Jmjd3 contributes to the control of gene expression in LPS-activated macrophages
2009; Springer Nature; Volume: 28; Issue: 21 Linguagem: Inglês
10.1038/emboj.2009.271
ISSN1460-2075
AutoresFrancesca De Santa, Vipin Narang, Zhei Hwee Yap, Betsabeh Khoramian Tusi, Thomas Burgold, Liv Austenaa, Gabriele Bucci, Marieta Cagánová, Samuele Notarbartolo, Stefano Casola, Giuseppe Testa, Wing‐Kin Sung, Chia‐Lin Wei, Gioacchino Natoli,
Tópico(s)Histone Deacetylase Inhibitors Research
ResumoArticle24 September 2009Open Access Jmjd3 contributes to the control of gene expression in LPS-activated macrophages Francesca De Santa Francesca De Santa Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Vipin Narang Vipin Narang Computational and Mathematical Biology, Genome Institute of Singapore, Singapore, Singapore Search for more papers by this author Zhei Hwee Yap Zhei Hwee Yap Genome Technology and Biology Group, Genome Institute of Singapore, Singapore, Singapore Search for more papers by this author Betsabeh Khoramian Tusi Betsabeh Khoramian Tusi Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Thomas Burgold Thomas Burgold Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Liv Austenaa Liv Austenaa Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Gabriele Bucci Gabriele Bucci Consortium for Genomic Technologies (COGENTECH), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Marieta Caganova Marieta Caganova IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, IFOM-IEO Campus, Milan, Italy Search for more papers by this author Samuele Notarbartolo Samuele Notarbartolo Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Stefano Casola Stefano Casola IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, IFOM-IEO Campus, Milan, Italy Search for more papers by this author Giuseppe Testa Giuseppe Testa Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Wing-Kin Sung Wing-Kin Sung Department of Computer Science, National University of Singapore, Singapore, Singapore Search for more papers by this author Chia-Lin Wei Corresponding Author Chia-Lin Wei Genome Technology and Biology Group, Genome Institute of Singapore, Singapore, Singapore Search for more papers by this author Gioacchino Natoli Corresponding Author Gioacchino Natoli Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Francesca De Santa Francesca De Santa Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Vipin Narang Vipin Narang Computational and Mathematical Biology, Genome Institute of Singapore, Singapore, Singapore Search for more papers by this author Zhei Hwee Yap Zhei Hwee Yap Genome Technology and Biology Group, Genome Institute of Singapore, Singapore, Singapore Search for more papers by this author Betsabeh Khoramian Tusi Betsabeh Khoramian Tusi Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Thomas Burgold Thomas Burgold Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Liv Austenaa Liv Austenaa Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Gabriele Bucci Gabriele Bucci Consortium for Genomic Technologies (COGENTECH), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Marieta Caganova Marieta Caganova IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, IFOM-IEO Campus, Milan, Italy Search for more papers by this author Samuele Notarbartolo Samuele Notarbartolo Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Stefano Casola Stefano Casola IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, IFOM-IEO Campus, Milan, Italy Search for more papers by this author Giuseppe Testa Giuseppe Testa Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Wing-Kin Sung Wing-Kin Sung Department of Computer Science, National University of Singapore, Singapore, Singapore Search for more papers by this author Chia-Lin Wei Corresponding Author Chia-Lin Wei Genome Technology and Biology Group, Genome Institute of Singapore, Singapore, Singapore Search for more papers by this author Gioacchino Natoli Corresponding Author Gioacchino Natoli Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy Search for more papers by this author Author Information Francesca De Santa1,‡, Vipin Narang2,‡, Zhei