Pou5f1, Nanog, Cdx2, Hoxb1, Hoxb9) as mouse embryos progress from 8-cell to blastocyst in culture. Only by the blastocyst stage, when the embryonic (Inner Cell Mass) and extra-embryonic (Trophoblast) lineages are compared, do we see the expected association between histone modifications previously linked to active and silent chromatin, and transcriptional state. To explore responses to an environmental signal, we exposed embryos to the histone deacetylase inhibitor, anti-epileptic and known teratogen valproic acid (VPA), during progression from 8-cell to morula stage. Such treatment increased H4 acetylation and H3 lysine 4 methylation at the promoters of Hoxb1 and Hoxb9, but not the promoters of Pou5f1, Nanog,Cdx2 or the housekeeping gene Gapdh. Despite the absence of detectable Hoxb transcription, these VPA-induced changes were heritable, following removal of the inhibitor, at least until the blastocyst stage. The selective hyperacetylation of Hoxb promoters in response to a histone deacetylase inhibitor, suggests that Hox genes have a higher turnover of histone acetates than other genes in the preimplantation embryo. To explain the heritability, through mitosis, of VPA-induced changes in histone modification at Hoxb promoters, we describe how an epigenetic feed-forward loop, based on cross-talk between H3 acetylation and H3K4 methylation, might generate a persistently increased steady-state level of histone acetylation in response to a transient signal.Enzyme-catalyzed, post-translational modifications of core histones have been implicated in the complex changes in gene expression that drive early mammalian development. However, until recently the small number of cells available from the preimplantation embryo itself has prevented quantitative analysis of histone modifications at key regulator genes. The possible involvement of histone modifications in the embryo's response to extracellular signals, or as determinants of cell fate or lineage progression, remains unclear. Here we describe the use of a recently-developed chromatin immunoprecipitation technique (CChIP) to assay histone modification levels at key regulator genes ( Specific post-translational modifications of core histones are a central component in the complex network of epigenetic mechanisms by which genes are activated and silenced Drosophila cells to allow immunoprecipitation of chromatin from as few as 100 mammalian cells Nanog and Cdx2 in the mouse blastocyst Changes in histone modification seem to be closely involved in the complex changes in gene expression that drive early development Hoxb1 and Hoxb9 promoters, but little or no effect at the promoters of other genes, both active and silent. Crucially, the VPA-induced changes in histone modification are transmitted, in the absence of inhibitor or of detectable transcription, through to the blastocyst stage, thereby meeting one of the conditions necessary for a predictive histone code.Here we use CChIP to define patterns of histone modification at key genes as mouse embryos progress from the 2-cell to blastocyst stage and to show how this pattern can be changed by an environmental agent, namely the deacetylase inhibitor and teratogen valproic acid (VPA) Valproic acid , like other short chain fatty acids, is a broad spectrum inhibitor of both class I and class II histone deacetylases (HDACs) and causes rapid hyperacetylation of bulk histones when applied to cultured cell lines The experimental design used to monitor changes in histone modification in the developing pre-implantation embryo in the presence and absence of VPA, is shown in Pou5f1 and Nanog, and Cdx2, a gene essential for trophectoderm formation Pou5f1 is expressed strongly and uniformly in all cells at the 8-cell stage and beyond, while Nanog and Cdx2 are expressed relatively weakly (Nanog) or not at all (Cdx2) at the 8-cell stage and in some cells, but not others, in the morula. We used RTQ-PCR to confirm expression of Pou5f1 and Nanog at the 8-cell to morula stage and to show that expression was moderately enhanced in VPA-treated morula-stage embryos  and only modest developmental changes from 8-cell to morula  and H3K9me2 (often associated with silent chromatin) at the promoters of o morula , S2. Simo morula . Levels s tested .Pou5f1, Nanog and Cdx2Pou5f1 and Nanog promoters are relatively rich in H4K8ac and H3K4me3, and depleted in H3K9me2, while in the trophectoderm, where they are silent, they are relatively rich in H3K9me2 and depleted in H4K8ac and H3K4me3 . It seems that global changes in histone modification are not necessarily reflected by changes at individual genes.We detected no significant change in histone acetylation at the promoters of st stage , S2. ThiHox gene expression in mouse embryos, ES cells Hoxb1 control elements in mouse ES cells Hox gene expression might account for some of the teratogenic effects of VPA Hoxb1 and Hoxb9 promoters and the Hoxb9 coding region in morula-stage embryos treated with VPA. The increase was consistent across all three Hoxb regions tested . In contrast, levels of the silencing mark H3K9me2 were consistently lower in VPA-treated morulae (P = 0.016). To ask whether the valproate-induced changes were contingent upon progression from 8-cell to morula, we treated a group of 8-cell embryos for just 2 h with 1 mM VPA. Both Hoxb1 and Hoxb9 showed increased levels of H4K8 acetylation after this brief treatment, whereas Gapdh did not . There is an indication that the lower levels of H3K9me2 following VPA treatment at the 8-cell to morula stage may also carry through to the blastocyst (P = 0.04). The retention of elevated levels of H4K8ac and H3K4me3 at Hox gene promoters following removal of VPA contrasts with the behaviour of bulk histones in cultured cell lines, where VPA-induced increases in both modifications were lost within minutes of removal of the inhibitor and unpublished results).The increased levels of H4 acetylation and H3K4 tri-methylation at controls . The difastocyst , though Hox genes tested in blastocysts, levels of H4K8ac, H3K4me3 and H3K9me2 generally showed little change as a result of VPA treatment at the 8-cell to morula stage  than in controls, while levels of H3K9me2 tend to be higher .Neither the VPA-induced changes in histone modifications at S.pombeXenopus has been used to show that some genes  can retain a memory of an active gene state, even in an inappropriate  cell lineage Xenopus embryo, there is no genomic transcription, so the memory mechanism involved does not require active transcription. Chromatin seems to have a role in this memory in that the variant histone H3.3 and specifically its methylatable lysine 4 residue, seems to be necessary for re-expression (memory) of the active state after progression through the early cleavage cycles The ability of histone deacetylase inhibitor to induce a mitotically heritable change in histone acetylation and gene expression was first demonstrated in the yeast Hoxb1 and Hoxb9 promoters without any detectable induction of transcription and increased expression of Nanog and Pou5f1 without any parallel increase in acetylation or H3K4 methylation. Similarly, in untreated morula stage embryos, a comparison of active and silent genes reveals no correlation between promoter levels of H4 acetylation and H3K4 tri-methylation and transcriptional status. Only by the blastocyst stage, when genes that are active or silent in the ICM and trophectoderm were compared, did the expected relationships between “active” marks and transcriptional activity become apparent. These findings may be due, at least in part, to differences between cells in gene expression patterns, even in the 8-cell embryo. If, for example, only a minority of blastomeres were to show enhanced Nanog expression in response to VPA, increased transcript levels would be readily detected in pooled samples, but increased promoter acetylation, even if it occurs in the minority of expressing cells, would not be.CChIP has allowed us, for the first time, to compare the expression and chromatin modification patterns of selected genes in the pre-implantation mouse embryo and to study the effects of the histone deacetylase inhibitor and teratogen valproic acid (VPA). There is, amongst the genes studied here, a surprising lack of correlation between transcriptional activity and the levels of histone modifications usually considered to be “activating”, namely H4 acetylation and H3K4 tri-methylation. Thus, VPA increased acetylation levels at the silent Hoxb promoters in VPA treated embryos shows that histone acetate groups are turning over, despite the absence of detectable transcription. None of the other genes tested, whether active  or silent (Cdx2) showed increased acetylation. This suggests a lack of turnover, though it remains possible that turnover is mediated by deacetylases resistant to inhibition by VPA, perhaps SirT1 or other sirtuins Hoxb promoters provides a system through which the promoter chromatin of selected genes can respond to environmental or developmental signals that act on the relevant modifying and demodifying enzymes. Perhaps, in the early embryo high turnover of histone acetates marks genes that are silent but “poised”, ready to be expressed later in development Pou5f1, Nanog and Cdx2, where differing expression patterns are already being put in place at the morula-blastocyst transition and earlier. We show that VPA-induced increases in histone acetylation, and contingent H3K4 methylation, at Hox gene promoters are not sufficient to override silencing signals and induce transcription. However, these VPA-induced changes are inherited, at least until the blastocyst stage and in the continuing absence of detectable transcription. This finding not only establishes that a change in histone modification level induced by an environmental agent can be heritable, but also provides a process by which the effects of a transient, possibly stage-specific, signal might be transmitted to a later developmental stage without initiating inappropriate transcription The increased level of H4 acetylation at In considering the mechanisms by which an induced increase in acetylation could be maintained across cell generations, it is important to remember that the measured level of acetylation represents a dynamic equilibrium between the activities of modifying and demodifying enzymes. Inhibition of deacetylases will shift the equilibrium to a more acetylated level, but following removal of the inhibitor, one would expect the steady state level at gene promoters to return rapidly to its pre-inhibition levels, exactly as seen in assays of bulk acetylation Hoxb genes respond to VPA with increased histone acetylation and H3K4 methylation, whereas other genes do not (unpublished results). However, in contrast to the situation in the embryo itself, these VPA-induced modifications seem not to be heritable through mitosis (unpublished results).The consistent differences between Hox genes and others, and