Fusarium graminearum, is a devastating disease of wheat. We developed near‐isogenic lines (NILs) differing in the two strongest known F. graminearum resistance quantitative trait loci (QTLs), Qfhs.ndsu‐3BS  and Qfhs.ifa‐5A, which are located on the short arm of chromosome 3B and on chromosome 5A, respectively. These NILs showing different levels of resistance were used to identify transcripts that are changed significantly in a QTL‐specific manner in response to the pathogen and between mock‐inoculated samples. After inoculation with F. graminearum spores, 16 transcripts showed a significantly different response for Fhb1 and 352 for Qfhs.ifa‐5A. Notably, we identified a lipid transfer protein which is constitutively at least 50‐fold more abundant in plants carrying the resistant allele of Qfhs.ifa‐5A. In addition to this candidate gene associated with Qfhs.ifa‐5A, we identified a uridine diphosphate (UDP)‐glycosyltransferase gene, designated TaUGT12887, exhibiting a positive difference in response to the pathogen in lines harbouring both QTLs relative to lines carrying only the Qfhs.ifa‐5A resistance allele, suggesting Fhb1 dependence of this transcript. Yet, this dependence was observed only in the NIL with already higher basal resistance. The complete cDNA of TaUGT12887 was reconstituted from available wheat genomic sequences, and a synthetic recoded gene was expressed in a toxin‐sensitive strain of Saccharomyces cerevisiae. This gene conferred deoxynivalenol resistance, albeit much weaker than that observed with the previously characterized barley HvUGT13248.Fusarium head blight, caused by  Fusarium graminearum frequently infects small grain cereals, particularly wheat  and on chromosome 5A (Qfhs.ifa‐5A)  that confer partial resistance . A highly DON‐inducible barley uridine diphosphate‐glycosyltransferase (UGT), capable of inactivating DON by transforming it to D3G, has been identified  constitutively more abundant for the resistant Qfhs.ifa‐5A allele. A UGT gene more responsive in the Fhb1 background – if Qfhs.ifa‐5A is also present – was identified and functionally tested by heterologous expression in yeast.This report investigates transcriptional differences of near‐isogenic lines (NILs) segregating for Fhb1 and Qfhs.ifa‐5A, we generated NILs from a cross of the susceptible spring wheat cv. Remus and the FHB resistant line CM‐82036, carrying the Fhb1 and Qfhs.ifa‐5A QTLs. Four NILs from a BC5F2 population were selected for the presence of the resistant alleles at both QTLs (NIL1), harbouring only the Fhb1 (NIL2) or only the Qfhs.ifa.5A (NIL3) resistance allele, or carrying the susceptible alleles at both QTLs (NIL4) . Intermediate resistance levels were observed in NIL2 (Fhb1) and NIL3 (Qfhs.ifa‐5A)  and earlier] contained only trace amounts of DON.F. graminearum vs. mock) and time point  in three individual replicates using the Affymetrix 61 K wheat GeneChip. Several examples are shown in Fig. F. graminearum infection in at least one time point when calculating the average (i.e. genotype‐independent) differences in transcript abundance between F. graminearum‐ and mock‐inoculated samples (adjusted P ≤ 0.05). The vast majority of these transcripts (792) were detected at 72 hai, less at 24 hai (62) and a few at 8 hai (29). The Fhb1‐ and Qfhs.ifa‐5A‐specific responses were generated by subtracting the F. graminearum/mock contrast of NIL4  from either NIL2  or NIL3  . We confirmed the observed differences by reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) for a representative group of probe sets showing the highest relative changes. The investigated transcripts comprise F. graminearum‐induced and QTL‐specific genes , and genes which are constitutively changed between mock‐treated lines for one of the QTLs . Potentially, observed transcript levels can be confounded by single‐feature polymorphisms (SFPs) that lead to different binding affinities to the probe sets. However, such SFPs were detected for only a few transcripts. Transcripts were functionally annotated and compared against available databases, and with annotations reported previously by Jia et al. , all showed a negative difference in response, indicating a higher induction or more sustained expression in the more susceptible genotype lacking Qfhs‐ifa.5A resistance. These 338 genes  encode a variety of defence‐related proteins.The contrast to detect Qfhs.ifa‐5A specific were identified when calculating the average differences in transcript abundance between F. graminearum‐ and mock‐inoculated samples across all lines. Most of the identified transcripts involved in the production of ethylene (ET) and JA showed higher accumulation for the susceptible Qfhs.ifa‐5A allele . Similarly, F. graminearum‐responsive transcripts potentially involved in DON detoxification were found to be predominantly more abundant in lines harbouring the susceptible Qfhs.ifa‐5A allele. These transcripts correspond to 12 UGTs , three MATE‐type transporter genes (three) and nine pleiotropic drug resistance transporter genes (18).Twenty‐one probe sets correspond to genes involved in the phenylpropanoid pathway, including multiple transcripts encoding phenylalanine ammonia lyases (PALs), the first dedicated step in the pathway. Only two more genes in this pathway that were not Qfhs.ifa‐5A‐specific contrast. These included the subclasses of chitinases, glucanases, thaumatin and thaumatin‐like proteins, as well as LTPs, which are involved in the early interaction with the pathogen .In contrast, transcripts corresponding to pathogenesis‐related proteins were, in most cases, detected as a general response to the pathogen and not by the F. graminearum were identified for Qfhs.ifa‐5A (8 hai), indicating higher accumulation for the resistant allele. These encoded a stress‐responsive A/B barrel domain‐containing protein, a calmodulin‐related calcium sensor protein, a glutathione‐S‐transferase (GST), a nonsymbiotic haemoglobin and one unknown protein. Notably, two of these genes also exhibited a negative difference at 72 hai, with higher accumulation in the genotype with the susceptible Qfhs.ifa‐5A allele . Fusarium graminearum‐responsive transcripts exhibiting a significant difference in response for Qfhs.ifa‐5A are summarized in Table At time points other than 72 hai, five probe sets with a positive difference in response to Qfhs.ifa‐5A , which is identical to the gene model of TaLTP9.4a  from the F. graminearum/mock contrast derived from NIL2 . We identified 16 transcripts exhibiting a differential response for Fhb1 . Of these 54 transcripts, 17 were constitutively more abundant in the presence of the resistant Fhb1 allele  and the region comprising the putative Fhb1 region spans about 274 kb (sts32 to umn10). From the genes represented on the 61 K wheat GeneChip, we mapped 35 with at least 98% identity to ctg0954b. Four of the 35 mapped transcripts were changed significantly for Fhb1 in our experiments, including TaAffx.12498.2.S1_at and Ta.22694.1.A1_at  and Ta.28185.1.S1_at and Ta.6066.2.S1_a_at  and NIL3 (Qfhs.ifa‐5A) from the contrast observed in NIL1 . The pathogen‐responsive and constitutively changed transcripts are given in Tables S6 and S7, respectively (see Supporting Information).We also investigated whether the combined QTLs in NIL1 exhibited interaction effects between F. graminearum‐responsive transcripts that showed interaction effects at 72 hai were similarly abundant at high levels in both lines lacking the 5A resistance (NIL2 and NIL4). Hence, the interaction contrast is generated by the difference between NIL1 and NIL3, indicating potential Fhb1 dependence . This contrast identified the same three UGTs, two GSTs, two P450 monooxygenases and a laccase protein, as well as three metabolism‐related proteins and four unknown. The annotated gene with the highest difference in response was TaAffx.111585.1.S1. A similar contrast was also generated for Qfhs.ifa‐5A in the background of Fhb1 , which, however, did not yield additional candidates.Twenty‐three of the lastN analysis identified TaAffx.111585.1.S1_at as well as Ta.12887.1.S1_at as close homologues to HvUGT13248, a barley gene encoding a UGT with a proven ability to inactivate DON  of cv. Chinese Spring showed that the ESTs corresponding to both probe sets originated from the same gene. The assembled gene model contained two introns, which separated a large first exon (705 bp) and two smaller exons of 385 and 338 bp in length. The encoded open reading frame (ORF) (1428 bp) was designated TaUGT12887 (GenBank accession JX624788). We used a collection of nulli‐tetra and ditelosomic lines to map TaUGT12887 to chromosome 5BL. Resequencing of PCR products from both parental genotypes of the mapping population showed that the coding sequence was identical in Chinese Spring and the two parental lines used in this study, CM‐82036 and Remus.BTaUGT12887 encodes a protein with the ability to inactivate DON, we recoded the sequence for optimal expression in yeast. The toxin‐sensitive Saccharomyces cerevisiae strain YZGA515 was transformed with a plasmid containing the custom synthesized ORF under the control of the strong constitutive ADH1 promoter. Spotting assays showed that TaUGT12887 conferred only weak resistance against DON compared with the barley DON‐inactivating HvUGT13248  reduces disease severity at a similar level to lines with Fhb1 (type II resistance) after point inoculation—a method more sensitive to the assessment of type II resistance. The high level of resistance conferred by Qfhs.ifa‐5A may have been more evident with the cautious single floret inoculation method used. Most of the F. graminearum‐responsive genes found represent a general response to the pathogen, regardless of resistance level and QTL.We investigated the effect of the F. graminearum–wheat interaction have led to the characterization of multiple defence‐associated gene classes that describe a general or genotype‐specific response of wheat to F. graminearum or DON , Ta.22694.1.A1_at (unknown), Ta.28185.1.S1_at (splicing factor), Ta.438.3.S1_at (nuclease) and Ta.9489.1.S1_at (regulatory gene). In addition, only a