Hwee Yap3, Betsabeh Khoramian Tusi1, Thomas Burgold1, Liv Austenaa1, Gabriele Bucci4, Marieta Caganova5, Samuele Notarbartolo1, Stefano Casola5, Giuseppe Testa1, Wing-Kin Sung6, Chia-Lin Wei 3 and Gioacchino Natoli 1 1Department of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Milan, Italy 2Computational and Mathematical Biology, Genome Institute of Singapore, Singapore, Singapore 3Genome Technology and Biology Group, Genome Institute of Singapore, Singapore, Singapore 4Consortium for Genomic Technologies (COGENTECH), IFOM-IEO Campus, Milan, Italy 5IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, IFOM-IEO Campus, Milan, Italy 6Department of Computer Science, National University of Singapore, Singapore, Singapore ‡These authors contributed equally to this work *Corresponding authors: Genome Technology and Biology Group, Genome Institute of Singapore, 60 Biopolis Street #02-01, Singapore 138672, Singapore. Tel.: +65 6478 8074; Fax: +65 6478 9059; E-mail: [email protected] of Experimental Oncology, European Institute of Oncology (IEO), IFOM-IEO Campus, Via Adamello 16, Milan 20139, Italy. Tel.: +39 02 5748 9953; Fax: +39 02 5748 9851; E-mail: [email protected] The EMBO Journal (2009)28:3341-3352https://doi.org/10.1038/emboj.2009.271 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Jmjd3, a JmjC family histone demethylase, is induced by the transcription factor NF-kB in response to microbial stimuli. Jmjd3 erases H3K27me3, a histone mark associated with transcriptional repression and involved in lineage determination. However, the specific contribution of Jmjd3 induction and H3K27me3 demethylation to inflammatory gene expression remains unknown. Using chromatin immunoprecipitation-sequencing we found that Jmjd3 is preferentially recruited to transcription start sites characterized by high levels of H3K4me3, a marker of gene activity, and RNA polymerase II (Pol_II). Moreover, 70% of lipopolysaccharide (LPS)-inducible genes were found to be Jmjd3 targets. Although most Jmjd3 target genes were unaffected by its deletion, a few hundred genes, including inducible inflammatory genes, showed moderately impaired Pol_II recruitment and transcription. Importantly, most Jmjd3 target genes were not associated with detectable levels of H3K27me3, and transcriptional effects of Jmjd3 absence in the window of time analysed were uncoupled from measurable effects on this histone mark. These data show that Jmjd3 fine-tunes the transcriptional output of LPS-activated macrophages in an H3K27 demethylation-independent manner. Introduction Inflammatory responses require the activation of a complex gene expression program that involves the inducible transcription of hundreds of genes whose products restrain microbial colonization, recruit and activate leukocytes, increase vascular permeability, amplify the response, and protect inflammatory and tissue cells from apoptosis (Medzhitov, 2008). Transcription factors belonging to the NF-kB/Rel, IRF and STAT families (Ivashkiv and Hu, 2004; Hayden et al, 2006; Honda and Taniguchi, 2006) are well-established regulators of inflammatory gene expression. However, knowledge on the essential transcriptional coregulators, chromatin modifiers and transcriptional circuits underlying inflammation is still rather primitive. We previously reported that the histone demethylase (HDM) Jmjd3 is quickly induced by NF-kB in primary mouse macrophages in response to inflammatory stimuli, whereas its paralog Utx is expressed at low and constant levels (De Santa et al, 2007). With the only exception of LSD1 (Shi et al, 2004), all known HDMs belong to the JmjC family, which includes 27 members in the current human genome annotation (Klose and Zhang, 2007; Shi and Whetstine, 2007; Cloos et al, 2008). The JmjC domain is a variant of a common structural motif found from bacteria to mammals, the 2-histidine-1-carboxylate facial triad (Koehntop et al, 2005), that serves as a platform for binding divalent iron. The metal centre of this motif is used to activate molecular oxygen and transfer an oxygen atom to the substrate. Priming of the metal centre for attack