The brain, spinal cord, and neural retina comprise the central nervous system (CNS) of vertebrates. Understanding the regulatory mechanisms that underlie the enormous cell-type diversity of the CNS is a significant challenge. Whole-genome mapping of DNase I-hypersensitive sites (DHSs) has been used to identify cis-regulatory elements in many tissues. We have applied this approach to the mouse CNS, including developing and mature neural retina, whole brain, and two well-characterized brain regions, the cerebellum and the cerebral cortex.in vivo and in vitro electroporation of the retina. Analysis of transcription factor consensus sites within the DHSs shows distinct region-specific profiles of transcriptional regulators particular to each region. Clustering developmentally dynamic DHSs in the retina revealed enrichment of developmental stage-specific transcriptional regulators. Additionally, we found reporter gene activity in the retina driven from several previously uncharacterized regulatory elements surrounding the neurodevelopmental gene Otx2. Identification of DHSs shared between mouse and human showed region-specific differences in the evolution of cis-regulatory elements.For the various regions and developmental stages of the CNS that we analyzed, there were approximately the same number of DHSs; however, there were many DHSs unique to each CNS region and developmental stage. Many of the DHSs are likely to mark enhancers that are specific to the specific CNS region and developmental stage. We validated the DNase I mapping approach for identification of CNS enhancers using the existing VISTA Browser database and with Overall, our results demonstrate the potential of genome-wide DNase I mapping to cis-regulatory questions regarding the regional diversity within the CNS. These data represent an extensive catalogue of potential cis-regulatory elements within the CNS that display region and temporal specificity, as well as a set of DHSs common to CNS tissues. Further examination of evolutionary conservation of DHSs between CNS regions and different species may reveal important cis-regulatory elements in the evolution of the mammalian CNS.The online version of this article (doi:10.1186/1756-8935-8-8) contains supplementary material, which is available to authorized users. The human central nervous system  contains billions of neurons, with hundreds of distinct types. Studies of neuronal morphology, neurotransmitter response, and single-cell electrophysiological analyses have traditionally been used to define neuronal diversity and led to estimates of different types of neurons ranging in the thousands. Recent large-scale molecular mapping studies, however, have shown an even greater complexity than that previously appreciated –3. The eSeveral previous studies have used comparative genomics approaches to identify cis-regulatory modules (CRMs) in both developing and mature CNS, based on the fact that these are frequently conserved across species –7. More While these advances have generated large numbers of potential CRMs, there are many reasons to suspect that this list of candidates is not yet comprehensive. Although many CRMs show substantial sequence conservation among species, recent estimates suggest that nearly 40% of CRMs active in the mouse are not active in humans, despite their conserved sequence, highlighting the limitation of inferring function from comparative sequence methods alone , 16. In To generate a more comprehensive view of CRMs, the use of DNase I hypersensitivity mapping at the genome scale (DNase-seq) has emerged as a powerful approach –22. DNasin vivo in the developing and mature mouse brain and three specific regions, the cerebral cortex, the cerebellum, and the developing neural retina. By comparing CNS DHSs with DHSs active in other mouse cell lines and tissues, we were able to delineate a core ‘regulome’ for the CNS. We were also able to carry out an analysis of transcription factor binding motifs in CRMs active within the developing and mature retina to identify stage-specific transcriptional regulators and confirmed that a number of these potential CRMs display enhancer activity in vitro and in vivo. Overall, our results demonstrate the power of genome-wide DNase I mapping to provide answers to questions of neuronal diversity, brain evolution, and the cis-regulation that underlies these processes.We therefore undertook a genome-scale, high-resolution mapping of accessible chromatin using DNase-seq to identify CRMs utilized We carried out DNase-seq  mouse brain, DNase I-hypersensitive regions align with ChIP-seq peaks from the EMCODE project [The brain and retina DHSs overlapped with many previously identified cis-regulatory regions of the genome . An exam project  for mark project , with thDscam, Dscaml1, complexins, contactins [To define DHSs unique to the CNS, we compared DHSs from the mature whole brain and mature retina with those from other mouse cell and tissue types  . This contactins , 27) andOlig1 and Olig2 genes, along with the DNase I signal and hotspots for E14.5 brain. There is a good correspondence between the H3K27ac, the P300, and the DNase I hypersensitivity. However, there are also some regions where one P300 ChIP study shows a peak that corresponds with a DNase I hotspot which is not present in the other P300 study. There are other regions with DNase I hypersensitivity that are also identified in both the P300 ChIP studies, but not with the H3K27ac ChIP-seq. Thus, there appears to be good agreement with our DNase data and other well-characterized marks of enhancers, but the DNase I signal encompasses a wider range of potential regulatory elements.The vast majority of DHSs in the CNS, CNS-core, and all CNS subregions are located either in introns or distal to gene transcription start sites (TSSs) and may be acting as remote enhancers . To deteOverall, comparing brain DHSs from E14.5 mouse to the H3K27ac ChIP-seq data at the same age . Thus, while DHSs provide an effective method for identifying putative enhancers, they are no better than H3K27ac ChIP-seq at discriminating those elements that are not confirmed by transgenic analysis.Neurod1 locus that is not present in the cerebellum  of developing and mature CNS samples Figure A. As expm Figure B even ths Figure C and agaWe hypothesized that the transcription factors regulating neuronal and glial gene expression might differ between these region-specific DHSs and reflect the specific complement of transcriptional regulators in these different brain regions. To test for enrichment of consensus binding sites in the region-specific DHSs, we created sets of DHS peaks that are (1) present in the retina, but not in the cerebellum or cortex; (2) present in the cortex, but not in the retina or cerebellum; and (3) present in the cerebellum, but not in the retina or cortex. These sets of DHSs were analyzed with the MEME suite (DREME and CentriMo –35), andRorb, Kcnn2, and Etv1, in the cerebral cortex  where the developmental processes are more synchronous.Neurog2 and Olig2, two transcription factors expressed in retinal progenitors and necessary for neurogenesis [Rho, Guca1a, and Guca1b, genes expressed specifically in developing and mature photoreceptors [We observed clear developmentally dynamic patterns of chromatin accessibility surrounding two key developmental genes and two genes highly expressed in mature retina Figure A. Neurogogenesis , displayogenesis ). The reeceptors , 46, 47:eceptors ).Figure k-means clustering to group all DHSs in the retina based on shared patterns of accessibility across P0, P7, and adult stages , we find that the majority of CRX and NRL binding occurs in DHSs active in late-enriched stages of retinal development  of motifs for various TFs known to be involved in retinal development and neurogenesis. The cluster enrichment of a selected subgroup of TFs important for retinal development is displayed in Figure Lhx2, Pou3f2) [Otx2, Crx) [P < 0.01; Additional file Rax, Otx2, Crx). Interestingly, many motifs enriched in temporally dynamic as well as retina-specific DHSs are recognized by TFs with as yet unexplored roles in retinal development.Given the enrichment of temporally patterned retina DHSs near specific classes of genes, we next sought to identify which transcription factors (TFs) could be controlling these patterns by analyzing TF binding motifs within the  Pou3f2) , 50, whex2, Crx) . When weOverall, these results show that temporal dynamics of specific developmental processes are reflected in temporal changes in chromatin accessibility surrounding genes involved in these processes. Furthermore, examination of TF motifs within temporally patterned DHSs can be used to identify the transcription factors that regulate these processes.Otx2 locus due to the critical role for this gene in retinal development and the enrichment of its binding motif in retina-specific DHSs [Otx2 locus contains many developmentally dynamic DHSs, which show either an increase or a decrease in accessibility across stages and were assigned to early or late clusters in our k-means analysis. The DHS map , which is highly active in P0 retina but has decreased activity in the P7 and adult retina, and Otx2 DHS-15 (approximately 79 kb upstream of Otx2), which shows the opposite pattern  for P300 binding  and othcer  reporter construct in retinal tissue. Each DHS with a P300 ChIP signal above the positive control  was cloned into a minimal promoter vector and was electroporated on the day of birth along with a transfection control plasmid constitutively expressing nuclear-CHERRY. We assayed reporter expression after 1 day (in vitro) or after 7 days (in vivo); the TATA box minimal promoter was used as a negative control . However, we found that several Otx2 DHSs robustly drive expression of the GFP reporter in the retina. Otx2 DHS-4, for example, drives GFP expression primarily in the progenitor zone (middle and outer retina) at P0  . Betweeet al.[We asked whether functional conservation of CNS regulatory elements between mouse and human varies by brain region. Vierstra et al. found thP value) are genes associated with NMDA receptors and their regulation. Figure Grin2a and Grin2b), critical for neuroplasticity and memory, and the neurexins (Nrxn1 and Nrxn3), highly differentially spliced genes involved in synapse formation [Comparing the proportions of shared DHSs within CNS regions revealed significant differences among their distributions . In light of recent evidence that disease-associated polymorphisms, identified by GWAS studies, are concentrated in DHSs [By comparing the DHSs from the brain and retina, we were able to delineate a core set of DHSs common to the CNS, the majority of which are shared between mouse and human, representing the accessible chromatin of the brain and retina. Not surprisingly, the core set is enriched for DHSs near neural genes, like neurotransmitter receptors and ion channels, and provides a new resource for studies of their regulation. In addition, there appear to be many regulatory elements that potentially regulate expression of neural-expressed genes involved in brain disorders  and retinal cell type (photoreceptor vs bipolar cell). It is likely that the same will hold true for other brain regions, and thus, the DHSs we have identified could potentially be involved in regulating gene expression in subsets of neurons within these regions. Further testing of candidate elements in transgenic assays will be needed to validate this possibility.In addition to the core set of DHSs common to all regions of the CNS, when we compared different regions of the CNS, we were able to identify DHSs unique to each region. Genes expressed in many regions of the CNS, like Opn1sw) are only present in these cells or are present in