GAL regulatory system of Saccharomyces cerevisiae, the inhibition of glucose is accomplished through two regulatory mechanisms: one through the transcriptional repressor Mig1p, and the other through regulating the amount of transcriptional activator Gal4p. However, the impact of stochasticity in gene expression and hierarchy in regulatory mechanisms on the phenotypic level is not clearly understood.Transcriptional regulation involves protein-DNA and protein-protein interactions. Protein-DNA interactions involve reactants that are present in low concentrations, leading to stochastic behavior. In addition, multiple regulatory mechanisms are typically involved in transcriptional regulation. In the GAL network. The mechanism involving Gal4p is the dominant mechanism, yielding low variability in the expression of GAL genes. The mechanism involving Mig1p is necessary to maintain the switch-like response of certain GAL genes. The number of binding sites for Mig1p and Gal4p further influences the expression of the genes, with extra binding sites lowering the variability of expression. Our experiments involving growth on various substrates show that the trends predicted in mean expression and its variability are transmitted to the phenotypic level.We address the question of quantifying the effect of stochasticity inherent in these regulatory mechanisms on the performance of various genes under the regulation of Mig1p and Gal4p using a dynamic stochastic model. The stochastic analysis reveals the importance of both the mechanisms of regulation for tight expression of genes in the The mechanisms involved in the transcriptional regulation and their variability set up a hierarchy in the phenotypic response to growth on various substrates. Structural motifs, such as the number of binding sites and the mechanism of regulation, determine the level of stochasticity and eventually, the phenotypic response. GAL network of Saccharomyces cerevisiae. In this work, we characterize the intrinsic noise of the GAL network in response to variations in glucose concentration.It is well known that gene expression is a highly stochastic, or noisy, process . The cauGAL system codes for genes that are responsible for protein expression involved in the Leloir pathway  to express GAL genes. The binding of Gal3p and Gal80p is initiated by intracellular galactose. The amount of intracellular galactose is controlled by the amount of permease Gal2p (synthesized by GAL2 gene), which transports it from the extracellular medium. However, in the presence of glucose, a kinase (Mig1p) binds to the upstream repressing sequence (URS) of certain GAL genes and GAL4 to repress their synthesis. Mig1p is a constitutively expressed [S. cerevisiae, three different mechanisms can be observed for glucose repression through Mig1p. Expression is repressed directly by binding of Mig1p to the URS of genes such as SUC2 and GAL4 [GAL/MEL regulon, Mig1p represses the structural  and regulatory  genes indirectly through Gal4p. In this case, Mig1p represses only the expression of the activator, and thus indirectly represses the structural genes (like in GAL2 and GAL7) [GAL1, MEL1) as well as a regulatory gene (GAL3) have a URS for Mig1p binding as well as an upstream activation sequence (UAS) for the transcriptional activator [The xpressed  global rxpressed -18. In txpressed ,17. Undexpressed  to repreand GAL4 ,20,21. Ind GAL7) . In addictivator ,20,21. Ictivator .GAL system has also been reported; however, studies have focused on the response of the system to galactose (inducer), and on the role of the Gal3p and Gal80p regulatory mechanisms [GAL genetic network [GAL network, we consider a mutant strain of S. cerevisiae lacking Gal80p. It should be noted that in the absence of Gal80p, the activity of the transcriptional activator Gal4p is solely controlled through the effect of glucose concentration. Further, the role of Gal3p is also negated due to the absence of Gal80p; thus, such a mutant strain will constitutively express GAL genes even in the absence of galactose, and will respond only to variations in glucose concentration. The stochastic analysis reveals mechanisms for which the effect of inherent stochasticity is high. Both the regulators, Gal4p and Mig1p, are essential for complete repression by glucose with low noise. We also present experiments to determine whether the noise in the gene expression can be correlated to the variability at the phenotypic level.Stochastic analysis of the chanisms -26. Othe network . In the GAL gene expression at various glucose concentrations. These distributions were computed as fractional gene expressions. The steady state distributions of fractional protein concentrations were computed from the fractional gene expression in the manner described in the section on simulation. Figure MEL1 gene. Shown in the figure are the expression of individual simulations , and the mean expression from these simulations. We also present representative distributions of the expression across the simulations at three specific glucose concentrations . These distributions may be taken to represent the distributions in a cell population. We have chosen a low glucose concentration (high expression), an intermediate concentration in the sensitive region for expression, and a high glucose concentration indicating low expression for the distributions. The maximum mean fractional expression of MEL1 is 70%, which matches reasonably well with steady-state experimental data [The stochastic model was simulated to obtain steady state distributions of the tal data . At loweGAL1 gene, and the results are shown in Figure H = 3.4). Figure Next, we conduct a similar analysis for the expression of SUC2 gene at various glucose concentrations. Suc2p has two binding sites for Mig1p and none for Gal4p. Suc2p is also fully expressed at low glucose concentrations, but the response is leaky (mean = 15%) at high concentrations. The response is closer to a Michaelis-Menten type response, with ηH = 1.5. The distribution shows similar characteristics to that of Gal2p.Figure GAL genes. Unlike other GAL genes, Gal4p demonstrates relatively broad distributions at all glucose concentrations, with the variability being highest at the intermediate concentrations.Figure GAL genes, our next studies attempt to decipher the individual role of each of the repressors, Gal4p and Mig1p. Figure MEL1 gene for an in silico mutant strain lacking the UAS for Gal4p. In such a strain, it can be observed from the figure that glucose cannot completely repress the expression of Mel1p. The degree of leakiness in the response is very high (mean = 40%). The histograms shown in panel 'b' demonstrate that there is high variability at all glucose concentrations. The variability at low glucose concentration (high expression) is comparable to or smaller than that in the original strain  at high concentrations, and glucose cannot completely repress the expression of Gal1p, either. An important observation is that the switch-like behavior of Gal1p is lost, and the variability is higher at all concentrations, but especially so at higher glucose levels. This implies that Gal4p is essential for complete repression of GAL genes and to obtain crisper switching between the expression states.Since there are two mechanisms of repression of in silico mutant strain lacking the URS for Mig1p. The expression is similar to that observed for the original GAL80 mutant strain, with marginally higher variability . The precultured cells were streaked on plates containing melibiose (for MEL1 response), galactose (for GAL2 response) and sucrose (for SUC2 response). Figure MEL1 predicted by the simulations. For the galactose plates, the cells precultured at intermediate glucose concentrations (1 mM) have the largest variability, and the variability reduces significantly in both directions. For the sucrose plates, the variability in colonies formed increases slightly in the direction of increasing precultured glucose concentration.The above simulation results demonstrate that stochastic effects in the gene expression are significant in the e for GAL responseMEL1, the variability increases with time for both preculturing states, which is similar to the trend observed (in steady state) in Figure GAL2, variability does change appreciably with time for preculturing at 27.8 mM glucose. However, the variability is high at lower times, and decreases significantly with time at 1 mM glucose. The high variability at lower times matches with the high variability observed at intermediate glucose concentrations (similar to 1 mM) in Figure Figure S. cerevisiae is capable of growing on various carbon sources in its natural habitat. The organism prefers to grow on glucose in the presence of other carbon sources such as galactose, melibiose and sucrose. This requires the existence of a transcriptional mechanism to regulate the uptake of the other sugars. This transcriptional mechanism is known and well-studied [MEL1 (for melibiose). In this case, the MEL1 gene has one binding site for both Mig1p and Gal4p. Also, for the regulation of GAL1 , there are two binding sites for Gal4p and one for Mig1p. In the case of SUC2 (for sucrose), there are two binding sites for Mig1p, and the regulation is independent of Gal4p. Thus, the regulation of SUC2 is controlled by only one mechanism, but with two binding sites. These varied mechanisms and their hierarchy allow the organism to efficiently utilize and switch from one carbon source to the other [-studied  in yeasthe other ,36-38.GAL and SUC2 genes. The analysis clearly demonstrates that the glucose repression on the uptake of other sugars is indeed noisy, resulting in high variability in the gene expression. Furthermore, the stochastic noise is directly dependent on the mechanism prevailing for a specific gene. The conservation of signal amplification and sensitivity observed in the steady state analysis was also confirmed in our stochastic simulations. For MEL1, the variability is high at low glucose concentrations, and lower at high glucose concentrations. For GAL1, the variability is lower than that for MEL1 at both extremes of glucose concentration. The only difference in the mechanism of glucose repression of GAL1 and MEL1 is the presence of an extra Gal4p binding site on GAL1. This additional binding site essentially helps in lowering the variability for GAL1 expression. This result is similar to one described in [SUC2 is regulated only by Mig1p with two binding sites, and the higher sensitivity associated with the Gal4p repression mechanism is not seen here. Since sucrose is a carbon source not linked to the galactose metabolic pathway, it may have evolved to be regulated only by Mig1p, so as to provide a graded response. Gal4p expression is leaky at high glucose concentrations, as it is a global transcriptional activator and needs to be available to switch on the system. There is variability at all glucose concentrations, which is the result of having only one binding site for Mig1p. Tight regulation of Gal4p is anyway not essential, since all that is required is a graded response to glucose.Our current study provides insights into the stochastic effects of the various mechanisms described above on the expression of ribed in , where iin silico mutant strains generated by eliminating individual mechanisms. The results clearly indicate that both the mechanisms are necessary for complete repression at high glucose concentrations. The mechanism involving Gal4p is a more dominant mechanism to regulate noise and stochastic effects than the mechanism involving Mig1p. However, at low glucose concentrations, the two binding