The heat shock response is characterized by the transcriptional activation of both hsp genes and noncoding and repeated satellite III DNA sequences located at pericentric heterochromatin. Both events are under the control of Heat Shock Factor I (HSF1). Here we show that under heat shock, HSF1 recruits major cellular acetyltransferases, GCN5, TIP60 and p300 to pericentric heterochromatin leading to a targeted hyperacetylation of pericentric chromatin. Redistribution of histone acetylation toward pericentric region in turn directs the recruitment of Bromodomain and Extra-Terminal (BET) proteins BRD2, BRD3, BRD4, which are required for satellite III transcription by RNAP II. Altogether we uncover here a critical role for HSF1 in stressed cells relying on the restricted use of histone acetylation signaling over pericentric heterochromatin (HC). Both events are under the control of Heat Shock Factor 1 (HSF1), a transcription factor present in the nucleus and cytoplasm of cell before heat shock4. Activation of pericentric HC is mainly observed at the 9q12 locus, and leads to the accumulation of long noncoding (lnc) satellite III (sat III) RNA. Sites of HSF1 accumulation at pericentric HC and of sat III sequences transcription form nuclear foci, also called nuclear stress bodies, (nSBs) in stressed cells. Transcriptional regulation of the heat shock response at hsp gene promoters, has been extensively studied as a model system of gene expression. Unlike hsp genes, sat III transcripts are transcribed from heterochromatic regions suggesting that different mechanisms may be involved in their transcriptional activation. In higher eukaryotes, RNA pol II (RNAP II) at hsp genes is transcriptionally engaged5 before heat shock, while no evidence exist that it is similarly engaged at sat III sequences.In human cells, the heat shock response (HSR) is characterized by the transcriptional activation of both protein coding heat shock (hsp) genes7. TIP60 has also been identified for its role in the spreading of PARP and subsequent recruitment of elongation factors such as P-TEFb and Spt6 and for its role in the recruitment of histone-modifying enzymes such as Trithorax and CREB-binding protein (CBP)8. Based on in vitro interaction assays, a binding between HSF1, the general transcription factors TATA-Binding Protein (TBP) and Transcription Factor IIB (TFIIB) has been reported in human cell extracts9. More recently a direct interaction between HSF1 and replication protein A (RPA), which is known to bind and stabilize single-strand DNA (ssDNA) has also been characterized10. The HSF1-RPA complex leads to preloading of RNAP II and opens the chromatin structure by recruiting a histone chaperone, FACT10.Several actors have been identified for their role in HSF1-mediated hsp genes activation in higher eukaryotes. In Drosophila, proteins have been identified for their essential role in the initial, HSF1-mediated, transcription independent, nucleosome disruption. They include GAGA factor  and poly(ADP)-ribose polymerase (PARP)11. The role of Histone Acetyl Transferases (HATs) is to catalyze lysine acetylation which, depending on the lysine targeted, could be a way to loosen histone/DNA interactions12. Acetylated residues also represent docking sites for bromodomain (Brd), a protein domain known to act as an acetyl-lysine binding domain. Brd-containing factors include proteins with multiple roles in transcriptional activation, like the HATs GCN5, PCAF (p300/CREB-binding protein-Associated Factor), p300 and like CBP (CREB Binding Protein), the BRG1 ATPase subunit of SWI/SNF nucleosome remodeling complex, or the TAF1 subunit of the general transcription factor TFIID (Transcription initiation factor TFIID subunit 1). They also include the “bromo- and extra terminal domains” (BET) family members, among which BRD2, BRD3, BRD4 and BRDT13. BRD4 and BRDT are key mediators for the assembly of the Positive Transcription Elongation Factor b complex (P-TEFb), an event required for the initiation of transcription elongation15. An intrinsic histone chaperone activity has also been characterized for BRD216. BET/histone interactions, which play important roles in physiological17 and in pathological settings18, can be selectively inhibited by the acetyl-lysine competitive inhibitor such as JQ120. As expected, histone acetylation plays an important role in the activation of hsp genes. Likewise, increased acetylation at histone H3 in the presence of HSF1 at the hsp70 gene is associated with increased recruitment of CBP and BRG121, while an interaction between HSF1, p300/CBP and STRAP (Stress-responsive activator of p300) promotes HSF1 binding and augments histone acetylation within hsp genes22. Important changes in chromatin acetylation have also been shown within nSBs, revealed by the appearance of HSF1-dependent acetylated foci at nSBs, colocalizing with foci enriched for the HAT p300/CBP2. However the role of the different actors described above, that set up and read acetylation marks, is currently unknown in the transcriptional activation of sat III sequences. The contrasting role of Trichostatin A (TSA), a potent inhibitor of Histone Deacetylases of Class I and II mammalian Histone Deacetylases (HDACs), on sat III and hsp70 transcriptional activation supports the existence of distinct specificities between sat III and hsp gene transcription. Indeed, while TSA promotes hsp 70 gene transcription even in the absence of stress, TSA inhibits sat III transcriptional up-regulation3.Histone modifications, and especially acetylation, are known to play an essential role in the control of gene regulationIn this context, investigating the mechanisms associated with HSF1-induced HC acetylation and sat III transcription represents an important goal in understanding the biology of HC. Here, we report that HSF1 directs several Histone Acetyl Transferases to pericentric HC in stressed cells. We also bring evidence that Bromodomain-containing proteins BRD2, BRD3 and BRD4 are specifically and massively recruited to pericentric HC in heat-shocked cells. Accordingly, we provide new evidence that unlike hsp70 gene, JQ1 specifically inhibits sat III transcription by RNA polymerase II (RNAP II), pointing to the existence of important differences in the mechanisms of activation of two major targets of the heat shock response.in situ approaches23.The transcriptional activation of pericentric HC is probably one of the most striking events occurring in response to stress. Unlike hsp genes, transcriptional activation at pericentric HC is thought to occur within compact chromatin, and the molecular mechanisms involved are still poorly characterized. Interestingly, the presence of a large number of transcribed units at the 9q12 locus, resulting in the formation of nSBs about 1 µm diameter, allows a characterization of the actors involved in the activation of these regions by 3. As shown in Fig. As a step toward a better characterization of chromatin acetylation at the 9q12 locus, analysis of specific histone marks was performed in heat-shocked cells. Unstressed cells display a homogenous distribution of acetylated chromatin  while no recruitment of the TFIID subunit TAF250, and of the chromatin modulator ATAD232, was observed, revealing the presence of certain, but not all, bromodomain proteins to nSBs , TBP and RNAPII, in a model where a heat shock gene was flanked by mating-type silencers36. We now broaden these observations to an endogenous locus in human cells, and confirm the capacity of HSF1 to promote a rapid deposition of active epigenetic marks at regions enriched in H3K9me3, thus reinforcing the classification of HSF1 in the category of pioneer transcription factors, able to access target element in nucleosomal DNA when other factors cannot37.The capacity of HSF1 to rapidly and efficiently activate the transcription of HC very likely relies on its capability, shown here, to interact with HATs, and to drive them to pericentric HC. Coimmunoprecipitation of p300/CBP with HSF1 has already been reported3 and is consistent with the active transcriptional state of HC upon HS. We show that acetylation targets lysine residues at the N terminal histone tails but also, in the case of H3K56, H3K64, H3K122, at histone fold motifs. Among the different acetylation marks we detect at nSBs, three of them have been related to heterochromatin maintenance. H3K4ac has been proposed to regulate heterochromatin