The establishment and maintenance of Epstein-Barr Virus (EBV) latent infection requires distinct viral gene expression programs. These gene expression programs, termed latency types, are determined largely by promoter selection, and controlled through the interplay between cell-type specific transcription factors, chromatin structure, and epigenetic modifications. We used a genome-wide chromatin-immunoprecipitation (ChIP) assay to identify epigenetic modifications that correlate with different latency types. We found that the chromatin insulator protein CTCF binds at several key regulatory nodes in the EBV genome and may compartmentalize epigenetic modifications across the viral genome. Highly enriched CTCF binding sites were identified at the promoter regions upstream of Cp, Wp, EBERs, and Qp. Since Qp is essential for long-term maintenance of viral genomes in type I latency and epithelial cell infections, we focused on the role of CTCF in regulating Qp. Purified CTCF bound ∼40 bp upstream of the EBNA1 binding sites located at +10 bp relative to the transcriptional initiation site at Qp. Mutagenesis of the CTCF binding site in EBV bacmids resulted in a decrease in the recovery of stable hygromycin-resistant episomes in 293 cells. EBV lacking the Qp CTCF site showed a decrease in Qp transcription initiation and a corresponding increase in Cp and Fp promoter utilization at 8 weeks post-transfection. However, by 16 weeks post-transfection, bacmids lacking CTCF sites had no detectable Qp transcription and showed high levels of histone H3 K9 methylation and CpG DNA methylation at the Qp initiation site. These findings provide direct genetic evidence that CTCF functions as a chromatin insulator that prevents the promiscuous transcription of surrounding genes and blocks the epigenetic silencing of an essential promoter, Qp, during EBV latent infection. Epstein-Barr Virus (EBV) establishes a latent infection that is associated with several lymphoid and epithelial cell malignancies. The latent virus persists as a circular minichromosome in the nucleus of infected cells. Epigenetic modifications of the viral DNA and chromatin are known to control viral gene expression and genome stability, but the nature and mechanisms of these epigenetic marks are not known. Here, we use viral genome-wide analysis to characterize patterns of DNA and histone methylation, and how these are organized by the chromatin boundary factor CTCF. Mutation of one such CTCF site at the EBV Q promoter results in aberrant accumulation of DNA CpG methylation and histone H3 K9 trimethylation, and the consequent silencing of Qp transcription. We conclude that CTCF chromatin insulator function is required for the epigenetic programming and stable maintenance of latent viral infection. Epstein-Barr Virus (EBV) is a human gamma herpesvirus that establishes latent infection in more than 90% of the adult population world-wide EBV latency gene expression programs have been categorized into four different types based primarily on the differential expression of the EBNA and LMP gene transcripts Latency type gene expression is regulated largely through differential promoter utilization The epigenetic control of EBV promoter utilization and latency type is evident in the differential pattern of DNA methylation between latency types, and by the ability of DNA methylation inhibitors to stimulate type III gene transcription from type I latently infected cells Cellular factors that regulate communication between promoters and enhancers, have also been implicated in the organization of chromatin structure An EBV genome-wide real-time PCR array was used to compare the patterns of several epigenetic marks between type I and type III latent virus genomes using the chromatin immunoprecipitation (ChIP) assay . For theA more detailed examination of the regions surrounding the major latency promoter elements reveals other features relevant to epigenetic regulation . While mCTCF binds consistently at Qp in both cell types . In typehttp://insulatordb.uthsc.edu). At least two candidate CTCF sites were identified at positions 43739 and 50082   . GALK incrf.gov) , PCR acrcrf.gov)  and sequcrf.gov) . As expecrf.gov) . We founWhile early passage cell pools showed little difference in GFP and EBNA1 expression between ΔCTCF and Wt rescue genomes, we observed a marked loss of GFP expression in the ΔCTCF relative to Wt rescue genomes after longer passages in culture . At 4 anThe loss of GFP expression and episome stability in 293 cell pools could be due to a deregulation of viral gene expression. Others have shown that EBV establishes a restricted pattern of latency gene expression in 293 cells, resembling a type I program with stable Qp utilization for EBNA1 expression To better understand the failure of ΔCTCF bacmids to sustain EBNA1 mRNA expression, we investigated the promoter utilization at 4, 8, and 16 weeks after transfection . EBNA1 m1C2W1W2), Wp (W0W1W2), or within the lytic transcripts of BFLF1 1C2W1W2, W0W1W2 and FQUK transcripts were elevated in ΔCTCF relative to Wt rescue, consistent with RT-PCR data showing elevated levels of Wp/Cp and Fp utilization in bacmid lacking the CTCF binding site. UK transcripts were similar in ΔCTCF and Wt rescue, consistent with observations from quantitative RT-PCR , Sav I and Sav III cells were cultured in suspension in RPMI 1640 medium supplemented with 10% fetal bovine serum and antibiotics in a 5% CO2 incubator at 37°C. Mutu-LCL were established by primary infection of peripheral blood mononuclear cells (PBMCs) with EBV virions derived from stimulated Mutu I cells.D98/HR1, HeLa and 293 cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum and antibiotics in a 5% COhttp://recombineering.ncifcrf.gov/) ggtcgctagatggcgcgggtgagg was mutated by single substitutions to ggtTgTtaTatTTTAcgggtgagg. The ΔEBNA1 binding site at 50142 gaaaag[gcgggatagcgtgcgctaccggatggcgggtaatacatgct]atccttaca was mutated by deletion and substitution with gaaag[tgcttgaaaaggcgcgg]atccttaca. Plasmid containing the Qp region (49712–50250) was subcloned by PCR into pBKSII using Asp718 and HinDIII sites. Recombinant human CTCF protein was expressed as an N-terninal hexa-histidine tagged fusion protein from a baculovirus expression virus in sf9 cells, as described previouslyEBV bacmid was a generous gift of Dr. H. –J. Delecluse ChIP assay followed the protocol provided by Upstate Biotechnology, Inc., with minor modifications as previously described 4, and 10,000 cpm of 32P-labeled DNA probe (∼0.1 ng). Reaction mixtures were incubated for 30 min at 25°C, electrophoresed in a 5% nondenaturing, polyacrylamide gel at 110 V, and visualized by PhosphorImager.EMSA assays with CTCF were described previously 32P]dATP  and 2 U of Klenow fragment (Roche) for 30 min at 25°C. Purified proteins were incubated in a reaction mixture containing 1XPBS, 5 mM MgCl2, 0.1 mM ZnSO4, 1 mM dithiothreitol, 0.1% NP-40, 10% glycerol, 1 µg bovine serum albumin, 0.4 µg poly(dI–dC), and 10,000 cpm of 32P-labeled probe. The protected probe was digested with different dilutions of DNase I (Sigma) and purified by phenol-chloroform extraction following proteinase K digestion. The DNA samples were then electrophoresed on a 7% denaturing, polyacrylamide sequencing gel at 33 mA and visualized by PhosphorImager.DNase I footprinting was performed as described previously 6 cells using Qiagen RNA extraction Kit according to manufacturer's protocol (Qiagen). After the extraction the RNA was incubated with 2 U DNAse I at 37°C for 30 minutes, following by the inactivation of the enzyme at 65°C for 10 minutes. The RNA was quantified and 2 µg of RNA was reverse transcribed using Super Script II Reverse Transcriptase from Invitrogen. 50 ng of cDNA was then analyzed by real time or conventional PCR. Primer sequences used for real time and conventional PCR are listened in tables 2 and 3  following by incubation with Proteinase K for 2 h at 50°C. The DNA was extracted by phenol-chloroform extraction and precipitated by ethanol. Titration of 6.6×10−7–6.6×10−12 g of Namalwa DNA were used to obtain a calibration curve using primers for EBV genome (48779 – 48834) and β-Actin. 6.6×10−9 g of DNA from EBV positive cells were then analyzed by real time PCR for EBV genome copy quantification. Primer sequences used for real time PCR were listed in For EBV genome quantification Namalwa titration was used, assuming that Namalwa cell lines contain 2 copies of EBV genome and each cell contain 6.6×106 cells were resuspended in Lysis Buffer  plus 0.7 µg/µl Proteinase K and incubated at 50°C overnight. The DNA was extracted by twice phenol-chloroform extraction followed by ethanol precipitation. The DNA was resuspended in 300 µl of TE buffer containing 20 µg/ml RNAse A and incubated at 37°C for 1 hr followed by DNA sonication to between 750 – 500 bp and DNA purification by phenol-chloroform extraction and ethanol precipitation. 8 µg of DNA were resuspended in Immunoprecipitation Buffer, IP buffer, , denaturated at 95°C for 10 min and incubated with 5 µg of 5-methylCytodine antibody (Abcam) or 5 µg mouse IgG (Upstate), overnight at 4°C. The immunocomplexes were precipited by adding 50 µl of Protein G Dynabeads (Invitrogen) for 2 hr at 4°C. The beads were collected by a magnetic rack and washed twice for 10 min with 1 ml of IP buffer. The DNA was eluted by incubating the beads with 250 µl of Proteinase digestion buffer  at 50°C for 3 hr with shacking. The DNA was then purified and then analyzed by real time PCR. Primer sequences are listed in 10×10Viral episomal DNA was extracted using the Hirt lysis method Table S1CTCF binding site identified by ChIP assay(0.04 MB DOC)Click here for additional data file.Table S2Primer sequences for Real Time PCR(0.06 MB DOC)Click here for additional data file.Table S3Primer sequences for RT-PCR(0.03 MB DOC)Click here for additional data file.Table S4Real time Primer sequences for Qp region(0.04 MB DOC)Click here for additional data file.Figure S1Histone modifications and copy number of type I and type III EBV latency. A) Histone modification analysis of Qp region in different latency types. ChIP assay was performed with Sav I (type I) or Sav III (type III) using antibodies for H3me2K4, H3me3K9, CTCF and EBNA1 ChIP DNA was assayed by real time PCR using primer sets for a region from −5.8 kb to +2.7 kb of Qp. B) ChIP assay as in A but using IgG as control for non-specific binding. C) EBV genome copy number in EBV positive cells  was quantified by real time PCR.