other cells in the population that do not express these genes. Further experiments involving isolation of individual cell populations of the brain or retina will be needed to determine the specificity of these DHSs.It is interesting that we are able to detect DNase I-seq peaks at the promoters and enhancers of genes expressed in only a subset of the total cell population within a given region of the CNS. There is a good correlation between the chromatin accessibility and the expression of a gene in a particular tissue, and in this report, we find that this correlation extends to regional differences within the CNS. The data also suggest that this correlation might extend to the level of specific cell types within the CNS regions. However, at this point, we do not know whether the DHSs near genes expressed in minority neuronal populations  were used for all experiments, housed in the University of Washington Department of Comparative Medicine. All experiments were carried out according to approved protocols by the University of Washington Institutional Animal Care and Use Committee (IACUC) protocol #2448-08.3 pieces) and suspended in 3 mL homogenization buffer . Tissue was Dounce-homogenized with 5 to 10 strokes with loose, type A pestle (brain tissues) or with 5 and 25 strokes of loose and tight pestle, respectively (retina tissues), followed by filtration through a 100-μm filter. Nuclei suspension was then cryopreserved by addition of DMSO to 10%, controlled freeze to -80°C, and subsequently stored in liquid nitrogen. After thaw and before DNase I treatment, buffer was exchanged with 15 mL sucrose buffer , and nuclei were collected by centrifugation  and resuspended in 10 mL fresh sucrose buffer. Nuclei were passed through a 20-μm filter and centrifuged . The pelleted nuclei were washed with 10 mL of buffer A  and resuspended to two million nuclei per mL.Retinal and brain tissue was dissected  and subsequently treated with proteinase K and RNase A at 55°C. Small (<750 bp) DNA fragments were recovered via sucrose ultracentrifugation and subsequently end-repaired and ligated with Illumina-compatible adaptors. A detailed description of the mapping of DNase I-hypersensitive sites is available in reference [Nuclei were incubated at 37°C for 3 min with limiting concentrations of DNase I enzyme in buffer A supplemented with Caeference .Sequence reads (36 bp) were mapped to the human (GRCh37) and mouse (NCBI37) genomes using bowtie v 0.12.7) . Sequenc2.7 . Seqhttp://www.uwencode.org/proj/hotspot/) was used to detect distinct regions of chromatin accessibility. Localized enrichments of sequence tags are identified based on a binomial distribution model computed against a local background model surrounding each tag. Regions of enrichment are termed hotspots and are further internally scanned for the local maxima; 150-bp windows around the local maxima are called as peaks. To generate a false discovery rate , simulated datasets are generated based on random reads at equal sequencing depth to each sample dataset and the simulated data was subsequently scanned for hotspots to determine an estimate FDR. Dataset quality is also measured using a SPOT  score defined as the percentage of tags that fall into hotspots (http://www.uwencode.org/proj/hotspot/).The Hotspot algorithm  for each TF and used a cumulative hypergeometric distribution to calculate a P value for the enrichment of that TF’s motifs within DHSs assigned to specific k-means clusters (or retina-specific DHSs) compared to the overall prevalence of its binding sites in master-list DHSs. P values were corrected for multiple testing using the Bonferroni method.Putative transcription factor binding sites were identified by scanning the entire mouse genome for consensus sequences using the FIMO tool from the MEME Suite (version 4.6)  with defEf1a-promoter driving nuclear CHERRY red fluorescent protein. Retinal explants (dissected retina tissue cultured in vitro) were electroporated in PBS with 2 μL DNA (2.33 g/μL ECR-GFP plus 1 g/μL mCherry control plasmid) using an ECM830 Square Wave Electroporation System  with the following settings: 35 V, 5 pulses, 50 ms/pulse. Retinas were then cultured in six-well tissue culture plates with 1 mL of Neurobasal media, with 1% FBS , 1 mM l-glutamine , N2 (Invitrogen), and 1% penicillin-streptomycin (Invitrogen) for 24 to 36 h. Brains were dissected from mice and sliced with a McIlwain Tissue Chopper  set to 300-μm sections. Individual brain sections were electroporated as above and cultured on 0.4-μm Millicell cell culture inserts  in six-well tissue culture plates containing 1 mL neurobasal media (above) for 36 h. For in vivo electroporations, P0 mice were anesthetized on ice and 1 μL DNA (2.7 g/μL ECR-GFP and 0.3 g/μL mCherry control plasmid) was injected into the vitreous of the eye . Electroporation was performed with head paddles connected to the ECM830 Square Wave Electroporation System with the following settings: 90 V, 5 pulses, 50 ms/pulse, 950-ms intervals. Mice were revived at 37°C and retinas were harvested 7 days later.Reporter plasmids contain the experimental DHS sequence immediately upstream of a minimal promoter containing TATA box driving nuclear GFP; control plasmids contain the Retinas or brain explants were fixed with 2% paraformaldehyde. Retinas were cryoprotected in 30% sucrose/PBS at 4°C overnight, embedded in OCT compound , and sectioned at 12 μm using a cryostat. Immunohistochemistry (IHC) was carried out using chicken anti-GFP , rabbit anti-RFP , biotinylated anti-Otx2 . All secondary antibodies were from Life Technologies  or Jackson ImmunoResearch  and used at 1:500. Imaging was performed using an Olympus FluoView confocal microscope .Otx2 ChIP: P0 and adult retinas were digested with papain into single cell suspension and fixed with 0.5% formaldehyde, 10 min, rotating at room temperature (RT). Sonication  was performed: 12 pulses of 100 J, 35 amplitude with a 45-s offset at 4°C. Immunoprecipitation performed with 20 μL anti-rabbit IgG magnetic beads  and 2 μg goat anti-hOTX2 antibody (R&D Systems BAF1979) or 2 μg goat IgG (R&D Systems AB-108-C) against chromatin from 1e6 cells (P0) per IP according to LowCell# ChIP Kit . DNA sequences were quantified with Bio-Rad CFX96 thermocycler using SsoFast EvaGreen Supermix  according to the manufacturer’s instructions. All values were expressed as a percentage of input DNA averaged from at least three biologically independent experiments.MW is a graduate student in Molecular and Cellular Biology at the University of Washington. JB was a post-doctoral fellow in the Department of Biological Structure at the University of Washington and is now an assistant professor in Ophthalmology at the University of Colorado, Denver. AL was a post-doctoral fellow in the Department of Biological Structure at the University of Washington and is now an assistant professor at the University of California, Davis. JV and KS are post-doctoral fellows in the Department of Genome Sciences at UW. RT, PS, RS, and TC are scientists in the Department of Genome Sciences at UW. RH is a research associate professor of Medical Genetics at UW. MB is an associate professor of Pediatrics at the University of Washington Medical School and a member of the Fred Hutchinson Cancer Research Center. JS is a professor of Genome Sciences at the University of Washington. TR is a professor of Biological Structure at the University of Washington.Additional file 1: Table S1: Sequencing and accessibility information of DNase I hypersensitivity sequencing of samples analyzed in this manuscript. Basic tissue, sequencing and data accessibility information in tabular format. (DOCX 118 KB)Additional file 2: Figure S2: Genomic partition, Gene Ontology Molecular Function enrichment, and Gene Ontology analysis. (A) Genomic partition of the cortex, cerebellum, and retina DHSs. Distribution of DHSs present in mature cerebral cortex and cerebellum brain regions and mature retina relative to genomic features. (B) Gene Ontology Molecular Function enrichment of CNS-core DHSs. (C) Gene Ontology analysis from GREAT, Molecular Function category, of the CNS-core set of DHSs showing enrichment near neuronal genes. (JPEG 262 KB)Additional file 3: Table S2: Genomic intervals for ‘CNS core regulome’ DNase I-hypersensitive sites. Genomic interval file in tabular format for DHSs common to CNS, but not other tissues. (TXT 152 KB)Additional file 4: Figure S4: DNase I hypersensitivity corresponds to enhancer regions identified in previous studies and confirmed in transgenic mice for the VISTA Browser project . Three dAdditional file 5: Figure S5: Sets of enriched DHSs between CNS tissues and CentriMo analysis. (A) The sets of DHSs enriched for the cerebral cortex, cerebellum, or retina were analyzed with the MEME suite (DREME and CentriMo), and we found a distinct pattern of enrichment for transcription factor motifs in the different sets. EGR1 sites and bHLH transcription factor sites were highly enriched in the cerebral cortex, whereas Crx sites were over-represented in DHSs from the retina. (B) CentriMo analysis for EGR1 and E-box sites in cerebral cortical DHSs shows enrichment near the central regions of these DHSs for the over-represented transcription factor motifs, consistent with their role as enhancers. Below: GO enrichment terms associated with EGR1 and E-box sites in the cortex. (JPEG 284 KB)Etv1 and (B) Kcnn2 and accompanying in situ data . Black arrows point to cell layers with positive (dark purple) in situ signal. In situ data from 2014 Allen Institute for Brain Science. Available from http://mouse.brain-map.org/[Opn4 (ganglion cells), Th (amacrine cells), Gnat2 and Opn1mw (cone photoreceptors). Tan box indicates DHS at the promoter of each gene. (PDF 4 MB)Additional file 6: Figure S6: DNase I hypersensitivity at the promoters of cell type-specific genes. DNase I landscape ; cerebellum, Cbm (green)) surrounding the gene bodies of (A) -map.org/. (C) DNaAdditional file 7: Table S3: Genomic intervals for retinal specific DNase I-hypersensitive sites. Genomic interval file in tabular format for DHSs unique to the mouse retina, but not other tissues. (TXT 69 KB)Additional file 8: Figure S8: Gene ontology enrichment of retinal specific DHSs. Gene ontology (A) biological process and (B) disease ontology categories for genes associated with retina-specific DHSs as determined by GREAT analysis. (PDF 3 MB)Neurog2 and Olig2, and three photoreceptor genes expressed in the mature retina: Rho and Guca1a/b. RNA-seq data previously generated by [Additional file 9: Figure S9:  categories for genes associated with DHSs within each k-means temporal cluster of retinal DHSs  as determined by GREAT analysis. E, early clusters; M, mid-clusters; L, late clusters; O, other cluster groups; C, constitutive cluster group. (JPEG 579 KB)Additional file 10: Figure S10: Gene ontology analysis of genes near Nrl and Crx. RNA-seq data previously generated by [Additional file 11: Figure S11: ) indicated as color intensity for each transcription factor (rows) within each temporal cluster group (columns) from P0, P7, and adult stages of mouse retina. E, early clusters; M, mid-clusters; L, late clusters; O, other cluster groups; C, constitutive cluster group. (JPEG 694 KB)Additional file 12: Figure S12: Transcription factor binding motif enrichment within temporal clusters of total retinal DHSs. Transcription factor binding motif enrichment . (DOCX 64 KB)Otx2 DHS reporter expression and transcription factor binding.  Panels show representative images of expression from empty minimal reporter constructs (TATA) with no Otx2 DHS insert (green) co-immunostained for transfection control plasmid  and endogenous OTX2 (white). (A) Expression from TATA in electroporated P0 retinal explants cultured 24 h in vitro (N = 3). (B) Expression from TATA in retinas electroporated in vivo at P0 and harvested at P7 (N = 3). ONL, outer nuclear layer; NBL, neuroblastic layer; GCL, ganglion cell layer; INL, inner nuclear layer. (C) Quantification of the percentage of GFP+ cells that co-express OTX2+ for Otx2 DHSs, non-specific control plasmid (TATA), and transfection control (CHERRY) in P7 retina in vivo (N = 2 to 5). *P < 0.01; error bars ± SD.  Chromatin immunoprecipitation for OTX2 or IgG control from P0 (D) and Adult (E) whole retina tissue shown as a percentage of input DNA. Assayed regions are Otx2 DHSs 2, 4, 12, and 15 with the MyoD promoter serving as a negative control. N = 3 ± S.D. *P < 0.05; **P < 0.01; ***P < 0.001, ****P < 0.0001. Error bars ± SD. (F) Chromatin immunoprecipitation for P300 or IgG control shown as a percentage of input DNA for Otx2 DHSs, the Otx2 promoter (pOtx2), previously described enhancers , and a positive control promoter (pIrbp). N = 2 to 4 ± S.D. (G) Inset from Figure Otx2 DHS #15 reporter construct (green) co-immunostained for transfection control plasmid  and endogenous OTX2 (white). Arrows indicate examples of triple positive labeled cells. (PDF 3 MB)Additional file 14: Figure S14.: N = 2 to . *P < 0.P values resulting from pairwise chi-squared on DHS distribution into conservation categories. Related to Figure Additional file 15: Table S5: Additional file 16: Table S6: Mouse DHS alignment and conservation with human DHSs. The numbers of mouse DHSs falling into each category of alignment and DHS conservation in humans. (DOCX 77 KB)