formation, and more specifically the switch between active and inactive HC formation at S phase in the yeast S. pombe, allowing the binding of Chp2 and Swi6, the yeast homologs of HP1, to H3K9me2/338. A very recent work now also points to the contrasting role of two other acetylation marks in mammals. Indeed, H3K18 deacetylation at pericentric HC has been found to stabilize pericentric HC and to prevent mitotic errors and cellular senescence39 while, conversely, a new study points to the importance of TIP60-dependent H3K12 acetylation, through BRD2 interaction, to stabilize pericentric HC in Suv39H−/− mouse cells40.Acetylation of all four H2A, H2B, H3 and H4 histones at nSBs has already been reported, more specifically at H4K8 and K16S. pombe41 and mammals42 in HC maintenance and establishment through the recruitment of proteins, such as HP1 (Heterochromatin Protein 1)43. Finally, based on the recent finding that deacetylation of H3K18 at pericentric HC seems to prevent mitotic errors and cellular senescence39, histone acetylation at pericentric HC may promote a rapid activation of cell cycle checkpoints and be used as a sensor of stress.The contrasting roles of histone acetylation in these different biological contexts, might reflect the dual roles of pericentric HC activation. Indeed, in stressed cells, pericentric HC transcriptional activation and sat III RNA accumulation may serve, through the transient recruitment of transcription factors and of RNA maturation factors at nSBs, as an efficient way to trigger global and rapid changes in gene expression. Conversely, transient accumulation of pericentric RNA also participates, as in the yeast 44.Our observation that HSF1 overexpression is sufficient to trigger chromatin acetylation and sat III accumulation at nSBs suggests that active HSF1 is able to drive HATs to nSBs, even in the absence of stress, with possible impact in tumors where HSF1 overexpression and nuclear localization is associated with reduced cell survival16. Likewise, BRD4 activates transcriptional elongation through different ways. One, specifically found at enhancers, relies on the acetyl binding property of the BRD4 bromodomain and involves the histone chaperone activity of BET proteins45. A second way, not impaired by JQ1, would rely on the capacity of BRD4 to recruit the P-TEFb complex allowing RNAP II phosphorylation and release from pausing at transcription start sites46. The new observation we make that RNAP II accumulation at the 9q12 locus is impaired in JQ1 treated cells, reveals a new specific role for Brd proteins in RNAP II recruitment at pericentric HC in stressed cells and indicates that the histone chaperone function of BET proteins is preferentially required for HC transcription . Co-immunoprecipitations were performed in fibroblast-like Monkey Cos7 cells.Heat-shock experiments were performed by immersion of the culture flasks in a warm water bath. Unless otherwise stated, heat-shock was performed for 1 h at 43 °C.19 was achieved by treating the cells at a concentration of 500 nM, 5 min before heat shock.JQ1 treatmentTransient transfections were performed using lipofectamine®2000 solution (ThermoFisher Scientific).54, at a final concentration of 5 mM in the culture medium 5 min before stress. Triptolide was then maintained during the heat-shock treatment.Transcription inhibition was performed with adding Triptolide, an inhibitor of RNAP II initiation56. pH2.3 clone corresponding to human hsp70 sequence57 was provided by R.I. Morimoto . The plasmids expressing the mouse HSF1-GFP, DBD-TRIM-GFP and FLAG-HSF1 were developed in our laboratory58. Detection of hsp70 and sat III RNA was performed with plasmid pH2.3 and pHuR9859.Expression plasmids for human CBP-Ha, Ha-Gcn5 and p300 were provided by A. Harel-Bellan , and H. Stunnenberg , respectively. Human pcDNA Ha‐tagged TIP60 expression plasmids are described in60, with the following antibodies: rabbit anti-HSF1  (1:100), mouse anti-HSF1 , rabbit anti-H3K4ac , rabbit anti-H3K9ac Abcam ab39917 (1:200), rabbit anti-H3K18ac Abcam ab1191 (1:50), rabbit anti-H3K27ac active motif 39133 (1:800), rabbit anti-H3K56ac Epitomics 2134-1 (1:250), rabbit anti-H3K64ac Active Motif 39546 (1:1000), rabbit anti-H3K122ac R. Schneider (1:100), rabbit anti-H4 K5ac Epitomics 1808-1 (1:500), rabbit anti-H4K8ac Epitomics 1796-1 (1:800), rabbit anti-H4K12ac , rabbit anti-H4K16ac Millipore 07–329 (1:200), rabbit anti-H4K20ac Abcam ab19855 (1:200), anti-acetyl lysin Millipore, 06–933 (1:200), anti-Gcn5 Millipore 07–038 (1:50), anti-Tip60 Santa Cruz sc-20698 (1:100), anti-Brd2 , anti-Brd4  (1:300), and mouse anti-ATAD2  (1:200). Anti-RNAP II Ser 2 (1:400) was obtained, from M. Vigneron  (1:400). Anti-TAF1 was kindly provided by L. Tora . Anti-Brd3 (1:100) was made by immunizing a rabbit with the peptides QLKKGGKQASASYDSC and CQAAKSKEELAQEK, followed by immuno-purification of the serum on the immobilized peptides.Immunofluorescence was performed on formaldehyde-fixed cells as previously described60. Cells were analyzed using an apotome microscopy system  or a confocal laser scanning microscope (CLSM)  using a Plan-Apochromat 63x Oil objective (NA = 1.4). Confocal excitation wavelengths were 488 nm (FITC), 543 nm (Cy3), and 730 nm (DAPI- biphotonic excitation). The pinhole diameter was set to 1 Airy Unit for the confocal channels and fully opened for 2P excitation.Secondary anti-rat or -rabbit antibodies conjugated to either Dylight 549 (KPL) or Alexa 488 (Molecular Probes) were used. DNA was counterstained with DAPI (250 ng/mL). RNA FISH alone and in combination with immunofluorescence or DNA FISH was performed as described previously57 or pHuR9859 probe labeled with- α32 P dCTP.Total RNAs were extracted with Tri-reagent® (Sigma-Aldrich) following the manufacturer’s instructions. For Northern blot analysis, 10 µg of RNA were loaded on a 1% agarose denaturing gel. Before transfer, the gel was treated with 75 mM NaOH for 10 min, and washed in 0.5 M Tris/1.5 M NaCl, pH 7.0. The RNAs were then transferred onto Hybond N membrane (Amersham Biosciences) and hybridized with pH2.3Cos cells were plated in 10 cm dishes and transfected with 3 μg of a pcDNA-FLAG plasmid expressing a Human HSF1 (FLAG in Cter) or an empty vector as negative control, and a plasmid expressing either HA-p300 (5 µg), HA-GCN5 (4 µg) or HA-TIP60 (4 µg). Cells were lysed 24 h after transfection by incubation for 1 h on ice in LSDB500  containing 0.2% NP‐40, 1 mM DTT, 200 μg/ml DNaseI, 100 μg/ml RNaseA, 50 ng/ml TSA and protease inhibitors cocktail . After centrifugation, the lysate was incubated with 1 μg of anti‐FLAG antibody (Sigma) for 1 h on ice. Protein G‐Sepharose beads (Amersham) were then added and incubated at 4 °C on a rotoshake for 1 h. After three washes with LSDB250 (same as the lysis buffer with 250 mM KCl), complexes were recovered by adding Laemmli sample buffer and analyzed by Western blots. Western blot was performed with a rat anti-HA (3F10) antibody (Roche).52.Cells were cross-linked with 1% formaldehyde at room temperature for 10 min before the addition of 125 mM glycine (Sigma) at room temperature for 5 min. After washing, cell were resuspended into cytosol lysis buffer  at 4 °C for 5 min. Nuclei were resuspended in nuclei lysis buffer  and then sonicated at 4 °C for 19 min with a BioRuptor sonicator (Diagenode) to obtain fragments between 200 and 800 base pairs. Samples were incubated overnight with the anti-H3 (Abcam), anti-H3K9Me3 (Abcam), anti-IgG (Diagenode) antibodies and immunoprecipitated using the OneDay ChIP kit (Diagenode), following the manufacturer instructions. Immunoprecipitated DNA was analyzed by qPCR, using primers. IP-PHUR1 (Fwd): AATCAACCCGAGTGCAATCGAATGGAATCG and IP-PHUR2 (Rev) TCCATTCCATTCCTGTACTCGG)Standard deviations were calculated from 3 independent experiments.Supplementary Figures