sites present for Mig1p are sufficient to lower the noise in the expression. This is in contrast to the expression of MEL1 (which has only one binding site for Mig1p) at low glucose, which shows high variability. To conclude, the study indicates that multiple mechanisms tightly regulate the variability and expression at high glucose concentrations, while multiple binding sites for the regulators control the variability at lower glucose concentrations. The motif of multiple regulatory mechanisms having a role in reducing variability has been observed in simulations on the Gal3p and Gal80p mechanisms in a wild type strain of S. cerevisiae [The roles of the various mechanisms were also investigated by simulating the stochastic model on revisiae ,25.MEL1 expression in the simulations at low glucose concentrations. This was also observed in the growth experiments on melibiose for cells precultured on low glucose concentrations. The dynamics of the growth experiments also confirmed that the cells demonstrate similar variability as observed in simulations, and tend towards the variability observed in the low glucose limit with time.We also conducted growth experiments on agar plates to investigate the variability at the phenotypic level. Such experiments would indicate if the noise introduced at the transcriptional level is transmitted to the phenotype. Our experiments indicate a similar trend in the variability in growth as that of the simulated gene expression for all the three substrates. For example, high variability was observed in the Specific mechanisms utilized by the cell to regulate expression of different genes responsible for the uptake of various carbon sugars by glucose demonstrate different levels of noise. The hierarchy in the variability introduced in the transcriptional mechanism sets up a corresponding hierarchy in the uptake of different sugars. In the case of glucose repression, the variability is highest for sucrose at high glucose concentrations, followed by galactose and melibiose. This results in sucrose being taken up before galactose and melibiose. However, at low glucose concentrations, the variability observed for growth on galactose was lower than that observed for melibiose, resulting in galactose being taken up before melibiose. Thus, the prevailing mechanisms result in a hierarchical uptake of sugars, in the order glucose, sucrose, galactose and melibiose.GAL/SUC2 genes. The experiments showed the same trends as the simulations, but with slightly lower variability, possibly implying modulation of the noise through metabolism and cell division, leading to the phenotypic response.Thus, the different mechanisms demonstrated different noise characteristics at the gene expression level, and this differentiation was carried through to the phenotypic level of growth. This may have important implications on the understanding of the effect of 'intrinsic' and 'extrinsic' noise ,2 in theTranscriptional regulation involves protein-DNA interactions, and these involve reactants that are present in low concentrations, leading to the presence of stochasticity. This stochasticity may influence the phenotypic response of an organism. We have demonstrated that the stochasticity at the transcriptional level in glucose repression on the uptake of other substrates in yeast is transmitted to its growth. This implies that the intrinsic noise propagates through the metabolism and growth. The mechanisms involved in the transcriptional regulation and their variability set up a hierarchy in the phenotypic response. More experiments are needed in a single cell to measure the variability at the transcriptional, translational and metabolic levels. Further, studies on mutants obtained by disrupting specific mechanisms will provide more insights into the relationship between mechanisms and stochasticity. Studies on other transcriptional regulation systems and organisms are needed to generalize the relationship between noise and the phenotypic response. Finally, simulations incorporating models of metabolism  and dynaGAL network in the mutant strain of S. cerevisiae that we consider for the stochastic modeling is shown in Figure GAL and SUC2 genes. It should be noted that SUC2 has two URS for Mig1p, while GAL1, GAL4 and MEL1 have one each. The Gal4p synthesized interacts with the UAS of the GAL genes. In this case, MEL1 has one binding site for Gal4p, while the remaining seven GAL genes, including GAL1 and GAL2, have two binding sites. To reiterate, glucose represses the GAL and SUC2 genes by two mechanisms – by recruiting the repressor Mig1p into the nucleus, and by repressing the synthesis of the transcriptional activator Gal4p. These mechanisms were incorporated into our stochastic model to obtain insights on their relative importance.The schematic of the We assume a Mig1p concentration in the nucleus as an input to our stochastic model. This nuclear Mig1p is related to the extracellular glucose (Glu) through a steady state Michaelis-Menten type relationship as given below:max is the maximum concentration of Mig1p in the cell , and Ks is the half-saturation constant. We then consider all the interactions described above  to speed up the simulation was not necessary. Since the system is stochastic, each run is a particular realization of the true dynamics of the system. Thus, the results over multiple (500) runs in an ensemble were averaged to obtain the mean values and distributions of the component populations. Past studies on steady state GAL gene response to glucose concentrations [GAL system is responsive is approximately 0.1 mM to 15 mM. This range was used to set the initial condition for the equivalent Mig1p nuclear concentrations using Equation (1).All the simulations were performed using the direct method of the stochastic simulation algorithm (SSA) of Gillespie ,7. For ttrations  have demGAL80 mutant strain of S. cerevisae Sc285 with genotype MATa ura3-52 leu2-3 2-112gal80 [MEL1 in its genome, and can grow on melibiose as a carbon source.112gal80  was usedThe strains were stored in 20% (v/v) glycerol at -80°C in micro centrifuge tubes. The cells were precultured in YPD broth and streaked out onto YPD plate, from which a single colony was picked up to inoculate the shake flask.A cotton-stoppered, 500 ml Erlenmeyer flask containing 100 ml medium of following composition: 25 mg/L adenine, 5.0 g/L Yeast extract, 10.0 g/L peptone and 30.0 g/L glycerol was used. The pH was adjusted to 5.5 with 1 M HCl. The cells were grown in a shake flask at 240 rpm on a rotary shaker at 30°C for 12–16 h, until the cell concentration reached 1.0–1.5 OD at 600 nm. Subsequently, the bioreactor was inoculated with 10% cell mass of OD 1.0 at 600 nm.S. cerevisae was grown in a batch bioreactor until biomass reached about 0.5–0.85 OD at 600 nm in a medium of composition 25 mg/L adenine, 5.0 g/L Yeast extract, 10.0 g/L peptone and 30.0 g/L glycerol. After this, the bioreactor was operated in a fed-batch mode by maintaining different average glucose concentrations (± 10%). The glucose concentration in the reactor was maintained by continuous feeding of standard glucose solution using calibrated peristaltic pumps (Watson Marlow 101U) through a feedback control mechanism. Different average glucose concentrations (with a set point for each) were maintained by altering the feed rate and the concentration of standard glucose solution. Steady state glucose concentration was thus maintained by feeding two standard glucose solutions (10 and 100 fold of required concentrations) by using peristaltic pumps. The glucose concentrations maintained in the fed-batch reactor were 0, 1, 4.4, 10 and 27.8 mM.Initially, The inoculums from the fermentor, on reaching a fixed steady state glucose concentration, were streaked onto plates containing three different carbon sources, melibiose, galactose and sucrose at 20 g/L. The colonies were counted as colony forming units (CFU) beginning from 44 hours up to the time that the colonies reached a steady state number. It should be noted that the colonies were counted for different preculturing states depending on the glucose concentration used for their growth in the fed-batch reactor. Ten experiments with three sets in each of these experiments were carried out. Thus, the data is presented as a mean of thirty plates with their respective standard deviations, normalized with respect to the maximum number of colonies formed on the individual plates.VP and KVV conceived the modeling and experimental study, developed the models and experimental protocol and conducted all the analysis together.Supplementary information. Lists (1) the initial conditions used in the simulations. (2) the reaction scheme and parameter values used in the simulations. (3) Figure S1, expression for Gal1p for in silico mutant lacking Mig1p.Click here for file


It has been well established that prolactin (PRL) signals through the long form of its receptor (PRL-RL) and activates the Jak/Stat pathway for transcription of PRL target genes. However, signaling pathways mediated through the short PRL-R isoform (PRL-RS) remains controversial. Our recent finding that PRL signaling through PRL-RS represses two transcription factors critical for follicular development lead us to examine other putative PRL/PRL-RS target transcription factors in the decidua and ovary, two well-known target tissues of PRL action in reproduction.In this investigation we used mice expressing PRL-RS on a PRL-R knockout background and a combo protein/DNA array to study the transcription factors regulated by PRL through PRL-RS only.We show that PRL activation of the PRL-RS receptor either stimulates or inhibits the DNA binding activity of a substantial number of transcription factors in the decidua as well as ovary. We found few transcription factors to be similarly regulated in both tissues, while most transcription factors are oppositely regulated by PRL in the decidua and ovary. In addition, some transcription factors are regulated by PRL only in the ovary or only in the decidua. Several of these transcription factors are involved in physiological pathways known to be regulated by PRL while others are novel.Our results clearly indicate that PRL does signal through PRL-RS in the decidua as well as the ovary, independently of PRL-RL, and activates/represses transcription factors in a tissue specific manner. This is the first report showing PRL/PRL-RS regulation of specific transcription factors. Many of these transcription factors were not previously known to be PRL targets, suggesting novel physiological roles for this hormone. PRL is a polypeptide hormone known to exert a great number of biological functions by regulating transcription of genes involved in several physiological pathways [rev in ]. PRL reWhile these studies show a distinct physiological function of PRL-RS, the signaling mechanisms involved in these processes remain largely unidentified. Since one major end-point of hormone signaling is activation/deactivation of transcription factors, detection of the transcription factors regulated specifically after PRL-RS activation can provide important information on PRL action through PRL-RS. Traditionally, transcription factor activity has been analyzed by electrophoretic mobility shift assay (EMSA) or by detection of specific posttranslational modifications known to affect DNA binding activity, such as phosphorylation. Unfortunately, these methods can detect activation of only one transcription factor/reaction and also require a large amount of sample. Here, we used a high throughput protein/DNA array approach in which the activities of a large number of transcription factors can be