Ribosomal RNA (rRNA) gene (rDNA) transcription by RNA Polymerase I (Pol I) drives cell growth and underlies nucleolar structure and function, indirectly coordinating many fundamental cellular processes. The importance of keeping rDNA transcription under tight control is reflected by the fact that deranged Pol I transcription is a feature of cancer and other human disorders. In this review, we discuss multiple aspects of rDNA function including the relationship between Pol I transcription and proliferative capacity, the role of Pol I transcription in mediating nucleolar structure and integrity, and rDNA/nucleolar interactions with the genome and their influence on heterochromatin and global genome stability. Furthermore, we discuss how perturbations in the structure of the rDNA loci might contribute to human disease, in some cases independent of effects on ribosome biogenesis. In human cells 300 copies of the rRNA genes are arranged in repeated arrays located in nucleolar organizer regions (NORs) on the short arms of the acrocentric chromosomes-5. Pol IrDNA transcription underpins the structure of the nucleoli, which form around active clusters of rDNA. Howevervice versa[Recent evidence also suggests that the epigenetic status of the rDNA repeats and the integrity of the nucleolus can modulate cellular homeostasis beyond ribosome biogenesis and nucleolar stress. Spatial organization of the genome around the nucleoli and the interactions of specific chromatin domains with the nucleoli are both suggested to affect the various functions of the nucleoli and ice versa,52. Furtice versa. Somewhaice versa. Epigeneice versa, with thice versa,56-58.This review provides an overview of the mechanisms that regulate rDNA transcription. We will discuss the spatial organization of the nucleoli around actively transcribed rDNA and their potential functional interactions with the rest of the genome, and the notion of rDNA instability promoting genome-wide instability and influencing cellular functions such as, maintainance of heterochromatin, DNA damage response and aging. Further, we present our current knowledge of human diseases specifically associated with deregulated Pol I transcription.In addition to RNA Polymerase I, optimal rRNA gene transcription requires a number of accessory factors that facilitate Pol I recruitment, initiation, promoter escape, elongation, termination and re-initiation,59,60. PDespite the high demand for rRNA synthesis, only a subset of rRNA genes is transcribed at any given time. In mammalian cells, rDNA chromatin can exist in at least four distinct states,79. The The nucleolus is the subnuclear site of ribosome biogenesis and its formation around active NORs requires ongoing rDNA transcription. The structure of nucleoli is highly dynamic and is tightly coordinated with cell cycle progression. Nucleoli disassemble at the onset of mitosis coinciding with inactivation of Pol I transcription and reassemble during telophase as rDNA transcription is reinitiated. The rate of rRNA gene transcription reaches a maximum in S- and G2 phases, is halted at the onset of mitosis and slowly reactivated as cell enter G1. Cell cyOnce established, the nucleolus comprises three major structural and functional subcompartments defined by their texture and contrast using electron microscopy, the fibrillar centre (FC), the dense fibrillar component (DFC) and the granular component (GC),24,94,95The organization, size and protein composition of the nucleoli change dramatically during the cell cycle and under different cellular conditions, including stress and viral infections,98-102. Nucleolar integrity is tightly linked to rRNA gene transcription and ribosome biogenesis. Downregulation of Pol I transcription seems to be a major strategy to maintain cellular homeostasis under adverse growth conditions or metabolic deficits,109-113.Virtually any major cellular perturbation that uncouples the processes driving ribosome synthesis and assembly results in the activation of a regulated series of events that are controlled through the nucleoli, which activate cell cycle checkpoints leading to cell cycle arrest or cell death,115-117.The structure of the genome is highly dynamic and is closely coupled with gene regulation. It is now well accepted that the nucleus is organized into chromosome territories and transcription factories in which functionally related genes cluster together allowing their concerted regulation. Furthern/(CATTC)n-types) and genes belonging to the zinc finger, olfactory receptor and immunoglobulin gene families[The periphery of the nucleolus contains satellite DNA repeats, which are thought to be involved in the formation of perinucleolar heterochromatic domains surrounding the nucleolus as a dense shell. CpG-metfamilies. In addifamilies. Similarfamilies.Genes enriched in the NADs are characterized by repressive histone marks and a lack of gene expression. Indeed, the NADs have been proposed to serve as a distinct nuclear space with a primary function in maintaining repressive chromatin states,141. ForTaken together, the observed enrichment of heterochromatic regions and transcriptionally repressed genes in perinucleolar domain suggests that the NADs represent a novel mechanism of gene silencing. However, more experiments need to be performed to completely rule out the possibility of nucleolar localization being a mere consequence of inactive gene expression. Additionally, the composition of NADs in disease and their biological relevance are not well understood.The repetitive nature of the rDNA leaves them vulnerable to loss or gain of rDNA copies through a high rate of recombination,152, altThe importance of rDNA copy number and rDNA integrity is implied by the existence of a well-regulated maintenance system in yeast which keeps rDNA copy number at a uniform level, as well as the tight regulation of rDNA recombination. During Drosophila, general heterochromatin content appears to be directly influenced by the number of rDNA copies suggesting that rDNA instability may affect heterochromatin maintenance[Several studies in yeast suggest a strong correlation between rDNA copy number and genome integrity,162. In ntenance. Consistntenance. The relntenance,83.In addition to the role of rDNA in regulating heterochromatin, silent rDNA repeats are required for DNA damage repair, an essential pathway for preventing genome instability. The influence of rDNA on DNA damage has been shown utilizing yeast strains that have reduced rDNA copy numbers, in which most or all copies are actively transcribed,164. TheUpregulation of Pol I activity is common in diseases associated with profound changes in cellular growth, such as cardiac disease and cancPol I activity is frequently deregulated in cancers by oncogene activation and/or tumor suppressor inactivation,18,20 anFurther, deregulation of rDNA transcription contributes to the pathology of several rare human genetic disorders (reviewed in). These Cohesinopathies are another group of human congenital disorders associated with deregulated Pol I transcription. Cornelia de Lange and Roberts Syndrome are associated with mutations in genes encoding either regulators or structural components of the cohesin complex, which is critical for sister chromatid cohesion, chromosome segregation during S phase, chromosome condensation, DNA damage repair and gene regulation including Pol I transcription of the rRNA,177,178.Downregulation of rRNA synthesis and nucleolar size has also been observed during aging,182. AltThe evolving paradigm of the nucleolus being a key regulator of cellular homeostasis implicates nucleolar stress resulting from deregulation of rDNA transcription in the development of these diseases. Unquestionably, further mechanistic investigations are required in order to examine how perturbations of rDNA stability and function, independent of ribosome biogenesis and nucleolar stress, are involved in the aetiology of these diseases.Taken together, this review advocates a role for the nucleoli in genome organization and the regulation of gene expression beyond its classic role in ribosome biogenesis and nucleolar stress response. Variation in rDNA copy number alters the ratio of active to silent rDNA repeats, which in turn can alter heterochromatin content. As such, changes at rDNA loci could affect gene expression and alter global genomic stability driving an imbalance in cellular homeostasis leading to disease. Analysis of rDNA/nucleolus interactions with chromatin domains concomitant with genome-wide gene expression analyses under various cellular conditions are the next steps necessary to understand rDNA/nucleolar functions at genome-wide as well as system levels.ATM: CCTF, CCCTC-binding factor; DFC: Dense fibrillar component; FC: Fibrillar centre; GC: Granular component; HR: Homologous recombination; IGS: Intergenic spacer; LADs: Lamina associated domains; lncRNA: Long noncoding RNA; NADs: Nucleolar associated domains; NOR: Nucleolar organizer region; NoRC: Nucleolar repressive complex; NoDS: Nucleolar detention sequence; PIC: Preinitiation complex; Pol: RNA polymerase I; rDNA: Ribosomal gene; RNPs: Ribonucleoproteins; RP: Ribosomal proteins; rRNA: Ribosomal RNA; SL-I: Selectivity factor 1; UBF: Upstream binding factor.The authors declare that they have no competing interests.JD and ES contributed equally to writing this review. RDH provided substantive intellectual input. All authors read and approved the final manuscript.ES received her PhD from Monash University  in 2003 and was awarded a Cancer Research UK Postdoctoral Fellowship to undertake research in Dr. Gordon Peters laboratory at the London Research Institute . Since 2006, she worked as a Senior Research Scientist in the Growth Control Laboratory at the Peter MacCallum Cancer Institute . Her studies examine epigenetic regulation of ribosomal gene (rDNA) transcription  with a focus on the use of next generation sequencing to characterize the chromatin state of the rDNA. Further, she made seminal contributions in identifying deregulation of Pol I transcription as a requirement for malignant transformation and the therapeutic efficacy of targeting Pol I transcription in cancer .


