P. falciparum, which revealed a complex occupancy profile consisting of conserved and parasite-specific features. In contrast to other histone variants, PfH3.3 primarily demarcates euchromatic coding and subtelomeric repetitive sequences. Stable occupancy of PfH3.3 in these regions is largely uncoupled from the transcriptional activity and appears to be primarily dependent on the GC-content of the underlying DNA. Importantly, PfH3.3 specifically marks the promoter region of an active and poised, but not inactive antigenic variation (var) gene, thereby potentially contributing to immune evasion. Collectively, our data suggest that PfH3.3, together with other histone variants, indexes the P. falciparum genome to functionally distinct domains and contribute to a key survival strategy of this deadly pathogen.Histones, by packaging and organizing the DNA into chromatin, serve as essential building blocks for eukaryotic life. The basic structure of the chromatin is established by four canonical histones , while histone variants are more commonly utilized to alter the properties of specific chromatin domains. H3.3, a variant of histone H3, was found to have diverse localization patterns and functions across species but has been rather poorly studied in protists. Here we present the first genome-wide analysis of H3.3 in the malaria-causing, apicomplexan parasite,  In all eukaryotes, chromatin enables high-degree compaction of the genetic material while allowing access to the DNA for essential cellular processes such as transcription, replication, DNA damage response and repair. Canonical histones, namely H2A, H2B, H3 and H4, are mainly synthesized and assembled into nucleosomes in a replication-coupled manner to form the basic layout of the newly synthetized chromatin121Given their essential function, histones are among the most conserved proteins and their evolutionary origins can be traced back to archaea, where one or two histone like proteins are present78101112Saccharomyces cerevisiae and the algal protist, Cyanidioschyzon merolae, possess only one form of non-centromeric H3 that is needed for both replication-coupled and replication-independent assembly1115Tetrahymena thermophila (Superphylum Alveolata)11181920Phylogenetically earlier organisms, such as Arabidopsis thaliana revealed that H3.3 is mainly deposited into the coding sequence of transcribed genes, promoters of active and inactive genes, transcription start and end sites as well as further gene regulatory elements in euchromatic regions816222426272829262831A. thaliana33D. melanogasterT. thermophilaD. melanogaster and in T. thermophila results in meiotic defects17363239Genome-wide studies performed mainly in animals, but also in Plasmodium falciparum. Besides the canonical histones four histone variants are encoded in the Plasmodium genome, i.e. PfH2A.Z, PfH2B.Z, PfCenH3 and PfH3.341P. falciparum has an extremely AT-rich genome (on average ~80% adenine and thymine bases) where different genomic regions have distinctly different base composition. Earlier we observed an intriguing correlation between the base composition of these genomic regions and the incorporation of various histone variantsPfCenH3 into a ~2kb long extremely AT-rich (~97% AT) sequence present once on every chromosome434445PfH2A.Z and the Plasmodium-specific variant PfH2B.Z464748Plasmodium epigenome into functionally distinct domains and the rough blueprint of the epigenome is sketched by the AT-content of the underlying sequence. Here, in further support of this model, we show that PfH3.3 demarcates euchromatic coding sequences and subtelomeric repeat regions in a GC-content coupled-manner. Furthermore, we observe that PfH3.3 specifically associates with the promoter region of the single expressed or poised - but not inactive - antigenic variation gene (var gene). This suggests that PfH3.3 might contribute to the epigenetic memory of var gene expression and hence could underlie a major defense mechanism of this deadly human pathogen.In this article, we focus on the localization and role of H3.3 in the apicomplexan parasite, Plasmodium falciparum’s histone variant PfH3.3 (PF3D7_0617900) and its single copy canonical counterpart PfH3 (PF3D7_0610400) both encode for a 136 amino-acid protein. They only differ in eight amino acids including an Apicomplexan specific feature for PfH3.3, namely the substitution of the conserved amino acids RY with KF at position 54 and 5542P. falciparum 3D7 parasite line that expresses an episomal Ty1-tagged copy of PfH3.3 under the control of the chloroquine resistance transporter promoter (similar to pARL-1a-Ty1-H2A in ref. PfH3.3-Ty1) that, after single crossover integration, resulted in a P. falciparum NF54-DCJ parasite linePfH3.3-Ty1 under control of the endogenous promoter . First we performed a test ChIP-seq on native chromatin isolated from the episomal Ty1-PfH3.3 line for two intraerythrocytic stages (30 and 40 hours post-invasion) in duplicates . Additionally, we performed ChIP-seq using an anti-H3 antibody that recognizes the C-terminal end of both PfH3.3 and PfH3 (referred to as H3core control) and RNA-seq on the same four parasite populations. The resulting PfH3.3 ChIP-seq data were normalized either to input or H3core ChIP-seq data to correct for any biases due to differential cross-linking, PCR amplification efficiency and reduced mapability of the highly AT-rich sequences, typical to the P. falciparum genomePfH3.3 ChIP-seq data from episomal and endogenously PfH3.3-tagged parasites displayed a highly similar localization pattern  are only temporarily marked or completely depleted of PfH3.3  during the intraerythrocytic cycle and matched with the respective PfH3.3 occupancy values  for upstream regions, coding sequences and downstream regions  for each gene against PfH3.3 occupancy in coding, upstream or downstream regions for each stage separately  silent genes 525354var gene family members is expressed in any given parasite while other family members remain transcriptionally silenced. Importantly, var genes are expressed in a clonally variant fashionvar gene is reactivated over several generation by a yet elusive mechanisms of epigenetic memory57Persistence of PfH3.3 in var gene regulation, we specifically looked at the var2CSA locus of CSA-selected  . The var2CSA gene in non-CSA selected parasites, however, is clearly less enriched in PfH3.3 and only shows PfH3.3 coverage at the var2CSA intron and the second exon . Subsequently, half of the culture was treated with blasticidin (2.5 mM), which forced a switch to another var promoter (PF3D7_0223500) driving expression of the BSD resistance cassettevar2CSA or PF3D7_0223500 was confirmed by RT-qPCR . The switch from var2CSA expression to PF3D7_0223500 was confirmed at the epigenetic level: CSA-selected parasites showed high H3K9ac recoveries for the whole promoter region of the var2CSA gene and low H3K9me3 recoveries for the promoter as well as coding sequences   and non-CSA gene   require transcriptional activity and PfH3.3 occupancy is rather influenced by the GC-content of DNA sequence.Here we present the first genome-wide analysis of histone variant H3.3 in the apicomplexan parasite,  regions  and only regions . In manyng genes , Fig. S6ying DNA . This coelements . Furthere PfH3.3 . These oPfH3.3 at euchromatic intergenic regions we observe clear PfH3.3 marking at subtelomeric repeat sequences. In mammals, PfH3.3 is also deposited at repressive chromatin regions and in mouse embryonic cells additionally at the telomeres262831PfH3.3 might serve a conserved telomere associated function. However, unlike in mouse embryonic stem cells31PfH3.3 occupancy seems to be uncoupled from K9me3 marking. In fact, PfH3.3 appears to be specifically depleted from most heterochromatic regions - marked by K9me3 and HP1 in P. falciparum - except for the subtelomeric repetitive regions  this is the stage when extensive replication of the parasites genome begins; ii) this is the transcriptionally most active stage. Notably, in contrast to many other eukaryotic cells, cell division during the intraerythrocytic cycle of P. falciparum does not only double the DNA content, but rather results in 8 to 32 genome copies. Given that an enormous amount of histones needed to support the synthesis of the new chromatin it is conceivable that both H3 variants (PfH3 and PfH3.3) need to be incorporated during replication to the newly synthetized chromatin, after which PfH3.3 might be preferentially retained at GC-rich sequences. A similar phenomenon is observed in mouse spermatocytes, when almost all histones are replaced by protamine to form a highly compact paternal chromatin, nucleosomes that are retained at unmethylated CpG-rich sequences, primarily contain H3.3PfH3.3 to promoter regions could be explained by temporal transcriptional activity, typical to intraerythrocytic stages of malaria parasites . Therefore, additional experiments will have to be performed to determine whether this protein supports the incorporation of both PfH3 and PfH3.3 during replication or yet unidentified PfH3/PfH3.3-specific chaperons exist that ensure differential genomic incorporation of PfH3 and PfH3.3.In mammalian cells, the different distribution patterns of H3.3 compared to H3 is achieved by histone- and site-specific chaperones. While H3.3 is deposited at euchromatic regions by the chaperone Histone regulator A (HIRA) replication-independently20261819PfH3.3 incorporation to the promoter region of the active, but not inactive var gene , while the poised var gene carries H3K9ac and H3K4me25871PfH2A.Z/PfH2B.Z, PfH3.3 is retained at this AT-rich var promoter even after active transcription has ceased . This raises the exciting possibility that PfH3.3 may contribute to the epigenetic memory of var gene expression57var gene over several generations. In line with such hypothesis, nuclear transplantation experiments in Xenopus laevis showed that H3.3 mediates the epigenetic memory of certain genes; and H3.3 overexpression in nuclear transplant embryos even enhances this effect73742576var2CSA gene during the second half of the intraerythrocytic cycle. All in all, these observations made in other organisms support the idea of a potential role of PfH3.3 in epigenetic memory in P. falciparum. In order to investigate the functional relevance of PfH3.3 in parasite biology in general or in var gene regulation in particular, we made several attempts to tag PfH3.3 with a AID-GFP-glmS78PfH3.3. Accordingly, whether PfH3.3 is critical for “epigenetic memory” of var gene expression and how it could excerpt such function will be the subject of future studies.Of particular interest is the specific var gene . This isPfH3.3 has a complex localization pattern that consists of conserved, but also parasite-specific features. It demarcates euchromatic coding and subtelomeric repetitive sequences in a GC-content coupled manner. Within the coding sequences it might promote the process of transcription by forming labile nucleosomes, as has been reported for other organisms. The strong marking of subtelomeric repetitive sequences resembles findings from higher eukaryotes, which could elude to a conserved, yet uncharacterized function. Excitingly, PfH3.3 specifically marks the promoter region of the var gene in its active and poised, but not inactive state and could contribute to the process of antigenic variation which is critical to immune evasion of this deadly pathogen.In summary, we show that PfH3.3, 3D7 P. falciparum parasites were transfected with a pARL-1a- plasmidPfH3.3 (PF3D7_0617900) under the control of the chloroquine resistance transporter promoter  and a CTG instead of an ATG as “start codon” (PfH3.3-Ty1 under the endogenous promoter (ATG as start codon) and a non-functional untagged PfH3.3 (CTG as start codon) PfH3.3 protein on Western blot (In order to profile t codon” . Parasitt codon) . A clonat codon) . Expressern blot .