between different cell types and developmental stages, may reflect the families of enzymes present at selected genomic regions, something that is amenable to experimental testing.If this model, or something like it, is correct, then the ability to put in place a heritable epigenetic change in response to an environmental agent, or to respond to that agent at all, will depend on the presence of a group of enzymes of defined specificity , perhapsHox gene hyperacetylation, raises the possibility that transient exposure to VPA at the very earliest stages of pregnancy could result in disorganised Hox gene expression at later stages, with consequent morphological abnormalities Hoxb1 or Hoxb9 in VPA treated embryos grown to the hatching blastocyst stage, suggesting that silencing signals still predominate. It will be important to determine, perhaps through re-implantation of VPA-treated embryos, whether the induced changes in Hox gene acetylation reported here can alter spatial or temporal patterns of Hox gene expression later in development when silencing signals are being overridden.Valproic acid (VPA) is a widely used preventive treatment for seizures and bipolar disorder Rabbit polyclonal antisera to H4K8ac, H4K16ac and H3K4me3 were raised by immunization with synthetic peptides conjugated to ovalbumin as previously described Balb/c mouse embryos  were thawed and cultured in M16 medium  for differing times up to the (expanded) blastocyst stage. Isolation of the inner cell mass (ICM) was by immunosurgery Cryopreserved 2-cell Drosophila SL2 cells, cultured as previously described 7 cells/ml and 1 ml aliquots were mixed with a small number  of mouse cells derived from pooled 8-cell, morula or blastocyst stage embryos. Chromatin was prepared from the mixed sample and immunoprecipitated exactly as previously described Drosophila DNA controls run in parallel to monitor cross-hybridization For the CChIP procedure, Pou5f1, Nanog, Cdx2 and ActB were quantified by real time PCR using SYBR Green PCR master mix and an ABI 7900 Detection System . The primer sequences are listed in Supplementary RNA was prepared from mouse embryos as previously described M KCl, 20 mM NaCl, 10 mM Tris-HCl, pH 8, 0.5 mM EDTA, 0.1% Triton X-100) was added for 10 minutes at room temperature. Excess KCM was removed with PVA absorption spears . 50 µl rabbit antibody in 1% BSA/KCM, was added and incubated in a humid chamber at 4°C for 1 hour. Slides were rinsed in KCM, followed by 50 µl of FITC-conjugated goat anti-rabbit IgG antibody (Sigma) at×50 dilution in 1% BSA/KCM. After 1 hour as above, slides were rinsed, fixed in 4% formaldehyde in KCM for 10 minutes and counterstained with DAPI (1 µg/ml) in Vectorshield .Immunofluorescence labelling of embryos was performed as previously described Figure S1CChIP analysis of H4K16 acetylation at selected promoters in pooled 8-cell embryos or morulae.(0.05 MB PDF)Click here for additional data file.Figure S2Expression of Hoxb1 and Hoxb9 assayed by radioactive PCR in embryos cultured with or without valproic acid.(0.06 MB PDF)Click here for additional data file.Figure S3Examples of gels resolving 32P-labelled PCR products generated from antibody bound and unbound fractions derived by CChIP.(0.19 MB PDF)Click here for additional data file.Table S1ChIP data; 8-cell(0.05 MB DOC)Click here for additional data file.Table S2ChIP data; morula(0.05 MB DOC)Click here for additional data file.Table S3ChIP data; fractionated blastocyst(0.08 MB DOC)Click here for additional data file.Table S4ChIP data; whole blastocyst(0.05 MB DOC)Click here for additional data file.Table S5Primers used for CChIP analysis(0.03 MB DOC)Click here for additional data file.Table S6Primers used for expression analysis(0.03 MB DOC)Click here for additional data file.


Drosophila Notch and epidermal growth factor receptor pathways, and relate to the relationship between them. We then mainly focus on the involvement of Groucho (Gro)/TLE, a global developmental corepressor, in these pathways. In particular, we discuss Gro/TLE's role at the junction between these two signal transduction cascades.In this minireview, we briefly revisit the  Drosophila now suggest that inputs from signalling pathways also converge on general transcriptional coregulators, in a way that can alter gene expression programmes involving large arrays of genes. Below, we focus on the antagonism between the Notch (N) and the epidermal growth factor receptor (EGFR) pathways, and on the role played by the universal transcriptional corepressor Groucho (Gro) as a focal point for cross-pathway regulation.A handful of signalling pathways are repeatedly used to generate the innumerable varied cell types that comprise the bodies of all multicellular organisms. Mounting evidence suggests that the genesis of cellular complexity by so few pathways relies to a large extent on the crosstalk between them, that is, on the integration of signals transduced by these cascades, and on their combinatorial activities. For crosstalk to occur, whether with synergistic or antagonistic outcomes, signalling pathways must act on common cellular targets. In the nucleus, for example, diverse signalling pathways often impinge on a given promoter, which thus integrates the multiple cues required for stringent control of that specific gene. Recent findings from ICD) and allowing it to shuttle into the nucleus. In the nuclear compartment, NICD associates with the DNA-binding transcription factor Suppressor of Hairless (Su(H); CBF1/RBP-Jk in vertebrates), and jointly they transduce the N signal by directly activating target gene expression (ICD complex are the Enhancer of split (E(spl)) gene products, which are the ultimate executioners of the N signalling cascade and are therefore considered to be its major effectors. The E(spl) proteins (HES in vertebrates) are transcriptional repressors that have been best characterised in the context of neurogenesis as suppressors of proneural gene expression.The highly conserved N signalling pathway participates in multiple developmental processes that entail cell fate determination in both invertebrates and higher organisms. This pathway is perhaps best known for its role in ‘lateral inhibition’, a biological setting in which the N cascade allows a signal emanating from one cell to alter the differentiation state of its initially equivalent neighbours. It thus acts to prevent groups of equipotent cells from all acquiring identical fates, thereby ensuring cellular diversity. In brief, the binding of one of two ligands, Serrate or Delta , to the N receptor triggers a proteolytic cleavage of N, releasing its intracellular domain  and the Gro-related gene (Grg) proteins – similarly play transcriptional regulatory roles in diverse developmental stages of higher organisms, including in neurogenesis  proteins rely on the general corepressor Gro for silencing transcription of their targets, implicating Gro as a key effector of N signalling  repressors but also with Su(H) itself, allowing the latter to maintain N target genes in a silenced state in the absence of N signalling . For lacβ and Wingless (Wg)/Wnt signalling pathways, respectively  and Tcf/Lef, the nuclear transducers of the Dpp/TGF-The developmental roles played by the EGFR pathway and its involvement in intercellular communication and cell fate specification have been extensively reviewed elsewhere . Brieflyin vivo in response to EGFR signalling , in which the phospho-acceptor residues found in the two MAPK sites have been substituted for by negatively charged Aspartate (D) amino acids. Reciprocally, blocking phosphorylation of the MAPK sites by changing these residues to Alanine (A) (GroAA) leads to the overpotentiation of Gro's corepressor capacity  e.g. .Drosophila transcriptional repressors, leads to their nuclear export and degradation (MAPK phosphorylation of Yan and Capicua (Cic), two The long-standing antagonistic relationship between the EGFR and Notch signalling pathways, and the opposing effects exerted by these signal transduction cascades, have been well documented in various developmental settings and organisms , yet theDrosophila developmental processes, in which the antagonism between the two cascades has previously been established and studied, are the prefiguring of the veins in the wing imaginal disc and of the mesothorax bristles in the adult fly. In these settings, the N pathway acts as an anti-vein and anti-bristle determinant and, in both cases, EGFR signalling functions to overcome the influence of the N pathway in a temporally and spatially restricted fashion, allowing veins and bristles to form (DD), the presumed end product of MAPK signalling vis-à-vis Gro, generates similar effects to those seen when the EGFR pathway is overactivated, that is, extra vein material and supernumerary bristles are formed  has several possible biological implications. Below, we elaborate on three of these:RTK signalling effectively alters cell fates, a phenomenon that entails eliciting extensive changes to gene expression programmes involving the transcription of numerous genes e.g. .Wing vein patterning illustrates the above point. It turns out that besides the E(spl) proteins, there are at least two other Gro-dependent repressors that obstruct wing vein formation, namely Brk and Cic  is changed to a stronger Gro-binding motif (WRPW), this protein no longer alternates between its two modes of gene regulation; rather, it now behaves as a constitutive repressor . ConversRTK signalling could also be at the basis of the switch between long- and short-range repression. It appears that many repressors possess more than one repression domain. Brk and Su(H), for example, rely both on Gro as well as on a second corepressor called CtBP for turning off gene expression . We propIn summary, phosphorylation of Gro can provide a dynamic molecular switch that alternates between transcriptional repression and activation, or between different modes of repression.β and Wg/Wnt pathways. The regulation of Gro by RTK signalling is expected to similarly affect the transcriptional output of these cascades  at which these pathways interconnect have remained largely elusive . In a licascades  (Hasson D-Pax2 in the Drosophila eye, that depends on the combined binding of effectors of both the N and EGFR pathways, together with tissue-specific transcription factors (Needless to say, crosstalk between pathways also takes place at other focal points. As mentioned above, target promoters frequently integrate distinct signals in the nucleus. A nice example for this is the expression of A general coregulator such as Gro/TLE makes a perfect integrator of inputs from RTK signal transduction cascades with multiple signalling networks. Remarkably, post-transcriptional phosphorylation also modulates the activity of several other global cofactors. For example, CtBP activity is modified by HIPK2 phosphorylation (