Ifnb1) in activated macrophages is only partially understood. Here we report that TRIM33 deficiency results in high, sustained expression of Ifnb1 at late stages of toll-like receptor-mediated activation in macrophages but not in fibroblasts. In macrophages, TRIM33 is recruited by PU.1 to a conserved region, the Ifnb1 Control Element (ICE), located 15 kb upstream of the Ifnb1 transcription start site. ICE constitutively interacts with Ifnb1 through a TRIM33-independent chromatin loop. At late phases of lipopolysaccharide activation of macrophages, TRIM33 is bound to ICE, regulates Ifnb1 enhanceosome loading, controls Ifnb1 chromatin structure and represses Ifnb1 gene transcription by preventing recruitment of CBP/p300. These results characterize a previously unknown mechanism of macrophage-specific regulation of Ifnb1 transcription whereby TRIM33 is critical for Ifnb1 gene transcription shutdown.Despite its importance during viral or bacterial infections, transcriptional regulation of the interferon-β gene  in macrophages is a critical immune event. Here, Ferri et al. show that, at late phases of macrophages activation, TRIM33 bound to a distal repressor element suppresses Ifnb1 transcription by preventing recruitment of CBP/p300. Ifna and 1 Ifnb genes in mice) gene transcription in myeloid and non-myeloid cellsIfn gene transcription is shut down3In response to viral or bacterial infections, germ line-encoded pattern recognition receptors induce type I interferon  to form the Ifnb1 enhanceosome. This enhanceosome recruits CBP/p300 for acetylation of histone H3 and permits recruitment of chromatin-remodeling complexes for initiation of transcriptionAmong type I Ifnb1 gene transcription has only been observed in the modulation of the transcriptional activity of IRF3. In monocytes, a PU.1-binding site located in the enhanceosome is constitutively bound by a IRF8/PU.1 myeloid complex that creates a preformed activation complex. This myeloid complex can recruit IRF3 through direct IRF3/IRF8 interaction and is involved in the rapid transcription of Ifnb1 observed in monocytes after pathogenic stimulationIfnb1 transcriptionIfnb1 gene transcription in fibroblasts89Ifnb1 transcription cycle and of monocyte/macrophage-specific regulation of Ifnb1 transcription.Monocyte/macrophage-specific regulation of The tripartite motif (TRIM) protein family is characterized by a shared N-terminal structure consisting of a RING, two B-box domains, a coiled-coil domain and, for most of the TRIM family members, an E3 ubiquitin ligase activityIfnb1 gene transcription after recruitment to an Ifnb1 regulatory sequence.A large number of TRIM proteins are innate immune enhancers and act at multiple levels in signalling pathways, including production and action of IFN-β to confer antiviral and antibacterial cytokine production17131313Ifnb1 during the late phases of macrophages activation. Our results indicate that TRIM33, recruited by PU.1 on a distal regulatory element of the Ifnb1 gene, regulates Ifnb1 enhanceosome loading and shuts down Ifnb1 gene transcription during the late stages of lipopolysaccharide (LPS) activation of macrophages by preventing recruitment of CBP/p300.We have previously shown that TRIM33 can interact with PU.1 to repress its transcriptional activity during haematopoiesis and suggested that TRIM33 may regulate myeloid fate and have a role in macrophagesTrim33fl/fl mice were crossed with mice expressing the Cre recombinase from the endogenous Lysozyme2 locus (LyzCre) to generate Trim33−/− mice. Trim33−/− mice did not display developmental abnormalities and were healthy. PCR genotyping of purified haematopoietic cell populations showed deletion of Trim33 in monocytes, bone-marrow-derived macrophages (BMDM), peritoneal macrophages (PMs) and neutrophils but not in myeloid progenitors, nor in B lymphocytes  or Trim33−/− BMDM and PM, upon TLR4 activation by LPS or upon TLR3 activation by poly(I:C). These two activation protocols mimic bacterial (LPS) or viral (poly(I:C)) infections. A rapid early increase of Ifnb1 mRNA levels was observed in WT and Trim33−/− BMDM or PM activated by LPS (Ifnb1 mRNA levels decreased from 2 h in WT BMDM (or 4 h in WT PM) and returned to basal levels by 6 h in WT BMDM and PM. In contrast, although Ifnb1 mRNA levels decreased between 2 and 4 h, Ifnb1 mRNA levels began to rise significantly by 6 h and remained high 24 h after LPS activation in Trim33−/− BMDM or PM  with Ifnb1 mRNA levels being much higher at all late time points in poly(I:C) treated Trim33−/− BMDM compared with WT (Ifnb1 mRNA levels in Trim33−/− BMDM activated with LPS correlated with increased levels of secreted IFN-β (−1) which did not activate Ifnb1 in WT BMDM, we showed, in LPS-activated Trim33−/− BMDM, an increased expression of genes whose transcription is dependent on IFN-β and occurs late during LPS activation   , indicatIfnb1 expression were addressed in ‘rescue' experiments in which TRIM33 protein was added back to LPS-activated immortalized Trim33−/− macrophages (iM)Trim33−/− iM bore the expected deletion of Trim33 gene and these cells expressed no TRIM33 protein  construct was expressed in Trim33−/− iM and cells were stimulated with LPS , the ubiquitin ligase activity (flag-ΔUb) or the Smad-interaction domain (flag-ΔSmad) were expressed in Trim33−/− iM . Recently, the human homologue of ICE, named L2, has been shown to be a virus-inducible enhancer of Ifnb1 in non-myeloid cells where it displays promoter and enhancer activitiesTRIM33 ChIP-seq data obtained from the RAW 264.7 murine macrophage cell line or from BMDM treated with LPS for 24 h showed a single TRIM33 peak at the fnb1 TSS . InteresIfnb1 promoter and at ICE was constitutive and detected in WT and Trim33−/− BMDM  following LPS activation  were studied. Whereas TRIM33 deficiency did not modify kinetics of Ifnb1 mRNA levels in response to poly(I:C) in normal NIH3T3 cells (Ifnb1 mRNA levels in NIH3T3 PU.1+ cells treated with poly(I:C) but did not result in sustained expression of Ifnb1 during the late stages of poly(I:C) activation  or the fragment containing the Ifnb1 promoter alone as a control sequence (Ifnb1 Prom-Luc). Transcriptional activity of ICE-reporter (ICE-Ifnb1 Prom-Luc) showed four to tenfold less activity than the promoter reporter in LPS-activated RAW 264.7 cells indicating that ICE functions as a cis-acting transcriptional repressor element in RAW 264.7 cells  activation. In LPS-activated RAW WT cells, Ifnb1 mRNA levels increased during the first 4 h and then gradually decreased , kinetics of Ifnb1 mRNA levels were similar  activation. In the two clones high and sustained Ifnb1 mRNA levels were found but only after LPS or poly(I:C) activation, with the highest increase being obtained during the late phase of activation  -binding on ICE 10 h after LPS activation  were performed in WT and Trim33−/− BMDM before and after LPS activation. ICE  and blue (WT) curves). These results indicate that ICE interacts with the Ifnb1 proximal region in a constitutive and TRIM33-independent manner, but that this interaction is strengthened following LPS stimulation.To investigate how TRIM33 bound to ICE, that is located 15 kb upstream of the ion. ICE , top andnb1 gene , bottom,Ifnb1 transcription, we studied the effect of TRIM33 deficiency on temporal binding of p65 and c-jun to the enhanceosome as these two factors are major regulators of its activity. As anticipated, in WT BMDM, c-jun and p65 were recruited to the Ifnb1 promoter at early times following LPS activation (1 h), but their binding returned to basal levels after 24 h . Treatment of LPS-activated WT BMDM with trichostatine A, an inhibitor of HDACs, did not result in sustained Ifnb1 expression, whereas it did alter expression of genes regulated by HDACs -activated RAW cells where both alleles of ICE are deleted using lentiviral delivery of Cas9 and gRNA indicates that ICE has a repressor activity when these cells are stimulated. Although the lentiviral system used might increase the frequency of off-target effects30+ and TRIM33− NIH3T3 cells indicates that TRIM33-mediated repression of Ifnb1 is dependent on PU.1 expression. However, in fibroblasts activated with poly(I:C), PU.1 expression associated with TRIM33 deficiency does not result in the sustained Ifnb1 expression observed in poly(I:C)-activated Trim33−/− BMDM. This result suggests that factors present in BMDM, but not in fibroblasts, are required for Ifnb1 repression during the late stages of BMDM activation. Amongst haematopoietic-restricted PU.1 partners, IRF8 and IRF3 that regulate Ifnb1 activation in BMDM6Ifnb1 repression during the late stages of BMDM activation.Transient transfection in fibroblast cell lines shows that ICE acts as an enhancer of Ifnb1. Interestingly, TRIM33 deficiency did not alter the initial activation of Ifnb1 transcription after LPS activation of macrophages but impaired repression of Ifnb1 expression during the late stages of this activation. Together with the continuous binding of TRIM33 on ICE, the complementation of the Ifnb1 phenotype in macrophages by re-expression of TRIM33 in Trim33−/− immortalized macrophages suggested a direct role of TRIM33 in Ifnb1 transcription and indicated that the activity and/or substrate availability of TRIM33 regulated Ifnb1 expression at the end of LPS activation of macrophages.Several TRIM proteins regulate production and action of IFN-β during innate immune response. TRIM6 is a positive regulator of IFN-β and is critical for IFN-β-mediated antiviral responseIfnb1 expression through modulation of CBP/p300 recruitment and/or activity at the Ifnb1 promoter. First, TRIM33 inactivation led to recruitment of CBP/p300 at the Ifnb1 promoter and enhanced H3ac. Second, CBP/p300 activity is critical for enhanced Ifnb1 transcription at late stages as chemical inhibition of CBP/p300 enzymatic activity could restore the regulated expression of Ifnb1 gene in Trim33−/− macrophages. As CBP/p300 acts as a switch to initially turn on Ifnb1 gene transcriptionIfnb1 transcription during the late stages of LPS activation of macrophages by impairing the CBP/p300 recruitment and activity at the Ifnb1 promoter. This TRIM33-dependent