Enhancers and promoters are cis-acting regulatory elements associated with lineage-specific gene expression. Previous studies showed that different categories of active regulatory elements are in regions of open chromatin, and each category is associated with a specific subset of post-translationally marked histones. These regulatory elements are systematically activated and repressed to promote commitment of hematopoietic stem cells along separate differentiation paths, including the closely related erythrocyte (ERY) and megakaryocyte (MK) lineages. However, the order in which these decisions are made remains unclear.To characterize the order of cell fate decisions during hematopoiesis, we collected primary cells from mouse bone marrow and isolated 10 hematopoietic populations to generate transcriptomes and genome-wide maps of chromatin accessibility and histone H3 acetylated at lysine 27 binding (H3K27ac). Principle component analysis of transcriptional and open chromatin profiles demonstrated that cells of the megakaryocyte lineage group closely with multipotent progenitor populations, whereas erythroid cells form a separate group distinct from other populations. Using H3K27ac and open chromatin profiles, we showed that 89% of immature MK (iMK)-specific active regulatory regions are present in the most primitive hematopoietic cells, 46% of which contain active enhancer marks. These candidate active enhancers are enriched for transcription factor binding site motifs for megakaryopoiesis-essential proteins, including ERG and ETS1. In comparison, only 64% of ERY-specific active regulatory regions are present in the most primitive hematopoietic cells, 20% of which containing active enhancer marks. These regions were not enriched for any transcription factor consensus sequences. Incorporation of genome-wide DNA methylation identified significant levels of de novo methylation in iMK, but not ERY.Our results demonstrate that megakaryopoietic profiles are established early in hematopoiesis and are present in the majority of the hematopoietic progenitor population. However, megakaryopoiesis does not constitute a “default” differentiation pathway, as extensive de novo DNA methylation accompanies megakaryopoietic commitment. In contrast, erythropoietic profiles are not established until a later stage of hematopoiesis, and require more dramatic changes to the transcriptional and epigenetic programs. These data provide important insights into lineage commitment and can contribute to ongoing studies related to diseases associated with differentiation defects.The online version of this article (10.1186/s13072-018-0195-z) contains supplementary material, which is available to authorized users. Hematopoiesis is the process by which proliferating hematopoietic stem cells (HSC) undergo continued transcriptional and epigenetic changes associated with lineage-restriction and cell-specific function, giving rise to all of the cell types in the hematopoietic system. Classical models of hematopoiesis describe a series of progressively restricted cell fate decisions in which HSC give rise to multipotent progenitors (MPP), which in turn give rise to common myeloid progenitors (CMP) and common lymphoid progenitors (CLP). In myelopoiesis, CMP differentiate into granulocyte–macrophage progenitors (GMP), from which mature granulocytes and monocytes are generated, and megakaryocytic erythroid progenitors (MEP), which have classically been described as the common erythrocyte and megakaryocyte progenitor –4.Erythro-megakaryopoiesis describes the subset of cell fate decisions associated with the production of erythroblasts  and megakaryocytes . The process of erythroid and megakaryocyte commitment is accompanied by the substitution of GATA1 for GATA2 at key chromatin regulatory sites to increase expression of linage-specific transcription factors including FLI1 and ETS in the MK lineage, and KLF1 and GATA1 in the ERY lineage –8. TradiDetermining when lineage specification occurs is important for understanding mechanisms behind diseases with maturation defects. These diseases include anemias, thrombocytopenias, neutropenias, myelodysplastic and myeloproliferative disorders, and hematologic malignancies –20. FluoTo characterize the relationships between hematopoietic populations and the changes that accompany erythro-megakaryopoiesis, we enriched 10 primary hematopoietic cell populations from C57BL6 mice for RNA-Seq, ATAC-Seq, ChIP-Seq, and MBD-Seq analysis. These data have been integrated into transcriptional and epigenetic maps to identify candidate regulatory elements in the ERY and MK lineages and to study the establishment and maintenance of these regulatory elements during erythro-megakaryopoiesis. Our results demonstrate that committed megakaryocytes have similar transcriptional and epigenetic profiles to hematopoietic stem and progenitor cells, but do not represent a “default” developmental program. By contrast, the erythroid population is the most dissimilar from hematopoietic stem and progenitor populations and requires more extensive changes to the transcriptional and epigenetic programs to permit erythropoiesis.RNA-Seq was performed on two biological replicates of total RNA isolated from 10 populations of primary mouse bone marrow separated on the basis of cell surface marker expression  clustered with progenitors , while lineage-restricted erythroid (CFU-E and ERY) and granulocyte populations grouped in different clusters Fig. a. We usep value < 0.001)  , 81% were also expressed in the iMK population  Fig. . Of the 01) Fig. . These rWe generated maps of accessible chromatin regions using the Assay for Transposase-Accessible Chromatin (ATAC) on the same populations of primary mouse bone marrow cells used for transcriptional profiling. Unsupervised hierarchical clustering of the average ATAC-Seq signal in each peak region showed that, like the transcriptional profiles, the ATAC profiles of iMK and CFU-MK clustered with LSK, CMP, and GMP, while the ATAC profiles of the erythroid, neutrophil, and monocyte populations clustered separately Fig. a. These Of ~ 64,000 ATAC-Seq peaks in LSK, more than 95% of these were present in the set of ~ 100,000 CMP peaks Fig. d. We conp value 0.002, CMP p value 0.002) . More ERY-specific AREs (84%) were present in CMP, but this was significantly lower than the 97% of iMK-specific AREs . Approximately 14% of ERY-specific AREs were established de novo in CFU-E , whereas only 1% of iMK-specific AREs are established de novo in CFU-MK   . We gene02) Fig. d. In conic AREs 8% were prPoised enhancers are defined as regions of accessible chromatin that contain histone H3 monomethylated at lysine 4 H3K4me1), but they can be distinguished from active enhancers by the absence of H3K27 acetylation . To dete, but theSlc4a1, encoding the anion transporter BAND3, is strongly induced during erythroid maturation. This induction was accompanied by the ERY-specific induction of transposase-accessible chromatin around the promoter and 3′ end of the gene, as well as in the upstream noncoding gene Bloodlinc  was expressed and in regions of open chromatin in LSK, CMP, and MK-committed populations, but was repressed and in non-accessible chromatin regions in erythroid cells , and AREs outside of this region were defined as candidate enhancer elements (cEE). Based on these criteria, approximately 85% (1800) of ERY-specific and 55% (3500) of iMK-specific AREs established during differentiation were categorized as cPE Fig. a, with pIn addition to AREs established at different stages of hematopoiesis, we plotted the proximity of active, poised, and inactive AREs to the nearest TSS. As with ERY-specific AREs established during differentiation, ERY-specific active, poised, and inactive AREs were almost exclusively categorized as cPE Fig. b. While q-value ≤ 0.05) in the set of AREs , ETS1 , and PU.1 . In addition to those found in poised iMK-specific AREs, active iMK-specific AREs also had matches to motifs for RUNX1  and RUNX2 . ERY-specific AREs established in CMP were enriched for TFBS motifs of the master regulators GATA1, GATA2, and NF-E2   that were significantly enriched (Tfr2) gene  protein to enrich methylated DNA fragments, followed by next-generation sequencing (MBD-Seq) . We idenene Fig. c.Fig. 8OBecause changes in DNA methylation are associated with differences in expression, we compared the overlap of DNA methylation peaks with cell-specific ARE profiles. We found that 43 (2.1%) of 2098 ERY-specific AREs established in ERY overlapped with ERY DNA methylation peaks Table . 