The cohesin complex consists of multiple core subunits that play critical roles in mitosis and transcriptional regulation. The cohesin-associated protein Wapal plays a central role in off-loading cohesin to facilitate sister chromatid separation, but its role in regulating mammalian gene expression is not understood. We used embryonic stem cells as a model, given that the well-defined transcriptional regulatory circuits were established through master transcription factors and epigenetic pathways that regulate their ability to maintain a pluripotent state.cis-regulatory element (CRE) with the c-Fos promoter.RNAi-mediated depletion of Wapal causes a loss of pluripotency, phenocopying loss of core cohesin subunits. Using chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq), we determine that Wapal occupies genomic sites distal to genes in combination with CTCF and core cohesin subunits such as Rad21. Interestingly, genomic sites occupied by Wapal appear enriched for cohesin, implying that Wapal does not off-load cohesin at regions it occupies. Wapal depletion induces derepression of Polycomb group (PcG) target genes without altering total levels of Polycomb-mediated histone modifications, implying that PcG enzymatic activity is preserved. By integrating ChIP-seq and gene expression changes data, we identify that Wapal binding is enriched at the promoters of PcG-silenced genes and is required for proper Polycomb repressive complex 2 (PRC2) recruitment. Lastly, we demonstrate that Wapal is required for the interaction of a distal Collectively, this work indicates that Wapal plays a critical role in silencing of PcG target genes through the interaction of distal CREs with promoters.The online version of this article (doi:10.1186/s13072-016-0063-7) contains supplementary material, which is available to authorized users. The cohesin complex plays a critical role in connecting distal cis-acting DNA elements to gene promoters by facilitating DNA loops [cis-regulatory elements by “looping-out” the intervening chromatin segment [Gene expression is regulated by the complex interplay of NA loops –3. In adNA loops . In mitoNA loops , 6. Whil segment –4, 7. In segment , 8–10. D segment , 8, 11.  segment , 13. HowSmc1a, Smc3, and Rad21 . A subset of patients with Cornelia de Lange Syndrome (CdLS), characterized by microcephaly, cognitive impairment, abnormal facies, and other malformations has mutations within core subunits of the cohesin complex including iewed in , 15. Howiewed in , 17. GivCompared to the core cohesin subunits, far less is known about the role of Wapal in transcriptional regulation. In mammals, Wapal plays a role in off-loading cohesin to prevent chromatin condensation , implyincis-regulatory elements (CREs) termed Polycomb response elements (PREs) [How Polycomb complexes are targeted to specific genomic regions remains a critical question within epigenetics given their important role in cellular differentiation . In Dross (PREs) , 19. In s (PREs) , nonspecs (PREs) , interacs (PREs) , 23, or pluripotency, or the ability to differentiate into all three primitive germ layers  that give rise to the embryo, and self-renewal, or the ability to propagate indefinitely in an undifferentiated state. The transcriptional and epigenetic pathways that regulate both functions have been well defined through a series of genome-wide approaches. Our study demonstrates that Wapal plays a central role in regulating transcription by assisting in Polycomb group-mediated gene silencing.To better delineate the role of Wapal in mammalian transcriptional regulation, we have chosen murine ESCs as a model system. ESCs are unique because they possess two critical properties: IRES:EGFP) cassette inserted into the Oct4 locus, allowing EGFP expression to be a surrogate marker for pluripotency. The same approach has been used to generate a flow cytometry-based assay to quantitate changes in pluripotency [p value <0.08). Taken together, the changes in cell morphology and alkaline phosphatase activity and decrease in Oct4:IRES:EGFP expression indicate that Wapal depletion induces a loss of pluripotency and subsequent differentiation of ESCs. Similar results have been observed by other groups when depleting Smc3, Smc1a [Depletion of cohesin complex core subunits  causes a loss of pluripotency when depleted in ESCs , 11. To ipotency , 27. AftWe further examined the effects of each shRNA on known pluripotency markers Fig. b. Both sGiven the role of Wapal in off-loading cohesin from sister chromatids at the end of mitosis, we assessed the cell cycle to determine whether Wapal depletion induced aneuploidy, which could cause a loss of pluripotency. We measured DNA content by propidium iodide (PI) staining in ESCs 48 h after Wapal depletion and assessed the percent of cells in different stages of the cell cycle or that exhibited aneuploidy Fig. e. We choTo ensure that the pluripotency changes were not secondary to off-target effects of our shRNAs, we generated an ESC line expressing an epitope (v5)-tagged version of full-length Wapal immune to shRNA #2 by mutating every third base in each codon within the shRNA recognition site. This strategy prevents recognition by the shRNA without altering the linear amino acid sequence. Infection with shRNA #2 on wild-type cells caused a loss of pluripotency as assessed by cell morphology that was completely rescued in cells expressing the full-length Wapal immune to the shRNA  were in extragenic regions, and only a small percentage 5 %) were within gene promoters Fig. a. Wapal  % were wRecently, it has been demonstrated that Wapal antagonizes cohesin binding to chromatin during interphase, similar to its role during mitosis , 12, 13.To assess transcriptome changes after Wapal depletion, we hybridized cDNA generated from ESCs 48 h post-lentiviral infection to microarrays. We chose a short time frame to minimize differentiation effects and enrich for target genes. Wapal-depleted cells showed no changes in morphology as compared to empty vector at this early time point . As a first step, we focused on gene sets derived from pluripotent cells as being more pertinent to our analyses. In particular, we noted that Polycomb-marked genes were derepressed after Wapal depletion in pluripotent cells [To identify pathways or groups of genes dysregulated by Wapal depletion, we utilized gene set enrichment analysis (GSEA) that measures whether a defined set of genes shows a statistically significant, concordant difference between two biological conditions . We scrent cells –38. We ont cells . Two genTo validate whether Wapal depletion causes a derepression of PcG-silenced genes, we downloaded two different, independent gene sets where PcG target genes were identified , 40. BotRecent studies in Drosophila  indicateWe also hypothesized that if Ring1b genomic changes were secondary to differentiation of ESCs, rather than genomic occupancy induced by Wapal, we should see altered Ring1b occupancy at genes either upregulated or downregulated during differentiation. To address this, we identified the top 1000 genes upregulated or downregulated by depletion of Nanog or Oct4 and assessed changes in Ring1b occupancy  of Wapal-binding sites are within promoters. In fact, only 3 % of the high-confidence PcG-marked genes (1455) we identified had a Wapal-binding site within 2 kb of the transcriptional start site  had combined Wapal-/CTCF-binding sites outside the promoter but within 10 kb of the TSS. In both cases , we were unable to identify a Wapal-binding site within 10 kb of the gene, but nonetheless noted evidence of Wapal enrichment around the TSS  48 h after depleting Wapal with two shRNAs  were included as a negative control; we observed no enrichment at these loci using a nonspecific IgG (data not shown). Given the results of our GSEA, we focused on the PRC2 mark H3K27me3. In addition, per the classical models of PcG action in mammals PRC1 activity is preceded by PRC2-mediated deposition of H3K27me3 [HoxD1 and Lefty1) showed a statistically significant reduction in Suz12 occupancy with only one shRNA, but the trend was consistent with the second shRNA. Collectively, these data demonstrate that Wapal depletion is associated with decreased PRC2 