recruitment and activity of CBP/p300 could be direct and/or mediated by trans-acting factors such as p65 that are, in Trim33−/− BMDM, loaded onto the enhanceosome during the late phases of LPS activation. However, as PU.1 can interact with CBP/p300 Ifo/J, The Jackson Laboratory). Male and female mice of 8–12 months of age were used. Experiments were performed in compliance with European legislation and with the Ethics Committee of the French Ministry of Agriculture (Agreement B9203202).To generate deletion of −1 mouse CSF1 (Miltenyi Biotec). On day 7, BMDM were activated with LPS or poly(I:C) in IMDM supplemented with 2.5% FCS and 1% PS. For PMs, cells were collected with cold PBS and cultured for 2 h in RPMI 1640 medium (Life Technologies), supplemented with 10% FCS. All adherent cells expressed F4/80 and were considered as PMs. PM were activated with LPS or poly(I:C) in RPMI 1640 medium supplemented with 2.5% FCS and 1% PS. WT and Trim33−/− iM were generated as previously describedTrim33−/− mice were cultured in IMDM supplemented with 10% FCS and 50 ng ml−1 mouse CSF1 (Peprotech). After overnight culture, the cells were transduced with a lentiviral vector expressing the SV40 large T antigen (SFFV.Tag LV). Cells were then cultured in a chemically defined medium  supplemented with CSF1 (50 ng ml−1). Single clones were isolated that expressed F4/80, CD11b but not Gr1. The resulting clones, termed iM, were maintained in IMDM supplemented with 20% foetal bovine serum, 1% PS and 50 ng ml−1 CSF1. iM were activated with LPS in IMDM supplemented with 2.5% FCS and 1% PS. For cell lines, RAW 264.7 and NIH3T3 cells were grown in DMEM (Life Technologies) supplemented with 10% FCS and 1% PS. RAW 264.7 cells were activated with LPS in DMEM supplemented with 1% FCS. Unless otherwise indicated, LPS (Sigma Aldrich) was used at 100 ng ml−1, poly(I:C) at 30 μg ml−1, TSA (Sigma Aldrich) at 10 nM, and C646 (Merck Millipore) at 10 μM. NIH3T3 were transfected with 10 μg ml−1 poly(I:C) (Sigma Aldrich).For BMDM generation, mouse bone marrow (BM) was flushed out of the tibiae, femora and humeri using a syringe with PBS, filtered through a 70-μm mesh filter (Becton Dickinson) to remove debris and pelleted by centrifugation. Adherent cells were removed by incubating BM cells on culture dishes for 3 h in IMDM supplemented with 10% foetal calf serum (FCS), 1% penicillin/streptomycin  and 10 μM thioglycerol (Sigma Aldrich), then non-adherent cells were cultured for 7 days on culture dishes in the same medium supplemented with 25 ng mlCells were fixed with 1% formaldehyde for 10 min at 37 °C, lysed in SDS lysis buffer ) and sonicated. Supernatant was diluted 10 times in IP dilution buffer ) and immunoprecipitations were carried out overnight with specific antibodies. Immunoprecipitated chromatin was collected using Protein A Agarose/Salmon Sperm DNA beads (Millipore) and, after washing and elution, reverse cross-linking was carried out with 0.2 M NaCl at 65 °C overnight. The chromatin was then digested by 20 μg of Proteinase K (Invitrogen) for 1 h at 45 °C and isolated by phenol–chloroform extraction. PCR reactions were performed using SYBR Green PCR Master Mix (Applied Biosystems) and specific primers sequences are listed below.For sequencing, 10 ng of purified DNA from ChIP was adapter-ligated, PCR amplified and sequenced on the Illumina Genome Analyzer IIx as single-end 50 base reads following Illumina's instructions. Sequence reads were mapped to reference genome mm9/NCBI37 using Bowtie v0.12.7.3C-seq was performed as previously describedTrim33−/− BMDM supernatants were collected after LPS stimulation. ELISA was performed with LegendMax Mouse IFN-β kit (Biolegend), according to manufacturer's instructions.WT and Total RNA was extracted with the RNeasy Plus Micro kit (Qiagen) and reverse transcribed with random primers and Superscript III (Life Technologies). Quantitative PCR was performed using the Power SYBR green PCR master mix (Applied Biosystems) in the 7900HT Fast Real-Time or the StepOne PCR Systems (Applied Biosystems). Primer sequences are listed below.Trim33−/− iM, the lentiviral vector was constructed by inserting full-length flag-TRIM33 from the pSG5-flag-TRIM33  and cloned in the pSuper vector (OligoEngine) downstream the H1 promoter. H1-shRNATRIM33 fragment was then cloned in the pTRIP/ΔU3-EF1α-GFP, where the GFP coding sequence is under the control of the EF1α promoter. All the constructs were verified by sequencing. Lentiviral vector production and cells transduction were carried out as describedFor rescue experiments in M33 ref.  into theIfnb1 promoter was obtained by PCR on genomic DNA and cloned into the pGL3 Basic vector (Promega) using KpnI and XhoI sites. A 408 bp DNA fragment spanning the −15 kb PU.1/TRIM33-binding site was obtained by PCR of genomic DNA and cloned upstream of the 391 bp DNA fragment of murine Ifnb1 promoter into the pGL3 Basic vector. After sequencing of the different constructs, RAW 264.7 cells were transfected using JetPEI (PolyPlus-transfection) with the indicated constructs and the TK-renilla reporter vector (Promega), as an internal control for normalization. Twenty-four hours after transfection, cells were activated with LPS (100 ng ml−1) and luciferase activity was determined using the dual-luciferase reporter assay (Promega). Firefly luciferase activity was normalized with respect to renilla luciferase activity.A 391 bp DNA fragment of murine http://tools.genome-engineering.org), to minimize the number of off-target sites. Sequences for gRNA 5A2 and 3A are 5′- TTCTCGTTCATTGTTAGCGA -3′ and 5′- CTCTAGTTTAGACGTTTAAC -3′, respectively. Singles gRNA were then cloned into the LentiCRISPRv2 (Addgene) vector, which contains the human CAS9-coding sequence, using BsmBI cloning sites. Lentivirus were produced and co-transduced into RAW 264.7 and NIH3T3. After puromycin selection , cells were clonally isolated by fluorescence-activated cell sorting and individual clones were screened for deletion by PCR using the following primers: F5-1:5′- TGACGACAAATGTGGTACTGG -3′; F3-1:5′- CAGGCGAAAGGGAAACTAAA -3′; and R3-1:5′- TAGGAGTGGCAGATGGGAAG -3′.Two guides RNA (gRNA) were designed to delete a 1,361 bp fragment containing the −15 kb region using the CRISPR Design Tool (−/− RAW 264.7 cells were identified by PCR using the following primers: F: 5′- AGCAAAGCCGAAGAAGACAC -3′ and R: 5′- GGAAGAGGACGAGAGAACCA -3′.Deletions of 100 or 176 bp were carried out using one gRNA guide (5′- AAACACGTTAGTTGTCAGAC -3′) flanking the PU.1/TRIM33 site at the ICE and cloned into the LentiCRISPRv2 vector as above. After puromycin selection and clonal isolation, WT and ICEAll the clones were verified by sequencing of a 2 kb genomic region containing the ICE.Total proteins from BMDM or iM were extracted using lysis buffer  and complete protease inhibitor cocktail (Roche)). Proteins were separated on 3–8% Tris-Acetate gel or 4–12% Bis Tris gel (Life Technologies), transferred on a nitrocellulose membrane (Amersham) and blotted with the indicated antibodies. Following staining with HRP-coupled secondary antibodies, the proteins were detected with ECL Prime by chemiluminescence (Amersham).The following antibodies were used in western blot and ChIP: TRIM33 ; A301-059A, Bethyl for ChIP (1:100)), c-JUN ), p65 ), RNA Pol II ), H3K4me3 ), PU.1 ), p300 , AcH3 ), H3K4me1 ), Flag ), CBP ), β-actin ).; Ifnb1 EcoRI primer: 5′- AATGATACGGCGACCACCGAACACTCTTTCCCTACACGACGCTCTTCCGATCT -3′ followed by a 4nt tag and 5′- GGGTGTTCTATTTTATCAATAC -3′; Ifnb1 DpnII primer: 5′- CAAGCAGAAGACGGCATACGATTTCTAGAGTTTCCGACTCTG -3′.The following primer sequences were used in quantitative RT-PCR, ChIP and 3C-seq: Ifnb1 F: 5′- CTGGCTTCCATCATGAACAA -3′; Ifnb1 R: 5′- AGAGGGCTGTGGTGGAGAA -3′; Ifna F: 5′- GGATGTGACCTTCCTCAGACTC -3′; Ifna R: 5′- ACCTTCTCCTGCGGGAATCCAA -3′; Hprt F: 5′- GCTGGTGAAAAGGACCTCT -3′; Hprt R: 5′- CACAGGACTAGAACACCTGC -3′; Ifnb1 promoter F: 5′- CCACCTGTTGTTCATGATGG -3′; Ifnb1 promoter R: 5′- CATTCTCACTGCAGCCTTTG -3′; Ifnb1 ICE F: 5′- CCAAGGGTTGCGTAATGAAC -3′; Ifnb1 ICE R: 5′- CCCGATCTTCAAATCCAGTC -3′; Ifnb1 +2,5 kb F: 5′- TAGCTTCCATGCCCAGTTTC -3′; Ifnb1 +2,5 kb R: 5′- CCAAAAGTATGCCCATCACC -3′; β-globin major promoter F: 5′- GAAGCCTGATTCCGTAGAGC -3′; β-globin major promoter R: 5′- CAACTGATCCTACCTCACCTTATATGC -3′; ICE EcoRI primer: 5′- AATGATACGGCGACCACCGAACACTCTTTCCCTACACGACGCTCTTCCGATCT -3′ followed by a 4nt tag and 5′- GATGGCACACCTTGAATTC -3′; ICE DpnII primer: 5′- CAAGCAGAAGACGGCATACGAGACGACAAATGTGGTACTG -3′Accession codes: High throughput sequencing data have been deposited in the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo), under accession codes GSE73322 (3c-seq) and GSE43654 (ChIP-seq).How to cite this article: Ferri, F. et al. TRIM33 switches off Ifnb1 gene transcription during the late phase of macrophage activation. Nat. Commun. 6:8900 doi: 10.1038/ncomms9900 (2015).Supplementary Figures 1-4 and Supplementary References