few genes identified here as QTL specific were identified in other comparable studies also using the Affymetrix GeneChip. The F. graminearum‐responsive Ta.24715.1.S1_at, encoding a peroxidase, was more abundant for Fhb1 at the earliest observed time point of 8 hai and was also found to be upregulated in response to DON, but only for the susceptible genotype Superb . In addition, Xiao et al. : This probe set corresponds to a putative tau‐class GST, which was also identified by other contrasts. Two further GSTs  were identified in the additional Fhb1 contrast, in the background of Qfhs.ifa‐5A. Transcriptomic studies in DON‐infected barley indicate that the F. graminearum virulence factor DON most probably depletes the glutathione pool. The involvement of GSTs in the detoxification of DON is still unproven, but the nonenzymatic formation of DON–glutathione adducts has been shown  at all observed time points in the line harbouring the resistant Fhb1 allele. This gene is located close to the predicted location of Fhb1 between sts142 and sts32 —for which the probe sets have been designed—and one antisense transcript (cv. Chinese Spring), are present in the database. It is unclear whether there is a meaningful (short) ORF or whether the transcript has a regulatory role.Lemmens et al.  have proF. graminearum at 72 hai for Qfhs.ifa‐5A, indicating higher accumulation in the presence of the susceptible Qfhs.ifa‐5A allele. One hypothesis that explains the first counterintuitive observation of a higher upregulation of multiple defence‐related genes in the susceptible cultivar is that the fungal biomass is higher in the lines lacking the Qfhs.ifa‐5A resistance allele. Consequently, more plant tissue is undergoing a defence response at a given time.We identified 338 genes exhibiting a negative difference in response for Qfhs.ifa‐5A proposed in the literature, none has been identified in this study. Steiner et al. .A promising , et al.  also repet al.  cultivar Chinese Spring. Positional cloning efforts in resistant germplasm, combined with functional validation of candidates in wheat using TILLING  populations or virus‐induced gene silencing, will provide further insights into the resistance mediated by both QTLs. For Qfhs.ifa‐5A, additional efforts are required to resolve the locus in the centromeric region of chromosome 5A.Taken together, this study presents candidate genes associated with the 2 progeny of BC5 plants were screened for the presence of the homozygous resistance QTL. NIL1 possessing the resistance alleles of Fhb1 and Qfhs.ifa‐5A, NIL2 possessing Fhb1, NIL3 carrying the Qfhs.ifa‐5A resistance allele and NIL4 carrying the susceptible alleles at Fhb1 and Qfhs.ifa‐5A were selected  markers barc186, gwm1057, barc56, gwm293, barc117 and gwm129 on the short arm of chromosome 5A, and barc1, barc180, barc40 and gmw156 on the long arm of 5A. The four NILs, CM‐82036 and Remus were also genotyped with DArT markers by Triticarte Pty. Ltd.  of a DH line identified from a CM‐82036  by Remus population. Selfed Fet al. (F. graminearum suspension or water (about 200 conidia). Five heads per genotype, treatment and replication were harvested at 8, 24 and 72 hai. Treated spikelets were separated into lemma, palea and the subtending section of the rachis and the reproductive tissues, immediately frozen in liquid nitrogen and stored at −80 °C. For phenotype scoring, one central spikelet (two florets) of 50 wheat heads per genotype was inoculated on the field with 100 conidia per floret, and diseased spikelets were counted after 12, 18 and 24 days. The inoculum concentration used for phenotyping allowed better differentiation of phenotypes between NILs. Statistically significant differences between all lines were calculated using pairwise t‐tests (adjusted P ≤ 0.05). The experiment was repeated twice.The glasshouse experiment and inoculum production were conducted as described in Steiner et al.  with modet al. . RNA was extracted according to Steiner et al. . We empl  and packages of the Bioconductor suite (http://www.bioconductor.org). We used the ‘gcrma’ package . SFPs were detected between the lines by applying an algorithm described by Xu et al.  at 8, 24 and 72 hai were calculated by subtracting the genetic background provided by NIL4  from NIL2 (Fhb1) and NIL3 (Qfhs‐ifa.5A). In addition, we generated contrasts from NIL1 and NIL2/NIL3 to yield QTL‐specific contrasts in the background of the respective other QTL resistance allele. For the identification of transcripts exhibiting constitutive expression changes, mock‐inoculated samples were compared, whereas, for the F. graminearum response, transcript abundances of mock samples were subtracted from those of F. graminearum‐inoculated samples.QTL‐specific, constitutive and F. graminearum‐responsive transcripts identified in the single QTL contrasts  from the abundance differences observed in the contrast between the double QTL line NIL1 relative to NIL4. Similarly, we calculated the interaction effects comparing mock‐inoculated samples. The average F. graminearum response was calculated as the mean F. graminearum response of all four investigated genotypes.Interactions of the two QTLs were assessed by subtracting abundance differences of http://harvest.ucr.edu, Version 1.57). Ontologies, UniProt annotations and EC numbers were acquired with the Blast2GO web tool (http://www.blast2go.org). Genes were considered to be ‘unknown’ if none of the search results exhibited E ≤ 10−10. Mapping positions to the identified gene transcripts were assigned on the basis of BlastN search results against the mapped EST database hosted on the Graingenes website (http://wheat.pw.usda.gov/GG2). Moreover, we mapped transcripts (>98% identity) to the wheat contig ctg0954b, which harbours Fhb1 .Selected candidates were mapped using a collection of Chinese Spring nulli‐tetra and ditelosomic lines and deletion lines of chromosome 5A . Ta.1282.4.S1_at and TaUGT12887 was optimized for expression in S. cerevisiae by reverse translating the amino acid sequence . The gene was custom synthesized  including flanking 5′ HindIII and 3′ NotI restriction sites, which were used to clone the ORF into the HindIII/NotI cloning sites of yeast expression vector pYAK7 in frame behind the N‐terminal c‐Myc epitope tag  was transformed with the resulting construct, pWS60, and also with pWS1921, harbouring the previously reported DON‐UGT HvUGT13248  = 0.7]. Three microlitres of liquid culture dilutions (OD600 = 0.05 and 0.005) were spotted on plates with yeast extract–peptone–dextrose (YPD) medium containing increasing amounts of DON, and incubated for 3 days at 30 °C. Immunodetection was performed with a primary mouse anti‐c‐Myc antibody  and a horseradish peroxidase (HRP)‐conjugated secondary anti‐mouse antibody . For signal detection, we used the SuperSignal West Pico chemiluminescent substrate .Crystallized DON was dissolved in water to a stock concentration of 10 g/L. Yeast transformants were selected on synthetic complete medium lacking leucine, and grown in liquid selective medium to the exponential growth phase [optical density at 600 nm (ODm in assay) in combination with the MESA GREEN qPCR Mastermix Plus . The reference genes glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH), transcription enhancer factor‐1 (TEF1) and ubiquitin were used for normalization. Calculations of the relative fold changes were conducted as described in Pfaffl . One microgram of total RNA was subjected to reverse transcription . RT‐qPCRs were performed for 45 cycles in triplicate using 1 μL of cDNA and primers  confirmation of selected transcripts.Click here for additional data file.Fig. S2 Multiple alignment of the resequenced wheat lipid transfer proteins (LTPs) corresponding to Ta.1282.4.S1_at in cv. CM‐82036 and Remus.Click here for additional data file.Fig. S3 Immunoblotting of the N‐terminal c‐Myc antibody used to detect uridine diphosphate‐glycosyltransferase (UGT) expression levels (A) and the corresponding Coomassie‐stained acryl amide gel as loading control (B).Click here for additional data file.Table S1 Number of transcripts exhibiting a significant difference in response to Fusarium graminearum for either Fhb1 or Qfhs.ifa‐5A at the given time point.Click here for additional data file.Table S2 Functional classification of Fusarium graminearum‐responsive transcripts and transcripts showing constitutive quantitative trait locus (QTL)‐specific differences in transcript abundance .Click here for additional data file.Table S3 Transcripts exhibiting a negative difference in response for the resistant allele of Qfhs.ifa‐5A  after inoculation with Fusarium graminearum spores at 72 h after inoculation.Click here for additional data file.Table S4 Transcripts exhibiting constitutive differences in transcript abundance for Qfhs.ifa‐5A .Click here for additional data file.Table S5 Transcripts exhibiting constitutive differences in transcript abundance for Fhb1 .Click here for additional data file.Table S6 Fusarium graminearum‐responsive transcripts exhibiting nonadditive effects between Fhb1 and Qfhs.ifa‐5A .Click here for additional data file.Table S7 Transcripts exhibiting nonadditive effects between Fhb1 and Qfhs.ifa‐5A  in mock‐inoculated samples.Click here for additional data file.Table S8 Transcripts exhibiting a significant difference in response between NIL1 and NIL3 yielding Fhb1‐related transcripts in the presence of the Qfhs.ifa‐5A resistance allele  after inoculation with Fusarium graminearum spores.Click here for additional data file.Table S9 Transcripts exhibiting a significant difference in response between NIL1 and NIL2 yielding Qfhs.ifa‐5A‐related transcripts in the presence of the Fhb1 resistance allele  after inoculation with Fusarium graminearum spores.Click here for additional data file.Table S10 Sequences of primers used for sequencing and mapping of Ta.1282.4.S1_at and TaUGT12887.Click here for additional data file.Table S11 Sequences of primers used for the validation of transcript abundances by reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR).Click here for additional data file.