by molecular oxygen depends on binding to the substrate and in some cases to a 2-oxoacid cofactor (most often α-ketoglutarate). Although the spectrum of metabolic transformations carried out by oxygenases with a 2-histidine-1-carboxylate facial triad is extremely broad (Loenarz and Schofield, 2008), JmjC proteins in animals can apparently catalyse only hydroxylation reactions. If the target of hydroxylation is a methyl–lysine, an unstable hydroxy-methyl group is generated that is released as a formaldehyde molecule, thus eventually restoring the unmethylated state of the lysine residue (Tsukada et al, 2006). Overall, JmjC family HDMs are characterized by a high level of specificity both regarding the target lysine in the amino-terminal histone tails, and the level of methylation (mono-, di- and tri-methylation) they can reverse. The closely related Jmjd3 and Utx specifically demethylate trimethylated lysine 27 in histone H3 (H3K27me3), a chromatin modification associated with transcriptional repression (Agger et al, 2007; De Santa et al, 2007; Lan et al, 2007; Lee et al, 2007). Hence, NF-kB-induced Jmjd3 upregulation links inflammation to the control of a histone modification involved in lineage determination, differentiation and tissue homeostasis (Kirmizis et al, 2004; Boyer et al, 2006; Bracken et al, 2006; Lee et al, 2006; Sparmann and van Lohuizen, 2006; Burgold et al, 2008; Sen et al, 2008), which may provide a mechanistic connection between chronic inflammation and the associated alterations of differentiation (e.g. metaplasia, discussed in De Santa et al, 2007). However, the specific contribution, if any, of Jmjd3 induction to innate immunity and inflammation remains unknown. In this study, we investigated the genomic distribution of Jmjd3, its relationship to H3K27me3, H3K4me3 and RNA polymerase II (Pol_II) occupancy, and finally its role in controlling the gene expression program of lipopolysaccharide (LPS)-activated macrophages. The data we report here show the involvement of Jmjd3 in tuning the transcriptional output of LPS-stimulated macrophages and suggest that this activity is largely independent of H3K27me3 demethylation. Results Analysis of Jmjd3 genomic distribution in LPS-activated macrophages Jmjd3 induction by LPS depends on multiple evolutionary conserved binding sites for the transcription factor NF-kB (De Santa et al, 2007), which map to a region of the gene containing a large CpG island and multiple peaks of H3K4 trimethylation (Supplementary Figure 1), likely representing alternative sites of transcriptional initiation. The presence of multiple conserved binding sites for an inflammatory transcription factor such as NF-kB (Hayden et al, 2006) suggests a possible involvement of Jmjd3 in the control of inflammatory gene expression programs. To address this possibility we analysed the genomic distribution of newly synthesized Jmjd3 in mouse bone marrow-derived macrophages using chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-Seq). Macrophages were stimulated with LPS and interferon gamma (IFNγ) for 2 h, corresponding to the peak of Jmjd3 induction in these cells (Figure 1A). We generated a set of ∼8 million high quality and uniquely aligned reads. Using a very restrictive false discovery rate (FDR)=0.1% (clusters of tags with ⩾9 overlaps) as a cutoff, we identified 4398 peaks, whereas considering clusters of ⩾7 tags 14 013 peaks were detected (Supplementary Table I). Subsequent analyses were carried out considering an FDR=0.1%. Validation by ChIP–qPCR on a representative set of Jmjd3 targets in stimulated versus unstimulated macrophages showed good correlation with peak calling with an overall validation rate of about 93% (Supplementary Figure 2A). Jmjd3 binding preferentially occurred at transcription start sites (TSS), often extending at various distance inside the coding region (Figure 1B; Supplementary Figure 3). Fifty-four per cent of the Jmjd3 peaks were within ±0.5 kb from a mapped TSS, and more than 70% of them were within 2.5 kb, a value much higher than expected for a random distribution. Figure 1.Genomic distribution