(5.07 MB TIF)Click here for additional data file.Figure S2EBNA2 RNA expression and promoter utilization in different EBV cell lines. Real time PCR was used to validate the pattern of gene expression program I or III in EBV positive cells , by measuring Cp and Qp promoter utilization and EBNA2 expression assays, as indicated.(0.70 MB TIF)Click here for additional data file.Figure S3Lytic replication was assayed in Rescue or ΔCTCF 293 cells pool. Virus reactivation was evaluated by real time PCR measuring Zta mRNA expression in Rescue or ΔCTCF 293 cells pool. Sodium butyrate (NaB) and phorbol ester (TPA) was used to activate lytic replication from Wt rescue or ΔCTCF 293 cells pools as a positive control.(2.85 MB TIF)Click here for additional data file.Figure S41C2W1W2 (Cp initiation), W0W1W2 (Wp initiation), BFLF1 (lytic gene adjacent to Qp), UK (EBNA1 mRNA in both type I and type III), and control cellular GAPDH.RNA expression and promoter utilization in GalK-Qp mutated bacmids. A) Quantitative RT-PCR was used to measure the abundance of EBNA2, EBNA3A and EBNA3C mRNA relative to bacmid GFP for Wt rescue or GalK-Qp bacmids in 293 cell pools. B) Same as in A, except mRNA for EBNA1-transcripts were measured relative to GFP. C) RT-PCR was measured for Wt rescue or GalK-Qp bacmids in 293 cell pools, as well as for type I (Mutu I) or type III (Mutu-LCL) controls. RNA was analyzed for initiation the junction specific transcripts QUK (Qp initiation), C(4.37 MB TIF)Click here for additional data file.Figure S5Change in Epigenetic patterns in Cp and Wp in mutated bacmids. A) The epigenetic pattern of Cp and Wp in Wt rescue and ΔCTCF bacmids in 293 cell pools was analyzed by MeDIp assay (A), H3me2K4 (B) and H3me3K9 (C) ChIp assay at 8 weeks (top panel) or 16 weeks (lower panel) after transfection.(4.54 MB TIF)Click here for additional data file.Figure S6Control ChIP for (3.04 MB TIF)Click here for additional data file.Figure S7Normalization of RNA for RT-PCR. A) GFP mRNA was normalized relative to cellular Actin at 8 and 16 weeks after transfection into 293 cells for Wt rescue (black) and ΔCTCF (red) bacmids. B) GFP mRNA was normalized relative to the DNA copy number for each bacmid with same samples as shown in panel A.(0.14 MB TIF)Click here for additional data file.


Drosophila boundary element–associated factor (BEAF) subfamily. We identify a dual-core BEAF binding signature at 1,720 sites genome-wide, defined by five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers. Dual-cores are tightly linked to hundreds of genes highly enriched in cell-cycle and chromosome organization/segregation annotations. siRNA depletion of BEAF from cells leads to cell-cycle and chromosome segregation defects. Quantitative RT-PCR analyses in BEAF-depleted cells show that BEAF controls the expression of dual core–associated genes, including key cell-cycle and chromosome segregation regulators. beaf mutants that impair its insulating function by preventing proper interactions of BEAF complexes with the dual-cores produce similar effects in embryos. Chromatin immunoprecipitations show that BEAF regulates transcriptional activity by restricting the deposition of methylated histone H3K9 marks in dual-cores. Our results reveal a novel role for BEAF chromatin dual-cores in regulating a distinct set of genes involved in chromosome organization/segregation and the cell cycle.Chromatin insulators/boundary elements share the ability to insulate a transgene from its chromosomal context by blocking promiscuous enhancer–promoter interactions and heterochromatin spreading. Several insulating factors target different DNA consensus sequences, defining distinct subfamilies of insulators. Whether each of these families and factors might possess unique cellular functions is of particular interest. Here, we combined chromatin immunoprecipitations and computational approaches to break down the binding signature of the  Drosophila, we have combined computational and experimental approaches to address the function of the boundary element-associated factor (BEAF) subfamily of insulators. We identify hundreds of BEAF dual-cores that are defined by a particular arrangement of DNA sequence motifs bracketing nucleosome binding sequences, and that mark the genomic BEAF binding sites. BEAF dual-cores are close to hundreds of genes that regulate chromosome organization/segregation and the cell cycle. Since BEAF acts by restricting the deposition of repressing epigenetic histone marks, which affects the accessibility of chromatin, its depletion affects the expression of cell-cycle genes. Our data reveal a new role for BEAF in regulating the cell cycle through its binding to highly conserved chromatin dual-cores.The genome of eukaryotes is packaged in chromatin, which consists of DNA, histones, and accessory proteins. This leads to a general repression of genes, particularly for those exposed to mostly condensed, heterochromatin regions. DNA sequences called chromatin insulators/boundary elements are able to insulate a gene from its chromosomal context by blocking promiscuous heterochromatin spreading. No common feature has been identified among the insulators/boundary elements known so far. Rather, distinct subfamilies of insulators harbor different DNA consensus sequences targeted by different DNA-binding factors, which confer their insulating activity. Determining whether distinct subfamilies possess distinct cellular functions is important for understanding genome regulation. Here, using  cis-regulatory elements that regulate the accessibility of promoters of genes controlling chromosome organization/segregation and the cell cycle.Chromatin Dual-Cores define new potent nucleosome-associated Chromatin insulators/boundary elements (BEs) ,2 are deDrosophila, insulating factors include dCTCF [No common signature and/or mechanism of action has been identified among characterized insulators/boundary elements . Rather,de dCTCF ,15, Zw5 de dCTCF , boundarde dCTCF , and thede dCTCF ,19, whicde dCTCF –22. Whetyeast [BEAF blocks both enhancer–promoter communication ,23–25 anyeast , confirmdual-core elements genome-wide that share an unusual organization conserved over 600 bp. The dual-core signature consists of five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers. BEAF dual-cores juxtapose to hundreds of genes highly enriched in gene annotations regulating chromosome organization/segregation and the cell cycle. Accordingly, BEAF depletion leads to cell-cycle and chromosome segregation defects. Quantitative RT-PCR analyses further show that dual-cores regulate the expression of key cell-cycle genes including cdk7 and mei-S332. These results are also reproduced in embryos expressing truncated beaf mutants, which abolish the proper targeting of BEAF to dual-cores and its insulating activity. Chromatin immunoprecipitation analyses show that BEAF acts by restricting the deposition of methylated H3K9 marks in dual-cores. Our data reveal a new role for BEAF in regulating chromosome organization/segregation and the cell cycle through its binding to highly conserved chromatin dual-cores.Here we have combined computational and experimental approaches to address the function of BEAF binding sites in vivo. We have identified ≈1,720 BEAF d ∼ 10–25 pM) BEAF in vitro binding sites, which we call single elements. A computational scan of the Drosophila genome revealed thousands of single elements, yet immunostaining analysis demonstrated that they were not good predictors for BEAF binding in vivo. For example, Chromosome 4 was found to contain hundreds of single elements, yet immunodetection analysis showed only three major BEAF signals on this chromosome . Moreover, 1,226 additional “dual-core–like” elements have a second cluster of two (instead of three) CGATAs. These elements include all characterized BEAF insulators whose activity involves a second, lower-affinity CGATA cluster (Kd ∼ 400–600 pM) where BEAF binding is abolished when the first high-affinity cluster is mutated [The DNA-binding activity of BEAF has been well-characterized in vitro ,20,23,24romosome A. Interescs' and other characterized BEAF insulators, these spacers were found to be relatively AT-rich [Detailed analysis by alignment of all 1,720 dual-core and dual-core–like elements showed a highly organized distribution of their 12,058 CGATAs, which preferentially segregate into two clusters separated by spacers of approximately 200 bp B. For sc AT-rich ,26. Scanactin promoter region, which contains six unclustered CGATA motifs, was not bound by BEAF . We note that computer analysis occasionally retrieved minor peaks present in the shoulder of the major BEAF peaks  and ChIP-on-chip D and 2. p-value = 6.7e-119)  (.7e-119) A, and mop-value < 1e-6; beaf mutants [Strikingly, genes containing a dual-core near their promoter were statistically enriched in gene-class ontology (GO) groups that include the cell cycle, chromosome organization/segregation, apoptosis, and sexual reproduction  where BEAF does not bind . Accordingly, Western blot analysis showed that Cdk7 and Mei-S332 protein levels increased under these conditions , similar to what is found by depleting DREF. Strikingly, BEAF depletion further impaired the expression of cdk7 by approximately 5-fold  compared to the expression of the identical construct in control cells . We conclude that, although