histone modification (Maison et al, 2002; Muchardt et al, 2002; Maison et al, 2011). While HP1 proteins may bind multiple families of RNA species (Piacentini et al, 2009), mouse HP1α was shown to specifically bind pericentromeric RNA transcripts from the major satellites, a family of repeats particularly abundant in pericentromeric heterochromatin (Maison et al, 2011; Maison et al, 2016). Beyond their role in structuring heterochromatin, HP1 proteins also function as regulators of euchromatic transcription. For instance, at the promoters of many inducible genes involved in development or in cellular defence, they participate in the transient silencing of transcription while awaiting stimulation (Mateescu et al, 2008; Sridharan et al, 2013; Harouz et al, 2014; Huang et al, 2017; Sun et al, 2017; Casale et al, 2019). But HP1 proteins, in particular HP1γ in mammals, are also present inside the coding region of genes (Vakoc et al, 2005), a localization which is not always correlated with H3K9me3 (Sridharan et al, 2013). The association of HP1γ with transcribed genes is consistent with a role for this protein in co-transcriptional mechanisms such as termination (Skourti-Stathaki et al, 2014), and regulation of alternative splicing (Allo et al, 2009; Saint-Andre et al, 2011; Ameyar-Zazoua et al, 2012; Smallwood et al, 2012; Yearim et al, 2015). Splicing is a maturation process of RNA polymerase II transcripts catalysed by the Spliceosome complex and leading to the formation of mature mRNA by excision of introns and joining of exons. Most human genes undergo alternative splicing which gives rise to multiple mRNAs from a single gene locus (Pan et al, 2008; Wang et al, 2008). As splicing is mostly co-transcriptional and occurs in the close vicinity of chromatin, it is influenced by a large number of chromatin-associated factors (Luco et al, 2011; Allemand et al, 2016). In this context, we have shown earlier that recruitment of AGO proteins and HP1γ to CD44 and other genes favours intragenic H3K9 methylation and affects the outcome of alternative splicing by targeting the spliceosome to specific sites inside the gene body (Saint-Andre et al, 2011). Our study on the CD44 gene also unveiled an interaction between intragenic chromatin and pre-mRNA which was dependent on HP1γ and seemed to modulate the outcome of splicing (Saint-Andre et al, 2011). To gain further understanding of this HP1-dependent relationship between chromatin and transcripts, we have here analysed the genome-wide association of HP1γ with RNA by a chromatin-enriched RNA immunoprecipitation (RNAchIP) assay. We find that HP1γ is preferentially targeted to intronic regions on RNA, due to the presence therein of hexameric motifs. Consequently, HP1γ-bound RNAs are also enriched in B4 SINEs, a family of euchromatic transposable repeat elements which harbours high proportions of these hexameric motifs. The consequence of this RNA binding by HP1γ is a tethering of unspliced pre-mRNA to chromatin via the intronic region. This way, HP1γ limits the usage of intronic cryptic splice sites. These observations reconcile the heterochromatic and euchromatic functions of HP1, by showing that its role in mRNA maturation, alike its role in heterochromatin structuring, relies on its ability to associate with repeat-encoded RNAs. Results HP1γ associates with chromatin-enriched RNA To better understand the relationship between HP1γ and RNA in the functionality of this protein, beyond its classical role in heterochromatin formation, we used a genome-wide approach to identify chromatin-bound RNA species interacting with HP1γ (RNAchIP; Fig 1A). For this, we used a modification of our previously described strategy to solubilize native chromatin and produce chromatin-enriched RNA fragments suitable for immunoprecipitation (Saint-Andre et al, 2011) (Fig EV1C). HP1γ −/− (KO cells) mouse embryonic fibroblast (MEF)-derived cell lines, re-complemented with FLAG-tagged HP1γ (HP1γ cells) as previously described were used in these assays (Harouz et al, 2014). These cells expressed ectopic FLAG-tagged HP1γ at a level similar to that of endogenous HP1γ in WT MEFs (Fig