occupancy and activity at derepressed genes.To determine whether Wapal depletion caused global alterations in the activity of either PRC1 or PRC2 complexes, we measured protein levels by Western blotting with antibodies specific for PRC marks (H2Aub1 and H3K27me3) along with core subunits Ring1B (PRC1), Ezh2 (PRC2), and Suz12 (PRC2). In each case, we saw trivial changes in the protein expression of the histone marks and core PRC subunits  to directly measure the interaction frequency between the two. An area spanning 26 kb downstream of the c-Fos promoter was probed for interactions using an anchor primer near the TSS. An interaction was observed between the c-Fos promoter and the Wapal/CTCF distal CRE in cells infected with the empty vector, but no interactions were observed with primers flanking the Wapal-binding site  has been used as a model to understand Wapal’s role during interphase. Genetic deletion of Wapal  or a doml allele  increasel allele . This hiEzh2/−− and EED/−− ESCs are pluripotent even though they lack PRC2 activity [EED/−− cells that lack H3K27me3. It is more likely that improper genomic localization of PRC2, rather than a complete loss of its activity, results in a loss of pluripotency. Polycomb-silenced genes are typically developmental regulators [Our data demonstrate that Wapal plays a critical role in regulating the epigenome, specifically PRC2-mediated H3K27me3 deposition. Whether this ultimately causes Wapal-depleted cells to lose pluripotency remains an unresolved issue. Deletion of either the catalytic subunit (Ezh2) or the core subunit (EED) required for PRC2 stability causes an early embryonic lethal phenotype in mice. However, both activity , 53. In gulators , 55, andgulators .c-Fos expression, with Wapal depletion associated with decreased H3K27me3 at the promoter and reduced interaction with a CTCF-occupied distal CRE. Given the high degree of overlap between Wapal and CTCF (97 %), we hypothesize two different models to explain how Wapal and CTCF may facilitate Polycomb targeting to effect gene silencing  can be inv-en gene complex [Seminal studies in Drosophila regarding the role of cohesin in regulating gene expression and the epigenome demonstrate some similar but also disparate results from our own. In Drosophila, cohesin depletion decreased long-range interactions between a PRE and the  complex , which i complex , 59, 60  complex . While t complex . In mamm complex , 63, med complex . In cont complex . In fact complex , 65. Thucis-regulatory regions with gene promoters. However, the role of the cohesin off-loading protein Wapal in regulating gene expression has not been described. We show that Wapal depletion by RNAi induces the differentiation of ESCs, implying that it is required for pluripotency much like other core cohesin subunits. Wapal co-occupies genomic sites distal to genes in conjunction with other core cohesin subunits and CTCF, and at genomic sites it occupies Wapal does not appear to promote cohesin of off-loading. Wapal depletion causes a global derepression of Polycomb-silenced genes, although overall PcG activity remains unchanged. Lastly, Wapal facilitates the interaction between a distal CRE with the c-Fos promoter. Collectively, these data demonstrate that Wapal plays a critical role in PcG-mediated gene silencing.The core cohesin subunits play a critical role in transcriptional regulation by facilitating DNA loops that permit the interaction of distal Gelatin-adapted ESCs (129SVJ-dervied) were cultured as previously . Bright-Total RNA and total protein were collected as previously , 66. If 3VO4. Protease inhibitors were added to buffer as follows: 1:1000 DTT , 5:1000 PMSF , and 1:1000 Protease Inhibitor Cocktail . Equal protein (10–50 μg) was run using standard SDS-PAGE separation techniques and blotted. Protein quantification was performed with Image J. All samples were normalized to a loading control  and an empty vector or normal control.Whole-cell extracts were prepared from cells by lysis in 50 mM Tris (pH 8.0), 10 % glycerol, 0.7 % NP-40 substitute , 0.1 mM EDTA, 250 mM NaCl, 50 mM NaF, and 0.1 mM NaSmc3 , Sall4 , Wapal , Scc1/Rad21 , Nanog , GAPDH-HRP , total H3 , H3K27me3 , H3K4me3 , anti-Brachyury , anti-v5 , Streptavidin–HRP , anti-Suz12 , anti-Ezh2 , anti-Ring1b , anti-H2Aub1 .RNAi/TRC consortium lentiviral constructs were obtained from either Open Biosystems or Sigma-Aldrich. The following constructs were utilized: pLKO.1 is the parental viral vector without shRNA. Sall4 (TRCN0000097821) was used as a positive control and can be rescued by the Sall4b cDNA , indicatAntibody-mediated ChIP and biotin-mediated ChIP (bioChIP) were performed similar to our previous work . PrimersThe Wapal open reading frame was obtained from Open Biosystems and used as a template to perform site-directed mutagenesis to disrupt the third base pair of each codon within the shRNA complementary region, thus making it immune to the shRNA without altering the protein-coding sequence. A c-terminal v5 epitope tag was added to aid in detection. The cDNA was then cloned into the pPyCAG iH vector  was cloned into the Cas9-expressing vector px459 [IRES:EGFP cassette flanked by homology regions from Oct4 was obtained from Addgene (plasmid #48681) and co-transfected along with the guide RNA into 1–2 × 106 wild-type ESCs. Transfected cells were selected with puromycin (2 μg/mL first 2 days only) and G418 (500 μg/mL daily) until single colonies appeared. Individual clones were isolated and cells that expressed GFP only in the pluripotent state were isolated, expanded, and used for subsequent experiments.To generate the or px459 . A homolp value of 10−5). For each sample , at least 50 million aligned reads were used for peak calling, and peak calling was performed by comparing experimental samples to input as a control . Annotation of peaks was done using CisGenome [Biotin-mediated Chromatin IP (bioChIP) was performed similar to previously , 25. BriisGenome . PublishisGenome  when comhttp://www.broadinstitute.org/gsea/msigdb/collections.jsp).Total RNA was extracted using Trizol from ESCs infected with either empty vector (pLKO.1) or two, independent shRNAs to Wapal or Smc3. Two biological replicates for each shRNA or empty vector were used. Probes were generated and hybridized to Affymetrix Mouse Genome 430 2.0 arrays as previously . CEL fil6 cells were resuspended, and 1.9 mL of cold 70 % ethanol was added in a dropwise fashion to fix cells. Cells were incubated at −20 °C for 30 min, spun down, and RNase digested at 37 °C for 30 min. Cells were stained with propidium iodide  for at least 5 min. Flow cytometry was performed and data was analyzed with FlowJo.ESCs were trypsinized, counted, and washed with PBS-/-. In 100 μL of PBS-/-, 0.5–1 × 106 mESC were fixed in a 1 % formaldehyde/10 % FBS solution for 10 min. Nuclei were isolated and digested with 1000U HaeIII (NEB R0108M) overnight at 37 °C while shaking. DNA was ligated with 7000 units of T4 DNA ligase (NEB M0202M) for 5 h at 16 °C. Samples were then de-cross-linked overnight at 65 °C. Following two phenol/chloroform extractions, the library was precipitated with a standard ethanol/sodium acetate protocol.For each library generated, approximately 15 × 10Twenty micrograms of BAC RP23206I6 (CHORI) was digested with 1250U HaeIII overnight at 37 °C while shaking. Digested BAC DNA was phenol/chloroform extracted then ethanol precipitated. BAC was then ligated with 7000 units of T4 DNA ligase overnight in a 16 °C water bath. Ligated BAC template was phenol/chloroform extracted and ethanol precipitated. Template was resuspended in TE.Template and BAC loading were normalized by subtracting the CT of the internal primers from the CT of the test primers yielding ∆CT. The ∆CT from the BAC was then subtracted from ∆CT of the template to remove random interactions and PCR biases from the test primers. Interaction frequency is then determined by the value of 2^−(∆CT of template - ∆CT of BAC).t test comparisons were made and p values <0.05 were considered significantly different. All error bars are SEM derived from three experiments unless specifically stated otherwise.Unless otherwise stated, All microarray and ChIP-seq data are available in GEO Omnibus (GSE63325).


Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene  at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies. Gene expression starts with transcription, a multi-step process that produces an RNA molecule that is complementary to the gene. Cells often control the amount of gene expression by controlling the amount of RNA produced through interactions between regulatory DNA and proteins involved in transcription. While the identity of the molecules that take part in this regulatory process is known for a number of different genes, their dynamics in cells is still poorly understood. We show theoretically that the cell-to-cell variability in the number of nascent RNA molecules, those still in the process of being synthesized by the RNA polymerase, carries the signature of transcriptional dynamics in cells. We analyze published nascent RNA distributions for a set of yeast genes and show that the data is inconsistent with a single-step model of transcription initiation. Instead we propose a coarse-grained model where initiation happens not in one but in two sequential steps. Our analytical framework can be used to extract quantitative information about the dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells. Transcription is a multi-step process that leads to the production of messenger RNA (mRNA) molecules from its DNA template. Genetic experiments on cells have identified the key molecular components of transcription, while biochemical studies with purified components have uncovered the basic mechanisms governing their dynamics and interactions in vitro. Still an important question that remains is whether the same mechanisms are also operational in cells. One approach to unraveling the mechanisms of transcription in cells is to measure the outputs of this process, either the proteins that correspond to the genes being transcribed, or the actual mRNA molecules. This idea has motivated numerous experiments that count protein –3, and min vivo by counting cytoplasmic RNA in single cells has led to important insights, a key limitation is that processes that are downstream from transcription initiation can mask the signature of transcriptional dynamics in measurements of the cell-to-cell variability of mRNA and protein abundances. A striking example of this is the recent finding that spatial and temporal averaging, i.e., the process of accumulation and diffusion of mRNA transcripts during nuclear cycles, significantly reduces the variability in mRNA copy number expected from stochastic transcription initiation , the probability of finding two or more RNAP molecules at the first base of the gene would be 5×10−5. This justifies one of the main assumptions of our model, namely that we can ignore the constraint that no base can be occupied by more than one polymerase. The assumption will be valid as long as the initiation time scale is slower than the elongation time scale, and it makes the model analytically tractable. As described in the results section, this assumption leads to simple formulas for the first two moments of the nascent RNA distribution in the large gene-length limit, and to a set of L2 linear equations in the case of stochastic elongation. As shown in the Our model  makes thtributed  and giveAs argued above, we expect the approximations made in our model to be reasonable for all but the strongest promoters characterized by very fast initiation ,44.In order to test this intuition, we compare the analytic predictions of our model with numerical simulations of a more realistic one  reaches steady state.E. coli and yeast cells [kP-= 4/sec, kP+= 0.01/sec, k = 80 bp/sec, as was reported for ribosomal promoters in E.Coli [L = 2000 bases and a polymerase whose DNA footprint is 30 bases.We consider four different transcription initiation rates, spanning the typically observed values in st cells ,19,23,25n E.Coli . We alsokOFF = 5/min, kON = 0.435/min, k = 0.8kb/min, kLOAD= 5/min and kESC= 0/min; kOFF, kON, k, and kLOAD are the characteristic rates for the PDR5 promoter, as reported in [kLOAD= 0.14/min, kESC= 0.14/min, kOFF = 0/min, kON = 0/min, k = 0.8kb/min; these are characteristics of yeast genes, such as MDN1 [kLOAD= 0.09/min, kESC= 0/min, kOFF = 0/min, kON = 0/min, k = 0.8kb/min, which are characteristics of the yeast gene RPB1 [We generated the plots for the Fano factor versus gene length , for the as MDN1 . For theene RPB1 .kINI/kOFF, where kINI = kLOADkESC/ (kLOAD+ kESC) is the average rate of initiation), or by modulating the burst frequency (kON),  [kOFF and kON. In the first case, we change the burst size by changing kOFF and taking the other parameter values to be, kON = 0.435/min, k = 0.8kb/min, L = 4436 bps, kINI= 5/min as reported for PDR5[kON, where the other parameters are, kOFF = 5/min, k = 0.8kb/min, L = 4436 bps, kINI= 5/min as reported for PDR5 [max, where meanmax is the maximum of the mean number of nascent RNAs which is obtained when there is no transcriptional regulation and the promoter is always active.Genes that are transcribed from a promoter that switches between an active and an inactive state can be regulated by changing the rates of switching between these two states, either by modulating the burst size (given by dulated) ,58. In oWe analyze the measured nascent RNA distributions for twelve different constitutively expressed yeast genes reported in reference . By applWhen considering experiments that count nascent RNAPs it is important to be mindful of the fact that the number of RNAP molecules along a gene is not necessarily equal to the nascent RNA counts. Transcribing RNAPs have partial nascent transcripts attached to them depending on how far along the gene they have progressed , as shown in In addition to the mean, we analyze the Fano factor of the nascent mRNA distributions as well, and compare it with the prediction from our model of transcriptional regulation . It is tWe also analyze the Fano factor of the nascent mRNA distributions of the other six genes: RPB1, MDN1, PUP1, PRE7, PRE3, PRP8, which initiate transcription at a different mean rate . Their lIt is to be noted that it might be difficult to experimentally tell the difference between a mature RNA or a single nascent RNA, there are 12 different genes for which data is available  and our kLOAD and kESC, respectively. For the gene RPB1 initiation is a one-step process, while for others  three steps are required to account for the measured nascent RNA distribution. We further assume that mRNA is degraded with a constant probability γ per unit time per molecule. The degradation rates of the twelve genes used in the calculation are those reported in reference [if only the initiation process was contributing to this variability. The fact that we observe a discrepancy between the mRNA variability calculated in this way and the measured mRNA variability . In other words an mRNA molecule is produced in two sequential steps, e.g., by first assembling the transcriptional machinery at the promoter DNA, followed by RNA polymerase escaping the promoter. These two steps are parameterized by the kinetic rates eference . Given teference –41. The iability  is indicS1 TextCalculation of the first two moments of the nascent RNA distribution.(DOCX)Click here for additional data file.S1 Fig1, from which transcription initiation occurs with a constant probability per unit time k1,ini, and state 2, from which transcription does not initiate. The promoter switches from state 1 to state 2 with probability per unit time k12, and from 2 to 1 with probability per unit time k21. After initiation each RNAP molecule hops from one base pair to the next along the gene at a rate k per unit time. Each RNAP molecule has a finite DNA footprint of 30 bp and it can pause at any site with a rate kP+ and come out of the pause with a rate kP-. An RNAP molecule cannot move forward if another one occupies the bases in front of it. The length of the gene is L. (B) No-traffic model. The elongation process is uniform with each RNAP molecule occupying one base pair. (C) Using the Gillespie algorithm [kP-= 4/sec, kP+= 0.01/sec, k = 80 bp/sec [E.Coli. The length of the gene is L = 2000 bps and the footprint of one RNAP molecule is 30 bps.The promoter switches between two states: state lgorithm ,46, we s0 bp/sec  as repor(EPS)Click here for additional data file.S2 FigHere we plot the mean of the nascent RNA distribution for all twelve genes: MDN1, PRP8, RPB1, PUP1, PRE3, PRE7, KAP104, TAF5, TAF6, TAF12, RPB2, RPB3, reported by Gandhi et al. . For six(EPS)Click here for additional data file.S3 FigHere we show 6 different constitutively expressed genes in yeast : RPB1, M(EPS)Click here for additional data file.S4 FigkOFF = 5/min, kON = 0.435/min, k = 0.8kb/min, kLOAD= 5/min and kESC= 0/min, which are characteristic of the PDR5 promoter in yeast, as reported in [kLOAD= 0.14/min, kESC= 0.14/min, kOFF = 0/min, kON = 0/min, k = 0.8kb/min, characteristic of MDN1 promoter, which we find by analyzing the data reported in ref. [kLOAD= 0.09/min, kESC= 0/min, kOFF = 0/min, kON = 0/min, k = 0.8kb/min, which are characteristics of the yeast gene RPB1, obtained by analyzing the data published in ref. [We compare the Fano factors for the one step, two-step and ON-OFF models with stochastic and deterministic elongation respectively, when the gene length is tuned. When the gene length is of the order of few thousand bases, the elongation process essentially becomes deterministic and hence the Fano factors for the models become similar for all the initiation models considered. To illustrate this point for the ON-OFF model we use the following parameters:  in ref. . For the in ref. .(EPS)Click here for additional data file.