easily detected using biotin-labeled DNA binding probes and a chemiluminescence method. We took advantage of this array and the availability of transgenic mice expressing only PRL-RS in the PRLR null background , which a+/- oocytes derived from 129SV pure background mice [Transgenic mice expressing only PRL-RS were previously generated by microinjecting the eF1-PRLR-PR-1 transgenic construct encoding the mouse cDNA for PRL-RS into fertilized PRLRund mice . Animalsund mice . Mice weFemale PRL-RS transgenic mice were mated with vasectomized males to induce pseudopregnancy. Progesterone pellet  was implanted sc in each mouse on the day when the vaginal plug was found. Decidualization was induced with intrauterine administration of sesame oil on day 4. On day 9 of pseudopregnancy, mice were injected with ergocryptine  to block PRL secretion. After 6 hrs, a single i.p. injection of either recombinant oPRL (60 ug) purchased from Dr. Arieh Gertler  or vehicle (0.1%BSA in PBS) was given to the mice. Mice were sacrificed after 30 and 120 min to obtain ovaries and decidua that were frozen in liquid nitrogen and stored at -80°C until processing for RNA or protein extraction. All experimental procedures were performed in accordance with the principles of the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee.Nuclear extracts from decidual and ovarian tissue were prepared as previously described . Three a32P ATP  to a specific activity of more than 8000 cpm/fmol. Five micrograms of nuclear extract are incubated with 1 μg of poly (dI-dC) (Amersham) and 50 fmol of probe in 1× binding buffer on ice for 30 min. Cold competitor probes are added to a final concentration of 2.5 pmol. Samples are run on a 4% nondenaturing polyacrylamide gel in 0.5× Tris borate EDTA buffer at 200 V for 2–3 h. The gels are then dried and analyzed by autoradiography.Five picomoles of RUNX1 annealed oligonucleotide probes are labeled using 10 U of T4 polynucleotide kinase  and 25 μCi of γ-To determine the downstream signaling pathways of PRL/PRL-RS, we examined the activities of transcription factors in the ovary and decidua of PRL-RS expressing mice using the TranSignal protein/DNA array. For this purpose Fig. , deciduaNext, we categorized transcription factors as those either activated or inhibited by PRL in the decidua and ovary at early (30 min) and late 120 min) time points. As shown in Fig. 0 min timAs shown in Fig. We also analyzed the transcription factors repressed by PRL/PRL-RS Fig. . In the Surprisingly, we found only six transcription factors similarly regulated by PRL in the ovary as well as in the decidua of PRL-RS expressing mice Table . WhereasRUNX1 is another transcription factor whose activity was modulated similarly in the decidua and ovary. This transcription factor was transiently activated at 30 min in decidua while it remained activated in the ovary for a longer time. We further examined the regulation of RUNX1 in the decidual nuclear extracts from mice expressing only PRL-RS by EMSA analysis. A double-stranded DNA oligo corresponding to RUNX1 binding sites was used as the probe. We found a single DNA/protein complex that was markedly increased by PRL treatment at 30 min only Fig. . This coIn sharp contrast, we found a significant number of transcription factors, including Foxo4, NFkB, NFY and Smad3/4, to be inhibited in the decidua and to be stimulated in the ovary suggesting that PRL may activate/deactivate similar transcription factors through PRL-RS in an opposite way depending on the target tissue Table .Although some transcription factors have redundant action, we grouped them Table  accordinIn the decidua, close analysis of the array data revealed that, a majority of the transcription factors regulated by PRL is involved in growth and differentiation Table . We founIn contrast to the decidua, a significant number of transcription factors regulated by PRL in the ovary are involved in glucose metabolism Table . This fuWe have recently provided evidence that PRL activation of PRL-RS regulates the transcriptional activity of several genes. We haveWe found indeed that PRL markedly affects the activation or inhibition of DNA binding activity of a substantial number of transcription factors. Some were regulated similarly in both ovary and decidua, while others were regulated in an opposite way. The large majority however, was regulated solely in either the ovary or decidua, suggesting that PRL may affect several signaling pathways in a tissue specific manner. One possibility for these differential effects may be the cellular milieu of the target tissue such as the level of expression of specific transcription factors and/or availability of scaffolding proteins. Scaffolding proteins are known to interact with signaling molecules and prevent their degradation and/or facilitate their localization at specific sites .When organized by function, it became apparent that the PRL/PRL-RS regulated transcription factors are involved in glucose metabolism, growth and differentiation, the immune response, inflammation and development and steroidogenesis. While some of these transcription factors were previously shown to be regulated by PRL ,36,46, tPRL is thought to play a role in modulating the immune response in utero during pregnancy ,54. ImpoAnother intriguing finding of this investigation is that several members of the Pax family are activated by PRL in the decidua as well as the ovary. Pax genes are regulators of tissue development and cellular differentiation in embryos, acting to promote cell proliferation, cell-lineage specification, migration and survival. The function of these Pax members in the adult ovary and decidua is not known. However, several investigations indicate that the regulation of these transcription factors involves methylation. Pax2 is silenced by hypermethylation of its promoter . SeparatAnother transcription factor activated in both decidua and ovary is RUNX1. This protein belongs to a family of runt-related transcription factors and binds to a consensus sequence known as a PEPB2 site. RUNX1/PEBP2 is critical for hematopoiesis and plays an important role in differentiation of many cell types by recruiting corepressors and coactivaotors . Most imIn conclusion, we have found that DNA binding activities of several transcription factors are either activated or inhibited by PRL in the decidua and ovary of PRLR-/-RS mice. Transcription factors, such as the Pax family and RUNX1/PEBP2, are similarly regulated in both decidua and ovary, suggesting that some of the PRL/PRL-RS signaling pathways are common to both of these reproductive tissues. However, several transcription factors are differentially regulated in the decidua and ovary by PRL/PRL-RS, suggesting tissue specific signaling pathways and gene expression. Overall, our results have suggested several possible mechanisms of PRL signaling through PRL-RS in two reproductive tissues. Further studies should reveal the specific signaling pathways involved in the regulation of these transcription factors and their downstream genes.The authors declare that they have no competing interests.YSD performed experiments on ovarian tissue, analysis and interpretation of data and manuscript drafting. AS performed experiments on decidual tissue, analysis and interpretation of data. JH participated in maintenance of transgenic mice colony. CS participated in designing the study. JL and AMS participated in many aspects of this work. GG conceived the idea of the study, participated in its design, data analysis and drafted the manuscript. All authors read and approved the final manuscript.Table 4. Transcription factors not regulated by PRL in the decidua of PRLR-/-RS mice.Click here for fileTable 5. Transcription factors not regulated by PRL in the ovary of PRLR-/-RS mice.Click here for file


Drosophila and mammals, however, have shown that it targets many genes, which can switch state during development. Genetic evidence indicates that critical for the active state of PcG target genes are the histone methyltransferases Trithorax (TRX) and ASH1. Here we analyze the repertoire of alternative states in which PcG target genes are found in different Drosophila cell lines and the role of PcG proteins TRX and ASH1 in controlling these states. Using extensive genome-wide chromatin immunoprecipitation analysis, RNAi knockdowns, and quantitative RT–PCR, we show that, in addition to the known repressed state, PcG targets can reside in a transcriptionally active state characterized by formation of an extended domain enriched in ASH1, the N-terminal, but not C-terminal moiety of TRX and H3K27ac. ASH1/TRX N-ter domains and transcription are not incompatible with repressive marks, sometimes resulting in a “balanced” state modulated by both repressors and activators. Often however, loss of PcG repression results instead in a “void” state, lacking transcription, H3K27ac, or binding of TRX or ASH1. We conclude that PcG repression is dynamic, not static, and that the propensity of a target gene to switch states depends on relative levels of PcG, TRX, and activators. N-ter TRX plays a remarkable role that antagonizes PcG repression and preempts H3K27 methylation by acetylation. This role is distinct from that usually attributed to TRX/MLL proteins at the promoter. These results have important implications for Polycomb gene regulation, the “bivalent” chromatin state of embryonic stem cells, and gene expression in development.Polycomb (PcG) regulation has been thought to produce stable long-term gene silencing. Genomic analyses in  Drosophila and mammals, however, have shown that it targets many genes that can switch state during development. Here we analyze the repertoire of alternative states in which PcG target genes are found in different Drosophila cell lines. In addition to the known repressed state, PcG targets can be found in a transcriptionally active state characterized by formation of an extended chromatin domain enriched in features that antagonize PcG repression. These features include the ASH1 protein, the N-terminal, but not C-terminal moiety of TRX, and histone H3K27 acetylation. ASH1/TRX N-ter domains and transcription are not incompatible with repressive marks, sometimes resulting in a “balanced” state modulated by both repressors and activators. Often, however, loss of PcG repression results instead in a “void” state, lacking transcription, H3K27acetylation, and other marks of active chromatin or binding of TRX or ASH1. The propensity of a target gene to switch states depends on relative levels of PcG, TRX, and activators.Polycomb (PcG) regulation has been thought to produce stable long-term gene silencing. Genomic analyses in  Drosophila homeotic (HOX) genes for which PcG genes were first discovered  activity. Complexes containing TRX have been found to methylate histone H3K4 Drosophila is not known. Certain Drosophila genes have been found to bind PcG proteins while remaining functional and sustaining transcriptional activity The hallmark of a locus repressed by PcG mechanisms is its residence within a broad chromatin domain enriched in trimethylated H3K27, often including more than one gene To screen for alternative chromatin states of PcG target genes we determined the genomic distributions of PC, E(Z) and H3K27me3 in three different cultured cell lines: ML-DmBG3-c2 cells (hereafter BG3 cells) derived from larval brain and ventral ganglion, ML-DmD23-c4 cells from imaginal wing disc (hereafter D23 cells) and Sg4 cells  by the stringent criterion of two-fold enrichment. We found 99 such domains in Sg4 cells, 107 domains in BG3 cells and 89 domains in D23 cells. These we call Class I PcG target