TCP genes has been characterised from tomato (Solanum lycopersicum). Here we report several functional features of the members of the entire family present in the tomato genome.TCP proteins are plant-specific transcription factors, which are known to have a wide range of functions in different plant species such as in leaf development, flower symmetry, shoot branching, and senescence. Only a small number of Solanum lycopersicum SlTCP genes, most of which have not been described before. Phylogenetic analysis clearly distinguishes two homology classes of the SlTCP transcription factor family - class I and class II. Class II differentiates in two subclasses, the CIN-TCP subclass and the CYC/TB1 subclass, involved in leaf development and axillary shoots formation, respectively. The expression patterns of all members were determined by quantitative PCR. Several SlTCP genes, like SlTCP12, SlTCP15 and SlTCP18 are preferentially expressed in the tomato fruit, suggesting a role during fruit development or ripening. These genes are regulated by RIN (RIPENING INHIBITOR), CNR (COLORLESS NON-RIPENING) and SlAP2a (APETALA2a) proteins, which are transcription factors with key roles in ripening. With a yeast one-hybrid assay we demonstrated that RIN binds the promoter fragments of SlTCP12, SlTCP15 and SlTCP18, and that CNR binds the SlTCP18 promoter. This data strongly suggests that these class I SlTCP proteins are involved in ripening. Furthermore, we demonstrate that SlTCPs bind the promoter fragments of members of their own family, indicating that they regulate each other. Additional yeast one-hybrid studies performed with Arabidopsis transcription factors revealed binding of the promoter fragments by proteins involved in the ethylene signal transduction pathway, contributing to the idea that these SlTCP genes are involved in the ripening process. Yeast two-hybrid data shows that SlTCP proteins can form homo and heterodimers, suggesting that they act together in order to form functional protein complexes and together regulate developmental processes in tomato.We have identified 30 The comprehensive analysis we performed, like phylogenetic analysis, expression studies, identification of the upstream regulators and the dimerization specificity of the tomato TCP transcription factor family provides the basis for functional studies to reveal the role of this family in tomato development. Antirrhinum majus, and PROLIFERATING CELL FACTORS (PCFs) from rice are plant-specific transcription factors characterized by the TCP domain, a motif encompassing a non-canonical basic-helix-loop-helix (bHLH) structure SlTCP12, n plants . As expeSlTCP12, -15 and -18 in the ripening mutants and SlAP2a transgenic knockdown plants indicates that they are regulated by key ripening regulatory genes, but does not show if this regulation is direct. In order to find out if these transcription factors could be directly interacting with the promoters of SlTCP12, SlTCP15 and SlTCP18, we used yeast-one-hybrid assays to identify transcription factors binding to the promoter fragments of the ripening related SlTCP genes. The promoter elements used were upstream of the transcription start sites and were as follows: 568-bp promoter fragment for SlTCP12, 500-bp fragment of SlTCP15 and 473-bp fragment of SlTCP18. In this assay we detected interaction between the ripening regulator RIN and the promoters of SlTCP12, -15 and -18 as well as between CNR and the promoter of TCP18 , also named CEJ1 (COOPERATIVELY REGULATED BY ETHYLENE AND JASMONATE 1). In Arabidopsis, DEAR1 expression is induced by pathogen infection [To identify more potential regulators of tomato g frames . All tranfection .SlTCP15 and SlTCP18 promoter fragments, in addition to the AtDEAR1/CEJ1 protein, are nine. SlTCP12 and SlTCP15 share additional 11 common interactions. SlTCP12 and SlTCP18 have 32 common interactions, among which one encodes a member of the DREB subfamily A-2 of ERF/AP2 transcription factor family, another ethylene responsive gene. The fact that both SlTCP12 and SlTCP18 have more common interactions compared to SlTCP12 and SlTCP15 or SlTCP18 and SlTCP15, may be due to their expression overlap during ripening.The common interactions for SlTCP12, -15 and -18. Whether the same trend can be observed with tomato transcription transcription factors may be further investigated. In the absence of a comprehensive library of cloned tomatos TFs, we speculate that the same would be observed for tomato TFs. Recent studies in Arabidopsis show that many binding sites can be occupied by transcription factors, in a dynamic manner without them necessarily being active regulators of the bound promoter [SlTCP promoters versus the tested 1379 Arabidopsis transcription factors from the REGIA collection. The results did not show significant enrichment per GO category, suggesting that these TCPs may be involved in or regulated by many different processes.This experiment demonstrates that there are a lot of Arabidopsis transcription factors binding the promoter elements of promoter . We perfArabidopsis proteins binding SlTCP12 promoter fragment are transcription factors known to be part of the ethylene signal transduction pathway. These include an ERF/AP2 transcription factor gene encoding for RELATED TO AP2 3 (RAP2.3), other genes from the ERF (ETHYLENE RESPONSE TRANSCRIPTION FACTOR) family, and ETHYLENE-INSENSITIVE3-LIKE 2 (EIL2). Several AP2/ERF proteins also bind SlTCP12, SlTCP15 and SlTCP18 independently , expressed in all tissues, but has high expression in green fruits and leaves [The Arabidopsis DEAR1, also named CEJ1 binds the promoter fragments of rs Table . LeT12 ier Table . LeT12[4ble LeT12 is a clad leaves .SlTCP18 promoter fragment. The tomato homolog of SVP, JOINTLESS (J) binds to both SlTCP12 and SlTCP18 promoter fragments  binds the ts Table . In Arabm stages , while imeristem .TCP12, -15 and -18 . AtTCP6 and -13 bind the SlTCP18 promoter, while AtTCP1, -3, -4, -8, and -19 bind the SlTCP15 promoter. The SlTCP12 promoter did not show any binding activity with Arabidopsis TCP transcription factors.The protein-DNA interaction assay performed with the Arabidopsis TF collection and the tomato promoter fragments show that there are Arabidopsis TCPs binding the promoter fragments of tomato SlTCP12, -15 and -18 by the Arabidopsis TCP transcription factors are seen also with most of their tomato SlTCP homologs. SlTCP1, -6, -9, which are class II TCP TFs, bind the class I SlTCP12 promoter . The material was stored at -80°C until further use.TCP gene sequences found in BAC sequences only, the genomic DNA sequence was taken as template for the design of RACE primers. For this purpose, RNA was isolated of tomato fruit pericarp of 7 developmental stages (7 days after pollination to red ripe). mRNA was isolated using the RNAeasy Plant MiniKit (50) (QIAGEN). All RNA’s were mixed together in equal amounts. 5’and 3’ RACE-ready cDNA libraries were made from RNA using the Clontech SMART RACE cDNA amplification kit . Full-length open reading frames were amplified by PCR using ESTs (when containing the entire orf), 5’ and 3’ RACE clones, or genomic DNA. Genomic DNA was isolated from leaves using an earlier described method [attL1 and attL2 sites in pENTR/D-TOPO using an Invitrogen pENTR™ Directional TOPO® Cloning Kits (http://www.lifetechnologies.com), producing GATEWAY Entry vectors. Ligated products were transformed into E. coli DH5α competent cells by electroporation. All entry clones were checked by sequencing analysis . (DYEnamic ET Terminator (Dett) Cycle Sequencing Kit from Amersham Biosciences, GE Healthcare). The messenger RNA sequences of the first identified 24 SlTCPs have been deposited in GenBank under accession numbers listed in Table In order to identify tomato genes putatively encoding TCP transcription factors, the Solanaceae Genomics Network (SGN)-Unigene database (for ESTs) as well as the BAC sequences deposited by the International Tomato Sequencing Project were searched using the TBLASTN algorithm with Arabidopsis TCP proteins or TCP domains as query sequence. For thus identified unigenes, EST clones, when available, were obtained from the Boyce Thompson Institute for Plant Research at Cornell University  and resequenced to confirm identity and to establish the presence or absence of 5’ and 3’ ends of an open reading frame. Where either one or both open reading frame ends were not present or where no EST was available, mRNA sequence was extended by 5’ and/or 3’ RACE, as was done for three partial cDNA sequences deposited earlier in GenBank . For thd method . All seqA. thaliana TCP proteins. Multiple sequence alignment was performed with Muscle [Tomato TCP protein sequences were compared with all 24 h Muscle  as impleh Muscle . Phylogeh Muscle  using thExtraction of total RNA was performed with the use of TriPure reagent  according to the manufacturer’s protocol. DNAase treatment was performed with DNAase I  according to the protocol. RNeasy columns  were used to purify the RNA. Quantitative and qualitative concentration measurements were performed using Nanodrop ND-1000 Spectrophotometer .Cnr, rin, nor, and the knock down SlAP2a, RNA from Breaker + 7 was isolated using an InviTrap® Spin Plant RNA Mini Kit (http://www.invitek.de). The RNA quality was checked on gel and cDNA was synthesised using TaqMan® Reverse Transcription Reagents (Invitrogen™) with 1 μg of RNA. The qRT-PCR was performed on BIORAD iQ5 using SYBR-green fluorescence dye. The program used was as described before for the TCPs qRT-PCR.For determination of transcript concentrations, quantitative real time RT-PCR was performed in two biological replicates. Briefly, 1 μg of total RNA was used for cDNA synthesis using the TaqMan Reverse Transcription Reagents kit . Primers were designed using Beacon Designer  and purchased from Biolegio . Real-Time PCR was performed in a MyIQ Single-Color Real-Time PCR Detection System  using the following program: 3 min denaturation at 94°C, 40 cycles of 15 sec at 94°C and 30 sec at 60°C, followed by a melting curve gradient to analyze the specificity of the primer pairs for a particular gene. No Template-Controls served as blanks and β-Actin was used as reference gene based on least variation observed in the 11 tomato tissues. For the ripening mutants, t-values of the 11 samples were measured in duplo and averaged, followed by calculation of the relative gene expression using the 2 -δCt method for the expression of SlTCP transcripts in the different tomato organs and the 2 -δδCt method [SlTCPs in the ripening mutants. Analysis of the reaction efficiency was performed with the LinRegPCR program [Sequences of the primer pairs used are listed in Additional file t method  for the  program .MATα) and all prey vectors into strain PJ69-4a  and pADGAL4 prey vector . The bait vectors were transformed into yeast strain PJ69-4α (4a (MATa ) and selhttp://www.clontech.com). This system uses the antibiotic Aureobasidin A resistance as a reporter. PJ69-4A yeast strain was used for the transcription factor baits and PJ69-α for the TCP12, -15 and -18 promoter reporter constructs. The single promoter fragments were cloned into the pAbAi reporter vector, which was made Gateway compatible. Primers used for the promoter elements cloning can be seen in Additional file DNA-protein interactions between Arabidopsis transcription factors and the single tomato TCP12, TCP15 and TCP18 proteins