Gas1 (growth arrest-specific 1) gene is known to inhibit cell proliferation in a variety of models, but its possible implication in regulating quiescence in adult tissues has not been examined to date. The knowledge of how Gas1 is regulated in quiescence may contribute to understand the deregulation occurring in neoplastic diseases.Gas1 expression has been studied in quiescent murine liver and during the naturally synchronized cell proliferation after partial hepatectomy. Chromatin immunoprecipitation at nucleosomal resolution (Nuc-ChIP) has been used to carry out the study preserving the in vivo conditions. Transcription has been assessed at real time by quantifying the presence of RNA polymerase II in coding regions (RNApol-ChIP). It has been found that Gas1 is expressed not only in quiescent liver but also at the cell cycle G1/S transition. The latter expression peak had not been previously reported. Two nucleosomes, flanking a nucleosome-free region, are positioned close to the transcription start site. Both nucleosomes slide in going from the active to the inactive state and vice versa. Nuc-ChIP analysis of the acquisition of histone epigenetic marks show distinctive features in both active states: H3K9ac and H3K4me2 are characteristic of transcription in G0 and H4R3me2 in G1/S transition. Sequential-ChIP analysis revealed that the “repressing” mark H3K9me2 colocalize with several “activating” marks at nucleosome N-1 when Gas1 is actively transcribed suggesting a greater plasticity of epigenetic marks than proposed until now. The recruitment of chromatin-remodeling or modifying complexes also displayed distinct characteristics in quiescence and the G1/S transition.Gas1 is transcribed at the G1/S transition suggests that the gene may exert a novel function during cell proliferation. Transcription of this gene is modulated by specific “activating” and “repressing” epigenetic marks, and by chromatin remodeling and histone modifying complexes recruitment, at specific nucleosomes in Gas1 promoter.The finding that  Once this process is completed, cells exit from the cycle to return to reversible growth arrest in an active process that requires growth-inhibitory gene products  offers the advantages of analyzing cell cycle events in a synchronized cell population proliferating in an organism (reviewed in Gas1 to Gas6). Among these growth arrest-specific (Gas) genes, only Gas1 demonstrated the ability to inhibit cell proliferation when over-expressed in normal and transformed cell lines Gas1 include promotion of apoptosis Special attention has been paid to the identification of genes expressed in quiescent conditions to discover genes that could arrest proliferation of growing cells, for instance, during the development of cancer. Schneider and co-workers Gas1 is a pleiotropic gene, which exerts its functions according to the tissue, the developmental stage or the cellular context. However, no data on the implication of Gas1 in the maintenance of quiescence of adult tissues, such as liver, are available to date.Therefore, it seems evident that The control of eukaryotic cell proliferation requires the integration of several signals that specify a precise transcriptional program, which obviously ought to be developed in a chromatin context  in mice. The semi-quantitative and quantitative RT-PCR  and that its expression decreases rapidly to reach a minimum at around 7 h after PH. The gene also shows an unexpected maximum of expression at 24 h after PH, a time at which hepatocytes are about to enter S-phase, as estimated by analysis of the induction of cell cycle markers CycE2 and CycB1 , while in the active state during liver quiescence, the SNF2 h-containing ISWI remodeling complex appears bound to the promoter. In the second active state, at 24 h after PH, a BRM-containing remodeler binds the promoter. These results strongly suggest that a remodeling of the promoter chromatin is required for Gas1 to be expressed, and this process is accomplished in different ways in both transcriptionally active states. The other complexes checked, i.e., those containing BRG1 or MTA1, do not participate in these remodeling events under these conditions.As we have pointed out before, eukaryotic gene transcription is regulated in a chromatin context, so we wondered whether the changes in http://wwwmgs.bionet.nsc.ru/mgs/programs/phase/), showed a high probability of nucleosomal presence between −50 to −200 from TSS, a moderate probability between −300 to −500, and a low probability between +50 to +200 . First, the theoretical prediction of nucleosome positioning near the TSS, based on the PHASE program algorithm  and histone deacetylases (HDAC) to the gene promoter by chromatin immunoprecipitation (ChIP) assay at mononucleosomal resolution. We again selected the two transcriptional active states of Gas1, quiescence and G1/S transition, and the transcriptional inactive state, at 7 h after PH.To explore the epigenetic regulation of Gas1 promoter in quiescent liver. GCN5 binding was less clear at 7 h after PH and it was released from the promoter at the G1/S transition. By contrast, CBP is constitutively bound to Gas1 promoter at the three selected time-points. The other HAT complexes analyzed, p300 and PCAF, do not seem to be implicated in the acetylation pattern of Gas1 promoter in the time-points under study (data not shown). In relation with the HDACs, mSIN3A was bound to Gas1 promoter in quiescence and at 7 h, and the ChIP analysis only gave a faint band 24 h after PH. It is also noteworthy that the NCoR-containing histone deacetylase complex was constitutively bound to Gas1 promoter at the three selected transcriptional states (data not shown).The preliminary studies, using standard ChIP assay, indicated that GCN5 was bound at the Gas1 TSS vicinity. Consequently, we analyzed the recruitment/release of those histone modifying complexes to the specific nucleosomes located at the Gas1 is repressed. Afterwards, the SIN3A complex remains bound to nucleosome N-1, although it was released from nucleosome N+1 in the G1/S transition, when Gas1 is again active  and “active characteristic marks”  not only in neighbor nucleosomes, but also within the same nucleosome. Some “activating” marks are present at both analyzed nucleosomes when Gas1 is transcriptionally active in quiescence and in G1/S transition. It is noteworthy that some distinctive, specific marks have been found at the different transcriptional states, as it happens with the presence of H3K9ac in nucleosome N+1 in quiescence or with H4R3me2 in nucleosomes N-1 and N+1 in the G1/S transition.These results suggest that the regulation of the epigenetic marks level along the Gas1 as an additional mechanism to regulate precisely the level of transcription in response to cellular stimuli during quiescence and G1/S transition. By this mechanism, the hepatocyte could maintain the desired level of transcript depending on whether one or two alleles are transcribed and, at the same time, the transcriptional activity of each allele may be epigenetically regulated through independent ways. Gimelbrant and co-workers Gas2 and Gas6, members of the growth arrest-specific gene family, as genes that are expressed monoallelically.The presence of “inactivating” and “activating” epigenetic marks within the same nucleosome could be due to a monoallelic gene expression of Gas1 promoter in the same nucleosome, but being located at different alleles, we perform a sequential-ChIP analysis  at nucleosome N-1 by combining Nuc-ChIP assay, using an antibody against H3K9me2, followed by reimmunoprecipitation with an antibody against H3K27ac, H3K4me3 or H4R3me2, and vice versa.To check if the “activating” and “repressing” marks are coexisting in The results represented in Gas1 in cell growth control in vitro, in programmed cell death and in mouse development in vivo, no data on the role of Gas1 in the quiescence of adult tissues is available. On the other hand, over-expression of Gas1 inhibits cell proliferation in different tumor cell lines Gas1 in tumor gene therapy have been advanced In spite of the extensive information on the role of the Gas1 is expressed not only in quiescent liver but also actively transcribed just before entering S phase. This is the first time that expression of Gas1 at that moment of the cell cycle is described, because, to date it has only been showed expression of the gene when cell lines are arrested in G0Gas1 expression could be playing a dual role in liver cell cycle control, acting to keep liver quiescence, and to control the pass of the cells through the G1/S checkpoint. The expression of Gas1 would facilitate the arrest of the cell cycle before entering S phase if cell damage has occurred. In this context it should be noted that overexpression of Gas1 in cultured cells causes arrest of the cell cycle at the interface G1/S In the present study, using the natural synchrony of the cell proliferation model after partial hepatectomy, we describe that c-Myc, which is expressed during liver regeneration in a biphasic manner, is distinctly activated in both expression waves Gas1 in quiescence and in the G1/S transition are also different.We have described that Gas1 is not linked to the TGF-β signalling pathway p15, p21, p27, Gadd34, Gadd45, and Gadd153. c-MYC represses transcription of these genes by at least two distinct mechanisms. One mechanism requires DNA binding of the MYC–MAX complex to Inr element in their promoters, and the inhibition of transcriptional activators via the C-terminal domain of c-MYC. The other mechanism is dependent on c-MYC binding to the SP1 transcription factor via the c-MYC central region inhibiting SP1 transcriptional activity  (data not shown), and contains several putative binding sequences for SP1 binding (as estimated by TESS software http://www.cbil.upenn. edu/cgi-bin/tess/tess) (data not shown). Nevertheless, it cannot be ruled out that other mechanisms might be also acting in Gas1 transcriptional regulation since it has been reported that MYC-box 2 (MB2) and leucine repeat C-terminal region (LZ) of c-MYC are required for Gas1 repression in Myc-transformed Rat-1a fibroblast cell lines It has been described that c-MYC and v-Src-repressed expression of after PH , suggestGas1 nucleosome N+1 in quiescence and G1/S transition  is flanked by nucleosomes N-1 and N+1. These results are in agreement with the findings obtained by genome-wide mapping which have revealed that N-1 and N+1 nucleosomes delimiting a NFR is an evolutionarily conserved, common feature of promoters. In our case, both nucleosomes reside in canonical locations at specific distances from the TSS ansition . The obsansition . In our ansition . This slansition .Gas1 promoter and proximal transcribed region.It is noteworthy that a differential binding of the BRM catalytic subunit to N-1 and N+1 nucleosomes occurred at the analyzed transcriptional states. The data of Gas1 TSS at the three transcriptional states. On the other hand, the mSIN3A-containing HDAC complex is mainly bound to the analyzed nucleosomes when Gas1 is repressed at 7 h post-HP, while the low signal obtained for the binding of NCoR-containing HDAC complexes make it difficult the drawing of definite conclusions. At any rate, the present data suggest that the regulated acquisition of histone epigenetic marks depends on the balanced and controlled activities of the opposite histone modifying enzymes activities acting over the same specific nucleosome.There is a plethora of data showing that covalent histone modifications play a crucial role in transcriptional regulation  epigenetic marks, as expected since the gene is transcriptional active, but also the known “inactivating” mark H3K4me2. The widespread monoallelic expression analysis on human autosomes, carried out by Gimelbrant and co-workers Gas2 and Gas6, two members of the growth arrest-specific gene family, belong to the group of genes expressed monoallelically. This finding opens the hypothesis that Gas1 could be differentially regulated in both alleles by differential epigenetic marks leading to a monoallelic gene expression. However, the results obtained by sequential-ChIP  and were authorized by the Ethics Committee for Animal Experimentation of the University of Valencia . Eight to ten week-old CD1 male mice were used in the experiments. The partial hepatectomy (PH) technique was based on the classical method described by Higgins and Anderson 260/A280, and by visualizing RNA on agarose formaldehyde gel electrophoresis. 