The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false positives and false negatives in current approaches. Computational methods to reduce false positives are to look for over-representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments.R database that are over-represented in an experimental set compared to a random set of promoters and identifies cross-species conservation of the predicted transcription factor binding sites. The algorithm has been evaluated with expression and chromatin-immunoprecipitation on microarray data. We also implement and demonstrate the importance of matching the random set of promoters to the experimental promoters by GC content, which is a unique feature of our tool.We have developed a novel tool, "CORE_TF" (Conserved and Over-REpresented Transcription Factor binding sites) that identifies common transcription factor binding sites in promoters of co-regulated genes. To improve upon existing binding site predictions, the tool searches for position weight matrices from the TRANSFAC. It provides a table of over-represented transcription factor binding sites in the users input genes' promoters and a graphical view of evolutionary conserved transcription factor binding sites. In our test data sets it successfully predicts target transcription factors and their binding sites.The program CORE_TF is accessible in a user friendly web interface at  They also tend to target more precise the TFBS instead of just containing a TFBS region. However, finding TFBS can be extremely difficult since they may be less than 12–14 bp long and their consensus binding sites may be fairly loose [There are both experimental and computational approaches to identify transcription factors (TF) and their relevant binding sites. In the wet lab, hypothesis driven techniques, such as deletion constructs with luciferase reporter assays and chromatin-immunoprecipitation on microarrays (ChIP-on-chip), can be used to identify TF binding site (TFBS) regions. Luciferase assays can prove that a specific region has regulatory function, but is laborious and time consuming. ChIP-on-chip is more global, but requires prior knowledge of which TF to target using a specific antibody and is laborious, time consuming, and expensive. Faster and cheaper One method to identify TFBS for known TF is using position weight matrices (PWM) . PWM sumde novo) TFBS is by searching for conservation between orthologous promoters [An additional method to look for new , 6 different Match  ,14 settiDepending on the selections from page 1, page 2 presents text boxes to paste in lists of fasta format sequences or Ensembl gene IDs, or radio-buttons to select a certain number of random promoters for the appropriate species, or species based check boxes for pre-constructed runs or %GC matched runs. If CORE_TF must retrieve promoters there are two options to define promoter sequences. The first option is to call a promoter as exon 1 plus a user defined number of base-pairs (bp) upstream. The second option is to define a promoter sequence as a user specified number of bp before and after the start of exon 1. The pre-constructed (approximately 3000 promoters) and pre-retrieved sets to match %GC on  are based on 1000 bp upstream of exon 1 and exon 1 sequence.If requested, page 3 figure  uses EnsH. sapiens, P. troglodytes, M. musculus, R. norvegicus, B. taurus, C. familiaris, and G. gallus) or enter user defined orthologous sequences in fasta format. There is also the option to define promoters as was done in page 2. If the user skipped over-representation analysis there is a list of TFBS to chose from for analysis, otherwise CORE_TF uses TFBS selection from page 3.Page 4 gives the user the opportunity to use Ensembl defined orthologs or aligned genomic regions in a selection of species  microarray. As a positive control set for TF binding, we took those promoters from the ChIP-on-chip data that showed enrichment for MyoD or Myog binding sites . We re-analyzed the Affymetrix expression data by applying a RMA summarization and normalization and using the R package limma ,20 to fiFor the 200 most significantly induced genes, we evaluated whether their promoters contained MyoD or Myog binding sites according to the ChIP-on-chip data. We expect that the smaller more specific lists would have a higher percent of promoters with true binding sites (significant on the ChIP-on-chip platform) and therefore likely to contain more significantly over-representation TFBS in our predictions. We found that as a general trend this is true that the smaller more specific expression lists contain a higher percent of true positives (significant ChIP-on-chip genes)  compared to 2000 purely random mouse Ensembl promoters. We varied the promoter size to include exon 1 plus an additional number of bp upstream; 500, 1000, 2000, and 4000. Analysis showed that with a Match setting to minimize false positives a promoter size of 2000 bp + exon 1 was best, whereas with a Match setting to minimize the sum of false positives and negatives a promoter size of 1000 bp + exon 1 was preferable  on all Cao et al ChIP data. We then compared that to runs with the option to get random promoters of approximately equal %GC content compared to the experimental set (the Similar %GC option).To give wet-lab confirmation to the results of the CORE_TF conservation predictions we used the TransFactor kit with double stranded DNA designed on a LAMA4 (ENSG00000112769) MyoD predicted TFBS conserved between human, chimp, and dog  through the oPOSSUM website . We chosWe also chose to evaluate CORE_TF versus an additional easily viewable cross-species conservation program, ConTra . As a teWe have developed a series of web pages to identify TFBS in two sequential processes. First, pages 1 to 3 allow a user to predict TF that regulate a set of co-regulated genes. This is done by identifying TFBS that are over-represented in the promoters of an experimental (e.g. similar expressed genes from microarray data) compared to a random data set, taking GC content into account if requested. These results are displayed in a sortable table in page 3 figure . SecondlAlternatively, if a user did not wish to look at a list of promoters, but just a single promoter they could look purely for cross-species conserved TFBS by skipping straight to page 4 from page 1. They must then provide which promoter they want to search and a set of TFBS from a web displayed list. In theory they could paste the sequences conserved in the alignments back into the over-representation pages to find TFBS over-represented in conserved regions .To evaluate the performance of our tool we first used the Cao et al 2006 ChIP-on-chip data as a positive control. We tested whether the promoters in the ChIP pull-down were enriched for the binding sites for the transcription factors targeted in the ChIP experiments compared to a random set of promoters. To evaluate the effect of matching promoters for %GC content, CORE_TF was run with a purely random selected set of promoters (FAST option) and a random set of promoters with matched %GC content as controls (similar %GC option). Using both sets of random promoters, CORE_TF found a significant over-representation  for theStrikingly, by ranking TFBS on p-value, we demonstrate that the target transcription factors were higher ranked with the %GC matched promoters as control rather than with the purely random set of control promoters table , indicatTo demonstrate that our algorithm is able to find shared regulatory sites in co-regulated genes identified in expression microarray data we evaluated whether genes for which the expression level increased upon MyoD or Myog activation were enriched for MyoD or Myog binding sites. We ran sets consisting of the 20, 50, 100, and 200 genes most significantly affected by MyoD or Myog activation versus a random set of approximately equal %GC content  . EnsemblThe top 10 ranked genes of the Myog-induced genes were inspected for the presence of MYOGENIN_Q6 motifs. To this end, all available orthologs for the mouse genes were retrieved. All conserved TFBS and their conservation scores are reported in Table To confirm a CORE_TF conserved TFBS in the lab we looked at a MyoD predicted TFBS in the LAMA4 promoter. Using Ensembl defined genomic alignments we found the matrix MyoD_Q6_01 conserved in human, chimp, and dog (figure We compared the performance of CORE_TF (using a random set with similar %GC) to oPOSSUM, a webtool with similar objectives as ours. oPOSSUM looks for over-represented JASPAR PWM in pre-defined species alignments, but is limited to specific species alignments (e.g. human-mouse) and use of the smaller JASPAR PWM database. We used the previously mentioned expression microarray datasets for the evaluation of both programs performances. Our runs on the oPOSSUM website showed that our binomial test performs similar to their Fisher test  with greater flexibility in input data, coverage of a larger number of species, more intuitive output, and the option to account for GC content.Our tool is provided as a web service free to all non-commercial users.Project name: CORE_TFProject home page: Operating system(s): LinuxProgramming language: Perl (we used 5.8.4)Other requirements: TransFac Professional (we used 11.2), BLASTz, sorttable.js, Math::Cephes (Perl module), Apache (we used 1.3.33)License: GNU General Public License, v3 Any restrictions to use by non-academics: none for website use, TransFac Professional license for a local installMH, JD, GO, and PH conceived of the primary concepts of the software. MH and MG did the primary programming and debugging. MV performed all primary installations on the web-server and helped in debugging code. MH, MG, and PH performed the software evaluation on expression and ChIP-on-chip data. Wet-lab work was done by MH. Manuscript drafting was done by MH, MG, JD, GO, and PH. All authors read and approved the final manuscript.Overlap of most significant expression genes in ChIP-on-chip data. Indicated are the size of the lists for the top expressed genes and the percent of those contained in the significant ChIP-on-chip genes (true-positives). There is a trend that the smaller more selective expression gene lists contain a higher percent of true positives.Click here for fileConsistency of TF identification in different random set sizes. Indicated are the number of TF that occur in 1, 2, or 3 out of 3 total runs. As expected, the larger the random set size  the larger the consistency over runs. However, as indicated by the y-axis scale, this is not a very large effect.Click here for fileOptimal promoter size. The p-value and frequency of promoters with size 500, 1000, 2000, and 4000 bp and exon 1 with Match settings to minimize false positives (Min_pos) or minimize the sum of false positives and negatives (Min_sum). Overall, we see a promoter of (A) 1000 bp + exon 1 works best for Min_sum runs and (B) 2000 bp + exon 1 works best for Min_pos runs. As expected, (C and D) frequency of TFBS hit increases as