Networks of interacting transcription factors are central to the regulation of cellular responses to abiotic stress. Although the architecture of many such networks has been mapped, their dynamic function remains unclear. Here we address this challenge in archaea, microorganisms possessing transcription factors that resemble those of both eukaryotes and bacteria. Using genome-wide DNA binding location analysis integrated with gene expression and cell physiological data, we demonstrate that a bacterial-type transcription factor (TF), called RosR, and five TFIIB proteins, homologs of eukaryotic TFs, combinatorially regulate over 100 target genes important for the response to extremely high levels of peroxide. These genes include 20 other transcription factors and oxidative damage repair genes. RosR promoter occupancy is surprisingly dynamic, with the pattern of target gene expression during the transition from rapid growth to stress correlating strongly with the pattern of dynamic binding. We conclude that a hierarchical regulatory network orchestrated by TFs of hybrid lineage enables dynamic response and survival under extreme stress in archaea. This raises questions regarding the evolutionary trajectory of gene networks in response to stress. Complex circuits of genes rather than a single gene underlie many important processes such as disease, development, and cellular damage repair. Although the wiring of many of these circuits has been mapped, how circuits operate in real time to carry out their functions is poorly understood. Here we address these questions by investigating the function of a gene circuit that responds to reactive oxygen species damage in archaea, microorganisms that represent the third domain of life. Members of this domain of life are excellent models for investigating the function and evolution of gene circuits. Components of archaeal regulatory machinery driving gene circuits resemble those of both bacteria and eukaryotes. Here we demonstrate that regulatory proteins of hybrid ancestry collaborate to control the expression of over 100 genes whose products repair cellular damage. Among these are other regulatory proteins, setting up a stepwise hierarchical circuit that controls damage repair. Regulation is dynamic, with gene targets showing immediate response to damage and restoring normal cellular functions soon thereafter. This study demonstrates how strong environmental forces such as stress may have shaped the wiring and dynamic function of gene circuits, raising important questions regarding how circuits originated over evolutionary time. All organisms encounter reactive oxygen species (ROS) originating from biotic and abiotic sources. ROS are produced at relatively low levels as natural byproducts of aerobic respiration, Fenton reactions, or other biotic sources e.g. stress-responsive TFs) typically resemble those of bacteria at the amino acid sequence level Relative to the other domains of life, the function of TFs that control the oxidant response in archaea remain understudied. To our knowledge, only a few transcription factors have been characterized to date H. salinarum as a model, both to characterize the genome-wide binding dynamics of an ROS-responsive transcription factor, and to analyze regulatory network function during ROS stress in archaea. This hypersaline adapted archaeal model organism encounters high levels of abiotic oxidants in its natural salt lake environment, where intense solar radiation and desiccation are frequent Here we use the salt-loving archaeon H. salinarum2O2 and paraquat). Genes directly and indirectly controlled by RosR in response to oxidant encode macromolecular repair functions. In the current study, we ask which of these genes are direct targets of RosR regulation. Integrated analysis of genome-wide binding location time course data with gene expression data demonstrates that RosR binds and regulates over 100 target genes. These encode molecular repair functions and a surprisingly high number of other TFs. RosR binds many of these sites in the absence of stress. Upon exposure to H2O2, RosR disengages from DNA at most loci. However, at other loci, RosR-DNA binding is dynamic following peroxide exposure, with locus-specific differences in TF occupancy over time. RosR binding is mediated via a 20-bp palindromic cis-regulatory binding sequence. Integration of data generated here in the context of other existing systems biology datasets reveals extensive combinatorial binding of RosR with multiple Tfb proteins throughout the regulon. We conclude that RosR is a master regulator of a hierarchy of TFs that performs global, dynamic physiological readjustment in response to oxidative stress.However, this network was computationally inferred from gene expression data. To experimentally characterize TFs with putative involvement in this network, our previous work identified the winged helix-turn-helix DNA-binding TF RosR. This TF dynamically regulates expression of more than 300 genes in response to oxidative stress in 2O2 over time (see 2O2 (2O2 and remained low for the duration of the time course. (2) At other sites (90 genes), RosR binding was initially lost in the presence of H2O2, but binding recovered within 60 minutes . RosR binding ability in the absence of stress (clusters 1 and 4 at the 0 time point) was previously validated by ChIP-qPCR Previous work demonstrated that the RosR transcription factor is required for the differential expression of genes in response to ROS time see . A total, which was detected nearby 37 of RosR-bound loci , but not detectable elsewhere in the genome  was fused to GFP. Promoter activity was assayed in the ΔrosR vs parent strain in the absence of stress, when RosR binding activity was evident in ChIP-chip experiments for these promoters. Ptrh4 activity is significantly higher in the ΔrosR strain relative to the parent and the empty vector background control  this cis-regulatory sequence carries significant importance in the function of the RosR regulatory network.To validate the function of this computationally predicted binding site experimentally, the native genomic promoter (TATA box and putative cis-regulatory sequence) of  control . This su control . Togethehsp4 and hsp5, peroxidase perA), translation , DNA replication, cell growth and division, and transcription  are downregulated upon ROS exposure, whereas stress response genes  are upregulated . Relative to the isogenic parent strain, both TF knockout strains are significantly impaired for growth in response to oxidative stress induced by addition of H2O2 to the cultures , a peak association is detected at distances of 65–75 bp  and TFIIB (Tfb)]. In contrast, most repressor TFs inhibit gene expression by binding downstream of the PIC 65–75 bp . This reckground ; and (c)p<5.68×10−3; see p-value calculation and p<2.05×10−4 for TfbB; p<2.16×10−7 for TfbG). In contrast, predictions from the model did not match experimental observations regarding combinatorial control by RosR-TfbD and RosR-TfbF pairs ”; bF pairs . Of all H. salinarum RosR is a bifunctional regulator that directly controls a large hierarchy of transcription factors in combination with Tfb proteins to enable extreme oxidative stress survival. The majority of these sites are bound in the absence of stress, with RosR released from DNA in the presence of oxidant. A subset of loci exhibits the opposite binding pattern. We show that RosR binds to a ∼20 bp imperfect palindrome cis-regulatory sequence and directly activates or represses genes encoding functions in transcription, macromolecular repair and central cellular physiology. We demonstrate that RosR regulates genes encoding TFs that are also required for oxidative stress survival. Such regulation is conducted in concert with Tfb proteins. We conclude that RosR plays an important role in a large transcriptional network that enables a rapid response to extreme oxidative stress followed by re-establishment of homeostasis.Data and analyses presented here suggest that H. salinarumThe function of gene products in the RosR regulon reported here reflects the observations from our previous work The integrated genome-wide analysis presented here suggests hypotheses for the RosR biochemical mechanism. Dynamic TF-DNA binding analysis suggests a differential preference in RosR promoter occupancy, as some promoters are re-bound while homeostasis is restored, whereas a small subset of other sites are bound only in the presence of peroxide , suggest2O2 and re-occupied by RosR within 60 minutes of oxidant exposure  may enable RosR to re-bind. For example, ΔperA mutants experience high intracellular H2O2 concentrations during mid-log phase growth, whereas H2O2 is cleared from the H. salinarum parent strain within the time frame tested here In contrast to RosR targets in Cluster 3, a significant fraction of sites are bound in the absence of H2O2 challenge than the parent strain  and binds together with only one other Tfb protein at its target promoters H. salinarum iron regulators Idr1 and Idr2 only regulate one other TF each via TF-TF network interactions.Integrated analysis of several genome-wide binding location and gene expression datasets for TFIIB homologs Escherichia coli, where SoxS regulates at least four other TF-coding genes . Δura3, the parent strain, and transcription factor deletion strain derivatives thereof, were grown in CM supplemented with 0.05 mg/mL uracil to complement the auxotrophy. In-frame gene deletion strains  were constructed using the pop-in/pop-out gene deletion strategy described previously ura3ΔrosR, referred to throughout as ΔrosR for brevity, was constructed previously H. salinarum strains