Complex genetic networks consist of structural modules that determine the levels and timing of a cellular response. While the functional properties of the regulatory architectures that make up these modules have been extensively studied, the evolutionary history of regulatory architectures has remained largely unexplored. Here, we investigate the transition between direct and indirect regulatory pathways governing inducible resistance to the antibiotic polymyxin B in enteric bacteria. We identify a novel regulatory architecture—designated feedforward connector loop—that relies on a regulatory protein that connects signal transduction systems post-translationally, allowing one system to respond to a signal activating another system. The feedforward connector loop is characterized by rapid activation, slow deactivation, and elevated mRNA expression levels in comparison with the direct regulation circuit. Our results suggest that, both functionally and evolutionarily, the feedforward connector loop is the transitional stage between direct transcriptional control and indirect regulation. A regulatory protein can activate the expression of a target gene either directly, i.e., by binding to the gene's promoter, or indirectly, i.e., by altering the expression of regulators, which, in turn, bind to the target gene's promoter and induce or inhibit its transcription. Indirect regulatory circuits can contain multiple components and functional elements, such as feedforward and feedback loops. The complex structure of indirect regulation raises the question of its evolutionary origins. Here, we study the dynamic and evolutionary properties of regulatory architectures that involve members of the recently emerged class of bacterial proteins termed connectors. Such proteins post-translationally modulate the activity of two-component systems and phosphorelays, which constitute the prevalent form of bacterial signal transduction. We describe a novel connector-mediated regulatory circuit that combines the structural and functional properties of direct and indirect regulation. Our results indicate that this architecture is the evolutionary link between direct and connector-dependent regulatory designs. Related organisms often express orthologous genes in response to a particular cellular or environmental cue. However, the regulatory mechanisms promoting expression of these genes can be drastically different, ranging from direct transcriptional control to multi-stage architectures involving feedback loops, feedforward loops and regulatory cascades A prevalent form of bacterial signal transduction is the two-component system and its more complex version, the phosphorelay Salmonella enterica serovar Typhimurium, where it enables expression of genes controlled by the PmrA/PmrB two-component regulatory system in response to the low Mg2+ signal that activates the PhoP/PhoQ system pbgP  in the bacterium Yersinia pestis also promotes pbgP expression and demonstrates polymyxin B resistance in response to Fe3+ and/or low Mg2+, even though it lacks pmrDY. pestis pbgP promoter harbors binding sites for both the PhoP and the PmrA proteins Yersinia-like direct transcription regulation circuit, which was reconstructed in an engineered S. enterica strain, to the connector-mediated pathway of wild-type S. enterica demonstrated that the latter pathway exhibits heightened induction ratios, which results in increased levels of polymyxin B resistance The related enteric species Y. pestis, and an indirect branch that is analogous to the connector-mediated pathway of S. enterica. Even though the simultaneous presence of direct and indirect branches of regulation also characterizes one of the most abundant network motifs  In this paper, we identify a novel regulatory architecture that combines structural and functional features of the direct regulation circuit and the connector-mediated pathway. The novel architecture, termed feedforward connector loop, possesses a direct regulatory branch, like that in pmrD gene, and of PhoP and PmrA boxes in the pmrD and pbgP promoters among enteric bacteria    because z both directly, by binding to its promoter region, and indirectly, by promoting expression of gene y encoding a transcriptional regulator Y that also binds to the promoter of gene z (The feedforward loop (FFL) is one of the most abundant network motifs in prokaryotic regulatory circuits f gene z . FFLs exK. pneumoniae  to promote expression of a connector protein  that activates another transcription factor  at the post-translational level  was the main input for the regulatory circuits. An additional input was the level of PmrA-P, which reflects the activity of the PmrA/PmrB system (stimulated by Fe3+); in the FFL case, the second input was the level of activated (phosphorylated) protein Y  possessed a high ability to promote both activation and deactivation delays , and S6,pbgP expression in the different architectures  for 4 h and then switched to inducing conditions  at time 0, the pbgP mRNA level rose much faster in the FCL than in the connector-mediated pathway  This rapid activation was ascribed to the direct regulation branch because the connector-mediated pathway, which lacks a direct regulation branch  whereas the FCL does not , suggest pathway . , which pression  [2]. At tivation , and S6,tivation . The pre2+-dependent polymyxin B resistance in enteric bacteria. First, the Klebsiella and Salmonella lineages diverged after their common ancestor had split from the Yersinia lineage  may come from the fact that Mg2+ normally neutralizes the negative charges in the LPS 2+ are low, the LPS is chemically modified by PmrA-activated gene products that neutralize these negative charges pbgP, as well as the heightened pbgP expression level promoted by the FCL architecture  with the MasterPure RNA purification kit (Epicentre Technologies) according to the manufacturer's recommended protocol.To isolate the RNA used in the S1 nuclease assay , overnigpbgP promoter regions of K. pneumoniae were generated by PCR using the primers 3249 (5′-TTCGTGACAGGAACGCATCT′-3′) and 3250 (5′-GGGCGCGAAAAAGGCAAAAA-3′). S1 nuclease reactions were performed as described previously Double stranded DNA probes to the K. pneumoniae pbgP promoter region probe was generated as described in S. enterica PhoP and PmrA proteins were purified as described previously DNase I footprinting was performed as described previously K. pneumoniae strains harboring the pAG, pAG-rpsM, pAG-KlebsiellapmrD plasmids were grown in N-minimal media, pH 7.7 or 5.8, containing 38 mM glycerol with either 10 µM MgCl2, 10 mM MgCl2 or 10 µM MgCl2 and 100 µM FeSO4 and supplemented with 10 µg/ml tetracycline. GFP expression was analyzed following 4 hours of growth at 37°C using a Becton Dickinson fluorescent-activated cell sorter. Assays were performed in triplicate. Error bars  were concatenated to infer the molecular phylogeny for the eight enteric species Pseudomonas aeruginosa as the outgroup.Identification of protein orthologs and putative transcription factor binding sites is described in pmrD transcription , 7874 , 8813 , 8816 . Data were normalized with the values corresponding to 16S RNA, and represent five independent experiments with the highest and lowest outliers omitted. Error bars , 6523 , 3023 , 3024 . Data were normalized with the values corresponding to 16S RNA. Measurements were done in duplicate; error bars  that describe concentration dynamics for the main chemical components of the three regulatory circuits. The FCL model comprises five ODEs for the PmrD, PmrA, PmrA-P, the PmrD/PmrA-P complex, and inactive level+(activatedlevel−inactive level)/10; deactivation time was defined in an analogous way. Activation and deactivation delays correspond to situations when the PhoP-P input of the circuits was activated and deactivated, respectively.All computations were performed in MATLAB R2007a . In delay distribution computations, the delays were defined as the differences between the activation and deactivation times for the FCL (or FFL) and those for the direct regulation circuit. Activation time was defined as the time required to reach an activation level equal to The delay distributions for the FCL  were simFigure S1E. coli K-12, S. enterica serovar Typhimurium strain LT2, and K. pneumoniae strain KC2668. The sequences were aligned using Clustal W 1.83.Alignment of the amino acid sequences for the PmrD proteins from (0.01 MB PDF)Click here for additional data file.Figure S2pmrD promoter in K. pneumoniae is PhoP-dependent but PmrA-independent. mRNA levels of pmrD were measured by real-time PCR analysis using isolated RNA from wild-type (EG13127) and isogenic phoP (EG15289) and pmrA (EG13129) K. pneumoniae strains following growth in N-minimal medium, pH 7.7, containing 38 mM glycerol with 50 µM Mg2+ and 100 µM Fe3+ Click here for additional data file.Figure S3K. pneumoniae pbgP promoter performed for the non-coding strands. (A) Footprinting analysis of the pbgP promoter with increasing amounts of the PhoP protein . (B) DNase footprinting analysis of the pbgP promoter with increasing amounts of the PmrA protein . Solid vertical lines correspond to regions of the pbgP promoter protected by the PhoP and PmrA proteins. The start and end positions of the protected regions are given relative to the transcription start site immediately downstream of the protected region Click here for additional data file.Figure S4Delay length distributions for the feedforward connector loop (FCL) and feedforward loop (FFL). Activation and deactivation delays correspond to the situations when the PhoP-P input of the circuits was activated and deactivated, respectively. The delays are defined as differences between the activation and deactivation times for the FCL (or FFL) and those for the direct regulation circuit . The dis(0.01 MB PDF)Click here for additional data file.Figure S5Delay length distributions for the feedforward connector loop (FCL) and feedforward loop (FFL). Activation and deactivation delays correspond to the situations when the PhoP-P input of the circuits was activated and deactivated, respectively. The delays are defined as differences between the activation and deactivation times for the FCL (or FFL) and those for the direct regulation circuit . The dis(0.01 MB PDF)Click here for additional data file.Figure S6Delay length distributions for the feedforward connector loop (FCL) and feedforward loop (FFL). Activation and deactivation delays correspond to the situations when the PhoP-P input of the circuits was activated and deactivated, respectively. The delays are defined as differences between the activation and deactivation times for the FCL (or FFL) and those for the direct regulation circuit . The dis(0.01 MB PDF)Click here for additional data file.Table S1List of strains and plasmids used in this study.(0.14 MB PDF)Click here for additional data file.Table S2Intervals used for model parameter sampling. The superscripts for the parameters are omitted, because the same intervals are used to sample the corresponding parameters for the FCL, FFL, and the direct regulation circuit.(0.06 MB PDF)Click here for additional data file.Text S1Supplementary methods.(0.08 MB PDF)Click here for additional data file.