BEAF regulates DREF-mediated activation, it additionally positively regulates the expression of cdk7, as found for other genes associated with a dual-core lacking a DREF consensus. In support of this conclusion, we obtained a similar result for snf, which is transcribed in opposite orientation relative to cdk7  [scs′–scs pair of insulators [beaf gene itself, called BEAF-32A [cis-regulatory element that regulates the expression of nearby genes.Results of our in silico analysis reveal ∼1,720 BEAF dual-cores in the nization E. Genomenization , yet thinization . Furthered data) . Since tsulators . Other pBEAF-32A , which dBEAF-32A . Taken tmei-S332 and cdk7 within the appropriate window for cell-cycle progression [Drosophila embryos during early development shows that the 545 genes associated with dual-cores are positively correlated with beaf expression . This strict correlation further indicates that BEAF has a global positive role on gene expression genome-wide, and similar analyses did not reveal any significant correlation change between genes whose TSS is closely juxtaposed (<100 bp) to dual-cores, including snf or cdk7 (cdk7 and mei-S332 are enriched for positively correlated genes (see our database for a detailed list). Taken together, our results show that BEAF could play an important role in chromosome organization during the cell cycle through a regulated switch involving the BEAF–DREF competition: According to such a mechanism, BEAF would restrict the deposition of H3K9me3, allowing dual-core–associated genes to remain in a potentially active state, while controlling the time of activation of cell-cycle GOs by DREF. Accordingly, BEAF depletion leads to down-regulation of genes associated with a dual-core lacking a DREF element , but to increased expression of CG32676, mei-S332, cdk7, CG10944, and ser, which are under the control of DREF-associated dual-cores  [Drosophila [It is intriguing that the spacers of dual-cores are well-conserved. One possibility is that they may be preferentially bound by a nucleosome, as recently shown for CTCF insulators . Supportscs′) ,24,26 (Fscs′) –44, as iscs′) ,13,42,43Our model whereby dual-cores regulate the deposition of specific epigenetic marks is in agreement with the activity of other known insulators ,7,9–11. http://www.sfu.ca/~eemberly/insulator/. Additional information, including DNA sequences of single elements, dual-cores or dual-core–like elements, and their position relative to genes or other genomic features (GOs) can be directly retrieved from our Web site.All genome-wide predictions and analyses are available on our Web site: like element as a single BEAF element (three CGATAs) associated with a second nearby (<800 bp) cluster of two CGATA sites within a 100-bp window. 1,226 BEAF elements fit into this classification, including all previously identified dual-cores ). The position of each CGATA site within a dual-core sequence was analyzed relative to the position of the rightmost site of the first BEAF single element. In x-axis) of all the CGATA locations in the first BEAF single element of the dual-core. This removes any ambiguity in defining the starting position of the sequence, allowing more precise mapping of dual-cores with respect to gene promoters.Each single BEAF element that was not a part of a dual-core element was analyzed for the presence of a “dual-core–like” signature. We define single elements as consisting of three CGATAs within 200 bp, and a dual-core–p-value was found to reach a flat minima for 600 bp < L < 3,000 bp. For larger L values, the predictions decreased in significance, eventually becoming no more significant than chance. There are 1,720 dual-cores, L = 800 bp with a p-value of 1e-9, in the sequenced Drosophila melanogaster genome.We predicted dual-cores by pairing together the genome-wide set of 7,045 single BEAF elements that were separated by a spacer <L bp. The statistical significance of the number of predicted dual-cores as a function of spacer length L was assessed by comparing it to the expected number for randomly spaced elements. The p-value was found to decrease in statistical significance, yet 850 dual-cores reside within 2,000 bp of a promoter. Additional dual-cores are found close to genes or groups of genes (see our database).The statistical significance of the number of dual-cores within +/− d bp of a promoter was assessed by comparing it to the number expected for randomly placed elements. Out of 1,720 dual-core elements, 545 fall within +/− 500 bp of a promoter. Beyond this distance, the p-value for N can then be evaluated using the expected number E as a function of W and DC. We find W = 10 kb and DC = 2 to yield the statistically most significant BEAF dual-core distribution in pairs .In order to analyze the distribution of dual-cores, we calculated the statistical significance for a minimum number of dual-cores, 2, 3,…x dual-cores (DC) to be found along 5, 10, …100 kbp of DNA (W). For a given W and DC, we predicted N, the frequency of dual-cores for a certain DNA length. To assess the significance of N, we compared it to the number of randomly distributed elements for the same DNA length. If the probability of a random dual-core element to occur within a window of size W is p, then the probability that there are ≥DC elements in W is P = B, where B is the binomial distribution. The expected number of domain predictions for these random elements is then E = Nwin(W)*P, where Nwin(W) is the number of non-overlapping windows of size W in the entire genome. The p-value = B, where x is the number of genes within the given GO class in a set of N predicted genes, and p is the probability of that GO class in the entire annotation. See our database for a complete listing of all GO analyses of positively correlated genes with or without BEAF dual-cores or DREF elements in their promoters.The statistical significance of a GO class was assessed using the binomial distribution, Drosophila gene expression data ). For a given pair of genes, we calculated the Pearson correlation coefficient between their respective expression profiles. We then calculated the correlation coefficient between a given set of genes and a given reference gene. To test whether two sets of genes had statistically different correlation coefficient profiles, we used the Kolmogorov-Smirnov test, which assigns a p-value to the likelihood that two samples of a continuous random variable come from the same parent distribution.Genome-wide ion data  coveringbeaf siRNA-treated cells. Equivalent amounts of chromatin samples were sonicated using a Diagenode Bioruptor and immunoprecipitated with 4 μl of anti-H3K9me3 (Abcam). Precipitated DNA was analyzed by real-time PCR in parallel with genomic DNA using a Roche Light Cycler and a Light Cycler FastStart DNA Master SYBR green kit. The amplified DNA fragments (<250 bp) cover regions corresponding to the indicated elements (Drosophila melanogaster genome sequence (Flybase release 4.3) from Nimblegen Systems . To calculate whether the levels of enrichment are statistically significant for each array, a normal distribution was calculated, with the assumption that the mode and median absolute deviation of the normalized log2 ratios are the average and the standard deviation of the normal distribution, respectively. Assuming that the normal distribution covers the entire background noise , a p-value was calculated for each oligonucleotide signal. For the two replicate samples of each profile, each pair of probe p-values were then combined using a Chi Square law with 4 degrees of freedom. Finally, correction for multiple testing [p-values. Only oligonucleotides with final p-values (for combined replicates) < 1E-04 were considered to be significantly enriched for the signal.Chromatin immunoprecipitation (ChIP) was done according to the Upstate protocol using control or elements  and 7. Celements  or not  supplemented with 10% Fetal Bovine Serum  and 1% penicillin/streptomycin . Cells were diluted to a final concentration of 1 × 106 cells/ml in SDM without FBS, and 400 μl of 2 μM beaf32, dref or cdk7 double-stranded RNAs (dsRNA) were added directly to 10 ml of cells which were then plated on 75-cm2 T-flasks (Sarstedt), immediately followed by vigorous agitation. dsRNAs were synthesized using full-length cDNAs of the above genes as templates. Primers consisted of a complementary template portion, a floating end with a T7 promoter and an EcoR1 site located at the other end. 5 μg of DNA template were transcribed for 2 hours at 37 °C in the presence of 0.5 mM rNTPs, 10 mM DTT, 120 units RNAse inhibitor, 60 units T7 polymerase in its 1× buffer in a 100 μl final volume. cDNA degradation was performed for 30 to 40 minutes at 37 °C in the presence of 4 units RQDNase in a 400 μl final volume of the recommended buffer. dsDNAs were then extracted with phenol/chloroform, ethanol-precipitated, and solubilized in 20 μl TE, pH 7.5. The resulting sequences were checked for potential off-target effects by performing searches with dsCheck [http://dsCheck.RNAi.jp/). Treated cells were incubated for 2 hours at 25°C, followed by addition of 20 ml of SDM containing FBS, and cells were incubated for an additional 5 days. Depletion of beaf32 mRNA was assayed by RT-PCR at 1, 3, or 5 days after treatment. Cells were grown for 5–6 days, and samples were recovered for total RNA, immunostaining, or immunoblotting analysis. FACS analyses were performed after resuspending control or BEAF-depleted cells and staining their DNA with propidium iodide. Analysis of gene expression was performed by quantitative RT-PCR on cDNAs prepared by RT-PCR from BEAF-depleted or control cells (+5–6 days), untreated or treated with AA (5 μM) for 24 hours. Each measurement was reproduced three times and in two independent RNA extraction experiments. For gene expression analysis, cDNAs prepared from control or BEAF-depleted cells were quantified in parallel with genomic DNA by RT-PCR using a Qiagen Light Cycler. Transfections of plasmids were performed using Lipofectamine (Invitrogen) for 2 hours according to the manufacturer's instructions, 48 hours before RNA purification. Measurements of gene expression for the transfected (wild-type or mutant) constructs were performed using primers that specifically amplify cDNAs from the tags introduced at the 5′ and 3′ borders  of the tags. For other analyses, two primer pairs were used alone or in combination to confirm the specific increase/decrease in gene expression, using actin as a control. For quantitative RT-PCR analysis in embryos, males with the BID transgene on Chromosome 2  were crossed with virgin females harboring an embryonic da-GAL4 driver (daughterless) on Chromosome 3. The corresponding measurements were compared to those from embryos expressing da-GAL4 alone or from BID2B embryos without a da-GAL4 driver.For siRNA treatments, exponentially growing  dsCheck   mutant sequence is TAgCGATA and disrupts DREF binding but preserves the CGATA consensus of BEAF. The BEAF site mutant was produced by mutagenesis of two of the CGATA consensus in one cluster of the dual-core, using the ttATA mismatches critical for BEAF binding [For mutagenesis of Dcore38_D, a genomic DNA fragment harboring the first exons of  binding ,23–25.