harboring plasmids were cultured in CM supplemented with 20 µg/mL mevinolin for plasmid maintenance. H2O2 was added to mid-logarithmic phase cultures to 25 mM or at inoculation at 5 or 6 mM to test oxidative stress response as displayed in the figures.Strains of H. salinarum harboring VNG0258H::myc  by virtue of the myc epitope tag as described previously H. salinarum genome was tiled every 30 bp in triplicate. Randomly selected regions of the genome were spotted in quadruplicate. The custom tiling microarray design can be accessed via Agilent Technologies AMADID 026819, Gene Expression Omnibus (GEO) platform accession GPL18848. Dye swaps were conducted to correct for bias in incorporation. Seven biological replicate experiments were conducted for VNG0258::myc in the absence of H2O2 and three in the presence of H2O2. These experiments yielded a total of at least 18 replicate intensity data points per 30-bp genomic region per condition. DNA fragments were directly labeled with Cy3 and Cy5 dyes (Kreatech) as described previously H. salinarum genome  in at least one time point. Composite p-values for multiple binding peaks nearby the same gene within a given time point were calculated using Fisher's combined probability test Resultant slides were scanned and processed with Feature Extraction software (Agilent). Raw probe intensities were first normalized within each array using density-weighted loess 2O2 at 0, 10, 20, and 60 min; GEO accession GSE33980). These correlations are referred to throughout as “GE-ChIP correlations”. GE-ChIP correlations were calculated separately for each of the ΔrosR deletion and isogenic parent backgrounds as an additional metric for the impact of RosR binding on gene expression. Significance of the difference in GE-ChIP correlations between the parent and ΔrosR strains was calculated using Student's t-test. Genes with strong GE-ChIP correlations (Cs≥0.6) were interpreted as directly activated by RosR, whereas anticorrelations (Cs≤−0.6) were interpreted as repressed. Statistical overrepresentation in archaeal clusters of orthologous genes (arCOG) functional categories Time course profiles of processed ChIP-chip binding data were grouped using Spearman correlated complete linkage hierarchical clustering to identify various dynamic binding patterns. To determine the dynamic relationship between binding and gene expression, each gene in each dynamic binding cluster was correlated to expression data under the same culturing conditions as ChIP-chip from a previous study p<0.01 were selected using the R bioconductor MeDiChI package rosR). Significance of these correlations was computed from by comparing 10,000-fold resampled data to actual data  samples were prepared over the time course described above and subjected to quantitative real-time PCR analysis (qPCR) using SYBR green as previously described H. salinarum Δura3 parent, TF deletion strains Δura3ΔVNG0194 and Δura3Δhrg (deletion of VNG0917G), and the complementation strains . Growth rates were calculated from the slope of the log2 transformed data during logarithmic growth. Reported in the figures are ratios of the growth rates of each strain under H2O2 stress relative to the same strain's growth rate without stress. All growth data are provided in H. salinarum genome sequence 250 bp upstream and downstream of each of the 189 ChIP-chip binding loci  in the absence of stress in 50 mL CM. Samples were collected, washed and fixed as previously described ura3 harboring the empty vector (i.e. GFP-encoding gene with no promoter) or vector containing GFP-encoding gene driven by the strong constitutive Pfdx promoter To validate the predicted cis-regulatory binding sequence, a 200-bp region containing the putative cis-sequence and TATA box of S1 TablePrimers and archaeal strains used in this study.(XLS)Click here for additional data file.S2 TableExpression data and associated RosR binding sites for each gene. Contains binding location coordinates, coordinate-to-gene distances, and binding peak p-values for each gene at each time point.(XLS)Click here for additional data file.S3 TableDetailed annotations for genes nearby RosR binding sites.(XLS)Click here for additional data file.S4 TableDistances between RosR binding locations, each of the five TFB binding locations and associated gene identifiers.(XLS)Click here for additional data file.S5 TableRosR cis-regulatory binding motif sequences and genome coordinates.(XLSX)Click here for additional data file.S6 TableRaw growth data for all TF deletion strains grown under oxidative stress conditions.(XLSX)Click here for additional data file.


Nucleosome positioning dictates the DNA accessibility for regulatory proteins, and thus is critical for gene expression and regulation. It has been well documented that only a subset of nucleosomes are reproducibly positioned in eukaryotic genomes. The most prominent example of phased nucleosomes is the context of genes, where phased nucleosomes flank the transcriptional starts sites (TSSs). It is unclear, however, what factors determine nucleosome positioning in regions that are not close to genes. We mapped both nucleosome positioning and DNase I hypersensitive site (DHS) datasets across the rice genome. We discovered that DHSs located in a variety of contexts, both genic and intergenic, were flanked by strongly phased nucleosome arrays. Phased nucleosomes were also found to flank DHSs in the human genome. Our results suggest the barrier model may represent a general feature of nucleosome organization in eukaryote genomes. Specifically, regions bound with regulatory proteins, including intergenic regions, can serve as barriers that organize phased nucleosome arrays on both sides. Our results also suggest that rice DHSs often span a single, phased nucleosome, similar to the H2A.Z-containing nucleosomes observed in DHSs in the human genome. The fundamental unit of chromatin is the nucleosome, which consists of 147 bp of DNA wrapped around a histone octamer containing four core histones . Nucleosome positioning in the genome affects the DNA accessibility for regulatory proteins, and thus is critical for gene expression and regulation. Genomic regions associated with regulatory proteins are associated with a pronounced sensitivity to DNase I digestion, and are thus called DNase I hypersensitive sites (DHSs). It is well known that only a subset of nucleosomes are reproducibly positioned in eukaryotic genomes. However, it is less clear what factors determine genome-wide nucleosome positioning, especially in intergenic regions. We mapped both nucleosome positioning and DHS datasets across the rice genome. We discovered that DHSs located in a variety of contexts, both genic and intergenic, were flanked by strongly phased nucleosome arrays. We confirmed the same association of DHSs with phased nucleosomes in the human genome. We conclude that genomic loci associated with a diverse set of regulatory proteins are major determinants of nucleosome phasing, and this is true in both genic and intergenic regions. In vitro nucleosome assembly studies in yeast (Saccharomyces cerevisiae) and Caenorhabditis elegans have confirmed the DNA sequence preferences in nucleosome formation in vivo is determined by several factors that can override the sequence preferences, including gene transcription, action of nucleosome remodeling complexes, and presence of histone variants and histone modifications in vivo nucleosome positions in S. cerevisiaeThe fundamental unit of chromatin is the nucleosome, which consists of 147 bp of DNA wrapped around a histone octamer containing four core histones  Relationships between nucleosome organization and gene expression have been well demonstrated in several model eukaryotes. Phased nucleosome arrays have been observed on both sides of the promoters of active genes Oryza sativa) has been used as model species for plant genome research. The rice genome is relatively small (∼400 Mb) and is one of the best sequenced genomes in higher eukaryotes Rice  in the rice genome We also examined nucleosome phasing surrounding TSSs in the rice genome independently of DHSs. Clearly-phased nucleosomes were detected downstream of TSSs of expressed genes , but notWe noticed that the average lengths of most DHSs in different genomic regions, except for those located in proximal promoters, were similar in the rice genome, with ∼50% DHSs in the size of 35–150 bp. In contrast, the lengths of DHSs in proximal promoters were more variable, including ∼79% DHSs >150 bp . We suspp<0.001). In addition, 5,197 and 2,898 of the IPA1-binding sites contain the IPA1-binding motif, GTAC, and another over-represented motif, TGGGC[C/T], respectively We wanted to examine if phased nucleosomes are associated with the binding sites of specific rice transcription factors. IDEAL PLANT ARCHITECTURE1 (IPA1), a member of the SPL transcription factor family, is a key regulator in determining plant architecture and enhancing grain yield in rice Mapping of both DNase-seq and MNase-seq datasets revealed peaked MNase-seq reads from both forward and reverse strands on both sides of DHSs . These rThe DHSs in intergenic regions were associated with a unique nucleosomal positioning pattern. The intergenic DHSs lacked the forward MNase-seq peak and the reverse MNase-seq peak, respectively, on the two sides of the DHSs , suggestSince the DHSs in proximal promoters were more variable in lengths , we furtThe sizes of 2,495 DHSs  in proximal promoters were <140 bp, which is shorter than the sequences required to wrap a single nucleosome. These DHSs did not appear to span a nucleosome, but appeared to be enriched in the 3′ portion of the -1 nucleosome  or were