+ red blood cells. Culturing occurred in 250 ml tissue culture flasks placed in candle jars or in an incubator with a gas composition of 3% O2, 4% CO2 and 93% N2 and incubated at 37 °C. Parasites were synchronized with sorbitol treatments and Percoll gradient centrifugations as described previouslyPfH3.3 profiles early ring (10 hpi), late ring (20 hpi), trophozoite (30 hpi) and schizont stages (40 hpi) were collected. For the ChIP-qPCR investigation of PfH3.3 var2CSA- or PF3D7_0223500-selected parasites, ring, trophozoite and schizont stages were collected at 15, 25 and 40 hpi. Parasite cultures used for RNA extraction or native ChIP were immediately placed and processed on ice (4 °C). Parasite cultures used for cross-linked ChIP were immediately treated with 1% formaldehyde  and incubated at 37 °C for 10 min while shaking. Cross-linking reaction was quenched by adding 0.125 M glycine . All subsequent steps were performed at 4 °C unless noted otherwise. To remove contaminating human white blood cells that would cause considerable background upon sequencing, all blood used for collection of sequence samples was filtered through Plasmodipure filters (EuroProxima).Parasites were cultured in standard RPMI medium supplemented with 10% human serum (AB), 0.2% NaHCO3 and 2.5% human OBlood and serum was obtained from the local blood bank (Sanquin) with the full consent of healthy donors to use this material for malaria research.PfH3.3-Ty1 expressing NF54-DCJ P. falciparum parasites were selected for var2CSA expression by affinity purification as in ref. var2CSA panning was repeated several times to achieve an increasing proportion of var2CSA-expressing parasites. Subsequent switching to PF3D7_0223500 expression, was enforced by culturing the parasites with 2.5 mM Blasticidinvar gene selection was confirmed via RNA extraction  followed by cDNA synthesis and subsequent quantitative real time PCR (qPCR) using var gene specific primers71Endogenously P. falciparum genomic DNA served as standard . SYBRgreen supermix (BioRad) and primers were mixed according to manufacturer’s instructions. qPCR was performed in 96 well plate format (BioRad) using the following protocol: 95 °C for 3 min,  39x, 95 °C 1 min, 65 °C 1 min and a gradient from 65 °C to 94.5 °C with a 0.5 °C increase every 10 sec.To synthesize the cDNA 0.5–5 ug of total RNA were mixed with random hexamer primers , OligodT12-18  and dNTPs  and incubated for 5 min at 70 °C. Then first strand synthesis was performed for 1 hour at 42 °C in First Strand Buffer supplemented with DTT (10 mM), Superscript III (200 units) and RNasin Plus RNase inhibitor , after which superscript III was inactivated by incubation at 70 °C for 15 minutes. RT “minus” reactions (where Superscript III was replaced by water) were performed in all cases as negative control. Subsequent qPCR was performed using the CFX96 Real Time Systems C1000 Touch Thermal Cycler. PfH3.3 episomally (~60 ug) and the NF54-DCJ parasites expressing PfH3.3-Ty1 under the endogenous promoter respectively, were separated on 18% SDS-PAGE and transferred to a protean nitrocellulose membrane . The membrane was probed with mouse anti-Ty1 antibody , the 3D7 parasites expressing Ty1-PfH3.3 sequence specific hybridization probe was generated by PCR labelling using Digoxigenin-11-dUTP (DIG)  according to manufacturer’s instructions. The PCR reaction was performed using the Advantage® cDNA PCR Kit and Polymerase Mix (Clonetech) and a DIG-dUTP to dTTP ratio of 1:3 under the following PCR conditions: 5 min 95 °C; 30 s 95 °C, 30 s 52 °C, 90 s 68 °C (35 cycles); 5 min 68 °C , prehybridization  and probe-DNA hybridization  were all performed according to manufacturer’s recommendations. The detection of the hybridized probe was performed using the DIG Wash and Block Buffer Set, the anti-Digoxigenin antibody  and the CDP-Star Kit (ready-to-use) from Roche according to manufacturer’s instructions.Genomic DNA was isolated from native nuclei using phenol-chloroform extraction as described previouslyin 68 °C . Gel pre2, 0.2% NP-40, Roche Protease Inhibitor Cocktail) respectively, native nuclei  or formaldehyde-cross-linked nuclei (ChIP-seq experiment) were separated from cell debris using a 0.25 M sucrose buffer cushion as described beforePfH3.3 profiles, enzymatic MNase digestion of cross-linked chromatin was performed using digestion buffer  with 0.5 U MNase  in 150 μl aliquots for 6 min (10 hpi), 12 min (20 hpi) or 13 min (30 hpi & 40 hpi) at 37 °C. Optimal digestion times were empirically determined for each stage by test-digestion of a nuclear aliquot for varying times, to obtain primarily mono-nucleosomal DNA fragments. Digestion reactions were stopped by adding 150 μl quenching solution  and placing the samples on ice. Subsequently, digested chromatin was sonicated for 6 × 10 sec  to free cross-linked chromatin from nuclear membranes. Approximately 200 ng of digested DNA-containing cross-linked chromatin was incubated in ChIP buffer  with 2 μg of anti-Ty1, 1 ug of anti-histone H3  or 1 μg polyclonal anti-IgG (Upstate 12-370) overnight at 4 °C while rotating, followed by the addition of 10 μl ProtA and 10 ul ProtG dynabeads  and further incubation at 4 °C for 2 h. For the H3core antibody (capturing histone PfH3 and PfH3.3) one ChIP reaction was performed, for PfH3.3 (anti-Ty1) four ChIP reactions were performed in parallel per life cycle stage to ensure sufficient amount of DNA for subsequent ChIP-seq. After washing with three different buffers  immunoprecipitated chromatin was eluted using 1% SDS and 0.1 M NaHCO3. Immunoprecipitated chromatin was decross-linked  overnight at 45 °C shaking, followed by DNA isolation via QIAquick column purification (Qiagen).After erythrocyte and parasite lysis using 0.05% saponin and a hypotonic buffer , 7 min  and 10 min (40 hpi Var2CSA-selected parasites) at 37 °C in a waterbath and regular tapping of the tubes . Subsequent chromatin extraction was performed in low-salt buffer  and incubated with 1 volume ChIP incubation buffer . For native ChIP reactions, native chromatin (containing about 200 ng DNA) from each time point of var2CSA- and PF3D7_0223500-selected parasites was incubated with 2 μg of anti-Ty1 (PfH3.3), 0.5 μl polyclonal H2A.ZTo investigate PfH3.3-Ty1, H2A.Z, H3K9ac and H3K9me3 xlChIP was performed as described above. qPCR (Bio-Rad MyIQ) on each ChIP sample was performed alongside with a 1:10 dilution series of the corresponding input DNA to calculate recovery as a percentage of input. Recovery values for the var2CSA locus where normalized across the different samples using primer sets for regions that are invariably marked with the histone mark/variant. For H3K9ac, PfH2A.Z and PfH3.3 primers for actin  and seryl-tRNA synthetase , HSP70  and 18s RNA  were chosen. For H3K9me3 primers for bp the telomere repetitive regions, a rifin  and AP2G . Normalization factor was calculated by dividing the average of the recoveries from the control primers within each sample with the average of the average recovery values across the samples. Finally, all recovery values from the var2CSA specific primers have been multiplied by the normalization factors. The normalization factor scaled between 0.75 and 1.8.To test relative enrichment of PfH3.3-Ty1 expressing NF54-DCJ P. falciparum parasites 3 ng of ChIP DNA were end repaired, extended with 3′ A-overhangs, and ligated to barcoded NextFlex adapters (Bio Scientific)PfH3.3-expressing parasites were generated as described in refs For each sequencing library generated from endogenously The preparation of strand-specific RNA-seq libraries was performed as in ref. PfH3.3-Ty1 expressing NF54-DCJ parasites were sequenced on a HiSeq 2000 system (Illumina) to obtain 100 bp single-end reads (TruSeq SE Cluster Kit v2). ChIP-seq libraries generated from episomal Ty1-PfH3.3 expressing parasites were sequenced on a GAIIx system (Illumina) to obtain 75 bp single-end reads (Standard Cluster Generation KitV4). 100 bp and 75 bp reads respectively were mapped against the Plasmodium falciparum genome assembly (PlasmoDB v6.1) for ChIP samples or against the Plasmodium falciparum annotated transcripts (PlasmoDB v6.1) for mRNA samples using BWAChIP-seq and strand-specific RNA-seq libraries generated from endogenously PfH3.3 values were divided through the respective PfH3core values and log2 was calculated . Bedgraph.gz files were generated using bedtools v2.20.1. Genome browser tracks were set to ‘mean’ as windowing function and ‘16’ as smoothing window function.To visualize ChIP-seq and RNA-seq data in the UCSC browser, all libraries were normalized to the amount of mapped reads per million (RPM). For log2 ratio tracks PfH3.3 with the GC-content of the Plasmodium falciparum genome the genome was binned into 150 bp windows and the GC-content as well as PfH3.3 and PfH3core tags were calculated in each window. PfH3.3 tags were divided through PfH3core tags , log2 transformed and plotted over the GC-content as a density plot using the python package seaborn 0.6.0 in ipython notebook (Enthought Canopy 1.5.5.3123). For more detailed analysis the 150 bp windows were intersected with different chromatin domains e.g. heterochromatic or euchromatic regions (at least 50% overlap) using bedtools v2.20.1 and depicted as described above.To investigate the correlation of PfH3.3 in upstream regions, downstream regions or coding sequences with mRNA abundance, the genome was subdivided as describedPfH3.3 and PfH3core tags were calculated within each upstream region, downstream region and coding sequence, normalized to the amount of reads per kb per million mapped reads (RPKM) and PfH3.3/PfH3core ratios (PfH3.3 occupancy) were determined. To assess RNA abundance of each gene, RNA-seq data were mapped against the annotated P. falciparum transcriptome, tags were counted for each transcript  and RPKM values were calculated. To illustrate the dynamics of RNA expression during intraerythrocytic development, the relative RNA abundance was calculated by dividing the amount of tags per transcript of each intraerythrocytic stage through the sum of tags per transcript of all four investigated intraerythrocytic stages . Subsequently, RNAs were clustered into eight groups according to their expression pattern throughout the intraerythrocytic cycle using k-means clustering (Multi Experiment Viewer 4.9.0). Clusters were visualized in a heatmap. After calculating the relative PfH3.3 occupancy in upstream regions for all four intraerythrocytic stages as described above, the profile of PfH3.3 occupancy in upstream regions was matched to the respective RNA expression profile. The same method was applied to coding sequences and downstream regions.In order to investigate the correlation of PfH3.3 enrichment and gene expression level, RPKM values were log10 transformed and plotted against log2 transformed PfH3.3 occupancy levels of matching upstream region, downstream region and coding sequence.To illustrate the relationship between ChIP-seq and RNA-seq datasets will be available via GEO (accession number GSE80466) and PlasmoDB.How to cite this article: Fraschka, S. A.-K. et al. H3.3 demarcates GC-rich coding and subtelomeric regions and serves as potential memory mark for virulence gene expression in Plasmodium falciparum. Sci. Rep.6, 31965; doi: 10.1038/srep31965 (2016).