Sox10, a member of the Sry-related HMG-Box gene family, is a critical transcription factor for several important cell lineages, most notably the neural crest stem cells and the derivative peripheral glial cells and melanocytes. Thus far, only a handful of direct target genes are known for this transcription factor limiting our understanding of the biological network it governs.We describe identification of multiple direct regulatory target genes of Sox10 through a procedure based on function and conservation. By combining RNA interference technique and DNA microarray technology, we have identified a set of genes that show significant down-regulation upon introduction of Sox10 specific siRNA into Schwannoma cells. Subsequent comparative genomics analyses led to potential binding sites for Sox10 protein conserved across several mammalian species within the genomic region proximal to these genes. Multiple sites belonging to 4 different genes  were shown to directly interact with Sox10 by chromatin immunoprecipitation assay. We further confirmed the direct regulation through the identified cis-element for one of the genes, extracellular superoxide dismutase, using electrophoretic mobility shift assay and reporter assay.In sum, the process of combining differential expression profiling and comparative genomics successfully led to further defining the role of Sox10, a critical transcription factor for the development of peripheral glia. Our strategy utilizing relatively accessible techniques and tools should be applicable to studying the function of other transcription factors. Identifying direct targets of a given transcription factor is a key step in dissecting the regulatory network wherein the detailed function of the transcription factor can be understood. Early attempts included ectopic over-expression followed by expression profiling for identification of candidate genes which in turn entailed extensive sequence analyses of the promoter region . AnotherSeveral recently developed molecular tools and technical advances have been paving ways for achieving the same goal in a more efficient high throughput manner. For example, microarray expression profiling has dramatically improved the differential screening, and completion of genome sequencing for several species now allows a rapid identification of genes in the genomic context as well as the application of comparative genomics analysis for conserved DNA elements. Furthermore, RNA interference (RNAi) revolutionized somatic cell genetics making it possible to perform loss-of-function experiments conveniently while chromatin immunoprecipitation (ChIP) assay provides a reasonably efficient means of testing direct interaction between proteins and DNA within cellular context. It had been envisioned that these tools, in variable combinations ,4, can bSox10 is an HMG box family transcription factor expressed in neural crest stem cells and a subset of neural crest derived lineages -9. At thTo date, only a handful of direct regulatory targets of Sox10 are known. In the glial lineage, genes such as the P0, myelin basic protein (MBP), ciliary neurotrophic factor (CNTF) and connexin 32 (Cx32) have been shown to be direct targets of Sox10 -17. In mHere, we describe a stream-lined procedure in identifying such direct regulatory targets. We took advantage of RNAi technique to screen for genes in a Schwannoma cell line that are down-regulated upon introducing small interfering RNA (siRNA) specific for Sox10. Genes with significant level of down-regulation were subjected to comparative genomics analysis for identification of potential Sox10 binding sites that are conserved in multiple mammalian species. Next, we confirmed the physical interaction between Sox10 and the predicted target binding sites by chromatin immunoprecipitation (ChIP) assay leading to identification of at least 4 novel direct regulatory targets, proteolipid protein (PLP), Sox10, extracellular superoxide dismutase (SOD3), and pleiotrophin (Ptn). We further confirm the direct regulation of SOD3 by Sox 10 through the identified cis-element using electrophoretic mobility shift assay (EMSA) and reporter assay.We have previously reported a case of apparent transdifferentiation of RT4D6 Schwannoma cells into smooth muscle cells upon introduction of an siRNA specific to the transcription factor Sox10 . AccompaGene expression profiling was carried out with two independent pairs of RT4D6 cell samples each treated with wild type and mutant siRNAs, and 88 affy probes that showed at least 4-fold down-regulation upon introduction of Sox10 specific siRNA were identified  reasoning that functional Sox10 binding sites are likely conserved across mammalian species. This led to further refinement of putative targets to 95 candidate sites belonging to 24 genes  for the 44 genes. The MATCH program  in the Tnal 10.2  was usednal 10.2 ,25. As tWe next set up a filtering step utilizing the cross-species conservation information. The phastCons score database  of 9 speFor subsequent examination by chromatin immunoprecipitation (ChIP) assay, only the genes with 6-fold or higher level of change from the microarray screen were considered. The identified TFBS were visually inspected for 100% sequence conservation of the core sequence in human, mouse, rat, and dog, and 23 different putative Sox binding sites in 10 genes were finally selected for ChIP assay. The name of the genes and genomic locations for the candidate TFBS are listed in the table E. coli strain BL21 was transformed with the construct, and protein was purified using glutathione agarose beads after IPTG induction.For the production of GST fusion protein, HMG domain of Sox10 (a.a. 80~185) was PCR amplified from a mouse cDNA clone and inserted into pGEX-4T-1 plasmid vector (Amersham). The sequences of primers used were 5'-AGAATTCCTCAGCGGCTACGACTGGACG-3' and 5'-ACTCGAGCTGGGCTGCCTTCCCGTTC-3' . The E. pFlag-Sox10 was generated by PCR amplifying the full length Sox10 with a pair of PCR primers, 5'-AGCGGCCGCGCCGAGGAACAAGACCTATC-3' and 5'-AGTCGACCTCTAAGGTCGGGATAGAGTCG-3' and inserting the product into pFLAG-CMV vector (Invitrogen) in frame with the FLAG epitope.5 RT4D6 cells on a 100 mm dish using Effectene transfection reagent (Qiagen). Typically, cells pooled from 4 plates (2 × 106 cells) were processed per assay. ChIP assays were performed using EZ ChIP™ Chromatin immunoprecipitation kits (Upstate) following the manufacturer's protocols except for the elution of protein-DNA complex. Specifically, immunoprecipitated DNA-protein complexes were eluted twice by adding 100 μl elution buffer containing 3× FLAG peptide  for 30 min at 4°C. Detailed protocols are available upon request. For the 23 potential Sox10 binding sites subjected to ChIP assay, only 17 PCR primer pairs were used as cis-elements within 200 bp were examined with the same primer pairs. Primer pairs used for PCR are listed in the additional data file For the ChIP assay, 2 μg of pFlag-Sox10 plasmid was transfected into 5 × 1032P end-labeled for use as the probe. Unlabeled oligonucleotide probe and core sequence mutated oligonucleotide probe  were used as the specific and non-specific competitors respectively. 32P end-labeled probe was incubated with GST-SOX10 HMG protein for 25 min at room temperature in 35 μl of reaction mixture . For competition experiments, GST-SOX10 HMG protein was preincubated with specified amount of unlabeled probe for 15 min before exposure to the labeled specific probe. The protein-DNA complexes were resolved by 5% polyacrylamide gel electrophoresis in 1× TBE buffer.A pair of oligonucleotides  encompassing the candidate SOX10 binding sequence (-590 to -610 5' to the transcription initiation site of SOD3) were hybridized and Fluorescence real time PCR was performed with SYBR Green PCR master mix (Applied Biosystems) following the manufacturer's protocol. Oligonucleotide primers used as part of the ChIP assay are listed in additional data file 4 RT4D6 cells were seeded on 12 well plates 24 hours prior to transfection. SOD3 reporter plasmids (300 ng), pCMV β-gal plasmids (50 ng), and specified combinations of wild type and mutant siRNAs (combined concentration of 100 nM) were transfected using Lipofectamine 2000 (Invitrogen). After 24 hours, cells were harvested and luciferase activity was measured and normalized using Luciferase Assay System (Promega)The promoter region of SOD3 (-739 to +76) was PCR amplified with a pair of primers  using the BAC clone CH230-303F11  as the template. The resulting DNA fragment was inserted into the 5' region of pGL3 basic promoter (Promega) to produce the wild type reporter construct. The reporter construct with mutated Sox10 binding site was generated using QuickChangeII Site-directed Mutagenesis Kit and a pair of mutagenizing oligonucleotide primers . For reporter assays, 3 × 10KL carried out the ChIP assay and contributed to preparing the manuscript. SN carried out bioinformatics analyses. EC carried out the RTPCR and reporter assay. IS carried out the EMSA. JL organized the data and contributed to preparing the manuscript. SL designed the bioinformatics analyses and drafted the manuscript. JK designed the 'wet-lab' experiments and drafted the manuscript.Genes down-regulated by Sox10 specific siRNA. The 88 probe sets/genes that show more than 4 fold down-regulation by wild type Sox10 siRNA. 44 genes in bold letters with RefSeq ID were subsequently subjected to comparative genomics analysis.Click here for fileCandidate Sox10 protein binding sites. The 95 conserved putative SOX10 binding sites. The location of each site on rat chromosome is indicated and linked to UCSC genome browser.Click here for fileOligonucleotide primers used for ChIP assay. List and sequence of oligonucleotide primers used for ChIP assay.Click here for fileOligonucleotide primers used for RT-PCR. List and sequence of oligonucleotide primers used for RT-PCRassay.Click here for file