were identified and characterized using yeast-one hybrid assay, which was based on Clontech’s Matchmaker Gold Yeast One-Hybrid (Y1H) System  [The transcription factors (TFs) library (The REGIA TF ORF Library) used for the yeast one-hybrid screen contains a set of s (ORFs) . After iThe ORFs of the single tomato candidate TFs were amplified and independently cloned in pCR™8/GW/TOPO vector (Invitrogen). All entry clones were controlled by sequencing analysis, then recombined into the pADGAL4 vector  and transformed into PJ69-4A yeast strain . In summSlTCP12, -15 and -18 in the ripening mutants Cnr, rin and the SlAP2a knockdown plants. Default settings were used with nested network style.Cytoscape v3.0.2  was usedBiNGO 2.8 plugin for Cytoscape  was usedThe data sets supporting the results of this article are included within the article and its additional files .The authors declare that they have no competing interests.Authors, who have made substantial contributions to conception, design of experiments, acquisition of data, analysis and interpretation of data: VP, RD, AB, RK. Authors who have contributed to performing experiments: VP, ML, MB, JB. Authors who have been involved in drafting the manuscript: VP and RM. Authors who have revised it critically: VP, RM, RK, GA. Authors who have given final approval of the version to be published: all. Authors who agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: all.Chromosomal location of the tomato TCP genes. “1a-d”, “19a:, and “28a” depict the extra copies of the respective genes in the tomato reference genome.Click here for fileSlTCP1, -19, and -28 found in the tomato reference genome. Table S4. Tomato TCP genes and TCP-like sequences, corresponding SGN-Unigenes, (re) sequenced EST clones, and RACE experiments performed to obtain full-length cDNAs. Table S5. Sequences of primers used for amplification of open reading frames. Table S6. Sequences of primers used for quantitative RT-PCR. Table S7. Primers used for amplification of TCP12, TCP15 and TCP18 promoters. Table S8. Primers used for the amplification of the ORF of tomato candidate genes.Additional  copies of Click here for fileScoring tables for the yeast two-hybrid experiments. Every experiment was performed in triplicate and interactions were scored as positive if growth occurred on both selection media in at least two of the three experiments.Click here for fileYeast one-hybrid results, representing the binding of the Arabidopsis transcription factor proteins and the cloned corresponding tomato homologous proteins to the tomato TCP12, -15, and -18 promoter elements independently. ”-” represents no binding, “+” represents binding.Click here for file


Bordetella pertussis, we propose an optimized mathematical framework for signal transduction through BvgAS two-component system. The response of the network output to the sensory input has been demonstrated in steady state. An analysis in terms of local sensitivity amplification characterizes the nature of the molecular switch. The sensitivity analysis of the model parameters within the framework of various correlation coefficients helps to decipher the contribution of the modular structure in signal propagation. Once classified, the model parameters are tuned to generate the behavior of some novel strains using simulated annealing, a stochastic optimization technique.To understand the switching of different phenotypic phases of  Living systems sustain in a diverse and dynamically changing niche that they have to cope with to survive. As a result, every organism adopts specialized communication machinery that helps in responding to alteration of the immediate environment. Any rapid or slow change in the surroundings is taken care of through intracellular signal transduction pathways and a number of genetic switches . The sigBordetella pertussis—a gram-negative human pathogen, shows dormancy in the atmospheric environment (∼25°C) and becomes virulent inside host (∼37°C) ..BordetelcyaA and ptxA, containing low-affinity binding sites and are activated at high level of phosphorylated BvgA. Class 2 gene fhaB possesses high-affinity BvgA binding site and produces transcripts at a very small level of BvgA-P. The unique class 3 gene bipA starts transcription at a moderately low level of BvgA-P bound at the low-affinity binding site, but get repressed at a high level of BvgA-P. frlAB belongs to class 4 gene and shows repression at high BvgA-P. The class 3 and class 4 gene expression are not observed in B. pertussis through temperature elevation. Only class 3 gene are expressed in B. pertussis for intermediate level of MgSO4. Thus, at a low level of BvgA, B. pertiussis expresses class 1, 2 and 3 minimally but class 4 genes maximally. Class 2 and 3 genes show a high level of transcription in contrast with the low level of class 1 and 4 expression at the moderate level of BvgA. Finally, at a high level of BvgA, class 1 and 2 genes show maximum and class 4 genes minimum level of expression with low level of class 3 gene expression.Depending on the transcription factor binding affinity, the variety of the Bvg-regulated genes are classified into four classes –9. ClassB. pertussis and its effect on the downstream differential gene regulation. In one of our earlier communications, we have theoretically identified the temperature mediated molecular switch that controls the signaling mechanism -ATP and the phosphotransfer kinetics was monitored for 5 min, and the relative amount of phosphorylation was noted at different time points. The relative amount of [γ−32P]-ATP thus incorporated in BvgA was quantified using phosphoimager. The total protein concentration of BvgS and BvgA used were 0.8 μM and 2.1 μM, respectively. The experimental results suggest that the mutant R152H behaves almost like the WT strain whereas the mutant T194M is heavily impaired in its ability to get phosphorylated. This happens as both arginine (R) and histidine (H) are positively charged and are good acceptor of the negatively charged phosphate group. On the other hand, when the polar threonine (T) residue is replaced by methionine (M) it heavily impairs the phosphorylation capacity.The previous subsection describes how efficiently one can decipher the set of reaction rate constants (on assay . The wilfhaB (class 2) and bipA (class 3) gene as their promoters have high affinity binding sites and show quick response even under low levels of induction [in vitro transcription assay are available for these classes of genes [To examine the performance of the phosphorylated BvgA, one can construct a transcription assay as BvgA on phosphorylation acts as a transcription factor for its promoter and the promoters of the downstream genes. Among the four classes of downstream genes, we opted for nduction . Also, rof genes . High afof genes . This inof genes , i.e., bin vitro phosphorylation assay  at different promoters gets affected. The three strains WT, R152H and T194M have been used to observe the effect on DNA-protein interaction through in vitro transcription assay. At this point, it is important to mention that the experimental data does not have any error bar that gives an estimation of the uncertainty in the experimental results. The snapshots of the profile of cost function and the parameter optimization with respect to SA steps are shown in in vitro experimental results  shows maximum response to the extra-cellular stimulus.The present study undertakes the temperature mediated activation and virulence of BvgAS cascade in the light of sensitivity based optimization. The kinetic model of BvgAS has been simulated for a broad range of sensor protein autophosphorylation that mimics the kinetics of the same quantity due to temperature elevation. The sharp switch in the phosphorylated response regulator is a consequence of positive feedback operative on the in vitro experimental results. In addition, it helps in the understanding of different nature of attenuation or delay during activation of the mutants. Quantification of such delay is biologically important as a delay in the switch due to temperature elevation brings in obstruction in the virulence to be triggered within the host. The obstruction might become operative in the level of protein-protein interaction or the protein-DNA interaction. Although, in vivo fabrication of such synthetic network is difficult, it may be a good starting point in understanding the functionality of stimulus mediated in vivo systems. Future experiments leading to target characterization and quantitative measurements of such interactions will help one to build more efficient models.The development of sharp switch has been extensively investigated in the light of sensitivity analysis coupled with stochastic optimization. Our analysis shows the phosphotransfer module to be the more sensitive compared to the autoregulation module. The parameter sensitivity opens up the avenue to classify and explain the role of model parameters in accord with their influence on the steady state dynamics. Once classified, it is possible to tune the most sensitive parameters to regenerate the experimental profiles computationally. The simulated annealing based stochastic optimization performed on three different strains of BvgA  could successfully reproduce the characteristics of S1 Textin vitro phosphorylation assay and the kinetics of in vitro transcription assay.The text contains the detailed kinetic mechanism of BvgAS two-component system, the kinetics of (PDF)Click here for additional data file.S1 Figin vitro phosphorylation assay results (The cost function and the optimization profiles of the kinetic parameters associated with the simulation of  results  as a fun(TIF)Click here for additional data file.S2 Figin vitro transcription assay results of fhaB (The cost function and the optimization profiles of the kinetic parameters  associat of fhaB  as a fun(TIF)Click here for additional data file.S3 Figin vitro transcription assay results of bipA (The cost function and the optimization profiles of the kinetic parameters  associat of bipA  as a fun(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Activation of eukaryotic