Here we investigate this conversion process through integrative transcriptomic and epigenomic analyses. We find in SSCs that promoters essential to maintenance and differentiation of embryonic stem cells (ESCs) are enriched with histone H3-lysine4 and -lysine 27 trimethylations. These bivalent modifications are maintained at most somatic promoters after conversion, bestowing MASCs an ESC-like promoter chromatin. At enhancers, the core pluripotency circuitry is activated partially in SSCs and completely in MASCs, concomitant with loss of germ cell-specific gene expression and initiation of embryonic-like programs. Furthermore, SSCs in vitro maintain the epigenomic characteristics of germ cells in vivo. Our observations suggest that SSCs encode innate plasticity through the epigenome and that both conversion of promoter chromatin states and activation of cell type-specific enhancers are prominent features of reprogramming.Spermatogonial stem and progenitor cells (SSCs) generate adult male gametes. During  Spermatogonial stem cells (SSCs) spontaneously convert to multipotent adult spermatogonial-derived stem cells (MASCs). Here, the authors reveal the dynamics of bivalent histone H3-lysine4 and -lysine27 methylation signatures at somatic gene promoters in SSCs and ESC-like promoter chromatin states in MASCs. During  animals 12. To daPou5f1 , which forms the core pluripotency circuitry with Sox2 and Nanog in ESCs to sustain stem cell self-renewal and control the expression of many differentiation genes8Zbtb16, Piwil2, and Piwil4) but repress regulators of somatic cell development 1011One possible explanation for the spontaneous loss of lineage commitment is that SSCs may preserve a latent ESC-like gene expression programme. Indeed, upon germline specification in the mouse embryo, somatic genes are mainly repressed in primordial germ cells (PGCs), while several ESC signature transcription factors exhibit transcriptional activation and their expressions are preserved at modest levels in spermatogonia, which include SSCs in the adult testis56cis-regulation to gene expression by recruiting specific transcription factors, and enhancer activity is tightly controlled in a cell-type-specific manner21Among other potential regulators that enable spontaneous conversion of SSCs into MASCs, the covalent modification of histones plays a key role in cell-type specification1314151719in vitro-cultured SSCs, which are capable of unipotent differentiation to all germ cells in mouse testicular transplantation, closely align with MASCs and ESCs in global gene expression and histone modification. In SSCs and differentiating germ cells, unlike differentiated somatic cells , K4me3 and K27me3 co-localize to a significant number of developmental gene promoters, and half of these promoters are bivalently modified in both MASCs and ESCs. Notably, in MASCs, which function like pluripotent stem cells , K27me3 is erased from many genes crucial to early embryogenesis and stem cell maintenance but is acquired at promoters of most germ cell-specific genes. These selective epigenetic alterations closely correlate with gene expression changes and endow MASCs with an ESC-like promoter chromatin. This phenomenon distinguishes SSC conversion from fibroblast to iPS cell reprogramming, which involves global epigenetic changes and reconstitution of promoter bivalency. At enhancer loci, we note that the germ cell epigenetic ‘signature' is largely lost in MASCs compared with SSCs, while ESC-specific enhancers are partially activated after reprogramming. Notably, unipotent SSCs share substantial enhancer activity with multipotent MASCs and pluripotent ESCs. These shared active enhancers are predicted targets of many ESC signature transcriptional regulators, including the core pluripotency regulators, such as Pou5f1, Sox2 and Nanog. Taken together, our results reveal the topography of global histone modifications in mouse SSCs and MASCs, and indicate developmental lineage-associated epigenetic signature changes before and after spontaneous SSC cell fate transitions.On the basis of these observations, we hypothesize that the epigenetic landscape of SSCs is plastic, and under certain yet unrecognized conditions may cause conversion back to an ESC-like state. Here we perform transcriptome sequencing (RNA-seq) and find that MASCs are distinguished from SSCs by reactivation of somatic lineage-specific genes and repression of spermatogenesis regulators. To elucidate the epigenomic regulation underlying SSC conversion, we describe the results of chromatin immunoprecipitation followed by sequencing (ChIP-seq) on SSCs and MASCs to identify changes in transcription-associated histone modifications at both promoters and enhancers of reprogrammed genes. This genome-wide study reveals that long-term tetO-4F2A)1242426We first compared global gene expression among MASCs, ESCs and iPS cells using hierarchical clustering of RNA-seq results collected from each cell type . To ensuin vitro expansion37r) between in-house and published data  and repression (K27me3). ChIP-seq results from MASCs, ESCs2930293233242627hed data . Thus, oin vitro-cultured SSCs were epigenomically distant from any adult germ cells, including pachytene spermatocytes2424262930In comparison with differentiated somatic or germ cells in our study, we noticed that the SSC transcriptome was very close to MASCs, ESCs and iPS cells, irrespective of genetic background or source of data  of SSC bivalent genes also possessed bivalent promoter modifications in MASCs and ESCs ; such gePou5f1, Nanog and Fgf4; Zbtb16, Piwil2, and Piwil4; To elucidate the genomic regulation underlying spontaneous reprogramming of SSCs, we then focused on 1,651 genes differentially expressed (>2-fold change) among SSCs, MASCs and ESCs . Hierarclow, either K4me3+K27me3 or univalent K27me3) from active promoters , a specific PRIM cutoff was identified for each cell type  largely shared K4me3- and/or K27me3-marked promoter chromatin between SSCs and MASCs  and Nanog  to an active state (MASCs), with complete erasure of K27me3 , indicating that promoter chromatin state-associated transcriptional activation is both selective and gene specific. However, genes functioning in embryonic differentiation to somatic lineages maintained their bivalent promoter modifications in both SSCs and MASCs, consistent with recent observations in freshly isolated mouse spermatogoniaTherefore, chromatin state changes were restricted to only ESC signature genes  typically regulate embryonic differentiation towards both germline and somatic lineages  after reprogramming. However, most ESC signature genes and germ cell-specific genes varied their K27me3 modifications at promoters, while a few of them either acquired or erased K4me3 modification in MASCs. These two histone modifications significantly affected the promoter chromatin states of only a small subset of transcriptionally activated genes (class I), but more prominently affected the repressed genes (class II).Thus, the majority of somatic genes with bivalent promoter modifications in SSCs remained epigenetically repressed in MASCs, irrespective of their transcriptional activities .A total of 5,383 enhancers were found to be active in at least one of the three cell types  and were associated with 1,651 differentially expressed genes . As predSox2 and Pou5f1) could bind at these common active enhancers, indicating that an ESC-like transcriptional programme might be partially active in SSCs   , as wellfication 3940.Usf1/2 and Gata2/4) but not the core pluripotency transcription factors , suggesting that the ESC differentiation apparatus rather than the pluripotency circuitry could be responsible for the activation of such enhancers (Prdm14 and Foxh1) or signalling regulators  enriched at MASC-specific enhancers (M enhancers), indicating their potential roles in SSC conversion (Unlike shared active enhancers in MASCs and ESCs (ME and SME enhancers), the 1,581 ESC-specific enhancers (E enhancers) were potential targets of many regulators of late embryogenesis  and target-specific transcription factors, with cooperative effects on active transcription or silencing of these genes, respectively, in a cell-type-specific manner.In summary, MASCs underwent erasure of germline-specific enhancers and partial establishment of enhancers that resemble those of ESCs. This process would be prospectively enabled by binding of ESC signature transcription factors. The change in enhancer activity closely correlated with alterations in expression of nearby genes and could support SSC reprogramming along with changes in promoter chromatin state. In contrast to the regulation at enhancers, bivalently marked somatic gene promoters could be modulated by both PRC2 . In-depth analysis of the SSC epigenome, coupled with transcriptional profiling by RNA-seq and motif analysis, suggested that while the chromatin of somatic gene promoters exhibits stable bivalency, promoters and enhancers of signature genes for ESCs and germ cells undergo significant chromatin state changes after SSC conversion into a multipotent state . This epFirst, transcriptome analysis revealed that SSC reprogramming involves not only activation of somatic cell- and ESC-expressed genes but also repression of germ cell differentiation regulators. During early embryogenesis, many of these activated genes are expressed in the inner cell mass, the embryonic origin of ESCs, and are considered to be hallmarks of stem cell pluripotency. This suggests that SSCs reach a multipotent developmental state through both recovery of broad embryonic developmental programmes and erasure of adult germ cell characteristics.Second, SSCs preserve ESC-like epigenetic features at both promoter and enhancer regions. In contrast to the differentiated cell types that we analysed, promoters of regulatory genes involved in embryonic differentiation maintained K4me3+K27me3 bivalent modifications in SSCs as in ESCs. Moreover, many ESC signature genes were also poised with bivalent modifications in SSCs. Furthermore, our motif study of active enhancers shared among SSCs, MASCs and ESCs suggests that the core pluripotency circuitry is partially active in SSCs. This unique epigenetic milieu could facilitate a transcriptional switch in the presence of internal or external stimuli and endow SSCs with unusual developmental flexibility with respect to lineage development beyond the germline.Active) or