regions . They inDrosophila genome.Pairwise comparison of PcG target regions detected in any two cell lines shows that their repertoire is relatively stable but we found a total of 53 cases in which PcG protein binding and H3K27me3 was lost in one of the lines . Each ceWe then asked whether the differences in PcG binding in the three cell lines were reflected by changes in transcriptional activity of the underlying genes and/or in the binding of TrxG proteins. In the analyses that follow, we used the presence of RNA Pol II at the Transcription Start Site (TSS) and H3K4me3 at position +500 bp as criteria to distinguish transcriptionally active from inactive genes , containing the three homeotic genes Ubx, abd-A and Abd-B. In two cell lines, BG3 and D23, all three genes are PcG-repressed: they bind PcG proteins at the known or presumed PREs and the entire BX-C domain of more than 320 kb is highly enriched for H3K27me3  derepression was accompanied by binding of ASH1, indicating strong correlation. We then asked if regions that bind ASH1/TRX N-ter in Sg4 cells but no longer do so in BG3 cells generally acquire PcG and H3K27me3 and vice versa. From 44 regions that bind ASH1 and TRX N-ter in Sg4 cells, 29 no longer bind these proteins in BG3 cells and seven of these simultaneously acquire PC and H3K27me3 . ConversThe comparison of PcG and trxG landscapes in different cell lines showed that in half of the cases the loss of PcG and H3K27me3 was not associated with the gain of an ASH1/TRX N-ter domain and vice versa . Strikinhedgehog (hh) locus in D23 cells , also an essential developmental gene. In this case PcG regulation of tio was evident in the BG3 line . In fact all ASH1/TRX N-ter domains are co-extensive with domains of H3K27ac  which reCoextensive binding of ASH1/TRX N-ter and H3K27ac suggests that HAT activity responsible for H3K27 acetylation may constitute a part of ASH1 or/and TRX N-ter protein complexes. To explore this possibility we examined the effect of TRX knock-down on the level of H3K27ac within ASH1/TRX N-ter domains. The knock-down of TRX results in simultaneous loss of ASH1 thus the effect of loss of both proteins was assayed in this experiment. Only a modest decrease in H3K27ac was found , despitePsc gene, which encodes a core component of the PRC1 complex. In all cell lines examined, this gene has prominent PcG binding and H3K27me3 but remains functionally active and produces PSC protein. A prominent peak of Pol II is found at the TSS of Psc and of the adjacent, closely related and divergently transcribed Su(z)2 gene. The presence of ASH1 and TRX N-ter in a narrow region at the 5′ end of Psc  and, unlike TRX, ASH1 is not generally recruited to PREs in the repressive state. However, exceptional sites that bind both ASH1 and H3K27me3 are very instructive . One of d of Psc  supportsengrailed (en) and invected (inv), two genes encoding related homeodomain factors with important roles in metazoan development. Their regulation by PcG mechanisms is well known Another region of this kind contains The above examples indicate that, while most PcG/TrxG target loci in cultured cells are dominated by either the PcG or TrxG function, some genes display both simultaneously, or at least they can alternate from one state to the other. We call such a condition a “balanced” state and speculate that in this state PcG and TrxG proteins act in concert with other positive and negative regulators of transcription, keeping one epigenetic activity from “overcoming” the other.To what extent are the alternative chromatin states described above dependent on the relative cellular levels of PcG and TrxG proteins? Do these levels directly determine their binding equilibrium at target genes? To address these questions we looked for changes in the genomic distributions of PC, TRX N-ter, ASH1, H3K27me3 and H3K27ac in BG3 cells subjected to RNAi against TRX or PC.The knock-down of TRX led to a substantial drop in the amount of TRX N-ter and ASH1 bound to transcriptionally active PcG targets . This, hSimilar to TRX knock-down, the reduction of PC levels to 20% of wild type  did not trx mutations act as suppressors of Pc mutations. We thus wondered whether the derepression of target genes caused by the reduction of PC levels in the cell culture is also sensitive to the level of TRX. Comparison of the effects of single PC knockdown versus simultaneous knockdown of PC and TRX on the expression levels of Abd-B and mirr genes indicates that this is indeed the case. Double knockdown of PC and TRX resulted in two- to four-fold lower transcription than knockdown of PC alone  PcG and TrxG regulation are tightly coupled. Considering the role of MLL1 in the regulation of HOX genes Consistent with genetic evidence, the presence of ASH1 and TRX at PcG target regions is linked to their transcriptional activity. However the two proteins show important differences in their behavior: binding of ASH1 is limited to transcriptionally active  PcG targets and is not detected at completely repressed target loci. TRX is more complex. Both N-ter and C-ter parts of the protein associate with PREs regardless of the transcriptional status of their target genes and bind in the vicinity of TSS specifically when a target gene is transcriptionally active. In addition, the N-ter moiety of TRX together with ASH1 forms broad domains that encompass transcriptionally active PcG target genes. The different behavior of N-ter and C-ter parts of TRX may account for the discrepancy between reports of the co-localization of TRX and PcG proteins at many chromosomal sites Whether the C-ter or N-ter specific antibodies were used, the number of TRX bound regions detected in our experiments is small compared to the number of active genes. This argues against a general role for TRX in transcription and is consistent with the limited number of regions detected on polytene chromosomes by various antibodies directed against C-ter or N-ter TRX ash1 mutant larvae The RNAi knockdown experiments show that the broad binding of ASH1 and TRX N-ter within transcriptionally active PcG target regions is interdependent. This is consistent with the reported dissociation of ASH1 from polytene chromosomes of the salivary gland cells subjected to TRX RNAi Drosophila the HAT responsible for bulk acetylation of H3K27 is CREB-binding protein (dCBP), encoded by the nejire gene. Direct association of both TRX and ASH1 with dCBP was previously reported trx mutant larvae was recently reported trx mutation may have been indirect.A histone mark associated with ASH1/TRX N-ter domains is H3K27ac. Acetylation of H3K27 can antagonize PcG activity by competing with the placement of the H3K27me3 mark, which in our model is needed for effective contact of the PRE complex with the promoter, as well as for stable PcG binding. In fact, targeting a histone H3 acetylase to a PRE is sufficient to prevent the epigenetic maintenance of repression Our microarray data show that narrower peaks of H3K27ac are also found near numerous active promoters, at genes not known to be PcG targets. We speculate that a role of H3K27ac at these promoters may be to antagonize dimethylation of H3K27, which is abundantly distributed throughout the genome Drosophila cultured cells most PcG target genes are either completely repressed or fully derepressed or entirely devoid of both PcG and TrxG regulation. However in about 5% of cases, exemplified by the Psc-Su(z)2 or inv-en loci, we see that binding of PcG complexes does not result in complete transcriptional silencing and can coexist with binding of ASH1/TRX N-ter. Whether a PcG target gene is capable of and will assume a “balanced” state may depend on the nature of the PRE, its binding complexes, and the promoter of the target gene. More likely, however, the major determinants are the nature and concentration of activators and repressors that act in concert with PcG/TrxG. Consistent with this idea, in imaginal disc cells, which are controlled by much more complex regulatory networks than cultured cells, simultaneous presence of both PcG and TrxG proteins at the transcriptionally active PcG target genes appears to be more common In Drosophila, in cases such as those of the Psc-Su(z)2 or inv-en loci, the balanced action of PcG and TrxG results in chromatin states similar in appearance to the “bivalent” state. We suppose that, like the “balanced” chromatin state of Drosophila PcG targets, the “bivalent” domains of embryonic stem cells would be associated with both PcG proteins and mammalian orthologs of TRX and ASH1.Much has been said about the “bivalent” state, i.e. containing both PcG repressive marks and transcriptional activity marks, as characteristic of genes in mammalian embryonic stem cells hh gene lacks PcG binding and H3K27me3 but remains transcriptionally inactive PcG target genes may also assume a state lacking both PcG and TrxG proteins. The fact that in several instances the same locus resides in this “void” chromatin state in cultured cells of completely different origin argues against it being a product of genomic aberrations. Several experimental observations also indicate that the “void” state is not a peculiarity of cultured cells. Thus, in salivary glands the Sex combs reduced (Scr), Antennapedia (Antp) and Abd-B, the HOX genes whose derepression in heterozygous +/Pc− flies gives the famous Polycomb phenotype, are also among the genes most sensitive to PC knockdown in BG3 cells. The sensitivity of these genes cannot be explained by intrinsically poor recruitment of PcG proteins as Abd-B and Scr are controlled by multiple strong PREs capable of robust repression when placed next to a reporter gene In line with the finding that the lack of PcG repression in the “void” chromatin state does not automatically lead to the activation of a target gene is the observation that PC knock-down elicits a very specific genomic response. Remarkably Drosophila tiling arrays v1.0 (Forward)  and primary data processing were done as described (D. melanogaster Apr. 2004 (dm2) genome assembly and genome annotation version 4.3 were used.The description of cell lines and culturing conditions are detailed in escribed , Text S1escribed . The res6 cells was isolated using Trizol (Invitrogen) and 3 µg were used for random primed synthesis of cDNA with First Strand Synthesis Kit . The control reaction, omitting reverse transcriptase, was always run in parallel. cDNA was purified with QIAquick PCR Purification Kit (Qiagen) and eluted with 100 µl of elution buffer. qPCR was done with the Mx3000P instrument (Stratagene) in total volume of 10 µl containing 2.5 µl of cDNA solution, 1xSYBR Green PCR Master Mix (ABgene), 100 nM of corresponding primers and 100 nM of ROX as a reference dye. The sequences of primers are given in RpL32 cDNA. The RNAi was performed as described Total RNA from 5×10Microarray data are available from GEO under accession numbers GSE18100, GSE18176 and GSE18177.Figure S1Binding of H3K27me3 to Class II PcG target regions is generally weaker and more narrow than in Class I target regions. For each Class I (blue) and Class II (pink) target region the average extent of H3K27me3 enrichment  and the length of methylation domain  were computed and the density of observations plotted. The smoothed density estimates were plotted as dashed lines.(1.63 MB TIF)Click here for additional data file.Figure S2Changes in chromatin states of PcG target regions among cell lines. (A) Comparison of Class I PcG target regions with the absolute number of changes from one cell line to another as indicated. Grey indicates sites that do not change, white sites that lose E(Z) binding, black sites that lose all PcG binding. (B) Venn diagram representation of the overlap between Class I PcG target regions in Sg4 (red), BG3 (green), and D23 (blue) cell lines. The total number of sites (in parentheses) and the number of common regions for each cell line is indicated.