the promoters become larger.Click here for fileTransFactor_LAMA4_MyoD. Set-up and data analysis of MyoD binding a LAMA4 promoter derived sequence with the TransFactor kit.Click here for fileCao_et_al_2006_ChIP_CORE_TF. CORE_TF run results to identify over-represented TFBS in MyoD/Myog ChIP-on-chip data.Click here for fileCORE_TF using random FAST runs vs runs with similar %GC. It is visible that in all ChIP-on-chip data tested the runs on purely random Ensembl promoters (FAST runs) has a bias towards high and low p-values while the random set with a similar %GC follows a more normal distribution. This could account for false positives in the FAST runs.Click here for fileCao_et_al_2006_expression_CORE_TF. CORE_TF run results to identify over-represented TFBS in expression array data.Click here for fileoPOSSUM runs on expression data. Custom oPOSSUM runs using the top 10, 20, 50, 100, and 200 genes from Cao et al 2006 expression data. oPOSSUM supplies (A) Fisher and (B) z-scores. (C) We also used their hits in the experimental and background data to generate a binomial test p-value similar to our program. (D) Frequency of TFBS hits overall declines as we stray from the top hits, as expected, but this is not an entirely smooth curve.Click here for fileCORE_TF vs oPOSSUM. CORE_TF and oPOSSUM binomial test p-values for the top 20, 50, 100, and 200 genes from Cao et al 2006 expression data for over-expression (A) of MyoD or Myog in the appropriately induced cell line. We see comparable results in the top 20, 50, 100, and 200 sets, but better overall performance in oPOSSUM for Myog and in CORE_TF for MyoD. Frequency (B) of MyoD or Myog hits was also plotted. As expected, the smaller more significant lists generally have higher frequency and more significant p-values than larger less specific lists. Frequency of TFBS in the promoters was also overall higher in experimental data than random promoters as expected. The oPOSSUM MyoD frequency was the only plot that did not seem concordant.Click here for fileIdentifying MyoD TFBS conserved in the LAMA4 promoter with ConTra and CORE_TF. Many conserved sites were found identically between the two programs. Shown here is the most conserved TFBS found, a MyoD TFBS conserved between human, chimp, and dog in (B) CORE_TF and also macaque in (A) ConTra. Though found by both programs, CORE_TF also identifies the site is on both strands of the DNA.Click here for file


Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes.70 factors of Escherichia coli RNA polymerase. It detects promoter-specific motifs by consecutively matching three patterns, consisting of an UP-element, required for interaction with the α subunit, and then optimally-separated patterns of -35 and -10 boxes, required for interaction with the σ70 subunit of RNA polymerase. Analysis of 43 bacterial genomes revealed that the frequency of candidate sequences depends on the A+T content of the DNA under examination. The accuracy of in silico prediction was experimentally validated for the genome of a hyperthermophilic bacterium, Thermotoga maritima, by applying a cell-free expression assay using the predicted strong promoters. In this organism, the strong promoters govern genes for translation, energy metabolism, transport, cell movement, and other as-yet unidentified functions.We describe a new triad pattern algorithm that predicts strong promoter candidates in annotated bacterial genomes by matching specific patterns for the group I σThe triad pattern algorithm developed for predicting strong bacterial promoters is well suited for analyzing bacterial genomes with an A+T content of less than 62%. This computational tool opens new prospects for investigating global gene expression, and individual strong promoters in bacteria of medical and/or economic significance. Research in bioinformatics has developed approximate matching methods to detect conserved sequences in nucleic acids .70 and α subunits of bacterial RNA polymerase. Theoretically, the presence of a UP element may not be essential for relatively strong promoter activity if two -35 and -10 boxes are well conserved and optimally distanced. Similarly, the presence of a well conserved UP element may compensate for a poor -35 box in some promoters. However, it seems very likely that the strongest promoters probably possess all three essential sequences. The specific interaction between the UP element and the α subunit significantly amplifies the association of RNA polymerase with promoter DNA -L-methionine , and E. coli S30 cell-free extracts. The reaction mixture was incubated at 37°C for 90 min, and heated to 65°C for 10 min. After centrifuging, the supernatant was precipitated with acetone, and then protein samples were separated by SDS-PAGE and bound to 3 MM paper. The ArgC protein synthesized in vitro was quantified by counting the radioactivity of the corresponding band with a PhosphorImager 445 SI (Molecular Dynamics).PCR-generated linear DNA fragments carrying a promoter region fused to the eviously . The celby Pratt . Proteinby Pratt . TypicalT. maritima promoter consensus sequences was generated at the WebLogo site as described in . The distribution of strong promoter candidates in real genomes indicates that the critical point of the A+T content is close to 62%  of strong promoter candidates in 300-bp random-generated sequences corresponding to upstream regions of bacterial genes satisfies the "exponential low" of the form N(A+T) = exp [cT. pallidum (~47% AT-rich genome), which belongs to a distinct phylum of Spirochetes that appear to use different DNA patterns for the promotion and regulation of transcription [Another important aspect of the quality of detection is the location of candidate sequences with regard to coding regions in the genome analyzed. We compared the frequencies of strong promoter-like patterns identified upstream and downstream of the initiation codon in all the genomes. The frequency of candidate sequences was clearly greater in the upstream region of ORFs in most of the genomes with an A+T content of less than 62% , which highlights the fact that -35 and -10 boxes and UP-like element all contribute to the high promoter activity in the thermophilic host.Two regions, (2.4 and 4.2) of the four domains of σectively . Severalectively . These D70 factor of E. coli RNA polymerase. The first step in the proposed triad pattern approach involves matching the UP element located 300 bp upstream of a gene-coding sequence, and then matching two optimally separated -35 and -10 boxes.Bacterial promoters can be arbitrarily classified as weak, moderate and strong promoters, depending on the level of expression of mRNAs or of the corresponding proteins. We have developed an algorithm that can predict strong promoters in bacterial genomes by matching the triad pattern specific for the group I σThe accuracy of the computational prediction of bacterial promoters depends on the A+T content of the genomes, which means that the matrix has to be adjusted to account for this factor in the DNA under analysis . The dattac, can be used as an reasonable criterion for identifying real strong promoters recognized by an Eσ70-like RNA polymerase. In particular, using the scores applied to genomes analysis (see Tables M. tuberculosis (~34% AT-rich genome) that encodes a variety of σ factors, including σA that recognizes the promoters possessing typical -10 and -35 boxes [M. tuberculosis.The choice of the matching score is yet another difficulty in identifying DNA-binding sites including promoters, as the highest score may not be the one most biologically relevant for genome-scale predictions ,62. It i35 boxes . HoweverT. maritima. Though this approach does not exclude possible masking effects of E. coli repressors or activators in the extracts, it is relatively simple, timesaving and informative, all of which are major advantages for evaluating computational predictions. Using the two well-characterized strong promoters (Ptac and PargC) as references, high activity has been demonstrated for 11 out of 13 candidate sequences of T. maritima. This is quite a low proportion; however, it suggests that the detection accuracy by the triad pattern algorithm might be close to 85%. The limitations of the algorithm in terms of specificity and sensitivity of the virtual prediction of putative strong promoters might be further experimentally evaluated by analysis of bacterial genomes with high-throughput methods.The possibility of applying linear PCR-generated molecules for cell-free protein synthesis, without needing to perform DNA cloning in bacteria, is a prerequisite for assessing gene expression on a genome-wide scale. As a first step in this direction, we tested reporter-gene fusions to evaluate the strength of the promoters identified in the genome of T. maritima RNA polymerase α subunit binds to the PargG promoter described here under PTM1780 [E. coli subunit and crucial for DNA recognition [argG promoter harbors a UP-like element, and is able to direct high gene expression in vitro. Moreover, this element appears to compensate for a poor -35 box or non-optimal 20-bp spacer of this promoter (see Table T. maritima promoters, allow us to assume that the presence of a UP-like element with less than 5 mismatches out of 17 nucleotides is essential for the strength of most strong promoters. This is consistent with the conservation of DNA interaction amino acids in the α subunit of the hyperthermoiphilic RNA polymerase. However, sequence-independent upstream DNA interactions within the C-terminal domain of the α subunit could often be required to initiate transcription in E. coli cells [This study offers the first insight into the organization and distribution of strong promoters in hyperthermophilic organisms, which probably constitute the longest lineage in the microbial world . Overall PTM1780 . It has ognition ,66, or tognition . The Parli cells . TherefoT. maritima direct the transcription of genes involved in tRNA, ribosome synthesis, energy metabolism, transport, and cell movement (see Table Vibrio vulnificus [T. maritima transcriptome have indicated that ABC transporters could play a major role in its ecology [The strong promoters of lnificus . In this ecology . FurtherT. maritima ORFs transcribed from putative strong promoters are preceded by a highly conserved Shine-Dalgarno site located 7–9 nucleotides from the ATG initiation codon, which is a characteristic feature of elevated protein synthesis in gram-negative and gram-positive bacteria [The strong promoter candidates prediction could contribute to the wide-scale genome expression analysis of evolutionarily distant bacteria, especially of those that possess an A+T DNA content lower than 62%. As a complement to DNA microarrays, it could help to elucidate the overall response of bacterial genomes to various environmental stresses. Moreover, the triad pattern algorithm can be used to extract the DNA region that carries translational