EV1A). In these experiments, cells were treated or not with the phorbol ester PMA. This activator of the PKC signalling pathway was previously shown to induce HP1γ phosphorylation in its hinge region and to modulate its activity on a subset of responsive genes (Harouz et al, 2014). Nuclei were isolated to obtain a chromatin-enriched RNA fraction, (Fig EV1A and B). Our procedure included a step of limited crosslinking in order to stabilize association of HP1γ with RNA. We therefore expected to detect both direct and indirect HP1γ-RNA associations. Yet, the anti-FLAG antibody did not precipitate any of the HP1γ-interacting proteins we tested, suggesting that our highly stringent immunoprecipitation conditions eliminated most indirect interactions (HP1α, H3, RNA polymerase II) (Fig EV1B). RNAs present in the chromatin fraction (input) or collected by HP1γ RNAchIP (IP) were then analysed by Illumina sequencing in biological triplicates and reads were mapped onto the mouse genome (RNAchIP-seq). At most genes, RNA levels in the IP were correlated with levels in the input (see example of the stress-responsive Fosl1 gene Fig 1B left). Normalized read counts per gene body (input and IP) confirmed this correlation genome-wide in both unstimulated and PMA-stimulated cells (Fig 1C). RNAchIP experiments were then repeated using either HP1γ cells or the parental HP1γ KO cells. RT–qPCR at the Fosl1 gene (Fig 1B; arrow) and at other genes (Fig EV2) confirmed the dependence of HP1γ RNAchIP signal on the presence of HP1γ (Figs 1D and EV2B). Interestingly, at a small number of genes, many of which encoding histones, the IP signal from the HP1γ RNAchIP data seemed uncoupled from that of the input, indicating that HP1γ does not equally associate to all transcripts (Hist1h4a and Hist1h3a, Fig 1B right). Figure 1. HP1γ associates with RNA on chromatin Scheme of the strategy used to assay HP1γ association with RNA on chromatin. Genome views of RNA read density profiles on representative loci enriched (left) or low (right) in FLAG-HP1γ RNAchIP (IP) relative to input RNA (inp.) in overlaid replicate samples from HP1γ cells stimulated or not with PMA. Orange arrow, position of the locus analysed in D. Genome-wide scatter plot of IP and input normalized (norm.) RNA read counts per gene for the mean of the triplicates; n = 21,754; r, Pearson's correlation coefficient between IP and input. Relative quantities of RNA in the RNAchIP samples, detected by RT–qPCR with primers aligning on the intron1 of the Fosl1 gene as depicted in B (orange arrow). n = 3 independent experiments. Heat maps of RNA IP and input signal centred on the summit of RNAchIP peaks of IP versus input detected by MACS2 analysis (10,013 and 2,609 peaks) for a representative replicate (replicate 1). Black arrows represent peak summit. Percentage of RNAchIP peaks overlapping with indicated chromatin features in MEF samples from ENCODE database (green bars). Overlaps was evaluated by comparison with the list of merged peaks whose genomic location was randomized among genes with one hundred permutations (random, grey bars). Data information: Histograms represent mean and SD Dots represent individual data points. P-values indicate a significantly higher difference between IP in HP1γ and in KO cells (***P < 0.001; two-tailed Student's t-test). Source data are available online for this figure. Source Data for Figure 1 [embr202052320-sup-0004-SDataFig1.xlsx] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Western blot analysis and RNA profiles in RNAchIP input and IP fractions Top panels, western blot analysis of HP1γ and HP1β protein levels in MEF-derived cells expressing or not FLAG-tagged HP1γ (HP1γ or KO, respectively), compared to WT MEF-derived cells. Bottom panel, total protein staining by Ponceau S. Panel is a section of the blot centred on 17 kD, showing Histones. Detection of RNA polymerase II (Pol II), FLAG-tagged HP1γ, HP1α and Histone H3 (H3) in the RNAchIP fractions depicted in Fig 1 by Western blot analysis in the HP1γ-expressing cells (HP1γ), compared to KO cells (KO). Virtual gel profiles showing the size range of RNA fragments in both input and IP representative samples from HP1γ cells, obtained by Bioanalyzer (Agilent). Source data are available online for this figure. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. HP1γ associates with RNA and DNA at different genomic loci A. Genome views of RNA read density in overlaid replicates as in (Fig 1B) on representative gene loci bearing a peak (blue arrows) identified by MACS2, together with a neighbouring intronic region (yellow arrow). B, C. Relative quantities of RNA (B) and DNA (C) in the RNAchIP samples versus input in both HP1γ and KO cell lines, detected by RT–qPCR (B) and by PCR (C), at the peak (blue) and in the intron (yellow) regions delineated in (A). n = 3–5 independent experiments. Data information: Histograms represent mean and SD; dots represent individual data points; P-values indicate significantly higher difference between IP in HP1γ and in KO cells (**P < 0.005, ***P < 0.001; two-tailed Student's t-test). Download figure Download PowerPoint Within individual genes, the distribution of HP1γ-associated RNA fluctuated along the gene body (Fig 1B). To detect regions of local enrichment, we searched for peaks appearing in IP but not in the input RNA. By merging peaks conserved in at least two of the three replicates, we identified 10,013 and 2,603 peaks, in unstimulated and in PMA-stimulated samples, respectively (Fig 1E and examples Fig EV2; blue highlights). Several peaks were validated by RT–qPCR (Fig EV2A; arrows and Fig EV2B). Importantly, DNA regions encompassing these peaks were likewise enriched in the HP1γ immunoprecipitations, sustaining a model where HP1γ links RNA to chromatin (Fig EV2C). When exploring ChIP data from MEF cells available in the ENCODE database, we did not see a clear co-distribution of the peaks of HP1γ-bound RNA with H3K9me3 and H3K27me3 (Fig 1F). We note however that the very broad peaks yielded by these histone marks known to be recognized by HP1γ may have interfered with this analysis. In contrast, we observed a clear overlap of HP1γ peaks with sites enriched in RNA polymerase II and H3K4me3 histone marks, clearly linking RNA-associated HP1γ with active transcription. HP1γ-associated RNA is enriched in CACACA and GAGAGA motifs We next investigated whether sequence specific motifs could be found within the peaks. Peak-motif analysis using RSAT on stranded sequences of all RNA peaks revealed a significant enrichment in CACACA motifs (e-val. 4.8 e-88) and to a lesser extend in GAGAGA motifs (e-val. 1.8 e-10) (Fig 2A and 2B and Appendix Fig S1). These motifs were oriented, as we observed no enrichment in the complementary (antisense) motifs (Fig 2B). Consistent with an enrichment in CACACA sequences in the RNA co-immunoprecipitating with HP1γ, we observed an accumulation of reads around all intronic CACACA or GAGAGA motifs at expressed genes, visualized by an increased average distribution of the reads from IP compared to input RNA around these motifs (Fig 2C, and Fig EV3B), and the clustering of reads at a large number of intragenic loci centred on CACACA motifs (Fig EV3A). Noticeably, we did not find any enrichment within exons (Fig 2D) or in the TGTGTG motif, antisense to the CACACA motif (Fig 2C). The same patterns were observed with the GAGAGA motif (Fig EV3B). Enrichment within introns only is consistent with an average of 3 motifs per intron but only 0.1–0.2 motif per exon (Appendix Table S1), indicating that exons are in average devoid of such motifs. Figure 2. HP1γ-associated RNA is enriched in CACACA and GAGAGA motifs A. Consensus motifs discovered among RNAchIP peaks with the RSAT pipeline, together with the percentage of peaks containing at least one exact hexameric CACACA or GAGAGA motifs or both. e-val. represents the expected number of patterns which would be returned at random for a given probability. B. RNAchIP peaks are enriched in CACACA and GAGAGA motifs. Number of peaks containing at least one exact hexameric motif as indicated, compared to the average number of motifs in peaks whose genomic location was randomized among genes with ten permutations (random, light bars, represent mean and SD). C, D. Distribution