Schizosaccharomyces pombe and humans. As a result, fission yeast has emerged as an important model system for dissecting mechanisms governing heterochromatin integrity. Thus far, over 50 proteins have been found to contribute to heterochromatin assembly at fission yeast centromeres. However, previous studies have not been exhaustive, and it is therefore likely that further factors remain to be identified.Heterochromatin plays important roles in the regulation and stability of eukaryotic genomes. Both heterochromatin components and pathways that promote heterochromatin assembly, including RNA interference, RNAi, are broadly conserved between the fission yeast To gain a more complete understanding of heterochromatin assembly pathways, we have performed a systematic genetic screen for factors required for centromeric heterochromatin integrity. In addition to known RNAi and chromatin modification components, we identified several proteins with previously undescribed roles in heterochromatin regulation. These included both known and newly characterised splicing-associated proteins, which are required for proper processing of centromeric transcripts by the RNAi pathway, and COP9 signalosome components Csn1 and Csn2, whose role in heterochromatin assembly can be explained at least in part by a role in the Ddb1-dependent degradation of the heterochromatin regulator Epe1.This work has revealed new factors involved in RNAi-directed heterochromatin assembly in fission yeast. Our findings support and extend previous observations that implicate components of the splicing machinery as a platform for RNAi, and demonstrate a novel role for the COP9 signalosome in heterochromatin regulation.The online version of this article (doi:10.1186/s13059-014-0481-4) contains supplementary material, which is available to authorized users. Schizosaccharomyces pombe, which possesses relatively simple but conserved RNAi and chromatin modification pathways, making it a powerful model system for dissecting mechanistic principles of eukaryotic heterochromatin assembly.Heterochromatin is a condensed form of chromatin of fundamental importance to the regulation and stability of eukaryotic genomes. It is characterised by methylation of histone H3 on lysine 9, a specific chromatin signature that facilitates binding of chromodomain proteins and other factors to create a transcriptionally repressive chromatin state . EvidencIn fission yeast, domains of constitutive heterochromatin are found at centromeres, telomeres, and the silent-mating-type locus. H3K9 methylation is mediated by a sole H3K9 methyltransferase, Clr4, which can be recruited to chromatin via both RNAi-dependent and independent pathways . At centRaf1 complex is therefore thought to represent a specialised paralog of the canonical Cul4-Ddb1DCAF complex, in which Rik1 and Raf1 substitute for Ddb1 and the DCAF, respectively [in vitro [in vivo [in vivo ubiquitination substrates have not yet been identified. Interestingly, in addition to the essential role of CLRC in H3K9 methylation, a role in maintaining robust heterochromatin has also recently been uncovered for the canonical Cul4-Ddb1DCAF complex [ddb1Δ mutant cells are largely alleviated by deletion of Epe1, consistent with a model in which Cul4-Ddb1Cdt2 contributes to the integrity of heterochromatin by mediating the ubiquitination, and hence degradation, of Epe1 bound within the interior of heterochromatin domains [The H3K9 methyltransferase Clr4 is found within a multi-protein complex called CLRC, all members of which are required for heterochromatin assembly -22. In aectively ,26. Consin vitro , and mut[in vivo , althoug complex . Deletio complex -30. Hete domains .Rapid progress in the identification of factors required for heterochromatin assembly in fission yeast has been made through a combination of genetic and biochemical approaches. The use of reporter genes to monitor heterochromatin integrity has proved a particularly powerful tool: because genes embedded in heterochromatin are typically repressed or ‘silenced’ (a phenomenon termed position effect variegation), loss of silencing represents a convenient indicator of defective heterochromatin . Previouade6+ reporter gene inserted into the heterochromatic outer repeats of centromere 1  inserted close to centromere 1 allowed selection for cen1:ade6+, while the ade6-210 mutant allele at the endogenous ade6+ locus was selected via an adjacent ura4+ cassette. The tester strain also contained the P56Q allele of the ribosomal protein gene rpl42+, which confers robust and recessive resistance to cyclohexamide thereby providing a means of selecting against diploids [mat2(P) and mat3(M), marked with LEU2+, to allow selection for homogeneity in mating-type. High throughput crossing of the tester strain to the deletion library  followed by direct plating on selective media allowed us to select haploid cells bearing both a single gene deletion and the cen1:ade6+ reporter gene  and via growth on media lacking supplementary adenine . Scoring was done semi-quantitatively on a scale of 1 to 4, where 4 is equivalent to wild-type silencing, and 1 indicates strong ade6+ de-repression , and two affecting both the centromere and silent mating-type locus . Sde2, a protein recently implicated in heterochromatic silencing particularly at telomeres [cen1:ade6+ marker gene silencing that are detectable via both pale colour on plates containing limiting adenine, and increased growth in the absence of adenine  is disrupted upon deletion of Ddb1, Csn2 or (to a lesser extent) Csn1, as evidenced by increased growth on media lacking uracil, and reduced growth in the presence of the counter-selective drug FOA . Interestingly, levels of Epe1 were also found to be elevated in csn1Δ/spd1Δ and csn2Δ/spd1Δ mutant cells, although to a lesser extent than in ddb1Δ/spd1Δ cells; this is consistent with a model in which deletion of Csn1 or Csn2 impairs the function of the Cul4-Ddb1Cdt2 complex in removal of Epe1 from heterochromatin. We also tested whether deletion of Epe1 can rescue the heterochromatic silencing defect in csn1Δ and csn2Δ mutant cells, by analysing expression of the silent mating-type reporter gene mat3-M:ura4+. csn1Δ/spd1Δ and csn2Δ/spd1Δ double mutants displayed silencing defects similar to those seen in the csn1Δ and csn2Δ single mutants, confirming that, as for ddb1Δ, the observed effects on silencing are independent of Spd1-mediated effects on cell cycle  where RNAi is known to be recruited via the RNA-binding protein Mmi1; this supports a specific role for this splicing factor in facilitating RNAi at many, but not all, target sites, arguing against the possibility of an indirect effect on RNAi in general [cwf10-1 mutation specifically impairs splicing of cryptic introns but not annotated introns. This was striking since the presence of cryptic splice sites in target transcripts has been shown to increase the efficiency of RNAi in Cryptococcus [S. cerevisiae (which lacks RNAi), whereby inefficient splicing appears to act as a regulatory signal flagging transcripts for degradation by the RNA surveillance machinery [We have found that cells lacking Smd3, Saf1 or Saf5 all exhibit compromised centromeric heterochromatin associated with a defect in the RNAi pathway. Smd3 is a core snRNP protein involved in splicing, while Saf1 is a splicing-associated factor . Here we general . Third, tococcus . Althougachinery . This hiago1+ and arb2+ or ers1+) with intron-less versions, suggesting that the deficiencies in heterochromatin are at least partly due to impaired splicing of mRNA encoding components of the RNAi pathway [Recently, it has been reported that deletion of yet another fission yeast splicing factor, Cwf14, also causes defects in centromeric heterochromatin assembly. However, heterochromatin defects in cells lacking Cwf14 could be partially rescued by replacement of genomic copies of intron-containing RNAi genes  is associated with reduced heterochromatin integrity [in vitro [We have found that deletions of two of the predicted six components of the fission yeast COP9 signalosome, Csn1 and Csn2, cause defects in heterochromatin. Deletions of two other signalosome components represented in the library, Csn5 and Csn7, did not affect heterochromatin. This is consistent with previous studies that reported UV sensitivity and S-phase delay phenotypes for cells lacking Csn1 or Csn2, but not Csn3, Csn4 or Csn5 . Moreoventegrity ,30. How in vitro . NonetheCdt2 complex upon deletion of Csn1 or Csn2. However, the lack of dependence on Csn5, which is the subunit harbouring the isopeptidase activity that cleaves Nedd8 from cullins [Cullin neddylation promotes activation of CRL E3 ubiquitin ligase activity, and the COP9 signalosome mediates cullin deneddylation ,66. Loss cullins , indicat cullins ,69. Anot cullins .The genetic screen reported here uncovered several novel factors affecting heterochromatin integrity in fission yeast. We identified known and newly-characterised splicing factors that support processing of heterochromatic transcripts by the RNAi pathway, and elucidated a previously undescribed role for COP9 signalosome components in heterochromatin regulation. The critical bridging protein Stc1 was also identified in this screen and described elsewhere . Our finS. pombe [S. pombe strains used are listed in Additional file A near genome-wide haploid deletion library was constructed and supplied by the Bioneer Corporation and the Korea Research Institute of Biotechnology and Bioscience . The delS. pombe . Other SManipulations were carried out using a Singer RoToR colony pinning robot, essentially as described previously . First, act1+ or tRNA) in IP over input. Northern analysis of centromeric siRNAs and qRT-PCR analysis of transcripts were performed as described previously [ChIP was performed as described previously , fixing eviously . qPCR prImmunoaffinity purifications for mass-spec analysis were performed essentially as described previously . Briefly