signals; this is useful for investigating ORFs located downstream from the corresponding strong promoters (see Table bacteria . This inbacteria . FurtherFurther quantitative assessment of a dynamic and complicated mechanism of protein-DNA and protein-protein interactions involved in transcription might help to develop a more advantageous multi-pattern tool using both DNA and protein parameters to provide a comprehensive prediction of the strength of promoter activity in bacterial cells.T. maritima promoters. The data indicate that strong promoters govern the transcription of genes coding vital functions, and of genes coding as-yet unknown functions in this hyperthermophilic bacterium. This algorithm is simple to use and flexible, and it could be further adapted to meet the requirements of a genome of interest if its promoter-specific motifs differ from consensi recognized by Eσ70-like RNA polymerase.The triad pattern algorithm developed predicts strong promoter candidates by matching UP-like elements and identifying the presence of -35 and -10 boxes optimally distanced from each other in the annotated bacterial genomes. The presence of strong promoters is a function of the A+T content of the bacterial genome, and the number of false-positives is greater for genomes that have an A+T content higher than 62%. The prediction algorithm has been validated by cell-free experimental dissection of putative . It takes several seconds to analyze the annotated genome sequence available from databases.The algorithm is freely accessible for non-commercial use at the web-site The authors declare that they have no competing interests.MD developed the algorithm and performed the computational analysis; AM conducted cell-free experiments; VS designed the project, contributed to the development of the algorithm and data analysis, and wrote the manuscript.ReadMe. Contains information to use the algorithm.Click here for fileSoftware "strong_promoters.doc". The Text-format provides the list of putative strong promoter sequences with total and individual scores obtained for each consensus. The Word-format provides the tabulated list of putative strong promoters and their total score.Click here for file


It is essential to understand the network of transcription factors controlling self-renewal of human embryonic stem cells (ESCs) and human embryonal carcinoma cells (ECs) if we are to exploit these cells in regenerative medicine regimes. Correlating gene expression levels after RNAi-based ablation of OCT4 function with its downstream targets enables a better prediction of motif-specific driven expression modules pertinent for self-renewal and differentiation of embryonic stem cells and induced pluripotent stem cells.de novo approach, thus enabling us to define six distinct modules of OCT4 binding and regulation of its target genes.We initially identified putative direct downstream targets of OCT4 by employing CHIP-on-chip analysis. A comparison of three peak analysis programs revealed a refined list of OCT4 targets in the human EC cell line NCCIT, this list was then compared to previously published OCT4 CHIP-on-chip datasets derived from both ES and EC cells. We have verified an enriched POU-motif, discovered by a CDKN1B, CDKN1C, CDK6 and MAPK4) and developmental processes . A comparison of positively regulated OCT4 targets common to EC and ES cells identified genes such as NANOG, PHC1, USP44, SOX2, PHF17 and OCT4, thus further confirming their universal role in maintaining self-renewal in both cell types. Finally we have created a user-friendly database (http://biit.cs.ut.ee/escd/), integrating all OCT4 and stem cell related datasets in both human and mouse ES and EC cells.A selection of these targets has been validated, like NANOG, which harbours the evolutionarily conserved OCT4-SOX2 binding motif within its proximal promoter. Other validated targets, which do not harbour the classical HMG motif are USP44 and GADD45G, a key regulator of the cell cycle. Over-expression of GADD45G in NCCIT cells resulted in an enrichment and up-regulation of genes associated with the cell cycle (In the current era of systems biology driven research, we envisage that our integrated embryonic stem cell database will prove beneficial to the booming field of ES, iPS and cancer research. Human embryonic stem cells (hESCs), derived from the inner cell mass (ICM) of the blastocyst, have the ability to differentiate into all cell types and thus hold great potential for regenerative medicine and studying early development EC cells may be a useful model in deciphering regulatory networks associated with self-renewal and pluripotency OCT4  was first isolated from mouse ES cells based on its ability to bind the octamer sequence “ATGCAAAT” Several downstream targets of OCT4 in human ES OCT4 knockdowns have been performed with NCCIT cells et al. performed RNAi-based knockdowns for Oct4 and Nanog and compared the differential expression pattern with potential binding sites of these factors, using a ChIP-PET approach Oct4 levels in mES cells with expression profiling to identify new Oct4 regulated genes Oct4 and Sox2 knockdowns induced by shRNA in mES cells, Walker et al. reported a set of predicted targets of pluripotency In order to study gene regulation of transcription factors and their direct targets, it is essential to correlate ChIP-on-chip assays to gene knockdown experiments, specific for the transcription factor under investigation. RNAi-based NANOG, SOX2, LEFTY2 and FGF2Prior to hybridising the samples onto the NimbleGen-promoter array we performed ChIP-RT-PCR experiments to compare the amplified input (control) DNA with that of OCT4-bound DNA in order to assess the quality of the samples. To achieve this, primers flanking the OCT4-SOX2 binding motifs within the promoter of established OCT4 downstream target genes such as We compared the targets identified by three independent peak analysis programs, including MA2C, TAMALPAIS and an in-house developed peak analysis tool for ratio distribution dependent interval analysis, referred to as brute-force Comparing the three different peak analysis programs, we noticed a significant number of targets were identified exclusively by one program, for peaks detected in up to 3 biological replicates . This waEarly studies in mouse showed that a strong enhancer element for OCT4 binding is the octamer motif OCT4, SOX2 and NANOG were identified as targets with the highest peak scores. In order to define a threshold for the scores obtained, we defined an OCT4 motif, based on the targets obtained by the three distinct programs  upon differentiation of ESC and EC cells as a result of ablating OCT4 function GADD45G coding sequence into the pIRES2-eGPF vector and screened for fluorescence as a control for transfection efficiency . However, down-regulation of pluripotency associated genes such as EMP1, RIN2, TNC, KLHL5, FOXB1, PKD1L2, GPC6 and CBR3 were up-regulated whilst GSPT2, HESX1, RHCE, RHD, SFRP2 and GDF3 were down-regulated in OCT4 depleted NCCIT cells.This set consist of 65 genes in total, of these IL1, COL4A1, PLAU, TPM1, SYTL2, CDC42EP1, KDELR3, KLNK10, H2AFY, SLC7A7, LGI1, BAG3, PACS1, MAP3K8, TOM1L2, LBR, KCTD10, ZFP90, EPHB3, and WDR1 were up-regulated whilst, SCGB2A2, GABRA5, FRAT2, RAB25, CSPG5, MAD2L2, SPTBN2, C20orf12, PHC1, MYCN, TUB, GPR3 and TIMP4 were down-regulated in OCT4 depleted NCCIT cells. For these regulated genes, we investigated if within the respective promoter regions where putative indirect OCT4 binding activity could be confirmed, one could also detect an enrichment of known transcription factor binding sites by adopting a de novo motif discovery approach. Our hypothesis was that some of these sites might recruit OCT4 into a complex, which is not dependent on direct OCT4-DNA interaction for activating or repressing downstream target genes. Using this strategy, we identified four significant motifs predicted to be the binding sites for transcription factors such as REST, TCF3, NR2F1, p53, NF-kB, LF-A1, RUNX1 and PAX5  physically contacting the promoter regions of these target genes. Of these genes, and PAX5 .ATTTGAAATGCAAAT) shown to co-operatively bind two OCT4 molecules was first identified within the first intron of the Osteopontin gene ATXN3, CIR, FLJ16611 and SPIC. However, none of these genes were significantly regulated upon knockdown of OCT4 in NCCIT cells.The PORE sequence  was discovered after the PORE sequence was identified. Like the PORE motif, the MORE sequence also co-operatively binds to two OCT4 molecules ATPBD4, C14orf94, CLLU1, DHDDS, SNX20, ORFA17, REM2, SERPINB7, UBE2C and GSPT2. Interestingly GSPT2, which encodes a GTP-binding protein that plays an essential role at the G1 to S-phase transition in human cells is also regulated by OCT4 under module 3 (conserved SOX2 binding motif but lacking OCT4). Furthermore, knockdown of OCT4 in NCCIT resulted in a down-regulated expression of GSPT2 and UBE2C. To further describe these modules in silico we aligned the sequences under the respective OCT4 binding peak regions of selected genes within each module (This motif (More PORE- h module .http://biit.cs.ut.ee/escd/). The new database provides easy access to transcription factor binding data together with various perturbation experiments. ESCDb gathers mainly two types of data – chromatin immunoprecipitation array-based data on transcription factor targets and gene specific knockdown of pluripotency associated factors  as well as growth factor (FGF2) withdrawal and cytokine (BMP4 and ACTIVIN A) stimulation of human ES cells. We have gathered data for mouse and human, and to complement embryonic stem cell experiments we also gathered data from human embryonal carcinoma cells (NCCIT and NTERA2).We are in an era of high-throughput functional genomics and systems biology-driven research where large datasets are usually needed and provided as supplementary tables in most publications. Though useful, such tables in isolation are of limited use for making cross-references across other related datasets. Furthermore, as similar approaches have recently been adopted in constructing the HaemAtlas which serves as a reference library for gene expression in human blood cells and as a resource for identifying key genes with roles in blood cell function ESCDb offers a summarized view of multiple pluripotency related datasets. Individual genes are described as a row in the output table. A colour-scheme helps to illustrate the potential regulatory relations between genes. In the gene-expression datasets often more than one probe-set represent a gene and we treat each individual probe-set individually. We kept the same order of probe-sets in the output table for easier comparisons between probe-sets in all available datasets. Further details are given in numerical form when a given cell of the table is pointed with a cursor. The database can be queried with any widely used gene or protein identifier or Gene Ontology terms.The