1 µg of total RNA was retrotranscribed to cDNA using Superscript II RNase H- , following the manufacturer's instructions, with random hexamers  to prime the elongation reaction. cDNA was analyzed by PCR, the products were size-fractionated by 2% agarose gel electrophoresis, stained with ethidium bromide, photographed with Gel Doc XR+ image analyzer  and quantified by ImageJ software (http://rsbweb.nih.gov/ij/). The quantitative real time RT-PCR (qRT-PCR) were conducted in an ABI GeneAmp 7000 Sequence Detection System  and analyzed with the ABI Prism Software (Applied Biosystems), and the relative expression values were calculated according to the 2–ΔΔCt method.The livers were harvested from anaesthetized mice and snap-frozen in liquid nitrogen. After disrupting 30 mg of tissue in an Ultra-Turrax homogenizer , RNA was extracted and purified with the Illustra RNAspin Mini RNA Isolation Kit  according to the manufacturer's instructions. The RNA concentration was estimated by measuring the absorbance at 260 nm, and its purity double-checked by calculating the ratio AGas1, forward 5′-CGAACACTGCAGGTCCACCAAG-3′, reverse 5′-TCGCACACGCAGTCGTTGAG-3′, amplicon position +937/+1209 and size 273 bp; 18S rRNA, forward 5′-TGGTTGATCCTGCCAGTAGC-3′, reverse 5′- CTCTCCGGAATCGAACCCTG-3′, amplicon position +1327/+1580 and size 254 bp. The primer pairs used for qRT-PCR: Gas1, forward 5′-TGGATGAGGACGCCCATG-3′, reverse 5′- GGAACTCGGA CAAACTTTTCCA-3′, amplicon position +426/+477 and size 52 bp; 18S rRNA, forward 5′-CACGGCCGGTACAGTGAAA-3′, reverse 5′-AGAGGAGCGAGCGACCAA-3′, amplicon position +67/+138 and size 72 bp.The primer pairs used for RT-PCR were: xg for 10 min at 4°C, and supernatants containing the soluble proteins were recovered. Protein quantification was performed with the Protein Assay Reagent kit (Bio-Rad) according to the manufacturer's instructions.Fragments of 100 mg of liver were homogenized in 1 ml of ice-cold PBS, supplemented with 2 µl of protease inhibitor cocktail (Sigma). After centrifuging and removing PBS, total protein extraction was performed in RIPA buffer , keeping the samples 2 h under rotation at 4°C. The lysates were centrifuged at 14,000Thirty µg of total protein extracts were run in 12% SDS-PAGE and transferred to a 0.45 µm-pore nitrocellulose membrane, incubated with primary antibodies , washed and incubated with rabbit anti-goat HRP-conjugated secondary antibody  and developed with ECL advance detection kit , according to the manufacturer's instructions.The ChIP and RNApol ChIP procedures were performed according to Sandoval and co-workers xg for 10 min, and the supernatants were 10-fold diluted with dilution buffer  supplemented with protease inhibitor cocktail (Sigma). Aliquots from the diluted supernatants  were incubated under rotation for 2 h at 4°C with Dynabeads-protein G (Invitrogen) and 2 µg of the corresponding antibodies. The chromatin fragment/antibody/protein G-Dynabead immunocomplexes, were recovered and washed twice with ice-cold low-salt buffer , twice with ice-cold high-salt buffer , twice with ice-cold LiCl buffer  and finally once with TE buffer. The immunoselected chromatin was eluted from the Dynabead-protein G in two consecutive steps, by adding 50 µl of elution buffer , vortexing and incubating for 10 min at 65°C. The resulting 100 µl fraction (IP fraction) was incubated at 65°C overnight to reverse formaldehyde cross-links, in the presence of proteinase K (0.4 mg/ml). An aliquot of the cross-linked chromatin was treated as above, but in the absence of antibody (NA fraction), and the first supernatant was saved as the Input fraction. The DNA  was purified with PCR purification kit  and used for PCR analysis of the target gene. Analysis of constitutively expressed β-Actin and of repressed α-Actin genes were used, respectively, as positive and negative controls of the experiment. The entire experiment was repeated at least three times.Cross-linked chromatin was disrupted on ice with 8 pulses, 10 s each, in a Vibra-Cell VCX-500 sonicator  to obtain chromatin fragments of 300–500 bp. The fragmented chromatin was centrifuged at 14,000To study nucleosome positioning as well as to detect histone modifications or modifier binding at the level of single nucleosomes, we employed the Nuc-ChIP technique. To do this, the sonication step of the standard ChIP protocol was replaced by extensive micrococcal nuclease digestion (see below) of isolated cross-linked nuclei. The resulting chromatin fragments are thus enriched in mononucleosomes, which were immunoprecipitated with the desired antibody. The detection of the immunoprecipitated DNA was carried out by PCR by using 1:500 dilutions of the Input and 1:30 dilutions of the IP and NA fractions. The integrated density of the electrophoretic bands (obtained by ImageJ software) of IP fraction was subtracted from that of the NA fraction, and the result normalized by that of the Input fraction. The entire experiments were repeated at least three times.For the sequential ChIP analysis of histone epigenetic marks colocalization, aliquots from the mononucleosome-enriched chromatin fragments  were incubated with the first antibody and, after washings steps, the immunocomplexes were eluted by adding 75 µl TE buffer supplemented with 10 mM DTT and incubating for 30 min at 37°C. The resulting fractions were diluted 20 times with sequential ChIP dilution buffer , supplemented with protease inhibitor cocktail. Afterwards, the diluted samples were reimmunoprecipitated with a second antibody, and the standard procedure was followed. The sequential ChIP results were always confimed by changing the order in which the antibodies were used. To subtract the background signal to the IP samples, the integrated density of the electrophoretic bands of a parallel sample in which first antibody is replaced by IgG was used.The antibodies used in our experiments were: α-CBP (sc-369), α-GCN5 (sc-303), α-PCAF (sc-8999), α-P300 (sc-584), α-mSIN3A (sc-994), α-NCoR (sc-8994), α-BRM (sc-710), α-BRG1 (sc-0768), α-MTA1 (sc-0813), α-SP1 (sc-59), α-CREB (sc-186), α-C/EBPβ (sc-150) and α-RNApol II (sc-899) from Santa Cruz; α-SNF2H (ab3749), α-H3K9ac (ab12181-50), α-H3K27ac (ab4729-100), α-H3K4me3 (ab1012-100), α-H4R3me2 (ab5823) and α-H3 (ab1791-22) from Abcam; α-H3K14ac (07-353), α-H4K5ac (07-327) and α-H3K9me2 (05-685) from Upstate-Millipore.Gas1, forward 5′-CGAACAATGCAGGTCCACCAAG-3′, reverse 5′-TCGCACACGCAGTCGTTGAG-3′, amplicon position +937/+1209 and size 273 bp; α-Actin, forward 5′- GAGAAGATCTGGCACCACAC-3′, reverse 5′- CCCAGAATCCAACACGATC-3′, amplicon position +1325/+1759 and size 434 bp; β-Actin, forward 5′-TGTGCTGTCCCTGTATGCCTC-3′, reverse 5′-GGCCATCTCCTGCTCGAAG-3′, amplicon position +1993/+2263 and size 270 bp. The primer pairs used for standard ChIP were: Gas1, forward 5′- AACAACAGGCTGGACCAATAGC-3′, reverse 5′- ATGCATGCATAGAAAAGCAAACAACA-3′, amplicon position −252/−78 and size 174 bp; α-Actin, forward 5′-CACCTGACCACAGGGCTACC-3′, reverse 5′- AACTGGCTCCAAGGCTCACG-3′, amplicon position −484/−257 and size 227 bp; β-Actin, forward 5′-TCTGGCTTTCCGGCTATTGC -3′, reverse 5′- AGTTTTGGCGATGGGTGCTG-3′, amplicon position −540/−234 and size 306 bp.The primer pairs used for RNApol ChIP were: To estimate the position of nucleosomes, isolated nuclei from cross-linked cells were incubated with micrococcal nuclease to obtain mononucleosomes. After purification of the mononucleosomal DNA, PCR analysis were performed using primers that amplified short tiled fragments covering the region comprised between (500 and +300 bp relative to the transcription start site (TSS) of Gas1. Consequently, amplification products were expected at regions in which a nucleosome was protecting DNA from nuclease digestion.Nuclei were obtained from cross-linked livers, as described above, and nuclear pellets were resuspended in RSB buffer for micrococcal nuclease digestion. 7×108 nuclei were centrifuged at 2,000xg for 3 min at 4°C and washed with 1 ml of ice-cold RSB buffer . After collecting nuclei by centrifugation, the pellets were resuspended in 0.5 ml RSB buffer without protease inhibitors, and incubated with 150 units of micrococcal nuclease  for 30 min. Reaction was stopped by adding 1/10 volume EDTA 100 mM and putting the samples on ice. Nuclei were incubated at 65(C for 2 h to reverse cross-links in the presence of proteinase K (50 µg/ml). Finally, DNA was extracted and purified by phenol-chloroform extraction and precipitated with ethanol. The DNA pellet was resuspended in 100 µl of TE buffer and incubated 1 h at 37 was included to normalize the results for each primer pair in the PCR reaction. The entire experiments were repeated at least three times.The primer pairs used for nucleosomal positioning were: amplicon  and amplicon +125 (for nucleosome N+1) were used.Figure S1Gas1 promoter occupation by chromatin remodelling complexes after PH. The samples immunoprecipitated with indicated antibodies were analyzed by PCR using primers of the Gas1 promoter region (Top panel). The integrated density of electrophoretic bands, obtained by ImageJ software analysis of the PCR signals, from the NA fraction was subtracted from that of IP fraction and normalized by that of input fraction. These images are representative of at least three different experiments.(TIF)Click here for additional data file.Figure S2Gas1 promoter occupation by transcription factors after PH. The samples were processed as described in (TIF)Click here for additional data file.