profiles of average RNAchIP signal in both IP (warm colours) and input (blue colours) over ± 2 kb centred on intronic (C) and exonic (D) CACACA hexameric motifs oriented in the same orientation (sense) or in opposite orientation (antisense) relative to the overlapping annotated transcript. The antisense CACACA motifs were obtained by querying the transcript sequences with the TGTGTG motif. E. Left, representative example of a locus on the Ppp3ca gene surrounding an RNAchIP merged peak, showing the RNAchIP read density as in Fig 1B. Red and orange arrowheads correspond to oriented CACACA and GAGAGA motifs, respectively. Right, sequence of the RNA surrounding a CACACA motif highlighted in red, as well as a neighbouring imperfect motif. The sequence was used to design a CACACA-containing RNA oligonucleotide probe (CACACA), compared to a control probe (no-CA) devoid of any related motif. F–H. Gel mobility shift assays of bacterially expressed, purified HP1γ-Hinge domain, fused to GST proteins and tested for its direct interaction with the Cy3-labelled RNA (F and H) or dsDNA (G) oligonucleotide probes depicted in (C). The probes in the gels, either free (white arrowheads) or as shifted protein/RNA complexes (red arrowheads) were detected by their Cy3 fluorescence. Representative of three independent experiments. Source data are available online for this figure. Source Data for Figure 2 [embr202052320-sup-0005-SDataFig2.xlsx] Download figure Download PowerPoint Click here to expand this figure. Figure EV3. CACACA and GAGAGA motifs in RNAchIP peaks and by Gel shift assays Heat maps of IP and input signal in replicate sample 1 for each condition over ± 2 kb centred (arrows) on intragenic sense or antisense CACACA hexameric motifs (red or black arrows, respectively), corresponding to the average profiles depicted in Fig 2B. Distribution profiles of average RNAchIP signal in both IP (warm colours) and input (blue colours) over ± 2 kb centred on intronic (left) and exonic (right) GAGAGA hexameric motifs oriented in the same orientation (sense) or in opposite orientation (antisense) relative to the overlapping annotated transcript. The antisense GAGAGA motifs were obtained by querying the transcript sequences with the TCTCTC motif. Left, schematic representation of the GST-HP1γ constructions used in gel mobility shift assay, depicting the chromo- and chromoshadow-globular domains, as well as the unstructured Hinge domain (CD, CSD, Hinge, respectively). Right, Coomassie blue staining of the bacterially expressed, purified GST-HP1 fusion proteins used in Figs 2D, EV2C and 3H. Gel mobility shift assay performed in the same conditions as in Fig 2, on the indicated dsDNA oligonucleotide probe. The probes in the gels, either free (white arrowheads) or as shifted protein/DNA complexes (blue arrowheads) were detected by their Cy3 fluorescence. Download figure Download PowerPoint To test whether these motifs contribute to the association between HP1γ and RNA, we identified a representative RNA peak within the Ppp3ca gene which overlaps both CACACA and GAGAGA motifs (Fig 2E) and then used the sequence overlapping one of these motifs in vitro as an RNA probe in gel mobility shift assays. This probe was compared to a control RNA devoid of any CA motif ("no-CA"; Fig 2E and F). We conducted our experiments with bacterially expressed purified GST-HP1γ Hinge region (covering amino acids 65–110; Fig EV3C), a domain of HP1γ earlier shown to have more DNA- and RNA-binding activity in vitro than the full-length proteins (Maison et al, 2002; Muchardt et al, 2002) and compare Figs EV3D and 2F). This purified GST-HP1γ Hinge domain (Fig EV3C) readily bound the CACACA-containing RNA probe (red arrowhead Fig 2F, lanes 2 and 3) while showing essentially no binding to a dsDNA oligonucleotide with the same sequence (Fig 2G) or for the "no-CA" control RNA probe (Fig 2F, lanes 4 and 5, compared to lanes 2 and 3). Consistent with this, in competition assays, the GST-HP1γ-Hinge binding to the CACACA-containing RNA probe was competed away by an excess of unlabelled CACACA-containing RNA (Fig 2H, lanes 3 and 4), but not by