(0.66 MB TIF)Click here for additional data file.Figure S3All cell lines contribute to the non-redundant catalogue of PcG target genes. The changes in the numbers of high-confidence (red line), low-confidence (blue line), and grand total number (green line) of PcG target genes were monitored as the data set (x-axis) from an additional cell line was considered. Each panel represents different sequence of comparisons indicated above the graphs. Note that irrespective of the direction of comparison the total number of targets (green line) continues to grow indicating that more potential PcG targets are yet to be discovered.(0.21 MB TIF)Click here for additional data file.Figure S4RpL32 transcript. The mean and the scatter (error bars) from the two independent experiments is shown. Note the log10 scale of the y-axis. The Wilcoxon rank sum test shows that the difference in the expression levels between the two groups is statistically significant .Binding of H3K4me3 to transcription units peaks at position +500 and in combination with binding of Pol II at TSS indicates transcriptional activity. (A) H3K4me3/Input ratios from 10 kb windows centered on TSS of transcription units bound by Pol II and H3K4me3 were collected and superimposed into a single scatter plot. The color  indicates the density of observations. The binding of H3K4me3 within a transcription unit is most prominent around position +500 . The weaker peak of H3K4me3 upstream of TSS is due to a high number of closely juxtaposed divergently transcribed genes. (B) The expression of eight randomly chosen transcription units from a group that binds Pol II at TSS and H3K4me3 around position +500 in Sg4 cells (red bars) was compared to expression of eight randomly chosen transcription units that show no binding of these proteins in Sg4 cells (blue bars). The abundance of transcripts in the total mRNA pool from Sg4 cells was assayed by qRT-PCR and normalized to the abundance of (1.39 MB TIF)Click here for additional data file.Figure S5Bithorax-Complex in D23 cells. The results of ChIP-chip experiments with antibodies indicated on the right of each graph were expressed as smoothed ChIP/Input signal ratios averaged for two independent experiments and plotted along Drosophila release 2004 coordinates. The positions and the exon structure of annotated transcripts are shown above (transcription left to right) and below (transcription right to left) the coordinate scale (in bp). As is evident from the presence of PC and H3K27me3 and complete absence of Pol II and H3K4me3, all genes of the BX-C are fully repressed in this cell line.(0.20 MB TIF)Click here for additional data file.Figure S6Knock-down of PC and ASH1 by RNAi. To estimate the efficiency of RNAi knock-downs serial dilutions of total nuclear protein from cells treated with dsRNA specific to the protein of interest were separated by SDS-PAGE and transferred to the membrane alongside with equal amounts of total nuclear protein from cells treated with mock (LacZ) dsRNA. Western blots were stained with anti-PC (A) and anti-ASH1 (B) antibodies and the intensity of the signals compared. The amounts of nuclear protein loaded (in µl) are indicated above each lane. The leftmost lane of each western blot shows the migration of molecular weight standards (in kDa). (C) Coomassie staining of SDS-PAGE of equal amounts of total nuclear protein from corresponding cells (indicated above the lanes) served as loading control.(1.83 MB TIF)Click here for additional data file.Figure S7tiptop can assume a “void” state. The tiptop gene is PcG-silenced in BG3 cells (B) with PC and TRX bound at PRE (blue shade) but is in the “void” state in Sg4 cells (A), with no PC and H3K27me3 but also lacking TRX, ASH1, Pol II, and H3K4me3.The gene (0.18 MB TIF)Click here for additional data file.Figure S82-transformed H3K9ac/Input ratios from 10 kb windows centered on these TSS were collected and superimposed into a single scatter plot. The color  indicates the density of observations. Both within and outside ASH1/TRX N-ter domains the distribution of H3K9ac peaks around position +450 downstream of the TSS .The distribution of H3K9ac within and outside ASH1/TRX N-ter domains is similar. The TSS of active transcription units within (A) and outside ASH1/TRX N-ter domains (B) were defined based on Pol II and H3K4me3 binding. The log(0.16 MB TIF)Click here for additional data file.Figure S9en-inv locus in D23 cells. In these cells the locus resides in fully repressed state as judged by the presence of H3K27me3, PC and E(Z) (not shown) and complete absence of H3K4me3 and Pol II.The (0.13 MB TIF)Click here for additional data file.Figure S10mirr gene after various RNAi treatments. The histogram shows the mean of two independent qRT-PCR experiments with error bars indicating the scatter.The expression of (0.22 MB TIF)Click here for additional data file.Figure S11The binding sites recognized by both rabbit polyclonal and rat monoclonal anti-ASH1 antibodies include all the broadest and strongest ASH1 binding regions. The set of ASH1 bound regions detected in Sg4 cells with rabbit polyclonal antibodies was compared to the set of regions detected in these cells with rat monoclonal antibodies. The length (B) and the extent of ASH1 binding (A) within the regions unique to the polyclonal antibody data set (empty bars) and the regions recognized by both antibodies (red bars) were computed and the frequencies of observations plotted. The extent of ASH1 binding detected with polyclonal antibodies was defined as average smoothed IP/Input ratio for the six consecutive features which showed the strongest binding within the bound region. The reciprocal comparison and the comparisons in BG3 and D23 cells gave the same results (data not shown).(0.42 MB TIF)Click here for additional data file.Figure S12Comparison of antibodies against TRX C-ter raised by Poux et al. (2002) and Beisel at al. (2007). The Venn diagrams illustrate overlapping between sets of regions detected by the two antibodies in Sg4 cells (A) within PcG target regions, (B) outside PcG target regions. The total number of regions in the group and percentage of overlapping are indicated.(0.33 MB TIF)Click here for additional data file.Table S1PcG target regions.(0.02 MB XLS)Click here for additional data file.Table S2Class I high-confidence PcG target genes.(0.09 MB XLS)Click here for additional data file.Table S3Class I low-confidence PcG target genes.(0.07 MB XLS)Click here for additional data file.Table S4Class II high-confidence PcG target genes.(0.03 MB XLS)Click here for additional data file.Table S5Class II low-confidence PcG target genes.(0.05 MB XLS)Click here for additional data file.Table S6List of computationally defined PREs.(0.03 MB XLS)Click here for additional data file.Table S7List of PcG target regions found in void state.(0.02 MB XLS)Click here for additional data file.Table S8List of Class I PcG target regions in “balanced” state.(0.02 MB XLS)Click here for additional data file.Table S9List of antibodies used.(0.02 MB XLS)Click here for additional data file.Table S10List of microarray data sets.(0.03 MB XLS)Click here for additional data file.Table S11List of oligonucleotide primers used in this work.(0.02 MB XLS)Click here for additional data file.Table S12List of Max Distance parameter values used in definition of bound regions.(0.01 MB XLS)Click here for additional data file.Text S1Supplemental notes.(0.05 MB DOC)Click here for additional data file.


A substantial fraction of extragenic Pol II transcription sites coincides with transcriptional enhancers, which may be relevant for functional annotation of mammalian genomes. cis-regulatory genomic regions. Discrimination between lncRNAs generated by canonical RNA genes and products of transcribed enhancers will provide a framework for experimental approaches to lncRNAs and help complete the annotation of mammalian genomes.Mammalian genomes are pervasively transcribed outside mapped protein-coding genes. One class of extragenic transcription products is represented by long non-coding RNAs (lncRNAs), some of which result from Pol_II transcription of bona-fide RNA genes. Whether all lncRNAs described insofar are products of RNA genes, however, is still unclear. Here we have characterized transcription sites located outside protein-coding genes in a highly regulated response, macrophage activation by endotoxin. Using chromatin signatures, we could unambiguously classify extragenic Pol_II binding sites as belonging to either canonical RNA genes or transcribed enhancers. Unexpectedly, 70% of extragenic Pol_II peaks were associated with genomic regions with a canonical chromatin signature of enhancers. Enhancer-associated extragenic transcription was frequently adjacent to inducible inflammatory genes, was regulated in response to endotoxin stimulation, and generated very low abundance transcripts. Moreover, transcribed enhancers were under purifying selection and contained binding sites for inflammatory transcription factors, thus suggesting their functionality. These data demonstrate that a large fraction of extragenic Pol_II transcription sites can be ascribed to  Mammalian genomes contain vast intergenic regions that are extensively transcribed and generate various types of short and long non-coding RNAs (ncRNAs). Although in some cases specific functions have been assigned to intergenic transcripts, the functional significance of this transcriptional output remains largely unknown, and the possibility exists that part of this transcription reflects noise generated by random collisions of the transcriptional machinery with the genome to generate meaningless transcription. In this study we used chromatin signatures to characterize extragenic transcription sites targeted by RNA Polymerase II (RNA Pol II) in a highly regulated response—endotoxin activation of macrophages. We found that a significant portion of extragenic transcription sites are associated with the chromatin signature characteristic of enhancers. Consistent with their chromatin signature, we found that these extragenic transcription sites are under purifying selection and contain binding sites for inflammatory transcription factors, as well as for PU.1, a hematopoietic transcription factor that marks enhancers in macrophages. Moreover, much of this extragenic transcription is regulated by stimulation. We also identified hundreds of transcribed regions with a signature of canonical RNA genes. Our data indicate that extragenic transcription sites can be efficiently classified using chromatin signatures, which will be relevant for functional annotation of mammalian genomes. A most striking finding of modern genomic biology has been the identification of a large amount of transcription that occurs outside mapped protein-coding genes and generates a heterogeneous spectrum of transcripts Regarding functionality, the two extreme views are that most of this extragenic non-coding transcription merely represents noise, namely the consequence of unscheduled but productive collisions of RNA polymerases with random genomic regions, and that most of the products of non-coding transcription are functional RNA molecules exerting downstream functions cis-regulatory elements like LCRs and enhancers. Unidirectional transcription of the β-globin LCR by RNA Pol_II Drosophila requires intergenic transcription through the PRE, indicating that in some cases transcription