Candida albicans to survive in the presence of nitrosative stress during the initial contact with the host immune system is crucial for its ability to colonize mammalian hosts. Thus, this fungus must activate robust mechanisms to neutralize and repair nitrosative-induced damage. Until now, very little was known regarding the regulatory circuits associated with reactive nitrogen species detoxification in fungi. To gain insight into the transcriptional regulatory networks controlling nitrosative stress response (NRS) in C. albicans a compilation of transcriptional regulator-defective mutants were screened. This led to the identification of Cwt1p as a negative regulator of NSR. By combining genome-wide location and expression analyses, we have characterized the Cwt1p regulon and demonstrated that Cwt1p is directly required for proper repression of the flavohemoglobin Yhb1p, a key NO-detoxification enzyme. Furthermore, Cwt1p operates both by activating and repressing genes of specific functions solicited upon NSR. Additionally, we used Gene Set Enrichment Analysis to reinvestigate the C. albicans NSR-transcriptome and demonstrate a significant similarity with the transcriptional profiles of C. albicans interacting with phagocytic host-cells. In summary, we have characterized a novel negative regulator of NSR and bring new insights into the transcriptional regulatory network governing fungal NSR.The ability of  In mammalian cells, NO is produced by a high-output form of nitric oxide synthase 2− to create the strong oxidant peroxynitrite (ONOO−), which has fungicidal activity Phagocytic cells such as macrophages and neutrophils form a part of the innate immune system and are considered as the first line of host defense against pathogens. These cells are able to ingest and subsequently kill infectious agents by producing a variety of toxic chemicals. The most important of these are antimicrobial peptides, nitric oxide (NO), the super oxide anion  and RNS generated by phagocytic cells. The protective role of RNS against C. albicans was established in many infection models including murine oral candidiasis C. albicans to survive in the presence of nitrosative stress during the initial contact with the host immune systems is important for colonization. Thus, this fungus must activate robust mechanisms to detoxify RNS and repair NO-induced damage. Previous works had highlighted the role of the flavohemoglobin enzyme Yhb1p in NO detoxification through its action in converting the short NO radical to nitrate C. albicans yhb1 homozygous mutant is hypersensitive to NO killing and exhibits a slightly reduced virulence in a tail vein model of disseminated candidiasis YHB1 is highly induced by NO and its inducibility under nitrosative stress depends on the Zn(II)2-Cys6 transcription factor (TF) Cta4p YHB1 gene has been identified.C. albicans we screened a compilation of mutants from various publicly available libraries C. albicans. Cwt1p was previously characterized as a regulator of cell wall damage and morphogenesis in C. albicansC. albicans and other pathogenic fungi. This study also demonstrates that Cwt1p plays a role in both activating and repressing genes of specific functions solicited upon nitrosative stress response. Using Gene Set Enrichment Analysis (GSEA) we have revisited the C. albicans transcriptome in response to nitrosative stress and revealed a correlation with transcriptional programs of C. albicans interacting with host-cells, such as poly-morphonuclear neutrophils C. albicans through the release of NRS.Compared to mechanisms controlling the detoxification of ROS in fungi, very little is known regarding the transcriptional circuits and signaling pathways associated with RNS detoxification. To gain insight into these regulatory mechanisms in cta4 and cwt1 . Since Cta4p was already characterized as a master modulator of nitrosative stress response in C. albicans we focused our investigation on the TF Cwt1p.We screened a compilation of 390 mutants, representing 229 transcriptional regulators, from publicly available libraries . The abiand cwt1 . As showC. albicans, we set out to determine its genomic occupancy using chromatin immunoprecipitation coupled to microarray analysis (ChIP-Chip). Binding locations were determined in duplicate ChIP-chip experiments using microarrays containing 5449 70-mer oligonucleotides representative of all intergenic regions. Statistical analysis using the Welch t-test with a false-discovery rate of 5% and 1.6 fold enrichment cutoff provided evidence for Cwt1p binding at 130 intergenic regions corresponding to 141 gene promoters (11 probes in our dataset represent shared promoter regions of two divergent ORFs)   and S3.To assess the reliability of the ChIP-Chip assays, the immunoprecipitated DNA from two additional independent ChIP experiments were quantified using qPCR. A total of 10 promoters were randomly selected and pairs of primers were designed to amplify 100–200 bp regions surrounding the probes that showed a significant Cwt1p-binding enrichment. The results obtained for the 10 selected promoters confirmed the binding of Cwt1p to the promoters identified by ChIP-Chip .The target sites of Cwt1p were analyzed for the presence of potential consensus sequence binding motifs using the motif-based sequence analysis tool MEME . Cwt1p also bound the promoter regions of the azole transporters Cdr1p and Mdr1p in addition to six genes (among fifteen) that were constitutively activated in azole-resistant clinical strains overexpressing Mdr1p  To explore the biological processes controlled by the TF Cwt1p, we conducted a gene ontology (GO) investigation by analyzing the 141 genes whose promoters are associated with Cwt1p. As shown in YHB1 promoter using tiled ChIP-qPCR, and the enrichment signal overlaps precisely with the carbon source response elements (CSREs) motif (AANYCCGA) recognized by the homologue of Cwt1p in S. cerevisiae, Rds2p YHB1 promoter was augmented significantly when Candida cells were challenged with DPTA NONOate compared to the control experiment suggesting a dynamic regulatory role of this TF during NSR. In order to assess the contribution of Cwt1p to the transcriptional regulation of this key NO-detoxifying enzyme, qPCR was used to evaluate the expression level of YHB1 in wt and cwt1 mutants under nitrosative stress condition at different time-points . As anticipated, the transcript levels of the YHB1 in wt cells were significantly induced when cells were challenged with the NO donor DPTA NONOate  in addition to genes related to oxidative stress response . Transcript levels of genes involved in cell detoxification process such as the flavohemoglobin YHB1 and the glutaredoxin TTR1 were also induced  and nitrogen utilization . Gene Set Enrichment Analysis (GSEA) was used to determine whether defined lists (or sets) of genes exhibit a statistically significant bias in their distribution within a ranked gene list C. albicans as reported previously by Hromatka et al. in vivo infection models including a reconstituted human oral epithelium model (RHE) As a preliminary step to assess the contribution of the TF Cwt1p on the NSR transcriptional program, NO-responsive genes were first identified by analyzing the transcriptional profile of wt r 15 min . In resp induced . RepressFTR1, PGA10, HEM3 and RBT5), oxidative stress  lipid catabolism (LIP5 and LIP3) and the flavohemoglobin YHB1 . Using the same criteria as described above Cwt1p was found to bind 9 promoter regions among the 94 genes requiring Cwt1p for their proper activation  and amino acid metabolism  of the TFs annotated in CGD and 40% (229/500) of proteins annotated to the GO term “regulation of gene expression”. This extensive investigation led to the identification of two transcriptional regulators, Cwt1p and Cta4p acting antagonistically in the control of nitrosative stress tolerance in C. albicans. Cta4p is a Zn(II)2-Cys6 TF that plays a dominant role in nitrosative stress resistance through a direct transcriptional activation the NO-detoxifying enzyme Yhb1p S. cerevisiae Rds2p that plays a key role in regulating genes involved in gluconeogenesis, TCA and glyoxylate cycles CWT1 has been a subject of many functional characterizations in C. albicans, which showed that this TF is required for cell wall remodeling, morphogenesis and virulence S. cerevisiae, to the modulation of nitrosative stress has not been reported. Our work therefore uncovers a new regulator of fungal NSR.Although the critical role of NSR in mediating the killing and inhibition of fungal pathogens such as PCK1 (Phosphoenolpyruvate carboxykinase) and FBP1 , in addition to many genes implicated in the TCA cycle  and gluconeogenesis/glycolysis common genes . Expression levels of some of those genes including the key gluconeogenic enzyme, Phosphoenolpyruvate carboxykinase PCK1 were also downregulated in the cwt1 strain suggesting that Cwt1p is directly mediating their transcriptional activation  specifically in C. albicans. The list of Cwt1p promoter targets was also enriched for genes from a variety of GO categories that has not previously been related to Rds2p, including translation, proteolysis, vesicle-mediated transport and amino acid metabolism .Our genome-wide location profiling demonstrated that Cwt1p occupies the promoters of key gluconeogenesis specific genes, including tivation . The roltabolism . AnotherC. albicansCandida cells upregulates genes involved in the oxidative stress response, iron uptake and sulfur assimilation. In our investigation we reexamined the C. albicans nitrosative stress transcriptome using GSEA. This method ranks genes according to their changes in transcript abundance and compares them to a large collection (5689 gene sets) of expression profiling, ChIP-chip, and GO categories data in addition to different S. cerevisiae synthetic genetic interactions and protein-protein interaction datasets. This analysis revealed an interesting and unexpected correlation with the ciclopirox olamine expression profiling data  and cwt1 mutant (Homann TF collection 600) of 0.1 in 100 ml fresh YPD buffered with 50 mM sodium phosphate pH 7.3, grown at 30°C to an OD600 of 0.8 and then split into two 50 ml cultures. DPTA NONOate was added to the experimental culture to a final concentration of 0.1 mM. An equal volume of YPD buffered with 50 mM sodium phosphate pH 7.3 was added to the control culture. For the profiling experiment using microarray, the cultures were incubated for 15 min. Different time-points  were considered for the qPCR experiment assessing the transcript level of YHB1. Cells were harvested by centrifugation and stored at −80°C.For general propagation and maintenance conditions, the strains were cultured at 30°C in yeast-peptone-dextrose (YPD) medium supplemented with uridine . For gene expression profiling of cells treated with Dipropylenetriamine NONOate , saturated overnight cultures of the wild type SN152 . cDNA was synthesized from 2 µg of total RNA using the reverse-transcription system 15, 20 ng random octamers, 0.5 mM dNTPs, 200 units Superscript III reverse transcriptase; Invitrogen). The mixture was incubated for 30 min at 50°C. cDNAs were then treated with 2U of RNase H (Promega) for 20 min at 37°C followed by heat inactivation of the enzyme at 80°C for 10 min. Aliquots were used for qPCR, which was performed using the Mx3000P QPCR System (Agilent) with the QuantiTect SYBR Green PCR master mix (Qiagen). Cycling was 10 min at 95°C followed by 40 cycles . Samples were done in triplicate and means were used for calculations. Fold changes were estimated by using the coding sequence of the CWT1 was TAP-tagged in vivo with a TAP-URA3 PCR product in BWP17 strain as described by in vivo TAP-tagged Cwt1p was ChIPed as described for chip-chip. PCR reactions were performed exactly as for gene expression analysis. Fold enrichments of tested promoter sequences were estimated by using the coding sequence of the C. albicans ACT1 ORF as a reference. Oligonucleotide sequences are given in ChIP-qPCR was performed using 1 ng of TAP-ChIPed DNA or total genomic DNA extracted from the whole cell extract (WCE). Cycling was for 15 min at 95°C, followed by 45 cycles of 95°C for 10 s, 58°C for 15 s, and 72°C for 15 s. All samples were tested in duplicates and means were used for further calculations.2O. RNA quality and integrity were assessed using an Agilent 2100 bioanalyzer.Total RNA was extracted as described in 21 in the presence of Cy3 or Cy5-dCTP (Invitrogen) and 400 U of Superscript III reverse transcriptase (Invitrogen) in a 40 µl reaction. After cDNA synthesis, template RNA was degraded by adding 2.5 units RNase H (Promega) and 1 µg RNase A (Pharmacia) followed by incubation for 15 min at 37°C. The labeled cDNAs were purified with QIAquick PCR Purification Kit (Qiagen). DNA microarrays were processed and analyzed as above for ChIP-chip experiment. NO-responsive transcripts and genes requiring Cwt1p for their proper regulation were determined using the Welch t-test with a false-discovery rate of 5% and 1.5 fold enrichment cutoff.cDNA labeling and microarray production were performed as described by Nantel et al. http://www.broadinstitute.org/gsea/for details Gene Set Enrichment Analysis (GSEA), was used to determine whether defined lists (or sets) of genes exhibit a statistically significant bias in their distribution within a ranked gene list (see http://meme.sdsc.edu/meme/cgi-bin/meme.cgi). Sequences of 300 bp upstream the ATG of 17 Cwt1p targets (using a 3 fold binding enrichment cutoff) were extracted and used for min-motif detection. The motifs were allowed to have any sequence and length (between 6 and 20 bp) and could be present anywhere in the sequence.For de novo identification of the consensus-binding site, the sequences were analyzed with the Multiple EM for Motif Elucidation (MEME) program Click here for additional data file.Table S1C. albicans mutant collections using 0.3 mM and 1 mM of DPTA NONOate.Results of the primary screen of (XLS)Click here for additional data file.Table S2ChIP-chip datasets showing DNA loci occupied by Cwt1p.(XLSX)Click here for additional data file.Table S3Gene Ontology analysis of Cwt1p-targets.(XLSX)Click here for additional data file.Table S4The five most significant motifs in the core promoter sequences from −300 bp to 0 bp relative to the ATG codon, of 17 Cwt1p-bound promoter regions (top ranked Cwt1p targets) as identified by the MEME algorithm.(DOCX)Click here for additional data file.Table S5C. albicans exposed to DPTA NONOate for 15 min.NO-responsive genes identified by analyzing the transcriptional profile of wt (XLSX)Click here for additional data file.Table S6GSEA analysis of DPTA NONOate-differentially expressed in the wt strain.(XLSX)Click here for additional data file.Table S7cwt1 mutant after treatment with 0.1 mM DPTA NONOate during 15 min.Genes differentially expressed in (XLSX)Click here for additional data file.Table S8List of primers used in this study.(DOCX)Click here for additional data file.