p<0.005, Kolmogorov–Smirnov test). We also calculated linker lengths in the human genome using human MNase-seq data We observed a superposition between the forward and reverse MNase-seq reads in genic and promoter regions, which indicates very little or no space between 5' ends of forward and reverse oriented reads . HoweverA weakness of the above method of calculating linker length is that it is influenced by the severity of MNase digestion as MNase can either digest into the nucleosome core DNA or fail to completely digest the linker DNA. Thus, we used an alternative method to estimate the linker lengths in different genomic regions in rice. Since the position of the nucleosome center (dyad), which can be identified as the middle position of each paired-end read, is not affected by different levels of MNase digestion, we can calculate the spacing of between two adjacent nucleosomes using the midway point between paired MNase-seq reads rather than 5' ends. We found that the average spacing between two nucleosomes adjacent to intergenic DHSs was ∼191 bp , which iWe exploited the genomic datasets from the human genome to examine a similar association of DHSs with nucleosome positioning. Human CD4+ T cell line has been extensively used in epigenomics profiling, including histone modifications Since H2A.Z-associated nucleosomes were found in regions that were previously thought to be nucleosome free, we investigated if DHSs in the human genome span H2A.Z-associated nucleosomes. Mapping of H2A.Z ChIP-seq dataset Drosophila melanogasterC. elegansArabidopsis thalianaGenome-wide nucleosome positioning maps have been generated in several eukaryotes, including yeast A barrier model was proposed to explain genome-wide nucleosome positioning S. cerevisiae strains containing large genomic regions from other yeast species Gaffney et al. (2012) recently mapped nucleosomes surrounding the binding sites of 35 different transcription factors in human lymphoblastoid cell lines. Strongly positioned nucleosome arrays were found to flank the binding sites, including those at least 1 kb away from a known TSS Drosophila were quantitatively correlated with DNA accessibility in chromatin that can be measured by the DNase I sensitivity A. thaliana, APETALA1 and SEPALLATA3, were covered by DHSs We demonstrate that DHSs in the rice genome are flanked by phased nucleosome arrays on both sides , which iDrosophila, the binding sites of transcription factors that are flanked with strongly positioned nucleosome arrays were more sensitive to DNase I digestion and have more pronounced DNase I footprints A DHS-based barrier can be permanent, such as the promoters associated with constitutively expressed genes, or be temporarily, such as binding sites of transcription factors associated with tissue- or organ-specific gene expression. Regulatory proteins can bind DNA tightly or loosely , which may result in “hard” barriers or “soft” barriers. Hard barriers will result in well positioned and well phased nucleosome arrays; whereas soft barriers may result in “fuzzy” and less phased nucleosome arrays. In In summary, we demonstrate that DHSs located across the rice genome are flanked by strongly phased nucleosome arrays. We confirmed the same phenomenon in the human genome by analyzing publically available datasets. Our results support the barrier model for nucleosome organization as a general feature of eukaryote genomes. We propose that genome-wide nucleosome positioning in the eukaryotic genomes is orchestrated by genomic regions associated with regulatory proteins.Rice cultivar “Nipponbare” seeds were germinated at room temperature for three days. Germinated seeds were then sowed in soil to continue to grow in the greenhouse. The seedlings continued to grow for two weeks under 12 hrs day/night cycles and 32°C/27°C corresponding to day and night, respectively. The seedlings were then harvested for nuclei isolation, the same growing stage/condition used for developing DNase-seq and RNA-seq datasets previously http://www.r-project.org/).We mapped the MNase-seq reads to the rice genome (TIGR release 5) using MAQ software MNase-seq data has been deposited to NCBI under accession number GSE53027.Figure S1Patterns of nucleosome positioning around DHSs in the rice genome. The nucleosome positioning profiles were shown around the DHSs located in (A) proximal promoters (within 200 bp upstream of a TSS); (B) distal promoters (200–1000 bp upstream of a TSS); (C) within genes; (D) downstream regions of genes (within 200 bp downstream of gene transcription); (E) intergenic region and (F) 10,000 randomly selected genomic regions. Y-axes show normalized reads (read number in per bp genome in per million reads) within 1 kb upstream and downstream around the DHSs. Ellipses indicate the nucleosomes within (grey) and outside (black) of DHSs. Arrows in (a-d) indicate the direction of gene transcription. Paired MNase-seq reads were used in mapping nucleosome positioning.(PDF)Click here for additional data file.Figure S2An illustration of mapping phased nucleosomes with different linker lengths. After MNase digestion, linker DNA was presumably digested and the remaining DNA fragments wrapped on nucleosome core were included in library construction. Longer linkers between adjacent nucleosomes may cause a shift between the sequence reads derived from the forward and reverse strands, respectively.(PDF)Click here for additional data file.


BCL2 gene and the promoter to regulate BCL2 expression during early apoptosis. In the present study, we used chromosome conformation capture (3C) assays and molecular analyses to further investigate the function of the SATB1-mediated higher-order chromatin structure in co-regulation of the anti-apoptotic BCL2 gene and the pro-apoptotic NOXA gene located 3.4Mb downstream on Chromosome 18. We demonstrated that the mbr enhancer spatially juxtaposed the promoters of BCL2 and NOXA genes through SATB1-mediated chromatin-loop in Jurkat cells. Decreased SATB1 levels switched the mbr-BCL2 loop to mbr-NOXA loop, and thus changed expression of these two genes. The SATB1-mediated dynamic switch of the chromatin loop structures was essential for the cooperative expression of the BCL2 and NOXA genes in apoptosis. Notably, the role of SATB1 was specific, since inhibition of SATB1 degradation by caspase-6 inhibitor or caspase-6-resistant SATB1 mutant reversed expression of BCL-2 and NOXA in response to apoptotic stimulation. This study reveals the critical role of SATB1-organized higher-order chromatin structure in regulating the dynamic equilibrium of apoptosis-controlling genes with antagonistic functions and suggests that aberrant SATB1 expression might contribute to cancer development by disrupting the co-regulated genes in apoptosis pathways.Aberrant expression of special AT-rich binding protein 1 (SATB1), a global genomic organizer, has been associated with various cancers, which raises the question of how higher-order chromatin structure contributes to carcinogenesis. Disruption of apoptosis is one of the hallmarks of cancer. We previously demonstrated that SATB1 mediated specific long-range chromosomal interactions between the mbr enhancer located within 3’-UTR of the  Special AT-rich binding protein 1 (SATB1) is a global genomic organizer that integrates higher-order chromatin organization with regulation of gene expression . SATB1 iDisruption of apoptosis is a hallmark of cancer that reinforces tumorigenesis and resistance to cytotoxic cancer therapies . MembersBCL2 encodes BCL2 anti-apoptotic protein, a key regulator of apoptosis [BCL2 gene is an enhancer element that contains an SATB1 binding sequence [BCL2 promoter during the early apoptotic response of Jurkat cells [NOXA gene that is 3.4Mb downstream of the BCL2 gene on Chromosome 18 also physically interacted with the mbr enhancer, suggesting that the NOXA gene is targeted by the mbr enhancer. Consistently, bioinformatic analysis revealed that the NOXA gene promoter contained SATB1 binding sequences, indicating a potential functional role of SATB1 in the regulation of NOXA expression.Proto-oncogene poptosis . Our presequence ,24. SATBat cells . This fiNOXA gene encodes NOXA, a BH3-only pro-apoptotic protein that functions as sensitizer to neutralize anti-apoptotic BCL2 family proteins [BCL2 and pro-apoptotic NOXA genes to participate in the control of apoptosis. Aberrant SATB1 expression could disrupt apoptosis by disturbing the balance between anti-apoptotic and pro-apoptotic genes, and thus contribute to cancer development. To test this hypothesis, we focused on two questions: 1) Whether SATB1-mediated long-range chromatin conformation is involved in the regulation of cooperative expression of the BCL2 and NOXA genes, and 2) whether altered SATB1 expression changes the cellular apoptotic response by switching the balance between BCL2 and NOXA.The proteins . Our priHuman T lymphoid cell line Jurkat which is kindly supplied by Dr. Krontiris’ Laboratory at City of Hope National Medical Center in Los Angeles, USA were maintained in RPMI 1640 medium supplemented with 10% FBS, 10 mM HEPES, 100unit/ml penicillin and 100mg/ml streptomycin and cultured at 37°C in a humidified atmosphere of 5% CO2. Cells were passaged every 2–3 days for exponential growth. For apoptosis assay, Jurkat cells were exposed to 5μM camptothecin and the equal volume of DMSO as the vehicle control. In another assay, Jurkat cells were preincubated with 10μM Z-VEID-fmk for 30 min for prevention the influence of protease then induced Apoptosis.SATB1-specific sh-RNA plasmids were prepared as described in our previous article . The SATCellular protein was isolated using lysis buffer  