236 11.Table 5CWe compared the overlap of DNA methylation peaks with active, poised, and inactive regions in LSK. We found that nearly 20% of active and poised ERY-specific regulatory elements overlapped with regions of DNA methylation in LSK, whereas ~ 8% of active and poised iMK-specific regulatory elements overlapped with DNA methylation in LSK Table . We concIt is well known that megakaryocytes and stem cells share a number of molecular features , 11. BotThe clearest distinction between MPP and megakaryocytes is in their DNA methylation profiles, where de novo methylation increases in CFU-MK but subsequently decreases in iMK . SimilarIn contrast to megakaryopoiesis, erythropoiesis is associated with significant changes in the chromatin, enhancer/promoter, transcriptional, and methylation profiles. Our data suggest that, for most elements, these profiles are first detected at the level of the MEP, where the MEP ATAC-Seq profile clusters with that of mature erythroid cells. These data are consistent with the greater frequency of erythroid committed cells in the human MEP population . Based oThe classic model of hematopoiesis is displayed as a hierarchy in which multipotent progenitors traverse a series of oligopotent and bipotent intermediates that progressively restrict lineage potential , 45, 46.The data and analysis in this paper were generated as part of ValIdated Systematic IntegratiON of hematopoietic epigenomes (VISION) project, which aims to generate comprehensive catalogs of genomic regulatory elements in mouse and human hematopoietic cells and to conduct integrative statistical modeling and machine learning to predict regulatory interactions that are then validated by manipulating hematopoiesis at the genetic level. The data come both from efforts in the project laboratories, such as those generated for this paper, and also from other laboratories and consortia such as the Encyclopedia of DNA Elements (ENCODE) project and the NIH Roadmap Epigenomics Project –24. ThesOur studies confirm that the majority of the epigenetic and transcriptional profiles found in the hematopoietic stem cell population are present in megakaryocytes, while the erythropoietic epigenetic and transcriptional programs are not established until erythroid-lineage commitment. By performing “bulk” analyses, we achieve greater depth of coverage that emphasizes the dominant characteristics of cell populations. In contrast, single-cell analyses can resolve finer distinctions between populations, but at a lower depth of coverage.All primary hematopoietic cell populations were enriched from 5-to-8-week-old C57BL6 male mice. LSK, CMP, MEP, GMP, CFU-E, ERY, CFU-MK, and iMK populations were collected from bone marrow. Following collection, samples were lineage-depleted using antibodies Fig.  and conjCells were sorted into media and total RNA was extracted using TRIzol and the Ambion PureLink RNA Mini Kit , treated with DNase to remove genomic DNA using the DNA-free Kit . Sequencing libraries were then constructed from 100 ng of treated, total RNA using the ScriptSeq Complete Kit (Illumina cat# BHMR1224) according to manufacturer’s specifications. In brief, the RNA was subjected to rRNA depletion using the Ribo-Zero removal reagents and fragmented. First-strand cDNA was then synthesized using a 5’ tagged random hexamer and reverse transcription, followed by annealing of a 5’ tagged, 3’-end blocked terminal-tagged oligo and second-strand synthesis. The Di-tagged cDNA fragments were purified, barcoded, and PCR-amplified for 15 cycles.https://www.encodeproject.org/pipelines/ENCPL002LPE/). In brief, reads were mapped to the mouse genome (mm10 assembly) using STAR v2.5.4 [The size and quality of each library were then evaluated by Bioanalyzer 2100 , and quantified using qPCR. Libraries were sequenced in paired-end mode on the NextSeq500 to generate 2 × 75 bp reads using Illumina-supplied kits as appropriate. The sequence reads were processed using the ENCODE3 long RNA-Seq pipeline  and centrifuged as above. For each transposition reaction, cells were suspended in the following mix: 25 µl 2X Tagment DNA buffer (Illumina cat# FC-121-1030), 3 µl Tn5 Transposase (Illumina cat#FC-121-1030), 22 µl nuclease-free H2O and incubated for 30 min at 37 °C. Reactions were terminated by adding 5 µl of 1% SDS solution and purified using SPRIselect beads (Beckman Coulter cat #B23318) at a 1:1 ratio according to manufacturer’s instructions, followed by a right side size selection using SPRIselect beads (Beckman Coulter cat #B23318) at a 0:5 ratio according to manufacturer’s instructions. Following purification, library fragments were amplified using 1 × NEBnext PCR master mix and 1.25 μM of custom Nextera PCR primers 1 and 2  using th6 cells were fixed in 0.4% formaldehyde  for 15 min before quenching in 125 mM glycine for 5 min. Cells were washed in 2X PIC  and stored at − 80°C. Cells were resuspended in lysis buffer  and sonicated (QSonica) to a size range of 200–500 bp as determined by electrophoresis. Immediately prior to immunoprecipitation, an aliquot was removed to be sequenced as a matched input control. Immunoprecipitation was performed using H3K27ac (Abcam ab4729) supplemented with 50 mM sodium butyrate. DNA fragments were purified with AMPure XP beads  according to the manufacturer’s protocol. Sequencing was performed at the NIH Intramural Sequencing Center on an Illumina HiSeq2500. Chromatin immunoprecipitations were repeated to generate four biological replicates. Sequence data were aligned to the mm10 genome using Bowtie v2.3.4 [Approximately 20 × 10e v2.3.4  and peake v2.3.4 . Peaks pWe accessed indexing-first immunoprecipitation (iChIP) experimental data performed by Lara-Astiaso et al.  from GenGenomic DNA was isolated from enriched cells with the QIAGEN Puregene kit and sonicated to 200- to 400-bp fragments (QSonica). MBD2 enrichment was performed with the Active Motif Methyl Collector kit. Approximately 1 μg of sonicated genomic DNA was incubated with MBD2-His-conjugated protein and magnetic beads according to the manufacturer’s protocol. After enrichment, both the methylated fraction and supernatant fractions were purified with QIAGEN DNA purification columns. Quantitative PCR amplification of the differentially methylated regions regulating the imprinting of Snrpn and Rasgrf1 and the unmethylated CpG island promoter of Actb was performed with SYBR Green PCR master mix (Applied Biosystems) and was used to validate the enrichment of methylated DNA using the MBD2-pull-down approach.Two biological replicates of each enriched cell population and one supernatant sample per cell type were submitted for high-throughput sequencing analysis. Between 225 and 540 ng of MBD2-enriched DNA and 1 μg of supernatant for each cell type were used to construct DNA libraries according to the Illumina protocol. The libraries were sequenced on the Illumina Genome Analyzer platform, and 36-bp single-end reads were used to uniquely identify the MBD2-bound fraction of the mouse genome. Sequenced reads were mapped to the mouse genome (UCSC assembly mm10) using Bowtie v2.3.4 . Peaks fPermutation tests were performed using the R package regioneR v1.10.0. Transcription factor enrichment analyses were performed in HOMER v3.18 . Graphs Additional file 1: Fig. S1. Establishment of ERY and iMK enhancer/promoter regions throughout hematopoiesis. (A–C) LSK-accessible cell-specific and shared AREs were compared against the indexing-first H3K4me1 and H3K27ac chromatin immunoprecipitation profiles in (A) LT_HSC, (B) ST_HSC, and (C) MPP . (D) Z-score of overlap between ARE and iChIP peaks. Hashed bars indicate p-value >0.05.Additional file 2: Fig. S2. Locus-specific example of (epi) genomic correlations. (A) Erythroid specific induction of AREs and expression (Slc4a1) and constitutive AREs and expression (Slc25a39). RNA-Seq and ATAC-Seq are shown for all 10 cell types, and histone modifications for 5 cell types of most relevance. (B) Retention of AREs and expression from LSKs in MKs and loss in ERY. RNA-Seq and ATAC-Seq are shown for all 10 cell types, and histone modifications for 5 cell types of most relevance. Tracks are displayed on the mm10 genome.Additional file 3. Enrichment scores of transcription factor binding sites in AREs. Unabridged list of enriched transcription factor binding sites in AREs. Scores are reported as q-values.Additional file 4. RNA-Seq data with TPM calculations. Transcripts per million counts are included for each RNA-Seq replicate.Additional file 5. Peak data from ATAC-Seq experiments. The presence of ATAC-Seq peaks in each cell type is indicated by a 1 (present) or 0 (not present).Additional file 6. Peak data from ChIP-Seq and MBD-Seq experiments. Peaks from H3K27ac ChIP-Seq and MBD-Seq are presented in BED format. Note that due to file restrictions, MBD-Seq data for CFU-MK is included across two workbooks.