Nr2F1 (Coup-TF1) and the Hoxa5 gene, respectively. Both genes are transcriptionally activated by all-trans retinoic acid (RA) and display increased levels of the permissive H3K9/K14ac and tri-methylated histone H3 lysine 4 epigenetic marks in response to RA. However, while in response to RA the PRC2 and H3K27me3 marks are greatly decreased at the Hoxa5 promoter, these marks are initially increased at the Nr2F1 promoter. Functional depletion of the essential PRC2 protein Suz12 by short hairpin RNA (shRNA) technology enhanced the RA-associated transcription of Nr2F1, Nr2F2, Meis1, Sox9 and BMP2, but had no effect on the Hoxa5, Hoxa1, Cyp26a1, Cyp26b1 and 2RARβ transcript levels in wild-type embryonic stem cells. We propose that PRC2 recruitment attenuates the RA-associated transcriptional activation of a subset of genes. Such a mechanism would permit the fine-tuning of transcriptional networks during differentiation.Polycomb proteins play key roles in mediating epigenetic modifications that occur during cell differentiation. The Polycomb repressive complex 2 (PRC2) mediates the tri-methylation of histone H3 lysine 27 (H3K27me3). In this study, we identify a distinguishing feature of two classes of PRC2 target genes, represented by the  All-traES cells ,3. SeverES cells . DiffereES cells ,6.The Polycomb Repressive Complex 2 (PRC2) is a multi-protein complex that confers transcriptional repression via the placement of the repressive H3K27me3 histone mark. Indeed, Polycomb repressive complexes (PRC1/2) silence many genes in ES cells . The PRCtrans RA mediates most biological effects of retinol  and its natural and synthetic analogs, retinoids, exert profound effects on many biological processes . The  retinol , and haspathways . The actpathways . These npathways ,20. We hnd RARβ2 , and thand RARβ2 ,25. It iNr2F1 and Nr2F2 are also referred to as the chicken-ovalbumin upstream promoter-transcription factors (Coup-TF1/2). Nr2F1 and Nr2F2 belong to a diverse group of nuclear receptors, which are termed orphan nuclear receptors because physiological ligands have not yet been identified  and Nr2F2 , have been identified, but homologs have been cloned from numerous species , but later in development, Nr2F1 is expressed mainly in the nervous system, whereas Nr2F2 is predominantly expressed in the mesenchyme of internal organs such as the pancreas  and Hoxa5 genes. We further evaluate both transcriptionally permissive and PRC2-associated repressive epigenetic marks. We extend these findings to ES cells, and delineate the functional role of Suz12 in the RA-induced transcription of the genes Nr2F1, Nr2F2, Meis1, Sox9, BMP2, Hoxa5, Hoxa1, Cyp26a1, Cyp26b1 and 2RARβ. Our findings identify PRC2 dynamics as a distinguishing feature between two classes of RA-inducible stem cell genes, both of which include several key regulators of differentiation. We suggest that in addition to maintaining transcriptional repression, the PRC2 complex attenuates the transcriptional activation of specific genes during stem cell differentiation.In this study, we first evaluate the effects of RA on the epigenetic states of RARα−/−, 2RARβ−/− and RARγ−/− embryonal teratocarcinoma stem cells were cultured as described  . WT (J1) and knockdown ES lines were cultured as described (F9 wild-type (WT), escribed . The RARescribed . Total cGeneration of viral particles and transduction of ES cells was previously described ,43. In b−2 U Taq DNA polymerase . Each cycle included 94°C for 30 s (denaturation), 58–64°C for 45 s  and 72°C for 1 min (extension). The number of cycles required for PCR amplification in the linear range was determined experimentally for each gene. For semiquantitative PCR, amplified PCR products were resolved on 2% agarose gels and visualized by staining with ethidium bromide. Primer pairs were either obtained from published articles or designed using the PrimerSelect program (DNAstar). The primer pairs were evaluated using in silico PCR analysis (http://genome.ucsc.edu/) to avoid pseudogenes. All primers were designed to anneal to different exons to avoid any contribution of genomic DNA to the signal. Sequences of gene-specific primers are specified in the Supplementary Table S1. All gene expression amplicons were validated by sequencing.Total RNA (3 μg) was used to synthesize cDNA with random primers. The cDNA synthesis was performed at 42°C for 1 h in a final volume of 20 μl using qScript . PCR were performed using 2.5 × 10n = 3) were normalized to 36B4 transcript levels and statistical significance was determined by t-test (P < 0.05). The chromatin immunoprecipitation (ChIP) signals in the biological triplicates (n = 3) were normalized to percent input and statistical significance was determined by t-test (P < 0.05). The standard error of the mean was determined for each of the data sets (plotted as error bars in the graphs), and P-values <0.05 between compared samples were assigned statistical significance.The transcript levels in the biological triplicates  was screened. A PCR product containing ∼1 kb of mouse Nr2F1 promoter sequence was used as the probe for library screening. Positive plaques were further screened three times, and phage DNA was isolated using the Wizard kit . The positive clones were verified by sequencing a portion of each clone. The inserts in the positive plaques were cloned into the pGL3 firefly luciferase reporter plasmid , and assayed for RA responsiveness. This identified a 1 kb region as sufficient for RA-induced transcription. The fragment corresponded to a 1 kb region of sequence located ∼2.4 kb upstream of the P3 RefSeq transcriptional start site .To isolate the mouse Nr2F1 gene using the Lipofectamine method   . The rep6 F9 cells were performed with 2 µg of antibody (Ab) per ChIP. Antibodies: H3K27me3 ; H3K4me3 ; H3K9/K14ac ; Suz12 ; Ring1B (Supplementary Table S2. Each ChIP assay was performed at least three times starting with independently propagated cells each time (n ≥ 3).ChIP assays were performed as previously described ,46,47. I; Ring1B ; polII-CThe sodium dodecyl sulphate–polyacrylamide gel electrophoresis and western blot analyses were performed as described ,49 usinghttp://www.ncbi.nlm.nih.gov/geo/) and viewed in the integrated genome viewer (IGV 2.0)  . Where aIGV 2.0) .Nr2F1 and Hoxa5 transcript levels in response to RA treatment of F9 WT embryonal carcinoma stem cells . In the 2RARβ−/− and RARγ−/− cell lines, Nr2F1 transcript levels were induced by RA to only 43 and 57% of WT levels, respectively (RARα−/− cell line displayed Nr2F1 transcript levels similar to those of WT (97%). For comparison, we measured the increase in Hoxa5 transcript levels in response to RA (Hoxa5 gene is directly activated by RA in vivo and in cultured ES cells (Hoxa5 transcript levels were potently induced by 24 h of RA treatment (∼8-fold). Consistent with Hoxa5 expression being dependent on RARβ2 . Therefore, we conclude that both RARγ and RARβ2 (or downstream targets thereof) are required for the full RA-associated increases in Nr2F1 and Hoxa5 transcript levels to be achieved.To evaluate the involvement of the RA receptors RARα, RARβisotypes ,39–41,53ectively C, left. se to RA C, right.ES cells . In F9 Won RARβ2 , we founectively B. In conNr2F1 transcriptional activation, we isolated ∼14 kb of the mouse Nr2F1 locus from a phage library. The phage DNA that contained portions of 5′ Nr2F1 sequence , the 1 kb construct was assayed for RA responsiveness in F9 WT cells  positioned at 383–441 bp upstream of the P1 promoter (2.4 kb upstream of the P3 RefSeq promoter).To delineate the region responsible for the RA induction of WT cells B. Using lleagues , RA incrlleagues A. A 5′-tnduction B. These Nr2F1 RefSeq promoter (P3), thereby confirming upstream promoter activity also in ES cells. Interestingly, Nr2F2 displayed similar transcriptional activity upstream of the RefSeq promoter, whereas both Hoxa5 and Cyp26a1 displayed only background levels of transcription upstream of their respective RefSeq promoters  as compared with the Hoxa5 and Cyp26a1 promoters, which possess a modestly lower CpG density  as compared with a random 1000 bp sequence (∼62 CpGs) (Nr2F1 gene. We found that the Nr2F1 IEE (P1/P2) and the RefSeq promoter (P3) displayed only minimal DNA methylation both before and after RA treatment of F9 WT cells . Thus, changes in DNA methylation are not involved in the changes in Nr2F1 transcript levels after RA addition.DNA methylation, which occurs at CpG dinucleotides, is an epigenetic modification that can potently repress transcription. Regulation by DNA methylation is particularly pronounced in genes with CpG-rich promoter regions. We therefore evaluated the CpG content proximal to the RefSeq TSS of 62 CpGs) C. As proNr2F1 gene in F9 embryonal carcinoma stem cells. As discussed above, three distinct Nr2F1 TSSs have been detected in differentiating ES cells  in parallel  and the IEE (P1/P2). Similarly, RA-associated increases in the PRC repressive epigenetic marks occurred in the three F9 RAR knockout lines, RARα−/−, 2RARβ−/− and RARγ−/− at the Nr2F1 gene . We found that RA had no effect on the overall interaction between Suz12 and EZH2 core components. Transcriptional activation is often associated with a decrease in epigenetic repressive marks, as we observed for the Hoxa5 gene  and IEE (P1/P2) after RA addition, we observed an increase in Nr2F1 transcript levels in response to RA  displayed increased levels of the H3K9/K14ac and H3K4me3 marks  on RA treatment of F9 WT cells (Nr2F1 RefSeq promoter (P3) on a 24 h RA treatment of F9 WT cells , and a large increase in polII-CTD association (>5-fold) with the promoter and the RARE  RNA polymerase II (polII-CTD). The WT cells C, left. WT cells C, left. reatment C, left. the RARE C, left. and RARE C, left. ut cells C, right.WT cells C. The hixa5 gene D, bottomNr2F1 promoter were dramatically different from those of the Hoxa5 promoter  in each of the Suz12 knockdown lines were reduced by >90% relative to each of the shLuc (control) transfected parent cell lines  transfected ES cells . The ChIP-seq analysis confirmed recruitment of RNA polII to the Nr2F1, Nr2F2, Sox9, Hoxa5 and Cyp26a1 proximal promoter regions in response to RA treatment . Importantly, with respect to Hoxa5 and Cyp26a1, RNA polII was not only recruited to the proximal promoter regions in response to RA but was also detected distributed throughout the length of each gene, consistent with highly active transcription. In contrast, recruitment of RNA polII appeared to be lower at both the promoters .A recently published ChIP-seq analysis of RARγ, RXRα and RNA polII in F9 WT cells  allowed Hoxa5 and Cyp26a1 . The Cyp26a1 distal RARE showed association with both RARγ and RXRα, whereas the proximal RARE was predominantly associated with RXRα. This may indicate that the proximal RARE is primarily bound by RARα or RARβ, whereas the distal RARE is primarily bound by RARγ. Neither RARγ nor RXRα was associated with the Nr2F1, Nr2F2 and Sox9 genes . This suggests that Nr2F1, Nr2F2 and Sox9 are secondary targets in the RA signaling cascade. Alternatively, Nr2F1, Nr2F2 and Sox9 could be targeted by different RAR and RXR isotypes (e.g. RARα or RARβ dimerized with RXRβ or RXRγ).The co-localization of RARγ and RXRα at specific genomic locations is strong evidence for an RARE. Indeed, the ChIP-seq analysis confirmed the previously identified RAREs in Nr2F1, Nr2F2, Meis1, Sox9 and BMP2, that is activated during RA-induced stem cell differentiation. The RA-associated transcriptional activation of Nr2F1 occurs along with both increases in PRC and H3K27me3 repressive marks and permissive histone marks such as H3K4me3. The differential effects of Suz12 depletion on Nr2F1/Nr2F2 and Hoxa5/Hoxa1 (Drosophila (Hoxa1 (Hoxa5 (Nr2F1 and Nr2F2). We show here that such genes initially display an increase in PRC2 levels concurrent with transcriptional activation. We