The androgen receptor (AR) is a steroid-activated transcription factor that binds at specific DNA locations and plays a key role in the etiology of prostate cancer. While numerous studies have identified a clear connection between AR binding and expression of target genes for a limited number of loci, high-throughput elucidation of these sites allows for a deeper understanding of the complexities of this process.We have mapped 189 AR occupied regions (ARORs) and 1,388 histone H3 acetylation (AcH3) loci to a 3% continuous stretch of human genomic DNA using chromatin immunoprecipitation (ChIP) microarray analysis. Of 62 highly reproducible ARORs, 32 (52%) were also marked by AcH3. While the number of ARORs detected in prostate cancer cells exceeded the number of nearby DHT-responsive genes, the AcH3 mark defined a subclass of ARORs much more highly associated with such genes – 12% of the genes flanking AcH3+ARORs were DHT-responsive, compared to only 1% of genes flanking AcH3−ARORs. Most ARORs contained enhancer activities as detected in luciferase reporter assays. Analysis of the AROR sequences, followed by site-directed ChIP, identified binding sites for AR transcriptional coregulators FoxA1, CEBPβ, NFI and GATA2, which had diverse effects on endogenous AR target gene expression levels in siRNA knockout experiments.We suggest that only some ARORs function under the given physiological conditions, utilizing diverse mechanisms. This diversity points to differential regulation of gene expression by the same transcription factor related to the chromatin structure. One potential way to better assign functionality to transcription factor binding sites, including ARORs, is to make use of chromatin analyses. In particular, ARORs containing active histone modifications may function as enhancers, modulating transcription at a distance.The ‘textbook’ paradigm of gene regulation by steroid hormone receptors entails the binding of receptors to hormone response elements located 5′-upstream of the transcription start sites (TSSs) of responsive genes, followed by the recruitment of non-DNA-binding coactivators or corepressors. These latter factors modify histones and interact with the basal transcriptional machinery to modulate transcriptional initiation Histone covalent modifications, also referred to as chromatin epigenetics, are involved in normal somatic cell development as well as in the progression of diseases, such as cancer With the development of high throughput methodologies such as ChIP-chip  occupied by the AR in at least two of the three experiments, with 62 [level 1 (L1) ARORs] being reproduced in all three, and 127 [level 2 (L2) ARORs] being reproduced in exactly two of the three  that covers sequences on chromosomes 19 and 20 that total about 3% of the human genome, including the classical AR target gene he three . Their che three . FrequenTwo replicates for the AcH3 ChIP-chip experiment were sufficient because they were highly reproducible – of 1,388 regions identified within the two replicates, 1,189 (>90%) were in common . This hiKLK3, PYGB, TGM2 and SERINC3)   verifiedAR occupancy at many regions identified in our study does not lead to alterations in expression of nearby genes as a function of DHT exposure. Since AcH3 is a landmark of functional enhancers both upstream and downstream from the AROR). This is highly enriched (p = 1.4×10−7 by Fisher exact test) relative to AcH3- ARORs and control regions without an AROR, for both of which only 1.1% of adjacent genes were DHT-stimulated. There was no statistically significant relationship between repressed genes and association with ARORs, irrespective of their acetylation status. Conversely, 11 of 24 up-regulated genes were adjacent to an acetylated AROR, while only 1 of 24 was adjacent to an un-acetylated AROR , non-acetylated AROR(s), or no AROR at all. The results  show thated AROR . While tted AROR .PYGB AROR cluster in We additionally found that acetylated ARORs  were morTK) minimal promoter/luciferase reporter and subjected to luciferase assay in the presence or absence of DHT exposure as described in We suspected that in many of the ARORs in C4-2B cells, the AR was present in complexes devoid of transcriptional trans-activation activity. We therefore tested the DHT-mediated enhancer activity of 61 ARORs in transient transfection reporter assays. These 61 ARORs, encompassing all of the highly reproducible (L1) ARORs except A059 (which is in the middle of a simple tandem repeat), were cloned upstream of a thymidine kinase (i) the ARORs might be enriched for binding sites for both AR as well as AR-coregulators, and (ii) the presence of such sites modulated responsiveness to DHT in a combinatorial manner. To pursue the first hypothesis, we searched for TF binding motifs enriched in the highly reproducible L1 ARORs compared to 6200 equivalent random size-matched sequences from the interrogated genomic territory on chromosomes 19 and 20. These analyses excluded the un-cloned AROR A059, as well as A002 that contains 14 tandem canonical AREs and would skew the analysis. The set of 60 L1 ARORs were investigated using both scanning and de novo motif-finding approaches. Scanning approaches using the Cis-Elements Annotation System (CEAS) website −12), HNF3α/FoxA1-binding motif , NFI-binding motif , GRE , and C/EBP   . Nonethede novo motif-finding approaches, as well as two alternate ARE versions from the ConSite de novo motifs with consensus sequences of TTGCTT (2.2-fold) and TTGGCAAATA (3.2-fold)  . The TTGnd Oct-1 .TTGCTT (4.6-fold enrichment) or TTGGCAAATA (4.6-fold) de novo motifs. The remaining two did not have AREs, but instead consisted of a FoxA1-binding site with either TTGCTT (4.6-fold) or the TTGGCAAATA (11.3-fold) motifs  . Two of ) motifs .PSA/KLK3 locus. As shown in PSA (A042), PYGB (A129) and TGM2 (A156), and Oct-1 interaction was weakly observed on several ARORs with only one of three different antibody preparations. Interestingly, we could not always predict occupancy of any particular AROR by FoxA1, C/EBP, NFI and Oct-1 by the presence or absence of consensus motifs. This finding is suggestive of complex protein-protein interactions in the recruitment of a given TF to a specific AROR, and further indicates that the site-specific arrangement of TFs creates a diversity of site-specific AR-mediated transcriptional responses. For example, A129 exhibited relatively high NFI and GATA2 occupancy, but low to intermediate C/EBPβ and FoxA1 levels, A107 had relatively high C/EBPβ, low to intermediate NFI and FoxA1 and negligible GATA2, whereas A140, was highly occupied by FoxA1 after DHT treatment but had low NFI, intermediate C/EBPβ and extremely low GATA2 occupancy levels.The identification of motifs enriched in ARORs , and in The C4-2B cell line was originally obtained from LNCaP cells grown as xenografts after castration PSA/KLK3 (near A042), KLK2 (near A043), TGM2 (near A156), TMPRSS2 (chromosome 21) and FKBP51 (chromosome 6)] ]  in a mansome 6)]  and 6. FThe functional consequences of AR engagement at DNA target sites, which normally follow androgen binding to the AR, are of major importance in the development of the male phenotype and in diseases such as PCa. The prostate is a well-established AR target tissue, where it is known that 5α-reductase converts circulating testosterone to the more potent androgen, DHT. DHT in turn binds the AR, mediates its nuclear translocation, and promotes prostate differentiation via specific gene expression modulation. With respect to PCa, increased life-time exposure to enhanced AR activity predisposes men to the disease In this report, we employed ChIP-chip to map the locations of both the AR and AcH3 onto 3% of the genome in C4-2B prostate cancer cells. AR occupancy exhibited higher inter-experimental variance than AcH3, but was still sufficiently reproducible to detect 62 regions occupied in every one of three independent experiments. Aside from their exclusion from exonic sequences, ARORs were distributed randomly with respect to gene locations. Of the 62 highly reproducible ARORs, 32 were also marked by AcH3. The significance of AROR acetylation was apparent when examining gene expression patterns of neighboring genes – 12% of genes adjacent to AcH3+ARORs were DHT-stimulated, compared to only 1% of genes adjacent to AcH3- ARORs  While the number of differentially regulated genes on chromosomes 19 and 20 is small, this roughly 10-fold enrichment in functionality of ARORs with the acetylation mark illustrates the utility of combining ChIP studies of individual transcription factors with epigenetic markers in the same cells.We detected many more ARORs than DHT-responsive genes, leaving most ARORs without any detectable function in C4-2B cells. We can speculate that these ARORs, particularly the acetylated ones, modulate transcription at levels undetectable in our assay, or target microRNAs or other un-annotated transcripts. They may even function as enhancers of genes over large linear DNA distances or on other chromosomes in vitro assays. Indeed, even ARORs that had no enhancer activity in C4-2B cells turned out to be active in other prostate cancer cell lines (data not shown). As stated above, whether an AROR is engaged, poised, or transcriptionally inactive, even in a non-chromosomal context after transient transfection, likely depends on the differential occupancy of the AROR by trans-acting factors, which modify the transcriptional activity of the AR. This was best exemplified by the siRNA knockdown of specific AR coregulators that had differential effects on different DHT-responsive genes -mediated C/EBPs (CCAAT-enhancer-binding proteins) belong to a family of transcription factors that interact with the CCAAT box motif, which is present in the promoters of several genes. In LNCaP cells, C/EBPα over-expression inhibited the expression of PSA Nuclear factor I (NFI) belongs to a family of transcription factors that play important roles in modulating transcription of differentiation-associated genes PSA, TGM2 and PYGB), indicating that this transcriptional coregulator might potentiate the AR-mediated transcription of these genes. However, depending on the specific enhancer, GATA2 might have enhancing or repressing activities on gene expression, as was shown in the siRNA knockdown experiments.GATA family members are able to bind to DNA at their response elements and mediate the ‘opening’ of compacted chromatin Oct-1 was proposed as an AR collaborator We have previously reported that at some loci (but not at others) gene-wide histone H3 acetylation leads to efficient AR-mediated transcription in C4-2B but not in LNCaP cells The interplay between the AR and potential coregulators is