of Jmjd3 in LPS-stimulated macrophages. (A) Jmjd3 induction in primary mouse bone marrow-derived macrophages. A western blot (left) and an RT–qPCR (right) analysis are shown. (B) Genome-wide analysis of Jmjd3 association with TSSs. Macrophages were stimulated with LPS+γIFN for 2 h and distribution of Jmjd3 peaks relative to mapped TSS was determined. (C) Jmjd3 binding to a representative region of mouse chromosome 5. The y axis indicates the number of tags in peaks. (D) A zoomed-in view of the same region shows the association of Jmjd3 with the TSSs of two genes. (E) Kinetics of Jmjd3 recruitment. TSS1 of Arhgef3, which was negative for Jmjd3 in the ChIP-Seq data, was used as a negative control. Guanylate-binding protein 6 (Gbp6) encodes an antiviral GTPase representing one of the most abundant proteins induced by LPS+γIFN. Error bars: s.e.m. from a triplicate experiment. (F) Abrogation of ChIP signals in Jmjd3 knockout macrophages. Anti-Jmjd3 ChIP was carried out in wild type and Jmjd3−/− foetal liver-derived macrophages. Download figure Download PowerPoint Using ±100 kb around promoters as cutoff, we found 4331 Jmjd3 peaks (98.5%) associated with 3339 genes (based on the annotated TSSs from the DBTSS database; Supplementary Table I). The binding of Jmjd3 to a large (0.85 Mbp) representative region of chr5 is shown as an example in Figure 1C and a zoomed-in view of the same region is shown in Figure 1D. The kinetic profile of Jmjd3 recruitment to individual target genes closely mirrored the behaviour of bulk Jmjd3 protein levels (Figure 1E) and ChIP signals were dependent on the presence of Jmjd3, as indicated by their abrogation in Jmjd3 knockout macrophages (Figure 1F; Supplementary Figure 2B). In activated macrophages, newly synthesized Jmjd3 is rapidly recruited to the TSSs of thousands of genes (Supplementary Table I) including those encoding LPS-inducible immune response and inflammatory mediators such as chemokines (e.g. Cxcl2, Cxcl11, Ccl5), cytokines (e.g. Tnfα, Il6, Il27), proteins or enzymes involved in microbial recognition and killing (Nos2, Nod1, Nod2, multiple 2′–5′ oligoadenylate synthetase family members, antiviral GTPases such as Gbp2-6), adhesion molecules and immuno-receptors (Sdc4, Icam1, Cd40, Cd83, Cd86), complement components (C3), growth factors (Vegfa, Vegfc), signal transducers and transcription factors (Jak2, Socs1, Socs3, Nfkbie, Nfkbiz, Bcl3, Jun, Junb, Irf2, Irf7), histo-compatibility molecules (H2-Q2) and enzymes involved in prostaglandin biosynthesis (Ptgs2). Thus, widespread Jmjd3 genomic recruitment in activated macrophages seems to provide a satisfactory explanation of the evolutionary link of Jmjd3 gene expression to NF-kB activation in vertebrates. Jmjd3 distribution correlates with gene activity To systematically analyse the transcriptional state of Jmjd3-bound TSSs, we first generated genomic maps of H3K4me3, a histone mark associated with the TSSs of genes that are either active or poised for activation (Kouzarides, 2007; Ruthenburg et al, 2007), in both unstimulated and 4 h LPS+IFNγ-stimulated primary macrophages. More than 17 million uniquely aligned sequencing reads were obtained in each condition, corresponding to 21 418 and 19 631 peaks, respectively (FDR 0.1%) (Supplementary Tables II and III); 18 618 (87%) peaks were overlapping between unstimulated and stimulated macrophages and they were used for further analyses (Supplementary Table IV). In agreement with previous gene-specific data (Foster et al, 2007), H3K4me3 was quickly upregulated at several LPS+IFNγ-inducible genes: based on the total tag counts within each H3K4me3 cluster, H3K4me3 intensity increased more than 2-fold at the TSS of 173 genes and more than 1.5-fold at 496 genes (Supplementary Table IV; Supplementary Figure 4), thus indicating a very dynamic behaviour of H3K4me3 in this system (as opposed to what was found during embryonic stem cell differentiation) (Mikkelsen et al, 2007; Zhao et al, 2007). The observation that in yeast H3K4me3 completely turns over completely in less than 2 h (Seward et al, 2007) also supports the dynamic nature of this