demonstrate that PRC2 attenuates transcription of Nr2F1 and describe the associated epigenetic changes (Nr2F1 (iib) and genes rapidly induced by RA i.e. Hoxa5 (iia) during stem cell differentiation.In the current study, we have identified a novel class of PRC target genes, represented by a5/Hoxa1  and 6 poosophila ,59. It ia , as is typically seen for genes, with the potential for transcriptional activation or repression depending on the ES cell differentiation state (Nr2F1 and Nr2F2 promoter regions are enriched for CpG sites relative to the Cyp26a1 and Hoxa5 promoter regions (Nr2F1 promoter (high CpG content), but resulted in a decrease in PRC proteins located at the Hoxa5 promoter (low CpG content). This difference could be related to the presence or absence of a TATA-box, which specifies a distinct TSS only in promoters with low CpG content . The key role of PRC2 . These features are similar to the features recently described for PRC-associated permissive enhancers , and thus far no functional element has been identified that can explain the RA responsiveness of Nr2F1 . This is the first study identifying the Nr2F1 promoter region that is responsible for the RA induction. The complex dynamics of PRC association in response to RA suggest that several transcription factors cooperate to regulate the transcription of the Nr2F1 gene. Thus, it will be important to identify the cis-regulatory DNA elements responsible for the RA-associated PRC recruitment and to characterize the transcription factors that recognize these DNA elements.The role of Nr2F1 in RA-associated endodermal differentiation of ES cells  and the elements , yet the unknown ,34. The Nr2F1 (Coup-TF1) and the Hoxa5 gene, respectively. We conclude that PRC1/2, in addition to specifying transcriptional repression, can function to attenuate transcriptional activation by RA of specific genes during stem cell differentiation. Attenuation of the maximal transcriptional activation may allow for more exquisite precise regulation of commitment to a specific differentiation pathway.Here we identify PRC2 dynamics as a distinguishing feature between two classes of PRC2 target genes represented by the Supplementary Data are available at NAR Online: Supplementary Tables 1 and 2 and Supplementary Figures 1–5.National Institutes of Health (NIH) [R01 CA043796 to L.J.G.]; WCMC funds; NIH [T32 CA062948 to Y.Z. for a portion of this research]; Fonds de Recherche en Santé du Québec [PF1-Benoit-25389 to Y.D.B]. Funding for open access charge: The University of Nottingham's open access publishing fund .Conflict of interest statement. None declared.


Drosophila embryo have shown that a substantial proportion of uninduced genes have pre-assembled RNA polymerase-II transcription initiation complex (PIC) bound to their promoters. These constitute a subset of promoter proximally paused genes for which mRNA elongation instead of promoter access is regulated. This difference can be described as a rearrangement of the regulatory topology to control the downstream transcriptional process of elongation rather than the upstream transcriptional initiation event. It has been shown experimentally that genes with the former mode of regulation tend to induce faster and more synchronously, and that promoter-proximal pausing is observed mainly in metazoans, in accord with a posited impact on synchrony. However, it has not been shown whether or not it is the change in the regulated step per se that is causal. We investigate this question by proposing and analyzing a continuous-time Markov chain model of PIC assembly regulated at one of two steps: initial polymerase association with DNA, or release from a paused, transcribing state. Our analysis demonstrates that, over a wide range of physical parameters, increased speed and synchrony are functional consequences of elongation control. Further, we make new predictions about the effect of elongation regulation on the consistent control of total transcript number between cells. We also identify which elements in the transcription induction pathway are most sensitive to molecular noise and thus possibly the most evolutionarily constrained. Our methods produce symbolic expressions for quantities of interest with reasonable computational effort and they can be used to explore the interplay between interaction topology and molecular noise in a broader class of biochemical networks. We provide general-purpose code implementing these methods.Recent whole genome polymerase binding assays in the  Gene activation is an inherently random process because numerous diffusing proteins and DNA must first interact by random association before transcription can begin. For many genes the necessary protein–DNA associations only begin after activation, but it has recently been noted that a large class of genes in multicellular organisms can assemble the initiation complex of proteins on the core promoter prior to activation. For these genes, activation merely releases polymerase from the preassembled complex to transcribe the gene. It has been proposed on the basis of experiments that such a mechanism, while possibly costly, increases both the speed and the synchrony of the process of gene transcription. We study a realistic model of gene transcription, and show that this conclusion holds for all but a tiny fraction of the space of physical rate parameters that govern the process. The improved control of cell-to-cell variations afforded by regulation through a paused polymerase may help multicellular organisms achieve the high degree of coordination required for development. Our approach has also generated tools with which one can study the effects of analogous changes in other molecular networks and determine the relative importance of various molecular binding rates to particular system properties. Drosophila melanogaster and showed that regulation of polymerase release from the promoter is widespread during development. Their data shows that some 15% of tissue-specific genes bind polymerase to their promoters in all tissues, even though each gene only allows polymerase to proceed through the coding sequence in a specific tissue  To motivate this idea, the preloaded, paused polymerase is described as a “loaded gun” ready to shoot off a single transcript as soon as it is induced. Experiments with heat shock genes – the first class of genes for which paused promoters were identified – show evidence of rapid induction consistent with this idea first transcript would be produced more quickly. Surprisingly then, it was also observed by Yao et al. subsequent polymerases are recruited rapidly to promoters of induced, elongation-regulated genes as well as the first, preloaded Pol II – a phenomenon not accounted for by the loaded gun metaphor. Since most genes must be transcribed several times in order to produce functional levels of mRNA, changes in speed of induction as a whole are likely to be of more physiological consequence than changes in the time at which the first, pre-paused transcript releases.It remains an open question why expression of some genes is controlled further downstream than others. Several groups have postulated that pausing may ready a polymerase for rapid induction When whole-genome studies extended the observation of pausing to cover many key developmental regulatory genes Recent work by Darzacq and colleagues The idea that gene expression itself is intrinsically variable (rather than variable as a result of extrinsic fluctuations in upstream quantities) is well established and is a recent focus of theoretical and experimental interest – see D. melanogaster and humans than E. coli or S. cerevisiae.Populations of single-celled organisms have been shown to take advantage of noisy gene expression to achieve clonal yet phenotypically heterogeneous populations Here we investigate mathematically whether the significant change in the coordination of expression observed in experiment topological rearrangement of the other. Because the same set of transition rates completely parametrize both chains, (see We do this by constructing continuous-time Markov chain models of PIC assembly with states that correspond to joint configurations of the promoter and the enhancer. The (random) time taken for the chain to pass from a “start” state to an “end” state corresponds to the elapsed time between successive transcription events. The models we construct for the two different modes of regulation have a common set of transition rates, but the particular mode of regulation dictates that certain transitions are disallowed, resulting in two chains with different sets of states accessible from the “start” state. We describe this situation by saying that each model is a ins, see  we can mactive state (in which transcription can occur), and an inactive state (in which it does not), as shown by Raj et al. small stochastic deviations from the differential equations that capture the average behaviors at equilibrium Although there has recently been much work modeling different sources of stochasticity in gene expression, most models refrain from a detailed representation of the different protein–DNA complexes involved in favor of more abstract approximations We model the intrinsic noise of regulation and polymerase recruitment using biologically-derived Markov chain models. We focus on this particular piece of the larger process of expression in greater detail than has been done previously in order to provide a detailed mathematical investigation of the role of promoter proximal pausing. Unlike simulation methods, our approach provides a tractable way to compute analytic expressions for which interpretation is direct and reliable. Moreover, it does not depend on small-noise or equilibrium assumptions, or require the passage to a continuum limit. Furthermore, the structure of the models we use is determined by biological realism rather than being constrained by mathematical tractability. Our approach is most similar to that of As a prelude to describing the actual Markov chain model of transcriptional regulation we analyze, we describe a general approach to modeling promoters, enhancers and their interactions, and illustrate this approach with a toy model of transcription that is not too cumbersome to draw – see promoter chain and an enhancer chain  We choose the regulated step according to the regulation mechanism that we are modeling.Next, to model the regulatory interaction between enhancer and promoter, we designate a particular configuration of the enhancer as the scaffold, returning the composite chain to an intermediate state (and possibly leading to successive rounds of reinitiation and thus a “burst” of transcription products – i.e. multiple mRNA molecules being transcribed per promoter opening event).The composite stochastic process that records the states of both the promoter and enhancer chains is our resulting Markov chain model of transcription. Varying the regulated step leads to alternative topologies for this chain. We stress that, as we change the choice of regulated step, the underlying promoter and enhancer chains remain the same. In particular, the same set of rate parameters are used in both schemes and they have the same meaning. This permits meaningful comparison of different methods of regulation. Two possible regulated steps, labeled “IR gated” and “ER gated”, are shown along with the corresponding Markov chains in Formally, the general composite Markov chain model is constructed as follows. Consider two promoter configurations, say, Because there are generally only two promoters per gene active at the same time in a given nucleus, binding of a general transcription factor (TF) at one locus does not decrease the total concentration of the TF in the nucleus sufficiently to affect the rate of binding at the homologous locus. Furthermore, since the observed timescales of variability