This rewiring was brought about by the exaptation of a transposable element (TE) called Izanagi, which is co-opted to act as a silencer to turn off the dmrt1bY gene after it performed its function in sex determination.Sex determination relies on a hierarchically structured network of genes, and is one of the most plastic processes in evolution. The evolution of sex-determining genes within a network, by neo- or sub-functionalization, also requires the regulatory landscape to be rewired to accommodate these novel gene functions. We previously showed that in medaka fish, the regulatory landscape of the master male-determining gene Rex1, has been incorporated into Izanagi. The insertion of Rex1 brought in a preformed regulatory element for the transcription factor Sox5, which here functions in establishing the temporal and cell-type-specific expression pattern of dmrt1bY. Mutant analysis demonstrates the importance of Sox5 in the gonadal development of medaka, and possibly in mice, in a dmrt1bY-independent manner. Moreover, Sox5 medaka mutants have complete female-to-male sex reversal.We now show that a second TE, dmrt1 transcriptional activity during primary sex determination due to exaptation of the Rex1 transposable element.Our work reveals an unexpected complexity in TE-mediated transcriptional rewiring, with the exaptation of a second TE into a network already rewired by a TE. We also show a dual role for Sox5 during sex determination: first, as an evolutionarily conserved regulator of germ-cell number in medaka, and second, by de novo regulation of The online version of this article (10.1186/s12915-018-0485-8) contains supplementary material, which is available to authorized users. Sex determination (SD) is one of the most plastic processes in evolution. The trigger for the bipotential undifferentiated embryonic gonad anlage to develop into either testis or ovary can be provided by various signals from the environment, the genetic constitution of the individual, or a mixture of both , 2. StudClearly, such a high turnover of genetic determinants can work only if the evolutionary innovations are accompanied by the ability of the respective genes to neo-functionalize or sub-functionalize quickly and efficiently , 9. In admrt1bY of the teleost fish Oryzias latipes (medaka) arose approximately 5 to 10 million years ago from an autosome encompassing the dmrt1 gene. Dmrt1 is a highly conserved transcription factor that usually functions at a downstream position of the sexual regulatory cascade. In medaka, the dmrt1 gene was locally duplicated and inserted into another chromosome that became the Y-chromosome  region in Fig. Rex1 TE [α], and two within repeat 3 [γ] of the dmrt1bY proximal promoter region  and zebrafish (59), but there are fewer in cavefish (20), cod (15) and gar (10). They are scarce in stickleback (3), platyfish (4), and Amazon molly (2), and absent in fugu, tetraodon, and coelacanth  of the Rex1 element  and fibroblast (OLF) cells, were transfected with a FLAG-tagged version of Sox5 and then subjected to chromatin immunoprecipitation (ChIP) using an anti-FLAG antibody. DNA amplification with specific sets of primers from the Sox5 immunoprecipitates revealed that the Sox5 protein binds to the predicted sites  of the dmrt1bY promoter. However, much stronger binding is apparent for the proximal site [α] , they do not appear to be functional, as Sox5 does not target them in the ChIP experiments (data not shown).To assess the relevance of the predicted Sox5 binding sites [see Fig.  in the dsox5 binding region [β] has previously been shown to overlap with a high affinity binding site for Dmrt1  and [β]  had intermediate transcriptional activity  when fused to a minimal thymidine kinase promoter. Neither the [β] or a mutated version of the [α] region ([α]-MUT) were able to modulate the activity of the minimal thymidine kinase promoter  at different time points post-transfection . Our findings reveal that the transcription of dmrt1bY is highly repressed in the presence of Sox5  was monitored  are maternally deposited, their lower relative abundances compared to controls after Sox5 overexpression are likely attributable to a total arrest of background transcription after zygotic transcription started, or possibly accelerated mRNA decay.Next, to validate our in vitro results, Sox5 coding mRNAs were microinjected into one-cell-stage embryos, and the expression of red Fig. . The resion Fig. . Overexpion Fig. . Our in sox5 mRNA, which is expressed in embryonic and early larval development, has a distinct spatially and temporarily restricted expression pattern  reporter. The reliability of sox5 gene expression was confirmed by comparison of the observed fluorescence with the in situ hybridization data  is also expressed in germ cells at that specific stage of development  Kb Dmrt1bY prom . For Fig. Cre-NLS protein was performed .I-SceI meganuclease was injected into the cytoplasm of one-cell-stage medaka embryos (Carbio strain). Adult F0 fish were mated to each other and the offspring were tested for the presence of the transgene by checking for fluorescence. Siblings from positive F1 fish were raised to adulthood and tested again for fluorescence.To generate stable transgenic lines, the meganuclease protocol was used . BrieflyFor PGC visualization and counting, the GFP-nos1 3′ UTR construct that includes the mmGFP5 ORF cloned upstream of the 3′ UTR of the zebrafish nanos1 gene , 69 was For imaging, embryos, hatchlings, or tissues were mounted with 1.2% low melting temperature agarose. Confocal pictures and image stacks were acquired using Nikon C1 (eclipse Ti) confocal laser scanning and the NIS element AR software.RNA whole-mount in situ hybridizations were performed as previously described . HybridiThe Sox5 medaka mutant strain (N541S) has been deposited with the National Bioresource Center .Additional file 1: Figure S1.dmrt1bY promoter. Bold underlined: Rex1 element. Bold red: location of sox5 binding sites. Dmrt1bY exon 0 is in blue letters. Primer sets used for ChIP are provided. (DOCX 169 kb)Annotation of the Additional file 2: Figure S2.Gallus gallus (U88211); Maui from Fugu rubripes (AF086712); Jockey, R1, and I from Drosophila melanogaster ; and Tad1 from Neurospora crassa (L25662). The degree of amino acid conservation between sequences is shown at the foot of the alignment. (JPG 811 kb)Comparison of medaka Rex1 reverse-transcriptase (RT) sequence with other non-LTR retrotransposons. RT conserved domains are given according to Malik, Burke, and Eickbush . RT sequAdditional file 3: Table S1.Rex1 elements containing Sox5 binding sites in the medaka genome. (PDF 70 kb)Location and adjacent genes of Additional file 4: Figure S3.SOX5 protein expression in fetal mouse gonads. Double immunofluorescence of SOX5 (green) and MVH (red) on sagittal sections of 13.5 dpc (left panel) and 14.5 dpc (middle panel) mouse testes, as well as 14.5 dpc mouse ovaries (right panel). Gonads are demarcated with dotted lines. The lower panels are a higher magnification image of the area marked by a square in the upper panels. Scale bars 100 μm (upper panels) and 30 μm (lower panels). (JPG 1710 kb)Additional file 5:Supporting data. Raw supporting data for Figs.