S-associated RNA polymerase form (EσS) during transition from exponential to stationary phase. We identified 63 binding sites for EσS overlapping known or putative promoters, often located upstream of genes (encoding either ORFs or non-coding RNAs) showing at least some degree of dependence on the σS-encoding rpoS gene. EσS binding did not always correlate with an increase in transcription level, suggesting that, at some σS-dependent promoters, EσS might remain poised in a pre-initiation state upon binding. A large fraction of EσS-binding sites corresponded to promoters recognized by RNA polymerase associated with σ70 or other σ factors, suggesting a considerable overlap in promoter recognition between different forms of RNA polymerase. In particular, EσS appears to contribute significantly to transcription of genes encoding proteins involved in LPS biosynthesis and in cell surface composition. Finally, our results highlight a direct role of EσS in the regulation of non coding RNAs, such as OmrA/B, RyeA/B and SibC.In bacteria, selective promoter recognition by RNA polymerase is achieved by its association with σ factors, accessory subunits able to direct RNA polymerase “core enzyme” (E) to different promoter sequences. Using Chromatin Immunoprecipitation-sequencing (ChIP-seq), we searched for promoters bound by the σ Binding to any of the different alternative σ factors creates different RNA polymerase holoenzymes (Eσ), proficient in specific promoter recognition and transcription initiation. After the process of transcription initiation has taken place, the σ factor dissociates from the holoenzyme, and the core enzyme carries out transcription elongationEscherichia coli, including σ70 (or σD), the “housekeeping” σ factor devoted to transcription of a large part of the genome and of most essential genes. In contrast, alternative σ factors are responsible for the transcription of smaller subsets of genes, fulfilling specific roles or belonging to defined functional groupsS, strongly affects cell survival during stress conditions, such as starvation, oxidative stress, and exposure to either low or high pH, and controls expression of virulence factors in several pathogensS is considered the master regulator of the so-called “general stress response” and, consistently, it is induced in response to any stressful event leading to reduction in specific growth rate4Bacteria are constantly exposed to changes and fluctuations in their environment, to which they can adapt by reprogramming their gene expression through various mechanisms, including use of alternative σ factors. σ factors are accessory subunits of bacterial RNA polymerase that associate, in a 1:1 stoichiometric ratio, to the core enzyme (E), S and σ70 appear to recognize very similar promoter sequencesS and Eσ70in vitroin vivo is mediated by accessory regulatory proteins70 or σS can be achieved by deviations from a common consensus sequence6S binding and it is a common feature in σS-dependent promotersin vitro by either Eσ70 or EσS by run-off transcription microarray (ROMA); we confirmed the importance of sequence elements important for promoter recognition by σS, such as the presence of C residues at positions -13 and -12 C element, and suggested that an A/T-rich discriminator region would favour transcription initiation by EσSin vitroInterestingly, σS at early stationary phase, i.e., at a moment in which σS accumulates inside the bacterial cell. Our results led to identification of novel σS-dependent genes, and provided insight on regulation of non-coding RNAs by σS. We could also show that a significant subset of EσS-bound promoters controls genes whose expression is σS-independent, suggesting considerable overlap in promoter recognition by different σ factors.In this work, we used Chromatin-Immunoprecipitation-sequencing (ChIP-seq) to identify promoters bound by EσS antibodies suitable for immunoprecipitation were available at the time of this study, we decided to utilize anti-6xHis-tag antibodies targeting a histidine-tagged σS protein (σS-His6). In order to study promoter binding by σS-His6 without perturbing σS physiological levels or rpoS gene expression, we constructed a strain carrying a chromosomal rpoSHis6 allele, i.e., an otherwise wild type rpoS allele with 6 codons for histidine at its 3` end, as described in Materials and Methods. We verified the effects of the rpoS allele replacement on specific growth rate . No differences were detected in either specific growth rate or intracellular σS amounts in the two strains (S (or σS-His6) increased significantly at the end of the exponential phase, (compare points 1 and 2): at this point, bacterial cells were growing at a specific growth rate of 0.32 (±0.02) h−1. Cells were collected at the growth stage corresponding to point 2 in Since no anti-σwth rate  and checantibody , inset.  strains . WesternS activity in vivo, we tested the activity of HPII catalase, encoded by the rpoS-dependent katE gene and a marker for rpoS functionalityrpoSHis6, while, in contrast, HPII catalase specific activity was almost totally abolished in an rpoS null mutant strain, as expected . To this purpose, we performed qRT-PCR experiments comparing the relative abundance of the promoter region of the σS-dependent dps gene (Pdps) to coding sequences within the rpoB and the yeeJ genes. Both the Pdps/rpoB and Pdps/yeeJ ratios approached 1 in the Input sample, while being 10-fold higher in the σS-His6 immunoprecipitation sample  and of the σS-IP sample (σS-His6 immunoprecipitated DNA) were used to prepare sequencing libraries. The libraries were sequenced into 4 separate lanes of the same GAIIx run. We obtained more than 50 million mapping reads for both the input samples (corresponding to a sequencing depth of 543-fold the E. coli genome); for the first and the second IP samples, more than 26 and 32 million mapping reads were obtained, respectively. Identification of the DNA regions more represented in the σs-IP sample, corresponding to potential binding sites for EσS, was carried out using the CisGenome softwarei.e., regions of the genome significantly enriched  in the σs-IP sample as compared to the Input sample. Almost all peaks detected (72/78) corresponded to DNA regions ≤400 bp-long or slightly larger, consistent with the DNA fragment sizes obtained after DNA sonication . Three enriched regions were slightly larger in size (500-700 bp), while only three regions had sizes larger than 1kbp . The last one encompassed a DNA region including five different ORFs and several non-coding and regulatory elements, making it impossible to identify a putative binding site for EσS; thus, this DNA fragment was excluded from further analysis and is listed, together with intragenic peaks, in S binding site analysis shown in S binding peaks obtained from the CisGenome analysis is shown in i.e., peaks) are reported for the known rpoS-dependent genes osmB, dps, osmE and csrA .Two replicates of the Input sample , they were not considered for further characterization within this study. However, even assuming that all the intragenic peaks are artefacts of ChIP-seq, the resulting percentage of false positives (19%) would still be lower than what reported for similar studiesThe large majority (63 out of 78) of the σrpoS-dependent in previous reports, as listed in dsrB-yodD intergenic region, rpoS-dependent, thus suggesting that EσS binding was due the presence of an rpoS-dependent promoter within the intergenic region. As an example, we assigned the putative EσS binding site in the osmE-nadE intergenic region to osmE, since its promoter is σS-dependent141550 out of the 63 peaks corresponding to known or putative promoter regions could unequivocally be attributed to one specific gene, based on the DNA sequence covered by the peak, the direction of transcription of the neighbouring genes, the distance to the nearest ORFs and, when available, the presence of an experimentally determined transcription start site within the boundaries of the peak. Of the 50 genes unequivocally identified, 27 had been shown to be at least partially rpoS-dependent  and lipopolysaccharide (LPS) biogenesis , suggesting that EσS might be important for the expression of cell surface-related genes in response to growth cessation.Altogether, the peaks identified in the ChIP-seq experiment overlapped with the promoters of 36 genes that had been shown to be at least partially rpoS-dependent genes included known or putative promoters recognized by Eσ70, in agreement with previous results indicating extensive cross-recognition between EσS and Eσ70 regulons7E (ytfJ and lpxP) and σH  (The majority of the intergenic regions not linked to nd rpmE) , 2, .S-binding sites are associated with promoters directing transcription of rpoS-independent genes. Alternatively, regulation of these genes by σS might have been overlooked in previous investigations of the rpoS regulon, mostly carried out as whole genome transcription analysis comparing an rpoS mutant to its parental strain1415161718S-binding sites, we measured relative expression of 10 genes whose promoters, according to our ChIP-seq results, are recognized by EσS, by performing qRT-PCR experiments comparing E. coli MG1655 to its otherwise isogenic rpoS mutant. As control genes in the qRT-PCR experiment, we chose 4 genes previously proposed to be rpoS-dependent: dps, ycgB, rssA and bsmA1516rpoS-dependent, were selected based either on their function or on promoter features: lpp encodes Braun lipoprotein, which bridges the outer membrane to peptidoglycan and is extremely abundant in E. colissrA is a transfer-messenger RNA (tmRNA)-encoding gene; uxaB is involved in galacturonate metabolism; ybiI is a gene of unknown function whose promoter had been indicated as putative EσS-dependent through bioinformatics predictionydbK is an oxidative stress-related geneygjR, like ybiI, is an unknown function gene with a known transcription start siteS-dependent promoters, such as the −13C.The results of our ChIP-seq experiments seem to indicate that a large percentage of EσrpoS-dependent gene expression for dps, ycgB, ybiI and ydbK, suggesting that the latter two are yet unidentified members of the rpoS regulon. In contrast, the expression of the remaining genes was not affected by the lack of a functional rpoS gene, at least in the conditions tested. To further investigate whether these genes showed any kind of dependence on σS, we tested their expression levels in a rpoS-overexpressing strain (MG1655/pBADrpoS) grown to early stationary phase in LB medium supplemented with 0.1% arabinose. Although intracellular σS amounts were almost 10-fold higher in the pBADrpoS-bearing strains compared to MG1655, no significant changes in relative expression levels were detected for any of the genes tested (data not shown).Results of the qRT-PCR experiments  could deS promoter binding and EσS-dependent transcription, even for genes previously described as rpoS-dependent, such as rssA and bsmA . EσS was clearly more efficient than Eσ70 in promoting open complex formation at the ybiI, ydbK and bsmA promoters  overlaps a small ORF, ibsC, reading in the opposite direction, and