acquisition of the K4me3 modification (MASCModified). Conversely, promoters of germ cell differentiation genes are largely repressed by K27me3 modification (MASCRepressive) or silenced through loss of K4me3 modification (MASCUnmodified). Such a pattern suggests that H3K4- and H3K27-specific methyltransferases and demethylases may play active roles in SSC reprogramming, by facilitating chromatin state changes at selected promoters. Further study of enzymes that mediate H3K4 and H3K27 covalent modifications, particularly those specifically expressed in SSCs, may be useful to improve reprogramming efficiency.Third, after SSC reprogramming, most pluripotency-associated gene promoters switch to active chromatin states through erasure of K27me3 modification (MASCStable I and MASCStable II). Transcription of these poised genes is potentially regulated by both PRC2 (Suz12)-mediated K27me3 and by transcription factors at both promoters and enhancers. Many such transcription factors undergo significant increases in expression (class I) or decreases in expression (class II), as well as chromatin state changes during reprogramming, for example, the core pluripotency regulators  and several ESCs differentiation factors. Our results suggest that epigenetic switches could associate with the expression changes of key transcription factors and initiate SSC reprogramming, while somatic genes represent downstream targets of this hierarchical networkFourth, somatic genes generally retain bivalent promoter modifications in MASCs, despite changes in expression after SSC reprogramming  (OG)C57Bl6 (B6), C57Bl6/129S mix (129mix)Gt(ROSA26)Sor-lacZ (Rosa). All mouse experiments were performed in accordance with institutional and national guidelines and regulations including the Weill Cornell Medical College Institutional Animal Care and Use Committee.SSCs were prepared from mice over 3 months old from the following strains: −1; Sigma-Aldrich). The cell suspension was then collected and plated in gelatin-coated plate in SSC medium containing StemPro-34 (Invitrogen) and supplements as follows: D(+) glucose, 6 mg ml−1; BSA, 0.50%; insulin, 25 μg ml−1 (Sigma-Aldrich); MEM nonessential amino acids, 1 × (Gibco); MEM vitamin solution, 1 × (Gibco); penicillin (100 U ml−1)/streptomycin (100 μg ml−1)/amphotericin (0.2 μg ml−1) (Media-tech); fetal bovine serum, 1%; L-glutamine, 2 mM (Media-tech); bovine holo-transferrin, 100 μg ml−1 (Sigma-Aldrich); β-oestradiol, 30 ng ml−1 ; progesterone, 60 ng ml−1 ; putrescine, 60 μM (Research Organics); sodium selenite, 30 nM (Sigma-Aldrich); pyruvic acid, 30 μg ml−1 (Sigma-Aldrich); D(L)-lactic acid, 1 μg ml−1 (Baker); β-mercaptoethanol, 50 μM (Gibco); ascorbic acid, 100 μM (EMD); D-biotin, 10 μg ml−1 ; human glial cell-derived neurotrophic factor (GDNF), 10 ng ml−1 (R&D Systems); human basic fibroblast growth factor, 10 ng ml−1 ; and mouse epidermal growth factor, 20 ng ml−1 (R&D Systems). SSC colonies start to appear after about 1 week. The primary SSCs were then transferred to MEF (Millipore #PMEF-CF)-coated plate and expanded. The SSC identities of cultured cells were evaluated by several cell surface antigens  and supplements as follows: KnockOut Serum Replacement, 10% (Gibco); penicillin (100 U ml−1)/streptomycin (100 μg ml−1)/amphotericin (0.2 μg ml−1) (Media-tech); L-glutamine, 2 mM (Media-tech); MEM nonessential amino acids, 1 × (Gibco); β-mercaptoethanol, 50 μM (Gibco); and ESGRO leukaemia inhibitory factor, 1,000 U ml−1 (Millipore). All the cells applied to further experiments were serially passaged and collected within 20 passages.MASCs were derived from SSC conversion after long-term − HSCs, whole bone marrow cells from C57BL/6J mice were isolated and Lin− cells were enriched by mouse Lineage Cell Depletion Kit (Miltenyi Biotec). Purified cells were plated with E4ORF1-HUVECs in X-Vivo serum-free media (Lonza) supplemented with 10 ng ml−1 of soluble Kit Ligand (sKitL) (Biosource)− cells and plated with new E4ORF1-HUVECs feeders every 7 days. Total haematopoietic cell expansion was enriched for Lin− cells by mouse Lineage Cell Depletion Kit before collection.To expand mouse Lin7 cells per experiment were crosslinked for 15 min in 1% paraformaldehyde, washed and lysed. Chromatin was sheared using a Bioruptor to create fragments of ∼150 bp (base pairs), incubated with about 2–5 μg antibody bound to 75 μl Dynabeads M-280 (Invitrogen) and rotated overnight at 4 °C, then washed and eluted. The eluted chromatin was reverse-crosslinked and column-purified. ChIP was performed using the following antibodies: H3K4me3 (Abcam ab8580); H3K27me3 (Abcam ab6002); and H3K27ac (Abcam ab4729).ChIP was performed as previously describedhttp://www.illumina.com/). Sequencing service was performed on an Illumina Hiseq 2000 sequencer according to the standard Illumina protocol.ChIP samples were prepared for sequencing using Illumina TruSeq DNA Sample Preparation Kit according to the standard preparation protocol (RNA was isolated using QIAGEN RNeasy kit. RNA samples were prepared for sequencing using Illumina TruSeq RNA Sample Preparation Kit and were sequenced on an Illumina HiSeq 2000.http://picard.sourceforge.net/). Unique reads that mapped to a single best-matching location with no more than 4% of the read length of mismatches were kept and used to study genome-wide enrichment of specific histone modification. Sequence data were visualized with IGV by normalizing to 1 million readsChIP-seq reads were aligned to the reference mouse genome  using the BWA programme (version 0.5.9)t=5, with t as the significance negative log P value (ratio) threshold for peaks) (FDR<0.05). Cell-type-specific bivalent genes were identified as promoters carrying both K4me3 and K27me3 peaks in at least two biological repeats of certain cell type. PRIM K4me3 and K27me3 (PRIMs) was calculated as a' equals to 0.001. Only promoters with detectable K4me3 or K27me3 peaks were evaluated by PRIMs.The software ChIPseeqer-2.0 was applied to the ChIP-seq data with sequencing data from input DNA as control to identify genomic enrichment (peak) of specific histone modificationsAn enhancer region was defined as any genomic locus with H3K27ac enrichment but without K4me3 enrichment (K27ac+K4me3−). To identify enhancer loci, K4me3 peaks were extended 1 kb each way. All H3K27ac peaks not overlapping with extended K4me3 peaks, known gene body, transcription start and end site were selected. Selected H3K27ac peaks within a 500-bp interval were merged together as enhancers. Enhancer regions identified from SSC, MASC and ESCP value <0.05) between any pair of the three cell types were selected. These gene expression values were used as input for hierarchical clustering  by Cluster 3.0  analysis was done using iPAGE with mouse GO annotation databaseEnrichment of known motifs within promoter and enhancer regions was analysed with HOMER with default parameters and a fragment size of 200 bp. All known motifs used in our study were defined by HOMER.Statistical analysis was performed in R (version 3.2.1) statistical frameworkAccession codes: ChIP-seq and RNA-seq have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE78127 (RNA-seq) and GSE78129 (ChIP-seq).How to cite this article: Liu, Y. et al. Epigenetic profiles signify cell fate plasticity in unipotent spermatogonial stem and progenitor cells. Nat. Commun. 7:11275 doi: 10.1038/ncomms11275 (2016).Supplementary Figures 1-12List of RNA-seq datasets showing number of biological replicates and data sources.List of ChIP-seq datasets showing number of biological replicates and sources.Promoters with histone modification peak.Promoters with K4me3+K27me3 bivalent histone modification peaks in ESC, MASC, SSC, iPS, and MEF. .Genes selected by differential expression among SSC, MASC, and ESC.Enhancers associated with differentially expressed genes among SSC, MASC, and ESC. (Number of transcription factor peaks within one enhancer is shown in the table).Motif enrichment at selected promoters and cell type-specific enhancers.


The original message appears to be simply a letter or photograph, yet a person with the requisite knowledge can decode it to find hidden information. As it turns out, cells may have developed a form of steganography. Recent studies have shown that a single transcription factor (TF) can exhibit different dynamics in response to different stimuli  and thatTo understand this process, the authors studied Msn2, a yeast transcription factor that responds to different stresses with distinct dynamic expression patterns: short-duration repeated pulses, a single pulse with a dose-dependent duration, or a single pulse with a dose-dependent amplitude . Using ain silico analysis showed that slow promoters would be activated in only approximately half the cells. Further analysis revealed a strong correlation between the timescale of promoter activation and noise. A brief, low amplitude signal led to weak, but uniform, expression from LF promoters. A longer, higher amplitude signal generated a stronger, uniform expression. In contrast, in the absence of sufficient signal there was no expression from an HS promoter, and even after sufficient signal there was only heterogeneous expression from the promoter  showed that, indeed, chromatin remodeling occurred more quickly at fast promoters. Furthermore, knockouts of two different chromatin remodeling complexes (SWI/SNF and SAGA) led to a slower promoter activation and an increase in both intrinsic and total noise for a fast promoter.SIP18, the product of which provides protection from reactive oxygen species, would only be activated during the long periods of Msn2 nuclear localization caused by oxidative stress. The LF promoter driving HXK1, a protein important for growth on non-fermentable carbon sources, is conversely primed for the quick response to glucose starvation; however, the link to function is not as clear for the other promoters tested. The tools and models described in this study provide a conceptual framework for understanding how promoters decode TF dynamics. Yet, exploring how these different promoter classes are used for different cellular functions and outcomes is a key unanswered question. Compiling the wealth of data available about TF binding affinities and dynamics, promoter architecture, and noise contributions into more realistic models may provide the next level of insight into how cells ‘decide' at what time and to what level gene expression is regulated.