containing protease inhibitors. After boiling for 5min in loading buffer, equal amounts (30μg/lane) of proteins were fractionated on SDS-PAGE gels and transferred to polyvinylidene difluoride membranes. Non-specific protein interactions were blocked by incubation in 5% nonfat dry milk in TST buffer  at room temperature for 1h. Membranes were then incubated with anti-SATB1 and anti-β-Actin antibody (Sigma) in fresh blocking buffer at 4°C overnight. After washing with PBS containing 0.1% (v/v) Tween20, the membranes were incubated with peroxidase-conjugated individual secondary antibodies for 1h at room temperature. The blots were developed with ECL reagent (Amersham Biosciences). Prestained markers (NEB) were used as internal molecular weight standards.Total RNA was isolated with Trizol reagent (Invitrogen) according to the manufacturer’s protocol. cDNA was synthesized from total RNA (1μg) using applied biosystem cDNA Synthesis Kit according to the Manufacturer’s instructions. The reaction was incubated at 25°C for 10 min, then applied to 37°C for 2 hour, and finally 85°C for 5min. The reactions were stored at -20°C before use. The real-time PCR amplification conditions were 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s, annealing at 60°C for 1 min. Primers are listed in supporting information: To construct the plasmid SATB1-D254A  which is resistant to caspase-6 cleavage, the SATB1 overexpression plasmid pEGFP-C1-SATB1 (generous gifts from Dr. Krontiris laboratory) was used by using the QuikChange® Site-Directed Mutagenesis Kit (Stratagene). The primer is as follows with the mutated bases in underline: 5’-GATATGATGGTTGAAATGGCTAGTCTTTCTGAGCTATC-3’. Plasmid was confirmed by sequencing (BGI).Apoptosis assays were performed using two Apoptosis Detection Kits (Keygentech) according to the manufacturer’s instructions. Briefly, cells were collected and washed twice with PBS, gently resuspended in binding buffer. Annexin V-FITC and propidium iodide (PI) were then added and incubated in the dark for 15min at room temperature, followed by immediate analysis on FACScan (Becton Dickinson). Another apoptosis detection kit is Annexin V-PE/7-AAD, and the manufacture instruction is similar to the protocol above. The percentage of apoptosis was computed using Cell Quest software (Becton Dickinson).ChIP assay was conducted with the ChIP assay kit essentially as described in the manufacturer (Upstate). Detailed experimental procedures are described in our previous report article . For quaAse I fragments or Hind III fragments within the ubiquitously expressed PGK1 locus that had been checked to be transcribed stably in our experiment systems.The 3C protocol was adapted from published article with modifications. Detailed experimental procedures are described in our previous article . Quantithttp://www.genomatix.de/index.html) to analyze the promoter regions of Bcl2 family members for the binding sites of SATB1). The NOXA gene located 3.4Mb downstream of the BCL2 gene was of particular interest. NOXA has 3 sequences that are possible SATB1 binding sites. These sequences are NOXA-SBS1 to NOXA-SBS3, and are located within the 3.5kb region upstream of the NOXA transcription start site. The illustrations of three SATB1 binding sites in NOXA promoter have been shown in in vivo. The precipitated products were weak for SBS2. No specific precipitate was detected for SBS3 . Targeting efficiency was confirmed by Western blot analysis . The effBCL2 and NOXA genes to apoptosis stimulation, we analyzed the change in chromatin loops, transcriptional activity of the genes, and SATB1 level in Jurkat cells treated with camptothecin. Cells were incubated with 5μM camptothecin and harvested after 2 h, when early apoptosis occurred and SATB1 was degraded to 70%.  must participate in further tethering the mbr-NOXA interaction and maintain the NOXA chromatin loop at higher levels after the departure of the SATB1/SATB1 complex. We propose that a potential candidate protein might be a transcription factor that could function as chromatin organizer by forming protein polymers and recruiting other transcription factors and cofactors to the binding sites. A spectrum of MAR-binding proteins, including Cux/CDP , SAF-A , Bright The cooperative regulation of the BCL2 and NOXA genes by SATB1 at higher-order chromatin structure levels is significant in view of the profound association of SATB1 with cancer development and chemotherapy resistance ,16. In tIt is important to point out that the role of SATB1 was specific, since transfection of cells with mutant SATB1 that were resistant to caspase-6 or treatment with caspase-6 inhibitor restored BCL-2 expression and reduced NOXA levels in Jurkat cells treated with camptothecin, and thus significantly suppressed apoptosis. Therefore, the SATB1 level profoundly influenced cell fate by co-regulating expression of the anti-apoptotic BCL2 gene and the pro-apoptotic NOXA gene during the cellular response to apoptosis. Recent studies have shown that many types of cancer cells aberrantly express SATB1 , which iGenome-wide investigation of chromatin interaction and the interactome has revealed widespread stimulus-responsive promoter-promoter and promoter-enhancer interactions between co-regulated genes. This suggests the role of such a “multi-gene complex” in providing a topological framework for the transcription of co-regulated genes . The intS1 Fig(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 FigThe scheme of NOXA gene with the positions of restriction sites and positions of primers were indicated in A. The three SATB1 binding sites as been showed as N-SBS1、N-SBS2 and N-SBS3 (A). The 3C experiments were performed as described in the ‘Materials and Methods’ section. Physical interactions between mbr and NOXA-1/NOXA-2 were then determined by specific PCRs that detected hybrid fragments containing either mbr sequences or NOXA-1/NOXA-2 sequences. 3C data demonstrated that the mbr specifically interacted with NOXA-1 , but not with NOXA-2 . PCR products from HindIII digested cross-linked chromatin without ligation and non-crosslinked genomic DNA with or without ligation were used as negative controls. The bands shown in the bottom panels of B represented the PCR products from genomic DNA that was not cut by any restriction enzyme, which were used as the loading control. The bands shown in C are PCR products from BAC plasmids that showed the primers for 3C experiments are pretty good.(TIFF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S5 Fig(A) Western blot determined SATB1 expression level in Jurkat cells treated with camptothecin for three trials. (B) Gray analysis of SATB1 expression level in the stimulation of camptothecin showed SATB1 degraded to 70%. (C) SATB1 mRNA level decreased to 50% in the stimulation of camptothecin.(TIFF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Drosophila melanogaster follicle cells, ori62, which coincides with the yellow-g2 transcription unit and exhibits transcription-dependent origin firing. Within a 10-kb genomic fragment that contains ori62 and is sufficient for amplification, RNA-sequencing analysis revealed that all detected RNAs mapped solely to the yellow-g2 gene. To determine whether transcription is required in cis for ori62 firing, we generated a set of tagged yellow-g2 transgenes in which we could prevent local transcription across ori62 by deletions in the yellow-g2 promoter. Surprisingly, inhibition of yellow-g2 transcription by promoter deletions did not affect ori62 firing. Our results reveal that transcription in cis is not required for ori62 firing, raising the possibility that a trans-acting factor is required specifically for the activation of ori62. This finding illustrates that a diversity of mechanisms can be used in the regulation of metazoan DNA replication initiation.The mechanisms that underlie metazoan DNA replication initiation, especially the connection between transcription and replication origin activation, are not well understood. To probe the role of transcription in origin activation, we exploited a specific replication origin in  DNA replication initiation occurs at specific genomic sites called origins of replication, and proper activation of these origins is essential for the precise duplication of the genome in dividing cells. In eukaryotes, replication initiation first requires the loading of the minichromosome maintenance (MCM)2-7 replicative helicase complex to origins of replication through the cooperative activities of the origin recognition complex (ORC) and the replication initiation factors Cdt1 and Cdc6 , and onlDrosophila follicle cell gene amplification system provides a powerful model for the study of individual metazoan origins in vivo  are repeatedly activated during follicle cell differentiation while whole-genome replication is shut off  and the 10-kb locus was then liberated and cloned into the NotI and NheI sites of the pCaSpeR4-SHWBS P-element transformation vector to generate pCaSpeR4-10kb-tag.All constructs used in this study were derived from the PCRA10kb plasmid, which contains the 10-kb genomic locus corresponding to the central amplified region in escribed . To geneyellow-g2 promoter deletions, a NotI/ApaI fragment containing a partial 5′ fragment of the yellow-g2 coding sequence and 2.5-kb of upstream sequence was subcloned into pBluescriptSK to generate pBS-NotI/ApaI. Using site-directed mutagenesis, a 214-bp deletion was made to generate pBS-NotI/ApaI-Δ214, and a 1226-bp deletion was made to generate pBS-NotI/ApaI-Δ1226. The 214-bp deletion corresponds to the coordinates −120 to +94 relative to the transcription start site. The 1226-bp deletion corresponds to the