>10 Mbp for Chromosome 2 and X chromosome, showing striking correspondence . For mapping of nucleases-sensitive sites  before analysis by Southern blotting. Western blotting was performed as previously described [Immunostaining analyses were performed using affinity-purified mouse or rabbit anti-BEAF-32B (1:100) as previously described ,49, usinve sites , freshlyescribed ,20,23,24escribed ,24 usingFigure S1 Plots showing the distribution of all 12,058 CGATA motifs from dual-cores (A) and the locations of their AT-rich spacers (B) as in (C) CGATA motifs in the second cluster are enriched near the border of the spacer (+200–300 bp), while fewer localize at larger distances.(89 KB PDF)Click here for additional data file.Figure S2Immunoblotting experiment showing the protein levels of BEAF, MEI-S332, and CDK7 compared to loading controls , after siRNA-mediated depletion of BEAF or control treatment. 1.0, 3.0: standard, or 3-fold excess protein loading, respectively.(43 KB PDF)Click here for additional data file.Figure S3eye locus from ChIP-on-chip data. Note that promoter regions often fit into discrete H3K9me3 peaks distinct from the major H3K9me3 peaks of repressed loci  that are also enriched for the H3 methylK27 mark (see text).(C) Profile of H3K9me3 and position of BEAF Dcores on the X chromosome corresponding to the Xdcore_38D region  or to the (156 KB PDF)Click here for additional data file.Figure S4The figure shows one of the exceptions for a region where some BEAF binding is detected (graph in green) by genome-wide ChIP-on-chip analysis  yet which is not included in our database of dual-cores . This region was not scored in the dual-core database because the second CGATA in the first cluster is 103 bp away (‘out') instead of the defined window of 100 bp. TSSs and primary transcript are depicted on the top graphs .(474 KB TIF)Click here for additional data file.Figure S5The panel shows an alignment of ChIP-on-chip analysis (graphs in green) using anti-BEAF antibodies that recognize the BEAF-32A splicing variant (‘+32A') or not (−32A). The red bars mark the position of significant peaks over the same region of the X chromosome  as shown in (1.4 MB TIF)Click here for additional data file.Figure S6p-Values for gene annotations (GOs) of BEAF dual-cores-only  versus dual-cores containing additional TATCGATA consensus sites for DREF  [p-values is shown for each independent GO category and highlights a greater enrichment for BEAF dual-cores–only sites in chromosome organization (left) and for dual-cores–DREF sites in cell-cycle and apoptosis (right). DREF competes with BEAF for binding to a nested consensus sequence [beaf and dref [s-DREF”) . The ratsequence  present and dref ,50 (see (124 KB PDF)Click here for additional data file.Figure S7p-value of 0.004 according to the Kolmogorov-Smirnov test) than BEAF, which has a more global positive effect on gene expression .(A) BEAF dual-cores have a global positive impact on transcription. Distribution of correlation coefficients between the expression profile of genes with (red) or without (black) BEAF dual-cores (i) in their promoters see . “+” andsnf and cdk7 (TSS corresponds to the first colored bp).(B) Distribution of the BEAF  and DREF  motifs in the Dual-core 38_D with respect to (309 KB PDF)Click here for additional data file.Figure S8cdk7 and mei-S332 promoter regions (A) as well as in >20 cell-cycle regulatory genes (B) (see our Web site for a list). Predicted NPSs are indicated by purple boxes below dual-cores (A) or as an overlay of predicted NPSs (B). The relative position of nuclease-resistant cores is indicated ). These predictions fit with the positions of AT-rich dual-core spacers  Relative positioning of CGATA BEAF consensus binding motifs and the position of putative NPSs predicted by submitting dual-core sequences to available databases ,43 in thcers see D.PvuII +NotI or EcoRI) which cuts into the first CGATA cluster or 50 bp 3′ of the second CGATA cluster, respectively (see dotted lines below the autoradiogram). The dual-core spacer fits into a nuclease-resistant core region bracketed by hypersensitive sites. Note that these features are not found in the naked DNA control, where genomic DNA was first purified before MNase digestion.(C) Mapping of the accessibility of naked DNA control (top photograph) and of chromatin by nuclease digestion of nuclei Click here for additional data file.Table S1p-value is given in the fifth column. GO terms have been binned into larger categories. Low-scoring GO classes underrepresented in the set of dual-core target genes are shown at the bottom. See our database for a complete listing and additional GO analysis.GO terms for 1,720 BEAF dual-core target genes, which contain a dual-core within +/− 1,000 bp of their promoter.671 dual-core elements hit one promoter in the genome. The second column gives the number of annotated genes in that GO class, the third column gives the number of genes in dual-core/promoter sets in that GO class, the fourth column shows the expected number of genes in the predicted set given the observed class frequency. The corresponding (25 KB DOC)Click here for additional data file.


Plasmodium falciparum revealing unique binding preferences for the majority of these DNA binding proteins. In addition to high affinity primary motif interactions, we also observe interactions with secondary motifs. The ability of a number of ApiAP2 proteins to bind multiple, distinct motifs significantly increases the potential complexity of the transcriptional regulatory networks governed by the ApiAP2 family. Using these newly identified sequence motifs, we infer the trans-factors associated with previously reported plasmodial cis-elements and provide evidence that ApiAP2 proteins modulate key regulatory decisions at all stages of parasite development. Our results offer a detailed view of ApiAP2 DNA binding specificity and take the first step toward inferring comprehensive gene regulatory networks for P. falciparum.The molecular mechanisms underlying transcriptional regulation in apicomplexan parasites remain poorly understood. Recently, the Apicomplexan AP2 (ApiAP2) family of DNA binding proteins was identified as a major class of transcriptional regulators that are found across all Apicomplexa. To gain insight into the regulatory role of these proteins in the malaria parasite, we have comprehensively surveyed the DNA-binding specificities of all 27 members of the ApiAP2 protein family from  Plasmodium falciparum is the main cause of the devastating human disease malaria. This parasitic organism has a complex lifecycle spanning a variety of different cell types in the mosquito vector and human host. To adapt and survive in these different environments, the parasite precisely regulates the transcription of genes throughout its lifecycle. However, the mechanisms governing transcriptional regulation in P. falciparum are poorly understood. To date, a single family of specific transcription factors, the Apicomplexan AP2 (ApiAP2) proteins, has been identified. These DNA binding proteins are likely to play a major role in coordinating the development of this parasite and are therefore of major interest. Here, we determine the DNA binding specificities for the entire P. falciparum ApiAP2 family of DNA binding proteins. Our results demonstrate that these proteins bind diverse DNA sequence motifs and co-occur in functionally related sets of genes. By mapping these sequences throughout the parasite genome, we can begin to establish a regulatory network underlying parasite development. This study represents the first characterization of a family of DNA binding proteins in P. falciparum and provides an important step towards understanding gene regulation in this parasite. Plasmodium falciparum is responsible for the majority of human malaria cases and causes approximately 1 million deaths every year P. falciparum includes three developmental stages, which occur in its mosquito vector, the human liver, and human blood. Within each developmental stage the parasite undergoes major morphological changes that are accompanied by precisely timed transcription of genes that are necessary for parasite growth, differentiation, and replication. Detailed transcriptome and proteome studies have been conducted across the different stages of the life cycle P. falciparum, very little is known regarding the mechanism of transcriptional regulation, including transcription factor binding and sequence specificity.P. falciparum appears to resemble that of other eukaryotic organisms, with general transcription factors coordinating