in induction are shorter than the expected timescale for protein translation and folding, we neglect any feedback from mRNA synthesis which might modify the transition rates. This allows us, in particular, to assume that the jump rates of the Markov chain are homogeneous in time.We now apply this framework to examine a model of transcription that is more interesting and detailed than the toy model used above for illustrative purposes.in vivo indicate that the components of this complex assemble on the promoter DNA Many general transcription factors (TFs), such as the protein complexes TFIIA, TFIIB, etc., function together in a coordinated fashion to form the pre-initiation complex (PIC) necessary for the proper activation of transcription initiation regulated and require that the enhancer reach its permissive state , then its behavior will be well approximated by our minimal model, with the transition from active to inactive corresponding to the rate for this limiting step.elongation regulation (ER), and any regulation acting upstream of PIC assembly initiation regulation (IR).For many paused genes, it is the phosphorylation event which is believed to be regulated Finally, the scaffold of transcriptional machinery that facilitates polymerase binding does not necessarily dissociate when transcription begins. Thus, reinitiation may occur by binding new polymerases (at step 5) which must still reload TFIIH which was evicted during promoter escape in order to proceed to step 6 and so on back to step 8. Repeated cycles of reinitiation may lead to a burst of mRNAs synthesized from a single promoter opening event. We denote by rase see . The scaOur aim is not to present a definitive model of PIC assembly itself. Rather, we seek to understand the impact of different modes of regulation on a reasonable model that incorporates sufficient detail and to develop tools that can analyze effectively models of this complexity.induction we mean the first time at which all the components required for expression of a particular gene become available, and by expression we mean the time when transcription of the first nascent mRNA transcript begins.) We use the mean delay to explore the hypothesis that the mechanism of elongation regulation is faster than that of initiation regulation, even when there is no polymerase initially bound  magnitude of the stochastic effects.)However, the ratio of this quantity for the IR scheme to its counterpart for ER scheme does not depend on our choice of time scale. For any time We use our model to examine how these three important system properties – speed, synchrony, and transcript count variability – depend on the jump rates and how they differ between an IR and an ER regulation scheme. In both cases, the delay infinitesimal generator matrix that has off-diagonal entries stopped when it hits state Denote by In principle, the transform In particular, the mean and variance of It is not necessary to carry out the differentiation in equation (2) explicitly, since (2) becomesEquation (1) is known as the Feynman–Kac formula pinch points)  see . A schemate 7 of  we suppoOur approach has several advantages. Firstly, once we have derived symbolic expressions for features of interest, it is straightforward to substitute in a large number of possibilities for the transition rate vector in order to understand how those features vary with respect to the values of the transition rates. This would be computationally impossible using simulation and at best very expensive using a numerical version of the naive Feynman–Kac approach. Secondly, we are able to differentiate the symbolic expressions with respect to the transition rate parameters to determine the sensitivity with respect to the values of the parameters. It would be even more infeasible to use simulation or a numerical Feynman–Kac approach to perform such a sensitivity analysis.in vivo and observations in embryonic Drosophila transcription. These data do not allow us to uniquely estimate all 14 binding reaction rates in our model of PIC assembly, but they do constrain key properties, including the time scale of the rate-limiting reactions and the ratio of forward to backward reaction rates for both early binding events and later promoter engagement events. We chose parameters to be consistent with these measurements, and chose enhancer activation and deactivation rates to be consistent with induction times estimated in Drosophila To get an initial sense of the differences between these two schemes of regulation, we first compared the transcriptional behaviors for a best-guess set of parameters, guided by measurements of promoter binding and escape rates by Darzacq et al. We used the following rate parameters for the model of We found the probability density of the amount of time it takes the system to go from induced to actively transcribing, shown in We also described the number of mRNA produced over a given period of time at one choice of In this example, Our predictions for the time of expression and the number of transcripts in the previous subsection depended on the chosen parameter values such as the association rate of different GTFs and the average burst size of the gene expression. The values of such parameters can, for the most part, be only very approximately estimated. Moreover, they may be expected to vary considerably between different genes and different species.Since a single vector of parameters simultaneously specifies our models for the two regulation mechanisms, we can systematically explore all possible combinations of promoter strength and enhancer activation rates and ask in each of these cases how the two mechanisms compare in terms of speed, synchrony and variability in transcript counts.all of parameter space.  Furthermore, independent draws of new sets of 10,000 parameter vectors and substitutions give nearly identical results, confirming that our results are not sensitive to the specifics of the sample. Additionally, discarding parameter vectors for which the enhancer dynamics are significantly slower than for the promoter chain  than does the ER scheme. Intermediate values of To further understand why elongation regulation results in faster, more synchronous, and more consistent gene expression over a wide range of parameters we investigated alternative post-initiation regulatory schemes. This allows us to explore how changing certain properties of the model of PIC assembly (the promoter chain) will affect the results: Is the difference large because there are many steps between the IR step and the ER step, or is it because there is no allowed transition leading backward out of the state immediately before the regulated step? To explore these questions, we made modifications to the toy model of First note that, as is shown in We performed the same analysis after adding a reverse transition from state 3 back to state 2 see . The resWe also investigated the case in which the relative sensitivity that is analogous to the “percent variation explained” in classical analysis of variance. Our analytic solutions for the quantities of interest make this computation possible. For example, let Small variations in rate parameters between cells will occur if the number of TF or Pol II molecules is small, so it is of interest to investigate how robust the properties of each regulation scheme are to such variation and which jump rates affect each scheme the most. To measure this sensitivity, we compute the gradient of a quantity of interest (e.g. the mean induction speed) with respect to the vector of jump rates, square the entries, and normalize so that the entries sum to one, giving a quantity we refer to as To explore the sensitivity across parameter space, we computed relative sensitivities for each of the three system properties to all 16 parameters at each of the 10,000 random vectors of transition rates described above. Each of the system properties showed surprisingly similar sensitivity profiles, so we only discuss the results for the mean time to transcription. Marginal distributions of sensitivity of mean time to transcription to each parameter are shown in As one might expect, for a given parameter vector the parameters to which the behavior of the models are most sensitive are generally those that happen to take the smallest value (and are thus rate-limiting): for each parameter vector, we recorded the sizes of the two parameters with the highest and second highest sensitivity values and found that their sample means were Two further observations are evident from this analysis. First, we see which transitions in the process of activating the gene are most sensitive to small fluctuations . As is apparent from Second, we also observe that the complex assembly steps which may occur in arbitrary arrival order, namely the recruitment of TFIIE or TFIIF .Our investigation required us to introduce a general probabilistic framework for analyzing system properties of protein–DNA interactions. Stochastic effects, resulting from molecular fluctuations, are increasingly understood to play important roles in gene control and expression . Different assembly topologies, such as sequential versus arbitrary association mechanisms for the component TFs Figure S1Sample chip. Identification of Paused Polymerase in Drosophila by Chip-chip: (A) Gene models are shown top, aligned to Pol II chromatin immunoprecipitation signal measurements from whole genome tiling array, showing locations in the genome where Pol II is bound in each of three specific tissues — the dorsal ectoderm, the neurogenic ectoderm, and the mesoderm, from Zeitlinger 2007. pnr is expressed only in the dorsal ectoderm — the promoter (highlighted region) is silent in the other tissues. (B) Genome data as in (A) for the region around the gene tup. In this case the promoter region is bound in all three tissue types, even though the rest of the gene is only transcribed in the dorsal ectoderm.(0.53 MB EPS)Click here for additional data file.Figure S2Paramater Histograms. Histograms of the distributions of those parameter values where the IR scheme is faster than the ER scheme (top row), more synchronous the ER scheme (middle row) or less noisy in terms of total transcripts than the ER scheme (bottom row).(0.30 MB EPS)Click here for additional data file.Figure S3k, bk, ck, and dk depend only on the distributions of both adjacent chains Xk and Xk+1, while the behavior of X between pinch points pk-1 and pk only depends on the distribution of Xk.Pinchpoint schema. A schematic of the decomposition. The probabilities a(0.30 MB EPS)Click here for additional data file.Figure S432 which enables polymerase to exit the paused state and return to a pre-initiated state. (B) Effect of the added transition on the structure of the composite Markov chains. (C) Comparison between the models over all of parameter space when the transition k32 is added. (D) Schematic of changing the regulated step to control promoter escape rather than release from pausing. (E) Resulting composite Markov chains for regulating promoter escape. (F) Comparison between the models over all of parameter space when promoter escape is the regulated step.Change topology. Effect of regulated step. (A) Adding a transition k(1.25 MB EPS)Click here for additional data file.Figure S5Sensitivity analysis for variance in transcription time. The details are the same as for (0.47 MB EPS)Click here for additional data file.Figure S6Sensitivity analysis for transcript count variability. The details are the same as for (0.47 MB EPS)Click here for additional data file.Text S1Derivation of equations and detailed mathematical approach for rapid inversion of large transition matrices.(0.45 MB PDF)Click here for additional data file.Text S2Matlab code to implement the analyses described in the main text and outlined in detail in (6.06 MB ZIP)Click here for additional data file.