BRLF1 revealed that interaction with a methylated CpG ZRE (RpZRE3) is key to overturning the epigenetic silencing of the gene.Epstein Barr virus (EBV) infects the majority of the human population, causing fatal diseases in a small proportion in conjunction with environmental factors. Following primary infection, EBV remains latent in the memory B cell population for life. Recurrent reactivation of the virus occurs, probably due to activation of the memory B-lymphocytes, resulting in viral replication and re-infection of B-lymphocytes. Methylation of the viral DNA at CpG motifs leads to silencing of viral gene expression during latency. Zta, the key viral protein that mediates the latency/reactivation balance, interacts with methylated DNA. Zta is a transcription factor for both viral and host genes. A sub-set of its DNA binding sites (ZREs) contains a CpG motif, which is recognised in its methylated form. Detailed analysis of the promoter of the viral gene Here we question whether we can use this information to identify which host genes contain promoters with similar response elements. A computational search of human gene promoters identified 274 targets containing the 7-nucleotide RpZRE3 core element. DNA binding analysis of Zta with 17 of these targets revealed that the flanking context of the core element does not have a profound effect on the ability of Zta to interact with the methylated sites. A second juxtaposed ZRE was observed for one promoter. Zta was able to interact with this site, although co-occupancy with the RpZRE3 core element was not observed.This research demonstrates 274 human promoters have the potential to be regulated by Zta to overturn epigenetic silencing of gene expression during viral reactivation from latency. Epstein Barr virus (EBV) infects and causes several diseases in humans including Burkitt's lymphoma, nasopharyngeal carcinoma, Hodgkin's disease, post-transplant lymphoproliferative disorder and infectious mononucleosis (glandular fever) EBV latency is disrupted following physiological activation of B-lymphocytes, through the expression of Zta  BSLF2+BMLF1BRLF1BZLF1BHLF1 and BHRF1EBNA1 promoter Fp BRRF1BMRF1DHRS9 EGR1CIITAIL-8IL-10IL-13 BZLF1 gene, Zp, which contains the two functional ZIIIA and ZIIIB ZREs within a total span of 20 nucleotides Zta has the unusual feature of interacting with a sub-set of ZREs that contain a methylated CpG motif BRLF1, EBV BRRF1 and human EGR1BRLF1 provided compelling evidence that the interaction of Zta with a methylated ZRE was instrumental in reactivating EBV into lytic cycle in B-lymphocytes BRLF1 gene includes three ZREs in the promoter proximal region BRRF1 gene contains two CpG-containing ZREs, which Zta only interacts with in their methylated states EGR1 gene, which is recognized by Zta in its methylated form, suggests that EBV may overturn epigenetic silencing of host genes in order to modulate the host cell environment To date, three genes with CpG-containing ZREs have been studied: EBV TCGCGAA), which was clearly shown to be required for overturning epigenetic silencing of BRLF1 in B-lymphocytes The RpZRE3 core element (TCGCGAA RpZRE3 core element.Promoter regions (defined as −500 to +1 base pairs from the transcription start site) of a sample of human protein-coding genes in Ensembl (49) An identical screen was undertaken on the entire human genome from Ensembl (50) 32P]ATP (30 µCi) using polynucleotide kinase (Roche). 12 pmol of the complementary oligonucleotide strand was added and incubated with the labelled single strand at 95°C for 2 minutes, 65°C for 10 minutes, and 37°C for 30 minutes to anneal the strands. The concentration of the probe was 33.3 nM. The oligonucleotides comprised the core 7-mer sequence surrounded by 20 nucleotides corresponding to the cognate flanking sequence for each promoter and were synthesised. Where indicated in the figure, the central CpG motif was methylated on both cytosines during synthesis (Sigma).Double stranded labelled DNA probes were made by labelling 6 pmol of oligonucleotide (27 nt long) at the 5′ end with [γ-in vitro translated using wheatgerm extract (Promega). This was incubated with the labelled probe  for 30 minutes at room temperature, before the sample was fractionated on an 8% native polyacrylamide gel at 100 volts for 1 hour. Following detection of the radio labelled DNA using a Storm phosphorimager, the relative signals were quantitated using ImageQuant software .Zta protein was Competition EMSAs were carried out with a 100X excess of a double stranded non-labelled oligonucleotide in addition to the standard EMSA reaction. Equal quantities of each of the complementary oligonucleotides were annealed in the same manner as the labelled probes and diluted to yield a solution concentration of 3.33 µM. This was added to the EMSA reaction at a final concentration of 333 nM (i.e. 100X excess).The oligonculeotides used were double strand versions of the following sequences (5′-3′):GGTGCGTCACTCGCGAAGTGGAATTTGXPC GCTTCCCGGCTCGCGAAAGGGAGGACCZC3H8 TCCCCCACTGTCGCGAAGCTCCCGCCCHDAC2 CCGCGGCGTCTCGCGAAGGGAGGGGCGMNT GGGCGGCTCCTCGCGAAGCTCCACGGCCyclin L2 GTTTATAGCATCGCGAATTTTGAGTGCRpZRE3 CCGGGGAGGCTCGCGAATCGCGGTCCACAPN2 CGTCCCAGCGTCGCGAACCACAGCGGCCDO1 GGCGCGCAGCTCGCGAAATGCCCGGCGFALZ GCTTCGGCCCTCGCGAAACTCCGCCCGKIF1B TCGGCCGGGCTCGCGAAGGGACGCCCGLLGL1 GGATCCCGGGTCGCGAAGGGCAGCCCALMO4 CCTCCTGCTCTTCGCGAACCGCCCCGCMBD4 AGCCGCTCCCTCGCGAAAGTTGGCCCCPLEKHJ1 CTTCCTGGGGTCGCGAACTTCCCGGGCPRKD1 CGCCCGCTACTCGCGAAGCCCAGCCCGSEC14L GCTGCGCTTCTCGCGAAAGGGCAGGCATADA3L CCGCGCCCCATCGCGAAGATCCGGAGCTOP2B GGTGCGTCACTCGCGAAGGGAGGGGCGXPCLMNTR CCGCGGCGTCTCGCGAAGTGGAATTTGMNTLXPCR GGTGCGTCACCCCCTTAGTGGAATTTGXPC Mcore GGTCCGCCTCCCCCTTAGTGGAATTTGXPCLM3Mcore GATCCACCCCTTAGAGGAAAACATACGAP1 mut The transcription factor binding site prediction program PROMO BRLF1 promoter (Rp), formed the RpZRE3-promoter-6 data-set.The initial search for RpZRE3-core elements in a sample of human promoters revealed 67 genes with an RpZRE3-core element. 5 of these that are involved in gene regulation were selected for DNA binding analysis. These 5 genes were ZC3H8 (ENSG00000144161), HDAC2 (ENSG00000196591), XPC (ENSG00000154767), MNT (ENSG00000070444) and CyclinL2 (ENSG00000116148) and together with the RpZRE3 element from the viral in vitro translated Zta protein. Zta protein/DNA complexes formed readily with the oligonucleotides from all six promoters (DNA-binding assays were undertaken with the methylated forms of each of the 5 human promoters using 27mer double strand oligonucleotides encompassing the 7-nucleotide core element and 10-nucleotide flanking region on each side together with RpZRE3 from Rp. Electrophoretic mobility shift assays (EMSA) were undertaken with romoters . The speBRLF1 promoter (RpZRE3-promoter-6 data-set), these form the RpZRE3-promoter-18 data-set.The complete human genome was scanned for additional RpZRE3-core elements. This resulted in a data-set of 274 genes, denoted the RpZRE3-promoter-274 data-set see . To asseOligonucleotides were designed using the same principles with 10 nucleotides of flanking sequence on each side of the RpZRE3-core element, and the ability of Zta to interact with each site in its methylated form was assessed. The EMSA analysis revealed that all twelve of the newly identified methylated promoters were recognised by Zta .This analysis demonstrated that for the RpZRE3-promoter-18 data-set, the flanking sequence surrounding the methylated core 7-mer element did not have a profound effect on the ability of Zta to interact with promoters and therefore the core element was sufficient to facilitate binding. This was illustrated by the generation of a PWM using sequence from the RpZRE3-promoter-18 data-set , which rZta is able to recognise many response elements which do not contain a CpG motif and therefore do not have a methylated core element (Class I ZREs) A series of DNA binding experiments with the oligonucleotides representing the human promoters in the RpZRE3-promoter-18 data-set, were undertaken comparing non-methylated with methylated RpZRE3-core elements. The interaction of Zta with the sites was quantitative, as demonstrated by the reduction in complex formation as the Zta protein concentration was titrated on each of the methylated promoters . All barZta displayed a reproducible interaction with the non-methylated XPC oligonucleotide; the interaction reached 50% of the binding observed with the methylated oligonucleotide . This raTo identify whether sequences conferring methylation independent recognition was confined to a specific flank of XPC, a series of mutant oligonucleotide probes that exchanged flanking sequences between XPC and a class III site (MNT), that is not recognised when non-methylated, were designed. Analysis of the interaction with Zta by EMSA revealed that the ability to confer Zta binding resided within the 5′ sequence of XPC; the hybrid XPCLMNTR was able to bind but MNTLXPCR was not . This suCCGCCT). The ability of Zta to interact with the double AP1/core mutant site (XPCLM3Mcore) was compared with the core-only mutant. Dual mutation of the predicted AP1 site and the core abrogated the ability of Zta to interact with the XPC oligonucleotide, demonstrating that binding of Zta to the non-methylated XPC site resided with this AP1 site .BZLF1 and a cellular promoter EGR1. In the BZLF1 promoter, the elements are situated with 12 base pairs between the central nucleotides; both can be occupied simultaneously and both are functionally relevant EGR1 promoter, the elements are immediately adjacent with just 8 nucleotides between the central nucleotides in vivoXPC promoter places the elements 8 nucleotides apart, identical to EGR1, and there is no evidence from the DNA-binding experiments for simultaneous occupation of the sites. As one ZRE is recognised in a methylation-dependent manner and the other is recognised when non-methylated, it is possible that the arrangement may provide a fail-safe mechanism to ensure that the gene is regulated in both its methylated and non-methylated states.The identification of two adjacent juxtaposed ZREs in the XPC promoter presents an interesting problem; can both sites be occupied at once? The natural juxtaposition of ZREs has been previously described for a viral promoter; in vivo is technically challenging given both the scarcity of memory B-lymphocytes in peripheral circulation and the infrequency of latency disruption in vivo.The simple computational approach taken to identify the location of this methylation-dependent and novel DNA-binding motif in human gene promoters identified 274 genes potentially regulated by overcoming epigenetic silencing during the viral replicative cycle. Given the known functions of Zta in reprogramming viral gene expression, disrupting cell cycle control and replicating viral DNA, it is interesting to note that the Gene Ontology terms that are over-represented in the RpZRE3-promoter-274 gene-set are largely involved in transcription, chromatin re-modelling and mitosis. This strongly suggests that Zta may activate this set of cellular genes in order to accomplish these functions. Testing whether these genes are activated during latency disruption in memory B-lymphocytes cis-regulator module which may illuminate the regulation of these host and viral genes.The co-location of the RpZRE3 core element in 274 human promoters is unlikely to have been driven by an evolutionary advantage to the virus, but may reflect the involvement of a cellular transcription factor interacting with the same element. This would suggest that this set of genes share a common mode of regulation during human development or differentiation. Furthermore, it will be interesting to question whether co-occurrence of the RpZRE3-core element with other transcription factor binding sites forming a co-operative Table S1RpZRE3-promoter-274 data-set: A table of genes which contain a RpZRE3 core element in the 500 bp promoter region.(0.05 MB XLS)Click here for additional data file.