may provide an anti-silencing mechanism SRG1 through the promoter of SER3 in yeast interferes with binding of transcription factors and subsequent activation, thus providing a paradigmatic example of transcriptional interference mediated by non-coding transcription Ubx gene in Drosophila is repressed by non-coding transcription elongating from the upstream bxd locus, which results in complementary and non-overlapping patterns of expression of Ubx mRNA and bxd ncRNAs per se (rather than the transcripts) mediates downstream effects. For instance, intergenic transcription extending in the yeast PHO5 promoter is required for nucleosome eviction and gene activation; however, increasing the level of the unstable lncRNA generated in this region didn't affect gene activation CCND1 gene trans to coactivate the homeodomain TF Dlx-2 The concept that transcription has roles other than generating functional products mainly stems from the analysis of S. Pombe: transiently inducible non-coding Pol_II transcription upstream of the fbp1 locus caused a wave of chromatin remodeling preceding, and required for, binding of activating transcription factors to cognate sites in the fbp1 promoter fbp1 was not directly addressed.Mechanistically, transcriptional elongation causes a broad spectrum of effects to the underlying chromatin template, including chromatin remodeling, nucleosome eviction, and changes in the acetylation and methylation state of histone tails cis-regulatory regions (like enhancers and LCRs) is completely unknown.In spite of all these observations, it is still unclear to what extent each of these reports represents an anecdotal description of uncommon gene regulatory mechanisms or conversely a paradigmatic example of a more general contribution of non-coding transcription to gene control. Moreover, the extent to which transcription occurring outside protein-coding genes indicates underlying RNA genes rather than Pol_II elongation along distant Here we took advantage of a dataset of extragenic Pol_II sites in a model of highly regulated gene expression (endotoxin-stimulated primary macrophages). Using chromatin signatures we discriminated between transcribed enhancers and transcription start sites (TSS) of RNA genes. Remarkably, 70% of extragenic transcription sites (which were frequently up- or down-regulated by endotoxin stimulation) corresponded to genomic regions with an enhancer-type chromatin signature. These Pol_II peaks overlapped with annotated lncRNAs, were associated with binding sites for inflammatory transcription factors, and displayed enhancer activity in reporter assays. We also identified about 700 extragenic Pol_II clusters with a typical signature of active TSS and highly enriched for CpG islands, thus likely representing the 5′ end of bona fide RNA-coding genes. Overall, enhancers overlap a sizeable fraction of extragenic transcription sites in higher eukaryotes.We first determined the genomic distribution of RNA Pol_II in unstimulated and activated mouse macrophages . These ChIP-Sequencing datasets  1, −2, and −3) indicated in fbp1 gene in S.Pombe, a rapidly induced, low-level upstream transcription (which is required for chromatin opening at the locus) precedes downstream gene activation and is turned off when the gene is activated To start characterizing the properties of the extragenic transcription described above, we first analyzed kinetics of induction of the corresponding ncRNA relative to that of the downstream coding gene. We carried out quantitative RT-PCR with primers designed in regions contained within the extragenic Pol_II peaks. In the case of Ccl5 we explored the three regions of extragenic transcription , the upstream non-coding transcripts were very unstable, being reduced by 80% to 90% after a 30 min actinomycinD treatment  . High inAnother interesting property of some of the upstream transcripts is that, unlike mRNAs, they are poorly sensitive to DRB treatment . DRB is Browsing through the data indicated some major challenges towards a systematic and correct identification of extragenic Pol_II peaks. The most obvious one was represented by the extension of elongating Pol_II molecules several kilobases beyond the end of annotated protein coding genes, namely in regions that by definition are extragenic. This is most likely due to the lack of specific and strong termination signals for RNA Pol_II. Moreover, alternative TSSs located upstream of the annotated ones contribute to create ambiguity in extragenic Pol_II peak annotation. To systematically annotate sites of extragenic transcription, we first filtered out all Pol_II signals overlapping UCSC known genes as well as peaks within 10 kb from the 3′ end of annotated genes . It is important to stress that because of this design, our analysis does not take into account gene boundaries, which represent a major source of long and short non-coding RNAs The initial list was eventually curated for additional filtering , leading to 4,588 high-confidence extragenic Pol_II peaks. Using a statistical approach for ChIP-Seq data analysis hi/H3K4me3lo domains) Chromatin signatures generated by specific combinations of post-translational modifications of core histone tails are powerful and sensitive indicators of functionality In order to assign the extragenic Pol_II clusters in our dataset to either TSSs of lncRNA genes or to enhancers, we used a machine-learning algorithm . The algp<1e-3) as compared to only 11/2,236 enhancer clusters   . The assp<1e-7 when compared to the expected fraction), while association with constitutive and repressed genes was underrepresented in a statistically significant manner . Ser5 phosphorylation by TFIIH occurs at the transition to transcription initiation and is maintained throughout the length of transcribed genes to be then removed by a phosphatase at the very 3′ end p values associated with the corresponding Pol_II peaks . In most cases, however, low density of tags precluded the identification of well-defined transcripts. Importantly, the extragenic regions associated with RNA-Seq tags displayed median Pol_II signals about 1.5 orders of magnitude higher than the regions for which transcripts could not be detected at this sequencing depth  we could detect 225,439 transcripts corresponding to 13,702 RefSeq genes and 28,247 UCSC known genes. We found RNA-Seq tags overlapping 193/484 promoters and 369/1,660 enhancers active in untreated macrophages  and it was progressively diluted moving outwards.We next cloned some of these predicted enhancer sequences in a plasmid bearing a minimal promoter driving luciferase expression and tested their ability to increase reporter gene activity. All the sequences tested increased basal expression and some provided responsiveness to LPS stimulation . The firFinally, we evaluated the degree of overlap between extragenic Pol_II and binding of the transcription factor PU.1, which (in addition to being recruited to active promoters) is very extensively associated with enhancers in macrophages Enhancer functionality depends on the transcription factor binding sites (TFBS) contained in their sequence. TFs activated by stimulation with LPS+IFNγ include NF-kB/Rel family members In the inducible group we found a strong enrichment for IRFs and STAT1 (which bind related sites and were recognized by five distinct PWMs), as well as for NF-kB/Rel (identified by four PWMs) . MoreoveWe next evaluated if the identified TFBSs are functional. Some of the inducible Pol_II peaks in the region upstream of Ccl5 scored positively for IRF3 (as well as other IRFs) and NF-kB, which are known to coregulate Ccl5 expression As we could detect thousands of enhancer-associated extragenic Pol_II peaks with distinct behaviors, some degree of functional heterogeneity is expected. Moreover, definitive understanding of the function of each extragenic transcription site would require dedicated genetic approaches to interfere with Pol_II loading and/or elongation . On the contrary, ncRNAs generated from canonical RNA genes in most cases act at a distance from the production site (e.g. Neat1) cis, as in the case of Xist and Air, they coat  broad chromosomal regions, thus in fact exerting an activity that extends far beyond the borders of their site of synthesis.First, can enhancers be classified on the basis of being transcribed or not, and do Pol_II-transcribed enhancers represent a functionally and mechanistically homogeneous group? A simple model, compatible with a large body of experimental data, is that functionality of transcribed enhancers and LCRs indeed depends on the directional movement of Pol_II along their sequence In this context, it appears very relevant to bring into focus the conceptual and technical problems related to the mechanistic dissection of the ncRNAs generated at regulatory regions. Assessing the functionality of these ncRNAs will require that their specific elimination or depletion be dissociated from any effect on the underlying transcription. Therefore, knocking-in transcriptional terminators to interfere with Pol_II elongation  One important aspect of extragenic transcription, and particularly the fraction not associated with putative RNA-coding genes bearing a promoter signature at their 5′ end, is that it should be unambiguously distinguished from the transcriptional noise that may arise from spontaneous collisions of the Pol_II transcriptional machinery with some genomic sequences The direct evidence arguing against the possibility that extragenic Pol_II reflects transcriptional noise comes from four groups of data we obtained in this study: (1) the presence of an enhancer-associated chromatin signature An additional aspect worthy of attention is that at least in this system extragenic Pol_II peaks are more frequently repressed than induced by stimulation. While in many cases repression correlated with downregulation of the associated genes, in several others it correlated with gene activation of a neighboring gene. A reasonable hypothesis in this case is that, similar to what was described in other models cis-regulatory elements.In conclusion, our study demonstrates that the pervasive transcription occurring in mammalian genomes sc-372). The anti-acetylH3K9 antibody used in Bone marrow cells isolated from female Fvb/Hsd mice were plated in 10 cm plates in 5 ml of BM-medium . Cultures were fed with 2.5 ml of fresh medium every 2 d. Stimulations were carried out at day 7. Raw264.7 were cultured in high glucose DMEM containing 10% low endotoxin FCS. Clones stably expressing dominant negative IRF3 and IkBα super-repressor were a gift of G. Cheng (UCLA) sc-899). The Ser5-Pol II ChIP-Seq datasets were generated using the Ab5131 antibody from Abcam, which recognizes the RNA Pol_II CTD repeat YSPT(phospho)SPS. The H3K4me3, H3K4me1, and PU.1 datasets used in this study were all obtained in unstimulated cells, and antibodies were from Abcam  or Santa Cruz (PU.1 sc-352). Datasets are available for download from NCBI's Gene Expression Omnibus , accession numbers GSE17631, GSE19553, GSE19991.The RNA Pol_II and H3K4me3 ChIP-Sequencing datasets are described in Nuclei were isolated as described in the section below and total nuclear RNA was extracted using Trizol. After quality control, RNA was processed following the same standard Solexa protocol recommended for mRNA sequencing. The dataset is available for download from GEO, accession number GSE20370.2, 1 M urea, 1% NP-40, 100 µg/ml yeast tRNA). Chromatin was then pelleted in microfuge at 4°C and nascent transcripts extracted in Trizol. As control of the lack of genomic DNA contamination, Q-PCR was also carried out on RNA that was not reverse-transcribed. The sequences of the primers used are in RNA was extracted from macrophages using Trizol (Invitrogen) and reverse transcribed with random hexamers. In some experiments oligo-dT or gene specific oligonucleotides were used to prime the reverse transcription, as indicated in the text. For isolation of nascent transcripts, cells were lysed in HB buffer  containing 0.3 M sucrose and 0.8% NP40. Nuclei were then pelleted through a 0.9 M sucrose cushion in HB buffer and then resuspended in 100 µl of NRB ; lysis was carried out by addition of 750 µl of NLB  or vectors containing the specified genomic regions  with LipFigure S1Inducible extragenic Pol_II peaks occurring upstream of LPS-inducible genes. (A) The Cxcl9-Cxcl11 chemokine gene clusters with two Pol_II peaks upstream of Cxcl11 highlighted. (B–D) Three additional representative genomic regions are shown. The extragenic Pol_II peaks are indicated by horizontal lines. Peaks can also be detected at lower levels in unstimulated macrophages.