coordinates −1132 to +94. The BbvCI/ApaI fragment containing the promoter deletion was then liberated from each deletion construct to replace the corresponding fragment in PCRA-10kb-tag to generate PCRA-10kb-tag-214 and PCRA-10kb-tag-1226. The NotI/AvrII fragment containing one SHWBS and the 10-kb locus was then liberated from each plasmid and cloned into the NotI and NheI sites of the pCaSpeR4-SHWBS P-element transformation vector to generate pCaSpeR4-Δ214 and pCaSpeR4-Δ1226.To generate the P-element transposon constructs were sent to BestGene Inc.  for individual injections into 1118w embryos to establish at least two independent transformation lines per construct. To examine the effects of the Suppressor of Hairy-wing (Su(Hw)) chromatin insulator, transposons on the nd2 chromosome were crossed into the 2su(Hw)Sbsbd-2/TM6, 5su(Hw) background.The RNA-seq analysis of total RNA extracted from purified 16C follicle cells has been described previously (yellow-g2 transcript levels were determined by quantitative polymerase chain reaction (qPCR) using total cDNA samples and a primer set specific for the tag within the transgenic yellow-g2 transcript. In addition, a primer set that did not discriminate between the endogenous and transgenic yellow-g2 transcripts was used to measure the total amount of yellow-g2 transcript. Primer sequences are available on request. Each transcription analysis experiment was performed in biological duplicates.Total RNA was isolated from 30−50 stage 12 egg chambers using TRIzol reagent (Invitrogen). One microgram of total RNA was reverse transcribed using AMV reverse transcriptase (Promega) to generate single-stranded cDNA. Transgenic 62D amplification. Each DNA sample was internally normalized to the copy number of a nonamplified control locus at 93F. Primer sequences are available on request. Fold amplification at a given developmental stage was determined relative to preamplification stage 1−8 egg chamber DNA. Each amplification assay was performed in biological triplicates.Genomic DNA was isolated from staged egg chambers and quantified using relative qPCR as described . A primeori62 firing, we first identified the RNAs within the 10-kb amplification-sufficient DAFC-62D region previously characterized ) insulator binding sites (SHWBS) to protect from inhibitory position effects  mutant backgrounds. We found that the yellow-g2 transgene was transcribed and amplified to comparable levels in both the wild-type and su(Hw) mutant backgrounds  mutant backgrounds, confirming that our findings were not confounded by the presence of the Su(Hw) insulator. If transcription were required in cis for origin firing at stage 13, then inhibition of transcription of yellow-g2 should have inhibited stage 13 origin firing. Thus, from these results we conclude that transcription is not required in cis for origin activation at DAFC-62D.To test whether transcription is required in r region . Reverseion line  and the ion line . We obseDAFC-62D uniquely among the amplicons requires transcription for late stage origin firing, yet this transcription is not required in cis? We propose that transcription is required to produce a trans-acting factor that is required specifically for DAFC-62D origin firing in the developmental time window after stage 10B. Such an origin-specific, trans-acting regulatory factor would add to the growing list of mechanisms by which metazoan origins are regulated, emphasizing the idea that individual origins can exhibit unique and distinct mechanisms of regulation. The diversity in the molecular mechanisms that regulate metazoan origin firing is made evident by several Drosophila amplicons. These include a local replication enhancer that activates origin firing at DAFC-66D (DAFC-34B (DAFC-22B (trans-acting regulators of origin activity could be cell-type specific, allowing an additional level of regulation of origin activation. Thus, identification of this trans-acting factor required at DAFC-62D and its mechanism of action will prove extremely valuable in our understanding of metazoan DNA replication initiation regulation.How is it possible that DAFC-34B , and locOur results provide perspective not only on the widespread plasticity in mechanisms that control metazoan origin activation but also on the implications of replication initiation errors in genome instability and cancer progression. Misregulation of origin activation can lead to genome instability due to unreplicated or amplified chromosomal regions (


Through systematic experiments and modeling, we analyse how roadblocking by the lac repressor (LacI) in Escherichia coli cells is controlled by promoter firing rate, the concentration and affinity of the roadblocker protein, the transcription-coupled repair protein Mfd, and promoter–roadblock spacing. Increased readthrough of the roadblock at higher RNAP fluxes requires active dislodgement of LacI by multiple RNAPs. However, this RNAP cooperation effect occurs only for strong promoters because roadblock-paused RNAP is quickly terminated by Mfd. The results are most consistent with a single RNAP also sometimes dislodging LacI, though we cannot exclude the possibility that a single RNAP reads through by waiting for spontaneous LacI dissociation. Reducing the occupancy of the roadblock site by increasing the LacI off-rate (weakening the operator) increased dislodgement strongly, giving a stronger effect on readthrough than decreasing the LacI on-rate (decreasing LacI concentration). Thus, protein binding kinetics can be tuned to maintain site occupation while reducing detrimental roadblocking.Genomic DNA is bound by many proteins that could potentially impede elongation of RNA polymerase (RNAP), but the factors determining the magnitude of transcriptional roadblocking  Collisions between RNAP and other RNA and DNA polymerases have been intensively studied . HoweverEscherichia colilac repressor (LacI) can cause more than 80% reduction in transcription downstream of its binding site both in vitro and in vivo  and LexA cause a substantial pause in transcription of bacterial and eukaryotic RNAPs in vitro  RNAP flux (promoter strength); (ii) roadblocker concentration; (iii) roadblocker affinity; (iv) Mfd; and (v) promoter–roadblock spacing. We used a modular, chromosomally integrated promoter–spacer–roadblock–lacZ reporter system to measure roadblocking and analysed the results by stochastic simulations. Our large body of observations was consistent with a relatively simple model of roadblocking and defined the critical role of the kinetics of the roadblocker protein.It is unclear how these processes act in combination to determine roadblocking outcomes lacZ reporter constructs  were integrated into the λ attB site of E. coli MG1655 rph+ ΔlacIZYA, or a derivative with an in-frame deletion of the mfd gene. Details of DNA constructions are given in Supplementary Data. Growth rate was similar in all strains.All Cells were grown at 37°C in minimal medium for microtiter plate-based LacZ assays as previously described . Backgroet al. . Three different lac operators  were placed in the untranslated region between the promoter and lacZ  and a 102 bp promoter–operator spacing. Roadblocking ranged from 96% for the weaker promoters (Rf = 0.04) to 73% (Rf = 0.27) for the strongest promoter, confirming the RNAP cooperation effect seen previously in vitro and in vivo . This and any other elongating RNAP on the promoter–spacer–operator DNA segment were advanced by 1 bp in each time step (1/40 s)  . Althoug(1/40 s) ,38, we tlus RNAP , and conlus RNAP ,32. RNAPTk or moved forward to dislodge the roadblock protein , while if multiple RNAPs were queued at the roadblock, a different rate kMD (multiple dislodgement) was applied to allow for RNAP cooperation. Note that we did not explicitly model backtracking at the roadblock. Binding of the LacI roadblocker to its 20 bp operator occurred with a rate Bk (s−1) as long as the operator was free of RNAP. The same Bk value was used for each of the three lac operators, and was calculated as the product of the LacI concentration and its on-rate constant 2.51 × 106 M−1s−1, obtained from in vivo imaging experiments estimating a single LacI tetramer in the cell takes ∼4 min to find its operator   with a rate n Figure . We allooperator . The five Figure . The dis) Figure , calcula1 and O2 .pL and pBla promoters, for which in vivo firing rates have been estimated under different growth conditions by comparison with ribosomal RNA promoters . To obtain estimates of ‘absolute’ firing rates (Supplementary Table S1), we included in our promoter series the λromoters .Rf) was determined in the simulations by comparing the number of RNAPs passing the operator per unit time in the presence of LacI with the number passing in its absence. Simulations were repeated in a Monte Carlo simulated annealing approach to find values of Tk, kSD and kMD that could best reproduce the observed Rf versus promoter strength data for the Oid roadblock with 250 nM LacI. The model gave a reasonable fit to the data .kSD in the stochastic model means that the best fit is obtained with a single RNAP being able to actively dislodge the LacI roadblock at Oid, that is, increasing its rate of unbinding. While some effect of a single RNAP on roadblock dissociation seems intuitive, direct evidence for dislodgement by a single RNAP has been lacking.The non-zero value obtained for Fk = 0.00033 s−1), 220-fold slower than Tk. Thus, the ∼4% Rf seen with this promoter is due to three processes: (i) ‘dislodgement’ of the roadblock by a single RNAP, (ii) ‘escape’ due to spontaneous roadblocker unbinding that allows an RNAP paused at the roadblock to resume elongating, and (iii) ‘avoidance’ of the roadblock, where the RNAP arrives at an unoccupied operator. Avoidance must be exceedingly rare in our experiment, since Oid is only Uk/(Bk+Uk) = 0.07% unoccupied  = 0.67% readthrough . Thus, the majority of the readthrough seen must be due to single RNAP dislodgement. This was confirmed by our simulations, where for weak promoters, dislodgement of the roadblocker by a single RNAP contributed to 73% of the transcripts passing the lac operator . It is possible for the model to give a reasonable fit to the data with urements  indicateeriments . Lower RA number of mechanisms might cause the increase in readthrough with increased RNAP flux Figure . MultiplkMD) estimated to be ∼17-fold higher than by a single RNAP  = 1/exp(gap/(1/Fk)) (pL (Fk = 0.2 s−1), the lac operator is unoccluded 78% of the time. Thus, this non-queue-associated occlusion effect is small.The RNAP-density mechanisms for cooperation alter the magnitude of readthrough but their effect is largely dependent on multiple RNAP dislodgement. We distinguish two mechanisms, ‘occlusion’ and ‘facilitated absence’. Occlusion occurs when the next RNAP behind an RNAP that has just traversed the operator follows so closely that there is no room for LacI to bind to the operator. The occlusion effect is large Figure  and D pr/(1/kF))  indicateOid and 250 nM LacI, this effect is small .Facilitated absence occurs when the gap between an RNAP crossing the operator and the next RNAP is large enough that LacI ‘could’ bind but does not do so before the second RNAP reaches the operator. For l Figure  and D bein vitro . Instead, the simulations indicate that this increased readthrough is due mainly to facilitated absence, where RNAPs get through before LacI can rebind after a dislodgement event , the average time for LacI rebinding is ∼27 s. Thus, at high firing rates, a LacI dislodgement will lead to additional RNAPs passing the operator before it refills.The lack of effect of reduced LacI concentration on slower promoters indicates that the increased readthrough for strong promoters is not simply due to low natural occupation of the operator by LacI (the absence mechanism). Even at 15 nM LacI, t Figure . For λpLm Figure . At 15 nTk, kSD and kMD similar to those obtained from fitting of the 250 nM LacI concentration alone . Since these parameters are derived from a larger data set, they were used in subsequent modeling.Repeating the parameter fitting using the data from all five LacI concentrations  is the same for different operators, the effect of these DNA sequence changes is to increase the rate of LacI unbinding from the operator, Uk. We found large increases in readthrough with decreasing LacI–operator affinity for both slow- and fast-firing promoters  and O2 (47% of Rf = 25%) for weak promoters at 250 nM LacI .Optimal fits to our experimental data were obtained with the rate of termination Δmfd reporter strain and repeated our assays using 11 promoters, two lac operators and three LacI concentrations. Deletion of mfd had a strong effect on roadblocking  and ∼80% for O2 (compared to ∼25–40% for +mfd). Strikingly, the effect of increasing promoter strength was quite different to the +mfd case, with a decrease in Rf seen at the highest promoter activities.To quantitate the role of Mfd in this termination, we constructed a g Figure  and B. FkSD and kMD  suggest that forward translocation of the stalled RNAP by Mfd may sometimes lead to dislodgement of the roadblock without termination.These data could be well fitted by the stochastic model by reducing the rate of termination, combined with small decreases in Δmfd cells is due to promoter clogging, where the queue of RNAPs builds up at the roadblock and extends backward along the DNA, eventually preventing RNAP binding to the promoter. Once the RNAP flux is sufficient to clog the promoter, the rate of RNAP passage past the roadblock cannot increase  is quite low, even for the highest promoter strengths, but increases substantially with shorter spacers Figure . Howevers Figure  and F. IRf with increasing promoter strength. A 1-RNAP spacing should also lead to promoter clogging and reduced Rf at high promoter strengths.Our modeling Figures  and 6A pOid to either 60 bp (2-RNAP) or 30 bp (1-RNAP) and measured readthrough using 10 promoters and 250 nM LacI  were increased some 2.1-fold and 5.3-fold, respectively , roadblocker concentration (Bk), roadblocker unbinding (Uk) and termination (Tk)—can be well explained by remarkably straightforward mechanisms of RNAP–roadblock interaction , Uk and kSD are likely to fall outside these ranges for some DNA-binding protein/operator combinations. Can we predict the level of roadblocking in these cases? Higher unbinding rates should lead to increased readthrough, and we can calculate using the analytical model  that increasing Uk 10-fold beyond that for LacI-O2 (to Uk ∼0.1 s−1) should increase Rf from 26% to 68%, while a 100-fold increase in Uk (to Uk = 1.0 s−1) would almost eliminate roadblocking (Rf = 98%). A 100-fold increase in Uk would give an affinity comparable to the weak lacO3 operator  gave a ∼3-fold increase in kSD for a 10-fold increase in Uk and a calculated Rf = 72% (at 250 nM LacI), only slightly above the 68% without an increase in kSD. Thus, once unbinding is fast, dislodgement becomes unimportant.The critical variables in the low RNAP flux regime are s Figure . Althougoperator . AllowinkMD the critical parameter. We estimate kMD to be ∼4- to 10-fold higher than kSD if all stalled RNAPs are subject to the same termination rate, and 2- to 4-fold higher than kSD if RNAPs can protect each other from termination . Frequent dislodgement and high flux can make rebinding of the roadblock protein limiting, so that Bk also becomes significant in this regime. For weaker DNA-binding proteins, spontaneous unbinding can also be significant. Increasing Uk to 0.1 s−1 would increase λpL readthrough from Rf = 45% (for O2) to Rf = 73% (at 250 nM LacI). If kMD values continue to rise with increasing Uk , then readthrough increases further; to Rf = 91% for λpL (if kMD = 0.1 s−1).In the high RNAP flux regime, most readthrough relies on roadblock dislodgement by multiple RNAPs, making in vivo on-rate  suggests that the first model cannot be the sole explanation of cooperation. If trailing RNAPs simply increase the frequency of dislodgement attempts by the leading RNAP, then the ratio kMD/kSD should be the same for the three different lac operators. Instead, kSD is more sensitive to increased binding affinity than kMD; in both the standard and the termination protection models, kSD drops ∼4.2-fold from O2 to Oid, while kMD drops only ∼1.7-fold . A worse fit to the data was obtained if the kMD/kSD ratio was held fixed across all three operators. This suggests that some extra dislodgement capability is available with multiple RNAPs, supporting the second ‘combined push’ model.Our analysis indicates that multiple RNAPs also actively increase the dislodgement of the roadblock, as opposed to acting solely by protecting each other from termination. Two non-exclusive basic mechanisms might account for increased dislodgement. (i) In the presence of RNAP restart factors, the leading RNAP can make multiple attempts to transcribe into the roadblock, with the overall success of dislodgement proportional to the number of attempts. In this mechanism, the trailing RNAP aids dislodgement by acting as a restart factor by pushing a backtracked leading RNAP forward . (ii) ThpL promoter, giving a flux of ∼1 RNAP per 5 s, roadblocking by LacI caused loss of 75% to 25% of the transcribing RNAPs, depending on the binding site and LacI concentration. Maximizing cooperation by lowering the termination rate also did not eliminate roadblocking because expression from stronger promoters became limited by promoter clogging  of DNA binding proteins, such that spontaneous unbinding and dislodgement is relatively fast (see above), is a more effective and general mechanism than RNAP cooperation, reducing roadblocking of both weak and strong promoters. However, without compensatory mechanisms, increasing the unbinding rate of a DNA-binding protein from a particular site in order to avoid roadblocking would reduce the site occupancy and may compromise its normal function. Our analysis shows that roadblocking and site occupancy are, to some degree, independently tunable —has little effect on roadblocking unless the promoter is very strong . Whether stronger protein–DNA interactions could reduce dislodgement further to increase roadblock regulation is not clear. Thus, to give a regulatory range comparable to promoter repression, multiple roadblocks may be required.On the other hand, what does our analysis tell us about how roadblocking could be maximized for regulatory purposes both in natural and engineered systems? The maximum roadblocking effect we saw was a 25-fold inhibition of transcription from weak to moderate strength promoters when the strongest roadblocker affinity was used , NusG, pppGp(p) and DksA (Other factors beyond those we tested are likely to be important for roadblocking. The DNA sequence upstream of the roadblock could affect RNAP–roadblock collisions, RNAP termination probabilities and the probabilities of backtracking and restart ,23,52. Aand DksA  are likeand DksA  are alsoIt is also conceivable that dislodgement rates could be different for protein–DNA complexes with the same overall unbinding rate, possibly due to different arrangements of strong and weak atomic level protein–DNA contacts, supported by the strong effect of orientation on roadblocking by dCas9–RNA complexes ,16. FurtSupplementary Data are available at NAR Online.