S. cerevisiae relies on pA site recognition by 3′ end processing factors. Here we demonstrate the existence of two alternative termination mechanisms that rescue polymerases failing to disengage from the template at pA sites. One of these fail-safe mechanisms is mediated by the NRD complex, similar to termination of short noncoding genes. The other termination mechanism is mediated by Rnt1 cleavage of the nascent transcript. Both fail-safe termination mechanisms trigger degradation of readthrough transcripts by the exosome. However, Rnt1-mediated termination can also enhance the usage of weak pA signals and thereby generate functional mRNA. We propose that these alternative Pol II termination pathways serve the dual function of avoiding transcription interference and promoting rapid removal of aberrant transcripts.Transcription termination of RNA polymerase II (Pol II) on protein-coding genes in  S. cerevisiae transcribes genes for either polyadenylated mRNA or noncoding RNA such as small nuclear RNA (snRNA) and small nucleolar RNA (snoRNA). In both cases, Pol II termination is tightly coupled to RNA 3′ end formation, although different cis- and trans-acting elements are involved. Transcription termination of mRNA-encoding genes relies on recognition of the polyadenylation (pA) signal by cleavage/polyadenylation complexes that trigger Pol II disassembly from the template by a still-uncertain mechanism (RNA polymerase II (Pol II) in echanism . Accordiechanism .S. cerevisiae occurs by a different, specific mechanism involving the NRD complex (NRD) comprising Nrd1, Nab3, and Sen1 , this is activated and the transcript is stabilized by Pap1-mediated polyadenylation. Finally we analyze the crosstalk between pA-dependent, NRD- and Rnt1-induced termination pathways in the endogenous NPL3 locus and on reporter plasmids.We present evidence that malfunction of pA-dependent termination due to either mutations in CYC1 transcribed from the GAL1 promoter, and the endogenous PMA1 gene , possibly reflecting promoter-restricted Pol II. While the single rat1-1 mutant showed little increase in transcription beyond the poly(A) signal, the single sen1-1 mutant did show some increase over probes 4–8, indicating a defect in Pol II termination. Higher readthrough levels were detected on the double rat1-1 sen1-1 mutant . Together, these results indicate that, in the absence of functional Rat1, NRD acts to terminate readthrough transcription.We have shown that Sen1 cooperates with Rat1 to promote Pol II transcription termination on two different mRNA-coding genes, MA1 gene . To exteonstruct A. As in 1 mutant B, indica1 mutant . To testmination . TRO anaGFP gene C, simila1 sen1-1 B. The rarat1-1. Chromatin immunoprecipitation (ChIP) analysis across endogenous ADH1 in wild-type (WT) cells reveals Nrd1 signal spread across the whole gene peaking at the 5′ region and downstream of the pA signal  or S5-P (3E8) CTD-specific antibodies was performed on ADH1. As shown above, shifting rat1-1 cells to nonpermissive temperature resulted in Pol II readthrough signals over probes A3 and A4  and higher levels than in the WT at A3 and A4 . Thirteen clones had 3′ ends in the first cluster region around the fifth Nrd1-binding site, while 19 were in the second cluster mainly at the first UCUU Nab3 sequence. A further five clones mapped around 70 nt downstream of the last UCUU repeat. The precise sites are indicated by arrows  to evaluate their impact on termination. Mutations in the second cluster clearly reduced the number of transcripts ending in this region, as shown by a decrease in the intensity of the 670 bp PCR product  to form a new plasmid, pKGGRCS . This argues against a role for Rnt1 in recruiting Sen1 to mediate termination. To establish that RCS generates an entry site for Rat1, we performed TRO analysis on a strain bearing a methionine-repressible RAT1 gene (MET1p::RAT1). This depletes Rat1 from the cell after 20 hr growth in media with methionine , presumably because nascent RNA is rapidly degraded. The upper band observed in ΔpA corresponds to a cryptic pA site previously reported for pGCYC1-512  polymerase, did not alter levels of kanMX4 mRNA (data not shown). These results show that RCS can enhance utilization of cryptic pA sites.In parallel with steady-state analysis of pGCYC1RCS, we assessed mRNA accumulation in the pKGG-based system used above for termination assays . Even thmination B, it onlA levels C and 5D.nstructs E. In conGAL10 mRNA displayed heterogeneous 3′ ends over the poly(A) signal region . However, 3 out of 16 clones analyzed were longer, indicating that a proportion of polymerases fail to terminate at the pA site. This is in agreement with recent studies showing that NPL3 pA signal is weak and Npl3 itself regulates pA site usage by competing with 3′ end cleavage and polyadenylation factors , although a small proportion still continued until GIP17 pA site  with a rat1-1 allele causes a strong increase in mRNA encoding gene readthrough transcription. Significantly, these readthrough transcripts end at Nrd1- and Nab3-binding sites. Discrimination between pA- or NRD-dependent termination correlates with the phosphorylation state of Pol II CTD. Nrd1 binds preferentially to S5-P CTD, and S2-P antagonizes this interaction  using genome-wide analysis has revealed that several additional genes to NPL3 generate 3′ flanking region Pol II profiles and detectible readthrough transcripts in rnt1Δ strains, indicative of Rnt1-dependent termination.g to RNA . Indeed,g to RNA . Interesg to RNA . PossiblS. cerevisiae must reflect strong evolutionary selection to maintain separate transcription units.Overall, the data presented in these studies demonstrate the existence of three different termination pathways for protein-coding genes. The dominant pA-dependent mechanism exclusively generates stable mRNA. If Pol II fails to terminate at the pA site, then NRD- or Rnt1-mediated termination may act as a fail-safe mechanism. This remarkable triplication of Pol II termination mechanisms in Strains, plasmids, and oligonucleotides used in this study are listed in Yeast strains were grown in minimal media at 25°C and transferred to 37°C 2.5 hr prior to TRO or ChIP assays. YCBA63 and DRY41-4A were grown in selective media with 2% galactose to OD 0.6 and then transferred to selective media with 2% glucose for 20 hr at 25°C and 2.5 hr at 37°C. YAEH97 was grown at 30°C to OD 0.5 in SD medium lacking methionine, then diluted to OD 0.03 and grown for 20 hr in medium supplemented with methionine (5 mM).ChIP was performed as described , with moTRO analysis and probes for pKGG, pGCYC1, and derived plasmids are as described . LocatioLM-RT-PCR was performed as described  with mod3′RACE was performed with the primers listed in cRT-PCR was performed as previously described , with 5 Northern blot was performed from 8 μg of total RNA or 400 ng of mRNA isolated with Oligotex mRNA kit.qRT-PCR employed 1 μg of total RNA with oligodT and SuperScript III following the manufacture indications. Two microliters were used as template for each quantitative PCR, using primers as listed in


Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial. Numerous target gene scoring methods have been used in the past. However, their suitability for the task and their performance remain unclear, because a thorough comparative assessment of these methods is still lacking. Here we present a systematic evaluation of computational methods for defining TF targets based on ChIP-seq data. We validated predictions based on 68 ChIP-seq studies using a wide range of genomic expression data and functional information. We demonstrate that peak-to-gene assignment is the most crucial step for correct target gene prediction and propose a parameter-free method performing most consistently across the evaluation tests. a priori definition of any parameter — all parameters are ‘learned’ from the data. This and other methods tested were implemented in a freely available software package for future testing and application to other ChIP-seq datasets.Transcription factors (TFs) are the main regulators of gene transcription. Thus, knowing the genes that are targeted by a specific TF is of utmost importance for understanding developmental processes, cellular stress response, or disease etiology. Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) allows for measuring the genome-wide binding of TFs. Several computational methods have been used for inferring the genes that are targeted by TFs using this binding information, but a thorough evaluation of their performance has not been performed so far. Here we present an assessment of a range of TF-target-calling methods using 68 ChIP-seq datasets. It turns out that the first step of the scoring, the assignment of binding events to genes, is the most important for correctly calling target genes. Our evaluation revealed important performance differences between the target-calling methods, with some simplistic methods exhibiting a particularly poor performance compared to more elaborate scorings. One of the methods is particularly attractive, because it does not require the  Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) is a powerful technique for the genome-wide profiling of protein-DNA interactions, histone modifications, and nucleosome positions Published ChIP-seq studies often use ad-hoc approaches for calling target genes, such as defining as targets all genes with a peak within a certain distance from the transcription start site (TSS) www.cellularnetworks.org. This package greatly facilitates the parallel testing of the various methods on any given ChIP-seq dataset.In this study, we systematically compared different TF target prediction approaches. We focused on the genome-wide prediction of targets using ChIP-seq data alone, because it is often the case that only this data is available. ChIP-seq-based target scores can be later integrated with expression data or any other information that could enhance target identification, if such data is available We have structured the TF-target scoring based on ChIP-seq data into three main steps that are conducted on a gene-by-gene basis : (1) peaWe distinguish window-based and window-free peak-to-gene assignment methods. The simplest approach is to define a fixed window around each TSS and assign all peaks within this window to the corresponding gene  and downstream (3′) of the TSSs. Thereby it implicitly accounts for a potential bias for preferential upstream (or downstream) binding.In the simplest approach, which we refer to as Binary , each ge Linear, , which iak Chen,  [12]. WesestGene , which iBinary and Chen methods do not require any integration; Binary does not depend on the number of peaks and Chen assigns only one peak to each gene. All other methods assume an additive influence of multiple binding events on the gene expression. Therefore, the total TF-gene association score is computed as the sum of the individual scores of the peaks assigned to that gene.The last step consists of integrating the scores of all peaks that have been assigned to a given gene. The Binary and Linear methods we tested several window sizes and chose the optimal one for the comparison with other methods . TF-target calling was performed using two sets of ChIP-seq experiments: The first is the HemoChIP compendium consisting of 53 ChIP-seq studies performed in mouse hematopoietic cells We have evaluated six target assignment methods that are representative for the different classes of methods being used in the recent literature . For theAn obvious way to compare the performance of target scoring methods would be to use known target genes as benchmarks. However, since relatively few