Bacillus subtilis. By using promoter-GFP fusions and live cell array technology, we demonstrate a temporally controlled transcriptional activation of the bmrCD genes in response to antibiotics that target protein synthesis. Intriguingly, bmrCD expression only occurs during the late-exponential and stationary growth stages, irrespective of the timing of the antibiotic challenge. We show that this is due to tight transcriptional control by the transition state regulator AbrB. Moreover, our results show that the bmrCD genes are co-transcribed with bmrB (yheJ), a small open reading frame immediately upstream of bmrC that harbors three alternative stem-loop structures. These stem-loops are apparently crucial for antibiotic-induced bmrCD transcription. Importantly, the antibiotic-induced bmrCD expression requires translation of bmrB, which implies that BmrB serves as a regulatory leader peptide. Altogether, we demonstrate for the first time that a ribosome-mediated transcriptional attenuation mechanism can control the expression of a multidrug ABC transporter.Expression of particular drug transporters in response to antibiotic pressure is a critical element in the development of bacterial multidrug resistance, and represents a serious concern for human health. To obtain a better understanding of underlying regulatory mechanisms, we have dissected the transcriptional activation of the ATP-binding cassette (ABC) transporter BmrC/BmrD of the Gram-positive model bacterium  Since the clinical introduction of antibiotics, bacteria have evolved efficient mechanisms to counteract their effects. This has resulted in the emergence of multidrug resistant (MDR) bacterial pathogens, which is a serious threat to public health as the treatment of infections caused by these pathogens is becoming increasingly difficult . An effeBacillus subtilis, a close relative of notoriously drug-resistant Gram-positive bacteria, such as Staphylococcus aureus, is equipped with an extensive repertoire of genes that encode for potential drug efflux transporters  at 37°C. Where appropriate, the growth medium was supplemented with antibiotics: ampicillin 100 μg/ml, spectinomycin 100 μg/ml, chloramphenicol 5 μg/ml (B. subtilis) or 10 μg/ml (E. coli).The plasmids and bacterial strains used in this study are listed in Supplementary Table S1, and the primers used for plasmid constructions in Supplementary Table S2. bmrB or bmrC were amplified by polymerase chain reaction (PCR) with the Phusion Hot Start High-Fidelity DNA Polymerase , and then joined with the pBaSysBioII plasmid by Ligation-Independent Cloning (LIC). The resulting plasmids were used to transform B. subtilis 168 as described by Kunst and Rapoport  fusions were constructed using the integrative pBaSysBioII plasmid, as described previously . The 5′ Rapoport . Since peviously .AscI LIC site was introduced into pRM3 by primer overlap. Two different fragments of bmrB including the 5′ untranslated region (UTR) and coding sequence, either with or without the terminator region, were amplified by PCR. The resulting DNA fragments were fused by overlap-PCR with gfpmut3, including an optimal ribosome-binding site (RBS) . PCR fragments of these synthesized sequences, consisting of the front part of bmrB, were fused to the terminator region and GFP by overlap-PCR.Plasmid pRM3 was constructed by combining the backbone of plasmid pHB201  with thete (RBS) . The reste (RBS)  resultinB. subtilis were performed as described previously (600nm) and GFP fluorescence  at 5- or 10-min intervals. The promoter-GFP strains were either exposed to subinhibitory concentrations of antibiotics or mock-treated during early exponential growth (OD600 nm corrected for 1 cm path length = ∼0.5). Background fluorescence from the isogenic wild-type control strain not expressing GFP was subtracted. Arbitrary transcriptional activity units (TAU) represent the increase in GFP expression levels during each 5- or 10-min interval and were calculated using the equation: (GFPt − GFPt−1)/OD600t . Expression of the transcriptional-GFP fusions on pRM3 was induced in exponentially growing cells (OD600 nm ∼0.18) by the addition of xylose  either with or without supplementation of lincomycin (0.75 μg/ml). For the experiments with strains containing pRM3-borne gfp fusions, the final GFP activity levels were corrected by subtracting the GFP activity levels measured for non-induced cultures. The xylose-induced GFP activity levels thus obtained were expressed in arbitrary units (AU). Data depicted in the LCA figures were derived from one of three representative experiments.LCA analyses with eviously ,14,15. AB. subtilis 168 was inoculated from an overnight culture into 150 ml LB medium to an OD600 of 0.05. During exponential growth (OD600 ∼0.6) the cells were either mock-treated or exposed to lincomycin (0.5 μg/ml) for 150 min. Next, total RNA was extracted according to the method described by Eymann et al. (bmrB/bmrC/bmrD operon (in short bmrBCD) with Taq polymerase (Life Technologies) and the quantitative PCR (qPCR) primers bmrB-foward and bmrC-reverse (Supplementary Table S2). Real-time qPCR  was performed according to the manufacturer's protocol using an Applied Biosystems 7500 Real-time PCR system and the primer sets specified in Supplementary Table S2. The recF and ssrA genes were included as reference genes. Data analysis was carried out using the accompanying Applied Biosystems software package (v.2.0.5).bmrCD genes was carefully mapped using data from a recent high-resolution transcriptional analysis of B. subtilis grown under 104 different conditions (bmrC and bmrD genes are co-transcribed with bmrB (previously known as yheJ), a small and as yet uncharacterized gene of 162 bp that ends 119 bp upstream from the start codon of bmrC , is located ∼70 bp upstream of bmrB  carrying the 5′bmrC-gfp fusion were exposed to subinhibitory concentrations of the ribosome-targeted antibiotics chloramphenicol (0.2 μg/ml), erythromycin (0.02 μg/ml), lincomycin (0.2 μg/ml), kanamycin (0.2 μg/ml) or gentamycin (0.2 μg/ml). The addition of chloramphenicol or erythromycin resulted in a slight reduction in the growth rate and a slight drop in the maximum OD600 nm that was reached  after which it gradually returned to baseline levels. Interestingly, while the presence of 0.2 μg/ml lincomycin had no inhibitory effect on growth, it triggered the strongest induction of the 5′bmrC-gfp fusion , the transcriptional activity was doubled, and a further 10-fold increase (2 μg/ml) induced the bmrC promoter activity to ∼17 800 TAU. This relatively high concentration of lincomycin slightly affected growth of the 168 BSBII-5′bmrC-gfp strain  was administered to the culture at the start of the experiment -inducible transcriptional 5′-GFP fusion,B. subtilis 168 BSBII spac-gfp, was included in the experiments . A final concentration of 0.1 mM IPTG was added to exponentially growing bacteria, which, as expected, resulted in an immediate and strong induction of the spac-gfp fusion. Importantly, the addition of lincomycin (2.0 μg/ml) had no detectable effect on the IPTG-induced GFP production, excluding a direct inhibitory effect of this antibiotic on the production of active GFP. Taken together, these observations imply that bmrCD expression is induced by a subset of ribosome-targeted antibiotics, and that this induction is limited to the late transition and early stationary growth phases.To exclude the possibility that the delayed transcriptional response of the 5′bmrC-gfp fusion was constructed to monitor bmrCD transcription and, for this purpose, the 600-bp region directly upstream of bmrC was cloned into pBaSysBioII. As a consequence, transcription of the 5′bmrC-gfp fusion was not only subject to control by the upstream bmrBCD promoter region, but also to possible regulatory sequences within bmrB . Together these findings verified that transcription of the bmrBCD operon is indeed initiated from a promoter upstream of bmrB. This conjoint promoter is activated during the transition- and early-stationary growth stages and is not responsive to ribosome-targeted antibiotics, such as lincomycin.Conceivably, the terminator structure within e Figure . The addn Figure  and D. Ts Figure . This wabmrBCD operon was strictly retained to the transition and early-stationary growth stages, we investigated whether this could be attributed to the transition state regulator AbrB. Supporting this idea, a potential AbrB-binding site upstream of bmrB was previously identified in a genome-wide AbrB-binding study , this resulted in a strong expression of GFP. However, in contrast to the abrB-proficient strain, which displayed the reported delay, the transcriptional activation of the 5′bmrC-gfp fusion in response to lincomycin was considerably faster in the ΔabrB strain  or a truncated version (1–109 bp) of the bmrB-coding sequence, the latter lacking the terminator region. In addition, the 5′ UTR, assigned according to the B. subtilis Expression Data Browser , we wondered whether bmrB could encode a potential leader peptide involved in the regulation of transcriptional read-through via its translation.With the aid of pRM3-bmrB is translated and could thus function as a regulatory leader peptide, we constructed a translational BmrB-GFP fusion, using the pMUTIN-GFP chromosomal integration plasmid . However, cells containing this construct showed no detectable GFP fluorescence. To test whether this was due to low-level expression of BmrB-GFP from the single-copy gene fusion on the chromosome, the in-frame bmrB-gfp fusion was PCR-amplified and cloned in plasmid pRM3. Upon induction with xylose, cells containing the resulting construct (pRM3-bmrB-gfpInframe) were fluorescent, which showed that the bmrB open reading frame is translated and, therefore, has the intrinsic potential to act as a regulatory leader peptide .To determine whether  plasmid . In thisbmrB could function as a leader peptide, we constructed the transcriptional pRM3-bmrBΔstart-gfp fusion in which we prevented the translation of bmrB. This was done by mutating the annotated GTG start codon to GTC, the insertion of a TAG stop codon downstream of the mutated start codon, and four point mutations in the 5′-UTR that either remove alternative start codons or potential RBSs , thereby preventing binding of the antibiotic compound. While ermC provides cross-resistance to several ribosome-targeted antibiotics, including lincomycin, expression of ermC can only be induced by erythromycin   Figure . The facbmrB. The BmrB peptide would then serve as a regulatory leader peptide. In this system, the ribosomes translating the BmrB leader peptide would act as a sensor for the presence of ribosome-targeted antibiotics. By inhibiting protein synthesis, these antibiotics will interfere with the efficient translation of bmrB. In turn, this would promote the formation of the anti-termination structure, thereby stimulating the transcription of the downstream bmrCD genes.Taken together, these observations show that the antibiotic-mediated regulation of transcriptional termination occurs during the translation of bmrB for controlling the transcription of bmrCD led us to ask the question whether the codon composition of bmrB could be important for its function as a regulatory leader peptide. This question was relevant since we have identified five rare codons within the bmrB coding sequence upstream of the regulatory terminator region   . Rare cocat.de/) –27, and,t Figure . Next, t2 Figure . FurtherbmrCD is constrained to the transition and stationary growth phases due to a tight control by the transition state regulator AbrB. In addition, we have demonstrated that antibiotic-induced expression of bmrCD is regulated via a ribosome-mediated transcription termination mechanism, a regulatory mechanism that has not been previously described for MDR-ABC transporters.In the present study, we have employed promoter-GFP fusions to monitor the expression dynamics of the genes encoding the MDR-ABC transporter BmrCD upon exposure to antibiotics. This real-time