very likely promoter/enhancer specific and dependent on the phenotypes of the target genes as was shown in the present study. Some of the AR coregulators may be considered pioneer factors, laying the groundwork to allow efficient AR-mediated gene modulation as was proposed for ER activity per se. Silent occupied regions may act as a reservoir of bound AR, possibly poised to mediate gene expression as conditions change. Such conditions may include chromatin modifications and/or the recruitment of coregulators, since most of the AR occupied regions were also co-occupied by other TFs, which in several cases acted as coregulators of gene expression. We conclude that the diversity of site-specific functions of the AR points to differential regulation of gene expression by the same transcription factor related to chromatin structure and the presence of coregulators.Over the past several decades a general concept of TF-mediated gene regulation has emerged. It depicts a sequence of events, starting with TFs binding at specific regions of DNA, followed by the covalent modification of chromatin proteins (primarily histones) to ‘open’ chromatin, and ultimately allowing the nearby docking of the transcription machinery. This is not necessarily always the case and alternative insights were recently revealed by the use of new high throughput techniques, which allow the agnostic mapping of such regions and an unbiased appreciation of their functions. In the current study, many AR binding sites were not associated with nearby gene expression despite the fact that they had intrinsic positive regulatory activity in chromatin-independent assays. On the other hand, those that regulated the expression of nearby genes were 10-times more likely marked by acetylation, indicating that AR occupancy and associated histone acetylation are both necessary requirements for positive regulatory competence. The existence of silent AR occupied regions, however indicates that AR occupancy does not necessarily lead to the recruitment of histone acetylases and subsequent nearby gene expression. Therefore, the rate-limiting step for AR-mediated gene expression is not occupancy Human prostate cancer C4-2B cells, obtained from ViroMed Laboratories , were maintained in RPMI 1640 supplemented with 5% (v/v) fetal bovine serum (FBS). Antibodies were anti-AR (N20), anti-HNF-3α (H-120), anti-NFI (H-300), anti-C/EBPβ (C19), anti-GATA2 (H116), normal rabbit IgG , anti-Oct1  and anti-AcH3-K9/K14 . Pre-designed SMARTpool siRNA reagents against FoxA1, NFI, C/EBPβ, GATA2 and nonspecific siRNA were purchased from Dharmacon . AR siRNA was described previously 5′-GCGGTGACCCGGGAGATCTGAATTC-3′, and oJW103, 5′-GAATTCAGATC-3′), and amplified by ligation-mediated PCR (LM-PCR). NimbleGen Systems  labeled amplified input DNA and ChIP DNA with Cy3 and Cy5, respectively, and hybridized to genomic tilling arrays HG17Tilling Set 35.C4-2B cells were cultured in phenol red-free RPMI 1640 supplemented with 5% charcoal/dextrane-stripped FBS (CSS) for 3 days. After 4 h DHT (10 nM) treatment, ChIP was conducted as described previously iM were determined for 600-bp windows centered on each probe i, for all windows containing three or more probes. Statistically significant windows were determined by comparing actual moving average values to a null distribution obtained by permutation sampling as follows. For each actual moving average iM containing j probes, we computed a null moving average *iM by randomly sampling j log-ratio values from the entire dataset. This was performed 10,000 times to generate a distribution of permuted moving averages. A normal QQ-plot (û) and variance (2ô) parameters and performed a one-sided normal test to calculate individual window p-values based on this null distribution. To accommodate the large number of statistical tests, we used the method of Benjamini et. al. to control the False Discovery Rate (FDR) at an FDR (α) level of 0.005. Regions with at least four consecutive probes were considered a peak, then peaks were extended until the first insignificant probe was encountered, and overlapping peaks were merged. For AcH3 occupied regions, final peaks were those that were called in both of the replicate experiments. For AR occupied regions (ARORs), L1 peaks were defined as those present in all three replicate experiments, while L2 peaks were those present in two of three.Normalized log-ratios for each replicate were obtained from NimbleGen and processed independently. Moving averages  QQ-plot  indicateDistance to TSSs and genomic contexts were determined relative to transcript annotations in the “Known Genes” track of the UCSC Genome Browser. 6,200 randomized ARORs were generated by picking random regions from chromosomes 19 and 20 with properties matched to the actual L1 ARORs – they had the same relative frequencies on chromosome 19 vs. 20, were matched for size, and were required to have no more than one missing probe. 95% confidence intervals are defined as the TSS distances of the upper 2.5% and the lower 2.5% of the randomized ARORs.ChIP analyses on LNCaP or C4-2B cells were performed as described previously Hormone-depleted C4-2B cells were treated with DHT (10 nM) or an equal volume of ethanol for 16 hr. Total RNA from three biological replicates was extracted using Aurum Total RNA Kit  and hybridized to Human-6 v2 Expression BeadChIP  at the USC/Norris Cancer Center Core Facility. To determine differentially expressed genes, we performed a two-tailed t-test  across three DHT− replicates and three DHT+ replicates for all 46,713 array features having a valid measurement for all 6 replicates. To determine an empirical null distribution, we generated 1,000 permutations of the data matrix by randomly shuffling the three DHT− and three DHT+ assignments for each array feature (yielding roughly 47 million randomized features). An adjusted p-value was then determined for each feature. These p-values were converted to Expectation values  by multiplying by the number of features . A signiDistances were calculated between each pair of AcH3−ARORs (3C) and each pair of AcH3+ARORs (3D). Chromosome and size-matched randomized ARORs were generated as described above. For each trial, we generated sets with the same number of ARORs as the experimentally determined set. For 95% confidence interval, we took the upper 2.5% and the lower 2.5% of distances for 10,000 independent trials.The AROR sequences (an average of 500 bp fragment surrounding the AROR peak center) were PCR amplified from C4-2B genomic DNA and subcloned upstream of a thymidine kinase (TK) minimal promoter-luciferase vector [from Dr Axel Schönthal (USC)] in both directions. The primers for subcloning are listed in 5 cells/well) were plated in 12-well plates and grown in phenol red-free RPMI 1640 containing 5% CSS for 2 days. Cells were then transfected with AROR containing TK-luciferase reporter plasmids using Lipofectamine LTX Reagent  according to the manufacturer's protocol. After transfection, cells were treated with DHT (10 nM) or ethanol vehicle for 24 h. Luciferase activities were measured as previously described C4-2B  to 6,200 size-matched controls. Each was run to search for motifs of various lengths . The “-T 20” command line parameter was used for BioProspector, and “-t 30” was used for MDScan. Because these de novo search programs produce a large number of highly similar versions of the same motif , we performed a clustering technique to collapse them to a relatively non-redundant set. We examined each possible pair of motifs A and B, and measured the Jaccard distance between the two motifs, which is defined as count(A&B) is the number of overlapping binding sites between A and B, and count(A|B) is the total number of binding sites predicted for either motif. Motifs were then clustered using linear hierarchical clustering (average linkage), and motifs with an average Jaccard distance of 0.1 or less were grouped together. The motif with the lowest average distance to all other motifs in the cluster was recorded as the prototype and used for all subsequent analysis. Weeder produced 23 clusters, and BioProspector and MDScan results were combined to yield 22. To this we added 2 versions of the ARE motif from JASPAR/ConSite, and 5 motifs identified by CEAS  BioProspector, MDScan, and Weeder were used to identify motifs For each of the constructs with luciferase data, we counted the number of predicted binding sites within the construct for each of the 52 motifs enriched in ARORs , and separately counted the number of predicted sites for each of the 1,326 unique motif pairs . For each motif or motif pair, we generated an enrichment significance score by performing a one-sided non-parametric t-test  between the binding site counts in the 18 strongly induced ARORs vs. the 19 non-responsive ARORs. Adjusted p-values were determined by randomly permuting the non-responsive and responsive labels 1,000 times for each motif, and determining an empirical distribution of t-test scores, which was used to calibrate the actual t-test scores.5 cells/well) were plated in 6-well plates and grown in phenol red-free RPMI 1640 containing 5% CSS for 2 days. Cells were transfected with the siRNA duplexes as indicated at a final concentration of 100 nM using Oligofectamine Reagent (Invitrogen) according to the manufacturer's instructions. After transfection, cells were grown in phenol red-free RPMI 1640 containing 5% CSS for 48 hr and then treated with DHT (10 nM) or ethanol vehicle for additional 18 hr. Total RNA extraction and protein extraction were conducted respectively for further assessment.C4-2B . cDNA was prepared using iScript cDNA Synthesis Kit (Bio-Rad), and qPCR was conducted using TaqMan PCR Master Mix or SYBR Green PCR Master Mix . The primers and probes are listed in Immunoblotting were performed as previously described using the indicated antibodies Figure S1AROR distribution in Chromosomes 21 & 22 of LNCaP cells. Data from Wang et al (0.09 MB TIF)Click here for additional data file.Figure S2Conservation of AR ChIP and AcH3 ChIP non-coding regions. Cumulative distribution plots of 28-way phastCons conservation scores (UCSC phastCons28way) are plotted for the non-exonic ARORs, Estrogen Receptor Occupied Regions (ERORs) from (7.99 MB TIF)Click here for additional data file.Figure S3Hormone-dependent gene expression in C4-2B cells. Illumina expression arrays were used to measure expression levels of 46,713 transcripts in three replicates before and three replicates after DHT exposure in C4-2B cells. The student's t-test was used to determine statistical significance, and p-values were adjusted based on random permutations of the full dataset. This volcano plot shows the E-value  plotted against the mean fold change. 