the recruitment of RNA polymerase II to core promoter elements cis-acting sequences required for gene expression have successfully identified specific enhancer and repressor sequences upstream of the core promoter elements kahrp) gbp130) pfcds) hsp) family rif genes var genes: the SPE1, CPE, and SPE2 motifs, of which the SPE2 motif has been hypothesized to be involved in silencing of var gene expression pfs16, pfs25pgs28P. falciparum genome have identified a number of potential cis-elements that may play a role in gene regulation trans-factors have been largely unsuccessful Basic transcriptional control in P. falciparum, containing Apetala2 (AP2) domains Plasmodium spp. and in other Apicomplexans including Theileria, CryptosporidiumToxoplasmaP. falciparum was initially predicted to contain 26 ApiAP2 factors, each containing one to three AP2 domains Toxoplasma the family is expanded to over 50 ApiAP2 proteins th highly conserved ApiAP2 protein (PF13_0267), which agrees with recent Pfam predictions for this protein P. falciparum, where they are expressed throughout the entire developmental lifecycle Recently, a large protein family was identified in P. berghei orthologue of PF14_0633 (PBANKA_132980) is essential for the formation of sporozoites P. berghei protein, AP2-O [PBANKA_090590 (PF11_0442)], as an activator of genes required for invasion of the mosquito midgut during the mosquito stage of the life cycle Previously, we established that two ApiAP2 proteins, PF14_0633 and PFF0200c, bind DNA with high sequence selectivity P. falciparum. Our results reveal a complex array of DNA sequence elements, with the majority of proteins binding to unique sequences. We demonstrate several cases where multiple AP2 domains within the same ApiAP2 protein are capable of binding distinct DNA sequences. The identification of these unique sequence motifs sheds light on the molecular mechanisms of transcriptional regulation by assigning correlations between putative cis-acting sequences in Plasmodium and the trans-factors that will bind at those sequences genome-wide. Our data reveal the likely identity of trans-acting ApiAP2 factors that specifically bind to previously described cis-motifs, illuminating some of the previously unknown effectors of plasmodial gene expression. For the many new motifs we identified, we predict putative targets for each of the ApiAP2 proteins. This work represents the first comprehensive analysis of the ApiAP2 DNA binding proteins in P. falciparum and provides a crucial missing link toward understanding their role in the regulation of parasite development.Here we biochemically and computationally characterize the global DNA binding specificities for the entire ApiAP2 protein family from P. falciparum AP2 domains, we used protein binding microarrays (PBMs), which enable simultaneous screening of all possible DNA sequences up to ten nucleotides in length without sequence bias The 27 plasmodial ApiAP2 proteins vary drastically in size , howeverP. falciparum AP2 domains revealed motifs for 20 out of the 27 ApiAP2 proteins . Domain 2 of PFD0985w has three predicted secondary motifs in addition to the primary motif. A plot of the E-scores for all ungapped 8-mers reveals that the top 100 matches to both the primary motif and one of the secondary motifs  are relaPlasmodiumtrans-factors that bind to these motifs have mostly remained elusive. For three cases, we now establish plausible links between the newly identified AP2 DNA sequence motifs and these previous reports. Militello et al. identified a specific motif, (A/G)NGGGG(C/A) , upstream of 8 out of 18 Plasmodium heat shock genes pf13_0235, hsp86 (pf07_0029), and hsp70 (pf08_0054), two heat shock genes containing one or more G-boxes exhibit a strong positive correlation (r = 0.93) during the asexual blood stage hsp gene expression. We performed EMSAs using both G-box elements of the hsp86 upstream region and found that PF13_0235_D1 interacts specifically with the G-box and deletion of both G-boxes is required to completely eliminate binding , msp10 (pff0995c) and rhopH 3 (pfi0265c)  , and no gbp130 (pf10_0159), GTATT, was previously found to be bound by unknown nuclear factors in a sequence-specific manner gbp130 and the purified AP2 domain from PF11_0091 confirm its ability to interact with this sequence  As a test of the ability of our ApiAP2 proteins to bind to the ScanACE predicted targets we selected a putative target for the newly annotated ApiAP2 protein PF13_0267;  binding . Althougcis-elements cis-elements in their upstream regions. To narrow the ScanACE list to a more informative subset of putative target genes we used relative mRNA abundance profiles to define relationships between co-expressed Plasmodium genes While the ScanACE analysis provides a list of all occurrences for each motif, it is unlikely that the ApiAP2 proteins are binding to all possible motif occurrences, and instead that they bind to a smaller subset of promoters. Proteins that are co-localized in the cell or form sub-cellular structures such as the ribosome have been found to be transcriptionally co-regulated in other organisms such as yeast, and often are regulated by the same pf13_0235, the gene for the ApiAP2 factor that binds the G-box, shows a strong positive correlation , which is involved in sequestration of infected red blood cells in the vasculature var genes have been divided into groups based on their location along the chromosome , the direction of transcription, and the sequences of their intron and 5′ and 3′ untranslated regions var promoters  which is shifted from trophozoite to late schizont stage Plasmodium spp. and will likely bind the same motifs. It follows that the ApiAP2 proteins may regulate similar or related target genes in P. vivax compared to P. falciparum. We calculated activity profiles using the P. vivax asexual blood stage transcriptome data P. falciparum, most of the motifs are associated with activation or repression of target genes at one or more timepoints. However, a comparison of the target gene lists for each motif in P. falciparum and P. vivax shows that the conservation of putative targets is low, ranging from 0 to 53% , as well as the CACACA motif, which is bound by PF13_0026  using the PBMs is comparable to results obtained using the same technology with other transcription factor families from yeast and mouse (40–85% successful) hsp86  cis-elements bound by individual AP2 domains may be conserved across species, our predicted IDC target gene sets for ApiAP2 proteins appear to differ extensively. This is common in other eukaryotic organisms, where DNA binding domains are highly conserved across species, but downstream target genes are divergent Plasmodium spp., as demonstrated by the divergence of IDC target gene sets in P. vivax and P. yoelii compared to P. falciparum, which contrasts with the almost perfect conservation of AP2 domains and the similar temporal expression of ApiAP2 genes. We previously showed that the orthologous AP2 domains from PF14_0633 in P. falciparum and its distant Apicomplexan relative C. parvum (cgd2_3490) bind virtually identical sequence elements C. parvumP. falciparumP. falciparum, P. vivax, P. yoelii, and P. knowlesiin vivo by this factor. This is also likely to be true for other ApiAP2 factors and further work identifying functional binding sites will clarify the level of conservation of transcription factor binding sites among the different Plasmodium spp.The extremely high level of conservation (>95% identity) for each AP2 domain across all P. falciparum prompted the hypothesis that combinatorial gene regulation plays an important role in the parasite in vivoin vivo data will be required to fully elucidate the role of individual ApiAP2 motif occurrences on target gene functions; however it is evident from our data that the ApiAP2 factors have sufficient diversity in sequence recognition to potentially regulate all P. falciparum genes.Combinatorial gene regulation is an important aspect of transcription in many organisms. It controls the level of gene expression, the precise timing of expression, and determines the ability of a regulatory circuit to respond differently to a wide variety of extracellular signals. The finding that there are a relatively small number of specific transcription factors in Plasmodium family of DNA binding proteins and provides a starting point to investigate transcriptional control in P. falciparum. These results provide an important step toward understanding the role of these proteins as major regulators throughout all stages of parasite development in Plasmodium spp. and other related Apicomplexan species.While it is clear that the ApiAP2 factors bind DNA and recent work has begun to explore their contribution to transcriptional regulation Plasmodium spp. orthologues, as well as using structural predictions from the online secondary structure prediction server Jpred3 P. falciparum as well as one from P. berghei were cloned into pGEX-4T1 (GE Life Sciences) to create N-terminal glutathione S-transferase (GST) fusions . For public access, all motifs have been deposited in the UniPROBE database All constructs in http://www.idtdna.com) and annealed with complementary oligonucleotides to create double-stranded probes  was performed according to the manufacturer's instructions. The Chemiluminescent Nucleic Acid Detection Module (Pierce) was used according to the manufacturer's instructions to visualize the probes.N-terminal GST fusions of the ApiAP2 domains were purified as described above. Single-stranded HPLC purified 5′ biotinylated oligonucleotides were purchased from Integrated DNA Technologies .2 kb-long upstream regions (or up to the nearest ORF) were first extracted from whole genome sequences and associated gene annotation data (PlasmoDB 6.0). PBM motifs were trimmed down to their 6 most informative consecutive motif positions (motif cores). Then, we determined