approach revealed that the antibiotic-induced expression profile of bmrBCD operon is regulated via the transcriptional regulator AbrB, which is known to repress stationary phase-specific genes during exponential growth of B. subtilis (bmrCD expression. Nevertheless, although the AbrB-regulated promoter upstream of bmrB controls the growth phase-dependent expression of bmrCD, this promoter is unresponsive to the antibiotics that induce bmrCD expression. By constructing several different transcriptional-fusions, we have now demonstrated that the antibiotic-induced expression of these two MDR-transporter genes is regulated via the transcriptional terminator located within the coding sequence of bmrB. In the absence of antibiotics, such as lincomycin, transcription of the bmrBCD operon will be terminated at this terminator site, thereby minimizing expression of bmrCD. In contrast, when the cells are challenged with translational inhibitors, such as lincomycin, erythromycin or chloramphenicol, transcriptional termination is prevented and the bmrCD transporter genes are transcribed. Moreover, our study shows that translation of bmrB is essential for the antibiotic-controlled transcription of bmrCD. From these observations we conclude that BmrB acts as a regulatory leader peptide, and that the extent of bmrCD transcription is related to the efficiency of bmrB translation. Intriguingly, kanamycin and gentamycin, both targeting the 30S ribosomal subunit, were unable to induce the 5′bmrC-gfp fusion, whereas chloramphenicol, erythromycin and lincomycin, which target the 50S subunit, did induce the 5′bmrC-gfp fusion. This difference between antibiotics that target the 50S and 30S ribosomal subunits could relate to their exact binding sites on the ribosome and/or mechanisms of action in relation to bmrB translation, but this is presently highly speculative and further research is needed to explain this observation.Our study shows that the promoter controlling transcription of the subtilis , and thaE. coli (B. subtilis bmrBCD operon, as described here, differs from that of the E. coli trp operon in that ribosome stalling is caused by the inhibitory effect of the ribosome-targeted antibiotics on protein synthesis instead of a tryptophan shortage. However, as described for the trp operon, it seems likely that the ribosome translational stalling will allow structural organization of the anti-terminator in bmrB, thereby promoting the transcription of bmrCD as schematically represented in Figure bmrB transcription was completely lost upon methylation of the ribosome by ErmC (Figure bmrCD transcription.While we have identified the first leader peptide-based transcriptional control system for the expression of an MDR transporter, it should be noted that leader peptide-based regulatory mechanisms have previously been reported to control various other processes in bacteria. These mechanisms rely on the fact that bacterial transcription and translation are coupled. The best-known example of leader peptide control involving transcriptional termination is the operon responsible for tryptophan synthesis in E. coli ,30. In tE. coli . This riE. coli . The regC Figure . The funC Figure  within oB. subtilis vmlR ABC transporter gene, which is involved in resistance to lincomycin and virginiamycin M (vmlR (vmlR lacks an ORF, indicating that the expression of this gene is not regulated via ribosome-mediated transcriptional termination. In addition, vmlR lacks the typical anti-terminator structure that is normally required for such transcriptional attenuation mechanisms, and the precise mechanism regulating vmlR expression remains to be determined (ermC, encoding the erythromycin ribosomal methylase C, is regulated via another class of leader peptide-based systems. Under non-inducing conditions efficient translation of the leader peptide of ermC promotes formation of a stem-loop structure that protects the RBS so that translation of ermC is prevented (ermC leader peptide, giving time for the formation of an alternative stem-loop structure (ermC (ermC encourages the theory that translational leader peptides can act as efficient sensors for ribosome-targeted antibiotics. This is further supported by the translation-mediated attenuation mechanism controlling transcription of cat-86, a plasmid-borne chloramphenicol resistance gene in B. subtilis. Expression of cat-86 is induced when the ribosome, while translating the leader peptide, is stalled by chloramphenicol (vmlR, ermC and cat-86, the bmrCD genes are, to the best of our knowledge, the first MDR-ABC transporter genes to be regulated via ribosome-mediated transcriptional attenuation. In fact, the termination system controlling the expression of bmrCD is the first documented transcriptional attenuation mechanism encoded in the B. subtilis genome that is controlled by a leader peptide. However, it should be noted that other transcriptional attenuation systems have previously been characterized in B. subtilis. One example is the tryptophan synthesis operon, which is dependent on an RNA-binding protein to control expression of downstream genes (B. subtilis has several transcriptional systems that are controlled by riboswitches (Transcriptional termination has previously been reported as a regulatory mechanism in the expression of several resistance genes, including the amycin M ,34. Thesrevented . The pretructure . As a rere (ermC . The regphenicol . Despiteam genes . Additioswitches . These Rswitches .bmrCD expression is presently elusive. Fast and efficient detoxification of the cellular interior is essential when bacteria are exposed to antibiotics. Therefore, most drug efflux systems show an immediate transcriptional response toward the toxic compounds they expel. However, as a soil bacterium, B. subtilis can be confronted with a large variety of different antimicrobial compounds and this is probably the reason why it possesses a multitude of efflux pumps (B. subtilis. The transcriptional response of bmrCD is strongest upon exposure to lincomycin, an antibiotic produced by Streptomyces lincolnensis, which is also a soil-dwelling bacterium (vmlR occurs during the early- and mid-exponential growth phases and declines in the late-exponential growth phase (bmrCD expression during the late-exponential and stationary growth stages. In addition, lmrB, encoding an additional lincomycin exporter, follows a similar expression profile as vmlR (vmlR and lmrB seem to be transcribed predominantly in the exponential growth phase, it appears that expression of bmrCD would be redundant during this point in growth and more effective during later growth stages. An alternative explanation for the strictly growth phase-dependent expression profile of bmrCD is a potential role of the respective transporter in sporulation. Transcript levels of bmrCD increase during sporulation (bmrCD was shown to significantly reduce spore formation in a kinB mutant B. subtilis strain (et al. subsequently demonstrated that BmrD interacts with KinA, one of the key regulators of the sporulation process, and they postulated that this interaction might inhibit the activation of KinA by trapping it to the inner surface of the cytoplasmic membrane.The biological relevance for the observed strictly growth phase-dependent ux pumps . Consequacterium . Interesth phase . It thus as vmlR ,43. Sincrulation , and thes strain . Kumano bmrCD genes as uncovered in the present study points toward a role of BmrCD in antibiotic efflux during the post-exponential stages of growth. This mechanism is apparently tailored for the optimal extrusion of ribosome-targeted antibiotics and represents the first ribosome-mediated transcriptional attenuation system described for an MDR-ABC transporter.In conclusion, the regulatory mechanism controlling the transcription of the Supplementary Data are available at NAR Online.


Transcription control insights using blood ChIP sequencing compendium. in vivo. Although this technology has led to many important discoveries, the rapidly increasing number of publicly available ChIP-sequencing datasets still remains a largely unexplored resource. Using a compendium of 144 publicly available murine ChIP-sequencing datasets in blood, we show that systematic bioinformatic analysis can unravel diverse aspects of transcription regulation; from genome-wide binding preferences, finding regulatory partners and assembling regulatory complexes, to identifying novel functions of transcription factors and investigating transcription dynamics during development.Transcription factors play a key role in the development of a disease. ChIP-sequencing has become a preferred technique to investigate genome-wide binding patterns of transcription factors  Moreover, dysregulation of transcription has been widely proven to be a major contributor to human pathologies, with the recent development of small molecule drugs targeting protein interactions between transcriptional regulators generating much excitement.cis-regulatory DNA elements and trans-acting transcription factors (TFs) representing the fundamental basis of transcriptional control, the delineation of comprehensive collection of regulatory sequences together with knowledge of the TFs bound to them will be essential to gain global insights into transcriptional control mechanisms. Over the past 10 years, chromatin immunoprecipitation (ChIP) followed by microarray (ChIP-chip) or sequencing (ChIP-Seq) have become the most widely used approaches for genome wide identification and characterization of in vivo protein–DNA interactions. Due to the rapid drop in the cost of high throughput sequencing, ChIP sequencing has become the method of choice for the generation of high resolution maps of genome-wide protein–DNA interactions in mammalian systems.3With the interaction between 10 and only a few larger studies reporting 10 or more factors12 or a single factor across multiple cell types.13 We have previously shown14 that unlike gene expression data, ChIP-Seq datasets produced by different laboratories can be readily integrated. This analysis revealed that genome wide transcription factor binding profiles are largely governed by cellular context. We recently reported a TF ChIP-Seq compendium containing 144 publicly available studies pertaining to the mouse blood system.15 Using this dataset, here we show how concerted bioinformatic analysis of such a high quality hand-curated compendium can reveal previously unknown aspects of transcriptional control. This includes identification of those TF-bound sites most likely to be functional, prediction of TF interactions and multicomponent complexes, specific functionality of individual TFs and the dynamics of transcriptional regulation during differentiation and development.To gain a holistic view of transcriptional control during development, it is essential to generate genome scale maps of key transcription factors across multiple cell types. However, generating such genome-scale maps in many different cell types remains a daunting task for individual research groups due to limited human and financial resources. Moreover, each individual TF requires careful validation of antibody reagents, which limits the potential throughput of large-scale initiatives. Indeed, bespoke protocols are often developed by individual groups with specialist expertise, so that published ChIP-Seq studies commonly report binding maps for less than a handful of TFs15 to obtain 270 261 regulatory regions with at least one factor binding. We classified peaks into two groups: promoter and enhancer peaks by defining the peaks within 1 kb of TSS as promoter peaks. 