552 transcripts up-regulated at the E = 150 level , along with 416 transcripts down-regulated , are shown in the upper two quadrants, and of the well-studied DHT-responsive genes in prostate cancer are labeled.(2.42 MB TIF)Click here for additional data file.Figure S4Predicted ARE and coregulator binding sites in L1 ARORs. Binding sites for the ARE and 3 coregulator motifs are shown for the AROR constructs either highly induced (upper left), weakly induced (lower left), unchanged (upper right), and repressed (lower right) in our luciferase reporter activity assays. The strongly induced ARORs had a total of 70 sites in 18 ARORs (3.9 sites per AROR), while the weakly induced had 35 sites in 21 ARORs (1.7 sites per AROR) and the unchanged had 20 sites in 19 ARORs (1.6 sites per AROR).(40.51 MB TIF)Click here for additional data file.Figure S5Normality of permuted tiling array moving averages. Individual log-ratios from one of the AR-ChIP NimbleGen arrays were permuted into “randomized” moving averages . 10,000 randomized moving averages were sampled and plotted against the standard normal. The results show that they largely follow a normal distribution. The deviating right tail comes from enriched probes, but represent only a small fraction of all values and do not effect the statistical testing described above which aims to reject against the background distribution, i.e. the bulk of moving average scores.(0.88 MB TIF)Click here for additional data file.Figure S6Occupancy of AR and coregulators in LNCaP and C4-2B cells. LNCaP and C4-2B cells were cultured in hormone-depleted medium for 3 days and then treated with 10 nM DHT or ethanol vehicle for 4 h. Conventional site-specific ChIP assays were performed with indicated antibodies. The values are presented as percentage of input.(3.54 MB TIF)Click here for additional data file.Figure S7Occupancy of AR and coregulators at PSA and TGM loci. C4-2B cells were cultured in hormone-depleted medium for 3 days and then treated with 10 nM DHT or ethanol vehicle for 4 h. Conventional site-specific ChIP assays were performed with indicated antibodies. PSA and TGM ARORs were re-plotted from (51.64 MB TIF)Click here for additional data file.Table S1189 L1/L2 ARORs and 1,189 AcH3 occupied regions(0.60 MB XLS)Click here for additional data file.Table S2Hormone-dependent gene expression in C4-2B cells(4.91 MB XLS)Click here for additional data file.Table S3Sequence motifs enriched in ARORs(0.08 MB XLS)Click here for additional data file.Table S4ChIP-PCR, probes and primers(0.04 MB XLS)Click here for additional data file.Table S5Luciferase reporter constructs, probes and primers(0.03 MB XLS)Click here for additional data file.Table S6Quantitative PCR, probes and primers(0.03 MB XLS)Click here for additional data file.Table S7IlluminaGX.txt.gz. Illumina microarray raw expression data of C4-2B cells. Text file.(3.08 MB GZ)Click here for additional data file.


Poisson distribution. A Poisson mixture model was then developed to distinguish Pol II binding changes in transcribed region using an empirical approach and an expectation-maximization (EM) algorithm developed for estimation and inference. In order to achieve a global maximum in the M-step, a particle swarm optimization (PSO) was implemented. We applied this model to Pol II binding data generated from hormone-dependent MCF7 breast cancer cells and antiestrogen-resistant MCF7 breast cancer cells before and after treatment with 17β-estradiol (E2). We determined that in the hormone-dependent cells, ~9.9% (2527) genes showed significant changes in Pol II binding after E2 treatment. However, only ~0.7% (172) genes displayed significant Pol II binding changes in E2-treated antiestrogen-resistant cells. These results show that a Poisson mixture model can be used to analyze ChIP-seq data.We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) in transcribed regions, measured using ChIP-seq . The statistical model assumes that the number of Pol II-targeted sequences contained within each genomic region follows a  Massively parallel sequencing is a high-throughput technology capable of sequencing hundreds of thousands of DNA fragments in a single experiment. Combined with antibody-based chromatin immunoprecipitation assay (or ChIP-seq assay), this technology has been demonstrated to be a comprehensive, quantitative and cost-effective approach for mapping protein-DNA interaction on a genome-wide scale . One ChIcis-acting DNA elements sharing certain sequence features. While such algorithms can identify DNA binding sites for highly specific transcription factors, current approaches are not appropriate for identifying binding sites for the general transcriptional machinery, such as RNA polymerase II (Pol II), which typically does not display high sequence specificity. In addition, as Pol II activity likely extends beyond the promoter/transcription start site of active genes, algorithms for assessing long-range Pol II binding are needed. Therefore, in this study, we have proposed a mixture model-based analysis for identifying differences in Pol II distribution in combined 5'-end, open reading frame (ORF), and 3'-untranscribed regions of active genes. Our strategy is based on the underlying assumption that the number of Pol II-target sequences follows a Poisson distribution and can be used to identify differentially transcribed genes under different experimental conditions. Furthermore, our proposed methodology can be used for making statistical inferences in experiments for which replicates are not available.Due to the large amount of data generated in high-throughput sequencing experiments, innovative computational and statistical approaches are required to identify biological signals from ChIP-seq data. To date, several approaches have been applied to identify genomic regions containing a high concentration of sequence hits, i.e., "ChIP-seq peaks" . An undeet al. [et al. [et al. [t-statistic to address the problem of multiple comparisons through permutation analysis. These approaches yielded only p-values representing the probability of having an observed expression difference of a given gene, if the status is assumed to be the same before and after a treatment. However, for Pol II binding data derived from ChIP-seq, these approaches cannot be used to estimate the status change. A more appropriate choice analyzing ChIP-seq Pol II binding data may be a Bayes or an empirical Bayes approach. In this regard, Efron et al. [Bayes model for calculating the probability of a differentially expressed gene given the observed data. As the empirical distribution for their t-like statistic for each gene does not share variation information, it works well only in situations involving at least a few replicates. Newton et al. [Bayes model for cDNA microarray experiments with only one replicate. According to their method, if a gene is differentially expressed under two conditions, its level of expression is independently generated from the same distribution [Gamma distribution with mean μg, and μg itself follows an inverse Gamma distribution (prior component). This hierarchical model is often referred to as the Gamma-Gamma model. Specifically, all genes share the same distribution for the within-gene sampling errors, a crucial feature in their method, as no replicates were available in their data example. Kendziorski et al. [Gamma-Gamma model to situations where replicates were available. In addition, they developed a log-normal model for the observational component and a normal model for the prior component. They demonstrated a comparable performance for a lognormal-normal model and a Gamma-Gamma model. A major advantage in the methods proposed by Newton et al. [et al. [Bayes approach. The model pools the variation information across all genes, making it well suited for data sets containing only a few replicates , and we have successfully utilized this model framework to test the correlation among genome wide gene expression, DNA methylation, and histone acetylation [To date statistical models for ChIP-seq data are very limited. Nevertheless, new algorithms for ChIP-seq can be developed using existing framework for identifying differentially expressed genes from microarray data. For example, Kerr et al.  and Wolf [et al.  employed [et al.  used a tn et al.  proposedn et al.  proposedribution ; otherwii et al.  further n et al.  and Kend [et al.  is that tylation ,9.Poisson mixture model, within the same empirical Bayes framework for identifying gene targets with differential Pol II binding activities in breast cancer MCF7 cell line under various conditions. ChIP-seq data processing, normalization, and statistical methods are proposed in the method section; analysis of ChIP-seq data from breast cancer cell lines (MCF7 and its tamoxifen-resistant subline OHT-MCF7) before and after treatment with 17β-estradial (E2), are presented in the result section, with conclusions at the end of the paper.In the current paper, we propose a different model, -8 mol/L) for three hours. Then, cells were cross-linked with 1% formaldehyde and chromatin immunoprecipitation was done as previously described [As described by Fan et al , MCF7 huescribed . The antcis-acting DNA element with specific sequence features usually described by a position weight matrix (PWM). A hypothetical distribution of ChIP-seq-derived DNA fragments corresponding to transcription factors and RNA polymerase II (Pol II) is shown in Figs. Most DNA binding proteins, such as transcription factors, bind to Poisson distribution, we developed a mixture model to identify differences in Pol II binding under two conditions, control vs. treatment. Denote yij as the Pol II quantity for gene i  under condition j . In this application, j = 1, 2 indicates two biological conditions, MCF7 control (vehicle-treated) and MCF7 treated (3 hour E2 treatment), respectively. Marginally, yij follows a Poisson distribution,Based on the assumption that the number of Pol II-binding fragments detected within gene transcript region, including 5'- and 3'-untranslated regions and open reading frames, follows a λij denotes the expected quantity of Pol II binding for gene i under condition j . For the i-th gene, if Pol II binding quantities within the gene transcript region are statistically different between the two biological conditions, yij follows marginal distributions with different parameters λi1 and λi2; conversely, if the number of Pol II binding does not demonstrate a significant difference, yij follows the same distribution with a unified λ. A mixture model is proposed to estimate the posterior probability of differential Pol II binding quantity.where λi1 and λi2 follow the same Gamma distribution (2). The selection of Gamma distribution is based on two considerations. Firstly, the two parameter Gamma distribution is a very flexible function which can be used to describe a wide range of distribution shapes. Secondly, the Gamma distribution is a conjugate distribution of Poisson for the λ parameters. Hence, the follow-up expectation step in an E-M algorithm has a close form solution.For unified Pol II binding quantity between two conditions, λi1, λi2) are distributed independently.Otherwise,  are independently distributed . Therefore, the joint distribution of Pol II binding quantity is modeled by a mixture of uniform binding events (p = 0) and differential binding events (p = 1).Denote Zi as missing data.Based on equation (4), we implement E-M algorithm by treating E-step of the algorithm is specified as:The M-step, the parameters in the Gamma distribution  are estimated by maximizing the two likelihood functions in Equation 6.In the Gamma parameters can be trapped into local optimum, causing either slow convergence or failure to converge on the global optimal solution. In order to overcome these difficulties, we utilize particle swarm optimization (PSO), an artificial intelligence approach that mimics a behavior of swarm-forming agents, providing a good balance between global optimum searching and computation efficiency [In the M-step, the optimization procedure is challenging, because searching for the optimal solutions for ficiency .