There are nine hot spots of DSBs located in human rDNA units3–6. Here we describe that the profiles of these hot spots coincide with the profiles of γ-H2AX or H2AX, strongly suggesting a high level of in vivo breakage inside rDNA genes. The data were confirmed by microscopic observation of the largest γ-H2AX foci inside nucleoli in interphase chromosomes. In metaphase chromosomes, we observed that only some portion of rDNA clusters possess γ-H2AX foci and that all γ-H2AX foci co-localize with UBF-1 binding sites, which strongly suggests that only active rDNA units possess the hot spots of DSBs. Both γ-H2AX and UBF-1 are epigenetically inherited and thus indicate the rDNA units that were active in the previous cell cycle. These results have implications for diverse fields, including epigenetics and cancer genomics.Endogenous hot spots of DNA double-strand breaks (DSBs) are tightly linked with transcription patterns and cancer genomics Here, we report that the hot spots are generated in vivo only inside transcriptionally active rDNA clusters. Together with the data that rDNA units are among the most fragile sites in the human genomein vivo DNA breakage. We also propose a model to explain the possible origin and the properties of hot spots of DSBs inside rDNA units.DNA breakage occurs during different physiological events in nuclei, including replication, transcription, and genome rearrangements. Exogenous factors, such as chemicals, radiation, viral infection, and a high-salt environment, also challenge the DNA integrity. Massive DNA fragmentation has been described in cancer cells and during the early course of a neurodegenerative disease135in vivo DSBs+ T lymphocytes practically coincide  using anti-γ-H2AX antibodies and a fluorescently labeled rDNA probe. We observed that the largest γ-H2AX foci are located mainly inside central areas of the nucleoli. The rest of the γ-H2AX foci are scattered inside the nucleus. The data obtained in interphase chromosomes from two different cell lines are shown in in vivo breakage inside IGS regions  in only the portion of rDNA clusters that possessed breaks in the previous cell cycle, we analyzed the metaphase chromosomes for individual rDNA clusters. Using combined immuno-FISH, we detected that, in the tested cell line, among seven rDNA clusters—which are visualized as double green dots, each one corresponding to one of the homologous chromosomes—there were only three clusters that co-localized with γ-H2AX foci . InteresTo test the supposition that only transcriptionally active rDNA clusters possess γ-H2AX marks, we determined whether the major γ-H2AX foci are co-localized with UBF-1 (Pol I-specific upstream binding factor) binding sites. It is known that UBF-1 plays an epigenetic role in the formation and maintenance of active rRNA gene chromatin11We also observed that γ-H2AX foci in metaphase chromosomes coincide with the binding sites of UBF-1 . At the To answer the question about the nature of Pleiades, which are present only in active rDNA clusters, we analyzed the binding profiles of three master regulators of gene expression in human cells. Our previous data indicate that the binding sites of CTCF precisely coincide with these hot spots of DSBs, suggesting a role for these nine regions of IGS in the regulation of expression of rDNA clusters1419We also found that the binding sites of the TCF7L2 (Transcription factor 7-like 2) master regulator overlaps with five hot spots of DSBs , shares a large proportion of target genes with GATA3 and was found to be located in nucleoliin vivo breakage of rDNA units. At the same time, the binding sites of KAP1, TCF7L2, GATA3, and T-bet transcription factors coincide with different sets of particular hot spots of DSBs inside rDNA units. Previously, these master regulators were considered as important regulators only in specialized cell types. Our data on their binding inside rDNA expand the functional role of these factors. The sizes and distribution of peaks shown for DSBs and γ-H2AX marks practically coincide, which suggests in vivo only in active rDNA clusters and play an important role in epigenetic regulation of these genes that produce about 80% of cellular RNA. The question arises regarding the origin and apparent role of these chromosomal breaks. Our data suggest that Pleiades are not the result of replication-mediated stress, because they occur in the resting CD4+ cells, which are not replicating, and that is why we suppose that these hot spots are connected with transcription. At present, however, we cannot determine what comes first: the transcription or the breakage? Whether R-loops can trigger genomic instability and are a source of the DSBs that occur at numerous transcribed regions has been discussed for many years2413in vivo in order to quickly load some preexisting multimeric protein complexes at particular regulatory regions inside the IGS ; GSM776558 (for GATA3 in Th1 cells); GSM776557 (for T-bet in Th1 cells); GSE74954 (for PARP1 in HEK293T cells). For profiling of DSBs in HEK293T cells, the data with accession number GSE49302 were used.Profiles of DSBs, H2A, H2AX, KAP1, TCF7L2, PARP1, and GATA3 along rDNA units were created from raw reads as described previously3 cells were used. The centrifugation was performed for 5 min at 2000 rpm.Immortalized human B-lymphocytes, and Epstein Barr virus-transformed GM06895 and GM06891 cell lines were obtained from the Coriell collection . Cells were grown in RPMI medium  with 10% heat-inactivated fetal bovine serum, plus penicillin and kanamycin. Before preparation of spreads, cells were incubated with Colcemid (Gibco) at 100 ng/mL for 8 h. Then the cells were suspended in 1 mL of 0.075 М KCl and incubated for 20 min at 37°С. The spreads were prepared using a Shandon Cytospin 4 (Thermo Scientific) centrifuge. For each slide, about 10 × 10in situ suppression) was performed by hybridization of a probe for 1 h at 42°С in a wet chamber. Consecutive washes were performed at 42°С in a solution containing 50% formamide and 2xSSC (three times for 3 min each), followed by washing with 2xSSC (two times for 3 min each). Finally, the slides were washed in 0.2xSSC for 3 min. Fluorescein avidin DCS (Vector Laboratories) and biotinylated anti-avidin antibody  were used for staining. The slides were mounted in DAPI/DABCO. The analysis was performed with a fluorescent Olympus BX53 microscope using the coordinates selected after immunostaining.Immunostaining was performed using primary mouse monoclonal antibodies to γ-H2AX  in KCM buffer  containing 3% BSA. Incubation was performed for 1 h at room temperature. Incubation with secondary antibodies  was performed under the same conditions. The spreads were fixed in 4% (v/v) formaldehyde in KCM buffer, and mounted in DAPI/DABCO. The slides were analyzed using a fluorescent Olympus BX53 microscope. Plasmid pHr13, containing ~10 kb of the human rDNA regionThe slides were prepared as described above. Immunostaining was performed using primary rabbit monoclonal antibodies to UBF1  and primary mouse monoclonal antibodies to γ-H2AX  as described above. The staining was performed by incubation with secondary antibodies FITC-conjugated goat anti-rabbit immunoglobulin  and Alexa 555-conugated rabbit anti-mouse immunoglobulin, Abcam, 150126, for visualization of γ-H2AX foci).How to cite this article: Tchurikov, N. A. et al. Hot spots of DNA double-strand breaks in human rDNA units are produced in vivo. Sci. Rep.6, 25866; doi: 10.1038/srep25866 (2016).