encoding a toxic peptideS might bind the promoter regions of omrA (but not omrB), and of ryeB and sibC, rather than ryeA and ibsC  and those controlling genes whose expression is not affected by lack of a functional rpoS gene (9 promoters). In good agreement with the previously proposed consensus for σS4810S-dependent genes  control transcription of genes regulated by the σS-encoding rpoS gene  appear to be rpoS-dependent in Salmonella TyphimuriumS in this bacterium.In this work, we used a ChIP-seq approach in order to identify promoters bound by Eσial cell . The expd by EσS , 2, ; theriments . Howeverctor see , suggeston by σS . At leaspromoter . AlthougS also seems to extend to the alternative factors σE and σH . Our results would support the notion that, rather than being part of an adaptive response triggered by exposure to specific environmental stresses, the rpoS gene activates, in response to reduction in growth rate, a variety of stress-related genes, thus allowing the bacterial cells to “brace themselves” for any stressful conditions that might arise. However, promoter binding by EσS does not necessarily translate in increased transcription levels for EσS-dependent genes, suggesting that, upon binding, EσS might be unable to initiate transcription efficiently at some promoters. For the bsmA promoter, this hypothesis would fit with the results of in vitro promoter interaction studies  in order to form a complex proficient in transcription initiation.At least 10 of the  studies  and withrpoS regulon, our results suggest direct involvement of σS in the expression of genes involved in biogenesis and structure of the LPS and outer membrane proteins , carrying an rpoS gene in which a 6-histidine tag is added to an otherwise wild type allele, was constructed following the genetic procedures described for allele replacement41ccdB gene under the control of a rhamnose inducible promoter were amplified by PCR from the pKD45 plasmid. The first 45 nucleotides of either primer used for amplification  had been added by PCR amplification, using the rpoS_IF and rpoS_IR primers , the MG1655-rpoSHis6 strain was grown in 50 ml LB medium at 37 °C with vigorous shaking to an OD600 = 3.0. In order to enrich the amount of RNA polymerase bound to promoters, cells were treated with rifampicin, which inhibits transcription initiation blocking RNA polymerase at the transcription start site, following the protocol describedg). A 250 μl-fraction of the sample was treated with 100 μg/ml Proteinase K and 5 mM CaCl2 for two hours at 42 °C, and then at 65 °C overnight, to remove proteins non specifically bound to DNA. DNA was recovered by phenol-chloroform extraction and analyzed on a 2% agarose gel to verify DNA fragmentation. The sample was mixed at a 5:1 (vol:vol) ratio with protein A/G agarose slurry and incubated for 2 h at 4 °C on a rotating wheel to clear the sample and reduce unspecific binding. Subsequently, the agarose beads were separated from the lysate by centrifugation at 10000Xg. The cleared lysate was then incubated at 4 °C overnight on a rotating wheel with 5 μl of antibody . The rest of the procedure was carried out as previously describedFor immunoprecipitation of the σrpoSHis6 was sonicated and 200 μl were taken to be used as a control in sequencing reactions (Input=non-immunoprecipitated DNA). The Input and immunoprecipitated DNA samples were analyzed with the Agilent Bioanalyzer using the High Sensitivity DNA kit (Agilent Technologies). Five IP samples were pooled on the same DNA purification column  to reach 5 ng of total DNA, which is the minimum amount for sequencing library preparation. Two pools of IP DNAs were produced. Prior to sequencing libraries construction, quantitative Real Time reverse transcriptase-PCR (qRT-PCR) was carried out to assess the enrichment of the promoter region of the rpoS-dependent dps gene in the immunoprecipitated samples in comparison to the Input sample. The sequences of the primers used for qRT-PCR are listed in DNA from untreated MG1655-Illumina libraries were prepared either from 5 ng of each of the two pools of immunoprecipitated-DNA (RpoS-IP) or from 5 ng of the two control DNA (Input) following the Illumina TruSeq ChIP-seq DNA sample preparation kit; then each library was sequenced in a lane of a single strand 51 bp Illumina run on a GAIIx sequencer. Raw data are publicly available at Sequence Reads Archive under accession number BioProject SRP041323; BioSample SRS595203; Experiment SRX523029; Run1 SRR1265068; Run2 SRR1271103.Escherichia coli MG1655 genome using BowtieRaw reads were mapped against the ®-Oligo buffer (Ambion) at 45 °C and signals were detected using a Chemi nucleic acid detect wmodule (Thermo Scientific Pierce). GFP reporter assays were performed as previously describedFor all gene expression experiments, bacterial strains were grown in LB medium to OD600nm = 3.0. For qRT-PCR, RNA was extracted and experiments performed as previously described4 reactivity assays were performed as previously described32P-labeled DNA was produced by PCR after 5`-phosphorylation of the primer complementary to the coding strand , in a final reaction volume of 10 μl. The reaction mixture was loaded onto a 5% native polyacrylamide gel after addition of 2.5 μl of heparin-supplemented loading bufferRNA polymerase reconstitution, gel mobility shift and KMnOrand see  in order4 reactivity assays, 50 nM of either form of RNA polymerase (EσS and Eσ70) were incubated with about 3 nM of labeled promoter DNA for 20 min at 37 °C in K-glu100 buffer without DTT for complex formation. KMnO4 was added to a final concentration of 10 mM and the reaction was stopped after 30 seconds by adding 2 mM DTT. Samples were phenol-extracted and precipitated, treated with 1 mM piperidine, resuspended in pure formamide blue before being loaded onto a 7% polyacrylamide denaturing gel. A DNA ladder was generated for each labeled DNA fragment by partial G/A sequencing using formic acid and piperidine.For KMnOomrA promoter was carried out by generation of PCR products with mutagenic primers carried the desired substitutions, as previously describedDetermination of HPII catalase activity and Western blot experiments were carried out as previously described51How to cite this article: Peano, C. et al. Characterization of the Escherichia coli σS core regulon by Chromatin Immunoprecipitation-sequencing (ChIP-seq) analysis.. Sci. Rep.5, 10469; doi: 10.1038/srep10469 (2015).


Arabidopsis thaliana during onset and progression of leaf senescence imply a central role for transcription factors. While many transcription factors are themselves up- or down-regulated during senescence, the bZIP transcription factor G-box-binding factor 1 (GBF1/bZIP41) is constitutively expressed in Arabidopsis leaf tissue but at the same time triggers the onset of leaf senescence, suggesting posttranscriptional mechanisms for senescence-specific GBF1 activation. Here we show that GBF1 is phosphorylated by the threonine/serine CASEIN KINASE II (CKII) in vitro and that CKII phosphorylation had a negative effect on GBF1 DNA-binding to G-boxes of two direct target genes, CATALASE2 and RBSCS1a. Phosphorylation mimicry at three serine positions in the basic region of GBF1 also had a negative effect on DNA-binding. Kinase assays revealed that CKII phosphorylates at least one serine in the basic domain but has additional phosphorylation sites outside this domain. Two different ckII α subunit1 and one α subunit2 T-DNA insertion lines showed no visible senescence phenotype, but in all lines the expression of the senescence marker gene SAG12 was remarkably diminished. A model is presented suggesting that senescence-specific GBF1 activation might be achieved by lowering the phosphorylation of GBF1 by CKII.Massive changes in the transcriptome of  Onset and progression of leaf senescence is accompanied by a massive change in the transcriptome, implying an important role of transcription factors in this process ,2,3,4. ACAT2 by the bZIP transcription factor GBF1 at bolting time appears to be the initial step for elevation of hydrogen peroxide contents, whereas the loss of APX1 activity is not achieved on the transcriptional level. Inhibition of the APX activity is most likely mediated through its own substrate H2O2 in which APX is sensitive to its own substrate exactly in this developmental time window. APX regulation is therefore believed to build a positive feed-back loop to enhance the increase of hydrogen peroxide at this time point of development ATP (5000 Ci mmol−1) and one of the substrates with the protein amount of 25 or 50 μg. The kinase assay with purified GBF1 proteins was conducted using a final protein amount of 0.5 μg. Casein  was used as a control protein. The reaction was terminated by the addition of 4× SDS sample loading buffer. After electrophoresis on 10% (w/v) SDS-PAGE, the phosphorylated substrates were visualized by autoradiography.Phosphorylation activities of mammalian Casein Kinase II  was assayed at 30 °C for 30 min in a final volume of 20 μL reaction buffer  containing 200 μM ATP and 50 μCi [γ-™ cDNA Synthesis Kit  according to the manufacturer’s instructions. For the qRT-PCR the iQ™ SYBR® Green Supermix (Bio-Rad) was used following protocol of BIO-RAD. ACTIN2 was chosen as reference gene for senescence since the variation of ACTIN2 expression over different leaf and plant stages in Arabidopsis was very low in contrast to other housekeeping genes [ACTIN2 expression according to [RNA was isolated from pooled leaf material using leaf No. 6 and 7 with the PURESCRIPT RNA Isolation Kit . cDNA was polmerized using the iScriptng genes . Expressrding to . The folet al. [Hydrogen peroxide content was determined as described in Smykowski et al. . Whole AOne of the major regulatory mechanisms governing plant senescence is transcriptional reprogramming governed by a high number of different transcription factors. Many of the senescence-regulating transcription factors are themselves induced or repressed on the transcriptional level; however, at some point, several transcription factors have to be regulated on the posttranscriptional level and GBF1 belongs to these factors. One possible regulatory mechanism is the activation or inactivation by phosphorylation by different kinases and CKII appears to be one of the senescence-regulating kinases modulating the DNA-binding activity of GBF1. But how the activity of CKII is controlled during development and onset of senescence and which other kinases are involved in the modulation of GBF1 activity still has to be elucidated.