α1, β1) and  are factorized in equation 6, the PSO optimization procedures are conducted independently using the following four steps.Because the likelihood functions of  or .Step 1: 100 particles  were initially randomly distributed in 2-dimensional parameter spaces .Zi can be interpreted as the probability of differential Pol II binding between two conditions. Although the model derivation is based on ChIP-seq data from MCF7 cells before and after treatment with vehicle or E2 for 3 hours, it can also be equally applied to OHT-MCF7 (+/- E2 treatment). In practice, the solution of the E-M algorithm converges in only 5 to 6 cycles.At the convergence, We tested our model on the Pol II binding quantity in MCF7 and OHT-MCF7 breast cancer cell lines (+/- E2 treatment) derived from the use of ChIP-seq technology. Among all the DNA fragments detected in each sample, we selected only those with high sequencing and matching quality that could be mapped to unique genomic locus. This pre-filtering step sufficiently removed background detection noise, and 2.59, 2.52, 3.00, and 1.33 million reads passed the above filter in MCF7 control, MCF7 E2-treated, OHT-MCF7 control, and OHT-MCF7 E2-treated samples, respectively. In order to compare Pol II binding quantity within a specific genomic region across multiple samples, the number of detected Pol II fragments was normalized using the total number of matched fragments in each sample, based on the assumption that the total number of DNA-binding Pol II would be similar in different cell types under different biological conditions.As the Pol II binding quantity in each gene transcript region reflects the expression level of the corresponding gene, we analyzed whole genome Pol II binding distributions for the four breast cancer cell samples. For MCF7, E2 treatment resulted in a slightly higher Pol II quantity distribution Fig. , decreasThis observation is consistent with the nature of the MCF7 cell line and the OHT-MCF7 subline, representing hormone-dependent and -independent breast cancer, respectively, and was also seen in the mean values and standard deviations for these samples Table . After nPoisson mixture model was used to identify estrogen-induced differences in Pol II binding quantity in the two cell lines. The results are shown as a scatter plot of the (log2) number of fragments in control and E2-treated samples , or minor change , where Zi is a posterior probability that the Pol II binding quantity changed after E2 treatment. This result demonstrates a critical feature of the Poisson mixture model: more weight is given to high abundant signals, while additional penalties are imposed on genes with low abundant quantities. The motivation is that additional relative changes are required to separate low abundant signals from background noise, because high signals are less sensitive to background noise. Consistent with previous observations [Because no replicates were available for the test data, a rvations , wild-tyα targets in hormone-dependent breast cancer [Fragment distribution in the gene transcript region of two genes falling in the "major change" catergories, PgR (progesterone receptor) and MYC, two well known ERt cancer ,15 are sPoisson mixture model to identify estrogen-induced changes in Pol II binding quantity in wild type MCF7 cells and OHT-resistant MCF7 cells. Despite having only one replicate available, our model successfully identified genes with different Pol II binding quantities from data derived using ChIP-seq technology. This model can distinguish differentially expressed Pol II activities from unchanged Pol II activities using a posterior probability calculated through an empirical Bayes approach. The empirical Bayes approach utilizes a combination of E-M and PSO algorithms for estimation and optimization in detection of the differential Pol II binding in two biological conditions. In this model, small signals require large changes in binding quantity to reach the same level of significance. The proposed model is unique in its ability to handle the ChIP-seq data without replicates and thus is an excellent tool for laboratories to evaluate preliminary ChIP-seq results. However, it is important to point out that despite the fact that no replicates are required to calculate changing probability, the use of biological replicates to capture persistent measurements in response to certain treatments are strongly encouraged.We report a The authors declare that they have no competing interests.YL and LL designed the study. WF, YL and LL designed and performed the computational modeling and drafted the manuscript. JW, THH, and KPN performed ChIP-seq experiment. All the authors read and approved the final manuscript.


Notch1 along with two downstream transcription factors hes1 and RBP-Jk forms an intricate network of positive and negative feedback loops, and we have implemented a systems biology approach to computationally study this gene regulation network. Our results indicate that the system exhibits bistability and is capable of switching states at a critical level of Notch signaling initiated by its ligand Delta in a particular range of parameter values. In this mode, transient activation of Delta is also capable of inducing prolonged high expression of Hes1, mimicking the “ON” state depending on the intensity and duration of the signal. Furthermore, this system is highly sensitive to certain model parameters and can transition from functioning as a bistable switch to an oscillator by tuning a single parameter value. This parameter, the transcriptional repression constant of hes1, can thus qualitatively govern the behavior of the signaling network. In addition, we find that the system is able to dampen and reduce the effects of biological noise that arise from stochastic effects in gene expression for systems that respond quickly to Notch signaling.The Notch signaling pathway controls numerous cell fate decisions during development and adulthood through diverse mechanisms. Thus, whereas it functions as an oscillator during somitogenesis, it can mediate an all-or-none cell fate switch to influence pattern formation in various tissues during development. Furthermore, while in some contexts continuous Notch signaling is required, in others a transient Notch signal is sufficient to influence cell fate decisions. However, the signaling mechanisms that underlie these diverse behaviors in different cellular contexts have not been understood. This work thus helps our understanding of an important cell fate control system and begins to elucidate how this context dependent signaling system can be modulated in different cellular settings to exhibit entirely different behaviors. Drosophila eye. Furthermore, malfunctioning of Notch signaling is implicated in various cancers. To better understand the underlying mechanisms that allow the network to function distinctly in different contexts, we have mathematically modeled the behavior of the Notch network, encompassing the Notch gene along with two of its downstream effector transcription factors, which together form a network of positive and negative feedback loops. Our results indicate that the qualitative and quantitative behavior of the system can readily be tuned based on key parameters to reflect its multiple roles. Furthermore, our results provide insights into alterations in the signaling system that lead to malfunction and hence disease, which could be used to identify potential drug targets for therapy.The Notch signaling pathway is an evolutionarily conserved signaling system that is involved in various cell fate decisions, both during development of an organism and during adulthood. While the same core circuit functions in various different cellular contexts, it has experimentally been shown to elicit varied behaviors and responses. On the one hand, it functions as a cellular oscillator critical for somitogenesis, whereas in other situations, it can function as a cell fate switch to pattern developing tissue, for example in the  Cells continuously receive signals from their microenvironments – including factors present in the extracellular matrix, soluble media, and surrounding cells – which collectively influence cell function and behavior via activating intracellular signal transduction and gene regulation networks. These networks generally involve complex, nonlinear interactions of proteins, such as phosphorylation cascades , cytoplasmic protein (Hcp), and nuclear protein (Hnp), respectively, which are assumed to undergo first order degradation kinetics. ktrHc denotes the translation constant (min−1) for conversion of cytoplasmic hes1 mRNA into cytoplasmic protein. Transcriptional and translational delay times are incorporated into the model, as these are processes that inherently involve delays between initiation and the production of a molecule of mRNA or protein, as previously described hes1 mRNA concentration HcmD , which is the concentration of mRNA present when the process of translation was initiated instead of the concentration at the present time. kniHcp denotes the nuclear import rate in units of min−1. A dilution factor of 7 is again used to incorporate differences in nuclear