modification and suggests that steady-state levels measured by ChIP may in fact reflect a dynamic equilibrium. Visual browsing through the data suggested a strong correlation between Jmjd3 binding and H3K4me3 positivity and levels (Figure 2A). To assess the correlation between Jmjd3 and H3K4me3 genome-wide in a quantitative manner, we sorted all 19 631 H3K4me3 peaks in LPS-stimulated cells in the order of their total tag counts (the number of tags that form the peak). Then we calculated the percentage of peaks in each bin that overlap a Jmjd3 peak. Almost 100% of the highest H3K4me3 peaks were found associated with a Jmjd3 peak (Figure 2B; Supplementary Table IV). The association with Jmjd3 decreased steadily in the H3K4me3 peaks with lower tag counts (Figure 2B). The correlation was much weaker when pre-stimulation H3K4me3 levels were considered (data not shown). Importantly, not all strongly positive H3K4me3 genes were bound by Jmjd3 (Figure 2A; Supplementary Table IV) and Jmjd3 seemed to bind preferentially to TSSs with upregulated H3K4me3 levels: out of the 173 genes with ⩾2-fold increase in H3K4me3, 106 genes (61%) were bound by Jmjd3. Some specific examples are shown in Figure 2C and Supplementary Figures 5 and 6. Figure 2.Jmjd3 association with transcriptionally active and inducible genes. (A) Jmjd3 association with H3K4me3-positive genes. Jmjd3 peaks and H3K4me3 peaks (in unstimulated and LPS-stimulated macrophages) at a representative region of chr 11 are shown. (B) Correlation between H3K4me3 levels and Jmjd3 binding in LPS-stimulated macrophages. H3K4me3 peaks were grouped in bins of decreasing total tag count from left to right. The y axis indicates the per cent of H3K4me3 peaks overlapping Jmjd3 peaks. (C) Association between Jmjd3 and H3K4me3 at representative genes. (D) Correlation between intensity of Jmjd3 binding and high levels of H3K4me3. (E) Correlation between Pol_II level and Jmjd3 binding at 2 h after LPS stimulation. Genes were grouped in bins of decreasing Pol_II intensity from left to right. The y axis shows the per cent of active, RNA Pol_II-positive genes that are associated with Jmjd3. Download figure Download PowerPoint We next measured the correlation between the levels of Jmjd3 and those of H3K4me3 after LPS stimulation. Figure 2D shows a box plot of the number of overlapping tags in Jmjd3 peaks and the total tag counts of the associated H3K4me3 cluster. It seems that the intensity of the Jmjd3 ChIP signal is positively correlated with H3K4me3 ChIP intensity after LPS treatment, indicating that Jmjd3 binds to active genes in a manner somehow proportional to the intensity of gene activity. As the distribution of H3K4me3 and Jmjd3 often overlaps and because newly synthesized Jmjd3 is transiently incorporated in H3K4 HMT complexes (De Santa et al, 2007), a simple possibility is that Jmjd3 is preferentially recruited to sites of active H3K4me3 deposition or turnover by association with H3K4 histone methyltransferases. Indeed, some of the genes showing the highest levels of Jmjd3 recruitment were those undergoing massive H3K4me3 increase after stimulation (e.g. Ccl5, Nos2, Cd40) (Figure 2C; Supplementary Figure 6; Supplementary Table IV). To assess the correlation between Jmjd3 recruitment and gene activity in a more direct manner, we generated genomic maps of total Pol_II in unstimulated, 2 and 4 h LPS-stimulated primary macrophages. About 9 million uniquely aligned sequencing reads were obtained in unstimulated macrophages, whereas more than 12 million reads were obtained in stimulated macrophages at both time points. Pol_II transcriptional activity was indicated by the detection of Pol_II signals within the internal regions of several inducible genes including Nos2, Ccl5 and Irf1 (Supplementary Figure 7). Using a high stringency cutoff (FDR=0.1%), we found a total of 55 600 Pol_II peaks in the unstimulated macrophage library and 57 201 and 57 514 peaks in the 2- and 4 h-stimulated libraries, respectively. In each library >70% of the peaks were located ±10 kb of known TSSs, as compared with 26% association with random peaks in simulation