all core PBM motif occurrences in the 2 kb regions using the ScanACE approach k-means approach together with the Pearson correlation. Motif scanning was performed using the ScanACE approach k-means. At a given motif score threshold, motif over-representation in each cluster was assessed using the hypergeometric distribution; to correct for multiple testing , we used the Benjamini-Hochberg procedure; corrected p-values corresponding to an estimated overall FDR of 0.25 were considered significant and the genes associated with motif occurrences in these clusters were extracted. Because the k-means is dependent on initialization, we repeated the entire procedure 10 times; genes extracted 3 times or more (out of 10) were considered as candidate target genes. Thus, only genes associated with the considered motif and that are consistently found as co-expressed together with other genes sharing that same motif end up as candidate target genes in our analysis. More detailed methods are available in the In order to identify candidate target genes for each AP2, we reasoned that these target genes should be co-expressed and should share the AP2 binding site we identified using PBMs. Thus, we first identified groups of genes that are co-expressed across multiple experimental conditions or multiple time points . Then for each AP2 motif, we determined in which of the groups the motif was over-represented. For each of these groups, we extracted the genes associated with one or more motif occurrences. Thus, target genes in this definition can come from multiple co-expression groups . In order to define co-expressed gene groups, we used the www.plasmodb.org) accession numbers for genes and proteins discussed in this publication are: hsp86 (PF07_0029); hsp70 (PF08_0054); msp1 (PFI1475w); msp10 (PFF0995c); rhopH 3 (PFI0265c); gbp130 (PF10_0159); AP2-O (PBANKA_090590); AP2-Sp (PBANKA_132980).PlasmoDB Click here for additional data file.Figure S1Size distribution of ApiAP2 proteins. Proteins range in size from 200 amino acids to over 4000, with the four smallest ApiAP2 proteins having less than 500 amino acids. Bars are colour coded based on the number of AP2 domains in each protein.(0.38 MB TIF)Click here for additional data file.Figure S2E. coli and tested in duplicate on protein binding microarrays (PBMs). D1 indicates the AP2 domain closest to the N-terminus; D2 and D3 are the AP2 domains following D1 going from the N- to C-terminus of the protein; DLD indicates two domains and a short linker region (Domain - Linker - Domain); ext at the end of the domain number indicates an extension of the original cloned domain at either or both of the N- and C-termini. Enrichment scores above 0.450 were considered significant, and no result indicates an E-score below this cut-off. PFL1900w_DLD has a poly-asparagine tract that increases its linker length by 39 amino acids. This expanded linker is absent in the P. berghei orthologue (PB000218.00.0) of PFL1900w, while the AP2 domain sequences are 99% identical. To test the effect of the PFL1900w expanded linker on DNA binding we generated a GST fusion DLD construct for the shorter P. berghei orthologue. Both constructs exhibited identical DNA binding specificity.ApiAP2 domains tested on PBMs. ApiAP2 proteins are listed in order of size. Domains were cloned into pGEX-4T1 to produce N-terminal GST fusions. Proteins were expressed and purified from (0.26 MB PDF)Click here for additional data file.Figure S3AP2 domain secondary motifs. Secondary motifs found for ApiAP2 proteins and their associated enrichment scores. E-scores greater than 0.450 were considered significant. The final column lists the relationship of the secondary motif to the primary motif using the following descriptions: end modification is a change in nucleotide specificity at either or both the 5′ and 3′ ends of the motif, alternate recognition interface is a motif that is unrelated to the primary motif, variable spacer distance is an insertion or deletion in the middle of the motif, and core change is a change in nucleotide specificity in the middle of the motif.(0.26 MB PDF)Click here for additional data file.Figure S4Plasmodium regulatory motifs. A) Partial sequences of the EMSA probes from the upstream sequences of rhopH3, msp1 and msp10. The PF10_0075_D3 motif is underlined in red and mutations are underlined in black. EMSAs using these probes illustrate the ability of PF10_0075_D3 to specifically bind the target sequences (shifted complexes are denoted with an arrow). No binding was observed with an unrelated oligonucleotide. Probes are biotin labeled and all competitors are unlabeled. B) EMSA using the gbp130 upstream sequence demonstrates that PF11_0091 binds to this sequence, but not to a non-specific probe. Sequences are indicated as in (A).PF10_0075_D3 and PF11_0091 bind (1.84 MB TIF)Click here for additional data file.Figure S5pf13_0267 and a predicted target gene, pfc0975c, show similar timing pfc0975c, a putative target of PF13_0267. Biotinylated probe is specifically shifted with increasing amounts of the purified protein (designated by the arrow) and this shift is competed with unlabeled probe DNA. No shift is observed with an unrelated oligonucleotide (data not shown). Partial probe sequences are shown below the gel, with the PF13_0267 target motif underlined in red and mutations of the motif underlined in black.PF13_0267 binds to a sequence upstream of a ScanACE predicted target gene. A) Transcript abundance profiles of (0.63 MB TIF)Click here for additional data file.Figure S6Correlation of ApiAP2 mRNA abundance and expression of putative target genes. Average targets represents the average mRNA abundance profiles during the IDC for all genes in (1.01 MB PDF)Click here for additional data file.Figure S7Activity profiles for AP2 motifs using perturbation data. To refine our list of target genes we defined activity profiles for each motif using the IDC perturbation data (2.25 MB TIF)Click here for additional data file.Figure S8Overlap between predicted IDC and perturbation co-expressed targets. Blue circles represent the predicted IDC co-expressed targets and the yellow shaded circles are the perturbation co-expressed targets. The overlap between the two gene lists is shown in green. The numbers indicate the number of unique gene IDs in each dataset. The AP2 domain that binds to the corresponding motif is listed above each Venn diagram.(4.62 MB TIF)Click here for additional data file.Figure S9P. vivax samples. A) A comparison of IDC mRNA abundance profiles for the P. falciparum ApiAP2 proteins P. vivax orthologs P. falciparum and P. vivax, activity profiles were defined using three P. vivax isolates ApiAP2 IDC expression and activity profiles for the AP2 motifs in (1.85 MB TIF)Click here for additional data file.Figure S10P. falciparum lifecycle and target gene predictions. To identify motifs that function in different stages we used data from across the lifecycle Activity profiles for AP2 motifs in different stages of the (1.04 MB TIF)Click here for additional data file.Figure S11P. yoelii liver stage. Motifs for the P. falciparum ApiAP2 proteins were used to establish activity profiles in the P. yoelii liver stage (LS) AP2 motif activity profiles during the (1.26 MB TIF)Click here for additional data file.Figure S12P. falciparum IDC ApiAP2 regulatory network. ApiAP2 genes are targets of other ApiAP2 factors. ApiAP2 genes are arranged in order of expression during the IDC in a clockwise manner starting at PF07_0126. Arrows pointing away from an ApiAP2 gene indicate that it potentially regulates the target factor by binding to motifs in the target upstream region. ApiAP2 genes coloured in green are expressed during the IDC, but did not exhibit DNA binding specificity on the PBMs. Genes in yellow are expressed during the IDC and bind to specific DNA sequences and genes in blue also bind DNA, but are not expressed during the IDC.(0.67 MB TIF)Click here for additional data file.Figure S13www.ebi.ac.uk/clustalw) of the AP2 domains for these three factors demonstrates a high level of similarity (52%) in the predicted β-sheets ; which likely contain the DNA binding residues. Identical residues are highlighted in red and similar residues in yellow. Secondary structure predictions were made using Jpred3 ApiAP2 proteins that bind the CACACA motif. A) Three of the ApiAP2 factors that bind the CACACA motif are expressed in the late stages of the IDC as shown by mRNA abundance profiles (0.84 MB TIF)Click here for additional data file.Table S1Similarity E-values calculated using the STAMP tool between ApiAP2 position weight matrices.(0.02 MB XLS)Click here for additional data file.Table S2Distribution and accessibility of ApiAP2 binding sites.(0.05 MB PDF)Click here for additional data file.Table S3P. falciparum and P. vivax.Conservation of IDC ApiAP2 targets between (0.06 MB PDF)Click here for additional data file.Table S4Oligonucleotides used in EMSAs.(0.08 MB PDF)Click here for additional data file.Dataset S1Position weight matrices for all AP2 domains that bound DNA in PBM experiments.(0.06 MB XLS)Click here for additional data file.Dataset S2Position weight matrices for all secondary AP2 motifs.(0.10 MB XLS)Click here for additional data file.Dataset S3ScanACE results for all AP2 motifs. ScanACE results for all ApiAP2 motifs. The location, orientation, and sequence of each motif match in upstream regions are indicated.(8.80 MB ZIP)Click here for additional data file.Dataset S4P. falciparum genome.Motif occurrence across the (2.12 MB XLS)Click here for additional data file.Dataset S5IDC co-expressed predicted targets for all motifs.(1.71 MB ZIP)Click here for additional data file.Dataset S6Perturbation co-expressed predicted targets for all motifs.(1.49 MB ZIP)Click here for additional data file.Dataset S7Sporozoite and zygote predicted targets of ApiAP2 proteins.(0.18 MB ZIP)Click here for additional data file.