target genes have been validated in small scale studies, we have chosen to conduct a range of unbiased tests employing different genome-wide expression datasets and using functional consistency of the predicted targets as a criterion.First, we used perturbation expression profiling data, i.e. measurements of differential expression after inhibiting a specific TF either through RNA interference (RNAi) or genetic modifications. Functional targets of a TF are expected to respond stronger to the perturbation of a TF than non-targets. Even though perturbation experiments have indirect effects on genes that are not direct targets of the TF, a bigger overlap with perturbation expression data is indicative of better target scoring Binary and Chen performed worst suggesting that considering peak distances from the TSS (neglected by Binary) as well as proper peak-to-gene assignment are crucial aspects of target scoring. In addition, the Chen method suffers from the fact that the scoring is based on one peak only and thus is very sensitive to experimental variation in ChIP-seq data, such as miscalled peaks. The best performing method in this test was ClosestGene  of genes . This gisestGene . SimilarsestGene .ClosestGene performs worse than Linear, Ouyang and Cheng on the HemoChIP data  that a given TF would change its activity. As an effect true targets of the TF should exhibit differential expression between these two conditions. This latter analysis rests on more physiological conditions than engineered TF activity changes, but it has the drawback that several TFs may change their activity between the two cell states. Thus, observed expression differences cannot exclusively be attributed to the TF of interest. This analysis was performed similarly as for perturbation expression data . The reshIP data . SimilarhIP data .ClosestGene had the largest number of relevant GO terms in both cases (HemoChIP and ESChIP). Similar conclusions could be drawn when comparing other sets of target genes . The relatively weak performance of Binary and Chen correlates with the fact that they both ignore the number of peaks in the proximity of a gene. Similar conclusions could be drawn when comparing other sets of target genes  used in this method. Methods using smaller window sizes  or relying on fewer proximal peaks (Chen) show a smaller bias in favor of gene-rich regions. Importantly, Ouyang, which assigns all peaks to each gene (but with decreasing weight) shows a stronger bias than methods considering only peaks in the proximity of the TSS. Compared to the other methods, ClosestGene shows a bias in favor of gene-poor regions, which can be explained by the fact that in a gene-poor region more peaks are likely to be assigned to a single gene. These gene density biases are persistent across different sets of targets  increases the likelihood of finding a peak close to any gene in such region. In order to test the relevance of this issue for target gene identification we compared the gene densities around the highest scoring targets for the different scoring methods . The str targets .In this study we have presented the first systematic comparison of ChIP-seq based TF-target prediction methods using a wide range of ChIP-seq studies from different biological systems. We have developed robust evaluation tests that account for several biases and potential pitfalls. Structuring the target scoring process into three phases helped systematizing the methods and it revealed the peak-to-gene assignment as the most important step. Although all methods resulted in TF-target predictions that are better than random, we observed important differences between the methods.Binary and Chen clearly performed the worst. In contrast, all other methods accounting for the number and distance of peaks around a TSS consistently performed better across all our tests. All of these methods  performed similarly, but we observed a small advantage of ClosestGene especially in the expression data tests and GO enrichment. Although the performance gain was small it was consistent when changing thresholds (see ClosestGene approach presented in this study is a combination of three ideas that have not been used in this combination in the past: (1) assigning peaks to their closest gene, (2) scoring peaks based on the distribution of all peaks around the TSSs, and (3) considering (summing) all peaks assigned to a particular gene. Contrary to all other methods tested, ClosestGene does not require the definition of any parameters, giving it another advantage.A common principle underlying all of these methods is the scoring of putative TF-targets by correlating the positions of TF binding sites with the positions of TSSs. Even though genomic distance is not a perfect predictor for TF-target interactions  this approach yields statistically significant predictions. Individual targets would however have to be validated through functional assays. Out of six compared methods, olds see . This inAn obvious limitation of all methods tested herein is their inability to predict distal regulatory events. Matching long-range DNA interaction data obtained through methods such as 5C Cheng), methods assessed in this paper use the list of identified peaks as input. Since all target prediction methods were faced with the same set of peaks, the evaluation tests allow for a fair comparison of the predictions.Another factor influencing the quality of the TF target prediction from ChIP-seq data is the quality of peak calling itself. Different peak calling algorithms result in different numbers of identified peaks and/or different peak intensities, which in turn influences the performance of target identification methods. All, but one  or pileup height at peak summit as returned by MACS (ESChIP). Peaks with intensity above 200 were excluded from the analysis.www.ensembl.org) using the R package biomaRt For the perturbation analysis, 13 HemoChIP related and 8 ESChIP related expression datasets were collected . In the For the activity analysis of HemoChIP TFs, the normalized expression data were obtained from For the activity analysis of ESChIP TFs, we compared expression profiles for ES cells and differentiated (MEF) cells www.ensembl.org) using the R package biomaRt Genomic positions of the transcripts were retrieved from the Ensembl64 database .For the A gene was designated as a target with a score 1 if there was at least one peak within the window, and as a non-target with a score 0 otherwise. For this approach we chose a window , i.e. spanning the region from 5 kb upstream of the TSS to 5 kb downstream of the TSS .Linear the window was set to   score was implemented as described in http://archive.gersteinlab.org/proj/tftarget/. There are two variants of this method: (1) using a binding profile inferred directly form the wiggle file or (2) using a list of identified peaks as input. We used the first approach and following the original publication, we set the window size to  to calculate binding profile around the TSS.The target identification from profiles (TIP) method was implemented as described in The TF-gene association score was implemented as described in In the first step, each peak was assigned to its closest gene. When a particular gene was scored, only the peaks assigned to that gene, i.e. peaks closer to that gene than to any other, were considered. The peak scoring was derived from the observed peak-to-gene distance distribution.The peak-to-gene distance distribution was obtained by computing the distances between each TSS and all peaks within 1 Mb upstream and 1 Mb downstream of the TSS. Separate distributions for upstream and downstream binding were generated and the distances were pooled across all chromosomes. This distribution approximates the distribution of the distances of all peaks to all genes. The restriction of the distance to 1 Mb was applied in order to minimize artifacts for genes located close to the telomeres and due to different lengths of the chromosomes.Subsequently, individual peaks were scored using the cumulative distance distributions by computing the fraction of peaks observed at the given distance from the TSS or closer (again separately for upstream and downstream binding). This fraction , the overlaps between the top 500 genes ranked according to the TF-gene score in the representative ChIP-seq dataset and the top 500 of genes ranked according to the TF-gene score in other ChIP-seq studies for the same TF were calculated. For op genes .For each TF, for which perturbation expression data were available, the overlap between the top 500 genes ranked according to the TF-gene association score and the top 500 genes ranked according to the fold change between perturbation and control conditions was calculated. In case of the activity analysis respective fold changes from different conditions were used. Bindings were handled in the same way as for the consistency analysis. The overlap between the target scorings and expression data not only depends on the performance of the scoring methods but also on the compatibility between the ChIP-seq data and the expression data (e.g. depending on the cell types measured). In order to correct for this effect the average overlap achieved by all methods for a particular ChIP-seq - expression data pair was subtracted. Hence, this measures how much better (or worse) than average each method performed on a given dataset.We have validated that our conclusions are robust with respect to the choice of this set of top genes  and S6.For each TF the GO enrichment was analyzed using the ‘biological process’ GO category. Genes were ranked according to the target score and the enrichment of top 500 genes with specific GO functions was quantified with Fisher's exact test using the ‘elim’ algorithm from the topGO R package op genes  or p-valop genes .Figure S1Performance of Binary for different window sizes. Overlap of the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Overlap of the top 500 targets with the top 500 genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S2Performance of Linear for different window sizes. Overlap of the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Overlap of the top 500 targets with the top 500 genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S3Specificity of TF-target prediction methods. High specificity of target predictions means that there should be higher congruence with perturbation expression data from the same factor than with perturbation data from different factors. Here, we compare measuring consistency based on the overlap of the top 500 genes  with measuring consistency based on the sum of rank products . Specificity is quantified as the difference between the scorings obtained for matching data (matching ChIP-seq and expression data) versus non-matching data, expressed as the –log10 A, C) refers to comparing matching and non-matching pairings from the same cellular system. ‘Other category’  refers to comparing matching pairings with pairing HemoChIP ChIP-seq data with ES cell expression data and vice versa. Larger differences are expected in the latter case. ‘All’ using all genes for the scoring; ‘5000’ and ‘2000’ using only the top 5000 (or 2000) most variable genes. The dashed line corresponds to p-value = 0.05.(PDF)Click here for additional data file.Figure S4Significance of the targets recovery within top 500 genes. Z-scores of the overlap between the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments, (C) between erythroid and myeloid cells, (D) between undifferentiated (ES) and differentiated (MEF) cells. The difference between this visualization and (PDF)Click here for additional data file.Figure S5Targets recovery within different sets of genes (perturbation). Overlap of the top  300,  500 or  1000 targets with the respective number of genes differentially expressed in  HemoChIP and  ESChIP TF perturbation experiments.