We identified significant physical interactions between risk regions and their potential target genes including CAPG at 2p11.2, C2orf43 at 2p24.1, RFX6 at 6q22.1, NFASC at 1q32.1, MYC at 8q24.1 and AGAP7P at 10q11.23. Most of the interaction peaks were co-localized to regions of active histone modification and transcription factor binding sites. Expression quantitative trait locus (eQTL) analysis showed suggestive eQTL signals at rs1446669, rs699664 and rs1078004 for CAPG (p < 0.004), rs13394027 for C2orf43 (p = 2.25E-27), rs10993994 and rs4631830 for AGAP7P (p < 8.02E-5). Further analysis revealed an enhancer activity at genomic region surrounding rs4631830 which was expected to disrupt HOXB-like DNA binding affinity. This study identifies a set of candidate genes and their potential regulatory variants, and provides additional evidence showing the role of long-range chromatin interactions in prostate cancer etiology.Genome-wide association studies have identified more than 100 common single nucleotide polymorphisms (SNPs) that are associated with prostate cancer risk. However, the vast majority of these SNPs lie in noncoding regions of the genome. To test whether these risk SNPs regulate their target genes through long-range chromatin interactions, we applied capture-based 3C sequencing technology to investigate possible  Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) that are associated with various diseases1245678910456789101213151617Chromosome Conformation Capture (3C)18212224252627To examine the possible interactions between risk SNP loci and candidate target genes, we designed a SureSelect custom target enrichment assay covering the ten risk loci across entire LD blocks. EcoRI site at risk regions of interest. The on-target sequences were further grouped as cis- and trans-interactions, which contributed to 91.2% and 9.8% of interactions, respectively. The sequence mapping data for the six cell lines is summarized in To map the sequencing data, we first trimmed adaptor and low quality sequences, and then submitted ∼200 million read pairs per library for sequence alignments. Mappable read pairs accounted for ∼82% of all submitted sequences. Among these, ∼23.4% (∼38.17 million read pairs/library) was mapped to the target regions. These on-target sequences were defined as paired sequence reads with at least one end mapped to ≤250 bp to the nearest r = 0.90) between original and the repeat library. This correlation was further enhanced if considering the dataset with at least three read counts in both data sets (r = 0.93). Meanwhile, we also observed significant correlation between the LNCaP and other cell lines tested  and 5′-end of USP39 (chr2: 85841299–85871912)  , Table 2r2 = 0.89) has been linked to prostate cancer aggressivenessNFASC  and BE9-10 (chr1: 204482247–204489040) with various strengths of signals among different cell lines  at this locus were associated with an increased risk of developing prostate cancer63451497238 . The mos51466745 . ChIP-se51466745 . For E1051466745 MYC and PVT126MYC gene region with prostate cancer risk region 1 (E128-129) in the LNCaP cancer cell line but not in LCLs. In contrast, strong interactions with the MYC region were at E72U and E39–40 in LCLs but not in prostate cancer cell lines. Additionally, LCL cell lines had three regions  showing unique interactions with a genomic region at chr8: 128565240–128578550 which covered the 5′-end of gene CASC8 (LOC727677). As expected, the interaction pattern and intensity in normal prostate epithelial cell lines BPH-1 and RWPE-1 always remained between cancer cell lines and LCLs.This risk locus has been extensively reported in previous studies4815163637383637UBE2Q1 and KCNN3 at 1q21.3, HS1BP3, C2orf43 at 2p24.1, TRIP13 at 5p15.33, RFX6, GPRC6A at 6q22.1, ANTXRLP1 at 10q11.23 and non-coding RNA LOC338694 at 11q13.2 (HS1BP3 (chr2: 20849923–20864079) and two separate regions at the 5′-end of gene C2orf43 (chr2: 21111995–21122083 and chr2: 21130466–21184716). The interaction hot spot at fragment chr2: 21111995–21122083 was only observed in prostate cancer cell lines LNCaP and DU-145. Other interaction hot spots at multiple EcoRI fragments (chr2: 21130466–21184716) were observed in prostate-derived cell lines , but not in LCLs. Bait fragment AE2D (chr5: 1275548) demonstrated an interaction with the 3′-end of gene TRIP13 (chr5: 919694–928218) at 5p15.33. For the 10q11.23 locus, E7U and E9-10 showed interactions with the 5′-end of the gene ANTXRLP1. E4-5 and E12-13 exhibited physical contact with the non-coding RNA LOC338694. Multiple bait fragments AE5-7 (chr1: 154844039–154859292) and AE11-12 (chr1: 154888031–154904153) at 1q21.3 showed higher contact frequency with multiple locations of KCNN3.In addition to the interactions listed above, we also observed significant interactions at other gene loci including  11q13.2 . Among tCAPG, C2orf43, NFASC, RFX6, MYC, PVT1, and AGAP7P, we performed eQTL analysis using RNA-seq and SNP genotyping data from 467 normal prostate tissues. We observed suggestive evidence of the SNP-gene expression associations at most loci tested (2 = 1), rs699664  and rs1078004  were associated with the expression of CAPG . rs13394027  was associated with the expression of C2orf43 . rs10808556 and rs6983267 (8q24.21) were associated with the expression of PVT1 with p value = 8.38E-4 and 1.75E-3, respectively. rs10993994 and rs4631830 (10q11.23) were associated with the expression of AGAP7P with p value = 4.06E-07 and 8.02E-5, respectively. We found no evidence of association between either SNP (rs6983267 and rs10808556) and MYC  or SNP rs4245739 (1q32.1) and NFASC .To determine whether the risk SNPs and their LD SNPs were associated with expression levels of target genes i tested . For exaCAPG; C2orf43-associated SNP rs13394027 was located in the high interaction fragment AE3-4 at 2p24.1. AGAP7P-associated SNP rs10993994 and rs4631830 were mapped to high contact fragments E10U and E10D, respectively , rs1078004 (BE7-8 fragment) and rs699664 (BE8-9 fragment) at 2p11.2 resided in an interaction peak with the 5′-end of ectively . In addiectively . Consistectively . To deteectively . Howeverectively . The redUBE2Q1, TRIP13, RFX6, TIMM23, MYC, PVT1, CASC8 (LOC727677) and AGAP7P were up-regulated and the remaining GPRC6A, CAPG, C2orf43, NFASC, KCNN3, PIK3C2B, and PPP1R15B were down-regulated. In particular, the genes GPRC6A, TRIP13, CAPG, PVT1, and C2orf43 demonstrated over 2 fold changes with p value < = 3.56E-10. Further exon level analysis showed that the expression differences in some genes were driven by a portion of entire coding exons  in 15 of 17 selected genes at gene level . Among tng exons . For exaCAPG gene locus at 2p11.2. The bait fragment E9U was selected because of its higher interaction signal and strong histone modification peaks in addition to transcription factor  binding sites. After normalizing Ct values to an internal randomly ligated control, our 3C-qPCR demonstrated stronger signals at EcoRI fragments containing T6 (chr2: 85644150) and T7 (chr2: 85654414)  around 5654414) , which wGWAS have identified thousands of variants associated with hundreds of disease traits. The next critical step in deciphering these associations is to develop high-throughput methods to link cis- regulatory elements to their target genes and determine how these variants alter gene expression. Capture-based 3C-seq, by incorporation of target capture into the 3C-seq technology, allows for the systematic and relatively straightforward identification of the long-range interactions with multiple regulatory regions in one assay24252627PPP1R15B/PIK3C2B, NFASC at 1q32.1, C2orf43 at 2p24.1, CAPG at 2p11.2, RFX6 and GPRC6A at 6q22.1, MYC and PVT1 at 8q24.21, AGAP7P at 10q11.23. Of those, the most significant and consistent interactions across different cell lines were at CAPG locus at 2p11.2. CAPG encodes a member of the gelsolin/villin family of actin-regulatory proteins; the encoded protein contributes to the control of actin-based motility in non-muscle cellsCAPG is also a candidate tumor suppressorNFASC were prostate tissue-specific because they were only found in prostate-derived cell lines. NFASC encodes an L1 family immunoglobulin cell adhesion molecule with multiple IGcam and fibronectin domains. The protein functions in neurite outgrowth, fasciculation, and organization of the axon initial segment (AIS) and nodes of Ranvier on axons during early developmentBy applying the capture-based 3C seq, we were able to find the physically interacting genes at risk SNP loci. The candidate target genes included AGAP7P. This interaction appears to be prostate-specific. The SNP rs4631830, located in a chromatin region with multiple transcription factor binding signals, is predicted to disrupt HOXB-like DNA-binding motif and shows enhancer activity. Therefore, integration of 3C-seq, eQTL and epigenomic analyses will facilitate discovery of candidate causal SNPs and their potential target genes.Although GWAS identified numerous SNPs with an increased risk for PC, there is still a challenge to identify the functional SNPs, largely because the risk SNPs are not necessarily causative. 