and cytoplasmic volumes.A set of differential equations was developed to track changes in the concentrations of various species in the nucleus and cytoplasm of a cell as a function of time following activation of Notch by its ligand. The cell is modeled as a 10 µm diameter sphere with a 5 µm diameter nucleus. Numerous processes were modeled as terms in the differential equation system, including transcription, translation, transport, degradation or - in the case of Notch - receptor cleavage , Text S1notch1, hes1, and RBP-Jκ are based on the states of their respective promoters. Previous promoter analysis has been complemented with Genomatix Suite Gene2Promoter transcription factor (TF) binding site prediction software to identify potential TF binding sites in the promoters of the three genes in the model.The transcription rates for hes1 transcription is repressed by its own gene product through Hes1 protein binding to sites in the hes1 promoter termed N-boxes. Through a series of binding and transcriptional activity assays, the study determined that Hes1 bound strongly to three N-boxes found upstream of the transcriptional start site and repressed transcription of the hes1 mRNA up to 40-fold. Also, while the work concluded that there was a synergistic rather than an additive effect of the N-box binding dependent repression of gene expression, further mathematical analysis has indicated that there is no or very weak synergy among the different binding sites hes1 promoter, and it was also shown to have two adjacent RBP-Jκ binding sites hes1 promoter to have three equivalent N-boxes where the Hes1 protein can bind and repress transcription, as well as two equivalent RBP-Jκ sites. The presence of all other positive regulators of transcription is lumped into a constant basal rate of transcription.Takebayashi et al. notch1 promoter sequence was analyzed in the Gene2Promoter software. One putative Hes1 site (N-box) and two putative RBP-Jκ sites were found in the ∼1 kb notch1 promoter analyzed. This may imply that notch1 is both positively and negatively regulated by its own gene product. To test this, a transcriptional activity experiment using the dual luciferase assay system was conducted. The promoter of murine notch1notch1 promoter is regulated negatively by Hes1 and RBP-Jκ in the absence of NICD but is positively regulated by NICD in the presence of RBP-Jκ  and RBP-Jκ. It is assumed that Hes1 can bind and repress transcription of the corresponding promoter only in its homodimer form, and the dimerization reaction is assumed to be at steady state over timescales of protein transcription, translation and import, driven by mass action kinetics such that the concentration of the dimer is given by:i] is the probability of the promoter being in state i, and vi is the activation rate of gene transcription associated to the promoter being in that state i. When the promoter is empty, the gene activation rate is assumed to be the basal transcription rate (Vb) for that promoter. When a Hes1 dimer is bound to an N-box, the rate is reduced by a factor rN that takes into account the repressive effect of the Hes1 transcription factor, and when RBP-Jκ is bound, the rate is reduced by a factor rR. Furthermore, when the promoter is in its maximally activated state with the NICD bound to the RBP-Jκ and no Hes1 dimers bound, the activation rate is assumed to be at its maximum and is given by (Vmax+Vb). In the case of multiple RBP-Jκ binding sites, an additional factor tc (<1) is used to account for states where not all RBP-Jκ sites bind NICD to represent the decrease from the maximum possible activation rate. For a detailed expression of transcription rates please refer to Supplemental Materials (The level of promoter activation (i.e. rate of mRNA synthesis) is modeled by an approach termed BEWARE aterials .n, Cr and Cnr - please refer to Although explicit parameters have been included to account for cooperative binding for Hes1 dimers to multiple N-boxes and for RBP-Jκ binding  is estimated from the results of Takebayashi et al. fNcp), a lumped parameter of this constant with the Delta concentration is used to report the strength of the Delta signal (kfNcp*Delp). The initial parameters for which the experimentally determined values are not accurately available were later subjected to sensitivity analysis (See The repression constant of Hes1 dimer bound to an N-box (rwww.berkeleymadonna.com) with the Runge Kutta 4 module at a step size of 1 min. To arrive at realistic initial conditions for the model, the initial concentrations of all species were set to 0 with zero Delta signal, and the simulations were run until the various species attained steady state concentration levels. These steady state values  with a moderate strength Delta signal, the deterministic model predicts a transient peak in the Hes1 expression that eventually decays to its low steady state value . HoweverWe also examined the effect of stochastics on transient activation of the network. Simulations were run using the parameter values as in the deterministic model for various Delta pulse durations ranging from 10 minutes to 3000 minutes and >40 trajectories per input duration value were analyzed. For Delta pulse durations of less than 500 minutes, the stochastic simulations followed the prediction of the deterministic model (data not shown). However, for a 500-minute Delta pulse, even though the deterministic model predicts a transient Hes1 peak that does not attain the maximum possible expression level, a small percentage of the stochastic trajectories in fact did switch to the “ON” state (corresponding to high Hes1 expression levels) (data not shown). Also, as the duration of the Delta pulse is increased, the percentage of trajectories that remain in the “ON” state for the simulated 15,000 minutes progressively increases even though the deterministic model predicts that the system would revert back to the “OFF” state within that time. Furthermore, the average first passage time (FPT) of the trajectories that do switch state increases as the Delta pulse duration increases . It is la), the maximal transcription rates (Vmax), and the repression constant of Hes1 (rNbox). NICD has a long half-life of a few hours under normal physiological conditions a) of NICD to RBP-Jκ in the model is 108 M−1, but as Ka increases – denoting stronger interactions of NICD with the promoter – bifurcation analysis demonstrates that the OFF-ON transition occurs at accordingly lower values of the Delta signal (kDelp) (maxh) to indicate a stronger promoter shifts the OFF-ON transitions to lower Delta signal strengths  . Similartrengths .Nbox). As the Hes1 repression constant (rNbox) is progressively decreased (or the repressive strength of Hes1 progressively increased) from 0.3 to 0.1, the final steady state concentrations of Hes1 progressively decrease for a given level of Delta signaling , it once again undergoes a transition to a stable oscillatory response where the Hes1 levels in the cell oscillate about a low mean steady state value (Nbox (hes1 models showing sustained oscillations have focused exclusively on the low rNbox region (i.e. rNbox = 0) of such a phase plot Interestingly, the response of the deterministic model was most sensitive to the extent to which Hes1 binding reduced or repressed expression of target genes , the Hes1 expression level functions as a bistable switch in response to varying the strength of the Delta signal, thereby providing an unambiguous fate switch that is insensitive to the presence of small fluctuations in input signal (a (the association binding constant of NICD to RBP-Jκ) and Vmax  can shift the region of bistability, thus changing the sensitivity of the system to the Delta signal, but the qualitative nature of the gene network remains the same for a broad range of these parameter values. Positive feedback loops with nonlinearity can yield bistability The model predicts that for low repressive strengths of Hes1 , the cell can transition from being a bistable system, to a brief region of monostability, and finally to an oscillator  may enhance the repression due to Hes1 such that the value of rNbox is very low.By increasing the repressive strength of the Hes1 dimer by 10-fold Click here for additional data file.Figure S1Transcriptional analysis of the Notch1 promoter. Relative fold changes in the activity of the Notch1 promoter in the presence of exogenous Hes1 (H), dNHes1 (dN), RBP-Jκ(R) and NICD (N) are shown. Relative amounts of plasmids used in each case encoding the respective cDNA are indicated. For example H0.1dN1R0.5N0.5 indicates Hes1 = 0.1 µg, dNHes1 = 1 µg, RBP-Jk = 0.5 µg and NICD = 0.5 µg in a total of 4 µg transfection.(2.52 MB TIF)Click here for additional data file.Figure S2Bifurcation Analysis. (A) Bifurcation analysis of how the switching points vary with the Hes1 DNA association constant (Kn). Varying the association constant over two orders of magnitude causes a slight shift in the strength of the Delta signal required to switch the system state. Thus, stronger DNA association of Hes1  increases the threshold values of Delta signal strength (Kdelp) required to turn the system ON. (B) Bifurcation analysis of how the switching points vary with the Hes1 dimerization constant (KaHp). Varying the dimerization constant over two orders of magnitude causes a slight increase in the strength of the Delta signal required to switch the system state to ON. (C) Bifurcation analysis of how the switching points vary with the repression constant of RBP-Jκ (rR). Increasing the repression constant from 0 to 0.5 (corresponding to a decrease in the RBP-Jκ repressive strength), has very little effect on the threshold of Delta signaling strength required to turn the system ON.(0.82 MB TIF)Click here for additional data file.Figure S3−4, but no qualitative change in the switch. Increasing the degradation constant by 10-fold (kdNcp = 0.0014 to 0.014) however, causes complete suppression of the switch. (B) Analysis of how the threshold value of Delta signal required to switch the system from OFF to ON increases with the increasing degradation constant of NICD .Effect of half-life of NICD on the Hes1 switch. (A) Changing the half life from 3 hrs (kdNcp = 0.00385) to 8 hrs (0.0014) due to the effect of GSK3β causes a slight increase in the steady state Hes1 concentration in response to a Delta signal of kDelp = 5×10(0.52 MB TIF)Click here for additional data file.Figure S4Nbox) on the high steady state values of Hes1 expression.Decreasing rNbox progressively decreases the steady state concentrations of Hes1 in the rNbox range of 0.3 to 0.1.Effect of repression through Hes1 (r(0.42 MB TIF)Click here for additional data file.