(0.55 MB TIF)Click here for additional data file.Figure S2Nascent, chromatin associated transcripts at the Ccl5 (top) and Cxcl11 (bottom) loci were measured in LPS+γIFN-stimulated cells as indicated. The amplicons indicated are: TSS (transcription start site); A and B (corresponding to two regions contained within the −1 peak in (0.25 MB TIF)Click here for additional data file.Figure S3Stability of representative RNAs originating from extragenic Pol_II transcription sites. (A) Macrophages were stimulated with LPS for 2 h and then treated for 30 min with actinomycinD (ActD). Stability of the upstream non-coding transcripts is compared to that of the neighboring protein-coding gene. At each panel the y-axis on the left (in red) indicates the mRNA levels relative to those of the housekeeping gene TBP, while the y-axis on the right (light blue) indicates the levels of the neighboring upstream RNA generated by extragenic transcription. (B) Stability of annotated ncRNAs, including Neat, Xist, two Fantom transcripts, and two Linc RNAs.(0.61 MB TIF)Click here for additional data file.Figure S4An enhancer-associated chromatin signature in the transcribed region upstream of Ccl5. The three main sites of extragenic transcription are indicated by shaded blue boxes. The two tracks at the bottom show the ChIP-Seq profiles of PU.1 in the same region. PU.1 is a hematopoietic Ets family member highly expressed in macrophages and showing a widespread association with enhancers Click here for additional data file.Figure S5Canonical lncRNA genes have a typical promoter chromatin signature at their 5′ end. ChIP-Seq profiles at two representative genes, Malat1 (top) and Neat1 (bottom). The green box indicates a CpG island.(0.80 MB TIF)Click here for additional data file.Figure S6Examples of extragenic Pol_II peaks in predicted enhancers overlapping annotated lincRNAs. Four representative regions are shown. The H3K4me3/H3K36me3 domains from Guttman et al. (1.24 MB TIF)Click here for additional data file.Figure S7Correlation between total Pol_II and phospho-Ser5 Pol_II at extragenic regions with a promoter prediction. The graphs display the distance between extragenic Pol_II peaks predicted as promoters/TSSs and the closest phospho-Ser5 Pol_II peak.(0.27 MB TIF)Click here for additional data file.Figure S8Chromosome conformation capture (3C) assay at the Cxcl11 and Ccl5 loci. The position of the anchor (constant) primer (red asterisk) and the Hind III restriction sites used is indicated. Inverted images of ethidium bromide-stained agarose gels are shown. n.s., non-specific band.(0.55 MB TIF)Click here for additional data file.Table S1A curated dataset of extragenic Pol_II peaks in mouse macrophages. Peaks were divided in constitutive, inducible, and repressed according to Pol_II behavior in response to stimulation.(0.42 MB XLS)Click here for additional data file.Table S2Intergenic lncRNAs datasets used in this study. The dataset termed “Ponjavic” is based on two datasets generated by the FANTOM consortium (0.39 MB XLS)Click here for additional data file.Table S3RNA Pol_II clusters overlapping annotated intergenic lncRNAs. Clusters from (0.13 MB XLS)Click here for additional data file.Table S4Promoter and enhancer predictions. The extragenic Pol_II peaks were analyzed for chromatin signatures of enhancers or promoters using a machine-learning algorithm (described in the (1.29 MB XLS)Click here for additional data file.Table S5Association between the transcriptional activity of enhancer-type extragenic Pol_II clusters with the expression of the associated protein-coding genes. Extragenic Pol_II peak clusters with a signature of enhancer were assigned to the neighboring protein coding gene when distant less than 20 kb. Transcriptional activity of the assigned coding gene was evaluated on the basis of the Pol_II tag counts at ±500 bp surrounding their TSS in untreated and LPS-treated macrophages.(0.09 MB XLS)Click here for additional data file.Table S6Phospho-Ser5 Pol_II datasets. Peaks detected in untreated as well as LPS treated (2 h) macrophages against their input are listed.(9.40 MB XLS)Click here for additional data file.Table S7Validation of 100 ncRNAs associated with predicted enhancers. The table shows the genomic location of the regions, the Q-RT-PCR data, the sequence of the primers used, and the corresponding peak in (0.03 MB XLS)Click here for additional data file.Table S8Enrichment of TFBSs in the datasets of predicted enhancers and promoters. The table shows the results of the Clover analysis carried out using a library of 338 high-quality PWMs Click here for additional data file.Table S9Primers used in this study. Primers used are shown with reference to each figure.(0.04 MB XLS)Click here for additional data file.Table S10Regions used in the luciferase assay and relative cloning primers.(0.02 MB XLS)Click here for additional data file.Text S1Computational methods. Chromosome conformation capture (3C) assay.(0.08 MB DOC)Click here for additional data file.


We find that a majority of Arp6 binding sites coincide with binding sites of Swr1, the catalytic subunit of SWR-C, and with the histone H2A variant Htz1 (H2A.Z) deposited by SWR-C. However, Arp6 binding detected at centromeres, the promoters of ribosomal protein (RP) genes, and some telomeres is independent of Swr1 and Htz1 deposition. Given that RP genes and telomeres both show association with the nuclear periphery, we monitored the ability of Arp6 to mediate the localization of chromatin to nuclear pores. Arp6 binding is sufficient to shift a randomly positioned locus to nuclear periphery, even in a swr1Δ strain. Arp6 is also necessary for the pore association of its targeted RP promoters possibly through cell cycle-dependent factors. Loss of Arp6, but not Htz1, leads to an up-regulation of these RP genes. In contrast, the pore-association of GAL1 correlates with Htz1 deposition, and loss of Arp6 reduces both GAL1 activation and peripheral localization. We conclude that Arp6 functions both together with the nucleosome remodeler Swr1 and also without it, to mediate Htz1-dependent and Htz1-independent binding of chromatin domains to nuclear pores. This association is shown to have modulating effects on gene expression.Actin-related proteins are ubiquitous components of chromatin remodelers and are conserved from yeast to man. We have examined the role of the budding yeast actin-related protein Arp6 in gene expression, both as a component of the SWR1 complex (SWR-C) and in its absence. We mapped Arp6 binding sites along four yeast chromosomes using chromatin immunoprecipitation from wild-type and  GAL1 promoter, where it binds together with Swr1 and the histone H2A variant H2A.Z. Indeed, loss of Arp6 and nuclear envelope binding delays galactose-induced activation of GAL1. The two opposing functions of Arp6 correlate with different pathways of anchoring mediated by Arp6: one requires that the intact SWR-C deposits H2A.Z, while in the second Arp6 may mediate promoter binding to nuclear pore baskets directly. In both cases, Arp6 is implicated in the spatial localization of chromatin within the interphase nucleus, which has functional consequences on expression. This identifies a novel function for actin-related proteins.Actin and the structurally similar actin-related proteins (ARPs) are major components of nucleosome remodeling complexes in the nucleus. Here we show that budding yeast Arp6 has functions independent of its catalytic chromatin remodeling partner, Swr1. Arp6 binds to multiple promoters and subtelomeric zones at which Swr1 does not bind, and this association increases in strains lacking an intact SWR-C remodeling complex. We show that Arp6 can mediate the association of the ribosomal protein gene promoters to which it binds, with the nuclear envelope. The loss of Arp6 led to an up-regulation of these ribosomal protein genes, yet had the opposite effect on the galactose-induced  Genomic DNA is complexed with histones and non-histone proteins to form chromatin, which is organized into active and inactive domains within the interphase nucleus INO1, GAL1, and HXK1 form a stable association with the nuclear pore complex (NPC) upon activation. In some cases, this interaction ensures maximal expression and fine-tuning of induction rates In both vertebrates and yeast, transcriptionally inactive heterochromatin is enriched around the nucleolus or at the nuclear envelope (NE). In vertebrates, perinuclear anchoring appears to require the nuclear lamina, while in yeast integral proteins of the inner nuclear membrane tether repressed chromatin domains peripherally Besides nuclear pore proteins, little is known about the components that position active chromatin domains within the nucleus. Nuclear actin and myosin, as well as myosin-like and actin-related proteins have been proposed as candidates that could contribute to the organization of transcription in the interphase nucleus S. cerevisiae alone harbors ten ARP genes, numbered from 1 to 10, with ascending degrees of dissimilarity to actin In all organisms from yeast to man, the actin family includes a number of proteins that are structurally similar to actin, called actin-related proteins or ARPs. The yeast Arp6 is an evolutionarily conserved nuclear ARP In vitro analyses have indicated that Arp6 contributes to transcriptional regulation by mediating Htz1 deposition in vivo is very limited.Nucleosomes containing acetylated H2A.Z are specifically enriched at promoters in higher eukaryotes, and consistently, Htz1 is found in nucleosomes flanking the nucleosome-free region located at transcription start sites in yeast ARP gene deletion are more extensive than those provoked by loss of a complex-specific ATPase subunit. Indeed, ARP4 is essential for viability in yeast, and the protein is a component of both INO80- and SWR-C remodeling complexes and the NuA4 histone acetyltransferase, which carry out distinct nuclear functions In addition to a role in chromatin modulating complexes, some nuclear ARPs are thought to have functions beyond that of the remodeling complex to which they belong. In other cases, such as Arp4 , ARP proteins are involved in multiple complexes, so that the phenotypes associated with arp6 deletion reduces the association of RP genes with the NPC. This leads to a slight elevation in RP gene expression. We argue that Arp6 not only modulates local chromatin organization by facilitating Htz1 deposition, but also contributes to long-range chromatin organization that can fine-tune expression levels independently of SWR-C.Here we have localized Arp6 along budding yeast chromosomes, both in the absence and presence of Swr1. We find that