3C-seq technology was able to define the candidate functional SNPs into one or several restriction fragments. By integrating with eQTL analysis, our study is able to identify candidate SNPs that may regulate target gene expressions. By further integrating with ChIP-seq data, our study is able to refine these candidate SNPs that are overlapped with active histone modifications, and transcription factor binding including FOXA1 and HOXB13, the critical transcription factors during prostate cancer development. Following this workflow, our study has identified several candidate SNPs and their target genes. For example, the DNA fragment containing rs4631830 at 10q11.23 is physically interacted with non-coding gene CAPG. The second model is a one to many model: one regulatory fragment interacts with several target genes simultaneously. An example is at the 6q22.1 locus where fragment E16-17 interacts with both RFX6 and GPRC6A. The third is a many to many models; many regulatory fragments interact with multiple genes. Examples include the loci at 1q32.1 for PIK3C2B and NFASC, and at 8q24 for MYC and PVT1Our data demonstrated a complex chromatin interaction at the risk loci. Depending on complexity, these long-range interactions may be classified into three models. The first is a one to one model: one predicted regulatory fragment interacts with one specific promoter fragment. An example is 2q11.3 locus where E8-9 fragment shows strong interaction signals at the 5′ end of Although the capture-based 3C-seq is able to link risk SNPs found in regulatory elements to the target genes, thereby providing a possible regulatory mechanism, there are still some limitations of this technology. The first limitation is weak signal in the detection of significant chromatin interactions. Our data show that interaction signals are often embedded within a large amount of background noises. Sometimes, the signals are hardly distinguishable from nearby background in read count-based heat maps. Because all 3C assays are based on proximity ligation, significant improvement of ligation specificity is technically challenging. Therefore, increasing sequencing depth and improving capture efficiency may partially solve the problem. The second limitation is generally low resolution which makes it difficult for fine mapping of regulatory elements and determination of candidate genes. Our data show that the captured interactions are more likely involved in multiple bait fragments and many continuous fragments at target gene loci. To increase the mapping resolution, one approach is to use more frequent cutter or different restriction enzymes. Another approach is to take advantage of existing epigenomic data from a large variety of resources. Our analysis suggests that interaction peaks are usually accompanied with chromatin modification and/or transcription factor binding sites, indicating that these interaction fragments may contain functional cis-acting elements. Further integration of different approaches into a streamlined workflow will enable translation of GWAS discovery into underlying biological mechanisms.In summary, this is the first capture-based high throughput 3C study to systematically investigate the long-range chromatin interactions at the multiple prostate cancer risk loci. By integrative analysis of the 3C-seq, eQTL and ChIP-seq data, we were able to define several fragments containing genetic variants that may contribute to expression variation of the candidate genes, hence, increasing the risk of prostate cancer. By examining multiple cell lines, we were able to identify cell line and cell type-specific chromatin interactions. Our study reveals a set of candidate genes and their potential regulatory variants and provides additional evidence showing the role of long-range chromatin interactions in prostate cancer etiology. Further understanding genetic effect and biological mechanism of these chromatin interactions will shed light on the newly discovered regulatory role of the risk loci in prostate cancer etiology and progression.http://www.genome.gov/gwastudies303143444546474849http://hic.umassmed.edu/welcome/welcome.php), with the settings of GM06690 cell line and 100 kb resolution observed data for each of the loci, for some evidence of interactions even though the resolution was low. Finally we checked eQTL sources from the U of Chicago (http://eqtl.uchicago.edu/Home.html) for eQTL hits in our loci. We used these limited datasets to identify 10 loci that may have chromatin interactions. For each SNP we determined the best LD block that included each risk SNP and all LD SNPs using the HapMap LD Phased track on the UCSC browser as well as Haploview . The LD SNPs included in each block were defined by choosing the outermost SNPs that were in LD with the risk SNP. We expanded all selected regions to >20 kb by centering on the original LD block. We combined the LD regions when two LD blocks overlapped. Size of the LD regions ranged from 20–476 kb. For each LD region all of the EcoRI sites were mapped using the REBASE (The Restriction Enzyme Database) track on the UCSC browser. At each EcoRI site, 250 bp on each side of the cut site were chosen for the probe design. Agilent Design Service was used to design the probes tiled at 5X. Any probes expanded into or across the EcoRI cut sites were removed.The target regions for the probe design were based on prostate GWAS risk SNPs reported in et al.Cell culture, formaldehyde crosslinking, and 3C library preparation were performed as described in Du Target enriched sequencing libraries were prepared and sequenced on an Illumina Genome Analyzer HiSeq 2000 with 100 bp PE reads. Each library was sequenced in one lane. Bowtie (v.2.0) was used to align each end of the 100 bp pairs to the human hg19 reference sequence; the seed for alignment was 22 bases with no mismatches allowed. A pipeline of PERL scripts was created to extract interactions from the mapped sequences. We excluded the following sequences: both read ends (R1 and R2) mapped to outside of the capture region and duplicate reads aligning to the same genome location. The resulting alignments were assigned to the respective restriction fragments derived from EcoRI digestion sites. Informative sequence pairs were defined as those with at least one end mapped to within 250 bp of the nearest EcoRI sites of target regions. Cis-interactions were defined as one end mapped on the target regions, the other mapped either inside or outside of the probe regions on the same chromosome. To correct for potential differences in probe capture efficiency, read count for each probe region were normalized to median read counts across all probes. The probes which were located in repeat sequences or had low capture efficiency (lower 22 percentile) were removed. The significant interactions at each probe region were determined using fourSig softwareCAPG and risk locus 2q11.3. The anchor primer at 2q11.3 was designed at the position (chr2: 85778503) named E9U. Nine test primers were designed on 2q11.3 around nine EcoRI cutting sites from position chr2: 85625540 (T3) to position chr2: 85679686 (T12) which corresponded to the promoter and nearby region of CAPG (Primers were designed for the detection of interactions between  of CAPG . Each tehttp://www.gencodegenes.org), and histone modifications including active enhancers , active promoters (H3K4me3) and (H3K4me2), which usually overlapped with transcription factor binding regions515253To evaluate functional relevance of the chromatin interactions, we aligned our interaction peaks with the genomic locations of GENCODE genes  of LNCaP cells per well were applied to reverse transfection with luciferase reporter plasmids together with pGL75 using X-tremeGENE HP DNA Transfection Reagent  following the manufacturer’s instructions. 48 hours later, cells were analyzed for luciferase activities using Dual-Glo® Luciferase Assay System (Promega). All data came from five replicate wells.SNPs rs4631830 and rs13394027 were selected for enhancer reporter assay. The SNP-centered DNA fragment (~500 bp) was amplified from human genomic DNA and cloned into upstream of the SV40 promoter in the pGL3 promoter vector . For allele-specific enhancer activity, site-directed mutagenesis  was used to create allele-specific construct. The oligo sequences used in this mutagenesis were listed in How to cite this article: Du, M. et al. Chromatin interactions and candidate genes at ten prostate cancer risk loci. Sci. Rep.6, 23202; doi: 10.1038/srep23202 (2016).