current version of the database comprises gene expression data from 18 mouse transcription factor-targeting experiments for 14 known factors ChIP-based studies on the transcription factor OCT4 have been carried out by others We previously demonstrated that NCCIT cells are a useful model system for investigating pathways involved in maintaining self-renewal The HMG factor SOX2, is known to form a heterodimer with OCT4 which results in a protein-protein-DNA complex required for transcriptional regulation of genes such as Utf1, Fbx15, Sox2 and Nanog in silico-derived SOX2 motif for target identification instead of peak regions, unveiled 108 SOX2-motif related putative binding sites out of 497 total binding sites and 161 binding sites linked to an OCT4 motif. However, this is only a fraction of the putative SOX2 binding sites identified in hES cells, thus suggesting distance related effects and/or other SOX2 motifs not discovered with our analysis. Additionally, one has to bear in mind that all thresholds defined for the OCT4 and SOX2 PWMs are arbitrarily set and therefore can only provide a prediction for a bona fide functional binding event, hence further experimental validation will be needed.Boyer and colleagues CTGCATATGCAT) motif within the Bmp4 promoter, Kang et al. To identify binding modules, where the octamer element is not present, we also screened the 497 target genes for the presence of PORE or MORE motifs as an addition to target genes defined by module 4. We identified 4 putative target genes harbouring a PORE motif (module 5) and 10 target genes, which contained a MORE motif (module 6). Using a previously identified MORE  which allows rapid and convenient access and comparisons between published datasets related to embryonic stem cell biology. We anticipate that this study will aid in increasing our meager understanding of self-renewal in ES, EC, iPS and cancer cells.NCCIT cells were grown in high-glucose DMEM supplemented with 10% FCS , 2 mM glutamine, and penicillin/streptomycin on conventional tissue culture plastic surfaces.7–1×108 cells for each Immunoprecipitation. Cells were chemically crosslinked by the addition of one-tenth volume of fresh 11% formaldehyde solution for 10 min at room temperature. Cells were rinsed twice with 1× PBS and harvested using a silicon scraper and flash frozen in liquid nitrogen and stored at −80°C prior to use. Cells were resuspended, subjected to lysis buffers, and sonicated to solubilize and shear crosslinked DNA. Sonication conditions vary depending on cells, culture conditions, crosslinking, and equipment. We used a BRANSON 250 and sonicated at power 3 for 11:00 min with 30% Duty Cycle at 4°C while samples were immersed in an ice bath. The resulting wholecell extract (WCE) was incubated overnight at 4°C with 100 µl of Dynal Protein G magnetic beads that had been preincubated with 10 µg of OCT4 antibody (insert). Beads were washed five times with RIPA buffer and once with TE containing 50 mM NaCl. Bound complexes were eluted from the beads by heating at 65°C with occasional vortexing, and crosslinking was reversed by overnight incubation at 65°C. Whole-cell extract DNA (reserved from the sonication step) was also treated for crosslink reversal. Immunoprecipitated DNA and whole-cell extract DNA were then purified by treatment with RNase A, proteinase K, multiple phenol:chloroform:isoamyl alcohol extractions and precipitation with ethanol. Purified DNA was amplified using a one-stage random PCR protocol. For ChIP-on-chip assay three biological replicate ChIP experiments were performed. Labelling and hybridisation of ChIP-DNA was done by the NimbleGen company. Using the NimbleGen human promoter tiling arrays (HG18) we screened 6517 putative promoter regions more, with a median probe spacing of 100 bp, compared to the OCT4 ChIP-on-Chip done by Boyer et al. Though the chip was covering only 4250 bp, these probes were within the most abundant TF binding sites, using TRANSFAC Human NCCIT cells were grown to a final count of 5×10For the Bandshift assays, nuclear extracts were prepared from NCCIT cells, using the method of Dignam et al. 50 µl streptavidin conjugated Dynabeads  were washed with PBS-BSA  for each sample. Biotinylated USP44 promoter fragment DNA (100 pmol) was incubated with the streptavidin beads for 4 h at 4°C with rotation. Dynabead·DNA complexes were extensively washed with PBS-BSA to remove unbound DNA. Beads were added to 1000 µg Nuclear Extract of NCCIT cells . Samples were incubated for 8 h at 4°C with rotation. Dynabead-DNA-protein complexes were separated using the Dynabead magnetic station and then washed three times with ice cold Buffer D, adding 300X scrambled oligos each time. Samples were transferred to fresh microfuge tubes prior to final wash to avoid eluting plastic bound proteins. Dynabead-DNA-protein complexes were eluted in SDS-reducing sample buffer by heating at 95°C. Duplicate samples were pooled and equal volumes loaded onto 10% polyacrylamide gels for SDS-PAGE. Samples were transferred to nitrocellulose membranes and subjected to Western blot analysis. Western blotting was performed according to standard procedures and using chemiluminescence detection (ECL – Amersham). Antibodies used were OCT4 (sc-8629) and PARP1 (sc-7150) both from Santa Cruz.RNA was reversely transcribed using MMLV (USB) and oligo-dT priming. Real-time RT-PCR was carried out on Applied Biosystems 7900 instrumentation in 20 µl reactions containing 10 µl of SYBR Green PCR mix (ABI), 0.375 µM of each primer, and diluted cDNA. All primer pairs used were confirmed to approximately double the amount of product within one cycle and to yield a single product of the predicted size. Primer sequences are provided in http://www.illumina.com/pages.ilmn?ID=5). All basic expression data analyses were carried out using the manufacturer's software BeadStudio 1.0. Raw data were background-subtracted and normalised using the “rank invariant” algorithm, by which negative intensity values may arise. These and values below the detection limit were arbitrarily set to the level of threshold detection (S = 20) in order to avoid nonsense values for expression ratios. Differentially expressed genes were required to change by at least 50% at P<0.01 according to an Illumina custom model In all microarray experiments, biotin-labelled cRNA was generated employing a linear amplification kit (Ambion #IL1791) with 300 ng of genomic DNA-free, quality-checked total RNA as input. Chip hybridisations, washing, Cy3-streptavidin (Amersham Biosciences) staining, and scanning was performed on the Illumina BeadStation 500 platform employing reagents and following protocols supplied by the manufacturer. cRNA samples were hybridised as biological triplicates on Illumina human-8 BeadChips. Due to an at least 20-fold feature redundancy quantitative expression data can be obtained ((Ct(input)-Ct(ChIP)). Melting curves of each amplified sample indicated formation of a single product in all cases. All samples were analysed as duplicates.Duplicates of each sample were analyzed in a quantitative PCR reaction using the Applied Biosystems 7900 sequence detector and QPCR SYBR Green PCR mix (ABI). Data was analyzed with a threshold set in the linear range of amplification. The cycle number that any particular sample crossed that threshold (Ct) was then used to determine fold difference (enrichment). Fold difference was calculated as 2The NimbleGen human promoter tiling array utilized in this study is a two-array set. Three replicates of the ChIP vs. Input experiment were performed resulting in a total set of six arrays. Each array was analysed separately. Because NimbleGen did not deliver array images, the array images were reconstructed based on the intensity values using Bioconductors image function These 31 experiments  to make two sequences equal at most 2 to de novo motif discovery analysis based on specific promoter regions of OCT4 target genes derived from those genes which were at least 2 fold differentially regulated in NCCIT cells. By taking the genomic positions of the identified peaks as a reference (that is to a peak score of at least 0.5), we assembled the sub-sequences underlying the peaks. The selected sub-sequences were used as input for the TAMO package, a de novo motif discovery framework minaligndiff function of the TAMO distribution and when motifs occur with an alignment difference <0.2, only the motif with the highest bit score is further considered. Secondly, we computed entropy of the dimer distribution of the motif sequence as a measure for the motif complexity. Motifs with complexity score <1.0 were discarded.In order to screen for putative transcription factor binding sites other than OCT4 and SOX2, we performed a The discovered motifs were compared against two existing databases of known motifs using the STAMP tool Three programs were adopted in this study:Based on the quantile normalized data, for each oligonucleotide a fold-enrichment was calculated by dividing the signal intensity from the immunoprecipitated sample by the signal intensity of the whole-genome sample. For each array, the total ChIP/IP ratio distribution was examined in order to obtain array specific threshold values for the upper 0.01 and for the upper 0.05 quantile. A potential binding event is defined with respect to the estimated average fragment size of the sonicated genomic DNA (550 bp) in relation to the distance of oligonucleotides relative to the promoter regions of the examined TSSs (distances between oligonucleotides is 100 bp). Therefore, a potential binding event is defined as at least three oligonucleotides that fulfil the following criteria: a centre oligonucleotide has a ChIP/IP ratio within the upper 0.01 quantile of the total ratio distribution and one upstream and one downstream neighbour each within a distance of max. 1000 bp have a ChIP/IP ratio within the upper 0.05 quantile. All identified peaks were linked to the closest transcription start site (TSS), if one exists within a distance of 8 kb. Genomic positions of transcription start sites are based on Ensembl We used MA2C with standard settings for first normalizing our PairData files for each of the five experiments and thereafter searched for peaks We used a web version of the TAMALPAIS program for analysing already normalized files provided by NimbleGen Document S1Quality control of the OCT4 ChIP-chip data.(2.75 MB DOC)Click here for additional data file.Table S1Genes correlated with an OCT4 bound region in NCCIT, NTERA2 and H9 cells.(0.04 MB XLS)Click here for additional data file.Table S2Genes identified in the 6 different OCT4 binding modules.(0.19 MB XLS)Click here for additional data file.Table S3Regulated genes in NCCIT cells upon OCT4 and SOX2 knockdowns in relation to the 6 different OCT4 binding modules.(0.03 MB XLS)Click here for additional data file.Table S4Oligonucleotides and primers used in this study.(0.03 MB XLS)Click here for additional data file.