the DNA- or the "no-CA" RNA probes (Fig 2H, lanes 5–10). Mutation of positively charged basic lysine residues in the Hinge domain, previously shown to be involved in RNA binding in Swi6 (Keller et al, 2012) led to a complete loss of RNA binding in GST-HP1γ-Hinge (Fig 2F, lanes 6–9). HP1γ has therefore the capacity of directly interacting with RNA in a sequence specific manner, suggesting that the RNA enrichment seen in IP is, at least in some instances, due to direct HP1γ/RNA associations at specific positions enriched in CACACA motifs. HP1γ-associated RNA is enriched in SINE repeat motifs Heterochromatin-based silencing mechanisms may occur within active chromatin on repeated sequences such as interspersed repeats (SINEs, LINEs) or endogenous retroviruses (LTRs) (Leung & Lorincz, 2012; Bulut-Karslioglu et al, 2014). We therefore asked whether HP1γ-associated RNA could be enriched over different classes of repeated elements annotated in the RepeatMasker database. When aligned onto repeats with our multimapping alignment procedure, sequencing reads showed best enrichment on LTR and SINE repeats (Fig 3A). Figure 3. HP1γ-associated RNA is enriched in oriented SINE repeat motifs Fold enrichment values on all repeat masker classes and on Refseq genes, on the basis of all uniquely aligned reads, shown as mean and SD, n = 6 biological replicates. Dashed line represents an IP/Input ratio of 1, taken as a reference. Pie chart of the distribution of RNA sequencing reads in IP per repeat masker classes counted as in (A), as a percentage of all repeats. Profiles of average RNAchIP signal as in Fig 2B over ± 2 kb centred on intragenic LINE, LTR or SINE repeats annotated in the Repeat masker database (n = 34,000). All repeats are in the same orientation (sense) relative to the overlapping annotated transcript, unless otherwise specified (antisense, shuffled), and as depicted by an oriented white box overlapping the transcript in 5′–3′ orientation (black line with arrowhead). Profiles of average RNAchIP signal, as in (C) centred on the 5 major families of intragenic SINE repeats. Proportions of the families of CACACA-containing SINE repeats within each family, as a percentage of total intragenic sense SINEs. Profiles of average RNAchIP signal, as in (D) centred on the subset of intragenic B4 SINEs containing a consensus CACACA motif. Genomic position of a B4 SINE repeat and CACACA consensus motifs at the location of the RNAchIP peak depicted in Fig 2C. North-western blot assay showing direct association between the indicated bacterially expressed, purified GST-fusion proteins and in vitro transcribed, biotinylated RNA probes based on the sequence of the B4 SINE depicted in (G). Top panels, binding of a 256nt probe corresponding to the CACACA-containing B4 SINE sequence, was compared to an identical B4 SINE deleted of its CACACA by truncation of its 3′ portion (B4 SINE-Δ3′). RNA probes hybridized on the membranes were detected by their Cy3 fluorescence. Total GST-fusion protein loading was visualized by Ponceau S staining. Representative of two independent experiments. Source data are available online for this figure. Source Data for Figure 3 [embr202052320-sup-0006-SDataFig3.zip] Download figure Download PowerPoint We then focused on SINEs, LINEs, and LTRs which are the most abundant repeats within gene bodies (Figs 3B and EV3A). Profile plots for IP read counts over these repeats confirmed that the best enrichments were to be found in the neighbourhood of SINEs (Fig 3C—note that the body of the various repeats are plotted as valleys because multimapping reads have been filtered away). Enrichment was most obvious on the B4 SINE family, and also more pronounced on the B3 and RSINE families, while undetected on the B1 and B2 SINEs (Fig 3D). Consistent with a preference of HP1γ for CACACA sequences, B3, B4 and RSINEs contain this hexamer motif in their sequence more frequently than other SINEs (Fig 3E). In addition, average profiles for IP read counts over CACACA-containing B4 SINEs showed a better enrichment than that observed over B4 SINEs in genera
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