7.5% of the total peaks belonged to promoters and all non-promoter peaks were classified as putative enhancers. The hierarchical clustering of enhancers clustered them according to the cell type (14We collected genome-wide binding patterns (peaks) of 144 publicly available murine ChIP-sequencing datasets for 53 transcription factors in 15 major blood lineages and leukemiaell type  irrespecet al., 2009).The promoter regions did not show a strong cell type specific clustering but clustered into two major clusters . ClusterAs Pu.1 peaks in both promoters as well as enhancers cluster according to the factor rather than the cell type, we characterized them in more detail. The 5-way Venn diagram of Pu.1 in promoter regions showed a high overlap of binding sites with about 50% of peaks present in all cell types  whereas 8 To check if TFs have distinct interacting partners in promoter and enhancer regions, we calculated cis-regulatory motif enrichment in promoter and enhancer regions separately for each factor using HOMER software. The sequence motif of the transcription factor chipped was enriched in both promoters and enhancers in most samples. Most samples also exhibited promoter-specific and enhancer-specific sequence motifs  motif is enriched only for Pu.1 enhancers in B cells while MafA (macrophage activating factor) motif is only enriched in Pu.1 enhancers in macrophages.It is well established that transcription factors bind to different interacting partners in a cell type specific manner to drive gene expression.Taken together, the data support the suggestion that tissue-specificity is a common feature of enhancers rather than promoters.p-value: 6.6 × 10–18), ‘hematopoiesis’  and ‘blood vessel development’  demonstrating that hematopoietic regulatory genes have more binding sites in their gene loci. In an individual ChIP-sequencing experiment, most gene loci are associated with only one peak with an average of 1.8 peaks per gene. Genes with more than 5 peaks in their gene locus were enriched for hematopoietic functions. Transcription factor gene loci have an average of 2.5 peaks per gene, in agreement with previously reported suggestions that TF gene loci have a higher number of regulatory elements than average. This difference is statistically significant even after correcting for the gene length .We mapped peaks across 15 blood lineages to their nearest genes resulting in an average of 13.5 peaks per gene. The 19 869 unique gene loci were associated with peaks ranging from a single peak to over 200 peaks. The 726 genes with more than 50 peaks in their gene loci are enriched for functional categories ‘transcription regulation’  for all samples (y axis) highlighted in yellow. The enriched motifs are useful in three ways. Firstly, they validate the chipped TF e.g. the Cepb motif is enriched in the two samples CebpA and CebpB  can accurately predict the presence of CpG islands in promoters suggesting that they are structural elements critical for CpG island function. In line with this, transcription factors such as the three ETS factors Erg, Fli1 and Pu.1 were enriched for CpG rich regions. Interestingly, peaks of components of the Ldb1 complex  occurred significantly less often than expected by chance in CpG rich regions .Physical interaction of TFs is an important aspect in determining tissue specific gene expression, and cooperative binding to DNA may be subject to spatial constraints. For each TF pair, we mapped the sequence motifs to peaks bound by both TFs and calculated the distance between two motifs. We selected motif pairs displaying a specific distance preference in at least two independent ChIP-seq experiments. Importantly, this analysis recovered previously known spacing of 8–10 bps between GATA and E-box motifs involved in binding of Gata1/Scl/E2A/Lmo2 multiprotein complexes.et al., 2008).32 The second pattern, GATA and CTG or GTC, had a 9 bp gap mapping to GATA and a half Ebox binding as a part of the Ldb1 complex. The final pattern, Gfi1b and (A/T)GC, had a 2 bp gap . We found 3 binding distance preferences; the first pattern, GATA and GAT, had a 3/4 bp gap consistent with Gata factors binding as homo-dimers validated by the crystal structure ’, ‘immune system development ’, ‘B cell receptor signalling pathway ’ are enriched in genes near Pu.1 peaks in HPC7 and B cells and not in macrophages while macrophage specific functional categories such as ‘endocytosis ’ and ‘inflammatory response ’ are over-represented in genes near Pu.1 peaks in HPC7 and macrophages and not in B cells. This is a strong indicator of lineage priming in the progenitor cells and therefore provides global confirmation for one of the most hotly debated topics in stem cell biology.TFs are major determinants of cell fate and lineage choice. However, most lineage determining TFs are expressed across multiple lineages, suggesting that combinatorial interactions are critical in determining cell type specificity. By merging datasets from different studies, the TF ChIP-seq compendium serves as an excellent resource in the study of genome wide binding patterns of the same TF in multiple cell types. Grouping the genome wide binding patterns of Pu.1 in haematopoietic progenitor cells (HPCs) along with two mature cell types (macrophages and B cells) highlights that cell type specific, as well as ubiquitous binding events are present in both promoters and enhancers with ubiquitous binding events being more common in promoters. T and B cells specific functional categories such as ‘lymphocyte activation  or under-represented (blue) compared to 100 randomizations. Using HOMER8 and based on gene context or repeat elements, peaks were sorted into 9 categories: 3′ UTR, 5′ UTR, exons, introns, intergenic regions, promoters, LINE, SINE and LTR. CpG islands were downloaded from UCSC. A list of transcription factors in mouse was downloaded from RIKEN.29 To find distance preferences between pairs of TFs, the sequences for peaks bound by both transcription factors were obtained using UCSC Galaxy and the binding locations of each sequence motif were determined using TFSBsearch.30cis-Regulatory sequence motifs were downloaded from the JASPAR library26 and the motifs were searched in peaks using TFSBsearch;30 over-representation was calculated with respect to 100 random sequence sets of the same number and lengths of real peak sequences. Functional enrichment was calculated using DAVID.31 Most analysis was done using Perl, MATLAB and R scripts.The Genome-wide binding patterns of 53 transcription factors in 15 major blood lineages and leukaemia were obtained from The advent of next generation sequencing technologies has led to a dramatic shift in modern biological research, where bioinformatic processing and interpretation of large-scale datasets are rapidly replacing data generation as the major bottleneck. Moreover, bioinformatic analysis of genome-scale datasets is often restricted to the particular context of the paper that first reported them, even though the raw data are made publicly available in online repositories. Consequently, a whole potential treasure trove of biological insights remains essentially unexplored.To ameliorate this situation, progress on two fronts will be vital. Firstly, significant efforts need to be invested into the generation of data integration platforms that facilitate cross-referencing between the multiple independent studies. Secondly, bioinformatic analysis strategies need to be developed to facilitate extraction of novel biological hypotheses from integrated genome-scale resources.In this paper, we have addressed the latter issue and provided seven examples of bioinformatic analysis that together have allowed us to develop a number of new hypotheses on transcriptional control mechanisms with the potential to transform our understanding of blood cell development. Importantly, both the procedures outlined as well as the take-home messages learned should be readily transferable to the exploitation of ChIP-Seq datasets in other cellular systems, and thus have the potential to significantly advance our understanding of a wide range of both normal and pathological cellular processes.


The aryl hydrocarbon receptor (AhR) is a member of the family of basic helix-loop-helix transcription factors that is widely expressed in most differentiated mammalian cells and implicated in a variety of cellular processes, such as cell cycle progression, cell migration, carcinogenesis and immune functions. Upon the binding of its synthetic or natural ligands, the cytosolically-located AhR is activated and translocated into the nucleus where it dimerizes with ARNT  and specifically binds to a consensus DNA sequence (5′-TNGCGTG-3′) known as an AhR-responsive element (AHRE) or dioxin response element (DRE), leading to changes in target gene transcription. The best characterized high-affinity ligands for the AhR are synthetic halogenated aromatic hydrocarbons and polycyclic aromatic hydrocarbons such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), and a growing number of natural products such as arachidonic acid metabolites, indoles, tetrapyrroles and polyphenolics are found to be able to act as AhR ligands as well. However, the physiological or pathological relevance of those natural ligands remained to be fully addressed [Considerable AhR target genes identified by genome-wide gene expression profiling have morphogenetic functions, indicating that AhR may play a role in embryonic development. In fact, the developmental toxicity of TCDD has been well documented. By RNA sequencing-based analysis, it was implicated that the overall effect of TCDD-driven AhR activation was to maintain the pro-proliferative state of mouse embryonic stem cells (ESCs) and inhibit their differentiation into cardiomyocytes. Remarkably, in TCDD-treated AhR-positive differentiating ESCs, key pluripotency-associated transcription factors such as Oct4, Sox2, Nanog, Klf4 were all predicted to be activated at the transcriptional level [The results from a subsequent study showed that AhR expression was maintained in a repressed but poised state in mouse ESCs which allowed for its quick activation during differentiation. Such repression resulted from the direct binding of the Oct4-Nanog-Sox2 complexes onto the AhR distal promoter region, along with PcG-mediated repression and pausing of unproductive RNA polymerase II molecules on the TSS (transcription start site) proximal promoter region. Release of the core pluripotency factors (particularly Oct4) from their binding sites can switch AhR expression from repression to activation . This inPOU5F1 gene) promoter in vivo and act as a transcriptional repressor of POU5F1 [in vitro and in vivo [POU5F1 promoter, and such effect appeared to be specific for ITE but not other tested tryptophan derivatives [Recently, we showed that, compared to differentiated human cell lines, human ESCs and embryonal carcinoma cells (ECCs) possess the highest Oct4 mRNA levels yet the lowest AhR mRNA levels, and among various normal human tissues, placenta derived from Oct4-deficient trophectoderm exhibited the highest level of AhR mRNA. During RA-induced differentiation of human ESCs and ECCs, the Oct4 mRNA level decreased concurrently with an increase in AhR mRNA level, implicating a strong negative correlation between these two factors at the transcriptional level . Such obf POU5F1 . Such ref POU5F1  that pos in vivo . In variivatives .Thus, these new findings raise an interesting possibility of reciprocal suppression between AhR and Oct4. The chicken-and-the-egg question then follows: who plays a leading role in differentiation? Since Oct4 is highly expressed while AhR is undetectable in ESCs, it is likely that a reduction in the Oct4 level may come ahead of an increase in the AhR level. Once a small number of AhR molecules are generated, they can bind to the Oct4 promoter and inhibit its transcription in a ligand-specific manner, eliciting a feed-forward effect that drives accelerated reduction of Oct4 and differentiation. In such a postulated model, the concentration of the Oct4-suppressive ligands (such as ITE) may be a key accessory factor that regulates the progression of differentiation. In future studies, it will be important to fully decipher the cross-talk between AhR and Oct4, and how it is regulated by tryptophan-derived ligands and other endogenous AhR ligands in the context of embryonic development and lineage-specific differentiation.