experiments. Moreover, >99% of the peaks were associated with gene regions (±100 kb of a gene) whereas less than 1% of Pol_II peaks were found in gene deserts. Out of the 17 389 genes associated with Pol_II, 3992 genes (23%) showed more than a two-fold increase in the total tag count within the corresponding Pol_II peaks at 2 h after LPS simulation and 1510 of them (1510/3992; 38%) were also bound by Jmjd3. Reciprocally, when the 3339 genes bound by Jmjd3 were considered, 73% of them (2438) showed an increase in Pol_II activity at 2 h after LPS (>25% increase in the total tag count in their peaks), thus indicating that Jmjd3 binding is biased towards a subset of genes whose transcription is induced or increased by LPS. To measure the correlation between transcriptional activity and Jmjd3 binding in a quantitative manner, we first grouped genes in bins of decreasing Pol_II tag count and then we calculated the percentage of genes in each bin that are bound by Jmjd3. Seventy-eight per cent of the genes with the highest total Pol_II tag counts at 2 h after LPS stimulation were Jmjd3 targets (Figure 2E). The association with Jmjd3 decreased steadily in genes with lower tag counts. The correlation was much weaker with the pre-stimulation Pol_II library, and slightly weaker with the 4 h LPS-stimulated library (data not shown). Overall, these data indicate that Jmjd3 is preferentially recruited to sites of high and inducible Pol_II occupancy and gene activity. H3K27me3 status at Jmjd3 target genes The only known substrate of Jmjd3 is H3K27me3, and the simplest prediction consistent with its reported biochemical activity as a H3K27 demethylase is that Jmjd3 is recruited to genes associated with basal H3K27me3 levels to reduce them and enable or enhance transcriptional activation. To test this prediction we generated H3K27me3 genomic maps in unstimulated and LPS-stimulated macrophages; 9.7 million and 14 million uniquely aligned sequences were obtained from the anti-H3K27me3 ChIP in untreated and LPS-treated cells, corresponding to 59 684 and 89 093 peaks, respectively, at an FDR of 0.1% (Supplementary Tables V and VI). Similarly to other systems, H3K27me3 peaks were in fact often part of broad regions (previously defined as broad local enrichment, BLOCs) (Pauler et al, 2009) of average size of 21.2 and 27.8 kb (in untreated and LPS-treated macrophages, respectively) (Supplementary Table VII and Supplementary data). The percentage of H3K27me3 peaks contained within the 5733 BLOCs identified was 72.3 and 66.6% in untreated and LPS-treated macrophages, respectively. The behaviour of peaks within and outside these broad regions was however comparable (see Supplementary data) and therefore we will refer to peaks rather than BLOCs in the following section of the manuscript. Jmjd3 target genes were in the majority of cases (2963/3339; 88%) not associated with any H3K27me3 peak within ±1 kb already before LPS stimulation and therefore before induction of Jmjd3 (Figure 3A; Supplementary Table VII). At a few genes with multiple TSSs with differential H3K27me3 association, Jmjd3 seemed to be selectively recruited at the H3K27me3-negative TSS (Supplementary Table VIII). Therefore, Jmjd3 recruitment to target genes does not rely on pre-existing H3K27me3, and at most recruitment sites Jmjd3 will not encounter H3K27me3. Considering all (genic and extragenic) Jmjd3 peaks, only a minority of them (511/4398; 11.6%) was associated with a neighbouring (±500 bp) H3K27me3 peak (Supplementary Table VIII). Of these peaks, only 83 (16.3%) decreased by more than two-fold after a 4 h LPS stimulation. This value is similar to the frequency of a two-fold reduction of H3K27me3 peaks observed elsewhere in the genome after LPS treatment (17.6%, corresponding to 10 385 out of a total of 59 173 peaks). Figure 3B shows the comparison of intensity changes between overall H3K27me3 peaks (shown in red) and peaks overlapping with Jmjd3 (in blue). The change of H3K27me3 patterns on stimulation is extremely similar in the two groups. Figure 3.Jmjd3 binding and H3K27me3. (A) Lack of basal H3K27me3 at Jmjd3-bound