(PDF)Click here for additional data file.Figure S6Targets recovery within different sets of genes (activity). Overlap of the top  300,  500 or  1000 targets with the respective number of genes differentially expressed  between erythroid and myeloid cells or  between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S7Functional homogeneity of targets for different sets of targets. Number of significantly enriched GO terms among top  300,  500 or  1000 targets specific for a given cellular system and specific for the opposite cellular system for HemoChIP  and ESChIP . The specific terms are hematopoiesis or embryonic development related GO terms for HemoChIP and ESChIP, respectively.(PDF)Click here for additional data file.Figure S8Functional homogeneity of the targets for different enrichment significance thresholds. Number of significantly enriched GO terms specific for a given cellular system and specific for the opposite cellular system for HemoChIP  and ESChIP . The significantly enriched GO terms were defined as the ones with p–value<0.05A,B) or p–value<0.0001C,D) of the Fisher's exact test.(PDF)Click here for additional data file.Figure S9Consistency of target gene predictions for different sets of targets. Independent ChIP-seq experiments are available for some of the factors measured in the hematopoietic system. Consistency of target predictions is quantified as the overlap between the top (A) 300, (B) 500 or (C) 1000 target genes. Results are summarized based on intersecting targets from pairs of ChIP-seq experiments measuring the same transcription factor (‘same TF’), using ChIP-seq experiments from different factors, but the same system  and using ChIP-seq experiments from a different system . Numbers on the right are rounded p-values, measuring the significance of the difference between the overlaps (t-test). P-values for the comparison ‘same TF’ versus ‘HemoChIP’ are generally less significant than ‘HemoChIP’ versus ‘ESChIP’, because the number of comparisons (observations) is smaller.(PDF)Click here for additional data file.Figure S10Characterization of binding events with different numbers of co-factors. Density distribution of binding sites around TSSs as a function of the number of factors binding (as shown in legend). Figure shows a fragment of the density distribution plot; the density below the lines sums to 1 for distances from −1 Mb to 1 Mb from the TSS.(PDF)Click here for additional data file.Figure S11Characterization of binding events with different intensities. Density distribution of peaks around TSSs as a function of normalized peak intensity  for (A) HemoChIP and (B) ESChIP. Figure shows fragments of density distribution plots; the below the density lines sums to 1 for distances from −1 Mb to 1 Mb from the TSS.(PDF)Click here for additional data file.Figure S12Combination of peak-scoring criteria. Peaks are scored based on their distance to the TSS (Distance), intensity (Intensity) or number of co-binding factors at the same site (Co-binding) or combinations thereof (as indicated). Peak-to-gene assignment and score integration is done using the ClosestGene scheme. Overlap of the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Overlap of the top 500 targets with the top 500 genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S13Gene density in target regions for different sets of targets. Gene density (number of genes inside 1 Mb regions around the target gene's TSS) of the regions harboring the top 300 , 500  or 1000  genes across the studies in the HemoChIP  and ESChIP datasets .(PDF)Click here for additional data file.Figure S14Characterization of transcriptional regulators. Fraction of peaks binding at promoters, or co-occurring with P300 or CTCF binding. Overlap encompasses peaks falling in more than one of those categories. Regulators are grouped depending on the cellular system in which the corresponding ChIP-seq study has been conducted .(PDF)Click here for additional data file.Figure S15Performance of ClosestGene for different scorings. Z-scores of the overlap between the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments, (C) between erythroid and myeloid cells, (D) between undifferentiated (ES) and differentiated (MEF) cells. ClosestGene corresponds to ClosestGene using TF-specific peak-to-gene distance distribution. ClosestGene (symmetric) corresponds to the variant where a distribution symmetric around a TSS . ClosestGene (unspecific) corresponds to the variant where a distribution specific for another TF is used for peak scoring. Linear 1Mb corresponds to the variant where peaks assigned to the TSS are scored using linearly decreasing weights.(PDF)Click here for additional data file.Figure S16Peak-to-gene distance distribution. Peak-to-gene distance distribution for (A) OCT4 and (B) P300 used for peak scoring.(PDF)Click here for additional data file.Figure S17Performance of the OCT4 target prediction using different distributions. Z-score representing the significance of the overlap between top 500 targets and top 500 genes differentially expressed after Oct4 knock-down (‘perturbation’) or between ES and undifferentiated (MEF) cells (‘activity’) when scoring OCT4 peaks using OCT4 (‘OCT4’) or P300 (‘P300’) distributions.(PDF)Click here for additional data file.Figure S18Overlap between targets and differentially expressed genes. Median overlap between top 300, 500 and 1000 targets with the respective number of genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Median overlap between top 300, 500 and 1000 targets with the respective number of genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Table S1Transcription factors and data included in the study.(PDF)Click here for additional data file.Text S1Supporting text. (1) TF characterization based on ChIP-seq studies. (2) Importance of using TF-specific peak-to-gene distance distributions. (3) Incorporation of peak height or binding of co-factors does not improve target prediction. (4) Significance and robustness of evaluation methods. (5) Ranking is biased by non-changing genes. (6) Q-value calculation for ClosestGene scores.(PDF)Click here for additional data file.


Tri-methylated H3 lysine 4 (H3K4me3) is associated with transcriptionally active genes, but its function in the transcription process is still unclear. Point mutations in the catalytic domain of ATX1 (ARABIDOPSIS TRITHORAX1), a H3K4 methyltransferase, and RNAi knockdowns of subunits of the AtCOMPASS–like (Arabidopsis Complex Proteins Associated with Set) were used to address this question. We demonstrate that both ATX1 and AtCOMPASS–like are required for high level accumulation of TBP (TATA-binding protein) and Pol II at promoters and that this requirement is independent of the catalytic histone modifying activity. However, the catalytic function is critically required for transcription as H3K4me3 levels determine the efficiency of transcription elongation. The roles of H3K4me3, ATX1, and AtCOMPASS–like may be of a general relevance for transcription of Trithorax-activated eukaryotic genes. We provide a definitive answer to the question regarding the role of histone H3 lysine 4 tri-methylation marks in the transcription of two ATX1-regulated genes. Despite the proven correlation between the gene transcriptional activity and the level of H3K4me3 modification on the nucleosomes, whether H3K4me3 contributes to, or simply “registers,” active transcription has remained unclear. Another broader-relevance question is whether histone-modifying proteins are required for recruitment of the general transcription machinery, thus playing roles beyond their catalytic activity. Using a combination of gene deletion and specific point mutation analyses, we untangle overlapping effects and reveal that H3K4me3 is not required for TBP/Pol II recruitment to promoters but is critical as an activating mark for transcription elongation. The existing hitherto ambiguity about the role of H3K4me3 as an activating mark has been largely due to the unknown duality of the ATX1/AtCOMPASS functions: facilitating PIC assembly and producing H3K4me3 as an activating mark for transcription elongation. The H3K4me3 mark is generally associated with transcriptionally active genes The eukaryotic histone methyltransferases responsible for the H3K4me3 mark have diverged both evolutionarily and functionally into two families The mechanism of ATX1-dependent gene regulation in Arabidopsis involves features that are both similar and different from yeast Set1 and mammalian MLL models. A distinguishing feature of ATX1-dependent gene regulation is that ATX1 has dual roles upstream and downstream of the transcription start sites (TSS) of regulated genes Both SET and TRX family proteins  operate within specific complexes, called COMPASS or COMPASS-LIKE, respectively. Both types of complexes share three conserved subunits, WDR5- ASH2L- RbBp5 that are critical for methyltransferase activity of the respective SET or TRX catalytic subunit heat shock 70 (hsp70) gene Although it has been well established that knockdown or deletion of a COMPASS or COMPASS-like subunit results in reduced mRNA and H3K4me3 levels of specific genes RKY70  and LTP7  FLC gene Here, we study the roles of the Arabidopsis ATX1/COMPASS-like during the specific stages of transcription of two ATX1-regulated genes, WTo distinguish the discrete transcription stages dependent on H3K4me3 levels from effects caused by the structural disruption of ATX1/AtCOMPASS–like, we used a combination of RNAi-mediated knockdowns of AtCOMPASS–like subunits and specific point mutations to inactivate the catalytic domain of ATX1. We demonstrate that ATX1, AtCOMPASS–like, and H3K4me3 have distinct effects on PIC formation and the transition to transcription elongation. ATX1 and AtCOMPASS–like are required for efficient PIC formation. In contrast to the MLL1-regulated gene model AtWDR5-RNAi, AtASH2-RNAi or AtRbBp5-RNAi constructs. Knockdown lines produced less transcripts from the respective subunit genes, confirming efficient knockdown of their target mRNAs  regions of Wand LTP7 . AtWDR5 and LTP7  and a TAand LTP7 .WDR5-interacting) peptide of MLL1 The ATX1-AtWDR5 interaction was mapped further by Y2-H analysis and in pull down assays . DetaileRKY70 and LTP7 genes in atx1 mutant and wild type backgrounds, determined by ChIP-PCR with antiWDR5 antibodies, were strongly diminished in atx1 relative to the wild type background  to the target genes or whether the presence of AtWDR5 was needed for recruiting ATX1. The AtWDR5 levels at the Wckground . The resAtWDR5-RNAi knockdown lines  as this modification marks the transition of Pol II from the promoter (promoter clearance) to the sites of transcription initiation e levels , Table 1e levels , and thee levels  suggesteWRKY70 and LTP7 genes were strongly decreased in the RNAi lines . Less Pol II at the genes' 3, has a lesser effect on the Ser5P Pol II levels during the promoter clearance and transcription initiation, but is critically required for productive transcription elongation at the ATX-regulated genes.Collectively, the results indicate that AtCOMPASS–like plays a role at the promoters (lower TBP and Pol II levels in ATX1 T-DNA insertion mutant (atx1) or RNAi lines for depleting individual subunits of ATX1/AtCOMPASS–like decreases the amounts of both the intact complex and of H3K4me3, making it impossible to elucidate which of these alterations were affecting transcription. To distinguish the effects caused by changes in the structure of the ATX1/AtCOMPASS–like complex from