The three-dimensional (3D) organization of chromosomes can be probed using methods like Capture-C. However, it is unclear how such population-level data relate to the organization within a single cell, and the mechanisms leading to the observed interactions are still largely obscure. We present a polymer modeling scheme based on the assumption that chromosome architecture is maintained by protein bridges, which form chromatin loops. To test the model, we perform FISH experiments and compare with Capture-C data. Starting merely from the locations of protein binding sites, our model accurately predicts the experimentally observed chromatin interactions, revealing a population of 3D conformations.The online version of this article (doi:10.1186/s13059-016-0909-0) contains supplementary material, which is available to authorized users. The three-dimensional (3D) spatial organization of mammalian chromosomes in vivo is a topic of fundamental importance in cell biology –5. Undercis-regulatory elements [DNA regions where the binding of a transcription factor (TF) can regulate the expression of a gene that is tens or hundreds of kilobase pairs (kbp) away]  , 18.α and β globin loci, which have long been model systems for understanding cis-regulatory interactions [The pattern of interactions revealed by most 3C-based experiments is an average over a large population of cells, yet it has become clear that there is a remarkable variability in both chromosomal conformation and chromatin interactions between different cells , 20. Thuractions , 21–30. Our model builds on the minimal assumption that the spatial organization of eukaryotic chromosomes is maintained largely through the action of proteins or protein complexes, which can form bridges by simultaneously binding to more than one site in the genome, and forming loops from the intervening chromatin , 31–36. cis-regulatory binding are not well characterized, and the DHS approximation avoids the need to make any assumptions. One factor that most certainly has a chromatin architectural role is the CCCTC-binding factor (CTCF) [To keep our model as simple as possible, we use the locations of DNase1 hypersensitive sites (DHSs) as a proxy for binding sites of a generic type of protein bridge, which we imagine is made up from complexes of TFs and other DNA-binding proteins. The choice of DHSs as binding sites is justified due to their well-documented tendency to correlate with open chromatin, or euchromatin, and with peaks in ChIP-seq data for many TFs , such asr (CTCF) , 42–44. r (CTCF) , 44, 45.r (CTCF) , 42. Intr (CTCF) . To reflr (CTCF) . We inclr (CTCF) .α globin locus , two copies of the α globin gene  and two θ globin genes . Expression of the genes in the cluster is controlled by several regulatory elements: the multi-species conserved elements R1–4 and the mouse specific R(m). Some of these are contained within the introns of Nprl3, one of several widely expressed genes that surround the locus; the R2 element  is thought to be particularly important for globin regulation [111First, we use our model to predict the folding of a 400-kbp region around the mouse gulation , 23, 27.As anticipated, one of the main strengths of our approach is that it naturally outputs information on each member of the population of chromatin conformations , which we can then further interrogate. A clustering analysis  of conformations that do not fit into any of these four clusters. It is also interesting to note that the ζ gene and Mpg seldom interact with the elements (these genes are not widely expressed in adult erythroid cells). The arrangement within the domains can be further probed by looking at which promoters are directly interacting with the different regulatory elements in each conformation , which showed that the gene situated linearly closer to the enhancer elements, Hba-a1, is always expressed at a higher level [In the most common representative structure, which accounts for 53 % of the total observed conformations for the locus, there is a single globular domain containing the promoters of the globin genes, the promoters of the two neighboring genes er level .cis-regulatory landscapes in mouse erythroblasts . Figure 3Mpg promoter. The results show that, remarkably, with the sole input of the ChIP-seq and DNase-seq data giving the locations of the protein binding sites, we can reproduce to a good accuracy the Capture-C profiles. In particular, we reproduce the contacts between the α promoters and the five known regulatory elements; we also reproduce that there is some interaction between the regulatory elements and the Nprl3 promoter , and is controlled by interactions with a series of DHSs in a region known as the locus control region (LCR) [α globin locus, the β globin genes are surrounded on either side by a condensed chromatin region, containing genes that are not expressed in erythroid cells. As with the α globin case, we use ChIP-seq and DNase-seq data to label a bead-and-spring polymer that represents the gene locus  , 24. Unlous work , 24, conhe locus  and the he locus ), is neaα globin locus . This indication that the β globin locus is less compact than the α globin case is borne out in measurements of the overall 3D size of the simulated loci  than inα globin locus, our simulations predict contact patterns that are in good agreement with Capture-C data, both for the β Major and Minor gene promoters . When repeating the analysis for the β globin locus, we find that the loss of non-local contacts is even more dramatic  gene in both mouse erythroblasts and embryonic stem cells. This gene encodes a mitochondrial protein essential for iron import into mitochondria; however, much less is known about this locus than about the α or β globin, and so our results represent a true prediction of its folding. The input data used were similar to that of the globin loci, and are given in Additional file Slc25a37 and Entpd4; the Slc25a37 promoter interacts strongly across the Slc25a37 gene, but also with two distinct regions between the nearby Synb and Gm16677 genes .α globin locus, we find that, surprisingly, for both the CTCF and DHS knock-outs the same folded structures can still form , the CTCF sites near the Hbq1 and Hbq2 promoters, and within the introns of the Nprl3 gene , and also within the less understood Slc25a37 (Mitoferrin1) locus. Our model is built on the hypothesis that there exist architectural protein bridges, which we assume are either CTCF or generic bridges made up by complexes of TFs and other DNA-binding proteins. The only inputs we require are ChIP-seq data for CTCF binding and the map of DHSs, which we take as a proxy for the location of the binding sites for the generic protein bridges (DHS bridges). Importantly, our approach differs from other recent polymer modeling studies that also have predictive power [In this work, we have shown that a minimal polymer model informed by large bioinformatic data sets on protein binding can successfully reproduce the pattern of Capture-C contacts observed in the well-studied ve power , 29, 36,Our model generates a population of conformations, hence we can predict, for instance, the distribution of distances between selected targets on the globin locus. These results compare very favorably with our FISH measurements, which allow us to estimate the physical size of the beads in our coarse-grained polymer . The packing we obtain (15.8 nm for 400 bp) is consistent with open chromatin, which is reasonable since the region we focus on is highly active.α and β globin loci, the analysis suggests that there is a split in these structures between two main types: those in which there is a single globular domain that includes the active genes together with their regulatory elements, and those where the globule splits into two microdomains. The single globule structures are favored by bridging, while the competing structure requires less bending and looping, and costs less entropy. .That our model generates a population of conformations, rather than a single average conformation, is important because it gives an estimate of the stochasticity and fluctuations in in vivo 3D organization. A key result of our model is that the conformations of the loci we studied can be grouped into a handful of representative structures, which account for different fractions of the whole population. In both the y with n .) There α globin genes using techniques that permit imaging of the locus during erythroid differentiation in live cells. Another application of the work might be to provide some explanation of how the Hba-x gene is silenced in adult erythroblasts: in all of our predicted conformations, it does not contact the known enhancer elements nor the surrounding gene promoters. It may also be informative to repeat the modeling for primitive erythroblasts, when sufficient protein binding and DNase hypersensitive data become available for that cell type.In future work, it will be interesting to compare these predictions with experimentally determined chromatin dynamics through cell differentiation, for example, examining the α globin locus, which has been shown experimentally to result in a 50 % reduction of α globin RNA levels [α thalassemia that results from a deletion of the equivalent HS-40 element in humans [α promoters interact with the remaining regulatory elements. We can make our model more complex by replacing DHS binding proteins with bridges that bind to specific TF binding sites. For instance, GATA1 and Klf1 are a minimal set of TFs . Thus, we use a model with three protein species, binding strongly to GATA1, Klf1 and CTCF sites, respectively (no longer considering hypersensitive sites), and weakly to H3K4me1 modified regions . Therefore, our approach can be generalized to accommodate more biological detail in a modular fashion, where this detail is known.As we have seen, our model can be further exploited to predict the organizational consequence of the knock-out of proteins such as CTCF (or our generic DHS bridge). Similarly, one can perform an in silico experiment that follows the consequences of modifying some genomic region within a locus. An intriguing example is the deletion of the R2 (HS-26) hypersensitive site in the A levels  (a much n humans ). RemoviWe anticipate that the main application of our in silico chromosome folding model will be to investigate regions of mammalian and other eukaryotic genomes that are currently poorly characterized. The approach relies only on DNase hypersensitivity and protein binding data, which are available genome-wide for many organisms and cell types. Our technique is fast and inexpensive, so that it can be used to predict the organization of a large number of wild-type and modified genomic loci prior to, for example, a combination of detailed Capture-C, 5C or FISH experiments, directing focus to those regions whose predicted structure was deemed to be of particular interest. The ease with which genome modifications can be incorporated makes it highly applicable for investigation of the effect on 3D chromatin structure of, for example, single nucleotide polymorphisms at enhancers, which have been implicated in many diseases.In the present work, we focused on looping interactions within a gene locus, at a sub-TAD length scale. Polymer models, and the principle of protein bridges driving chromatin conformations, can easily be adapted to treat larger looping and organization at the chromosome and genome scale, and this will be the subject of a future study.The chromatin fiber is modeled as a simple coarse-grained bead-and-spring polymer, where each bead represents 400 bp of DNA, or roughly two nucleosomes. The positions of the beads are updated via a molecular dynamics scheme (Langevin dynamics) using the LAMMPS   softwareThe Capture-C data shown in Figs. α globin locus in mouse erythroblasts, where the α genes are active. Genomic locations of the probes are given in Additional file λ-red-mediated recombination using oligonucleotide sequences shown in Additional file Figure 3α and β globin genes) were settled on poly-l-lysine coated coverslips, fixed with 4 % paraformaldehyde in 0.25 M HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) and permeabilized with 0.2 % Triton-X 100. FISH was performed using 7-kbp plasmid FISH probes, labeled with either Cy3-dCTP  or digoxygenin 11-dUTP (Roche Life Science). The genomic locations of the FISH probes are shown in Additional file z-step size of 200 nm. Images were restored by deconvolution using Huygens Professional software (Scientific Volume Imaging). Probe signal pairs were analyzed using a specifically designed Fiji algorithm that measures the 3D Euclidean distance (in microns) between thresholded signal centroids. Each measurement was adjusted to account for chromatic shift by using a displacement vector calculated from 0.1-µm Tetraspeck™ microspheres (Life Technologies) collected using the same imaging parameters as in the experiments.In vitro cultured mouse fetal liver cells , GSE21877 (Scl/TAL1 ChIP-seq for Ter119+ cells), GSE20478 (Klf1 ChIP-seq for Ter119+ cells), GSE47492 , GSE47758 (Capture-C data for the α and β globin loci in Ter119+ and mES cells) and GSE67959 (Capture-C data for mitoferrin1 in Ter119+ and mES cells). Other data sets used were obtained from the ENCODE project .The data sets supporting the results of this article are available in the Edinburgh DataShare repository [imulator , which iNo ethical approval was required for this study.