Many components of the RNA polymerase II transcription machinery have been identified in kinetoplastid protozoa, but they diverge substantially from other eukaryotes. Furthermore, protein-coding genes in these organisms lack individual transcriptional regulation, since they are transcribed as long polycistronic units. The transcription initiation sites are assumed to lie within the 'divergent strand-switch' regions at the junction between opposing polycistronic gene clusters. However, the mechanism by which Kinetoplastidae initiate transcription is unclear, and promoter sequences are undefined.50), and H3 histones were assessed in Leishmania major using microarrays hybridized with DNA obtained through chromatin immunoprecipitation (ChIP-chip). The TBP and SNAP50 binding patterns were almost identical and high intensity peaks were associated with tRNAs and snRNAs. Only 184 peaks of acetylated H3 histone were found in the entire genome, with substantially higher intensity in rapidly-dividing cells than stationary-phase. The majority of the acetylated H3 peaks were found at divergent strand-switch regions, but some occurred at chromosome ends and within polycistronic gene clusters. Almost all these peaks were associated with lower intensity peaks of TBP/SNAP50 binding a few kilobases upstream, evidence that they represent transcription initiation sites.The chromosomal location of TATA-binding protein (TBP or TRF4), Small Nuclear Activating Protein complex (SNAPThe first genome-wide maps of DNA-binding protein occupancy in a kinetoplastid organism suggest that H3 histones at the origins of polycistronic transcription of protein-coding genes are acetylated. Global regulation of transcription initiation may be achieved by modifying the acetylation state of these origins. Trypanosoma brucei), Chagas disease (Trypanosoma cruzi), and leishmaniasis (Leishmania major), which kill approximately 400,000 people per year. The parasites are transmitted to their preferred hosts by different insect vectors where they reside and replicate as host-adapted and vector-adapted forms, respectively, with remarkably different morphologies.Kinetoplastids are early-branching protists with unusual mechanisms of gene expression. While some are harmless free-living organisms, other members of this group infect a range of plants and animals, causing significant human disease in the form of African Sleeping Sickness  RNA that is attached to the 5' end of each individual messaged before it can be translated. While bacteria use polycistronic transcription as a method of co-regulating genes within an operon, kinetoplastid genes are not typically arranged by function [L. major are arranged in only two long gene clusters units, on opposite strands separated by a 'divergent strand-switch' region [Kinetoplastids display peculiar molecular mechanisms, especially when it comes to gene expression. In the nucleus, functionally unrelated genes are transcribed polycistronically  and are function , and it ' region . TranscrOf all the models of eukaryotic transcription initiation, the TATA box promoter serves as the most visible . The TATc), which in humans is a five-member complex, is involved in initiating the transcription of small nuclear (sn)RNAs from promoters containing a proximal sequence element (PSE) with or without a TATA box, depending on the gene being transcribed [c, consisting of the three core proteins, retains its ability to initiate snRNA transcription [Drosophila and kinetoplastids [Aside from protein-coding genes, a whole host of non-coding RNAs must be transcribed, with many more still being discovered . Ribosomnscribed . A minimcription , and theplastids ,14.Eukaryotic DNA is wrapped around nucleosomes composed of H2A, H2B, H3, and H4 histone proteins, with an additional H1 histone contributing to larger order structures. By substituting histone variants, by modifying these histones, or by changing the layout of nucleosomes, eukaryotes can regulate access of chromatin to proteins involved in transcription initiation. There are a wide variety of modifications that can be made to histones, and some of these alterations can be used as markers for gene expression. In model organisms, acetylated histone H3 is found at the 5' ends of transcription start sites , and hasTrichomonas vaginalis, a more early-diverging protist, uses Inr elements, suggesting that transcription from Inr-containing promoters may represent an ancient process lost in kinetoplastids [Kinetoplastids do not employ canonical TATA box elements as part of their transcription initiation, and the Inr element used with the SL RNA promoter is widely divergent. Interestingly, plastids . HoweverT. brucei variant surface glycoprotein and EP/PARP/procyclin genes [50 bind to snRNA and SL RNA gene promoters [50 binding to tRNA or snRNA promoters in T. brucei [50, the putative homologues of general transcription factors TFIIA [There are few well-defined promoters in kinetoplastids, including promoters for non-coding RNA genes -20, and in genes , which fin genes . Severalromoters ,24, alth. brucei . TBP was. brucei . In addirs TFIIA , TFIIB [rs TFIIA ,28, and rs TFIIA ,30 all hMuch of the work towards characterizing these proteins has come from binding studies and affinity purification strategies ,27,31-35Trypanosoma cruzi [et al. for a brief summary [In terms of chromatin structure, kinetoplastids possess a standard nucleosome core and their chromatin appears to undergo some sort of condensation in response to histone H1 , althougma cruzi ,39. Post summary .L. major genome is 32.8 Mbp, so the Nimblegen microarrays [Chromatin immunoprecipitation (ChIP) has emerged as a powerful tool for analysis of interplay between chromatin structure and transcription initiation. This technique utilizes co-precipitation of DNA-binding proteins and their associated DNA sequences from live cells. In a recent adaptation of this technology (ChIP-chip), the immunoprecipitated DNA can be non-specifically amplified and hybridized to microarrays containing oligonucleotide probes tiled across the genome. Since current technology allows the creation of microarrays containing several hundred thousand to millions of oligonuceotides, ChIP-chip affords high resolution genome-wide interrogation of protein-DNA interactions on a small number of microarrays. The entire roarrays  used in Leishmania tarentolae TBP and L. major SNAP50 have been used for ChIP of L. tarentolae chromatin [9/K14 acetylated Tetrahymena histone H3 have been used for similar analyses in T. cruzi [L. major homologues of these targets (data not shown), although the exact residues of L. major H3 that are acetylated are not known. PCR assays of L. major chromatin precipitated using the antisera against TBP and SNAP50 showed enriched amplification of the U2 snRNA/tRNA promoter region when compared to a control region from within the larger chromosome (chr)1 polycistronic gene cluster  H3, or acetyl-H3 were used to precipitate the chromatin. The precipitated DNA was subsequently unlinked and amplified before fluorescent labeling and two-color microarray hybridization. Generally each microarray was hybridized with an experimental sample and input chromatin to control for genomic DNA copy number and biases introduced due to cross-linking, amplification, and other technical manipulations; although sometimes experimental samples were compared directly on the same array. The signal obtained for acetyl-H3 was normalized to total H3 signal and the resulting ratio at each oligonucleotide position was visualized relative to the annotated L. major Friedlin sequence assembly v5.0 using Nimblegen's SIGNALMAP software  represent the enrichment of chromatin at each position of the chromosome when specifically precipitated with these antibodies compared to a mock precipitation. Peaks in the ChIP-chip ratios were identified using the MPEAK algorithm [PEAK relies on the fact that multiple probes will bind the same chromatin fragment if the probes are designed to be close together along the genomic sequence, with maximal binding seen where the probe sequence and the binding element overlap. The program first identifies triangular shapes with heights above a specific threshold, and then determines the exact peak position by analyzing the triangular shape. Using parameters in line with the size of sheared chromatin (~300 bp) and the distance between tiled probes used for this study (85 bp), binding peaks were predicted genome-wide for acetyl-H3, TBP and SNAP50. A genome-wide summary of all the acetyl-H3:H3 peaks and selected TBP/SNAP50 peaks can be found in Additional file lgorithm . MPEAK rL. major genome contained peaks of H3 acetylation, as illustrated by two sites on chr27  of peaks of H3 acetylation were found on each chromosome. Only 184 peaks of H3 acetylation were found in the entire genome . Each of the 58 divergent strand-switch regions in the –13 of pehr27 Fig . Closer hr27 Fig  and 3C. hr27 Fig . An addihr27 Fig . Thus, eEach acetyl-H3 peak usually covered 4–6 kb and generally overlapped the first one or two genes in each polycistronic gene cluster, as well as the upstream intergenic region. Peaks of H3 acetylation were not observed near tRNA/snRNA clusters found within convergent strand switch regions  peaks were usually observed , indicating very similar DNA-binding patterns genome-wide, and so only the TBP peaks are shown in Figs 50 binding at all acetyl-H3 peaks within the genome. There is an apparent polarity of binding signal relative to the direction of transcription, with TBP and SNAP50 peaks overlapping ~1–2 kb upstream of the peak of H3 acetylation.The great majority 174/184) of the acetyl-H3 peaks contained clear TBP-and SNAP see Fig , while tgion Fig  and 3C. ters Fig , where t/184 of tThe experiments above where performed using rapidly growing 'mid-log' procyclic promastigotes. Although kinetoplastids do not regulate transcription on the level of individual genes, it has been proposed that overall transcription rates may slow when cells approach stationary phase and begin to undergo metacyclogenesis . To detei.e. either an intense peak or background signal), the TBP  ratios appear to reflect at least two different levels of binding, such that very large peaks occur at certain sites, while a number of much smaller peaks are distributed throughout the rest of the genome .In contrast to the relatively binary nature of the acetyl-H3:H3 ratio  TBP and SNAP50 binding at rRNA genes. No effect on rRNA expression was detected when TBP was knocked down due to RNA interference in T. brucei [50 did not bind above background to the rRNA promoter region [50 binding to the rRNA locus  length are peppered throughout the genome, and almost half of the predicted sites of transcription initiation lack such G-tracts (data not shown). Furthermore, the positions of acetyl-H3 peaks contain shorter G-tract lengths on average than the surrounding regions . Two such G-tracts were found within the 73-bp sequence from the chr1 strand-switch region  and they species . While G see Fig . Thus, tL. major promastigotes showed acetylated histone H3 peaks at the 5' ends of all polycistronic protein-coding gene clusters. These peaks occurred within all divergent strand-switch regions, at some telomeres, and at a few other sites within the gene clusters. The level of H3 acetylation was higher at these sites in rapidly growing cells than in stationary ones. Substantial TBP and SNAP50-binding peaks were associated with tRNA, snRNA, and SL RNA gene promoter regions, confirming previous small-scale studies. Less intense peaks were found immediately upstream of the H3 acetylation regions associated with the putative transcription initiation sites for the protein-coding genes, as has been observed in other eukaryotes. G-tracts were potentially associated with these TBP and SNAP50-binding regions, although similar sequences were also found in other regions of the genome.Genome-wide ChIP-chip analysis of More than half of all mammalian transcripts are produced from promoters with no known promoter elements – a crucial fact that went unappreciated until genome-wide studies were undertaken. Since kinetoplastid protozoa share conserved transcription factors with other eukaryotes, but lack complex transcriptional regulation, they may serve as a model for studying these mechanisms involved in non-conventional transcription initiation. On the other hand, if aspects of kinetoplastid transcription turn out to be distinct among eukaryotes, then these mechanisms could provide good targets for clinical therapy. The first whole-genome studies aimed at understanding the mechanisms of kinetoplastid transcription initiation are presented here.50 in kinetoplastid transcription have been the subject of a several studies, with apparently contradictory observations. This study confirms the expected role of TBP and SNAP50 in binding to the SL RNA promoter, as observed in several different kinetoplastids using a variety of different experimental techniques [50 are involved in transcription of snRNAs [T. brucei, SNAP50 did not bind to an snRNA promoter under conditions where it efficiently bound the SL RNA gene promoter [50 bind universally to all snRNA promoter regions, and both proteins are found at all tRNA and 5S rRNA promoter regions as well. This contrasts with ChIP data from L. tarentolae, where only TBP was observed binding to those sites [The roles of TBP and SNAPchniques ,24,35,46f snRNAs ,24. Howepromoter . In the T. brucei had no effect on steady-state rRNA levels [50 did not bind above background to the rRNA promoter region [T. brucei rRNA promoter is reported to bind SNAP50 in vitro [50 apparently bind within the rRNA gene coding sequence, but not the promoter. One possible explanation is that it may represent precipitation of rRNAs along with TBP/SNAP50 nascent polypeptides, although how this RNA would be labeled and why it was not also seen with H3 is not clear. Alternatively, the apparent pattern of binding may be merely an artifact caused by repetitive sequences, although this does not appear to be the case for the SL RNA locus. However, transcription of rRNAs is notably distinct from organism to organism even among closely-related crown-group eukaryotes, and species-specific effects may explain the apparent differences between T. brucei and L. major. With key components still being identified [With regards to rRNA transcription, TBP knockdown by RNA interference in A levels , and TBPr region , even thin vitro . The datentified , the ful9/K14 acetylation of histone H3 is a marker for sites of active transcription initiation in other eukaryotic systems. Our observation that similar H3 acetylation is found at all divergent strand-switch regions, as well as a few other sites throughout the L. major genome, in a polarity consistent with expected direction of transcription, suggests that this acetylation represents the first marker for sites of RNA polymerase II-mediated transcription initiation of protein-coding genes in kinetoplastids. This conclusion is bolstered by our finding that peaks of TBP binding were observed immediately upstream of the vast majority of acetylated regions. This suggests that histone acetylation is a marker for open chromatin, which makes specific regions of the genome available for binding of transcription initiation complexes, as observed in other organisms. Since TBP and SNAP50 signals were correlated genome-wide, and both were associated with H3 acetylation at the 5' end of polycistronic gene clusters, it seems likely that SNAP50 has a role as a general transcription factor in kinetoplastid transcription, an adaptation that would be unique among all model eukaryotes.KThe hypothesis that kinetoplastids regulate overall transcription rates according to cell density  led to tEME and MAST analysis of sequences at sites of enhanced ChIP signal easily recovered known tRNA promoter elements; but no clear motifs were discovered for protein-coding gene transcription initiation sites. The only identifiable motif found in a majority of the isolated sequences was a G-tract (or C-tract) longer than 10 nucleotides. Similar elements were found within the transcription-enhancing 73-bp sequence derived from the L. major chr1 strand-switch region [Leishmania species [50 peaks is not indicative of typical promoter-directed initiation from a single initiation point. This is consistent with the finding that the chr1 strand-switch region contains several distinct transcription sites in both directions [Aside from identifying the sites of transcription initiation, one of the main goals of this research is to identify DNA elements responsible for recruiting RNA polymerases to those sites. Several different methods have been used successfully in other organisms to identify regulatory elements from an enriched selection of sequences likely to contain similar elements. Mh region  which is species . Howeverrections .L. major revealed an unusually high AT composition, a lack of putative hairpins and a strong curvature of the DNA [in vitro bending assays or resolving the structure of the protein-bound DNA. Other secondary structures, like triple helices for example, are difficult to predict bioinformatically at present.Previous bioinformatic analyses of several divergent strand-switch regions from  the DNA . Our dat the DNA  and bindL. major. They also extend our understanding of the roles of TBP and SNAP50 in L. major transcription. TBP and SNAP50 appear to bind to all RNA polymerase II and III promoters and appear to have identical binding patterns genome-wide, laying open the interesting possibility that SNAPc may serve as a general transcription factor for protein-coding transcription in these organisms. The identification of acetylated H3 histones at divergent strand-switch regions and at a few other sites throughout the genome suggests that chromatin accessibility may restrict transcription initiation of protein-coding genes in kinetoplastids to a few select sites throughout the genome. Changes in acetylation between rapidly-dividing cells and stationary ones may constitute evidence of wholesale regulation of transcriptional rates by modulation of chromatin availability at these sites. Future whole-genome studies of epigenetic features and chromatin remodeling proteins are likely to shed more light on the mechanisms of kinetoplastid transcription initiation. The transcriptional machinery of kinetoplastids is highly reduced, making complete binding maps of all known kinetoplastid transcription factors and epigenetic factors likely within the near future once suitable antibodies are developed.The use of ChIP-chip analysis to probe genome-wide transcription factor occupancy has been applied for the first time to the study of kinetoplastids. The results confirm many conclusions made from previous small-scale studies and suggest that there are only 184 transcription-initiation sites for protein-coding genes in L. major cells were used instead of L. tarentolae. For each IP, 2.5 × 109 L. major cells were incubated with 1% formaldehyde at 25°C for 15 min. Next, 2.5 ml of 2.5 M glycine was added and incubation continued at 25°C for an additional 5 min. The cells were pelleted and washed once with 20 ml 1× phosphate buffered saline (PBS), pelleted again and washed once more with 2 ml PBS. After the final pelleting, the cells were re-suspended in 400 μl ChIP lysis buffer  and an equal volume of glass beads was added. The sample was shaken continuously for 30 min on a vortex at 4°C. The lysate was recovered by piercing the bottom of the tube and collecting drops. The samples were sheared on a Misonix Microson model #XL2007 sonicator with 6–10 s pulses at 12 Watts, followed by a 15 s pause after each pulse. The lysate was centrifuged at 10,000 rpm for 10 min at 4°C. The supernatant was taken and its protein concentration determined. One mg of protein was used for each immunoprecipitation reaction. One-fortieth of the amount of lysate that would be used for an immunoprecipitation was reverse cross-linked (65°C overnight), and the DNA was purified on a Qiagen QiaQuick® spin column and analyzed by agarose gel electrophoresis to determine the average genomic fragment size achieved by sonication (300 bp).ChIPs were performed as previously described , except Tetrahymena unmodified  and H9/K14-acetylated  histone H3 sequence were used to precipitate chromatin that was subsequently amplified and used to create fluorescently labeled probes used in two-color microarray hybridization, as were antisera against recombinant L. tarentolae TBP and L. major SNAP50 [Commercial antisera against peptides representing r SNAP50 . Each an50 experiments, 'mock' immunoprecipitations were performed in which input chromatin was subjected to immunoprecipitation conditions without the use of antiserum. For acetyl-H3 experiments, the antiserum against unmodified H3 peptides was used as a control.A ChIP sample and a control sample were generated for each experiment. For the TBP and SNAPL. major genome assembly (version 5.0) was used to design 50-nt-long tiled probes interspersed by 35-bp gaps, yielding a final custom-designed Nimblegen microarray containing 387,865 'top strand' probes (as determined by the orientation of the v5.0 genome sequence available at the Sanger website). The probe design data and all microarray data are available from the NCBI Gene Expression Omnibus (Accession number GSE13415).The 32.8 Mbp DITPAD PRO or the R statistical package, and viewed graphically (from a gff file format) using Nimblegen's SIGNALMAP software. A combination of R and PERL scripts were written for microarray data analysis. Scripts for these analyses are available upon request. All experiments were performed in triplicate, using different cell cultures, except for the comparison of mid-log versus stationary cultures, which was done only once.The microarray data was analyzed in raw form using EEAK algorithm was used to identify peaks in the ChIP data [ERL script was written to extract the 300 nucleotides surrounding each MPEAK-predicted TBP/SNAP50 peak and the resulting sequences were divided into different groups. Sequences representing peaks near SL RNAs, and rRNA genes were put into two different bins; those representing tRNA, 5S rRNA, and snRNA genes were put into a third bin; while a fourth bin represented all other peaks. A fifth bin represented peaks found within divergent strand-switch regions, and a final bin represented a random selection of sites throughout the genome as a control. The sequences in each bin were submitted to the MEME/MAST algorithm for motif discovery, and the resulting motifs were analyzed manually to recover already identified promoter elements and to determine whether any of the proposed motifs represented a newly discovered promoter element. For analysis of G-tract distribution, a PERL script was written to systematically scan the genome sequence and count the number of uninterrupted Gs (or Cs) beginning at each base and to report the maximum length of G or C tracts in a window, this was used to demonstrate the lower average length of maximum G and C tracts at sites of H3 acetylation. A separate PERL script was written to measure inherent curvature of the genomic sequence based on previously published algorithm using a dinucleotide stacking model [The MPhIP data . The prong model . This mo50): small nuclear activating protein complex subunit 50; (snRNA): small nuclear RNA; (snoRNA): small nucleolar RNA; (tRNA): transfer RNA; (TBP): TATA-binding protein.(ChIP): Chromatin immunoprecipitation; (chr): chromosome; (rRNA): ribosomal RNA; (SL RNA): spliced leader RNA;  of associated TBP/SNAP50 peak(s); a systematic name; type (see below); strand ; number of genes in the associated polycistronic gene cluster; and a comment noting any other features (e.g. RNA genes) associated with the entry. The codes used to describe the type of peak are D (divergent strand-switch region), C (convergent strand-switch region), T (telomeric), R (downstream of an RNA gene cluster); and I . The total number of each peak type is shown at the bottom of the table.Click here for file