The new IVT-SAPAS provides a rapid and high-parallel procedure for APA library construction with low-input sample, which may be a new robust approach for studying APA.Gene transcribing with alternative polyadenylation (APA) sites leads to mRNA isoforms, which may encode different proteins or harbor different 3'UTRs. APA plays an important role in regulating gene expression network among various physiological processes, such as development, immune responses and cancer. Several methods of library construction for APA study have been developed to apply high-throughput sequencing. However, the requirement of high-input RNA and time-consuming nature of the current methods limited the studies of APA for the samples difficult to obtain. Here, we describe a new method based on our SAPAS in combining  Transcription termination by RNA Pol II (RNAPII) in eukaryotes is conducted with recognizing the poly(A) signals by cleavage and polyadenylation complex factors. More than half of the genes harbor alternative polyadenylation (APA) sites. Tandem APA sites located in 3'UTR region can lead to transcription of different mRNA isoforms with various 3'UTRs. Various biological effects associated with tandem APA were investigated, including cancer transformation , 2, embrin vitro transcription and magnetic bead-based purification and size selection. With the new method, as low as 200 ng of total RNA is sufficient to obtain 3' end library, benefiting from the good linear amplification of in vitro-transcription and high recovery rate of magnetic bead-based method. At the same time, the magnetic bead-based purification and size selection method also makes the library preparation high-parallel.Noticed the important function of APA, several groups , 11–14 d® Mini kits, and maintained in RNase-free water. The quality of the samples was checked with agarose gel electrophoresis and OD260/280 ratio greater than two.A breast cancer cell line MCF7  was cultured in Dulbecco’s modified Eagle’s medium (DMEM), and a human normal mammary epithelial cell line MCF10A  was cultured in monolayer in DMEM/F12. Total RNA was extracted from the cells using QIAGEN RNeasy1) RNA fragmentation. About 200 ng total RNA for each sample was randomly fragmented with heating at 80°C for 30 minutes. 2) The first round of reverse transcription. An anchored oligo d(T) tagged with Illumina A adaptor and T7 promoter was used for the first strand cDNA synthesis, and then the second strand was synthesized with RNase H, E. coli DNA polymerase and DNA ligase. The frayed termini of double-stranded cDNA were polished with T4 DNA polymerase. Subsequently, the products were purified with Agencourt RNAClean XP kit (Beckman Coulter). 3) In vitro transcription. RiboMAX™ Large Scale RNA Production System-T7 (Promega) was used to perform in vitro transcription with the manufacturer's manual, and then the template cDNA was removed with RQ1 RNase-Free DNase (Promega). The RNA product was also purified with Agencourt RNAClean XP kit (Beckman Coulter). 4) The second round of reverse transcription. Random primers tagged with part of Illumina B adaptor were used for the first strand cDNA synthesis, and then the product was purified with MinElute PCR Purification Kit (Qiagen). 5) PCR amplification. Illumina adaptor B with 6-mer barcode and modified oligo d(T) tagged with Illumina adaptor A were used to perform the PCR amplification. Finally, size selection of 200-500bp fragments of the PCR product was performed with AMPure XP Beads (Beckman Coulter), and then the quality of the library was checked with Agilent 2100 Bioanalyzer. The details of protocol and primers were shown in Twelve libraries with different barcodes were pooled together and sequenced with Hiseq 2500 with rapid run mode. To overcome the problem of the homogeneous nucleotide T of the first 20 bases, 20 dark cycles were taken and then 55 bp were further sequenced.The raw reads were mapped to the human genome (hg19) using Bowtie  and inteA dataset of genes was constructed by obtaining the genes with the largest 3’UTR for each stop codon from UCSC known genes. The merged poly(A) sites were mapped to these 3'UTRs of the new dataset, and the poly(A) sites mapped to a single 3’UTR were used for further analysis. The expression levels of poly(A) sites were calculated as the number of raw reads scaled to the sample with the lowest number of reads.To reduce the variance of 3’UTR length across genes, we standardized the length by designating the longest 3’UTR as 1.0 and calculated the weighted mean of 3’UTR length with multiple APA sites for each gene.r was calculated using the number of reads in the table as the values and the scores for rows and columns as coordinates; and 4) We calculated the statistic M2 = (n-1)r2, which is an approximated chi-square distribution with df = 1 for large samples. P-value was obtained, and the Benjamin-Hochberg FDR was estimated with R software.A test of linear trend alternative to independence  was usedin vitro transcription with magnetic bead based purification and size selection, we developed IVT-SAPAS  sequencing, including our SAPAS method , requireVT-SAPAS . BrieflyUsing IVT-SAPAS, with 200 ng of initial total RNA, we profiled poly(A) sites of a human mammary epithelial cell line (MCF10A) and a breast cancer cell line (MCF7) with Hiseq 2500 rapid mode. Three biological replicates were performed for each cell line. On average, 27.9 million raw reads for each sample were obtained . In totaTo check the poly(A) site sequencing efficacy of IVT-SAPAS, we first annotated the reads and poly(A) sites to known poly(A) sites, 3'UTRs, introns, CDS, 1 kb downstream, and noncoding gene and intergenic regions. We found that 90.5% of reads could be mapped to poly(A) sites in the UCSC and Tian's poly(A) databases , and 35%2) of poly(A) site expression levels  sites to these 3'UTRs of the new dataset. In total, 10,341 genes were mapped with 9,812 UCSC known gene clusters. We created a scatter plot and calculated pairwise correlation coefficients (Rn levels . The avehttp://david.abcc.ncifcrf.gov/) revealed that mitotic cell-cycle-related genes were enriched in genes with shorter 3'UTRs in MCF7 , including CCND1 and CKD6, which is consistent with our previous study using SAPAS [The 3'UTRs with multiple poly(A) sites were defined as tandem 3'UTRs. These poly(A) sites were tandem APA sites. The data were used for the subsequent analysis. We first calculated the mean standardized 3'UTR length of each gene in each sample. Both box  and scatng SAPAS .We found 511 UCSC known gene clusters with different stop codons with reads mapped to 3'UTR regions, which can lead to truncated protein. We summed the poly(A) reads of each gene for each sample as their expression levels. Then, with the mean expression level of each cell line, we compared the gene expression between MCF7 and MCF10A and found 72 significant gene clusters  . Among tTMPRSS3  are usually amplified for poly(A) sites sequencing, then more abundant total RNA are usually required for these methods than RNA-seq. Actually tens of μg total RNA are required for previous methods to construct poly(A) sites sequencing library , 11, 14.In this method, we directly add the Illumina adaptors by reverse transcription and PCR, which reduces the difficulty of the experiment comparing to 3P-seq  and 3'REWe found genome wide shorter 3'UTRs in cancer cell than normal cell, which is consistent with previous findings , 2. FurtS1 FigThe poly(A) sites were classified into eight classes as described in text.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigThe red lines show the diagonal lines.(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Tablep values were corrected by bonferroni method. The genes confirmed by qRT-PCR were labeled by yellow color.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.