Nutrition and metabolism are known to influence chromatin biology and epigenetics through post-translational modifications, yet how this interaction influences genomic architecture and connects to gene expression is unknown. Here we consider, as a model, the metabolically-driven dynamics of H3K4me3, a histone methylation mark that is known to encode information about active transcription, cell identity, and tumor suppression. We analyze the genome-wide changes in H3K4me3 and gene expression in response to alterations in methionine availability in both normal mouse physiology and human cancer cells. Surprisingly, we find that the location of H3K4me3 peaks is largely preserved under methionine restriction, while the response of H3K4me3 peak width encodes almost all aspects of H3K4me3 biology including changes in expression levels, and the presence of cell identity and cancer-associated genes. These findings may reveal general principles for how nutrient availability modulates specific aspects of chromatin dynamics to mediate biological function. Methionine availability is known to affect the global levels of histone methylation. Here the authors investigate the metabolically driven dynamics of H3K4me3 and find that methionine availability influences peak width, which is linked to changes in expression of genes associated with cell fate and cancer. This nexus is essential in numerous biological contexts, including maintaining different stages of pluripotency8, mediating an immune response10, promoting or suppressing cancer progression16, and transducing information about metabolic health and longevity from parent to offspring19. The molecular foundation of this interaction is in large part determined by the modifications on chromatin.Genes interact with environmental factors such as nutrition to shape the epigenome that together influences gene activity and organismal physiology. Metabolism is also shaped by genes and environment and has a substantial contribution to epigenetics20 and its concentration can fluctuate in physiological conditions around values that can limit the activity of histone methyltransferases21.Chromatin is affected by metabolism through changes in the concentrations of metabolites that serve as substrates and cofactors for post-translational modifications. These concentrations are dynamic and are mediated by changes in metabolic pathway activity or flux that arise from transcriptional programs and nutrient availability. For example, histone methylation requires S-adenosylmethionine (SAM) as the universal methyl donor. SAM is derived from methionine22. Recently work from us and others has shown that dietary modulation of methionine concentrations that approach the lower end of what can be observed in humans leads to bulk changes in the levels of histone methylation23. Other studies have reported similar findings in that changes to SAM levels or to the levels of alpha-ketoglutarate that modify the activity of demethylase enzymes induce global changes in the levels of histone modifications32. When these modifications are known to mark key aspects of chromatin status, global changes could have broad consequences to epigenomic programs. How these bulk changes to the levels of post-translational modifications on chromatin alter the genomic architecture of histone marks and relate to gene expression is, however, largely unknown.In plasma, methionine is in some reports the most dynamic of the 20 amino acids and the variation can to a large extent be explained by diet22. In addition, there are numerous lines of evidence indicating that the structural features of H3K4me3, such as the width or breadth of the peak as deposited over a genic region, encode information such as gene activity, and gene function such as the presence of a developmental program, cell type identity, or a tumor suppressor37. Thus, changes in H3K4me3 may be relevant to developmental transitions and tumor suppression. How metabolic dynamics that occur due to differences in nutritional status or metabolic pathway activity might affect these programs and gene activity related to H3K4me3 is largely unknown.One attractive model to investigate this interaction at the genome scale is the tri-methylation of histone H3 on lysine 4 (H3K4me3). The global  levels of this mark are dynamically and reversibly responsive to the levels of methionine22. In this present study, we question whether changes in methionine availability that are known to affect global levels of H3K4me3 affect specific aspects of the genomic architecture and gene expression regulation. We consider a mouse model of dietary methionine restriction (MR) and focused our analysis on liver. In this organ, this diet results in changes to bulk levels of H3K4me3. Similar changes occur in cultured human cancer cells (HCT116) subjected to acute MR in culture media, that together provide a complementary set of two species, environmental conditions, biological statuses , models (in vitro and in vivo) and two tissues. We study genome-wide H3K4me3 dynamics using a quantitative ChIP-seq analysis that considers peak geometry and characterize the connection to gene expression dynamics. We find that height and area of the peaks are overall reduced, which account for most of the global changes. Strikingly, however, while the most conserved feature of H3K4me3 dynamics is the peak width, changes in peak width but not other features of peak geometry reflect important cellular processes previously linked to H3K4me3, including cell identity-related gene expression programs and the dynamics of gene expression.We have shown previously that methionine availability modulates bulk levels of H3K4me3 by modifying SAM concentrations22. Aligning the reads to a reference genome followed by peak calling identified a set of H3K4me3 peaks, i.e., genomic regions significantly enriched  with ChIP-seq reads in comparison to the control ) = 4.25 for gained peaks compared to 7.76, 8.05, and 8.98 for three random gene sets of identical size, Supplementary Fig. 10) = 3.00 for lost peaks compared to 7.12, 6.05, and 5.08 for three random gene sets with identical size, Supplementary Fig. 38, that most peaks  covered promoter regions with a smaller subset of peaks  that appeared on non-promoter regions such as intergenic regions and introns  between high and low methionine conditions  to map genomic locations enriched with H3K4me3 in HCT116 cells cultured under high (100 μM) and low, MR (3 μM) methionine availability which is known to lead to several-fold global changes in the bulk levels of H3K4me3rol Fig. . Comparirol Fig. , genomicrol Fig. , and therol Fig.  between rol Fig.  while anrol Fig. , c. Gene41. Thus, this mouse model of liver physiology in combination with the human cancer cell model provides a breadth of model systems covering both short-term and long-term nurtrient alterations, both in vitro and in vivo systems, and both pathological and healthy contexts. We focused on liver in profiling the epigenomics and transcriptomics because it is the metabolic organ which is most responsive to metabolic reprogramming and there are liver-related phenotypes associated with MR related to metabolic health40. Livers were obtained from C57BL6 adult mice on a diet with either high methionine (0.84% w/w) or low, MR methionine (0.12% w/w) for 12 weeks, and ChIP-seq of H3K4me3 was conducted ) = 3.09 for gained peaks compared to 4.03, 4.76, and 3.39 for three random gene sets with identical size, Supplementary Fig. 10) = 14.62 for lost peaks compared to 16.58, 15.75, and 13.27 for three random gene sets with identical size, Supplementary Fig. To determine whether this conservation in H3K4me3 dynamics under MR extends to normal physiology and under a longer-term alteration in nutrient availability that has beneficial health effects, we investigated the effects of MR on H3K4me3 in vivo. Under these conditions, MR has been shown to alter methionine metabolism, improve metabolic physiology in liver, extend life-span, and reduce global levels of H3K4me3ted Fig. . QuantitP-values < 1e-323 for all comparisons, Supplementary Fig. To assess the robustness of the quantitative peak descriptors to variation in ChIP-seq data analysis methodologies, we repeated the calculations using several different peak-calling algorithms. Although the total number of peaks  and robust  height, area, and width  pathways was used to quantify association of this category with biological functions. In cultured cancer cells, the change in peak width exhibited the strongest signal for enrichment of specific biological processes as measured by the number of significantly enriched pathways  in both models Fig.  relativedth Fig.  using thTo further quantify the information contained in peak width, we annotated the significantly enriched pathways in each case by classifying them into categorical subsets involving fundamental cellular function, response to stimulus, cell type , transcriptional regulation, differentiation and development, and cancer Fig. . We next44, we searched for transcription factor (TF) binding motifs enriched in the subsets of the different geometrical features of H3K4me3 peaks that change or remain consistent during MR. Peak subsets with the 500 most sensitive peaks or 500 most robust peaks were considered in this analysis  in peaks with sensitive width , suggesting that the role of H3K4me3 dynamics in relation to TF binding has a tissue-specific function. The top scoring TF motifs in cancer cells that associated with sensitive H3K4me3 peak width dynamics tended to involve cancer-associated TFs such as c-MYC and ETS in cancer cells  in peaks with sensitive width in human cancer cells, while the liver-specific TFs, HNF4a  in peaks with robust width in mouse liver. Such patterns were not observed in the TFs used as negative controls  under high and low methionine conditions and correlated the gene expression levels with H3K4me3 peak features again in both