Current methods for analyzing the dynamics of natural regulatory networks, and quantifying synthetic circuit function, are limited by the lack of well-characterized genetic measurement tools. Fluorescent reporters have been used to measure dynamic gene expression, but recent attempts to monitor multiple genes simultaneously in single cells have not focused on independent, isolated measurements. Multiple reporters can be used to observe interactions between natural genes, or to facilitate the 'debugging' of biologically engineered genetic networks. Using three distinguishable reporter genes in a single cell can reveal information not obtainable from only one or two reporters. One application of multiple reporters is the use of genetic noise to reveal regulatory connections between genes. Experiments in both natural and synthetic systems would benefit from a well-characterized platform for expressing multiple reporter genes and synthetic network components.Escherichia coli with single-cell fluorescence imaging and time-lapse microscopy. The three spectrally distinct reporters allow independent monitoring of genetic regulation and analysis of genetic noise. As a novel application of this tool we show that the presence of genetic noise can reveal transcriptional co-regulation due to a hidden factor, and can distinguish constitutive from regulated gene expression.We describe such a plasmid-based platform for the design and optimization of synthetic gene networks, and for analysis of endogenous gene networks. This network scaffold consists of three distinguishable fluorescent reporter genes controlled by inducible promoters, with conveniently placed restriction sites to make modifications straightforward. We quantitatively characterize the scaffold in We have constructed a general chassis where three promoters from natural genes or components of synthetic networks can be easily inserted and independently monitored on a single construct using optimized fluorescent protein reporters. We have quantitatively characterized the baseline behavior of the chassis so that it can be used to measure dynamic gene regulation and noise. Overall, the system will be useful both for analyzing natural genetic networks and assembling synthetic ones. In many cases, the behavior of designed genetic networks differs significantly from initial model predictions.Synthetic biology requires the assembly of regulatory networks encoded in DNA -4. Such It would be helpful to have a tool for characterizing the quantitative behavior of both natural and synthetic genetic networks. Here we present a three-color genetic reporter that can be used to monitor dynamic gene expression in single bacterial cells. This "three-color scaffold" is contained on a single DNA construct. The system has been designed to minimize spurious interactions between reporters, maximize signal, and support modular additions. We show that interactions between the reporters are minimal when controlled by different transcription factors. This tool can measure multiple network properties in parallel to characterize multi-component systems.Biocompatibility. The scaffold must be genetically stable and non-toxic to the cells carrying it. (2) Distinctness. It must be possible to completely separate the fluorescent signals of the three genetic reporters. (3) Independence. The genetic expression of each reporter must be made as independent as possible without spurious cross talk between components. (4) Modularity. To make the three-color scaffold generally useful for natural and synthetic biological applications, it must be straightforward to change the various genetic elements.We followed four principles in the sequence design of the three-color scaffold. (1) The construct contains a set of three operons , each containing a single reporter gene , and the activator AraC additionally regulates the rfp gene at the P3 promoter. Combinatorial promoters that accept multiple genetic inputs similar to P3 are ubiquitous in genomes and are useful for creating synthetic networks [We demonstrate the response of the scaffold using three synthetic promoters regulated by three inducible transcription factors Figure . TetR renetworks -22. We uMeasurements using fluorescent reporter proteins in individual microbes ,20,23-30There are many ways to detect regulatory interactions, but context-preserving tools for distinguishing the directionality and causality of regulatory connections within a larger network are lacking. In the engineering field of system identification theory, white noise is often used as an input to characterize system response . AnalogoPreviously, we showed that the maximum cross correlation between a repressor and its target gene occurs with a delay due the time required for a transcriptional event to result in a functional repressor . Here weWe designed and built the three-color fluorescent reporter scaffold Figure  using cuWe used the three reporters to monitor regulation by the transcription factors AraC, LacI, and TetR. Each transcription factor can be controlled independently by the addition of chemical inducers L-ara, IPTG, and aTc, respectively. Repressors TetR and LacI control separate promoters P1 and P2) expressing CFP and YFP, respectively . These results show that the design of the scaffold provides genetic isolation, enabling independent control of the three reporter genes.Two inducible genes are independent when induction of one gene does not affect the expression of the other. We first verified that each of the three inducers  only affected the expression of genes regulated by their corresponding transcription factor (not shown). We then characterized the scaffold in single ribed in ) using qs Figure . The autt Figure , and 2E,p Figure , and 2H,G Figure  and 2E. a Figure , and 2I-rfp as an asymmetric AND [The combinatorial LacI/AraC-regulated promoter controls tric AND  gate , due to the increased noise in LacI concentration at low protein copy number. Second, we measured the correlation between yfp and rfp in a strain containing a deletion of the entire lac operon (ΔlacI). Third, we switched the LacI/AraC controlled rfp promoter with a constitutive promoter containing no LacI operators (ΔlacO). In both the second and third experiments LacI regulatory connections were disconnected, and the extra correlation between yfp and rfp disappeared  without requiring any external perturbation or induction.We hypothesized that noise in LacI might be used to detect the co-regulation of its targets. We performed three additional experiments to confirm that the added correlation was indeed due to LacI repression at the rs Table . First, n MG1655  as oppos1 strain ) and no n Figure . As expeyfp and rfp due to LacI co-regulation should persist over time in growing cells. We measured three-color fluctuations in a growing microcolony with time-lapse microscopy  the cross correlation function would be asymmetric due to the delay of indirect regulation ,33. A stp Figure . The expp Figure  agrees wyfp and rfp) genes and the constitutive control (cfp) gene are similar to each other , and the cross correlation has an asymmetric shape. This asymmetry occurs due to fluctuations in LacI. Too see this, consider the extrinsic fluctuations in the expression of all genes. If a cell has a momentary global increase in gene expression at time t = 0, the three reporters will be transiently expressed highly--but so will LacI. Once this transient pulse has matured into a functional LacI protein (1t = t), it will repress its targets and the yfp and rfp (but not cfp) levels will eventually drop (2t = t). As a result, the correlation between the future (2t = t) yfp and rfp levels and the present (t = 0) cfp level will be less than if yfp or rfp were unregulated. In other words, the positive half of the cross correlations R and R will reach zero faster than the negative half. The mathematical model reproduces this asymmetry  and R. These results show that fluctuations in LacI simultaneously affect only the two target genes (yfp and rfp) but not the constitutive gene (cfp). Thus the three-color reporter can be used to distinguish genes regulated by noisy transcription factors from constitutive ones.The cross correlations between the LacI regulated  can be switched to appropriate natural promoters. The activity of multiple promoters could be quantified at the same time by using the promoters provided here as a baseline control. Promoters could be screened for quantitative changes in activity in various environmental conditions. For example, cells carrying a version of the scaffold that monitors three regulated promoters could be measured by fluorescence activated cytometry in different media conditions to determine a particular promoter activity state . In this way the reporter scaffold can be used to monitor and compare multiple context-sensitive natural promoter responses within the same regulatory network.The scaffold will aid synthetic network construction and optimization. Previously constructed synthetic networks can be analyzed in the same way as natural networks, by inserting their regulatory promoters into the framework to control expression of the three fluorescent proteins. Monitoring multiple network promoters in this way can provide insight into internal variables of the system that would otherwise be unknown. Alternatively, synthetic gene networks can be built directly into the scaffold reporter backbone by using the unique restriction sites between each genetic element to create transcriptional fusions of network genes. These network genes can be paired with target promoters of the three operons, adding additional operons to build more complex networks as necessary. To test the modularity of specific parts, a synthetic network of transcription factors and promoters could be characterized first individually  and then again