genes. Jmjd3 and H3K27me3 ChIP-Seq profiles at two representative regions of chr5 and chr11. (B) Reduction in H3K27me3 is statistically similar at Jmjd3-bound and non-bound regions. The x axis indicates fold changes in H3K27me3 levels in response to LPS stimulation. The y axis shows the fraction of H3K27me3 peaks. (C) Reduction in H3K27me3 at Nos2 and Upp1 after LPS stimulation. The H3K27me3 peak downregulated after LPS is indicated by a shaded box. (D) Reduction in H3K27me3 at Upp1 and Nos2 in LPS-stimulated macrophages reflects nucleosome loss. ChIP–qPCRs were carried out with antibodies against H3K27me3 or total H3. H3K27me3/H3 ratios were calculated by dividing H3K27me3 signals by the signal obtained with the anti-H3 antibody. The data refer to one representative experiment out of four with qualitatively similar results. Download figure Download PowerPoint Taken together, these data show that there is no statistically significant increase in the probability of H3K27me3 reduction at peaks lying close to Jmjd3 as compared with the distant ones. However, some H3K27me3 peaks underwent a rapid reduction following LPS stimulation, and a fraction of these peaks were Jmjd3-associated. Therefore, we sought to understand the molecular basis of this reduction and whether it was due to enzymatic demethylation. Reduced H3K27me3 signals at these peaks after LPS may reflect enzymatic demethylation, histone exchange or nucleosome loss. To discriminate among these possibilities, we analysed two Jmjd3-associated genes among those showing the highest stimulus-induced reduction, Nos2 and Upp1 (Figure 3C; Supplementary Figure 6). In both cases, H3K27me3 downregulation perfectly mirrored the reduction in the total H3 levels that accompanied gene induction (Figure 3D, upper and middle panels). In fact, when H3K27me3 ChIP data were normalized to total H3, no difference was found in untreated and treated cells (Figure 3D, bottom panel), suggesting that nucleosome loss rather than enzymatic demethylation is the mechanism underlying the observed reductions of H3K27me3. Similar data were observed with all the other genes analysed (data not shown). Therefore, nucleosome depletion at inducible genes is a widespread occurrence in LPS-stimulated macrophages, possibly because of the extensive nucleosome displacement linked to massive Pol_II elongation (Supplementary Figure 7); conversely, we could not get evidence supporting the occurrence of H3K27me3 demethylation in the first 4 h after LPS treatment. The Jmjd3-mediated H3K27me3 demethylation, we previously reported at the Bmp2 gene, in fact occurs with much slower kinetics (De Santa et al, 2007). We surmise that the slow rate of the H3K27me3 demethylation reaction, which in vitro requires a high enzyme-to-substrate ratio and a long incubation time (Agger et al, 2007; De Santa et al, 2007; Lan et al, 2007; Lee et al, 2007), combined with the short duration of the encounter with Jmjd3, makes Jmjd3-mediated H3K27me3 demethylation in the very first hours after LPS extremely unlikely. The few kinetic studies published insofar for JmjC-catalysed histone demethylation reactions are compatible with this interpretation, as they reported very slow substrate turnover rates (0.01 min−1) (Culhane and Cole, 2007). Finally, it should be noticed that increased H3K27 methylation (appearance of new peaks or BLOCs and increased intensity of the pre-existing ones) was more common than its loss or reduction (Figure 3B; Supplementary Table VII). This increase in H3K27 methylation was however strictly gene specific and occurred without any global change in H3K27me3 and Ezh2 levels, which remain both constant after LPS stimulation (De Santa et al, 2007). Overall, these data suggest that in this system and in this window of time Jmjd3 regulates transcription independently of H3K27me3 demethylation. This possibility is consistent both with the previous observation that most genes downregulated in macrophages depleted of Jmjd3 by retroviral RNA interference were not obvious or reported polycomb targets (De S
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