Drosophila, in the development of the peripheral nervous system. First, these factors regulate transcription of scute during the singling out of sensory organ precursors from clusters of cells expressing the proneural genes achaete and scute. This effect is possibly mediated through binding sites for NF-κB/Rel proteins in a regulatory module of the scute gene required for maintenance of scute expression in precursors as well as repression in cells surrounding precursors. Second, genetic evidence suggests that the receptor Toll-8, Relish, Dif and Dorsal, and the caspase Dredd pathway are active over the entire imaginal disc epithelium, but Toll-8 expression is excluded from sensory organ precursors. Relish promotes rapid turnover of transcripts of the target genes scute and asense through an indirect, post-transcriptional mechanism. We propose that this buffering of gene expression levels serves to keep the neuro-epithelium constantly poised for neurogenesis.Two distinct roles are described for Dorsal, Dif and Relish, the three NF-κB/Rel proteins of  This is a conserved process involving an indirect auto-regulatory loop whereby the proneural genes repress their own transcription. Cells chosen to be precursors sustain high threshold levels of expression and signal to the cells surrounding them causing them to gradually lose proneural gene expression Drosophila thorax is a useful paradigm for understanding the control of neuronal precursor development ac and sc are expressed in small clusters of cells from which one or two sensory organ precursors (SOP) are singled out asense (ase) and senseless (sens)The array of sensory bristles on the sc gene, the SOP element, which mediates lateral inhibition. It drives auto-regulation of sc in the SOP itself and is probably a target for repression in the inhibited cells. The enhancer bears binding sites for Ac-Sc/Da (E boxes) and a potential site for repression by the E(spl) bHLH proteins, targets of Notch signalling (N box). Three α boxes, motifs resembling the consensus binding sequence for transcription factors of the NF-κB/Rel family and a T-rich motif of unknown function were also found. Mutation of the α boxes in the sc SOP enhancer demonstrated a role for these sequences for maintenance of transcription in SOPs and for repression in inhibited cells Drosophila: dorsal (dl), Dorsal related immunity factor (Dif) and Relish (Rel). All three are involved in innate immunity and dl is also required for dorso-ventral polarity of the embryo Culi and Modolell Drosophilasc via the α boxes in the SOP enhancer might be direct. In addition, Toll-8 and Relish act to maintain low levels of expression over most of the epithelium of the target genes sc, ase and sens, by promoting rapid turnover of mRNA. This is mediated by a post-transcriptional mechanism affecting both RNA stability and translation. It appears to involve a heptamer nucleotide motif present in the coding regions similar to that described for the indirect regulation by NF-κB of MyoD and Sox9 in mammalian cells Toll-1 is known to initiate signalling leading to activation and nuclear translocation of the NF-κB/Rel proteins in sc promoter, we examined mutants of the three NF-κB/Rel genes for perturbations in the patterns of the large bristles (macrochaetes) of the notum. Flies homozygous for loss of function alleles of dl, Dif and Rel were found to display ectopic bristles on the notum in dorsocentral, scutellar and lateral regions , bristles are missing in the lateral notum of 4/+dl flies . No involvement of cactus in bristle patterning could be detected from visible phenotypes in loss-of-function clones or in over-expression experiments (not shown).Previous work has shown that Dorsal and Dif are sequestered in the cytoplasm by the IκB factor Cactus whereas Relish has its own IκB domain Toll-8 (10% heminota) and this trait was used to detect interacting components. The bristle phenotype of flies simultaneously heterozygous for either EP1412Dredd and 1Toll-8 (57% heminota), or E20Rel and 1Toll-8 (55% heminota), was significantly enhanced compared to the single heterozygotes . These results suggest a link between Toll-8 and NF-κB/Rel signalling via the Dredd pathway.Ectopic bristles were also observed in flies heterozygous for We conclude from these observations that NF-κB/Rel proteins are active in the imaginal epithelium and that the receptor Toll-8 also plays a role. The mutant phenotypes suggest a function for Dorsal, Dif and Relish in repression of the neural fate.ac-sc expression. Therefore sca-Gal4, which is expressed in all proneural clusters on the notum, was used to over-express Rel, Dorsal and Dif.Ectopic bristles in the loss-of-function mutants are generally observed close to extant ones, suggesting that they arise from the proneural clusters of Over-expression of full-length Rel at 25°C resulted in loss of bristles in the lateral notum and ectopic bristles in the medial notum . Over-exWe conclude that the NF-κB/Rel proteins can promote or repress the neural fate in a region-specific manner and that differences are apparent between the activities of the three proteins.Toll-8 in the larval wing disc. We employed MD806, a Gal4 insertion in the 5′UTR of Toll-8. Toll-8-Gal4 drives GFP reporter gene expression at very high levels in the lateral halves of the notum in discs at the third larval instar . This suggests that Toll-8 is expressed in the medial notum but that the levels there are much lower. We also used Toll-8-Gal4 to drive UAS-Flp and obtain mitotic recombination with a cuticular marker 36af and found labelled cells over the entire notum in these flies . Overall these results suggest that Toll-8 activity is likely to be higher in the lateral notum. It is noteworthy that the bristle phenotypes seen after loss or gain of function of NF-κB/Rel gene activity display differences between the medial and lateral notum.Genetic interactions suggested a role for Toll-8 in NF-κB/Rel regulation of the neural fate, so we examined the expression pattern of l instar . In situpression , see alsse flies . This alToll-8 differ in the SOP with respect to cells surrounding it. Expression of Toll-8, visualized with Toll-8-Gal4>UAS-GFP, is gradually excluded from the SOPs as they arise . Furthermore, whereas mis-expression of E(spl)m8 using the SOP-specific neur-Gal4 driver results in a complete loss of notal bristles . The significance of this, as discussed below, may lie in the fact that a further round of neurogenesis takes place some hours later, when precursors of the small bristles arise.Expression of Toll-8 is non-uniform in the disc epithelium and transcription is down regulated in the SOPs. If Toll-8 is affecting the activity of one or more NF-κB/Rel proteins, this would suggest, by extrapolation, a similar discontinuity between the levels of NF-κB/Rel proteins in the SOP compared to the cells surrounding it.Expression of sc a direct target of NF-κB/Rel signalling? The SOP enhancer of sc (sc-SOPE) sc we employed sc-SOPE-lacZ, a construct containing the native SOPE m7, a transcription factor that would bind the intact E or N boxes in n domain .sc-SOPE-lacZ staining in Bx-Gal4>UAS-Rel-VP16 flies were close to the positions of extant SOPs  were used to prime cDNA synthesis and detect transcript levels. Transcripts specific for F-κB/Rel . This reckground . Unexpece mutant . GFP traMyoD and Sox9 in C2C12 cells, an effect dependent on a heptamer motif, ACTACAG, present in the coding sequence of both genes sc, ase and GFP reveals the presence of a similar, but slightly modified motif, in which six of the seven nucleotides are conserved , whereas ectopic bristles are found on the medial notum (where Toll-8 levels are low).If NF-κB/Rel proteins both activate and repress sc SOP enhancer sc by the E(spl) proteins, targets of Notch signalling in non-SOP cells, is already known to require the activity of Groucho A second means by which NF-κB/Rel proteins could act differently in SOP and in non-SOP cells, may be the presence/absence of co-factors. It has been shown that Dorsal can be converted from an activator to a repressor by association with the co-repressor Groucho sc, ase and sens (and GFP) accumulate in Rel and Toll-8 mutants as a result of increased transcript stability. Transcript stability correlates with the presence of a six or seven nucleotide motif in the transcribed sequence of these genes. The motif is present in sc, ase and sens, but not ac the transcription of which is unaffected in Rel mutants. The motif is almost identical to the heptamer in MyoD and Sox9 that is associated with transcript stability after inhibition of NF-κB/Rel signalling in C2C12 cells sc mutant with a truncated sc transcript lacking one of the two motifs present in the coding sequence of this gene, has a phenotype similar to Rel and Toll-8 mutants and an increase in sc mRNA. Rabinow et al (1993) suggested that increased stability of the transcripts rather than increased transcription underlies this phenotype. We note the presence of the heptamer in a number of genes involved in sensory organ patterning suggesting possible regulation by NF-κB/Rel of a battery of genes in the imaginal epithelium. A similar motif is present in other vertebrate targets of NF-κB/Rel Drosophila and mammals and possibly even jellyfish. Sitcheran et al Rel and Toll-8 mutants the accumulated transcripts are not translated. This must be an effect of the mutants because ectopic expression in wild-type flies allows translation and ectopic bristle formation.Transcripts for Toll-8 expression is extinguished in the SOPs after their formation. Factors