transcription is an intricate process that relies on a multitude of regulatory proteins forming complexes on chromatin. Chromatin modifications appear to play a guiding role in protein-complex assembly on chromatin. Together, these processes give rise to stochastic, often bursting, transcriptional activity. Here we present a model of eukaryotic transcription that aims to integrate those mechanisms. We use stochastic and ordinary-differential-equation modeling frameworks to examine various possible mechanisms of gene regulation by multiple transcription factors. We find that the assembly of large transcription factor complexes on chromatin via equilibrium-binding mechanisms is highly inefficient and insensitive to concentration changes of single regulatory proteins. An alternative model that lacks these limitations is a cyclic ratchet mechanism. In this mechanism, small protein complexes assemble sequentially on the promoter. Chromatin modifications mark the completion of a protein complex assembly, and sensitize the local chromatin for the assembly of the next protein complex. In this manner, a strict order of protein complex assemblies is attained. Even though the individual assembly steps are highly stochastic in duration, a sequence of them gives rise to a remarkable precision of the transcription cycle duration. This mechanism explains how transcription activation cycles, lasting for tens of minutes, derive from regulatory proteins residing on chromatin for only tens of seconds. Transcriptional bursts are an inherent feature of such transcription activation cycles. Bursting transcription can cause individual cells to remain in synchrony transiently, offering an explanation of transcriptional cycling as observed in cell populations, both on promoter chromatin status and mRNA levels. Transcription initiation is an important process that contributes to determining mRNA and eventually protein levels. In multicellular organisms transcription activity of a single gene is regulated by many different signals. This leads to multiple transcription factors binding to the same promoter. Here we study fundamental aspects of this regulation. We show that the formation of a single regulating protein complex, consisting of tens of proteins is a very inefficient mechanism: it is slow and hard to regulate. The formation of small complexes, each leaving a histone modification on the promoter solves these problems. Optimally complexes are assembled in a strict order and new histone modifications sensitize the chromatin for the formation of the next complex. This leads to a cyclic, ordered series of irreversible events that is fast and can be tightly regulated—a cyclic ratchet mechanism; like a mechanical ‘clock’. In the ratchet model, one particular chromatin state allows for RNA polymerase rebinding, which makes bursts in mRNA production a basic feature of the ratchet mechanism. Such bursting transcriptional activity has indeed been observed for eukaryotic genes. The duration of the entire transcription cycle can easily become tens of minutes, even though single proteins reside on the chromatin for only tens of seconds. Although the formation time of protein complexes on chromatin can be highly variable, due to invariable stochasticity in protein binding and disassociation, the duration of the entire transcription cycle can be very precise. As a consequence, cells that are activated at the same moment in time display synchronous transcriptional activity for several transcription cycles. This provides an explanation for transient transcription cycles observed at the level of cell populations. Eukaryotic transcription depends on dozens of proteins, including transcription factors (TFs), chromatin remodellers and RNA polymerase II components –7. It isEven though we refer by ‘transcription regulation’ only to regulation of the transcription process itself and not to the regulation of gene expression through transcription, the complexity of eukaryotic transcription regulation is immense. It varies with gene function. ‘House-keeping’ genes maintain nucleosome-free regulatory regions permissive to transcription , 10. GenRegulation of gene activity involves protein complex formation on chromatin and changes in nucleosome states , 15. GenIn some cases transcription dynamics at the cell population level proceeds in an oscillatory fashion –28 at frDespite many recent experimental studies, we lack a realistic, dynamic picture of transcription initiation that integrates time scales of nucleosome modifications, protein complex formation, RNA polymerase assembly and its escape from regulatory regions, mRNA elongation and mRNA-concentration dynamics. This hiatus precludes resolving a number of issues: do cyclic transcriptional mechanisms perform better than the reversible ones found in prokaryotes; and are they perhaps even essential? How well can an ordered transcription cycle be regulated by many factors in comparison to a random set of equilibrium binding events? Is irreversibility essential for transcription regulation by multiple TFs? Does such organization of transcription imply special regulatory and stochastic properties? Do the stochastic dynamics of the transcription cycle model agree with single-cell and population-level studies of transcription when realistic kinetic parameters are considered? Is the seconds-timescale of molecular events in agreement with transcriptional cycling of tens of minutes?Using a variety of experimental data and observations, we here compare a number of transcription activation mechanisms in terms of their ability to be both fast enough and responsive to multiple factors. We show that fully reversible transcription activation mechanisms would not work for promoters regulated by many TFs, which is expected for conditionally regulated genes in mammals. Rather, a transcription activation clock model with irreversible ratchets does resolve diffusion limitations, the required multi-factorial regulation as well as the experimental observations on transcriptional cycling and bursting.n TFs, the activity of the gene Φ(T), with T as the vector of TF concentrations, corresponds to the product of the saturation ϕi(Ti) of the individual, independent TF-binding sites, i.e. v as the transcription rate and k′ as the apparent transcription rate constant. Here ϕ(Ti) denotes the saturation function of a single site, in the case of an activating TF it becomes i for TFi. Taking the product of the saturation functions of the individual, independent sites has the effect that saturating 10 activating sites by 50% leads to an activity of the gene of only 0.1% (T) = 0.75, each individual site would have to be virtually saturated, e.g. ϕi(Ti) = 0.98, which is unrealistic. The tendency of requiring enhanced saturation of individual binding sites is shown in In the equilibrium-binding mechanism, TFs populate individual binding sites in a concentration-dependent manner. For convenience, we define every protein involved in transcription regulation as a TF, regardless of whether it acts merely as a scaffold, a modifier of a nucleosome or enzyme. For ϕi(Ti) = 0.98. This analysis shows that increasing the single-site saturation, which is sufficient to produce significant activation of transcription, causes the gene to loose sensitivity to regulators. Thus, in case of the equilibrium binding mechanism, genes become progressively harder to regulate when the number of regulatory factors increases. This finding is highly relevant as the number of TF-binding sites actively regulating many eukaryotic genes is readily around ten or more. A genome-wide analysis of data from ENCODE project shows in Drosophila that the number of functional TF-binding sites per enhancer sequence is between 2 and 15 with the average around 8 . An. An49]. In the proposed mechanism, the sequence of chromatin modifications proceeds in a defined order. When the activation and inactivation routes would cross however, as would happen in a random mechanism, this would obviously disturb the progress marking, causing the system to erroneously move backward or forward, and skip steps. These considerations may explain the preference for a unique order of histone mark addition/removal and specificity of each partially assembled complex to a chromatin state.n identical first-order reactions follows an Erlang distribution ” returns the value of variable λ that minimizes the function f(λ).The value of the interaction coefficient, (PDF)Click here for additional data file.S2 Figet al. . Elongation and export times were estimated from data available in literature [s38-41] and modeled as a multi-step process (N = 30). A 30 min life-time was assumed; degradation was modeled as multi-step process with (N = 20). For models used to simulate data, additional parameters as well as parameters different from the main model are given. For simulation of the Karpova et al. data, the initiation rate constant was taken to be faster than the RNA polymerase II off-rate. The rates of mRNA elongation (modeled as N = 30 process) and degradation (modeled as N = 20 process) were adjusted to fit the data.Protein association and dissociation rate constants and concentrations were calculated in the same way as shown in (PDF)Click here for additional data file.S5 Tableet al. (2007) [s42], which suggests that the average rate for DNA loop formation of comparable length in vitro is at least faster than 60 seconds. The loop was assumed to be stable (Kd = 1012) due to a big protein interaction surface. The polymerase modification step and promoter step were modelled explicitly. The protein concentrations were adjusted to fit experimental data.Protein association and dissociation rate constants and concentrations were calculated in the same way as shown in (PDF)Click here for additional data file.S6 TableSpecifications (structures and parameters) of each model are described, and the model states plotted in the main text figures are listed.(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file.S5 Text(PDF)Click here for additional data file.S6 Text(PDF)Click here for additional data file.S1 Dataset(ZIP)Click here for additional data file.