effects caused by diminished H3K4me3 levels, we constructed an ATX1 mutant transgene, ATX1-set, containing point mutations expected to inactivate its catalytic methyltransferase domain while maintaining its structural integrity. This catalytically inactive ATX1-set gene contains 5 tyrosine to alanine mutations at positions that are evolutionarily conserved in ATX1 and MLL1 (see Methods). One of the conserved tyrosines (ATX1 Y1015) is known to be essential for the methyltransferase activity of the SET domain of human SET7/9 atx1::ATX1-set lines was verified by immunoblot analysis  indicated the WRKY 70 and LTP7 transcripts were not restored to their wild type levels  methyltransferase activity in vivo as ATX1 target genes had H3K4me3 levels that were identical to those in the atx1 background. The apparent structural integrity of the ATX1-set mutant supported, first, by its ability to be correctly recruited to its target genes and second, by its ability to efficiently recruit AtWDR5  levels at the promoters were similar to the wild type  at the promoter, the H3K4me3 mark generated by ATX1/AtCOMPASS–like is not required for transcription initiation, but is an activating mark for transcription elongation. The mechanisms by which H3K4me3 affects transition to productive transcription elongation remain to be established. H3K4me3 may be responsible for the generation of a chromatin structure at the 5′-end to ensure optimal Pol II release into productive elongation and/or recruitment of pre-mRNA processing and elongation factors to the 5′ regions of genes Lastly, our finding that the ATX1-Win domain is a functional counterpart of the Win domain in MLL1  [21], [2AtWDR5a-RNAi, AtASH-RNAi, and AtRbBp5-RNAi transgenic lines, are provided in ST 1. Genes used in this study have the following IDs: ATX1 (At2g31650), WDR5a (At3g49660), AtASH2 (At1g51450), AtRbBp5 (At3g21060), TBP(At3g13445), WRKY70 (AT3G56400), LTP7 (AT2G15050).Arabidopsis plants (WS ecotype) were grown for 14–21 d in potting soil in growth rooms at 22°C with a 12-h light photoperiod. Descriptions of all the cloning vectors and primers used in this study, as well as plasmid construction and generation of The AH190 strain was transformed with one of the following bait constructs: pGBKT-AtWDR5, pGBKT-AtASH2, or pGBKT-AtRbBp5, then transformed with one of the prey constructs: pGADT7-ATX1, pGADT7-ATX1N, pGADT7-ATX1C, pGADT7-ATX1DH, pGADT7-ATX1win, pGADT7-ATX1SET, pGADT7-AtAsh2, or pGADT7-AtRbBp5. Yeast were scored for protein interactions by their ability to grow on SD medium lacking Trp, Leu, His, and Ade.E. coli containing the His-Tag or GST vectors. The beads were washed five times , and the remaining proteins eluted from the washed beads in SDS-loading buffer, separated on a 12% PAGE/SDS gel, and analyzed by anti-GST  or anti-His antibody .For GST-bead pull down assays, GST beads were incubated with 2 µg of each GST fusion protein, washed, and then incubated with 3 µg of a His-fusion protein overnight at 4°C. Mock controls used extracts prepared from Analyses were performed as described earlier The ChIP assay was performed using a modified method ATX1-set is a synthetic gene expressed from the MAS promoter and encoding the wild type ATX1 protein with a N-terminal HA fusion, except for the mutations noted above.The ATX1-setm mutant contains 5 tyrosine to alanine substitutions: Y927A, Y945A, Y954A, Y1013A, and Y1015A. The Arabidopsis thaliana. Search parameters used were: no restrictions on protein molecular weight or pI, enzymatic specificity was set to trypsin with up to 3 missed cleavage sites, carbamidomethylation of C was selected as a fixed modification. Mass accuracy settings were 0.15 daltons for peptide mass.A Q-TOF Ultima tandem mass spectrometer (Waters) with electrospray ionization was used to analyze the eluting peptides. The stained bands were excised and subjected to LC/MS as described −ΔΔCt calculation according to the manufacturer's software , where ΔΔCt is the difference in the threshold cycles and the reference housekeeping gene; ACT7 was used as an internal control for ChIP experiments and immunoprecipitated DNA was expressed as a percent of input DNA.Total RNA isolation and reverse transcription with oligo(dT)  were performed as described previously Primers used for the various cloning and analytical procedures are in Figure S1AtWDR5-RNAi (wR1 and wR2), two transgenic AtASH2-RNAi (a2R7 and a2R16), and two transgenic AtRbBP5-RNAi  lines used in this study. Transgenic plants transformed with the empty vector (Con) were used as a control; B) Flowering phenotypes in the RNAi-lines, in atx1, and control backgrounds.A) Relative mRNA levels in the two transgenic (PDF)Click here for additional data file.Figure S2Interactions between ATX1, AtWDR5a, AtASH2, and AtRbPB5; Identification of the ATX1-Win AtWDR5-binding domain. A) Proteins from total cellular extracts retained by TAP-tagged-AtWDR5a in a pull-down experiment. Gel stained by Coomassie blue (left) and western blot assay with antiATX1 antibody (right). Arrows point to ATX1 (120 kD) and degradation products. Identities were established by MS ; B) Sche(PDF)Click here for additional data file.Figure S3Identification of AtWDR5 interacting protein bands by MS. A) Coverage map of the ∼120 kD band reacting with the antiATX1 antibody (see arrow in (PDF)Click here for additional data file.Figure S4The amount of ATX1 at the promoter regions of three genes in AtASH2-deficient lines was determined by ChIP-PCR using antiATX1 antibody. The primers were located within the promoter regions (Region 1 in (PDF)Click here for additional data file.Figure S5atx1 mutant plants expressing the synthetic HA-tagged ATX1-wtSET domain protein or the HA-tagged ATX1 with Tyr/Ala substitutions in the SET domain (set). A) Western blot assay with antiHA antibody illustrating the expression of HA-ATX1-wtSET in two atx1 transgenic lines (HA-ATX1 and HA-ATX2) and in two atx1 lines expressing the HA-tagged ATX1-set mutant proteins (setm1 and setm2) transformed with the respective constructs. The levels of histone H3 expression detected by antiH3 antibodies are shown as loading controls; B) Flowering time phenotypes of plants from the transformed lines shown in (A). Transgenic atx1 plants expressing the HA-ATX1-setm do not rescue the early flowering caused by the atx1 mutation, while transformants expressing the wt SET restored wild type flowering; C) expression of the WRKY70 and LTP7 genes in set1 and set2 mutant lines in the atx1 background and in the atx1::HA-ATX1-wtSet background. The higher transcript levels from the WRKY70 and LTP7 genes may result from higher ATX1 protein produced in the transgenic lines.Transgenic (PDF)Click here for additional data file.Table S1Primers used for the various cloning and analytical procedures.(PDF)Click here for additional data file.Text S1Interaction between ATX1 and AtWDR5a; Identification of the ATX1-Win AtWDR5-binding domain. Interactions between ATX1 and AtASH2 or AtRbPB5.(PDF)Click here for additional data file.


Arabidopsis H3 variants in order to find core properties of this class of histones. Here we present genome-wide maps of H3.3 and H3.1 enrichment and the dynamic changes of their profiles upon cell division arrest. We find H3.3 enrichment to positively correlate with gene expression and to be biased towards the transcription termination site. In contrast with H3.1, heterochromatic regions are mostly depleted of H3.3. We report that, in planta, dynamic changes in H3.3 profiles are associated with the extensive remodeling of the transcriptome that occurs during cell differentiation. We propose that H3.3 dynamics are linked to transcription and are involved in resetting covalent histone marks at a genomic scale during plant development. Our study suggests that H3 variants properties likely result from functionally convergent evolution.In animals, replication-coupled histone H3.1 can be distinguished from replication-independent histone H3.3. H3.3 variants are enriched at active genes and their promoters. Furthermore, H3.3 is specifically incorporated upon gene activation. Histone H3 variants evolved independently in plants and animals, and it is unclear whether different replication-independent H3.3 variants developed similar properties in both phyla. We studied  Histone proteins are assembled into nucleosomes to build the skeleton of chromosomes. Beyond their role as DNA scaffold, histones participate in the regulation of gene activity. Studies in animals have shown that the deposition of two different histone H3 variants, H3.1 and H3.3, requires distinct pathways and results in distinct profiles throughout the genome. H3 variants evolved independently in plants and animals. Hence, H3 variants' properties shared by plants and animals would reflect core functions that have been selected during evolution. Our study indicates that these core properties include the high enrichment of H3.3 at active genes and a relative low deposition of H3.3 over regions deprived of genes or with inactive genes. In contrast with H3.1, H3.3 incorporation is dynamic and accompanies global changes of gene activity at major developmental transitions. We anticipate that the dynamic link between H3.3 variants and transcription enables remodeling of histone modifications that contribute to developmental transitions. Histones are not static scaffolding proteins but dynamic actors involved in many aspects of chromatin related functions. They are targets of chromatin modifiers that deposit covalent modifications on histone tails and thereby influence chromatin properties and affect transcriptional and translational activities. Histones H3 can be subdivided into several classes. In addition to the centromeric variant CENH3 Drosophila H3.3 is enriched in euchromatic regions, at loci of active gene expression Drosophila, induction of gene expression leads to enrichment of H3.3 To date, many studies in animal species show that H3.3, in contrast to H3.1, is distinctly distributed along the genome. Drosophila and mammals In mammals, H3.3 enrichment has also been detected over telomeres, repressed genes and pericentric heterochromatin, resulting from deposition by distinct chaperone complexes Based on DNA sequences and gene structure, it is clear that H3.1 and H3.3 have evolved separately in animals and plants Arabidopsis H3.3 and H3.1 enrichment in chromatin and clarify their specificities regarding genomic features and cell cycle regulation.In contrast to animals, the knowledge about plant histone H3 is limited. Although it is likely that plant H3.1 expression is coupled to the cell cycle as in animals Arabidopsis genome contains three H3.3 genes encoding the same protein and we tagged HTR5 (HISTONE THREE RELATED 5), the most highly expressed gene of the family HTR13, one of the five genes encoding the unique H3.1 protein in ArabidopsisTo generate genome-wide maps of H3 variant localization, we performed Chromatin-Immunoprecipitation followed by deep sequencing (ChIP-Seq). We detected the localization of fusion proteins between a green fluorescent protein (GFP) tag and histone variants expressed under their endogenous promoter in transgenic plants. As a positive control, we used an H3 antibody recognizing the C-terminal part of H3, while an anti-IgG antibody was used as a negative control. The Arabidopsis nuclear chromosomes , defined in a previous study combining twelve different covalent histone marks CS1 and CS2 are predominantly associated with genes These results motivated the analysis of the H3 variant distribution over general genomic features including protein-coding genes, TEs and inter-annotation regions (IRs) . The medDrosophila and mammals In summary, the even distribution