most Arp6 co-localizes with Swr1, being enriched in the promoters of divergently transcribed genes. This correlates with the deposition of the histone H2A variant H2A.Z/Htz1, and is consistent with the proposal that Arp6, as part of SWR-C, contributes to transcriptional regulation by exchanging H2A for Htz1 ARP6 or SWR1, and the functionality of the fusion constructs was confirmed by monitoring growth rates and sensitivity to DNA damaging agents (www.ncbi.nlm.nih.gov/geo under the access number GSE9213).We determined the localization of Arp6 and Swr1 along budding yeast chromosomes by chromatin immunoprecipitation using high-density microarray chips (ChIP-chip assay). A 3FLAG epitope tag was introduced at the 3′ end of the genomic copy of either g agents . GenomicArp6 binding sites were found widely dispersed along chromosomes 3, 4, 5, and 6R, occasionally spreading over regions of several kb . Among tIn conclusion, our ChIP-chip analysis suggests that Arp6 binds chromatin as a component of SWR-C at most euchromatic sites, and in particular in the promoters of divergently transcribed genes. However, at centromeres, some telomeres and select promoters, Arp6 appears to bind independently of Swr1 . This is unlikely to reflect an indirect effect on chromatin, since the association of Ino80 or topoisomerase II with chromatin was unaffected by arp6 deletion . This result suggests that the physical association of Arp6 with chromatin is at least in part independent of SWR-C, and is consistent with the ChIP-chip data which show partially non-overlapping distributions of Arp6 and Swr1 . Consistent with the reduced level of chromatin-bound Arp6 . This expression microarray analysis was repeated at least three times for each strain, and the statistical differences were determined by t-test. Changes with p<0.05 were considered significant. We found a larger number of genes to be differentially regulated in both arp6Δ and swr1Δ cells , and 56 out of 375 genes induced in swr1Δ  To examine the independent contributions of Arp6 and Swr1 to gene expression, we performed a yeast whole-genome microarray with wild-type, 1Δ cells   in both  strains . Intriguarp6Δ cells we found 21 ribosomal protein genes , even after deletion of SWR1  and UBX3 (YDL091C)  . These gPES4) at which we have inserted 4 LexA binding sites and a lac operator array that allows visualization with GFP-LacI (PES4 locus in the outermost nuclear zone (zone 1) in both G1- and S-phase cells (swr1). Expression of LexA alone does not shift the position of the tagged locus, allowing us to conclude that LexA targeted Arp6 is sufficient to favor the association of a chromatin locus with the NE. This is independent of Htz1 deposition, since it requires the catalytic activity of Swr1. We note that there is a low level of endogenous Arp6 detected near the PES4 locus in swr1 mutant cells  LYS2 locus bound by LexA-Arp6 not only accumulated in the nuclear peripheral zone like the PES4 locus  but also colocalized significantly with clustered NPC  to the NE was indeed lost in S-phase arp6Δ cells . Taken together our data argue that Arp6, while being sufficient to relocate loci to the NE . Intriguingly, we again see that LexA-Arp6 anchoring activity was dependent on Mlp1 and Mlp2 exclusively in S-phase cells. We conclude that Arp6 is able to mediate association with nuclear pores in an Mlp1/Mlp2-dependent manner, yet again our data indicate that a second pathway for Arp6 binding is functional in G1 phase. Arp6 may interact with the coiled-coil proteins Mlp1 and Mlp2 directly, although it is more likely to bind through Yra1 . The role of Arp6 at telomeres is therefore not restricted to the deposition of Htz1.ChIP-chip analysis showed that the SWR-C-independent binding of Arp6 is observed at RP genes and in some subtelomeric zones, which are both depleted for Htz1 The RP genes are among the most important genes for cell metabolism, and the fine-tuning of RP gene transcription responds to a variety of environmental effects, ultimately coordinated by the TORC1 complex (Target of Rapamycin) RPP1A, RPL4B, and RPP2B, which belong to a subgroup that binds neither Hmo1, Fhl1, nor Rap1 RPP1A . The perinuclear binding activity may thus reflect a unique domain of Arp6 or a binding partner with affinity for the NPC.In addition to facilitating mRNA processing and export, the association of euchromatic domains with the NPC may facilitate the formation of nuclear subcompartments by creating boundaries arp6 mutants show hypersensitivity to various DNA damaging agents, Arp6 may also contribute to repair through its perinuclear relocalization activity.The positioning of chromatin in the interphase nucleus not only influences transcription, mRNA processing and export, but genome stability as well. Several laboratories have reported that critically short telomeres, irreparable DNA double-strand breaks and collapsed replication forks shift to the NPC for a repair pathway controlled by SUMO-dependent ubiquitin ligase We have recently analyzed chicken DT40 cells carrying a conditional knockout for Arp6, and found that the radial distribution of chromosome territories was altered in the absence of Arp6 . We therefore entertain the hypothesis that the contribution of Arp6 to long-range chromatin organization is evolutionarily conserved. In vertebrates there is as yet no compelling data implicating the NPC in gene expression or DNA repair, although other intranuclear structures such as PML bodies or transcription factories may replace pores in this function. We note that the reduction of human Arp4 by siRNA, unlike the loss of BRG-1, BRM, or Tip49, causes an expansion of the nuclear volume occupied by individual chromosomes . Sequence and position of oligonucleotides on the microarrays are available from Affymetrix. ChIP was carried out as previously described 600 = 1.0 using TRIzol (Invitrogen). Microarray detection was performed as previously described For microarray analysis, total RNAs were prepared from cultures grown at 30°C in YPD medium to ODThe native molecular mass of complexes was monitored by gel filtration analysis according to Harata et al. 8cells) was then re-suspended in 1 ml 50 mM Hepes-KOH pH 7.5, 2.5 mM MgCl2, 10 mM glycerol 2-phosphate, 0.1 mM Na3VO4, 0.25% Triton X-100, 300 µg/ml benzamidine, 1 µg/ml pepstatin A, 2 µg/ml antipain, 0.5 µg/ml leupeptin, 100 µg/ml TPCK, 50 µg/ml TLCK.The chromatin fractionation assay was performed as previously described 600 = 1.0 by using RNeasy Mini Kit (Qiagen). Total RNAs were reverse-transcribed using the High Capacity cDNA Reverse Transcription kit (ABI), and subjected to quantitative real-time PCR with a SYBR Green Master Mix (ABI Prism 7000 Sequence Detector System and Software). ChIP was performed as for ChIP-chip analysis, but purified ChIP DNA was subjected to quantitative real-time PCR rather than microarray hybridization. For the primer sets, see For quantitative RT-PCR analysis, total RNAs were prepared from cultures grown at 30°C in YPD or YPG medium to ODFigure S1The functionality of the tagged Arp6 and Swr1 was confirmed by monitoring cell growth and sensitivity to hydeoxyurea (HU). Five-fold serial dilutions of each strain were plated on YPD with or without 50 mM HU and incubated at 30°C or 37°C for 3 days.(1.09 MB TIF)Click here for additional data file.Figure S2swr1 cells (bottom) are compared. The position of Tel 3L, Tel 3R, CEN3, and the RP gene are shown under the panels.Localization of Arp6 and Swr1 on chromosome 3. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in (9.62 MB TIF)Click here for additional data file.Figure S3swr1 cells (bottom) in the whole chromosome region are compared. The position of Tel 4L, Tel 4R, CEN4, SWR1, and RP genes are shown under the panels.Localization of Arp6 and Swr1 on chromosome 4. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in (2.67 MB TIF)Click here for additional data file.Figure S4SWR1 gene of chromosome 4. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) are compared. The position and orientation of the SWR1 gene is shown.Localization of Arp6 and Swr1 on the region including the (1.86 MB EPS)Click here for additional data file.Figure S5swr1 cells (bottom) are compared. The position of Tel 5L, Tel 5R, CEN5, and the RP genes are shown under the panels.Localization of Arp6 and Swr1 on chromosome 5. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in (5.97 MB TIF)Click here for additional data file.Figure S6swr1 cells (bottom) in the region 228K-244K of Chr 6R were compared. The orientation of transcription of the genes of Watson strand and Crick strand in the region was shown by arrows in the map over the panels. Regions of divergent promoters are indicated with gray shadow.Preferential localization of Arp6 and Swr1 in the 5′ end of genes. Vertical bars represent the binding ratio of proteins in each locus. The binding of Arp6-Flag (Top), Swr1-Flag (middle), and Arp6-Flag in (1.29 MB EPS)Click here for additional data file.Figure S7p-value <0.025) in wild-type (A) and in swr1 cells (B) are represented as scatterplots versus the Swr1 binding log2 ratio in each Arp6 binding locus of wild-type cells. The yellow lines represent the hypothetical pattern of the data if Arp6 and Swr1 bind equally on the chromosomes.Correlation of the localizations of Arp6 and Swr1. The Arp6-binding log2 ratios of Arp6-binding loci (change (2.49 MB EPS)Click here for additional data file.Figure S8GAL1, SWR1, and ribosomal protein (RPL13A and RPS16B) genes was analyzed using ChIP with an anti-Htz1 antibody  and quantified using real-time quantitative PCR in wild-type (WT), arp6, and swr1 cells. The values are indicated as percentage of input DNA obtained by ChIP with anti-Htz1 antibody. The data points represent the mean ± SD for at least three independent experiments.ChIP analysis for Htz1 in cells lacking Arp6 or Swr1. Htz1 association to the promoter of (0.90 MB EPS)Click here for additional data file.Figure S9RDS1 (YCR106W) and UBX3 (YDL091C) in arp6- and htz1-deletion mutants. The same amount of total RNA from wild-type, arp6, and htz1 cells was analyzed using real-time quantitative RT–PCR. The ACT1 gene was analyzed as a control. The relative amount of the transcript of the genes to ACT1 is shown. The data points represent the mean ± SD for at least three independent experiments.Quantitative analysis of (0.87 MB EPS)Click here for additional data file.Figure S10GAL1 gene in arp6 cells. The association of GAL1 gene with NPC was analyzed using ChIP with an antibody against nuclear pore complex proteins  in wild-type (WT) and arp6 cells grown on the glucose- or galactose containing media. Immunoprecipitated DNA was quantified using real-time PCR probed for GAL1 gene. The percentage of recovered DNA over input is plotted relative to wild-type cells on glucose as 1. The data points represent the mean ± SD for at least three independent experiments.ChIP analysis for nuclear pore complex with (0.85 MB EPS)Click here for additional data file.Table S1Presence of Arp6 in nonrepetitive 10 kb subtelomere zones.(0.05 MB DOC)Click here for additional data file.Table S2arp6Δ and swr1Δ cells.Microarray analysis in (1.10 MB XLS)Click here for additional data file.Table S3Binding of Arp6 and Swr1 on ribosomal protein genes.(0.04 MB DOC)Click here for additional data file.Table S4arp6Δ and swr1Δ cells.Expression of RP genes in (0.04 MB XLS)Click here for additional data file.Table S5Genes markedly down-regulated in arp6 cells.(0.09 MB DOC)Click here for additional data file.Table S6Strains used in this study.(0.06 MB DOC)Click here for additional data file.Text S1Primer sequences.(0.04 MB DOC)Click here for additional data file.