after they are connected.The noise correlation method complements modern bioinformatic and experimental high-throughput techniques for analyzing natural transcriptional networks: (1) Comparing noise correlation between pairs of uncharacterized promoters versus their correlations with a constitutive promoter can suggest co-regulation--even when the co-regulating factor is unknown. (2) Putative transcription factor binding sites may be analyzed in our three-color reporter scaffold against a confirmed target promoter of the same transcription factor. For example, more than 200 novel CRP binding sites have been predicted based on species comparison ; our novin vivo applications. We show that a noise source such as LacI can be used as an input to a synthetic regulatory network, and the propagation of this noise can be monitored to confirm the intended network structure. Correlation analysis of genetic noise can suggest the presence of regulatory connections even when no suitable condition is known--it doesn't require finding a specific mutation  or chemical signal  to perturb the regulatory interaction in question.We present a three-color scaffold for monitoring gene expression, where the individual genes are independent of each other. With this system it is possible to insert three transcription units directly into a plasmid so that they can be analyzed simultaneously using fluorescent protein reporters. Our scaffold provides a biocompatible, distinct, independent, and modular platform for a wide variety of The 3,518 base-pair scaffold was constructed by total synthesis by DNA2.0 in Menlo Park, CA. The sequence was cloned into the plasmid vector pDrive, and confirmed by automated sequencing. The annotated sequence of the scaffold in plasmid pZS2-123 is supplied  to control cfp, PLlacO-1 (P2) to control yfp, and Plac/ara-1 (P3) to control rfp [cfp and yfp [rfp [lac/ara-1 promoter is weaker than the PLtetO-1 and PLlacO-1 promoters. Terminators RNAI and TSAL [cfp. Terminators TR2-17 [yfp and rfp.Unique restriction sites allow for insertion and swapping of genetic elements between the promoters, protein coding regions, and terminators of the construct. The three fluorescent protein sequences were codon optimized for expression in bacteria. We created a modified codon usage table by averaging the two codon usage frequency tables of Gram-negative subtilis . We furt[E. coli . Each pr variant  for back variant , and inc variant . For the variant . The codtrol rfp . To cont and yfp , and theyfp [rfp . These wand TSAL  terminats TR2-17 , TL17 [5s TR2-17 , BS7 [50s TR2-17  terminatrfp and yfp with the LacI regulation of rfp removed (Table ΔlacO), we placed the rfp gene under the control of the (constitutive due to the absence of cI) P(R) promoter from phage λ [The initial synthetic construct was cloned into the modular pZ* expression vector system  using thed Table , ΔlacO,  phage λ :cccgggcatacgttaaatctatcaccgcaagggataaatatctaacaccgtgcgtgttgactattttacctctggcggtgataatggttgcatgcctaggThis promoter sequence was synthesized and cloned in between the restriction sites XmaI and AvrII to create plasmid pZS2-12R.E. coli MG1655 [yfp and rfp from plasmid pZS2-123 in the presence of low levels of LacI .We used wild-type i MG1655  for the g DH5aZ1  strain b-3 min-1. Our scaffold uses the Venus YFP protein and Cherry RFP protein, both of which are known to have maturation times of 15 minutes [It is essential for the fluorescent reporters to mature quickly and uniformly for correlation measurements to be accurate. In our movies the time between cell divisions was 87 ± 25 minutes, corresponding to a protein dilution rate of 7.97 × 10 minutes . If one cfp , Yellow/yfp , and Crimson/rfp . To prevent photobleaching, all images were collected as ordered exposures of , with minimal light exposure. We verified the fluorescent field provided by the Lambda LS light source and liquid light guide with fluorescent slides . The field was found to be extremely flat (std/mean ≈ %3) when centered in all three colors.Single-cell measurements were acquired on an Olympus IX-81 inverted fluorescence microscope at 100× magnification, with a Hammamatsu Orca ER CCD camera (2 × 2 binning) using custom microscope acquisition software. Phase-contrast images were acquired to measure cell morphology, position, and image quality. Fluorescent excitation was performed with a Lambda LS Xenon lamp  with a liquid light guide and fluorescent filter cubes  for Cyan/cfp, yfp, and rfp in all three filter cubes  was used for all cell experiments. All enzymes for plasmid construction and modification were obtained from New England Biolabs. MG1655Z1 cells containing the plasmid pZS2-123 were grown to saturation overnight in LB with 50 mg/mL kanamycin at 37°C and diluted 100× into non-fluorescent M9 minimal medium  containiMG1655 cells containing the plasmid pZS2-123 were grown to saturation overnight in LB at 37°C and diluted 1000× into non-fluorescent M9 arabinose minimal media (as above with 0.2% L-ara instead of glycerol). Cells were grown for 3 hours at 32°C, then diluted 100× and transferred to L-ara media pads made with 1.5% low melting point agarose. These pads were placed inside a glass Wilco dish chamber and sealed. Time-lapse images were acquired at 10 minute intervals in a 32°C temperature controlled chamber.We used custom software and the Matlab  Image Processing Toolbox to segment the phase contrast images and collect corresponding pixels from each of the three fluorescent images. The program identified individual cells on phase contrast images by progressive watershed thresholds. Shapes were filtered based on morphological properties to eliminate non-cell objects, clumps of cells, and misshapen cells. For each cell, a background value of the minimum pixel contained in the bounding box was recorded for each color. Collected fields of cells were examined by eye to check for errors in segmentation and acquisition. For the time-lapse experiment, we identified cell division events and tracked lineages of cells (lines of daughter and parent cells) during microcolony growth ,27. We eWe tested for sources of systematic or experimental error. Parabolic fluorescent field correction did not change the qualitative relationships or reduce variation. The overall fluorescence variation between fields of cells remained small: Each frame analyzed was within one standard deviation of the mean over all frames of the same color. Normalization to account for morphological factors such as size and shape did not qualitatively change our results or decrease the observed variation. As a final correction, we removed outlying cells and non-cell objects from the processed data that were more than three standard deviations from the median of the 500-1000 cells. Previous noise measurements have used similar corrections ,29.For each processed data set, we calculated the normalized Pearson and Spearman correlation coefficient between each pair of colors Table . Using tth and 900th values of the sorted list of resampled correlation coefficients).To calculate the errors in correlation and partial correlation coefficients, we uniformly re-sampled 1,000 data sets (bootstrap sampling with replacement) of the same size and recomputed the correlation coefficients for each sample. The errors reported in Table For the time-lapse movies, we calculated the Pearson correlation coefficient for each pair of colors over the cells present in the entire microcolony at each time point Figure . The errWe calculated the cross correlation function between each cell lineage for all three pairs of colors for five microcolonies grown in identical conditions. Each microcolony analyzed contained 62-124 cell lineages. The data shown in Figure f(t)and g(t) is defined asThe cross correlation between two discrete signals N is the number of time points. This function is normalized to Here, We adapted this standard formula to accommodate tree-structured (branching) data as in . The folcells Nis the number of cells at the end of the movie and i kis the branching point between i fand i+1f. As in the non-branched case, the function is normalized byHere System dynamics were modeled using stochastic differential equations Figure . We apprLY = -0.007 1/min, gLR = -0.007 1/min, gAR = 0.0007 1/min, WE = 6.39x10-3, WL = 100, WA = WC = WY = WR = 1.where L = LacI, A = AraC, C = CFP, Y = YFP, and R = RFP. Cross correlations are calculated as in  with modThe authors declare that they have no competing interests.RSC designed the DNA sequences, performed the experiments and drafted the manuscript. MJD carried out the time-lapse data analysis and mathematical modeling, and helped write the manuscript. MBE conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.pZS2-123 annotated sequence. Sequence of the three-color reporter construct in a 6,806 base-pair plasmid containing a Kanamycin resistance gene and an SC101 origin of replication. Sequence +1 begins at the single NotI restriction site at the beginning of the 3,518 base-pair three color reporter scaffold (the three color scaffold sequence ends at the NheI site). All genetic elements described in the Methods section are annotated as sequence features, along with the primers used for sequence verification.Click here for fileFigure S1: Total genetic noise is controlled by induction. The total genetic noise, calculated as the standard error divided by the mean, is plotted for each of the conditions in Figure cfp, yellow to noise in yfp, and red to noise in rfp. In each case, the noise is maximal in the fully induced state. The noise of each color is only affected by the associated inducer(s): aTc for cfp, IPTG for yfp, and both IPTG and L-ara for rfp.Click here for fileTimelapse movie of 3 color network, yellow and cyan channels.Click here for fileTimelapse movie of 3 color network, yellow and red channels.Click here for file