specific to the SOP presumably allow translation of the transcripts. We therefore suggest that high levels of Relish provided by Toll-8 in non-SOP cells might be required for post-transcriptional regulation of neuronal genes.Promotion of a rapid turnover of transcripts of neuronal genes presumably does not take place in the SOPs where high levels of the protein products of these genes are required. Accordingly sens and ase is restricted to SOPs where they are activated by high levels of Ac and Sc ac-sc in proneural clusters would counteract the effects of NF-κB/Rel to allow selection of SOPs. After selection of SOPs for the large sensory bristles is finished, Toll-8 expression is maintained in the epithelium, suggesting that high levels of NF-κB/Rel are still required for continued transcript turnover. Continuous buffering of neuronal gene expression presumably continues until the next round of neurogenesis that takes place after pupariation when precursors for the small bristles form. Therefore we hypothesize that NF-κB/Rel plays a subtle role in maintaining steady state levels of expression of many genes required for neural development. The maintenance of low levels of expression of neuronal genes would keep the tissue poised for neurogenesis that takes place in repeated rounds. Perhaps low levels of expression of neuronal genes are characteristic of neuro-epithelia in general.In wild-type animals expression of neuronal precursor genes such as Drosophila is as follows. The neuro-epithelium of the imaginal discs expresses neuronal genes. Prior to development of SOPs, high levels of Toll-8 maintain high levels of Rel and result in nuclear accumulation of NF-κB/Rel. Through an unknown transcriptional target(s), Relish promotes rapid turnover of neuronal transcripts by a post-transcriptional mechanism. This might be mediated by a specific sequence in the coding regions of target genes. Activation of ac and sc in proneural clusters by regulatory proteins of the notal prepattern counteracts the effects of Relish. After singling out of SOPs by Notch-mediated lateral inhibition, Toll-8 expression ceases in the SOPs. Reduced levels of signal uncover a trans-activator function for NF-κB/Rel that, synergistically with Sc, helps to maintain high levels of sc expression in the SOP, possibly through direct binding to consensus sequences in the sc SOP enhancer. The NF-κB/Rel proteins may also directly repress sc in non-SOP cells of the proneural clusters. It remains to be seen to what extent each of the three proteins participates in these two processes.Our hypothesis concerning the dual role of NF-κB/Rel in neurogenesis in 1 cn sca1/Cyo, dl4pr1cn1wxwxtbw1/CyO, Dif1 cn bw/CyO, Df(2L)J4/Cyo-GFP, Df(2L)TW119/Cyo, RelE20/TM6b , Toll-81/TM6b, DreddEP1412, sc[HwUa] and sc[HwUa]; RelE20. Strains used for NFκB/Rel misexpression were sca[537.4]Gal4>GFP, Bx[MS1096] Gal4, pnr[MD237]Gal4>GFP, Mae-UAS.6.11-dlUY2278, Mae-UAS.6.11-DifLA00958, UAS-HA-Rel and UAS-HA-VP16-Rel. lacZ reporter strains used were sc-SOPE-lacZ, sc-SOPEα3−-lacZ and neur[A101]-lacZ. Other strains used for ectopic expression were UAS-E(spl)m8, UAS-E(spl)m7ACT, UAS-ac, UAS-sc and UAS-HAase. The genotype y f36a abx>f+>Gal4; UAS Flp; Toll-8-Gal4 was used to ascertain the extent of Toll-8 expression. See FlyBase for a description of mutants (http://flybase.bio.indiana.edu/).Flies were maintained on standard cornmeal-agar medium at 18°C and Oregon-R was used as a control. Strains used were: dlUAS-constructs for ectopic expression of haemagglutin (HA)-tagged Rel were generated by standard techniques. The VP16 TA domain was PCR-amplified from a fly bearing the Ubx-VP16 fusion, and cloned in frame into pHA-Rel to generate an N-terminal HA-VP16-Rel fusion.Toll-8 by plasmid rescue and used to generate the 1Toll-8 deletion by standard P-element excision. Several P[w-] strains were established and the extents of the deletions were confirmed by PCR. Primer pair U6 (CTCAGCCACCGCCACCTCAT) and L10 (GGTGACAAGCGGAGAGCATTG) was used to determine the precise breakpoints of the R5A strain and primer pair U5 (AGCCCTCAGCAAGACGGTG) and L5 (AAGATTCCTGGGGGCCAGTAC) was used to generate a probe for in situ hybridization.The fly strain MD806 CyO-GFP (Chr II) or TM6b,Tb (Chr III) chromosomes and animals of the appropriate genotypes were selected as Non-GFP or Non-Tb individuals. Triple null animals were selected as Non-GFP, Non-Tb animals and confirmed by single-fly genomic PCR using primers specific for dorsal , Dif , Rel . The unrelated third chromosome gene delilah  was used as an independent control.All mutant chromosomes used for the scoring were placed over either 4/dl1dl (146 hemithoraces). Statistical analysis was performed using Student's T-test. Pie charts were generated using Microsoft Excel.Females grown at 18°C were examined for ectopic bristles and 200 hemithoraces were scored for each genotype, except Primary antibodies used included Anti-GFP-Alexa 488 (Molecular Probes), anti-Senseless (Bellen lab), anti-βgal 40-1a, anti-Achaete and Anti-Hindsight (DSHB). Secondary antibodies coupled to Alexa-488, Alexa-546 and Alexa-647 (Molecular Probes) were used. Fluorescence images were taken with a Leica microscope using FW4000 software. Images were processed with Adobe Photoshop.in situ hybridization using standard techniques. Full-length cDNA clones of sc and ase and a 1kb PCR-amplified fragment of sens were used as templates to generate DIG-labelled RNA probes. All samples were processed in identical fashion. The experiments were repeated at least 4 times.Samples were fixed overnight in 4% formaldehyde and then processed for sca[537.4]>GFP, UAS-ac, UAS-sc and UAS-HA-ase second-chromosome insert stocks were established individually with E20Rel on the third chromosome. Gal4>UAS crosses were set up simultaneously as the E20Gal4>UAS; Rel crosses. 10 wandering larvae were collected for each pair and processed for semi-quantitative RT-PCR. RNA was extracted using TRIzol reagent (Life Technologies) and 200ng of this RNA was used for reverse transcription using gene-specific reverse primers and SuperScript™ II RNAseH− Reverse Transcriptase (Life Technologies). Total RNA was treated with DNAse I to remove possible contaminating genomic DNA. Transcripts of the ribosomal protein RP49 were used as RNA loading controls. PCR amplification using gene-specific forward and reverse primers (25 cycles) was performed using Taq DNA polymerase (Roche) on an Eppendorf Mastercycler. Since the amplified transcripts represented a mixture of endogenous and ectopic RNA, we repeated the reverse priming with primers located in the SV40 tail of the UAS constructs. Subsequent PCR using gene-specific primers then resulted in the exclusive amplification of ectopic (UAS) transcripts.Primers used were SV401 CCGGTAGGTAGTTTGTCC, SV402 GGGGCCTTCACAAAGATC, RP49-5′ ATGACCATCCGCCCAGCATAC, RP49-3′ TTACCTCGTTCTTCTTGAGAC, Acf1 GCTTGCAGAAAGTTCTTCATG, Acr1 GTTTTTTTCAGGTCGTCCTG, Scf1 CCATGTCATCGAGTGTGC, Scr1 ACTGTGACTGCTGGACTC, Asef3 GGCACAACCAGCAGAATC, Aser3 CTTCTTGAATCCGGGAAG, Gfp1 AGGAGAAGAACTTTTCACTG, Gfp2 CCCTTGTTAATAGAATCGAGscute gene from the HwUa allele was amplified using primers Scf1 (CCATGTCATCGAGTGTGC) and Copiar1 (GTGCTGGTGTTGCAGTTG). This PCR fragment was sequenced and the exact breakpoint of the copia insertion was established.The Figure S1Toll-8. The insertion in MD806 maps 160bp upstream of the site of initiation of transcription and 640bp upstream of the translation start site and so was used to generate Toll-8 mutants by imprecise excision (A), several mutant lines were established and the extents of the deletions were confirmed by genomic PCR. Several primer pairs spanning the length of the Toll-8 gene and its upstream sequences were used to test the R5A strain and a 1.8kb U6L10 fragment normally spanning 4kb in wild-type flies was cloned and sequenced to confirm the extent of the deletion. This mutant strain was found to have a deletion of 2.24kb in the Toll-8 gene and was renamed 1Toll-8. (B), 1Toll-8 flies lack detectable transcript as judged by in situ hybridization with a U5L5 probe (C) and are predicted to be protein-null due to the absence of the usual translational start site.Recovery of a null allele of (0.29 MB TIF)Click here for additional data file.Figure S2Toll-8 and Relish mutants. The neuronal-specific reporter gene sc-SOPE, lacZ is found in a normal complement of precursors in wild type (A), 1Toll-8 (B) and E20Rel (C) mutants. sc-SOPE-lacZ contains three E-boxes, which are binding sites for bHLH proteins such as Scute and Asense. Over-expression of ase in wild-type animals leads to ectopic expression of sc-SOPE-lacZ, presumably due to generation of functional Ase protein (see sc-SOPE-lacZ (A–C), leading to the conclusion that Ase function in these discs is still confined to the SOPs. Staining with an antibody against Achaete shows a pattern of expression in the normal numbers and positions of the bristle precursors in wild type (D), 1Toll-8 (E) and E20Rel (F) mutants.The protein products of neuronal precursor genes are confined to sensory organ precursors in tein see . However(0.63 MB TIF)Click here for additional data file.Figure S3Relish.Relish transcripts are reduced in 1Toll-8 homozygotes (A), so Toll-8 may affect transcription of Relish. However, over-expression of Toll-8 in Gal4-C765>UAS-Toll-8 does not lead to a concomitant elevation in levels of Relish transcripts (B), indicating that the role of Toll-8 may be confined to maintenance of Relish transcript levels.Toll-8 is required to maintain transcription of (0.08 MB TIF)Click here for additional data file.