cancer cells and mouse liver  between all three peak size descriptors and gene expression levels in both high and low methionine conditions  > 0) and 1161 genes downregulated  < 0) under MR  > 0) and 431 downregulated  < 0) genes in mouse liver   Fig.  in diverP-value = 1.82e-12 for upregulated genes and 1.89e-98 for downregulated genes in human cancer cells, Fig. P-value < 0.05, Spearman’s rank correlation coefficient > 0.1) with alterations in gene expression levels, and this correlation was consistent in both models  and gene expression  in response to MR . Plated cells were first cultured in 100 micromolar methionine and switched to 3 micromolar for 24 h upon harvesting lysates. Seven-week-old male C57BL/6J mice were randomized and fed with either 0.84% (w/w) methionine control diet or 0.12% (w/w) methionine MR diet for 12 weeks and fasted before being sacrificed. Mice were randomized to minimize difference in body weight between the control and MR groups. No blinding was used. All animal procedures were approved by the Institutional Animal Care and Use Committee of the Orentreich Foundation for the Advancement of Science (Permit Number 0511MB). Input chromatin was pooled from all samples in each replicate.7 cells and the Millipore ChIPAb + H3K4me3 antibody  with Protein A Agarose beads . In addition, we applied a spike-in normalization strategy for generating quantitative ChIP-seq data59, in which spike-in Drosophila chromatin and spike-in antibody for Drosophila H2Av  was mixed with chromatin from the HCT116 cells before the chromatin IP step with a fixed ratio  as a reference. Libraries were prepared according to Illumina instructions and sequenced on the Illumina HiSEQ 2500 sequencer in Rapid Run Mode at the Duke GCB Sequencing Shared Resource. Reads were aligned to a combinational genome consisting of the human reference genome hg19 and the Drosophila reference genome dm6 using Bowtie260. Alignment files were then filtered according to their alignment scores and down-sampled to ensure each file contains the same amount of unique Drosophila reads. Finally, reads mapped to hg19 in the normalized alignment files were kept for peak-calling and other following analyses. Replicate 1 for high methionine condition was not used due to abnormally high fraction of Drosophila-originated reads in this sample. Processing of alignment files was done using SAMtools61. Peaks were called using the --broad mode of MACS262 and filtered with the criterion that enrichment Q-value is smaller than 10−5. For mouse liver, ChIP-seq reads were aligned to the mouse reference genome mm8 and normalized by sequencing depth.ChIP was performed using 1.5 × 1063 to generate a combinational peak set for following annotation and computation of peak descriptors. Peaks were assigned to genes with TSS closest to center of the peak region using Homer44. Peak height, area, and width were computed on this combinational peak set using the extended reads coverage files  generated by MACS2. Height was computed by searching for position with highest read coverage. Area was computed by integrating the reads coverage file over the peak region. Width was quantified by first normalizing reads coverage profile of each peak to a probability distribution function  and then calculating standard deviation accordingly. Percentage peak areas on each type of genomic elements were computed using C++ codes with genomic element annotation files in the HOMER package and reads coverage files generated by MACS2 as inputs. Replicates were merged by computing average between them.H3K4me3 peak regions called for different methionine conditions and replicates were merged using the bedtools merge command in BEDTools package64, MUltiScale enrIchment Calling for ChIP-seq65, and MACS2 either with (MACS2.broad) or without (iMACS2.narrow) the -broad option. Default parameters for each of these methods were used. Generation of unions and intersections of the peak sets and quantification of reads mapped to peak regions was conducted with BEDTools using the commands bedtools merge, bedtools intersect, and bedtools coverage. Extended read coverage profiles were generated using MACS2 as described previously. Raw read coverage profiles were generated from the normalized alignment files using the command bedtools genomecov in BEDTools without applying the model in MACS2 for extension of reads to whole fragments. For the comparison of H3K4me3 changes between peak-calling algorithms, peaks called by the two algorithms assigned to the same gene were compared with each other.H3K4me3 peaks were called using Bayesian Change Pointhttp://software.broadinstitute.org/gsea/downloads.jsp). Gene sets H , C1 , CP , CP:BIOCARTA (BioCarta gene sets), CP:KEGG (KEGG gene sets), CP:REACTOME (Reactome gene sets), C3 (motif gene sets), C4 , and C5 (GO gene sets) in the MSigDB 6.0 (http://software.broadinstitute.org/gsea/msigdb/index.jsp) were included in the analysis. Genes were ranked in ascending order according to changes in height, area, or width of H3K4me3 peaks associated with them  or according to the Wald statistic . The ranked lists of genes were used as input to the GSEA algorithm with the parameter “enrichment statistic” set to “classic”. For all other parameters, the default values were used. TF binding motif enrichment analysis was done by the module findMotifsGenome.pl in HOMER. Lists of tumor suppressors and oncogenes were obtained from the databases TSGene 2.066 and ONGene67.Pathway enrichment analysis with GSEA was done using the tool “Run GSEAPreranked” in the javaGSEA Desktop Application (https://github.com/ncbi/sra-tools) before the alignment. Peaks were called using MACS2 without the --broad option and filtered with the criterion that the enrichment Q-value is smaller than 10−5. H3K4me3 peaks bound by a TF were defined as those H3K4me3 peaks overlapping with peaks called from the corresponding TF ChIP-seq data. Fold enrichment of TF binding in a H3K4me3 peak subset was defined as the ratio of fraction of peaks bound by the TF in this H3K4me3 peak subset relative to the fraction of peaks bound by the TF in the complete set of H3K4me3 peaks, that is:Raw read files in SRA or FASTQ format for the TF ChIP-seq experiments were downloaded using the accession numbers in Supplementary Fig. Q-values were computed by correcting P-values from one-sided Fisher’s exact test using Benjamini–Hochberg procedure.Enrichment Total RNA from HCT116 cells and mouse liver under high and low methionine conditions was extracted using the PARIS kit , polyA selected and then sent to the Weill-Cornell Epigenomics core (HCT116) and the Duke GCB Sequencing Shared Resource (mouse liver) for library preparation and sequencing. Libraries were sequenced either on the Illumina HiSEQ 2500 sequencer in Rapid Run Mode (HCT116) or on the Illumina HiSEQ 4000 sequencer (mouse liver).68. Number of reads mapped to each gene feature was first quantified using HTSeq69 with the input of GTF files obtained from the UCSC Table Browser and then normalized using DESeq270. Differential expression analysis was done with DESeq2. P-values were adjusted using the Benjamini–Hochberg procedure. Genes with adjusted differential expression P-values smaller than 0.05 were considered as differentially expressed.Raw reads were aligned to the human reference genome hg19 and mouse reference genome mm8, respectively, using TopHat248.The list of fitness genes was extracted from Table P-values were adjusted using the Benjamini–Hochberg procedure. P-value < 0.05 or Q-value < 0.05 was considered statistically significant.The sample sizes were selected to enable evaluation of statistical significance of difference between groups. Comparison between replicates was performed before the following analysis to ensure that the variance was similar between the groups. For ChIP-seq, we performed two biological replicates in human cancer cells and two technical replicates in mouse liver. For RNA-seq, we performed two biological replicates in human cancer cells and six biological replicates in mouse liver. For multiple hypothesis testing, the http://software.broadinstitute.org/software/igv/).Heatmaps and average profiles showing the H3K4me3 ChIP-seq signal were generated with the commands plotHeatmap and plotProfile in deepTools. Other heatmaps and density scatter plots were created using MATLAB. All bar graphs, pie graphs, and box plots were created using GraphPad Prism. In all box plots, the boxes extend between the 25th and 75th percentiles, the center line shows the median, and the whiskers represent the minimal and maximal values. ChIP-seq tracks were created using Integrative Genomics Viewer .Codes are available at GitHub page of the Locasale Lab . Raw data are available at the Gene Expression Omnibus database with accession number GEO: GSE103602. Peak height, area, width, and gene expression values in different conditions are available in Supplementary Data Processed data are available at GitHub page of the Locasale Lab (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1