of H3.1 over all genomic features, similar to H3, suggests that H3.1 serves as a rather static chromatin backbone. In contrast, H3.3 appears to be more associated with active genes (CS1) than with repressed ones (CS2) and is depleted in regions of constitutive heterochromatin (CS3), including centromeres and TEs. H3.3 deposition at active genes appears to be conserved in yeast, We next investigated the enrichment profiles of the H3 variants over protein-coding genes and their flanking intergenic sequences in dividing tissue . To reveAccording to our results on CS1 and CS2 , H3.3 miIn agreement with our observations, plant H3.3 is associated with several histone marks, which are correlated with active gene expression Arabidopsis RNAPII enrichment over genes using data published previously Animal H3.3 enrichment shows a positive correlation to gene expression C. elegans H3.3 In summary, plant H3.3 enrichment positively correlates with gene expression and gene length and appears to gradually increase towards the distal gene end, reaching a maximum immediately upstream of the TTS. This profile appears to be similar to that reported for Drosophila and mammals is not limited to the coding regions but is also high on cis-regulatory elements, repressed genes and telomeres Arabidopsis genome lacks long distance acting enhancers, which are common in mammals and Drosophila.While plant and animal H3.3 obviously share similar features, we observed important differences as well. H3.3 enrichment in Arabidopsis genome . The SAM and the primordia comprise dividing cells s genome . Amongstll cycle . TogetheTo investigate H3.3 and H3.1 dynamics during the developmental transition from primordia to differentiated leaves, we selected two subsets of genes, according to their higher expression levels (at least five-fold) in either dividing or non-dividing tissue. Having gene sets that were preferentially expressed in either of the two tissue types, we could examine the changes in H3 variant levels that accompanied repression  and induWe conclude that the repression of gene expression during leaf differentiation is linked with a decrease in the H3.3 level, but not H3.1 level, suggesting that H3.3 may contribute to developmental transitions. Differentiation also requires the induction of gene expression, which correlates with gain of H3.3 enrichment at the 3′ end of some genes. H3.1 enrichment on the other hand, is not significantly affected by developmental transitions and appears to be a relatively stable chromatin component. This property would support a role of H3.1 in propagation of epigenetic patterns of histone modification through division, in agreement with the preference of H3.1 over H3.3 enrichment at heterochromatic regions, which need to be maintained in a transcriptional silent state.Arabidopsis H3.3 is dynamically deposited over gene bodies and its enrichment is linked to gene expression. Thus, the incorporation specificities observed for H3.3 and H3.1 are largely similar between animals and plants, suggesting a functional convergence during evolution of H3 variants in eukaryotes. Whether this convergence was driven by the conservation of the distinct mechanisms that incorporate H3.1 and H3.3 remains to be established since we currently lack biochemical characterization of histone H3 chaperones in plants.There are remarkable similarities between H3 variants in animals and plants, yet phylogenetic analyses indicate that amongst metazoa and plantae, H3.1 and H3.3 variants evolved independently. However, both share similar features such as specific amino acid changes at positions 31 and 87 and the absence of introns in H3.1 Arabidopsis remains unclear.Our study outlines a specific enrichment of H3.3 culminating towards the 3′ end of genes, a phenomenon that might be linked with gene length. Noteworthy, active marks such as H3K4me3 and H3K4me36 are enriched towards the 5′ part of genes, in contrast to H3.3. Although we observe a correlation with RNAPII, the origin and significance of H3.3 enrichment at the 3′ end of genes in in planta. We find that gene expression changes during differentiation are reflected in H3.3 enrichment. This dynamic replacement of H3.3 potentially allows covalent histone marks present in the chromatin of dividing cells to be remodeled in order to allow repression or expression of a new repertoire of genes that participate to the differentiation program. Hence, H3 variant replacement might serve as a mechanism that enables reprogramming at developmental transitions by globally facilitating dynamics of covalent marks.Our study addresses genome-wide dynamics of H3.3 and H3.1 enrichment during differentiation We used homozygous transgenic plants constructed by Mathieu Ingouff Nuclei enrichment was performed as previously described http://www.illumina.com/applications/sequencing/.ilmn). For mRNA-Seq libraries, briefly, 10 µg of total RNA was purified to yield poly-A containing mRNA molecules using poly-T oligo-attached magnetic beads. Following purification, the mRNA was fragmented into small pieces using divalent cations under elevated temperature. Then the cleaved RNA fragments were copied into first strand cDNA using Superscript II Reverse Transcriptase (Invitrogen) and random primers. This was then followed by second strand cDNA synthesis using DNA Polymerase I and RNaseH. These cDNA fragments then underwent an end repair process, the addition of a single ‘A’ base, and then ligation of adapters specific for sequencing flow cell. These products were then purified by gel excision and enriched by PCR with Phusion polymerase (Fermentas) to create the final cDNA library. This library was validated by loading 1 µl of the re-suspended constructs onto an Agilent Technologies 2100 Bioanalyzer DNA-1000 microfluidic chip. The final products showed a distinct band at 200–300 bp and were subsequently sequenced on an Illumina Genome Analyser IIx.mRNA-Seq and ChIP-Seq libraries of template molecules suitable for high-throughput sequencing were constructed according to the guide lines described in the Illumina website . This library was validated by loading 1 µl of the re-suspended constructs onto an Agilent Technologies 2100 Bioanalyzer DNA-1000 microfluidic chip. The final products showed a distinct band at 200–300 bp and were subsequently sequenced on an Illumina Genome Analyser IIx.Arabidopsis genome (TAIR9) using CASAVA v1.7. The number of mapped reads varied from 228 k to 11.9 M for the first biological replicates, and from 15.1 M to 20.7 M for the second (The ChIP-Seq reads were mapped onto the e second .http://genome.gis.a-star.edu.sg/).These files were then converted to 10 bp density WIG files using MACS v1.4.0 The mRNA-Seq reads were mapped onto TAIR9 using recommended settings of TopHat v1.2.0 http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/).The quality of the mapped reads was assessed using FastQC v.0.9.0 (http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO series accession number GSE36631.The raw reads and processed files from both ChIP-Seq and mRNA-Seq experiments have been deposited in the NCBI Gene Expression Omnibus (GEO)  regions used in For the scatterplots of All the data used in the boxplots and scatterplots are available in ftp://ftp.arabidopsis.org/home/tair/Software/UpdateCoord/”. Based on the authors suggestion, the number of sites per matrix was limited to 200,000 following the “restriction” procedure (http://www.athamap.de/restriction_scores.php), giving ∼4.7 millions sites. The 2,390,614 non-exonic sites were then used to generate an average profile as described above  and presented in Predicted Transcription Factor Binding Sites (TFBS) data from AthaMap database Figure S1Genome browser snapshot of 31 kb on chromosome 3. Profiles of H3 (blue), H3.1 (orange), H3.3 (green) and IgG (grey) obtained from two independent biological replicates in the two tissue types. The region includes several protein-coding genes  having the following average FPKM values per condition (div/nonDiv): AT3G14820 (0/0), AT3G14830 (18.6/19.7), AT3G14840 (19.8/38.2), AT3G14850 (1.7/1.1), AT3G14855 (0/0), AT3G14860 (12.0/13.9), AT3G14870 (1.4/3.7). Vertical dashed grey lines correspond to the TTS of the genes.(EPS)Click here for additional data file.Figure S2Genomic distribution of H3.3 and H3.1 enrichment in non-dividing tissue. (A–D) Replicate of (EPS)Click here for additional data file.Figure S3Genomic distribution of H3.3 and H3.1 enrichment over Transcription Factor Binding Sites (TFBS). Profiles of H3.3 (green), H3.1 (orange), H3 (blue) and IgG (dashed grey) over non-exonic TFBS extracted from AthaMap (EPS)Click here for additional data file.Figure S4Enrichment profiles of marks and RNAPII over genes. (A) Average profile of H3 enrichment in dividing cells over the protein-coding genes grouped according to their expression levels into six different subsets see . (B) Ave(EPS)Click here for additional data file.Figure S5H3.3 and H3.1 enrichment over genes in non-dividing tissue. (A) Average profile of H3.3 (green), H3.1 (orange), H3 (blue) and IgG (dashed grey) enrichment over gene bodies . (B–E) Average profile of H3.3 (B), H3.1 (C), H3 (D) and IgG (E) enrichment over the protein-coding genes grouped according to their expression levels into six different subsets .(EPS)Click here for additional data file.Figure S6Correlation of H3.1 and H3.3 enrichment with gene length. Average profile of H3.3 (A), H3.1 (B), H3 (C), IgG (D) from dividing tissue over the protein-coding genes grouped according to their length into five different subsets . Non-dividing profiles (not shown) are almost identical.(EPS)Click here for additional data file.Figure S7Dynamics of H3.3 and H3.1 enrichment during development over different sets of genes. Average profile of H3.3 (green) and H3.1 (orange) enrichment in dividing tissue (plain line) compared to non-dividing tissue (dashed line) over genes that are cell cycle regulated according to CycleBase (A), genes that are ON (FPKM>3) in dividing tissue and OFF (FPKM<1) in non-dividing tissue (B) and vice-versa (C) and genes that present similar expression in both tissue types and are either highly expressed (D), or have low expression levels (E). (F–G) Snapshots of the UCSC genome browser showing H3 (blue), H3.1 (orange), H3.3 (green), IgG (grey) and mRNA-Seq (black) over representative protein-coding genes  more expressed in dividing tissue than non-dividing tissue (F) or the opposite (G). The corresponding FPKM values per condition (div/nonDiv) are: AT3G14600 (126.9/422.4), AT3G14610 (1.3/2.4), AT3G14620 (8.7/3.3), AT1G20440 (1768.8/134.5), AT1G20450 (503.1/94.9). Horizontal dashed grey lines correspond to half the scale of each graph.(EPS)Click here for additional data file.Table S1Overview of sequencing libraries analyzed.(DOC)Click here for additional data file.Table S2www.cyclebase.org with ranks from 1 to 20,945 reflecting the magnitude of cell-cycle dependent regulation. FPKM values are mean values obtained from the HTR5::GFP and HTR13::GFP mRNA-Seq libraries.Expression of the top-most cell-cycle dependent and independent genes in dividing and non-dividing tissue. CycleBase ranks were extracted from (DOC)Click here for additional data file.Table S3Tab-delimited file containing FPKM values for each TAIR9 annotation. The last column contains the information whether the gene was kept or not for the downstream analyses based on expression variation between replicates from the same tissue see .(GZ)Click here for additional data file.Table S4Tab-delimited file containing the average signal of all datasets used in the boxplots and scatterplots over each annotation , inter-annotation and CS segment.(GZ)Click here for additional data file.