GC-rich and AT-rich chromatin domains display distinct chromatin conformations and are marked by distinct patterns of histone modifications, and the histone deacetylase Rpd3p is an attenuator of these differences. Base-composition varies throughout the genome and is related to organization of chromosomes in distinct domains (isochores). Isochore domains differ in gene expression levels, replication timing, levels of meiotic recombination and chromatin structure. The molecular basis for these differences is poorly understood.RPD3 activates expression of GC-rich genes more strongly than AT-rich genes. Analyses of effects of the histone deacetylase inhibitor trichostatin A, global patterns of Rpd3p binding and effects of deletion of RPD3 on histone H4 acetylation confirmed that conformation and activity of GC-rich chromatin are more sensitive to Rpd3p-mediated deacetylation than AT-rich chromatin.We have compared GC- and AT-rich isochores of yeast with respect to chromatin conformation, histone modification status and transcription. Using 3C analysis we show that, along chromosome III, GC-rich isochores have a chromatin structure that is characterized by lower chromatin interaction frequencies compared to AT-rich isochores, which may point to a more extended chromatin conformation. In addition, we find that throughout the genome, GC-rich and AT-rich genes display distinct levels of histone modifications. Interestingly, elimination of the histone deacetylase Rpd3p differentially affects conformation of GC- and AT-rich domains. Further, deletion of We find that GC-rich and AT-rich chromatin domains display distinct chromatin conformations and are marked by distinct patterns of histone modifications. We identified the histone deacetylase Rpd3p as an attenuator of these base composition-dependent differences in chromatin status. We propose that GC-rich chromatin domains tend to occur in a more active conformation and that Rpd3p activity represses this propensity throughout the genome. Chromosomes are characterized by regions that differ in base composition ,2. TheseSaccharomyces cerevisiae range in size from 5-90 kb . Values for the individual parameters k and L cannot be directly obtained from this analysis and the combined parameter [k × L-3] will be referred to as the apparent compaction factor. However, if k is known to be constant, as appears to be the case in the present study (above), variations in this combined parameter can be interpreted as differences in the value of L.This model describes chromatin in terms of three key features: flexibility, apparent circularity and level of compaction (expressed in nm/kb). The parameter -linking . FinallyS is smaller, P < 0.05). Second, the GC-rich domain appears to be in a circular conformation, with an apparent circle size of around 200 kb reflecting the presence of constraints on the chromatin path, consistent with our previous findings  was obtained for the GC-rich domain than for the AT-rich domain (P < 0.01).When interaction frequencies for the GC- and AT-rich domains, from three independent cultures were fitted to equation 1 Figure ; Table 1findings . For thek × L-3] for the GC- and AT-rich domain (Table k) or compaction (L). Since no difference in cross-linking efficiency between GC- and AT-rich domains could be detected, this analysis indicates that there is a 2.5-fold difference in the value of L-3 (average of three independent yeast cultures) and thus an approximately 1.4-fold difference in the value of L. In other words, the contour length of 1 kb of chromatin in the GC-rich isochore region is approximately 40% larger than the contour length of 1 kb of chromatin in the AT-rich isochore.The difference in the value of [in Table  could reWe next examined functional differences between GC- and AT-rich isochores by determining the relationship between base composition of genes and their transcriptional activity throughout the genome in wild-type yeast cells. First, genes were divided into categories based on the average base composition of the surrounding 4 kb region . Genes were then divided into six groups, approximately equal in size, based on regional base composition Figure . Genes let al. [et al. [Next we determined average steady-state mRNA levels of genes in each group. The transcriptional activity of each gene is known from data obtained by Bernstein et al. . Using t [et al.  reportedet al. [Histone modifications can affect the conformation of chromatin fibers and are correlated with gene expression . Givet al.  to deterWe again divided all genes into six groups based on their base composition, exactly as described above. For each group we determined the average level of each of 11 histone modifications Figure  and provrpd3Δ mutant.The histone deacetylase Rpd3p acts as a repressor of a number of specific target genes throughout the genome -39. In arpd3Δ mutant than in the wild type . We conclude that Rpd3p differentially affects the conformation of these GC-rich and AT-rich domains, which results in further exaggeration of their difference in conformation. These observations are important for two reasons. First, they reveal a previously unrecognized base-composition-sensitive effect of this histone deacetylase. Second, they suggest that Rpd3p normally acts to keep the two types of isochore domains from being even more different in conformation than they would otherwise tend to be.As discussed above, a difference in interaction frequency between GC- and AT-rich domains could result either from a difference in chromatin compaction or a difference in cross-linking efficiency, and the two possibilities can be distinguished by assessing the efficiency of restriction digestion. When such analysis was performed for rpd3Δ cells, interaction frequencies were fitted to equation 1 . Application of the restriction digestion assay described above further reveals that, as for wild-type cells, this difference is not ascribable to a differential change in efficiency of cross-linking efficiency (k) .To more fully characterize chromatin conformation in 1 Figure ; Table 1L of the chromatin fiber in the GC- and AT-rich isochores has changed in rpd3Δ cells. Specifically, in rpd3Δ cells, the value of L is 1.7-fold higher, and compaction correspondingly lower, in the GC-rich isochore compared to the AT-rich isochore.We conclude that the difference in relative compaction RPD3 on transcription throughout the yeast genome.Our results suggest that Rpd3p activity differentially affects GC-rich and AT-rich chromatin. We next tested whether this effect was also reflected in differential modulation of expression of GC- and AT-rich genes throughout the yeast genome. This question was addressed using a genome-wide dataset generated in Tsukiyama's laboratory  that desRPD3. We calculated the average change in transcription in rpd3Δ versus wild-type cells along chromosome III as a function of gene position along the chromosome. Comparison of average base composition with average global change in transcription shows that deletion of RPD3 had little effect on transcription of the central AT-rich isochore domain. In contrast, transcription in the GC-rich isochore domains was significantly more increased .First, we examined whether the genes in the relatively large 90 kb GC-rich and AT-rich isochores along chromosome III are differentially affected by deletion of RPD3 has a general differential effect on expression levels of GC- and AT-rich genes throughout the genome. We calculated the effect of deletion of RPD3 on expression of the same six groups of genes with different base compositions as described above  = 0.08) but that the magnitude of this effect varies in proportion to GC content. More GC-rich genes are significantly more up-regulated in rpd3Δ cells than more AT-rich genes  on expression of GC- and AT-rich genes in wild-type cells. Bernstein and co-workers [To determine whether the base composition-dependent effect of -workers  have ana-workers .HDA1. Using an expression dataset obtained by Bernstein et al. [HDA1 does not result in base composition-dependent changes in gene expression  and, therefore, have similar protein densities. Histones are the most abundant chromatin proteins, and thus our results suggest that GC-rich and AT-rich regions have similar levels of histone binding. Consistent with this hypothesis, Nagy et al.  found noan cells , similaret al. [The base composition-dependent effect of Rpd3p activity affects expression of genes independent of their steady state level of expression. Genes with the same steady state expression level in wild type are more strongly repressed by Rpd3p when they are GC-rich than when they are AT-rich. This implies that GC-rich genes are intrinsically more active, consistent with higher steady state levels of H3 and H4 acetylation, as we observed here, and that Rpd3p acts as an attenuator of these genes. Based on these considerations, we propose that chromatin status is regulated through a homeostatic and highly dynamic mechanism involving counteracting activating and repressing activities. A similar model of dynamic global acetylation and deacetylation has been proposed by Katan-Khaykovich and Struhl  and by Cet al. .The findings described here uncover novel GC content-dependent differences in chromatin conformation, regulation, histone modification status and transcription. These findings are significant from four perspectives. First, they provide new information about the nature and functional significance of base composition variation along chromosomes. Second, they show that GC-rich chromatin is most acetylated and is attenuated by the histone deacetylase Rpd3p, thus providing a first clue as to how regions with different base-compositions are differentially regulated. Third, they show that the roles of Rpd3p can be separated into independent components, with a GC-content-dependent global activity layered on top of its targeted and gene specific effects. Finally, they suggest that functional and structural differences between GC- and AT-rich regions are determined in part by balances between activating and repressing activities.MATa, ho::LYS2, lys2, ura3, nuc1::LEU2. The genotype of the rpd3Δ strain (JDY172) is MATa, ho::LYS2, lys2, ura3, nuc1::LEU2, rpd3::KanMX4. The RPD3 gene was deleted using a standard PCR based gene disruption strategy [Yeast strains were derived from SK1. The genotype of the wild-type strain (NKY2997) is: strategy .rpd3Δ cultures. Data from wild-type cells and rpd3Δ cells was normalized so that the average Log of the fold difference in interaction frequencies in rpd3Δ cells compared to wild-type cells for loci in the AT-rich domain of chromosome III was zero.Nuclei were isolated from G1-arrested cells and 3C analysis was performed as described previously . A contrThe chromosomal position of each open reading frame was defined as its 5' start site. Base composition was determined using EMBOSS Isochore  with a wrpd3Δ mutant compared to wild type was obtained from a dataset generated by Fazzio and co-workers [RPD3 along chromosomes III (Figure For each open reading frame the fold change in transcription in an -workers  and log-I Figure . A windorpd3Δ cells was determined using data obtained by Bernstein et al. [et al. [Steady-state transcription levels in exponentially growing wild-type cells were obtained by Bernstein and co-workers . Histonen et al. . Rpd3p bn et al. ,42. Data [et al. . Data werpd3Δ cells. Additional data file rpd3Δ cells. Additional data file The following additional data are available with the online version of this paper. Additional data file rpd3Δ cells.Methodology used to quantify cross-linking efficiency in GC- and AT-rich domains in wild-type and Click here for filerpd3Δ cells. For details see Additional data file 1.Relationship between the level of cross-linking and the efficiency with which chromatin is digested and the digestion efficiencies of GC- and AT-rich chromatin in wild-type and Click here for fileLevel of seven histone modifications in relation to base composition.Click here for file