PU.1 is a hematopoietic lineage-specific transcription factor belonging to the Ets family. We investigated the role of PU.1 in the expression of OX40L in dendritic cells (DCs), because the regulatory mechanism of cell type-specific expression of OX40L, which is mainly restricted to antigen-presenting cells, is largely unknown despite the critical involvement in Th2 and Tfh development. PU.1 knockdown decreased the expression of OX40L in mouse DCs. Chromatin immunoprecipitation (ChIP) assays demonstrated that PU.1 constitutively bound to the proximal region of the OX40L promoter. Reporter assays and electrophoretic mobility shift assays revealed that PU.1 transactivated the OX40L promoter through direct binding to the most-proximal Ets motif. We found that this Ets motif is conserved between mouse and human, and that PU.1 bound to the human OX40L promoter in ChIP assay using human monocyte-derived DCs. ChIP assays based on ChIP-seq datasets revealed that PU.1 binds to several sites distant from the transcription start site on the OX40L gene in addition to the most-proximal site in mouse DCs. In the present study, the structure of the OX40L promoter regulated by PU.1 is determined. It is also suggested that PU.1 is involved in mouse OX40L expression via multiple binding sites on the gene. When immature DCs recognize microbial structures such as pathogen-associated molecular patterns (PAMPs) using pattern recognition receptors, they develop into mature DCs with up-regulation of MHC and costimulatory molecules. The OX40 ligand  is a costimulatory molecules that is mainly expressed on APCs, including mature DCs, B cells, and macrophages234OX40L promoter are linked to the susceptibility to systemic lupus erythematosus (SLE) and myocardial infarction25OX40L promoter is associated with immune-related diseases.Despite the importance of OX40L in immune responses, the mechanism of the transcriptional regulation of OX40L gene remains to be elucidated. Previous studies showed that OX40L expression in DCs is up-regulated by several stimulatory signalings, including thymic stromal lymphopoietin (TSLP), CD40, PAMPs, and IFN-α18192021PU.1 is a hematopoietic lineage-specific transcription factor that belongs to the Ets family. It has been proposed that graded levels of PU.1 expression by hematopoietic progenitors are determinative of their lineage commitment because high levels of PU.1 direct macrophage differentiation and low levels are sufficient for fetal B cell development273132In this study, we investigated whether PU.1 regulates the expression of OX40L in DCs. We found that PU.1 binds to the Ets motif located in the 5′-flanking region proximal to the transcriptional start site and transactivates the OX40L gene both in mouse and human DCs.To evaluate the effect of PU.1 suppression on OX40L expression, BMDCs were transfected with PU.1 small interfering RNA (siRNA) and stimulated with potent activators of DCs such as LPS (a ligand for TLR4), CpG (for TLR9), and poly I:C (for TLR3). We observed approximately 6- to 10-fold increases in OX40L mRNA levels after TLR ligand-induced maturation of bone marrow-derived DCs (BMDCs)  as used in the previous studyRecently, Helft in vitro generated DCs but also in primary DCs.We also evaluated the effect of PU.1 knockdown on OX40L expression in primary cells with using DCs freshly isolated from mouse spleen. As shown in To investigate whether PU.1 binds to the endogenous OX40L promoter in chromosomal DNA, we initially performed chromatin immunoprecipitation (ChIP) assays using BMDCs  and spleIn experiments by using mouse dendritic cell line, JAWS II, we obtained similar results to those of BMDCs. Briefly, the mRNA level of OX40L was decreased by siRNA-mediated PU.1 knockdown , and PU.cis-element(s) were located between −136 and +71. To assess the effect of PU.1 on OX40L promoter activity, we carried out luciferase assays using the human embryonic kidney (HEK293T) cells, which are useful for evaluation of the transactivation activity of hematopoietic cell-specific transcription factors. Luciferase activity driven by the shortest promoter (−136/+71) was significantly augmented with ectopic PU.1 to a similar extent as those driven by the reporter plasmids carrying longer promoter regions (cis-element(s) reside within the −136/+71 region, which is functional as a promoter in OX40L-expressing cells.Therefore, we performed luciferase assays using JAWS II cells in order to evaluate the activity of the OX40L promoter. When JAWS II cells were transfected with a series of reporter vectors carrying various lengths of the mouse OX40L 5′-flanking regions, we found that the luciferase activities were enhanced as long as reporter genes contained the segment from −136 to +71 bp . This re regions . These rOX40L promoter. We termed these motifs Ets 1–6, respectively. To examine whether these Ets motifs are recognized by PU.1, electrophoretic mobility shift assay (EMSAs) were performed using the two probes underlined in lanes 5–8). Next, we performed EMSAs with competitive double-stranded oligonucleotides to further identify the PU.1-binding site in this region. The shifted band completely disappeared upon the addition of an excess amount of the wild-type (WT) competitor or Ets 5-mutated competitor but was only slightly diminished by a mutant competitor lacking Ets 6 . These results indicate that PU.1 binds to the OX40L gene via multiple sites in mouse DCs.Although we identified the sequence TTCC at −58/−55 as a CD80 and CD86 genes, which provide co-stimulatory signals by engaging CD28 or CTLA-4 on T cells38We and others have demonstrated that PU.1 plays critical roles in DC functions. Briefly, PU.1 positively regulates the expression of CIITA, which is mainly involved in the transcriptional regulation of MHC class IIcis-elements for PU.1-mediated basal transcription and NF-κB-mediated up-regulation might be a useful tool to reveal the cross-talk between PU.1 and NF-κB. It has been reported that IL-33 enhanced the OX40L expression in DCs and induced Th2 responsesThe siRNA-mediated PU.1 knockdown significantly decreased the expression of OX40L in both of immature and mature DCs . AdditioWe showed that PU.1 specifically bound to −58/−55 TTCC of the mouse OX40L promoter by EMSA . As show+ monocytes and monocyte-derived macrophages (UCSC Genome Browser on Human GRCh39/hg19 Assembly). Involvement of PU.1 in OX40L expression through multiple sites might be observed in mouse gene but not human gene.Finally, we searched other PU.1-binding peaks outside of the minimum promoter on the OX40L gene in mouse DCs by using published ChIP-seq datasets and found the presence of several peaks. As expected from the datasets, further enriched binding of PU.1 was detected at these putative regions in our experimental condition using BMDCs and splenic DCs. In contrast to the high homology between mouse and human at just upstream of the transcription start site, the candidate sites possessing similar sequences as identified at −4.9 kb and +3.7 kb on the mouse OX40L gene were not identified in human gene so far. Furthermore, enriched PU.1-binding peak was not found on the OX40L gene in published ChIP-seq data using human CD14Besides mature DCs, B cells and macrophages express OX40LIn vivo blockade of OX40-OX40L interactions resulted in a marked reduction in the infiltration of effector T cells into sites of inflammation and, therefore, in reduced clinical symptoms in several T cell-mediated diseases46BALB/c and C57BL/6 mice were purchased from Japan SLC . All animal experiments were performed in accordance with the approved guidelines of the Institutional Review Board of Juntendo University School of Medicine, Tokyo, Japan, and of Tokyo University of Science, Tokyo, Japan.+ cells were isolated by using the MACS separation system with anti-mouse CD11c MicroBeads (#130-052-001) and an autoMACS . Splenic DCs were also isolated from mouse spleen by using the MACS separation system with anti-CD11c MicroBeasBMDCs were generated from the femoral and tibial bone marrow cells of female mice as described previously2.Mouse DC line JAWSII  were maintained in MEM-α (GIBCO) supplemented with 20% FCS, 4 mM L-glutamine, 1 mM sodium pyruvate, 100 U/mL of penicillin, 100 μg/mL streptomycin, and 5 ng/mL of murine GM-CSF. All cells were incubated at 37 °C in a humidified atmosphere in the presence of 5% COLPS (#L3024) and poly I:C (#P0913) were purchased from Sigma-Aldrich. CpG-ODN (#tlrl-1826) was obtained from InvivoGen .7 BMDCs using a Mouse Dendritic cell Nucleofector kit  using Nucleofector II (Lonza) set at Y-001.PU.1 siRNA  and control siRNA  were obtained from Invitrogen . A 5 μl of aliquot of 20 μmol/L siRNA was introduced into 1 × 10Total RNA was extracted from BMDCs using an RNeasy Micro Kit  according to the manufacturer’s instructions. Complementary DNA was synthesized from 2 μg of total RNA and amplified using a ReverTra Ace qPCR RT kit . Quantitative real-time PCR was performed using an Applied Biosystems StepOne real-time PCR system. Relative mRNA levels were obtained after normalization to the GAPDH transcript. The TaqMan ID numbers for the genes analyzed in the present study are as follows: mOX40L, Mm0043721_m1; mPU.1, Mm00488142_m1; hPU.1, Hs02786711_m1; mGAPDH, 4352339E; hGAPDH, 4326317E. For detection of the human OX40L transcripts, the following primers were used with SYBR Green PCR Master Mix  was used to stain cell-surface OX40L after blocking Fc receptors with 2.4G2 (BD PharMingen). PerCP-conjugated anti-I-A/I-E (MHCII) , PE Cy7-conjugated anti-CD11c , and APC Cy7-conjugated anti-CD11b  were used to characterize BMDCs. Cell-surface fluorescence was detected using MACSQuant (Miltenyi Biotech) and analyzed by Flowjo .OX40L were amplified from mouse genomic DNA by using PCR and inserted into the multi-cloning site of pGL-4 Basic  to generate reporter plasmids. Mutant reporter plasmids were generated by using a PrimeSTAR Mutagenesis basal kit . The nucleotide sequences of the primers are listed in Various lengths of the 5′ flanking region of Cells of 293T were transfected with 500 ng reporter plasmid, 500 ng expression plasmid, and 10 ng pRL-null (Promega) using FuGENE HD (Promega). JAWSII cells were transfected with 1 μg reporter plasmid and 50 ng pRL-CMV (Promega). At 48 h after transfection, luciferase activity was determined with using Micro Lumat Plus  and a Dual-luciferase assay kit (Promega), as described previouslyAn EMSA was performed based on a method described previouslyChIP assays were performed as described previouslyp values < 0.05 considered significant.Statistical analysis was performed using a two-tailed Student’s t-test with How to cite this article: Yashiro, T. et al. Critical Role of Transcription Factor PU.1 in the Function of the OX40L/TNFSF4 Promoter in Dendritic cells. Sci. Rep.6, 34825; doi: 10.1038/srep34825 (2016).